These datasets are used in machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning. Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high-quality training datasets.[1] High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce.[2][3][4][5]

Many organizations including governments publish and share their datasets . The datasets are classified, based on the licenses, as Open data and Non-Open data.

The datasets from various governmental-bodies are presented in List of open government data sites. The datasets are ported on open data portals. They are made available for searching, depositing and accessing through interfaces like Open API. The datasets are made available as various sorted types and subtypes.

List of sorting used for datasets

Type Subtypes
Specific category Finance, Economics, Commerce, Societal, Health, Academy, Sports, Food, Agriculture, Travel, Geospatial, Political, Consumer, Transport, Logistics, Environmental, Real-Estate, Legal, Entertainment, Energy, Hospitality
Scope Supranational Union, National, Subnational, Municipality, Urban, Rural
Language Mandarin Chinese, Spanish, English, Arabic, Hindi, Bengali
Type Tabular, Graph, Text, Image, Sound, Video
Usage Training, validating, and testing
File-Formats CSV, JSON, XML, KML, GeoJSON, Shapefile, GML
Licenses Creative-Commons, GPL, Other Non-Open data licenses
Last-Updated Last-Hour, Last-Day, Last-Week, Last-Month, Last-Year
File-Size Minimum, Maximum, Range
Status Verified, In-Preparation, Deactivated(or Deprecated)
Number of records 100s, 1000s, 10000s, 100000s, Millions
Number of variables Less than 10, 10s, 100s, 1000s, 10000s
Services Individual, Aggregation

The data portal is classified based on its type of license. The open source license based data portals are known as open data portals which are used by many government organizations and academic institutions.

List of open data portals

See also: Open data portal

Portal-Name License List of Installations of the Portal Typical Usages
Comprehensive Knowledge Archive Network (CKAN) AGPL

Data repository for government or non-profit organisations, Data Management Solution for Research Institutes
DKAN GPL Data repository for government or non-profit organisations, Data Management Solution for Research Institutes
Dataverse Apache

Data Management Solution for Research Institutes
DSpace BSD Data Management Solution for Research Institutes
OpenML BSD Data Management Solution to share datasets, algorithms, and experiments results through APIs.

List of portals suitable for multiple types of applications

See also: machine learning

The data portal sometimes lists a wide variety of subtypes of datasets pertaining to many machine learning applications.

Academic Torrents
Amazon Datasets
Awesome Public Datasets Collection
Datahub – Core Datasets
Global Open Data Index – Open Knowledge Foundation Archived 25 May 2020 at the Wayback Machine
Google Dataset Search
Hugging Face
IBM's Data Asset Exchange
Jupyter – Tutorial Data
Machine learning datasets
Major Smart Cities with Open Data
Microsoft Datasets
Open Data Inception
Papers with Code
Penn Machine Learning Benchmarks
Public APIs
Registry of Open Access Repositories 
REgistry of REsearch Data REpositories 
UCI Machine Learning Repository Archived 26 June 2020 at the Wayback Machine
Speech Dataset
Visual Data Discovery

List of portals suitable for a specific subtype of applications

See also: Machine learning

The data portals which are suitable for a specific subtype of machine learning application are listed in the subsequent sections.

Image data

Main article: List of datasets in computer vision and image processing

Text data

These datasets consist primarily of text for tasks such as natural language processing, sentiment analysis, translation, and cluster analysis.


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Amazon reviews US product reviews from None. 233.1 million Text Classification, sentiment analysis 2015 (2018) [6][7] McAuley et al.
OpinRank Review Dataset Reviews of cars and hotels from and TripAdvisor respectively. None. 42,230 / ~259,000 respectively Text Sentiment analysis, clustering 2011 [8][9] K. Ganesan et al.
MovieLens 22,000,000 ratings and 580,000 tags applied to 33,000 movies by 240,000 users. None. ~ 22M Text Regression, clustering, classification 2016 [10] GroupLens Research
Yahoo! Music User Ratings of Musical Artists Over 10M ratings of artists by Yahoo users. None described. ~ 10M Text Clustering, regression 2004 [11][12] Yahoo!
Car Evaluation Data Set Car properties and their overall acceptability. Six categorical features given. 1728 Text Classification 1997 [13][14] M. Bohanec
YouTube Comedy Slam Preference Dataset User vote data for pairs of videos shown on YouTube. Users voted on funnier videos. Video metadata given. 1,138,562 Text Classification 2012 [15][16] Google
Skytrax User Reviews Dataset User reviews of airlines, airports, seats, and lounges from Skytrax. Ratings are fine-grain and include many aspects of airport experience. 41396 Text Classification, regression 2015 [17] Q. Nguyen
Teaching Assistant Evaluation Dataset Teaching assistant reviews. Features of each instance such as class, class size, and instructor are given. 151 Text Classification 1997 [18][19] W. Loh et al.
Vietnamese Students’ Feedback Corpus (UIT-VSFC) Students’ Feedback. Comments 16,000 Text Classification 1997 [20] Nguyen et al.
Vietnamese Social Media Emotion Corpus (UIT-VSMEC) Users’ Facebook Comments. Comments 6,927 Text Classification 1997 [21] Nguyen et al.
Vietnamese Open-domain Complaint Detection dataset (ViOCD) Customer product reviews Comments 5,485 Text Classification 2021 [22] Nguyen et al.
ViHOS: Hate Speech Spans Detection for Vietnamese Social Media Texts Comments Containing 26k spans on 11k comments Text Span Detection 2021 [23] Hoang et al.

News articles

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
NYSK Dataset English news articles about the case relating to allegations of sexual assault against the former IMF director Dominique Strauss-Kahn. Filtered and presented in XML format. 10,421 XML, text Sentiment analysis, topic extraction 2013 [24] Dermouche, M. et al.
The Reuters Corpus Volume 1 Large corpus of Reuters news stories in English. Fine-grain categorization and topic codes. 810,000 Text Classification, clustering, summarization 2002 [25] Reuters
The Reuters Corpus Volume 2 Large corpus of Reuters news stories in multiple languages. Fine-grain categorization and topic codes. 487,000 Text Classification, clustering, summarization 2005 [26] Reuters
Thomson Reuters Text Research Collection Large corpus of news stories. Details not described. 1,800,370 Text Classification, clustering, summarization 2009 [27] T. Rose et al.
Saudi Newspapers Corpus 31,030 Arabic newspaper articles. Metadata extracted. 31,030 JSON Summarization, clustering 2015 [28] M. Alhagri
RE3D (Relationship and Entity Extraction Evaluation Dataset) Entity and Relation marked data from various news and government sources. Sponsored by Dstl Filtered, categorisation using Baleen types not known JSON Classification, Entity and Relation recognition 2017 [29] Dstl
Examiner Spam Clickbait Catalogue Clickbait, spam, crowd-sourced headlines from 2010 to 2015 Publish date and headlines 3,089,781 CSV Clustering, Events, Sentiment 2016 [30] R. Kulkarni
ABC Australia News Corpus Entire news corpus of ABC Australia from 2003 to 2019 Publish date and headlines 1,186,018 CSV Clustering, Events, Sentiment 2020 [31] R. Kulkarni
Worldwide News – Aggregate of 20K Feeds One week snapshot of all online headlines in 20+ languages Publish time, URL and headlines 1,398,431 CSV Clustering, Events, Language Detection 2018 [32] R. Kulkarni
Reuters News Wire Headline 11 Years of timestamped events published on the news-wire Publish time, Headline Text 16,121,310 CSV NLP, Computational Linguistics, Events 2018 [33] R. Kulkarni
The Irish Times Ireland News Corpus 24 Years of Ireland News from 1996 to 2019 Publish time, Headline Category and Text 1,484,340 CSV NLP, Computational Linguistics, Events 2020 [34] R. Kulkarni
News Headlines Dataset for Sarcasm Detection High quality dataset with Sarcastic and Non-sarcastic news headlines. Clean, normalized text 26,709 JSON NLP, Classification, Linguistics 2018 [35] Rishabh Misra


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Enron Email Dataset Emails from employees at Enron organized into folders. Attachments removed, invalid email addresses converted to or ~ 500,000 Text Network analysis, sentiment analysis 2004 (2015) [36][37] Klimt, B. and Y. Yang
Ling-Spam Dataset Corpus containing both legitimate and spam emails. Four version of the corpus involving whether or not a lemmatiser or stop-list was enabled. 2,412 Ham 481 Spam Text Classification 2000 [38][39] Androutsopoulos, J. et al.
SMS Spam Collection Dataset Collected SMS spam messages. None. 5,574 Text Classification 2011 [40][41] T. Almeida et al.
Twenty Newsgroups Dataset Messages from 20 different newsgroups. None. 20,000 Text Natural language processing 1999 [42] T. Mitchell et al.
Spambase Dataset Spam emails. Many text features extracted. 4,601 Text Spam detection, classification 1999 [43] M. Hopkins et al.

Twitter and tweets

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
MovieTweetings Movie rating dataset based on public and well-structured tweets ~710,000 Text Classification, regression 2018 [44] S. Dooms
Twitter100k Pairs of images and tweets 100,000 Text and Images Cross-media retrieval 2017 [45][46] Y. Hu, et al.
Sentiment140 Tweet data from 2009 including original text, time stamp, user and sentiment. Classified using distant supervision from presence of emoticon in tweet. 1,578,627 Tweets, comma, separated values Sentiment analysis 2009 [47][48] A. Go et al.
ASU Twitter Dataset Twitter network data, not actual tweets. Shows connections between a large number of users. None. 11,316,811 users, 85,331,846 connections Text Clustering, graph analysis 2009 [49][50] R. Zafarani et al.
SNAP Social Circles: Twitter Database Large Twitter network data. Node features, circles, and ego networks. 1,768,149 Text Clustering, graph analysis 2012 [51][52] J. McAuley et al.
Twitter Dataset for Arabic Sentiment Analysis Arabic tweets. Samples hand-labeled as positive or negative. 2000 Text Classification 2014 [53][54] N. Abdulla
Buzz in Social Media Dataset Data from Twitter and Tom's Hardware. This dataset focuses on specific buzz topics being discussed on those sites. Data is windowed so that the user can attempt to predict the events leading up to social media buzz. 140,000 Text Regression, Classification 2013 [55][56] F. Kawala et al.
Paraphrase and Semantic Similarity in Twitter (PIT) This dataset focuses on whether tweets have (almost) same meaning/information or not. Manually labeled. tokenization, part-of-speech and named entity tagging 18,762 Text Regression, Classification 2015 [57][58] Xu et al.
Geoparse Twitter benchmark dataset This dataset contains tweets during different news events in different countries. Manually labeled location mentions. location annotations added to JSON metadata 6,386 Tweets, JSON Classification, Information Extraction 2014 [59][60] S.E. Middleton et al.
Sarcasm, Perceived and Intended, by Reactive Supervision (SPIRS) Intended and perceived sarcastic tweets along with their context collected using reactive supervision; an equal number of negative (non-sarcastic) samples 30,000 Tweet IDs, CSV Classification 2020 [61][62] B. Shmueli et al.
Dutch Social media collection This dataset contains COVID-19 tweets made by Dutch speakers or users from Netherlands. The data has been machine labeled classified for sentiment, tweet text & user description translated to English. Industry mention are extracted 271,342 JSONL Sentiment, multi-label classification, machine translation 2020 [63][64][65] Aaaksh Gupta, CoronaWhy
ReactionGIF dataset A dataset of 30K tweets and their GIF reactions Classified for sentiment, reaction, and emotion 30,000 Tweet IDs, JSONL Classified for sentiment, reaction, and emotion 2021 [66][67] B. Shmueli et al.


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
NPS Chat Corpus Posts from age-specific online chat rooms. Hand privacy masked, tagged for part of speech and dialogue-act. ~ 500,000 XML NLP, programming, linguistics 2007 [68] Forsyth, E., Lin, J., & Martell, C.
Twitter Triple Corpus A-B-A triples extracted from Twitter. 4,232 Text NLP 2016 [69] Sordini, A. et al.
UseNet Corpus UseNet forum postings. Anonymized e-mails and URLs. Omitted documents with lengths <500 words or >500,000 words, or that were <90% English. 7 billion Text 2011 [70] Shaoul, C., & Westbury C.
NUS SMS Corpus SMS messages collected between two users, with timing analysis. ~ 10,000 XML NLP 2011 [71] KAN, M
Reddit All Comments Corpus All Reddit comments (as of 2015). ~ 1.7 billion JSON NLP, research 2015 [72] Stuck_In_the_Matrix
Ubuntu Dialogue Corpus Dialogues extracted from Ubuntu chat stream on IRC. 930 thousand dialogues, 7.1 million utterances CSV Dialogue Systems Research 2015 [73] Lowe, R. et al.
Dialog State Tracking Challenge The Dialog State Tracking Challenges 2 & 3 (DSTC2&3) were research challenge focused on improving the state of the art in tracking the state of spoken dialog systems. Transcription of spoken dialogs with labelling DSTC2 contains ~3.2k calls – DSTC3 contains ~2.3k calls Json Dialogue state tracking 2014 [74] Henderson, Matthew and Thomson, Blaise and Williams, Jason D


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
FreeLaw Filtered data from Court Listener, part of the FreeLaw project. Cleaned and normalized text 4,940,710 Json NLP, linguistics 2020 [75] T. Hoppe
Pile of Law Corpus of legal and administrative data Cleaned, normalized, and privatized ~50,000,000 Json NLP, linguistics, sentiment 2022 [76][77] L. Zheng; N. Guha; B. Anderson; P. Henderson; D. Ho
Caselaw Access Project All official, book-published state and federal United States case law — every volume or case designated as an official report of decisions by a court within the United States. Cleaned and normalized text ~10,000 Json NLP, linguistics 2022 [78] A. Aizman; S. Chapman; J. Cushman; K. Dulin; H. Eidolon; et al.

Other text

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Web of Science Dataset Hierarchical Datasets for Text Classification None. 46,985 Text Classification,


2017 [79][80] K. Kowsari et al.
Legal Case Reports Federal Court of Australia cases from 2006 to 2009. None. 4,000 Text Summarization,

citation analysis

2012 [81][82] F. Galgani et al.
Blogger Authorship Corpus Blog entries of 19,320 people from Blogger self-provided gender, age, industry, and astrological sign. 681,288 Text Sentiment analysis, summarization, classification 2006 [83][84] J. Schler et al.
Social Structure of Facebook Networks Large dataset of the social structure of Facebook. None. 100 colleges covered Text Network analysis, clustering 2012 [85][86] A. Traud et al.
Dataset for the Machine Comprehension of Text Stories and associated questions for testing comprehension of text. None. 660 Text Natural language processing, machine comprehension 2013 [87][88] M. Richardson et al.
The Penn Treebank Project Naturally occurring text annotated for linguistic structure. Text is parsed into semantic trees. ~ 1M words Text Natural language processing, summarization 1995 [89][90] M. Marcus et al.
DEXTER Dataset Task given is to determine, from features given, which articles are about corporate acquisitions. Features extracted include word stems. Distractor features included. 2600 Text Classification 2008 [91] Reuters
Google Books N-grams N-grams from a very large corpus of books None. 2.2 TB of text Text Classification, clustering, regression 2011 [92][93] Google
Personae Corpus Collected for experiments in Authorship Attribution and Personality Prediction. Consists of 145 Dutch-language essays. In addition to normal texts, syntactically annotated texts are given. 145 Text Classification, regression 2008 [94][95] K. Luyckx et al.
PushShift Archives of social media websites, including Reddit, Twitter, and Hackernews. Text extracted and normalized from WARCs ~100,000,000 posts Json NLP, sentiment, linguistics 2022 [96][97] J. Baumgartner
SEC Filings EDGAR | Company Filings Text extracted. csv NLP
CNAE-9 Dataset Categorization task for free text descriptions of Brazilian companies. Word frequency has been extracted. 1080 Text Classification 2012 [98][99] P. Ciarelli et al.
Sentiment Labeled Sentences Dataset 3000 sentiment labeled sentences. Sentiment of each sentence has been hand labeled as positive or negative. 3000 Text Classification, sentiment analysis 2015 [100][101] D. Kotzias
BlogFeedback Dataset Dataset to predict the number of comments a post will receive based on features of that post. Many features of each post extracted. 60,021 Text Regression 2014 [102][103] K. Buza
PubMed Central PubMed® comprises more than 35 million citations for biomedical literature from MEDLINE, life science journals, and online books. None 35 Million Text NLP
USPTO The United States Patent and Trademark Office Text NLP
PhilPapers Open access collection of philosophy publications Text NLP
Book Corpus A popular large-scale text corpus. None Text NLP 2015 [104] Zhu, Yukun, et al.
Stanford Natural Language Inference (SNLI) Corpus Image captions matched with newly constructed sentences to form entailment, contradiction, or neutral pairs. Entailment class labels, syntactic parsing by the Stanford PCFG parser 570,000 Text Natural language inference/recognizing textual entailment 2015 [105] S. Bowman et al.
DSL Corpus Collection (DSLCC) A multilingual collection of short excerpts of journalistic texts in similar languages and dialects. None 294,000 phrases Text Discriminating between similar languages 2017 [106] Tan, Liling et al.
Urban Dictionary Dataset Corpus of words, votes and definitions User names anonymised 2,580,925 CSV NLP, Machine comprehension 2016 May [107] Anonymous
T-REx Wikipedia abstracts aligned with Wikidata entities Alignment of Wikidata triples with Wikipedia abstracts 11M aligned triples JSON and NIF [4] NLP, Relation Extraction 2018 [108] H. Elsahar et al.
General Language Understanding Evaluation (GLUE) Benchmark of nine tasks Various ~1M sentences and sentence pairs NLU 2018 [109][110][111] Wang et al.
Contract Understanding Atticus Dataset (CUAD) (formerly known as Atticus Open Contract Dataset (AOK)) Dataset of legal contracts with rich expert annotations ~13,000 labels CSV and PDF Natural language processing, QnA 2021 The Atticus Project
Vietnamese Image Captioning Dataset (UIT-ViIC) Vietnamese Image Captioning Dataset 19,250 captions for 3,850 images CSV and PDF Natural language processing, Computer vision 2020 [112] Lam et al.
Vietnamese Names annotated with Genders (UIT-ViNames) Vietnamese Names annotated with Genders 26,850 Vietnamese full names annotated with genders CSV Natural language processing 2020 [113] To et al.
Vietnamese Constructive and Toxic Speech Detection Dataset (UIT-ViCTSD) Vietnamese Constructive and Toxic Speech Detection Dataset 10,000 Vietnamese users' comments on online newspapers on 10 domains CSV Natural Language Processing 2021 [114] Nguyen et al.
PG-19 A set of books extracted from the Project Gutenberg books library Text Natural Language Processing 2019 Jack W et al.
Deepmind Mathematics Mathematical question and answer pairs. Text Natural Language Processing 2018 [115] D Saxton et al.

Sound data

These datasets consist of sounds and sound features used for tasks such as speech recognition and speech synthesis.


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Zero Resource Speech Challenge 2015 Spontaneous speech (English), Read speech (Xitsonga). None, raw WAV files. English: 5h, 12 speakers; Xitsonga: 2h30, 24 speakers WAV (audio only) Unsupervised discovery of speech features/subword units/word units 2015 [116][117] Versteegh et al.
Parkinson Speech Dataset Multiple recordings of people with and without Parkinson's Disease. Voice features extracted, disease scored by physician using unified Parkinson's disease rating scale. 1,040 Text Classification, regression 2013 [118][119] B. E. Sakar et al.
Spoken Arabic Digits Spoken Arabic digits from 44 male and 44 female. Time-series of mel-frequency cepstrum coefficients. 8,800 Text Classification 2010 [120][121] M. Bedda et al.
ISOLET Dataset Spoken letter names. Features extracted from sounds. 7797 Text Classification 1994 [122][123] R. Cole et al.
Japanese Vowels Dataset Nine male speakers uttered two Japanese vowels successively. Applied 12-degree linear prediction analysis to it to obtain a discrete-time series with 12 cepstrum coefficients. 640 Text Classification 1999 [124][125] M. Kudo et al.
Parkinson's Telemonitoring Dataset Multiple recordings of people with and without Parkinson's Disease. Sound features extracted. 5875 Text Classification 2009 [126][127] A. Tsanas et al.
TIMIT Recordings of 630 speakers of eight major dialects of American English, each reading ten phonetically rich sentences. Speech is lexically and phonemically transcribed. 6300 Text Speech recognition, classification. 1986 [128][129] J. Garofolo et al.
Arabic Speech Corpus A single-speaker, Modern Standard Arabic (MSA) speech corpus with phonetic and orthographic transcripts aligned to phoneme level. Speech is orthographically and phonetically transcribed with stress marks. ~1900 Text, WAV Speech Synthesis, Speech Recognition, Corpus Alignment, Speech Therapy, Education. 2016 [130] N. Halabi
Common Voice A public domain database of crowdsourced data across a wide range of dialects. Validation by other users . English: 1,118 hours MP3 with corresponding text files Speech recognition 2017 June (2019 December) [131] Mozilla
LJSpeech A single-speaker corpus of English public-domain audiobook recordings, split into short clips at punctuation marks. Quality check, normalized transcription alongside the original. 13,100 CSV, WAV Speech synthesis 2017 [132] Keith Ito, Linda Johnson
Arabic Speech Commands Dataset Collected from 30 contributors and grouped into 40 keywords. Raw WAV files 12,000 WAV, CSV Speech recognition, keyword spotting 2021 [133] Abdulkader Ghandoura


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Geographic Origin of Music Data Set Audio features of music samples from different locations. Audio features extracted using MARSYAS software. 1,059 Text Geographic classification, clustering 2014 [134][135] F. Zhou et al.
Million Song Dataset Audio features from one million different songs. Audio features extracted. 1M Text Classification, clustering 2011 [136][137] T. Bertin-Mahieux et al.
MUSDB18 Multi-track popular music recordings Raw audio 150 MP4, WAV Source Separation 2017 [138] Z. Rafii et al.
Free Music Archive Audio under Creative Commons from 100k songs (343 days, 1TiB) with a hierarchy of 161 genres, metadata, user data, free-form text. Raw audio and audio features. 106,574 Text, MP3 Classification, recommendation 2017 [139] M. Defferrard et al.
Bach Choral Harmony Dataset Bach chorale chords. Audio features extracted. 5665 Text Classification 2014 [140][141] D. Radicioni et al.

Other sounds

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
UrbanSound Labeled sound recordings of sounds like air conditioners, car horns and children playing. Sorted into folders by class of events as well as metadata in a JSON file and annotations in a CSV file. 1,059 Sound


Classification 2014 [142][143] J. Salamon et al.
AudioSet 10-second sound snippets from YouTube videos, and an ontology of over 500 labels. 128-d PCA'd VGG-ish features every 1 second. 2,084,320 Text (CSV) and TensorFlow Record files Classification 2017 [144] J. Gemmeke et al., Google
Bird Audio Detection challenge Audio from environmental monitoring stations, plus crowdsourced recordings 17,000+ Classification 2016 (2018) [145][146] Queen Mary University and IEEE Signal Processing Society
WSJ0 Hipster Ambient Mixtures Audio from WSJ0 mixed with noise recorded in the San Francisco Bay Area Noise clips matched to WSJ0 clips 28,000 Sound (WAV) Audio source separation 2019 [147] Wichern, G., et al., Whisper and MERL
Clotho 4,981 audio samples of 15 to 30 seconds long, each audio sample having five different captions of eight to 20 words long. 24,905 Sound (WAV) and text (CSV) Automated audio captioning 2020 [148][149] K. Drossos, S. Lipping, and T. Virtanen

Signal data

Datasets containing electric signal information requiring some sort of signal processing for further analysis.


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Witty Worm Dataset Dataset detailing the spread of the Witty worm and the infected computers. Split into a publicly available set and a restricted set containing more sensitive information like IP and UDP headers. 55,909 IP addresses Text Classification 2004 [150][151] Center for Applied Internet Data Analysis
Cuff-Less Blood Pressure Estimation Dataset Cleaned vital signals from human patients which can be used to estimate blood pressure. 125 Hz vital signs have been cleaned. 12,000 Text Classification, regression 2015 [152][153] M. Kachuee et al.
Gas Sensor Array Drift Dataset Measurements from 16 chemical sensors utilized in simulations for drift compensation. Extensive number of features given. 13,910 Text Classification 2012 [154][155] A. Vergara
Servo Dataset Data covering the nonlinear relationships observed in a servo-amplifier circuit. Levels of various components as a function of other components are given. 167 Text Regression 1993 [156][157] K. Ullrich
UJIIndoorLoc-Mag Dataset Indoor localization database to test indoor positioning systems. Data is magnetic field based. Train and test splits given. 40,000 Text Classification, regression, clustering 2015 [158][159] D. Rambla et al.
Sensorless Drive Diagnosis Dataset Electrical signals from motors with defective components. Statistical features extracted. 58,508 Text Classification 2015 [160][161] M. Bator


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Wearable Computing: Classification of Body Postures and Movements (PUC-Rio) People performing five standard actions while wearing motion trackers. None. 165,632 Text Classification 2013 [162][163] Pontifical Catholic University of Rio de Janeiro
Gesture Phase Segmentation Dataset Features extracted from video of people doing various gestures. Features extracted aim at studying gesture phase segmentation. 9900 Text Classification, clustering 2014 [164][165] R. Madeo et a
Vicon Physical Action Data Set Dataset 10 normal and 10 aggressive physical actions that measure the human activity tracked by a 3D tracker. Many parameters recorded by 3D tracker. 3000 Text Classification 2011 [166][167] T. Theodoridis
Daily and Sports Activities Dataset Motor sensor data for 19 daily and sports activities. Many sensors given, no preprocessing done on signals. 9120 Text Classification 2013 [168][169] B. Barshan et al.
Human Activity Recognition Using Smartphones Dataset Gyroscope and accelerometer data from people wearing smartphones and performing normal actions. Actions performed are labeled, all signals preprocessed for noise. 10,299 Text Classification 2012 [170][171] J. Reyes-Ortiz et al.
Australian Sign Language Signs Australian sign language signs captured by motion-tracking gloves. None. 2565 Text Classification 2002 [172][173] M. Kadous
Weight Lifting Exercises monitored with Inertial Measurement Units Five variations of the biceps curl exercise monitored with IMUs. Some statistics calculated from raw data. 39,242 Text Classification 2013 [174][175] W. Ugulino et al.
sEMG for Basic Hand movements Dataset Two databases of surface electromyographic signals of 6 hand movements. None. 3000 Text Classification 2014 [176][177] C. Sapsanis et al.
REALDISP Activity Recognition Dataset Evaluate techniques dealing with the effects of sensor displacement in wearable activity recognition. None. 1419 Text Classification 2014 [177][178] O. Banos et al.
Heterogeneity Activity Recognition Dataset Data from multiple different smart devices for humans performing various activities. None. 43,930,257 Text Classification, clustering 2015 [179][180] A. Stisen et al.
Indoor User Movement Prediction from RSS Data Temporal wireless network data that can be used to track the movement of people in an office. None. 13,197 Text Classification 2016 [181][182] D. Bacciu
PAMAP2 Physical Activity Monitoring Dataset 18 different types of physical activities performed by 9 subjects wearing 3 IMUs. None. 3,850,505 Text Classification 2012 [183] A. Reiss
OPPORTUNITY Activity Recognition Dataset Human Activity Recognition from wearable, object, and ambient sensors is a dataset devised to benchmark human activity recognition algorithms. None. 2551 Text Classification 2012 [184][185] D. Roggen et al.
Real World Activity Recognition Dataset Human Activity Recognition from wearable devices. Distinguishes between seven on-body device positions and comprises six different kinds of sensors. None. 3,150,000 (per sensor) Text Classification 2016 [186] T. Sztyler et al.
Toronto Rehab Stroke Pose Dataset 3D human pose estimates (Kinect) of stroke patients and healthy participants performing a set of tasks using a stroke rehabilitation robot. None. 10 healthy person and 9 stroke survivors (3500–6000 frames per person) CSV Classification 2017 [187][188][189] E. Dolatabadi et al.
Corpus of Social Touch (CoST) 7805 gesture captures of 14 different social touch gestures performed by 31 subjects. The gestures were performed in three variations: gentle, normal and rough, on a pressure sensor grid wrapped around a mannequin arm. Touch gestures performed are segmented and labeled. 7805 gesture captures CSV Classification 2016 [190][191] M. Jung et al.

Other signals

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Wine Dataset Chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. 13 properties of each wine are given 178 Text Classification, regression 1991 [192][193] M. Forina et al.
Combined Cycle Power Plant Data Set Data from various sensors within a power plant running for 6 years. None 9568 Text Regression 2014 [194][195] P. Tufekci et al.

Physical data

Datasets from physical systems.

High-energy physics

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
HIGGS Dataset Monte Carlo simulations of particle accelerator collisions. 28 features of each collision are given. 11M Text Classification 2014 [196][197][198] D. Whiteson
HEPMASS Dataset Monte Carlo simulations of particle accelerator collisions. Goal is to separate the signal from noise. 28 features of each collision are given. 10,500,000 Text Classification 2016 [197][198][199] D. Whiteson


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Yacht Hydrodynamics Dataset Yacht performance based on dimensions. Six features are given for each yacht. 308 Text Regression 2013 [200][201] R. Lopez
Robot Execution Failures Dataset 5 data sets that center around robotic failure to execute common tasks. Integer valued features such as torque and other sensor measurements. 463 Text Classification 1999 [202] L. Seabra et al.
Pittsburgh Bridges Dataset Design description is given in terms of several properties of various bridges. Various bridge features are given. 108 Text Classification 1990 [203][204] Y. Reich et al.
Automobile Dataset Data about automobiles, their insurance risk, and their normalized losses. Car features extracted. 205 Text Regression 1987 [205][206] J. Schimmer et al.
Auto MPG Dataset MPG data for cars. Eight features of each car given. 398 Text Regression 1993 [207] Carnegie Mellon University
Energy Efficiency Dataset Heating and cooling requirements given as a function of building parameters. Building parameters given. 768 Text Classification, regression 2012 [208][209] A. Xifara et al.
Airfoil Self-Noise Dataset A series of aerodynamic and acoustic tests of two and three-dimensional airfoil blade sections. Data about frequency, angle of attack, etc., are given. 1503 Text Regression 2014 [210] R. Lopez
Challenger USA Space Shuttle O-Ring Dataset Attempt to predict O-ring problems given past Challenger data. Several features of each flight, such as launch temperature, are given. 23 Text Regression 1993 [211][212] D. Draper et al.
Statlog (Shuttle) Dataset NASA space shuttle datasets. Nine features given. 58,000 Text Classification 2002 [213] NASA


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Volcanoes on Venus – JARtool experiment Dataset Venus images returned by the Magellan spacecraft. Images are labeled by humans. not given Images Classification 1991 [214][215] M. Burl
MAGIC Gamma Telescope Dataset Monte Carlo generated high-energy gamma particle events. Numerous features extracted from the simulations. 19,020 Text Classification 2007 [215][216] R. Bock
Solar Flare Dataset Measurements of the number of certain types of solar flare events occurring in a 24-hour period. Many solar flare-specific features are given. 1389 Text Regression, classification 1989 [217] G. Bradshaw
CAMELS Multifield Dataset 2D maps and 3D grids from thousands of N-body and state-of-the-art hydrodynamic simulations spanning a broad range in the value of the cosmological and astrophysical parameters Each map and grid has 6 cosmological and astrophysical parameters associated to it 405,000 2D maps and 405,000 3D grids 2D maps and 3D grids Regression 2021 [218] Francisco Villaescusa-Navarro et al.

Earth science

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Volcanoes of the World Volcanic eruption data for all known volcanic events on earth. Details such as region, subregion, tectonic setting, dominant rock type are given. 1535 Text Regression, classification 2013 [219] E. Venzke et al.
Seismic-bumps Dataset Seismic activities from a coal mine. Seismic activity was classified as hazardous or not. 2584 Text Classification 2013 [220][221] M. Sikora et al.
CAMELS-US Catchment hydrology dataset with hydrometeorological timeseries and various attributes see Reference 671 CSV, Text, Shapefile Regression 2017 [222][223] N. Addor et al. / A. Newman et al.
CAMELS-Chile Catchment hydrology dataset with hydrometeorological timeseries and various attributes see Reference 516 CSV, Text, Shapefile Regression 2018 [224] C. Alvarez-Garreton et al.
CAMELS-Brazil Catchment hydrology dataset with hydrometeorological timeseries and various attributes see Reference 897 CSV, Text, Shapefile Regression 2020 [225] V. Chagas et al.
CAMELS-GB Catchment hydrology dataset with hydrometeorological timeseries and various attributes see Reference 671 CSV, Text, Shapefile Regression 2020 [226] G. Coxon et al.
CAMELS-Australia Catchment hydrology dataset with hydrometeorological timeseries and various attributes see Reference 222 CSV, Text, Shapefile Regression 2021 [227] K. Fowler et al.
LamaH-CE Catchment hydrology dataset with hydrometeorological timeseries and various attributes see Reference 859 CSV, Text, Shapefile Regression 2021 [228] C. Klingler et al.

Other physical

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Concrete Compressive Strength Dataset Dataset of concrete properties and compressive strength. Nine features are given for each sample. 1030 Text Regression 2007 [229][230] I. Yeh
Concrete Slump Test Dataset Concrete slump flow given in terms of properties. Features of concrete given such as fly ash, water, etc. 103 Text Regression 2009 [231][232] I. Yeh
Musk Dataset Predict if a molecule, given the features, will be a musk or a non-musk. 168 features given for each molecule. 6598 Text Classification 1994 [233] Arris Pharmaceutical Corp.
Steel Plates Faults Dataset Steel plates of 7 different types. 27 features given for each sample. 1941 Text Classification 2010 [234] Semeion Research Center

Biological data

Datasets from biological systems.


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Age Dataset A structured general-purpose dataset on life, work, and death of 1.22 million distinguished people. Public domain. A five-step method to infer birth and death years, gender, and occupation from community-submitted data to all language versions of the Wikipedia project. 1,223,009 Text Regression, Classification 2022 Paper[235]


Amoradnejad et al.
Synthetic Fundus Dataset[237] Photorealistic retinal images and vessel segmentations. Public domain. 2500 images with 1500*1152 pixels useful for segmentation and classification of veins and arteries on a single background. 2500 Images Classification, Segmentation 2020 [238] C. Valenti et al.
EEG Database Study to examine EEG correlates of genetic predisposition to alcoholism. Measurements from 64 electrodes placed on the scalp sampled at 256 Hz (3.9 ms epoch) for 1 second. 122 Text Classification 1999 [239] H. Begleiter
P300 Interface Dataset Data from nine subjects collected using P300-based brain-computer interface for disabled subjects. Split into four sessions for each subject. MATLAB code given. 1,224 Text Classification 2008 [240][241] U. Hoffman et al.
Heart Disease Data Set Attributed of patients with and without heart disease. 75 attributes given for each patient with some missing values. 303 Text Classification 1988 [242][243] A. Janosi et al.
Breast Cancer Wisconsin (Diagnostic) Dataset Dataset of features of breast masses. Diagnoses by physician is given. 10 features for each sample are given. 569 Text Classification 1995 [244][245] W. Wolberg et al.
National Survey on Drug Use and Health Large scale survey on health and drug use in the United States. None. 55,268 Text Classification, regression 2012 [246] United States Department of Health and Human Services
Lung Cancer Dataset Lung cancer dataset without attribute definitions 56 features are given for each case 32 Text Classification 1992 [247][248] Z. Hong et al.
Arrhythmia Dataset Data for a group of patients, of which some have cardiac arrhythmia. 276 features for each instance. 452 Text Classification 1998 [249][250] H. Altay et al.
Diabetes 130-US hospitals for years 1999–2008 Dataset 9 years of readmission data across 130 US hospitals for patients with diabetes. Many features of each readmission are given. 100,000 Text Classification, clustering 2014 [251][252] J. Clore et al.
Diabetic Retinopathy Debrecen Dataset Features extracted from images of eyes with and without diabetic retinopathy. Features extracted and conditions diagnosed. 1151 Text Classification 2014 [253][254] B. Antal et al.
Diabetic Retinopathy Messidor Dataset Methods to evaluate segmentation and indexing techniques in the field of retinal ophthalmology (MESSIDOR) Features retinopathy grade and risk of macular edema 1200 Images, Text Classification, Segmentation 2008 [255][256] Messidor Project
Liver Disorders Dataset Data for people with liver disorders. Seven biological features given for each patient. 345 Text Classification 1990 [257][258] Bupa Medical Research Ltd.
Thyroid Disease Dataset 10 databases of thyroid disease patient data. None. 7200 Text Classification 1987 [259][260] R. Quinlan
Mesothelioma Dataset Mesothelioma patient data. Large number of features, including asbestos exposure, are given. 324 Text Classification 2016 [261][262] A. Tanrikulu et al.
Parkinson's Vision-Based Pose Estimation Dataset 2D human pose estimates of Parkinson's patients performing a variety of tasks. Camera shake has been removed from trajectories. 134 Text Classification, regression 2017 [263][264][265] M. Li et al.
KEGG Metabolic Reaction Network (Undirected) Dataset Network of metabolic pathways. A reaction network and a relation network are given. Detailed features for each network node and pathway are given. 65,554 Text Classification, clustering, regression 2011 [266] M. Naeem et al.
Modified Human Sperm Morphology Analysis Dataset (MHSMA) Human sperm images from 235 patients with male factor infertility, labeled for normal or abnormal sperm acrosome, head, vacuole, and tail. Cropped around single sperm head. Magnification normalized. Training, validation, and test set splits created. 1,540 .npy files Classification 2019 [267][268] S. Javadi and S.A. Mirroshandel


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Abalone Dataset Physical measurements of Abalone. Weather patterns and location are also given. None. 4177 Text Regression 1995 [269] Marine Research Laboratories – Taroona
Zoo Dataset Artificial dataset covering 7 classes of animals. Animals are classed into 7 categories and features are given for each. 101 Text Classification 1990 [270] R. Forsyth
Demospongiae Dataset Data about marine sponges. 503 sponges in the Demosponge class are described by various features. 503 Text Classification 2010 [271] E. Armengol et al.
Farm animals data PLF data inventory (cows, pigs; location, acceleration, etc.). Labeled datasets. List is constantly updated Text Classification 2020 [272] V. Bloch
Splice-junction Gene Sequences Dataset Primate splice-junction gene sequences (DNA) with associated imperfect domain theory. None. 3190 Text Classification 1992 [248] G. Towell et al.
Mice Protein Expression Dataset Expression levels of 77 proteins measured in the cerebral cortex of mice. None. 1080 Text Classification, Clustering 2015 [273][274] C. Higuera et al.


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
UCI Mushroom Dataset Mushroom attributes and classification. Many properties of each mushroom are given. 8124 Text Classification 1987 [275] J. Schlimmer
Secondary Mushroom Dataset Mushroom attributes and classification Simulated data from larger and more realistic primary mushroom entries. Fully reproducible. 61069 Text Classification 2020 [276][277] D. Wagner et al.


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Forest Fires Dataset Forest fires and their properties. 13 features of each fire are extracted. 517 Text Regression 2008 [278][279] P. Cortez et al.
Iris Dataset Three types of iris plants are described by 4 different attributes. None. 150 Text Classification 1936 [280][281] R. Fisher
Plant Species Leaves Dataset Sixteen samples of leaf each of one-hundred plant species. Shape descriptor, fine-scale margin, and texture histograms are given. 1600 Text Classification 2012 [282][283] J. Cope et al.
Soybean Dataset Database of diseased soybean plants. 35 features for each plant are given. Plants are classified into 19 categories. 307 Text Classification 1988 [284] R. Michalski et al.
Seeds Dataset Measurements of geometrical properties of kernels belonging to three different varieties of wheat. None. 210 Text Classification, clustering 2012 [285][286] Charytanowicz et al.
Covertype Dataset Data for predicting forest cover type strictly from cartographic variables. Many geographical features given. 581,012 Text Classification 1998 [287][288] J. Blackard et al.
Abscisic Acid Signaling Network Dataset Data for a plant signaling network. Goal is to determine set of rules that governs the network. None. 300 Text Causal-discovery 2008 [289] J. Jenkens et al.
Folio Dataset 20 photos of leaves for each of 32 species. None. 637 Images, text Classification, clustering 2015 [290][291] T. Munisami et al.
Oxford Flower Dataset 17 category dataset of flowers. Train/test splits, labeled images, 1360 Images, text Classification 2006 [292][293] M-E Nilsback et al.
Plant Seedlings Dataset 12 category dataset of plant seedlings. Labelled images, segmented images, 5544 Images Classification, detection 2017 [294] Giselsson et al.
Fruits 360 dataset Database with images of 120 fruits and vegetables. 100x100 pixels, White background. 82213 Images (jpg) Classification 2017–2019 [295][296] Mihai Oltean, Horea Muresan


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Ecoli Dataset Protein localization sites. Various features of the protein localizations sites are given. 336 Text Classification 1996 [297][298] K. Nakai et al.
MicroMass Dataset Identification of microorganisms from mass-spectrometry data. Various mass spectrometer features. 931 Text Classification 2013 [299][300] P. Mahe et al.
Yeast Dataset Predictions of Cellular localization sites of proteins. Eight features given per instance. 1484 Text Classification 1996 [301][302] K. Nakai et al.

Drug discovery

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Tox21 Dataset Prediction of outcome of biological assays. Chemical descriptors of molecules are given. 12707 Text Classification 2016 [303] A. Mayr et al.

Anomaly data

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Numenta Anomaly Benchmark (NAB) Data are ordered, timestamped, single-valued metrics. All data files contain anomalies, unless otherwise noted. None 50+ files CSV Anomaly detection 2016 (continually updated) [304] Numenta
Skoltech Anomaly Benchmark (SKAB) Each file represents a single experiment and contains a single anomaly. The dataset represents a multivariate time series collected from the sensors installed on the testbed. There are two markups for Outlier detection (point anomalies) and Changepoint detection (collective anomalies) problems 30+ files (v0.9) CSV Anomaly detection 2020 (continually updated)

[305] [306]

Iurii D. Katser and Vyacheslav O. Kozitsin
On the Evaluation of Unsupervised Outlier Detection: Measures, Datasets, and an Empirical Study Most data files are adapted from UCI Machine Learning Repository data, some are collected from the literature. treated for missing values, numerical attributes only, different percentages of anomalies, labels 1000+ files ARFF Anomaly detection 2016 (possibly updated with new datasets and/or results)


Campos et al.

Question answering data

This section includes datasets that deals with structured data.

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
DBpedia Neural Question Answering (DBNQA) Dataset A large collection of Question to SPARQL specially design for Open Domain Neural Question Answering over DBpedia Knowledgebase. This dataset contains a large collection of Open Neural SPARQL Templates and instances for training Neural SPARQL Machines; it was pre-processed by semi-automatic annotation tools as well as by three SPARQL experts. 894,499 Question-query pairs Question Answering 2018 [308][309] Hartmann, Soru, and Marx et al.
Vietnamese Question Answering Dataset (UIT-ViQuAD) A large collection of Vietnamese questions for evaluating MRC models. This dataset comprises over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia. 23,074 Question-answer pairs Question Answering 2020 [310] Nguyen et al.
Vietnamese Multiple-Choice Machine Reading Comprehension Corpus(ViMMRC) A collection of Vietnamese multiple-choice questions for evaluating MRC models. This corpus includes 2,783 Vietnamese multiple-choice questions. 2,783 Question-answer pairs Question Answering/Machine Reading Comprehension 2020 [311] Nguyen et al.
Open-Domain Question Answering Goes Conversational via Question Rewriting An end-to-end open-domain question answering. This dataset includes 14,000 conversations with 81,000 question-answer pairs. Context, Question, Rewrite, Answer, Answer_URL, Conversation_no, Turn_no, Conversation_source

Further details are provided in the project's GitHub repository and respective Hugging Face dataset card.

Question Answering 2021 [312] Anantha and Vakulenko et al.
UnifiedQA Question-answer data Processed dataset Question Answering 2020 [313] Khashabi et al.

Dialog or instruction prompted data

This section includes datasets that ...

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Taskmaster "The Taskmaster corpus consists of THREE datasets, Taskmaster-1 (TM-1), Taskmaster-2 (TM-2), and Taskmaster-3 (TM-3), comprising over 55,000 spoken and written task-oriented dialogs in over a dozen domains."[314] Taskmaster-1: goal-oriented conversational dataset. It includes 13,215 task-based dialogs comprising six domains.

Taskmaster-2: 17,289 dialogs in the seven domains (restaurants, food ordering, movies, hotels, flights, music and sports).

Taskmaster-3: 23,757 movie ticketing dialogs.

Taskmaster-1 and Taskmaster-2: conversation id, utterances, Instruction id

Taskmaster-3: conversation id, utterances, vertical, scenario, instructions.

For further details check the project's GitHub repository or the Hugging Face dataset cards (taskmaster-1, taskmaster-2, taskmaster-3).

Dialog/Instruction prompted 2019 [315] Byrne and Krishnamoorthi et al.
DrRepair A labeled dataset for program repair. Pre-processed data Check format details in the project's worksheet. Dialog/Instruction prompted 2020 [316] Michihiro et al.
Natural Instructions v2 Large dataset that covers a wider range of reasoning abilities Each task consists of input/output, and a task definition.

Additionally, each ask contains a task definition.

Further information is provided in the GitHub repository of the project and the Hugging Face data card.

Input/Output and task definition 2022 [317] Wang et al.
LAMBADA " LAMBADA is a collection of narrative passages sharing the characteristic that human subjects are able to guess their last word if they are exposed to the whole passage, but not if they only see the last sentence preceding the target word."[318] Information about this dataset's format is available in the HuggingFace dataset card and the project's website.

The dataset can be downloaded here, and the rejected data here.

2016 [319] Paperno et al.
FLAN A re-preprocessed version of the FLAN dataset with updates since the original FLAN dataset was released is available in Hugging Face:
  1. test data
  2. train data
  3. validation data

The scripts to process the data are available in the GitHub repo mentioned on the paper:

Another FLAN GitHub repo was created as well. This is the one associated with the dataset card in Hugging Face.

2021 [320] Wei et al.


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
MITRE ATTACK The ATT&CK is a globally-accessible knowledge base of adversary tactics and techniques. Data can be downloaded from these two GitHub repositories: version 2.1 and version 2.0 [321] MITRE ATTACK
CAPEC Common Attack Pattern Enumeration and Classification Data can be downloaded from CAPEC's website:

Mechanisms of Attack Domains of Attack

[322] CAPEC
CVE CVE is a list of publicly disclosed cybersecurity vulnerabilities that is free to search, use, and incorporate into products and services. Data can be downloaded from: Allitems [323] CVE
CWE Common Weakness Enumeration data. Data can be downloaded from:

Software Development Hardware Design[permanent dead link]Research Concepts

[324] CWE
MalwareTextDB Annotated database of malware texts. The GitHub repository of the project contains the data to download. [325] Kiat et al.
USENIX Security Symposium proceedings Collection of security proceedings from USENIX Security Symposium – technical sessions from 1995 to 2022. This data is not pre-processed. 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,

2009, 2010 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022.

[326] USENIX Security Symposium
APTNotes Collection of public documents, whitepapers and articles about APT campaigns. All the documents arepublicly available data. This data is not pre-processed. The GitHub repository of the project contains a file with links to the data stored in box.

Data files can also be downloaded here.

[327] APT Notes
arXiv Cryptography and Security papers Collection of articles about cybersecurity This data is not pre-processed. All articles available here. [328] arXiv
Security eBooks for free Small collection of security eBooks, and security presentations publicly available. This data is not pre-processed. [329][330][331][332][333][334][335][336][337][338][339][340]
National Cyber Security strategy repository Repository of worldwide strategy documents about cybersecurity. This data is not pre-processed. [341]
Cyber Security Natural Language Processing Data about cybersecurity strategies from more than 75 countries. Tokenization, meaningless-frequent words removal. [342] Yanlin Chen, Yunjian Wei, Yifan Yu, Wen Xue, Xianya Qin
APT Reports collection Sample of APT reports, malware, technology, and intelligence collection Raw and tokenize data available. All data is available in this GitHub repository. [citation needed] blackorbird
Offensive Language Identification Dataset (OLID) Data available in the project's website.

Data is also available here.

[343] Zampieri et al.
Cyber reports from the National Cyber Security Centre This data is not pre-processed. Threat reports, reports and advisory, news, blog-posts, speeches.

Alternate list of reports.

APT reports by Kaspersky This data is not pre-processed. [345]
The cyberwire This data is not pre-processed. Newsletters, podcasts, and stories. [346]
Databreaches news This data is not pre-processed. News, list of news from Aug 2022 to Feb 2023 [347]
Cybernews This data is not pre-processed. News, curated list of news [348]
Bleepingcomputer This data is not pre-processed. News [349]
Therecord This data is not pre-processed. Cybercrime news [350]
Hackread This data is not pre-processed. Hacking news [351]
Securelist This data is not pre-processed. APT reports, archive, DDOS reports, incidents, Kaspersky security bulletin, industrial threats, malware-reports, opinions, publications, research, and SAS. [352]
Stucco project The Stucco project collects data not typically integrated into security systems. This data is not pre-processed Project's website with data informationReviewed source with links to data sources [353]
Farsightsecurity Website with technical information, reports, and more about security topics. This data is not pre-processed Technical information, research, reports. [354]
Schneier Website with academic papers about security topics. This data is not pre-processed Papers per category, papers archive by date. [355]
Trendmicro Website with research, news, and perspectives bout security topics. This data is not pre-processed Reviwed list of Trendmicro research, news, and pespectives. [356]
The Hacker News News about cybersecurity topics. This data is not pre-processed data breaches, cyberattacks, vulnerabilities, malware news. [357]
Krebsonsecurity Security news and investigation This data is not pre-processed curated list of news [358]
Mitre Defend Matrix of Defend artifacts json files [359]
Mitre Atlas Mitre Atlas is a knowledge base of adversary tactics, techniques, and case studies for machine learning (ML) systems based on real-world observations. This data is not pre-processed [360]
Mitre Engage MITRE Engage is a framework for planning and discussing adversary engagement operations that empowers you to engage your adversaries and achieve your cybersecurity goals. This data is not pre-processed [361]
Hacking Tutorials This data is not pre-processed [362]

Climate and sustainability

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
TCFD reports Database of company reports that include TCFD-related disclosures. This data is not pre-processed Direct link to reportsCurated list of reports [363] TCFD Knowledge Hub
Corporate Social Responsibility Reports A listing of responsibility reports on the internet. This data is not pre-processed Curated list of reports [364] ResponsibilityReports
The Intergovernmental Panel on Climate Change (IPCC) A collection of comprehensive assessment reports about knowledge on climate change, its causes, potential impacts and response options This data is not pre-processed ReportsCurated list of reports [365] IPCC
Alliance for Research on Corporate Sustainability This data is not pre-processed Curated list of blog posts [366] ARCS
ESG corpus: Knowledge Hub of the Accounting for Sustainability This data is not pre-processed Guides, case studies, blogs, and reports & surveys. [367] Mehra et al.
CLIMATE-FEVER A dataset adopting the FEVER methodology that consists of 1,535 real-world claims regarding climate-change collected on the internet. Each claim is accompanied by five manually annotated evidence sentences retrieved from the English Wikipedia that support, refute or do not give enough information to validate the claim totalling in 7,675 claim-evidence pairs.[368] Dataset HF card, and project's GitHub repository. [369] Diggelmann et al.
Climate News dataset A dataset for NLP and climate change media researchers The dataset is made up of a number of data artifacts (JSON, JSONL & CSV text files & SQLite database) Climate news DB, Project's GitHub repository [370] ADGEfficiency
Climatext Climatext is a dataset for sentence-based climate change topic detection. HF dataset [371] University of Zurich
GreenBiz Collection of articles and news about climate and sustainability This data is not pre-processed Curated list of climate articlesCurated list of sustainability articles [372]
Top research pre-prints in climate and sustainability List of pre-prints from researchers in the reuters hot list This data is not pre-processed Curated list of pre-prints [373] Maurice Tamman
ARCS This data is not pre-processed Curated list of corporate sustainability blogs [374]
GreenBiz Website with articles about climate and sustainability This data is not pre-processed [375] GreenBiz
CSRWIRE This data is not pre-processed Curated list of articles [376] CSRWIRE
CDP Articles about climate, water, and forests This data is not pre-processed [377] CDP

Code data

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
The Stack A 3.1 TB dataset consisting of permissively licensed source code in 30 programming languages. Filtered through license detection and deduplication. 6 TB, 51.76B files (prior to deduplication); 3 TB, 5.28B files (after). 358 programming languages. Parquet Language modeling, autocompletion, program synthesis. 2022 [378][379] D. Kocetkov, R. Li, L. Ben Allal, L. von Werra, H. de Vries
GitHub repositories This data is not pre-processed Curated lis of repositories from GitHub: 61 62 63 64 65 66 67 68 69 70 71 , 72, 73, 74, 75, 76, 77 101
IBM Public GitHub repositories This data is not pre-processed Curated list of repositories from GitHub
RedHat Public GitHub repositories This data is not pre-processed Curated list of repositories from GitHub
StackExchange Public files This data is not pre-processed Curated list of files from
Gitlab Public repositories This data is not pre-processed Curated list of repositories from Gitlab: 1 2
Ansible Collections public repositories This data is not pre-processed Curated list of repositories from GitHub.
CodeParrot GitHub Code Dataset This data is not pre-processed Curated list of repositories from Hugging Face: 1 2 3 4 5 6 7 8 9 10
OKD The Community Distribution of Kubernetes that powers Red Hat OpenShift This data is not pre-processed List of GitHub repositories of the project
OpenShift The developer and operations friendly Kubernetes distro List of GitHub repositories of the project
Kubernetes This data is not pre-processed List of GitHub repositories of the project
Red Hat Developer GitHub home of the Red Hat Developer program This data is not pre-processed List of GitHub repositories of the project
Red Hat


This data is not pre-processed List of GitHub repositories of the project
Kubernetes SIGs This data is not pre-processed List of GitHub repositories of the project
Konveyor This data is not pre-processed List of GitHub repositories of the project
RedHat Marketplace This data is not pre-processed List of GitHub repositories of the project
Redhat blog This data is not pre-processed [380]
Kubernetes io This data is not pre-processed [381]
Docs Openshift This data is not pre-processed [382]
cncf io This data is not pre-processed [383]
Kubernetes presentations List of publicly available Kubernetes presentations This data is not pre-processed data link
Red Hat Open Innovation Labs This data is not pre-processed List of GitHub repositories of the project
Red Hat Demos This data is not pre-processed List of GitHub repositories of the project
Red Hat OpenShift Online This data is not pre-processed List of GitHub repositories of the project
Software Collections This data is not pre-processed List of GitHub repositories of the project
Red Hat Insights This data is not pre-processed List of GitHub repositories of the project
Red Hat Government This data is not pre-processed List of GitHub repositories of the project
Red Hat Consulting This data is not pre-processed List of GitHub repositories of the project
Red Hat Communities of Practice This data is not pre-processed List of GitHub repositories of the project
Red Hat Partner Tech This data is not pre-processed List of GitHub repositories of the project
Red Hat Documentation This data is not pre-processed List of GitHub repositories of the project
IBM This data is not pre-processed List of GitHub repositories of the project
IBM Cloud This data is not pre-processed List of GitHub repositories of the project
Build Lab Team This data is not pre-processed List of GitHub repositories of the project
Terraform IBM Modules This data is not pre-processed List of GitHub repositories of the project
Cloud Schematics This data is not pre-processed List of GitHub repositories of the project
OCP Power Demos This data is not pre-processed List of GitHub repositories of the project
IBM App Modernization  This data is not pre-processed List of GitHub repositories of the project
Kubernetes OperatorHub  This data is not pre-processed List of GitHub repositories of the project
Cloud Native Computing Foundation (CNCF)  This data is not pre-processed List of GitHub repositories of the project
Operator Framework This data is not pre-processed List of GitHub repositories of the project [384]
GitHub repositories referenced in This data is not pre-processed List of GitHub repositories in
Red Hat Communities of Practice This data is not pre-processed List of GitHub repositories of the project
Red Hat partner This data is not pre-processed List of GitHub repositories of the project
IBM Repositories This data is not pre-processed List of GitHub repositories for the project
Build Lab Team This data is not pre-processed List of GitHub repositories for the project
Operator Framework This data is not pre-processed List of GitHub repositories for the project
GitHub repositories This data is not pre-processed List of GitHub repositories for the project
Red Hat This data is not pre-processed List of GitHub repositories of the project
Kubernetes Patterns This data is not pre-processed List of GitHub repositories of the project
Kubernetes Deployment & Security Patterns This data is not pre-processed List of GitHub repositories of the project
Kubernetes for Full-Stack Developers This data is not pre-processed List of GitHub repositories of the project
Load Balancer Cloudwatch Metrics This data is not pre-processed GitHub repository of the project
Dynatrace This data is not pre-processed [5]
AIOps Challenge 2020 Data This data is not pre-processed GitHub repository of the project
Loghub This data is not pre-processed List of repositories
HTML Pages This data is not pre-processed List of HTML pages
Opensift ebooks This data is not pre-processed [385]
Kubernetes ebooks This data is not pre-processed Kubernetes Patterns, Kubernetes Deployment, Kubernetes for Full-Stack Developers
Kubernetes for Full-Stack Developers This data is not pre-processed Kubernetes for Full-Stack Developers
List of public and licensed Github repositories This data is not pre-processed List of repositories

Multivariate data


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Dow Jones Index Weekly data of stocks from the first and second quarters of 2011. Calculated values included such as percentage change and a lags. 750 Comma separated values Classification, regression, Time series 2014 [386][387] M. Brown et al.
Statlog (Australian Credit Approval) Credit card applications either accepted or rejected and attributes about the application. Attribute names are removed as well as identifying information. Factors have been relabeled. 690 Comma separated values Classification 1987 [388][389] R. Quinlan
eBay auction data Auction data from various objects over various length auctions Contains all bids, bidderID, bid times, and opening prices. ~ 550 Text Regression, classification 2012 [390][391] G. Shmueli et al.
Statlog (German Credit Data) Binary credit classification into "good" or "bad" with many features Various financial features of each person are given. 690 Text Classification 1994 [392] H. Hofmann
Bank Marketing Dataset Data from a large marketing campaign carried out by a large bank . Many attributes of the clients contacted are given. If the client subscribed to the bank is also given. 45,211 Text Classification 2012 [393][394] S. Moro et al.
Istanbul Stock Exchange Dataset Several stock indexes tracked for almost two years. None. 536 Text Classification, regression 2013 [395][396] O. Akbilgic
Default of Credit Card Clients Credit default data for Taiwanese creditors. Various features about each account are given. 30,000 Text Classification 2016 [397][398] I. Yeh
StockNet Stock movement prediction from tweets and historical stock prices None Text NLP 2018 [399] Yumo Xu and Shay B. Cohen


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Cloud DataSet Data about 1024 different clouds. Image features extracted. 1024 Text Classification, clustering 1989 [400] P. Collard
El Nino Dataset Oceanographic and surface meteorological readings taken from a series of buoys positioned throughout the equatorial Pacific. 12 weather attributes are measured at each buoy. 178080 Text Regression 1999 [401] Pacific Marine Environmental Laboratory
Greenhouse Gas Observing Network Dataset Time-series of greenhouse gas concentrations at 2921 grid cells in California created using simulations of the weather. None. 2921 Text Regression 2015 [402] D. Lucas
Atmospheric CO2 from Continuous Air Samples at Mauna Loa Observatory Continuous air samples in Hawaii, USA. 44 years of records. None. 44 years Text Regression 2001 [403] Mauna Loa Observatory
Ionosphere Dataset Radar data from the ionosphere. Task is to classify into good and bad radar returns. Many radar features given. 351 Text Classification 1989 [260][404] Johns Hopkins University
Ozone Level Detection Dataset Two ground ozone level datasets. Many features given, including weather conditions at time of measurement. 2536 Text Classification 2008 [405][406] K. Zhang et al.


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Adult Dataset Census data from 1994 containing demographic features of adults and their income. Cleaned and anonymized. 48,842 Comma separated values Classification 1996 [407] United States Census Bureau
Census-Income (KDD) Weighted census data from the 1994 and 1995 Current Population Surveys. Split into training and test sets. 299,285 Comma separated values Classification 2000 [408][409] United States Census Bureau
IPUMS Census Database Census data from the Los Angeles and Long Beach areas. None 256,932 Text Classification, regression 1999 [410] IPUMS
US Census Data 1990 Partial data from 1990 US census. Results randomized and useful attributes selected. 2,458,285 Text Classification, regression 1990 [411] United States Census Bureau


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Bike Sharing Dataset Hourly and daily count of rental bikes in a large city. Many features, including weather, length of trip, etc., are given. 17,389 Text Regression 2013 [412][413] H. Fanaee-T
New York City Taxi Trip Data Trip data for yellow and green taxis in New York City. Gives pick up and drop off locations, fares, and other details of trips. 6 years Text Classification, clustering 2015 [414] New York City Taxi and Limousine Commission
Taxi Service Trajectory ECML PKDD Trajectories of all taxis in a large city. Many features given, including start and stop points. 1,710,671 Text Clustering, causal-discovery 2015 [415][416] M. Ferreira et al.
METR-LA Speed from loop detectors in the highway of Los Angeles County. Average speed in 5 minutes timesteps. 7,094,304 from 207 sensors and 34,272 timesteps Comma separated values Regression, Forecasting 2014 [417] Jagadish et al.
PeMS Speed, flow, occupancy and other metrics from loop detectors and other sensors in the freeway of the State of California, U.S.A.. Metric usually aggregated via Average into 5 minutes timesteps. 39,000 individual detectors, each containing years of timeseries Comma separated values Regression, Forecasting, Nowcasting, Interpolation (updated realtime) [418] California Department of Transportation


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Webpages from Common Crawl 2012 Large collection of webpages and how they are connected via hyperlinks None. 3.5B Text clustering, classification 2013 [419] V. Granville
Internet Advertisements Dataset Dataset for predicting if a given image is an advertisement or not. Features encode geometry of ads and phrases occurring in the URL. 3279 Text Classification 1998 [420][421] N. Kushmerick
Internet Usage Dataset General demographics of internet users. None. 10,104 Text Classification, clustering 1999 [422] D. Cook
URL Dataset 120 days of URL data from a large conference. Many features of each URL are given. 2,396,130 Text Classification 2009 [423][424] J. Ma
Phishing Websites Dataset Dataset of phishing websites. Many features of each site are given. 2456 Text Classification 2015 [425] R. Mustafa et al.
Online Retail Dataset Online transactions for a UK online retailer. Details of each transaction given. 541,909 Text Classification, clustering 2015 [426] D. Chen
Freebase Simple Topic Dump Freebase is an online effort to structure all human knowledge. Topics from Freebase have been extracted. large Text Classification, clustering 2011 [427][428] Freebase
Farm Ads Dataset The text of farm ads from websites. Binary approval or disapproval by content owners is given. SVMlight sparse vectors of text words in ads calculated. 4143 Text Classification 2011 [429][430] C. Masterharm et al.
The Pile Assembling several large datasets of diverse and unstructured texts Various (removing HTML and Javascript from websites, removing duplicated sentences) 825 GiB English text JSON Lines[431][432] Natural Language Processing, Text Prediction 2021 [433][431] Gao et al.
OSCAR Large collection of monolingual corpora extracted from web data (Common Crawl dumps) covering 150+ languages Various (filtering, language classification, adult-content detection and other labelling) 3.4 TB English text, 1.4 TB Chinese text, 1.1 TB Russian text, 595 MB German text, 431 MB French text, and data for 150+ languages (figures for version 23.01) JSON Lines[434] Natural Language Processing, Text Prediction 2021 [435][436] Ortiz Suarez, Abadji, Sagot et al.
OpenWebText An open-source recreation of the WebText corpus. The text is web content extracted from URLs shared on Reddit with at least three upvotes. Extracted non-HTML content, deduplicated, and tokenized. 8,013,769 Documents, 38GB Text Natural Language Processing, Text Prediction 2019 [437][438] A. Gokaslan, V. Cohen
ROOTS A well-documented and representative multilingual dataset with the explicit goal of doing good for and by the people whose data was collected. Extracted non-HTML content, cleaned out UI and ads, deduplicated, removed PII, and tokenized. 1.6 TB, 59 languages. Parquet Natural Language Processing, Text Prediction 2022 [439][440] H. Laurençon, L. Saulnier, T. Wang, C. Akiki, A. Villanova del Moral, T. Le Scao


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Poker Hand Dataset 5 card hands from a standard 52 card deck. Attributes of each hand are given, including the Poker hands formed by the cards it contains. 1,025,010 Text Regression, classification 2007 [441] R. Cattral
Connect-4 Dataset Contains all legal 8-ply positions in the game of connect-4 in which neither player has won yet, and in which the next move is not forced. None. 67,557 Text Classification 1995 [442] J. Tromp
Chess (King-Rook vs. King) Dataset Endgame Database for White King and Rook against Black King. None. 28,056 Text Classification 1994 [443][444] M. Bain et al.
Chess (King-Rook vs. King-Pawn) Dataset King+Rook versus King+Pawn on a7. None. 3196 Text Classification 1989 [445] R. Holte
Tic-Tac-Toe Endgame Dataset Binary classification for win conditions in tic-tac-toe. None. 958 Text Classification 1991 [446] D. Aha

Other multivariate

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Housing Data Set Median home values of Boston with associated home and neighborhood attributes. None. 506 Text Regression 1993 [447] D. Harrison et al.
The Getty Vocabularies structured terminology for art and other material culture, archival materials, visual surrogates, and bibliographic materials. None. large Text Classification 2015 [448] Getty Center
Yahoo! Front Page Today Module User Click Log User click log for news articles displayed in the Featured Tab of the Today Module on Yahoo! Front Page. Conjoint analysis with a bilinear model. 45,811,883 user visits Text Regression, clustering 2009 [449][450] Chu et al.
British Oceanographic Data Centre Biological, chemical, physical and geophysical data for oceans. 22K variables tracked. Various. 22K variables, many instances Text Regression, clustering 2015 [451] British Oceanographic Data Centre
Congressional Voting Records Dataset Voting data for all USA representatives on 16 issues. Beyond the raw voting data, various other features are provided. 435 Text Classification 1987 [452] J. Schlimmer
Entree Chicago Recommendation Dataset Record of user interactions with Entree Chicago recommendation system. Details of each users usage of the app are recorded in detail. 50,672 Text Regression, recommendation 2000 [453] R. Burke
Insurance Company Benchmark (COIL 2000) Information on customers of an insurance company. Many features of each customer and the services they use. 9,000 Text Regression, classification 2000 [454][455] P. van der Putten
Nursery Dataset Data from applicants to nursery schools. Data about applicant's family and various other factors included. 12,960 Text Classification 1997 [456][457] V. Rajkovic et al.
University Dataset Data describing attributed of a large number of universities. None. 285 Text Clustering, classification 1988 [458] S. Sounders et al.
Blood Transfusion Service Center Dataset Data from blood transfusion service center. Gives data on donors return rate, frequency, etc. None. 748 Text Classification 2008 [459][460] I. Yeh
Record Linkage Comparison Patterns Dataset Large dataset of records. Task is to link relevant records together. Blocking procedure applied to select only certain record pairs. 5,749,132 Text Classification 2011 [461][462] University of Mainz
Nomao Dataset Nomao collects data about places from many different sources. Task is to detect items that describe the same place. Duplicates labeled. 34,465 Text Classification 2012 [463][464] Nomao Labs
Movie Dataset Data for 10,000 movies. Several features for each movie are given. 10,000 Text Clustering, classification 1999 [465] G. Wiederhold
Open University Learning Analytics Dataset Information about students and their interactions with a virtual learning environment. None. ~ 30,000 Text Classification, clustering, regression 2015 [466][467] J. Kuzilek et al.
Mobile phone records Telecommunications activity and interactions Aggregation per geographical grid cells and every 15 minutes. large Text Classification, Clustering, Regression 2015 [468] G. Barlacchi et al.

Curated repositories of datasets

As datasets come in myriad formats and can sometimes be difficult to use, there has been considerable work put into curating and standardizing the format of datasets to make them easier to use for machine learning research.

See also


  1. ^ Wissner-Gross, A. "Datasets Over Algorithms". Retrieved 8 January 2016.
  2. ^ Weiss, G. M.; Provost, F. (1 September 2003). "Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction". Journal of Artificial Intelligence Research. AI Access Foundation. 19: 315–354. doi:10.1613/jair.1199. ISSN 1076-9757. S2CID 2344521.
  3. ^ Turney, Peter (2000). "Types of cost in inductive concept learning". arXiv:cs/0212034.
  4. ^ Abney, Steven (17 September 2007). Semisupervised Learning for Computational Linguistics. CRC Press. ISBN 978-1-4200-1080-0.
  5. ^ Žliobaitė, Indrė; Bifet, Albert; Pfahringer, Bernhard; Holmes, Geoff (2011). "Active Learning with Evolving Streaming Data". Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science. Vol. 6913. Berlin, Heidelberg: Springer Berlin Heidelberg. pp. 597–612. doi:10.1007/978-3-642-23808-6_39. ISBN 978-3-642-23807-9. ISSN 0302-9743.
  6. ^ McAuley, Julian; Targett, Christopher; Shi, Qinfeng; Anton van den Hengel (2015). "Image-based Recommendations on Styles and Substitutes". arXiv:1506.04757 [cs.CV].
  7. ^ "Amazon review data". Retrieved 8 October 2021.
  8. ^ Ganesan, Kavita; Zhai, Chengxiang (2012). "Opinion-based entity ranking". Information Retrieval. 15 (2): 116–150. doi:10.1007/s10791-011-9174-8. hdl:2142/15252. S2CID 16258727.
  9. ^ Lv, Yuanhua, Dimitrios Lymberopoulos, and Qiang Wu. "An exploration of ranking heuristics in mobile local search." Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. ACM, 2012.
  10. ^ Harper, F. Maxwell; Konstan, Joseph A. (2015). "The MovieLens Datasets: History and Context". ACM Transactions on Interactive Intelligent Systems. 5 (4): 19. doi:10.1145/2827872. S2CID 16619709.
  11. ^ Koenigstein, Noam, Gideon Dror, and Yehuda Koren. "Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy." Proceedings of the fifth ACM conference on Recommender systems. ACM, 2011.
  12. ^ McFee, Brian, et al. "The million song dataset challenge." Proceedings of the 21st international conference companion on World Wide Web. ACM, 2012.
  13. ^ Bohanec, Marko, and Vladislav Rajkovic. "Knowledge acquisition and explanation for multi-attribute decision making." 8th Intl Workshop on Expert Systems and their Applications. 1988.
  14. ^ Tan, Peter J., and David L. Dowe. "MML inference of decision graphs with multi-way joins." Australian Joint Conference on Artificial Intelligence. 2002.
  15. ^ "Quantifying comedy on YouTube: why the number of o's in your LOL matter". Metatext NLP Database. Retrieved 26 October 2020.
  16. ^ Kim, Byung Joo (2012). "A Classifier for Big Data". Convergence and Hybrid Information Technology. Communications in Computer and Information Science. Vol. 310. pp. 505–512. doi:10.1007/978-3-642-32692-9_63. ISBN 978-3-642-32691-2.
  17. ^ Pérezgonzález, Jose D.; Gilbey, Andrew (2011). "Predicting Skytrax airport rankings from customer reviews". Journal of Airport Management. 5 (4): 335–339.
  18. ^ Loh, Wei-Yin, and Yu-Shan Shih. "Split selection methods for classification trees." Statistica sinica(1997): 815–840.
  19. ^ Lim, Tjen-Sien; Loh, Wei-Yin; Shih, Yu-Shan (2000). "A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms". Machine Learning. 40 (3): 203–228. doi:10.1023/a:1007608224229. S2CID 17030953.
  20. ^ Kiet Van Nguyen, Vu Duc Nguyen, Phu X. V. Nguyen, Tham T. H. Truong, Ngan Luu-Thuy Nguyen. "UIT-VSFC: Vietnamese Students’ Feedback Corpus for Sentiment Analysis
  21. ^ Ho, Vong Anh; Nguyen, Duong Huynh-Cong; Nguyen, Danh Hoang; Pham, Linh Thi-Van; Nguyen, Duc-Vu; Nguyen, Kiet Van; Nguyen, Ngan Luu-Thuy (2020). "Emotion Recognition for Vietnamese Social Media Text". Computational Linguistics. Communications in Computer and Information Science. Vol. 1215. pp. 319–333. arXiv:1911.09339. doi:10.1007/978-981-15-6168-9_27. ISBN 978-981-15-6167-2. S2CID 208202333.
  22. ^ Nhung Thi-Hong Nguyen, Phuong Ha-Dieu Phan, Luan Thanh Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen (24 April 2021). "Vietnamese Open-domain Complaint Detection in E-Commerce Websites". arXiv:2104.11969 [cs.CL].((cite arXiv)): CS1 maint: multiple names: authors list (link)
  23. ^ Phu Gia Hoang, Canh Duc Luu, Khanh Quoc Tran, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen (26 January 2023). "ViHOS: Hate Speech Spans Detection for Vietnamese". arXiv:2301.10186 [cs.CL].((cite arXiv)): CS1 maint: multiple names: authors list (link)
  24. ^ Dermouche, Mohamed; Velcin, Julien; Khouas, Leila; Loudcher, Sabine (2014). "A Joint Model for Topic-Sentiment Evolution over Time". 2014 IEEE International Conference on Data Mining. IEEE. pp. 773–778. doi:10.1109/icdm.2014.82. ISBN 978-1-4799-4302-9.
  25. ^ Rose, Tony; Stevenson, Mark; Whitehead, Miles (2002). "The Reuters Corpus Volume 1-from Yesterday's News to Tomorrow's Language Resources" (PDF). LREC. 2. S2CID 9239414. Archived from the original (PDF) on 6 August 2019.
  26. ^ Amini, Massih R.; Usunier, Nicolas; Goutte, Cyril (2009). "Learning from Multiple Partially Observed Views – an Application to Multilingual Text Categorization". Advances in Neural Information Processing Systems. 22: 28–36.
  27. ^ Liu, Ming; et al. (2015). "VRCA: a clustering algorithm for massive amount of texts". Proceedings of the 24th International Conference on Artificial Intelligence. AAAI Press. Archived from the original on 5 November 2021. Retrieved 6 August 2019.
  28. ^ Al-Harbi, S; Almuhareb, A; Al-Thubaity, A; Khorsheed, M. S.; Al-Rajeh, A (2008). "Automatic Arabic Text Classification". Proceedings of the 9th International Conference on the Statistical Analysis of Textual Data, Lyon, France.
  29. ^ "Relationship and Entity Extraction Evaluation Dataset: Dstl/re3d". GitHub. 17 December 2018.
  30. ^ "The Examiner – SpamClickBait Catalogue".
  31. ^ "A Million News Headlines".
  32. ^ "One Week of Global News Feeds".
  33. ^ Kulkarni, Rohit (2018), Reuters News-Wire Archive, Harvard Dataverse, doi:10.7910/DVN/XDB74W
  34. ^ "IrishTimes – the Waxy-Wany News".
  35. ^ "News Headlines Dataset For Sarcasm Detection". Retrieved 27 April 2019.
  36. ^ Klimt, Bryan, and Yiming Yang. "Introducing the Enron Corpus." CEAS. 2004.
  37. ^ Kossinets, Gueorgi; Kleinberg, Jon; Watts, Duncan (2008). "The Structure of Information Pathways in a Social Communication Network". arXiv:0806.3201 [physics.soc-ph].
  38. ^ Androutsopoulos, Ion; Koutsias, John; Chandrinos, Konstantinos V.; Paliouras, George; Spyropoulos, Constantine D. (2000). "An evaluation of Naive Bayesian anti-spam filtering". In Potamias, G.; Moustakis, V.; van Someren, M. (eds.). Proceedings of the Workshop on Machine Learning in the New Information Age. 11th European Conference on Machine Learning, Barcelona, Spain. Vol. 11. pp. 9–17. arXiv:cs/0006013. Bibcode:2000cs........6013A.
  39. ^ Bratko, Andrej; et al. (2006). "Spam filtering using statistical data compression models" (PDF). The Journal of Machine Learning Research. 7: 2673–2698.
  40. ^ Almeida, Tiago A., José María G. Hidalgo, and Akebo Yamakami. "Contributions to the study of SMS spam filtering: new collection and results."Proceedings of the 11th ACM symposium on Document engineering. ACM, 2011.
  41. ^ Delany; Jane, Sarah; Buckley, Mark; Greene, Derek (2012). "SMS spam filtering: methods and data". Expert Systems with Applications. 39 (10): 9899–9908. doi:10.1016/j.eswa.2012.02.053. S2CID 15546924.
  42. ^ Joachims, Thorsten. A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization. No. CMU-CS-96-118. Carnegie-mellon univ pittsburgh pa dept of computer science, 1996.
  43. ^ Dimitrakakis, Christos, and Samy Bengio. Online Policy Adaptation for Ensemble Algorithms. No. EPFL-REPORT-82788. IDIAP, 2002.
  44. ^ Dooms, S. et al. "Movietweetings: a movie rating dataset collected from twitter, 2013. Available from"
  45. ^ RoyChowdhury, Aruni; Lin, Tsung-Yu; Maji, Subhransu; Learned-Miller, Erik (2017). "Twitter100k: A Real-world Dataset for Weakly Supervised Cross-Media Retrieval". arXiv:1703.06618 [cs.CV].
  46. ^ "huyt16/Twitter100k". GitHub. Retrieved 26 March 2018.
  47. ^ Go, Alec; Bhayani, Richa; Huang, Lei (2009). "Twitter sentiment classification using distant supervision". CS224N Project Report, Stanford. 1: 12.
  48. ^ Chikersal, Prerna, Soujanya Poria, and Erik Cambria. "SeNTU: sentiment analysis of tweets by combining a rule-based classifier with supervised learning." Proceedings of the International Workshop on Semantic Evaluation, SemEval. 2015.
  49. ^ Zafarani, Reza, and Huan Liu. "Social computing data repository at ASU." School of Computing, Informatics and Decision Systems Engineering, Arizona State University (2009).
  50. ^ Data Science Course by DataTrained Education "IBM Certified Data Science Course[dead link]." IBM Certified Online Data Science Course
  51. ^ McAuley, Julian J.; Leskovec, Jure. "Learning to Discover Social Circles in Ego Networks". NIPS. 2012: 2012.
  52. ^ Šubelj, Lovro; Fiala, Dalibor; Bajec, Marko (2014). "Network-based statistical comparison of citation topology of bibliographic databases". Scientific Reports. 4 (6496): 6496. arXiv:1502.05061. Bibcode:2014NatSR...4E6496S. doi:10.1038/srep06496. PMC 4178292. PMID 25263231.
  53. ^ Abdulla, N., et al. "Arabic sentiment analysis: Corpus-based and lexicon-based." Proceedings of the IEEE conference on Applied Electrical Engineering and Computing Technologies (AEECT). 2013.
  54. ^ Abooraig, Raddad, et al. "On the automatic categorization of Arabic articles based on their political orientation." Third International Conference on Informatics Engineering and Information Science (ICIEIS2014). 2014.
  55. ^ Kawala, François, et al. "Prédictions d'activité dans les réseaux sociaux en ligne." 4ième conférence sur les modèles et l'analyse des réseaux: Approches mathématiques et informatiques. 2013.
  56. ^ Sabharwal, Ashish; Samulowitz, Horst; Tesauro, Gerald (2015). "Selecting Near-Optimal Learners via Incremental Data Allocation". arXiv:1601.00024 [cs.LG].
  57. ^ Xu et al. "SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter (PIT)" Proceedings of the 9th International Workshop on Semantic Evaluation. 2015.
  58. ^ Xu et al. "Extracting Lexically Divergent Paraphrases from Twitter" Transactions of the Association for Computational (TACL). 2014.
  59. ^ Middleton, Stuart E; Middleton, Lee; Modafferi, Stefano (2014). "Real-Time Crisis Mapping of Natural Disasters Using Social Media" (PDF). IEEE Intelligent Systems. 29 (2): 9–17. doi:10.1109/MIS.2013.126. S2CID 15139204.
  60. ^ "geoparsepy". 2016. Python PyPI library
  61. ^ Shmueli, Boaz; Ku, Lun-Wei; Ray, Soumya (2020). "Reactive Supervision: A New Method for Collecting Sarcasm Data". Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics. pp. 2553–2559. doi:10.18653/v1/2020.emnlp-main.201. S2CID 221970454.
  62. ^ Shmueli, Boaz. "SPIRS Sarcasm Dataset". GitHub.
  63. ^ Gupta, Aakash (2020). "Dutch social media collection". COVID-19 Data Hub. doi:10.5072/FK2/MTPTL7. Retrieved 11 November 2023.
  64. ^ "Streamlit". Retrieved 18 December 2020.
  65. ^ "Dutch Social media collection". Retrieved 18 December 2020.
  66. ^ Shmueli, Boaz; Ray, Soumya; Lun-Wei (2021). "Happy Dance, Slow Clap: Using Reaction GIFs to Predict Induced Affect on Twitter". Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Vol. Association for Computational Linguistics. As. pp. 395–401. doi:10.18653/v1/2021.acl-short.50. S2CID 235125510.
  67. ^ Shmueli, Boaz (5 May 2023), ReactionGIF, retrieved 6 October 2023
  68. ^ Forsyth, E., Lin, J., & Martell, C. (2008, June 25). The NPS Chat Corpus. Retrieved from
  69. ^ Sordoni, Alessandro; Galley, Michel; Auli, Michael; Brockett, Chris; Ji, Yangfeng; Mitchell, Margaret; Nie, Jian-Yun; Gao, Jianfeng; Dolan, Bill (2015). "A Neural Network Approach to Context-Sensitive Generation of Conversational Responses". arXiv:1506.06714 [cs.CL].
  70. ^ Shaoul, C. & Westbury C. (2013) A reduced redundancy USENET corpus (2005–2011) Edmonton, AB: University of Alberta (downloaded from
  71. ^ KAN, M. (2011, January). NUS Short Message Service (SMS) Corpus. Retrieved from Archived 29 June 2018 at the Wayback Machine
  72. ^ Stuck_In_the_Matrix. (2015, July 3). I have every publicly available Reddit comment for research. ~ 1.7 billion comments @ 250 GB compressed. Any interest in this? [Original post]. Message posted to
  73. ^ Lowe, Ryan; Pow, Nissan; Serban, Iulian; Pineau, Joelle (2015). "The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems". arXiv:1506.08909 [cs.CL].
  74. ^ Jason Williams Antoine Raux Matthew Henderson, "[1]", Dialogue & Discourse | April 2016 .
  75. ^ Hoppe, Travis (16 December 2021), The-Pile-FreeLaw, retrieved 11 January 2023
  76. ^ Zheng, Lucia; Guha, Neel; Anderson, Brandon R.; Henderson, Peter; Ho, Daniel E. (21 June 2021). "When does pretraining help?". Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law. New York, NY, USA: ACM. pp. 159–168. doi:10.1145/3462757.3466088. ISBN 9781450385268. S2CID 233296302.
  77. ^ "pile-of-law/pile-of-law · Datasets at Hugging Face". 4 July 2022. Retrieved 11 January 2023.
  78. ^ "About | Caselaw Access Project". Retrieved 11 January 2023.
  79. ^ K. Kowsari, D. E. Brown, M. Heidarysafa, K. Jafari Meimandi, M. S. Gerber and L. E. Barnes, "HDLTex: Hierarchical Deep Learning for Text Classification", 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 364–371. doi:10.1109/ICMLA.2017.0-134
  80. ^ K. Kowsari, D. E. Brown, M. Heidarysafa, K. Jafari Meimandi, M. S. Gerber and L. E. Barnes, "Web of Science Dataset", doi:10.17632/9rw3vkcfy4.6
  81. ^ Galgani, Filippo, Paul Compton, and Achim Hoffmann. "Combining different summarization techniques for legal text." Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data. Association for Computational Linguistics, 2012.
  82. ^ Nagwani, N. K. (2015). "Summarizing large text collection using topic modeling and clustering based on MapReduce framework". Journal of Big Data. 2 (1): 1–18. doi:10.1186/s40537-015-0020-5.
  83. ^ Schler, Jonathan; et al. (2006). "Effects of Age and Gender on Blogging" (PDF). AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs. 6. Archived from the original (PDF) on 14 November 2020. Retrieved 6 August 2019.
  84. ^ Anand, Pranav, et al. "Believe Me-We Can Do This! Annotating Persuasive Acts in Blog Text."Computational Models of Natural Argument. 2011.
  85. ^ Traud, Amanda L., Peter J. Mucha, and Mason A. Porter. "Social structure of Facebook networks." Physica A: Statistical Mechanics and its Applications391.16 (2012): 4165–4180.
  86. ^ Richard, Emile; Savalle, Pierre-Andre; Vayatis, Nicolas (2012). "Estimation of Simultaneously Sparse and Low Rank Matrices". arXiv:1206.6474 [cs.DS].
  87. ^ Richardson, Matthew; Burges, Christopher JC; Renshaw, Erin (2013). "MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text". EMNLP. 1.
  88. ^ Weston, Jason; Bordes, Antoine; Chopra, Sumit; Rush, Alexander M.; Bart van Merriënboer; Joulin, Armand; Mikolov, Tomas (2015). "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks". arXiv:1502.05698 [cs.AI].
  89. ^ Marcus, Mitchell P.; Ann Marcinkiewicz, Mary; Santorini, Beatrice (1993). "Building a large annotated corpus of English: The Penn Treebank". Computational Linguistics. 19 (2): 313–330.
  90. ^ Collins, Michael (2003). "Head-driven statistical models for natural language parsing". Computational Linguistics. 29 (4): 589–637. doi:10.1162/089120103322753356.
  91. ^ Guyon, Isabelle, et al., eds. Feature extraction: foundations and applications. Vol. 207. Springer, 2008.
  92. ^ Lin, Yuri, et al. "Syntactic annotations for the google books ngram corpus." Proceedings of the ACL 2012 system demonstrations. Association for Computational Linguistics, 2012.
  93. ^ Krishnamoorthy, Niveda; et al. (2013). "Generating Natural-Language Video Descriptions Using Text-Mined Knowledge". AAAI. 1. Archived from the original on 6 August 2019. Retrieved 6 August 2019.
  94. ^ Luyckx, Kim, and Walter Daelemans. "Personae: a Corpus for Author and Personality Prediction from Text[dead link]." LREC. 2008.
  95. ^ Solorio, Thamar, Ragib Hasan, and Mainul Mizan. "A case study of sockpuppet detection in wikipedia." Workshop on Language Analysis in Social Media (LASM) at NAACL HLT. 2013.
  96. ^ "Pushshift Files". Archived from the original on 12 January 2023. Retrieved 12 January 2023.
  97. ^ Baumgartner, Jason; Zannettou, Savvas; Keegan, Brian; Squire, Megan; Blackburn, Jeremy (23 January 2020). "The Pushshift Reddit Dataset". arXiv:2001.08435 [cs.SI].
  98. ^ Ciarelli, Patrick Marques, and Elias Oliveira. "Agglomeration and elimination of terms for dimensionality reduction." Intelligent Systems Design and Applications, 2009. ISDA'09. Ninth International Conference on. IEEE, 2009.
  99. ^ Zhou, Mingyuan, Oscar Hernan Madrid Padilla, and James G. Scott. "Priors for random count matrices derived from a family of negative binomial processes." Journal of the American Statistical Association just-accepted (2015): 00–00.
  100. ^ Kotzias, Dimitrios, et al. "From group to individual labels using deep features." Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015.
  101. ^ Ning, Yue; Muthiah, Sathappan; Rangwala, Huzefa; Ramakrishnan, Naren (2016). "Modeling Precursors for Event Forecasting via Nested Multi-Instance Learning". arXiv:1602.08033 [cs.SI].
  102. ^ Buza, Krisztian. "Feedback prediction for blogs."Data analysis, machine learning and knowledge discovery. Springer International Publishing, 2014. 145–152.
  103. ^ Soysal, Ömer M (2015). "Association rule mining with mostly associated sequential patterns". Expert Systems with Applications. 42 (5): 2582–2592. doi:10.1016/j.eswa.2014.10.049.
  104. ^ Zhu, Yukun, et al. "Aligning books and movies: Towards story-like visual explanations by watching movies and reading books." Proceedings of the IEEE international conference on computer vision. 2015.
  105. ^ Bowman, Samuel R.; Angeli, Gabor; Potts, Christopher; Manning, Christopher D. (2015). "A large annotated corpus for learning natural language inference". arXiv:1508.05326 [cs.CL].
  106. ^ "DSL Corpus Collection". Retrieved 22 September 2017.
  107. ^ "Urban Dictionary Words and Definitions".
  108. ^ H. Elsahar, P. Vougiouklis, A. Remaci, C. Gravier, J. Hare, F. Laforest, E. Simperl, "T-REx: A Large Scale Alignment of Natural Language with Knowledge Base Triples", Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018).
  109. ^ Wang, Alex; Singh, Amanpreet; Michael, Julian; Hill, Felix; Levy, Omer; Bowman, Samuel R. (2018). "GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding". arXiv:1804.07461 [cs.CL].
  110. ^ "Computers Are Learning to Read—But They're Still Not So Smart". Wired. Retrieved 29 December 2019.
  111. ^ "GLUE Benchmark". Retrieved 25 February 2019.
  112. ^ Quan, Hoang Lam; Quang, Duy Le; Van Kiet, Nguyen; Ngan, Luu-Thuy Nguyen. "UIT-ViIC: A Dataset for the First Evaluation on Vietnamese Image Captioning".
  113. ^ To, Quoc Huy; Nguyen, Van Kiet; Nguyen, Luu Thuy Ngan; Nguyen, Gia Tuan Anh (2020). "Gender Prediction Based on Vietnamese Names with Machine Learning Techniques". Proceedings of the 4th International Conference on Natural Language Processing and Information Retrieval. pp. 55–60. arXiv:2010.10852. doi:10.1145/3443279.3443309. ISBN 9781450377607. S2CID 224814110.
  114. ^ Nguyen, Luan Thanh; Van Nguyen, Kiet; Nguyen, Ngan Luu-Thuy (18 March 2021). "Constructive and Toxic Speech Detection for Open-Domain Social Media Comments in Vietnamese". Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices. Lecture Notes in Computer Science. Vol. 12798. pp. 572–583. arXiv:2103.10069. doi:10.1007/978-3-030-79457-6_49. ISBN 978-3-030-79456-9. S2CID 232269671.
  115. ^ Saxton, David, et al. "Analysing Mathematical Reasoning Abilities of Neural Models." International Conference on Learning Representations. 2018.
  116. ^ M. Versteegh, R. Thiollière, T. Schatz, X.-N. Cao, X. Anguera, A. Jansen, and E. Dupoux (2015). "The Zero Resource Speech Challenge 2015," in INTERSPEECH-2015.
  117. ^ M. Versteegh, X. Anguera, A. Jansen, and E. Dupoux, (2016). "The Zero Resource Speech Challenge 2015: Proposed Approaches and Results," in SLTU-2016.
  118. ^ Sakar, Betul Erdogdu; et al. (2013). "Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings". IEEE Journal of Biomedical and Health Informatics. 17 (4): 828–834. doi:10.1109/jbhi.2013.2245674. PMID 25055311. S2CID 15491516.
  119. ^ Zhao, Shunan, et al. "Automatic detection of expressed emotion in Parkinson's disease." Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on. IEEE, 2014.
  120. ^ Used in: Hammami, Nacereddine, and Mouldi Bedda. "Improved tree model for Arabic speech recognition." Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on. Vol. 5. IEEE, 2010.
  121. ^ Maaten, Laurens. "Learning discriminative fisher kernels." Proceedings of the 28th International Conference on Machine Learning (ICML-11). 2011.
  122. ^ Cole, Ronald, and Mark Fanty. "Spoken letter recognition." Proc. Third DARPA Speech and Natural Language Workshop. 1990.
  123. ^ Chapelle, Olivier; Sindhwani, Vikas; Keerthi, Sathiya S. (2008). "Optimization techniques for semi-supervised support vector machines" (PDF). The Journal of Machine Learning Research. 9: 203–233.
  124. ^ Kudo, Mineichi; Toyama, Jun; Shimbo, Masaru (1999). "Multidimensional curve classification using passing-through regions". Pattern Recognition Letters. 20 (11): 1103–1111. Bibcode:1999PaReL..20.1103K. CiteSeerX doi:10.1016/s0167-8655(99)00077-x.
  125. ^ Jaeger, Herbert; et al. (2007). "Optimization and applications of echo state networks with leaky-integrator neurons". Neural Networks. 20 (3): 335–352. doi:10.1016/j.neunet.2007.04.016. PMID 17517495.
  126. ^ Tsanas, Athanasios; et al. (2010). "Accurate telemonitoring of Parkinson's disease progression by noninvasive speech tests". IEEE Transactions on Biomedical Engineering (Submitted manuscript). 57 (4): 884–893. doi:10.1109/tbme.2009.2036000. PMID 19932995. S2CID 7382779.
  127. ^ Clifford, Gari D.; Clifton, David (2012). "Wireless technology in disease management and medicine". Annual Review of Medicine. 63: 479–492. doi:10.1146/annurev-med-051210-114650. PMID 22053737.
  128. ^ Zue, Victor; Seneff, Stephanie; Glass, James (1990). "Speech database development at MIT: TIMIT and beyond". Speech Communication. 9 (4): 351–356. doi:10.1016/0167-6393(90)90010-7.
  129. ^ Kapadia, Sadik, Valtcho Valtchev, and S. J. Young. "MMI training for continuous phoneme recognition on the TIMIT database." Acoustics, Speech, and Signal Processing, 1993. ICASSP-93., 1993 IEEE International Conference on. Vol. 2. IEEE, 1993.
  130. ^ Halabi, Nawar (2016). Modern Standard Arabic Phonetics for Speech Synthesis (PDF) (PhD Thesis). University of Southampton, School of Electronics and Computer Science.
  131. ^ Ardila, Rosana; Branson, Megan; Davis, Kelly; Henretty, Michael; Kohler, Michael; Meyer, Josh; Morais, Reuben; Saunders, Lindsay; Tyers, Francis M.; Weber, Gregor (13 December 2019). "Common Voice: A Massively-Multilingual Speech Corpus". arXiv:1912.06670v2 [cs.CL].
  132. ^ "The LJ Speech Dataset". Retrieved 13 April 2022.
  133. ^ Ghandoura, Abdulkader; Hjabo, Farouk; Al Dakkak, Oumayma (June 2021). "Building and benchmarking an Arabic Speech Commands dataset for small-footprint keyword spotting". Engineering Applications of Artificial Intelligence. 102: 104267. doi:10.1016/j.engappai.2021.104267. ISSN 0952-1976. S2CID 235637809.
  134. ^ Zhou, Fang, Q. Claire, and Ross D. King. "Predicting the geographical origin of music." Data Mining (ICDM), 2014 IEEE International Conference on. IEEE, 2014.
  135. ^ Saccenti, Edoardo; Camacho, José (2015). "On the use of the observation-wise k-fold operation in PCA cross-validation". Journal of Chemometrics. 29 (8): 467–478. doi:10.1002/cem.2726. hdl:10481/55302. S2CID 62248957.
  136. ^ Bertin-Mahieux, Thierry, et al. "The million song dataset." ISMIR 2011: Proceedings of the 12th International Society for Music Information Retrieval Conference, 24–28 October 2011, Miami, Florida. University of Miami, 2011.
  137. ^ Henaff, Mikael; et al. (2011). "Unsupervised learning of sparse features for scalable audio classification" (PDF). ISMIR. 11.
  138. ^ Rafii, Zafar (2017). "Music". MUSDB18 – a corpus for music separation. doi:10.5281/zenodo.1117372.
  139. ^ Defferrard, Michaël; Benzi, Kirell; Vandergheynst, Pierre; Bresson, Xavier (6 December 2016). "FMA: A Dataset For Music Analysis". arXiv:1612.01840 [cs.SD].
  140. ^ Esposito, Roberto; Radicioni, Daniele P. (2009). "Carpediem: Optimizing the viterbi algorithm and applications to supervised sequential learning" (PDF). The Journal of Machine Learning Research. 10: 1851–1880.
  141. ^ Sourati, Jamshid; et al. (2016). "Classification Active Learning Based on Mutual Information". Entropy. 18 (2): 51. Bibcode:2016Entrp..18...51S. doi:10.3390/e18020051.
  142. ^ Salamon, Justin; Jacoby, Christopher; Bello, Juan Pablo. "A dataset and taxonomy for urban sound research." Proceedings of the ACM International Conference on Multimedia. ACM, 2014.
  143. ^ Lagrange, Mathieu; Lafay, Grégoire; Rossignol, Mathias; Benetos, Emmanouil; Roebel, Axel (2015). "An evaluation framework for event detection using a morphological model of acoustic scenes". arXiv:1502.00141 [stat.ML].
  144. ^ Gemmeke, Jort F., et al. "Audio Set: An ontology and human-labeled dataset for audio events." IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). 2017.
  145. ^ "Watch out, birders: Artificial intelligence has learned to spot birds from their songs". Science | AAAS. 18 July 2018. Retrieved 22 July 2018.
  146. ^ "Bird Audio Detection challenge". Machine Listening Lab at Queen Mary University. 3 May 2016. Retrieved 22 July 2018.
  147. ^ Wichern, Gordon; Antognini, Joe; Flynn, Michael; Licheng Richard Zhu; McQuinn, Emmett; Crow, Dwight; Manilow, Ethan; Jonathan Le Roux (2019). "WHAM!: Extending Speech Separation to Noisy Environments". arXiv:1907.01160 [cs.SD].
  148. ^ Drossos, K., Lipping, S., and Virtanen, T. "Clotho: An Audio Captioning Dataset" IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). 2020.
  149. ^ Drossos, K., Lipping, S., and Virtanen, T. (2019). Clotho dataset (Version 1.0) [Data set]. Zenodo.
  150. ^ The CAIDA UCSD Dataset on the Witty Worm – 19–24 March 2004,
  151. ^ Chen, Zesheng, and Chuanyi Ji. "Optimal worm-scanning method using vulnerable-host distributions." International Journal of Security and Networks 2.1–2 (2007): 71–80.
  152. ^ Kachuee, Mohamad, et al. "Cuff-less high-accuracy calibration-free blood pressure estimation using pulse transit time[permanent dead link]." Circuits and Systems (ISCAS), 2015 IEEE International Symposium on. IEEE, 2015.
  153. ^ PhysioBank, PhysioToolkit. "PhysioNet: components of a new research resource for complex physiologic signals." Circulation. v101 i23. e215-e220.
  154. ^ Vergara, Alexander; et al. (2012). "Chemical gas sensor drift compensation using classifier ensembles". Sensors and Actuators B: Chemical. 166: 320–329. doi:10.1016/j.snb.2012.01.074.
  155. ^ Korotcenkov, G.; Cho, B. K. (2014). "Engineering approaches to improvement of conductometric gas sensor parameters. Part 2: Decrease of dissipated (consumable) power and improvement stability and reliability". Sensors and Actuators B: Chemical. 198: 316–341. doi:10.1016/j.snb.2014.03.069.
  156. ^ Quinlan, John R (1992). "Learning with continuous classes" (PDF). 5th Australian Joint Conference on Artificial Intelligence. 92.
  157. ^ Merz, Christopher J.; Pazzani, Michael J. (1999). "A principal components approach to combining regression estimates". Machine Learning. 36 (1–2): 9–32. doi:10.1023/a:1007507221352.
  158. ^ Torres-Sospedra, Joaquin, et al. "UJIIndoorLoc-Mag: A new database for magnetic field-based localization problems." Indoor Positioning and Indoor Navigation (IPIN), 2015 International Conference on. IEEE, 2015.
  159. ^ Berkvens, Rafael, Maarten Weyn, and Herbert Peremans. "Mean Mutual Information of Probabilistic Wi-Fi Localization." Indoor Positioning and Indoor Navigation (IPIN), 2015 International Conference on. Banff, Canada: IPIN. 2015.
  160. ^ Paschke, Fabian, et al. "Sensorlose Zustandsüberwachung an Synchronmotoren."Proceedings. 23. Workshop Computational Intelligence, Dortmund, 5.-6. Dezember 2013. KIT Scientific Publishing, 2013.
  161. ^ Lessmeier, Christian, et al. "Data Acquisition and Signal Analysis from Measured Motor Currents for Defect Detection in Electromechanical Drive Systems."
  162. ^ Ugulino, Wallace, et al. "Wearable computing: Accelerometers’ data classification of body postures and movements Archived 25 September 2020 at the Wayback Machine." Advances in Artificial Intelligence-SBIA 2012. Springer Berlin Heidelberg, 2012. 52–61.
  163. ^ Schneider, Jan; et al. (2015). "Augmenting the senses: a review on sensor-based learning support". Sensors. 15 (2): 4097–4133. Bibcode:2015Senso..15.4097S. doi:10.3390/s150204097. PMC 4367401. PMID 25679313.
  164. ^ Madeo, Renata CB, Clodoaldo AM Lima, and Sarajane M. Peres. "Gesture unit segmentation using support vector machines: segmenting gestures from rest positions." Proceedings of the 28th Annual ACM Symposium on Applied Computing. ACM, 2013.
  165. ^ Lun, Roanna; Zhao, Wenbing (2015). "A survey of applications and human motion recognition with Microsoft Kinect". International Journal of Pattern Recognition and Artificial Intelligence. 29 (5): 1555008. doi:10.1142/s0218001415550083.
  166. ^ Theodoridis, Theodoros, and Huosheng Hu. "Action classification of 3d human models using dynamic ANNs for mobile robot surveillance Archived 6 August 2019 at the Wayback Machine."Robotics and Biomimetics, 2007. ROBIO 2007. IEEE International Conference on. IEEE, 2007.
  167. ^ Etemad, Seyed Ali, and Ali Arya. "3D human action recognition and style transformation using resilient backpropagation neural networks." Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on. Vol. 4. IEEE, 2009.
  168. ^ Altun, Kerem; Barshan, Billur; Tunçel, Orkun (2010). "Comparative study on classifying human activities with miniature inertial and magnetic sensors". Pattern Recognition. 43 (10): 3605–3620. Bibcode:2010PatRe..43.3605A. doi:10.1016/j.patcog.2010.04.019. hdl:11693/11947.
  169. ^ Nathan, Ran; et al. (2012). "Using tri-axial acceleration data to identify behavioral modes of free-ranging animals: general concepts and tools illustrated for griffon vultures". The Journal of Experimental Biology. 215 (6): 986–996. doi:10.1242/jeb.058602. PMC 3284320. PMID 22357592.
  170. ^ Anguita, Davide, et al. "Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine." Ambient assisted living and home care. Springer Berlin Heidelberg, 2012. 216–223.
  171. ^ Su, Xing; Tong, Hanghang; Ji, Ping (2014). "Activity recognition with smartphone sensors". Tsinghua Science and Technology. 19 (3): 235–249. doi:10.1109/tst.2014.6838194. S2CID 62751498.
  172. ^ Kadous, Mohammed Waleed. Temporal classification: Extending the classification paradigm to multivariate time series. Diss. The University of New South Wales, 2002.
  173. ^ Graves, Alex, et al. "Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks." Proceedings of the 23rd international conference on Machine learning. ACM, 2006.
  174. ^ Velloso, Eduardo, et al. "Qualitative activity recognition of weight lifting exercises."Proceedings of the 4th Augmented Human International Conference. ACM, 2013.
  175. ^ Mortazavi, Bobak Jack, et al. "Determining the single best axis for exercise repetition recognition and counting on smartwatches Archived 4 November 2021 at the Wayback Machine." Wearable and Implantable Body Sensor Networks (BSN), 2014 11th International Conference on. IEEE, 2014.
  176. ^ Sapsanis, Christos, et al. "Improving EMG based Classification of basic hand movements using EMD." Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE. IEEE, 2013.
  177. ^ a b Andrianesis, Konstantinos; Tzes, Anthony (2015). "Development and control of a multifunctional prosthetic hand with shape memory alloy actuators". Journal of Intelligent & Robotic Systems. 78 (2): 257–289. doi:10.1007/s10846-014-0061-6. S2CID 207174078.
  178. ^ Banos, Oresti; et al. (2014). "Dealing with the effects of sensor displacement in wearable activity recognition". Sensors. 14 (6): 9995–10023. Bibcode:2014Senso..14.9995B. doi:10.3390/s140609995. PMC 4118358. PMID 24915181.
  179. ^ Stisen, Allan, et al. "Smart Devices are Different: Assessing and MitigatingMobile Sensing Heterogeneities for Activity Recognition."Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems. ACM, 2015.
  180. ^ Bhattacharya, Sourav, and Nicholas D. Lane. "From Smart to Deep: Robust Activity Recognition on Smartwatches using Deep Learning."
  181. ^ Bacciu, Davide; et al. (2014). "An experimental characterization of reservoir computing in ambient assisted living applications". Neural Computing and Applications. 24 (6): 1451–1464. doi:10.1007/s00521-013-1364-4. hdl:11568/237959. S2CID 14124013.
  182. ^ Palumbo, Filippo; Barsocchi, Paolo; Gallicchio, Claudio; Chessa, Stefano; Micheli, Alessio (2013). "Multisensor Data Fusion for Activity Recognition Based on Reservoir Computing". Evaluating AAL Systems Through Competitive Benchmarking. Communications in Computer and Information Science. Vol. 386. pp. 24–35. doi:10.1007/978-3-642-41043-7_3. ISBN 978-3-642-41042-0.
  183. ^ Reiss, Attila, and Didier Stricker. "Introducing a new benchmarked dataset for activity monitoring."Wearable Computers (ISWC), 2012 16th International Symposium on. IEEE, 2012.
  184. ^ Roggen, Daniel, et al. "OPPORTUNITY: Towards opportunistic activity and context recognition systems." World of Wireless, Mobile and Multimedia Networks & Workshops, 2009. WoWMoM 2009. IEEE International Symposium on a. IEEE, 2009.
  185. ^ Kurz, Marc, et al. "Dynamic quantification of activity recognition capabilities in opportunistic systems." Vehicular Technology Conference (VTC Spring), 2011 IEEE 73rd. IEEE, 2011.
  186. ^ Sztyler, Timo, and Heiner Stuckenschmidt. "On-body localization of wearable devices: an investigation of position-aware activity recognition." Pervasive Computing and Communications (PerCom), 2016 IEEE International Conference on. IEEE, 2016.
  187. ^ Zhi, Ying Xuan; Lukasik, Michelle; Li, Michael H.; Dolatabadi, Elham; Wang, Rosalie H.; Taati, Babak (2018). "Automatic Detection of Compensation During Robotic Stroke Rehabilitation Therapy". IEEE Journal of Translational Engineering in Health and Medicine. 6: 2100107. doi:10.1109/JTEHM.2017.2780836. ISSN 2168-2372. PMC 5788403. PMID 29404226.
  188. ^ Dolatabadi, Elham; Zhi, Ying Xuan; Ye, Bing; Coahran, Marge; Lupinacci, Giorgia; Mihailidis, Alex; Wang, Rosalie; Taati, Babak (23 May 2017). "The toronto rehab stroke pose dataset to detect compensation during stroke rehabilitation therapy". Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare. ACM. pp. 375–381. doi:10.1145/3154862.3154925. ISBN 9781450363631. S2CID 24581930.
  189. ^ "Toronto Rehab Stroke Pose Dataset".
  190. ^ Jung, Merel M.; Poel, Mannes; Poppe, Ronald; Heylen, Dirk K. J. (1 March 2017). "Automatic recognition of touch gestures in the corpus of social touch". Journal on Multimodal User Interfaces. 11 (1): 81–96. doi:10.1007/s12193-016-0232-9. ISSN 1783-8738. S2CID 1802116.
  191. ^ Jung, M.M. (Merel) (1 June 2016). "Corpus of Social Touch (CoST)". University of Twente. doi:10.4121/uuid:5ef62345-3b3e-479c-8e1d-c922748c9b29. ((cite journal)): Cite journal requires |journal= (help)
  192. ^ Aeberhard, S., D. Coomans, and O. De Vel. "Comparison of classifiers in high dimensional settings." Dept. Math. Statist., James Cook Univ., North Queensland, Australia, Tech. Rep 92-02 (1992).
  193. ^ Basu, Sugato. "Semi-supervised clustering with limited background knowledge." AAAI. 2004.
  194. ^ Tüfekci, Pınar (2014). "Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods". International Journal of Electrical Power & Energy Systems. 60: 126–140. doi:10.1016/j.ijepes.2014.02.027.
  195. ^ Kaya, Heysem, Pınar Tüfekci, and Fikret S. Gürgen. "Local and global learning methods for predicting power of a combined gas & steam turbine." International conference on emerging trends in computer and electronics engineering (ICETCEE'2012), Dubai. 2012.
  196. ^ Baldi, Pierre; Sadowski, Peter; Whiteson, Daniel (2014). "Searching for exotic particles in high-energy physics with deep learning". Nature Communications. 5: 2014. arXiv:1402.4735. Bibcode:2014NatCo...5.4308B. doi:10.1038/ncomms5308. PMID 24986233. S2CID 195953.
  197. ^ a b Baldi, Pierre; Sadowski, Peter; Whiteson, Daniel (2015). "Enhanced Higgs Boson to τ+ τ− Search with Deep Learning". Physical Review Letters. 114 (11): 111801. arXiv:1410.3469. Bibcode:2015PhRvL.114k1801B. doi:10.1103/physrevlett.114.111801. PMID 25839260. S2CID 2339142.
  198. ^ a b Adam-Bourdarios, C.; Cowan, G.; Germain-Renaud, C.; Guyon, I.; Kégl, B.; Rousseau, D. (2015). "The Higgs Machine Learning Challenge". Journal of Physics: Conference Series. 664 (7): 072015. Bibcode:2015JPhCS.664g2015A. doi:10.1088/1742-6596/664/7/072015.
  199. ^ Baldi, Pierre; Cranmer, Kyle; Faucett, Taylor; Sadowski, Peter; Whiteson, Daniel (2016). "Parameterized neural networks for high-energy physics". The European Physical Journal C. 76 (5): 235. arXiv:1601.07913. Bibcode:2016EPJC...76..235B. doi:10.1140/epjc/s10052-016-4099-4. S2CID 254108545.
  200. ^ Ortigosa, I.; Lopez, R.; Garcia, J. "A neural networks approach to residuary resistance of sailing yachts prediction". Proceedings of the International Conference on Marine Engineering MARINE. 2007.
  201. ^ Gerritsma, J., R. Onnink, and A. Versluis.Geometry, resistance and stability of the delft systematic yacht hull series. Delft University of Technology, 1981.
  202. ^ Liu, Huan, and Hiroshi Motoda. Feature extraction, construction and selection: A data mining perspective. Springer Science & Business Media, 1998.
  203. ^ Reich, Yoram. Converging to Ideal Design Knowledge by Learning. [Carnegie Mellon University], Engineering Design Research Center, 1989.
  204. ^ Todorovski, Ljupčo; Džeroski, Sašo (1999). "Experiments in Meta-level Learning with ILP". Principles of Data Mining and Knowledge Discovery. Lecture Notes in Computer Science. Vol. 1704. pp. 98–106. doi:10.1007/978-3-540-48247-5_11. ISBN 978-3-540-66490-1. S2CID 39382993.
  205. ^ Wang, Yong. A new approach to fitting linear models in high dimensional spaces. Diss. The University of Waikato, 2000.
  206. ^ Kibler, Dennis; Aha, David W.; Albert, Marc K. (1989). "Instance-based prediction of real-valued attributes". Computational Intelligence. 5 (2): 51–57. doi:10.1111/j.1467-8640.1989.tb00315.x. S2CID 40800413.
  207. ^ Palmer, Christopher R., and Christos Faloutsos. "Electricity based external similarity of categorical attributes." Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2003. 486–500.
  208. ^ Tsanas, Athanasios; Xifara, Angeliki (2012). "Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools". Energy and Buildings. 49: 560–567. doi:10.1016/j.enbuild.2012.03.003.
  209. ^ De Wilde, Pieter (2014). "The gap between predicted and measured energy performance of buildings: A framework for investigation". Automation in Construction. 41: 40–49. doi:10.1016/j.autcon.2014.02.009.
  210. ^ Brooks, Thomas F., D. Stuart Pope, and Michael A. Marcolini. Airfoil self-noise and prediction. Vol. 1218. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1989.
  211. ^ Draper, David. "Assessment and propagation of model uncertainty." Journal of the Royal Statistical Society, Series B (Methodological) (1995): 45–97.
  212. ^ Lavine, Michael (1991). "Problems in extrapolation illustrated with space shuttle O-ring data". Journal of the American Statistical Association. 86 (416): 919–921. doi:10.1080/01621459.1991.10475132.
  213. ^ Wang, Jun, Bei Yu, and Les Gasser. "Concept tree based clustering visualization with shaded similarity matrices." Data Mining, 2002. ICDM 2003. Proceedings. 2002 IEEE International Conference on. IEEE, 2002.
  214. ^ Pettengill, Gordon H.; Ford, Peter G.; Johnson, William T. K.; Raney, R. Keith; Soderblom, Laurence A. (1991). "Magellan: Radar Performance and Data Products". Science. 252 (5003): 260–265. Bibcode:1991Sci...252..260P. doi:10.1126/science.252.5003.260. PMID 17769272. S2CID 43398343.
  215. ^ a b Aharonian, F.; et al. (2008). "Energy spectrum of cosmic-ray electrons at TeV energies". Physical Review Letters. 101 (26): 261104. arXiv:0811.3894. Bibcode:2008PhRvL.101z1104A. doi:10.1103/PhysRevLett.101.261104. hdl:2440/51450. PMID 19437632. S2CID 41850528.
  216. ^ Bock, R. K.; et al. (2004). "Methods for multidimensional event classification: a case study using images from a Cherenkov gamma-ray telescope". Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 516 (2): 511–528. Bibcode:2004NIMPA.516..511B. doi:10.1016/j.nima.2003.08.157.
  217. ^ Li, Jinyan; et al. (2004). "Deeps: A new instance-based lazy discovery and classification system". Machine Learning. 54 (2): 99–124. doi:10.1023/b:mach.0000011804.08528.7d.
  218. ^ Villaescusa-Navarro, Francisco; al., et (2022). "The CAMELS Multifield Data Set: Learning the Universe's Fundamental Parameters with Artificial Intelligence". The Astrophysical Journal Supplement Series. 259 (2): 61. arXiv:2109.10915. Bibcode:2022ApJS..259...61V. doi:10.3847/1538-4365/ac5ab0. S2CID 237604997.
  219. ^ Siebert, Lee, and Tom Simkin. "Volcanoes of the world: an illustrated catalog of Holocene volcanoes and their eruptions." (2014).
  220. ^ Sikora, Marek; Wróbel, Łukasz (2010). "Application of rule induction algorithms for analysis of data collected by seismic hazard monitoring systems in coal mines". Archives of Mining Sciences. 55 (1): 91–114.
  221. ^ Sikora, Marek, and Beata Sikora. "Rough natural hazards monitoring." Rough Sets: Selected Methods and Applications in Management and Engineering. Springer London, 2012. 163–179.
  222. ^ Addor, Nans; Newman, Andrew J.; Mizukami, Naoki; Clark, Martyn P. (20 October 2017). "The CAMELS data set: catchment attributes and meteorology for large-sample studies". Hydrology and Earth System Sciences. 21 (10): 5293–5313. Bibcode:2017HESS...21.5293A. doi:10.5194/hess-21-5293-2017. ISSN 1607-7938.
  223. ^ Newman, A. J.; Clark, M. P.; Sampson, K.; Wood, A.; Hay, L. E.; Bock, A.; Viger, R. J.; Blodgett, D.; Brekke, L.; Arnold, J. R.; Hopson, T. (14 January 2015). "Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance". Hydrology and Earth System Sciences. 19 (1): 209–223. Bibcode:2015HESS...19..209N. doi:10.5194/hess-19-209-2015. ISSN 1607-7938.
  224. ^ Alvarez-Garreton, Camila; Mendoza, Pablo A.; Boisier, Juan Pablo; Addor, Nans; Galleguillos, Mauricio; Zambrano-Bigiarini, Mauricio; Lara, Antonio; Puelma, Cristóbal; Cortes, Gonzalo; Garreaud, Rene; McPhee, James (13 November 2018). "The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset". Hydrology and Earth System Sciences. 22 (11): 5817–5846. Bibcode:2018HESS...22.5817A. doi:10.5194/hess-22-5817-2018. ISSN 1607-7938. S2CID 133955609.
  225. ^ Chagas, Vinícius B. P.; Chaffe, Pedro L. B.; Addor, Nans; Fan, Fernando M.; Fleischmann, Ayan S.; Paiva, Rodrigo C. D.; Siqueira, Vinícius A. (8 September 2020). "CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil". Earth System Science Data. 12 (3): 2075–2096. Bibcode:2020ESSD...12.2075C. doi:10.5194/essd-12-2075-2020. ISSN 1866-3516. S2CID 234737197.
  226. ^ Coxon, Gemma; Addor, Nans; Bloomfield, John P.; Freer, Jim; Fry, Matt; Hannaford, Jamie; Howden, Nicholas J. K.; Lane, Rosanna; Lewis, Melinda; Robinson, Emma L.; Wagener, Thorsten (12 October 2020). "CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain". Earth System Science Data. 12 (4): 2459–2483. Bibcode:2020ESSD...12.2459C. doi:10.5194/essd-12-2459-2020. ISSN 1866-3516. S2CID 226192657.
  227. ^ Fowler, Keirnan J. A.; Acharya, Suwash Chandra; Addor, Nans; Chou, Chihchung; Peel, Murray C. (6 August 2021). "CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia". Earth System Science Data. 13 (8): 3847–3867. Bibcode:2021ESSD...13.3847F. doi:10.5194/essd-13-3847-2021. ISSN 1866-3516. S2CID 238796784.
  228. ^ Klingler, Christoph; Schulz, Karsten; Herrnegger, Mathew (16 September 2021). "LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe". Earth System Science Data. 13 (9): 4529–4565. Bibcode:2021ESSD...13.4529K. doi:10.5194/essd-13-4529-2021. ISSN 1866-3516. S2CID 240533508.
  229. ^ Yeh, I–C (1998). "Modeling of strength of high-performance concrete using artificial neural networks". Cement and Concrete Research. 28 (12): 1797–1808. doi:10.1016/s0008-8846(98)00165-3.
  230. ^ Zarandi, MH Fazel; et al. (2008). "Fuzzy polynomial neural networks for approximation of the compressive strength of concrete". Applied Soft Computing. 8 (1): 488–498. Bibcode:2008ApSoC...8...79S. doi:10.1016/j.asoc.2007.02.010.
  231. ^ Yeh, I. "Modeling slump of concrete with fly ash and superplasticizer." Computers and Concrete5.6 (2008): 559–572.
  232. ^ Gencel, Osman; et al. (2011). "Comparison of artificial neural networks and general linear model approaches for the analysis of abrasive wear of concrete". Construction and Building Materials. 25 (8): 3486–3494. doi:10.1016/j.conbuildmat.2011.03.040.
  233. ^ Dietterich, Thomas G., et al. "A comparison of dynamic reposing and tangent distance for drug activity prediction Archived 7 December 2019 at the Wayback Machine." Advances in Neural Information Processing Systems (1994): 216–216.
  234. ^ Buscema, Massimo, William J. Tastle, and Stefano Terzi. "Meta net: A new meta-classifier family."Data Mining Applications Using Artificial Adaptive Systems. Springer New York, 2013. 141–182.
  235. ^ Amoradnejad, Issa; Amoradnejad, Rahimberdi; et al. (2022). "Age dataset: A structured general-purpose dataset on life, work, and death of 1.22 million distinguished people". Workshop Proceedings of the 16th International AAAI Conference on Web and Social Media (ICWSM). ICWSM. 3: 1–4. doi:10.36190/2022.82. S2CID 249668669.
  236. ^ "Age Dataset". GitHub. 7 June 2022.
  237. ^ "Synthetic Fundus Dataset". Archived from the original on 29 November 2021. Retrieved 22 February 2023.
  238. ^ Lo Castro, Dario; et al. (2020). "A visual framework to create photorealistic retinal vessels for diagnosis purposes". Journal of Biomedical Informatics. 108: 103490. doi:10.1016/j.jbi.2020.103490. PMID 32640292. S2CID 220429697.
  239. ^ Ingber, Lester (1997). "Statistical mechanics of neocortical interactions: Canonical momenta indicatorsof electroencephalography". Physical Review E. 55 (4): 4578–4593. arXiv:physics/0001052. Bibcode:1997PhRvE..55.4578I. doi:10.1103/PhysRevE.55.4578. S2CID 6390999.
  240. ^ Hoffmann, Ulrich; Vesin, Jean-Marc; Ebrahimi, Touradj; Diserens, Karin (2008). "An efficient P300-based brain–computer interface for disabled subjects". Journal of Neuroscience Methods. 167 (1): 115–125. CiteSeerX doi:10.1016/j.jneumeth.2007.03.005. PMID 17445904. S2CID 9648828.
  241. ^ Donchin, Emanuel; Spencer, Kevin M.; Wijesinghe, Ranjith (2000). "The mental prosthesis: assessing the speed of a P300-based brain-computer interface". IEEE Transactions on Rehabilitation Engineering. 8 (2): 174–179. doi:10.1109/86.847808. PMID 10896179. S2CID 84043.
  242. ^ Detrano, Robert; et al. (1989). "International application of a new probability algorithm for the diagnosis of coronary artery disease". The American Journal of Cardiology. 64 (5): 304–310. doi:10.1016/0002-9149(89)90524-9. PMID 2756873.
  243. ^ Bradley, Andrew P (1997). "The use of the area under the ROC curve in the evaluation of machine learning algorithms" (PDF). Pattern Recognition. 30 (7): 1145–1159. Bibcode:1997PatRe..30.1145B. doi:10.1016/s0031-3203(96)00142-2. S2CID 13806304.
  244. ^ Street, W. N.; Wolberg, W. H.; Mangasarian, O. L. (1993). "Nuclear feature extraction for breast tumor diagnosis". In Acharya, Raj S; Goldgof, Dmitry B (eds.). Biomedical Image Processing and Biomedical Visualization. Vol. 1905. pp. 861–870. doi:10.1117/12.148698. S2CID 14922543.
  245. ^ Demir, Cigdem, and Bülent Yener. "Automated cancer diagnosis based on histopathological images: a systematic survey." Rensselaer Polytechnic Institute, Tech. Rep (2005).
  246. ^ Abuse, Substance. "Mental Health Services Administration, Results from the 2010 National Survey on Drug Use and Health: Summary of National Findings, NSDUH Series H-41, HHS Publication No.(SMA) 11-4658." Rockville, MD: Substance Abuse and Mental Health Services Administration 201 (2011).
  247. ^ Hong, Zi-Quan; Yang, Jing-Yu (1991). "Optimal discriminant plane for a small number of samples and design method of classifier on the plane". Pattern Recognition. 24 (4): 317–324. Bibcode:1991PatRe..24..317H. doi:10.1016/0031-3203(91)90074-f.
  248. ^ a b Li, Jinyan, and Limsoon Wong. "Using rules to analyse bio-medical data: a comparison between C4. 5 and PCL." Advances in Web-Age Information Management. Springer Berlin Heidelberg, 2003. 254–265.
  249. ^ Güvenir, H. Altay, et al. "A supervised machine learning algorithm for arrhythmia analysis."Computers in Cardiology 1997. IEEE, 1997.
  250. ^ Lagus, Krista, et al. "Independent variable group analysis in learning compact representations for data." Proceedings of the International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning (AKRR'05), T. Honkela, V. Könönen, M. Pöllä, and O. Simula, Eds., Espoo, Finland. 2005.
  251. ^ Strack, Beata, et al. "Impact of HbA1c measurement on hospital readmission rates: analysis of 70,000 clinical database patient records." BioMed Research International 2014; 2014
  252. ^ Rubin, Daniel J (2015). "Hospital readmission of patients with diabetes". Current Diabetes Reports. 15 (4): 1–9. doi:10.1007/s11892-015-0584-7. PMID 25712258. S2CID 3908599.
  253. ^ Antal, Bálint; Hajdu, András (2014). "An ensemble-based system for automatic screening of diabetic retinopathy". Knowledge-Based Systems. 60 (2014): 20–27. arXiv:1410.8576. Bibcode:2014arXiv1410.8576A. doi:10.1016/j.knosys.2013.12.023. S2CID 13984326.
  254. ^ Haloi, Mrinal (2015). "Improved Microaneurysm Detection using Deep Neural Networks". arXiv:1505.04424 [cs.CV].
  255. ^ ELIE, Guillaume PATRY, Gervais GAUTHIER, Bruno LAY, Julien ROGER, Damien. "ADCIS Download Third Party: Messidor Database". Retrieved 25 February 2018.((cite web)): CS1 maint: multiple names: authors list (link)
  256. ^ Decencière, Etienne; Zhang, Xiwei; Cazuguel, Guy; Lay, Bruno; Cochener, Béatrice; Trone, Caroline; Gain, Philippe; Ordonez, Richard; Massin, Pascale (26 August 2014). "Feedback on a Publicly Distributed Image Database: The Messidor Database". Image Analysis & Stereology. 33 (3): 231–234. doi:10.5566/ias.1155. ISSN 1854-5165.
  257. ^ Bagirov, A. M.; et al. (2003). "Unsupervised and supervised data classification via nonsmooth and global optimization". Top. 11 (1): 1–75. CiteSeerX doi:10.1007/bf02578945. S2CID 14165678.
  258. ^ Fung, Glenn, et al. "A fast iterative algorithm for fisher discriminant using heterogeneous kernels."Proceedings of the twenty-first international conference on Machine learning. ACM, 2004.
  259. ^ Quinlan, John Ross, et al. "Inductive knowledge acquisition: a case study." Proceedings of the Second Australian Conference on Applications of expert systems. Addison-Wesley Longman Publishing Co., Inc., 1987.
  260. ^ a b Zhou, Zhi-Hua; Jiang, Yuan (2004). "NeC4. 5: neural ensemble based C4. 5". IEEE Transactions on Knowledge and Data Engineering. 16 (6): 770–773. CiteSeerX doi:10.1109/tkde.2004.11. S2CID 1024861.
  261. ^ Er, Orhan; et al. (2012). "An approach based on probabilistic neural network for diagnosis of Mesothelioma's disease". Computers & Electrical Engineering. 38 (1): 75–81. doi:10.1016/j.compeleceng.2011.09.001.
  262. ^ Er, Orhan, A. Çetin Tanrikulu, and Abdurrahman Abakay. "Use of artificial intelligence techniques for diagnosis of malignant pleural mesothelioma."Dicle Tıp Dergisi 42.1 (2015).
  263. ^ Li, Michael H.; Mestre, Tiago A.; Fox, Susan H.; Taati, Babak (25 July 2017). "Vision-Based Assessment of Parkinsonism and Levodopa-Induced Dyskinesia with Deep Learning Pose Estimation". Journal of Neuroengineering and Rehabilitation. 15 (1): 97. arXiv:1707.09416. Bibcode:2017arXiv170709416L. doi:10.1186/s12984-018-0446-z. PMC 6219082. PMID 30400914.
  264. ^ Li, Michael H.; Mestre, Tiago A.; Fox, Susan H.; Taati, Babak (May 2018). "Automated assessment of levodopa-induced dyskinesia: Evaluating the responsiveness of video-based features". Parkinsonism & Related Disorders. 53: 42–45. doi:10.1016/j.parkreldis.2018.04.036. ISSN 1353-8020. PMID 29748112. S2CID 13666294.
  265. ^ "Parkinson's Vision-Based Pose Estimation Dataset | Kaggle". Retrieved 22 August 2018.
  266. ^ Shannon, Paul; et al. (2003). "Cytoscape: a software environment for integrated models of biomolecular interaction networks". Genome Research. 13 (11): 2498–2504. doi:10.1101/gr.1239303. PMC 403769. PMID 14597658.
  267. ^ Javadi, Soroush; Mirroshandel, Seyed Abolghasem (2019). "A novel deep learning method for automatic assessment of human sperm images". Computers in Biology and Medicine. 109: 182–194. doi:10.1016/j.compbiomed.2019.04.030. ISSN 0010-4825. PMID 31059902. S2CID 146809768.
  268. ^ "soroushj/mhsma-dataset: MHSMA: The Modified Human Sperm Morphology Analysis Dataset". Retrieved 3 May 2019.
  269. ^ Clark, David, Zoltan Schreter, and Anthony Adams. "A quantitative comparison of dystal and backpropagation." Proceedings of 1996 Australian Conference on Neural Networks. 1996.
  270. ^ Jiang, Yuan, and Zhi-Hua Zhou. "Editing training data for kNN classifiers with neural network ensemble." Advances in Neural Networks–ISNN 2004. Springer Berlin Heidelberg, 2004. 356–361.
  271. ^ Ontañón, Santiago, and Enric Plaza. "On similarity measures based on a refinement lattice." Case-Based Reasoning Research and Development. Springer Berlin Heidelberg, 2009. 240–255.
  272. ^ "PLF data inventory". GitHub. 5 November 2021.
  273. ^ Higuera, Clara; Gardiner, Katheleen J.; Cios, Krzysztof J. (2015). "Self-organizing feature maps identify proteins critical to learning in a mouse model of down syndrome". PLOS ONE. 10 (6): e0129126. Bibcode:2015PLoSO..1029126H. doi:10.1371/journal.pone.0129126. PMC 4482027. PMID 26111164.
  274. ^ Ahmed, Md Mahiuddin; et al. (2015). "Protein dynamics associated with failed and rescued learning in the Ts65Dn mouse model of Down syndrome". PLOS ONE. 10 (3): e0119491. Bibcode:2015PLoSO..1019491A. doi:10.1371/journal.pone.0119491. PMC 4368539. PMID 25793384.
  275. ^ Langley, PAT (2014). "Trading off simplicity and coverage in incremental concept learning" (PDF). Machine Learning Proceedings. 1988: 73. Archived from the original (PDF) on 6 August 2019. Retrieved 6 August 2019.
  276. ^ "Mushroom Data Set 2020". Retrieved 6 April 2021.
  277. ^ Wagner, Dennis; Heider, Dominik; Hattab, Georges (14 April 2021). "Mushroom data creation, curation, and simulation to support classification tasks". Scientific Reports. 11 (1): 8134. Bibcode:2021NatSR..11.8134W. doi:10.1038/s41598-021-87602-3. ISSN 2045-2322. PMC 8046754. PMID 33854157.
  278. ^ Cortez, Paulo, and Aníbal de Jesus Raimundo Morais. "A data mining approach to predict forest fires using meteorological data." (2007).
  279. ^ Farquad, M. A. H.; Ravi, V.; Raju, S. Bapi (2010). "Support vector regression based hybrid rule extraction methods for forecasting". Expert Systems with Applications. 37 (8): 5577–5589. doi:10.1016/j.eswa.2010.02.055.
  280. ^ Fisher, Ronald A (1936). "The use of multiple measurements in taxonomic problems". Annals of Eugenics. 7 (2): 179–188. doi:10.1111/j.1469-1809.1936.tb02137.x. hdl:2440/15227.
  281. ^ Ghahramani, Zoubin, and Michael I. Jordan. "Supervised learning from incomplete data via an EM approach Archived 22 April 2017 at the Wayback Machine." Advances in neural information processing systems 6. 1994.
  282. ^ Mallah, Charles; Cope, James; Orwell, James (2013). "Plant leaf classification using probabilistic integration of shape, texture and margin features". Signal Processing, Pattern Recognition and Applications. 5: 1.
  283. ^ Yahiaoui, Itheri, Olfa Mzoughi, and Nozha Boujemaa. "Leaf shape descriptor for tree species identification Archived 6 August 2019 at the Wayback Machine." Multimedia and Expo (ICME), 2012 IEEE International Conference on. IEEE, 2012.
  284. ^ Tan, Ming, and Larry Eshelman. "Using weighted networks to represent classification knowledge in noisy domains." Proceedings of the Fifth International Conference on Machine Learning. 2014.
  285. ^ Charytanowicz, Małgorzata, et al. "Complete gradient clustering algorithm for features analysis of x-ray images." Information technologies in biomedicine. Springer Berlin Heidelberg, 2010. 15–24.
  286. ^ Sanchez, Mauricio A.; et al. (2014). "Fuzzy granular gravitational clustering algorithm for multivariate data". Information Sciences. 279: 498–511. doi:10.1016/j.ins.2014.04.005.
  287. ^ Blackard, Jock A.; Dean, Denis J. (1999). "Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables". Computers and Electronics in Agriculture. 24 (3): 131–151. CiteSeerX doi:10.1016/s0168-1699(99)00046-0. S2CID 13985407.
  288. ^ Fürnkranz, Johannes. "Round robin rule learning."Proceedings of the 18th International Conference on Machine Learning (ICML-01): 146—153. 2001.
  289. ^ Li, Song; Assmann, Sarah M.; Albert, Réka (2006). "Predicting essential components of signal transduction networks: a dynamic model of guard cell abscisic acid signaling". PLOS Biol. 4 (10): e312. arXiv:q-bio/0610012. doi:10.1371/journal.pbio.0040312. PMC 1564158. PMID 16968132.
  290. ^ Munisami, Trishen; et al. (2015). "Plant Leaf Recognition Using Shape Features and Colour Histogram with K-nearest Neighbour Classifiers". Procedia Computer Science. 58: 740–747. doi:10.1016/j.procs.2015.08.095.
  291. ^ Li, Bai (2016). "Atomic potential matching: An evolutionary target recognition approach based on edge features". Optik. 127 (5): 3162–3168. Bibcode:2016Optik.127.3162L. doi:10.1016/j.ijleo.2015.11.186.
  292. ^ Razavian, Ali, et al. "CNN features off-the-shelf: an astounding baseline for recognition." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2014.
  293. ^ Nilsback, Maria-Elena, and Andrew Zisserman. "A visual vocabulary for flower classification."Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on. Vol. 2. IEEE, 2006.
  294. ^ Giselsson, Thomas M.; et al. (2017). "A Public Image Database for Benchmark of Plant Seedling Classification Algorithms". arXiv:1711.05458 [cs.CV].
  295. ^ Muresan, Horea; Oltean, Mihai (2018). "Fruit recognition from images using deep learning". Acta Univ. Sapientiae, Informatica. 10 (1): 26–42. doi:10.2478/ausi-2018-0002.
  296. ^ Oltean, Mihai; Muresan, Horea (2017). "A dataset with fruit images on Kaggle".
  297. ^ Nakai, Kenta; Kanehisa, Minoru (1991). "Expert system for predicting protein localization sites in gram-negative bacteria". Proteins: Structure, Function, and Bioinformatics. 11 (2): 95–110. doi:10.1002/prot.340110203. PMID 1946347. S2CID 27606447.
  298. ^ Ling, Charles X., et al. "Decision trees with minimal costs." Proceedings of the twenty-first international conference on Machine learning. ACM, 2004.
  299. ^ Mahé, Pierre, et al. "Automatic identification of mixed bacterial species fingerprints in a MALDI-TOF mass-spectrum." Bioinformatics (2014): btu022.
  300. ^ Barbano, Duane; et al. (2015). "Rapid characterization of microalgae and microalgae mixtures using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS)". PLOS ONE. 10 (8): e0135337. Bibcode:2015PLoSO..1035337B. doi:10.1371/journal.pone.0135337. PMC 4536233. PMID 26271045.
  301. ^ Horton, Paul; Nakai, Kenta (1996). "A probabilistic classification system for predicting the cellular localization sites of proteins" (PDF). ISMB-96 Proceedings. 4: 109–15. PMID 8877510. Archived from the original (PDF) on 4 November 2021. Retrieved 6 August 2019.
  302. ^ Allwein, Erin L.; Schapire, Robert E.; Singer, Yoram (2001). "Reducing multiclass to binary: A unifying approach for margin classifiers" (PDF). The Journal of Machine Learning Research. 1: 113–141.
  303. ^ Mayr, Andreas; Klambauer, Guenter; Unterthiner, Thomas; Hochreiter, Sepp (2016). "DeepTox: Toxicity Prediction Using Deep Learning". Frontiers in Environmental Science. 3: 80. doi:10.3389/fenvs.2015.00080.
  304. ^ Lavin, Alexander; Ahmad, Subutai (12 October 2015). "Evaluating Real-Time Anomaly Detection Algorithms -- the Numenta Anomaly Benchmark". 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA). pp. 38–44. arXiv:1510.03336. doi:10.1109/ICMLA.2015.141. ISBN 978-1-5090-0287-0. S2CID 6842305.
  305. ^ Iurii D. Katser; Vyacheslav O. Kozitsin. "SKAB GitHub repository". GitHub. Retrieved 12 January 2021.
  306. ^ Iurii D. Katser; Vyacheslav O. Kozitsin (2020). "Skoltech Anomaly Benchmark (SKAB)". Kaggle. doi:10.34740/KAGGLE/DSV/1693952. Retrieved 12 January 2021. ((cite journal)): Cite journal requires |journal= (help)
  307. ^ Campos, Guilherme O.; Zimek, Arthur; Sander, Jörg; Campello, Ricardo J. G. B.; Micenková, Barbora; Schubert, Erich; Assent, Ira; Houle, Michael E. (2016). "On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study". Data Mining and Knowledge Discovery. 30 (4): 891. doi:10.1007/s10618-015-0444-8. ISSN 1384-5810. S2CID 1952214.
  308. ^ Ann-Kathrin Hartmann, Tommaso Soru, Edgard Marx. Generating a Large Dataset for Neural Question Answering over the DBpedia Knowledge Base. 2018.
  309. ^ Tommaso Soru, Edgard Marx. Diego Moussallem, Andre Valdestilhas, Diego Esteves, Ciro Baron. SPARQL as a Foreign Language. 2018.
  310. ^ Kiet Van Nguyen, Duc-Vu Nguyen, Anh Gia-Tuan Nguyen, Ngan Luu-Thuy Nguyen. A Vietnamese Dataset for Evaluating Machine Reading Comprehension. COLING 2020.
  311. ^ Kiet Van Nguyen, Khiem Vinh Tran, Son T. Luu, Anh Gia-Tuan Nguyen, Ngan Luu-Thuy Nguyen. Enhancing Lexical-Based Approach With External Knowledge for Vietnamese Multiple-Choice Machine Reading Comprehension. IEEE Access. 2020.
  312. ^ Anantha, Raviteja; Vakulenko, Svitlana; Tu, Zhucheng; Longpre, Shayne; Pulman, Stephen; Chappidi, Srinivas (2020). "Open-Domain Question Answering Goes Conversational via Question Rewriting". arXiv:2010.04898 [cs.IR].
  313. ^ Khashabi, Daniel; Min, Sewon; Khot, Tushar; Sabharwal, Ashish; Tafjord, Oyvind; Clark, Peter; Hajishirzi, Hannaneh (November 2020). "UNIFIEDQA: Crossing Format Boundaries with a Single QA System". Findings of the Association for Computational Linguistics: EMNLP 2020. Online: Association for Computational Linguistics: 1896–1907. arXiv:2005.00700. doi:10.18653/v1/2020.findings-emnlp.171. S2CID 218487109.
  314. ^ Taskmaster, Google Research Datasets, 17 December 2022, retrieved 7 January 2023
  315. ^ Byrne, Bill; Krishnamoorthi, Karthik; Sankar, Chinnadhurai; Neelakantan, Arvind; Duckworth, Daniel; Yavuz, Semih; Goodrich, Ben; Dubey, Amit; Cedilnik, Andy; Kim, Kyu-Young (1 September 2019). "Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset". arXiv:1909.05358 [cs.CL].
  316. ^ Yasunaga, Michihiro; Liang, Percy (21 November 2020). "Graph-based, Self-Supervised Program Repair from Diagnostic Feedback". International Conference on Machine Learning. PMLR: 10799–10808. arXiv:2005.10636.
  317. ^ Wang, Yizhong; Mishra, Swaroop; Alipoormolabashi, Pegah; Kordi, Yeganeh; Mirzaei, Amirreza; Arunkumar, Anjana; Ashok, Arjun; Dhanasekaran, Arut Selvan; Naik, Atharva; Stap, David; Pathak, Eshaan; Karamanolakis, Giannis; Lai, Haizhi Gary; Purohit, Ishan; Mondal, Ishani (24 October 2022). "Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks". arXiv:2204.07705 [cs.CL].
  318. ^ Paperno, Denis; Kruszewski, Germán; Lazaridou, Angeliki; Pham, Quan Ngoc; Bernardi, Raffaella; Pezzelle, Sandro; Baroni, Marco; Boleda, Gemma; Fernández, Raquel (7 August 2016), The LAMBADA dataset, doi:10.5281/zenodo.2630551, retrieved 7 January 2023
  319. ^ Paperno, Denis; Kruszewski, Germán; Lazaridou, Angeliki; Pham, Ngoc Quan; Bernardi, Raffaella; Pezzelle, Sandro; Baroni, Marco; Boleda, Gemma; Fernández, Raquel (August 2016). "The LAMBADA dataset: Word prediction requiring a broad discourse context". Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Berlin, Germany: Association for Computational Linguistics: 1525–1534. doi:10.18653/v1/P16-1144. hdl:10230/32702. S2CID 2381275.
  320. ^ Wei, Jason; Bosma, Maarten; Zhao, Vincent; Guu, Kelvin; Yu, Adams Wei; Lester, Brian; Du, Nan; Dai, Andrew M.; Le, Quoc V. (10 February 2022). "Finetuned Language Models are Zero-Shot Learners". arXiv:2109.01652. ((cite journal)): Cite journal requires |journal= (help)
  321. ^ "Working with ATT&CK | MITRE ATT&CK®". Retrieved 14 January 2023.
  322. ^ "CAPEC - Common Attack Pattern Enumeration and Classification (CAPEC™)". Retrieved 14 January 2023.
  323. ^ "CVE - Home". Retrieved 14 January 2023.
  324. ^ "CWE - Common Weakness Enumeration". Retrieved 14 January 2023.
  325. ^ Lim, Swee Kiat; Muis, Aldrian Obaja; Lu, Wei; Ong, Chen Hui (July 2017). "MalwareTextDB: A Database for Annotated Malware Articles". Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Vancouver, Canada: Association for Computational Linguistics: 1557–1567. doi:10.18653/v1/P17-1143. S2CID 7816596.
  326. ^ "USENIX". USENIX. Retrieved 19 January 2023.
  327. ^ "APTnotes | Read the Docs". Retrieved 19 January 2023.
  328. ^ "Cryptography and Security authors/titles recent submissions". Retrieved 19 January 2023.
  329. ^ "Holistic Info-Sec for Web Developers - Fascicle 0". Retrieved 20 January 2023.
  330. ^ "Holistic Info-Sec for Web Developers - Fascicle 1". Retrieved 20 January 2023.
  331. ^ Vincent, Adam. "Web Services Web Services Hacking and Hardening" (PDF).
  332. ^ McCray, Joe. "Advanced SQL Injection" (PDF).
  333. ^ Shah, Shreeraj. "Blind SQL injection discovery & exploitation technique" (PDF).
  334. ^ Palcer, C. C. "Ethical hacking" (PDF). textfiles.
  335. ^ "Hacking Secrets Revealed - Information and Instructional Guide" (PDF).
  336. ^ Park, Alexis. "Hack any website" (PDF).
  337. ^ Cerrudo, Cesar; Martinez Fayo, Esteban. "Hacking Databases for Owning your Data" (PDF). blackhat.
  338. ^ O'Connor, Tj. "Violent Python-A Cookbook for Hackers, Forensic Analysts, Penetration Testers and Security Engineers" (PDF). Github.
  339. ^ Grand, Joe. "Hardware Reverse Engineering: Access, Analyze, & Defeat" (PDF). blackhat.
  340. ^ Chang, Jason V. "Computer Hacking: Making the Case for National Reporting Requirement" (PDF).
  341. ^ "National Cybersecurity Strategies Repository". ITU. Retrieved 20 January 2023.
  342. ^ Chen, Yanlin (31 August 2022), Cyber Security Natural Language Processing, retrieved 20 January 2023
  343. ^ Zampieri, Marcos; Malmasi, Shervin; Nakov, Preslav; Rosenthal, Sara; Farra, Noura; Kumar, Ritesh (16 April 2019). "Predicting the Type and Target of Offensive Posts in Social Media". arXiv:1902.09666 [cs.CL].
  344. ^ "Threat reports". Retrieved 20 January 2023.
  345. ^ "Category: APT reports | Securelist". Retrieved 23 January 2023.
  346. ^ "Your Cybersecurity News Connection - Cyber News | CyberWire". The CyberWire. Retrieved 23 January 2023.
  347. ^ "News". 21 August 2016. Retrieved 23 January 2023.
  348. ^ "Cybernews". Cybernews.
  349. ^ "BleepingComputer". BleepingComputer. Retrieved 23 January 2023.
  350. ^ "Homepage". The Record from Recorded Future News. Retrieved 23 January 2023.
  351. ^ "HackRead | Latest Cyber Crime - InfoSec- Tech - Hacking News". 8 January 2022. Retrieved 23 January 2023.
  352. ^ "Securelist | Kaspersky's threat research and reports". Retrieved 31 January 2023.
  353. ^ Harshaw, Christopher R.; Bridges, Robert A.; Iannacone, Michael D.; Reed, Joel W.; Goodall, John R. (5 April 2016). "GraphPrints". Proceedings of the 11th Annual Cyber and Information Security Research Conference. CISRC '16. New York, NY, USA: Association for Computing Machinery. pp. 1–4. doi:10.1145/2897795.2897806. ISBN 978-1-4503-3752-6.
  354. ^ "Farsight Security, cyber security intelligence solutions". Farsight Security. Retrieved 13 February 2023.
  355. ^ "Schneier on Security". Retrieved 13 February 2023.
  356. ^ "#1 in Cloud Security & Endpoint Cybersecurity". Trend Micro. Retrieved 13 February 2023.
  357. ^ "The Hacker News | #1 Trusted Cybersecurity News Site". The Hacker News. Retrieved 13 February 2023.
  358. ^ "Krebs on Security – In-depth security news and investigation". Retrieved 25 February 2023.
  359. ^ "MITRE D3FEND Knowledge Graph". Retrieved 31 March 2023.
  360. ^ "MITRE | ATLAS™". Retrieved 31 March 2023.
  361. ^ "MITRE Engage™ | An Adversary Engagement Framework from MITRE". Retrieved 1 April 2023.
  362. ^ "Hacking Tutorials - The best Step-by-Step Hacking Tutorials". Hacking Tutorials. Retrieved 1 April 2023.
  363. ^ "TCFD Knowledge Hub". TCFD Knowledge Hub. Retrieved 3 February 2023.
  364. ^ "". Retrieved 3 February 2023.
  365. ^ "About — IPCC". Retrieved 20 February 2023.
  366. ^ "Alliance for Research on Corporate Sustainability | ARCS serves as a vehicle for advancing rigorous academic research on corporate sustainability issues". Retrieved 2 March 2023.
  367. ^ Mehra, Srishti; Louka, Robert; Zhang, Yixun (26 March 2022). "ESGBERT: Language Model to Help with Classification Tasks Related to Companies Environmental, Social, and Governance Practices". Embedded Systems and Applications: 183–190. arXiv:2203.16788. doi:10.5121/csit.2022.120616. ISBN 9781925953657. S2CID 247825524.
  368. ^  This article incorporates text available under the CC BY 4.0 license.
  369. ^ Diggelmann, Thomas; Boyd-Graber, Jordan; Bulian, Jannis; Ciaramita, Massimiliano; Leippold, Markus (2 January 2021). "CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims". arXiv:2012.00614 [cs.CL].
  370. ^ "climate-news-db". Retrieved 3 February 2023.
  371. ^ "Climatext". Retrieved 19 February 2023.
  372. ^ "Greenbiz". Retrieved 2 March 2023.
  373. ^ "Explore the @Reuters Hot List of 1,000 top climate scientists". Reuters. Retrieved 22 March 2023.
  374. ^ "Blogs | Alliance for Research on Corporate Sustainability". Retrieved 27 March 2023.
  375. ^ "Greenbiz". Retrieved 29 March 2023.
  376. ^ "CSR News". Retrieved 29 March 2023.
  377. ^ "CDP Homepage". Retrieved 29 March 2023.
  378. ^ de Vries, Harm (2022). "The Stack: 3 TB of permissively licensed source code". arXiv:2211.15533 [cs.CL].
  379. ^ "The Stack Dedup". Huggingface. Retrieved 29 August 2023.
  380. ^ "Hybrid cloud blog". Retrieved 9 April 2023.
  381. ^ "Production-Grade Container Orchestration". Kubernetes. Retrieved 9 April 2023.
  382. ^ "Home | Official Red Hat OpenShift Documentation". Retrieved 9 April 2023.
  383. ^ "Cloud Native Computing Foundation". Cloud Native Computing Foundation. Retrieved 9 April 2023.
  384. ^ CNCF Community Presentations, Cloud Native Computing Foundation (CNCF), 11 April 2023, retrieved 11 April 2023
  385. ^ "Red Hat - We make open source technologies for the enterprise". Retrieved 1 May 2023.
  386. ^ Brown, Michael Scott, Michael J. Pelosi, and Henry Dirska. "Dynamic-radius species-conserving genetic algorithm for the financial forecasting of Dow Jones index stocks[dead link]." Machine Learning and Data Mining in Pattern Recognition. Springer Berlin Heidelberg, 2013. 27–41.
  387. ^ Shen, Kao-Yi; Tzeng, Gwo-Hshiung (2015). "Fuzzy Inference-Enhanced VC-DRSA Model for Technical Analysis: Investment Decision Aid". International Journal of Fuzzy Systems. 17 (3): 375–389. doi:10.1007/s40815-015-0058-8. S2CID 68241024.
  388. ^ Quinlan, J. Ross (1987). "Simplifying decision trees". International Journal of Man-Machine Studies. 27 (3): 221–234. CiteSeerX doi:10.1016/s0020-7373(87)80053-6.
  389. ^ Hamers, Bart; Suykens, Johan AK; De Moor, Bart (2003). "Coupled transductive ensemble learning of kernel models" (PDF). Journal of Machine Learning Research. 1: 1–48.
  390. ^ Shmueli, Galit, Ralph P. Russo, and Wolfgang Jank. "The BARISTA: a model for bid arrivals in online auctions." The Annals of Applied Statistics(2007): 412–441.
  391. ^ Peng, Jie, and Hans-Georg Müller. "Distance-based clustering of sparsely observed stochastic processes, with applications to online auctions." The Annals of Applied Statistics (2008): 1056–1077.
  392. ^ Eggermont, Jeroen, Joost N. Kok, and Walter A. Kosters. "Genetic programming for data classification: Partitioning the search space."Proceedings of the 2004 ACM symposium on Applied computing. ACM, 2004.
  393. ^ Moro, Sérgio; Cortez, Paulo; Rita, Paulo (2014). "A data-driven approach to predict the success of bank telemarketing". Decision Support Systems. 62: 22–31. doi:10.1016/j.dss.2014.03.001. hdl:10071/9499. S2CID 14181100.
  394. ^ Payne, Richard D.; Mallick, Bani K. (2014). "Bayesian Big Data Classification: A Review with Complements". arXiv:1411.5653 [stat.ME].
  395. ^ Akbilgic, Oguz; Bozdogan, Hamparsum; Balaban, M. Erdal (2014). "A novel Hybrid RBF Neural Networks model as a forecaster". Statistics and Computing. 24 (3): 365–375. doi:10.1007/s11222-013-9375-7. S2CID 17764829.
  396. ^ Jabin, Suraiya. "Stock market prediction using feed-forward artificial neural network." Int. J. Comput. Appl. (IJCA) 99.9 (2014).
  397. ^ Yeh, I-Cheng; Che-hui, Lien (2009). "The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients". Expert Systems with Applications. 36 (2): 2473–2480. doi:10.1016/j.eswa.2007.12.020. S2CID 15696161.
  398. ^ Lin, Shu Ling (2009). "A new two-stage hybrid approach of credit risk in banking industry". Expert Systems with Applications. 36 (4): 8333–8341. doi:10.1016/j.eswa.2008.10.015.
  399. ^ Yumo Xu and Shay B. Cohen. 2018. Stock Movement Prediction from Tweets and Historical Prices. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1970–1979, Melbourne, Australia. Association for Computational Linguistics.
  400. ^ Pelckmans, Kristiaan; et al. (2005). "The differogram: Non-parametric noise variance estimation and its use for model selection". Neurocomputing. 69 (1): 100–122. doi:10.1016/j.neucom.2005.02.015.
  401. ^ Bay, Stephen D.; et al. (2000). "The UCI KDD archive of large data sets for data mining research and experimentation". ACM SIGKDD Explorations Newsletter. 2 (2): 81–85. CiteSeerX doi:10.1145/380995.381030. S2CID 534881.
  402. ^ Lucas, D. D.; et al. (2015). "Designing optimal greenhouse gas observing networks that consider performance and cost". Geoscientific Instrumentation, Methods and Data Systems. 4 (1): 121. Bibcode:2015GI......4..121L. doi:10.5194/gi-4-121-2015.
  403. ^ Pales, Jack C.; Keeling, Charles D. (1965). "The concentration of atmospheric carbon dioxide in Hawaii". Journal of Geophysical Research. 70 (24): 6053–6076. Bibcode:1965JGR....70.6053P. doi:10.1029/jz070i024p06053.
  404. ^ Sigillito, Vincent G., et al. "Classification of radar returns from the ionosphere using neural networks." Johns Hopkins APL Technical Digest10.3 (1989): 262–266.
  405. ^ Zhang, Kun, and Wei Fan. "Forecasting skewed biased stochastic ozone days: analyses, solutions and beyond." Knowledge and Information Systems14.3 (2008): 299–326.
  406. ^ Reich, Brian J., Montserrat Fuentes, and David B. Dunson. "Bayesian spatial quantile regression." Journal of the American Statistical Association (2012).
  407. ^ Kohavi, Ron (1996). "Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid". KDD. 96.
  408. ^ Oza, Nikunj C., and Stuart Russell. "Experimental comparisons of online and batch versions of bagging and boosting." Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2001.
  409. ^ Bay, Stephen D (2001). "Multivariate discretization for set mining". Knowledge and Information Systems. 3 (4): 491–512. CiteSeerX doi:10.1007/pl00011680. S2CID 10945544.
  410. ^ Ruggles, Steven (1995). "Sample designs and sampling errors". Historical Methods. 28 (1): 40–46. doi:10.1080/01615440.1995.9955312.
  411. ^ Meek, Christopher, Bo Thiesson, and David Heckerman. "The Learning Curve Method Applied to Clustering." AISTATS. 2001.
  412. ^ Fanaee-T, Hadi; Gama, Joao (2013). "Event labeling combining ensemble detectors and background knowledge". Progress in Artificial Intelligence. 2 (2–3): 113–127. doi:10.1007/s13748-013-0040-3. S2CID 3345087.
  413. ^ Giot, Romain, and Raphaël Cherrier. "Predicting bikeshare system usage up to one day ahead." Computational intelligence in vehicles and transportation systems (CIVTS), 2014 IEEE symposium on. IEEE, 2014.
  414. ^ Zhan, Xianyuan; et al. (2013). "Urban link travel time estimation using large-scale taxi data with partial information". Transportation Research Part C: Emerging Technologies. 33: 37–49. doi:10.1016/j.trc.2013.04.001.
  415. ^ Moreira-Matias, Luis; et al. (2013). "Predicting taxi–passenger demand using streaming data". IEEE Transactions on Intelligent Transportation Systems. 14 (3): 1393–1402. doi:10.1109/tits.2013.2262376. S2CID 14764358.
  416. ^ Hwang, Ren-Hung; Hsueh, Yu-Ling; Chen, Yu-Ting (2015). "An effective taxi recommender system based on a spatio-temporal factor analysis model". Information Sciences. 314: 28–40. doi:10.1016/j.ins.2015.03.068.
  417. ^ H. V. Jagadish, Johannes Gehrke, Alexandros Labrinidis, Yannis Papakonstantinou, Jignesh M. Patel, Raghu Ramakrishnan, and Cyrus Shahabi. Big data and its technical challenges. Commun. ACM, 57(7):86–94, July 2014.
  418. ^ Caltrans PeMS
  419. ^ Meusel, Robert, et al. "The Graph Structure in the Web—Analyzed on Different Aggregation Levels."The Journal of Web Science 1.1 (2015).
  420. ^ Kushmerick, Nicholas. "Learning to remove internet advertisements." Proceedings of the third annual conference on Autonomous Agents. ACM, 1999.
  421. ^ Fradkin, Dmitriy, and David Madigan. "Experiments with random projections for machine learning."Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2003.
  422. ^ This data was used in the American Statistical Association Statistical Graphics and Computing Sections 1999 Data Exposition.
  423. ^ Ma, Justin, et al. "Identifying suspicious URLs: an application of large-scale online learning."Proceedings of the 26th annual international conference on machine learning. ACM, 2009.
  424. ^ Levchenko, Kirill, et al. "Click trajectories: End-to-end analysis of the spam value chain." Security and Privacy (SP), 2011 IEEE Symposium on. IEEE, 2011.
  425. ^ Mohammad, Rami M., Fadi Thabtah, and Lee McCluskey. "An assessment of features related to phishing websites using an automated technique."Internet Technology And Secured Transactions, 2012 International Conference for. IEEE, 2012.
  426. ^ Singh, Ashishkumar, et al. "Clustering Experiments on Big Transaction Data for Market Segmentation." Proceedings of the 2014 International Conference on Big Data Science and Computing. ACM, 2014.
  427. ^ Bollacker, Kurt, et al. "Freebase: a collaboratively created graph database for structuring human knowledge." Proceedings of the 2008 ACM SIGMOD international conference on Management of data. ACM, 2008.
  428. ^ Mintz, Mike, et al. "Distant supervision for relation extraction without labeled data." Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2-Volume 2. Association for Computational Linguistics, 2009.
  429. ^ Mesterharm, Chris, and Michael J. Pazzani. "Active learning using on-line algorithms Archived 22 September 2017 at the Wayback Machine."Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2011.
  430. ^ Wang, Shusen; Zhang, Zhihua (2013). "Improving CUR matrix decomposition and the Nyström approximation via adaptive sampling" (PDF). The Journal of Machine Learning Research. 14 (1): 2729–2769. arXiv:1303.4207. Bibcode:2013arXiv1303.4207W.
  431. ^ a b "The Pile". Retrieved 14 April 2022.
  432. ^ "JSON Lines". Retrieved 14 April 2022.
  433. ^ Gao, Leo; Biderman, Stella; Black, Sid; Golding, Laurence; Hoppe, Travis; Foster, Charles; Phang, Jason; He, Horace; Thite, Anish; Nabeshima, Noa; Presser, Shawn (31 December 2020). "The Pile: An 800GB Dataset of Diverse Text for Language Modeling". arXiv:2101.00027 [cs.CL].
  434. ^ "OSCAR". Retrieved 12 August 2023.
  435. ^ Ortiz Suarez, Pedro, et al. "[2]." Asynchronous Pipeline for Processing Huge Corpora on Medium to Low Resource Infrastructures. CMLC-7, 2019.
  436. ^ Abadji, Julien, et al. "[3]." Towards a Cleaner Document-Oriented Multilingual Crawled Corpus. LREC, 2022.
  437. ^ Cohen, Vanya. "OpenWebTextCorpus". OpenWebTextCorpus. Retrieved 9 January 2023.
  438. ^ "openwebtext · Datasets at Hugging Face". 16 November 2022. Retrieved 9 January 2023.
  439. ^ Saulnier, Lucile (2023). "The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset". arXiv:2303.03915 [cs.CL].
  440. ^ "BigScience Data · Datasets at Hugging Face". 29 August 2023. Retrieved 29 August 2023.
  441. ^ Cattral, Robert; Oppacher, Franz; Deugo, Dwight (2002). "Evolutionary data mining with automatic rule generalization" (PDF). Recent Advances in Computers, Computing and Communications: 296–300. S2CID 18625415. Archived from the original (PDF) on 6 August 2019.
  442. ^ Burton, Ariel N.; Kelly, Paul H.J. (2006). "Performance prediction of paging workloads using lightweight tracing". Future Generation Computer Systems. Elsevier BV. 22 (7): 784–793. doi:10.1016/j.future.2006.02.003. ISSN 0167-739X.
  443. ^ Bain, Michael; Muggleton, Stephen (1994). "Learning optimal chess strategies". Machine Intelligence. Oxford University Press, Inc. 13: 291–309. doi:10.1093/oso/9780198538509.003.0012. ISBN 978-0-19-853850-9.
  444. ^ Quilan, J.R (1983). "Learning Efficient Classification Procedures and Their Application to Chess End Games". Machine Learning – Learning Efficient Classification Procedures and Their Application to Chess End Games. Vol. 1. pp. 463–482. doi:10.1007/978-3-662-12405-5_15. ISBN 978-3-662-12407-9. ((cite book)): |journal= ignored (help)
  445. ^ Shapiro, Alen D. (1987). Structured induction in expert systems. Addison-Wesley Longman Publishing Co., Inc.
  446. ^ Matheus, Christopher J.; Rendell, Larry A. (1989). "Constructive Induction on Decision Trees" (PDF). IJCAI. 89.[dead link]
  447. ^ Belsley, David A., Edwin Kuh, and Roy E. Welsch. Regression diagnostics: Identifying influential data and sources of collinearity. Vol. 571. John Wiley & Sons, 2005.
  448. ^ Ruotsalo, Tuukka; Aroyo, Lora; Schreiber, Guus (2009). "Knowledge-based linguistic annotation of digital cultural heritage collections" (PDF). IEEE Intelligent Systems. 24 (2): 64–75. doi:10.1109/MIS.2009.32. hdl:1871.1/9f6091aa-9596-46a9-9251-f11edeeb28b7. S2CID 6667472. Archived from the original (PDF) on 16 August 2017. Retrieved 6 December 2018.
  449. ^ Li, Lihong; Chu, Wei; Langford, John; Wang, Xuanhui (2011). "Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms". Proceedings of the fourth ACM international conference on Web search and data mining. pp. 297–306. arXiv:1003.5956. doi:10.1145/1935826.1935878. ISBN 9781450304931. S2CID 744200.
  450. ^ Yeung, Kam Fung, and Yanyan Yang. "A proactive personalized mobile news recommendation system." Developments in E-systems Engineering (DESE), 2010. IEEE, 2010.
  451. ^ Gass, Susan E.; Roberts, J. Murray (2006). "The occurrence of the cold-water coral Lophelia pertusa (Scleractinia) on oil and gas platforms in the North Sea: colony growth, recruitment and environmental controls on distribution". Marine Pollution Bulletin. 52 (5): 549–559. Bibcode:2006MarPB..52..549G. doi:10.1016/j.marpolbul.2005.10.002. PMID 16300800.
  452. ^ Gionis, Aristides; Mannila, Heikki; Tsaparas, Panayiotis (2007). "Clustering aggregation". ACM Transactions on Knowledge Discovery from Data. 1 (1): 4. CiteSeerX doi:10.1145/1217299.1217303. S2CID 433708.
  453. ^ Obradovic, Zoran, and Slobodan Vucetic.Challenges in Scientific Data Mining: Heterogeneous, Biased, and Large Samples. Technical Report, Center for Information Science and Technology Temple University, 2004.
  454. ^ Van Der Putten, Peter; van Someren, Maarten (2000). "CoIL challenge 2000: The insurance company case". Published by Sentient Machine Research, Amsterdam. Also a Leiden Institute of Advanced Computer Science Technical Report. 9: 1–43.
  455. ^ Mao, K. Z. (2002). "RBF neural network center selection based on Fisher ratio class separability measure". IEEE Transactions on Neural Networks. 13 (5): 1211–1217. doi:10.1109/tnn.2002.1031953. PMID 18244518.
  456. ^ Olave, Manuel; Rajkovic, Vladislav; Bohanec, Marko (1989). "An application for admission in public school systems" (PDF). Expert Systems in Public Administration. 1: 145–160.
  457. ^ Lizotte, Daniel J.; Madani, Omid; Greiner, Russell (2012). "Budgeted Learning of Naive-Bayes Classifiers". arXiv:1212.2472 [cs.LG].
  458. ^ Lebowitz, Michael (1986). "Concept learning in a rich input domain: Generalization-based memory". Machine Learning: An Artificial Intelligence Approach. 2: 193–214. ISBN 9780934613002.
  459. ^ Yeh, I-Cheng; Yang, King-Jang; Ting, Tao-Ming (2009). "Knowledge discovery on RFM model using Bernoulli sequence". Expert Systems with Applications. 36 (3): 5866–5871. doi:10.1016/j.eswa.2008.07.018.
  460. ^ Lee, Wen-Chen; Cheng, Bor-Wen (2011). "An intelligent system for improving performance of blood donation". Journal of Quality Vol. 18 (2): 173.
  461. ^ Schmidtmann, Irene, et al. "Evaluation des Krebsregisters NRW Schwerpunkt Record Linkage Archived 6 December 2018 at the Wayback Machine." Abschlußbericht vom 11 (2009).
  462. ^ Sariyar, Murat; Borg, Andreas; Pommerening, Klaus (2011). "Controlling false match rates in record linkage using extreme value theory". Journal of Biomedical Informatics. 44 (4): 648–654. doi:10.1016/j.jbi.2011.02.008. PMID 21352952.
  463. ^ Candillier, Laurent, and Vincent Lemaire. "Design and Analysis of the Nomao challenge Active Learning in the Real-World." Proceedings of the ALRA: Active Learning in Real-world Applications, Workshop ECML-PKDD. 2012.
  464. ^ Marquez, Ivan Garrido. "A Domain Adaptation Method for Text Classification based on Self-adjusted Training Approach." (2013).
  465. ^ Nagesh, Harsha S., Sanjay Goil, and Alok N. Choudhary. "Adaptive Grids for Clustering Massive Data Sets." SDM. 2001.
  466. ^ Kuzilek, Jakub, et al. "OU Analyse: analysing at-risk students at The Open University." Learning Analytics Review (2015): 1–16.
  467. ^ Siemens, George, et al. Open Learning Analytics: an integrated & modularized platform[permanent dead link]. Diss. Open University Press, 2011.
  468. ^ Barlacchi, Gianni; De Nadai, Marco; Larcher, Roberto; Casella, Antonio; Chitic, Cristiana; Torrisi, Giovanni; Antonelli, Fabrizio; Vespignani, Alessandro; Pentland, Alex; Lepri, Bruno (2015). "A multi-source dataset of urban life in the city of Milan and the Province of Trentino". Scientific Data. 2: 150055. Bibcode:2015NatSD...250055B. doi:10.1038/sdata.2015.55. ISSN 2052-4463. PMC 4622222. PMID 26528394.
  469. ^ Vanschoren J, van Rijn JN, Bischl B, Torgo L (2013). "OpenML: networked science in machine learning". SIGKDD Explorations. 15 (2): 49–60. arXiv:1407.7722. doi:10.1145/2641190.2641198. S2CID 4977460.
  470. ^ Olson RS, La Cava W, Orzechowski P, Urbanowicz RJ, Moore JH (2017). "PMLB: a large benchmark suite for machine learning evaluation and comparison". BioData Mining. 10 (1): 36. arXiv:1703.00512. Bibcode:2017arXiv170300512O. doi:10.1186/s13040-017-0154-4. PMC 5725843. PMID 29238404.
  471. ^ "Off The Shelf Datasets". Appen. Retrieved 30 December 2020.
  472. ^ "Open Source Datasets". Appen. Retrieved 30 December 2020.