Clear format and fix #291

This commit is contained in:
Xiaming Chen 2017-04-07 16:47:40 +08:00
parent 2be9cd5374
commit f96c461782

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@ -25,8 +25,8 @@ Biology
* `1000 Genomes <http://www.1000genomes.org/data>`_ * `1000 Genomes <http://www.1000genomes.org/data>`_
* `American Gut (Microbiome Project) <https://github.com/biocore/American-Gut>`_ * `American Gut (Microbiome Project) <https://github.com/biocore/American-Gut>`_
* `Broad Cancer Cell Line Encyclopedia (CCLE) <http://www.broadinstitute.org/ccle/home>`_
* `Broad Bioimage Benchmark Collection (BBBC) <https://www.broadinstitute.org/bbbc>`_ * `Broad Bioimage Benchmark Collection (BBBC) <https://www.broadinstitute.org/bbbc>`_
* `Broad Cancer Cell Line Encyclopedia (CCLE) <http://www.broadinstitute.org/ccle/home>`_
* `Cell Image Library <http://www.cellimagelibrary.org>`_ * `Cell Image Library <http://www.cellimagelibrary.org>`_
* `Complete Genomics Public Data <http://www.completegenomics.com/public-data/69-genomes/>`_ * `Complete Genomics Public Data <http://www.completegenomics.com/public-data/69-genomes/>`_
* `EBI ArrayExpress <http://www.ebi.ac.uk/arrayexpress/>`_ * `EBI ArrayExpress <http://www.ebi.ac.uk/arrayexpress/>`_
@ -64,12 +64,13 @@ Biology
* `The Catalogue of Life <http://www.catalogueoflife.org/content/annual-checklist-archive>`_ * `The Catalogue of Life <http://www.catalogueoflife.org/content/annual-checklist-archive>`_
* `The Personal Genome Project <http://www.personalgenomes.org/>`_ or `PGP <https://my.pgp-hms.org/public_genetic_data>`_ * `The Personal Genome Project <http://www.personalgenomes.org/>`_ or `PGP <https://my.pgp-hms.org/public_genetic_data>`_
* `UCSC Public Data <http://hgdownload.soe.ucsc.edu/downloads.html>`_ * `UCSC Public Data <http://hgdownload.soe.ucsc.edu/downloads.html>`_
* `Universal Protein Resource (UnitProt) <http://www.uniprot.org/downloads>`_
* `UniGene <http://www.ncbi.nlm.nih.gov/unigene>`_ * `UniGene <http://www.ncbi.nlm.nih.gov/unigene>`_
* `Universal Protein Resource (UnitProt) <http://www.uniprot.org/downloads>`_
Climate/Weather Climate/Weather
--------------- ---------------
* `Actuaries Climate Index <http://actuariesclimateindex.org/data/>`_ * `Actuaries Climate Index <http://actuariesclimateindex.org/data/>`_
* `Australian Weather <http://www.bom.gov.au/climate/dwo/>`_ * `Australian Weather <http://www.bom.gov.au/climate/dwo/>`_
* `Aviation Weather Center - Consistent, timely and accurate weather information for the world airspace system <https://aviationweather.gov/adds/dataserver>`_ * `Aviation Weather Center - Consistent, timely and accurate weather information for the world airspace system <https://aviationweather.gov/adds/dataserver>`_
@ -95,6 +96,7 @@ Complex Networks
* `AMiner Citation Network Dataset <http://aminer.org/citation>`_ * `AMiner Citation Network Dataset <http://aminer.org/citation>`_
* `CrossRef DOI URLs <https://archive.org/details/doi-urls>`_ * `CrossRef DOI URLs <https://archive.org/details/doi-urls>`_
* `DBLP Citation dataset <https://kdl.cs.umass.edu/display/public/DBLP>`_ * `DBLP Citation dataset <https://kdl.cs.umass.edu/display/public/DBLP>`_
* `DIMACS Road Networks Collection <http://www.dis.uniroma1.it/challenge9/download.shtml>`_
* `NBER Patent Citations <http://nber.org/patents/>`_ * `NBER Patent Citations <http://nber.org/patents/>`_
* `Network Repository with Interactive Exploratory Analysis Tools <http://networkrepository.com/>`_ * `Network Repository with Interactive Exploratory Analysis Tools <http://networkrepository.com/>`_
* `NIST complex networks data collection <http://math.nist.gov/~RPozo/complex_datasets.html>`_ * `NIST complex networks data collection <http://math.nist.gov/~RPozo/complex_datasets.html>`_
@ -111,7 +113,7 @@ Complex Networks
* `UCI Network Data Repository <https://networkdata.ics.uci.edu/resources.php>`_ * `UCI Network Data Repository <https://networkdata.ics.uci.edu/resources.php>`_
* `UFL sparse matrix collection <http://www.cise.ufl.edu/research/sparse/matrices/>`_ * `UFL sparse matrix collection <http://www.cise.ufl.edu/research/sparse/matrices/>`_
* `WSU Graph Database <http://www.eecs.wsu.edu/mgd/gdb.html>`_ * `WSU Graph Database <http://www.eecs.wsu.edu/mgd/gdb.html>`_
* `DIMACS Road Networks Collection <http://www.dis.uniroma1.it/challenge9/download.shtml>`_
Computer Networks Computer Networks
----------------- -----------------
@ -130,15 +132,10 @@ Computer Networks
* `UCSD Network Telescope, IPv4 /8 net <http://www.caida.org/projects/network_telescope/>`_ * `UCSD Network Telescope, IPv4 /8 net <http://www.caida.org/projects/network_telescope/>`_
Contextual Data
---------------
* `Context-aware data sets from five domains <https://github.com/irecsys/CARSKit/tree/master/context-aware_data_sets>`_
Data Challenges Data Challenges
--------------- ---------------
* `Bruteforce Database <https://github.com/duyetdev/bruteforce-database>`_
* `Challenges in Machine Learning <http://www.chalearn.org/>`_ * `Challenges in Machine Learning <http://www.chalearn.org/>`_
* `CrowdANALYTIX dataX <http://data.crowdanalytix.com>`_ * `CrowdANALYTIX dataX <http://data.crowdanalytix.com>`_
* `D4D Challenge of Orange <http://www.d4d.orange.com/en/home>`_ * `D4D Challenge of Orange <http://www.d4d.orange.com/en/home>`_
@ -150,9 +147,9 @@ Data Challenges
* `Netflix Prize <http://netflixprize.com/leaderboard.html>`_ * `Netflix Prize <http://netflixprize.com/leaderboard.html>`_
* `Space Apps Challenge <https://2015.spaceappschallenge.org>`_ * `Space Apps Challenge <https://2015.spaceappschallenge.org>`_
* `Telecom Italia Big Data Challenge <https://dandelion.eu/datamine/open-big-data/>`_ * `Telecom Italia Big Data Challenge <https://dandelion.eu/datamine/open-big-data/>`_
* `Yelp Dataset Challenge <http://www.yelp.com/dataset_challenge>`_
* `Bruteforce Database <https://github.com/duyetdev/bruteforce-database>`_
* `TravisTorrent Dataset - MSR'2017 Mining Challenge <https://travistorrent.testroots.org/>`_ * `TravisTorrent Dataset - MSR'2017 Mining Challenge <https://travistorrent.testroots.org/>`_
* `Yelp Dataset Challenge <http://www.yelp.com/dataset_challenge>`_
Earth Science Earth Science
------------- -------------
@ -216,7 +213,6 @@ Energy
* `WHITED <http://nilmworkshop.org/2016/proceedings/Poster_ID18.pdf>`_ * `WHITED <http://nilmworkshop.org/2016/proceedings/Poster_ID18.pdf>`_
Finance Finance
------- -------
@ -224,12 +220,12 @@ Finance
* `Google Finance <https://www.google.com/finance>`_ * `Google Finance <https://www.google.com/finance>`_
* `Google Trends <http://www.google.com/trends?q=google&ctab=0&geo=all&date=all&sort=0>`_ * `Google Trends <http://www.google.com/trends?q=google&ctab=0&geo=all&date=all&sort=0>`_
* `NASDAQ <https://data.nasdaq.com/>`_ * `NASDAQ <https://data.nasdaq.com/>`_
* `NYSE Market Data <ftp://ftp.nyxdata.com>`_ (see FTP link on `RAW <https://raw.githubusercontent.com/caesar0301/awesome-public-datasets/master/README.rst>`_)
* `OANDA <http://www.oanda.com/>`_ * `OANDA <http://www.oanda.com/>`_
* `OSU Financial data <http://fisher.osu.edu/fin/fdf/osudata.htm>`_ * `OSU Financial data <http://fisher.osu.edu/fin/fdf/osudata.htm>`_
* `Quandl <https://www.quandl.com/>`_ * `Quandl <https://www.quandl.com/>`_
* `St Louis Federal <https://research.stlouisfed.org/fred2/>`_ * `St Louis Federal <https://research.stlouisfed.org/fred2/>`_
* `Yahoo Finance <http://finance.yahoo.com/>`_ * `Yahoo Finance <http://finance.yahoo.com/>`_
* `NYSE Market Data <ftp://ftp.nyxdata.com>`_ (see FTP link on `RAW <https://raw.githubusercontent.com/caesar0301/awesome-public-datasets/master/README.rst>`_)
GIS GIS
@ -263,9 +259,9 @@ GIS
Government Government
---------- ----------
* `OpenDataSoft's list of 1,600 open data <https://www.opendatasoft.com/a-comprehensive-list-of-all-open-data-portals-around-the-world/>`_
* `Open Data for Africa <http://opendataforafrica.org/>`_
* `A list of cities and countries contributed by community <https://github.com/caesar0301/awesome-public-datasets/blob/master/Government.rst>`_ * `A list of cities and countries contributed by community <https://github.com/caesar0301/awesome-public-datasets/blob/master/Government.rst>`_
* `Open Data for Africa <http://opendataforafrica.org/>`_
* `OpenDataSoft's list of 1,600 open data <https://www.opendatasoft.com/a-comprehensive-list-of-all-open-data-portals-around-the-world/>`_
Healthcare Healthcare
@ -289,10 +285,13 @@ Image Processing
* `10k US Adult Faces Database <http://wilmabainbridge.com/facememorability2.html>`_ * `10k US Adult Faces Database <http://wilmabainbridge.com/facememorability2.html>`_
* `2GB of Photos of Cats <http://137.189.35.203/WebUI/CatDatabase/catData.html>`_ or `Archive version <https://web.archive.org/web/20150520175645/http://137.189.35.203/WebUI/CatDatabase/catData.html>`_ * `2GB of Photos of Cats <http://137.189.35.203/WebUI/CatDatabase/catData.html>`_ or `Archive version <https://web.archive.org/web/20150520175645/http://137.189.35.203/WebUI/CatDatabase/catData.html>`_
* `Adience Unfiltered faces for gender and age classification <http://www.openu.ac.il/home/hassner/Adience/data.html>`_
* `Affective Image Classification <http://www.imageemotion.org/>`_ * `Affective Image Classification <http://www.imageemotion.org/>`_
* `Animals with attributes <http://attributes.kyb.tuebingen.mpg.de/>`_ * `Animals with attributes <http://attributes.kyb.tuebingen.mpg.de/>`_
* `Caltech Pedestrian Detection Benchmark <https://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/>`_
* `Chars74K dataset, Character Recognition in Natural Images (both English and Kannada are available) <http://www.ee.surrey.ac.uk/CVSSP/demos/chars74k/>`_ * `Chars74K dataset, Character Recognition in Natural Images (both English and Kannada are available) <http://www.ee.surrey.ac.uk/CVSSP/demos/chars74k/>`_
* `Face Recognition Benchmark <http://www.face-rec.org/databases/>`_ * `Face Recognition Benchmark <http://www.face-rec.org/databases/>`_
* `GDXray: X-ray images for X-ray testing and Computer Vision <http://dmery.ing.puc.cl/index.php/material/gdxray/>`_
* `ImageNet (in WordNet hierarchy) <http://www.image-net.org/>`_ * `ImageNet (in WordNet hierarchy) <http://www.image-net.org/>`_
* `Indoor Scene Recognition <http://web.mit.edu/torralba/www/indoor.html>`_ * `Indoor Scene Recognition <http://web.mit.edu/torralba/www/indoor.html>`_
* `International Affective Picture System, UFL <http://csea.phhp.ufl.edu/media/iapsmessage.html>`_ * `International Affective Picture System, UFL <http://csea.phhp.ufl.edu/media/iapsmessage.html>`_
@ -301,17 +300,17 @@ Image Processing
* `Several Shape-from-Silhouette Datasets <http://kaiwolf.no-ip.org/3d-model-repository.html>`_ * `Several Shape-from-Silhouette Datasets <http://kaiwolf.no-ip.org/3d-model-repository.html>`_
* `Stanford Dogs Dataset <http://vision.stanford.edu/aditya86/ImageNetDogs/>`_ * `Stanford Dogs Dataset <http://vision.stanford.edu/aditya86/ImageNetDogs/>`_
* `SUN database, MIT <http://groups.csail.mit.edu/vision/SUN/hierarchy.html>`_ * `SUN database, MIT <http://groups.csail.mit.edu/vision/SUN/hierarchy.html>`_
* `The Oxford-IIIT Pet Dataset <http://www.robots.ox.ac.uk/~vgg/data/pets/>`_
* `YouTube Faces Database <http://www.cs.tau.ac.il/~wolf/ytfaces/>`_
* `Adience Unfiltered faces for gender and age classification <http://www.openu.ac.il/home/hassner/Adience/data.html>`_
* `The Action Similarity Labeling (ASLAN) Challenge <http://www.openu.ac.il/home/hassner/data/ASLAN/ASLAN.html>`_ * `The Action Similarity Labeling (ASLAN) Challenge <http://www.openu.ac.il/home/hassner/data/ASLAN/ASLAN.html>`_
* `The Oxford-IIIT Pet Dataset <http://www.robots.ox.ac.uk/~vgg/data/pets/>`_
* `Violent-Flows - Crowd Violence \ Non-violence Database and benchmark <http://www.openu.ac.il/home/hassner/data/violentflows/>`_ * `Violent-Flows - Crowd Violence \ Non-violence Database and benchmark <http://www.openu.ac.il/home/hassner/data/violentflows/>`_
* `Visual genome <http://visualgenome.org/api/v0/api_home.html>`_ * `Visual genome <http://visualgenome.org/api/v0/api_home.html>`_
* `Caltech Pedestrian Detection Benchmark <https://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/>`_ * `YouTube Faces Database <http://www.cs.tau.ac.il/~wolf/ytfaces/>`_
Machine Learning Machine Learning
---------------- ----------------
* `Context-aware data sets from five domains <https://github.com/irecsys/CARSKit/tree/master/context-aware_data_sets>`_
* `Delve Datasets for classification and regression (Univ. of Toronto) <http://www.cs.toronto.edu/~delve/data/datasets.html>`_ * `Delve Datasets for classification and regression (Univ. of Toronto) <http://www.cs.toronto.edu/~delve/data/datasets.html>`_
* `Discogs Monthly Data <http://data.discogs.com/>`_ * `Discogs Monthly Data <http://data.discogs.com/>`_
* `eBay Online Auctions (2012) <http://www.modelingonlineauctions.com/datasets>`_ * `eBay Online Auctions (2012) <http://www.modelingonlineauctions.com/datasets>`_
@ -322,8 +321,8 @@ Machine Learning
* `Machine Learning Data Set Repository <http://mldata.org/>`_ * `Machine Learning Data Set Repository <http://mldata.org/>`_
* `Million Song Dataset <http://labrosa.ee.columbia.edu/millionsong/>`_ * `Million Song Dataset <http://labrosa.ee.columbia.edu/millionsong/>`_
* `More Song Datasets <http://labrosa.ee.columbia.edu/millionsong/pages/additional-datasets>`_ * `More Song Datasets <http://labrosa.ee.columbia.edu/millionsong/pages/additional-datasets>`_
* `New Yorker caption contest ratings <https://github.com/nextml/caption-contest-data>`_
* `MovieLens Data Sets <http://grouplens.org/datasets/movielens/>`_ * `MovieLens Data Sets <http://grouplens.org/datasets/movielens/>`_
* `New Yorker caption contest ratings <https://github.com/nextml/caption-contest-data>`_
* `RDataMining - "R and Data Mining" ebook data <http://www.rdatamining.com/data>`_ * `RDataMining - "R and Data Mining" ebook data <http://www.rdatamining.com/data>`_
* `Registered Meteorites on Earth <http://healthintelligence.drupalgardens.com/content/registered-meteorites-has-impacted-earth-visualized>`_ * `Registered Meteorites on Earth <http://healthintelligence.drupalgardens.com/content/registered-meteorites-has-impacted-earth-visualized>`_
* `Restaurants Health Score Data in San Francisco <http://missionlocal.org/san-francisco-restaurant-health-inspections/>`_ * `Restaurants Health Score Data in San Francisco <http://missionlocal.org/san-francisco-restaurant-health-inspections/>`_
@ -347,6 +346,7 @@ Museums
Natural Language Natural Language
---------------- ----------------
* `Automatic Keyphrase Extracttion <https://github.com/snkim/AutomaticKeyphraseExtraction/>`_
* `Blogger Corpus <http://u.cs.biu.ac.il/~koppel/BlogCorpus.htm>`_ * `Blogger Corpus <http://u.cs.biu.ac.il/~koppel/BlogCorpus.htm>`_
* `CLiPS Stylometry Investigation Corpus <http://www.clips.uantwerpen.be/datasets/csi-corpus>`_ * `CLiPS Stylometry Investigation Corpus <http://www.clips.uantwerpen.be/datasets/csi-corpus>`_
* `ClueWeb09 FACC <http://lemurproject.org/clueweb09/FACC1/>`_ * `ClueWeb09 FACC <http://lemurproject.org/clueweb09/FACC1/>`_
@ -361,37 +361,36 @@ Natural Language
* `Hansards text chunks of Canadian Parliament <http://www.isi.edu/natural-language/download/hansard/>`_ * `Hansards text chunks of Canadian Parliament <http://www.isi.edu/natural-language/download/hansard/>`_
* `Machine Comprehension Test (MCTest) of text from Microsoft Research <http://research.microsoft.com/en-us/um/redmond/projects/mctest/index.html>`_ * `Machine Comprehension Test (MCTest) of text from Microsoft Research <http://research.microsoft.com/en-us/um/redmond/projects/mctest/index.html>`_
* `Machine Translation of European languages <http://statmt.org/wmt11/translation-task.html#download>`_ * `Machine Translation of European languages <http://statmt.org/wmt11/translation-task.html#download>`_
* `Multi-Domain Sentiment Dataset (version 2.0) <http://www.cs.jhu.edu/~mdredze/datasets/sentiment/>`_
* `Microsoft MAchine Reading COmprehension Dataset (or MS MARCO) <http://www.msmarco.org/dataset.aspx>`_ * `Microsoft MAchine Reading COmprehension Dataset (or MS MARCO) <http://www.msmarco.org/dataset.aspx>`_
* `Multi-Domain Sentiment Dataset (version 2.0) <http://www.cs.jhu.edu/~mdredze/datasets/sentiment/>`_
* `Open Multilingual Wordnet <http://compling.hss.ntu.edu.sg/omw/>`_
* `Personae Corpus <http://www.clips.uantwerpen.be/datasets/personae-corpus>`_ * `Personae Corpus <http://www.clips.uantwerpen.be/datasets/personae-corpus>`_
* `SaudiNewsNet Collection of Saudi Newspaper Articles (Arabic, 30K articles) <https://github.com/ParallelMazen/SaudiNewsNet>`_ * `SaudiNewsNet Collection of Saudi Newspaper Articles (Arabic, 30K articles) <https://github.com/ParallelMazen/SaudiNewsNet>`_
* `SMS Spam Collection in English <http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/>`_ * `SMS Spam Collection in English <http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/>`_
* `Universal Dependencies <http://universaldependencies.org>`_
* `USENET postings corpus of 2005~2011 <http://www.psych.ualberta.ca/~westburylab/downloads/usenetcorpus.download.html>`_ * `USENET postings corpus of 2005~2011 <http://www.psych.ualberta.ca/~westburylab/downloads/usenetcorpus.download.html>`_
* `Webhose - News/Blogs in multiple languages <https://webhose.io/datasets>`_
* `Wikidata - Wikipedia databases <https://www.wikidata.org/wiki/Wikidata:Database_download>`_ * `Wikidata - Wikipedia databases <https://www.wikidata.org/wiki/Wikidata:Database_download>`_
* `Wikipedia Links data - 40 Million Entities in Context <https://code.google.com/p/wiki-links/downloads/list>`_ * `Wikipedia Links data - 40 Million Entities in Context <https://code.google.com/p/wiki-links/downloads/list>`_
* `Universal Dependencies <http://universaldependencies.org>`_
* `WordNet databases and tools <http://wordnet.princeton.edu/wordnet/download/>`_ * `WordNet databases and tools <http://wordnet.princeton.edu/wordnet/download/>`_
* `Open Multilingual Wordnet <http://compling.hss.ntu.edu.sg/omw/>`_
* `Automatic Keyphrase Extracttion <https://github.com/snkim/AutomaticKeyphraseExtraction/>`_
* `News/Blogs in multiple languages <https://webhose.io/datasets/>`_
Neuroscience Neuroscience
------------- -------------
* `Allen Institute Datasets <http://www.brain-map.org/>`_ * `Allen Institute Datasets <http://www.brain-map.org/>`_
* `Brain Catalogue <http://braincatalogue.org/>`_ * `Brain Catalogue <http://braincatalogue.org/>`_
* `Brainomics <http://brainomics.cea.fr/localizer>`_ * `Brainomics <http://brainomics.cea.fr/localizer>`_
* `CodeNeuro Datasets <http://datasets.codeneuro.org/>`_ * `CodeNeuro Datasets <http://datasets.codeneuro.org/>`_
* `Collaborative Research in Computational Neuroscience (CRCNS) <http://crcns.org/data-sets>`_ * `Collaborative Research in Computational Neuroscience (CRCNS) <http://crcns.org/data-sets>`_
* `FCP-INDI <http://fcon_1000.projects.nitrc.org/index.html>`_ * `FCP-INDI <http://fcon_1000.projects.nitrc.org/index.html>`_
* `Human Connectome Project <http://www.humanconnectome.org/data/>`_ * `Human Connectome Project <http://www.humanconnectome.org/data/>`_
* `NDAR <https://ndar.nih.gov/>`_ * `NDAR <https://ndar.nih.gov/>`_
* `NIMH Data Archive <http://data-archive.nimh.nih.gov/>`_
* `NeuroData <http://neurodata.io>`_ * `NeuroData <http://neurodata.io>`_
* `Neuroelectro <http://neuroelectro.org/>`_
* `NIMH Data Archive <http://data-archive.nimh.nih.gov/>`_
* `OASIS <http://www.oasis-brains.org/>`_ * `OASIS <http://www.oasis-brains.org/>`_
* `OpenfMRI <https://openfmri.org/>`_ * `OpenfMRI <https://openfmri.org/>`_
* `Neuroelectro <http://neuroelectro.org/>`_
* `Study Forrest <http://studyforrest.org>`_ * `Study Forrest <http://studyforrest.org>`_
@ -419,9 +418,9 @@ Public Domains
* `Archive.org Datasets <https://archive.org/details/datasets>`_ * `Archive.org Datasets <https://archive.org/details/datasets>`_
* `CMU JASA data archive <http://lib.stat.cmu.edu/jasadata/>`_ * `CMU JASA data archive <http://lib.stat.cmu.edu/jasadata/>`_
* `CMU StatLab collections <http://lib.stat.cmu.edu/datasets/>`_ * `CMU StatLab collections <http://lib.stat.cmu.edu/datasets/>`_
* `Data.World <https://data.world>`_
* `Data360 <http://www.data360.org/index.aspx>`_ * `Data360 <http://www.data360.org/index.aspx>`_
* `Datamob.org <http://datamob.org/datasets>`_ * `Datamob.org <http://datamob.org/datasets>`_
* `Data.World <https://data.world>`_
* `Google <http://www.google.com/publicdata/directory>`_ * `Google <http://www.google.com/publicdata/directory>`_
* `Infochimps <http://www.infochimps.com/>`_ * `Infochimps <http://www.infochimps.com/>`_
* `KDNuggets Data Collections <http://www.kdnuggets.com/datasets/index.html>`_ * `KDNuggets Data Collections <http://www.kdnuggets.com/datasets/index.html>`_
@ -477,8 +476,8 @@ Social Networks
* `Skytrax' Air Travel Reviews Dataset <https://github.com/quankiquanki/skytrax-reviews-dataset>`_ * `Skytrax' Air Travel Reviews Dataset <https://github.com/quankiquanki/skytrax-reviews-dataset>`_
* `Social Twitter Data <http://snap.stanford.edu/data/egonets-Twitter.html>`_ * `Social Twitter Data <http://snap.stanford.edu/data/egonets-Twitter.html>`_
* `SourceForge.net Research Data <http://www3.nd.edu/~oss/Data/data.html>`_ * `SourceForge.net Research Data <http://www3.nd.edu/~oss/Data/data.html>`_
* `Twitter Data for Sentiment Analysis <http://help.sentiment140.com/for-students/>`_
* `Twitter Data for Online Reputation Management <http://nlp.uned.es/replab2013/>`_ * `Twitter Data for Online Reputation Management <http://nlp.uned.es/replab2013/>`_
* `Twitter Data for Sentiment Analysis <http://help.sentiment140.com/for-students/>`_
* `Twitter Graph of entire Twitter site <http://an.kaist.ac.kr/traces/WWW2010.html>`_ * `Twitter Graph of entire Twitter site <http://an.kaist.ac.kr/traces/WWW2010.html>`_
* `Twitter Scrape Calufa May 2011 <http://archive.org/details/2011-05-calufa-twitter-sql>`_ * `Twitter Scrape Calufa May 2011 <http://archive.org/details/2011-05-calufa-twitter-sql>`_
* `UNIMI/LAW Social Network Datasets <http://law.di.unimi.it/datasets.php>`_ * `UNIMI/LAW Social Network Datasets <http://law.di.unimi.it/datasets.php>`_
@ -523,11 +522,11 @@ Social Sciences
* `Texas Inmates Executed Since 1984 <http://www.tdcj.state.tx.us/death_row/dr_executed_offenders.html>`_ * `Texas Inmates Executed Since 1984 <http://www.tdcj.state.tx.us/death_row/dr_executed_offenders.html>`_
* `Titanic Survival Data Set <https://github.com/caesar0301/awesome-public-datasets/tree/master/Datasets>`_ or `on Kaggle <https://www.kaggle.com/c/titanic/data>`_ * `Titanic Survival Data Set <https://github.com/caesar0301/awesome-public-datasets/tree/master/Datasets>`_ or `on Kaggle <https://www.kaggle.com/c/titanic/data>`_
* `UCB's Archive of Social Science Data (D-Lab) <http://ucdata.berkeley.edu/>`_ * `UCB's Archive of Social Science Data (D-Lab) <http://ucdata.berkeley.edu/>`_
* `Uppsala Conflict Data Program <http://ucdp.uu.se/>`_
* `UCLA Social Sciences Data Archive <http://dataarchives.ss.ucla.edu/Home.DataPortals.htm>`_ * `UCLA Social Sciences Data Archive <http://dataarchives.ss.ucla.edu/Home.DataPortals.htm>`_
* `UN Civil Society Database <http://esango.un.org/civilsociety/>`_ * `UN Civil Society Database <http://esango.un.org/civilsociety/>`_
* `Universities Worldwide <http://univ.cc/>`_ * `Universities Worldwide <http://univ.cc/>`_
* `UPJOHN for Labor Employment Research <http://www.upjohn.org/services/resources/employment-research-data-center>`_ * `UPJOHN for Labor Employment Research <http://www.upjohn.org/services/resources/employment-research-data-center>`_
* `Uppsala Conflict Data Program <http://ucdp.uu.se/>`_
* `World Bank Open Data <http://data.worldbank.org/>`_ * `World Bank Open Data <http://data.worldbank.org/>`_
* `WorldPop project - Worldwide human population distributions <http://www.worldpop.org.uk/data/get_data/>`_ * `WorldPop project - Worldwide human population distributions <http://www.worldpop.org.uk/data/get_data/>`_
@ -594,8 +593,8 @@ Complementary Collections
* `Data Packaged Core Datasets <https://github.com/datasets/>`_ * `Data Packaged Core Datasets <https://github.com/datasets/>`_
* `Database of Scientific Code Contributions <https://mozillascience.org/collaborate>`_ * `Database of Scientific Code Contributions <https://mozillascience.org/collaborate>`_
* DataWrangling: `Some Datasets Available on the Web <http://www.datawrangling.com/some-datasets-available-on-the-web>`_
* A growing collection of public datasets: `CoolDatasets. <http://cooldatasets.com/>`_ * A growing collection of public datasets: `CoolDatasets. <http://cooldatasets.com/>`_
* DataWrangling: `Some Datasets Available on the Web <http://www.datawrangling.com/some-datasets-available-on-the-web>`_
* Inside-r: `Finding Data on the Internet <http://www.inside-r.org/howto/finding-data-internet>`_ * Inside-r: `Finding Data on the Internet <http://www.inside-r.org/howto/finding-data-internet>`_
* OpenDataMonitor: `An overview of available open data resources in Europe <http://opendatamonitor.eu>`_ * OpenDataMonitor: `An overview of available open data resources in Europe <http://opendatamonitor.eu>`_
* Quora: `Where can I find large datasets open to the public? <http://www.quora.com/Where-can-I-find-large-datasets-open-to-the-public>`_ * Quora: `Where can I find large datasets open to the public? <http://www.quora.com/Where-can-I-find-large-datasets-open-to-the-public>`_