Awesome Public Datasets

这是一个收集和整理自互联网上的大规模公共数据集的列表，涵盖气候、经济、能源、金融、生物、物理、医疗健康、地理空间/GIS等多个领域。

This is a list of large-scale public datasets collected and curated from the internet, covering multiple fields such as climate, economics, energy, finance, biology, physics, healthcare, and geospatial/GIS.

原始信息汇总

数据集概述

气候数据集

• Australian Weather: http://www.bom.gov.au/climate/dwo/
• Climate data: http://www.cru.uea.ac.uk/cru/data/temperature/ and ftp://ftp.cmdl.noaa.gov/
• Global climate data since 1929: http://www.tutiempo.net/en/Climate
• NOAA Bering Sea Climate: http://www.beringclimate.noaa.gov/
• NOAA climate datasets: http://ncdc.noaa.gov/data-access/quick-links
• WU Historical Weather Worldwide: http://www.wunderground.com/history/index.html

经济学数据集

• American Economic Ass. (AEA): http://www.aeaweb.org/RFE/toc.php?show=complete
• EconData (UMD): http://inforumweb.umd.edu/econdata/econdata.html
• Internet Product Code Database: http://www.upcdatabase.com/
• World bank: http://data.worldbank.org/indicator

能源数据集

• AMPds: http://ampds.org/
• BLUEd: http://nilm.cmubi.org/
• COMBED: http://combed.github.io/
• Dataport: https://dataport.pecanstreet.org/
• ECO: http://www.vs.inf.ethz.ch/res/show.html?what=eco-data
• iAWE: http://iawe.github.io/
• HFED: http://hfed.github.io/
• Plaid: http://plaidplug.com/
• REDD: http://redd.csail.mit.edu/
• UK-Dale: http://www.doc.ic.ac.uk/~dk3810/data/

金融数据集

• CBOE Futures Exchange: http://cfe.cboe.com/Data/
• Google Finance: https://www.google.com/finance
• Google Trends: http://www.google.com/trends?q=google&ctab=0&geo=all&date=all&sort=0
• NASDAQ: https://data.nasdaq.com/
• OANDA: http://www.oanda.com/
• OSU Financial data: http://fisher.osu.edu/fin/osudata.htm or http://fisher.osu.edu/fin/fdf/osudata.htm
• Quandl: http://www.quandl.com/
• St Louis Federal: http://research.stlouisfed.org/fred2/
• Yahoo Finance: http://finance.yahoo.com/

生物学数据集

• CRCNS: http://crcns.org/data-sets
• Gene Expression Omnibus: http://www.ncbi.nlm.nih.gov/geo/
• Human Microbiome Project: http://www.hmpdacc.org/reference_genomes/reference_genomes.php
• MIT Cancer Genomics Data: http://www.broadinstitute.org/cgi-bin/cancer/datasets.cgi
• NIH Microarray data: ftp://ftp.ncbi.nih.gov/pub/geo/DATA/supplementary/series/GSE6532/
• Protein structure: http://www.infobiotic.net/PSPbenchmarks/
• Protein Data Bank: http://pdb.org/
• Public Gene Data: http://www.pubgene.org/
• Stanford Microarray Data: http://smd.stanford.edu/
• UniGene: http://www.ncbi.nlm.nih.gov/unigene
• The Personal Genome Project: http://www.personalgenomes.org/ or https://my.pgp-hms.org/public_genetic_data
• 1000 Genomes: http://www.1000genomes.org/data
• UCSC Public Data: http://hgdownload.soe.ucsc.edu/downloads.html

物理学数据集

• NASA: http://nssdc.gsfc.nasa.gov/nssdc/obtaining_data.html

医疗保健数据集

• EHDP Large Health Data Sets: http://www.ehdp.com/vitalnet/datasets.htm
• Gapminder: http://www.gapminder.org/data/
• Medicare Data File: http://go.cms.gov/19xxPN4

地理空间/GIS数据集

• EOSDIS: http://sedac.ciesin.columbia.edu/data/sets/browse
• Factual Global Location Data: http://www.factual.com/
• Geo Spatial Data: http://geodacenter.asu.edu/datalist/
• OpenStreetMap (a free map worldwide): http://wiki.openstreetmap.org/wiki/Downloading_data
• GeoNames (over eight million placenames): http://www.geonames.org/
• BODC (marine data of nearly 22,000 oceanographic vars): http://www.bodc.ac.uk/data/where_to_find_data/
• GADM (Global Administrative Areas database): http://www.gadm.org/
• twofishes (Foursquares coarse geocoder): https://github.com/foursquare/twofishes
• Natural Earth (vectors and rasters of the world): http://www.naturalearthdata.com/
• tz_world (timezone polygons): http://efele.net/maps/tz/world/
• TIGER/Line (official United States boundaries and roads): http://www.census.gov/geo/maps-data/data/tiger-line.html

交通运输数据集

• Airlines Data (2009 ASA Challenge): http://stat-computing.org/dataexpo/2009/the-data.html
• Bike Share Data Systems: https://github.com/BetaNYC/Bike-Share-Data-Best-Practices/wiki/Bike-Share-Data-Systems
• Edge data for US domestic flights 1990 to 2009: http://data.memect.com/?p=229
• Half a million Hubway rides: http://hubwaydatachallenge.org/trip-history-data/
• NYC Taxi Trip Data 2013 (FOIA/FOIL): https://archive.org/details/nycTaxiTripData2013
• OpenFlights (airport, airline and route data): http://openflights.org/data.html
• RITA Airline On-Time Performance Data: http://www.transtats.bts.gov/Tables.asp?DB_ID=120
• RITA transport data collection: http://www.transtats.bts.gov/DataIndex.asp
• Transport for London: http://www.tfl.gov.uk/info-for/open-data-users/our-feeds
• U.S. Freight Analysis Framework: http://ops.fhwa.dot.gov/freight/freight_analysis/faf/index.htm
• Marine Traffic - ship tracks, port calls and more: https://www.marinetraffic.com/de/p/api-services

政府数据集

• Archive-it: https://www.archive-it.org/explore?show=Collections
• Australia: https://data.gov.au/
• Australia: http://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/3301.02009?OpenDocument
• Canada: http://www.data.gc.ca/default.asp?lang=En&n=5BCD274E-1
• Chicago: https://data.cityofchicago.org/
• FDA: https://open.fda.gov/index.html
• Fed Stats: http://www.fedstats.gov/cgi-bin/A2Z.cgi
• Guardian world governments: http://www.guardian.co.uk/world-government-data
• HUD: http://www.huduser.org/portal/datasets/pdrdatas.html
• London Datastore, U.K: http://data.london.gov.uk/dataset
• Glasgow, Scotland, UK: http://data.glasgow.gov.uk/
• Netherlands: https://data.overheid.nl/
• New Zealand: http://www.stats.govt.nz/browse_for_stats.aspx
• NYC betanyc: http://betanyc.us/
• NYC Open Data: http://nycplatform.socrata.com/
• OECD: http://www.oecd.org/document/0,3746,en_2649_201185_46462759_1_1_1_1,00.html
• RITA: http://www.transtats.bts.gov/OT_Delay/OT_DelayCause1.asp
• San Francisco Data sets: http://datasf.org/
• The World Bank: http://wdronline.worldbank.org/
• U.K. Government Data: http://data.gov.uk/data
• U.S. Census Bureau: http://www.census.gov/data.html
• U.S. American Community Survey: http://www.census.gov/acs/www/data_documentation/data_release_info/
• U.S. Federal Government Agencies: http://www.data.gov/metric
• U.S. Federal Government Data Catalog: http://catalog.data.gov/dataset
• U.S. Open Government: http://www.data.gov/open-gov/
• UK 2011 Census Open Atlas Project: http://www.alex-singleton.com/2011-census-open-atlas-project/
• United Nations: http://data.un.org/
• US CDC Public Health datasets: http://www.cdc.gov/nchs/data_access/ftp_data.htm
• Open Government Data (OGD) Platform India: http://www.data.gov.in/

数据挑战

• Challenges in Machine Learning: http://www.chalearn.org/
• ICWSM Data Challenge (since 2009): http://icwsm.cs.umbc.edu/
• Kaggle Competition Data: http://www.kaggle.com/
• KDD Cup by Tencent 2012: https://www.kddcup2012.org/
• Netflix Prize: http://www.netflixprize.com/leaderboard
• Yelp Dataset Challenge: http://www.yelp.com/dataset_challenge

机器学习数据集

• eBay Online Auctions: http://www.modelingonlineauctions.com/datasets
• IMDb database: http://www.imdb.com/interfaces
• Keel Repository: http://sci2s.ugr.es/keel/datasets.php
• Lending Club Loan Data: https://www.lendingclub.com/info/download-data.action
• Machine Learning Data Set Repository: http://mldata.org/
• Million Song Dataset: http://blog.echonest.com/post/3639160982/million-song-dataset
• More Song Datasets: http://labrosa.ee.columbia.edu/millionsong/pages/additional-datasets
• MovieLens Data Sets: http://datahub.io/dataset/movielens
• RDataMining R and Data Mining ebook data: http://www.rdatamining.com/data
• Registered meteorites on Earth: http://www.analyticbridge.com/profiles/blogs/registered-meteorites-that-has-impacted-on-earth-visualized
• SF restaurants dataset: http://missionlocal.org/san-francisco-restaurant-health-inspections/
• UCI Machine Learning Repository: http://archive.ics.uci.edu/ml/
• University of Toronto Delve Datasets: http://www.cs.toronto.edu/~delve/data/datasets.html
• Yahoo Ratings and Classification Data: http://webscope.sandbox.yahoo.com/catalog.php?datatype=r

自然语言处理数据集

• 40 Million Entities in Context: https://code.google.com/p/wiki-links/downloads/list
• ClueWeb09 FACC: http://lemurproject.org/clueweb09/FACC1/
• ClueWeb12 FACC: http://lemurproject.org/clueweb12/FACC1/
• Flickr personal taxonomies: http://www.isi.edu/natural-language/download/flickr/flickr_taxonomies.html
• Google Books Ngrams: http://aws.amazon.com/datasets/8172056142375670
• Google Web 5gram, 2006 (1T): https://catalog.ldc.upenn.edu/LDC2006T13
• Gutenberg eBooks List: http://www.gutenberg.org/wiki/Gutenberg:Offline_Catalogs
• Hansards: http://www.isi.edu/natural-language/download/hansard/
• Machine Translation: http://statmt.org/wmt11/translation-task.html#download
• SMS Spam Collection: http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/
• USENET corpus: http://www.psych.ualberta.ca/~westburylab/downloads/usenetcorpus.download.html
• WordNet: http://wordnet.princeton.edu/wordnet/download/

图像处理数据集

• 2GB of photos of cats: http://137.189.35.203/WebUI/CatDatabase/catData.html
• Face Recognition Benchmark: http://www.face-rec.org/databases/
• ImageNet: http://www.image-net.org/

时间序列数据集

• Time Series data Library: https://datamarket.com/data/list/?q=provider:tsdl
• UC Riverside Time Series: http://www.cs.ucr.edu/~eamonn/time_series_data/

社会科学数据集

• China Hotel Checkin/out data: http://www.360doc.com/content/13/1105/13/7863900_326788919.shtml
• CMU Enron Email: http://www.cs.cmu.edu/~enron/
• Facebook Social Networks (since 2007): http://law.di.unimi.it/datasets.php
• Facebook100 (2005): https://archive.org/details/oxford-2005-facebook-matrix
• Foursquare (2010,2011): http://www.public.asu.edu/~hgao16/dataset.html
• Foursquare (UMN/Sarwat, 2013): https://archive.org/details/201309_foursquare_dataset_umn
• General Social Survey (GSS): http://www3.norc.org/GSS+Website/
• GetGlue (users rating TV shows): http://bit.ly/1aL8XS0
• GitHub Archive: http://www.githubarchive.org/
• ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/index.jsp
• Mobile Social Networks (UMASS): https://kdl.cs.umass.edu/display/public/Mobile+Social+Networks
• PewResearch Internet Project: http://www.pewinternet.org/datasets/pages/2/
• Social Networking: http://www.cs.cmu.edu/~jelsas/data/ancestry.com/
• SourceForge Graph: http://www.nd.edu/~oss/Data/data.html
• Titanic Survival Data Set: https://github.com/caesar0301/awesome-public-datasets/blob/master/Datasets/titanic.csv.zip
• Twitter Graph: http://an.kaist.ac.kr/traces/WWW2010.html
• UC Berkeleys D-Lab Achive: http://ucdata.berkeley.edu/
• UCLA Social Sciences Data Archive: http://dataarchives.ss.ucla.edu/Home.DataPortals.htm
• UNIMI Social Network Datasets: http://law.di

搜集汇总

数据集介绍

构建方式

Awesome Public Datasets 数据集是通过整合来自博客、问答平台以及用户反馈的公开数据源构建而成。该数据集涵盖了多个领域，包括气候、经济、能源、金融、生物学、物理学、医疗保健、地理空间、交通、政府、数据挑战、机器学习、自然语言处理、图像处理、时间序列、社会科学、复杂网络、计算机网络、博物馆等多个主题。每个数据源均经过筛选和整理，确保其可用性和相关性。

特点

该数据集的特点在于其广泛的数据覆盖范围和多样化的数据来源。它不仅包含了来自政府机构、学术研究机构和企业的数据，还涵盖了多个学科领域的数据集。数据集中的每个条目都附有详细的来源链接，便于用户直接访问原始数据。此外，数据集中的大部分数据是免费的，尽管部分数据可能需要付费获取。这种多样性和开放性使得该数据集成为研究人员和数据科学家的宝贵资源。

使用方法

用户可以通过访问 Awesome Public Datasets 的 GitHub 页面，浏览不同主题下的数据集链接。每个数据集都附有详细的描述和来源链接，用户可以根据需求选择合适的数据集进行下载和使用。对于需要进一步处理的数据，用户可以使用常见的数据分析工具（如 Python、R 或 Excel）进行清洗和分析。此外，数据集中的链接可以直接跳转到原始数据源，方便用户获取最新的数据更新。

背景与挑战

背景概述

Awesome Public Datasets 是一个广泛收集和整理公共数据源的资源库，涵盖了从气候、经济、能源到生物、物理、医疗保健等多个领域的数据集。该数据集由GitHub用户caesar0301于2014年创建，旨在为研究人员、数据科学家和开发者提供一个便捷的公共数据访问平台。其核心研究问题在于如何高效地整合和分类来自不同领域的公开数据，以便于跨学科的研究和应用。该数据集的影响力体现在其广泛的应用场景中，包括机器学习、自然语言处理、图像处理等，为相关领域的研究提供了丰富的数据支持。

当前挑战

Awesome Public Datasets 面临的挑战主要体现在两个方面。首先，数据集所涵盖的领域极为广泛，如何确保数据的准确性和时效性是一个持续的挑战。其次，数据集的构建过程中，数据来源的多样性和格式的不统一性增加了数据整理和分类的难度。此外，部分数据集可能涉及版权或隐私问题，如何在合法合规的前提下提供数据访问也是一个需要解决的问题。这些挑战不仅影响了数据集的使用效率，也对数据集的长期维护提出了更高的要求。

常用场景

经典使用场景

Awesome Public Datasets 数据集广泛应用于跨学科研究，尤其是在气候、经济、能源、金融、生物学、物理学、医疗保健、地理信息系统、交通运输、政府数据、机器学习、自然语言处理、图像处理、时间序列分析、社会科学、复杂网络、计算机网络等领域。研究者可以通过该数据集获取全球范围内的公开数据，进行数据分析和模型训练，推动科学研究和技术创新。

实际应用

在实际应用中，Awesome Public Datasets 数据集为政府、企业和研究机构提供了丰富的数据支持。例如，政府可以利用该数据集中的交通数据进行城市规划；企业可以通过金融数据集进行市场分析和风险评估；医疗机构可以借助医疗数据集进行疾病预测和健康管理。这些应用场景展示了该数据集在推动社会经济发展中的重要作用。

衍生相关工作

该数据集衍生了许多经典的研究工作。例如，在机器学习领域，基于该数据集的公开数据，研究者开发了多种预测模型和算法；在自然语言处理领域，Google Books Ngrams 数据集被广泛用于语言模型训练；在图像处理领域，ImageNet 数据集推动了深度学习在计算机视觉中的应用。这些衍生工作不仅推动了相关领域的技术进步，也为后续研究提供了宝贵的参考。