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Web Science 2014 Data Challenge

The datasets described below are used in the Web Science 2014 Data Challenge. For more, information, please the call for participation. For updates, see the Data Challenge section of the Web Science 2014 website.

There are 4 datasets in this collection. Each is available as a .tar.gz file containing either .json or .csv files. When the JSON format is used, each .json file contains a single JSON object. The format of that object is dependent on the dataset. See below for details. The datasets have been prepared by Dimitar Nikolov.

1. Web Traffic

A collection of Web (HTTP) requests for the month of November 2009. This is a small sample of the larger click dataset, documented here. (More on Web Traffic project).

JSON object format:

    'timestamp': 123456789, # Unix timestamp
    'from': '...', # the referrer host
    'to': '...', # the target host
    'count': 1234 # the number of request between the referrer and target hosts that occurred within the given hour

The data has been aggregated for every hour of the day. Thus, if more than one request occurred from the same referrer host to the same target host between, say, 2pm and 3pm, this is reflected in the ‘count’ field of the JSON object with a timestamp for 2pm, rather than by a different JSON object with a different timestamp.

Dataset statistics:

  • Dataset size: 235M requests
  • File size: 2.7GB uncompressed
  • Time period: Nov 1, 2009 – Nov 22, 2009

Data: web-clicks-nov-2009.tgz (321MB)

If you use this dataset in your research, please cite either or both of these papers:

    title = {Ranking Web Sites with Real User Traffic},
    author = {Meiss, M. and Menczer, F. and Fortunato, S. and Flammini, A. and Vespignani, A.},
    booktitle = {Proc. First ACM International Conference on Web Search and Data Mining (WSDM)},
    url = {http://informatics.indiana.edu/fil/Papers/click.pdf},
    pages = {65--75},
    year = 2008
    title = {Modeling Traffic on the Web Graph},
    author = {Meiss, M. and Goncalves, B. and Ramasco, J. and Flammini, A. and Menczer, F.},
    booktitle = {Proc. 7th Workshop on Algorithms and Models for the Web Graph (WAW)},
    series = {Lecture Notes in Computer Science},
    url = {http://informatics.indiana.edu/fil/Papers/abc.pdf},
    pages = {50--61},
    volume = 6516,
    year = 2010


2. Twitter

A collection of records extracted from tweets for the month of November 2012 containing both #hashtags and URLs as part of the tweet. (More on Truthy project)

JSON object format:

    'timestamp': 123456789, # Unix timestamp
    'user_id': 12345, # an integer uniquely identifying the user who tweeted
    'hashtags': ['...', '...', '...'], # a list of hashtags used in the tweet
    'urls': ['...', '...', '...'] # a list of links used in the tweet

Dataset statistics:

  • Dataset size: 27.8M tweets
  • File size: 3.5GB uncompressed
  • Time Period: Nov 1, 2012 – Nov 30, 2012

Data: tweets-nov-2012.json.gz (865MB)

If you use this dataset in your research, please cite either or both of these papers:

    author = {McKelvey, Karissa and Menczer, Filippo},
    title = {Design and prototyping of a social media observatory},
    booktitle = {Proceedings of the 22nd international conference on World Wide Web companion},
    series = {WWW '13 Companion},
    pages = {1351--1358},
    url = {http://dl.acm.org/citation.cfm?id=2487788.2488174},
    year = 2013
    Author = {Karissa McKelvey and Filippo Menczer},
    Title = {{Truthy: Enabling the Study of Online Social Networks}},
    Booktitle = {Proc. 16th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion (CSCW)},
    Url = {http://arxiv.org/abs/1212.4565},
    Year = 2013


3. Social Bookmarking

A collection of bookmarks from GiveALink.org for the month of November 2009. (More on GiveALink project)

JSON object format:

    'timestamp': 123456789, # Unix timestamp for when the URL was posted
    'url': '...', # the URL that was bookmarked
    'hashtags': ['...', '...', '...'] # a set of tags attached to the URL by the (anonymous) user

Dataset statistics:

  • Dataset size: 61,665 posts (approximately 430,000 triples)
  • File size: 12MB uncompressed
  • Time period: Nov 1, 2009 – Nov 30, 2009

Data: givealink-nov-2009.tgz (2MB)

If you use this dataset in your research, please cite either or both of these papers:

    author = {Markines, B. and Stoilova, L. and Menczer, F.},
    title = {Bookmark hierarchies and collaborative recommendation},
    booktitle = {Proc. 21st National Conference on Artificial Intelligence (AAAI-06)},
    pages = {1375--1380},
    publisher = {AAAI Press},
    url = {http://www.aaai.org/Papers/AAAI/2006/AAAI06-216.pdf},
    year = 2006
    Author = {Stoilova, Lubomira and Holloway, Todd and Markines, Ben and Maguitman, Ana G. and Menczer, Filippo},
    Title = {GiveALink: Mining a Semantic Network of Bookmarks for Web Search and Recommendation},
    Booktitle = {Proc. KDD Workshop on Link Discovery: Issues, Approaches and Applications (LinkKDD)},
    Url = {http://informatics.indiana.edu/fil/Papers/givealink-linkkdd.pdf},
    Year = 2005


4. Publications

Metadata for the complete set of all PubMed records through 2012 (with part of 2013 available as well), including title, authors, and year of publication. All data provided originates from NLM’s PubMed database (as downloaded April 24, 2013 from the NLM FTP site) and was retrieved via the Scholarly Database.

CSV format:

PubMed ID1,title1,year of publication1,author1|author2|author3|…
PubMed ID2,title2,year of publication2,author4|author1|author5|…

Dataset statistics:

  • Dataset size: 21.5 mil publications and 10.8 mil authors
  • File size: 3.1GB uncompressed
  • Time period: 1809 – 2013

Data: publications-1809-2013.tar.gz (1.4GB)

If you use this dataset in your research, please cite either or both of these papers:

  author    = {Light, Robert P., David E. Polley and Katy Börner},
  title     = {Open Data and Open Code for Big Science of Science Studies},
  booktitle = {Proceedings of International Society of Scientometrics and Informetrics Conference},
  year      = {2013},
  pages     = {1342--1356},
  url       = {http://cns.iu.edu/docs/publications/2013-light-sdb-sci2-issi.pdf}
  author  = {Rowe, Gavin La, Sumeet Adinath Ambre, John W. Burgoon, Weimao Ke, and Katy Börner},
  title   = {The Scholarly Database and its Utility for Scientometrics Research"},
  journal = {Scientometrics},
  year   = {2009},
  volume = {79},
  number = {2},
  month  = {May},
  url    = {http://cns.iu.edu/docs/publications/2009-larowe-sdb.pdf}


Scholarometer is a social tool to facilitate citation analysis and help evaluate the impact of an author’s publications. One of the promises of Web Science is to leverage the wisdom of the crowds to give rise to emergent, bottom-up semantics, by making it easy for users to express relationships between arbitrary kinds of objects. Rather than starting with an ontology that determines the kinds of objects and relationships to be described and reasoned about, the idea is to give users the freedom to annotate arbitrary objects with arbitrary predicates, along with incentives for such annotations. Social tagging systems for images are one example, where the motivation can stem from the wish to organize and share one’s photos or from entertaining games to guess one another’s tags. Here we explore a similar approach in the domain of scholarly publications. We describe a system called Scholarometer, which provides a service to scholars by computing citation-based impact measures. This motivates users to provide disciplinary annotations for authors, which in turn can be used to compute for the first time measures that allow to compare authors’ impact across disciplinary boundaries. We show how this crowdsourcing approach can lead to emergent semantic networks to study interdisciplinary annotations and trends. To learn more please visit http://scholarometer.indiana.edu/about.html

Project Members:

Fil Menczer
Jasleen Kaur
Mohsen JafariAsbagh
Mohsen JafariAsbagh


Diep Hoang
Snehal Patil
Xiaoling Sun
Lino Possamai

Project Publications: