Academics Torrents was established to meet the demands of science in the age of big data. It utilizes a scalable BitTorrent platform that distributes the burden of hosting data, eliminating the risk of data loss due to the rise and fall of dataset hosting providers. Researchers are empowered to replicate data they are working with and share large datasets without incurring the high costs usually associated with commercial providers.

Academic Torrents is a product of the Institute for Reproducible Research (a U.S. 501(c)3 nonprofit).

Twitter: @academictorrent
Facebook: AcademicTorrents


Joseph Paul Cohen        Henry Z Lo        Jonathan Nogueira


Personal Statement

  • This service is designed to facilitate storage of all the data used in research, including datasets as well as publications. There are many advantages of using bittorrent technology to disseminate this work.

  • Distributed storage and content delivery provided by anyone. Files can be securely downloaded from other users of the system. They can share the file for a day or a year.

  • Mirroring the content can be done from a desktop computer anywhere. Everyone surrounding this computer will have local access to the data automatically and securely.

  • Bundles of files, not just papers, or any size can be disseminated in this way as long as at least one person can become a seed for that data.

  • Torrent technology allows a group of editors to “seed” their own peer-reviewed published articles with just a torrent client. Each editor can have part or all of the papers stored on their desktops and have a torrent tracker to coordinate the delivery of papers without a dedicated server.

  • One aim of this site is to create the infrastructure to allow open access journals to operate at low cost. By facilitating file transfers, the journal can focus on its core mission of providing world class research. After peer review the paper can be indexed on this site and disseminated throughout our system.

  • Large dataset delivery can be supported by researchers in the field that have the dataset on their machine. A popular large dataset doesn’t need to be housed centrally. Researchers can have part of the dataset they are working on and they can help host it together.

  • Libraries can host this data to host papers from their own campus without becoming the only source of the data. So even if a library’s system is broken other universities can participate in getting that data into the hands of researchers.

-Joseph Paul Cohen 2013 joseph /at/ josephpcohen.com


Please cite Academic Torrents:

  • Henry Z. Lo. and Cohen, Joseph Paul “Academic Torrents: Scalable Data Distribution.” Neural Information Processing Systems Challenges in Machine Learning (CiML) Workshop, 2016, http://arxiv.org/abs/1603.04395.

  • Cohen, Joseph Paul, and Henry Z. Lo. “Academic Torrents: A Community-Maintained Distributed Repository.” Annual Conference of the Extreme Science and Engineering Discovery Environment, 2014, http://doi.org/10.1145/2616498.2616528.

download bibtex file academictorrents.bib

 title = {Academic Torrents: A Community-Maintained Distributed Repository},
 author = {Cohen, Joseph Paul and Lo, Henry Z.},
 booktitle = {Annual Conference of the Extreme Science and Engineering Discovery Environment},
 doi = {10.1145/2616498.2616528},
 url = {http://doi.acm.org/10.1145/2616498.2616528},
 year = {2014}

 title = {Academic Torrents: Scalable Data Distribution},
 author = {Lo, Henry Z. and Cohen, Joseph Paul},
 booktitle = {Neural Information Processing Systems Challenges in Machine Learning (CiML) workshop},
 arxivId = {1603.04395},
 url = {http://arxiv.org/abs/1603.04395},
 year = {2016}


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We have an issue tracker here: https://github.com/AcademicTorrents/academictorrents-docs/issues
We have a contact email here: contact@academictorrents.com


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