Berkeley Institute for Data Science explained

Berkeley Institute for Data Science
Parent Organization:University of California, Berkeley
Leader Title:Faculty Director
Leader Name:Fernando Pérez
Established:November 2013

The Berkeley Institute for Data Science (BIDS) is a central hub of research and education within University of California, Berkeley designed to facilitate data-intensive science and earn grants to be disseminated within the sciences.[1] [2] BIDS was initially funded by grants from the Gordon and Betty Moore Foundation and the Sloan Foundation as part of a three-year grant with data science institutes at New York University and the University of Washington.[3] [4] [5] The objective of the three-university initiative is to bring together domain experts from the natural and social sciences, along with methodological experts from computer science, statistics, and applied mathematics.[6] Saul Perlmutter established BIDS in 2013 and stepped down as the Faculty Director in December 2023.[7] The initiative was announced at a White House Office of Science and Technology Policy event to highlight and promote advances in data-driven scientific discovery, and is a core component of the National Science Foundation's strategic plan for building national capacity in data science.[8] [9] [10]

Working groups

When BIDS was founded in 2013, there were six working groups across the three universities included in the original Moore/Sloan grant, referred to as the Moore-Sloan Data Science Environments (MSDSE).[11] The aim of the MSDSE was to address the major challenges facing advances in data-intensive research, including careers, education and training, tools and software, reproducibility and open science, physical and intellectual space, and data science studies.[12] The efforts from these working groups led to the founding of the Academic Data Science Alliance (ADSA)[13] in 2019. BIDS is a founding member of ADSA.[14]

Notable fellows

A primary objective of BIDS is to build a community of data science fellows and senior fellows across academic disciplines. The 23 current fellows constitute the majority of the onsite liveware at the Institute, which supports a number of notable initiatives (via Fellow support). The following list is a subset of notable fellows to date:

External links

Notes and References

  1. News: White House to Universities: We Need More Data Scientists. Ungerleider. Neal. 13 November 2013. Fast Company. 25 October 2015.
  2. Book: Suthaharan, Shan. Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning. Springer. 2015. 9781489976413. 10.
  3. News: NYU Part of Initiative to Harness Potential of Data Scientists, Big Data with Support from Moore, Sloan Foundations. 12 November 2013. New York University. 26 October 2015.
  4. News: UW, Berkeley, NYU collaborate in $37.8M data science initiative. https://web.archive.org/web/20200814101617/https://honingds.com/blog/uw-berkeley-nyu-collaborate-in-37-8m-data-science-initiative/. dead. 14 August 2020. 7 November 2013. University of Washington eScience Institute. 26 October 2015.
  5. Data science: Industry allure. Baker. Monya. 8 April 2015. Nature. 520. 7546. 253–255. 10.1038/nj7546-253a. 25859590 . free.
  6. Web site: Examples of Big Data Initiatives and Funding Projects. 2015. 26 October 2015. Data Sharing for Demographic Research. Eunice Kennedy Shriver National Institute of Child Health and Human Development.
  7. Web site: 12 December 2023 . BIDS Community Celebrates Saul Perlmutter's Tenure as the Founding Faculty Director . 2 October 2024 . . article . Berkeley, California.
  8. News: Program Seeks to Nurture 'Data Science Culture' at Universities. Lohr. Steve. 12 November 2013. New York Times. 25 October 2015.
  9. Web site: Data to Knowledge to Action. 12 November 2013. 25 October 2015. https://web.archive.org/web/20170128053645/https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/Data2Action%20Announcements.pdf. 2017-01-28. National Archives. Office of Science and Technology Policy. live.
  10. Book: Final Report from StatSNSF subcommittee. Johnstone. Iain. National Science Foundation. 5 November 2015. Roberts. Fred. 18 July 2014. https://web.archive.org/web/20160305133942/https://www.nsf.gov/mps/members_only/presentations_0714/johnstone-roberts_statsnsf-to-post.pdf. 5 March 2016. dead.
  11. Web site: MSDSE Archive . 2 October 2024 . Academic Data Science Alliance.
  12. Web site: MSDSE Archive - Themes . 2 October 2024 . Academic Data Science Alliance.
  13. Web site: About ADSA . 2 October 2022 . Academic Data Science Alliance.
  14. Web site: Founding Members . 2 October 2022 . Academic Data Science Alliance.
  15. News: Deciding Force: What we learned from Ferguson. Allred. Cathy. 17 September 2014. Daily Herald. 5 November 2015.
  16. News: Decriminalize dissent. McMillan. Cecily. 17 September 2015. Al Jazeera America. 6 November 2015. Gould-Wartofsky. Michael.
  17. News: $6M for UC Berkeley and Cal Poly to Expand and Enhance Open-Source Software for Scientific Computing and Data Science. 7 July 2015. Business Wire. 5 November 2015.
  18. News: IPython founder details road map for interactive computing platform. Krill. Paul. 14 February 2014. InfoWorld. 6 November 2015.
  19. Google Earth Engine Brings Big Data to Environmental Activism. Strickland. Eliza. 16 April 2014. IEEE Spectrum. 5 November 2015.
  20. Putting women at the controls at NASA. Benderly. Beryl. 13 July 2015. Science. 5 November 2015.
  21. Book: Effective Computation in Physics. Scopatz. Anthony. O'Reilly Media. 2015. 9781491901595. Kathryn. Huff.
  22. Web site: Women in Data Science: Kathryn Huff. 14 September 2014. 6 November 2015. Center for Data Science. New York University. Lowery. Jack.
  23. Web site: Berkeley Institute for Data Science. Berkeley Institute for Data Science.
  24. Web site: Anomaly - Precision Payments brought to healthcare. Anomaly - Precision Payments brought to healthcare.
  25. Web site: From Bioinformatics to Natural Language Processing with Leonard Apeltsin. James Le.
  26. Book: Apeltsin, Leonard. Data Science Bookcamp: Five Python Projects. Manning Publishing. 2021. 9781617296253.
  27. Book: Bressert, Eli. SciPy and NumPy: An Overview for Developers. O'Reilly Media. 2012. 9781449361624. 43.
  28. Web site: scikit-image. 5 November 2015. Python Package Index.
  29. Web site: Laura Waller. Berkeley Institute for Data Science.

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