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Collections as Data: National
Forum 2: Panel 3
Computationally Consumable Content
Mary Elings
Assistant Director and Head of Technical Services
Bancroft Library, UC Berkeley
UNIVERSITY LIBRARY STRATEGIC PLAN
Our vision for the future
2017-2021
Collections as Data Projects
FSM Digital Archive Hackathon 2014
http://research-it.berkeley.edu/publications/hackfsm-bootstrapping-library-hackathon-eight-short-weeks
ArchExtract 2014-2015
Collections as Data Projects
https://saaers.wordpress.com/2016/05/24/using-nlp-to-support-dynamic-arrangement-description-and-
discovery-of-born-digital-collections-the-archextract-experiment/
New Netherland Project 2016-2017
Collections as Data Projects
http://dutch.berkeley.edu/about-new-netherland/
“Liberating Textual Data”
Computational social scientists working with D-Lab, BIDS,
and the Social Science Matrix to capture digital texts from
online government records and transform them into
research-ready formats.
APRIL 30, 2018
https://mybinder.org/v2/gh/Goodly/CapitolQuery_SSRC/master
https://github.com/Goodly/CapitolQuery_SSRC
https://oac.cdlib.org/ark:/13030/tf6c60112x/?brand=oac4Spans E4-7, E9, Cantilever, Suspended, Yerba Buena 4, Yerba Buena 1 Upper and Lower Decks;
Anchor Arms East and West, Cantilever East and West Arms, Upper Deck Approach, East Bay
Crossing, Towers E2, E3, 1935-36 -- No. 222-429
The Future…
 Secure Computational Environments
 Mechanisms for Download and Contribution
 Support for New Research Ready Projects
 Partnering with Campus Courses
Thank you
Mary Elings
Assistant Director and Head of Technical Services
Bancroft Library, UC Berkeley

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Collections as Data National Forum (Elings)

  • 1. Collections as Data: National Forum 2: Panel 3 Computationally Consumable Content Mary Elings Assistant Director and Head of Technical Services Bancroft Library, UC Berkeley
  • 2.
  • 3. UNIVERSITY LIBRARY STRATEGIC PLAN Our vision for the future 2017-2021
  • 4. Collections as Data Projects FSM Digital Archive Hackathon 2014 http://research-it.berkeley.edu/publications/hackfsm-bootstrapping-library-hackathon-eight-short-weeks
  • 5. ArchExtract 2014-2015 Collections as Data Projects https://saaers.wordpress.com/2016/05/24/using-nlp-to-support-dynamic-arrangement-description-and- discovery-of-born-digital-collections-the-archextract-experiment/
  • 6. New Netherland Project 2016-2017 Collections as Data Projects http://dutch.berkeley.edu/about-new-netherland/
  • 7. “Liberating Textual Data” Computational social scientists working with D-Lab, BIDS, and the Social Science Matrix to capture digital texts from online government records and transform them into research-ready formats. APRIL 30, 2018 https://mybinder.org/v2/gh/Goodly/CapitolQuery_SSRC/master https://github.com/Goodly/CapitolQuery_SSRC
  • 8. https://oac.cdlib.org/ark:/13030/tf6c60112x/?brand=oac4Spans E4-7, E9, Cantilever, Suspended, Yerba Buena 4, Yerba Buena 1 Upper and Lower Decks; Anchor Arms East and West, Cantilever East and West Arms, Upper Deck Approach, East Bay Crossing, Towers E2, E3, 1935-36 -- No. 222-429
  • 9. The Future…  Secure Computational Environments  Mechanisms for Download and Contribution  Support for New Research Ready Projects  Partnering with Campus Courses
  • 10. Thank you Mary Elings Assistant Director and Head of Technical Services Bancroft Library, UC Berkeley

Editor's Notes

  1. 2
  2. What we did learn from these various projects was: 1) We need to be more engaged with researchers and partners to achieve successful outcomes Looking at various projects around computational text analysis of digital archives at Berkeley, it often feels as if we are approaching them from different ends of the problem, one as content providers and the other content consumers. We need to be sure to communicate with data consumers about the work we are doing and understand their work and needs, and hopefully meet somewhere in the middle. We want to be sure we are not duplicating effort, especially because this work is resource intensive. 2) We need to better understand our role and the work we own in this space Our goal is to create data from our digitized primary sources and born digital collections. That means we need to determine how much processing to apply to make the data useful to researchers. Where does our work end and the consumers work begin? How do we find the edges of what is too little and too much processing for the broadest usability. Knowing the costs of doing more processing, we need to explore what is feasible. Do we simply clean up the OCR, do we label data, extract entities, or create databases? What is the right approach, or are there multiple approaches? How do we scale our work, given the size of our collections, or is scaling important? Perhaps we need to think about right fit cleaning and processing. We need to help consumers understanding what is lost when cleaning and processing occur. We need to document the work we do so it is clear what has been done to a collection. We also want to lower barriers to use and make it easier for consumers to access this data. What we provide now needs work and could be informed by our consumers. 3) We need to build capacity for this type of work within communities (why we are here today) The work that will come out of this forum will move the needle quite a bit. The framework this project is building will support all of us. I am honored to be part of that work and contribute what little we have learned in our on efforts. We are excited to learn from the Collections as Data Always Already Computational outcomes. We want to take those outcomes back home to our campus partners to better support these types of efforts at UC Berkeley. Thomas asked us to think about what surprised us in our work around Collections as Data and I think what surprised me was the enthusiasm shown by the students, their excitement around the content (they think our data is cool), and their eagerness to do work no one has done yet.