[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
10.1145/3343413.3377979acmconferencesArticle/Chapter ViewAbstractPublication PagesirConference Proceedingsconference-collections
keynote

Big Data, Little Data, or No Data? Why Human Interaction with Data is a Hard Problem

Published: 14 March 2020 Publication History

Abstract

Enthusiasm for big data is obscuring the complexity and diversity of data in scholarship and the challenges of human interaction and retrieval. Data practices are local, varying from field to field, individual to individual, and country to country. As the number and variety of research partners expands, so do the difficulties of sharing, reusing, and sustaining access to data. Information retrieval is hindered by the lack of agreement on what are "data." Complexities of human interaction with data will be illustrated with empirical examples from environmental sciences, sensor networks, astronomy, biomedicine, and other fields. Unless larger questions of knowledge infrastructures and stewardship are addressed by research communities, "no data" often becomes the norm. Implications for policy and practice in the information sciences will be explored, drawing upon the presenter's book, Big Data, Little Data, No Data: Scholarship in the Networked World (MIT Press, 2015), and subsequent research.

References

[1]
Christine L. Borgman. 2015. Big data, little data, no data: Scholarship in the networked world. MIT Press, Cambridge, MA.
[2]
Christine L. Borgman. 2019. The Lives and After Lives of Data. Harvard Data Science Review 1, 1. https://doi.org/10.1162/99608f92.9a36bdb6
[3]
Christine L. Borgman, Peter T. Darch, Ashley E. Sands, and Milena S. Golshan. 2016. The durability and fragility of knowledge infrastructures: Lessons learned from astronomy. In Proceedings of the Association for Information Science and Technology, 1--10. Retrieved from http://dx.doi.org/10.1002/pra2.2016.14505301057
[4]
Irene V. Pasquetto, Christine L. Borgman, and Morgan F. Wofford. 2019. Uses and Reuses of Scientific Data: The Data Creators' Advantage. Harvard Data Science Review 1, 2. https://doi.org/10.1162/99608f92.fc14bf2d
[5]
Irene V. Pasquetto, Bernadette M. Randles, and Christine L. Borgman. 2017. On the Reuse of Scientific Data. Data Science Journal 16. https://doi.org/10.5334/dsj-2017-008
[6]
Dan Turello. 2019. How to Think About Data: A Conversation with Christine Borgman | Insights: Scholarly Work at the John W. Kluge Center. Retrieved February 7, 2019 from //blogs.loc.gov/kluge/2019/02/how-to-think-about-data-a-conversation-with-christine-borgman/
[7]
Jillian C. Wallis, Elizabeth Rolando, and Christine L. Borgman. 2013. If We Share Data, Will Anyone Use Them? Data Sharing and Reuse in the Long Tail of Science and Technology. PLOS ONE 8, 7: e67332. https://doi.org/10.1371/journal.pone.0067332
[8]
Morgan F. Wofford, Bernadette M. Boscoe, Christine L. Borgman, Irene V. Pasquetto, and Milena S. Golshan. 2019. Jupyter notebooks as discovery mechanisms for open science: Citation practices in the astronomy community. Computing in Science & Engineering: 1--1. https://doi.org/10.1109/MCSE.2019.2932067

Cited By

View all
  • (2024)Participatory Observation Methods Within Data-Intensive Science: Formal Evaluation and Sociotechnical InsightWisdom, Well-Being, Win-Win10.1007/978-3-031-57850-2_19(253-269)Online publication date: 15-Apr-2024
  • (2022)Forgetting Practices in the Data SciencesProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517644(1-19)Online publication date: 29-Apr-2022
  • (2020)Interrogating Data ScienceCompanion Publication of the 2020 Conference on Computer Supported Cooperative Work and Social Computing10.1145/3406865.3418584(467-473)Online publication date: 17-Oct-2020

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences
CHIIR '20: Proceedings of the 2020 Conference on Human Information Interaction and Retrieval
March 2020
596 pages
ISBN:9781450368926
DOI:10.1145/3343413
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 March 2020

Check for updates

Author Tags

  1. collaboration
  2. data
  3. data science
  4. information policy
  5. knowledge infrastructures
  6. research
  7. scholarly communication
  8. science

Qualifiers

  • Keynote

Conference

CHIIR '20
Sponsor:

Acceptance Rates

Overall Acceptance Rate 55 of 163 submissions, 34%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)33
  • Downloads (Last 6 weeks)0
Reflects downloads up to 22 Dec 2024

Other Metrics

Citations

Cited By

View all
  • (2024)Participatory Observation Methods Within Data-Intensive Science: Formal Evaluation and Sociotechnical InsightWisdom, Well-Being, Win-Win10.1007/978-3-031-57850-2_19(253-269)Online publication date: 15-Apr-2024
  • (2022)Forgetting Practices in the Data SciencesProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517644(1-19)Online publication date: 29-Apr-2022
  • (2020)Interrogating Data ScienceCompanion Publication of the 2020 Conference on Computer Supported Cooperative Work and Social Computing10.1145/3406865.3418584(467-473)Online publication date: 17-Oct-2020

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media