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The Local Goes Global

The Local Goes Global. How do data come to be shared?. Ann Zimmerman Research Assistant Professor ASIST Data Summit, April 10, 2010. Data collections Research Community Reference. Why is it hard to share data? Why is data sharing more common in some fields than others?. Science-based.

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The Local Goes Global

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  1. The Local Goes Global How do data come to be shared? Ann Zimmerman Research Assistant Professor ASIST Data Summit, April 10, 2010

  2. Data collections • Research • Community • Reference

  3. Why is it hard to share data? • Why is data sharing more common in some fields than others?

  4. Science-based Technical Social DATA Organizational Legal Political

  5. The Local goes global: some examples

  6. Implications • Who is involved • What count as data • What gets shared

  7. Step 2: A data curator scans journals for data, extracts data and descriptive information, and enters it all into a computer. Step 1: Scientist publishes a a paper in a journal. Step 4: Other people use the data. They provide input that results in additions of new types of data or corrections to the database. Step 3: Data are integrated with other data into one database and made available to anyone via the Internet.

  8. Step 2: Scientist submits the data associated with the paper to a repository as a requirement of publication. Step 1: Scientist publishes a a paper in a journal. Step 4: Other people use the data. They provide input that results in additions of new types of data or corrections to the database. Step 3: Data are integrated with other data into one database and made available to anyone via the Internet.

  9. Step 1: Individuals get together to decide upon standard data collection protocols. Step 2: Every laboratory uses the agreed upon methods to collect data. Step 4: Other people use the data. They provide input that influences how new data are collected or that result in corrections to existing data. Step 3: Data are integrated and made available to anyone via the Internet.

  10. Implications • Who is involved • What count as data • What gets shared

  11. The local struggles to go global

  12. Materials science data Images Graphs Spectra Columns of numbers

  13. Shared Needs • Long term access to data • Finding data later • Understanding the context of the data to use it in a meaningful way • Accessing data, and information about data, from multiple locations

  14. Open Questions • What makes it hard to share data? • How does the “state” of data affect sharing? • What makes documentation sufficient for reuse?

  15. Developing Capacity • Open Data IGERT (Margaret Hedstrom) http://opendata.si.umich.edu • “i-School” Masters programs

  16. Acknowledgments • Data Summit organizers & attendees • Research participants • Dharma Akmon & Morgan Daniels, PhD students, UM School of Information • NSF Grants OCI 0724300 and IIS 0085981 (Gary Olson, PI)

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