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Metadata for Research Data

Metadata for Research Data

Presentation given to the LIS853: Metadata course, UW-Madison iSchool. October 2015

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Brianna Marshall

October 26, 2015
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Transcript

  1. Brianna Marshall | @notsosternlib | UW Libraries Image adapted from

    Flickr user lianhua (CC BY NC)
  2. about me Brianna Marshall Digital Curation Coordinator, UW Libraries Lead,

    Research Data Services MLS/MIS, May 2014 Indiana University School of Informatics + Computing    
  3. what is research data? “the recorded factual information commonly accepted

    in the scientific community as necessary to validate research findings.” INCLUDES: code, figures, statistics, interviews, transcripts EXCLUDES: preliminary analyses, drafts of papers, plans for further research, communication + peer reviews, physical samples -  OMB Circular, White House
  4. data vs. metadata vs. “research outputs” vs. “supporting materials” Image

    courtesy of Flickr user bruce_aldridge (CC BY NC)
  5. http://www.icpsr.umich.edu/icpsrweb/ ICPSR/help/cb9721.jsp#code example codebook

  6. (necessary to read coded SPSS responses.)

  7. federal funding requirements around research data Data Management Plans (DMPs)

    required by most federal funders since 2011 Office of Science and Technology Policy (OSTP) memorandum •  Released by the White House •  Published spring 2013; took effect fall 2015 •  Requires open sharing of published articles and data •  Publication repository is provided; data repository is not •  Most require that researchers provide access to “all materials needed to reproduce published findings” •  Applies to agencies with $100M + in R&D
  8. capturing metadata for research data Image courtesy of Flickr user

    thomashawk (CC BY NC)
  9. the who, what, why, where, when Important information to capture

    about data may include: •  Principal investigator •  Funding sources •  Data collector/producer •  Project description •  Sample and sampling procedures •  Weighting •  Substantive, temporal, and geographic coverage of the data collection •  Data source(s) •  Unit(s) of analysis/observation •  Variables •  Technical information on files •  Data collection instruments Adapted from ICPSR
  10. datacite [ https://schema.datacite.org/ ]

  11. Image courtesy of J. Haugen, National Science Foundation technology  

  12. Image courtesy of Flickr user stewf (CC BY NC) +

    passion  
  13. + structure   Image courtesy of Flickr user zigazou76 (CC

    BY)
  14. Image used courtesy of Flickr user nationaleyeinstitute (CC BY) handwritten

    notes  
  15. electronic lab notebooks www.labarchives.com    

  16. metadata + repositories

  17. example metadata fields (dublin core)

  18. data curation profiles toolkit [ http://datacurationprofiles.org/ ]

  19. readme files Screenshot of M. Burkert’s README: http://minds.wisconsin.edu/bitstream/handle/1793/71768/README_BURKERT.txt?sequence=4

  20. what’s in a good readme file? •  Names + contact

    info for people associated with the project" •  List of files, including a description of their relationship to one another" •  Copyright + licensing information" •  Limitations of the data" •  Funding sources / institutional support" " tl;dr !! Any information necessary for someone with no knowledge of the research to understand and / or replicate the work."
  21. what do I tell researchers? •  Describe project/folder level vs.

    item-level description •  Advise them to start conversation + remain aware of community standards •  I have to make tough choices as repository manager –  If a dataset doesn’t have metadata, I won’t preserve it –  Sometimes data (even with detailed metadata!) is too big for our repository platform •  Data without the contextual information needed to interpret it (and ultimately reproduce the research results) is useless"
  22. final thoughts •  In research, sharing data isn’t yet commonplace

    - much less capturing metadata" •  Federal funding requirements necessitate data sharing but don’t provide guidance on metadata to support it" •  Metadata standards are disciplinary; can be hard to give generalizable advice" •  We need new information professionals willing to wade into the mess and help us solve this!"
  23. (meta)data questions? Brianna Marshall brianna.marshall@wisc.edu @notsosternlib Research Data Services researchdata.wisc.edu

    @UWMadRschSvcs