Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Coming to an Understanding: a Cross-institutional Examination of Assessments of Data Curation Needs


Published on

Data curation has emerged as a strategic growth area for academic libraries. Many libraries have conducted needs assessments as a precursor towards developing services; however there have been few comparisons of the findings across institutions. This panel brings together four librarians from different institutions to discuss both common and distinct findings from their respective needs assessments. The panelists will speculate on the application of these findings at their specific libraries and in academic libraries generally.

Published in: Education, Technology
  • Be the first to comment

  • Be the first to like this

Coming to an Understanding: a Cross-institutional Examination of Assessments of Data Curation Needs

  1. 1. COMING TO ANUNDERSTANDINGA Cross-institutional Examinationof Assessments of Data CurationNeedsJake Carlson - Purdue UniversityDianne Dietrich - Cornell UniversityGail Steinhart - Cornell UniversityAlison Valk - Georgia Institute of TechnologyStephanie Wright - University of Washington
  2. 2. Dianne DietrichPlanning & Data ManagementPlans
  3. 3. Planning and Data ManagementPlansMay 2010October2010December2010January2011NSF pressreleaseindicating intentto require datamanagementplans withgrantproposals.NSF releasesspecifics fordatamanagementplanrequirement.Cornell surveydistributed toPIs and Co-PIsof NSF grants.NSFrequirementgoes intoeffect.
  4. 4. Planning and Data ManagementPlans How prepared are researchers to address datamanagement plan requirements? What is the potential impact of researcherplans on existing Cornell services?
  5. 5. Planning and Data ManagementPlans0%10%20%30%40%50%60%70%80%90%100%Each bar represents a question where respondents were asked to select "Yes", "No", or "Im not sure"Percentage of respondents who answered "Im not sure"for questions where that was an optionAdapted from Steinhart, et al. (2012) Prepared to Plan? A Snapshot of Research Readinessto Address Data Management Planning Requirements. Journal of eScience Librarianship 1(2).
  6. 6. Planning and Data ManagementPlans0% 10% 20% 30% 40% 50%No dataUp to 1 GB1 GB - 100 GB100 GB - 1 TB1 TB - 100 TBMore than 100 TBResponses to the question: "Given the NSF expectation toshare data ... how much data would you intend to share?"Adapted from Steinhart, et al. (2012) Prepared to Plan? A Snapshot of Research Readinessto Address Data Management Planning Requirements. Journal of eScience Librarianship 1(2).
  7. 7. Planning and Data ManagementPlansYes30%Im not sure61%No: 9%I do not planto createmetadata26%Im not sureif I plan tocreate metadata32% I do plan tocreate metadata42%Have you produced or do you anticipateproducing metadata for this project?Adapted from Steinhart, et al. (2012) Prepared to Plan? A Snapshot of Research Readinessto Address Data Management Planning Requirements. Journal of eScience Librarianship 1(2).If you plan oncreatingmetadata, doesit conform toknownstandards inyour discipline?
  8. 8. Planning and Data ManagementPlans010203040506070OwninfrastructureCampus solution CommercialsolutionNumberofresponsesBackup StrategyAnticipated Backup Strategy by Size of DataMore than 100 TB1 TB - 100 TB100 GB - 1 TB1 GB - 100 GBUp to 1 GBAdapted from Steinhart, et al. (2012) Prepared to Plan? A Snapshot of Research Readinessto Address Data Management Planning Requirements. Journal of eScience Librarianship 1(2).
  9. 9. Stephanie WrightManagement
  10. 10. Management: UW Background Services Survey &Interviews
  11. 11. Management: OrganizationSurvey Guidance on dataorganization (filestructure, filenaming, etc.) ranked13th out of 14 Tracking updates todata (versioning)ranked 8thImage Credit: radrice “data cat finds no data”
  12. 12. Management: OrganizationInterviews Whatever makessense to organizer Moreplanning, betterorganization Especially true oflarger, well-fundedprojects“But that really wassort of something weaddressed after thefact, after we startedto go, „Huh, I‟mnaming them thisway, you‟re namingthem that way, and Ihave no idea whatyour namingconventions mean.‟”
  13. 13. Management: DescriptionSurvey 1/3 didn‟t know ofmetadata standard 16% were able toidentify metadatastandard Metadata serviceranked 10th out of 14Image & Quote Credit: NYU Health Sciences Libraries “Data Sharing and ManagementSnafu in 3 Short Acts”“Everything youneed to know aboutthe data is in thearticle.”
  14. 14. Management: DescriptionInterviews Documentation isbiggest challenge indata management Recognize role ofmetatadata Time consuming, noimmediate benefit Data planning vs. dataforensics“If I was gonna make(the data) availableto other people, Iwould feel someresponsibility indocumenting it alittle bit better.”(Social Sciences)
  15. 15. Management: SummaryServices needed: Training on bestpractices or generalstrategies Tools that integratedescription andorganization of datainto the workflow“I kind of feel like we’rejust making our waythrough the wilderness.And if there weresomebody who couldkind of hold our handsand say, „Look, datamanagement is importantand here are somestrategies for going aboutit…‟ That would begreat.”
  16. 16. Jake CarlsonSharing
  17. 17. Sharing: PurdueBackground on Purdue‟swork:Primarily InterviewDriven• Data Curation Profiles• Data ManagementPlans• Data InformationLiteracy
  18. 18. Sharing Willingness to ShareGenerally, faculty are opento sharing their data withothers.There is an “undergroundeconomy” of data sharing.Factors in deciding whetheror not to share:What will this person dowith my data?How much time & effort willit take me?Image Credit: andrew_mc_d “Share”
  19. 19. Sharing
  20. 20. Sharing ControlIssues in sharing data publicly:Timing over when to release data.Use - If anyone can get the data, anyonecan use it for whatever they want toMisinterpretation - there‟s no guaranteethat someone won‟t misconstrue the data
  21. 21. Sharing AttributionGenerally expressed as need for othersto cite the data set (though not always)“So for in my personal opinion, data citationswon‟t help me too much. Paper citations countfor everything. It counts for impact of the paper, itcounts for tenure, it counts for the profile of mywork.”- Professor of Biochemistry
  22. 22. Sharing Documentation andDescription"If you ask someone if you cansee their raw data, you might aswell be asking if you can look attheir underwear. Its reallyproblematic."- Agronomy Professor
  23. 23. Sharing Services for Data Sharing at PurdueConsultation & Collaboration with Data Producers Support "local" sharing Workflows Documentation Description Support "external" sharing Workflows Documentation Description
  24. 24. Alison ValkPreservation
  25. 25. Background“Develop campuspartnerships tocollect, manage, share, and preserve Georgia Techdigital research data.”“Improve and develop newresources & services toassist researchers withdata stewardship”Preservation
  26. 26. IRB-approved research to determinegaps in data curation servicesprovided to researchers.Data assessment surveySeries of campus wide interviewsNSF DMP content analysisPreservation
  27. 27. By combining information gatheredvia the survey and the interviews, wedeveloped a clearer picture of theresearch data curation needs oncampus.Out of 77 who completedsurvey-o 44 agreed to be interviewedo 26 interviews completedPreservation
  28. 28. Interview TeamChris DotySusan ParhamElizabeth RolandoAlison Valk10 Interview questions“How important is it for you toarchive / preserve your data?”“How important is it for you orothers to have access to your dataover the long-term?”PreservationTranscribeinterviewsWeb application forQualitative & Mixed Methods researchVisualize major discussion pointsor code correlationsCode
  29. 29. Correlation betweencost of working withdata –to how stronglyparticipants feel datashould bepreserved…Preservation
  30. 30. Storage prices no longercost prohibitivePreservation
  31. 31. Lack of metadata orcuration =unusable dataData is often “lost”when project participantssuch as grad students leaveinstitutionComputingprofessor:“I don’t want tomicromanage myresearch assistants”Preservation
  32. 32. Some researchersare usingCloud basedtools, such asDropBox etc. forarchiving –Little concern forsecurity risksassociated.Preservation
  33. 33. Next Steps:Select Case studies-o Researchers have volunteered to allow usto archive their research data.Increased Outreach- New Serviceso Customized DMPtoolo Departmental Data Management Workshopso More robust web presenceo Proof-of-concept Library hostedResearch Data RepositoryPreservation
  34. 34. Questions? Jake Carlson @jrcarlso Dianne Dietrich @nemka Gail Steinhart @gailst Alison Valk @valkcano Stephanie Wright @shefw