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Acrl march2015 final

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Talk titled "Roles for Libraries in Providing Research Data Management Services" for presentation at the ACRL conference in Portland, OR, on 03/28/15. Presented by Nicole Vasilevsky (Oregon Health & Science University), Victoria Mitchell (University of Oregon) and Jeremy Kenyon (University of Idaho).

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Acrl march2015 final

  1. 1. Roles for Libraries in Providing Research Data Management Services Nicole Vasilevsky, Oregon Health & Science University Victoria Mitchell, University of Oregon Jeremy Kenyon, University of Idaho
  2. 2. Nicole Vasilevsky Project Manager, Biocurator and Ontologist, Ontology Development Group, OHSU Victoria Mitchell Social Science Data & Government Documents Librarian, University of Oregon Jeremy Kenyon Research Librarian, University of Idaho Library
  3. 3. 1 | Data services at UO Library 2 | UI support for documentation 3 | OHSU data management trainings
  4. 4. Do you have experience in data management training?
  5. 5. Why do our patrons need to know about data management?
  6. 6. Why? Researcher Perspective Version control Track processes for reproducibility Quality Control Stay Organized Save Time and Stress Avoid Data Loss Format data for reuse (by self, team, or others) Document for own recollection, accountability, reuse
  7. 7. Funding mandates http://www.economist.com/news/briefing/21588057-scientists- think-science-self-correcting-alarming-degree-it-not-trouble Reproducibility Why? Funding mandates
  8. 8. Libraries can help!
  9. 9. At the UO Libraries Data Services
  10. 10. The UO Environment • No campus-wide research data policy • Library leading on research data management and preservation • Collaborating with campus IT, Research Services
  11. 11. The UO Environment • Digital Scholarship Center • Open Access Publishing • Digital Collections • Institutional Repository • Interactive Media Development • Data Services • Science Data Services Librarian • Social Science Data Librarian
  12. 12. Services • Data Management Plans – Consultation and review
  13. 13. Data Management Web Pages
  14. 14. Services • Consultations with faculty • Special projects – Southern Voting Project
  15. 15. Education • Workshops • Presentations in classes and new faculty orientations • 1-credit course in research data management for grad students
  16. 16. Graduate Seminar in Data Management • 2 iterations so far • 1st: Spring 2013 – 1 credit course, LIB 407/507 • Made it available to upper-division undergrads; none signed up • 2nd Spring 2014 – 1 credit course, LIB 607
  17. 17. Graduate Seminar in Data Management Based course around creation of a DMP for a funding agency • Students registering for the course were strongly encouraged to have a research project already in mind or underway • Also used, in part and with modification, the education modules created by DataONE
  18. 18. • Natural disaster • Facilities infrastructure failure • Storage failure • Server hardware/software failure • Application software failure • External dependencies (e.g. PKI failure) • Format obsolescence • Legal encumbrance • Human error • Malicious attack by human or automated agents • Loss of staffing competencies • Loss of institutional commitment • Loss of financial stability • Changes in user expectations and requirements Data Loss CCimagebySharynMorrowonFlickr CCimagebymomboleumonFlickr Slide adapted from DataONE Education Module: Why Data Management. DataOne. Retrieved March 21, 2013
  19. 19. Spreadsheet for Help with Organizing Research Project: [Name of research project] Name: [Your name] Dates: [when you'll be conducting your research, e.g. 7/14- 1/15] Project Data Folder: [e.g. dissertation_coldfusion _data] Research Process/Method / Data Source Collection Dates Storage Format Original Format Working Format Access Format Preservation Format(s) File Naming Convention Folder / Convention Versioning Strategy Storage Location Who can help? Access restrictions? Who needs access? Software / Tools Required Metadata Schema Notes
  20. 20. LIB 607 v.3 • Changed to Data Management for the Social Sciences (and Digital Humanities) • Less emphasis on DMP per funder requirements • More time to address issues specific to the social sciences and humanities
  21. 21. @ the University of Idaho Library Research Data Services Credit: University of Idaho Creative Services
  22. 22. University of Idaho Characteristics: • Public, comprehensive, land-grant university • Strong emphasis on agriculture, environmental science, engineering • Recent emphasis on developing research data and research cyberinfrastructure, including library research data services, INSIDE Idaho, the geospatial data repository, and NKN, a multi-disciplinary institutional data repository
  23. 23. How do we move from this?
  24. 24. To this?
  25. 25. To this?
  26. 26. Research Data Services at the U-Idaho Library Appointments & Consultations Northwest Knowledge Network (institutional data repository) Embedded Services (Buy-outs of librarian time)Tool & Technology Support: IQ-Station, ESRI Products, DMPTool, Metadata editors Website: Data Management Best Practices Guide Instruction & Workshops Many modes of service Raise awareness of research data management & our services Create a culture of documentation Transform thinking across disciplines about data distribution & publishing
  27. 27. Focus: creating a culture of documentation FISH502 “One-shot” Instruction Session - Class participants: fisheries biology and statistics graduate students - Exercise: 1) review the following spreadsheet 2) identify the information needed to re-use this dataset
  28. 28. Focus: creating a culture of documentation Research consultation: environmental modelling Post-doc from a multi-institutional project was primary contact for several teams Consultation on metadata was made towards the end of project Producing 6 discrete collections of data as netCDF (format required by funder) Repository required ISO 19115 XML metadata for describing whole collections
  29. 29. Focus: creating a culture of documentation Challenges: Understanding the standard Attribute Conventions for Dataset Discovery ISO 19115-2 Codelists and controlled vocabularies Rules for free-text fields what does a good title look like? Placement of content should variables be listed in keywords, title, or description? Responsibilities who should create XML files – the researcher or us?
  30. 30. Focus: creating a culture of documentation Re-use and comprehension of data requires good documentation Researchers often have idiosyncratic and localized, i.e. customized, documentation practices Content standards are often not well-known among researchers Disciplinary content standards are necessary for enabling advanced modes of data access Library services must emphasize documentation
  31. 31. Future Directions Fienberg, S.E. et al. (1985). Sharing Research Data. Washington, D.C: National Academies Press. http://www.nap.edu/catalog/2033/sharing- research-data
  32. 32. at Oregon Health & Science University Research Data Management Efforts
  33. 33. What would you do with $1k today to make research communication better that doesn’t involve building another tool?
  34. 34. 1| Workshops with the library 2| Individual consultations
  35. 35. Gummy Bear: the Groundbreaking Paper
  36. 36. Your Data: Gummy Bear Raw Data Bounces Amplitude Color 15 4 blue 43 3 red 58 9 green 75 82 purple Materials: • Haribo Gummi Bears Sugar Free, 5 lb bag, Amazon.com (UPC: 422384500110) • SpringOMatic 3000 (ICanPickleThat, Portland, OR) http://laughingsquid.com/the-anatomy-of-a-gummy- bear-by-jason-freeny/
  37. 37. Figure 1. A) Gummy skeleton with belly button annotated with red arrow B) Springiness by sample color. Methods Section: Haribo Gummi Bears (Sugar Free) were purchased from Amazon.com (UPC: 422384500110). Gummy bears were placed in the SpringOMatic 3000 (ICanPickleThat, Portland OR) according to the manufactures instructions. The Gummy Anatomy (Jason Freeny) image was cropped in PPT (Microsoft) and annotate to highlight the bellybutton. Gummy Bear Final Figure 0 2 4 6 8 10 12 14 16 blue red green purple Springiness(bounces/length) Sample Color A B Figure legends/metadat a Manipulating images Attribution Metadata about research resources
  38. 38. Group 1: Gummy Bear Final Data 0 2 4 6 8 10 12 14 16 blue red green purple 4 3 9 82 15 43 58 75 Springiness (Bounces/Amplitude) 15 4 blue 43 3 red 58 9 green 75 82 purple Methods: A schematic of a Gummi Bear was cropped to indicate where the belly button is located (Fig. 1). At this point, raw experimental data showing the bounce, amplitude, and color were analyzed and the springiness calculated for each color of bear. This was accomplished by dividing the bounce by the amplitude and plotting this against bear color. Fig. 1 Belly button of Haribo Sugar Free Gummi Bear What is missing? A.Image manipulation B. Attribution C. Figure Legends D.Metadata about resources
  39. 39. Figure 1. A) Gummy skeleton with belly button annotated with red arrow B) Springiness by sample color. Methods Section: Haribo Gummi Bears (Sugar Free) were purchased from Amazon.com (UPC: 422384500110). Gummy bears were placed in the SpringOMatic 3000 (ICanPickleThat, Portland OR) according to the manufactures instructions. Group 2: Gummy Bear Final Data 0 2 4 6 8 10 12 14 16 blue red green purple Springiness(bounces/length) Sample Color A B What is missing? A.Image manipulation B. Attribution C. Figure Legends D.Metadata about resources
  40. 40. Figure 2: Schematic depiction of Haribo Gummi Bear umbilical skeletal anatomy. Methods & Materials Gummi Bears were obtained through Amazon in 3 kg bags. Lot and temperature during transport data were not made available. Bears were housed in a plastic bowl in accordance with IACUC policy and national standards for gummi bear care. They were housed at room temperature on a natural light cycle. Food and water were provided ad libitum (consumption was not monitored) Each bear was sampled only once to reduce costs Group 3: Gummy Bear Final Data What is missing? A.Image manipulation B. Attribution C. Figure Legends D.Metadata about resources
  41. 41. Belly Button 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 blue red green purple Springiness(bounces/amplitude) Gummy Bear Color (a) (b) Fig. 1. (a) schematic of the anatomy of a gummy bear (adapted from 1). (b) springiness of bear by color using spring-o-matic. Methods: Insert the sample of interest, specifically a colored gummy bear (Haribo, Japan). Position the probe above the sample. Press "Tickle" and the SpringOMatic (ICanPickleThat, Portland) will poke the belly button a standard depth of 1 cm. Record the number of bounces and the amplitude of the largest bounce in cm. From these values, the springiness can be calculated (bounce/amplitude). What is missing? A.Image manipulation B. Attribution C. Figure Legends D.Metadata about resources Group 4: Gummy Bear Final Data
  42. 42. GUMMY BEARS TAUGHT US… • People see the same data very differently • “Detailed” means different things… • Metadata?!? • File management is difficult • Workflow Vasilevsky N; Wirz J, Champieux R, Hannon T, Laraway B Banerjee K, Shaffer C, and Haendel M. Lions, Tigers, and Gummi Bears: Springing Towards Effective Engagement with Research Data Management (2014). Scholar Archive. Paper 3571.
  43. 43. CONSULTATIONS Researcher + 2-3 from Data Stewardship Team
  44. 44.  Researchers DO need assistance:  Finding and choosing data standards  File versioning  Applying metadata to facilitate data sharing  “Gummi Bear” themed data management exercise resonated well with students  Lack of awareness of services and expertise offered by the Library Conclusions
  45. 45. OHSU New Directions  OHSU Library is developing data services for researchers  BD2K educational grants in collaboration with DMICE www.ohsu.edu/xd/education/library/data
  46. 46. Acknowledgements OHSU Melissa Haendel Robin Champieux Jackie Wirz Kyle Banerjee Bryan Laraway Chris Shaffer Kaiser Todd Hannon UO Brian Westra Karen Estlund Cathy Flynn- Purvis John Russell Idaho Bruce Godfrey Nancy Sprague Lynn Baird Greg Gollberg Luke Sheneman Steven Daley-Laursen
  47. 47. Contact us Nicole Vasilevsky vasilevs@ohsu.edu @N_Vasilevsky Thank you Victoria Mitchell vmitch@uoregon.edu @VictoriaStap Jeremy Kenyon jkenyon@uidaho.edu @jr_kenyon

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