Diese Präsentation wurde erfolgreich gemeldet.
Wir verwenden Ihre LinkedIn Profilangaben und Informationen zu Ihren Aktivitäten, um Anzeigen zu personalisieren und Ihnen relevantere Inhalte anzuzeigen. Sie können Ihre Anzeigeneinstellungen jederzeit ändern.

Slides | Research data literacy and the library

1.006 Aufrufe

Veröffentlicht am

Slides from the Dec. 8, 2016 Library Connect webinar "Research data literacy and the library" with Christian Lauersen, Sarah J. Wright and Anita de Waard. See the full webinar at: http://libraryconnect.elsevier.com/library-connect-webinars?commid=226043

Veröffentlicht in: Bildung
  • Als Erste(r) kommentieren

Slides | Research data literacy and the library

  1. 1. Teaching Data Information Literacy Sarah J. Wright Life Sciences Librarian for Research, Cornell University
  2. 2. What I’ll talk about Data Information Literacy IMLS-funded Data Information Literacy research project needs identified approaches lessons learned
  3. 3. DIL + Related Literacies Data Literacy Access, assess, manipulate, summarize, and present data Statistical Literacy Think critically about basic stats in everyday media Information Literacy Think critically about concepts; read, interpret, evaluate information Data Information Literacy The ability to use, understand, and manage dataSchield, Milo. "Information literacy, statistical literacy and data literacy." I ASSIST Quarterly 28.2/3 (2004): 6-11.
  4. 4. Discovery & Acquisition Databases & Data formats Data Conversion & Interoperability Data Processing & Analysis Data Visualization & Representation Data Management & Organization Data Quality & Documentation Metadata & Description Cultures of Practice Ethics & Attribution Data Curation & Re-use Data Preservation Carlson, J., Fosmire, M., Miller, C. C., & Nelson, M. S. (2011). Determining data information literacy needs: A study of students and research faculty. Portal: Libraries & the Academy, 11(2), 629-657.
  5. 5. Cornell University University of Minnesota University of Oregon Purdue University 1 Purdue University 2 Natural Resources Civil Engineering Ecology Electrical & Computer Engineering Agricultural & Biological Engineering Longitudinal data of fisheries and water quality Real-time sensor data on bridge structures Climate change and plant growth data Software code in community service projects Simulation data of hydrological processes http://datainfolit.org
  6. 6. Cornell University University of Minnesota University of Oregon Purdue University 1 Purdue University 2 for credit course online modules seminar workshop series embedded librarian Data sharing Databases Data ownership Long-term access Cultures of Practice Metadata Documenta-tion & organization Standard Operating Procedures Metadata http://datainfolit.org
  7. 7. Courses Developed at Cornell: NTRES 6600: Research Data Management Seminar Six session, 1-credit mini-course for grad students in Natural Resources BIOG 3020: Seminar in Research Skills for Biologists 1-credit semester long course for undergraduates involved in research; data management portion of course
  8. 8. Lessons Learned • The competencies were almost universally considered important by students and faculty interviewed. • Students were considered lacking in these competencies. • Faculty assumed that students have or should have acquired the competencies earlier. • Lack of formal training for students working with data. http://www.slideshare.net/asist_org/rdap-15-lessons-learned-from-the-data-information-literacy-project
  9. 9. PhD comics, http://www.phdcomics.com/comics.php?f=1323 http://www.phdcomics.com/comics/archive.php/tellafriend.php?comicid=1323 Lessons Learned • Needs may not be as complex as you might think.
  10. 10. Lessons Learned • Learning is largely self-directed through “trial and error.” • Training often at point of need, often in the context of the immediate issue. • Faculty were often unsure of best practices or how to approach the competencies themselves. http://www.slideshare.net/asist_org/rdap-15-lessons-learned-from-the-data-information-literacy-project
  11. 11. DIL Resources Data Information Literacy Project Website: http://www.datainfolit.org/ Book: http://www.thepress.purdue.edu/titles/format/9781612493527 Data Q (for your data questions): http://researchdataq.org/
  12. 12. Contact Information SARAH J. WRIGHT Life Sciences Librarian for Research Cornell University sjw256@cornell.edu
  13. 13. Digital Social Science Lab - connecting academia with data literacy Christian Lauersen Copenhagen University Library Email: cula@kb.dk Twitter: @clauersen Library Connect Webinar Dec 8th 2016 Research Data Literacy and The Library
  14. 14. Why? The master’s thesis case
  15. 15. Kub kort Hvorfor?3 Data Labs Humanities Social Sciences Natural and Health Science
  16. 16. An open platform for education and events on digital methods Hardware and software for harvesting, cleaning, analyzing and visualizing data A dynamic and aesthetically inspiring learning environment
  17. 17. What we do •Events and instruction •Facilitating and curating •Community building
  18. 18. The library as hub: Community and peer-to-peer
  19. 19. The Space: •Flexibility •Functionality •Inspiration
  20. 20. An alternative to the classic learning setup
  21. 21. The Evolving DSSL Network DSSL Aalborg University DTU Faculty members Students Ethnographic Exploratorium ETHOS Lab Teaching and learning unit Faculty BADASS Higher education Danish Business Authority Open Data Network Libraries and archives Society
  22. 22. Hvad er Digital Social Science Lab? • Et fysisk rum til understøttelse af forskning, uddannelse og læring • Relevant software og hardware + vejledning og support • En platform for digitale metoder og værktøjer indenfor samfundsvidenskaben Key to impact? Stakeholders Ownership Collaboration
  23. 23. Challenges in the process • ”Is this a library task?” • ”On the expense of what?” • How do we get the relevant skills? • How do we talk about this project? • How do we position ourselves toward the local research and educational environment?
  24. 24. What we’ve learned • It’s not enough to provide access to software and hardware • Skill development is a long process and has to be in context of need and resources • The facilitating role is a good way to create value • Network is key • The Library is a very strong platform for bringing people together within academia • Library support of data literacy might not fit with all subjects
  25. 25. Digital Social Science Lab http://kub.kb.dk/DSSL Christian Lauersen Mail: cula@kb.dk Twitter: @clauersen The Library Lab https://christianlauersen.net Thanks for listening
  26. 26. | 29 Elsevier‘s RDM Program: Ten Habits of Highly Effective Data Anita de Waard VP Research Data Collaborations Elsevier RDM Services a.dewaard@elsevier.com December 8, 2016
  27. 27. | 30 https://www.elsevier.com/connect/10-aspects-of-highly-effective-research-data 10.Integrateupstreamanddownstream –makemetadatatoserveuse. Save Share Use 9. Re-usable (allow tools to run on it) 8. Reproducible 7. Trusted (e.g. reviewed) 6. Comprehensible (description / method is available) 5. Citable 4. Discoverable (data is indexed or data is linked from article) 3. Accessible 1. Stored (existing in some form) 2. Preserved (long-term & format-independent) A Maslow Hierarchy for Research Data:
  28. 28. | 31 Store, Preserve: Data Rescue Award
  29. 29. | 32 Store: Hivebench www.hivebench.com
  30. 30. | 33 https://data.mendeley.com/ Linked to published papers – or not Linked to Github – or not Versioning and provenance tracking Store, Access: Mendeley Data Different Licenses: GNU-PL, CC-BY CC0, etc
  31. 31. | 34 Access, Cite: Data Linking • Integrated in paper submission process • Supplementary data is never behind a firewall • Closely integrated with > 150 databases
  32. 32. | 35 Access, Discover: Scholix/DLIs • ICSU-WDS/RDA Publishing Data Service Working group, merged with National Data Service pilot • Cross-stakeholder – with input from CrossRef, DataCite, OpenAIRE, Europe PubMed Central, ANDS, PANGAEA, Thomson Reuters, Elsevier, and others • Proposed long-term architecture and interoperability framework: www.scholix.org • Operational prototype at http://dliservice.research-infrastructures.eu/#/api (including 1.4 Million links from various sources)
  33. 33. | 36 Cite: Force11 https://www.elsevier.com/connect/data-citation-is-becoming-real-with-force11-and-elsevier
  34. 34. | 37 Discover: DataSearch https://datasearch.elsevier.com
  35. 35. | 38 Data articles Software articles Method articles Protocols Video articles Hardware articles Lab resources Full Research paper • Brief article types designed to communicate a specific element of the research cycle • Complementary to full research papers • Easy to prepare and submit • Peer-reviewed and indexed • Receive a DOI and fully citable • Allow citable post-publication updates • Primarily Open Access (CC-BY) • Published in Multidisciplinary and domain-specific journals https://www.elsevier.com/books-and-journals/research-elements Review: Research Elements
  36. 36. | 39 Reuse: Cortex Registered Reports 39 • Two-step submission process: • Method and proposed analysis are submitted for pre-registration • Paper is conditionally accepted • Research is executed • Full paper submitted, accepted provided that protocol is followed • All experimental data made available Open Access Featured in The Guardian:
  37. 37. | 40 Research article published Initial inquiry Share, publish and link data Monitor progress and provide guidance Generate reports 111110 00011 1101110 0000 001 10011 1 011100 101 Metrics for Institutions: Data Lighthouse What? Service for Research Institutes (esp. librarians) to engage with researchers throughout the research data life cycle. How? Offer service for Librarians to interact with researchers regarding the RDM Process to: • Offer solutions to store, share, link and publish data • Monitor progress report on posting, citation, downloads of dataset • Provide monthly reporting DATA LIGHTHOUSE
  38. 38. | 41 10.Integrateupstreamanddownstream –makemetadatatoserveuse. Save Share Use 9. Re-usable 8. Reproducible 7. Trusted 6. Comprehensible 5. Citable 4. Discoverable 3. Accessible 1. Stored 2. Preserved https://www.elsevier.com/connect/10-aspects-of-highly-effective-research-data A Maslow Hierarchy for Research Data: Data at Risk Reproducibility Papers Data Lighthouse

×