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.

R2DaLT: thoughts about data literacy - Koltay


Published on

Presented at LILAC 2019

Published in: Education
  • Did u try to use external powers for studying? Like ⇒ ⇐ ? They helped me a lot once.
    Are you sure you want to  Yes  No
    Your message goes here
  • Earn Up To $316/day! Social Media Jobs from the comfort of home! ★★★
    Are you sure you want to  Yes  No
    Your message goes here
  • Be the first to like this

R2DaLT: thoughts about data literacy - Koltay

  2. 2. CONTENTS • Research 2.0 • The changing view on data, • Data literacy, • The sequence of supporting researchers, • RDM, • Data curation, • Contents and courses, • Conclusion. 2
  3. 3. RESEARCH 2.0 • Data-intensive research • Open Science = Open Data = Open Access to scholarly publications Research 2.0 requires a high level of attention to data management and data curation, data citation and data quality (Vilar, & Zabukovec, 2019). 3
  4. 4. ABUNDANCE OF DATA Source and consequence of Information overload → data overload 4
  5. 5. NEW VIEWS ON DATA With its perceived importance, the views on data have changed. • Is data something different from information? 5
  6. 6. Digital, humanists often assume: Data can be interpreted as texts, Texts can be interpreted as data. 6 IS THE DIK(W) PYRAMID STILL VALID?
  7. 7. DATA IS… • Any information in binary digital form (Digital Curation Centre). 7
  8. 8. • Information literacy is related not only to print, but data, images, etc. (CILIP, 2018). ← Documents, not born digital may become digital at some point. 8
  9. 9. PRIMARY SOURCE DATA • Humanities – unstructured data (from archives and manuscripts), • Social sciences – tabulated data (from surveys, polls, census), • Natural sciences – tabulated data (from controlled studies) (Fontichiaro et al, 2017). 9
  10. 10. RESEARCH DATA • More specific than ‘data’ = ‘data collected as part of a research project’ • data collected for curation and preservation, can become research data, • research data is archived for curation and preservation. 10
  11. 11. DATA LITERACY = competences needed for any work with research data (Schneider, 2013). = the ability to process, sort and filter vast quantities of information, for which competences for searching, filtering, processing, creation and synthesizing information are needed (Koltay, 2015). 11
  12. 12. DATA LITERACY • Is cognate to information literacy. • Is compatible with the information literacy focus of academic librarianship. 12
  13. 13. DATA LITERACY • Focuses on data quality. It involves elements of • Statistical literacy, • Numeracy, • Data governance principles, • Data science, • and Open Data. 13
  14. 14. DATA LITERACY Is relevant • in the context of education and training (of researchers, students, etc.) • in the context of providing data services of informational and/or technical nature (for librarians and other providers, technical staff, etc.) 14
  15. 15. DATA LITERACY • Data literacy education’s main targets are students. • Librarians and teaching staff members also should be data literate, but educating the latter is a delicate issue. 15
  16. 16. FUNDAMENTAL TOPICS • The concept of data, • Critical thinking, • Ethical issues, • Research Data Management (RDM), • Data quality • Data citation • Data visualization • Metadata (Ridsdale et al., 2015). 16
  17. 17. OTHER IMPORTANT TOPICS • Big data, little data, no data (Borgman, 2015). • The existence of grey data (useful data, produced by universities outside their research realm, but not subjected to peer review, similarly to grey literature (Borgman, 2018). 17
  18. 18. INCLUDE FAIR PRINCIPLES AND RDA • Findable, • Accessible, • Interoperable, • and Re-usable. Mention the existence of Research Data Alliance ( 18
  19. 19. DATA GOVERNANCE • Provides answers about the availability and access possibilities, provenance, trustworthiness and meaning. Provenance as indicator of quality: • Who generated it? • Who funded the study where the data come from? • What was the research agenda? (Koltay, 2016; Fontichiaro et al., 2017) 19
  20. 20. HOW TO TEACH DATA LITERACY? • Include mechanics related to research data; • Focus on practice; • Use real world data, when appropriate (Ridsdale et al. 2015). 20
  21. 21. THE SEQUENCE OF SUPPORTING RESEARCHERS 1. Data literacy instruction, 2. Research Data Management, 3. Data curation, 4. Data preservation. 21
  22. 22. • RDM is an integral element of everyday research work for many researchers → supporting it should become routine activity in academic libraries. 22
  23. 23. RDM • Academic libraries show varied levels of readiness, but they provide a wide array of informational RDM services in many countries = • Informational services (with high frequency), • Technological/technical services (with lower frequency). 23
  24. 24. INFORMATIONAL SERVICES Consulting with staff and students • On Data Management Plans, • On metadata standards, Providing • reference support (for finding and citing) datasets, 24
  25. 25. TECHNOLOGICAL SERVICES • Creating or transforming metadata for datasets, • identifying datasets that could be candidates for repositories, • selecting and preparing datasets for deposit, • deaccessioning, or deselecting datasets. 25
  26. 26. DATA CURATION Data curation is close to technological RDM services The encompassing work and actions taken by curators of a data repository in order to provide meaningful and enduring access to data (Johnston et al., 2018). 26
  27. 27. TARGET AUDIENCE • Anyone wanting to support researchers in storing, managing, archiving and sharing research data = a data supporter = (data) librarians, IT staff and researchers 28
  28. 28. Acquiring the basic knowledge and skills = essentials that enable data supporters to take the first steps towards supporting researchers in storing, managing, archiving and sharing their research data. 29
  29. 29. A DATA SUPPORTER • Shows proactive attitude to improve data services; • Sees data and information services as part of larger whole in which decisions are made; • Can handle questions efficiently and knows when to address a dedicated expert; • Is cooperative. 30
  30. 30. UNDERSTANDS / KNOWS • The structure of a data management plans; • The various ways to store, backup, organize and document research data; • Types of archives, data publication and data citation; • How to advise researchers in balancing legislation and practice; (Verbakel, & Grootveld, 2016). 31
  31. 31. 32
  32. 32. FOR LIBRARIANS Do-It-Yourself Research Data Management Training Kit for Librarians • Data management planning • Organising & documenting data • Data storage & security • Ethics & copyright • Data sharing
  34. 34. DATA CURATION PROFILES • Can be used to provide a foundational base of information about a particular set of data • that may be curated by an academic library or other institution (Carlson, 2010). 35
  35. 35. AN EXCERPT FROM A PROFILE Linguistics / Etymology • Significant amount of data; • Composed of videos, spreadsheets and a finalized report captured in MS Word files; • Lack the means of managing, curating and sharing this data effectively. 36
  36. 36. GENERAL ADVISE Ensure that researchers understand • Libraries will be responsible stewards of their information. 37
  37. 37. • They will not be taking ownership of the material, • Original owners will be able to access their access content • or take it out of library systems • at any point and in-perpetuity 38
  38. 38. • Relieving researchers’ technical and administrative burdens is a respectable goal. • Transcending this status by achieving true collaboration with researchers, requires focused and intensive work of library managers, who must understand the advantages of RDM (Burton & Lyon, 2017, Koltay, 2019). 39
  39. 39. TELL TO ACADEMIC LIBRARIES THAT THEY SHOULD… • Fill gaps at all levels of professional skills • Change librarians’ self-identification • Gain an appreciation of technical and socio-ethical issues related to research data (Robinson & Bawden, 2017). 40
  40. 40. NOT ONLY FOR DATA LIBRARIANS • There is a clear need for teaching data literacy to academic librarians (Koltay, 2017). • At smaller institutions, there is a greater need for diverse and generalized skills, • Being data literate is a need for all future academic librarians (Morrison & Weech, 2018). 41
  41. 41. LITERATURE Borgman, C. L. (2015). Big data, little data, no data: Scholarship in the networked world. MIT press. Borgman, C. L. (2018). Open Data, Grey Data, and Stewardship: Universities at the Privacy Frontier. Berkeley Technology Law Journal, 3(2), 287-336. Fontichiaro, K., Lennex, A., Hoff, T., Hovinga, K., & Oehrli, J. A. (Eds.). (2017). Data Literacy in the Real World: Conversations & Case Studies. Michigan Publishing, University of Michigan Library. Koltay, T. (2015). Data literacy: in search of a name and identity. Journal of Documentation, 71(2), 401–415. Koltay, T. (2016). Data governance, data literacy and the management of data quality. IFLA Journal, 42 (4):303-312. Koltay, T. (2017). Data literacy for researchers and data librarians. Journal of Librarianship and Information Science, 49(1) 3–14. Koltay, T. (2019). Accepted and emerging roles of academic libraries in supporting Research 2.0. Journal of Academic Librarianship 45(2), 75-80. Morrison, L. & Weech, T. (2018). Reading data: the missing literacy from LIS education. In: Lelde, Petrovska; Baiba, Īvāne-Kronberga; Zane, Melde (eds.) The Power of Reading : Proceedings of the XXVI Bobcatsss Symposium, Riga: University of Latvia, pp. 75-80. Rice, R. (2014). Research Data MANTRA: A Labour of Love. Journal of eScience Librarianship 3(1): e1056. Ridsdale Ch. et al. (2015). Strategies and best practices for data literacy education: Knowledge synthesis report. Halifax, NS: Dalhousie University. Robinson, L. and Bawden, D. (2017). 'The story of data': a socio-technical approach to education for the data librarian role in the CityLIS library school at City, University of London. Library Management, 38 (6-7), 312- 322. Schneider, R. (2013). Research data literacy. In Kurbanoglu, S. et al. (Eds.), Worldwide Commonalities and Challenges in Information Literacy Research and Practice (pp. 134–140). Springer International. Vilar, P., & Zabukovec, V. (2019). Research data management and research data literacy in Slovenian science. Journal of Documentation, 75(1), 24-43, 42
  42. 42. Tibor Koltay Professor Eszterházy Károly University Email: koltay.tibor@uni- Telephone: +36 7066687302 Twitter: @ktiborH