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Rscd 2017 bo f data lifecycle data skills for libs

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Birds of a Feather session on the role of the Librarian in the data lifecycle at the Research Support Community Day, Sydney 2017.

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Rscd 2017 bo f data lifecycle data skills for libs

  1. 1. Research Data Lifecycle: Data Skills for Librarians Kathryn Unsworth (ANDS) RSCD - BoF 13 February 2017
  2. 2. What data skills do librarians require? There’s a matrix of possibilities - it’s complex! https://etherpad.openstack.org/p/RSCD_2017_Data_lifecycle_&_libs
  3. 3. Elements of the data skills for librarians matrix • Current librarian role - where and at what level it connects with research/researchers – data-related scope • Future aspirations of the librarian – data-related scope • Aspects of the Data Lifecycle the Library provides services for • RDM maturity of the institution (dictates services provided) • Research intensity of the institution (dictates services provided) = data skills and knowledge required
  4. 4. Where and at what level you connect with your research communities: ● Based mainly in the library? ● Embedded in research teams/labs? ● Hot desking it out of faculty spaces? ● Roving in a Research Commons (not only the Library)? ● Undertaking research support services (e.g. data consultations, data collection/collating, data cleaning) out in the (research) field? ● A combination?
  5. 5. Which parts of the Data Lifecycle your Library actively supports with services: ● Data seeking for analysis ● Data documentation (metadata) ● Data citation ● Data storage and backups ● Data sharing ● Data archiving and preservation ● Data and teaching ● Data cleaning ● Data visualisation ● DMP tools and advice + any intended new services
  6. 6. Your role - what proportion is research & data related? Hybrid or specialist role? Librarian Liaison Librarian Metadata Librarian Subject Librarian Research Librarian Scholarly Communications Librarian Repository Manager Repository Officer Data Librarian Research Data Management – Librarian Data Science Librarian and more… Same title, but different responsibilities at University X vs University Y
  7. 7. Discussion: Are there other elements that we can add to the matrix? https://etherpad.openstack.org/p/RSCD_2017_Data_lifecycle_&_libs
  8. 8. How do librarian roles and data skills and knowledge intersect with the Data Lifecycle?
  9. 9. Data-related skills and knowledge for librarians across the Data Lifecycle
  10. 10. Domain knowledge – best practice (current research methods and data models) Connecting researchers to research / research data “Map knowledge/data gaps” “Identify emerging disciplinary cross-overs” “Assist in the formulation and refinement of innovative research questions” “Digital tools to automate Literature reviews – Meta, CHORUS system?” “Applying network analysis to visualise trends in emerging research” “Tools to map key research terms in articles – where are the terms appearing?” “Text and data mining techniques for refining research questions”
  11. 11. Project planning • project management (Prince, Agile, Waterfall, Critical Path, etc.) • Business analysis – requirements gathering • Problem solving, troubleshooting Collaboration tools/platforms (OSF, Confluence, Syncplicity, Google Apps) RDMPs (DMPtool, DMPonline) • Project governance – roles and responsibilities • Data standards • Data organisation – file formats, file naming conventions, versioning, etc. • Ethics and privacy – consent for sharing • Copyright and other IP, Licensing • Data storage • Data security Funder and publisher requirements for data Digital literacies training
  12. 12. Data search/discovery: • Discovery tools and services • Locate existing data • Full text search • Text and data mining • Web APIs to discover, extract, enrich existing data Data organisation Data collection methods – generating new data, transforming legacy data, sharing/exchanging data, purchasing data Metadata capture and creation tools and services Metadata standards: • Data description • Controlled vocabularies • Metadata modeling • Interoperability Patten recognition Collaboration tools and platforms Databases, including relational Version tracking Reference/citation management Storage options for working, master, raw, sensitive and big data Data appraisal and selection Licensing – data access/sharing agreements Data security
  13. 13. Storage options for active data, collaborative research, data and metadata flows Data security Access rules Data cleaning Data aggregation Machine learning/algorithms – graphical modeling Scripting/coding Data mapping across data sources Data transforms, e.g. raster to shape files Lab notebooks (eLNs) Data screening and preparation Iterative data changes prompted by analysis Preparing data for long-term preservation and sharing Process documentation – process diagrams, workflows, tools and automation
  14. 14. Data visualisation Storage options for active data Data security, including access controls Data manipulation Text and data mining Scripting/coding Machine learning Analysis: Statistical, Spatial, Image Analysis documentation Modeling Interpretation Database programming (querying DBs) Problem solving/troubleshooting Analytical thinking
  15. 15. Why share data? Author/Creator rights Data catalogs and portals Sensitive data Access rules Metadata standards • Descriptive metadata • Controlled vocabularies Persistent identifiers (DOIs, ORCIDs) Data citation Data licensing Performance/Impact metrics Programming – front-end – editing web page source code, incorporate forms, multimedia Contributor badges Communication Storytelling Data visualisation Client engagement Advocacy
  16. 16. Persistent identifiers (DOIs, ORCIDs) Using tools to identify file formats Conversion to access and preservation formats/mediums Batch/automation Data decoding Data warehousing Data archives and repositories Long-term archival storage for final-state data Metadata standards • Descriptive metadata for discovery • Provenance and other administrative metadata Disposition – disposing of obsolete or redundant data, or archival retention
  17. 17. Licensing – legal framework around how data can be (re)used Reuse documentation (code, simulations, models, protocols, workflows, etc.) Impact and assessment metrics (Altmetrics, PlumX, ImpactStory) Data for teaching Data citation – how and why to cite data
  18. 18. Whole of lifecycle activities • Describing and contextualising data (metadata, documentation, associated research outputs) • Managing data quality • Storage, Back ups and Security
  19. 19. Are you kidding me? Who has the capacity to attain all these skills?
  20. 20. Teams, not unicorns “Team-building is another important tactic in tackling the skills gap. There is little point looking for the great, single all-rounder who can do everything – the mythical unicorn. Even if such people existed (and they may) they would be too expensive as they can walk into any job. It is much more profitable to look across the skill-set required and build a team to fulfil it.” Read more at: http://www.techweekeurope.co.uk/e-management/skills/bridging- data-science-skills-gap-requires-team-effort-160818#msRDJHrzR8QUhLHa.99 Copyrighted Image - Data Science Roles https://libraryconnect.elsevier.com/articles/learning- about-research-data-lab-pitt-ischool
  21. 21. So what’s the minimum data skills requirement for librarians? Is there an optimal level? Maybe even an aspirational level? Are we talking about all librarians or only those with data- related responsibilities? As an academic librarian is it ok to just be “data aware” or do we all need to be “data savvy” or maybe something in between? Discussion… https://etherpad.openstack.org/p/RSCD_2017_Data_lifecycle_&_libs
  22. 22. What is a Data Savvy Librarian? “...librarians need increasingly to become data-savvy themselves and to have a deeper understanding of the research data lifecycle in order to enhance the services they offer.” “...the main requirement is a basic familiarity with how various software tools can transform data.” And, “...to learn the basics of some of the latest tools for extracting, analyzing, storing, and visualizing data.” “...working directly with messy, unavailable or difficult to-access data it is possible to have a more complete vision of the different issues the researchers have to face when working with data.” Barbaro, A. (2016). On the importance of being a data-savvy librarian. Journal of EAHIL, 12(1):24-27
  23. 23. Then there’s the question of Data Science in Libraries…
  24. 24. The research librarian of the future: data scientist and co-investigator There remains something of a disconnect between how research librarians themselves see their role and its responsibilities and how these are viewed by their faculty colleagues. Jeannette Ekstrøm, Mikael Elbaek, Chris Erdmann and Ivo Grigorov imagine how the research librarian of the future might work, utilising new data science and digital skills to drive more collaborative and open scholarship. Arguably this future is already upon us but institutions must implement a structured approach to developing librarians’ skills and services to fully realise the benefits.
  25. 25. Core duties versus ‘stretch’ services The research librarian community is not in consensus as to what exactly are the emerging roles of future librarians in a rapidly evolving digital scholarship environment (see #libraryfutures). Added to the polarised views within that community, a recent survey shows there is also a clear gap in perception and expectations between librarians and faculty staff. While librarians surveyed agreed that “information literacy” and “aiding students one-on-one in conducting research” are primary and essential roles, they viewed “supporting faculty research” as less important than their faculty colleagues. So does this present an opportunity in the digital age?
  26. 26. The Role of Librarians in Data Science: A Call to Action “All of this hesitancy on the part of librarians to participate in the data movement is happening at a time when we have seen an increase in the money and involvement in data initiatives from a range of other professions and academic disciplines (e.g. computer science, informatics, etc.). For me, this is an especially critical moment for librarians to talk about data and actively plan and implement our strategies collectively. I want to share with you a proposed framework for the librarian’s role in data science. I come to the discussion with the fear that data science is an evolving academic discipline being defined solely by computer science and that the field of library and information science is being left behind. I would argue that the principles and values of the field of library and information science that form the core of our profession need to be part of this new discipline and that we can add unique perspectives and roles.” (Opinion piece by Elaine R. Martin, 2015)
  27. 27. Data Science – is there a future where you see librarians filling the DS skills gap? What’s your next data skills challenge? https://etherpad.openstack.org/p/RSCD_2017_Data_lifecycle_&_libs Discussion points:
  28. 28. Acknowledgements Barbaro, A. (2016). On the importance of being a data-savvy librarian. Journal of EAHIL, 12(1):24-27 https://www.researchgate.net/publication/299394172_On_the_importance_of_being_a_data-savvy_librarian Ekstrom, J., Elbeaek, M., Erdmann, C., & Grigorov, I. (2016). The research librarian of the future: data scientist and co-investigator, The Impact Blog LSE. http://blogs.lse.ac.uk/impactofsocialsciences/2016/12/14/the-research-librarian-of-the-future-data-scientist- and-co-investigator/ Faundeen, J.L., Burley, T.E., Carlino, J.A., Govoni, D.L., Henkel, H.S., Holl, S.L., Hutchison, V.B., Martín, Elizabeth, Montgomery, E.T., Ladino, C.C., Tessler, Steven, and Zolly, L.S., 2013, The United States Geological Survey Science Data Lifecycle Model: U.S. Geological Survey Open-File Report 2013–1265, 4 p., https://doi.org/10.3133/ofr20131265. Macrae, D. (2015). Why Bridging The Data Science Skills Gap Requires A Team Effort. TechWeek Europe. http://www.techweekeurope.co.uk/e-management/skills/bridging-data-science-skills-gap-requires-team-effort- 160818#25vTmJ6UpzSfI20F.99 Martin, Elaine R. (2015). "The Role of Librarians in Data Science: A Call to Action." Journal of eScience Librarianship 4(2): e1092. http://dx.doi.org/10.7191/jeslib.2015.1092 Library Journal Research. (2015). Bridging the Librarian-Faculty Gap in the Academic Library. Gale Cengage Learning. https://s3.amazonaws.com/WebVault/surveys/LJ_AcademicLibrarySurvey2015_results.pdf University of Central Florida Libraries Research Lifecycle Committee. (2012). The research lifecycle at UCF [Online Graphic]. Retrieved (February, 13, 2017) from library.ucf.edu/ScholarlyCommunication/ResearchLifecycleUCF.php
  29. 29. With the exception of logos, third party images or where otherwise indicated, this work is licensed under the Creative Commons Australia Attribution 3.0 Licence. ANDS is supported by the Australian Government through the National Collaborative Research Infrastructure Strategy Program. Monash University leads the partnership with the Australian National University and CSIRO. Kathryn Unsworth - ANDS Outreach Officer and Data Librarian kathryn.unsworth@ands.org.au

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