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Workshop 4: Open Science & Open Data for Librarians/Ina Smith

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AOSP participation during SCECSAL XXIII, Entebbe, Uganda (23-28 April 2018)

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Workshop 4: Open Science & Open Data for Librarians/Ina Smith

  1. 1. Workshop 4: Open Science & Open Data for Librarians 24 April 2018 14:00 – 17:30 XXIII SCECSAL Conference, Entebbe, Uganda 24 April 2018
  2. 2. Programme 14:00 – 14:30 Introduction to Open Science/Open Data 14:30 – 15:00 Data informing the library profession 15:00 – 15:40 Data in support of research 15:40 – 16:00 Health Break 16:00 – 17:00 Working with data – tools & applications 17:00 – 17:30 Towards a data strategy for your library & institution
  3. 3. Data Stakeholders • Governments (policy) • Institutions (policy & strategy) • Research Offices (reporting, impact) • Researchers (collecting data in an ethical and trusted way so that it can be re-used) • Statisticians (processing, analysing and visualising data) • System engineers (to maintain a network and allow for data to be digitally transmitted) • Librarians (managing and organizing the data, and making sure it is digitally preserved for the unforeseeable future)
  4. 4. Why Librarians as Data Partners? • Information standards • Organizational skills • Setting up file structures (organizing information) • Knowledge of workflows • Knowledge of collection management • Describing data using established metadata schemes & controlled vocabulary • Collection curation/preservation
  5. 5. Role of Librarians • Advocate for transparency, openness in research, access to data • Initiating conversation on Open Science Open Data Policy & Strategy - implement • Develop own data skills (data skills but also informed on copyright, licensing, citation) • Increase visibility of research data • Manage & register trusted data repositories • Recommend trusted data repositories • Promote & support proper research data management planning among researchers
  6. 6. Data Skills for Librarians (1) • Data terminology • Unix-style command line interface, allowing librarians to efficiently work with directories and files, and find and manipulate data • Cleaning and enhancing data in OpenRefine and spreadsheets • Git version control system and the GitHub collaboration tool • Web scraping and extracting data from websites • Scientific writing in useful, powerful, and open mark-up languages such as LaTeX, XML, and Markdown • Formulating and managing citation data, publication lists, and bibliographies in open formats such as BiBTeX, JSON, XML and using open source reference management tools such as JabRef and Zotero
  7. 7. Data Skills for Librarians (2) • Transforming metadata documenting research outputs into open plain text formats for easy reuse in research information systems in support of funder compliance mandates and institutional reporting • Scholarly identity with ORCiD and managing reputation with ORCiD- enabled scholarly sharing platforms such as ScienceOpen • Authorship, contributorship, and copyright ownership in collaborative research projects • Demonstrating best practices in attribution, acknowledgement, and citation, particularly for non-traditional research outputs (software, datasets) • Identifying reputable Open Access publications and Open Institutional/Open Data repositories • Scholarly annotation and open peer review • Investigating and managing copyright status of a work, and evaluating conditions for Fair Use
  8. 8. Introduction to Open Science/ Open Data
  9. 9. Types of data • Government data • Communication data (mobile phones) • Internet data • Statistical data • Research data (social & natural sciences) • Discipline specific • And more …
  10. 10. What is “data” and why “data”?
  11. 11. Open Science Defined “Open Science is the practice of science in such a way that others can collaborate and contribute, where research data, lab notes and other research processes are freely available, under terms that enable reuse, redistribution and reproduction of the research and its underlying data and methods.” - FOSTER Project, funded by the European Commission
  12. 12. Open Science Research Lifecycle (Foster)
  13. 13. Original Research Data Lifecycle image from University of California, Santa Cruz http://guides.library.ucsc.edu/datamanagement/ Repositories Repositories Tools Plan Policy&Infrastructure
  14. 14. Data Activity
  15. 15. Activity http://bit.ly/scecsal2018
  16. 16. Data in support of research
  17. 17. Fears Researchers Experience • Getting scooped • Time & effort by researcher • Someone else finding a path-breaking application of the data that researcher hasn’t considered • Fear of problems/errors in the measurement process being exposed • Confidentiality/privacy of respondents - ethics clearance • Intellectual Property Rights – signed away, little understanding, no IP in place
  18. 18. • When should research data be open? • When should research data be closed?
  19. 19. • IP, Copyright, Licensing, Citations, Persistent Identifiers (DOIs), Metadata Standards • Dataverse https://dataverse.org/ • CKAN https://ckan.org/ • DKAN https://getdkan.org/ • Nesstar http://www.nesstar.com/software/publisher.html https://www.coretrustseal.org/about/ Implement & Manage Trusted Data Repositories
  20. 20. Data Repositories vs Social Media • Social media sites/3rd party software: • Connect researchers sharing interests • Marketing data • Sites belong to third parties – and data • Repository: • Supports export/harvesting of metadata • Offers long-term preservation • Non-profit – no advertisements • Uses open standards and protocols • Copyright
  21. 21. Recommend Trusted Data Repositories https://www.re3data.org/ Find more repositories, datasets
  22. 22. Register Data Initiatives • re3data.org https://www.re3data.org/ • Open Data Barometer https://opendatabarometer.org/ • Global Open Data Index https://index.okfn.org/ • African Open Science Platform http://africanopenscience.org.za/ • Dataverse …. And more …
  23. 23. Research Data Management https://github.com/DMPRoadmap Research Proposal Ethics Committee Funder Data Server & Repository Etc.
  24. 24. More on DCC Web Page
  25. 25. Health Break 15:40 – 16:00
  26. 26. Working with data – tools & applications
  27. 27. Working with Data • Using R, Python, ggplot and more .. • Collection e.g. Survey • Normalisation & Cleaning e.g. OpenRefine • Analysis • Visualisation • Preservation • Mining
  28. 28. Data Visualisation • Static: http://r-statistics.co/Top50-Ggplot2- Visualizations-MasterList-R-Code.html • Dynamic: https://blog.profitbricks.com/39- data-visualization-tools-for-big-data/
  29. 29. https://www.targetmap.com/viewer.aspx?reportId=56245 Please note: this is just a preview and data still to be cleaned and updated and corrected. African Open Science Platform (AOSP) Landscape Study
  30. 30. Data Mining • Set of methods to analyse data from various dimensions and perspectives, finding previously unknown hidden patterns, classifying and grouping the data and summarizing the identified relationships The tasks of data mining are twofold: • Create predictive power using features to predict unknown or future values of the same or other feature • Create a descriptive power, find interesting, human-interpretable patterns that describe the data
  31. 31. https://www.youtube.com/watch?v=W44q6qszdqY
  32. 32. https://my.rapidminer.com/nexus/acco unt/index.html
  33. 33. Self- & Lifelong Learning • Bachelor of Science in Data Science, Sol Plaatje University (South Africa) • Coursera Data Science • Coursera Research Data Management and Sharing • Foster Open Science Courses • Masters Program in Biodiversity Informatics, Prof Jean Ganglo, University of Abomey- Calavi (Benin) • MANTRA for Researchers • MANTRA for Librarians • Agricultural Information Management Standards (AIMS) • Author Carpentry • Data Carpentry • Library Carpentry • WDS Training Resources • UCT eResearch
  34. 34. http://www.dcc.ac.uk/resources/meta data-standards/list
  35. 35. Towards a data strategy for your library & institution
  36. 36. Open Science Open Data Statement
  37. 37. Open Science Open Data Policy http://learn-rdm.eu/wp- content/uploads/red_LEARN_Elements_of_the_Content_of_a_RDM_Policy.pdf
  38. 38. Endorse the Accord Call to Endorse
  39. 39. AOSP Focus Areas Policy Infrastructur Capacity Building Incentives
  40. 40. Library Frameworks • Policy • Infrastructure • Capacity Building/CPD • Incentives
  41. 41. Awareness – start the conversation • To begin …. • What data repositories? Which data type? Which metadata standards? • Data web page • Market services re data support • Meet with stakeholders at institution • Form a committee to implement strategy, policy, etc. • Implement Research Data Management Plans • Implement Institutional Data Repository
  42. 42. http://internationaldataweek.org/
  43. 43. Thank you Ina Smith Project Manager, African Open Science Platform Project, Academy of Science of South Africa (ASSAf) ina@assaf.org.za Susan Veldsman Director, Scholarly Publishing Programme, Academy of Science of South Africa (ASSAf) susan@assaf.org.za Visit http://africanopenscience.org.za

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