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RDM for Librarians

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RDM for Librarians

  1. 1. Research Data Management for librarians Michael Day and Marieke Guy Digital Curation Centre (DCC)
  2. 2. About this course  Short presentations with exercises and discussion  Five main sections ― Research data and RDM (30 mins) ― Data Management Planning (30 mins) ― Data sharing (20 mins) ― Skills (30 mins) ― RDM at Cardiff (30 mins)  Coffee break halfway through, after DMP
  3. 3. Introductions Introduce yourself and offer a reflection on the questions:  What is your understanding of research?  Do you know anything about data management?  What do you want to find out today?  Do you see a role for librarians in supporting RDM?
  4. 4. Digital Curation Centre (DCC)  Consortium comprising units from the Universities of Bath (UKOLN), Edinburgh (DCC Centre) and Glasgow (HATII)  Launched 1st March 2004 as a national centre for solving challenges in digital curation that could not be tackled by any single institution or discipline  Funded by JISC with additional HEFCE funding from 2011 for targeted institutional development  Support selection of tools: DAF, CARDIO, DMP Online, tools and metadata schema catalogues  Offer advice and support through ‘How to Guides’, ‘Briefing papers’ and Web site
  5. 5. Assess Needs Make the case Develop support and services RDM policy development DAF & CARDIO assessments Guidance and training Workflow assessment DCC support team Advocacy with senior management Institutional data catalogues Pilot RDM tools Customised Data Management Plans …and support policy implementation Support from the DCC
  6. 6. Research data and RDM
  7. 7. Exercise: What are research data?  In pairs, list as many types of data as you can, focusing (if appropriate) on the subject areas you support  You have 5 minutes
  8. 8. What are research data?  http://www.youtube.com/watch?v=2JBQS0qKOBU  Video from DCC – first 3.10 minutes
  9. 9. What are research data? All manner of things produced in the course of research
  10. 10. Defining research data  Research data are collected, observed or created, for the purposes of analysis to produce and validate original research results  Both analogue and digital materials are 'data'  Lab notebooks and software may be classed as 'data'  Digital data can be: ― created in a digital form ('born digital') ― converted to a digital form (digitised)
  11. 11. Types of research data  Instrument measurements  Experimental observations  Still images, video and audio  Text documents, spreadsheets, databases  Quantitative data (e.g. household survey data)  Survey results & interview transcripts  Simulation data, models & software  Slides, artefacts, specimens, samples  Sketches, diaries, lab notebooks …
  12. 12. What is data management? “the active management and appraisal of data over the lifecycle of scholarly and scientific interest” Digital Curation Centre
  13. 13. What is involved in RDM?  Data Management Planning  Creating data  Documenting data  Accessing / using data  Storage and backup  Sharing data  Preserving data Create Document Use Store Share Preserve
  14. 14. RDM principles and advice to share with researchers See in particular: UK Data Archive, Managing and sharing data: best practice for researchers http://data-archive.ac.uk/media/2894/managingsharing.pdf n.b. Data Management Planning and Data Sharing are covered in separate sections
  15. 15. Data creation  Decide what data will be created and how - this should be communicated to the whole research team  Develop procedures for consistency and data quality  Choose appropriate software and formats - some are better for long-term preservation and reuse  Ensure consent forms, licences and partnership agreements don’t limit options to share data if desired
  16. 16. Documentation  Collect together all the information users would need to understand and reuse the data  Create metadata at the time - it’s hard to do later  Use standards where possible  Name, structure and version files clearly
  17. 17. Access and use  Restrict access to those who need to read/edit data  Consider the data security implications or where you store data and from which devices you access files  Choose appropriate methods to transfer / share data ― filestores & encrypted media rather than email & Dropbox
  18. 18. Storage and backup  Use managed services where possible e.g. University filestores rather than local or external hard drives  Ask the local IT team for advice  3… 2… 1… backup! ― at least 3 copies of a file ― on at least 2 different media ― with at least 1 offsite
  19. 19. Data selection  It’s not possible to keep everything. Select based on: ― What has to be kept e.g. data underlying publications ― What legally must be destroyed ― What can’t be recreated e.g. environmental recordings ― What is potentially useful to others ― The scientific or historical value ― ... How to select and appraise research data: www.dcc.ac.uk/resources/how-guides/appraise-select-research-data
  20. 20. Data preservation  Be aware of requirements to preserve data  Consult and work with experts in this field  Use available subject repositories, data centres and structured databases ― http://databib.org
  21. 21. Data Management Planning
  22. 22. Data Management Planning DMPs are written at the start of a project to define:  What data will be collected or created?  How the data will be documented and described?  Where the data will be stored?  Who will be responsible for data security and backup?  Which data will be shared and/or preserved?  How the data will be shared and with whom?
  23. 23. Why develop a DMP? DMPs are often submitted with grant applications, but are useful whenever researchers are creating data. They can help researchers to:  Make informed decisions to anticipate & avoid problems  Avoid duplication, data loss and security breaches  Develop procedures early on for consistency  Ensure data are accurate, complete, reliable and secure
  24. 24. Which funders require a DMP? www.dcc.ac.uk/resources/policy-and-legal/ overview-funders-data-policies
  25. 25. What do research funders want?  A brief plan submitted in grant applications, and in the case of NERC, a more detailed plan once funded  1-3 sides of A4 as attachment or a section in Je-S form  Typically a prose statement covering suggested themes  Outline data management and sharing plans, justifying decisions and any limitations
  26. 26. Five common themes / questions  Description of data to be collected / created (i.e. content, type, format, volume...)  Standards / methodologies for data collection & management  Ethics and Intellectual Property (highlight any restrictions on data sharing e.g. embargoes, confidentiality)  Plans for data sharing and access (i.e. how, when, to whom)  Strategy for long-term preservation
  27. 27. Exercise: My DMP - a satire  Read through the satirical DMP  Highlight examples of bad practice  Suggest alternative methods / approaches  You have 15 minutes My Data Management Plan – a satire, Dr C. Titus Brown http://ivory.idyll.org/blog/data-management.html
  28. 28. A useful framework to get started Think about why the questions are being asked Look at examples to get an idea of what to include www.icpsr.umich.edu/icpsrweb/content/datamanagement/dmp/framework.html
  29. 29. Help from the DCC https://dmponline.dcc.ac.uk www.dcc.ac.uk/resources/how-guides/develop-data-plan
  30. 30. How DMPonline works Create a plan based on relevant funder / institutional templates... ...and then answer the questions using the guidance provided
  31. 31. Supporting researchers with DMPs Various types of support could be provided by libraries:  Guidelines and templates on what to include in plans  Example answers, guidance and links to local support  A library of successful DMPs to reuse  Training courses and guidance websites  Tailored consultancy services  Online tools (e.g. customised DMPonline)
  32. 32. Tips to share: writing DMPs  Keep it simple, short and specific  Seek advice - consult and collaborate  Base plans on available skills and support  Make sure implementation is feasible  Justify any resources or restrictions needed Also see: http://www.youtube.com/watch?v=7OJtiA53-Fk
  33. 33. Data sharing
  34. 34. What is data sharing? “… the practice of making data used for scholarly research available to others.” [Wikipedia] Who’s involved?  the data sharer  the data repository  the secondary data user  support staff!
  35. 35. Reasons to share data BENEFITS  Avoid duplication  Scientific integrity  More collaboration  Better research  More reuse & value  Increased citation 9-30% increase depending on e.g. discipline (Piwowar et al, 2007, 2013) DRIVERS  Public expectations  Government agenda  Content mining ― http://www.jisc.ac.uk/news/stories/2 012/03/textmining.aspx  RCUK Data Policy ― www.rcuk.ac.uk/research/Pages/Data Policy.aspx  Institutional Policy
  36. 36. The expectation of public access The RCUK Common Principles state that: “Publicly funded research data are a public good, produced in the public interest, which should be made openly available with as few restrictions as possible in a timely and responsible manner that does not harm intellectual property.”
  37. 37. Exercise: barriers to data sharing Constraints on data sharing Possible solutions / approaches  Briefly list some reasons why certain data can’t be shared and consider whether any actions could be taken to reduce or overcome these restrictions  You have 10 minutes
  38. 38. Managing restrictions on sharing Ethics Balance data protection with data sharing  Informed consent – cover current and future use  Confidentiality – is anonymisation appropriate?  Access control – who, what, when? IPR  Clarify copyright before research starts  Consider licensing options e.g. Creative Commons
  39. 39. Select formats for data sharing It’s better to use formats that are:  Unencrypted  Uncompressed  Non-proprietary/patent-encumbered  Open, documented standard  Standard representation (ASCII, Unicode) Type Recommended Avoid for data sharing Tabular data CSV, TSV, SPSS portable Excel Text Plain text, HTML, RTF PDF/A only if layout matters Word Media Container: MP4, Ogg Codec: Theora, Dirac, FLAC Quicktime H264 Images TIFF, JPEG2000, PNG GIF, JPG Structured data XML, RDF RDBMS Further examples: http://www.data-archive.ac.uk/create-manage/format/formats-table Research360
  40. 40. How to share research data  Use appropriate repositories ― http://databib.org or http://www.re3data.org  License the data so it is clear how it can be reused ― www.dcc.ac.uk/resources/how-guides/license-research-data  Make sure it’s clear how to cite the data ― http://www.dcc.ac.uk/resources/how-guides/cite-datasets
  41. 41. Skills
  42. 42. How are libraries engaging in RDM? Library IT Research Office The library is leading on most DCC institutional engagements. They are involved in:  defining the institutional strategy  developing RDM policy  delivering training courses  helping researchers to write DMPs  advising on data sharing and citation  setting up data repositories  ... www.dcc.ac.uk/community/institutional-engagements
  43. 43. Why should libraries support RDM? RDM requires the input of all support services, but libraries are taking the lead in the UK – why? ― existing data and open access leadership roles ― often run publication repositories ― have good relationships with researchers ― proven liaison and negotiation skills ― knowledge of information management, metadata etc ― highly relevant skill set
  44. 44. Exercise: skills to support RDM  Based on the activities we discussed earlier, consider who may have relevant skills or expertise to share.  You have 15 minutes Activity Library and LRC IT Services (OBIS) Research Business Development Office Copyright Data citation Information literacy Data storage Digital preservation Metadata ...
  45. 45. Possible Library RDM roles  Leading on local (institutional) data policy  Bringing data into undergraduate research-based learning  Teaching data literacy to postgraduate students  Developing researcher data awareness  Providing advice, e.g. on writing DMPs or advice on RDM within a project  Explaining the impact of sharing data, and how to cite data  Signposting who in the Uni to consult in relation to a particular question  Auditing to identify data sets for archiving or RDM needs  Developing and managing access to data collections  Documenting what datasets an institution has  Developing local data curation capacity  Promoting data reuse by making known what is available RDMRose Lite
  46. 46. An exciting opportunity  Leadership  Providing tools and support  Advocacy and training  Developing data informatics capacity & capability “Researchers need help to manage their data. This is a really exciting opportunity for libraries….” Liz Lyon, VALA 2012
  47. 47. Potential challenges  Librarians are already over-taxed! ― Other challenges in supporting research (Auckland, 2012) ― Getting up-to-speed and keeping up-to-date  How deep is our understanding of research, especially scientific research and our level of subject knowledge?  Translating library practices to research data issues  Will researchers look to libraries for this support?  Still need to resource and develop infrastructure RDMRose Lite
  48. 48. RDM at Cardiff
  49. 49. Exercise: supporting RDM at Cardiff?  In small groups, discuss which activities you think should fall within your role and which shouldn’t.  Do you feel confident to support RDM?  How would you like to see things develop?  You have 15 minutes
  50. 50. Conclusion
  51. 51. Summary  In the light of external drivers, researchers at Cardiff need support for RDM  The library has a key role in shaping services for researchers in this area  Library staff have an opportunity to apply their skills in a new and exciting way 
  52. 52. Feedback  Has the event met your expectations? ― If not, what would you have liked to see more / less of?  Was the content useful?  Did you like the mix of exercises?
  53. 53. Acknowledgement Ideas and content have been taken from various courses: ― Skills matrix, ADMIRe project, University of Nottingham http://admire.jiscinvolve.org/wp/2012/09/18/rdmnottingham-training-event ― DIY Training Kit for Librarians, University of Edinburgh http://datalib.edina.ac.uk/mantra/libtraining.html ― Managing your research data, Research360, University of Bath http://opus.bath.ac.uk/32296 ― RDMRose Lite, University of Sheffield http://rdmrose.group.shef.ac.uk/?page_id=364 ― RoaDMaP training materials, University of Leeds http://library.leeds.ac.uk/roadmap-project-outputs ― SupportDM modules, University of East London http://www.uel.ac.uk/trad/outputs/resources

Editor's Notes

  • For this we are just going to show the first 3 minutes of this video as we think most of you already know this and there is more information in the handbook
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