1. Research Data Management
RDMRose: Research Data Management for LIS
Session: RDMRose Lite
Research Data Management
Apr-19
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2. Learning outcomes At the end of the session you will be able to:
discuss the definition of ‘Research Data Management’ and ‘Digital curation’
outline the research process
reflect on the nature of research data
be able to explain the DCC Curation Lifecycle Model
describe the strategic context within which RDM has appeared on the agenda and the key drivers and issues for researchers
analyse the role of the LIS professional in relation to what the literature has suggested about LIS roles in research support and RDM in particular
Apr-19
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3. Session Overview Background: RDMRose Research and research data
Research Data Management and Digital Curation
The data curation lifecycle
The LIS role in RDM
RDMRose again
Reflection
Apr-19
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4. Background: RDMRose
RDMRose: Research Data Management for LIS Professionals
Funded by JISC, for one year, July 2012-
White Rose University libraries + Information School at the University of Sheffield
RDM module as an OER for
CPD
LIS educators to reuse
By repurposing existing content
By creating new tailored content, including two substantial case studies
Apr-19
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5. The team Information School authors
Andrew Cox,
Eddy Verbaan,
Barbara Sen,
University of Sheffield Library in 2012
University Libraries in York and Leeds in 2013
Open to postgraduate students in the Information School
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6. Activity 1
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7. Activity 1 What is your understanding of research?
What is your experience with research?
What do you think Research Data Management might be?
You have 10 minutes
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8. What is research ?
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9. The research cycle (RIN, 2010)
Conceptualising and networking
Proposal writing and research design
Collecting and analysing data
Infrastructuring
Documenting and describing
Publishing and reporting
Engaging and translating
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10. Features of research Cyclic Iterative Non-linear
Complex through collaboration
Large scale
Remote
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11. Complexity of information practices
Information flow maps for life science research (RIN, 2009) e.g. in neuroscience illustrate
Multiple data sources, of different types
Visual images, quantitative data, secondary data
Storage devices
Multiple analytic tools
Some requiring grid power
Supporting complex scholarly communication
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12. Activity 2
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13. Name some types of data!
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14. A list we came up with earlier...
Weather measurements
Photographs
Results from experiments
Government records
GIS data
Simulation data
Log data
Field notes
Software
Images (e.g. brain scans)
Quantitative data (e.g. household survey data)
Historical documents
Moving images
Physical objects: such as bones or blood samples
Digitised photos / born digital photos
Social media data: tweets
Metadata
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15. What is data? Some researchers use other terms, eg “sources”
Complex: data can be produced from other data
Massive
Fragile
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16. What is research data management?
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17. RDM: definition
“Research data management concerns the organisation of data, from its entry to the research cycle through to the dissemination and archiving of valuable results.” (Whyte & Tedds, 2011)
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18. Digital curation
“Digital curation, broadly interpreted, is about maintaining and adding value to a trusted body of digital information for current and future use.” (DCC 101:6)
Managing digital material from the point it is created
Adding value so that it can be used and re-used
Includes the destruction of data
Beyond archiving and preservation
“Digital curation is concerned with actively managing data for as long as it continues to be of scholarly, scientific, research and/or administrative interest, with the aim of supporting reproducibility of results, reuse of and adding value to that data, managing it from its point of creation until it is determined not to be useful, and ensuring its long-term accessibility and preservation, authenticity and integrity.”
Apr-19
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19. DCC Curation Lifecycle Model
Curation lifecycle is based on the OAIS Reference Model
But includes activities that take place outside the archival system: the research lifecycle
In particular: the creation of data, the use and reuse of data
Apr-19
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20. Target audience
The model is an idealised situation: curation is planned from the very beginning, and planned for throughout the lifecycle
You can start at any point and use the model to identify gaps and undertake appropriate actions
According to the DCC (2012c) this model is relevant to:
Data creators
Data archivists
Data (re)users
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21. DCC Curation Lifecycle Model
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22. Actions Three sets of actions:
Full Lifecycle Actions (4): apply to all stages in the lifecycle
Sequential Actions (8): key actions needed as data move through their lifecycle
Occasional Actions (3): only occur when special conditions are met, but they do not apply to all data
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23. The context of Research data management
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24. Context “Data deluge” Funders’ mandates Institutional policies
Escience, cyberscholarship, e-research
Collaborations
Multiple forms of complex data
Huge cost of research
Funders’ mandates
Research Councils UK Common Principles on Data Policy:
EPSRC principles and expectations:
Institutional policies
UoS:
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Dec-2012
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25. Activity 3
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26. Activity 3
Read your University’s Research Data Management policy. If not available, use the University of Sheffield’s policy:
What are the two most important points you pick up from this document?
According to this policy, what are the incentives to take Research Data Management seriously?
You have 15 minutes.
Apr-19
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27. Incentive 1: Direct benefits to researchers
Improve the quality of research data
Provide access to reliable working data
Allow conclusions to be validated externally
Apply good record-keeping standards to data capture including in lab and field electronic notebooks, which enables scientists to draw conclusions from reliable and trustworthy working research data
Enable large amounts of data to be analysed and developed across different locations by maintaining consistency in working practices and interpretations
Manage relationships between different versions of dynamic or evolving datasets, and facilitates linkage with other related research and between primary, secondary and tertiary data
Ensure valuable knowledge and data originating from short-term research projects does not become obsolete or inaccessible when funding expires
Allow data sets to be combined in new and innovative ways
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28. Incentive 2: ‘Public good’ obligations
Demonstrate Return on Investment
Open Access
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29. Incentive 3: Compliance reasons
Compliance with funding body requirements
Legal requirements
Publishers’ requirements
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30. Some issues for researchers
The nature of data
How important is it relative to doing the research; projects only get short term funding
Is infrastructure available?
Lack of RDM knowledge and skills
No checking of compliance
Legal, ethical and commercial motives
Desire to keep control over data
Informal sharing practices already exist
Lack of reuse culture
Apr-19
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31. Activity 4
Apr-19
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32. Activity 4: The researcher’s viewpoint
Which issues with RDM are the most critical barriers in your opinion?
Which of the arguments in favour of RDM would play best with researchers?
You have 5 minutes.
Apr-19
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33. The LIS role in RDM
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34. Library roles to support research
Offering advice on funding sources
Embedded or support roles conducting literature reviews or current awareness alerts for research projects or groups
Information literacy training
Supporting REF
Bibliometrics and measuring impact
Bibliographic software training
Advocacy for open access / institutional repository
Offering data analysis advice
Offering advice on copyright issues
Offering advice on archiving of research records (e.g. correspondence)
Apr-19
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35. Possible Library RDM roles
Leading on local (institutional) data policy
Bringing data into undergraduate research-based learning, promoting data information literacy
Teaching data literacy to postgraduate students
Developing researcher data awareness
Providing researcher data advice, e.g. on writing Data Management plans or advice on RDM within a project
Explaining the impact of sharing data, and how to cite data
Signposting who in the institution should be consulted 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
Apr-19
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36. Why librarians might have an important role because of
Their knowledge of and networks within disciplinary communities; their liaison and negotiation skills
The strong LIS professional network to copy best practice across institutions
Their contact with many students and researchers in a way other support services do not
Their generic knowledge of good information management practices
Understanding that research data management as a form of Information Literacy
Their existing data and open access leadership roles
Relevance of collection development practices; their understanding of metadata
Apr-19
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37. Challenges We 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?
Complexity and scale of issues
Marked disciplinary differences in information practice
Goes wide and deep: to every researcher in our institution
Translating library practices to research data issues
Will researchers look to libraries for this support? Historic failure to engage researchers in library services
Computing services, Research support services
Resources, infrastructure, management structures have to be found
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38. Activity 5
Apr-19
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39. Activity 5: Reflection
Which aspects of support to research are you most interested in, and why?
Which aspects of RDM support are you most interested in, and why?
How do they fit into your future role as a (digital) librarian?
Apr-19
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40. RDMRose
Apr-19
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41. Learning outcomes
The learning outcomes of the learning materials will be for learners to develop the ability to:
explain the diverse nature of research across academic disciplines and specialities and discuss different conceptions of research data,
analyse the context in which research data management has become an issue,
discuss the role of a range of professional services, including libraries, in RDM,
reflect for themselves as individuals and for information professionals in general on the role and priority of supporting research data management,
explain and apply the key concepts of research data management and data curation to real world case studies and professional practice,
and understand how to keep knowledge acquired on the module up-to-date.
Apr-19
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42. Course overview Introductions, RDM, and the role of LIS
The nature of research and the need for RDM
The DCC curation lifecycle model
Key institutions and projects in RDM
What is data?
Managing data
Case studies: research projects
Case study: Institutional context, and conclusions
Apr-19
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43. Sources
Apr-19
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44. Sources
Slide “digital curation” is based on DC101 What is digital curation?
Slides incentives 1-3 are based on DC101 Incentives for digital curation
Slides on the DCC Curation lifecycle model are based on DCC Curation Lifecycle Model
Image of the OAIS model is taken from The Consultative Committee for Space Data Systems (2012). Recommendation for Space Data System Practices. Reference Model for an Open Archival Information System (OAIS). Recommended practice, issue 2. Washington, D.C.: CCSDS Secretariat, CCSDS M-2 Magenta Book, p. 4-1,
Apr-19
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45. References
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46. References
Auckland, M. (2012) Re-Skilling for Research: An Investigation into the Role and Skills of Subject and Liaison Librarians Required to Effectively Support the Evolving Information Needs of Researchers. London: Research Libraries UK. Retrieved from   
DCC (2012a). Digital 101 materials. Edinburgh: Digital Curation Centre,
DCC (2012b) DCC Charter and Statement of Principles. Edinburgh: Digital Curation Centre,
DCC (2012c) Lifecycle Model FAQ. Edinburgh: Digital Curation Centre,
Harvey, R. (2010) Digital curation: a how-to-do-it manual. London: Facet.
Michener, W.K. et al. (1997) Nongeospatial metadata for the ecological sciences. Ecological Applications 7.1:
Pryor, G. (2012). Managing Research Data. London: Facet.
RIN. (2010). Open to All? Case studies of openness in research. London. Retrieved from
RIN. (2009). Patterns of information use and exchange : case studies of researchers in the life sciences. London. Retrieved from
Whyte, A., & Tedds, J. (2011). Making the case for Research Data Management. Edinburgh. Retrieved from
Apr-19
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