RDMRose 1.2 Research and researchers

Research and researchers
May-15
Research Data Management
Workshop 1.2
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose

Session 1.2 overview
• Research in Higher Education
• Being a researcher
• Incentives for RDM
• Data curation profiles
• Data asset framework

RESEARCH IN HIGHER EDUCATION

Research in HEIs
• Research is important to universities!
• There are many stakeholders
– Senior researchers/research groups
– Early career researchers
– Postgraduate Research Students
– Departmental administrators
– Data specialists
• Research funding is often project based
May-15

Types of academic discipline
(Biglan, 1973)
• Academic disciplines
are different; one
classic taxonomy is
based on the following
factors:
– Hard (paradigmatic) –
soft (non-paradigmatic)
– Pure – applied
– Living – non-living
Hard and pure
Natural
sciences and
mathematics
Hard and
applied
Science-based
professions
Soft and pure
Humanities
and social
sciences
Soft and
applied
Social
professions
May-15
Hard knowledge
Soft knowledge
Pureknowledge
Appliedknowledge

Academic tribes
(Becher &Trowler, 2001)
• Academic disciplines could be seen as “global
tribes”
• They share:
– A sense of identity and personal commitment
– Myths
– A sense of what is a “contribution”
– Social networks, with gatekeepers
– Formal communication channels: journals and
conferences
• Peer review
– An “invisible college” and informal networks
May-15

Disciplinary differences
• “Disciplines differ in the ways they structure
themselves, establish identities, maintain
boundaries, regulate and reward practitioners,
manage consensus and dissent, and
communicate internally and externally.” (Klein,
1996, p. 55)
May-15

“Blurring, cracking and crossing”
(Klein, 1993)
• Disciplines change
• Disciplines overlap
• Most disciplines are a mix of hard/soft; pure/applied
• Proliferation of specialities
– 1000 maths journals with 4500 subtopics (Becher &
Trowler, 2001, p. 14)
– “Research tracks and specialties grow, split, join, adapt
and die” (Klein, 1996, p. 55)
• Theories and methods may be greater common ground
than subject
• Interdisciplinarity as a major creative strategy
May-15

Example: Geography
• Has fundamental internal divisions: Physical
and human
• Are diverse internationally: different origins: in
Germany, earth science, in France history
• Has seen many new specialties: “human,
cultural, economic, political, urban, and
regional geography as well as biogeography,
geomorphology, climatology, environmental
science and cartography” (Klein, 1996, p. 41)
May-15

Specialisation, fragmentation,
hybridisation, fluidity
• Have material impact on LIS collection
– The way that journals change titles, are
superceded
– “Scatter” (Palmer, 2010) creates much of the work
for LIS in facilitating access to the vastly complex
body of academic knowledge
May-15

Activity: Analysing an academic
department
• How would you characterise the subject as a
whole?
• Can you identify some specialities? Do you
know of any very new specialities?
• Identify some examples of interdisciplinarity
or links between this Department and others.
• Share your thoughts with a colleague who
works to support a different discipline
May-15

BEING A RESEARCHER

Video about the daily life of an
academic researcher
• Watch the following two videos:
– http://www.youtube.com/watch?v=sQ_ZzP7g7TQ
– http://www.youtube.com/watch?v=Nc-
5VXdNGkw
May-15

Video about the daily life of an
academic researcher
• What does the video tell us about the
academic (in particular, scientific) community?
• What does the video tell us about the
personal motives of researchers?
• What is the concept of research and research
data that comes to the fore in this video?
• What other key points did you pick up from
the video?
May-15

INCENTIVES FOR RDM
May-15

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
May-15

Incentive 2: ‘Public good’ obligations
• Demonstrate Return on Investment
• Open Access
May-15

Incentive 3: Compliance reasons
• Compliance with funding body requirements
• Legal requirements
• Publishers’ requirements
May-15

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
May-15

Good research practice
Open access
Other priorities
Nature of data
Lack of RDM knowledge
& skills
Legal, ethical & commercial
exceptions
Good
Research Data
Management
practices
Academic culture & lack
of reuse culture
Force field analysis of RDM
May-15
Data preservation
Data storage and security
Compliance
The strengths of these forces differ in different contexts

DATA CURATION PROFILES
May-15

Data Curation Profiles
• http://datacurationprofiles.org/
• http://docs.lib.purdue.edu/dcp/
• http://datalib.edina.ac.uk/mantra/libtraining/
CurationProfiles/DCP-interview-PPLS-
Donnelly.pdf
May-15

DATA ASSET FRAMEWORK
May-15

Research data interviews
• Witt and Carlson (2007) offer an overview of the research
data interview:
1. What is the story of the data?
2. What form and format are the data in?
3. What is the expected lifespan of the dataset?
4. How could the data be used, reused, and repurposed?
5. How large is the dataset, and what is its rate of growth?
6. Who are the potential audiences for the data?
7. Who owns the data?
8. Does the dataset include any sensitive information?
9. What publications or discoveries have resulted from the data?
10. How should the data be made accessible?
May-15

Data Asset Framework (DAF)
• “A framework developed by the JISC-funded DAFD
project to identify data assets held within Higher
and Further Educational institutions and to
explore how they are managed. The framework is
structured around audit at departmental or unit
level with results being amassed to obtain an
institutional or national perspective.” (Jones,
Ross, & Ruusalepp, 2009, p. 6)
• http://www.data-audit.eu/documents.html
• http://www.data-
audit.eu/docs/DAF_Implementation_Guide.pdf
May-15

Four stages of DAF
• Stage 1 Plan the audit
– Appoint an auditor
– Establish a business case
– Conduct initial research
– Set up audit
• Stage 2 Identify and classify data assets
– Analysis of documentary sources
– Conduct a written survey
– Interviews
– Prepare the data asset inventory
• Vital, important and minor
– Approve and finalise the asset classification
May-15

DAF 4 stages
• Stage 3 Assess the management of data assets
– Collect data on each data asset (Audit form 3: Jones,
Ross, & Ruusalepp, 2009, pp. 45-51)
• Description, provenance, ownership, location, retention,
management
• Stage 4 Reporting results and making
recommendations
– Produce audit report
• Brief overview of the organisation
• Profile of data holdings
• Recommendations for improved asset management
– Meet with management and finalise report
May-15

A data asset record
• Examine the example from the DAF
methodology of a completed extended audit
form 3 for a data asset (Jones, Ross, &
Ruusalepp, 2009, pp.49-51).
• How often is the asset updated?
• How is this asset backed up?
• What file format is it in?
• Did you find the form very “technical”?
May-15

University of Hertfordshire data asset
survey results
• Another useful resource to explore is
http://research-data-
toolkit.herts.ac.uk/2012/08/data-asset-
survey-results/
• It illustrates the range of formats, scale of data
etc. being held by researchers in one
institution
May-15

REFERENCES
May-15

References
• Becher, T., & Trowler, P.R. (2001). Academic Tribes and
Territories: Intellectual Enquiry and the Culture of
Disciplines (2nd ed.). Philadelphia; Buckingham: Society for
Research into Higher Education; Open University Press.
• Biglan, A. (1973). The characteristics of subject matter in
different academic areas. Journal of Applied
Psychology, 57(3), 195-203.
• Jones, S., Ross, S., & Ruusalepp, R. (2009). Data Audit
Framework Methodology (draft for discussion, version 1.8).
Glasgow: HATII. Retrieved from http://www.data-
audit.eu/DAF_Methodology.pdf.

References
• Klein, J. T. (1993). Blurring, cracking, and crossing: permeation and
the fracturing of discipline. In E. Messer-Davidow, D. R. Shumway, &
D. Sylvan (Eds.), Knowledges: Historical and Critical Studies in
Disciplinarity (pp. 185-211). Charlottesville: University Press of
Virginia.
• Klein, J. T. (1996). Crossing Boundaries: Knowledge, Disciplinarities,
and Interdisciplinarities. Charlottesville: University Press of Virginia.
• Palmer, C. L. (2010). Information research on interdisciplinarity. In R.
Frodeman, J. T. Klein, & C. Mitcham (Eds.), The Oxford Handbook of
Interdisciplinarity. Oxford; New York: Oxford University Press.
• Witt, M., & Carlson, J. R. (2007). Conducting a Data
Interview. Scientist. West Lafayette, Indiana: Purdue University
Libraries. Retrieved from
http://docs.lib.purdue.edu/lib_research/81/.
May-15

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