rdmrose 1.2 research and researchers

15 Apr 2023

Research and researchers

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


15 Apr 2023

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


15 Apr 2023

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 pureNatural

sciences and mathematics

Hard and applied

Science-based professions

Soft and pureHumanities and social sciences

Soft and appliedSocial

professions


Hard knowledge

Soft knowledge

Pure

kno

wle

dge

Appl

ied

know

ledg

e

15 Apr 2023

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


15 Apr 2023

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)


15 Apr 2023

“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 Learning material produced by RDMRose

http://www.sheffield.ac.uk/is/research/projects/rdmrose

15 Apr 2023

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)


15 Apr 2023

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


15 Apr 2023

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


BEING A RESEARCHER


15 Apr 2023

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


http://www.youtube.com/watch?v=sQ_ZzP7g7TQ

http://www.youtube.com/watch?v=Nc-5VXdNGkw

15 Apr 2023

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?


15 Apr 2023

INCENTIVES FOR RDM


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

Learning material produced by RDMRose http://www.sheffield.ac.uk/is/research/projects/rdmrose Apr 15, 2023

Incentive 2: ‘Public good’ obligations

• Demonstrate Return on Investment

• Open Access


Incentive 3: Compliance reasons

• Compliance with funding body requirements

• Legal requirements

• Publishers’ requirements


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


15 Apr 2023

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

Data preservation

Data storage and security

Compliance

The strengths of these forces differ in different contexts

DATA CURATION PROFILES


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


http://datacurationprofiles.org/

http://docs.lib.purdue.edu/dcp/

http://datalib.edina.ac.uk/mantra/libtraining/CurationProfiles/DCP-interview-PPLS-Donnelly.pdf



DATA ASSET FRAMEWORK


15 Apr 2023

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?


15 Apr 2023

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_Implementati

on_Guide.pdf Learning material produced by RDMRose http://www.sheffield.ac.uk/is/research/projects/rdmrose

http://www.data-audit.eu/documents.html

http://www.data-audit.eu/docs/DAF_Implementation_Guide.pdf

http://www.data-audit.eu/docs/DAF_Implementation_Guide.pdf

15 Apr 2023

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 Learning material produced by RDMRose

http://www.sheffield.ac.uk/is/research/projects/rdmrose

15 Apr 2023

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


15 Apr 2023

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”?


15 Apr 2023

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


http://research-data-toolkit.herts.ac.uk/2012/08/data-asset-survey-results/



REFERENCES


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.


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/.


http://docs.lib.purdue.edu/lib_research/81/

rdmrose 1.2 research and researchers

Data & Analytics

rdmrose http

overview research

heis research

research tracks

academic disciplines

social sciences soft

mathematics hard

social professions