This document discusses research data management from a disciplinary perspective. It begins with an overview of case studies on disciplinary practice from various sources. It then groups disciplines into Arts & Humanities, Social Sciences, Sciences & Engineering, and Life Sciences. For each group, it discusses common practices, challenges, and examples. It also discusses a research data typology commissioned by RLUK to help librarians understand researchers' data needs and types of data across disciplines. Overall, the document provides a high-level overview of differences in research data management practices across broad disciplinary categories.

1. Research Data Management from
a disciplinary perspective
S a rah Jones
D i g i tal Curat i o n Cent re
s a rah .jones@glasgow. a c . u k
Twi t ter : @sjDCC
S téphane Goldste i n
Research I n format i o n Netwo r k
s tephane.go l d stein@re s e a rc h i nfonet . o rg
Twi t ter : @stephgold7

2. Disclaimer
Practice varies greatly by
discipline and sub‐discipline
so it’s hard to generalise
Apologies for any sweeping
statements and groupings
that don’t fit your model
Image credit: Sweep by Judy Van der Velden CC‐BY‐NC‐ND
www.flickr.com/photos/judy‐van‐der‐velden/6757403261

3. Case studies on disciplinary practice
 RIN Information Seeking and Sharing Behaviour
www.rin.ac.uk/our‐work/using‐and‐accessing‐information‐resources
– Life sciences
– Humanities
– Physical sciences
 RIN Open Science Case studies
www.rin.ac.uk/our‐work/data‐management‐and‐curation/open‐science‐case‐studies
 SCARP case studies www.dcc.ac.uk/resources/case‐studies/scarp
 Knowledge Exchange Incentives and motivations for sharing research
data (forthcoming)
 RLUK research data typology (more from Stephane)

4. Groups and disciplines
 Arts & Humanities
– Creative arts, languages, philosophy, archaeology…
 Social Science
– Economics, history, politics, business, psychology...
 Sciences & Engineering
– Physics, astronomy, earth sciences, computing…
 Life Sciences
– Biology, ecology, medical and veterinary science…

5. Arts & Humanities
 Outputs may not be termed
‘data’ e.g. sketches, writing,
performance, artefacts, ‘work’
 Focus on literary outputs &
manuscripts in some disciplines
 More use of standard tools e.g.
Word, Excel – less likely to adapt
technologies to fit
 Arguably lower awareness and
uptake of RDM overall

6. Creative Arts
 Several RDM projects in the creative arts e.g. Kultivate,
KAPTUR, VADS4R, CAiRO training...
 Resistance to term ‘data’ – too scientific
 Importance of personal websites for profile as work is
also conducted outside of academia
 Visual Arts Data Service ‐ www.vads.ac.uk
 Institutional repositories at arts schools accept a broader
range of outputs and display content more visually to fill
the void e.g. http://research.gold.ac.uk

7. Sonic Arts Research Unit
 Collaboration with IR as a
result of losing data
 Tension between providing
access in a visual / usable
way and preserving data
 Still use soundcloud and
personal websites for
access, but these link to
‘master’ copy of data held
in IR for preservation
www.dcc.ac.uk/resources/developing‐rdm‐services/repository‐radar

8. Digital Humanities
 Intentional creation of resources rather than just data as
by‐product of research process
 More use of standards e.g. XML & TEI in language
resources, image standards and capture quality for
digitisation, Dublin Core metadata…
 Often include technical experts in project team
 Links with cultural heritage collections
 Negotiating copyright often a major issue
 Sustainability a big challenge

9. Mapping Edinburgh’s Social History
Historical maps overlaid these with all kinds of
open data to chart how the town has changed
through time
 Uses open source tools
 Allows you to overlay maps
 Picks up on common themes
www.mesh.ed.ac.uk

10. Social Sciences
 Greater awareness and acceptance
of RDM by community
 Methodology is as much a factor in
determining difference as discipline
 Nature of data often poses
challenges for sharing
 Lots of reuse of large survey data
 Established metadata standards e.g.
Data Documentation Initiative (DDI)
 Strong international data centre
infrastructure

11. Public health
 Ethics predominant concern
– How to negotiate consent
– How to store, transfer & handle data securely
– How to anonymise and share data
 Data integration / linking and curation of longitudinal studies is
major concern as data added to over decades
 Need for data havens to help control access to data – role for
unis e.g. Grampian Data Safe Haven
 UK Data Service ‐ http://ukdataservice.ac.uk

12. Twenty‐07: Public health study
 Longitudinal study following 4510 people from West of Scotland
over 20 years to investigate the reasons for differences in health
 Undertook interviews, questionnaires, physical measurements,
blood samples etc
 Strict access controls and guidelines for data collection
 Data managed within the MRC Social and Public Health Sciences
Unit and accessible under a data sharing agreement ‐
http://2007study.sphsu.mrc.ac.uk/Revised‐Data‐Sharing‐Policy‐has‐
been‐launched.html

13. Life Sciences
 Funders arguably more demanding in
terms of data sharing policy
 Sharing can be problematic / resisted
given the nature of the data, fear of
misuse or loss of control over IPR
 Data sharing agreements and access
committees more common
 Data integration & mining key drivers
 Research is well‐resourced so greater
capacity to fund local solutions and
tools for RDM during projects

14. Genetics
 Vast quantities of data and rapid growth
– DNA sequence data is doubling every 6‐8 months
 Well established public databases for gene sequences e.g.
GenBank www.ncbi.nlm.nih.gov/genbank
– However even this is on short‐term project funding!
 Need accession number to publish so driver for sharing and
established workflow
 European Data Infrastructure projects too e.g. ELIXIR

15. Neuroscience
 Large data volumes due to use of medical imaging
 Moving towards larger cohort studies integrating wider range of data types,
which strains the balance with ethical requirements around personal data
 Costs of data gathering and advances in analysis technology are making field
more data intensive ‐ computational methods
 Small interdisciplinary teams provide the human infrastructure for RDM, but
historically low funder investment in data management at lab level
 Disciplinary archives are immature, and has encouraged tendency for labs to
treat longitudinal datasets as intellectual capital

16. OMERO – Open Microscopy Environment
 Monash e‐Research Centre
helps groups to adopt (and if
needed adapt) existing
technological solutions
 Partnered a research group to
implement OMERO, a secure
central repository to help
researchers organise, analyze
and share images
 Resulting tool more
sustainable as tailored to
specific community need
www.dcc.ac.uk/resources/developing‐rdm‐services/improving‐rdm‐monash

17. Science & Engineering
 Large scale can mean RDM is built in
as standard and sharing part of
workflow e.g. facilities science
 Often early adopters and advocates
of new technologies e.g. the Grid,
wikis & Arxiv in particle physics
 Archiving established in some cases
as data can’t be recreated e.g. NERC
data centres for Earth Sciences
 Commercial sensitivities can place
restrictions on sharing in some fields
Industry
partners

18. Mechanical Engineering
 Several RDM projects at Bath e.g. ERIM, REDm‐MED
 Concept of repository well established in industrial engineering
– Product Lifecycle Management (PLM) systems
 Preservation issues as data is challenging e.g. CAD files
 Less information sharing than other disciplines
– Commercial sensitivities preclude sharing
– Consultancy‐style research can lead to internal‐only results
– Data generated from private systems, so less applicable to others

19. Crystallography
 X‐ray examinations, images and videos of crystal structures,
chemical crystallography diffraction images
 Established metadata standards e.g. Crystallographic
Information Framework (CIF)
 Advocates of open science and use of related tools
 UsefulChem ‐ http://usefulchem.wikispaces.com
 LabTrove ‐ www.labtrove.org
 eCrystals Archive and Crystallography Open Database (COD)
 National Crystallography Service ‐ www.ncs.ac.uk

20. Astronomy
 Established data standards (e.g. FITS and NOA) maintained by
community
 Access to facilities requires the deposit of raw data, although this
can be embargoed
 International data centres e.g. Sloan Digital Sky Survey ‐
www.sdss.org
 Large volumes of data so transfer can be difficult
 Few IPR issues compared to other disciplines
 Data products are not always shared

21. Galaxy Zoo
 Citizen Science project started to
classify a million galaxies imaged by
the Sloan Digital Sky Survey
 Over 50 million classifications in the
first year, contributed by more than
150,000 people
 Classifications were as good as those
from professional astronomers
 Further projects in astronomy,
climatology, biology, humanities… www.galaxyzoo.org

22. Research data typology
 Commissioned by RLUK
 Aim: to help librarians improve their ability to
engage with researchers on RDM matters; and
to enable them to acquire a better
understanding of the needs of researchers
 A resource structured around a suggested
typology of research data, looking at different
ways in which data might be categorised

23. Broad data types
1. How do researchers generate and process data, and
for what purpose?
1.1 Method of creation and collection of research data:
where the data comes from
1.2 Readiness of research data: extent to which data
has been processed
1.3 Use of research data: researchers' main purpose for
accessing and using data
2. In what file formats, media and volumes do researchers
generate data?
2.1 Medium and format for research data: objects in which
data is captured and recorded, electronic storage and file
types
2.2 Electronic data volumes: size of files (this is subjective,
and based largely on the perception of researchers
3. How do researchers manage and store their data? 3.1 Storage of research data: where and how data is kept
3.2 Types of metadata: not an exhaustive list, but these are
widely‐recognised metadata standards
3.3 Metadata standards
3.4 Degree of openness: founded on Royal Society's
categorisation of 'intelligent openness'
3.5 Licensing of research data: legal rights appertaining the
use of the data

24. An expandable resource
 A scaffold onto which disciplinary examples can be
hung
 Dynamic resource: community input (from librarians,
but maybe others too?), crowdsourcing
 Turning it into an online interactive tool
 Refreshing, curating, adapting the resource
 Basic introduction at
http://www.powtoon.com/show/fZDm1s0W6TI/research‐data‐typology‐for‐rluk‐
draft/

25. Conclusions
 Lots of work still to do!
 Domains different in all respects: data, methods, key
RDM concerns, level of infrastructure and support…
 Differences exist at sub‐discipline level
 Need to understand the area
 Developing and using RLUK’s typology

26. How to plug the gaps?
 Dozens of different repositories or databases specialising
in sub‐domains or data types, but still major gaps
– Shared services?
– Institutional services – specialising rather than generic?
– Role of publishers and learned societies?
– Funder calls for domain specific infrastructure?
– Unis to support ground‐up development of tools / services?
• How can the sector help domain‐specific solutions to
mature and thrive?