A presentation offering an introduction to managing and sharing research data given at the Czech Open Science days as part of the EC-funded FOSTER project.

1. Managing and Sharing Research Data
Sarah Jones
DCC, University of Glasgow
sarah.jones@glasgow.ac.uk
Twitter: @sjDCC
#fosteropenscience
Open Science Days 2015, 21st & 23rd April, Prague & Brno http://foster.czu.cz/?r=6661

2. RDM, OPEN SCIENCE, OPEN DATA...
Some definitions and clarifications
Image CC-BY-NC-SA by Tom Magllery www.flickr.com/photos/lwr/13442910354

3. What is Research Data Management?
The active management of data throughout the lifecycle
Create
Document
Use
Store
Share
Preserve
• Data Management Planning
• Creating data
• Documenting data
• Accessing / using data
• Storage and backup
• Selecting what to keep
• Sharing data
• Data licensing and citation
• Preserving data
• …
CC-BY-NC-SACC-BY-NC-SA

4. What is open science?
“science carried out and communicated in a
manner which allows others to contribute,
collaborate and add to the research effort, with
all kinds of data, results and protocols made
freely available at different stages of the
research process.”
Research Information Network, Open Science case studies
www.rin.ac.uk/our-work/data-management-and-curation/
open-science-case-studies

5. Open data
 make your stuff available on the Web (whatever format) under an open licence
 make it available as structured data (e.g. Excel instead of a scan of a table)
 use non-proprietary formats (e.g. CSV instead of Excel)
 use URIs to denote things, so that people can point at your stuff
 link your data to other data to provide context
Tim Berners-Lee’s proposal for five star open data - http://5stardata.info
“Open data and content can be freely used,
modified and shared by anyone for any purpose”
http://opendefinition.org

6. Data sharing: degrees of openness
Open Restricted Closed
Content that can be
freely used, modified
and shared by anyone
for any purpose
Limits on who can use the data,
how or for what purpose
- Charges for use
- Data sharing agreements
- Restrictive licences
- Peer-to-peer exchange
- …
Five star open data

Unable to share
Under embargo

7. WHY MANAGE AND SHARE DATA?
Benefits and drivers
Image CC-BY-NC-SA by wonderwebby www.flickr.com/photos/wonderwebby/2723279491

8. Why YOU need a Data
Management Plan
http://blogs.ch.cam.ac.uk/pmr/2
011/08/01/why-you-need-a-data-
management-plan
What if this was your laptop?

9. Data sharing boosts citation
A study that analysed the citation counts of 10,555 papers on gene
expression studies that created microarray data, showed:
“studies that made data available in a public repository
received 9% more citations than similar studies for
which the data was not made available”
Data reuse and the open data citation advantage,
Piwowar, H. & Vision, T. https://peerj.com/articles/175

10. Returns for institutions
“If an institution spent A$10 million on data, what would
be the return? The answer is: more publications; an
increased citation count; more grants; greater profile;
and more collaboration.”
Dr Ross Wilkinson, ANDS
www.ariadne.ac.uk/issue72/oar-2013-rpt

11. Expectations of public access
“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.”
RCUK Common Principles on Data Policy
www.rcuk.ac.uk/research/Pages/DataPolicy.aspx

12. Funder imperatives...
“The European Commission’s vision is
that information already paid for by the
public purse should not be paid for
again each time it is accessed or used,
and that it should benefit European
companies and citizens to the full.”
http://ec.europa.eu/research/participants/data/
ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-
oa-pilot-guide_en.pdf

13. Increased use and economic benefit
Up to 2008
• Sold through the US Geological
Survey for US$600 per scene
• Sales of 19,000 scenes per year
• Annual revenue of $11.4 million
Since 2009
• Freely available over the internet
• Google Earth now uses the images
• Transmission of 2,100,000
scenes per year.
• Estimated to have created value for
the environmental management
industry of $935 million, with direct
benefit of more than $100 million
per year to the US economy
• Has stimulated the development of
applications from a large number of
companies worldwide
The case of NASA Landsat satellite imagery of the Earth’s surface:
http://earthobservatory.nasa.gov/IOTD/view.php?id=83394&src=ve

14. Validation of results
www.guardian.co.uk/politics/2013/apr/18/uncovered-error-george-osborne-austerity
“It was a mistake in a spreadsheet that could
have been easily overlooked: a few rows left
out of an equation to average the values in a
column.
The spreadsheet was used to draw the
conclusion of an influential 2010 economics
paper: that public debt of more than 90% of
GDP slows down growth. This conclusion was
later cited by the International Monetary Fund
and the UK Treasury to justify programmes
of austerity that have arguably led to riots,
poverty and lost jobs.”

15. More scientific breakthroughs
www.nytimes.com/2010/08/13/health/research/13alzheimer.html?pagewanted=all&_r=0
“It was unbelievable. Its not science
the way most of us have practiced in
our careers. But we all realised that
we would never get biomarkers unless
all of us parked our egos and
intellectual property noses outside
the door and agreed that all of our
data would be public immediately.”
Dr John Trojanowski, University of Pennsylvania

16. Reasons to manage and share data
Direct benefits for you
• To make your research
easier!
• Stop yourself drowning in
irrelevant stuff
• Make sure you can
understand and reuse
your data again later
• Advance your career –
data is growing in
significance
Research integrity
• To avoid accusations of
fraud or bad science
• Evidence findings and
enable validation of
research methods
• Meet codes of practice
on research conduct
• Many research funders
worldwide now require
Data Management and
Sharing Plans
Potential to share data
• So others can reuse and
build on your data
• To gain credit – several
studies have shown
higher citation rates
when data are shared
• For greater visibility,
impact and new
research collaborations
• Promote innovation and
allow research in your
field to advance faster

17. HOW TO MANAGE RESEARCH DATA?
Questions to consider
Image CC-BY-NC-SA by Leo Reynolds www.flickr.com/photos/lwr/13442910354

18. What file formats are most appropriate?
• Do you have a choice or do the instruments you use only export in certain
formats?
• What is common in your field? Try to use something that is accepted and
widespread
• Does your data centre recommend formats? If so it’s best to use these.
If you want your data to be re-used and sustainable in the long-term, you
typically want to opt for open, non-proprietary formats.
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
www.data-archive.ac.uk/create-manage/format/formats-table

19. How will you name your files?
• Keep file and folder names short, but meaningful
• Agree a method for versioning
• Include dates in a set format e.g. YYYYMMDD
• Avoid using non-alphanumeric characters in file names
• Use hyphens or underscores not spaces e.g. day-sheet, day_sheet
• Order the elements in the most appropriate way to retrieve the record
www.jiscdigitalmedia.ac.uk/guide/choosing-a-file-name
Example from ARM Climate
Research Facility
www.arm.gov/data/docs/plan

20. Can others understand the data?
Think about what is needed in order to find, evaluate,
understand, and reuse the data.
• Have you documented what you did and how?
• Did you develop code to run analyses? If so, this should
be kept and shared too.
• Is it clear what each bit of your dataset means? Make
sure the units are labelled and abbreviations explained.
• Record metadata so others can find your work e.g.
title, date, creator(s), subject, format, rights…,

21. Where will you store the data?
• Your own device (laptop, flash drive, server etc.)
– And if you lose it? Or it breaks?
• Departmental drives or university servers
• “Cloud” storage
– Do they care as much about your data as you do?
The decision will be based on how sensitive your data are,
how robust you need the storage to be, and
who needs access to the data and when

22. Who will do the backup?
• Use managed services where possible (e.g. University
filestores rather than local or external hard drives), so
backup is done automatically
• 3… 2… 1… backup!
at least 3 copies of a file
on at least 2 different media
with at least 1 offsite
• Ask central IT team for advice

23. Which data need to be kept?
Five steps to follow
① Could this data be re-used
② Must it be kept as evidence or for legal reasons
③ Should it be kept for its potential value
④ Consider costs – do benefits outweigh cost?
⑤ Evaluate criteria to decide what to keep
5 steps to decide what data to keep
www.dcc.ac.uk/resources/how-guides/five-steps-decide-what-data-keep

24. Can you publish / share your data?
• Who owns the data?
• Have you got consent for sharing?
• Do any licences you’ve signed permit sharing?
• Is the data in suitable formats?
• Is there enough documentation?

25. Data Management Plans
It’s useful to consider these issues and the approaches you will
take in a Data Management Plan. Many research funders and
universities now ask for these.
• What types of data will the project generate/collect?
• What standards will be used?
• How will this data be shared/made available?
• If not, why? e.g. ethics & IP issues, embargoes, confidentiality
• How will this data be curated and preserved?
www.dcc.ac.uk/resources/data-management-plans/checklist

26. SUPPORT FOR IMPLEMENTATION
Good practice, tools, infrastructure & services
Image under licence by DCC

27. Managing and sharing data: a best
practice guide
http://data-archive.ac.uk/media/2894/managingsharing.pdf

28. Support on Data Management Plans
• Checklist on what to include
• How to guide on developing a plan
• Guidance on assessing plans (forthcoming)
• Webinars and training materials
• DMPonline tool
• Example DMPs
www.dcc.ac.uk/resources/data-management-plans

29. DMPonline
• Presents requirements from funders
• Guidance from funder, uni, discipline…
• Example answers
• Ability to share plans with collaborators
• Export into a variety of formats
• …
https://dmponline.dcc.ac.uk

30. Metadata standards catalogue
• Good metadata is key for research data access and re-use
• Many disciplines have formalised community metadata standards
• Use relevant standards for interoperability
www.dcc.ac.uk/resources/metadata-standards

31. www.dcc.ac.uk/resources/how-guides/license-research-data
Data licensing guidance and tools
Check out the EUDAT licensing wizard:
http://ufal.github.io/lindat-license-selector

32. Data repositories
http://databib.org
http://service.re3data.org/search
• Does your publisher or funder suggest a repository?
• Are there data centres or community databases for your discipline?
• Does your university offer support for long-term preservation?

33. HOW ARE OTHER UNIS SUPPORTING RDM?
Examples from the UK
Image CC-BY by GotCredit www.flickr.com/photos/jakerust/16844834832

34. Components of RDM services
www.dcc.ac.uk/resources/developing-rdm-services

35. Institutional RDM policies
www.dcc.ac.uk/resources/policy-and-legal/institutional-data-policies

36. Early research data policies
“Statement of commitment”
 Infrastructure  policy
“10 commandments”
mutual promises
aspirational
Baseline of RCUK Code
+ procedures & support
legal tone / language
a section in uni DM policy
useful guide as appendix
Based on Edin.
with a few
additions

37. RDM strategies and roadmaps
A series of blog posts
www.dcc.ac.uk/news
Links to example roadmaps
http://tiny.cc/EPSRCroadmaps

38. University of Bath RDM roadmap
• Based on Monash University RDM strategy
• Identifies the current position and proposes activity
• Defines roles and responsibilities and timeframes
www.bath.ac.uk/rdso/University-of-Bath-Roadmap-for-EPSRC.pdf

39. Guidance webpages
www.gla.ac.uk/datamanagement
www.bath.ac.uk/research/data

40. Online training
http://datalib.edina.ac.uk/mantra

41. Training for librarians
• Data Intelligence for librarians, 3TU, Netherlands
http://dataintelligence.3tu.nl/en/about-the-course
• DIY Training Kit for Librarians, University of Edinburgh
http://datalib.edina.ac.uk/mantra/libtraining.html
• RDM for librarians, DCC
http://www.dcc.ac.uk/training/rdm-librarians
• RDMRose, University of Sheffield
http://rdmrose.group.shef.ac.uk
• SupportDM modules, University of East London
http://www.uel.ac.uk/trad/outputs/resources

42. Data Management Planning support
• Guidelines / templates on what to include in plans
• Example answers, guidance and links to local support
• A library of successful DMPs to reuse
• Tailored consultancy services
• Online tools (e.g. customised DMPonline)
• Links / flags embedded in grant systems
• ...

43. Research data storage
Blue Peta at Bristol
1st 5TB free per Data Steward then
£400 per TB p.a. for disk storage;
tape backup £40 per TB
http://data.bris.ac.uk
• Use of HPC facility
• £2m funding to date
• 3 machine rooms – resilience
(tape archive 2012)
• Available to all researchers
for research data

44. Data storage at Hertfordshire
• Acquired 100TB of tier 2 storage which will be backed
up to tape for device level recovery
• Default offer will be 50GB but established an RDM
triage system and will accommodate < 5TB on the
basis of need
• 10TB of Arkivum A-stor for 10 years for archival
storage bolted onto Dspace institutional repository in
order to support long term preservation of datasets
www.herts.ac.uk/rdm

45. Institutional data repositories
Not intended to
replace national,
subject or other
established data
repositories
Acknowledge
hybrid environment
http://datashare.is.ed.ac.uk
www.dspace.cam.ac.uk
https://databank.ora.ox.ac.uk
Research Data at Essex &
DataPool at Southampton

46. Data catalogues
• Use of Current Research Information Systems (CRIS) e.g.
PURE and Symplectic
• Use of repository platforms e.g. ePrints, Dspace, CKAN
• No definitive standard – use of DataCite, CERIF, DDI…
• UK national Research Data Discovery Service pilot:
http://rdds.jiscinvolve.org/wp

47. Thanks – any questions?
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