1. Jonathan Rans Digital Curation Centre
Introduction to Research Data Management Anglia Ruskin University 1st June 2015
Jonathan Rans
Digital Curation Centre
This work is licensed under the Creative Commons Attribution 2.5 UK: Scotland License.

2. Who we are
The (Est. 2004) is:
A national-level centre of expertise in digital preservation with a particular focus on Research Data Management (RDM)
Working closely with a number of UK institutions to boost RDM capability across the HE sector
Also involved in a variety of national and international collaborations

3. What will we cover? Definitions that we work to
Why take a formal approach to managing your data?
What does Research Data Management encompass?

4. Definitions of research data?
“Research data, unlike other types of information is collected, observed, or created, for purposes of analysis to produce original research results.”
“Research data is defined as recorded factual material commonly retained by and accepted in the scientific community as necessary to validate research findings; although the majority of such data is created in digital format, all research data is included irrespective of the format in which it is created.“
“Evidence which is used or created to generate new knowledge and interpretations. ‘Evidence’ may be intersubjective or subjective; physical or emotional; persistent or ephemeral; personal or public; explicit or tacit; and is consciously or unconsciously referenced by the researcher at some point during the course of their research.”

5. So, what might this include?
Instrument measurements
Experimental observations
Still images, video and audio
Text documents, spreadsheets, databases
Quantitative data (e.g. survey data)
Survey results & interview transcripts
Simulation data, models & software
Slides, artefacts, specimens, samples
Questionnaires
Sketches, diaries, lab notebooks …
Anything & everything produced in the course of research

6. Data management is part of
What is research data management?
Plan
Create
Use
Appraise
Deposit and Publish
Discover and Reuse
“an explicit process covering the creation and stewardship of research materials to enable their use for as long as they retain value.”
Data management is part of
good research practice 6

7. Why manage research data?
To make research easier!
To stop yourself drowning in irrelevant stuff
In case you need the data later
To avoid accusations of fraud or bad science
To share data so others can use and learn from it
To get credit for producing the data
Because somebody else said to do so
Data is increasing in significance. It will unquestionably matter to your research careers, more than it does to your supervisors’ generation.
Learn good data habits now! You’ll need them later.

8. RCUK Common Principles in brief
Make data openly available where possible
Have policies & plans. Preserve data of long-term value
Metadata for discovery / reuse. Link to data from publications
Be mindful of legal, ethical and commercial constraints
Allow limited embargoes to protect the effort of creators
Acknowledge sources to recognise IP and abide by T&Cs
Ensure cost-effective use of public funds for RDM

9. Ultimately funders expect:
Data management plans
timely release of data
once patents are filed or on (acceptance for) publication
open data sharing
minimal or no restrictions if possible
preservation of data
typically years if of long-term value
See the RCUK Common Principles on Data Policy:

10. Why make data available?

11. Sharing leads to breakthroughs
“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
/13alzheimer.html?pagewanted=all&_r=0
...and increases the speed of discovery

12. Benefits of data sharing (3)
“There is evidence that studies that make their data available do indeed receive more citations than similar studies that do not.”
Piwowar H. and Vision T.J 2013 "Data reuse and the open data citation advantage“
... more citations
9% - 30% increase

13. What is involved in RDM? Data Management Planning Data creation
Annotating / documenting data
Analysis, use, versioning
Storage and backup
Publishing papers and data
Preparing for deposit
Archiving and sharing
Licensing
Citing…
Plan
Create
Use
Appraise
Deposit and Publish
Discover and Reuse

14. Data management planning
All RCUK funders expect a DMP to be produced as part of project development, most expect it to be submitted with the grant application
Examples of good DMPs are available here:
The DCC provides an online tool to guide you through the process of developing a funder-specific DMP

15. Data creation Adopt file naming conventions:
Design a good project folder structure
Ensure consent forms, licences and partnership agreements don’t restrict opportunities to share data

16. Some formats are better for long-term
It’s preferable to opt for formats that are:
Uncompressed
Non-proprietary
Open, documented
Standard representation (ASCII, Unicode)
Data centres may have preferred formats for deposit e.g.
Type
Recommended
Non-preferred
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
Some formats are better for data sharing and long-term preservation than others.
It’s preferable to use formats that are uncompressed (e.g. large, high-quality files like .wav), non-proprietary (i.e. open) standards that are documented and well-understood. This aids preservation and interoperability.
Some data centres have preferred formats for deposit so it’s worthwhile encouraging researchers to consult these to check.
Further examples:

17. Documentation and metadata
Metadata: basic info e.g. title, author, dates, access rights...
Documentation: context, workflows, methods, code, data dictionary...
Use standards wherever possible for interoperability
To make sure their data can be understood by themselves, their community and others, researchers should create metadata and documentation.
Metadata is basic descriptive information to help identify and understand the structure of the data e.g. title, author...
Documentation provides the wider context. It’s useful to share the methodology / workflow, software and any information needed to understand the data e.g. explanation of abbreviations or acronyms
There are lots of standards that can be used. The DCC started a catalogue of disciplinary metadata standards which is now being taken forward as an international initiative via an RDA working group
metadata-standards

18. Data creation: documentation
Collect together all the information users would need to find and understand the data
Create metadata as you go, it’s more time-consuming and less effective to do it at the end of a project
Use standards where possible
Data Documentation Initiative
DCC Metadata Catalogue
Name, structure and version files clearly

19. Where to store data? Your own device (PC, flash drive, etc.)
And if you lose it? Or it breaks?
Departmental drive or university filestore
Should be more robust with automated back-up
“Cloud” storage
Do they care as much about your data as you do?

20. Storage and backup
Use managed services where possible e.g. shared drives rather than local or external hard drives
Consider the security implications of where you store data and how you transfer it
3… 2… 1… backup!
at least 3 copies of a file
on at least 2 different media
with at least 1 offsite

21. Appraisal and deposit
Relevance to Mission – including any legal/funder requirement to retain the data beyond its immediate use.
Scientific or Historical Value – significance and relationship to publications etc.
Uniqueness – can it be found elsewhere / if we don’t preserve it, who will?
Potential for Redistribution – quality / IP / ethical concerns are addressed.
Non-Replicability – either impossible to replicate (e.g. atmospheric or social science data) or not financially viable.
Economic Case – costs of managing and preserving the resource stack up well against potential future benefits.
Full Documentation – surrounding / contextual information necessary to facilitate future discovery, access, and reuse is adequate.
2. Prioritise based on relationship with publications, e.g. underpins scientific record (c.f. Sarah Callaghan, Preparde)
5. Privilege irreproducible data…
How to Appraise & Select Research Data for Curation
Angus Whyte, Digital Curation Centre, and Andrew Wilson, Australian National Data Service (2010)

22. Data repositories Zenodo OpenAIRE-CERN joint effort
Zenodo
OpenAIRE-CERN joint effort
Multidisciplinary repository
Multiple data types
Publications
Long tail of research data
Citable data (DOI)
Links to funder, publications, data & software
The EC guidelines suggest selecting a suitable repository. The Databib and Re3data lists can be useful for this. They allow you to search and browse by subject. Re3data also allows you to restrict the search by certificates, open access repositories and persistent identifiers.

23. Why hand data over for preservation?
To preserve the data themselves “Data rot”
Bitwise preservation
Format migration
To preserve contextual information
Often held in a researcher’s head
Notes often aren’t detailed enough
Protecting digital objects requires specialist skills and particular information to be captured
The aim is to enable the reuse of data
Not everything can, or should be preserved!

24. RDM and sharing : a best practice guide

25. Tools for managing data
managing-active-research-data

26. Thank-you for listening!
Jonathan Rans
@JNRans
Image Credits
Harvey Rutt, Southampton. Recovered from:
Pile of flash drives:
Dalian University fire:
Metadata devil: