Practical Strategies for Research Data Management

Practical Strategies for Research
Data Management
5th November 2018
Dan Crane, Research Support Librarian
library-research-support@open.ac.uk

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http://www.open.ac.uk/library-research-support/

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What is Research Data Management?
Planning for data management
Useful resources
Questions (and feedback)
WHAT WE’LL COVER
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• Data collection
• Documentation and metadata
• Ethics and legal
• Storage and backup
• Selection and preservation
• Data sharing
• Responsibilities and resources

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Aims of the session:
• Know what RDM is
• Be aware of some of the drivers and benefits
• What a Data Management Plan (DMP) is
• How to approach some of the key aspects of
data management
• Where to go for more information

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But first…
…introductions

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…and, what do you want to get out of
the session?

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WHAT IS RESEARCH DATA MANAGEMENT?
Research data is defined as the evidence base on which academic
researchers build their analytic or other work. Such data may be in any
form, but may include “digital information created directly from research
activities such as experiments, analysis, surveys, measurements,
instrumentation and observations; data resulting from automated or
manual data reduction and analysis including the inputs and outputs of
simulations and models”
RCUK / UKRI Common principles on data policy
WHAT DO WE MEAN BY ‘DATA’
Open University Research Data Management Policy

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Research data are the evidence that underpins the answer to the research question, and
can be used to validate findings regardless of its form (e.g. print, digital, or physical). These
might be quantitative information or qualitative statements collected by researchers in the
course of their work by experimentation, observation, modelling, interview or other methods,
or information derived from existing evidence. Data may be raw or primary (e.g. direct from
measurement or collection) or derived from primary data for subsequent analysis or
interpretation (e.g. cleaned up or as an extract from a larger data set), or derived from
existing sources where the rights may be held by others. Data may be defined as ‘relational’
or ‘functional’ components of research, thus signalling that their identification and value lies
in whether and how researchers use them as evidence for claims.
They may include, for example, statistics, collections of digital images, sound recordings,
transcripts of interviews, survey data and fieldwork observations with appropriate
annotations, an interpretation, an artwork, archives, found objects, published texts or a
manuscript.
WHAT DO WE MEAN BY ‘DATA’
Concordat on Open Research Data
https://www.ukri.org/files/legacy/documents/concordatonopenresearchdata-pdf/

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“Research data management concerns the
organisation of data, from its entry to the research
cycle through to the dissemination and archiving of
valuable results. It aims to ensure reliable
verification of results, and permits new and
innovative research built on existing information."
Digital Curation Centre (2011)
Making the Case for Research Data Management
http://www.dcc.ac.uk/sites/default/files/documents/publications/Making%20the%20case.pdf

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UK Data Archive Data Lifecycle model
Design research
Plan data
management
Plan consent for
sharing
Locate existing data
Collect data
Capture and create
metadata
Creating data
http://www.data-archive.ac.uk/create-manage/life-cycle

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Enter data, digitise,
transcribe, translate
Check, validate,
clean data
Anonymise data
Describe data
Manage and store
data
Processing data

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Interpret data
Produce research
outputs
Author publications
Prepare data for
publications
Analysing data

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Migrate data to best
format
Migrate data to
suitable medium
Back-up and store
data
Create metadata
and documentation
Archive data
Preserving data

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Distribute data
Share data
Control access
Establish copyright
Assign licences
Promote data
Giving access
to data

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Follow-up research
New research
Undertake research
reviews
Scrutinise findings
Teach and learn
Re-using data

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Why spend time and effort on this?
• So you can work efficiently and
effectively - save time and reduce
frustration
• Because your data is precious
• To enable data re-use and sharing
• To meet funders’ and institutional
requirements

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What does the OU expect?
“Research data must be managed to the highest standards
throughout their lifecycle in order to support excellence in
research practice.”
“In keeping with OU principles of openness, it is expected
that research data will be open and accessible to other
researchers, as soon as appropriate and verifiable, subject
to the application of appropriate safeguards relating to the
sensitivity of the data and legal and commercial
requirements.”
OU Research Data Management Policy, November 2016
http://www.open.ac.uk/library-research-support/sites/www.open.ac.uk.library-research-
support/files/files/Open-University-Research-Data-Management-Policy.pdf

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What do funders expect?
“Good data management is
fundamental to all stages of the
research process and should be
established at the outset.”
“Open access to research data is an
enabler of high quality research, a
facilitator of innovation and safeguards
good research practice.”

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What does the OU provide?
• Support from the library research support
team and website
library-research-support@open.ac.uk
http://www.open.ac.uk/library-research-support/

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What does the OU provide?
• A research data repository, (ORDO) for
secure, long-term storage of data, meeting
funder requirements
https://ou.figshare.com/

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Discussion
• What type of data do you create/use?
• What data management challenges do you
face?
For 5 minutes

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“Start as you mean to go on”
Thinking about the
requirements at the beginning
of the project will limit the
work needed during and at
the end.
Finish
PLANNING FOR DATA MANAGEMENT

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The Data Management Plan
a project document that describes:
• the data that a project will collect
• how data will be stored, transferred, backed up during
the project
• how data will be archived at the end of the project
• how access will be granted to data where appropriate
Data Management Plans
What are they?

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• Make informed decisions to anticipate and avoid
problems
• Develop procedures early on for consistency
• Avoid duplication, data loss and security breaches
• Tell your funder that you know what you are doing
(and get funding!)
• Save time and effort – make your life easier!
Why write one?

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• As soon as possible
• When submitting funding bids
• But if you’re not applying for funding, do one too
• Update it during your work
When do I need to do one?

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Data Collection
What data will you collect or create?
How will the data be collected or created?

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Data Collection
Documentation and Metadata
What documentation and metadata will accompany the data?

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Data Collection
Documentation and Metadata
What documentation and metadata will accompany the data?
Ethics and Legal Compliance
How will you manage any ethical issues?
How will you manage copyright and Intellectual Property Rights
(IPR) issues?

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Storage and Backup
How will the data be stored and backed up during the research?
How will you manage access and security?

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Storage and Backup
Selection and Preservation
Which data should be retained, shared, and/or preserved?
What is the long-term preservation plan for the dataset?

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Storage and Backup
Data Sharing
How will you share the data?
Are any restrictions on data sharing required?

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Storage and Backup
Data Sharing
How will you share the data?
Are any restrictions on data sharing required?
Responsibilities and Resources
Who will be responsible for data management?
What resources will you require to deliver your plan?

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The basics
Data collection
• What data will I have?
• How many files?
• How big will they be?
• What format(s) will I use?
• How will it be collected?
• How will it be organised?

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Organising your data
Data collection
Filing is more than saving files, it’s making
sure you can find them later in your project
• Naming
• Directory Structure
• File Types
• Versioning
All these help to keep your data safe and
accessible.

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File naming
Data collection
Decide on a file naming convention at the start of your project. Useful file
names are:
• consistent.
• meaningful to you and your colleagues.
• allow you to find the file easily.
Agree on the following elements of a file name:
• Vocabulary
• Punctuation
• Dates (YYYY-MM-DD)
• Order
• Numbers
• Version information
Ideally you should be able to tell what’s in a file before opening it.
Tip: create a readme file detailing the naming scheme.

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File naming: an example
Data collection
Slides-RDM-PracticalStrategiesForRDM-2018-11.ppt
Slides-RDM-PracticalStrategiesForRDM-2018-11.ppt
type of document
general area of
work / topic
specific area of work / title
date

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File naming: what to avoid…
Data collection
Dan.doc
My paper.doc
Results.xls
August Mtg.doc
20June.csv
IMPORTANT.pdf
Article_Manuscript October_FINAL.doc
Article_Manuscript October_FINAL FINAL.doc
Article_Manuscript October_FINAL FINALv1.doc
Article_Manuscript October_FINAL FINALv2.doc
Article_Manuscript October_FINAL FINALv2 last version.doc

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Folder structure
Data collection
An example:
by Nikola Vukovic : http://nikola.me/folder_structure.html

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Documentation and metadata
What do others need to understand your data?
Metadata is additional information that is required to make
sense of your files – it’s data about data.
Project level
1. For what purpose was data created
2. What does the dataset contain
3. How was data collected
4. Who collected the data and when
5. How was the data processed
6. What possible manipulations were
done to the data
7. What were the quality assurance
procedures
8. How can data be accessed
CESSDA ERIC: https://www.cessda.eu/Research-Infrastructure/Training/Expert-Tour-Guide-on-Data-Management
Supporting documentation
• Working papers or laboratory
books
• Questionnaires or interview
guides
• Final project reports and
publications
• Catalogue metadata
• READ ME file

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What do others need to understand your data?
Embedded documentation
• code, field and label
descriptions
• descriptive headers or
summaries
• recording information in the
Document Properties function
of a file (Microsoft)
Metadata is additional information that is required to make
sense of your files – it’s data about data.
Object level
CESSDA ERIC: https://www.cessda.eu/Research-Infrastructure/Training/Expert-Tour-Guide-on-Data-Management

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Guidance on disciplinary metadata standards: http://www.dcc.ac.uk/resources/metadata-standards
Disciplinary standards

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Imagine you have just downloaded the
data sample sheet from a repository...
1. What contextual or explanatory
information is missing?
2. Is there anything odd about the data that
needs clarifying?
3. What additional metadata would you like
to see supplied?
Discussion

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Personal and sensitive data
Ethics and legal
• Legal: See ‘OU Data protection procedures’ and start with the
DPIA screening questions
Link to intranet (requires log-in) http://intranet6.open.ac.uk/governance/data-protection/
Working with personal data?
• Ethical: See Human Research ethics guidance and the ‘HREC
Project Registration and Risk Checklist’
Link to website http://www.open.ac.uk/research/ethics/human-research

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Ethics and legal
When working with research participants....
• Inform your participants what will happen with the data during
and after the project
• Ensure you have obtained their consent
• Consider who needs access to the data
• Can data be anonymised or pseudonymised?
• Pre-planning and agreeing with participants during the
consent process, on what may and may not be recorded or
transcribed, can be more effective than anonymisation
For more information, see the UK Data Service guidance:
https://www.ukdataservice.ac.uk/manage-data/legal-ethical/consent-data-sharing/gaining-consent

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Ethics and legal
Managing sensitive data
• If possible, collect the necessary data without using
personally identifying information
• There is a difference between pseudonymisation and
anonymisation
• Pseudonymise or anonymise your data upon collection or
as soon as possible thereafter
• Avoid transmitting unencrypted personal data electronically
• Consider whether you need to keep original collection
instruments (recordings, surveys etc.) once they have been
transcribed and quality assured.

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Ethics and legal
Other issues:
• Copyright – of your data and any you re-use
• Collaborations

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Data storage and security
Storage and backup
• Data size, complexity
• Where will you be working, collecting and
accessing your data?
• Are you working with anyone?

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Storage and backup
There are several storage options available to you:
• OU networked file storage
• OneDrive
• SharePoint
• STEM Specialist Support Unit
• ORDO
• Cloud based services (DropBox, Google Drive
etc.)
Tip: See the comparison guide

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Data storage and security - collaboration
Storage and backup
• OneDrive
• SharePoint
• ORDO
• Zendto (for one-off transfers)
• Be wary of Dropbox & similar

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Selection and preservation
What data should be retained, preserved and
shared?
• What data must be retained/destroyed for
contractual, legal, or regulatory purposes?
• How will you decide what other data to keep?
• What are the foreseeable research uses for the
data?
• How long will the data be retained and
preserved?

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A quick overview
Data sharing
• Supporting publication or end of project
• In a trusted repository
• With metadata
• As open as possible
• Available for 10 years
• Discoverable, accessible, citeable
• With a licence
• Data access statement in publications
“Researchers will, wherever possible, make their research data open and
usable within a short and well defined period, which may vary by subject and
disciplinary area and reflect the resources available to them to do so. Data
supporting publications should be accessible by the publication date and
should be in a citeable form“

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A quick overview
Data sharing
• Benefits: transparency, re-use, impact
• Required by many funders and publishers
• Lots of tools and guidance to help researchers
• Come to our session on 27 Nov ‘Making your research
data open’ or see our online training sessions and
recordings:
• ‘Data sharing: how, what and why?’ Online / Recording
• ‘Data sharing: legal and ethical issues’ Online / Recording

Responsibilities and
resources

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Responsibilities and resources
Who will be responsible for data management?
• Who is responsible for implementing the DMP, and ensuring
it is reviewed and revised?
• Who will be responsible for each data management activity?
• How will responsibilities be split across partner sites in
collaborative research projects?
What resources will you require to deliver your plan?
• Is additional specialist expertise (or training for existing
staff) required?
• Do you require hardware or software which is additional or
exceptional to existing institutional provision?
• Will charges be applied by data repositories?

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So, there’s a lot to think about…

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How we can help
• Data Management Plan checking
• Support with setting up new projects
• Advice on preparation of data for sharing
• Data Repository (ORDO)
• Online guidance
• Enquiries

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Tips
• Keep it simple, short and specific
• Seek advice - consult and
collaborate
• Base plans on available skills
and support
• Make sure implementation is
feasible
• Justify any resources or
restrictions needed

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Activity
Think about your own
research.
Have a go at writing your
own DMP…

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DMP Online
https://dmponline.dcc.ac.uk
• A web-based tool to
help you write DMPs
according to different
requirements.
• DCC, funder and OU
guidance.

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Now for a game…
Image:‘Bingo’byJagobaMartínezathttps://flic.kr/p/5dwjVt

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Now for a game…
With thanks to Georgina Parsons: Parsons, Georgina (2017): Writing a DMP - workshop materials.
figshare.https://doi.org/10.6084/m9.figshare.5044930.v2Retrieved: 16:00, Aug 15, 2017 (GMT)
• Take a bingo card and an example DMP.
• Each square contains a positive quality:
good DMPs will do all/most of these.
• Read each square and if it is true for the
example DMP, mark it with a cross.
• The first person to get five crosses in a row
(vertical, horizontal, or diagonal) calls
“Bingo!” and gets a prize.

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Links
USEFUL RESOURCES
• The OU Library Research Support website: http://www.open.ac.uk/library-
research-support/research-data-management
• Open Research Data Online (ORDO): https://ou.figshare.com
• Digital Curation Centre: http://www.dcc.ac.uk/
• DMP Online: https://dmponline.dcc.ac.uk/
• UK Data Archive: http://www.data-archive.ac.uk/
• MANTRA: http://datalib.edina.ac.uk/mantra/
• CESSDA ERIC training: https://www.cessda.eu/Research-
Infrastructure/Training/Expert-Tour-Guide-on-Data-Management
• The Orb: http://open.ac.uk/blogs/the_orb
• OU Human Research Ethics Committee:
http://www.open.ac.uk/research/ethics/
• OU Data Protection: http://intranet6.open.ac.uk/governance/data-
protection/advice-and-resources (if clicking on the link doesn’t work, copy and paste the address)
• OU Information Security: http://intranet6.open.ac.uk/it/main/information-
security (if clicking on the link doesn’t work, copy and paste the address)

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3 TAKE HOME POINTS
1. Start early to help you work better and
protect your precious data
2. Write a Data Management Plan
3. Don’t be shy. Ask for help!

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FEEDBACK
How did we do?
Before you go, please fill out our very short feedback form to
tell us:
• One thing you liked about today's session
• One thing you would change
Use the paper form, or online form at: https://bit.ly/2EZu0K6

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IMAGE CREDITS
Unless otherwise stated, all images are by
Jørgen Stamp at http://www.digitalbevaring.dk

Practical Strategies for Research Data Management

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