| www.eudat.eu | 2nd Session: July 14, 2016. In this webinar, Sarah Jones (DCC) and Marjan Grootveld (DANS) talked through the aspects that Horizon 2020 requires from a DMP. They discussed examples from real DMPs and also touched upon the Software Management Plan, which for some projects can be a sensible addition

1. How to write a
Data Management Plan
Sarah Jones (DCC)
Marjan Grootveld (DANS)
both involved in EUDAT and OpenAIRE
This work is licensed under the Creative
Commons CC-BY 4.0 licence

2. Open Access Infrastructure
for Research in Europe
www.openaire.eu
Who we are
Research Data Services, Expertise &
Technology https://www.eudat.eu

3. Joint webinar held on 26 May 2016 covering:
• Reasons to manage data
• Horizon 2020 Open Research Data Pilot
• How to manage and share data
• EUDAT & OpenAIRE services
Slides, webinar recording and Q&A document online
www.openaire.eu/research-data-management-an-
introductory-webinar-from-openaire-and-eudat
Introduction to RDM

4. • What is a DMP and why write one?
• Requirements under Horizon 2020
• Example plans
• Lessons and guidance
Overview

5. WHAT IS A DMP & WHY WRITE ONE?
Image CC-BY-NC-SA by Leo Reynolds www.flickr.com/photos/lwr/13442910354

6. A DMP is a brief plan to define:
• how the data will be created
• how it will be documented
• who will be able to access it
• where it will be stored
• who will back it up
• whether (and how) it will be shared & preserved
DMPs are often submitted as part of grant applications, but
are useful whenever researchers are creating data.
Data Management Plans

7. Why manage data?
NON PECUNIAE INVESTIGATIONIS CURATORE
SED VITAE FACIMUS PROGRAMMAS DATORUM PROCURATIONIS
(Not for the research funder, but for life we make data management plans)
• Make your research easier
• Stop yourself drowning in irrelevant stuff
• Save data for later
• Avoid accusations of fraud or bad science
• Write a data paper
• Share your data for re-use
• Get credit for it

8. CREATING
DATA
PROCESSING
DATA
ANALYSING
DATA
PRESERVING
DATA
GIVING
ACCESS TO
DATA
RE-USING
DATA
Research data lifecycle
CREATING DATA: designing research,
DMPs, planning consent, locate existing
data, data collection and management,
capturing and creating metadata
RE-USING DATA: follow-
up research, new
research, undertake
research reviews,
scrutinising findings,
teaching & learning
ACCESS TO DATA:
distributing data,
sharing data,
controlling access,
establishing copyright,
promoting data PRESERVING DATA: data storage, back-
up & archiving, migrating to best format
& medium, creating metadata and
documentation
ANALYSING DATA:
interpreting, & deriving
data, producing outputs,
authoring publications,
preparing for sharing
PROCESSING DATA:
entering, transcribing,
checking, validating and
cleaning data, anonymising
data, describing data,
manage and store data
Ref: UK Data Archive: http://www.data-archive.ac.uk/create-manage/life-cycle

9. What data organisation would a re-user like?
Planning trick 1: think backwards
CREATING
DATA
PROCESSING
DATA
PRESERVING
DATA
GIVING
ACCESS TO
DATA
RE-USING
DATA

10. Data organisation exercises
Design a data organisation for
the project (folder structure,
file naming convention, …)
Research Data Netherlands data support training:
http://datasupport.researchdata.nl/en/start-de-cursus/iii-onderzoeksfase/organising-data/

11. Data organisation
http://datasupport.researchdata.nl/en/start-de-cursus/iii-onderzoeksfase/organising-data

12. Planning trick 2: include stakeholders
Institution
RDM policy
Facilities
€$£
Research funders
Publishers
Data Availability
policy
Commercial partners
https://www.openaire.eu/briefpaper-rdm-infonoads

13. Responsibilities in RDM
https://www.openaire.eu/briefpaper-rdm-infonoads

14. A DMP is about ‘keeping’ data
• Storing data < > archiving data
• Archived data < > findable data
• Findable < > accessible
• Accessible < > understandable
• Understandable < > usable
• A USB stick is not safe
• A persistent ID is essential but no guarantee for usability
• Data in a proprietary format is not sustainable

15. • Findable
– Assign persistent IDs, provide rich metadata, register in a
searchable resource,...
• Accessible
– Retrievable by their ID using a standard protocol, metadata remain
accessible even if data aren’t...
• Interoperable
– Use formal, broadly applicable languages, use standard
vocabularies, qualified references...
• Reusable
– Rich, accurate metadata, clear licences, provenance, use of
community standards...
www.force11.org/group/fairgroup/fairprinciples
Making data FAIR

16. How to deal with data and context?
• Versioning, back-up, storage and archiving
– During the project and in the long term
• Ethics, consent forms, legal access
• Security and technical access
• Usage licences

17. What should be preserved and shared?
• The data needed to validate results in scientific publications (minimally!).
• The associated metadata: the dataset’s creator, title, year of publication,
repository, identifier etc.
– Follow a metadata standard in your line of work, or a generic standard, e.g.
Dublin Core or DataCite, and be FAIR.
– The repository will assign a persistent ID to the dataset: important for
discovering and citing the data.
• Documentation: code books, lab journals, informed consent forms – domain-
dependent, and important for understanding the data and combining them with
other data sources.
• Software, hardware, tools, syntax queries, machine configurations – domain-
dependent, and important for using the data. (Alternative: information about the
software etc.)
Basically, everything that is needed to replicate a study should be available. Plus
everything that is potentially useful for others.
Research Data Alliance (RDA) http://rd-alliance.github.io/metadata-directory/standards/
FAIR Guiding Principles for scientific data management & stewardship http://www.nature.com/articles/sdata201618
How to select and appraise research data:www.dcc.ac.uk/resources/how-guides/appraise-select-research-data

18. DMPS IN HORIZON 2020
Image “Open Data” CC BY 2.0 by http://www.descrier.co.uk

19. Some funders that require DMPs

20. Common themes in DMPs
1. Description of data to be collected / created
(i.e. content, type, format, volume...)
2. Standards / methodologies for data collection & management
3. Ethics and Intellectual Property
(highlight restrictions on data sharing e.g. embargoes, confidentiality)
4. Plans for data sharing and access
(i.e. how, when, to whom)
5. Strategy for long-term preservation
Start planning and communicating early

21. Horizon 2020: Open Research Data Pilot
http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/o
a_pilot/h2020-hi-oa-data-mgt_en.pdf
• Open access to research data refers to the right to
access and re-use digital research data. Openly
accessible research data can typically be accessed,
mined, exploited, reproduced and disseminated free
of charge for the user.
• The use of a Data Management Plan (DMP) is
required for projects participating in the Open
Research Data Pilot, detailing what data the project
will generate, whether and how they will be exploited
or made accessible for verification and re-use, and
how they will be curated and preserved.

22. Who’s involved in this pilot?
Current situation:
• Researchers funded by Horizon 2020 within 9 specified call
areas - https://www.openaire.eu/opendatapilot
• Opt out and opt in are possible.
• A DMP per dataset
As of 2017:
• European Cloud Initiative to give Europe a global lead in
the data-driven economy.
• For new projects open data will become the default option.
The pilot will be extended to cover all call areas. Opting out
remains possible.
• http://europa.eu/rapid/press-release_IP-16-1408_en.htm

23. Open, unless…
• The EC’s goal is Open Access to research data: as
open as possible, as closed as necessary.
• Grant Agreement, Art. 29.3, Open Access to research
data:
• When applicable: explain in the DMP why you need to
(partially) opt out.

24. Timing the DMP
• Note that the Commission does NOT require applicants
to submit a DMP at the proposal stage (see next slide).
• A DMP is therefore NOT part of the evaluation.
• DMPs are a deliverable for those in the pilot (due by
month 6).
• Note that the Commission requires updates. A DMP is a
living or “active” document.

25. Proposal phase
Where relevant*, H2020 proposals can include a section on data management which is
evaluated under the criterion ‘Impact’.
• What types of data will the project generate/collect?
• What standards will be applied?
• How will this data be exploited &/or shared/made accessible for verification and
reuse?
• If data cannot be made available, why not?
• How will this data be curated and preserved?
Your data management policy should reflect the current state of consortium agreements
on RDM.
* For “Research and Innovation actions” and “Innovation Actions”
http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-data-mgt_en.pdf

26. Initial DMP (at 6 months)
The DMP should address the points below on a dataset by dataset
basis:
• Dataset reference and name
• Data set description
• Standards and metadata
• Data sharing
• Archiving and preservation (including storage and backup)
See Annex 1 at:
http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020
-hi-oa-data-mgt_en.pdf

27. More elaborate DMP
Scientific research data should be easily:
1. Discoverable
Are the data discoverable and identifiable by a standard mechanism e.g. DOIs?
2. Accessible
Are the data accessible and under what conditions e.g. licenses, embargoes?
3. Assessable and intelligible
Are the data and software assessable and intelligible to third parties for peer-review? E.g. can
judgements be made about their reliability and the competence of those who created them?
4. Useable beyond the original purpose for which it was collected
Are the data properly curated and stored together with the minimum software and documentation to
be useful by third parties in the long-term?
5. Interoperable to specific quality standards
Are the data and software interoperable, allowing data exchange? E.g. were common formats and
standards for metadata used?
See Annex 2 at:
http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-
hi-oa-data-mgt_en.pdf

28. DMPonline
A web-based tool to help researchers write DMPs
Includes a template for Horizon 2020
Guidance from EUDAT and OpenAIRE being added
https://dmponline.dcc.ac.uk

29. How the tool works
Click to write a
generic DMP
Or choose your
funder to get their
specific template
Pick your uni to
add local
guidance and to
get their template
if no funder
applies
Choose any
additional
optional
guidance

30. EUDAT guidance

31. OpenAIRE support
• Summary on the Open Research Data pilot
https://www.openaire.eu/opendatapilot
• Brief guide on developing a DMP
https://www.openaire.eu/opendatapilot-dmp
• Selecting a data repository
https://www.openaire.eu/opendatapilot-repository
• Developing guidance to add to DMPonline
• Will be adding an ‘export to Zenodo’ feature in early 2017 to
allow DMPs to be published and assigned a DOI

32. Deliver the DMP and keep it up to date
• EC: “Since DMPs are expected to mature during the
project, more developed versions of the plan can be
included as additional deliverables at later stages. (…)
New versions of the DMP should be created whenever
important changes to the project occur due to inclusion
of new data sets, changes in consortium policies or
external factors.”
Focus on how you
will ensure your data
are “FAIR”

33. Active DMPs
• Interested in ways to support this active quality, where
“active” is understood as “able to evolve and be
monitored”?
• Join the RDA’s Active Data Management Plans interest
group https://rd-alliance.org/groups/active-data-
management-plans.html
• And see recordings, slides and notes of the international
and interdisciplinary ADMP Workshop 28-30 June 2016
https://indico.cern.ch/event/520120

34. Option: add SSI template for
software projects
Two templates available for Software Management Plans in
DMPonline courtesy of SSI
www.software.ac.uk/resources/guides/software-management-plans

35. EXAMPLE PLANS

36. Example plans
• 108 DMPs from the National Endowment for the Humanities
www.neh.gov/divisions/odh/grant-news/data-management-plans-successful-
grant-applications-2011-2014-now-available
• 20+ scientific DMPs submitted to the NSF (USA) provided by UCSD
– http://libraries.ucsd.edu/services/data-curation/data-management/ dmp-
samples.html
• Example DMP collection from Leeds University
• https://library.leeds.ac.uk/research-data-tools
• Further examples:
• www.dcc.ac.uk/resources/data-management-plans/guidance-examples

37. Example: OpenMinTed
OpenMinTed aims to
create an infrastructure for
Text and Data Mining
(TDM) of scientific and
scholarly content
Have adopted their own
structure to create a ‘Data
and Software
Management Plan’
http://openminted.eu

38. Example: OpenMinTed –
Data chapter
Six high-level datasets identified:
1. Scholarly publications
2. Language and knowledge resources
3. Services and workflows
4. Automatically and manually generated annotations
5. Consortium publications
6. Metadata
Described in a table
per dataset
(see illustration)

39. OpenMinTed – Software examples

40. Example: CAPSELLA
CAPSELLA aims to develop ICT
solutions for farmers and other
actors engaged in agrobiodiversity
Devised a questionnaire to collate
datset information from project
partners
Identified 13 datasets, 6 of which
are imported as is, 3 aggregated, 3
transformed and 1 generated
www.capsella.eu

41. Example dataset record

42. Data description examples
The final dataset will include self-reported demographic and
behavioural data from interviews with the subjects and laboratory data
from urine specimens provided.
From NIH data sharing statements
Every two days, we will subsample E. affinis populations growing under our
treatment conditions. We will use a microscope to identify the life stage
and sex of the subsampled individuals. We will document the information
first in a laboratory notebook and then copy the data into an Excel
spreadsheet. The Excel spreadsheet will be saved as a comma separated
value (.csv) file.
From DataOne – E. affinis DMP example

43. Metadata examples
Metadata will be tagged in XML using the Data Documentation Initiative (DDI)
format. The codebook will contain information on study design, sampling
methodology, fieldwork, variable-level detail, and all information necessary for
a secondary analyst to use the data accurately and effectively.
From ICPSR Framework for Creating a DMP
We will first document our metadata by taking careful notes in the laboratory notebook
that refer to specific data files and describe all columns, units, abbreviations, and
missing value identifiers. These notes will be transcribed into a .txt document that will
be stored with the data file. After all of the data are collected, we will then use EML
(Ecological Metadata Language) to digitize our metadata. EML is one of the accepted
formats used in ecology, and works well for the types of data we will be producing. We
will create these metadata using Morpho software, available through KNB. The
metadata will fully describe the data files and the context of the measurements.
From DataOne – E. affinis DMP example

44. Data sharing examples
We will make the data and associated documentation available to users under a data-
sharing agreement that provides for: (1) a commitment to using the data only for research
purposes and not to identify any individual participant; (2) a commitment to securing the data
using appropriate computer technology; and (3) a commitment to destroying or returning the
data after analyses are completed.
From NIH data sharing statements
The videos will be made available via the bristol.ac.uk website (both as streaming media
and downloads) HD and SD versions will be provided to accommodate those with lower
bandwidth. Videos will also be made available via Vimeo, a platform that is already well
used by research students at Bristol. Appropriate metadata will also be provided to the
existing Vimeo standard.
All video will also be available for download and re-editing by third parties. To facilitate this
Creative Commons licenses will be assigned to each item. In order to ensure this usage is
possible, the required permissions will be gathered from participants (using a suitable
release form) before recording commences.
From University of Bristol Kitchen Cosmology DMP

45. Examples restrictions
Because the STDs being studied are reportable diseases, we will be collecting
identifying information. Even though the final dataset will be stripped of identifiers
prior to release for sharing, we believe that there remains the possibility of
deductive disclosure of subjects with unusual characteristics. Thus, we will make
the data and associated documentation available to users only under a data-
sharing agreement.
From NIH data sharing statements
1. Share data privately within 1 year.
Data will be held in Private Repository, but metadata will be public
2. Release data to public within 2 years.
Encouraged after one year to release data for public access.
3. Request, in writing, data privacy up to 4 years.
Extensions beyond 3 years will only be granted for compelling cases.
4. Consult with creators of private CZO datasets prior to use.
Pis required to seek consent before using private data they can access
From Boulder Creek Critical Zone Observatory DMP

46. Archiving examples
The investigators will work with staff at the UKDA to determine what to
archive and how long the deposited data should be retained. Future long-
term use of the data will be ensured by placing a copy of the data into the
repository.
From ICPSR Framework for Creating a DMP
Data will be provided in file formats considered appropriate for long-term
access, as recommended by the UK Data Service. For example, SPSS Portal
format and tab-delimited text for qualitative tabular data and RTF and
PDF/A for interview transcripts. Appropriate documentation necessary to
understand the data will also be provided. Anonymised data will be held
for a minimum of 10 years following project completion, in compliance
with LSHTM’s Records Retention and Disposal Schedule. Biological samples
(output 3) will be deposited with the UK BioBank for future use.
From Writing a Wellcome Trust Data Management and Sharing Plan

47. Share your example DMPs!
Send us links to your
DMPs
We will add them to
the DCC list
Aim to cover wide
range of disciplines
and funders
www.dcc.ac.uk/
share-DMPs

48. LESSONS AND RESOURCES
Image ‘Energy Resources | Energie Quelle’ CC-BY-NC by K. H. Reichert www.flickr.com/photos/reupa/19502634575

49. Tips for writing DMPs
• Seek advice - consult and collaborate
• Consider good practice for your field
• Base plans on available skills & support
• Make sure implementation is feasible
• Think about things early…

50. Plan to share data from the outset
• Negotiation on licenses and consent agreement may
preclude later sharing if not careful
• Costings can’t be included retrospectively
• Useful to consider data issues at the consortium
negotiation stage to make sure potential issues are
identified and sorted asap
Decisions made early on affect what you can do later

51. Sharing data: what is meant?
With collaborators while
research is active
Data are mutable
(Open) data sharing
Data are stable,
searchable, citable, clearly
licensed

52. Storing data: what is meant?
Storing and backing up files
while research is active
Likely to be on a networked
filestore or hard drive
Easy to change or delete
Archiving or preserving
data in the long-term
Likely to be deposited in a
digital repository
Safeguarded and preserved

53. Archiving, repositories, ehm?
• Horizon 2020 ORD pilot participants are asked to “deposit
your data in a research data repository”: a digital archive
collecting and displaying datasets and their metadata.
• Select a data repository that will preserve your data,
metadata and possibly tools in the long term.
• It is advisable to contact the repository of your choice
when writing the first version of your DMP.
• Repositories may offer guidelines for sustainable data
formats and metadata standards, as well as support for
dealing with sensitive data and licensing.

54. Where to find a repository?
• More information: https://www.openaire.eu/opendatapilot-repository
• Zenodo: http://www.zenodo.org
• Re3data.org: http://www.re3data.org

55. Searching with Re3data.org
www.fosteropenscience.eu/content/re3data-demo

56. How to select a repository?
• Certification as a ‘Trustworthy Digital Repository’ with an explicit
ambition to keep the data available in long term.
• Matches your particular data needs: e.g. formats accepted; mixture of
Open and Restricted Access.
• Provides guidance on how to cite the deposited data.
• Gives your submitted dataset a persistent and globally unique identifier
for sustainable citations and to link back to particular researchers and
grants.
www.openaire.eu/opendatapilot-repository
Data Seal of Approval
nestor seal
ISO 16363

57. Keep everything? For always?
• When regenerating data would be cheaper than archiving, don’t
archive. Select what data you’ll need and want to retain.
• 10 years is often stated in data policies and academic codes, but data
can be valuable for ages, in climatology, sociology, health sciences,
astronomy, linguistics, … Look beyond minimal retention periods
where relevant.
• Explain your selection criteria in the DMP.
DCC How-to guide: http://www.dcc.ac.uk/resources/how-guides/appraise-select-data
RDNL Selection criteria: http://www.researchdata.nl/en/services/data-
management/selecting-research-data/

58. Licensing research data
• Horizon 2020 guidelines point to CC-BY or CC-0
• EUDAT licensing wizard help you pick licence for data & software
http://ufal.github.io/public-license-selector
• DCC How-to guide helps you to license data
www.dcc.ac.uk/resources/how-guides/license-research-data

59. Metadata standards
Metadata Standards Directory
• Broad, disciplinary listing of
standards and tools
• Maintained by RDA group
http://rd-alliance.github.io/metadata-
directory
Biosharing
• A portal of data standards,
databases, and policies
• Focused on life, environmental
and biomedical sciences
https://biosharing.org

60. • How to develop a DMP
www.dcc.ac.uk/resources/how-guides/develop-data-plan
• RDM brochure and template
https://dans.knaw.nl/en/about/organisation-and-policy/information-
material?set_language=en
• OpenAIRE guidelines
• www.openaire.eu/opendatapilot-dmp
• ICPSR framework for a DMP
www.icpsr.umich.edu/icpsrweb/content/datamanagement/dmp/fram
ework.html
Guidelines on DMPs

61. • Guidelines on Data Management
in Horizon 2020
• Provides summary of
requirements
• Includes templates for DMPs
http://ec.europa.eu/research/participants/data/r
ef/h2020/grants_manual/hi/oa_pilot/h2020-hi-
oa-data-mgt_en.pdf
EC guidance

62. KEY MESSAGES
Image “Fishbone” CC BY-NC-ND 2.0 by ttps://www.flickr.com/photos/mrjnl/

63. Key messages
• Data management is part of good research practice whether you plan
to make the data open or not – it benefits you!
• The process of planning and reflecting are most important. Think
about the desired end result and plan for this.
• Approach the DMP in whatever way best fits your project
– adopt a different template to suit
– add sections / elements e.g. ethics, software
– decide whether to describe each dataset in detail
– focus effort on datasets you’ll create rather than reuse…

64. www.eudat.eu www.openaire.eu
Thanks – any questions?
Contact us:
Marjan Grootveld: marjan.grootveld@dans.knaw.nl
Sarah Jones: sarah.jones@glasgow.ac.uk
Acknowledgements:
Thanks to DANS and DCC for reuse of slides, and to the OpenMinTeD and
CAPSELLA projects for sharing their Data Management Plans

65. www.eudat.eu www.openaire.eu
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