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Basis : 3,699 Horizon 2020 signed grant agreements
Calls in core-areas: opt out 35% (149/431 proposals)
Other areas: voluntary opt in 13% (409/3,268 proposals)
In multi-beneficiary projects it is also possible for specific beneficiaries to keep their data closed if relevant provisions are made in the consortium agreement and are in line with the reasons for opting out
Please take time to read Background information and the guidance in the Annex, because the questions in the template are not all clear on their own.
What metadata will be created? In case metadata standards do not exist in your discipline, please outline what type of metadata will be created and how. Are the data produced and/or used in the project discoverable with metadata, identifiable and locatable by means of a standard identification mechanism (e.g. persistent and unique identifiers such as Digital Object Identifiers)? What naming conventions do you follow? Will search keywords be provided that optimize possibilities for re-use? Do you provide clear version numbers?
Metadata is needed to locate and understand the data. When you are deciding what information to capture, think about what others would need in order to find, evaluate, understand, and reuse your data; the EC template also mentions keywords. Also get others to check your metadata to improve the quality and make sure it’s understandable to others. Standards should be used where possible.
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
For Accessibility the Guidance contains more questions:
Which data produced and/or used in the project will be made openly available as the default? If certain datasets cannot be shared (or need to be shared under restrictions), explain why, clearly separating legal and contractual reasons from voluntary restrictions. Note that in multi-beneficiary projects it is also possible for specific beneficiaries to keep their data closed if relevant provisions are made in the consortium agreement and are in line with the reasons for opting out. How will the data be made accessible (e.g. by deposition in a repository)? What methods or software tools are needed to access the data?Is documentation about the software needed to access the data included? Is it possible to include the relevant software (e.g. in open source code)? Where will the data and associated metadata, documentation and code be deposited? Preference should be given to certified repositories which support open access where possible. Have you explored appropriate arrangements with the identified repository?If there are restrictions on use, how will access be provided?Is there a need for a data access committee?Are there well described conditions for access (i.e. a machine readable license)? How will the identity of the person accessing the data be ascertained?
Are the data produced in the project interoperable, that is allowing data exchange and re-use between researchers, institutions, organisations, countries, etc. (i.e. adhering to standards for formats, as much as possible compliant with available (open) software applications, and in particular facilitating re-combinations with different datasets from different origins)? What data and metadata vocabularies, standards or methodologies will you follow to make your data interoperable? Will you be using standard vocabularies for all data types present in your data set, to allow inter-disciplinary interoperability? In case it is unavoidable that you use uncommon or generate project specific ontologies or vocabularies, will you provide mappings to more commonly used ontologies?
How will the data be licensed to permit the widest re-use possible? When will the data be made available for re-use? If an embargo is sought to give time to publish or seek patents, specify why and how long this will apply, bearing in mind that research data should be made available as soon as possible. Are the data produced and/or used in the project useable by third parties, in particular after the end of the project? If the re-use of some data is restricted, explain why. How long is it intended that the data remains re-usable? Are data quality assurance processes described?
Guidance from the DCC can also help researchers to understand data licensing. This guide outlines the pros and cons of each approach e.g. the limitations of some CC options
The OA guidelines under Horizon 2020 point to CC-0 or CC-BY as a straightforward and effective way to make it possible for others to mine, exploit and reproduce the data. See p11 at: http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-pilot-guide_en.pdf
Contrary to some other funders, the EC does not require a DMP at the proposal stage.
First of all, what is a Data Management Plan (dmp)?
Essentially, most funders just want evidence at the grant stage that data has been considered – how much will be generated? Usually just a 1-2 page summary covering the expected data to be produced through research along with an idea of what might be shared and how and when it will be shared.
It is important to stress that funders aren’t expecting something carved in stone at this stage. Projects often change quite radically from what is submitted at the proposal stage and this is ok. Researchers just need to be able to provide evidence that they have thought about the data they might be generating and how it will be managed and shared.
In terms of preservation, it is important to remember that not ALL data has to be retained. Selecting what data needs to be kept is something that only the researcher can do. Essentially, he/she will need to retain any data that underpins published findings to allow for validation of results. Additional data that is not required for validation purposes but is deemed to have longer-term value might also be worth keeping.
DMPs are often submitted as part of grant applications, but are useful whenever you’re creating data. Some HEIs are introducing policies that require DMPs for all research undertaken by staff – whether externally funded or not.
[final bullet] Acting on requests from the community, DMPonline will add an ‘export to Zenodo’ feature alongside the other export options. You might want to use this to increase your project’s transparancy, share good practices, or maybe because you write your DMP as a (kind of ) data paper, which is interesting in its own right. At the moment there are a few H2020 DMPs in Zenodo and figshare.
Web-based tool to help researchers write Data Management and Sharing Plans according to different funder / institutional requirements
There are various templates in DMP Online based on different funder requirements and institutional customisations.
We’re currently enhancing it with practical examples, boilerplate text and tailored support. TEDDINET may wish to develop discipline specific guidance within the tool for future related projects.
You may get the feeling that there is so much to do and to know. It is important to realise that you don’t have to build or buy all data services. Instead, institutes and academic communities should support researchers to find & use what is there already. That holds for the repositories that I mentioned, but also for the services that our sister project EUDAT offers.
Note that these are all ”technical” services. The notion “RDM” has different meanings in EUDAT and OpenAIRE.
B2STAGE was conceived to deal with modern day research challenges. As hardware and research software improve, scope for research is broadening. Communities now pursue large-scale simulations, for example developing models for climate simulation encompassing the whole of the Earth, as opposed to isolated regions. Scientists simulate not only organs in the human body, but also their interactions. Similarly, earthquake data are now collected and processed for areas as large as entire continents. The common requirement of such research challenges is that they generate and process increasing volumes of data, with typical workflows requiring data to be processed in a distributed fashion, so as to cope with the pace of data generation. Efficient transfer of data to high performance computing (HPC) workspaces is essential especially in distributed. In order for this to be possible, data need to be transferred in an efficient way to the high-performance or high-throughput computing resources, and this is where B2STAGE comes in. The service also takes care of depositing the computation output from the HPC facilities to EUDAT. B2STAGE can also be used to deposit the community data into the EUDAT facilities. B2STAGE uses the established gridFTP protocol to ensure high-speed transfer between the sites. Data transfer is reliable and requires very little user interaction. B2STAGE also assigns PIDs to computational output that the user elects to inject back into the EUDAT datacentres.
When you are interested in learning more about EUDAT services you can contact CINECA.
OpenAIRE and Eudat services and tools to support FAIR DMP implementation
Credits: OpenAIRE team
Sarah Jones, Data Curation Centre, DCC (UK)
Marjon Grootweld, Dans (NL)
Natalia Manola, ATR (GR)
FAIR Data Management: best practices and open
Paola Gargiulo, OpenAIRE NOAD/Cineca
OpenAIRE and Eudat services and tools to
support FAIR DMP implementation
• The Open Research Data Pilot
• The data management plan
• OPENAIRE tools and services for the Data Pilot
• EUDAT data services
Open Research Data Pilot (2015-
To make the research data
generated by selected
Horizon 2020 projects
accessible with as few
restrictions as possible,
while at the same time
protecting sensitive data
information already paid for by the public
should not be paid for again.
Open data is data that is free to access and
To whom does the Data Pilot
Current situation 2015-2016:
• Researchers funded by Horizon 2020 within 9 specified call areas.
• Opt out and opt in are possible and are being used
• Call areas: https://www.openaire.eu/opendatapilot
As of 2017:
• European Cloud Initiative to give Europe a global lead in the data-
• Open data will become the default option. The pilot will be extended
to cover all call areas. Opting out remains possible.
• Press release: http://europa.eu/rapid/press-release_IP-16-
Daniel Spichtinger (EC) at OpenCon 14-11-15: 3,699 Horizon 2020 signed grant agreements – 149/431 projects in core areas opted out - 409/3268 projects in
other areas opted in 4
Which research has to partipate in the
pilot? (2015- 2016)
• Future and Emerging Technologies
• Research infrastructures – (new: coverage of the whole area)
• Leadership in enabling and industrial technologies – Information and
• Nanotechnologies, Advanced Materials, Advanced Manufacturing and Processing,
and Biotechnology: ‘nanosafety’ and ‘modelling’ topics (new)
• Societal Challenge: Food security, sustainable agriculture and forestry, marine and
maritime and inland water research and the bioeconomy - selected topics as
specified in the work programme (new)
• Societal Challenge: Climate Action, Environment, Resource Efficiency and Raw
materials – except raw materials
• Societal Challenge: Europe in a changing world – inclusive, innovative and
• Science with and for Society 5
Open Data by Default from 2017
Two types of data:
Data, including metadata, needed to validate
the results in scientific publications
Other data, including metadata, as specified
in the Data Management Plan, like raw data
The following slides come from the EC’s open access team
and provide an overview to the key points. Content from
Jean-Francois Dechamp and colleagues.
RDA National Event in Italy, 14-15 November 2016 8
Openly accessible and minable.
Eligible costs for APCs.
Openly accessible research data can
typically be accessed, mined,
exploited, reproduced and
disseminated free of charge for the
Three top reasons to opt out
Whether a (proposed) project participates
in the ORD or chooses to opt out does
not affect the evaluation of that project.
Proposals will not be penalised for opting
Reasons for opting out:
• participation is incompatible with the Horizon 2020 obligation to
protect results that can reasonably be expected to be commercially or
• participation is incompatible with the need for confidentiality in
connection with security issues;
• participation is incompatible with rules on protecting personal data;
• the project will not generate / collect any research data; or
• there are other legitimate reasons not to take part in the Pilot.
• Note that partial opt out is possible – and preferable to full opt out!
– assign persistent IDs, provide rich metadata, register in a searchable
– Retrievable by their ID using a standard protocol, metadata remain accessible
even if data aren’t...
– Use formal, broadly applicable languages, use standard vocabularies,
– Rich, accurate metadata, clear licences, provenance, use of community
• Use metadata and specify standards for metadata creation (if
any). If there are no standards in your discipline describe what
type of metadata will be created and how
• Search keywords
• Persistent and unique identifiers such as DOIs or other
• File and folder naming conventions
• Versioning of the datasets and clear version numbers
Metadata and documentation
• Metadata and documentation is needed to find and
understand research data
• Think about what others would need in order to find,
evaluate, understand, and reuse your data
• Get others to check the metadata to improve quality
• Use standards to enable interoperability
Where to find metadata standards
Broad, disciplinary listing of
standards and tools
Maintained by RDA group
A portal of data standards,
databases, and policies for life,
environmental and biomedical
• Explain which data can’t be shared openly, if any
• Specify how access will be provided in case of restrictions,
e.g., through a data committee, a license, or arranged with the
• Will methods or software tools needed to access the data (if
any) be included or documented?
• Deposit the data and associated metadata, documentation and
code preferably in certified repositories which support Open
Where to find a repository?
More information: https://www.openaire.eu/opendatapilot-repository
What to deposit?
a. the data needed to validate the results
presented in scientific publications, including the
b. any other data, including the metadata, as
specified in the DMP;
c. plus for a-b the documentation and the tools
that are needed to validate the results, e.g.
specialised software or software code,
algorithms and analysis protocols (when
possible, these instruments themselves).
Re3data is a registry of data repositories
RDM Seminar @ ISERD, Tel Aviv - Oct 1, 2016
Searching with Re3data.org
• Interoperability on data and metadata, on data exchange
formats and protocols
• Specify what data and metadata vocabularies, standards or
methodologies you will follow to facilitate interoperability
• Standard vocabulary to allow inter-disciplinary interoperability
or a mapping from your vocabulary to more commonly used
Aim for compliance to globally accepted practices
RDM Seminar @ ISERD, Tel Aviv - Oct 1, 2016 24
• Clarify licences early on
• License the data to permit the widest reuse possible
• Specify a data embargo, if needed
• If data re-use by third parties is restricted, explain why
• How long will the data remain reusable?
• Describe data quality assurance processes
RDM Seminar @ ISERD, Tel Aviv - Oct 1, 2016 25
License research data openly
DCC guide outlines the pros and cons of
each approach and gives practical advice
on how to implement your licence
CREATIVE COMMONS LIMITATIONS
What counts as commercial?
ND No Derivatives
Severely restricts use
These clauses are not open licenses
Horizon 2020 Open Access
guidelines point to:
RDM Seminar @ ISERD, Tel Aviv - Oct 1, 2016 26
What is a data management plan?
A plan written at the start of a project to define:
• how the data will be created?
• how it will be documented?
• who will 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
The DMP is a living document.
You are not required to provide
detailed answers to all the
questions in the first version of
the DMP (due M6)
Explain any selection criteria in the DMP
When to submit the DMP
• Note that the Commission does NOT require applicants to submit a
DMP at the proposal stage.
• A DMP is therefore NOT part of the evaluation.
• DMPs are a deliverable
• Note that the Commission requires updates. A DMP is a living or
What aspects of RDM should be in a DMP?
What data will be created (format, types, volume...)
Standards and methodologies to be used (incl. metadata)
How ethics and Intellectual Property will be addressed
Plans for data sharing and access
Strategy for long-term preservation Create
A DMP is a plan to share!
What should be deposited?
• The data needed to validate results in scientific publications (minimally!).
• The associated metadata: the dataset’s creator, title, year of publication, repository,
• 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
• Documentation like code books, lab journals, informed consent forms – domain-
dependent, and important for understanding the data and combining them with other
• Software, hardware, tools, syntax queries, machine configurations – domain-
dependent, and important for using the data. (Alternative: information about the
Basically, everything that is needed to replicate a study should be available for others.
Research Data Alliance (RDA) http://rd-alliance.github.io/metadata-directory/standards/
FAIR Guiding Principles for scientific data management http://www.nature.com/articles/sdata201618 31
Archive the data openly,
• Confidentiality and security issues can be good reasons not to
publish or share – all – data. Note in the DMP the reasons for
not giving access, and deposit that part of the data under a
Restricted Access regime.
• When regenerating data would be cheaper than archiving, don’t
archive. Spend time on selecting what data you’ll need and
want to retain. Motivate your criteria in the DMP.
For selection criteria see https://www.openaire.eu/opendatapilot
Grant Agreement, Art. 29.3, Open Access to research data:
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
• Figshare is not a Trustworthy Digital Repository
• a persistent identifier is essential but no guarantee for usability
• Data in a proprietary format are not sustainable
How much does it cost? Who pays?
• What are the costs for making data FAIR in your project?
• Resources needed for long term preservation
• Check the UK Data Service Costing model
• The High Level Expert Group on the European Open Science Cloud
recommends that “well budgeted data stewardship plans should be
made mandatory and we expect that on average about 5% of
research expenditure should be spent on properly managing and
• Who pays? How?UKDS model http://www.data-archive.ac.uk/create-manage/planning-for-sharing/costing
& SUPPORTS THE OPEN DATA
Human Network e-infrastructure
NOADS: National Open Access Desks
Monitor and foster the adoption of Open
Access policies at the local level
Support researchers at the implementation of
the Open Data Pilot
FP7 post grant APCs Pilot
e-infrastructure for monitoring impact of OA
mandates and research projects
OpenAIRE guidelines for metadata exchange
Zenodo Repository for the deposition of research
THE POINT OF REFERENCE FOR OPEN ACCESS IN EUROPE
50 Partners: EU countries, data centers, universities, libraries, repositories
Open Access infrastructure
for research in Europe
Integrated Scientific Information System
RDM Seminar @ ISERD, Tel Aviv - Oct 1, 2016
17.3 mi unique publications
760+ validated data providers
370Κ publications linked to
projects from 6 funders
28 K datasets linked to
3.5K links to software
Software Facilities Methods
From January 2017
Briefing papers, factsheets,
webinars, workshops, FAQs
• Open Research Data Pilot
• Creating a data
• Selecting a data repository
• Personal data
Developing guidance to add
RDM Seminar @ ISERD, Tel Aviv - Oct 1, 2016 40
Information at the OpenAIRE website
• Open Research Data Pilot
• What is the pilot? Which H2020 strands are required to participate? What
practical steps should the researcher take?
• Create a Data Management Plan
• Information about how to create a Data Management Plan. First steps; When to
write and revise your Data Management Plan
• Select a Data Repository
• Information about how to select a repository
• Frequently Asked Questions about the Open Research Data
• E.g. about Pilot, Zenodo and data
management, but also about OA
publications and how to make your
repository OpenAIRE compatible.
• Various presenters; slides included.
OpenAIRE Research Data Management Briefing
• This extensive briefing paper gives an overview of
Research Data Management with practical sections
about data management planning, and archiving the
research data for reuse.
• Zenodo for all types of publications, data and software
• Claiming – linking research results
• Amnesia, an anonymization tool for all
• Data providers – Interoperability Guidelines, validation,…
• Project coordinators – reporting
• Funders and institutions – monitoring
• Research communities – gathering, monitoring all research
Multi-disciplinary repository used for the long-tail of research
• An OpenAIRE-CERN joint effort
• Multidisciplinary repository accepting
– Multiple data types
– Software – link to Github
• Assigns a Digital Object Identifier (DOI), up t 50GB per
• Links funding, publications, data & software
What is DMPonline?
• A web-based tool to help researchers write Data
Management and Sharing Plans
• Includes requirements and guidance from funders,
universities and other groups
• Developed by the Digital Curation Centre
How to write a DMP
• Template available from https://dmponline.dcc.ac.uk/
• And from a few national DMPonline sites, e.g. in Spain and Belgium
See https://www.openaire.eu/opendatapilot-dmp - Spain: http://pgd.consorciomadrono.es/ - Belgium: pilot and therefore limited to authorised persons 48
Deliver the DMP
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
Where to find a repository?
More information: https://www.openaire.eu/opendatapilot-repository
Zenodo: http://www.zenodo.org/ 54
How to select a repository?
Main criteria for choosing a data repository:
• Certification as a ‘Trustworthy Digital Repository’, with an explicit
ambition to keep the data available in the long term.
• Network of trustworthy digital repositories for long-term preservation of the data
after the research is finished.
• Three common certification standards for TDRs:
Data Seal of Approval: http://datasealofapproval.org/en/
nestor seal for DIN 31644: http://www.langzeitarchivierung.de/Subsites/nestor/EN/nestor-Siegel/siegel_node.html
ISO 16363: http://www.iso16363.org/
Main criteria for choosing a data repository:
• Certification as a ‘Trustworthy Digital Repository’, with an explicit ambition
to keep the data available in the long term.
• Matches your particular data needs and is FAIR compliant: e.g. certain file
formats; mixture of Open and Restricted Access. So contact the repository
of your choice when writing the first version of your DMP, or earlier.
• Provides guidance on metadata and on how to cite the data that has been
• Gives your submitted dataset a persistent and globally unique identifier:
for sustainable citations – both for data and publications – and to link back
to particular researchers and grants.
How to select a repository?
EUDAT offers common data services
to both research communities and
individuals through a network of 35
EUDAT offers data
EUDAT services are designed, built and implemented based on user
B2 SERVICE SUITE
• Store and exchange data with
colleagues and team members,
including research data not finalized
• share data with fine-grained access
• synchronize multiple versions of data
across different devices
e.g. B2DROP – a solution for
researchers and scientists to:
20GB storage per user
Living objects, so no PIDs
Versioning and offline use
B2DROP is hosted at the Jülich Supercomputing Centre
Daily backups of all files in B2DROP are taken and kept on tape
• move large amounts of data between
data stores and high-performance
• re-ingest computational results back
• deposit large data sets into EUDAT
resources for long-term preservation
reliable and light-weight
manages permanent PIDs
e.g. B2STAGE - Facilitating communities to: