5
2. Standards and metadata:
Grantees should describe the protocols used to structure their data and indicate the
metadata standards applied. This will allow other scientists to make an assessment, to
attempt to reproduce the conclusions derived from the dataset (and possibly even the
dataset itself), and potentially reuse the data for further research. If available, grantees
should provide a reference to the community data standards with which their data
conform and that make them interoperable with other datasets of similar type.
3. Name and persistent identifier for the datasets:
Grantees should plan to use repositories that will provide a unique and persistent
identification (an identifier) of their datasets and derived data products, and a stable
resolvable link to where they (or, as a minimum, their metadata) can be directly
accessed.
4. Curation and preservation methodology:
Grantees should provide information on the standards that will be used to ensure the
integrity of their datasets, and the period during which they will be maintained.
Grantees should also explain whether and how their datasets will be preserved and
kept accessible in the longer term. If applicable, they should detail the criteria for
prioritisation, appraisal and selection of the datasets to be retained. If raw data cannot
be stored (e.g. because they are too large or modified in (quasi-)real-time), grantees
should describe what data products will be derived, and how these will be preserved
and kept accessible. If available, grantees should provide a reference to the public data
repository in which their datasets or data products will reside.
5. Data sharing methodology
Grantees should provide information on how their datasets and/or data products can
be accessed, including the terms-of-use or the licence under which they can be
accessed and re-used, and information on any restrictions that may apply. It is also
important to specify and justify the timing of data sharing. This could be, for example,
as soon as possible after the data collection, or at the end of the project. For data that
underlie publications it could be, for example, at the time of publication or pre-
publication.
Grantees should demonstrate that their approach to data management planning is in line
with the FAIR principles by providing adequate information on these five topics.
The ERC does not prescribe a specific format for the DMPs that its grantees need to submit,
because practices and standards differ widely across disciplines. However, ERC grantees are
encouraged to use the ERC template that is available on the ERC website:
ERC Data Management Plan Template:
http://erc.europa.eu/sites/default/files/document/file/ERC-Data-Management-
Plan.docx
A very convenient on-line tool to formulate a DMP according to the requirements of the ERC
(as laid down in the template) and of several other research funding organisations is
provided by the Digital Curation Centre:
DMPonline tool: https://dmponline.dcc.ac.uk
The ARGOS tool (a joint effort of OpenAIRE and EUDAT) allows generating machine
actionable DMPs: