1.1.1 1.1.1.1

1.1.2 1.1.2.1

1.1.3 1.1.3.1

1.1.4 1.1.4.1

1.1

1.2.1 1.2.1.1

1.2.2 1.2.2.1

1.2.3 1.2.3.1 For example, decisions may be based on staff expertise within the host organisation, a preference for open formats, standards accepted by data repositories, widespread usage within the research community, or on the software or equipment that will be used. Give preference to open and standard formats as they facilitate sharing and long-term re-use of data (several repositories provide lists of such ‘preferred formats’).

1.2.4 1.2.4.1 they can be expressed in storage space required (bytes), and/or in numbers of objects, files, rows, and columns.

1.2

1

2.1.1 2.1.1.1

2.1.2 2.1.2.1 Use community metadata standards where these are in place

2.1.3 2.1.3.1 Mentioning for example conventions, version control, and folder structures. Consistent, well-ordered research data will be easier to find, understand, and re-use.

2.1.4 2.1.4.1 This may include information on the methodology used to collect the data, analytical and procedural information, definitions of variables, units of measurement, and so on.

2.1.5 2.1.5.1 For example in a database with links to each item, a ‘readme’ text file, file headers, code books, or lab notebooks.

2.1 For example the methodology of data collection and way of organizing data

2.2.1 2.2.1.1 This may include processes such as calibration, repeated samples or measurements, standardised data capture, data entry validation, peer review of data, or representation with controlled vocabularies.

2.2

2

3.1.1 3.1.1.1 3.1.1.2 It is recommended to store data in least at two separate locations. Give preference to the use of robust, managed storage with automatic backup, such as provided by IT support services of the home institution. Storing data on laptops, stand-alone hard drives, or external storage devices such as USB sticks is not recommended.

3.1

3.2.1 3.2.1.1

3.2.2 3.2.2.1

3.2.3 3.2.3.1 Consider data protection, particularly if your data is sensitive for example containing personal data, politically sensitive information, or trade secrets.

3.2.4 3.2.4.1

3.2

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4.1.1 4.1.1.1

4.1.2 4.1.2.1

4.1 Ensure that when dealing with personal data data protection laws (for example GDPR) are complied with: ›› Gain informed consent for preservation and/or sharing of personal data. ›› Consider anonymisation of personal data for preservation and/or sharing (truly anonymous data are no longer considered personal data). ›› Consider pseudonymisation of personal data (the main difference with anonymisation is that pseudonymisation is reversible). ›› Consider encryption which is seen as a special case of pseudonymisation (the encryption key must be stored separately from the data, for instance by a trusted third party).

4.2.1.1 4.2.1.1.1 Meaning who will have the rights to control access:

4.2.1.2 4.2.1.2.1

4.2.1.3 4.2.1.3.1 Access restrictions

4.2.1.4 4.2.1.4.1

4.2.1 Make sure to cover these matters of rights to control access to data for multi-partner projects and multiple data owners, in the consortium agreement.

4.2.2.1 4.2.2.1.1 true for example Database Directive, sui generis rights

4.2.2.2 4.2.2.2.1 4.2.2.2.2

4.2.2

4.2.3.1 4.2.3.1.1

4.2.3

4.2

4.3.1 4.3.1.1 4.3.1.2

4.3 Consider whether ethical issues can affect how data are stored and transferred, who can see or use them, and how long they are kept. Demonstrate awareness of these aspects and respective planning. • Follow the national and international codes of conducts and institutional ethical guidelines, and check if ethical review (for example by an ethics committee) is required for data collection in the research project.

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5.1.1 5.1.1.1 For example by deposit in a trustworthy data repository, indexed in a catalogue, use of a secure data service, direct handling of data requests, or use of another mechanism.

5.1.2 5.1.2.1

5.1.3 5.1.3.1

5.1.4 5.1.4.1

5.1.5 5.1.5.1 true

5.1.6 5.1.6.1 5.1.6.2

5.1.7 5.1.7.1 5.1.7.2 For example to publish, protect intellectual property, or seek patents.

5.1.8 5.1.8.1

5.1.9 5.1.9.1 true true

5.1.10 5.1.10.1

5.1.11 5.1.11.1

5.1

5.2.1 5.2.1.1 5.2.1.2

5.2.2 5.2.2.1

5.2.3 5.2.3.1

5.2.4 5.2.4.1

5.2.5 5.2.5.1

5.2.6 5.2.6.1 false

5.2.7 5.2.7.1 It is recommended to demonstrate that the repositories policies and procedures (including any metadata standards, and costs involved) have been checked.

5.2 For example a data repository or archive

5.3.1 5.3.1.1 Another mechanism 5.3.1.2

5.3.2 5.3.2.1 Consider the sustainability of software needed for accessing the data.

5.3

5.4.1 5.4.1.1 Persistent identifiers should be applied so that data can be reliably and efficiently located and referred to. Persistent identifiers also help to track citations and re-use.

5.4.2 5.4.2.1 Typically, a trustworthy, long-term repository will provide a persistent identifier.

5.4 Such as a Digital Object Identifier (DOI)

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6.1.1 6.1.1.1 6.1.1.2 Name responsible individual(s) where possible. For example data capture, metadata production, data quality, storage and backup, data archiving, and data sharing.

6.1.2 6.1.2.1 true

6.1.3 6.1.3.1

6.1.4 6.1.4.1 Consider regular updates of the DMP

6.1 For example role, position, and institution. i.e. the data steward

6.2.1 6.2.1.1 Carefully consider and justify any resources needed to deliver the data. These may include storage costs, hardware, staff time, costs of preparing data for deposit, and repository charges.

6.2.2 6.2.2.1 true

6.2.3 6.2.3.1

6.2 For example financial and time

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