Austrian FWF – maDMP mapping

We performed a mapping for each section using te Evaluation Rubric of the FWF template.

Section: I General Information

Subsection: I.1 Administrative information

Question or Requirement information

Evaluation Rubric

DCS

OSTrails AP

Provide information such as name of principal investigator, FWF project number, and version of DMP

Contains the minimal information required to identify the principal investigator and the references of the project as well as the version of the DMP.

dmp/contact/name, dmp/project/number, dmp/project/description, dmp/project/funding/funder_id, dmp/project/funding/grant_id,
Subsection: I.2 Data management responsibilities and resources

Question or Requirement information

Evaluation Rubric

DCS

OSTrails AP

Who (for example, role, position, and institution) will be responsible for data management?

Clearly outlines the roles and responsibilities for data management, naming responsible individual(s) and clearly indicates who is responsible for day-to-day implementation and adjustments to the DMP.

contributor/contributor_id, contributor/role, contributor/affiliation, contributor/name,

What resources will be dedicated to data management and ensuring that data will be FAIR (Findable, Accessible, Interoperable, Re-usable)?

Provides clear estimates of the resources and costs (for example, staff time and repository charges) that will be dedicated to data management and ensuring that data will be FAIR and describes how these costs will be covered. Alternatively, there is a statement that no additional resources are needed.

cost/description, cost/value, cost/title,

Section: II Data Characteristics

Subsection: II.1 Data description and collection or re-use of existing data

Question or Requirement information

Evaluation Rubric

DCS

OSTrails AP

How will new data be collected or produced and/or how will existing data be re-used?

Gives clear details of where the existing data come from and how new data will be collected or produced. It clearly explains methods and software used.

dataset/methodology

What data (types, formats, and, volumes) will be collected or produced?

Explains, if existing data are re-used, how these data will be accessed and any constraints on their re-use.

dataset/is_reused, dataset/distribution /data_access,

Clearly describes or lists what data types will be generated (for example, numeric, textual, audio, or video) and their associated data formats.

dataset/type , dataset/distribution /format

Explains why certain formats have been chosen and indicates if they are in open and standard format. If a proprietary format is used, it explains why.

dataset/distribution /description,

dataset/distribution /format_justification

Provides information about the estimated data volume.

dataset/distribution/byte_size

Section: III Documentation and Data Quality

Subsection: III.1 Metadata and documentation

Question or Requirement information

Evaluation Rubric

DCS

OSTrails AP

What metadata and documentation (for example, the methodology of data collection and way of organising the data) will accompany the data?

Clearly outlines the metadata that will accompany the data, with reference to good practice in the community (for example, uses metadata standards where they exist).

datset/metadata

Indicates how the data will be organised during the project (for example, naming conventions, version control strategy, and folder structures).

dataset/metadata dataset/data_quality_assurance

Clearly outlines the documentation needed to enable data re-use, stating where the information will be recorded (for example, a database with links to each item, a ‘readme’ text file, code books, or lab notebooks).

dataset/methodology
Subsection: III.2 Data quality control

Question or Requirement information

Evaluation Rubric

DCS

OSTrails AP

What data quality control measures will be used?

Clearly describes the approach taken to ensure and document quality control in the collection of data during the lifetime of the project.

dataset/methodology

Section: IV Data Storage, Sharing, and Long-Term Preservation

Subsection: IV.1 Data storage and backup during the research process

Question or Requirement information

Evaluation Rubric

DCS

OSTrails AP

How will the data and metadata be stored and backed up during the research process?

The location where the data and backups will be stored during the research activities.

dataset/distribution /host/url

How often backups will be performed.

dataset/distribution /host/backup_type

The use of robust, managed storage with automatic backup (for example, storage provided by the home institution).

dataset/distribution /host/backup_frequency, dataset/distribution /host/backup_type

Explains why institutional storage will not be used (and for what part of the data) and describes the (additional) locations, storage media, and procedures that will be used for storing and backing up data during the project.

dataset/distribution /host/description, dataset/distributions /host/url , dataset/distribution /access_url

How will data security and protection of sensitive data be taken care of during the research?

How the data will be recovered in the event of a technical incident.

dataset/distribution /host/title, dataset/distribution /host/description,

dataset/distribution /host/recovery_plan
Subsection: IV.2 Data sharing and long-term preservation

Question or Requirement information

Evaluation Rubric

DCS

OSTrails AP

How and when will the data be shared? Are there restrictions to data sharing or embargo reasons?

Clearly describes how and when the data will be made discoverable and shared.

dataset/distroibution/, dataset/distributions/host/, dataset/distribution /license/start_date, dataset/issued

In which repository will the data be archived and made available for re-use? What persistent identifier (e.g., DOI) and which usage licence (e.g., CC BY) will be used?

Specifies a repository for data re-use and explains which persistent identifiers (PIDs) are provided for the data and under which licence the data will be made available. (see FWF Open Access Policy for Research Data).

dataset/distribution /host/url, dataset/distribution /licenselicense_ref

Clearly explains, if applicable, why data sharing is limited or not possible, and who can access the data under which conditions (for example, only members of certain communities or via a sharing agreement).

dataset/security_and _privacy, dataset/personal_data, dataset/sensitive_data

Explains what actions will be taken to overcome or to minimise data sharing restrictions.

dataset/distribution /license/start_date

What methods and software tools are needed to access and use the data?

Clearly indicates which specific tools or software (for example, specific scripts, codes, or algorithms developed during the project, version of the software) potential users may need to access, interpret, and (re-) use the data.

dataset/technical_resouce/*

Provides details on how the data, accompanying documentation, and any other required technology such as copies of software in specific versions will be archived in the long term.

dataset/distribution/host/url

How will data for preservation be selected, and where will the data be preserved long-term?

Provides details of which (versions of) data and accompanying documentation will be retained or destroyed, and explains the rationale (for example, contractual, legal requirements, or regulatory purposes).

dataset/preservation_statement

dmp/related_policy

Provides details of what data collected or created in the project will be preserved in the long term and clearly indicates for how long. This should be in alignment with institutional, or national policies and/or legislation, or community standards.

dataset/preservation_statement, dataset/distribution /license/start_date, dataset/data_quality_assurance