https://datacitation.openconcept.no/Recommendations-for-Data-Repositories

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        <h3>Recommendation 1 – Request data to be cited</h3>\n
        \n
        <p>Data repositories should request that data are cited whenever they are used or mentioned. Citing data is a sound scholarly practice. Enforcing the citation of data relies on contracts, licences, terms of use, or suggested best practices. Requesting data to be cited could also be stated in related documents included in the data package download folder, such as user terms and conditions, codebooks and data reports.&nbsp;</p>\n
        \n
        <h3><a name="_e5pmhf4701mt"></a>Recommendation 2 – Provide all six core data citation components</h3>\n
        \n
        <p>Data repositories should ensure that all data published are citable by providing at least the core components of a data citation, as listed above. Data repositories should check the quality of the metadata useful for the citation (e.g., explicit dataset title). Moreover, they are responsible for assigning a PID and providing the version (or publication date) for published datasets. Finally, for embargoed data, data repositories should provide a PID that allows data authors to cite the data in their research article. Reservation of a PID before the data are accessible should therefore be possible.</p>\n
        \n
        <h3><a name="_r5tv0kls4gs3"></a>Recommendation 3 – Provide data versioning and access information</h3>\n
        \n
        <p>Data citations should include the version of a dataset (or a publication date) to ensure reproducibility. Therefore, data users may need to access specific versions of datasets, and information about changes and versions of data should be available. This could be provided through specific PIDs for each version of the dataset and/or a changelog, or versioning information text for concept PIDs. If a dataset or version of a dataset is no longer available, the PID assigned in the data citation should still lead to metadata about the dataset on the repository websites (e.g., a tombstone page). Moreover, data repositories should ensure access to metadata about all published versions of a dataset.</p>\n
        \n
        <h3><a name="_wrpfy536syly"></a>Recommendation 4 – Provide data citation suggestions including all six core components</h3>\n
        \n
        <p>User-friendly access to citation information for each dataset should be provided on the repository website. The data repositories should provide a data citation suggestion for each dataset. This suggestion should always include at least the six core components, and be visually noticeable. Providing this citation in multiple formats (e.g., APA, Chicago, and Harvard) is considered a good practice.</p>\n
        \n
        <h3><a name="_4syaly57w3ip"></a>Recommendation 5 – Facilitate data citation for authors through export and download of metadata for reference management software</h3>\n
        \n
        <p>Data repositories should make it easy for users to automatically export and download data citation core components to reference management software. This will ensure that data can be cited in the same manner as other sources (such as journal articles, books, etc.) (Bornatici &amp; Fedrigo, 2023). It is recommended that data repositories use formats that are compatible with commonly used reference management software (e.g., Endnote, Mendeley and Zotero), such as the RIS format (Research Information System Document). These files should include at least the six core components needed to build a data citation.</p>\n
        \n
        <h3><a name="_p44ak2lcxv4v"></a>Recommendation 6 – Make citation information and other publication metadata accessible through generic structured metadata files</h3>\n
        \n
        <p>For the implementation of the FAIR principles, it is important to make sure that the six core components of a data citation are openly accessible together with other relevant information about the data publication as linked open metadata, thus providing a structured way of accessing a description of the data.</p>\n
        \n
        <p>At least one generic metadata container should be provided with the data publication in a standardised and machine-actionable linked data format, for example, JSON-LD or RDF/XML. The actual metadata standards implemented may vary, but should at the very least cover the basic needs of a data repository. An example of this is the DCAT standard (Albertoni et al., 2020). In specialised repositories, the schema used may depend on what relevant community standards are in use, for example, DDI-Lifecycle for the social sciences (DDI Alliance, 2024).</p>\n
        \n
        <p>In addition to metadata standards established specifically for describing data, it is also advisable to provide metadata files or tags that use a generic metadata schema such as Schema.org (Payne &amp; Verhey, 2022). This makes it more likely that infrastructures that are not specialised in research data will be able to reuse the six core components of a data citation and other information.</p>\n
        \n
        <p>The metadata contents should reflect community standards and needs, while also providing generic information such as details on access methods, licensing and conditions for data reuse. Ideally, it should provide all structured information included in the data repository entry itself.</p>\n
        \n
        <h3><a name="_172gzfirb165"></a>Recommendation 7 – Make data publications and relations to other entities machine-actionable through rich metadata and PIDs</h3>\n
        \n
        <p>In addition to the six core components of a data citation, the generic metadata containers should express relations between the data publication and other entities, using machine-actionable PIDs when possible. Examples of such PIDs and entities are ORCID for expressing relations to individuals, such as data authors and other contributors, ROR ID for expressing relations to organisations, such as research institutions, infrastructures or funders and DOI for expressing relations to publications and other digital objects. An example of expressing relations to publications would be including PIDs of publications citing the data as relations of a specific “cited by” type in the metadata. Such relations may also be enhanced over time by external metadata contributors.</p>\n
        \n
        <p>Data may be published with different hierarchical relations and varying granularity. Whenever possible, this should be expressed with PIDs and machine-actionable metadata. For example, a DOI could be provided pointing to the conceptual level of a data publication, with other DOIs pointing to specific versions or subsets of the data publication. In such cases, the latter type of PID would most often be the appropriate choice for inclusion in a data citation.</p>\n
        \n
        <h3><a name="_6uaupg3xw3"></a>Recommendation 8 –&nbsp; Raise awareness about data citation</h3>\n
        \n
        <p>Data repositories should inform data users on data citation best practices, for example through their general information materials or accompanying dataset documentation. Data repositories should also target data producers specifically and encourage them to cite their data and report the shared datasets alongside traditional research outputs. Similarly, data repositories should suggest to research institutions to include data collection and sharing, as well as data citations, in their research assessment process. In addition to creating specific documentation and teaching materials, seminars and presentations could be conducted to inform different stakeholders like researchers, students, institutions, funders, and journals on best practices. Data repositories could thus emphasise the benefits of correct data citation, such as aligning with open data principles and fostering better research practices. Recognising data authors for sharing data gives them more incentives to share their data, which ultimately increases repository content.</p>\n
        """
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        <h3>Recommendation 1 – Request data to be cited</h3>\n
        \n
        <p>Data repositories should request that data are cited whenever they are used or mentioned. Citing data is a sound scholarly practice. Enforcing the citation of data relies on contracts, licences, terms of use, or suggested best practices. Requesting data to be cited could also be stated in related documents included in the data package download folder, such as user terms and conditions, codebooks and data reports.&nbsp;</p>\n
        \n
        <h3><a name="_e5pmhf4701mt"></a>Recommendation 2 – Provide all six core data citation components</h3>\n
        \n
        <p>Data repositories should ensure that all data published are citable by providing at least the core components of a data citation, as listed above. Data repositories should check the quality of the metadata useful for the citation (e.g., explicit dataset title). Moreover, they are responsible for assigning a PID and providing the version (or publication date) for published datasets. Finally, for embargoed data, data repositories should provide a PID that allows data authors to cite the data in their research article. Reservation of a PID before the data are accessible should therefore be possible.</p>\n
        \n
        <h3><a name="_r5tv0kls4gs3"></a>Recommendation 3 – Provide data versioning and access information</h3>\n
        \n
        <p>Data citations should include the version of a dataset (or a publication date) to ensure reproducibility. Therefore, data users may need to access specific versions of datasets, and information about changes and versions of data should be available. This could be provided through specific PIDs for each version of the dataset and/or a changelog, or versioning information text for concept PIDs. If a dataset or version of a dataset is no longer available, the PID assigned in the data citation should still lead to metadata about the dataset on the repository websites (e.g., a tombstone page). Moreover, data repositories should ensure access to metadata about all published versions of a dataset.</p>\n
        \n
        <h3><a name="_wrpfy536syly"></a>Recommendation 4 – Provide data citation suggestions including all six core components</h3>\n
        \n
        <p>User-friendly access to citation information for each dataset should be provided on the repository website. The data repositories should provide a data citation suggestion for each dataset. This suggestion should always include at least the six core components, and be visually noticeable. Providing this citation in multiple formats (e.g., APA, Chicago, and Harvard) is considered a good practice.</p>\n
        \n
        <h3><a name="_4syaly57w3ip"></a>Recommendation 5 – Facilitate data citation for authors through export and download of metadata for reference management software</h3>\n
        \n
        <p>Data repositories should make it easy for users to automatically export and download data citation core components to reference management software. This will ensure that data can be cited in the same manner as other sources (such as journal articles, books, etc.) (Bornatici &amp; Fedrigo, 2023). It is recommended that data repositories use formats that are compatible with commonly used reference management software (e.g., Endnote, Mendeley and Zotero), such as the RIS format (Research Information System Document). These files should include at least the six core components needed to build a data citation.</p>\n
        \n
        <h3><a name="_p44ak2lcxv4v"></a>Recommendation 6 – Make citation information and other publication metadata accessible through generic structured metadata files</h3>\n
        \n
        <p>For the implementation of the FAIR principles, it is important to make sure that the six core components of a data citation are openly accessible together with other relevant information about the data publication as linked open metadata, thus providing a structured way of accessing a description of the data.</p>\n
        \n
        <p>At least one generic metadata container should be provided with the data publication in a standardised and machine-actionable linked data format, for example, JSON-LD or RDF/XML. The actual metadata standards implemented may vary, but should at the very least cover the basic needs of a data repository. An example of this is the DCAT standard (Albertoni et al., 2020). In specialised repositories, the schema used may depend on what relevant community standards are in use, for example, DDI-Lifecycle for the social sciences (DDI Alliance, 2024).</p>\n
        \n
        <p>In addition to metadata standards established specifically for describing data, it is also advisable to provide metadata files or tags that use a generic metadata schema such as Schema.org (Payne &amp; Verhey, 2022). This makes it more likely that infrastructures that are not specialised in research data will be able to reuse the six core components of a data citation and other information.</p>\n
        \n
        <p>The metadata contents should reflect community standards and needs, while also providing generic information such as details on access methods, licensing and conditions for data reuse. Ideally, it should provide all structured information included in the data repository entry itself.</p>\n
        \n
        <h3><a name="_172gzfirb165"></a>Recommendation 7 – Make data publications and relations to other entities machine-actionable through rich metadata and PIDs</h3>\n
        \n
        <p>In addition to the six core components of a data citation, the generic metadata containers should express relations between the data publication and other entities, using machine-actionable PIDs when possible. Examples of such PIDs and entities are ORCID for expressing relations to individuals, such as data authors and other contributors, ROR ID for expressing relations to organisations, such as research institutions, infrastructures or funders and DOI for expressing relations to publications and other digital objects. An example of expressing relations to publications would be including PIDs of publications citing the data as relations of a specific “cited by” type in the metadata. Such relations may also be enhanced over time by external metadata contributors.</p>\n
        \n
        <p>Data may be published with different hierarchical relations and varying granularity. Whenever possible, this should be expressed with PIDs and machine-actionable metadata. For example, a DOI could be provided pointing to the conceptual level of a data publication, with other DOIs pointing to specific versions or subsets of the data publication. In such cases, the latter type of PID would most often be the appropriate choice for inclusion in a data citation.</p>\n
        \n
        <h3><a name="_6uaupg3xw3"></a>Recommendation 8 –&nbsp; Raise awareness about data citation</h3>\n
        \n
        <p>Data repositories should inform data users on data citation best practices, for example through their general information materials or accompanying dataset documentation. Data repositories should also target data producers specifically and encourage them to cite their data and report the shared datasets alongside traditional research outputs. Similarly, data repositories should suggest to research institutions to include data collection and sharing, as well as data citations, in their research assessment process. In addition to creating specific documentation and teaching materials, seminars and presentations could be conducted to inform different stakeholders like researchers, students, institutions, funders, and journals on best practices. Data repositories could thus emphasise the benefits of correct data citation, such as aligning with open data principles and fostering better research practices. Recognising data authors for sharing data gives them more incentives to share their data, which ultimately increases repository content.</p>\n
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          <h3>Recommendation 1 – Request data to be cited</h3>\n
          \n
          <p>Data repositories should request that data are cited whenever they are used or mentioned. Citing data is a sound scholarly practice. Enforcing the citation of data relies on contracts, licences, terms of use, or suggested best practices. Requesting data to be cited could also be stated in related documents included in the data package download folder, such as user terms and conditions, codebooks and data reports.&nbsp;</p>\n
          \n
          <h3><a name="_e5pmhf4701mt"></a>Recommendation 2 – Provide all six core data citation components</h3>\n
          \n
          <p>Data repositories should ensure that all data published are citable by providing at least the core components of a data citation, as listed above. Data repositories should check the quality of the metadata useful for the citation (e.g., explicit dataset title). Moreover, they are responsible for assigning a PID and providing the version (or publication date) for published datasets. Finally, for embargoed data, data repositories should provide a PID that allows data authors to cite the data in their research article. Reservation of a PID before the data are accessible should therefore be possible.</p>\n
          \n
          <h3><a name="_r5tv0kls4gs3"></a>Recommendation 3 – Provide data versioning and access information</h3>\n
          \n
          <p>Data citations should include the version of a dataset (or a publication date) to ensure reproducibility. Therefore, data users may need to access specific versions of datasets, and information about changes and versions of data should be available. This could be provided through specific PIDs for each version of the dataset and/or a changelog, or versioning information text for concept PIDs. If a dataset or version of a dataset is no longer available, the PID assigned in the data citation should still lead to metadata about the dataset on the repository websites (e.g., a tombstone page). Moreover, data repositories should ensure access to metadata about all published versions of a dataset.</p>\n
          \n
          <h3><a name="_wrpfy536syly"></a>Recommendation 4 – Provide data citation suggestions including all six core components</h3>\n
          \n
          <p>User-friendly access to citation information for each dataset should be provided on the repository website. The data repositories should provide a data citation suggestion for each dataset. This suggestion should always include at least the six core components, and be visually noticeable. Providing this citation in multiple formats (e.g., APA, Chicago, and Harvard) is considered a good practice.</p>\n
          \n
          <h3><a name="_4syaly57w3ip"></a>Recommendation 5 – Facilitate data citation for authors through export and download of metadata for reference management software</h3>\n
          \n
          <p>Data repositories should make it easy for users to automatically export and download data citation core components to reference management software. This will ensure that data can be cited in the same manner as other sources (such as journal articles, books, etc.) (Bornatici &amp; Fedrigo, 2023). It is recommended that data repositories use formats that are compatible with commonly used reference management software (e.g., Endnote, Mendeley and Zotero), such as the RIS format (Research Information System Document). These files should include at least the six core components needed to build a data citation.</p>\n
          \n
          <h3><a name="_p44ak2lcxv4v"></a>Recommendation 6 – Make citation information and other publication metadata accessible through generic structured metadata files</h3>\n
          \n
          <p>For the implementation of the FAIR principles, it is important to make sure that the six core components of a data citation are openly accessible together with other relevant information about the data publication as linked open metadata, thus providing a structured way of accessing a description of the data.</p>\n
          \n
          <p>At least one generic metadata container should be provided with the data publication in a standardised and machine-actionable linked data format, for example, JSON-LD or RDF/XML. The actual metadata standards implemented may vary, but should at the very least cover the basic needs of a data repository. An example of this is the DCAT standard (Albertoni et al., 2020). In specialised repositories, the schema used may depend on what relevant community standards are in use, for example, DDI-Lifecycle for the social sciences (DDI Alliance, 2024).</p>\n
          \n
          <p>In addition to metadata standards established specifically for describing data, it is also advisable to provide metadata files or tags that use a generic metadata schema such as Schema.org (Payne &amp; Verhey, 2022). This makes it more likely that infrastructures that are not specialised in research data will be able to reuse the six core components of a data citation and other information.</p>\n
          \n
          <p>The metadata contents should reflect community standards and needs, while also providing generic information such as details on access methods, licensing and conditions for data reuse. Ideally, it should provide all structured information included in the data repository entry itself.</p>\n
          \n
          <h3><a name="_172gzfirb165"></a>Recommendation 7 – Make data publications and relations to other entities machine-actionable through rich metadata and PIDs</h3>\n
          \n
          <p>In addition to the six core components of a data citation, the generic metadata containers should express relations between the data publication and other entities, using machine-actionable PIDs when possible. Examples of such PIDs and entities are ORCID for expressing relations to individuals, such as data authors and other contributors, ROR ID for expressing relations to organisations, such as research institutions, infrastructures or funders and DOI for expressing relations to publications and other digital objects. An example of expressing relations to publications would be including PIDs of publications citing the data as relations of a specific “cited by” type in the metadata. Such relations may also be enhanced over time by external metadata contributors.</p>\n
          \n
          <p>Data may be published with different hierarchical relations and varying granularity. Whenever possible, this should be expressed with PIDs and machine-actionable metadata. For example, a DOI could be provided pointing to the conceptual level of a data publication, with other DOIs pointing to specific versions or subsets of the data publication. In such cases, the latter type of PID would most often be the appropriate choice for inclusion in a data citation.</p>\n
          \n
          <h3><a name="_6uaupg3xw3"></a>Recommendation 8 –&nbsp; Raise awareness about data citation</h3>\n
          \n
          <p>Data repositories should inform data users on data citation best practices, for example through their general information materials or accompanying dataset documentation. Data repositories should also target data producers specifically and encourage them to cite their data and report the shared datasets alongside traditional research outputs. Similarly, data repositories should suggest to research institutions to include data collection and sharing, as well as data citations, in their research assessment process. In addition to creating specific documentation and teaching materials, seminars and presentations could be conducted to inform different stakeholders like researchers, students, institutions, funders, and journals on best practices. Data repositories could thus emphasise the benefits of correct data citation, such as aligning with open data principles and fostering better research practices. Recognising data authors for sharing data gives them more incentives to share their data, which ultimately increases repository content.</p>\n
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          <h3>Recommendation 1 – Request data to be cited</h3>\n
          \n
          <p>Data repositories should request that data are cited whenever they are used or mentioned. Citing data is a sound scholarly practice. Enforcing the citation of data relies on contracts, licences, terms of use, or suggested best practices. Requesting data to be cited could also be stated in related documents included in the data package download folder, such as user terms and conditions, codebooks and data reports.&nbsp;</p>\n
          \n
          <h3><a name="_e5pmhf4701mt"></a>Recommendation 2 – Provide all six core data citation components</h3>\n
          \n
          <p>Data repositories should ensure that all data published are citable by providing at least the core components of a data citation, as listed above. Data repositories should check the quality of the metadata useful for the citation (e.g., explicit dataset title). Moreover, they are responsible for assigning a PID and providing the version (or publication date) for published datasets. Finally, for embargoed data, data repositories should provide a PID that allows data authors to cite the data in their research article. Reservation of a PID before the data are accessible should therefore be possible.</p>\n
          \n
          <h3><a name="_r5tv0kls4gs3"></a>Recommendation 3 – Provide data versioning and access information</h3>\n
          \n
          <p>Data citations should include the version of a dataset (or a publication date) to ensure reproducibility. Therefore, data users may need to access specific versions of datasets, and information about changes and versions of data should be available. This could be provided through specific PIDs for each version of the dataset and/or a changelog, or versioning information text for concept PIDs. If a dataset or version of a dataset is no longer available, the PID assigned in the data citation should still lead to metadata about the dataset on the repository websites (e.g., a tombstone page). Moreover, data repositories should ensure access to metadata about all published versions of a dataset.</p>\n
          \n
          <h3><a name="_wrpfy536syly"></a>Recommendation 4 – Provide data citation suggestions including all six core components</h3>\n
          \n
          <p>User-friendly access to citation information for each dataset should be provided on the repository website. The data repositories should provide a data citation suggestion for each dataset. This suggestion should always include at least the six core components, and be visually noticeable. Providing this citation in multiple formats (e.g., APA, Chicago, and Harvard) is considered a good practice.</p>\n
          \n
          <h3><a name="_4syaly57w3ip"></a>Recommendation 5 – Facilitate data citation for authors through export and download of metadata for reference management software</h3>\n
          \n
          <p>Data repositories should make it easy for users to automatically export and download data citation core components to reference management software. This will ensure that data can be cited in the same manner as other sources (such as journal articles, books, etc.) (Bornatici &amp; Fedrigo, 2023). It is recommended that data repositories use formats that are compatible with commonly used reference management software (e.g., Endnote, Mendeley and Zotero), such as the RIS format (Research Information System Document). These files should include at least the six core components needed to build a data citation.</p>\n
          \n
          <h3><a name="_p44ak2lcxv4v"></a>Recommendation 6 – Make citation information and other publication metadata accessible through generic structured metadata files</h3>\n
          \n
          <p>For the implementation of the FAIR principles, it is important to make sure that the six core components of a data citation are openly accessible together with other relevant information about the data publication as linked open metadata, thus providing a structured way of accessing a description of the data.</p>\n
          \n
          <p>At least one generic metadata container should be provided with the data publication in a standardised and machine-actionable linked data format, for example, JSON-LD or RDF/XML. The actual metadata standards implemented may vary, but should at the very least cover the basic needs of a data repository. An example of this is the DCAT standard (Albertoni et al., 2020). In specialised repositories, the schema used may depend on what relevant community standards are in use, for example, DDI-Lifecycle for the social sciences (DDI Alliance, 2024).</p>\n
          \n
          <p>In addition to metadata standards established specifically for describing data, it is also advisable to provide metadata files or tags that use a generic metadata schema such as Schema.org (Payne &amp; Verhey, 2022). This makes it more likely that infrastructures that are not specialised in research data will be able to reuse the six core components of a data citation and other information.</p>\n
          \n
          <p>The metadata contents should reflect community standards and needs, while also providing generic information such as details on access methods, licensing and conditions for data reuse. Ideally, it should provide all structured information included in the data repository entry itself.</p>\n
          \n
          <h3><a name="_172gzfirb165"></a>Recommendation 7 – Make data publications and relations to other entities machine-actionable through rich metadata and PIDs</h3>\n
          \n
          <p>In addition to the six core components of a data citation, the generic metadata containers should express relations between the data publication and other entities, using machine-actionable PIDs when possible. Examples of such PIDs and entities are ORCID for expressing relations to individuals, such as data authors and other contributors, ROR ID for expressing relations to organisations, such as research institutions, infrastructures or funders and DOI for expressing relations to publications and other digital objects. An example of expressing relations to publications would be including PIDs of publications citing the data as relations of a specific “cited by” type in the metadata. Such relations may also be enhanced over time by external metadata contributors.</p>\n
          \n
          <p>Data may be published with different hierarchical relations and varying granularity. Whenever possible, this should be expressed with PIDs and machine-actionable metadata. For example, a DOI could be provided pointing to the conceptual level of a data publication, with other DOIs pointing to specific versions or subsets of the data publication. In such cases, the latter type of PID would most often be the appropriate choice for inclusion in a data citation.</p>\n
          \n
          <h3><a name="_6uaupg3xw3"></a>Recommendation 8 –&nbsp; Raise awareness about data citation</h3>\n
          \n
          <p>Data repositories should inform data users on data citation best practices, for example through their general information materials or accompanying dataset documentation. Data repositories should also target data producers specifically and encourage them to cite their data and report the shared datasets alongside traditional research outputs. Similarly, data repositories should suggest to research institutions to include data collection and sharing, as well as data citations, in their research assessment process. In addition to creating specific documentation and teaching materials, seminars and presentations could be conducted to inform different stakeholders like researchers, students, institutions, funders, and journals on best practices. Data repositories could thus emphasise the benefits of correct data citation, such as aligning with open data principles and fostering better research practices. Recognising data authors for sharing data gives them more incentives to share their data, which ultimately increases repository content.</p>\n
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