Best practice recommendations for end users on PID usage & implementation

This section comprises the core content of this report with recommendations organised across nine categories based on their respective topics, guiding end users in their PID practices. The recommendations are derived from use case engagements and align with the key areas of focus explored in those cases. All nine categories of recommendations are presented in a short introductory paragraph, followed by a table. The table helps address the questions ‘what’ - ‘why’ - ‘for whom’ - ‘about’ - ‘from where’. In other words, what is the recommendation, why is this recommendation part of good research practices, for whom is the recommendation for, what is the recommendation about as defined in keywords and lastly, from where has this recommendation been derived.

A PID is applied to a clearly defined digital or a digital representation of an object so that it can be referred to. The recommendations below apply to the infrastructure and practices necessary for using a PID. However, other aspects of PID management must consider the digital objects' lifecycles and how they are changed for research, deposit, curation, preservation, and reuse. Each change to the content defined as part of a digital object (e.g., data, metadata, and associated documentation) becomes a part of its provenance. Based on the impact of those changes on the object, its version and PID should be described and detailed with sufficient and appropriate information. This is particularly important as various actors may apply different versions and criteria for when a new PID is needed.

1. Identifiers for different Object Types and Purposes

In research, different types of objects require specific identifiers to ensure clarity, provenance, and long-term accessibility. People involved in research processes should be identified using ORCID, a system designed specifically for unambiguous and persistent identification of individuals. For software, SWHID is the preferred identifier, recognising the critical role software plays in data collection, analysis, and dissemination. Unlike data, software is an executable tool with complex architecture and an evolving lifecycle, making traditional data identification methods unsuitable. Additionally, any data referenced in a scientific knowledge graph should have a dereferenceable URI alongside existing PIDs. This allows researchers to create unique, accessible URIs for all resources within a project, ensuring better data management and interoperability.

Recommendation - What?	Problem Statement -Why?	Stakeholder affected - for whom?	Keywords - About?	Source - From where?¹
A: Identifiers for different Object Types and Purposes
A1. Use ORCID to identify people involved in research processes, as ORCID is specially designed for identifying people.	To ensure provenance, people need to be persistently and unambiguously identified.	Researcher	Identification of people	B²
A2: Use SWHID for identifying software.	“If you don’t have the software, you don’t have the data³” : digital data is collected, created, visualised, analysed, interpreted, and disseminated via software. Archiving and referencing (pieces of) software source code is a prerequisite for more robust data workflows. Solutions developed for data are not appropriate for software, as there are significant differences between these outputs (EOSC Executive Board & EOSC Secretariat, 2020⁴): Software is an executable tool The software relies on a very complex architecture Software may have a very complex lifecycle: from a “one shot” project to software that will evolve over several decades	Research Software Contributor Research Software User	Identification of software antifacts Referencing Traceability	C⁵
A3: Assign a dereferenceable URI⁶ to any data referenced in a scientific knowledge graph, in addition to existing PIDs.	The dereferenceable URI allows researchers to reserve a root URI. Based on this root URI, researchers can create unique URIs (equivalent to an http domain name) for all resources produced/used within a project.	Research Software Engineer to provide services that support the use of dereferenceable URIs Researcher to make use of dereferenceable URIs	Scientific knowledge graph Exposure to structured, interlinked, and semantically rich research resources	D⁷

2. Workflows and Scientific Reproducibility Practices

Ensuring scientific reproducibility requires well-documented and accessible computational workflows. Researchers should register their workflows in established repositories, such as the WorkflowHub and Galaxy, recognising them as valuable scholarly outputs that encompass diverse research materials. The Signposting approach enables precise referencing by linking specific parts of an HTML page to PIDs, improving navigation and reproducibility. Additionally, integrating SWHIDs into journals and repositories strengthens research software identification, ensuring the integrity of source code and allowing citation of entire programs or specific components. This integration enhances Software Heritage coverage, improves archival reliability, and enriches the services available to researchers, ultimately supporting more robust scientific reproducibility practices.

Recommendation - What?	Problem statement - Why?	Stakeholder affected - For whom? Keywords - About?	Keywords - About?	Source - From where?⁸
B: Workflows and Scientific Reproducibility Practices
B1: Register computational workflows in established registries and repositories (e.g., WorkflowHub⁹, Galaxy¹⁰).	Registering computational workflows furthers scientific reproducibility. Computational workflows can themselves be considered scholarly products, encompassing various types of research outputs (PDFs, text files, images, audio, etc.).	Data Owner Data Depositor Repository and Registry Managers	Computational workflows	E¹¹
B2: Use the Signposting approach¹² to allow navigation from an HTML landing page to a specific resource part, where PIDs are leveraged in the redirection.	Allows referencing to a specific part of an HTML page. More precise and targeted references increase the reproducibility of research.	Researcher	Computational workflows Referencing subsets of resources More targeted	F¹³
B3: Integrate SWHIDs into existing workflows within journals and repositories.	Integration of SWHIDs cater for robust and precise identification of research software, which enriches the services offered to the end users and supports scientific reproducible practices. Software identification calls for specific tooling. One should be able to cite the whole or parts of a source code. The integrity of the source code should be ensured: An external identifier may point to an object that may be altered without any notification of the change, whereas an intrinsic identifier reflects such modifications. Such integration improves Software Heritage coverage by pushing content directly into the archive. Enhancing end users’ set of services and scientific reproducibility.	Service Provider	Software deposit Intrinsic identifier Interconnected and interoperable academic ecosystem Referencing software artifacts More exact identification	P¹⁴

3. Versioning Practices

In some cases, any change to the bits comprising an object (as defined through integrity measures such as a checksum) may trigger the creation of a new PID. In other environments, changes considered to be minor (e.g., a spelling correction in metadata or documentation) may only be recorded through the ‘change log’ metadata within a PID. Defining and communicating the approach to change, versioning, and minting new PIDs for derived digital objects becomes a dependency for applying the more dynamic recommendations below, including granularity, citation, and maintenance.

Recommendation - What?	Problem statement - Why?	Stakeholder affected - For whom?	Keywords - About?	Source - From where?¹⁵
C: Versioning Practices
C1: Develop a versioning policy/procedure of PIDs and referenced objects that provides clear guidance on: Who is allowed to request changes; Who will record these changes; and Where these changes are recorded. Recommendations C1-C4 should be considered and applied jointly.	How (which rules and by whom) versioning is handled must be clear to ensure the integrity of the data and documentation material by maintaining a structured history of changes, enabling traceability, and preventing data loss or corruption.	Data Owner Data Depositor Repository Manager	Versioning policy	L¹⁶
C2: Communicate the boundaries constituting a minor change or a significant change.	Significant and/or minor changes may lead to other versioning actions, such as generating a new PID.	Data Owner Data Depositor Data Administrator	Data augmentation	L
C3: Check if the repository communicates whether a significant change (and/or a minor change) leads to generating a new PID.	Proper versioning practices help improve the reproducibility of research as, if cited correctly, researchers can see which version of the data was accessed for which analysis. A new PID resolves to a complete version history of the object system, supports resource discovery, and simplifies citations for data collection users. Data producers also benefit directly through the increased visibility of their work.	Data Owner Data Depositor	Versioning instructions	N¹⁷
C4: Communicate file naming conventions clearly and apply them across all copies and versions in all formats and locations.	It must be clear to all parties what the most recent data version is. A file naming convention is a structured and consistent approach to naming files to improve organisation, traceability, and reproducibility of research data. A well-designed convention helps researchers quickly identify file contents, version history, and relationships between files.	Data Owner Data Depositor Data Curator	File naming convention	M¹⁸

4. Data Granularity Practices

Effective versioning practices ensure that data is structured at the right level of detail, balancing usability, efficiency, and reproducibility. The appropriate data granularity depends on the type of data and its intended re-use, enabling accurate citation, validation, and interoperability. Machine-actionable identifiers further enhance data discoverability by allowing citations at various levels of granularity, from entire datasets to specific subsets. Research communities often establish best practices for granularity, ensuring consistency and credibility across studies. Additionally, PIDs should be assigned to subsets of datasets, workflows, and software components to guarantee provenance and recognition for contributors. Standards like W3C PROV help maintain context and traceability, particularly in complex research environments where datasets are combined and analysed. By adhering to these principles, researchers enhance reproducibility, improve data citation, and facilitate more precise referencing of digital resources, including audiovisual content and software artifacts.

Recommendation - What?	Problem statement - Why?	Stakeholder affected - For whom?	Keywords - About?	Source - From where?¹⁹
D: Data Granularity Practices
D1: Consider what best serves the needs of potential re-use. The correct data granularity level also depends on the data type at hand.	Proper data granularity practices ensure that data is structured at the appropriate level of detail, balancing usability, efficiency, and reproducibility. This will lead to enhanced data reusability and interoperability, e.g. through more accurate data interpretation and increased computational performance and analysis, but also data validation.	Researcher	Granularity Re-usability	G²⁰
D2: Enable more machine-actionability of the data by using machine-actionable identifiers. This makes citation at various levels of granularity possible, even to the most fine-grained level.	Unlike people, machines can deal with very high levels of granularity and support discovery, accessibility, and interoperability.	Researcher	Granularity Machine-actionability	G
D3: Allow for citation at several levels: The semantic artefact The versioning of entities inside the artefact Linking between deprecated and valid entities	Large datasets associated with satellites (or other instruments) commonly have good data management plans and DOIs that can be referenced. However, the entire dataset is not widely used in any research effort. Navigating different levels of granularity and accurately identifying the specific data used is crucial for ensuring the citation of subsets of the datasets and, thus, reproducibility.	Researcher	Exact data citation Granularity Versioning Semantic artefact	G
D4: Find out if your research community has defined granularity practices and adhere to them if available. These practices are often determined through consensus or the best practices shared within the community.	Some research areas have agreed-upon guidelines or standards for how detailed or broad data or findings should be presented. For example, in fields like data science or machine learning, researchers may have standards on granular data, e.g., whether to use raw or aggregated data. If such standards or practices have been established, it is essential to follow them to ensure consistency, reliability, and alignment with peers' expectations. Following these practices can improve the credibility of findings and make research comparable with others in the field.	Research community Researcher	Version granularity	G
D5: Use handles to refer to a specific part of a lengthy video, as handles can address video fragments.	This procedure allows very exact citations to specific parts of audiovisual resources.	Researcher	Citation granularity Audiovisual resource	G
D6: Assign a PID and new metadata to the subsets of a dataset used in an analysis to guarantee the provenance of the resulting data. This metadata needs to contain both context and provenance information.	In the Virtual Research Environment, a dataset can be filtered and composed from multiple other datasets and analysed by services, resulting in a new dataset. Ensuring the provenance of this new dataset is essential for providing recognition to all involved research contributors. Recommended standards, such as the World Wide Web Consortium (W3C) PROV²¹, are available.	Researcher	Dataset granularity Research analysis Metadata Provenance	G
D7: Assign a PID to collections and related components. A workflow is a collection that also contains executables.	Data-centric solutions may not adequately address the unique requirements of different software components. It is worth noting that software can be compared to a "data type", and once the source code is converted into binaries, it then turns into an "executable"²²	Researcher	Workflow granularity Software	G

5. Complex Data Citation Practices

Accurate and consistent citation is crucial for research transparency, particularly when handling complex data. To maintain consistency, only one PID of the same type, such as a DOI, should be assigned to each unique digital object. Assigning multiple PIDs of the same type can lead to fragmentation and errors in citation matching. It’s also essential to properly credit contributors using systems like the Contributor Role Taxonomy (CRediT) to ensure clear attribution within research teams. Dynamic citations, such as those using tools like Zotero or OpenCitations, allow for flexibility in citing evolving datasets and resources. By incorporating versioning and linking multiple identifiers to the same object, researchers can track the history of the data and ensure they are referencing the correct version, enhancing accessibility and reproducibility. Additionally, linking research objects when multiple identifiers exist ensures better discoverability and citation consistency, especially when datasets are updated or modified over time.

Recommendation - What?	Problem statement - Why?	Stakeholder affected - For whom?	Keywords - About?	Source - From where?²³
E: Complex Data Citation Practices
E1: Only assign one PID of the same type for each unique digital object to allow consistent citations.	If several PIDs of the same type (e.g., DOIs) are assigned per record, they become less likely to be matched correctly. Fragmentation of citation records undermines the effectiveness of PIDs, which are designed to ensure accurate and consistent referencing.	Researcher	Data citation	A²⁴
E2: Use Contributor Role Taxonomy (CRediT)²⁵ to cater for proper accreditation within a research group or project.	Proper citation, no matter how complex, ensures research transparency. This makes it easier for researchers to be as accurate as possible in identifying the datasets or partial datasets used in their research. Clearly defining roles at the start of a research project and making adjustments as needed throughout can help streamline the process, reducing confusion and minimising the risk of disputes during the reporting and writing stages.	Researcher	Data citation Contribution roles	G
E3: Make use of dynamic citations in cases where the source or reference material might change or be updated over time, e.g., by making use of Zotero²⁶ or OpenCitations²⁷.	Collections are living objects, i.e., they change depending on needs. A dynamic citation is flexible and adaptable, typically used in contexts where the source or reference material might change or be updated over time.	Researcher	Dynamic citation	G
E4: Cite dynamic data dynamically via query: data + timestamp or TMS (time management system) when it follows a versioned history.	Using the query to identify the dataset provides valuable semantic information on how the dataset was constructed, rather than just having a raw data dump. Additionally, scalable dynamic data citation enables users to re-execute the query with the original timestamp to retrieve the original data while accessing the latest version with all updates and changes. This allows for a comparison of the resulting differences. This approach is supported by the RDA Data Citation WG²⁸.	Researcher	Dynamic citation Query	G
E5: Add a link between the research objects if multiple identifiers point to the same object. Versioning adds a layer of complexity.	Linking multiple identifiers to the same object ensures that researchers can access the object through various citation systems or databases, increasing its visibility and accessibility. Linking identifiers allows for better tracking of the object’s history, especially when versioning adds complexity.	Researcher	Data citation Linking objects	G

6. PID Ownership and Maintenance

Managing the ownership and responsibility of PIDs is essential for ensuring accurate and reliable data citation. To streamline this process, PID management should be bundled with existing agreements, such as software licenses, to clarify ownership and transfer responsibilities. While automation can simplify PID minting and metadata management, human oversight remains critical to maintaining quality and accuracy. Automated systems can populate and update metadata fields, but regular reviews by a human owner are necessary to ensure precision. Proper budgeting for these human-curation processes is essential when implementing PIDs to ensure long-term data integrity.

Recommendation - What?	Problem statement - Why?	Stakeholder affected - For whom?	Keywords - About?	Source - From where?²⁹
F: PID Ownership and Maintenance
F1: Manage PID responsibility and ownership by bundling these with other existing agreements, such as software licenses.	There is often confusion about PID and PID metadata ownership, responsibility, and transfer. Using already-existing agreement infrastructures like software licences can make this easier to address and track.	Researcher Data Curator	PID ownership Licensing	H³⁰
F2: Make sure to allow automation of PID minting and metadata management, but human curation remains crucial in ensuring quality and must be budgeted for.	Many metadata fields can be automatically populated and updated when integrating PIDs into research workflows. However, they must be periodically reviewed by a human owner to ensure accuracy, which should be budgeted for when implementing PIDs.	Data Curator Researcher Research Funder / Research Performing Organisation	Human data curation Data quality assurance	H

7. Kernel Metadata Practices

Kernel metadata practices are crucial for ensuring that data is well-documented and accessible without compromising security or privacy. To avoid complications with permissions, sensitive information should not be included in kernel metadata. Any additional, sensitive metadata can be stored separately, ensuring proper access controls. Ownership and access decisions should lie with the data owner and provider, not upstream actors, and researchers should consult institutional and national stakeholders for guidance on best practices. As discussions on standardising kernel metadata continue at local and international levels, it is important to define whether data is "open" or "closed" in the kernel metadata to clarify terms of use. Kernel metadata plays a key role in describing physical objects, such as instruments or samples, ensuring traceability, reproducibility, and proper interpretation of data by providing essential context like calibration records and environmental conditions.

Recommendation - What?	Problem statement - Why?	Stakeholder affected - For whom?	Keywords - About?	Source - From where?³¹
G: Kernel Metadata Practices
G1: Avoid sensitive metadata in the kernel metadata.	Having no sensitive data in the kernel removes the need for authentication and permissions for kernel metadata. Additional layers (separate files) of non-kernel metadata can be deployed if necessary. Furthermore, the decision to grant access does not lie with the upstream actors (Provider, Authority) but with the Owner and the Provider³²	PID Manager to enact/enable Researcher to make use	Sensitive metadata	I³³
G2: Consult institutional and national stakeholders on recommended kernel metadata practices.	Discussions on attributes to be included in the kernel metadata are ongoing; for now, agreement on kernel metadata takes place at the local/national level. Work is in progress on reaching mutually agreed-upon practices and kernel metadata content at an EOSC/international level.	Researcher	Agreed kernel metadata practices	I
G3: Include the notion of “open” or “closed” in the kernel metadata.	Kernel metadata, with its machine-readable metadata, efficiently manages vast amounts of records. Defining whether it is open or closed is crucial for identifying the terms of use and conditions for querying data.	PID Service Provider to enact/enable Researcher to define	Open vs. closed	I
G4: Include kernel metadata, especially when describing physical objects, e.g., instruments or samples.	For instruments and samples, this metadata ensures that their use, conditions, and modifications can be traced over time, which is critical for reproducibility and verifying results. Furthermore, these types of objects have variable properties depending on their conditions or handling. Including kernel metadata allows researchers to accurately interpret data by providing detailed context, such as calibration records for instruments or environmental conditions for samples. This level of detail ensures that the data is understood and used appropriately.	PID Service Provider to enact/enable PID User to describe	Describing physical objects	I

8. Sensitive Data Practices

Managing sensitive data requires a comprehensive lifecycle approach, especially when assigning PIDs and updating metadata. It is essential to evaluate the compatibility of predefined terms, licenses, and rights-related information with the repository's terms of deposit, ensuring that rights are properly conveyed to data re-users. Sensitive digital objects must be protected with robust rights management metadata, and any pre-existing PIDs or provenance history should be carefully considered when updating records. Transparency in provenance is critical to maintaining trust. Repositories may alter object structures during curation, which could necessitate updates to metadata or the assignment of new PIDs. Additionally, versioning models must be defined and consistently applied to digital objects to ensure accurate tracking of changes. Any sensitivity assessments should be clearly communicated, along with any access barriers, to ensure researchers understand the conditions for discovering, accessing, and reusing sensitive data.

Recommendation - What?	Problem statement - Why?	Stakeholder affected - For whom?	Keywords - About?	Source - From where?³⁴
H: Sensitive Data Practices
H1: Take a lifecycle perspective on sensitive metadata issues for PIDs.	Assigning PIDs and updating metadata should consider the digital objects deposited alongside repositories' curation and preservation steps.	Data Curator PID Manager	Data quality assurance	J³⁵
H2: Evaluate predefined terms and conditions, licences, or other rights-related information and whether these are compatible with the terms of deposit and rights required by the repository, including rights to be passed on to data re-users.	Rights management metadata recorded with the PID protects sensitive digital objects.	Data Curator PID Manager	Data quality assurance	J
H3: Consider if one or more PIDs are already assigned to one or more parts of the object and whether the existing PID approach will be continued or replaced by one set by the repository.	If the repository applies its own PIDs, historical digital object records may need to be updated to point to newly minted PIDs.	Data Curator PID Manager	Data quality assurance	J
H4: Evaluate any pre-existing provenance history of custody and change and consider: How will the pre-deposit provenance and history be retained and communicated to end users? If and how will this influence the repository’s own approach to recording provenance information?	Transparency of provenance is critical to trust in sensitive digital objects.	Data Curator PID Manager	Data quality assurance Provenance	J
H5: Define a straightforward object model (data, metadata, and associated documentation) for the deposit and whether the existing object model structure will be maintained or revised by the repository during curation, quality, and compliance activities.	Digital objects may be reorganised, restructured, or relabelled to align with repository practice, triggering a need to update change logs or generate a new PID.	Data Curator PID Manager	Data quality assurance Object model definition	J
H6: Evaluate whether a deposit includes an associated version model for defining how the identifiers have been assigned and the terms by which they, or related metadata, would be changed in response to changes in the object. Decide whether this versioning model will be retained or if the repository's versioning practice will be applied.	Change and versioning rules for digital objects must be consistently applied and communicated to end users. These rules influence updates to change logs and new PIDs.	Data Curator PID Manager	Data quality assurance Version model	J
H7: Evaluate whether there is a pre-existing assessment of the digital objects’ sensitivity and whether the repository will accept the assessment method and outcome provided or apply its approach to sensitivity assessment moving forward. It must then be recorded whether the repository’s assessment concludes that some or all digital objects are sensitive.	Any inconsistency in approach or sensitivity assessment between the time of deposit and subsequent access and reuse should be documented, justified, and communicated.	Data Curator PID Manager	Data quality assurance Sensitivity assessment	J
H8: Communicate sensitivity assessments and any barriers (to humans and or machines) to access digital objects.	Clarity on whether a researcher can immediately or potentially access a digital object for some sensitivity-related reason reduces the barriers to discovering, accessing, and reusing digital objects relevant to research.	Data Curator PID Manager Researcher	Data quality assurance Access Restrictions	K³⁶

9. Instrument Practices

Defining clear guidelines for when changes to an instrument require a new PID is essential for maintaining consistency and clarity in research workflows. A shared understanding of what the PID identifies and how versioning is applied ensures all stakeholders are on the same page. Organisations should develop comprehensive policies for assigning PIDs to instruments, ensuring that the process aligns with both internal practices and external standards. The PIDINST metadata schema provides a domain-agnostic approach, helping organisations implement consistent PID practices that foster the sharing of best practices and encourage collaboration across research domains.

Recommendation - What?	Problem statement - Why?	Stakeholder affected - For whom?	Keywords - About?	Source - From where?³⁷
I: Instrument Practices
I1: Define and agree when changes to an instrument require a new PID.	There is a need to obtain a clear understanding of what the PID identifies and how versioning is applied to all stakeholders.	Research Infrastructure Manager Instrument Owner PID Manager Data Curator Researcher	Agreed instrument practices	O³⁸
I2: Develop guidance and policies for the assigning of PIDs for instruments within the organisation.	PIDINST provides high-level domain-agnostic approaches. Applying the PIDINST metadata schema requires implementing organisational decisions effectively and consistently. This encourages the sharing and establishment of best practices within domains.	Research Infrastructure Manager Instrument Owner PID Manager Data Curator	PID guidance PID (minting) policy	O

Sources (‘From where?’):

A)   LifeWatch ERIC Metadata Catalogue
B)   ORCID community
C)   INRIA/Software Heritage use case
D)   INRAE/DipSO (department for Open Science) use case
E)   WorkflowHub - a registry of computational FAIR workflows
F)   Signposting
G)   PIDs in complex data citation workshop
H)   “EOSC Compliant PID Implementations - Practical Guidelines for Implementing Best Practices” workshop. Material: van Lieshout, N., van Horik, R., & Hugo, W. (2023). EOSC Compliant PID Implementations - Practical Guidelines for Implementing Best Practices. Webinar, 21 November 2023. Zenodo. https://doi.org/10.5281/zenodo.10245076 Recording: https://fair-impact.eu/events/fair-impact-events/eosc-compliant-pid-implementations-practical-guidelines-implementing-best
I)   Joint FAIR-IMPACT / FAIRCORE4EOSC³⁹ - Kernel metadata recommendations. Nordling, J., Hugo, W., Bingert, S., L'Hours, H., Ramezani, P., van Horik, R., Matthiesen, M., Lager, L., Caminha Juaçaba Neto, R., & van Lieshout, N. (2025). Kernel Metadata - Agreed Recommendations on Metadata for Persistent Identifiers. Zenodo. https://doi.org/10.5281/zenodo.15096004
J)   Sensitive Data & Persistent Identifiers (PIDs) Repository Lifecycle Factors. https://doi.org/10.5281/zenodo.12774337
K)   Sensitive Data: Persistent Identifier (PID) Semantics Gaps. https://doi.org/10.5281/zenodo.12774041
L)   CESSDA Training Team (2024). CESSDA Data Archiving Guide version 3.0. Bergen, Norway: CESSDA ERIC. https://dag.cessda.eu/Chapter-4/5-Updates-and-versioning
M)   Versioning - UK Data Service https://ukdataservice.ac.uk/learning-hub/research-data-management/format-your-data/versioning/
N)   UESSEX-UKDS/CESSDA use case
O)   UKRI-STFC
P)   INRIA/Software Heritage: Software Heritage and IPOL, a fruitful collaboration towards reproducible research

All source definitions compiled as a list below the table. All sources are in addition also referenced as footnotes upon first appearance.
ORCID community: https://orcid.org/
Turello, D. (2019, February 7). How to Think About Data: A Conversation with Christine Borgman [Webpage]. Insights. Scholarly Work at the John W. Kluge Center. //blogs.loc.gov/kluge/2019/02/how-to-think-about-data-a-conversation-with-christine-borgman/
EOSC Executive Board & EOSC Secretariat. (2020). Scholarly infrastructures for research software. Report from the EOSC Executive Board Working Group (WG) Architecture Task Force (TF) SIRS. European Commission. Directorate General for Research and Innovation. https://data.europa.eu/doi/10.2777/28598
INRIA/Software Heritage, SIRS report
A dereferenceable URI is an identifier that resolves to a webpage
INRAE/DipSO (a set of software services)
Source definitions below the table
https://workflowhub.eu/
https://dockstore.org/organizations/iwc
WorkflowHub - a registry of computational FAIR workflows
Signposting is an approach to make the scholarly web more friendly to machines. More information: https://researchlibrary.lanl.gov/projects/signposting-the-web/
https://signposting.org/adopters/#workflowhub
https://blog.zenodo.org/2024/10/21/2024-10-21-swh/
Source definitions below the table
CESSDA Training Team (2024). CESSDA Data Archiving Guide version 3.0. Bergen, Norway: CESSDA ERIC https://dag.cessda.eu/Chapter-4/5-Updates-and-versioning
UESSEX-UKDS/CESSDA use case
https://ukdataservice.ac.uk/learning-hub/research-data-management/format-your-data/versioning/
Source definitions below the table
PIDs in complex data citation workshop
https://www.w3.org/TR/prov-overview/
An executable file (EXE file) is a computer file that contains an encoded sequence of instructions that the system can execute directly when the user clicks the file icon. https://www.techtarget.com/whatis/definition/executable-file-exe-file
Source definitions below the table
LifeWatch ERIC Metadata Catalogue
https://credit.niso.org/