ARDC > Fair toolFair tool Total across FAIR 0/12 Answered Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Title of Assessment I am analysing data for Findable The data has sufficiently rich metadata and a unique and persistent identifier to be easily discovered by others. This includes assigning a persistent identifier (like a DOI or Handle), having rich metadata to describe the data and making sure it is findable through disciplinary local or international discovery portals. 1 Does the dataset have any identifiers assigned? What is this? Close Findable Learn more about identifiers, metadata describing datasets and repositories and registries. Related Resources Featured Page Citation and Identifiers Enabling connections — now and into the future. PDF Using ORCID and Persistent Identifiers to Connect, Link, Cite and Credit Research The ARDC’s Natasha Simons presented a session on Persistent Identifiers at the Research Support Community… ARDC DataCite DOI Service Uniquely identifying digital research objects. ARDC Handle Service Creating references to your research objects that can be maintained, even if the location changes. Featured Page Metadata Enabling the discovery and reuse of research data ARDC Research Data Australia Find, access, reuse and attribute data from Australian research organisations. Featured Page Making Data FAIR Making data FAIR can accelerate your research impact. This is your guide to making data… Featured Page FAIR Data Sharing your data accelerates your research impact. Featured Page Good Data Practices Manage your research data efficiently. PDF Making Your Data FAIR: A Flowchart Use this flowchart to consider how you can make your research data more FAIR. Featured Page Guide to Choosing a Data Repository It can be important to publish your data when you’ve completed your research. Find out… Globally unique, citable, and persistent (e.g. DOI, PURL, ARK or Handle) Web Address (URL) Local Identifier No Identifier 2 Is the dataset identifier included in all metadata records/files describing the data? Yes No 3 How is the data described with metadata? Comprehensively using a formal machine-readable metadata schema Comprehensively, but in a text-based, non-standard format Brief title and description The data is not described 4 What type of repository or registry is the metadata record in? Data is in one place but discoverable through several registries Generalist public repository Domain-specific repository Local institutional repository The data is not described in any repository Findable meter Accessible The data is retrievable by humans and machines through a standardised communication protocol, with authentication and authorisation where necessary. The data does not necessarily have to be open. Data can be sensitive due to privacy concerns, national security or commercial interests. When it’s not able to be open, there should be clarity and transparency around the conditions governing access and reuse. 5 How accessible is the data? What is this? Close Accessible Learn more about accessibility of data, availability of data online and metadata records. Related Resources Page Standardised Communications Protocols ARDC guidance for research software developers on what these protocols are in practice Video Standardisation of Spatial Data Quality Watch this presentation to find out why the standardisation of spatial data quality is important. Featured Page Data Governance Learn about data governance, its importance and its application and find related resources. Video Data Governance and Data Quality Watch this ARDC presentation on data governance and its impact on data quality. PDF Research Data Rights Management Guide A practical guide for people and organisations working with data. Page Working with Sensitive Data Are you working with sensitive data? Explore this guide to the ethical and legal considerations… Featured Page Making Data FAIR Making data FAIR can accelerate your research impact. This is your guide to making data… Featured Page FAIR Data Sharing your data accelerates your research impact. Featured Page Good Data Practices Manage your research data efficiently. PDF Making Your Data FAIR: A Flowchart Use this flowchart to consider how you can make your research data more FAIR. Publicly accessible Fully accessible to persons who meet explicitly stated conditions, e.g. ethics approval for sensitive data A de-identified / modified subset of the data is publicly accessible Embargoed access after a specified date Unspecified conditional access e.g. contact the data custodian for access Access to metadata only No access to data or metadata 6 Is the data available online without requiring specialised protocols or tools once access has been approved? Standard web service API (e.g. OGC) Non-standard web service (e.g. OpenAPI/Swagger/informal API) File download from online location By individual arrangement No access to data 7 Will the metadata record be available even if the data is no longer available? Yes No Unsure Accessible meter Interoperable The associated data and metadata uses a ‘formal, accessible, shared, and broadly applicable language for knowledge representation’. This involves using community accepted languages, formats and vocabularies in the data and metadata. Metadata should reference and describe relationships to other data, metadata and information through identifiers. 8 What (file) format(s) is the data available in? What is this? Close Interoperable Learn more about file formats for data, different types of vocabularies, ontologies and tagging schemas for data and linking metadata to other data and metadata. Related Resources Video Unpacking Interoperability What does interoperability mean — and how does it help make data FAIR? PDF Guide to Vocabularies and Research Data This guide explains what vocabularies are and how they are useful for supporting research. It… Page Community-Endorsed Data Standards Data standards endorsed by research communities help share data and meet specific needs. Featured Page Citation and Identifiers Enabling connections — now and into the future. PDF Using ORCID and Persistent Identifiers to Connect, Link, Cite and Credit Research The ARDC’s Natasha Simons presented a session on Persistent Identifiers at the Research Support Community… PDF DOI Decision Tree for Data Managers A pathfinder for data managers seeking to use a persistent identifier for their data. Video Identifiers for Instruments Webinar Learn all about why persistent identifiers (PIDs) for instruments benefit your research. This 2019 webinar… Featured Page Making Data FAIR Making data FAIR can accelerate your research impact. This is your guide to making data… Featured Page FAIR Data Sharing your data accelerates your research impact. Featured Page Good Data Practices Manage your research data efficiently. PDF Making Your Data FAIR: A Flowchart Use this flowchart to consider how you can make your research data more FAIR. ARDC Identifier Services We provide a range of services for research organisations to create and manage persistent identifiers… In a structured, open standard, machine-readable format In a structured, open standard, non-machine-readable format Mostly in a proprietary format 9 What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements? Standardised open and universal using resolvable global identifiers linking to explanations Standardised vocabularies/ontologies/schema without global identifiers No standards have been applied in the description of data elements Data elements not described 10 How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)? Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF). The metadata record includes URI links to related metadata, data and definitions There are no links to other metadata Interoperable meter Reusable The associated metadata provides rich and accurate information, and the data comes with a clear usage licence and detailed provenance information. Reusable data should maintain its initial richness. For example, it should not be diminished for the purpose of explaining the findings in one particular publication. It needs a clear machine readable licence and provenance information on how the data was formed. It should also use discipline-specific data and metadata standards to give it rich contextual information that will allow reuse. 11 Which of the following best describes the license/usage rights attached to the data? What is this? Close Reusable Learn more about data license and usage rights and data provenance information. Related Resources PDF Research Data Rights Management Guide A practical guide for people and organisations working with data. Featured Page Data Provenance Documentation that builds trust, credibility and reproducibility in research data. Featured Page Metadata Enabling the discovery and reuse of research data Featured Page Making Data FAIR Making data FAIR can accelerate your research impact. This is your guide to making data… Featured Page FAIR Data Sharing your data accelerates your research impact. Featured Page Good Data Practices Manage your research data efficiently. PDF Making Your Data FAIR: A Flowchart Use this flowchart to consider how you can make your research data more FAIR. Standard machine-readable license (e.g. Creative Commons) Standard text based license Non-standard machine-readable license (clearly indicating under what conditions the data may be reused) Non-standard text-based license No license 12 How much provenance information has been captured to facilitate data reuse? Fully recorded in a machine readable format Fully recorded in a text format Partially recorded No provenance information is recorded Reusable meter Get Personalised Resources And Save Your Results Get personalised resources that will help you level up in making your data FAIR. You’ll receive an email with the list of best resources for you based on your answers. Additionally, you’ll get a unique link to be able to access your assessment at a later time. Job Role Email Address Remind me again in six months to reassess my data Sign me up for ARDC Connect Full Name First Name Last Name I am interested in: All Biological and Biotechnological Sciences Engineering Humanities, Arts and Social Sciences (HASS) Indigenous Studies Mathematical-Information and Computing Sciences Medical and Health Sciences Physical-Chemical and Earth Sciences Tool Disclaimer The ARDC FAIR data self assessment tool has been developed by the ARDC. It is provided purely for educational and informational purposes. It is based on our interpretation of the FAIR Data Principles with the acknowledgement that there are other interpretations of the principles. Other tools like the CSIRO 5 star data rating tool and the DANS FAIRdat tool provided valuable inspiration in developing this tool. The scores arising from this tool are intended for self assessment purposes only and to trigger thinking and discussion around possible ways of making data more FAIR. The code for this tool is available for reuse on Github.
