FAIR: Findable

This section will describe what it means for a scholarly resource to be findable.

For data & software to be findable:

1. (meta)data are assigned a globally unique and eternally persistent identifier or PID
2. data are described with rich metadata
3. (meta)data are registered or indexed in a searchable resource
4. metadata specify the data identifier

Persistent identifiers (PIDs) 101

A persistent identifier (PID) is a long-lasting reference to a (digital or physical) resource:

• Designed to provide access to information about a resource even if the resource it describes has moved location on the web
• Requires technical, governance and community to provide the persistence
• There are many different PIDs available for many different types of scholarly resources e.g. articles, data, samples, authors, grants, projects, conference papers and so much more

Different types of PIDs

PIDs have community support, organizational commitment and technical infrastructure to ensure persistence of identifiers. They often are created to respond to a community need. For instance, the International Standard Book Number or ISBN was created to assign unique numbers to books, is used by book publishers, and is managed by the International ISBN Agency. Another type of PID, the Open Researcher and Contributor ID or ORCID (iD) was created to help with author disambiguation by providing unique identifiers for authors. The ODIN Project identifies additional PIDs along with Wikipedia’s page on PIDs.

Digital Object Identifiers (DOIs)

The DOI is a common identifier used for academic, professional, and governmental information such as articles, datasets, reports, and other supplemental information. The International DOI Foundation (IDF) is the agency that oversees DOIs. CrossRef and Datacite are two prominent not-for-profit registries that provide services to create or mint DOIs. Both have membership models where their clients are able to mint DOIs distinguished by their prefix. For example, DataCite features a statistics page where you can see registrations by members.

Anatomy of a DOI

A DOI has three main parts:

• Proxy or DOI resolver service
• Prefix which is unique to the registrant or member
• Suffix, a unique identifier assigned locally by the registrant to an object

In the example above, the prefix is used by the Australian National Data Service (ANDS) now called the Australia Research Data Commons (ARDC) and the suffix is a unique identifier for an object at Griffith University. DataCite provides DOI display guidance so that they are easy to recognize and use, for both humans and machines.

Exploring Hydroshare

Rich Metadata

More and more services are using common schemas such as DataCite’s Metadata Schema or Dublin Core to foster greater use and discovery. A schema provides an overall structure for the metadata and describes core metadata properties. While DataCite’s Metadata Schema is more general, there are discipline specific schemas such as Data Documentation Initiative (DDI) and Darwin Core.

Thanks to schemas, the process of adding metadata has been standardized to some extent but there is still room for error. For instance, DataCite reports that links between papers and data are still very low. Publishers and authors are missing this opportunity.

Challenges: Automatic ORCID profile update when DOI is minted RelatedIdentifiers linking papers, data, software in Zenodo

Connecting research outputs

DOIs are everywhere. Examples.

• Resource IDs (articles, data, software, …)
• Researcher IDs
• Organisation IDs, Funder IDs
• Projects IDs
• Instrument IDs
• Ship cruises IDs
• Physical sample IDs,
• DMP IDs…
• videos
• images
• 3D models
• grey literature

Provenance?

Provenance refers to the data lineage (inputs, entitites, systems, etc.) that ultimately impact validation & credibility. A researcher should comply to good scientific practices and be sure about what should get a PID (and what not).

Metadata is central to visibility and citability – metadata behind a PID should be provided with consideration.

Policies behind a PID system ensure persistence in the WWW - point. At least metadata will be available for a long time.

Machine readability will be an essential part of future discoverability – resources should be checked and formats should be adjusted (as far possible).

Metrics (e.g. altmetrics) are supported by PID systems.

Publishing behaviour of researchers

According to: Questionnaire and Dataset of the TIB Survey 2017 on information procurement and publishing behavior of researchers in the natural sciences and engineering:

• Responses from 1400 scientists in the natural sciences & engineering (across Germany)
• 70% of the researchers are using DOIs for journal publications
• Less than 10% use DOIs for research data
• 56% answered that they don’t know about the option to use DOIs for other publications (datasets, conference papers etc.)
• 57% stated no need for DOI counselling services
• 40% of the questioned researchers need more information
• 30% cannot see a benefit from a DOI

Choosing the right repository

Some things to check:

• Ask your colleagues & collaborators
• determining the right repo for your research
• That data are kept safe in a secure environment and data are regularly backed up and preserved (long-term) for future use
• Data can be easily discovered by search engines and included in online catalogues
• Intellectual property rights and licencing of data are managed
• Access to data can be administered and usage monitored
• That visibility of data can be enhanced to enable more use and citation

The decision for or against a specific repository depends on various criteria, e.g.

• Data quality
• Discipline
• Institutional requirements
• Reputation (researcher and/or repository)
• Visibility of research
• Legal terms and conditions
• Data value (FAIR Principles)

Some recommendations: → look for the usage of PIDs → look for the usage of standards (DataCite, Dublin Core, discipline-specific metadata → look for licences offered → look for certifications (DSA / Core Trust Seal, DINI/nestor, WDS, …)

Data Journals

Another method available to researchers to cite and give credit to research data is to author works in data journals or supplemental approaches used by publishers, societies, disciplines, and/or journals.

Articles in data journals allow authors to:

• Describe their research data (including information about process, qualities, etc)
• Explain how the data can be reused
• Improve discoverability (through citation/linking mechanisms and indexing)
• Provide information on data deposit
• Allow for further (peer) review and quality assurance
• Offer the opportunity for further recognition and awards

Examples:

• Nature Scientific data - published by Nature and established in 2013
• Geoscience Data Journal - published by Wiley and established in 2012
• Journal of Open Archaeology Data - published by Ubiquity and established in 2011
• Biodiversity Data Journal - published by Pensoft and established in 2013.
• Earth System Science Data - published by Copernicus Publications and established in 2009

Also, the following study discusses data journals in depth and reviews over 100 data journals: Candela, L. , Castelli, D. , Manghi, P. and Tani, A. (2015), Data Journals: A Survey. J Assn Inf Sci Tec, 66: 1747-1762. doi:10.1002/asi.23358

How does your discipline share data

Does your discipline have a data journal? Or some other mechanism to share data? For example, the American Astronomical Society (AAS) via the publisher IOP Physics offers a supplement series as a way for astronomers to publish data.