The persistent identifier is defined. Learners will be able to define the structure of an identifier and explain its importance.

Agenda

In this tutorial, we will cover:

1. Persistent identifiers and the FAIR Principles
2. Using persistent identifiers (PIDs)
3. The structure of persistent identifiers
4. Useful Resources

Persistent identifiers and the FAIR Principles

Data Identifiers relate to the following 5 FAIR Principles (Table 5.1). We will discuss and signpost these in this episode.

Table 5.1: The 15 FAIR Guiding Principles. Principles relating to data identifiers are highlighted in black.

Using persistent identifiers (PIDs)

Identifiers are an important theme within the FAIR principles, arguably being foundational; they are considered two of the pillars for the FAIR principles since they are crucial for Findable (F) and Accessible (A) principles.

Identifiers are an eternal reference to a digital resource such as a dataset and its metadata. They provide the information required to reliably identify, verify and locate your research data.

Commonly, a persistent identifier is a unique record ID in a database, or a unique URL that takes a researcher to the data in question in a database. Persistent identifiers (PIDs) have to be unique so that only one dataset can be identified by this identifier. In addition to the identifier being unique, it needs to be persistent. When depositing or hosting data, you should ensure the longevity of this persistence meets your requirements, which may require reading specific database policies regarding identifier policy.

Since FAIR permits the withdrawal of data, the FAIR Principles combat the potential for broken URLs by stating: “Metadata are accessible, even when the data are no longer available.” (FAIR Principle A2). This means the link (PID) remains valid, displaying all the original metadata of the record even though the data is no longer available.

It is important to note that when you upload your data to a public repository, the repository will create this ID for you automatically.

Based on how to FAIR, many resources can help you find databases to assign PIDs to your data. One of these resources is FAIRsharing something we’ve already encountered in the previous episodes. FAIRsharing, provides a list of databases grouped by domains and organisations.

The structure of persistent identifiers

To ensure that a PID is globally unique, commonly it is supplied as a unique URL. For the case of a record in a biological database, the use of a URL ensures that the database record ID is associated with the database name or often some derivation of this. This is often enough to ensure the uniqueness of the PID for any future scenario.

Commonly, for things like publications, a DOI is used for the PID, where DOI stands for Digital Object Identifier. An example is shown below where the PID is constructed from 3 pieces of information: the resolver service, the prefix (namespace) and the suffix (dataset ID).

Resolver service: the domain/service/institution hosting the PID e.g. [https://www.doi.org] (doi.org)

Prefix: a unique number referring to the publisher. This is also known as the namespace.

Suffix: the unique dataset number

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For biological data, PIDs usually require a resolver that can deal with multiple resolving locations, which means that if a database changes its name or internal structure, the new and old variations of the PID remain valid and take the user to the (meta)data. One commonly used resolver service is called identifiers.org which maintains a list of database namespaces (prefix’s) as a persistent record. If a database changes its name, it keeps the original namespace operational or alternatively arranges for redirection from the original.

Examples of using identifiers.org to construct a PID are given below for 2 different databases, Ensembl and WikiPathways, respectively. The namespace is given as the database name in these examples.

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Question

Access the preprint for Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data. This paper makes 10 recommendations for PID best practice.

Locate the lesson for “Do not reassign or delete identifiers”. Which PID is used as an example of a “tombstone page”? Which FAIR Principle does this relate to?

Solution

Tombstone PID: https://www.uniprot.org/uniprotkb/A0AV18

This relates directly to the FAIR Principle A2. “Metadata are accessible, even when the data are no longer available” Note that other FAIR Principles are also illustrated.

The Tombstone page is “retrievable by their identifier using a standardised communications protocol”(FAIR Principle A1), which in this case is http(s). The page contains metadata which “are assigned a globally unique and persistent identifier” (FAIR Principle F1). Also the metadata “clearly and explicitly includes the identifier of the data they describe” (FAIR Principle F3) noting that the identifier itself is featured on the webpage.

Additionally, the webpage features a link to the updated UniProt record thereby “metadata include qualified reference to other (meta)data” (FAIR Principle I3)

Useful Resources

• More on identifiers: RDMkit and FAIR Cookbook
• Nick Juty, Sarala M. Wimalaratne, Stian Soiland-Reyes, John Kunze, Carole A. Goble, Tim Clark; Unique, Persistent, Resolvable: Identifiers as the Foundation of FAIR. Data Intelligence 2020; 2 (1-2): 30–39. Juty et al. 2020

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Key points

• PIDs are eternal and unique.

• PIDs are commonly URLs in the Life Sciences.

Frequently Asked Questions

References

1. Juty, N., S. M. Wimalaratne, S. Soiland-Reyes, J. Kunze, C. A. Goble et al., 2020 Unique, Persistent, Resolvable: Identifiers as the Foundation of FAIR. Data Intelligence 2: 30–39. 10.1162/dint_a_00025

Glossary

FAIR

Findable, Accessible, Interoperable, Reusable

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Citing this Tutorial

1. Robert Andrews, Nick Juty, Munazah Andrabi, Nicola Soranzo, Sara Morsy, Kellie Snow, Korneel Hens, Philippe Rocca-Serra, Laura Cooper, Xenia Perez Sitja, Andrew Mason, Branka Franicevic, Saskia Lawson-Tovey, Katarzyna Kamieniecka, Khaled Jum'ah, Krzysztof Poterlowicz, Persistent Identifiers (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/fair/tutorials/fair-persistent-identifiers/tutorial.html Online; accessed TODAY
2. Hiltemann, Saskia, Rasche, Helena et al., 2023 Galaxy Training: A Powerful Framework for Teaching! PLOS Computational Biology 10.1371/journal.pcbi.1010752
3. Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

BibTeX

@misc{fair-fair-persistent-identifiers,
author = "Robert Andrews and Nick Juty and Munazah Andrabi and Nicola Soranzo and Sara Morsy and Kellie Snow and Korneel Hens and Philippe Rocca-Serra and Laura Cooper and Xenia Perez Sitja and Andrew Mason and Branka Franicevic and Saskia Lawson-Tovey and Katarzyna Kamieniecka and Khaled Jum'ah and Krzysztof Poterlowicz",
	title = "Persistent Identifiers (Galaxy Training Materials)",
	year = "",
	month = "",
	day = ""
	url = "\url{https://training.galaxyproject.org/training-material/topics/fair/tutorials/fair-persistent-identifiers/tutorial.html}",
	note = "[Online; accessed TODAY]"
}
@article{Hiltemann_2023,
	doi = {10.1371/journal.pcbi.1010752},
	url = {https://doi.org/10.1371%2Fjournal.pcbi.1010752},
	year = 2023,
	month = {jan},
	publisher = {Public Library of Science ({PLoS})},
	volume = {19},
	number = {1},
	pages = {e1010752},
	author = {Saskia Hiltemann and Helena Rasche and Simon Gladman and Hans-Rudolf Hotz and Delphine Larivi{\`{e}}re and Daniel Blankenberg and Pratik D. Jagtap and Thomas Wollmann and Anthony Bretaudeau and Nadia Gou{\'{e}} and Timothy J. Griffin and Coline Royaux and Yvan Le Bras and Subina Mehta and Anna Syme and Frederik Coppens and Bert Droesbeke and Nicola Soranzo and Wendi Bacon and Fotis Psomopoulos and Crist{\'{o}}bal Gallardo-Alba and John Davis and Melanie Christine Föll and Matthias Fahrner and Maria A. Doyle and Beatriz Serrano-Solano and Anne Claire Fouilloux and Peter van Heusden and Wolfgang Maier and Dave Clements and Florian Heyl and Björn Grüning and B{\'{e}}r{\'{e}}nice Batut and},
	editor = {Francis Ouellette},
	title = {Galaxy Training: A powerful framework for teaching!},
	journal = {PLoS Comput Biol} Computational Biology}
}

                   

Funding

These individuals or organisations provided funding support for the development of this resource

DASH UK

This Fellowship was funded through the ELIXIR-UK DaSH project as part of the UKRI Innovation Scholars: Data Science Training in Health and Bioscience call (DaSH). (MR/V038966/1). The project aims to embed Research Data Management (RDM) know-how into UK universities and institutes by producing and delivering training in FAIR data stewardship using ELIXIR-UK knowledge and resources.

Galaxy Administrators: Install the missing tools

You can use Ephemeris's shed-tools install command to install the tools used in this tutorial.

shed-tools install [-g GALAXY] [-a API_KEY] -t <(curl https://training.galaxyproject.org/training-material/api/topics/fair/tutorials/fair-persistent-identifiers/tutorial.json | jq .admin_install_yaml -r)

Alternatively you can copy and paste the following YAML

---
install_tool_dependencies: true
install_repository_dependencies: true
install_resolver_dependencies: true
tools: []

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