The concept of data registration is defined as well as ways in which data registration can be achieved. Learners will be able to describe why indexed data repositories are important as well as resources enabling you to choose a searchable repository.
Data registration relates to the following 3 FAIR Principles (Table 3.1).
We will discuss and signpost these in this Episode.
The FAIR Guiding Principles
To be Findable:
F1. (meta)data are assigned a globally unique and persistent identifier F2. data are described with rich metadata (defined by R1 below) F3. metadata clearly and explicitly include the identifier of the data it describes F4. (meta)data are registered or indexed in a searchable resource
To be Accessible:
A1. (meta)data are retrievable by their identifier using a standardized communications protocol A1.1 the protocol is open, free, and universally implementable A1.2 the protocol allows for an authentication and authorization procedure, where necessary A2. metadata are accessible, even when the data are no longer available
To be Interoperable:
I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation. I2. (meta)data use vocabularies that follow FAIR principles I3. (meta)data include qualified references to other (meta)data
To be Reusable:
R1. meta(data) are richly described with a plurality of accurate and relevant attributes R1.1. (meta)data are released with a clear and accessible data usage license R1.2. (meta)data are associated with detailed provenance R1.3. (meta)data meet domain-relevant community standards
Table 3.1: The 15 FAIR Guiding Principles. Principles relating to data registration in black.
What is data deposition and registration?
Data deposition and registration refer to the process of uploading data to a searchable resource and providing appropriate metadata to facilitate its discoverability.
For example, a data repository, where data and metadata can be uploaded, may enable it to be discovered, preserved and accessed. Here we use the general term data repository to describe any online storage location that can host deposited (meta)data.
In the context of FAIR, data deposition relates to a number of the Guiding Principles. Firstly, “(meta)data are registered or indexed in a searchable resource, Indexed in a searchable resource: a resource where (meta)data are organised so that they can be queried based on defined fields, ” (FAIR Principle F4). Searchable (indexed) metadata enables humans and computers to query and discover data of interest, though this depends on what is indexed. Here, indexing refers to a process that occurs within the architecture of the data repository (local indexing) where metadata are organised so that they can be queried based on a defined field. It is worth noting that community resources focused on a particular domain (for example, the human database in Ensembl) are better indexed for a particular community, rather than generic repositories (for example, Zenodo) which may not index the community specific components, and may focus on higher level metadata. Indexing by an internet search engine is another example of this. Google (and other search engines, such as Yahoo and Yandex) have an agreed vocabulary (schema.org), within web pages, that are ‘scraped’ and indexed. While the focus of this vocabulary was originally intended for commercial products, community-specific efforts to facilitate discipline-specific indexing are underway (for example, Bioschemas).
Why should I upload my data to a data repository?
Data repositories are generally preferred to file storage systems (such as Dropbox) or sharing data on an ad hoc basis since they often better support FAIR best practice. Repositories will assign citable, “globally unique and persistent identifiers” (FAIR Principle F1) to data, and in some cases enable a data submitter to apply a data usage licence through association with the resource (FAIR Principle R1.1).
Although not exclusively, data repositories support the creation of metadata through curation interfaces providing drop-downs and text fields for metadata entry and validation. Often in the case of a domain or data-specific data repository, such as BioStudies shown in the previous Episode, drop-downs for metadata curation will link community-endorsed vocabularies (FAIR Principle R1.3).
Types of data repository
General public data repositories, such as Zenodo, are multidisciplinary and permit registration and upload of open and closed access (meta)data. Metadata curation is relatively high level and made searchable via indexing. Relating to data in the Life Sciences, Zenodo is often used to publish and provide citable URLs to supplementary data within articles, usually in instances where a domain repository does not exist.
Institutional repositories work similarly and provide an online archive for hosting, indexing and preserving research output specific to an institution. Typically these house more than data, providing a repository often for documents and articles. Institutions will have their own systems supported locally or buy into company solutions.
Discipline-specific repositories cater for communities and datatypes, and typically provide web interfaces to annotate rich metadata at the point when data are submitted. Examples of these belong to the suite of data repositories at the European Bioinformatics Institute (EBI) where rich metadata creation is supported by teams of curators.
Question
An example of a discipline-specific repository is ArrayExpress database. ArrayExpress stores data from high-through functional genomics assays, such as RNAseq, ChIPseq and expression microarrays.
The data submission interface of ArrayExpress is called Annotare. Without creating a login, what help is given to a person looking to submit a dataset for the first time?
Finding more help on how to upload data to specific repositories
The FAIR Cookbook is an online open resource housing specific ‘how to’ guides or recipes. Use the FAIR Cookbook to find two recipes for “depositing data to Zenodo” and “registering datasets with Wikidata”, respectively.
Choosing the right data repository for your data
FAIRsharing helps researchers identify suitable data repositories, standards and policies relating to their data. Use this resource to identify data repositories for proteomic data.
Access the search bar for the FAIRsharing database registry. Search for proteomics and select “repository” under “Record Type”.
Please also consider filling out the Feedback Form as well!
Key points
A good way to FAIRify your (meta)data is through submission to a public repository if it indexes and exposes the appropriate level of metadata to serve your specific use case or serve your envisaged users.
Use Repositories that support controlled access to data if necessary.
FAIRsharing is a useful resource to locate relevant public repositories.
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Citing this Tutorial
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, Data Registration (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/fair/tutorials/fair-data-registration/tutorial.html Online; accessed TODAY
Hiltemann, Saskia, Rasche, Helena et al., 2023 Galaxy Training: A Powerful Framework for Teaching! PLOS Computational Biology 10.1371/journal.pcbi.1010752
Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012
@misc{fair-fair-data-registration,
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 = "Data Registration (Galaxy Training Materials)",
year = "",
month = "",
day = ""
url = "\url{https://training.galaxyproject.org/training-material/topics/fair/tutorials/fair-data-registration/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
Robert Andrews
Nick Juty
Munazah Andrabi
Nicola Soranzo
Sara Morsy
Kellie Snow
Korneel Hens
Philippe Rocca-Serra
Xenia Perez Sitja
Andrew Mason
Branka Franicevic
Saskia Lawson-Tovey
Katarzyna Kamieniecka
Khaled Jum'ah
Krzysztof PoterlowiczQuestions:
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