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HAL
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# What about metadata?

Tovo Rabemanantsoa

► To cite this version:

Tovo Rabemanantsoa. What about metadata?. 2024. hal-04645703

HAL Id: hal-04645703

https://hal.inrae.fr/hal-04645703v1

Submitted on 12 Jul 2024

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Distributed under a Creative Commons Attribution - ShareAlike 4.0 International License
> What about the metadata?

REACTION 2024

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## Definition

Data that provides information about other data

It can describe a collection, a single resource, or a component part of a larger resource

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# What is metadata?

## Types of metadata

* Descriptive (title, abstract, author, keywords,...)

* Structural (how compound objects are put together)

* Administrative (how it was created, file type, access rights,...)

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# What is metadata?

## How does it look like?

## README content

Following fasta sequences read as follows:

(Example from Acnodon oligacanthus)

>1302|69|[C/A]|AOL

TGCAGGTTATTTAACTCTTTTTCATAATGCACCTCATACATAGTAACCATTTTCAAACATGTCTTTCACCACCTGCGACGTGATATATTCAAAGTCTTATAGTTACAGTACATACTTTTTGTAGTTAAAATGGAAGAAGTGGCA

- The number '1302' is the identifiant of the sequence corresponding RAD-tag within the 'catalog_index' table of the Stacks database. This basically serves as marker ID.

- The number '69' is the position of the expected SNP marker. Please note that Stacks numerotation starts from 0; so a SNP at position '69' actually corresponds to the 70th nucleotide within the 145 bp sequence.

- Information within brackets [C/A] correspond to the two expected alleles for the SNP.

- 'AOL' is a descriptive code relative to the species 'A'cnodon 'OL'igacanthus (First letter of genus, first and second letter of species)

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## README

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# Why is it matters?

## Role of the metadata

At least, gives an answer to the:

- What?

- How?

- Who?

Provides:

- Differentiation between datasets

- Context (scope, units,...)

- Reassurance of reliability

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a keystone of FAIR data

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# Why is it matters?

## Reproducibility and reusability

* Metadata enforces the FAIRness of your data

* FAIRness is a major step to reproducibility and reusability

* Reproducibility and reusability leads to a stronger science

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# Why is it matters?

## It's not (only) about the money

Not having or having poor quality metadata

- Is a waste of time (to find/retrieve, to understand/authenticate/analyse)

- Is a waste of resources (redundant copies, collection of existing data)

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It costs money (€10bn/y in Europe)

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Some good practices

Scaffold a strategy

* Define a data governance strategy

- Build a cross-functional data team

* Adopt a standard

* Deploy a metadata management tool

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# Some good practices

## Stick to the plan

* Regularly assess your alignment with your strategy

* Make sure that your teammates are:

* Up to date on their skills

* Have the resources to implement the strategy

* Still ok with the strategy

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# Some good practices

Stay agile

If the strategy no longer suits you (or you team):

- Change it!

- Work out the change with your data team

- Communicate vastly

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**Datacite Metadata Schema:** a list of core metadata properties chosen for an accurate and consistent identification of a resource for citation and retrieval purposes, along with recommended use instructions - https://schema.datacite.org/

**Dublin Core:** A basic, domain-agnostic standard which can be easily understood and implemented, and as such is one of the best known and most widely used metadata standards - http://dublincore.org/

**RO-Crate:** RO-Crate is a community effort to establish a lightweight approach to packaging research data with their metadata - https://www.researchobject.org/ro-crate/

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And tools

**ODAM:** Open Data for Access and Mining is an Experimental Data Table Management System (EDTMS) - https://inrae.github.io/ODAM/

**MOLGENIS:** a data platform for researchers to accelerate scientific collaborations and for bioinformaticians who want to make researchers happy - https://molgenis.org/

**PDBx/mmCIF:** a standard Protein Data Bank archive format - https://mmcif.wwpdb.org/

**Dspace:** The DSpace digital asset management system that powers your Institutional Repository - https://wiki.lyrasis.org/display/DSDOC8x/

**DRYAD:** Open data publishing platform and a community committed to the open availability and routine re-use of all research data - https://datadryad.org

**FAIR Aware:** FAIR-Aware helps you assess your knowledge of the FAIR Principles, and better understand how making your data(set) FAIR can increase the potential value and impact of your data - https://fairaware.dans.knaw.nl/

**Recherche Data gouv:** Un écosystème au service du partage et de l'ouverture des données de la recherche - https://recherche.data.gouv.fr

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References

* PwC EU Services (2018). Cost of not having FAIR research data - Cost-Benefit analysis for FAIR research data. DOI: 10.2777/02999

* NISO Press (2004). Understanding Metadata. ISBN: 1-880124-62-9

* Anne Marie Smith, Ph.D. (2017). Turning Data into Knowledge: Creating and Implementing a Meta Data Strategy

* Pasquetto, I V et al 2017 On the Reuse of Scientific Data. Data Science Journal, 16: 8, pp. 1–9, DOI: 10.5334/dsj-2017-008

* Barriocanal, Elena & Sánchez-Alonso, Salvador & Sicilia, M.. (2017). Deploying Metadata on Blockchain Technologies. 38-49. DOI: 10.1007/978-3-319-70863-8_4

* Iturbide, M., Fernández, J., Gutiérrez, J.M. et al. Implementation of FAIR principles in the IPCC: the WGI AR6 Atlas repository. Sci Data 9, 629 (2022). https://doi.org/10.1038/s41597-022-01739-y

* Metadata Standards Catalog: https://rdamsc.bath.ac.uk/

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