15  Archives and online repositories

Making research data available to others for reuse is essential to scientific progress and the main purpose of archiving open research data (ORD). This is recognized in the funding regulations of the Swiss National Science Foundation (SNSF).

We recommend to plan early for the archiving of research outputs — deciding what will be archived, how, when, and where. At a minimum, a data archive should involve raw data as well as any relevant derivatives, metadata files and documentation, and when applicable, any code involved in processing the data or making it usable. Without the adequate context, provided by metadata and documentation, archiving data results in a data dump of little scientific value.

15.1 Types of data archives

Data temperature by frequency of access

We can broadly classify the data archives by how frequently we expect it be accessed. Table 15.1 summarizes different ‘temperatures’ of the data defined by access frequency (Pernet et al. 2023). Irrespective of the temperature, any data archiving should comply with the pertinent legal governance requirements. The contents and steps described in this primer can apply to any type of archive, but the presented use-case fits into the warm archive category. It should be noted that an archive does not necessarily make data publicly available immediately and most repositories allow restricting access, for example by requiring an application or applying an embargo until a publication is released.

Table 15.1: An overview of different types of data archives depending on the frequency of access.
Feature \ Temperature Cold Warm Hot
Access frequency Very low Regular Constant
Back-ups No Yes Yes
Costs Cheap Expensive Expensive
Content All High-utility High-utility
Duration Long Long Short/Medium
Medium Tape Disk/Server/Cloud Disk/Server/Cloud
Online No Yes Yes

  • Cold data are not expected to be accessed in a long time but they should still be curated, unlike data considered disposable (Pernet et al. 2023). In some contexts, rarely accessed data can be essential. Collecting new data is usually much more expensive than archiving existing data, so it is generally advisable to cold-archive everything by default. Tapes are relatively cheap and robust to data quality deterioration if stored in facilities that ensure data integrity preservation. Cold data tends to involve larger storage volume than warm and hot data, as it includes all data, including that of limited utility. Therefore it is often not cost-effective to make cold data available online.

  • Warm data are expected to be accessed regularly and they are long-term archived in online servers or cloud repositories. Usually there are multiple back-up copies in physically separated locations to protect data integrity. Warm data are considered of high-utility for research reuse.

  • Hot data are accessed constantly for a specific period of time, for example, while a project is running to share it with collaborators. Hot data are stored in relatively expensive online servers or cloud repositories, and are more likely to move location than cold and warm data.

15.2 Open Research Data (ORD) online repositories

Open research data repositories aim at preserving and making data freely accessible to anyone, promoting transparency and scientific collaboration. Online repositories are meant to host warm data that will be accessed. They are too expensive and not conceived for storing cold data that will remain for long time without being used. Nonetheless, a good online repository should offer security and guaranty that the data will be findable and accessible for a long period of time.

SNSF requirements and FAIR repositories

A key requirement in a repository is that it should provide a persistent identifier to the data and metadata and facilitate making the archived data “FAIR enough”. These can be determining factors when evaluating the suitability of a repository, although assessing its FAIRness may not be a straightforward task. For example, the Swiss National Science Foundation defined a set of minimal criteria for the repositories to fulfill to be considered as ‘FAIR enough’ (Milzow et al. 2020). The criteria in that report were:

  • Globally unique and persistent identifiers are attributed to data sets (e.g. DOI)

  • Upload of intrinsic (e.g. author’s name, content of dataset, associated publication, etc.) and submitter-defined (e.g. definition of variable names, etc.) metadata possible

  • Reuse defined (e.g. Creative Commons, Open Data Commons, etc.)

  • Citation information and metadata always (even in the case of datasets with restricted access) publicly accessible

  • Intrinsic metadata submitted via standardized template/mask (to ensure machine readability and interoperability)

  • Long-term preservation plan for the archived data in place

Initiatives like the CoreTrustSeal make these criteria more explicit by defining and providing certifications based on how well repositories can ensure long-term storage and accessibility of data. These certifications also try to integrate FAIR-enabling assessments, taking into account repository features that help publishing FAIRer data.

Finding a repository

The SNSF requires researchers to submit data associated with the project’s publications into public repositories. Researchers are therefore encouraged to think about the field-specific options available in the early stages of project planning:

  • The SNSF provides an overview of data repositories and links to relevant institutional, generalist and discipline-specific repositories.
  • The website https://www.re3data.org/, (Pampel et al. 2013) also listed on the SNSF page, allows to explore the available options for a variety of domains. This data base allows browsing by subject, content type and country, as well as other filters (e.g., versioning, metadata standards, data licenses and many others).

Repositories of special interest for preclinical neuroimaging

  • EBRAINS. EBRAINS is a platform and repository created as a core aim of the EU-funded Human Brain Project to promote advancements in scientific and industrial research in neuroscience, computing, and brain-related medicine. It gathers a broad range of data and tools, including computing resources. It is designed to accommodate large volumes of data. The platform offers EBRAIN Curation Services accessible via a request form.

  • Image Data Resource (IDR) is a public repository of reference (microscopy) image datasets from published studies. It is led by PIs from Dundee University and the European Molecular Biology Laboratory - European Bioinformatics Institute. The accepted images must be reference images according to the criteria of the Euro-BioImaging - Elixir Image Data Strategy. For other images, the IDR website points at project repositories of BioStudies and Dryad. Metadata guidelines are provided together with examples and templates of annotations.

  • OpenNeuro. OpenNeuro is the former openfMRI repository, from which the Brain Imaging Data Structure (BIDS) guidelines emerged. It offers DOIs to dataset snapshots (versions) and although it has a strong focus on human neuroimaging, there are also animal imaging datasets published. It is focused exclusively on neuroimaging data of different modalities (MRI, PET, MEG, EEG, and iEEG). It is free, maintained by the Stanford Center for Reproducible Neuroscience, and endorsed by NIH as a data-sharing resource.