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Metadata And Data Brokering Online Tool

Plug in your research data. Publish it FAIR.

Madbot connects the places your data already lives (a cluster, Galaxy, S3, OMERO) to the repositories that publish it. Describe it once with standard metadata, check it against ENA, Zenodo and Recherche Data Gouv requirements, and submit it in one click.

Open madbot →
See how it works
  • Your data stays where it is
  • Free and open source
  • Hosted in France by IFB

Animated example in four steps. 1: you tell madbot where your data is stored, and nothing is copied. 2: you describe it with metadata fields that are checked as you type. 3: madbot translates each field into the format of the repository you chose. 4: madbot fetches the files, checks them and submits them, and the repository returns an identifier. Three examples: sequencing reads from a cluster to ENA, RNA-seq counts from Galaxy to Zenodo, and microscopy images from S3 to Recherche Data Gouv.

madbot todaylive from the madbot API

191researchers
151workspaces
1.6Kdatalinks
21.5Kmetadata values

How it works

Four steps from lab bench to repository

Link it, describe it, publish it, repeat it. Each step builds on the one before, so nothing gets typed twice.

01

Map your project, bring your team

Shape your project as a tree that fits how you work: name each level and nest it as deep as you need, following the ISA model, your field’s conventions or simply your lab’s habits. Each node holds its own description, samples, datalinks and metadata. The project lives in your team’s workspace, a shared space where colleagues you invite build it with you.

Project tree in madbot: S. salivarius conjugative elements, with a literature review and methods split into data analysis, data collection and a study design with lab experiments and a strain collection
madbot / Oral streptococci lab

02

Link data where it already lives

Point madbot at files on a cluster, a NAS, Galaxy, OMERO or an S3 bucket. Madbot doesn’t store your files: datalinks record exactly where each dataset sits and whether it is still accessible.

Datalinks table listing eight paired fastq.gz files; the details of B50_R1.fastq.gz give its path on core.cluster.france-bioinformatique.fr and show it is accessible
madbot / S. salivarius conjugative elements

03

Describe once, inherit everywhere

Describe your data with fields from madbot’s own referential or from community standards such as Dublin Core, Darwin Core or ENA checklists, all mapped to the repositories’ formats. A value set on a parent node flows down to its children, where you can still override it, and every entry is checked as you type.

Metadata of the Lab experiments node: an isolation source set on the node itself, then a collection date, geographic location, host scientific name and subject inherited from the Methods node, greyed out with an Inherited value from node Methods tooltip
madbot / S. salivarius conjugative elements / Methods / Study design / Lab experiments

04

Publish in one click

Choose the datalinks and the repository. Madbot fetches the files, maps your metadata to the repository’s format, validates it and submits it. The DOI or accession number comes back into madbot, linked to your data.

Submissions list with ENA, Recherche Data Gouv and Zenodo submissions, all marked Deposited
madbot / Submissions

Across disciplines

Not just genomics

Madbot was born in bioinformatics, but its workflow isn’t tied to DNA. Plugins bring each field its own storage, standards and repositories; each route below works with today’s plugins.

Genomics

Sequencing runs

Paired-end reads described with ENA checklists and submitted as samples and runs.

Data SSHFS Metadata ENA checklists Madbot assembles data and metadata into an ENA submission. Published on ENA

Bioimaging

Microscopy screens

Images stay in OMERO; madbot describes the experiment and publishes it.

Data OMERO Metadata Dublin Core Madbot translates Dublin Core to Recherche Data Gouv’s format, then assembles the deposit. Published on Recherche Data Gouv

Omics analyses

Workflow outputs

Results from a Galaxy history, described once and published with a DOI.

Data Galaxy Metadata madbot referential Madbot translates its own fields to Zenodo’s format, then assembles the deposit. Published on Zenodo

Biodiversity

Occurrence data

Species observations described with Darwin Core terms, the standard of biodiversity data.

Data S3 Metadata Darwin Core Madbot translates Darwin Core to Zenodo’s format, then assembles the deposit. Published on Zenodo

Research software

Code and notebooks

A release from your repository, archived with its authors and license.

Data GitLab Metadata Dublin Core Madbot translates Dublin Core to Zenodo’s format, then assembles the deposit. Published on Zenodo

Your field

Build it yourself

Occurrence data bound for GBIF? Microscopy images for the BioImage Archive? Each new route is a plugin, and the next ones are best built with the people who need them.

Write your own plugin → or tell us what you need

Plugins

Plugs into what you already use

Every connection is a small Python plugin. There are three kinds, and your institute can write its own.

Connect data from

Storage types you can create datalinks on.

Describe with

Field sets that extend the metadata catalogue.

Publish to

Repositories madbot can submit to directly.

Preview Still evolving: it works, and your feedback helps us stabilize it.community Built and maintained by partners outside the core team.

Browse all plugins on GitLab ↗ Write your own ↗

Metadata

Metadata that finally makes sense

One shared catalogue of fields, mapped to each repository’s own format. Values link to the authorities researchers already trust, so a name stays a person and a place stays a place. Every entry is checked as you type: change any field of the example, or try your own ORCID iD.

ORCID
people
ROR
institutions
DOI
datasets and papers
PubMed
publications
NCBI Taxonomy
organisms
EDAM · OLS
ontology terms
Methods / Study design /Lab experimentsTry it! Every field can be edited
Geographic location (country and/or sea)
enasample
↳ inherited from Methods
Collection date
enasample
✓ valid ISO 8601 date
Scientific name
enasample
✓ NCBI Taxonomy 1304
Dataset contacts
recherche data gouvgeneral
✓ Thomas DENECKER, from ORCID

Workspaces

As open as possible, as closed as necessary

Each team gets its own workspace. Invite people, give them a role on each node, and keep sensitive data restricted while the rest stays open to collaborators.

Role on a node
ownerFull access to the node, can delete it and can manage members
managerFull access to the node, and can manage members
contributorFull access to the node, but cannot delete it
collaboratorCan view the node, but not modify any content

For developers and institutions

Built to fit your infrastructure

Madbot is open source and designed to work with your own tools, not only through its interface.

API

A documented REST API

An OpenAPI 3 API, documented with Swagger, lets scripts and lab information systems (LIMS) work with madbot directly.

Plugins

Extend it in Python

The plugin API and its development guide let you add a connection without touching madbot’s core. Partners already maintain their own.

Security

Credentials in a vault

The credentials madbot uses to reach your storage are kept in HashiCorp Vault.

Quality

Checked on every change

Each change goes through automated linting, dependency and security scans, and tests before it is released.

License

Open source, BSD-3-Clause

The code is public on GitLab. Read it, reuse it, deploy it or contribute to it.

Archive

Preserved on Software Heritage

Madbot’s source code is archived by Software Heritage, the universal archive of software source code.

Developer documentation ↗ Source code on GitLab ↗ Software Heritage ↗

Roadmap 2026

What’s next for madbot

Q1

  • Import metadata when creating a datalink
  • Batch metadata editing
  • Duplicate and move nodes
  • Personal access tokens for the API

Q2

  • SSH key authentication for SSHFS
  • Recherche Data Gouv submission plugin
  • Accession numbers and DOIs shown on submissions, data and metadata
  • Suggested metadata values and sample–data associations
  • Easier sample–data associations, with guidance for paired data

Q3

  • Faster lists, project trees and metadata pages
  • Plugin maturity shown in the app (Stable or Preview)
  • A profile with ORCID and ROR identifiers
  • Smoother editing: visible multi-selection, accurate unsaved-changes warnings, fixed rich-text editors
  • Clear error messages when a plugin connection fails

Q4

  • Batch editing everywhere, in one fast table view
  • A faster application start-up
  • Metadata entry: autosave, clearer validation, a single window to add fields
  • Create datalinks and samples in bulk, with clear errors when something fails
  • Typed data associations (paired, assembly…)
  • A clearer interface: guided discovery of madbot’s vocabulary, separate user and workspace settings
  • A documented API path for data-producing platforms

Want the details? Follow the epics on Taiga ↗

Team

Meet the people behind madbot

Developers and project leads at the Institut Français de Bioinformatique, in Paris and Strasbourg, dedicated to making your research experience better.

Thomas Denecker

Project owner

Paris

“This is awesome! Let’s try it!”

Julien Seiler

Project owner

Strasbourg

“It’s great but…”

Baptiste Rousseau

Full-stack developer

Paris

“I may have gone down a rabbit hole… but look what I found!”

Elora Vigo

Full-stack developer

Strasbourg

“Isn’t this a bit off-center?”

Meet the whole team →

National working group

Shaped with institutes across France

Researchers, data-producing platforms and national infrastructures meet in madbot’s working group. Its members try madbot on their own use cases, tell us what to fix, and take it back to their communities.

Join the working group
Meet the members →
  • Institut Français de Bioinformatique
  • ELIXIR France
  • CNRS
  • INRAE
  • Inserm
  • France Génomique
  • France-BioImaging
  • GenomiqueENS
  • ProFI, proteomics
  • IGBMC
  • CIRAD
  • Université Clermont Auvergne
  • Aix-Marseille Université

Link your first dataset in minutes.

Madbot is free and open source, and runs on French public research infrastructure.

Open madbot →
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