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NOMAD makes materials science data FAIR

The NOvel Materials Discovery (NOMAD) is a data management platform for materials science data. Here, NOMAD is a web-application and database that allows to centrally publish data. But you can also use the NOMAD software to build your own local NOMAD Oasis.

NOMAD More than 12 million of simulations from over 400 authors world-wide

  • Free publication and sharing of data
  • Manage research data though its whole life-cycle
  • Extracts rich metadata from data automatically
  • All data in raw and machine processable form
  • Use integrated tools to explore, visualize, and analyze

History of NOMAD

NOMAD was founded 2014 as a repository for sharing electronic structure code files.

The first NOMAD-coe project (2016-2019) extended the raw-file repository. Parsers were developed to automatically extract rich metadata and transform all data into a common machine processable archive. The encyclopedia allowed to explore and visualize data from a materials and properties perspective. The ai toolkit was established to run analysis notebooks directly on NOMAD servers.

The current official installation of NOMAD (v0.x) is the result of a consolidation process that integrated the NOMAD-CoE services repository, archive, encyclopedia, and ai toolkit. Here, we also introduced the NOMAD Oasis and allowed to the local use of NOMAD.

Since then, NOMAD is developed by and fair-di e.V. as a general platform for managing materials science data. Upcoming NOMAD version including this NOMAD (v1) extend NOMAD towards material science synthesis, experiments, and computational material science at different scales.

How does NOMAD work?

Managing data based on automatically extract rich metadata

how does nomad work

NOMAD is based on a bottom-up approach. Instead of only managing data of a specific predefined format, we use parsers and processing to support an extendable variety of data formats. Uploaded raw files are analysed and files with a recognized format are parsed. Parsers are small programs that transform data from the recognized mainfiles into a common machine processable version that we call archive. The information in the common archive representation drives everything else. It is the based for our search interface, the representation of materials and their properties, as well as all analytics.

A common hierarchical machine processable format for all data

archive example

The archive is a hierarchical data format with a strict schema. All the information is organized into logical nested sections. Each section comprised a set of quantities on a common subject. All sections and quantities are supported by a formal schema that defines names, descriptions, types, shapes, and units. We sometimes call this data archive and the schema metainfo.

Datamodel: uploads, entries, files, datasets

Uploaded raw files are managed in uploads. Users can create uploads and use them like projects. You can share them with other users, incrementally add and modify data in them, publish (incl. embargo) them, or transfer them between NOMAD installations. As long as an upload is not published, you can continue to provide files, delete the upload again, or test how NOMAD is processing your files. Once an upload is published, it becomes immutable.


NOMAD's main entities

An upload can contain an arbitrary directory structure of raw files. For each recognized mainfile, NOMAD creates an entry. Therefore, an upload contains a list of entries. Each entry is associated with its mainfile, an archive, and all other auxiliary files in the same directory. Entries are automatically aggregated into materials based on the extract materials metadata. Entries (of many uploads) can be manually curated into datasetsfor which you can also get a DOI.

Using NOMAD software locally (the Oasis)

The software that runs NOMAD is Open-Source and can be used independently of the NOMAD central installation at We call any NOMAD installation that is not the central one a NOMAD Oasis.

oasis use-cases

NOMAD Oasis use-cases

There are several use-cases how the NOMAD software could be used. Of course other uses and hybrids are imaginable:

  • Academia: Use the Oasis for local management of unpublished research data
  • Mirror: Use the Oasis as a mirror that hosts a copy of all published NOMAD data
  • Industry: Use of Oasis to manage private data and full internal use of published data in compliance with strict privacy policies
  • FAIRmat: Use Oasis to form a network of repositories to build a federated data infrastructure for materials science. This is what we do in the FAIRmat project.


A containerized cloud enabled architecture

NOMAD is a modern web-application that requires a lot of services to run. Some are NOMAD specific, others are 3rd party products. While all services can be traditionally installed and run on a single sever, NOMAD advocates the use of containers and operating NOMAD in a cloud environment.

nomad architecture

NOMAD architecture

NOMAD comprises two main services, its app and the worker. The app services our API, graphical user interface, and documentation. It is the outward facing part of NOMAD. The worker runs all the processing (parsing, normalization). Their separation allows to scale the system for various use-cases.

Other services are:

  • rabbitmq: a task queue that we use to distribute tasks for the worker containers
  • mongodb: a no-sql database used to maintain processing state and user-metadata
  • elasticsearch: a no-sql database and search engine that drives our search
  • a regular file system to maintain all the files (raw and archive)
  • jupyterhub: run ai toolkit notebooks
  • keycloak: our SSO user management system (can be used by all Oasises)
  • a content management system to provide other web-page content (not part of the Oasis)

All NOMAD software is bundled in a single NOMAD docker image and a Python package (nomad-lab on pypi). The NOMAD docker image can be downloaded from our public registry. NOMAD software is organized in multiple git repositories. We use continuous integration to constantly provide the latest version of docker image and Python package.

NOMAD uses a modern and rich stack frameworks, systems, and libraries

Besides various scientific computing, machine learning, and computational material science libraries (e.g. numpy, skikitlearn, tensorflow, ase, spglib, matid, and many more), Nomad uses a set of freely available technologies that already solve most of its processing, storage, availability, and scaling goals. The following is a non comprehensive overview of used languages, libraries, frameworks, and services.

nomad stack

NOMAD components and dependencies

Python 3

The backend of nomad is written in Python. This includes all parsers, normalizers, and other data processing. We only use Python 3 and there is no compatibility with Python 2. Code is formatted close to pep8, critical parts use pep484 type-hints. Pycodestyle, pylint, and mypy (static type checker) are used to ensure quality. Tests are written with pytest. Logging is done with structlog and logstash (see Elasticstack below). Documentation is driven by Sphinx.


Celery (+ rabbitmq) is a popular combination for realizing long running tasks in internet applications. We use it to drive the processing of uploaded files. It allows us to transparently distribute processing load while keeping processing state available to inform the user.

Elasticsearch is used to store repository data (not the raw files). Elasticsearch enables flexible, scalable search and analytics.


Mongodb is used to store and track the state of the processing of uploaded files and the generated entries. We use mongoengine to program with mongodb.


Keycloak is used for user management. It manages users and provides functions for registration, forgetting passwords, editing user accounts, and single sign-on to fairdi@nomad and other related services.


The ReSTful API is build with the FastAPI framework. This allows us to automatically derive a OpenAPI description of the nomad API. Fruthermore, you can browse and use the API via OpenAPI dashboard.


The elastic stack (previously ELK stack) is a centralized logging, metrics, and monitoring solution that collects data within the cluster and provides a flexible analytics front end for that data.

Javascript, React, Material-UI

The frontend (GUI) of nomad@FAIRDI is built on the React component framework. This allows us to build the GUI as a set of re-usable components to achieve a coherent representations for all aspects of nomad, while keeping development efforts manageable. React uses JSX (a ES6 variety) that allows to mix HTML with Javascript code. The component library Material-UI (based on Google's popular material design framework) provides a consistent look-and-feel.


To run a nomad@FAIRDI instance, many services have to be orchestrated: the nomad app, nomad worker, mongodb, Elasticsearch, Keycloak, RabbitMQ, Elasticstack (logging), the nomad GUI, and a reverse proxy to keep everything together. Further services might be needed (e.g. JypiterHUB), when nomad grows. The container platform Docker allows us to provide all services as pre-build images that can be run flexibly on all types of platforms, networks, and storage solutions. Docker-compose allows us to provide configuration to run the whole nomad stack on a single server node.

kubernetes + helm

To run and scale nomad on a cluster, you can use kubernetes to orchestrated the necessary containers. We provide a helm chart with all necessary service and deployment descriptors that allow you to set up and update nomad with only a few commands.


Nomad as a software project is managed via GitLab. The nomad@FAIRDI project is hosted here. GitLab is used to manage versions, different branches of development, tasks and issues, as a registry for Docker images, and CI/CD platform.