datalab is a user-friendly, open-source platform that can capture all the experimental data and metadata produced in a scientific lab, targeted (broadly) at materials chemistry but with customisability and extensability in mind. datalab records data and metadata securely and makes it accessible and reusable by both humans and machines via the web UI and API, respectively. datalab can be self-hosted and managed deployments are also available.
You can try the demo deployment at demo.datalab-org.io and read the online documentation at docs.datalab-org.io with release notes and changelog available on GitHub and online.
Features:
- Capture and store sample and device metadata
- Connect and sync raw data directly and from laboratory instruments
- Built-in support for multiple characterisation techniques (XRD, NMR, echem, TEM, TGA, Mass Spec, Raman and more).
- Capture scientific context: store the graph of relationships between research objects.
- Python API for programmatic access to your lab's data enabling custom analysis and automation.
- Join the datalab federation: you can add your datalab to the federation for additional shared features.
- Plugin ecosystem allowing for custom data blocks, AI integration and other instance-specific code.
- Deployment and infrastructure automation via Ansible playbooks.
Datalab-subtitled.mp4
Note
You may be looking for the identically named project DataLab for signal processing, which also has plugins, clients and other similar concepts!
To set up your own datalab instance or to get started with development, you can follow the installation and deployment instructions in the online documentation.
We can also provide paid managed deployments via datalab industries ltd.: contact us at [email protected].
The datalab architecture is shown below:
graph TD
classDef actor fill:#0066CC,fill-opacity:0.3,stroke:#333,stroke-width:2px,color:#000;
classDef clientInterface fill:#00AA44,fill-opacity:0.3,stroke:#333,stroke-width:2px,color:#000;
classDef coreComponent fill:#FF6600,fill-opacity:0.3,stroke:#333,stroke-width:2px,color:#000;
classDef umbrellaLabel fill:#666666,fill-opacity:0.3,stroke:#666,stroke-width:1px,color:#000,rx:5,ry:5,text-align:center;
classDef subgraphStyle fill:#f9f9f9,fill-opacity:0.1,stroke:#ccc,stroke-width:1px;
subgraph ExternalActors [External actors]
direction TB
User[User]
Machine[Machine]
end
class User,Machine actor;
class ExternalActors subgraphStyle;
UmbrellaDesc["Raw instrument data,<br>annotations, connections"]
class UmbrellaDesc umbrellaLabel;
subgraph ClientInterfaces [Client interfaces]
direction TB
BrowserApp[_datalab_<br>Browser app]
PythonAPI[_datalab_<br>Python API]
end
class BrowserApp,PythonAPI clientInterface;
class ClientInterfaces subgraphStyle;
subgraph Backend
direction TB
RESTAPI[_datalab_<br>REST API]
MongoDB[MongoDB Database]
DataLake[Data Lake]
end
class RESTAPI,MongoDB,DataLake coreComponent;
class Backend subgraphStyle;
User <-- "User data I/O" --> UmbrellaDesc;
Machine <-- "Machine data I/O" --> UmbrellaDesc;
UmbrellaDesc <-- "_via_ GUI" --> BrowserApp;
UmbrellaDesc <-- "_via_ scripts" --> PythonAPI;
BrowserApp <-- "HTTP (Data exchange)" --> RESTAPI;
PythonAPI <-- "API calls (Data exchange)" --> RESTAPI;
RESTAPI <-- "Annotations, connections" --> MongoDB;
RESTAPI <-- "Raw and structured characterisation data" --> DataLake;
linkStyle 0 stroke:#666,stroke-width:3px
linkStyle 1 stroke:#666,stroke-width:3px
linkStyle 2 stroke:#666,stroke-width:3px
linkStyle 3 stroke:#666,stroke-width:3px
linkStyle 4 stroke:#666,stroke-width:3px
linkStyle 5 stroke:#666,stroke-width:3px
linkStyle 6 stroke:#666,stroke-width:3px
linkStyle 7 stroke:#666,stroke-width:3px
click PythonAPI "https://github.com/datalab-org/datalab-api" "datalab Python API on GitHub" _blank
click BrowserApp "https://github.com/datalab-org/datalab/tree/main/webapp" "datalab Browser App on GitHub" _blank
click RESTAPI "https://github.com/datalab-org/datalab/tree/main/pydatalab" "pydatalab REST API on GitHub" _blank
The main aim of datalab is to provide a platform for capturing the significant amounts of long-tail experimental data and metadata produced in a typical lab, and enable storage, filtering and future data re-use by humans and machines. datalab is targeted (broadly) at materials chemistry labs but with customisability and extensability in mind.
The platform provides researchers with a way to record sample- and cell-specific metadata, attach and sync raw data from instruments, and perform analysis and visualisation of many characterisation techniques in the browser (XRD, NMR, electrochemical cycling, TEM, TGA, Mass Spec, Raman).
Importantly, datalab stores a network of interconnected research objects in the lab, such that individual pieces of data are stored with the context needed to make them scientifically useful.
This software is released under the conditions of the MIT license. Please see LICENSE for the full text of the license.
We are available for consultations on setting up and managing datalab deployments, as well as collaborating on or sponsoring additions of new features and techniques. Please contact Josh or Matthew on their academic emails, or join the public datalab Slack workspace.
This software was conceived and developed by:
- Prof Joshua Bocarsly (Department of Chemistry, University of Houston, previously Department of Chemistry, University of Cambridge)
- Dr Matthew Evans (MODL-IMCN, UCLouvain & Matgenix)
with support from the group of Professor Clare Grey (University of Cambridge), and major contributions from:
plus many contributions, feedback and testing performed by other members of the community, in particular, the groups of Prof Matt Cliffe (University of Nottingham) and Dr Peter Kraus (TUBerlin).
A full list of code contributions can be found on GitHub.
Contributions to datalab have been supported by a mixture of academic funding and consultancy work through datalab industries ltd.
In particular, the developers thank:
- Initial proof-of-concept funding from the European Union's Horizon 2020 research and innovation programme under grant agreement 957189 (DOI: 10.3030/957189), the Battery Interface Genome - Materials Acceleration Platform (BIG-MAP), as an external stakeholder project.
- The BEWARE fellowship scheme of the Wallonie recherche SPW for funding under the European Commission's Marie Curie-Skłodowska Action (COFUND 847587).