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What CDLA-Permissive-2.0 is
The Community Data License Agreement (CDLA) is an agreement for sharing and using data. The Linux Foundation announced CDLA-Permissive-2.0 on 22 June 2021; the CDLA project identifies its release month as June 2021. The project describes it as a shorter, simpler rewrite of version 1.0, intended to make open data easier to share and use, including in artificial-intelligence and machine-learning work.
The SPDX license identifier for this version is CDLA-Permissive-2.0, which can be used in dataset catalogs, manifests, and license-scanning metadata.
What you can do with data under the agreement
Section 1.1 says a Data Recipient may use, modify, and share the Data made available under the agreement, provided the recipient follows its terms. In practical terms, the agreement allows analysis and adaptation, as well as redistribution, subject to its data-sharing condition.
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- Use scikit-learn to track an example ML project end to end
- Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
- Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
- Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
- Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
When you redistribute the dataset
Section 2.1 permits sharing data with or without modifications if you make the agreement text available with the shared data. A practical distribution should include the license text or a reliable copy of it alongside the dataset and preserve the agreement’s disclaimer language. CDLA-Permissive-2.0 does not require an attribution notice as a condition of redistribution; removing mandatory attribution was a deliberate change from the earlier permissive version.
When you train a model or analyze the data
Section 3.1 says the agreement imposes no restrictions or obligations on the use, modification, or sharing of “Results.” The CDLA FAQ explains that a trained machine-learning model will typically be a Result. Accordingly, CDLA-Permissive-2.0 itself does not require you to release a model trained on covered data, nor does it impose an agreement obligation on using or distributing that model or insights produced through analysis.
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This is a statement about the agreement’s treatment of Results, not a universal answer to whether a particular training workflow is lawful. The provider can grant only rights it has. Copyright ownership, privacy and publicity rights, contractual terms, export controls, and the provenance of the dataset may create independent obligations or restrictions.
What to check before using or sharing a dataset
- Confirm the applicable license. Check that the specific data you received is offered under CDLA-Permissive-2.0, rather than relying on a general description of a project or catalog.
- Record provenance. Keep track of where the data came from and the license terms attached to it. The agreement cannot fix unclear ownership or missing permissions upstream.
- Review independent legal issues. Assess privacy, copyright, publicity, contractual, regulatory, and other restrictions relevant to the data and your intended use.
- If redistributing data, include the agreement text. Make the CDLA-Permissive-2.0 text available with the data and retain its disclaimer language. This applies whether or not you modified the dataset.
- Review every license in a combined collection. Check the terms for each dataset and how the combined collection will be distributed; do not assume one component’s license governs all the others.
CDLA-Permissive-2.0 compared with version 1.0
| Consideration | CDLA-Permissive-1.0 | CDLA-Permissive-2.0 |
|---|---|---|
| Relationship between versions | Earlier permissive version; remains a valid agreement. | Released in June 2021 as a thorough rewrite of version 1.0. |
| Length and approach | More detailed provisions. | Shorter, streamlined text intended to be easier for data scientists and lawyers to understand. |
| Attribution on redistribution | Included attribution-style requirements. | No mandatory attribution notice; the stated redistribution condition is making the agreement text available with the data. |
| Use of computational Results | The CDLA project says version 2.0 retained explicit permission to use Results without restriction or obligation; a version-specific comparison beyond that is not stated here. | Section 3.1 expressly says the agreement imposes no restriction or obligation on the use, modification, or sharing of Results. |
The CDLA project recommends considering version 2.0 for new collaborations. That recommendation does not invalidate version 1.0 or change the terms attached to data already offered under it. Check the actual license accompanying a dataset rather than treating the two versions as interchangeable.
Compatibility with CC0 and other licenses
The CDLA project’s compatibility information lists CC0-1.0 as compatible, with the condition that the CDLA-Permissive-2.0 text be made available when redistributing the CDLA-covered data. This specific example should not be generalized to every Creative Commons license, government dataset, database right, or proprietary license. For a mixed-license collection, assess each source’s terms and the planned form of redistribution.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When CDLA-Permissive-2.0 may fit a project
It is a candidate for a new open-data collaboration when providers want recipients to use, modify, and redistribute data, while keeping agreement obligations for computational Results—including typical trained models—separate from obligations for the data itself. Its redistribution condition is concise: make the agreement available with shared data. A provider should still establish that it has the rights needed to grant the intended permissions, and recipients should evaluate the independent legal and provenance issues relevant to their use.
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