Our approach

We believe everyone can benefit from opening, sharing, and collaborating around data to make better decisions, improve efficiency, and help tackle some of the world’s most pressing societal challenges.

Set data collaboration principles

We adopted five principles to guide our participation in data collaborations: open; usable; empowering; secure; and private. These principles underpin our participation, and we hope other organizations can build on them to share their data responsibly.

Engage partnerships and explore projects

We believe success will depend on building deep collaborations with others from industry, government, and civil society around the world. This includes work with leading organizations in the open data movement, such as the Open Data Institute and The GovLab at New York University.

Make data sharing easier

We're committed to investing in the essential assets that will make data sharing easier, including the necessary tools; frameworks; and templates. This is especially important when it comes to opening and collaborating around data to solve important societal issues.

Closing the data divide

Access to data is a big challenge. The benefits for organizations of all sizes and the broader community are significant if we can work together to make progress on open data.

Aerial view of people walking on a street.

Industry Data for Society Partnership

Working across industry to make private sector data more open and accessible for societal good.

Line drawing of a neighborhood connected to a cloud with lines.

The open data opportunity

The importance behind data sharing explained

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Open data stories

Stories of open data and data sharing driving change

Microsoft Data for Society catalog

Explore datasets, use cases, and more in our Microsoft Data for Society repository. ​

Visit Data for Society on GitHub

Legal frameworks

Data sharing agreements can take months to draw up, oftentimes deterring organizations from sharing data at all. As a first step toward building better processes and tools, we're sharing a set of data agreements to govern the sharing of data, particularly in the context of training AI models.

CDLA Permissive 2.0

The Community Data License Agreement (CDLA) Permissive 2.0 is an open data agreement designed to make it easier to share and collaborate with open data.

C-UDA 1.0

The Computational Use of Data Agreement (C-UDA) 1.0 is intended for use with datasets that may include material not owned by the data provider, but where it may have been assembled lawfully from publicly accessible sources.


The Data Use Agreement for Open AI Model Development (DUA-OAI) provides terms to govern the sharing of data by an organization with another for the purpose of allowing that second organization to use the data to train an AI model, where the trained model is open sourced.


The Data Use Agreement for Data Commons (DUA-DC) can be used by multiple parties who want to share data through a common, Application Programming Interface (API)-enabled database.


Learn more about the tools and practices we employ to enable more secure and streamlined access to data.

Differential privacy

Differential privacy introduces statistical noise–slight alterations–to mask datasets and protect the privacy of individuals.

Learn about differential privacy

Azure confidential computing

Confidential computing helps to protect sensitive data in the cloud by offering security through data-in-use encryption–additional protection for your data while it's being processed.

Read about Azure confidential computing

Azure Open Datasets

A curated collection of publicly available datasets that are ready to use in machine learning workflows and easy to access from Azure services.

Review the Azure Open Datasets

Researcher tools

Explore a collection of datasets, code, and models from Microsoft Research for the broader academic community to advance state-of-the-art research across all disciplines.

Explore researcher tools