Data for Society
Using open data to accelerate the development of solutions to solve society's most pressing challenges.
Open Data for Social Impact Framework
The Open Data for Social Impact Framework is a tool to help leaders put data to work to solve pressing societal issues.
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
When we launched the Open Data Campaign, 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.
Year one in review
Sharing 10 lessons learned from the first year of the campaign to help other organizations of all sizes unlock the power of data.
The open data opportunity
The importance behind data sharing explained
Open data stories
Stories of open data and data sharing driving change
20 data collaborations by 2022
Explore projects in the areas of sustainability; health; and equity and inclusion using open data and data sharing models that we've helped to launch against our commitment of 20 data collaborations by 2022.
Electric Vehicle (EV) Charging Infrastructure Pilot
The EV Charging Infrastructure Pilot of the London Data Commission combines public and private datasets into a series of layered maps to help analyze and unlock EV charging market constraints.
London Air Quality project
This project led by The Alan Turing Institute uses collated air pollution data sources to forecast air quality and help decision makers improve air quality over London.
The OS-Climate Initiative, led by the Linux Foundation, empowers the investment community to better address climate risk and opportunity using open-source analytics and open data.
Microsoft partnered with WRI India and BlackRock on the Wave2Web Hack, a Data Science and Predictive Modeling Hackathon, with participants developing algorithms to forecast reservoir water availability in India and dashboards to enable visualizations on this data.
Microsoft Nonprofit Innovation Hub
The Nonprofit Innovation Hub is an open-source GitHub repository with lightweight solutions that enable nonprofits to innovate.
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.
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.
Learn more about the tools and practices we employ to enable more secure and streamlined access to data.
Differential privacy introduces statistical noise–slight alterations–to mask datasets and protect the privacy of individuals.
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.
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.
Microsoft Research Open Data
A collection of free datasets from Microsoft Research to advance state-of-the-art research in areas such as natural language processing, computer vision, and domain specific sciences.