---
title: "Data collaboratives"
chunk: 2/2
source: "https://en.wikipedia.org/wiki/Data_collaboratives"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:48:56.315156+00:00"
instance: "kb-cron"
---

AI4BetterHearts: A global data cooperative established by the Novartis Foundation and Microsoft to improve cardiovascular health with the aim of using AI and data analytics to tackle heart disease.
The Chicago Data Collaborative: An effort by newsrooms, academics, and non-profit organizations to source data from public agencies, organize and document the data, and link it for a better and comprehensive understanding of the criminal justice system.
The Counter Trafficking Data Collaborative: A data collaborative working to curb human trafficking through data contributed by various countries and is maintained by the International Organization for Migration (IOM) and Polaris.
CubeIQ: An offline intelligence and measurement company helping marketers understand the true impact of their cross-channel advertising in the offline world. Their "Data For Good" program provides access to anonymous, privacy-compliant location data for academic research and humanitarian initiatives related to human mobility.
Data Collaborative for Justice: A project at the John Jay College that leverages community data to research the operations of the criminal justice system and create informed and transparent frameworks for criminal justice reform.
The Health Data Collaborative: A multi-agency, multilateral effort active in five African countries that provides a collaborative platform to leverage technical and financial resources at all levels alongside country-owned strategies and plans for collecting, storing, analyzing, and using data to improve health outcomes, with specific focus on UN SDG targets and communities that are left behind.
International Network for Data on Impact and Government Outcomes (INDIGO): An initiative of the Government Outcomes Lab (GO Lab) at the Blavatnik School of Government at the University of Oxford that builds an interdisciplinary network of data stewards to address social problems collaboratively.
InfoSum: A UK based company that enables a decentralized and trusted data ecosystem to enable companies to do more with customer data without actually sharing the data.
The Mobility Data Collaborative: A partnership among mobility operators, data aggregators, public agencies, academia and others to provide solutions and common framework to ensure safe, equitable and livable streets for all.
Water Data Collaborative: Works towards their mission to grow and maintain an inclusive community of water scientist data generators to provide data that enable the protection and restoration of our nation's waterways.

== Risks, challenges, and ethical considerations ==
Data collaboratives have significant challenges related to data security, data privacy, commercial risk, reputational concerns and regulatory uncertainty. In addition, there exist concerns about the lack of trust among individuals, institutions and governments.

=== Risks ===
Commercial Risks: "Corporations are concerned about brand reputation, data rights and the disclosure of proprietary or commercially sensitive information."
Security Risks: Vulnerable data structures, lacking security expertise and processes can put all members of a data collaborative at risk.
Regulatory Risks: Fragmented legal and regulatory frameworks hinder data sharing across sectors and sovereign borders. Varying definitions of privacy and data holder rights exposes data holders to significant compliance risks and liabilities.
Privacy and Ethical Risks: Collaborative data use can expose individual identities, infringing on privacy and security. Additionally, protecting vulnerable populations from discrimination and human rights violations through the sharing of non-personal but demographically identifiable data is often a major issue.

=== Mitigating privacy protection issues ===
Privacy preserving computation (PPC) presents data in forms that can be shared, analyzed, and operated on by multiple stakeholders without the raw information. To do so, PPC seeks to control the environment within which the data is operated on (Trusted Execution Environment) and strips the data of identifying traits (Differential Privacy). Protecting the data via Homomorphic Encryption techniques, PPC allows users to execute operations and see their outcomes without exposing the source data. Through secure Multi-Party Computation, different groups can combine data to work in a decentralized and collaborative manner.
PPC techniques are already being leveraged by governments and large corporations. In 2015, the Estonian government worked with the private firm, Sharemind, to analyze tax and education records through Multi-Party Computation for the Private Statistics Project. An external audit by the European Commission PRACTICE project found that the Private Statistics Project did not expose any personal data.
In 2019, Google released its Private Join and Compute protocol to open-source, allowing users to use Homomorphic Encryption and Multi-Party Computation. In the same year, ten pharmaceutical companies formed the Melloddy consortium to use blockchain technology to train a drug discovery algorithm via shared data.

=== Mitigating power asymmetries ===
Power imbalances can occur when stronger parties manipulate, exclude, or pressure weaker members of the data collaborative. From a classical viewpoint, power refers to the influence a person or group has over another. Examining collaborative governance, Dave Egan, Evan E. Hjerpe, and Jesse Abrams suggest a three-phased approach to power: power over refers to the ability to control the behavior of others, power for looks at the ability to authorize the participation of stakeholders, and power to considers the ability to measure another entity's ability to realize its goals.
Power imbalances can arise from disparities in authority, resources, legitimacy or trust between parties. The more actors in the data collaborative or more incentives of data use, the increased likelihood for conflicting interests. Oftentimes, data is viewed as an organizational asset, and opening it up to new uses by others means relinquishing control over the data and ceding this autonomy to the collaborative, resulting in the "control and generativity challenge." Data stewards can help reduce the power imbalances by reducing bias influences, follow operating procedures, and provide issue resolution and remediation.

== See also ==
Big Data
Data sharing
Open collaboration
Dispersed knowledge
Digital collaboration
Mass collaboration
Open innovation

== References ==

== External links ==