Data Cooperatives

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"Cooperative structures could enable the creation of open data and personal data stores for mutual benefit; they could rebalance what many perceive as asymmetric relationship between data subjects (people with personal data) and data users (people who use data to develop services and products)." (

2. Ledgerback:

"Data Cooperatives are “[c]orporations and other important dataholders group together to create “data pools” with shared data resources.”²⁶ Data Cooperatives can be considered a type of platform cooperative, a cooperative-run enterprise or similar that operates, maintains, shares, or creates a platform (webiste and/or application) to connect individuals together or to provide services.

This type of data trust is one of the best models for ensuring data usage based on individual consent because they can develop “common standards to ensure data security, compliance and technical treatment.” (


"In summary data cooperatives;

  1. are owned by their membership and therefore should be more accountable;
  2. have the potential put a halt to the over collection of personal data through representing data subjects and
  3. advocating on their behalf;
  4. can create value for their membership;
  5. can form around single issues or scale with many data subjects;
  6. can become representative and be used to create change;
  7. could help their membership to understand how data is used – data literacy;
  8. can liberate personal data on members behalf through Subject Access Requests;
  9. can encourage better data and context to be produced by data subjects;
  10. build trust and consent within the organisation and
  11. can be a blend of open data and personal data organisations "



First six collated by Trebor Scholz and Igor Calzada [1]:

Salus Coop, [2] is a non-profit data cooperative for health research (referring to health data and lifestyle-related data more broadly, such as data that captures the number of steps a person takes in a day), founded in Barcelona in September 2017. Salus aims to create a citizen-driven collaborative governance model and management of health data. It legitimizes citizens’ rights to control their health records while facilitating data sharing to accelerate public research innovation in healthcare.

MIDATA, [3], is a ‘health data cooperative’ started in 2015. enables citizens to securely store, manage and control access to their personal data by helping them to establish and own national/regional not-for-profit MIDATA cooperatives. MIDATA cooperatives act as the fiduciaries for their members’ data. MIDATA offers a platform on which user-members can securely store copies of their medical records, genomes, and mHealth data. Members might decide to give their physicians access to all personal data through the platform. In contrast, a not-for-profit cancer research institute could be given access to only medical and dietary information. Members could deny access to a for-profit drug company. Members’ revenues from the sale of data are donated to public research.

LBRY, [4] states: “We think users should own their content (and their privacy) instead of handing it over to a corporate giant and their advertising buddies. If you think we’re paranoid, there are dozens of examples of companies abusing users and acting against their interests. It’s not paranoia if they’re actually out to get you.”

Polypoly, [5]is a data cooperative that ensures that personal data no longer leaves a device, whether mobile phone, computer, or web-enabled toaster. PolyPod, the member of the co-op, has a private server that stores his/her data, and that is controlled by him/her., [6]currently transitioning to a data co-op, is a full-stack development collective that works with industry-leading projects in Web3 by using a decentralized manner by builders worldwide through smart contracts, blockchain, and Ethereum.

Eva Coop: To serve such a vision of a digital commonwealth, localized data need to be federated into data ecosystems. Distributed ledgers are likely able to help. Communities will be using their data gathered in local repositories. This is the case with, a Montreal-based data cooperative: It provides the digital infrastructure for cooperatives of drivers without accessing local passenger data. is built on the EOSIO blockchain protocol as a way to show how the cooperative model could mark a new blockchain-based iteration of the ‘sharing economy’ driven by a democratically centralized system that respects the privacy of workers and meets local needs. Local communities have more input, and drivers are treated more fairly, riding members maintain their privacy, and comforted by a locally supported app. Such federated, democratically centralized data ecosystems can be arranged by sectors (e.g., health-related data, environmental data, transport, and mobility data, energy and consumption data). Communities can then decide to release those data meaningfully. "

See also:

  • The Good Data which allows people to control data flow at a browser level with benefits going to social causes and
  • the Swiss-based Health Bank where personal health data is aggregated for the advancement of medicine,
James Hazard: "At the initiative of the Broad Institute, they formed The Global Alliance for Genomics and Health (GA4GH), to improve standards and interoperability. John Wilbanks is working on this and the GA4GH has done a model patient consent." [7]
James Hazard: "Working with people from Kansas City, the UMKC Law School, Code for America, and MIT Media Lab on model data sharing agreements for municipalities." Data sharing agreements are licenses and municipalities are working the same issues of public/private/commons. [8]
  • The Ubiquitous Commons project: "designing a legal+technological toolkit with which you reappropriate data you produce (social networks, wearables, biotech, IoT, sensors, domotics...), you establish a identity+trust+responsibility model and you distribute access through a p2p infrastructure"
  • Mnémotix, in France "invents solidarity and cooperative smart data"

Apps and Shared Data Platforms


Igor Calzada, 2023:

(in the context of E-Diasporas)

"Data cooperatives are organizations that allow individuals to share their personal data for mutual benefit (Calzada, 2021b). For e-diasporas, data cooperatives could be used to collect and analyze data on their members to better understand their needs and provide more effective support. Additionally, data cooperatives could provide businesses with access to shared resources, such as marketing and sales data, customer databases, and other tools to improve their operations and reach new markets. The Calzada, 2021b, Calzada, 2020 and Bühler et al. (2023c) collected several case studies about data cooperatives that could be inspirational for e-diaspora development worldwide. To illustrate the potential of data co-operatives as a disruptive technology, several cases are listed below.

Calzada, 2021b, Calzada, 2020 identified six active cases:

(i) Salus,

(ii) Driver Seat,

(iii) My Data,

(iv) LBRY,

(v), and

(vi) Polypoly.

Bühler et al. (2023c) in a recent policy brief published by G20 identified ten transformative use cases of data co-operatives from Asia and Africa, with limited examples from Europe and the US:

(i) M-Pesa (mobile money in Kenya),

(ii) e-Kutir (digital agriculture in India),

(iii) Farmerline (collaborative land management in Ghana),

(iv) SOLshare (decentralized renewable energy in Bangladesh), (v) Nubank (Fintech for financial inclusion in Brazil),

(vi) Halodoc (telemedicine in Indonesia),

(vii) Zenzeleni (community networks in Africa),

(viii) GemeinWerk (construction industry in Bavaria, Germany),

(ix) Salus (healthcare in Barcelona, Catalonia), and

(x) Driver’s Seat (ride-hailing, USA). can illustrate the potential of data co-operatives as disruptive technologies by creating a framework for a citizen-driven collective health data management and governance model. In the next methodological and comparative section, the Datafund case will be included as well.

Data cooperatives are a type of cooperative that allows individuals to share and control their data collectively (Bühler et al., 2023c).

Positive contributions of data cooperatives in addressing the limits of HD can be listed as follows:

1. Data sovereignty: Data cooperatives empower e-diaspora communities to take control of their data and leverage it for their benefit, increasing their economic and social capital (Calzada, 2021b).

2. Data sharing: Data cooperatives enable e-diaspora communities to collaboratively and securely share data, facilitating the development of new products and services tailored to the community’s specific needs (Bühler et al., 2023a).

3. Privacy protection: Data cooperatives provide e-diaspora communities with greater privacy protection by allowing them to control how their data is collected, used, and shared (Calzada, 2022c).

4. Collective bargaining: Data cooperatives, as a subcategory of platform cooperatives (Calzada, 2020), enable e-diaspora communities to negotiate better terms with data buyers and sellers, increasing the value of their data and ensuring a fair share of the benefits (Mannan & Pek, 2023).

Regarding negative contributions of this disruptive technology:

1. Technical complexity: Data cooperatives can be technically complex and require significant technical expertise to set up and maintain, limiting accessibility for e-diaspora communities with limited technical knowledge (Orgad & Bauböck, 2018).

2. Legal and regulatory challenges: The legal and regulatory frameworks governing data collection, use, and sharing can be complex and vary across jurisdictions, creating challenges for data cooperatives operating in e-diaspora contexts (TAPP, 2023).

3. Risk of exploitation: Data cooperatives can be vulnerable to exploitation and manipulation by bad actors, posing a risk to the security and privacy of e-diaspora communities (Cheney-Lippold, 2016).

4. Data quality issues: Data cooperatives rely on the quality and accuracy of the data shared by community members, which can vary and create challenges for data analysis and utilization (Loukissas, 2019)."



Simon Grant

"Information which might be relevant to anyone buying anything is valuable, and can be sold. Naturally, the more money is at stake, the higher the price of information relevant to that purchase. Some information about a person can be used in this way over and over again.

Given this, it should be possible for people themselves to profit from giving information about themselves. And in small ways, they already do: store cards give a little return for the information about your purchases. But once the information is gathered by someone else, it is open for sale to others. One worry is that, maybe in the future if not right away, that information might enable some “wrong” people to know what you are doing, when you don’t want them to know.

Can an individual manage all that information about themselves better, both to keep it out of the wrong hands, and to get a better price for it from those to whom it is entrusted? Maybe; but it looks like a daunting task. As individuals, we generally don’t bother. We give away information that looks trivial, perhaps, for very small benefits, and we lose control of it.

It’s a small step from these reflections to the idea of people grouping together, the better to control data about themselves. What they can’t practically do separately, there is a chance of doing collectively, with enough efficiencies of scale to make it worthwhile, financially as well as in terms of peace of mind. You could call such a grouping a “personal data cooperative” or a “personal information mutual”, or any of a range of similar names.

Compared with gathering and holding data about the public domain, personal information is much more challenging."

Annemarie Naylor

From a summary of the Berlin Open : Data : Cooperation event on the 20th October 2014, by Annemarie Naylor et al:

"Our modern, technologised society exists on data. Our everyday interactions leave a trace that is often invisible and unknown to us. The services that we interact with, the daily transactions that we make and the way we negotiate through our everyday generate data, building a picture of who we are and what we do. This data also enables aggregators to predict, personalise and intervene seamlessly and sometimes invisibly. Even for the most technically literate, keeping track of what we do and don’t give away is daunting. There is a need to stem the unbridled exploitation of personal data by both public and private organisations, to empower individuals to have more control over the data they create, and for people to have more of a say in the services that are built upon and informed by this data. Data cooperatives may help rebalance the relationship between those that create data and those that seek to exploit it whilst also creating the environment for fair and consensual exchange.


Cooperation for the creation of common good is a widely understood concept and in a world where value is often extracted by large organisations with opaque processes and ethics, they are starting to be seen as a way of reinvigorating value transactions within smaller, often under-represented communities of interest, and between organisations that create and use data.

Finding already existing data cooperatives is not easy. Examples such as The Good Data which allow people to control data flow at a browser level and the Swiss-based Health Bank are two known examples, and as the principles of data custodianship for social good become understood there is little to challenge that more would develop.

There are organisations that exhibit cooperative traits but may not themselves be cooperatives or co-owned structures. Open Street Map (OSM) is a resource that is essentially created and administered by the community, with the underlying motivation for OSM being for common good. The open source movement was cited as being the largest example of technological cooperativism, although the largest platform on which cooperative endeavour is expressed (GitHub) is a privately owned Silicon Valley entity.

There are many versions of coops. These have traditionally come out of the needs of the membership who subscribe to them. Structures of these cooperatives have generally been organised around a single class of member – workers, producers, consumers, etc. The single class structure, although creating an equitable environment for those that are members of a particular coop, can tend towards self interest and although they may be bound by the notion of the common good, the mechanism for the creation of the common good or commons is seldom explicit.

Internationally the creation of new forms of cooperatives that explicitly express the development of common good across multiple classes of stakeholders are more abundant. Social co-ops in Italy and Solidarity coops in Canada often provide services such as healthcare, education and social care. Could these types of cooperative be more relevant for our networked and distributed age?

Michel Bauwens founder of the P2P Foundation talks about the creation of these new forms of cooperatives, and how it is necessary to wean ourselves off the notion of cooperativism as a means of participation in a capitalist economy, to one that builds a commons both material and immaterial. This commons would be subscribed to by other commons creating entities and licenced to non-commons creating organisations.

Would a data cooperative necessarily adopt these newer forms of distributed and commons creating structure? There appears to be a consensus that commons creating, multi-stakeholders cooperatives are positive, but is this model easily understood? And can individual circumstances especially when dealing with communities based around sensitive issues, create an environment for sharing beyond a single class? A single class cooperative may seem to be a simpler, immediate solution for a community of people who have specific needs and issues and where strong trust relationships need to be maintained.

It is understood that personal data empowerment is not just about selling data to the highest bidder and any organisation acting as a data intermediary would need to be able to accommodate the complexity of reasons as to why people donate or give. Even though economic gain might seem an obvious attraction for people, motivations are more complex and often financial incentives can be detrimental to the process of participation and giving.

From The Good Data’s perspective data cooperatives should split the data layer from the service layer. The cooperative should control the data layer and enable/choose others to build the service layer as it is likely that data cooperatives would not have the capacity or expertise to create end to end solutions.

The structure of the data cooperative should encourage maximum participation and consent, although 100% participation and engagement is unrealistic. Flat structures have a tendency towards hierarchy through operational efficiency and founder endeavour. Even though the majority of members align with the aims of the cooperative, it doesn’t necessarily mean that they want to be constantly encumbered with the burden of governance.

A certain pragmatism and sensitivity needs to be adopted to the model of cooperative that a group may want to adopt. There are examples of communities maintaining informality to enable themselves to be less burdened by expectation, to maintain independence or minimise liability. Advocates of data cooperatives need to be sensitive to this.


Data Cooperatives need to have a simplicity of purpose. What do they do, for whom and why? Is the building of data cooperative around particular issue enough? Or do we need to take a look at the data cooperative as being a platform that allows the representation of personal data across a broader portfolio of interests?

Although the there is a tendency to see a data cooperative as being a mechanism to generate bulk, high worth data that can then be used to draw down value from large organisations, a more appropriate application might be in enabling a smaller community of interest, perhaps around a particular health condition, to draw down certain services or to negotiate for a better deal. The notion of withholding data from public service providers might be seen to be detrimental to the delivery of that service, but it could also create a more balanced decision making process. It is also known that many providers of service collect more data than they actually need for the delivery of that service. Empowering people to take more control over their data may create a situation where the practice of excessive data gathering is curtailed.

Data literacy

Ideally for a data cooperative to be most effective, the level of data literacy amongst members would need to be raised so that members could make more informed decisions about what data was given away or used. This ideal might be difficult to achieve without a broader awareness raising campaign about the power of personal data. The revealing of the ways that security agencies collect data by Edward Snowdon was sensational and although it highlighted that we unintentionally give away a lot, it didn’t build a wider popular discourse around protection and usage of personal data.

Raising the level of data awareness amongst cooperative members would create more informed decision making, but this task would need to be delivered in a nuanced way and ultimately some people might not engage. This could be the case with people who are dependant on service and have little power or real choice as to their decisions.

For a data cooperative to represent its membership and control the flow of data it needs to have legitimacy, know and understand the data assets of the membership, and have the authority to negotiate with those data assets on the members behalf.

Decisions around data sharing and understanding the potential consequences are difficult and complex. As an intermediary the cooperative would need to ensure that individual members were able to give informed consent. We have to know what we have and what it does for us, in order to utilise it.

Mechanisms of consent

There already exist mechanisms for the creation of consent. These by and large create the environment for proxy voting in decision making processes. A mechanism such as Liquid Feedback – popularised by the Pirate Parties, where an individual bestows voting rights to a proxy who aligns to their position, is a representative democracy process, the ‘liquid’ element allows proxy rights to be revoked at any point in the decision making process. Other mechanisms might follow along the lines of the Platform Preferences initiative developed by W3C, which sought to create privacy policies that could be understood by browsers which was ultimately considered too difficult to implement. A potentially easier solution might work on the basis of preset preferences based on trusted individuals or the creation of archetype or persona based preferences that people can select.

Can one organisation be representative of the broader range of ethical positions held within a membership structure? For practical reasons the data cooperative might have a high level ethical policy but individuals within the cooperative are empowered to make data sharing choices based on their personal ethical standpoint. This could be enabled by proxy or preset data sharing preferences.

The alternative to having data coops with high level ethical aims that also represent multiple ethical standpoints could be to have smaller federated or distributed niche organisations where individuals could allow the organisation to use their data on their behalf.

Right to personal data

In order for an individual to allow an organisation to use data on their behalf we need to have control over our individual personal data. Legislation in many countries offers a framework about how personal data is used and shared amongst organisations, but these don’t necessarily create a mechanism that allows users to retrieve their data and use it for other purposes. Often within the End User License Agreement (EULA) or Terms of Service that come with software products an individual may find that their data is inexorably tied up with the function of the service. A function of a data cooperative might be to help individuals understand these agreements and add to the commons of knowledge about them.

How would the argument for greater individual data rights be made when service providers see that personal data mediated through their product part of their intellectual property? Work has been done through the midata initiative and the development of personal data passports – where individuals grant rights to organisations to use the data for delivery of service. UK Government has supported this initiative, but has backed away from underpinning the programme with changes in legislation. This lack of regulatory enforcement may limit the efficacy of any initiative that seeks to grant individuals’ rights and agency over their data.

The development of a personal data licence may aid the creation of data cooperatives but the form of the licence and the mechanism for compliance might be weakened without an underpinning regulatory framework. At present there is a certain level of cynicism around voluntary codes of practice where power imbalances exist between stakeholders. The lack of legislation might also create a chilling effect on the ability of data cooperatives to gain the trust of their membership.

Data empowerment is promoted in Project VRM (Vendor Relation Management) developed by Doc Searls at Harvard University. The ability for an individual to have control over their data is an integral component of developing an open market for personal data-based services and theoretically giving more choice. The criticism voiced about midata and Project VRM is that they are too individualistic and focus on economic rather than social transaction with ethical aims. Even with these criticisms the development of a market logic to enable large organisations to engage with the process of individual data empowerment might be beneficial for the long term aims of data cooperatives and for the development of innovative service for social good.

Ultimately if the individual isn’t able to have control over their data or the data derived from them then the function of the cooperative would be inhibited.

Creating value from data

It could emerge that scale could dictate the eventual form of the data cooperative. Many potential clients of a data cooperative might require this, which would see the need to build a data asset that contained upwards of 500,000 users. The Good Data cooperative’s aim is to achieve this scale to become viable.

A challenge that all data cooperatives would face would be how they maintain a relationship with their membership so that service based upon, or value that is extracted from the data is not subject to unforeseen supply-side problems. If a data cooperative represented its membership and entered into licensing relationships with third party organisations on behalf of its membership, what would be reasonable for a client to expect, especially if individual members had the rights to revoke access to data at anytime? With larger scale data cooperatives this may not be too much of a problem as scale has the potential to damp down unforeseen effects. The Good Data proposes to get around these issues by only holding data for a limited amount of time essentially minimising disruptions in data supply by creating a buffer.

Smaller scale data cooperatives, especially ones that are created around single issues may have difficulty in engaging in activity that requires service guarantees. Developing a mechanism for federation, cumulatively creating data at scale might be a potential solution, but creating a federated system of consent may be more difficult to achieve. As suggested previously economic activity might be a low priority for such organisations where the main purpose might be to represent members and create the environment for informed service decisions.

The challenge facing federated data cooperatives and how they interact is undefined. It has been noted that building distributed and federated systems is difficult, and that centralised systems persist due to operational efficiencies. The advent of alternative forms of ‘block chain’ transaction could enable distributed organisations to coexist using ‘rules based’ or algorithmic democracy. But alternative transaction systems and currencies often face challenges when they interface with dominant and established forms of currency and value."

More Information

See also:

For more, see also the diigo bookmark collection on Data Ownership: