How open data communities are like polycules
Here we would like to explore some of the curious similarities between open data communities and polycules, something that might seem like comparing apples and oranges, but where there is actually an interesting degree of similarity (fun fact, apples and oranges are also far more similar than the saying would imply).
Open data communities are networks in which users create, maintain and share data in an open and collaborative way. (e.g. the Human Genome Project, Github repositories)
A polycule is a network of interconnected romantic and/or intimate partners in a polyamourous relationship.
Just from these simple definitions we can already see some similarities emerging: both groups can be described as networks of individuals who share and collaborate to create a common environment, it’s just that one is doing it for data and the other for intimacy.
Let’s look in a bit more detail at some areas of overlap.
Structural similarities
Decentralised
Polycules can be described as a non-hierarchical webs of relationships consisting of multiple partners each of whom may have their own connections. Though there can be a nucleus that a polycule develops around (e.g. a couple who each have multiple connections), there is no centralisation (i.e. concentration of authority or decision making in a single location), rather a grouping of fluid and dynamic connections.
Open data communities similarly work on a decentralised collaboration model. Individuals, organisations and governments contribute and share data typically without rigid top down control.
In both cases the networks grow organically as more participants join and connect (and can similarly shrink organically).
Fluid and adaptable
In both cases, fluidity and adaptability are essential to a successful community.
In open data, projects and collaborations are often dynamic. New datasets and collaborators will emerge, data standards will change and evolve, and new challenges will arise that communities will need to pivot to address (for example, adapting data sharing to the Covid-19 pandemic).
In polycules, relationships will also change and evolve, such as going through different stages of intimacy and intensity. New connections can form whilst others may shift and evolve requiring the polycule to adapt to changing circumstances and composition.
Transparency and trust
In both communities, transparency and trust are essential. Successful polycules are built on open communication, consent and transparency around relationships and boundaries.
Similarly, transparency is core to the ethos of open data. Data should be openly shared with clear licensing and standards, and it is essential to build trust among both users and contributors.
Collaboration, not ownership
In contrast to the exclusivity that defines traditional monogamy (where partners ‘belong’ to each other), polycules are built on shared connections and mutual support. There is little to no element of ownership.
Similarly in open data, datasets are considered communal resources to be maintained and shared collaboratively rather than proprietary assets.
Overlaps between communities
In both cases, the boundaries of communities can be fuzzy. There can be multiple overlapping relationship networks, for example, one person may be on the periphery of multiple polycules. And in data communities, participants may belong to multiple initiatives and contribute to a variety of projects.
Shared challenges
Miscommunication and trust
Both concepts are vulnerable to loss of trust. Careful communication is required to avoid destructive drama in polycules, resulting from excessive jealousy, mismatched communications and unclear boundaries.
Misuse of data can erode trust in open data communities and poor metadata or lack of context around datasets can lead to misrepresentation.
Both rely on clear norms and transparency to function and bad actors or human error can disrupt harmony.
Scaling complexity
In polycules, more partners results in exponentially more relationship dynamics to navigate: scheduling can become tricky and emotional labour can increase significantly. As networks grow there is also more risk that not everyone shares the same communication style or commitment, which can also cause problems.
In open data, more contributors can result in fragmentation of the community, and may lead to duplicate datasets or conflicting standards. Maintaining quality control can become harder as the community grows.
In both cases decentralisation can enable freedom but struggles with coordination at large scales.
Burnout and maintenance labour
In polycules, the maintenance required to successfully maintain multiple relationships can cause exhaustion and burnout. Similarly in open data communities, especially in volunteer projects, burnout from maintenance labour can lead to collaborators leaving, causing projects to collapse.
Both systems can suffer from a ‘free rider problem’, where participants utilize the benefits of the community without contributing back, increasing the maintenance labour on others.
Power imbalances
In polycules, hierarchies can still develop (e.g. primary and secondary partners) which if not managed carefully can lead to power imbalances, and bad actors can take advantage of less experienced members.
In open data, well resourced institutions can dominate the discourse and shape the community in their favour (e.g. by influencing standards in their favour), and are more able to extract data without contributing.
Even though both systems tend to be non-hierarchical, power dynamics can still emerge that need careful management by the group as a whole.
Stigma and legitimacy
Open data can often be seen as niche and unreliable compared to proprietary data. Governments and organisations may also resist moves to open data due to bureaucracy, fear of backlash, or lack of understanding of the benefits.
Polycules can experience similar marginalisation and stigmatisation by wider society. This can lead to a lack of both cultural understanding and cultural scripts, often leaving new participants to have to learn from scratch.
Both systems often have to ‘prove their value’ and fight for cultural acceptance.
Shared solutions
In both cases user protocols can reduce friction and drama in the community. In polycules this could be relationship agreements (whether formal or informal), and in open data this could take the form of licences and governance documentation. Moderation tools can also help, along with the presence of mediators and curators.
Both systems also benefit greatly from education and onboarding (e.g. polyamoury guides or FAQs and the Open Data Handbook or the Collaborative Data guidebook).
Summary
Both systems are experiments in reimagining traditional structures (monogamy/proprietary data) as collaborative networks. In both cases the biggest challenge is managing the dynamics in complex, collaborative, decentralised systems and proving that they can be valuable and sustainable without collapsing.
An open data community is like a ‘polycule of knowledge’: interconnected, built on trust, and thriving when participants freely share and collaborate without silos.
If you’d like advice or help with open data, whether it’s establishing a community, help with publishing tools or just guidance on best practice, get in touch with us at Register Dynamics.
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