Open Data or Closed Doors? 3 Powerful Insights on Who Controls Access and Sharing

II.A. Infrastructure and Portal Ownership

In digital biodiversity infrastructures, the entity that owns or operates the platform wields significant influence over access conditions and the pace of data curation. Control over hosting environments, authentication systems, and primary access points allows these actors to set priorities and determine the terms of engagement.

Even in systems branded as community-driven, decision-making power may reside with a small administrative group. The architecture—APIs, rate limits, uptime commitments—reflects the priorities of those operators, meaning that a single policy or technical change can redefine who participates in data-driven research.

II.B. Curation, Standards, and Editorial Bias

Standards make data interoperable, enabling datasets from different sources to work together. Yet every standard encodes choices: what counts as valid, how it is labeled, and what is left undefined. These decisions can privilege well-documented subjects while sidelining data that does not fit established schemas.

During curation, content outside the dominant taxonomies or formats—such as local classification systems or unconventional field observations—may be altered or removed to fit the standard. While this may improve technical uniformity, it can also erase valuable nuance, narrowing the diversity of information preserved.

II.C. Funding Flows and Citation Economies

Financial support determines which datasets remain accessible and up to date. Resources typically flow toward projects with established visibility, creating a self-reinforcing loop: well-funded datasets attract more citations, which in turn secure additional funding and policy influence.

Meanwhile, valuable but less-visible datasets—often maintained by smaller teams—may receive limited attention and risk obsolescence. Without systems that recognize and resource these contributors, open data ecosystems risk becoming concentrated around a small set of dominant actors.

III.A. Informed Consent in Data Collection

The assumption that digitized data can always be freely shared ignores the necessity of informed consent—particularly for datasets tied to culturally significant areas or sensitive ecological sites.

Consent is not a static agreement. It should evolve alongside the ways data is reused, reinterpreted, or combined with other sources. Without this flexibility, contributors may lose control over how their information is represented and applied.

Clear, revisitable consent protocols build confidence and reduce the risk of ethical breaches, ensuring that openness does not come at the expense of autonomy.

III.B. Provenance as Accountability

Provenance is the documented record of where data comes from, how it has been modified, and who has been responsible for its stewardship. It is critical for trust, reproducibility, and recognition.

Yet, during aggregation or integration, provenance is often lost or stripped away—breaking the ability to verify, attribute, or contest data use. Once that link is gone, accountability becomes harder to enforce.

III.C. Fair and Equitable Benefit Sharing

For openness to be more than a one-way extraction of information, benefits must flow back to the contributors. These can include shared research findings, co-authorship opportunities, improved conservation outcomes, or technical tools tailored to local needs.

Benefit sharing can strengthen trust and encourage participation, turning data release into a mutual exchange rather than a unilateral transaction.

Mechanisms for benefit sharing should be embedded into agreements and workflows from the start, not treated as an afterthought.

Without them, open data infrastructures risk becoming extractive systems that erode the very relationships needed for their sustainability.

IV.A. Licensing as a Tool of Control

Licenses can enable openness, but they can also limit it. Clauses restricting commercial use, derivative works, or redistribution may narrow who can apply the data and under what circumstances.

Complex legal language can discourage smaller organizations or researchers without legal expertise from engaging with a dataset, even when it is technically “open.”

Clarity, simplicity, and transparency in licensing not only broaden potential use but also reduce misunderstandings and legal risks for all parties involved.

IV.B. Governance Structures and Decision-Making

Every open data platform operates under a governance model—formal or informal—that determines how priorities are set, disputes are resolved, and changes are implemented.

When governance is concentrated within a small set of actors, decision-making may reflect narrow perspectives. This can limit innovation, exclude valuable contributions, and reduce trust in the system.

IV.C. Geography and Concentration of Infrastructure

Hosting locations, funding sources, and maintenance responsibilities influence not only performance and reliability, but also governance and data flow patterns. Infrastructure concentration in a few regions can lead to dependency on external decision-making and technical standards.

Distributed models—where data storage, processing, and governance are shared—can help balance influence and increase resilience against technical or policy shifts in any single location.

Such distribution also supports redundancy, reduces single points of failure, and fosters local capacity to manage and adapt systems as needs evolve.

In practice, infrastructure geography is as much a strategic choice as it is a technical one, with lasting effects on how “open” an open data ecosystem can be.

V.A. Redefining “Open” with Equity at Its Core

The conventional definition of “open” often stops at accessibility—whether the data can be viewed or downloaded. This view is incomplete. A richer definition includes the capacity for diverse actors to influence priorities, participate in governance, co-create standards, and benefit from outcomes.

Equity-centered openness acknowledges that not all participants enter the data ecosystem with equal resources, capacities, or influence. Without mechanisms to level the playing field, the promise of openness risks reproducing existing disparities.

Embedding equity requires revisiting governance charters, funding criteria, and operational norms so that they explicitly include provisions for representation, fair attribution, and benefit sharing. Infrastructures that adopt this approach not only expand participation but also enhance the quality and relevance of the data they host.

V.B. Building Participatory Governance Models

Governance is where the principles of openness are either reinforced or undermined. A participatory model ensures that decision-making reflects the diversity of those affected by data use—not just the perspectives of technical or administrative elites.

Such models can incorporate mechanisms like rotating leadership roles, open nomination processes, and public consultation periods for policy changes. These tools prevent the consolidation of power and encourage transparent, accountable processes.

When governance structures are inclusive, they tend to produce more adaptive and context-aware policies. This adaptability strengthens the system’s ability to address emerging challenges—whether technological, ecological, or social—without compromising its core values.

V.C. Investing in Local Infrastructure and Skills

Equity in open data is impossible without equitable access to the tools, infrastructure, and expertise needed to participate fully. This means going beyond one-off workshops or temporary funding streams and committing to long-term investment in locally controlled infrastructure.

Such investment includes physical assets like servers and high-speed connectivity, but also human capacity—data managers, curators, and technical specialists embedded within local institutions. These teams ensure that data is not only stored but actively managed, interpreted, and used to address local priorities.

By strengthening local infrastructure, data ecosystems reduce dependency on external platforms, enhance resilience against geopolitical or market shifts, and foster innovations that emerge from on-the-ground realities rather than imported templates.

Ultimately, this approach transforms data contributors into co-governors, ensuring that their role extends far beyond initial collection.

V.D. Embedding Ethical Frameworks in Data Workflows

Ethics cannot be an afterthought applied at the point of data release; it must be embedded in every stage of the data lifecycle. This means designing workflows that include explicit steps for informed consent, culturally appropriate metadata, and transparent documentation of data provenance.

Operationalizing ethics might involve automated systems that prompt curators to review consent terms before ingestion, metadata fields that capture community permissions, and dashboards that display benefit-sharing commitments in real time.

Such integration turns ethics from a reactive compliance measure into a proactive, visible safeguard—one that strengthens trust between data providers, managers, and users.

V.E. Moving from Extraction to Reciprocity

Historically, many data systems have functioned on an extractive model: data flows outward from contributors to centralized repositories, while benefits flow in the opposite direction, often bypassing the origin entirely. A reciprocal model reverses this logic.

Reciprocity involves tangible returns—shared analytical tools, access to processed datasets, capacity-building programs, or revenue-sharing agreements where commercial applications emerge from shared resources.

This shift is more than ethical; it is strategic. When communities see direct, sustained benefits from participation, they are more likely to continue contributing high-quality, timely data. Trust deepens, collaborations endure, and the system as a whole becomes stronger.

Ultimately, reciprocity transforms open data from a static repository into a living network of mutual investment and shared outcomes.