Citizen data as a catalyst for gender equality: Transforming data ecosystems through collaboration

1 Introduction

Traditional data sources often fail to capture the nuanced dimensions of gender inequality, particularly those experienced in informal economies, unpaid care work, or in contexts of gender-based violence. The termination of the Demographic and Health Survey (DHS) Programme in early 2025, the primary source of reliable and comparable gender-specific data for many governments, has intensified interest in the role of alternative data sources for monitoring progress toward gender equality. ³ The discontinuation of these surveys exacerbates pre-existing gender data gaps and creates a significant vacuum in the availability of essential evidence needed to inform and evaluate gender-related policies.

In this context, novel data sources, especially citizen data, have gained prominence as potential complements to official statistics. Citizen data—often produced through the efforts of women's rights organizations and grassroots movements—can serve as a catalyst for empowerment and advocacy. By capturing the lived experiences, needs, and perspectives of women and gender-diverse individuals, citizen data provides critical insights that often remain invisible in conventional administrative datasets. ⁴ With the arrival of technological advancements, citizen data, such as crowd-sourced data or digital storytelling platforms, have established itself as an alternative data source documenting lived realities that are systematically underrepresented in official statistics.^5,6 Mobile platforms like HarassMap in Egypt or SafetiPin in India collect citizen-reported incidents of harassment, helping to map unsafe urban spaces and inform urban design from a gender-sensitive perspective. ⁷ Citizen data initiatives rooted in participatory methods ensure that women and gender-diverse individuals actively contribute to shaping the narratives and policies that affect them. This helps democratize knowledge production and challenges dominant power structures that often exclude marginalized voices.^8,9 Feminist participatory action research has been used in contexts like Southeast Asia to collect narratives from women laborers and informal workers, empowering them to engage with policymakers directly. ¹⁰

When gender-disaggregated citizen data is collected from the ground up, it can provide real-time, granular, and context-specific information which can validate and inform the development of policies in areas such as reproductive health, education access, labor rights, and digital inclusion. ¹¹ This kind of granular, localized data is particularly important for developing gender-responsive budgeting and impact assessments. ¹² This bottom-up monitoring can complement official data and ensure that the implementation of gender policies is not merely symbolic.

Intersectional data collection, which consists of recognizing the interlocking systems of oppression related to race, class, disability or sexuality, is critical for effective gender policy. Citizen data is often considered more flexible and adaptable than institutional data systems, making it well-suited to capture these complex, overlapping forms of discrimination. ¹³ Projects like “Our Data Bodies” in the U.S. document how marginalized communities experience surveillance and exclusion in digital systems, offering an intersectional critique of data justice. ¹⁴

By bringing previously marginalized voices into the data ecosystem, such initiatives offer opportunities for more inclusive and participatory data production processes. When institutionalized effectively, they hold the potential to contribute to gender-transformative outcomes, including the increased use of gender data in policy design, implementation, and accountability mechanisms.

However, while citizen data presents valuable opportunities, it also poses significant challenges. Issues related to data quality, interoperability, standardization, and the protection of privacy and agency of contributors must be carefully addressed. Recognizing both the promise and the limitations of these emerging data practices is essential for building robust, equitable, and sustainable gender data ecosystems.

In recent years, there has been a discernible shift in the landscape of data governance and production, marked by the increasing engagement of NSOs and development partners in the realm of citizen data. This trend signals a move toward institutionalized collaboration that bridges the divide between official statistics and grassroots data initiatives, with the shared goal of enhancing data inclusivity, responsiveness, and utility for sustainable development.

Historically, citizen data has existed on the margins of formal statistical systems, often perceived as anecdotal, unstandardized, or lacking methodological rigor. ¹⁵ However, the growing recognition of persistent data gaps, particularly in tracking the Sustainable Development Goals (SDGs) and the growing interest in inclusive data has prompted NSOs and multilateral agencies to acknowledge the complementarity of citizen data in filling critical voids in official statistics.^16,17 For example, the UNECE's Road Map on Statistics for SDGs and UN Stats’ Global Data Platform has endorsed the inclusion of non-traditional data sources, including citizen data (referred to as citizen-generated data), as part of modern statistical infrastructure. ¹⁸

This recognition has catalyzed a shift from parallel, disconnected data ecosystems to more integrated, multi-stakeholder models of data production and governance. ¹⁹ NSOs are increasingly partnering with CSOs, community-based initiatives, and citizen science projects to co-design data collection frameworks, share standards, and harmonize outputs. In this spirit, the Collaborative on Citizen Data was formed in 2023 bringing stakeholders from CSOs, NSOs, UN entities and intergovernmental organizations together to advance citizen.

NSOs are increasingly exploring innovative institutional mechanisms to legitimize and incorporate citizen data into NSS. These include for example multi-stakeholder data collaboratives, where citizen data producers are recognized as official contributors to the data ecosystem, methodological guidelines for assessing the quality and usability of citizen data (e.g., interoperability, metadata, reliability), or open data platforms and portals that feature both official and citizen data streams, such as Kenya's National SDG Reporting Platform. ²⁰

Development partners, including UN agencies, donor governments, and philanthropic foundations, have played a critical intermediary role in this transformation. They have provided technical, financial, and normative support to both NSOs and citizen data initiatives, creating enabling environments for collaboration.

While institutional collaboration is growing, scholars and practitioners caution that the integration of citizen data into official systems must avoid co-optation or depoliticization of grassroots knowledge.^9,21 There are concerns that institutional actors may selectively adopt citizen data only when it aligns with predefined priorities or technical standards, potentially marginalizing the emancipatory and political dimensions of citizen-led knowledge production.

Therefore, the institutionalization of citizen data requires reflexive governance frameworks that uphold ethical principles, ensure data sovereignty for communities, and safeguard pluralism in data epistemologies. ²²

The expanding engagement of NSOs and development partners with citizen data efforts is not merely a technical or logistical evolution—it reflects a deeper epistemological shift in how knowledge for development is defined, produced, and valued. It aligns with calls for inclusive gender-transformative data governance that democratizes evidence production, supports data justice, and enhances the relevance of national data systems to local and marginalized populations. ¹³

2 Case studies

The following case studies are presented to demonstrate the impact and application of citizen data in advancing gender equality. While they do not serve to validate the Copenhagen or Participation for Transformation Frameworks directly, they exemplify the principles and practices encouraged by these frameworks.

3 Conceptual frameworks: Copenhagen and participation for transformation

Strong governance structures and evidence-based policy frameworks are essential for sustaining gender-responsive data systems. ²⁸ In March 2025, the 56th session of the United Nations Statistical Commission endorsed the Copenhagen Framework on Citizen Data, ¹ developed by the Collaborative on Citizen Data. ²⁹ The framework has set a foundation of the citizen data definition, as well as a set of principles and key considerations for governments and NSOs in considering the reuse of that data. This marks a major milestone signaling global recognition of citizen data as a key resource within the national data ecosystem and highlights the interest in sustainable and inclusive production and use of data for decision making.

It also represents a critical step toward formalizing the integration of citizen data into official statistical systems. It responds to growing interest in more inclusive, participatory, and timely data sources to complement traditional national statistics and address persistent data gaps.

The Framework defines “Citizen Data” to data originating from initiatives in which citizens are engaged at various stages of the data value chain, guided by principles that promote inclusive, responsible, professional and ethical production and use, regardless of whether or not these data are integrated into official statistics.

There are two important dimensions in this definition: one related to the notion of meaningful engagement and another on when engagement should happen. Meaningful engagement empowers citizens to become data agents and have an impact on the issues that matter to them and affect them, such as local and national policies. The Framework also encourages citizen to engage throughout the entire data value chain.

The framework is aligned with the Beijing Platform for Action document in its underscoring of the importance of collaboration between NSOs, CSOs, and data intermediaries to build trust, align data quality standards, and foster innovation. It reframes citizen data not merely as supplementary, but as a legitimate and potentially transformative input into official statistics, capable of enhancing inclusivity, responsiveness, and local ownership in data governance.

In addition, the Participation for Transformation Framework ² emphasises the co-creation of data by communities and institutions. It advocates for mutual accountability, community ownership, and the strategic use of citizen data for systemic transformation. This complements the former by operationalizing inclusive data practices at national and sub-national levels.

4 Enabling factors for transformation

The growing global momentum around citizen data has empowered an increasing number of countries to establish and strengthen national citizen data partnerships. These partnerships play a critical role in positioning citizen data as a credible and complementary source within both official and informal monitoring, planning, and reporting processes, particularly in advancing gender equality and the SDGs.

One of the key enablers of this transformation is the formation of the Collaborative on Citizen Data ²⁹ and the implementation of the Copenhagen Framework on Citizen Data, ¹ which provides a clear, inclusive, and adaptable structure to guide national-level efforts. The framework highlights eight key pillars—Whole-of-Society engagement, trust, legal and regulatory frameworks, human capital, institutional data governance, fit-for-purpose data quality assurance, an open civic space and sustainable funding. As countries adopt and operationalize this framework, several enabling factors have proven essential to driving transformation within national data ecosystems.

Building on these enabling factors, the transformative potential of citizen data can only be realized through an integrated and transparent approach that embeds fairness and equity at every step. Transparency and fairness are essential to building trust among all gender data stakeholders. A process that is open, equitable, and respectful fosters long-term collaboration, encourages knowledge sharing, and strengthens mutual accountability. This not only enhances the legitimacy of the data collected but also ensures that it reflects the realities of those most affected by gender inequalities.

Enabling transformation through citizen data requires more than technical guidance, it demands a holistic, inclusive, and ethically grounded approach. By embedding principles of full participation, inclusivity, strategic alignment, intersectionality, capacity development, ethics, and transparency, countries can foster an environment in which citizen data contributes meaningfully to advancing gender equality. ² These efforts, when coordinated effectively, can lead to more gender-responsive and transformative data ecosystems that leave no one behind.

5 Principles: Data governance, ethics, and quality assurance

Citizen data initiatives should be guided by key principles that promote inclusive, responsible, professional and ethical production and use of citizen data. Just as how official statistics are guided by the Fundamental Principles of Official Statistics, citizen data principles are the highest standard to hold citizen data producers responsible. The Copenhagen Framework on Citizen Data ¹ included 13 principles:

Independence: Data collection should be initiated and conducted free of any unwanted political pressure or by donors and other organizations.

The 13 principles are drawn from multiple existing sets principles and frameworks including the Fundamental Principles of Official Statistics, A human rights-based approach to data, ³⁴ the CARE and FAIR principles, and other considerations.

The set of principles has a large overlap with the FPOS, but the specificities of citizen data are reflected in a few key principles. For example, participation and inclusion, which are the key elements that define citizen data, are covered under principle 3. Self-definition and self-identification, under principle 7, where citizens and communities assert agency in defining identities, sometimes different from definitions used in official statistics. Data sovereignty (principle 8) recognizes collective ownership and control, often extremely relevant for indigenous population. Ethical and safe production and use (principle 10) is important for both official statistics and citizen data, but for citizen data, there is an explicit human rights framing and heightened sensitivity to power dynamics and harm, especially when citizens are both data producers and subjects. Attribution (principle 12) is more emphasized for citizen data where contributors are credited.

6 Conclusions and recommendations

As the global community intensifies efforts to achieve the SDGs and promote gender equality, the inclusion of citizen data in national and global data ecosystems is becoming increasingly indispensable. Throughout this paper, gender is understood to encompass the experiences of women, men, and gender-diverse individuals. Citizen data provides a unique and powerful complement to official statistics, offering disaggregated, context-specific insights into the lived experiences of women and marginalized groups. Through meaningful engagement and collaboration, such data can expose systemic gaps, drive policy reform, and ensure development outcomes reflect the realities of those most often left behind.

The growing use of citizen data signals a necessary shift towards a more inclusive and participatory approach to data production and use. Evidence from countries like Senegal, Uganda, Kenya, and India has demonstrated how citizen-led data initiatives can spotlight inequalities, inform program design, and influence high-level policy decisions. Yet, the path forward requires more than sporadic innovations. It demands systems that institutionalize citizen engagement, resources that sustain collaborative processes, and safeguards that uphold ethical data use. Sustaining momentum in this area means recognizing that data justice is central to gender justice.

Continued citizen engagement is foundational to nurturing inclusive gender data ecosystems. Engagement must go beyond short-term consultations and evolve into structured, long-term mechanisms for feedback and co-creation. Citizens must be empowered not just as respondents but as producers and users of data, contributing to every stage of the data value chain. The process of generating citizen data must also embed gender-sensitive methodologies and ensure that women and girls, particularly from marginalized communities, are actively involved in shaping the questions, interpreting the findings, and using the results to demand change.

Such sustained participation not only improves data quality and relevance but also fosters trust and accountability between institutions and communities. Trust, in turn, reinforces the legitimacy of data, encouraging both government uptake and community ownership. This participatory ethos transforms data collection from an extractive exercise into a collaborative process of knowledge generation and mobilization.