-
Notifications
You must be signed in to change notification settings - Fork 0
3.1 Advancing Critical Data Literacies Through Citizen Science
Note: Content in this section is adapted from: Atenas, J. (2025). Advancing critical data literacies through citizen science. Libraries and positive climate action webinars, British Library. Zenodo. https://doi.org/10.5281/zenodo.17902124 ``
This unit explores the relationship between data literacy, critical data literacy, and citizen science as approaches to fostering meaningful participation in a data-driven society. It examines how citizens can engage with scientific research while developing the skills required to collect, analyse, interpret, communicate, and critically evaluate data.
Citizen science refers to research that involves the active participation of the general public, including non-professional or amateur contributors, across a wide range of disciplines such as ecology, health, astronomy, and social sciences. Its definition varies, but it broadly centres on collaboration between scientists and the public.
In practice, citizen science serves multiple roles. It is often used as a research methodology, where volunteers contribute to activities such as data collection and classification, significantly expanding the scale and scope of scientific studies. This approach allows researchers to gather data across large geographic areas and long time periods—something that would be difficult for individual researchers to achieve alone. Citizen science can also involve community-led research, where participants investigate issues affecting their own environments, such as health or environmental risks. In addition to its research value, it plays an important educational role by helping participants better understand the scientific process and raising awareness of key issues. For this reason, it is increasingly incorporated into educational settings, including school curricula.
Data literacy is commonly understood as the ability to:
- Read data
- Understand data
- Create data
- Communicate information derived from data
Data literacy extends beyond technical competencies and includes the social skills required to participate effectively in a datafied society.
Prado and Marzal (2013) define data literacy as the skillset that enables individuals to access, interpret, critically assess, manage, handle, and ethically use data.
Critical data literacy refers to:
The ability to critically engage with datafication by reflecting on the societal implications of data processing and implementing this understanding in practice (Brand & Sander, 2020).
Critical data literacy recognises:
- Power relationships embedded in data systems
- Social and political implications of data collection
- Issues of surveillance, bias, exclusion, and representation
- Ethical uses of data
Citizen science is a research approach that actively involves members of the public in scientific inquiry.
Participants may contribute to:
- Problem definition
- Data collection
- Quality assurance
- Data analysis
- Interpretation of findings
- Dissemination of results
This collaborative approach increases accessibility, inclusivity, and community engagement in research.
Public participation plays an increasingly important role in shaping how research is conducted and how evidence is used in society. Initiatives such as Citizen Science and Civic Monitoring demonstrate two distinct but complementary ways in which non-experts contribute to knowledge production and accountability.
Citizen Science is typically organised by academic or scientific institutions and involves volunteers contributing to research by collecting or classifying data. Its primary aim is to expand the scale of data available for scientific inquiry, enabling researchers to study phenomena across broader geographical areas and longer timeframes. In doing so, it also promotes public engagement with science, fostering awareness, education, and scientific literacy.
In contrast, Civic Monitoring (also referred to as civic environmental monitoring or citizen sensing) emerges from grassroots initiatives. It is driven by communities seeking to document and respond to issues that directly affect them, such as environmental degradation, pollution, or public health risks. Rather than contributing primarily to academic knowledge, civic monitoring focuses on producing evidence for advocacy, accountability, and legal action, often in contexts where institutional monitoring is insufficient or absent. While both approaches rely on public participation, they differ significantly in their objectives, methodologies, and outcomes. Citizen science extends the reach of formal research systems, whereas civic monitoring challenges power structures and seeks social or environmental justice. Together, they illustrate the diverse ways in which data practices can support both knowledge generation and democratic participation.
| Feature | Citizen Science | Civic Monitoring |
|---|---|---|
| Primary Driver | Driven by scientific institutions or researchers | Driven by local communities or concerned citizens |
| Main Goal | To scale up data collection for broad research and conservation | To provide actionable evidence against environmental injustices or crimes |
| Methodology | Top-down: designed by scientists and executed by volunteers | Bottom-up: defined by lived experience, senses, or simple sensors |
| Data Use | Academic journals, policy reports, and global databases | Legal evidence, court litigation, and local advocacy |
| Participation | Voluntary participation of non-professional researchers | Form of active citizenship and civic engagement |
| Role of Participants | Contributions at different stages of the research process (e.g. data collection, classification, analysis) | Citizens monitor public policies, services, and public spending |
| Purpose and Outcomes | Supports research, innovation, education, and public engagement | Focuses on accountability, governance, and social justice |
| Tools and Practices | Structured research tools and scientific protocols | Uses shared methods and tools to evaluate public services and governance |
Despite these differences, both approaches intersect in meaningful ways. They both rely on distributed data collection, often use similar tools (such as sensors, mobile devices, or digital platforms), and can influence policy and governance. In some cases, citizen science projects may evolve into more activist forms, while civic monitoring initiatives may contribute to scientific understanding.
Volunteer thinking activities use collective intelligence to process and interpret data.
Benefits include:
- Reducing digital inequities
- Supporting dataset preparation and classification
- Enhancing machine learning through crowdsourced contributions
- Building community engagement
Key stakeholders include:
- Researchers
- Participants
- Community organisations
- Funders
flowchart TD
%% Shared foundation
A[Public Participation in Data] --> B[Data Collection & Observation]
B --> C[Citizen Science]
B --> D[Civic Monitoring]
%% Citizen Science path
C --> C1[Institution-Led Research]
C1 --> C2[Scientific Methods]
C2 --> C3[Large-Scale Data Collection]
C3 --> C4["Knowledge Production<br/>Education & Engagement"]
%% Civic Monitoring path
D --> D1[Community-Led Action]
D1 --> D2[Lived Experience & Local Knowledge]
D2 --> D3[Targeted Evidence Gathering]
D3 --> D4["Advocacy, Accountability<br/>Legal Action"]
%% Intersections
C3 --> E[Shared Tools & Platforms]
D3 --> E
E --> F[Data Informs Policy & Governance]
F --> G[Social Impact]
%% Feedback loop
G --> A
%% Styling (pastel + black text)
classDef base fill:#e6f7ff,stroke:#444,color:#000;
classDef cs fill:#e6ffe6,stroke:#444,color:#000;
classDef cm fill:#ffe6f0,stroke:#444,color:#000;
classDef shared fill:#fff5e6,stroke:#444,color:#000;
classDef outcome fill:#f3e6ff,stroke:#444,color:#000;
class A,B base;
class C,C1,C2,C3,C4 cs;
class D,D1,D2,D3,D4 cm;
class E,F shared;
class G outcome;
Citizen science projects can be understood through five levels of participation.
flowchart LR
A[Contractual] --> B[Contributory]
B --> C[Collaborative]
C --> D[Co-Created]
D --> E[Collegial]
A --> A1[Communities request research from professionals]
B --> B1[Scientists design projects and citizens contribute data]
C --> C1[Citizens contribute to data collection analysis and dissemination]
D --> D1[Citizens and scientists jointly design and implement research]
E --> E1[Independent citizen-led research]
style A fill:#FFE4E1,color:#000
style B fill:#E6F2FF,color:#000
style C fill:#E6FFE6,color:#000
style D fill:#FFF5E1,color:#000
style E fill:#F3E6FF,color:#000
style A1 fill:#FDEDEC,color:#000
style B1 fill:#EBF5FB,color:#000
style C1 fill:#E8F8F5,color:#000
style D1 fill:#FEF9E7,color:#000
style E1 fill:#F5EEF8,color:#000
| Model | Description |
|---|---|
| Contractual | Communities request research from professionals |
| Contributory | Scientists design projects and citizens contribute data |
| Collaborative | Citizens contribute to data collection, analysis, and dissemination |
| Co-Created | Scientists and citizens jointly design and implement projects |
| Collegial | Independent research conducted by non-credentialed researchers |
--
Citizen science can be used to develop a broad range of data competencies.
flowchart LR
A[Data Collection]
--> B[Data Analysis]
--> C[Data Curation]
--> D[Data Visualisation]
--> E[Data Storytelling]
--> F[Data Publication]
style A fill:#FFE4E1,color:#000
style B fill:#E6F2FF,color:#000
style C fill:#E6FFE6,color:#000
style D fill:#FFF5E1,color:#000
style E fill:#F3E6FF,color:#000
style F fill:#E0F7FA,color:#000
| Skill Area | Description |
|---|---|
| Data Collection | The ability to gather data using surveys, interviews, questionnaires, sensors, open data portals, and data mining techniques |
| Data Analysis | The ability to review data, identify patterns, generate insights, and answer research questions |
| Data Curation | The ability to organise datasets, maintain records, create databases, and preserve data for future use |
| Data Visualisation | The ability to present findings graphically, communicate patterns effectively, and support decision-making |
| Data Storytelling | The ability to communicate narratives emerging from data and explain findings to different audiences |
| Data Publication | The ability to share datasets openly, deposit data in repositories, and enable reuse and transparency |
Libraries play a critical role in enabling citizen science through:
- Infrastructure provision
- Training and skills development
- Community engagement
- Partnership building
- Research support
flowchart TD
A[Libraries]
--> B[Infrastructure]
A --> C[Training]
A --> D[Partnerships]
A --> E[Scientific Literacy]
A --> F[Community Engagement]
B --> G[Citizen Science Capacity Building]
C --> G
D --> G
E --> G
F --> G
style A fill:#FFE4E1,color:#000
style B fill:#E6F2FF,color:#000
style C fill:#E6FFE6,color:#000
style D fill:#FFF5E1,color:#000
style E fill:#F3E6FF,color:#000
style F fill:#E0F7FA,color:#000
style G fill:#E8F8F5,color:#000
A major challenge for citizen science initiatives is the lack of:
- Training opportunities
- Organisational support
- Sustainable infrastructure
Libraries can address this through:
- Skills programmes
- Project planning support
- Scientific literacy frameworks
- Community-based learning activities
Different groups may require different engagement methods.
| Group | Data Collection | Data Creation | Data Analysis | Outputs |
|---|---|---|---|---|
| Children | Guided observation activities | Child-friendly applications | Simple dashboards | Stories, drawings, audio reports |
| Teenagers | Mobile data collection | Sensors, smartphones, AR/VR | Spreadsheet and beginner coding activities | Videos, blogs, infographics |
| Adults | Community observation projects | Surveys, recordings, photographs | Data cleaning and interpretation | Community reports |
| Senior Citizens | Simplified and analogue tools | Assisted digitisation | Guided exploration sessions | Oral histories and presentations |
| People with Disabilities | Accessible and adaptive tools | Voice commands, tactile technologies | Multimodal learning environments | Easy-read and multimedia outputs |
| Mixed Communities | Community events and shared datasets | Open data practices | Collaborative workshops | Reports, exhibitions, podcasts |
flowchart LR
%% LEVELS
A[Basic]
B[Intermediate]
C[Proficient]
D[Advanced]
A --> B --> C --> D
%% CRITICAL + RESEARCH
A --> A1["Critical & Research<br/>Understand thinking and methods"]
B --> B1["Critical & Research<br/>Verify evidence and structure projects"]
C --> C1["Critical & Research<br/>Analyse phenomena and replicate studies"]
D --> D1["Critical & Research<br/>Produce original research and insights"]
%% DATA PRACTICES
A --> A2["Data Practices<br/>Organise and identify datasets"]
B --> B2["Data Practices<br/>Source and manage data"]
C --> C2["Data Practices<br/>Integrate and curate datasets"]
D --> D2["Data Practices<br/>Create complex data systems"]
%% ANALYSIS + STATISTICS
A --> A3["Analysis & Statistics<br/>Basic quantitative and qualitative skills"]
B --> B3["Analysis & Statistics<br/>Apply statistical techniques and tools"]
C --> C3["Analysis & Statistics<br/>Model and forecast data"]
D --> D3["Analysis & Statistics<br/>Advanced modelling and programming"]
%% VISUALISATION + COMMUNICATION
A --> A4["Communication<br/>Create basic charts"]
B --> B4["Communication<br/>Develop infographics"]
C --> C4["Communication<br/>Professional visualisation tools"]
D --> D4["Communication<br/>Advanced visual storytelling"]
%% STYLING
style A fill:#FFE4E1,color:#000
style B fill:#E6F2FF,color:#000
style C fill:#E6FFE6,color:#000
style D fill:#F3E6FF,color:#000
style A1 fill:#FDEDEC,color:#000
style B1 fill:#EBF5FB,color:#000
style C1 fill:#E8F8F5,color:#000
style D1 fill:#F5EEF8,color:#000
style A2 fill:#FDEDEC,color:#000
style B2 fill:#EBF5FB,color:#000
style C2 fill:#E8F8F5,color:#000
style D2 fill:#F5EEF8,color:#000
style A3 fill:#FDEDEC,color:#000
style B3 fill:#EBF5FB,color:#000
style C3 fill:#E8F8F5,color:#000
style D3 fill:#F5EEF8,color:#000
style A4 fill:#FDEDEC,color:#000
style B4 fill:#EBF5FB,color:#000
style C4 fill:#E8F8F5,color:#000
style D4 fill:#F5EEF8,color:#000
flowchart LR
A["Basic<br/>Understand critical thinking"]
--> B["Intermediate<br/>Verify information using data"]
B --> C["Proficient<br/>Analyse regional phenomena using data"]
C --> D["Advanced<br/>Present findings using advanced statistical modelling"]
style A fill:#FFE4E1,color:#000
style B fill:#E6F2FF,color:#000
style C fill:#E6FFE6,color:#000
style D fill:#F3E6FF,color:#000
flowchart LR
A["Basic<br/>Quantitative and qualitative analysis"]
--> B["Intermediate<br/>Use SPSS or NVivo"]
B --> C["Proficient<br/>Discipline-specific analytical tools"]
C --> D["Advanced<br/>Develop databases and analytical systems"]
style A fill:#FFE4E1,color:#000
style B fill:#E6F2FF,color:#000
style C fill:#E6FFE6,color:#000
style D fill:#F3E6FF,color:#000
flowchart LR
A["Basic<br/>Organise datasets"]
--> B["Intermediate<br/>Identify and organise sources"]
B --> C["Proficient<br/>Use digital curation tools"]
C --> D["Advanced<br/>Develop databases and metadata systems"]
style A fill:#FFE4E1,color:#000
style B fill:#E6F2FF,color:#000
style C fill:#E6FFE6,color:#000
style D fill:#F3E6FF,color:#000
flowchart LR
A["Basic<br/>Identify datasets"]
--> B["Intermediate<br/>Select datasets from portals"]
B --> C["Proficient<br/>Merge and compare datasets"]
C --> D["Advanced<br/>Create complex datasets"]
style A fill:#FFE4E1,color:#000
style B fill:#E6F2FF,color:#000
style C fill:#E6FFE6,color:#000
style D fill:#F3E6FF,color:#000
flowchart LR
A["Basic<br/>Locate CSV files"]
--> B["Intermediate<br/>Extract datasets from PDFs"]
B --> C["Proficient<br/>Extract data from multiple sources"]
C --> D["Advanced<br/>Develop datasets using advanced methods"]
style A fill:#FFE4E1,color:#000
style B fill:#E6F2FF,color:#000
style C fill:#E6FFE6,color:#000
style D fill:#F3E6FF,color:#000
flowchart LR
A["Basic<br/>Create charts and graphics"]
--> B["Intermediate<br/>Develop simple infographics"]
B --> C["Proficient<br/>Use professional design software"]
C --> D["Advanced<br/>Create complex visualisations"]
style A fill:#FFE4E1,color:#000
style B fill:#E6F2FF,color:#000
style C fill:#E6FFE6,color:#000
style D fill:#F3E6FF,color:#000
flowchart LR
A["Basic<br/>Understand scientific methods"]
--> B["Intermediate<br/>Structure research projects"]
B --> C["Proficient<br/>Replicate published studies"]
C --> D["Advanced<br/>Produce original research"]
style A fill:#FFE4E1,color:#000
style B fill:#E6F2FF,color:#000
style C fill:#E6FFE6,color:#000
style D fill:#F3E6FF,color:#000
flowchart LR
A["Basic<br/>Averages, median and mode"]
--> B["Intermediate<br/>Correlation, regression and chi-square"]
B --> C["Proficient<br/>Forecasting and modelling"]
C --> D["Advanced<br/>Complex statistical programming and modelling"]
style A fill:#FFE4E1,color:#000
style B fill:#E6F2FF,color:#000
style C fill:#E6FFE6,color:#000
style D fill:#F3E6FF,color:#000
A citizen science partnership involving communities in Woodbridge and Sutton Hoo demonstrated how local engagement can contribute to scientific knowledge.
- Expansion of monitoring activities across multiple sites
- Identification of thriving bat populations
- Detection of one of the UK's rarest bat species
- Collaboration between community members and researchers
flowchart LR
A[Community Participation]
--> B[Data Collection]
B --> C[Bat Monitoring]
C --> D[Data Analysis]
D --> E[Scientific Findings]
E --> F[Conservation Insights]
- How does critical data literacy differ from traditional data literacy?
- Which citizen science model would be most appropriate for your community?
- How can libraries support inclusive citizen science initiatives?
- Which data skills are most important for participants in your context?
- What ethical considerations arise when collecting and sharing community-generated data?
- Adewumi, B., Onwuka, E. C., & Idowu, D. O. (2021). The Missing Link: Capacity Development for Citizen Science. Library Management.
- Alving, B. E. (2021). Increasing Scientific Literacy with Citizen Science. LIBER Citizen Science Working Group.
- Brand, U., & Sander, I. (2020). Critical Data Literacy.
- Cigarini, A., Bonhoure, I., Vicens, J., & Perelló, J. (2021). Public Libraries Embrace Citizen Science: Strengths and Challenges. Library & Information Science Research.
- Jenkins, S. (2024). Driving Citizen Science Success Through Academic Libraries. Elsevier.
- Millett, A. C., Burrows, K., Caldwell, N., & Richards, S. (2025). Suffolk Libraries: Enhancing Well-being Within Its Community. The Journal of Positive Psychology.
- Prado, J., & Marzal, M. (2013). Data Literacy Frameworks and Information Literacy Programmes.