CitiObs Environmental Monitoring Toolkit
  • Welcome!
    • Glossary
    • About
    • Why this Toolkit?
    • Contributing
  • Environmental monitoring
    • What to consider before conducting environmental monitoring?
    • How to ask the right questions in air quality monitoring?
    • Who is who in air quality environmental monitoring?
  • Sensing devices
    • What are we talking about when we say sensing device?
    • What are the main elements of a sensing device?
    • What variables can be measured with air quality sensing devices?
    • What other methods can be used for air quality monitoring?
    • What aspects to consider when doing mobile monitoring?
    • What technical aspects to consider when choosing an air quality sensing device?
    • What non-technical aspects to consider when choosing sensing devices?
    • What aspects to consider when writing guidance on how to install sensing devices?
  • Data platforms
    • What are the main components of a sensor data platform?
    • What aspects to consider when choosing an infrastructure or platform?
    • What to consider for increasing interoperability among sensor data infrastructures?
    • What should be considered when building a sensor data platform for COs?
    • How to add a real-time alerting system on the web?
  • Managing data
    • How do we increase traceability in data collected by citizens?
    • How can I analyse and visualize my data?
    • How do we increase transparency in data collected by citizens?
    • How to make data accessible to non-experts in a clear and understandable format?
    • What is data quality? How can we increase data quality in citizen gathered data?
    • What are the main aspects you need to consider when managing citizen collected data?
    • Why is it important to document context and how does it help better understand collected data?
  • Community
    • How to address the lack of confidence in the use of sensor technology?
    • How to promote involvement and participation in environmental monitoring to minimize data gaps?
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Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Executive Agency (REA).

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  • Description
  • Why is this relevant?
  • How can this be done?
  • Useful resources
  • You might also be interested in….

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  1. Managing data

How to make data accessible to non-experts in a clear and understandable format?

Description

Data accessibility refers to the ease with which data can be accessed, retrieved, used, and understood by individuals, systems, or applications. It involves ensuring that data is available and usable by those who need it, promoting openness, transparency, and inclusivity. Making Citizen Science data readily available to non-experts in a clear and understandable format is crucial for maximizing its impact and promoting transparency. Understandable data facilitates participatory decision-making processes where community members can actively engage in discussions, voice concerns, and contribute to solutions based on their understanding of the data.

Why is this relevant?

Accessible and understandable data empower non-experts to make informed decisions about their communities, environment, and personal activities. It promotes awareness and encourages behavioral changes to address local issues. Data transparency is crucial when involving citizen scientists as it builds trust, encourages participation, and increases credibility. When non-experts can comprehend the data, they are more likely to trust the findings and engage in discussions about the implications and possible actions. Accessible data ensures that people of diverse backgrounds, abilities, and expertise can participate in Citizen Science projects.

How can this be done?

Making citizen collected data more accessible and transparent to non-experts involves several key strategies to ensure that the information is easy to understand and use. Some effective approaches are:

  1. Clear and simple language:

    1. Avoid jargon and technical terms: explain scientific concepts and findings in plain, simple language that is easily understandable to the public. Define any specialized terms or acronyms used in the data and provide explanations.

    2. Use clear and intuitive visualizations such as charts, graphs, maps and infographics to present the data (e.g. bar charts for comparison, line graphs for trends).

    3. Personal stories can make complex data more relatable and memorable. Present real-life examples, case studies, or narratives that illustrate the importance of the data and how it relates to everyday experiences.

    4. Use of local language.

  2. Data platforms and data licensing:

    1. Provide interactive tools and platforms where non-experts can explore data visually. Interactive maps, dashboards and charts allow users to customize their data views, making the information more engaging and accessible.

    2. Host the data on open-access platforms and repositories that allow free and unrestricted access. Platforms like Zenodo, or institutional repositories provide a stable and accessible environment for data sharing.

    3. Clearly define the data licensing terms, specifying how the data can be used, shared, and attributed. Open licenses, such as Creative Commons licenses, promote data accessibility by providing clear guidelines for data usage.

  3. Provide context to the data:

    1. Provide information about the research objectives, methods, and the real-world implications of the data on the community or environment. Clearly communicate the methods and protocols used to collect and analyse data.

    2. Provide detailed documentation alongside the data, explaining the context, methodology, and limitations of the data collection process. Clear documentation enhances data accessibility by providing essential background information for interpretation.

    3. Ensure that the data and research findings are openly accessible to the public. Publish data, reports, and research papers in open-access formats, allowing anyone to explore the information without restrictions. Provide access to the raw data for those who want to explore it in more detail.

    4. Explain how to interpret and use the data.

  4. Data quality assurance:

    1. Implement robust data quality assurance processes to maintain the accuracy and reliability of the data. Clearly document quality control measures to increase confidence in the data's integrity, enhancing accessibility for users.

    2. If applicable, visualize the uncertainty associated with the data. Uncertainty visualizations help non-experts understand the reliability and limitations of the data, promoting a more nuanced interpretation.

  5. Community engagement:

    1. Involve the community in data interpretation and analysis. Organize workshops, webinars, or community events where researchers can interact directly with non-experts. These interactions provide opportunities for participants to ask questions, gain insights, and learn about the data directly from the experts involved in the research. Community engagement fosters a sense of ownership and understanding of the data.

    2. Develop interactive tools, dashboards and simulations that allow users to explore and analyse the data visually. Interactive tools enhance data accessibility by enabling users to customize their data views and derive insights based on their specific interests. Ensure these tools are designed with accessibility features in mind.

    3. Establish feedback mechanisms where non-experts can ask questions, provide feedback, and request clarification. Respond to inquiries promptly and consider incorporating user suggestions to improve the accessibility of the data.

  6. Accessible formats:

    1. Ensure that the data and related materials are accessible to people with disabilities. Use accessible formats for visual content, provide alternative text for images, use readable fonts, and ensure compatibility with screen readers for online platforms.

    2. Create visualizations that are easy to interpret and understand. Use clear labels, simple shapes, and high colour contrast. Interactive elements should be keyboard accessible and provide textual descriptions.

    3. Provide captions for videos and transcriptions for audio content to make them accessible to individuals with hearing impairments, and alternative text for the visually impaired. This ensures that everyone can access the information presented in multimedia formats.

    4. Implement keyboard navigation: ensure that all interactive elements, such as buttons and links, can be accessed and activated using a keyboard alone, without requiring a mouse or touch input.

    5. Ensure colour contrast: use high colour contrast for text and background elements to improve readability, especially for individuals with visual impairments. WCAG (Web Content Accessibility Guidelines) provides standards for colour contrast ratios.

    6. If applicable, provide translations of the data and related materials into multiple languages spoken within the community. Consider the linguistic diversity of the target audience. Ensure translations maintain accuracy and cultural relevance.

  7. Diverse outreach channels:

    1. Use various communication channels such as social media, blogs, podcasts, and traditional media to reach diverse audiences.

    2. Tailor communication strategies for different demographics.

    3. Emphasize the relevance and importance of science for everyone, regardless of background.

    4. Collaborate with libraries, educational institutions, and local organizations to promote data literacy and provide training on accessing and understanding Citizen Science data.

Useful resources

  • ECSA WG Storytelling and other arts: The purpose of this working group is to explore and gain knowledge and confidence in the way stories are an ally in science communication, specifically in Citizen Science.

  • Storytelling with Data: Storytelling with Data is a website and book by Cole Nussbaumer Knaflic, offering practical tips and resources on how to visualize data effectively and tell compelling stories with data. Website: Storytelling with Data

  • The Open Knowledge Foundation: The Open Knowledge Foundation provides resources on open data and data visualization. Their guides offer practical advice on making data accessible and understandable to various audiences. Website: Open Knowledge Foundation

  • Google Data Studio: Google Data Studio is a free tool for creating interactive and shareable dashboards. It's user-friendly and allows for the creation of visually appealing data visualizations that can be embedded into websites or shared online. Website: Google Data Studio

  • PlainLanguage.gov - A U.S. government website providing guidelines and resources for writing in plain language.

  • The A-Z of alternative words - A resource for finding plain English alternatives to complex words.

You might also be interested in….

  • What are the main aspects you need to consider when managing citizen collected data?

  • What is data quality? How can we increase data quality in citizen gathered data?

  • How can I analyse and visualize my data?

  • How to promote involvement and participation in environmental monitoring to minimize data gaps?

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