TAPIS: A Simple Web Tool for Analyzing Citizen-Generated Data

Curated on Posted on by Stefaan Verhulst

Tool by CityObs: “Citizen observatories and communities collect valuable environmental data — but making sense of this data can be tricky, especially if you’re not a data expert. That’s why we created TAPIS: a free, easy-to-use web tool developed within the CitiObs project to help you view, manage, and analyze data collected from sensors and online platforms.

Why We Built TAPIS

The SensorThings API is a standard for sharing sensor data, used by many observatories. However, tools that help people explore this data visually and interactively have been limited. Often, users had to dig into complicated URLs and query parameters such as “expand”, “select”, “orderby” and “filter” to extract the data they needed, as illustrated in tutorials and examples such as the ones collected by SensorUp [1].

TAPIS changes that. It gives you a visual interface to work with sensor data from different API standards (such as SensorThings API, STAplus, OGC API Features/Records, OGC Catalogue Service for the Web, S3 Services, Eclipse Data Connectors, and STAC) and data file formats (such as CSV, JSON, JSON-LD, GeoJSON, and GeoPackage). You can load the data into tables, filter or group it, and view it as maps, bar charts, pie charts, or scatter plots — all in your browser, with no installation required.

Key Features

  • Connects to online data sources (like OGC APIs, STAC, SensorThings, and CSV files)
  • Turns raw data into easy-to-read tables
  • Adds meaning to table columns
  • Visualizes data with different chart types
  • Links with MiraMon to create interactive maps

TAPIS is inspired by the look and feel of Orange Data Mining (a popular data science tool) — but runs entirely in your browser, making it accessible for all users, even those with limited technical skills…(More)”

Leveraging Citizen Data to Improve Public Services and Measure Progress Toward Sustainable Development Goal 16

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Paper by Dilek Fraisl: “This paper presents the results of a pilot study conducted in Ghana that utilized citizen data approaches for monitoring a governance indicator within the SDG framework, focusing on indicator 16.6.2 citizen satisfaction with public services. This indicator is a crucial measure of governance quality, as emphasized by the UN Sustainable Development Goals (SDGs) through target 16.6 Develop effective, accountable, and transparent institutions at all levels. Indicator 16.6.2 specifically measures satisfaction with key public services, including health, education, and other government services, such as government-issued identification documents through a survey. However, with only 5 years remaining to achieve the SDGs, the lack of data continues to pose a significant challenge in monitoring progress toward this target, particularly regarding the experiences of marginalized populations. Our findings suggest that well-designed citizen data initiatives can effectively capture the experiences of marginalized individuals and communities. Additionally, they can serve as valuable supplements to official statistics, providing crucial data on population groups typically underrepresented in traditional surveys…(More)”.

Co-Designing AI Systems with Value-Sensitive Citizen Science

Curated on Posted on by Stefaan Verhulst

Paper by Sachit Mahajan and Dirk Helbing: “As artificial intelligence (AI) systems increasingly shape everyday life, integrating diverse community values into their development becomes both an ethical imperative and a practical necessity. This paper introduces Value Sensitive Citizen Science (VSCS), a systematic framework combining Value Sensitive Design (VSD) principles with citizen science methods to foster meaningful public participation in AI. Addressing critical gaps in existing approaches, VSCS integrates culturally grounded participatory methods and structured cognitive scaffolding through the Participatory Value-Cognition Taxonomy (PVCT). Through iterative value-sensitive participation cycles guided by an extended scenario logic (What-if, If-then, Then-what, What-now), community members act as genuine coresearchers-identifying, translating, and operationalizing local values into concrete technical requirements. The framework also institutionalizes governance structures for ongoing oversight, adaptability, and accountability across the AI lifecycle. By explicitly bridging participatory design with algorithmic accountability, VSCS ensures that AI systems reflect evolving community priorities rather than reinforcing top-down or monocultural perspectives. Critical discussions highlight VSCS’s practical implications, addressing challenges such as power dynamics, scalability, and epistemic justice. The paper concludes by outlining actionable strategies for policymakers and practitioners, alongside future research directions aimed at advancing participatory, value-driven AI development across diverse technical and sociocultural contexts…(More)”.


Mapping local knowledge supports science and stewardship

Curated on Posted on by Stefaan Verhulst

Paper by Sarah C. Risley, Melissa L. Britsch, Joshua S. Stoll & Heather M. Leslie: “Coastal marine social–ecological systems are experiencing rapid change. Yet, many coastal communities are challenged by incomplete data to inform collaborative research and stewardship. We investigated the role of participatory mapping of local knowledge in addressing these challenges. We used participatory mapping and semi-structured interviews to document local knowledge in two focal social–ecological systems in Maine, USA. By co-producing fine-scale characterizations of coastal marine social–ecological systems, highlighting local questions and needs, and generating locally relevant hypotheses on system change, our research demonstrates how participatory mapping and local knowledge can enhance decision-making capacity in collaborative research and stewardship. The results of this study directly informed a collaborative research project to document changes in multiple shellfish species, shellfish predators, and shellfish harvester behavior and other human activities. This research demonstrates that local knowledge can be a keystone component of collaborative social–ecological systems research and community-lead environmental stewardship…(More)”.

Combine AI with citizen science to fight poverty

Curated on Posted on by Stefaan Verhulst

Nature Editorial: “Of the myriad applications of artificial intelligence (AI), its use in humanitarian assistance is underappreciated. In 2020, during the COVID-19 pandemic, Togo’s government used AI tools to identify tens of thousands of households that needed money to buy food, as Nature reports in a News Feature this week. Typically, potential recipients of such payments would be identified when they apply for welfare schemes, or through household surveys of income and expenditure. But such surveys were not possible during the pandemic, and the authorities needed to find alternative means to help those in need. Researchers used machine learning to comb through satellite imagery of low-income areas and combined that knowledge with data from mobile-phone networks to find eligible recipients, who then received a regular payment through their phones. Using AI tools in this way was a game-changer for the country.Can AI help beat poverty? Researchers test ways to aid the poorest people

Now, with the pandemic over, researchers and policymakers are continuing to see how AI methods can be used in poverty alleviation. This needs comprehensive and accurate data on the state of poverty in households. For example, to be able to help individual families, authorities need to know about the quality of their housing, their children’s diets, their education and whether families’ basic health and medical needs are being met. This information is typically obtained from in-person surveys. However, researchers have seen a fall in response rates when collecting these data.

Missing data

Gathering survey-based data can be especially challenging in low- and middle-income countries (LMICs). In-person surveys are costly to do and often miss some of the most vulnerable, such as refugees, people living in informal housing or those who earn a living in the cash economy. Some people are reluctant to participate out of fear that there could be harmful consequences — deportation in the case of undocumented migrants, for instance. But unless their needs are identified, it is difficult to help them.Leveraging the collaborative power of AI and citizen science for sustainable development

Could AI offer a solution? The short answer is, yes, although with caveats. The Togo example shows how AI-informed approaches helped communities by combining knowledge of geographical areas of need with more-individual data from mobile phones. It’s a good example of how AI tools work well with granular, household-level data. Researchers are now homing in on a relatively untapped source for such information: data collected by citizen scientists, also known as community scientists. This idea deserves more attention and more funding.

Thanks to technologies such as smartphones, Wi-Fi and 4G, there has been an explosion of people in cities, towns and villages collecting, storing and analysing their own social and environmental data. In Ghana, for example, volunteer researchers are collecting data on marine litter along the coastline and contributing this knowledge to their country’s official statistics…(More)”.

Why these scientists devote time to editing and updating Wikipedia

Curated on Posted on by Stefaan Verhulst

Article by Christine Ro: “…A 2018 survey of more than 4,000 Wikipedians (as the site’s editors are called) found that 12% had a doctorate. Scientists made up one-third of the Wikimedia Foundation’s 16 trustees, according to Doronina.

Although Wikipedia is the best-known project under the Wikimedia umbrella, there are other ways for scientists to contribute besides editing Wikipedia pages. For example, an entomologist could upload photos of little-known insect species to Wikimedia Commons, a collection of images and other media. A computer scientist could add a self-published book to the digital textbook site Wikibooks. Or a linguist could explain etymology on the collaborative dictionary Wiktionary. All of these are open access, a key part of Wikimedia’s mission.

Although Wikipedia’s structure might seem daunting for new editors, there are parallels with academic documents.

For instance, Jess Wade, a physicist at Imperial College London, who focuses on creating and improving biographies of female scientists and scientists from low- and middle-income countries, says that the talk page, which is the behind-the-scenes portion of a Wikipedia page on which editors discuss how to improve it, is almost like the peer-review file of an academic paper…However, scientists have their own biases about aspects such as how to classify certain topics. This matters, Harrison says, because “Wikipedia is intended to be a general-purpose encyclopaedia instead of a scientific encyclopaedia.”

One example is a long-standing battle over Wikipedia pages on cryptids and folklore creatures such as Bigfoot. Labels such as ‘pseudoscience’ have angered cryptid enthusiasts and raised questions about different types of knowledge. One suggestion is for the pages to feature a disclaimer that says that a topic is not accepted by mainstream science.

Wade raises a point about resourcing, saying it’s especially difficult for the platform to retain academics who might be enthusiastic about editing Wikipedia initially, but then drop off. One reason is time. For full-time researchers, Wikipedia editing could be an activity best left to evenings, weekends and holidays…(More)”.

Citizen science as an instrument for women’s health research

Curated on Posted on by Stefaan Verhulst

Paper by Sarah Ahannach et al: “Women’s health research is receiving increasing attention globally, but considerable knowledge gaps remain. Across many fields of research, active involvement of citizens in science has emerged as a promising strategy to help align scientific research with societal needs. Citizen science offers researchers the opportunity for large-scale sampling and data acquisition while engaging the public in a co-creative approach that solicits their input on study aims, research design, data gathering and analysis. Here, we argue that citizen science has the potential to generate new data and insights that advance women’s health. Based on our experience with the international Isala project, which used a citizen-science approach to study the female microbiome and its influence on health, we address key challenges and lessons for generating a holistic, community-centered approach to women’s health research. We advocate for interdisciplinary collaborations to fully leverage citizen science in women’s health toward a more inclusive research landscape that amplifies underrepresented voices, challenges taboos around intimate health topics and prioritizes women’s involvement in shaping health research agendas…(More)”.

Quality Assessment of Volunteered Geographic Information

Curated on Posted on by Stefaan Verhulst

Paper by Donia Nciri et al: “Traditionally, government and national mapping agencies have been a primary provider of authoritative geospatial information. Today, with the exponential proliferation of Information and Communication Technologies or ICTs (such as GPS, mobile mapping and geo-localized web applications, social media), any user becomes able to produce geospatial information. This participatory production of geographical data gives birth to the concept of Volunteered Geographic Information (VGI). This phenomenon has greatly contributed to the production of huge amounts of heterogeneous data (structured data, textual documents, images, videos, etc.). It has emerged as a potential source of geographic information in many application areas. Despite the various advantages associated with it, this information lacks often quality assurance, since it is provided by diverse user profiles. To address this issue, numerous research studies have been proposed to assess VGI quality in order to help extract relevant content. This work attempts to provide an overall review of VGI quality assessment methods over the last decade. It also investigates varied quality assessment attributes adopted in recent works. Moreover, it presents a classification that forms a basis for future research. Finally, it discusses in detail the relevance and the main limitations of existing approaches and outlines some guidelines for future developments…(More)”.

Citizen scientists will be needed to meet global water quality goals

Curated on Posted on by Stefaan Verhulst

University College London: “Sustainable development goals for water quality will not be met without the involvement of citizen scientists, argues an international team led by a UCL researcher, in a new policy brief.

The policy brief and attached technical brief are published by Earthwatch Europe on behalf of the United Nations Environment Program (UNEP)-coordinated World Water Quality Alliance that has supported citizen science projects in Kenya, Tanzania and Sierra Leone. The reports detail how policymakers can learn from examples where citizen scientists (non-professionals engaged in the scientific process, such as by collecting data) are already making valuable contributions.

The report authors focus on how to meet one of the UN’s Sustainable Development Goals around improving water quality, which the UN states is necessary for the health and prosperity of people and the planet…

“Locals who know the water and use the water are both a motivated and knowledgeable resource, so citizen science networks can enable them to provide large amounts of data and act as stewards of their local water bodies and sources. Citizen science has the potential to revolutionize the way we manage water resources to improve water quality.”…

The report authors argue that improving water quality data will require governments and organizations to work collaboratively with locals who collect their own data, particularly where government monitoring is scarce, but also where there is government support for citizen science schemes. Water quality improvement has a particularly high potential for citizen scientists to make an impact, as professionally collected data is often limited by a shortage of funding and infrastructure, while there are effective citizen science monitoring methods that can provide reliable data.

The authors write that the value of citizen science goes beyond the data collected, as there are other benefits pertaining to education of volunteers, increased community involvement, and greater potential for rapid response to water quality issues…(More)”.

Toward a citizen science framework for public policy evaluation

Curated on Posted on by Stefaan Verhulst

Paper by Giovanni Esposito et al: “This study pioneers the use of citizen science in evaluating Freedom of Information laws, with a focus on Belgium, where since its 1994 enactment, Freedom of Information’s effectiveness has remained largely unexamined. Utilizing participatory methods, it engages citizens in assessing transparency policies, significantly contributing to public policy evaluation methodology. The research identifies regional differences in Freedom of Information implementation across Belgian municipalities, highlighting that larger municipalities handle requests more effectively, while administrations generally show reluctance to respond to requests from perceived knowledgeable individuals. This phenomenon reflects a broader European caution toward well-informed requesters. By integrating citizen science, this study not only advances our understanding of Freedom of Information law effectiveness in Belgium but also advocates for a more inclusive, collaborative approach to policy evaluation. It addresses the gap in researchers’ experience with citizen science, showcasing its vast potential to enhance participatory governance and policy evaluation…(More)”.