Citizen science in environmental and ecological sciences

How can citizen science reduce psychological distance to science? Insights from three projects in contested environmental contexts

Citizen science (CS) is a participatory mode of knowledge production, enabling non-scientific actors to contribute to and sometimes contest scientific agendas and interpretations, making it a way to bridge science and society. This paper examines how that potential unfolds by analysing the individual perspectives of citizen scientists through the lens of Psychological Distance to Science (PSYDISC). Drawing on three case studies of contested environmental CS, we identify which contextual aspects of CS shape citizen scientists' experiences of social, spatial, temporal, or hypothetical distance to relevant science, and how these experiences may relate to trust. Our findings underscore the role of science communication as both a channel for dissemination, and as a constitutive element of participatory research; crucial for reducing psychological distance and enabling socially robust knowledge production, especially in contested, policy-relevant science settings.

Practical recommendations from a multi-perspective needs and challenges assessment of citizen science games

Citizen science games are an increasingly popular form of citizen science, in which volunteer participants engage in scientific research while playing a game. Their success depends on a diverse set of stakeholders working together–scientists, volunteers, and game developers. Yet the potential needs of these stakeholder groups and their possible tensions are poorly understood. To identify these needs and possible tensions, we conducted a qualitative data analysis of two years of ethnographic research and 57 interviews with stakeholders from 10 citizen science games, following a combination of grounded theory and reflexive thematic analysis. We identify individual stakeholder needs as well as important barriers to citizen science game success. These include the ambiguous allocation of developer roles, limited resources and funding dependencies, the need for a citizen science game community, and science–game tensions. We derive recommendations for addressing these barriers.

A Polycentric Governance Lens on Data Infrastructures

Funding policies for data infrastructure promote open data sharing to drive positive social impact. However, concerns regarding the long-term management of data within and across distributed infrastructures can hinder data sharing. We draw upon the concept of polycentric governance to demonstrate how collaborative practices of data curation, in preparing and maintaining data for (future) sharing, provide a solid foundation for understanding data governance within data infrastructures. Based on a qualitative case study of a distributed ecological network, we investigate the conditions under which data are managed as a shared resource by local actors to ensure the long-term (re)usability of data. We contribute to CSCW by conceptualising data curation as a complex form of governance practice with multiple centres of decision-making, each of which operates with some degree of autonomy in data infrastructures. A polycentric governance lens on data infrastructures advances the CSCW conception of data curation as a collective governance practice that can cultivate a data democracy culture within and across organisations, empower individuals to be accountable for their data, and foster a mindset shift toward decentralised data governance.

iNaturalist accelerates biodiversity research

Abstract. Participatory citizen science is expanding, with iNaturalist emerging as one of the most widely used platforms globally. However, its application

A Data Utopia for Science-of-Science

Here I want to briefly sketch out a vision for how to solve a key set of problems facing science-of-science researchers, using the relatively new idea of a ‘data trust.’ In my ideal wor…

Data‐ and code‐archiving in the British Ecological Society journals: Present status and recommendations for future improvements

Abstract Data‐ and code‐archiving are important components of open science, as both make research more transparent, reproducible, accountable and credible, allowing future researchers to build on previous work. Despite progress in implementing data‐ and code‐archiving policies in journals publishing ecology and evolution research, issues remain. To be more useful to future researchers, archived data and code must not only be archived but also meet good practice standards. We collected data from 1861 papers published between 2017 and 2024 in the seven British Ecological Society (BES) journals, during a hackathon event. We systematically checked associated data and/or code, metadata, help files and annotations to assess archiving practices. We determined if and where data and code files were archived, whether they could be located, downloaded and opened, and whether they had associated READMEs, digital object identifiers (DOI) and licences. We also recorded the file extensions used to save data/code files, and which programming languages code was written in. 93% of the 1861 papers we examined used data and ~90% used code. While 97% of the 1735 papers that used data also archived it, only 35% of the 1670 papers that used code also archived code. Over 85% of archived data and code could be located, downloaded and opened. Reusability, however, was more limited; around a third of papers did not have a README or similar to explain their data/code files, and the quality of READMEs varied substantially. We recommend that researchers archive their code and that archived code be explicitly mentioned in the Data (or Code) Availability statement. We also encourage researchers to provide more accessible and informative READMEs for data and code. To help achieve these recommendations, we advocate that journals employ Data/Code editors to review data and code quality, research institutions deliver more training in open science practices, and funding bodies set clear expectations on open data and code practices.

Assessing nature's contributions to people

Recognizing culture, and diverse sources of knowledge, can improve assessments , A major challenge today and into the future is to maintain or enhance beneficial contributions of nature to a good quality of life for all people. This is among the key motivations of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), a joint global effort by governments, academia, and civil society to assess and promote knowledge of Earth's biodiversity and ecosystems and their contribution to human societies in order to inform policy formulation. One of the more recent key elements of the IPBES conceptual framework ( 1 ) is the notion of nature's contributions to people (NCP), which builds on the ecosystem service concept popularized by the Millennium Ecosystem Assessment (MA) ( 2 ). But as we detail below, NCP as defined and put into practice in IPBES differs from earlier work in several important ways. First, the NCP approach recognizes the central and pervasive role that culture plays in defining all links between people and nature. Second, use of NCP elevates, emphasizes, and operationalizes the role of indigenous and local knowledge in understanding nature's contribution to people.

Democratizing Data

Democratizing Data builds a community-driven data ecosystem by identifying how datasets are used and reducing barriers to accessing high-quality public data. The initiative enhances the discoverability, usability, and relevance of data for researchers, policymakers, and stakeholder communities. A suite of tools and strategic partnerships supports this work by connecting users to the data, insights, and networks needed to inform decisions and generate impact.

Civic, Citizen and Grassroots Science: Towards a Transformative Scientific Research Model | Request PDF

Request PDF | On Jan 1, 2013, Jessica McCallum Breen and others published Civic, Citizen and Grassroots Science: Towards a Transformative Scientific Research Model | Find, read and cite all the research you need on ResearchGate

Professionalising Community Management Roles in Interdisciplinary Research Projects

In this article we discuss community management in interdisciplinary research teams, focusing on recognising and professionalising roles referred to here as the Research Community Managers (RCM). Drawing insights and examples from research and data science projects, we discuss how RCM roles address some of the research’s most pressing challenges, from promoting best practices for open research and reproducibility to engaging diverse stakeholders in community-led research and ensuring fair recognition for their contributions. We offer a Community Maturation Indicator and share examples of projects from The Alan Turing Institute, the UK's national institute for data science and Artificial Intelligence (AI), where institutionally supported RCM roles were established. With the aim to integrate RCM expertise in teams involved in data science and AI research, we provide an RCM Skills and Competencies Framework. We also propose a roadmap for professionalising RCM roles by improving recognition and rewards, potential career paths and organisational support structures. To systematically sustain and progress these roles, we recommend institutional investment in establishing RCM teams that are empowered to prioritise collaboration, transparency and community-based approaches in interdisciplinary projects, such as in data science and AI. As a team, RCMs are well placed to connect disparate teams, initiatives and resources across the organisation, building more resilient research communities that can achieve greater innovation, improved project outcomes and a strongly connected ecosystem, with impacts extending beyond their narrow contexts.

Knowledge infrastructures for the Anthropocene

The technosphere metabolizes not only energy and materials, but information and knowledge as well. This article first examines the history of knowledge about large-scale, long-term, anthropogenic environmental change. In the 19th and 20th centuries, major systems were built for monitoring both the environment and human activity of all kinds, for modeling geophysical processes such as climate change, and for preserving and refining scientific memory, i.e. data about the planetary past. Despite many failures, these knowledge infrastructures also helped achieve notable successes such as the Limited Test Ban Treaty of 1963, the ozone depletion accords of the 1980s, and the Paris Agreement on climate change of 2015. The article’s second part proposes that knowledge infrastructures for the Anthropocene might not only monitor and model the technosphere’s metabolism of energy, materials and information, but also integrate those techniques with new accounting practices aimed at sustainability. Scientific examples include remarkable recent work on long-term socio-ecological research, and the assessment reports of the Intergovernmental Panel on Climate Change. In terms of practical knowledge, one key to effective accounting may be ‘recycling’ of the vast amounts of ‘waste’ data created by virtually all online systems today. Examples include dramatic environmental efficiency gains by Ikea and United Parcel Service, through improved logistics, self-provision of renewable energy, and feedback from close monitoring of delivery trucks. Blending social ‘data exhaust’ with physical and environmental information, an environmentally focused logistics might trim away excess energy and materials in production, find new ways to re-use or recycle waste, and generate new ideas for eliminating toxic byproducts, greenhouse gas emissions and other metabolites.

Knowledge infrastructures for the Anthropocene

The technosphere metabolizes not only energy and materials, but information and knowledge as well. This article first examines the history of knowledge about large-scale, long-term, anthropogenic environmental change. In the 19th and 20th centuries, major systems were built for monitoring both the environment and human activity of all kinds, for modeling geophysical processes such as climate change, and for preserving and refining scientific memory, i.e. data about the planetary past. Despite many failures, these knowledge infrastructures also helped achieve notable successes such as the Limited Test Ban Treaty of 1963, the ozone depletion accords of the 1980s, and the Paris Agreement on climate change of 2015. The article’s second part proposes that knowledge infrastructures for the Anthropocene might not only monitor and model the technosphere’s metabolism of energy, materials and information, but also integrate those techniques with new accounting practices aimed at sustainability. Scientific examples include remarkable recent work on long-term socio-ecological research, and the assessment reports of the Intergovernmental Panel on Climate Change. In terms of practical knowledge, one key to effective accounting may be ‘recycling’ of the vast amounts of ‘waste’ data created by virtually all online systems today. Examples include dramatic environmental efficiency gains by Ikea and United Parcel Service, through improved logistics, self-provision of renewable energy, and feedback from close monitoring of delivery trucks. Blending social ‘data exhaust’ with physical and environmental information, an environmentally focused logistics might trim away excess energy and materials in production, find new ways to re-use or recycle waste, and generate new ideas for eliminating toxic byproducts, greenhouse gas emissions and other metabolites.

Doing Data Science on the Shoulders of Giants: The Value of Open Source Software for the Data Science Community

Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally supported, but many of whom do this maintenance on a purely volunteer basis. The health of the data science ecosystem depends on the support of open source projects, on an individual and institutional level.

Why Do We Call It Participatory Science? | Smithsonian Environmental Research Center