Text (Instruction Step Text)
From The Embassy of Good Science
Describe the actions the user should take to experience the material (including preparation and follow up if any). Write in an active way.
- ⧼SA Foundation Data Type⧽: Text
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Read the sustainability and eco-justice in everyday research competency profile. +
'''Life Cycle Assessment (LCA)''' is a systematic method used to evaluate the environmental impacts of a product, process, or service throughout its entire life cycle from raw material extraction, production, and distribution to use and final disposal. It helps identify where the greatest environmental impacts occur and supports more sustainable and informed decision-making.
Why Conduct LCAs (Purpose)
*Identify areas of high environmental impact within production processes
*Quantify key impacts like greenhouse gas emissions, water use, and energy consumption
*Identify opportunities for waste reduction, energy savings, and the use of more sustainable materials
Benefits of LCA (output)
*Provides data-driven insights for informed decision-making
*Guides process and product design improvements
*Enables hotspot analysis to focus sustainability efforts
*Helps set concrete sustainability goals and measure progress
Please see the following interactive videos for a real-life explanation of how LCA is applied in practice across different supply chains. +
Doing research with communities affected by climate change: Climate-conscious methodologies matrix (for researchers and ethics reviewers) +
Please go through the PowerPoint presentation. <span lang="EL"><span lang="EN-US">(Please click on the bottom right of the slides to expand it to full screen and improve your experience).</span></span> +
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<span lang="EN-GB">Before delivering your training session(s), take time to familiarize yourself with the RE4GREEN course materials.</span>
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*<span lang="EN-GB">Begin with the '''introductory micromodule''' to understand the course scope and approach.</span>
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*<span lang="EN-GB">Explore the available materials and resources.</span>
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<span lang="EN-GB">The RE4GREEN course is designed as a '''flexible toolbox'''. Trainers are encouraged to:</span>
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*Explore the '''learning needs''' and perspectives of learners.
*<span lang="EN-GB">Select content based on the intended '''learning goals'''</span>
*<span lang="EN-GB">Select content based on their '''target audience''' </span>
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'''<span lang="EN-GB">About the course</span>'''
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<span lang="EN-GB">The course follows a progressive learning logic that moves from foundational values to systemic analysis and critical engagement, then to future‑oriented reflection, and finally to practical action.</span>
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<span lang="EN-GB">The course is structured as follows:</span>
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'''<span lang="EN-GB">Section 1: Embodying Sustainability Values</span>'''
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*<span lang="EN-GB">Introduces ethical and conceptual foundations</span>
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*<span lang="EN-GB">Covers key principles such as environmental justice, planetary health, and care-based approaches</span>
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'''<span lang="EN-GB">Section 2: Embracing Complexity</span>'''
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*<span lang="EN-GB">Focuses on systems thinking and interconnected sustainability challenges</span>
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*<span lang="EN-GB">Includes analytical exercises and reflection tools</span>
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'''<span lang="EN-GB">Section 3: Envisioning Sustainable Futures</span>'''
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*<span lang="EN-GB">Explores transformative thinking and future perspectives</span>
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*<span lang="EN-GB">Encourages reflection on alternative research pathways</span>
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'''<span lang="EN-GB">Section 4: Acting for Sustainability</span>'''
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*<span lang="EN-GB">Focuses on practical implementation</span>
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*<span lang="EN-GB">Translates concepts into actionable changes in research practices</span>
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<span lang="EN-GB">Although designed as a sequence, each micromodule is '''self-contained.''' Trainers and learners can:</span>
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*<span lang="EN-GB">Follow structured learning paths based on target audience, or</span>
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*<span lang="EN-GB">Select modules based on specific needs and interest</span>
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Audio Activity
Listen to the podcast episode featuring Rose Bernabe.
While listening, consider:
* What experiences shaped Rose’s approach to research?
* What helped her find her voice?
* How did support from others influence her journey? +
The Stories Behind (the) Research – Ep. 2. From Skepticism to Action: Embracing a culture of humility in research +
Listen to the podcast.
While listening, consider:
*How did skepticism influence Joeri’s career?
*What transformed skepticism into action?
*What values drive his work today? +
The Stories Behind (the) Research, Ep 3. Beyond the Ivory Tower: The courage to feel when researching tough topics +
Listen to the following podcast. +
Being a scientist entails a commitment to exploring the unknown, uncovering truths, and contributing to the collective understanding of the world. To become a scientist, one typically pursues higher education in a scientific field, such as obtaining a bachelor's degree, followed by a master's or doctoral degree.
Beyond formal education, curiosity, critical thinking skills, and a passion for discovery are essential qualities. Scientists have a responsibility to conduct research ethically, with integrity, and to communicate their findings accurately. They must also engage in peer review, collaborate with others, and contribute to the scientific community's body of knowledge. Ultimately, being a scientist is not just a profession but a calling a dedication to advancing human understanding and improving the world through rigorous inquiry and discovery.
==== Skills, knowledge, responsibilities ====
Any Scientist should be able to:
* critically and responsibly review scientific results for research and education purposes, i.e., carry out various types of literature reviews, e.g., systematic, scoping, etc.; and know the differences between them,
* publish the scientifically sound results of your work in domestic and international scientific journals (in English),
* identify and solve key ethical dilemmas regarding scientific publishing,
* provide scientifically sound reviews of scientific manuscripts,
* conduct independent and ethically correct research, design basic or applied research in your field of expertise,
* secure funding in a transparent way with a declared conflict of interest, and be able to write your own grant applications,
* present the unbiased results of your scientific work at conferences, workshops, and scientific meetings with a declared conflict of interest,
* assess new perspectives for paradigm shifts in your field of expertise, and
* last but not least: +
Open Science represents a new approach to the scientific process based on cooperative work and new ways of diffusing knowledge by using digital technologies and new collaborative tools.
==== What is open science? ====
Open Science is the practice of science in such a way that others can collaborate and contribute, where research data, lab notes and other research processes are freely available, under terms that enable reuse, redistribution and reproduction of the research and its underlying data and methods ([https://www.fosteropenscience.eu/foster-taxonomy/open-science-definition FOSTER Open Science Definition]).
According to the [https://www.fosteropenscience.eu/taxonomy/term/7 FOSTER taxonomy], "Open science is the movement to make scientific research, data and dissemination accessible to all levels of an inquiring society." It can be defined as a grouping of principles and practices:
* Principles: Open Science is about increased transparency, re-use, participation, cooperation, accountability and reproducibility for research. It aims to improve the quality and reliability of research through principles like inclusion, fairness, equity, and sharing. Open Science can be viewed as research simply done properly, and it extends across the Life and Physical Sciences, Engineering, Mathematics, Social Sciences, and Humanities ([https://opensciencemooc.eu/ Open Science MOOC]).
* Practices: Open Science includes changes to the way science is done - including opening access to research publications, data-sharing, open notebooks, transparency in research evaluation, ensuring the reproducibility of research (where possible), transparency in research methods, open source code, software and infrastructure, citizen science and open educational resources.
[[File:Et2.png|center|frameless|600x600px]]
==== What are the benefits? ====
Open Science is about increased rigour, accountability, and reproducibility for research. It is based on the principles of inclusion, fairness, equity, and sharing, and ultimately seeks to change the way research is done, who is involved and how it is valued. It aims to make research more open to participation, review/refutation, improvement and (re)use for the world to benefit.
==== What is it about? ====
Open Science is not different from traditional science and applies to all research disciplines. It just means to carry out the research in a more transparent and collaborative way. Open Science is about extending the principles of openness to the whole research cycle, fostering sharing and collaboration as early as possible, thus entailing a systemic change to the way science and research are done.
There are several definitions of "''openness''" with regards to various aspects of science; the [http://opendefinition.org/ Open Definition] defines it thus: "Open data and content can be freely used, modified, and shared by anyone for any purpose". Open Science encompasses a variety of practices, usually including areas like open access to publications, open research data, open source software/tools, open workflows, citizen science, open educational resources, and alternative methods for research evaluation, including open peer review.
==== The Open Science Training Handbook ====
Want to learn more? FOSTER consortium created an open, living handbook on Open Science training – [https://book.fosteropenscience.eu/ The Open Science Training Handbook]. This book offers comprehensive guidance and resources for Open Science instructors and trainers, as well as anyone interested in improving levels of transparency and participation in research practices. The focus of the handbook is not only spreading the ideas of Open Science, but showing how to spread these ideas most effectively.
Further reading:
The best thing you can do now to understand this particular issue better is to read this paper by Jean-Claude Burgelman and his colleagues: [https://www.frontiersin.org/articles/10.3389/fdata.2019.00043/full Open Science, Open Data, and Open Scholarship: European Policies to Make Science Fit for the Twenty-First Century].
Just read. You don't have to do anything else.
[1] FOSTER consortium. (2018). What is open science? https://zenodo.org/records/2629946
[2] FOSTER consortium. (2018). The open science training handbook. https://doi.org/10.5281/zenodo.1212496
Writing, publishing, and presenting science is one of the basic skills of a researcher and academic professional.
A scientist/lecturer at a university who does not write or otherwise present (promote or defend) the results of his or her work in front of a professional community or wider public is a pre-extinction species that cannot withstand evolution.
Writing a high-quality and highly valued professional text is a very demanding activity. Your writing skills improve with each paper you publish. Article writing is a skill that can be learned. The speed of mastering the writing of research articles is different for different individuals and depends on many factors.
However, when it comes to scientific writing, there is only one acceptable approach: responsible scientific writing. +
Amber Wutich, Professor of Anthropology, Arizona State University, describes her personal experiences working with communities in environmentally fragile settings. +
Laboratories consume a huge amount of plastic, the majority of which is single use, and not recycled. Green Labs Austria presents the problem of plastic waste from labs and gives guidelines on where to start in addressing the problem in a lab (Green Labs Austria, 2024. ''Pioneering sustainability in scientific research.'' ''MIT Science Policy Review''). Through a background study, they evaluate what plastic materials can be recycled, which ones can be replaced and how can plastic materials be recycled for greener labs ([https://www.youtube.com/watch?v=aojnkoh4fPA Tackling the plastic problem in the lab]).
'''Watch this video and familiarize yourself with the types of plastic materials used in labs which can be recycled or replaced as well as the steps involved in the setting up of a plastic recycling pipeline.''' +
Research plays a crucial role in shaping the pathways through which societies confront pressing global challenges, including climate change, biodiversity loss, pollution, and other forms of environmental degradation. At the same time, research, like all human activities, can leave environmental and social footprints, both through its practices and through the applications it enables. These impacts are not inherent to research but can arise unintentionally or out of a lack of considerations for these impacts. Therefore, strengthening integrity in the context of environmental and climate concerns requires supporting practices that responsibly account for and minimise such impacts. Research that aims to serve the public good must uphold high standards of rigour, transparency, and responsibility while avoiding actions that may contribute to environmental or social harm.
The European Code of Conduct for Research Integrity (ECoC) (ALLEA, 2023) sets out the foundational principles of reliability, honesty, respect, and accountability. These principles remain central to all research activities within the European Research Area and beyond. The ECoC recognises in its 2017 revision (ALLEA, 2017) that ecosystems, cultural heritage, and the environment are aspects that must be respected in research practice. However, as environmental and climate concerns become increasingly urgent, there is a need for additional guidance that translates these broad principles into operational responsibilities for the research community. In parallel, the European Union has established the principle of Do No Significant Harm (DNSH). within its regulatory framework under the EU Taxonomy
Regulation (EU, 2020). This ensures that publicly funded activities do not undermine key environmental objectives such as climate change mitigation and adaptation, biodiversity protection, or pollution prevention. While DNSH provides an important legal benchmark, it is narrowly defined and primarily tailored to investment and economic activities. Recognising this limitation, the European Commission will soon be issuing dedicated guidance for research, offering a broader and more flexible framework for identifying, assessing, and mitigating environmental harms throughout the research lifecycle. This guidance extends beyond DNSH by addressing a wider range of environmental ethics considerations, including research topics, methods, practices and new ethics requirements. Together, these developments emphasise the need to embed environmental and climate considerations at the heart of research integrity and practice.
This Annex is intended to complement the ECoC. It offers interpretive guidance on how the existing principles and practices of the ECoC should be understood and applied through an environmental and climate lens. These interpretations remain fully compatible with the freedom of research and do not restrict the independence with which researchers formulate questions and pursue knowledge. Following the structure of the ECoC, this Annex is organised into three sections: Principles, Good Research Practices, and Violations of Research Integrity. Each section provides insights on how environmental and climate considerations can be integrated into established research integrity standards
Dave wall's interview is on how research and innovation related to climate and environmental challenges should be conducted responsibly and ethically. Dave Wall's interview, as an example of the benefits of citizen science initiatives. Click and watch the video below. +
The seven Assessment List for Trustworthy Artificial Intelligence (ALTAI) requirements, established by the European Commission’s High-Level Expert Group on AI, define the core technical, organizational, and ethical standards for human-centric AI systems. The framework establishes that trustworthy AI must be lawful, ethical, and robust across its operational lifecycle. The foundational requirements begin with '''Human Agency and Oversight''', which mandates that systems support human autonomy rather than replacing critical decision-making, guarding against challenges like automation bias. '''Technical Robustness and Safety''' requires systems to maintain accuracy, resilience against adversarial attacks, and reliable fallback mechanisms. '''Privacy and Data Governance''' enforces data protection standards, ensuring full control over personal data, lawful processing, and proper consent mechanisms.
Building upon governance and system integrity, '''Transparency''' requires clear explainability of model outputs, traceability of algorithmic decisions, and honest communication about system capabilities and limitations. '''Diversity, Non-Discrimination, and Fairness''' targets algorithmic bias and proxy discrimination, requiring representative training datasets and equitable outcomes across protected demographic groups.
At a broader level, '''Societal and Environmental Well-Being''' evaluates the systemic impacts of AI deployment, including psychological impacts, democratic discourse, and the ecological footprint of model training and inference. Finally, '''Accountability''' establishes mechanisms for auditability, risk assessment, and clear human oversight to ensure legal and moral responsibility when harms or errors occur. +
The transition from ethics issue identification to concrete system safeguards relies on the '''AIOLIA Portfolio of Measures''', a decision-support repository of 122 technical (TECH), organizational (ORG), and combined (BOTH) interventions organized across the seven ALTAI requirements. To prevent mechanical compliance, learners apply a structured '''4-step process''': first, prioritizing the ALTAI requirements identified during evaluation; second, targeted browsing of portfolio sections corresponding to those high-priority requirements; third, selecting, combining, and adapting measures to fit the specific operational context; and fourth, reviewing the set for ethical urgency and technical feasibility to maintain a streamlined, non-redundant list.
A central core of the module is transforming one-sentence measure summaries into '''fully specified, operational ethics interventions'''. A complete measure must go beyond a description to define why it is relevant, how it will be technically or organizationally achieved, how fulfillment will be assessed or audited, what operational challenges or risks exist if neglected, and which specific stakeholder roles (e.g., QA teams, ML engineers, compliance officers) hold primary accountability.
Finally, the module highlights that technical safeguards and organizational governance are inherently interdependent. A technically robust system operating in a poorly governed organization remains an ethical risk, just as strong policy controls cannot rescue a flawed technical architecture. By teaching learners to balance design controls (like confidence scores or oversight overrides) with organizational protocols (like mandatory human sign-offs and bias audits), Module D prepares the selected measures for real-world deployment and downstream tension analysis. +
Operationalising trustworthy AI requires navigating fundamental ethical tensions where fulfilling one requirement directly constrains another. Across six real-world deployment contexts, specific friction points emerge:
* '''Human-in-the-Loop vs. Professional Deskilling (GPAI):''' Reliance on automated decision-support systems erodes the foundational domain expertise human monitors need to perform meaningful oversight.
* '''Benchmarking Safety vs. User Autonomy & Market Dynamics (PLAY AI Companion):''' Lowering safety guardrails to retain users seeking uncritical validation creates commercial survival at the cost of reinforcing manipulative inputs.
* '''Unsafe Behaviour vs. Human Autonomy (Emotional AI):''' Balancing an adult user's agency to control personal roleplay against the system's obligation to prevent longer-term psychological harm.
* '''Algorithmic Moderation: Safety vs. Privacy (Conversational AI):''' Detecting subtle behavioral harms requires processing intimate user interaction data, directly violating GDPR privacy and data minimisation mandates.
* '''Distributed Responsibility in Decision Support Systems (Clinical DSS):''' Reliance on high-confidence automated risk flags splits liability across software developers, system designers, and clinical staff, leaving no single point of clear ownership when errors occur.
* '''Cognitive Traps & Proxy Indicators (HR & Security DSS):''' Automated systems convert temporary situational factors—such as fatigue during long shifts—into rigid, unappealable risk scores that permanently impact employee opportunities.
To resolve these trade-offs without relying on passive compliance checklists, practitioners deploy dual technical and organisational measures from the AIOLIA Portfolio. Technical safeguards include enforcing active human interaction checkpoints ('''MS102'''), designing interfaces that discourage automatic default approvals ('''MS105'''), protecting data integrity ('''MS61'''), and ensuring users are not systemically penalized for rejecting AI recommendations ('''MS106'''). These are complemented by organisational mechanisms, including mandatory training on system limitations ('''MS107/MS108'''), explicit assignment of decision ownership ('''MS110'''), transparent logging and data processing documentation ('''MS56/MS66'''), and multi-layered escalation pathways ('''MS112/MS113''') that maintain responsibility across system updates and operational handovers ('''MS114''').
Environmental and climate considerations are increasingly recognised as integral to responsible research practice. However, as recent research has highlighted, there is a structural gap in the current ethics landscape (Bourban, 2026) and governance (RE4GREEN, 2025). Existing Research Ethics (RE) literature and frameworks focus primarily on human participants and the ethical acceptability of research involving them, while environmental and climate ethics focus on ecological systems, justice, and sustainability. The two domains rarely intersect. As a result, researchers often lack clear ethical foundations for understanding how their work may engage environmental or climate responsibilities such as questions of responsibility, environmental and climate justice, or long-term environmental implications.
The European Commission’s (EC) forthcoming Guidance Note on assessing and managing risks of environmental harm in EU-funded research represents an important development bridging this gap. It offers a clear and practical framework to help applicants, researchers, and ethics evaluators identify, assess, and manage potential environmental harms within EU-funded research. This guide builds on and complements that work. Environmental and climate considerations in RE extend beyond questions of harm. Research activities can raise ethical concerns even when no significant environmental harm is expected, such as when research assumptions, models, or design choices may influence long-term environmental or societal trajectories. This starting guide aims to embed key ethical considerations that are not always captured by harm-based assessments alone into RE practice, helping researchers and evaluators identify relevant environmental and climate considerations during project design, execution and evaluation. It does so by structuring the guidance around three complementary dimensions of environmentally and climate-responsible research. Given the diversity of research domains, methods, and contexts, it is not possible to define a single set of actions that would be appropriate in all cases. For this reason, the guide adopts a reflective approach based on guiding questions and illustrative examples, rather than prescriptive rules. This approach is intended to support context-sensitive ethical reflection, enabling researchers and evaluators to identify relevant environmental and climate considerations in light of their specific research activities.
Towards an expanded scope of research ethics committees: Integrating environmental and climate ethics +
Research Ethics Committees (RECs) were originally established to protect the rights, dignity, and well being of human participants, particularly within medical and health-related research. Their core procedures and review practices developed in response to concrete risks associated with biomedical experimentation, with a primary focus on informed consent, the proportionality of risks and benefits, and the protection of vulnerable individuals. Over time, ethical review in research has broadened beyond individual participants to include animals, communities, and society at large, reflecting growing awareness of the social, distributive, and long-term impacts of research activities. In parallel, environmental degradation, climate change, and biodiversity loss have increasingly been recognised as factors that shape human well-being and societal resilience. Environmental and climate ethics have therefore emerged as a relevant extension of contemporary research ethics, grounded in the interdependence of human and ecological systems.
The institutionalisation of ethics review has, however, developed unevenly across disciplines and national contexts. While RECs are firmly established in biomedical research and in parts of the social sciences, this is not the case across all research domains. In many technical disciplines, engineering fields, and parts of the environmental sciences (especially where research does not involve direct interaction with human participants) formal institutional REC structures are weakly institutionalised or absent. Even where RECs exist, their review practices do not always systematically address environmental or climate considerations.
As a result, research activities with potential environmental or climate impacts often fall outside established ethics review practices despite their ethical relevance. Deliverable Findings from RE 4GREEN D1.31 confirm that existing research ethics and integrity guidance largely treats environmental considerations peripherally, and that training resources for ethics reviewers rarely address environmental or climate ethics in a structured way. These developments highlight a gradual expansion of ethical expectations in research, alongside persistent gaps in how environmental and climate considerations are reflected in ethics review practice. This underlines the need for clear, proportionate, and supportive guidance that helps RECs integrate such considerations into existing review processes, without altering their core role or increasing administrative burden.
Engaging with citizen scientists: Integrating environmental and climate considerations into research practice +
Design and planning This subsection outlines key environmental and climate considerations that should be taken into account when designing and planning citizen science activities.
'''1. Clarify purpose, roles and expectations'''
Define your project’s scientific and societal objectives, including any environmental or climate implications and explain why citizen science is appropriate (for example extended spatial or temporal coverage, access to local knowledge, or empowerment of affected communities). Clearly specify the roles and status of citizen scientists, including whether they act primarily as data contributors, co-researchers, co authors, or participants in decision-making or governance processes. Describe what citizen scientists will do in practice, what skills or training are required, and what level of effort and responsibility is expected. Prepare clear, non-technical descriptions of roles, rights, and responsibilities that can be reused in information sheets, consent materials, and terms and conditions.
'''2. Embed DNSH and environmental ethics from the outset'''
Conduct a DNSH-oriented scoping of risks, including possible ecological disturbance, social and distributive impacts (who benefits, who may be harmed), and risks associated with the collection and use of data. 1The DNSH principle originates from EU sustainability policy and is used here as a high-level ethical lens to help researchers reflect on whether research activities could cause significant environmental or social harm. This guide does not provide legal guidance on DNSH compliance under the EU Taxonomy or related instruments, nor does it place such responsibilities on citizen scientists. Researchers remain responsible for addressing any formal DNSH or regulatory requirements applicable to their projects
These may include misuse or secondary use of data, for example where environmental or spatial data contribute to surveillance, inform investment or planning decisions that accelerate green gentrification, or enable exploitation of natural resources. Adjust protocols to minimise harm and document mitigation measures. For projects in health and life sciences or areas involving biosecurity, ensure that applicable regulatory requirements are met and approved by local biosecurity committees before involving citizen scientists, and make clear which activities must be carried out or supervised by qualified professionals.
'''3. Plan for inclusion and fairness'''
Identify groups most affected by environmental or climate implications that may arise through project activities and plan for their proactive inclusion, including vulnerable communities, youth, local experts and under-represented groups that are historically or structurally under-represented in research participation, knowledge production, or decision making processes relevant to the project domain. Offer diverse participation modes (digital and non digital, synchronous and asynchronous) and budget for accessibility needs such as language support, interpretation, childcare or travel. Aim to design participation protocols and digital tools (such as apps and platforms) in ways that are accessible, usable, and inclusive across different levels of digital literacy, abilities, and technological access. Anticipate power imbalances and consider structures, such as advisory boards or steering groups, in which citizen scientists participating in the research can influence design, governance, and communication.
'''4. Fieldwork in natural landscapes'''
For field-based projects in natural landscapes, conduct a basic ecological and health-and-safety risk assessment in collaboration with local communities, land managers or park authorities. Agree on a simple code of conduct for fieldwork and communicate it clearly to all citizen scientists and participants before activities begin.
