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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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.''' +
'''Greening labs''' involves reducing environmental impact by implementing sustainable practices within laboratory settings. In this regard, several small actions that are '''ecofriendly''' can be considered in lab activities to contribute to environmental sustainability.
'''Watch the video on “Green Labs from the Faculty of Science and Engineering of the University of Groningen” and pay attention to the everyday small actions that can be adopted to improve lab efficiency and make lab research more environmentally friendly.''' +
Read the slides carefully, focusing on the goals and methods of the two types of innovation and the differences between them. You can expand the slides to full screen by clicking the button in the '''bottom-right''' corner. +
Read the slides carefully and learn about the concept of climate mainstreaming within organisations and the key steps required for its successful implementation. You can expand the slides to full screen by clicking the button in the '''bottom-right''' corner. +
We will begin by watching a short video on '''environmental justice'''. After watching the video, you will be asked to complete a brief questionnaire based on the content you’ve just seen. +
The purpose of this exercise is to facilitate an understanding of sustainability as a wicked problem. At the end of the video, some questions will help you reflect on what you have seen. +
Please watch the video carefully! The purpose of this exercise is to assess your understanding of the concept of circularity and its role in addressing today’s environmental challenges. Circularity is essential because it helps reduce resource extraction, waste, and pollution while keeping materials in use for as long as possible. By fostering more sustainable production and consumption patterns, circularity contributes to protecting ecosystems, supporting equitable economies, and achieving the Sustainable Development Goals. +
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<span lang="EN-US">After taking the “Sustainability and Eco-Justice in Everyday Research” course learners will be able to:</span>
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* <span lang="EN-US">Explain key concepts and ethical frameworks linking '''research, sustainability, and environmental justice'''.</span>
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* <span lang="EN-US">Analyse how research and innovation contribute to '''environmental impacts, inequalities, and sustainability transitions'''.</span>
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* <span lang="EN-US">Apply '''systems thinking and intersectional perspectives''' to sustainability challenges in research contexts.</span>
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* <span lang="EN-US">Integrate '''justice-oriented and climate-conscious approaches''' into research design, methodology, and innovation.</span>
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* <span lang="EN-US">Implement '''practical strategies for sustainable research practices''' in laboratories, fieldwork, technology use, and research culture.</span>
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In this activity, you’ll explore the environmental impacts associated with AI technologies. Concerns include electricity use, water consumption, rare earth mining, and e-waste. Possible solutions and governance ideas are also explored. Interactive exercises to test your understanding are also included.
You can expand the image by clicking on the button on the bottom right-hand corner. +
In ecology, field research aims to understand how ecosystems work, respond, and change. But whether we’re conducting observational surveys or setting up experiments, field activities can unintentionally damage the ecosystems that we want to protect. This raises a central question of how we can minimize the environmental impact of our fieldwork in accordance with ethical standards.
To be able to answer this question, watch the video on “Doing science responsibly: Minimizing ecological footprints in field research” to familiarize yourself with basic actions that can be implemented to minimize the environmental impacts of field research activities. Note down those actions that you find the most relevant to your research.
You can expand the image by clicking on the button on the bottom right-hand corner. +
<span lang="EN-US">Open the podcast episode below through the linke and proceed to the next step.</span> +
An AI system, as defined in the EU AI Act, provides the foundational reference point for determining the scope of AI governance and related ethics obligations. This definition is crucial because it establishes clear criteria for distinguishing AI-based technologies from other software systems, ensuring consistent interpretation across research, development, and policy contexts. A precise understanding of this definition helps stakeholders assess whether a system is subject to specific legal, ethical, and compliance requirements under EU rules. According to the definition of an AI system in the EU AI Act:
''‘AI system’ means a <u>machine-based system</u> that is designed to operate with <u>varying levels of autonomy</u> and that may exhibit <u>adaptiveness</u> after deployment, and that, for <u>explicit or implicit objectives</u>, <u>infers</u>, from the input it receives, how to generate <u>outputs</u> such as predictions, content, recommendations, or decisions that can <u>influence physical or virtual environments</u>.''
Source: European Union. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 on artificial intelligence (EU AI Act). Official Journal of the European Union. Article 3(1) <u>https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689</u> +
Listen to the episode and consider both system dynamics and ethical dimensions
If you prefer reading, you can consult the transcript.
Guiding prompt while listening: Pay attention not only to how the food system works, but also to:
* Who has influence within the system
* Whose knowledge is valued or overlooked
* Who benefits from current practices and proposed solutions +
Before we start, take a moment to reflect.
You can click on the bottom right button to expand the window. +
Stories influence how people process information. Rather than presenting disconnected facts, stories organise information into causal sequences that help audiences understand how ideas and events relate to one another. Stories in communication have been shown to improve comprehension and engagement, and can make information more accessible to non-expert audiences (Dahlstrom, 2014)However, storytelling is not only used to communicate research more effectively, but also to support reflexivity among researchers.
By structuring research as a story, researchers are encouraged to reflect on:
* how they define the problem
* which perspectives are included or excluded
* what is important or relevant to them personally
* how their findings may be interpreted
This means storytelling is not just a communication tool, but also a way to critically examine how research is framed and what it means for those who are involved in it.
Watch the following video explaining why stories are powerful tools for communication. +
The research team within TIER2 present an analytical framework that supports epistemic diversity by examining the potential relevance and degree of feasibility of reproducibility for different modes of knowledge production. The research team find current general typologies with the same aim wanting. They propose top-down derived enumerative lists of kinds of reproducibility organised according to vaguely defined fields, disciplines, methods or so-called research types.
Current typologies cannot sufficiently characterise different kinds of research and their varying research context at the granularity needed to deal with how epistemic diversity and reproducibility relate. They also do not clarify the prevailing conceptual confusion surrounding reproducibility and replication. To clarify matters, they propose ''redoing'' to commonly describe the acts of reproducing and replicating and ''enabling'' to describe the acts of making something reproducible and replicable. They suggest mapping practices and epistemic functions to characterise what parts of a study should be redone or enabled and for what intended purposes. The research team propose knowledge production modes (KPM) as an organising construct to situate redoing and enabling within knowledge production’s epistemic, social, and contextual conditions. Epistemologies determine epistemic norms and criteria. Social conditions influence how research is organised, practised, rewarded, reported, and discussed. Contextual conditions put boundaries and restrictions on research, for example, due to subject matter, environment, availability of resources, and technologies, which are the ‘local’ conditions. Their framework clarifies the potential ''relevance'' of redoing and the degree of ''feasibility'' of redoing and enabling for a specific knowledge production mode. Relevance comprises research goals and epistemology. Epistemology is the basic assumption behind knowledge production modes. It determines how knowledge claims are produced and justified with systems of justification, the criteria for good/trustworthy research, and, thus, the epistemic norms. Different ways of knowing have different epistemic norms, practices, and criteria. Feasibility comprises the nature and complexity of the subject under investigation, the necessary investment for redoing or enabling, and the degree of theoretical and methodological uncertainty associated with the actual research. The proposed framework works bottom-up in that knowledge production modes are not defined a priori but derived from the analytical framework. The framework, therefore, supports epistemic diversity by being open and non-hierarchical and working at a sufficient level of granularity to discern the diverse conditions of knowledge production. The research team propose a framework that can clarify, not a cookbook. Enabling in some form always seems relevant in empirical work irrespective of ways of knowing—the same is not true for redoing.
For the full paper click here: [https://osf.io/preprints/metaarxiv/ujnd9_v1 MetaArXiv Preprints - Knowledge Production Modes: The Relevance and Feasibility of Reproducibility]
'''Reference'''
Ulpts, S., & Schneider, J. W. (2023, September 25). Knowledge Production Modes: The Relevance and Feasibility of Reproducibility. '"`UNIQ--nowiki-00000002-QINU`"'
The ethical conduct of research is crucial for maintaining the integrity of science. [https://oir.nih.gov/sourcebook/ethical-conduct/responsible-conduct-research-training Responsible Conduct of Research] (RCR) advances scientific goals, fosters a collaborative research environment, and builds public trust in scientific advances that benefit society. Conversely, unethical research practices such as data fabrication and falsification lead to the dissemination of false hypotheses and unreliable data, which harms the search for valid knowledge. Similarly, plagiarism and harassment undermine respect and trust among researchers, while fraudulent or socially irresponsible research weakens public trust and support for science. [https://oir.nih.gov/sourcebook/ethical-conduct/responsible-conduct-research-training The goals of RCR training] include developing a culture of integrity in science and improving knowledge and awareness about the conduct of research.
RCR training and education should be continuous and extend beyond the academic programme throughout a scientist's career. This education can take place in a variety of contexts, such as seminars, workshops, conferences on research ethics and informal mentoring sessions, training courses and laboratory meetings where ethical behaviour and practises are discussed.
As described by van den Hoven and colleagues, multiple factors influence research integrity (RI) training (learning objectives), RI learning (learning outcomes), and changes in RI behaviour (learning outcomes).'"`UNIQ--ref-00000025-QINU`"' Through these, it is possible to promote trustworthy science, responsible research practices, and high integrity/ethical standards. "Training effects" Can be conceptualised through the (intended) impacts of RI training on various performance levels, including individual, institutional, and societal levels.'"`UNIQ--ref-00000026-QINU`"' Furthermore, the authors distinguish among intended training effects (for example changes in behaviour);training input and output (that is training focus/content and learners orientating themselves towards this content);outcomes (that is, learners change their behaviour);and training impact (manifestation of the outcome, such as decreases in misconduct).
[https://nap.nationalacademies.org/read/21896/chapter/15 Effective education in research ethics and integrity aims to achieve several crucial goals.]Defining goals for teaching research integrity and research ethics is crucial to promote and foster responsible research practices and a trustworthy research ecosystem. The main goals to achieve in training RE/RI are related to promoting knowledge (in relation to responsible research practices, norms, and guidelines), skills (in relation to ethical decision-making, problem solving and critical thinking), ‘theoretical’ attitude (in relation to what should be done to foster responsible research) and ‘practical’ behaviour (in relation to how researchers behave in their daily practice).'"`UNIQ--ref-00000027-QINU`"'
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Bloom's Taxonomy is a well-known educational framework that offers a methodical way to classify learning objectives according to cognitive difficulty. (e.g., Adams, 2015).'"`UNIQ--ref-0000003A-QINU`"' It is a hierarchical framework that uses cognitive complexity to classify learning objectives. Benjamin Bloom created it in the 1950s, and it is now a vital instrument in educational theory and practice. The taxonomy is divided into six stages: remembering, understanding, applying, analysing, evaluating, and creating. The levels are arranged from lower to higher order cognitive skills. Fundamentally, remembering entails recollecting words, information, and fundamental ideas. Understanding is more than just remembering concepts,it also involves understanding meanings. Applying necessitates applying knowledge to novel contexts or problem-solving. Analysing means dissecting data into its constituent elements and identifying connections between them. Making decisions based on standards and criteria is the process of evaluating. Creating, in the end, involves coming up with original concepts and/or interpretations. The goal of applying Bloom's Taxonomy to training aims and results is to enhance comprehension by considering the knowledge, skills, and competencies that the specific training programmes were created to impart. The Remembering, Understanding, Applying, Analysing, Evaluating, and Creating domains of Bloom's Taxonomy each reflect a different cognitive process and the depth and complexity of learning.
[[File:BloomsTaxonomy.jpg|alt=|center|frame|Fig 21. Bloom’s Taxonomy (taken from the Centre for teaching, Vanderbilt University. '"`UNIQ--nowiki-0000003B-QINU`"') ]]
All taxonomic levels are relevant irrespective of the study or career level. However, the taxonomic levels may mean different things for different individuals. For example, application of knowledge may mean engaging with research designs, but senior researchers often use more complex designs than students still learning how to do research. Nevertheless, it is essential that the learning extends beyond remembering and understanding, and that the complexity of activities at all levels gradually grow as the individual gains experience, knowledge and confidence.
*<span lang="EN-GB"> R'''emembering and understanding:''' focus on memorizing key ethics concepts and theories. For example, students should master basic principles and terminology related to ethics and integrity.</span>
*<span lang="EN-GB">'''Applying and analysing''': engage in practical applications and critical thinking. Apply ethics concepts to real-life scenarios, such as conducting experiments and analyzing data.</span>
*'''Evaluating and creating''': evaluate research findings and create new knowledge. Encourage learners to think critically and innovate in ethical dilemmas.
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Bloom's Taxonomy is a well-known educational framework that offers a methodical way to classify learning objectives according to cognitive difficulty. (e.g., Adams, 2015).'"`UNIQ--ref-0000005D-QINU`"' It is a hierarchical framework that uses cognitive complexity to classify learning objectives. Benjamin Bloom created it in the 1950s, and it is now a vital instrument in educational theory and practice. The taxonomy is divided into six stages: remembering, understanding, applying, analysing, evaluating, and creating. The levels are arranged from lower to higher order cognitive skills. Fundamentally, remembering entails recollecting words, information, and fundamental ideas. Understanding is more than just remembering concepts, it also involves understanding meanings. Applying necessitates applying knowledge to novel contexts or problem-solving. Analysing means dissecting data into its constituent elements and identifying connections between them. Making decisions based on standards and criteria is the process of evaluating. Creating, in the end, involves coming up with original concepts and/or interpretations. The goal of applying Bloom's Taxonomy to training aims and results is to enhance comprehension by considering the knowledge, skills, and competencies that the specific training programmes were created to impart. The Remembering, Understanding, Applying, Analysing, Evaluating, and Creating domains of Bloom's Taxonomy each reflect a different cognitive process and the depth and complexity of learning.
[[File:BloomsTaxonomy.jpg|alt=|center|frame|Fig 21. Bloom’s Taxonomy (taken from the Centre for teaching, Vanderbilt University. '"`UNIQ--nowiki-0000005E-QINU`"') ]]
All taxonomic levels are relevant irrespective of the study or career level. However, the taxonomic levels may mean different things for different individuals. For example, application of knowledge may mean engaging with research designs, but senior researchers often use more complex designs than students still learning how to do research. Nevertheless, it is essential that the learning extends beyond remembering and understanding, and that the complexity of activities at all levels gradually grow as the individual gains experience, knowledge and confidence.
''Remembering and understanding:''
Here, the focus is on memorising key facts, concepts and theories relevant to the field of research and innovation. Understanding these foundational elements is critical to moving forward. For example, undergraduate students need to master the basic principles and terminology related to ethics and integrity to effectively navigate through more complex topics later. Similarly, individuals pursuing a PhD or who are new to academia need a solid understanding of basic concepts before they can conduct more in-depth analyses and applications, such as mastering the ethics of their own PhD research. Moreover, senior researchers may need to understand the basic concept of supervision and mentoring practices when it comes to supervising a team and PhD candidates.
''Apply and analyse:''
Learning should always be an active endeavour irrespective of career or studies applying and analysing knowledge. This is where the emphasis shifts to practical application and critical thinking. Early career researchers, junior professors and academics need competencies for applying the ethics and integrity concepts they have learnt to real-life scenarios in connection to conducting experiments, collecting data and critically analysing the results to gain meaningful insights. Through these activities, participants develop the skills necessary to contribute to the advancement of their field and address research questions with greater depth and sophistication. In terms of research ethics and integrity, this involves applying such knowledge and values to every step of the research.
''Evaluate and create:''
The highest level in Bloom’s Taxonomy involves evaluating existing knowledge and creating new knowledge. All researchers play a critical role in shaping the direction of research and innovation. They are responsible for assessing the validity and significance of research findings and identifying areas for further investigation and innovation. By synthesising existing knowledge and developing new ideas, theories or methods, researchers develop their field forward and inspire the next generation of researchers and innovators. All RE/RI training should include components, which encourage learners to extend their thinking to evaluation and creation. In practice, this involves having such a robust knowledge base and values so that even when encountering new ethical dilemmas or being posed with a novel potentially integrity-threatening situation, they can rely on having the ‘tools’ to handle the situation.
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Across disciplines, case-based learning (CBL) is a well-established method that encourages higher levels of cognition by having students apply their knowledge to real-world or fictional situations (see Bloom's Taxonomy or Relational/extended abstract levels of learning in SOLO taxonomy).
Learners usually '''work in groups''' on case studies, which are narratives with one or more characters and/or scenarios. The cases pose a disciplinary issue or issues, to which learners come up with remedies while working with an instructor.'"`UNIQ--ref-00000052-QINU`"''"`UNIQ--ref-00000053-QINU`"''"`UNIQ--ref-00000054-QINU`"' Case studies are an effective teaching tool that engages learners, stimulates critical thinking, and enables a deeper understanding of real-life situations. The use of case studies is a deliberate process designed to promote active engagement, critical thinking, and deeper understanding among students. Prior research (e.g., Löfström & Tammeleht, 2023 citing: Bagdasarov et al., 2012;Johnson et al., 2012;McWilliams & Nahavandi, 2006;Nonis & Swift, 2001;O'Leary & Cotter, 2000) has identified the use of cases to be beneficial in RE/RI teaching/learning.'"`UNIQ--ref-00000055-QINU`"' Understanding why and how learning occurs is essential for improving teaching, and as a result, understanding how learners learn can be accessed through an awareness of learning within the framework of research ethics and integrity.'"`UNIQ--ref-00000056-QINU`"'
'''It begins with the careful selection''' of relevant case studies that align with the learning objectives of the course. These cases should not only be current and authentic but should also reflect the students' interests and experiences and provide them with a tangible connection to the course material. RE/RI case-studies can be easily found on [https://embassy.science/wiki-wiki/index.php/Main_Page the Embassy of Good Science].
'''Once a suitable case study has been selected''', the teaching process usually begins with an introduction to the case (e.g., providing context and background information. This first step is crucial to ensuring that students understand the importance of the case study and its relevance to the wider course material. This practice will help students to get acquainted with the topic. In addition to case-studies, also vignettes have been used in RE/RI education to reflect on real-life situations including an explicit or implicit conflict. Trainers may identify a specific ethical/integrity issue on which learners are asked to reflect on.'"`UNIQ--ref-00000057-QINU`"'
'''As learners delve into the case study''', they are asked to actively engage with the material. This means more than just passive reading, learners are encouraged to take notes, ask questions, and identify important themes or patterns contained in the case study. By promoting active reading practises, instructors aim to encourage the development of deeper understanding of the complexity of real-world problems and the various factors at play.
'''The initial reading''' is often followed by common discussion and analysis. With the guidance of the trainer, learners are encouraged to share their interpretations of the case study and explore different perspectives. Discussions can be structured around questions, which encourage critical thinking, consider alternative viewpoints and evaluate the implications of different approaches in order to move from uni- and multistructural levels to relational and extended abstract levels.
Central to the case study approach is the opportunity for learners to '''apply theoretical concepts and principles to real-life situations'''. Instructors help learners make connections between the case study and the course material by encouraging them to analyse the case through the lens of relevant theories, models or frameworks. This process not only deepens students' understanding of theoretical concepts, but also enhances their ability to apply these concepts in practical contexts.
In addition, case studies provide a platform to foster problem-solving skills. Learners are tasked with finding creative solutions to the challenges presented in the case, evaluating the feasibility of various options, and developing a reasoned plan of action. Through this process, learners learn to deal with complex problems, weigh competing interests, and make informed decisions based on facts and analyses.
'''Finally''', case studies can serve as a valuable assessment tool, allowing instructors to evaluate learners' mastery of the content of the course and their ability to apply theoretical concepts to real-world scenarios. Assignments may include written reflections, group presentations, or class discussions based on the case study so that students can demonstrate their learning and receive constructive feedback from fellow students and instructors.
Case-based approaches are utilised in [[Initiative:639c9790-bf80-4f21-9fc0-8027b4c0cfe0|ENERI]], [[Initiative:Fa186292-623f-4b6f-a21e-44250c057f15|RID-SSISS]], [[Initiative:0582c7af-35eb-4def-b74e-c884f29965da|Path2Integrity]], [[Initiative:F9656f91-a514-44ff-9264-d6b3414fdddc|INTEGRITY]] and [[Initiative:8eed30fd-c2ed-44d1-9752-753092bd350e|VIRT<sup>2</sup>UE]].
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