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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<span lang="EN-US">TIER2's Decision Aid provides clarity on the meaning, relevance, and feasibility of ‘reproducibility’ for researchers to aid them in identifying what type of reproducibility is relevant for their research and indicate what they must consider regarding how feasible such ‘reproducibility’ would be for them.</span> +
In order to make an initiative page for your project, you need to be logged into the Embassy using an ORCiD.
If you don't yet have an ORCiD, you can sign up for one [https://orcid.org/register here].
Once you have an ORCiD, follow the steps in the video below to sign in to the Embassy. +
"Many research fields are currently reckoning with issues of poor levels of reproducibility. Some label it a "crisis", and research employing or building Machine Learning (ML) models is no exception. Issues including lack of transparency, data or code, poor adherence to standards, and the sensitivity of ML training conditions mean that many papers are not even reproducible in principle. Where they are, though, reproducibility experiments have found worryingly low degrees of similarity with original results. Despite previous appeals from ML researchers on this topic and various initiatives from conference reproducibility tracks to the ACM's new Emerging Interest Group on Reproducibility and Replicability, we contend that the general community continues to take this issue too lightly. Poor reproducibility threatens trust in and integrity of research results. Therefore, in this article, we lay out a new perspective on the key barriers and drivers (both procedural and technical) to increased reproducibility at various levels (methods, code, data, and experiments). We then map the drivers to the barriers to give concrete advice for strategies for researchers to mitigate reproducibility issues in their own work, to lay out key areas where further research is needed in specific areas, and to further ignite discussion on the threat presented by these urgent issues."
Find the full paper here: [https://arxiv.org/abs/2406.14325 Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers]
References:
Semmelrock, H., Ross-Hellauer, T., Kopeinik, S., Theiler, D., Haberl, A., Thalmann, S., & Kowald, D. (2024). Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers. ''arXiv preprint arXiv:2406.14325''. +
Want to contribute to the Embassy of Good Science? It's simple! All you need is an ORCiD login and you can get started right away.
See the video below for detailed instructions on how to use your ORCiD to log into the Embassy.
<div class="video-button" data-href="https://www.youtube.com/embed/8s2hroYxT3I">
<span class="video-button-label">ORCiD Login</span>
<span class="video-button-duration">0:47 min</span>
</div> +
In order to add any resources, you need to be logged into the Embassy using an ORCiD.
If you don't yet have an ORCiD, you can sign up for one [https://orcid.org/register here].
Once you have an ORCiD, follow the steps in the video below to sign in to the Embassy. +
To make a module, you must first navigate to the training section of the Embassy. You can do this using the tabs at the top of the home page. +
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This project proposes to use AI and machine learning (ML) algorithms to analyse large datasets of malaria transmission in rural regions of Sub-Saharan Africa. Data will be collected on environmental variables, human behaviour, and mosquito population dynamics to build predictive models. The goal is to use AI to identify hotspots of malaria transmission, optimise intervention strategies, and contribute to global malaria eradication efforts.
This is a hypothetical case but draws inspiration from current discussions regarding ethics dumping and the fair and equitable use of AI technologies in research.
As you work through the module, we invite you to consider the ethics issues that are associated with this type of study from a variety of perspectives as well as how they might be addressed by a research ethics committee. +
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How confident do you feel about navigating ethics issues related to the use of AI technologies in healthcare domains? Select the most appropriate response in the anonymous poll. +
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Research governance can be thought of as the broad range of regulations, principles, processes and systems that help to ensure good practice in the management and conduct of research. Governance regulations and systems aim to:
*safeguard the interests of those who are affected by the research (for example, participants, researchers, animals, environments, society, and institutions),
*foster accountability and trustworthiness, and
*promote high-quality research.
It is often repeated that research ethics was ‘born in scandal’ because its evolution has been repeatedly triggered by revelations about exploitation of participants in research. For instance, early medical experiments undertaken by physicians and biomedical scientist involved the use of vulnerable individuals (like orphaned children or prisoners) as ‘human guinea pigs’. History shows us that many of the early ethics codes and governance mechanisms were developed in response to such scandals in research.
For instance, the Nuremberg Code was formulated in 1947, as a direct response to the abhorrent medical experiments by Nazi and Japanese doctors during the Second World War. While major scandals in research may not be commonplace nowadays, the development and refinement of research ethics codes and processes is ongoing as new ethical challenges and problems come to light.
Today, there are a multitude of ethics codes, policies and systems for research governance at international, national, organisational, and institutional levels. Finding out which governance mechanisms are relevant to a research study is of primary importance for all researchers when designing and conducting research. +
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Before you begin to work your way through the rest of this module, take a moment to think about how you feel about the increasing use of AI technologies in healthcare.
*Are you excited and looking forward to seeing how it develops?
*Are you against the use of AI technologies in healthcare?
*Or do you have some reservations, but think it can be beneficial?
We will ask you to reflect on this again at the end of the module.
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'''Feedback'''
Your opinions and assumptions about the use of XR and related ethics issues will likely be influenced by your prior experiences and understanding. How do you think your current understanding will impact upon your decision-making?
As you work through this module try to keep these thoughts in mind and notice whether your opinions or assumptions about XR in research change. Even if you don’t have any experience of XR, as a REC member who is reviewing this project proposal, you are being asked to make an evidence-informed and balanced judgement call.
If you are unfamiliar with the use of XR, it might be helpful to watch the following video about some of the benefits that XR has to offer.<div><div></div></div> +
<span lang="EN-US">'''Introduce yourself and share the plan for the session presented below:'''</span>
*<span lang="EN-US">Ice-breaker</span>
*<span lang="EN-US">Short introductory lecture</span>
*<span lang="EN-US">Case discussion in smaller groups</span>
*<span lang="EN-US">Plenary discussion</span> +
<span lang="EN-US">I</span>t is strongly recommended to circulate the relevant [https://classroom.eneri.eu/node/377 "technology basics" e-modules] among participants prior to the training. Trainers can consult [https://www.irecs.eu/irecs-training-materials this page] to get some guidance on how to navigate the concent of the modules. Trainers should select the most pertinent modules from those developed by the project, aligning their choices with the session's specific topic.
[[File:Tr1.png|center|frameless|300x300px]][https://www.irecs.eu/irecs-training-materials Overview of irecs Training Materials on ENERI Classroom] +
*Start by introducing yourself and the aim of the session:
''Aim: To introduce the ethical challenges related to gene editing. Using a real case, it aims to encourage reflection on ethical issues related to this technology in an interactive way.''<div>
*Continue with an ice-breaker to warm up your audience. Do that before introducing the topic. Ask participants one of these questions, encourage participants to share their answers with all the group:
*What’s one word that comes to mind when you think of gene editing?
*What's one thing about gene editing that makes you feel excited or uneasy
*Continue with a short introduction of the session and plan: Short introduction to gene editing
</div>
#Our focus today: mind-mapping
#Mind mapping in smaller groups
#Plenary discussion<div></div>
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<div>
*Start by introducing yourself and the aim of the session:
''Aim: To introduce the ethical challenges related to gene editing. Using a real case, it aims to encourage reflection on ethical issues related to this technology in an interactive way.''
<div>
*Continue with an ice-breaker to warm up your audience. Do that before introducing the topic. Ask participants one of these questions, encourage participants to share their answers with all the group:
*What’s one word that comes to mind when you think of gene editing?
*What's one thing about gene editing that makes you feel excited or uneasy
*Continue with a short introduction of the session and plan:
</div>
#Short introduction to gene editing
#Our focus today: mind-mapping
#Mind mapping in smaller groups
#Plenary discussion
<div></div> +
<div>
<span lang="EN-US">Before participating in this activity participants prepare themselves by completing (one of) the e-modules:</span>
</div><div>
*<span lang="EN-US">[https://classroom.eneri.eu/node/236 AI In Healthcare: Technology Basics]</span>
*<span lang="EN-US">[https://classroom.eneri.eu/node/238 AI In Healthcare: Ethics Issues]</span>
*[https://classroom.eneri.eu/node/401 Case studies: AI in Healthcare]
</div> +
<div>
<span lang="EN-US">Before participating in this activity participants prepare themselves by completing (one of) the e-modules:</span>
</div><div>
*<span lang="EN-US">[https://classroom.eneri.eu/node/236 AI In Healthcare: Technology Basics]</span>
*<span lang="EN-US">[https://classroom.eneri.eu/node/238 AI In Healthcare: Ethics Issues]</span>
*[https://classroom.eneri.eu/node/401 Case studies: AI in Healthcare]
</div> +
How can we make sure we're not cutting corners on important safety and ethical standards while we rush through scientific research? During the COVID-19 pandemic, fast scientific work helped us understand the virus, create treatments and vaccines, and figure out how to stay safe. But there were some big questions about protecting the people involved in the research. Should scientists use information and samples from patients who were too sick to agree to it? Should people in studies get a fake treatment if there's already a good one available? Is it okay to intentionally expose healthy volunteers to the virus for research purposes? Watch this annotated video and learn more about ethics and integrity challenges for research during global crises.
Click below to watch the annotated video! +
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Biobanks are specialized repositories that collect and store biological samples and information from various sources (animals, plants, microorganisms, humans, etc.). The focus of this module is on human biobanks, which collect and store biological samples from human donors (e.g., saliva, urine, blood) and health-related data (e.g., health records, family history, lifestyle, genetic, occupational, residential information, etc.) for research purposes and the development of new diagnostic procedures, preventive measures, and treatments. Some biobanks collect health-related data from donors throughout their life, and researchers may continue to access and make use of this data after donors’ demise.
These repositories play a crucial role in advancing biomedical research, by providing scientists with access to a diverse array of high-quality biological materials and continuously updated health-related data. Biobanks ensure the preservation of sample integrity, allowing for researchers to conduct longitudinal studies and other investigations into various health conditions at multiple scales.
For instance, by analyzing samples and data, researchers can search for biological markers, investigate the relationship between biological markers and the sensitivity of diseases to treatment, the aggressiveness of diseases, progression, risk of death, as well as study the genetic and environmental factors that influence the development of certain diseases.
Legal instruments and guidance govern biobank operations to protect the autonomy and dignity of donors, along with their fundamental rights (e.g., private life and data protection) while also advancing the societal benefit of conducting research to address the key public health challenges.
Overall, biobanks contribute significantly to the progress of medical science and personalized medicine. +
