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Open dataset of kinetics, kinematics, and electromyography of above-knee amputees during stand-up and sit-down
After above-knee amputation, the biological knee and ankle are replaced with prostheses. The mobility level of individuals with amputation is related, in part, to the functionality of their prostheses. To understand healthcare needs of amputees, as well as design new, more helpful prostheses, we need to understand the biomechanical effects of using current prosthetic devices. Here we present a dataset of kinetic, kinematic, electromyographic, and video recordings of nine above-knee amputees during the stand-up and sit-down movements. This dataset represents the first repository of amputee biomechanics during stand-up and sit-down with their passive, microprocessor-controlled prostheses, which are still the standard of care after above-knee amputation. The biomechanics were captured using a 12-camera motion capture system with two force plates and four EMG sensors on the intact lower limb. The dataset can serve as a reference when designing next-generation powered prostheses and controllers, to inform prosthetic prescription, and to improve amputee rehabilitation.
Developing practices for FAIR and linked data in Heritage Science
Heritage Science has a lot to gain from the Open Science movement but faces major challenges due to the interdisciplinary nature of the field, as a vast array of technological and scientific methods can be applied to any imaginable material. Historical and cultural contexts are as significant as the methods and material properties, which is something the scientific templates for research data management rarely take into account. While the FAIR data principles are a good foundation, they do not offer enough practical help to researchers facing increasing demands from funders and collaborators. In order to identify the issues and needs that arise “on the ground floor”, the staff at the Heritage Laboratory at the Swedish National Heritage Board took part in a series of workshops with case studies. The results were used to develop guides for good data practices and a list of recommended online vocabularies for standardised descriptions, necessary for findable and interoperable data. However, the project also identified areas where there is a lack of useful vocabularies and the consequences this could have for discoverability of heritage studies on materials from areas of the world that have historically been marginalised by Western culture. If Heritage Science as a global field of study is to reach its full potential this must be addressed.
Environment scan of generative AI infrastructure for clinical and translational science
This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the CTSA Program led by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) at the United States. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis reveals that 53% of institutions identified data security as a primary concern, followed by lack of clinician trust (50%) and AI bias (44%), which must be addressed to ensure the ethical and effective implementation of GenAI technologies.
Tracing inclusivity at UNFCCC conferences through side events and interest group dynamics
Inclusivity and transparency are the foundations of procedural justice in climate governance. However, concerns persist around the influence of business interest groups at United Nations Framework Convention on Climate Change (UNFCCC) Conferences of Parties (COPs). COPs have increased in size and complexity, obscuring agendas and organizational relationships. Here we analyse the discourse and networks of actors at COP side events from 2003 to 2023 using machine learning-based topic modelling and social network analysis. We trace how discussions on energy, food and forests have evolved. Focusing on energy topics, we show that fossil fuel lobbyists gain COP access through developed-country business non-governmental organizations (NGOs) and developing-country governments. Their nominators focus on renewable energy and system approaches but are peripheral in the anti-fossil fuel discourse which grew from a collaborative network of environmental NGOs. Despite data availability challenges, systematically tracing the inclusivity of COP processes can uncover power dynamics at the highest levels of climate governance.
Causation versus prediction in travel mode choice modeling
This study discusses and analyzes the difference between causal and predictive modeling to model travel mode choice. Causal modeling is expressed through causal discovery and causal inference, used to extract causal relationships in mode choice decision making and estimate causal effects between variables. Predictive modeling is expressed through artificial neural networks. When modeling travel mode choice in three Chicago neighborhoods, we find that both causal and predictive modeling approaches perform well and are useful for their modeling purposes. We also note that the study of mode choice behavior through causal modeling is under-explored while it could transform our understanding of mode choice behavior. Further research is needed to realize the full potential of these techniques in modeling mode choice.
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