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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.
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.
‘Social media is quite good in informing patients but can be quite dangerous because we’re doing clinical work’
AK Let’s go back to the start, what was your undergraduate experience like? FE It was generally positive, it was obviously interrupted by COVID-19, which…
Higher income is associated with greater life satisfaction, and more stress
Is there a cost to our well-being from increased affluence? Drawing upon responses from 2.05 million U.S. adults from the Gallup Daily Poll from 2008 to 2017 we find that with household income above ~$63,000 respondents are more likely to experience stress. This contrasts with the trend below this threshold, where at higher income the prevalence of stress decreases. Such a turning point for stress was also found for population sub-groups, divided by gender, race, and political affiliation. Further, we find that respondents who report prior-day stress have lower life satisfaction for all income and sub-group categories compared to the respondents who do not report prior-day stress. We find suggestive evidence that among the more satisfied, healthier, socially connected, and those not suffering basic needs deprivations, this turn-around in stress prevalence starts at lower values of income and stress. We hypothesize that stress at higher income values relates to lifestyle factors associated with affluence, rather than from known well-being deprivations related to good health and social conditions, which may arise even at lower income values if conventional needs are met.
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