This matrix is generic. It is a tool for data stewards or other research supporters to assist researchers in taking appropriate measures for the safe use and protection of data about people in scientific research. It is a template that you can adjust to the context of your own institution, faculty and / or department by taking into consideration your setting’s own policies, guidelines, infrastructure and technical solutions. In this way you can more effectively determine the appropriate technical and organizational measures to protect the data based on the context of the research and the risks associated with the data.
Organizations feel an urgency to develop and implement applications based on foundation models: AI-models that have been trained on large-scale general data and can be finetuned to domain-specific tasks. In this process organizations face many questions, regarding model training and deployment, but also concerning added business value, implementation risks and governance. They express a need for guidance to answer these questions in a suitable and responsible way. We intend to offer such guidance by the question matrix presented in this paper. The question matrix is adjusted from the model card, to match well with development of AIapplications rather than AI-models. First pilots with the question matrix revealed that it elicited discussions among developers and helped developers explicate their choices and intentions during development.
MULTIFILE
Aim: To investigate the effects of exercise on salivary concentrations of inflammatory markers by analyzing a panel of 25 inflammatory markers in subjects who had participated in bicycle ergometer tests varying in workload and hydration status. Methods: Fifteen healthy young men (20-35 years) had performed 4 different exercise protocols of 1 hour duration in a randomly assigned cross-over design, preceded by a rest protocol. Individual workloads depended on participant's pre-assessed individual maximum workload (Wmax): rest (protocol 1), 70% Wmax in hydrated (protocol 2) and dehydrated (protocol 3) state, 50% Wmax (protocol 4) and intermittent 85%/55% Wmax in 2 min blocks (protocol 5). Saliva samples were collected before (T0) and immediately after exercise (T1), and at several time points after exercise (2 hours (T3), 3 hours (T4), 6 hours (T5) and 24 hours (T6)). Secretory Leukocyte Protease Inhibitor (SLPI), Matrix Metallopeptidase-9 (MMP-9) and lactoferrin was analyzed using a commercial ELISA kit, a panel of 22 cytokines and chemokines were analyzed using a commercial multiplex immunoassay. Data was analyzed using a multilevel mixed linear model, with multiple test correction. Results: Among a panel of 25 inflammatory markers, SLPI concentrations were significantly elevated immediately after exercise in all protocols compared to rest and higher concentrations reflected the intensity of exercise and hydration status. MMP-9 showed a significant increase in the 70% Wmax dehydrated, 50% Wmax and intermittent protocols. Conclusions: Salivary concentrations of SLPI and MMP-9 seem associated with exercise intensity and hydration status and may offer non-invasive biomarkers to study (local) inflammatory responses to different exercise intensities in human studies. sa
In the last decade, the automotive industry has seen significant advancements in technology (Advanced Driver Assistance Systems (ADAS) and autonomous vehicles) that presents the opportunity to improve traffic safety, efficiency, and comfort. However, the lack of drivers’ knowledge (such as risks, benefits, capabilities, limitations, and components) and confusion (i.e., multiple systems that have similar but not identical functions with different names) concerning the vehicle technology still prevails and thus, limiting the safety potential. The usual sources (such as the owner’s manual, instructions from a sales representative, online forums, and post-purchase training) do not provide adequate and sustainable knowledge to drivers concerning ADAS. Additionally, existing driving training and examinations focus mainly on unassisted driving and are practically unchanged for 30 years. Therefore, where and how drivers should obtain the necessary skills and knowledge for safely and effectively using ADAS? The proposed KIEM project AMIGO aims to create a training framework for learner drivers by combining classroom, online/virtual, and on-the-road training modules for imparting adequate knowledge and skills (such as risk assessment, handling in safety-critical and take-over transitions, and self-evaluation). AMIGO will also develop an assessment procedure to evaluate the impact of ADAS training on drivers’ skills and knowledge by defining key performance indicators (KPIs) using in-vehicle data, eye-tracking data, and subjective measures. For practical reasons, AMIGO will focus on either lane-keeping assistance (LKA) or adaptive cruise control (ACC) for framework development and testing, depending on the system availability. The insights obtained from this project will serve as a foundation for a subsequent research project, which will expand the AMIGO framework to other ADAS systems (e.g., mandatory ADAS systems in new cars from 2020 onwards) and specific driver target groups, such as the elderly and novice.
