PUMA publications for /group/simtech/DataWed Nov 05 15:30:21 CET 2025Journal of Children’s Orthopaedics053179–188Impact of body weight and age on plantar pressure in typically developing children: Normative data and methodological considerations192025children normalisation normative parameter weight data Pedobarography chronological body age Purpose: Pedobarography is frequently employed for the identification and characterisation of foot pathologies in paediatrics. However, the lack of standardised normalisation methods presents a challenge for cross-age comparisons. This cross-sectional study provides normative plantar pressure data for typically developing children aged 4–17 years and compares normalisations and explanatory powers of parameters measuring peak and total load. Methods: Dynamic foot pressure of 101 typically developing children aged 4–17 years was measured at self-selected speed using the mid-gait protocol. They were divided into five age groups: 4–6, 7–8, 9–11, 12–14 and 15–17 years old. Force and pressure variables measuring peak and total load were normalised by body weight or scaled by maximum value and the foot region where the peak pressure occurred was identified. Results: The absolute values demonstrated an increase in load with advancing age. In contrast, when normalised to body weight, peak pressure and pressure time integrals decreased. The scaled peak pressure showed a load shift to the forefoot. The results indicate that the normalised parameters exhibit superior qualitative significance, suggesting a more dynamic gait pattern and improved morphology of the foot in relation to body weight with increasing age. Conclusions: This study shows that standardisation of the measurement protocol is imperative because results in typically developing children can vary depending up parameter selection and normalisation technique. Level of evidence: 3Tue Jan 09 16:52:45 CET 2024Advancing Research Data Management in Universities of Science and Technology2020myOwn RDM, Data Management Research Tue Oct 10 10:32:37 CEST 2023DatasetRelated to: Rettberg, J.; Wittwar, D.; Buchfink, P.; Brauchler, A.; Ziegler, P.; Fehr, J.; Haasdonk, B.: Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar. Mathematical and Computer Modelling of Dynamical Systems, 2023. doi: 10.1080/13873954.2023.2173238Replication Data for: Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar2023jrettberg data This dataset includes the system matrices in velocity formulation and a parallel coordinates plot of the sensitivity analysis from the paper titled "Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar". The system matrices can be used to perform further system reformulations according to the paper or model reductions for the basis generation variants mentioned in the paper. The parallel coordinates plot is an interactive way to visualize the multitude of numerical experiments. For this purpose, the desired combination possibilities can simply be marked. The color of the path is a measure of the relative error due to the model reduction. The plot is in .html file format and can be opened with any browser. For visualisation purposes all error values >1 were set to 1. Tue Feb 23 06:44:07 CET 2021Science Editing02116--23An analysis of data paper templates and guidelines: types of contextual information described by data journals72020journal documentation data diss Fri Jun 29 10:07:39 CEST 2018Secaucus, NJ, USAThe VLDB Journal126881--906A Survey on Provenance: What for? What Form? What from?262017provenance data Provenance refers to any information describing the production process of an end product, which can be anything from a piece of digital data to a physical object. While this survey focuses on the former type of end product, this definition still leaves room for many different interpretations of and approaches to provenance. These are typically motivated by different application domains for provenance (e.g., accountability, reproducibility, process debugging) and varying technical requirements such as runtime, scalability, or privacy. As a result, we observe a wide variety of provenance types and provenance-generating methods. This survey provides an overview of the research field of provenance, focusing on what provenance is used for (what for?), what types of provenance have been defined and captured for the different applications (what form?), and which resources and system requirements impact the choice of deploying a particular provenance solution (what from?). For each of these three key questions, we provide a classification and review the state of the art for each class. We conclude with a summary and possible future research challenges.Tue Jun 26 09:18:47 CEST 2018IEEE Internet Computing05364-69The Social Compute Unit152011social computing workflow data Collaboration soc2018 network Fri Mar 23 14:15:39 CET 2018PeerJ Preprints12e2630v1Software vs. data in the context of citation42016forschungsdaten citation software data Software is data, but it is not just data. While "data" in computing and information science can refer to anything that can be processed by a computer, software is a special kind of data that can be a creative, executable tool that operates on data. However, software and data are similar in that they both traditionally have not been cited in publications. This paper discusses the differences between software and data in the context of citation, by providing examples and referring to evidence in the form of citations. Tue Mar 20 14:17:35 CET 2018Recommendation on Research Data Collections2017forschungsdaten data colection rda Recent efforts of the Research Data Alliance have established a conceptual model for the management of research data that promotes the use of digital objects, transcending the traditional notion of files and decoupling questions of access and use from location and storage. In this context, the need for building aggregations or collections of such objects has become an essential element. However, contemporary work on object collections focuses on primarily describing such collections through metadata, whereas research data management practice requires not only to describe collections, but to make them actionable by automated processes to be able to cope with ever increasing amounts and volumes of data. To this effect, this recommendation provides a comprehensive model for actionable collections and a technical interface specification to enable client-server interaction. It also reports on first adoption and implementation efforts across communities and institutions and provides perspectives on the use of data types in connection with collection structures, highlighting pathways for possible future work.