PUMA publications for /group/researchcode/frameworkTue Jun 18 16:31:36 CEST 2024BMC Medical Research Methodology163--Facilitating harmonized data quality assessments. A data quality framework for observational health research data collections with software implementations in R212021framework forschungsdaten health quality No standards exist for the handling and reporting of data quality in health research. This work introduces a data quality framework for observational health research data collections with supporting software implementations to facilitate harmonized data quality assessments.Tue Sep 12 15:52:49 CEST 2017Computational Materials Science 218 - 230AiiDA: automated interactive infrastructure and database for computational science 1112016framework forschungsdaten hpc-computing metadata software reproducibility softwareLizenzMotivation repository Abstract Computational science has seen in the last decades a spectacular rise in the scope, breadth, and depth of its efforts. Notwithstanding this prevalence and impact, it is often still performed using the renaissance model of individual artisans gathered in a workshop, under the guidance of an established practitioner. Great benefits could follow instead from adopting concepts and tools coming from computer science to manage, preserve, and share these computational efforts. We illustrate here our paradigm sustaining such vision, based around the four pillars of Automation, Data, Environment, and Sharing. We then discuss its implementation in the open-source AiiDA platform (http://www.aiida.net), that has been tuned first to the demands of computational materials science. AiiDA’s design is based on directed acyclic graphs to track the provenance of data and calculations, and ensure preservation and searchability. Remote computational resources are managed transparently, and automation is coupled with data storage to ensure reproducibility. Last, complex sequences of calculations can be encoded into scientific workflows. We believe that AiiDA’s design and its sharing capabilities will encourage the creation of social ecosystems to disseminate codes, data, and scientific workflows. Tue Sep 05 11:25:43 CEST 2017cite arxiv:1611.03543Comment: 14 pages, 6 figuressignac - A Simple Data Management Framework2016framework forschungsdaten engineering largeData Researchers in the field of computational physics, chemistry, and materials science are regularly posed with the challenge of managing large and heterogeneous data spaces. The amount of data increases in lockstep with computational efficiency multiplied by the amount of available computational resources, which shifts the bottleneck within the scientific process from data acquisition to data post-processing and analysis. We present a framework designed to aid in the integration of various specialized data formats, tools and workflows. The signac framework provides all basic components required to create a well-defined and thus collectively accessible data space, simplifying data access and modification through a homogeneous data interface, largely agnostic of the data source, i.e., computation or experiment. The framework's data model is designed not to require absolute commitment to the presented implementation, simplifying adaption into existing data sets and workflows. This approach not only increases the efficiency with which scientific results can be produced, but also significantly lowers barriers for collaborations requiring shared data access.