PUMA publications for /group/simtech/SoftwareFri Jan 16 13:06:27 CET 2026SoftwareReplication Data for: An entropy-based evaluation of conceptual constraints in hybrid hydrological models2025myown software Hybrid Models for Hydrology: This repository contains code for the paper "When physics gets in the way: an entropy-based evaluation of conceptual constraints in hybrid hydrological models" submitted to Hydrology and Earth Systems Science (HESS). In this repository we have included the 'Hy2DL' and 'unite_toolbox' libraries in the versions they used for the 'hybrid_models' project. Fri Jan 16 12:50:33 CET 2026SoftwareUNITE Toolbox2024myown software smdi UNITE Toolbox: Unified diagnostic evaluation of scientific models based on information theoryThe UNITE Toolbox is a Python library for incorporating Information Theoryinto data analysis and modeling workflows. The toolbox collects different methods of estimating information-theoretic quantitiesin one easy-to-use Python package. Currently, UNITE includes functions to calculate entropy H(X),Kullback-Leibler divergence D_{KL}(p||q), and mutual information I(X; Y),using three methods: Kernel density-based estimation (KDE), Binning using histograms, k-nearest neighbor-based estimation (k-NN). DaRUS: This is the DaRUS archive for the UNITE toolbox. This archive is currently in version 0.1.9 which is the latest version as of 13.05.2024. This archive will be updated semi-regularly. Check the Github repository for the latest version. Installation: Although the code is still highly experimental and in very active development,a release version is available on PyPI and can be installed using pip. pip install unite_toolbox. Check the pyproject.toml for requirements. Note: pip will install the latest version which might not exactly match this archive. How-to: In the documentation please findtutorials onthe general usage of the toolbox and some applications. Fri Jun 28 06:14:58 CEST 2024Datenbank-Spektrum06Research Data Management in Simulation Science: Infrastructure, Tools, and Applications2024myown forschungsdaten publist simtech software 04Research Data Management (RDM) has gained significant traction in recent years, being essential to allowing research data to be, e.g., findable, accessible, interoperable, and reproducible (FAIR), thereby fostering collaboration or accelerating scientific findings. We present solutions for RDM developed within the DFG-Funded Cluster of Excellence EXC2075 Data-Integrated Simulation Science (SimTech). After an introduction to the scientific context and challenges faced by simulation scientists, we outline the general data management infrastructure and present tools that address these challenges. Exemplary domain applications demonstrate the use and benefits of the proposed data management software solutions. These are complemented by additional measures for enablement and dissemination to foster the adoption of these techniques.Fri Oct 07 10:51:20 CEST 2022Umgang mit Forschungssoftware an der Universität Stuttgart2020myown forschungsdaten publist simtech software Wed Jul 27 10:03:53 CEST 2022Beijing1 Online Ressource (xxv, 571 pages)Software engineering at Google: lessons learned from programming over time2020documentation software diss Tue Jun 28 06:45:33 CEST 202202FAIR4RS: Adoption support2022forschungsdaten software diss fair Tue Jun 28 06:40:41 CEST 202203{A Survey on Adoption Guidelines for the FAIR4RS Principles}2022forschungsdaten software diss fair Tue Jun 28 06:38:58 CEST 202203{A Survey on Adoption Guidelines for the FAIR4RS Principles: Dataset}2022forschungsdaten software diss fair Tue Jun 28 06:34:26 CEST 2022FAIR Principles for Research Software (FAIR4RS Principles)2021forschungsdaten software diss fair Mon Jul 19 08:38:08 CEST 2021Proceedings of the {ACM} 2011 conference on Computer supported cooperative work - {CSCW} {\textquotesingle}11Scientific software production2011forschungsdaten development software success diss Mon Jul 19 08:36:21 CEST 2021CoRRHow to Scale a Code in the Human Dimensionabs/1301.70642013forschungsdaten development software success diss Sat Jun 05 17:44:14 CEST 2021Briefings in Bioinformatics014693-699Top considerations for creating bioinformatics software documentation192017forschungsdaten software recommendation diss {Investing in documenting your bioinformatics software well can increase its impact and save your time. To maximize the effectiveness of your documentation, we suggest following a few guidelines we propose here. We recommend providing multiple avenues for users to use your research software, including a navigable HTML interface with a quick start, useful help messages with detailed explanation and thorough examples for each feature of your software. By following these guidelines, you can assure that your hard work maximally benefits yourself and others.}Thu May 27 14:40:06 CEST 2021F1000Research06876Four simple recommendations to encourage best practices in research software62017forschungsdaten documentation software Thu May 27 14:24:06 CEST 2021{236 GRAYS INN RD, FLOOR 6, LONDON WC1X 8HL, ENGLAND}{GIGASCIENCE}{DEC 4}{Ten recommendations for software engineering in research}{Editorial Material}{3}2014forschungsdaten documentation software recommendation diss {Research in the context of data-driven science requires a backbone of well-written software, but scientific researchers are typically not trained at length in software engineering, the principles for creating better software products. To address this gap, in particular for young researchers new to programming, we give ten recommendations to ensure the usability, sustainability and practicality of research software.}Thu May 27 14:15:39 CEST 2021PLOS Computational Biology0441-10Ten simple rules for making research software more robust132017forschungsdaten documentation software recommendation diss Software produced for research, published and otherwise, suffers from a number of common problems that make it difficult or impossible to run outside the original institution or even off the primary developer’s computer. We present ten simple rules to make such software robust enough to be run by anyone, anywhere, and thereby delight your users and collaborators.Thu May 27 14:08:47 CEST 20212018 IEEE 14th International Conference on e-Science (e-Science)1019-20Lesson Development for Open Source Software Best Practices Adoption2018forschungsdaten software opensource diss The "ELIXIR Training Platform" is partnering with The Carpentries (Software and Data Carpentry) to train life science researchers in computing and data management skills. The "ELIXIR Software development best practices" group, which is part of the ELIXIR Tools Platform, has proposed "Four simple recommendations to encourage best practices in research software" aiming to help researchers and developers to adopt Open Source Software (OSS) practices and thus improve the quality and sustainability of research software. In order to encourage researchers and developers to adopt the four recommendations (4OSS) and build FAIR software, we are developing specific and practical training materials, taking advantage of the Carpentries approach and experience in training material development and maintenance.Thu May 27 14:06:05 CEST 20212018 IEEE 14th International Conference on e-Science (e-Science)101-8The Role of Data Stewardship in Software Sustainability and Reproducibility2018forschungsdaten software steward Software and computational tools are instrumental for scientific investigation in today's digitized research environment. Despite this crucial role, the path towards implementing best practices to achieve reproducibility and sustainability of research software is challenging. Delft University of Technology has begun recently a novel initiative of data stewardship - disciplinary support for research data management, one of the main aims of which is achieving reproducibility of scientific results in general. In this paper, we aim to explore the potential of data stewardship for supporting software reproducibility and sustainability as well. Recently, we gathered the key stakeholders of the topic (i.e. researchers, research software engineers, and data stewards) in a workshop setting to understand the challenges and barriers, the support required to achieve software sustainability and reproducibility, and how all the three parties can efficiently work together. Based on the insights from the workshop, as well as our professional experience as data stewards, we draw conclusions on possible ways forward to achieve the important goal of software reproducibility and sustainability through coordinated efforts of the key stakeholders.Thu May 27 14:05:13 CEST 2021{GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND}{BRIEFINGS IN BIOINFORMATICS}{JUL}{4}{693-699}{Top considerations for creating bioinformatics software documentation}{Article}{19}2018forschungsdaten documentation software {Investing in documenting your bioinformatics software well can increase its impact and save your time. To maximize the effectiveness of your documentation, we suggest following a few guidelines we propose here. We recommend providing multiple avenues for users to use your research software, including a navigable HTML interface with a quick start, useful help messages with detailed explanation and thorough examples for each feature of your software. By following these guidelines, you can assure that your hard work maximally benefits yourself and others.}Thu May 27 13:59:05 CEST 2021{PeerJ} Computer Science09e163Towards computational reproducibility: researcher perspectives on the use and sharing of software42018forschungsdaten software survey Thu May 27 11:02:12 CEST 2021Computing in Science Engineering01139-52Attributing and Referencing (Research) Software: Best Practices and Outlook From Inria222020forschungsdaten susi software diss zitieren Software is a fundamental pillar of modern scientific research, across all fields and disciplines. However, there is a lack of adequate means to cite and reference software due to the complexity of the problem in terms of authorship, roles, and credits. This complexity is further increased when it is considered over the lifetime of a software that can span up to several decades. Building upon the internal experience of Inria, the French research institute for digital sciences, we provide in this article a contribution to the ongoing efforts in order to develop proper guidelines and recommendations for software citation and reference. Namely, we recommend: first, a richer taxonomy for software contributions with a qualitative scale; second, to put humans at the heart of the evaluation; and third, to distinguish citation from reference.