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@ -115,19 +117,19 @@ Nutzer*innen zu helfen, die theoretischen Grundlagen nachvollziehbar zu machen.
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<meta name="citation_reference" content="citation_title=Introducing the FAIR Principles for Research Software;,citation_abstract=Research software is a fundamental and vital part of research, yet significant challenges to discoverability, productivity, quality, reproducibility, and sustainability exist. Improving the practice of scholarship is a common goal of the open science, open source, and FAIR (Findable, Accessible, Interoperable and Reusable) communities and research software is now being understood as a type of digital object to which FAIR should be applied. This emergence reflects a maturation of the research community to better understand the crucial role of FAIR research software in maximising research value. The FAIR for Research Software (FAIR4RS) Working Group has adapted the FAIR Guiding Principles to create the FAIR Principles for Research Software (FAIR4RS Principles). The contents and context of the FAIR4RS Principles are summarised here to provide the basis for discussion of their adoption. Examples of implementation by organisations are provided to share information on how to maximise the value of research outputs, and to encourage others to amplify the importance and impact of this work.;,citation_author=Michelle Barker;,citation_author=Neil P. Chue Hong;,citation_author=Daniel S. Katz;,citation_author=Anna-Lena Lamprecht;,citation_author=Carlos Martinez-Ortiz;,citation_author=Fotis Psomopoulos;,citation_author=Jennifer Harrow;,citation_author=Leyla Jael Castro;,citation_author=Morane Gruenpeter;,citation_author=Paula Andrea Martinez;,citation_author=Tom Honeyman;,citation_publication_date=2022-10-14;,citation_cover_date=2022-10-14;,citation_year=2022;,citation_fulltext_html_url=https://www.nature.com/articles/s41597-022-01710-x;,citation_issue=1;,citation_doi=10.1038/s41597-022-01710-x;,citation_issn=2052-4463;,citation_volume=9;,citation_language=en-US;,citation_journal_title=Scientific Data;,citation_journal_abbrev=Sci Data;,citation_publisher=Nature Publishing Group;">
<meta name="citation_reference" content="citation_title=Introducing the FAIR Principles for research software;,citation_abstract=Research software is a fundamental and vital part of research, yet significant challenges to discoverability, productivity, quality, reproducibility, and sustainability exist. Improving the practice of scholarship is a common goal of the open science, open source, and FAIR (Findable, Accessible, Interoperable and Reusable) communities and research software is now being understood as a type of digital object to which FAIR should be applied. This emergence reflects a maturation of the research community to better understand the crucial role of FAIR research software in maximising research value. The FAIR for Research Software (FAIR4RS) Working Group has adapted the FAIR Guiding Principles to create the FAIR Principles for Research Software (FAIR4RS Principles). The contents and context of the FAIR4RS Principles are summarised here to provide the basis for discussion of their adoption. Examples of implementation by organisations are provided to share information on how to maximise the value of research outputs, and to encourage others to amplify the importance and impact of this work.;,citation_author=Michelle Barker;,citation_author=Neil P. Chue Hong;,citation_author=Daniel S. Katz;,citation_author=Anna-Lena Lamprecht;,citation_author=Carlos Martinez-Ortiz;,citation_author=Fotis Psomopoulos;,citation_author=Jennifer Harrow;,citation_author=Leyla Jael Castro;,citation_author=Morane Gruenpeter;,citation_author=Paula Andrea Martinez;,citation_author=Tom Honeyman;,citation_publication_date=2022-10-14;,citation_cover_date=2022-10-14;,citation_year=2022;,citation_fulltext_html_url=https://www.nature.com/articles/s41597-022-01710-x;,citation_issue=1;,citation_doi=10.1038/s41597-022-01710-x;,citation_issn=2052-4463;,citation_volume=9;,citation_language=en-US;,citation_journal_title=Scientific Data;,citation_journal_abbrev=Sci Data;,citation_publisher=Nature Publishing Group;">
<meta name="citation_reference" content="citation_title=Projektmanagement und Digital Humanities: Zur klugen Gestaltung der Zusammenarbeit;,citation_abstract=Die Professionalisierung des Projektmanagements in den Digital Humanities: Theorie und Praxis zum Weiterdenken.;,citation_editor=Fabian Cremer;,citation_editor=Swantje Dogunke;,citation_editor=Anna Maria Neubert;,citation_editor=Thorsten Wübbena;,citation_publication_date=2024-04;,citation_cover_date=2024-04;,citation_year=2024;,citation_doi=10.14361/9783839469675;,citation_language=de-DE;">
<meta name="citation_reference" content="citation_title=The Journal of Open Source Software (JOSS): Bringing Open-Source Software Practices to the Scholarly Publishing Community for Authors, Reviewers, Editors, and Publishers;,citation_abstract=Introduction: Open-source software (OSS) is a critical component of open science, but contributions to the OSS ecosystem are systematically undervalued in the current academic system. The Journal of Open Source Software (JOSS) contributes to addressing this by providing a venue (that is itself free, diamond open access, and all open-source, built in a layered structure using widely available elements/services of the scholarly publishing ecosystem) for publishing OSS, run in the style of OSS itself. A particularly distinctive element of JOSS is that it uses open peer review in a collaborative, iterative format, unlike most publishers. Additionally, all the components of the process—from the reviews to the papers to the software that is the subject of the papers to the software that the journal runs—are open. Background: We describe JOSS’s history and its peer review process using an editorial bot, and we present statistics gathered from JOSS’s public review history on GitHub showing an increasing number of peer reviewed papers each year. We discuss the new JOSSCast and use it as a data source to understand reasons why interviewed authors decided to publish in JOSS. Discussion and Outlook: JOSS’s process differs significantly from traditional journals, which has impeded JOSS’s inclusion in indexing services such as Web of Science. In turn, this discourages researchers within certain academic systems, such as Italy’s, which emphasize the importance of Web of Science and/or Scopus indexing for grant applications and promotions. JOSS is a fully diamond open-access journal with a cost of around US$5 per paper for the 401 papers published in 2023. The scalability of running JOSS with volunteers and financing JOSS with grants and donations is discussed.;,citation_author=Patrick Diehl;,citation_author=Charlotte Soneson;,citation_author=Rachel C. Kurchin;,citation_author=Ross Mounce;,citation_author=Daniel S. Katz;,citation_publication_date=2025-02-04;,citation_cover_date=2025-02-04;,citation_year=2025;,citation_fulltext_html_url=https://www.iastatedigitalpress.com/jlsc/article/id/18285/;,citation_issue=2, 2;,citation_doi=10.31274/jlsc.18285;,citation_issn=2162-3309;,citation_volume=12;,citation_language=en-US;,citation_journal_title=Journal of Librarianship and Scholarly Communication;,citation_publisher=Iowa State University Digital Press;">
<meta name="citation_reference" content="citation_title=Endings Principles for Digital Longevity;,citation_abstract=Enabling Sustainable Digital Humanities Projects;,citation_author=Endings Project Team;,citation_publication_date=2023-03-03;,citation_cover_date=2023-03-03;,citation_year=2023;,citation_fulltext_html_url=https://endings.uvic.ca/principles.html;,citation_language=en-US;">
<meta name="citation_reference" content="citation_title=Leitlinien zur Sicherung guter wissenschaftlicher Praxis;,citation_abstract=The DFG´s Code of Conduct “Safeguarding Good Research Practice” represents the consensus among the member organisations of the DFG on the fundamental principles and standards of good practice and are upheld by these organisations. These guidelines underline the importance of integrity in the everyday practice of research and provide researchers with a reliable reference with which to embed good research practice as an established and binding aspect of their work.;,citation_publication_date=2024-09;,citation_cover_date=2024-09;,citation_year=2024;,citation_fulltext_html_url=https://zenodo.org/records/14281892;,citation_doi=10.5281/zenodo.14281892;,citation_language=de-DE;,citation_publisher=Deutsche Forschungsgemeinschaft;">
<meta name="citation_reference" content="citation_title=Open Source Research Software;,citation_author=Wilhelm Hasselbring;,citation_author=Leslie Carr;,citation_author=Simon Hettrick;,citation_author=Heather Packer;,citation_author=Thanassis Tiropanis;,citation_publication_date=2020-08;,citation_cover_date=2020-08;,citation_year=2020;,citation_fulltext_html_url=https://ieeexplore.ieee.org/document/9153295/;,citation_issue=8;,citation_doi=10.1109/MC.2020.2998235;,citation_issn=0018-9162, 1558-0814;,citation_volume=53;,citation_journal_title=Computer;,citation_journal_abbrev=Computer;">
<meta name="citation_reference" content="citation_title=Four Simple Recommendations to Encourage Best Practices in Research Software;,citation_abstract=Scientific research relies on computer software, yet software is not always developed following practices that ensure its quality and sustainability. This manuscript does not aim to propose new software development best practices, but rather to provide simple recommendations that encourage the adoption of existing best practices. Software development best practices promote better quality software, and better quality software improves the reproducibility and reusability of research. These recommendations are designed around Open Source values, and provide practical suggestions that contribute to making research software and its source code more discoverable, reusable and transparent. This manuscript is aimed at developers, but also at organisations, projects, journals and funders that can increase the quality and sustainability of research software by encouraging the adoption of these recommendations.;,citation_author=Rafael C. Jiménez;,citation_author=Mateusz Kuzak;,citation_author=Monther Alhamdoosh;,citation_author=Michelle Barker;,citation_author=Bérénice Batut;,citation_author=Mikael Borg;,citation_author=Salvador Capella-Gutierrez;,citation_author=Neil Chue Hong;,citation_author=Martin Cook;,citation_author=Manuel Corpas;,citation_author=Madison Flannery;,citation_author=Leyla Garcia;,citation_author=Josep Ll. Gelpí;,citation_author=Simon Gladman;,citation_author=Carole Goble;,citation_author=Montserrat González Ferreiro;,citation_author=Alejandra Gonzalez-Beltran;,citation_author=Philippa C. Griffin;,citation_author=Björn Grüning;,citation_author=Jonas Hagberg;,citation_author=Petr Holub;,citation_author=Rob Hooft;,citation_author=Jon Ison;,citation_author=Daniel S. Katz;,citation_author=Brane Leskošek;,citation_author=Federico López Gómez;,citation_author=Luis J. Oliveira;,citation_author=David Mellor;,citation_author=Rowland Mosbergen;,citation_author=Nicola Mulder;,citation_author=Yasset Perez-Riverol;,citation_author=Robert Pergl;,citation_author=Horst Pichler;,citation_author=Bernard Pope;,citation_author=Ferran Sanz;,citation_author=Maria V. Schneider;,citation_author=Victoria Stodden;,citation_author=Radosław Suchecki;,citation_author=Radka Svobodová Vařeková;,citation_author=Harry-Anton Talvik;,citation_author=Ilian Todorov;,citation_author=Andrew Treloar;,citation_author=Sonika Tyagi;,citation_author=Maarten Van Gompel;,citation_author=Daniel Vaughan;,citation_author=Allegra Via;,citation_author=Xiaochuan Wang;,citation_author=Nathan S. Watson-Haigh;,citation_author=Steve Crouch;,citation_publication_date=2017-06-13;,citation_cover_date=2017-06-13;,citation_year=2017;,citation_fulltext_html_url=https://f1000research.com/articles/6-876/v1;,citation_doi=10.12688/f1000research.11407.1;,citation_issn=2046-1402;,citation_volume=6;,citation_language=en-US;,citation_journal_title=F1000Research;,citation_journal_abbrev=F1000Res;">
<meta name="citation_reference" content="citation_title=Jupyter Notebooks – a Publishing Format for Reproducible Computational Workflows;,citation_abstract=It is increasingly necessary for researchers in all fields to write computer code, and in order to reproduce research results, it is important that this code is published. We present Jupyter notebooks, a document format for publishing code, results and explanations in a form that is both readable and executable. We discuss various tools and use cases for notebook documents.;,citation_author=Thomas Kluyver;,citation_author=Benjamin Ragan-Kelley;,citation_author=Fernando Pérez;,citation_author=Brian Granger;,citation_author=Matthias Bussonnier;,citation_author=Jonathan Frederic;,citation_author=Kyle Kelley;,citation_author=Jessica Hamrick;,citation_author=Jason Grout;,citation_author=Sylvain Corlay;,citation_author=Paul Ivanov;,citation_author=Damián Avila;,citation_author=Safia Abdalla;,citation_author=Carol Willing;,citation_author=Jupyter team;,citation_editor=Fernando Loizides;,citation_editor=Birgit Scmidt;,citation_publication_date=2016;,citation_cover_date=2016;,citation_year=2016;,citation_fulltext_html_url=https://eprints.soton.ac.uk/403913/;,citation_doi=10.3233/978-1-61499-649-1-87;,citation_language=en-US;,citation_conference=IOS Press;">
<meta name="citation_reference" content="citation_title=Towards FAIR Principles for&nbsp;Research&nbsp;Software;,citation_abstract=The FAIR Guiding Principles, published in 2016, aim to improve the findability, accessibility, interoperability and reusability of digital research objects for both humans and machines. Until now the FAIR principles have been mostly applied to research data. The ideas behind these principles are, however, also directly relevant to research software. Hence there is a distinct need to explore how the FAIR principles can be applied to software. In this work, we aim to summarize the current status of the debate around FAIR and software, as basis for the development of community-agreed principles for FAIR research software in the future. We discuss what makes software different from data with regard to the application of the FAIR principles, and which desired characteristics of research software go beyond FAIR. Then we present an analysis of where the existing principles can directly be applied to software, where they need to be adapted or reinterpreted, and where the definition of additional principles is required. Here interoperability has proven to be the most challenging principle, calling for particular attention in future discussions. Finally, we outline next steps on the way towards definite FAIR principles for research software.;,citation_author=Anna-Lena Lamprecht;,citation_author=Leyla Garcia;,citation_author=Mateusz Kuzak;,citation_author=Carlos Martinez;,citation_author=Ricardo Arcila;,citation_author=Eva Martin Del Pico;,citation_author=Victoria Dominguez Del Angel;,citation_author=Stephanie Sandt;,citation_author=Jon Ison;,citation_author=Paula Andrea Martinez;,citation_author=Peter McQuilton;,citation_author=Alfonso Valencia;,citation_author=Jennifer Harrow;,citation_author=Fotis Psomopoulos;,citation_author=Josep Ll. Gelpi;,citation_author=Neil Chue Hong;,citation_author=Carole Goble;,citation_author=Salvador Capella-Gutierrez;,citation_publication_date=2020-06-12;,citation_cover_date=2020-06-12;,citation_year=2020;,citation_fulltext_html_url=https://doi.org/10.3233/DS-190026;,citation_issue=1;,citation_doi=10.3233/DS-190026;,citation_issn=2451-8484;,citation_volume=3;,citation_language=en-US;,citation_journal_title=Data Science;,citation_publisher=SAGE Publications;">
<meta name="citation_reference" content="citation_title=Ten Simple Rules for Documenting Scientific Software;,citation_author=Benjamin D. Lee;,citation_publication_date=2018-12-20;,citation_cover_date=2018-12-20;,citation_year=2018;,citation_fulltext_html_url=https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006561;,citation_issue=12;,citation_doi=10.1371/journal.pcbi.1006561;,citation_issn=1553-7358;,citation_volume=14;,citation_language=en-US;,citation_journal_title=PLOS Computational Biology;,citation_journal_abbrev=PLOS Computational Biology;,citation_publisher=Public Library of Science;">
<meta name="citation_reference" content="citation_title=Four simple recommendations to encourage best practices in research software;,citation_abstract=Scientific research relies on computer software, yet software is not always developed following practices that ensure its quality and sustainability. This manuscript does not aim to propose new software development best practices, but rather to provide simple recommendations that encourage the adoption of existing best practices. Software development best practices promote better quality software, and better quality software improves the reproducibility and reusability of research. These recommendations are designed around Open Source values, and provide practical suggestions that contribute to making research software and its source code more discoverable, reusable and transparent. This manuscript is aimed at developers, but also at organisations, projects, journals and funders that can increase the quality and sustainability of research software by encouraging the adoption of these recommendations.;,citation_author=Rafael C. Jiménez;,citation_author=Mateusz Kuzak;,citation_author=Monther Alhamdoosh;,citation_author=Michelle Barker;,citation_author=Bérénice Batut;,citation_author=Mikael Borg;,citation_author=Salvador Capella-Gutierrez;,citation_author=Neil Chue Hong;,citation_author=Martin Cook;,citation_author=Manuel Corpas;,citation_author=Madison Flannery;,citation_author=Leyla Garcia;,citation_author=Josep Ll. Gelpí;,citation_author=Simon Gladman;,citation_author=Carole Goble;,citation_author=Montserrat González Ferreiro;,citation_author=Alejandra Gonzalez-Beltran;,citation_author=Philippa C. Griffin;,citation_author=Björn Grüning;,citation_author=Jonas Hagberg;,citation_author=Petr Holub;,citation_author=Rob Hooft;,citation_author=Jon Ison;,citation_author=Daniel S. Katz;,citation_author=Brane Leskošek;,citation_author=Federico López Gómez;,citation_author=Luis J. Oliveira;,citation_author=David Mellor;,citation_author=Rowland Mosbergen;,citation_author=Nicola Mulder;,citation_author=Yasset Perez-Riverol;,citation_author=Robert Pergl;,citation_author=Horst Pichler;,citation_author=Bernard Pope;,citation_author=Ferran Sanz;,citation_author=Maria V. Schneider;,citation_author=Victoria Stodden;,citation_author=Radosław Suchecki;,citation_author=Radka Svobodová Vařeková;,citation_author=Harry-Anton Talvik;,citation_author=Ilian Todorov;,citation_author=Andrew Treloar;,citation_author=Sonika Tyagi;,citation_author=Maarten Van Gompel;,citation_author=Daniel Vaughan;,citation_author=Allegra Via;,citation_author=Xiaochuan Wang;,citation_author=Nathan S. Watson-Haigh;,citation_author=Steve Crouch;,citation_publication_date=2017-06-13;,citation_cover_date=2017-06-13;,citation_year=2017;,citation_fulltext_html_url=https://f1000research.com/articles/6-876/v1;,citation_doi=10.12688/f1000research.11407.1;,citation_issn=2046-1402;,citation_volume=6;,citation_language=en-US;,citation_journal_title=F1000Research;,citation_journal_abbrev=F1000Res;">
<meta name="citation_reference" content="citation_title=Jupyter Notebooks – a publishing format for reproducible computational workflows;,citation_abstract=It is increasingly necessary for researchers in all fields to write computer code, and in order to reproduce research results, it is important that this code is published. We present Jupyter notebooks, a document format for publishing code, results and explanations in a form that is both readable and executable. We discuss various tools and use cases for notebook documents.;,citation_author=Thomas Kluyver;,citation_author=Benjamin Ragan-Kelley;,citation_author=Fernando Pérez;,citation_author=Brian Granger;,citation_author=Matthias Bussonnier;,citation_author=Jonathan Frederic;,citation_author=Kyle Kelley;,citation_author=Jessica Hamrick;,citation_author=Jason Grout;,citation_author=Sylvain Corlay;,citation_author=Paul Ivanov;,citation_author=Damián Avila;,citation_author=Safia Abdalla;,citation_author=Carol Willing;,citation_author=Jupyter team;,citation_editor=Fernando Loizides;,citation_editor=Birgit Scmidt;,citation_publication_date=2016;,citation_cover_date=2016;,citation_year=2016;,citation_fulltext_html_url=https://eprints.soton.ac.uk/403913/;,citation_doi=10.3233/978-1-61499-649-1-87;,citation_language=en-US;,citation_conference=IOS Press;">
<meta name="citation_reference" content="citation_title=Towards FAIR principles for&nbsp;research&nbsp;software;,citation_abstract=The FAIR Guiding Principles, published in 2016, aim to improve the findability, accessibility, interoperability and reusability of digital research objects for both humans and machines. Until now the FAIR principles have been mostly applied to research data. The ideas behind these principles are, however, also directly relevant to research software. Hence there is a distinct need to explore how the FAIR principles can be applied to software. In this work, we aim to summarize the current status of the debate around FAIR and software, as basis for the development of community-agreed principles for FAIR research software in the future. We discuss what makes software different from data with regard to the application of the FAIR principles, and which desired characteristics of research software go beyond FAIR. Then we present an analysis of where the existing principles can directly be applied to software, where they need to be adapted or reinterpreted, and where the definition of additional principles is required. Here interoperability has proven to be the most challenging principle, calling for particular attention in future discussions. Finally, we outline next steps on the way towards definite FAIR principles for research software.;,citation_author=Anna-Lena Lamprecht;,citation_author=Leyla Garcia;,citation_author=Mateusz Kuzak;,citation_author=Carlos Martinez;,citation_author=Ricardo Arcila;,citation_author=Eva Martin Del Pico;,citation_author=Victoria Dominguez Del Angel;,citation_author=Stephanie Sandt;,citation_author=Jon Ison;,citation_author=Paula Andrea Martinez;,citation_author=Peter McQuilton;,citation_author=Alfonso Valencia;,citation_author=Jennifer Harrow;,citation_author=Fotis Psomopoulos;,citation_author=Josep Ll. Gelpi;,citation_author=Neil Chue Hong;,citation_author=Carole Goble;,citation_author=Salvador Capella-Gutierrez;,citation_publication_date=2020-06-12;,citation_cover_date=2020-06-12;,citation_year=2020;,citation_fulltext_html_url=https://doi.org/10.3233/DS-190026;,citation_issue=1;,citation_doi=10.3233/DS-190026;,citation_issn=2451-8484;,citation_volume=3;,citation_language=en-US;,citation_journal_title=Data Science;,citation_publisher=SAGE Publications;">
<meta name="citation_reference" content="citation_title=Ten simple rules for documenting scientific software;,citation_author=Benjamin D. Lee;,citation_publication_date=2018-12-20;,citation_cover_date=2018-12-20;,citation_year=2018;,citation_fulltext_html_url=https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006561;,citation_issue=12;,citation_doi=10.1371/journal.pcbi.1006561;,citation_issn=1553-7358;,citation_volume=14;,citation_language=en-US;,citation_journal_title=PLOS Computational Biology;,citation_journal_abbrev=PLOS Computational Biology;,citation_publisher=Public Library of Science;">
<meta name="citation_reference" content="citation_title=Ten Simple Rules for the Open Development of Scientific Software;,citation_author=Andreas Prlić;,citation_author=James B. Procter;,citation_publication_date=2012-12-06;,citation_cover_date=2012-12-06;,citation_year=2012;,citation_fulltext_html_url=https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002802;,citation_issue=12;,citation_doi=10.1371/journal.pcbi.1002802;,citation_issn=1553-7358;,citation_volume=8;,citation_language=en-US;,citation_journal_title=PLOS Computational Biology;,citation_journal_abbrev=PLOS Computational Biology;,citation_publisher=Public Library of Science;">
<meta name="citation_reference" content="citation_title=Software Citation Principles;,citation_abstract=Software is a critical part of modern research and yet there is little support across the scholarly ecosystem for its acknowledgement and citation. Inspired by the activities of the FORCE11 working group focused on data citation, this document summarizes the recommendations of the FORCE11 Software Citation Working Group and its activities between June 2015 and April 2016. Based on a review of existing community practices, the goal of the working group was to produce a consolidated set of citation principles that may encourage broad adoption of a consistent policy for software citation across disciplines and venues. Our work is presented here as a set of software citation principles, a discussion of the motivations for developing the principles, reviews of existing community practice, and a discussion of the requirements these principles would place upon different stakeholders. Working examples and possible technical solutions for how these principles can be implemented will be discussed in a separate paper.;,citation_author=Arfon M. Smith;,citation_author=Daniel S. Katz;,citation_author=Kyle E. Niemeyer;,citation_publication_date=2016;,citation_cover_date=2016;,citation_year=2016;,citation_fulltext_html_url=https://peerj.com/articles/cs-86;,citation_doi=10.7717/peerj-cs.86;,citation_issn=2376-5992;,citation_volume=2;,citation_language=en-US;,citation_journal_title=PeerJ Computer Science;,citation_journal_abbrev=PeerJ Comput. Sci.;,citation_publisher=PeerJ Inc.;">
<meta name="citation_reference" content="citation_title=Good Enough Practices in Scientific Computing;,citation_abstract=Author summary Computers are now essential in all branches of science, but most researchers are never taught the equivalent of basic lab skills for research computing. As a result, data can get lost, analyses can take much longer than necessary, and researchers are limited in how effectively they can work with software and data. Computing workflows need to follow the same practices as lab projects and notebooks, with organized data, documented steps, and the project structured for reproducibility, but researchers new to computing often don’t know where to start. This paper presents a set of good computing practices that every researcher can adopt, regardless of their current level of computational skill. These practices, which encompass data management, programming, collaborating with colleagues, organizing projects, tracking work, and writing manuscripts, are drawn from a wide variety of published sources from our daily lives and from our work with volunteer organizations that have delivered workshops to over 11,000 people since 2010.;,citation_author=Greg Wilson;,citation_author=Jennifer Bryan;,citation_author=Karen Cranston;,citation_author=Justin Kitzes;,citation_author=Lex Nederbragt;,citation_author=Tracy K. Teal;,citation_publication_date=2017-06-22;,citation_cover_date=2017-06-22;,citation_year=2017;,citation_fulltext_html_url=https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005510;,citation_issue=6;,citation_doi=10.1371/journal.pcbi.1005510;,citation_issn=1553-7358;,citation_volume=13;,citation_language=en-US;,citation_journal_title=PLOS Computational Biology;,citation_journal_abbrev=PLOS Computational Biology;,citation_publisher=Public Library of Science;">
<meta name="citation_reference" content="citation_title=Software citation principles;,citation_abstract=Software is a critical part of modern research and yet there is little support across the scholarly ecosystem for its acknowledgement and citation. Inspired by the activities of the FORCE11 working group focused on data citation, this document summarizes the recommendations of the FORCE11 Software Citation Working Group and its activities between June 2015 and April 2016. Based on a review of existing community practices, the goal of the working group was to produce a consolidated set of citation principles that may encourage broad adoption of a consistent policy for software citation across disciplines and venues. Our work is presented here as a set of software citation principles, a discussion of the motivations for developing the principles, reviews of existing community practice, and a discussion of the requirements these principles would place upon different stakeholders. Working examples and possible technical solutions for how these principles can be implemented will be discussed in a separate paper.;,citation_author=Arfon M. Smith;,citation_author=Daniel S. Katz;,citation_author=Kyle E. Niemeyer;,citation_publication_date=2016;,citation_cover_date=2016;,citation_year=2016;,citation_fulltext_html_url=https://peerj.com/articles/cs-86;,citation_doi=10.7717/peerj-cs.86;,citation_issn=2376-5992;,citation_volume=2;,citation_language=en-US;,citation_journal_title=PeerJ Computer Science;,citation_journal_abbrev=PeerJ Comput. Sci.;,citation_publisher=PeerJ Inc.;">
<meta name="citation_reference" content="citation_title=Good enough practices in scientific computing;,citation_abstract=Author summary Computers are now essential in all branches of science, but most researchers are never taught the equivalent of basic lab skills for research computing. As a result, data can get lost, analyses can take much longer than necessary, and researchers are limited in how effectively they can work with software and data. Computing workflows need to follow the same practices as lab projects and notebooks, with organized data, documented steps, and the project structured for reproducibility, but researchers new to computing often don’t know where to start. This paper presents a set of good computing practices that every researcher can adopt, regardless of their current level of computational skill. These practices, which encompass data management, programming, collaborating with colleagues, organizing projects, tracking work, and writing manuscripts, are drawn from a wide variety of published sources from our daily lives and from our work with volunteer organizations that have delivered workshops to over 11,000 people since 2010.;,citation_author=Greg Wilson;,citation_author=Jennifer Bryan;,citation_author=Karen Cranston;,citation_author=Justin Kitzes;,citation_author=Lex Nederbragt;,citation_author=Tracy K. Teal;,citation_publication_date=2017-06-22;,citation_cover_date=2017-06-22;,citation_year=2017;,citation_fulltext_html_url=https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005510;,citation_issue=6;,citation_doi=10.1371/journal.pcbi.1005510;,citation_issn=1553-7358;,citation_volume=13;,citation_language=en-US;,citation_journal_title=PLOS Computational Biology;,citation_journal_abbrev=PLOS Computational Biology;,citation_publisher=Public Library of Science;">
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Zusammengefasst sollte die Dokumentation alle <strong>W-Fragen</strong> beantworten
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<p>Achten Sie dabei auf Dateigrößen und Formate (Bilder als PNG/JPG, Diagramme wenn möglich als SVG für Langlebigkeit). Falls Diagramme der Architektur oder Workflow-Abbildungen hilfreich sind, können diese mit simplen Mitteln erstellt werden<a href="#fn3" class="footnote-ref" id="fnref3" role="doc-noteref"><sup>3</sup></a>.</p>
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