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OpenStats: how to combine statistics and research data management (RDM) to leverage efficient scientific data analysis by guided statistics

Krämer, Konrad 1; Tremouilhac, Pierre 1; Mauz, Fabian; Grathwol, Christoph 1; Jung, Nicole ORCID iD icon 2; Bräse, Stefan ORCID iD icon 2
1 Institut für Biologische und Chemische Systeme (IBCS), Karlsruher Institut für Technologie (KIT)
2 Institut für Organische Chemie (IOC), Karlsruher Institut für Technologie (KIT)

Abstract:

We developed OpenStats, a user-friendly web application that brings the power of the R language to researchers through a high-level interface and broad support for statistical methods such as t-tests and ANOVA. OpenStats was integrated into our electronic lab notebook Chemotion ELN via its third-party API, enabling direct data exchange with it. A pivotal feature of OpenStats is its ability to record each analysis step in a structured history. This record allows users to retrace their work and enables automatic replay of the entire analysis, promoting reproducibility and long-term data integrity. In the modern research landscape, Research Data Management (RDM) tools like electronic lab notebooks (ELNs) are crucial for generating reproducible, repeatable, and transparently documented data. However, integrating statistical analysis tools into RDM systems remained a challenge. We demonstrated how OpenStats can be seamlessly linked to Research Data Management (RDM) systems, using Chemotion ELN as a reference implementation, by our application to a standard dose–response assay scenario. This linkage allows the integration of statistical data analysis in the form of a closed, traceable workflow into the RDM world. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000194979
Veröffentlicht am 06.07.2026
Originalveröffentlichung
DOI: 10.1186/s13321-026-01241-2
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Biologische und Chemische Systeme (IBCS)
Institut für Organische Chemie (IOC)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2026
Sprache Englisch
Identifikator ISSN: 1758-2946
KITopen-ID: 1000194979
Erschienen in Journal of Cheminformatics
Verlag SpringerOpen
Band 18
Heft 1
Seiten 86
Vorab online veröffentlicht am 01.07.2026
Nachgewiesen in OpenAlex
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