Summary

This code of conduct compiles existing guidelines for planning, conducting and reporting animal experiments and distills them into key recommendations, in the legal and institutional context in Switzerland. The main focus is on enabling researchers to produce research data than can be reused by others.

Each chapter begins with a concise recommendation on the topic, followed by conceptual clarifications, a selection of relevant resources, and/or more specific practical tips. The appendix contains a glossary of the key terms per chapter.

Part I Basic considerations

We begin by introducing basic concepts and definitions: the 3R principles of replacement, reduction and refinement; different aspects of reproducibility; the FAIR principles and open research data; how preregistration and data management plans can contribute to reproducibility and the 3R principles; and relevant legal and ethical frameworks in Switzerland. Key recommendations in this area are:

• Apply the principles of Replacement, Reduction, and Refinement to all experimental design and data practices. Refer to Swiss 3RCC and partners for 3R tools and guidance.

• Manage research data as if it would become of public domain.

• Document data in a way that it can or could be reused by researchres outside you group.

• Share all metadata and, whenever possible, also all data.

• Consider routinely preregistering every experiment and analysis.

• Aim for a realistic degree of FAIRness in your data, depending on the available resources and required capabilities.

• Use data management plans as a living, editable document to guide data management in a project. Use version control and track changes in this document.

• Consult a data steward for assistance in writing and implementing a data management plan.

• Consider the relevant legal and ethical guidelines already before starting your research project.

Part II Data Organization and Metadata

The second part describes how to find and use discipline-specific standards for organizing and documenting data and metadata and provides practical guidance on working with tables and spreadsheets, metadata companion files and controlled vocabularies. Key recommendations in this area are:

• Adopt data and metadata standards that are actively maintained and used by a broad community.

• If no established standard is available, adapt an existing standard rather than create a new one.

• Produce documentation that can be understood by collaborators outside your group as early as possible.

• Utilize the wide range of existing templates as well as discipline-specific reporting guidelines.

• Structure and format the tables so that they can be machine-readable.

• Use interoperable file formats.

• Include a data dictionary describing the data.

• Always keep the raw data and keep track of changes.

• Plan the content of metadata companion files early on and follow standards when available. Automate the generation of these files.

• Adopt a controlled vocabulary, and consider adopting more complex structures like taxonomies or ontologies.

Part III Archiving, Sharing and Publishing

The third part describes how to choose file formats and use version control for the purpose of archiving, sharing and publishing data, and introduces different types of data archives and online repositories available for research data. Key recommendations in this area are:

• Prioritize long-term sustainability and interoperability when choosing file format.

• Use formats widely used in your data community.

• Use Git for version control when producing research code, and consider its use also for other text data and metadata tables and files.

• Begin with simple workflows without trying to utilize all the advance features from the start.

• Plan ahead for archiving research outputs, defining what, how, when, and where.

• Treat metadata and documentation with the same importance as the data itself.