NEW PUBLICATION: A practical guide to data management and sharing for biomedical laboratory researchers, read more here

We want to share the recently published article by Fouad et al., 2024, A practical guide to data management and sharing for biomedical laboratory researchers, as it discusses the importance of effective data management and sharing in research with a key focus on the research data organization system. Most importantly this article provides strategies for effective data sharing, which has become a crucial part of academic research due to new funder and journal mandates aimed at improving transparency, reproducibility, and reducing waste.

Embracing data management and sharing practices from the start can unlock the full potential of research data and contribute to advancing the field. A well-organized data collection, management, and sharing have tremendous value for both the data creators and the broader research community. It provides mechanisms for compliance with data sharing mandates, facilitates training of new lab members, and eases reviewing and integrating old data. Furthermore, it increases reproducibility, collaboration, and citations.

The article by Fouad et al., 2024 emphasizes the importance of creating documentation and standard operating procedures (SOPs) for the entire data workflow, from data collection to management and sharing, (figure 3). It provides practical guidance on developing a data management and sharing (DMS) plan, as well as mechanisms for compliance with data sharing mandates. These include the key concepts that relate to research data organization, including machine-readable vs. human-readable data, data standards, data dictionaries, and unique subject identifiers. Here we recommend adopting established data standards in the field, creating a data dictionary to document data elements, and developing a system for unique subject identification. Standardized file organization, with an experimental catalog and subject catalog, is presented as a simple yet effective data organization system that can be tailored to individual lab needs, see Folder organization tree of digital files in figure 4.

In figure Fig. 6 & 7 we provide an example of a subject catalogue and a data dictionary respectively, using Excel spreadsheet.

In summary, each experiment contains a subject list file providing essential information for each subject. This includes a unique subject identifier, experimental variables such as group allocation, species, strain, and any parameters necessary to understand the experiment. The data dictionary template is provided which follows the inclusion of SCI community recommended minimal required variables and their descriptions as described in table 4. 

Creating a standard subject catalogue and data dictionary for routine data elements involves documenting essential information such as variable names, definitions, units of measurement, and permitted values. This ensures that all lab members use consistent terminology and have a shared understanding of the data, which is essential for seamless data workflows within the lab. It also promotes data sharing among lab members and the wider research community by enhancing the interoperability and reusability of the data, as others can easily comprehend and use the data with standardized definitions and formats.