Researcher

A person engaged in conducting research, possibly recognized as an occupation by a formal job title. While a researchers produces and uses data, they are not necessarily data scientists (see: data scientist).

Researchers advance scientific knowledge in public or private organizations by conducting complex experiments that can generate large amounts of data. They design, perform, reproduce experimental studies, collect and analyse results. They publish their results in peer-reviewed journals, private reports, and knowledge management systems. Researchers use a mix of unstructured formats such as PowerPoint, Excel, and Word, alongside structured systems like LIMS, internal databases, and ELNs. They often develop and use domain-specific abbreviations that may not be widely understood outside their immediate teams. Researchers actively seek advanced tools such as knowledge graphs, AI-powered insights, and new technologies to enhance discovery. However, their focus on scientific inquiry can sometimes lead to gaps in data management practices, such as limited coordination with IT for large data handling or storage optimization.

Upside

Establishing common documentation standards and adopting interoperable, FAIR-aligned practices would streamline data management across disciplines. Legible and well-structured ELNs would enhance data discoverability and reuse, while open, accessible file formats would enable seamless information exchange and integration. These improvements would not only reduce data loss and duplication but also accelerate scientific insight, collaboration, and regulatory readiness."

Downside

There is currently no agreed standard practice for scientific documentation. Different roles—chemists, molecular biologists, in vivo scientists, and others from junior interns to senior researchers—apply diverse and inconsistent approaches to data governance, with no shared guidance on how specific data types should be managed. Electronic lab notebooks (ELNs) often lack clarity, as important details are buried in truncated spreadsheets or hidden behind undefined acronyms. Moreover, the prevalence of encrypted or vendor-locked file types restricts open access, hindering data parsing, interoperability, and reuse.

FAIR data principles streamline the research lifecycle from data creation to knowledge sharing. Enhanced metadata makes datasets easier to locate and assess for relevance, while reliable access ensures seamless collaboration across projects. Interoperability connects results across disciplines, fostering integrative discoveries. Alignment with community standards further promotes data reuse, reducing redundancy and supporting transparency. By embedding FAIR practices early—through standardized tools, ontologies, and interoperable systems—researchers can minimize downstream cleanup, improve data quality, and accelerate innovation across the scientific ecosystem.