Renaming lab data or experiment results refers to systematically changing identifiers assigned to raw data files, derived datasets, or experimental runs. Unlike simple file renaming, this typically occurs within formal data management systems (like ELNs or LIMSs) and involves updating metadata to reflect changes while preserving critical traceability links to the original source. The core purpose is improving clarity or consistency in project organization without losing context.

In practice, this might involve a researcher renaming complex instrument output files according to a project-approved schema (e.g., changing Run_001.fastq to ProjectAlpha_HumanSample1_SeqRun1.fastq) within a genomic database. A pharmaceutical team might standardize compound screening results in their LIMS, updating BatchX_TestY to include assay date and plate position (20240624_CYP3A4_Plate01_A01).

While renaming enhances organization and discoverability, manual processes risk human error and breaking vital data provenance links. Maintaining a clear audit trail documenting all name changes is crucial for reproducibility and meeting regulatory compliance (e.g., FDA ALCOA+ principles). The trend is towards automated, rule-based renaming upon data ingestion to minimize risks and ensure consistency.