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Data & research · A worked example

Keep research-run exports connected to the experiment log

A CSV called results tells you little after several experimental runs. A stable run identifier can link the export to the record that explains it.

By TreatFiles6 min readResearchers · Lab coordinators

Experiment-log entries pairing runs with replicates
Illustrated workflow. The names and files in this story are fictional.

Instrument or analysis exports can accumulate several files called results.csv in a day. Months later, it is the experiment log that explains the conditions and sample handling. A stable filename can point back to that record without trying to reproduce the entire experiment in a name.

Name copied CSV exports from a fictional Cedar experiment using checked run and replicate identifiers. Run07 and Run08 each have two replicate exports.

TreatFiles does not validate experimental design, identify samples or interpret measurement values. The identifiers come from the checked run log.

What the names need to say

Use stable identifiers already defined in the experiment record. Run and replicate labels should have a consistent meaning across the dataset. Keep detailed conditions in the log rather than making filenames so long that they are difficult to compare.

Experiment_Run_Replicate
Four files, before and after
Original filenameProposed filename
results.csvCEDAR_Run07_Rep01.csv
results (1).csvCEDAR_Run07_Rep02.csv
test-output.csvCEDAR_Run08_Rep01.csv
results-new.csvCEDAR_Run08_Rep02.csv

The examples show complete filenames. In TreatFiles’ New File/Folder Name column, normally enter the base name only; the extension is stored separately.

Connect the export to the run record

  1. Match exports to the run log

    Record which original filename belongs to which run and replicate. Work on copied exports, leaving instrument-managed or analysis-managed source locations unchanged.

  2. Export the file list in Rename mode

    List the copied CSV folder in TreatFiles and create an XLSX or CSV naming export.

  3. Enter the checked identifiers

    Populate New File/Folder Name using the experiment, run and replicate codes. Keep fixed-width labels where they improve sorting.

  4. Resolve missing or ambiguous mappings

    Do not assign a replicate number because a file happens to appear second in the folder. Resolve uncertainty using the experiment record before naming it.

  5. Rename and retain the cross-reference

    Import the edited list, review the mapping and apply the rename. Keep the mapping with the run record and check any analysis scripts intentionally using the copied files.

If you need the application controls alongside the example, keep the TreatFiles renaming guide or FAQ and user guide open as a reference.

The exceptions matter

A name is not a provenance record

Retain the experiment log and source-to-export mapping needed to interpret the data.

Replicate and sample are different concepts

Use the labels defined by the experiment rather than borrowing terms that imply a different relationship.

Scripts can depend on the old paths

Update and verify references for copied datasets deliberately; do not rename live analysis inputs without checking.

A sample to work from

The Research run exports tab in this workbook contains the example above. Adapt the idea to your own files, or share it with the people who agree your naming convention.

Download the sample workbook (.xlsx) ↓

This is a planning worksheet, not a TreatFiles import file. Generate a rename-ready export in the app, then put your chosen base names into its New File/Folder Name column.

Questions you might have

Can TreatFiles infer a run from measurement values?

The example uses a checked run log, not content-based identification.

Is this naming pattern a research standard?

No. It is a fictional example; use identifiers and requirements appropriate to the actual project.

Should conditions be encoded in every name?

A stable identifier linked to a complete record is often easier to maintain. Choose the convention with the people who will interpret the dataset.