Datasets

A dataset is one CMS sample — a simulated signal/background, or a chunk of real data — identified by its DAS name. Datasets are declared per era in datasets.yaml files. Thanks to the configuration merge, the lists from the framework and the analysis are concatenated, so all datasets for an era are available together.

The split rule (where a dataset belongs)

Kind of sample	Goes in
SM background, real data	FLAF/config/<era>/datasets.yaml (framework — common to all analyses)
Signal, custom/CI sample	<analysis>/config/<era>/datasets.yaml (analysis-specific)

This keeps the common SM samples in one shared place while each analysis owns its signals.

Dataset entry format

DatasetName:
  generator: powheg          # madgraph, powheg, pythia, ...
  mass: 125                  # optional (signals)
  spin: 0                    # optional (resonant signals)
  crossSection: 1pb          # a value, or a key into crossSections13p6TeV.yaml
  nanoAOD:
    v12:                     # NanoAOD campaign tag (matches the era)
      - /DAS/path/to/dataset/NANOAODSIM
    v15:
      - /DAS/path/to/dataset/NANOAODSIM

For custom/local samples (e.g. CI test inputs) that are not official DAS datasets, point at your own storage instead:

custom_CI:
  generator: powheg
  mass: 125
  spin: 0
  crossSection: 1pb
  fs_nanoAOD: T3_CH_CERNBOX:/store/user/<user>/
  dirName: "directory_name"

Cross-sections

MC datasets reference a cross-section, either inline (crossSection: 1pb) or by a key into FLAF/config/crossSections13p6TeV.yaml (13.6 TeV; crossSections13TeV.yaml for Run 2). For signals whose normalisation is set elsewhere, a placeholder such as 1pb is conventional.

Adding a dataset

1. Choose the file by the split rule above.
2. Add the entry in the right era's datasets.yaml, following the format.
3. Make sure the crossSection resolves — add it to crossSections13p6TeV.yaml if needed.
4. For Run3_2024 signals, check the actual DAS name: the naming changed (e.g. VBF drops the _fixedTauDecays suffix and uses a _Par- form). The conventions are summarised in the analysis docs and the project notes.
5. Validate (below).

Validate the dataset config

The same check the CI runs (ds-consistency-check) verifies that MC entries have a generator and a resolvable cross-section, that names are well-formed, etc.:

python3 test/checkDatasetConfigConsistency.py \
  --exception config/dataset_exceptions.yaml \
  Run3_2022 Run3_2022EE Run3_2023 Run3_2023BPix Run3_2024 Run3_2025

Run it after editing any datasets.yaml. Known, intentional exceptions live in config/dataset_exceptions.yaml. See CI / GitHub Actions.

Adding a new era

1. Create FLAF/config/<new_era>/ with at least datasets.yaml and global.yaml.
2. Create <analysis>/config/<new_era>/ with the analysis-specific overrides and signals.
3. Add the era to test-setup-loading.yaml in each affected analysis (so CI loads Setup.py for it and catches config errors early).
4. Add the era to the *_eras variable in the relevant .github/integration_cfg.yaml if it should be part of CI runs. See Integration pipeline.

See also Eras & periods.