Adapt these examples to files you actually have. Each workflow names the input, output, and check that makes completion meaningful. OpenScience will need the appropriate model, tools, data, and service access.
Literature question to evidence table
Investigate whether method A improves measurement reliability over
method B for my stated population. Agree on inclusion criteria, search
primary sources, and save an evidence table with study design, sample
size, outcome, limitations, source URL, and reading status. Write a short
synthesis and list gaps in the search.
Check that each conclusion has supporting evidence and that inaccessible full text is marked. Continue with Literature review.
Raw table to reproducible analysis
Inspect data/measurements.csv and methods/data-dictionary.md. Audit types,
missingness, duplicates, units, and group structure. Propose cleaning
before changing anything. Then save a script, cleaned table, diagnostic
figure, and methods note, and verify a rerun from the original inputs.
Check input and output counts and the exclusion record. See Data analysis and Statistics.
Paper to a bounded reproduction
Use papers/study.pdf and the connected code folder to reproduce its main
reported metric. Identify exact and missing inputs, select a testable
acceptance criterion, and run a small pilot. Ask before paid compute.
Report matched results, deviations, and unavailable checks separately.
Check that partial reproduction is described accurately. See Reproduction.
Molecule collection to a property table
Check RDKit readiness, validate data/molecules.csv, and compute the agreed
descriptors. Keep original identifiers and SMILES, save invalid rows with
reasons, and produce a comparison table, script, and visual summary.
Check representation choices and failed molecules. See Molecular research.
Sequence file to a quality report
Inspect data/sequences.fasta. Report record counts, lengths, alphabet,
duplicates, gaps, and malformed records. Preserve the source and propose
an alignment or downstream analysis only after the quality report.
Check whether a preview is sampled and whether an existing alignment is valid. See Genomics.
Dataset to a model baseline
Plan a baseline for data/training.csv. Define the prediction unit,
leakage-safe split, metrics, and resource budget. Run a pilot, then save
the code, split identifiers, predictions, metrics, and model-loading
instructions. Keep the final evaluation set out of tuning.
Check the split and held-out evaluation. See Machine learning.
Finished analysis to a report
Use the verified files in results/ to draft a report with question,
methods, findings, figures, limitations, and sources. Link every figure
and table to its input or script. Do not add results that were not run.
Render the deliverable with available tools and inspect the final file.
Check captions, references, output rendering, and unsupported claims. See Writing and Figures.
Long experiment to a reliable handoff
Inspect the existing compute job before starting anything new. Recover
its outputs, verify expected files, save completed Results, and write a
handoff with the job identifier, commands, findings, failures, and next
step. Identify any remote resources still requiring attention.
Check delivery separately from command completion. See Jobs and Context.