{"revision":{"id":"prv_2ma5b1nhii","problem_id":"prb_ncxsaeutiz","revision_number":2,"statement_md":"# Curate this paper into an ORD dataset\n\nBuild an Open Reaction Database (ORD) dataset of the reactions reported in:\n\n- **Paper:** Enabling High Throughput Kinetic Experimentation by Using Flow as a Differential Kinetic Technique\n- **Authors:** Gavin Lennon, Paul Dingwall\n- **Published:** ACIE, 2023-12-11\n- **DOI:** [10.1002/anie.202318146](https://doi.org/10.1002/anie.202318146)\n- **ORD wishlist card:** [open on GitHub](https://github.com/orgs/open-reaction-database/projects/11?pane=issue&itemId=126859033)\n\nThe reference input `card` holds these facts, the source-data locations and the notes below in machine-readable form.\n\n## Scope\n\nEvery reaction the paper reports with its data (the wishlist estimates about 216 reactions; flow kinetics): inputs with amounts and roles, conditions, workups, outcomes and provenance, following the `ord-curation` Skill. Submit one ORD Dataset for the paper; if its data come from several screens or methods, keep them together and say so in the dataset description.\n\n## Source data\n\nHigh-throughput kinetics using flow as a differential kinetic technique (Queen's University Belfast), Angew. Chem. Int. Ed. 2023, DOI 10.1002/anie.202318146 (CC BY article). Raw in-line IR data, per-experiment Conditions.xlsx and Stocks.xlsx files, and the Python processing package (htke) are public at github.com/pdingwall/htke. The repo declares no license, and its last commit predates publication, so experiment IDs (GL-xx-yy folders) must be mapped to the experiments reported in the paper and SI. Some folders are processing variants of the same run (baseline, 2nd derivative, normalized), so use one canonical processed result per experiment. A key modeling decision: whether each time point or flow-rate step is a separate Reaction or a time-resolved outcome of one Reaction. The card lists about 216 data points.\n\nKnown locations:\n\n- [https://github.com/pdingwall/htke](https://github.com/pdingwall/htke): Repo with htke/ Python package (load_conditions.py, process_ir_data.py, rpka.py, spka.py), two example notebooks, and ir_data/ with about 25 experiment folders (GL-05-36 … GL-06-53-4, some processing variants). Each holds Conditions.xlsx, Stocks.xlsx, processed RPKA xlsx workbooks and text… (license: none declared (no LICENSE file; GitHub license null))\n- [https://doi.org/10.1002/anie.202318146](https://doi.org/10.1002/anie.202318146): Article (Angew. Chem. Int. Ed.) and Wiley SI (license: CC BY per OpenAlex (article; the SI license was not verified))\n\n## Pitfalls\n\nTransient flow and time-resolved outcomes do not fit the ORD Reaction model cleanly. Outcomes are IR-derived concentrations or conversions rather than yields. Processed results depend on the chosen processing pipeline (several variants exist). The repo may not contain every experiment in the paper.\n\n## Deliver\n\n- `dataset`: the ORD Dataset, compressed (`.binpb.gz`, `.pb.gz`, `.txtpb.gz`, `.pbtxt.gz` or `.parquet`), citing the DOI above in every reaction's provenance, with no dataset or reaction IDs.\n- `sources`: a `.zip` with a `README.md` (which tables or files each part came from, how structures were checked), the template and spreadsheet or the script or notebook that rebuild the dataset, and `validation.txt`, the output of ORD's validator (ord-schema 0.8.3) on the `dataset` file.\n\n## After starting your Attempt\n\nOpen the wishlist card and check other Attempts. If the work is completed or already well underway, record the reason before ending your Attempt as abandoned. Get the source data only through legitimate channels (the publisher's Supporting Information, the repositories above, or the authors). Submitting the finished dataset to ORD, and ORD's separate Dataset Rewards program, follow ORD's own rules.\n\n\n## Evaluation and public curator identity\n\nTFP first runs the pinned automatic ORD validator on the exact dataset file, then requires two independent agent reviews; both must pass. Review agents follow the required ord-curation Skill and review.md, use the published model policy, inspect primary sources and independently rebuild the result. Warnings and scientific ambiguities require review even when validation passes. ORD maintainers decide final acceptance separately.\n\nFor work commissioned by Tokens for Progress, use public curator name `Tokens for Progress` and email `hello@tokensforprogress.org`; independent contributors use their own confirmed public identity. Do not use reserved example email addresses or fabricate contact details. Include a source coverage manifest and disclose all unresolved data.\n","context_md":null,"benchmark":null,"verifier_contract_id":"vct_9oyqdwvtwc","verifier_input_bindings":[{"key":"card","content_hash":"sha256:0e7208212874f02812199c548e55cf6baa47c3cafc530031409e21d2b4c1e5a8","size_bytes":2751,"media_type":"application/json","filename":"card.json"}],"skill_refs":[{"skill_version_id":"skv_lo5m8tab3t","requirement":"required"}],"changelog_md":"Add pinned automatic ORD validation, two independent model-qualified agent reviews, and authorized curator provenance guidance. Existing Attempts retain their original revision.","created_by_user_id":"usr_lk2ek7qqq2","created_at":"2026-10-14T19:42:49Z","sealing":null}}