{"revision":{"id":"prv_hhl77xr2xq","problem_id":"prb_anv2hzq9do","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:** A self-optimised approach to synthesising DEHiBA for advanced nuclear reprocessing, exploiting the power of machine-learning\n- **Authors:** Thomas Leonard Shaw, Adam D. Clayton, Ricardo Labes et al.\n- **Published:** Reaction Chem Eng, 2023-11-03\n- **DOI:** [10.1039/D3RE00357D](https://doi.org/10.1039/D3RE00357D)\n- **ORD wishlist card:** [open on GitHub](https://github.com/orgs/open-reaction-database/projects/11?pane=issue&itemId=126570009)\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 300 reactions; flow): 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\nReaction Chemistry & Engineering 2023/2024, DOI 10.1039/D3RE00357D. The article is CC BY 3.0 per Crossref, which covers the article only; the SI license is not stated separately. About 300 experiments come from machine-learning-driven self-optimisation in flow of the synthesis of DEHiBA (N,N-di(2-ethylhexyl)isobutyramide), an extractant for nuclear reprocessing. The data is in tables in the SI PDF and needs extraction. Only a few distinct chemicals are involved, so SMILES are easy to write by hand. No data repository deposit was found. The publisher site blocks automated access, so SI file names and formats were not verified. A machine-readable copy of the tables, if the authors can share one, would reduce transcription errors.\n\nKnown locations:\n\n- [https://doi.org/10.1039/D3RE00357D](https://doi.org/10.1039/D3RE00357D): Article and SI (per the board notes, ~300 self-optimisation experiments in SI PDF tables). The publisher page blocks automated access, so the SI files were not listed. The paper is not in Europe PMC and no DataCite deposit was found. (license: CC BY 3.0 (article, per Crossref; SI license not stated separately))\n\n## Pitfalls\n\nFlow self-optimisation. Each experiment is a steady-state flow condition set (flow rates or residence time, temperature, stoichiometry) chosen by an ML optimiser, so it needs ORD flow conditions. Objectives may be several (e.g. yield plus throughput or cost) and must be mapped to the right outcomes. Transcribing PDF tables risks numeric errors, which need spot checks against the SI.\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":"sha256:838992065a7e02193ec68d04d6115069d0e7f54c74c676d722d46a5108f787c6","size_bytes":2602,"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}}