{"revision":{"id":"prv_bqdqnxbmgy","problem_id":"prb_5f5ty4g03f","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:** Doing More with Less, On Time and In Full: An Intelligent Multiattribute Process Optimization Platform\n- **Authors:** Reihaneh Soleimany, Wenyao Zhang, Jee Seong Kwak et al.\n- **Published:** OPRD, 2024-02-26\n- **DOI:** [10.1021/acs.oprd.3c00392](https://doi.org/10.1021/acs.oprd.3c00392)\n- **ORD wishlist card:** [open on GitHub](https://github.com/orgs/open-reaction-database/projects/11?pane=issue&itemId=126570026)\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: 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\nPaper: Soleimany et al., Org. Process Res. Dev. (published online 2024), doi:10.1021/acs.oprd.3c00392 (closed access). It describes an intelligent multiattribute process-optimization platform. The underlying experiment-level reaction data may not be in the SI; the curator needs to confirm whether per-experiment conditions and outcomes are tabulated. If they are not, the data must come from the authors. Optimization campaigns often record several responses per experiment (yield, impurity levels and so on), and each should become a separate ProductMeasurement.\n\nKnown locations:\n\n- [https://doi.org/10.1021/acs.oprd.3c00392](https://doi.org/10.1021/acs.oprd.3c00392): OPRD article and Supporting Information; contents not verified (license: ACS publisher terms (closed article))\n\n## Pitfalls\n\nAlgorithm-driven optimization campaigns may include sequential or iterative experiments, possibly in flow, where time ordering and multiple responses per run matter. Multi-objective outcomes (yield, purity, impurities) have to be mapped onto ORD measurement types.\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:fac2dc2eba055d1b5bcedba2183abc509dab74df119934a7f984abd09a1053e0","size_bytes":2007,"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}}