{"revision":{"id":"prv_a25cxmzbcy","problem_id":"prb_4twpsh82rh","revision_number":1,"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:** Identifying opportunities for late-stage C-H alkylation with high-throughput experimentation and in silico reaction screening\n- **Authors:** David F. Nippa, Kenneth Atz, Alex T. Müller et al.\n- **Published:** Communications Chemistry, 2023-11-20\n- **DOI:** [10.1038/s42004-023-01047-5](https://doi.org/10.1038/s42004-023-01047-5)\n- **ORD wishlist card:** [open on GitHub](https://github.com/orgs/open-reaction-database/projects/11?pane=issue&itemId=126569963)\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 1,104 reactions; HTE): 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\nLate-stage Minisci-type C-H alkylation HTE (Roche/ETH). The data is SURF-formatted TSV, published as Nature Communications Chemistry Supplementary Data 2-8 and mirrored in the GitHub repo ETHmodlab/minisci (minisci/data/) and on Zenodo (10.5281/zenodo.8344586). Columns cover conditions (temperature, time, atmosphere, scale, concentration, mixing) and CAS, SMILES and equivalents for starting materials, reagent, catalyst, additive and up to two solvents. Outcomes are the columns product_mono, product_di and product_non, apparently relative fractions, plus a binary success label. Pitfalls: column definitions are not explicitly documented, so derive their meaning (e.g. whether outcome fractions are LCMS area ratios) from the paper's methods. Most products are not structurally characterized and are only labelled mono- or di-alkylation, so product SMILES must be generated from reactant SMILES and regiochemistry is often undetermined. Document how products are represented. The paper's decoy reactions (368) and literature reactions (45) are distinct subsets; decide explicitly whether to include them (decoys should likely be excluded, and literature reactions belong to other publications). Prefer the Nature SI copy (CC-BY) over the AGPL-licensed repo copy as the source. Cite DOI 10.1038/s42004-023-01047-5.\n\nKnown locations:\n\n- [https://www.nature.com/articles/s42004-023-01047-5](https://www.nature.com/articles/s42004-023-01047-5): Article. The data-availability statement lists Supplementary Data 2-8 as SURF TSV files: all reactions, literature (45), experimental (691), literature-validation reactions, decoy (368), carboxylic acids, substrates (license: CC-BY 4.0 (gold OA article; SI assumed to follow article license, confirm))\n- [https://github.com/ETHmodlab/minisci/tree/master/minisci/data](https://github.com/ETHmodlab/minisci/tree/master/minisci/data): Repo data dir: 20230830_minisci_pub_{all_data,hte,hte_new,literature,decoy}.tsv, template_acids.tsv, template_substrates.tsv. The HTE TSV has 620 data rows; columns include rxn_id, conditions, startingmat/reagent/catalyst/additive/solvent CAS+SMILES+eq, product_mono, product_di, product_non, binary (license: AGPL-3.0 (repository license, code-oriented))\n- [https://zenodo.org/records/8344587](https://zenodo.org/records/8344587): Zenodo archive (concept DOI 10.5281/zenodo.8344586) of repo v1.0, minisci-v.1.0.zip 13.8 MB (not downloaded) (license: other-open (Zenodo metadata))\n\n## Pitfalls\n\nProducts are uncharacterized (mono/di labels only) and regiochemistry is ambiguous, so product identifiers are a curation decision rather than extracted fact. Outcomes are semi-quantitative fractions, not isolated yields. The decoy set and literature-sourced subsets must be separated. SURF column semantics are not documented.\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## Before you start\n\nOpen the wishlist card. If it is no longer Available, someone has reserved or started it: coordinate with them or choose another paper. 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","context_md":null,"benchmark":null,"verifier_contract_id":"vct_4t6ri8zibj","verifier_input_bindings":[{"key":"card","content_hash":"sha256:fb84a4b459b4a8d2789ac4ca1c3c145c77ad11357666be0d38b68d50051e9e68","size_bytes":3929,"media_type":"application/json","filename":"card.json"}],"skill_refs":[{"skill_version_id":"skv_k5sivlohu1","requirement":"required"}],"changelog_md":null,"created_by_user_id":"usr_llep2e937g","created_at":"2026-09-25T19:27:21Z","sealing":null}}