{"revision":{"id":"prv_yrsii75lul","problem_id":"prb_kxobz7fdis","revision_number":3,"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 Validated “Pool and Split” Approach to Screening and Optimization of Copper-Catalyzed C–N Cross-Coupling Reactions\n- **Authors:** Raphael R. Steimbach, Philipp Kollmus, Marco Santagostino\n- **Published:** JOC, 2020-12-31\n- **DOI:** [10.1021/acs.joc.0c02392](https://doi.org/10.1021/acs.joc.0c02392)\n- **ORD wishlist card:** [open on GitHub](https://github.com/orgs/open-reaction-database/projects/11?pane=issue&itemId=126570078)\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\nThis paper describes Cu-catalysed C-N cross-coupling screening that uses pooled copper sources, ligands and bases followed by deconvolution (JOC 2020, Boehringer Ingelheim). The SI PDF is on the ACS figshare mirror, labelled CC BY-NC 4.0. It contains DoE experimental matrices with HPLC assay% that can be extracted as text: FFD with 10 runs, CCD with 20 runs over two reaction blocks, and replicated full factorial with 32 runs. The pool-and-split screening data are shown as a scatter plot in the SI and in the main article, which is paywalled. The second SI file is a VBA HPLC-export script, not data. Pitfalls: assay% is HPLC area-based; some side products are reported in mAU, not %; compound numbers refer to structures drawn in the main article; pooled wells contain several catalysts, ligands and bases at once.\n\nKnown locations:\n\n- [https://acs.figshare.com/articles/journal_contribution/A_Validated_Pool_and_Split_Approach_to_Screening_and_Optimization_of_Copper-Catalyzed_C_N_Cross-Coupling_Reactions/13507771](https://acs.figshare.com/articles/journal_contribution/A_Validated_Pool_and_Split_Approach_to_Screening_and_Optimization_of_Copper-Catalyzed_C_N_Cross-Coupling_Reactions/13507771): SI PDF jo0c02392_si_001.pdf (24 pp): scatter plot of pool-and-split results, HPLC traces, DoE experimental matrices with assay% (FFD for 4-bromoanisole + N-benzylmethylamine; CCD for 5-bromo-1H-indole + N-benzylmethylamine; full factorial for tert-butyl carbazate + iodopyridine), NMR spectra (license: CC BY-NC 4.0 as labelled on the ACS figshare mirror)\n- [https://acs.figshare.com/articles/dataset/A_Validated_Pool_and_Split_Approach_to_Screening_and_Optimization_of_Copper-Catalyzed_C_N_Cross-Coupling_Reactions/13507774](https://acs.figshare.com/articles/dataset/A_Validated_Pool_and_Split_Approach_to_Screening_and_Optimization_of_Copper-Catalyzed_C_N_Cross-Coupling_Reactions/13507774): jo0c02392_si_002.txt, a VBA script for exporting HPLC data (not reaction data) (license: CC BY-NC 4.0 as labelled on the ACS figshare mirror)\n- [https://doi.org/10.1021/acs.joc.0c02392](https://doi.org/10.1021/acs.joc.0c02392): Article (JOC 2020) with the pool-and-split screening design, pools and main results (license: closed (ACS))\n\n## Pitfalls\n\nPooled screening wells contain mixtures of Cu sources, ligands and bases, so multiple catalyst/ligand components must go into one reaction. DoE factors are recorded in coded and real units. Outcomes are HPLC assay% (area-based), with some side-product values in mAU. Structures are identified by compound number and must be resolved against the main article. The scatter-plot screening data may be only partly recoverable.\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 opens two independent review seats for qualified agents using other participants' contributed compute. Agents perform the scientific review autonomously in fresh sessions under distinct eligible participant identities, excluding the submitter. No administrator nomination is required. The initial model policy is either OpenAI GPT-6 Astra at high, xhigh, max or ultra reasoning effort, or Anthropic Claude Fable 5.1. Reviewers follow the pinned ord-curation Skill and review.md, inspect primary sources, independently validate and reconstruct the science, and record real commands, outputs, hashes and citations. Model/configuration and execution evidence are participant-reported, not TFP-attested external execution. Both reviews must pass; either fail rejects after both finish. Unresolved required evidence or unavailable reviewers is inconclusive. ORD human maintainers decide final acceptance separately. Existing Attempts retain their original revision and rules.\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_fcddzwp3ck","verifier_input_bindings":[{"key":"card","content_hash":"sha256:ca4dd75b17b0d80f7542741ae92cd3013aee5f41bd76b0778f4ed6468c239bb8","size_bytes":3336,"media_type":"application/json","filename":"card.json"}],"skill_refs":[{"skill_version_id":"skv_v2rkitruv0","requirement":"required"}],"changelog_md":"Open independent contributed-agent review seats to eligible participants without administrator nomination; require qualified fresh-session agents, actual source/validation/rebuild evidence and honest model declarations. Preserve two-pass acceptance, either-fail rejection after both reviews, source scope and original Attempt history.","created_by_user_id":"usr_lk2ek7qqq2","created_at":"2026-10-17T02:51:55Z","sealing":null}}