{"revision":{"id":"prv_emzqjrhkgt","problem_id":"prb_7v6k9n2nnh","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:** On the Use of Multicomponent Reactions in High-throughput Synthesis, And, the Synthesis of Some Selectively Modified Carbohydrates\n- **Published:** PhD Thesis, 2024-01-15\n- **DOI:** [10.33612/diss.849176860](https://doi.org/10.33612/diss.849176860)\n- **ORD wishlist card:** [open on GitHub](https://github.com/orgs/open-reaction-database/projects/11?pane=issue&itemId=126569976)\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 (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\nAutomated high-throughput synthesis using multicomponent reactions (MCRs), from Part I of the PhD thesis by M. Ahmadianmoghaddam, University of Groningen, 2024 (doi:10.33612/diss.849176860). Part II, on carbohydrate modification, is out of scope. The thesis PDF (16 MB) is openly downloadable from the Groningen research portal; no separate data deposit was found. The format of the HTE data (numeric tables vs product-detection heat maps) and the number of reactions are not yet established. If the thesis chapters were published as journal articles, their SI may be the better source. Cite the thesis DOI and any chapter article DOIs.\n\nKnown locations:\n\n- [https://research.rug.nl/en/publications/on-the-use-of-multicomponent-reactions-in-high-throughput-synthes/](https://research.rug.nl/en/publications/on-the-use-of-multicomponent-reactions-in-high-throughput-synthes/): University of Groningen research portal record (doi:10.33612/diss.849176860); complete thesis PDF (16 MB, not downloaded) and propositions PDF (license: unknown (thesis 'Final publisher's version', no licence shown on the landing page))\n- [https://research.rug.nl/files/849176862/Complete_thesis.pdf](https://research.rug.nl/files/849176862/Complete_thesis.pdf): Complete thesis PDF, 16 MB (Part I: automated high-throughput synthesis with multicomponent reactions) (license: unknown)\n\n## Pitfalls\n\nNanoscale MCR screens are often reported qualitatively (product detected or not detected by MS), which gives weak ORD outcomes. The thesis may duplicate data from published chapter papers, so dataset boundaries must be decided. Thesis citation rather than a journal DOI.\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":"sha256:8289399a6be351d8578fbebde92f9e746ac500ba976658d37f27103590fda86d","size_bytes":2597,"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}}