{
  "problem": {
    "id": "prb_58netrrftb",
    "slug": "ord-holland-moritz-2025-rave-ms-optimization",
    "title": "Accelerating Cross-Modality Reaction Optimization via Robotically Automated Vacuum Enabled Direct-inject Mass Spectrometry (RAVE MS)",
    "summary": "Curate this paper's reaction data into a validated Open Reaction Database dataset. Reaction Chem Eng 2025, DOI 10.1039/D5RE00248F. About 96 reactions · HTE. ORD wishlist priority High, difficulty Normal. Source data: plate images or heatmaps.",
    "structure": "SOLVE"
  },
  "revision": {
    "sealing": null,
    "id": "prv_hidz3tzzy1",
    "problem_id": "prb_58netrrftb",
    "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:** Accelerating Cross-Modality Reaction Optimization via Robotically Automated Vacuum Enabled Direct-inject Mass Spectrometry (RAVE MS)\n- **Authors:** Daniel A. Holland-Moritz, Sarah R. Moor, Joseph B. Parry et al.\n- **Published:** Reaction Chem Eng, 2025-08-06\n- **DOI:** [10.1039/D5RE00248F](https://doi.org/10.1039/D5RE00248F)\n- **ORD wishlist card:** [open on GitHub](https://github.com/orgs/open-reaction-database/projects/11?pane=issue&itemId=126806413)\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 96 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\nSource: React. Chem. Eng. 10.1039/D5RE00248F (Merck & Co. / University of Cambridge), with ChemRxiv preprint 10.26434/chemrxiv-2025-g242r (CC-BY-NC 4.0). The work covers reaction optimisation analysed by robotically automated, vacuum-enabled direct-inject MS (RAVE MS) alongside other analytical methods. The reaction data (one 96-well plate) are in the SI only as a plate image. The journal article is publisher-licensed; the preprint SI may be the more accessible copy. Check whether the image shows numeric per-well values or only a colour scale before starting. Tabular data from the authors would make this much more reliable.\n\nKnown locations:\n\n- [https://doi.org/10.1039/D5RE00248F](https://doi.org/10.1039/D5RE00248F): Reaction Chemistry & Engineering article; the SI has the 96-well plate data as an image (license: RSC terms of use (closed; per Crossref))\n- [https://doi.org/10.26434/chemrxiv-2025-g242r](https://doi.org/10.26434/chemrxiv-2025-g242r): ChemRxiv preprint of the same work (Crossref: is-preprint-of 10.1039/D5RE00248F); may carry its own SI (license: CC-BY-NC 4.0 (per Crossref))\n\n## Pitfalls\n\nOutcomes are MS-based (relative ion response or cross-modality comparison), not isolated yields, so the analysis type and any calibration must be stated. Values read from a colour scale are approximate. The plate layout (row and column to conditions) must be reconstructed from the SI text.\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": "sha256:9af5d5e064953b9472b10a96555a24d41c62c8c162f896cda826c726b3361ae4",
        "size_bytes": 2530,
        "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"
  }
}