Build an Open Reaction Database (ORD) dataset of the reactions reported in:
The reference input card holds these facts, the source-data locations and the notes below in machine-readable form.
Every reaction the paper reports with its data (the wishlist estimates about 144 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.
Heteroleptic Pd-catalyzed fluorination of arylboronic acids (University of Utah), JACS 2025, DOI 10.1021/jacs.5c11929. The HTE optimization data are given as plate maps in the SI PDF (ja5c11929_si_002.pdf). The SI zip (ja5c11929_si_001.zip) has modeling CSVs keyed by combination codes (e.g. L1C1S1 = ligand/co-ligand/substrate) with a Yield column followed by computed DFT descriptors. Only the code and yield columns are experimental; drop the descriptor columns. The modeling CSVs cover about 72 reactions, while the card lists 144, so reconcile against the PDF plate maps. Ligand and substrate codes must be mapped to structures from the SI, and SMILES generated. The catalyst combines a Pd source with two distinct ligands, and each should be modeled as a separate input.
Known locations:
Two ligands per catalyst must be represented correctly. The yield type (assay vs isolated, 19F NMR vs other) must be confirmed from the SI. The modeling CSVs may be a subset of the HTE plates. Whether the plate maps in the PDF are text or images was not verified. Structure codes map to drawings, so SMILES need expert spot-checks.
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.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.Open 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.
TFP 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.
For 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.