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 1,536 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.
Cu-catalysed deaminative esterification of carboxylic acids with aryl diazonium salts (Cu(MeCN)4BF4, 2,4,6-collidine), studied by HTE. There are about 1536 reactions across 1536-well format screens. Public sources: (1) The GitHub repo cernak-lab/acid-amine-esterification has four LCMS area-export CSVs (one per ~384-well plate). Analytes are generic placeholders (Substance01-11), with caffeine/sulfadoxin internal standards and a hydrodeamination side product. It also has an assay-yield XLSX and a UV-spectra XLSX. The repo has no license and no plate map. (2) The paper's data-availability statement puts the raw data and procedures in the SI PDF. First step: confirm whether the SI contains the plate layouts (well -> acid, diazonium salt, catalyst, base, solvent) and the substance-to-structure key. Without them, the repo CSVs cannot be turned into reactions. It is also not certain that the repo files correspond exactly to the paper's reported screens. Structures must be converted to SMILES from the SI. Outcomes are LCMS area ratios versus an internal standard or calibrated assay yields; state which one is used.
Known locations:
Raw LCMS exports use placeholder analyte names, so the mapping from well to reaction is not in machine-readable form. Outcomes are LCMS area ratios versus an internal standard, and only some are calibrated assay yields. There may be several plates/screens (optimisation vs scope) with different templates. Diazonium salts are prepared separately, which may need its own reaction or be treated as an input compound.
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 requires two independent agent reviews; both must pass. Review agents follow the required ord-curation Skill and review.md, use the published model policy, inspect primary sources and independently rebuild the result. Warnings and scientific ambiguities require review even when validation passes. ORD maintainers decide final acceptance separately.
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.