# Curate this paper into an ORD dataset

Build an Open Reaction Database (ORD) dataset of the reactions reported in:

- **Paper:** Kinetic and Data-Driven Reaction Analysis for Pharmaceutical Process Development
- **Authors:** Ke Wang, Lu Han, Jason G. Mustakis et al.
- **Published:** I&EC, 2019-09-19
- **DOI:** [10.1021/acs.iecr.9b03578](https://doi.org/10.1021/acs.iecr.9b03578)
- **ORD wishlist card:** [open on GitHub](https://github.com/orgs/open-reaction-database/projects/11?pane=issue&itemId=126570097)

The reference input `card` holds these facts, the source-data locations and the notes below in machine-readable form.

## Scope

Every 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.

## Source data

Source: Wang et al., Ind. Eng. Chem. Res. 2019 (doi:10.1021/acs.iecr.9b03578). Methods paper comparing kinetic and data-driven (semiparametric) modeling of dynamic reaction profiles. The single industrial case study is a chemoselective methyl ester hydrolysis studied over a designed set of conditions with time-course sampling. The article is closed-access. The SI is one PDF (~1 MB) with no machine-readable data file. Reaction profiles are expected to be available mainly as figures, and substrate structure disclosure has not been verified.

Known locations:

- [https://api.figshare.com/v2/articles/9941501](https://api.figshare.com/v2/articles/9941501): ACS SI PDF ie9b03578_si_001.pdf (1.06 MB); contents not inspected (license: Labeled CC BY-NC 4.0 on figshare (ACS SI mirror); not verified against ACS terms)
- [https://doi.org/10.1021/acs.iecr.9b03578](https://doi.org/10.1021/acs.iecr.9b03578): Ind. Eng. Chem. Res. 2019 article (closed access); kinetic and semiparametric modeling of reaction profiles (license: ACS standard (closed))

## Pitfalls

Time-course (kinetic) outcomes: each DoE run has multiple timepoints that need ORD ProductMeasurement/time handling. Values digitized from plots are approximate. The substrate may be a proprietary pharmaceutical intermediate with an undisclosed structure. Only one reaction type is involved, and conditions vary by DoE factors.

## Deliver

- `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.

## After starting your Attempt

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.


## Evaluation and public curator identity

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.