{"revision":{"id":"prv_nbma4c9xrn","problem_id":"prb_cecr7nfsar","revision_number":1,"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:** Streamlining the Automated Discovery of Porous Organic Cages\n- **Authors:** Annabel R. Basford, Steven P. Bennett, Muye Xiao et al.\n- **Published:** ChemRxiv, 2023-11-16\n- **DOI:** [10.26434/chemrxiv-2023-63ckv](https://doi.org/10.26434/chemrxiv-2023-63ckv)\n- **ORD wishlist card:** [open on GitHub](https://github.com/orgs/open-reaction-database/projects/11?pane=issue&itemId=126569965)\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 366 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\nSelf-assembled porous organic cage synthesis (imine condensation of di-/tri-topic aldehydes and amines) run by HTE on an Opentrons platform: 366 precursor combinations (codes A1-U34). The preprint (doi 10.26434/chemrxiv-2023-63ckv, CC-BY 4.0) was published as Chem. Sci. 2024, doi 10.1039/D3SC06133G (CC-BY 3.0); the dataset should cite the journal DOI. Inputs: GitHub GreenawayLab/Streamlining-Automated-Discovery-POCs, where HT_Cage_Assembly_Optimisation/precursors.json maps precursor codes to SMILES and precursor_combination_map_to_exp_data.json maps each combination to experiment code, plate and well. The repo has no license file, so bind the copies on Zenodo 10.5281/zenodo.10675206 (CC-BY-4.0) instead, together with precursor_positions.json from Zenodo 10.5281/zenodo.10138085. Raw NMR/HRMS/turbidity archives are large (0.2-4 GB); do not bind them, only reference them. Outcome labels (cage formation and purity by 1H NMR/HRMS, solubility by turbidity) are not in the mapping JSON and must be taken from the paper and ESI PDF. Conditions (solvent, concentration, temperature, time, stoichiometry) come from the ESI experimental section.\n\nKnown locations:\n\n- [https://github.com/GreenawayLab/Streamlining-Automated-Discovery-POCs](https://github.com/GreenawayLab/Streamlining-Automated-Discovery-POCs): Code repo: precursor_combination_map_to_exp_data.json (366 combos A1-U34 -> experiment code/plate/formulation + raw-data paths) and HT_Cage_Assembly_Optimisation/precursors.json (precursor codes Di*/Tri* -> SMILES); Opentrons protocols with substance_locations.json (license: none declared (no LICENSE file; GitHub license API 404))\n- [https://zenodo.org/records/10675206](https://zenodo.org/records/10675206): Experimental data (version cited by Chem Sci): precursor_combination_map_to_exp_data.json (129 KB), nmr.zip (211 MB), ms.zip (4.0 GB), turbidity.zip (1.4 GB) (license: CC-BY-4.0)\n- [https://zenodo.org/records/10138085](https://zenodo.org/records/10138085): Earlier data version: precursor_positions.json (8 KB plate layouts by experiment code) plus the same raw NMR/HRMS/turbidity zips (license: CC-BY-4.0)\n- [https://europepmc.org/article/PMC/PMC11062116](https://europepmc.org/article/PMC/PMC11062116): Chem Sci 2024 article (doi 10.1039/D3SC06133G) and its ESI PDF (SC-015-D3SC06133G-s001.pdf); probable source of per-combination outcome labels. Only the filename was listed; contents not inspected (license: CC-BY-3.0 (article, per Crossref))\n- [https://doi.org/10.26434/chemrxiv-2023-63ckv](https://doi.org/10.26434/chemrxiv-2023-63ckv): ChemRxiv preprint (the DOI on the wishlist card) (license: CC-BY-4.0 (per Crossref))\n\n## Pitfalls\n\nOutcomes are qualitative/categorical (cage formed, clean, soluble), not yields. Product cage structures (topology such as Tri2Di3 / Tri4Di6) may be assigned only by HRMS mass, so product SMILES may be absent or ambiguous. The reactions are plate-based, and the well-to-combination mapping must be joined across the two JSON files. Precursor codes use mixed SMILES styles (some Kekulé, some stereo) that need canonicalisation.\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## Before you start\n\nOpen the wishlist card. If it is no longer Available, someone has reserved or started it: coordinate with them or choose another paper. 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","context_md":null,"benchmark":null,"verifier_contract_id":"vct_4t6ri8zibj","verifier_input_bindings":[{"key":"card","content_hash":"sha256:44c9b797c35d40539ba0efc2f48582b8c44eba991eb5e16ccc7174126e1fc7d3","size_bytes":4063,"media_type":"application/json","filename":"card.json"}],"skill_refs":[{"skill_version_id":"skv_k5sivlohu1","requirement":"required"}],"changelog_md":null,"created_by_user_id":"usr_llep2e937g","created_at":"2026-09-25T19:27:21Z","sealing":null}}