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ord-data
Getting the Data
The datasets live under data/ and are stored with
Git LFS. LFS reads are redirected to the
Hugging Face mirror
via .lfsconfig, so dataset objects are fetched from Hugging
Face's CDN rather than from GitHub's shared (and limited) LFS bandwidth. This is
automatic — you do not need to configure anything.
Option 1: Clone the repository
git clone https://github.com/open-reaction-database/ord-data.git
With Git LFS installed, this pulls every dataset object from the Hugging Face mirror and gives you the full Git history with the data in place.
Option 2: Download only the data (a subset, or without Git history)
pip install huggingface_hub
python scripts/download_from_huggingface.py
(If you use uv, uv run scripts/download_from_huggingface.py
installs the dependencies for you.)
The script mirrors the data/ directory from the Hugging Face dataset into your
local checkout. Pass --allow-pattern 'data/4d/*.parquet' (repeatable) to download
only a subset, or --output-dir <path> to write somewhere other than the
repository root. To skip LFS entirely during the clone and fetch the data
afterward:
GIT_LFS_SKIP_SMUDGE=1 git clone https://github.com/open-reaction-database/ord-data.git
cd ord-data
python scripts/download_from_huggingface.py
You can also browse and download datasets directly from the Hugging Face dataset page.
For how this LFS / Hugging Face mirror setup works (and what it means for contributors), see Git LFS and the Hugging Face mirror below.
Data Manipulation
The ord-data repository contains the Open Reaction Database (ORD) as Parquet files stored in the
data directory. Each file holds one dataset: the reactions are serialized Protobuf
messages (one per row) and the dataset name/description/ID travel in the file metadata, so those
scalars and every reaction survive a round trip through ord_schema. (Dataset.reaction_ids,
which names reactions stored outside the dataset, is not persisted — the reaction_id column is
the source of truth — and a dataset with no reactions cannot be written at all.) The user can
convert the data into human readable text format, *.pbtxt.
# import requirements
from ord_schema.datasets import load_dataset
from ord_schema.message_helpers import save_message
# load the parquet file as a Dataset proto
# (as_dataset materializes it; the default is a streaming DatasetView)
dataset = load_dataset("input_fname.parquet", as_dataset=True)
# save the ord file as human readable text
save_message(dataset, "output_fname.pbtxt")
datasets.load_dataset returns a DatasetView for Parquet, which takes the
dataset's scalars and row count from the file footer and reads reactions on
demand. (Note the name collision: ord_schema.parquet.load_dataset always
materializes, which on the largest file is a silent 1.8M-reaction load rather
than an error.) Iteration, len, indexing, and slicing over reactions behave
like a list, so read-only code needs no change — and the USPTO grants file,
1.8M reactions in 1.1 GB, opens in milliseconds rather than deserializing up
front. Two places the resemblance stops: indexing returns a freshly
deserialized copy rather than a live sub-message, so mutating it changes
nothing on disk, and reactions never compares equal to a list — compare
against list(view.reactions). Pass as_dataset=True (above) when you need the
protobuf surface: serializing or mutating the dataset as a whole. If you already
hold a view, to_proto() does the same thing.
Beyond plain iteration, a view can look a reaction up by ID with get_reaction,
yield IDs alone with iter_reaction_ids, and read one row group at a time with
iter_reactions(row_group=...), which yields (reaction_id, Reaction) pairs
rather than bare reactions. Row groups are the unit of parallelism for fanning
out over a large file; view.num_row_groups gives the upper bound, and an index
outside it raises IndexError.
We can also convert ORD data into JSON format.
# import requirements
import json
from ord_schema.datasets import load_dataset
from google.protobuf.json_format import MessageToJson
input_fname = "sample_file.parquet"
dataset = load_dataset(input_fname)
# take one reaction message from the dataset for example
rxn = dataset.reactions[0]
rxn_json = json.loads(
MessageToJson(
message=rxn,
always_print_fields_with_no_presence=False,
preserving_proto_field_name=True,
indent=2,
sort_keys=False,
use_integers_for_enums=False,
descriptor_pool=None,
float_precision=None,
ensure_ascii=True,
)
)
print(
f"We have converted the {input_fname} to JSON format shown as below, \n{rxn_json}"
)
Retired dataset IDs
Two datasets are consolidations of shards that were once published separately, and a consolidated output carries a new ID derived from the sorted IDs of its sources:
| Dataset | ID | Sources |
|---|---|---|
uspto-grants |
ord_dataset-1158e351757f315b93cbcbe7bc55f38e |
489 monthly uspto-grants-YYYY_MM datasets |
Training data from https://doi.org/10.1039/C8SC04228D |
ord_dataset-e7830cd6b11158b43994ccfb5ee9acb3 |
10 (N/10) shards |
retired_datasets.csv maps each of the 499 retired IDs to the dataset that
replaced it. Every other dataset kept its ID.
Per-reaction patent provenance for the USPTO data is on Reaction.provenance.patent, so the
monthly bucket a reaction came from is recoverable from the consolidated dataset.
Git LFS and the Hugging Face mirror
Dataset files under data/ are stored with Git LFS. Clone and fork
traffic was dominating GitHub's shared LFS bandwidth quota, so the repository is
configured to keep that traffic off GitHub while leaving GitHub authoritative
for the data:
- Reads come from Hugging Face.
.lfsconfigpointslfs.urlat the Hugging Face mirror, so clones and forks fetch LFS objects from HF's CDN instead of GitHub. - GitHub remains the source of truth. LFS objects are always written to
GitHub (storage there is fine; only download bandwidth was the problem), and
the mirror workflow copies them to
Hugging Face after every merge to
main. Hugging Face is purely a read replica — every object is always retrievable from GitHub. - LFS is scoped to
data/(see.gitattributes). A new dataset staged at the repository root is an ordinary Git file, so submissions can be pushed from a fork with no LFS configuration; the submission workflow turns the file into an LFS object when it moves it intodata/.
For contributors
Submitting a new dataset: nothing special is required — stage your file at the repository root and open a PR (see CONTRIBUTING.md and the Submission Workflow).
Editing a file that already lives under
data/from a fork: that file is an LFS object, so point LFS uploads at your own fork once before pushing (you cannot write to the canonical repository's LFS store):git config lfs.pushurl https://github.com/<your-username>/ord-data.git/info/lfs
For maintainers (CI)
Freshly pushed objects are not on the Hugging Face mirror until the post-merge
mirror job runs, so CI and the mirror override the read endpoint back to GitHub
at runtime (git config lfs.url …):
validation.ymlpulls only each matrix shard's objects from GitHub, sparsely, instead of the whole dataset in every job.submission.ymlreads from GitHub so fork and branch submissions are validated before their bytes reach Hugging Face.huggingface_mirror.ymlreads the to-be-mirrored objects from GitHub.
Contributing
Please see the Submission Workflow documentation. Make sure to review the license and terms of use.
Maintainer notes
Skipping the Update submission step
The submission workflow's Update submission step runs
scripts/process_dataset.py --update --cleanup
to assign reaction/dataset IDs and timestamps to newly
submitted files and rewrite them to the canonical on-disk format. For
maintainer PRs that touch dataset files but should not be re-processed
this way — e.g., format conversions or mass migrations of already-finalized
data — apply the skip-update-submission label to the PR. Note that the
label leaves such a PR with no dataset validation inside the submission
workflow: Validate submission runs only for fork PRs and for this
workflow's own re-runs, so on a labeled maintainer PR both steps sit out.
The corpus sweep in validation.yml
covers the result on merge to main.
Converting datasets to Parquet
Published datasets are stored as Parquet only, and new submissions are written
that way by process_dataset.py, so data/ holds no .pb.gz to convert.
scripts/convert_to_parquet.py handles the
case where one lands there anyway: it globs data/*/ord_dataset-*.pb.gz,
converts each 1:1 (carrying the existing dataset_id), and refuses to overwrite
an existing output whose reaction count disagrees with its input. Converting the
corpus is this script's job, not a side effect of editing one dataset, which is
why process_dataset.py leaves an edited file in whatever format it has.
It needs ord_schema, which comes from the pipeline dependency group. Because
it reads .pb.gz content, pull the inputs first. With data/ Parquet-only the
converter has nothing to do and says so — these steps are worth running only
once a .pb.gz has actually appeared:
uv sync --only-group pipeline
git lfs pull --include="data/*/ord_dataset-*.pb.gz"
uv run --no-sync python scripts/convert_to_parquet.py --dry-run # preview
uv run --no-sync python scripts/convert_to_parquet.py
git rm data/*/*.pb.gz # keep only the Parquet versions
Commit the new .parquet files (they become LFS objects), push them (see
Pushing new LFS objects), and open the PR with the
skip-update-submission label. Validation runs against the full dataset on
merge to main.
Pushing new LFS objects
.lfsconfig routes LFS reads to the Hugging Face mirror and
deliberately sets no pushurl, so a plain push would try to upload new objects
to HF — which you cannot write. Point LFS uploads at GitHub for the push, and
make sure git can authenticate to github.com over HTTPS for the LFS API
(the LFS endpoint is HTTPS even when your git remote is SSH). The simplest
auth is the GitHub CLI:
git config lfs.pushurl https://github.com/open-reaction-database/ord-data.git/info/lfs
gh auth setup-git # let git use your gh token for github.com over HTTPS
git push -u origin <branch>
Or, as a one-off without persisting any config:
git -c lfs.pushurl=https://github.com/open-reaction-database/ord-data.git/info/lfs \
-c 'credential.https://github.com.helper=!gh auth git-credential' \
push -u origin <branch>
Reads stay on the mirror; only your uploads go to GitHub. On merge to main,
huggingface_mirror.yml copies the new objects to Hugging Face.
License
This repository carries two licenses, because it holds both data and code:
| what | license | file |
|---|---|---|
The datasets under data/ (and the repository metadata describing them) |
CC-BY-SA-4.0 | LICENSE |
The code under scripts/ and .github/ |
Apache-2.0 | LICENSE-CODE |
If you are using ORD data, CC-BY-SA-4.0 is the license that applies to you; it
is what CITATION.cff and the Hugging Face
mirror declare.
The code carries a separate license because Creative Commons licenses are not intended for software — Creative Commons recommends against it — and because the project's other code repositories, including ord-schema, are Apache-2.0.
Citation
If you use this dataset, please cite the Open Reaction Database paper:
@article{kearnes2021ord,
author = {Kearnes, Steven M. and Maser, Michael R. and Wleklinski, Michael and Kast, Anton and Doyle, Abigail G. and Dreher, Spencer D. and Hawkins, Joel M. and Jensen, Klavs F. and Coley, Connor W.},
title = {The Open Reaction Database},
journal = {Journal of the American Chemical Society},
year = {2021},
volume = {143},
number = {45},
pages = {18820--18826},
doi = {10.1021/jacs.1c09820},
}
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