OpenProteinSet Archive

This is an upload-friendly mirror of OpenProteinSet/OpenFold training data components. The source tree is kept intact inside archive shards rather than expanded as hundreds of thousands of repository files.

The main use case is simple: search metadata.csv, download the shard or part files you need with the Hugging Face Python API, then extract or reassemble the original file.

What Is Included

Component	Files	Size
alignment_data/	524,454	600.05 GiB
pdb_data/	3	52.22 GiB
duplicate_pdb_chains.txt	1	4.31 MiB

File types:

Type	Files	Size
.a3m	393,000	535.65 GiB
.hhr	131,454	64.40 GiB
.zip	1	51.77 GiB
.json	2	456.83 MiB
.txt	1	4.31 MiB

Packaging summary:

Item	Count / Size
Original files	524,458
Original payload	652.27 GiB
Tar shards	31
Split large-file parts	3
Archived tar payload	601.38 GiB
Metadata generated	2026-05-24T22:58:05Z

Repository Layout

README.md
_MANIFEST.json
metadata.csv
shards.csv
parts.csv
shards/
  shard-00000.tar
  shard-00001.tar
  ...
large_files/
  pdb_data/pdb_mmcif.zip.part-00000
  pdb_data/pdb_mmcif.zip.part-00001
  pdb_data/pdb_mmcif.zip.part-00002

Most files live inside shards/*.tar. The large pdb_data/pdb_mmcif.zip file is stored as byte parts under large_files/ so no single uploaded object is excessively large.

Metadata Tables

metadata.csv is the default table shown in the Dataset Viewer. It has one row per original file.

Column	Meaning
path	Original relative path in the OpenProteinSet tree.
storage_type	tar for files inside shards/*.tar, parts for files split into byte parts.
shard_path	Tar shard to download when storage_type == "tar".
member_path	Path of the file inside the tar shard.
parts_count	Number of parts when storage_type == "parts".
part_paths	Semicolon-separated part paths for split files.
top_level, directory, filename, extension	Path fields for filtering.
size_bytes, size_human, modified_utc	File size and timestamp captured during packaging.

shards.csv has one row per tar shard. parts.csv has one row per split file part. _MANIFEST.json contains the packaging summary used to build this card.

Install

Use recent versions of the Hugging Face clients:

# pip install -U huggingface_hub datasets

All examples below use Python APIs only.

Inspect The File List

Load the metadata table with datasets:

from datasets import load_dataset

repo_id = "LiteFold/OpenProteinSet-archive"
files = load_dataset(repo_id, "files", split="train")

print(files)
print(files[0])

For quick inspection without materializing the whole table:

from datasets import load_dataset

repo_id = "LiteFold/OpenProteinSet-archive"
files = load_dataset(repo_id, "files", split="train", streaming=True)

for row in files:
    if row["extension"] == ".a3m":
        print(row["path"], row["shard_path"], row["size_human"])
        break

Download One File From A Tar Shard

This downloads the shard that contains the file, then extracts only that member.

from pathlib import Path
import tarfile

from datasets import load_dataset
from huggingface_hub import hf_hub_download

repo_id = "LiteFold/OpenProteinSet-archive"
out_dir = Path("./openproteinset")

files = load_dataset(repo_id, "files", split="train", streaming=True)
row = next(item for item in files if item["extension"] == ".a3m")

if row["storage_type"] != "tar":
    raise ValueError(f"{row['path']} is not stored in a tar shard")

shard = hf_hub_download(
    repo_id=repo_id,
    repo_type="dataset",
    filename=row["shard_path"],
)

with tarfile.open(shard) as archive:
    archive.extract(row["member_path"], path=out_dir)

print(out_dir / row["path"])

Reassemble pdb_mmcif.zip

pdb_data/pdb_mmcif.zip is split into three parts. Reassemble it with the paths listed in metadata.csv:

from pathlib import Path

from datasets import load_dataset
from huggingface_hub import hf_hub_download

repo_id = "LiteFold/OpenProteinSet-archive"
out_path = Path("./openproteinset/pdb_data/pdb_mmcif.zip")
out_path.parent.mkdir(parents=True, exist_ok=True)

files = load_dataset(repo_id, "files", split="train")
row = files.filter(lambda item: item["path"] == "pdb_data/pdb_mmcif.zip")[0]

with out_path.open("wb") as dst:
    for part_path in row["part_paths"].split(";"):
        part = hf_hub_download(
            repo_id=repo_id,
            repo_type="dataset",
            filename=part_path,
        )
        with Path(part).open("rb") as src:
            while chunk := src.read(8 * 1024 * 1024):
                dst.write(chunk)

print(out_path)

Download And Restore Everything

This pulls the full repository snapshot, extracts all tar shards, and reassembles any split large files.

from pathlib import Path
import csv
import tarfile

from huggingface_hub import snapshot_download

repo_id = "LiteFold/OpenProteinSet-archive"
snapshot = Path(snapshot_download(repo_id=repo_id, repo_type="dataset"))
out_dir = Path("./openproteinset")
out_dir.mkdir(parents=True, exist_ok=True)

for shard in sorted((snapshot / "shards").glob("*.tar")):
    with tarfile.open(shard) as archive:
        archive.extractall(out_dir)

with (snapshot / "metadata.csv").open(newline="") as handle:
    for row in csv.DictReader(handle):
        if row["storage_type"] != "parts":
            continue

        target = out_dir / row["path"]
        target.parent.mkdir(parents=True, exist_ok=True)
        with target.open("wb") as dst:
            for part_path in row["part_paths"].split(";"):
                with (snapshot / part_path).open("rb") as src:
                    while chunk := src.read(8 * 1024 * 1024):
                        dst.write(chunk)

print(out_dir)

Notes

• This is a raw mirror, not a cleaned or reformatted training set.
• The alignment tree is preserved under its original relative paths inside the tar shards.
• Tar shards are uncompressed. The goal is predictable extraction and straightforward random access to individual members.
• Check the upstream OpenProteinSet/OpenFold data terms and cite the original resources as appropriate for your use case.