LiteFold/protenix-data

Protenix Data

This dataset preserves the source folder as tar shards. metadata.csv has one row per original file and is configured as the default Dataset Viewer table, while the actual bytes live in shards/*.tar.

Summary

Original files	931,270
Original payload	1.05 TiB (1,150,960,949,915 bytes)
Tar shards	52
Archive size	1.05 TiB (1,152,633,989,120 bytes)
Max shard payload	20.00 GiB
Metadata generated	2026-05-24T16:01:55Z

Source Layout

Directory	Files	Size
common	9	1.18 GiB
indices	4	28.58 MiB
mmcif	220,113	282.13 GiB
mmcif_bioassembly	219,912	34.89 GiB
mmcif_msa_template	475,724	624.02 GiB
posebusters_bioassembly	308	41.52 MiB
posebusters_mmcif	429	360.26 MiB
recentPDB_bioassembly	8,808	1.44 GiB
rna_msa	5,959	39.07 GiB
search_database	4	88.75 GiB

Common File Types

Extension	Files	Size
.a3m	479,867	658.57 GiB
.cif	220,543	283.34 GiB
.fasta	4	88.75 GiB
.pkl.gz	229,028	36.37 GiB
.csv	1,816	4.53 GiB
.pkl	2	255.97 MiB
.json	4	58.64 MiB
.csv.gz	1	25.70 MiB
.txt	2	20.70 MiB
.npz	2	435.23 KiB
	1	1.50 KiB

Shards

Shard	Files	Payload	Archive
shards/shard-00000.tar	3,743	19.99 GiB	20.00 GiB
shards/shard-00001.tar	2,219	19.69 GiB	19.69 GiB
shards/shard-00002.tar	26,895	20.00 GiB	20.04 GiB
shards/shard-00003.tar	17,553	20.00 GiB	20.03 GiB
shards/shard-00004.tar	23,622	19.91 GiB	19.95 GiB
shards/shard-00005.tar	21,546	20.00 GiB	20.03 GiB
shards/shard-00006.tar	18,365	20.00 GiB	20.03 GiB
shards/shard-00007.tar	12,514	20.00 GiB	20.02 GiB
shards/shard-00008.tar	16,486	20.00 GiB	20.03 GiB
shards/shard-00009.tar	14,994	20.00 GiB	20.02 GiB
shards/shard-00010.tar	12,684	19.99 GiB	20.01 GiB
shards/shard-00011.tar	10,438	19.97 GiB	19.99 GiB
shards/shard-00012.tar	13,431	20.00 GiB	20.02 GiB
shards/shard-00013.tar	9,903	20.00 GiB	20.01 GiB
shards/shard-00014.tar	10,788	20.00 GiB	20.02 GiB
shards/shard-00015.tar	9,355	20.00 GiB	20.02 GiB
shards/shard-00016.tar	142,265	20.00 GiB	20.24 GiB
shards/shard-00017.tar	81,080	20.00 GiB	20.13 GiB
shards/shard-00018.tar	15,468	20.00 GiB	20.02 GiB
shards/shard-00019.tar	14,558	20.00 GiB	20.02 GiB
shards/shard-00020.tar	14,807	20.00 GiB	20.02 GiB
shards/shard-00021.tar	13,074	20.00 GiB	20.02 GiB
shards/shard-00022.tar	12,558	19.99 GiB	20.01 GiB
shards/shard-00023.tar	15,179	19.99 GiB	20.02 GiB
shards/shard-00024.tar	13,741	20.00 GiB	20.02 GiB

Only the first 25 shards are shown here. See shards.csv for all 52 shards.

Metadata

metadata.csv columns:

Column	Description
path	Original relative path in the source folder.
shard_path	Tar shard containing the file.
member_path	Path of the file inside the tar shard.
top_level	First directory under the source folder.
directory	Parent directory of the file.
filename	File basename.
extension	File extension, preserving compound suffixes such as .csv.gz.
size_bytes	Original file size in bytes.
size_human	Human-readable original file size.
modified_utc	Local file modification timestamp captured during packaging.

shards.csv lists one row per tar shard. _MANIFEST.json contains the aggregate build summary.

Download Everything

pip install -U huggingface_hub
hf download LiteFold/protenix-data --repo-type dataset --local-dir ./protenix-data

Extract all shards:

mkdir -p ./data
for shard in ./protenix-data/shards/*.tar; do
  tar -xf "$shard" -C ./data
done

Use With datasets

Use the datasets API to query file metadata, then use huggingface_hub to download the shard that contains the file.

from datasets import load_dataset
from huggingface_hub import hf_hub_download
import tarfile

files = load_dataset("LiteFold/protenix-data", "files", split="train")
row = files[0]

shard = hf_hub_download(
    repo_id="LiteFold/protenix-data",
    repo_type="dataset",
    filename=row["shard_path"],
)

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

For streaming metadata:

from datasets import load_dataset

files = load_dataset("LiteFold/protenix-data", "files", split="train", streaming=True)
for row in files:
    print(row["path"], row["shard_path"])
    break

Notes

The tar shards are uncompressed by design. This keeps packaging and random extraction simple and avoids spending CPU compressing data that is often already compressed.