Update dataset card

README.md

@@ -50,6 +50,10 @@ configs:
   data_files:
   - split: train
     path: data/mgnify_short/train-*
+- config_name: mgnify_long
+  data_files:
+  - split: train
+    path: data/mgnify_long/train-*
 - config_name: rcsb_index
   data_files:
   - split: train
@@ -78,6 +82,10 @@ configs:
   data_files:
   - split: train
     path: data/mgnify_short_index/train-*
+- config_name: mgnify_long_index
+  data_files:
+  - split: train
+    path: data/mgnify_long_index/train-*
 ---
 
 # AtlasFold-Data
@@ -104,10 +112,11 @@ and each manifest entry was rebuilt from its row and compared with the original
 | `disordered_pdb_af2` | AlphaFold2 (ColabFold) predictions for PDB chains with many unresolved residues | 11,828 |  | train | 0.34 GiB | Monomer stages 1 to 4 |
 | `disordered_pdb_afm` | AlphaFold-Multimer predictions of PDB complexes | 29,431 | 176,472 | train | 1.79 GiB | Multimer stages 1 to 3 |
 | `mgnify_short` | AlphaFold2 predictions of MGnify sequences shorter than 200 residues (OpenFold3) | 430,418 |  | train | 5.04 GiB | All stages |
+| `mgnify_long` | AlphaFold2 predictions of MGnify sequences of 200 or more residues (OpenFold3) | 16,099,404 |  | train | 526.45 GiB | All stages |
 | `rcsb_multimer_templates` | Template chains for `rcsb_multimer` (OpenFold3) | 950,691 | | train | 16.91 GiB | Multimer stages 1 to 3 |
 | `rcsb_multimer_template_hits` | Template alignments per `rcsb_multimer` entity | 89,255 | | train | 0.07 GiB | Multimer stages 1 to 3 |
 
-Not yet published: `disordered_pdb_esm`, `mgnify_long`.
+`disordered_pdb_esm` is not included: the released AtlasFold configurations do not use it, and its ESMFold predictions of PDB chains with unresolved regions duplicate what `disordered_pdb_af2` covers.
 
 "Samples" counts the chain and interface samples that AtlasFold draws from each complex. Every
 subset also has a `<subset>_index` config with one row per training sample in AtlasFold's order.
@@ -549,12 +558,21 @@ documentation asks users to check the upstream terms before redistribution or co
 Converted from the AtlasFold release at commit `444f376d85b9954a5f2f5f3f8b3cbcae1201ebb1` with the `atlasfold_data`
 package in [Synthyra/DatasetDev](https://github.com/Synthyra/DatasetDev).
 
-| Subset | Drive file | Archive SHA-256 | Converted with |
+`mgnify_long` could not be downloaded from the release Drive folder because of
+Google Drive download quotas, so it was rebuilt from the same source AtlasFold used: the OpenFold3
+AlphaFold2 MGnify predictions on the AWS Registry of Open Data. The rebuild parses each entry's
+`best_structure_relaxed.pdb` with AtlasFold's own reader (`scripts/preprocess/af2/a1_process.py`) and
+writes the released LMDB and manifest formats. The same procedure reproduced the released `mgnify_short`
+exactly: all 430,418 LMDB values byte for byte, `manifest.msgpack` byte for byte, and
+the uncompressed `manifest.json.zst` text.
+
+| Subset | Source | SHA-256 | Converted with |
 | --- | --- | --- | --- |
-| `rcsb` | `1TEH73v9oxA1oYYnsPZntHqES_04vEz8P` | `905b248e1baee4e7e8b7a3536e5596af9c7cbf2c249b30b50fa7c45f5ebd9562` | pyarrow 25.0.1, datasets 4.8.5, NumPy 2.5.3 |
-| `rcsb_multimer` | `1aN9zUL4JokQc0L6AWVUNlsnftQBi8pjr` | `c29a7cf85dc9a4ca46523fa65b74e2a19dd382c90d08c2f15a209e86686b682a` | pyarrow 25.0.1, datasets 4.8.5, NumPy 2.5.3 |
-| `cameo_val` | `10fhgH7nnVA022nvN-v3bTg1Xor97t2Ne` | `0e9c635b9a1196630d5c540e1a2b5abb718b5325163b9443da2608405629857c` | pyarrow 25.0.1, datasets 4.8.5, NumPy 2.5.3 |
-| `rcsb_multimer_val` | `17meo4uBvvFfB2M-uor17KWwqQDYdSGFI` | `be0300ebee91c93b92d9a5ebcd8c1b94c41e26e0261b139607630435ce0f2ac4` | pyarrow 25.0.1, datasets 4.8.5, NumPy 2.5.3 |
-| `disordered_pdb_af2` | `1f79fRsVOK5SloBo-wVYLuAXDyBX5bOTR` | `3adc4928e7da6cf22eaed2462bb1728eb914cb307f85703a3524a1ba428c8c69` | pyarrow 25.0.1, datasets 4.8.5, NumPy 2.5.3 |
-| `disordered_pdb_afm` | `1f7pw1T7Bdho3r2P7cT5KTv4AvkY4joqb` | `589ea53a7a18cc19d854b75c1c8e398a11487fdc829970431880a26e65d95c10` | pyarrow 25.0.1, datasets 4.8.5, NumPy 2.5.3 |
-| `mgnify_short` | `1VN9XQsd9d5ulsyO4XICXhIMYqqVk7Ag_` | `b7da9a2f9ce2453fa57428060e0ab47afe3939a0153dae0cd3cc8adfbccc1879` | pyarrow 25.0.1, datasets 4.8.5, NumPy 2.5.3 |
+| `rcsb` | Drive file `1TEH73v9oxA1oYYnsPZntHqES_04vEz8P` | archive `905b248e1baee4e7e8b7a3536e5596af9c7cbf2c249b30b50fa7c45f5ebd9562` | pyarrow 25.0.1, datasets 4.8.5, NumPy 2.5.3 |
+| `rcsb_multimer` | Drive file `1aN9zUL4JokQc0L6AWVUNlsnftQBi8pjr` | archive `c29a7cf85dc9a4ca46523fa65b74e2a19dd382c90d08c2f15a209e86686b682a` | pyarrow 25.0.1, datasets 4.8.5, NumPy 2.5.3 |
+| `cameo_val` | Drive file `10fhgH7nnVA022nvN-v3bTg1Xor97t2Ne` | archive `0e9c635b9a1196630d5c540e1a2b5abb718b5325163b9443da2608405629857c` | pyarrow 25.0.1, datasets 4.8.5, NumPy 2.5.3 |
+| `rcsb_multimer_val` | Drive file `17meo4uBvvFfB2M-uor17KWwqQDYdSGFI` | archive `be0300ebee91c93b92d9a5ebcd8c1b94c41e26e0261b139607630435ce0f2ac4` | pyarrow 25.0.1, datasets 4.8.5, NumPy 2.5.3 |
+| `disordered_pdb_af2` | Drive file `1f79fRsVOK5SloBo-wVYLuAXDyBX5bOTR` | archive `3adc4928e7da6cf22eaed2462bb1728eb914cb307f85703a3524a1ba428c8c69` | pyarrow 25.0.1, datasets 4.8.5, NumPy 2.5.3 |
+| `disordered_pdb_afm` | Drive file `1f7pw1T7Bdho3r2P7cT5KTv4AvkY4joqb` | archive `589ea53a7a18cc19d854b75c1c8e398a11487fdc829970431880a26e65d95c10` | pyarrow 25.0.1, datasets 4.8.5, NumPy 2.5.3 |
+| `mgnify_short` | Drive file `1VN9XQsd9d5ulsyO4XICXhIMYqqVk7Ag_` | archive `b7da9a2f9ce2453fa57428060e0ab47afe3939a0153dae0cd3cc8adfbccc1879` | pyarrow 25.0.1, datasets 4.8.5, NumPy 2.5.3 |
+| `mgnify_long` | rebuilt from `s3://openfold3-data/monomer_distillation_sets_v2/long_monomers/raw/` | entry list `6c9697f29f845180ea37fd795d561c3f67bc179ff4eecc75f0a7f9b5747846b6` | pyarrow 25.0.1, datasets 4.8.5, NumPy 2.5.3 |