Update README.md

README.md

@@ -91,31 +91,13 @@ for example in ds.take(3):
 
 ### 1. `eukaryote_generator` — Eukaryote Genomes
 
-**Source:** [GenerTeam/pretrain_data_eukaryote](https://huggingface.co/datasets/GenerTeam/pretrain_data_eukaryote) · MIT License
+**Source:** [GenerTeam/pretrain_data_eukaryote](https://huggingface.co/datasets/GenerTeam/pretrain_data_eukaryote)
 
-**What it contains:** Genomic sequences from eukaryotic organisms (fungi, plants, animals, and more), packaged by the GenerTeam as part of their [GENERATOR](https://arxiv.org/abs/2502.07272) genomic foundation model. This is the main part is Carbon training data, which is focused on Eukaryote species. Each row carries full taxonomic metadata, gene type, strand orientation, and chromosome coordinates alongside the raw sequence. 
+Genomic sequences from eukaryotic organisms (fungi, plants, animals, and more), packaged by the GenerTeam as part of their [GENERator](https://arxiv.org/abs/2502.07272) genomic foundation model. This is the main part is Carbon training data, which is focused on Eukaryote species. Each row carries full taxonomic metadata, gene type, strand orientation, and chromosome coordinates alongside the raw sequence. 
 We filter out sequences longer than 100 kbp, as excluding them improves training performance on DNA donwstream benchmarks. For long-context training, you can concatenate genes from the same contig to build longer samples.
 
-**Schema:**
 
-```
-begin_of_sequence   bool     — special boundary marker
-end_of_sequence     bool     — special boundary marker
-begin_of_gene       bool     — marks gene start
-end_of_gene         bool     — marks gene end
-gene_type           str      — e.g. "protein_coding", "ncRNA", "pseudogene"
-species_type        str      — e.g. "fungi", "plant", "vertebrate"
-strand              str      — "+" or "-" (which DNA strand)
-sequence            str      — the raw nucleotide string (A/T/G/C/N...)
-molecule_type       str      — e.g. "genomic DNA"
-topology            str      — "linear" or "circular"
-taxonomy            str      — full NCBI taxonomy lineage
-record_id           str      — upstream GenBank/RefSeq accession
-start               int      — coordinate start on chromosome
-end                 int      — coordinate end on chromosome
-```
-
-**Example row (sequence truncated):**
+**Example row:**
 ```json
 {
   "species_type": "fungi",
@@ -131,42 +113,24 @@ end                 int      — coordinate end on chromosome
 
 ### 2. `mrna_evo2` — Messenger RNA Sequences
 
-**Source:** [arcinstitute/opengenome2](https://huggingface.co/datasets/arcinstitute/opengenome2) · Apache-2.0 License · `mrna` split
-
-**What it contains:** Processed mRNA sequences — the "final edited" version of a gene after the cell has removed introns (non-coding interruptions) and kept only the exons. This is the sequence that gets read to produce protein. These sequences are shorter and more information-dense than raw genomic DNA.
+**Source:** [arcinstitute/opengenome2](https://huggingface.co/datasets/arcinstitute/opengenome2) (`mrna` split)
 
-**Schema:**
-```
-text        str   — the raw nucleotide sequence
-metadata    dict  — {"source": "data_mrna_train_chunk1.jsonl.gz", "record": "..."}
-```
-
-**Stats:** 52.7 M rows · mean 2,200 nt · min 64 nt · max ~100 K nt · 115.9 Gbp total
+Processed mRNA sequences — the "final edited" version of a gene after the cell has removed introns (non-coding interruptions) and kept only the exons. This is the sequence that gets read to produce protein. These sequences are shorter and more information-dense than raw genomic DNA.
 
 
 ### 3. `mrna_splice_evo2` — Augmented mRNA Transcripts
 
-**Source:** [arcinstitute/opengenome2](https://huggingface.co/datasets/arcinstitute/opengenome2) · Apache-2.0 · `mrna_splice_promoter` split
+**Source:** [arcinstitute/opengenome2](https://huggingface.co/datasets/arcinstitute/opengenome2) (`mrna_splice_promoter`)
 
-**What it contains:** The same mRNA transcripts as `mrna_evo2`, with augmentations applied by the Evo2 team: each transcript gets an extra 1,024 bp of upstream promoter sequence prepended, and an extra 32 bp of flanking sequence around each exon boundary to expose splice sites. The resulting exon chunks are concatenated with a special `@` separator into a single stitched sequence per transcript.
+The same mRNA transcripts as `mrna_evo2`, with augmentations applied by the Evo2 team: each transcript gets an extra 1,024 bp of upstream promoter sequence prepended, and an extra 32 bp of flanking sequence around each exon boundary to expose splice sites. The resulting exon chunks are concatenated with a special `@` separator into a single stitched sequence per transcript.
 
 Sequences here are systematically longer than `mrna_evo2` — the extra promoter and splice flanks are the only difference, not a different set of genes.
 
-**Stats:** 56.9 M rows · mean 3,470 nt · min 96 nt · max ~122 K nt · 197.4 Gbp total
-
 ### 4. `prokaryote_evo2` — Prokaryote Genomes
 
-**Upstream:** [arcinstitute/opengenome2](https://huggingface.co/datasets/arcinstitute/opengenome2) · Apache-2.0 License · `gtdb_v220_imgpr` split
+**Upstream:** [arcinstitute/opengenome2](https://huggingface.co/datasets/arcinstitute/opengenome2) (`gtdb_v220_imgpr` split)
 
-**What it contains:** Long chromosomal chunks from bacteria and archaea, sourced from [GTDB v220](https://gtdb.ecogenomic.org/) (a curated taxonomy of ~85 K prokaryote genomes) and [IMG/PR](https://img.jgi.doe.gov/) (a DOE database of environmental prokaryote sequences). Prokaryote genomes are compact — genes sit back-to-back with minimal intergenic space — so these sequences are biologically rich per nucleotide.
-
-**Schema:** identical to the mRNA subsets
-```
-text        str   — nucleotide sequence (can be megabases long)
-metadata    dict  — {"source": "gtdb_..._train_chunk1.jsonl.gz", "record": "GCF_..."}
-```
-
-**Stats:** 17.4 M rows · mean 20,533 nt · min 20 nt · **max ~16 M nt** · 357.5 Gbp total
+Long chromosomal chunks from bacteria and archaea, sourced from [GTDB v220](https://gtdb.ecogenomic.org/) (a curated taxonomy of ~85 K prokaryote genomes) and [IMG/PR](https://img.jgi.doe.gov/) (a DOE database of environmental prokaryote sequences). Prokaryote genomes are compact — genes sit back-to-back with minimal intergenic space — so these sequences are biologically rich per nucleotide.
 
 
 ## ⚗️ Carbon Training Mixture
@@ -200,3 +164,14 @@ In Carbon we added optional metadata tags to a fraction of eukaryote sequences,
 ```
 
 At inference time you can prompt with any combination of tags or none at all. The three OpenGenome2 subsets are tokenized as plain `<dna>SEQUENCE</dna>` without metadata conditioning.
+
+## License
+
+This dataset is a mirror of two upstream sources with different permissive licenses:
+
+| Subset | License | Upstream |
+|---|---|---|
+| `eukaryote_generator` | [MIT](https://huggingface.co/datasets/GenerTeam/pretrain_data_eukaryote) | GenerTeam/pretrain_data_eukaryote |
+| `mrna_evo2` | [Apache-2.0](https://huggingface.co/datasets/arcinstitute/opengenome2) | arcinstitute/opengenome2 |
+| `mrna_splice_evo2` | [Apache-2.0](https://huggingface.co/datasets/arcinstitute/opengenome2) | arcinstitute/opengenome2 |
+| `prokaryote_evo2` | [Apache-2.0](https://huggingface.co/datasets/arcinstitute/opengenome2) | arcinstitute/opengenome2 |