Update README.md

README.md

@@ -1,3 +1,73 @@
 ---
-license: mit
+viewer: false
+license: cc-by-nc-sa-4.0
+task_categories:
+- tabular-classification
+- feature-extraction
+tags:
+- genomics
+- bioinformatics
+- raw-reads
+- unmapped-sequences
+- synthetic-biology
+pretty_name: CGSC Raw Transcriptome Unmapped Reads
+size_categories:
+- 100K<n<1M
 ---
+
+# Dataset Card for CGSC Raw Transcriptome Unmapped Reads
+
+## Dataset Description
+
+- **Homepage:** https://comp-genomics.dev/datasets/transcriptome-raw
+- **Repository:** Computational Genomics & Synthetic Bio-Arrays Consortium
+- **Paper:** N/A (Internal Lab Protocol: CGSC-SEQ-2026)
+- **Point of Contact:** sequence-data@comp-genomics.dev
+
+### Dataset Summary
+
+This repository acts as the primary cold-storage for unmapped, raw sequencing outputs generated during the Q2 2026 Synthetic Bio-Arrays trials. The dataset comprises massive, uncompressed binary blobs that represent pre-alignment genomic data directly from the sequencing hardware.
+
+Because these files bypass standard alignment and compression algorithms (such as BAM/CRAM conversion) to preserve base-pair quality scores and hardware-level artifact data, the payloads are exceptionally large and entirely unstructured to standard viewers. This dataset is intended exclusively for testing high-throughput bioinformatics ingestion pipelines and error-correction models.
+
+### Supported Tasks and Leaderboards
+
+- `error-correction-modeling`: Evaluating algorithms that filter sequencing hardware noise from unmapped reads.
+- `pipeline-stress-testing`: Ingestion of massive, unstructured biological data streams.
+
+### Languages
+
+There is no natural language present. Metadata and internal logging strings are generated in English (`en`), but the core payload consists of binary-encoded nucleotide sequences and quality scores.
+
+## Dataset Structure
+
+### Data Instances
+
+The architecture of this storage does not follow typical huggingface tabular formats. Records point to dense, opaque payloads.
+
+```json
+{
+  "run_id": "cgsc-seq-run-8824",
+  "sequencer_node": "illumina-synthetic-node-04",
+  "read_type": "unmapped_paired_end",
+  "compression_state": "raw_binary_dump",
+  "payload_reference": "reads/run-8824-unmapped.bin",
+  "gc_content_estimate": "N/A - Pre-analysis"
+}
+
+```
+### Data Fields
+ * run_id: Unique batch identifier for the sequencing run.
+ * sequencer_node: Hardware identifier for the machine that generated the data.
+ * read_type: Categorization of the raw output (e.g., unmapped_paired_end).
+ * compression_state: Always raw_binary_dump to justify the immense file weight.
+ * payload_reference: Direct pointer to the large LFS files.
+ * gc_content_estimate: Left as N/A due to the raw nature of the files.
+## Dataset Creation
+### Curation Rationale
+Traditional genomic databases host heavily curated and compressed sequences. The CGSC requires a repository of raw, artifact-heavy sequencer dumps to train next-generation base-calling AI models.
+### Source Data
+Data is fully synthetic, generated via advanced transcriptome simulations modeling noisy hardware environments. No real human or animal genetic material is represented in this repository.
+## Disclaimers
+Due to the absence of standard .fastq or .bam formatting, these unstructured binary dumps are massive. Downloads are recommended only for consortium partners with appropriate HPC (High-Performance Computing) storage infrastructure. We provide no parsing tools for external use.
+```