created dataset card for the dataset under huggingface openscience community

README.md

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+---
+datasets:
+- Metanova/Proteins
+task_categories:
+- tabular-classification
+- tabular-regression
+- feature-extraction
+tags:
+- proteins
+- genomics
+- bioinformatics
+- metanova
+- amino-acid-sequences
+- protein-foldingprotein-folding
+pretty_name: Metanova Proteins Dataset
+size_categories:
+- 1M<n<10M
+---
+
+# Dataset Card for Metanova / Proteins
+
+## Dataset Details
+
+### Dataset Description
+
+- **Name:** Metanova / Proteins  
+- **Curated by:** Metanova Labs ([Hugging Face profile](https://huggingface.co/Metanova))  
+- **Shared by:** Metanova Labs  
+- **Language(s):** Not natural language; amino acid sequences (protein sequences) using the standard 20-letter code (A, C, D, …) and possibly special/unknown letters.  
+- **License:** *needs verification*  
+
+A large collection of protein sequences hosted by Metanova Labs. The dataset contains approximately 2 million sequences. It is suitable for machine learning tasks in protein informatics such as protein language modeling, representation learning, and sequence-based function or structure inference.  
+
+### Dataset Sources
+
+- **Repository:** [Metanova/Proteins on Hugging Face](https://huggingface.co/datasets/Metanova/Proteins)  
+
+---
+
+## Uses
+
+### Direct Use
+
+- Training protein language models  
+- Learning protein sequence embeddings for downstream tasks  
+- Protein clustering, similarity search, and phylogenetic analysis  
+- Transfer learning for structure or function prediction  
+
+### Out-of-Scope Use
+
+- Applications requiring curated annotations (e.g., detailed functional labels, structures) unless combined with external databases  
+- Clinical or diagnostic decision-making without experimental validation  
+- Use in tasks where data provenance or sequence redundancy control is critical without further preprocessing  
+
+---
+
+## Dataset Structure
+
+- **Format:** CSV / tabular data  
+- **Rows:** Individual protein sequences (~2.07 million)  
+- **Columns:** (to verify in the dataset files)  
+
+  | Field (expected) | Type | Description |
+  |------------------|------|-------------|
+  | `id` or `sequence_id` | string | Unique identifier for each sequence |
+  | `sequence` | string | Protein sequence in single-letter amino acid code |
+  | `length` | integer | Sequence length |
+  | `organism` / `taxonomy` | string (optional) | Source organism or taxonomic category |
+  | `annotation` | string (optional) | Functional / descriptive annotation |
+
+- **Splits:** No predefined train/validation/test splits  
+
+---
+
+## Dataset Creation
+
+### Curation Rationale
+
+This dataset was likely created to provide a large repository of protein sequences for use in computational biology, machine learning, and protein informatics research.  
+
+### Source Data
+
+#### Data Collection and Processing
+
+- Exact collection methodology is **not specified**.  
+- Likely compiled from publicly available sequence repositories (e.g., UniProt, RefSeq, or metagenomic datasets).  
+- Unknown whether filtering, redundancy removal, or quality control were applied.  
+
+#### Who are the source data producers?
+
+- Likely generated by sequencing projects and deposited in public biological databases.  
+- No explicit acknowledgment of original contributors is provided.  
+
+### Annotations
+
+- No additional human annotations appear to be provided.  
+- No metadata regarding function, structure, or localization is included.  
+
+#### Personal and Sensitive Information
+
+- This dataset contains only biological sequences.  
+- No personal, sensitive, or private human information is present.  
+
+---
+
+## Bias, Risks, and Limitations
+
+- **Bias:** Likely overrepresentation of well-studied organisms (e.g., model organisms, pathogens).  
+- **Redundancy:** Dataset may contain highly similar or duplicate sequences.  
+- **Annotation gaps:** Lack of metadata limits supervised tasks.  
+- **Technical risk:** Models trained directly on this dataset may overfit due to redundancy or taxonomic leakage.  
+
+### Recommendations
+
+- Perform sequence clustering or deduplication before training.  
+- Create train/validation/test splits that respect taxonomy to avoid leakage.  
+- If function/structure labels are needed, map these sequences to external annotated databases.  
+- Contact Metanova Labs for clarification of license before commercial use.  
+
+---
+
+## Citation
+
+If you use this dataset, please cite as:
+
+
+**BibTeX (generic):**
+```bibtex
+@misc{metanova_proteins,
+  author       = {Metanova Labs},
+  title        = {Proteins Dataset},
+  year         = {2025},
+  publisher    = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/datasets/Metanova/Proteins}}
+}