Build, manage, and explore omics datasets with BioMiner Indexd
Explore our curated collection of omics datasets
Whole exome sequencing of 12 breast AdCCs with comprehensive clinical and molecular data.
Learn how to create and import custom datasets into BioMiner Indexd
To create a dataset, you need two essential files:
Dataset description file containing metadata and configuration
key: my_dataset
name: My Research Dataset
description: Comprehensive analysis of...
citation: Author et al. Journal 2024
pmid: 12345678
groups: PUBLIC; RESEARCH;
tags: disease:Cancer; organ:Lung;
total: 100
is_filebased: false
version: v1.0.0
license: CC-BY-4.0Tab-separated file with sample metadata and clinical information
#Patient ID Age Gender Diagnosis
#Unique ID Age Sex Disease
#STRING NUMBER STRING STRING
#1 1 1 1
PATIENT_ID AGE GENDER DIAGNOSIS
SAMPLE001 45 Female Cancer
SAMPLE002 52 Male ControlUse our conversion script to transform your files into BioMiner-compatible format:
Ensure your dataset.txt and metadata_table.tsv files are properly formatted
python examples/build_dataset.py convert /path/to/input /path/to/output --version v1.0.0
./biominer-indexd-cli index-datasets --datasets-dir datasets
Your dataset is now available in the BioMiner interface and API
Guidelines for creating high-quality datasets
Keep raw data separate from processed files. Use clear, consistent naming conventions for all files.
Ensure data quality by checking for missing values, format consistency, and logical relationships.
Provide comprehensive documentation including data collection methods, processing steps, and quality metrics.
Ensure compliance with data privacy regulations and obtain proper consent for data sharing.
Use semantic versioning for dataset updates and maintain backward compatibility when possible.
Follow established community standards for data formats, metadata, and sharing practices.
Tools and documentation to help you get started
Comprehensive guides and tutorials for dataset creation and management.
Read Documentation