NIH Cloud Lab

For NIH-affiliated researchers and trainees exploring cloud-based bioinformatics, AI/ML, and data science workflows in a secure, NIH‑approved environment.

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Start Here

• Learn the program & request an account: NIH Cloud Lab
• Extramural account sign‑up instructions: Sign up (Extramural)
• CSP-specific tutorial repositories:
  • AWS: STRIDES/NIHCloudLabAWS
  • Azure: STRIDES/NIHCloudLabAzure
  • Google Cloud: STRIDES/NIHCloudLabGCP

Accounts typically provide up to $500 in credits for up to 90 days of exploration. Use public/non‑sensitive data only.

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What Is NIH Cloud Lab?

NIH Cloud Lab is developed by NIH’s Center for Information Technology (CIT) Cloud Services Team to support the STRIDES Initiative mission of modernizing biomedical research through the cloud. It offers short‑term, low‑risk access to AWS, Azure, and Google Cloud along with curated, hands‑on tutorials.

You’ll get:

• A temporary cloud account (AWS / Azure / GCP)
• Guardrails for spend tracking and program duration
• Curated tutorials and notebooks for common research tasks
• Guidance on beginner cloud operations and Jupyter environments

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Tutorial Catalog (Topics)

Use the CSP repositories for full lists.Highlights include:

1. Artificial Intelligence & Machine Learning

Cloud‑based AI/ML workflows across Azure, AWS, and GCP. Includes model training, inference, imaging, and Generative AI examples.

Subtopics

• Machine Learning & AI (general workflows, model pipelines)
  • Drug Discovery
  • AlphaFold
• Medical Imaging (MONAI, NVIDIA models)
  • Spleen Segmenttion with Liver
• Generative AI (PubMed chatbot, Bedrock, Vertex AI, Azure OpenAI)
  • PubMed Chatbot
  • BedRock
  • Vertex AI HuggingFace

To explore more tutorials on this topic, please visit the cloud platform repositories:

• Generative AI on GCP
• Generative AI on AWS
• Generative AI on Azure

2. Biomedical Workflows

Scientific pipelines commonly used in bioinformatics and computational biology.

Subtopics

• RNA‑Seq
  • RNA-Seq Pipeline on Azure
  • RNA-Seq Pipeline on GCP
  • RNA-Seq Pipeline on AWS
• Single‑Cell RNA‑Seq (scRNA‑seq analysis)
  • scRNA-seq Pipeline
• Long‑Read Sequencing (Nanopore tutorials)
  • Analysis
• Proteomics (AlphaFold, cloud‑native workflows)
  • AlphaFold
  • Protein Expression
• Genomics
  • Genomics Pipeline
  • Variant Calling
  • Somatic Varinat Analysis
• GWAS (cloud‑based genotype/phenotype workflows)
  • GWAS on Azure
  • Deep Learning GWAS on GCP
  • GWAS on AWS
• Metagenomics, ATAC‑seq, other multi‑omics pipelines
  • ATAC-Seq Analysis
  • Metagenomics Analysis
• SARS‑CoV‑2 lineage workflows
  • Pangolin Pipeline
• BLAST & ElasticBLAST
  • NCBI BLAST on Azure
  • Elastic Blast

3. Workflow Languages & Pipeline Orchestration

Cloud execution of workflow languages and HPC‑style batch processing.

Subtopics

• Nextflow
• Sagemaker
• SnakeMake
• Cromwell
• Platform-specific batch orchestration:
  • AWS Batch
  • Google Batch
  • Azure Batch
  • HPC cluster configuration:
    • AWS
    • GCP
    • Azure

4. Clinical & Healthcare Informatics

Tutorials focused on clinical data systems and secure healthcare analytics.

Subtopics

• FHIR on Azure
• AWS HealthLake

5. Data Retrieval & Cloud‑Native Storage

Accessing public datasets and performing scalable cloud queries.

• COVID-19 Data Lake
• Genome Aggregation Database
• Open Data on AWS

Subtopics

• SRA Data (NCBI)
• BigQuery
• Azure Synapse

6. Cloud Platform Fundamentals & Environment Setup

Guides to using Jupyter, VMs, shutdown guards, billing insights, and CSP‑specific tooling.(AWS, Azure, GCP)

Subtopics

• Jupyter Notebook setup (AWS, Azure, GCP)
• VM usage and auto‑shutdown
• Billing dashboards
• Service-specific beginner workflows

7. Specialized & Emerging Topics

Advanced or niche tutorials covering newer research technologies.

Subtopics

• Advanced biomarker discovery
• GenAI‑assisted scientific tooling (RAG chatbot, AI enabled Visualization, SQL chatbot, etc.)
• AI‑enabled visualization pipelines

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How‑To Docs & Onboarding Aids

Each CSP repository includes practical “how‑to” docs (e.g., notebook environments, auto‑shutdown for VMs/notebooks, billing intros, and environment setup). Start with the README in each repo and follow links to the docs/ folder.

• AWS: STRIDES/NIHCloudLabAWS
• Azure: STRIDES/NIHCloudLabAzure
• Google Cloud: STRIDES/NIHCloudLabGCP

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Program Terms & Data Use

• Credits & Duration: Up to $500 for up to 90 days (varies by program/use case)
• Data: Cloud Lab is for public/non‑sensitive data (no PHI/PII)
• Purpose: Training, prototyping, benchmarking, and early experimentation — not production
• Terms: See program terms/conditions in each CSP repo’s docs/ folder

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Support & Next Steps

• General questions & support: [email protected]
• Broader STRIDES information: NIH STRIDES Initiative
• Transitioning to long‑term cloud use: Request a consultation via NIH channels to explore enterprise accounts and options tailored to your data and workload requirements.