SOLUTIONS for LIFE SCIENCE, GENOMICS, AND HEALTHCARE

From research to care, we transform how data drives discovery.

Managing life-changing research with results comprised of petabytes of data spread across different storage repositories requires a comprehensive and sustainable solution.

Whether meeting NIH data requirements or streamlining collaboration between teams, Diskover transforms complex datasets into actionable insights through specialized metadata harvesting and workflows.

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BAM Plugin

Grant Plugin

POWERING THE FUTURE OF LIFE SCIENCE AND HEALTHCARE DATA

From data silos to discovery-driven insight.

Research teams and healthcare professionals are overwhelmed by the sheer volume of microscopy images, genomic data, and clinical trial files generated every day.

We help life science and healthcare organizations turn this fragmented, unstructured data into clear, actionable insight. From sequencing and imaging to analytics and long-term data retention, our solutions bring visibility, automation, and intelligence to every stage of the data lifecycle.

Whether it’s integrating genomic datasets, managing clinical research archives, or preparing data for AI-driven discovery, we make it simple to access, organize, and use your data — securely, efficiently, and at scale.

From workflow pain to performance gain.

Workflow pain.

Fragmented research data. Genomic sequences, imaging results, and clinical records often reside across disconnected systems, making it difficult to locate, correlate, and reuse valuable datasets.
Data sprawl and versioning chaos. Repeated experiments and file duplication inflate storage costs and complicate traceability.
Limited visibility and control. Distributed data environments obscure lineage, metadata, and lifecycle status — slowing collaboration and discovery.
Compliance risk. Inconsistent file organization and ad-hoc retention practices hinder reproducibility and audit readiness
High operational costs. Manual data management, inefficient tiering, and duplicated files inflate costs and hinder agility.

Performance gain.

Unified visibility across research, clinical, and analytics environments — enabling teams to find, compare, and reuse trusted data faster.
Metadata enrichment brings together context from genomics, imaging, and clinical data, making datasets searchable, comparable, and ready for AI and analytics.
Automated lifecycle management streamlines tiering, versioning, and retention to reduce storage costs while keeping validated, high-value datasets accessible for research and reuse.
Accelerated discovery through curated, metadata-driven datasets that simplify integration across experiments, cohorts, and modalities.
AI-ready insight by feeding high-quality, context-rich data into machine learning pipelines for predictive modeling, diagnostics, and personalized medicine.

Example of data lifecycle optimization for genomics.

Sample Collection

Data Sequencing

Analysis & Modeling

Clinical Correlation

AI-Driven Insight

Publication & Sharing

Archive & Retention

No matter the data type, format, or platform — from sequencing and imaging systems to lab management and analytics tools — we connect every stage of your data lifecycle. From capture to archive, Diskover helps research and healthcare organizations locate, enrich, and automate data movement and curation — ensuring data integrity, collaboration, and readiness for AI-driven discovery and clinical insight.

Unify.
We bring together fragmented data — delivering unified visibility across research, genomics, and healthcare teams. With secure, read-only access and powerful indexing, you can find and share the right data faster, without disrupting workflows.
Curate.
Our smart filtering, tagging, and metadata enrichment turn raw experimental and clinical data into searchable, high-value assets. This accelerates research analysis, enables rapid data retrieval for AI and modeling.
Orchestrate.
Policy-driven automation manages data movement, versioning, and retention — from active research to long-term archives. Keep datasets accessible while reducing storage costs and maintaining compliance with regulatory data practices.

USING DISKOVER TO OPERATIONALIZE LIFE SCIENCE DATA PIPELINES

USE CASE | World-renowned institute builds key infrastructure for clinical and research pathology teams.

CHALLENGE

No single owner of pathology data pipelines.
Applications in play create unwieldy structures on storage systems.
Fractured procurement throws more capacity issues at the problem.

MANAGE

Rapid index of 4.7PB, 27 million file dataset.
Identified 1PB of files older than 5 years old for immediate tiering/archiving.
Identified hotspots of unmanaged application temporary and cache data.

AUTOMATE

Estached policy-driven rules for critical data vs application temporary data.
Tiering policy manages data lifecycle between filesystem and cloud.
Operationalized complex data flows by understanding pathology apps and genomics pipelines.

BENEFITS

Reclaimed 10% of data estate—$750K—within days and identification of pipelines that do not clean up data.
Protects sensitive data with role-based access control and comprehensive audit trails.
Enabled strategic data estate planning and reduces complexity of lifecycle management.

BAM/SAM METADATA ENRICHMENT PLUGIN FOR OPTIMIZED GENOMICS PIPELINES

BAM plugin | Optimize every data pipeline in your genomics ecosystem.

The BAM Metadata Enrichment Plugin extends Diskover’s indexing capabilities into bioinformatics pipelines by harvesting alignment and command-line metadata directly from BAM and SAM files—without requiring any read/write access, ensuring full data integrity.

Talk with us about your use cases

How it works.

Harvests key genomic metadata. Using Python’s pysam, the plugin extracts attributes such as sample ID, sequencing platform, alignment method, and genome build—plus the MD5 checksum of the original command line to verify lineage and detect redundant or derived files.
Enriches Diskover indexes with scientific context. Extracted BAM and SAM metadata are indexed and correlated across datasets, making genomic files searchable, comparable, and reportable without exposing raw data.
Automates lifecycle and analysis workflows. Researchers can leverage agentic workflows to drive policy-based lifecycle management, streamline validation, and surface context-rich data for downstream AI and modeling pipelines.

Why it matters.

Transforms static files into actionable insight. Turns unstructured genome sequence data into structured, metadata-rich datasets—ready for analytics, machine learning, and reproducible research.
Accelerates research and reproducibility. Enables teams to quickly trace lineage, validate file integrity, and eliminate redundant data, ensuring confidence in every analysis.
Bridges data management and AI-readiness.
By unifying scientific metadata with operational context, Diskover connects the data that fuels genomic discovery, automation, and AI-driven precision analysis.

Rich BAM attributes.

Screenshot showing Diskover’s BAM/SAM Metadata Enrichment Plugin in action. The interface displays indexed BAM file attributes and harvested metadata fields, including sequencing platform, alignment method, genome build, and MD5 checksum. These enriched fields make genomic data searchable, reportable, and AI-ready for downstream analysis and automation.

BAM plugin overview.

END-TO-END SCIENTIFIC GRANT DATA MANAGEMENT

GRANT plugin | Linking datasets, grants, and research outcomes.

The Grant plugin brings transparency and intelligence to scientific data management by linking research datasets to grant metadata—enabling realiable compliance with NIH policies, accurate cost tracking, and automated lifecycle oversight.

How it works.

Centralizes grant metadata. Collects and organizes grant IDs, group numbers, and funding references into searchable datasets without requiring access to raw research files.
Associates cost and storage data. Links infrastructure usage and storage costs directly to grants for real-time visibility and fiscal accountability.
Supports NIH-aligned data management. Maps metadata and lifecycle practices to NIH DMS Policy requirements—from proposal through publication.
Enables policy-driven automation. Uses agentic workflows to automate data curation, retention, and reporting tasks based on grant and project policies.
Maintains integrity and traceability. Keeps research data read-only while associating grants, projects, and datasets through secure metadata mapping.

Why it matters.

Simplifies compliance. Helps research teams meet and exceed NIH data-management and sharing requirements effortlessly.
Improves visibility. Provides grant-level insight into data usage, cost, and project outputs without exposing sensitive research content.
Reduces operational waste. Minimizes redundant storage and manual reporting, freeing funds for active research instead of infrastructure.
Accelerates collaboration. Links investigators, datasets, and grant information in one unified index for faster discovery and validation.
Drives accountability through automation. Ensures reproducibility, transparency, and efficient use of grant resources with automated lifecycle tracking.

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Community Edition on GitHub

A free edition with no time limit available on GitHub.

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