LIFE SCIENCE DATA MANAGEMENT

Your research data, in full view.

Life science data management from sequencer to publication: Diskover indexes sequencing, imaging, and clinical research files across HPC, lab NAS, object, and cloud, in place. Researchers find data by study, research IT sees cost by lab, and data stewards see where likely patient files sit.

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Life science solution brief
Healthcare solution brief

$542K

of data estate reclaimed within days of the first index

Diskover life science customer

$250K

in storage overspend avoided at a cancer research institute

Diskover life science customer

27M

pathology files indexed across 3 sites

Diskover life science customer

THE PROBLEM

Researchers see the tip. Storage holds the rest.

Sequencers and imaging systems add data every day, and pipeline output outlives the analysis. In one Diskover life science customer index, 21% of 2.5 PB was duplicate copies.

Diagram of research storage: current study data above the surface, raw reads, duplicate BAM files, imaging data, and model checkpoints below.

HOW IT WORKS

From scattered files to clear action.

01

CONNECT

Point Diskover at HPC file systems, lab NAS, object, cloud, and archive. Indexing runs in place, so images and sequencing output stay where instruments and pipelines expect them.

Global visibility

02

CATALOG

Every file becomes a searchable record with rich metadata, such as name, path, owner, and dates, enriched with study and sample names from the path, directory lineage, and classification tags.

Metadata catalog

03

ORGANIZE

Tag by study, protocol, project, and status. Build saved searches for scanner output still on premium storage, or every file tied to a closed study.

Data intelligence

04

ACTIVATE

Run tiering, removal, and packaging by policy with an audit trail, and deliver curated imaging and sequencing metadata to analytics and AI pipelines through the API.

Orchestration

BUILT FOR LIFE SCIENCE

Diskover speaks life science.

Explore the plugin suite
Explore life science plugins

Adds sequencing details.

The BAM info plugin adds pipeline, tool, and version to each file.

Tracks storage by lab.

Breadcrumb adds owner and cost center from each project folder, so each lab sees what it spends on storage.

Knows your file types.

File Kind groups FASTQ, BAM, CRAM, VCF, and DICOM files into the categories you define.

Gives visibility without access.

Technicians see their group’s data in Diskover without direct file system access.

WHO FEELS IT

Three teams. Three different pains.

RESEARCHERS • SCIENTISTS

“Where is the data from that study?”

  • Data sits in many labs, instruments, and shares.
  • Each team keeps its own copy.
  • Folder names make sense only to the person who made them.

With Diskover: find study data by study, protocol, or owner, across every lab and instrument.

RESEARCH IT • BIOINFORMATICS

“Who owns these files?”

  • Storage growth outpaces budget.
  • Intermediate files outlive the analysis.
  • Researchers leave. Their data stays.

With Diskover: see the owner and cost center of every file, and move finished projects by policy.

COMPLIANCE • DATA STEWARDSHIP

“Where is our patient data?”

  • Patient identifiers appear in file names.
  • Funders ask what was kept and shared.
  • Each study and grant has its own retention dates.

With Diskover: find likely patient files from metadata, and keep a record of what was kept and shared.

WHY IT MATTERS

Fund research, not storage.

Gartner expects demand for data center storage capacity to grow 19.5% a year through 2029. Research budgets do not grow that fast. With context on every file, labs put a cost on storage, keep fast storage for active studies, and answer funders with confidence.

Explore data intelligence

Free up fast storage.

Finished studies and pipeline leftovers move to lower tiers by policy, based on real age and use.
Data tiering

Cost every lab.

See storage cost by lab, grant, or cost center, so each group knows what it spends.
Chargeback

Reuse past studies.

Data from earlier studies stays findable by study, protocol, or owner, so teams build on it instead of starting over.
Saved searches

Pass the audit.

Show funders and auditors what was kept, where it lives, and who owns it.
Retention policy

AI READINESS

Prepare research data for AI before it reaches a model.

Explore AI-ready data

01

TAG

Rules tag QC-passed runs and reference datasets as they are indexed. Breadcrumb adds the owner and cost center from each project folder.

02

CURATE

Select sequencing, imaging, and lab data by study, instrument, or file type. BAM info and File Kind add the details you filter on.

03

GOVERN

Flag likely patient and clinical files from names and paths, so rules leave them out of the training set. Every dataset keeps its lineage.

04

DELIVER

Send curated file lists to your research and AI platforms as Parquet, or copy the data to where the model runs, by policy.

REAL-WORLD USE CASE

$542K reclaimed, within days of the first index.

A cancer research institute, with 4.7 PB of research and clinical storage across 3 sites.

27M

pathology files

4.7 PB

research storage

3

sites

1 PB

over 5 years old

THE CHALLENGE

  • Research and clinical data growing across 3 sites.
  • Temporary and test data filled core storage.
  • AI pipelines needed data, without storage overspend.

HOW THEY USE DISKOVER

  • Indexed pathology files across all 3 sites in one metadata catalog.
  • Found 0.5 PB of temporary, transient, and test data.
  • Flagged 1 PB of files over 5 years old for tiering.

$542K

of data estate reclaimed within days of the first index

$250K

in storage overspend avoided, with AI data pipelines kept running

0.5 PB

reclaimed from core research and clinical storage

See how teams like yours did it

FAQ

Common questions.

Scattered data points flowing into a single stream that becomes organized data paths across a world map.

See it on your own research data.

We will index a sample of your storage and show you what is recoverable against your current storage spend.

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