Life science data sourcing for drug discovery
Identify internal, public and licensed biomedical data that fits your scientific question, then define a practical path to access and downstream use.
Ardigen provides life science data sourcing for discovery and translational teams. We assess scientific fit, cohort and modality coverage, access conditions, provenance and known limitations. The resulting shortlist supports an informed decision about data acquisition, analysis and model development.
Where discovery programs lose time before analysis begins
The required evidence is not available
A program needs a modality, cohort, perturbation or outcome that the current data estate does not contain
Relevant public data is difficult to evaluate
Repositories differ in population, assay, annotation, update cadence, access conditions and suitability for the intended question
Licensed data requires a feasibility path
Scientific fit, permitted use, application steps, cost and delivery format must be understood before a team commits
Internal assets are hard to discover
Potentially useful assay, compound, imaging, omics or clinical data may exist across programs without a complete inventory or clear owner
Acquisition is separated from downstream use
A dataset can look valuable in isolation but fail to support the planned analysis, model or validation strategy
Start with the scientific decision
Life science data sourcing begins by defining the decision the data must support. Ardigen translates the scientific question into required entities, modalities, population or experimental coverage, labels, metadata, quality thresholds and permitted uses.
Candidate sources are evaluated against those requirements. The assessment records supported uses, material bias or coverage gaps, access steps and downstream preparation needs. The result is a defensible sourcing plan with a clear basis for each recommendation.
When a selected source needs ingestion, harmonization, multimodal integration or a new technical environment, that work is scoped as a clear handoff to the relevant engineering service.
What we help you do
Map your data landscape
Inventory what internal data exists, what public datasets are relevant, and what third-party partnerships your program needs – before committing to a data architecture.
Integrate public repositories
Connect TCGA, DepMap, CCLE, JUMP-CP, OpenTargets, and others as governed, versioned data products – not one-off downloads.
Establish third-party data access
Scope and manage data-sharing agreements with UK Biobank, Genomics England, Tempus, and other curated sources; build the ingestion pipelines to make that data usable.
Govern and unify internal data
Bring proprietary experimental, clinical, and compound data under a single governed framework – queryable across programs rather than locked in project-specific silos.
Match data to the scientific question
Assess whether available data modalities and coverage support the hypothesis being tested – before modeling begins.
Build on the right infrastructure
Select and configure Google Cloud, AWS, or Databricks based on your workload, compliance requirements, and team environment.
Three source categories, one feasibility view
Internal
data
Inventory proprietary experimental, assay, compound, screening, imaging, omics and relevant clinical assets. Record ownership, location, provenance, usage constraints and the metadata required to judge whether each asset can support the program.
Assay results
Compound libraries
Screening outputs
Clinical trial data
Imaging datasets
Omics profiles
Public
data
Evaluate open and controlled-access repositories relevant to the question. Examples may include DepMap, CCLE, TCGA, Open Targets, GEO, JUMP Cell Painting or population resources, depending on the required modality and access conditions. Repository names should appear on the live page only when their use is accurate for the offer.
CCLE
DepMap
TCGA
OpenTargets
PheWAS
JUMP-CP
Licensed and partner
data
Define the scientific, legal and technical requirements for third-party data. Ardigen can review the provider landscape, formulate feasibility questions, document permitted-use requirements and delivery specifications, and coordinate the access process. Any statement about a license or provider relationship requires prior verification and approval.
UK Biobank
Genomics England
Tempus
IBD Plexus
Built on proven cloud platforms
Scalable storage, BigQuery analytics, and Life Sciences APIs for genomics workloads – on HIPAA-eligible infrastructure.
S3, Redshift, and HealthLake for large-scale biomedical storage, processing pipelines, and secure data sharing.
A unified data and AI platform for large-scale engineering, feature stores, and governed data products across distributed teams.
Selected data modalities
Ardigen sources and integrates data across the full range of biomedical modalities relevant to AI-driven drug discovery
Genomics
Transcriptomics
Proteomics
scRNA
Spatial transcriptomics
Phenotypic profiling
Biomedical imaging
Drug structure registration
High-throughput screens
Clinical & unstructured records
Assess your data sourcing gaps.
Concrete, reusable deliverables
Data requirements brief. Decision-linked requirements for modalities, entities, labels, metadata, coverage, quality and permitted use.
Source landscape and shortlist. Relevant internal, public and third-party candidates with rationale for inclusion or exclusion.
Dataset feasibility matrix. Scientific fit, known limitations, provenance, access conditions, update cadence and downstream preparation needs.
Access and partnership plan. Required applications, approvals, licensing questions, delivery expectations and stakeholder dependencies.
Engineering handoff specification. Source interfaces, expected formats, metadata and acceptance criteria for the next implementation stage.
Outcomes the service is designed to support
Better data investment decisions
Teams can compare sources against the actual scientific and analytical need before committing budget or engineering time.
Fewer late feasibility
surprises
Access restrictions, coverage gaps, bias and missing metadata are identified before they block modeling or validation.
A cleaner route to implementation
The selected source arrives with documented expectations for ingestion, harmonization, integration and governance.
Clear provenance from the start
Source, version, permitted use and known limitations remain attached to the sourcing decision.
Frequently Asked Questions
How do you decide whether a dataset is relevant?
We start with the scientific decision and required evidence. Candidate sources are compared on modality, cohort or experimental coverage, labels, metadata, quality, bias, provenance, permitted use and expected preparation work.
Can Ardigen help with controlled-access or licensed data?
Yes, within an agreed scope. Support can cover feasibility, provider questions, access requirements, technical delivery expectations and coordination with client legal, privacy and governance teams. Access proceeds after the required approval or agreement is in place.
How is data sourcing different from data harmonization?
Sourcing identifies and secures fit-for-purpose data. Harmonization standardizes and curates data after it is available. The sourcing deliverable defines what should be obtained and the implementation handoff; the Data Management page owns the harmonization service.
Does the service include ingestion and integration?
The sourcing engagement can define and scope the work. Technical ingestion, cross-source harmonization and multimodal integration are delivered through the relevant engineering services, with separate ownership, estimates and acceptance criteria.
Can you assess whether our existing data is sufficient for an AI use case?
Yes. The assessment can compare current assets with the coverage, labels, metadata and quality required by the selected use case, then identify the additional data that would materially change feasibility.
When should a program start with AI and data strategy consulting?
Start with AI & Data Strategy Consulting when the organization still needs to prioritize use cases, define governance, assess readiness or agree an implementation roadmap. Start with Data Sourcing & Partnerships when the scientific question is defined and the immediate need is to identify, assess or access suitable datasets.
Related capabilities
Contact
Talk to our team about your data sourcing needs
Every discovery program depends on data that was sourced, connected, and governed before modeling began. If your current data foundation doesn’t match the scientific questions your programs are asking, the gap is addressable.
Send us a message and we will contact you back within 48 hours.
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