Your DRE, your way.

Users Included

The Aridhia DRE provides flexible user licensing, tailored to organisational needs. Our single-user Project plan is ideal for individual researchers conducting pilot studies, whereas larger research groups can opt to license on a per-team or per-user basis, and our highest tier allows unlimited users for enterprise deployments supporting large-scale global collaborations.

For biomedical and life sciences organisations, this scalability means that a small lab can begin exploring datasets independently, and as collaborative needs grow (such as when running multi-site clinical trials), the platform seamlessly accommodates expanding teams. This ensures data governance and security remain consistent regardless of scale.

Number of Workspaces

Workspaces are secure, isolated analytical sandboxes where research teams collaborate on sensitive data. The Aridhia DRE offers one workspace on our entry-level plan making it ideal for a smaller-scale research project, multiple workspaces for Premium subscriptions enabling parallel research streams, and unlimited workspaces for Enterprise customers.

This enables compartmentalised research (such as running separate workspaces for genomics analysis, clinical trial data, and biomarker discovery) while maintaining strict data governance between projects. Each workspace operates as an independent trusted research environment with its own storage, database, user permissions, and audit trail, allowing data controllers to maintain complete oversight while enabling researchers to work efficiently on distinct scientific questions without cross-contamination of sensitive datasets.

Native Workspaces Features

Workspaces have a number of tools and features built-in, which form the foundational analytical capability of the DRE. They provide researchers with immediate access to data management, collaboration tools and analysis capabilities, without requiring additional software installation. This includes workspace file organisation, data table creation wizards that automatically detect data types and preserve metadata, activity tracking for team collaboration, audited inbound and outbound airlocks for secure data transfer, and built-in statistical modules offering over 20 data science methods (including logistic regression, ANOVA, and chi-squared tests).

These native features mean that upon receiving approved access to a dataset, approved users can immediately begin exploratory analysis, generate preliminary statistics and collaborate with colleagues. All of this takes place within a secure, compliant environment that maintains complete audit trails for regulatory purposes.

R Development Environment

The R Development Environment is a fully-featured IDE running natively within secure workspaces, providing biomedical researchers with familiar tools for statistical analysis, visualisation and reproducible research. Pre-installed with extensive packages commonly used in life sciences (including those for survival analysis, genomics, and clinical trial statistics), the environment connects directly to workspace file systems and databases, ensuring data never leaves the secure boundary.

For researchers analysing clinical or genomic datasets, or developing predictive models, this means they can write and execute R code, create publication-quality visualisations, build web applications for interactive data exploration and maintain version-controlled scripts via integrated Git repositories. Meanwhile, the Aridhia DRE ensures full compliance with data governance requirements and maintaining reproducibility standards expected in peer-reviewed research.

Jupyter Lab

Jupyter Lab provides an interactive computational environment supporting Python, R and other languages, enabling researchers to combine executable code, rich text documentation and visualisations in a single document. This is invaluable for creating reproducible analysis pipelines, documenting each step of data preprocessing, statistical analysis and machine learning model development, in a format that can be easily shared and replicated by collaborators.

The notebooks connect directly to workspace data storage and databases. They support background execution for long-running analyses, as well as integrating with machine learning frameworks for developing predictive models for applications (such as tumor recurrence prediction or drug response analysis) while ensuring that sensitive patient data remains within the secure DRE boundary.

Collabora Office

Collabora Office integration enables researchers to open, view, and edit Microsoft Office documents (Word, Excel, PowerPoint) directly within workspaces without requiring local software installation or data export.

This addresses a critical workflow need as research protocols, data dictionaries, clinical study reports and regulatory submission documents frequently arrive as Office files alongside raw data. With Collabora, a researcher can review a clinical trial protocol, annotate a data dictionary or edit a manuscript draft, all without breaking the secure workspace boundary. As a result, it eliminates the security risk of downloading sensitive documents to local machines, while enabling seamless collaboration on documentation that accompanies research data.

Tooling Compute Cluster

Compute cluster configuration ranges from shared clusters on our SaaS DRE plans to dedicated clusters for enterprise customers requiring guaranteed computational capacity and isolation. Shared clusters provide cost-efficient access to scalable computing, with resources dynamically allocated based on demand across multiple private workspaces.

Alternatively, dedicated clusters can be deployed to ensure that computationally intensive research, such as large-scale genomic analysis, imaging AI model training, or clinical trial simulations, receives guaranteed resources without contention from other projects. For pharmaceutical companies running drug discovery computations or research hospitals developing clinical AI applications, dedicated compute clusters provide the predictable performance and isolation required for time-sensitive research and regulatory-grade reproducibility.

Hosting Deployment

The Aridhia DRE offers flexible hosting deployment options. These range from multi-tenant SaaS hubs where multiple organisations share optimised infrastructure for cost-effective access, to dedicated single-tenancy hubs providing complete isolation for enterprises with stringent security or regulatory requirements. For healthcare and life sciences organisations handling Protected Health Information (PHI), genetic data, or pharmaceutical intellectual property, dedicated single-tenancy deployment ensures that infrastructure, encryption keys, and administrative access are entirely segregated from other customers.

The platform operates on Microsoft Azure, with hosting available in multiple regions including North America, UK, Europe, and Australia, enabling organisations to meet data residency requirements under regulations like GDPR while benefiting from Azure’s enterprise-grade security certifications.

Storage Size for Files

File storage provides organised, hierarchical storage for research data files within workspaces. Our file storage options scale from 50GB for Project tier to 5TB for Premium and up to >PB (petabyte) levels for Enterprise deployments.

When working with high-dimensional data such as genomic sequencing files, medical imaging datasets or longitudinal clinical records, adequate file storage is essential. The storage is integrated with workspace tools, meaning R scripts, Python notebooks and analytical applications can directly read and write files without complex data transfer processes. Files pass through audited airlocks when entering and/or leaving workspaces, ensuring complete traceability of data movement while also providing the capacity needed for modern multi-modal research combining clinical, genomic, imaging, and biobank data.

Storage Size for Blobs

Blob storage provides capacity for large, unstructured data objects. This starts at 1TB on the Project plan, up to 5TB on Premium, scaling all the way to >PB (petabyte) levels for Enterprise customers. This storage space is designed for massive datasets such as raw sequencing files, medical imaging archives (CT, MRI, PET scans), digital pathology images and video data.

For precision oncology programs or neuroimaging consortia, blob storage accommodates the enormous data volumes generated by modern biomedical research (a single whole-genome sequence can exceed 100GB, and comprehensive imaging studies multiply this substantially). The blob storage integrates with workspace analytical tools and supports efficient streaming access, enabling researchers to process large datasets without the delays and security risks associated with moving data between systems.

Storage Type

Configurable storage enables organisations to architect data infrastructure matching their specific research profile, balancing high-performance storage for frequently accessed analytical datasets against cost-optimised storage for archival data. Our storage type configurations range from small to extended pools on our SaaS-level offerings, through to fully configurable hybrid arrangements for Enterprise deployments.

For those with diverse data portfolios spanning active clinical trials, historical study archives and biosample inventories, hybrid storage configurations ensure that current research projects have immediate access to high-speed storage, while maintaining cost-effective long-term retention of valuable historical datasets that may inform future research.

Security Compliance

We’re deeply committed to the protection, privacy and the security of your data. As such, data within our platform is carefully looked after, and we’re very open about what provisions we have put in place to ensure all data is protected to help you meet your compliance obligations under UK and EU law, international standards, and sector-specific standards.

The Aridhia DRE maintains comprehensive security certifications, including ISO 27001 for information security management, ISO 27701 for privacy information management, HITRUST certification for healthcare data protection and Cyber Essentials Plus. The platform is designed for compliance with GDPR data protection requirements and HIPAA regulations for Protected Health Information.

Aira (AI Research Assistant)

Aira is a secure, offline Large Language Model (LLM) framework embedded within the Aridhia DRE,including ISO 27001 for information security management, ISO 27701 for privacy information management, HITRUST certification for healthcare data protection and Cyber Essentials Plus. The platform is designed for compliance with GDPR data protection requirements and HIPAA regulations for Protected Health Information. These certifications provide assurance that research involving sensitive patient data meets the highest security standards, which is essential for institutional review board approvals and regulatory submissions, as well as maintaining trust with data contributors.

Aira accelerates development by assisting with code generation for R, Python and SQL analytics, helping both novice and expert users rapidly develop analytical pipelines. With OpenAI-compatible APIs, dynamic GPU/CPU scheduling and support for custom models, Aira enables applications from clinical trial analysis to genomic research annotation, all while maintaining complete audit trails, ISO/HIPAA/GDPR compliance, and absolute data sovereignty.

Local Back-ups

Local back-ups provide automated data protection within the workspace environment, maintaining rolling backups that enable recovery from accidental data loss or corruption. For research teams working on extended analysis projects, local backups ensure that weeks or months of analytical work (e.g. scripts, derived datasets, intermediate results) remain protected against inadvertent deletion or system issues.

Workspaces maintain 14-day rolling backups, enabling rapid recovery of workspace contents without requiring formal data restoration procedures. This protection layer is essential for biomedical researchers where lost analysis work could delay critical research timelines or compromise the reproducibility of findings approaching publication or regulatory submission.

Remote Back-ups

Remote back-ups extend data protection beyond the primary workspace environment, replicating critical data to geographically separated storage for disaster recovery and business continuity purposes. For healthcare and life sciences organisations with regulatory obligations to protect research data, remote backups provide assurance that even catastrophic infrastructure failures will not result in permanent data loss.

This is particularly important for organisations managing irreplaceable datasets (e.g. clinical trial records, longitudinal cohort data, biobank sample information), where data loss would have serious research and, potentially, patient-safety implications. Remote back-up options ensure organisational data resilience appropriate to the criticality of research programs.

Virtual Machine Allowance

Virtual Machines (VMs) enable researchers to deploy specialised analytical software, run computationally intensive processes, and access computing resources beyond standard workspace infrastructure. VM allowance provides dedicated compute capacity within workspaces. Not included in the Project tier, allowance ranges from 200 hours on Premium to an unlimited allocation for Enterprise customers.

Developing machine learning models requires running complex statistical simulations, or processing genomic data through bioinformatics pipelines, VM allocation determines the capacity for intensive computational work. To optimise costs, VMs automatically shut down outside working hours by default, ensuring organisations pay only for actual research activity while maintaining the flexibility to scale computing power as research demands require.

Virtual Machine Type

VM types range from dedicated Linux VMs to specialised Data Science Virtual Machines (DSVMs) pre-configured with comprehensive analytical toolkits. The Azure DSVM comes pre-installed with machine learning frameworks (TensorFlow, PyTorch), development environments (VS Code, PyCharm, Jupyter Lab), bioinformatics tools, and the MLflow SDK for experiment tracking.

DSVMs provide immediate access to the complete toolkit needed for advanced analytics. Researchers can develop deep learning models for medical image analysis and run bioinformatics pipelines for genomic data, without the complexity of software installation and configuration. For Enterprise users, flexibility is provided via Windows and Linux options to accommodate different researcher preferences and software compatibility requirements.

Virtual Machine Compute

VM compute options scale from fixed Linux configurations through to GPU machines for more demanding workloads. This allows researchers to match processing power to analytical requirements by using standard configurations for routine data processing, then scaling up for computationally demanding tasks. Examples of these would include training neural networks, running genome-wide association studies, or processing large imaging datasets.

For an organisation such as a precision medicine program analysing multi-omic data, unlimited compute allocation removes bottlenecks that could otherwise delay research timelines. This enables rapid iteration on analytical approaches, and supports the computational demands of modern biomedical AI/ML applications.

Deploying your own Containerised Apps

Container deployment capability enables researchers to package and run their own custom applications within workspaces using Docker containerisation technology. This means proprietary analytical tools, custom bioinformatics pipelines, drug development tools (DDTs) and specialised visualisation applications can be deployed within their trusted research environment.

A genomics team might deploy a custom variant calling pipeline, while a clinical informatics group runs specialised phenotyping algorithms, all within the security of private workspaces. This extensibility transforms the Aridhia DRE from a fixed analytical platform into a flexible research infrastructure that adapts to the specific methodological requirements of diverse biomedical research programs.

Sharing your Containerised Apps within your Organisation

Organisational app sharing allows custom-built containerised applications to be distributed across workspace within an enterprise deployment. This creates an internal app catalogue tailored to organisational research needs, with access to workspace native features such as shared files, database and audit. Thus, analytical methods can be standardised. For example, a validated drug response prediction tool developed by one team can be formally reviewed, approved, and made available to all researchers across the organisation.

This promotes methodological consistency, reduces duplication of development effort, and ensures that best-practice analytical approaches propagate across research programs, while maintaining the security and audit controls essential for regulated research environments.

FAIR Data Catalogue

Dedicated FAIR Data Services provides organisations with their own instance of our comprehensive metadata catalogue, data discovery, and data access management system. FAIR (Findable, Accessible, Interoperable, Reusable) Data Services enables data owners to create rich metadata catalogues describing their datasets, with customisable templates, data dictionaries and governance workflows.

FAIR provides a secure space for data owners to make datasets discoverable to authorised researchers, while maintaining complete control over access policies. Features include AI-powered semantic search for intuitive data discovery, cohort builder tools for visualising and subsetting datasets before requesting access, as well as configurable data access request (DAR) workflows that can accommodate complex multi-stage approval processes required by institutional review boards and data governance committees.

Automated Data Request Workflows

Governed Data Request Workflows provide automated, auditable processes for requesting, reviewing, and approving access to sensitive datasets. Built into FAIR via an integrated business process management engine, these support complex multi-stage approval processes. Workflows can include auto-approval configuration, screening stages, data usage committee reviews, and data usage agreements, along with features like quorum rules, reminder timers and automatic escalation.

This addresses the fundamental challenge of enabling data sharing while maintaining governance. Researchers can discover datasets of interest, submit structured access requests with project details and intended uses, and have those requests routed through appropriate approval chains. Data owners maintain complete control over their data, with full audit trails of all access decisions supporting regulatory compliance and demonstrating responsible data stewardship to data contributors.

Federated Analysis

The Federated Node is an open-source component developed by Aridhia, which enables organisations to participate in federated data analysis networks while maintaining complete control over their data. Deployed within the data holder’s own infrastructure, the Federated Node accepts approved analytical queries, executes them against local data, and returns only aggregate results while the underlying patient-level data never leaves the secure environment.

For hospitals, research institutions, and pharmaceutical companies seeking to contribute to multi-site studies without exposing sensitive data, the Federated Node can be integrated with the Aridhia DRE to provide the technical infrastructure for trustworthy participation. Premium and Enterprise users benefit from the opportunity to join federated networks, find out more about Federation in the DRE here.

Federated Learning

Aridhia have partnered with Flower.ai to provide out-of-the-box federated learning support for Premium and Enterprise customers. A flexible deployment approach allows for the use of Flower SuperGrid or an Aridhia DRE-hosted SuperLink for federated learning aggregation.

Workspaces contribute to the network as nodes, ensuring models are trained securely across organisations while data remains in situ.

Custom Portal Page

Available for Premium and Enterprise customers, our portal pages provide a customised entry point to the DRE, featuring tailored branding and navigation to relevant Workspaces and FAIR Data Services instances. This creates a professional, on-brand data research environment that reflects organisational identity, while providing users with streamlined access to relevant resources.

The portal serves as a central landing point, where researchers can authenticate via single sign-on. They can then discover available datasets, access their workspace projects and engage with the research community, all within a cohesive interface that reinforces ownership of the research environment and simplifies user orientation.

White-label Service

Our white-labeling service is perfect for Enterprise customers who wish to fully brand their research environment with their own identity, using their own logos, colors, terminology and domain names. This creates the appearance of a proprietary institutional research platform, while benefiting from Aridhia’s enterprise-grade infrastructure and continuous development and support.

For prestigious research institutions, teaching hospitals and pharmaceutical companies, white-labeling supports brand consistency and institutional identity without the substantial investment required to build and maintain comparable infrastructure internally. These customers are able to present their own branded platforms as purpose-built institutional resources while leveraging the full capabilities, security certifications, and ongoing development of the underlying Aridhia DRE platform.

Platform Service Desk

Premium and Enterprise customers receive dedicated Service Level Agreements (SLAs) with guaranteed response times, resolution commitments for technical issues as well as defined escalation paths, ensuring critical issues receive prioritised attention. This enables research programs to maintain momentum despite occasional technical challenges, so time-sensitive research (e.g. clinical trials with enrolment deadlines, regulatory submission timelines, grant-funded projects with fixed schedules) can rely on predictable support responsiveness, which is operationally essential.

Aridhia’s experienced Service Desk handles extensive support volumes across our global customer base, providing assistance ranging from routine data access questions to complex infrastructure configurations, so research teams can keep their focus on scientific questions rather than technical troubleshooting.

Hybrid Compute

Hybrid Compute allows researchers to securely offload computational workloads to alternative infrastructure outside the Aridhia DRE. Making use of existing investments in on-premises systems or other cloud resources, this flexibility means research teams can leverage specialist hardware, institutional HPC clusters, or dedicated GPU resources for intensive tasks like machine learning model training or large-scale genomic analysis, while maintaining the security, governance, and audit capabilities of the Aridhia DRE.

Hybrid Compute ensures organisations get maximum value from their existing infrastructure investments without compromising on compliance or data protection.

Data Integration Experts

Dedicated data integration experts provide specialised consulting support for complex data engineering challenges, such as ETL pipeline development, data model transformation, system integration, bespoke application development and analytical workflow optimisation.

Data integration is frequently a critical bottleneck. Electronic health records require transformation into research-ready formats, clinical data needs to be mapped to common data models (such as OMOP, CDISC, FHIR), genomic data gets integrated with clinical phenotypes, and automated pipelines are established from source systems to research workspaces.

Network Controlled Access

Network-restricted workspaces provide specialised environments for data transformation and quality assurance, as well as any preparation activities that must occur before datasets are ready for research use and require further restrictions on access from a dedicated enterprise VPN.

For organisations contributing data to collaborative networks, our network-restricted workspaces enable data preparation and sensitive data research to remain within the boundaries of the trusted research environment. Find out more on our Workspaces page.

Single Sign-On

Single Sign-On (SSO) integration enables users to authenticate to the Aridhia DRE using their organisational credentials, typically through integration with enterprise identity providers such as Azure Active Directory or institutional authentication systems. SSO simplifies user management (new researchers gain access through standard institutional provisioning), enhances security (authentication policies are centrally managed), and improves user experience (researchers don’t need separate credentials for the research platform).

SSO also enables sophisticated arrangements like cross-platform authentication between related TRE deployments and external systems, as demonstrated by the single sign-on connection between the European Platform for Neurodegenerative Diseases (EPND) and the AD Workbench (ADDI), enabling researchers to seamlessly move between related resources.

Advanced Cloud Services

By integrating advanced Azure services, customers have access to sophisticated machine learning operations (MLOps) workflows within the Aridhia DRE. Workspace environments connect with Azure Machine Learning for scalable model training, experiment tracking, and production deployment.

For life sciences organisations developing AI/ML applications (e.g. drug response prediction, medical image analysis, clinical risk scoring), MLOps integration provides the infrastructure for professional-grade model development. This covers experiment tracking via MLflow, access to scalable GPU compute clusters, version-controlled model registries and deployment pipelines.

The integration maintains the DRE’s security boundary while enabling researchers to leverage Azure’s enterprise ML infrastructure, supporting the complete lifecycle from experimental development through to validated production models suitable for clinical application or regulatory submission.

3rd Party Platform Integrations

A range of third-party platform integrations are available, extending DRE connectivity to external systems including business intelligence tools (Qlik, PowerBI), data visualisation platforms, clinical data standards tools (OHDSI ATLAS for OMOP exploration), and other specialised research applications. For customers with established analytical toolchains, integrations ensure the Aridhia DRE complements rather than replaces existing investments.

Researchers can push data to familiar visualisation tools, connect to institutional reporting systems or leverage specialized domain applications, while maintaining the security and governance framework of the Aridhia DRE. Integration capabilities also support connections to external data sources, enabling automated data pipelines from electronic health record systems, laboratory information systems and external research databases into secure workspace environments.

XNAT Hosting

XNAT hosting provides integrated support for the XNAT imaging informatics platform, which is an open-source system widely used in biomedical imaging research for archiving, processing, and securely distributing medical imaging data.

For research programs working with radiology images, digital pathology or neuroimaging, XNAT integration enables sophisticated imaging workflows. These include automated DICOM import with de-identification, cohort selection and extraction, image processing pipelines, and secure delivery of imaging data to DRE workspaces for integrated analysis with clinical and genomic data.

Reviewer Users

When carrying out research in a Workspace, a common requirement is for a guest user to be able to view analysis results. Our review user functionality enables organisations to grant limited read-only access to external collaborators for review and comment purposes.

The number of reviewer users available is dictated by the customer’s tier of Aridhia DRE subscription, with Project workspaces having one permitted reviewer user and the Premium and Enterprise tiers being unlimited. As such, customers on a Project plan are able to have their work reviewed on a small scale, and customers with a larger-scale operation are able to structure their reviewer role allocations as required.