DataHub – The Digital Platform for Research and Study Data

In modern clinical trials, data from various sources—study sponsors, CROs, laboratories—and a wide range of analytical systems all converge. What remains are Excel spreadsheets, data silos, and manual processes that hinder collaboration, impede reproducibility, and delay results.

As a central platform, DataHub brings order to your clinical trial data.

Consistent data from the start to the end of the study

Modern studies examine numerous parameters that are collected by various specialized laboratories. This requires close cooperation between sponsors, contractors, CROs, and analytical laboratories. The resulting data is available in various formats and must be consolidated, stored in a structured manner, and evaluated—all in a way that is traceable and auditable at all times.

In practice, however, Excel spreadsheets, proprietary formats, and exported text files dominate, which are often combined manually or only partially automated. This approach is time-consuming, error-prone, and barely scalable.

DataHub puts an end to this Excel chaos.

As a central platform for study data, DataHub enables smooth collaboration among all parties involved – from the beginning of the study to its completion. Data collection is structured and uniform, regardless of source or format. Changes during the course of the study, e.g., in evaluation criteria or evaluation logic, can be flexibly mapped and are documented transparently with versioning.

This creates a reliable, consistent database as the basis for robust study results. DataHub offers comprehensive functions for analysis and visualization – from classic statistical methods to AI-supported evaluation of complex data patterns.

Versatile Functions & High Expandability

DataHub provides you with the most essential features for the structured collection and evaluation of study data. The platform can be flexibly adapted to your studies.

Study Database – Central management of studies, study participants, and users
Patient Visit Coordination Portal – Management of subjects, multicenter studies, and subject appointments
Individual Configuration of Study Structure – sites, subjects, visits, laboratory, etc.
Survey Module – creation and evaluation of medical history forms and study-related questionnaires
Secure & Independently Hosted – simple on-premise operation with Docker installation, full control over sensitive data, role-based user management
Traceability & Audit Trail – Every analysis step documented and versioned, ideal for non-linear processes such as adjustment of data selection limits (gating) in flow cytometry
Analysis & Visualization – Integrated statistical evaluations and visualizations
AI-Supported Analytics – Data analytics based on voice input, reproducible analysis scripts that can be refined manually
Human & Machine Labeling – Manual and AI-supported annotations for images, videos, and time series
Open & Expandable – Modular architecture on an open source basis – flexibly adaptable to new studies, data formats, and analysis methods

DataHub for Industrial Companies

Immatics – Immunomonitoring and Multicenter Studies
Complex FACS data had to be analyzed consistently despite varying selection criteria, and all adjustments had to be documented in a traceable manner. At the same time, centralized donor management across multiple study centers was required.

Microcoat – Sample Data and Automated Reporting
The manual management, verification, and analysis of sample data were time-consuming and increased the risk of errors. Data imports, quality checks, analyses, and customer reports were largely automated.

Acousia Therapeutics – Clinical Trial Data and Local AI
Sensitive clinical data on listening ability had to be securely managed and flexibly analyzed using statistical and AI-based methods. Complete local processing ensures control over all research data.

DataHub in Research Projects

HOLOPROTEOME – Imaging Data and AI-Based Tumor Diagnostics
Imaging data from various modalities and medical annotations had to be accurately aligned and prepared for AI development. This enabled end-to-end processing, from data acquisition to the automatic detection of relevant tumor regions.

SYMPATHJA – Sensor-Based Motion Analytics
Large volumes of heterogeneous motion, sensor, and survey data had to be securely collected, synchronized, and jointly evaluated. An end-to-end infrastructure connects mobile data collection, automated processing, and AI-assisted analysis.

TruVac – Consolidation of Clinical and Analytical Data
To enable a detailed analysis of virus-specific immune responses, donor questionnaires as well as clinical and analytical data had to be centrally consolidated. The structured database allows for a joint evaluation of the various measurement and study data.