Recommendations on standardization of datasets
Service Description
Overview
This service delivers specialized technical guidance on data handling, data architecture, and dataset standardization tailored for digital health applications. Proper data structure is critical when training and validating robust machine learning algorithms. Through tailored evaluations, our experts review your existing data collection workflows and propose unified schemas, interoperable formats, and clean data storage models. The primary objective is to help organizations eliminate data silos, streamline machine learning pipelines, and ensure their medical datasets meet modern data management standards such as making research data findable, accessible, interoperable, and reusable (FAIR).
How can the service help you?
Disorganized, non-standardized datasets introduce training errors, inflate pre-processing overhead, and create regulatory hurdles during product validation. This service establishes a solid structural foundation for your data engineering efforts.
- Before: Fragmented data stored in non-standard formats with inconsistent metadata, requiring extensive manual cleaning before every machine learning experiment.
- After: A clean, optimized data infrastructure plan aligned with machine learning requirements and data governance standards.
It equips engineering and clinical teams with concrete recommendations to optimize data retrieval, improve model scalability, and ensure long-term data reusability.
How will the service be delivered?
The service is conducted remotely through structured technical consultations and document reviews. Clients share their proposed data collection plans, sample schema files, or description of existing database architectures. After analysing the setup, our experts deliver a comprehensive data standardization report and review findings.
Additional information
Provider description
Operating from the Department of Artificial Intelligence in Biomedical Engineering at Friedrich-Alexander-Universität Erlangen-Nürnberg, we are a service provider node within the TEF-Health consortium. The research group specializes in machine learning, biomedical signal processing, and multimodal sensor synchronization. Our team provides testing infrastructure and scientific support for evaluating medical devices, wearables, and contactless sensing systems.
Technical details
Our recommendations leverage established open-source data formats, healthcare interoperability guidelines, and FAIR data principles.
- Input Data Requirements: Proposed data management plans, storage schemas, API documentation, or anonymized sample datasets.
- Core Tasks & Processing: Metadata audit, data model mapping, analysis of storage/retrieval speed, evaluation of machine learning feature compatibility, and FAIRness compliance review.
- Outputs: A structured data infrastructure proposal containing dataset standardization guidelines, recommended metadata schemas, and actionable pre-processing recommendations.
Service customization
Clients can tailor the advisory scope based on their current stage of development. Startups and SMEs can opt for a high-level review of a single collection plan or request an in-depth infrastructure audit covering multi-modal sensor inputs, database pipelines, and machine learning storage architectures.
Provider & Contact
The final price will be determined in the contract between the service provider and the applicant.