We turn unstructured public and proprietary data into analysis-ready, FAIR* datasets: quality-controlled, annotated, standardized and traceable.
Our experts support the data foundation of your project by:
- extracting, reformatting and migrating biological and biomedical data between systems, to ensure data consistency and query traceability;
- ensuring the long-term management and storage of biodata;
- reaching data interoperability targets through data annotation, data standardization and federated databases – from local to international scales and within regulated or academic environments (see more on our sensitive data expertise);
- defining and implementing Data Management Plans (DMP) for research proposals and funding applications.
*FAIR: Findable, Accessible, Interoperable and Reusable
Making your data FAIR to let you focus on what you do best: research and discovery
The challenge
Companies, universities, funders, datasets managers, and publishers increasingly expect data to be FAIR, to improve the reliability and innovation potential of data analysis through well-annotated data and reproducibility.
Our solution
Whatever biological or biomedical data you have, we have a proven expertise in making them FAIR and packaging them in a publication-ready way. This is what we do as part of a number of endeavours, such as several European projects on diabetes, cancer, arthritis, as well as other projects dealing with multiple -omics or data from diverse biological origin (e.g. plants, bacteria, fungi).
Harmonizing your datasets to advance healthcare
The challenge
Internal resources are lacking to standardize multiple datasets in order to aggregate them.
Our solution
We organize large databases containing routine health information, clinical trial or research datasets to enable merging them with public datasets.
Supporting the identification of anti-infective and pro-metabolic natural products
The challenge
Integrate large volumes of heterogenous in silico and chemo-biological data to enable their sharing between partners and subsequent data analysis.
Our solution
We build an RDF database that integrates and centralizes data from all partners, and allows data management and sharing following FAIR principles. We enable access of all partners to the resulting database through the development of a user-friendly web page. This is what we are doing as part of a Sinergia project led by the University of Geneva and the ETH Zurich for instance, with the creation and mining of a library of more than 15,500 plants to find new molecules to treat tuberculosis infection as well as obesity and their associated co-morbidities.
