Research Data Management (RDM)

Unlock the full potential of your research data with our Engineering Services

With the advent of machine learning and AI, data has proven to be a major asset for decision making and exploring scientific hypotheses. However, leveraging data is still not an easy task, especially across department and unit boundaries due to several reasons: Relationships between data are often missing or hard to discover, metadata is inconsistent between different sources and is sometimes missing or incorrect, data provenance is not always established. Our RDM team will help you to organize and manage your data so you can focus on the research questions and data analysis that you care about.

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What we offer

Our RDM Professional Engineering Services include

Semantic Modelling

A semantic model is a formal specification of entities (such as genes, proteins, and assays) and relations between them, in a given domain. In contrast to other types of data models, it specifies the meaning, or semantics, of domain-specific concepts.

Having a semantic model will enable you to standardize and harmonize structured data so that it is comprehensible for both humans and machines. It is an essential component to make your data more FAIR and it facilitates data integration, within and across R&D data domains.

Knowledge Graphs

A knowledge graph typically connects entities from multiple domains, with formal semantics provided by a semantic model. It serves as a flexible framework for data integration, harmonization, analytics, and sharing of data from multiple sources.

A knowledge graph contains data from multiple sources organized as a graph that can be queried and navigated in a uniform manner.

The Hyve designs and builds knowledge graphs that may cover distinct biomedical domains, typically from the R&D pharma departments such as: small and large molecule research, omics, clinical, manufacturing etc..

FAIR by Design solutions

Design your own scalable research data management solution. The Hyve provides software engineering services to build or customize open source tools and enable integrations with existing data sources, in order to support knowledge creation and collaboration.

We created a platform - Fairspace - that can be used by data stewards to design data models, and by researchers to annotate and publish their existing research data as digital FAIR data objects. We also have the capabilities and knowledge of using other open source data management tools such as Gen3, iRODs and COLID.

RDM Science Community Initiatives

The Hyve is a member of Pistoia Alliance and FAIRplus

Pistoia Alliance is a global, not-for-profit, members’ organization with more than 100 members collaborating to lower barriers to innovation in life science and healthcare R&D. PA projects intend to help to overcome common obstacles to innovation and to transform R&D. The Hyve is a member of the alliance and contributes to the FAIR principles initiatives within the community.

The FAIRplus consortium develops tools and guidelines to make life science data more FAIR. The project has 22 partners from academia and industry, and runs from January 2019 to December 2022. The Hyve continues to contribute to the FAIRification of IMI datasets, the FAIRplus data management plan and the FAIR cookbook recipes.

Are you facing these challenges?

  • Data is difficult to find for researchers
  • Users are unable to access the data which is managed by others.
  • Data does not integrate easily and is unable to reuse. Data formats, encoding and semantics are unclear, not specified or not harmonised.
  • The process to query and retrieve data is complicated. Data analysis scripts and dashboards are difficult to create or reuse.

Let's collaborate to overcome these challenges!

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