Unlocking the power of data for scientific discovery
Data Fairification makes research data fair, which translates into data that is accessible, findable, interoperable and reusable. This data can then, in turn, be made accessible to researchers for them to use it by combining it with their own data or re-use it in different contexts. Fairification of data leads to extensive knowledge sharing and creates improved opportunities for innovation.
Entity Extraction is used for identifying, extracting and classifying key data elements from text into pre-defined categories.
Data Lake Consulting is being increasingly used for improving analytics and for gaining business intelligence.
Transforming Data into Discovery with FAIR Principles
Enabling you to drive groundbreaking research, accelerate drug discovery, and deliver life-changing healthcare solutions.
Enhancing data discoverability, enabling scientists to easily locate and access relevant datasets for their projects.
Metadata and unique identifiers
Detailed metadata and permanent identifiers (e.g., DOIs) allow datasets to be found through search engines and repositories.
Centralized or distributed data repositories with effective search functionality promote the discoverability of datasets.
Adopting common terminologies and ontologies across domains aids in efficient data discovery and interoperability.
Indexing and ranking
Efficient indexing and ranking of datasets based on relevance, quality, and other factors help data scientists find the most suitable data for their research.
Conveniently obtain and use datasets, fostering collaboration and accelerating research progress
Clear access protocols
Establishing transparent and standardized access mechanisms, such as APIs or web services, simplifies data retrieval.
Authentication and authorization
Implementing secure, user-friendly authentication and authorization systems maintain data privacy while granting appropriate access.
Open and machine-readable formats
Providing data in open and machine-readable formats ensures compatibility across different platforms and tools.
Data licensing and usage policies
Clearly defined licensing terms and usage policies enable data scientists to understand their rights and responsibilities when using the data.
Facilitating seamless data integration and analysis, enabling users to efficiently combine and utilize diverse datasets
Standardized data formats
Adopting widely-used data formats and file types promotes compatibility and simplifies data exchange between systems.
Common vocabularies and ontologies
Using shared terminologies and data models enhances semantic interoperability and understanding across datasets.
Metadata schema and mappings
Employing consistent metadata schema and providing mappings to other related schema improves data discoverability and integration.
Data provenance tracking
Recording the lineage and history of datasets, including changes and transformations, supports data traceability and reproducibility.
leverage existing datasets and methodologies, maximizing resource efficiency and promoting reproducible research
Providing detailed documentation, including methodology, data processing steps, and variables, facilitates data comprehension and reuse.
Sharing transparent, reusable workflows and codebases enables data scientists to build upon existing work and maintain consistency.
Implementing version control for datasets ensures tracking of changes and supports long-term data maintenance and reuse.
Clear licensing and citation guidelines
Establishing well-defined licensing terms and citation practices acknowledges the work of data creators and encourages responsible data sharing and reuse.
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