Consolidating data using metadata guadeloupe friendship online dating site com

Posted by / 12-Mar-2020 02:49

Consolidating data using metadata

The FAIR data principles are guidelines that define the criteria for achieving shareable and reusable data.

However, this still leaves the details of how to actually achieve this in practice.

Biomedical informatics has traditionally adopted a linear view of the informatics process (collect, store and analyse) in translational medicine (TM) studies; focusing primarily on the challenges in data integration and analysis.

However, a data management challenge presents itself with the new lifecycle view of data emphasized by the recent calls for data re-use, long term data preservation, and data sharing.

From its inception to its use and completion, research data will likely undergo multiple transformations in its format, application, use, and perhaps even its purpose.

Data lifecycle management focuses on the data itself as assets and acknowledges that managing data requires managing its lifecycle.

Furthermore, there is an increasingly widespread recognition that data could potentially be reused and repurposed to support new areas of research.

This framework is designed to incorporate different types of metadata models, i.e.

There is currently a lack of dedicated infrastructure focused on the ‘manageability’ of the data lifecycle in TM research between data collection and analysis.

Current community efforts towards establishing a culture for open science prompt the creation of a data custodianship environment for management of TM data assets to support data reuse and reproducibility of research results.

This lays the foundation to our approach to identify and describe component data management services and resources that are necessary for data custodianship.

We also use this approach to define the metadata necessary to manage data at each stage, which we developed into a metadata framework discussed below. Each stage has its own data form, data service (top blue boxes) and data storage resource (bottom grey boxes).

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Leaving data management planning as an afterthought severely reduces the long term value of research data assets and limits the potential for reuse.

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