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title: Our Research data management concept | ||
title: Our Research Data Management concept | ||
summary: "" | ||
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DataPLANT’s research data management (RDM) concept focusses on FAIR compliance, data and service interoperability, community participation, knowledge transfer, and long-term sustainability. | ||
It is tailored to the plant community and aims to establish sustainable RDM processes and habits. DatapPLANT sees RDM as a collaborative and continuous process. | ||
DataPLANT’s Research Data Management (RDM) concept focusses on FAIR compliance, data and service interoperability, community participation, knowledge transfer, and long-term sustainability. | ||
It is tailored to the plant community and aims to establish sustainable RDM processes and habits. | ||
DataPLANT sees RDM as a collaborative and continuous process. | ||
The goal is to transform static research artifacts into dynamic, growing research objects that are continuously extended, annotated, processed, analyzed, and managed alongside ongoing research activities, ensuring that research artifacts are produced in a FAIR manner. | ||
The core technology for DataPLANT’s strategy is the ARC. | ||
ARCs provide a set of guidelines for combining data, metadata, and workflows in a file system layout that is easy to understand and ensures that changes to data, metadata, or workflows are well-defined and consistent. | ||
The core technology for DataPLANT’s strategy is the Annotated Research Context (ARC). | ||
ARCs provide a set of guidelines for combining data, metadata, and workflows in a file system layout that is easy to understand and ensures that changes to data, metadata, or workflows are consistently documented. | ||
ARCs are based on international, open standards, like ISA, CWL and RO Crate. | ||
DataPLANT’s RDM concept seamlessly integrates with national and international networked infrastructures with a common architecture. | ||
Collaborations within and outside the NFDI are fostered to facilitate (re)use of data and services across consortia. |