A Lightweight Framework for Research Data Management. Nikolov, D. & Tuna, E. In Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning) (PEARC '19), pages 1-4, 2019. Association for Computing Machinery (ACM). ![A Lightweight Framework for Research Data Management [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper doi abstract bibtex We describe a framework for managing live research data involving two major components. First, a system for the scalable scheduling and execution of automated policies for moving, organizing, and archiving data. Second, a system for managing metadata to facilitate curation and discovery with minimal change to existing workflows. Our approach is guided by four main principles: 1) to be non-invasive and to allow for easy integration into existing workflows and computing environments; 2) to be built on established, cloud-aware, open-source tools; 3) to be easily extensible and configurable, and thus, adaptable to different academic disciplines; and 4) to integrate with and take advantage of infrastructure and services available on academic campuses and research computing environments. These principles give our solution a well-defined place along the spectrum of research data management software such as sophisticated electronic lab notebooks and science gate-ways. Our lightweight and flexible data management framework provides for curation and preservation of research data within a lab, department or university cyberinfrastructure.
@inproceedings{
title = {A Lightweight Framework for Research Data Management},
type = {inproceedings},
year = {2019},
pages = {1-4},
publisher = {Association for Computing Machinery (ACM)},
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abstract = {We describe a framework for managing live research data involving two major components. First, a system for the scalable scheduling and execution of automated policies for moving, organizing, and archiving data. Second, a system for managing metadata to facilitate curation and discovery with minimal change to existing workflows. Our approach is guided by four main principles: 1) to be non-invasive and to allow for easy integration into existing workflows and computing environments; 2) to be built on established, cloud-aware, open-source tools; 3) to be easily extensible and configurable, and thus, adaptable to different academic disciplines; and 4) to integrate with and take advantage of infrastructure and services available on academic campuses and research computing environments. These principles give our solution a well-defined place along the spectrum of research data management software such as sophisticated electronic lab notebooks and science gate-ways. Our lightweight and flexible data management framework provides for curation and preservation of research data within a lab, department or university cyberinfrastructure.},
bibtype = {inproceedings},
author = {Nikolov, Dimitar and Tuna, Esen},
doi = {10.1145/3332186.3333157},
booktitle = {Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning) (PEARC '19)}
}
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