E-RPID PEARC 2019 - The Digital Object Architecture and Enhanced Robust Persistent Identification of Data. Quick, R., Lannom, L., Krenz, M., & Luo, Y. In Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning) - PEARC '19, pages 1-4, 2019. ACM Press. ![E-RPID PEARC 2019 - The Digital Object Architecture and Enhanced Robust Persistent Identification of Data [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex The expansion of the research community's ability to collect and store data has grown much more rapidly than its ability to catalog, make accessible, and make use of data. Recent initiatives in Open Science and Open Data have attempted to address the problems of making data discoverable, accessible and reusable at internet scales. The Enhanced Robust Persistent Identification of Data (E-RPID) project's goal is to address these deficiencies and enable options for data interoperability and reusability in the current research data landscape by utilizing Persistent Identifiers (PIDs) and a kernel of state information available with PID resolution. To do this requires integrating a set of preexisting software systems along with a small set of newly developed software solutions. The combination of these software components and the core principles of making data FAIR (findable, accessible, interoperable and reusable) will allow us to use Persistent Identifiers to create an end-to-end fabric capable of realizing the Digital Object Architecture for researchers. This poster will acquaint the audience to the concepts of the Digital Object Architecture, describe the software service architecture necessary to enable this architecture, outline the existing E-RPID testbed that is available for experimental usage from the Jetstream cloud environment, and describe the diverse set of use cases already using E-RPID to enhance their data accessibility, interoperability and reusability. It will focus on how the Digital Object Architecture and E-RPID testbed would interact with XSEDE resources and how E-RPID could assist with interoperability, reusability and reproducibility of HPC workflows.
@inproceedings{
title = {E-RPID PEARC 2019 - The Digital Object Architecture and Enhanced Robust Persistent Identification of Data},
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year = {2019},
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abstract = {The expansion of the research community's ability to collect and store data has grown much more rapidly than its ability to catalog, make accessible, and make use of data. Recent initiatives in Open Science and Open Data have attempted to address the problems of making data discoverable, accessible and reusable at internet scales. The Enhanced Robust Persistent Identification of Data (E-RPID) project's goal is to address these deficiencies and enable options for data interoperability and reusability in the current research data landscape by utilizing Persistent Identifiers (PIDs) and a kernel of state information available with PID resolution. To do this requires integrating a set of preexisting software systems along with a small set of newly developed software solutions. The combination of these software components and the core principles of making data FAIR (findable, accessible, interoperable and reusable) will allow us to use Persistent Identifiers to create an end-to-end fabric capable of realizing the Digital Object Architecture for researchers. This poster will acquaint the audience to the concepts of the Digital Object Architecture, describe the software service architecture necessary to enable this architecture, outline the existing E-RPID testbed that is available for experimental usage from the Jetstream cloud environment, and describe the diverse set of use cases already using E-RPID to enhance their data accessibility, interoperability and reusability. It will focus on how the Digital Object Architecture and E-RPID testbed would interact with XSEDE resources and how E-RPID could assist with interoperability, reusability and reproducibility of HPC workflows.},
bibtype = {inproceedings},
author = {Quick, Rob and Lannom, Larry and Krenz, Marina and Luo, Yu},
doi = {10.1145/3332186.3333255},
booktitle = {Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning) - PEARC '19}
}
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