Accelerating the adoption of research data management strategies

Johanne Medina; Abdul Wahab Ziaullah; Heesoo Park; Ivano E. Castelli; Arif Shaon; Halima Bensmail; Fedwa El-Mellouhi

doi:10.1016/j.matt.2022.10.007

Accelerating the adoption of research data management strategies

Johanne Medina^*, Abdul Wahab Ziaullah, Heesoo Park, Ivano E. Castelli, Arif Shaon, Halima Bensmail, Fedwa El-Mellouhi^*

^*Corresponding author for this work

Research output: Contribution to journal › Comment/debate › Research › peer-review

Abstract

The need for good research data management (RDM) practices is becoming more recognized as a critical part of research. This may be attributed to the 5V challenge in big data: volume, variety, velocity, veracity, and value. The materials science community is no exception to these challenges as it heralds its new paradigm of data-driven science, which uses artificial intelligence to accelerate materials discovery but requires massive datasets to perform effectively. Hence, there are efforts to standardize, curate, pre-serve, and disseminate these data in a way that is findable, acces-sible, interoperable, and reusable (FAIR). To understand the cur-rent state of data-driven materials science and learn about the challenges faced with RDM, we gather user stories of researchers from small-and large-scale projects. This enables us to provide relevant recommendations within the data-driven research life cy-cle to develop and/or procure an effective RDM system following the FAIR guiding principles.

Original language	English
Journal	Matter
Volume	5
Issue number	11
Pages (from-to)	3614-3642
Number of pages	29
ISSN	0959-9428
DOIs	https://doi.org/10.1016/j.matt.2022.10.007
Publication status	Published - 2022

Keywords

FAIR guiding principles
Research data management
Data-driven research
Materials science
Batteries
Perovskite crystal structure
Photovoltaic devices

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.matt.2022.10.007

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title = "Accelerating the adoption of research data management strategies",

abstract = "The need for good research data management (RDM) practices is becoming more recognized as a critical part of research. This may be attributed to the 5V challenge in big data: volume, variety, velocity, veracity, and value. The materials science community is no exception to these challenges as it heralds its new paradigm of data-driven science, which uses artificial intelligence to accelerate materials discovery but requires massive datasets to perform effectively. Hence, there are efforts to standardize, curate, pre-serve, and disseminate these data in a way that is findable, acces-sible, interoperable, and reusable (FAIR). To understand the cur-rent state of data-driven materials science and learn about the challenges faced with RDM, we gather user stories of researchers from small-and large-scale projects. This enables us to provide relevant recommendations within the data-driven research life cy-cle to develop and/or procure an effective RDM system following the FAIR guiding principles.",

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author = "Johanne Medina and Ziaullah, {Abdul Wahab} and Heesoo Park and Castelli, {Ivano E.} and Arif Shaon and Halima Bensmail and Fedwa El-Mellouhi",

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T1 - Accelerating the adoption of research data management strategies

AU - Medina, Johanne

AU - Ziaullah, Abdul Wahab

AU - Park, Heesoo

AU - Castelli, Ivano E.

AU - Shaon, Arif

AU - Bensmail, Halima

AU - El-Mellouhi, Fedwa

PY - 2022

Y1 - 2022

N2 - The need for good research data management (RDM) practices is becoming more recognized as a critical part of research. This may be attributed to the 5V challenge in big data: volume, variety, velocity, veracity, and value. The materials science community is no exception to these challenges as it heralds its new paradigm of data-driven science, which uses artificial intelligence to accelerate materials discovery but requires massive datasets to perform effectively. Hence, there are efforts to standardize, curate, pre-serve, and disseminate these data in a way that is findable, acces-sible, interoperable, and reusable (FAIR). To understand the cur-rent state of data-driven materials science and learn about the challenges faced with RDM, we gather user stories of researchers from small-and large-scale projects. This enables us to provide relevant recommendations within the data-driven research life cy-cle to develop and/or procure an effective RDM system following the FAIR guiding principles.

AB - The need for good research data management (RDM) practices is becoming more recognized as a critical part of research. This may be attributed to the 5V challenge in big data: volume, variety, velocity, veracity, and value. The materials science community is no exception to these challenges as it heralds its new paradigm of data-driven science, which uses artificial intelligence to accelerate materials discovery but requires massive datasets to perform effectively. Hence, there are efforts to standardize, curate, pre-serve, and disseminate these data in a way that is findable, acces-sible, interoperable, and reusable (FAIR). To understand the cur-rent state of data-driven materials science and learn about the challenges faced with RDM, we gather user stories of researchers from small-and large-scale projects. This enables us to provide relevant recommendations within the data-driven research life cy-cle to develop and/or procure an effective RDM system following the FAIR guiding principles.

KW - FAIR guiding principles

KW - Research data management

KW - Data-driven research

KW - Materials science

KW - Batteries

KW - Perovskite crystal structure

KW - Photovoltaic devices

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DO - 10.1016/j.matt.2022.10.007

M3 - Comment/debate

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VL - 5

SP - 3614

EP - 3642

JO - Matter

JF - Matter

IS - 11

ER -

Accelerating the adoption of research data management strategies

Abstract

Keywords

UN SDGs

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