The Case for Open Research in Materials Science

The Case for Open Research in Materials Science#

This article is authored by Stephen Pryce, Data Curator Cultural Focus, Henry Royce Institute

Materials science is a critical discipline with far-reaching implications: for technology, industry, and society. As materials science shifts toward data-driven, informatics-based approaches, the open publication of research data becomes central to its progress. This revolution in the materials field demands a dynamic shift in how researchers approach data sharing and transparency. Naturally, researchers will want to ask what the benefits and imperatives are around open data.

Benefits#

The collective benefits#

  • Cultivating a culture of collaboration, transparency & open peer review

  • Enhancing the reusability of research

  • Improved identification of technical errors and questionable research practices

  • Preventing unnecessary repetition of negative findings: ‘the file drawer problem’

  • Extracting maximum value from research data using AI and Machine Learning

  • Enabling researchers worldwide to tackle global issues which disproportionately affect developing countries, such as:

    • Climate change

    • Sustainable resource management

    • Global health crises

The economic benefit#

The economic benefits of open data are significant. In the field of materials science, publicly accessible data from initiatives such as The Materials Project and the Open Quantum Materials Database (OQMD) lower the barrier to entry for publicly funded research, small businesses and startups: accelerating the commercialisation of materials science [1, 2]. These shared databases have driven technological advancements across sectors including energy, electronics, and transportation. High-throughput simulation studies, enabled by the OQMD, have led to the identification of promising alternatives to industry standard materials. For example, researchers have used OQMD to identify promising alternatives to traditional Li-Ion and Li2O batteries, which are safer, longer-lasting, and provide a higher voltage [3]. In the automotive industry, researchers have used OQMD to identify promising, affordable, Mg-based alloy alternatives to aluminium and steel, without compromising on strength and ductility [3, 4]. These materials may form the basis for next generation automobile manufacture. Similar methods have been used in the discovery, design and selection of electrocatalysts for use in crucial hydrogen energy conversion reactions [5, 6].

Imperatives#

Open data is moving from an ideal to a standard practice. Key funders are providing clear guidance. For instance, UKRI’s open data policy emphasizes that publicly funded research data are a public good and should be made openly available with minimal restrictions[7].

Benefits to individuals#

Jobs in materials science research within the public sector are increasingly prioritising a commitment to open research and experience in open publication of data. As such, junior researchers are well-placed to gain experience with open data to benefit their careers.

Research practices towards opening up data#

Best practice#

The success of open data depends on several key requirements, including:

  • Sufficiently descriptive metadata

  • Sufficient and user-friendly annotations

  • Open, non-proprietary file formats

Overcoming potential barriers#

Naturally, some researchers may hold reservations about adopting open data as standard practice. It is important for data curation services at key institutions to identify and resolve these issues as they emerge. Some common barriers are listed with potential solutions below:

Barrier: Researchers may be unfamiliar with data sharing methods
Solution: Institutions to roll out ‘How to Share Data’ training programmes and provide access to a suitable platform such as a generalist repository.

Barrier: Time constraints
Solution: Institutions often offer research data management services and are increasingly formalising the role of data stewards. These professionals play a key role in preparing data for sharing and providing training to help researchers effectively manage and share their data. Many institutions already deliver training on topics such as actionable and effective research data management planning.

Barrier: Concerns about intellectual property or ‘being scooped’
Solution: Apply Creative Commons Attribution Licensing and stipulate an embargo when publishing data openly in repositories.

Barrier: Lack of Incentives
Solution: Institutions should promote the benefits of open research and make examples of good practice in research data publishing.

Implications for Royce and the wider materials science research space#

As science continues to evolve, open research practices such as open publication of data will drive this evolution and accelerate scientific discovery. The Henry Royce Institute is well placed to coordinate efforts as institutions continue to incorporate open publication of data into standard practice. There may be issues along the way. The Henry Royce Institute Data Curation team is here to embrace these issues and provide solutions.

If you have any questions or concerns regarding open research practices and the open publishing of research data, please contact: datacuration@royce.ac.uk

References#

1

The Materials Project. This work is licensed under a Creative Commons Attribution 4.0 License. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/. URL: https://next-gen.materialsproject.org/.

2

The Open Quantum Materials Database. This work is licensed under a Creative Commons Attribution 4.0 License. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/. URL: https://oqmd.org/.

3(1,2)

James E. Saal, Scott Kirklin, Muratahan Aykol, Bryce Meredig, and C. Wolverton. Materials design and discovery with high-throughput density functional theory: the open quantum materials database (oqmd). JOM, 65(11):1501–1509, 09 2013. doi:10.1007/s11837-013-0755-4.

4

Jian-Feng Nie. Precipitation and hardening in magnesium alloys. Metallurgical and Materials Transactions A, 43(11):3891–3939, 07 2012. doi:10.1007/s11661-012-1217-2.

5

Rui Ding, Junhong Chen, Yuxin Chen, Jianguo Liu, Yoshio Bando, and Xuebin Wang. Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation. Chemical Society Reviews, 53(23):11390–11461, 2024. doi:10.1039/d4cs00844h.

6

Priyanka Sinha, M.V. Jyothirmai, B. Moses Abraham, and Jayant K. Singh. Machine learning driven advancements in catalysis for predicting hydrogen evolution reaction activity. Materials Chemistry and Physics, 326:129805, 10 2024. doi:10.1016/j.matchemphys.2024.129805.

7

UKRI. Publishing your research findings. Making your research data open. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/. URL: https://www.ukri.org/manage-your-award/publishing-your-research-findings/making-your-research-data-open/.

Contents