[Lauren Cadwallader]

We hope you are enjoying P22 and have had a chance to attend a few of the sessions so far! We are diving into our asynchronous discussion, which you can join here in the mailing list or through our slack in the #rda-plenary-22 channel. Our next featured group is the RDA group FAIR Principles for Research Hardware IG.

FAIR Principles for Research Hardware IG

The FAIR4RH Interest Group aims to provide a definition of research hardware (RH) and contribute to better recognition of RH as a valid research output. The IG also aims to raise awareness about the importance of sharing exhaustive documentation of RH which contributes to RH reproducibility. RH can also be strongly interdependent with software (in some cases software is required to run the hardware) so computational reproducibility is essential to achieve reproducibility of an RH or of a method that utilizes RH.

Briefly, tell us about your work/organization and how it’s related to computational reproducibility; What are you trying to address and how?

While scientific advancement (and therefore reproducibility) is often dependent on specific Research Hardware (physical object developed as part of or for a research process, RH), information about the RH is generally lacking, and efforts to provide it are inadequately incentivized in the current academic landscape. At the same time, development and dissemination of hardware (from prototypes towards market-ready designs) could be facilitated using open source hardware standards and FAIR principles.

By defining research hardware (our first output currently under review, https://www.rd-alliance.org/group_output/research-hardware-definition/ ) and by adopting the FAIR principles to RH, we aim at raising awareness on the importance of sharing an exhaustive documentation of RH within FAIR4RH IG. Concurrently, we hope to contribute to better recognition of RH as a valid research output. We are not dealing with “computational reproducibility” per se. However, FAIR principles for RH can contribute to the RH reproducibility, especially in the sense of proper documentation (“To improve reproducibility, it is imperative that these novel instruments are properly documented” [Bowman, R. W. (2023). Improving instrument reproducibility with open source hardware. Nature Reviews Methods Primers, 3(1), 27.]. Moreover, RH can be strongly interdependent with software (in some cases software is required to run the hardware) so computational reproducibility is essential to achieve reproducibility of an RH or of a method that utilizes RH.

What is your/your organization’s vision when it comes to computational reproducibility (e.g., all scholarship is computationally reproducible by default)?

We cannot speak on behalf of all members of RDA FAIR4RH IG, but yes all scholarship should be computationally reproducible by default. However, we do not feel that RH should be reproducible by default as RH definition:

(1) do not envision RH to be documented at all and (2) do not consider RH complimented with the FAIR digital assets (hardware design files, software, documentation, and branding). Disseminated research hardware and especially scientific hardware should be FAIR and reproducible.

What are some of the challenges you see to achieving this vision?

Two main challenges we can think of are that: (1) RH can be dependent on hardware components that are not always easily accessible and (2) that hardware reproducibility can come with costs.

What would you like to ask the members of our Interest Group?

Are FAIR principles required for reproducible research? Would you consider expanding reproducibility outside of computational realm and how do you see RH reproducibility? Does the RH definition make sense in your reproducibility context? What should we consider for aligning FAIR principles for RH?

rd-alliance.org

FAIR Principles for Research Hardware Members

FAIR Principles for Research Hardware Members

Discussion