This blog is part of a series on open hardware and key messages for public policy. Read the introduction and access other #OHpolicy blogs here.
By Shannon Dosemagen, Alexandra Novak, Jenny Molloy, Anne Bowser, Alison Parker
In the last months of the Trump Administration in the US, the authors hosted a workshop that led to a set of key messages for public policy audiences, a thirteen-part blog series on open hardware for science, and recommendations for how public policy can reflect its value. As of writing in January 2021, the new Administration has taken a strong perspective on the critical role of science for evidence-based policy, within the first week of office. Reflecting on the workshop and blogs, we propose that emphasizing open hardware in the new science policy agenda is an immense opportunity to advance research and innovation infrastructure while addressing other critical priorities. The thirteen blog posts in this series describe key messages in complementary ways, propose new ways of thinking about hardware for science, and outline bold recommendations for increasing the impact of open hardware on science and society.
As COVID-19 swept the globe during 2020, the open hardware community was ready to mobilize, meeting supply chain shortages for personal protective equipment (PPE) by quickly innovating on open source designs and distributed manufacturing practices. As Michael Weinberg writes, this readiness resulted from the open hardware community’s foundation in — and experience with — collaborative and collective endeavors. Sabrina Merlo found that the culture of innovation, rapid creativity and value creation inherent in many maker communities supported the design and production of open hardware during COVID-19, and was further re-enforced by pandemic response. Makerspaces, digital fabrication spaces, and internet access were key to this success and demonstrated a model for cultivating open source innovation and entrepreneurship that could support future crisis response.
Alicia Gibb described the landscape of innovation that open hardware helps create. She connected the dots between innovation and open hardware’s ability to encompass classic values of science such as attribution through citation, and quality assurance through repeatability and replicability. To see the increased success of open hardware in these types of scientific collaborations, Angela Eaton and Shannon Dosemagen left us with recommendations for how to broaden our ideas of inclusivity, including with thoughts on where and how open hardware for science and the resulting data can be useful. Broadening out can increase our potential to create collective, multi-sector solutions for our most pressing societal challenges. COVID-19 demonstrated the potential for open hardware to address other pressing challenges facing the new Administration, including economic stimulation, racial injustice, and the climate crisis.
Alexandra Novak explored the value of distributed manufacturing in open hardware and wrote that it can generate new jobs in science instrumentation production and calibration, and support industry growth by cutting manufacturing costs and innovating more efficiently. Joshua Pearce concurred and argued that funding open hardware for science leads to higher return on investment because open tools can be used and built upon by other scientists, making the process cheaper and providing plenty of room for flexibility and customization. Nadya Peek proposed that tailoring science hardware to custom research applications relies on the collective development of reliable designs and infrastructure that open hardware promotes, with return on investment also coming from speeding up the research process. She warns that incentives to maintain open systems and ensure reproducibility are critical, but challenging in our dominant scientific culture.
The value of reproducibility — both in research institutions and in other environments — emerged in the series as a key theme. With conservation technology as the backdrop, Andrew Hill and Alasdair Davies found that in their work outside the confines of research labs, open hardware for science supports scientific reproducibility in three key ways. More experiments are repeatable thanks to accessible methods and instruments; open hardware tools are exposed to greater public scrutiny, which can improve the quality and reproducibility of data; and, the collaborative economy of open hardware provides greater support for reuse and repurposing instruments in new types of experiments. Tarunima Prabhakar connected us back to a 17th-century experiment to make the case that to produce, share and interpret data, it is essential to consider scientific instruments as part of the data infrastructure. Therefore, open hardware plays a critical role in any open data commons. Because “there is no such thing as raw data,” creating better reproducibility for scientific data requires a cyclical focus on considering data in relation to the hardware producing it.
And that brings us to open hardware as infrastructure. Many times we tend to think about open hardware for science as objects — microscopes, spectrometers, water sensors — while overlooking the value of open hardware as part of scientific research infrastructure, or the social and technical support that enables scientific work to happen. To address this, Luis Felipe Murillo takes us on a journey down into the Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN) where he examines how large research facilities rely on open hardware as critical infrastructure, through examples such as the White Rabbit project — which ensures that the CERN network keeps time to the nearest nanosecond — and even a radiation-resistant power supply for the emergency lighting in the CERN tunnels. Greg Austic identified open hardware for science as akin to building blocks that should serve as a public resource: providing the foundation for researchers and practitioners to solve complex societal problems.
As many authors in this series argue and Martin Häuer, Paul Jerchel, and Jaime Arredondo drive home — there is no science without sharing. In their article, they discuss how openness fosters efficiency and cost savings, and the ability for scientific sectors to scale. They make the case that collaboration leads to higher quality work, creates a high return on investment, and increases the likelihood of positive social impact. Similarly, Julieta Arancio reminds us that — regardless of whether we are creating large, collaborative communities, generating research agendas, or building the infrastructure on which that research sits — open hardware for science can help us to prioritize impactful research. We enable more local research and collaboration through the use of adaptive and modifiable technologies.
The recognition of open hardware’s value to promote collaboration and drive down the cost of scientific equipment is catalyzing the rapid growth of the field and movement. From a government and policy perspective, COVID-19 demonstrated the value of decentralized manufacturing processes for open hardware; this can now be leveraged to provide better access to scientific tools. We also recognize open hardware for science as a vehicle to build stronger solutions to the societal dilemmas we face, while uplifting innovation, creativity, and lifelong learning across the globe. We encourage readers to consider each of the thirteen articles in this series in full to better understand the successes, challenges, and impacts of open hardware for science across diverse fields. Most importantly, we hope the reader considers the policy recommendations outlined by each author that indicates open hardware needs policy attention now in order to reach its full potential in science and beyond.