Download PDF Fall Bridge on Open Source Hardware September 15, 2017 Volume 47 Issue 3 The articles in this issue look at how the development and use of free and open source hardware (FOSH or simply “open hardware”) are changing the face of science, engineering, business, and law. Impacts of Open Source Hardware in Science and Engineering Friday, September 15, 2017 Author: Joshua M. Pearce There is an opportunity to radically reduce the costs of experimental research while improving it by supporting the development of free and open source hardware (FOSH) for science and engineering. By harnessing a scalable open source method, federal funding is spent just once for the development of scientific equipment and then a return on this investment is realized by direct digital replication of scientific devices for only the costs of -materials. FOSH for science and engineering has been growing at a rapid pace and already supports many fields. Scaled peer production and digital replication reduce traditional costs by 90–99 percent, making scientific equipment much more accessible not only for research but also for preparation of the next generation of scientists and engineers as research-grade tools are available for science, technology, engineering, and math (STEM) education. I propose four straightforward and negative-net-cost policies to support FOSH development and improve access to scientific tools in the United States. The policies will directly save millions in research and STEM education expenditures, while providing researchers and students access to better equipment, which will promote advances in technology and concomitant benefits for the US economy. The Problem of High-Cost Proprietary Scientific Equipment Scientists all over the world have limited access to the best scientific tools largely because of the inflated -prices of proprietary scientific equipment for experimental research (Pearce 2014). This slows the rate of scientific development in every field. Even American scientists, who have dominated research expenditures for decades and have the most well-equipped research labs in the world (NSB 2012), rarely have access to a complete collection of the best tools to do their work. In addition, the high costs of scientific instruments often limit access to exciting and engaging labs in both K–12 and university education (Gutnicki 2010) as there is simply not enough lab-grade equipment available at reasonable prices for everyone to use. This -weakens recruitment into STEM fields and results in a drain on scientific talent for the future. Historically, the scientific community had to choose one of two suboptimal paths to participate in state-of-the-art experimental research: (1) purchase high-cost proprietary tools or (2) develop equipment largely from scratch in their own labs, which can have an enormous time investment penalty resulting in high personnel costs. The high cost of modern scientific tools thus excludes many potential scientists from participating in the scientific endeavor and slows technical progress in all laboratories. Solution: Free and Open Source Hardware for Science and Engineering A new option is emerging: low-cost, often highly sophisticated and customized scientific equipment is being developed as free and open source hardware, similar to free and open source software (FOSS) (Fisher and Gould 2012; Pearce 2012). FOSS is computer software that is available in source code form and can be used, -studied, -copied, modified, and redistributed either without restriction or with restrictions only to ensure that further recipients have the same rights under which it was obtained: free, or libre. Under similar rights, FOSH provides the “code” for hardware—including the bill of materials, schematics, instructions, computer--aided designs (CAD), and other information needed to -recreate a physical -artifact—and enables improved product innovation in a wide range of fields (Fisher and Gould 2012; Hienerth et al. 2014; Pearce 2014). FOSH in the Laboratory The open source paradigm combines three-dimensional (3D) printing with open source microcontrollers running on FOSS (Pearce 2012, 2014), and hundreds of scientific tools have already been developed to allow free access to plans (Pearce 2014). For example, in DNA nanotechnology labs, gel scanners, horizontal electrophoresis gel molds, and homogenizers for generating DNA-coated particles can all be fabricated using an open source approach for up to 90 percent less than commercially offered tools (Damase et al. 2015). Similarly, there is a long list of open source money-saving tools for biology labs—microscopes, centrifuges, hot plates, magnetic stirrers, waveform generators, EEGs, and Skinner boxes, to name a few (Baden et al. 2015). Scientists and research engineers design, share, and build on one another’s work to develop scientific tools (Harnett 2011). For example, open source micro-controllers like the Arduino are being used for a variety of chemical educational tools, from simple -colorimeters and pH meters to automated titrators, data loggers, and generic control devices for automated assays (Urban 2014). An open source Python framework has been developed for the Arduino that offers even more flexibility for applications, such as high-voltage power supplies, pressure and mass flow controllers, syringe pumps, multiposition valves, and data recording systems (-Koenka et al. 2014). Open source microcontrollers can also be used for more sophisticated and targeted applications like the electrochemical pretreatment of boron-doped diamond electrodes (Rosa et al. 2017) and radial stretching systems with force sensors (Schausberger et al. 2015), saving hundreds of dollars, and a robot-assisted mass -spectrometry assay platform (Chiu and Urban 2015) and -automated peptide synthesizer (Gali 2017), saving over $25,000. -Other groups are developing open source electronics to provide Big Data–like Internet of Things (IoT) meter -devices for smart and energy-efficient buildings (Pocero et al. 2017) and sensor and computational platforms for smart cities (Jiang and Claudel 2017). Open source electronics also drive open source 3D printers like the replicating rapid prototyper (RepRap) (-Anzalone et al. 2015; Jones et al. 2011), which can be used to manufacture high--quality scientific tools. Even complex manufacturing designs are free, and it is just as easy to replicate an $850 magnetic test tube rack as it is to make an inexpensive rack. Researchers at the University of Washington became frustrated with the exorbitant prices for commercial magnet racks and designed an open sourced 3D-printable tube magnet rack. As the magnets are available for about $6 each it is possible to economically justify the purchase of a $500 RepRap 3D printer (Wittbrodt et al. 2013) for a lab to download, print, and avoid the cost of a single commercial magnetic rack. The 3D printer can then be used to make a long list of progressively more sophisticated and costly tools (Baden et al. 2015; Pearce 2014). And sharing digital designs and 3D printers can be used to attempt new experiments with, for example, chemical reactionware (Kitson et al. 2013; Symes et al. 2012), or polymer laser–welded heat exchangers (Arie et al. 2017), which are fabricated with an open source system shown in figure 1. The most powerful and expensive open source scientific equipment combines 3D printing and open source electronics. For example, several approaches have been shown to decrease costs for microfluidics platforms, saving researchers $2,000 or more (Pearce et al. 2016; Tothill et al. 2017). A single automated device such as a filter wheel changer can be built in a day for $50, replacing inferior commercial tools that cost $2,500 and have long lead times (Pearce 2012). Not only has a 3D-printable open source optics library been developed (Zhang et al. 2013) and expanded (Gopalakrishnan and Gühr 2015; Salazar-Serrano et al. 2017), but scientists are pushing ever more complex tools such as an automated 3D microscope, saving several thousand to over $10,000 (Wijnen et al. 2016a). Open source 3D printers driven by open source electronics can even become scientific tools themselves, as when they are used to make thin silica gel layers in -planar chromatography (Fichou and Morlock 2017). This is particularly easy if they are controlled with -Franklin, an open source 3D motion control software suite (Wijnen et al. 2016b) that has been used to control an automated mapping four-point probe (-Chandra et al. 2017) and a 3D scientific platform (figure 2; Zhang et al. 2016), which can be used for laboratory auto-stirring and measuring as well as automated fluid handling and even shaking and mixing, taking the place of -dedicated open source tools like the simple mixer shown in figure 3 (Dhankani and Pearce 2017). FOSH in the Classroom FOSH methods offer the potential to radically reduce the costs of not only doing science but also training future scientists (Pearce 2013). For example, open source electronics can aid in chemical education (Urban 2014), and FOSH can be used to help teach young students programming skills (Hill and Ciccarelli 2013), reduce costs in physics education (Zhang et al. 2013), and teach mechatronics (Kentzer et al. 2011). An entire university classroom of physics optics setups can be printed in-house for $500 using a selection of predesigned components from the open source optics library on an open source 3D printer, replacing $15,000 of commercial equipment (Zhang et al. 2013). This would save over $66 million if scaled only to the basic physics labs in US degree-granting institutions, and over $500 million if scaled to all US public and private secondary schools. Scaling and Returns on Investment in FOSH Clearly there is an enormous return on investment (ROI) possible for funders of scientific research and for STEM education by investing in the development of FOSH for all the sciences, basic and applied (Pearce 2016). To fully take advantage of the scalability of FOSH for the benefit of both areas, the United States must implement policies that allow knowledge to scale horizontally. Such scaling will be accomplished by federal funding spent only once for the development of scientific equipment, followed by an immediate ROI through the digital replication of devices for no more than the costs of materials. In this way research-grade scientific instruments will be much more accessible at every level of the educational system and a greater percentage of America’s scientists will be able to participate in experimental science. The ROI thus goes beyond simply funding laboratories themselves. Improvements in science lead to improvements in technology, which can in turn enhance virtually every aspect of the economy (Salter and Martin 2001). Historically the ROI were on the order of 20–70 percent (Salter and Martin 2001), but a study of the ROI for funders on the design of an open source syringe pump (Wijnen et al. 2014) found that it was as much as 1,000 percent after only a few months (Pearce 2016). Four Policies to Accelerate FOSH Development To foster double- and triple-digit returns on investment, four policies are needed to support scientific FOSH development in the United States: Form a task force of the National Academies of Sciences and Engineering (NAS and NAE) to identify the best opportunities to realize strategic national goals and a high ROI for the creation of open source scientific hardware. The country’s largest current expenditures on equipment should be determined along with likely future expenditures. This goes beyond cataloguing the largest single-point expenditures to tools used across many disciplines and found in labs throughout the country. The value, VUSA, can be maximized by Pearce Equation where NUSA is the total number of labs in the United States, cj is the cost per unit of j instrument, and nj is the number of j instruments in i lab. The resulting list of high-ROI equipment can then be compared against the existing (and rapidly growing) list of libre hardware to determine the primary targets for policy 2 (below). Beyond the highest national value equipment, the task force could also rank all science-based purchases from internationally sourced suppliers by value (following the equation above), so that equivalent (or superior) open source devices could be identified as either existing or needing to be developed for policy 2. Such information could assist national goals such as improving balance of trade and reshoring manufacturing in the United States (see policy 4). 2. Earmark federal funding for the development of FOSH scientific equipment identified from policy 1. This can be accomplished with a combination of traditional calls for proposals for academic grants and programs like the Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) programs. National contests like the XPRIZE or “first to make” can promote progress toward specific technical goals by offering “bounties.” All federal funding for scientific hardware should be directed to FOSH projects via a purchasing preference. Last, because of the high ROI of such projects, funding for FOSH scientific equipment design should be prioritized over current offerings for hardware purchase. As with proprietary tools, all FOSH scientific designs should be vetted, tested, and validated. This work needs to be funded with high priority. This step will largely eliminate the technical risks for labs to adopt the use of the hardware, while at the same time ensuring that scientific equipment no longer becomes obsolete, as proprietary systems do when a company loses key personnel, discontinues a product line, or goes out of business. 3. Create a national free online database of tested, -vetted, and validated FOSH, with the bill of mate-rials, digital designs, instructions for assembly and operation, and source code for all software and firmware. Efforts on this front have been started at the University of -California, Berkeley’s Tekla Labs, which created a library of open source documents for the construction of more than 150 quality pieces of lab equipment. Similarly, the NIH 3D Print Exchange in the custom labware category has begun doing this on the national level (https://3dprint.nih.gov/) (Coakley et al. 2014; Coakley and Hurt 2016). To be included in the database FOSH tools must undergo peer review. To this end scientific publishers are providing venues for scientists and engineers to publish their validated studies of scientific FOSH with new journals such as HardwareX (Elsevier) and the Journal of Open Hardware (Ubiquity) as well as open access journals that support open hardware such as PLoS One and more conventional specialty journals and instrumentation journals (e.g., Journal of Laboratory Automation). Papers that do not disclose the source of the hardware should be discouraged as largely unhelpful to the scientific enterprise. 4. To provide incentives for US entrepreneurs to scale production of manufactured components that are not easily digitally distributed (e.g., microcontrollers, -sensors, actuators—often called “vitamins” as they are necessary to make a piece of FOSH equipment), enact purchasing policy preferences at all levels of government for validated FOSH (policies 2 and 3) for government labs and all government-funded projects. As the -United States has some of the world’s leading open source firms (e.g., Red Hat is a $1 billion/year FOSS company, and FOSH companies like Sparkfun -Electronics and Adafruit Industries produce millions of dollars of revenue a year), a preferential purchasing policy would also support “Made in the USA”–related employment. Last, to bring traditionally closed source companies to the open source way, consider tax benefits to save companies development money (e.g., an enhanced tax deduction for FOSH release of valuable hardware determined by policy 1). Conclusions It is well established that knowledge sharing via networked science has incredible power and scales well (Lang 2011; Salter and Martin 2001; Woelfle et al. 2011). “Crowd science” (Young 2010), “citizen science” (Wiggins and Crowston 2011), “networked science” (Nielsen 2011), and “massively collaborative science” (Franzoni and Sauermann 2014) will all benefit from low-cost scientific hardware, enabling practitioners and collaborators to go far beyond what is possible with software and computer simulation alone. Latterly scaled peer production and replication of free and open source scientific hardware generally provide savings of 90–99 percent of the traditional costs, making scientific equipment much more accessible for both research and STEM education. To accelerate the development and use of FOSH for science, this paper suggests four negative-net-cost federal policies: 1formation of an NAS/NAE task force to identify opportunities to realize strategic national goals and a high return on investment for creation of FOSH scientific tools, 2. a shift in federal funding from proprietary equipment to the development of scientific FOSH, 3. creation of a free online catalogue of validated scientific FOSH with vetted peer-reviewed designs, and 4. purchasing policy preferences for FOSH for all government-funded projects as well as tax incentives for businesses to adopt FOSH protocols. Given the incredible ROI of the open source paradigm (a minimum of 100 percent ROI), the policies can be implemented at no net cost. References Anzalone G, Wijnen B, Pearce JM. 2015. Multi-material additive and subtractive prosumer digital fabrication with a free and open-source convertible delta RepRap 3-D printer. Rapid Prototyping Journal 21(5):506–519. Arie MA, Shooshtari AH, Tiwari R, Dessiatoun SV, Ohadi MM, Pearce JM. 2017. Experimental characterization of heat transfer in an additively manufactured polymer heat exchanger. Applied Thermal Engineering 113:575–584. Baden T, Chagas AM, Gage G, Marzullo T, Prieto-Godino LL, Euler T. 2015. Open labware: 3-D printing your own lab equipment. PLoS Biology 13(3). Chandra H, Allen SW, Oberloier SW, Bihari N, Gwamuri J, Pearce JM. 2017. Open-source automated mapping four-point probe. Materials 10(2):110. Chiu SH, Urban PL. 2015. Robotics-assisted mass -spectrometry assay platform enabled by open-source electronics. Bio-sensors and Bioelectronics 64:260–268. Coakley MF, Hurt DE, Weber N, Mtingwa M, Fincher EC, Alekseyev V, Chen DT, Yun A, Gizaw M, Swan J, Yoo TS. 2014. The NIH 3D print exchange: A public resource for bioscientific and biomedical 3D prints. 3D Printing and Additive Manufacturing 1(3):137–140. Coakley M, Hurt DE. 2016. 3D printing in the laboratory: Maximize time and funds with customized and open-source labware. Journal of Laboratory Automation 21(4):489–495. Damase TR, Stephens D, Spencer A, Allen PB. 2015. Open source and DIY hardware for DNA nanotechnology labs. Journal of Biological Methods 2(3):e24. Dhankani KC, Pearce JM. 2017. Open source laboratory sample rotator mixer and shaker. HardwareX 1:1–12. Fichou D, Morlock GE. 2017. Open-source-based 3D printing of thin silica gel layers in planar chromatography. -Analytical Chemistry 89(3):2116–2122. Fisher D, Gould P. 2012. Open-source hardware is a low-cost alternative for scientific instrumentation and research. Modern Instrumentation 1(2):8–20. Franzoni C, Sauermann H. 2014. Crowd science: The organization of scientific research in open collaborative projects. Research Policy 43(1):1–20. Gali H. 2017. An open-source automated peptide synthesizer based on Arduino and Python. SLAS Technology: Translating Life Sciences Innovation. DOI: https://doi.org/10.1177/2472630316685844. Gopalakrishnan M, Gühr M. 2015. A low-cost mirror mount control system for optics setups. American Journal of -Physics 83(2):186–190. Gutnicki J. 2010. The evolution of teaching science. Innovative Educator, February 28. Available at http://theinnovativeeducator.blogspot.com/2010/02/evolution- of-teaching-science.html. Harnett C. 2011. Open source hardware for instrumentation and measurement. IEEE Instrumentation & Measurement Magazine 14(3):34–38. Hienerth C, von Hippel E, Berg Jensen M. 2014. User community vs. producer innovation development efficiency: A first empirical study. Research Policy 43(1):190–201. Hill L, Ciccarelli S. 2013. Using a low-cost open source hardware development platform in teaching young students programming skills. Proceedings of the 13th annual ACM SIGITE Conference on Information Technology Education (pp. 63–68), October 11–13, 2012, Calgary. Jiang J, Claudel C. 2017. A high performance, low power computational platform for complex sensing operations in smart cities. HardwareX 1:22–37. Jones R, Haufe P, Sells E, Iravani P, Olliver V, Palmer C, -Bowyer A. 2011. RepRap: The replicating rapid prototyper. Robotica 29(01):177–191. Kentzer J, Koch B, Thiim M, Jones RW, Villumsen E. 2011. An open source hardware-based mechatronics project: The replicating rapid 3-D printer. Proceedings of the 4th International Conference on Mechatronics (ICOM), May 17–19, Kuala Lumpur. Kitson PJ, Symes MD, Dragone V, Cronin L. 2013. Combining 3D printing and liquid handling to produce user--friendly reactionware for chemical synthesis and purification. Chemical Science 4(8):3099–3103. Koenka IJ, Sáiz J, Hauser PC. 2014. Instrumentino: An open-source modular Python framework for controlling -Arduino-based experimental instruments. Computer -Physics Communications 185(10):2724–2729. Lang T. 2011. Advancing global health research through digital technology and sharing data. Science 331:714–717. Laureto JJ, Dessiatoun SV, Ohadi MM, Pearce JM. 2016. Open source laser polymer welding system: Design and characterization of linear low-density polyethylene multilayer welds. Machines 4(3):14. NSB [National Science Board]. 2012. Science and Engineering Indicators 2012. Arlington VA: National Science Foundation. Nielsen M. 2011. Reinventing Discovery: The New Era of Networked Science. Princeton University Press. Pearce JM. 2012. Building research equipment with free, open-source hardware. Science 337(6100):1303–1304. Pearce JM. 2013. Open-source hardware for research and education. Physics Today 66(11):8. Pearce JM. 2014. Open-Source Lab: How to Build Your Own Hardware and Reduce Research Costs. Amsterdam: -Elsevier. Pearce JM. 2016. Return on investment for open source -hardware development. Science and Public Policy 43(2):192–195. Pearce JM, Anzalone NC, Heldt CL. 2016. Open-source wax RepRap 3-D printer for rapid prototyping paper-based microfluidics. Journal of Laboratory Automation 21(4):510–516. Pocero L, Amaxilatis D, Mylonas G, Chatzigiannakis I. 2017. Open source IoT meter devices for smart and energy--efficient school buildings. HardwareX 1:54–67. Rosa TR, Betim FS, de Queiroz Ferreira R. 2017. Development and application of a labmade apparatus using open-source “Arduino” hardware for the electrochemical pretreatment of boron-doped diamond electrodes. Electrochimica Acta 231:185–189. Salazar-Serrano LJ, Torres JP, Valencia A. 2017. A 3D -printed toolbox for opto-mechanical components. PLoS One 12(1):e0169832. Salter AJ, Martin BR. 2001. The economic benefits of -publicly funded basic research: A critical review. Research Policy 30:509–532. Schausberger SE, Kaltseis R, Drack M, Cakmak UD, Major Z, Bauer S. 2015. Cost-efficient open source desktop size radial stretching system with force sensor. IEEE Access 3:556–561. Symes MD, Kitson PJ, Yan J, Richmond CJ, Cooper GJ, Bowman RW, Vilbrandt T, Cronin L. 2012. Integrated 3D-printed reactionware for chemical synthesis and analysis. Nature Chemistry 4(5):349–354. Tothill AM, Partridge M, James SW, Tatam RP. 2017. Fabrica-tion and optimisation of a fused filament 3D-printed microfluidic platform. Journal of Micromechanics and Microengineering 27(3):035018. Urban PL. 2014. Open-source electronics as a technological aid in chemical education. Journal of Chemical Education 91(5):751–752. Wiggins A, Crowston K. 2011. From conservation to crowdsourcing: A typology of citizen science. Paper presented at the 44th Hawaii International Conference on System Sciences (HICSS), January 4–7, Kauai. Wijnen B, Hunt EJ, Anzalone GC, Pearce JM. 2014. Open-source syringe pump library. PLoS One 9(9):e107216. Wijnen B, Petersen EE, Hunt EJ, Pearce JM. 2016a. Free and open-source automated 3-D microscope. Journal of -Microscopy 264(2):238–246. Wijnen B, Anzalone GC, Haselhuhn AS, Sanders PG, Pearce JM. 2016b. Free and open-source control software for 3-D motion and processing. Journal of Open Research Software 4(1):e2. Wittbrodt BT, Glover AG, Laureto J, Anzalone GC, -Oppliger D, Irwin JL, Pearce JM. 2013. Life-cycle economic -analysis of distributed manufacturing with open-source 3-D -printers. Mechatronics 23(6):713–726. Woelfle M, Olliaro P, Todd MH. 2011. Open science is a research accelerator. Nature Chemistry 3(10):745–748. Young J. 2010. Crowd science reaches new heights. Chronicle of Higher Education, May 28. Available at http://chronicle.com/article/The-Rise-of-Crowd-Science/ 65707/. Zhang C, Anzalone NC, Faria RP, Pearce JM. 2013. Open-source 3D-printable optics equipment. PLoS One 8(3):e59840. Zhang C, Wijnen B, Pearce JM. 2016. Open-source 3-D platform for low-cost scientific instrument ecosystem. Journal of Laboratory Automation 21(4):517–525.  The term “libre” has been adopted to convey the freedom (of access, use, and discovery) that comes with free and open source materials, not only the lack of cost.  Acadey 96 well plate/0.2 mL strip tube magnet rack (www.-thingiverse.com/thing:79430).  US Department of Education, National Center for Education Statistics, Digest of Education Statistics (http://nces.ed.gov/-programs/digest/2013menu_tables.asp) . About the Author:Joshua M. Pearce is a professor cross-appointed in the Departments of -Materials Science & Engineering and Electrical & Computer Engineering at the -Michigan -Technological University, and -director, Michigan Tech Open -Sustainability Technology Lab.