Lessons from 17th Century Scientific Experiments: Data Commons Need Open Hardware

Model of Boyle’s Air Pump (CC-BY-SA-3.0, source)

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 Tarunima Prabhakar, Research Lead at Tattle Civic Tech and non-resident fellow at Carnegie India

Anxieties around a ‘Data-fied’ Society, mired as they are in broader concerns of rising inequality, have resulted in proposals focused exclusively on data management. Through a historical lens, this article aims to assert the often ignored relationship between data and hardware. It contends that a policy kit to equitably distribute the benefits of an increasingly Data-fied society must include support for open hardware principles.

Robert Boyle’s experiments with the pneumatic pump are an important landmark in the annals of science. As a founding fellow of the Royal Society in 1650[1], he emphasized experimental evidence as the basis for knowledge. In his treatise, the ‘New Experiments Physico-Mechanical Touching The Air’ he describes forty-three experiments with a ‘pneumatical engine’ (air pump). The air pump allowed the first controlled experiments with differential air pressure. In one experiment he placed a candle in the pump while removing the air from the glass tube. With the expulsion of air, appearance of the flame changed and was eventually extinguished [2]. He also used the pump to observe the effect of reducing air pressure on magnets and barometers. These experiments, amongst other learnings, led to the Boyle’s law- a law that describes the inverse relation between pressure and volume of air. His experiments, and the knowledge produced from them, was contingent on the instrument. This dependence irked Thomas Hobbes [3]- he launched public attacks on the integrity of the air pump and the conclusions of Boyle’s experiments [4]. The integrity of the pump, or the lack thereof, was a recurring explanation in Boyle’s discussion of the results of his experiments [5]. In one experiment, Boyle explained a deviation from the expected result by the impossibility of preventing small leaks of outside air into the glass tube. Boyle, like Hobbes, recognized the role of the air pump in production of facts. The data from the experiments were derived from the instrument he co-engineered. The difference was that while Hobbes denigrated it, Boyle as well as other members of the Royal Society celebrated it as an enabler of the experimental life.

It speaks to the success of Boyle’s intellectual project that we barely recognize it even though we live by it. Instrumentation hardware such as air pollution monitors, fitness and health trackers, road traffic sensors surround us. Our hand held computational devices enroll us in population scale randomized experiments. On one hand, the measurements from this hardware are used to generate and test hypothesis. At the same time, there is sufficient trust in fact making through experiments, that we rely on the resultant data for personal as well as societal scale planning. In the three hundred and fifty years since Boyle’s experiments, measurement and tracking hardware has become more complex and miniaturized. But the relationship between data and hardware remains unchanged- data is contingent on the hardware that it is generated from. There is no such thing as raw data.

Various groups have proposed greater data sharing as a method to further innovation, economic growth and social good. In an odd inversion, we have become so preoccupied with the possibilities of data that we have forgotten how it is produced. To translate an audio or visual into a stream of data points that can be fed to a computer, an intermediating hardware is necessary. This hardware can be configured in multiple ways- it can create a new data point for every millisecond of an audio or for every second. If the audio is meant for use by older people, the device might discard high pitch notes altogether (since the ability to hear high frequency notes declines with age) [7]. Take another example, the two images below [8] are of the same wall. Without knowing how the two were produced, one might be hard pressed to understand which of the two are more appropriate representations.

Interpretability, and consequently, utility of data, will always be limited without knowing how the data is produced. Trusting data in absence of this knowledge requires trusting the integrity of those who share the data. It also requires trusting their expertise in knowing everything about the capabilities and limits of their hardware. This is an unreasonable expectation of any expert.

The delinking of data from the systems that produce them explains why some policy positions on ‘data commons’ often appear vexing or untenable. Since data is always contingent on a physical apparatus, boundaries between the two are necessarily contrived. Proposals aiming to promote innovation through data sharing clash with the intellectual property rights framework that guides development of the underlying hardware. These proposals have to draw arbitrary boundaries between raw and processed data. The former are understood to be more shareable as they are imbued with lesser skill/agency from the data collector while the latter are construed as more proprietary. These boundaries are inevitably confusing because ‘raw data’ is a vacuous conceptual category. The danger of chasing two rabbits is that one might not catch either.

In some scenarios, enforcing data sharing through these contrived boundaries could be a necessity. But these data sharing mechanisms must come with a recognition of the underlying compromise. In accepting the opacity of the data creating apparatus, one accepts limited interpretability and consequently utility of the data. Oddly, it also requires trust in the entity sharing the data (that they are sharing what they claim to be sharing), when the calls for sharing data may be coming from a position of mistrust.

The source and resolution of these paradoxical outcomes is in the lessons that were patently clear in early scientific experiments- data is always contingent on the instruments that produce it. Consequently, we must re-direct some of the attention away from the data, towards the hardware that produces them. True data commons are data resources that no one entity can claim possession of and are collectively managed and can be used by anyone. These are only possible when the hardware through which they are produced are part of a commons. A policy kit to equitably distribute the benefits of an increasingly Data-fied society must include the management of hardware.

— — — — — — — — — — — — — — — — — — — — — — — — — —

The Air Pump was seventeenth century “Big Science.” [9] Boyle was the Earl of Cork. He could afford the risk as well as cost of construction of the first Air Pump. Despite Boyle’s extensive diagrams, documentation and proselytizing, constructing the Air Pump remained prohibitively expensive. In the decade after the Boyle first presented his experiments, there were less than ten air pumps in all of Europe [10].

Boyle was working during and after the Thirty Years’ War- at a time when the idea of knowledge through experimentation was contested. He needed to document and open his hardware design and experiments to convince people not only of his theories about air and vacuum (that emerged from the experiments), but also of the merits of the scientific enterprise. It can be easy to forget that there was a time when the scientific enterprise had to stake a claim for legitimacy in the public eye. Boyle and other members of the Royal Society were a minority that had to convince the public that the experiments were superior to theological discussions for discovering ‘truths’ about the world. Openness of experiments was necessary for Boyle to convert people to his way of life.

The scientific enterprise is no longer contested. Terms and methodologies from natural sciences have carried over into policy and economic planning. Trust in scientific expertise has meant that researchers are not obligated to share all details of their process to build support for their work. However, openness of research process has multiplicative effects- it enables reproducibility, which results in more robust data and science. It also enables more people to replicate the work and adapt it. In an alternate world, an Earl in 17th century English Society may have kept a machine he funded, to himself, for personal gains and the amusement of nobility and friends. Progress in chemistry might have slowed down by a few years, or a few decades. And this world would have lost a rich public dialogue on construction of hardware, consequences of experiments and theories of knowledge.

Open hardware policies that supports public interest innovation need considerable thought and work. Governments play an active role in shaping the direction of science and technology through public funding and industry policies. Both of these are avenues to incentivize more open hardware. Government ownership of hardware is neither necessary nor sufficient to ensure open hardware adoption, but governments can lead the way by adopting open hardware in their procurement process. They can also strengthen existing cultures of open hardware manufacturing by creating and advocating standards for performance and quality control. Finally, they can support and protect frameworks that facilitate consensual data sharing between different open hardware owners to build data as a commons.

Notes:

[1] (2004, August). London Royal Society. Retrieved December 30, 2020, from https://mathshistory.st-andrews.ac.uk/Societies/RS/

[2] West. B. J. (2005, January 01). Robert Boyle’s landmark book of 1660 with the first experiments on rarified air. Retrieved January 02, 2021, from https://journals.physiology.org/doi/full/10.1152/japplphysiol.00759.2004

[3] Thomas Hobbes, in defense an absolute sovereign was also defending the existing order of knowledge making that originated from treatise and dialogue often tied with the Church.

[4] Shapin, S., & Schaffer, S. (1985). Leviathan and the Air-Pump: Hobbes, Boyle, and the Experimental Life. Princeton; Oxford: Princeton University Press. doi:10.2307/j.ctt7sv46

[5] ibid

[6] Boyle, R. Chapter 5, New experiments Physico-Mechanical Touching The Air : Whereunto is Added a Defence of the Author’s Explication of the Experiments Against the Objections of Franciscus Linus and Thomas Hobbs; London: printed by Miles Flesher, 1682. ETH-Bibliothek Zürich, Rar 2168, https://doi.org/10.3931/e-rara-16019 / Public Domain Mark. Accessed at: https://www.e-rara.ch/zut/doi/10.3931/e-rara-16019

[7] The Scientific American. (May, 2013). Sonic Science: The High-Frequency Hearing Test. Retrieved January 01, 2021, from https://www.scientificamerican.com/article/bring-science-home-high-frequency-hearing/

[8] Wikimedia Commons, CC-BY-SA-3.0. Accessed at: https://en.wikipedia.org/wiki/Aliasing#/media/File:Moire_pattern_of_bricks_small.jpg

[9] Page 38–39, Shapin, S., & Schaffer, S. (1985). Leviathan and the Air-Pump: Hobbes, Boyle, and the Experimental Life. Princeton; Oxford: Princeton University Press. doi:10.2307/j.ctt7sv46

[10] ibid

Journal of Open Hardware, an Open Access initiative run by the Global Open Science Hardware community and published by Ubiquity Press.

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store