The miracle of the commons and the democratization of science

In the early days of scientific inquiry, it was quite common for a “citizen scientist” — one without an affiliation to an academic institution and sometimes no formal scientific training 新しいタブ／ウィンドウで開く — to contribute significantly to scientific discovery. Noteworthy examples include Leonardo da Vinci 新しいタブ／ウィンドウで開く, Antonie van Leeuwenhoek 新しいタブ／ウィンドウで開く, Benjamin Franklin 新しいタブ／ウィンドウで開く, Mary Somerville 新しいタブ／ウィンドウで開く, Mary Anning 新しいタブ／ウィンドウで開く, Charles Darwin 新しいタブ／ウィンドウで開く, Gregor Mendel 新しいタブ／ウィンドウで開く and Thomas Edison 新しいタブ／ウィンドウで開く. Even Marie Curie 新しいタブ／ウィンドウで開く, although on staff at the École Normale Supérieure 新しいタブ／ウィンドウで開く, conducted much of her work without official support, in a leaky, uninsulated shed.

The contributions made by these scientists were transformative and even led to the birth of entire scientific disciplines.

Over time, however, it became increasingly difficult for “scientific outsiders” to make such contributions. New findings were largely built on an earlier body of knowledge, and accessing that knowledge was hard — not only requiring years of learning but having much of that learning locked within costly books and journals available only to a few. Moreover, much of scientific experimentation relied on highly specialized and expensive equipment and reagents. In addition, high-impact scientific publication was largely disseminated via selective publication venues whose reviewers were insiders who preferentially accepted papers from other insiders.

Open science reemerges

An exception to this trend has been emerging in the field of machine learning (ML), where the past decade has seen an explosion of work by many diverse researchers. For example, an analysis 新しいタブ／ウィンドウで開く of the NeurIPS conference 新しいタブ／ウィンドウで開く (among the premier in ML) shows that in 2013, papers came from about 200 institutions, compared to about 1,300 in 2020. This growth was enabled by the ready availability of three important resources:

• Extensive open-source code (starting with OpenCV 新しいタブ／ウィンドウで開く and then TensorFlow 新しいタブ／ウィンドウで開く and PyTorch 新しいタブ／ウィンドウで開く) has hugely reduced both the time and expertise needed for writing ML code.

• Cloud computing has eliminated the need to obtain access to expensive compute infrastructure.

• Large, high-quality data sets: ImageNet 新しいタブ／ウィンドウで開く, published in 2012, was one of the most significant catalysts of the current AI revolution; and the multi-petabyte, freely available web crawling data provided by the nonprofit CommonCrawl 新しいタブ／ウィンドウで開く is one of the key data sets for training the current generative AI models.

All of these reduced the barrier to entry for newcomers in the field and allowed people from all over the world to both utilize the technology and contribute new insights.

Reading the above description, with its emphasis on compute infrastructure and data, one might believe that this type of impact is specific to the ML field. I argue that high-quality, accessible data can have a huge impact in other disciplines. The biomedical sciences have a rich history of such projects, starting with the Human Genome Project 新しいタブ／ウィンドウで開く (HGP), a community-driven effort that brought together more than 2,000 researchers from around the world. The economic, scientific and medical impacts of this effort are enormous. As one example, early drugs were found largely by serendipity, and their molecular and protein targets were often unknown; post-HGP, the targets are known 新しいタブ／ウィンドウで開く for almost all new drugs. From an economic perspective, a 2011 report 新しいタブ／ウィンドウで開く (updated 新しいタブ／ウィンドウで開く in 2013) shows that by 2010, the HGP — which cost $3.8 billion in investment — gave rise (directly and indirectly) to an economic (output) impact of $965 billion, personal income exceeding $293 billion, and 4.3 million job years of employment. The ongoing Moore’s law for genome sequencing 新しいタブ／ウィンドウで開く owes its origins to technology created as part of the HGP.

The HGP was the model for multiple other “open science” projects in biomedical science, many of which have had huge impact in their own right. For example, The Cancer Genome Atlas 新しいタブ／ウィンドウで開く (TCGA) has produced 新しいタブ／ウィンドウで開く over 2.5 petabytes of data, spanning 11,000 patients and 33 tumor types. By providing a rich “molecular catalog” of cancer, it has enabled 新しいタブ／ウィンドウで開く scientists from all over the world to study cancer processes and derive important insights. For instance, we now know that cancer can be driven by a range of both genetic and epigenetic changes and that cancers of different tissues are often actually driven by the same alterations and can be biologically more similar to each other than to other tumors of the same tissue of origin. This insight has been the underpinning of the field of precision oncology.

In a more recent example, the UK Biobank 新しいタブ／ウィンドウで開く (UKB) contains deep, high-quality data on 500,000 anonymized individuals, spanning genetics, multiple phenotypic modalities (including imaging and ‘omics) and longitudinal health follow-up. Due to its open nature, the Biobank was able to attract $480 million in external investment over the past five years (supplementing $40 million in core funding). The richness of the cohort has therefore increased considerably over time, translating into increased impact: in 2015, fewer than 100 publications cited the cohort; in 2022, that number was over 2,200. The UKB played a critical role in understanding the population impact of COVID-19, including long-term consequences on brain 新しいタブ／ウィンドウで開く and cardiovascular 新しいタブ／ウィンドウで開く health. A key factor in the explosive utilization of the UKB resource is its ease of access while preserving privacy. Similar data sets of high potential value (such as deCODE 新しいタブ／ウィンドウで開く or the Million Veterans Program 新しいタブ／ウィンドウで開く) have had considerably less impact because they have been locked away and out of reach for most researchers.

Enabling open science

Recent developments have the potential to enable a sea change whereby researchers from all over the world and diverse backgrounds can contribute to our scientific knowledge. However, we must be vigilant and proactive in ensuring that these tools help decrease rather than increase the gap between the haves and the have-nots, in which capabilities are available disproportionately to certain groups or deployed disproportionately to certain problem domains versus others. We need to make sure we create knowledge and tools that are egalitarian, open and universal.

One important trend is the ongoing shift towards open access knowledge, including both open courseware and the increasing adoption (sometimes even mandated 新しいタブ／ウィンドウで開く) of early and open access publishing, whether in preprint servers or open access journals. This is particularly critical for researchers who aren’t affiliated with wealthy academic institutions and industries.

Another, equally important transformation is the democratization of programming — a critical 21^st-century skill that has been consistently challenging to teach to a broader audience. Higher-level programming languages were the first step in making programming more accessible, followed by increasingly large code libraries. But these are nothing compared to the phase transition that is now upon us with the remarkable advent of generative AI models that are able to produce working code 新しいタブ／ウィンドウで開く based on a simple verbal description. We have barely begun to appreciate the magnitude of impact of that capability. Importantly, while early generative AI models are largely proprietary, there are recent efforts 新しいタブ／ウィンドウで開く to create open-access, open-source, multi-language models with liberal intellectual property (IP) rights on both the model and its derivatives. Such open tools will help ensure that the benefits of AI are broadly accessible.

Equally critical for democratization of science will be generating and making available data resources that empower diverse research communities. It has become common to view data as “the new oil 新しいタブ／ウィンドウで開く.” But how do we move beyond a limited set of proprietary “oil wells” to “alternative energy” resources that are more broadly available? I believe that areas of key societal challenges — climate change, renewable energy, sustainable agriculture, poverty, health equity or new therapeutics — could be hugely accelerated by the creation of large, high-quality accessible data resources that can enable broad scientific discovery. A thoughtful and rigorous creation of high-quality data resources is critical here, because data that are biased, faulty or not representative of the complexity of the problem (e.g., the diversity of the human population) could lead us astray 新しいタブ／ウィンドウで開く.

A 25th-anniversary reflection on the Human Genome Project 新しいタブ／ウィンドウで開く provides a valuable perspective on the ingredients necessary to enable projects of this level of impact:

• Creating a collaborative, shared resource —moving away from “individual researchers toiling away in isolation to answer a small set of scientific questions” and “focusing instead on the discovery of fundamental information that would inform many follow-on investigations.”

• Sharing highly open data, including “policies that shortened the time between the generation and release of data.”

• Planning for data analysis from the start.

• Fostering the creation and scaling of enabling technical innovations.

• Proactively addressing broader societal and ethical implications.

• Being audacious, quantitative and flexible in setting project goals, with a strong grounding in “explicit milestones, quality metrics and assessments” and a “willingness to iterate plans as needed.”

These guidelines could and should serve as a North Star in developing a new constellation of open-access, community-led, “big science” efforts to create technologies and data sets in key challenge areas. As discussed above, these initiatives can be implemented effectively via collaborative, community-driven efforts: a miracle of the commons 新しいタブ／ウィンドウで開く. While not inexpensive, we have seen that these efforts can easily pay for themselves many times over in terms of economic, scientific and societal benefits. Done right, these projects will enable a dramatically larger set of researchers to contribute their energy, enthusiasm and intellect toward these areas, and will help generate new and impactful solutions towards some of the most pressing societal problems.