C. elegans researchers, and were introduced and defended for the HGP by the nematode biologists John Sulston and Robert Waterston. In the C. elegans community, and subsequently in the HGP, daily sharing served the pragmatic goals of quality control and project coordination. Yet in the HGP human genome, we also argue, the Bermuda Principles addressed concerns about gene patents impeding scientific advancement, and were aspirational and flexible in implementation and justification. They endured as an archetype for how rapid data sharing could be realized and rationalized, and permitted adaptation to the needs of various scientific communities. Yet in addition to the support of Sulston and Waterston, their adoption also depended on the clout of administrators at the US National Institutes of Health (NIH) and the UK nonprofit charity the Wellcome Trust, which together funded 90% of the HGP human sequencing effort. The other nations wishing to remain in the HGP consortium had to accommodate to the Bermuda Principles, requiring exceptions from incompatible existing or pending data access policies for publicly funded research in Germany, Japan, and France. We begin this story in 1963, with the biologist Sydney Brenner’s proposal for a nematode research program at the Laboratory of Molecular Biology (LMB) at the University of Cambridge. We continue through 2003, with the completion of the HGP human reference genome, and conclude with observations about policy and the historiography of molecular biology.The Bermuda Principles for DNA sequence data sharing are an enduring legacy of the Human Genome Project (HGP). They were adopted by the HGP at a strategy meeting in Bermuda in February of 1996 and implemented in formal policies by early 1998, mandating daily release of HGP-funded DNA sequences into the public domain. The idea of daily sharing, we argue, emanated directly from strategies for large, goal-directed molecular biology projects first tested within the “community” of  Homo sapiens and five model organisms between 1990 and 2003 and helped to refine many key genomics technologies.1 The HGP culminated in a draft human sequence completed in 2001 . A more refined human reference genome was published in pieces through 2004 , with the model organism sequences all made publicly available by 2002 . The HGP was the nonprofit and publicly funded effort that generated the first genomic reference sequences for  by 2002 .2 This d4 Drafted and ratified as the official HGP-wide data-sharing policies at the 1996 International Strategy Meeting for Human Genome Sequencing at the Hamilton Princess Hotel in Bermuda, which was sponsored by the British nonprofit philanthropic funder the Wellcome Trust, the Bermuda Principles stood at the core of the strategy that the HGP’s leaders crafted to decipher the six target genomes rapidly, to high quality, and with minimal restrictions on data access .5 The Principles were applied first to HGP human genomic sequences and, throughout 1998, to the remainder of the model organisms included in the HGP: yeast (Saccharomyces cerevisiae), bacteria (Escherichia coli), nematode worm (Caenorhabditis elegans), fruit fly (Drosophila melanogaster), and mouse (Mus musculus)  . They hausculus) .6 Since the conclusion of the HGP, the NIH in particular has continued to write rapid data-sharing mandates into its grant requirements, especially in genomics, with additional public and private funders and large communities of researchers, such as in proteomics, following suit with their own sets of policies, recommendations, and requirements  framework of “interactional co-production” pioneered by Jasanoff and developed with San-Hyun Kim , 2015.8 governed , 2005. SA related line of inquiry by legal and policy analysts has built on broader theoretical frameworks of the commons, especially the genome commons, and has explored its nature and function in biology from the late twentieth century to the present . The cen9 The necessity of rapid sharing to monitor the HGP’s progress and quality became apparent under these uncharted circumstances, giving rise to what Hilgartner calls the “‘when’ question” regarding the mandated release of HGP-funded data  still sometimes fell short. Following the 1996 International Strategy Meeting for Human Genome Sequencing in Bermuda, the daily-sharing standard remained but a rhetorical aspiration in many other places. Some center leaders never even attempted to abide by it, trying to release data as rapidly as possible but treating daily sharing as unrealistic or unwise, despite the HGP’s explicit policy calling for it. Practices also diverged for model species after 1998, particularly in the HGP’s laboratories located outside the US and Great Britain ; and the “completion” of the HGP was hailed in 2003 . These accomplishments, in turn, served as grounds for the Wellcome Trust and the NIH to extend the Bermuda Principles to other genomics and large-scale “community resource projects” in biology  to include multicellular animals (metazoans)  at the University of Cambridge in England, within the Division of Molecular Genetics led by Francis Crick of DNA-structure fame, Sydney Brenner spent the latter half of the 1960s building a research program on the physiology and genetics of tazoans) , p. 286.briggsae , p. 475,oratory” , p. 413.oratory” . As the oratory” , Chap. 9C. elegans, from its genetics to its biochemical and cellular environments through cell lineage maps and behavioral phenotypes  in Woods Hole, Massachusetts became a postdoctoral fellow in 1972 to work on muscle mutants , p. 88.  systems , p. 93.  systems .Worm Breeder’s Gazette), and later through worm-strain sharing centers  and electronic databases   .18 There rapidly , p. 100.C. elegans suggested that a fixed cell lineage generated the entire complement of cells in embryogenesis, implying no further divisions after hatching  inserted into the genomes of bacterial hosts  on Long Island. CSHL had been home to Max Delbrück’s phage group after the Second World War , 2010, aical map , p. 103.ical map , below. rway too , p. 48. rway too .22 Accor release . Watson’C. elegans sequencing,” ran from 1993 through 1998 with Waterston as the Principal Investigator  and the US NIH, Waterston and Sulston scaled up their preliminary sequencing efforts, building on existing genetic and physical maps , with the Centre also leading the worldwide distribution of C. elegans clones Although Waterston’s records “lack notes on some of the crucial decisions” about the evolution of his and Sulston’s sharing practices from this point forward (Waterston to authors Feb 2017), we have utilized grant applications and other sources to reconstruct the process.phrap (or “Phil’s revised assembly program”) handled clone alignment; and phred (“Phil’s revised editor”), developed in its early phases with the bioinformaticist LaDeana Hillier and first tested on worm clones sequenced by Waterston in St. Louis and Leroy Hood, Lee Rowen, and Maynard Olson in Seattle, assigned quality scores to bases, outperforming the software installed on the sequencing machines from the then-predominant commercial manufacturer, Applied Biosystems (ABI) -Autofinish, a graphical tool for visualizing and editing contigs during finishing  and “unfinished” data. Ideally, finished sequences were to contain minimal errors , no gaps, and annotations flagging matches between sequences and known genes or other functional elements. There was, as of yet, no common standard for maximum error rate in finished sequence, but unfinished data—essentially, contigs assembled by computers when the clones went through the early sequencing machines—contained more errors. The laboratory of the computational biologist Phil Green, first at Washington University in St. Louis and later at the University of Washington in Seattle, developed the predominant algorithms and software for aiding in clone alignment and the burdensome human labor of finishing, the major bottleneck in completing any genome , Chap. 2C. elegans and human sequences to GenBank, also sharing the nematode data with their community through ACeDB and the Worm Breeder’s Gazette, but they did not want colleagues building on unreliable or low-quality data . Despite holding back their unfinished sequences, however, release of finished data was rapid, even for sequences that were too difficult to complete in a timely manner. These recalcitrant data were annotated as such and released to the public domain as quickly as possible, with the explicit intent “clear that we will submit the sequence as soon as it is ready, not waiting for publication or imposing any other delay” (Waterston to authors Jan 2017). Almost always, this type of release occurred before the NIH-DOE 6-month requirement.Through 1993, unfinished sequences were simply too unreliable for Sulston and Waterston to share, either with collaborators or more publicly. They released their finished, or “completed,” phred and phrap were improved and adapted for human sequencing (Waterston to authors Feb 2017). After the 1994 paper announcing the completion of 2.2 continuous Mb of nematode DNA Contigs longer than 1 Kb  will be posted immediately … after … checking, but independently of whether the data has been reviewed further for content. This is a policy that was instituted for the C. elegans genome in the past 2 years” . Pragmatic justifications for data sharing thus prevailed in nematode biology, spilling into human biology through the Cambridge and St. Louis sequencing centers. But in its generalization to all of the HGP’s human-sequencing centers, the daily-sharing standard, as enshrined in the Bermuda Principles, encountered new complications. The origins of these complications reached to the very foundations of the HGP: its dual roles as a publicly funded infrastructure project and as a driver of economic growth, and, perhaps counter-intuitively, the attendant ambiguities about sharing and the commons that had plagued it from the beginning.Both Sulston and Waterston strove for daily release, but pre-publication sharing was the more important outcome. It created common resources in the nematode and human genomes, and in so doing helped to coordinate these large-scale, distributed, and novel projects while also helping to delineate, for the data’s users, between sequences of unfinished and finished quality. It must be noted that many in the worm community were dedicated to a “democratic” biology, as the FOSS bioinformatics teams had been. They were skeptical of the patent claims proliferating on DNA sequences, such as those for the breast cancer genes nd BRCA2 . For Joh28 In May of that year, Robert Sinsheimer, a molecular biologist and also the chancellor of the University of California at Santa Cruz, pitched an idea for a project to sequence the human genome (Technologies for Detecting Heritable Mutations in Human Beings). Reading a draft of this document, Charles DeLisi, the new OHER director, was struck with his own idea for a “dedicated human genome project” within the DOE  engaged in a competition with the Atlas of Protein Sequence and Structure creator, Margaret Dayhoff, for NIH funding to host the US database that would become GenBank , civil servants at the Los Alamos National Laboratory (LANL) and elsewhere were tasked with investigating the biological effects of radiation . Under t GenBank . To make GenBank . The LAN30 Dayhoff’s team, in contrast, proposed paper distribution, as had been the case with Atlas of Protein Sequence and Structure , and grew into one of the most trafficked thoroughfares to GenBank by the late 1980s  before some published sequences appeared in the public domain . Also in 1986, the National Research Council (NRC) of the US National Academy of Sciences investigated the feasibility of mapping and sequencing large genomes, with Sydney Brenner representing the viewpoints of the UK mappers and sequencers and Maynard Olson bringing further first-hand experience to the study.33 In 1987, a DOE committee recommended a 15-year project to map and sequence the human genome . Shortly thereafter, the DOE and the NIH began allocating explicit funding for mapping and sequencing, securing their first official HGP budgets from Congress . John Sulston (who was still at the LMB) discussed data sharing and the technologies likely to be required for successful and efficient human genome sequencing, and other attendees included Christian Burks (GenBank), Walter Gilbert (Harvard), Ruth Kirschstein (NIH), and James Watson (CSHL). No one in the yeast community, such as Maynard Olson, attended, and John Sulston was the only physical mapper present.The US HGP continued to gestate through 1986 and 1987. In 1986, the DOE announced its Human Genome Initiative (HGI), allocating $5.3 million to the national laboratory network for resource and technology development .In 1988, in a crucial pair of reports, both the NRC and the OTA formally endorsed a human genome project. The NRC report enjoyed input from Sydney Brenner and Maynard Olson. Alongside investment in DNA sequencing technologies, the NRC recommended the production of a reference human genome via the map-then-sequence strategy . In the late 1980s, various credible views existed on how best to handle genomic data. Some experts, such as John Sulston, Robert Waterston, and others in the C. elegans community, veered far from the academic norm of sharing data at the time of journal publication. Other individuals, including Maynard Olson, tended to hem much closer to it.Large-scale genome sequencing, in short, was uncharted territory. In England, Rodger Staden had helped Frederick Sanger begin computerizing his sequencing methods in the late 1970s , 1986. Tuencing” , p. 1; aMapping and Sequencing the Human Genome, pp. 82–84, 91). It also noted that the project might generate “materials of potential commercial value,” such as “clones that encode previously undiscovered hormones, growth factors, or mediators of immunity” (p. 99). But on the matters of “copyright protection of the data and … intellectual property,” the NRC was strikingly vague (pp. 99–100). “Sequences and material generated by … publicly funded projects should and even must be made freely available,” the report asserted (p. 8). But no timeline was provided, despite the report’s insistence that “a single, unified policy must prevail if the information is to be accurately acquired, stored, analysed, and distributed” (p. 85).Beyond exhortations for sharing data, the 1988 NRC report failed to articulate a clear data-sharing policy. It stressed the creation of a commons, “to be used by biomedical scientists to accelerate the understanding of human biology and the application of this knowledge to human health,” alongside a central database for sharing maps and sequences (Diamond v. Chakrabarty (447 U.S. 303 (1980)), which declared that a General Electric patent, claiming a bacterium engineered to digest hydrocarbons, was valid: the first US patent on a man-made living object . The historian Nicolas Rasmussen has chronicled the history of rDNA cloning for many such therapeutically valuable proteins, including for insulin, interferon, tissue plasminogen activator, erythropoietin, and human growth hormone (2014). These developments reflected the sparkling commercial potential of the new molecular biology, but in turn fueled uncertainty and outright alarm regarding the issues of sharing and secrecy. A 1982 summit in California had already addressed the widespread concerns about biologists in start-up advisory roles . The OTA report envisioned a “clear role for congressional oversight … in ensuring that data are shared promptly and fully,” but suggested only that the “disclosure of data should not be long delayed by policies designed to encourage patenting,” because “data per se are not eligible for patent protection” . A timeline and policy for data sharing in genome projects once again remained elusive, as had occurred with the NRC’s report on the same topic, from that same year.The 1988 OTA report, Understanding Our Genetic Inheritance, implicitly acknowledged the NIH as the lead agency, with an NIH-to-DOE funding ratio of 2:1 (pp. 30–37). The National Science Foundation (NSF), National Institute for Standards and Technology, and several Department of Defense research and development agencies were also expected to play less conspicuous roles . Partly to encourage data sharing and quality control, a digitally compatible language of “sequence-tagged sites” (STS) would help to decouple physical mapping from material clones . An initial 3% of the budget was dedicated to addressing the ethical, legal, and social implications (ELSI) —a number raised to 5% in 1992 .40 By 1990, the DOE had established three genome analysis centers at the Berkeley, Livermore, and Los Alamos National Laboratories, working mostly on physical mapping and technology development . The NIH had planned between 10 and 20 Genome Science and Technology (GESTEC) centers of its own, each set to address a discrete task such as the physical mapping of a single chromosome. The concurrent efforts in Britain in human and model organism genomics were also highly advanced, beyond the C. elegans tradition that had been spearheaded by Sydney Brenner and perpetuated by John Sulston, Alan Coulson, and others. For example, the Austrian geneticist Hans Lehrach led the Genome Analysis Laboratory at London’s Imperial Cancer Research Fund (ICRF), boasting a “reference library” of data and materials for physical mapping in the fission yeast, S. pombe (41 Another effort in the U.K. was the MRC-funded Human Genome Mapping Program (HGMP), which was heavily orchestrated by Brenner and the geneticist Walter Bodmer, then the director of research at the ICRF, and focused on the localization and sequencing of highly expressed genes ; and after the Bermuda meetings, Généthon-identified genetic markers became a standard mechanism for coordinating human sequencing, allowing HGP centers to identify and “claim” regions of the genome that they planned to sequence .Genomics in France and Japan around 1990 each reflected a similar diversity of actors, funders, and institutions. A private-sector collaboration between the French Muscular Dystrophy Association (AFM), the Centre d’Etude du Polymorphisme Humain , and the laboratory Généthon began in 1990 and was already producing, and would continue to produce, world-class genetic maps  as well 43 The Japanese-US competition in genomics was particularly intense, given the trade tensions between the two nations that had lasted throughout the 1980s. Yet as the leader of the Japanese sequencing projects, Akiyoshi Wada had stimulated international efforts to organize a coordinated human genome project, visiting the US in 1986 to promote such collaborations and also to help squeeze genomics funding from his own government . With support from various other agencies, including the Ministry of Education, Science, and Culture (or Monbusho), in 1991 the STA expanded its DNA sequencing program to include projects in universities and also to mapping in human chromosomes 21 and 22  for a p1 and 22 , p. 217.1 and 22 .Mapping Our Genes, pp. 157, 189). The Genatlas, which collated genetic and clinical information, grew out of the 1987 HGMW in Paris; and in 1990, the Howard Hughes Medical Institute (HHMI), later joined by the DOE, the NIH, and several other benefactors, established the Genome Database (GDB) at Johns Hopkins University, a repository for maps and related data including STS mapping markers (Online Mendelian Inheritance in Man (OMIM) (44 McKusick himself founded the Human Genome Organization (HUGO) with backing from the HHMI in 1988  , p. 481. in 1988 , p. 44.  in 1988 , p. 219.G-Nome News  . Francis Collins from the NIH participated in these deliberations (Collins interview 2012); and Jane Rogers, who organized the sequencing groups at the Sanger Centre and was thus instrumental in discussions about ramping up the human efforts, recalls that Robert Waterston (who had been visiting the UK), David Bentley, and Richard Durbin were also involved, the latter two as the heads of human genetics and informatics at the Sanger Centre, respectively  (Rogers interview 2012). The Wellcome Trust had a tradition of small workshops, called the “Dormy House workshops. They would last two or three days, there would be about 50 participants…. And we usually did it in a very nice hotel in the Cotswolds, it’s called Dormy House” . The inaugural International Strategy Meeting for Human Genome Sequencing, held at the Hamilton Princess Hotel in Bermuda from February 25–28, 1996, grew directly out of this Dormy House tradition.On those four days, the weather in Bermuda happened to be horrible , p. 117.45 The meeting’s opening session incorporated an “enumeration of existing plans,” especially from the “Sanger Centre, St Louis, MIT, LBL [Lawrence Berkeley], Japan, Germany, France, [and] TIGR ” laboratories (1996 Wellcome Trust Programme). The people delivering these updates were mostly white and male, and all hailed from rich nations, yet the size of the assembly was still impressive, given that a large number of genomics experts believed that the mapping-to-sequencing scale-up in the human genome would require a reduction in numbers both to streamline the workflow and to concentrate expertise existing “at the center director level” (Understanding Our Genetic Inheritance), and a decade after the first whispers of generating a human reference genome within the US DOE, this was the project’s largest organizational meeting to date.The approximately 50 invitees included genetic and physical mappers, sequencers, bioinformatics and computer experts, and administrators from public, nonprofit, corporate, or other organizations already affiliated with the HGP or intent upon becoming so  boasted considerable attendance, including Frank Laplace as the administrator for the German human genome program (DHGP) (1996 Bermuda Programme and Participants List). DHGP biologists in attendance included Hans Lehrach, formerly of the ICRF in London, and André Rosenthal, from the Max Planck Institut für Molekulare Genetik (MPI) in Berlin and the Institute of Molecular Biotechnology (IMB) in Jena, respectively . Yoshiyu46Germany, France, and Japan each had established human sequencing programs soon before the first Bermuda meeting. Germany had arrived late to postwar molecular genetics, a relic of its Nazi past . The 8-yThe bulk of the Bermuda discussion regarded pragmatics, particularly for quality control, data sharing, and coordination . In response, a member of the worm project noted how in the nematode, “34 Mb had been sequenced by the two centres (WashU and the Sanger Centre), 6000 genes had been identified,” and “key features of the strategy were the use of multilevel maps (cosmids and YACs) to resolve difficulties and provide a range of resources for use by the scientific community” (1996 Wellcome Trust Bermuda Meeting Report). Along these lines, another member described the worm community’s data-sharing norms:[W]e rather piously … go on about our communal effort, but actually, we all subscribe to this…. The entire worm community was involved in putting together the genome map, and I do mean the entire, that’s not just … the two central map assembly and subsequently sequencing labs, but also from the beginning of the map, and now the central labs strictly refrain from participating to their advantage in the use of the data. That’s not to say they can’t use it, but they strictly refrain from using it before anybody else does…. That’s why I draw attention to the way that the data is released. And this has allowed, and encouraged, the effect by which the community of molecular geneticists in the worm … have made the links between genes, [on] the functional side, [and] the genetic map and the physical, and subsequently sequenced map…. It’s a communal property. Pre-publication sharing had clearly traveled from the larger nematode laboratories to the smaller ones. Analyses took a back seat to sharing, and all the community’s centers enjoyed equal access to the sequences. While there were certainly political, and strikingly Mertonian , underto47 Waterston, Sulston, Michael Morgan, and Susan Wallace all remember that the “interlude” in Bermuda in 1996 was meant to include Craig Venter, who had to leave the meeting early ” (Collins 1996 Notes). The 1 Kb criterion, representing the shortest contigs thought likely to be accurate, came directly from the worm, as well as from the human sequencing Waterston and Sulston had piloted together following their successful nematode work . Maynardng early , p. 46.  meeting . WhetherArabidopsis project had recently adopted a 3-month sharing policy. Why not follow one of these alternate strategies, cutting the non-worm biologists some slack? Why daily release? There were other ways. As a fourth individual in Session III noted directly to Sulston and Waterston:Concerns over data quality were paramount. “We have to agree on public release being very important,” one delegate had conceded in Session I, but “we can debate whether or not that should be done hourly, weekly, monthly … every three weeks or so it has to be done” (1996 Bermuda Meeting Transcript). In Session III, one attendee asked, “Why can’t you just finish your BAC … so what if it takes another week? Is that that bad? I don’t want to see sloppy stuff out there. There’s enough of that out there now.” A second cautioned: “All I want to do is make sure that we don’t get so enthusiastic about instantaneous [release], that we run into some other realistic problems about whether people can deliver on that.” Sulston and Waterston’s clout on the issue was clear, but deep reservations about daily data sharing remained. “You’ve got me basically won over,” a third person said. “The two of you are powerful enough that I’m basically won over though I’m a little uncomfortable…. [I]f I’ve got 10 megabases of unfinished but … publicly available sequence … there’s the possibility of … mismatch.” The mouse and human STS projects had just moved from 90- to 30-day release—clearing the NIH-DOE 6-month requirement easily, but also enabling “the chance to make sure” that the data to be released “was somewhat cleaned up for errors and nonsenses”—and the NSF Many people are just, frankly, uncomfortable around the table, not putting out as much data as you two guys do, with the notion of baring their soul on a nightly basis, because then it becomes infinitely obvious that things have screwed up in the past 2 weeks. And I’m quite serious. It’s just too early to press the notion of nightly … what we ought to do is set a precedent of regular [release]. BRCA2 project involving the Sanger Centre, there was definitely a trade-off in balancing the immediate sharing of unfinished data with the later release of more curated data. But, “experience … had shown that such information [released daily] could be effectively utilised by both academic and commercial groups” (1996 Wellcome Trust Bermuda Meeting Report). The Sanger Centre had released 2.7 Mb of finished human sequence into the public domain by 1996, with the goal of scaling up from 20 Mb per year to 100 Mb annually in the next 3 years; and in Session IV, on “Informatics,” a member of the Washington University Genome Sequencing Center noted that its goal was “to provide immediate data release with local annotation of sequence,” the discrete “units of release” including BACs (for human sequence) and “cosmids which could be updated into larger contigs” (Ibid.). Along with James Watson, moreover, Sulston and Waterston chaired the 1996 Bermuda meeting’s fifth and final session, focused on “[c]o-ordination,” “[f]unding agency viewpoints,” and “[c]riteria for comparing cost/accuracy/value of sequence data” . The issues of data release and developing a consistent data-sharing policy for the HGP emerged as key discussion points yet again, with Sulston taking notes on the white board while Waterston directed the conversation and edited the wording for a draft data-release strategy as the final session progressed ” onto individual laboratory websites, with the “immediate submission of finished annotated sequence” to the three public nucleotide databases . As Mark[D]ata should be released regularly and very quickly from large-scale sequencing projects, perhaps as frequently as daily but maybe weekly would do; this refers to preliminary data  which would be put up locally automatically; it was also agreed that finished, annotated sequence would be immediately submitted to databases. C. elegans genome project, with the logic once again including, “the need to have buffers between different aspects of the operation” given that the “different cosmids have different problems,” and “we don’t have all parts of the pipeline in perfect balance all the time” . Nevertheless, Olson had had some an inkling of what was to come in Bermuda before the meeting that February, and he explained to Waterston afterward that, “I do not believe that any scientists should ever be forced into what amounts to real-time release of raw data” (Olson to Waterston 1996). He was furious, hated the Bermuda Principles, and also noted later that, “I do not like manifestos and ‘unanimous’ pronouncements on highly technical issues. Most people are simply being rail-roaded under these circumstances, in response to [an] unwise conversion of a technical issue into a moral one” (Ibid.). Olson had no intention of patenting sequences, and favored open access to data , p. 940.Science. J. Craig Venter and Mark Adams of TIGR, and David Bentley of the Sanger Centre, published dueling opinion pieces on the issue, with Venter and Adams arguing against the Bermuda Principles and Bentley supporting daily release . Sequence volume, including the actual-to-promised output ratios for sequences from each center, would be monitored over the Human Sequencing and Mapping Index . Waterston, similarly, remembers that if the data “wasn’t out there, it didn’t count” as a part of the human reference sequence , labeled “punishment by public humiliation” in 2011 .48 Some sequence . In 2001h teeth” , p. 1192primary genomic” in front of “sequence” . He was chronicling the mood in the room, which apparently favored the idea that: “Primary means in the absence of additional experimental information about function or diagnostic utility.” Moreover, the 1996 discussants elaborated: “Endorse the principle that genomic sequence, in the absence of any additional experimental information about functional or diagnostic significance, is not an appropriate subject for patent protection” .Yet further reasoning for daily sharing centered on patents. “We want immediate data released for more than one reason,” a Session III participant noted, “for reasons of coordination as well as” (1996 Bermuda Meeting Transcript). The speaker was cut off. Francis Collins’s notes from that session, however, reveal the reference. The interlude from earlier in the meeting had brought up the patenting elephant: “No patenting of genomic sequence & fundamental info,” Collins’s minutes from that moment read (Collins 1996 Notes). They continued—”Genes should be freely available for research work”—and from Session V, the “final session,” Collins took down the newly-drafted Bermuda Principles with key clarifications: “Aim to have all human primary genomic sequence from large scale sequencing centers freely available and in the public domain for both research and development, in order to maximize its benefit to society.” To the first white board version of the Principles, Collins had added the phrase, “human Science stoking further controversy through news stories published as the incidents unfolded  rejected the initial Venter-Adams patent applications in 1992 . But disr’s ESTs . A year BRCA1 and BRCA2 genes, responsible for inherited risks of breast and ovarian cancers, was smoldering between Myriad Genetics in Utah and OncorMed in Maryland, alongside parallel clashes over BRCA2 in the UK and elsewhere .From 1991 to 1993, several companies began seeking patents on cDNAs . These results validated what Aristides Patrinos of the DOE called “the religious issue” . One Bermuda attendee remarked in 1996 that, “early release … is the most effective way of ensuring … rapid exploitation by the whole community of workers in human biology” . While those without reliable Internet access would certainly have missed out on many of these benefits, scientists in the larger HGP centers would not have held much of an advantage over smaller HGP groups given this equal access to the sequences. neurons . He knews issue” , or getts issue” , p. 1192s issue” . Public Human Genome News, the other from the Wellcome Trust—which also advanced two additional goals that had been included in the inaugural white board draft of the Principles handwritten by John Sulston: “to encourage research and development and to maximize the benefit to society” . Unfinished, raw “sequence assemblies” of the designated length were to be “released as soon as possible” onto individual laboratory websites, while “in some centres, assemblies of greater than 1 kb would be released automatically on a daily basis,” and any “finished annotated sequence” was to “be submitted immediately to the public databases” (1996 NIH Bermuda Meeting Report). While the 1996 handwritten Principles had not acknowledged the issue of laboratory size explicitly, the report of the 1996 meeting confirmed that one of the major goals was “to prevent such [large scale] centres [from] establishing a privileged position in the exploitation and control of human sequence information” (1996 Wellcome Trust Bermuda Meeting Report). The point about exploitation was affirmed in two April press releases after the 1996 meeting—one in the HGP newsletter Human Genome News and Wellcome Trust statements, contain sections on participant endorsement, technical standards, and project coordination (Reaping the Benefits of Genomic and Proteomic Research), did contain sections on patenting, with the NCHGR, reflecting the wording from Collins’s notes, explicitly discouraging patents on DNA sequences of unknown function and the NRC writing rather vaguely that, “it was … agreed that patents should not be sought” .55 While the HGP’s most formal announcement of the Bermuda Principles did occur through HUGO on the ORNL website, the most likely sources for the NRC’s 2006 account are the NCHGR’s statement on patenting and the Wellcome Trust’s press release, both from April 1996.The most public statement of the Bermuda Principles, however, was issued by HUGO after apparent input and revision from the 1996 Bermuda meeting participants . This stdination . Additiodination  and the Mapping and Sequencing the Human Genome; Mapping Our Genes; Understanding Our Genetic Inheritance). Yet the period from 1990 to 1992 had shown a real jumble of data-release strategies amongst the HGP’s early mapping and sequencing centers, with some centers releasing data every 40,000 base pairs, another every 100,000, and other PIs worrying about the implications of pre-publication data release for junior investigators whose careers rested on gaining credit for analytical articles  had asked investigators to use a private database for the initial housing of their data, from which the data was moved to a public space “accessible to any qualified researcher” within one year or at the time of journal publication, allowing for the “timely sharing of information while affording investigators some proprietary protection for their results” . The French CEPH infrastructure, however, despite reaching to North America and Africa, had been judged as too small for a scale-up to the broader HGP, and there was no enforcement mechanism.57Hans Lehrach’s ICRF “reference library” policies had similarly given collaborators between 6 months and 1 year to report gene hunt data gleaned from reference library resources , p. 140.The situation was similar in Britain. The Resource Centre of the MRC Human Genome Mapping Program (HGMP), a complement to the research arm of the project focused on medically-relevant genes , p. 536,Most attendees, including our interviewees, saw the 1996 International Strategy Meeting in Bermuda as crucial, enabling the alignment of working priorities and technological strategies . The mee58 The 1997 participants, in turn, solidified the standard for what was to count as “finished” sequence: 99.99% accuracy, with no more than one error per 10 Kb, reflecting the inclinations of the attendees from 1996 . This standard was met with general agreement—except from Craig Venter, who hated it—and was especially lauded by Francis Collins and Maynard Olson, who insisted on producing a reference genome that would be analytically useful for generations  and phrap  programs were to be included with finished sequence, as it was released to the public databanks (1997 Meeting Report Summary). Moreover, a “data exchange exercise” was agreed upon for quality assessments, wherein “raw sequence data would be exchanged among sequencing centres,” the “centres would reassemble the data and identify outright discrepancies or ambiguities with reference to the sequence submitted to the database,” and “the same data sets would be sent to two centres which would hopefully engender competition to detect errors” (1997 Bermuda Meeting Report).From 1996 through 1998, the details of the Bermuda Principles were modified slightly as the sequencing and assembly technologies improved and the HGP’s sequencing efforts continued apace. In 1997, the daily-release trigger was extended to 2 Kb . The goal for minimizing gaps in the finished sequence was “set at zero [gaps],” with “only 1 gap in 250 kb of ‘finished’ sequence” deemed “allowable” and an aim “to reduce this to 1 in 1 Mb or better” articulated (1997 Meeting Report Summary).Finally, in 1997, the Human Sequencing and Mapping Index shifted from Susan Wallace’s office at HUGO to the NIH National Center for Biology Information (NCBI), with additional changes to where unfinished sequence was to be reported and which of the HGP’s organisms, alongside human, the Bermuda Principles were to be applied . In 2009, echoing the title of a 2003 book by Andrew Brown, the LMB worm biologist Sydney Brenner wrote of his participation in the HGP that: “In the beginning was the worm…” , p. 413.Human Genome News reported that the Principles “were endorsed unanimously by the attendees” , and the fearlessly contrary Maynard Olson does not believe “that there was even majority support at Bermuda for these principles that were unanimously adopted” . The meeting report noted that “all participants voted, on a personal basis to endorse these principles,” yet “some centres may find it difficult to implement thse [sp.] principles because of legal constraints and it was agreed that all participants should be given an opportunity to comment on the final Bermuda statement before it was released” (1996 Wellcome Trust Bermuda Meeting Report). The bioinformaticist LaDeana Hillier recalls that the group sat in a U-shape, and it felt much “like the United Nations…. It was … basically going around the group and asking each … are you willing to agree?” (Hillier interview 2012). tendees” , p. 19. tendees” , but Fraadopted” . In 2012C. elegans, of the sharing norms developed within this community, and as John Sulston recalled in 2011: “The two of us [he and Waterston], were … promoting the map…. [T]hen as it got bigger we pasted it on the wall…. So it [the first Bermuda meeting] was a … third session … of trying to listen to the anxieties, to reassure people, and then … distill onto the board” . Jane Rogers liked releasing data as rapidly as possible, so long as users understood that the quality would vary (Rogers interview 2012). Eric Lander loved the Principles too, and he remembers having discussed the idea with Sulston and Waterston before the first Bermuda meeting . Michaele board” . While oBRCA2, and following discussion among the trustees and the board of governors at the Wellcome Trust, the Trust adopted the Bermuda Principles relatively swiftly, and in 1996 deployed them as conditions on their HGP human sequencing grants . “We did have in-house questions … as to how all of this was going to work,” Michael Morgan remembers, but when “it was suggested that this data could be captured in some way by nefarious individuals who would then go ahead and patent it,” the relevant leaders at the Trust were persuaded . Indeed, “there was nobody at the Sanger Centre who was going to do sequencing other than … by those principles because they were the ones who were driving the principles” (Ibid.). By May 1996, “assembled data” from contigs longer than 1 Kb was placed on the Sanger Centre’s “ftp site as it becomes available,” with daily updates and the caveat that “this data is clearly marked on our site as incomplete data, subject to error and possibly even to misidentification. People use it at their own risk,” though further curated sequences were submitted “to the public databases after they are finished and annotated in a timely manner” . Drawing on the daily release of sequences from prior work on  manner” . In 1998 manner” , contingBRCA2 in the UK had only strengthened Sulston’s resolve, and with leverage from the St. Louis-Merck Gene Index Project, he and Waterston convinced Mike Stratton, of the UK Cancer Research Campaign, to implement immediate release of his data associated with this gene . Sulston and Waterston’s pressure swayed Collins, who became the chief architect of the NIH’s response to the Bermuda Principles.Roadblocks to this policy existed, however, including in the US. Maynard Olson, Mark Adams, and Craig Venter had already vehemently vocalized their reservations regarding data quality, and Francis Collins even went through an “evolutionary process” (Collins interview 2012). In the 1980s, Collins had found himself in the midst of several heated races to find disease-associated genes, including those for cystic fibrosis and neurofibromatosis. While working on the gene for Huntington’s disease, Collins’s team at the University of Michigan had broached collaboration with Sulston. “John’s very clear statement was … we have to make the data available to everybody…. And … that was the first time for me as a gene hunter in the human genetics community that this immediate access had been proposed as a requirement of a collaboration.” At first, Collins was “taken aback,” but “with a bit of pain and suffering” was convinced, especially for sequences of unknown function: “It seemed to me by about 1991, 1992 … there’s no justification for holding onto this” (Collins interview 2012). The patent battle over Science in 1996, neither the NIH nor the DOE could “legally restrict researchers or their institutions from applying for patents” . The statement acknowledged that, “grantees have the right … to retain title to subject inventions,” but it also warned, reflecting Collins’s notes (Collins 1996 notes), the transcript from the 1996 meeting , and the contemporaneous press release from the Wellcome Trust (1996 Wellcome Trust Press Release), that:Raw human genomic DNA sequence, in the absence of additional demonstrated biological information, lacks … specific utility and therefore is an inappropriate material for patent filing. NIH is concerned that patent applications on large blocks of primary genomic DNA sequence could have a chilling effect on the development of future inventions. (Ibid.)59 Plenty of gene hunts were, in reality, occurring in the HGP’s centers, but raw sequence patents do appear to have been avoided (The NIH would “monitor grantee activity … to learn whether or not attempts are being made to patent large blocks of primary … sequence” (Ibid.). Violators could have lost their funding, yet it does seem that the NCHGR’s grantees complied voluntarily, at least with the patenting provision. No Determination of Exceptional Circumstances (DEC), which could have allowed the NIH to circumvent the Bayh-Dole Act in order to prohibit incompletely characterized EST patents, was ever filed .The situation in the DOE was even more complicated. “I think at that point since the people we were imposing upon had all been [a] part of the decision there was not a lot of objection,” Francis Collins recalled (Collins interview 2012). This was certainly true of Elbert Branscomb, who supported the Principles . But whiIn continental Europe and Japan, a much larger gulf existed between the HGP scientists and their funders. Guyer had written in 1996 that the Principles were “understood to be the sense of the attendees and that different organizations/agencies/countries might be under … constraints that might or might not allow them to adopt this as policy” (Guyer to Collins 1996). At the first Bermuda meeting, some had certainly felt blind-sided. Andreas Weller, Frank Laplace, and Ursula Hurtenbach of the German BMBF recall their “perception … that the speed of publication of results was an important issue,” but that in 1996 no international consensus had yet been reached (BMBF to KMJ 2012 and 2013). Along similar lines, Masahira Hattori from Japan remembers that most of the British and American delegates had seemed aware of the “outline of the Bermuda Rule,” hence the criticism that his team had received for not having declared the sequencing of human chromosome 21 before beginning it . Yet not60 Yet no such grace period, as had been discussed in Session III of the 1996 Bermuda meeting (1996 Wellcome Trust Bermuda Meeting Report), existed in many other patent jurisdictions, so outside the US the Bermuda Principles really did endanger even patents that reflected genuine novelty , their protein products, disease-causing sequence mutations, manipulation techniques (such as rDNA), and drugs developed from these inventions . In the ng field , but to results” , p. 111.results” , p. 536.62 In 1996 and 1997, under the administration of Frank Laplace, the BMBF awarded 60 peer-reviewed grants, three of which constituted the German Genomic DNA Sequencing Consortium led by André Rosenthal, Hans Lehrach, and Helmut Blöcker.63 The main goal of the consortium was “to establish a national research network (DHGP) for the identification of medically relevant genes and … analysis of their structure, function, and regulation.” A second purpose was “to increase knowledge on the etiology and pathology of diseases with high socioeconomic relevance,” and a third was the “development of new products and services by the German pharmaceutical and biotech” industries , and the Max-Planck Society, three predominant research patrons in Germany, published the funding concept that launched the German Human Genome Project (DHGP), aimed at “the development and strengthening of the German scientific community in the area of human genome research at large” .62 In 19dustries .64 The development resembled the British HGMP and the French CEPH arrangements from before 1995, especially the “reference library” that Hans Lehrach had run at the ICRF in London. All of the RZPD’s users, including the DHGP centers, were obliged to deposit their new data, including DNA sequences, into the database. The data’s producers could request up to 6 months of confidentiality, after which 3 months’ privileged access was granted to the Förderverein “for bilateral co-operations … or for licensing negotiations with the owners of the data” (BMBF to KMJ 2012 and 2013). The Förderverein funded no sequencing, but got a head start in commercialization. After the period of privileged industry access, all data were released to the public domain.Early on, the BMBF received the criticism “that [German] industry had declined to contribute to the [national] human genome programme” , p. 111.65 “There is discussion on how to get industry money into the entire project and then … industry is likely to request … a few months in which they can access the data,” the 1996 meeting transcript read . The speaker may or may not have understood, though, that German companies were not funding the DHGP, but rather helping in efforts towards downstream development. Preceding the 1996 meeting, the BMBF had thought its data-sharing arrangement was “quite in line with the time frames discussed in the scientific community” (BMBF to KMJ 2012 and 2013). It was, indeed, in accordance with general standards in genetics and genomics, yet several voices in Bermuda deemed the plan intolerable . After the second Bermuda meeting, in March 1997, he informed Michael Morgan of a new working arrangement he had broached with Schering, which would divide his time between industrial research and the DHGP . As a ha summit” . The BMBIt is essential that all of the participants in the international Human Genome Program have the same policy with regard to the release of human DNA sequence data. We urge the BMBF to reconsider its decision and bring its policies into line with those of the other participants…. It is our opinion that, by definition, continuation of such restrictions on the immediate availability of the sequence data would mean that the German program is not, in fact, participating in the Human Genome Project as it is defined and practiced in the rest of the world. The warning of HGP exclusion was coupled to one from André Rosenthal, who “threatened to refuse his approved ministry [DHGP] grant of nearly DM20 million (US$12 million) to set up his sequencing laboratory unless he was allowed to publish his data immediately on the Internet” , p. 536.Nature had reported a few months earlier that, “the threat by geneticists in the United States and Britain to exclude German scientists from international collaborations in the Human Genome Project, and to block their access to vital biological material, outweighed the advantage to industry” the Förderverein’s privileged data-reading time presumably conferred . The transcript of the 1998 meeting indicated that “the [German] Genomic Consortium can now fully adhere to the Bermuda principles,” a notice that was met with applause and soon confirmed by the meeting report . In 2013, the BMBF wrote that it had been unable to trace any patents directly to the short-lived delay-for-review data access policy, despite this having been the policy’s original, putative objective (BMBF to KMJ 2012 and 2013).By the 1998 Bermuda meeting, the BMBF’s privileged data-access policy was formally lifted. It remained in effect though 2004 for the other DHGP laboratories, but it no longer applied to the DNA Sequencing Consortium (BMBF to KMJ 2012 and 2013). onferred , p. 536.human genome database that will provide an efficient source of information for researchers after the human genome has been sequenced”  for an undefined period of time before permitting their deposit into the international DDBJ database. Thus, completed sequence contigs may often be inaccessible for many months. We have been unsuccessful in our efforts to learn the reasons for the JST review of the data. The 1998 Bermuda Meeting Report had noted that, “the requirement to release finished data via the JST database … resulted in a delay of approximately 3 months in the release of data,” and the 1997 Meeting Report had confirmed the JST’s checking period of 3 months. This was hardly the “undefined period of time” before public release that the warning letter above reported, and the 1998 meeting transcript indicated that the JST did not intend to unduly delay its release of finished data . Nevertheless, the potentially six-month delay, incorporating the time that investigators had to report their sequences, was consistent with the assertion that “completed sequence contigs may often be inaccessible for many months” . “It would be unfortunate,” the letter from Collins, Patrinos, and Morgan elaborated, “if the current situation of less than complete compliance [with the Bermuda Principles] were to be perpetuated in Japan, as the consequences for international cooperation and good will in this noble enterprise might well be significant” . There was no explicit threat of HGP exclusion akin to the one that had been levied against the BMBF in 1997, but perhaps that warning was implied.Nature reported a massive “human genome” funding boost in Japan, up 162.5% from 1997 despite a relatively austere budget overall . The central policy question was whether a single sequencing center, akin to the situation in the UK, was preferable over distributed centers, such as in the American HGP. The 1995 planning report recommended the latter option, at which point the experienced French mapper, Jean Weissenbach, suggested that Egly attend the first Bermuda meeting (Weissenbach interview 2012). The second planning report, published after the meeting, reversed the conclusion from the original report, recommending the centralized national sequencing center that became Genoscope. Both of the plans, however, emphasized data sharing. As the 1995 report stated: “A very large sequencing center must supply databases to a very significant level and in so doing gain international gratitude for the effort made in France” (1995 French HGP Planning Report). The 1996 report noted, similarly, that, “the product of a sequencing center is sequencing data, which are then made into public data” (1996 French HGP Planning Report). Following the Bermuda discussion in 1996, Jean-Marc Egly reported the immediate data-release policy of the HGP to the French basic research agency, the CNRS (Weissenbach interview 2012). Weissenbach recalled in 2012 that the CNRS immediately acquiesced to the Bermuda Principles, but the actual implementation was more complicated. The main problem, once again, was patenting.Nature reported of Genoscope that “a decision about whether to place all its data on the Internet or to grant French industry privileged access is due this month” . Under Jean Weissenbach’s leadership, all of the in-house projects would immediately adhere to the Bermuda Principles, but the policies for external collaborations, not entirely under Weissenbach’s control, would be much harder to dictate. In June 1997, s month” , p. 536.Pyrococcus abysii . Venter even planned on “patenting cDNA’s in a limited number for new, exciting discoveries that we make with the genome,” and after having disagreed with the publicly funded HGP’s technical and organizational strategies for years, considered Celera as an opportunity to profit while also doing science his own way (Ibid.).69 Venter felt that the yeast genome could have been the first one completed had the effort not been so fragmented, and he argued forcefully that the HGP was suffering from the same defects.The new company, Celera, was to utilize a markedly different sequencing strategy from that of the HGP. Following projects by Frederick Sanger on bacteriophage and other viruses in the 1980s, Whole Genome Shotgun (WGS) sequencing was tested once again in the bacterium  by 2000 . WGS seq by 2000 , Chap. 770 As was the case with Celera, the HGP moved to the ABI Prism 3700 sequencers, such that these machines now powered both sides of the sequencing race [A]re the goals of this [private] initiative realistic or just an optimistic vision? Will this private sector initiative duplicate the federal program and make it redundant or is it another approach that can complement the federal program and make it stronger? Is the pace and the cost of the federal program increased by the bureaucratic nature of any federal program or does the timetable and cost reflect what is necessary to do a thorough job? And will the federal program utilize the latest technology described in the private sector announcement? . While the HGP’s daily data-release policy had indeed induced its own share of friction about data quality in 1996, in the face of the Celera race, even Maynard Olson worried more about the gaps that Venter’s WGS sequencing would leave . In 2002, Olson also recalled that Congressman Vernon Ehlers, who held a PhD in physics, wanted to see how both the public and private projects proceeded, in order to assess “how the Celera versus public-sector competition played out” , and the WGS strategy would leave gaps that only the public effort could close . Celera’s quarterly sharing promises, moreover, were in no way binding, meaning that researchers outside the company might be left with either a genome behind a pay wall, or no genome at all, if the Celera effort did prove to be unsuccessful. “Daily release … was arrived at because of the great interest in the scientific community in gaining access to this highly valuable information,” Francis Collins explained, and “any delay [in data release] can result in wasted effort in research” (Ibid.). He also warned that “if business demands were to change or personnel were to change or the [Celera] stockholders were to decide it’s not such a good thing to be giving this all away anymore, one would not want to see a circumstance where the publicly-funded effort … dropped the ball. We don’t intend to drop the ball” (p. 79).Francis Collins in particular argued for the necessity of a genome commons, transforming the public HGP from possibly superfluous, given the private sector’s efforts, to essential. The HGP leadership’s rhetorical strategy ranged from discouraging patenting, to expediting downstream research, to loftier and more general claims about publicly funded science conducted for the greater good (These arguments employed daily data sharing to emphasize the differences between the HGP and Celera. They relied on the relative speeds (and mechanisms) by which data would be shared in the public domain, convincing the members of the House Committee on Science that both the public and private efforts should continue. The science writer James Shreeve observed that in this turning point, the most important thing, for Collins in particular, “was about community, about rules that applied to all, about the sacrifice of individual motives for the collective good.71 “Public funding of the HGP,” the project’s 1998 progress report echoed, “is predicated on the belief that public availability of the human sequence at the earliest possible time will lead to the greatest public good” . Similarly, Aristides Patrinos of the DOE wrote in his answers to the post-hearing questions that “we are not, at the NIH, allowed to deny our grantees the opportunity to file for intellectual property rights on things they discover with NIH funds, because of the Bayh-Dole Act,” but “as a practical matter … the publicly supported sequencing community has agreed to a 24 h data release policy, and we are not aware that there have been any patent filings” (p. 94). Collins and Patrinos, however, were likely referring to patents on uncharacterized ESTs, the only kinds of patents that the Bermuda Principles—at least in the United States—really threatened. The HGP was actually generating plenty of patents on genetic material, including in its own laboratories, and stimulating much downstream patenting as well . Similar dialogues had taken place during the HGP’s early planning sessions, in the Bermuda meetings, and afterwards, especially in reference to the biologically and commercially valuable research the HGP would ideally fuel downstream. Aristides Patrinos noted in his own post-hearing questions that, “the sequence itself is publicly accessible,” so “future investigators, who figure out the value of a particular gene sequence and/or turn that … information into a pharmaceutical or a new diagnostic, may decide they have added enough value to meet the patent criteria … and file for a patent” (p. 94). While the arguments defending the HGP against Celera indeed proved successful, therefore, they did create some misconceptions. Rhetoric related to the genome commons and commercialization in 1998 largely (though with exceptions) lacked the nuance of previous deliberations, including those in Bermuda, when the HGP’s leadership had hardly proven itself as wholly opposed to genomic patenting and commercialization.The blurriness between the public and private efforts, moreover, went even farther than this. In his own set of post-hearing questions and answers, Craig Venter claimed in 1998 that “we [at Celera] are not planning to seek patents on broad sets of ESTs similar to what was done at NIH [in 1991 and 1992],” but rather they hoped “to fully characterize a small subset of key genes for which we will seek to identify and understand their biological significance” . Venter, it appears, did not like either extreme. Ultimately, the issue led to the termination of his partnership with HGS, as Venter described in his post-hearing questions in 1998: on “June 20, 1997, The Institute for Genomic Research (TIGR) and Human Genome Science[s] (HGS) ended a collaborative arrangement that required TIGR to forego payments totalling [sp.] $38 million. The primary reason for my choosing to end this relationship and access to significant financial resources was a philosophical disagreement about the public release of DNA sequence data. The day after this relationship was terminated, TIGR made the largest deposit of DNA sequence data into the public domain in history” . 1998 and afterwards. These purposes were the defense of the HGP under an imminent threat, alongside the creation of a genome commons before—as many in the HGP’s leadership feared—Celera locked up the human genome sequence in a restrictive database, effectively hijacking the basis for future discoveries.This constructed contrast between the two projects, however, and the associated public attention, obscured (and continues to obscure) the detailed history of the pragmatic issues, in particular data quality and project coordination, for which the Bermuda Principles were initially employed. Daily data release had always faced opposition, for instance from Mark Adams, Craig Venter (who had hated the notion well before Celera), and Maynard Olson, each of whom had harbored serious concerns about data quality.Science and the HGP’s in Nature  and Celera’s scientists, regarding the relative quality and completeness of the two rival genomes . The central outcome of the meeting was the formalization of the concept of “community resource projects,” to which the Bermuda Principles were officially extended and applied. These kinds of projects—similar to their exemplar, the Human Genome Project—were “specifically devised and implemented to create a set of data, reagents or other material whose primary utility will be as a resource for the broad scientific community” (Ibid.). Two such projects underway by 2003 were the Single Nucleotide Polymorphism (SNP) Consortium, focused on finding single-base pair variations in human genomes, and the International HapMap Consortium, devoted to finding other common human genomic variants  are highly amenable to pre-publication sharing. But others, such as patient health records and identifiable genomic sequences, are—for good reason—subject to privacy and ethical constraints that rarely, if ever, allow them to be shared even at publication. The strategies and policies for effective data release in any project must therefore depend on numerous factors, including but not limited to: data types; technological feasibility and implementation; community buy-in; project organization and goals; funding structures; local and international policies and laws; benefits and drawbacks of commercialization; and risks and rewards for all of the stakeholders, including for research subjects and those who stand to benefit or be harmed by the investigations at hand . The choC. elegans biology, so much so that one attendee at the 1996 Bermuda meeting remarked, “I feel slightly uncomfortable about the notion that we would hang the human genome on the worm! Although, of course, historically, for the groups concerned, it is very much the way it’s come about” . Nevertheless, despite this remarkably strong connection, daily data release was far from inevitable. Historical actors all work within the finite resources available to them, and no moment in the past has offered infinite possibilities . As the worm community coalesced and grew through the 1980s and 1990s, Sulston and Waterston convinced others, including Collins, of the advantages of daily data sharing, resulting in the NIH and the Wellcome Trust adopting the Bermuda Principles as HGP policies quickly relative to the project’s other major funders.ital age , 2017. Iital age . This ap77 This next generation of scholarship is adding to perspectives already present in the many popular accounts  sequencing (The historiography of the HGP is maturing, complemented by new archives and digital resources . A notabaccounts  and the accounts , 2005, saccounts . This moaccounts . Furtherquencing , 2016; tquencing ; human vC. elegans molecular biology to human genomics. In so doing, we have attempted to lay bare the political valence of the human genome, especially regarding the patenting of genetic materials and the attendant commercialization structures historically associated with molecular biology (see also, Our history links the HGP directly to nematode biology, and less directly to the practices and leadership of other influential model organism communities. Most of the rich historiography of model organisms has explored how practices, materiality, politics, theories, moral economies, and cultures, including norms of rapid and pre-publication data sharing, have evolved in relatively coherent and discrete communities (see, for instance, 78 The preferred paths to these outcomes, moreover, were always contested and contingent, with pragmatic and principled arguments by the varied stakeholders, whether over data release and quality, patenting and the genome commons, or other issues, vying for supremacy in the administration of the project. For Sulston and Waterston, daily data release, or at least working towards it, fulfilled what we have termed the “pragmatic” goals of quality control and project coordination, building on their vast experience with sharing in the nematode worm community. But the Bermuda Principles’ supporters also argued that, especially for the human genome, this strategy was the right way of doing publicly funded science, because it prevented overly broad patents on genes, promoted follow-on research and commercialization, and ensured broad access to data that might tell us something fundamental about humanity. Daily sharing, however, was only one strategy for rapid sharing; its consequences for the commons and for patenting were just one orientation, a particular policy configuration, for how public science might be translated into principled social outcomes.In what sense were the Bermuda Principles truly “principled,” akin to what Aristides Patrinos described as “religious”  and ElbeWere the Bermuda Principles optimal? We do not know. It was precisely because the Principles absorbed and embodied so many scientific and principled meanings over time, which together constituted a multifaceted and coherent stance towards public science and the commons, that they engendered such great conflict, and yet, especially during the Venter race in 1998 and afterward, were reinforced. Moreover, it was through their aspirational and paradigmatic nature that the Bermuda Principles could repeatedly be so imperfectly implemented in policy and in practice, yet still remain as a spiritual rallying cry for the HGP, one that eventually stood in as a justification for the project and proved consistent with its public works mandate from the very beginning. In this sense, the Bermuda Principles functioned like a boundary object , or perh79There is also no doubt that the DOE, German, French, and Japanese scientists and administrators could have been treated more considerately, nor that the outcome was heavily biased towards the preferences and philosophies of Anglo-American scientists and institutions. Even Aristides Patrinos felt that the Bermuda Principles sounded “like a declaration of war, an attack against individuals or groups that for whatever reason could not endorse something like this because their governments had different notions” . The hegAt least in terms of the goals set in relation to them, however, the Bermuda Principles were a wild success. In the face of the private threat from Celera Genomics, the HGP remained on-task and efficient. The project was completed ahead of schedule, achieving widely accepted quality standards by the time of its publications and making primary DNA sequences and much else available in the public domain. The policy implications were profound, largely because pre-publication sharing proved useful for industry as well as for science . In 1996


Artificial intelligence (AI) and Machine learning (ML) systems in medicine are poised to significantly improve health care, for example, by offering earlier diagnoses of diseases or recommending optimally individualized treatment plans. However, the emergence of AI/ML in medicine also creates challenges, which regulators must pay attention to. Which medical AI/ML-based products should be reviewed by regulators? What evidence should be required to permit marketing for AI/ML-based software as a medical device (SaMD)? How can we ensure the safety and effectiveness of AI/ML-based SaMD that may change over time as they are applied to new data? The U.S. Food and Drug Administration (FDA), for example, has recently proposed a discussion paper to address some of these issues. But it misses an important point: we argue that regulators like the FDA need to widen their scope from evaluating medical AI/ML-based products to assessing systems. This shift in perspective—from a product view to a system view—is central to maximizing the safety and efficacy of AI/ML in health care, but it also poses significant challenges for agencies like the FDA who are used to regulating products, not systems. We offer several suggestions for regulators to make this challenging but important transition. Artificial intelligence (AI), particularly its subset Machine learning (ML), has the potential to improve health care systems worldwide, for example, by optimizing workflows in hospitals, providing more accurate diagnoses, and bringing better medical treatments to patients. However, medical AI/ML also creates new challenges that we, as a society, and especially current regulators like the U.S. Food and Drug Administration (FDA), need to face: Which medical AI/ML-based products should undergo review by regulators? What evidence should regulators require makers of AI/ML-based software as a medical device (SaMD) to submit as a prerequisite to permit marketing? How to ensure the improvement of AI/ML-based SaMD’s performance in real-time while safeguarding their safety and effectiveness?2”. Many patients and physicians are particularly concerned about such “autonomous” devices. The current AI/ML-based SaMD that received marketing authorization by the FDA have what the FDA has called “locked” algorithms—they do not evolve over time and do not use new data to alter their performance3. If the algorithm changes through usage, such SaMD will, at present, likely require another FDA round of review3. Thus, AI/ML makers will probably be inclined not to update their SaMD—both to reduce the cost and effort, but also because there are risks to proposing an update  if FDA does not approve the update or there is a significant delay4. For these reasons, the most valuable asset of AI/ML, its ability to improve by learning from data, may not be fully harnessed.Some AI/ML-based SaMD have already received marketing authorization in the U.S., including IDx-DR in 2018, the first AI/ML diagnostic that provides a screening decision for the eye disease diabetic retinopathy, which its maker claims is the “first ever autonomous AI system cleared by the FDA to provide a diagnostic decision4. In this article, we make a more fundamental point: The FDA and its sister regulators in other countries have primarily been product regulators—they review and ultimately approve or reject medical products such as drugs and devices. AI/ML-based SaMD and perhaps other future ways in which AI/ML will be incorporated in medical products will require the agency and its sister regulators to shift more towards a “system” view. Even IDx-DR, the most “autonomous” AI/ML-based product authorized for marketing by the FDA, is not an island. It is one part of a larger system involving various kinds of human involvement—from health care teams inputting the data to physicians reacting to the AI recommendation to insurers deciding whether to reimburse only for certain courses of action. It is the entire system that must be evaluated—a concept that we call the “system approach”. We discuss how an agency like the FDA should think about AI/ML in a systemic way and how this puts pressure, for example, on traditional concepts of the limits of the FDA’s jurisdiction—in particular, the current approach that the FDA does not regulate the practice of medicine5. While a full-scale move into the system approach is currently infeasible for regulators, we discuss how they might take further steps in this direction, which can improve the public’s confidence in the use of AI in health care.Elsewhere we have discussed the FDA’s recent attempt to wrestle with what we call “the update problem”, its treatment of “locked” versus “adaptive” algorithms, and have made proposals for how the FDA’s approach might be improved, such as through continuous monitoring processes that consider specific risks of AI/ML systemsSeveral medical AI/ML-based products must undergo review by regulators. For example, in the U.S., a medical device is defined in Section 201(h) of the Federal Food, Drug, and Cosmetic Act and is “an instrument, apparatus, implement, machine, contrivance, implant, in vitro reagent, or other similar or related article, including any component, part, or accessory, which is (…) intended for use in the diagnosis of disease or other conditions, or in the cure, mitigation, treatment, or prevention of disease (…), which does not achieve its primary intended purposes through chemical action within or on the body of man (…), and which is not dependent upon being metabolized for the achievement of its primary intended purposes”.7. While not everyone is supportive of the SaMD construct, given that the FDA has committed to it, in what follows we examine how it can be enriched by going beyond a pure product worldview.Under U.S. law, some AI/ML-based software functions may not fall under the device definition (1)(E) of the Federal Food, Drug, and Cosmetic Act), but some do. In particular, the term “Software as a Medical Device” (SaMD) is used to refer to software that is on its own a medical device, “without being part of a hardware medical device”8. There is no reason to expect that the adoption and impact of AI/ML-based SaMD will be consistent, or even improve performance, across all settings. A good cautionary tale comes from the use of computer-aided detection (CAD) for mammography, which was, in particular, financially encouraged by the Centers for Medicare and Medicaid Services in the early 2000s as a way to improve breast cancer detection. As a study in JAMA Internal Medicine showed, because of the way physicians interacted with CAD they performed no better (and in some ways worse) when CAD was introduced9. Human judgment also introduces well-known biases into an AI/ML environment, including, for example, inability to reason with probabilities provided by AI/ML systems, over extrapolation from small samples, identification of false patterns from noise, and undue risk aversion10. Even with a single user in a single setting, there may be poor consistency: over time—both in terms of experience but even in the course of the day—we may see more risk aversion or alert fatigue of physicians. Judges, for example, have been documented to have a different tolerance for risk during the course of a single day11. These make it much more difficult for regulators to decide whether permitting marketing authorization is warranted, but also, for example, for a purchaser of an AI/ML system to determine whether it will add value to that individual practice or hospital. Regulators like the FDA have already started considering some of these issues, such as requiring training programs and human factors validation testing12. However, there are more nuanced and possibly complex systemic issues to consider.AI/ML-based SaMD raise new challenges for regulators. As compared to typical drugs and medical devices, we argue that due to their systemic aspects, AI/ML-based SaMD will present more variance between performance in the artificial testing environment and in actual practice settings, and thus potentially more risks and less certainty over their benefits. Variance can increase due to human factors or the complexity of these systems and how they interact with their environment. Unlike drugs, the usage of software and generally Information Technologies (IT) is known to be highly affected by organizational factors such as resources, staffing, skills, training, culture, workflow, and processes 13, because (1) they have the capacity to continuously learn, (2) they have the potential to become ubiquitous in medical interactions and make recommendations , and (3) the way they reach their recommendations is often opaque to physicians. For example, the latter factor creates particular puzzles for regulators: Do different physicians interact differently with the same algorithm if they believe the basis for its decision-making is explainable (even if they do not themselves understand the basis for that decision) as opposed to when they believe the algorithm is more opaque?AI/ML-based SaMD also differ from other medical technologies, such as the da Vinci surgical systemVariance will likely further increase as future AI/ML-based SaMD begin to interact with physicians dynamically, for example, by responding to the physician’s manipulations and possibly also becoming better attuned to the preferences of each individual user. The more human-AI interactions in decision-making, the more uncertainty as to what outcomes the AI/ML-based SaMD  will actually produce in clinical settings due to factors outlined above. Thus, even attempts to engage in human factors testing will have difficulties determining outcomes if evaluation is not done in actual practice settings since outcomes are likely to vary much more than, say, the use of a drug on a particular type of patient.14. “Strong” and “weak” refer to the skill or lack thereof of the human, but the key focal point is the “process”. Kasparov made this observation during a 2005 chess tournament in which the winners were, counterintuitively, two amateur chess players who used three computers and who were able to better manipulate and coach their computers to take an in-depth look into positions than chess Grandmasters and participants with greater computational power14. Kasparov’s observation can also be seen in other situations in daily life. For example, most people use Google as a search platform, but some are quicker (and more efficient) in identifying the information needed by entering the “right” key words into the engine. The key insight here is that we cannot know whether the AI/ML-based SaMD will improve outcomes without knowing more about the process.These insights are well captured by the so-called Kasparov’s Law, named for the chess player Garry Kasparov: the idea that a weak human cooperating with a machine under a good process is superior to a strong computer alone and, surprisingly, to a strong human with a machine under a weak process3. Imagine the FDA permitted marketing of such an AI/ML-based SaMD. The FDA then asks what should happen if the company retrained its algorithm using additional data and “the revised algorithm has the same sensitivity and false-alarm rate as the previous version”, but it can now alert “15 minutes prior to the onset of physiologic instability, which the previous version of the algorithm could not do”3?To see this in the context of health care, consider an example the FDA gives in a recent discussion paper: The FDA considers an AI/ML-based SaMD that “receives electrocardiogram, blood pressure, and pulse-oximetry signals from a primary patient monitor” and then “signals are processed and analyzed to detect patterns that occur at the onset of physiologic instability”, with an alert for the physician warning “that prompt clinical action is needed to prevent potential harm to the patient”This seems like a great advantage, but without robust human factors testing, we cannot know if the new version is actually better. It is possible that the way humans react to these alerts going off further away in time from the signs of instability makes the algorithm less effective; for example, because the alert and the instability are no longer paired so closely in time, the users begin to doubt the probative value of the alerts and begin discounting them more often.In addition to the uncertainty introduced by human users’ reactions to AI/ML output, the system view reveals additional complex and interacting elements to consider—interactions between different parts of the care team with the AI, the payment structure, possibly data providers, software components providers, and trainers. For example, an AI/ML-based SaMD might be approved by regulators on the understanding that a physician can always overrule its recommendations. But what happens if that, while formally true, is nonetheless in practice a fairly rare occurrence because a payer will only reimburse for that which is recommended by the AI/ML system? Should that be considered as part of the regulatory approval process?The key insight that the health care impact of AI/ML-based SaMD depends on many factors of a broader system indicates that the regulators’ focus should be on designing an appropriate process for managing this new environment taking a more system approach than a product one. Unless regulatory review is attuned to such system aspects as outlined above, it will be woefully incomplete. The insight also helps us see that, perhaps paradoxically, more autonomous AI/ML systems can create more, rather than less, predictability in the emerging human-AI environment.3—the reimbursement decisions of insurers, the effects of court decisions on liability, any behavioral biases in the process, data quality of any third-party providers, any (possibly proprietary) machine learning algorithms developed by third parties, and many others. In a full system approach, the regulator would then issue a limited regulatory authorization that tracks factors like the ones discussed above. Indeed, the regulator might even require approval to come at the level of a specific hospital, possibly with specific trained and authorized users, including, among other things, detailed hospital-level information about how the AI/ML-based SaMD is integrated into the workflow and staffing levels of that hospital, how the practice style and training of the physicians at that hospital interact with it, how the payers in that market authorize or do not authorize reimbursement for actions that deviate from its recommendations, and how the tort law in that jurisdiction intersects with provider decision-making.Clearly, taking the system perspective seriously makes the job of regulators like the FDA evaluating AI/ML-based SaMD much more difficult. A full system approach would require the regulator to collect data on a myriad of information beyond its current regulatory gaze and perhaps even beyond its legal mandate, requiring additional statutory authority15. To act lawfully, clinics need to get a license from the HFEA to carry out one or both of these techniques—they need to show the capability to perform MST and/or PNT15. In addition, they also must receive another approval from the HFEA when using one of these techniques for a particular patient15. Something similar could, in theory, be done for AI/ML-based SaMD, but the burden on the regulator would be much more demanding since in making such local evaluations it must consider far more facets of health care delivery, insurance, and law, and do so for a much wider set of technologies. Such an approach would also raise difficult questions about how to far upstream regulators would need to go, for example, in validating “golden datasets” as ground truth comparators.This would be a huge change from what the FDA and its peer regulators in other countries currently consider as part of their review. One could, perhaps, find some loose analogies in the way the United Kingdom’s specialized Human Fertilisation and Embryology Authority (HFEA) licenses individual clinics for particular reproductive technology uses such as maternal spindle transfer (MST) or pronuclear transfer (PNT), two mitochondrial replacement techniques to prevent the transmission of serious mitochondrial disease from a mother to her infant17. But a full system approach would require far more than this. Finally, a full system approach would require the FDA at least to take some steps in contravention of one of its shibboleths: that the FDA does not regulate the practice of medicine5. Such multi-faceted changes in the processes and possibly mandates of the regulators may be necessary in the world of increasingly sophisticated AI/ML systems, but a full change may not be possible or even desirable as it may limit innovation or negatively affect the behavior of stakeholders.Moreover, because the system itself may change, even if the AI/ML remains “locked”, a full system approach would not treat premarket approval even at the level of a hospital as a “one and done”, but instead tentative and subject to reevaluation. To be sure, the FDA currently monitors the safety of medical products through its Sentinel program, and in September 2019, the FDA announced its goal to enlarge Sentinel to three distinct coordinating centers with more monitoring capabilities18. The Perfect, however, must not be the enemy of the Good. If regulators cannot realistically take the full system approach, they can at least somewhat widen their perspective. Doing so is also in harmony with the FDA’s increasing emphasis in its AI/ML-based SaMD publications of implementing more “real-world performance monitoring”19.We believe that a full system approval and monitoring approach is out of reach for today’s regulators, especially because of expertise, resources, political obstacles, and the difficulties in controlling human behavior12. Regulators could require more, such as ongoing system monitoring, periodic retraining, software and usage inspections, review of aggregate usage statistics 4. They could also demand data and model validation and robustness analysis  of the AI/ML, such as due to data quality or adversarial attack issues4. Further, regulators could also require testing variants that provide humans with different degrees of freedom: For example, users’ discretion can be more or less limited in cases where devices provide probabilistic recommendations such as IDx-DR; or the AI/ML-based SaMD may provide more or fewer alternative recommended courses of action or even usage parameter choices. Regulators may also simply require clinical trials of the AI/ML-based SaMD as used in actual planned clinical settings. Moreover, regulators could also request data collected outside traditional clinical trials such as from Fitbits and other wearables capturing users’ behavioral changes over time as well as electronic health records capturing all decisions that may be related to the use of an AI/ML-based SaMD20.Regulators like the FDA can, for example, demand fuller human factors analysis of how actual physicians and others of the health care team such as nurses react to outputs of particular AIs and require training for users to help minimize variance. As it stands, the FDA does not regulate the practice of medicine and thus does not oversee the training of medical professionals. However, even with those restrictions in place, it can require the AI maker to set up a training program for their product, such as in the case of IDx-DR where the FDA required a training program including instructions on how to acquire and process quality imagesIn a sense, it may be useful to reframe what the hospitals and practices are doing: they are not merely buying an AI/ML-based tool, but hiring one. Cognitive testing of a physician will not tell you how they will do when added to a preexisting team in a particular health system, and employees need to be continuously assessed. The same is true for AI/ML-based SaMD.3. The agency is currently developing a strategy for how to regulate “unlocked” or “adaptive” AI/ML algorithms—algorithms that may change as they are applied to new data3.All AI/ML-based SaMD that the FDA has thus far reviewed have been cleared or approved as “locked” algorithms, which it defines as “an algorithm that provides the same result each time the same input is applied to it and does not change with use”This is a welcome development. Much of the value of medical AI/ML-based SaMD is in their ability to update as new and hopefully more representative data, for example, becomes available. Unfortunately, the task of reviewing such an “update” is much more difficult when we consider the system rather than the product perspective.4. We articulated some key features that risk monitoring should focus on  and suggested some ways to implement it4. Our goal is to emphasize that the tasks adopted by a true system view for regulating adaptive AI/ML-based SaMD are indeed even more demanding since they consider many more facets of the delivery of care. If there were no human involvement, for a hypothetically truly fully autonomous AI , the update could be approved based on retesting a reference set of patients, all prior patients, or even on simulated patient data. But when humans are involved, the system perspective requires considering how the update interacts with human usage and organizational factors. To be sure, in some instances, AI/ML-based SaMD updates will have no effect or improve things from a human factor perspective. The point is that determining the effects of the update is much more challenging for a regulator when there is significant human involvement in decision-making.Elsewhere we have suggested that regulators should prioritize risk monitoring to address the “update problem”AI/ML-based SaMD pose new safety challenges for regulators. They need to make a difficult choice: either largely ignore systemic and human factor issues with each approval and subsequent update or require the maker to conduct significant organizational and human factors validation testing with each update resulting in increased cost and time, which may, in turn, chill the desire of the maker to engage in potentially very beneficial innovations or possible updates. Striking the right balance is a challenge that may take time to resolve. However, ignoring all systemic aspects of AI/ML-based SaMD, such as those we outlined, may not be an option.