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Early warning signs

Science

Epidemiology Modeling an emerging infectious disease is an inexact science. At an early stage of an epidemic, we only have sparse data, little knowledge of the mechanisms driving emergence, and an urgent need to devise control measures that will be effective. Using epidemiological incidence reports, Brett and Rohani have developed a detection algorithm for disease (re)emergence that is agnostic to the mechanisms involved. This supervised statistical learning algorithm was trained on data collected for mumps outbreaks in England and resurgent pertussis in the United States. The algorithm successfully anticipated reemergence of mumps 4 years in advance, which would have given plenty of time for mitigation efforts to be implemented. The algorithm also performed well for vector-borne diseases, including dengue in Puerto Rico, and predicted the rapid emergence of plague in Madagascar. The success of this approach stems from the common statistical properties of incidence data across disease emergence contexts and has obvious application for monitoring waves of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reemergence. PLOS BIOL. 18 , e3000697 (2020).


Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions

Science

From February to April 2020, many countries introduced variations on social distancing measures to slow the ravages of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Publicly available data show that Germany has been particularly successful in minimizing death rates. Dehning et al. quantified three governmental interventions introduced to control the outbreak. The authors predicted that the third governmental intervention—a strict contact ban since 22 March—switched incidence from growth to decay. They emphasize that relaxation of controls must be done carefully, not only because there is a 2-week lag between a measure being enacted and the effect on case reports but also because the three measures used in Germany only just kept virus spread below the growth threshold. Science , this issue p. [eabb9789][1] ### INTRODUCTION When faced with the outbreak of a novel epidemic such as coronavirus disease 2019 (COVID-19), rapid response measures are required by individuals, as well as by society as a whole, to mitigate the spread of the virus. During this initial, time-critical period, neither the central epidemiological parameters nor the effectiveness of interventions such as cancellation of public events, school closings, or social distancing is known. ### RATIONALE As one of the key epidemiological parameters, we inferred the spreading rate λ from confirmed SARS-CoV-2 infections using the example of Germany. We apply Bayesian inference based on Markov chain Monte Carlo sampling to a class of compartmental models [susceptible-infected-recovered (SIR)]. Our analysis characterizes the temporal change of the spreading rate and allows us to identify potential change points. Furthermore, it enables short-term forecast scenarios that assume various degrees of social distancing. A detailed description is provided in the accompanying paper, and the models, inference, and forecasts are available on GitHub ([https://github.com/Priesemann-Group/covid19\_inference\_forecast][2]). Although we apply the model to Germany, our approach can be readily adapted to other countries or regions. ### RESULTS In Germany, interventions to contain the COVID-19 outbreak were implemented in three steps over 3 weeks: (i) Around 9 March 2020, large public events such as soccer matches were canceled; (ii) around 16 March 2020, schools, childcare facilities, and many stores were closed; and (iii) on 23 March 2020, a far-reaching contact ban ( Kontaktsperre ) was imposed by government authorities; this included the prohibition of even small public gatherings as well as the closing of restaurants and all nonessential stores. From the observed case numbers of COVID-19, we can quantify the impact of these measures on the disease spread using change point analysis. Essentially, we find that at each change point the spreading rate λ decreased by ~40%. At the first change point, assumed around 9 March 2020, λ decreased from 0.43 to 0.25, with 95% credible intervals (CIs) of [0.35, 0.51] and [0.20, 0.30], respectively. At the second change point, assumed around 16 March 2020, λ decreased to 0.15 (CI [0.12, 0.20]). Both changes in λ slowed the spread of the virus but still implied exponential growth (see red and orange traces in the figure). To contain the disease spread, i.e., to turn exponential growth into a decline of new cases, the spreading rate has to be smaller than the recovery rate μ = 0.13 (CI [0.09, 0.18]). This critical transition was reached with the third change point, which resulted in λ = 0.09 (CI [0.06, 0.13]; see blue trace in the figure), assumed around 23 March 2020. From the peak position of daily new cases, one could conclude that the transition from growth to decline was already reached at the end of March. However, the observed transient decline can be explained by a short-term effect that originates from a sudden change in the spreading rate (see Fig. 2C in the main text). As long as interventions and the concurrent individual behavior frequently change the spreading rate, reliable short- and long-term forecasts are very difficult. As the figure shows, the three example scenarios (representing the effects up to the first, second, and third change point) quickly diverge from each other and, consequently, span a considerable range of future case numbers. Inference and subsequent forecasts are further complicated by the delay of ~2 weeks between an intervention and the first useful estimates of the new λ (which are derived from the reported case numbers). Because of this delay, any uncertainty in the magnitude of social distancing in the previous 2 weeks can have a major impact on the case numbers in the subsequent 2 weeks. Beyond 2 weeks, the case numbers depend on our future behavior, for which we must make explicit assumptions. In sum, future interventions (such as lifting restrictions) should be implemented cautiously to respect the delayed visibility of their effects. ### CONCLUSION We developed a Bayesian framework for the spread of COVID-19 to infer central epidemiological parameters and the timing and magnitude of intervention effects. With such an approach, the effects of interventions can be assessed in a timely manner. Future interventions and lifting of restrictions can be modeled as additional change points, enabling short-term forecasts for case numbers. In general, our approach may help to infer the efficiency of measures taken in other countries and inform policy-makers about tightening, loosening, and selecting appropriate measures for containment of COVID-19. ![Figure][3] Bayesian inference of SIR model parameters from daily new cases of COVID-19 enables us to assess the impact of interventions. In Germany, three interventions (mild social distancing, strong social distancing, and contact ban) were enacted consecutively (circles). Colored lines depict the inferred models that include the impact of one, two, or three interventions (red, orange, or green, respectively, with individual data cutoff) or all available data until 21 April 2020 (blue). Forecasts (dashed lines) show how case numbers would have developed without the effects of the subsequent change points. Note the delay between intervention and first possible inference of parameters caused by the reporting delay and the necessary accumulation of evidence (gray arrows). Shaded areas indicate 50% and 95% Bayesian credible intervals. As coronavirus disease 2019 (COVID-19) is rapidly spreading across the globe, short-term modeling forecasts provide time-critical information for decisions on containment and mitigation strategies. A major challenge for short-term forecasts is the assessment of key epidemiological parameters and how they change when first interventions show an effect. By combining an established epidemiological model with Bayesian inference, we analyzed the time dependence of the effective growth rate of new infections. Focusing on COVID-19 spread in Germany, we detected change points in the effective growth rate that correlate well with the times of publicly announced interventions. Thereby, we could quantify the effect of interventions and incorporate the corresponding change points into forecasts of future scenarios and case numbers. Our code is freely available and can be readily adapted to any country or region. [1]: /lookup/doi/10.1126/science.abb9789 [2]: https://github.com/Priesemann-Group/covid19_inference_forecast [3]: pending:yes


Immigrants help make America great

Science

I am a scientist. I am an American. And I am the product of special expert visas and chain migration—among the many types of legal immigration into the United States. On 22 June, President Trump issued a proclamation that temporarily restricts many types of legal immigration into the country, including that of scientists and students. This will make America neither greater nor safer—rather, it could make America less so. The administration claims that these restrictions are necessitated by the coronavirus disease 2019 (COVID-19) outbreak to prevent threats to American workers. This reasoning is flawed for science and engineering, where immigrants are critical to achieving advances and harnessing the resulting economic opportunity for all Americans. For decades, the United States has inspired both immigrants and nonimmigrants to make substantial contributions to science and technology that benefit everyone. Preventing highly skilled scientists and postdocs from entering the United States directly threatens this enterprise. My uncle, a geologist, came to the United States in the 1960s to work at NASA. He then taught at Appalachian State University in North Carolina and later served as lead geochemist for the state of California. He sponsored my father to come to America in 1968. Leaving Mumbai, a city of millions, and arriving in Hickory, a town of thousands in North Carolina, my father came home to a place he had never been before. My parents worked in furniture factories and textile mills to put us though college and ensure we had opportunities. Today, my sister works at the U.S. Centers for Disease Control and Prevention, and I have the privilege of leading the American Association for the Advancement of Science (AAAS, the publisher of Science ). We exist because of the Immigration and Nationality Act of 1965 and our parents' belief in the vision of the United States as a shining city on a hill. My family's story is repeated by thousands of American scientists. These stories include uncertainty when an immigrant's status in America is in question. This uncertainty causes stress and the possibility that immigrants will leave and take their skills, talents, and humanity elsewhere. For the successful, these stories culminate with relief, celebration, and the pride of becoming a naturalized citizen. As President Reagan said, the United States is the one place in the world where “anybody from any corner of the world can come…to live and become an American.” Naturalized citizens love the United States deeply because they chose to be American. They and other immigrants make huge contributions to science and engineering. According to the National Science Foundation, more than 50% of postdocs and 28% of science and engineering faculty in the United States are immigrants. Of the Nobel Prizes in chemistry, medicine, and physics awarded to Americans since 2000, 38% were awarded to immigrants to the United States. I don't know the number of prizes given to second-generation Americans but Steven Chu—current chair of the AAAS Board of Directors—is among them. The incredible achievements of the American scientific enterprise speak volumes about the vision and forethought of the American people who have worked to create a more perfect union. Suspending legal immigration is self-defeating and breaks a model that is so successful that other nations are copying it. As Thomas Donohue, chief executive officer of the U.S. Chamber of Commerce, said regarding the administration's proclamation, “Putting up a ‘not welcome’ sign for engineers, executives, IT experts, doctors, nurses, and other workers won't help our country, it will hold us back. Restrictive changes to our nation's immigration system will push investment and economic activity abroad, slow growth, and reduce job creation.” To develop treatments and vaccines for COVID-19, cure cancers, go to Mars, understand the fundamental laws of the universe and human behavior, develop artificial intelligence, and build a better future, we need the brain power of the descendants of Native Americans, Pilgrims, Founding Mothers and Fathers, Enslaved People, Ellis Island arrivals, and immigrants from everywhere. The United States has thrived as a crossroads where people are joined together by ideas and contribute by choice to the freedom and opportunity provided by this wonderful, inspiring, and flawed country that is always striving to live up to its aspirations. Scientists, look around your labs and offices. Think about your collaborations and friendships. We must ensure that this “temporary” restriction on legal immigration does not become permanent. Now is the time to speak up for your immigrant colleagues and for America.


How to build a more open justice system

Science

![Figure][1] GRAPHIC: DAVIDE BONAZZI/SALZMANART Modern governments gather information across an extraordinary range of activities and use this information to direct policy. Whether a central bank monitoring inflation or a health agency monitoring disease, these entities typically publicly disclose the information gathered so that their actions can be reviewed and evaluated by others. But in many respects, the justice system is a glaring exception. In the United States, a range of technical and financial obstacles blocks large-scale access to public court records—all but foreclosing their use to direct policy. Yet a growing body of empirical legal research demonstrates that systematic analyses of court records could improve legal practice and the administration of justice. And although much of the legal community resists quantitative approaches to law, we believe that even the skeptics will be receptive to quantitative feedback—so long as it is straightforward, apolitical, and incontrovertible. We offer an example of this kind of feedback as well as a collaborative research agenda to dismantle access barriers to court records and enable the public to analyze them. Although court records in the United States sit in the public domain, federal courts charge $0.10 per printed page to view any record online ([ 1 ][2]). Accessing a single case might cost $10 or more. Accessing all cases from a given year would cost millions of dollars ([ 2 ][3]). To be sure, the federal judiciary releases inhouse studies that use federal court records, as well as a database of basic information about each case, such as the subject matter (e.g., tort, contract, civil rights) and disposition (e.g., settled, transferred, jury verdict) ([ 3 ][4]). The federal judiciary has steadfastly refused, however, to make the underlying public court records freely accessible. Selective access is not the approach taken by the rest of the U.S. federal government: Congressional records are freely available at [congress.gov][5]. Executive agencies' records are freely available at [regulations.gov][6]. It's hard to conceive of a compelling argument for selective access to judicial records that does not apply equally to selective access to congressional records or federal agencies. More to the point, it's hard to conceive of a reason why public records should not generally be accessible to the public. There are some alternative sources for court records, but barriers to systematic analysis remain. Commercial legal services have directly purchased many court records, but they impose their own fees, prohibit bulk downloads, and thus foreclose systematic analysis even for subscribers. Individual judges and commercial services occasionally grant ad hoc fee reductions for research purposes, but these grants are rare, cumbersome to acquire, limited to subsets of the data, and always come with the condition that the underlying records are not disclosed to the public ([ 4 ][7]). An open alternative, Free Law Project , maintains a crowdsourced repository of free court records, but coverage remains too low to support systematic research. The lack of access to court records seemingly undercuts any claim that the courts are truly “open” ([ 5 ][8], [ 6 ][9]). It surely conflicts with researchers' conception of openness. Scientific practice is grounded on a commitment to sharing data and enabling others to replicate findings. But the law's conception of openness is different, a commitment to carrying out public acts in a public space. A scientist might restrict access to a lab and still claim that the research she conducts there is “open.” Closed proceedings in a legal setting, on the other hand, are only tolerated in extraordinary circumstances. Also in contrast to scientific practice, much of the legal profession resists quantitative or evidence-based approaches to improving legal practice and instead prefers to rely on personal experience and professional judgment ([ 7 ][10]). In a recent Supreme Court case challenging the constitutionality of partisan gerrymandering, Chief Justice John Roberts summarily dismissed empirical approaches to gerrymandering as “sociological gobbledygook” that any “intelligent man on the street” would denigrate as “a bunch of baloney” ([ 8 ][11]). Such skepticism is by no means confined to the United States. France, for example, has recently prohibited the publication of any statistical analysis of a judge's or clerk's decisions “with the object or effect of evaluating, analyzing, comparing or predicting their actual or supposed professional practices.” Violators face up to 5 years in prison ([ 9 ][12]). We believe that these differences help explain why the lack of large-scale access to data is not viewed as a priority—or even as a concern—by much of the legal community. The differences in priorities reflect not just commitments to different values but different conceptions of the same values. Yet, if court records are to be truly accessible and evaluable by the public, the legal and scientific communities must cooperate, and appreciate the values that the other holds dear. Access to justice is a fundamental right and the foundation of any fair and legitimate justice system. But how can one quantify and empirically evaluate this concept? Consider court fees. For a litigant without means, court fees are a substantial barrier to the civil justice system. Anyone who files a lawsuit in federal court must pay a $400 filing fee, along with other costs related to litigation such as formal service of the complaint. Litigants in need can file an application to waive court fees, but there is no uniform standard to review these requests ([ 10 ][13]). Application forms differ by district. Most ask the applicant to list sources of income, assets, and cash on hand—and then leave the decision to the judge's discretion. Individual judges thus have considerable power over whether to grant or deny access to the justice system. How do judges exercise this power? This is but one of the myriad questions that is difficult, and arguably impossible, to answer without easy access to structured court records. Even with free access to the data, the answer would be difficult to infer without being able to computationally analyze the text of the court records. In this case, the analysis is straightforward. When a party submits a fee waiver request, the case docket report adds a separate entry for that request, and the textual summary accompanying the entry typically includes some reference to whether the request was granted or denied. We analyzed these entries to compute the grant rate of each federal judge in 2016. Average grant rates naturally differ among federal districts because cases are not randomly assigned to districts. However, once a case is filed in, say, San Francisco, it is then randomly assigned to one of the judges sitting in the federal district that includes San Francisco. Thus, if all judges reviewed fee waiver applications under the same standard, then grant rates should not systematically differ within districts. We find, however, that they do (see the figure). At the 95% confidence level, nearly 40% of judges—instead of the expected 5%—approve fee waivers at a rate that statistically significantly differs from the average rate for all other judges in their same district. In one federal district, the waiver approval rate varies from less than 20% to more than 80%. These findings were recently presented to a group of federal judges who are responsible for amending the rules in their local district. On learning of the inconsistent treatment of fee waiver requests, these judges expressed interest in using our data to improve the decision-making process ([ 11 ][14]). We count this as an early and encouraging validation of our claim that judges will be especially receptive to quantitative feedback that is straightforward, apolitical, and incontrovertible. Going forward, we believe that the best way to provide the judiciary with quantitative feedback is to develop a forum where individuals can collaborate and build on each other's efforts. With this vision in mind, we propose a three-pronged collaborative research agenda to empower the public to access and analyze court records. ### Make court records free In theory, Congress could make federal records free by repealing the laws that authorize the judiciary to charge for access ([ 12 ][15]), or the Judicial Conference of the United States (the policy-making body of the federal judiciary) could stop charging fees. Both Congress and the courts have rejected calls to do so. A principal reason, it seems, is money. About 2% of the federal judiciary's budget comes from online record access fees ($145 million in fiscal year 2019). The judiciary is naturally unwilling to forgo this revenue without a commensurate increase from Congress, and Congress, for its part, is unwilling to increase funding. The stalemate persists because not enough judges, members of Congress, and people realize that this is an issue of legitimacy, not just an issue of money. To break this impasse, we believe that organizations outside government should directly purchase and publicize court records. The most impactful first step is to make docket reports accessible. A docket report is essentially a lawsuit's table of contents. It lists the case title, presiding judge, subject matter of the suit, and information on the plaintiffs, defendants, and their attorneys. A docket report also gives the date that a document was filed, along with a summary of the document that can be analyzed to extract important features of a case. The data for the figure, for example, were constructed by parsing docket reports, not the underlying court records. Though docket reports represent only a fraction of all court records, acquiring them will be expensive. The docket reports used in the figure, which cover all cases filed in 2016, cost more than $100,000. ![Figure][1] Inconsistency in judicial fee waiver decisions Litigants filed 34,001 applications to waive court fees in U.S. federal courts in 2016. For visual simplification, we show only the 294 judges (out of 1742 total) who ruled on at least 35 applications. We would expect 5% of judges to differ from their within-district peers at 95% confidence. Instead, we find that nearly 40% of judges differ. GRAPHIC: X. LIU/ SCIENCE ### Link data in a knowledge network Because court records are mostly unstructured text, researchers will need to dedicate extensive time and resources to organizing the data. Documents must be analyzed using natural language processing; entities must be disambiguated; and events, such as the filing of a fee waiver, must be classified using machine learning. The docket reports should also be linked to external metadata such as information on judges, litigants, and lawyers. By linking court records to outside data sources, individual users can conduct more powerful searches, such as for litigation against big tech firms or for suits currently pending against the federal government. Although we already have solutions to many of the problems associated with organizing and classifying the data, for many more we will need additional research. For example, it is straightforward to link the presiding judge of each case to outside data on the judge's characteristics such as age, gender, and appointing president. By contrast, to assemble information about litigants and lawyers, researchers will need to make considerable progress on named-entity recognition techniques while protecting litigants' and third parties' privacy. We believe that an open and collaborative platform is the best way to make substantial and rapid progress on these challenges. ### Empower the public The ultimate goal must be to enable the public to directly evaluate and engage with the work of the courts. To this end, we should create applications that not only support scholars and researchers who may want to analyze the data but also enable members of the judiciary, entrepreneurs, journalists, potential litigants, and concerned citizens to learn more about the functioning of the courts. To support inquiries made by the public, we should develop applications that can process natural language queries such as “What are the most recent data privacy cases?” or “How often do police officers invoke qualified immunity?” Funding the efforts we propose will be challenging because the cause does not slot nicely into standard philanthropic categories. To carry out our proposals, the academic community should partner with other stakeholders such as nongovernmental organizations, law firms, legal clinics, and other advocacy groups. Indeed, we believe that one of the main reasons why past calls for change failed is because they were not coordinated. Opening up court records could lead to some flawed or misleading analyses, yet such problems apply to any setting with open data. No one can control what people do with congressional records, federal agency records, census data, etc. Nevertheless, these data are—and should remain—available to everyone. As in any discipline, standards and best practices eventually emerge, and there is already a thriving literature of empirical legal studies. Many scholars have engaged with these data, albeit on a smaller scale. Thus, for the most part, standards and best practices already exist ([ 13 ][16]). We believe that the judiciary should be shielded from outside pressures so that it can decide cases according to the law, not the latest poll. But the judiciary also acts on behalf of the public. Its independence must therefore be balanced with commensurate transparency. Ultimately, the judiciary's principal asset is not its annual appropriation from Congress or the revenue generated by access fees, but the public trust. And the most effective way to cultivate this trust—to promote transparency, dismantle barriers to access ([ 14 ][17], [ 15 ][18]), and build an open knowledge network—is to do it together. 1. [↵][19]Public Access to Court Electronic Records (PACER), “PACER user manual for CM/ECF courts” (United States Courts, 2019). 2. [↵][20]United States Courts, Federal judicial caseload statistics 2018 (2018); [www.uscourts.gov/statistics-reports/federal-judicial-caseload-statistics-2018][21]. 3. [↵][22]1. W. Hubbard , J. Empir. Leg. Stud. 14, 474 (2017). [OpenUrl][23] 4. [↵][24]1. J. B. Gelbach , Yale Law J. 121, 2270 (2011). [OpenUrl][25] 5. [↵][26]1. A. Bronstad , “PACER fees harm judiciary's credibility, Posner says in class action brief,” 25 January 2019; [www.law.com/2019/01/25/pacer-fees-harm-judiciarys-credibility-posner-says-in-class-action-brief/][27]. 6. [↵][28]1. L. Doggett, 2. M. J. Mucchetti , Tex. Law Rev. 69, 643 (1990). [OpenUrl][29] 7. [↵][30]1. H. F. Lynch et al ., Science 367, 1078 (2020). [OpenUrl][31][Abstract/FREE Full Text][32] 8. [↵][33]Gill v. Whitford, Transcript of oral argument at 38 and 40, no. 16-1161, 138 S. Ct. 1916 (2018). 9. [↵][34]1. J. Tashea , “France bans publishing of judicial analytics and prompts criminal penalty,” ABA Journal, 7 June 2019; [www.abajournal.com/news/article/france-bans-and-creates-criminal-penalty-for-judicial-analytics][35]. 10. [↵][36]1. A. Hammond , Yale Law J. 128, 1478 (2018). [OpenUrl][37] 11. [↵][38]Owing to the preliminary nature of discussions, the identities of courts and judges are not reported, but Science has confirmed this claim. 12. [↵][39]28 U.S. Codes §§ 1913, 1914, 1926, 1930, 1932. 13. [↵][40]1. W. Baude et al ., Univ. Chic. Law Rev. 84, 37 (2017). [OpenUrl][41] 14. [↵][42]1. A. Madison , “Team tapped to review PACER amid fee dispute (corrected),” Bloomberg Law, 9 January 2020; . 15. [↵][43]1. A. Kragie , “Court transparency bill calls for live audio, free PACER,” 2 March 2020; [www.law360.com/articles/1249148][44]. Acknowledgments: We thank K. Sanga for valuable feedback. This research was supported by a gift from John and Leslie McQuown and by the National Science Foundation Convergence Accelerator Program under grant no. 1937123. The data and code used for this article, along with full replication instructions and additional discussion of the analyses, are available at and at Zenodo (10.5281/zenodo.3905128). [1]: pending:yes [2]: #ref-1 [3]: #ref-2 [4]: #ref-3 [5]: http://congress.gov [6]: http://regulations.gov [7]: #ref-4 [8]: #ref-5 [9]: #ref-6 [10]: #ref-7 [11]: #ref-8 [12]: #ref-9 [13]: #ref-10 [14]: #ref-11 [15]: #ref-12 [16]: #ref-13 [17]: #ref-14 [18]: #ref-15 [19]: #xref-ref-1-1 "View reference 1 in text" [20]: #xref-ref-2-1 "View reference 2 in text" [21]: http://www.uscourts.gov/statistics-reports/federal-judicial-caseload-statistics-2018 [22]: #xref-ref-3-1 "View reference 3 in text" [23]: {openurl}?query=rft.jtitle%253DJ.%2BEmpir.%2BLeg.%2BStud.%26rft.volume%253D14%26rft.spage%253D474%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [24]: #xref-ref-4-1 "View reference 4 in text" [25]: {openurl}?query=rft.jtitle%253DYale%2BLaw%2BJ.%26rft.volume%253D121%26rft.spage%253D2270%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [26]: #xref-ref-5-1 "View reference 5 in text" [27]: http://www.law.com/2019/01/25/pacer-fees-harm-judiciarys-credibility-posner-says-in-class-action-brief/ [28]: #xref-ref-6-1 "View reference 6 in text" [29]: {openurl}?query=rft.jtitle%253DTex.%2BLaw%2BRev.%26rft.volume%253D69%26rft.spage%253D643%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [30]: #xref-ref-7-1 "View reference 7 in text" [31]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DLynch%26rft.auinit1%253DH.%2BF.%26rft.volume%253D367%26rft.issue%253D6482%26rft.spage%253D1078%26rft.epage%253D1080%26rft.atitle%253DOvercoming%2Bobstacles%2Bto%2Bexperiments%2Bin%2Blegal%2Bpractice%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.aay3005%26rft_id%253Dinfo%253Apmid%252F32139532%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [32]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEzOiIzNjcvNjQ4Mi8xMDc4IjtzOjQ6ImF0b20iO3M6MjI6Ii9zY2kvMzY5LzY1MDAvMTM0LmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ== [33]: #xref-ref-8-1 "View reference 8 in text" [34]: #xref-ref-9-1 "View reference 9 in text" [35]: http://www.abajournal.com/news/article/france-bans-and-creates-criminal-penalty-for-judicial-analytics [36]: #xref-ref-10-1 "View reference 10 in text" [37]: {openurl}?query=rft.jtitle%253DYale%2BLaw%2BJ.%26rft.volume%253D84%26rft.spage%253D37%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [38]: #xref-ref-11-1 "View reference 11 in text" [39]: #xref-ref-12-1 "View reference 12 in text" [40]: #xref-ref-13-1 "View reference 13 in text" [41]: {openurl}?query=rft.jtitle%253DUniv.%2BChic.%2BLaw%2BRev.%26rft.volume%253D84%26rft.spage%253D37%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [42]: #xref-ref-14-1 "View reference 14 in text" [43]: #xref-ref-15-1 "View reference 15 in text" [44]: http://www.law360.com/articles/1249148


The ontogeny of a mammalian cognitive map in the real world

Science

The presence of a cognitive map is essential to our ability to navigate through areas we know because it facilitates the use of spatial knowledge to derive new routes. Whether such maps exist in nonhuman animals has been debated, largely because of the difficulty of demonstrating qualifying components of the map outside of a laboratory. In two studies on Egyptian fruit bats, Harten et al. and Toledo et al. together show that this species's navigational strategies meet the requirements for the use of a cognitive map of their environment, confirming that this skill occurs outside of humans (see the Perspective by Fenton). Science , this issue p. [194][1], p. [188][2]; see also p. [142][3] How animals navigate over large-scale environments remains a riddle. Specifically, it is debated whether animals have cognitive maps. The hallmark of map-based navigation is the ability to perform shortcuts, i.e., to move in direct but novel routes. When tracking an animal in the wild, it is extremely difficult to determine whether a movement is truly novel because the animal’s past movement is unknown. We overcame this difficulty by continuously tracking wild fruit bat pups from their very first flight outdoors and over the first months of their lives. Bats performed truly original shortcuts, supporting the hypothesis that they can perform large-scale map-based navigation. We documented how young pups developed their visual-based map, exemplifying the importance of exploration and demonstrating interindividual differences. [1]: /lookup/doi/10.1126/science.aay3354 [2]: /lookup/doi/10.1126/science.aax6904 [3]: /lookup/doi/10.1126/science.abd1213


Monkeying with the piano

Science

Neuroscience The anatomical organization of auditory cortical pathways in nonhuman primates (NHPs) shows remarkable similarities with humans. So why don't NHPs have a more speech-like communication system? Archakov et al. trained macaques to perform an auditory-motor task using a purpose-built piano. Mapping brain activity by functional magnetic resonance imaging showed that sound sequences activated the auditory midbrain and cortex. More importantly, sound sequences that had been learned by self-production also activated motor cortex and basal ganglia. This shows that monkeys can form auditory-motor links and that this is not the reason why they do not speak. Instead, the origin of speech in humans may have required the evolution of a command apparatus that controls the upper vocal tract. Proc. Natl. Acad. Sci. U.S.A. 117 , 15242 (2020).


Cognitive map-based navigation in wild bats revealed by a new high-throughput tracking system

Science

The presence of a cognitive map is essential to our ability to navigate through areas we know because it facilitates the use of spatial knowledge to derive new routes. Whether such maps exist in nonhuman animals has been debated, largely because of the difficulty of demonstrating qualifying components of the map outside of a laboratory. In two studies on Egyptian fruit bats, Harten et al. and Toledo et al. together show that this species's navigational strategies meet the requirements for the use of a cognitive map of their environment, confirming that this skill occurs outside of humans (see the Perspective by Fenton). Science , this issue p. [194][1], p. [188][2]; see also p. [142][3] Seven decades of research on the “cognitive map,” the allocentric representation of space, have yielded key neurobiological insights, yet field evidence from free-ranging wild animals is still lacking. Using a system capable of tracking dozens of animals simultaneously at high accuracy and resolution, we assembled a large dataset of 172 foraging Egyptian fruit bats comprising >18 million localizations collected over 3449 bat-nights across 4 years. Detailed track analysis, combined with translocation experiments and exhaustive mapping of fruit trees, revealed that wild bats seldom exhibit random search but instead repeatedly forage in goal-directed, long, and straight flights that include frequent shortcuts. Alternative, non–map-based strategies were ruled out by simulations, time-lag embedding, and other trajectory analyses. Our results are consistent with expectations from cognitive map–like navigation and support previous neurobiological evidence from captive bats. [1]: /lookup/doi/10.1126/science.aay3354 [2]: /lookup/doi/10.1126/science.aax6904 [3]: /lookup/doi/10.1126/science.abd1213


Feasibility of blood testing combined with PET-CT to screen for cancer and guide intervention

Science

Cancers diagnosed early are often more responsive to treatment. Blood tests that detect molecular markers of cancer have successfully identified individuals already known to have the disease. Lennon et al. conducted an exploratory study that more closely reflects the way in which such blood tests would be used in the future. They evaluated the feasibility and safety of incorporating a multicancer blood test into the routine clinical care of 10,000 women with no history of cancer. Over a 12-month period, the blood test detected 26 cancers of different types. A combination of the blood test and positron emission tomography–computed tomography (PET-CT) imaging led to surgical removal of nine of these cancers. Use of the blood test did not result in a large number of futile follow-up procedures. Science , this issue p. [eabb9601][1] ### INTRODUCTION The goal of earlier cancer detection is to identify the disease at a stage when it can be effectively treated, thereby offering the patient a better chance of long-term survival. Adherence to screening modalities known to decrease cancer mortality such as colonoscopy, mammography, low-dose computed tomography, and Pap smears varies widely. Moreover, the majority of cancer types are diagnosed only when symptoms occur. Multicancer blood tests offer the exciting possibility of detecting many cancer types at a relatively early stage and in a minimally invasive manner. ### RATIONALE Evaluation of the feasibility and safety of multicancer blood testing requires prospective interventional studies. We designed such a study to answer four critical questions: (i) Can a multicancer blood test detect cancers not previously detected by other means? (ii) Can a positive test result lead to surgical intervention with curative intent? (iii) Can testing be incorporated into routine clinical care and not discourage patients from undergoing recommended screening tests such as mammography? (iv) Can testing be performed safely, without incurring a large number of unnecessary, invasive follow-up tests? ### RESULTS We evaluated a blood test that detects DNA mutations and protein biomarkers of cancer in a prospective, interventional study of 10,006 women who were 65 to 75 years old and who had no prior history of cancer. Positive blood tests were followed by diagnostic positron emission tomography–computed tomography (PET-CT), which served to independently confirm and precisely localize the site and extent of disease if present. The study design incorporated several features to maximize the safety of testing to the participants. Of the 10,006 enrollees, 9911 (99.1%) could be assessed with respect to the four questions posed above. (i) Detection: Of 96 cancers incident during the study period, 26 were first detected by blood testing and 24 additional cancers by conventional screening. Fifteen of the 26 patients in whom cancer was first detected by blood testing underwent PET-CT imaging, and 11 patients developed signs or symptoms of cancer after the blood test that led to imaging procedures other than PET-CT. The specificity and positive predictive value (PPV) of blood testing alone were 98.9% and 19.4%, respectively, and combined with PET-CT, the specificity and PPV increased to 99.6% and 28.3%. The blood test first detected 14 of 45 cancers (31%) in seven organs for which no standard-of-care screening test is available. (ii) Intervention: Of the 26 cancers first detected by blood testing, 17 (65%) had localized or regional disease. Of the 15 participants with positive blood tests as well as positive PET-CT scans, 9 (60%) underwent surgery with curative intent. (iii) Incorporation into clinical care: Blood testing could be combined with conventional screening, leading to detection of more than half of the total incident cancers observed during the study period. Blood testing did not deter participants from undergoing mammography, and surveys revealed that 99% of participants would join a similar, subsequent study if offered. (iv) Safety: 99% of participants did not require any follow-up of blood testing results, and only 0.22% underwent an unnecessary invasive diagnostic procedure as a result of a false-positive blood test. ### CONCLUSION A minimally invasive blood test in combination with PET-CT can safely detect and precisely localize several types of cancers in individuals not previously known to have cancer, in some cases enabling treatment with intent to cure. Further studies will be required to assess the clinical utility, risk-benefit ratio, and cost-effectiveness of such testing. ![Figure][2] Overview of cancers detected by blood testing. Twenty-six cancers (blue dots) in 10 organs were first detected by blood testing. The blue dots with the red halo represent 12 of the 26 cancers that were surgically treated with intent to cure. Nine of these 12 were detected by the combination of the blood test and PET-CT, with the remaining three identified by the blood test combined with another imaging modality. Cancer treatments are often more successful when the disease is detected early. We evaluated the feasibility and safety of multicancer blood testing coupled with positron emission tomography–computed tomography (PET-CT) imaging to detect cancer in a prospective, interventional study of 10,006 women not previously known to have cancer. Positive blood tests were independently confirmed by a diagnostic PET-CT, which also localized the cancer. Twenty-six cancers were detected by blood testing. Of these, 15 underwent PET-CT imaging and nine (60%) were surgically excised. Twenty-four additional cancers were detected by standard-of-care screening and 46 by neither approach. One percent of participants underwent PET-CT imaging based on false-positive blood tests, and 0.22% underwent a futile invasive diagnostic procedure. These data demonstrate that multicancer blood testing combined with PET-CT can be safely incorporated into routine clinical care, in some cases leading to surgery with intent to cure. [1]: /lookup/doi/10.1126/science.abb9601 [2]: pending:yes


Improve alignment of research policy and societal values

Science

Historically, scientific and engineering expertise has been key in shaping research and innovation (R&I) policies, with benefits presumed to accrue to society more broadly over time ([ 1 ][1]). But there is persistent and growing concern about whether and how ethical and societal values are integrated into R&I policies and governance, as we confront public disbelief in science and political suspicion toward evidence-based policy-making ([ 2 ][2]). Erosion of such a social contract with science limits the ability of democratic societies to deal with challenges presented by new, disruptive technologies, such as synthetic biology, nanotechnology, genetic engineering, automation and robotics, and artificial intelligence. Many policy efforts have emerged in response to such concerns, one prominent example being Europe's Eighth Framework Programme, Horizon 2020 (H2020), whose focus on “Responsible Research and Innovation” (RRI) provides a case study for the translation of such normative perspectives into concrete policy action and implementation. Our analysis of this H2020 RRI approach suggests a lack of consistent integration of elements such as ethics, open access, open innovation, and public engagement. On the basis of our evaluation, we suggest possible pathways for strengthening efforts to deliver R&I policies that deepen mutually beneficial science and society relationships. Alignment of R&I objectives with societal benefits, which transcend exclusive economic value, is a globally relevant concern ([ 3 ][3]). Aspiration of stronger science and society interrelationships have been visible in U.S. research management efforts, as well as in Canada and Europe. In H2020, to which the European Commission (EC) allocated nearly €80 billion for the 2014–2020 funding period, the EC enumerated RRI as a priority across all of H2020 activities (a “cross-cutting issue”) to deepen science and society relationships and be responsive to societal challenges. To date, €1.88 billion have been invested across 200 different R&I areas (e.g., quantum computing, graphene nanotechnology, human brain research, artificial intelligence) in more than 1100 projects related to various dimensions of RRI (see the figure). Inclusion of RRI in H2020 reflected the commitment of the European Union (EU) to the precautionary principle with regard to R&I policy, and the deepening commitment of the EC to mainstream concerns related to science and society integration ([ 4 ][4], [ 5 ][5]). RRI principles and practices have been designed to enhance inclusive and democratic modes of conducting R&I to reflect current forms and aspirations of society ([ 4 ][4]). Formal adoption and exploitation of RRI in H2020 coalesced around six thematic domains of responsibility (“keys”): public engagement, gender equality, science education and science literacy, open access, ethics, and governance ([ 6 ][6]). As a relatively young concept, these six keys cover only a part of RRI as it is discussed in the academic literature. Their integration in the European R&I ecosystem was advanced by various political- and policy-level ambitions ([ 3 ][3]–[ 5 ][5]). The forthcoming Ninth Framework Programme, Horizon Europe (2021–2027), includes further mention of RRI, as well as additional efforts to increase responsiveness of science to society through elements of the so-called “three O's agenda” (i.e., open innovation, open science, openness to the world) ([ 7 ][7]). Despite this fairly extensive history of EC investment in mainstreaming activities, a recent survey of more than 3100 European researcher recipients of H2020 funding showed that a vast majority of respondents were not familiar with the concept of RRI ([ 8 ][8]). Although these findings by no means suggest that researchers are irresponsible, they raise questions about the success of the EC approach to embedding normative targets for responsibility into R&I. The need for systematic evaluation is clear ([ 9 ][9]). Our study contributes to a legacy of research on the efficacy of framework programmes in light of various EC ambitions ([ 10 ][10]). To answer our question about policy integration and implementation of RRI in H2020, we conducted a mixed method investigation in three stages: (i) desktop research, (ii) interviews, and (iii) case research [see supplementary materials (SM) S10 for details]. First, we collected and reviewed relevant documentation of the four H2020 Programme Sections (Excellent Science, Industrial Leadership, Societal Challenges, Diversity of Approaches) and 19 respective subthemes available on the websites of the EC. This included reviews of documents at the following levels: policy, scoping, work package, calls, projects, proposal templates, and evaluations. Review of documents extended to all three periods of H2020 (2014–2015, 2016–2017, and 2018–2020) and employed the six EC RRI keys as indicators. Second, we conducted interviews with representatives ( n = 257) of seven stakeholder groups within the 19 subthemes of H2020. Third, using natural language processing algorithms, we obtained and analyzed texts describing project objectives of all the H2020 projects (ongoing and finished, n = 13,644) available on the CORDIS Portal, which provides information on EU-funded R&I activities. We examined how proposal language and RRI policies translate into project activities across H2020 using text-mining approaches. We carried out keyword frequency analysis by applying a selection of 10 to 12 keywords (SM S8) associated with each of the six RRI keys. This resulted in an “RRI score” for each of six keys for each H2020 project (SM S13). This subsequent case research covered all three H2020 periods (i.e., 2014–2015, 2016–2017, and 2018–2020). At each of these stages we produced reports for each corresponding subtheme (SM S11). The resulting body of 19 reports was then systematically reviewed for levels of policy integration. The policy-integration levels were qualitatively assessed with the EC's own indicator assessment ([ 6 ][6]). ![Figure][11] How well is Responsible Research and Innovation represented in Horizon 2020? Limited high-quality reference to Responsible Research and Innovation (RRI) suggests that it has largely been referred to without proper understanding, or as an empty signifier. Data combine all four Horizon 2020 (H2020) program sections and reflect the amount and quality of representation of six RRI keys and three “O's,” across three levels: samples of internal H2020 program documents, H2020 stakeholder interviews, and H2020 project objectives. Comparison across keys within a given level is straightforward; all values are drawn from the same underlying materials. Comparison across levels within a given key should focus on relative proportions of the four colors within a given level, not on absolute values; analyses drew upon different types and amounts of underlying materials in each level. See supplementary materials for details. GRAPHIC: X. LIU/ SCIENCE This assessment demonstrates which elements of the RRI framework were initially defined by the policy-makers (desktop level), which RRI attributes the stakeholders were most aware of (interview level), and which RRI elements were manifested in project proposals (case level) (SM S12; see the figure). RRI as a concept has been present in most of the four Programme Sections of H2020, and particular RRI policy elements emerge as prominent in certain subthemes, especially those addressing societal challenges or explicitly promoting the uptake of RRI. But RRI overall has largely been referred to either without proper understanding of its definition, or as empty signifier, suggesting lack of compliance with the EC's interpretation of the RRI concept (see the figure; SM S9). Integration of the three O's agenda, contemplated as a successor to the RRI framework, lagged behind that of the six RRI keys; a finding consistent with introduction of the agenda in the later stages of H2020. Our results suggest that the integration of the RRI framework into H2020 has fallen short of stated EC ambitions. Our data show substantial discrepancies between the inclusion of RRI concepts within official subtheme documents (e.g., on policy and work programme levels), and awareness of RRI by interviewees working on projects funded by such subthemes (see the figure). Absence of RRI keys across the majority of programme subtheme evaluation criteria is a telling example. Such evidence suggests that (i) the RRI framework is still an evolving concept, the development of which hinders its proper understanding by those who are supposed to use it; (ii) such individuals have only superficial understanding of the notion for its effective exploitation; and (iii) although the RRI framework is present on the declarative, strategic policy level (scoping and subtheme general description), it wanes in funding calls (policy operationalization) and is largely absent in evaluation criteria used in proposal assessment. Collectively, these points further suggest that applicants have little in the way of consistently aligned incentives to regard RRI as relevant in proposal design and submission. Although (i) and (ii) are primarily a matter of a lack of adequate information, awareness and training, (iii) points to limitations of European science policy efforts related to the pursuit of RRI. Such translation failures are typically caused by interplay of different logics of negotiation at the different levels ([ 11 ][12]), a linear model of innovation appealing to scientific excellence in R&I ([ 12 ][13]), actors' resistance to change, path dependencies, cognitive boundaries, and competing policy agendas ([ 13 ][14]). As the issues covered by RRI are normatively claimed to be of high relevance by political decision-makers, as evidenced in several EC documents, we conclude that the problem is one of policy integration strategy and implementation ([ 14 ][15]). The lack of clarity in conceptualizing RRI for research policy and governance, the limited understanding among key stakeholders, and the concept's conflation with other—often conflicting—policy goals (e.g., scientific excellence, economic value, technological readiness) hinder the emergence of a specific RRI-oriented policy frame ([ 15 ][16]). Such conflicting policy goals are palpable at the core of European research funding (e.g., supporting either mission-oriented innovation or curiosity-driven basic research in key funding instruments) and highlight the structural tensions between the normative ideals and potential instrumentalization ([ 3 ][3]). There are some limitations of this study that must be taken into account when interpreting results. First, the measurements were cross-sectional and though representative, are not exhaustive. Generalizability of findings could be increased if the study were to extend in a longitudinal fashion and possibly to better elaborate causal relationships among factors. Second, although we employed mixed methods in our investigation, the number of interviews and case studies could be further increased to provide additional qualitative information about the dynamics of RRI at the project level. Third, as the framework programme remains ongoing, our analysis was not able to evaluate the entire H2020 corpus. Although the results indicate evidence of patchy RRI implementation, highlighting the need for more consistent support to help align EC science policy and societal values, the progress made is nontrivial, given the history of science ([ 1 ][1]). A clear discrepancy exists between the expressed strong normative position on RRI and its integration in concrete policies and practices. Fully integrating RRI as a strong normative position into research funding and governance is a necessary but not sufficient first step to creating a working policy system that drives RRI integration. Longer-lived investments are needed for building a shared understanding and awareness of the relevance of responsibility in R&I among key stakeholders. Integrating responsibility into research funding further requires RRI to shift from a “cross-cutting issue” to a “strategic concern” that receives consistent and sustained embedding in call texts and project selection criteria. This will require “policy entrepreneurs” who can stimulate interactions across subthemes to foster alignment of RRI integration and translation. In addition, a range of integration policies are required at the system level and within subthemes, in which the issue of RRI is adopted as a goal. This is pertinent as, in case of such integration failures, it is often the normative position that is called into question instead of the implementation strategy, or actual integration pathway. The EC would benefit from enhancing previous efforts to integrate RRI and so affirm its role as a leader of ethically acceptable and societally responsible R&I on the world stage. Otherwise Europe needlessly undercuts its ability to direct research toward tackling societal challenges in ways compatible with its values. [science.sciencemag.org/content/369/6499/39/suppl/DC1][17] 1. [↵][18]1. M. Polanyi, 2. J. Ziman, 3. S. Fuller , Minerva 38, 1 (2000). [OpenUrl][19][CrossRef][20][Web of Science][21] 2. [↵][22]1. N. Mejlgaard et al ., Science 361, 761 (2018). [OpenUrl][23][FREE Full Text][24] 3. [↵][25]1. R. von Schomberg, 2. J. Hankins 1. R. von Schomberg , in International Handbook on Responsible Innovation: A Global Resource, R. von Schomberg, J. Hankins, Eds. (Edward Elgar, 2019), pp. 12–32. 4. [↵][26]1. R. Owen, 2. P. Macnaghten, 3. J. Stilgoe , Sci. Public Policy 39, 751 (2012). [OpenUrl][27][CrossRef][28][Web of Science][29] 5. [↵][30]1. D. Simon, 2. S. Kuhlmann, 3. J. Stamm, 4. W. Canzler 1. R. Owen, 2. M. Pansera , in Handbook on Science and Public Policy, D. Simon, S. Kuhlmann, J. Stamm, W. Canzler, Eds. (Edward Elgar, 2019), pp. 26–48. 6. [↵][31]DGRI, “Indicators for promoting and monitoring responsible research and innovation: Report from the expert group on policy indicators for responsible research and innovation” (Report, European Commission, 2015); [http://ec.europa.eu/research/swafs/pdf/pub\_rri/rri\_indicators\_final\_version.pdf][32]. 7. [↵][33]DGRI, Open innovation, open science, open to the world: A vision for Europe” (Directorate-General for Research and Innovation, European Union, 2016); . 8. [↵][34]1. S. Bührer et al ., “Monitoring the evolution and benefits of responsible research and innovation: Report on the researchers' survey – Study” [Report KI-1-18-886-EN-N, Directorate-General for Research; Innovation (European Commission), 2018]. 9. [↵][35]1. A. Rip , J. Responsib. Innov. 3, 290 (2016). [OpenUrl][36] 10. [↵][37]1. H. Rodríguez, 2. E. Fisher, 3. D. Schuurbiers , Res. Policy 42, 1126 (2013). [OpenUrl][38] 11. [↵][39]1. M. Howlett, 2. J. Vince, 3. P. Del Río , Politics Gov. 5, 69 (2017). [OpenUrl][40] 12. [↵][41]1. K. Rommetveit, 2. R. Strand, 3. R. Fjelland, 4. S. Funtowicz , “What can history teach us about the prospects of a European research area? Joint Research Centre scientific and policy reports” (Report JRC84065, European Commission, 2013). 13. [↵][42]1. H. Colebatch , Public Policy Admin 33, 365 (2017). [OpenUrl][43] 14. [↵][44]1. B. G. Peters et al ., Designing for Policy Effectiveness: Defining and Understanding a Concept (Cambridge Univ. Press, 2018). 15. [↵][45]1. R. Owen, 2. E.-M. Forsberg, 3. C. Shelley-Egan , “RRI-practice policy recommendations and roadmaps: Responsible research and innovation in practice” (Report, RRI-Practice Project, 2019); [www.rri-practice.eu/wp-content/uploads/2019/06/RRI-Practice\_Policy\_recommendations.pdf][46]. Acknowledgments: This project received funding from the EU's Horizon 2020 research and innovation programme under grant agreement no. 741402. We acknowledge all the consortium members who contributed to the data collection and writing of the reports (SM S11), which this study is based on. We express our gratitude to H. Tobi and N. Mejlgaard, as well as to the reviewers, for their helpful and constructive comments. 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A child of the slums

Science

I scrambled up a ladder to the tin roof of our house, clutching a book about the evolution of animals. I was 10 years old, and I'd just finished cooking dinner for my entire family—a task that was my daily responsibility. From my perch, I could look out at the slum where we lived in a small town in India. But that wasn't what drew me to the roof: We didn't have any lamps in our house, so I needed sunlight to read my book. I didn't know it at the time, but that study routine was my ticket to a career as a scientist. > “I hope others can take inspiration from my story and realize … they, too, can persevere.” My father—a laborer—didn't let me attend school initially. I was always jealous of my younger brother when he set off to school each day. So, one day, when I was 5 years old, I followed him and hid under the teacher's desk. She noticed me and sent me home. But the next day, she called my father and told him that he should put me in school. Much to my delight, my father said yes. I had a passion for learning, and—despite the hunger pangs I went to school with most days—I quickly shot to the top of my class. When I was 10 years old, my father sent me to a better school outside our neighborhood, one that was mostly attended by students from wealthier families. I was at the top of the class there, too. But I was treated poorly by classmates who saw me as a child of the slums. I also suffered from embarrassment during biology labs because I was very short—due to malnutrition, I suspect—and I had to stand on a chair to see into the microscope. When I graduated from high school, I wanted to become an engineer. My father was eager for me to attend university, but he told me I couldn't study engineering because it was for boys; he said I should study food science instead. My initial reaction was that food science was the last thing I wanted to study. After a childhood preparing meals for my family, there was nothing I hated more than cooking. I enrolled in a food science program anyway, and I quickly discovered that food science wasn't so bad after all. It was a real science—something akin to chemistry—that involved hypothesis testing and experimentation. Soon enough, I was hooked. While attending university, I lived in a hostel near campus, paying my tuition and living expenses with the help of student loans my father secured for me as well as my side job as a research assistant. My room had a lamp, and I was thankful every night that I had light to study under—something I have learned to never take for granted. In the years that followed, I received a Ph.D. in food engineering and was appointed to a faculty position—milestones that felt far removed from my beginnings in the slums. But shortly thereafter, I began a collaboration that brought me back to my roots. I worked with a company that wanted to tackle malnutrition in India's slums. When representatives from the company first approached me, they said, “You'd need to go to the slums and talk with people”—thinking that I'd never done that before. “That's no problem,” I replied. “I grew up in the slums.” As part of my work with the company, I modified the ingredients in a traditional Indian flatbread called chapati, which I'd made every day growing up. I realized it was the perfect vehicle to introduce more nutrition into the diet of poor people, because it was a staple eaten at every meal. I experimented with the ingredients and landed on a recipe that replaced wheat flour with cheap, locally grown grains that contain more minerals, protein, and dietary fiber. Other researchers laughed at me when I started to work on chapati because they didn't think there'd be much science, or innovation, associated with it. But I've since proved them wrong. My work has won numerous national and international awards, and companies, nonprofit organizations, and government agencies have all sought my expertise. In my life, I've faced poverty, hunger, and discrimination. But I didn't let them hold me back. I pushed through the obstacles and learned lessons from them that helped propel me forward. I hope others can take inspiration from my story and realize that—despite the challenges they may be facing—they, too, can persevere.