Open science in sociology. What, why and now.

WHAT

By now you’ve heard the term “open science”. Although it has no global definition, its advocates tend toward certain agreements. Most definitions focus on the practical aspects of accessibility.

“…the practice of science in such a way that others can collaborate and contribute, where research data, lab notes and other research processes are freely available, under terms that enable reuse, redistribution and reproduction of the research and its underlying data and methods.”


FORSTER, open science teaching resource

Some definitions enter the realm of ethics, feminism and social justice.

“…to imagine and design inclusive infrastructures, practices, and workflows for scientific practice that intentionally enable meaningful participation and redress (these new) forms of exclusion.


Denisse Albornoz,OCSDNet

Whatever the ontology, open science is inevitably something that challenges the status quo in science. Usage of term indicates there is something undesirable about science, otherwise advocates would simply advocate “science”.

The “open” part of the concept refers to any number of things depending on whom you ask. Commonly it means:

Open access – making the results of scientific techniques, research and theory accessible to everyone; as opposed to only in paywalled journals.

Transparency <open process> – making all methods, code, data and any biases or conflicts of interest known before and after the research is conducted. So long as doing this does not harm human subjects or violate any laws.

Open source – on the technology side of science, all programs, apps, algorithms, tools and scripts should be transparent and usable by others. This means that when a scientist develops a new technology, anyone else’s technologies can interact and interface with it. Moreover, anyone can modify the technology to better suit their own needs.

Open academia <open communication/democracy/feminism> – allowing anyone to participate in academia. That inequalities, prejudice and domination that take place in the social world are eliminated from the academic world, if not that the academic world has a goal of eliminating them in the social world. That everyone has the same place in scientific discussions, and no science is conducted by pressuring others or taking advantage of existing power structures. That no science takes place in secret, except for research that requires obfuscation for its completion.

Again, the definitions can cover a broad range. The above are just a snippet, although they strike me as the most common usages; except for ‘open academia’, this is reserved for certain justice motivated scholars.

WHY

Although I do not proclaim to be the arbiter or knower of right or wrong in academia (and life in general), the following facts seem wrong to me.

Double-work and the co-opting of journals

Scientists provide their work as editors and reviewers, because the peer review and publication process is the centerpiece of all of science. Peer reviewers and editors are the only consistent form of quality control in science. The academic journal was a functional response to previous forms of knowledge transmission that required direct scientist/practitioner to student interactions which were geographically limited and reached a very narrow audience.

The journal made it possible to transmit knowledge across the globe. Moreover, the journal reduced the simultaneous discovery and re-discovery problems of science, because no one could prove they discovered something first, and others worked on problems that were already solved unknowingly. It represents one of the first ‘open science’ movements because it was driven by the idea that science was at an impasse and could only move forward through transparent and open exchange of ideas arbitrated by being part of the public record through publishing.

Ironically, the journal format came full circle and began to undermine science. After over two centuries of journals run by non-profit academic associations, for-profit publishing houses began ‘offering’ their services to meet the growing global demand for journals and their content and the rising costs of editing and distribution. In many cases, these publishing houses were able to purchase the journals by offering the academic societies the exclusive right to determine what went in them. Within just 30 years, five conglomerates owned the titles, content or certain features of over 50% of all journal articles published globally.

The content, as always, is still a product of the scientists and the voluntary work of editors and peer reviewers. The publishing houses make large profits, but pay nothing to these workers. The editors and peer reviewers earn their income from universities mostly. The very universities that pay high fees to purchase the right to provide the journals in their libraries. This is a double tax on the universities – paying the producers of content to produce and then paying the distributors of that content to consume it. The content does not change at any point in between these two forms of payment, in other words, the publishers do not add any scientific value to this content.

Matters got even worse with the publishing houses over the past decades. As creative and deceitful capitalists, the publishing houses realized they could generate even more profit by collaborating with the private sector. For example pharmaceutical companies’ profits were directly determined by the findings of studies published in journals. Pharmaceutical companies, or any companies whose profits were determined by the outcomes of scientific experiments, would be willing to invest in shaping those outcomes if they could. Enter a novel concept pioneered by Elsevier: selling journals or journal space to private companies to boost their profits. Win-win for them.

Publish-or-perish begets questionable research practices

Thanks to the advent of the scientific journal, knowledge could be evaluated, used and further transmitted across space and time. The utility of the journal and other forms of academic publication such as books, proved so effective that they became the primary source for others to evaluate the importance of scientists and their work. This gave rise to the norm we are all familiar with, publish-or-perish.

In a survey of psychologists, John et al. (2012) found that 50% claimed they had selectively reported studies that supported their hypothesis (as in, selectively excluding those that didn’t). Moreover, 35% admitted to reporting unexpected findings as having been predicted from the start. Nearly 2% outright admitted to faking data.

Publish-or-perish and questionable research practices have a causal relationship. Except for occasional sociopathic or psychotic individuals, there is no reason for a scientist to engage in questionable research practices. No reason, except scientists’ very existence on scientists may depend on it. So many studies in reality lead to results that go in all directions, support the null or (most importantly) do not provide groundbreaking new results.

Through the peer review and editorial process, journals select studies that are path-breaking. Studies that will move knowledge forward and be of the greatest interest to readers. When faced with prospects of not getting tenured, not getting grant funding and being forced out of academia, a human’s (scientist’s) rational calculations change. Suddenly, rounding that p-value from 0.054 to < 0.05 or even adding some cases to the data becomes a cognitively defensible decision.

Like any profession, science is competitive. Those who publish more, or get more citations to their publications tend to get ahead. Those who don’t, don’t. Professional athletes use incredible tactics to gain competitive advantage. Of course steroids are well-known, but other tactics are much harder to detect. For example, endurance athletes often use blood transfusions to boost recovery and performance. This is what it means to be human, scientist or not.

One of the most radical events in the social and behavioral sciences is Diederik Stapel’s entire career faking data and results that were published in at least 54 articles that consumed millions of Euro in funding. It took almost two decades for critics and whistleblowers to finally out him. Psychology is not alone. In political science LaCour and Green published a study in Science that attitudes toward gay marriage could be changed if heterosexual people listened to a homosexual person’s story, but it turns out LaCour fabricated results of a follow up survey that never took place as uncovered by Broockman. In economics Reinhart and Rogoff published numerous studies identifying a negative impact of high debt rates on national economic growth, when in fact several points in their dataset had conspicuously missing values. When these values were added there was no longer support for their claim as identified by Herndon, Ash and Pollin.

I suspect that most questionable research practices are not intentional. The sociopathic (~psychotic) Stapel’s of the world are rare. This pressure to find a job after doing doctoral studies and then to get tenured, means a trade off between conducting science in its ideal form – so learning as much as possible about the existing literature on a subject, mastering the necessary methods to perform the research and executing the research, possibly with several iterations, and facing the prospect of null results – with science in a form that will lead to publication as fast as possible.

This ‘fast as possible’ leads to amateur science. For example, in the rush to get my first publication I attempted to use “multiple imputation”, but lacked the time to properly learn this method. Instead I simply generated several datasets and averaged them into one and re-ran the analysis on this one. This was not an intentional misuse of a method. It is a questionable research practice as a result of context. Think about matrix algebra. It is the basis of many advanced statistical techniques regularly used by social scientists. How many of us have a strong grasp of matrix mathematics? I don’t. And yet I’ve published several studies using structural equation modeling.

WHAT & WHY in SOCIOLOGY

I am aware of nothing about sociology that suggests it needs a special adaptation of open science. Most research cannot be strictly delineated as sociology or not sociology anyways. The boundaries of a discipline, especially within the social sciences, exist mostly in the institutional structure of universities. Eliason suggested that sociology is unique because it overemphasizes quantitative techniques, has needlessly long articles, lacks writing for the popular press and emphasizes research at the expense of teaching. In my experience the previous sentence perfectly describes all social and behavioral science disciplines at once. Even article length, something I thought might be peculiar to sociology, is not special. Political science and management research have very long articles. Consider that and ASR and ESR for example, limit words to 9,000 and 8,000 or less – this is relatively average if not short for social science.

Actually, I would argue the most unique thing about sociology at the moment relates to open science. Two points in particular: (A) that sociology has not had the same incredible scandals as other disciplines and (B) that sociology lags behind other social sciences in promoting open science.

A lack of scandals, not scandalousness

Could sociologists be more scientific and ethical in their research behaviors than those in other disciplines? Given identical institutional and career structures that favor productivity and innovation over replicating or checking each other’s work, I doubt it. Sociology journals and their editors, for example, rarely retract articles despite evidence of serious methodological mistakes. Carina Mood once accurately pointed out mistakes in the interpretation of odds-ratios in some American Sociological Review articles, but the editors refused to publish her comments, much less consider retractions. She shared her exchange with ASR in an email to me and discusses some of it in a working paper. An exceptional recent event was the retraction of one of Legewie’s sociological studies, but this required he himself to initiate the retraction after someone pointed out errors in his work. Until 2020, the Retraction Watch database (www.retractiondatabase.org) listed no retractions from the top sociology journals, and only two among the well-known, one in Sociology and another in Social Indicators Research.

This year, something new happened. Five articles published in Social Problems, Criminology, and Law & Society Review were retracted. These articles had the common co-author Eric Stewart. It turns out that the data he provided were faked. There is no other logical conclusion that this after exceptionally rigorous work by Pickett (a co-author of Stewart) provided evidence that the Stewart studies had consistently incorrect means and standard deviations, unverifiable surveys (sources, methods, original materials), magically changing case numbers despite identical statistical results, sometimes half the data had duplicate cases and impossible clustering structures in the data.

As an aside, one of Pickett’s findings was that the data had non-uniform terminal digit distributions. This means that the right-most digits in the reported statistics differs markedly from a uniform distribution. In particular, at the third-digit numbers should be uniformly distributed with 0-9 appearing roughly 10% of the time. In one of the papers, zeros appear less than 2% of the time. If you are considering faking data, keep in mind that it is roughly impossible to do it in a way that cannot be detected by careful investigation. Any algorithm used to generate results (even copying and pasting) leaves is statistical marks.

Perhaps we sociologists should be partly relieved, as this is just confirmation that we are as much a part of social science and its problems, as any other discipline. However, the Stewart retractions which should have been breaking news for sociology, went mostly unnoticed. The results of the investigation leading to the retractions is not published in a flagship sociology journal where it belongs. Instead it appears in Econ Journal Watch – something unlikely to be read by any sociologist. Moreover, the retraction notices from the original journals do not cite outright fraud. Stewart continues to promote his work in print claiming the main findings still hold, and several other of his studies with similar irregularities have not been retracted.

Another, extremely important event was a case of ethnomethodological research conducted by Lindsay, Boghossian, and Pluckrose in the mid 2010s. This is sociological self-examination at its best, although their backgrounds are mostly outside of the discipline of sociology. They wrote a series of 20 papers presenting fake results and making arguably unethical claims. They invented the papers to mimic the style of articles published in journals well-known for sociological research on topics of identity, hegemony and marginalization. Seven of their papers were published or had revise and resubmit recommendations before whistleblowing forced them to cancel the project. Some highlights: one paper contained sections from Hitler’s Mein Kampf. Another suggested men should be trained similar to dogs to prevent rape, and a third that white men should be forced to sit in chains on the floors of university classrooms, instead of normal desks. I am not commenting on the merit contained in these ideas, only that they all contained faked data, non-existent methods or conclusions not supported by the data. That these studies easily flew under the radar of a number of high impact journals points out how easy it is to publish without doing the necessary research work.

Lagging behind closed doors

October 6th, 2020. I entered the search terms “open science” (with quotations to search the exact phrase) and “sociology” (with quotations to only return results that contain the word) into Google Scholar. Six pages of results without a single sociology journal. On page 7, Merton’s “Priorities in scientific discovery: a chapter in the sociology of science” appears. Publication date 1957.

In 1973, Wilson, Smoke and Martin found that 80% of studies published in the top three sociology journals of that time rejected the null hypothesis, in other words they had p-values below a threshold. This suggests publication bias, if not p-hacking. Sahner (Table 5) analyzed all article submissions to the Zeitschrift für Soziologie, 1972-1980. Of those that contained significance tests, 70% were significant at p < 0.05 suggesting that authors prefer to submit significant results. More recently, Gerber and Malhotra (2008) reviewed articles published in American Journal of Sociology, American Sociological Review and The Sociological Quarterly, and specifically looked at the boundary of t = 1,96 (i.e., p<0.05) to find that as many as 4-out-of-5 studies were ‘significant’. This suggests publication bias as well. Sociology has yet to have a systematic review of p-hacking by comparing p-values within ‘significant’ results. Meanwhile psychology and political science for example are teeming with papers on “p-hacking” and “publication bias”.

Sociology is rather intransparent. An estimated 78% of the major sociology journals have long-standing transparency policies. Unfortunately, these policies are mostly artifacts on paper without much enforcement. For example, only 37% of sociology articles published in the mainstream journals between 2012-2014 include shared data and/or materials. In 2015, a small group of sociologists tried to obtain materials from the authors of 53 prominent sociological studies. They obtained these from just 19%, and only 20% of all the authors they contacted bothered to respond despite several requests. This suggests sociologists are free to hide the data and materials that led to their findings without recourse, despite such guidelines.

Other disciplines have embraced the Transparency and Openness Promotion Guidelines (TOP). The TOP guidelines with help of the Center for Open Science support journals to improve science. Journals can become signatories of TOP, and in doing so they either adopt and enforce new transparency guidelines, or certify that they already meet certain transparency standards. Most of the top psychology journals and several political science journals signed on. Other major journals such as the Journal of Applied Econometrics and later the American Economic Review adopted their own enforced transparency guidelines.

Until 2017, the only higher ranking sociology journals that signed TOP were Sociological Methods and Research and American Journal of Cultural Sociology. In 2017, Elsevier dictated that all its journals adopt guidelines and this added Social Science Research to the list. At the time of writing this, the flagship journals American Journal of Sociology and American Sociological Review neither signed TOP nor enforce their own guidelines. Of top German sociology journals, the Kölner Zeitschrift für Soziologie und Sozialpsychologie is the only signatory.

If intransparency is pervasive in sociology, then research cannot be (a) checked for errors, (b) reproduced or (c) simply critiqued. Even when exact reproducibility is not the goal, as often is the case with context-specific interpretive research, most research methods remain shrouded in mystery. This requires readers to take a giant leap to trust what others report. Part of the problem is that sociologists express little interest in reproduction or checking others’ works. There are few replications in the history of sociology, and if anything, they decreased over time until recently. For example, searching the articles in American Journal of Sociology and American Sociological Review reveals 22 replication studies from 1950-1980 and only 8 from 1981-2010.

Something telling about a lack of willingness to open sociology comes from sociology’s most ‘powerful’ society, the American Sociological Association. They collectively petitioned the US government to not make data transparency a requirement attached to grant funding in 2019.

NOW

What to do about it? Here are some simple steps to consider.

Transparency

Make all the materials – research design, methodological steps, data (when legally and ethically possible), analyses, conflict of interest and any software code – available online. The practical reason is that others can follow your work and expand it in the future. Doubly practical is that you don’t need to respond to email requests for your materials. So long as you are not a deceitful sociopath, you want others interested in your work and to replicate your work. Even if a study, seems to ‘prove you wrong’, the fact that it replicated your work is evidence of how important your work is and the topic of study. You are a piece of a much larger community of knowledge construction. Constructive exchange can lead to collaboration with critics to generate better future research without personal conflicts.

The immediate value of transparency is that being transparent forces you to be careful. Knowing everything will be public information increases the value of attention to detail. Put in its converse: not sharing your workflow publicly can indirectly foster lower quality standards, in addition to creating possibilities for misconduct. All this enables rather than hinders knowledge, and increases inter-researcher trust.

Transparency should not be much extra work. During the research process you should take high quality notes for yourself. You will often return to your data and research in the future and thus need those notes. This is a best practice with or without sharing your work. When you engage in this best practice, you have a deep familiarity with your data and can draw meaningful conclusions and easily redact identifying characteristics in your data in the case of qualitative research. In case you cannot share data, you can still reveal the design and expectations; or allow controlled access to the data. Human subjects must be protected at all costs, and yes this often means data sharing is not possible .

The ‘transparency work’ of the qualitative research process can be reduced by software platforms that provide semi-automated annotation and coding. Even if you do not share data, you can build an open workflow from the beginning that allows others to understand every step of the data generating process. However, this work can also be extremely tedious and the incentives not immediately clear. More fruitful discussion if not research assistant funding is needed in this area moving forward.

If you are using quantitative methods, immediately stop hiding your work. If you ran 100 models and 99 did not support your hypothesis, then this is your finding. If a journal does not want to publish this, point the editors and reviewers to the importance of null results and the problems of publication bias. If they still refuse, consider boycotting this journal and sharing your negative experience in public.

Preregistration

Preregistration can drastically reduce bias and hacking prior to collecting data. When you clearly outline your plans including how you will analyze the data, before conducting the research, there is little room for hacking so long as you stick to the plan. Moreover, preregistration can be done directly with a journal although sociology journals are laggards here because they generally do not offer this option. In a preregistration, even if you just put an pre-analysis plan or research design and goals online, you must think much harder about factors such as meaning, causality, inter-subjectivity and ‘how the world probably works’. You cannot hide behind results in this process and therefore you must anticipate counterarguments and explore counterfactual logic. This improves the clarity of theory and research, creating an immense gain in efficiency and effectiveness.

Regardless of the methods you use there are many opportunities to take advantage of preregistration. Some forms of qualitative research, for example those involving grounded theory and interpretivist methods, require decisions during the research process that cannot be foreseen. This uncertainty can be outlined in a preregistration stating explicitly when flexibility is and is not admissible. Moreover, simply putting a qualitative research plan online prior to conducting the research is equivalent to a pre-analysis plan. This research design need not compromise your data collection work because you can register the plan on a platform like the Open Science Framework and then embargo it, so that it is preserved but not made public until after the research concludes. Some scholars using quantitative methods might assume that preregistration is not possible because they work with secondary survey data. But the regularity and release of these survey data are known in advance, and these scholars can preregister their studies before the next round of data are collected with the knowledge of which questions and countries will be available.

Decommodify science

The central functions of the scientific publishing industry are printing and disseminating knowledge, which historically solved a problem of how to share knowledge across universities and countries. The business functions of publishing, however, come with harmful byproducts. Publishing firms extract profits from scientists twice. First, scientists provide free labor in the form of editing and peer reviewing, in addition to producing the results for the articles to be printed. Next, researchers, or their employers, must purchase the product of their own labor; labor not paid for by the publishers. The journal article as a product comes at a high cost, and often only in packages of journals meaning that universities have to pay for extra material their scholars do not use.

Sometimes publishing houses neglect science in favor of profits, but Elsevier has been particularly problematic. They sponsored weapon fairs, created and sold ‘fake’ journals to pharmaceutical companies to publish ‘results’ supporting their drugs, purchased the Social Science Research Network and created paywalls or removed legally shared working versions of articles, charge fees for open access articles, and actively lobbied against open access legislation (For a concise summary with links see Tal Yarkoni’s blog entry). This brought massive counter movements against Elsevier in the scientific community (for example, The Cost of Knowledge). You can take action and refuse to review for or publish with unethical publishers if you feel it is justified. Thus, you should inform yourself about the publishers. Your libraries are a source of information, because they deal with the business side of publishers.

If you are in Europe, check if your institution is a signatory of ProjektDEAL. A consortium of universities are collectively bargaining with publishers via ProjektDEAL demanding that publishers reduce fees and eliminate the double paying of universities. The primary objective is that publishers sign country-wide subscription agreements that enable access for all universities at once. Wiley agreed to such a model and this marks a paradigm change. It indicates how the publishing industry looks in the future, so long as the OS Movement proceeds. If you are not in Europe, consider starting a similar initiative, for example the entire University of California system of 10 universities, 5 medical centers and several research institutions that collectively produce roughly 10% of the world’s academic publications recently followed ProjektDEAL and boycotted Elsevier.

You can work around the publishing business. Prior to submitting an article or after it is published, you have the right to share a preprint – a draft of the paper you share publicly so long as it is not published elsewhere or sold for profit. Posting preprints reduces the power that publishing firms have over science, in addition to giving others immediate access to your work. But simply posting preprints on your academic website is not open enough. Use a preprint service, for example through the Open Science Framework, to ensure that your preprints appear in search engines such as Google Scholar. SocArXiv for example, is the go to location for sociology. This enables scholars to find and directly access research results based on the words they contain, uninhibited by paywalls – a crucial aspect to practicing sociology in the Global South. Preprint services are free and open access.

P-hacking. Religion and science aren’t that different

Science and religion parted ways long ago. This is a historical struggle over power. If science claims to disprove that the earth is the center of the universe or that evolution undermines creation, it might falsify religious doctrine, said to be the word of a God or Gods and thus the ultimate Truth. Religions rely on their claims to this Truth to convert people to submit to their institutions. If science undermines this Truth, it undermines religious power. And power is something that changes human behavior; they might lie, cheat, steal and kill to get or preserve it.

Wikimedia Commons: Thinker; Passion

But science and religion followers are not that different. Actually, they are the same. They are human.

Power is another way of describing status and prestige. In science, we know all about status. Scientific status comes from recognition. From making scientific discoveries and claims that garner attention. In particular, attention in the form of citations.

The absence of market prices results in prestige becoming the main reward and high prestige becoming the measure of exceptional ability. Rent seeking in academia, therefore, produces ego-maniacs and much destructive behavior

Sørensen (1996, p. 1358)

The seeking of status, what economists and Sørensen label as a form of ‘rent-seeking’, is presumably the reason scientists p-hack, and engage in other forms of malpractice. In some cases they ‘must’ p-hack in order to meet the demands of reviewers. Mostly, statistical research requires significance stars to attain publication. This is changing with the Open Science Movement in recent times, but only in the margins. Research using qualitative methods also requires its own form of statistical significance ‘p-hacking’. To be published, a paper must extract novel ideas from observational data, whether these reflect the actual data or are even based on actual data at all seems to be irrelevant as long as the story looks good to reviewers. Just like the significance stars that look all sparkly and comforting to reviewers of quantitative research.

So humans (scientists) cheat to attain status; intentionally or even unintentionally — without malicious intent because they are conditioned to play with their data until the stars appear. Therefore it should be no surprise to humans (scientists) that other humans (religious followers) also cheat.

If p-hacking in science is playing around with models so that they represent the data in a way that matches the researcher’s desire for status, rather than portray the results of scientific tests, then p-hacking in religion must be to interpret the dictates of God (or Gods) to fit one’s, or one’s group’s own status goals.

The conflict of science and religion it like p-hacking. Its a power struggle. Who has the power to make claims about the way the world is and the way it should be? Religious followers would attribute this authority to God, and then themselves as seekers and messengers of God. Science followers attribute this to factual knowledge about the world and then themselves as the testers and reducers of uncertainty to ‘uncover’ those facts. In both cases the process is corrupted by status seeking, a fundamental fallibility of humans. When acting as scientists and spiritual seekers, we are fundamentally still primates, and as such tend toward hierarchy, with many of us human-primates willing to cause harm to others in order to attain higher and higher positions.

For religious followers to gain status through p-hacking they would have to adjust the ‘word of God’ or the ultimate Truth in a way that it (a) is no longer a religious or spiritual truth so that it (b) serves their own ends. Do we have evidence of this practice? Wars fought in the name of religion do not really fit the criteria, as wars can be justified as right, as God’s (or the Gods’) will, for example Christian New Testament Revelation 19:11 about the righteous warring against the (presumably) non-righteous (i.e., ‘evil’); Christian/Jewish Old Testament Deuteronomy 20 calls Israelites to war against cities that do not accept their terms; Islam Qur’an 22:39 advocates war in self-defense and possibly 4:74 to fight in the name of God; and Buddhism taking the stance that war might be necessary in defense but not justified as an aggressor.

The point is that it is difficult to find direct evidence of p-hacking by religious followers in order to gain status for themselves or a group. The same problem lies with detecting p-hacking in scientists. Given that all sides in all wars tend to claim righteousness under God (or Gods) it seems obvious that some (or all) are misinterpreting what should be God’s will for their own gain. Given that so many p-values lay below 0.05 in published research, we can assume that not all are derived from a clean research design, method and presentation of results.

Openness is not needed because we are untrustworthy; it is needed because we are human

(Nosek, Spies and Motyl 2012, p. 626)

It is not only religious and scientific institutions that are antagonistic given their seeking of power. Political institutions, who wield a monopoly on force in the modern world divided into sovereign nation states, also do not always get along with both religious and scientific institutions as they have their own p-values to guard.

Novel coronavirus pandemic and the limits of open science

German version available.

On January 30th, 2019, the WHO declared a global health emergency based on scientific evidence of a rapidly spreading coronavirus from the SARS family (Sars-CoV-2, and the disease Covid-19). The evidence the WHO used to declare this emergency came almost entirely from Chinese data.

The Chinese data demonstrated an alarming spread rate in January, as shown in Figure 1. Without the Chinese data, there would have been little cause for alarm as all other countries combined had barely 90 known cases in that period, and not a single death.

Figure 1. The spread of Covid-19 leading to the WHO emergency declaration Janurary 30th. Johns Hopkins data.

In early January, the Chinese government took measures to block news and data1 related to the virus; however, Chinese scientists still managed to follow open science practices (updated news on this here) including sharing partial-gene sequence data with the world. This allowed the WHO to take appropriate measures and enabled scientists in Germany to develop tests to identify the novel coronavirus. The German team shared publicly their methods on the WHO website on January 13th. Technology and global communications have evolved to the point where governments can slow but not stop the free flow of information.

Sharing all data and findings is the best form of science, but not always practiced. The Open Science Movement has the goal of changing this. If everyone in the world has equal access to the theory, methods, data and results of all other scientific research, quality and efficiency increases exponentially. This is evidenced in the open science practices behind the global fight against Covid-19 that saved and will save lives, potentially millions of them.

Figure 2 is a simulation predicting how many people would die of the virus in any given country depending on when governments follow WHO recommended operating procedures, as in: issue stay-at-home orders, engage in widespread testing and quarantine both individuals with the virus and those they were in contact with. ‘Day 0’ in Figure 2 is the moment when there are at least 3 symptomatic cases per million people, usually about 2 months after the first case in a country but much faster if several cases arrive at once.

Figure 2. The impact of government intervention in reducing deaths from Covid-19. Source: Gabriel Goh & author’s calculations
(*indicates a predicted death toll)

The reader should keep in mind that Figure 2 is a simplified simulation. The reality of the situation is extremely complex. In particular, governments do not go from normal operations to full lockdown of society in one day, this usually proceeds in stages. Nonetheless, this simulation comes from the best known predictive epidemiology models and helps demonstrate how even one day of indecision can cost thousands of lives.

In response to this outbreak in China and rapid appearance of Covid-19 globally, South Korea followed the WHO’s standard emergency operating procedures. Meaning: Test everyone possible, isolate all cases, restrict travel and gatherings, close non-essential businesses. The virus was contained and only 200 people died. Of course, previous virus outbreaks heightened their preparedness level. Germany was also well prepared given its rapid development of tests, and because they learned from the experience of Italy as Europe’s ‘ground zero’.

Roughly speaking, Italy crossed the ‘Day 0’ threshold in Figure 2 around February 15th. It was the least prepared as a country because it was the first in Europe, and a place where people from around the globe flock as tourists if not football fans. Thus, Italy’s case is not a story of major government failure, also given that there were reasons to be suspicious of the Chinese case.

The ‘Day 0’ threshold came around March 2nd in Germany, and ‘Day 0’ was around March 8th in New York at least on paper. But New York only started testing people with success around March 1st because the CDC’s own test kits released in early February failed. This left plenty of time, in ‘pandemic terms’, to source the accurate tests being deployed in the rest of the world since January. This did not happen in New York or the US as a whole. Thus, New York was caught completely unprepared but not because the virus was a surprise arrival.

When combined with the data from China, South Korea and several other countries, the WHO upgraded the global emergency to a global pandemic on March 12th. New York had issued a state of emergency but only gave stay at home orders as of March 20th. It was not until a week later that most schools were closed and police authorized to enforce these orders (the blue arrow around ‘Day 31’ in Figure 2). Despite massive open science efforts channeled through the WHO, New York and much of the US simply failed to heed obvious scientific evidence and predictions. This is even more shocking because Seattle, not New York, was ‘ground zero’ in the US. Washington State as a whole implemented early and successful emergency measures.

Reviewing the failures of countries, states or cities to immediately take drastic emergency measures before or on January 30th (global emergency) or March 12th (pandemic) is not the subject of this blog post. The world had access to all the same data and knowledge of how to test for the virus thanks to open science practices, the WHO and several partner organizations and websites.

The message I want to convey is that open science is not enough. Its limits are found in governments. In many countries, science has little place in government decision making. This is perhaps understandable in a dysfunctional authoritarian regime where nearly all political decisions are made to maintain and concentrate power. This is certainly a reason that the worst horrors of the virus are yet to come in sub-Saharan Africa and Central Asia. But it is shocking in democracies where there are throngs of scientists and agencies tasked with monitoring and advising the government on what to do to protect its people.

The United States had ample information that Covid-19 was in the US and spreading rapidly, how to design effective tests, and exactly what to do to reduce the spread of the virus and its death toll months before any major actions were taken – the same information Washington State used to stem the spread. But this scientific information, shared at a scale and speed not seen before in world history, was simply not enough.

The Open Science Movement has ethical principles underlying its open access, data, methods and sharing recommendations. It is not just that open science practices make science more reliable and effective; they promote social justice, or scientific justice if you will. When every scientist in the world can access all the information that every other scientist in the world has, there is scientific equality. While rich universities boycott Elsevier, poorer universities cannot even afford a subscription. Thus, open access would bring a global North-South and a endowed v. not-endowed university equality to the world. But it can’t bring justice to those who are potential virus victims.

In the case of the Covid-19 pandemic, open science looked to undermine the bickering and buffonery of governments at first, but it could only ring the doorbell. Some governments simply refused to open the door, to take action. This begs the question if the Open Science Movement needs to adopt principles of political action that extend beyond policies promoting transparency and reproducibility. Does the Open Science Movement need to push governments to adopt administrative, if not constitutional procedures that make governments accountable to scientists in a natural disaster or emergency like a hurricane or pandemic?

I say yes from an ethical stand point. But its not so simple. As soon as we start pushing things like procedural reform, deep-pocketed special interests get involved and it gets ugly. As scientists we are not likely suited to mudslinging and political maneuvering. Not to mention that the more time we spend on lobbying, the less time we have for science. There are some of us with the ability to lead the Movement and influence governments, but most of us are ill-equipped to combat the powers-that-be behind politics.

That brings up the end game question: Is it enough to give the right answers to governments even if they ignore them? Have we done our duty as scientists if we just ‘show up at the doorstep’ and let government officials decide if we get to come in?

1 The original news article was deleted from the Chinese News Agencies’ website but can be found in the Internet Archive.

2 Source: Goh, Gabriel. “COVID Epidemic Calculator“. Day 0 is at least 3 symptomatic cases per million people, meaning there are potentially hundreds infected given the incubation period. Parameters used for predictions: 106 mil. Population, a single initial case, contagiousness per person of 2.2, rate of transmission 0.73, incubation period 5.2 days and mortality rate 2%.

A note from me: I have sought to capture the empirical evidence and historical timeline as accurate as possible, but any errors in this blog post are my own.

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