Reproducibility of findings is a core foundation of science. If scientific results only hold true in some labs but not in others, then how can researchers feel confident about their discoveries? How can society put evidence-based policies into place if the evidence is unreliable?
Recognition of this “crisis” has prompted calls for reform. Researchers are feeling their way, experimenting with different practices meant to help distinguish solid science from irreproducible results. Some people are even starting to reevaluate how choices are made about what research actually gets tackled. Breaking innovative new ground is flashier than revisiting already published research. Does prioritizing novelty naturally lead to this point?
Incentivizing the wrong thing?
One solution to the reproducibility crisis could be simply to conduct lots of replication studies. For instance, the scientific journal eLife is participating in an initiative to validate and reproduce important recent findings in the field of cancer research. The first set of these “rerun” studies was recently released and yielded mixed results. The results of 2 out of 5 research studies were reproducible, one was not and two additional studies did not provide definitive answers.
But there’s at least one major obstacle to investing time and effort in this endeavor: the quest for novelty. The prestige of an academic journal depends at least partly on how often the research articles it publishes are cited. Thus, research journals often want to publish novel scientific findings which are more likely to be cited, not necessarily the results of newly rerun older research.
Genetics researcher Barak Cohen at Washington University in St. Louis recently published a commentary analyzing this growing push for novelty. He suggests that progress in science depends on a delicate balance between novelty and checking the work of other scientists. When rewards such as funding of grants or publication in prestigious journals emphasize novelty at the expense of testing previously published results, science risks developing cracks in its foundation.
One of his main concerns is that scientific papers now inflate their claims in order to emphasize their novelty and the relevance of biomedical research for clinical applications. By exchanging depth of research for breadth of claims, researchers may be at risk of compromising the robustness of the work. By claiming excessive novelty and impact, researchers may undermine its actual significance because they may fail to provide solid evidence for each claim.
Prestigious journals often now demand complete scientific stories, from basic molecular mechanisms to proving their relevance in various animal models. Unexplained results or unanswered questions are seen as weaknesses. Instead of publishing one exciting novel finding that is robust, and which could spawn a new direction of research conducted by other groups, researchers now spend years gathering a whole string of findings with broad claims about novelty and impact.
Balancing fresh findings and robustness
A challenge for editors and reviewers of scientific manuscripts is assessing the novelty and likely long-term impact of the work they’re assessing. The eventual importance of a new, unique scientific idea is sometimes difficult to recognize even by peers who are grounded in existing knowledge. Many basic research studies form the basis of future practical applications. One recent study found that of basic research articles that received at least one citation, 80 percent were eventually cited by a patent application. But it takes time for practical significance to come to light.
A collaborative team of economics researchers recently developed an unusual measure of scientific novelty by carefully studying the references of a paper. They ranked a scientific paper as more novel if it cited a diverse combination of journals. For example, a scientific article citing a botany journal, an economics journal and a physics journal would be considered very novel if no other article had cited this combination of varied references before.
This measure of novelty allowed them to identify papers which were more likely to be cited in the long run. But it took roughly four years for these novel papers to start showing their greater impact. One may disagree with this particular indicator of novelty, but the study makes an important point: It takes time to recognize the full impact of novel findings.
Realizing how difficult it is to assess novelty should give funding agencies, journal editors and scientists pause. Progress in science depends on new discoveries and following unexplored paths – but solid, reproducible research requires an equal emphasis on the robustness of the work. By restoring the balance between demands and rewards for novelty and robustness, science will achieve even greater progress.
Lingulodinium polyedrum is a unicellular marine organism which belongs to the dinoflagellate group of algae. Its genome is among the largest found in any species on this planet, estimated to contain around 165 billion DNA base pairs – roughly fifty times larger than the size of the human genome. Encased in magnificent polyhedral shells, these bioluminescent algae became important organisms to study biological rhythms. Each Lingulodinium polyedrum cell contains not one but at least two internal clocks which keep track of time by oscillating at a frequency of approximately 24 hours. Algae maintained in continuous light for weeks continue to emit a bluish-green glow at what they perceive as night-time and swim up to the water surface during day-time hours – despite the absence of any external time cues. When I began studying how nutrients affect the circadian rhythms of these algae as a student at the University of Munich, I marveled at the intricacy and beauty of these complex time-keeping mechanisms that had evolved over hundreds of millions of years.
I was prompted to revisit the role of Beauty in biology while reading a masterpiece of scientific writing, “Dreams of a Final Theory” by the Nobel laureate Steven Weinberg in which he describes how the search for Beauty has guided him and many fellow theoretical physicists to search for an ultimate theory of the fundamental forces of nature. Weinberg explains that it is quite difficult to precisely define what constitutes Beauty in physics but a physicist would nevertheless recognize it when she sees it.Over the course of a quarter of a century, I have worked in a variety of biological fields, from these initial experiments in marine algae to how stem cells help build human blood vessels and how mitochondria in a cell fragment and reconnect as cells divide. Each project required its own set of research methods and techniques, each project came with its own failures and successes. But with each project, my sense of awe for the beauty of nature has grown. Evolution has bestowed this planet with such an amazing diversity of life-forms and biological mechanisms, allowing organisms to cope with the unique challenges that they face in their respective habitats. But it is only recently that I have become aware of the fact that my sense of biological beauty was a post hoc phenomenon: Beauty was what I perceived after reviewing the experimental findings; I was not guided by a quest for beauty while designing experiments. In fact, I would have been worried that such an approach might bias the design and interpretation of experiments. Might a desire for seeing Beauty in cell biology lead one to consciously or subconsciously discard results that might seem too messy?
One such key characteristic of a beautiful scientific theory is the simplicity of the underlying concepts. According to Weinberg, Einstein’s theory of gravitation is described in fourteen equations whereas Newton’s theory can be expressed in three. Despite the appearance of greater complexity in Einstein’s theory, Weinberg finds it more beautiful than Newton’s theory because the Einsteinian approach rests on one elegant central principle – the equivalence of gravitation and inertia. Weinberg’s second characteristic for beautiful scientific theories is their inevitability. Every major aspect of the theory seems so perfect that it cannot be tweaked or improved on. Any attempt to significantly modify Einstein’s theory of general relativity would lead to undermining its fundamental concepts, just like any attempts to move around parts of Raphael’s Holy Family would weaken the whole painting.
Can similar principles be applied to biology? I realized that when I give examples of beauty in biology, I focus on the complexity and diversity of life, not its simplicity or inevitability. Perhaps this is due to the fact that Weinberg was describing the search of fundamental laws of physics, laws which would explain the basis of all matter and energy – our universe. As cell biologists, we work several orders of magnitude removed from these fundamental laws. Our building blocks are organic molecules such as proteins and sugars. We find little evidence of inevitability in the molecular pathways we study – cells have an extraordinary ability to adapt. Mutations in genes or derangement in molecular signaling can often be compensated by alternate cellular pathways.
This also points to a fundamental difference in our approaches to the world. Physicists searching for the fundamental laws of nature balance the development of fundamental theories whereas biology in its current form has primarily become an experimental discipline. The latest technological developments in DNA and RNA sequencing, genome editing, optogenetics and high resolution imaging are allowing us to amass unimaginable quantities of experimental data. In fact, the development of technologies often drives the design of experiments. The availability of a genetically engineered mouse model that allows us to track the fate of individual cells that express fluorescent proteins, for example, will give rise to numerous experiments to study cell fate in various disease models and organs. Much of the current biomedical research funding focuses on studying organisms that provide technical convenience such as genetically engineered mice or fulfill a societal goal such as curing human disease.
Uncovering fundamental concepts in biology requires comparative studies across biology and substantial investments in research involving a plethora of other species. In 1990, the National Institutes of Health (NIH – the primary government funding source for biomedical research in the United States) designated a handful of species as model organisms to study human disease, including mice, rats, zebrafish and fruit flies. A recent analysis of the species studied in scientific publications showed that in 1960, roughly half the papers studied what would subsequently be classified as model organisms whereas the other half of papers studied additional species. By 2010, over 80% of the scientific papers were now being published on model organisms and only 20% were devoted to other species, thus marking a significant dwindling of broader research goals in biology. More importantly, even among the model organisms, there has been a clear culling of research priorities with a disproportionately large growth in funding and publications for studies using mice. Thousands of scientific papers are published every month on the cell signaling pathways and molecular biology in mouse and human cells whereas only a minuscule fraction of research resources are devoted to studying signaling pathways in algae.
The question of whether or not biologists should be guided by conceptual Beauty leads us to the even more pressing question of whether we need to broaden biological research. If we want to mirror the dizzying success of fundamental physics during the past century and similarly advance fundamental biology, then we need substantially step-up investments in fundamental biological research that is not constrained by medical goals.
Murder your darlings. The British writer Sir Arthur Quiller Crouch shared this piece of writerly wisdom when he gave his inaugural lecture series at Cambridge, asking writers to consider deleting words, phrases or even paragraphs that are especially dear to them. The minute writers fall in love with what they write, they are bound to lose their objectivity and may not be able to judge how their choice of words will be perceived by the reader. But writers aren’t the only ones who can fall prey to the Pygmalion syndrome. Scientists often find themselves in a similar situation when they develop “pet” or “darling” hypotheses.
How do scientists decide when it is time to murder their darling hypotheses? The simple answer is that scientists ought to give up scientific hypotheses once the experimental data is unable to support them, no matter how “darling” they are. However, the problem with scientific hypotheses is that they aren’t just generated based on subjective whims. A scientific hypothesis is usually put forward after analyzing substantial amounts of experimental data. The better a hypothesis is at explaining the existing data, the more “darling” it becomes. Therefore, scientists are reluctant to discard a hypothesis because of just one piece of experimental data that contradicts it.
In addition to experimental data, a number of additional factors can also play a major role in determining whether scientists will either discard or uphold their darling scientific hypotheses. Some scientific careers are built on specific scientific hypotheses which set apart certain scientists from competing rival groups. Research grants, which are essential to the survival of a scientific laboratory by providing salary funds for the senior researchers as well as the junior trainees and research staff, are written in a hypothesis-focused manner, outlining experiments that will lead to the acceptance or rejection of selected scientific hypotheses. Well written research grants always consider the possibility that the core hypothesis may be rejected based on the future experimental data. But if the hypothesis has to be rejected then the scientist has to explain the discrepancies between the preferred hypothesis that is now falling in disrepute and all the preliminary data that had led her to formulate the initial hypothesis. Such discrepancies could endanger the renewal of the grant funding and the future of the laboratory. Last but not least, it is very difficult to publish a scholarly paper describing a rejected scientific hypothesis without providing an in-depth mechanistic explanation for why the hypothesis was wrong and proposing alternate hypotheses.
For example, it is quite reasonable for a cell biologist to formulate the hypothesis that protein A improves the survival of neurons by activating pathway X based on prior scientific studies which have shown that protein A is an activator of pathway X in neurons and other studies which prove that pathway X improves cell survival in skin cells. If the data supports the hypothesis, publishing this result is fairly straightforward because it conforms to the general expectations. However, if the data does not support this hypothesis then the scientist has to explain why. Is it because protein A did not activate pathway X in her experiments? Is it because in pathway X functions differently in neurons than in skin cells? Is it because neurons and skin cells have a different threshold for survival? Experimental results that do not conform to the predictions have the potential to uncover exciting new scientific mechanisms but chasing down these alternate explanations requires a lot of time and resources which are becoming increasingly scarce. Therefore, it shouldn’t come as a surprise that some scientists may consciously or subconsciously ignore selected pieces of experimental data which contradict their darling hypotheses.
Let us move from these hypothetical situations to the real world of laboratories. There is surprisingly little data on how and when scientists reject hypotheses, but John Fugelsang and Kevin Dunbar at Dartmouth conducted a rather unique study “Theory and data interactions of the scientific mind: Evidence from the molecular and the cognitive laboratory” in 2004 in which they researched researchers. They sat in at scientific laboratory meetings of three renowned molecular biology laboratories at carefully recorded how scientists presented their laboratory data and how they would handle results which contradicted their predictions based on their hypotheses and models.
In their final analysis, Fugelsang and Dunbar included 417 scientific results that were presented at the meetings of which roughly half (223 out of 417) were not consistent with the predictions. Only 12% of these inconsistencies lead to change of the scientific model (and thus a revision of hypotheses). In the vast majority of the cases, the laboratories decided to follow up the studies by repeating and modifying the experimental protocols, thinking that the fault did not lie with the hypotheses but instead with the manner how the experiment was conducted. In the follow up experiments, 84 of the inconsistent findings could be replicated and this in turn resulted in a gradual modification of the underlying models and hypotheses in the majority of the cases. However, even when the inconsistent results were replicated, only 61% of the models were revised which means that 39% of the cases did not lead to any significant changes.
The study did not provide much information on the long-term fate of the hypotheses and models and we obviously cannot generalize the results of three molecular biology laboratory meetings at one university to the whole scientific enterprise. Also, Fugelsang and Dunbar’s study did not have a large enough sample size to clearly identify the reasons why some scientists were willing to revise their models and others weren’t. Was it because of varying complexity of experiments and models? Was it because of the approach of the individuals who conducted the experiments or the laboratory heads? I wish there were more studies like this because it would help us understand the scientific process better and maybe improve the quality of scientific research if we learned how different scientists handle inconsistent results.
In my own experience, I have also struggled with results which defied my scientific hypotheses. In 2002, we found that stem cells in human fat tissue could help grow new blood vessels. Yes, you could obtain fat from a liposuction performed by a plastic surgeon and inject these fat-derived stem cells into animal models of low blood flow in the legs. Within a week or two, the injected cells helped restore the blood flow to near normal levels! The simplest hypothesis was that the stem cells converted into endothelial cells, the cell type which forms the lining of blood vessels. However, after several months of experiments, I found no consistent evidence of fat-derived stem cells transforming into endothelial cells. We ended up publishing a paper which proposed an alternative explanation that the stem cells were releasing growth factors that helped grow blood vessels. But this explanation was not as satisfying as I had hoped. It did not account for the fact that the stem cells had aligned themselves alongside blood vessel structures and behaved like blood vessel cells.
Even though I “murdered” my darling hypothesis of fat –derived stem cells converting into blood vessel endothelial cells at the time, I did not “bury” the hypothesis. It kept ruminating in the back of my mind until roughly one decade later when we were again studying how stem cells were improving blood vessel growth. The difference was that this time, I had access to a live-imaging confocal laser microscope which allowed us to take images of cells labeled with red and green fluorescent dyes over long periods of time. Below, you can see a video of human bone marrow mesenchymal stem cells (labeled green) and human endothelial cells (labeled red) observed with the microscope overnight. The short movie compresses images obtained throughout the night and shows that the stem cells indeed do not convert into endothelial cells. Instead, they form a scaffold and guide the endothelial cells (red) by allowing them to move alongside the green scaffold and thus construct their network. This work was published in 2013 in the Journal of Molecular and Cellular Cardiology, roughly a decade after I had been forced to give up on the initial hypothesis. Back in 2002, I had assumed that the stem cells were turning into blood vessel endothelial cells because they aligned themselves in blood vessel like structures. I had never considered the possibility that they were scaffold for the endothelial cells.
This and other similar experiences have lead me to reformulate the “murder your darlings” commandment to “murder your darling hypotheses but do not bury them”. Instead of repeatedly trying to defend scientific hypotheses that cannot be supported by emerging experimental data, it is better to give up on them. But this does not mean that we should forget and bury those initial hypotheses. With newer technologies, resources or collaborations, we may find ways to explain inconsistent results years later that were not previously available to us. This is why I regularly peruse my cemetery of dead hypotheses on my hard drive to see if there are ways of perhaps resurrecting them, not in their original form but in a modification that I am now able to test.
Fugelsang, J., Stein, C., Green, A., & Dunbar, K. (2004). Theory and Data Interactions of the Scientific Mind: Evidence From the Molecular and the Cognitive Laboratory. Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale, 58 (2), 86-95 DOI: 10.1037/h0085799
We often laud intellectual diversity of a scientific research group because we hope that the multitude of opinions can help point out flaws and improve the quality of research long before it is finalized and written up as a manuscript. The recent events surrounding the research in one of the world’s most famous stem cell research laboratories at Harvard shows us the disastrous effects of suppressing diverse and dissenting opinions.
The infamous “Orlic paper” was a landmark research article published in the prestigious scientific journal Nature in 2001, which showed that stem cells contained in the bone marrow could be converted into functional heart cells. After a heart attack, injections of bone marrow cells reversed much of the heart attack damage by creating new heart cells and restoring heart function. It was called the “Orlic paper” because the first author of the paper was Donald Orlic, but the lead investigator of the study was Piero Anversa, a professor and highly respected scientist at New York Medical College.
Anversa had established himself as one of the world’s leading experts on the survival and death of heart muscle cells in the 1980s and 1990s, but with the start of the new millennium, Anversa shifted his laboratory’s focus towards the emerging field of stem cell biology and its role in cardiovascular regeneration. The Orlic paper was just one of several highly influential stem cell papers to come out of Anversa’s lab at the onset of the new millenium. A 2002 Anversa paper in the New England Journal of Medicine – the world’s most highly cited academic journal –investigated the hearts of human organ transplant recipients. This study showed that up to 10% of the cells in the transplanted heart were derived from the recipient’s own body. The only conceivable explanation was that after a patient received another person’s heart, the recipient’s own cells began maintaining the health of the transplanted organ. The Orlic paper had shown the regenerative power of bone marrow cells in mouse hearts, but this new paper now offered the more tantalizing suggestion that even human hearts could be regenerated by circulating stem cells in their blood stream.
A 2003 publication in Cell by the Anversa group described another ground-breaking discovery, identifying a reservoir of stem cells contained within the heart itself. This latest coup de force found that the newly uncovered heart stem cell population resembled the bone marrow stem cells because both groups of cells bore the same stem cell protein called c-kit and both were able to make new heart muscle cells. According to Anversa, c-kit cells extracted from a heart could be re-injected back into a heart after a heart attack and regenerate more than half of the damaged heart!
These Anversa papers revolutionized cardiovascular research. Prior to 2001, most cardiovascular researchers believed that the cell turnover in the adult mammalian heart was minimal because soon after birth, heart cells stopped dividing. Some organs or tissues such as the skin contained stem cells which could divide and continuously give rise to new cells as needed. When skin is scraped during a fall from a bike, it only takes a few days for new skin cells to coat the area of injury and heal the wound. Unfortunately, the heart was not one of those self-regenerating organs. The number of heart cells was thought to be more or less fixed in adults. If heart cells were damaged by a heart attack, then the affected area was replaced by rigid scar tissue, not new heart muscle cells. If the area of damage was large, then the heart’s pump function was severely compromised and patients developed the chronic and ultimately fatal disease known as “heart failure”.
Anversa’s work challenged this dogma by putting forward a bold new theory: the adult heart was highly regenerative, its regeneration was driven by c-kit stem cells, which could be isolated and used to treat injured hearts. All one had to do was harness the regenerative potential of c-kit cells in the bone marrow and the heart, and millions of patients all over the world suffering from heart failure might be cured. Not only did Anversa publish a slew of supportive papers in highly prestigious scientific journals to challenge the dogma of the quiescent heart, he also happened to publish them at a unique time in history which maximized their impact.
In the year 2001, there were few innovative treatments available to treat patients with heart failure. The standard approach was to use medications that would delay the progression of heart failure. But even the best medications could not prevent the gradual decline of heart function. Organ transplants were a cure, but transplantable hearts were rare and only a small fraction of heart failure patients would be fortunate enough to receive a new heart. Hopes for a definitive heart failure cure were buoyed when researchers isolated human embryonic stem cells in 1998. This discovery paved the way for using highly pliable embryonic stem cells to create new heart muscle cells, which might one day be used to restore the heart’s pump function without resorting to a heart transplant.
The dreams of using embryonic stem cells to regenerate human hearts were soon squashed when the Bush administration banned the generation of new human embryonic stem cells in 2001, citing ethical concerns. These federal regulations and the lobbying of religious and political groups against human embryonic stem cells were a major blow to research on cardiovascular regeneration. Amidst this looming hiatus in cardiovascular regeneration, Anversa’s papers appeared and showed that one could steer clear of the ethical controversies surrounding embryonic stem cells by using an adult patient’s own stem cells. The Anversa group re-energized the field of cardiovascular stem cell research and cleared the path for the first human stem cell treatments in heart disease.
Instead of having to wait for the US government to reverse its restrictive policy on human embryonic stem cells, one could now initiate clinical trials with adult stem cells, treating heart attack patients with their own cells and without having to worry about an ethical quagmire. Heart failure might soon become a disease of the past. The excitement at all major national and international cardiovascular conferences was palpable whenever the Anversa group, their collaborators or other scientists working on bone marrow and cardiac stem cells presented their dizzyingly successful results. Anversa received numerous accolades for his discoveries and research grants from the NIH (National Institutes of Health) to further develop his research program. He was so successful that some researchers believed Anversa might receive the Nobel Prize for his iconoclastic work which had redefined the regenerative potential of the heart. Many of the world’s top universities were vying to recruit Anversa and his group, and he decided to relocate his research group to Harvard Medical School and Brigham and Women’s Hospital 2008.
There were naysayers and skeptics who had resisted the adult stem cell euphoria. Some researchers had spent decades studying the heart and found little to no evidence for regeneration in the adult heart. They were having difficulties reconciling their own results with those of the Anversa group. A number of practicing cardiologists who treated heart failure patients were also skeptical because they did not see the near-miraculous regenerative power of the heart in their patients. One Anversa paper went as far as suggesting that the whole heart would completely regenerate itself roughly every 8-9 years, a claim that was at odds with the clinical experience of practicing cardiologists. Other researchers pointed out serious flaws in the Anversa papers. For example, the 2002 paper on stem cells in human heart transplant patients claimed that the hearts were coated with the recipient’s regenerative cells, including cells which contained the stem cell marker Sca-1. Within days of the paper’s publication, many researchers were puzzled by this finding because Sca-1 was a marker of mouse and rat cells – not human cells! If Anversa’s group was finding rat or mouse proteins in human hearts, it was most likely due to an artifact. And if they had mistakenly found rodent cells in human hearts, so these critics surmised, perhaps other aspects of Anversa’s research were similarly flawed or riddled with artifacts.
At national and international meetings, one could observe heated debates between members of the Anversa camp and their critics. The critics then decided to change their tactics. Instead of just debating Anversa and commenting about errors in the Anversa papers, they invested substantial funds and efforts to replicate Anversa’s findings. One of the most important and rigorous attempts to assess the validity of the Orlic paper was published in 2004, by the research teams of Chuck Murry and Loren Field. Murry and Field found no evidence of bone marrow cells converting into heart muscle cells. This was a major scientific blow to the burgeoning adult stem cell movement, but even this paper could not deter the bone marrow cell champions.
The skeptics who had doubted Anversa’s claims all along may now feel vindicated, but this is not the time to gloat. Instead, the discipline of cardiovascular stem cell biology is now undergoing a process of soul-searching. How was it possible that some of the most widely read and cited papers were based on heavily flawed observations and assumptions? Why did it take more than a decade since the first refutation was published in 2004 for scientists to finally accept that the near-magical regenerative power of the heart turned out to be a pipe dream.
One reason for this lag time is pretty straightforward: It takes a tremendous amount of time to refute papers. Funding to conduct the experiments is difficult to obtain because grant funding agencies are not easily convinced to invest in studies replicating existing research. For a refutation to be accepted by the scientific community, it has to be at least as rigorous as the original, but in practice, refutations are subject to even greater scrutiny. Scientists trying to disprove another group’s claim may be asked to develop even better research tools and technologies so that their results can be seen as more definitive than those of the original group. Instead of relying on antibodies to identify c-kit cells, the 2014 refutation developed a transgenic mouse in which all c-kit cells could be genetically traced to yield more definitive results – but developing new models and tools can take years.
The scientific peer review process by external researchers is a central pillar of the quality control process in modern scientific research, but one has to be cognizant of its limitations. Peer review of a scientific manuscript is routinely performed by experts for all the major academic journals which publish original scientific results. However, peer review only involves a “review”, i.e. a general evaluation of major strengths and flaws, and peer reviewers do not see the original raw data nor are they provided with the resources to replicate the studies and confirm the veracity of the submitted results. Peer reviewers rely on the honor system, assuming that the scientists are submitting accurate representations of their data and that the data has been thoroughly scrutinized and critiqued by all the involved researchers before it is even submitted to a journal for publication. If peer reviewers were asked to actually wade through all the original data generated by the scientists and even perform confirmatory studies, then the peer review of every single manuscript could take years and one would have to find the money to pay for the replication or confirmation experiments conducted by peer reviewers. Publication of experiments would come to a grinding halt because thousands of manuscripts would be stuck in the purgatory of peer review. Relying on the integrity of the scientists submitting the data and their internal review processes may seem naïve, but it has always been the bedrock of scientific peer review. And it is precisely the internal review process which may have gone awry in the Anversa group.
Just like Pygmalion fell in love with Galatea, researchers fall in love with the hypotheses and theories that they have constructed. To minimize the effects of these personal biases, scientists regularly present their results to colleagues within their own groups at internal lab meetings and seminars or at external institutions and conferences long before they submit their data to a peer-reviewed journal. The preliminary presentations are intended to spark discussions, inviting the audience to challenge the veracity of the hypotheses and the data while the work is still in progress. Sometimes fellow group members are truly skeptical of the results, at other times they take on the devil’s advocate role to see if they can find holes in their group’s own research. The larger a group, the greater the chance that one will find colleagues within a group with dissenting views. This type of feedback is a necessary internal review process which provides valuable insights that can steer the direction of the research.
Considering the size of the Anversa group – consisting of 20, 30 or even more PhD students, postdoctoral fellows and senior scientists – it is puzzling why the discussions among the group members did not already internally challenge their hypotheses and findings, especially in light of the fact that they knew extramural scientists were having difficulties replicating the work.
“I think that most scientists, perhaps with the exception of the most lucky or most dishonest, have personal experience with failure in science—experiments that are unreproducible, hypotheses that are fundamentally incorrect. Generally, we sigh, we alter hypotheses, we develop new methods, we move on. It is the data that should guide the science.
In the Anversa group, a model with much less intellectual flexibility was applied. The “Hypothesis” was that c-kit (cd117) positive cells in the heart (or bone marrow if you read their earlier studies) were cardiac progenitors that could: 1) repair a scarred heart post-myocardial infarction, and: 2) supply the cells necessary for cardiomyocyte turnover in the normal heart.
This central theme was that which supplied the lab with upwards of $50 million worth of public funding over a decade, a number which would be much higher if one considers collaborating labs that worked on related subjects.
In theory, this hypothesis would be elegant in its simplicity and amenable to testing in current model systems. In practice, all data that did not point to the “truth” of the hypothesis were considered wrong, and experiments which would definitively show if this hypothesis was incorrect were never performed (lineage tracing e.g.).”
Discarding data that might have challenged the central hypothesis appears to have been a central principle.
According to the whistleblower, Anversa’s group did not just discard undesirable data, they actually punished group members who would question the group’s hypotheses:
“In essence, to Dr. Anversa all investigators who questioned the hypothesis were “morons,” a word he used frequently at lab meetings. For one within the group to dare question the central hypothesis, or the methods used to support it, was a quick ticket to dismissal from your position.“
The group also created an environment of strict information hierarchy and secrecy which is antithetical to the spirit of science:
“The day to day operation of the lab was conducted under a severe information embargo. The lab had Piero Anversa at the head with group leaders Annarosa Leri, Jan Kajstura and Marcello Rota immediately supervising experimentation. Below that was a group of around 25 instructors, research fellows, graduate students and technicians. Information flowed one way, which was up, and conversation between working groups was generally discouraged and often forbidden.
Raw data left one’s hands, went to the immediate superior (one of the three named above) and the next time it was seen would be in a manuscript or grant. What happened to that data in the intervening period is unclear.
A side effect of this information embargo was the limitation of the average worker to determine what was really going on in a research project. It would also effectively limit the ability of an average worker to make allegations regarding specific data/experiments, a requirement for a formal investigation.“
This segregation of information is a powerful method to maintain an authoritarian rule and is more typical for terrorist cells or intelligence agencies than for a scientific lab, but it would definitely explain how the Anversa group was able to mass produce numerous irreproducible papers without any major dissent from within the group.
In addition to the secrecy and segregation of information, the group also created an atmosphere of fear to ensure obedience:
“Although individually-tailored stated and unstated threats were present for lab members, the plight of many of us who were international fellows was especially harrowing. Many were technically and educationally underqualified compared to what might be considered average research fellows in the United States. Many also originated in Italy where Dr. Anversa continues to wield considerable influence over biomedical research.
This combination of being undesirable to many other labs should they leave their position due to lack of experience/training, dependent upon employment for U.S. visa status, and under constant threat of career suicide in your home country should you leave, was enough to make many people play along.
Even so, I witnessed several people question the findings during their time in the lab. These people and working groups were subsequently fired or resigned. I would like to note that this lab is not unique in this type of exploitative practice, but that does not make it ethically sound and certainly does not create an environment for creative, collaborative, or honest science.”
Foreign researchers are particularly dependent on their employment to maintain their visa status and the prospect of being fired from one’s job can be terrifying for anyone.
This is an anonymous account of a whistleblower and as such, it is problematic. The use of anonymous sources in science journalism could open the doors for all sorts of unfounded and malicious accusations, which is why the ethics of using anonymous sources was heavily debated at the recent ScienceOnline conference. But the claims of the whistleblower are not made in a vacuum – they have to be evaluated in the context of known facts. The whistleblower’s claim that the Anversa group and their collaborators received more than $50 million to study bone marrow cell and c-kit cell regeneration of the heart can be easily verified at the public NIH grant funding RePORTer website. The whistleblower’s claim that many of the Anversa group’s findings could not be replicated is also a verifiable fact. It may seem unfair to condemn Anversa and his group for creating an atmosphere of secrecy and obedience which undermined the scientific enterprise, caused torment among trainees and wasted millions of dollars of tax payer money simply based on one whistleblower’s account. However, if one looks at the entire picture of the amazing rise and decline of the Anversa group’s foray into cardiac regeneration, then the whistleblower’s description of the atmosphere of secrecy and hierarchy seems very plausible.
The investigation of Harvard into the Anversa group is not open to the public and therefore it is difficult to know whether the university is primarily investigating scientific errors or whether it is also looking into such claims of egregious scientific misconduct and abuse of scientific trainees. It is unlikely that Anversa’s group is the only group that might have engaged in such forms of misconduct. Threatening dissenting junior researchers with a loss of employment or visa status may be far more common than we think. The gravity of the problem requires that the NIH – the major funding agency for biomedical research in the US – should look into the prevalence of such practices in research labs and develop safeguards to prevent the abuse of science and scientists.
The family of cholesterol lowering drugs known as ‘statins’ are among the most widely prescribed medications for patients with cardiovascular disease. Large-scale clinical studies have repeatedly shown that statins can significantly lower cholesterol levels and the risk of future heart attacks, especially in patients who have already been diagnosed with cardiovascular disease. A more contentious issue is the use of statins in individuals who have no history of heart attacks, strokes or blockages in their blood vessels. Instead of waiting for the first major manifestation of cardiovascular disease, should one start statin therapy early on to prevent cardiovascular disease?
If statins were free of charge and had no side effects whatsoever, the answer would be rather straightforward: Go ahead and use them as soon as possible. However, like all medications, statins come at a price. There is the financial cost to the patient or their insurance to pay for the medications, and there is a health cost to the patients who experience potential side effects. The Guideline Panel of the American College of Cardiology (ACC) and the American Heart Association (AHA) therefore recently recommended that the preventive use of statins in individuals without known cardiovascular disease should be based on personalized risk calculations. If the risk of developing disease within the next 10 years is greater than 7.5%, then the benefits of statin therapy outweigh its risks and the treatment should be initiated. The panel also indicated that if the 10-year risk of cardiovascular disease is greater than 5%, then physicians should consider prescribing statins, but should bear in mind that the scientific evidence for this recommendation was not as strong as that for higher-risk individuals.
Using statins in low risk patients
The recommendation that individuals with comparatively low risk of developing future cardiovascular disease (10-year risk lower than 10%) would benefit from statins was met skepticism by some medical experts. In October 2013, the British Medical Journal (BMJ)published a paper by John Abramson, a lecturer at Harvard Medical School, and his colleagues which re-evaluated the data from a prior study on statin benefits in patients with less than 10% cardiovascular disease risk over 10 years. Abramson and colleagues concluded that the statin benefits were over-stated and that statin therapy should not be expanded to include this group of individuals. To further bolster their case, Abramson and colleagues also cited a 2013 study by Huabing Zhang and colleagues in the Annals of Internal Medicine which (according to Abramson et al.) had reported that 18 % of patients discontinued statins due to side effects. Abramson even highlighted the finding from the Zhang study by including it as one of four bullet points summarizing the key take-home messages of his article.
The problem with this characterization of the Zhang study is that it ignored all the caveats that Zhang and colleagues had mentioned when discussing their findings. The Zhang study was based on the retrospective review of patient charts and did not establish a true cause-and-effect relationship between the discontinuation of the statins and actual side effects of statins. Patients may stop taking medications for many reasons, but this does not necessarily mean that it is due to side effects from the medication. According to the Zhang paper, 17.4% of patients in their observational retrospective study had reported a “statin related incident” and of those only 59% had stopped the medication. The fraction of patients discontinuing statins due to suspected side effects was at most 9-10% instead of the 18% cited by Abramson. But as Zhang pointed out, their study did not include a placebo control group. Trials with placebo groups document similar rates of “side effects” in patients taking statins and those taking placebos, suggesting that only a small minority of perceived side effects are truly caused by the chemical compounds in statin drugs.
Admitting errors is only the first step
Whether 18%, 9% or a far smaller proportion of patients experience significant medication side effects is no small matter because the analysis could affect millions of patients currently being treated with statins. A gross overestimation of statin side effects could prompt physicians to prematurely discontinue medications that have been shown to significantly reduce the risk of heart attacks in a wide range of patients. On the other hand, severely underestimating statin side effects could result in the discounting of important symptoms and the suffering of patients. Abramson’s misinterpretation of statin side effect data was pointed out by readers of the BMJ soon after the article published, and it prompted an inquiry by the journal. After re-evaluating the data and discussing the issue with Abramson and colleagues, the journal issued a correction in which it clarified the misrepresentation of the Zhang paper.
Fiona Godlee, the editor-in-chief of the BMJ also wrote an editorial explaining the decision to issue a correction regarding the question of side effects and that there was not sufficient cause to retract the whole paper since the other points made by Abramson and colleagues – the lack of benefit in low risk patients – might still hold true. Instead, Godlee recognized the inherent bias of a journal’s editor when it comes to deciding on whether or not to retract a paper. Every retraction of a peer reviewed scholarly paper is somewhat of an embarrassment to the authors of the paper as well as the journal because it suggests that the peer review process failed to identify one or more major flaws. In a commendable move, the journal appointed a multidisciplinary review panel which includes leading cardiovascular epidemiologists. This panel will review the Abramson paper as well as another BMJ paper which had also cited the inaccurately high frequency of statin side effects, investigate the peer review process that failed to identify the erroneous claims and provide recommendations regarding the ultimate fate of the papers.
Reviewing peer review
Why didn’t the peer reviewers who evaluated Abramson’s article catch the error prior to its publication? We can only speculate as to why such a major error was not identified by the peer reviewers. One has to bear in mind that “peer review” for academic research journals is just that – a review. In most cases, peer reviewers do not have access to the original data and cannot check the veracity or replicability of analyses and experiments. For most journals, peer review is conducted on a voluntary (unpaid) basis by two to four expert reviewers who routinely spend multiple hours analyzing the appropriateness of the experimental design, methods, presentation of results and conclusions of a submitted manuscript. The reviewers operate under the assumption that the authors of the manuscript are professional and honest in terms of how they present the data and describe their scientific methodology.
In the case of Abramson and colleagues, the correction issued by the BMJ refers not to Abramson’s own analysis but to the misreading of another group’s research. Biomedical research papers often cite 30 or 40 studies, and it is unrealistic to expect that peer reviewers read all the cited papers and ensure that they are being properly cited and interpreted. If this were the expectation, few peer reviewers would agree to serve as volunteer reviewers since they would have hardly any time left to conduct their own research. However, in this particular case, most peer reviewers familiar with statins and the controversies surrounding their side effects should have expressed concerns regarding the extraordinarily high figure of 18% cited by Abramson and colleagues. Hopefully, the review panel will identify the reasons for the failure of BMJ’s peer review system and point out ways to improve it.
It is difficult to obtain precise numbers to quantify the actual extent of severe research misconduct and fraud since it may go undetected. Even when such cases are brought to the attention of the academic leadership, the involved committees and administrators may decide to keep their findings confidential and not disclose them to the public. However, most researchers working in academic research environments would probably agree that these are rare occurrences. A far more likely source of errors in research is the cognitive bias of the researchers. Researchers who believe in certain hypotheses and ideas are prone to interpreting data in a manner most likely to support their preconceived notions. For example, it is likely that a researcher opposed to statin usage will interpret data on side effects of statins differently than a researcher who supports statin usage. While Abramson may have been biased in the interpretation of the data generated by Zhang and colleagues, the field of cardiovascular regeneration is currently grappling in what appears to be a case of biased interpretation of one’s own data. An institutional review by Harvard Medical School and Brigham and Women’s Hospital recently determined that the work of Piero Anversa, one of the world’s most widely cited stem cell researchers, was significantly compromised and warranted a retraction. His group had reported that the adult human heart exhibited an amazing regenerative potential, suggesting that roughly every 8 to 9 years the adult human heart replaces its entire collective of beating heart cells (a 7% – 19% yearly turnover of beating heart cells). These findings were in sharp contrast to a prior study which had found only a minimal turnover of beating heart cells (1% or less per year) in adult humans. Anversa’s finding was also at odds with the observations of clinical cardiologists who rarely observe a near-miraculous recovery of heart function in patients with severe heart disease. One possible explanation for the huge discrepancy between the prior research and Anversa’s studies was that Anversa and his colleagues had not taken into account the possibility of contaminations that could have falsely elevated the cell regeneration counts.
Improving the quality of research: peer review and more
Despite the fact that researchers are prone to make errors due to inherent biases does not mean we should simply throw our hands up in the air, say “Mistakes happen!” and let matters rest. High quality science is characterized by its willingness to correct itself, and this includes improving methods to detect and correct scientific errors early on so that we can limit their detrimental impact. The realization that lack of reproducibility of peer-reviewed scientific papers is becoming a major problem for many areas of research such as psychology, stem cell research and cancer biology has prompted calls for better ways to track reproducibility and errors in science.
One important new paradigm that is being discussed to improve the quality of scholar papers is the role of post-publication peer evaluation. Instead of viewing the publication of a peer-reviewed research paper as an endpoint, post publication peer evaluation invites fellow scientists to continue commenting on the quality and accuracy of the published research even after its publication and to engage the authors in this process. Traditional peer review relies on just a handful of reviewers who decide about the fate of a manuscript, but post publication peer evaluation opens up the debate to hundreds or even thousands of readers which may be able to detect errors that could not be identified by the small number of traditional peer reviewers prior to publication. It is also becoming apparent that science journalists and science writers can play an important role in the post-publication evaluation of published research papers by investigating and communicating research flaws identified in research papers. In addition to helping dismantle the Science Mystique, critical science journalism can help ensure that corrections, retractions or other major concerns about the validity of scientific findings are communicated to a broad non-specialist audience.
In addition to these ongoing efforts to reduce errors in science by improving the evaluation of scientific papers, it may also be useful to consider new pro-active initiatives which focus on how researchers perform and design experiments. As the head of a research group at an American university, I have to take mandatory courses (in some cases on an annual basis) informing me about laboratory hazards, ethics of animal experimentation or the ethics of how to conduct human studies. However, there are no mandatory courses helping us identify our own research biases or how to minimize their impact on the interpretation of our data. There is an underlying assumption that if you are no longer a trainee, you probably know how to perform and interpret scientific experiments. I would argue that it does not hurt to remind scientists regularly – no matter how junior or senior- that they can become victims of their biases. We have to learn to continuously re-evaluate how we conduct science and to be humble enough to listen to our colleagues, especially when they disagree with us.
The patient has verified his or her identity, the surgical site, the type of procedure, and his or her consent. Check.
The surgical site is marked on a patient if such marking is appropriate for the procedure. Check.
The probe measuring blood oxygen content has been placed on the patient and is functioning. Check.
All members of the surgical and anesthesia team are aware of whether the patient has a known allergy? Check.
These were the first items on a nineteen-point World Health Organization (WHO) surgical safety checklist from an international research study to evaluate the impact of routinely using checklists in operating rooms. The research involved over 7,500 patients undergoing surgery in eight hospitals (Toronto, Canada; New Delhi, India; Amman, Jordan; Auckland, New Zealand; Manila, Philippines; Ifakara, Tanzania; London, England; and Seattle, WA) and was published in the New England Journal of Medicine in 2009.
Some of the items on the checklist were already part of standard care at many of the enrolled hospitals, such as the use of oxygen monitoring probes. Other items, such as ensuring that there was a contingency plan for major blood loss prior to each surgical procedure, were not part of routine surgical practice. The impact of checklist implementation was quite impressive, showing that this simple safety measure nearly halved the rate of death in surgical patients from 1.6% to 0.8%. The infection rate at the site of the surgical procedure also decreased from 6.2% in the months preceding the checklist introduction to a mere 3.4%.
Checklists as a Panacea?
The remarkable results of the 2009 study were met with widespread enthusiasm. This low-cost measure could be easily implemented in hospitals all over the world and could potentially lead to major improvements in patient outcomes. It also made intuitive sense that encouraging communication between surgical team members via checklists would reduce complications after surgery.
A few weeks after the study’s publication, the National Patient Safety Agency (NPSA) in the United Kingdom issued a patient safety alert, requiring National Health Service (NHS) organizations to use the WHO Surgical Safety Checklist for all patients undergoing surgical procedures. In 2010, Canada followed suit and also introduced regulations requiring the use of surgical safety checklists. However, the data for the efficacy of such lists had only been obtained in observational research studies conducted in selected hospitals. Would widespread mandatory implementation of such a system in “real world” community hospitals also lead to similar benefits?
A recently published study in the New England Journal of Medicine lead by Dr. David Urbach at the University of Toronto has now reviewed the surgery outcomes of hospitals in Ontario, Canada, comparing the rate of surgical complications during three-month periods before and after the implementation of the now mandatory checklists. Nearly all the hospitals reported that they were adhering to the checklist requirements and the vast majority used either a checklist developed by the Canadian Patient Safety Institute, which is even more comprehensive than the WHO checklist or other similar checklists. After analyzing the results of more than 200,000 procedures at 101 hospitals, Urbach and colleagues found no significant change in the rate of death after surgery after the introduction of the checklists (0.71% versus 0.65% – not statistically significant). Even the overall complication rates or the infection rates in the Ontario hospitals did not change significantly after surgical teams were required to complete the checklists.
Check the Checklist
The discrepancy in the results between the two studies is striking. How can one study demonstrate such a profound benefit of introducing checklists while a second study shows no significant impact at all? The differences between the two studies may hold some important clues. The 2009 study had a pre-checklist death rate of 1.6%, which is more than double the pre-checklist death rate in the more recent Ontario study. This may reflect the nature and complexity of the surgeries surveyed in the first study and also the socioeconomic differences. A substantial proportion of the patients in the international study were enrolled in low-income or middle-income countries. The introduction of a checklist may have been of much greater benefit to patients and hospitals that were already struggling with higher complication rates.
Furthermore, as the accompanying editorial by Dr. Lucian Leape in the New England Journal of Medicine points out, assessment of checklist implementation in the recent study by Urbach and colleagues was based on a retrospective analysis of self-reports by surgical teams and hospitals. Items may have been marked as “checked” in an effort to rush through the list and start the surgical procedures without the necessary diligence and time required to carefully go through every single item on the checklist. In the 2009 WHO study, on the other hand, surgical teams were aware of the fact that they were actively participating in a research study and the participating surgeons may have therefore been more motivated to meticulously implement all the steps on a checklist.
One of the key benefits of checklists is that they introduce a systematic and standardized approach to patient care and improve communication between team members. It is possible that the awareness of surgical teams in the Ontario hospitals in regards to patient safety and the need for systematic communication was already raised to higher level even before the introduction of the mandatory checklists so that this mandate may have had less of an impact.
The study by Urbach and colleagues does not prove that safety checklists are without benefit. It highlights that there is little scientific data supporting the use of mandatory checklists. Since the study could not obtain any data on how well the checklists were implemented in each hospital, it is possible that checklists are more effective when team members buy into their value and do not just view it as another piece of mandatory and bureaucratic paperwork.
Instead of mandating checklists, authorities should consider the benefits of allowing surgical teams to develop their own measures that improve patient safety and team communication. The safety measures will likely contain some form of physical or verbal checklists. By encouraging surgical teams to get involved in the development process and tailor the checklists according to the needs of individual patients, surgical teams and hospitals, they may be far more motivated to truly implement them.
Optimizing such tailored checklists, understanding why some studies indicate benefits of checklists whereas others do not and re-evaluating the efficacy of checklists in the non-academic setting will all require a substantial amount of future research before one can draw definitive conclusions about the efficacy of checklists. Regulatory agencies in Canada and the United Kingdom should reconsider their current mandates. Perhaps an even more important lesson to be learned is that health regulatory agencies should not rush to enforce new mandates based on limited scientific data.
Urbach DR, Govindarajan A, Saskin R, Wilton AS, & Baxter NN (2014). Introduction of surgical safety checklists in Ontario, Canada. The New England Journal of Medicine, 370 (11), 1029-38 PMID: 24620866
I will be facilitating the discussion at this session, which will take place at noon on Saturday, March 1, just before the final session of the conference. The title of the session is rather vague, and the purpose of the session is for attendees to exchange their views on whether we can agree on certain scientific and journalistic standards for science blogging.
Individual science bloggers have very different professional backgrounds and they also write for a rather diverse audience. Some bloggers are part of larger networks, others host a blog on their own personal website. Some are paid, others write for free. Most bloggers have developed their own personal styles for how they write about scientific studies, the process of scientific discovery, science policy and the lives of people involved in science. Considering the heterogeneity in the science blogging community, is it even feasible to identify “standards” for scientific blogging? Are there some core scientific and journalistic standards that most science bloggers can agree on? Would such “standards” merely serve as informal guidelines or should they be used as measures to assess the quality of science blogging?
These are the kinds of questions that we will try to discuss at the session. I hope that we will have a lively discussion, share our respective viewpoints and see what we can learn from each other. To gauge the interest levels of the attendees, I am going to pitch a few potential discussion topics on this blog and use your feedback to facilitate the discussion. I would welcome all of your responses and comments, independent of whether you intend to attend the conference or the session. I will also post these questions in the Science Online discussion forum.
One of the challenges we face when we blog about specific scientific studies is determining how much background reading is necessary to write a reasonably accurate blog post. Most science bloggers probably read the original research paper they intend to write about, but even this can be challenging at times. Scientific papers aren’t very long. Journals usually restrict the word count of original research papers to somewhere between 2,000 words to 8,000 words (depending on each scientific journal’s policy and whether the study is a published as a short communication or a full-length article). However, original research papers are also accompanied four to eight multi-paneled figures with extensive legends.
Nowadays, research papers frequently include additional figures, data-sets and detailed descriptions of scientific methods that are published online and not subject to the word count limit. A 2,000 word short communication with two data figures in the main manuscript may therefore be accompanied by eight “supplemental” online-only figures and an additional 2,000 words of text describing the methods in detail. A single manuscript usually summarizes the results of multiple years of experimental work, which is why this condensed end-product is quite dense. It can take hours to properly study the published research study and understand the intricate details.
Is it enough to merely read the original research paper in order to blog about it? Scientific papers include a brief introduction section, but these tend to be written for colleagues who are well-acquainted with the background and significance of the research. However, unless one happens to blog about a paper that is directly related to one’s own work, most of us probably need additional background reading to fully understand the significance of a newly published study.
An expert on liver stem cells, for example, who wants blog about the significance of a new paper on lung stem cells will probably need substantial amount of additional background reading. One may have to read at least one or two older research papers by the authors or their scientific colleagues / competitors to grasp what makes the new study so unique. It may also be helpful to read at least one review paper (e.g. a review article summarizing recent lung stem cell discoveries) to understand the “big picture”. Some research papers are accompanied by scientific editorials which can provide important insights into the strengths and limitations of the paper in question.
All of this reading adds up. If it takes a few hours to understand the main paper that one intends to blog about, and an additional 2-3 hours to read other papers or editorials, a science blogger may end up having to invest 4-5 hours of reading before one has even begun to write the intended blog post.
What strategies have science bloggers developed to manage their time efficiently and make sure they can meet (external or self-imposed) deadlines but still complete the necessary background reading?
Should bloggers provide references and links to the additional papers they consulted?
Should bloggers try to focus on a narrow area of expertise so that over time they develop enough of a background in this niche area so that they do not need so much background reading?
Are there major differences in the expectations of how much background reading is necessary? For example, does an area such as stem cell research or nanotechnology require far more background reading because every day numerous new papers are published and it is so difficult to keep up with the pace of the research?
Is it acceptable to take short-cuts? Could one just read the paper that one wants to blog about and forget about additional background reading, hoping that the background provided in the paper is sufficient and balanced?
Can one avoid reading the supplementary figures or texts of a paper and just stick to the main text of a paper, relying on the fact that the peer reviewers of the published paper would have caught any irregularities in the supplementary data?
Is it possible to primarily rely on a press release or an interview with the researchers of the paper and just skim the results of the paper instead of spending a few hours trying to read the original paper?
Or do such short-cuts compromise the scientific and journalistic quality of science blogs?
Would a discussion about expectations, standards and strategies to manage background reading be helpful for participants of the session?