|Year : 2019 | Volume
| Issue : 2 | Page : 93-99
The use of distributed consensus algorithms to curtail the spread of medical misinformation
Mateusz Plaza1, Lorenzo Paladino2, Ijeoma Nnodim Opara3, Michael S Firstenberg4, Benjamin Wilson5, Thomas J Papadimos6, Stanislaw P Stawicki7
1 Philadelphia College of Osteopathic Medicine, Philadelphia, USA
2 Department of Emergency Medicine, SUNY Downstate Health Sciences University, Brooklyn, New York, USA
3 Department of Medicine and Pediatrics, and Global Health Alliance, Wayne State University, Detroit, Michigan, USA
4 Department of Cardiovascular and Thoracic Surgery, The Medical Center of Aurora, Aurora, Colorado, USA
5 Founder, Lynx Core Development, Charlotte, North Carolina, USA
6 Department of Anesthesiology, The Ohio State University College of Medicine, Columbus, Ohio, USA
7 Department of Research and Innovation, St. Luke's University Health Network, Bethlehem, Pennsylvania, USA
|Date of Submission||18-Aug-2019|
|Date of Decision||20-Aug-2019|
|Date of Acceptance||21-Aug-2019|
|Date of Web Publication||29-Aug-2019|
Dr. Stanislaw P Stawicki
Department of Research and Innovation, St. Luke's University Health Network, Bethlehem, Pennsylvania
Source of Support: None, Conflict of Interest: None
|How to cite this article:|
Plaza M, Paladino L, Opara IN, Firstenberg MS, Wilson B, Papadimos TJ, Stawicki SP. The use of distributed consensus algorithms to curtail the spread of medical misinformation. Int J Acad Med 2019;5:93-9
|How to cite this URL:|
Plaza M, Paladino L, Opara IN, Firstenberg MS, Wilson B, Papadimos TJ, Stawicki SP. The use of distributed consensus algorithms to curtail the spread of medical misinformation. Int J Acad Med [serial online] 2019 [cited 2023 Feb 3];5:93-9. Available from: https://www.ijam-web.org/text.asp?2019/5/2/93/265681
| Introduction|| |
Medical misinformation (MEMI) occurs when individuals propagate health-related claims as “medical fact,” without proper scientific verification that the content being “broadcasted” is indeed true., Lack of rigorous scientific verification of medical information that is shared across a broad range of modern media platforms results in a potentially dangerous status quo.,, Implications of MEMI can be both serious and unpredictable., The antifluoride and antivaccination movements provide examples of the consequences of the lack of appropriate built-in safety mechanisms and scientific consensus (SCS) implementation processes at the level of media outlets, which subsequently led to the dangerous spread of fear-based MEMI.,,,,,, From a definitional standpoint, SCS refers to an agreement among scientific community members about which scientific claims (e.g., statements proposing an explanation about an empirical phenomenon) constitute a scientific fact (a true, proven claim). The authors of this editorial propose that the development of a distributed mechanism for medical SCS (MSCS) determination is urgently needed to help curb the escalating phenomenon of MEMI associated with the exponential growth of Internet-based media platforms. Further, it is proposed that blockchain technology (BCT)-based MSCS verification can be reasonably implemented and fill the much needed scientific consensus-building gap. Moreover, this can be accomplished in a way that is constructive and nonoppressive from the standpoint of preserving scientific freedom, while at the same time reducing the potential for harm resulting from “unchecked” dissemination of MEMI across various contemporary media platforms.
To reach SCS, the scientific community's members use their collective scientific training, experience, and knowledge to verify claims through the use of the scientific method – specifically, often involving a comprehensive and objective assessment of the peer-reviewed literature. With the accumulation of sufficient research intended to verify a particular claim, the scientific community can increase the cumulative probability that the claim is indeed true. In a way, one might reframe the above by stating that when each member of the scientific community is “more than 50% certain” of the claim's validity, then a consensus has been reached that the claim may be valid. With increasing degrees of probability, one can also begin to interpret a particular claim as a “scientific fact.” Of great importance, and to be determined at a later time, is the decision regarding which members of the scientific community are eligible to determine a “claim's” validity, and what percentage of that suitable/qualified expert group would be required to cause a claim to become a “fact.” Regardless of the above considerations, without SCS, a claim remains “a claim” and does not become admitted into the realm of “facts.”,
| Case Study #1|| |
In an article published on March 1, 2018, the Fluoride Action Network (FAN) propagated the idea that Dr. Howard Hu's 2017 research on fluorinated tap water proved that “fluoride is neurotoxic and has no place in the public water supply.” However, Dr. Hu himself did not make such a statement. In fact, in a 2017 commentary dedicated to his scientific investigations, Dr. Hu explicitly stated that his research results “did not provide enough information to suggest there is no safe level of fluoride exposure” and that the possibility of the tap water's neurotoxicity “should be studied further.” Dr. Hu intended his claim to undergo additional investigation, using the rigorous process of SCS building through repeated experimentation and validation of his original findings. He did not present his claim as a “scientific fact” verified by SCS, nor did he suggest that people should stop drinking fluorinated water.
The FAN, however, interpreted and released their “processed information” as being factual. This, in turn, set the stage for the public to propagate potential MEMI that implied there was SCS confirming the neurotoxicity of fluorinated tap water. As can be seen in this example, the FAN's original good intentions led to both confusion and the propagation of MEMI across various social media platforms via the phenomenon of “content adjacency” where the original information intended to convey one message is now being used to convey another message accompanied by content that may be conflicting or outright damaging in nature. Furthermore, once widely disseminated, MEMI may be very difficult to rectify (especially considering that high-profile challenges to established lines of thinking often generate significant interest and reporting in both the conventional journalistic media and the less rigorously reviewed “social media” outlets [often turning the more controversial, outlandish, or dramatic highlights– and potentially incorrectly so – into brief “sound bites” or “tweets”])., Such developments may render the scientific community powerless to restore the content back into the realm of MSCS building. Yet all of the above transpired despite Dr. Hu's clear message that his team's work was not conclusive, and that further validation and verification of their findings is required. It is important to highlight that there is ample evidence showing that safe and appropriate fluoride administration improves dental health by inhibiting tooth demineralization, enhancing remineralization, as well as inhibiting bacterial metabolism and reducing microbial adherence to teeth., Thus, while the scientific benefits of fluoridation are well established, evidence regarding any potential harm is questionable at best.
| Case Study #2|| |
In 1998, a medical researcher, Andrew Wakefield, published a paper in The Lancet strongly suggesting that measles, mumps, and rubella (MMR) vaccines cause autism. After the paper's publication, independent scientists pointed out critical methodological errors that essentially invalidated Wakefield's claims. Due to the irrefutable proof of these errors, The Lancet retracted Wakefield's study from publication., However, this retraction occurred too late, and the public had already been widely exposed to Wakefield's faulty paper, with some individuals and organizations actively using it to support the now prevalent misinformation that MMR vaccines may cause autism. The antivaccine movement contributed to parents worldwide deciding to stop vaccinating their children. In the United Kingdom, the MMR vaccination rate dropped from 92% in 1996 to 85% in 2002. In the United States population, there was a noticeable drop in MMR vaccination rate between 1996 and 2002. The decrease in the vaccination rates of children likely contributed to the 2014–2015 measles outbreak in the United States, where an estimated 125 people contracted the disease,, as well as the more recent, much larger outbreak of 2019., This latest episode also centered around communities, with known vaccination resistance resulting in more than 1100 occurrences, and confirmed cases spreading to thirty states thus far. The largest spike of measles in decades, prompted the Centers for Disease Control and Prevention to release the press statement “U. S. measles cases in first five months of 2019 surpass total cases per year for past 25 years,” demonstrating the long-lasting repercussions and residual damage of MEMI.,,
The above example emphasizes that members of the scientific community must be vigilant when conducting the peer-review process, especially when highly controversial topics and findings have the potential of introducing public health dangers through the propagation of MEMI across various social media channels. Medical institutions, public organizations, and scientific journals must ensure that their members strictly abide by the established MSCS processes. In the case of Andrew Wakefield, the scientific community essentially permitted the conduct of research and subsequent publication of results independent of the collective scrutiny of other scientists., This complacency resulted in an environment without proper checks and balances, up to and including the eventual publication of Wakefield's research in one of the most prestigious medical journals in the world, The Lancet., As it became apparent, the journal did not stop to ascertain that Wakefield's claims were verified by MSCS; instead, the journal assumed that Wakefield's work was legitimate and proceeded to publish it. It was only after the public was exposed to MEMI for more than a decade that scientists realized that Wakefield's claims were fundamentally flawed. By then, it was too late to rectify the damage created by the antivaccine movement.
Given both of the above case studies, how can we effectively apply “lessons learned” to combat the inception and spread of MEMI and ensure the proper attainment of MSCS? One suggestion is to explore the applications of BCT because its decentralized consensus methodology is inherently suitable to facilitate other types of consensus building. In brief, BCT is a computationally governed public ledger that permanently records all transactions in a chain of interlinked data packets (also known as, blocks)., As such, the blockchain utilizes a consensus mechanism that verifies and adds new transactions to the chain of previous blocks, all based on the ability of the participating actors to “agree on validity” of the new block as the “next in line” for inclusion. This consensus mechanism ensures “the common, unambiguous ordering of transactions and blocks and guarantees the integrity and consistency of the blockchain across geographically distributed nodes.”
In the jargon of BCT, “distributed nodes” are decentralized junctions that allow for the node operators to use the blockchain to process, transmit, or receive specified transactions. Node operators (e.g., people or institutions) are the active participants of the blockchain. Collectively, all the nodes within the network help facilitate the computational operation of the blockchain through simple yet very powerful consensus mechanism(s). Finally, the node operators participate in a voting system to agree upon the way the consensus mechanism will manage the blockchain. The MSCS BCT model presents points that must be carefully fleshed out, such as who and what authority will define who the scientific community and node operators are, and what the qualifying criteria will be. We must avoid straight democracy (e.g., the largest number of opinions decides), as that is not necessarily synonymous with the presence of factual correctness.
It is also critically important to recognize that not all claims contrary to the prevailing SCS/MSCS turn out to be false. History is replete with erroneous ideas that were wholly and completely accepted by the scientific community at the time. Dissenting opinions often take a long time to gain momentum against the tide. As technology improves, it will undoubtedly lead to new discoveries that may contradict our previous understanding of a subject. The scientific community must be conscientious that we do not build a mechanism that acts as a barrier to the changing perspectives of medicine and science as our understanding evolves through progress. Thus, as our knowledge of science, disease, and treatments evolves, it is inevitable that our understanding of disease and treatment will as well. We must be careful that the majority consensus does not act as a barrier to make it more difficult to change the past dogma which is believed to be true when there is a dissenting opinion or a contrarian theoretical proposal.
There is limited research on the relationship between BCT and SCS/MSCS building. Such a relationship, however, inherently makes sense. If one views the scientists (or members of the scientific community) as the node operators of the blockchain who use the ledger to transact their scientific research information, review, and verification, then BCT can be helpful in enhancing the attainment of SCS/MSCS in several ways. First, implementation of BCT-based SCS/MSCS could help secure the process of consensus building. Second, it would make the process of SCS/MSCS more objective and less biased. Third, it would provide scientists with anonymity while also preventing specific agendas from overtaking the fairness of the process through random assignment and wide geographic/institutional distribution of nodes. Fourth, BCT could both encourage communication and increase the speed and quality of communication between scientists. Finally, the availability of a decentralized consensus-building capacity may help improve the general access to robust mechanisms intended specifically for SCS/MSCS creation.
In terms of practical implications in which BCT may improve the security and efficiency of SCS/MSCS, key benefits include the synergy between cryptographic security and immutability of the record. This, in turn, prevents any dishonest or malignant actors from distorting or altering the data. Thus, no one without permission to access the blockchain can access (or share) the stored data. Moreover, any changes or edits within the data are permanently recorded and readily trackable. The blockchain, thus, allows for the secure storing and sharing of research work, with full transparency, among a specific community of scientists or experts.,, Importantly, once scientists believe that they have come to SCS/MSCS about a certain claim, they can use their node operator voting rights to formally “confirm or reject” the scientific claim as a “scientific fact.” At the conclusion of this voting process, BCT has the capability of adding a cryptographically immutable and secure stamp to the scientific research information. This stamp would certify the veracity of the scientific facts. It goes without saying that the veracity of “scientific facts” can be revisited at future points as new data presents itself, perhaps through the introduction of required periodic re-review of scientific evidence within a specific topic area.
The security of blockchain thus enables an effective process of SCS/MSCS, at virtually all levels of the scientific process – from hypothesis generation, to data collection, to data analysis, and finally publication of results. Of importance, BCT may serve to restrict the access and distribution of preliminary research information to only those scientists who are actively involved in the research initiative and/or its peer evaluation, thus reducing the risk of uncontrolled release of invalid or unproven results. If a consensus can be established regarding the validity of research results, then a subsequent decision to broadcast these results beyond the scientific community may follow. Further, the blockchain-based consensus verification stamp can be used to provide an immutable certificate of authenticity, guaranteeing that the scientific claims have been deemed valid by a community of qualified/appropriate experts in the field. This goes beyond the standard “two-reviwer” process of the current peer evaluation paradigm. Such prevetting mechanism would give medical journals a readily accessible method of proceeding only with submissions that have attained community-based SCS/MSCS. Moreover, the stamp would provide the public with an external standard by which to verify whether a specific scientific claim is valid and/or verified. Finally, postpublication community-based SCS/MSCS formation can be utilized to raise any “red flags” about the already-published research. In other words, scientists who identify certain research reports to be methodologically flawed or internally inconsistent will have an avenue of alerting both the scientific community and the public about such concerns. The anonymity and distributed nature of this process will be crucial to its functioning, including the element of random selection of content expert reviewers,,,, as well as the de-stigmatization of the important function of scientific whistle-blowing.,,
| Case Study #3|| |
One powerful example of a misguided SCS/MSCS is the 1955 scientific consensus that bacteria were not the cause of peptic ulcers. Due to this consensus, most members of the scientific community remained skeptical of Dr. Robin Warren and Dr. Barry Marshall's research which argued that Helicobacter pylori was the cause of peptic ulcers., By being in the minority, both the physician–scientists faced significant stigma and prejudice; the scientists prejudged their theories to be wrong and thus did not take their research findings seriously. Fortunately, Dr. Warren and Dr. Marshall persisted and, in the course of 30 years, collected a substantial amount of evidence consistently proving their theory. In 1980, the scientific community finally committed to a new MSCS, the one in which peptic ulcers were determined to be caused by H. pylori.
The above example points out that scientific communities can incorrectly apply SCS/MSCS, and worse yet, use it to prejudge efforts to improve the very same SCS/MSCS. The result of such prejudice can lead to decades of MEMI as we have seen in the case of Dr. Warren and Dr. Marshall's experience. Blockchain can help scientific communities mitigate this problem in the first place. In the case of H. pylori and its association with peptic ulcers, the provision of a way for scientists to reach alternative conclusions without the fear of being judged and ridiculed, may have saved innumerable lives during an unnecessarily long process of breaking “old habits.” It is well established that people find it more difficult to unlearn or “let go” of the existing knowledge than to learn completely new facts , – a problem well exemplified with the instances of MEMI outlined in our earlier case studies.
Finally, BCT may help improve the SCS/MSCS process by increasing the speed and efficiency of communication between scientists, as well as improving the scientists' access to the SCS/MSCS infrastructure. Aided by the “instant nature” of Internet technology, scientists can send data to each other seamlessly, regardless of where a researcher is located geographically. All one would need to participate in the blockchain science consensus process is an Internet-enabled computer with a suitable software interface. With these fundamental tools, scientists can send and receive research/data from colleagues and participate in the blockchain voting process that would help verify the accuracy of scientific claims and the validity of the corresponding results. The larger the number of scientists who work on developing a given consensus, the more efficient and robust the SCS/MSCS process becomes. Consequently, the scientific community would become more effective in intercepting and/or stopping MEMI before it spreads and leads to public harm. Very relevant in this particular context is the broad topic of “data sharing.”, Of central importance to verification/validation of scientific results is the scientific community's ability to perform independent evaluation of the underlying data,,, especially when one considers the widespread “pressure to publish” among researchers in the competitive academic environment of today leading to an entire spectrum of problems, beginning with subtle biases and ending with outright scientific misconduct.,, Yet, despite the clear need for independent access to source information accumulated during medical research, significant barriers persist for peer reviewers tasked with determining the validity of reported results.,,,,, Within this realm, BCT may be one of the most important, and the most disruptive, influences/practical solutions to these chronic problems.,,,,
The security, anonymity, speed, and accessibility of BCT can help the medical community implement a dynamic consensus protocol that can rapidly adapt to today's changing social, scientific, and political landscape. Various social media platforms have been abused by malignant actors to efficiently propagate MEMI in the recent past., In 2018, a Pew Research Center study determined that only 26% of Americans can distinguish facts from opinion in the news. Moreover, the same authors indicated that social movements have a relatively easier time swaying the public to believe in medical misinformation. Hoping to instill positive societal change, technology companies have started to invest in BCT designed to counteract this dangerous trend. For example, in 2017, IBM (International Business Machines Corporation, Armonk, New York) filed a patent called “Blockchain for Open Scientific Research,” which aims to improve scientific research efforts through BCT. Nevertheless, BCT development in this important area is still in its infancy and we have likely only seen a small glimpse of its potential. In the long run, it is possible that social media postings of medical and/or scientific nature could undergo a continuous pre- and post-publication, blockchain-based consensus evaluation for validity, with posts deemed invalid being tagged as having “high probability” of constituting MEMI.
| Conclusion|| |
The current commentary intends to send a clear message to our readers – judicious application of BCT to help build SCS/MSCS has the potential to become the most powerful tool in the fight against medical misinformation and the propagation of false, misinterpreted, and/or outright harmful medical claims. Further research and development is required in this critically important and rapidly emerging area of public health – locally, nationally, and internationally.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Ioannidis JP, Stuart ME, Brownlee S, Strite SA. How to survive the medical misinformation mess. Eur J Clin Invest 2017;47:795-802.
Oyeyemi SO, Gabarron E, Wynn R. Ebola, twitter, and misinformation: A dangerous combination? BMJ 2014;349:g6178.
Stawicki TT, Peck GL, Galwankar SC, Bahner DP, Papadimos JS, Papadimos TJ, et al
. From “pearls” to “tweets:” How social media and web-based applications are revolutionizing medical education. Int J Acad Med 2018;4:93-7. [Full text]
Waszak PM, Kasprzycka-Waszak W, Kubanek A. The spread of medical fake news in social media – The pilot quantitative study. Health Policy Technol 2018;7:115-8.
Chou WS, Oh A, Klein WM. Addressing health-related misinformation on social media. JAMA 2018;320:2417-8.
Stawicki SP, Firstenberg MS, Papadimos TJ. The growing role of social media in international health security: The good, the bad, and the ugly, in global health security: Recognizing vulnerabilities, Creating opportunities. Masys AJ, Izurieta R, Reina Ortiz M, editors. Global Health Security. Cham, Switzerland: Springer International Publishing; 2020. p. 447.
Bode L, Vraga EK. In related news, that was wrong: The correction of misinformation through related stories functionality in social media. J Commun 2015;65:619-38.
Barraza L, Orenstein DG, Campos-Outcalt D. Denialism and its adverse effect on public health. Jurimetrics 2012;53:307.
Carstairs C. Cities without cavities: Democracy, risk, and public health. J Can Stud 2010;44:146-70.
Howat P, Binns C, Jancey J. New international review supports community water fluoridation as an effective and safe dental health promotion measure. Health Promot J Austr 2015;26:1-3.
Dubé E, Vivion M, MacDonald NE. Vaccine hesitancy, vaccine refusal and the anti-vaccine movement: Influence, impact and implications. Expert Rev Vaccines 2015;14:99-117.
Mavragani A, Ochoa G. The internet and the anti-vaccine movement: Tracking the 2017 EU measles outbreak. Big Data Cogn Comput 2018;2:2.
Jacobson RM, Targonski PV, Poland GA. A taxonomy of reasoning flaws in the anti-vaccine movement. Vaccine 2007;25:3146-52.
Kuhn TS. The Structure of Scientific Revolutions. Chicago, Illinois: University of Chicago Press; 2012.
Gilbert G.N. The transformation of research findings into scientific knowledge. Soc Stud Sci 1976;6:281-306.
O'Connor C, Weatherall J.O. The Misinformation Age: How False Beliefs Spread. New Haven, Connecticut: Yale University Press; 2019.
Kolstø SD. Scientific literacy for citizenship: Tools for dealing with the science dimension of controversial socioscientific issues. Sci Educ 2001;85:291-310.
Bell RL, Lederman NG. Understandings of the nature of science and decision making on science and technology based issues. Sci Educ 2003;87:352-77.
Gallaugher J. Advertising and Social Networks: A Work in Progress, in Getting the Most Out of Information Systems. Boston, MA: Unnamed Publisher; 2013.
Dalal M, Clark M, Quiñonez RB. Pediatric oral health: Fluoride use recommendations. Pediatrics 2019;36:1-2.
Loskill P, Zeitz C, Grandthyll S, Thewes N, Müller F, Bischoff M, et al.
Reduced adhesion of oral bacteria on hydroxyapatite by fluoride treatment. Langmuir 2013;29:5528-33.
Hussain A, Ali S, Ahmed M, Hussain S. The anti-vaccination movement: A regression in modern medicine. Cureus 2018;10:e2919.
Godlee F, Smith J, Marcovitch H. Wakefield's article linking MMR vaccine and autism was fraudulent. BMJ 2011;342:c7452.
Dyer C. Lancet retracts Wakefield's MMR paper. BMJ 2010;340:c696.
Poland GA, Spier R. Fear, misinformation, and innumerates: How the Wakefield paper, the press, and advocacy groups damaged the public health. Vaccine 2010;28:2361-2.
Pergam SA, Englund JA, Kamboj M, Gans HA, Young JH, Hill JA, et al.
Preventing measles among immunosuppressed cancer and hematopoietic cell transplant patients: A position statement by the American Society for Transplantation and Cellular Therapy. Biol Blood Marrow Transplant 2019. pii: S1083-8791(19)30506-3.
Patel M, Lee AD, Redd SB, Clemmons NS, McNall RJ, Cohn AC, et al.
Increase in measles cases – United States, January 1-April 26, 2019. MMWR Morb Mortal Wkly Rep 2019;68:402-4.
Deresinski S. The return of measles. Intern Med Alert 2019;41:13.
Dees P, Berman DM. The media's role in vaccine misinformation. In: Vaccinophobia and Vaccine Controversies of the 21st
Century. New York, New York: Springer; 2013. p. 383-98.
Larson HJ, Cooper LZ, Eskola J, Katz SL, Ratzan S. Addressing the vaccine confidence gap. Lancet 2011;378:526-35.
Wakefield AJ, Harvey P, Linnell J. MMR – Responding to retraction. Lancet 2004;363:1327-8.
Leask J, Booy R, McIntyre PB. MMR, Wakefield and the lancet: What can we learn? Med J Aust 2010;193:5-7.
Eggertson L. Lancet retracts 12-year-old article linking autism to MMR vaccines. CMAJ 2010;182:E199-200.
Stawicki SP, Firstenberg MS, Papadimos TJ. What's new in academic medicine? Blockchain technology in health-care: Bigger, better, fairer, faster, and leaner. Int J Acad Med 2018;4:1.
Zyskind G, Nathan O. Decentralizing Privacy: Using Blockchain to Protect Personal Data. In 2015 IEEE Security and Privacy Workshops. IEEE; 2015.
Baliga A. Understanding Blockchain Consensus Models, in Persistent; 2017.
Liu J, Li X, Ye L, Zhang H, Du X, Guizani M. BPDS: A Blockchain Based Privacy-Preserving Data Sharing for Electronic Medical Records. In 2018 IEEE Global Communications Conference (GLOBECOM). IEEE; 2018.
Pilkington M. Blockchain Technology: Principles And Applications. Olleros FX, Shegu M, Eds. Research Handbook on Digital Transformations. Northampton, Massachusetts: Edward Elgar Publishing; 2016. p. 225.
Benchoufi M, Porcher R, Ravaud P. Blockchain protocols in clinical trials: Transparency and traceability of consent. F1000Res 2017;6:66.
Dai H, Young HP, Durant TJ, Gong G, Kang M, Krumholz HM, et al
. TrialChain: A blockchain-based platform to validate data integrity in large, biomedical research studies. arXiv preprint arXiv: 1807.03662; 2018.
Herrera-Joancomartí J. Research and challenges on bitcoin anonymity. In: Data Privacy Management, Autonomous Spontaneous Security, and Security Assurance. Cham, Switzerland: Springer; 2014. p. 3-16.
Seebacher S, Schüritz R. Blockchain Technology as An Enabler of Service Systems: A Structured Literature Review. In International Conference on Exploring Services Science. Springer; 2017.
Gipp B, Breitinger C, Meuschke N, Beel J. Cryptsubmit: Introducing Securely Timestamped Manuscript Submission and Peer Review Feedback Using The Blockchain. In Proceedings of the 17th
ACM/IEEE Joint Conference on Digital Libraries. IEEE Press; 2017.
Haffar S, Bazerbachi F, Murad MH. Peer review bias: A critical review. Mayo Clin Proc 2019;94:670-6.
Faunce TA, Jefferys S. Whistleblowing and scientific misconduct: Renewing legal and virtue ethics foundations. Med Law 2007;26:567-84.
Rhodes R, Strain JJ. Whistleblowing in academic medicine. J Med Ethics 2004;30:35-9.
Hellstrom PM. This year's Nobel prize to gastroenterology: Robin Warren and Barry Marshall awarded for their discovery of Helicobacter pylori
as pathogen in the gastrointestinal tract. World J Gastroenterol 2006;12:3126-7.
Altman LK. Nobel Came After Years of Battling the System. New York, New York: New York Times; 2005.
Allee V. 12 principles of knowledge management. Train Dev 1997;51:71-4.
Gersten R, Brengelman SU. The quest to translate research into classroom practice: The emerging knowledge base. Remedial Spec Educ 1996;17:67-74.
Zollman KJ. The communication structure of epistemic communities. Philos Sci 2007;74:574-87.
Contreras JL, Reichman JH. Sharing by design: Data and decentralized commons. Science 2015;350:1312-4.
Moore GM, Cseri I. Systems and Methods for Providing Distributed, Decentralized Data Storage and Retrieval; 2012.
Kothari CR. Research Methodology: Methods and Techniques. New Delhi, India: New Age International; 2004.
Bayliss HR, Wilcox A, Stewart GB, Randall NP. Does research information meet the needs of stakeholders? Exploring evidence selection in the global management of invasive species. Evid Policy 2012;8:37-56.
Fontanarosa PB, DeAngelis CD. Publication of clinical trials in JAMA: Information for authors. JAMA 2008;299:95-6.
Fanelli D. Do pressures to publish increase scientists' bias? An empirical support from US states data. PLoS One 2010;5:e10271.
Martinson BC, Anderson MS, de Vries R. Scientists behaving badly. Nature 2005;435:737-8.
Fanelli D. How many scientists fabricate and falsify research? A systematic review and meta-analysis of survey data. PLoS One 2009;4:e5738.
van Panhuis WG, Paul P, Emerson C, Grefenstette J, Wilder R, Herbst AJ, et al
. A systematic review of barriers to data sharing in public health. BMC Public Health 2014;14:1144.
Conradie P, Choenni S. On the barriers for local government releasing open data. Gov Inf Q 2014;31:S10-7.
Molloy JC. The open knowledge foundation: Open data means better science. PLoS Biol 2011;9:e1001195.
Nosek BA, Alter G, Banks GC, Borsboom D, Bowman SD, Breckler SJ, et al
. SCIENTIFIC STANDARDS. Promoting an open research culture. Science 2015;348:1422-5.
Treloar A. The research data alliance: Globally co-ordinated action against barriers to data publishing and sharing. Learn Publ 2014;27:S9-13.
Barry E, Bannister F. Barriers to open data release: A view from the top. Inf Polity 2014;19:129-52.