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On the reproducibility of science: unique identification of research resources in the biomedical literature

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Scientific reproducibility has been at the forefront of many news stories and there exist numerous initiatives to help address this problem. We posit that a contributor is simply a lack of specificity that is required to enable adequate research reproducibility. In particular, the inability to uniquely identify research resources, such as antibodies and model organisms, makes it difficult or impossible to reproduce experiments even where the science is otherwise sound. In order to better understand the magnitude of this problem, we designed an experiment to ascertain the “identifiability” of research resources in the biomedical literature. We evaluated recent journal articles in the fields of Neuroscience, Developmental Biology, Immunology, Cell and Molecular Biology and General Biology, selected randomly based on a diversity of impact factors for the journals, publishers, and experimental method reporting guidelines. We attempted to uniquely identify model organisms (mouse, rat, zebrafish, worm, fly and yeast), antibodies, knockdown reagents (morpholinos or RNAi), constructs, and cell lines. Specific criteria were developed to determine if a resource was uniquely identifiable, and included examining relevant repositories (such as model organism databases, and the Antibody Registry), as well as vendor sites. The results of this experiment show that 54% of resources are not uniquely identifiable in publications, regardless of domain, journal impact factor, or reporting requirements. For example, in many cases the organism strain in which the experiment was performed or antibody that was used could not be identified. Our results show that identifiability is a serious problem for reproducibility. Based on these results, we provide recommendations to authors, reviewers, journal editors, vendors, and publishers. Scientific efficiency and reproducibility depend upon a research-wide improvement of this substantial problem in science today.

Material Type: Reading

Authors: Gregory M. LaRocca, Holly Paddock, Laura Ponting, Matthew H. Brush, Melissa A. Haendel, Nicole A. Vasilevsky, Shreejoy J. Tripathy

The Open Science Training Handbook

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Open Science, the movement to make scientific products and processes accessible to and reusable by all, is about culture and knowledge as much as it is about technologies and services. Convincing researchers of the benefits of changing their practices, and equipping them with the skills and knowledge needed to do so, is hence an important task.This book offers guidance and resources for Open Science instructors and trainers, as well as anyone interested in improving levels of transparency and participation in research practices. Supporting and connecting an emerging Open Science community that wishes to pass on its knowledge, the handbook suggests training activities that can be adapted to various settings and target audiences. The book equips trainers with methods, instructions, exemplary training outlines and inspiration for their own Open Science trainings. It provides Open Science advocates across the globe with practical know-how to deliver Open Science principles to researchers and support staff. What works, what doesn’t? How can you make the most of limited resources? Here you will find a wealth of resources to help you build your own training events.

Material Type: Reading

Author: FOSTER Open Science

Five selfish reasons to work reproducibly

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And so, my fellow scientists: ask not what you can do for reproducibility; ask what reproducibility can do for you! Here, I present five reasons why working reproducibly pays off in the long run and is in the self-interest of every ambitious, career-oriented scientist.A complex equation on the left half of a black board, an even more complex equation on the right half. A short sentence links the two equations: “Here a miracle occurs”. Two mathematicians in deep thought. “I think you should be more explicit in this step”, says one to the other.This is exactly how it seems when you try to figure out how authors got from a large and complex data set to a dense paper with lots of busy figures. Without access to the data and the analysis code, a miracle occurred. And there should be no miracles in science.Working transparently and reproducibly has a lot to do with empathy: put yourself into the shoes of one of your collaboration partners and ask yourself, would that person be able to access my data and make sense of my analyses. Learning the tools of the trade (Box 1) will require commitment and a massive investment of your time and energy. A priori it is not clear why the benefits of working reproducibly outweigh its costs.Here are some reasons: because reproducibility is the right thing to do! Because it is the foundation of science! Because the world would be a better place if everyone worked transparently and reproducibly! You know how that reasoning sounds to me? Just like yaddah, yaddah, yaddah …It’s not that I think these reasons are wrong. It’s just that I am not much of an idealist; I don’t care how science should be. I am a realist; I try to do my best given how science actually is. And, whether you like it or not, science is all about more publications, more impact factor, more money and more career. More, more, more… so how does working reproducibly help me achieve more as a scientist.

Material Type: Reading

Author: Florian Markowetz

Harry Potter and the Methods of Reproducibility

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"Harry Potter and the Methods of Reproducibility -- A brief Introduction to Open Science" gives a brief overview of Open Science, particularly reproducibility, for newcomers to the topic. It introduces the concept of questionable research practices (QRPs) and Open Science solutions to these QRPs, such as preregistrations, registered reports, Open Data, Open Code, and Open Materials.

Material Type: Lesson

Author: Mariella Paul

Open science challenges, benefits and tips in early career and beyond

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The movement towards open science is a consequence of seemingly pervasive failures to replicate previous research. This transition comes with great benefits but also significant challenges that are likely to affect those who carry out the research, usually early career researchers (ECRs). Here, we describe key benefits, including reputational gains, increased chances of publication, and a broader increase in the reliability of research. The increased chances of publication are supported by exploratory analyses indicating null findings are substantially more likely to be published via open registered reports in comparison to more conventional methods. These benefits are balanced by challenges that we have encountered and that involve increased costs in terms of flexibility, time, and issues with the current incentive structure, all of which seem to affect ECRs acutely. Although there are major obstacles to the early adoption of open science, overall open science practices should benefit both the ECR and improve the quality of research. We review 3 benefits and 3 challenges and provide suggestions from the perspective of ECRs for moving towards open science practices, which we believe scientists and institutions at all levels would do well to consider.

Material Type: Reading

Authors: Christopher Allen, David M. A. Mehler

A checklist is associated with increased quality of reporting preclinical biomedical research: A systematic review

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Irreproducibility of preclinical biomedical research has gained recent attention. It is suggested that requiring authors to complete a checklist at the time of manuscript submission would improve the quality and transparency of scientific reporting, and ultimately enhance reproducibility. Whether a checklist enhances quality and transparency in reporting preclinical animal studies, however, has not been empirically studied. Here we searched two highly cited life science journals, one that requires a checklist at submission (Nature) and one that does not (Cell), to identify in vivo animal studies. After screening 943 articles, a total of 80 articles were identified in 2013 (pre-checklist) and 2015 (post-checklist), and included for the detailed evaluation of reporting methodological and analytical information. We compared the quality of reporting preclinical animal studies between the two journals, accounting for differences between journals and changes over time in reporting. We find that reporting of randomization, blinding, and sample-size estimation significantly improved when comparing Nature to Cell from 2013 to 2015, likely due to implementation of a checklist. Specifically, improvement in reporting of the three methodological information was at least three times greater when a mandatory checklist was implemented than when it was not. Reporting the sex of animals and the number of independent experiments performed also improved from 2013 to 2015, likely from factors not related to a checklist. Our study demonstrates that completing a checklist at manuscript submission is associated with improved reporting of key methodological information in preclinical animal studies.

Material Type: Reading

Authors: Doris M. Rubio, Janet S. Lee, Jill Zupetic, John P. Pribis, Joo Heung Yoon, Kwonho Jeong, Kyle M. Holleran, Nader Shaikh, SeungHye Han, Tolani F. Olonisakin

The citation advantage of linking publications to research data

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Efforts to make research results open and reproducible are increasingly reflected by journal policies encouraging or mandating authors to provide data availability statements. As a consequence of this, there has been a strong uptake of data availability statements in recent literature. Nevertheless, it is still unclear what proportion of these statements actually contain well-formed links to data, for example via a URL or permanent identifier, and if there is an added value in providing them. We consider 531,889 journal articles published by PLOS and BMC which are part of the PubMed Open Access collection, categorize their data availability statements according to their content and analyze the citation advantage of different statement categories via regression. We find that, following mandated publisher policies, data availability statements have become common by now, yet statements containing a link to a repository are still just a fraction of the total. We also find that articles with these statements, in particular, can have up to 25.36% higher citation impact on average: an encouraging result for all publishers and authors who make the effort of sharing their data. All our data and code are made available in order to reproduce and extend our results.

Material Type: Reading

Authors: Barbara McGillivray, Giovanni Colavizza, Iain Hrynaszkiewicz, Isla Staden, Kirstie Whitaker

Open science saves lives: Lessons from the COVID-19 pandemic

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In the last decade Open Science principles have been successfully advocated for and are being slowly adopted in different research communities. In response to the COVID-19 pandemic many publishers and researchers have sped up their adoption of Open Science practices, sometimes embracing them fully and sometimes partially or in a sub-optimal manner. In this article, we express concerns about the violation of some of the Open Science principles and its potential impact on the quality of research output. We provide evidence of the misuses of these principles at different stages of the scientific process. We call for a wider adoption of Open Science practices in the hope that this work will encourage a broader endorsement of Open Science principles and serve as a reminder that science should always be a rigorous process, reliable and transparent, especially in the context of a pandemic where research findings are being translated into practice even more rapidly. We provide all data and scripts at https://osf.io/renxy/.

Material Type: Reading

Authors: Clemence Leyrat, Cooper Smout, Corentin Segalas, Eric Billy, Haiting Jiang, Lonni Besancon, Maxime Deforet, Nathan Peiffer-Smadja, Paolo Masuzzo

Preliminary analysis of COVID-19 academic information patterns: a call for open science in the times of closed borders

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The Pandemic of COVID-19, an infectious disease caused by SARS-CoV-2 motivated the scientific community to work together in order to gather, organize, process and distribute data on the novel biomedical hazard. Here, we analyzed how the scientific community responded to this challenge by quantifying distribution and availability patterns of the academic information related to COVID-19. The aim of this study was to assess the quality of the information flow and scientific collaboration, two factors we believe to be critical for finding new solutions for the ongoing pandemic. The RISmed R package, and a custom Python script were used to fetch metadata on articles indexed in PubMed and published on Rxiv preprint server. Scopus was manually searched and the metadata was exported in BibTex file. Publication rate and publication status, affiliation and author count per article, and submission-to-publication time were analysed in R. Biblioshiny application was used to create a world collaboration map. Preliminary data suggest that COVID-19 pandemic resulted in generation of a large amount of scientific data, and demonstrates potential problems regarding the information velocity, availability, and scientific collaboration in the early stages of the pandemic. More specifically, the results indicate precarious overload of the standard publication systems, significant problems with data availability and apparent deficient collaboration. In conclusion, we believe the scientific community could have used the data more efficiently in order to create proper foundations for finding new solutions for the COVID-19 pandemic. Moreover, we believe we can learn from this on the go and adopt open science principles and a more mindful approach to COVID-19-related data to accelerate the discovery of more efficient solutions. We take this opportunity to invite our colleagues to contribute to this global scientific collaboration by publishing their findings with maximal transparency.

Material Type: Reading

Authors: Homolak, Kodvanj, Virag

Influenza Research Database: an integrated bioinformatics resource for influenza research and surveillance

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Background The recent emergence of the 2009 pandemic influenza A/H1N1 virus has highlighted the value of free and open access to influenza virus genome sequence data integrated with information about other important virus characteristics. Design The Influenza Research Database (IRD, http://www.fludb.org) is a free, open, publicly-accessible resource funded by the U.S. National Institute of Allergy and Infectious Diseases through the Bioinformatics Resource Centers program. IRD provides a comprehensive, integrated database and analysis resource for influenza sequence, surveillance, and research data, including user-friendly interfaces for data retrieval, visualization and comparative genomics analysis, together with personal log in-protected ‘workbench’ spaces for saving data sets and analysis results. IRD integrates genomic, proteomic, immune epitope, and surveillance data from a variety of sources, including public databases, computational algorithms, external research groups, and the scientific literature. Results To demonstrate the utility of the data and analysis tools available in IRD, two scientific use cases are presented. A comparison of hemagglutinin sequence conservation and epitope coverage information revealed highly conserved protein regions that can be recognized by the human adaptive immune system as possible targets for inducing cross-protective immunity. Phylogenetic and geospatial analysis of sequences from wild bird surveillance samples revealed a possible evolutionary connection between influenza virus from Delaware Bay shorebirds and Alberta ducks. Conclusions The IRD provides a wealth of integrated data and information about influenza virus to support research of the genetic determinants dictating virus pathogenicity, host range restriction and transmission, and to facilitate development of vaccines, diagnostics, and therapeutics.

Material Type: Data Set

Authors: Bateman A, Duvvuri Vrsk, Kirkemo Am, Lin Mi, Martin Dm, Matthews L, Moghadas Sm, Rice P, Tan G, Vita R

Order without Intellectual Property Law: Open Science in Influenza

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Today, intellectual property (IP) scholars accept that IP as an approach to information production has serious limits. But what lies beyond IP? A new literature on “intellectual production without IP” (or “IP without IP”) has emerged to explore this question, but its examples and explanations have yet to convince skeptics. This Article reorients this new literature via a study of a hard case: a global influenza virus-sharing network that has for decades produced critically important information goods, at significant expense, and in a loose-knit group—all without recourse to IP. I analyze the Network as an example of “open science,” a mode of information production that differs strikingly from conventional IP, and yet that successfully produces important scientific goods in response to social need. The theory and example developed here refute the most powerful criticisms of the emerging “IP without IP” literature, and provide a stronger foundation for this important new field. Even where capital costs are high, creation without IP can be reasonably effective in social terms, if it can link sources of funding to reputational and evaluative feedback loops like those that characterize open science. It can also be sustained over time, even by loose-knit groups and where the stakes are high, because organizations and other forms of law can help to stabilize cooperation. I also show that contract law is well suited to modes of information production that rely upon a “supply side” rather than “demand side” model. In its most important instances, “order without IP” is not order without governance, nor order without law. Recognizing this can help us better ground this new field, and better study and support forms of knowledge production that deserve our attention, and that sometimes sustain our very lives.

Material Type: Reading

Author: Amy Kapczynski