All resources in Researchers

Effect of Population Heterogenization on the Reproducibility of Mouse Behavior: A Multi-Laboratory Study

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In animal experiments, animals, husbandry and test procedures are traditionally standardized to maximize test sensitivity and minimize animal use, assuming that this will also guarantee reproducibility. However, by reducing within-experiment variation, standardization may limit inference to the specific experimental conditions. Indeed, we have recently shown in mice that standardization may generate spurious results in behavioral tests, accounting for poor reproducibility, and that this can be avoided by population heterogenization through systematic variation of experimental conditions. Here, we examined whether a simple form of heterogenization effectively improves reproducibility of test results in a multi-laboratory situation. Each of six laboratories independently ordered 64 female mice of two inbred strains (C57BL/6NCrl, DBA/2NCrl) and examined them for strain differences in five commonly used behavioral tests under two different experimental designs. In the standardized design, experimental conditions were standardized as much as possible in each laboratory, while they were systematically varied with respect to the animals' test age and cage enrichment in the heterogenized design. Although heterogenization tended to improve reproducibility by increasing within-experiment variation relative to between-experiment variation, the effect was too weak to account for the large variation between laboratories. However, our findings confirm the potential of systematic heterogenization for improving reproducibility of animal experiments and highlight the need for effective and practicable heterogenization strategies.

Material Type: Reading

Authors: Benjamin Zipser, Berry Spruijt, Britta Schindler, Chadi Touma, Christiane Brandwein, David P. Wolfer, Hanno Würbel, Johanneke van der Harst, Joseph P. Garner, Lars Lewejohann, Niek van Stipdonk, Norbert Sachser, Peter Gass, Sabine Chourbaji, S. Helene Richter, Vootele Võikar

An open source pharma roadmap

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In an Essay, Matthew Todd and colleagues discuss an open source approach to drug development. This Essay outlines how open source methods of working could be applied to the discovery and development of new medicines. There are many potential advantages of an open source approach, such as improved efficiency, the quality and relevance of the research, and wider participation by the scientific and patient communities; a blend of traditional and innovative financing mechanisms will have to be adopted. To evaluate properly the effectiveness of an open source methodology and its potential as an alternative model of drug discovery and development, we recommend that new projects be trialed and existing projects scaled up. Where we stand The scientific and medical community has discovered and developed many groundbreaking medicines that have had a major impact on public health. However, drug development is challenged by a widening gap between health needs and the pharmaceutical industry’s motives and business model, alongside a decrease in efficiency per research dollar spent in medicinal product research and development (R&D), a trend known colloquially as Eroom’s Law. Such fundamental challenges result in frequent high-level calls for new initiatives to develop therapeutics and bring them to market. These include market push and pull mechanisms such as priority review vouchers, advance market commitments, and public R&D funding. New organizational models have also emerged, including public–private partnerships (PPPs) and not-for-profit product development partnerships (PDPs) (for example, the Drugs for Neglected Diseases Initiative [DNDi], the Medicines for Malaria Venture [MMV], and the Global Alliance for Tuberculosis Drug Development [TB Alliance]) that often apply a full “de-linkage” model in which the price of medicines and the cost of R&D are uncoupled.

Material Type: Reading

Authors: Els Torreele, Jaykumar Menon, John McKew, John Wilbanks, Manica Balasegaram, Matthew H. Todd, Peter Kolb, Piero Olliaro, Tomasz Sablinski, Zakir Thomas

What incentives increase data sharing in health and medical research? A systematic review

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The foundation of health and medical research is data. Data sharing facilitates the progress of research and strengthens science. Data sharing in research is widely discussed in the literature; however, there are seemingly no evidence-based incentives that promote data sharing. Methods A systematic review (registration: doi.org/10.17605/OSF.IO/6PZ5E) of the health and medical research literature was used to uncover any evidence-based incentives, with pre- and post-empirical data that examined data sharing rates. We were also interested in quantifying and classifying the number of opinion pieces on the importance of incentives, the number observational studies that analysed data sharing rates and practices, and strategies aimed at increasing data sharing rates. Results Only one incentive (using open data badges) has been tested in health and medical research that examined data sharing rates. The number of opinion pieces (n = 85) out-weighed the number of article-testing strategies (n = 76), and the number of observational studies exceeded them both (n = 106). Conclusions Given that data is the foundation of evidence-based health and medical research, it is paradoxical that there is only one evidence-based incentive to promote data sharing. More well-designed studies are needed in order to increase the currently low rates of data sharing.

Material Type: Reading

Authors: Adrian G. Barnett, Anisa Rowhani-Farid, Michelle Allen

Reproducible and reusable research: are journal data sharing policies meeting the mark?

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Background There is wide agreement in the biomedical research community that research data sharing is a primary ingredient for ensuring that science is more transparent and reproducible. Publishers could play an important role in facilitating and enforcing data sharing; however, many journals have not yet implemented data sharing policies and the requirements vary widely across journals. This study set out to analyze the pervasiveness and quality of data sharing policies in the biomedical literature. Methods The online author’s instructions and editorial policies for 318 biomedical journals were manually reviewed to analyze the journal’s data sharing requirements and characteristics. The data sharing policies were ranked using a rubric to determine if data sharing was required, recommended, required only for omics data, or not addressed at all. The data sharing method and licensing recommendations were examined, as well any mention of reproducibility or similar concepts. The data was analyzed for patterns relating to publishing volume, Journal Impact Factor, and the publishing model (open access or subscription) of each journal. Results A total of 11.9% of journals analyzed explicitly stated that data sharing was required as a condition of publication. A total of 9.1% of journals required data sharing, but did not state that it would affect publication decisions. 23.3% of journals had a statement encouraging authors to share their data but did not require it. A total of 9.1% of journals mentioned data sharing indirectly, and only 14.8% addressed protein, proteomic, and/or genomic data sharing. There was no mention of data sharing in 31.8% of journals. Impact factors were significantly higher for journals with the strongest data sharing policies compared to all other data sharing criteria. Open access journals were not more likely to require data sharing than subscription journals. Discussion Our study confirmed earlier investigations which observed that only a minority of biomedical journals require data sharing, and a significant association between higher Impact Factors and journals with a data sharing requirement. Moreover, while 65.7% of the journals in our study that required data sharing addressed the concept of reproducibility, as with earlier investigations, we found that most data sharing policies did not provide specific guidance on the practices that ensure data is maximally available and reusable.

Material Type: Reading

Authors: Jessica Minnier, Melissa A. Haendel, Nicole A. Vasilevsky, Robin E. Champieux

No evidence of publication bias in climate change science

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Non-significant results are less likely to be reported by authors and, when submitted for peer review, are less likely to be published by journal editors. This phenomenon, known collectively as publication bias, is seen in a variety of scientific disciplines and can erode public trust in the scientific method and the validity of scientific theories. Public trust in science is especially important for fields like climate change science, where scientific consensus can influence state policies on a global scale, including strategies for industrial and agricultural management and development. Here, we used meta-analysis to test for biases in the statistical results of climate change articles, including 1154 experimental results from a sample of 120 articles. Funnel plots revealed no evidence of publication bias given no pattern of non-significant results being under-reported, even at low sample sizes. However, we discovered three other types of systematic bias relating to writing style, the relative prestige of journals, and the apparent rise in popularity of this field: First, the magnitude of statistical effects was significantly larger in the abstract than the main body of articles. Second, the difference in effect sizes in abstracts versus main body of articles was especially pronounced in journals with high impact factors. Finally, the number of published articles about climate change and the magnitude of effect sizes therein both increased within 2 years of the seminal report by the Intergovernmental Panel on Climate Change 2007.

Material Type: Reading

Authors: Christian Harlos, Johan Hollander, Tim C. Edgell

Ten Simple Rules for Reproducible Computational Research

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Replication is the cornerstone of a cumulative science. However, new tools and technologies, massive amounts of data, interdisciplinary approaches, and the complexity of the questions being asked are complicating replication efforts, as are increased pressures on scientists to advance their research. As full replication of studies on independently collected data is often not feasible, there has recently been a call for reproducible research as an attainable minimum standard for assessing the value of scientific claims. This requires that papers in experimental science describe the results and provide a sufficiently clear protocol to allow successful repetition and extension of analyses based on original data. The importance of replication and reproducibility has recently been exemplified through studies showing that scientific papers commonly leave out experimental details essential for reproduction, studies showing difficulties with replicating published experimental results, an increase in retracted papers, and through a high number of failing clinical trials. This has led to discussions on how individual researchers, institutions, funding bodies, and journals can establish routines that increase transparency and reproducibility. In order to foster such aspects, it has been suggested that the scientific community needs to develop a “culture of reproducibility” for computational science, and to require it for published claims. We want to emphasize that reproducibility is not only a moral responsibility with respect to the scientific field, but that a lack of reproducibility can also be a burden for you as an individual researcher. As an example, a good practice of reproducibility is necessary in order to allow previously developed methodology to be effectively applied on new data, or to allow reuse of code and results for new projects. In other words, good habits of reproducibility may actually turn out to be a time-saver in the longer run. We further note that reproducibility is just as much about the habits that ensure reproducible research as the technologies that can make these processes efficient and realistic. Each of the following ten rules captures a specific aspect of reproducibility, and discusses what is needed in terms of information handling and tracking of procedures. If you are taking a bare-bones approach to bioinformatics analysis, i.e., running various custom scripts from the command line, you will probably need to handle each rule explicitly. If you are instead performing your analyses through an integrated framework (such as GenePattern, Galaxy, LONI pipeline, or Taverna), the system may already provide full or partial support for most of the rules. What is needed on your part is then merely the knowledge of how to exploit these existing possibilities.

Material Type: Reading

Authors: Anton Nekrutenko, Eivind Hovig, Geir Kjetil Sandve, James Taylor

Meta-assessment of bias in science

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Numerous biases are believed to affect the scientific literature, but their actual prevalence across disciplines is unknown. To gain a comprehensive picture of the potential imprint of bias in science, we probed for the most commonly postulated bias-related patterns and risk factors, in a large random sample of meta-analyses taken from all disciplines. The magnitude of these biases varied widely across fields and was overall relatively small. However, we consistently observed a significant risk of small, early, and highly cited studies to overestimate effects and of studies not published in peer-reviewed journals to underestimate them. We also found at least partial confirmation of previous evidence suggesting that US studies and early studies might report more extreme effects, although these effects were smaller and more heterogeneously distributed across meta-analyses and disciplines. Authors publishing at high rates and receiving many citations were, overall, not at greater risk of bias. However, effect sizes were likely to be overestimated by early-career researchers, those working in small or long-distance collaborations, and those responsible for scientific misconduct, supporting hypotheses that connect bias to situational factors, lack of mutual control, and individual integrity. Some of these patterns and risk factors might have modestly increased in intensity over time, particularly in the social sciences. Our findings suggest that, besides one being routinely cautious that published small, highly-cited, and earlier studies may yield inflated results, the feasibility and costs of interventions to attenuate biases in the literature might need to be discussed on a discipline-specific and topic-specific basis.

Material Type: Reading

Authors: Daniele Fanelli, John P. A. Ioannidis, Rodrigo Costas

The influence of journal submission guidelines on authors' reporting of statistics and use of open research practices

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From January 2014, Psychological Science introduced new submission guidelines that encouraged the use of effect sizes, estimation, and meta-analysis (the “new statistics”), required extra detail of methods, and offered badges for use of open science practices. We investigated the use of these practices in empirical articles published by Psychological Science and, for comparison, by the Journal of Experimental Psychology: General, during the period of January 2013 to December 2015. The use of null hypothesis significance testing (NHST) was extremely high at all times and in both journals. In Psychological Science, the use of confidence intervals increased markedly overall, from 28% of articles in 2013 to 70% in 2015, as did the availability of open data (3 to 39%) and open materials (7 to 31%). The other journal showed smaller or much smaller changes. Our findings suggest that journal-specific submission guidelines may encourage desirable changes in authors’ practices.

Material Type: Reading

Authors: David Giofrè, Geoff Cumming, Ingrid Boedker, Luca Fresc, Patrizio Tressoldi

The Weak Spots in Contemporary Science (and How to Fix Them)

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In this review, the author discusses several of the weak spots in contemporary science, including scientific misconduct, the problems of post hoc hypothesizing (HARKing), outcome switching, theoretical bloopers in formulating research questions and hypotheses, selective reading of the literature, selective citing of previous results, improper blinding and other design failures, p-hacking or researchers’ tendency to analyze data in many different ways to find positive (typically significant) results, errors and biases in the reporting of results, and publication bias. The author presents some empirical results highlighting problems that lower the trustworthiness of reported results in scientific literatures, including that of animal welfare studies. Some of the underlying causes of these biases are discussed based on the notion that researchers are only human and hence are not immune to confirmation bias, hindsight bias, and minor ethical transgressions. The author discusses solutions in the form of enhanced transparency, sharing of data and materials, (post-publication) peer review, pre-registration, registered reports, improved training, reporting guidelines, replication, dealing with publication bias, alternative inferential techniques, power, and other statistical tools.

Material Type: Reading

Author: Jelte M. Wicherts

Public Availability of Published Research Data in High-Impact Journals

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Background There is increasing interest to make primary data from published research publicly available. We aimed to assess the current status of making research data available in highly-cited journals across the scientific literature. Methods and Results We reviewed the first 10 original research papers of 2009 published in the 50 original research journals with the highest impact factor. For each journal we documented the policies related to public availability and sharing of data. Of the 50 journals, 44 (88%) had a statement in their instructions to authors related to public availability and sharing of data. However, there was wide variation in journal requirements, ranging from requiring the sharing of all primary data related to the research to just including a statement in the published manuscript that data can be available on request. Of the 500 assessed papers, 149 (30%) were not subject to any data availability policy. Of the remaining 351 papers that were covered by some data availability policy, 208 papers (59%) did not fully adhere to the data availability instructions of the journals they were published in, most commonly (73%) by not publicly depositing microarray data. The other 143 papers that adhered to the data availability instructions did so by publicly depositing only the specific data type as required, making a statement of willingness to share, or actually sharing all the primary data. Overall, only 47 papers (9%) deposited full primary raw data online. None of the 149 papers not subject to data availability policies made their full primary data publicly available. Conclusion A substantial proportion of original research papers published in high-impact journals are either not subject to any data availability policies, or do not adhere to the data availability instructions in their respective journals. This empiric evaluation highlights opportunities for improvement.

Material Type: Reading

Authors: Alawi A. Alsheikh-Ali, John P. A. Ioannidis, Mouaz H. Al-Mallah, Waqas Qureshi

Clinical trial registration and reporting: a survey of academic organizations in the United States

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Many clinical trials conducted by academic organizations are not published, or are not published completely. Following the US Food and Drug Administration Amendments Act of 2007, “The Final Rule” (compliance date April 18, 2017) and a National Institutes of Health policy clarified and expanded trial registration and results reporting requirements. We sought to identify policies, procedures, and resources to support trial registration and reporting at academic organizations. Methods We conducted an online survey from November 21, 2016 to March 1, 2017, before organizations were expected to comply with The Final Rule. We included active Protocol Registration and Results System (PRS) accounts classified by ClinicalTrials.gov as a “University/Organization” in the USA. PRS administrators manage information on ClinicalTrials.gov. We invited one PRS administrator to complete the survey for each organization account, which was the unit of analysis. Results Eligible organization accounts (N = 783) included 47,701 records (e.g., studies) in August 2016. Participating organizations (366/783; 47%) included 40,351/47,701 (85%) records. Compared with other organizations, Clinical and Translational Science Award (CTSA) holders, cancer centers, and large organizations were more likely to participate. A minority of accounts have a registration (156/366; 43%) or results reporting policy (129/366; 35%). Of those with policies, 15/156 (11%) and 49/156 (35%) reported that trials must be registered before institutional review board approval is granted or before beginning enrollment, respectively. Few organizations use computer software to monitor compliance (68/366; 19%). One organization had penalized an investigator for non-compliance. Among the 287/366 (78%) accounts reporting that they allocate staff to fulfill ClinicalTrials.gov registration and reporting requirements, the median number of full-time equivalent staff is 0.08 (interquartile range = 0.02–0.25). Because of non-response and social desirability, this could be a “best case” scenario. Conclusions Before the compliance date for The Final Rule, some academic organizations had policies and resources that facilitate clinical trial registration and reporting. Most organizations appear to be unprepared to meet the new requirements. Organizations could enact the following: adopt policies that require trial registration and reporting, allocate resources (e.g., staff, software) to support registration and reporting, and ensure there are consequences for investigators who do not follow standards for clinical research.

Material Type: Reading

Authors: Anthony Keyes, Audrey Omar, Carrie Dykes, Daniel E. Ford, Diane Lehman Wilson, Evan Mayo-Wilson, G. Caleb Alexander, Hila Bernstein, James Heyward, Jesse Reynolds, Keren Dunn, Leah Silbert, M. E. Blair Holbein, Nidhi Atri, Niem-Tzu (Rebecca) Chen, Sarah White, Yolanda P. Davis

Public Data Archiving in Ecology and Evolution: How Well Are We Doing?

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Policies that mandate public data archiving (PDA) successfully increase accessibility to data underlying scientific publications. However, is the data quality sufficient to allow reuse and reanalysis? We surveyed 100 datasets associated with nonmolecular studies in journals that commonly publish ecological and evolutionary research and have a strong PDA policy. Out of these datasets, 56% were incomplete, and 64% were archived in a way that partially or entirely prevented reuse. We suggest that cultural shifts facilitating clearer benefits to authors are necessary to achieve high-quality PDA and highlight key guidelines to help authors increase their data’s reuse potential and compliance with journal data policies.

Material Type: Reading

Authors: Dominique G. Roche, Loeske E. B. Kruuk, Robert Lanfear, Sandra A. Binning

Re-run, Repeat, Reproduce, Reuse, Replicate: Transforming Code into Scientific Contributions

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Scientific code is different from production software. Scientific code, by producing results that are then analyzed and interpreted, participates in the elaboration of scientific conclusions. This imposes specific constraints on the code that are often overlooked in practice. We articulate, with a small example, five characteristics that a scientific code in computational science should possess: re-runnable, repeatable, reproducible, reusable and replicable. The code should be executable (re-runnable) and produce the same result more than once (repeatable); it should allow an investigator to reobtain the published results (reproducible) while being easy to use, understand and modify (reusable), and it should act as an available reference for any ambiguity in the algorithmic descriptions of the article (replicable).

Material Type: Reading

Authors: Fabien C. Y. Benureau, Nicolas P. Rougier

The Post-Embargo Open Access Citation Advantage: It Exists (Probably), It’s Modest (Usually), and the Rich Get Richer (of Course)

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Many studies show that open access (OA) articles—articles from scholarly journals made freely available to readers without requiring subscription fees—are downloaded, and presumably read, more often than closed access/subscription-only articles. Assertions that OA articles are also cited more often generate more controversy. Confounding factors (authors may self-select only the best articles to make OA; absence of an appropriate control group of non-OA articles with which to compare citation figures; conflation of pre-publication vs. published/publisher versions of articles, etc.) make demonstrating a real citation difference difficult. This study addresses those factors and shows that an open access citation advantage as high as 19% exists, even when articles are embargoed during some or all of their prime citation years. Not surprisingly, better (defined as above median) articles gain more when made OA.

Material Type: Reading

Author: Jim Ottaviani

Choice of analysis pathway dramatically affects statistical outcomes in breaking continuous flash suppression

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Breaking Continuous Flash Suppression (bCFS) has been adopted as an appealing means to study human visual awareness, but the literature is beclouded by inconsistent and contradictory results. Although previous reviews have focused chiefly on design pitfalls and instances of false reasoning, we show in this study that the choice of analysis pathway can have severe effects on the statistical output when applied to bCFS data. Using a representative dataset designed to address a specific controversy in the realm of language processing under bCFS, namely whether psycholinguistic variables affect access to awareness, we present a range of analysis methods based on real instances in the published literature, and indicate how each approach affects the perceived outcome. We provide a summary of published bCFS studies indicating the use of data transformation and trimming, and highlight that more compelling analysis methods are sparsely used in this field. We discuss potential interpretations based on both classical and more complex analyses, to highlight how these differ. We conclude that an adherence to openly available data and analysis pathways could provide a great benefit to this field, so that conclusions can be tested against multiple analyses as standard practices are updated.

Material Type: Reading

Authors: Guido Hesselmann, Isabell Wartenburger, James Allen Kerr, Philipp Sterzer, Romy Räling

Comparison of registered and published outcomes in randomized controlled trials: a systematic review

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Clinical trial registries can improve the validity of trial results by facilitating comparisons between prospectively planned and reported outcomes. Previous reports on the frequency of planned and reported outcome inconsistencies have reported widely discrepant results. It is unknown whether these discrepancies are due to differences between the included trials, or to methodological differences between studies. We aimed to systematically review the prevalence and nature of discrepancies between registered and published outcomes among clinical trials. Methods We searched MEDLINE via PubMed, EMBASE, and CINAHL, and checked references of included publications to identify studies that compared trial outcomes as documented in a publicly accessible clinical trials registry with published trial outcomes. Two authors independently selected eligible studies and performed data extraction. We present summary data rather than pooled analyses owing to methodological heterogeneity among the included studies. Results Twenty-seven studies were eligible for inclusion. The overall risk of bias among included studies was moderate to high. These studies assessed outcome agreement for a median of 65 individual trials (interquartile range [IQR] 25–110). The median proportion of trials with an identified discrepancy between the registered and published primary outcome was 31 %; substantial variability in the prevalence of these primary outcome discrepancies was observed among the included studies (range 0 % (0/66) to 100 % (1/1), IQR 17–45 %). We found less variability within the subset of studies that assessed the agreement between prospectively registered outcomes and published outcomes, among which the median observed discrepancy rate was 41 % (range 30 % (13/43) to 100 % (1/1), IQR 33–48 %). The nature of observed primary outcome discrepancies also varied substantially between included studies. Among the studies providing detailed descriptions of these outcome discrepancies, a median of 13 % of trials introduced a new, unregistered outcome in the published manuscript (IQR 5–16 %). Conclusions Discrepancies between registered and published outcomes of clinical trials are common regardless of funding mechanism or the journals in which they are published. Consistent reporting of prospectively defined outcomes and consistent utilization of registry data during the peer review process may improve the validity of clinical trial publications.

Material Type: Reading

Authors: Christopher W. Jones, Lukas G. Keil, Melissa C. Caughey, Timothy F. Platts-Mills, Wesley C. Holland

Toward Reproducible Computational Research: An Empirical Analysis of Data and Code Policy Adoption by Journals

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Journal policy on research data and code availability is an important part of the ongoing shift toward publishing reproducible computational science. This article extends the literature by studying journal data sharing policies by year (for both 2011 and 2012) for a referent set of 170 journals. We make a further contribution by evaluating code sharing policies, supplemental materials policies, and open access status for these 170 journals for each of 2011 and 2012. We build a predictive model of open data and code policy adoption as a function of impact factor and publisher and find higher impact journals more likely to have open data and code policies and scientific societies more likely to have open data and code policies than commercial publishers. We also find open data policies tend to lead open code policies, and we find no relationship between open data and code policies and either supplemental material policies or open access journal status. Of the journals in this study, 38% had a data policy, 22% had a code policy, and 66% had a supplemental materials policy as of June 2012. This reflects a striking one year increase of 16% in the number of data policies, a 30% increase in code policies, and a 7% increase in the number of supplemental materials policies. We introduce a new dataset to the community that categorizes data and code sharing, supplemental materials, and open access policies in 2011 and 2012 for these 170 journals.

Material Type: Reading

Authors: Peixuan Guo, Victoria Stodden, Zhaokun Ma

Four simple recommendations to encourage best practices in research software

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Scientific research relies on computer software, yet software is not always developed following practices that ensure its quality and sustainability. This manuscript does not aim to propose new software development best practices, but rather to provide simple recommendations that encourage the adoption of existing best practices. Software development best practices promote better quality software, and better quality software improves the reproducibility and reusability of research. These recommendations are designed around Open Source values, and provide practical suggestions that contribute to making research software and its source code more discoverable, reusable and transparent. This manuscript is aimed at developers, but also at organisations, projects, journals and funders that can increase the quality and sustainability of research software by encouraging the adoption of these recommendations.

Material Type: Reading

Authors: Alejandra Gonzalez-Beltran, Allegra Via, Andrew Treloar, Bérénice Batut, Bernard Pope, Björn GrüningJonas Hagberg, Brane Leskošek, Carole Goble, Daniel S. Katz, Daniel Vaughan, David Mellor, Federico López Gómez, Ferran Sanz, Harry-Anton Talvik, Horst Pichler, Ilian Todorov, Jon Ison, Josep Ll. Gelpí, Leyla Garcia, Luis J. Oliveira, Maarten van Gompel, Madison Flannery, Manuel Corpas, Maria V. Schneider, Martin Cook, Mateusz Kuzak, Michelle Barker, Mikael Borg, Monther Alhamdoosh, Montserrat González Ferreiro, Nathan S. Watson-Haigh, Neil Chue Hong, Nicola Mulder, Petr Holub, Philippa C. Griffin, Radka Svobodová Vařeková, Radosław Suchecki, Rafael C. Jiménez, Robert Pergl, Rob Hooft, Rowland Mosbergen, Salvador Capella-Gutierrez, Simon Gladman, Sonika Tyagi, Steve Crouchc, Victoria Stodden, Xiaochuan Wang, Yasset Perez-Riverol

P values in display items are ubiquitous and almost invariably significant: A survey of top science journals

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P values represent a widely used, but pervasively misunderstood and fiercely contested method of scientific inference. Display items, such as figures and tables, often containing the main results, are an important source of P values. We conducted a survey comparing the overall use of P values and the occurrence of significant P values in display items of a sample of articles in the three top multidisciplinary journals (Nature, Science, PNAS) in 2017 and, respectively, in 1997. We also examined the reporting of multiplicity corrections and its potential influence on the proportion of statistically significant P values. Our findings demonstrated substantial and growing reliance on P values in display items, with increases of 2.5 to 14.5 times in 2017 compared to 1997. The overwhelming majority of P values (94%, 95% confidence interval [CI] 92% to 96%) were statistically significant. Methods to adjust for multiplicity were almost non-existent in 1997, but reported in many articles relying on P values in 2017 (Nature 68%, Science 48%, PNAS 38%). In their absence, almost all reported P values were statistically significant (98%, 95% CI 96% to 99%). Conversely, when any multiplicity corrections were described, 88% (95% CI 82% to 93%) of reported P values were statistically significant. Use of Bayesian methods was scant (2.5%) and rarely (0.7%) articles relied exclusively on Bayesian statistics. Overall, wider appreciation of the need for multiplicity corrections is a welcome evolution, but the rapid growth of reliance on P values and implausibly high rates of reported statistical significance are worrisome.

Material Type: Reading

Authors: Ioana Alina Cristea, John P. A. Ioannidis

The cumulative effect of reporting and citation biases on the apparent efficacy of treatments: the case of depression

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Evidence-based medicine is the cornerstone of clinical practice, but it is dependent on the quality of evidence upon which it is based. Unfortunately, up to half of all randomized controlled trials (RCTs) have never been published, and trials with statistically significant findings are more likely to be published than those without (Dwan et al., 2013). Importantly, negative trials face additional hurdles beyond study publication bias that can result in the disappearance of non-significant results (Boutron et al., 2010; Dwan et al., 2013; Duyx et al., 2017). Here, we analyze the cumulative impact of biases on apparent efficacy, and discuss possible remedies, using the evidence base for two effective treatments for depression: antidepressants and psychotherapy.

Material Type: Reading

Authors: A. M. Roest, J. A. Bastiaansen, M. R. Munafò, P. Cuijpers, P. de Jonge, Y. A. de Vries