scholarly journals Reproducibility for everyone: a community-led initiative with global reach in reproducible research training

2020 ◽  
Author(s):  
Susann Auer ◽  
Nele Haelterman ◽  
Tracey Lynn Weissgerber ◽  
Jeffrey C Erlich ◽  
Damar Susilaradeya ◽  
...  
Keyword(s):  
Scientific Method ◽  
Scientific Community ◽  
Research Training ◽  
Reproducible Research ◽  
Scientific Process ◽  
Research Practices ◽  
Pharmaceutical Drug ◽  
Shared Set ◽  
Scientific Disciplines ◽  
Global Initiative

Reproducibility is a cornerstone of the scientific method and sets apart science from pseudoscience. Unfortunately, a majority of scientists have experienced difficulties in reproducing their own or someone else’s results. This inability to confirm scientific findings negatively impacts individual scientists, funding bodies, academic journals, pharmaceutical drug development and the public’s perception of science. Factors causing irreproducible results can arise from nearly every aspect of the scientific process, and typically reflect a lack of in-depth training in reproducible research practices. Here, we present the Reproducibility for Everyone (R4E) initiative, a collaboration between researchers from diverse scientific disciplines and industry partners who aspire to promote open and reproducible research practices. We have developed a customizable workshop series targeting researchers at all levels and across disciplines. Our workshop series covers the conceptual framework of reproducible research practices followed by an overview of actionable research practices. To date, we have reached more than 2000 researchers through over 25 workshops held at international conferences and local meetings. By incorporating further contributions from the scientific community, we hope to expand this valuable resource for teaching transparent and reproducible research practices. Our initiative demonstrates how a shared set of materials may form the basis for a global initiative to improve reproducibility in science. The workshop materials, including accompanying resources, are available under a CC-BY 4.0 license at www.repro4everyone.org.

scholarly journals Current Protocols: Open and Reproducible Research on OSF

2019 ◽  
Author(s):  
Ian Sullivan ◽  
Alexander Carl DeHaven ◽  
David Thomas Mellor
Keyword(s):  
Data Management ◽  
Open Source ◽  
Scientific Community ◽  
Management Plan ◽  
Version Control ◽  
Reproducible Research ◽  
Research Practices ◽  
Share Data

By implementing more transparent research practices, authors have the opportunity to stand out and showcase work that is more reproducible, easier to build upon, and more credible. The scientist gains by making work easier to share and maintain within their own lab, and the scientific community gains by making underlying data or research materials more available for confirmation or making new discoveries. The following protocol gives the author step by step instructions for using the free and open source OSF to create a data management plan, preregister their study, use version control, share data and other research materials, or post a preprint for quick and easy dissemination.

The Fight Against Doubt

2018 ◽  
Author(s):  
Inmaculada de Melo-Martín ◽  
Kristen Intemann
Keyword(s):  
Climate Change ◽  
Scientific Community ◽  
Vaccine Safety ◽  
False Beliefs ◽  
Scientific Process ◽  
The Public ◽  
Scientific Consensus ◽  
Scientific Claims

Current debates about climate change or vaccine safety provide an alarming illustration of the potential impacts of dissent about scientific claims. False beliefs about evidence and the conclusions that can be drawn from it are commonplace, as is corrosive doubt about the existence of widespread scientific consensus. Deployed aggressively and to political ends, ill-founded dissent can intimidate scientists, stymie research, and lead both the public and policymakers to oppose important policies firmly rooted in science. To criticize dissent is, however, a fraught exercise. Skepticism and fearless debate are key to the scientific process, making it both vital and incredibly difficult to characterize and identify dissent that is problematic in its approach and consequences. Indeed, as de Melo-Martín and Intemann show, the criteria commonly proposed as means of identifying inappropriate dissent are flawed, and the strategies generally recommended to tackle such dissent are not only ineffective but could even make the situation worse. The Fight against Doubt proposes that progress on this front can best be achieved by enhancing the trustworthiness of the scientific community and being more realistic about the limits of science when it comes to policymaking. It shows that a richer understanding is needed of the context in which science operates so as to disarm problematic dissent and those who deploy it in the pursuit of their goals.

The New Statistics with R

2021 ◽  
Author(s):  
Andy Hector
Keyword(s):  
Linear Model ◽  
Evolutionary Biology ◽  
Environmental Science ◽  
Research Training ◽  
Model Analysis ◽  
Scientific Data ◽  
Information Criteria ◽  
Reproducible Research ◽  
Data Sets ◽  
R Programming

Statistics is a fundamental component of the scientific toolbox, but learning the basics of this area of mathematics is one of the most challenging parts of a research training. This book gives an up-to-date introduction to the classical techniques and modern extensions of linear-model analysis—one of the most useful approaches in the analysis of scientific data in the life and environmental sciences. The book emphasizes an estimation-based approach that takes account of recent criticisms of overuse of probability values and introduces the alternative approach using information criteria. The book is based on the use of the open-source R programming language for statistics and graphics, which is rapidly becoming the lingua franca in many areas of science. This second edition adds new chapters, including one discussing some of the complexities of linear-model analysis and another introducing reproducible research documents using the R Markdown package. Statistics is introduced through worked analyses performed in R using interesting data sets from ecology, evolutionary biology, and environmental science. The data sets and R scripts are available as supporting material.

scholarly journals Putting open science into practice: A social dilemma?

First Monday ◽  
2014 ◽  
Author(s):  
Kaja Scheliga ◽  
Sascha Friesike
Keyword(s):  
Scientific Community ◽  
Social Dilemma ◽  
Digital Technologies ◽  
Research Process ◽  
Open Science ◽  
Best Interest ◽  
Research Practices ◽  
Individual Scientist ◽  
The Individual ◽  
Opening Up

Digital technologies carry the promise of transforming science and opening up the research process. We interviewed researchers from a variety of backgrounds about their attitudes towards and experiences with openness in their research practices. We observe a considerable discrepancy between the concept of open science and scholarly reality. While many researchers support open science in theory, the individual researcher is confronted with various difficulties when putting open science into practice. We analyse the major obstacles to open science and group them into two main categories: individual obstacles and systemic obstacles. We argue that the phenomenon of open science can be seen through the prism of a social dilemma: what is in the collective best interest of the scientific community is not necessarily in the best interest of the individual scientist. We discuss the possibilities of transferring theoretical solutions to social dilemma problems to the realm of open science.

scholarly journals Teaching reproducible research for medical students and postgraduate pharmaceutical scientists

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Andreas D. Meid
Keyword(s):  
Doctoral Students ◽  
Scientific Community ◽  
Natural Sciences ◽  
Interdisciplinary Teams ◽  
Reproducible Research ◽  
Pharmaceutical Sciences ◽  
The Public ◽  
Academic Settings ◽  
Research Findings ◽  
Good Research

AbstractIn medicine and other academic settings, (doctoral) students often work in interdisciplinary teams together with researchers of pharmaceutical sciences, natural sciences in general, or biostatistics. They should be fundamentally taught good research practices, especially in terms of statistical analysis. This includes reproducibility as a central aspect. Acknowledging that even experienced researchers and supervisors might be unfamiliar with necessary aspects of a perfectly reproducible workflow, a lecture series on reproducible research (RR) was developed for young scientists in clinical pharmacology. The pilot series highlighted definitions of RR, reasons for RR, potential merits of RR, and ways to work accordingly. In trying to actually reproduce a published analysis, several practical obstacles arose. In this article, reproduction of a working example is commented to emphasize the manifold facets of RR, to provide possible explanations for difficulties and solutions, and to argue that harmonized curricula for (quantitative) clinical researchers should include RR principles. These experiences should raise awareness among educators and students, supervisors and young scientists. RR working habits are not only beneficial for ourselves or our students, but also for other researchers within an institution, for scientific partners, for the scientific community, and eventually for the public profiting from research findings.

scholarly journals Report on the Meeting of the Political Science Members of the National Academy of Sciences

2013 ◽  
Vol 47 (01) ◽  
pp. 237-238
Author(s):  
David Laitin ◽  
Gary King
Keyword(s):  
Political Science ◽  
Scientific Community ◽  
Scientific Evidence ◽  
Natural Sciences ◽  
The Political ◽  
National Academy Of Sciences ◽  
Public Issues ◽  
Scientific Disciplines ◽  
The Many ◽  
Academy Of Sciences

With assistance of the APSA, the political science members of the National Academy of Sciences (NAS) held their standing meeting at the annual APSA convention in Chicago. The purposes of these meetings are two-fold: First, as required, to discuss ways that political science can fulfill the NAS mission in providing scientific evidence to address consequential public issues that come from queries posed by various agencies of government; and second, to increase the presence of political scientists in the Academy, where membership from our discipline is, in our view, much lower than political scientists' contributions to the scientific community, and does not adequately recognize the many political scientists who merit election. While we have made some progress toward this second goal, it is a complicated battle: 2,179 members and 437 foreign associates across scientific disciplines have been elected to and currently serve in the NAS, but only 21 are political scientists. Although the science-based mission of NAS does not seek to represent all of the highly pluralistic discipline of political science, far more research relying on methods that are recognized in the natural sciences is produced in our field than is presently represented in the NAS.

The Revolutionary Conservatism of Normal Science

2020 ◽  
pp. 167-160
Author(s):  
Evgeniy V. Maslanov ◽  
Keyword(s):  
Scientific Community ◽  
Public Life ◽  
Scientific Research ◽  
Normal Science ◽  
Public Attention ◽  
Research Practices ◽  
Technical Problems ◽  
Leading Position ◽  
Science Subject

The article analyzes the functioning of normal science. It has conservative fea­tures and implies the restriction of research practices to solving puzzle, rarely reflects on the ontological assumptions of its own paradigm. Such functioning of normal science allows it to solve a large number of various scientific and sci­entific-technical problems. As a result, normal science is developing quite rapidly. At the same time, revolutionary features can be distinguished in its func­tioning. They are associated with the struggle of each specific normal science for its position in the field of science, in the desire, through the dissemination of the results of its research beyond the scientific community and active participa­tion in the examination, to enlist the support of extra-scientific actors. Alliances with extra-scientific actors allow normal sciences to actively participate in the struggle for the redistribution of public attention and the financing of scientific research. With the help of such alliances, they are trying to introduce the results of their research into industry and public life. The success of such implementa­tions leads to an active redistribution of positions in the field of science. In this case, the revolutionary element of normal science is associated not with the de­sire to reconsider the fundamental ideas underlying it, but in the desire to rebuild the system of relations within the field of science, to take a leading position in it. As a result, it is concluded that the successful functioning of normal science is associated with the desire to make permanent micro-revolutions in the field of science, subject to a conservatively protective attitude to the fundamental as­sumptions of its own paradigm.

scholarly journals Open Science in the Humanities, or: Open Humanities?

Publications ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 65 ◽  
Author(s):  
Marcel Knöchelmann
Keyword(s):  
Open Access ◽  
Peer Review ◽  
Scholarly Communication ◽  
Open Science ◽  
Essential Elements ◽  
Research Practices ◽  
Open Research ◽  
Scientific Disciplines ◽  
Communication Practices ◽  
Do So

Open science refers to both the practices and norms of more open and transparent communication and research in scientific disciplines and the discourse on these practices and norms. There is no such discourse dedicated to the humanities. Though the humanities appear to be less coherent as a cluster of scholarship than the sciences are, they do share unique characteristics which lead to distinct scholarly communication and research practices. A discourse on making these practices more open and transparent needs to take account of these characteristics. The prevalent scientific perspective in the discourse on more open practices does not do so, which confirms that the discourse’s name, open science, indeed excludes the humanities so that talking about open science in the humanities is incoherent. In this paper, I argue that there needs to be a dedicated discourse for more open research and communication practices in the humanities, one that integrates several elements currently fragmented into smaller, unconnected discourses (such as on open access, preprints, or peer review). I discuss three essential elements of open science—preprints, open peer review practices, and liberal open licences—in the realm of the humanities to demonstrate why a dedicated open humanities discourse is required.

scholarly journals Do oncology researchers adhere to reproducible and transparent principles? A cross-sectional survey of published oncology literature

BMJ Open ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. e033962 ◽  
Author(s):  
Corbin Walters ◽  
Zachery J Harter ◽  
Cole Wayant ◽  
Nam Vo ◽  
Michael Warren ◽  
...  
Keyword(s):  
Random Sample ◽  
Systematic Reviews ◽  
Primary Outcome ◽  
Reproducible Research ◽  
Cross Sectional Survey ◽  
Research Practices ◽  
Cross Sectional ◽  
Raw Data ◽  
Open Access Journals ◽  
Meta Analyses

ObjectivesAs much as 50%–90% of research is estimated to be irreproducible, costing upwards of $28 billion in USA alone. Reproducible research practices are essential to improving the reproducibility and transparency of biomedical research, such as including preregistering studies, publishing a protocol, making research data and metadata publicly available, and publishing in open access journals. Here we report an investigation of key reproducible or transparent research practices in the published oncology literature.DesignWe performed a cross-sectional analysis of a random sample of 300 oncology publications published from 2014 to 2018. We extracted key reproducibility and transparency characteristics in a duplicative fashion by blinded investigators using a pilot tested Google Form.Primary outcome measuresThe primary outcome of this investigation is the frequency of key reproducible or transparent research practices followed in published biomedical and clinical oncology literature.ResultsOf the 300 publications randomly sampled, 296 were analysed for reproducibility characteristics. Of these 296 publications, 194 contained empirical data that could be analysed for reproducible and transparent research practices. Raw data were available for nine studies (4.6%). Five publications (2.6%) provided a protocol. Despite our sample including 15 clinical trials and 7 systematic reviews/meta-analyses, only 7 included a preregistration statement. Less than 25% (65/194) of publications provided an author conflict of interest statement.ConclusionWe found that key reproducibility and transparency characteristics were absent from a random sample of published oncology publications. We recommend required preregistration for all eligible trials and systematic reviews, published protocols for all manuscripts, and deposition of raw data and metadata in public repositories.

On the Literature of B-Spline Interpolation Functions

2009 ◽  
pp. 214-222
Author(s):  
Carlo Ciulla
Keyword(s):  
Scientific Community ◽  
Polynomial Interpolation ◽  
Spline Interpolation ◽  
Spline Functions ◽  
Great Effort ◽  
Formal Definition ◽  
B Spline ◽  
B Splines ◽  
Scientific Disciplines ◽  
Definition Of

This chapter reviews the extensive and comprehensive literature on B-Splines. In the forthcoming text emphasis is given to hierarchy and formal definition of polynomial interpolation with specific focus to the subclass of functions that are called B-Splines. Also, the literature is reviewed with emphasis on methodologies and applications of B-Splines within a wide array of scientific disciplines. The review is conducted with the intent to inform the reader and also to acknowledge the merit of the scientific community for the great effort devoted to B-Splines. The chapter concludes emphasizing on the proposition that the unifying theory presented throughout this book has for what concerns two specific cases of B-Spline functions: univariate quadratic and cubic models.

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