Reproducibility in plant pathology: where do we stand and a way forward.

2021 ◽  
Author(s):  
Adam H. Sparks ◽  
Emerson del Ponte ◽  
Kaique S. Alves ◽  
Zachary S. L. Foster ◽  
Niklaus J. Grünwald
Keyword(s):  
Plant Pathology ◽  
Open Source ◽  
Open Science ◽  
Scientific Study ◽  
Reproducible Research ◽  
Research Practices ◽  
Open Research ◽  
Computational Code ◽  
Share Data ◽  
Scientific Results

Abstract Open research practices have been highlighted extensively during the last ten years in many fields of scientific study as essential standards needed to promote transparency and reproducibility of scientific results. Scientific claims can only be evaluated based on how protocols, materials, equipment and methods were described; data were collected and prepared; and, analyses were conducted. Openly sharing protocols, data and computational code is central for current scholarly dissemination and communication, but in many fields, including plant pathology, adoption of these practices has been slow. We randomly selected 300 articles published from 2012 to 2018 across 21 journals representative of the plant pathology discipline and assigned them scores reflecting their openness and reproducibility. We found that most of the articles were not following protocols for open science, and were failing to share data or code in a reproducible way. We also propose that use of open-source tools facilitates reproducible work and analyses benefitting not just readers, but the authors as well. Finally, we also provide ideas and tools to promote open, reproducible research practices among plant pathologists.

scholarly journals How open science helps researchers succeed

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Erin C McKiernan ◽  
Philip E Bourne ◽  
C Titus Brown ◽  
Stuart Buck ◽  
Amye Kenall ◽  
...  
Keyword(s):  
Open Access ◽  
Open Source ◽  
Open Data ◽  
Open Science ◽  
Review Literature ◽  
Research Practices ◽  
Media Attention ◽  
Open Research ◽  
Job Opportunities ◽  
Funding Opportunities

Open access, open data, open source and other open scholarship practices are growing in popularity and necessity. However, widespread adoption of these practices has not yet been achieved. One reason is that researchers are uncertain about how sharing their work will affect their careers. We review literature demonstrating that open research is associated with increases in citations, media attention, potential collaborators, job opportunities and funding opportunities. These findings are evidence that open research practices bring significant benefits to researchers relative to more traditional closed practices.

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.

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 The case for free and open source software in research and scholarship

2021 ◽  
Vol 379 (2197) ◽  
Author(s):  
Laura Fortunato ◽  
Mark Galassi
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Research Process ◽  
Open Science ◽  
Support Staff ◽  
Research Quality ◽  
Open Research ◽  
The Common

Free and open source software (FOSS) is any computer program released under a licence that grants users rights to run the program for any purpose, to study it, to modify it, and to redistribute it in original or modified form. Our aim is to explore the intersection between FOSS and computational reproducibility. We begin by situating FOSS in relation to other ‘open’ initiatives, and specifically open science, open research, and open scholarship. In this context, we argue that anyone who actively contributes to the research process today is a computational researcher, in that they use computers to manage and store information. We then provide a primer to FOSS suitable for anyone concerned with research quality and sustainability—including researchers in any field, as well as support staff, administrators, publishers, funders, and so on. Next, we illustrate how the notions introduced in the primer apply to resources for scientific computing, with reference to the GNU Scientific Library as a case study. We conclude by discussing why the common interpretation of ‘open source’ as ‘open code’ is misplaced, and we use this example to articulate the role of FOSS in research and scholarship today. This article is part of the theme issue ‘Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico ’.

scholarly journals A practical guide for improving transparency and reproducibility in neuroimaging research

2016 ◽  
Author(s):  
Krzysztof J. Gorgolewski ◽  
Russell A. Poldrack
Keyword(s):  
Open Science ◽  
Science Data ◽  
Research Practices ◽  
Practical Advice ◽  
The Public ◽  
Practical Guide ◽  
Open Research

AbstractRecent years have seen an increase in alarming signals regarding the lack of replicability in neuroscience, psychology, and other related fields. To avoid a widespread crisis in neuroimaging research and consequent loss of credibility in the public eye, we need to improve how we do science. This article aims to be a practical guide for researchers at any stage of their careers that will help them make their research more reproducible and transparent while minimizing the additional effort that this might require. The guide covers three major topics in open science (data, code, and publications) and offers practical advice as well as highlighting advantages of adopting more open research practices that go beyond improved transparency and reproducibility.

scholarly journals Open Research: Examples of good practice, and resources across disciplines

2020 ◽  
Author(s):  
Emily Kate Farran ◽  
Priya Silverstein ◽  
Aminath A. Ameen ◽  
Iliana Misheva ◽  
Camilla Gilmore
Keyword(s):  
Case Studies ◽  
Good Practice ◽  
Open Data ◽  
Open Science ◽  
Open Education ◽  
Research Practices ◽  
Open Research

Open research is best described as “an umbrella term used to refer to the concepts of openness, transparency, rigor, reproducibility, replicability, and accumulation of knowledge” (Crüwell et al., 2019, p. 3). Although a lot of open research practices have commonly been discussed under the term “open science”, open research applies to all disciplines. If the concept of open research is new to you, it might be difficult for you to determine how you can apply open research practices to your research. The aim of this document is to provide resources and examples of open research practices that are relevant to your discipline. The document lists case studies of open research per discipline, and resources per discipline (organised as: general, open methods, open data, open output and open education).

scholarly journals Evolution of Data Formats in Very-High-Energy Gamma-ray Astronomy

Universe ◽  
2021 ◽  
Vol 7 (10) ◽  
pp. 374
Author(s):  
Cosimo Nigro ◽  
Tarek Hassan ◽  
Laura Olivera-Nieto
Keyword(s):  
Open Source ◽  
Gamma Ray ◽  
Future Generation ◽  
Open Science ◽  
High Energy ◽  
Next Generation ◽  
Data Format ◽  
Gamma Ray Astronomy ◽  
Very High Energy ◽  
Scientific Results

Most major scientific results produced by ground-based gamma-ray telescopes in the last 30 years have been obtained by expert members of the collaborations operating these instruments. This is due to the proprietary data and software policies adopted by these collaborations. However, the advent of the next generation of telescopes and their operation as observatories open to the astronomical community, along with a generally increasing demand for open science, confront gamma-ray astronomers with the challenge of sharing their data and analysis tools. As a consequence, in the last few years, the development of open-source science tools has progressed in parallel with the endeavour to define a standardised data format for astronomical gamma-ray data. The latter constitutes the main topic of this review. Common data specifications provide equally important benefits to the current and future generation of gamma-ray instruments: they allow the data from different instruments, including legacy data from decommissioned telescopes, to be easily combined and analysed within the same software framework. In addition, standardised data accessible to the public, and analysable with open-source software, grant fully-reproducible results. In this article, we provide an overview of the evolution of the data format for gamma-ray astronomical data, focusing on its progression from private and diverse specifications to prototypical open and standardised ones. The latter have already been successfully employed in a number of publications paving the way to the analysis of data from the next generation of gamma-ray instruments, and to an open and reproducible way of conducting gamma-ray astronomy.

scholarly journals A practical guide for transparency in psychological science

2018 ◽  
Author(s):  
Olivier Klein ◽  
Tom Elis Hardwicke ◽  
Frederik Aust ◽  
Johannes Breuer ◽  
Henrik Danielsson ◽  
...  
Keyword(s):  
Management Practices ◽  
Open Science ◽  
Research Management ◽  
Reproducible Research ◽  
Research Practices ◽  
Psychological Science ◽  
Practical Guide ◽  
Study Protocols ◽  
User Friendly ◽  
Incremental Step

The credibility of scientific claims depends upon the transparency of the research products upon which they are based (e.g., study protocols, data, materials, and analysis scripts). As psychology navigates a period of unprecedented introspection, user-friendly tools and services that support open science have flourished. There has never been a better time to embrace transparent research practices. However, the plethora of decisions and choices involved can be bewildering. Here we provide a practical guide to help researchers navigate the process of preparing and sharing the products of their research. Being an open scientist means adopting a few straightforward research management practices, which lead to less error prone, reproducible research workflows. Further, this adoption can be piecemeal – each incremental step towards complete transparency adds positive value. Transparent research practices not only improve the efficiency of individual researchers, they enhance the credibility of the knowledge generated by the scientific community.

scholarly journals Using R in hydrology: a review of recent developments and future directions

2019 ◽  
Author(s):  
Louise J. Slater ◽  
Guillaume Thirel ◽  
Shaun Harrigan ◽  
Olivier Delaigue ◽  
Alexander Hurley ◽  
...  
Keyword(s):  
Open Source ◽  
Data Science ◽  
Meteorological Data ◽  
Central Place ◽  
Open Science ◽  
Reproducible Research ◽  
Data Archives ◽  
Integrated Development Environments ◽  
Recent Developments ◽  
Short Courses

Abstract. The open-source programming language R has gained a central place in the hydrological sciences over the last decade, driven by the availability of diverse hydro-meteorological data archives and the development of open-source computational tools. The growth of R's usage in hydrology is reflected in the number of newly published hydrological packages, the strengthening of online user communities, and the popularity of training courses and events. In this paper, we explore the benefits and advantages of R's usage in hydrology, such as: the democratization of data science and numerical literacy, the enhancement of reproducible research and open science, the access to statistical tools, the ease of connecting R to and from other languages, and the support provided by a growing community. This paper provides an overview of important packages at every step of the hydrological workflow, from the retrieval of hydro-meteorological data, to spatial analysis and cartography, hydrological modelling, statistics, and the design of static and dynamic visualizations, presentations and documents. We discuss some of the challenges that arise when using R in hydrology and useful tools to overcome them, including the use of hydrological libraries, documentation and vignettes (long-form guides that illustrate how to use packages); the role of Integrated Development Environments (IDEs); and the challenges of Big Data and parallel computing in hydrology. Last, this paper provides a roadmap for R's future within hydrology, with R packages as a driver of progress in the hydrological sciences, Application Programming Interfaces (APIs) providing new avenues for data acquisition and provision, enhanced teaching of hydrology in R, and the continued growth of the community via short courses and events.

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