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.

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 Creating and sharing reproducible research code the workflowr way

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1749 ◽  
Author(s):  
John D. Blischak ◽  
Peter Carbonetto ◽  
Matthew Stephens
Keyword(s):  
Open Source ◽  
Source Code ◽  
R Package ◽  
Version Control ◽  
Regular Part ◽  
Reproducible Research ◽  
Careful Attention ◽  
Research Practice ◽  
Development History ◽  
Novice Users

Making scientific analyses reproducible, well documented, and easily shareable is crucial to maximizing their impact and ensuring that others can build on them. However, accomplishing these goals is not easy, requiring careful attention to organization, workflow, and familiarity with tools that are not a regular part of every scientist's toolbox. We have developed an R package, workflowr, to help all scientists, regardless of background, overcome these challenges. Workflowr aims to instill a particular "workflow" — a sequence of steps to be repeated and integrated into research practice — that helps make projects more reproducible and accessible.This workflow integrates four key elements: (1) version control (via Git); (2) literate programming (via R Markdown); (3) automatic checks and safeguards that improve code reproducibility; and (4) sharing code and results via a browsable website. These features exploit powerful existing tools, whose mastery would take considerable study. However, the workflowr interface is simple enough that novice users can quickly enjoy its many benefits. By simply following the workflowr "workflow", R users can create projects whose results, figures, and development history are easily accessible on a static website — thereby conveniently shareable with collaborators by sending them a URL — and accompanied by source code and reproducibility safeguards. The workflowr R package is open source and available on CRAN, with full documentation and source code available at https://github.com/jdblischak/workflowr.

scholarly journals Information Integration for Machine Actionable Data Management Plans

2017 ◽  
Vol 12 (1) ◽  
pp. 22-35 ◽  
Author(s):  
Tomasz Miksa ◽  
Andreas Rauber ◽  
Roman Ganguly ◽  
Paolo Budroni
Keyword(s):  
Data Management ◽  
Information Integration ◽  
Open Science ◽  
Management Plan ◽  
Reproducible Research ◽  
Free Form ◽  
Text Documents ◽  
Data Repositories ◽  
Standard Data ◽  
Management Plans

Data management plans are free-form text documents describing the data used and produced in scientific experiments. The complexity of data-driven experiments requires precise descriptions of tools and datasets used in computations to enable their reproducibility and reuse. Data management plans fall short of these requirements. In this paper, we propose machine-actionable data management plans that cover the same themes as standard data management plans, but particular sections are filled with information obtained from existing tools. We present mapping of tools from the domains of digital preservation, reproducible research, open science, and data repositories to data management plan sections. Thus, we identify the requirements for a good solution and identify its limitations. We also propose a machine-actionable data model that enables information integration. The model uses ontologies and is based on existing standards.

scholarly journals OceanNETs Data Management Plan

2020 ◽  
Author(s):  
Lisa Paglialonga ◽  
Carsten Schirnick
Keyword(s):  
Data Management ◽  
Scientific Community ◽  
Management Plan ◽  
Research Data ◽  
Data Handling ◽  
Research Project ◽  
Project Duration ◽  
Data Output ◽  
Close Cooperation

This is the data management plan for the research project OceanNETs. It compiles OceanNETs research data output and describes the data handling during and after the projects duration with the aim to make OceanNETs research data FAIR – sustainably available for the scientific community. This data management plan is a living document; it will be continously developed in close cooperation with the consortium members throughout the project duration.

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.

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