Making Reproducible Research Simple Using RMarkdown and the OSF

AbstractIn the past few decades, deep learning algorithms have become more prevalent for signal detection and classification. To design machine learning algorithms, however, an adequate dataset is required. Motivated by the existence of several open-source camera-based hand gesture datasets, this descriptor presents UWB-Gestures, the first public dataset of twelve dynamic hand gestures acquired with ultra-wideband (UWB) impulse radars. The dataset contains a total of 9,600 samples gathered from eight different human volunteers. UWB-Gestures eliminates the need to employ UWB radar hardware to train and test the algorithm. Additionally, the dataset can provide a competitive environment for the research community to compare the accuracy of different hand gesture recognition (HGR) algorithms, enabling the provision of reproducible research results in the field of HGR through UWB radars. Three radars were placed at three different locations to acquire the data, and the respective data were saved independently for flexibility.

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Towards Reproducible Research of Event Detection Techniques for Twitter

2019 6th Swiss Conference on Data Science (SDS) ◽

10.1109/sds.2019.000-5 ◽

2019 ◽

Author(s):

Andreas Weiler ◽

Harry Schilling ◽

Lukas Kircher ◽

Michael Grossniklaus

Keyword(s):

Event Detection ◽

Reproducible Research ◽

Detection Techniques

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Reproducible research: Geophysics papers of the future — Introduction

Geophysics ◽

10.1190/geo2017-0918-spseintro.1 ◽

2017 ◽

Vol 82 (6) ◽

pp. WBi-WBii ◽

Cited By ~ 2

Author(s):

Filippo Broggini ◽

Joseph Dellinger ◽

Sergey Fomel ◽

Yang Liu

Keyword(s):

Reproducible Research ◽

The Future

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The New Statistics with R

10.1093/oso/9780198798170.001.0001 ◽

2021 ◽

Cited By ~ 1

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.

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Roundtable: Challenges in repeatable experiments and reproducible research in data science

Proceedings of Moscow Institute of Physics and Technology ◽

10.53815/20726759_2021_13_2_100 ◽

2021 ◽

Vol 13 (2) ◽

pp. 100-108

Author(s):

K.V. Vorontsov ◽

V.I. Iglovikov ◽

V.V. Strijov ◽

A.E. Ustuzhanin ◽

A.S. Khritankov

Keyword(s):

Data Science ◽

Reproducible Research

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Bridging the gap: A geroscience primer for neuroscientists with potential collaborative applications

The Journals of Gerontology Series A ◽

10.1093/gerona/glab314 ◽

2021 ◽

Author(s):

Jessica M Hoffman ◽

Caesar M Hernandez ◽

Abbi R Hernandez ◽

Jennifer L Bizon ◽

Sara N Burke ◽

...

Keyword(s):

Neurodegenerative Diseases ◽

Disease Diagnosis ◽

The State ◽

Reproducible Research ◽

Collaborative Effort ◽

Important Impact ◽

Collaborative Applications ◽

The Many ◽

Biology Of Aging

Abstract While neurodegenerative diseases can strike at any age, the majority of afflicted individuals are diagnosed at older ages. Due to the important impact of age in disease diagnosis, the field of neuroscience could greatly benefit from the many of the theories and ideas from the biology of aging – now commonly referred as geroscience. As discussed in our complementary perspective on the topic, there is often a “silo-ing” between geroscientists who work on understanding the mechanisms underlying aging and neuroscientists who are studying neurodegenerative diseases. While there have been some strong collaborations between the biology of aging and neuroscientists, there is still great potential for enhanced collaborative effort between the two fields. To this end, here, we review the state of the geroscience field, discuss how neuroscience could benefit from thinking from a geroscience perspective, and close with a brief discussion on some of the “missing links” between geroscience and neuroscience and how to remedy them. Notably, we have a corresponding, concurrent review from the neuroscience perspective. Our overall goal is to “bridge the gap” between geroscience and neuroscience such that more efficient, reproducible research with translational potential can be conducted.

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