Writing a reproducible paper with RStudio and Quarto

Bauer, Paul C.; Landesvatter, Camille

doi:10.31219/osf.io/ur4xn

Cited by 3 publications

(2 citation statements)

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“…In 2022, the company behind RStudio (Posit) released Quarto, the conceptual successor of R Markdown, but with enhanced support for other computer languages like Python, Julia, and Observable JavaScript [ 60 ].…”

Section: Recommendationsmentioning

confidence: 99%

The five pillars of computational reproducibility: bioinformatics and beyond

Ziemann,

Poulain,

Bora

2023

Briefings in Bioinformatics

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Computational reproducibility is a simple premise in theory, but is difficult to achieve in practice. Building upon past efforts and proposals to maximize reproducibility and rigor in bioinformatics, we present a framework called the five pillars of reproducible computational research. These include (1) literate programming, (2) code version control and sharing, (3) compute environment control, (4) persistent data sharing and (5) documentation. These practices will ensure that computational research work can be reproduced quickly and easily, long into the future. This guide is designed for bioinformatics data analysts and bioinformaticians in training, but should be relevant to other domains of study.

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Section: Recommendationsmentioning

confidence: 99%

The five pillars of computational reproducibility: bioinformatics and beyond

Ziemann,

Poulain,

Bora

2023

Briefings in Bioinformatics

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“…Snippets of R code may be executed by a simple click, without leaving the R Markdown document (extension Rmd). For an introduction to R Markdown, see Bauer and Landesvatter (2023) and for a short tutorial, see Frank and Hartgerink (2017).…”

Section: Acknowledgementsmentioning

confidence: 99%

Programmatic access to open statistical data for population studies: The SDMX standard

Willekens

2023

DemRes

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BACKGROUNDThe public sector publishes vast amounts of open data and metadata. APIs (application programming interfaces) are transforming the way data are collected, documented, and disseminated. The transformation is slow, however, due to differences in communication protocol, data definition, and data format. The development is of particular relevance to demography, being a data-intensive science. It paves the way to the automation of data acquisition and the integration of data acquisition and data analysis. Together with the parallel development of literate programming, which allows the integration of text and computer code in a single document, programmatic access to data makes workflows transparent, verifiable, and easy to replicate by others. The Statistical Data and Metadata Exchange (SDMX) standard, which has emerged as a popular option for data and metadata exchange, makes finding and retrieving data and metadata easy and swift. Query strings form URLs with a standardised syntax. OBJECTIVEThe aim of this paper is to describe the SDMX standard and demonstrate its benefits to our profession by retrieving demographic data and the associated metadata from online databases disseminated by a variety of data providers. The software environment used is R. CONTRIBUTIONThis is the first review of the SDMX standard aimed at the study of population. The paper includes the R code to access databases and download data and metadata. The paper includes several hyperlinks to relevant documents issued by data providers, giving readers immediate access to the referenced material.

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DASCore: a Python Library for Distributed Fiber Optic Sensing

Chambers,

Jin,

Tourei

et al. 2024

Seismica

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In the past decade, distributed acoustic sensing (DAS) has enabled many new monitoring applications in diverse fields including hydrocarbon exploration and extraction; induced, local, regional, and global seismology; infrastructure and urban monitoring; and several others. However, to date, the open-source software ecosystem for handling DAS data is relatively immature. Here we introduce DASCore, a Python library for analyzing, visualizing, and managing DAS data. DASCore implements an object-oriented interface for performing common data processing and transformations, reading and writing various DAS file types, creating simple visualizations, and managing file system-based DAS archives. DASCore also integrates with other Python-based tools which enable the processing of massive data sets in cloud environments. DASCore is the foundational package for the broader DAS data analysis ecosystem (DASDAE), and as such its main goal is to facilitate the development of other DAS libraries and applications.

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Writing a reproducible paper with RStudio and Quarto

Cited by 3 publications

References 0 publications

The five pillars of computational reproducibility: bioinformatics and beyond

The five pillars of computational reproducibility: bioinformatics and beyond

Programmatic access to open statistical data for population studies: The SDMX standard

DASCore: a Python Library for Distributed Fiber Optic Sensing

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