I teach R to a lot of scientists, those that are new to science (i.e. students)as well as more established scientists, new to R.I find that after all their struggles of dealing with dates,or remembering where to put the comma, they’re so grateful to actual have an analysis,that they often forget or aren’t aware of the next steps.

GigaScience has always had a focus on reproducibility rather than subjective impact, and it can be challenging for our reviewers to judge this, especially now that more and more tools are being created – bringing data science to the masses.  This also means more efficiency and ease is required especially when multiple collaborators and contributors on a specific project are involved.

The National Academies of Sciences, Engineering, and Medicine (NASEM) hosted the two-day virtual workshop “Changing the Culture of Data Management and Sharing” on 28th-29th April 2021 to discuss the challenges and opportunities for establishing effective data management and sharing practices and exploring the question of universal availability of scientific data.

GigaScience Press and River Valley Technologies, with the help of Stencila, launch their first interactive “ Executable Research Article ” from their new scientific journal, GigaByte.

The COVID-19 pandemic has dramatically impacted all of our lives in a very short period of time.Spring and summer are usually very busy as students prepare to go the field to engage in various data collection efforts.The pandemic has also disrupted these carefully planned activities as travel is suspended and local and remote field stations have closed indefinitely.A lost field season can be a major setback for a dissertation timeline and

The R language has become very popular among scientists and analystsbecause it enables the rapid development of software and empowersscientific investigation. However, regardless of the language used,data analysis is usually complicated. Because of various projectcomplexities and time constraints, analytical software often reflectsthese challenges. “What did I measure? What analyses are relevant tothe study? Do I need to transform the data?

Out today in GigaScience is ShinyLearner, a new tool to make it easier to perform benchmark comparisons of classification algorithms. This tool stands out by making this process super systematic and reproducible, and despite needing to interface with many different libraries and languages it uses software containers (and a CodeOcean demo) so end users don’t need to worry about this complexity.

Studies of muscle physiology often rely on closed-source, proprietary software for not only recording data but also for data wrangling and analyses. Although specialized software might be necessary to record data from highly-specialized equipment, data wrangling and analyses should be free from this constraint.

To the uninitiated, software testing may seem variously boring, daunting or bogged down in obscure terminology. However, it has the potential to be enormously useful for people developing software at any level of expertise, and can often be put into practice with relatively little effort. Our 1-hour Call will include two speakers and at least 20 minutes for Q &

Ambitious workflows in R, such as machine learning analyses, can be difficult to manage. A single round of computation can take several hours to complete, and routine updates to the code and data tend to invalidate hard-earned results. You can enhance the maintainability, hygiene, speed, scale, and reproducibility of such projects with the drake R package.