相关论文: The Software Behind the Stats: A Student Explorati…

The use of statistical software in academia and enterprises has been evolving over the last years. More often than not, students, professors, workers, and users, in general, have all had, at some point, exposure to statistical software.…

Born in the late 20s, R is one of the most popular software for statistical computing and graphics. With the development of information technology and the advent of the big data era, great changes have taken place in the R ecosystem. Based…

R is a language and environment for statistical computing and graphics, which provides a wide variety of statistical tools (modeling, statistical testing, time series analysis, classification problems, machine learning, ...), together with…

Traditionally, statistical computing courses have taught the syntax of a particular programming language or specific statistical computation methods. Since the publication of Nolan and Temple Lang (2010), we have seen a greater emphasis on…

Big data are data on a massive scale in terms of volume, intensity, and complexity that exceed the capacity of standard software tools. They present opportunities as well as challenges to statisticians. The role of computational…

Science advances not only through the accumulation of facts but also through the evolution of tools. Crucially, tools are rarely used in isolation. They form tool portfolios, combinations shaped by a discipline's workflows and analytical…

Modern research in the sciences, engineering, humanities, and other fields depends on software, and specifically, research software. Much of this research software is developed in universities, by faculty, postdocs, students, and staff. In…

A growing number of students are completing undergraduate degrees in statistics and entering the workforce as data analysts. In these positions, they are expected to understand how to utilize databases and other data warehouses, scrape data…

Software now lies at the heart of scholarly research. Here we argue that as well as being important from a methodological perspective, software should, in many instances, be recognised as an output of research, equivalent to an academic…

Software engineering research is evolving and papers are increasingly based on empirical data from a multitude of sources, using statistical tests to determine if and to what degree empirical evidence supports their hypotheses. To…

With the advent of open source software, a veritable treasure trove of previously proprietary software development data was made available. This opened the field of empirical software engineering research to anyone in academia. Data that is…

This Innovative Practice Full Paper presents an approach of using software development artifacts to gauge student behavior and the effectiveness of changes to curriculum design. There is an ongoing need to adapt university courses to…

Different communities rely heavily on software, but use quite different software development practices. {\bf Objective}: We wanted to measure the state of the practice in the area of statistical software for psychology to understand how it…

Software is now a vital scientific instrument, providing the tools for data collection and analysis across disciplines from bioinformatics and computational physics, to the humanities. The software used in research is often home-grown and…

As computer systems become more and more complex, software and tools lag more and more behind. This is especially true for scientific software that often demands high performance, and thus needs to take advantage of parallelisms, memory…

The R software has become popular among researchers due to its flexibility and open-source nature. However, researchers in the fields of public health and epidemiological studies are more customary to commercial statistical softwares such…

Keeping up with the research literature plays an important role in the workflow of scientists - allowing them to understand a field, formulate the problems they focus on, and develop the solutions that they contribute, which in turn shape…

Keeping pace with rapidly evolving technology is a key challenge in teaching statistics. To equip students with essential skills for the modern workplace, educators must integrate relevant technologies into the statistical curriculum where…

Software is the key crosscutting technology that enables advances in mathematics, computer science, and domain-specific science and engineering to achieve robust simulations and analysis for science, engineering, and other research fields.…

Scientific software-defined as computer programs, scripts, or code used in scientific research, data analysis, modeling, or simulation-has become central to modern research. However, there is limited research on the readability and…