Databases are useful for both storing and using data effectively. Using a …
Databases are useful for both storing and using data effectively. Using a relational database serves several purposes. It keeps your data separate from your analysis. This means there’s no risk of accidentally changing data when you analyze it. If we get new data we can rerun a query to find all the data that meets certain criteria. It’s fast, even for large amounts of data. It improves quality control of data entry (type constraints and use of forms in Access, Filemaker, etc.) The concepts of relational database querying are core to understanding how to do similar things using programming languages such as R or Python. This lesson will teach you what relational databases are, how you can load data into them and how you can query databases to extract just the information that you need.
Software Carpentry lesson that teaches how to use databases and SQL In …
Software Carpentry lesson that teaches how to use databases and SQL In the late 1920s and early 1930s, William Dyer, Frank Pabodie, and Valentina Roerich led expeditions to the Pole of Inaccessibility in the South Pacific, and then onward to Antarctica. Two years ago, their expeditions were found in a storage locker at Miskatonic University. We have scanned and OCR the data they contain, and we now want to store that information in a way that will make search and analysis easy. Three common options for storage are text files, spreadsheets, and databases. Text files are easiest to create, and work well with version control, but then we would have to build search and analysis tools ourselves. Spreadsheets are good for doing simple analyses, but they don’t handle large or complex data sets well. Databases, however, include powerful tools for search and analysis, and can handle large, complex data sets. These lessons will show how to use a database to explore the expeditions’ data.
The adoption of reproducibility remains low, despite incentives becoming increasingly common in …
The adoption of reproducibility remains low, despite incentives becoming increasingly common in different domains, conferences, and journals. The truth is, reproducibility is technically difficult to achieve due to the complexities of computational environments.To address these technical challenges, we created ReproZip, an open-source tool that packs research along with all the necessary information to reproduce it, including data files, software, OS version, and environment variables. Everything is then bundled into an .rpz file, which users can use to reproduce the work with ReproUnzip and an unpacker (Docker, Vagrant, and Singularity). The .rpz file is general and contains rich metadata: more unpackers can be added as needed, better guaranteeing long-term preservation.However, installing the unpackers can still be burdensome for secondary users of ReproZip bundles. In this paper, we will discuss how ReproZip and our new tool ReproServer can be used together to facilitate access to well-preserved, reproducible work. ReproServer is a cloud application that allows users to upload or provide a link to a ReproZip bundle, and then interact with/reproduce the contents from the comfort of their browser. Users are then provided a stable link to the unpacked work on ReproServer they can share with reviewers or colleagues.
Reproducibility is unquestionably at the heart of science. Scientists face numerous challenges …
Reproducibility is unquestionably at the heart of science. Scientists face numerous challenges in this context, not least the lack of concepts, tools, and workflows for reproducible research in today's curricula.This short course introduces established and powerful tools that enable reproducibility of computational geoscientific research, statistical analyses, and visualisation of results using R (http://www.r-project.org/) in two lessons:1. Reproducible Research with R MarkdownOpen Data, Open Source, Open Reviews and Open Science are important aspects of science today. In the first lesson, basic motivations and concepts for reproducible research touching on these topics are briefly introduced. During a hands-on session the course participants write R Markdown (http://rmarkdown.rstudio.com/) documents, which include text and code and can be compiled to static documents (e.g. HTML, PDF).R Markdown is equally well suited for day-to-day digital notebooks as it is for scientific publications when using publisher templates.2. GitLab and DockerIn the second lesson, the R Markdown files are published and enriched on an online collaboration platform. Participants learn how to save and version documents using GitLab (http://gitlab.com/) and compile them using Docker containers (https://docker.com/). These containers capture the full computational environment and can be transported, executed, examined, shared and archived. Furthermore, GitLab's collaboration features are explored as an environment for Open Science.Prerequisites: Participants should install required software (R, RStudio, a current browser) and register on GitLab (https://gitlab.com) before the course.This short course is especially relevant for early career scientists (ECS).Participants are welcome to bring their own data and R scripts to work with during the course.All material by the conveners will be shared publicly via OSF (https://osf.io/qd9nf/).
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