Research data management is a critical component of any research project. The process of research data management starts in the planning phases of a project. Not later on when you have a paper to publish or grant requirements to fulfill and thus need to deposit data. Those who think about and plan for data management processes early on in the process will find that it's easier to fulfill later-stage requirements. This guide segment of the guide will review:
Throughout this guide, we will be referring to the FAIR principles. This is an acronym for ideal conditions of data once a project is complete. The data should be Findable, Accessible, Interoperable, and Reusable.
Reproducible Research with R and RStudio by
Praise for previous editions: "Gandrud has written a great outline of how a fully reproducible research project should look from start to finish, with brief explanations of each tool that he uses along the way... Advanced undergraduate students in mathematics, statistics, and similar fields as well as students just beginning their graduate studies would benefit the most from reading this book. Many more experienced R users or second-year graduate students might find themselves thinking, 'I wish I'd read this book at the start of my studies, when I was first learning R!'...This book could be used as the main text for a class on reproducible research ..." (The American Statistician) Reproducible Research with R and R Studio, Third Edition brings together the skills and tools needed for doing and presenting computational research. Using straightforward examples, the book takes you through an entire reproducible research workflow. This practical workflow enables you to gather and analyze data as well as dynamically present results in print and on the web. Supplementary materials and example are available on the author's website. New to the Third Edition Updated package recommendations, examples, URLs, and removed technologies no longer in regular use. More advanced R Markdown (and less LaTeX) in discussions of markup languages and examples. Stronger focus on reproducible working directory tools. Updated discussion of cloud storage services and persistent reproducible material citation. Added discussion of Jupyter notebooks and reproducible practices in industry. Examples of data manipulation with Tidyverse tibbles (in addition to standard data frames) and pivot_longer() and pivot_wider() functions for pivoting data. Features Incorporates the most important advances that have been developed since the editions were published Describes a complete reproducible research workflow, from data gathering to the presentation of results Shows how to automatically generate tables and figures using R Includes instructions on formatting a presentation document via markup languages Discusses cloud storage and versioning services, particularly Github Explains how to use Unix-like shell programs for working with large research projects
Research Data Management: A Primer by
Abstract
Meeting the challenges of 21st century research require sound research data management. By carefully planning, documenting, and preserving data, the goals of having reproducible and transparent research data are far easier to meet. Further, well-managed data are easier to use and reuse, which translates to more collaboration for researchers and maximum return-on-investment for funders. This primer will cover the basics of research data management, with the goal of helping researchers and those that support them become better data stewards.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 Generic License.