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Managing Research Data: Workshop Descriptions

Bite size learning opportunities!

Descriptions

Workshop Title Description Instructor(s)
Open Science:  The role of publishers & funders in reproducibility A research project is reproducible if the researcher provides a set of files and instructions to enable a second researcher (including you, the researcher) to recreate the final reported results. Over approximately the last decade, research funders and, increasingly, scholarly publishers have been implementing policies and supporting technologies to facilitate reproducibility. This workshop will define reproducibility, provide a history and overview of publishers' and funders' policies and practices aiming to encourage data and code sharing, discuss some obstacles and challenges, and describe innovations in workflows and collaborations between researchers, funders, and publishers driving reproducibility forward Clarke Iakovakis
An Introduction to Electronic Lab Notebooks

Electronic lab notebooks (ELNs) are a data management software platform for organizing research records. At it's simplest, it replicates the function of a paper lab notebook but has several advantages over traditional paper records. This workshop will explore those advantages and suggest some options for adopting ELNs for your research.

Kay Bjornen
An Introduction to LaTeX

LaTeX is a typesetting system for producing technical documents and is an important document standard in a number of disciplines. LaTeX gives authors more flexibility and control than other word processing software, but is less intuitive for first time users. This workshop will cover basic use of LaTeX editors, as well as an introduction to the markup language of LaTeX. Participants will gain a basic understanding of how to use LaTeX to create documents, how to write and format text, and how to format non-standard typesettings such as expressions, equations, and non-Latin scripts. We will use the free Overleaf LaTeX text editor, which does not require installing any software ahead of time

Clarke Iakovakis
Reproducibility for Everyone Reproducibility for Everyone (R4E) is a community led education initiative to increase adoption of open research practices at scale. Research reproducibility is enhanced by good data management practices and transparent research methods. This workshop will use an adapted R4E curriculum to introduce research tools and practices for improved research reproducibility.  Topics will include an introduction to reproducibility and why it is important, protocol, data and reagent sharing, data visualization, publishing and bioinformatics. Kay Bjornen
Introduction to Tidy Data and the Tidyverse in R 
Tidy data is a data format that is consistent and machine readable. In this workshop we will explore what makes data "tidy" beginning with good practices for setting up spreadsheets using historical weather data from the OSU Agricultural Research Station that is being curated for use by the OSU Library. We will then explain why R is the right tool for data cleaning, visualization and analysis and discuss the advantages of learning how to use this powerful tool rather than be frustrated by the limitations of Excel.
Kay  Bjornen

Cleaning messy data with R

Cleaning data, whether it is your own or someone else's, is usually the most difficult and painstaking part of data analysis. In spreadsheets it may involve going cell by cell to look for errors and inconsistencies followed by cutting and pasting which can also introduce errors. In this workshop, we will show you how to use R to clean, reformat and subset data quickly and without introducing the errors that Excel can be prone to. We will use real life messy weather data to demonstrate how to use the power of R to arrive at a clean and manageable data set.

Clarke Iakovakis
Data Analysis with R Cleaning data, whether it is your own or someone else's, is usually the most difficult and painstaking part of data analysis. In spreadsheets it may involve going cell by cell to look for errors and inconsistencies followed by cutting and pasting which can also introduce errors. In this workshop, we will show you how to use R to clean, reformat and subset data quickly and without introducing the errors that Excel can be prone to. We will use real life messy weather data to demonstrate how to use the power of R to arrive at a clean and manageable data set. Phil Alderman
Data Visualization with R Visualization of data is easy to do in R. Graphs can be built layer by layer with as much detail as necessary using a variety of plot types, some which are not available in Excel. Once you have created your graphs and made them look professional, it is particularly convenient to be able to save the scripts and reproduce them with new data sets. The last of our data series will again use the OSU weather data to show how easy it is to make terrific looking visualizations in R. Kay Bjornen