LibGuides: Research Data and Reproducibility: Processing Data

What is data processing?

Data processing is the step where raw data is cleaned, organized, and transformed into a format that can be analyzed. This may include correcting errors, removing duplicates, formatting values, or combining data from multiple sources. Processing should be done carefully and documented clearly so others can understand what was done and why. Good data processing helps ensure accurate, reliable results and supports reproducibility.

Cleaning, Transforming, and Validating Data

Tidy Data
This article in the Journal of Statistical Software by data scientist Hadley Wickham outlines a standard method for displaying multivariate data in the form of a data matrix where each variable is a column, each observation is a row, and each type of observational unit is a table. It makes it easy to tidy messy datasets and to develop tools for data analysis.
CSV on the Web: A Primer
The CSV on the Web Working Group has developed standard ways to express useful metadata about CSV files and other kinds of tabular data. This primer takes you through the ways in which these standards work together, covering what we mean by "tabular data" and "CSV", where files that provide metadata about CSV live, how to create a schema to validate the content of a CSV file, how to specify how a CSV file should be converted to RDF or JSON, and how to provide other documentation and metadata about a CSV file.
Data Cleaning with Excel
This guide from the Galter Health Sciences Library at Northwestern's Feinberg School of Medicine offers tricks and tips for cleaning and organizing tabular data in Microsoft Excel.
Cleaning Spreadsheet Data with OpenRefine
This guide from the Galter Health Sciences Library at Northwestern's Feinberg School of Medicine can be used to learn the basic functions of OpenRefine.

Exploring and Analyzing Data

Data Exploration

Datasette: a tool for exploring and publishing data. It helps people take data of any shape or size, analyze and explore it, and publish it as an interactive website and accompanying API.
VisiData: a free, open-source tool that lets you quickly open, explore, summarize, and analyze datasets in your computer’s terminal. VisiData works with CSV files, Excel spreadsheets, SQL databases, and many other data sources.

Qualitative Analysis Tools

ATLAS.ti Qualitative Data Analysis Software: a qualitative data analysis and management tool that helps users to systematically arrange, reassemble, and manage large bodies of textual, graphical, audio and video data.
NVivo Qualitative Data Analysis Software: a software program used for qualitative and mixed-methods research. Specifically, it is used for the analysis of unstructured text, audio, video, and image data, including (but not limited to) interviews, focus groups, surveys, social media, and journal articles.
Taguette: free and open-source tool for qualitative research. You can import your research materials, highlight and tag quotes, and export the results!

Statistical Tools

Posit Workbench (formerly RStudio Statistics Software): the premier development experience for data scientists who use R and Python.
SAS Statistics Software: SAS Statistics is a software package used for statistical analysis. OHSU licenses the Windows version only. It is available for OHSU employees and students. The software is licensed only for academic purposes.
SPSS Statistics Software: a software package used for statistical analysis. It is available for OHSU students and employees for both work and personal computers. The software is licensed only for academic purposes.

Data Analysis Resources

Data Scavenger Hunt: Data Exploration of NHANES includes materials from the OHSU BioData Club workshop, which introduced data exploration using the publicly available NHANES (National Health and Nutritional Examination Survey) to learn about answering questions about diabetes and depression.
A Dive into Logistic Regression Modeling: Materials from the OHSU BioData club workshop, which introduced the mathematics of logistic regression for binary classification modeling, and how to prevent the harms of overfitting with cross validation in R.
LinkedIn Learning: Data Analysis : A collection of data analysis videos and courses, curated by OHSU Career and Workplace Enhancement (CWE) Staff.

Visualizing Data

Tableau
Tableau is an application designed to connect to many different data resources for the purpose of creating meaningful graphs and charts and present them in reports or dashboards. This link describes Tableau licensing options available to OHSU members facilitated by OHSU's Business Intelligence community.
Data Storytelling
Materials from the OHSU BioData Club workshop, which covered techniques for creating figures that communicate findings clearly. Discusses the role of color, annotations, and simplifying figures to communicate effectively.
A Practical Introduction to Data Visualization with R and ggplot2
Materials from the OHSU BioData Club workshop, which introduced beginners to practical hands-on examples using R and ggplot2, tools for making impactful dat visualizations, to one's own data analyses, and provided experienced users an opportunity to test their knowledge and learn new tricks and techniques.
LinkedIn Learning: Data Visualization
A collection of data visualization videos and courses, curated by OHSU Career and Workplace Enhancement (CWE) Staff.

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