Upcoming Workshops

[Online] The sharing of research data has become increasingly important to federal and private funders and journal publishers. With the 2023 NIH Data Management and sharing Policy (https://sharing.nih.gov/), there are legitimate concerns about how data from human beings can ethically and safely be shared. This workshop will talk about the various challenges that arise when considering how to share human participants data and potential ways that these challenges can be addressed, and even solved, with resources available at Duke and beyond (data sharing consent language, controlled access repositories, de-identification help, etc.). While support resources are still evolving, you are not alone in determining how to share human participants data the right and ethical way.

Data Management

[Online] Tableau is a software package that is increasingly popular for creating striking visualizations, such as charts and graphs, from tabular data. It also has an increasing number of capabilities to create maps. Source data can include native geospatial files (such as shapefiles or GeoJSON files), but also tabular data (such as CSV or Excel files) that include locational values, such as place names or coordinate data. This workshop will cover how to create maps in Tableau and on ways to manipulate the data and to effectively symbolize it on a map.

Mapping & GIS

[Online] This workshop will help you get started telling stories with maps on the ArcGIS StoryMaps platform. This easy-to-use web application combines interactive maps with narrative text, images, and videos to provide a powerful communication tool for any project with a spatial component. We will explore the capabilities of the platform, share best practices for designing effective stories, and guide participants through the process of creating their own story maps. No previous experience with GIS is necessary.

Mapping & GIS

[Online]Work together on a basic Python Pandas assignment featuring plotting and working with Dataframes with the benefit of live (online) support. Attendees are expected to have some previous experience with both Python and Pandas or should watch the supporting Python for Data Science introductory video in advance of this workshop (see registration for details). Active participation is required.

Data Science Data Visualization

[Online] This workshop will explore strategies and best practices for sharing and publishing data to support open science, reproducibility, and future innovation. Topics covered will include the use of data and metadata standards to support interoperability and harmonization. The workshop includes an overview of repository options, examples of disciplinary repositories, and data publishing methods to increase the impact of research projects and support the FAIR Guiding Principles (i.e., Findable, Accessible, Interoperable, and Reusable).

Data Management

[Online] This workshop uses a case-study approach to present tips and tricks for common R operations such as data-scraping, ingesting multiple files, cleaning column names, separating multi-value fields, uniting variable values, and nesting data. Some experience using the Tidyverse and ggplot is recommended.

Data Science

[Hybrid] This workshop will provide an overview of the Duke Research Data Repository and how it can help Duke researchers comply with funder and journal policies as well as meet growing standards around data stewardship and sharing, such as the FAIR Guiding Principles. The general functionalities of the platform as well as tips for submitting data will be discussed.

Data Management

[Online] Python can be a great option for exploration, analysis and visualization of tabular data, such as spreadsheets and CSV files, if you know which tools to use and how to get started. This workshop will take you through some practical examples of using Python and specifically the Pandas module to load data from files and transform it into a standard “tidy” format, so it's ready to analyze and visualize. We will visualize some tidy data in Seaborn, and also be learning how to merge two datasets, like a database JOIN. Note: Some experience in Python is strongly recommended.

Data Science