Category Archives: data science

Computational Reproducibility Pilot – Code Ocean Trial

A goal of Duke University Libraries (DUL) Code Ocean Logois to support the  growing and changing needs of the Duke research community. This can take many forms. Within Data and Visualization Services, we provide learning opportunities, consulting services, and computational resources to help Duke researchers implement their data-driven research projects. Monitoring and assessing new tools and platforms also helps DUL stay in tune with changing research norms and practices. Today the increasing focus on the importance of transparency and reproducibility has resulted in the development of new tools  and resources to help researchers produce and share more reproducible results. One such tool is Code Ocean.

Code Ocean is a computational reproducibility platform that employs Docker technology to execute code in the cloud. The platform does two key things—it integrates the metadata, code, data and dependencies into a single ‘compute capsule’, ensuring that the code will run—and it does this in a single web interface that displays all inputs and results. Within the platform, it is possible to develop, edit or download the code, run routines, and visualize, save or download output, all from a personal computer. Users or reviewers can upload their own data and test the effects of changing parameters or modification of the code. Users can also share their data and code through the platform. Code Ocean provides a DOI for all capsules facilitating attribution and a permanent connection to any published work.

In order to help us understand and evaluate the usefulness of the Code Ocean platform to the Duke research community, DUL will be offering trial access to the Code Ocean cloud-based computational reproducibility platform starting on October 1, 2018. To learn more about what is included in the trial access and to sign up to participate, visit the Code Ocean pilot portal page.

If you have any questions, contact askdata@duke.edu.

Fall Data and Visualization Workshops

2017 Data and Visualization Workshops

Visualize, manage, and map your data in our Fall 2017 Workshop Series.  Our workshops are designed for researchers who are new to data driven research as well as those looking to expand skills with new methods and tools. With workshops exploring data visualization, digital mapping, data management, R, and Stata, the series offers a wide range of different data tools and techniques. This fall, we are extending our partnership with the Graduate School and offering several workshops in our data management series for RCR credit (please see course descriptions for further details).

Everyone is welcome at Duke Libraries workshops.  We hope to see you this fall!

Workshop Series by Theme

Data Management

09-13-2017 – Data Management Fundamentals
09-18-2017 – Reproducibility: Data Management, Git, & RStudio 
09-26-2017 – Writing a Data Management Plan
10-03-2017 – Increasing Openness and Reproducibility in Quantitative Research
10-18-2017 – Finding a Home for Your Data: An Introduction to Archives & Repositories
10-24-2017 – Consent, Data Sharing, and Data Reuse 
11-07-2017 – Research Collaboration Strategies & Tools 
11-09-2017 – Tidy Data Visualization with Python

Data Visualization

09-12-2017 – Introduction to Effective Data Visualization 
09-14-2017 – Easy Interactive Charts and Maps with Tableau 
09-20-2017 – Data Visualization with Excel
09-25-2017 – Visualization in R using ggplot2 
09-29-2017 – Adobe Illustrator to Enhance Charts and Graphs
10-13-2017 – Visualizing Qualitative Data
10-17-2017 – Designing Infographics in PowerPoint
11-09-2017 – Tidy Data Visualization with Python

Digital Mapping

09-12-2017 – Intro to ArcGIS Desktop
09-27-2017 – Intro to QGIS 
10-02-2017 – Mapping with R 
10-16-2017 – Cloud Mapping Applications 
10-24-2017 – Intro to ArcGIS Pro

Python

11-09-2017 – Tidy Data Visualization with Python

R Workshops

09-11-2017 – Intro to R: Data Transformations, Analysis, and Data Structures  
09-18-2017 – Reproducibility: Data Management, Git, & RStudio 
09-25-2017 – Visualization in R using ggplot2 
10-02-2017 – Mapping with R 
10-17-2017 – Intro to R: Data Transformations, Analysis, and Data Structures
10-19-2017 – Developing Interactive Websites with R and Shiny 

Stata

09-20-2017 – Introduction to Stata
10-19-2017 – Introduction to Stata