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Category: Data Science

Teaching data science fundamentals through realistic synthetic clinical cardiovascular data | bioRxiv

Editorial Comments: “This is a paper that my colleagues and I have been working on to teach issues with using clinical data in data science. We built a realistic synthetic dataset and coursework to help students understand issues in predicting cardiovascular risk. The dataset and course material is freely available through my GitHub here:” […]

Data Analysis and Engagement – Does Caring About the Analysis Matter? · Simply Statistics

Editorial Comments: “This is a good discussion about the value of analysis that incorporates knowledge and care about the analysis. I find that my best collaborations are when my collaborator can communicate the importance of their problem and why what I do matters.” —Ted Laderas   Sometimes, when you’re recording a podcast, it’s actually difficult […]

I4OC: Initiative for Open Citations

Editor Comments:  “The potential utility and impact of open citation data is very exciting to imagine.  Access to so many bibliometric indicators is locked behind expensive sources, like Scopus and Web of Science.  Open citations would equalize access to this data, facilitate transparency, and enable the freer exploration of knowledge creation and communication processes” – […]

rOpenSci Interview with Jenny Bryan

Editor comments: “This is just an awesome and inspiring interview of one of the current leaders of data science and open research. I think anyone who thinks about data science problems or even just work life balance could learn something from Dr. Bryan and her work.” — Jessica Minnier   In this occasional series, we […]

(Re)usable Data Project

Editor Comments:  “This project, which I contribute to, aims to evaluate the reusability of biomedical data sources by evaluating their licensing.  Our approach was to translate frictionless licensing characteristics, as represented in frameworks like FAIR data, into a quantifiable rubric.” – Robin Champieux Inspired by the efforts of scientists around the world and the game-changing […]

The Practice of Reproducible Research

Editor Comments:  “This is a useful resource that includes many examples of reproducible studies and research, focusing on data-intensive sciences. It is filled with great advice and tips—especially the appendix, don’t miss it! The collection will be released as a book in 2018.” — Jessica Minnier This is the open, online version of the book […]

Bridging the Data Divide One Resource at a Time

Editor Comments:  “If you are looking to offer community data training events, this article gathers many resources like training materials and lessons that are made to be re-purposed. When I was organizing some introductory lessons for student groups in my library I found some particularly useful materials in the catalog of training materials. These materials […]

First Timers Only

Editor Comments: “This is a really nice guide for people who are first-timers to open source software (OSS) about how to contribute to projects and become involved in the OSS community. It’s made to be friendly and show that anyone can contribute positively to a project. It also provides some guidelines for OSS developers to […]

Open Data/Open Minds

Editor Comments:  “I’m blown away by the creativity and potential impact of this project from NEXMAP.  It provides an exciting framework and tools to facilitate data and scientific literacy by supporting citizen driven investigations of and story telling about the issues that matter to a local community.” – Robin Champieux First of all, welcome! And […]

What You Need to Know to Start a Career As a Data Scientist

Editor Comments: “This is a great post by Julia Silge on the challenges and rewards on moving from software development to data science. I especially like the section on needing creativity and communication to succeed.” – Ted Laderas Demand for developers with specialized skills is on the rise across the board, and companies are particularly interested […]