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Category: Data Science

(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 […]

Greg Wilson on “Good Enough Practices for Scientific Computing”

Editor Comments:  “Greg Wilson (formerly of Software Carpentry, now at DataCamp) talks about improving your practices around developing software and sharing data especially in an academic setting. Includes a candid discussion about git.” – Ted Laderas A recording of Greg Wilson’s talk for Oregon Health & Science University’s BioData Club about his paper “Good Enough […]

How Copyright Law Can Fix Artificial Intelligence’s Implicit Bias Problem

Editor Comments:  “New technologies present new opportunities for bias and ethical uncertainties. This article suggests that existing copyright laws can actually help mitigate bias in Artificial Intelligence systems.” — Sara Mannheimer As the use of artificial intelligence (AI) continues to spread, we have seen an increase in examples of AI systems reflecting or exacerbating societal bias, […]