Programming/Development: Journal of Open Source Software (JOSS), Qt, Julia vs. Python, and GitHub Stats

  • Who gets the credit?

    The primary goal of the Journal of Open Source Software (JOSS) is to give researchers who develop, contribute to, and maintain open source software a means to get citable credit for their work within today’s research ecosystem. In short, JOSS is a developer-friendly journal for publishing research software packages. It’s also an online academic journal (ISSN 2475-9066) with a formal peer review process that is designed to improve the quality of the software submitted by making sure it meets minimum standards and includes standard identifiers for content on digital networks (Digital Object Identifiers, or DOIs) for all accepted papers.

  • nanoQuill

    Qi, KDAB and Qt have now taken their collaboration one step further by introducing nanoQuill, otherwise known as “The Coloring Book of Life,” which is a crowdsourced coloring book and mobile app that gives anybody the opportunity to color cancer images to help annotate organelles inside those electron microscopy images. Qi is then able to take the crowdsourced annotations to measure a cell’s detail, render 3D images from the colored 2D images, and ultimately train new deep learning algorithms, all in the name of advancing cancer research.

  • An Unexpected C++ Journey

    Some of you may know that KDAB employees enjoy flexibility on working hours as well as location, and some choose to work from home, with the opportunity to share childcare, do part-time study or simply enjoy an out-of-the-way location. All that’s required is a decent bandwidth for KDAB work.

  • Julia vs. Python: Julia language rises for data science

    Of the many use cases Python covers, data analytics has become perhaps the biggest and most significant. The Python ecosystem is loaded with libraries, tools, and applications that make the work of scientific computing and data analysis fast and convenient.

    But for the developers behind the Julia language — aimed specifically at “scientific computing, machine learning, data mining, large-scale linear algebra, distributed and parallel computing”—Python isn’t fast or convenient enough. It’s a trade-off, good for some parts of this work but terrible for others.

  • Who contributed the most to open source in 2017? Let’s analyze GitHub’s data and find out.

    Note that analyzing GitHub doesn’t include top communities like Android, Chromium, GNU, Mozilla, nor the the Apache or Eclipse Foundation, and other projects that choose to run most of their activities outside of GitHub.

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