Tag Archives: reproducibility

This week we showcased a new way of peer reviewing software, testing code in an independent manner and providing a CODECHECK “certificate of reproducible computation” when the results in the paper can be reproduced. We’ve written a post on the CODECHECK process featuring a Q&A with CODECHECK founder Stephen Eglan, and here we’ll provide a follow […]

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Out today in GigaScience is ShinyLearner, a new tool to make it easier to perform benchmark comparisons of classification algorithms. This tool stands out by making this process super systematic and reproducible, and despite needing to interface with many different libraries and languages it uses software containers (and a CodeOcean demo) so end users don’t […]

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In this data-driven era, research is faced with new challenges, from sharing, storing and accessing data, including how to better integrate data to answer big questions in science. With many data repositories available, it is hard to maintain them all – some repositories are forced to close – meaning loss of access to invaluable datasets.  […]

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At GigaScience as our focus is on reproducibility rather than subjective impact, it can be challenging at times to judge this in our papers. Targeting the “bleeding edge” of data-driven research, more and more of our papers utilise technologies, such as Jupyter notebooks, Virtual Machines, and Containers such as Docker. Working these tools in to […]

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Yesterday we published the winning paper of the second GigaScience prize, with additional detail and coverage in GigaBlog describing why we and the judging panel found it so novel. This was an impressively case study in reproducibility, reassembling & reannotating around 700 microbial eukaryotic transcriptomes to demonstrate this approach can aid in revealing new biologically relevant […]

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As a journal focussed on open science we are big promoters of research parasites (and research on parasites), and try to feeds them with open data and tools. It is therefore appropriate this is the second year GigaScience has supported and sponsored the Research Parasite awards. In one of our Q&As, organisers Casey Greene and […]

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GigaScience is always trying to push the boundaries of how we disseminate reproducible research, and to adapt to the challenges of dealing with experiments become more data-intensive. We now showcase a new reproducible research platform we’ve been testing called Code Ocean, and have a Q&A with our Author Ruibang Luo on his experiences using it. […]

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While the Guardians of the Galaxy film franchise has just released its second film, the GigaScience Galaxy series has just published its 10th, 11th and 12th papers. And all without the need of expensive CGI, although we do have our GigaGalaxy server on standby for additional computational support. For those not part of their large […]

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This year has been an eventful one, probably too eventful for many.  For GigaScience it has been eventful too, although fortunately in a much more positive way than many have experienced. While there are fears of us entering a “post-truth” era, there is more need than ever for our role as promoters of transparency, reproducibility and providers of cold-hard data. We celebrated our birthday with Mickey Mouse, and experienced many other milestones. On the technical front, this year we have brought you better integration with citable and updatable methods, bigger better and broader data types, and much more. In the tradition of end-of-year-introspection, here is a summary of some of our 2016 achievements as we continue to push the boundaries of innovative publishing of all research objects and reproducible research.

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Our Editor in Chief Laurie Goodman gives her thoughts on the journal impact factor (jIF), and how we need to move beyond shallow, untransparent, gameable, out-of-date proxies that promote bad scientific practices.

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