The Digital Preservation Coalition (DPC) is pleased to announce that its new Beginners Guide to Computational Access is now publicly available.
This guide is the first in the Unit Testing for Scale and Profit series. In a project where changes are frequently made to research software, it is helpful to know that the code still works as expected. In our last two episodes, we looked at the benefits of having a set of unit tests and how we can use test parameterisation to write numerous tests efficiently. However, particularly with projects involving more than one contributor, it would be good to have assurance the software still works without everyone having to pull down all the changes and test them. In this guide, we'll be looking at…
In this guide, Heather Turner shares her top tips for how mentors can set up a successful short-term mentoring relationship.
This guide is the second in the Unit Testing for Scale and Profit series. As our code increases in size and particularly complexity, we should expect our number of tests to increase too, which means more time writing tests. Fortunately there is something that can help with this burden which we'll look at in this guide: parameterised tests!
This guide is the first in the Unit Testing for Scale and Profit series. In this guide we’ll look into techniques of automated testing to improve the predictability of a software change, make development more productive, and help us produce code that works as expected and yields desired results. We'll use Python for illustration purposes, but the concepts and approaches can be readily applied to many other languages.
A new guide has been published based on our experiences of planning recordings to processing the outputs.
A guide on planning and processing recordings of online sessions.
Photo by CHUTTERSNAP.
The SSI has developed and co-developed an array of training resources aimed at various levels of expertise. These resources are all listed here.
We think research reproducibility is super important! Reproducible research is necessary to ensure scientific outputs can be trusted and built upon in future work. An important aspect of reproducible research is computational reproducibility. Binder is a great tool to help you do this easily. Here we offer some top tips so you can make the most out of Binder.
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You've decided to make the leap and share your research data. You might do this to improve the transparency of your study, allowing others to reproduce the work. Or you want to set up new collaborations or allow others to build further on your research, speeding up scientific discovery. You may also want to obtain credit or visibility for all the work that you put into collecting and curating the data. Or you may simply want to share your data because others require your research outputs to be made publicly available to receive funding or to be published.