The call for submissions is now open for the DataTech20 conference in Glasgow on 16 March 2020.
Data Science for Doctors, developed by the Software Sustainability Institute’s Fellow Steve Harris, is a programme based on The Carpentries’ Data Carpentry aimed at teaching health care professionals basic concepts, skills and tools for working more effectively with data.
A Rice University scientist and his colleagues are using their search for dark matter in a study they hope will enhance all of data science.
By Grai Calvey, Fiona Jones and Heather Cooper
Drawing on Library Carpentry lessons, pedagogy and community.
Stop, collaborate and listen: Gender equality in social data science
Join us for an evening discussion with a panel of leading computational social scientists and data scientists on collaboration, equality, and skills future social scientists need to work with big data.
By Reka Solymosi, Software Sustainability Institute Fellow.
From 4 – 5 April 2019, I attended the Women in Data Science Zurich 2019 conference as an invited speaker to talk about my research involving ‘new’ forms of data. In particular the use of crowdsourced data collection methods to gain insight into people’s perceptions and subjective evaluations of their environments.
By Alex Morley, Institute Fellow & Mozilla Fellow
It’s not a new concept. But when people talk to me about improving the scientific process it really resonates with me when they talk about feedback loops. This framework is broad enough to encompass most ways in which we can think about improving science, but also makes explicit what actions need to be taken, and where bottlenecks are likely to arise. Here are a few examples of how people have used these cycles to make/explain progress/problems in scientific processes.
By Danny Wong, NIAA-HSRC & UCL-DAHR. I’ve recently had the great fortune of publishing a paper which had significant interest from the general news media. It even managed to get picked up by the BBC, The Guardian and all the major newspapers in the UK! As per usual, I’ve shared the source code for the analysis publicly, this time electing to serve it up on GitHub as a repository. I have included the manuscript as an .Rmd file, and the wrangling data wrangling and modelling code as a chunk located at the start of the .Rmd file.