Getting started with open source development

This morning I stumbled upon this really great blog post by Rachel Bilbro (@RebeccaBilbro), a data scientist and developer from District Data Labs ). It's a really nice piece about how to get started with open-source data and software development. Contributing to open-source software can be daunting, but as Rebecca points out, the barrier to starting is surprisingly low. You also don't have to be an expert programmer or know everything about GitHub to start contributing...  and that is a good thing! 

It's worth following @DistrictDataLab on Twitter. They are a data science research institute, data product incubator, and open source collaborative that seems to be working on really interesting projects.

Rebecca's blog post in a nutshell...

Step 1 – Buy into version control

Step 2 – Figure out the Git/GitHub basics

Step 3 – Find the README

Step 4 – Read the Issues

Step 5 – Fork It

Step 6 – Submit a Pull (Merge) request

Next Steps (advanced) – Learn Git Branching & Semantic Versioning

If you are still feeling overwhelmed and need a breather before you dive in, I can highly recommend watching Thrift Code
 
– Made with love at .Astronomy6.

 

 


 

Women In Science weekend

Last weekend I had the privilege of participating in the University of Melbourne's annual Women in Science (formerly Women in Physics – WIP) weekend getaway. I was invited to talk about my career path and to serve as a mentor for a new bunch of eager young women scientists. Not surprisingly we had a lot of fun. The first time I attended WIP was way back in 1997 (yes that's twenty years ago, if you can believe it?!) when I was a 3rd year undergrad in Physics. I remember getting a lift up to Daylesford with Virginia Kilborn (@astrogin); an Honours or PhD student at the time, and now a Professor in Astrophysics at Swinburne University of Technology and President of the Astronomical Society of Australia. I also remember being really timid and a little bit in awe of the women who talked about their careers. It might possibly have been the first time I realised that you really could have a career as an astronomer, as opposed to being a lecturer who just happened to teach astrophysics.

This year's cohort were just like me 20 years ago; somewhat overwhelmed by all the discussion, not really knowing what they wanted to do in the long-term, let alone where they were heading now, and dealing with all the negative things we told them about being a women in science. I can't help but feel like we ought to have focussed on the many wonderful reasons we put ourselves through PhDs and academic careers. Having said that we did have a lot of fun (dinner, drinks, boardgames, bike rides), and everything we talked about was really useful and productive. 

Machine learning: the power and promise of computers that learn by example

A few days ago the UK Royal Society published their two-year policy project on Machine learning: the power and promise of computers that learn by example.

The project began in November 2015 with the aim of  investigating the potential of machine learning over the next 5-10 years, and the barriers to realising that potential in the UK. As it carried out its investigation, the project engaged with key audiences – in policy communities, industry, academia, and the public – to raise awareness of machine learning, understand views held by the public and contribute to public debate about this technology, and identify the key social, ethical, scientific, and technical questions that machine learning presents.

The full report (PDF, 3.3Mb) published on the 25th April 2017, comes at a critical time in the rapid development and use of this technology, and the growing debate about how it will reshape the UK economy and people’s lives. 

The Second AeRAC meeting for 2017

Yesterday, Astronomy Australia Limited's (AAL) Astronomy e-Research Advisory Committee (AeRAC) , met to discuss the astronomy community's computing needs, and to review and recommend currently funded infrastructure projects. This was our second meeting for the year. 

Meetings tend to be quite long, often 2–3 hours each, but with so much to get through, time always seems to pass more quickly than it ought to. Given the upcoming Federal budget announcements, it's perhaps not surprising that a lot of discussion was centred around the National Collaborative Research Infrastructure Strategy (NCRIS), and what that might look like i for the 2017/2018 year.  As well as AAL, most of the high performance computing  and cloud infrastructure for research, such as NeCTAR, NCI, Pawsey, and ANDS are also funded through NCRIS. So the potential loss of funding, or access to services  is something the community (and AAL and AeRAC in particular,) needs to prepare for.

We also discussed the new Astronomy Data & Computing Services (ADACS) initiative, which launched earlier this year. Contract negotiations are complete with most of the new staff embedded at Swinburne and Curtain University. So far everything seems to be running pretty smoothly. ADACS has taken just over a year to get off the ground, from initial inception to launch and I think it's going to do a really great job of serving the community. I've been pushing for  something like ADACS (or something more ambitious) for the past few years, so I really pleased to see it finally have a presence. 

It's always really nice to hear where the All-Sky Virtual Observatory (ASVO) projects are at, especially since I'm not really involved in any of them. I've previously sat on ASVO–TAO selection panels and it was one of the projects I kept a close eye on when I worked at Swinburne Research but I was never directly involved in the projects.  The other projects are ASVO–SkyMapper, ASVO–AAT, and ASVO–MWA