Dr Marc Galland is a plant genomics researcher at the University of Amsterdam (UvA). He is the leader of the Amsterdam Science Park Study Group. “We are a community of researchers who perform computational data analysis as part of their research. The group promotes skill sharing and collaboration.”
“Mateusz Kuzak, Carlos Martinez, and me started this study group about one year ago,” says Galland. At the time, Kuzak and Martinez worked at the Netherlands eScience Center and Galland was a postdoc at the UvA, specialised in plant bioinformatics and data science. “We started the group because we felt the need to exchange experiences in computational data analysis. Also, while we organised programming trainings regularly, there was no continuous local support for experimental biologists at Science Park (UvA). So, we decided to launch a Mozilla Study Group. These are informal meetups of people from two research institutes (SILS and IBED), both located on the Science Park campus of the UvA. During these meetups, we share skills, stories, and ideas on using code for research, and explore open research practices. The goal is to create a friendly, no-pressure environment where people can share their work, ask for help on a coding problem, and learn and work together with their peers.”
Workshops
A year later, the Study Group’s administration consists of seven individuals from two UvA institutes: the Swammerdam Institute for Life Sciences (SILS) and the Institute for Biodiversity and Ecosystem Dynamics (IBED). “We are a mix of three PhD students, two postdocs, one data scientist, and one assistant professor, all with an interest in data-related training and learning from each other. We meet every week to exchange experiences and we organise periodic events such as one-hour interactive workshops, co-working sessions for peer-to-peer assistance, and Hacky Hour sessions. For instance, we recently organised a workshop on Comparative Genomics using the Circos software to compare genomes. This was a one-hour demo session where a skillful postdoc (Like Fokkens) showed how to visualise genomic data using circular representations.”
Another workshop addressed data stewardship and research data management. In the Hacky hours co-working sessions, researchers can walk in with a laptop and pose data-related questions. “We usually gather at the Oerknal Campus Café because it offers an informal atmosphere, a good Internet connection, and large tables where ten people easily fit. For now, those who regularly come are researchers that attend regular Study Group lessons. I am actively trying to invite and attract beginners. I have the impression that they feel not good enough to join these sessions or that they do not know exactly what a ‘Hacky Hour’ is. I am now trying to convince beginners to describe their issue on the Study Group Github page so I can pair-up experts and beginners during Hacky Hours. Finally, our group also acts as a local hub to set-up Software and Data Carpentry workshops, teaching wet-lab researchers computing skills,” explains Galland.
Open
“People are welcome to join our study group, regardless of their scientific research area, affiliation, or training level. The events are open to anyone and we advertise them broadly, for instance on our website, via the mailing lists of SILS and IBED, and on posters. I joined the recent FAIR data awareness event organised by ZonMw and DTL. It was very useful to hear about other people’s good practices and get feedback on ours. I am hoping that we will have more support in the next semester, so that we can dedicate more ‘helpdesk’ time for fellow researchers in need of support for data management and analysis. In addition, having more time will also prove useful to set-up regular Software and Data Carpentry workshops on broad (basic skills in programming) or specific topics (genomics). Eventually, we can truly reach out to people at other universities and institutes.”
More information
- Website of the Amsterdam Science Park study group
- More about Mozilla Study Groups
- More about Software and Data Carpentry