A Core Facility is a centralized institutional resource and includes a team of computational biologists available to work on a variety of projects with lab scientists. The descriptions here are drawn from experiences at an independent research institute (Whitehead Institute for Biomedical Research) and in a university setting (University of Cape Town). The funding model of the Core can be quite different depending on where it is located. It could be anything from funded by an institution in whole or part, partially or fully grant supported, fee for service or any combination of the above. Our focus here is how collaborations work in these facilities and how authorship is addressed.
Core facilities engage with the lab scientists at multiple stages of their research - preparation for a presentation, for grant proposals, and, to analyze data and co-author manuscripts. The group leader of the Core would likely promote the group's accomplishments with faculty and lab members and encourage them to include Core members as co-authors if they've made a significant contribution to their research.
Lab scientists often opt for a collaboration agreement, which means the analysis/support is free, but contributors get authorship on publications arising from the research. It is important for scientists to understand that the contributions from Core members are not just technical but also scientific. Few workflows are “one size fits all”, and algorithms are constantly evolving. Therefore, Core members must keep up-to-date with the latest methods and usually need to customize pipelines. Determining the most appropriate tool to use, including parameters, reporting on and presenting the results in a usable format, are key roles they play; they may also sometimes provide guidance on data interpretation. These all require intellectual input, making the Core member role more than just about technical assistance.
Core members should have a good overview of different bioinformatics techniques, specialist knowledge in one or more specific data-analysis workflows, some coding and statistical expertise, and good interpersonal skills. Ideally, they should want to help and be willing to work on other people’s projects rather than forge academic careers of their own. They still derive rewards from and recognition for their contributions, and increase their skills and experience in the process.