Ensuring long term provenance of research data is integral to best practices in research data management, and forms the basis of robust publications. Whether in the form of a thesis, journal article, conference presentation, teaching material or another form of dissemination, scientific publications generally contain visual data representations that present collected data, highlight trends, form conclusions, and suggest future research directions. Data visualisation is a vital part of scientific dissemination, and involves critical thinking on how to most effectively and truthfully present data, as well as conclusions in meaningful ways that are memorable, effective and responsible. In this course we will take “live data” and use data visualisation skills with the goal of building a broad and well-informed toolkit that aids researchers produce effectual figures for all types of scientific dissemination.
This seminar series is intended for those researchers who have datasets that need to be visualized and for those who have limited publishing experiences, e.g. MSc, PhD students, postdoctoral fellows, or other early-career researchers. The seminar series will consist of two days of lectures (October 17-18), followed by an interactive session on October 19 (Introduction to Python/Jupyter notebooks). Following each lecture day, participants will be assigned practical “homework” to be completed on their working datasets. On the final day (October 23), we will have an interactive presentation session where participants shall present and discuss their finished visualisations.
In order to participate in this course you must have a ready quantitative dataset, because the practical work requires working with live data. Participants will construct a visualization (e.g. manuscript figures, slide decks, conference poster) and presented it orally to the group on the final day.
BYOD: Bring your own data! It would be advisable to have a rough idea of what data will be presented and for what purpose. All participants must bring a computer to work on their own data.
RDM webinars: Suggested, not mandatory: Structure and Documenting, Data cleaning UiT Research Data Portal courses
Software: Access to and familiarity with one data processing and visualization software. In this course we will use Excel and Powerpoint, and we will also take first steps in Python/Jupyter notebooks.
Topics: Principles of Data visualization; FAIR data and Introduction to Research Data Management; Oral Presentation skills; Charts and Attributes; How Python and Jupyter notebooks can help us with Data Visualisation
Katie Smart (Academic librarian for physics and computer science at the University library, UiT)
Radovan Bast (Research Software Engineering group, UiT)
October 17 09:00 – 12:00 Universitetsbibliotekbygget Rom 244
October 18 09:00 – 12:00 Teorifagbygget hus 1 Rom 1.229
October 19 09:00 – 14:00 Universitetsbibliotekbygget Rom 344
October 23 12:00 – 16:00 Universitetsbibliotekbygget 132 Aud
October 17 13:00 – 15:00 Teorifagbygget hus 1 Rom 1.233
October 18 13:00 – 15:00 Teorifagbygget hus 1 Rom 1.221
October 19 14:00 – 16:00 Teorifagbygget hus 1 Rom 1.221