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| Semester | Frühjahrsemester 2023 |
| Angebotsmuster | Jedes Frühjahrsem. |
| Dozierende |
Aurelio Di Pasquale (aurelio.dipasquale@unibas.ch)
Manuel Hetzel (manuel.hetzel@unibas.ch, BeurteilerIn) Malin Michelle Ziehmer-Wenz (malin.ziehmer@unibas.ch) |
| Inhalt | Research data management concerns the organisation of data, from the time of data generation through the entire research cycle leading to archiving of data and dissemination of findings. Research data management can be a complex issue, but done correctly from the start, it will save a lot of time and hassle at the time when the data has to be analysed for a publication or thesis. In this course we will discuss and illustrate with practical examples ways to create, organise, share, and look after your research data. Our focus will be on quantitative research data and epidemiological field studies - though the principles apply to other types of data as well. We will contextualise the topics with requirements of funding bodies regarding the driving principle of data as a public good and ethical issues such as data protection and confidentiality. Summary of the course content: - Data flow and audit trail - Data collection, cleaning, storing - Electronic data capture incl. form design in ODK - Database formats, relational databases - Version control, GitHub - Data Management Plan - Data protection and integrity - Data storage, repositories, publishing, Open Science - Data visualization |
| Lernziele | Accurate, reliable and accessible research data forms the basis of our scientific work. In this course, students will learn the essentials of managing research data starting from data collection (with a focus on electronic data capture) to data cleaning, analysis, publishing and archiving. |
| Literatur | Useful literature: - S. Juul: Take good care of your data. Aarhus University, 2011. (available online: https://ph.au.dk/fileadmin/ph/Uddannelse/Software/TakeCareJan2011.pdf) - S. Juul & M. Frydenberg: An Introduction to Stata for Health Researchers. (Chapter 18) - C.O. Wilke: Fundamentals of Data Visualization. O'Reilly 2019. (available online: https://serialmentor.com/dataviz) |
| Bemerkungen | Additional lecturers: Moniek Bresser Marek Kwiatkowski Alexandra Kulinkina |
| Teilnahmebedingungen | 1) Students need their own laptop with Stata software installed 2) Basic working knowledge of Stata (e.g. from course 'Biostatistics') is a requirement. As an alternative, students who are well versed in R can use this software - though less support will be provided in this course. |
| Unterrichtssprache | Englisch |
| Einsatz digitaler Medien | kein spezifischer Einsatz |
| Intervall | Wochentag | Zeit | Raum |
|---|---|---|---|
| Block | Siehe Einzeltermine | ||
| Datum | Zeit | Raum |
|---|---|---|
| Donnerstag 20.04.2023 | 09.15-17.00 Uhr | Swiss TPH Neubau, Seminarraum 4 |
| Freitag 21.04.2023 | 09.15-13.00 Uhr | Swiss TPH Neubau, Seminarraum 4 |
| Donnerstag 27.04.2023 | 09.15-17.00 Uhr | Swiss TPH Neubau, Seminarraum 5 |
| Freitag 05.05.2023 | 09.15-17.00 Uhr | Swiss TPH Neubau, Seminarraum 5 |
| Freitag 12.05.2023 | 09.15-17.00 Uhr | Swiss TPH Neubau, Seminarraum 5 |
| Module |
Doktorat Epidemiologie: Empfehlungen (Promotionsfach: Epidemiologie) Lehrveranstaltungen Masterstudium Infektionsbiologie (Masterstudium: Infektionsbiologie) Modul: Biostatistics and Computing (Masterstudium: Epidemiologie) |
| Leistungsüberprüfung | Lehrveranst.-begleitend |
| Hinweise zur Leistungsüberprüfung | Satisfactory completion of assignments. Students have to complete and submit all assignments satisfactorily and in time to be awarded the credit points. |
| An-/Abmeldung zur Leistungsüberprüfung | Anm.: Belegen Lehrveranstaltung; Abm.: stornieren |
| Wiederholungsprüfung | keine Wiederholungsprüfung |
| Skala | Pass / Fail |
| Wiederholtes Belegen | beliebig wiederholbar |
| Zuständige Fakultät | Philosophisch-Naturwissenschaftliche Fakultät, studiendekanat-philnat@unibas.ch |
| Anbietende Organisationseinheit | Schweizerisches Tropen- und Public Health-Institut |