For this course it is not possible to enrol via Campus Management. Please enrol via the form "Modul-, Lehrveranstaltungs- und Prüfungsanmeldung" when you decided to take the course. You can find the form on the website of the Studienbüro Geschkult. Please submit this document to the Studienbüro within the same time frame you would have to enrol in your courses via Campus Management. The workload of the course equals 150 hours: attendance 30 hours, preparation and wrap-up 60 hours, Hausarbeit (3500 words) 60 hours. For questions regarding credits you will have to approach your BA- or MA advisor of your study program." Please register also via email: wissensgeschichte@geschkult.fu-berlin.de close

In the age of digital archaeology, proper management of data and their manipulation (collecting, storing, analysing, sharing and archiving) is a key skill. Too often, we are not sure how we used data to find answers. What about the results you obtained a year ago or those that you used when you collaborated with other persons? The replication of scientific findings is a standard by which scientific claims are evaluated. In the fields of science that cannot be replicated, there is a need for minimum standards to assert the conformity between data and results. In the realm of digital data, one candidate for this minimum standard is “reproducible research”, which requires that data sets and computer code be made available to others for verifying published results and conducting alternative analyses. In this course you will learn the ideas of reproducible research. Topics covered include managing data and literate programming for data analyses. You will learn to write dynamic documents using R and related tools, to publish documents to the web and to collaborate with others to produce data analysis. We make use of a wide range Free Source software (R, Git, pandoc) and the course is taught using a combination of lectures, practical sessions and tutorials realised on your own laptop. ? Requirement : No programming experience is necessary, but it will be expected to work in small teams to complete programming assignments. Students must bring their own computer (laptop) to each class. close

Bartling, Sönke, and Sascha Friesike (ed). Opening Science: The Evolving Guide on How the Internet Is Changing Research, Collaboration and Scholarly Publishing (Springer 2014) https://fu-berlin.primo.exlibrisgroup.com/permalink/49KOBV_FUB/5ami3a/cdi_oapen_doabooks_15596 Bashir Saghir and Dirk Eddelbuettel, Getting Started in R: Tinyverse Edition https://eddelbuettel.github.io/gsir-te/Getting-Started-in-R.pdf Hartmann, K., Krois, J., Waske, B. (2018): E-Learning Project SOGA: Statistics and Geospatial Data Analysis. Department of Earth Sciences, Freie Universitaet Berlin. Marwick, Ben. “Using R and Related Tools for Reproducible Research in Archaeology”, in: Kitzes, Justin (ed). The Practice of Reproducible Research (University of California Press, 2017) https://fu-berlin.primo.exlibrisgroup.com/permalink/49KOBV_FUB/1v1tp5h/alma9959228787502883 Marwick, Ben. “Computational Reproducibility in Archaeological Research: Basic Principles and a Case Study of Their Implementation.” Journal of archaeological method and theory 24.2 (2017): 424–450. https://fu-berlin.primo.exlibrisgroup.com/permalink/49KOBV_FUB/5ami3a/cdi_webofscience_primary_000399841400005CitationCount David Carlson, Quantitative methods in archaeology using R (Cambridge 2017). https://fu-berlin.primo.exlibrisgroup.com/permalink/49KOBV_FUB/1v1tp5h/alma9960117217202883 close