| Date | Topic | |
|---|---|---|
| Getting started | ||
| 1 | 2024-10-18 | Data and research data management |
| 2 | 2024-10-25 | Git and GitHub |
| Programming in R | ||
| 3 | 2024-11-08 | Introduction to R |
| 4 | 2024-11-15 | Best practices in programming |
| Data Science in R | ||
| 5 | 2024-11-22 | Data Science Workflow |
| 6 | 2024-11-29 | Count data and seriation |
| 7 | 2024-12-06 | Compositional data |
| Databases | ||
| 8 | 2024-12-13 | Databases (I) |
| 9 | 2024-12-20 | Databases (II) |
| GIS | ||
| 10 | 2025-01-10 | GIS (I) |
| 11 | 2025-01-17 | GIS (II) |
Data Management and Digital Archaeology
Course overview
040468 Datenmanagement und digitale Archäologie (ÜB, unterrichtet in Englisch) Übungen (3 CP)
Time slot / Zeitfenster: Fr 14-16 Uhr c.t.
Place / Ort: Raum 2
Course instructors / Kursleiter:
Andreas Angourakis
Tim Klingenberg
Support / Unterstützung: Thomas Rose
Course summary/Kurszusammenfassung:
The “Data Management and Digital Archaeology” MA course offers a comprehensive overview of data management and digital tools essential for archaeological research under the open science paradigm. It begins with foundational sessions on data management and the use of Git and GitHub for version control, helping students organize, maintain, and share their research data and analyses.
The course then delves into programming with R, covering basic R programming skills and best practices, followed by advanced topics like count data analysis, seriation techniques, and compositional data analysis tailored for archaeology. We then explore essential concepts in database management, offering two sessions to provide introductory and more advanced knowledge necessary for handling archaeological databases. The course concludes with a brief, but practical introduction to Geographic Information Systems (GIS), introducing students to GIS software and techniques used to analyse spatial data in archaeological research.
By the end, students will be equipped with enough digital skills to effectively manage, analyse, and interpret archaeological data under an open science framework while also being prepared to continue their own learning journey in this ever-evolving field.
Course schedule/Kursplan:
Evaluation / Kursbewertung
(Attendance and final examination)
Acknowledgements
The conception of the course structure, as well as the short summaries, exercises, and images shown in each chapter, greatly benefited from Large Language Models used as companion writer and programmer. As such, we own greatly to the current richness of reference information freely available on Internet.
The models and services used are:
- ChatGPT (GPT-4o) by OpenAI for brainstorming, text and code writing suggestions, collection and articulation of references.
- WebChatGPT, a free browser extension that enhances ChatGPT by providing Internet access directly within the chat interface, used to aid Internet search.
- Leonardo.ai (user tokens) for generating purely aesthetic visual assets.