180096 KU Big Data in Science (2022S)
Continuous assessment of course work
Labels
REMOTE
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Fr 11.02.2022 09:00 to Fr 18.02.2022 10:00
- Registration is open from Tu 22.02.2022 09:00 to Mo 28.02.2022 10:00
- Deregistration possible until Su 20.03.2022 23:59
Details
max. 25 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
All course units will be held via Zoom.
Thursday
10.03.
18:30 - 20:00
Digital
Thursday
17.03.
18:30 - 20:00
Digital
Thursday
24.03.
18:30 - 20:00
Digital
Thursday
31.03.
18:30 - 20:00
Digital
Thursday
07.04.
18:30 - 20:00
Digital
Thursday
28.04.
18:30 - 20:00
Digital
Thursday
05.05.
18:30 - 20:00
Digital
Thursday
12.05.
18:30 - 20:00
Digital
Thursday
19.05.
18:30 - 20:00
Digital
Thursday
02.06.
18:30 - 20:00
Digital
Thursday
09.06.
18:30 - 20:00
Digital
Thursday
23.06.
18:30 - 20:00
Digital
Thursday
30.06.
18:30 - 20:00
Digital
Information
Aims, contents and method of the course
Big Data has become ubiquitous in science, society, and politics, and has already changed the organization of science. Before the appearance of Big Data, the data has been produced in a more target directed way, e.g. in experimental settings. Such regimented data production appears to have changed with data producing technologies, such as the omic-technologies in the life sciences, which has led to the appearance of novel research areas, such as genomics, epigenomics, and proteomics. The huge amounts of data produced in those fields, become stored in databases, and shared among the community of researchers. Such data can ‘travel’ into different research contexts, becoming employed to study of diverse research questions. In recent years some philosophers of science have asked whether science is experiencing profound changes as a result of becoming data centric.This course addresses the notion of Big Data, the importance on algorithms and visualization for data-centric practices, and the impact of Big Data on scientific knowledge.
Assessment and permitted materials
- careful reading and active discussion of the literature
- questions in the Moodle platform
- active discussion in the Moodle platform
- (co-)chairing group discussion in the class
- a group project
- questions in the Moodle platform
- active discussion in the Moodle platform
- (co-)chairing group discussion in the class
- a group project
Minimum requirements and assessment criteria
Minimum requirements and assessment criteria:- active participation (30%),
- questions and online discussion of readings (30%),
- (co-)chairing a class (10%),
- a group project (30%).All aforementioned components of the course have to be fulfilled for the successful completion of the grade.One unexcused absence is permitted.Grading table
1 – (excellent) 90 – 100 points
2 – (good) 81 – 89 points
3 – (satisfactory) 71 – 80 points
4 – (sufficient) 61 – 70 points
5 – (insufficient) 0 – 60 points
- questions and online discussion of readings (30%),
- (co-)chairing a class (10%),
- a group project (30%).All aforementioned components of the course have to be fulfilled for the successful completion of the grade.One unexcused absence is permitted.Grading table
1 – (excellent) 90 – 100 points
2 – (good) 81 – 89 points
3 – (satisfactory) 71 – 80 points
4 – (sufficient) 61 – 70 points
5 – (insufficient) 0 – 60 points
Examination topics
The course does not have a final essay or an examination (see minimum requirements)
Reading list
Bechtel, William. 2020. “Data Journeys beyond Databases in Systems Biology: Cytoscape and NDEx.” In Data Journeys in the Sciences, 121–43. Springer, Cham.Kitchin, Rob. 2021. The Data Revolution: A Critical Analysis of Big Data, Open Data and Data Infrastructures. Second Edition. Thousand Oaks: SAGE Publications Ltd. (selected parts)Klingenstein, Sara, Tim Hitchcock, and Simon DeDeo. 2014. “The Civilizing Process in London’s Old Bailey.” Proceedings of the National Academy of Sciences 111 (26): 9419–24. https://doi.org/10.1073/pnas.1405984111.Leonelli, Sabina. 2016. Data-Centric Biology: A Philosophical Study. University of Chicago Press. (selected parts)———. 2021. “Data Science in Times of Pan(Dem)Ic.” Harvard Data Science Review, January. https://doi.org/10.1162/99608f92.fbb1bdd6.Lin, Chujun, and Mark Allen Thornton. 2021. “Fooled by Beautiful Data: Visualization Aesthetics Bias Trust in Science, News, and Social Media.” PsyArXiv. https://doi.org/10.31234/osf.io/dnr9s.Shen-Orr, Shai S., Ron Milo, Shmoolik Mangan, and Uri Alon. 2002. “Network Motifs in the Transcriptional Regulation Network of Escherichia Coli.” Nature Genetics 31 (1): 64–68. https://doi.org/10.1038/ng881.Stark, David C., and Noortje Marres. 2020. “Put to the Test: For a New Sociology of Testing” 71 (3): 423–43. https://doi.org/10.7916/d8-kkcr-7s54.Waller, Isaac, and Ashton Anderson. 2021. “Quantifying Social Organization and Political Polarization in Online Platforms.” Nature 600 (7888): 264–68. https://doi.org/10.1038/s41586-021-04167-x.Wills, Melissa. 2017. “Are Clusters Races? A Discussion of the Rhetorical Appropriation of Rosenberg Et Al.’S ‘Genetic Structure of Human Populations.’” Philosophy, Theory, and Practice in Biology 9 (12). http://dx.doi.org/10.3998/ptb.6959004.0009.012.
Association in the course directory
Last modified: Th 11.05.2023 11:27