-
Uddannelse
Find uddannelse
Studievejledning
Kvalitet
- Forskning
-
Samarbejde
Virksomheder
Kommuner og regioner
Gymnasier
Myndigheder
-
Om AU
Organisation
Kontakt
Om AU
- Organisation
- Ledelse
- Strategi
- AU i tal
- AU's historie
- Internationalt samarbejde
- Bæredygtighed
- Campus 2.0
- Diversitet og ligestilling
- Ledige stillinger
- Presse
- Kontakt
Scalable Reading of Structured Data
Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review
DOI
- https://doi.org/10.46430/phen0103
Forlagets udgivne version
- Max Odsbjerg Pedersen
- Josephine Møller Jensen ,
- Victor Harbo Johnston ,
- Alexander Ulrich Thygesen
- Helle Strandgaard Jensen
In this lesson, we introduce a workflow for scalable reading of structured data, combining close interpretation of individual data points and statistical analysis of the entire dataset. The lesson is structured in two parallel tracks:
A general track, suggesting a way to work analytically with structured data where distant reading of a large dataset is used as context for a close reading of distinctive datapoints.
An example track, in which we use simple functions in the programming language R to analyze Twitter data. Combining these two tracks, we show how scalable reading can be used to analyze a wide variety of structured data. Our suggested scalable reading workflow includes two distant reading approaches that will help researchers to explore and analyze overall features in large data sets (chronologically and in relation to binary structures), plus a way of using distant reading to select individual data points for close reading in a systematic and reproducible manner.
A general track, suggesting a way to work analytically with structured data where distant reading of a large dataset is used as context for a close reading of distinctive datapoints.
An example track, in which we use simple functions in the programming language R to analyze Twitter data. Combining these two tracks, we show how scalable reading can be used to analyze a wide variety of structured data. Our suggested scalable reading workflow includes two distant reading approaches that will help researchers to explore and analyze overall features in large data sets (chronologically and in relation to binary structures), plus a way of using distant reading to select individual data points for close reading in a systematic and reproducible manner.
| Originalsprog | Engelsk |
|---|---|
| Tidsskrift | Programming Historian |
| Vol/bind | 11 |
| ISSN | 2397-2068 |
| DOI | |
| Status | Udgivet - nov. 2022 |
Se relationer på Aarhus Universitet Citationsformater
ID: 296632355