Scalable Reading of Structured Data

Max Odsbjerg Pedersen; Josephine Møller Jensen; Victor Harbo Johnston; Alexander Ulrich Thygesen; Helle Strandgaard Jensen

doi:10.46430/phen0103

Scalable Reading of Structured Data

Max Odsbjerg Pedersen, Josephine Møller Jensen, Victor Harbo Johnston, Alexander Ulrich Thygesen, Helle Strandgaard Jensen

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

Abstract

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.

Original language	English
Journal	Programming Historian
Volume	11
ISSN	2397-2068
DOIs	https://doi.org/10.46430/phen0103
Publication status	Published - Nov 2022

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SHAPES: Sesame Street at 50: The Shapes of Digital Memory
Jensen, H. S. (PI), Jensen, J. M. (Participant), Thygesen, A. U. (Participant), Pedersen, M. O. (Participant) & Johnston, V. H. (Participant)
01/07/2020 → 17/07/2023
Project: Research

Cite this

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title = "Scalable Reading of Structured Data",

abstract = "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.",

author = "Pedersen, \{Max Odsbjerg\} and Jensen, \{Josephine M{\o}ller\} and Johnston, \{Victor Harbo\} and Thygesen, \{Alexander Ulrich\} and Jensen, \{Helle Strandgaard\}",

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AU - Pedersen, Max Odsbjerg

AU - Jensen, Josephine Møller

AU - Johnston, Victor Harbo

AU - Thygesen, Alexander Ulrich

AU - Jensen, Helle Strandgaard

PY - 2022/11

Y1 - 2022/11

N2 - 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.

AB - 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.

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DO - 10.46430/phen0103

M3 - Journal article

SN - 2397-2068

VL - 11

JO - Programming Historian

JF - Programming Historian

ER -

Scalable Reading of Structured Data

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SHAPES: Sesame Street at 50: The Shapes of Digital Memory

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