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Part 1: Document Description
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Citation |
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Title: |
Replication Data for: causalizeR: A text mining algorithm to identify causal relationships in scientific literature |
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Identification Number: |
doi:10.18710/PTQ8X7 |
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Distributor: |
DataverseNO |
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Date of Distribution: |
2021-06-16 |
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Version: |
1 |
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Bibliographic Citation: |
Ancin-Murguzur, Francisco Javier; Hausner, Vera Helene, 2021, "Replication Data for: causalizeR: A text mining algorithm to identify causal relationships in scientific literature", https://doi.org/10.18710/PTQ8X7, DataverseNO, V1 |
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Citation |
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Title: |
Replication Data for: causalizeR: A text mining algorithm to identify causal relationships in scientific literature |
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Identification Number: |
doi:10.18710/PTQ8X7 |
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Authoring Entity: |
Ancin-Murguzur, Francisco Javier (UiT The Arctic University of Norway) |
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Hausner, Vera Helene (UiT The Arctic University of Norway) |
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Producer: |
UiT The Arctic University of Norway |
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Date of Production: |
2021-05-18 |
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Software used in Production: |
R |
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Grant Number: |
369903 |
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Grant Number: |
296987 |
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Distributor: |
DataverseNO |
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Distributor: |
UiT The Arctic University of Norway |
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Access Authority: |
Ancin-Murguzur, Francisco Javier |
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Depositor: |
Ancin Murguzur, Francisco Javier |
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Date of Deposit: |
2021-04-30 |
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Holdings Information: |
https://doi.org/10.18710/PTQ8X7 |
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Study Scope |
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Keywords: |
Computer and Information Science, Earth and Environmental Sciences, text mining, causal link, algorithm, literature review |
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Abstract: |
Complex interactions among multiple abiotic and biotic drivers result in rapid changes in ecosystems worldwide. Predicting how specific interactions can cause ripple effects potentially resulting in abrupt shifts in ecosystems is of high relevance to policymakers, but difficult to quantify using data from singular cases. We present causalizeR (https://github.com/fjmurguzur/causalizeR), a text-processing algorithm that extracts causal relations from literature based on simple grammatical rules that can be used to synthesize evidence in unstructured texts in a structured manner. The algorithm extracts causal links using the relative position of nouns relative to the keyword of choice to extract the cause and effects of interest. The resulting database can be combined with network analysis tools to estimate the direct and indirect effects of multiple drivers at the network level, which is useful for synthesizing available knowledge and for hypothesis creation and testing. We illustrate the use of the algorithm by detecting causal relationships in scientific literature relating to the tundra ecosystem. |
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Date of Collection: |
2020-12-01-2020-12-01 |
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Geographic Coverage: |
Tundra ecosystem |
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Kind of Data: |
Algorithm in R language to perform bibliographic analyses |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Studies |
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https://github.com/fjmurguzur/causalizeR |
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https://doi.org/10.5281/zenodo.4817639 |
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Related Publications |
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Citation |
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Title: |
causalizeR: A text mining algorithm to identify causal relationships in scientific literature - Submitted for review |
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Bibliographic Citation: |
causalizeR: A text mining algorithm to identify causal relationships in scientific literature - Submitted for review |
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Label: |
00_README.txt |
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Notes: |
text/plain |
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Label: |
Script.R |
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Text: |
Script to process the bibliographic data with the causalizeR package |
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Notes: |
type/x-r-syntax |
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Label: |
causalizeR.zip |
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Text: |
Snapshot of the causalizeR package at the moment of publication |
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Notes: |
application/zip |
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Label: |
Data.zip |
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Text: |
bibliographic files in .bib format as downloaded from Scopus |
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Notes: |
application/zip |