causalizeR: a text mining algorithm to identify causal relationships in scientific literature
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10441578" target="_blank" >RIV/00216208:11320/21:10441578 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=UN9zCwNeLG" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=UN9zCwNeLG</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.7717/peerj.11850" target="_blank" >10.7717/peerj.11850</a>
Alternative languages
Result language
angličtina
Original language name
causalizeR: a text mining algorithm to identify causal relationships in scientific literature
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
PeerJ
ISSN
2167-8359
e-ISSN
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Volume of the periodical
9
Issue of the periodical within the volume
20.07.2021
Country of publishing house
US - UNITED STATES
Number of pages
10
Pages from-to
e11850
UT code for WoS article
000675410100010
EID of the result in the Scopus database
2-s2.0-85110722699