causalizeR: a text mining algorithm to identify causal relationships in scientific literature

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 dat...

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Veröffentlicht in:	PeerJ (San Francisco, CA) CA), 2021-07, Vol.9, p.e11850-e11850, Article e11850
Hauptverfasser:	Ancin-Murguzur, Francisco J, Hausner, Vera H
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Analysis Big data Bioinformatics Climate change Computational Biology Computational linguistics Computational Science Data mining Data Science Ecosystem Science Ecosystems Evidence synthesis Herbivores Hypotheses Language processing Literature review Matematikk og Naturvitenskap: 400 Mathematics and natural science: 400 Natural language interfaces Natural language processing Reindeer Scenarios Social network analysis Social networks Tundra Tundra ecology VDP Zoologiske og botaniske fag: 480 Zoology and botany: 480
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Beschreibung
Zusammenfassung:	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 (
ISSN:	2167-8359 2167-8359
DOI:	10.7717/peerj.11850