Replication Data for: 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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ftdatacite:10.18710/ptq8x7 2023-05-15T18:40:23+02:00 Replication Data for: causalizeR: A text mining algorithm to identify causal relationships in scientific literature Ancin-Murguzur, Francisco Javier Hausner, Vera Helene 2021 https://dx.doi.org/10.18710/ptq8x7 https://dataverse.no/citation?persistentId=doi:10.18710/PTQ8X7 unknown DataverseNO https://dx.doi.org/10.18710/ptq8x7/gcdugr https://dx.doi.org/10.18710/ptq8x7/yayofs https://dx.doi.org/10.18710/ptq8x7/edxm3k https://dx.doi.org/10.18710/ptq8x7/o34zju dataset Dataset 2021 ftdatacite https://doi.org/10.18710/ptq8x7 https://doi.org/10.18710/ptq8x7/gcdugr https://doi.org/10.18710/ptq8x7/yayofs https://doi.org/10.18710/ptq8x7/edxm3k https://doi.org/10.18710/ptq8x7/o34zju 2021-11-05T12:55:41Z 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. Dataset Tundra DataCite Metadata Store (German National Library of Science and Technology) |
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DataCite Metadata Store (German National Library of Science and Technology) |
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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. |
format |
Dataset |
author |
Ancin-Murguzur, Francisco Javier Hausner, Vera Helene |
spellingShingle |
Ancin-Murguzur, Francisco Javier Hausner, Vera Helene Replication Data for: causalizeR: A text mining algorithm to identify causal relationships in scientific literature |
author_facet |
Ancin-Murguzur, Francisco Javier Hausner, Vera Helene |
author_sort |
Ancin-Murguzur, Francisco Javier |
title |
Replication Data for: causalizeR: A text mining algorithm to identify causal relationships in scientific literature |
title_short |
Replication Data for: causalizeR: A text mining algorithm to identify causal relationships in scientific literature |
title_full |
Replication Data for: causalizeR: A text mining algorithm to identify causal relationships in scientific literature |
title_fullStr |
Replication Data for: causalizeR: A text mining algorithm to identify causal relationships in scientific literature |
title_full_unstemmed |
Replication Data for: causalizeR: A text mining algorithm to identify causal relationships in scientific literature |
title_sort |
replication data for: causalizer: a text mining algorithm to identify causal relationships in scientific literature |
publisher |
DataverseNO |
publishDate |
2021 |
url |
https://dx.doi.org/10.18710/ptq8x7 https://dataverse.no/citation?persistentId=doi:10.18710/PTQ8X7 |
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Tundra |
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Tundra |
op_relation |
https://dx.doi.org/10.18710/ptq8x7/gcdugr https://dx.doi.org/10.18710/ptq8x7/yayofs https://dx.doi.org/10.18710/ptq8x7/edxm3k https://dx.doi.org/10.18710/ptq8x7/o34zju |
op_doi |
https://doi.org/10.18710/ptq8x7 https://doi.org/10.18710/ptq8x7/gcdugr https://doi.org/10.18710/ptq8x7/yayofs https://doi.org/10.18710/ptq8x7/edxm3k https://doi.org/10.18710/ptq8x7/o34zju |
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1766229718804201472 |