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 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.全球范围内，多种非生物与生物驱动因子间的复杂相互作用正引发生态系统的快速变迁。预测特定相互作用如何催生连锁效应并可能导致生态系统猝变，对政策制定者而言具有重要现实意义，但仅依靠单一案例数据难以对此类效应进行量化。我们推出causalizeR（https://github.com/fjmurguzur/causalizeR）——一款基于简单语法规则从文献中提取因果关系的文本处理算法，可将非结构化文本中的研究证据整合为结构化形式。该算法通过选定关键词，依据名词与该关键词的相对位置提取目标因果关系中的原因与结果。生成的数据库可与网络分析工具结合，用以评估网络层级下多驱动因子的直接与间接效应，这对于整合现有研究知识、开展假说构建与检验均极具价值。我们以苔原生态系统相关科学文献为例，演示了该算法的实际应用。