DOI: 10.5061/dryad.9zw3r22q2
title: Supplemental data from: A conceptual classification scheme of invasion science

description: In the era of Big Data and global biodiversity decline, there is a
 pressing need to transform data and information into findable and
 actionable knowledge. We propose a conceptual classification scheme for
 invasion science that goes beyond hypothesis networks and allows to
 organize publications and datasets, guide research directions, and
 identify knowledge gaps. Combining expert knowledge with literature
 analysis, we identified five major research themes in this field: (1)
 introduction pathways, (2) invasion success and invasibility, (3) impacts
 of invasion, (4) managing biological invasions and (5) meta-invasion
 science. We divided these themes into ten broader research questions and
 linked them to 39 major hypotheses forming the theoretical foundation of
 invasion science. As artificial intelligence advances, such classification
 schemes will become important references for organizing scientific
 information. Our approach can be extended to other research fields,
 fostering cross-disciplinary connections to leverage the scientific
 knowledge needed to address Anthropocene challenges. The two datasets show
 the final classification scheme presented in this paper and the literature
 corpus used in this study. 
description: To develop a conceptual classification scheme encompassing all
 overarching themes and research questions in the field of invasion
 science, we combined a top-down with a bottom-up classification approach
 (please, see the manuscript for further information). We focused on 39
 major hypotheses forming the theoretical base of the discipline. This list
 is not exhaustive or final but serves as a starting point, comprising
 hypotheses selected through a prior consensus among experts.
 The literature corpus is from the Hi-Knowledge initiative https://hi-knowledge.org/ The included supplemental files contain all of the information necessary to support research findings.
description: # Supplemental data from: A conceptual classification scheme of invasion
 science
 [https://doi.org/10.5061/dryad.9zw3r22q2](https://doi.org/10.5061/dryad.9zw3r22q2) The included supplemental files contain all of the information necessary to support research findings. ## Description of the data and file structure Table S2 Identified connections between the 39 hypotheses and the 10 research questions. This table shows the final conceptual scheme and the four individual assessments. Table S3 The corpus used in this study.For more information: [https://hi-knowledge.org/](https://hi-knowledge.org/) ### Files and variables #### File: TableS2\_Musseau\_et\_al.\_BioScience.xlsx **Description:** Final conceptual scheme and individual assessments. ##### Variables * Hypothesis: list of 39 hypotheses, accronyms, full names and definitions * Five themes * Ten research questions * Krippendorff's alpha values: To assess uncertainty among experts' scores, we conducted an inter-rater reliability analysis using Krippendorff's α (*kripp.alpha* function, irr R package, Gamer et al. 2019). This index ranges from 0 (indicating no reliability) to 1 (indicating perfect reliability) and provides an overview of the main sources of debate among the experts for each hypothesis. * The first sheet is the final conceptual scheme, the last four sheets are the individual assessments #### File: TableS3\_Musseau\_et\_al.\_BioScience.xlsx **Description:** Literature corpus from Jeschke & Heger 2018 updated with the themes and research questions. Jeschke JM, Heger T. 2018. Invasion biology: hypotheses and evidence. CABI.