Data for: Evaluating how restoration frameworks bridge social and ecological dimensions: A mixed methods literature review

Data for : Evaluating how restoration frameworks bridge social and ecological dimensions: A mixed methods literature review Table S4. Articles from literature search: Without-Social dataset Table S5. Articles from literature search: With-Social dataset Table S6. Screened articles: The screened dataset of 392 articles selected by coauthors that were most relevant to restoration frameworks (with the Social block). The selection was carried out in Rayyan, which allowed us to double-check each co-authors selection. The table also indicates the citationrank for each paper. Table S7. Keyword list: Long list of the 2,593 unique keywords from the 392 screened articles dataset. Count refers to the number of articles the keyword appears in. Table S8. Word database: The original categories of the SER's ecological and social recovery wheels (Gann et al. 2019), including the 18 attribute and 36 subattribute categories, and the words placed in each category. The 2184 words not placed in any category are in the last row. Table S9. Wordrank trends: This table contains the counts in how many papers in the Social block dataset with social block mention the word at least once, in the earlier (2010-2014) and later (2020-2024) five-year periods (Figure 4 in main manuscript). RankFirstfive and RankLastfive is the rank of that word from most to least frequent, and Ranktrend is the change in rank between the two time periods. Table S10. In-depth analysis of 17 selected restoration frameworks out of the 50 top cited publications (citation by year rank)研究数据：《修复框架如何弥合社会与生态维度间的鸿沟：一项混合方法文献综述》 表S4 文献检索所得文章：无社会维度数据集 表S5 文献检索所得文章：含社会维度数据集 表S6 经筛选的文章：由合作者筛选出的392篇与修复框架（含社会模块）相关性最高的文章构成的筛选后数据集。本次筛选通过Rayyan平台完成，可对每位合作者的筛选结果进行复核。本表同时列出每篇论文的引用排名。 表S7 关键词列表：来自392篇经筛选文章数据集的2593个唯一关键词总清单。其中“出现频次”指该关键词在多少篇文章中出现过。 表S8 词汇数据库：社会-生态恢复轮的原始分类体系（Gann等人，2019），包含18个一级属性类别与36个二级子属性类别，以及归入各类别的词汇。未归入任何类别的2184个词汇列于最后一行。 表S9 词频排名趋势：本表统计了社会模块数据集内，在早期（2010-2014年）与后期（2020-2024年）两个五年时段中，至少被提及一次的词汇的出现论文数量（对应正文图4）。其中RankFirstfive与RankLastfive分别代表该词汇在早期、后期的词频从高到低排名，Ranktrend则代表两个时段间的排名变化幅度。 表S10 对50篇高被引文献（按年度引用量排名）中筛选出的17个修复框架的深度分析