A Systematic Review of Diversity and Inclusion Scholarly Sources in Natural Resources (1919 – 2018)

This dataset is a compilation of sources derived from a systematic review of diversity and inclusion in natural resource professions. For the purpose of this dataset, natural resource professions include forestry, fisheries, wildlife, rangelands and natural resource management. For the purpose of comprehensively reviewing sources that focus on the shifting demographics in natural resources, topics such as affirmative action, equal opportunity, cultural diversity, and workplace diversity were included.
This dataset contains 267 sources that were collected from October 2018 through February 2019. Each source and citation is in English. The sources within this dataset range from 1919 to 2018. The types of sources in this dataset include peer reviewed journal articles, theses and dissertations, technical reports, annotated bibliographies, conference papers, editorials/opinion pieces, magazine articles, newspapers and newsletters. Sources were gathered from four disciplinary journals: Fisheries, Journal of Forestry, Rangelands, Wildlife Society Bulletin; and 3 bibliographic databases: Web of Science, Google Scholar, and Education Resources Information Center (ERIC). Data collection procedures were developed by Hannah Rempel and Jasmine Brown. Data collection procedures were informed by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Five literature search strategies were used during our systematic review: (1) bibliographic databases and journals (2) backwards handsearching by checking reference lists (3) google searches (4) forward handsearching (5) opportunistic finds that were not found systematically.
The sources in this dataset were catalogued using Zotero. This dataset’s csv files were exported from Zotero. These csv files cannot be imported into Zotero or Mendeley but can be imported into Endnote. Please note that each individual source pdf is not included in this dataset. The csv files can be opened using readme or Microsoft Excel or Google Docs. The citations within the csv files are in English.