This article introduces a flexible and generic methodology called cascading citation expansion to improve the quality of constructing a bibliographic dataset for systematic reviews. The methodology simplifies the conceptualization of globalism and localism in science mapping and unifies them on a consistent and continuous spectrum. The authors demonstrate the application of this methodology to the research of literature-based discovery (LBD) and compare five datasets constructed based on three use scenarios: a conventional keyword-based search, an expansion process starting with a groundbreaking article, and an expansion process starting with a recently published review article by a prominent expert in the domain. The unique coverage of each dataset is inspected through network visualization overlays with reference to other datasets in a broad and integrated context. Systematic reviews play a critical role in scholarly communication by synthesizing findings from original research, assessing the degree of consensus or lack thereof, and identifying challenges and future directions. The use of science mapping tools has increased, allowing researchers to visualize the structure and dynamics of a research field. However, a common bottleneck in the current practice is the construction of a collection of scholarly publications as the input for scientometric analysis and visualization. The cascading citation expansion methodology addresses this by automatically expanding the initial dataset by retrieving additional records of relevant publications, moving forward into the future and backward into the past with reference to a given publication. This flexibility allows for a smooth transition between a local map approach and a global map approach, enhancing the capability of science mapping tools in the hand of end-users. The authors compare five datasets that focus on the research of literature-based discovery (LBD). LBD aims to foster new discoveries based on existing studies published in the literature. The methodology is applied to a systematic review of the landscape of LBD research, demonstrating how five datasets obtained from different strategies differ in terms of their coverage. Major clusters of the field and unique thematic patterns identified by individual datasets are visualized in the context of all datasets combined. The results show that the cascading citation expansion methodology provides a more comprehensive and accurate representation of the research field compared to conventional search strategies. The methodology also allows for the identification of areas that are uniquely covered by a particular dataset, improving the overall quality of systematic reviews.This article introduces a flexible and generic methodology called cascading citation expansion to improve the quality of constructing a bibliographic dataset for systematic reviews. The methodology simplifies the conceptualization of globalism and localism in science mapping and unifies them on a consistent and continuous spectrum. The authors demonstrate the application of this methodology to the research of literature-based discovery (LBD) and compare five datasets constructed based on three use scenarios: a conventional keyword-based search, an expansion process starting with a groundbreaking article, and an expansion process starting with a recently published review article by a prominent expert in the domain. The unique coverage of each dataset is inspected through network visualization overlays with reference to other datasets in a broad and integrated context. Systematic reviews play a critical role in scholarly communication by synthesizing findings from original research, assessing the degree of consensus or lack thereof, and identifying challenges and future directions. The use of science mapping tools has increased, allowing researchers to visualize the structure and dynamics of a research field. However, a common bottleneck in the current practice is the construction of a collection of scholarly publications as the input for scientometric analysis and visualization. The cascading citation expansion methodology addresses this by automatically expanding the initial dataset by retrieving additional records of relevant publications, moving forward into the future and backward into the past with reference to a given publication. This flexibility allows for a smooth transition between a local map approach and a global map approach, enhancing the capability of science mapping tools in the hand of end-users. The authors compare five datasets that focus on the research of literature-based discovery (LBD). LBD aims to foster new discoveries based on existing studies published in the literature. The methodology is applied to a systematic review of the landscape of LBD research, demonstrating how five datasets obtained from different strategies differ in terms of their coverage. Major clusters of the field and unique thematic patterns identified by individual datasets are visualized in the context of all datasets combined. The results show that the cascading citation expansion methodology provides a more comprehensive and accurate representation of the research field compared to conventional search strategies. The methodology also allows for the identification of areas that are uniquely covered by a particular dataset, improving the overall quality of systematic reviews.