Literature review matrix as a tool for collaboration and reproducible research synthesis

The application of literature review matrices as a collaborative and reproducible research synthesis tool holds significant promise across various disciplines. These matrices play a crucial role in the organization and synthesis of information from diverse sources, allowing researchers to discern patterns, identify gaps, and observe trends in existing literature (Kemmerer et al., 2021). In addition to enhancing the systematic approach to evidence synthesis, literature review matrices contribute to the transparency and reproducibility of research endeavors (Ghezzi-Kopel et al., 2021; Spillias et al., 2023).

Moreover, these matrices foster collaboration among researchers by facilitating joint analysis and interpretation of data, resulting in more comprehensive and reliable research outcomes (Murniarti et al., 2018). They prove instrumental in defining and summarizing effective confounding control strategies in systematic reviews of observational studies, thereby elevating the validity of the drawn conclusions. Furthermore, the incorporation of literature review matrices into the systematic review process aids in planning, identifying, evaluating, and synthesizing evidence, thereby bolstering the rigor, transparency, and replicability of the overall review process (Murniarti et al., 2018; Sjöö & Hellström, 2019).

In our paper, we propose a comprehensive workflow for creating literature matrices, utilizing a combination of conventional and artificial intelligence (AI) tools. This workflow encompasses various search services, a reference manager, a word processor, and illustrative AI tools to streamline the process of extracting and comparing information from research papers. By providing a structured framework for searching, organizing and analyzing the literature, a literature review matrix can facilitate the efficient screening and selection of relevant papers, as well as the extraction and synthesis of data.

References:

Ghezzi-Kopel, K., Ault, J., Chimwaza, G., Diekmann, F., Eldermire, E., Gathoni, N., … & Porciello, J. (2021). Making the case for librarian expertise to support evidence synthesis for the sustainable development goals. Research Synthesis Methods, 13(1), 77-87. https://doi.org/10.1002/jrsm.1528

Kemmerer, A., Vladescu, J., Carrow, J., Sidener, T., & Deshais, M. (2021). A systematic review of the matrix training literature. Behavioral Interventions, 36(2), 473-495. https://doi.org/10.1002/bin.1780

Murniarti, E., Nainggolan, B., Panjaitan, H., Pandiangan, L. E. A., Widyani, I. D. A., & Dakhi, S. (2018). Writing Matrix and Assessing Literature Review: A Methodological Element of a Scientific Project. Journal of Asian Development, 4(2), 133. https://doi.org/10.5296/jad.v4i2.13895

Sjöö, K., & Hellström, T. (2019). University–industry collaboration: A literature review and synthesis. Industry and Higher Education, 33(4), 275–285. https://doi.org/10.1177/0950422219829697

Spillias, S., Tuohy, P., Andreotta, M., Annand-Jones, R., Boschetti, F., Cvitanovic, C., Duggan, J., Fulton, E., Karcher, D., Paris, C., Shellock, R., & Trebilco, R. (2023). Human-AI Collaboration to Identify Literature for Evidence Synthesis [Preprint]. In Review. https://doi.org/10.21203/rs.3.rs-3099291/v1