Ten Simple Rules for Writing a Literature Review

By Marco Pautasso. PLoS Comput. Biol., 2013

329 citations recorded by the source corpus.

Literature reviews are in great demand in most scientific fields. Their need stems from the ever-increasing output of scientific publications [1]. For example, compared to 1991, in 2008 three, eight, and forty times more papers were indexed in Web of Science on malaria, obesity, and biodiversity, respectively [2]. Given such mountains of papers, scientists cannot be expected to examine in detail every single new paper relevant to their interests [3]. Thus, it is both advantageous and necessary to rely on regular summaries of the recent literature. Although recognition for scientists mainly comes from primary research, timely literature reviews can lead to new synthetic insights and are often widely read [4]. For such summaries to be useful, however, they need to be compiled in a professional way [5]. When starting from scratch, reviewing the literature can require a titanic amount of work. That is why researchers who have spent their career working on a certain research issue are in a perfect position to review that literature. Some graduate schools are now offering courses in reviewing the literature, given that most research students start their project by producing an overview of what has already been done on their research issue [6]. However, it is likely that most scientists have not thought in detail about how to approach and carry out a literature review. Reviewing the literature requires the ability to juggle multiple tasks, from finding and evaluating relevant material to synthesising information from various sources, from critical thinking to paraphrasing, evaluating, and citation skills [7]. In this contribution, I share ten simple rules I learned working on about 25 literature reviews as a PhD and postdoctoral student. Ideas and insights also come from discussions with coauthors and colleagues, as well as feedback from reviewers and editors.

Subfields: Information Systems and Management, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9f0a9545dcbcc4fca72eda0f5d1b9234ae9f80ad

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Citations in the corpus

SANRA—a scale for the quality assessment of narrative review articles (2019, background): “[13], for example, have provided guidance for writing narrative reviews, and Byrne [14] as well as Pautasso [15] has written, from different angles, thoughtful editorials on improving narrative reviews and presented lists of key features of writing a good review—lists that naturally overlap with SAN”
Which Type of Citation Analysis Generates the Most Accurate Taxonomy of Scientific and Technical Knowledge? (2015, background): “This is roughly the rate of summary articles anticipated by Price (1965). Second, almost half (48.”
Measurement of Trust in Automation: A Narrative Review and Reference Guide (2021, methodology): “This work follows the integrative narrative review methodological approach defined by Pautasso (2013) and outlined by Torraco (2005), wherein the trust measurement methods in question are inventoried and their value qualitatively – rather than quantitatively – compared.”
Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review (2017, methodology): “The whole process of the SLR is divided into three fundamental steps: (I) defining research questions, (II) conducting the search process for relevant publications, and (III) extracting necessary data from identified publications to answer the research questions [75, 109].”
PyZoBot: A Platform for Conversational Information Extraction and Synthesis from Curated Zotero Reference Libraries through Advanced Retrieval-Augmented Generation (2024, unknown): “Moreover, Python's flexibility, visualization capabilities, and extensive library support make it a preferred choice for this project.”