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Irreproducibility in searches of scientific literature: A comparative analysis.

Gabor PozsgaiGábor L LöveiLiette VasseurGeoff M GurrPéter BatáryJanos KorponaiNick A LittlewoodJian LiuArnold MóraJohn J ObryckiOlivia ReynoldsJenni A StockanHeather VanVolkenburgJie ZhangWen-Wu ZhouMin-Sheng You
Published in: Ecology and evolution (2021)
Repeatability is the cornerstone of science, and it is particularly important for systematic reviews. However, little is known on how researchers' choice of database, and search platform influence the repeatability of systematic reviews. Here, we aim to unveil how the computer environment and the location where the search was initiated from influence hit results.We present a comparative analysis of time-synchronized searches at different institutional locations in the world and evaluate the consistency of hits obtained within each of the search terms using different search platforms.We revealed a large variation among search platforms and showed that PubMed and Scopus returned consistent results to identical search strings from different locations. Google Scholar and Web of Science's Core Collection varied substantially both in the number of returned hits and in the list of individual articles depending on the search location and computing environment. Inconsistency in Web of Science results has most likely emerged from the different licensing packages at different institutions.To maintain scientific integrity and consistency, especially in systematic reviews, action is needed from both the scientific community and scientific search platforms to increase search consistency. Researchers are encouraged to report the search location and the databases used for systematic reviews, and database providers should make search algorithms transparent and revise access rules to titles behind paywalls. Additional options for increasing the repeatability and transparency of systematic reviews are storing both search metadata and hit results in open repositories and using Application Programming Interfaces (APIs) to retrieve standardized, machine-readable search metadata.
Keyphrases
  • systematic review
  • public health
  • machine learning
  • healthcare
  • minimally invasive
  • mental health
  • artificial intelligence
  • neural network