Using missing data patterns to detect incorrectly assigned articles in bibliographic datasets Simon Willemin simon.willemin@library.ethz.ch 0009-0003-5802-9113 ETH Library, ETH Zurich, Switzerland The DORA declaration and CoARA call for the use of bibliometric indicators based on open data. However, established scholarly metadata datasets are closed, and the quality of open datasets has not yet been thoroughly examined. In this paper, I present a method to detect errors in a dataset using missing data patterns. As an example, the method is applied to the affiliation metadata of publications associated with ETH Zurich. This allows me to identify a series of incorrectly affiliated papers. The method introduced in this paper is not specifically designed for affiliation data and can also be used to detect errors in other types of data. It could lead to corrections which will hopefully benefit providers as well as users of data. 1. Introduction The DORA declaration and CoARA call for the use of bibliometric indicators based on open data. Some institutions have recently taken up such calls by abandoning the use of proprietary bibliometric products (Sorbonne University, 2023), by unsubscribing from a commercial database (CNRS, 2024), or by introducing a ranking based on open data (Waltman et al., 2024). However, reservations against the use of open data still arise due to uncertainties regarding their quality compared to closed and more established datasets whose weaknesses and biases are better known. Recent projects, such as TOBI (Towards Open Bibliometric Indicators, https://eth-library.github.io/tobi/) in Switzerland and OPENBIB (Comparative Analysis and Curation of German Metadata in Open Bibliometric Data, https://www.dzhw.eu/en/forschung/projekt?pr_id=733) in Germany might allow for a smooth transition towards openness by evaluating the quality of open bibliometric databases or by establishing such a database. In the context of the project TOBI, tools to detect errors and to assess quality are developed. This paper introduces a method to detect a specific type of error in any dataset. It is based on the identification of patterns of missing data that are not – or rarely – due to a lack of information, but to errors. The method consists in (1) computing missing data patterns, (2) grouping computed values, (3) filtering for groups of values corresponding to the type of error to detect, and (4) manually identifying the cases corresponding to the type of error to detect. To the extent of my knowledge, this error detection method has not been previously employed nor described in the literature. As an example, the method is applied to the affiliation data of the bibliographic database Dimensions. The detection of affiliation errors can lead to improvements in the database and consequently to more reliable analyses, especially when it comes to collaborations or networks. It can also be useful for data analysts in a context where existing institution disambiguation systems are not sufficiently accurate (Donner et al., 2020). The specific application of the method that I present allows one to find a series of incorrectly affiliated papers (the erroneous affiliations they are associated with are given in Appendix 1). Most of the errors seem to be due to homonymy and to the format of data used as a source. I discuss both the results and potential improvements of the data they are associated with, as well as the method and further possible uses of missing data patterns in a bibliometric context.