## 2 Related Work  
There are various studies related to analysis of the Wikipedia references. One of the studies showed that a large number of academic and peer-reviewed publications used as sources in this collaborative knowledge base [17]. Other works showed that Wikipedia preferred referenced with higher academic status and accessibility of journal [9; 18]. There are also studies in a similar direction that concluded that Wikipedia can help assess the impact of scientific publications [19; 20]. Moreover based on Wikipedia references it is possible to identify the most influential journals [21]. There is also research that showed how special identifiers can be used to find and unify similar sources with different metadata in Wikipedia [8]. For instance, it can be useful when Wikipedia users provide some mistakes in metadata, but also when they translate and provide titles of the publications on their local languages. It is also important to note that in Wikipedia there are special tools for adding and editing references in articles with limited metadata [22]. There are also publications, which take into account the number of references to automatically assess the quality of information in Wikipedia articles [23], including approaches that used machine learning algorithms [24; 25] or synthetic measure [26].  
All previously mentioned studies did not analyze the quality of metadata of the Wikipedia references. One of the most relevant projects--WikiCite, which is an initiative to create a bibliographic database based on Wikidata [24]. The goals of this project include the improvement of citations in Wikipedia and other Wikimedia projects. However, not all citations from Wikipedia articles are extracted to Wikidata.  
Assessment of the quality of the bibliographic metadata is a known problem. However, there are few existing studies that are focused on quality assessment of metadata in other resources. Based on literature research, the same environment is examined and the focus is on ensuring the data quality of metadata in institutional information systems such as CRIS (e.g., Pure, Converis, Symplectic Elements, etc.).  
In research institutions, bad data quality can affect different areas. An improvement in data quality is therefore desirable and often meets with little resistance. However, some have found that improving data quality can be complex and time-consuming, but the opposite has to be analyzed and known only about the source of the data quality issues. This can be resolved using data profiling and data cleansing. Because only if the cause is remedied, a lasting improvement of the data quality can be achieved. For more details in this particular area, see the works from [5; 27; 28; 29; 30].  
The collection of publication data in CRIS can lead to quality errors. If errors are already made when collecting the data, this can have many negative effects [30]. Spelling errors and typos, incorrect values or missing and inconsistent values are just a few examples of errors in collecting research information into CRIS [31]. To ensure data quality, continuous analysis of research information during its integration into CRIS is required [29]. For CRIS, high data quality is one of the main criteria that determine whether the project is successful and the resulting information is complete, correct and consistent.