## 3 Methodology
The query used to download the data was:  
query <- \"(when:17 OR when:16 OR when:15 OR when:14)\"  
res_bulk <- tidy_cursored_search(query, qf = \"what:painting\", max_items = Inf)  
- The objective of the query is to filter the Europeana content first by period (14 th to 17 th century) and then to identify painting objects in these results. The data were collected by utilizing Europeana's Search API in a hierarchical structure in JSON format, and using EuropeanaR facilities we transformed them into a tidy format [33]. For the transformation from JSON to an R object we used jsonlite, an R package that consistently maps JSON data structures into R objects [34]. Since our study included only data that are public, we did not need explicit permission from the institutions that uploaded the metadata and furthermore, due to the fact that our study does not handle or reproduce the actual data (i.e., we do not modify the images of paintings and we do not post them to a different site), but focuses only on the metadata, which we process as explainedin the following, copyrights are not of importance. As mentioned above, the Renaissance is a broad movement including many sectors and fields. Our study focuses on paintings in order for our topics to have a common place of reference. However, since it is difficult to limit search results if you do not include specific search terms (e.g., artists' names), we also included post-Byzantine art as part of the European Renaissance. Furthermore, since a topic modeling analysis would have grouped words from different languages to different clusters and thus topic detection would be based on language and not on actual themes, we decided to include only the paintings that have been uploaded in the English language. Indeed, during our initial testing, it was made apparent that the resulting topics when multilingual descriptions are employed were completely different. After concluding with data collection and extraction of textual descriptions, we proceeded to the application of topic modeling to the collected textual data. Topic modeling is an unsupervised machine learning technique that takes as input a set of documents and learns a statistical model from it. Its primary objective is to identify clusters or groups of comparable subjects (i.e., words) within texts. It is considered as one of the most effective text mining techniques for data mining, discovery of latent data, and the identification of correlations between data and text documents [35]. For this study, as mentioned above, we utilized the Latent Dirichlet Allocation (LDA). LDA is an unsupervised machine learning technique that analyzes document collections and creates themes based on patterns [36]. Unsupervised machine learning techniques are descriptive tools that can help researchers to better understand the dataset at hand, by extracting frequent or repetitive patterns. In the case of text collections, unsupervised machine learning techniques such as document clustering or text extraction, can be used to create groups of highly similar documents or textual summaries for long documents or group of documents, respectively. LDA aims at finding concise descriptions of a collection's elements, usually consisting of words extracted from the descriptions. This technique allows for efficient processing of large collections while preserving the essential statistical relationships that are useful for fundamental tasks, such as classification, novelty detection, summarization, similarity and relevance judgments [30]. Our search returned approximately 17,000 results to us. This was the bulk download. Since we wanted only the English language descriptions, we used the CLD2 to filter out non-English languages and this also had the benefit of filtering out noisy results that would have otherwise been classified as NA. CLD2 is an R wrapper for Google's Compact Language Detector [37]. This resulted in the corpus of 5000 results. From this, we constructed a vocabulary of unique words contained in the results.