## 3 Results  
As mentioned in the methodology section, the first step of this study was the execution of the.eu domains discovery algorithm which took place in the beginning of April 2022. A total of 203,020 domains were discovered. The Web data extraction algorithm was executed from mid to late April 2022 on a random sample of 36,395 websites out of the 203,020 collected by the.eu domains discovery algorithm. Out of these, 29,290 were successfully crawled. From the rest, 617 denied access to the crawling algorithm through their robots.txt files and 6488 were unreachable. The total sampling size represents almost 18% of the discovered domains and more than 14% of \begin{table}
\begin{tabular}{c c} \hline \hline
**Data** & **Importance** \\ \hline Site hosted in country with plenty of data centers & Marginally relevant \\ Site hosted in country with medium amount of data centers & Relevant \\ Site hosted in any other country & Important \\ Country’s ccTLD with identical SLD exists & Relevant \\ Country’s ccTLD in the registrar’s website & Marginally relevant \\ Country’s coordinates detected in Google map link & Very important \\ Five or less email links with the country’s ccTLD & Important \\ More than five email links with the country’s ccTLD & Very important \\ Country’s telephone code detected in phone link & Very important \\ Country mentioned once in homepage or map pages & Marginally relevant \\ Country mentioned two to five times in homepage or map pages & Relevant \\ Country mentioned more than five times in homepage or map pages & Important \\ Country corresponds to registrar’s website language & Marginally relevant \\ Country corresponds to homepage language (English) & Marginally relevant \\ Country corresponds to homepage language (Non-English) & Important \\ Country corresponds to CC Index language (English) & Marginally relevant \\ Country corresponds to CC Index language (Non-English) & Important \\ Country corresponds to map page language (English) & Marginally relevant \\ Country corresponds to map page language (Non-English) & Important \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Importance of various examined data. successfully accessed domains. As such, it is deemed large enough to offer representative results. The number of domains involved in this research are presented in Table 3.  
In addition to discovering.eu domains, an alternative version of the discovery algorithm was used to discover the total amount of pages in the Common Crawl Index for every ccTLD belonging to an EU country. This gives us a metric of how many national domains each country has. By dividing each country's pages with the total of all EU countries the percentage of total ccTLDs from EU that each national ccTLD represents was derived. A sample of the information collected by the Web data extraction algorithm is presented in Table 4. The complete information is openly available at a link provided below, in the data availability statement of this article. As is apparent even from the small sample of Table 4, not all information regarding a domain was always available to be collected by the algorithm. Some were often available, while others seldomly appeared. Table 5 presents every collected variable with the absolute number and the percentage of total domains where it was successfully retrieved. In order to better visualize this information a chart is presented in Figure 5.  
Figure 5: Chart of percentage of variables’ appearances by successfully crawled domain. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Value** & **Percentage** \\ \hline Discovered domains & 203,020 & 100.00\% \\ Domains randomly sampled & 36,395 & 17.93\% \\ Domains successfully accessed & 29,290 & 14.43\% \\ Unreachable domains & 6488 & 3.20\% \\ Access not allowed by robots.txt & 617 & 0.30\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Number of domains involved in this research.