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When only the most common terms per publication are selected, does this result in a useful description of its content?</p><p>The data file OK_Computer_results contains a list of open access books and chapters descriptions found in the OAPEN Library, combined with Wikipedia entries found using the entity-fishing algorithm, plus several actions to filter out only the terms which describe the publication best. Each book or chapter is available in the OAPEN Library (www.oapen.org), see the column HANDLE/</p><p>The data file nerd_oapen_response_database contains the complete data set. The other text files contain R code to manipulate the file nerd_oapen_response_database.</p><p>Description of nerd_oapen_response_database:</p><p>The data is divided into the following columns:</p><p>Data\tDescription<br>OAPEN_ID\tUnique ID of the publication in the OAPEN Library<br>rawName\tThe entity as it appears in the text<br>nerd_score\tDisambiguation  confidence score<br>nerd_selection_score\tSelection confidence score, indicates how certain the disambiguated entity is actually valid for the text mention<br>wikipediaExternalRef\tID of the Wikipedia page<br>wiki_URL\tURL of the Wikipedia page<br>type\tNER class of the entity<br>domains\tDescription of subject domain</p><p>Each book may contain more than one occurrence of the same entity. The nerd_score and the nerd_selection_score may vary. This allows researchers to count the number of occurrences and use this as an additional method to assess the contents of the book. 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