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The documents in this collection describe the current knowledge in life sciences.
Let N be the number of documents in this collection and let m be the number of documents that mention disease term d and n the number of documents that mention gene term g.
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In this analysis, we are not concerned with the potential existence of other relevant documents in the collection that were not included in the systematic reviews.
One of the most colorful documents in the collection is a notebook filled with words and phrases that Joyce stored up for later use.
Further, there is a large variability in the size of documents in the collection.
where tf i,j means term frequency, N is a total number of documents in the collection, df i presents the document frequency, dl i document length and dl avg the average document length for the collection.
Figure 6 shows that the whole index is nearly linear with the size of M, namely the number of documents in the collection.
With respect to the number of documents in the collection (see the Section 3.2), we have received the matrix with 5,210 columns and 6,908,655 rows.
Many times, the inverse document frequency idf t = log(N/df t ) of a collection is used to determine weights, where N is the total number of documents in the collection.
IDF is used to measure the overall importance of the term within the whole collection, defined as the total number of documents in the collection divided by the total number of documents including that word.
(w_t) is the weight of query term t, which is given by: begin{aligned} w_t=log _{2}frac{N-df_{t}+0.5}{df_t+0.5} end{aligned} (2 where N is the number of documents in the collection, and (df_{t}) is the document frequency of query term t, which denotes the number of documents that t occurs.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com