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The full datasets have been submitted to the NCBI Gene Expression Omnibus (GEO).
Test runs were carried out on the full datasets and then on reduced data.
While any OCP data can be visualized with CATMAID, we have selected and grouped individual objects (Table 1 (available online only)) into four subcategories (axons, dendrites, synapses, and glia) from the full datasets to facilitate fast loading times in the web interface (Table 4 (available online only), with total number of objects in each category).
(a) shows the results for the full datasets and (b) for threshold equal to 3 casualties per event.
Our analysis, however, also confirmed that where non-trivial timing signatures for the full datasets exist these signatures are quite robust against uncertainties in timestamps of events.
However, these are random samples of the full datasets for each event and are therefore representative of the overall levels of cyber hate within the corpus of tweets.
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The full dataset is available online.
The full dataset is reported in Supplementary Table S3.
The full dataset is available on Figshare here.
Figure 4 shows the projection of the full dataset onto the dual-descriptor space of and.
Coefficient estimates for each model were determined using the full dataset and estimated via maximum likelihood.
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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