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For each of these tasks, we provide the details (experimental settings, labels, training and testing splits) to make results reproducible.
Second, for independent unitary tasks, we provide an optimal scheduling algorithm where the objective is to maximize the reliability subject to makespan minimization.
For each of these six tasks, we provide a tutorial that illustrates a basic R osetta protocol.
Based on the consideration that the taxonomy-based skill categories present a structure going from simple to complex motor tasks, we provide a formal procedure for guiding patients from the top left of the table to the bottom right.
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On the TIMIT task, we provide a detailed account of the factors in the proposed template matching methods that affect the rescoring performance, including local distances, number of GMMs employed for frame labeling, template selection, compression methods and their interactions with the local distances, and the percentage of selected template representatives.
To simplify this visualization task, we provide template HTML5 projects to render the exported JSON files.
For each task we provided a specific guideline.
For this task, we provided a flexible methodology and software.
In the annotation task, we provided each annotator annotation guidelines and an annotation tool that was developed specifically for this task.
Although each of the chimpanzees had extensive experience with this kind of visual search task, we provided preliminary training sessions before initiating the experimental sessions.
For the GO-term prediction task, we provided teams with the same <gene, document> pairs and asked their systems to return relevant GO terms.
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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