Table 1 shows a summary of our datasets.
"Implementation" section introduces the concept, followed by the description of our datasets and the experimental results in "Experimental results" section.
Also, some protein targets of our datasets only have 40 actives, so it is not allowed to use more than half of them to build a consensus query.
Given that both of our datasets have positive rank correlations for offline and Twitter data, now-casting seems feasible and we proceed to build a prediction model.
The guidelines are divided in overall guidelines of the consortium specialists, and more specific guidelines regarding the interoperability of our datasets.
As for the available experimental data, on the other hand, it is easy to see how their inherent incompleteness could be contributing to the noisiness of our datasets.
Both of our datasets have no systematic mass error, see Fig. 11 for the GNPS dataset; for datasets that show a systematic mass error, we expect worse identification rates for the naïve method.
The theoretical and methodological messages were that we need to do more than "eyeball" spatial patterns, we need to apply the proper analyses based on the characteristics of our datasets, and we need to ensure that our models, quantitative analyses, and resulting interpretations are based in the proper cultural and historical contexts.
The free form of the comment allows almost total flexibility to the size of the post: from single word (or even just a single emoticon or exclamation mark) to texts comprising of tens of thousands of words (in one of our datasets we discovered a comment of almost 150,000 characters length).
The MIR Flickr dataset was chosen because it has relevant depictions to three out of our four initial collections (UCID, Holidays and to some degree with UKBench), it has the same encoding (JPG) with half our collections (UKBench and Holidays) and a resolution of the same order of magnitude with three of our datasets (only Holidays has a significantly higher resolution).
We thus conducted Bayesian analyses with partitioning of our datasets in stems and loops.
