Next-generation sequencing is developing rapidly and many datasets have been generated in organisms whose mitochondrial genome is unknown [ 61].
Regarding healthcare issues, micro datasets have been generated for issues such as obesity, mental disorder, access to general practitioner (GP) services and lifestyle behaviour such as smoking and alcohol consumption [ 9, 13, 15, 16].
Two-dimensional artificial datasets have been generated to exhibit a set of properties describing global dataset characteristics: cluster alignment, label distribution, cluster morphism and cluster separability.
Through analyses of ligand promiscuity based on the PDB, an additional two datasets have been generated, containing 164 and 247 entries, respectively [18,19].
Sixteen artificial datasets have been generated covering all the combinations of these four binary meta-descriptors expecting to simulate a wide range of real datasets' structures arising in classification tasks.
Since sequencing of the entire rice genome was completed in 2005 IRGSP 20055), diverse omics datasets have been generated that include genome-wide expression analysis using microarrays as well as RNA-seq, re-sequencing of 3000 rice accessions, protein protein interactomes, gene regulatory networks, and metabolic pathways.
According to this second algorithm (referred into the article as "phenocopy method two", PM2), five additional datasets have been generated, with different markers associated to the affected status.
While multiple, largely consistent Ubx embryo datasets have been generated for modENCODE, the data analyzed here are from ChIPs performed with rabbit anti-Ubx (Ubx7701, http://intermine.modencode.org/release-22/portal.do?class=Antibody&externalids=KW0%3asBX-7701) as this is the most inclusive of the Ubx embryo data.
Massive datasets have been generated from the Tara Oceans project.
Such datasets have been generated for many tissue types as part of the ENCODE project [ 30].
