Various high-throughput experimental techniques are routinely used for generating large amounts of omics data.
Whilst NGS technologies are useful for generating large amounts of sequence data, the cost remains too high for most population genomics studies on nonmodel species.
For the past 30 years, new high-throughput technologies have been developed generating large amounts of data, distributed across many data sources on the Web, with a high degree of semantic heterogeneity and di erentlevelsofquality.
Recent technological advances have lead to the ability to generate large amounts of data for model and non-model organisms.
This is emerging as a new scientific discipline as fields such as systems biology generate large amounts of data, driving a demand for facilities and methods for storing, managing, and analyzing large datasets.
High volume imaging generates large amounts of data, but for preclinical SPECT/CT or PET/CT, this is well manageable within a contemporary standard information technology environment.
Anaesthesia Information Management Systems AIMSS) generate large amounts of data, which might be useful for quality assurance programs.
A typical processed lot generates large amounts of data that need to be analyzed and interpreted for process troubleshooting and continuous improvement purposes in addition to product release.
Modern particle physics experiments observing collisions of particle beams generate large amounts of data.
Functional neuroimaging often generates large amounts of data on regions of interest.
Multiparametric optical mapping generates large amounts of data per experiment, which makes data processing challenging.
