Recent work in computer vision has shown that common im-age datasets contain a non-trivial amount of near-duplicateimages. For instance CIFAR-10 has 3.3% overlap betweentrain and test images (Barz & Denzler, 2019). This results inan over-reporting of the generalization performance of ma-chine learning systems.

While WordNet attempts to organize the entire English language,[13] ImageNet is restricted to nouns (the idea being that nouns are things that pictures can represent). In the ImageNet hierarchy, every concept is organized under one of nine top-level categories: plant, geologic formation, natural object, sport, artifact, fungus, person, animal, and miscellaneous. Below these are layers of additional nested classes.

First, you'll need to clone your own copy of the template Google Sheet by clicking here, then pressing Use Template in the top right. This will add the sample spreadsheet to your personal Google account. Once you've done that, click here to open the starter app in Autocode. Poke around the source code if you'd like, then install the app to your Autocode account by pressing the green button. When prompted to link a Google Sheet, follow the instructions in the flow to link your Google account, then select the spreadsheet you just cloned.

Additional broader impacts will emerge from analyses of the whooping crane dataset. Through collaborations with endangered species biologists in the US Geological Survey, these analyses will have direct relevance to specific management actions for the whooping crane, such as the timing, group size, and composition of crane reintroductions and potentially their training with ultra-light aircraft.

The database should have an associated web site for access by internal researchers working on atlas construction and quite likely a separate Web site for public access to published datasets.

Just as comprehensive datasets of genomic sequence have revolutionalized biological discovery, large-scale quantitative measurements of gene expression and morphology will certainly be of great assistance in enabling computational embryology in the future. Such datasets will form the essential basis for systems level, computational models of molecular pathways and how gene expression concentrations and interactions alter to drive changes in cell shape, movement, connection, and differentiation. In this review, we discuss the strategies and methods used to generate such datasets.