Improving Recommending and Understanding Schemas and Their Provenance

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We are drowning in a sea of data. Increasingly, jobs that used to rely on other skills now rely on the ability to interpret, understand, and trust data. For example, many biologists now spend less time in the lab and more time trying to analyze the data that they have created. Increasingly, thanks to improved tools for creating data, they are expected to manage and understand this data by themselves. Unfortunately, this data is often exceedingly difficult to understand and trust, even for experts in the field. There are many reasons for this. One reason is that the representation of the data is often designed for computers, not for people (i.e., storage, not accessibility). Another reason for this is that users may spend a lot of time using systems where data is combined from multiple sources, leaving them stuck having trouble integrating data that they neither understand nor trust.