There are thousands of data repositories on the Web, providing access to millions of datasets. National and regional governments, scientific publishers and consortia, commercial data providers, and others publish data for fields ranging from social science to life science to high-energy physics to climate science and more. Access to this data is critical to facilitating reproducibility of research results, enabling scientists to build on others' work, and providing data journalists easier access to information and its provenance. In this paper, we discuss Google Dataset Search, a dataset-discovery tool that provides search capabilities over potentially all datasets published on the Web. The approach relies on an open ecosystem, where dataset owners and providers publish semantically enhanced metadata on their own sites. We then aggregate, normalize, and reconcile this metadata, providing a search engine that lets users find datasets in the “long tail” of the Web. In this paper, we discuss both social and technical challenges in building this type of tool, and the lessons that we learned from this experience.

State legislatures often rely on existing text when drafting new bills.Resource and expertise constraints, which often drive this copying behavior, can be taken advantage of by lobbyists and special interest groups.These groups provide model bills, which encode policy agendas, with the intent that the models become actual law.Unfortunately, model legislation is often opaque to the public-both in source and content.In this paper we present LOBBYBACK, a system that reverse engineers model legislation from observed text.LOBBYBACK identifies clusters of bills which have text reuse and generates "prototypes" that represent a canonical version of the text shared between the documents.We demonstrate that LOBBY-BACK accurately reconstructs model legislation and apply it to a dataset of over 550k bills.

Lexical simplification of scientific terms represents a unique challenge due to the lack of a standard parallel corpora and fast rate at which vocabulary shift along with research.We introduce SimpleScience, a lexical simplification approach for scientific terminology.We use word embeddings to extract simplification rules from a parallel corpora containing scientific publications and Wikipedia.To evaluate our system we construct SimpleSciGold, a novel gold standard set for science-related simplifications.We find that our approach outperforms prior context-aware approaches at generating simplifications for scientific terms.

State legislatures introduce at least 45,000 bills each year. However, we lack a clear understanding of who is actually writing those bills. As legislators often lack the time and staff to draft each bill, they frequently copy text written by other states or interest groups. However, existing approaches to detect text reuse are slow, biased, and incomplete. Journalists or researchers who want to know where a particular bill originated must perform a largely manual search. Watchdog organizations even hire armies of volunteers to monitor legislation for matches. Given the time-consuming nature of the analysis, journalists and researchers tend to limit their analysis to a subset of topics (e.g. abortion or gun control) or a few interest groups.

The exponentially growing complexity of modern processors intensifies verification challenges. Traditional pre-silicon verification covers less and less of the design space, resulting in increasing post-silicon validation effort. A critical challenge is the manual debugging of intermittent failures on prototype chips, where multiple executions of a same test do not yield a consistent outcome. We leverage the power of machine learning to support automatic diagnosis of these difficult, inconsistent bugs. During post-silicon validation, lightweight hardware logs a compact measurement of observed signal activity over multiple executions of a same test: some may pass, somemay fail. Our novel algorithm applies anomaly detection techniques similar to those used to detect credit card fraud to identify the approximate cycle of a bug's occurrence and a set of candidate root-cause signals. Compared against other state-of-the-art solutions in this space, our new approach can locate the time of a bug's occurrence with nearly 4x better accuracy when applied to the complex OpenSPARC T2 design.