This paper examines how social networks can be used to recruit and promote a crowdsourced citizen science project and compares this recruiting method to the use of tradition-al media channels including press releases, news stories, and participation campaigns. The target studied is Creek Watch, a citizen science project that allows anyone with an iPhone to submit photos and observations of their local waterways to authorities who use the data for water management, environmental programs, and cleanup events. The results compare promotional campaigns using a traditional press release with news pickups, a participation campaign through local organizations, and a social networking campaign through Facebook and Twitter. Results also include the trial of a feature that allows users to post automatically to Facebook or Twitter. Social networking is found to be a worthwhile avenue for increasing awareness of the project, increasing the conversion rate from browsers to participants, but that targeting existing communities with a participation campaign was a more successful means for increasing the amount of data collected by volunteers.

Citizen science projects can collect a wealth of scientific data, but that data is only helpful if it is actually used. While previous citizen science research has mostly focused on designing effective capture interfaces and incentive mechanisms, in this paper we explore the application of HCI methods to ensure that the data itself is useful. To provide a focus for this exploration we designed and implemented Creek Watch, an iPhone application and website that allow volunteers to report information about waterways in order to aid water management programs. Working with state and local officials and private groups involved in water monitoring, we conducted a series of contextual inquiries to uncover what data they wanted, what data they could immediately use, and how to most effectively deliver that data to them. We iteratively developed the Creek Watch application and website based on our findings and conducted evaluations of it with both contributors and consumers of water data, including scientists at the city water resources department. Our study reveals that the data collected is indeed useful for their existing practices and is already in use in water and trash management programs. Our results suggest the application of HCI methods to design the data for the end users is just as important as their use in designing the user interface.

The primary goal of the Midas project is to build a system that enables easy and scalable integration of unstructured and semi-structured information present across multiple data sources. As a first step in this direction, we have built a system that extracts and integrates information from regulatory filings submitted to the U.S. Securities and Exchange Commission (SEC) and the Federal Deposit Insurance Corporation (FDIC). Midas creates a repository of entities, events, and relationships by extracting, conceptualizing, integrating, and aggregating data from unstructured and semi-structured documents. This repository enables applications to use the extracted and integrated data in a variety of ways including mashups with other public data and complex risk analysis.

Recalls and defective parts are a major problem for manufacturers and a major challenge in information management. Increasingly complex supply chains have created a situation where parts' data is distributed across a volatile network of suppliers' databases. The challenge of parts tracking in this environment requires maintaining data views across constantly-restructuring networks of information systems as new suppliers are contracted and subassemblies are outsourced. In this paper we present a method for manufacturers to efficiently and automatically index all component parts in their products, allowing for complex changes to the supply chain, and enabling highly efficient reverse-lookups for recalls. Our method improves parts lookup and search time by subdividing all component part indexes into a distributed, versioned matrix of tables which models the structure of the supply chain. We introduce a method of reverse-indexing component parts using manufacturing time to pinpoint products for selective recalls, running in a fraction of the time of a traditional database search. We evaluate our method for applicability to the automotive industry using statistically re-generated data from a major automobile manufacturer to simulate a supply chain 7 layers deep, comprising 1000 individual component parts, indexing a distributed dataset totaling approximately 1TB in size.