Abstract — This paper presents Open Data Kit (ODK), an extensible, open-source suite of tools designed to build information services for developing regions. ODK currently provides four tools to this end: Collect, Aggregate, Voice, and Build. Collect is a mobile platform that renders application logic and supports the manipulation of data. Aggregate provides a “click-to-deploy” server that supports data storage and transfer in the “cloud” or on local servers. Voice renders application logic using phone prompts that users respond to with keypad presses. Finally, Build is a application designer that generates the logic used by the tools. Designed to be used together or independently, ODK core tools build on existing open standards and are supported by an open-source community that has contributed additional tools. We describe four deployments that demonstrate how the decisions made in the system architecture of ODK enable services that can both push and pull information in developing regions. I.

Data quality is critical for many information-intensive applications. One of the best opportunities to improve data quality is during entry. USHER provides a theoretical, data-driven foundation for improving data quality during entry. Based on prior data, USHER learns a probabilistic model of the dependencies between form questions and values. Using this information, USHER maximizes information gain. By asking the most unpredictable questions first, USHER is better able to predict answers for the remaining questions. In this paper, we use USHER’s predictive ability to design a number of intelligent user interface adaptations that improve data entry accuracy and efficiency. Based on an underlying cognitive model of data entry, we apply these modifications before, during and after committing an answer. We evaluated these mechanisms with professional data entry clerks working with real patient data from six clinics in rural Uganda. The results show that our adaptations has the potential to reduce error (by up to 78%), with limited effect on entry time (varying between-14 % and +6%). We believe this approach has wide applicability for improving the quality and availability of data, which is increasingly important for decision-making and resource allocation. ACM Classification: H5.2 [Information interfaces and presentation]:

In many disadvantaged communities worldwide, local low-resource organizations strive to improve health, education, infrastructure, and economic opportunity. These organizations struggle with becoming data-driven, because their communities still live outside of the reach of modern data infrastructure, which is crucial for delivering effective modern services. In this paper, we summarize some of the human, institutional and technical challenges that hinder effective data management in “first mile ” communities. These include the difficulty of deploying, cultivating and retaining expertise; oral traditions of knowledge acquisition and exchange; and mismatched incentives between top-down reporting requirements and local information needs. We propose a set of directions, drawing from projects that we have implemented. They include 1) separating the capture of data from its structuring, 2) applying intelligent automation to mitigate human, institutional and infrastructural constraints, and 3) deploying services in cloud infrastructure, opening up further opportunities for human and computational value addition. We illustrate these ideas in action with several projects, including Usher, a system for automatically improving data entry quality based on prior data, and Shreddr, a hosted paper form digitization service. We conclude by suggesting next steps for engaging in data management problems in the first mile. 1.

For low-resource organizations working in developing regions, infrastructure and capacity for data collection have not kept pace with the increasing demand for accurate and timely data. Despite continued emphasis and investment, many data collection efforts still suffer from delays, inefficiency and difficulties maintaining quality. Data is often still “stuck ” on paper forms, making it unavailable for decision-makers and operational staff. We apply techniques from computer vision, database systems and machine learning, and leverage new infrastructure – online workers and mobile connectivity – to redesign data entry with high data quality. Shreddr delivers self-serve, low-cost and on-demand data entry service allowing low-resource organizations to quickly transform stacks of paper into structured electronic records through a novel combination of optimizations: batch processing and compression techniques from database systems, automatic document processing using computer vision, and value verification through crowd-sourcing. In this paper, we describe Shreddr’s design and implementation, and measure system performance with a large-scale evaluation in Mali, where Shreddr was used to enter over a million values from 36,819 pages. Within this case study, we found that Shreddr can significantly decrease the effort and cost of data entry, while maintaining a high level of quality. 1.

In this paper we present a proposal for the implementation and evaluation of a novel method for automatically using data-rich text for filling form-based input interfaces. Our solution takes a text as input, extracts implicit data values from it and fills appropriate fields. For this task, we rely on knowledge obtained from values of previous submissions for each field, which are freely obtained from the usage of the interfaces. Our approach, called iForm, exploits features related to the content and the style of these values, which are combined through a Bayesian framework. Through extensive experimentation, we show that our approach is feasible and effective, and that it works well even when only a few previous submissions to the input interface are available. 1.