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From education to health to criminal justice, government regulation and policy decisions have important effects on social and individual experiences. New data science tools applied to data created by government agencies have the potential to enhance these meaningful decisions. However, certain institutional barriers limit the realization of this potential. First, we need to provide systematic training of government employees in data analytics. Second we need a careful rethinking of the rules and technical systems that protect data in order to expand access to linked individual-level data across agencies and jurisdictions, while maintaining privacy. Here, we describe a program that has been run for the last three years by the University of Maryland, New York University, and the University of Chicago, with partners such as Ohio State University, Indiana University/Purdue University, Indianapolis, and the University of Missouri. The program—which trains government employees on how to perform applied data analysis with confidential individual-level data generated through administrative processes, and extensive project-focused work—provides both online and onsite training components. Training takes place in a secure environment. The aim is to help agencies tackle important policy problems by using modern computational and data analysis methods and tools. We have found that this program accelerates the technical and analytical development of public sector employees. As such, it demonstrates the potential value of working with individual-level data across agency and jurisdictional lines. We plan to build on this initial success by creating a larger community of academic institutions, government agencies, and foundations that can work together to increase the capacity of governments to make more efficient and effective decisions.

Children exposed to "Sesame Street" were more likely to be enrolled in the correct grade level for their age at middle and high school

Montgomery County Executive, Maryland Governor disagree on Thin Blue Line Flag

Sought-for freedoms require action in both domains, she says

A major concern arising from ubiquitous tracking of individuals’ online activity is that algorithms may be trained to predict personal sensitive information, even for users who do not wish to reveal such information. Although previous research has shown that digital trace data can accurately predict sociodemographic characteristics, little is known about the potentials of such data to predict sensitive outcomes. Against this background, we investigate in this article whether we can accurately predict voting behavior, which is considered personal sensitive information in Germany and subject to strict privacy regulations. Using records of web browsing and mobile device usage of about 2,000 online users eligible to vote in the 2017 German federal election combined with survey data from the same individuals, we find that online activities do not predict (self-reported) voting well in this population. These findings add to the debate about users’ limited control over (inaccurate) personal information flows.

Mary Zaki and her colleagues published a working paper analyzing the effects of work requirements on SNAP participation, beneficiary composition, and labor supply

Phoenix Police Department has the most police shooting cases in 2018

Greywater is increasingly treated and reused for agricultural irrigation in off-grid communities in the Middle East and other water scarce regions of the world. However, there is a dearth of data regarding levels of antibiotics and herbicides in off-grid greywater treatment systems. To address this knowledge gap, we evaluated levels of these contaminants in two types of greywater treatment systems on four farms in the West Bank, Palestinian Territories. Samples of household greywater (influent, n = 23), treated greywater effluent intended for agricultural irrigation (n = 23) and pumped groundwater held in irrigation water ponds (n = 12) were collected from October 2017 to June 2018. Samples were analyzed using high performance liquid chromatography tandem mass spectrometry (LC-MS/MS) for the following antibiotics and herbicides: alachlor, ampicillin, atrazine, azithromycin, ciprofloxacin, erythromycin, linezolid, oxacillin, oxolinic acid, penicillin G, pipemidic acid, sulfamethoxazole, triclocarban, tetracycline, triflualin, and vancomycin. All tested antibiotics and herbicides were detected in greywater influent samples at concentrations ranging from 1.3 to 1592.9 ng/L and 3.1–22.4 ng/L, respectively. When comparing influent to effluent concentrations, removal was observed for azithromycin, alachlor, linezolid, oxacillin, penicillin G, pipemidic acid, sulfamethoxazole, triclocarban, and vancomycin. Removal was not observed for atrazine, ciprofloxacin, erythromycin, oxolinic acid, tetracycline, and trifluralin. Pond water also contained the majority of tested contaminants, but at generally lower concentrations. To our knowledge, this is the first description of an extensive array of antibiotics and herbicides detected in household greywater from off-grid treatment systems.

Yang's Freedom Dividend is not likely to reduce social inequality seriously