Quality Data is Key to Improving Education

The Data Quality Campaign (DQC) has been focused since 2005 on advocating for states to build robust state longitudinal data systems (SLDS). While states have made great progress in their data infrastructure, and should continue to emphasize this work, t data systems alone will not improve outcomes. It is time for both DQC and states to focus on building capacity to use the information that these systems are producing at every level – from classrooms to state houses. To impact system performance and student achievement, the ingrained culture must be replaced with one that focuses on data use for continuous improvement. The effective use of data to inform decisions, provide transparency, improve the measurement of outcomes, and fuel continuous improvement will not come to fruition unless there is a system wide focus on building capacity around the collection, analysis, dissemination, and use of this data, including through research

Workforce Data Quality Initiative

Texas’ Workforce Data Quality Initiative aimed to develop a comprehensive system for analysis of workforce and education participation and outcomes. In partnership with the Texas Workforce Commission, the Ray Marshall Center (RMC) is working to build, test, improve, and expand data linkages across linked individual-level, longitudinal education, and workforce records. Through this project, researchers would be able to identify and assess postsecondary pathways and transitions between education, employment, and other outcomes for students exiting the public school system as well as analyze the performance of the human capital development system in Texas, spanning secondary education through postsecondary education, and workforce training and employment.This report seeks to examine and analyze the postsecondary labor market outcomes of Texas high school graduates from the classes of 2008 and 2009. One advantage of looking at these two particular cohorts stems from differences in when they encountered the Great Recession: the class of 2008 graduated prior to the start of the recession in Texas and the class of 2009 graduated immediately after the start of the recession. It is likely that class of 2009 graduates factored in the regional changes in availability of employment as they weighed whether or not to apply for and enroll in college.Texas Workforce CommissionRay Marshall Center for the Study of Human Resource

Metrics for Measuring Data Quality - Foundations for an Economic Oriented Management of Data Quality

The article develops metrics for an economic oriented management of data quality. Two data quality dimensions are focussed: consistency and timeliness. For deriving adequate metrics several requirements are stated (e. g. normalisation, cardinality, adaptivity, interpretability). Then the authors discuss existing approaches for measuring data quality and illustrate their weaknesses. Based upon these considerations, new metrics are developed for the data quality dimensions consistency and timeliness. These metrics are applied in practice and the results are illustrated in the case of a major German mobile services provider

An intelligent linked data quality dashboard

This paper describes a new intelligent, data-driven dashboard for linked data quality assessment. The development goal was to assist data quality engineers to interpret data quality problems found when evaluating a dataset us-ing a metrics-based data quality assessment. This required construction of a graph linking the problematic things identified in the data, the assessment metrics and the source data. This context and supporting user interfaces help the user to un-derstand data quality problems. An analysis widget also helped the user identify the root cause multiple problems. This supported the user in identification and prioritization of the problems that need to be fixed and to improve data quality. The dashboard was shown to be useful for users to clean data. A user evaluation was performed with both expert and novice data quality engineers

Calibration and data quality of warm IRAC

We present an overview of the calibration and properties of data from the IRAC instrument aboard the Spitzer Space Telescope taken after the depletion of cryogen. The cryogen depleted on 15 May 2009, and shortly afterward a two-month- long calibration and characterization campaign was conducted. The array temperature and bias setpoints were revised on 19 September 2009 to take advantage of lower than expected power dissipation by the instrument and to improve sensitivity. The final operating temperature of the arrays is 28.7 K, the applied bias across each detector is 500 mV and the equilibrium temperature of the instrument chamber is 27.55 K. The final sensitivities are essentially the same as the cryogenic mission with the 3.6 μm array being slightly less sensitive (10%) and the 4.5 μm array within 5% of the cryogenic sensitivity. The current absolute photometric uncertainties are 4% at 3.6 and 4.5 μm, and better than milli-mag photometry is achievable for long-stare photometric observations. With continued analysis, we expect the absolute calibration to improve to the cryogenic value of 3%. Warm IRAC operations fully support all science that was conducted in the cryogenic mission and all currently planned warm science projects (including Exploration Science programs). We expect that IRAC will continue to make ground-breaking discoveries in star formation, the nature of the early universe, and in our understanding of the properties of exoplanets