Rhode Island's Longitudinal Data System (RILDS) uses the power of linked data to support the State’s policy and decision-making priorities. The RILDS centralizes more than 50 datasets from eleven sources over three decades. It currently links data from early childhood, through K-12 and postsecondary education, and into the workforce. Linking data over time and across sectors allows new information and patterns to emerge, cross-agency collaboration to evolve, and new insights to be discovered.
Integrated, longitudinal data systems are needed to ensure that informed decisions routinely occur throughout government with the speed and flexibility required for real-time decision making.
Since 2005, the U.S. Department of Education has awarded Statewide Longitudinal Data System (SLDS) grants to states to design, develop, implement, and expand K-12 and P-20W+ (early learning through the workforce) longitudinal data systems. These systems are intended to enhance the ability of states to efficiently and accurately manage, analyze, and use education data.
In 2009, the U.S. Department of Education awarded the Rhode Island Department of Education (RIDE) a SLDS grant. RIDE created the RILDS, then called the Rhode Island DataHUB, to integrate and link data along the education to workforce continuum.
In 2012, the U.S. Department of Education awarded Rhode Island another SLDS grant, and the U.S. Department of Labor awarded Rhode Island it's first Workforce Data Quality Initiative (WDQI) grant. This parallel effort supports the development and enhancements of statewide longitudinal data systems, integrating education and workforce data.
Over the past decade, the DataSpark has increased partnerships with state agencies, analyzed and visualized linked data, and fostered cross-sector problem solving. The RILDS is a State resource, used for the public good such as pioneering research on chronic absenteeism, evaluating the impact of elevated blood lead levels on academic achievements, analyzing workforce development efforts, and high quality academic research from faculty at the University of Rhode Island, Brown University, Portland State University, and elsewhere.
The information provided by DataSpark on the RILDS is for general informational purposes only. All information is provided in good faith, however we make no representation or warranty of any kind, express or implied, regarding the accuracy, adequacy, validity, reliability, availability or completeness of any administrative data provided to DataSpark on behalf of the source agency. Due to the timing of data collection by the originator; transfer to DataSpark; and analysis, vetting and publication by the RILDS, the information found within the RILDS may not match data publicly available elsewhere.
Although the data provided to DataSpark have been produced and processed from sources believed to be reliable, no warranty, expressed or implied, is made regarding accuracy, adequacy, completeness, legality, reliability or usefulness of any information. This disclaimer applies to both isolated and aggregate uses of the information. The information is provided on an "as is" basis. All warranties of any kind, express or implied, including but not limited to the implied warranties of merchantability, fitness for a particular purpose, freedom from contamination by computer viruses and non-infringement of proprietary rights are disclaimed. Changes may be periodically made to the information herein; these changes may or may not be incorporated in any new version of the publication.
Any opinions, advice, statements, services, offers, or other information or content expressed or made available by DataSpark and the RILDS, are those of the respective author(s) and do not necessarily state or reflect those of the data providing agency.