This data explores the U.S. Department of Health and Human Services (DHHS) Administration for Children and Families Administration on Children, Youth and Families Children's Bureau Adoption of Children with Public Child Welfare Agency Involvement by State for Fiscal Years 1995 - 2006. For Fiscal Years 1995 - 1997, The data for FY 1995-FY 1997 were reported by States to set baselines for the Adoption Incentive Program. They came from a variety of sources including the Adoption and Foster Care Analysis and Reporting System (AFCARS), court records, file reviews and legacy information systems. For Fiscal Years 1998 - 2006, Unless otherwise noted, the data come from the AFCARS adoption database. Because AFCARS adoption data are being continuously updated and cleaned, the numbers reported here may differ from data reported elsewhere. In addition, data reported for the Adoption Incentive Program will differ from these data because adoptions reported for that program are identified through a different AFCARS data element and must qualify in other ways to be counted toward the award of incentive funds. Counts include adoptions reported as of 6/1/2005. Where appropriate, AFCARS data have been adjusted for duplication.

This dataset explores the prior relationship of adoptive parents to their adoptee children during fiscal year 2006 (from October 1, 2005 to September 30, 2006). *The data from Maryland, Nebraska, New York, Ohio, Rhode Island and Washington was questionable due to the large percentage of missing data. *Iowa does not track non-welfare stepparent adoptions. Law defines relative as the fourth degree of consanguinity. *Nebraska includes great aunt/uncle, great grandparent, great great great grandparent, great great aunt/uncle, great great great grandparent, great great great aunt/uncle, adoptive sibling, biological sibling, first and second cousins, grandparent, parent-in-law, aunt/uncle. Fictive kin (ie. Godparents) are not included.

Abstract

Dataset quality ***: High quality dataset that was quality-checked by the EIDC team

This dataset tracks electric school bus (ESB) adoption across the United States. It tracks the number of “committed” ESBs at the school district level, as well as details about individual buses, including the bus manufacturer and funding source(s). It also tracks when each ESB passed through the phases of the adoption process and the current phase of each bus. The dataset contains school district socio-economic characteristics, like poverty rates, racial composition and air pollution to enable wider analysis including whether the transition to ESBs is happening equitably. This dataset was developed as part of WRI’s Electric School Bus Initiative.

Methodology

The dataset is organized by both school district and individual ESB and tracks the number of “committed” ESBs. An ESB is considered “committed” starting from the point when a school district or fleet operator has been awarded funding to purchase it or has made formal agreement to purchase it from a manufacturer or dealer. We would not consider an ESB “committed” if a school district or other fleet operator only expressed interest in ESBs or stated that they plan to acquire ESBs, without awarded funding or an agreement with a third party. The dataset also tracks the progress of each individual ESB through the four phases of the adoption process: “awarded,” “ordered,” “delivered,” and “operating.” It also contains school district characteristics including poverty, racial composition, air pollution, and locale (urban, suburban, town, or rural), to enable wider analysis of the adoption of ESBs, including the extent to which the transition to ESBs is happening equitably.

ESB-related data were collected from a variety of publicly available sources, including news articles, school websites, industry publications like School Bus Fleet magazine, and social media posts. Other demographic and economic data come from reputable, public datasets including the Environmental Protection Agency (EPA), U.S. Census, and National Center for Education Statistics. This dataset will be updated quarterly over the life of WRI’s to include new ESB commitments and additional indicators.

Usage

This dataset is the result of new data collection by WRI’s Electric School Bus Initiative, and is sourced from hundreds of news articles, school district webpages, and other online sources. To the best of our knowledge, these data are up to date as of March 2022, but represent a snapshot in time, in a rapidly evolving space. We will update this dataset quarterly for the duration of WRI’s Electric School Bus Initiative.

District-level Data on Electric School Bus Adoption:

This category includes the base table of this dataset, which comes from the district directory of the National Center for Education Statistics (NCES) for the 2020–21 school year. The approximately 19,500 LEAs in the United States make up the rows of this dataset. There are nine types of LEAs, including several types of public education-related entities beyond what is typically referred to as a “school district,” such as a state-operated agency or a service agency. This ESB adoption dataset includes all LEA types because there may eventually be ESBs owned by any of these LEA types. The dataset also includes any other entities (without LEA IDs) that have obtained electric school buses (i.e., private schools and private fleet operators).

The data also describe the social, economic, and demographic characteristics of the school district. As described in “Indicator Selection Criteria,” we tried to include data that would provide an adequately holistic understanding of socioeconomic and environmental health condition disparities among school districts, in alignment with wider thinking on the topic and what is relevant to ESBs, without including so many indicators that they burden nontechnical users with researching and selecting indicators. This section includes data on each school district’s number of enrolled students, whether the district is controlled by an Indian Tribe or the Bureau of Indian Education (Bureau of Indian Education n.d.), median household income, percentage of households below the federal poverty level, the distribution of the population among race and ethnic categories, the number of school students with a disability, and whether the school district was qualified for ESB funding from the American Rescue Plan. Also included are the variables; percent low-income, percent non-white and/or Hispanic, average ozone concentration (parts per billion, ppb), and average concentration of fine particulate matter (PM2.5, measured in micrograms per cubic meter, μg/ m3).

Utilities:

This category includes information on the electric power utilities operating in each school district. The “Utility name” variables include the names of all utility companies that operate within the boundaries of the school

Horse Racing Software Market Outlook

The global horse racing software market size was valued at approximately USD 180 million in 2023 and is forecasted to reach USD 340 million by 2032, growing at a compound annual growth rate (CAGR) of 7.2%. This promising growth trajectory is driven by a variety of factors including the increasing popularity of horse racing as a sport and betting activity, advancements in technology, and the rising demand for sophisticated betting and handicapping software solutions.

One of the key growth factors of the horse racing software market is the increasing adoption of digital platforms and mobile applications. As more users shift from traditional betting methods to online platforms, the demand for reliable, user-friendly, and feature-rich software solutions is steadily rising. This transition is not only making betting and handicapping more accessible but is also attracting a younger demographic, further fueling market expansion. Additionally, the COVID-19 pandemic accelerated the digital transformation across various sectors, including horse racing, thus boosting the market's growth.

Advancements in artificial intelligence (AI) and machine learning (ML) technologies are also playing a pivotal role in the evolution of horse racing software. AI-driven analytics and predictive algorithms have significantly improved the accuracy of handicapping and betting decisions. These technologies enable users to analyze large datasets, consider numerous variables, and generate insights that were previously unattainable. As a result, both novice and experienced bettors are increasingly relying on these advanced software solutions to enhance their betting strategies and outcomes.

The growth of the horse racing software market is further propelled by the rising globalization of horse racing events. Major events like the Kentucky Derby, the Royal Ascot, and the Melbourne Cup attract a global audience, driving cross-border betting activities. This international appeal is fostering the need for software solutions that can cater to diverse markets and comply with various regulatory frameworks. Additionally, the increasing number of racing clubs and betting agencies investing in cutting-edge software to improve their operations and customer experience is contributing to the market's expansion.

Regionally, North America holds a dominant position in the horse racing software market, supported by a strong tradition of horse racing and well-established betting infrastructure. The Asia Pacific region is expected to witness significant growth during the forecast period, driven by the rising popularity of horse racing in countries like Japan, Hong Kong, and Australia. Europe also presents substantial growth opportunities, with countries like the UK and France having a deep-rooted horse racing culture. The Middle East and Africa, while currently a smaller market, are showing potential for growth due to increasing investments in horse racing infrastructure and events.

Product Type Analysis

The horse racing software market is segmented into various product types such as handicapping software, betting software, simulation software, and others. Handicapping software is designed to assist bettors in analyzing racing data and making informed betting decisions. This type of software is increasingly popular among both casual and professional bettors due to its ability to process vast amounts of data and provide statistics, trends, and predictive analytics. The demand for handicapping software is expected to grow significantly as more bettors seek to enhance their betting strategies through data-driven insights.

Betting software, which facilitates the act of placing bets, is another critical segment. These platforms offer a seamless betting experience, often integrating live odds, race schedules, and secure payment gateways. Betting software is particularly popular among betting agencies and individual users who prefer the convenience of online betting. With the growing trend of mobile betting, software developers are focusing on creating mobile-friendly platforms that offer the same functionalities as their desktop counterparts, thereby expanding their user base.

Simulation software provides a virtual racing experience, allowing users to simulate horse races for entertainment or training purposes. This type of software is gaining traction among horse racing enthusiasts who enjoy the thrill of racing without the financial risks associated with real betting. Additionally, racing clubs and training centers use simulation sof