Seeking a comprehensive database that encompasses high school students, college attendees, young professionals, or individuals interested in continuing education opportunities?

We offer unparalleled access to premium student data lists, including detailed information on students by name, their parents, college attendees, graduates, and much more. Whether you're aiming to spearhead a direct mail initiative for college readiness programs, further education courses, or even school dance attire, our comprehensive database positions you to seamlessly connect with your ideal customer.

What sort of data do we have?

• College Bound HS Students
• K-12 Data
• College Student Mailing lists
• Homeschool Mailing Lists

We understand the challenges marketers face when reaching prospective students. Our solutions provide a data-driven, results-oriented roadmap to enrollment success. Accurate, demographics-rich student marketing data is critical to your school’s successful marketing plan, especially in today’s competitive environment. Our data alliances enable us to bring to market the most robust portfolio of data lists, including students and their parents, young adults, and working professionals for continuing education programs.

Why Buy Leads From Us? With McGRAW’s student leads, you can build a robust pipeline, drive enrollment growth, and achieve your institution's educational and financial objectives. Our education leads offer:

Targeted Outreach: Connect with students interested in specific programs and fields of study. Comprehensive Data: Gain insights into students' academic interests, career goals, and preferred locations. High Engagement Rates: Reach students who are actively exploring educational options, ensuring higher response rates. Scalable Solutions: Access a wide range of leads to match your institution's enrollment goals and capacity. Quick Integration: Seamlessly integrate leads into your CRM for efficient follow-up and management. Compliance and Accuracy: Ensure all leads are generated through compliant and ethical methods, providing accurate and reliable data. What other industries can utilize the data? There are obvious ways to utilize education data and leads, but there may be some additional industries that could benefit.

Book publishers Colleges Universities Religious Organizations Education Supply Companies Office Supply Companies Fundraising Product Companies

K12 - 現在の値は、過去のデータ、予測、統計、チャートや経済カレンダー - Jun 2025.Data for K12 including historical, tables and charts were last updated by Trading Economics this last June in 2025.

.K12.NY Whois Database, discover comprehensive ownership details, registration dates, and more for .K12.NY TLD with Whois Data Center.

This layer includes public K-12 public schools (including public preschools and specialty programs) and K-12 private schools in Hampton Roads. Private preschools and daycares are not included. The public schools data is comprehensive with source information obtained from each public school district in the region. The private school data was obtained from several sources and may not have identified every school. Sources consulted for private schools include the National Center for Education Statistics, Virginia Council for Private Education, and the Association of Christian Schools International. Data is updated for 2019-2020 school year.

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437 Global import shipment records of K12 mobile with prices, volume & current Buyer’s suppliers relationships based on actual Global import trade database.

lmms-lab/k12 dataset hosted on Hugging Face and contributed by the HF Datasets community

As of June 19, 116 school shooting incidents were recorded in K-12 schools in the United States in 2025. Within the provided time period, the greatest number of K-12 school shootings was recorded in 2023, at 350. The source defines a school shooting as every time a gun is brandished, fired, or a bullet hits school property for any reason, regardless of the number of victims (including zero), time, day or the week, or reason, including gang shootings, domestic violence, shootings at sports games and after hours school events, suicides, fights that escalate into shootings, and accidents.

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Sprague K-12 lead in drinking water test results

Utah School District and Charter School Expenditure Data

As of July 22, 2024, there have been a total of 270 school shootings in California since 1966, the most out of any state. Texas had the second highest number of school shootings within this time period, with 225 shootings. The source defines a school shooting as every time a gun is brandished, fired, or a bullet hits school property for any reason, regardless of the number of victims (including zero), time, day or the week, or reason, including gang shootings, domestic violence, shootings at sports games and after hours school events, suicides, fights that escalate into shootings, and accidents.

(Link to Metadata) FacilitiesSchools_PTSCHOOL is designed to provide point locations of every Vermont School along with the established school ID (PSID) for geographic analysis of school statistical data. In 1997 Vermont's Regional Planning Commisions digitized the locations of the schools in their region using orthophotos, existing GIS data, and road maps. Maps of school locations were sent to each School Supervisory Union for verification of school location and name. The point covers created by the RPCs were appended to one cover by VCGI. In 2003 the data layer was revised by VCGI using an updated list of schools from the VT Department of Education. In 2004 VT Department of Education updated and validated the locations of the schools. In 2014, VT Department of Education updated the representations of Vermont's public K12 school locations for school year 2014. As part of posting that 2014 update, VCGI exported pre-existing post-secondary school features to a new separate feature class to retain information on post-secondary schools. Therefore, as of the 2014 update, this data set changed from containing one feature class for all schools to containing two feature classes, one with the new K12 data (302 points) and the other with pre-existing post-secondary data. FacilitiesSchools_PTSCHOOL should be used for general mapping purposes only.

K12 - Текущие значения, предыдущие значения, прогнозы, графики и экономический календарь - Jul 2025.Data for K12 including historical, tables and charts were last updated by Trading Economics this last July in 2025.

This dataset contains the systematic map coded database (Microsoft Excel) in support of the publication in School Science and Mathematics Journal entitled, Citizen science in K-12 school-based learning settings. The dataset contains two excel sheets. The first sheet contains coded records that met the inclusion criteria that were extracted through the systematic mapping process as described in the aforementioned manuscript. Meanwhile, the second sheet contains the records excluded from the systematic map along with their reasons for exclusion. Items coded with ‘NA’ indicate that there was insufficient detail within the extracted records to populate these variables. All extracted manuscripts contained evidence related to both type and level of citizen science.

The train set is K12. The test set is MathVista.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study was designed to examine how districts that experienced an incident of school employee sexual misconduct in 2014 defined, interpreted, and implemented key elements of Title IX before, during, and after an incident. The study used a qualitative case study design with a purposeful sample of five districts recruited from a database of 459 districts who experienced a case of school employee sexual misconduct in 2014. The study was conducted between January 2016 and September 2017. Data collected included: 1) various district documents, 2) 41 interviews with primary actors (school employees and county officials directly involved in responding to the incident), 3) 10 focus groups with 51 secondary actors (school employees who were not directly involved with the incident but who might have been indirectly affected by it), and 4) offender, victim and district characteristics. Documents reviewed included written policies and protocols, training materials and handbooks for staff and students, case documents, and other guiding documents as applicable. In interviews and focus groups, participants were asked to discuss their knowledge of district policies and procedures, to describe the dissemination of and any changes to these policies and procedures, and to provide recommendations for improvement. To protect the confidentiality all district and participant identifying information is confidential and has been removed from any reporting.

This layer began as part of the Virginia.gov data development project in 2008-2009. VGIN was tasked to generate cartography and data for a specific application for location of Virginia state agency locations in relation to the project. In 2010, The State Broadband Data Development Program (SBDD) Community Anchor Institutions (CAI) dataset leveraged much of that data and catalogued broadband service to a specific feature. The points have since been checked and updated annually as part of the Virginia Landmark dataset. The data is matched to the Virginia Address Point dataset. There are numerous data sources associated to the point. The general development methodology is as follows: The facility data was extracted and geocoded to the VGIN Geocoding service. If results were positioned on the Road Centerline by the RCL locator, those features were subsequently updated and moved to match the address point in the data layer. Unmatched results were added to the map. Non-spatial address location data is harvested into a spreadsheet by the source agency and checked against the GIS data annually. Fields for Landmark Name, Place Type, Source, and Source Type can be used to differentiate specific data as stored in the dataset.Full VA Landmark Database: https://vgin.vdem.virginia.gov/datasets/VGIN::virginia-landmarks/explore

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The global education data security market is experiencing robust growth, driven by the increasing digitization of educational institutions and the rising concerns around student data privacy and compliance with regulations like FERPA and GDPR. The market's value in 2025 is estimated at $10 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, projecting a market value exceeding $30 billion by 2033. Key growth drivers include the expanding adoption of cloud-based learning platforms, the proliferation of connected devices within educational settings, and the increasing sophistication of cyber threats targeting sensitive student and institutional data. Market segmentation reveals a strong demand for cloud-based solutions across both K-12 and higher education sectors, reflecting a broader industry trend towards flexible and scalable IT infrastructure. However, factors like budget constraints within educational institutions, particularly in developing regions, and the complexities associated with integrating data security solutions into existing IT systems present significant restraints to market expansion. Leading vendors such as Cisco, Citrix, McAfee, and others are actively developing and marketing advanced security solutions tailored to the specific needs of the education sector, further fueling market competition and innovation. The North American market currently dominates the education data security landscape, fueled by high technology adoption rates and stringent data privacy regulations. However, significant growth opportunities exist in the Asia-Pacific region, driven by rapid digital transformation in education systems and increasing internet penetration across developing economies like India and China. The European market is also expected to witness substantial growth, influenced by the implementation of GDPR and a focus on digital learning initiatives. Further growth will be fueled by the development of AI-powered security solutions that can proactively identify and mitigate threats in real-time, combined with robust data loss prevention (DLP) mechanisms to protect sensitive information. The increasing adoption of multi-factor authentication (MFA) and zero-trust security models are also expected to significantly impact market growth in the coming years.