Success.ai’s Education Industry Data with B2B Contact Data for Education Professionals Worldwide enables businesses to connect with educators, administrators, and decision-makers in educational institutions across the globe. With access to over 170 million verified professional profiles, this dataset includes crucial contact details for key education professionals, including school principals, department heads, and education directors.

Whether you’re targeting K-12 educators, university faculty, or educational administrators, Success.ai ensures your outreach is effective and efficient, providing the accurate data needed to build meaningful connections.

Why Choose Success.ai’s Education Professionals Data?

1. Comprehensive Contact Information
2. Access verified work emails, direct phone numbers, and LinkedIn profiles for educators, administrators, and education leaders worldwide.

3. AI-driven validation guarantees 99% accuracy, ensuring the highest level of reliability for your outreach.

4. Global Reach Across Educational Roles

5. Includes profiles of K-12 teachers, university professors, education directors, and school administrators.

6. Covers regions such as North America, Europe, Asia-Pacific, South America, and the Middle East.

7. Continuously Updated Datasets

8. Real-time updates ensure that you’re working with the most current contact information, keeping your outreach relevant and timely.

9. Ethical and Compliant

10. Success.ai’s data is fully GDPR, CCPA, and privacy regulation-compliant, ensuring ethical data usage in all your outreach efforts.

Data Highlights:

• 170M+ Verified Professional Profiles: Includes educators and administrators across various levels of education.
• 50M Work Emails: Verified and AI-validated emails for seamless communication.
• 30M Company Profiles: Rich insights into educational institutions, supporting detailed targeting.
• 700M Global Professional Profiles: Enriched datasets for comprehensive outreach across the education sector.

Key Features of the Dataset:

1. Education Decision-Maker Profiles
2. Identify and connect with decision-makers at educational institutions, including principals, department heads, and education directors.

3. Reach K-12 educators, higher education faculty, and administrative professionals with relevant needs.

4. Advanced Filters for Precision Targeting

5. Filter by educational level, subject area, location, and specific roles to tailor your outreach campaigns for precise results.

6. AI-Driven Enrichment

7. Profiles are enriched with actionable data to provide valuable insights, ensuring your outreach efforts are impactful and effective.

Strategic Use Cases:

1. Educational Product and Service Marketing
2. Promote educational tools, software, or services to decision-makers in schools, colleges, and universities.

3. Build relationships with educators to present curriculum solutions, digital learning platforms, and teaching resources.

4. Recruitment and Talent Acquisition

5. Target educational institutions and administrators with recruitment solutions or staffing services for teaching and support staff.

6. Engage with HR professionals in the education sector to promote job openings and talent acquisition services.

7. Professional Development Programs

8. Reach educators and administrators to offer professional development courses, certifications, or training programs.

9. Provide online learning solutions to enhance the skills of educators worldwide.

10. Research and Educational Partnerships

11. Connect with education leaders for research collaborations, institutional partnerships, and academic initiatives.

12. Foster relationships with decision-makers to support joint ventures in the education sector.

Why Choose Success.ai?

1. Best Price Guarantee

2. Success.ai offers high-quality, verified data at the best possible prices, making it a cost-effective solution for your outreach needs.

3. Seamless Integration

4. Integrate this verified contact data into your CRM using APIs or download it in your preferred format for streamlined use.

5. Data Accuracy with AI Validation

6. With AI-driven validation, Success.ai ensures 99% accuracy for all data, providing you with reliable and up-to-date information.

7. Customizable and Scalable Solutions

8. Tailor data to specific education sectors or roles, making it easy to target the right contacts for your campaigns.

APIs for Enhanced Functionality:

1. Data Enrichment API

2. Enhance existing records in your database with verified contact data for education professionals.

3. Lead Generation API

4. Automate lead generation campaigns for educational services and products, ensuring your marketing efforts are more efficient.

Leverage Success.ai’s B2B Contact Data for Education Professionals Worldwide to connect with educators, administrators, and decision-makers in the education sector. With veri...

This point datalayer shows the locations of schools in Massachusetts. Schools appearing in this layer are those attended by students in pre-kindergarten through high school. Categories of schools include public, private, charter, collaborative programs, and approved special education. This data was originally developed by the Massachusetts Department of Environmental Protection’s (DEP) GIS Program based on database information provided by the Massachusetts Department of Education (DOE). The update published on April 17th, 2009 was based on listings MassGIS obtained from the DOE as of February 9th, 2009. The layer is stored in ArcSDE and distributed as SCHOOLS_PT. Only schools located in Massachusetts are included in this layer. The DOE also provides a listing of out-of-state schools open to Massachusetts' residents, particularly for those with special learning requirements. Please see http://profiles.doe.mass.edu/outofstate.asp for details. Updated September 2018.

ABSTRACT This article consists of a case study that aimed to identify health and education professionals’ perceptions of the School Health Program (PSE) actions in a suburban territory of Baixada Santista, São Paulo. Three educational counselors from two schools, a PSE articulator, a therapeutic companion, a psychologist, two nurses, and a community health worker were interviewed. The transcribed interviews were submitted to lexographic analysis and descending hierarchical classification in the software IRaMuTeQ-R. They were later analyzed based on the theoretical references on the PSE, school health, and intersectoriality. The results showed that the PSE actions focus on the matrix support meeting, referrals, vaccination verification, oral health, and eye health. The inadequate continuing training, poor knowledge of the PSE policy, and overwork compromise the full consideration of the program’s objectives, which, traversed by the pandemic, escalated the challenges faced by professionals. There is potential to be explored by the meeting of health and education. However, challenges involving these sectors, the traditional management rationale, the biological approach, and social participation should be overcome to advance towards intersectoral proposals to promote health and well-being.

This study is to understand how perceptions and the organization of school safety and security are associated with the level and type of law enforcement engagement in rural schools. A triangulation mixed methods design was used to collect and examine individual, school, and community level quantitative and qualitative data. The social-ecological theory of violence prevention guides the research by predicting that an interplay of factors at multiple levels influences the type and level of law enforcement engagement in rural schools. Specifically, it was predicted that the more organized and coordinated a school is in the area of safety and security, the more likely it is to be formally engaged with law enforcement. Formal engagement is defined as use of some version of the school resource officer (SRO) model or defined roles and responsibilities for law enforcement in schools that are articulated in documents such as a memorandum of agreement or understanding.

School safety research rarely considers the school security climate as a product of the simultaneous implementation of several school safety interventions. This is potentially problematic, as schools seldom employ only one safety intervention. Rather, schools today employ several interventions simultaneously to meet their safety and security needs. The purpose of this study is to investigate and identify effective types of school security climates and examine student growth within these climates. This multi-year project attempts to meet two goals: 1) Identify effective types of school security climates; and 2) Determine how the school security climate affects individual students. Data were collected from approximately 600 students attending 10 schools over the course of three years. Measures included an adapted version of the School Survey on Crime and Safety (SSOCS) and the Maryland Safe and Supportive Schools Survey (MDS3). The survey also included questions to obtain respondent demographics (age, gender, race/ethnicity) and other descriptive information about students and their experiences.

The global Cloud Security for Education market is experiencing robust growth, projected to reach $9.289 billion in 2025. While the exact Compound Annual Growth Rate (CAGR) isn't provided, considering the rapid adoption of cloud technologies in education and the increasing need for robust cybersecurity measures, a conservative estimate of the CAGR for the forecast period (2025-2033) would be around 15%. This growth is fueled by several key drivers. The increasing reliance on online learning platforms, the proliferation of sensitive student data requiring stringent protection, and the rising frequency and sophistication of cyber threats targeting educational institutions all necessitate strong cloud security solutions. Market segmentation reveals strong demand across both private and public cloud deployments, with a significant portion attributable to the K-12 and higher education sectors. Hybrid cloud solutions are also gaining traction, offering a balance between security and flexibility. Leading vendors like IBM, AWS, Microsoft Azure, and Google Cloud Platform are actively catering to this market, offering specialized security solutions tailored to the unique needs of educational institutions. However, factors such as budgetary constraints within some educational institutions and the complexity of integrating cloud security solutions into existing IT infrastructures can pose challenges to market expansion. Nevertheless, the long-term outlook remains positive, driven by ongoing technological advancements and growing awareness of cybersecurity risks within the education sector. The market's regional distribution likely mirrors global technology adoption patterns, with North America and Europe holding significant market shares initially. However, rapid growth is anticipated in Asia-Pacific regions like India and China, driven by increasing digitalization initiatives in education and government support for technological upgrades within schools and universities. The competitive landscape is marked by a mix of established players offering comprehensive security platforms and niche vendors focusing on specific security needs within the education sector. Future growth will depend on the continuous development of innovative security solutions addressing emerging threats, such as AI-powered threat detection and advanced data protection mechanisms, alongside efforts to make cloud security solutions more accessible and user-friendly for educational institutions with varying technical capabilities.

Total Special Educational Needs pupils in Maintained and Academy York Local Authority Schools (Excludes dual registered subsidiary pupils).

All data is taken from the January School Census.

Please note that, due to data protection requirements, we can't publish real values for number of SEN pupils in a certain school when those figures are < 5. Thus, those values have been converted to 9999.

Abstract copyright UK Data Service and data collection copyright owner.The National Pupil Database (NPD) is one of the richest education datasets in the world. It is a longitudinal database which links pupil characteristics to information about attainment for those who attend schools and colleges in England. There are a range of data sources in the NPD providing detailed information about children's education at different stages (pre-school, primary and secondary education and further education). Pupil level information was first collected in January 2002 as part of the Pupil Level Annual Schools Census (PLASC). The School Census replaced the PLASC in 2006 for secondary schools and in 2007 for nursery, primary and special schools. The School Census is carried out three times a year in the spring, summer and autumn terms (January, May and October respectively) and provides the Department for Education with both pupil and school-level data. The NPD is available through the UK Data Archive in three tiers. Tiers two and three are the most sensitive and must be accessed via the Archive's safe room, whereas tier four can be accessed remotely through the Archive's Secure Lab. Tier two contains individual pupil level data which is identifiable and sensitive. Individual pupil level extracts include sensitive information about pupils and their characteristics, including items described as 'sensitive personal data' within the UK Data Protection Act 1998 which have been recoded to become less sensitive. Examples of sensitive data items include ethnic group major, ethnic group minor, language group major, language group minor, Special Educational Needs and eligibility for Free School Meals. Tier three represents aggregated school level data which is identifiable and sensitive. Included are aggregated extracts of school level data from the Department of Education's School Level Database which include items described as 'sensitive personal data' within the Data Protection Act 1998 and could include small numbers and single counts. For example, there is 1 white boy eligible for Free School Meals in school x who did not achieve level 4 in English and maths at Key Stage 2. Tier four represents less sensitive data than tiers two and three. Included are individual pupil level extracts that do not contain information about pupils and their characteristics which are considered to be identifying or described as sensitive personal data within the Data Protection Act 1998. For example, the extracts may include information about pupil attainment, prior attainment, progression and pupil absences but do not include any identifying data items like names and addresses and any information about pupil characteristics other than gender. Extracts from the NPD are also available directly from the Department of Education through GOV.UK's National pupil database: apply for a data extract web page. The fourth edition (September 2017) includes a data file and documentation for the year 2016.

Description of the experiment setting: location, influential climatic conditions, controlled conditions (e.g. temperature, light cycle)In Fall of 2023 the USDA Food and Nutrition Service (FNS) conducted the fourth Farm to School Census. The 2023 Census was sent via email to 18,833 school food authorities (SFAs) including all public, private, and charter SFAs, as well as residential care institutions, participating in the National School Lunch Program. The questionnaire collected data on local food purchasing, edible school gardens, other farm to school activities and policies, and outcomes and challenges of participating in farm to school activities. A total of 12,559 SFAs submitted a response to the 2023 Census.Processing methods and equipment usedThe 2023 Census was administered solely via the web. The study team cleaned the raw data to ensure the data were as correct, complete, and consistent as possible. This process involved examining the data for logical errors and removing implausible values. The study team linked the 2023 Census data to information from the National Center of Education Statistics (NCES) Common Core of Data (CCD). Records from the CCD were used to construct a measure of urbanicity, which classifies the area in which schools are located.Study date(s) and durationData collection occurred from October 2, 2023 to January 7, 2024. Questions asked about activities prior to, during and after SY 2022-23. The 2023 Census asked SFAs whether they currently participated in, had ever participated in or planned to participate in any of 32 farm to school activities. Based on those answers, SFAs received a defined set of further questions.Study spatial scale (size of replicates and spatial scale of study area)Respondents to the survey included SFAs from all 50 States as well as American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, the U.S. Virgin Islands, and Washington, DC.Level of true replicationUnknownSampling precision (within-replicate sampling or pseudoreplication)No sampling was involved in the collection of this data.Level of subsampling (number and repeat or within-replicate sampling)No sampling was involved in the collection of this data.Study design (before–after, control–impacts, time series, before–after-control–impacts)None – Non-experimentalDescription of any data manipulation, modeling, or statistical analysis undertakenEach entry in the dataset contains SFA-level responses to the Census questionnaire for SFAs that responded. This file includes information from only SFAs that clicked “Submit” on the questionnaire. (The dataset used to create the 2023 Farm to School Census Report includes additional SFAs that answered enough questions for their response to be considered usable.)In addition, the file contains constructed variables used for analytic purposes. The file does not include weights created to produce national estimates for the 2023 Farm to School Census Report.The dataset identified SFAs, but to protect individual privacy the file does not include any information for the individual who completed the questionnaire. All responses to open-ended questions (i.e., containing user-supplied text) were also removed to protect privacy.Description of any gaps in the data or other limiting factorsSee the full 2023 Farm to School Census Report [https://www.fns.usda.gov/research/f2s/2023-census] for a detailed explanation of the study’s limitations.Outcome measurement methods and equipment usedNone

Attitude to data protection.

Topics: Occupational contact with personal data; self-assessment of willingness to provide information about personal matters regarding authorities; detailed determination of type and frequency of contacts with authorities; perceived disturbances by the requests for personal data by authorities; personal determination of wrong decisions by authorities due to incorrect storage of personal data; attitude to a data protection law and assessment of a government demand for storage of personal data; detailed determinations of those authorities to whom one would provide information without hesitation; assessment of the danger of abuse of data; attitude to a personal identification and a computer network of authorities; attitude to innovations and computers; attitude to protection of the private sphere; classification of activities in the areas private sphere and public; receipt of social services; type of borrowing and taxes paid; completed insurance policies; last medical treatment and number of visits to the doctor in the last year; last hospital stay; membership in clubs or citizen initiatives; self-assessment of status in various roles, such as e.g. patient, borrower, citizen, insurance policy holder or in occupation; satisfaction with democracy and the political system in the FRG; attitude to reforms and more social justice; relationship with neighborhood; assessment of the size of personal circle of friends.

Scales: attitudes to democracy and the social system.

Demography: age; sex; marital status; school education; vocational training; occupation; employment; household income; size of household; composition of household; head of household; self-assessment of social class.

This is a current list of approved security guard schools. The Security Guard Act of 1992 requires registration and training of security guards in New York State. The Division of Criminal Justice Services (DCJS) approves the private security training schools and provides administrative oversight for mandated security training.

ObjectivesWorld Health Organization issued Joint Statement on Data Protection and Privacy in the COVID-19 Response stating that collection of vast amounts of personal data may potentially lead to the infringement of fundamental human rights and freedoms. The Organization for Economic Cooperation and Development called on national governments to adhere to the international principles for data security and confidentiality. This paper describes the methods used to assist the Ministry of Health in bringing awareness of the data ownership, confidentiality and security principles to COVID-19 responders.MethodsThe Sierra Leone Epidemiological Data (SLED) Team data managers conducted training for groups of COVID-19 responders. Training included presentations on data confidentiality, information disclosure, physical and electronic data security, and cyber-security; and interactive discussion of real-life scenarios. A game of Jeopardy was created to test the participant’s knowledge.ResultsThis paper describes the methods used by the SLED Team to bring awareness of the DOCS principles to more than 2,500 COVID-19 responders.ConclusionSimilar efforts may benefit other countries where the knowledge, resources, and governing rules for protection of personal data are limited.

This data set contains special education enrolment data by exceptionality for both elementary and secondary publicly funded schools in Ontario, at the provincial level. Data includes: * Academic year * Areas of exceptionality * Elementary special education enrolment * Secondary special education enrolment * Total special education enrolment Source: as reported by school boards in the Ontario School Information System (OnSIS), October submission. The following school types are included: * public * catholic Data excludes private schools, school authorities, provincial/demonstration schools, Education and Community Partnership Program (ECPP) facilities, summer, night and adult continuing education day schools To protect student privacy, numbers are suppressed in categories with less than 10 students and enrolment totals have been rounded to the nearest five. Special education data on the identification of exceptionality and placement can vary by board, as boards may provide special education programs and services to students even if the students have not been identified as exceptional pupils by an Identification, Placement and Review Committee (IPRC). Non-identified category refers to students receiving special education programs and services who are not identified as exceptional pupils by an IPRC.

This dataset provides detail on the use of the IEP-504 exemption among school districts or local education agencies, including details on the software, provider, and assurances.

The Understanding School Safety and the Use of School Resource Officers in Understudied Settings project investigated school resource officers (SROs) within settings that have received almost no attention in the empirical literature: elementary schools and affluent, high performing school districts. This project was guided by four research questions: 1) Why and through what process were SROs implemented? 2) What roles and activities do SROs engage in within schools? 3) What impacts do SROs have on schools and students? 4) How do the roles and impacts of SROs differ across school contexts? Survey data come from the districts' SROs, and a sample of teachers, school leaders, students, and parents. Survey data was collected between spring of 2017 and fall of 2017.

Abstract copyright UK Data Service and data collection copyright owner.The National Pupil Database (NPD) is one of the richest education datasets in the world. It is a longitudinal database which links pupil characteristics to information about attainment for those who attend schools and colleges in England. There are a range of data sources in the NPD providing detailed information about children's education at different stages (pre-school, primary and secondary education and further education). Pupil level information was first collected in January 2002 as part of the Pupil Level Annual Schools Census (PLASC). The School Census replaced the PLASC in 2006 for secondary schools and in 2007 for nursery, primary and special schools. The School Census is carried out three times a year in the spring, summer and autumn terms (January, May and October respectively) and provides the Department for Education with both pupil and school-level data. The NPD is available through the UK Data Archive in three tiers. Tiers two and three are the most sensitive and must be accessed via the Archive's safe room, whereas tier four can be accessed remotely through the Archive's Secure Lab. Tier two contains individual pupil level data which is identifiable and sensitive. Individual pupil level extracts include sensitive information about pupils and their characteristics, including items described as 'sensitive personal data' within the UK Data Protection Act 1998 which have been recoded to become less sensitive. Examples of sensitive data items include ethnic group major, ethnic group minor, language group major, language group minor, Special Educational Needs and eligibility for Free School Meals. Tier three represents aggregated school level data which is identifiable and sensitive. Included are aggregated extracts of school level data from the Department of Education's School Level Database which include items described as 'sensitive personal data' within the Data Protection Act 1998 and could include small numbers and single counts. For example, there is 1 white boy eligible for Free School Meals in school x who did not achieve level 4 in English and maths at Key Stage 2. Tier four represents less sensitive data than tiers two and three. Included are individual pupil level extracts that do not contain information about pupils and their characteristics which are considered to be identifying or described as sensitive personal data within the Data Protection Act 1998. For example, the extracts may include information about pupil attainment, prior attainment, progression and pupil absences but do not include any identifying data items like names and addresses and any information about pupil characteristics other than gender. Extracts from the NPD are also available directly from the Department of Education through GOV.UK's National pupil database: apply for a data extract web page. The fourth edition (September 2017) includes a data file and documentation for the year 2016.

Data tracking the number of students enrolled in ministry defined secondary school courses at the provincial level. These ministry defined courses are for students in grades 9 to 12. They outline the mandatory knowledge and skills students must demonstrate by the end of the course. Includes: * course code * course description * grade * pathway or destination * enrolment Note: students enrolled in a course more than once are counted each time they enroll. Course enrolment data is reported by schools to the Ontario School Information System. The following secondary school types are included: * public * catholic To protect privacy, numbers are suppressed in categories with less than 10 students. Starting 2018-2019, enrolment numbers have been rounded to the nearest five. ## Related * College enrolment * College enrolments - 1996 to 2011 * University enrolment * Enrolment by grade in secondary schools * School enrolment by gender * Second language course enrolment * Enrolment by grade in elementary schools

Student enrolment data by school and grade as reported by school boards in the Ontario School Information System (OnSIS), October submission for the specified academic year. The data includes: * board number and name * school number, name, type, level and language * Enrolment by grade The following elementary and secondary school types are included: * public * Catholic The data excludes, private, publicly funded hospital and provincial schools, care and/or treatment, Education and Community Partnership Program (ECPP) facilities, summer schools, night schools and adult continuing education day schools. Includes Temporary Remote Learning Schools (TRLS). To protect privacy, numbers are suppressed in categories with less than 10 students. Numbers that represent 10 or more students are rounded to the nearest count of five.