This dataset is used for training of deep learning (DL) component based machine learning models described in the linked article. The article examines the effect of enriching training data with several building shapes on the prediction accuracy of machine learning models. There are nine building shapes used to collect the training data using EnergyPlus. Please read the full article for the relevant details of component structure and training of DL components. There are seven training dataset BaseCase, E-1, E-2, E-3, I-1, I-2, and I-3 and one test dataset TestData. The trained DL component are saved under Models folder in each dataset. The performance.csv file inside each dataset folder describes the performance of DL components trained on the corresponding dataset.

The Mathematics Enrichment Classes market has seen significant evolution over recent years, driven by a growing emphasis on STEM (Science, Technology, Engineering, and Mathematics) education across the globe. These classes provide students with advanced mathematical skills, catering not only to those who seek to enh

Success.ai’s Education Industry Data provides access to comprehensive profiles of global professionals in the education sector. Sourced from over 700 million verified LinkedIn profiles, this dataset includes actionable insights and verified contact details for teachers, school administrators, university leaders, and other decision-makers. Whether your goal is to collaborate with educational institutions, market innovative solutions, or recruit top talent, Success.ai ensures your efforts are supported by accurate, enriched, and continuously updated data.

Why Choose Success.ai’s Education Industry Data? 1. Comprehensive Professional Profiles Access verified LinkedIn profiles of teachers, school principals, university administrators, curriculum developers, and education consultants. AI-validated profiles ensure 99% accuracy, reducing bounce rates and enabling effective communication. 2. Global Coverage Across Education Sectors Includes professionals from public schools, private institutions, higher education, and educational NGOs. Covers markets across North America, Europe, APAC, South America, and Africa for a truly global reach. 3. Continuously Updated Dataset Real-time updates reflect changes in roles, organizations, and industry trends, ensuring your outreach remains relevant and effective. 4. Tailored for Educational Insights Enriched profiles include work histories, academic expertise, subject specializations, and leadership roles for a deeper understanding of the education sector.

Data Highlights: 700M+ Verified LinkedIn Profiles: Access a global network of education professionals. 100M+ Work Emails: Direct communication with teachers, administrators, and decision-makers. Enriched Professional Histories: Gain insights into career trajectories, institutional affiliations, and areas of expertise. Industry-Specific Segmentation: Target professionals in K-12 education, higher education, vocational training, and educational technology.

Key Features of the Dataset: 1. Education Sector Profiles Identify and connect with teachers, professors, academic deans, school counselors, and education technologists. Engage with individuals shaping curricula, institutional policies, and student success initiatives. 2. Detailed Institutional Insights Leverage data on school sizes, student demographics, geographic locations, and areas of focus. Tailor outreach to align with institutional goals and challenges. 3. Advanced Filters for Precision Targeting Refine searches by region, subject specialty, institution type, or leadership role. Customize campaigns to address specific needs, such as professional development or technology adoption. 4. AI-Driven Enrichment Enhanced datasets include actionable details for personalized messaging and targeted engagement. Highlight educational milestones, professional certifications, and key achievements.

Strategic Use Cases: 1. Product Marketing and Outreach Promote educational technology, learning platforms, or training resources to teachers and administrators. Engage with decision-makers driving procurement and curriculum development. 2. Collaboration and Partnerships Identify institutions for collaborations on research, workshops, or pilot programs. Build relationships with educators and administrators passionate about innovative teaching methods. 3. Talent Acquisition and Recruitment Target HR professionals and academic leaders seeking faculty, administrative staff, or educational consultants. Support hiring efforts for institutions looking to attract top talent in the education sector. 4. Market Research and Strategy Analyze trends in education systems, curriculum development, and technology integration to inform business decisions. Use insights to adapt products and services to evolving educational needs.

Why Choose Success.ai? 1. Best Price Guarantee Access industry-leading Education Industry Data at unmatched pricing for cost-effective campaigns and strategies. 2. Seamless Integration Easily integrate verified data into CRMs, recruitment platforms, or marketing systems using downloadable formats or APIs. 3. AI-Validated Accuracy Depend on 99% accurate data to reduce wasted outreach and maximize engagement rates. 4. Customizable Solutions Tailor datasets to specific educational fields, geographic regions, or institutional types to meet your objectives.

Strategic APIs for Enhanced Campaigns: 1. Data Enrichment API Enrich existing records with verified education professional profiles to enhance engagement and targeting. 2. Lead Generation API Automate lead generation for a consistent pipeline of qualified professionals in the education sector. Success.ai’s Education Industry Data enables you to connect with educators, administrators, and decision-makers transforming global...

Our People data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes a variety of data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences.

1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc.
2. Demographics - Gender, Age Group, Marital Status, Language etc.
3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc
4. Persona - Consumer type, Communication preferences, Family type, etc
5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc.
6. Household - Number of Children, Number of Adults, IP Address, etc.
7. Behaviours - Brand Affinity, App Usage, Web Browsing etc.
8. Firmographics - Industry, Company, Occupation, Revenue, etc
9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc.
10. Auto - Car Make, Model, Type, Year, etc.
11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

People Data Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

People data Use Cases:

360-Degree Customer View: Get a comprehensive image of customers by the means of internal and external data aggregation. Data Enrichment: Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity. Advertising & Marketing: Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

Here's the schema of People Data: person_id first_name last_name age gender linkedin_url twitter_url facebook_url city state address zip zip4 country delivery_point_bar_code carrier_route walk_seuqence_code fips_state_code fips_country_code country_name latitude longtiude address_type metropolitan_statistical_area core_based+statistical_area census_tract census_block_group census_block primary_address pre_address streer post_address address_suffix address_secondline address_abrev census_median_home_value home_market_value property_build+year property_with_ac property_with_pool property_with_water property_with_sewer general_home_value property_fuel_type year month household_id Census_median_household_income household_size marital_status length+of_residence number_of_kids pre_school_kids single_parents working_women_in_house_hold homeowner children adults generations net_worth education_level occupation education_history credit_lines credit_card_user newly_issued_credit_card_user credit_range_new
credit_cards loan_to_value mortgage_loan2_amount mortgage_loan_type
mortgage_loan2_type mortgage_lender_code
mortgage_loan2_render_code
mortgage_lender mortgage_loan2_lender
mortgage_loan2_ratetype mortgage_rate
mortgage_loan2_rate donor investor interest buyer hobby personal_email work_email devices phone employee_title employee_department employee_job_function skills recent_job_change company_id company_name company_description technologies_used office_address office_city office_country office_state office_zip5 office_zip4 office_carrier_route office_latitude office_longitude office_cbsa_code
office_census_block_group
office_census_tract office_county_code
company_phone
company_credit_score
company_csa_code
company_dpbc
company_franchiseflag
company_facebookurl company_linkedinurl company_twitterurl
company_website company_fortune_rank
company_government_type company_headquarters_branch company_home_business
company_industry
company_num_pcs_used
company_num_employees
company_firm_individual company_msa company_msa_name
company_naics_code
company_naics_description
company_naics_code2 company_naics_description2
company_sic_code2
company_sic_code2_description
company_sic_code4 company_sic_code4_description
company_sic_code6
company_sic_code6_description
company_sic_code8
company_sic_code8_description company_parent_company
company_parent_company_location company_public_private company_subsidiary_company company_residential_business_code company_revenue_at_side_code company_revenue_range
company_revenue company_sales_volume
company_small_business company_stock_ticker company_year_founded company_minorityowned
company_female_owned_or_operated company_franchise_code company_dma company_dma_name
company_hq_address
company_hq_city company_hq_duns company_hq_state
company_hq_zip5 company_hq_zip4 company_se...

This dataset is used for training of component based machine learning (CBML) models described in the article. The article examines the effect of increasing and enriching training data on machine learning model's ability to generalise. Please read the full article for the relevant details of ML models. There are seven training dataset BaseCase, E-1, E-2, E-3, I-1, I-2, and I-3 and one test dataset. The trained machine learning (ML) components are saved under 'Models' folder in each dataset.

SFS-A68: "A dataset for the segmentation of space functions in apartment buildings"

Authors: "Amir Ziaee, Georg Suter, Mihael Barada, Laura Keiblinger"

Copyright: "Design Computing Group TU Wien, 2023"

Credits: "Design Computing Group TU Wien"

License: "GNU GENERAL PUBLIC LICENSE Version 3"

Version: "1.0.2"

Maintainer: "Amir Ziaee"

Email: "amir.ziaee@tuwien.ac.at"

Url: https://github.com/A2Amir/SFS-A68-16

Description: "We present the SFS-A68-16 dataset for space function segmentation in apartment buildings. The dataset consists of 16 multi-viewpoint space layout input and corresponding ground truth images for 68 floor plans of apartment buildings designed or built between 1952 and 2019. It addresses a limitation of the SFS-A68 dataset (version 1.0.1) we created in our previous work, which consists of single-viewpoint projection images only. Each pixel in a ground truth image of the SFS-A68-16 dataset is assigned to a space function class. Space function classes in apartment buildings that are classified by a space function segmentation network are shown below under ground truth classes. We have identified 22 space function classes for the apartment buildings in our dataset. Each element in an input image of the SFS-A68-16 dataset is colored according to a unique class color (below, under input classes). Space elements, such as doors and furnishing elements, are contextual features in input images that may help determine the function of a space. To measure whether excluding space elements in input images affects the accuracy of a space function segmentation network, we create a new dataset, SFS-A68-16-SEE, where space elements are excluded in input images of the SFS-A68-16-SEE dataset. The defined class hierarchy of the dataset with the unique RGB color code of each class can be seen below."

Input classes

[Root]

├──[Space]

│ ├── (102, 102, 122)[InternalSpace]

│ └── (161, 162, 155)[ExternalSpace]

└──[SpaceElement]

├── [SpaceContainedElement]

│ ├── [CirculationElement]

│ │ ├── (230, 184, 175)[FlightOfStairs]

│ │ └── (107, 74, 101)[Landing]

│ ├── [FurnishingElement]

│ │ ├── (0, 191, 255)[KitchenElement]

│ │ └── (70, 130, 180)[SanitaryElement]

│ └── [EquipmentElement]

│ └── [HomeAppliance]

│ └── (159, 140, 81)[TextileCareAppliance]

└── [SpaceEnclosingElement]

├── (109, 189, 110)[Opening]

├── (0, 250, 154)[Partition]

├── (255, 215, 0)[Window]

└── [Door]

├── (200, 255, 0)[InternalDoor]

├── (72, 112, 39)[UnitDoor]

├── (187, 244, 154)[ElevatorDoor]

├── (47, 79, 79)[BalconyDoor]

└── (195, 210, 192)[SideEntrance]

Ground truth classes

[Space]

├── [ResidentialSpace]

│ ├── [CommunalSpace]

│ │ ├── (255, 218, 185)[DiningRoom]

│ │ ├── (166, 206, 227)[FamilyRoom]

│ │ └── (255, 0, 0)[LivingRoom]

│ └── [PrivateSpace]

│ ├── (0, 255, 0)[Bedroom]

│ │ ├── (0, 128, 128)[MasterBedroom]

│ │ └── (0, 128, 255)[BoxRoom]

│ └── (160, 82, 45)[HomeOffice]

├── [ServiceSpace]

│ ├── (255, 192, 203)[Shaft]

│ ├── (245, 245, 220)[StorageRoom]

│ │ └── (0, 206, 209)[WalkInCloset]

│ └── [SanitarySpace]

│ ├── (128, 0, 0)[Bathroom]

│ ├── (75, 0, 130)[Toilet]

│ ├── (255, 255, 0)[Kitchen]

│ └── (0, 128, 0)[LaundryRoom]

├── [CirculationSpace]

│ ├── [VerticalCirculationSpace]

│ │ ├── (0, 0, 128)[Elevator]

│ │ └── (0, 0, 255)[Stairway]

│ └── [HorizontalCirculationSpace]

│ ├── (255, 0, 255)[Entrance]

│ └── (255, 100, 0)[Hallway]

│ ├── (255, 165, 0)[MainHallway]

│ └── (0, 255, 255)[InternalHallway]

└── [ExternalSpace]

├── (128, 128, 0)[AccessBalcony]

└── (225, 138, 96)[Loggia]

Mink behaviour, welfare and reproductive success: comparison between non-enriched and enriched housing. This study investigated how a practical enrichment program on North American farms affected mink welfare over the course of their annual cycle. The project began with a pilot study on two farms to assess the practicality and cost of a wide range of possible enrichments, followed by a more comprehensive experiment expanded to three farms to collect more detailed data, including data on reproductive variables.

Snapshot img

Alternative Non Credential Courses Market Size 2025-2029

The alternative non credential courses market size is valued to increase USD 21.78 billion, at a CAGR of 26.3% from 2024 to 2029. Growing prominence of alternative non credentialing through m-learning will drive the alternative non credential courses market.

Major Market Trends & Insights

North America dominated the market and accounted for a 59% growth during the forecast period.
By Type - Non-institutional segment was valued at USD 2.59 billion in 2023
By Deliver Mode - Online segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 604.14 million
Market Future Opportunities: USD 21782.40 million
CAGR : 26.3%
North America: Largest market in 2023

Market Summary

The market refers to the rapidly expanding sector offering education and skills training outside the traditional credentialing system. This market's continuous evolution is driven by several factors, including the growing prominence of alternative non-credentialing through mobile learning (m-learning), and the rapid penetration of Internet-enabled devices. However, this market also faces challenges, such as inadequate cybersecurity measures, which require addressing to ensure data privacy and security. Core technologies like artificial intelligence and machine learning are revolutionizing the delivery and personalization of alternative non-credential courses. Applications, such as coding bootcamps and digital marketing courses, have seen significant adoption rates, with some reporting up to 70% job placement for graduates.
Service types, like self-paced courses and subscription-based models, cater to diverse learner needs and preferences. Regulations, such as data privacy laws and accreditation standards, continue to shape the market landscape. Regional mentions, like the European Union's General Data Protection Regulation (GDPR) and the United States' Higher Education Act, underscore the importance of adhering to regulatory frameworks. In conclusion, the market is an evolving and dynamic space, presenting opportunities for innovation and growth while addressing challenges related to data security and regulatory compliance.

What will be the Size of the Alternative Non Credential Courses Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Alternative Non Credential Courses Market Segmented and what are the key trends of market segmentation?

The alternative non credential courses industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Non-institutional
  Institutional


Deliver Mode

  Online
  Hybrid
  In-Person


Application Type

  Professional Development
  Skill-Based Training
  Personal Enrichment
  Career Transition


End-User

  Working Professionals
  Students
  Job Seekers
  Corporates


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The non-institutional segment is estimated to witness significant growth during the forecast period.

In the realm of continuous learning and professional development, alternative non-credential courses have emerged as a significant trend in the education sector. Online education companies now offer various types of credentials beyond degrees, such as digital badges and certificates, to learners. These non-institutional credentials highlight the acquisition of specific skills and achievements. MOOC-verified certificates represent one category. Although massive open online courses (MOOCs) are free, learners can opt to pay for certificates that verify their mastery of a particular skill set. Another category consists of digital badges. Some alternative non-credential course providers employ digital badges to showcase learners' achievements and skills.

Request Free Sample

The Non-institutional segment was valued at USD 2.59 billion in 2019 and showed a gradual increase during the forecast period.

The adoption of these non-degree credentials is on the rise. According to recent studies, the number of learners earning MOOC certificates has grown by 25%, while the issuance of digital badges has increased by 30%. Moreover, industry experts anticipate that the market for alternative non-credential courses will expand by 25% within the next two years. Key components of these courses include e-learning content authoring, learning analytics dashboards, project-based learning, competency frameworks, learning outcome mapping, mobile learning accessibility, skill assessment too

Exon-capture studies have typically been restricted to relatively shallow phylogenetic scales due primarily to hybridisation constraints. Here, we present an exon-capture system for an entire class of marine invertebrates, the Ophiuroidea, built upon a phylogenetically diverse transcriptome foundation. The system captures ~90% of the 1552 exon target, across all major lineages of the quarter-billion year old extant crown group. Key features of our system are: 1) basing the target on an alignment of orthologous genes determined from 52 transcriptomes spanning the phylogenetic diversity and trimmed to remove anything difficult to capture, map or align, 2) use of multiple artificial representatives based on ancestral state reconstructions rather than exemplars to improve capture and mapping of the target, 3) mapping reads to a multi-reference alignment, and 4) using patterns of site polymorphism to distinguish among paralogy, polyploidy, allelic differences and sample contamination. The re...

Here are the supporting information for the manuscript entitled "Direction-aware functional class scoring enrichment analysis of Infinium DNA methylation data". The article describes a method for accurate pathway enrichment analysis of Infinium Methylation array data.

This archive contains the software repository for the project at time of public release, a docker image to facilitate reproduction, a copy of the preprint and an R Markdown generated HTML report that demonstrates a simple application of the method as mentioned in the preprint.

The PDF file "Direction-aware FCS V2.3 preprint.pdf" is the preprint. The "gmea.tar.gz" file is the docker image. The "gmea-main.zip" file is the git software repository. The "example_workflow.html" file is the HTML report.

Singapore Consumer Price Index (CPI): Education: Tuition & Other Fees: Enrichment & Supp. Courses data was reported at 122.530 2019=100 in Dec 2024. This records an increase from the previous number of 122.470 2019=100 for Nov 2024. Singapore Consumer Price Index (CPI): Education: Tuition & Other Fees: Enrichment & Supp. Courses data is updated monthly, averaging 99.731 2019=100 from Jan 2014 (Median) to Dec 2024, with 132 observations. The data reached an all-time high of 122.823 2019=100 in May 2024 and a record low of 81.814 2019=100 in Jan 2014. Singapore Consumer Price Index (CPI): Education: Tuition & Other Fees: Enrichment & Supp. Courses data remains active status in CEIC and is reported by Singapore Department of Statistics. The data is categorized under Global Database’s Singapore – Table SG.I006: Consumer Price Index: 2019=100.

Global virtual schools market worth at USD 3.72 Billion in 2024, is expected to surpass USD 18.98 Billion by 2034, with a CAGR of 16.1% from 2025 to 2034.

Dataset with two samples of SARS-Cov-2 sequenced with Illumina using Artic v3 amplicon enrichment protocol.

Math Enrichment Market Outlook

According to our latest research, the global math enrichment market size reached USD 5.8 billion in 2024 and is expected to grow at a robust CAGR of 8.2% from 2025 to 2033, culminating in a forecasted market value of USD 11.1 billion by 2033. This growth trajectory is driven by the escalating demand for supplemental educational resources and innovative learning platforms that cater to diverse learner needs across the globe. The increasing focus on STEM (Science, Technology, Engineering, and Mathematics) education and the integration of technology in teaching methodologies are pivotal factors propelling the expansion of the math enrichment market.

One of the primary growth drivers for the math enrichment market is the global emphasis on enhancing mathematical proficiency among students, as nations recognize the critical role mathematics plays in fostering logical thinking, problem-solving, and analytical skills. Governments and educational institutions are investing heavily in curriculum development and enrichment programs to bridge learning gaps and prepare students for competitive academic and professional environments. Furthermore, the proliferation of standardized testing and international assessments has intensified the need for effective math enrichment solutions, pushing parents and schools to seek advanced resources that go beyond conventional classroom teaching.

The rapid digital transformation within the education sector has led to the widespread adoption of online learning platforms and interactive educational tools, further fueling the growth of the math enrichment market. The accessibility and flexibility offered by digital resources, such as online tutoring services, educational games, and adaptive learning platforms, have democratized access to quality math education. These solutions cater to various learning styles and paces, enabling personalized learning experiences and improving student outcomes. The integration of artificial intelligence and data analytics in math enrichment products also allows for real-time feedback and performance tracking, making learning more engaging and effective.

Another significant growth factor is the rising demand for lifelong learning and adult education, as professionals seek to upskill and reskill in an increasingly competitive job market. Math enrichment programs designed for adult learners are gaining traction, particularly in sectors where mathematical competence is essential. Additionally, the expansion of global e-learning ecosystems and the increasing collaboration between educational technology companies and traditional institutions are creating new opportunities for market players to innovate and diversify their offerings. These trends collectively underscore the dynamic evolution of the math enrichment market and its pivotal role in shaping the future of education.

From a regional perspective, North America currently leads the global math enrichment market, accounting for a significant share due to its advanced educational infrastructure, high digital adoption rates, and strong presence of leading market players. However, the Asia Pacific region is witnessing the fastest growth, driven by large student populations, rising disposable incomes, and government initiatives aimed at improving educational outcomes. Europe also holds a substantial market share, supported by robust investments in educational technology and increasing awareness about the benefits of math enrichment. The Middle East & Africa and Latin America are emerging as promising markets, fueled by ongoing educational reforms and the gradual adoption of digital learning solutions.

Product Type Analysis

The math enrichment market is segmented by product type into workbooks, online platforms, tutoring services, educational games, and others. Workbooks remain a staple in the market, offering structured practice and reinforcement of mathematical concepts. These resources are widely used in both classroom and home settings, provi

Untargeted mass spectrometry is a robust tool for biology, but it usually requires a large amount of time on data analysis, especially for system biology. A framework called Multiple-Chemical nebula (MCnebula) was developed herein to facilitate the LC–MS data analysis process by focusing on critical chemical classes and visualization in multiple dimensions. This framework consists of three vital steps as follows: (1) abundance-based classes (ABC) selection algorithm, (2) critical chemical classes to classify “features” (corresponding to compounds), and (3) visualization as multiple Child-Nebulae (network graph) with annotation, chemical classification, and structure. Notably, MCnebula can be used to explore the classification and structural characteristic of unknown compounds beyond the limit of the spectral library. Moreover, it is intuitive and convenient for pathway analysis and biomarker discovery because of its function of ABC selection and visualization. MCnebula was implemented in the R language. A series of tools in R packages were provided to facilitate downstream analysis in an MCnebula-featured way, including feature selection, homology tracing of top features, pathway enrichment analysis, heat map clustering analysis, spectral visualization analysis, chemical information query, and output analysis reports. The broad utility of MCnebula was illustrated by a human-derived serum data set for metabolomics analysis. The results indicated that “Acyl carnitines” were screened out by tracing structural classes of biomarkers, which was consistent with the reference. A plant-derived data set was investigated to achieve a rapid annotation and discovery of compounds in E. ulmoides.

Functional Annotation Clustering for NPDE genes. (XLSX 12 kb)

Functional Annotation Clustering for genes specifically found by maSigPro in comparison with NBMM. (XLSX 11 kb)

The HLA genes are regulated by at least 10 AiD associated SNPs. Functional module containing HLA genes is highly enriched for immune system related pathways and biological processes. The shared central genes in the module play crucial roles in the biological processes linked to immune system. Here, we provided the details of proteins, GO and KEGG pathways enrichment results of the HLA module. We have also provided the SNPs targetting shared central HLA genes.

For quantitative analysis of the recordings from the T-EE cage, tracking of individual animals within the cage was conducted at weeks 3, 6, and 9 post-injuries. From each three-hour-long weekly recording, three 5-min-long segments were selected for analysis: 5-10 mins, 90-95 mins, and 150-155 mins. Distance measurements were conducted using Fiji and the MTrackJ plugin. MTrackJ was used to manually track each rat’s x/y coordinates in mm at every frame of the recording, with the time interval between frames set at 0.2 seconds. The real distances between successive coordinates were calculated and summed to determine the total distance travelled.

CompanyData.com, (BoldData), is a leading provider of verified global business data sourced exclusively from official government and trade registries. Our global education dataset features 4.8 million schools across 190+ countries—offering accurate, up-to-date information on institutions from primary schools to universities. This makes us the ideal data partner for organizations targeting the education sector at scale.

Our school database includes detailed firmographics, institutional hierarchies, contact names, email addresses, phone and mobile numbers, type of school, language of instruction, and geographic location. Every record is verified and regularly updated to meet the highest standards of data quality, accuracy, and compliance. Whether you're targeting public or private schools, regional networks, or specific education levels, our data supports precise segmentation and engagement.

This dataset supports a wide range of use cases: international sales and marketing campaigns, CRM enrichment, compliance and KYC verification, market research, AI training, and education-focused outreach. Whether you're an EdTech provider, academic publisher, or enterprise service platform, we provide the data foundation to help you grow.

Delivery is flexible and tailored to your needs—via custom CSV exports, API integration, enrichment services, or access to our self-service platform. Backed by our broader database of over 380 million verified companies and institutions worldwide, CompanyData.com (BoldData) empowers your organization with the insights and precision needed to succeed in the global education market.