The Q-Herilearn scale is a probabilistic scale of summative estimates that measures different aspects of the learning process in Heritage Education. It consists of seven factors (Knowing, Understanding, Respecting, Valuing, Caring, Enjoying and Transmitting). Each dimension is measured by means of seven indicators scored on a 4-point frequency response scale (1 = Never or almost never; 2 = Sometimes; 3 = Quite often; 4 = Always or almost always). Sufficient evidence of content validity has been obtained through a concordance analysis —which employed multi-facet logistic models (Many Facet Rasch Model MFRM)— of the scores of 40 judges, who estimated the relevance, adequacy, and clarity of each item. The metric properties of the scores were determined using ESEM —Exploratory Structural Equation Modeling—, EGA Exploratory Graph Analysis and Network Analysis. The scale was calibrated using Item Response Theory models: the Nominal Response Model and the Graded Response Model.

The Atlas of Canada National Scale Data 1:5,000,000 Series consists of boundary, coast, island, place name, railway, river, road, road ferry and waterbody data sets that were compiled to be used for atlas medium scale (1:5,000,000 to 1:15,000,000) mapping. These data sets have been integrated so that their relative positions are cartographically correct. Any data outside of Canada included in the data sets is strictly to complete the context of the data.

In the United States in 2022, the majority of diagnostic vendors only shared data to health information exchanges (HIE) on a regional or state level. While around 30 percent said they contributed data to a private HIE.

USAGE OF DISSIMILARITY MEASURES AND MULTIDIMENSIONAL SCALING FOR LARGE SCALE SOLAR DATA ANALYSIS Juan M Banda, Rafal Anrgyk ABSTRACT: This work describes the application of several dissimilarity measures combined with multidimensional scaling for large scale solar data analysis. Using the first solar domain-specific benchmark data set that contains multiple types of phenomena, we investigated combination of different image parameters with different dissimilarity measure sin order to determine which combination will allow us to differentiate our solar data within each class and versus the rest of the classes. In this work we also address the issue of reducing dimensionality by applying multidimensional scaling to our dissimilarity matrices produced by the previously mentioned combination. By applying multidimensional scaling we can investigate how many resulting components are needed in order to maintain a good representation of our data (in an artificial dimensional space) and how many can be discarded in order to economize our storage costs. We present a comparative analysis between different classifiers in order to determine the amount of dimensionality reduction that can be achieved with said combination of image parameters, similarity measure and multidimensional scaling.

The MNIST Large Scale dataset is based on the classic MNIST dataset, but contains large scale variations up to a factor of 16. The motivation behind creating this dataset was to enable testing the ability of different algorithms to learn in the presence of large scale variability and specifically the ability to generalise to new scales not present in the training set over wide scale ranges.

The dataset contains training data for each one of the relative size factors 1, 2 and 4 relative to the original MNIST dataset and testing data for relative scaling factors between 1/2 and 8, with a ratio of $\sqrt[4]{2}$ between adjacent scales.

In this paper, we investigate the use of Bayesian networks to construct large-scale diagnostic systems. In particular, we consider the development of large-scale Bayesian networks by composition. This compositional approach reflects how (often redundant) subsystems are architected to form systems such as electrical power systems. We develop high-level specifications, Bayesian networks, clique trees, and arithmetic circuits representing 24 different electrical power systems. The largest among these 24 Bayesian networks contains over 1,000 random variables. Another BN represents the real-world electrical power system ADAPT, which is representative of electrical power systems deployed in aerospace vehicles. In addition to demonstrating the scalability of the compositional approach, we briefly report on experimental results from the diagnostic competition DXC, where the ProADAPT team, using techniques discussed here, obtained the highest scores in both Tier 1 (among 9 international competitors) and Tier 2 (among 6 international competitors) of the industrial track. While we consider diagnosis of power systems specically, we believe this work is relevant to other system health management problems, in particular in dependable systems such as aircraft and spacecraft. Reference: O. J. Mengshoel, S. Poll, and T. Kurtoglu. "Developing Large-Scale Bayesian Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft." Proc. of the IJCAI-09 Workshop on Self-* and Autonomous Systems (SAS): Reasoning and Integration Challenges, 2009 BibTex Reference: @inproceedings{mengshoel09developing, title = {Developing Large-Scale {Bayesian} Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft}, author = {Mengshoel, O. J. and Poll, S. and Kurtoglu, T.}, booktitle = {Proc. of the IJCAI-09 Workshop on Self-$\star$ and Autonomous Systems (SAS): Reasoning and Integration Challenges}, year={2009} }

This dataset is about books and is filtered where the book is Data just right : introduction to large-scale data & analytics, featuring 7 columns including author, BNB id, book, book publisher, and ISBN. The preview is ordered by publication date (descending).

This repository contains all of the data used in the manuscript "Linearizing the vertical scale of an interferometric microscope and its effect on step-height measurement," by Thomas A. Germer, T. Brian Renegar, Ulf Griesmann, and Johannes A. Soons, which has been published in Surface Topography: Metrology and Properties volume 12, number 2, article 025012 on 8 May 2024. The repository also contains a Python Jupyter notebook that performs the analysis of the data and generates the figures in the manuscript.

The Atlas of Canada National Scale Data 1:1,000,000 Series consists of boundary, coast, island, place name, railway, river, road, road ferry and waterbody data sets that were compiled to be used for atlas large scale (1:1,000,000 to 1:4,000,000) mapping. These data sets have been integrated so that their relative positions are cartographically correct. Any data outside of Canada included in the data sets is strictly to complete the context of the data.

This data set contains small-scale base GIS data layers compiled by the National Park Service Servicewide Inventory and Monitoring Program and Water Resources Division for use in a Baseline Water Quality Data Inventory and Analysis Report that was prepared for the park. The report presents the results of surface water quality data retrievals for the park from six of the United States Environmental Protection Agency's (EPA) national databases: (1) Storage and Retrieval (STORET) water quality database management system; (2) River Reach File (RF3) Hydrography; (3) Industrial Facilities Discharges; (4) Drinking Water Supplies; (5) Water Gages; and (6) Water Impoundments. The small-scale GIS data layers were used to prepare the maps included in the report that depict the locations of water quality monitoring stations, industrial discharges, drinking intakes, water gages, and water impoundments. The data layers included in the maps (and this dataset) vary depending on availability, but generally include roads, hydrography, political boundaries, USGS 7.5' minute quadrangle outlines, hydrologic units, trails, and others as appropriate. The scales of each layer vary depending on data source but are generally 1:100,000.

Methodology assessment of statistical capacity (scale 0 - 100) in Sudan was reported at 40 in 2020, according to the World Bank collection of development indicators, compiled from officially recognized sources. Sudan - Methodology assessment of statistical capacity (scale 0 - 100) - actual values, historical data, forecasts and projections were sourced from the World Bank on March of 2025.

This dataset is about book subjects and is filtered where the books includes Data just right : introduction to large-scale data & analytics, featuring 10 columns including authors, average publication date, book publishers, book subject, and books. The preview is ordered by number of books (descending).

Between January and September 2024, healthcare organizations in the United States saw 491 large-scale data breaches, resulting in the loss of over 500 records. This figure has increased significantly in the last decade. To date, the highest number of large-scale data breaches in the U.S. healthcare sector was recorded in 2023, with a reported 745 cases.

The global truck scale data management software market is anticipated to reach a valuation of USD 1.2 billion by 2033, expanding at a healthy CAGR of 7.2% from 2025 to 2033. The rising demand for efficient truck scale operations and the growing adoption of digital technologies in the logistics and transportation sectors are the key driving forces behind this growth. Furthermore, the increasing need for accurate and real-time data for decision-making and optimization is expected to propel the market forward. In terms of segmentation, the cloud-based deployment model is gaining traction due to its scalability, cost-effectiveness, and remote accessibility. The logistics and transportation segment holds a significant market share and is expected to maintain its dominance throughout the forecast period. Key vendors in the market include Mettler Toledo, Rice Lake Weighing Systems, Avery Weigh-Tronix, Cardinal Scale, and Leon Engineering SA. They are focused on providing advanced features such as real-time data monitoring, remote diagnostics, and integration with other systems to meet the evolving needs of customers.

Les écailles ont été prélevées sur le saumon dans l'océan Pacifique Nord-Est et analysées pour obtenir des informations sur l'âge. Ces données ont été recueillies dans le cadre de l'expédition en haute mer du golfe d'Alaska de l'Année internationale du saumon (IYS) menée en février et mars 2019, afin d'améliorer encore la compréhension des facteurs ayant une incidence sur la survie hivernale du saumon en début de mer.

This dataset is about book series and is filtered where the books is Data just right : introduction to large-scale data & analytics. It has 10 columns such as book series, earliest publication date, latest publication date, average publication date, and number of authors. The data is ordered by earliest publication date (descending).

Spatial coverage index compiled by East View Geospatial of set "Qatar 1:10,000 Scale Vector Data". Source data from QCGIS (publisher). Type: Topographic. Scale: 1:10,000. Region: Middle East.

This data set contains small-scale base GIS data layers compiled by the National Park Service Servicewide Inventory and Monitoring Program and Water Resources Division for use in a Baseline Water Quality Data Inventory and Analysis Report that was prepared for the park. The report presents the results of surface water quality data retrievals for the park from six of the United States Environmental Protection Agency's (EPA) national databases: (1) Storage and Retrieval (STORET) water quality database management system; (2) River Reach File (RF3) Hydrography; (3) Industrial Facilities Discharges; (4) Drinking Water Supplies; (5) Water Gages; and (6) Water Impoundments. The small-scale GIS data layers were used to prepare the maps included in the report that depict the locations of water quality monitoring stations, industrial discharges, drinking intakes, water gages, and water impoundments. The data layers included in the maps (and this dataset) vary depending on availability, but generally include roads, hydrography, political boundaries, USGS 7.5' minute quadrangle outlines, hydrologic units, trails, and others as appropriate. The scales of each layer vary depending on data source but are generally 1:100,000.

The Atlas of Canada National Scale Data 1:15,000,000 Series consists of boundary, coast and coastal islands, place name, railway, river, road, road ferry and waterbody data sets that were compiled to be used for atlas small scale (1:15,000,000 and 1:30,000,000) mapping. These data sets have been integrated so that their relative positions are cartographically correct. Any data outside of Canada included in the data sets is strictly to complete the context of the data.

A building is a structure that has a roof and walls and stands more or less permanently in one place.Small buildings have only their location recorded.A ‘building to scale’ is a structure that has one dimension larger than 50 metres for the 1: 20,000 scale and larger than 30 metres for the 1: 10,000 scale. Their extents are recorded.Additional Documentation

Building to Scale - User Guide (Word)

Building to Scale - Data Description (PDF) Building to Scale - Documentation (Word)

Status
Required: data needs to be generated or updated

Maintenance and Update Frequency
Not planned: there are no plans to update the data

Contact
Ontario Ministry of Natural Resources and Forestry - Provincial Mapping Unit, pmu@ontario.ca