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.

This dataset contains tabular files with information about the usage preferences of speakers of Maltese English with regard to 63 pairs of lexical expressions. These pairs (e.g. truck-lorry or realization-realisation) are known to differ in usage between BrE and AmE (cf. Algeo 2006). The data were elicited with a questionnaire that asks informants to indicate whether they always use one of the two variants, prefer one over the other, have no preference, or do not use either expression (see Krug and Sell 2013 for methodological details). Usage preferences were therefore measured on a symmetric 5-point ordinal scale. Data were collected between 2008 to 2018, as part of a larger research project on lexical and grammatical variation in settings where English is spoken as a native, second, or foreign language. The current dataset, which we use for our methodological study on ordinal data modeling strategies, consists of a subset of 500 speakers that is roughly balanced on year of birth. Abstract: Related publication In empirical work, ordinal variables are typically analyzed using means based on numeric scores assigned to categories. While this strategy has met with justified criticism in the methodological literature, it also generates simple and informative data summaries, a standard often not met by statistically more adequate procedures. Motivated by a survey of how ordered variables are dealt with in language research, we draw attention to an un(der)used latent-variable approach to ordinal data modeling, which constitutes an alternative perspective on the most widely used form of ordered regression, the cumulative model. Since the latent-variable approach does not feature in any of the studies in our survey, we believe it is worthwhile to promote its benefits. To this end, we draw on questionnaire-based preference ratings by speakers of Maltese English, who indicated on a 5-point scale which of two synonymous expressions (e.g. package-parcel) they (tend to) use. We demonstrate that a latent-variable formulation of the cumulative model affords nuanced and interpretable data summaries that can be visualized effectively, while at the same time avoiding limitations inherent in mean response models (e.g. distortions induced by floor and ceiling effects). The online supplementary materials include a tutorial for its implementation in R.

Hyper Scale Data Centres Market Outlook

The global market size for Hyper Scale Data Centres was valued at USD 35.6 billion in 2023 and is projected to reach USD 92.4 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 11.1% during the forecast period. The market is being driven by the increasing demand for scalable and efficient data handling capabilities, as well as the rising adoption of cloud services by enterprises worldwide.

One of the primary growth factors for the Hyper Scale Data Centres market is the exponential increase in data generation across various sectors. The proliferation of Internet of Things (IoT) devices, the rise of big data analytics, and the advancement in artificial intelligence and machine learning technologies have necessitated sophisticated data storage and processing solutions. Hyper Scale Data Centres, with their ability to scale resources seamlessly, offer a robust solution to manage these vast amounts of data efficiently, thereby fueling market growth.

Another significant growth driver is the increasing adoption of cloud computing services. As businesses continue to transition from traditional on-premises data centers to cloud-based solutions, the demand for Hyper Scale Data Centres has surged. Cloud service providers are investing heavily in hyper-scalable infrastructure to meet the growing needs of enterprises for high-performance computing, data storage, and network capabilities. This shift towards cloud-centric operations is expected to sustain the growth of the Hyper Scale Data Centres market over the forecast period.

The need for enhanced data security and regulatory compliance is also contributing to the market's expansion. Businesses are increasingly focusing on ensuring the security and integrity of their data amidst a growing number of cyber threats. Hyper Scale Data Centres offer advanced security features, including encryption, access controls, and multi-factor authentication, which are critical for industries such as BFSI, healthcare, and government. The ability of Hyper Scale Data Centres to provide robust security measures while maintaining operational efficiency is a key factor driving their adoption.

From a regional perspective, North America holds a significant share of the Hyper Scale Data Centres market, driven by the presence of major cloud service providers and technological advancements in the region. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, owing to the rapid digital transformation across emerging economies, increasing investments in data center infrastructure, and the growing demand for cloud-based services. Europe, Latin America, and the Middle East & Africa are also anticipated to contribute to market growth, albeit at varying growth rates.

Component Analysis

The Hyper Scale Data Centres market is segmented by component into hardware, software, and services. Each of these components plays a critical role in the overall functioning and efficiency of hyper-scale data centers. The hardware segment includes servers, storage devices, networking equipment, and other physical infrastructure essential for building and operating data centers. Due to the need for high-performance and reliable hardware, this segment is expected to hold a substantial market share.

Servers are the backbone of Hyper Scale Data Centres, providing the computational power required to process and analyze large datasets. With advancements in server technology, including higher processing power, energy efficiency, and scalability, the hardware segment continues to evolve. Additionally, the growing emphasis on environmentally sustainable data center operations has led to the adoption of energy-efficient servers and cooling systems, further driving the hardware market.

Software plays an equally important role in the Hyper Scale Data Centres ecosystem. This segment encompasses data center management software, virtualization software, and security solutions. Effective software solutions are crucial for managing the complex operations of hyper-scale data centers, ensuring optimal resource allocation, and maintaining high levels of security and compliance. With increasing cyber threats and the need for streamlined operations, the demand for advanced software solutions is on the rise.

The services component includes consulting, implementation, and maintenance services. As businesses continue to adopt hyper-scale data center solutions, the need for expert guidance and support becomes para

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.

The Large-Scale AI Models database documents over 200 models trained with more than 10²³ floating point operations, at the leading edge of scale and capabilities.

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

Data archived here were used to create the Roosevelt Island Ice Core gas age and ice age time scales. Data include methane concentrations, nitrogen and oxygen isotope ratios of N2 and O2, total air content and the D/H ratio of the ice. Derived products included here include ice age and gas age time scales.

The natural amenities scale is a measure of the physical characteristics of a county area that enhance the location as a place to live. The scale was constructed by combining six measures of climate, topography, and water area that reflect environmental qualities most people prefer. These measures are warm winter, winter sun, temperate summer, low summer humidity, topographic variation, and water area. The data are available for counties in the lower 48 States. The file contains the original measures and standardized scores for each county as well as the amenities scale.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Data file For complete information, please visit https://data.gov.

The Latin America Hyper-scale Data Center market will be USD 7077.1 million in 2024 and is estimated to grow at a compound annual growth rate (CAGR) of 5.6% from 2024 to 2031. The market is foreseen to reach USD 11429.7 million by 2031 owing to Investments in high-speed internet and telecommunications networks.

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. It has 1 row and is filtered where the book is Data just right : introduction to large-scale data & analytics. It features 7 columns including author, publication date, language, and book publisher.

This data set is a polygon feature that can be used to identify the location of landmarks (buildings and structures) that are permanent in nature. Buildings to scale must have one side larger than 50 meters for 1:20,000 scale data or one side larger than 30 meters for 1:10,000 data.

Please note that this data was collected with varying aerial photography dates and scales. Please use caution when interpreting data and results.

Supplementary tables can be used and are available for download from the additional documentation section. Supplementary look-up table descriptions are available in the data description document, which is available for download from the additional documentation section.

In Mexico Hyper Scale Data Center Market, The cloud and IT sector is expected to remain the largest consumer as cloud adoption grows.

In UK Hyper Scale Data Center Market, The cloud and IT sector is expected to remain the largest consumer as cloud adoption grows.

This collection is a legacy product that is no longer maintained. It may not meet current government standards. Users of Atlas of Canada National Scale Data 1:1,000,000 (release of May 2017) should plan to make the transition towards the new CanVec product. 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 includes validation of the reliability and validity of two self-designed scales, namely the Marriage and Childbearing Meaning Scale and the Marriage and Childbearing Intention Scale, both of which contain two dimensions. The Marriage and Childbearing Meaning Scale includes dimensions of marriage and fertility, while the Marriage and Childbearing Intention Scale includes dimensions of marriage and fertility. Two scales each have 20 questions, and we also collected demographic variables of gender and age.

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.

Automated in situ soil sensor network - the data set includes hourly and daily measurements of volumetric water content, soil temperature, and bulk electrical conductivity, collected at 42 monitoring locations and 5 depths (30, 60, 90, 120, and 150 cm) across Cook Agronomy Farm. Data collection was initiated in April 2007 and is ongoing. Description of data Tabular data CAF_sensors: folder with Daily and Hourly subfolders, each containing 42 '.txt' files of water content and temperature sensor readings. Each file represents readings from a single location, indicated in the file name (i.e. CAF003.txt) and in the 'Location' field of the table. Readings are organized by 'Date' (4/20/2007 - 6/16/2016), ‘Time’ (24 hr clock, only in hourly files), and with property (VW or T) and sensor 'Depth' as follows: VW_30cm: volumetric water readings at 30 cm depth (m^3/m^3) VW_60cm: volumetric water readings at 60 cm depth (m^3/m^3) VW_90cm: volumetric water readings at 90 cm depth (m^3/m^3) VW_120cm: volumetric water readings at 120 cm depth (m^3/m^3) VW_150cm: volumetric water readings at 150 cm depth (m^3/m^3) T_30cm: temperature readings at 30 cm depth (C) T_60cm: temperature readings at 60 cm depth (C) T_90cm: temperature readings at 90 cm depth (C) T_120cm: temperature readings at 120 cm depth (C) T_150cm: temperature readings at 150 cm depth (C) Volumetric water content readings are calibrated according to: Gasch, CK, DJ Brown, ES Brooks, M Yourek, M Poggio, DR Cobos, CS Campbell. 2017. A pragmatic, automated approach for retroactive calibration of soil moisture sensors using a two-step, soil specific correction. Computers and Electronics in Agriculture, 137: 29-40. Temperature readings are factory calibrated. CAF_BulkDensity.txt: file containing bulk density values ('BulkDensity' in g/cm^3) for sensor depths at each of the 42 instrumented locations at Cook Farm. Location is indicated in 'Location' field, and sample depths are defined (in cm) by the ’Depth’ field. CAF_CropID.txt: file containing crop codes for each sub-field (A, B and C) and strip (1-6 for A and B, 1-8 for C) at Cook Farm for 2007-2016. This is also part of the attribute table for 'CAF_strips.shp' CAF_CropCodes.txt: file containing crop code names and crop identities, used in 'CAF_CropID.txt' and 'CAF_strips.shp' CAF_ParticleSize.txt: file containing particle size fractions ('Sand', 'Silt', and 'Clay' as percent) for each 'Location' at sensor depths ('Depth', in cm). Spatial data All spatial data have spatial reference NAD83, UTM11N CAF_sensors.shp: file containing locations of each of the 42 monitoring locations, the 'Location' field contains the location name, which coincides with locations in tabular files. CAF_strips.shp: file containing areal extents of each sub-field, stip, and crop identities for 2007-2016. Crop identity codes are listed in 'CAF_CropCodes.txt' CAF_DEM.tif: file containing a 10 x 10 m elevation (in m) grid for Cook Farm. CAF_Spring_ECa.tif, CAF_Fall_ECa.tif: files containing 10 x 10 m apparent electrical conductivity (dS/m) grids to 1.5 m depth for spring and fall at Cook Farm. CAF_Bt_30cm.tif, CAF_Bt_60cm.tif, CAF_Bt_90cm.tif, CAF_Bt_120cm.tif, CAF_Bt_150cm.tif: files containing 10 x 10 m predictive surfaces for probability (0-1) of Bt horizon at the five sensor depths. Quality Control The Flags folder consists of the files containing the quality control flags for the Cook Farm Sensor Dataset. The nomenclature for the files indicates flags for either temperature (T) or water content (VW) and sensor depths. For example: T_30 is for the temperature data at 30cm. depth VW_120 is for the Volumetric water content at 120 cm. depth Files starting with “missing” contain flags (“M”) for locations and dates (mm/dd/yyyy) with missing data (NA in original dataset). Files starting with “range” contain flags for locations and dates (mm/dd/yyyy) with values outside acceptable ranges: Soil moisture (0-0.6 m^3/m^3) flagged as “C” Soil temperature (<0 deg. C) flagged as “D” Files starting with the name “flats” contain flags (“D”) for locations, dates (mm/dd/yyyy), and times (hh:mm) with constant values (within 1%) for a 24 hour period, as in Dorigo et al. 2013. Files starting with the name “spikes” contain flags (“D”) for locations, dates (mm/dd/yyy), and times (hh:mm) with sudden spikes in VWC readings. Files starting with the name “breaks” contain flags (“D”) for locations, dates (mm/dd/yyy), and times (hh:mm) with sudden breaks (jumps or drops) in VWC readings. Code (implemented in R) for the screening and flagging is included in “Code Snippet.txt” A list of the sensor versions as of 06/16/16 at each location and depth. Resources in this dataset:Resource Title: Data package for automated in situ soil sensor network. File Name: CAF_Sensor_Dataset.zipResource Description: Data file descriptions for Cook Farm sensor network data set (CAF_Sensor_Dataset). Data set compiled by Caley Gasch, under supervision of David Brown, Department of Crop and Soil Sciences, Washington State University, Pullman, WA. Updated: 04/01/2017 Tabular data: CAF_sensors: folder with Daily and Hourly subfolders, each containing 42 '.txt' files of water content and temperature sensor readings. Each file represents readings from a single location, indicated in the file name (i.e. CAF003.txt) and in the 'Location' field of the table. Readings are organized by 'Date' (4/20/2007 - 6/16/2016), ‘Time’ (24 hr clock, only in hourly files), and with property (VW or T) and sensor 'Depth' as follows: VW_30cm: volumetric water readings at 30 cm depth (m^3/m^3) VW_60cm: volumetric water readings at 60 cm depth (m^3/m^3) VW_90cm: volumetric water readings at 90 cm depth (m^3/m^3) VW_120cm: volumetric water readings at 120 cm depth (m^3/m^3) VW_150cm: volumetric water readings at 150 cm depth (m^3/m^3) T_30cm: temperature readings at 30 cm depth (C) T_60cm: temperature readings at 60 cm depth (C) T_90cm: temperature readings at 90 cm depth (C) T_120cm: temperature readings at 120 cm depth (C) T_150cm: temperature readings at 150 cm depth (C) Volumetric water content readings are calibrated according to: Gasch, CK, DJ Brown, ES Brooks, M Yourek, M Poggio, DR Cobos, CS Campbell. 2017. A pragmatic, automated approach for retroactive calibration of soil moisture sensors using a two-step, soil specific correction. Computers and Electronics in Agriculture, 137: 29-40. Temperature readings are factory calibrated. CAF_BulkDensity.txt: file containing bulk density values ('BulkDensity' in g/cm^3) for sensor depths at each of the 42 instrumented locations at Cook Farm. Location is indicated in 'Location' field, and sample depths are defined (in cm) by the ’Depth’ field. CAF_CropID.txt: file containing crop codes for each sub-field (A, B and C) and strip (1-6 for A and B, 1-8 for C) at Cook Farm for 2007-2016. This is also part of the attribute table for 'CAF_strips.shp' CAF_CropCodes.txt: file containing crop code names and crop identities, used in 'CAF_CropID.txt' and 'CAF_strips.shp' CAF_ParticleSize.txt: file containing particle size fractions ('Sand', 'Silt', and 'Clay' as percent) for each 'Location' at sensor depths ('Depth', in cm). Spatial data: all spatial data have spatial reference NAD83, UTM11N CAF_sensors.shp: file containing locations of each of the 42 monitoring locations, the 'Location' field contains the location name, which coincides with locations in tabular files. CAF_strips.shp: file containing areal extents of each sub-field, stip, and crop identities for 2007-2016. Crop identity codes are listed in 'CAF_CropCodes.txt' CAF_DEM.tif: file containing a 10 x 10 m elevation (in m) grid for Cook Farm. CAF_Spring_ECa.tif, CAF_Fall_ECa.tif: files containing 10 x 10 m apparent electrical conductivity (dS/m) grids to 1.5 m depth for spring and fall at Cook Farm. CAF_Bt_30cm.tif, CAF_Bt_60cm.tif, CAF_Bt_90cm.tif, CAF_Bt_120cm.tif, CAF_Bt_150cm.tif: files containing 10 x 10 m predictive surfaces for probability (0-1) of Bt horizon at the five sensor depths. (Dataset updated on 10/23/2017 to include QC information.)

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.

Abstract

Sustainable Development Goal (SDG) target 2.1 commits countries to end hunger, ensure access by all people to safe, nutritious and sufficient food all year around. Indicator 2.1.2, “Prevalence of moderate or severe food insecurity based on the Food Insecurity Experience Scale (FIES)”, provides internationally-comparable estimates of the proportion of the population facing difficulties in accessing food. More detailed background information is available at http://www.fao.org/in-action/voices-of-the-hungry/fies/en/

The FIES-based indicators are compiled using the FIES survey module, containing 8 questions. Two indicators can be computed:
1. The proportion of the population experiencing moderate or severe food insecurity (SDG indicator 2.1.2), 2. The proportion of the population experiencing severe food insecurity.

These data were collected by FAO through the Gallup World Poll. General information on the methodology can be found here: https://www.gallup.com/178667/gallup-world-poll-work.aspx. National institutions can also collect FIES data by including the FIES survey module in nationally representative surveys.

Microdata can be used to calculate the indicator 2.1.2 at national level. Instructions for computing this indicator are described in the methodological document available in the downloads tab. Disaggregating results at sub-national level is not encouraged because estimates will suffer from substantial sampling and measurement error.

Geographic coverage

National

Analysis unit

Individuals

Universe

Individuals of 15 years or older with access to landline and/or mobile phones.

Kind of data

Sample survey data [ssd]

Sampling procedure

With some exceptions, all samples are probability based and nationally representative of the resident adult population. The coverage area is the entire country including rural areas, and the sampling frame represents the entire civilian, non-institutionalized, aged 15 and older population. For more details on the overall sampling and data collection methodology, see the World poll methodology attached as a resource in the downloads tab. Specific sampling details for each country are also attached as technical documents in the downloads tab. Exclusions: NA Design effect: 1.31

Mode of data collection

Face-to-Face [f2f]

Cleaning operations

Statistical validation assesses the quality of the FIES data collected by testing their consistency with the assumptions of the Rasch model. This analysis involves the interpretation of several statistics that reveal 1) items that do not perform well in a given context, 2) cases with highly erratic response patterns, 3) pairs of items that may be redundant, and 4) the proportion of total variance in the population that is accounted for by the measurement model.

Sampling error estimates

The margin of error is estimated as 3.5. This is calculated around a proportion at the 95% confidence level. The maximum margin of error was calculated assuming a reported percentage of 50% and takes into account the design effect.