This dataset contains two tables: creative_stats and removed_creative_stats. The creative_stats table contains information about advertisers that served ads in the European Economic Area or Turkey: their legal name, verification status, disclosed name, and location. It also includes ad specific information: impression ranges per region (including aggregate impressions for the European Economic Area), first shown and last shown dates, which criteria were used in audience selection, the format of the ad, the ad topic and whether the ad is funded by Google Ad Grants program. A link to the ad in the Google Ads Transparency Center is also provided. The removed_creative_stats table contains information about ads that served in the European Economic Area that Google removed: where and why they were removed and per-region information on when they served. The removed_creative_stats table also contains a link to the Google Ads Transparency Center for the removed ad. Data for both tables updates periodically and may be delayed from what appears on the Google Ads Transparency Center website. About BigQuery This data is hosted in Google BigQuery for users to easily query using SQL. Note that to use BigQuery, users must have a Google account and create a GCP project. This public dataset is included in BigQuery's 1TB/mo of free tier processing. Each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery . Download Dataset This public dataset is also hosted in Google Cloud Storage here and available free to use. Use this quick start guide to quickly learn how to access public datasets on Google Cloud Storage. We provide the raw data in JSON format, sharded across multiple files to support easier download of the large dataset. A README file which describes the data structure and our Terms of Service (also listed below) is included with the dataset. You can also download the results from a custom query. See here for options and instructions. Signed out users can download the full dataset by using the gCloud CLI. Follow the instructions here to download and install the gCloud CLI. To remove the login requirement, run "$ gcloud config set auth/disable_credentials True" To download the dataset, run "$ gcloud storage cp gs://ads-transparency-center/* . -R" This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

Dataset Overview

This dataset contains augmented software architecture diagrams specifically focused on cloud service components from major cloud providers (AWS, Azure, GCP). The dataset was developed as part of a FIAP POS Tech Hackathon project for training computer vision models to detect and classify cloud architecture components in technical diagrams.

Dataset Contents

• 8k+ augmented images (PNG format) with corresponding Pascal VOC XML annotations
• Cloud service components from AWS, Azure, and GCP platforms
• 87 unique cloud service types including:
  • AWS services: EC2, S3, RDS, DynamoDB, Lambda, API Gateway, CloudFront, etc.
  • Azure services: Databricks, Storage, Managed Database, Load Balancer, etc.
  • GCP services: (various Google Cloud Platform components)

Data Augmentation Process

The dataset was created using a sophisticated augmentation pipeline that applies multiple transformations while preserving bounding box annotations:

Augmentation Techniques Applied:

• Brightness and contrast adjustments (±20%)
• Rotation (±15 degrees)
• Gaussian blur (sigma: 0.0-2.0)
• Gaussian noise (variance: 10-50)
• Elastic transformations (alpha: 1, sigma: 50)
• Grid distortions (num_steps: 5, distort_limit: 0.3)
• Optical distortions (distort_limit: 0.1, shift_limit: 0.1)

Quality Control:

• Relevance filtering: Only "High" relevance components were selected for augmentation
• Bounding box preservation: All transformations maintain accurate object detection annotations
• Format compatibility: Pascal VOC XML format for seamless integration with popular ML frameworks

Technical Specifications

• Image Format: PNG
• Annotation Format: Pascal VOC XML
• Augmentation Ratio: 10 variations per original image
• Image Categories: Cloud architecture components and services
• Bounding Box Accuracy: Preserved through geometric transformations

Use Cases

This dataset is ideal for: - Object detection in software architecture diagrams - Cloud service recognition in technical documentation - Automated diagram analysis tools - Computer vision research in technical domain - Training custom models for architecture diagram parsing

Dataset Structure

dataset_augmented/
├── image_xpto.png   # Augmented PNG images
├── image_xpto.xml   # Pascal VOC XML files

Machine Learning Applications

Perfect for training: - YOLO object detection models - Faster R-CNN for precise component detection - Custom CNN architectures for diagram analysis - Multi-class classification models

Quality Assurance

• All images maintain visual quality after augmentation
• Bounding boxes are accurately transformed with image modifications
• Consistent naming convention for easy batch processing
• Validated XML structure for error-free training

The Metropolitan Museum of Art, better known as the Met, provides a public domain dataset with over 200,000 objects including metadata and images. In early 2017, the Met debuted their Open Access policy to make part of their collection freely available for unrestricted use under the Creative Commons Zero designation and their own terms and conditions. This dataset provides a new view to one of the world’s premier collections of fine art. The data includes both image in Google Cloud Storage, and associated structured data in two BigQuery two tables, objects and images (1:N). Locations to images on both The Met’s website and in Google Cloud Storage are available in the BigQuery table. The meta data for this public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery . The image data for this public dataset is hosted in Google Cloud Storage and available free to use. Use this quick start guide to quickly learn how to access public datasets on Google Cloud Storage.

Company Datasets for valuable business insights!

Discover new business prospects, identify investment opportunities, track competitor performance, and streamline your sales efforts with comprehensive Company Datasets.

These datasets are sourced from top industry providers, ensuring you have access to high-quality information:

• Owler: Gain valuable business insights and competitive intelligence. -AngelList: Receive fresh startup data transformed into actionable insights. -CrunchBase: Access clean, parsed, and ready-to-use business data from private and public companies. -Craft.co: Make data-informed business decisions with Craft.co's company datasets. -Product Hunt: Harness the Product Hunt dataset, a leader in curating the best new products.

We provide fresh and ready-to-use company data, eliminating the need for complex scraping and parsing. Our data includes crucial details such as:

• Company name;
• Size;
• Founding date;
• Location;
• Industry;
• Revenue;
• Employee count;
• Competitors.

You can choose your preferred data delivery method, including various storage options, delivery frequency, and input/output formats.

Receive datasets in CSV, JSON, and other formats, with storage options like AWS S3 and Google Cloud Storage. Opt for one-time, monthly, quarterly, or bi-annual data delivery.

With Oxylabs Datasets, you can count on:

• Fresh and accurate data collected and parsed by our expert web scraping team.
• Time and resource savings, allowing you to focus on data analysis and achieving your business goals.
• A customized approach tailored to your specific business needs.
• Legal compliance in line with GDPR and CCPA standards, thanks to our membership in the Ethical Web Data Collection Initiative.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Unlock the power of data with Oxylabs' Company Datasets and supercharge your business insights today!

Flood susceptibility mapping is critical for disaster risk management in flood-prone regions, particularly the Nam Ngum River Basin in Lao PDR, which faces annual flooding due to monsoons and rainstorms. This study combines point-based data from publicly available open-source earth system datasets, historical flood observation datasets, Google’s cloud computing platform, and advanced remote sensing and deep learning techniques to create detailed flood susceptibility maps. Key datasets include digital elevation model (DEM), satellite-observed rainfall data, land use/land cover, and remote sensing image-derived indices such as NDVI and Sentinel-1 SAR imagery. The assessment employs various deep learning models, including Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Deep Neural Networks (DNN), to analyze hydro-meteorological and geomorphological parameters. These models were trained and tested using eleven flood conditioning variables and samples divided into training and testing datasets in a 70:30 ratio. Performance metrics such as accuracy, precision, and the area under the curve of Receiver Operating Characteristics (AUROC) were used to evaluate the models. The resulting flood susceptibility maps identify critical zones within the Nam Ngum River Basin at high risk of flooding, revealing that 36-53 % of the basin area is highly susceptible, especially in low-elevation and low-slope regions.Additionally, 85-93 % of the population is highly vulnerable to flooding within 261 to 296 km² of built-up area. Almost all of the critical facilities for health and education lie within the area, which is highly susceptible to flooding. The results of this study show the alarming situation of flood risk management. The study thus offers valuable insights for local authorities and stakeholders, enhancing flood risk management, emergency planning, and mitigation strategies. The findings provide essential information for policymakers, aiding disaster risk reduction and facilitating sustainable development planning in Lao PDR.

The AlphaFold Protein Structure Database is a collection of protein structure predictions made using the machine learning model AlphaFold. AlphaFold was developed by DeepMind , and this database was created in partnership with EMBL-EBI . For information on how to interpret, download and query the data, as well as on which proteins are included / excluded, and change log, please see our main dataset guide and FAQs . To interactively view individual entries or to download proteomes / Swiss-Prot please visit https://alphafold.ebi.ac.uk/ . The current release aims to cover most of the over 200M sequences in UniProt (a commonly used reference set of annotated proteins). The files provided for each entry include the structure plus two model confidence metrics (pLDDT and PAE). The files can be found in the Google Cloud Storage bucket gs://public-datasets-deepmind-alphafold-v4 with metadata in the BigQuery table bigquery-public-data.deepmind_alphafold.metadata . If you use this data, please cite: Jumper, J et al. Highly accurate protein structure prediction with AlphaFold. Nature (2021) Varadi, M et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Research (2021) This public dataset is hosted in Google Cloud Storage and is available free to use. Use this quick start guide to quickly learn how to access public datasets on Google Cloud Storage.

The object-based storage market is experiencing robust growth, driven by the increasing need for scalable, cost-effective, and durable data storage solutions. The market's expansion is fueled by the exponential growth of unstructured data generated by various sources, including cloud applications, IoT devices, and big data analytics initiatives. Businesses are increasingly adopting object storage to manage this data explosion, attracted by its pay-as-you-go pricing models, flexible scalability, and superior data management capabilities compared to traditional storage methods. Key players like Amazon, Google, and Microsoft are significantly contributing to market growth through their cloud-based object storage services, while established players like NetApp and Hitachi Vantara are adapting their offerings to compete in this dynamic landscape. The market is segmented based on deployment type (cloud, on-premise), industry vertical (healthcare, finance, media), and geographic location. We project a compound annual growth rate (CAGR) of approximately 18% from 2025 to 2033, reflecting continued market expansion. This rapid expansion is further propelled by several key trends, including the rise of hybrid and multi-cloud strategies, the increasing adoption of artificial intelligence and machine learning applications which generate massive datasets, and the growing importance of data security and compliance. However, the market also faces some restraints, such as the complexity of migrating legacy data to object storage, the need for skilled professionals to manage these systems effectively, and the ongoing concerns around data sovereignty and security regulations. Despite these challenges, the long-term outlook for object-based storage remains exceptionally positive, with the market poised for continued significant growth as the demand for scalable and cost-effective data storage continues to soar, exceeding traditional storage solutions' limitations. The competitive landscape remains dynamic, with both established vendors and new entrants vying for market share, fostering innovation and driving down costs.

Dataset

This dataset was created by Jay2K

Contents

The size of the Storage in Big Data Market was valued at USD 7.90 billion in 2023 and is projected to reach USD 18.89 billion by 2032, with an expected CAGR of 13.26% during the forecast period. The storage segment in the Big Data market is experiencing rapid growth due to the increasing volume of data generated by businesses across various industries. As organizations continue to adopt digital transformation strategies, the demand for efficient, scalable, and cost-effective data storage solutions rises. Traditional data storage methods are being complemented or replaced by cloud-based systems, data lakes, and hybrid models, allowing organizations to manage vast amounts of unstructured and structured data effectively. Cloud storage platforms such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud are leading the market by offering robust storage solutions that can scale in real-time, providing flexibility and security. The rise of Internet of Things (IoT) devices, artificial intelligence (AI), and machine learning (ML) technologies further fuel the need for advanced data storage options that can handle real-time processing and high throughput. As industries like healthcare, finance, and retail continue to generate massive datasets, the Big Data storage market is expected to evolve, with innovations like edge computing and distributed storage systems shaping the future landscape. Security, data governance, and compliance remain critical factors driving the development of storage technologies in the Big Data market. Key drivers for this market are: Surge in Data Generation and Storage Demand Increasing Adoption of Big Data Analytics Growing Popularity of Cloud-Based Storage Need for Data Security and Compliance. Potential restraints include: Data Privacy and Security Concerns Complexity and Cost of Implementing Big Data Storage Solutions Lack of Skilled Professionals. Notable trends are: Solid-State Drives (SSDs) and NVMe for High-Performance Storage Object Storage for Unstructured Data Management Artificial Intelligence (AI) for Storage Optimization Data Fabric for Storage Consolidation and Management.

Product Review Datasets: Uncover user sentiment

Harness the power of Product Review Datasets to understand user sentiment and insights deeply. These datasets are designed to elevate your brand and product feature analysis, help you evaluate your competitive stance, and assess investment risks.

Data sources:

• Trustpilot: datasets encompassing general consumer reviews and ratings across various businesses, products, and services.

Leave the data collection challenges to us and dive straight into market insights with clean, structured, and actionable data, including:

• Product name;
• Product category;
• Number of ratings;
• Ratings average;
• Review title;
• Review body;

Choose from multiple data delivery options to suit your needs:

1. Receive data in easy-to-read formats like spreadsheets or structured JSON files.
2. Select your preferred data storage solutions, including SFTP, Webhooks, Google Cloud Storage, AWS S3, and Microsoft Azure Storage.
3. Tailor data delivery frequencies, whether on-demand or per your agreed schedule.

Why choose Oxylabs?

1. Fresh and accurate data: Access organized, structured, and comprehensive data collected by our leading web scraping professionals.

2. Time and resource savings: Concentrate on your core business goals while we efficiently handle the data extraction process at an affordable cost.

3. Adaptable solutions: Share your specific data requirements, and we'll craft a customized data collection approach to meet your objectives.

4. Legal compliance: Partner with a trusted leader in ethical data collection. Oxylabs is a founding member of the Ethical Web Data Collection Initiative, aligning with GDPR and CCPA standards.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Join the ranks of satisfied customers who appreciate our meticulous attention to detail and personalized support. Experience the power of Product Review Datasets today to uncover valuable insights and enhance decision-making.

HPC Cloud Storage Market Outlook

According to our latest research, the Global HPC Cloud Storage market size was valued at $7.8 billion in 2024 and is projected to reach $23.6 billion by 2033, expanding at a robust CAGR of 13.2% during the forecast period of 2025–2033. One of the primary factors fueling the growth of the HPC Cloud Storage market globally is the accelerating demand for high-performance data processing and storage solutions across diverse industries, including scientific research, financial services, and healthcare. As organizations continue to generate and analyze massive datasets, the need for scalable, flexible, and cost-efficient storage infrastructure is pushing enterprises toward cloud-based HPC solutions. This paradigm shift is further amplified by advancements in artificial intelligence, machine learning, and big data analytics, which require robust computational resources and seamless data accessibility, making HPC Cloud Storage an essential component of modern digital strategies.

Regional Outlook

North America currently dominates the HPC Cloud Storage market, capturing the largest share of the global revenue in 2024, estimated at over 38%. The region’s leadership is attributed to its mature IT infrastructure, early adoption of advanced technologies, and the presence of key market players such as Amazon Web Services, Microsoft Azure, and Google Cloud. The strong ecosystem of research institutions, universities, and tech startups, combined with favorable government policies supporting innovation and digital transformation, further fuels market growth. Additionally, North American enterprises are increasingly leveraging HPC cloud storage for advanced analytics, scientific simulations, and risk modeling, making the region a hub for technological breakthroughs and large-scale cloud deployments.

In contrast, the Asia Pacific region is emerging as the fastest-growing market, projected to register a remarkable CAGR of 16.8% during 2025–2033. The surge in demand is driven by rapid digitalization, substantial investments in cloud infrastructure, and government initiatives promoting smart manufacturing and scientific research. Countries such as China, India, Japan, and South Korea are witnessing significant growth in sectors like healthcare, manufacturing, and financial services, where high-performance computing and storage are increasingly critical. The proliferation of data-intensive applications and the expansion of local cloud service providers are further accelerating the adoption of HPC Cloud Storage across the region, positioning Asia Pacific as a key growth engine in the coming years.

Meanwhile, emerging economies in Latin America and the Middle East & Africa are gradually embracing HPC Cloud Storage solutions, although market penetration remains relatively low compared to developed regions. Adoption challenges include limited high-speed connectivity, budget constraints, and a shortage of skilled IT professionals. However, localized demand is rising, especially in sectors such as oil & gas, government, and education, where large-scale data processing and secure storage are essential. Policy reforms aimed at digital transformation, coupled with international collaborations and investments in cloud infrastructure, are expected to gradually overcome these barriers, unlocking new growth opportunities for HPC cloud storage vendors in these regions.

Report Scope

Machine learning (ML) methods enable prediction of the properties of chemical structures without computationally expensive ab initio calculations. The quality of such predictions depends on the reference data that was used to train the model. In this work, we introduce the QCML dataset: A comprehensive dataset for training ML models for quantum chemistry. The QCML dataset systematically covers chemical space with small molecules consisting of up to 8 heavy atoms and includes elements from a large fraction of the periodic table, as well as different electronic states. Starting from chemical graphs, conformer search and normal mode sampling are used to generate both equilibrium and off-equilibrium 3D structures, for which various properties are calculated with semi-empirical methods (14.7 billion entries) and density functional theory (33.5 million entries). The covered properties include energies, forces, multipole moments, and other quantities, e.g. Kohn-Sham matrices. We provide a first demonstration of the utility of our dataset by training ML-based force fields on the data and applying them to run molecular dynamics simulations.

The data is available as TensorFlow dataset (TFDS) and can be accessed from the publicly available Google Cloud Storage at gs://qcml-datasets/tfds/. (See "Directory structure" below.)

For information on different access options (command-line tools, client libraries, etc), please see https://cloud.google.com/storage/docs/access-public-data.

• Storage API: Using the "directory structure" and "builder configurations" below, storage API links can be constructed, e.g.
  https://storage.mtls.cloud.google.com/qcml-datasets/tfds/qcml/dft_metadata/1.0.0/qcml-full.tfrecord-00010-of-00011
• gcloud: Our example_usage.py uses the gcloud command-line tool for download.
• Web access via Google Cloud Console is possible for any authenticated Cloud user: https://console.cloud.google.com/storage/browser/qcml-datasets/.

Directory structure

• gs://qcml-datasets (GCS Bucket)
  • tfds (TFDS data directory)
    • qcml (TFDS dataset name)
      • dft_atomic_numbers (TFDS builder config name)
        • 1.0.0 (Current version)
          • dataset_info.json
          • features.json
          • qcml-full.tfrecord-X-of-Y (TFDS data shards, see below)
      • ...
      • dft_positions
      • xtb_all

Builder configurations

Format: Builder config name: number of shards (rounded total size)

Semi-empirical calculations:

• xtb_all: 85000 (69 TB)

DFT calculations:

• dft_atomic_numbers: 11 (3 GB)
• dft_d4_atomic_charges: 11 (4 GB)
• dft_d4_c6_coefficients: 11 (4 GB)
• dft_d4_correction: 11 (8 GB)
• dft_d4_energy: 11 (2 GB)
• dft_d4_forces: 11 (7 GB)
• dft_d4_polarizabilities: 11 (4 GB)
• dft_force_field: 11 (18 GB)
• dft_force_field_d4: 110 (24 GB)
• dft_force_field_mbd: 110 (24 GB)
• dft_gfn0_dipole: 11 (3 GB)
• dft_gfn0_eeq_charges: 11 (4 GB)
• dft_gfn0_energy: 11 (2 GB)
• dft_gfn0_forces: 11 (7 GB)
• dft_gfn0_formation_energy: 11 (3 GB)
• dft_gfn0_orbital_energies_a: 11 (8 GB)
• dft_gfn0_orbital_occupations_a: 11 (8 GB)
• dft_gfn0_wiberg_bond_orders: 110 (29 GB)
• dft_gfn2_dipole: 11 (3 GB)
• dft_gfn2_energy: 11 (2 GB)
• dft_gfn2_forces: 11 (7 GB)
• dft_gfn2_formation_energy: 11 (3 GB)
• dft_gfn2_mulliken_charges: 11 (4 GB)
• dft_gfn2_orbital_energies_a: 11 (7 GB)
• dft_gfn2_orbital_occupations_a: 11 (7 GB)
• dft_gfn2_wiberg_bond_orders: 110 (29 GB)
• dft_is_outlier: 11 (2 GB)
• dft_mbd_c6_coefficients: 11 (4 GB)
• dft_mbd_correction: 11 (8 GB)
• dft_mbd_energy: 11 (2 GB)
• dft_mbd_forces: 11 (7 GB)
• dft_mbd_polarizabilities: 11 (4 GB)
• dft_metadata: 11 (11 GB)
• dft_multipole_moments: 11 (8 GB)
• dft_pbe0_core_hamiltonian_matrix: 110000 (30 TB)
• dft_pbe0_density_matrix_a: 110000 (30 TB)
• dft_pbe0_density_matrix_b: 110000 (3 TB)
• dft_pbe0_dipole: 11 (3 GB)
• dft_pbe0_electronic_free_energy: 11 (3 GB)
• dft_pbe0_energy: 11 (2 GB)
• dft_pbe0_forces: 11 (7 GB)
• dft_pbe0_formation_energy: 11 (3 GB)
• dft_pbe0_grid_density_a: 110000 (27 TB)
• dft_pbe0_grid_density_b: 110000 (3 TB)
• dft_pbe0_grid_density_gradient_a: 110000 (81 TB)
• dft_pbe0_grid_density_gradient_b: 110000 (10 TB)
• dft_pbe0_grid_density_laplacian_a: 110000 (27 TB)
• dft_pbe0_grid_density_laplacian_b: 110000 (3 TB)
• dft_pbe0_grid_kinetic_energy_density_a: 110000 (27 TB)
• dft_pbe0_grid_kinetic_energy_density_b: 110000 (3 TB)
• dft_pbe0_grid_points: 110000 (81 TB)
• dft_pbe0_grid_weight: 110000 (27 TB)
• dft_pbe0_guid: 11 (3 GB)
• dft_pbe0_hamiltonian_matrix_a: 110000 (30 TB)
• dft_pbe0_hamiltonian_matrix_b: 110000 (3 TB)
• dft_pbe0_has_equal_a_b_electrons: 11 (3 GB)
• dft_pbe0_hexadecapole: 11 (3 GB)
• dft_pbe0_hirshfeld_charges: 11 (4 GB)
• dft_pbe0_hirshfeld_dipoles: 11 (8 GB)
• dft_pbe0_hirshfeld_quadrupoles: 11 (11 GB)
• dft_pbe0_hirshfeld_spins: 11 (3 GB)
• dft_pbe0_hirshfeld_volume_ratios: 11 (4 GB)
• dft_pbe0_hirshfeld_volumes: 11 (4 GB)
• dft_pbe0_loewdin_charges: 11 (4 GB)
• dft_pbe0_loewdin_spins: 11 (3 GB)
• dft_pbe0_mulliken_charges: 11 (4 GB)
• dft_pbe0_mulliken_spins: 11 (3 GB)
• dft_pbe0_num_scf_iterations: 11 (3 GB)
• dft_pbe0_octupole: 11 (3 GB)
• dft_pbe0_orbital_coefficients_a: 110000 (30 TB)
• dft_pbe0_orbital_coefficients_b: 110000 (3 TB)
• dft_pbe0_orbital_energies_a: 110 (44 GB)
• dft_pbe0_orbital_energies_b: 11 (8 GB)
• dft_pbe0_orbital_occupations_a: 110 (44 GB)
• dft_pbe0_orbital_occupations_b: 11 (8 GB)
• dft_pbe0_overlap_matrix: 110000 (30 TB)
• dft_pbe0_quadrupole: 11 (3 GB)
• dft_pbe0_zero_broadening_corrected_energy: 11 (3 GB)
• dft_population_analysis: 11 (19 GB)
• dft_positions: 11 (7 GB)

Altman-Zscore Time Series for Ennoconn Corp. Ennoconn Corporation, together with its subsidiaries, manufactures and sells data storage, processing equipment, industrial motherboards, and network communication products in Taiwan, China, Europe, and internationally. The company also offers factory mechanical and electrical system services; and digitalization as a services, google cloud platform (GCP) integration services. In addition, it is also involved in the research, development, manufacturing, sales, import, and export of software and hardware products related to industrial computers and industrial control systems; marketing of industrial computers; manufacturing of electronic components, computer and peripheral equipment, and intelligence vehicle equipment; and wholesale and retail of telecommunications control RF equipment input and information software. Further, the company engages in multimedia product research and development and design; manufacturing business; high-tech industry plant operations; manufacturing system planning and integration services; manufacturing of power generation, transmission, and distribution machinery; general trade business; manufacture, processing, trading, and import/export of telecommunication machinery equipment; and design, research and development, and production of various molds, servers, and communication equipment. Additionally, it offers human-machine interface, industry 0, and other related products; network infrastructure, wireless communication solutions, and information security services; and general and personal investment, and investment consultancy activities. The company serves smart retail, smart manufacturing, smart finance, media and entertainment, and smart city industries. Ennoconn Corporation was incorporated in 1999 and is based in New Taipei City, Taiwan.

Land use/land cover (LULC) mapping in fragmented landscapes, characterized by multiple and small land uses, is still a challenge. This study aims to evaluate the effectiveness of Synthetic Aperture Radar (SAR) and multispectral optical data in land cover mapping using Google Earth Engine (GEE), a cloud computing platform allowing big geospatial data analysis. The proposed approach combines multi-source satellite imagery for accurate land cover classification in a fragmented municipal territory in Southern Italy over a 5-month vegetative period. Within the GEE platform, a set of Sentinel-1, Sentinel-2, and Landsat 8 data was acquired and processed to generate a land cover map for the 2021 greenness period. A supervised pixel-based classification was performed, using a Random Forest (RF) machine learning algorithm, to classify the imagery and derived spectral indices into eight land cover classes. Classification accuracy was assessed using Overall Accuracy (OA), Producer’s and User’s accuracies (PA, UA), and F-score. McNemar’s test was applied to assess the significance of difference between classification results. The optical integrated datasets in combination with SAR data and derivate indices (NDVI, GNDVI, NDBI, VHVV) produce the most accurate LULC map among those produced (OA: 89.64%), while SAR-only datasets performed the lowest accuracy (OA: 61.30%). The classification process offers several advantages, including widespread spectral information, SAR’s ability to capture almost all-weather, day-and-night imagery, and the computation of vegetation indices in the near infrared spectrum interval, in a short revisit time. The proposed digital techniques for processing multi-temporal satellite data provide useful tools for understanding territorial and environmental dynamics, supporting decision-making in land use planning, agricultural expansion, and environmental management in fragmented landscapes.

Net-Income Time Series for Ennoconn Corp. Ennoconn Corporation, together with its subsidiaries, manufactures and sells data storage, processing equipment, industrial motherboards, and network communication products in Taiwan, China, Europe, and internationally. The company also offers factory mechanical and electrical system services; and digitalization as a services, google cloud platform (GCP) integration services. In addition, it is also involved in the research, development, manufacturing, sales, import, and export of software and hardware products related to industrial computers and industrial control systems; marketing of industrial computers; manufacturing of electronic components, computer and peripheral equipment, and intelligence vehicle equipment; and wholesale and retail of telecommunications control RF equipment input and information software. Further, the company engages in multimedia product research and development and design; manufacturing business; high-tech industry plant operations; manufacturing system planning and integration services; manufacturing of power generation, transmission, and distribution machinery; general trade business; manufacture, processing, trading, and import/export of telecommunication machinery equipment; and design, research and development, and production of various molds, servers, and communication equipment. Additionally, it offers human-machine interface, industry 0, and other related products; network infrastructure, wireless communication solutions, and information security services; and general and personal investment, and investment consultancy activities. The company serves smart retail, smart manufacturing, smart finance, media and entertainment, and smart city industries. Ennoconn Corporation was incorporated in 1999 and is based in New Taipei City, Taiwan.

End-Period-Cash-Flow Time Series for Ennoconn Corp. Ennoconn Corporation, together with its subsidiaries, manufactures and sells data storage, processing equipment, industrial motherboards, and network communication products in Taiwan, China, Europe, and internationally. The company also offers factory mechanical and electrical system services; and digitalization as a services, google cloud platform (GCP) integration services. In addition, it is also involved in the research, development, manufacturing, sales, import, and export of software and hardware products related to industrial computers and industrial control systems; marketing of industrial computers; manufacturing of electronic components, computer and peripheral equipment, and intelligence vehicle equipment; and wholesale and retail of telecommunications control RF equipment input and information software. Further, the company engages in multimedia product research and development and design; manufacturing business; high-tech industry plant operations; manufacturing system planning and integration services; manufacturing of power generation, transmission, and distribution machinery; general trade business; manufacture, processing, trading, and import/export of telecommunication machinery equipment; and design, research and development, and production of various molds, servers, and communication equipment. Additionally, it offers human-machine interface, industry 0, and other related products; network infrastructure, wireless communication solutions, and information security services; and general and personal investment, and investment consultancy activities. The company serves smart retail, smart manufacturing, smart finance, media and entertainment, and smart city industries. Ennoconn Corporation was incorporated in 1999 and is based in New Taipei City, Taiwan.

The U.S. Geological Survey Oregon Water Science Center, in cooperation with The Klamath Tribes initiated a project to understand changes in surface-water prevalence of Klamath Marsh, Oregon and changes in groundwater levels within and surrounding the marsh. The initial phase of the study focused on developing datasets needed for future interpretive phases of the investigation. This data release documents the creation of a geospatial dataset of January through June maximum surface-water extent (MSWE) based on a model developed by Jones (2015; 2019) to detect surface-water inundation within vegetated areas from satellite imagery. The Dynamic Surface Water Extent (DSWE) model uses Landsat at-surface reflectance imagery paired with a digital elevation model to classify pixels within a Landsat scene as one of the following types: “not water”, “water – high confidence”, “water – moderate confidence”, “wetland – moderate confidence”, “wetland – low confidence”, and “cloud/shadow/snow” (Jones, 2015; Walker and others, 2020). The model has been replicated by Walker and others (2020) for use within the Google Earth Engine (GEE, https://code.earthengine.google.com/) online geospatial processing platform. The GEE platform was used to create 37 annual composite raster images of maximum surface water inundation within the Klamath Marsh during January through June 1985–2021. The dataset presented here includes surface area calculations of January through June MSWE in tabular (.csv) format, 37 years of composite January through June MSWE datasets in raster (.tif) and vector (.shp) format, and a study area polygon in vector (.shp) format. References Cited: Jones, J.W., 2015, Efficient Wetland Surface Water Detection and Monitoring via Landsat: Comparison with in situ Data from the Everglades Depth Estimation Network. Remote Sensing, 7, 12503–12538. Jones, J.W., 2019, Improved Automated Detection of Subpixel-Scale Inundation—Revised Dynamic Surface Water Extent (DSWE) Partial Surface Water Tests. Remote Sensing, 11, 374. https://doi.org/10.3390/rs11040374 Walker, J.J., Petrakis, R.E., and Soulard, C.E., 2020, Implementation of a Surface Water Extent Model using Cloud-Based Remote Sensing - Code and Maps: U.S. Geological Survey data release, https://doi.org/10.5066/P9LH9YYF.

Data Catalog Market was valued at USD 1.28 Billion in 2024 and is expected to reach USD 5.72 Billion by 2032, growing at a CAGR of 22.61% from 2026 to 2032.Global Data Catalog Market DriversThe Data Catalog Market faces several significant Drivers that can hinder its growth and expansionData Governance and Regulatory Compliance Mandates: The stringent demands of data governance and evolving regulatory frameworks like GDPR, CCPA, and HIPAA are paramount drivers for data catalog adoption. These regulations impose significant pressure on organizations to maintain a deep, auditable understanding of their data, particularly sensitive information like Personally Identifiable Information (PII). A data catalog acts as a single source of truth, centralizing metadata, tracking data lineage (the data's journey from source to use), and enforcing access controls and usage policies. This capability is essential for businesses to demonstrate compliance, mitigate the risk of hefty fines, and build customer trust by ensuring data is managed securely and ethically.Rapid Enterprise Adoption of Cloud and Hybrid Architectures: The accelerating migration of enterprise workloads and data to cloud platforms (AWS, Azure, Google Cloud) and the proliferation of hybrid cloud environments have created fragmented data landscapes, directly boosting the demand for data catalogs. As data silos multiply across different on premises databases, data lakes, and multi cloud services, finding and understanding specific datasets becomes a monumental task. Cloud native or cloud agnostic data catalogs provide a unified, scalable solution to discover and govern assets across these distributed architectures. They lower management overhead and leverage the cloud’s elasticity and security features, enabling seamless data discovery and access for global, remote teams.

Stock Price Time Series for Ennoconn Corp. Ennoconn Corporation, together with its subsidiaries, manufactures and sells data storage, processing equipment, industrial motherboards, and network communication products in Taiwan, China, Europe, and internationally. The company also offers factory mechanical and electrical system services; and digitalization as a services, google cloud platform (GCP) integration services. In addition, it is also involved in the research, development, manufacturing, sales, import, and export of software and hardware products related to industrial computers and industrial control systems; marketing of industrial computers; manufacturing of electronic components, computer and peripheral equipment, and intelligence vehicle equipment; and wholesale and retail of telecommunications control RF equipment input and information software. Further, the company engages in multimedia product research and development and design; manufacturing business; high-tech industry plant operations; manufacturing system planning and integration services; manufacturing of power generation, transmission, and distribution machinery; general trade business; manufacture, processing, trading, and import/export of telecommunication machinery equipment; and design, research and development, and production of various molds, servers, and communication equipment. Additionally, it offers human-machine interface, industry 0, and other related products; network infrastructure, wireless communication solutions, and information security services; and general and personal investment, and investment consultancy activities. The company serves smart retail, smart manufacturing, smart finance, media and entertainment, and smart city industries. Ennoconn Corporation was incorporated in 1999 and is based in New Taipei City, Taiwan.

Total-Long-Term-Assets Time Series for Ennoconn Corp. Ennoconn Corporation, together with its subsidiaries, manufactures and sells data storage, processing equipment, industrial motherboards, and network communication products in Taiwan, China, Europe, and internationally. The company also offers factory mechanical and electrical system services; and digitalization as a services, google cloud platform (GCP) integration services. In addition, it is also involved in the research, development, manufacturing, sales, import, and export of software and hardware products related to industrial computers and industrial control systems; marketing of industrial computers; manufacturing of electronic components, computer and peripheral equipment, and intelligence vehicle equipment; and wholesale and retail of telecommunications control RF equipment input and information software. Further, the company engages in multimedia product research and development and design; manufacturing business; high-tech industry plant operations; manufacturing system planning and integration services; manufacturing of power generation, transmission, and distribution machinery; general trade business; manufacture, processing, trading, and import/export of telecommunication machinery equipment; and design, research and development, and production of various molds, servers, and communication equipment. Additionally, it offers human-machine interface, industry 0, and other related products; network infrastructure, wireless communication solutions, and information security services; and general and personal investment, and investment consultancy activities. The company serves smart retail, smart manufacturing, smart finance, media and entertainment, and smart city industries. Ennoconn Corporation was incorporated in 1999 and is based in New Taipei City, Taiwan.