2683 Global import shipment records of Patch with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Dataset Card for Patch the Planet

  Dataset Description

This data was produced by ThinkOnward for the Patch the Planet Challenge, using a synthetic seismic dataset generator called Synthoseis. This dataset consists of 500 training volumes and 15 test volumes. You will also be provided with a training data generation code in the starter notebook to build the training data. This code allows experimentation with different-sized missing data volumes in the seismic data. The… See the full description on the dataset page: https://huggingface.co/datasets/thinkonward/patch-the-planet.

The Database Automation market, valued at $2.35 billion in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 24.38% from 2025 to 2033. This rapid expansion is driven by several key factors. The increasing complexity of database environments, coupled with the rising demand for faster application deployments and improved operational efficiency, are compelling organizations across various sectors to adopt automated solutions. The shift towards cloud-based deployments further fuels this growth, as cloud platforms offer scalable and cost-effective automation capabilities. Furthermore, stringent regulatory compliance requirements and the need to minimize human error in database management are pushing businesses towards automation. The market is segmented by component (Database Patch and Release Automation, Application Release Automation, Database Test Automation), services, deployment mode (cloud, on-premises), enterprise size (large enterprises, SMEs), and end-user industry (BFSI, IT & Telecom, E-commerce & Retail, Manufacturing, Government & Defense, Others). The BFSI sector is currently a major adopter, driven by the need for high transaction security and regulatory compliance, while the IT and Telecom sector shows significant potential for future growth. Leading vendors like IBM, Oracle, and Amazon Web Services are actively shaping the market landscape through continuous innovation and strategic partnerships. However, challenges remain. The initial investment required for implementing automation solutions can be substantial, posing a barrier for some SMEs. Additionally, the integration of automation tools with existing legacy systems can present technical complexities. Despite these restraints, the long-term prospects for the Database Automation market remain exceptionally positive, fueled by the ongoing digital transformation across industries and the increasing reliance on data-driven decision-making. The market’s continued growth is expected to be driven by advancements in AI and ML-powered automation, creating more sophisticated and efficient database management practices. Recent developments include: June 2023: Aquatic Informatics launched a new automated data validation tool, HydroCorrect, that can accelerate proactive monitoring and management of flooding, groundwater, and water quality in the Aquarius platform. With machine-learning technology, HydroCorrect will transform the QA/QC process with automation and standardized workflows that save time and improve data quality., May 2023: data.world, the data catalog platform, acquired the Mighty Canary technology and its incorporation into a new DataOps application. The application uses automation to surface contextual insights and real-time data quality updates directly to the BI, communications, and collaboration tools data consumers use.. Key drivers for this market are: Continuously Growing Volumes of Data Across Verticals, Increasing Demand for Automating Repetitive Database Management Processes. Potential restraints include: , Managing Identities Across Multiple Operation Environments. Notable trends are: IT and Telecommunication industry is Expected to Witness Significant Growth.

Who: USDA ARS and NDSU range and wildlife researchers, graduate students, and undergraduate techniciansWhat: Structural characteristics and community composition collected from southwestern North Dakota rangelands from 2017 through 2020Where: Hettinger Research Extension Center in Hettinger, North Dakota USA6, 65 ha patch-burn grazing pastures were the primary data collection locationsWhy: These two files come from a patch-burn grazing study in southwestern North Dakota that were comparing an iteration of patch-burn grazing with cattle to a version of patch-burn grazing with sheep for the grazing component. Feel free to contact me at jonathan.spiess@usda.gov or jwspiess@gmail.com.How: We used 0.5m x 0.5m quadrats to measure vegetation structure characteristics and community composition along 100m transects in patches (subsections) of larger pastures or management units. We measured 1 quadrat spaced every 10 m starting at 0 on both sides of the transect for 22 total quadrats per transect in patch-burn grazing pastures. Transects were distributed amongst patches of each pasture and management unit.Data were analyzed using a combination of mixed-effect models and ordinations to compare time since fire (TSF) and grazer type (cattle or sheep).17_18_19_20vegFG.csv is the primary dataset for this paper and repository here. We collected vegetation structure and community composition data in 2017, 2018, 2019, and 2020.Columns Year through PastPatch are various grouping variables used throughout the analysis.Pasture is the primary ID for a given unitBlock is the assigned set of pastures the pasture matchesTSF is the time since fire for a given locationUse is whether the pasture or management unit was managed for heterogeneity or homogeneityManagement is the grazer type for pbg pastures and hay or idle for management unitsPatch is a subsection of the pasture or management unitPastPatch is a combination of the pasture name with the patch numberVOR: Visual Obstruction Reading was measured using a Robel pole marked and recorded in 0.25 dm increments. We took four readings per quadrat and calculated an average score from these.MaxLive and MaxDead: these were the tallest living and tallest standing dead plant material within the quadrat measured in 0.25 dm increments using the Robel pole.LitMean: We measured litter depth using a ruler to the nearest cm in the four corners of each quadrat. After 2017, we started recording all four measurements instead of just recording the average of the four measurements.BGCover: bare ground cover is any exposed soil surface than can be seen when looking down on the quadrat. We expected this to be higher in recently burned patches.GCover: ground litter cover is any visible horizontal ground litter than can be seen when looking down on the quadrat. We expected this to be higher in recently burned patches.LitCover: vertical litter cover is any visible standing or vertical litter than can be seen when looking down on the quadrat. We expected this to be lower in recently burned patches.Columns ACMI through VIAM are the 4 letter species codes used during data collection on a tablet to record cover by cover class. The tablet was programmed to autorecord a '0' for species that were not present in the quadrat.Columns NatForb through NatShrub are the calculated cover values for finer scale groupings based on native and introduced status.Columns Forb through Litter are additional calculated cover values.RadGraph.csv was used to expedite making a community composition figure that is now in the supplemental materials for the paper.

Patch to assign a classification to otherwise unclassified backbone families which get pooled under the "incertae sedis" kingdom of the GBIF taxonomic backbone.

60628 Global import shipment records of Patch with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Comprehensive dataset of 2,196 Pumpkin patches in United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 32 Pumpkin patches in New Jersey, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

The P-Patch Community Gardening Program is made up of community managed open spaces throughout Seattle where gardeners use small plots of land to grow organic food, flowers, and herbs. All P-Patch gardens are open to the public to enjoy and are utilized as communal spaces, restorative spaces, learning and idea incubators, and venues for community gatherings. For more information, please see the Department of Neighborhoods P-Patch Community Gardening.

Displays data from DON.PPATCH. Labels are based on the attribute NAME. Each p-patch has an URL attribute, please use to find more information about specific p-patch.

Updated as needed, last update April 2025.

Adversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-learning model to misclassify it. However, their optimization is computationally demanding and requires careful hyperparameter tuning. To overcome these issues, we propose ImageNet-Patch, a dataset to benchmark machine-learning models against adversarial patches. It consists of a set of patches optimized to generalize across different models and applied to ImageNet data after preprocessing them with affine transformations. This process enables an approximate yet faster robustness evaluation, leveraging the transferability of adversarial perturbations.

We release our dataset as a set of folders indicating the patch target label (e.g., banana), each containing 1000 subfolders as the ImageNet output classes.

An example showing how to use the dataset is shown below.

code for testing robustness of a model

import os.path

from torchvision import datasets, transforms, models import torch.utils.data

class ImageFolderWithEmptyDirs(datasets.ImageFolder): """ This is required for handling empty folders from the ImageFolder Class. """

def find_classes(self, directory):
  classes = sorted(entry.name for entry in os.scandir(directory) if entry.is_dir())
  if not classes:
    raise FileNotFoundError(f"Couldn't find any class folder in {directory}.")
  class_to_idx = {cls_name: i for i, cls_name in enumerate(classes) if
          len(os.listdir(os.path.join(directory, cls_name))) > 0}
  return classes, class_to_idx

extract and unzip the dataset, then write top folder here

dataset_folder = 'data/ImageNet-Patch'

available_labels = { 487: 'cellular telephone', 513: 'cornet', 546: 'electric guitar', 585: 'hair spray', 804: 'soap dispenser', 806: 'sock', 878: 'typewriter keyboard', 923: 'plate', 954: 'banana', 968: 'cup' }

select folder with specific target

target_label = 954

dataset_folder = os.path.join(dataset_folder, str(target_label)) normalizer = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) transforms = transforms.Compose([ transforms.ToTensor(), normalizer ])

dataset = ImageFolderWithEmptyDirs(dataset_folder, transform=transforms) model = models.resnet50(pretrained=True) loader = torch.utils.data.DataLoader(dataset, shuffle=True, batch_size=5) model.eval()

batches = 10 correct, attack_success, total = 0, 0, 0 for batch_idx, (images, labels) in enumerate(loader): if batch_idx == batches: break pred = model(images).argmax(dim=1) correct += (pred == labels).sum() attack_success += sum(pred == target_label) total += pred.shape[0]

accuracy = correct / total attack_sr = attack_success / total

print("Robust Accuracy: ", accuracy) print("Attack Success: ", attack_sr)

See the included README.md for detailed information about the dataset.

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Wearable Patch Market Size 2025-2029

The wearable patch market size is forecast to increase by USD 8.59 billion at a CAGR of 14.1% between 2024 and 2029.

The market is experiencing significant growth due to the increasing prevalence of chronic diseases and the subsequent demand for continuous health monitoring solutions. These patches, which can be worn discreetly on the skin, offer real-time health data collection and analysis, enabling early detection and intervention. Furthermore, the shift towards cloud-based solutions is driving market expansion, as these technologies enable remote monitoring, data storage, and analysis. However, concerns regarding data privacy and security pose a significant challenge to market growth. Furthermore, the trend towards cloud-based healthcare services is gaining traction, enabling real-time data access and analysis for healthcare providers and patients. As companies seek to capitalize on this market opportunity, it is crucial to prioritize data protection measures and transparent data handling practices to build trust with consumers and regulatory bodies.
Effective navigation of these challenges, alongside continued innovation in patch technology and design, will be key to success in this dynamic and growing market. The market for electronic skin patches, including nicotine patches, is driven by the increasing prevalence of chronic diseases, which necessitate continuous monitoring and management.

What will be the Size of the Wearable Patch Market during the forecast period?

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The market is experiencing significant advancements, with key focus areas including user interface design and mobile app integration. Biosensing platforms are at the forefront of innovation, leveraging biodegradable materials and standardization for improved patch adhesion and dermal absorption. Manufacturing processes are refining, with sensor calibration and optical sensing techniques advancing organic electronics and electrochemical sensing. Biocompatible polymers and patch validation are crucial for ensuring patch sustainability and biocompatibility. Cloud-based platforms are transforming patch lifecycle management, enabling real-time data interpretation and patch prototyping. Microneedle patches are gaining traction, offering enhanced functionality and sustainability. The integration of biodegradable polymers and printable electronics is further driving market growth. The market is experiencing significant growth, particularly in the connected wearable segment, driven by advancements in myoelectric technology and Bluetooth connectivity.
Patch prototyping and biocompatibility testing are essential components of the wearable patch design process. Innovations in flexible circuits and sensor calibration are addressing challenges in patch manufacturing, while optical sensing and organic electronics are revolutionizing data interpretation. The market is witnessing a shift towards patch sustainability and patch validation, as companies prioritize biocompatible materials and efficient patch lifecycle management. The future of wearable patches lies in the seamless integration of these technologies, providing businesses with valuable insights and improved patient care solutions. Further, connected wearable patches deliver drugs, such as nicotine, through electronic skin, offering an alternative to traditional methods like nicotine patches.

How is this Wearable Patch Industry segmented?

The wearable patch 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.

Application

  Clinical
  Non-clinical


Technology

  Regular
  Connected


End-user

  Healthcare providers
  Individual consumers
  Fitness centers and sports organizations
  Research institutions


Type

  Glucose monitoring patches
  Cardiac monitoring patches
  Fitness and wellness patches
  Temperature monitoring patches
  Pain relief and drug delivery patches


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa



  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Application Insights

The clinical segment is estimated to witness significant growth during the forecast period. Wearable patches represent a significant advancement in healthcare technology, enabling continuous monitoring of patients' health conditions. These patches collect biometric data at regular intervals and transmit it to connected devices, such as smartphones and laptops, for real-time analysis. This remote monitoring eliminates the need for frequent physical consultations with healthcare professionals. In the realm of medical innovation, manufact

We conducted two experiments with a center patch, surrounded by four peripheral patches of two different sizes and distances from the center, to compare patch fidelity and patch size preference between the two bee species. The center and two of the peripheral patches were 9.14 m x 9.14 m and contained 100 plants (small), while the other two peripheral patches were 13.7 m x 13.7 m and contained 225 plants. One of the large and one of the small patches were located 9.14 m diagonally from the center patch, while the other large and small patches were located 18.3 m away. The five patches in the design were referred to, for identification purpose, as the center patch (C), the large near (LN), small near (SN), large far (LF) and small far (SF) patches. Peripheral patches of the same size were located on the same side of the center patch, in this case north. Individual bumble bees and honey bees foraging in one of the five alfalfa patches were caught and marked with uniquely numbered pa...

Intelligent Patch Panel Market Outlook

The global intelligent patch panel market size was estimated at USD 1.2 billion in 2023 and is projected to reach USD 3.8 billion by 2032, growing at a CAGR of 13.8% from 2024 to 2032. The significant growth in this market is driven primarily by the increasing demand for efficient data management solutions and the rising adoption of advanced networking systems across various industries.

One of the primary growth factors for the intelligent patch panel market is the exponential increase in data traffic globally. With the proliferation of IoT devices, cloud computing, and big data analytics, there is an ever-growing need for efficient data management and high-speed network infrastructure. Intelligent patch panels play a crucial role in managing and organizing cables, reducing downtime, and ensuring seamless data transmission, thereby meeting the rising demand for robust and efficient data networks.

Another critical factor contributing to the growth of the intelligent patch panel market is the burgeoning expansion of data centers worldwide. Data centers are the backbone of modern digital infrastructure, hosting a plethora of services ranging from cloud storage to complex computational tasks. Intelligent patch panels enable these data centers to optimize space, enhance performance, and reduce operational costs by streamlining cable management and improving network reliability. As companies and service providers continue to invest heavily in data center expansions, the demand for intelligent patch panels is set to rise significantly.

The increasing focus on network security and compliance is also driving the market for intelligent patch panels. With the rise in cyber threats and stringent regulatory requirements, organizations are seeking advanced solutions to secure their network infrastructure. Intelligent patch panels offer enhanced monitoring and management features, allowing for real-time tracking and automated alerts for any unauthorized access or anomalies in the network. This capability not only enhances security but also ensures compliance with regulatory standards, making intelligent patch panels a preferred choice for enterprises across various sectors.

From a regional perspective, North America holds the largest share in the intelligent patch panel market due to its advanced IT infrastructure and high adoption rate of new technologies. The presence of major market players, coupled with the significant investments in data centers and network security, further propels the market growth in this region. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The rapid digital transformation, rising investments in data centers, and increasing adoption of smart technologies in countries like China, India, and Japan are driving the market growth in this region.

The Automatic Patch Clamp technology represents a significant advancement in the field of electrophysiology, offering high-throughput capabilities and precision in measuring ion channel activity. This technology is particularly beneficial for pharmaceutical research and drug discovery, as it allows for the rapid screening of potential drug candidates and their effects on ion channels. By automating the traditionally labor-intensive patch clamp technique, researchers can achieve greater efficiency and consistency in their experiments. The integration of Automatic Patch Clamp systems in research facilities is expected to accelerate the pace of scientific discoveries and contribute to the development of new therapeutics.

Component Analysis

The intelligent patch panel market by component is divided into three primary segments: Hardware, Software, and Services. Each segment plays a critical role in the functionality and efficiency of intelligent patch panels, catering to the diverse needs of end-users across various industries.

The hardware segment constitutes the core physical components of an intelligent patch panel, including the modular units, connectors, and cables. This segment is crucial as it forms the foundation upon which the software and services operate. The demand for high-quality, reliable hardware is driven by the need for robust physical infrastructure to support high-speed data transmission and reduce downtime in network operations. Innovations in hardware design, such as modular and scalable components, are enhancing the efficiency and ease of i

A checklist providing patches to the automatically assembled GBIF backbone taxonomy.
Names and their classification in this list will take precedence over other backbone sources,
thus allowing small manual interventions in the backbone building process.
The list is hosted on github allowing for wider collaboration outside of the secretariat.
Names in this checklist should be removed once they appear in other trusted checklists.
When adding new names into this list all entries should be assigned some remarks why this
name exists and ideally a link to some other online resource via dc:references.

PatchDiscovery: Patch Presence Test for Identifying Binary Vulnerabilities Based on Key Basic Blocks

The source code is structured in the following way:
1. Preprocessing:PatchDiscovery preprocesses each input binary function (i.e., VF, PF and TF) by distilling each function’s semantics into a CFG and applying
instruction normalization and simplification to deal with the syntax gaps on instructions.
2. PatchAnalysis: PatchDiscovery identifies the scope of patch in PF and the scope of vulnerability in VF and selects the key basic blocks in PF and VF as their signatures for patch presence discovery, respectively.
3. PatchPresenceDscovery:PatchDiscovery determines whether a TF has been patched or not.

The dataset is structured in the following way:
1. Bin:the compiled binaries
2. _config.csv : There are four parts in the file, which are CVE id , the last vulnerable version , the first patched version , involved functions in order.
3. _func.csv : All involved functions in _config.cvs.
4. _version.csv : All binary versions to be analyzed.
5. gt.csv : optional , you can mark V and P for functions in target binary as ground truth.

This dataset provides information about the number of properties, residents, and average property values for Cabbage Patch Lane cross streets in Mountain City, TN.

Comprehensive dataset of 32 Pumpkin patches in Turkey as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

The global patch panel accessories market, valued at $3783 million in 2025, is projected to experience robust growth, driven by the increasing demand for high-speed data transmission and networking infrastructure across various sectors. The market's Compound Annual Growth Rate (CAGR) of 4.7% from 2025 to 2033 indicates a steady expansion, fueled by several key factors. The proliferation of electronic devices, the rise of data centers, and the expanding adoption of cloud computing are significantly boosting demand for reliable and efficient patch panel solutions. Further growth is expected from the increasing adoption of high-density patch panels to accommodate the rising number of network connections in modern infrastructure. Specific applications like amplifiers, recording equipment, and microphones in professional audio-visual settings contribute to market expansion, along with the ongoing upgrades and expansions of existing network infrastructure. Geographic segmentation reveals a strong presence in North America and Europe, with Asia-Pacific expected to emerge as a significant growth region due to rapid industrialization and infrastructure development in countries like China and India. While challenges such as fluctuating raw material prices and competition from alternative cabling solutions exist, the overall outlook for the patch panel accessories market remains positive, promising substantial growth opportunities over the forecast period. The market segmentation reveals a diverse landscape, with flat patch panels maintaining a significant market share due to their cost-effectiveness and ease of installation. However, the demand for angled and high-density patch panels is increasing rapidly due to their space-saving capabilities and enhanced connectivity features. Technological advancements, such as improved materials and miniaturization, contribute to the ongoing innovation within the patch panel accessories sector. Major players in the market are focused on developing advanced solutions to meet the growing demands of high-speed networks and evolving industry standards. Competitive strategies include product diversification, strategic partnerships, and geographical expansion. The market's future is marked by a continued focus on high-performance, reliable, and scalable patch panel solutions that cater to the needs of diverse industries, ultimately shaping the future of data transmission and network infrastructure.