Report Filter Definitions and Guidance Please note that all filter options are present in the dataset. For example, if you are looking at a dataset and a state is missing, it means there is no data for the year selected in that state - it does not use a list of all US states. Also note that if the data table disappears, there is no data available for the filter selections made.

Contains scans of a bin filled with different parts ( screws, nuts, rods, spheres, sprockets). For each part type, RGB image and organized 3D point cloud obtained with structured light sensor are provided. In addition, unorganized 3D point cloud representing an empty bin and a small Matlab script to read the files is also provided. 3D data contain a lot of outliers and the data were used to demonstrate a new filtering technique.

Dataset Card for No Language Left Behind (NLLB - 200vo)

  Dataset Summary

This dataset was created based on metadata for mined bitext released by Meta AI. It contains bitext for 148 English-centric and 1465 non-English-centric language pairs using the stopes mining library and the LASER3 encoders (Heffernan et al., 2022). The complete dataset is ~450GB. CCMatrix contains previous versions of mined instructions.

  How to use the data

There are two ways… See the full description on the dataset page: https://huggingface.co/datasets/yaya-sy/nllb-filtering.

Functional trait-based approaches are increasingly used for studying the processes underlying community assembly. The relative influence of different assembly rules might depend on the spatial scale of analysis, the environmental context and the type of functional traits considered. By using a functional trait-based approach, we aim to disentangle the relative role of environmental filtering and interspecific competition on the structure of European ant communities according to the spatial scale and the type of trait considered. We used a large database on ant species composition that encompasses 361 ant communities distributed across the five biogeographic regions of Europe; these communities were composed of 155 ant species, which were characterized by 6 functional traits. We then analysed the relationship between functional divergence and co-occurrence between species pairs across different spatial scales (European, biogeographic region and local) and considering different types of t...

Web Content Filtering Market Outlook

The global web content filtering market size was valued at approximately USD 3.5 billion in 2023 and is projected to reach about USD 8.6 billion by 2032, growing at a CAGR of 10.7% during the forecast period. This robust growth is primarily driven by the increasing need for sophisticated content control mechanisms to protect against online threats and ensure compliance with organizational policies. The surge in internet usage, coupled with the escalating threat of cyber-attacks and malware, has necessitated the deployment of advanced web filtering technologies across various sectors. As enterprises continue to digitalize their operations, the demand for effective web content filtering solutions is anticipated to witness substantial growth.

One of the primary growth factors in the web content filtering market is the rising awareness and concern over cybersecurity threats. With businesses and individuals increasingly relying on the internet for day-to-day operations, the risk of exposure to inappropriate or harmful content has become more pronounced. Organizations are investing heavily in web content filtering solutions to safeguard their networks from malware, phishing attacks, and other cyber threats. Moreover, the adoption of remote working models has further accentuated the need for robust web content controls to ensure that employees access only secure and relevant online resources while working outside the secure corporate network.

Another significant growth driver is the regulatory landscape compelling organizations to implement stringent web filtering mechanisms. Various governments and regulatory bodies worldwide have introduced laws mandating organizations to keep their digital environments secure, thereby boosting the demand for web content filtering solutions. For instance, the General Data Protection Regulation (GDPR) in Europe and the Children's Internet Protection Act (CIPA) in the United States require entities to employ measures that prevent access to inappropriate content, especially in sectors such as education and healthcare. Compliance with these regulations is not only a legal obligation but also a trust-building measure with consumers, driving market growth.

Technological advancements are also playing a pivotal role in propelling the web content filtering market. The integration of artificial intelligence and machine learning into web content filtering solutions has significantly enhanced their effectiveness and efficiency. These technologies enable real-time content analysis and adaptive filtering, ensuring that only relevant and safe content is accessible. Furthermore, the rise of cloud-based filtering solutions offers scalability and flexibility, making them particularly attractive to small and medium enterprises (SMEs) that may not have the resources for extensive on-premise solutions. As technology continues to evolve, it will likely spur further innovation in content filtering solutions, providing enhanced security and user experience.

Content-control Software plays a crucial role in the web content filtering landscape, offering organizations the ability to manage and restrict access to online content based on predefined policies. This software is essential for businesses aiming to protect their networks from harmful content and ensure compliance with industry regulations. By implementing content-control software, companies can effectively monitor and filter web traffic, preventing access to inappropriate or malicious websites. This not only enhances security but also boosts productivity by minimizing distractions and ensuring that employees focus on work-related tasks. As cyber threats continue to evolve, the demand for sophisticated content-control software is expected to rise, driving innovation and growth in the market.

Regionally, North America holds a significant share of the web content filtering market, attributed primarily to the region's technological advancements and high adoption rate of cybersecurity solutions. Europe follows closely, driven by stringent data privacy regulations and a strong emphasis on digital security. Meanwhile, the Asia Pacific region is expected to register the highest growth rate, fueled by increasing internet penetration, rising cyber threats, and growing awareness among businesses regarding the importance of cybersecurity. Emerging economies in this region are witnessing rapid digital transformation, which is expected to create lucrative opportunities for market players in the coming years.

Brazil Sales: General Use: Others nes: Apparatus for Filtering or Purifying Liquids data was reported at 315,557.283 BRL th in 2017. This records a decrease from the previous number of 503,589.053 BRL th for 2016. Brazil Sales: General Use: Others nes: Apparatus for Filtering or Purifying Liquids data is updated yearly, averaging 503,589.053 BRL th from Dec 2005 (Median) to 2017, with 13 observations. The data reached an all-time high of 741,897.000 BRL th in 2011 and a record low of 269,439.000 BRL th in 2006. Brazil Sales: General Use: Others nes: Apparatus for Filtering or Purifying Liquids data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Machinery and Equipment Sector – Table BR.RMB002: Machinery and Equipment Sales: General Use.

Next generation sequencing (NGS) technologies generate huge amounts of sequencing data. Several microbial genome projects, in particular fungal whole genome sequencing, have used NGS techniques, because of their cost efficiency. However, NGS techniques also demand for computational tools to process and analyze massive datasets. Implementation of few data processing steps, including quality and length filters, often leads to a remarkable improvement in the accuracy and quality of data analyses. Choosing appropriate parameters for this purpose is not always straightforward, as these will vary with the dataset. In this study we present the FastQFS (Fastq Quality Filtering and Statistics) tool, which can be used for both read filtering and filtering parameters assessment. There are several tools available, but an important asset of FastQFS is that it provides the information of filtering parameters that fit best to the raw dataset, prior to computationally expensive filtering. It generates statistics of reads meeting different quality and length thresholds, and also the expected coverage depth of the genome which would be left after applying different filtering parameters. The FastQFS tool will help researchers to make informed decisions on NGS reads filtering parameters, avoiding time-consuming optimization of filtering criteria.

R was used for the pipeline. All R code is provided for the creation of simulated datasets and filtering of those datasets.

We've also provide .012 data input files (.txt) with their env files (.env) and the outputs of baypass (.csv) and lfmm (calpval).

The name of the outputs look like this: emsim_156_6_0.5_0.1.txt.lfmm_env_2.calpval This naming convention is the same throughout.

emsim = name of the datastet E. microcarpa simulation

156 = # of individuals i.e., sample size

6 = number of individuals per population

0.5 = the missing data threshold (note, for coding purposes this is actually the % of data kept : 10% missing data will be 0.9) (one of 0.5, 0.6, 0.7 0.8, or 0.9)

0.1 = minor allele frequency (one of 0.1, 0.05, or 0.01)

Associated SNPs

V#####MT - SNPs associated with BIO5

V#####MP - SNPs associated with BIO14

This data set represents 3D point clouds acquired with LiDAR technology and related files from a subregion of 150*436 sqm in the ancient Nakadake Sanroku Kiln Site Center in South Japan. It is a densely vegetated mountainous region with varied topography and vegetation. The data set contains the original point cloud (reduced from a density of 5477 points per square meter to 100 points per square meter), a segmentation of the area based on characteristics in vegetation and topography, and filter pipelines for segments with different characteristics, and other data necessary. The data serve to test the AFwizard software which can create a DTM from the point cloud with varying filter and filter parameter selections based on varying segment characteristics (https://github.com/ssciwr/afwizard). The AFwizard adds flexibility to ground point filtering of 3D point clouds, which is a crucial step in a variety of applications of LiDAR technology. Digital Terrain Models (DTM) derived from filtered 3D point clouds serve various purposes and therefore, rather than creating one representation of the terrain that is supposed to be "true", a variety of models can be derived from the same point cloud according to the intended usage of the DTM. The sample data were acquired during an archaeological research project in a mountainous and densely forested region in South Japan -- the Nakadake-Sanroku Kiln Site Center: LiDAR data were acquired in a subregion of 0.5 sqkm, a relatively small area characterized by frequent and sudden changes in topography and vegetation. The point cloud is very dense due to the technology chosen (UAV multicopter GLYPHON DYNAMICS GD-X8-SP; LiDAR scanner RIEGL VUX-1 UAV). Usage of the data is restricted to the citation of the article mentioned below. Version 2.01: 2023-05-11; Article citation updated; 2022-07-21; Documentation (HowTo - Minimal Workflow) updated, data files tagged.

This archive contains the raw data and data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the StellaCam instrument on the Apache Point Observatory NMSU 1m telescope.

Full details about the raw data are available in Chanover, N. J. et al. Results from the NMSU-NASA Marshall Space Flight Center LCROSS observational campaign. J. Geophys. Res. (Planets) 116, E08003 (2011). https://doi.org/10.1029/2010JE003761

We use principal component analysis (PCA) filtering both to coregister the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) resulting in a non-detection of the LCROSS ejecta plume signal.

Full details are available in the published journal article:

Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" Remote Sensing 15, no. 1: 37. https://doi.org/10.3390/rs15010037

This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.

The identification of genetically encoded variants at the proteome level is an important problem in cancer proteogenomics. The generation of customized protein databases from DNA or RNA sequencing data is a crucial stage of the identification workflow. Genomic data filtering applied at this stage may significantly modify variant search results, yet its effect is generally left out of the scope of proteogenomic studies. In this work, we focused on this impact using data of exome sequencing and LC–MS/MS analyses of six replicates for eight melanoma cell lines processed by a proteogenomics workflow. The main objectives were identifying variant peptides and revealing the role of the genomic data filtering in the variant identification. A series of six confidence thresholds for single nucleotide polymorphisms and indels from the exome data were applied to generate customized sequence databases of different stringency. In the searches against unfiltered databases, between 100 and 160 variant peptides were identified for each of the cell lines using X!Tandem and MS-GF+ search engines. The recovery rate for variant peptides was ∼1%, which is approximately three times lower than that of the wild-type peptides. Using unfiltered genomic databases for variant searches resulted in higher sensitivity and selectivity of the proteogenomic workflow and positively affected the ability to distinguish the cell lines based on variant peptide signatures.

ReBeatICG database contains ICG (impedance cardiography) signals recorded during an experimental session of a virtual search and rescue mission with drones. It includes beat-to-beat annotations of the ICG characteristic points, made by a cardiologist, with the purpose of testing ICG delineation algorithms. A reference of synchronous ECG signals is included to allow comparison and mark cardiac events.

Raw data

The database includes 48 recordings of ICG and ECG signals from 24 healthy subjects during an experimental session of a virtual search and rescue mission with drones, described in [1]. Two segments of 5-minute signals are selected from each subject; one corresponding to baseline state (task BL) and the second one is recorded during higher levels of cognitive workload (task CW). In total, the presented database consisted of 240 minutes of ICG signals.

During the experiment, various signals were recorded, but here only ICG and ECG data are provided. Raw data was recorded with 2000Hz using the Biopac system.

Data Preprocessing (filtering)

Further, for the purpose of annotation by cardiologists, data were first downsampled to 250Hz instead of 2000Hz. Further, it was filtered with an adaptive Savitzky-Golay filter of order 3. “Adaptive'' refers to the adaptive selection of filter length, which plays a major role in the efficacy of the filter. The filter length was selected based on the first 3 seconds of each signal recording SNR level, following the procedure described below.

Starting from a filter length of 3 (i.e., the minimum length allowed), the length is increased in steps of two until signal SNR reaches 30 or the improvements are lower than 1% (i.e., the saturation of SNR improvement with further filter length increase). These values present a good compromise between reducing noise and over-smoothing of the signal (and hence potentially losing valuable details) and a lower filter length, thus reducing complexity. The SNR is calculated as a ratio between the 2-norm of the high and low signal frequencies considering 20Hz as cut-off frequency.

Data Annotation

In order to assess the performance of the ICG delineation algorithms, a subset of the database was annotated by a cardiologist from Lausanne University Hospital (CHUV) in Switzerland.

The annotated subset consists of 4 randomly chosen signal segments containing 10 beats from each subject and task (i.e., 4 segments from BL and 4 from CW task). Segments of signals with artifacts and very noisy were excluded when selecting the data for annotation, and in this case, 8 segments were chosen from the task with cleaner signals. In total, 1920 (80x24) beats were selected for annotation.

For each cardiac cycle, four characteristic points were annotated: B, C, X and O. The following definitions were used when annotating the data:

- C peak -- Defined as the peak with the greatest amplitude in one cardiac cycle that represents the maximum systolic flow.

- B point -- Indicates the onset of the final rapid upstroke toward the C point [3] that is expressed as the point of significant change in the slope of the ICG signal preceding the C point. It is related to the aortic valve opening. However, its identification can be difficult due to variations in the ICG signals morphology. A decisional algorithm has been proposed to guide accurate and reproducible B point identification [4].

- X point -- Often defined as the minimum dZ/dt value in one cardiac cycle. However, this does not always hold true due to variations in the dZ/dt waveform morphology [5]. Thus, the X point is defined as the onset of the steep rise in ICG towards the O point. It represents the aortic valve closing which occurs simultaneously as the T wave end on the ECG signal.

- O point -- The highest local maxima in the first half of the C-C interval. It represents the mitral valve opening.

Annotation was performed using open-access software (https://doi.org/10.5281/zenodo.4724843).

Annotated points are saved in separate files for each person and task, representing the location of points in the original signal.

Data structure

Data is organized in three folders, one for raw data (01_RawData), filtered data (02_FilteredData), and annotated points (03_ExpertAnnotations). In each folder, data is separated into files representing each subject and task (except in 03_ExpertAnnotations where 2 CW task files were not annotated due to an excessive amount of noise).

All files are Matlab .mat files.

Raw data and filtered data .mat files contain „ICG“, „ECG“ synchronized data, as well as “samplFreq“values. In filtered data final chosen Savitzky-Golay filter length (“SGFiltLen”) is provided too.

In Annotated data .mat file contains only matrix „annotPoints“ with each row representing one cardiac cycle, while in columns are positions of B, C, X and O points, respectively. Positions are expressed as a number of samples from the beginning of full database files (signals from 01_RawData and 02_FilteredData folders). In rare cases, there are less than 40 (or 80) values per file, when data was noisy and cardiologists couldn't annotate confidently each cardiac cycle.

-------------------

References

[1] F. Dell’Agnola, “Cognitive Workload Monitoring in Virtual Reality Based Rescue Missions with Drones.,” pp. 397–409, 2020, doi: 10.1007/978-3-030-49695-1_26.

[2] H. Yazdanian, A. Mahnam, M. Edrisi, and M. A. Esfahani, “Design and Implementation of a Portable Impedance Cardiography System for Noninvasive Stroke Volume Monitoring,” J. Med. Signals Sens., vol. 6, no. 1, pp. 47–56, Mar. 2016.

[3] A. Sherwood(Chair), M. T. Allen, J. Fahrenberg, R. M. Kelsey, W. R. Lovallo, and L. J. P. van Doornen, “Methodological Guidelines for Impedance Cardiography,” Psychophysiology, vol. 27, no. 1, pp. 1–23, 1990, doi: https://doi.org/10.1111/j.1469-8986.1990.tb02171.x.

[4] J. R. Árbol, P. Perakakis, A. Garrido, J. L. Mata, M. C. Fernández‐Santaella, and J. Vila, “Mathematical detection of aortic valve opening (B point) in impedance cardiography: A comparison of three popular algorithms,” Psychophysiology, vol. 54, no. 3, pp. 350–357, 2017, doi: https://doi.org/10.1111/psyp.12799.

[5] M. Nabian, Y. Yin, J. Wormwood, K. S. Quigley, L. F. Barrett, and S. Ostadabbas, “An Open-Source Feature Extraction Tool for the Analysis of Peripheral Physiological Data,” IEEE J. Transl. Eng. Health Med., vol. 6, p. 2800711, 2018, doi: 10.1109/JTEHM.2018.2878000.

The computer privacy screen filter market is experiencing robust growth, driven by increasing concerns about data breaches and visual hacking in both corporate and personal settings. The market, estimated at $5 billion in 2025, is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of 7% throughout the forecast period (2025-2033), reaching approximately $9 billion by 2033. This growth is fueled by several key trends: the rising adoption of remote work and hybrid work models, increasing awareness of cybersecurity threats, and the proliferation of sensitive data handled on laptops and desktops. Furthermore, the increasing use of sophisticated technology in privacy filters, such as anti-glare and blue light filtering features, enhances user experience and contributes to market expansion. Major players like 3M, HP, Dell, and Kensington continue to innovate, offering a diverse range of filter types catering to varied user needs and budgets. However, potential restraints include the relatively high cost of premium filters and the perception among some consumers that the filters affect screen clarity. Despite these challenges, the market's positive trajectory is expected to continue. Segmentation within the market includes different filter types (e.g., magnetic, adhesive), screen sizes, and application across various sectors (corporate, individual, educational). Regional variations are anticipated, with North America and Europe likely to maintain significant market shares due to heightened cybersecurity awareness and advanced technological adoption. The competitive landscape is characterized by both established players and emerging brands offering varying degrees of customization and functionality. This dynamic mix is driving innovation and competitive pricing, making privacy filters increasingly accessible across the consumer and commercial spectrum. Strategic partnerships and mergers and acquisitions could further consolidate the market in the coming years.

This contains two datasets used for demonstrate the dangers and solutions for post search filter in crosslinking mass-spectrometry. A dataset of search results for mycoplasma pneumonia acquisitions and escherichia coli. Both where searched against a combined database of all e.coli and mycoplasma pneumonia proteins.

These are results without any cut-off and are the base of testing if a filter or processing step is actually affecting decoys differently then target false positives. The idea being that these search results provide an decoy independent set of known false positive matches; all matches involving e.coli peptides to mycoplasma spectra and all matches involving mycoplasma pneumonia peptides to e.coli spectra. In the original use case the data where used to detect if a filter, that uses match external information to filter individual matches, results in an underrepresentation of decoys when compared to these secondary known false positives and how at least no contradiction was found when applying teh ec-filter style of applying the information.

The second dataset is a set of FDR results for 2% unique residue pair FDR of an yeast 26S Proteasome acquisition run with and without using the ec-filter and each of tzhese with and without xiFDR in built boosting. The spectra where searched against increasingly larger databases to show the effect of filtering the results depending on the database size – both in terms of present and assumed non-present proteins.

This data set represents 3D point clouds acquired with LiDAR technology and related files from a subregion of 150*436 sqm in the ancient Nakadake Sanroku Kiln Site Center in South Japan. It is a densely vegetated mountainous region with varied topography and vegetation. The data set contains the original point cloud (reduced from a density of 5477 points per square meter to 100 points per square meter), a segmentation of the area based on characteristics in vegetation and topography, and filter pipelines for segments with different characteristics, and other data necessary. The data serve to test the AFwizard software which can create a DTM from the point cloud with varying filter and filter parameter selections based on varying segment characteristics (https://github.com/ssciwr/afwizard). The AFwizard adds flexibility to ground point filtering of 3D point clouds, which is a crucial step in a variety of applications of LiDAR technology. Digital Terrain Models (DTM) derived from filtered 3D point clouds serve various purposes and therefore, rather than creating one representation of the terrain that is supposed to be "true", a variety of models can be derived from the same point cloud according to the intended usage of the DTM. The sample data were acquired during an archaeological research project in a mountainous and densely forested region in South Japan -- the Nakadake-Sanroku Kiln Site Center: LiDAR data were acquired in a subregion of 0.5 sqkm, a relatively small area characterized by frequent and sudden changes in topography and vegetation. The point cloud is very dense due to the technology chosen (UAV multicopter GLYPHON DYNAMICS GD-X8-SP; LiDAR scanner RIEGL VUX-1 UAV). Usage of the data is restricted to the citation of the article mentioned below. Version 2.01: 2023-05-11; Article citation updated; 2022-07-21; Documentation (HowTo - Minimal Workflow) updated, data files tagged.

As data sets continue to increase in size, there is growing interest in methods for prediction that are both flexible and interpretable. A flurry of recent work on this topic has focused on additive modeling in the regression setting, and in particular, on the use of data-adaptive non-linear functions that can be used to flexibly model each covariate’s effect, conditional on the other features in the model. In this paper, we extend this recent line of work to the survival setting. We develop an additive Cox proportional hazards model, in which each additive function is obtained by trend filtering, so that the fitted functions are piece-wise polynomial with adaptively-chosen knots. An efficient proximal gradient descent algorithm is used to fit the model. We demonstrate its performance in simulations and in application to a primary biliary cirrhosis data set, as well as a data set consisting of time to publication for clinical trials in the biomedical literature.

Spatio-temporal datasets are rapidly growing in size. For example, environmental variables are measured with increasing resolution by increasing numbers of automated sensors mounted on satellites and aircraft. Using such data, which are typically noisy and incomplete, the goal is to obtain complete maps of the spatio-temporal process, together with uncertainty quantification. We focus here on real-time filtering inference in linear Gaussian state-space models. At each time point, the state is a spatial field evaluated on a very large spatial grid, making exact inference using the Kalman filter computationally infeasible. Instead, we propose a multi-resolution filter (MRF), a highly scalable and fully probabilistic filtering method that resolves spatial features at all scales. We prove that the MRF matrices exhibit a particular block-sparse multi-resolution structure that is preserved under filtering operations through time. We describe connections to existing methods, including hierarchical matrices from numerical mathematics. We also discuss inference on time-varying parameters using an approximate Rao-Blackwellized particle filter, in which the integrated likelihood is computed using the MRF. Using a simulation study and a real satellite-data application, we show that the MRF strongly outperforms competing approaches. Supplementary materials include Python code for reproducing the simulations, some detailed properties of the MRF and auxiliary theoretical results.

ABSTRACT The aim of this study was to evaluate the performance of four ground filtering algorithms to generate digital terrain models (DTMs) from airborne light detection and ranging (LiDAR) data. The study area is a forest environment located in Washington state, USA with distinct classes of land use and land cover (e.g., shrubland, grassland, bare soil, and three forest types according to tree density and silvicultural interventions: closed-canopy forest, intermediate-canopy forest, and open-canopy forest). The following four ground filtering algorithms were assessed: Weighted Linear Least Squares (WLS), Multi-scale Curvature Classification (MCC), Progressive Morphological Filter (PMF), and Progressive Triangulated Irregular Network (PTIN). The four algorithms performed well across the land cover, with the PMF yielding the least number of points classified as ground. Statistical differences between the pairs of DTMs were small, except for the PMF due to the highest errors. Because the forestry sector requires constant updating of topographical maps, open-source ground filtering algorithms, such as WLS and MCC, performed very well on planted forest environments. However, the performance of such filters should also be evaluated over complex native forest environments.

More than one training run goes into making a large language model, but developers rarely release the small models and datasets they experiment with during the development process. How do they decide what dataset to use for pretraining or which benchmarks to hill climb on? To empower open exploration of these questions, we release DataDecide—a suite of models we pretrain on 25 corpora with differing sources, deduplication, and filtering up to 100B tokens, over 14 different model sizes ranging… See the full description on the dataset page: https://huggingface.co/datasets/allenai/DataDecide-data-recipes.

OpenWeb Ninja's Google Images Data (Google SERP Data) API provides real-time image search capabilities for images sourced from all public sources on the web.

The API enables you to search and access more than 100 billion images from across the web including advanced filtering capabilities as supported by Google Advanced Image Search. The API provides Google Images Data (Google SERP Data) including details such as image URL, title, size information, thumbnail, source information, and more data points. The API supports advanced filtering and options such as file type, image color, usage rights, creation time, and more. In addition, any Advanced Google Search operators can be used with the API.

OpenWeb Ninja's Google Images Data & Google SERP Data API common use cases:

• Creative Media Production: Enhance digital content with a vast array of real-time images, ensuring engaging and brand-aligned visuals for blogs, social media, and advertising.

• AI Model Enhancement: Train and refine AI models with diverse, annotated images, improving object recognition and image classification accuracy.

• Trend Analysis: Identify emerging market trends and consumer preferences through real-time visual data, enabling proactive business decisions.

• Innovative Product Design: Inspire product innovation by exploring current design trends and competitor products, ensuring market-relevant offerings.

• Advanced Search Optimization: Improve search engines and applications with enriched image datasets, providing users with accurate, relevant, and visually appealing search results.

OpenWeb Ninja's Annotated Imagery Data & Google SERP Data Stats & Capabilities:

• 100B+ Images: Access an extensive database of over 100 billion images.

• Images Data from all Public Sources (Google SERP Data): Benefit from a comprehensive aggregation of image data from various public websites, ensuring a wide range of sources and perspectives.

• Extensive Search and Filtering Capabilities: Utilize advanced search operators and filters to refine image searches by file type, color, usage rights, creation time, and more, making it easy to find exactly what you need.

• Rich Data Points: Each image comes with more than 10 data points, including URL, title (annotation), size information, thumbnail, and source information, providing a detailed context for each image.