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.

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.

Context

Cannabis is a genus of flowering plants in the family Cannabaceae.

Source: https://en.wikipedia.org/wiki/Cannabis

Content

In October 2016, Phylos Bioscience released a genomic open dataset of approximately 850 strains of Cannabis via the Open Cannabis Project. In combination with other genomics datasets made available by Courtagen Life Sciences, Michigan State University, NCBI, Sunrise Medicinal, University of Calgary, University of Toronto, and Yunnan Academy of Agricultural Sciences, the total amount of publicly available data exceeds 1,000 samples taken from nearly as many unique strains.

https://medium.com/google-cloud/dna-sequencing-of-1000-cannabis-strains-publicly-available-in-google-bigquery-a33430d63998

These data were retrieved from the National Center for Biotechnology Information’s Sequence Read Archive (NCBI SRA), processed using the BWA aligner and FreeBayes variant caller, indexed with the Google Genomics API, and exported to BigQuery for analysis. Data are available directly from Google Cloud Storage at gs://gcs-public-data--genomics/cannabis, as well as via the Google Genomics API as dataset ID 918853309083001239, and an additional duplicated subset of only transcriptome data as dataset ID 94241232795910911, as well as in the BigQuery dataset bigquery-public-data:genomics_cannabis.

All tables in the Cannabis Genomes Project dataset have a suffix like _201703. The suffix is referred to as [BUILD_DATE] in the descriptions below. The dataset is updated frequently as new releases become available.

The following tables are included in the Cannabis Genomes Project dataset:

Sample_info contains fields extracted for each SRA sample, including the SRA sample ID and other data that give indications about the type of sample. Sample types include: strain, library prep methods, and sequencing technology. See SRP008673 for an example of upstream sample data. SRP008673 is the University of Toronto sequencing of Cannabis Sativa subspecies Purple Kush.

MNPR01_reference_[BUILD_DATE] contains reference sequence names and lengths for the draft assembly of Cannabis Sativa subspecies Cannatonic produced by Phylos Bioscience. This table contains contig identifiers and their lengths.

MNPR01_[BUILD_DATE] contains variant calls for all included samples and types (genomic, transcriptomic) aligned to the MNPR01_reference_[BUILD_DATE] table. Samples can be found in the sample_info table. The MNPR01_[BUILD_DATE] table is exported using the Google Genomics BigQuery variants schema. This table is useful for general analysis of the Cannabis genome.

MNPR01_transcriptome_[BUILD_DATE] is similar to the MNPR01_[BUILD_DATE] table, but it includes only the subset transcriptomic samples. This table is useful for transcribed gene-level analysis of the Cannabis genome.

Fork this kernel to get started with this dataset.

Acknowledgements

Dataset Source: http://opencannabisproject.org/ Category: Genomics Use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - https://www.ncbi.nlm.nih.gov/home/about/policies.shtml - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset. Update frequency: As additional data are released to GenBank View in BigQuery: https://bigquery.cloud.google.com/dataset/bigquery-public-data:genomics_cannabis View in Google Cloud Storage: gs://gcs-public-data--genomics/cannabis

Banner Photo by Rick Proctor from Unplash.

Inspiration

Which Cannabis samples are included in the variants table?

Which contigs in the MNPR01_reference_[BUILD_DATE] table have the highest density of variants?

How many variants does each sample have at the THC Synthase gene (THCA1) locus?

Meet Earth EngineGoogle Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities and makes it available for scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth's surface.SATELLITE IMAGERY+YOUR ALGORITHMS+REAL WORLD APPLICATIONSLEARN MOREGLOBAL-SCALE INSIGHTExplore our interactive timelapse viewer to travel back in time and see how the world has changed over the past twenty-nine years. Timelapse is one example of how Earth Engine can help gain insight into petabyte-scale datasets.EXPLORE TIMELAPSEREADY-TO-USE DATASETSThe public data archive includes more than thirty years of historical imagery and scientific datasets, updated and expanded daily. It contains over twenty petabytes of geospatial data instantly available for analysis.EXPLORE DATASETSSIMPLE, YET POWERFUL APIThe Earth Engine API is available in Python and JavaScript, making it easy to harness the power of Google’s cloud for your own geospatial analysis.EXPLORE THE APIGoogle Earth Engine has made it possible for the first time in history to rapidly and accurately process vast amounts of satellite imagery, identifying where and when tree cover change has occurred at high resolution. Global Forest Watch would not exist without it. For those who care about the future of the planet Google Earth Engine is a great blessing!-Dr. Andrew Steer, President and CEO of the World Resources Institute.CONVENIENT TOOLSUse our web-based code editor for fast, interactive algorithm development with instant access to petabytes of data.LEARN ABOUT THE CODE EDITORSCIENTIFIC AND HUMANITARIAN IMPACTScientists and non-profits use Earth Engine for remote sensing research, predicting disease outbreaks, natural resource management, and more.SEE CASE STUDIESREADY TO BE PART OF THE SOLUTION?SIGN UP NOWTERMS OF SERVICE PRIVACY ABOUT GOOGLE

The Open Targets Platform is a comprehensive data integration tool that supports systematic identification and prioritisation of potential therapeutic drug targets. By integrating publicly available datasets including data generated by the Open Targets consortium, the Platform builds and scores target-disease associations to assist in drug target identification and prioritisation. It also integrates relevant annotation information about targets, diseases or phenotypes, variants, GWAS and molQTL studies, credible sets and drugs - as well as their most relevant relationships. The Platform is a freely available resource that is actively maintained with quarterly data updates. Data is available through an intuitive user interface, an API, and data downloads. The pipeline and infrastructure codebases are open-source and the licence allows the creation of self-hosted private instances of the Platform with custom data. To learn more about the Platform, visit our Platform documentation or join the Open Targets Community . 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 Google Maps dataset is ideal for getting extensive information on businesses anywhere in the world. Easily filter by location, business type, and other factors to get the exact data you need. The Google Maps dataset includes all major data points: timestamp, name, category, address, description, open website, phone number, open_hours, open_hours_updated, reviews_count, rating, main_image, reviews, url, lat, lon, place_id, country, and more.

The Open Banking API market is booming, projected to reach $70B by 2033 with a 25% CAGR. This comprehensive analysis explores market size, key trends, leading companies (Plaid, TrueLayer, etc.), and regional growth. Discover the opportunities and challenges in this rapidly evolving financial technology sector.

Explore the dynamic Open API market, its projected $1.8 trillion valuation by 2033 with a 25% CAGR. Discover key drivers, trends in cloud adoption, and insights into leading companies and regional growth.

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)

Context

This dataset has been created for an example of implementing predictive modeling in a dashboard.

Content

The online.csv file contains actual order data manually imported from the Google Store public access Google Analytics. This data can't be accessed via API, unfortunately.

The retail.csv is a heavily modified version of the UK retailer dataset, to approximate a retail location using another kind of POS for the Google store.

The KEY_SKU.csv file is the link between stock codes and product skus the permit joining the files.

The Marketing_Spend.csv file is a fake file containing marketing budgets for online and offline advertising. It was created to practice building a model predicting sales from the marketing budget.

Inspiration

Have fun!

List of search terms used to query Google Health Trends API, by category.

Field Name

Description

StateName

Name of the state (Oklahoma)

date

Date of the data point (YYYY-MM-DD)

covid-19_OK

The search interest in the term "COVID-19" in Oklahoma on the given date

sars-cov-2_OK

The search interest in the term "SARS-CoV-2" in Oklahoma on the given date

coronavirus_OK

The search interest in the term "coronavirus" in Oklahoma on the given date

Omicron_OK

The search interest in the term "Omicron" in Oklahoma on the given date

Delta_OK

The search interest in the term "Delta" in Oklahoma on the given date

Fever_OK

The search interest in the term "fever" in Oklahoma on the given date

fatigue_OK

The search interest in the term "fatigue" in Oklahoma on the given date

diarrhea_OK

The search interest in the term "diarrhea" in Oklahoma on the given date

pneumonia_OK

The search interest in the term "pneumonia" in Oklahoma on the given date

sore throat_OK

The search interest in the term "sore throat" in Oklahoma on the given date

loss of smell_OK

The search interest in the term "loss of smell" in Oklahoma on the given date

loss smell_OK

Another variation for tracking the search interest in "loss of smell" in Oklahoma on the given date

loss taste_OK

The search interest in the term "loss of taste" in Oklahoma on the given date

cough_OK

The search interest in the term "cough" in Oklahoma on the given date

nasal congestion_OK

The search interest in the term "nasal congestion" in Oklahoma on the given date

Pytrends is an unofficial Google Trends API for Python. It enables users to programmatically fetch Google Trends data, which can be useful for various applications such as market research, academic studies, and tracking public interest in specific topics over time. Benefits of Using Pytrends: Automated Data Collection: Pytrends allows for automated and repeatable data collection from Google Trends, saving time and effort compared to manual extraction.

Customizable Queries: Users can specify keywords, timeframes, geographic locations, and other parameters to tailor the data to their specific needs.

Integration with Data Analysis Tools: Pytrends data can be easily integrated with tools like pandas for further analysis, visualization, and reporting.

Real-Time Insights: By regularly updating and analyzing Google Trends data, users can gain real-time insights into public interest and behavior, which is valuable for decision-making and research.

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!

USPTO Patent Examiner Data System (PEDS) API Data contains data from the examination process of USPTO patent applications. PEDS contains the bibliographic, published document and patent term extension data tabs in Public PAIR from 1981 to present. There is also some data dating back to 1935.

Please cite this paper when using this dataset: N. Thakur, “Mpox narrative on Instagram: A labeled multilingual dataset of Instagram posts on mpox for sentiment, hate speech, and anxiety analysis,” arXiv [cs.LG], 2024, URL: https://arxiv.org/abs/2409.05292Abstract: The world is currently experiencing an outbreak of mpox, which has been declared a Public Health Emergency of International Concern by WHO. During recent virus outbreaks, social media platforms have played a crucial role in keeping the global population informed and updated regarding various aspects of the outbreaks. As a result, in the last few years, researchers from different disciplines have focused on the development of social media datasets focusing on different virus outbreaks. No prior work in this field has focused on the development of a dataset of Instagram posts about the mpox outbreak. The work presented in this paper (stated above) aims to address this research gap. It presents this multilingual dataset of 60,127 Instagram posts about mpox, published between July 23, 2022, and September 5, 2024. This dataset contains Instagram posts about mpox in 52 languages.For each of these posts, the Post ID, Post Description, Date of publication, language, and translated version of the post (translation to English was performed using the Google Translate API) are presented as separate attributes in the dataset. After developing this dataset, sentiment analysis, hate speech detection, and anxiety or stress detection were also performed. This process included classifying each post intoone of the fine-grain sentiment classes, i.e., fear, surprise, joy, sadness, anger, disgust, or neutralhate or not hateanxiety/stress detected or no anxiety/stress detected.These results are presented as separate attributes in the dataset for the training and testing of machine learning algorithms for sentiment, hate speech, and anxiety or stress detection, as well as for other applications.The 52 distinct languages in which Instagram posts are present in the dataset are English, Portuguese, Indonesian, Spanish, Korean, French, Hindi, Finnish, Turkish, Italian, German, Tamil, Urdu, Thai, Arabic, Persian, Tagalog, Dutch, Catalan, Bengali, Marathi, Malayalam, Swahili, Afrikaans, Panjabi, Gujarati, Somali, Lithuanian, Norwegian, Estonian, Swedish, Telugu, Russian, Danish, Slovak, Japanese, Kannada, Polish, Vietnamese, Hebrew, Romanian, Nepali, Czech, Modern Greek, Albanian, Croatian, Slovenian, Bulgarian, Ukrainian, Welsh, Hungarian, and Latvian.The following is a description of the attributes present in this dataset:Post ID: Unique ID of each Instagram postPost Description: Complete description of each post in the language in which it was originally publishedDate: Date of publication in MM/DD/YYYY formatLanguage: Language of the post as detected using the Google Translate APITranslated Post Description: Translated version of the post description. All posts which were not in English were translated into English using the Google Translate API. No language translation was performed for English posts.Sentiment: Results of sentiment analysis (using the preprocessed version of the translated Post Description) where each post was classified into one of the sentiment classes: fear, surprise, joy, sadness, anger, disgust, and neutralHate: Results of hate speech detection (using the preprocessed version of the translated Post Description) where each post was classified as hate or not hateAnxiety or Stress: Results of anxiety or stress detection (using the preprocessed version of the translated Post Description) where each post was classified as stress/anxiety detected or no stress/anxiety detected.All the Instagram posts that were collected during this data mining process to develop this dataset were publicly available on Instagram and did not require a user to log in to Instagram to view the same (at the time of writing this paper).

The Synthea Generated Synthetic Data in FHIR hosts over 1 million synthetic patient records generated using Synthea in FHIR format. Exported from the Google Cloud Healthcare API FHIR Store into BigQuery using analytics schema . 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 . This public dataset is also available in Google Cloud Storage and available free to use. The URL for the GCS bucket is gs://gcp-public-data--synthea-fhir-data-1m-patients. Use this quick start guide to quickly learn how to access public datasets on Google Cloud Storage. Please cite SyntheaTM as: Jason Walonoski, Mark Kramer, Joseph Nichols, Andre Quina, Chris Moesel, Dylan Hall, Carlton Duffett, Kudakwashe Dube, Thomas Gallagher, Scott McLachlan, Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record, Journal of the American Medical Informatics Association, Volume 25, Issue 3, March 2018, Pages 230–238, https://doi.org/10.1093/jamia/ocx079

This dataset is the resulting traffic volume data from a recent pilot of computer-vision sensors.

The following metrics were measured: -Volume counts of people, bikes, cars, trucks, and busses passing through pilot project areas -Volumes accounted for by mode and timestamped at up to 15-minute intervals -Desire lines and movement patterns accounted for by mode

The primary purpose of this pilot was to understand the impacts of temporary street-level changes that would be implemented to facilitate a safe re-opening in the context of Covid-19. A secondary objective of the project was to evaluate a privacy-oriented solution to data collection in the public realm.

Sensors were installed at three distinct locations: -In the Seaport district on a commercial street with bike lanes (Northern Avenue) -Downtown at a busy intersection next to the Boston Common (Tremont Street) -And in Jamaica Plain where the southwest corridor convenes with a blue bike station and T stop (Jackson Sq.)

Please complete this form to access the City of Boston Sandbox and access the data in API format: https://docs.google.com/forms/d/e/1FAIpQLScuEQEsmTToEMBRqvX7uhpCiWu165T4GciTCMEa2ylC2bT59w/viewform

Weekly data from the Google Analytics tag for the Open Data Portal at OpenData.fcgov.com.

Analytics shown are presumed to be non-City-employees, as these data come from computers external to the City network. Each day starting at the first day for which there are data is included, and the URL is either a specific page or "all", specifying that every page in the domain is included. Specific-page URLs are filtered to the main Portal page or data assets, so "all" may capture more pages than specified individually.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

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.