Pretrained models from the paper Big Transfer (BiT): General Visual Representation Learning downloaded from tensorflow hub for ease of use with kaggle notebooks. These models are the feature extraction ones.

The Landsat Image Mosaic of Antarctica (LIMA) is a seamless and virtually cloudless mosaic created from processed Landsat 7 ETM+ scenes. Processed Landsat Scenes (16 bit) are Level 1Gt NLAPS scenes converted to 16 bit, processed with sun-angle correction, and converted to reflectance values (Bindschadler 2008). Each Landsat scene is …

The highest clickthrough rate on Google Display Network - 1.08 percent - belongs to real estate advertising. For Google Search Network, dating and personals ads have the highest CTR, reaching 6.05 percent. The difference between Google’s Display Network (GDN) and Search Network is that the former offers placement of display ads on numerous websites, while the latter is dedicated to text advertising appearing alongside search engine results. What about mobile search advertising?
With the internet becoming more accessible via mobile devices, search advertising is adapting to this format as well, but mobile CTRs in Google AdWords are a bit different. The highest mobile rates on Search Network are attributed to travel and hospitality ads and hair salon advertising has the highest CTR on the Display Network. How popular is Google among users and advertisers?
In certain countries, Google is widely used, as proven by the high share of desktop search traffic originating from the search engine. In Brazil, India, Spain, Australia, Germany, France and Italy Google commands over 90 percent of the search traffic. When it comes to advertising, Google is definitely the leader when compared to other major players in the market. In fact, its advertising revenue was roughly eight times higher than that of its closest competitor, Baidu.

Global Mining Drill Bits Market was valued at USD 2.81 billion in 2024 and is expected to reach USD 4.32 billion by 2030 with a CAGR of 7.27% during the forecast period.

The statistic shows the demand growth for smartphone NAND memory bits worldwide from the first quarter of 2013 to the fourth quarter of 2015. In the fourth quarter of 2015, NAND demand from smartphones grew ** percent.

The data presented in this article is related to the research article entitled "Urban design using generative adversarial networks: optimising citizen health and wellbeing" (Wijnands et al 2018). The data consists of Google Street View (Google Maps, 2017) imagery (4,473,991 images, 8-bit JPEG at 256x256 resolution) from four headings (0, 90, 180, and 270 degrees) at 1,118,534 locations in the greater metropolitan area of Melbourne, Australia. Locations were determined using the nodes of the vector lines in the PSMA Street Network dataset (PSMA 2018) and data was post-processed by removing indoor images. Please cite this paper if you use the dataset.

The data is broken up into four archives, 000.zip, 090.zip, 180.zip, and 270.zip, containing the imagery from each compass heading. A csv file (contained in MelbourneStreetViewImagesData.zip) provides a mapping between the filenames, location names, direction, latitude, and longitude.

OUTDATED. See the current data at https://data.cityofchicago.org/d/hz9b-7nh8 -- Building footprints in Chicago. Metadata may be viewed and downloaded at http://bit.ly/HZVDIY. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ), is required.

The global market for multiplayer real-time online editing is experiencing robust growth, driven by the increasing adoption of cloud-based collaboration tools and the rising demand for seamless teamwork across geographically dispersed teams. The market, estimated at $15 billion in 2025, is projected to grow at a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033. This expansion is fueled by several key factors: the shift towards remote work models, the need for enhanced document version control, and the increasing integration of these tools with other productivity applications. The cloud-based segment currently dominates the market, owing to its scalability, accessibility, and cost-effectiveness. However, the web-based segment is witnessing significant growth due to its ease of use and browser compatibility. Large businesses currently account for a larger share of the market, but the SME segment is expected to witness faster growth in the coming years, driven by increasing affordability and awareness of the benefits of real-time collaborative editing. The competitive landscape is highly fragmented, with both established tech giants like Google and Microsoft, and specialized players such as Zoho and Bit.ai, vying for market share. Geographic growth is particularly strong in North America and Europe, reflecting the higher adoption rates of advanced technologies in these regions. However, the Asia-Pacific region is expected to exhibit high growth potential in the coming years, driven by rising internet penetration and increasing digital literacy. The market faces some restraints, including concerns about data security and privacy, and the need for robust integration with existing IT infrastructure. However, these challenges are being addressed through advancements in encryption technology and enhanced interoperability features. Continued innovation in user experience and the expansion into emerging markets will further propel the growth of the multiplayer real-time online editing market.

description: Building footprints in Chicago. Metadata may be viewed and downloaded at http://bit.ly/HZVDIY. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ), is required.; abstract: Building footprints in Chicago. Metadata may be viewed and downloaded at http://bit.ly/HZVDIY. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ), is required.

This statistic shows the number of servings of Bit-O-Honey chocolate and other candy eaten within one month in the United States in 2020. The data has been calculated by Statista based on the U.S. Census data and Simmons National Consumer Survey (NHCS). According to this statistic, **** million Americans consumed * or more servings in 2020.

Building footprints in Chicago. Metadata may be viewed and downloaded at http://bit.ly/HZVDIY. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ), is required.

Leaves from genetically unique Juglans regia plants were scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA). Soil samples were collected in Fall of 2017 from the riparian oak forest located at the Russell Ranch Sustainable Agricultural Institute at the University of California Davis. The soil was sieved through a 2 mm mesh and was air dried before imaging. A single soil aggregate was scanned at 23 keV using the 10x objective lens with a pixel resolution of 650 nanometers on beamline 8.3.2 at the ALS. Additionally, a drought stressed almond flower bud (Prunus dulcis) from a plant housed at the University of California, Davis, was scanned using a 4x lens with a pixel resolution of 1.72 µm on beamline 8.3.2 at the ALS Raw tomographic image data was reconstructed using TomoPy. Reconstructions were converted to 8-bit tif or png format using ImageJ or the PIL package in Python before further processing. Images were annotated using Intel’s Computer Vision Annotation Tool (CVAT) and ImageJ. Both CVAT and ImageJ are free to use and open source. Leaf images were annotated in following Théroux-Rancourt et al. (2020). Specifically, Hand labeling was done directly in ImageJ by drawing around each tissue; with 5 images annotated per leaf. Care was taken to cover a range of anatomical variation to help improve the generalizability of the models to other leaves. All slices were labeled by Dr. Mina Momayyezi and Fiona Duong.To annotate the flower bud and soil aggregate, images were imported into CVAT. The exterior border of the bud (i.e. bud scales) and flower were annotated in CVAT and exported as masks. Similarly, the exterior of the soil aggregate and particulate organic matter identified by eye were annotated in CVAT and exported as masks. To annotate air spaces in both the bud and soil aggregate, images were imported into ImageJ. A gaussian blur was applied to the image to decrease noise and then the air space was segmented using thresholding. After applying the threshold, the selected air space region was converted to a binary image with white representing the air space and black representing everything else. This binary image was overlaid upon the original image and the air space within the flower bud and aggregate was selected using the “free hand” tool. Air space outside of the region of interest for both image sets was eliminated. The quality of the air space annotation was then visually inspected for accuracy against the underlying original image; incomplete annotations were corrected using the brush or pencil tool to paint missing air space white and incorrectly identified air space black. Once the annotation was satisfactorily corrected, the binary image of the air space was saved. Finally, the annotations of the bud and flower or aggregate and organic matter were opened in ImageJ and the associated air space mask was overlaid on top of them forming a three-layer mask suitable for training the fully convolutional network. All labeling of the soil aggregate and soil aggregate images was done by Dr. Devin Rippner. These images and annotations are for training deep learning models to identify different constituents in leaves, almond buds, and soil aggregates Limitations: For the walnut leaves, some tissues (stomata, etc.) are not labeled and only represent a small portion of a full leaf. Similarly, both the almond bud and the aggregate represent just one single sample of each. The bud tissues are only divided up into buds scales, flower, and air space. Many other tissues remain unlabeled. For the soil aggregate annotated labels are done by eye with no actual chemical information. Therefore particulate organic matter identification may be incorrect. Resources in this dataset:Resource Title: Annotated X-ray CT images and masks of a Forest Soil Aggregate. File Name: forest_soil_images_masks_for_testing_training.zipResource Description: This aggregate was collected from the riparian oak forest at the Russell Ranch Sustainable Agricultural Facility. The aggreagate was scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 10x objective lens with a pixel resolution of 650 nanometers. For masks, the background has a value of 0,0,0; pores spaces have a value of 250,250, 250; mineral solids have a value= 128,0,0; and particulate organic matter has a value of = 000,128,000. These files were used for training a model to segment the forest soil aggregate and for testing the accuracy, precision, recall, and f1 score of the model.Resource Title: Annotated X-ray CT images and masks of an Almond bud (P. Dulcis). File Name: Almond_bud_tube_D_P6_training_testing_images_and_masks.zipResource Description: Drought stressed almond flower bud (Prunis dulcis) from a plant housed at the University of California, Davis, was scanned by X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 4x lens with a pixel resolution of 1.72 µm using. For masks, the background has a value of 0,0,0; air spaces have a value of 255,255, 255; bud scales have a value= 128,0,0; and flower tissues have a value of = 000,128,000. These files were used for training a model to segment the almond bud and for testing the accuracy, precision, recall, and f1 score of the model.Resource Software Recommended: Fiji (ImageJ),url: https://imagej.net/software/fiji/downloads Resource Title: Annotated X-ray CT images and masks of Walnut leaves (J. Regia) . File Name: 6_leaf_training_testing_images_and_masks_for_paper.zipResource Description: Stems were collected from genetically unique J. regia accessions at the 117 USDA-ARS-NCGR in Wolfskill Experimental Orchard, Winters, California USA to use as scion, and were grafted by Sierra Gold Nursery onto a commonly used commercial rootstock, RX1 (J. microcarpa × J. regia). We used a common rootstock to eliminate any own-root effects and to simulate conditions for a commercial walnut orchard setting, where rootstocks are commonly used. The grafted saplings were repotted and transferred to the Armstrong lathe house facility at the University of California, Davis in June 2019, and kept under natural light and temperature. Leaves from each accession and treatment were scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 10x objective lens with a pixel resolution of 650 nanometers. For masks, the background has a value of 170,170,170; Epidermis value= 85,85,85; Mesophyll value= 0,0,0; Bundle Sheath Extension value= 152,152,152; Vein value= 220,220,220; Air value = 255,255,255.Resource Software Recommended: Fiji (ImageJ),url: https://imagej.net/software/fiji/downloads

The data source in this research is primary data and the data collection tool (instrument) in this study uses a questionnaire consisting of six types of questionnaires obtained and developed from various sources. Primary data is collected by data collection tools or instruments in the form of questionnaires made in the form of Google forms which are distributed online and offline at the short link https://bit.ly/efektitasauditinternal.
The research implementation stage begins with the researcher sending a research permit letter from PAIRSI to the PAIRSI and SPI Whatsapp Group and providing an explanation before the study (informed consent) including the purpose, objectives and procedures of the study including inclusion and exclusion criteria for prospective respondents in the Whatsapp Group. The researcher explained about the research questionnaire in the form of a google form which was expected to be filled in by prospective respondents via the short link https://bit.ly/efektitasauditinternal

Occhiali_protettivi

## Overview

Occhiali_protettivi is a dataset for object detection tasks - it contains Goggles annotations for 1,611 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Contains 2 .csv files from the fit bit fitness tracker data (CC0: Public Domain, dataset made available through Mobius). Just completed my Google Professional data analyst certification and just used them to practice.

description: Street sweeping zones by Ward and Ward Section Number. For the corresponding schedule, see https://data.cityofchicago.org/d/izuq-yb9q. For more information about the City's Street Sweeping program, go to http://bit.ly/H2PHUP. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ).; abstract: Street sweeping zones by Ward and Ward Section Number. For the corresponding schedule, see https://data.cityofchicago.org/d/izuq-yb9q. For more information about the City's Street Sweeping program, go to http://bit.ly/H2PHUP. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ).

Alternative search engine DuckDuckGo's worldwide market share has steadily increased from July 2019 to January 2025 – the largest surge being in the United States, which surpassed the search engine’s growth in all other regions. Here, an increased uptake began roughly around the start of the COVID-19 pandemic in April 2020, continuing in the months leading up to the US elections and beyond, but later running a bit lower than three percent in since January 2023. Ecosia - the more popular alternative in Europe While Google is still the market leader by a long shot when it comes to search engines in the United States - where DuckDuckGo is also based, consumer trust in big tech has been waning and more are seeking out privacy-based alternatives. In Europe, Ecosia is the more popular option and has seen steady growth particularly in Germany and France. Despite not having privacy as its main selling point, the German-based company invests its profits into tree-planting and reforestation projects. Americans’ waning trust in the government The onslaught of the COVID-19 pandemic caught most governments off-guard, invoking a host of different responses and approaches across the globe. In the United States, overall trust in the government to deal appropriately with the pandemic fell by approximately 20 percent in the period between February 2020 and January 2021. In fact, among the various institutions of authority, Americans had the least trust in the government – even less than that towards the European Union. Conversely, the United Nations commanded the most public trust. As the COVID vaccination rollout continues across the country, young Americans aged 18 to 24 are also the most skeptical when it comes to the idea of vaccination passports for travel, with approximately 41 percent in support of the measure.

OUTDATED. See the current data at https://data.cityofchicago.org/d/hz9b-7nh8 -- Building footprints in Chicago. Metadata may be viewed and downloaded at http://bit.ly/HZVDIY. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ), is required.

Street sweeping schedule by Ward and Ward sections number. To find your Ward section, visit http://bit.ly/Hz0aCo. For more information about the City's Street Sweeping program, go to http://bit.ly/H2PHUP. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ), is required.

El mosaico de imágenes de Landsat de la Antártida (LIMA) es un mosaico continuo y prácticamente sin nubes creado a partir de escenas procesadas de ETM+ de Landsat 7. Las escenas de Landsat procesadas (16 bits) son escenas de NLAPS de nivel 1Gt convertidas a 16 bits, procesadas con corrección del ángulo del sol y convertidas a valores de reflectancia (Bindschadler 2008). Cada escena de Landsat se procesa con datos de elevación y corrección del ángulo del sol para garantizar que las características de la superficie se representen con precisión. El ángulo del sol en la Antártida da la apariencia de un sol poniente. Debido al ángulo bajo del Sol, a medida que Landsat pasa por la Antártida, los bordes externos del continente aparecen más brillantes que las áreas más cercanas al Polo Sur, por lo que las escenas tienen áreas claras y oscuras. Se corrigieron los ángulos del sol y las sombras incoherentes en estas escenas. Sin este proceso, el mosaico produciría un mosaico de escenas, ya que cada una tendría un lado más claro y otro más oscuro. Esta es una tabla que contiene metadatos de la colección de imágenes USGS/LIMA/SR.