A series of approximately 3250 navigational charts covering the world. The series is maintained by Admiralty Notices to Mariners issued every week. New editions or new charts are published as required. Two thirds of the series are now available in metric units.

In areas where the United Kingdom is, or until recently has been, the responsible hydrographic authority - i.e. Home Waters, some Commonwealth countries, British colonies, and certain areas like the Gulf, Red Sea and parts of the eastern Mediterranean - the Admiralty charts afford detailed cover of all waters, ports and harbours. These make up about 30 per cent of the total series. Modern charts in these areas usually have a source data diagram showing the sources from which the chart was compiled. The quantity and quality of the sources vary due to age and the part of the world the chart depicts. The other 70 per cent are derived from information on foreign charts, and the Admiralty versions are designed to provide charts for ocean passage and landfall, and approach and entry to the major ports.

The series contains charts on many different scales, but can be divided very broadly as follows:

Route planning 1:10 million Ocean planning 1:3.5 million Coast approach or landfall identification 1:1 million Coasting 1:300,000 to 1:200,000 Intricate or congested coastal waters 1:150,000 to 1:75,000 Port approach 1:50,000 or larger Terminal installation 1:12,500 or larger

Charts on scales smaller than 1:50,000, except in polar regions, are on Mercator projection. Since 1978 all charts on 1:50,000 and larger have been produced on Transverse Mercator projection. Prior to 1978 larger scale charts were on a modified polyconic projection referred to as 'gnomonic', not to be confused with the true Gnomonic projection.

Most of the detail shown on a chart consists of hydrographic information - soundings (selected spot depths) in metres (on older charts in fathoms or feet) reduced to a stated vertical datum; depth contours; dredged channels; and the nature of the seabed and foreshore. Features which present hazards to navigation, fishing and other marine operations are also shown. These include underwater rocks and reefs; wrecks and obstructions; submarine cables and pipelines and offshore installations. Shallow water areas are usually highlighted with pale blue tint(s). Also shown are aids established to assist the navigator - buoys, beacons, lights, fog signals and radio position finding and reporting services; and information about traffic separation schemes, anchorages, tides, tidal streams and magnetic variation. Outline coastal topography is shown especially objects of use as fixing marks. As a base for navigation the chart carries compass roses, scales, horizontal datum information, graduation (and sometimes land map grids), conversion tables and tables of tidal and tidal stream rates.

The Inland Electronic Navigational Charts (IENC) dataset is updated bi-monthly by the US Army Corps (USACE)/Army Geospatial Center (AGC), and part of U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). There is a partial delivery on the 1st and 15th business day of a month. If there is a need for a special chart delivery, occasionally there will be updates outside of the delivery cycle. The charts with data changes are released either as new editions or updates to existing IENCs on a regular delivery cycle. These IENCs were developed from available data used in maintenance of Navigation channels. These vector data, that make up the charts, can be downloaded as a geodatabase here: https://ienccloud.us/ienc/products/files/U37/ienc_master_dataset_gdb/USACE_IENC_Master_Service_gdb.zip. In addition, web mapping services of the feature classes/datasets can be found here: https://ienccloud.us/arcgis/rest/services/IENC_Feature_Classes. Users of these IENCs should be aware that some features and attribute information could have significant inaccuracies due to changing waterway conditions, inaccurate source data, or approximations introduced during chart compilation. Caution is urged in use of these IENCs or derived products for navigation planning or operation, or any decisions pertaining to or affecting safety of vessel operation. Only charts downloaded from the USACE chart server, https://ienccloud.us, and used in an Electronic Chart Display Information System (ECDIS) or Electronic Chart System (ECS), or official government chart books are suitable for navigation.

Graph and download economic data for Existing Home Sales (EXHOSLUSM495S) from Aug 2024 to Aug 2025 about headline figure, sales, housing, and USA.

Existing Home Sales in the United States decreased to 4000 Thousand in August from 4010 Thousand in July of 2025. This dataset provides the latest reported value for - United States Existing Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

640 Data New Models Chart

## Overview

640 Data New Models Chart is a dataset for object detection tasks - it contains Giantclams annotations for 590 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).

PublicationPrimahadi Wijaya R., Gede. 2014. Visualisation of diachronic constructional change using Motion Chart. In Zane Goebel, J. Herudjati Purwoko, Suharno, M. Suryadi & Yusuf Al Aried (eds.). Proceedings: International Seminar on Language Maintenance and Shift IV (LAMAS IV), 267-270. Semarang: Universitas Diponegoro. doi: https://doi.org/10.4225/03/58f5c23dd8387Description of R codes and data files in the repositoryThis repository is imported from its GitHub repo. Versioning of this figshare repository is associated with the GitHub repo's Release. So, check the Releases page for updates (the next version is to include the unified version of the codes in the first release with the tidyverse).The raw input data consists of two files (i.e. will_INF.txt and go_INF.txt). They represent the co-occurrence frequency of top-200 infinitival collocates for will and be going to respectively across the twenty decades of Corpus of Historical American English (from the 1810s to the 2000s).These two input files are used in the R code file 1-script-create-input-data-raw.r. The codes preprocess and combine the two files into a long format data frame consisting of the following columns: (i) decade, (ii) coll (for "collocate"), (iii) BE going to (for frequency of the collocates with be going to) and (iv) will (for frequency of the collocates with will); it is available in the input_data_raw.txt. Then, the script 2-script-create-motion-chart-input-data.R processes the input_data_raw.txt for normalising the co-occurrence frequency of the collocates per million words (the COHA size and normalising base frequency are available in coha_size.txt). The output from the second script is input_data_futurate.txt.Next, input_data_futurate.txt contains the relevant input data for generating (i) the static motion chart as an image plot in the publication (using the script 3-script-create-motion-chart-plot.R), and (ii) the dynamic motion chart (using the script 4-script-motion-chart-dynamic.R).The repository adopts the project-oriented workflow in RStudio; double-click on the Future Constructions.Rproj file to open an RStudio session whose working directory is associated with the contents of this repository.

Graph and download economic data for Monthly Supply of New Houses in the United States (MSACSR) from Jan 1963 to Aug 2025 about supplies, new, housing, and USA.

Information on water depth in river channels is important for a number of applications in water resource management but can be difficult to obtain via conventional field methods, particularly over large spatial extents and with the kind of frequency and regularity required to support monitoring programs. Remote sensing methods could provide a viable alternative means of mapping river bathymetry (i.e., water depth). The purpose of this study was to develop and test new, spectrally based techniques for estimating water depth from satellite image data. More specifically, a neural network-based temporal ensembling approach was evaluated in comparison to several other neural network depth retrieval (NNDR) algorithms. These methods are described in a manuscript titled "Neural Network-Based Temporal Ensembling of Water Depth Estimates Derived from SuperDove Images" and the purpose of this data release is to make available the depth maps produced using these techniques. The images used as input were acquired by the SuperDove cubesats comprising the PlanetScope constellation, but the original images cannot be redistributed due to licensing restrictions; the end products derived from these images are provided instead. The large number of cubesats in the PlanetScope constellation allows for frequent temporal coverage and the neural network-based approach takes advantage of this high density time series of information by estimating depth via one of four NNDR methods described in the manuscript: 1. Mean-spec: the images are averaged over time and the resulting mean image is used as input to the NNDR. 2. Mean-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is averaged to obtain the final depth map. 3. NN-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is then used as input to a second, ensembling neural network that essentially weights the depth estimates from the individual images so as to optimize the agreement between the image-derived depth estimates and field measurements of water depth used for training; the output from the ensembling neural network serves as the final depth map. 4. Optimal single image: a separate NNDR is applied independently to each image in the time series and only the image that yields the strongest agreement between the image-derived depth estimates and the field measurements of water depth used for training is used as the final depth map. MATLAB (Version 24.1, including the Deep Learning Toolbox) for performing this analysis is provided in the function NN_depth_ensembling.m available on the main landing page for the data release of which this is a child item, along with a flow chart illustrating the four different neural network-based depth retrieval methods. To develop and test this new NNDR approach, the method was applied to satellite images from the American River near Fair Oaks, CA, acquired in October 2020. Field measurements of water depth available through another data release (Legleiter, C.J., and Harrison, L.R., 2022, Field measurements of water depth from the American River near Fair Oaks, CA, October 19-21, 2020: U.S. Geological Survey data release, https://doi.org/10.5066/P92PNWE5) were used for training and validation. The depth maps produced via each of the four methods described above are provided as GeoTIFF files, with file name suffixes that indicate the method employed: American_mean-spec.tif, American_mean-depth.tif, American_NN-depth.tif, and American-single-image.tif. The spatial resolution of the depth maps is 3 meters and the pixel values within each map are water depth estimates in units of meters.

Income-Before-Tax Time Series for HubSpot Inc. HubSpot, Inc., together with its subsidiaries, provides a cloud-based customer relationship management (CRM) platform for businesses in the Americas, Europe, and the Asia Pacific. The company's CRM platform includes Marketing Hub, a toolset for marketing automation and email, social media, SEO, and reporting and analytics; Sales Hub offers email templates and tracking, conversations and live chat, meeting and call scheduling, lead and website visit alerts, lead scoring, sales automation, pipeline management, quoting, forecasting, and reporting; Service Hub, a service software designed to help businesses manage, respond, and connect with customers; and Content Hub enables businesses to create new and edit existing web content. It offers Operations Hub, which is designed for customer data to automate business processes, data cleanup, and provide customer insights and connections; and Commerce Hub, a B2B commerce suite. In addition, the company provides professional services to educate and train customers on how to utilize its CRM platform; and customer success, as well as phone and/or email and chat-based support services. It serves mid-market business-to-business companies. The company was incorporated in 2005 and is headquartered in Cambridge, Massachusetts.

Information on water depth in river channels is important for a number of applications in water resource management but can be difficult to obtain via conventional field methods, particularly over large spatial extents and with the kind of frequency and regularity required to support monitoring programs. Remote sensing methods could provide a viable alternative means of mapping river bathymetry (i.e., water depth). The purpose of this study was to develop and test new, spectrally based techniques for estimating water depth from satellite image data. More specifically, a neural network-based temporal ensembling approach was evaluated in comparison to several other neural network depth retrieval (NNDR) algorithms. These methods are described in a manuscript titled "Neural Network-Based Temporal Ensembling of Water Depth Estimates Derived from SuperDove Images" and the purpose of this data release is to make available the depth maps produced using these techniques. The images used as input were acquired by the SuperDove cubesats comprising the PlanetScope constellation, but the original images cannot be redistributed due to licensing restrictions; the end products derived from these images are provided instead. The large number of cubesats in the PlanetScope constellation allows for frequent temporal coverage and the neural network-based approach takes advantage of this high density time series of information by estimating depth via one of four NNDR methods described in the manuscript: 1. Mean-spec: the images are averaged over time and the resulting mean image is used as input to the NNDR. 2. Mean-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is averaged to obtain the final depth map. 3. NN-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is then used as input to a second, ensembling neural network that essentially weights the depth estimates from the individual images so as to optimize the agreement between the image-derived depth estimates and field measurements of water depth used for training; the output from the ensembling neural network serves as the final depth map. 4. Optimal single image: a separate NNDR is applied independently to each image in the time series and only the image that yields the strongest agreement between the image-derived depth estimates and the field measurements of water depth used for training is used as the final depth map. MATLAB (Version 24.1, including the Deep Learning Toolbox) source code for performing this analysis is provided in the function NN_depth_ensembling.m and the figure included on this landing page provides a flow chart illustrating the four different neural network-based depth retrieval methods. As examples of the resulting models, MATLAB *.mat data files containing the best-performing neural network model for each site are provided below, along with a file that lists the PlanetScope image identifiers for the images that were used for each site. To develop and test this new NNDR approach, the method was applied to satellite images from three rivers across the U.S.: the American, Colorado, and Potomac. For each site, field measurements of water depth available through other data releases were used for training and validation. The depth maps produced via each of the four methods described above are provided as GeoTIFF files, with file name suffixes that indicate the method employed: X_mean-spec.tif, X_mean-depth.tif, X_NN-depth.tif, and X-single-image.tif, where X denotes the site name. The spatial resolution of the depth maps is 3 meters and the pixel values within each map are water depth estimates in units of meters.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

Depreciation Time Series for Confluent Inc. Confluent, Inc. operates a data streaming platform in the United States and internationally. The company provides platforms that allow customers to connect their applications, systems, and data layers comprising Confluent Cloud, a managed cloud-native software-as-a-service (SaaS); and Confluent Platform, an enterprise-grade self-managed software. It also offers connectors for existing applications, and IT and cloud infrastructure; Apache Flink services that allows teams to create reusable data streams that can be delivered real-time; WarpStream, a bring your own cloud managed streaming service; and stream governance, a managed data governance suite that is designed for the intricacies of streaming data, which allows teams to accelerate data streaming initiatives without bypassing controls for risk management or regulatory compliance. In addition, the company offers professional services comprising packaged and residency offerings; education offerings consisting of training and certification guidance, technical resources, and access to hands-on training and certification exams; and certification programs. It serves banking and financial services, retail and ecommerce, manufacturing, automotive, telecommunication, gaming, insurance, and technology industries, as well as public sector. The company was formerly known as Infinitem, Inc. and changed its name to Confluent, Inc. in September 2014. Confluent, Inc. was incorporated in 2014 and is headquartered in Mountain View, California.

Price-Earnings-Ratio Time Series for Vonovia SE. Vonovia SE operates as an integrated residential real estate company in Europe. It operates through four segments: Rental, Value-Add, Recurring Sales, and Development. The company offers property management services; property-related services; and value-added services, including maintenance and modernization of residential properties, craftsmen and residential environment organization, condominium administration, cable TV, metering, energy supply, and insurances services. It also engages in the sale of individual condominiums and single-family houses; and project development activities to build new homes. In addition, the company engages in the sale of projects to investors, construction of rental apartments, and construction of new properties on existing land held in the portfolio. Further, it provides My Vonovia, a mobile application to organize service requests and appointments, track the status of requests online in real time, and view all Vonovia documents. The company was formerly known as Deutsche Annington Immobilien SE and changed its name to Vonovia SE in August 2015. Vonovia SE was founded in 1998 and is headquartered in Bochum, Germany.

Each of the five New York City Retirement Systems has its own Board of Trustees which, working with the Bureau of Asset Management and the Board’s consultants, makes decisions on the funds’ asset allocations based on factors including economic risk, return, performance, and beneficiary distributions. Data and further information is also available here: Asset Allocation : Office of the New York City Comptroller (nyc.gov). "1U.S. Fixed Income assets do not include cash. 2Market Value of private market investments are reported on a lagged basis. 3Cash includes Securities Lending, State Street Short Term and BNY‐Mellon CD accounts. 4Totals may not add due to rounding. 5Fiscal Year to Date begins July 1st. Information presented is current as of the date of this posting only. Past performance does not guarantee the future performance of any manager or strategy. The performance results and historical information provided herein may have been adversely or favorably impacted by events and economic conditions that will not prevail in the future. Therefore, these results are not indicative of the future performance of any strategy, index, fund, manager or group of managers."

New Home Sales in the United States increased to 800 Thousand units in August from 664 Thousand units in July of 2025. This dataset provides the latest reported value for - United States New Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The General Bathymetric Chart of the Oceans (GEBCO) One Minute Grid is a global terrain model for land and sea at one arc-minute intervals. The grid is largely based on the bathymetric contours contained in the Centenary Edition of the GEBCO Digital Atlas, existing grids are used in some areas. The land areas are based on the Global Land One-km Base Elevation (GLOBE) Project data set. The grid was originally released in 2003 and updated in 2008 to include data from the International Bathymetric Chart of the Arctic Ocean (IBCAO), for the region north of 64N and also updates for shallower water regions off India, the Korean Peninsula and around South Afriaca, using data extracted from Electronic Navigation Charts (ENCs). The grid is available to download, in netCDF format, for free from the internet. Free software is available for viewing and accessing data from the grid in netCDF and ASCII. This includes an option to export the grid in an ASCII form suitable for conversion to an ESRI raster. The grid is also included in the GEBCO Digital Atlas DVD. It is not intended to make any further updates to this data set. In 2009, GEBCO released a new bathymetric grid, the GEBCO_08 Grid. This is a global terrain model at 30 arc-second intervals. It is largely based on a database of ship-track soundings with interpolation between soundings guided by satellite derived-gravity data.

Graph and download economic data for Average Sales Price of Houses Sold for the United States (ASPUS) from Q1 1963 to Q2 2025 about sales, housing, and USA.

Information on water depth in river channels is important for a number of applications in water resource management but can be difficult to obtain via conventional field methods, particularly over large spatial extents and with the kind of frequency and regularity required to support monitoring programs. Remote sensing methods could provide a viable alternative means of mapping river bathymetry (i.e., water depth). The purpose of this study was to develop and test new, spectrally based techniques for estimating water depth from satellite image data. More specifically, a neural network-based temporal ensembling approach was evaluated in comparison to several other neural network depth retrieval (NNDR) algorithms. These methods are described in a manuscript titled "Neural Network-Based Temporal Ensembling of Water Depth Estimates Derived from SuperDove Images" and the purpose of this data release is to make available the depth maps produced using these techniques. The images used as input were acquired by the SuperDove cubesats comprising the PlanetScope constellation, but the original images cannot be redistributed due to licensing restrictions; the end products derived from these images are provided instead. The large number of cubesats in the PlanetScope constellation allows for frequent temporal coverage and the neural network-based approach takes advantage of this high density time series of information by estimating depth via one of four NNDR methods described in the manuscript: 1. Mean-spec: the images are averaged over time and the resulting mean image is used as input to the NNDR. 2. Mean-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is averaged to obtain the final depth map. 3. NN-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is then used as input to a second, ensembling neural network that essentially weights the depth estimates from the individual images so as to optimize the agreement between the image-derived depth estimates and field measurements of water depth used for training; the output from the ensembling neural network serves as the final depth map. 4. Optimal single image: a separate NNDR is applied independently to each image in the time series and only the image that yields the strongest agreement between the image-derived depth estimates and the field measurements of water depth used for training is used as the final depth map. MATLAB (Version 24.1, including the Deep Learning Toolbox) source code for performing this analysis is provided in the function NN_depth_ensembling.m available on the main landing page for the data release of which this is a child item, along with a flow chart illustrating the four different neural network-based depth retrieval methods. To develop and test this new NNDR approach, the method was applied to satellite images from the Colorado River near Lees Ferry, AZ, acquired in March and April of 2021. Field measurements of water depth available through another data release (Legleiter, C.J., Debenedetto, G.P., and Forbes, B.T., 2022, Field measurements of water depth from the Colorado River near Lees Ferry, AZ, March 16-18, 2021: U.S. Geological Survey data release, https://doi.org/10.5066/P9HZL7BZ) were used for training and validation. The depth maps produced via each of the four methods described above are provided as GeoTIFF files, with file name suffixes that indicate the method employed: Colorado_mean-spec.tif, Colorado_mean-depth.tif, Colorado_NN-depth.tif, and Colorado-single-image.tif. In addition, to assess the robustness of the Mean-spec and NN-depth methods to the introduction of a large pulse of sediment by a flood event that occurred partway through the image time series, depth maps from before and after the flood are provided in the files Colorado_Mean-spec_after_flood.tif, Colorado_Mean-spec_before_flood.tif, Colorado_NN-depth_after_flood.tif, and Colorado_NN-depth_before_flood.tif. The spatial resolution of the depth maps is 3 meters and the pixel values within each map are water depth estimates in units of meters.

Graph and download economic data for Housing Inventory: Active Listing Count in the United States (ACTLISCOUUS) from Jul 2016 to Sep 2025 about active listing, listing, and USA.

Inflation Rate in the United States increased to 2.90 percent in August from 2.70 percent in July of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.