CDS raw dataset

This dataset provides global maps describing the land surface into 22 classes, which have been defined using the United Nations Food and Agriculture Organization’s (UN FAO) Land Cover Classification System (LCCS). In addition to the land cover (LC) maps, four quality flags are produced to document the reliability of the classification and change detection. In order to ensure continuity, these land cover maps are consistent with the series of global annual LC maps from the 1990s to 2015 produced by the European Space Agency (ESA) Climate Change Initiative (CCI), which are also available on the ESA CCI LC viewer. To produce this dataset, the entire Medium Resolution Imaging Spectrometer (MERIS) Full and Reduced Resolution archive from 2003 to 2012 was first classified into a unique 10-year baseline LC map. This is then back- and up-dated using change detected from (i) Advanced Very-High-Resolution Radiometer (AVHRR) time series from 1992 to 1999, (ii) SPOT-Vegetation (SPOT-VGT) time series from 1998 to 2012 and (iii) PROBA-Vegetation (PROBA-V), Sentinel-3 OLCI (S3 OLCI) and Sentinel-3 SLSTR (S3 SLSTR) time series from 2013. Beyond the climate-modelling communities, this dataset’s long-term consistency, yearly updates, and high thematic detail on a global scale have made it attractive for a multitude of applications such as land accounting, forest monitoring and desertification, in addition to scientific research.

This entry covers single-level data aggregated on a monthly time resolution. Seasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect a distributions of outcomes. Given the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast, and used to predict the evolution of this state in time. While uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated. To this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment). The data includes forecasts created in real-time each month starting from the publication of this entry and retrospective forecasts (hindcasts) initialised over periods in the past specified in the documentation for each origin and system.

PDF. Link to Metadata. Order form for GIS Data on CD. Please note: Many GIS data layers are available for download at the St. Louis County GIS Service Center Open Data Site: http://openstlco.stlcogis.opendata.arcgis.com/.GIS Data CD Features:ArcGIS Shapefile formatState Plane Coordinate System, Missouri East, NAD1983 FeetCD 1 contains Base Map layers (e.g. jurisdictional boundaries, political areas, streets, etc.)CD 2 contains Parcel Data (e.g. parcel boundaries, ownership, valuation, etc.)Published: January 2019Cost: $15.27 eachTo order GIS Data CDs, please contact:Tracy HillImaging TechnicianSt. Louis County Records Center10275 Page Industrial CtSt. Louis, MO 63132Phone: 314.615.3715Fax: 314.615.3730Please note: Many GIS data layers are available for download at the St. Louis County GIS Service Center Open Data Site: http://data.stlouisco.com/.

CDS Keys

## Overview

CDS Keys is a dataset for object detection tasks - it contains Keys annotations for 1,060 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 [Public Domain license](https://creativecommons.org/licenses/Public Domain).

This catalogue entry provides Regional Climate Model (RCM) data on single levels from a number of experiments, models, domains, resolutions, ensemble members, time frequencies and periods computed over several regional domains all over the World in the framework of the Coordinated Regional Climate Downscaling Experiment (CORDEX). The term "single levels" is used to express that the variables are 2D-matrices computed on one vertical level which can be surface (or a level close to the surface) or a dedicated pressure level in the atmosphere. Multiple vertical levels are excluded from this catalogue entry. High-resolution Regional Climate Models (RCMs) can provide climate change information on regional and local scales in relatively fine detail, which cannot be obtained from coarse scale Global Climate Models (GCMs). This is manifested in better description of small-scale regional climate characteristics and also in more accurate representation of extreme events. Consequently, outputs of such RCMs are indispensable in supporting regional and local climate impact studies and adaptation decisions. RCMs are not independent from the GCMs, since the GCMs provide lateral and lower boundary conditions to the regional models. In that sense RCMs can be viewed as magnifying glasses of the GCMs. The CORDEX experiments consist of RCM simulations representing different future socio-economic scenarios (forcings), different combinations of GCMs and RCMs and different ensemble members of the same GCM-RCM combinations. This experiment design through the ensemble members allows for studies addressing questions related to the key uncertainties in future climate change. These uncertainties come from differences in the scenarios of future socio-economic development, the imperfection of regional and global models used and the internal (natural) variability of the climate system. This experiment design allows for studies addressing questions related to the key uncertainties in future climate change:

what will future climate forcing be? what will be the response of the climate system to changes in forcing? what is the uncertainty related to natural variability of the climate system?

The term "experiment" in the CDS form refers to three main categories:

Evaluation: CORDEX experiment driven by ECMWF ERA-Interim reanalysis for a past period. These experiments can be used to evaluate the quality of the RCMs using perfect boundary conditions as provided by a reanalysis system. The period covered is typically 1980-2010; Historical: CORDEX experiment which covers a period for which modern climate observations exist. Boundary conditions are provided by GCMs. These experiments, that follow the observed changes in climate forcing, show how the RCMs perform for the past climate when forced by GCMs and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1950-2005; Scenario: Ensemble of CORDEX climate projection experiments using RCP (Representative Concentration Pathways) forcing scenarios. These scenarios are the RCP 2.6, 4.5 and 8.5 scenarios providing different pathways of the future climate forcing. Boundary conditions are provided by GCMs. The period covered is typically 2006-2100.

In CORDEX, the same experiments were done using different RCMs (labelled as “Regional Climate Model” in the CDS form). In addition, for each RCM, there is a variety of GCMs, which can be used as lateral boundary conditions. The GCMs used are coming from the CMIP5 (5th phase of the Coupled Model Intercomparison Project) archive. These GCM boundary conditions are labelled as “Global Climate Model” in the form and are also available in the CDS. Additionally, the uncertainty related to internal variability of the climate system is sampled by running several simulations with the same RCM-GCM combination. On the forms, these are indexed as separate ensemble members (the naming convention for ensemble members is available in the documentation). For each GCM, the same experiment was repeatedly done using slightly different conditions (like initial conditions or different physical parameterisations for instance) producing in that way an ensemble of experiments closely related. More details behind these sequential ensemble numbers is available in the detailed documentation. The data are produced by the institutes and modelling centres participating in the different CORDEX domains with partial support from different international and national contributions including support from COPERNICUS for some of the EURO-CORDEX runs. The data can be used for commercial purposes (unrestricted use) with the exception of the simulations from the following RCMs: BOUN-RegCM4-3 model (for Central Asia and Middle East and North Africa domains) and RU-CORE-RegCM4-3 model (for South-East Asia domain). Precise terms of use are provided in the CORDEX licence.

The number of digital music album downloads in the United States amounted to **** million in 2024, marking a drop of more than ** percent from 2018. Over *** million digital music albums were downloaded in the U.S. year by year between 2011 and 2015, but the number then began to decrease annually and has failed to recover since.

Graph and download economic data for National Rate: 12 Month CD <100M (NDR12MCD) from Apr 2021 to Jul 2025 about CD, 1-year, deposits, rate, and USA.

CDS

## Overview

CDS is a dataset for object detection tasks - it contains Burglary Robbery Assault annotations for 7,462 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).

CDS Capstone

## Overview

CDS Capstone is a dataset for classification tasks - it contains Keys annotations for 1,095 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 [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Graph and download economic data for National Rate: 60 Month CD <100M (NDR60MCD) from Apr 2021 to Jul 2025 about CD, deposits, 5-year, rate, and USA.

CDS_project

## Overview

CDS_project is a dataset for object detection tasks - it contains Objects annotations for 4,055 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).

The National Automotive Sampling System (NASS) Crashworthiness Data System (CDS) is a nationwide crash data collection program sponsored by the U.S. Department of Transportation. It is operated by the National Center for Statistics and Analysis (NCSA) of the National Highway Traffic Safety Administration (NHTSA). The NASS CDS provides an automated, comprehensive national traffic crash database, and collects detailed information on a sample of all police-reported light ]motor vehicle traffic crashes. Data collection is accomplished at 24 geographic sites, called Primary Sampling Units (PSUs). These data are weighted to represent all police reported motor vehicle crashes occurring in the USA during the year involving passenger cars, light trucks and vans that were towed due to damage.

This resource includes two Jupyter Notebooks as a quick start tutorial for the ERA5 Data Component of the PyMT modeling framework (https://pymt.readthedocs.io/) developed by Community Surface Dynamics Modeling System (CSDMS https://csdms.colorado.edu/).

The bmi_era5 package is an implementation of the Basic Model Interface (BMI https://bmi.readthedocs.io/en/latest/) for the ERA5 dataset (https://confluence.ecmwf.int/display/CKB/ERA5). This package uses the cdsapi (https://cds.climate.copernicus.eu/api-how-to) to download the ERA5 dataset and wraps the dataset with BMI for data control and query (currently support 3 dimensional ERA5 dataset). This package is not implemented for people to use and is the key element to help convert the ERA5 dataset into a data component for the PyMT modeling framework.

The pymt_era5 package is implemented for people to use as a reusable, plug-and-play ERA5 data component for the PyMT modeling framework. This package uses the BMI implementation from the bmi_era5 package and allows the ERA5 datasets to be easily coupled with other datasets or models that expose a BMI.

HydroShare users can test and run the Jupyter Notebooks (bmi_era5.ipynb, pymt_era5.ipynb) directly through the "CUAHSI JupyterHub" web app with the following steps: - For the new user of the CUAHSI JupyterHub, please first make a request to join the "CUAHSI Could Computing Group" (https://www.hydroshare.org/group/156). After approval, the user will gain access to launch the CUAHSI JupyterHub. - Click on the "Open with" button. (on the top right corner of the page) - Select "CUAHSI JupyterHub". - Select "CSDMS Workbench" server option. (Make sure to select the right server option. Otherwise, the notebook won't run correctly.)

If there is any question or suggestion about the ERA5 data component, please create a github issue at https://github.com/gantian127/bmi_era5/issues

Cds Depot Counter

## Overview

Cds Depot Counter is a dataset for object detection tasks - it contains Plastic Bottles annotations for 826 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).

"No.O -ring-" was the most popular download album in Japan in 2024. The album by the Japanese boy band Number_i was downloaded ****** times from November 2023 to November 2024. It was followed by Hikaru Utada's "Science Fiction" and Kenshi Yonezu's "Lost Corner."

ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate covering the period from January 1940 to present. It is produced by the Copernicus Climate Change Service (C3S) at ECMWF and provides hourly estimates of a large number of atmospheric, land and oceanic climate variables. The data cover the Earth on a 31km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80km. ERA5 includes an ensemble component at half the resolution to provide information on synoptic uncertainty of its products. ERA5.1 is a dedicated product with the same horizontal and vertical resolution that was produced for the years 2000 to 2006 inclusive to significantly improve a discontinuity in global-mean temperature in the stratosphere and uppermost troposphere that ERA5 suffers from during that period. Users that are interested in this part of the atmosphere in this era are advised to access ERA5.1 rather than ERA5. ERA5 and ERA5.1 use a state-of-the-art numerical weather prediction model to assimilate a variety of observations, including satellite and ground-based measurements, and produces a comprehensive and consistent view of the Earth's atmosphere. These products are widely used by researchers and practitioners in various fields, including climate science, weather forecasting, energy production and machine learning among others, to understand and analyse past and current weather and climate conditions.

FYP AI CDS

## Overview

FYP AI CDS is a dataset for object detection tasks - it contains Cheating Student annotations for 2,652 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).

These are DNA coding sequences in the human genome build GRCh38, downloaded from ensembl with the following R script:

install biomartr 1.0.7 from CRAN

install.packages("biomartr", dependencies = TRUE)

Install Biostrings if not installed

if (!requireNamespace("BiocManager", quietly = TRUE)) { install.packages("BiocManager") }

Load required package

library(Biostrings) library(biomartr)

download the genome of Homo sapiens from ensembl

and store the corresponding genome CDS file in… See the full description on the dataset page: https://huggingface.co/datasets/MichelNivard/Human-genome-CDS-GRCh38.