Trolley Data Model

## Overview

Trolley Data Model is a dataset for object detection tasks - it contains CARTS annotations for 637 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).

This dataset includes the model data of a calibrated version of PLEXOS-World based on the 2015 calendar year. It furthermore includes supplementary material to the journal article titled 'Building and Calibrating a Country-Level Detailed Global Electricity Model Based on Public Data' that describes the model development and calibration process. The detailed global electricity model is capable of simulating the dispatch of over 30,000 existing power plants spread out over 164 countries and 258 regions, all using public data. The data includes the full model in PLEXOS format and in raw data format including all its input data to be able to recreate the model in a range of other modelling tools. Next to the power plant portfolio, the openly available input data consists of among others hourly demand profiles for all globally modeled countries, plant specific capacity factor profiles for renewables and a full global set of existing cross-border transmission capacities between countries and regions. Use of the data/model is upon citation of the journal paper and dataset following the underneath CC license without further restrictions. _ PLEXOS-World 2015 by Maarten Brinkerink and Paul Deane is licensed under a Creative Commons Attribution 4.0 International License.Based on a work at https://doi.org/10.1016/j.esr.2020.100592.

An ESRI GRID raster data model of the Mahogany bed structure was needed to perform overburden calculations in the Uinta Basin, Utah and Colorado as part of a 2009 National Oil Shale Assessment.

An updated Permit Data Model that includes relationships between the component feature classes. The Dissolved Use Impacts (SDOT.V_SU_PERMIT_USE_IMPACT_DISS) feature class is derived from dissolving the Use Impacts (SDOT.V_SU_PERMIT_USE_IMPACTS) feature class by Permit Number. The Impacts feature class is the Use Impact street line segments that are associated with any give Permit point (V_SU_PERMITS). The relationships connect the Permit points to the Dissolved Use Impacts and then the Dissolved Use Impacts to the component Use Impacts. This data model allows you to see all impacted street line segments associated with any given Permit easily, while also being able to drill down to any specific Use Impact for a given Permit. Service is constructed for use in the Right of Way Map. Data set to Nightly Refresh. Any Questions or Concerns contact the SDOT Street Use Data and GIS Team: Craig Moore/Bryan Bommersbach

The USGS National Hydrography Dataset (NHD) downloadable data collection from The National Map (TNM) is a comprehensive set of digital spatial data that encodes information about naturally occurring and constructed bodies of surface water (lakes, ponds, and reservoirs), paths through which water flows (canals, ditches, streams, and rivers), and related entities such as point features (springs, wells, stream gages, and dams). The information encoded about these features includes classification and other characteristics, delineation, geographic name, position and related measures, a "reach code" through which other information can be related to the NHD, and the direction of water flow. The network of reach codes delineating water and transported material flow allows users to trace movement in upstream and downstream directions. In addition to this geographic information, the dataset contains metadata that supports the exchange of future updates and improvements to the data. The NHD supports many applications, such as making maps, geocoding observations, flow modeling, data maintenance, and stewardship. For additional information on NHD, go to https://www.usgs.gov/core-science-systems/ngp/national-hydrography.

DWR was the steward for NHD and Watershed Boundary Dataset (WBD) in California. We worked with other organizations to edit and improve NHD and WBD, using the business rules for California. California's NHD improvements were sent to USGS for incorporation into the national database. The most up-to-date products are accessible from the USGS website. Please note that the California portion of the National Hydrography Dataset is appropriate for use at the 1:24,000 scale.

For additional derivative products and resources, including the major features in geopackage format, please go to this page: https://data.cnra.ca.gov/dataset/nhd-major-features Archives of previous statewide extracts of the NHD going back to 2018 may be found at https://data.cnra.ca.gov/dataset/nhd-archive.

In September 2022, USGS officially notified DWR that the NHD would become static as USGS resources will be devoted to the transition to the new 3D Hydrography Program (3DHP). 3DHP will consist of LiDAR-derived hydrography at a higher resolution than NHD. Upon completion, 3DHP data will be easier to maintain, based on a modern data model and architecture, and better meet the requirements of users that were documented in the Hydrography Requirements and Benefits Study (2016). The initial releases of 3DHP include NHD data cross-walked into the 3DHP data model. It will take several years for the 3DHP to be built out for California. Please refer to the resources on this page for more information.

The FINAL,STATIC version of the National Hydrography Dataset for California was published for download by USGS on December 27, 2023. This dataset can no longer be edited by the state stewards. The next generation of national hydrography data is the USGS 3D Hydrography Program (3DHP).

Questions about the California stewardship of these datasets may be directed to nhd_stewardship@water.ca.gov.

Open City Model is an initiative to provide cityGML data for all the buildings in the United States. By using other open datasets in conjunction with our own code and algorithms it is our goal to provide 3D geometries for every US building.

An ESRI TIN data model of the Mahogany Zone structure was needed to perform overburden calculations in the Piceance Basin, Colorado as part of a 2009 National Oil Shale Assessment.

This document is the data model/metadata for all Allegheny County Addressing datasets and tables. It was obtained from the Allegheny County GIS Portal: https://openac-alcogis.opendata.arcgis.com/documents/AlCoGIS::allegheny-county-addressing-data-model/explore

The Amsterdam Museum dataset describes more than 70.000 cultural heritage objects related to the city of Amsterdam described by the museum.

The metadata was retrieved from an XML Web API of the museum's Adlib collection database and converted to RDF compliant with the Europeana Data Model (EDM). This makes the Amsterdam Museum data the first of its kind to be officially converted and made available in this format.

Dataset Card for Sketch Data Model Dataset

  Dataset Summary

This dataset contains over 6M CAD 2D sketches extracted from Onshape. Sketches are stored as python objects in the custom SAM format. SAM leverages the Sketchgraphs dataset for industrial needs and allows for easier transfer learning on other CAD softwares.

  Supported Tasks and Leaderboards

Tasks: Automatic Sketch Generation, Auto Constraint

  Dataset Structure





  Data Instances

The… See the full description on the dataset page: https://huggingface.co/datasets/sketchai/sam-dataset.

Smart Home resident may be an Alzheimer patient needing continuous assistance and care giving. Because of forgetfulness, this person may realize activities of daily living erroneously. In order to assist this person automatically in Smart Home, all his performed actions and activities are observed through the embedded sensors of Smart Home, and applying the data mining techniques his activities are analyzed. Then information about his activities is provided and in the consequence, comparing learned correct patterns and current observations the Smart Home may infer provision of assistance to this person at the appropriate moment. In this paper we propose a data-driven activity modeling approach, which supports reasoning in correct realization of the activities. Activities are presumed as the series of fuzzy events that occur shortly one after another. Per each activity, we calculate a fuzzy conceptual structure, and the model of activity is represented in form of a multivariable problem.

DSM2 models, data, and other resources. The links are in chronological order, with most recent releases at the top.

As statistical units, the data includes the Berlin forecast rooms, district regions and planning rooms. They are described by attributes of the INSPIRE data model “Statistical Units”.

An ESRI GRID raster data model and TIN model of the overburden material above the LaClede bed of the Laney Member of the Eocene Green River Formation was needed to perform calculations in the Green River and Washakie Basins, southwestern Wyoming as part of a National Oil Shale Assessment.

PLEASE NOTE: This dataset has now been retired. [The Data is available from the Open Data Institute on the link shown http://pathway.theodi.org/assessments/46/report?token=-dUfiWaCsXG3p_L6CqnFfQ#detailTab but no longer held by the Environment Agency.]

Results from assessment of Open Data maturity for the Environment Agency at a national level carried out in 2015. The scores were provided by completion of the Open Data Institute's open data maturity model. The data includes summary scores, a breakdown of scores by theme, answers supplied and suggested improvements. Attribution statement: © Environment Agency copyright and/or database right 2016. All rights reserved.

This dataset contains output data files for the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) Regional Ocean Modelling System (ROMS) model runs. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Model output is limited to the SMODE field campaign region and uses 1hr ERA5 atmospheric forcings. ROMS model output files are provided in NetCDF format. Users should note that these files are very large and are encouraged to do analysis in the cloud rather than downloading.

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

The Department of Water Resources (DWR) has developed a new model, the Sacramento Valley Groundwater-Surface Water Simulation Model (SVSim). This new model will support two important DWR programs and has two main goals: 1) Water Transfer Program - develop a tool that meets essential modeling requirements for evaluating project-specific impacts of groundwater substitution transfers on stream depletion in the Sacramento Valley and: 2) Sustainable Groundwater Management Program - develop a tool for evaluating water budgets, surface water-groundwater interactions, and sustainable groundwater management scenarios in the Sacramento Valley. The intended users of SVSim are DWR, water transfer projects, Groundwater Sustainability Agencies, local agencies, and all other interested parties.

SVSim is an application of the Integrated Water Flow Model (IWFM-2015) numerical code and is based on DWR’s C2VSim model (2013). SVSim provides an updated analysis of geologic and hydrogeologic data for the Sacramento Valley and adjacent areas. The model domain includes the Sacramento Valley Groundwater Basin, the Redding Area Groundwater Basin, and the Delta. The southern model boundary lies between the Mokelumne and Calaveras Rivers. SVSim includes nine (9) layers of variable thickness that span the entire groundwater system. The base period of the model simulates conditions from 1973 to 2015.

A calibrated version of SVSim Version 1.0 is now available. The model input files, output files/data, and the executable program for SVSim Version 1.0 are available for download below. Version 1.0 supersedes the SVSim Beta Version released in April 2020). Documentation of SVSim model design, input data development, and model calibration and sensitivity analysis is also available.

The NOAA National Water Model Retrospective dataset contains input and output from multi-decade CONUS retrospective simulations. These simulations used meteorological input fields from meteorological retrospective datasets. The output frequency and fields available in this historical NWM dataset differ from those contained in the real-time operational NWM forecast model. Additionally, note that no streamflow or other data assimilation is performed within any of the NWM retrospective simulations

One application of this dataset is to provide historical context to current near real-time streamflow, soil moisture and snowpack conditions. The retrospective data can be used to infer flow frequencies and perform temporal analyses with hourly streamflow output and 3-hourly land surface output. This dataset can also be used in the development of end user applications which require a long baseline of data for system training or verification purposes.


Details for Each Version of the NWM Retrospective Output

CONUS Domain - CONUS retrospective output is provided by all four versions of the NWM

1. Version 3.0 - A 44-year (February 1979 through January 2023) retrospective simulation using version 3.0 of the National Water Model.
2. Version 2.1 - A 42-year (February 1979 through December 2020) retrospective simulation using version 2.1 of the National Water Model.
3. Version 2.0 - A 26-year (January 1993 through December 2018) retrospective simulation using version 2.0 of the National Water Model.
4. Version 1.2 - A 25-year (January 1993 through December 2017) retrospective simulation using version 1.2 of the National Water Model.
   
   

oCOUNS Domains - Only v3.0 of the NWM Retrospective data set provides coverage of the NWM Alaska, Hawaii and Puerto Rico / US Virgin Island domains.

CONUS Meteorological Forcing

1. Versions 3.0 and 2.1: NWM Retrospective simulations used forcing from the Office of Water Prediction Analysis of Record for Calibration (AORC) dataset. NWM v2.1 used AORC v1.0 for 1979-2006 and AORC v1.1 for 2007-2020, while NWM v3.0 used AORC v1.1 for the full v3.0 (1979-2023 period)

Important Warning - While the metadata tag in the NWM v3.0 forcing files label the files as “v2.1”, the files are in fact v3.0 forcing files. 2) Versions 2.0 and 1.2 - NWM Retrospective simulation uses forcing from the North American Land Data Assimilation System (NLDAS) dataset
oCONUS Meteorological Forcing

1. Version 3.0 - AORC Alaska forcing was used to drive the NWM Alaska simulation, while North American Regional Reanalysis (NARR) data along with precipitation from the Alaska Pacific River Forecast Center (APRFC) was used to drive the Hawaii retrospective simulation. Similarly, the Puerto Rico / US Virgin Island retrospective simulation was driven by NARR data along with precipitation from the Southeast River Forecast Center.
   
   

Formats - NWM Retrospective data is available in two formats, NetCDF and Zarr. The NetCDF files contain the full set of NWM output data, while the Zarr files contain a subset of NWM output fields that vary with model version.

1. NWM V3.0: All model output and forcing input fields are available in the NetCDF format. All model output fields along with the precipitation forcing field are available in the Zarr format.
2. NWM V2.1: All model output and forcing input fields are available in the NetCDF format. Many of the model output fields along with the precipitation forcing field are available in the Zarr format
3. NWM V2.0: All model output fields are available in NetCDF format. Model channel output including streamflow and related fields are available in Zarr format.
4. NWM V1.2: All model output fields are available in NetCDF format.
   
   A table listing the data available within each NetCDF and Zarr file is located in the 'documentation page'. This data includes meteorological NWM forcing inputs along with NWM hydrologic and land surface outputs, and varies by version number.

We present a novel and general methodology for modeling time-varying vector autoregressive processes which are widely used in many areas such as modeling of chemical processes, mobile communication channels and biomedical signals. In the literature, most work utilize multivariate Gaussian models for the mentioned applications, mainly due to the lack of efficient analytical tools for modeling with non-Gaussian distributions. In this paper, we propose a particle filtering approach which can model non-Gaussian autoregressive processes having cross-correlations among them. Moreover, time-varying parameters of the process can be modeled as the most general case by using this sequential Bayesian estimation method. Simulation results justify the performance of the proposed technique, which potentially can model also Gaussian processes as a sub-case.