tieba/upload-dataset-test dataset hosted on Hugging Face and contributed by the HF Datasets community

jasong03/data-upload dataset hosted on Hugging Face and contributed by the HF Datasets community

Sample Pet Trans (cli Upload)

## Overview

Sample Pet Trans (cli Upload) is a dataset for object detection tasks - it contains Autodistill Annoted annotations for 596 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).

COCO Upload

## Overview

COCO Upload is a dataset for object detection tasks - it contains COCO Upload annotations for 121,922 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 [MIT license](https://creativecommons.org/licenses/MIT).

Upload Philips

## Overview

Upload Philips is a dataset for object detection tasks - it contains Upload Philips annotations for 1,107 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 [MIT license](https://creativecommons.org/licenses/MIT).

Stepnosing Aug Upload

## Overview

Stepnosing Aug Upload is a dataset for object detection tasks - it contains Objects annotations for 884 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).

We provide MATLAB binary files (.mat) and comma separated values files of data collected from a pilot study of a plug load management system that allows for the metering and control of individual electrical plug loads. The study included 15 power strips, each containing 4 channels (receptacles), which wirelessly transmitted power consumption data approximately once per second to 3 bridges. The bridges were connected to a building local area network which relayed data to a cloud-based service. Data were archived once per minute with the minimum, mean, and maximum power draw over each one minute interval recorded. The uncontrolled portion of the testing spanned approximately five weeks and established a baseline energy consumption. The controlled portion of the testing employed schedule-based rules for turning off selected loads during non-business hours; it also modified the energy saver policies for certain devices. Three folders are provided: “matFilesAllChOneDate” provides a MAT-file for each date, each file has all channels; “matFilesOneChAllDates” provides a MAT-file for each channel, each file has all dates; “csvFiles” provides comma separated values files for each date (note that because of data export size limitations, there are 10 csv files for each date). Each folder has the same data; there is no practical difference in content, only the way in which it is organized.

This dataset was created using LeRobot.

  Dataset Structure

meta/info.json: { "codebase_version": "v2.1", "robot_type": "so101_follower_t", "total_episodes": 1, "total_frames": 416, "total_tasks": 1, "total_videos": 0, "total_chunks": 1, "chunks_size": 1000, "fps": 100, "splits": { "train": "0:1" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path":… See the full description on the dataset page: https://huggingface.co/datasets/pepijn223/test-dataset-upload.

This layer contains the average upload speed (mbps) per zip code.

this is a demo R&D for uploading dataset on Zenodo

CVAT Upload

## Overview

CVAT Upload is a dataset for object detection tasks - it contains 123 annotations for 1,370 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).

Demo to save data from a Space to a Dataset. Goal is to provide reusable snippets of code.

Documentation: https://huggingface.co/docs/huggingface_hub/main/en/guides/upload#scheduled-uploads Space: https://huggingface.co/spaces/Wauplin/space_to_dataset_saver/ JSON dataset: https://huggingface.co/datasets/Wauplin/example-commit-scheduler-json Image dataset: https://huggingface.co/datasets/Wauplin/example-commit-scheduler-image Image (zipped) dataset:… See the full description on the dataset page: https://huggingface.co/datasets/Wauplin/example-space-to-dataset-image-zip.

Note: This dataset has been superseded by the dataset found at "End-Use Load Profiles for the U.S. Building Stock" (submission 4520; linked in the submission resources), which is a comprehensive and validated representation of hourly load profiles in the U.S. commercial and residential building stock. The End-Use Load Profiles project website includes links to data viewers for this new dataset. For documentation of dataset validation, model calibration, and uncertainty quantification, see Wilson et al. (2022). These data were first created around 2012 as a byproduct of various analyses of solar photovoltaics and solar water heating (see references below for are two examples). This dataset contains several errors and limitations. It is recommended that users of this dataset transition to the updated version of the dataset posted in the resources. This dataset contains weather data, commercial load profile data, and residential load profile data. Weather The Typical Meteorological Year 3 (TMY3) provides one year of hourly data for around 1,000 locations. The TMY weather represents 30-year normals, which are typical weather conditions over a 30-year period. Commercial The commercial load profiles included are the 16 ASHRAE 90.1-2004 DOE Commercial Prototype Models simulated in all TMY3 locations, with building insulation levels changing based on ASHRAE 90.1-2004 requirements in each climate zone. The folder names within each resource represent the weather station location of the profiles, whereas the file names represent the building type and the representative city for the ASHRAE climate zone that was used to determine code compliance insulation levels. As indicated by the file names, all building models represent construction that complied with the ASHRAE 90.1-2004 building energy code requirements. No older or newer vintages of buildings are represented. Residential The BASE residential load profiles are five EnergyPlus models (one per climate region) representing 2009 IECC construction single-family detached homes simulated in all TMY3 locations. No older or newer vintages of buildings are represented. Each of the five climate regions include only one heating fuel type; electric heating is only found in the Hot-Humid climate. Air conditioning is not found in the Marine climate region. One major issue with the residential profiles is that for each of the five climate zones, certain location-specific algorithms from one city were applied to entire climate zones. For example, in the Hot-Humid files, the heating season calculated for Tampa, FL (December 1 - March 31) was unknowingly applied to all other locations in the Hot-Humid zone, which restricts heating operation outside of those days (for example, heating is disabled in Dallas, TX during cold weather in November). This causes the heating energy to be artificially low in colder parts of that climate zone, and conversely the cooling season restriction leads to artificially low cooling energy use in hotter parts of each climate zone. Additionally, the ground temperatures for the representative city were used across the entire climate zone. This affects water heating energy use (because inlet cold water temperature depends on ground temperature) and heating/cooling energy use (because of ground heat transfer through foundation walls and floors). Representative cities were Tampa, FL (Hot-Humid), El Paso, TX (Mixed-Dry/Hot-Dry), Memphis, TN (Mixed-Humid), Arcata, CA (Marine), and Billings, MT (Cold/Very-Cold). The residential dataset includes a HIGH building load profile that was intended to provide a rough approximation of older home vintages, but it combines poor thermal insulation with larger house size, tighter thermostat setpoints, and less efficient HVAC equipment. Conversely, the LOW building combines excellent thermal insulation with smaller house size, wider thermostat setpoints, and more efficient HVAC equipment. However, it is not known how well these HIGH and LOW permutations represent the range of energy use in the housing stock. Note that on July 2nd, 2013, the Residential High and Low load files were updated from 366 days in a year for leap years to the more general 365 days in a normal year. The archived residential load data is included from prior to this date.

The BuildingsBench datasets consist of: Buildings-900K: A large-scale dataset of 900K buildings for pretraining models on the task of short-term load forecasting (STLF). Buildings-900K is statistically representative of the entire U.S. building stock. 7 real residential and commercial building datasets for benchmarking two downstream tasks evaluating generalization: zero-shot STLF and transfer learning for STLF. Buildings-900K can be used for pretraining models on day-ahead STLF for residential and commercial buildings. The specific gap it fills is the lack of large-scale and diverse time series datasets of sufficient size for studying pretraining and finetuning with scalable machine learning models. Buildings-900K consists of synthetically generated energy consumption time series. It is derived from the NREL End-Use Load Profiles (EULP) dataset (see link to this database in the links further below). However, the EULP was not originally developed for the purpose of STLF. Rather, it was developed to "...help electric utilities, grid operators, manufacturers, government entities, and research organizations make critical decisions about prioritizing research and development, utility resource and distribution system planning, and state and local energy planning and regulation." Similar to the EULP, Buildings-900K is a collection of Parquet files and it follows nearly the same Parquet dataset organization as the EULP. As it only contains a single energy consumption time series per building, it is much smaller (~110 GB). BuildingsBench also provides an evaluation benchmark that is a collection of various open source residential and commercial real building energy consumption datasets. The evaluation datasets, which are provided alongside Buildings-900K below, are collections of CSV files which contain annual energy consumption. The size of the evaluation datasets altogether is less than 1GB, and they are listed out below: ElectricityLoadDiagrams20112014 Building Data Genome Project-2 Individual household electric power consumption (Sceaux) Borealis SMART IDEAL Low Carbon London A README file providing details about how the data is stored and describing the organization of the datasets can be found within each data lake version under BuildingsBench.

The EV Charging Dataset, used in this study, is a publicly available dataset on Kaggle that records real-world electric vehicle (EV) charging behavior and patterns across various locations. The dataset contains 26 key features, each providing valuable insights into the operational and environmental factors that influence EV charging loads. The features include vehicle-specific data, charging station details, and environmental metrics, which collectively contribute to a comprehensive understanding of the factors affecting EV charging demands and route optimization.

Vehicle ID: A unique identifier for each electric vehicle in the dataset, used for tracking individual vehicle charging behavior. Battery Capacity (kWh): The total energy storage capacity of the EV battery, typically measured in kilowatt-hours. State of Charge (SOC %): The current charge level of the vehicle's battery as a percentage of its total capacity. Energy Consumption Rate (kWh/km): The rate at which the vehicle consumes energy per kilometer traveled, modeled based on real-world driving conditions. Current and Destination Latitude/Longitude: Geographic coordinates providing the vehicle's current and intended location. Distance to Destination (km): The remaining distance to the vehicle’s destination, which influences the decision-making process for when to charge. Traffic Data: A count of vehicles on the road, providing insight into real-time congestion levels affecting the travel duration and energy consumption. Road Conditions: A categorical feature (Good, Average, Poor) representing the state of the road, which can impact vehicle energy efficiency. Charging Station ID: A unique identifier for each charging station where the vehicle connects for recharging. Charging Rate (kW): The rate at which power is delivered to the vehicle’s battery while charging, influencing the time required to fully charge. Queue Time (mins): The estimated waiting time before charging starts, influenced by the number of vehicles at the station. Station Capacity (EVs): The maximum number of vehicles a charging station can accommodate simultaneously. Time Spent Charging (mins): The duration for which a vehicle is connected to the charging station. Energy Drawn (kWh): The amount of energy transferred to the vehicle's battery during the charging session. Session Start Hour: The hour of the day when the charging session begins, represented as an integer from 0 to 23. Fleet Size: The total number of vehicles in the fleet, which provides insights into overall charging demand. Fleet Schedule: Indicates whether the fleet is on schedule or delayed (0 for on time, 1 for delayed). Temperature (°C), Wind Speed (m/s), and Precipitation (mm): Environmental variables that affect EV performance and energy usage during travel. Weekday: Coded as an integer from 0 to 6, representing the day of the week. Charging Preferences: A binary variable indicating whether a vehicle or user has any specific preferences for charging stations (0 for no preference, 1 for preference). Weather Conditions: The overall weather status (Clear, Cloudy, Rain, Storm), which influences travel and charging behavior. Charging Load (kW): The target label representing the load on the charging station, used for forecasting and demand prediction. This dataset is essential for the development of machine learning models aimed at predicting EV charging demand and optimizing charging infrastructure usage. By analyzing the features provided, the dataset enables researchers to investigate patterns in EV charging behavior and explore route optimization strategies in the context of IoT-enabled electric vehicle networks.

Location Dataset Description: The Location Dataset is a synthetic dataset designed for route optimization tasks, especially useful for logistics, fleet management, and EV route planning applications. The dataset consists of 30 key locations, each represented by its geographical coordinates and categorized based on its function (e.g., city, port, warehouse). This dataset allows for the computation of the optimal routes between locations using various optimization algorithms.

Location: A unique identifier for each point in the dataset, typically named after a city or functional node (e.g., A, B, C). Type: The type of location, which indicates its role in the network. Types include: City: Represents urban areas where fleet operations typically begin or end. Port: Represents seaports or inland ports where goods are transferred between modes of transport. Warehouse: Represents storage facilities that act as distribution points. Power Plant: Represents energy generation sites, often used in energy logistics planning. Industrial Zone: Represents areas designated for manufacturing and other industrial operations. Mining Site: Represents remote locations where resources are extracted. Latitude: The geographic coor...

In this upload we share processed crop type datasets from both France and Kenya. These datasets can be helpful for testing and comparing various domain adaptation methods. The datasets are processed, used, and described in this paper: https://doi.org/10.1016/j.rse.2021.112488 (arXiv version: https://arxiv.org/pdf/2109.01246.pdf).

In summary, each point in the uploaded datasets corresponds to a particular location. The label is the crop type grown at that location in 2017. The 70 processed features are based on Sentinel-2 satellite measurements at that location in 2017. The points in the France dataset come from 11 different departments (regions) in Occitanie, France, and the points in the Kenya dataset come from 3 different regions in Western Province, Kenya. Within each dataset there are notable shifts in the distribution of the labels and in the distribution of the features between regions. Therefore, these datasets can be helpful for testing for testing and comparing methods that are designed to address such distributional shifts.

More details on the dataset and processing steps can be found in Kluger et. al. (2021). Much of the processing steps were taken to deal with Sentinel-2 measurements that were corrupted by cloud cover. For users interested in the raw multi-spectral time series data and dealing with cloud cover issues on their own (rather than using the 70 processed features provided here), the raw dataset from Kenya can be found in Yeh et. al. (2021), and the raw dataset from France can be made available upon request from the authors of this Zenodo upload.

All of the data uploaded here can be found in "CropTypeDatasetProcessed.RData". We also post the dataframes and tables within that .RData file as separate .csv files for users who do not have R. The contents of each R object (or .csv file) is described in the file "Metadata.rtf".

Preferred Citation:

-Kluger, D.M., Wang, S., Lobell, D.B., 2021. Two shifts for crop mapping: Leveraging aggregate crop statistics to improve satellite-based maps in new regions. Remote Sens. Environ. 262, 112488. https://doi.org/10.1016/j.rse.2021.112488.

-URL to this Zenodo post https://zenodo.org/record/6376160

Electric power load (active power load) data is created based on hourly voltage (V), current(I) and power factor(pf) information available at 33/11 KV substation (Godishala, huzurabad, Telangana state, India). Hourly voltage, current and power factor information collected during the period from 01.01.2021 to 31.12.2021. Based these values (V,I and pf) 3-phase load on the substation at each hour calculated. This data consists hourly load, status of the day [weekday:0 and weekend:1], season[Winter:1, Summer:2 and Rainy:0], hourly temperature and humidity information. While preparing the data total 66 missing values are found. This missing data is due to shutdown of the substation for maintenance or power outage. Missing values are replaced with average of load at previous day and next day but at same time of load data missing (In case of Tuesday, Wednesday, Thursday and Friday). Whereas in case of missing load data related to Monday then average of load at Saturday and Tuesday considered, Similarly if the missing load data belongs to Saturday then average of load at Friday and Monday is considered. If the missing load data belongs to weekend then average of load at previous weekend and next weekend considered. This dataset consists total 8760 hourly load data values. Load data available in this data set are in kilo-watts, temperature in degree Fahrenheit and humidity in percentage. It has been observed that load data distribution has a mean value of 2130kW, standard deviation of 1302kW, minimum load is 412kW and peak load is 6306kW. Total number of hours substation shutdown happened in the year 2021 is 66 hours. During the year 2021, godishala town feeder (F1) has 25 outage hours, Bommakal feeder (F2) has 71 outage hours, Godishala rural feeder (F3) has 97outage hours and Raikal feeder(F4) has 46 outage hours.

Monitoring data from a subset of individual LV substations within NGED licence areas.

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. This layer contains the average upload speed (mbps) per County. Last Updated: Feature Service Layer Link: https://mdgeodata.md.gov/imap/rest/services/UtilityTelecom/MD_BroadbandSpeedTest/MapServer ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

We provide MATLAB binary files (.mat) and comma separated values files of data collected from a pilot study of a plug load management system that allows for the metering and control of individual electrical plug loads. The study included 15 power strips, each containing 4 channels (receptacles), which wirelessly transmitted power consumption data approximately once per second to 3 bridges. The bridges were connected to a building local area network which relayed data to a cloud-based service. Data were archived once per minute with the minimum, mean, and maximum power draw over each one minute interval recorded. The uncontrolled portion of the testing spanned approximately five weeks and established a baseline energy consumption. The controlled portion of the testing employed schedule-based rules for turning off selected loads during non-business hours; it also modified the energy saver policies for certain devices. Three folders are provided: “matFilesAllChOneDate” provides a MAT-file for each date, each file has all channels; “matFilesOneChAllDates” provides a MAT-file for each channel, each file has all dates; “csvFiles” provides comma separated values files for each date (note that because of data export size limitations, there are 10 csv files for each date). Each folder has the same data; there is no practical difference in content, only the way in which it is organized.