Commute mode is tracked by the American Community Survey (ACS) by asking respondents to provide the means of transportation usually used to travel the longest distance to work the prior week. A follow-up question asks about vehicle occupancy when "car, truck, van" is selected. This dataset tracks the sum of all individuals not selecting "car, truck, van" with one person in it. Transportation professionals often group travel modes into "single-occupancy vehicles" (SOV) and "non-single-occupancy vehicles" (non-SOV) because SOVs are a less efficient use of roadway and environmental resources. It also shows the share of modes that are classified as non-SOV.

Mode Leaves

## Overview

Mode Leaves is a dataset for object detection tasks - it contains Leaf annotations for 1,227 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 [ODbL v1.0 license](https://creativecommons.org/licenses/ODbL v1.0).

Mode is the Modular DeFi L2 building the Superchain alongside Optimism

Harvesting Mode

## Overview

Harvesting Mode is a dataset for object detection tasks - it contains Tomatoes annotations for 1,575 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).

interstellarninja/json-mode-dpo-prompts dataset hosted on Hugging Face and contributed by the HF Datasets community

This commuter mode share data shows the estimated percentages of commuters in Champaign County who traveled to work using each of the following modes: drove alone in an automobile; carpooled; took public transportation; walked; biked; went by motorcycle, taxi, or other means; and worked at home. Commuter mode share data can illustrate the use of and demand for transit services and active transportation facilities, as well as for automobile-focused transportation projects.

Driving alone in an automobile is by far the most prevalent means of getting to work in Champaign County, accounting for over 69 percent of all work trips in 2023. This is the same rate as 2019, and the first increase since 2017, both years being before the COVID-19 pandemic began.

The percentage of workers who commuted by all other means to a workplace outside the home also decreased from 2019 to 2021, most of these modes reaching a record low since this data first started being tracked in 2005. The percentage of people carpooling to work in 2023 was lower than every year except 2016 since this data first started being tracked in 2005. The percentage of people walking to work increased from 2022 to 2023, but this increase is not statistically significant.

Meanwhile, the percentage of people in Champaign County who worked at home more than quadrupled from 2019 to 2021, reaching a record high over 18 percent. It is a safe assumption that this can be attributed to the increase of employers allowing employees to work at home when the COVID-19 pandemic began in 2020.

The work from home figure decreased to 11.2 percent in 2023, but which is the first statistically significant decrease since the pandemic began. However, this figure is still about 2.5 times higher than 2019, even with the COVID-19 emergency ending in 2023.

Commuter mode share data was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Means of Transportation to Work.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (18 September 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (10 October 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (14 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (26 March 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (26 March 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).

In 2022, almost ** percent of workers in the U.S. commuted by personal vehicle on their own. During the same year, only *** percent of Americans workers traveled to work using public transportation. Meanwhile, around ** percent of U.S. Americans worked from home.

Proportion of school aged children in full time education travelling to school by the mode of travel that they usually use. Mode of transport is defined as six modes: cars, including vans and taxis, car share, public transport, walking, cycling, and other.

Source: Department for Transport (DfT)

Publisher: DCLG Floor Targets Interactive

Geographies: County/Unitary Authority, Government Office Region (GOR), National

Geographic coverage: England

Time coverage: 2007/08 to 2008/09

Detailed Active users (monthly) metrics and analytics for Mode Network, including historical data and trends.

This dataset contains model output from the Navy Coastal Ocean Model (NCOM) run during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. 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. NCOM model output consists of daily files during the deployment dates of the pilot campaign in Fall 2021, IOP1 in Fall 2022, and IOP2 in Spring 2023. Data consists of ocean variables such as salinity, sea water temperature, water depth, and surface wind stress, and are available in netCDF format.

The dataset contains information on the injured persons with different modes of transportation in U.S. It contains the number of injured persons on highway, rails, aviation, marine and pipeline. Highway transportation includes passenger cars, light trucks and vans, pedestrians, motorcycles, pedalcycles, medium and heavy trucks, buses and Others. Rail includes freight, passenger, and commuter rail (Trespassing), rail transit.

This dataset contains in-situ measurements of temperature, salinity, and velocity from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco, during an intensive observation period in the fall of 2022. The data are available in netCDF format with a dimension of time. 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. The target in-situ quantities were measured by Lagrangian floats, which were deployed from research vessels and retrieved 3-5 days later. The floats follow the 3D motion of water parcels at depths within or just below the mixed layer and carried a CTD instrument to measure temperature, salinity, and pressure, in addition to an ADCP instrument to measure velocity.

Dataset Card for gpu-mode-triton-augment-sample

This dataset has been created with distilabel.

  Dataset Summary

This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI: distilabel pipeline run --config "https://huggingface.co/datasets/winglian/gpu-mode-triton-augment-sample/raw/main/pipeline.yaml"

or explore the configuration: distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/winglian/gpu-mode-triton-augment-sample.

This dataset contains airborne hyperspectral imagery from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. 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. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes a hyperspectral camera operating in the visible to near-IR range (400-990 nm). Hyperspectral data are used by S-MODE to provide visible imagery of the kinematics of whitecaps and ocean color measurements. Level 1 data are available as zip files containing data in ENVI format and text files containing location and timing information.

No description found

The PortraitMode-400 dataset is a significant contribution to the field of video recognition, specifically focusing on portrait mode videos. Let me provide you with more details:

Dataset Overview: The PortraitMode-400 (PM-400) dataset is the first of its kind and is dedicated to portrait mode video recognition. It was created to address the unique challenges associated with recognizing videos captured in portrait mode.

Portrait mode videos are increasingly important due to the growing popularity of smartphones and social media applications.

Data Collection and Annotation:

The dataset consists of 76,000 videos collected from Douyin, a popular short-video application. These videos were meticulously annotated with 400 fine-grained categories.

Rigorous quality assurance measures were implemented to ensure the accuracy of human annotations.

Research Insights and Impact:

The creators of the dataset conducted a comprehensive analysis to understand the impact of video format (portrait mode vs. landscape mode) on recognition accuracy. They also explored spatial bias arising from different video formats. Key aspects of portrait mode video recognition were investigated, including data augmentation, evaluation procedures, the importance of temporal information, and the role of audio modality.

(1) [2312.13746] Video Recognition in Portrait Mode - arXiv.org. https://arxiv.org/abs/2312.13746. (2) Video Recognition in Portrait Mode | Papers With Code. https://paperswithcode.com/paper/video-recognition-in-portrait-mode. (3) Video Recognition in Portrait Mode - arXiv.org. https://arxiv.org/pdf/2312.13746.pdf. (4) undefined. https://doi.org/10.48550/arXiv.2312.13746.

No description found

This paper examines the effects of survey mode on patterns of survey response, paying special attention to the conditions under which mode effects are more or less consequential. We use the Youth Participatory Politics survey, a study administered either online or over the phone to 2,920 young people. Our results provide consistent evidence of mode effects. The internet sample exhibits higher rates of item non-response and “no opinion” responses, and considerably lower levels of differentiation in the use of rating scales. These differences remain even after accounting for how respondents selected into the mode of survey administration. We demonstrate the substantive implications of mode effects in the context of items measuring political knowledge and racial attitudes. We conclude by discussing the implications of our results for comparing data obtained from surveys conducted with different modes, and for the design and analysis of multi-mode surveys.