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).

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.

The Failure Mode Classification dataset released in the paper "MWO2KG and Echidna: Constructing and exploring knowledge graphs from maintenance data" by Stewart et al. The goal is to label a given observation (made by a maintainer) with the corresponding Failure Mode Code.

Each row contains an observation made by a maintainer, followed by a comma, followed by the Failure Mode, for example:

falure,Breakdown

As they are written in technical language, there are often spelling/grammatical/tokenisation errors made in the observations - these are typical of maintenance work orders.

The dataset comprises 502 (observation, label) pairs (for training), 62 pairs (for validation) and 62 pairs (for testing). The labels are taken from a set of 22 failure mode codes from ISO 14224. In order to pull a list of observations in which to label, we ran MWO2KG over the data once and exported a list of all entities labelled as ‘observation’ (such as ‘leaking’, ‘not working’) by the Named Entity Recognition model. We then removed all results that were incorrectly predicted as observations by the NER model and proceeded to label each observation with the most appropriate failure mode code using a text editor.

The source code of the above paper (which also includes this dataset) is located on GitHub.

The direct link to the data (train.txt, dev.txt, and test.txt) is available here.

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.

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

using an idea based on iterative gradient ascent. In this paper we develop a mean-shift-inspired algorithm to estimate the modes of regression functions and partition the sample points in the input space. We prove convergence of the sequences generated by the algorithm and derive the non-asymptotic rates of convergence of the estimated local modes for the underlying regression model.

Data collected monthly from urbanized area transit systems. The Monthly module includes a limited set of key indicators reported by transit properties. Data is reported on a monthly basis, by mode and type of service, for a calendar year. The four data items included are: Unlinked Passenger Trips, Vehicle Revenue Miles, Vehicle Revenue Hours, and Vehicles Operated in Maximum Service (Peak Vehicles). Monthly data are reported by mode and type of service.

This dataset supports measure M.A.1 of SD 2023. The source of the data is the American Community Survey. Each row is the five year estimate for Means of Transportation to Work for Austin. This dataset can be used to gain insight into the estimated mode split for the commute to work in Austin. View more details and insights related to this measure on the story page: https://data.austintexas.gov/stories/s/hm3r-8jfy

MODES applies three-dimensional linear wave theory for the decomposition of global circulation in terms of normal-mode functions (NMFs). NMFs used by MODES are eigensolutions of the linearized primitive equations in the terrain-following sigma coordinates and were derived by Kasahara and Puri (1981, Mon. Wea. Rev). The available data are three data sets (40 years), calculated from ERA5 reanalyses by modal filtering of certain wave components, here Kelvin waves (KW), Mixed Rossby-gravity waves (MRG) and Rossby wave n=1 (Rosn1).

Near-realtime modal decompositions of ECMWF deterministic forecasts, using the same tool (MODES) as has been used for the generation of the dataset are under this URL: https://modes.cen.uni-hamburg.de/

Dataset

This dataset was created by Lakshay Middha

Contents

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.

gearboxes in real industrial settings often operate under variable working conditions

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).

This dataset contains airborne visible 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 an IO Industries Flare 12M125-CL camera with 14mm lens mounted nadir in the aircraft in an orientation so that the short edge of the image was parallel with the aircraft heading. The camera was synchronized to a coupled GPS/IMU system with images taken at 5hz. Raw images were calibrated for lens distortion and boresight misalignment with the GPS/IMU. Images were georeferenced to the post-processed aircraft trajectory and exported with reference to WGS84 datum with a UTM zone 10 projection (EPSG 32610) at an altitude-dependent spatial resolution. Level 1 images are available in TIFF format.

This represents the Service data reported to the NTD by transit agencies to the NTD. In versions of the data tables from before 2014, you can find data on service in the file called "Transit Operating Statistics: Service Supplied and Consumed." If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.

This dataset contains data on transit agency employees as reported to the National Transit Database in the 2022 and 2023 report years. It is organized by agency, mode, type of service, and Employee Type (Full Time or Part Time Employee).

The NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis

This dataset is based on the 2022 and 2023 Employees database files, which are published to the NTD at https://transit.dot.gov/ntd/ntd-data.

Only Full Reporters report data on employees, and only for Directly Operated modes. Other reporter types, and Purchased Transportation service, do not appear in this file.

This repository contains all the raw data and raw images used in the paper titled 'Highly multi-mode hollow core fibres'. It is grouped into two folders of raw data and raw images. In the raw data there are a number of .dat files which contain alternating columns of wavelength and signal for the different measurements of transmission, cutback and bend loss for the different fibres. In the raw images, simple .tif files of the different fibres are given and different near field and far field images used in Figure 2.

This dataset includes the tables and supplementary information from the journal article. This dataset is associated with the following publication: Wallace, A., J. Pleil, S. Mentese, K. Oliver, D. Whitaker, and K. Fent. Calibration and performance of synchronous SIM/scan mode for simultaneous targeted and discovery (non-targeted) analysis of exhaled breath samples from firefighters. JOURNAL OF CHROMATOGRAPHY A. Elsevier Science Ltd, New York, NY, USA, 1516: 114-124, (2017).

This dataset is about book subjects. It has 11 rows and is filtered where the books is Fashioning spaces : mode and modernity in late nineteenth-century Paris. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This dataset is about books. It has 1 row and is filtered where the book is Average current-mode control of DC-DC power converters. It features 7 columns including author, publication date, language, and book publisher.