Dataset

This dataset was created by Shobhit Jaiswal

Released under CC0: Public Domain

Contents

This PDF contains additional information on the dataset groups and datasets in ModE-Sim and the variables therein.

Modal Service data and Safety & Security (S&S) public transit time series data delineated by transit/agency/mode/year/month. Includes all Full Reporters--transit agencies operating modes with more than 30 vehicles in maximum service--to the National Transit Database (NTD). This dataset will be updated monthly. The monthly ridership data is released one month after the month in which the service is provided. Records with null monthly service data reflect late reporting. The S&S statistics provided include both Major and Non-Major Events where applicable. Events occurring in the past three months are excluded from the corresponding monthly ridership rows in this dataset while they undergo validation. This dataset is the only NTD publication in which all Major and Non-Major S&S data are presented without any adjustment for historical continuity.

Collecty dataset is a dataset for multimodal transport analytics from mobile devices collected by users as they move through the transportation network. Each sample in dataset is labelled with a corresponding transport mode. Eight transport modes are present in the dataset: Car, Bus, Walking, Bicycle, Train, Tram, Running and Electric Scooter. During data collection, data from the accelerometer, magnetometer, and gyroscope sensors mounted within the mobile device were stored.

CITATION:

When incorporating these data into a research output, such as a publication or presentation, kindly cite the provided source and indicate that comprehensive details regarding the dataset are available within the same article:

Dataset for multimodal transport analytics of smartphone users - Collecty, M. Erdelić, T. Erdelić and T. Carić, Data in Brief, 2023

@article{ERDELIC2023109481, title = {Dataset for multimodal transport analytics of smartphone users - Collecty}, journal = {Data in Brief}, volume = {50}, pages = {109481}, year = {2023}, issn = {2352-3409}, doi = {https://doi.org/10.1016/j.dib.2023.109481}, url = {https://www.sciencedirect.com/science/article/pii/S2352340923005814}, author = {Martina Erdelić and Tomislav Erdelić and Tonči Carić} }

A detailed description of the data can be found here:

Tscharaktschiew, S., Müller, S. (2021): Ride to the hills, ride to your school: Physical effort and mode choice. Transportation Research D 98, 102983.

Müller, S., Mejía Dorantes, L., Kersten, E. (2020): Analysis of active school transportation in hilly urban environments: a case study of Dresden. Journal of Transport Geography (88), 102872.

Müller, S., Tscharaktschiew, S., Haase, K. (2008): Travel-to-school mode choice modelling and patterns of school choice in urban areas. Journal of Transport Geography 16(5), 342-357.

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).

Mode

## Overview

Mode is a dataset for instance segmentation tasks - it contains Dada WzkE Efxs H0Ij annotations for 300 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 [BY-NC-SA 4.0 license](https://creativecommons.org/licenses/BY-NC-SA 4.0).

氣胸_B MODE

## Overview

氣胸_B MODE is a dataset for object detection tasks - it contains Abcl annotations for 926 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).

Indoor Mode

## Overview

Indoor Mode is a dataset for instance segmentation tasks - it contains Indoor Objects annotations for 710 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 contains a suite of Saildrone in-situ measurements (including but not limited to temperature, salinity, currents, biochemistry, and meteorology) taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign spanning two weeks in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023. 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. Saildrones are wind-and-solar-powered unmanned surface vehicles rigged with atmospheric and oceanic sensors that measure upper ocean horizontal velocities, near-surface temperature and salinity, Chlorophyll-a fluorescence, dissolved oxygen concentration, 5-m winds, air temperature, and surface radiation. Acoustic Doppler Current Profiler (ADCP) data samples are available in their raw 1 Hz sampling frequency as well as 5 minute averages, the latter available with navigation data. Other measurements are available as raw files (1Hz or 20 Hz where applicable), as well as 1 minute averages. L1 data are available as a zip file.

This dataset contains Submersible Ultraviolet Nitrate Analyzer (SUNA) nitrate measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) 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. SUNA is a standalone optical nitrate sensor that mounts onto the shipboard CTD rosette cast from the R/V Oceanus. The SUNA measurements are calibrated against bottle nutrient samples taken from the underway flow-through system on the ship and later analyzed with a Lachat Nutrient Analyzer. From the Lachat data, the average concentration of nitrate+nitrite are used for each sample. Data are available in netCDF format.

This dataset is about books. It has 2 rows and is filtered where the author is Dorian Mode. It features 2 columns including publication date.

Scene Recognition Srs Mode

## Overview

Scene Recognition Srs Mode is a dataset for object detection tasks - it contains Classroom Kitchen Roadside annotations for 3,174 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).

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).

Arrival By Mode of Transportation

氣胸_M MODE

## Overview

氣胸_M MODE is a dataset for object detection tasks - it contains Objects annotations for 406 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 contains atmospheric sounding measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023. 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. Sounding profiles were collected using shipboard Windsond S1H3-S radiosondes launched from the R/V Oceanus cruise OC2108A, to a maximum elevation of at least 5 km above ground level (ABL). These measurements are used to understand the vertical structure of atmospheric temperature, winds, and moisture. The original 1Hz observations were gridded onto a uniform 20 m vertical grid. The data are available in netCDF format with dimensions of altitude and profile number.

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.

This dataset is about books. It has 1 row and is filtered where the book is Switch-mode power converters : design and analysis. It features 7 columns including author, publication date, language, and book publisher.

An open preclinical PET dataset. This dataset has been measured with the preclinical Siemens Inveon PET machine. The measured target is a (naive) rat with an injected dose of 21.4 MBq of FDG. The injection was done intravenously (IV) to the tail vein. No specific organ was investigated, but rather the glucose metabolism as a whole. The examination is a 60 minute dynamic acquisition. The measurement was conducted according to the ethical standards set by the University of Eastern Finland.

The dataset contains the original list-mode data, the (dynamic) sinogram created by the Siemens Inveon Acquisition Workplace (IAW) software (28 frames), the (dynamic) scatter sinogram created by the IAW software (28 frames), the attenuation sinogram created by the IAW software and the normalization coefficients created by the IAW software. Header files are included for all the different data files.

For documentation on reading the list-mode binary data, please ask Siemens.

This dataset can be used in the OMEGA software, including the list-mode data, to import the data to MATLAB/Octave, create sinograms from the list-mode data and reconstruct the imported data. For help on using the dataset with OMEGA, see the wiki.