cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3674276
title: Data on Memorable Tourism Experiences

description: This is a data collected from Indian and International tourist who have travelled for leisure. The focus is on memorable tourism experiences. It has also captured the visitors intention to revisit or extend their stay. It was collected from December 2018 - February 2020. I want research scholars, students, and academicians to make best use of this data and share any kind of analysis or papers published with me on chervendias@gmail.com.
description: {"references": ["Cheryl Dias & Venan Dias (2019) Memorable Tourism Experiences Data, DOI:10.5281/zenodo.3674276", "Cheryl Dias & Venan Dias (2019) Memorable tourism experience design an effective destination marketing tool, Anatolia, 30:4, 626-628, DOI: 10.1080/13032917.2019.1668727"]}
DOI: 10.5281/zenodo.4725222
title: Semantic Matching of GUI Events for Test Reuse: Are We There Yet?

description: This repository provides the replication package of our paper "Semantic Matching of GUI Events for Test Reuse: Are We There Yet?" presented at the 30th ACM/SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) 2021. It includes the codes, results, inputs and train datasets. A VM included that is ready to use. Everything is already installed in the VM. You can either create the environment by instructions in the README.md file or use the VM. APK of subjects are available in google drive. They are not required for running the framework.
DOI: 10.5281/zenodo.5517237
title: MITgcm 2.8deg Transport Matrix configuration

description: This repository contains transport matrices and associated data for the MITgcm 2.8deg configuration. For the most recent version of these files go to: http://kelvin.earth.ox.ac.uk/spk/Research/TMM/TransportMatrixConfigs/. The Transport Matrix Method driver code is available from https://github.com/samarkhatiwala/tmm.
DOI: 10.5285/ce8bf6be-4b5f-454c-9165-73ab4c3baf23
title: A Bathymetric Compilation of South Georgia, 1985-2015.

description: We present a new bathymetric compilation of the greater South Georgia region, here defined by a bounding box of ~900km (45W to 19W) by ~580km (63S to 50S) and covering an area of 530,000 km2. The region includes the South Georgia shelf, the Shag Rock shelf (to the west of South Georgia), the surrounding continental slopes and adjacent deep sea. This bathymetry grid was compiled from a variety of different data sources including multibeam swath bathymetry collected from scientific cruises undertaken by British Antarctic Survey (BAS), Alfred Wegener Institute (AWI) and the Institute of Geophysics, University of Texas. The grid has been constructed using a layered hierarchy dependent on accuracy of each dataset. The data is available as a 100m resolution GeoTIFF, ESRI ascii grid or KMZ file of elevation data along with a shapefile indicating the spatial coverage of all the contributing datasets. This work was supported by the National Environmental Research Council (grant number NE/L002531/1). For further information regarding the creation of this dataset please refer to doi:10.1038/srep33163.
DOI: 10.60635/c39g6t
title: A palynological zonation for the Neogene of the Solimões/Amazon basin, northwestern Amazonia Supplementary Dataset

description: dataset for A palynological zonation for the Neogene of the Solimões/Amazon basin, northwestern Amazonia. it containsSupplementary File 1. Dataset containing the counts of 152 taxa used in the biostratigraphic analysis. The counts include the site and stratigraphic level where the counts come from.Supplementary File 2. R code for importing CONOP data.Supplementary File 3. Files containing the line of correlation (LOC) of each section versus the composite section and the stratigraphy of each site in PDF and SDAR format.Supplementary File 4. Input file to be uploaded into R-GraphCor and contains the complete graphic correlation project, including the line of correlation (LOC) of each section and the composite section.Supplementary File 5. R-GraphCor, the set of functions in R to perform graphic correlation. To run it, simply dump the file into R.Supplementary File 6. Sequence of events of the composite section organized in alphabetic order.Supplementary File 7. R-code to transform any composite unit of the Amazonian CS into geological time by using the LOC of Amazonia vs Llanos and then the time calibration of the Llanos zonation (Jaramillo et al. 2011).
DOI: 10.6073/pasta/c3bb20a62edbf8548cbb30e79a689a5b
title: GOES-R Land Surface Products at AmeriFlux and NEON, Inc. Eddy Covariance Tower Locations

description: The terrestrial carbon cycle varies dynamically over short periods that can be difficult to observe. Geostationary (“weather”) satellites like the Geostationary Operational Environmental Satellite - R Series (GOES-R) deliver near-hemispheric imagery at a ten-minute cadence, and its Advanced Baseline Imager (ABI) measures visible and near-infrared spectral bands that can be used to estimate land surface properties and carbon dioxide flux. GOES-R data are designed for real-time dissemination and are difficult to link with eddy covariance time series of land-atmosphere carbon dioxide exchange. We compiled three-year time series of GOES-R land surface attributes including visible and near-infrared reflectances, land surface temperature, and downwelling shortwave radiation (DSR) at 318 ABI fixed grid pixels containing eddy covariance towers for years 2020-2022. We demonstrate how to best combine satellite and in-situ datasets, and show how ABI attributes useful for carbon cycle science vary across space and time. By connecting observation networks that infer rapid changes to the carbon cycle, we can gain a richer understanding of the processes that control it.
DOI: 10.6084/m9.figshare.24059154
title: Particle image velocimetry (PIV) data from a simulated settling column

description: Data from a particle image velocimetry (PIV) experiment with a simulation settling column (SETCOL), used to determining sinking speeds of phytoplankton and other particles. Velocity data were interpolated from slices to produce a 3-dimensional, 2-component flow field. In addition to the velocity field ("velocity_field"), calculated velocity magnitude ("umag") and vorticity ("vortz") fields are included.
DOI: 10.6084/m9.figshare.27215028
title: Supplementary Material for: Neonatal outcomes following two cases of maternal CAR-T therapy for high grade B-cell lymphoma

description: Introduction: Chimeric antigen receptor T-cells (CAR-T) targeting CD19 represent a significant advance in treatment for patients with relapsed/refractory B-cell malignancies. Although a significant minority of recipients are women during their reproductive years, there is a paucity of data regarding pregnancy and neonatal outcomes in women previously treated with CAR-T. This is important as maternal T-cells are known to cross the placenta and into breastmilk during pregnancy and breastfeeding, respectively. 
Case presentation: Here we present two successful pregnancies following CAR-T therapy where both neonates were initially breastfed. These represent the first cases of neonates born following CAR-T therapy comprehensively described in medical literature.
Conclusion: Pregnancy following CAR-T therapy does not appear to be associated with adverse neonatal outcomes. Further work is required to delineate the outcomes in this population.
DOI: 10.18419/darus-2019/7
title: 03_DSC08776.tif
DOI: 10.5281/zenodo.11099555
title: Scripts for Li 2024 JAMES WRF AH

description: WRF namelists and python scripts to reproduce the figures in the manuscript
DOI: 10.5281/zenodo.12706096
title: The grid-level fixed asset model developed for China from 1951 to 2020

description: This dataset was created by our submitted manuscript (with the same title as this dataset), in which the provincial-level and grid level fixed assets for China from 1951 to 2020 were developed.
DOI: 10.6084/m9.figshare.27247935.v1
title: scGraphformer's numerical source data for graphs and charts

description: This is the repository for numerical source data for graphs and charts of scGraphformer.
DOI: 10.6084/m9.figshare.3467747
title: Domestication influences choice behavior and performance of a generalist herbivore

description: Supplemental Data set for <i>Perspective in</i> <i>Plant Ecology, Evolution</i> <i>and Systematics</i> Paper: Domestication influences choice behavior and performance of a generalist herbivore xls-File: Associational resistance experiment: raw data of <i>G. raimondii </i>assoc. resistance experiment<br> FeedingChoiceAssay: raw data containing all collected data concerning the feeding choice of <i>S. littoralis</i> larvae<br> OlfactometerChoiceAssay: raw data containing all collected data concerning the choice of <i>S. littoralis</i> larvae in an olfactometerOvipositionChoiceAssay: raw data containing all collected data concerning the choice of <i>S. littoralis</i> females in an oviposition assay<br><br>PerformanceAssayLarvae: raw data containing all collected data concerning the performance of <i>S. littoralis</i> larvae on different cotton plants<br><br>VolatileRelease: raw data containing all collected concerning the volatile release of different cotton plants over 24h<br>
DOI: 10.18419/darus-2019/8
title: 01_DSC08771.tif
DOI: 10.18419/darus-2019/9
title: 10_DSC08792.tif
DOI: 10.18419/darus-2019/10
title: 12_DSC08796.tif
DOI: 10.18419/darus-2019/11
title: 04_DSC08778.tif
DOI: 10.18419/darus-2019
title: 2D pictures of MÜ68-18

description: &lt;p&gt;Dataset contains all pictures (.tif) that were taken during the &lt;a href="https://darus.uni-stuttgart.de/file.xhtml?fileId=49056&version=DRAFT"&gt;2D digitisation strategy&lt;/a&gt; for this object.&lt;/p&gt; &lt;p&gt;Order of data: object was digitised according to a digitisation schema visible within the Dataset&lt;/p&gt;&lt;ul&gt;&lt;li&gt;characteristic view,&lt;/li&gt; &lt;li&gt;front view,&lt;/li&gt; &lt;li&gt;left view,&lt;/li&gt; &lt;li&gt;back view,&lt;/li&gt; &lt;li&gt;right view,&lt;/li&gt;&lt;li&gt;bottom view,&lt;/li&gt; &lt;li&gt;upward view, &lt;/li&gt;&lt;li&gt;details (details 1, details 2, ..., details n)&lt;/li&gt;&lt;/ul&gt;&lt;p&gt; This schema is indicated within the pictures by inventory label and then labels according to view.&lt;/p&gt;&lt;p&gt; Further information one can find in the document: Setting and workflow for basic development of 2D.&lt;/p&gt;&lt;p&gt;This dataset is part of the digital gyroscope collection. For more information about 
the collection and its creation, please see the documentation dataset:
&lt;i&gt;Niklaus, Maria, 2021, "Information about the Gyrolog data repository", 
&lt;a href="https://doi.org/10.18419/darus-821"&gt;doi: 10.18419/darus-821&lt;/a&gt;, DaRUS.&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;To refer to the whole collection: Wagner, Jörg F.; Ceranski, Beate; Fritsch, Dieter; Mammadov, Gasim; Niklaus, Maria; Schweizer, Timo; Simon, Sven; Zhan, Kun, 2021, "Digital Gyroscope Collection Created by the Project 'Gyrolog'", &lt;a href="https://doi.org/10.18419/darus-gyrolog"&gt;doi: 10.18419/darus-gyrolog&lt;/a&gt;, DaRUS.&lt;/b&gt;&lt;/p&gt;
description: inertial platforms
DOI: 10.18419/darus-2019/12
title: 07_DSC08784.tif
DOI: 10.18419/darus-2040
title: Data for: On the role of density-driven dissolution of CO2 in karstification

description: &lt;b&gt;Summary:&lt;/b&gt;&lt;br&gt;
Data of the "density-driven dissolution of CO2 in karst water" column experiment.
A laboratory column was filled with tapwater (water level 5,55 m) and exposed it to an elevated gaseous CO2-concentration, roughly 50 times the current atmospheric concentration (20000 +/- 5000 ppm). 
After stripping the water with ambient air, it was initially in equilibrium with atmospheric conditions. Then, the concentration of dissolved CO2 was measured over a time period of 60 days in two different depths in the column.
Measuring points are in the column air, 1 m below water level and 0,15 m above ground. CO2-air concentration was provided using an air-membrane pump (KNF N86 KTE) and a 100 l TEDLAR bag (for more detailed information look into the related publication &lt;a href="https://doi.org/10.1002/essoar.10507661.1"&gt;Class 2021 et al.&lt;/a&gt;)&lt;br&gt;
&lt;br&gt;

&lt;b&gt;Raw Data:&lt;/b&gt;&lt;br&gt;
Raw data provided by GMP252 CO2 sensor probes, data accessed from ADL-MX Advanced Datalogger via ADL-C software&lt;br&gt;
&lt;u&gt;Sensor positions:&lt;/u&gt;&lt;br&gt;
CO2_10: column air&lt;br&gt;
CO2_20: 1 m below water surface&lt;br&gt;
CO2_40: 0.15 m above ground/5.40 m below water surface&lt;br&gt;
&lt;br&gt;
&lt;b&gt;Processed Data:&lt;/b&gt;&lt;br&gt;
Processed Data calculated after formula in the related publication &lt;a href="https://doi.org/10.1002/essoar.10507661.1"&gt;Class 2021 et al.&lt;/a&gt;&lt;br&gt;
MC: manufacturer correction&lt;br&gt;
OC: own correction&lt;br&gt;
&lt;br&gt;
&lt;b&gt;Sherwood:&lt;/b&gt;&lt;br&gt;
Calculations for Sherwood number. The density was calculated by an approach of &lt;a href="https://www.osti.gov/biblio/790022"&gt;Garcia 2001&lt;/a&gt;&lt;br&gt;
For further information look into the related publication &lt;a href="https://doi.org/10.1002/essoar.10507661.1"&gt;Class 2021 et al.&lt;/a&gt;&lt;br&gt;
&lt;br&gt;
&lt;b&gt;Buerkle2021a:&lt;/b&gt;&lt;br&gt;
The &lt;a href="https://git.iws.uni-stuttgart.de/dumux-pub/buerkle2021a"&gt;dumux-pub module&lt;/a&gt; used to simulate the different scenarios in the related publication &lt;a href="https://doi.org/10.1002/essoar.10507661.1"&gt;Class 2021 et al.&lt;/a&gt;
DOI: 10.18419/darus-2040/1
title: raw_data.tab

description: Raw data of the "density-driven dissolution of CO2 in karst water" column 60 days experiment
DOI: 10.18419/darus-2040/2
title: processed_data.tab

description: Detailled processing explained in related publication, Unit of CO2 values: mol CO2/mol H2O
DOI: 10.18419/darus-2040/3
title: sherwood.xlsx

description: Calculated sherwood number and required data to calculate sherwood number. 
Sim1: Simulation with measured atmospheric CO2 concentration.
Sim2: Simulation with (assumed) constant atmospheric CO2 concentration of 20,000 ppm
DOI: 10.18419/darus-2040/4
title: processing_script.ipynb

description: Processing script to reproduce the processed data from the raw data via Jupyter Notebook
DOI: 10.18419/darus-2040/5
title: buerkle2021a-master.tar

description: Dumux-pub-module mirror of https://git.iws.uni-stuttgart.de/dumux-pub/buerkle2021a 
Commit: 16eaaa4b
DOI: 10.18419/darus-2081/1
title: case3_small_onep_fracture_mpfa_2-00001.vtu

description: pressure solution (vtk file format) on the fractures for refinement level 2
DOI: 10.18419/darus-2081/10
title: case3_small_onep_fracture_mpfa_0-00001.vtu

description: pressure solution (vtk file format) on the fractures for refinement level 0
DOI: 10.18419/darus-2081/11
title: case3_small_onep_fracture_mpfa_1-00001.vtu

description: pressure solution (vtk file format) on the fractures for refinement level 1
DOI: 10.18419/darus-2081/4
title: case3_small_onep_fracture_mpfa_3-00001.vtu

description: pressure solution (vtk file format) on the fractures for refinement level 3
DOI: 10.18419/darus-2081/9
title: case3_small_onep_fracture_mpfa_4-00001.vtu

description: pressure solution (vtk file format) on the fractures for refinement level 4
DOI: 10.18419/darus-2081/2
title: case3_small_onep_matrix_mpfa_2-00001.vtu

description: pressure solution (vtk file format) in the porous matrix for refinement level 2
DOI: 10.18419/darus-2081/3
title: case3_small_onep_matrix_mpfa_4-00001.vtu

description: pressure solution (vtk file format) in the porous matrix for refinement level 4
DOI: 10.18419/darus-2081/5
title: case3_small_onep_matrix_mpfa_1-00001.vtu

description: pressure solution (vtk file format) in the porous matrix for refinement level 1
DOI: 10.18419/darus-2081/6
title: case3_small_onep_matrix_mpfa_0-00001.vtu

description: pressure solution (vtk file format) in the porous matrix for refinement level 0
DOI: 10.18419/darus-2081/7
title: case3_small_onep_fracture_mpfa_5-00001.vtu

description: pressure solution (vtk file format) on the fractures for refinement level 5
DOI: 10.18419/darus-2081/8
title: case3_small_onep_matrix_mpfa_3-00001.vtu

description: pressure solution (vtk file format) in the porous matrix for refinement level 3
DOI: 10.18419/darus-2081
title: Case 3 - Small Features

description: This dataset contains results related to the benchmark case 3 as described in detail in the &lt;a href="https://arxiv.org/abs/1809.06926"&gt;call for participation&lt;/a&gt;.
&lt;br&gt;&lt;br&gt;
The sub-folder "reference" contains solutions for pressure in both the fractures and the bulk porous matrix on five different refinement levels. The naming scheme is as follows: CASENAME_SIMULATIONTYPE_DOMAIN_REFINEMENT-00001.vtu, where CASENAME refers to the benchmark case (here: "case3_small"), SIMULATIONTYPE denotes the type of simulation carried out (here: "onep", a single-phase flow simulation), DOMAIN is either "fracture" or "matrix", and REFINEMENT is an integer number denoting the refinement level.
The last refinement level was used as reference in the &lt;a href="https://doi.org/10.1016/j.advwatres.2020.103759"&gt;related publication&lt;/a&gt;.
&lt;br&gt;&lt;br&gt;
The solutions were produced with the &lt;a href="https://dumux.org/"&gt;Dumux&lt;/a&gt; implementation of the mpfa scheme, denoted in the related publication by "USTUTT-MPFA". The results can be reproduced by getting the &lt;a href="https://git.iws.uni-stuttgart.de/dumux-pub/berre2020"&gt;source code&lt;/a&gt; to the Dumux implementation of this test case. Installation and execution instructions can be found therein. The .vtu-files can be visualized with the open-source software &lt;a href="https://www.paraview.org"&gt;ParaView&lt;/a&gt;.
DOI: 10.18419/darus-2081/12
title: case3_small_onep_matrix_mpfa_5-00001.vtu

description: pressure solution (vtk file format) in the porous matrix for refinement level 5
DOI: 10.18419/darus-1202/1
title: 00003781.JPG
DOI: 10.18419/darus-1202/2
title: 00003782.JPG
DOI: 10.18419/darus-1202/3
title: 00003775.JPG
DOI: 10.18419/darus-1202/4
title: 00003788.JPG
DOI: 10.18419/darus-1202/5
title: 00003790.JPG
DOI: 10.18419/darus-1202/6
title: 00003796.JPG
DOI: 10.18419/darus-1202/7
title: 00003776.JPG
DOI: 10.18419/darus-1202/8
title: 00003797.JPG
DOI: 10.18419/darus-1202/9
title: 00003778.JPG
DOI: 10.18419/darus-1202/10
title: 00003785.JPG
DOI: 10.18419/darus-1202/11
title: 00003789.JPG
DOI: 10.18419/darus-1202/12
title: 00003798.JPG
DOI: 10.18419/darus-1202/13
title: 00003774.JPG
DOI: 10.18419/darus-1202/14
title: 00003783.JPG
DOI: 10.18419/darus-1202/15
title: 00003795.JPG
DOI: 10.18419/darus-1202/16
title: 00003779.JPG
DOI: 10.18419/darus-1202/17
title: 00003791.JPG
DOI: 10.18419/darus-1202/18
title: 00003787.JPG
DOI: 10.18419/darus-1202/19
title: 00003773.JPG
DOI: 10.18419/darus-1202/20
title: 00003799.JPG
DOI: 10.18419/darus-1202/21
title: 00003794.JPG
DOI: 10.18419/darus-1202/22
title: 00003792.JPG
DOI: 10.18419/darus-1202/23
title: 00003786.JPG
DOI: 10.18419/darus-1202/24
title: 00003777.JPG
DOI: 10.18419/darus-1202/25
title: 00003780.JPG
DOI: 10.18419/darus-1202/26
title: 00003784.JPG
DOI: 10.18419/darus-1202
title: 2D pictures of KR22/1-20

description: &lt;p&gt;Dataset contains all pictures (.jpg; .arw) that were taken during the &lt;a href="https://darus.uni-stuttgart.de/file.xhtml?fileId=49056&version=DRAFT"&gt;2D digitisation strategy&lt;/a&gt; for this object.&lt;/p&gt; &lt;p&gt;Order of data: object was digitised according to a digitisation schema visible within the Dataset&lt;/p&gt;&lt;ul&gt;&lt;li&gt;characteristic view,&lt;/li&gt; &lt;li&gt;front view,&lt;/li&gt; &lt;li&gt;left view,&lt;/li&gt; &lt;li&gt;back view,&lt;/li&gt; &lt;li&gt;right view,&lt;/li&gt;&lt;li&gt;bottom view,&lt;/li&gt; &lt;li&gt;upward view, &lt;/li&gt;&lt;li&gt;details (details 1, details 2, ..., details n)&lt;/li&gt;&lt;/ul&gt;&lt;p&gt; This schema is indicated within the pictures by inventory label and then labels according to view.&lt;/p&gt;&lt;p&gt; Further information one can find in the document: Setting and workflow for basic development of 2D.&lt;/p&gt;&lt;p&gt;This dataset is part of the digital gyroscope collection. For more information about 
the collection and its creation, please see the documentation dataset:
&lt;i&gt;Niklaus, Maria, 2021, "Information about the Gyrolog data repository", 
&lt;a href="https://doi.org/10.18419/darus-821"&gt;doi: 10.18419/darus-821&lt;/a&gt;, DaRUS.&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;To refer to the whole collection: Wagner, Jörg F.; Ceranski, Beate; Fritsch, Dieter; Mammadov, Gasim; Niklaus, Maria; Schweizer, Timo; Simon, Sven; Zhan, Kun, 2021, "Digital Gyroscope Collection Created by the Project 'Gyrolog'", &lt;a href="https://doi.org/10.18419/darus-gyrolog"&gt;doi: 10.18419/darus-gyrolog&lt;/a&gt;, DaRUS.&lt;/b&gt;&lt;/p&gt;
description: gyro rotor
DOI: 10.18419/darus-1202/27
title: 00003793.JPG
DOI: 10.15468/dl.e543hq
title: Occurrence Download

description: A dataset containing 3477 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "License is one of (CC-BY 4.0, CC-BY-NC 4.0)",
 "OccurrenceStatus is Present",
 "TaxonKey is Ilex cassine L."
 ]
}

 The dataset includes 3477 records from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006844-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18419/darus-3236/2
title: dichasus-d002.tfrecords

description: The trajectory of the transmitter traces back overlapping rectangular shapes (vertical) on the so-called ramp (close underneath antenna array A).
DOI: 10.18419/darus-3236/1
title: spec.json

description: Machine-readable description file
DOI: 10.18419/darus-3236/3
title: dichasus-d004.tfrecords

description: The trajectory of the transmitter traces back overlapping rectangular shapes (horizontal) on the so-called ramp (close underneath antenna array A).
DOI: 10.18419/darus-3236/4
title: dichasus-d005.tfrecords

description: The transmitter moves along a zig-zag trajectory on the so-called ramp (close underneath antenna array A).
DOI: 10.18419/darus-2294
title: Code for: Lagrangian sensors in a stirred tank reactor: Comparing trajectories of 4D-Particle Tracking Velocimetry and Lattice-Boltzmann simulations

description: This dataset entails exemplary segments of raw data and MATLAB code that implements the following topics: (1) Raw data handling of the output files of DaVis (4D-PTV) and M-Star (Lattice-Boltzmann Large Eddy Simulation) for particle tracking in a stirred tank reactor, (2) Lagrangian evaluations of velocity and acceleration (3) Eulerian evaluations of Reynolds particle numbers and Stokes numbers and (4) additional calculations and evaluations based on numerically determined data to complement experimental data. The code was written by Sebastian Hofmann and Christian Weiland in the context of the DFG funded priority program SPP 2170 "InterZell" for the herein mentioned publication.
All data can also be accessed and collaboratively developed at &lt;a href="https://collaborating.tuhh.de/SebastianHofmannIMS/cej-data-4dptv-lbsimu"&gt;https://collaborating.tuhh.de/SebastianHofmannIMS/cej-data-4dptv-lbsimu&lt;/a&gt;.
description: MATLAB, 2021b
description: FlowFit DaVis, 10.1.2.73698
description: M-Star CFD, 3.0.19
DOI: 10.18419/darus-3236/5
title: dichasus-d006.tfrecords

description: The transmitter moves forth and back on the street (all the way to the left and all the way to the right).
DOI: 10.18419/darus-3236/6
title: dichasus-d007.tfrecords
DOI: 10.18419/darus-3236/7
title: dichasus-d008.tfrecords

description: The transmitter moves around on the street, and sometimes swerves onto the ramp area.
DOI: 10.18419/darus-3236/8
title: dichasus-d009.tfrecords

description: Again, the transmitter moves around on the street, and sometimes swerves onto the ramp area.
DOI: 10.18419/darus-3236/9
title: dichasus-d00a.tfrecords

description: Transmitter follows several trajectories that connect the ramp area below antenna A and the street (mostly towards antenna B).