An open-data initiative for the distributation of common datasets for the evaluation and validation of diffusion MRI processing methods. http://www.dkfz.de/en/medphysrad/projectgroups/dwi/DTI_projects.html#inhalt3

Imaging data sets for printed contrast phantoms across 4 timepoints and 3 frequencies. Printed contrast regions were analyzed for Backscatter signal within the region and CNR within the region compared to a background ROI.

Comprehensive financial and analytical metrics for Phantom, including key performance indicators, market data, and ecosystem analytics.

Analyzed data from the serpentine Doppler phantom. The variable Acq consists of structures of the image data as they were originally acquired, and Registered moves each dataset from Acq to their respective location within the co-registered volume. Each structure contains relevant metadata for reconstruction of the 3D volume.

43 measurements acquired in eight different sites within the IMAGEN-project, comprising the following 3 T scanner types: Siemens Verio and TimTrio; General Electric Signa Excite, and Signa HDx; Philips Achieva. Additionally one phantom data set was aquired on a 3 T Siemens Skyra resulting in 44 measurements.

Multi-slice radial2D Pulseq sequence and acquired phantom data including the trajectory information in the ISMRM raw data format.

This folder includes all the data for the paper titled: Partial Tidal Disruption Events: The Elixir of Life

Simulations were run with publicly available phantom code (https://github.com/danieljprice/phantom; https://phantomsph.readthedocs.io/en/latest/) and Kepler code (https://2sn.org/kepler/doc/Setup.html)

We used splash (https://github.com/danieljprice/splash.git) for making Figures 2, 7, 8, 9, 10, 12, 14, B2, F1.

We have three folders called s1_kepler, s3_kepler and s10_kepler. They contain the KEPLER models .wnd and .cnv files along with the dumps for ZAMS, MAMS and TAMS

We have saved the PHANTOM relaxed models in star_(mass_of_star)_(evolution_stage) along with mapping back evolution in KEPLER files (link.dat, .wnd, .cnv). We have also saved Makefile, star.in, star.setup, tde.comp files, linkg.

We have saved the PHANTOM simulation data in folders named star(mass_of_star)_(evolution_stage)_(beta_val). These include the .in, .tdeparams, ptok file, tde dump file used for analysis, KEPLER mapping back files (link.dat, .wnd, .cnv), .ev files.

We have saved the KEPLER models that had the same mass as the remnants in star(mass_of_star)_(evolution_stage)_(beta_val)_kepler folders. We provide the generator file, .wnd, .cnv files.

We have saved the KEPLER models that were generated by replacing the composition in the ptok files with the original star composition in the star_(mass_of_star)_(evolution_stage)_(beta_val)_strip

We have saved the KEPLER models that were run until the He4 in the central bin was same as remnant He4 in centre, in the star(mass_of_star)_(evolution_stage)_(beta_val)_he4_same

We have saved the PHANTOM simulation data for zero energy orbits run in star(mass_of_star)_(evolution_stage)_(beta_val)_zero folders.

We have saved the geodesic files in the geodesic folder. It includes the .py files used to run jobs in parallel, and interpolation files. We have also saved the rp values used in the simulations run.

We have saved the scripts used to run the KEPLER models (mapping back and stripping) in the python_code folder. We have also saved an EXCEL file which includes all the PHANTOM simulations data, along with the correct beta values shown in the paper.

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For the figures from the publication,

Figure 1 uses the data from s1_kepler, s3_kepler and s10_kepler folders.

Figure 2 uses data files present in the star(mass_of_star)_(evolution_stage)_(beta_val) folders. The folders for 3 solar MID have sub-folders starting with fig2 which contain the PHANTOM dump files.

Figure 3 uses the data from solar3_mid_beta(value) folders. We have included the .ev files which were used to make this plot.

Figure 4 uses the mass of the remnant as function of time. We have included the dump_info files that can be used to make this plot.

Figure 5 and 6 use the mass of the remnant. You can access this information from Table 1 and 2 of the paper.

Figure 7 uses the remnant files from the star1, star3 and star10 MID models.

Figure 8 uses the ptok files and tde dump files. These are all included in the folders.

Figure 9 uses dump files which are included in a sub-folder in the star3_mid_beta18 folder.

Figure 10 requires the dump files along with files including the binned rotation profile and the break-up velocities. All of this is present in the folders where .exact1 and .exact2 are the binned and break-up profiles.

Figure 11 can be generated by using the ptok files as they include binned rotation profile and radius.

Figure 12 uses data files from the star3_mid_beta18 folder. We have included the remnant files. The same folder includes vphi files for Figure 13.

Figure 14 requires the dump files along with tde.comp files. All tde.comp files are present in star_(mass_of_star)_(evolution_stage) folders. Dumps are in star_(mass_of_star)_(evolution_stage)_beta(value)folders.

Figure 15 uses the files provided in the folders ending with _he4_same.

Figure 16 and 17 can be generated by using the .wnd files provided in the star_(mass_of_star)_(evolution_stage) and star_(mass_of_star)_(evolution_stage)_beta(value) folders. The same files can be used for Figure 18 and Figure 19.

Figure 20 can be generated by using the files present in folders ending with _kepler.

Figure 21 uses files from folders ending with _strip.

Figure 22 uses ptok files which have been provided for all star10_tams_beta(value) folders

Figures A1, A2 can be generated using the ptok files and the original model files.

Figure B2 uses files from sub-folder called cut_plot in the star3_mid_beta15 folder.

Figure E1 uses .wnd files present in star(mass_of_star)_(evolution_stage) folders.

Figure E2 uses .wnd files from the star3_tams_beta25 (sub-folder, time_run).

Figure F1 uses files from zero and hyperbolic folders.

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To load the .wnd and .cnv, use windata and conv modules from python/source code written by Alexander Heger (KEPLER).
To load files starting with tde_0* use splash or sarracen (https://sarracen.readthedocs.io/en/latest/quick-start.html).
To load files starting with ptok (ASCII files), you can use splash or python.
To load .ev (ASCII files), you can use splash or python.
To load rem_* files or exact* files (ASCII) splash or python can be used.
To load link.dat file (ASCII) one can use splash or python.
To load KEPLER files containing @ one can use python. These are ASCII files. To convert any KEPLER files containing #, one would need to use Le-data filename to get it into ASCII form.

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orbit.tdeparams files contain the initial position and velocities required for zero energy orbit.
tde.tdeparams files contain the beta, pericentre and starting distance along with mass of SMBH (in code units).

star_00000.tmp files are the relaxed stellar profiles that were disrupted by the SMBH.
dump_info file is an ASCII file with information about the dumps such as the mass of the remnant, escape velocity, and maximum radius.

This database contains imaging and calibration data for phantoms contained in the NIST/NIBIB Phantom Lending Library (PLL). Description and access to the PLL can be found at https://www.nist.gov/programs-projects/nistnibib-medical-imaging-phantom-lending-library . Public analysis software written in Python can be found at https://github.com/MRIStandards/PhantomViewer . This database contains image sets from different scanners and different sites to be used for comparison and reference purposes. It is not meant to endorse any specific scanner or scan protocol.

Data for multi-site, multi-platform comparison of magnetic resonance imaging (MRI) T1 measurement using the International Society of Magnetic Resonance in Medicine/National Institute of Standards and Technology (ISMRM/NIST) system phantom. Includes data sets for T1 measurement by inversion recovery (IR) and variable flip angle (VFA) methods at 1.5 tesla and 3 tesla. At 1.5 T, data is from 2 different vendor systems, 9 total MRI machines. At 3 T, data is from 3 different vendor systems, 18 total MRI machines.

94 Global export shipment records of Phantom with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

250 Global import shipment records of Phantom with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The gel phantom was constructed to simulate diffusion of liquids inside plant stems, namely the flow of iodine-based contrast agents used in high resolution tomographic X-ray imaging of plants. In order to test different reconstruction methods, this radiation resistant phantom with similar diffusion properties was constructed.

A more detailed documentation can be found on arXiv: https://arxiv.org/abs/2003.02841

The phantom consists of a 50 ml Falcon test tube filled with agarose gel. After the agarose solidified, five cavities were made into the gel and filled with 20% sucrose solution to guarantee the diffusion by directing osmosis to the gel body. In addition densely punctured plastic straws were placed in the cavities to simulate cellular passages such as phloem plasmodesmata and to slow down the lateral diffusion.

The primary measurements consisted of 17 consecutive time frames, with initial stage of no contrast agent followed
by steady increase and diffusion into the gel body over time. Each round of measurements consist of 360 projections with a fanbeam microCT-scanner, but we used only the central plane of the cone beam, resulting in 2D fan beam geometry.

Data is given in two different resolutions corresponding to reconstructions of size 256 x 256 or 512 x 512, (GelPhantomData_b4.mat and GelPhantomData_b2.mat respectively). In addition to the primary measurements, a more densely sampled measurements from the first time step and an additional 18th time step are provided in GelPhantom_extra_frames.mat.

The measurements are stored in special data structures containing all the necessary metadata. In combination with the MATLAB toolboxes this allows for easy application of forward operators and reconstruction algorithms.

This is demonstrated using the included example codes. These require ASTRA Toolbox, Spot Linear Operator Toolbox and HelTomo Toolbox (v1) for MATLAB.

NOTE: Some of the metadata field names are different in HelTomo v2 and higher. Use of the data requires renaming (or adding) two fields in to the parameters:
numDetectorsPost = numDetectors;
effectivePixelSizePost = effectivePixelSize;

This dataset provides information about the number of properties, residents, and average property values for Phantom Lane cross streets in Stayton, OR.

Financing from the German Research Foundation (DFG) project number GRT 2260, BIOQIC is acknowledged

Copyright 2021 Physikalisch-Technische Bundesanstalt (PTB) If used in accordance with the supplied licence please cite the Digital Object Identifier (DOI) provided by Zenodo.

The dataset contains 3 files. They contain 3D MR golden-angle radial phase encoding [parallel cartesian readouts with phase encoding points assembled on a non-uniform grid] acquisition data of a standard ACR resolution phantom.

The dataset was acquired on a Siemens scanner and converted into ISMRMRD format using a converter ( https://github.com/ismrmrd/siemens_to_ismrmrd ).

Dataset name: PTB GRPE Resolution Phantom 3D File format: ISMRMRD (ISMRM Raw Data, http://ismrmrd.github.io/) File extension: .h5

Image/KSpace Data Dimension = 3D

Imaging Modality: MRI Institution: Physikalisch-Technische Bundesanstalt Scanner: SIEMENS Verio 3T

For details please refer to the file README.txt.

repeated texture regions

Phantom Forces

## Overview

Phantom Forces is a dataset for object detection tasks - it contains Players annotations for 1,036 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).

The RIDER PHANTOM PET-CT collection consists of repeat measurement PET/CT phantom scan collections carried out under the aegis of the Society of Nuclear Medicine (SNM) to discern the uniformity of clinical imaging instrumentation at various sites. They were obtained in cooperation with SNM as a resource for increased quantitative understanding of machine acquisition, analytic reproducibility and image processing.

The phantom was manufactured by Sanders Medical (www.sandersmedical.com) in December of 2006. The phantom was based on a NEMA NU-2 IQ phantom (manufactured by Data Spectrum, Durham NC), but with the central 5 cm diameter 'lung' cylinder of the IQ phantom removed. In addition the two larger fillable spheres were changed to hot spheres, as opposed to cold spheres as in the NEMA NU-2 specifications. Nominal target/background ratio was 4:1 with the initial background activity level set to be equivalent to 15 mCi in a 70 Kg patient, With the 271 day half-life of Ge-68 after 6 months the activity will be about 9.5 mCi. After a year it was 6 mCi.

About the RIDER project

The Reference Image Database to Evaluate Therapy Response (RIDER) is a targeted data collection used to generate an initial consensus on how to harmonize data collection and analysis for quantitative imaging methods applied to measure the response to drug or radiation therapy. The National Cancer Institute (NCI) has exercised a series of contracts with specific academic sites for collection of repeat "coffee break," longitudinal phantom, and patient data for a range of imaging modalities (currently computed tomography [CT] positron emission tomography [PET] CT, dynamic contrast-enhanced magnetic resonance imaging [DCE MRI], diffusion-weighted [DW] MRI) and organ sites (currently lung, breast, and neuro). The methods for data collection, analysis, and results are described in the new Combined RIDER White Paper Report (Sept 2008):

• RIDER White Paper: Combined contracts report ( Sept 2008) PDF

The long term goal is to provide a resource to permit harmonized methods for data collection and analysis across different commercial imaging platforms to support multi-site clinical trials, using imaging as a biomarker for therapy response. Thus, the database should permit an objective comparison of methods for data collection and analysis as a national and international resource as described in the first RIDER white paper report (2006):

• RIDER White Paper: Executive Summary PDF
• RIDER White Paper: Editorial in Nature.com

34 Global export shipment records of Phantom with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This dataset provides information about the number of properties, residents, and average property values for Phantom Heights Lane cross streets in Bar Harbor, ME.

This dataset provides information about the number of properties, residents, and average property values for Phantom Drive cross streets in Rancho Palos Verdes, CA.