cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.1476155
title: Machine Learning For Gravity Spy: Glitch Classification And Dataset

description: We present the first version of the training set used in the Gravity Spy citizen science project. This training set, discussed in detail here, was utilized to train the convolutional neural network employed in the Gravity Spy project. We anticipate moving forward to release more labelled Gravity Spy data sets, including a refined version of this training set which can be found here 10.5281/zenodo.1476551, and data sets containing the annotations provided by our citizen science volunteers.

<strong>Data Set Information</strong>

There are three files provided in this data set


	<strong>trainingset_v1d0_metadata.csv</strong>

	
		This file has three columns, <em>gravityspy_id, label, </em>and <em>sample_type.</em><em> gravityspy_id </em>is the unique 10 character hash given to every Gravity Spy sample. <em>label</em> is the string label of the sample. <em>sample_type </em>indicates whether this sample was used in the paper for testing training or validating the models. This is provided for those who would like to do direct comparisons to the network described in the paper.
	
	
	<strong>trainingsetv1d0.h5</strong>
	
		This file contains the exact arrays used in the paper for every Gravity Spy sample. Each Gravity Spy sample is defined by four different images with varying temporal duration, <em>0.5, 1.0, 2.0, and 4.0</em> second, respectively. This also determines the naming conventions of the PNGs: <em>interferometer_gravityspyid_spectrogram_duration.png (e.g. H1_Fv3p6eROvA_spectrogram_0.5.png, H1_Fv3p6eROvA_spectrogram_1.0.png, H1_Fv3p6eROvA_spectrogram_2.0.png, H1_Fv3p6eROvA_spectrogram_4.0.png</em>).
		This file contains all the information needed for each sample in the Gravity Spy dataset (i.e. the label, the sample type of the sample, the unique id of the sample, and the image data for that sample.
		
			/1080Lines/validation/xUEyaWr34c Group<br>
			/1080Lines/validation/xUEyaWr34c/0.5.png Dataset {1, 140, 170}<br>
			/1080Lines/validation/xUEyaWr34c/1.0.png Dataset {1, 140, 170}<br>
			/1080Lines/validation/xUEyaWr34c/2.0.png Dataset {1, 140, 170}<br>
			/1080Lines/validation/xUEyaWr34c/4.0.png Dataset {1, 140, 170}
		
		
	
	
	<strong>trainingsetv1d0.tar.gz</strong>
	
		Contains the raw PNGs of the Gravity Spy training set.
		The structure of the folder is <em>/"label"/"sample_type"/"pngs"</em>
	
	


<strong>Data Set Parsing Information</strong>

To read and crop out the plot axis and labels of the provided PNGs, the following small python code using scikit-image should work.

from skimage import io

image_data = io.imread("filename_of_image")

x=[66, 532]; y=[105, 671]

image_data = image_data[x[0]:x[1], y[0]:y[1], :3]
DOI: 10.5281/zenodo.1476156
title: Machine Learning For Gravity Spy: Glitch Classification And Dataset

description: We present the first version of the training set used in the Gravity Spy citizen science project. This training set, discussed in detail here, was utilized to train the convolutional neural network employed in the Gravity Spy project. We anticipate moving forward to release more labelled Gravity Spy data sets, including a refined version of this training set which can be found here 10.5281/zenodo.1476551, and data sets containing the annotations provided by our citizen science volunteers.

<strong>Data Set Information</strong>

There are three files provided in this data set


	<strong>trainingset_v1d0_metadata.csv</strong>

	
		This file has three columns, <em>gravityspy_id, label, </em>and <em>sample_type.</em><em> gravityspy_id </em>is the unique 10 character hash given to every Gravity Spy sample. <em>label</em> is the string label of the sample. <em>sample_type </em>indicates whether this sample was used in the paper for testing training or validating the models. This is provided for those who would like to do direct comparisons to the network described in the paper.
	
	
	<strong>trainingsetv1d0.h5</strong>
	
		This file contains the exact arrays used in the paper for every Gravity Spy sample. Each Gravity Spy sample is defined by four different images with varying temporal duration, <em>0.5, 1.0, 2.0, and 4.0</em> second, respectively. This also determines the naming conventions of the PNGs: <em>interferometer_gravityspyid_spectrogram_duration.png (e.g. H1_Fv3p6eROvA_spectrogram_0.5.png, H1_Fv3p6eROvA_spectrogram_1.0.png, H1_Fv3p6eROvA_spectrogram_2.0.png, H1_Fv3p6eROvA_spectrogram_4.0.png</em>).
		This file contains all the information needed for each sample in the Gravity Spy dataset (i.e. the label, the sample type of the sample, the unique id of the sample, and the image data for that sample.
		
			/1080Lines/validation/xUEyaWr34c Group<br>
			/1080Lines/validation/xUEyaWr34c/0.5.png Dataset {1, 140, 170}<br>
			/1080Lines/validation/xUEyaWr34c/1.0.png Dataset {1, 140, 170}<br>
			/1080Lines/validation/xUEyaWr34c/2.0.png Dataset {1, 140, 170}<br>
			/1080Lines/validation/xUEyaWr34c/4.0.png Dataset {1, 140, 170}
		
		
	
	
	<strong>trainingsetv1d0.tar.gz</strong>
	
		Contains the raw PNGs of the Gravity Spy training set.
		The structure of the folder is <em>/"label"/"sample_type"/"pngs"</em>
	
	


<strong>Data Set Parsing Information</strong>

To read and crop out the plot axis and labels of the provided PNGs, the following small python code using scikit-image should work.

from skimage import io

image_data = io.imread("filename_of_image")

x=[66, 532]; y=[105, 671]

image_data = image_data[x[0]:x[1], y[0]:y[1], :3]
DOI: 10.5281/zenodo.1478507
title: Crowdsourcing Storylines With Crowdtruth

description: Crowdsourced ground truth dataset for 1,204 sentences and 7,778 event pairs covering 22 news topics. The corpus was created by using the CrowdTruth methodology, as described in the following paper:


	Tommaso Caselli and Oana Inel: Crowdsourcing StoryLines: Harnessing the Crowd for Causal Relation Annotation. Events and Stories in the News Workshops, COLING 2018


If you find this data useful in your research, please consider citing:

<pre>@inproceedings{caselli2018crowdsourcing,
  title={Crowdsourcing StoryLines: Harnessing the Crowd for Causal Relation Annotation},
  author={Caselli, Tommaso and Inel, Oana},
  booktitle={Proceedings of the Workshop Events and Stories in the News 2018},
  pages={44--54},
  year={2018}
}
</pre>

Crowdsourcing results and evaluation against expert data are available in folder: <code>|--data/results/</code>

Expert ground truth data is available in folder: <code>|--data/ground_truth/</code>

Aggregated raw crowdsourcig data is available in folder: <code>|--data/aggregated_input/</code>

Raw crowdsourcig data is available in folder: <code>|--data/input/</code>

 

<strong>Running the notebooks</strong>

To run and regenerate the results, you need to install the stable version of the <em><strong>crowdtruth==2.0</strong></em> package from PyPI using:

pip install crowdtruth==2.0

<pre> </pre>
DOI: 10.5281/zenodo.1478508
title: Crowdsourcing Storylines With Crowdtruth

description: Crowdsourced ground truth dataset for 1,204 sentences and 7,778 event pairs covering 22 news topics. The corpus was created by using the CrowdTruth methodology, as described in the following paper:


	Tommaso Caselli and Oana Inel: Crowdsourcing StoryLines: Harnessing the Crowd for Causal Relation Annotation. Events and Stories in the News Workshops, COLING 2018


If you find this data useful in your research, please consider citing:

<pre>@inproceedings{caselli2018crowdsourcing,
  title={Crowdsourcing StoryLines: Harnessing the Crowd for Causal Relation Annotation},
  author={Caselli, Tommaso and Inel, Oana},
  booktitle={Proceedings of the Workshop Events and Stories in the News 2018},
  pages={44--54},
  year={2018}
}
</pre>

Crowdsourcing results and evaluation against expert data are available in folder: <code>|--data/results/</code>

Expert ground truth data is available in folder: <code>|--data/ground_truth/</code>

Aggregated raw crowdsourcig data is available in folder: <code>|--data/aggregated_input/</code>

Raw crowdsourcig data is available in folder: <code>|--data/input/</code>

 

<strong>Running the notebooks</strong>

To run and regenerate the results, you need to install the stable version of the <em><strong>crowdtruth==2.0</strong></em> package from PyPI using:

pip install crowdtruth==2.0

<pre> </pre>
DOI: 10.7910/dvn/pzhnu2/ankbpt
title: 112-5_0153.dcm
DOI: 10.7910/dvn/pzhnu2/gahqu3
title: 112-1_0014.dcm
DOI: 10.7910/dvn/pzhnu2/m0da27
title: 112-1_0070.dcm
DOI: 10.7910/dvn/pzhnu2/vg4q0b
title: 112-5_0059.dcm
DOI: 10.7910/dvn/pzhnu2/vjywez
title: 112-1_0124.dcm
DOI: 10.7910/dvn/pzhnu2/xpwy3d
title: 112-5_0043.dcm
DOI: 10.5258/soton/d06037
title: Data for Turbulence and dispersion below and above the interface of the internal and the external boundary layers

description: Data supporting the paper Sessa, V., Xie, Z-T., & Herring, S. (2018). Turbulence and dispersion below and above the interface of the internal and the external boundary layers. Journal of Wind Engineering and Industrial Aerodynamics, 182, 189-201. DOI: 10.1016/j.jweia.2018.09.021
DOI: 10.5281/zenodo.1477755
title: Melting Of 2D Molecular Crystals

description: Melting data
DOI: 10.7910/dvn/pzhnu2/4qmxtd
title: 112-5_0019.dcm
DOI: 10.7910/dvn/pzhnu2/fbrfis
title: 112-5_0025.dcm
DOI: 10.7910/dvn/pzhnu2/ggaxbt
title: 112-1_0057.dcm
DOI: 10.7910/dvn/pzhnu2/lgejo9
title: 112-1_0009.dcm
DOI: 10.7910/dvn/pzhnu2/mb9fr3
title: 112-1_0035.dcm
DOI: 10.7910/dvn/pzhnu2/nowobb
title: 112-1_0232.dcm
DOI: 10.7910/dvn/pzhnu2/rjhq10
title: 112-5_0124.dcm
DOI: 10.7910/dvn/pzhnu2/tzxd7u
title: 112-1_0056.dcm
DOI: 10.7910/dvn/pzhnu2/3juam3
title: 112-1_0059.dcm
DOI: 10.7910/dvn/pzhnu2/lcqjcr
title: 112-1_0136.dcm
DOI: 10.7910/dvn/pzhnu2/wtnyhx
title: 112-1_0119.dcm
DOI: 10.17632/sm952tc7kx.1
title: Data for: Adsorption of strontium and caesium onto an Na-illite and Na illite/Na-smectite mixtures: implementation and application of the multi-site ion-exchange model

description: Saturation curve of Na-illite vs. pH at I = 0.02 mol/kgw (NaCl) and S/L ratio = 19 g/L
DOI: 10.5281/zenodo.1415456
title: The Intermittency Regions Of Powder Snow Avalanches [Data-Set]

description: This data repository contains the data-sets presented in the publication:

Sovilla, B., McElwaine, J. N., & Köhler, A. (2018). The intermittency regions of powder snow avalanches. Journal of Geophysical Research: Earth Surface, 123,  https://doi.org/10.1029/2018JF004678.

This data set should be cited, together with the publication, as a:

B. Sovilla, J. N. McElwaine, and A. Köhler (2018), The intermittency regions of powder snow avalanches [Data set]. Zenodo. https://doi.org/10.5281/ zenodo.1415456.

Information on the data can be found in the Readme file or can be obtained by writing an e-mail at: avalanche.data@slf.ch.
DOI: 10.5281/zenodo.1476556
title: Simulated Tracer-Gas Distribution In A Multiscale Model Of The Human Lung During Multiple-Breath Nitrogene Washout

description: <em><strong>Content</strong></em>

<strong>baseline:</strong>


	inletFlow (ASCII format data for flow rate at the mouth in m^3/s, sampling frequency 1kHz)
	primary_results (ASCII format data table with four colums: time in seconds, N2 concentration (normalized), <em>empty </em>-1, pleural pressure in Pascal)


<strong>compliance modification (local):</strong>


	inletFlow (format as in baseline)
	primary_results (format as in basline)
	duct: unstructured VTK mesh data of several scalar quantities (airway dimensino, pressure, N2 concentration, flow velocity) witin the airway network. Sampling frequency 50Hz (separat vtk-file for each timestep). <em>Inspect for instance with the VisIt (Lawrence Livermore National Laboratory) free visualization software.</em>
	lobule: unstructured VTK mesh data of several scalar quantities (airway dimensino, pressure, N2 concentration, flow velocity) within the trumpet lobules.


<strong>compliance modification (regional):</strong>


	inletFlow (format as in baseline)
	primary_results (format as in basline)
	duct: (same format as described above)
	lobule: (same format as described above)


<strong>size modification (regional):</strong>


	inletFlow (format as in baseline)
	primary_results (format as in basline)


<strong>resistance modification (local):</strong>


	inletFlow (format as in baseline)
	primary_results (format as in basline)


<strong>healthy controls (local):</strong>


	inletFlow (format as in baseline)
	primary_results (format as in basline)
DOI: 10.5281/zenodo.1476557
title: Simulated Tracer-Gas Distribution In A Multiscale Model Of The Human Lung During Multiple-Breath Nitrogene Washout

description: <em><strong>Content</strong></em>

<strong>baseline:</strong>


	inletFlow (ASCII format data for flow rate at the mouth in m^3/s, sampling frequency 1kHz)
	primary_results (ASCII format data table with four colums: time in seconds, N2 concentration (normalized), <em>empty </em>-1, pleural pressure in Pascal)


<strong>compliance modification (local):</strong>


	inletFlow (format as in baseline)
	primary_results (format as in basline)
	duct: unstructured VTK mesh data of several scalar quantities (airway dimensino, pressure, N2 concentration, flow velocity) witin the airway network. Sampling frequency 50Hz (separat vtk-file for each timestep). <em>Inspect for instance with the VisIt (Lawrence Livermore National Laboratory) free visualization software.</em>
	lobule: unstructured VTK mesh data of several scalar quantities (airway dimensino, pressure, N2 concentration, flow velocity) within the trumpet lobules.


<strong>compliance modification (regional):</strong>


	inletFlow (format as in baseline)
	primary_results (format as in basline)
	duct: (same format as described above)
	lobule: (same format as described above)


<strong>size modification (regional):</strong>


	inletFlow (format as in baseline)
	primary_results (format as in basline)


<strong>resistance modification (local):</strong>


	inletFlow (format as in baseline)
	primary_results (format as in basline)


<strong>healthy controls (local):</strong>


	inletFlow (format as in baseline)
	primary_results (format as in basline)
DOI: 10.7910/dvn/pzhnu2/dcmblq
title: 112-5_0030.dcm
DOI: 10.7910/dvn/pzhnu2/lps8zx
title: 112-1_0197.dcm
DOI: 10.7910/dvn/pzhnu2/oe2fpa
title: 112-1_0012.dcm
DOI: 10.7910/dvn/pzhnu2/pgtbru
title: 112-5_0062.dcm
DOI: 10.7910/dvn/pzhnu2/qzmfgx
title: 112-5_0035.dcm
DOI: 10.7910/dvn/pzhnu2/wqyfja
title: 112-5_0179.dcm
DOI: 10.5281/zenodo.1415455
title: The Intermittency Regions Of Powder Snow Avalanches [Data-Set]

description: This data repository contains the data-sets presented in the publication:

Sovilla, B., McElwaine, J. N., & Köhler, A. (2018). The intermittency regions of powder snow avalanches. Journal of Geophysical Research: Earth Surface, 123,  https://doi.org/10.1029/2018JF004678.

This data set should be cited, together with the publication, as a:

B. Sovilla, J. N. McElwaine, and A. Köhler (2018), The intermittency regions of powder snow avalanches [Data set]. Zenodo. https://doi.org/10.5281/ zenodo.1415456.

Information on the data can be found in the Readme file or can be obtained by writing an e-mail at: avalanche.data@slf.ch.
DOI: 10.7910/dvn/pzhnu2/5cfuix
title: 112-1_0044.dcm
DOI: 10.7910/dvn/pzhnu2/9ho2nc
title: 112-5_0134.dcm
DOI: 10.7910/dvn/pzhnu2/dp9n4o
title: 112-1_0127.dcm
DOI: 10.7910/dvn/pzhnu2/gjxqcb
title: 112-5_0008.dcm
DOI: 10.7910/dvn/pzhnu2/jrvddz
title: 112-1_0200.dcm
DOI: 10.7910/dvn/pzhnu2/pg5dmn
title: 112-1_0085.dcm
DOI: 10.7910/dvn/pzhnu2/rxn9rs
title: 112-1_0240.dcm
DOI: 10.7910/dvn/pzhnu2/tpehz3
title: 112-1_0211.dcm
DOI: 10.7910/dvn/pzhnu2/ewf943
title: 112-1_0207.dcm
DOI: 10.7910/dvn/pzhnu2/i3oddn
title: 112-1_0027.dcm
DOI: 10.7910/dvn/pzhnu2/l9obhj
title: 112-1_0094.dcm
DOI: 10.7910/dvn/pzhnu2/lirnsi
title: 112-5_0136.dcm
DOI: 10.7910/dvn/pzhnu2/oijsyn
title: 112-5_0110.dcm
DOI: 10.7910/dvn/pzhnu2/pz86vv
title: 112-1_0004.dcm
DOI: 10.7910/dvn/pzhnu2/yfs8bo
title: 112-5_0221.dcm
DOI: 10.7910/dvn/pzhnu2/zpdpe8
title: 112-1_0096.dcm
DOI: 10.7910/dvn/pzhnu2/47mek6
title: 112-5_0233.dcm
DOI: 10.7910/dvn/pzhnu2/aawl9o
title: 112-5_0082.dcm
DOI: 10.7910/dvn/pzhnu2/gghitb
title: 112-5_0200.dcm
DOI: 10.7910/dvn/pzhnu2/kltbiw
title: 112-5_0151.dcm
DOI: 10.7910/dvn/pzhnu2/odrjh9
title: 112-5_0070.dcm
DOI: 10.7910/dvn/pzhnu2/ppurl1
title: 112-1_0116.dcm
DOI: 10.7910/dvn/pzhnu2/s4gpch
title: 112-1_0178.dcm
DOI: 10.7910/dvn/pzhnu2/tlxxxk
title: 112-1_0213.dcm
DOI: 10.7910/dvn/pzhnu2/xjmlna
title: 112-1_0036.dcm
DOI: 10.5281/zenodo.1477782
title: Experteninterview mit Michaela Voigt im Rahmen von Hamburg Open Science

description: Da sich die HAW Hamburg am hochschulübergreifenden Programm Hamburg Open Science beteiligt, wurde im Rahmen von Hamburg Open Science zur Entwicklung eines Konzeptes für eine Open-Access-freundliche Publikationskultur Michaela Voigt aus dem Team Universitätsverlag/Hochschulschriften/Open Access der Universitätsbibliothek der Technischen Universität Berlin interviewt. Das Experteninterview fand am 01.08.2018 an der UB der TU Berlin statt. Die Länge des Interviews betrug 49 Minuten. Das folgende Transkript wurde auf die relevanten Informationen gekürzt und zur besseren Lesbarkeit stilistisch bearbeitet.
DOI: 10.5281/zenodo.1477783
title: Experteninterview mit Michaela Voigt im Rahmen von Hamburg Open Science

description: Da sich die HAW Hamburg am hochschulübergreifenden Programm Hamburg Open Science beteiligt, wurde im Rahmen von Hamburg Open Science zur Entwicklung eines Konzeptes für eine Open-Access-freundliche Publikationskultur Michaela Voigt aus dem Team Universitätsverlag/Hochschulschriften/Open Access der Universitätsbibliothek der Technischen Universität Berlin interviewt. Das Experteninterview fand am 01.08.2018 an der UB der TU Berlin statt. Die Länge des Interviews betrug 49 Minuten. Das folgende Transkript wurde auf die relevanten Informationen gekürzt und zur besseren Lesbarkeit stilistisch bearbeitet.
DOI: 10.7910/dvn/pzhnu2/hwzos8
title: 112-1_0147.dcm
DOI: 10.7910/dvn/pzhnu2/imztja
title: 112-1_0248.dcm
DOI: 10.7910/dvn/pzhnu2/nbei7b
title: 112-1_0028.dcm
DOI: 10.7910/dvn/pzhnu2/oiojbd
title: 112-5_0205.dcm
DOI: 10.7910/dvn/pzhnu2/sxdklr
title: 112-5_0196.dcm
DOI: 10.7910/dvn/pzhnu2/tue0lp
title: 112-1_0234.dcm
DOI: 10.7910/dvn/pzhnu2/vg22mw
title: 112-5_0061.dcm
DOI: 10.7910/dvn/pzhnu2/ys5jmc
title: 112-5_0150.dcm
DOI: 10.5281/zenodo.1478517
title: H2020 Tweether Field Trial Results (Data Files)

description: ZIP file containing the CSV files of the different parameters captured during TWEETHER Field Trial (21/09/2018 to 04/10/2018).

Please contact TWEETHER consortium (www.tweether.eu) to request access.
DOI: 10.5281/zenodo.1478518
title: H2020 Tweether Field Trial Results (Data Files)

description: ZIP file containing the CSV files of the different parameters captured during TWEETHER Field Trial (21/09/2018 to 04/10/2018).

Please contact TWEETHER consortium (www.tweether.eu) to request access.
DOI: 10.7910/dvn/pzhnu2/0hxpii
title: 112-5_0049.dcm
DOI: 10.7910/dvn/pzhnu2/38whtz
title: 112-5_0235.dcm
DOI: 10.7910/dvn/pzhnu2/4ldr54
title: 112-1_0238.dcm
DOI: 10.7910/dvn/pzhnu2/a9owrj
title: 112-1_0074.dcm
DOI: 10.7910/dvn/pzhnu2/dv2jxm
title: 112-1_0106.dcm
DOI: 10.7910/dvn/pzhnu2/garavj
title: 112-5_0072.dcm
DOI: 10.7910/dvn/pzhnu2/ti6kxh
title: 112-5_0207.dcm
DOI: 10.7910/dvn/pzhnu2/yxvl5j
title: 112-5_0230.dcm
DOI: 10.7910/dvn/pzhnu2/9gb6jg
title: 112-5_0112.dcm
DOI: 10.7910/dvn/pzhnu2/al6ifi
title: 112-1_0041.dcm
DOI: 10.7910/dvn/pzhnu2/fuilbn
title: 112-5_0176.dcm
DOI: 10.7910/dvn/pzhnu2/jzggrz
title: 112-1_0022.dcm
DOI: 10.7910/dvn/pzhnu2/peux2l
title: 112-1_0169.dcm
DOI: 10.7910/dvn/pzhnu2/xfcsit
title: 112-1_0073.dcm
DOI: 10.7910/dvn/pzhnu2/0lo6w8
title: 112-5_0173.dcm
DOI: 10.7910/dvn/pzhnu2/cj5dbn
title: 112-1_0018.dcm
DOI: 10.7910/dvn/pzhnu2/dlmfit
title: 112-1_0199.dcm
DOI: 10.7910/dvn/pzhnu2/mxzfn2
title: 112-1_0208.dcm
DOI: 10.7910/dvn/pzhnu2/nh0mxn
title: 112-1_0183.dcm
DOI: 10.7910/dvn/pzhnu2/ns3bzx
title: 112-5_0194.dcm
DOI: 10.7910/dvn/pzhnu2/wxim0g
title: 112-5_0152.dcm
DOI: 10.7910/dvn/pzhnu2/x8sqye
title: 112-5_0104.dcm
DOI: 10.7910/dvn/pzhnu2/aemjrx
title: 112-1_0082.dcm
DOI: 10.7910/dvn/pzhnu2/cdrrtx
title: 112-1_0054.dcm
DOI: 10.7910/dvn/pzhnu2/extk4f
title: 112-1_0198.dcm
DOI: 10.7910/dvn/pzhnu2/lcmhnu
title: 112-1_0130.dcm
DOI: 10.7910/dvn/pzhnu2/scrlhk
title: 112-5_0234.dcm
DOI: 10.7910/dvn/pzhnu2/ysh3lu
title: 112-5_0142.dcm
DOI: 10.18119/n9pc7f
title: Extended Network of human ESRRA
DOI: 10.21421/d2/lfifzf
title: Data on plant growth-promoting broad-spectrum Streptomyces sp. in chickpea and its effect on grain yield 2013-14
DOI: 10.7910/dvn/pzhnu2/f5xexs
title: 112-5_0248.dcm
DOI: 10.7910/dvn/pzhnu2/hyh1a7
title: 112-1_0203.dcm
DOI: 10.7910/dvn/pzhnu2/iobue2
title: 112-5_0181.dcm
DOI: 10.7910/dvn/pzhnu2/spltta
title: 112-1_0179.dcm
DOI: 10.7910/dvn/pzhnu2/truzdk
title: 112-1_0024.dcm
DOI: 10.7910/dvn/pzhnu2/u21iq0
title: 112-5_0047.dcm
DOI: 10.7910/dvn/pzhnu2/yc6rso
title: 112-5_0188.dcm
DOI: 10.5281/zenodo.1472951
title: Single Amyloid Pet Scan On The Siemens Biograph Mmr

description: This zipped dataset consists of a full dynamic list-mode PET data of 60 minutes of acquisition, using an amyloid tracer, florbetapir, provided by Eli Lilly.  Also, the dataset includes normalisation files and the UTE mu-map, all needed for an independent image reconstruction using NiftyPET.

https://github.com/pjmark/NiftyPET

https://link.springer.com/article/10.1007/s12021-017-9352-y
DOI: 10.7910/dvn/pzhnu2/3d43b9
title: 112-1_0186.dcm
DOI: 10.7910/dvn/pzhnu2/bg8qeq
title: 112-1_0030.dcm
DOI: 10.7910/dvn/pzhnu2/cbxyp8
title: 112-1_0223.dcm
DOI: 10.7910/dvn/pzhnu2/ijfxa2
title: 112-5_0202.dcm
DOI: 10.7910/dvn/pzhnu2/kguckc
title: 112-5_0141.dcm
DOI: 10.7910/dvn/pzhnu2/wcndq3
title: 112-5_0051.dcm
DOI: 10.7910/dvn/pzhnu2/wkink5
title: 112-5_0065.dcm
DOI: 10.7910/dvn/pzhnu2/xelnwj
title: 112-1_0154.dcm
DOI: 10.7910/dvn/pzhnu2/8ruhjd
title: 112-5_0203.dcm
DOI: 10.7910/dvn/pzhnu2/gbxnl7
title: 112-5_0014.dcm
DOI: 10.7910/dvn/pzhnu2/h2o9h4
title: 112-5_0222.dcm
DOI: 10.7910/dvn/pzhnu2/imenud
title: 112-5_0067.dcm
DOI: 10.7910/dvn/pzhnu2/osphdu
title: 112-1_0191.dcm
DOI: 10.5281/zenodo.1478607
title: water level and discharge data in the Mekong Delta

description: The dataset contains water level and discharge data at multiple station within the Mekong Delta. The timezone is local Vietnamese time.
description: Data usage is restricted. Access to the data can only be authorized upon approval of the SIWRP.
DOI: 10.7910/dvn/pzhnu2/8uys62
title: 112-1_0216.dcm
DOI: 10.7910/dvn/pzhnu2/e0zobk
title: 112-5_0125.dcm
DOI: 10.7910/dvn/pzhnu2/ffmj6l
title: 112-1_0174.dcm
DOI: 10.7910/dvn/pzhnu2/gl84ol
title: 112-5_0180.dcm
DOI: 10.7910/dvn/pzhnu2/iemnpl
title: 112-5_0052.dcm
DOI: 10.7910/dvn/pzhnu2/mijpal
title: 112-5_0156.dcm
DOI: 10.7910/dvn/pzhnu2/wf8od4
title: 112-1_0224.dcm
DOI: 10.7910/dvn/pzhnu2/1zhucv
title: 112-1_0219.dcm
DOI: 10.7910/dvn/pzhnu2/845knq
title: 112-5_0066.dcm
DOI: 10.7910/dvn/pzhnu2/apse28
title: 112-1_0001.dcm
DOI: 10.7910/dvn/pzhnu2/djvp9v
title: 112-1_0078.dcm
DOI: 10.7910/dvn/pzhnu2/o4t8g5
title: 112-5_0197.dcm
DOI: 10.7910/dvn/pzhnu2/q6uosa
title: 112-5_0236.dcm
DOI: 10.7910/dvn/pzhnu2/rfoidn
title: 112-5_0016.dcm
DOI: 10.7910/dvn/pzhnu2/u5kinl
title: 112-5_0021.dcm