cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.2643947
title: Edge based large field of view observations with optical microscope of fatigue damage in composite materials during bending loading

description: Stitched microscope pictures ... References to this data-set should include a reference to the following paper in where the data has been used. [1] Mortensen U., Andersen, T.L., Mikkelsen, L.P., Observation of Edge Effect in Flexural Fatigue Test of Composites using Large Field of View Microscopy, <em>Journal of Composite Materials.</em> https://doi.org/10.1177/0021998320902233, 2020 The naming of the files is given in the following structure EI_05_A03_kxxx_stitch.tif where EI_05_A03 is the sample ID<br> kxxx indicate after how many cycles the stitched microscopy picture are taken where e.g. k000 is the initial picture, k001 is after 1 000 cycles ... k220 is after 220 000 cycles
DOI: 10.5281/zenodo.3052358
title: Inferring whole-genome histories in large population datasets: inferred tree sequences for Simons Genome Diversity Project

description: Tree sequences inferred for the SGDP autosomes using tsinfer version 0.1.4 and compressed using tszip. Tree sequences can  be decompressed as follows:

<pre><code class="language-bash">$ tsunzip sgdp_chr1.trees.tsz</code></pre>

Once decompressed, trees files can be loaded and processed using tskit. 

<pre><code class="language-python">import tskit
ts = tskit.load("sgdp_chr1.trees")
# ts is an instance of tskit.TreeSequence 
print("Chromosome 1 contains {} trees".format(ts.num_trees))</code></pre>

Metadata associated with individuals and populations was derived from the original source and converted to JSON form. For example, to access individual metadata we can use:

<pre><code class="language-python">import tskit
import json
ts = tskit.load("sgdp_chr1.trees")
ind = ts.individual(0)
metadata_dict = json.loads(ind.metadata)</code></pre>

The metadata_dict variable will now contain all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example,

<pre><code class="language-python">pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()]
ind_node = ts.node(ind.nodes[0])
ind_pop_metadata = pop_metadata[ind_node.population]</code></pre>

After this, the ind_pop_metadata variable will contain the population level metadata for individual ID 0.

The full data pipeline used to generate these tree sequences and associated metadata is available on GitHub.
description: AWW and CF would like to thank the Rhodes Trust for their generous support.
description: {"references": ["Kelleher et al 2019, Inferring the ancestry of everyone. https://doi.org/10.1101/458067"]}
DOI: 10.5281/zenodo.3052359
title: Inferring whole-genome histories in large population datasets: inferred tree sequences for Simons Genome Diversity Project

description: Tree sequences inferred for the SGDP autosomes using tsinfer version 0.1.4 and compressed using tszip. Tree sequences can  be decompressed as follows:

<pre><code class="language-bash">$ tsunzip sgdp_chr1.trees.tsz</code></pre>

Once decompressed, trees files can be loaded and processed using tskit. 

<pre><code class="language-python">import tskit
ts = tskit.load("sgdp_chr1.trees")
# ts is an instance of tskit.TreeSequence 
print("Chromosome 1 contains {} trees".format(ts.num_trees))</code></pre>

Metadata associated with individuals and populations was derived from the original source and converted to JSON form. For example, to access individual metadata we can use:

<pre><code class="language-python">import tskit
import json
ts = tskit.load("sgdp_chr1.trees")
ind = ts.individual(0)
metadata_dict = json.loads(ind.metadata)</code></pre>

The metadata_dict variable will now contain all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example,

<pre><code class="language-python">pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()]
ind_node = ts.node(ind.nodes[0])
ind_pop_metadata = pop_metadata[ind_node.population]</code></pre>

After this, the ind_pop_metadata variable will contain the population level metadata for individual ID 0.

The full data pipeline used to generate these tree sequences and associated metadata is available on GitHub.
description: AWW and CF would like to thank the Rhodes Trust for their generous support.
description: {"references": ["Kelleher et al 2019, Inferring the ancestry of everyone. https://doi.org/10.1101/458067"]}
DOI: 10.5281/zenodo.3055966
title: Austrian Science Fund (FWF) Publication Cost Data 2013 - 2018

description: The data set consists of the FWF publication cost data sets of the years 2013 to 2018 which are publicly available on Zenodo: https://zenodo.org/communities/fwf/?page=1&size=20 The data set includes payments for publications funded by the FWF programmes: Peer-reviewed Publications: https://www.fwf.ac.at/en/research-funding/fwf-programmes/peer-reviewed-publications/ and Stand-Alone Publications: https://www.fwf.ac.at/en/research-funding/fwf-programmes/stand-alone-publications/
description: Many thanks to Benedikt Raimann, Christopher Fabbro, Ünzüle Kirindi-Hentschel, Sasa Meischke-Ilic and Doris Haslinger for collecting the data and to Falk Reckling for his support in designing the data set.
DOI: 10.5281/zenodo.3066081
title: Training material for the course "Exome analysis with GALAXY"

description: Galaxy is an open source, web-based platform for data intensive biomedical research. It makes accessible bioinformatics applications to users lacking programming skills, enabling them to easily build analysis workflows for NGS data.<br>
 <br>
The course "<strong>Exome analysis using Galaxy</strong>" is aimed at PhD student, biologists, clinicians and researchers who are analysing, or need to analyse in the near future, high throughput exome sequencing data. The aim of the course is to make participants familiarise with the Galaxy platform and prepare them to work independently, using state-of-the art tools for the analysis of exome sequencing data.

The course will be delivered using a mixture of lectures and computer based hands-on practical sessions. Lectures will provide an up-to-date overview of the strategies for the analysis of exome next-generation experiments, starting from the raw sequence data. Analyses include sequence quality control, alignment to a reference genome, refinement of aligned sequences, variant calling, annotation and interpretation, and tools for visual inspection of results. Participants will apply the knowledge gained during the course to the analysis of Illumina’s real exome datasets, and implement workflows to reproduce the complete analysis. After the course, participants will be able to create pipeline for their individual analyses.

Those are the needed datasets for this course.
DOI: 10.5281/zenodo.641031
title: Training material for the course "Exome analysis with GALAXY"

description: Galaxy is an open source, web-based platform for data intensive biomedical research. It makes accessible bioinformatics applications to users lacking programming skills, enabling them to easily build analysis workflows for NGS data.<br>
 <br>
The course "<strong>Exome analysis using Galaxy</strong>" is aimed at PhD student, biologists, clinicians and researchers who are analysing, or need to analyse in the near future, high throughput exome sequencing data. The aim of the course is to make participants familiarise with the Galaxy platform and prepare them to work independently, using state-of-the art tools for the analysis of exome sequencing data.

The course will be delivered using a mixture of lectures and computer based hands-on practical sessions. Lectures will provide an up-to-date overview of the strategies for the analysis of exome next-generation experiments, starting from the raw sequence data. Analyses include sequence quality control, alignment to a reference genome, refinement of aligned sequences, variant calling, annotation and interpretation, and tools for visual inspection of results. Participants will apply the knowledge gained during the course to the analysis of Illumina’s real exome datasets, and implement workflows to reproduce the complete analysis. After the course, participants will be able to create pipeline for their individual analyses.

Those are the needed datasets for this course.
DOI: 10.7910/dvn/bz18ai
title: KUES Household Baseline Survey

description: This dataset was moved to: https://doi.org/10.34725/DVN/BZ18AIN/A
DOI: 10.5281/zenodo.2892415
title: Seismic and Pressure data for Wang and Tanimoto

description: Seismic and pressure data output files used for calculating half-space rigidity values at TA stations.
DOI: 10.5281/zenodo.2892416
title: Seismic and Pressure data for Wang and Tanimoto

description: Seismic and pressure data output files used for calculating half-space rigidity values at TA stations.
DOI: 10.5281/zenodo.3055965
title: Austrian Science Fund (FWF) Publication Cost Data 2013 - 2018

description: The data set consists of the FWF publication cost data sets of the years 2013 to 2018 which are publicly available on Zenodo: https://zenodo.org/communities/fwf/?page=1&size=20 The data set includes payments for publications funded by the FWF programmes: Peer-reviewed Publications: https://www.fwf.ac.at/en/research-funding/fwf-programmes/peer-reviewed-publications/ and Stand-Alone Publications: https://www.fwf.ac.at/en/research-funding/fwf-programmes/stand-alone-publications/
description: Many thanks to Benedikt Raimann, Christopher Fabbro, Ünzüle Kirindi-Hentschel, Sasa Meischke-Ilic and Doris Haslinger for collecting the data and to Falk Reckling for his support in designing the data set.
DOI: 10.7910/dvn/idtdmv
title: Journal of World Systems Research

description: :unav
DOI: 10.5281/zenodo.3051854
title: Inferring whole-genome histories in large population datasets: inferred tree sequences for 1000 Genomes

description: Tree sequences inferred for the 1000 Genomes phase 3 autosomes using tsinfer version 0.1.4 and compressed using tszip. Tree sequences can  be decompressed as follows:

<pre><code class="language-bash">$ tsunzip 1kg_chr1.trees.tsz</code></pre>

Once decompressed, trees files can be loaded and processed using tskit. 

<pre><code class="language-python">import tskit
ts = tskit.load("1kg_chr1.trees")
# ts is an instance of tskit.TreeSequence 
print("Chromosome 1 contains {} trees".format(ts.num_trees))</code></pre>

Metadata associated with individuals and populations was derived from the original source and converted to JSON form. For example, to access individual metadata we can use:

<pre><code class="language-python">import tskit
import json
ts = tskit.load("1kg_chr1.trees")
ind = ts.individual(0)
metadata_dict = json.loads(ind.metadata)</code></pre>

The metadata_dict variable will now contain all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example,

<pre><code class="language-python">pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()]
ind_node = ts.node(ind.nodes[0])
ind_pop_metadata = pop_metadata[ind_node.population]</code></pre>

After this, the ind_pop_metadata variable will contain the population level metadata for individual ID 0.

The full data pipeline used to generate these tree sequences and associated metadata is available on GitHub.
description: AWW and CF would like to thank the Rhodes Trust for their generous support.
description: {"references": ["Kelleher et al 2019, Inferring the ancestry of everyone. https://doi.org/10.1101/458067"]}
DOI: 10.5281/zenodo.3051855
title: Inferring whole-genome histories in large population datasets: inferred tree sequences for 1000 Genomes

description: Tree sequences inferred for the 1000 Genomes phase 3 autosomes using tsinfer version 0.1.4 and compressed using tszip. Tree sequences can  be decompressed as follows:

<pre><code class="language-bash">$ tsunzip 1kg_chr1.trees.tsz</code></pre>

Once decompressed, trees files can be loaded and processed using tskit. 

<pre><code class="language-python">import tskit
ts = tskit.load("1kg_chr1.trees")
# ts is an instance of tskit.TreeSequence 
print("Chromosome 1 contains {} trees".format(ts.num_trees))</code></pre>

Metadata associated with individuals and populations was derived from the original source and converted to JSON form. For example, to access individual metadata we can use:

<pre><code class="language-python">import tskit
import json
ts = tskit.load("1kg_chr1.trees")
ind = ts.individual(0)
metadata_dict = json.loads(ind.metadata)</code></pre>

The metadata_dict variable will now contain all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example,

<pre><code class="language-python">pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()]
ind_node = ts.node(ind.nodes[0])
ind_pop_metadata = pop_metadata[ind_node.population]</code></pre>

After this, the ind_pop_metadata variable will contain the population level metadata for individual ID 0.

The full data pipeline used to generate these tree sequences and associated metadata is available on GitHub.
description: AWW and CF would like to thank the Rhodes Trust for their generous support.
description: {"references": ["Kelleher et al 2019, Inferring the ancestry of everyone. https://doi.org/10.1101/458067"]}
DOI: 10.5281/zenodo.3066176
title: Scripts, models, and data for manuscript "On the Role of Stern- and Diffuse-Layer Polarization Mechanisms in Porous Media"

description: This repository contains Matlab scripts, Comsol Multiphysics models, and numerical simulation data used to generate the plots in the manuscript

Bücker, M., Flores Orozco, A., Undorf, S., and Kemna, A., 2019, <em>On the Role of Stern- and Diffuse-Layer Polarization Mechanisms in Porous Media</em>, submitted to JGR: Solid Earth.

If you find this data useful in your own research, please cite this manuscript.
DOI: 10.18112/openneuro.ds001894.v1.1.1
title: Longitudinal Brain Correlates of Multisensory Lexical Processing in Children
DOI: 10.17632/mm3f8t7jx9.1
title: Data for: Cross-Cultural Adaptation and Validation of the Voice Handicap Index in the Quebec French Population (VHI-QF)

description: Voice Handicap Index - Quebec French adaptation
DOI: 10.5063/f12z13t3
title: (:unkn)
DOI: 10.5063/f16q1vh0
title: (:unkn)
DOI: 10.5063/f1bg2m85
title: (:unkn)
DOI: 10.5063/f1g73c0w
title: (:unkn)
DOI: 10.5063/f1m043q6
title: (:unkn)
DOI: 10.5063/f1qr4vds
title: (:unkn)
DOI: 10.5063/f1z60mck
title: (:unkn)
DOI: 10.5281/zenodo.3066492
title: CRCN-NE Chromosomes DataSet

description: This dataset was obtained in partnership with The Biological Dosimetry Laboratory(CRCN-NE) and The Computing Department (DC) of the Rural Federal University of Pernambuco (UFRPE), both located in the city of Recife - Brazil.<br>
<br>
Here are 74 images of colored metaphases acquired with a Leica microscope. The labeling of these metaphases will also be found, in this case, their expected results. In addition, we used the adaptive thresholding algorithm on this dataset, the result is also found in this file. So, in total are 74 images of colored metaphases and 74 images of segmented metaphases.<br>
<br>
Finally, cuts of regions of chromosomes and non-chromosomes will be found for both the color image and the segmented image. Being present in the same quantity for both distributions, since they were obtained by the same technique.


	Cuttings of chromosome regions occurred using the Python OpenCv library, which swept the labeled image and captured the regions concerning the chromosomes. With these coordinates it was possible to perform the mapping for the colored and segmented images, making the extraction of these areas of interest.
	The cuts of the regions of non-chromosomes occurred with the aid of the LabelMe tool, in it was carried out the marking of 46 random points in the image. With this tool, it is possible to obtain an xml with the coordinates of these markings, which were used to extract the areas of interest.
DOI: 10.5281/zenodo.3066493
title: CRCN-NE Chromosomes DataSet

description: This dataset was obtained in partnership with the genetic cytology laboratory CRCN-NE and The Computing Department (DC) of the Federal Rural University of Pernambuco (UFRPE), both located in the city of Recife - Brazil.<br>
<br>
Here are 75 images of colored metaphases acquired with a Leica microscope. The labeling of these metaphases will also be found, in this case, their expected results. In addition, we used the adaptive thresholding algorithm on this basis, the result is also found in this file. So, in total are 75 images of colored metaphases and 75 images of segmented metaphases.<br>
<br>
Finally, cuts of regions of chromosomes and non-chromosomes will be found for both the color image and the segmented image. Being present in the same quantity for both distributions, since they were obtained by the same technique.


	Cuttings of chromosome regions occurred using the Python OpenCv library, which swept the labeled image and captured the regions concerning the chromosomes. With these coordinates it was possible to perform the mapping for the colored and segmented images, making the extraction of these areas of interest.
	The cuts of the regions of non-chromosomes occurred with the aid of the LabelMe tool, in it was carried out the marking of 46 random points in the image. With this tool, it is possible to obtain an xml with the coordinates of these markings, which were used to extract the areas of interest.
DOI: 10.7910/dvn/0b5zbq
title: Emery Thompson et al.: Males compromise the foraging success of their mates

description: Supporting Data for Emery Thompson et al.: Males compromise the foraging success of their mates
DOI: 10.7910/dvn/ygr4yb
title: Naturalized Citizens - US

description: :unav
DOI: 10.5063/f1pr7t9v
title: (:unkn)
DOI: 10.5063/f1th8k04
title: (:unkn)
DOI: 10.5063/f1vd6wsp
title: Example Water Tale

description: Demonstration of how to use Whole Tale to develop custom analysis and visualization for data published externally via DataONE. See https://wholetale.readthedocs.io/en/stable/users_guide/quickstart.html for more information.
DOI: 10.5281/zenodo.3066232
title: Dataset AEROARMS - Perception for robot operation

description: <strong>Dataset description: </strong>This dataset contains the images used for validating the perception algorithms to support aerial and ground robot operations.

<strong>Data description:</strong> The images where obtained using an Intel Realsense d435 camera. Additionally, the calibration of the camera is given in XML format.

<strong>Dataset size: </strong>72.8Mb
DOI: 10.17632/ms6gh965v6.1
title: Data for: An Intelligent Identification Method of the Edge Coherent Mode in EAST
DOI: 10.26304/361-u1476a-mspoint
title: Expedition 361 Hole U1476A Magnetic susceptibility (section half) data file

description: Hole U1476A MSPOINT - Magnetic susceptibility (section half) for Expedition 361 Hole U1476A
DOI: 10.26304/361-u1476a-spectro
title: Expedition 361 Hole U1476A Spectrophotometry data file

description: Hole U1476A SPECTRO - Spectrophotometry for Expedition 361 Hole U1476A
DOI: 10.26304/361-u1476a-titrator
title: Expedition 361 Hole U1476A Titration data file

description: Hole U1476A TITRATOR - Titration for Expedition 361 Hole U1476A
DOI: 10.26304/361-u1476b-spectro
title: Expedition 361 Hole U1476B Spectrophotometry data file

description: Hole U1476B SPECTRO - Spectrophotometry for Expedition 361 Hole U1476B
DOI: 10.26304/361-u1476d-mspoint
title: Expedition 361 Hole U1476D Magnetic susceptibility (section half) data file

description: Hole U1476D MSPOINT - Magnetic susceptibility (section half) for Expedition 361 Hole U1476D
DOI: 10.26304/361-u1476d-srm-sect
title: Expedition 361 Hole U1476D Magnetic remanence (SRM-longcore) data file

description: Hole U1476D SRM-SECT - Magnetic remanence (SRM-longcore) for Expedition 361 Hole U1476D
DOI: 10.5281/zenodo.3062725
title: Supplementary Files: processed model results for freshwater tracer concentration and filling/flushing time

description: FWdye_avg_241yr_300yr: monthly and inter-annual mean tracer concentrations for model years 241 to 300.

FWdye_outflux_avg_271yr_300yr: freshwater tracer sinks fluxes averaged for model years 271 to 300.

FW_flux_avg_001yr_060yr: monthly averaged freshwater fluxes with seasonal and inter-annual variability for one cycle of COREII.

filltime_basinWE_001yr_300yr: processed river water tracer filling time on shelf seas for model years 1 to 300.

filltime_basinWE_01yr_60yr_1yrimpulse: processed flushing time for one year released impulse test for model years 1 to 60.
DOI: 10.5281/zenodo.3062726
title: Supplementary Files: processed model results for freshwater tracer concentration and filling/flushing time

description: FWdye_avg_241yr_300yr: monthly and inter-annual mean tracer concentrations for model years 241 to 300.

FWdye_outflux_avg_271yr_300yr: freshwater tracer sinks fluxes averaged for model years 271 to 300.

FW_flux_avg_001yr_060yr: monthly averaged freshwater fluxes with seasonal and inter-annual variability for one cycle of COREII.

filltime_basinWE_001yr_300yr: processed river water tracer filling time on shelf seas for model years 1 to 300.

filltime_basinWE_01yr_60yr_1yrimpulse: processed flushing time for one year released impulse test for model years 1 to 60.
DOI: 10.26304/361-u1474a-spectro
title: Expedition 361 Hole U1474A Spectrophotometry data file

description: Hole U1474A SPECTRO - Spectrophotometry for Expedition 361 Hole U1474A
DOI: 10.26304/361-u1474b-spectro
title: Expedition 361 Hole U1474B Spectrophotometry data file

description: Hole U1474B SPECTRO - Spectrophotometry for Expedition 361 Hole U1474B
DOI: 10.26304/361-u1475b-spectro
title: Expedition 361 Hole U1475B Spectrophotometry data file

description: Hole U1475B SPECTRO - Spectrophotometry for Expedition 361 Hole U1475B
DOI: 10.26304/361-u1475c-spectro
title: Expedition 361 Hole U1475C Spectrophotometry data file

description: Hole U1475C SPECTRO - Spectrophotometry for Expedition 361 Hole U1475C
DOI: 10.26304/361-u1475d-spectro
title: Expedition 361 Hole U1475D Spectrophotometry data file

description: Hole U1475D SPECTRO - Spectrophotometry for Expedition 361 Hole U1475D
DOI: 10.26304/361-u1476a-srm-sect
title: Expedition 361 Hole U1476A Magnetic remanence (SRM-longcore) data file

description: Hole U1476A SRM-SECT - Magnetic remanence (SRM-longcore) for Expedition 361 Hole U1476A
DOI: 10.26304/361-u1476a-tcon
title: Expedition 361 Hole U1476A Thermal conductivity data file

description: Hole U1476A TCON - Thermal conductivity for Expedition 361 Hole U1476A
DOI: 10.26304/361-u1476b-mspoint
title: Expedition 361 Hole U1476B Magnetic susceptibility (section half) data file

description: Hole U1476B MSPOINT - Magnetic susceptibility (section half) for Expedition 361 Hole U1476B
DOI: 10.26304/361-u1476b-srm-sect
title: Expedition 361 Hole U1476B Magnetic remanence (SRM-longcore) data file

description: Hole U1476B SRM-SECT - Magnetic remanence (SRM-longcore) for Expedition 361 Hole U1476B
DOI: 10.26304/361-u1476b-titrator
title: Expedition 361 Hole U1476B Titration data file

description: Hole U1476B TITRATOR - Titration for Expedition 361 Hole U1476B
DOI: 10.26304/361-u1476c-mspoint
title: Expedition 361 Hole U1476C Magnetic susceptibility (section half) data file

description: Hole U1476C MSPOINT - Magnetic susceptibility (section half) for Expedition 361 Hole U1476C
DOI: 10.26304/361-u1476e-mspoint
title: Expedition 361 Hole U1476E Magnetic susceptibility (section half) data file

description: Hole U1476E MSPOINT - Magnetic susceptibility (section half) for Expedition 361 Hole U1476E