cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/the9-vqu
title: Layer-specific distributions of segmented cells in Area 44 (IFG) of BigBrain

description: This dataset contains cortical image patches of the cytoarchitectonic Area 44 (IFG), extracted from microscopic scans of the original tissue sections of the BigBrain model ([Amunts et al., 2013](https://www.science.org/doi/10.1126/science.1235381)), together with manual annotations of cortical layers and automatic segmentations of cell bodies, as well as individual and averaged reports of cortical thicknesses and densities of cells. The histological sections were prepared as described in the original publication of the BigBrain. The cell instance segmentations have been computed using openly accessible code ([https://github.com/FZJ-INM1-BDA/celldetection](https://github.com/FZJ-INM1-BDA/celldetection)) and a pre-trained model for Contour Proposal Networks (CPN; preprint at [https://arxiv.org/abs/2104.03393](https://arxiv.org/abs/2104.03393)).
DOI: 10.6084/m9.figshare.17161376
title: Anticuerpos anti-IgG en un modelo experimental de autoinmunidad inducida por IgY.xlsx

description: Base de datos para almacenar los resultados del proyecto: Anticuerpos anti-IgG en un modelo experimental de autoinmunidad inducida por IgY.
DOI: 10.6084/m9.figshare.17161376.v1
title: Anticuerpos anti-IgG en un modelo experimental de autoinmunidad inducida por IgY.xlsx

description: Base de datos para almacenar los resultados del proyecto: Anticuerpos anti-IgG en un modelo experimental de autoinmunidad inducida por IgY.
DOI: 10.25493/e1fy-2c4
title: Layer-specific distributions of segmented cells in Area FG1 (FusG) of BigBrain

description: This dataset contains cortical image patches of the cytoarchitectonic area FG1 (FusG), extracted from microscopic scans of the original tissue sections of the BigBrain model ([Amunts et al., 2013](https://www.science.org/doi/10.1126/science.1235381)), together with manual annotations of cortical layers and automatic segmentations of cell bodies, as well as individual and averaged reports of cortical thicknesses and densities of cells. The histological sections were prepared as described in the original publication of the BigBrain. The cell instance segmentations have been computed using openly accessible code ([https://github.com/FZJ-INM1-BDA/celldetection](https://github.com/FZJ-INM1-BDA/celldetection)) and a pre-trained model for Contour Proposal Networks (CPN; preprint at [https://arxiv.org/abs/2104.03393](https://arxiv.org/abs/2104.03393)).
DOI: 10.25493/pskr-2ca
title: Manifold-based Dictionary of the variability of the folding patterns of ten large cortical sulci

description: We are seven billion humans with unique fingerprints but also unique cortical folding patterns and functional architectures. Each sulcus morphological variability can be modeled by a low dimensional manifold, which has been computed from the HCP dataset for five main sulci of the left and right hemispheres: Central sulcus, Precentral gyrus, Superior Temporal Sulcus, Cingulate Sulcus, Lateral Sulcus. For each brain of the HCP dataset, the sulci have been automatically identified using the Morphologist toolbox of brainVISA software. Each brain has been affinely normalize to the Talairach standard space. Right sulci have been flipped to be comparable with left sulci. For each sulcus of interest, a pairwise distance matrix has been computed across subject after rigid aligment. Then a 6 dimensional manifold has been computed using Isomap algorithm. For each HCP subject, we deliver the coordinates in these manifolds for further studies assessing the links with behavior or functional organization.
DOI: 10.21233/xnx4-qx69
title: Toconce charcoal dataset

description: Raw data for the Toconce charcoal dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/tjsy-pa98
title: Toconce charcoal dataset

description: Raw data for the Toconce charcoal dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/evf0-mb08
title: Toconce charcoal dataset

description: Raw data for the Toconce charcoal dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/hmya-px66
title: Toconce charcoal dataset

description: Raw data for the Toconce charcoal dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/msvc-0d19
title: Toconce charcoal dataset

description: Raw data for the Toconce charcoal dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.6073/pasta/955ff0ecc46a0be0a3d251a6e02af908
title: Salt River Wetlands denitrification rate, dissimilatory nitrate reduction to ammonium rate, dissolved organic carbon concentration in June 2016 as well as soil porosity and bulk density

description: Raw and derived data used to calculate denitrification and dissimilatory nitrate to ammonium (DNRA) from push-pull experiments with added isotopically labelled nitrate. Experiments were conducted in 2016 in the Salt River Accidental Wetlands in three different patch types: Unvegetated, dominated by Ludwigia peploides, and dominated by Typha species (T. domingensis and T. latifolia). Data include start and end of incubation concentration of nitrate, ammonium, atom percent 15N in ammonium, dissolved organic carbon, excess mass 29-N2, and excess mass 30-N2. Soil data was collected from the same patch types including soil moisture, porosity, and bulk density.
DOI: 10.6084/m9.figshare.17161430
title: Base e-líquidos

description: Database of e-liquids in cell cultures
DOI: 10.6084/m9.figshare.17161430.v1
title: Base e-líquidos

description: Database of e-liquids in cell cultures
DOI: 10.6084/m9.figshare.17156420
title: Raw Data_Pharmacokinetics Profile of Squalene.pdf

description: the tabel icludes pharmacokinetic profile of Squalene in rats.
DOI: 10.6084/m9.figshare.17156420.v1
title: Raw Data_Pharmacokinetics Profile of Squalene.pdf

description: the tabel icludes pharmacokinetic profile of Squalene in rats.
DOI: 10.5281/zenodo.5773310
title: Delias oraia decontaminated FSCR

description: .
DOI: 10.5281/zenodo.5773309
title: Delias oraia decontaminated FSCR

description: .
DOI: 10.6084/m9.figshare.15025296.v4
title: Desmodus rotundus Occurrence Record Database

description: The common vampire bat (<i>Desmodus rotundus</i>) is a hematophagous bat species found across the North, Central, and South American continents. <i>Desmodus rotundus </i>is one of the only tree mammal species exclusively having a sanguivorous diet (i.e., blood). The species has a large distributional range and predates on a large range of vertebrate species. <i>Desmodus rotundus</i> is a know reservoir for the rabies virus and contributes to the continued spread of this pathogen across Latin America. Nevertheless, little is known about the historical distribuion of <i>D. rotundus</i> across it range. Historical occurrence data are critical for the assessment of past and current distributions for this species, and is necessary for a plethora of other ecological, biogeographic, and epidemiological studies. This is a dataset of <i>D. rotundus </i>historical occurrence including &gt;37,000 locality reports across the Americas to facilitate spatiotemporal studies of the species.<br>Data and metadata definitions. The following table provides standardized definitions of each occurrence metadata based on the the Darwin Core Archive format. Each piece of metadata for each occurrence is organized and recorded under the listed column headers.<br>Validation Code: This file contains code for usage and cleaning of the <i>Desmodus rotundus </i>record database. This code was also used for the Technical Validation process of the final <i>D. rotunudus</i> dataset.
DOI: 10.34740/kaggle/dsv/2912906
title: Cryptocurrency extra data - Binance Coin
DOI: 10.3886/e115063v1-104240
title: Social Determinants and Gun Homicides

description: Social determinants and gun homicide data.
DOI: 10.5517/ccdc.csd.cc28q416
title: CCDC 2106637: Experimental Crystal Structure Determination

description: Related Article: Subramanian Marappan|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28q48f
title: CCDC 2106644: Experimental Crystal Structure Determination

description: Related Article: Subramanian Marappan|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28q506
title: CCDC 2106667: Experimental Crystal Structure Determination

description: Related Article: Subramanian Marappan|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28q517
title: CCDC 2106668: Experimental Crystal Structure Determination

description: Related Article: Subramanian Marappan|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28q528
title: CCDC 2106669: Experimental Crystal Structure Determination

description: Related Article: Subramanian Marappan|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28q539
title: CCDC 2106670: Experimental Crystal Structure Determination

description: Related Article: Subramanian Marappan|2021|CSD Communication|||
DOI: 10.25493/f781-v5j
title: Layer-specific distributions of segmented cells in Area hOc3d (Cuneus) of BigBrain

description: This dataset contains cortical image patches of the cytoarchitectonic Area hOc3d (Cuneus), extracted from microscopic scans of the original tissue sections of the BigBrain model ([Amunts et al., 2013](https://www.science.org/doi/10.1126/science.1235381)), together with manual annotations of cortical layers and automatic segmentations of cell bodies, as well as individual and averaged reports of cortical thicknesses and densities of cells. The histological sections were prepared as described in the original publication of the BigBrain. The cell instance segmentations have been computed using openly accessible code ([https://github.com/FZJ-INM1-BDA/celldetection](https://github.com/FZJ-INM1-BDA/celldetection)) and a pre-trained model for Contour Proposal Networks (CPN; preprint at [https://arxiv.org/abs/2104.03393](https://arxiv.org/abs/2104.03393)).
DOI: 10.25493/j4hz-14x
title: Layer-specific distributions of segmented cells in Area hOc5 (LOC) of BigBrain

description: This dataset contains cortical image patches of the cytoarchitectonic Area hOc5 (LOC), extracted from microscopic scans of the original tissue sections of the BigBrain model ([Amunts et al., 2013](https://www.science.org/doi/10.1126/science.1235381)), together with manual annotations of cortical layers and automatic segmentations of cell bodies, as well as individual and averaged reports of cortical thicknesses and densities of cells. The histological sections were prepared as described in the original publication of the BigBrain. The cell instance segmentations have been computed using openly accessible code ([https://github.com/FZJ-INM1-BDA/celldetection](https://github.com/FZJ-INM1-BDA/celldetection)) and a pre-trained model for Contour Proposal Networks (CPN; preprint at [https://arxiv.org/abs/2104.03393](https://arxiv.org/abs/2104.03393)).
DOI: 10.25493/yfg8-3r7
title: Layer-specific distributions of segmented cells in Area Fp1 (FPole) of BigBrain

description: This dataset contains cortical image patches of the cytoarchitectonic Area Fp1 (FPole), extracted from microscopic scans of the original tissue sections of the BigBrain model ([Amunts et al., 2013](https://www.science.org/doi/10.1126/science.1235381)), together with manual annotations of cortical layers and automatic segmentations of cell bodies, as well as individual and averaged reports of cortical thicknesses and densities of cells. The histological sections were prepared as described in the original publication of the BigBrain. The cell instance segmentations have been computed using openly accessible code ([https://github.com/FZJ-INM1-BDA/celldetection](https://github.com/FZJ-INM1-BDA/celldetection)) and a pre-trained model for Contour Proposal Networks (CPN; preprint at [https://arxiv.org/abs/2104.03393](https://arxiv.org/abs/2104.03393)).
DOI: 10.25493/da0m-gm
title: Layer-specific distributions of segmented cells in Area FG3 (FusG) of BigBrain

description: This dataset contains cortical image patches of the cytoarchitectonic Area FG3 (FusG), extracted from microscopic scans of the original tissue sections of the BigBrain model ([Amunts et al., 2013](https://www.science.org/doi/10.1126/science.1235381)), together with manual annotations of cortical layers and automatic segmentations of cell bodies, as well as individual and averaged reports of cortical thicknesses and densities of cells. The histological sections were prepared as described in the original publication of the BigBrain. The cell instance segmentations have been computed using openly accessible code ([https://github.com/FZJ-INM1-BDA/celldetection](https://github.com/FZJ-INM1-BDA/celldetection)) and a pre-trained model for Contour Proposal Networks (CPN; preprint at [https://arxiv.org/abs/2104.03393](https://arxiv.org/abs/2104.03393)).
DOI: 10.25919/mw2e-zy17
title: Parkes observations for project P1050 semester 2021OCTS_07

description: This is a request for observing time for the timing campaign of new pulsar discoveries from the reprocessing of the low-latitude Galactic plane section of the HTRU survey (P630). We have now completed 90 % of the survey with GPU based coherent acceleration search, template bank and fast folding algorithm pipelines, and now have discovered 92 previously unknown pulsars and 8 "class A" pulsars awaiting confirmation. Interesting science can usually only be derived from a new pulsar after confirmation and a follow-up timing campaign is carried out. One year of initial timing is the minimum timespan required to fully characterise any newly-discovered pulsars, essential for deriving pulsar parameters such as the characteristic age, magnetic field strength, spin-down rate, as well as to detect any unexpected behaviour of the pulsar which might result from emission instabilities. Pulsars on the observing list here have been confirmed in the previous and current 2021APRS semesters, the follow-up timing in OCTS2021 will produce completely new and exciting results. In addition, this timing project will allow us to measure the true Galactic plane pulsar population and the boundaries of pulsar parameter space in the galactic plane.
DOI: 10.25493/35k1-btx
title: Layer-specific distributions of segmented cells in Area 7P (SPL) of BigBrain

description: This dataset contains cortical image patches of the cytoarchitectonic Area 7P (SPL), extracted from microscopic scans of the original tissue sections of the BigBrain model ([Amunts et al., 2013](https://www.science.org/doi/10.1126/science.1235381)), together with manual annotations of cortical layers and automatic segmentations of cell bodies, as well as individual and averaged reports of cortical thicknesses and densities of cells. The histological sections were prepared as described in the original publication of the BigBrain. The cell instance segmentations have been computed using openly accessible code ([https://github.com/FZJ-INM1-BDA/celldetection](https://github.com/FZJ-INM1-BDA/celldetection)) and a pre-trained model for Contour Proposal Networks (CPN; preprint at [https://arxiv.org/abs/2104.03393](https://arxiv.org/abs/2104.03393)).
DOI: 10.25493/pv5q-sfr
title: Layer-specific distributions of segmented cells in Area 4a (PreCG) of BigBrain

description: This dataset contains cortical image patches of the cytoarchitectonic Area 4a (PreCG), extracted from microscopic scans of the original tissue sections of the BigBrain model ([Amunts et al., 2013](https://www.science.org/doi/10.1126/science.1235381)), together with manual annotations of cortical layers and automatic segmentations of cell bodies, as well as individual and averaged reports of cortical thicknesses and densities of cells. The histological sections were prepared as described in the original publication of the BigBrain. The cell instance segmentations have been computed using openly accessible code ([https://github.com/FZJ-INM1-BDA/celldetection](https://github.com/FZJ-INM1-BDA/celldetection)) and a pre-trained model for Contour Proposal Networks (CPN; preprint at [https://arxiv.org/abs/2104.03393](https://arxiv.org/abs/2104.03393)).
DOI: 10.5281/zenodo.5773392
title: Supplemental Material Surveillance Improves Outcomes for Carriers of SDHB Pathogenic Variants

description: <strong>Supplemental table for Surveillance improves outcomes for carriers of <em>SDHB</em> pathogenic variants: a multi-center study</strong>
DOI: 10.5281/zenodo.5533996
title: Supplemental Material Surveillance Improves Outcomes for Carriers of SDHB Pathogenic Variants

description: <strong>Supplemental table for Surveillance improves outcomes for carriers of <em>SDHB</em> pathogenic variants: a multi-center study</strong>
DOI: 10.25493/evpc-mvh
title: Layer-specific distributions of segmented cells in Area hOc1 (V1, 17, CalcS) of BigBrain

description: This dataset contains cortical image patches of the cytoarchitectonic Area hOc1 (V1, 17, CalcS), extracted from microscopic scans of the original tissue sections of the BigBrain model ([Amunts et al., 2013](https://www.science.org/doi/10.1126/science.1235381)), together with manual annotations of cortical layers and automatic segmentations of cell bodies, as well as individual and averaged reports of cortical thicknesses and densities of cells. The histological sections were prepared as described in the original publication of the BigBrain. The cell instance segmentations have been computed using openly accessible code ([https://github.com/FZJ-INM1-BDA/celldetection](https://github.com/FZJ-INM1-BDA/celldetection)) and a pre-trained model for Contour Proposal Networks (CPN; preprint at [https://arxiv.org/abs/2104.03393](https://arxiv.org/abs/2104.03393)).
DOI: 10.25493/zxqx-jkp
title: Layer-specific distributions of segmented cells in Area hOc4d (Cuneus) of BigBrain

description: This dataset contains cortical image patches of the cytoarchitectonic Area hOc4d (Cuneus), extracted from microscopic scans of the original tissue sections of the BigBrain model ([Amunts et al., 2013](https://www.science.org/doi/10.1126/science.1235381)), together with manual annotations of cortical layers and automatic segmentations of cell bodies, as well as individual and averaged reports of cortical thicknesses and densities of cells. The histological sections were prepared as described in the original publication of the BigBrain. The cell instance segmentations have been computed using openly accessible code ([https://github.com/FZJ-INM1-BDA/celldetection](https://github.com/FZJ-INM1-BDA/celldetection)) and a pre-trained model for Contour Proposal Networks (CPN; preprint at [https://arxiv.org/abs/2104.03393](https://arxiv.org/abs/2104.03393)).
DOI: 10.4121/17021966.v1
title: Data underlying the publication: Establishing the Role of Elbow Muscles by Evaluating Muscle Activation and Co-contraction Levels at Maximal External Rotation in Fastball Pitching

description: This dataset contains muscle activation from eleven baseball pitchers throwing fifteen fastballs. MVC is also included. Videos of their baseball pitch are also included. <br>More information about this dataset can be found in this article:<br><br><br>
DOI: 10.4121/17021966.v2
title: Data underlying the publication: Establishing the Role of Elbow Muscles by Evaluating Muscle Activation and Co-contraction Levels at Maximal External Rotation in Fastball Pitching

description: This dataset contains muscle activation and video from eleven baseball pitchers throwing fifteen fastballs. MVC is also included. <br>Readme for more information.<br>More information about this dataset can be found in this article:10.3389/fspor.2021.698592<br><br><br>
DOI: 10.4121/17021966.v3
title: Data underlying the publication: Establishing the Role of Elbow Muscles by Evaluating Muscle Activation and Co-contraction Levels at Maximal External Rotation in Fastball Pitching

description: This dataset contains muscle activation and video from eleven baseball pitchers throwing fifteen fastballs. MVC is also included. <br>Readme for more information.<br>More information about this dataset can be found in this article:10.3389/fspor.2021.698592<br><br><br>
DOI: 10.4121/17021966
title: Data underlying the publication: Establishing the Role of Elbow Muscles by Evaluating Muscle Activation and Co-contraction Levels at Maximal External Rotation in Fastball Pitching

description: This dataset contains muscle activation and video from eleven baseball pitchers throwing fifteen fastballs. MVC is also included. <br>Readme for more information.<br>More information about this dataset can be found in this article:10.3389/fspor.2021.698592<br><br><br>
DOI: 10.5281/zenodo.5773401
title: ECCO v4 standard analysis sample

description: This version was generated in Julia from the ECCO output (version 4, release 2) archived @ https://doi.org/10.7910/DVN/NXYKDW
DOI: 10.5439/1835045
title: ARM: 60IOP24STDMOBV21VARANA

description: varanal
DOI: 10.5439/1835046
title: ARM: 180IOPSNDGV2VARANA

description: varanal
DOI: 10.5439/1835047
title: ARM: 180IOPSNDGV1VARANA

description: varanal
DOI: 10.5439/1835048
title: ARM: 60IOP21STDMOBV21VARANA

description: varanal
DOI: 10.5439/1835049
title: ARM: 180IOPSNDGV21VARANA

description: varanal
DOI: 10.5439/1835050
title: ARM: 180IOPSNDGV34VARANA

description: varanal
DOI: 10.5439/1835051
title: ARM: 60IOP23STDMOBV21VARANA

description: varanal
DOI: 10.6084/m9.figshare.17161552
title: Additional file 2 of Human resource and governance challenges in the delivery of primary eye care: a mixed methods feasibility study in Nigeria

description: Additional file 2.
DOI: 10.6084/m9.figshare.17161549
title: Additional file 1 of Human resource and governance challenges in the delivery of primary eye care: a mixed methods feasibility study in Nigeria

description: Additional file 1.
DOI: 10.6084/m9.figshare.17161549.v1
title: Additional file 1 of Human resource and governance challenges in the delivery of primary eye care: a mixed methods feasibility study in Nigeria

description: Additional file 1.
DOI: 10.6084/m9.figshare.17161552.v1
title: Additional file 2 of Human resource and governance challenges in the delivery of primary eye care: a mixed methods feasibility study in Nigeria

description: Additional file 2.
DOI: 10.15468/xj3bha
title: A new species of the genus Ptyctolaemus Peters, 1864 (Squamata, Agamidae) from Sagaing, Myanmar

description: This dataset contains the digitized treatments in Plazi based on the original journal article Liu, Shuo, Hou, Mian, Lwin, Ye Htet, Rao, Dingqi (2021): A new species of the genus Ptyctolaemus Peters, 1864 (Squamata, Agamidae) from Sagaing, Myanmar. Evolutionary Systematics 5 (2): 347-357, DOI: http://dx.doi.org/10.3897/evolsyst.5.75305, URL: http://dx.doi.org/10.3897/evolsyst.5.75305
DOI: 10.15468/29pyzu
title: New species and records of Chapsa (Graphidaceae) in China

description: This dataset contains the digitized treatments in Plazi based on the original journal article Dou *, Ming-Zhu, Li *, Min, Jia, Ze-Feng (2021): New species and records of Chapsa (Graphidaceae) in China. MycoKeys 85: 73-85, DOI: http://dx.doi.org/10.3897/mycokeys.85.76040, URL: http://dx.doi.org/10.3897/mycokeys.85.76040
DOI: 10.15468/c8kamq
title: A new species of Leonhardia Reitter, 1901 (Coleoptera, Leiodidae, Leptodirini) from Bosnia and Herzegovina, with a key to species of the genus

description: This dataset contains the digitized treatments in Plazi based on the original journal article Curcic 1, Srecko, Vesovic 1, Nikola, Vrbica 1, Maja, Popovic 1,2, Slađana, Radovanovic 3, Zeljko, Curcic 4, Nina B., Rađa 5, Tonci (2021): A new species of Leonhardia Reitter, 1901 (Coleoptera, Leiodidae, Leptodirini) from Bosnia and Herzegovina, with a key to species of the genus. Subterranean Biology 41: 69-85, DOI: http://dx.doi.org/10.3897/subtbiol.41.75613, URL: http://dx.doi.org/10.3897/subtbiol.41.75613