cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.tqff6p
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "MULTIPOLYGON (((148.510987 -33.245095, 148.510987 -33.24734, 148.513233 -33.24734, 148.513233 -33.245095, 148.510987 -33.245095)), ((148.513233 -33.24734, 148.513233 -33.249586, 148.51997 -33.249586, 148.51997 -33.24734, 148.513233 -33.24734)), ((148.51997 -33.249586, 148.51997 -33.251832, 148.515479 -33.251832, 148.515479 -33.256324, 148.51997 -33.256324, 148.51997 -33.258569, 148.522216 -33.258569, 148.522216 -33.249586, 148.51997 -33.249586)), ((148.522216 -33.249586, 148.526708 -33.249586, 148.526708 -33.251832, 148.528954 -33.251832, 148.528954 -33.245095, 148.522216 -33.245095, 148.522216 -33.249586)))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057721-240626123714530/datasets/export for details.
DOI: 10.5281/zenodo.13243284
title: NT1D CHU 7.850 MHz from April 8, 2024 Solar Eclipse

description: Note: NT1D collected data on multiple frequencies.

7.850 MHz USB.  Icom IC-7610 connected to dipole.  PC is Dell Inspiron 5567 Windows 10
DOI: 10.5281/zenodo.13243285
title: NT1D CHU 7.850 MHz from April 8, 2024 Solar Eclipse

description: Note: NT1D collected data on multiple frequencies.

7.850 MHz USB.  Icom IC-7610 connected to dipole.  PC is Dell Inspiron 5567 Windows 10
DOI: 10.15468/dl.jyhtv3
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((148.452596 -33.256324, 148.452596 -33.258569, 148.457088 -33.258569, 148.457088 -33.256324, 148.452596 -33.256324))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057722-240626123714530/datasets/export for details.
DOI: 10.5281/zenodo.13283379
title: BVOC Fluxes from Individual Branches in the Mixed Mediterranean Shrubbery of Ramat Hanadiv

description: Fluxes were derived from BVOCs sampled from individual branches in the mixed Mediterranean shrubbery of Ramat Hanadiv Nature Park. We conducted branch-enclosure sampling measurements on six selected branches of Phillyrea latifolia—the highest BVOC emitter in this park—during September–October 2020. These samples were then analyzed by gas chromatography-mass spectrometry to identify BVOCs and quantify their fluxes. The data was used for a publication  -https://doi.org/10.5194/egusphere-2024-529, 2024
DOI: 10.5281/zenodo.12754337
title: InLUT3D: Indoor Lodz University of Technology Point Cloud Dataset

description: Background

This resource contains Indoor Lodz University of Technology Point Cloud Dataset (InLUT3D) - a point cloud dataset tailored for real object classification and both semantic and instance segmentation tasks. Comprising of 321 scans, some areas in the dataset are covered by multiple scans. All of them are captured using the Leica BLK360 scanner.

Train/test split

The datset's authors impose the following train-test split:




Split
Setup range


train
from setup_0 to setup_300


test
from setup_301 to setup_320




The corresponding setups' objects are recommended for splits for the classificcation task.

Available categories

The points are divided into 18 distinct categories outlined in the label.yaml file along with their respective codes and colors. Among categories you will find:



ceiling,

floor,

wall,

stairs,

column,

chair,

sofa,

table,

storage,

door,

window,

plant,

dish,

wallmounted,

device,

radiator,

lighting,

other.


Challenges

Several challenges are intrinsic to the presented dataset:



Extremely non-uniform categories distribution across the dataset.

Presence of virtual images, particularly in reflective surfaces, and data exterior to windows and doors.

Occurrence of missing data due to scanning shadows (certain areas were inaccessible to the scanner's laser beam).

High point density throughout the dataset.


Dataset structure

The structure of the dataset is the following:

inlut3d.tar.gz/├─ setup_0/│  ├─ projection.jpg│  ├─ segmentation.jpg│  ├─ setup_0.pts├─ setup_1/│  ├─ projection.jpg│  ├─ segmentation.jpg│  ├─ setup_1.pts...




projection.jpg
A file containing a spherical projection of a corresponding PTS file.


segmentation.jpg
A file with objects marked with unique colours.


setup_x.pts
A file with point cloud int the textual PTS format.




Point characteristic

Each PTS file contains 8 columns:




Column ID
Description


1
X Cartesian coordinate


2
Y Cartesian coordinate


3
Z Cartesian Coordinate


4
Red colour in RGB space in the range [0, 255]


5
Green colour in RGB space in the range [0, 255]


6
Blue colour in RGB space in the range [0, 255]


7
Category code


8
Instance ID




 
description: Funding: This dataset is the result of the research project "Semantic analysis of 3D point clouds", co-funded by the Polish National Center for Research and Development under the LIDER XI program [grant number 0092/L-11/2019].
description: Zebranie oraz opracowanie niniejszego zbioru jet wynikiem projektu "Semantyczna analiza trójwymiarowych chmur punktów" współfinansowanego przez Narodowe Centrum Badań i Rozwoju w ramach programu LIDER XI [numer grantu 0092/L-11/2019].
DOI: 10.5281/zenodo.12804009
title: InLUT3D: Indoor Lodz University of Technology Point Cloud Dataset

description: Background

This resource contains Indoor Lodz University of Technology Point Cloud Dataset (InLUT3D) - a point cloud dataset tailored for real object classification and both semantic and instance segmentation tasks. Comprising of 321 scans, some areas in the dataset are covered by multiple scans. All of them are captured using the Leica BLK360 scanner.

Train/test split

The datset's authors impose the following train-test split:




Split
Setup range


train
from setup_0 to setup_300


test
from setup_301 to setup_320




The corresponding setups' objects are recommended for splits for the classificcation task.

Available categories

The points are divided into 18 distinct categories outlined in the label.yaml file along with their respective codes and colors. Among categories you will find:



ceiling,

floor,

wall,

stairs,

column,

chair,

sofa,

table,

storage,

door,

window,

plant,

dish,

wallmounted,

device,

radiator,

lighting,

other.


Challenges

Several challenges are intrinsic to the presented dataset:



Extremely non-uniform categories distribution across the dataset.

Presence of virtual images, particularly in reflective surfaces, and data exterior to windows and doors.

Occurrence of missing data due to scanning shadows (certain areas were inaccessible to the scanner's laser beam).

High point density throughout the dataset.


Dataset structure

The structure of the dataset is the following:

inlut3d.tar.gz/├─ setup_0/│  ├─ projection.jpg│  ├─ segmentation.jpg│  ├─ setup_0.pts├─ setup_1/│  ├─ projection.jpg│  ├─ segmentation.jpg│  ├─ setup_1.pts...




projection.jpg
A file containing a spherical projection of a corresponding PTS file.


segmentation.jpg
A file with objects marked with unique colours.


setup_x.pts
A file with point cloud int the textual PTS format.




Point characteristic

Each PTS file contains 8 columns:




Column ID
Description


1
X Cartesian coordinate


2
Y Cartesian coordinate


3
Z Cartesian Coordinate


4
Red colour in RGB space in the range [0, 255]


5
Green colour in RGB space in the range [0, 255]


6
Blue colour in RGB space in the range [0, 255]


7
Category code


8
Instance ID




 
description: Funding: This dataset is the result of the research project "Semantic analysis of 3D point clouds", co-funded by the Polish National Center for Research and Development under the LIDER XI program [grant number 0092/L-11/2019].
description: Zebranie oraz opracowanie niniejszego zbioru jet wynikiem projektu "Semantyczna analiza trójwymiarowych chmur punktów" współfinansowanego przez Narodowe Centrum Badań i Rozwoju w ramach programu LIDER XI [numer grantu 0092/L-11/2019].
DOI: 10.5281/zenodo.8131486
title: InLUT3D: Indoor Lodz University of Technology Point Cloud Dataset

description: Background

This resource contains Indoor Lodz University of Technology Point Cloud Dataset (InLUT3D) - a point cloud dataset tailored for real object classification and both semantic and instance segmentation tasks. Comprising of 321 scans, some areas in the dataset are covered by multiple scans. All of them are captured using the Leica BLK360 scanner.

Train/test split

The datset's authors impose the following train-test split:




Split
Setup range


train
from setup_0 to setup_300


test
from setup_301 to setup_320




The corresponding setups' objects are recommended for splits for the classificcation task.

Available categories

The points are divided into 18 distinct categories outlined in the label.yaml file along with their respective codes and colors. Among categories you will find:



ceiling,

floor,

wall,

stairs,

column,

chair,

sofa,

table,

storage,

door,

window,

plant,

dish,

wallmounted,

device,

radiator,

lighting,

other.


Challenges

Several challenges are intrinsic to the presented dataset:



Extremely non-uniform categories distribution across the dataset.

Presence of virtual images, particularly in reflective surfaces, and data exterior to windows and doors.

Occurrence of missing data due to scanning shadows (certain areas were inaccessible to the scanner's laser beam).

High point density throughout the dataset.


Dataset structure

The structure of the dataset is the following:

inlut3d.tar.gz/├─ setup_0/│  ├─ projection.jpg│  ├─ segmentation.jpg│  ├─ setup_0.pts├─ setup_1/│  ├─ projection.jpg│  ├─ segmentation.jpg│  ├─ setup_1.pts...




projection.jpg
A file containing a spherical projection of a corresponding PTS file.


segmentation.jpg
A file with objects marked with unique colours.


setup_x.pts
A file with point cloud int the textual PTS format.




Point characteristic

Each PTS file contains 8 columns:




Column ID
Description


1
X Cartesian coordinate


2
Y Cartesian coordinate


3
Z Cartesian Coordinate


4
Red colour in RGB space in the range [0, 255]


5
Green colour in RGB space in the range [0, 255]


6
Blue colour in RGB space in the range [0, 255]


7
Category code


8
Instance ID




 
description: Funding: This dataset is the result of the research project "Semantic analysis of 3D point clouds", co-funded by the Polish National Center for Research and Development under the LIDER XI program [grant number 0092/L-11/2019].
description: Zebranie oraz opracowanie niniejszego zbioru jet wynikiem projektu "Semantyczna analiza trójwymiarowych chmur punktów" współfinansowanego przez Narodowe Centrum Badań i Rozwoju w ramach programu LIDER XI [numer grantu 0092/L-11/2019].
DOI: 10.5281/zenodo.8131487
title: InLUT3D: Indoor Lodz University of Technology Point Cloud Dataset

description: Background

This resource contains Indoor Lodz University of Technology Point Cloud Dataset (InLUT3D) - a point cloud dataset tailored for real object classification and both semantic and instance segmentation tasks. Comprising of 321 scans, some areas in the dataset are covered by multiple scans. All of them are captured using the Leica BLK360 scanner.

Train/test split

The datset's authors impose the following train-test split:




Split
Setup range


train
from setup_0 to setup_300


test
from setup_301 to setup_320




The corresponding setups' objects are recommended for splits for the classificcation task.

Available categories

The points are divided into 18 distinct categories outlined in the label.yaml file along with their respective codes and colors. Among categories you will find:



ceiling,

floor,

wall,

stairs,

column,

chair,

sofa,

table,

storage,

door,

window,

plant,

dish,

wallmounted,

device,

radiator,

lighting,

other.


Challenges

Several challenges are intrinsic to the presented dataset:



Extremely non-uniform categories distribution across the dataset.

Presence of virtual images, particularly in reflective surfaces, and data exterior to windows and doors.

Occurrence of missing data due to scanning shadows (certain areas were inaccessible to the scanner's laser beam).

High point density throughout the dataset.


Dataset structure

The structure of the dataset is the following:

inlut3d.tar.gz/├─ setup_0/│  ├─ projection.jpg│  ├─ segmentation.jpg│  ├─ setup_0.pts├─ setup_1/│  ├─ projection.jpg│  ├─ segmentation.jpg│  ├─ setup_1.pts...




projection.jpg
A file containing a spherical projection of a corresponding PTS file.


segmentation.jpg
A file with objects marked with unique colours.


setup_x.pts
A file with point cloud int the textual PTS format.




Point characteristic

Each PTS file contains 8 columns:




Column ID
Description


1
X Cartesian coordinate


2
Y Cartesian coordinate


3
Z Cartesian Coordinate


4
Red colour in RGB space in the range [0, 255]


5
Green colour in RGB space in the range [0, 255]


6
Blue colour in RGB space in the range [0, 255]


7
Category code


8
Instance ID




 
description: Funding: This dataset is the result of the research project "Semantic analysis of 3D point clouds", co-funded by the Polish National Center for Research and Development under the LIDER XI program [grant number 0092/L-11/2019].
description: Zebranie oraz opracowanie niniejszego zbioru jet wynikiem projektu "Semantyczna analiza trójwymiarowych chmur punktów" współfinansowanego przez Narodowe Centrum Badań i Rozwoju w ramach programu LIDER XI [numer grantu 0092/L-11/2019].
DOI: 10.6084/m9.figshare.25532692.v3
title: Microbial acetate metabolism in the northwest Pacific Ocean

description: Data submitted for the manuscript "Microbial metabolism and environmental controls of acetate cycling in the northwest Pacific Ocean".
DOI: 10.15468/dl.v9wrxw
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((149.350916 -33.256324, 149.350916 -33.258569, 149.362145 -33.258569, 149.362145 -33.256324, 149.350916 -33.256324))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057723-240626123714530/datasets/export for details.
DOI: 10.6084/m9.figshare.25719786
title: Nano-anticoagulant based on carrier-free low molecular weight heparin and octadecylamine with an albumin shuttling effect

description: Source Data for "Nano-anticoagulant based on carrier-free low molecular weight heparin and octadecylamine with an albumin shuttling effect"
DOI: 10.6084/m9.figshare.25719786.v1
title: Nano-anticoagulant based on carrier-free low molecular weight heparin and octadecylamine with an albumin shuttling effect

description: Source Data for "Nano-anticoagulant based on carrier-free low molecular weight heparin and octadecylamine with an albumin shuttling effect"
DOI: 10.6084/m9.figshare.25702527
title: Case-3_GPlates Reconstruction_Liu SF.zip

description: Case-3_GPlates Reconstruction
DOI: 10.6084/m9.figshare.24262372
title: Single Cell Multiome Atlas of the Human Developing Retina

description: The development of the retina is under tight temporal and spatial control. To gain insights into the molecular basis of this process, we generated a single-nuclei multi-omic atlas of the human developing retina with approximately 220,000 nuclei from 14 donors aged between 8 and 23 weeks post-conception with matched macular and peripheral tissues from each donor. This atlas captures all major cell classes and over 60 cell types in the retina along with a large proportion of progenitors and cell-type-specific precursors. Cell trajectory analysis reveals a transition from continuous progression in early progenitor cells to a hierarchical development during the later stages of cell type specification. Both known and novel candidate transcription factors, along with gene regulatory networks that drive the transitions of various cell fates, are identified. Comparisons between the macular and peripheral retina indicate a largely consistent yet distinct developmental pattern. Furthermore, the retinoic acid pathway emerges as a potential key driver for retinal patterning. This multi-omic atlas of the developing retina offers unparalleled resolution into the transcriptional and chromatin accessibility landscapes during development, providing an invaluable resource for deeper insights into retinal development and associated diseases.
DOI: 10.6084/m9.figshare.24262372.v1
title: Single Cell Multiome Atlas of the Human Developing Retina

description: The development of the retina is under tight temporal and spatial control. To gain insights into the molecular basis of this process, we generated a single-nuclei multi-omic atlas of the human developing retina with approximately 220,000 nuclei from 14 donors aged between 8 and 23 weeks post-conception with matched macular and peripheral tissues from each donor. This atlas captures all major cell classes and over 60 cell types in the retina along with a large proportion of progenitors and cell-type-specific precursors. Cell trajectory analysis reveals a transition from continuous progression in early progenitor cells to a hierarchical development during the later stages of cell type specification. Both known and novel candidate transcription factors, along with gene regulatory networks that drive the transitions of various cell fates, are identified. Comparisons between the macular and peripheral retina indicate a largely consistent yet distinct developmental pattern. Furthermore, the retinoic acid pathway emerges as a potential key driver for retinal patterning. This multi-omic atlas of the developing retina offers unparalleled resolution into the transcriptional and chromatin accessibility landscapes during development, providing an invaluable resource for deeper insights into retinal development and associated diseases.
DOI: 10.6084/m9.figshare.26213156
title: Covalently linked organic molecular catalyst in conjugated organic polymer dots boosts photocatalytic hydrogen peroxide production and methanol oxidation in neutral condition

description: Raw data of experiments
DOI: 10.6084/m9.figshare.26213156.v1
title: Covalently linked organic molecular catalyst in conjugated organic polymer dots boosts photocatalytic hydrogen peroxide production and methanol oxidation in neutral condition

description: Raw data of experiments
DOI: 10.6084/m9.figshare.25378624
title: Site-selective α-C(sp3)–H arylation of dialkylamines via hydrogen atom transfer catalysis-enabled radical aryl migration

description: The uploaded dataset is the source data including the coordinates of the optimized structures.
DOI: 10.6084/m9.figshare.25378624.v1
title: Site-selective α-C(sp3)–H arylation of dialkylamines via hydrogen atom transfer catalysis-enabled radical aryl migration

description: The uploaded dataset is the source data including the coordinates of the optimized structures.
DOI: 10.6084/m9.figshare.26196002
title: Transparent Composites for Efficient Neutron Detection

description: The source data in Fig. 2c-e, Fig.4a-f, Fig. 5a-d, and Supplementary Figs. 4, 5, 6, 7, 8 are provided in this Source Data file.
DOI: 10.6084/m9.figshare.26196002.v1
title: Transparent Composites for Efficient Neutron Detection

description: The source data in Fig. 2c-e, Fig.4a-f, Fig. 5a-d, and Supplementary Figs. 4, 5, 6, 7, 8 are provided in this Source Data file.
DOI: 10.6084/m9.figshare.25645401
title: The DNA methylome of pediatric brain tumors appears shaped by structural variation and predicts survival

description: We obtained processed RNA-seq data for CBTN tumors from the CBTN Cavatica site on March 7, 2023, from both “CBTN” and “CBTN-X01” folders. As we observed extensive batch effects between the CBTN and CBTN-X01 RNA-seq datasets (due to the data for the CBTN dataset being generated much earlier than the CBTN-X01 dataset), we generated a batch effect-corrected RNA-seq dataset using the SVA package and ComBat algorithm in R Bioconductor, using histology as the experimental group (with histologies of &lt;20 tumors consolidated into an “other” group), and removing genes with non-zero values in &lt;20 tumors from the batch-correction.
DOI: 10.6084/m9.figshare.25645401.v1
title: The DNA methylome of pediatric brain tumors appears shaped by structural variation and predicts survival

description: We obtained processed RNA-seq data for CBTN tumors from the CBTN Cavatica site on March 7, 2023, from both “CBTN” and “CBTN-X01” folders. As we observed extensive batch effects between the CBTN and CBTN-X01 RNA-seq datasets (due to the data for the CBTN dataset being generated much earlier than the CBTN-X01 dataset), we generated a batch effect-corrected RNA-seq dataset using the SVA package and ComBat algorithm in R Bioconductor, using histology as the experimental group (with histologies of &lt;20 tumors consolidated into an “other” group), and removing genes with non-zero values in &lt;20 tumors from the batch-correction.
DOI: 10.25430/researchdata.cab.unipd.it.00001344
title: Dataset for the paper "Can we identify the shape of the active length vs streamflow relation in temporary streams? An empirical analysis"

description: This dataset includes data from 45 different catchments and case studies. The data includes joint observations of drainage density and streamflow at the outlet, and is used to evaluate the shape of the correlation between these two variables.
DOI: 10.6084/m9.figshare.25702521
title: Case-2-final.zip

description: Case-2_GPlates Reconstruction
DOI: 10.6084/m9.figshare.25702521.v1
title: Case-2-final.zip

description: Case-2_GPlates Reconstruction
DOI: 10.6084/m9.figshare.26014573
title: <i>Tenacibaculum maritimum</i> extracellular products

description: Proteomic approach of <i>in vitro</i> extracellular products of <i>Tenacibaculum maritimum</i> for protein identification.
DOI: 10.6084/m9.figshare.26014573.v2
title: <i>Tenacibaculum maritimum</i> extracellular products

description: Proteomic approach of <i>in vitro</i> extracellular products of <i>Tenacibaculum maritimum</i> for protein identification.
DOI: 10.6084/m9.figshare.26014573.v1
title: <b><i>Tenacibaculum maritimum</i></b><b> extracellular products </b>

description: Proteomic approach of <i>in vitro</i> extracellular products of <i>Tenacibaculum maritimum</i> for protein identification.
DOI: 10.15468/dl.tv3rmu
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((149.254347 -33.251832, 149.254347 -33.265307, 149.256592 -33.265307, 149.256592 -33.269798, 149.258838 -33.269798, 149.258838 -33.263061, 149.256592 -33.263061, 149.256592 -33.251832, 149.254347 -33.251832))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057724-240626123714530/datasets/export for details.
DOI: 10.60686/t-fsd3871
title: FSD3871 Citizens' Opinion: Presidential Election Surveys 2024
DOI: 10.6084/m9.figshare.25702524
title: Case-1-final.zip

description: Case-1_GPlates Reconstruction
DOI: 10.6084/m9.figshare.25702524.v1
title: Case-1-final.zip

description: Case-1_GPlates Reconstruction
DOI: 10.6084/m9.figshare.25532692.v4
title: Microbial acetate metabolism in the northwest Pacific Ocean

description: Data submitted for the manuscript "Microbial metabolism and environmental controls of acetate cycling in the northwest Pacific Ocean".
DOI: 10.15468/dl.t4cxrk
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((149.272313 -33.251832, 149.272313 -33.258569, 149.270067 -33.258569, 149.270067 -33.263061, 149.281296 -33.263061, 149.281296 -33.256324, 149.274559 -33.256324, 149.274559 -33.251832, 149.272313 -33.251832))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057726-240626123714530/datasets/export for details.