cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25582/data.2024-02.3041178/2521958
title: Data for EMSL Project 51619 from February 2024
DOI: 10.25582/data.2024-02.3041202/2521959
title: Data for EMSL Project 51619 from February 2024
DOI: 10.25582/data.2024-02.3041213/2521960
title: Data for EMSL Project 51619 from February 2024
DOI: 10.15468/dl.up9uem
title: Occurrence Download

description: A dataset containing 31 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ozoroa benguellensis (Engl.) R.Fern."
 ]
}

 The dataset includes 31 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004102-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-02.3041214/2521961
title: Data for EMSL Project 51619 from February 2024
DOI: 10.15468/dl.hwxpmz
title: Occurrence Download

description: A dataset containing 700 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Month is one of (January, February)",
 "OccurrenceStatus is Present",
 "TaxonKey is Calidris ruficollis (Pallas, 1776)",
 "Year 2011-2011"
 ]
}

 The dataset includes 700 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004101-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-02.3041215/2521962
title: Data for EMSL Project 51619 from February 2024
DOI: 10.15468/dl.mdsrta
title: Occurrence Download

description: A dataset containing 2477 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Micronia aculeata Guenée, 1857"
 ]
}

 The dataset includes 2477 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004103-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-02.3041216/2521963
title: Data for EMSL Project 51619 from February 2024
DOI: 10.15468/dl.n6n32g
title: Occurrence Download

description: A dataset containing 2676 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Month is one of (March, April, June, May, July, August, September, October, November, December)",
 "OccurrenceStatus is Present",
 "TaxonKey is Calidris ruficollis (Pallas, 1776)",
 "Year 2010-2010"
 ]
}

 The dataset includes 2676 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004100-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-02.3041231/2521964
title: Data for EMSL Project 51619 from February 2024
DOI: 10.25582/data.2024-02.3041267/2521965
title: Data for EMSL Project 51619 from February 2024
DOI: 10.25582/data.2024-02.3041286/2521966
title: Data for EMSL Project 51619 from February 2024
DOI: 10.25582/data.2024-02.3041287/2521967
title: Data for EMSL Project 51619 from February 2024
DOI: 10.25582/data.2024-02.3041288/2521968
title: Data for EMSL Project 51619 from February 2024
DOI: 10.25582/data.2024-02.3041306/2521969
title: Data for EMSL Project 51619 from February 2024
DOI: 10.25582/data.2024-02.3041314/2521970
title: Data for EMSL Project 51619 from February 2024
DOI: 10.25582/data.2024-02.3041316/2521971
title: Data for EMSL Project 51619 from February 2024
DOI: 10.25582/data.2024-02.3041317/2521972
title: Data for EMSL Project 51619 from February 2024
DOI: 10.25582/data.2024-02.3041318/2521973
title: Data for EMSL Project 51619 from February 2024
DOI: 10.25582/data.2024-02.3041329/2521974
title: Data for EMSL Project 51619 from February 2024
DOI: 10.25582/data.2024-02.3041273/2521975
title: Data for EMSL Project 60673 from February 2024
DOI: 10.25582/data.2024-02.3041285/2521976
title: Data for EMSL Project 60673 from February 2024
DOI: 10.25582/data.2024-02.3041352/2521977
title: Data for EMSL Project 60673 from February 2024
DOI: 10.25582/data.2024-02.3041362/2521978
title: Data for EMSL Project 60673 from February 2024
DOI: 10.5281/zenodo.14851412
title: VPS_Treg_PLOS_Biology_FCS_Gating
DOI: 10.5281/zenodo.14851411
title: VPS_Treg_PLOS_Biology_FCS_Gating
DOI: 10.5281/zenodo.14322456
title: BioSR for LLS-SIM

description: BioSR for LLS-SIM is a biological image dataset acquired using our home-built lattice light-sheet structured illumination microscopy (LLS-SIM). It currently includes paired diffraction-limited LLSM and LLS-SIM images of a variety of biology structures, constituting a high-quality volumetric SR dataset. The BioSR for LLS-SIM dataset originates from our paper [Qiao, C., Li, Z., Wang, Z., Lin, Y., ... & Li, D. (2024). Fast adaptive super-resolution lattice light-sheet microscopy for rapid, long-term, near-isotropic subcellular imaging. bioRxiv, 2024-05], serving as the supplementary data to execute meta-training or other proposed deep-learning-based algorithms, which provides sources for the community to try our methods, replicate our results, and develop their own methods for super-resolution lattice light-sheet microscopy.

The BioSR for LLS-SIM dataset includes 10 diverse task datasets from 10 distinct biological specimens (granular component, chromosomes, fibrillar center, fibrillarin, Lyso, MTs, F-actin in pollen tubes, inner mitochondrial membrane, ER in adherent Cos-7 cells and ER in mitotic Hela cells during metaphase). For each type of samples, we acquired raw LLS-SIM images from about 30-50 ROIs. For each ROI, five different levels of light intensity ranging from low to high fluorescence levels were acquired, and the images of the highest fluorescence level (i.e. the GT raw images) were reconstructed into high-quality GT LLS-SIM images via the conventional LLS-SIM reconstruction algorithm, which could be used as the groud truth in the training phase of deep-learning models. See training dataset.xls file for more specific information of the training datasets.

In this dataset, we also provide representative testing data, as well as the pre-trained meta-model and several representative finetuned models. See testing dataset and pre-trained models.xls file for more specific information of the demo testing data and pre-trained models.

The testing dataset and models were uploaded on the current repository, while we assigned each training dataset of a specific biological specimen to an exclusive repository with its own DOI and URL due to the quota policy of Zenodo, which are listed as follows:




 
Structure
URL


1
Granular Component
https://doi.org/10.5281/zenodo.14348076


2
Chromosomes
https://doi.org/10.5281/zenodo.14351353


3


Innermost Fibrillar Center



https://doi.org/10.5281/zenodo.14351668



4
Fibrillarin
https://doi.org/10.5281/zenodo.14373971


5
Lysosome
https://doi.org/10.5281/zenodo.14351377


6
Microtubules


https://doi.org/10.5281/zenodo.14373993



7
F-actin in pollen tubes
https://doi.org/10.5281/zenodo.14374023


8
Inner Mitochondrial Membrane
https://doi.org/10.5281/zenodo.14374065


9
ER in adherent Cos-7 cells
https://doi.org/10.5281/zenodo.14373955


10
ER in mitotic Hela cells during metaphase
https://doi.org/10.5281/zenodo.14436099
DOI: 10.5281/zenodo.14322457
title: BioSR for LLS-SIM

description: BioSR for LLS-SIM is a biological image dataset acquired using our home-built lattice light-sheet structured illumination microscopy (LLS-SIM). It currently includes paired diffraction-limited LLSM and LLS-SIM images of a variety of biology structures, constituting a high-quality volumetric SR dataset. The BioSR for LLS-SIM dataset originates from our paper [Qiao, C., Li, Z., Wang, Z., Lin, Y., ... & Li, D. (2024). Fast adaptive super-resolution lattice light-sheet microscopy for rapid, long-term, near-isotropic subcellular imaging. bioRxiv, 2024-05], serving as the supplementary data to execute meta-training or other proposed deep-learning-based algorithms, which provides sources for the community to try our methods, replicate our results, and develop their own methods for super-resolution lattice light-sheet microscopy.

The BioSR for LLS-SIM dataset includes 10 diverse task datasets from 10 distinct biological specimens (granular component, chromosomes, fibrillar center, fibrillarin, Lyso, MTs, F-actin in pollen tubes, inner mitochondrial membrane, ER in adherent Cos-7 cells and ER in mitotic Hela cells during metaphase). For each type of samples, we acquired raw LLS-SIM images from about 30-50 ROIs. For each ROI, five different levels of light intensity ranging from low to high fluorescence levels were acquired, and the images of the highest fluorescence level (i.e. the GT raw images) were reconstructed into high-quality GT LLS-SIM images via the conventional LLS-SIM reconstruction algorithm, which could be used as the groud truth in the training phase of deep-learning models. See training dataset.xls file for more specific information of the training datasets.

In this dataset, we also provide representative testing data, as well as the pre-trained meta-model and several representative finetuned models. See testing dataset and pre-trained models.xls file for more specific information of the demo testing data and pre-trained models.

The testing dataset and models were uploaded on the current repository, while we assigned each training dataset of a specific biological specimen to an exclusive repository with its own DOI and URL due to the quota policy of Zenodo, which are listed as follows:




 
Structure
URL


1
Granular Component
https://doi.org/10.5281/zenodo.14348076


2
Chromosomes
https://doi.org/10.5281/zenodo.14351353


3


Innermost Fibrillar Center



https://doi.org/10.5281/zenodo.14351668



4
Fibrillarin
https://doi.org/10.5281/zenodo.14373971


5
Lysosome
https://doi.org/10.5281/zenodo.14351377


6
Microtubules


https://doi.org/10.5281/zenodo.14373993



7
F-actin in pollen tubes
https://doi.org/10.5281/zenodo.14374023


8
Inner Mitochondrial Membrane
https://doi.org/10.5281/zenodo.14374065


9
ER in adherent Cos-7 cells
https://doi.org/10.5281/zenodo.14373955


10
ER in mitotic Hela cells during metaphase
https://doi.org/10.5281/zenodo.14436099
DOI: 10.5281/zenodo.14920897
title: BioSR for LLS-SIM

description: BioSR for LLS-SIM is a biological image dataset acquired using our home-built lattice light-sheet structured illumination microscopy (LLS-SIM). It currently includes paired diffraction-limited LLSM and LLS-SIM images of a variety of biology structures, constituting a high-quality volumetric SR dataset. The BioSR for LLS-SIM dataset originates from our paper [Qiao, C., Li, Z., Wang, Z., Lin, Y., ... & Li, D. (2024). Fast adaptive super-resolution lattice light-sheet microscopy for rapid, long-term, near-isotropic subcellular imaging. bioRxiv, 2024-05], serving as the supplementary data to execute meta-training or other proposed deep-learning-based algorithms, which provides sources for the community to try our methods, replicate our results, and develop their own methods for super-resolution lattice light-sheet microscopy.

The BioSR for LLS-SIM dataset includes 10 diverse task datasets from 10 distinct biological specimens (granular component, chromosomes, fibrillar center, fibrillarin, Lyso, MTs, F-actin in pollen tubes, inner mitochondrial membrane, ER in adherent Cos-7 cells and ER in mitotic Hela cells during metaphase). For each type of samples, we acquired raw LLS-SIM images from about 30-50 ROIs. For each ROI, five different levels of light intensity ranging from low to high fluorescence levels were acquired, and the images of the highest fluorescence level (i.e. the GT raw images) were reconstructed into high-quality GT LLS-SIM images via the conventional LLS-SIM reconstruction algorithm, which could be used as the groud truth in the training phase of deep-learning models. See training dataset.xls file for more specific information of the training datasets.

In this dataset, we also provide representative testing data, as well as the pre-trained meta-model and several representative finetuned models. See testing dataset and pre-trained models.xls file for more specific information of the demo testing data and pre-trained models.

The testing dataset and models were uploaded on the current repository, while we assigned each training dataset of a specific biological specimen to an exclusive repository with its own DOI and URL due to the quota policy of Zenodo, which are listed as follows:




 
Structure
URL


1
Granular Component
https://doi.org/10.5281/zenodo.14348076


2
Chromosomes
https://doi.org/10.5281/zenodo.14351353


3


Innermost Fibrillar Center



https://doi.org/10.5281/zenodo.14351668



4
Fibrillarin
https://doi.org/10.5281/zenodo.14373971


5
Lysosome
https://doi.org/10.5281/zenodo.14351377


6
Microtubules


https://doi.org/10.5281/zenodo.14373993



7
F-actin in pollen tubes
https://doi.org/10.5281/zenodo.14374023


8
Inner Mitochondrial Membrane
https://doi.org/10.5281/zenodo.14374065


9
ER in adherent Cos-7 cells
https://doi.org/10.5281/zenodo.14373955


10
ER in mitotic Hela cells during metaphase
https://doi.org/10.5281/zenodo.14436099
DOI: 10.5281/zenodo.14920947
title: BioSR for LLS-SIM

description: BioSR for LLS-SIM is a biological image dataset acquired using our home-built lattice light-sheet structured illumination microscopy (LLS-SIM). It currently includes paired diffraction-limited LLSM and LLS-SIM images of a variety of biology structures, constituting a high-quality volumetric SR dataset. The BioSR for LLS-SIM dataset originates from our paper [Qiao, C., Li, Z., Wang, Z., Lin, Y., ... & Li, D. (2024). Fast adaptive super-resolution lattice light-sheet microscopy for rapid, long-term, near-isotropic subcellular imaging. bioRxiv, 2024-05], serving as the supplementary data to execute meta-training or other proposed deep-learning-based algorithms, which provides sources for the community to try our methods, replicate our results, and develop their own methods for super-resolution lattice light-sheet microscopy.

The BioSR for LLS-SIM dataset includes 10 diverse task datasets from 10 distinct biological specimens (granular component, chromosomes, fibrillar center, fibrillarin, Lyso, MTs, F-actin in pollen tubes, inner mitochondrial membrane, ER in adherent Cos-7 cells and ER in mitotic Hela cells during metaphase). For each type of samples, we acquired raw LLS-SIM images from about 30-50 ROIs. For each ROI, five different levels of light intensity ranging from low to high fluorescence levels were acquired, and the images of the highest fluorescence level (i.e. the GT raw images) were reconstructed into high-quality GT LLS-SIM images via the conventional LLS-SIM reconstruction algorithm, which could be used as the groud truth in the training phase of deep-learning models. See training dataset.xls file for more specific information of the training datasets.

In this dataset, we also provide representative testing data, as well as the pre-trained meta-model and several representative finetuned models. See testing dataset and pre-trained models.xls file for more specific information of the demo testing data and pre-trained models.

The testing dataset and models were uploaded on the current repository, while we assigned each training dataset of a specific biological specimen to an exclusive repository with its own DOI and URL due to the quota policy of Zenodo, which are listed as follows:




 
Structure
URL


1
Granular Component
https://doi.org/10.5281/zenodo.14348076


2
Chromosomes
https://doi.org/10.5281/zenodo.14351353


3


Innermost Fibrillar Center



https://doi.org/10.5281/zenodo.14351668



4
Fibrillarin
https://doi.org/10.5281/zenodo.14373971


5
Lysosome
https://doi.org/10.5281/zenodo.14351377


6
Microtubules


https://doi.org/10.5281/zenodo.14373993



7
F-actin in pollen tubes
https://doi.org/10.5281/zenodo.14374023


8
Inner Mitochondrial Membrane
https://doi.org/10.5281/zenodo.14374065


9
ER in adherent Cos-7 cells
https://doi.org/10.5281/zenodo.14373955


10
ER in mitotic Hela cells during metaphase
https://doi.org/10.5281/zenodo.14436099
DOI: 10.15468/dl.jdeap7
title: Occurrence Download

description: A dataset containing 1137 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Plantas Acuáticas de la Orinoquía Colombiana"
 ]
}

 The dataset includes 1137 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004106-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6egszf
title: Occurrence Download

description: A dataset containing 22238 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pseudoeurycea Taylor, 1944"
 ]
}

 The dataset includes 22238 records from 82 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004104-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6xe22f
title: Occurrence Download

description: A dataset containing 11052460 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Brassicaceae"
 ]
}

 The dataset includes 11052460 records from 6623 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004095-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rknw2g
title: Occurrence Download

description: A dataset containing 15718951 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is International Barcode of Life project (iBOL)"
 ]
}

 The dataset includes 15718951 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018187-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vd2xyk
title: Occurrence Download

description: A dataset containing 2966 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-86.39329 32.73393,-77.33338 32.73393,-77.33338 39.05669,-86.39329 39.05669,-86.39329 32.73393))",
 "HasGeospatialIssue is false",
 "TaxonKey is Desmognathus marmoratus (Moore, 1899)"
 ]
}

 The dataset includes 2966 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010846-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sf6b4m
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "Geometry POLYGON((-103 -26,106 -26,106 28,-103 28,-103 -26))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Acer pauciflorum W.P.Fang"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010845-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zbkhfb
title: Occurrence Download

description: A dataset containing 64 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Linum littorale)",
 "Year is greater than or equal to 2000",
 "Year is less than or equal to 2025",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 64 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0073731-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ekua94
title: Occurrence Download

description: A dataset containing 3806 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (United States of America, Canada)",
 "DatasetKey is iNaturalist Research-grade Observations",
 "TaxonKey is Agrilus planipennis"
 ]
}

 The dataset includes 3806 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0073733-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h5fqmm
title: Occurrence Download

description: A dataset containing 64 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Linum littorale)",
 "Year is greater than or equal to 2000",
 "Year is less than or equal to 2025",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 64 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0073736-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18148517
title: RD Galaxy Dynamics Phase-Transition Dataset (SPARC + ATLAS³D)

description: This dataset contains the complete empirical foundation for the unified dynamical timescale τ, constructed from kinematic scale competition between the dynamical coherence length ξ_GL and the baryonic confinement scale r_core.

The dataset includes 165 SPARC rotation-supported disk galaxies and 50 ATLAS³D pressure-supported elliptical galaxies with TDGL (Time-Dependent Ginzburg–Landau) inspired fits to rotation curves and velocity dispersion profiles. All derived parameters (ξ_GL, r_core, τ) are provided with fit diagnostics (χ²_red, κ₁) along with Python scripts for full reproducibility.

Key empirical results demonstrate strong bimodality (Anderson-Darling A²=6.12, p<10⁻⁵): 97% of rotation-supported galaxies exhibit τ>0 (coherence-dominated regime, median +21.2 Myr), while 96% of pressure-supported galaxies exhibit τ<0 (confinement-dominated regime, median -13.0 Myr). Lenticular (S0) galaxies cluster near the structural boundary at τ≈0. The timescale τ correlates with long-term baryonic history (gas fraction, stellar age) but shows no correlation with instantaneous star formation or environment after morphology stratification.

Files included:• Raw SPARC and ATLAS³D datasets• TDGL fitting code (rotation curves and Jeans modeling)• Per-galaxy parameter tables for 215 galaxies (Tables A1, A2)• Publication-quality figures demonstrating bimodality and correlations• Complete methodology and results documentation

This dataset supports independent replication, verification, and extension of the unified dynamical timescale framework for galaxy dynamics across the Hubble sequence.
DOI: 10.6073/pasta/1b967f801641e793f9cd216625015dfc
title: Ground temperature profiles from DVDP borehole 11 at Explorers Cove, McMurdo Dry Valleys, Antarctica (2020-2025, ongoing)

description: The Dry Valley Drilling Project (DVDP) drilled multiple boreholes throughout Antarctica’s McMurdo Dry Valleys in the early 1970s, several of which remain open and accessible. DVDP borehole 11, with a total depth of 327.86 m, is located adjacent to the Explorers Cove Meteorological Station (EXEM), operated by the McMurdo Dry Valleys Long Term Ecological Research program (MCM LTER). In January 2020, the MCM LTER instrumented this borehole with a string of thermistors to monitor ground temperatures through the permafrost. Sensors were installed at depths of 1, 2, 3, 4, 5, 10, 20, and 30 m, providing ongoing measurements of subsurface thermal conditions at Explorers Cove.