cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10572199
title: StarDist_BF_Monocytes_dataset

description: This repository includes a StarDist deep learning model and its training and validation datasets for detecting mononucleated cells perfused over an endothelial cell monolayer. The model was trained on 27 manually annotated images and achieved an average F1 Score of 0.941. The dataset and model are helpful for biomedical research, especially in studying interactions between mononucleated and endothelial cells.

Specifications





Model: StarDist for mononucleated cell detection on endothelial cells




Training Dataset:






Number of Images: 27 paired brightfield microscopy images and label masks




Microscope: Nikon Eclipse Ti2-E, 20x objective




Data Type: Brightfield microscopy images with manually segmented masks




File Format: TIFF (.tif)






Brightfield Images: 16-bit




Masks: 8-bit





Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)





Training Parameters:






Epochs: 400




Patch Size: 992 x 992 pixels




Batch Size: 2





Performance:






Average F1 Score: 0.941




Average IoU: 0.831





Model Training: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki)



Reference

Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers

Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet

bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654
DOI: 10.5281/zenodo.10572200
title: StarDist_BF_Monocytes_dataset

description: This repository includes a StarDist deep learning model and its training and validation datasets for detecting mononucleated cells perfused over an endothelial cell monolayer. The model was trained on 27 manually annotated images and achieved an average F1 Score of 0.941. The dataset and model are helpful for biomedical research, especially in studying interactions between mononucleated and endothelial cells.

Specifications





Model: StarDist for mononucleated cell detection on endothelial cells




Training Dataset:






Number of Images: 27 paired brightfield microscopy images and label masks




Microscope: Nikon Eclipse Ti2-E, 20x objective




Data Type: Brightfield microscopy images with manually segmented masks




File Format: TIFF (.tif)






Brightfield Images: 16-bit




Masks: 8-bit





Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)





Training Parameters:






Epochs: 400




Patch Size: 992 x 992 pixels




Batch Size: 2





Performance:






Average F1 Score: 0.941




Average IoU: 0.831





Model Training: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki)



Reference

Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers

Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet

bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654
DOI: 10.5281/zenodo.10617531
title: StarDist_HUVEC_nuclei_dataset

description: This repository contains a StarDist deep learning model and its training and validation datasets for segmenting endothelial nuclei while ignoring cancer cells. The cancer cells were perfused over an endothelial cell monolayer. The initial dataset consisted of 17 images, where cancer cell nuclei were manually removed after segmentation with the StarDist Versatile Nuclei model. This dataset was augmented to 68 paired images using computational techniques like rotation and flipping. The model was trained for 200 epochs, achieving an average F1 Score of 0.976, demonstrating high accuracy in segmenting endothelial nuclei while excluding cancer cells.

Specifications





Model: StarDist for segmenting endothelial nuclei while ignoring cancer cells




Training Dataset:






Number of Original Images: 17 paired predictions of nuclei and label images




Augmented Dataset: Expanded to 68 paired images using rotation and flipping




Source Image Generation: Generated using a pix2pix model trained to predict nuclei from brightfield images of cancer cells on top of an endothelium (DOI: 10.5281/zenodo.10617532)




Target Image Generation: Masks obtained via manual segmentation




File Format: TIFF (.tif)






Brightfield Images: 8-bit




Masks: 8-bit





Image Size: 1024 x 1022 pixels (uncalibrated)





Training Parameters:






Epochs: 200




Patch Size: 1024 x 1024 pixels




Batch Size: 2





Performance:






Average F1 Score: 0.976




Average IoU: 0.927





Model Training: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki)





Reference

Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers


Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet

bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654
DOI: 10.5281/zenodo.10617532
title: StarDist_HUVEC_nuclei_dataset

description: This repository contains a StarDist deep learning model and its training and validation datasets for segmenting endothelial nuclei while ignoring cancer cells. The cancer cells were perfused over an endothelial cell monolayer. The initial dataset consisted of 17 images, where cancer cell nuclei were manually removed after segmentation with the StarDist Versatile Nuclei model. This dataset was augmented to 68 paired images using computational techniques like rotation and flipping. The model was trained for 200 epochs, achieving an average F1 Score of 0.976, demonstrating high accuracy in segmenting endothelial nuclei while excluding cancer cells.

Specifications





Model: StarDist for segmenting endothelial nuclei while ignoring cancer cells




Training Dataset:






Number of Original Images: 17 paired predictions of nuclei and label images




Augmented Dataset: Expanded to 68 paired images using rotation and flipping




Source Image Generation: Generated using a pix2pix model trained to predict nuclei from brightfield images of cancer cells on top of an endothelium (DOI: 10.5281/zenodo.10617532)




Target Image Generation: Masks obtained via manual segmentation




File Format: TIFF (.tif)






Brightfield Images: 8-bit




Masks: 8-bit





Image Size: 1024 x 1022 pixels (uncalibrated)





Training Parameters:






Epochs: 200




Patch Size: 1024 x 1024 pixels




Batch Size: 2





Performance:






Average F1 Score: 0.976




Average IoU: 0.927





Model Training: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki)





Reference

Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers


Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet

bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654
DOI: 10.5281/zenodo.13304399
title: StarDist_BF_cancer_cell_dataset_10x

description: This repository includes a StarDist deep learning model and its training dataset designed for segmenting cancer cells perfused over an endothelial cell monolayer captured at 10x magnification. The model was trained on 77 manually annotated images, with the dataset being computationally augmented during training by a factor of 8. The model was trained for 500 epochs and achieved an average F1 Score of 0.968, indicating high accuracy in segmenting cancer cells on endothelial cells.

Specifications





Model: StarDist for cancer cell segmentation on endothelial cells (10x magnification)




Training Dataset:






Number of Images: 77 paired brightfield microscopy images and label masks




Augmented Dataset: Computational augmentation by a factor of 8 during training




Microscope: Nikon Eclipse Ti2-E, 10x objective




Data Type: Brightfield microscopy images with manually segmented masks




File Format: TIFF (.tif)






Brightfield Images: 16-bit




Masks: 8-bit or 16-bit





Image Size: 1024 x 1022 pixels (pixel size: 1.3148 μm)





Training Parameters:






Epochs: 500




Patch Size: 992 x 992 pixels




Batch Size: 2





Performance:






Average F1 Score: 0.968




Average IoU: 0.882





Model Training: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki)





Reference

Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers


Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet

bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654
DOI: 10.5281/zenodo.13304398
title: StarDist_BF_cancer_cell_dataset_10x

description: This repository includes a StarDist deep learning model and its training dataset designed for segmenting cancer cells perfused over an endothelial cell monolayer captured at 10x magnification. The model was trained on 77 manually annotated images, with the dataset being computationally augmented during training by a factor of 8. The model was trained for 500 epochs and achieved an average F1 Score of 0.968, indicating high accuracy in segmenting cancer cells on endothelial cells.

Specifications





Model: StarDist for cancer cell segmentation on endothelial cells (10x magnification)




Training Dataset:






Number of Images: 77 paired brightfield microscopy images and label masks




Augmented Dataset: Computational augmentation by a factor of 8 during training




Microscope: Nikon Eclipse Ti2-E, 10x objective




Data Type: Brightfield microscopy images with manually segmented masks




File Format: TIFF (.tif)






Brightfield Images: 16-bit




Masks: 8-bit or 16-bit





Image Size: 1024 x 1022 pixels (pixel size: 1.3148 μm)





Training Parameters:






Epochs: 500




Patch Size: 992 x 992 pixels




Batch Size: 2





Performance:






Average F1 Score: 0.968




Average IoU: 0.882





Model Training: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki)





Reference

Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers


Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet

bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654
DOI: 10.15468/dl.bmcwn8
title: Occurrence Download

description: A dataset containing 871835 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Sideritis lanata L., Sideritis montana L., Sideroxylon persimile (Hemsl.) T.D.Penn., Sigesbeckia orientalis L., Sigmoidotropis speciosa (Kunth) A.Delgado, Silene diversifolia subsp. diversifolia, Silene chlorantha (Willd.) Ehrh., Eudianthe coeli-rosa (L.) Fenzl ex Endl., Silene conoidea L., Silene italica (L.) Pers., Silene latifolia Poir., Silene nutans L., Silene pendula L., Silybum marianum (L.) Gaertn., Sisymbrium erysimoides Desf., Sisymbrium orientale L., Sisymbrium polyceratium L., Sisymbrium turczaninowii Sond., Solandra grandiflora Sw., Sisyrinchium micranthum Cav., Solanum aviculare G.Forst., Solanum capsicoides All., Heliotropium sibiricum (L.) J.I.M.Melo, Argyranthemum foeniculaceum (Willd.) Webb ex Sch.Bip., Argyranthemum frutescens (L.) Sch.Bip., Argyreia nervosa (Burm.fil.) Bojer, Aristea compressa Buchinger ex Baker, Aristolochia littoralis Parodi, Aristolochia grandiflora Sw., Aristolochia labiata Willd., Aristolochia maxima Jacq., Aristolochia ringens Vahl, Aristotelia chilensis (Molina) Stuntz, Solanum pseudocapsicum L., Solanum chrysotrichum Schltdl., Solanum diphyllum L., Solanum donianum Walp., Solanum furcatum Dunal, Solanum laxum Spreng., Solanum lanceolatum Cav., Solanum linnaeanum Hepper & P.-M.L.Jaeger, Solanum mammosum L., Solanum marginatum L.fil., Solanum mauritianum Scop., Solanum melongena L., Solanum nigrescens M.Martens & Galeotti, Solanum villosum Mill., Solanum peruvianum L., Solanum pimpinellifolium L., Solanum robustum H.Wendl.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 871835 records from 4037 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009793-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9hqb6e
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Lepus americanus Erxleben, 1777)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil)"
 ]
 }
 },
 "Country is Brazil"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028984-241107131044228/datasets/export for details.
DOI: 10.15468/dl.gdq74f
title: Occurrence Download

description: A dataset containing 1399 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Gopherus morafkai Murphy, Berry, Edwards, Leviton, Lathrop & Riedle, 2011"
 ]
}

 The dataset includes 1399 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028985-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5xegq9
title: Occurrence Download

description: A dataset containing 2999 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Peru",
 "TaxonKey is Sicalis luteola (Sparrman, 1789)",
 "Year 2021-2024"
 ]
}

 The dataset includes 2999 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028986-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r99z8k
title: Occurrence Download

description: A dataset containing 21865 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Sinapis alba L.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 21865 records from 446 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028988-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5066/p1pcmuwb
title: Molecular resources combined with morphology and geography to provide insights into the evolutionary history of a wide-ranging freshwater mussel species complex (Unionida: Quadrula)

description: The freshwater mussel Quadrula quadrula of eastern North America occupies several isolated river drainages along the coastal Gulf of Mexico and northward in the Mississippi Basin and Great Lakes. Previous work has documented high morphological and genetic variation across its range but due to paraphyly of mitochondrial barcodes, variants are currently recognized as conspecific. Here we present sequence data from three genomes including male- and female-inherited mitochondrial markers and thousands of nuclear encoded SNPs that were used to disentangle the evolutionary history of this species complex. We also provide morphological and geographic data, which was analyzed with the molecular data in a comparative context to illuminate the group’s divergence history.
DOI: 10.15468/dl.nqcnne
title: Occurrence Download

description: A dataset containing 935 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Leptodon forbesi (Swann, 1922), Neochen jubata (Spix, 1825))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 {
 "or" : [
 "Continent is South America",
 "Continent is North America",
 "Continent is null"
 ]
 },
 {
 "not" : {
 "Country" : [
 "is United States of America"
 ]
 }
 },
 {
 "not" : {
 "Country" : [
 "is Canada"
 ]
 }
 }
 ]
}

 The dataset includes 935 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028989-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nhtucz
title: Occurrence Download

description: A dataset containing 140 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Soldanella minima Hoppe)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 140 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028990-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kc65wf
title: Occurrence Download

description: A dataset containing 163 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Peru",
 "OccurrenceStatus is Present",
 "TaxonKey is Tremarctos ornatus (F.G.Cuvier, 1825)",
 "Year 2014-2024"
 ]
}

 The dataset includes 163 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028991-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7939/dvn/nmtmy6
title: Social Cognitive Theory, leisure time physical activity, and mental health in Chinese university students

description: This data was collected in China with undergraduate students. The variables of interest are leisure time physical activity, self-efficacy, social support, intention to be physically active, access to places to be physically active, self-construal, subjective well-being (affect and satisfaction with life).
DOI: 10.15468/dl.wasxk8
title: Occurrence Download

description: A dataset containing 3799 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Soldanella pusilla Baumg.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 3799 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028992-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yvctdx
title: Occurrence Download

description: A dataset containing 156 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Annona purpurea Moç. & Sessé ex Dunal"
 ]
}

 The dataset includes 156 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028994-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c76ssv
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Spiraea cana Waldst. & Kit.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 19 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028996-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h6328u
title: Occurrence Download

description: A dataset containing 4052 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Stachys germanica L.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 4052 records from 220 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028999-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ctq5jz
title: Occurrence Download

description: A dataset containing 739 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Centropyge fisheri (Snyder, 1904)"
 ]
}

 The dataset includes 739 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028998-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hccx7c
title: Occurrence Download

description: A dataset containing 678 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Peru",
 "Geometry POLYGON((-76.32205 -13.87041,-76.62164 -14.58027,-70.03075 -19.26014,-68.39967 -12.89893,-71.7284 -12.50926,-76.32205 -13.87041))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Sicalis luteola (Sparrman, 1789)",
 "Year 2021-2024"
 ]
}

 The dataset includes 678 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029001-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e4452z
title: Occurrence Download

description: A dataset containing 372 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Calloplesiops altivelis (Steindachner, 1903)"
 ]
}

 The dataset includes 372 records from 47 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029000-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wny56a
title: Occurrence Download

description: A dataset containing 1187 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Stachys heraclea All.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 1187 records from 78 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029002-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.