cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0202992.10fu
title: SH0202992.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0202993.10fu
title: SH0202993.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0202994.10fu
title: SH0202994.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0202995.10fu
title: SH0202995.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.5281/zenodo.13443220
title: StarDist_TumorCell_nuclei

description: This repository contains a StarDist deep learning model designed for segmenting tumor cell nuclei from the DAPI channel in fluorescence microscopy images while excluding HUVEC nuclei. The model was trained to accurately detect individual tumor cell nuclei for subsequent measurement of CD44, ICAM1, ICAM2, or Fibronectin intensity around or under the nuclei. The model achieved an Intersection over Union (IoU) score of 0.558 and an F1 Score of 0.793, reflecting its capability to distinguish tumor cell nuclei from HUVEC nuclei.

Specifications





Model: StarDist for segmenting tumor cell nuclei from the DAPI fluorescence channel




Training Dataset:






Number of Images: 48 paired fluorescence microscopy images and label masks




Microscope: Spinning disk confocal microscope (3i CSU-W1) with a 20x objective, NA 0.8




Data Type: Fluorescence microscopy images of the DAPI channel with manually segmented masks




File Format: TIFF (.tif)






Fluorescence Images: 16-bit




Masks: 8-bit





Image Size: 920 x 920 pixels (Pixel size: 0.6337 x 0.6337 µm²)





Model Capabilities:






Segment Tumor Cell Nuclei: Detects individual tumor cell nuclei in the DAPI channel while distinguishing them from HUVEC nuclei




Measure Intensity: Enables measurement of CD44, ICAM1, ICAM2, or Fibronectin intensity around or under tumor cell nuclei in respective channels





Performance:






Average IoU: 0.558




Average F1 Score: 0.793





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.13443221
title: StarDist_TumorCell_nuclei

description: This repository contains a StarDist deep learning model designed for segmenting tumor cell nuclei from the DAPI channel in fluorescence microscopy images while excluding HUVEC nuclei. The model was trained to accurately detect individual tumor cell nuclei for subsequent measurement of CD44, ICAM1, ICAM2, or Fibronectin intensity around or under the nuclei. The model achieved an Intersection over Union (IoU) score of 0.558 and an F1 Score of 0.793, reflecting its capability to distinguish tumor cell nuclei from HUVEC nuclei.

Specifications





Model: StarDist for segmenting tumor cell nuclei from the DAPI fluorescence channel




Training Dataset:






Number of Images: 48 paired fluorescence microscopy images and label masks




Microscope: Spinning disk confocal microscope (3i CSU-W1) with a 20x objective, NA 0.8




Data Type: Fluorescence microscopy images of the DAPI channel with manually segmented masks




File Format: TIFF (.tif)






Fluorescence Images: 16-bit




Masks: 8-bit





Image Size: 920 x 920 pixels (Pixel size: 0.6337 x 0.6337 µm²)





Model Capabilities:






Segment Tumor Cell Nuclei: Detects individual tumor cell nuclei in the DAPI channel while distinguishing them from HUVEC nuclei




Measure Intensity: Enables measurement of CD44, ICAM1, ICAM2, or Fibronectin intensity around or under tumor cell nuclei in respective channels





Performance:






Average IoU: 0.558




Average F1 Score: 0.793





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.10611091
title: pix2pix_HUVEC_juctions_dataset

description: This repository contains a Pix2Pix deep learning model to generate synthetic PECAM-1 staining from brightfield images. The model was trained on an initial dataset of 484 paired brightfield and fluorescent microscopy images, which was computationally augmented by a factor of 6 to enhance model performance. The training was conducted over 245 epochs with a patch size of 512x512, a batch size of 1, and a vanilla GAN loss function. The final model was selected based on quality metric scores and visual comparison to ground truth images, achieving an average SSIM score of 0.273 and an LPIPS score of 0.360 on the test dataset.

Specifications





Model: Pix2Pix for generating synthetic PECAM staining from brightfield images




Training Dataset:






Original Dataset: 484 paired brightfield and fluorescent microscopy images




Augmented Dataset: Expanded to 2,904 paired images through computational augmentation




Microscope: Nikon Eclipse Ti2-E, brightfield/fluorescence microscope with a 20x objective




Data Type: Brightfield and fluorescent microscopy images




File Format: TIFF (.tif), 16-bit




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





Training Parameters:






Epochs: 245




Patch Size: 512 x 512 pixels




Batch Size: 1




Loss Function: Vanilla GAN loss function





Model Performance:






SSIM Score: 0.273




LPIPS Score: 0.360





Model Selection: The best model was chosen based on quality metric scores and visual inspection compared to ground truth images.




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.10611092
title: pix2pix_HUVEC_juctions_dataset

description: This repository contains a Pix2Pix deep learning model to generate synthetic PECAM-1 staining from brightfield images. The model was trained on an initial dataset of 484 paired brightfield and fluorescent microscopy images, which was computationally augmented by a factor of 6 to enhance model performance. The training was conducted over 245 epochs with a patch size of 512x512, a batch size of 1, and a vanilla GAN loss function. The final model was selected based on quality metric scores and visual comparison to ground truth images, achieving an average SSIM score of 0.273 and an LPIPS score of 0.360 on the test dataset.

Specifications





Model: Pix2Pix for generating synthetic PECAM staining from brightfield images




Training Dataset:






Original Dataset: 484 paired brightfield and fluorescent microscopy images




Augmented Dataset: Expanded to 2,904 paired images through computational augmentation




Microscope: Nikon Eclipse Ti2-E, brightfield/fluorescence microscope with a 20x objective




Data Type: Brightfield and fluorescent microscopy images




File Format: TIFF (.tif), 16-bit




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





Training Parameters:






Epochs: 245




Patch Size: 512 x 512 pixels




Batch Size: 1




Loss Function: Vanilla GAN loss function





Model Performance:






SSIM Score: 0.273




LPIPS Score: 0.360





Model Selection: The best model was chosen based on quality metric scores and visual inspection compared to ground truth images.




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.10617564
title: pix2pix_HUVEC_nuclei_immuno_cells_dataset

description: This repository contains a Pix2Pix deep learning model designed to generate synthetic nuclear staining from brightfield images of circulating immune cells. The model was trained on a dataset of 226 paired brightfield and fluorescent microscopy images, which were augmented computationally by a factor of 8 to enhance model performance. The model was trained over 400 epochs using a patch size of 512x512, a batch size of 1, and a vanilla GAN loss function. The final model was selected based on quality metric scores and visual comparison to ground truth images, achieving an average SSIM score of 0.756 and an LPIPS score of 0.130.

Specifications





Model: Pix2Pix for generating synthetic nuclear staining from brightfield images of circulating immune cells




Training Dataset:






Immune Cells: 226 paired brightfield and fluorescent microscopy images




Microscope: Nikon Eclipse Ti2-E, brightfield/fluorescence microscope with a 20x objective




Data Type: Brightfield and fluorescent microscopy images




File Format: TIFF (.tif), 16-bit




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





Training Parameters:






Epochs: 400




Patch Size: 512 x 512 pixels




Batch Size: 1




Loss Function: Vanilla GAN loss function





Model Performance:






Immune Cells:






SSIM Score: 0.756




LPIPS Score: 0.130






Model Selection: Models were chosen based on quality metric scores and visual inspection compared to ground truth images.




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.10617565
title: pix2pix_HUVEC_nuclei_immuno_cells_dataset

description: This repository contains a Pix2Pix deep learning model designed to generate synthetic nuclear staining from brightfield images of circulating immune cells. The model was trained on a dataset of 226 paired brightfield and fluorescent microscopy images, which were augmented computationally by a factor of 8 to enhance model performance. The model was trained over 400 epochs using a patch size of 512x512, a batch size of 1, and a vanilla GAN loss function. The final model was selected based on quality metric scores and visual comparison to ground truth images, achieving an average SSIM score of 0.756 and an LPIPS score of 0.130.

Specifications





Model: Pix2Pix for generating synthetic nuclear staining from brightfield images of circulating immune cells




Training Dataset:






Immune Cells: 226 paired brightfield and fluorescent microscopy images




Microscope: Nikon Eclipse Ti2-E, brightfield/fluorescence microscope with a 20x objective




Data Type: Brightfield and fluorescent microscopy images




File Format: TIFF (.tif), 16-bit




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





Training Parameters:






Epochs: 400




Patch Size: 512 x 512 pixels




Batch Size: 1




Loss Function: Vanilla GAN loss function





Model Performance:






Immune Cells:






SSIM Score: 0.756




LPIPS Score: 0.130






Model Selection: Models were chosen based on quality metric scores and visual inspection compared to ground truth images.




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.10621666
title: pix2pix_HUVEC_nuclei_cancer_cells_dataset

description: This repository contains a Pix2Pix deep learning model to generate synthetic nuclear staining from brightfield images. The model was trained on 258 paired brightfield and fluorescent microscopy images of circulating cancer cells perfused over an endothelial cell monolayer. To improve performance, the dataset was augmented computationally by a factor of 8. The model was trained over 400 epochs using a patch size of 512x512, a batch size of 1, and a vanilla GAN loss function. The final model was selected based on its performance metrics and visual fidelity when compared to ground truth images, achieving an average SSIM score of 0.755 and an LPIPS score of 0.120.

Specifications





Model: Pix2Pix for generating synthetic nuclear staining from brightfield images




Training Dataset:






Cancer Cells: 258 paired brightfield and fluorescent microscopy images




Microscope: Nikon Eclipse Ti2-E, brightfield/fluorescence microscope with a 20x objective




Data Type: Brightfield and fluorescent microscopy images




File Format: TIFF (.tif), 16-bit




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





Training Parameters:






Epochs: 400




Patch Size: 512 x 512 pixels




Batch Size: 1




Loss Function: Vanilla GAN loss function





Model Performance:






Circulating Cancer Cells:






SSIM Score: 0.755




LPIPS Score: 0.120






Model Selection: Models were selected based on quality metric scores and visual inspection compared to ground truth images.




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.10621667
title: pix2pix_HUVEC_nuclei_cancer_cells_dataset

description: This repository contains a Pix2Pix deep learning model to generate synthetic nuclear staining from brightfield images. The model was trained on 258 paired brightfield and fluorescent microscopy images of circulating cancer cells perfused over an endothelial cell monolayer. To improve performance, the dataset was augmented computationally by a factor of 8. The model was trained over 400 epochs using a patch size of 512x512, a batch size of 1, and a vanilla GAN loss function. The final model was selected based on its performance metrics and visual fidelity when compared to ground truth images, achieving an average SSIM score of 0.755 and an LPIPS score of 0.120.

Specifications





Model: Pix2Pix for generating synthetic nuclear staining from brightfield images




Training Dataset:






Cancer Cells: 258 paired brightfield and fluorescent microscopy images




Microscope: Nikon Eclipse Ti2-E, brightfield/fluorescence microscope with a 20x objective




Data Type: Brightfield and fluorescent microscopy images




File Format: TIFF (.tif), 16-bit




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





Training Parameters:






Epochs: 400




Patch Size: 512 x 512 pixels




Batch Size: 1




Loss Function: Vanilla GAN loss function





Model Performance:






Circulating Cancer Cells:






SSIM Score: 0.755




LPIPS Score: 0.120






Model Selection: Models were selected based on quality metric scores and visual inspection compared to ground truth images.




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.u8z3hh
title: Occurrence Download

description: A dataset containing 309 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Marsilea crenata C.Presl)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1945"
 ]
}

 The dataset includes 309 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009812-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ermq27
title: Occurrence Download

description: A dataset containing 1751 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Threatened Tree Species in the Philippines"
 ]
}

 The dataset includes 1751 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009813-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.edaqy3
title: Occurrence Download

description: A dataset containing 441 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Marsilea minuta L.)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1945"
 ]
}

 The dataset includes 441 records from 74 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009814-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15172294
title: Trajectories and RSA data
DOI: 10.5281/zenodo.15172295
title: Trajectories and RSA data
DOI: 10.15468/dl.b4x8sq
title: Occurrence Download

description: A dataset containing 1462849 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "OccurrenceStatus is Present",
 "TaxonKey is Mammalia"
 ]
}

 The dataset includes 1462849 records from 609 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009804-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.