cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.d732yv
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lampranthus proximus L.Bolus"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003842-230530130749713/datasets/export for details.
DOI: 10.15468/dl.jnex8z
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Geospiza fuliginosa Gould, 1837",
 "HasCoordinate is true",
 "Country is Japan",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0214566-220831081235567/datasets/export for details.
DOI: 10.15468/dl.hdq4ak
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rhizomys pruinosus Blyth, 1851",
 "HasCoordinate is true",
 "Country is Japan",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0214575-220831081235567/datasets/export for details.
DOI: 10.15468/dl.euzvn7
title: Occurrence Download

description: A dataset containing 28863 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Botswana",
 "TaxonKey is Insecta"
 ]
}

 The dataset includes 28863 records from 157 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007471-240425142415019/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qpps6z
title: Occurrence Download

description: A dataset containing 319 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "GadmGid is one of (COL.28.9_2, COL.28.33_2, COL.28.61_2, COL.28.36_2)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Mammalia"
 ]
}

 The dataset includes 319 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0265672-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s8j6fh
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sorex shinto Thomas, 1905",
 "HasCoordinate is true",
 "Country is Japan",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 9 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0214588-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.26wztz
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hieracium vorlichense P.D.Sell"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0247128-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x3tc3r
title: Occurrence Download

description: A dataset containing 834954 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Zimmerius Traylor, 1977, Stigmatura P.L.Sclater & Salvin, 1866, Pseudelaenia W.Lanyon, 1988, Euscarthmus Wied-Neuwied, 1831, Corythopis Sundevall, 1836, Pseudotriccus simplex (Berlepsch, 1901), Pseudotriccus ruficeps (Lafresnaye, 1843), Pseudotriccus pelzelni Taczanowski & Berlepsch, 1885, Pseudotriccus Taczanowski & Berlepsch, 1885, Pseudocolopteryx citreola (Landbeck, 1864), Pseudocolopteryx flaviventris (Orbigny & Lafresnaye, 1837), Pseudocolopteryx dinelliana Lillo, 1905, Pseudocolopteryx acutipennis (P.L.Sclater & Salvin, 1873), Pseudocolopteryx sclateri (Oustalet, 1892), Nesotriccus ridgwayi C.H.Townsend, 1895, Nesotriccus C.H.Townsend, 1895, Polystictus superciliaris (Wied-Neuwied, 1831), Polystictus pectoralis (Vieillot, 1817), Capsiempis Cabanis & Heine, 1859, Phaeomyias Berlepsch, 1902, Serpophaga Gould, 1839, Uromyias Hellmayr, 1927, Anairetes Reichenbach, 1850, Mecocerculus P.L.Sclater, 1862, Suiriri Orbigny, 1840, Camptostoma P.L.Sclater, 1857)"
 ]
}

 The dataset includes 834954 records from 450 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0221285-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2fq4pq
title: Occurrence Download

description: A dataset containing 1354 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Smilax subpubescens A.DC."
 ]
}

 The dataset includes 1354 records from 63 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0233316-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.erx4em
title: Occurrence Download

description: A dataset containing 283 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Monomorium floricola (Jerdon, 1851)"
 ]
}

 The dataset includes 283 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0221326-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5ft8d6
title: Occurrence Download

description: A dataset containing 499245 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Turdus viscivorus Linnaeus, 1758)",
 "HasCoordinate is true",
 "DecimalLatitude is greater than or equal to 29.93667",
 "DecimalLatitude is less than or equal to 51.91193",
 "DecimalLongitude is greater than or equal to -14.67522",
 "DecimalLongitude is less than or equal to 39.79752",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation)",
 "Year is greater than or equal to 1970",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 499245 records from 2035 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046734-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.27641673
title: Source Data

description: This dataset includes the source data of all the figures and tables in the article.
DOI: 10.6084/m9.figshare.27641673.v1
title: Source Data

description: This dataset includes the source data of all the figures and tables in the article.
DOI: 10.15468/dl.suaq8s
title: Occurrence Download

description: A dataset containing 610 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "TaxonKey is Hippophae rhamnoides L.",
 "Year 1972-2024"
 ]
}

 The dataset includes 610 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011589-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7gppkb
title: Occurrence Download

description: A dataset containing 3904 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Material sample, Specimen, Living Specimen, Occurrence evidence)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Comastoma (Wettst.) Toyok."
 ]
}

 The dataset includes 3904 records from 140 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011590-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.21823455.v96
title: The ReptileEvolution.com Project

description:   
Taxon exclusion is the number one problem currently vexing paleontology. The search for pterosaur, turtle, bat, snake, whale, reptile and tetrapod ancestors continues to unnecessarily befuddle paleontologists who continue to work from cherry-picked, textbook and traditional taxon lists that overlook and omit pertinent taxa. That’s why the large reptile tree (LRT), the nearly completely resolved cladogram at the heart of www.ReptileEvolution.com, includes 2212 taxa. Larger taxon numbers minimize taxon exclusion issues. Results indicate the simple act of adding taxa resolves all phylogenetic issues.
DOI: 10.5281/zenodo.14099923
title: FluoMix-dataset1
title: Adult_mouse_brain_focus_data_set(z0-z33)-dataset_1

description: Mouse tissues datasets stained with fluorescence dye to predict exact in-focus confocal image in tissue images.

In-focus and out-of-focus datasets were constructed to train deep learning.
DOI: 10.5281/zenodo.14099924
title: FluoMix-dataset1
title: Adult_mouse_brain_focus_data_set(z0-z33)-dataset_1

description: Mouse tissues datasets stained with fluorescence dye to predict exact in-focus confocal image in tissue images.

In-focus and out-of-focus datasets were constructed to train deep learning.
DOI: 10.15468/dl.2hpfmk
title: Occurrence Download

description: A dataset containing 14432677 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Bryophyta"
 ]
}

 The dataset includes 14432677 records from 2460 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011543-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xuztns
title: Occurrence Download

description: A dataset containing 682 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lycaena hermes (W.H.Edwards, 1870)"
 ]
}

 The dataset includes 682 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018592-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14827468
title: Supplementary Files for Re-Generating Manual Analyses in 'GelGenie: an AI-powered framework for gel electrophoresis image analysis'

description: This repository contains the project files and raw data necessary to recreate the segmentation analyses presented in our manuscript entitled 'GelGenie: an AI-powered framework for gel electrophoresis image analysis' (https://doi.org/10.1101/2024.09.06.611479).  The scripts required to process the data are all provided here: https://github.com/mattaq31/GelGenie/tree/main/python-gelgenie.

 

In more detail:





ladder_set_segmentation_analyses.zip contains:



The gelanalyzer project files for the ladder segmentation analysis of Figure 1, along with the exported band volume data.

The segmentation (marked as 'gelgenie') band volume computation data.

The corresponding ImageStudio project files and exported band volume data.



gelanalyzer_qualitative_analysis_test_set_projects.zip contains the GelAnalyzer project files created for the qualitative test set analysis of Fig. 3D.  These files were processed manually, no scripting was used.  qualitative_bands_identified.csv contains the results for each image.
DOI: 10.5281/zenodo.14827469
title: Supplementary Files for Re-Generating Manual Analyses in 'GelGenie: an AI-powered framework for gel electrophoresis image analysis'

description: This repository contains the project files and raw data necessary to recreate the segmentation analyses presented in our manuscript entitled 'GelGenie: an AI-powered framework for gel electrophoresis image analysis' (https://doi.org/10.1101/2024.09.06.611479).  The scripts required to process the data are all provided here: https://github.com/mattaq31/GelGenie/tree/main/python-gelgenie.

 

In more detail:





ladder_set_segmentation_analyses.zip contains:



The gelanalyzer project files for the ladder segmentation analysis of Figure 1, along with the exported band volume data.

The segmentation (marked as 'gelgenie') band volume computation data.

The corresponding ImageStudio project files and exported band volume data.



gelanalyzer_qualitative_analysis_test_set_projects.zip contains the GelAnalyzer project files created for the qualitative test set analysis of Fig. 3D.  These files were processed manually, no scripting was used.  qualitative_bands_identified.csv contains the results for each image.
DOI: 10.5281/zenodo.13218468
title: Segmented Gel Electrophoresis Images used for Training GelGenie Models

description: This repository contains gel images and corresponding hand-labelled segmentation maps (575 total) from various sources.  The images have various sizes, shapes, gel contents and imaging conditions.  In more detail:



The matthew_gels (301 total), matthew_gels_2 (85 total) and nathan_gels (37 total) folders contain images originating from previous experiments in the Dunn Lab. 

The quantitation_ladder_gels (35 total) were generated specifically for the GelGenie project and contain gel ladders in each well.  These were used to generate the data for Figure 1 in the main paper.

The stella_gels_for_finetuning (26 total) folder contains gel images provided to us from Siyuan Stella Wang (Wyss Institute/Dana-Farber Cancer Institute) which we used to finetune our U-Net model and produce the results in Figure 4C of the main paper.

The external_gels (25 total) images were gifted to us from 5 different researchers: Yichen Zhao (images generated at the University of Waterloo), Huangchen Cui (images generated at Tsinghua University), Thomas Mayer (images generated at the Technical University of Munich), Joana Reis (images generated at the Dana-Farber Cancer Institute) and Ricarda Törner (images generated at the Dana-Farber Cancer Institute).  These were used as an external unseen test set for our fine-tuned model.

The lsdb_gels (66 total) folder contains segmentation maps of images downloaded from the RGB Caps dataset, which is available from https://dbarchive.biosciencedbc.jp/en/rgp-caps/desc.html.  The original images have been shared with a Creative Commons Attribution-Share Alike 2.1 Japan license.  Permission was obtained from the data depositors for the training of our models on these images, as well as for the sharing of derived segmentation masks using the CC-BY license.  To use this portion of the dataset, you will need to download the original images from the website, and place them alongside our segmentation masks, mirroring the same setup used for all the other datasets in this repository.


The folders are organised as follows:



The images, val_images and test_images contain the original images, split into training/validation/testing partitions respectively (except for the lsdb_gels dataset).

The masks, val_masks and test_masks contain the corresponding segmentation maps for each image in the main folders (they have identical naming i.e. an image named test_image.tif will have a segmentation map labelled test_image.tif).

The external_gels folder does not have validation or test partitions.


The segmentation maps are 8-bit images, for which a white pixel (0) corresponds to a background pixel and a brown pixel (1) corresponds to a foreground pixel.

This is the main dataset used to train the GelGenie models, for which the corresponding source code and GUI can be downloaded at https://github.com/mattaq31/GelGenie.
DOI: 10.5281/zenodo.13218469
title: Segmented Gel Electrophoresis Images used for Training GelGenie Models

description: This repository contains gel images and corresponding hand-labelled segmentation maps (550 total) from various sources.  The images have various sizes, shapes, gel contents and imaging conditions.  In more detail:



The matthew_gels (301 total), matthew_gels_2 (85 total) and nathan_gels (37 total) folders contain images originating from previous experiments in the Dunn Lab. 

The quantitation_ladder_gels (35 total) were generated specifically for the GelGenie project and contain gel ladders in each well.  These were used to generate the data for Figure 1 in the main paper.

The stella_gels_for_finetuning (26 total) folder contains gel images provided to us from Siyuan Stella Wang (Wyss Institute/Dana-Farber Cancer Institute) which we used to finetune our U-Net model and produce the results in Figure 4C of the main paper.

The lsdb_gels (60 total) folder contains segmentation maps of images downloaded from the RGB Caps dataset, which is available from https://dbarchive.biosciencedbc.jp/en/rgp-caps/desc.html.  The original images have been shared with a Creative Commons Attribution-Share Alike 2.1 Japan license.  Permission was obtained from the data depositors for the training of our models on these images, as well as for the sharing of derived segmentation masks using the CC-BY license.  To use this portion of the dataset, you will need to download the original images from the website, and place them alongside our segmentation masks, mirroring the same setup used for all the other datasets in this repository.


The folders are organised as follows:



The images, val_images and test_images contain the original images, split into training/validation/testing partitions respectively (except for the lsdb_gels dataset).

The masks, val_masks and test_masks contain the corresponding segmentation maps for each image in the main folders (they have identical naming i.e. an image named test_image.tif will have a segmentation map labelled test_image.tif).


The segmentation maps are 8-bit images, for which a white pixel (0) corresponds to a background pixel and a brown pixel (1) corresponds to a foreground pixel.

This is the main dataset used to train the GelGenie models, for which the corresponding source code and GUI can be downloaded at https://github.com/mattaq31/GelGenie.
DOI: 10.5281/zenodo.14641893
title: Segmented Gel Electrophoresis Images used for Training GelGenie Models

description: This repository contains gel images and corresponding hand-labelled segmentation maps (575 total) from various sources.  The images have various sizes, shapes, gel contents and imaging conditions.  In more detail:



The matthew_gels (301 total), matthew_gels_2 (85 total) and nathan_gels (37 total) folders contain images originating from previous experiments in the Dunn Lab. 

The quantitation_ladder_gels (35 total) were generated specifically for the GelGenie project and contain gel ladders in each well.  These were used to generate the data for Figure 1 in the main paper.

The stella_gels_for_finetuning (26 total) folder contains gel images provided to us from Siyuan Stella Wang (Wyss Institute/Dana-Farber Cancer Institute) which we used to finetune our U-Net model and produce the results in Figure 4C of the main paper.

The external_gels (25 total) images were gifted to us from 5 different researchers: Yichen Zhao (images generated at the University of Waterloo), Huangchen Cui (images generated at Tsinghua University), Thomas Mayer (images generated at the Technical University of Munich), Joana Reis (images generated at the Dana-Farber Cancer Institute) and Ricarda Törner (images generated at the Dana-Farber Cancer Institute).  These were used as an external unseen test set for our fine-tuned model.

The lsdb_gels (66 total) folder contains segmentation maps of images downloaded from the RGB Caps dataset, which is available from https://dbarchive.biosciencedbc.jp/en/rgp-caps/desc.html.  The original images have been shared with a Creative Commons Attribution-Share Alike 2.1 Japan license.  Permission was obtained from the data depositors for the training of our models on these images, as well as for the sharing of derived segmentation masks using the CC-BY license.  To use this portion of the dataset, you will need to download the original images from the website, and place them alongside our segmentation masks, mirroring the same setup used for all the other datasets in this repository.


The folders are organised as follows:



The images, val_images and test_images contain the original images, split into training/validation/testing partitions respectively (except for the lsdb_gels dataset).

The masks, val_masks and test_masks contain the corresponding segmentation maps for each image in the main folders (they have identical naming i.e. an image named test_image.tif will have a segmentation map labelled test_image.tif).

The external_gels folder does not have validation or test partitions.


The segmentation maps are 8-bit images, for which a white pixel (0) corresponds to a background pixel and a brown pixel (1) corresponds to a foreground pixel.

This is the main dataset used to train the GelGenie models, for which the corresponding source code and GUI can be downloaded at https://github.com/mattaq31/GelGenie.