cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10572122
title: StarDist_BF_cancer_cell_dataset_20x

description: This repository contains a StarDist deep learning model and its training and validation datasets designed for segmenting cancer cells perfused over an endothelial cell monolayer captured at 20x magnification. Using computational methods, the initial dataset of 20 manually annotated images was augmented to 160 paired images. The model was trained over 400 epochs and achieved an average F1 Score of 0.921, demonstrating high accuracy in cell segmentation tasks.

Specifications





Model: StarDist for cancer cell segmentation on endothelial cells (20x magnification)




Training Dataset:






Number of Original Images: 20 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: 0.921




Average IoU: 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.13824989
title: VULTURE public dataset

description: This is the public dataset for VULTURE

the dataset is orgnized as following, can be used to detect 1-day  vulnerabilities in the wild.



aligned_patch_commits.zip : dataset for LLM based commit mapping



aligned_cpe: Each <product>.json file contains CVEs and related CPEs for the corresponding <product>.

aligned_patch: Each <product> directory contains directories named after related CVEs. Inside each <CVE> directory, there is a patch_info.json file that describes the CVE, and two directories: patch_before and patch_after, which store the code before and after the patch, respectively.



IoTList: dataset for selected TPLs



it contains the GitHub link for selected TPLs in TPLFilter step

https://anonymous.4open.science/r/Vulture-17BC/iotList.txt



signature.zip: dataset for TPLreuse detection & 1-day vulnerability detection



osscollector: contains the birth date and function hash

preprocessor: TPL information after filter

All of the above can be directly used by VULTURE




--------------------------- 

BENCHMARKS



Commit benchmark: https://anonymous.4open.science/r/Vulture-17BC/CommitBenckmark

Vulnerability benchmark: https://anonymous.4open.science/r/Vulture-17BC/VulnerabilityBenckmark.xlsx
DOI: 10.5281/zenodo.13824990
title: VULTURE public dataset

description: This is the public dataset for VULTURE

the dataset is orgnized as following, can be used to detect 1-day  vulnerabilities in the wild.



aligned_patch_commits.zip : dataset for LLM based commit mapping



aligned_cpe: Each <product>.json file contains CVEs and related CPEs for the corresponding <product>.

aligned_patch: Each <product> directory contains directories named after related CVEs. Inside each <CVE> directory, there is a patch_info.json file that describes the CVE, and two directories: patch_before and patch_after, which store the code before and after the patch, respectively.



IoTList: dataset for selected TPLs



it contains the GitHub link for selected TPLs in TPLFilter step

https://anonymous.4open.science/r/Vulture-17BC/iotList.txt



Results.zip: dataset for experiment result



res_TPL_detection: Results for TPL reuse detection

res_oneday_detection: Results for One-day vulnerability detection



signature.zip: dataset for TPLreuse detection & 1-day vulnerability detection



osscollector: contains the birth date and function hash

preprocessor: TPL information after filter

All of the above can be directly used by VULTURE




--------------------------- 

BENCHMARKS



Commit benchmark: https://anonymous.4open.science/r/Vulture-17BC/CommitBenckmark

Vulnerability benchmark: https://anonymous.4open.science/r/Vulture-17BC/VulnerabilityBenckmark.xlsx
DOI: 10.5281/zenodo.15172285
title: VULTURE public dataset

description: This is the public dataset for VULTURE

the dataset is orgnized as following, can be used to detect 1-day  vulnerabilities in the wild.



aligned_patch_commits.zip : dataset for LLM based commit mapping



aligned_cpe: Each <product>.json file contains CVEs and related CPEs for the corresponding <product>.

aligned_patch: Each <product> directory contains directories named after related CVEs. Inside each <CVE> directory, there is a patch_info.json file that describes the CVE, and two directories: patch_before and patch_after, which store the code before and after the patch, respectively.



IoTList: dataset for selected TPLs



it contains the GitHub link for selected TPLs in TPLFilter step

https://anonymous.4open.science/r/Vulture-17BC/iotList.txt



signature.zip: dataset for TPLreuse detection & 1-day vulnerability detection



osscollector: contains the birth date and function hash

preprocessor: TPL information after filter

All of the above can be directly used by VULTURE




--------------------------- 

BENCHMARKS



Commit benchmark: https://anonymous.4open.science/r/Vulture-17BC/CommitBenckmark

Vulnerability benchmark: https://anonymous.4open.science/r/Vulture-17BC/VulnerabilityBenckmark.xlsx
DOI: 10.5281/zenodo.13442127
title: StarDist_AsPC1_Lifeact

description: This repository includes a StarDist deep learning model designed for segmenting AsPC1 cells labeled with Lifeact from fluorescence microscopy images. The model distinguishes individual AsPC1 cells within clusters and separates them from the background. The model was trained on a small dataset and achieved an Intersection over Union (IoU) score of 0.884 and an F1 Score of 0.967, indicating high accuracy in cell segmentation.

Specifications





Model: StarDist for segmenting AsPC1 cells in fluorescence microscopy images




Training Dataset:






Number of Images: 10 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 AsPC1 Lifeact channel with manually segmented masks




File Format: TIFF (.tif)






Fluorescence Images: 16-bit




Masks: 8-bit





Image Size: 1024 x 1024 pixels (Pixel size: 0.6337 x 0.6337 µm²)





Model Capabilities:






Segment AsPC1 Cells: Detects individual AsPC1 cells from a cluster and separates them from the background




Measure Intensity: Enables measurement of CD44, ICAM1, ICAM2, or Fibronectin intensity under individual cells in respective channels





Performance:






Average IoU: 0.884




Average F1 Score: 0.967





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.13442128
title: StarDist_AsPC1_Lifeact

description: This repository includes a StarDist deep learning model designed for segmenting AsPC1 cells labeled with Lifeact from fluorescence microscopy images. The model distinguishes individual AsPC1 cells within clusters and separates them from the background. The model was trained on a small dataset and achieved an Intersection over Union (IoU) score of 0.884 and an F1 Score of 0.967, indicating high accuracy in cell segmentation.

Specifications





Model: StarDist for segmenting AsPC1 cells in fluorescence microscopy images




Training Dataset:






Number of Images: 10 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 AsPC1 Lifeact channel with manually segmented masks




File Format: TIFF (.tif)






Fluorescence Images: 16-bit




Masks: 8-bit





Image Size: 1024 x 1024 pixels (Pixel size: 0.6337 x 0.6337 µm²)





Model Capabilities:






Segment AsPC1 Cells: Detects individual AsPC1 cells from a cluster and separates them from the background




Measure Intensity: Enables measurement of CD44, ICAM1, ICAM2, or Fibronectin intensity under individual cells in respective channels





Performance:






Average IoU: 0.884




Average F1 Score: 0.967





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.10572230
title: StarDist_BF_Neutrophil_dataset

description: This repository includes a StarDist deep learning model and its training and validation datasets for detecting neutrophils perfused over an endothelial cell monolayer. The model was trained on 36 manually annotated images, achieving an average F1 Score of 0.969. The dataset and model are intended for use in biomedical research, particularly for analyzing interactions between neutrophils and endothelial cells.

Specifications





Model: StarDist for neutrophil detection on endothelial cells




Training Dataset:






Number of Images: 36 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: 0.969




Average IoU: 0.914





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.10572231
title: StarDist_BF_Neutrophil_dataset

description: This repository includes a StarDist deep learning model and its training and validation datasets for detecting neutrophils perfused over an endothelial cell monolayer. The model was trained on 36 manually annotated images, achieving an average F1 Score of 0.969. The dataset and model are intended for use in biomedical research, particularly for analyzing interactions between neutrophils and endothelial cells.

Specifications





Model: StarDist for neutrophil detection on endothelial cells




Training Dataset:






Number of Images: 36 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: 0.969




Average IoU: 0.914





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: 10.6084/m9.figshare.28748447
title: <b>F</b><b>低 </b><b>D</b><b>水平 </b><b>F</b><b>或 </b><b>T</b><b>反应 </b><b>O</b><b>f </b><b>I</b><b>颅骨 </b><b>A</b><b>神经瘤 </b><b>C</b><b>ombined </b><b>W</b><b>ith </b><b>C</b><b>erebral </b><b>I</b><b>nfarction： </b><b>A</b><b> </b><b>C</b><b>ase</b><b> </b><b>R</b><b>报告</b>

description: <b>视频 1：</b>植入分流器治疗动脉瘤，并保留动脉瘤内的造影剂。<b>视频</b><b> </b><b>2：</b>随访 12 个月，动脉瘤已完全消退。<b>视频</b><b> </b><b>3：</b>36个月时，动脉瘤保持消退，无复发迹象。
DOI: 10.15468/dl.q5ucfy
title: Occurrence Download

description: A dataset containing 3268 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Anura, Caudata)",
 "HasCoordinate is true",
 "Country is Japan",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 },
 "Year is greater than or equal to 1970",
 "CoordinateUncertaintyInMeters is less than 3000"
 ]
}

 The dataset includes 3268 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009807-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10572310
title: StarDist_Fluorescent_cells

description: This repository includes a StarDist deep learning model and its training and validation datasets for detecting fluorescently labeled cancer cells perfused over an endothelial cell monolayer. The model was trained on 66 images labeled with CellTrace and demonstrated high accuracy, achieving an average F1 Score of 0.877. The dataset and the trained model can be used for biomedical image analysis, particularly in cancer research.

Specifications





Model: StarDist for cancer cell detection




Training Dataset:






Number of Images: 66 paired fluorescent microscopy images and label masks




Microscope: Nikon Eclipse Ti2-E, 10x objective




Data Type: Fluorescent microscopy images with manually segmented masks




File Format: TIFF (.tif)






Brightfield Images: 16-bit




Masks: 8-bit





Image Size: 1024 x 1024 pixels (Pixel size: 1.3205 μm)





Training Parameters:






Epochs: 200




Patch Size: 1024 x 1024 pixels




Batch Size: 2





Performance:






Average F1 Score: 0.877




Average IoU: 0.646





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.10572309
title: StarDist_Fluorescent_cells

description: This repository includes a StarDist deep learning model and its training and validation datasets for detecting fluorescently labeled cancer cells perfused over an endothelial cell monolayer. The model was trained on 66 images labeled with CellTrace and demonstrated high accuracy, achieving an average F1 Score of 0.877. The dataset and the trained model can be used for biomedical image analysis, particularly in cancer research.

Specifications





Model: StarDist for cancer cell detection




Training Dataset:






Number of Images: 66 paired fluorescent microscopy images and label masks




Microscope: Nikon Eclipse Ti2-E, 10x objective




Data Type: Fluorescent microscopy images with manually segmented masks




File Format: TIFF (.tif)






Brightfield Images: 16-bit




Masks: 8-bit





Image Size: 1024 x 1024 pixels (Pixel size: 1.3205 μm)





Training Parameters:






Epochs: 200




Patch Size: 1024 x 1024 pixels




Batch Size: 2





Performance:






Average F1 Score: 0.877




Average IoU: 0.646





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.deedgb
title: Occurrence Download

description: A dataset containing 3268 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Anura, Caudata)",
 "HasCoordinate is true",
 "Country is Japan",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 },
 "Year is greater than or equal to 1970",
 "CoordinateUncertaintyInMeters is less than 3000"
 ]
}

 The dataset includes 3268 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009808-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bm6c8q
title: Occurrence Download

description: A dataset containing 94842 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Marrubium vulgare 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 94842 records from 584 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009789-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p5fhy5
title: Occurrence Download

description: A dataset containing 3236 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Cynoglossum amabile Stapf & J.R.Drumm.)",
 "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 3236 records from 160 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009809-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13442876
title: Stardist_MiaPaCa2_from_CD44

description: This repository contains a StarDist deep learning model designed for segmenting MiaPaCa2 cells from the CD44 channel in fluorescence microscopy images. The model is capable of accurately segmenting individual MiaPaCa2 cells while excluding HUVECs. Trained on a small dataset, the model achieved an Intersection over Union (IoU) score of 0.884 and an F1 Score of 0.950, indicating high precision in cell segmentation.

Specifications





Model: StarDist for segmenting MiaPaCa2 cells from the CD44 fluorescence channel




Training Dataset:






Number of Images: 8 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 CD44 channel, obtained after immunofluorescence staining with primary and secondary antibodies and 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 MiaPaCa2 Cells: Accurately detects individual MiaPaCa2 cells while ignoring HUVECs




Measure CD44 Intensity: Allows for the measurement of CD44 intensity around MiaPaCa2 cells, specifically from the CD44 channel





Performance:






Average IoU: 0.884




Average F1 Score: 0.950





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.13442877
title: Stardist_MiaPaCa2_from_CD44

description: This repository contains a StarDist deep learning model designed for segmenting MiaPaCa2 cells from the CD44 channel in fluorescence microscopy images. The model is capable of accurately segmenting individual MiaPaCa2 cells while excluding HUVECs. Trained on a small dataset, the model achieved an Intersection over Union (IoU) score of 0.884 and an F1 Score of 0.950, indicating high precision in cell segmentation.

Specifications





Model: StarDist for segmenting MiaPaCa2 cells from the CD44 fluorescence channel




Training Dataset:






Number of Images: 8 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 CD44 channel, obtained after immunofluorescence staining with primary and secondary antibodies and 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 MiaPaCa2 Cells: Accurately detects individual MiaPaCa2 cells while ignoring HUVECs




Measure CD44 Intensity: Allows for the measurement of CD44 intensity around MiaPaCa2 cells, specifically from the CD44 channel





Performance:






Average IoU: 0.884




Average F1 Score: 0.950





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.15166720
title: Emergent Inductance in Antiferromagnetic Systems with Spin-orbit Coupling: Raw Data
DOI: 10.5281/zenodo.15172288
title: Emergent Inductance in Antiferromagnetic Systems with Spin-orbit Coupling: Raw Data