cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.fuzay8
title: Occurrence Download

description: A dataset containing 475820 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Cardellina pusilla (A.Wilson, 1811)",
 "Year 2022-2025"
 ]
}

 The dataset includes 475820 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015783-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xdem78
title: Occurrence Download

description: A dataset containing 475820 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cardellina pusilla (A.Wilson, 1811)",
 "Year 2022-2025"
 ]
}

 The dataset includes 475820 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015786-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jsh4kn
title: Occurrence Download

description: A dataset containing 19724 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sabella pavonina Savigny, 1822"
 ]
}

 The dataset includes 19724 records from 141 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015823-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pxdgby
title: Occurrence Download

description: A dataset containing 2735376 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "StateProvince is Valle Del Cauca"
 ]
}

 The dataset includes 2735376 records from 733 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015760-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.awhj93
title: Occurrence Download

description: A dataset containing 357 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Dicotyles angulatus (Cope, 1889)",
 "Year 2009-2025"
 ]
}

 The dataset includes 357 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015825-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8tkr59
title: Occurrence Download

description: A dataset containing 13369 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cardellina rubrifrons (Giraud Jr, 1841)",
 "Year 2022-2025"
 ]
}

 The dataset includes 13369 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015829-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jzamhs
title: Occurrence Download

description: A dataset containing 475820 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cardellina pusilla (A.Wilson, 1811)",
 "Year 2022-2025"
 ]
}

 The dataset includes 475820 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015791-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tqgt73
title: Occurrence Download

description: A dataset containing 475820 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cardellina pusilla (A.Wilson, 1811)",
 "Year 2022-2025"
 ]
}

 The dataset includes 475820 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015795-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kvu2qv
title: Occurrence Download

description: A dataset containing 475820 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cardellina pusilla (A.Wilson, 1811)",
 "Year 2022-2025"
 ]
}

 The dataset includes 475820 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015799-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15191304
title: European (British) LD files for GhostKnockoffGWAS

description: This contains pre-processed LD files (Sigma matrix, S matrix, ...etc) computed on unrelated British samples of the UK-Biobank (n = 306604). It is intended to be used as an input to the GhostKnockoffGWAS pipeline. 



This is the output of applying solveblock executable directly on 306,604 unrelated British samples of the UK-Biobank.

Quasi-independent blocks are computed by applying the snp_ldsplit function with parameters thr_r2=0.01, max_r2=0.3, min_size = 500, and max_size = {1000, 1500, 3000, 6000, 10000}. 

SNPs with minor allele frequency less than 0.01 or Hardy-Weinburg equilibrium p-value less than 1e-6 are removed. 

Only HG19 coordinates are available. 

Knockoff optimization were carried out by the Knockoffs.jl julia package: https://github.com/biona001/Knockoffs.jl

The result (i.e. files available in this site) is saved in .csv and .h5 formatted files for easier access, which is directly readable by GhostKnockoffGWAS. 


Note: We previously released another set of EUR LD files. This set of LD files should be preferred over the previous one. The main difference with this entry is that the previous entry used quasi-independent blocks from LDetect computed on the 1000 genomes project. Here we compute the independent blocks using snp_ldsplit directly on the UK-Biobank British samples.
DOI: 10.5281/zenodo.15191305
title: European (British) LD files for GhostKnockoffGWAS

description: This contains pre-processed LD files (Sigma matrix, S matrix, ...etc) computed on unrelated British samples of the UK-Biobank (n = 306604). It is intended to be used as an input to the GhostKnockoffGWAS pipeline. 



This is the output of applying solveblock executable directly on 306,604 unrelated British samples of the UK-Biobank.

Quasi-independent blocks are computed by applying the snp_ldsplit function with parameters thr_r2=0.01, max_r2=0.3, min_size = 500, and max_size = {1000, 1500, 3000, 6000, 10000}. 

SNPs with minor allele frequency less than 0.01 or Hardy-Weinburg equilibrium p-value less than 1e-6 are removed. 

Only HG19 coordinates are available. 

Knockoff optimization were carried out by the Knockoffs.jl julia package: https://github.com/biona001/Knockoffs.jl

The result (i.e. files available in this site) is saved in .csv and .h5 formatted files for easier access, which is directly readable by GhostKnockoffGWAS. 


Note: We previously released another set of EUR LD files. This set of LD files should be preferred over the previous one. The main difference with this entry is that the previous entry used quasi-independent blocks from LDetect computed on the 1000 genomes project. Here we compute the independent blocks using snp_ldsplit directly on the UK-Biobank British samples.
DOI: 10.5281/zenodo.15171884
title: Ayurvedic Medicinal Raw Materials

description: The AyuFlora dataset is a high-quality image collection curated to support computer vision and deep learning research in the field of Ayurveda. It is specifically designed to aid in the identification and classification of Ayurvedic raw materials used in medicinal formulations. All images are captured under consistent, controlled lighting conditions to ensure high clarity, contrast, and background uniformity—making the dataset highly suitable for training reliable machine learning models.



Dataset Composition:

The dataset consists of more than 3,000 high-resolution images, divided into 10 distinct classes, each corresponding to a unique Ayurvedic raw material:





Vekhand




Manjistha




Jesthamadh




Gulvel




Ashwagandha




Sagargota




Niranjanphal




Madanphal




Kadukharik




Hirda



Each class contains more than 250 manually curated images—with some classes having 300 or more—ensuring balanced and robust representation across categories for effective model training and evaluation.



Image Characteristics:





Resolution: Typically 224x224 or higher




Lighting Conditions: Consistent and natural/artificial light to enhance visibility




Background: Uniform and noise-free to optimize feature extraction




Format: JPEG/PNG





Applications:

This dataset is ideal for a range of applications, including:





Image classification and recognition of herbal materials




Real-time raw material identification in IoT-enabled systems




Augmented reality support for Ayurvedic practitioners




Transfer learning involving botanical and natural product classification



Additional Files:

A CSV metadata file (ayuflora_dataset.csv) is included with the dataset, containing the following fields for each image: Image_ID, Class_Name, File_Path, and Format.
DOI: 10.5281/zenodo.15171885
title: Ayurvedic Medicinal Raw Materials

description: The AyuFlora dataset is a high-quality image collection curated to support computer vision and deep learning research in the field of Ayurveda. It is specifically designed to aid in the identification and classification of Ayurvedic raw materials used in medicinal formulations. All images are captured under consistent, controlled lighting conditions to ensure high clarity, contrast, and background uniformity—making the dataset highly suitable for training reliable machine learning models.



Dataset Composition:

The dataset consists of more than 3,000 high-resolution images, divided into 10 distinct classes, each corresponding to a unique Ayurvedic raw material:





Vekhand




Manjistha




Jesthamadh




Gulvel




Ashwagandha




Sagargota




Niranjanphal




Madanphal




Kadukharik




Hirda



Each class contains more than 250 manually curated images—with some classes having 300 or more—ensuring balanced and robust representation across categories for effective model training and evaluation.



Image Characteristics:





Resolution: Typically 224x224 or higher




Lighting Conditions: Consistent and natural/artificial light to enhance visibility




Background: Uniform and noise-free to optimize feature extraction




Format: JPEG/PNG





Applications:

This dataset is ideal for a range of applications, including:





Image classification and recognition of herbal materials




Real-time raw material identification in IoT-enabled systems




Augmented reality support for Ayurvedic practitioners




Transfer learning involving botanical and natural product classification



Additional Files:

A CSV metadata file (ayuflora_dataset.csv) is included with the dataset, containing the following fields for each image: Image_ID, Class_Name, File_Path, and Format.
DOI: 10.5281/zenodo.15191360
title: Supplementary R Scripts for Spectral Quantification of Lateral Flow Immunoassays

description: This archive contains anonymized R scripts used for the preprocessing, spectral normalization, principal component analysis (PCA), and machine learning prediction in the study titled “Portable Full-Spectrum Analysis with Machine Learning Framework for Quantitative Assessment of Lateral Flow Immunoassays.” No patient data are included due to privacy restrictions.
DOI: 10.5281/zenodo.15191361
title: Supplementary R Scripts for Spectral Quantification of Lateral Flow Immunoassays

description: This archive contains anonymized R scripts used for the preprocessing, spectral normalization, principal component analysis (PCA), and machine learning prediction in the study titled “Portable Full-Spectrum Analysis with Machine Learning Framework for Quantitative Assessment of Lateral Flow Immunoassays.” No patient data are included due to privacy restrictions.
DOI: 10.5281/zenodo.15191401
title: FAMR Vignette Sample Data
DOI: 10.5281/zenodo.15191402
title: FAMR Vignette Sample Data
DOI: 10.60893/figshare.pof.28392209
title: Granular particle sizes

description: Particle size distributions for silica gel particles and coffee grounds.
DOI: 10.15468/dl.2svv3y
title: Occurrence Download

description: A dataset containing 2744 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Geometry POLYGON((-25.7392 38.05644,17.67458 38.05644,17.67458 70.86677,-25.7392 70.86677,-25.7392 38.05644))",
 "TaxonKey is Aurelia aurita (Linnaeus, 1758)",
 "Year 1000-1989"
 ]
}

 The dataset includes 2744 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015833-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.64zucy
title: Occurrence Download

description: A dataset containing 13369 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cardellina rubrifrons (Giraud Jr, 1841)",
 "Year 2022-2025"
 ]
}

 The dataset includes 13369 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015832-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f5j8vn
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((27.73839 38.7893,31.16851 38.7893,31.16851 40.58504,27.73839 40.58504,27.73839 38.7893))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Sciurus vulgaris Linnaeus, 1758"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015831-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dtsf8y
title: Occurrence Download

description: A dataset containing 475776 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Machine Observation)",
 "OccurrenceStatus is Present",
 "TaxonKey is Cardellina pusilla (A.Wilson, 1811)",
 "Year 2022-2025"
 ]
}

 The dataset includes 475776 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015802-250402121839773/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.28773434
title: Aortomesenteric measurements data_FINAL 10042025.xlsx

description: The objective of this retrospective study was to analyze the aortomesenteric distance (AMD) and angle (AMA) measurements as well as demographic data in patients surgically treated for Superior Mesenteric Artery Syndrome (SMAS), also known as Wilkie’s Syndrome in a single treatment center in Slovakia, The National Institute of Cardiovascular Diseases in Bratislava. Our aim was to evaluate the accuracy and efficacy of computed tomography angiography (CTA) as the diagnostic modality used for these biomechanical measurements. Furthermore, we aimed to provide reference values of AMD and AMA measurements in SMAS patients both nationally and globally. Records of 50 patients surgically treated in the National Institute of Cardiovascular Diseases in Bratislava were retrieved from the database and studied. The technique of measuring both the distance and angle between the abdominal aorta and superior mesenteric artery using the Slovak software for viewing imaging outputs, TomoCon<b>®</b>, was described. Statistical analysis was performed on the obtained data. The mean AMD was 6.56 ± 2.15 mm, and the mean AMA was 17.56 ± 5.42 degrees, which correlated with literature-reported SMAS ranges. The mean patient age in our study population was 36.60 ± 15.18 years and there was a considerably high female-to-male ratio of 4.56:1. This significantly high prevalence of females in our study could correlate with the global high prevalence of women in Ehlers-Danlos syndrome (EDS), a multimorbid connective tissue disorder. Also, the relatively young mean age of our patients adds to the concept of the early disease onset, also typical in EDS patients. Based on our study, CTA can be considered an efficient and essential tool in the diagnostic process of SMAS. However, other dynamic imaging modalities in addition to CTA, such as upper GI barium study and doppler ultrasound are vital in order to make the diagnosis as accurate as possible. Genetic screening for connective tissue disease in SMAS patients is recommended and warrants further research.
DOI: 10.6084/m9.figshare.28773434.v1
title: Aortomesenteric measurements data_FINAL 10042025.xlsx

description: The objective of this retrospective study was to analyze the aortomesenteric distance (AMD) and angle (AMA) measurements as well as demographic data in patients surgically treated for Superior Mesenteric Artery Syndrome (SMAS), also known as Wilkie’s Syndrome in a single treatment center in Slovakia, The National Institute of Cardiovascular Diseases in Bratislava. Our aim was to evaluate the accuracy and efficacy of computed tomography angiography (CTA) as the diagnostic modality used for these biomechanical measurements. Furthermore, we aimed to provide reference values of AMD and AMA measurements in SMAS patients both nationally and globally. Records of 50 patients surgically treated in the National Institute of Cardiovascular Diseases in Bratislava were retrieved from the database and studied. The technique of measuring both the distance and angle between the abdominal aorta and superior mesenteric artery using the Slovak software for viewing imaging outputs, TomoCon<b>®</b>, was described. Statistical analysis was performed on the obtained data. The mean AMD was 6.56 ± 2.15 mm, and the mean AMA was 17.56 ± 5.42 degrees, which correlated with literature-reported SMAS ranges. The mean patient age in our study population was 36.60 ± 15.18 years and there was a considerably high female-to-male ratio of 4.56:1. This significantly high prevalence of females in our study could correlate with the global high prevalence of women in Ehlers-Danlos syndrome (EDS), a multimorbid connective tissue disorder. Also, the relatively young mean age of our patients adds to the concept of the early disease onset, also typical in EDS patients. Based on our study, CTA can be considered an efficient and essential tool in the diagnostic process of SMAS. However, other dynamic imaging modalities in addition to CTA, such as upper GI barium study and doppler ultrasound are vital in order to make the diagnosis as accurate as possible. Genetic screening for connective tissue disease in SMAS patients is recommended and warrants further research.
DOI: 10.5281/zenodo.10719187
title: Population-specific responses to developmental temperature in the arboviral vector Aedes albopictus: implications for climate change

description:  

This repository contains the Supplementary raw data associated to the published research article. The Supplementary Raw Data is organized in the following excel files:



Crema Wolb density.xlsx

Energy reserves and water content.xlsx

Fitness Comparisons Y0 vs Y2.xlsx

Fitness Foshan and Crema.xlsx

Foshan Wolb density.xlsx

Thermal traits.xlsx

Transcriptomics.xls


 

Citations:

* Research paper:  Carlassara M, Khorramnejad A, Oker H, Bahrami R, Lozada-Chávez AN, Mancini MV, Quaranta S, Body MJA, Lahondère C, Bonizzoni M. Population-specific responses to developmental temperature in the arboviral vector Aedes albopictus: Implications for climate change. Glob Chang Biol. 2024 Mar;30(3):e17226. doi: 10.1111/gcb.17226.

* This repository: Carlassara, M., Khorramnejad, A., Oker, H., Bahrami, R., Lozada-Chávez, A. N., Mancini, M. V., Quaranta, S., Body, M. J. A., Lahondère, C., & Bonizzoni, M. (2024). Population-specific responses to developmental temperature in the arboviral vector Aedes albopictus: implications for climate change [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10719188

* Github repository page:  https://github.com/BonizzoniLab/Carlassara_et_al_2024_raw_data
DOI: 10.5281/zenodo.10719188
title: Population-specific responses to developmental temperature in the arboviral vector Aedes albopictus: implications for climate change

description:  

This repository contains the Supplementary raw data associated to the published research article. The Supplementary Raw Data is organized in the following excel files:



Crema Wolb density.xlsx

Energy reserves and water content.xlsx

Fitness Comparisons Y0 vs Y2.xlsx

Fitness Foshan and Crema.xlsx

Foshan Wolb density.xlsx

Thermal traits.xlsx

Transcriptomics.xls


 

Citations:

* Research paper:  Carlassara M, Khorramnejad A, Oker H, Bahrami R, Lozada-Chávez AN, Mancini MV, Quaranta S, Body MJA, Lahondère C, Bonizzoni M. Population-specific responses to developmental temperature in the arboviral vector Aedes albopictus: Implications for climate change. Glob Chang Biol. 2024 Mar;30(3):e17226. doi: 10.1111/gcb.17226.

* This repository: Carlassara, M., Khorramnejad, A., Oker, H., Bahrami, R., Lozada-Chávez, A. N., Mancini, M. V., Quaranta, S., Body, M. J. A., Lahondère, C., & Bonizzoni, M. (2024). Population-specific responses to developmental temperature in the arboviral vector Aedes albopictus: implications for climate change [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10719188

* Github repository page:  https://github.com/BonizzoniLab/Carlassara_et_al_2024_raw_data
DOI: 10.5517/ccdc.csd.cc2lrz7l
title: CCDC 2406320: Experimental Crystal Structure Determination

description: Related Article: qin yushu|2024|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2lx2h3
title: CCDC 2410265: Experimental Crystal Structure Determination

description: Related Article: Jagadese VITTAL and Caroline Evania Mulijanto|2024|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2lx46w
title: CCDC 2410318: Experimental Crystal Structure Determination

description: Related Article: Dongjing Hong|2024|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2lxcyv
title: CCDC 2410558: Experimental Crystal Structure Determination

description: Related Article: Le Zhao|2024|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.