cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2fzy7m
title: CCDC 2264061: Experimental Crystal Structure Determination

description: Related Article: Karthik V, Ravi Singh K, Lohith T․N, Mofeli B. Leoma, Sridhar M․A, Sadashiva M․P|2025|J.Mol.Struct.|1322|140550|doi:10.1016/j.molstruc.2024.140550
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.cc2j7rr9
title: CCDC 2331161: Experimental Crystal Structure Determination

description: Related Article: Dipankar Srimani|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: 10.15468/dl.2vtc7s
title: Occurrence Download

description: A dataset containing 432 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Caracterización de la fauna epibentónica y demersal de la Reserva Natural Cordillera Beata"
 ]
}

 The dataset includes 432 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055774-241126133413365/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.28130591
title: Supplementary Material for: The Steep Ramp Test as Precursor to Assess Physical Fitness Before Esophagectomy in Cancer Patients

description: Abstract
Background
Maximum oxygen uptake (VO₂max) is a predictor for postoperative complications after esophagectomy. Cardiopulmonary Exercise Test (CPET) is the golden standard for measuring VO₂max. The alternative Steep Ramp Test (SRT) is less strenuous with several benefits, providing an estimation of VO₂max. This study aims to determine whether SRT is a reliable alternative for CPET to evaluate preoperative fitness.
Methods
A total of 113 patients were included in this study. The agreement between SRT and CPET was analyzed using a t-test, Intraclass Correlation Coefficient (ICC), and the Bland-Altmann analysis. The threshold for adequate preoperative fitness was set at 17.0 ml/kg/min
Results
The mean difference between CPET and SRT was 2.77 ml/kg/min (95% CI 2.14–3.41). The ICC was 0.79 (95% CI 0.70-0.85). The upper limit of agreement of the Bland-Altmann was 9.44. The addition of 9.44 to the CPET-threshold gives an SRT-threshold of 26.44 ml/kg/min. Thirty-one (27.4%) patients scored higher than the SRT-threshold. 
Conclusion
The SRT VO2max differs from VO₂max as measured by CPET. However, the difference was found to be clinically irrelevant for a substantial portion of patients. Hence, SRT is a promising alternative to CPET for determining physical fitness, and might render CPET obsolete for fit individuals.
DOI: 10.5281/zenodo.14592096
title: 호지 추측 보강
DOI: 10.5281/zenodo.14592097
title: 호지 추측 보강
DOI: 10.15468/dl.p4abrj
title: Occurrence Download

description: A dataset containing 1781 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Caracterización ecológica y de la diversidad en los ecosistemas de arrecifes de coral y pastos marinos en la jurisdicción marina de CORPAMAG, departamento del Magdalena, Caribe Colombiano"
 ]
}

 The dataset includes 1781 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055775-241126133413365/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.28130600
title: Supplementary Material for: Impact of patient characteristics and surgery-related risk factors on endophthalmitis after cataract surgery: a meta-analysis

description: Introduction
Cataracts are the leading cause of blindness worldwide, with a notably high incidence rate. Endophthalmitis is the most severe complication following cataract surgery , often resulting in profound vision loss. This study evaluates the impact of risk factors such as age, sex, diabetes mellitus (DM), hypertension, posterior capsule rupture (PCR), type of surgery, and use of intraocular lens (IOL) material on the risk of endophthalmitis after cataract surgery.
Methods
English and Chinese public databases were searched from inception to March 1, 2024. We included studies reporting the number of occurrences of endophthalmitis after cataract surgery at potential risk factors, including age, sex, DM status, hypertension status, intraoperative PCR, type of surgery and use of IOL material. The quality of the included studies was assessed using the Newcastle‒Ottawa Scale.
Results
A total of 57 studies were included and critically evaluated in the meta-analysis. The results showed that female sex was associated with a lower risk of endophthalmitis (odds ratio [OR]: 0.81; 95% CI: 0.75-0.87; p &lt; 0.001). Individuals with diabetes who underwent cataract surgery were found to have a greater risk of endophthalmitis (I2 = 95%; OR: 4.90; 95% CI: 2.41, 9.95; p &lt; 0.001), but the result may be influenced by publication bias. Individuals with hypertension (OR: 2.88; 95% CI: 1.53, 5.45; p = 0.001) and intraoperative PCR (OR: 9.18; 95% CI: 3.31, 25.43; p &lt; 0.001) were found to have a greater risk of endophthalmitis. Phacoemulsification significantly reduced the risk of endophthalmitis compared with extracapsular cataract extraction (ECCE) (OR: 0.62; 95% CI: 0.45, 0.85) based on network meta-analysis.
Conclusion
Male sex, hypertension, intraoperative PCR, and the use of the ECCE surgical approach are associated with a greater risk of postoperative endophthalmitis. Although an age-related trend in elevated risk was observed, this finding should be interpreted cautiously.
DOI: 10.6084/m9.figshare.28130600.v1
title: Supplementary Material for: Impact of patient characteristics and surgery-related risk factors on endophthalmitis after cataract surgery: a meta-analysis

description: Introduction
Cataracts are the leading cause of blindness worldwide, with a notably high incidence rate. Endophthalmitis is the most severe complication following cataract surgery , often resulting in profound vision loss. This study evaluates the impact of risk factors such as age, sex, diabetes mellitus (DM), hypertension, posterior capsule rupture (PCR), type of surgery, and use of intraocular lens (IOL) material on the risk of endophthalmitis after cataract surgery.
Methods
English and Chinese public databases were searched from inception to March 1, 2024. We included studies reporting the number of occurrences of endophthalmitis after cataract surgery at potential risk factors, including age, sex, DM status, hypertension status, intraoperative PCR, type of surgery and use of IOL material. The quality of the included studies was assessed using the Newcastle‒Ottawa Scale.
Results
A total of 57 studies were included and critically evaluated in the meta-analysis. The results showed that female sex was associated with a lower risk of endophthalmitis (odds ratio [OR]: 0.81; 95% CI: 0.75-0.87; p &lt; 0.001). Individuals with diabetes who underwent cataract surgery were found to have a greater risk of endophthalmitis (I2 = 95%; OR: 4.90; 95% CI: 2.41, 9.95; p &lt; 0.001), but the result may be influenced by publication bias. Individuals with hypertension (OR: 2.88; 95% CI: 1.53, 5.45; p = 0.001) and intraoperative PCR (OR: 9.18; 95% CI: 3.31, 25.43; p &lt; 0.001) were found to have a greater risk of endophthalmitis. Phacoemulsification significantly reduced the risk of endophthalmitis compared with extracapsular cataract extraction (ECCE) (OR: 0.62; 95% CI: 0.45, 0.85) based on network meta-analysis.
Conclusion
Male sex, hypertension, intraoperative PCR, and the use of the ECCE surgical approach are associated with a greater risk of postoperative endophthalmitis. Although an age-related trend in elevated risk was observed, this finding should be interpreted cautiously.
DOI: 10.15468/dl.hw48ma
title: Occurrence Download

description: A dataset listing the 4 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Argentina",
 "TaxonKey is Sus scrofa Linnaeus, 1758"
 ]
}

 The dataset's 4 records were derived from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055769-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14592135
title: PROGRESS OF MACHINE LEARNING IN THE FIELD OF INTRUSION DETECTION SYSTEMS

description: PROGRESS OF MACHINE LEARNING INTHE FIELD OF INTRUSION DETECTION SYSTEMSOuafae Elaeraj and Cherkaoui LeghrisL@M, RTM Team, Faculty of Sciences and Techniques Mohammedia,Hassan II University of Casablanca, MoroccoABSTRACTWith the growth in the use of the Internet and local area networks, malicious attacks and intrusions into computer systems are increasing. Implementing intrusion detection systems have become extremely important to help maintain good network security. Support vector machines (SVMs), a classic pattern recognition tool, have been widely used in intrusion detection. They can handle very large data with high efficiency, are easy to use, and exhibit good prediction behavior. This paper presents a new SVM model enriched with a Gaussian kernel function based on the features of the training data for intrusion detection. The new model is tested with the CICIDS2017 dataset. The test proves better results in terms of detection efficiency and false alarm rate, which can give better coverage and make detection more efficient.KEYWORDSIntrusion detection System, Support vector machines, Machine Learning.
DOI: 10.5281/zenodo.14592136
title: PROGRESS OF MACHINE LEARNING IN THE FIELD OF INTRUSION DETECTION SYSTEMS

description: PROGRESS OF MACHINE LEARNING INTHE FIELD OF INTRUSION DETECTION SYSTEMSOuafae Elaeraj and Cherkaoui LeghrisL@M, RTM Team, Faculty of Sciences and Techniques Mohammedia,Hassan II University of Casablanca, MoroccoABSTRACTWith the growth in the use of the Internet and local area networks, malicious attacks and intrusions into computer systems are increasing. Implementing intrusion detection systems have become extremely important to help maintain good network security. Support vector machines (SVMs), a classic pattern recognition tool, have been widely used in intrusion detection. They can handle very large data with high efficiency, are easy to use, and exhibit good prediction behavior. This paper presents a new SVM model enriched with a Gaussian kernel function based on the features of the training data for intrusion detection. The new model is tested with the CICIDS2017 dataset. The test proves better results in terms of detection efficiency and false alarm rate, which can give better coverage and make detection more efficient.KEYWORDSIntrusion detection System, Support vector machines, Machine Learning.
DOI: 10.15468/dl.v3qcym
title: Occurrence Download

description: A dataset containing 1374 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Composición de pesca de prospección para grandes pelágicos del Caribe colombiano, recolectados en el marco del Programa de Investigación Pesquera en Aguas Marinas Jurisdiccionales de Colombia INVEMAR – AUNAP (2013-2018)"
 ]
}

 The dataset includes 1374 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055777-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rmfkfk
title: Occurrence Download

description: A dataset containing 16335 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation, Material sample, Occurrence evidence)",
 "Continent is one of (North America, South America)",
 "Country is one of (Suriname, Belize, Argentina, Panama, Mexico, Brazil, Ecuador, Colombia, French Guiana, Costa Rica)",
 "DatasetKey is one of (iNaturalist Research-grade Observations, Observation.org, Nature data from around the World, Données du Parc amazonien de Guyane - Données sur la chasse Parc amazonien de Guyane 2010-2012, Données opportunistes faune du Groupe d'Étude et de Protection des Oiseaux en Guyane. - Données d'observations de Primates de Guyane. Extraction de Faune Guyane en 12/2019, Monitoreos trianuales de fauna y flora en el Valle del Cerrejón, La Guajira, Registro de avistamientos de fauna durante recorridos de control y vigilancia en el Parque Nacional Yasuní, Monitoreo de señales y huellas de fauna mediante senderos en el Refugio de Vida Silvestre Pambilar, Enquête chasse ONCFS Guyane 2000, Registros Primates Neotropicales Convocatoria APC, naturgucker)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Alouatta Lacépède, 1799"
 ]
}

 The dataset includes 16335 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055778-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jeg64b
title: Occurrence Download

description: A dataset containing 546 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Argentina",
 "TaxonKey is Sus scrofa Linnaeus, 1758"
 ]
}

 The dataset includes 546 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055779-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m4jy4r
title: Occurrence Download

description: A dataset containing 58 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Comunidad íctica de la Isla Cayo Serranilla durante la Expedición Seaflower 2017 - Proyecto Colombia BIO"
 ]
}

 The dataset includes 58 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055781-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.maa2e7
title: Occurrence Download

description: A dataset containing 122 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Comunidad íctica del atolón Albuquerque durante la Expedición Seaflower 2018 - Proyecto Colombia BIO"
 ]
}

 The dataset includes 122 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055784-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j2q433
title: Occurrence Download

description: A dataset containing 10730 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "OccurrenceStatus is Present",
 "TaxonKey is Paratrigona Schwarz, 1938"
 ]
}

 The dataset includes 10730 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055783-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60893/figshare.apl.28090049
title: Supplementary Materials

description: Supplementary Materials
DOI: 10.60893/figshare.apl.28045262
title: Supplementary Material

description: The preparation and characterization of NN single crystal
DOI: 10.60893/figshare.apl.28045262.v1
title: Supplementary Material

description: The preparation and characterization of NN single crystal
DOI: 10.15468/dl.x4zhkw
title: Occurrence Download

description: A dataset containing 2227 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Adenomera hylaedactyla (Cope, 1868)"
 ]
}

 The dataset includes 2227 records from 98 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055785-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60893/figshare.apm.28043435
title: Supplement Number 1

description: Supplementary Material-Chirality reversal of vortices induced by surface acoustic waves
DOI: 10.60893/figshare.apm.28043435.v1
title: Supplement Number 1

description: Supplementary Material-Chirality reversal of vortices induced by surface acoustic waves
DOI: 10.15468/dl.d9xmys
title: Occurrence Download

description: A dataset containing 106 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Condrictios del atolón Albuquerque durante la Expedición Seaflower 2018 - Proyecto Colombia BIO"
 ]
}

 The dataset includes 106 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055786-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60893/figshare.apr.27941334
title: SUPPLEMENTARY MATERIAL

description: See the additional detailed experimental data and demonstration videos to support the conclusions of the main manuscript.
DOI: 10.5281/zenodo.13305759
title: Multi-modality medical image dataset for medical image processing in Python lesson

description: This dataset contains a collection of medical imaging files for use in the "Medical Image Processing with Python" lesson, developed by the Netherlands eScience Center. 

The dataset includes:



SimpleITK compatible files: MRI T1 and CT scans (training_001_mr_T1.mha, training_001_ct.mha), digital X-ray (digital_xray.dcm in DICOM format), neuroimaging data (A1_grayT1.nrrd, A1_grayT2.nrrd). Data have been downloaded from here. 

MRI data: a T2-weighted image (OBJECT_phantom_T2W_TSE_Cor_14_1.nii in NIfTI-1 format). Data have been downloaded from here. 

Example images for the machine learning lesson: chest X-rays (rotatechest.png, other_op.png), cardiomegaly example (cardiomegaly_cc0.png).

Array data: Array data for the Intro to Medical Imaging lesson. Numpy arrays were created by processing and manipulation of publicly available data i.e. from the Schepp Logan phantom and from the NYU FastMRI dataset

Data for the anonymization exercises: ultrasound (identifiable_us.jpg) dowloaded from here, and DICOM data (our_sample_dicom.dcm) shared for this course specifically by a colleague

Histopathology data: histopathology slide images from openslide library samples in the freely distributable test data 


These files represent various medical imaging modalities and formats commonly used in clinical research and practice. They are intended for educational purposes, allowing students to practice image processing techniques, machine learning applications, and statistical analysis of medical images using Python libraries such as scikit-image, pydicom, and SimpleITK.
DOI: 10.5281/zenodo.13305760
title: Multi-modality medical image dataset for medical image processing in Python lesson

description: This dataset contains a collection of medical imaging files for use in the "Medical Image Processing with Python" lesson, developed by the Netherlands eScience Center. 

The dataset includes:



SimpleITK compatible files: MRI T1 and CT scans (training_001_mr_T1.mha, training_001_ct.mha), digital X-ray (digital_xray.dcm in DICOM format), neuroimaging data (A1_grayT1.nrrd, A1_grayT2.nrrd). Data have been downloaded from here. 

MRI data: a T2-weighted image (OBJECT_phantom_T2W_TSE_Cor_14_1.nii in NIfTI-1 format). Data have been downloaded from here. 

Example images for the machine learning lesson: chest X-rays (rotatechest.png, other_op.png), cardiomegaly example (cardiomegaly_cc0.png).

Additional anonymized data: TBA


These files represent various medical imaging modalities and formats commonly used in clinical research and practice. They are intended for educational purposes, allowing students to practice image processing techniques, machine learning applications, and statistical analysis of medical images using Python libraries such as scikit-image, pydicom, and SimpleITK.
DOI: 10.5281/zenodo.13305850
title: Multi-modality medical image dataset for medical image processing in Python lesson

description: This dataset contains a collection of medical imaging files for use in the "Medical Image Processing with Python" lesson, developed by the Netherlands eScience Center. 

The dataset includes:



SimpleITK compatible files: MRI T1 and CT scans (training_001_mr_T1.mha, training_001_ct.mha), digital X-ray (digital_xray.dcm in DICOM format), neuroimaging data (A1_grayT1.nrrd, A1_grayT2.nrrd). Data have been downloaded from here. 

MRI data: a T2-weighted image (OBJECT_phantom_T2W_TSE_Cor_14_1.nii in NIfTI-1 format). Data have been downloaded from here. 

Example images for the machine learning lesson: chest X-rays (rotatechest.png, other_op.png), cardiomegaly example (cardiomegaly_cc0.png).

Additional anonymized data: TBA


These files represent various medical imaging modalities and formats commonly used in clinical research and practice. They are intended for educational purposes, allowing students to practice image processing techniques, machine learning applications, and statistical analysis of medical images using Python libraries such as scikit-image, pydicom, and SimpleITK.
DOI: 10.5281/zenodo.13305860
title: Multi-modality medical image dataset for medical image processing in Python lesson

description: This dataset contains a collection of medical imaging files for use in the "Medical Image Processing with Python" lesson, developed by the Netherlands eScience Center. 

The dataset includes:



SimpleITK compatible files: MRI T1 and CT scans (training_001_mr_T1.mha, training_001_ct.mha), digital X-ray (digital_xray.dcm in DICOM format), neuroimaging data (A1_grayT1.nrrd, A1_grayT2.nrrd). Data have been downloaded from here. 

MRI data: a T2-weighted image (OBJECT_phantom_T2W_TSE_Cor_14_1.nii in NIfTI-1 format). Data have been downloaded from here. 

Example images for the machine learning lesson: chest X-rays (rotatechest.png, other_op.png), cardiomegaly example (cardiomegaly_cc0.png).

Additional anonymized data: TBA


These files represent various medical imaging modalities and formats commonly used in clinical research and practice. They are intended for educational purposes, allowing students to practice image processing techniques, machine learning applications, and statistical analysis of medical images using Python libraries such as scikit-image, pydicom, and SimpleITK.