cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/mkz3-w413
title: Geographic-based Auxiliary Routing Scheme in SDN-based Mega Constellation Networks

description: Recently, large-scale and mega low earth orbit (LEO) constellations have become the primary development trends in satellite networks.Considering the complex topology and limited processing capabilities of LEO satellites, traditional distributed routing algorithms with significant flooding overhead are not suitable for the era of mega constellations. In this context, centralized routing technology based on Software Defined Network (SDN) architecture has been widely applied. However, centralized routing technology exhibits a slower response to inter-satellite link (ISL) changes due to longer informing path, which induce that longer time is required to restore traffic in the event of ISL terminal or satellite /hardware faults. To this end, this paper proposes a lightweight Geographic-based Auxiliary Routing Scheme (GARS), adapted to Walker Delta constellation, as a traffic fast restoration strategy for SDN-based centralized routing scheme. This scheme has an ultralow computation complexity of O(1),enabling the rapid calculation and establishment of temporary paths to restore service during the SDN controller in ground station rerouting. In addition, an optimized geometric routing algorithm with anti-loop mechanism is proposed to enhance the stability and reliability of geometric routing algorithm. Simulation results show that (1) compared with traditional centralized routing system, the routing scheme proposed in this paper shortens interruption time of traffic by at least 65.5% and reduces mean packet loss ratio by at least 67.2% without increasing link load. (2) Compared to other traditional geometric routing algorithms without advanced anti-loop mechanism, GARS with the optimized anti-loop mechanism also effectively ensures the continuity of service, reducing packet loss rate and interruption time byat most 96%.
DOI: 10.5281/zenodo.14883163
title: Data for aggradation experiments performed at the Mountain Hydraulics Laboratory of the Politecnico di Milano

description: This repository contains raw data from aggradation experiments conducted at the Mountain Hydraulics Laboratory, Politecnico di Milano, Lecco campus (Italy). The experimental campaign includes 26 aggradation tests performed under supercritical flow conditions, with various controlling parameters. The aggradation experiments have been performed with particles made of Polyvinyl Chloride (PVC) with the size of 3.8 mm and density of 1443 kg/m3. Furthermore, the experiments are categorized into two groups based on the nominal initial channel slope (1.37% and 2.20%), seven groups based on water discharge (ranging from 0.003 m³/s to 0.009 m³/s), and five groups based on the load ratio (with values of 1.55, 1.85, 2.15, 2.7, and 3.2). The load ratio represents the ratio between the sediment inflow discharge and the initial sediment transport capacity of the flow.

Each Excel file presents the measured bed and water surface elevations at various times and locations during the experiment, all referenced to a horizontal datum. Additionally, the Excel file named "Key-parameters" provides the controlling parameter for each run.
DOI: 10.5281/zenodo.14883164
title: Data for aggradation experiments performed at the Mountain Hydraulics Laboratory of the Politecnico di Milano

description: This repository contains raw data from aggradation experiments conducted at the Mountain Hydraulics Laboratory, Politecnico di Milano, Lecco campus (Italy). The experimental campaign includes 26 aggradation tests performed under supercritical flow conditions, with various controlling parameters. The aggradation experiments have been performed with particles made of Polyvinyl Chloride (PVC) with the size of 3.8 mm and density of 1443 kg/m3. Furthermore, the experiments are categorized into two groups based on the nominal initial channel slope (1.37% and 2.20%), seven groups based on water discharge (ranging from 0.003 m³/s to 0.009 m³/s), and five groups based on the load ratio (with values of 1.55, 1.85, 2.15, 2.7, and 3.2). The load ratio represents the ratio between the sediment inflow discharge and the initial sediment transport capacity of the flow.

Each Excel file presents the measured bed and water surface elevations at various times and locations during the experiment, all referenced to a horizontal datum. Additionally, the Excel file named "Key-parameters" provides the controlling parameter for each run.
DOI: 10.18112/openneuro.ds005574.v1.0.2
title: The "Podcast" ECoG dataset for modeling neural activity during natural story listening
DOI: 10.6084/m9.figshare.28430045
title: Datafile 8. Functional distribution of expanded orthogroups identified in Chlorophyta and stramenopiles-alevolata

description: Functional distribution of expanded orthogroups identified in Chlorophyta and stramenopiles-alevolata
DOI: 10.5281/zenodo.14882988
title: Raw results of "PyJobShop: Solving scheduling problems with constraint programming in Python"

description: See https://github.com/PyJobShop/Experiments.
DOI: 10.5281/zenodo.14882989
title: Raw results of "PyJobShop: Solving scheduling problems with constraint programming in Python"

description: See https://github.com/PyJobShop/Experiments.
DOI: 10.15468/dl.kas5ja
title: Occurrence Download

description: A dataset containing 62 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Rhus typhina L."
 ]
}

 The dataset includes 62 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003058-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xp2wxa
title: Occurrence Download

description: A dataset containing 225 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "Country is Brazil",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Limnoperna fortunei (Dunker, 1857)"
 ]
}

 The dataset includes 225 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003060-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wqbbg7
title: Occurrence Download

description: A dataset containing 955 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Lepus saxatilis F.Cuvier, 1823"
 ]
}

 The dataset includes 955 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003061-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b7bspp
title: Occurrence Download

description: A dataset containing 29 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Vigna keraudrenii Du Puy & Labat"
 ]
}

 The dataset includes 29 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003063-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s7hr7k
title: Occurrence Download

description: A dataset containing 36567 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation, Specimen, Fossil, Living Specimen)",
 "Country is United States of America",
 "TaxonKey is Lepus Linnaeus, 1758"
 ]
}

 The dataset includes 36567 records from 131 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003064-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n58ncp
title: Occurrence Download

description: A dataset containing 50487 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Global soil organisms",
 "Month is January",
 "Year 2020-2020"
 ]
}

 The dataset includes 50487 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003062-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vf6kq7
title: Occurrence Download

description: A dataset containing 45431 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Denver Museum of Nature & Science - Arachnology"
 ]
}

 The dataset includes 45431 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003065-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14882563
title: Unsupervised Classification of Multi-Class Chart Images: A Comparison of Customized CNNs and Transfer Learning Techniques

description: Visualization has transformed into a crucial method for presenting complex data in a reasonable design, outperforming standard representation strategies in clearness and transparency. The feasibility of growing use cases of visualization has been further enhanced by the advent Machine Learning and Deep Learning algorithms and its types such as Transfer Learning which provides the capability reuse the solutions extracted from various sources to better train the data. This paper demonstrates financial, social economic, and political data visualization charts with 4 popular classes i.e., Histogram, Bar, Line, and Pie charts. For automatic feature extraction, VGG16 model has been implemented and applying principal component analysis (PCA) to extract signal from the feature descriptors, along with k-means clustering algorithm to cluster them into 4 groups. Finally for image classification three pre trained models are used that are RESTNET50, RESTNET50 V2, DenseNet along with Customized Convolutional Neural Network on the ChartVqa dataset of graph images for image classification with following accuracy 85.40%, 80.76%, 82.46%, and 90.71% respectively. By leveraging Transfer Learning and advanced neural network models, this study advances the capability to classify and interpret chart images without the need for labeled training data.
DOI: 10.5281/zenodo.14882564
title: Unsupervised Classification of Multi-Class Chart Images: A Comparison of Customized CNNs and Transfer Learning Techniques

description: Visualization has transformed into a crucial method for presenting complex data in a reasonable design, outperforming standard representation strategies in clearness and transparency. The feasibility of growing use cases of visualization has been further enhanced by the advent Machine Learning and Deep Learning algorithms and its types such as Transfer Learning which provides the capability reuse the solutions extracted from various sources to better train the data. This paper demonstrates financial, social economic, and political data visualization charts with 4 popular classes i.e., Histogram, Bar, Line, and Pie charts. For automatic feature extraction, VGG16 model has been implemented and applying principal component analysis (PCA) to extract signal from the feature descriptors, along with k-means clustering algorithm to cluster them into 4 groups. Finally for image classification three pre trained models are used that are RESTNET50, RESTNET50 V2, DenseNet along with Customized Convolutional Neural Network on the ChartVqa dataset of graph images for image classification with following accuracy 85.40%, 80.76%, 82.46%, and 90.71% respectively. By leveraging Transfer Learning and advanced neural network models, this study advances the capability to classify and interpret chart images without the need for labeled training data.
DOI: 10.15468/dl.gas3e6
title: Occurrence Download

description: A dataset containing 1430 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ixodes persulcatus Schulze, 1930"
 ]
}

 The dataset includes 1430 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003067-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ztaejm
title: Occurrence Download

description: A dataset containing 746 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Sri Lanka",
 "TaxonKey is Nisaetus kelaarti (Legge, 1878)",
 "Year 2000-2025"
 ]
}

 The dataset includes 746 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003069-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.63dtv8
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((13.41183 39.46632,11.45163 38.40364,11.21403 36.6609,13.47123 35.26672,17.03524 35.07249,17.74804 38.58959,13.41183 39.46632))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Ericaria amentacea (C.Agardh) Molinari & Guiry, 2020"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003070-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9wn8ee
title: Occurrence Download

description: A dataset containing 36 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((12.56523 55.69948,12.56627 55.69948,12.56627 55.70027,12.56523 55.70027,12.56523 55.69948))",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year 2019-2025"
 ]
}

 The dataset includes 36 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003072-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w3t5mt
title: Occurrence Download

description: A dataset containing 2796 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Buthus occitanus (Amoreux, 1789), Euscorpius flavicaudis (DeGeer, 1778))",
 "OccurrenceStatus is one of (Present)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil)"
 ]
 }
 },
 "Year is greater than or equal to 1970"
 ]
}

 The dataset includes 2796 records from 158 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003073-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.be32pu
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cybianthus jaramilloi Pipoly"
 ]
}

 The dataset includes 12 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003074-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14883320
title: Data regarding the manuscript: Carrier-envelope offset frequency measurement by means of an external optical resonator
DOI: 10.5281/zenodo.14883321
title: Data regarding the manuscript: Carrier-envelope offset frequency measurement by means of an external optical resonator
DOI: 10.6084/m9.figshare.28429931
title: Forecasting achievement of inactive disease in juvenile idiopathic arthritis with artificial intelligence

description: Objective. To seek for predictors of inactive disease (ID) in juvenile idiopathic arthritis (JIA) with artificial intelligence.Methods. The clinical charts of patients seen within 6 months after disease onset between 2007 and 2019 and with follow-up visits at 6, 12, 18, and 24 months were reviewed retrospectively. Sixty-eight potential predictors were recorded at each visit. The primary endpoint was ID at 24 months by 2004 Wallace criteria. Data obtained from diverse combinations of visits were examined to identify the best forecasting model. After pre-processing, the cohort was divided into training (50%) and testing (50%) cohorts. Multivariate time series forecasting, coupled with Random Forest method, was used to train the machine learning (ML) forecasting model. Predictive performance was assessed through Matthews correlation coefficient (MCC).Results. A total of 414 patients were included. The best performance in predicting ID at 24 months in the training cohort was provided by the 0-12 months interval (MCC = 0.68). In the testing cohort, the same ML model confirmed a high forecasting performance (MCC = 0.65). Assessment of feature importance and impact analysis showed that the most relevant predictor of ID was the physician’s global assessment (PhGA), followed by the count of active joints (AJC).Conclusion. PhGA and AJC values over the first 12 months were the strongest predictors of ID at 24 months. This finding highlights the importance of regular quantitative assessment of disease activity by the caring physician in monitoring the course of the patient toward achievement of complete disease quiescence.<br>
DOI: 10.6084/m9.figshare.28429931.v1
title: Forecasting achievement of inactive disease in juvenile idiopathic arthritis with artificial intelligence

description: Objective. To seek for predictors of inactive disease (ID) in juvenile idiopathic arthritis (JIA) with artificial intelligence.Methods. The clinical charts of patients seen within 6 months after disease onset between 2007 and 2019 and with follow-up visits at 6, 12, 18, and 24 months were reviewed retrospectively. Sixty-eight potential predictors were recorded at each visit. The primary endpoint was ID at 24 months by 2004 Wallace criteria. Data obtained from diverse combinations of visits were examined to identify the best forecasting model. After pre-processing, the cohort was divided into training (50%) and testing (50%) cohorts. Multivariate time series forecasting, coupled with Random Forest method, was used to train the machine learning (ML) forecasting model. Predictive performance was assessed through Matthews correlation coefficient (MCC).Results. A total of 414 patients were included. The best performance in predicting ID at 24 months in the training cohort was provided by the 0-12 months interval (MCC = 0.68). In the testing cohort, the same ML model confirmed a high forecasting performance (MCC = 0.65). Assessment of feature importance and impact analysis showed that the most relevant predictor of ID was the physician’s global assessment (PhGA), followed by the count of active joints (AJC).Conclusion. PhGA and AJC values over the first 12 months were the strongest predictors of ID at 24 months. This finding highlights the importance of regular quantitative assessment of disease activity by the caring physician in monitoring the course of the patient toward achievement of complete disease quiescence.<br>
DOI: 10.15468/dl.fqv7yq
title: Occurrence Download

description: A dataset containing 1616554 records in GBIF matching the query:

SELECT 
 GBIF_ISEA3HCode(
 8,
 decimalLatitude,
 decimalLongitude,
 COALESCE(coordinateUncertaintyInMeters, 1000)
 ) AS gridcode,
 "year", 
 countrycode,
 speciesKey,
 COUNT(*) AS occ_count
FROM occurrence
WHERE "year" &gt; 1969 AND "year" &lt; 2025
 AND hasCoordinate = TRUE
 AND speciesKey IS NOT NULL
 AND countryCode IS NOT NULL
 AND classKey = 359
GROUP BY 
speciesKey, 
"year",
countrycode,
gridcode;


 The dataset's 1616554 records were derived from 13059 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003066-250214102907787/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.25547139
title: Additional file 2 of DhuFAP: a platform for gene functional analysis in Dendrobium huoshanense

description: Supplementary Material 2
DOI: 10.6084/m9.figshare.25547139.v1
title: Additional file 2 of DhuFAP: a platform for gene functional analysis in Dendrobium huoshanense

description: Supplementary Material 2
DOI: 10.6084/m9.figshare.25546932
title: Additional file 1 of Low long-lasting insecticidal net use in malaria elimination areas in Southern Ethiopia: results from community based cross-sectional study

description: Additional file 1: Data S1. Data in Stata dta file type.
DOI: 10.6084/m9.figshare.25546932.v1
title: Additional file 1 of Low long-lasting insecticidal net use in malaria elimination areas in Southern Ethiopia: results from community based cross-sectional study

description: Additional file 1: Data S1. Data in Stata dta file type.
DOI: 10.6084/m9.figshare.26702311
title: Additional file 7 of Trx4, a novel thioredoxin protein, is important for Toxoplasma gondii fitness

description: Additional file 7: Table S3. The proximity proteins of Trx4 identified by mass spectrometry.
DOI: 10.6084/m9.figshare.26702311.v1
title: Additional file 7 of Trx4, a novel thioredoxin protein, is important for Toxoplasma gondii fitness

description: Additional file 7: Table S3. The proximity proteins of Trx4 identified by mass spectrometry.
DOI: 10.6084/m9.figshare.26702377
title: Additional file 3 of An adapted protocol to derive microglia from stem cells and its application in the study of CSF1R-related disorders

description: Supplementary Material 3.
DOI: 10.6084/m9.figshare.26702377.v1
title: Additional file 3 of An adapted protocol to derive microglia from stem cells and its application in the study of CSF1R-related disorders

description: Supplementary Material 3.
DOI: 10.6084/m9.figshare.26702380
title: Additional file 4 of An adapted protocol to derive microglia from stem cells and its application in the study of CSF1R-related disorders

description: Supplementary Material 4.
DOI: 10.6084/m9.figshare.26702380.v1
title: Additional file 4 of An adapted protocol to derive microglia from stem cells and its application in the study of CSF1R-related disorders

description: Supplementary Material 4.
DOI: 10.6084/m9.figshare.26702383
title: Additional file 5 of An adapted protocol to derive microglia from stem cells and its application in the study of CSF1R-related disorders

description: Supplementary Material 5.
DOI: 10.6084/m9.figshare.26702383.v1
title: Additional file 5 of An adapted protocol to derive microglia from stem cells and its application in the study of CSF1R-related disorders

description: Supplementary Material 5.
DOI: 10.6084/m9.figshare.26702434
title: Additional file 7 of Metabolome and transcriptome analyses reveal changes of rapeseed in response to ABA signal during early seedling development

description: Supplementary Material 7
DOI: 10.6084/m9.figshare.26702434.v1
title: Additional file 7 of Metabolome and transcriptome analyses reveal changes of rapeseed in response to ABA signal during early seedling development

description: Supplementary Material 7
DOI: 10.6084/m9.figshare.26702437
title: Additional file 8 of Metabolome and transcriptome analyses reveal changes of rapeseed in response to ABA signal during early seedling development

description: Supplementary Material 8
DOI: 10.6084/m9.figshare.26702437.v1
title: Additional file 8 of Metabolome and transcriptome analyses reveal changes of rapeseed in response to ABA signal during early seedling development

description: Supplementary Material 8