cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.r8hbh7
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aphyosemion georgiae Lambert & Géry, 1968",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((46.8009 -14.7158,46.1382 -15.1614,46.1382 -15.9401,50.8081 -15.9401,50.9712 -15.4415,50.3203 -12.5851,49.5255 -11.523,49.0875 -11.4772,48.2794 -11.911,48.1654 -12.5557,47.3945 -13.3102,47.2457 -14.0495,46.8009 -14.7158))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053891-250525065834625/datasets/export for details.
DOI: 10.15468/dl.ymgaun
title: Occurrence Download

description: A dataset containing 88181 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Lynx lynx (Linnaeus, 1758)"
 ]
}

 The dataset includes 88181 records from 241 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053882-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.52xddr
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aphyosemion georgiae Lambert & Géry, 1968",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((54.0789 -15.6465,54.0525 -15.9401,54.9903 -15.9401,54.9893 -15.6985,54.6906 -15.4224,54.2949 -15.439,54.0789 -15.6465))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053893-250525065834625/datasets/export for details.
DOI: 10.15468/dl.5cea4g
title: Occurrence Download

description: A dataset containing 30093 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "Month is September",
 "TaxonKey is Fungi",
 "Year is 2021"
 ]
}

 The dataset includes 30093 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053892-250525065834625/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.27860982
title: MHSI-Net.zip

description: Street identification in historical maps is essential for understanding urban historical landscapes and advancing heritage conservation. Traditional methods extract street characteristics through manually feature engineering, but limits the accuracy and generalizability. While deep learning methods have achieved automatic extraction of complex deep features, they still struggle to capture spatiotemporal variations in streets, resulting in limited performance and interpretability.To address this challenge, this study proposes a novel Multi-modal Historical Street Identification Network (MHSI-Net) that integrates street-related semantics to identify streets from specific construction periods in historical maps. It comprises three primary modules: multi-modal representation learning, multi-modal interactive fusion, and an interpretable attention-enhanced method. These modules are designed to extract features of street morphology from street images and network structure from street graph, facilitate cross-scale semantic fusion and capture local-global connections, and ultimately improve identification accuracy and interpretability.In the task of identifying Yuan streets (1267–1368) on the 1937 Beijing map, our model based on MHSI-Net demonstrated superior performance, outperforming existing benchmarks across various metrics, confirming the feasibility of multi-modal representation learning for fine-grained historical street identification. Additionally, the interpretable attention-enhanced method further improves both interpretability and accuracy regarding the morphological characteristics and spatial distribution of Yuan streets.
DOI: 10.6084/m9.figshare.27860982.v2
title: MHSI-Net.zip

description: Street identification in historical maps is essential for understanding urban historical landscapes and advancing heritage conservation. Traditional methods extract street characteristics through manually feature engineering, but limits the accuracy and generalizability. While deep learning methods have achieved automatic extraction of complex deep features, they still struggle to capture spatiotemporal variations in streets, resulting in limited performance and interpretability.To address this challenge, this study proposes a novel Multi-modal Historical Street Identification Network (MHSI-Net) that integrates street-related semantics to identify streets from specific construction periods in historical maps. It comprises three primary modules: multi-modal representation learning, multi-modal interactive fusion, and an interpretable attention-enhanced method. These modules are designed to extract features of street morphology from street images and network structure from street graph, facilitate cross-scale semantic fusion and capture local-global connections, and ultimately improve identification accuracy and interpretability.In the task of identifying Yuan streets (1267–1368) on the 1937 Beijing map, our model based on MHSI-Net demonstrated superior performance, outperforming existing benchmarks across various metrics, confirming the feasibility of multi-modal representation learning for fine-grained historical street identification. Additionally, the interpretable attention-enhanced method further improves both interpretability and accuracy regarding the morphological characteristics and spatial distribution of Yuan streets.
DOI: 10.6084/m9.figshare.27860982.v3
title: MHSI-Net.zip

description: Street identification in historical maps is essential for understanding urban historical landscapes and advancing heritage conservation. Traditional methods extract street characteristics through manually feature engineering, but limits the accuracy and generalizability. While deep learning methods have achieved automatic extraction of complex deep features, they still struggle to capture spatiotemporal variations in streets, resulting in limited performance and interpretability.To address this challenge, this study proposes a novel Multi-modal Historical Street Identification Network (MHSI-Net) that integrates street-related semantics to identify streets from specific construction periods in historical maps. It comprises three primary modules: multi-modal representation learning, multi-modal interactive fusion, and an interpretable attention-enhanced method. These modules are designed to extract features of street morphology from street images and network structure from street graph, facilitate cross-scale semantic fusion and capture local-global connections, and ultimately improve identification accuracy and interpretability.In the task of identifying Yuan streets (1267–1368) on the 1937 Beijing map, our model based on MHSI-Net demonstrated superior performance, outperforming existing benchmarks across various metrics, confirming the feasibility of multi-modal representation learning for fine-grained historical street identification. Additionally, the interpretable attention-enhanced method further improves both interpretability and accuracy regarding the morphological characteristics and spatial distribution of Yuan streets.
DOI: 10.15468/dl.86qm9m
title: Occurrence Download

description: A dataset containing 795538 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Xeno-canto - Bird sounds from around the world"
 ]
}

 The dataset includes 795538 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053889-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8zm2fh
title: Occurrence Download

description: A dataset containing 146 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Angola",
 "TaxonKey is Xenopus petersii Bocage, 1895"
 ]
}

 The dataset includes 146 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053896-250525065834625/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.29348963
title: Supplementary Material for: A case of epiretinal membrane masquerading as foveal hypoplasia uncovered by optical coherence tomography angiography

description: Introduction
This case report describes a patient whose misdiagnosis of foveal hypoplasia was uncovered by optical coherence tomography angiography (OCTA) findings that suggested the presence of an Epiretinal membrane (ERM) over foveal hypoplasia. 
Case Presentation 
A 67-year-old man with no significant past medical history was referred to our retina clinic with a diagnosis of foveal hypoplasia. He had been experiencing significant vision loss for more than one year. OCT demonstrated absence of the foveal depression in both eyes. A subtle ERM was identified in the left eye OCT, but the presence of an ERM in the right eye OCT was equivocal. As such, it was unclear whether flattening of the fovea was attributable to hypoplasia or ERM based on OCT alone. This prompted further investigation with OCTA, which showed the presence of the FAZ in both eyes. The diagnosis of stage 2 epiretinal membrane OU was made based on OCTA findings. The patient underwent cataract extraction with intraocular lens implantation, pars plana vitrectomy, and epiretinal membrane peel, which resulted in improvement of visual symptoms and visual acuity. Follow up OCT showed normalization of the foveal pit in the right eye greater than the left eye.
Conclusion 
This case demonstrates the importance of utilization of OCTA in differentiating true foveal hypoplasia from this foveal “pseudohypoplasia” exhibited by our patient.
DOI: 10.6084/m9.figshare.29348963.v1
title: Supplementary Material for: A case of epiretinal membrane masquerading as foveal hypoplasia uncovered by optical coherence tomography angiography

description: Introduction
This case report describes a patient whose misdiagnosis of foveal hypoplasia was uncovered by optical coherence tomography angiography (OCTA) findings that suggested the presence of an Epiretinal membrane (ERM) over foveal hypoplasia. 
Case Presentation 
A 67-year-old man with no significant past medical history was referred to our retina clinic with a diagnosis of foveal hypoplasia. He had been experiencing significant vision loss for more than one year. OCT demonstrated absence of the foveal depression in both eyes. A subtle ERM was identified in the left eye OCT, but the presence of an ERM in the right eye OCT was equivocal. As such, it was unclear whether flattening of the fovea was attributable to hypoplasia or ERM based on OCT alone. This prompted further investigation with OCTA, which showed the presence of the FAZ in both eyes. The diagnosis of stage 2 epiretinal membrane OU was made based on OCTA findings. The patient underwent cataract extraction with intraocular lens implantation, pars plana vitrectomy, and epiretinal membrane peel, which resulted in improvement of visual symptoms and visual acuity. Follow up OCT showed normalization of the foveal pit in the right eye greater than the left eye.
Conclusion 
This case demonstrates the importance of utilization of OCTA in differentiating true foveal hypoplasia from this foveal “pseudohypoplasia” exhibited by our patient.
DOI: 10.6084/m9.figshare.29348966
title: Supplementary Material for: Virtual Reality- Based Running Exposure To Target The Acute Urge To Be Physically Active In Anorexia Nervosa: A Case Series

description: Introduction: Anorexia Nervosa (AN) is a severe and complex eating disorder, whose incidence has increased following the Covid 19 pandemic, straining specialized healthcare systems worldwide. Although patients often resort to compulsive exercise as a means of weight control, prolonged fasting intensifies the urge to move, leading to increased motor restlessness. Despite progress in understanding AN, interventions often overlook the crucial role that the urge to be physically active plays in reinforcing the eating disorder itself. This case series tested the feasibility of a Virtual Reality-based Running Exposure (VR-RE) protocol to target the acute urge to be physically active in inpatients with severe AN undergoing rehabilitation.
Case Presentations: Three female inpatients with severe AN (Body Mass Index ≤ 15 kg/m2), aged 21, 23 and 26, participated in a 4-week VR-RE protocol, involving four sessions of simulated jogging in a VR environment. Pre- and post-intervention assessments evaluated eating disorder symptoms and traits characteristics, such as compulsive exercise and physical activity addiction. Although treatment outcomes were only partially achieved and there was little change observed in clinical parameters such as weight and body-mass index, reductions in the acute urge to move were observed for all after each VR session. Additionally, reliable and clinically relevant changes were observed in drive for thinness, body- image dissatisfaction, interoceptive deficits and emotional regulation. Two patients also reported a decrease in both trait- and addictive physical activity at the end of the protocol and rehabilitation program. Qualitative feedback revealed perceived synchronization with the avatar and endorsed the protocol’s potential for outpatient settings. No cybersickness was reported.
Conclusion: This study presents a structured VR-based protocol targeting the acute urge to be physically active in severe AN, integrated into a rehabilitation setting. Future research with longer, personalized, and controlled trials is needed to validate its clinical utility.
DOI: 10.6084/m9.figshare.29348966.v1
title: Supplementary Material for: Virtual Reality- Based Running Exposure To Target The Acute Urge To Be Physically Active In Anorexia Nervosa: A Case Series

description: Introduction: Anorexia Nervosa (AN) is a severe and complex eating disorder, whose incidence has increased following the Covid 19 pandemic, straining specialized healthcare systems worldwide. Although patients often resort to compulsive exercise as a means of weight control, prolonged fasting intensifies the urge to move, leading to increased motor restlessness. Despite progress in understanding AN, interventions often overlook the crucial role that the urge to be physically active plays in reinforcing the eating disorder itself. This case series tested the feasibility of a Virtual Reality-based Running Exposure (VR-RE) protocol to target the acute urge to be physically active in inpatients with severe AN undergoing rehabilitation.
Case Presentations: Three female inpatients with severe AN (Body Mass Index ≤ 15 kg/m2), aged 21, 23 and 26, participated in a 4-week VR-RE protocol, involving four sessions of simulated jogging in a VR environment. Pre- and post-intervention assessments evaluated eating disorder symptoms and traits characteristics, such as compulsive exercise and physical activity addiction. Although treatment outcomes were only partially achieved and there was little change observed in clinical parameters such as weight and body-mass index, reductions in the acute urge to move were observed for all after each VR session. Additionally, reliable and clinically relevant changes were observed in drive for thinness, body- image dissatisfaction, interoceptive deficits and emotional regulation. Two patients also reported a decrease in both trait- and addictive physical activity at the end of the protocol and rehabilitation program. Qualitative feedback revealed perceived synchronization with the avatar and endorsed the protocol’s potential for outpatient settings. No cybersickness was reported.
Conclusion: This study presents a structured VR-based protocol targeting the acute urge to be physically active in severe AN, integrated into a rehabilitation setting. Future research with longer, personalized, and controlled trials is needed to validate its clinical utility.
DOI: 10.15468/dl.t85h4a
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aphyosemion georgiae Lambert & Géry, 1968",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((46.8392 -11.8033,47.0739 -12.037,47.4888 -12.0535,47.8142 -11.7664,47.8606 -11.3694,47.5422 -11.0403,47.0944 -11.1109,46.8389 -11.3512,46.8392 -11.8033))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053895-250525065834625/datasets/export for details.
DOI: 10.6092/unibo/amsacta/8388
title: QUANTHEM. Data for the basis-minimized stabilizer asymmetry in qubit systems. Version 1

description: The dataset contains data generated in the framework of Horizon Europe ERC QUANTHEM project. The data were the result of numerical computations of nonstabilizerness for several classes of quantum states in qubit systems. Specifically, the data contains the value of the so-called basis-minimized stabilizer asymmetry and other nonstabilizerness measures, such as the stabilizer Rényi entropies.
DOI: 10.5281/zenodo.15688048
title: Surface texture measurements_2

description: The data of the surface texture measurements of the samples were collected, which is important for characterizing the relationship between the surface texture and the friction properties on the ice, as well as for improving the theoretical models of the ice friction.

Data collected are direct measurements using both 2D and 3D surface texture measurement equipment.

The dataset does not include data for reuse.

The data are useful for researchers working in the field of surface measurement as well as for researchers studying processes related to ice friction.
DOI: 10.5281/zenodo.15688047
title: Surface texture measurements_2

description: The data of the surface texture measurements of the samples were collected, which is important for characterizing the relationship between the surface texture and the friction properties on the ice, as well as for improving the theoretical models of the ice friction.

Data collected are direct measurements using both 2D and 3D surface texture measurement equipment.

The dataset does not include data for reuse.

The data are useful for researchers working in the field of surface measurement as well as for researchers studying processes related to ice friction.
DOI: 10.5281/zenodo.13837157
title: Soil chemistry, organic carbon pool, microbial biomass and necromass, enzymes activity and greenhouse gas (CO2, CH4, and N2O) fluxes after a long-term N fertilization of a Scots pine forest in Finland

description: The dataset contains data analyzed in a scientific manuscript Nitrogen fertilization of boreal forest soil increases soil carbon pool through elevated microbial necromass formation but also modifies tree secondary metabolism, in submission.

This study was established in a boreal Scots pine forest "Karstula" located in Central Finland (62°54’43.343″N; 24°34’16.021″E). Measurements and sampling were conducted in two treatments, control without nitrogen addition and nitrogen fertilization treatment which was performed seven times from 1960 to 2020. The soil CO2 emissions, and the CH4 and N2O net exchange data were collected from manual chamber measurements on control and N fertilization plots in 2021, 2022, ad 2023. Soils and roots were sampled from two soil layers, the organic layer and upper mineral layer (0–20 cm) in September 2021. 

The dataset "Karstula_SOM" contains soil pH, C and N content, stable and labile organic carbon pool, mineral-associated organic matter (MAOM) and particulate organic matter (POM), condensed tannins (CT), lignin, inorganic nitrogen (ammonium, nitrate), Fungal biomass (ergosterol) and necromass markers (aminosugar glucosamine) for mineral and organic layers.

The dataset "Karstula_NeedleCT" contains condensed tannins (CT) from tree needles.

The dataset "Karstula_Enzyme_activity" contains different enzyme activites of mineral and organic layers.

The dataset"Karstula_GHG_temp.swc" includes CO2, CH4 and N2O fluxes, soil temperature and water content data measured from control and N fertilization plots, this dataset is part of published dataset DOI:10.5281/zenodo.13889763.

The microbial community data used in this manuscript is part of the dataset in the publication DOI: 10.1111/gcb.17516.
DOI: 10.5281/zenodo.13837158
title: Soil chemistry, organic carbon pool, microbial biomass and necromass, enzymes activity and greenhouse gas (CO2, CH4, and N2O) fluxes after a long-term N fertilization of a Scots pine forest in Finland

description: The dataset contains data analyzed in a scientific manuscript Nitrogen fertilization of boreal forest soil increases soil carbon pool through elevated microbial necromass formation but also modifies tree secondary metabolism, in submission.

This study was established in a boreal Scots pine forest "Karstula" located in Central Finland (62°54’43.343″N; 24°34’16.021″E). Measurements and sampling were conducted in two treatments, control without nitrogen addition and nitrogen fertilization treatment which was performed seven times from 1960 to 2020. The soil CO2 emissions, and the CH4 and N2O net exchange data were collected from manual chamber measurements on control and N fertilization plots in 2021, 2022, ad 2023. Soils and roots were sampled from two soil layers, the organic layer and upper mineral layer (0–20 cm) in September 2021. 

The dataset "Karstula_SOM" contains soil pH, C and N content, stable and labile organic carbon pool, mineral-associated organic matter (MAOM) and particulate organic matter (POM), condensed tannins (CT), lignin, inorganic nitrogen (ammonium, nitrate), Fungal biomass (ergosterol) and necromass markers (aminosugar glucosamine) for mineral and organic layers.

The dataset "Karstula_NeedleCT" contains condensed tannins (CT) from tree needles.

The dataset "Karstula_Enzyme_activity" contains different enzyme activites of mineral and organic layers.

The dataset"Karstula_GHG_temp.swc" includes CO2, CH4 and N2O fluxes, soil temperature and water content data measured from control and N fertilization plots, this dataset is part of published dataset DOI:10.5281/zenodo.13889763.

The microbial community data used in this manuscript is part of the dataset in the publication DOI: 10.1111/gcb.17516.
DOI: 10.5281/zenodo.15688092
title: GBIF data summaries: GBIF national node type records: Sweden

description: GBIF—the Global Biodiversity Information Facility—is an international network and research infrastructure funded by the world__s governments and aimed at providing anyone, anywhere, open access to data about all types of life on Earth. https://www.gbif.org
DOI: 10.25592/uhhfdm.12643
title: An Interview About The History of Nyandu Kundaa Kaabiila in Berending Pirang (NCAC_RDD_TAPE_0873A)

description: Foundation of compound by informant 
 Informant's paternal genealogy
 Migration of the 2nd informant's parents from Timbuktu to Kaabu 
 2nd Informant's paternal genealogy


<em><sup>Alternative names: Joof, Fatty, Numo, Bayo, Abdoulie, Abdulie, Abdullahi, Bojang</sup></em>
description: Published by NCAC - National Digital Archive of The Gambia, initiated by University of Hamburg, Asia-Africa-Institute, Germany, funded by Gerda Henkel Foundation, Germany