cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.a3cs6d
title: Occurrence Download

description: A dataset containing 2747 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Continent is Europe",
 "Geometry POLYGON((-13.32082 33.66493,58.30918 31.00907,56.09217 62.04739,27.82696 59.93454,22.38299 59.04466,20.3438 55.86499,16.82379 55.01062,11.18493 54.80557,11.18493 55.59159,10.61516 58.42654,4.49353 55.8286,0.91875 50.28769,-11.59301 48.14282,-13.32082 33.66493))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Verbena officinalis"
 ]
}

 The dataset includes 2747 records from 148 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012950-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/bprmy76fdv
title: Dataset of physical-geographical predictors

description: The data represents a critical section of the Gidra River, which is located in the western part of Slovakia. The primary input for creating the physical-geographical predictors was the airborne laser scanned LiDAR digital elevation model (DMR 5.0) with a resolution of 1 m, provided by the Geodetic and Cartographic Institute, Bratislava as well as the orthophotos from 2023 (15 cm pixel). The dataset provides raw orthophoto map, LiDAR DEM and shapefile that were the basis for creating predictors. These predictors can be further used to train flood scenarios for predicting the fluvial flood extent as well as flow depth. Seven raster predictors for fluvial flood extent modelling at 1 m resolution were created from the orthophoto source file and LiDAR DEM: Height Above Nearest Drainage, Euclidean Distance to Drainage, Surface roughness, Stream Power Index, Topographic Wetness Index, Normalized Difference Vegetation Index, and Slope. The data repository has the following structure:
- Orthophoto_Gidra.tif (aerial orthophoto, RGB+NIR),
- DEM_Gidra.tif (elevation grid),
- Shapefile_Gidra.shp (shapefile, LineString geometry),
- HAND_Gidra.tif (Height Above Nearest Drainage),
- EucDist_Gidra.tif (Euclidean distance to drainage),
- Roughness_Gidra.tif (Manning’s n roughness),
- SPI_Gidra.tif (Stream Power Index),
- TWI_Gidra.tif (Topographic Wetness Index),
- NDVI_Gidra.tif (Normalized Difference Vegetation Index),
- Slope_Gidra.tif.
DOI: 10.17632/bprmy76fdv.1
title: Dataset of physical-geographical predictors

description: The data represents a critical section of the Gidra River, which is located in the western part of Slovakia. The primary input for creating the physical-geographical predictors was the airborne laser scanned LiDAR digital elevation model (DMR 5.0) with a resolution of 1 m, provided by the Geodetic and Cartographic Institute, Bratislava as well as the orthophotos from 2023 (15 cm pixel). The dataset provides raw orthophoto map, LiDAR DEM and shapefile that were the basis for creating predictors. These predictors can be further used to train flood scenarios for predicting the fluvial flood extent as well as flow depth. Seven raster predictors for fluvial flood extent modelling at 1 m resolution were created from the orthophoto source file and LiDAR DEM: Height Above Nearest Drainage, Euclidean Distance to Drainage, Surface roughness, Stream Power Index, Topographic Wetness Index, Normalized Difference Vegetation Index, and Slope. The data repository has the following structure:
- Orthophoto_Gidra.tif (aerial orthophoto, RGB+NIR),
- DEM_Gidra.tif (elevation grid),
- Shapefile_Gidra.shp (shapefile, LineString geometry),
- HAND_Gidra.tif (Height Above Nearest Drainage),
- EucDist_Gidra.tif (Euclidean distance to drainage),
- Roughness_Gidra.tif (Manning’s n roughness),
- SPI_Gidra.tif (Stream Power Index),
- TWI_Gidra.tif (Topographic Wetness Index),
- NDVI_Gidra.tif (Normalized Difference Vegetation Index),
- Slope_Gidra.tif.
DOI: 10.82201/wdy9sr
title: Active Control of Separated Flows on 3D Wings Using Deep Reinforcement Learning (DRL)

description: In this work, deep reinforcement learning (DRL) is applied to active flow control (AFC) over a three-dimensional SD7003 wing at a Reynolds number of Re = 60,000 and angle of attack of AoA = 14◦. In the uncontrolled baseline case, the flow exhibits massive separation and a fully turbulent wake. Using a GPU-accelerated CFD solver and multi-agent training, DRL discovers control strategies that enhance lift (ΔCl = 79%), reduce drag (ΔCd = −39%), and improve aerodynamic efficiency (ΔE = 408%). Flow visualizations confirm reattachment of the separated shear layer, demonstrating the potential of DRL for complex and turbulent flows.
description: SOD2D, 1.0
description: SmartiRedis, 0.5.3
description: RedisAI, 1.2.7
description: SmartSim, 0.7.0
description: TF-Agents, 0.19.0
description: Gmsh, 4.11.1
description: Paraview, 5.12.0
DOI: 10.17632/2wnpp94m2h
title: A dataset on cox1 sequences of limno-terrestrial rotifers

description: The dataset provides sequences of the cytochrome c oxidase subunit I gene (cox1) of rotifers (Rotifera) isolated from limno-terrestrial habitats: moss, lichens, leaf and mixed litter, soil, and other periodically moistened substrates.
Samples were collected across continental Europe (Belgium, Croatia, Denmark, Finland, France, Germany, Poland, Switzerland, Montenegro, Ukraine), Great Britain, Madeira, Turkey, Mediterranean islands (Mallorca, Malta, Pantelleria, Sardinia), the European Arctic (Iceland, Svalbard and Jan Mayen), and Greenland, representing a latitudinal gradient with approximately the same number of samples from each 5-degree zone: N 30-35; N 35-40; N 40-45; N 45-50; N 50-55; N 55-60; N 60-65; N 65-70; N 70-75; N 75-80. 
All rotifers, along with other microscopic organisms of similar size, were isolated from the samples by washing with running water through a system of metal sieves, followed by flotation and centrifugation in a sugar solution. DNA was extracted using standard Qiagen DNeasy Blood & Tissue Kits. PCR of cox1 gene was performed using BcdF01 and BcdR04 pair of primers (modified version of Folmer's primers described in Folmer et al. 1994). The amplified fragments were sequenced used Oxford Nanopore technology.

The dataset includes:
- An Excel file (“Sample list.xlsx”) containing sample IDs, collection sites (country or region), and their geographic coordinates (latitude and longitude);
- A ZIP archive with FASTA files of the sequences, where samples with all rotifer sequences are stored as separate files. The FASTA filenames correspond to the sample IDs.

The dataset was created under the ROTISFERA project: “Global patterns of microinvertebrate distribution: Does diversity decrease poleward in rotifers (Rotifera, Bdelloidea)?” NDICI-GEO-NEAR/2022/434-092-0068 (funded by the EU).


References:
Folmer, O., M. Black, W. Hoeh, R. Lutz & R. C. Vrijenhoek, 1994. DNA primers for amplifcation of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Molecular Marine Biology and Biotechnology 3: 294–299.
DOI: 10.5281/zenodo.10576402
title: INS test

description: test
DOI: 10.17632/2wnpp94m2h.1
title: A dataset on cox1 sequences of limno-terrestrial rotifers

description: The dataset provides sequences of the cytochrome c oxidase subunit I gene (cox1) of rotifers (Rotifera) isolated from limno-terrestrial habitats: moss, lichens, leaf and mixed litter, soil, and other periodically moistened substrates.
Samples were collected across continental Europe (Belgium, Croatia, Denmark, Finland, France, Germany, Poland, Switzerland, Montenegro, Ukraine), Great Britain, Madeira, Turkey, Mediterranean islands (Mallorca, Malta, Pantelleria, Sardinia), the European Arctic (Iceland, Svalbard and Jan Mayen), and Greenland, representing a latitudinal gradient with approximately the same number of samples from each 5-degree zone: N 30-35; N 35-40; N 40-45; N 45-50; N 50-55; N 55-60; N 60-65; N 65-70; N 70-75; N 75-80. 
All rotifers, along with other microscopic organisms of similar size, were isolated from the samples by washing with running water through a system of metal sieves, followed by flotation and centrifugation in a sugar solution. DNA was extracted using standard Qiagen DNeasy Blood & Tissue Kits. PCR of cox1 gene was performed using BcdF01 and BcdR04 pair of primers (modified version of Folmer's primers described in Folmer et al. 1994). The amplified fragments were sequenced used Oxford Nanopore technology.

The dataset includes:
- An Excel file (“Sample list.xlsx”) containing sample IDs, collection sites (country or region), and their geographic coordinates (latitude and longitude);
- A ZIP archive with FASTA files of the sequences, where samples with all rotifer sequences are stored as separate files. The FASTA filenames correspond to the sample IDs.

The dataset was created under the ROTISFERA project: “Global patterns of microinvertebrate distribution: Does diversity decrease poleward in rotifers (Rotifera, Bdelloidea)?” NDICI-GEO-NEAR/2022/434-092-0068 (funded by the EU).


References:
Folmer, O., M. Black, W. Hoeh, R. Lutz & R. C. Vrijenhoek, 1994. DNA primers for amplifcation of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Molecular Marine Biology and Biotechnology 3: 294–299.
DOI: 10.15468/dl.36n9vy
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gripopteryx coruja"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012953-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/xgh4xvm3rm
title: Rerouting TSP with Drones

description: No description
DOI: 10.17632/xgh4xvm3rm.1
title: Rerouting TSP with Drones

description: No description
DOI: 10.17632/ry3mx8xp8z
title: Reliability and Validity of Tamil Barratt's Impulsiveness Scale (BIS) -11 Tool and Dataset

description: Interpretability and comparability study Raw Data
DOI: 10.17632/ry3mx8xp8z.1
title: Reliability and Validity of Tamil Barratt's Impulsiveness Scale (BIS) -11 Tool and Dataset

description: Interpretability and comparability study Raw Data
DOI: 10.17632/y8p6nyxrtw
title: Impact of haematocrit on the accuracy of spot blood glucose measurements in dogs using two veterinary hand-held glucometers

description: Raw data
DOI: 10.17632/y8p6nyxrtw.1
title: Impact of haematocrit on the accuracy of spot blood glucose measurements in dogs using two veterinary hand-held glucometers

description: Raw data
DOI: 10.17632/x2ksy2dwfb
title: Research on the Thermal Fatigue Behaviour of Titanium Microalloyed Die Steel

description: This study presents the following data and experimental images: the calculated data of the thermal fatigue damage factor, the Origin plots for damage factor analysis, the Origin plots of EDS analysis prior to thermal fatigue, images of the crack surface morphology, images of the cross - sectional morphology of cracks, micrographs of extraction replicas, and TEM images following thermal fatigue cycles
DOI: 10.17632/x2ksy2dwfb.1
title: Research on the Thermal Fatigue Behaviour of Titanium Microalloyed Die Steel

description: This study presents the following data and experimental images: the calculated data of the thermal fatigue damage factor, the Origin plots for damage factor analysis, the Origin plots of EDS analysis prior to thermal fatigue, images of the crack surface morphology, images of the cross - sectional morphology of cracks, micrographs of extraction replicas, and TEM images following thermal fatigue cycles
DOI: 10.17632/gzpgnj49tf
title: COPSOQ RU Validation

description: Data collected for validation of the Russian version of the COPSOQ 3 questionnaire. It consists of two datasets. One was used for exploratory factor analisys (EFA) and the other one was used for confirmatory factor analisys.
DOI: 10.17632/gzpgnj49tf.1
title: COPSOQ RU Validation

description: Data collected for validation of the Russian version of the COPSOQ 3 questionnaire. It consists of two datasets. One was used for exploratory factor analisys (EFA) and the other one was used for confirmatory factor analisys.
DOI: 10.17632/sz6nx423h6
title: Peruvian nursing theses 2020-2025

description: The Peruvian nursing theses from 2020 to 2025, comprising a total of 6157 works including titles, keywords, abstracts, Sustainable Development Goals (SDGs) alignment, and health priorities addressed, illustrate a broad and strategic research effort aligned with national and global health challenges. These theses cover diverse topics such as nursing workforce well-being, stress management during the COVID-19 pandemic, community and rural health nursing, nutrition, mental health, chronic disease prevention, and health services management.
DOI: 10.17632/sz6nx423h6.1
title: Peruvian nursing theses 2020-2025

description: The Peruvian nursing theses from 2020 to 2025, comprising a total of 6157 works including titles, keywords, abstracts, Sustainable Development Goals (SDGs) alignment, and health priorities addressed, illustrate a broad and strategic research effort aligned with national and global health challenges. These theses cover diverse topics such as nursing workforce well-being, stress management during the COVID-19 pandemic, community and rural health nursing, nutrition, mental health, chronic disease prevention, and health services management.
DOI: 10.5281/zenodo.17398468
title: Metadata for Project ICOLMA: Impact of COVID-19 on livelihoods, mobility, and accessibility of marginalised groups

description: This deposit contains metadata from the data collected within the project Impact of COVID-19 on Livelihoods, Mobility, and Accessibility of Marginalized Groups (ICOLMA), conducted through a collaboration between the Federal University of ABC (UFABC, Brazil), Technische Universität Dortmund (Germany), and the University of Cape Town (South Africa).

The files includes the survey questionnaire applied in the city of São Paulo using the Maptionnaire platform, which integrates open- and closed-ended questions with georeferenced information about respondents’ daily activities, mobility patterns, and access to essential services before, during, and after the COVID-19 pandemic. 

They also include the data dictionary, developed to ensure the standardization of column names in the cleaned dataset.

The raw data collected with Maptionnaire and the cleaned dataset are not publicly available due to the presence of potentially sensitive information. In accordance with the ethical approvals obtained for the ICOLMA project, these data cannot be openly shared. Access to these data may be granted upon justified request and approval by the research team.

Keywords: COVID-19, São Paulo, mobility, accessibility, livelihoods, marginalized groups, Maptionnaire, urban studies, mixed methods.
DOI: 10.5281/zenodo.17398469
title: Metadata for Project ICOLMA: Impact of COVID-19 on livelihoods, mobility, and accessibility of marginalised groups

description: This deposit contains metadata from the data collected within the project Impact of COVID-19 on Livelihoods, Mobility, and Accessibility of Marginalized Groups (ICOLMA), conducted through a collaboration between the Federal University of ABC (UFABC, Brazil), Technische Universität Dortmund (Germany), and the University of Cape Town (South Africa).

The files includes the survey questionnaire applied in the city of São Paulo using the Maptionnaire platform, which integrates open- and closed-ended questions with georeferenced information about respondents’ daily activities, mobility patterns, and access to essential services before, during, and after the COVID-19 pandemic. 

They also include the data dictionary, developed to ensure the standardization of column names in the cleaned dataset.

The raw data collected with Maptionnaire and the cleaned dataset are not publicly available due to the presence of potentially sensitive information. In accordance with the ethical approvals obtained for the ICOLMA project, these data cannot be openly shared. Access to these data may be granted upon justified request and approval by the research team.

Keywords: COVID-19, São Paulo, mobility, accessibility, livelihoods, marginalized groups, Maptionnaire, urban studies, mixed methods.
DOI: 10.15468/dl.g34cjn
title: Occurrence Download

description: A dataset containing 141 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Philippines",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Chrysocolaptes haematribon, Chrysocolaptes lucidus haematribon)",
 "Year 2000-2009"
 ]
}

 The dataset includes 141 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012954-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/7jgcdt2vxd
title: Social Media as a Bank Run Catalyst

description: The data in this repository produces the findings in the paper, "Social Media as a Bank Run Catalyst".

The repository's main README file is readme_all.html in the main high-level folder. The README file explains in detail how to interpret and use the data in this repository.

The repository is structured into two main sub-directories: "./cross_sectional_hourly/", which produces the cross-sectional and hourly results, and "./high_frequency/", which produces the high-frequency results.
DOI: 10.17632/7jgcdt2vxd.2
title: Social Media as a Bank Run Catalyst

description: The data in this repository produces the findings in the paper, "Social Media as a Bank Run Catalyst".

The repository's main README file is readme_all.html in the main high-level folder. The README file explains in detail how to interpret and use the data in this repository.

The repository is structured into two main sub-directories: "./cross_sectional_hourly/", which produces the cross-sectional and hourly results, and "./high_frequency/", which produces the high-frequency results.
DOI: 10.6084/m9.figshare.26362876
title: CoAP_UAD: CoAP Under Attack Dataset. A public dataset for the detection of attacks in IoT networks using CoAP protocol

description: This dataset is registered at "Registro Oficial de Propiedad Intelectual, Evidencias y Secretos Empresariales de la Universidad de León" under registry number: 2024 - 000004<br><br>The CoAP_UAD dataset is designed to evaluate security in networks using the CoAP protocol. This dataset includes three files containing normal and malicious traffic, focusing on specific vulnerabilities of the CoAP protocol as described in its RFC, such as Denial of Service, Man-in-the-Middle, and Cross-Protocol attacks. It is intended for use in the development and testing of Machine Learning models for Intrusion Detection Systems in IoT environments, both domestic and industrial.
DOI: 10.6084/m9.figshare.26362876.v2
title: CoAP_UAD: CoAP Under Attack Dataset. A public dataset for the detection of attacks in IoT networks using CoAP protocol

description: This dataset is registered at "Registro Oficial de Propiedad Intelectual, Evidencias y Secretos Empresariales de la Universidad de León" under registry number: 2024 - 000004<br><br>The CoAP_UAD dataset is designed to evaluate security in networks using the CoAP protocol. This dataset includes three files containing normal and malicious traffic, focusing on specific vulnerabilities of the CoAP protocol as described in its RFC, such as Denial of Service, Man-in-the-Middle, and Cross-Protocol attacks. It is intended for use in the development and testing of Machine Learning models for Intrusion Detection Systems in IoT environments, both domestic and industrial.
DOI: 10.6084/m9.figshare.26362876.v1
title: CoAP_UAD: CoAP Under Attack Dataset. A public dataset for the detection of attacks in IoT networks using CoAP protocol

description: This dataset is registered at "Registro Oficial de Propiedad Intelectual, Evidencias y Secretos Empresariales de la Universidad de León" under registry number: 2024 - 000004<br><br>The CoAP_UAD dataset is designed to evaluate security in networks using the CoAP protocol. This dataset includes three files containing normal and malicious traffic, focusing on specific vulnerabilities of the CoAP protocol as described in its RFC, such as Denial of Service, Man-in-the-Middle, and Cross-Protocol attacks. It is intended for use in the development and testing of Machine Learning models for Intrusion Detection Systems in IoT environments, both domestic and industrial.
DOI: 10.17632/zn7t5rrcwk.1
title: Culture as Social Infrastructure for Innovation

description: README — Replication Package
Paper Title
Culture as Social Infrastructure for Innovation
Authors
Aaron Hui Lin (Shanghai Lixin University of Accounting and Finance)
Teng Zhong (University of International Business and Economics)
Qianhui Yan (Fudan University)
Maohua Wu (Nanjing University of Finance & Economics)
1. Overview
This replication package provides all the publicly available data, code, and instructions require to reproduce the main empirical results of the above-mentioned research paper. The package is designed to facilitate transparency and enable independent verification of all statistical findings reported in the study.
DOI: 10.17632/pgz887nv5v
title: Dataset. Accelerate Flash Removal of PFAS from Soil by Human-guided Bayesian Optimization and Interpretable Machine Learning

description: The study hypothesizes that coupling human-in-the-loop decision-making with Bayesian optimization and interpretable neural representations enables rapid identification of optimal FJH parameters and molecular-level insights into PFAS degradation mechanisms. Specifically, we posit that functional group composition, rather than chain length, primarily governs the defluorination efficiency under FJH conditions. The dataset contains 80 experimental records across four representative PFAS compounds. Each record combines: Molecular descriptors: molecular weight, number of fluorine atoms, mean C–F bond energy, polarity indices, and functional group identity. Experimental parameters: applied voltage, resistance, pulse duration, temperature rise rate, and calculated energy input.
DOI: 10.15468/dl.uevfua
title: Occurrence Download

description: A dataset containing 2276 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Iran (Islamic Republic of)",
 "Year is 2019",
 "InstitutionCode is iNaturalist"
 ]
}

 The dataset includes 2276 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012955-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/pgz887nv5v.1
title: Dataset. Accelerate Flash Removal of PFAS from Soil by Human-guided Bayesian Optimization and Interpretable Machine Learning

description: The study hypothesizes that coupling human-in-the-loop decision-making with Bayesian optimization and interpretable neural representations enables rapid identification of optimal FJH parameters and molecular-level insights into PFAS degradation mechanisms. Specifically, we posit that functional group composition, rather than chain length, primarily governs the defluorination efficiency under FJH conditions. The dataset contains 80 experimental records across four representative PFAS compounds. Each record combines: Molecular descriptors: molecular weight, number of fluorine atoms, mean C–F bond energy, polarity indices, and functional group identity. Experimental parameters: applied voltage, resistance, pulse duration, temperature rise rate, and calculated energy input.
DOI: 10.17632/zk6kzk38yc
title: MMEJ replication forks

description: western blot for "Microhomology-mediated end joining acts directly on replication forks to repair single-ended double strand breaks"