cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17299544
title: Director networks, Absorptive capacity, and Internationalization Scope

description: Against the backdrop of China's economic transformation, the director network—as a manifestation of informal institutions and a vehicle for social capital—serves as a crucial channel for firms to access external resources, thereby facilitating successful overseas market expansion. A firm's position within director networks reflects its network power, which influences the scope of corporate internationalization through resource integration and knowledge absorption effects. This study employs social network analysis methodology, utilizing data from Chinese A-share listed companies from 2012 to 2022, to empirically examine the influence mechanism of a firm's director network position on its internationalization scope and to explore the mediating role of absorptive capacity in this relationship. The findings reveal that the network centrality positively promotes the internationalization scope, while structural holes negatively affect it. Absorptive capacity partially mediates the relationship between director network position and its internationalization scope. This study expands the research on director networks and corporate internationalization, offering insights for Chinese enterprises in network construction and the pursuit of international expansion.
DOI: 10.5281/zenodo.17299545
title: Director networks, Absorptive capacity, and Internationalization Scope

description: Against the backdrop of China's economic transformation, the director network—as a manifestation of informal institutions and a vehicle for social capital—serves as a crucial channel for firms to access external resources, thereby facilitating successful overseas market expansion. A firm's position within director networks reflects its network power, which influences the scope of corporate internationalization through resource integration and knowledge absorption effects. This study employs social network analysis methodology, utilizing data from Chinese A-share listed companies from 2012 to 2022, to empirically examine the influence mechanism of a firm's director network position on its internationalization scope and to explore the mediating role of absorptive capacity in this relationship. The findings reveal that the network centrality positively promotes the internationalization scope, while structural holes negatively affect it. Absorptive capacity partially mediates the relationship between director network position and its internationalization scope. This study expands the research on director networks and corporate internationalization, offering insights for Chinese enterprises in network construction and the pursuit of international expansion.
DOI: 10.15468/dl.th7cdt
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Skeletonema subsalsum",
 "Country is United States of America",
 "HasCoordinate is true"
 ]
}

 The dataset includes 22 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063793-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z4dnux
title: Occurrence Download

description: A dataset containing 971771 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Pinus sylvestris)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 971771 records from 2081 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063758-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15801665
title: Data from: Warmer climates induce cellular and reproductive costs in a lizard despite buffering effects of thermo-hydroregulation strategies and forest cover

description: Ecological impacts of climate warming in ectotherms can manifest across various biological levels with heat stress disrupting cellular processes, functional traits and life-history strategies. Using space-for-time substitutions where one examines geographic variation in population phenotypes allows to make predictions for biotic responses to climate warming over time. Yet, this approach requires a multivariate description of the local environment to infer a potential causal relationship between climate conditions and phenotypes. Here, we examined the geographical variation in cellular, morphological, physiological, behavioural and reproductive traits of adult female common lizards (Zootoca vivipara) across 14 natural populations along an environmental gradient with contrasting daytime and nighttime temperatures, forest cover and free-standing water access. We found significant geographical variations in all female traits, except resting metabolic rates. Geographic trait variation was mainly correlated with local thermal conditions and forest cover, but not with access to free-standing water. Females from populations with warm nighttime temperatures had lower total evaporative water losses, while females from populations exposed to higher daytime temperatures had lower preferred body temperatures. Females from populations with higher forest cover had a higher body condition at capture and an earlier emergence time. In addition, female telomere length (a biological marker of ageing) was higher in populations with lower temperatures and a high forest cover. Reproductive traits were mainly determined by the forest cover and to a lesser extent by nighttime local temperature. Females from populations with high forest cover had delayed parturition dates, higher reproductive investment and a higher post-partum condition. However, the number of viable juveniles at birth was negatively correlated with high local nighttime temperatures. Our integrative study suggests that local diurnal and nocturnal thermal conditions as well as forest cover can lead to changes in cellular, physiological, behavioural and reproductive traits. While thermal stress and habitat alteration are growing threats to terrestrial ectotherms, taking into account intraspecific variations in functional and reproductive traits together with changes in diurnal, nocturnal temperatures and vegetation will be essential for predicting ectotherms responses to current and future climates.
DOI: 10.5281/zenodo.18174171
title: Supporting dataset for 3D mapping of organic carbon stocks in the Legarée mudflat (Seine Estuary, France)

description: This dataset contains the data supporting the study “Hyperspectral imaging and 3D sediment modeling for organic carbon stock mapping in an estuarine environment” (Barrois et al., 2026, submitted to Journal of Soils and Sediments). The files include all raw and processed data used to build the 3D sedimentary model of the Legarée mudflat (Seine Estuary, France) and to estimate the organic carbon (OC) stocks from hyperspectral imaging (HSI) predictive model outputs.

The dataset consists of three main files: (i) “Barrois_et_al_DEM_layer.zip” is a compressed archive containing ten shapefiles, each representing one tie layer of the 3D sedimentary model, (ii) “Barrois_et_al_Supplementary_Table.xlsx” is an Excel file compiling metadata for sediment cores, destructive measurements of SOC and DBD along the master core, and predicted values derived from HSI models along all cores, and (iii) “Barrois_et_al_Data_Read_me.xlsx”, a Excel file detailing all variables and attributes. 

These datasets allow replication of the 3D stock interpolation workflow and comparison between conventional and HSI-assisted methods. All files are provided in open and interoperable formats, suitable for use in GIS and data analysis environments.

Please cite this dataset as: Barrois, Barrois, J.-M., Debret, M., Jacq, K., Portet-Koltalo, F., Coynel, A., Mourier, B., Winiarski, T., Baudin, F., & copard, . yoann . (2025). Supporting dataset for 3D mapping of organic carbon stocks in the Legarée mudflat (Seine Estuary, France) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.18174172

*Files will be made available in open-access upon publication
DOI: 10.5281/zenodo.18174172
title: Supporting dataset for 3D mapping of organic carbon stocks in the Legarée mudflat (Seine Estuary, France)

description: This dataset contains the data supporting the study “Hyperspectral imaging and 3D sediment modeling for organic carbon stock mapping in an estuarine environment” (Barrois et al., 2026, submitted to Journal of Soils and Sediments). The files include all raw and processed data used to build the 3D sedimentary model of the Legarée mudflat (Seine Estuary, France) and to estimate the organic carbon (OC) stocks from hyperspectral imaging (HSI) predictive model outputs.

The dataset consists of three main files: (i) “Barrois_et_al_DEM_layer.zip” is a compressed archive containing ten shapefiles, each representing one tie layer of the 3D sedimentary model, (ii) “Barrois_et_al_Supplementary_Table.xlsx” is an Excel file compiling metadata for sediment cores, destructive measurements of SOC and DBD along the master core, and predicted values derived from HSI models along all cores, and (iii) “Barrois_et_al_Data_Read_me.xlsx”, a Excel file detailing all variables and attributes. 

These datasets allow replication of the 3D stock interpolation workflow and comparison between conventional and HSI-assisted methods. All files are provided in open and interoperable formats, suitable for use in GIS and data analysis environments.

Please cite this dataset as: Barrois, Barrois, J.-M., Debret, M., Jacq, K., Portet-Koltalo, F., Coynel, A., Mourier, B., Winiarski, T., Baudin, F., & copard, . yoann . (2025). Supporting dataset for 3D mapping of organic carbon stocks in the Legarée mudflat (Seine Estuary, France) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.18174172

*Files will be made available in open-access upon publication
DOI: 10.57702/kpy9x99b
title: ogbn-products

description: Graph Neural Networks (GNNs) have become a popular approach for various applications, ranging from social network analysis to modeling chemical properties of molecules.
DOI: 10.57702/7yydmvsg
title: ICFHR 2020 Competition on Image Retrieval for Historical Handwritten Fragments

description: A large dataset consisting of more than 120 000 fragments of about 9800 writers from about 20 000 document images.
DOI: 10.57702/39vnh99q
title: Materials Project Dataset

description: The Materials Project dataset contains theoretical crystal structure information using Density-Functional Theory based convex hull construction in composition, energy space that predicts compositions and corresponding lowest energy of formation atomic configurations that form the vertices of the convex hull.
DOI: 10.15468/dl.j533dt
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((89.69985 24.906783, 89.62243100000001 24.901206, 89.54694000000001 24.884614, 89.475251 24.857419, 89.40914600000001 24.820297, 89.350262 24.774171, 89.30005 24.720186, 89.259744 24.65968, 89.230327 24.594152, 89.212509 24.52522, 89.206709 24.454586, 89.213052 24.383991, 89.23136 24.315171, 89.261166 24.249817, 89.301721 24.189532, 89.352019 24.135791, 89.41081800000001 24.08991, 89.47667300000001 24.053009, 89.547972 24.025988, 89.622974 24.009508, 89.69985 24.00397, 89.776726 24.009508, 89.85172799999999 24.025988, 89.923027 24.053009, 89.988882 24.08991, 90.047681 24.135791, 90.097979 24.189532, 90.13853400000001 24.249817, 90.16834 24.315171, 90.18664800000001 24.383991, 90.19299100000001 24.454586, 90.187191 24.52522, 90.16937299999999 24.594152, 90.139956 24.65968, 90.09965 24.720186, 90.04943799999999 24.774171, 89.990554 24.820297, 89.924449 24.857419, 89.85276 24.884614, 89.777269 24.901206, 89.69985 24.906783))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0079747-251120083545085/datasets/export for details.
DOI: 10.57702/gate1h27
title: Heart Disease Dataset

description: Heart disease dataset is used to detect the existence of heart disease in patients. The dataset contains 70+ features.
DOI: 10.57702/bn24mst1
title: Task-oriented multi-user semantic communications for vqa
DOI: 10.57702/gnjynwr9
title: Maze

description: The dataset used for the maze exploration task, containing a 9x9 maze with walls and reward locations.
DOI: 10.57702/xwvk8t5s
title: REVEALING CHOICE BRACKETING

description: The dataset used in the Revealing Choice Bracketing experiment, consisting of choices from five decision rounds, each consisting of one or two subdecisions.
DOI: 10.57702/rmt51vvl
title: Corridor Environment

description: The corridor environment is a simple environment where the agent has to determine whether the rewarding cell (colored yellow) is at the top or bottom, based on the color of the cell it has seen at the start (either ”blue” or ”red”).
DOI: 10.57702/3s872amd
title: BSDS300 and BSDS500

description: The BSDS300 and BSDS500 datasets are benchmarks for image segmentation.
DOI: 10.5281/zenodo.18173516
title: Synthetic and Augmented Transposable Element Datasets for Classification and Curation Benchmarks

description: Directory structure and dataset contents

The Classification directory contains three FASTA files:





r.1.5_all_dataaug.fasta:Database including real and synthetic sequences, where synthetic data were generated using data augmentation techniques.




r.1.5_all_GAN_GROUPED_calibrated_augmented.fasta:Database including real and synthetic sequences, where synthetic data were generated using a grouped GAN approach (two GANs: one trained on Class I TEs and one on Class II TEs).




r.1.5_all_GAN_SIMPLE_calibrated_augmented.fasta:Database including real and synthetic sequences, where synthetic data were generated using a single, simple GAN model.



The Curation directory contains two FASTA files:





synthetic_dataset_with_imperfections.fasta:Database including real and synthetic sequences, generated across five experimental scenarios, where synthetic sequences were further modified by introducing imperfections and fragmentations at levels of 0.15 and 0.3.




synthetic_dataset_without_imperfections.fasta:Database including real and synthetic sequences, generated across the same five experimental scenarios, without introducing imperfections or fragmentations.
DOI: 10.57702/ckt9uowr
title: Caltech101

description: The dataset used in the paper is Caltech101, which is a natural image classification dataset. It contains 101 categories of natural images.
DOI: 10.5281/zenodo.18173515
title: Synthetic and Augmented Transposable Element Datasets for Classification and Curation Benchmarks

description: Directory structure and dataset contents

The Classification directory contains three FASTA files:





r.1.5_all_dataaug.fasta:Database including real and synthetic sequences, where synthetic data were generated using data augmentation techniques.




r.1.5_all_GAN_GROUPED_calibrated_augmented.fasta:Database including real and synthetic sequences, where synthetic data were generated using a grouped GAN approach (two GANs: one trained on Class I TEs and one on Class II TEs).




r.1.5_all_GAN_SIMPLE_calibrated_augmented.fasta:Database including real and synthetic sequences, where synthetic data were generated using a single, simple GAN model.



The Curation directory contains two FASTA files:





synthetic_dataset_with_imperfections.fasta:Database including real and synthetic sequences, generated across five experimental scenarios, where synthetic sequences were further modified by introducing imperfections and fragmentations at levels of 0.15 and 0.3.




synthetic_dataset_without_imperfections.fasta:Database including real and synthetic sequences, generated across the same five experimental scenarios, without introducing imperfections or fragmentations.
DOI: 10.57702/2wyd7urn
title: MHDeep Dataset

description: The dataset used in the MHDeep framework, containing physiological signals and ambient information collected from wearable medical sensors.
DOI: 10.57702/jnpf7mrl
title: Decathlon dataset

description: A large annotated medical image dataset for the development and evaluation of segmentation algorithms.
DOI: 10.57702/zbst77op
title: BioText dataset

description: The BioText dataset contains more than 3,500 text samples classified into one of eight classes, which specify the type of semantic relationship between disease and treatment entities appearing in the text.
DOI: 10.57702/s395nvm2
title: DSTC2

description: The DSTC2 dataset is a dataset for dialogue state tracking and user simulation.
DOI: 10.57702/glamrp6h
title: R-MVSNet

description: Recurrent multi-view stereo network for high-resolution depth inference
DOI: 10.3886/e242569
title: National Syndromic Surveillance Program (NSSP) Mental Health-Related Emergency Department Visit Rates

description: Data Source Description: Syndromic surveillance provides public health officials with a timely system for detecting, understanding, and monitoring health threats. By tracking symptoms and conditions reported by patients in emergency departments (EDs), public health officials can monitor trends in critical areas. When people seek treatment in the ED, the facility sends de-identified dataâ€”including chief complaint, diagnosis codes, patient characteristics, and locationâ€”to state and local health departments to share with CDC.


Indicator definitions: Definitions include both (1) visits with acute mental health crises or evaluations where the sole or primary reason for the visit is related to mental health, and (2) visits where a mental health condition may not be the sole reason for the visit but was noted to be present in the discharge diagnosis or chief complaint.

ED data are displayed as rates.

* The rate explains the number of ED visits related to a specific mental health condition out of every 100,000 ED visits. The monthly rate of ED visits related to a condition is calculated as the number of ED visits related to a condition as a fraction of the total number of ED visits in a given month multiplied by 100,000. ED visits with and without mental health conditions where patients were under 12 years old and where age, sex, and race and ethnicity were unknown were included in the denominators when calculating rates for the overall population.

* Rates are also calculated within demographic groups such as age, sex, and race and ethnicity. This is done by comparing the number of ED visits related to a condition within a demographic group as a fraction of the total number of ED visits for that demographic group in a given month multiplied by 100,000.



Data quality and methodology: More than 6,900 health care facilities covering 50 states, the District of Columbia, and Guam contribute data to NSSP daily. More than 80% of U.S. emergency departments send data to NSSP. Data are collected continuously, and for the purposes of this data channel, updated monthly. Monthly counts that are less than 10 either for the complete population or for specific demographic groups are not shown to protect confidentiality and privacy. Note, rate information may change as data are dynamically adjusted as electronic health record information is updated. However, these changes will not typically affect overall trends.


For additional information, please see: Mental Health Data Sources.

Updated monthly.â€¯
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description: (Downloaded 2026-01-06)
DOI: 10.57702/xvb6gj6r
title: A Neural Attention Model for Categorizing Patient Safety Events

description: Patient Safety Event reports are narratives describing potential adverse events to the patients and are important in identifying, and preventing medical errors.
DOI: 10.57702/24j54ouz
title: Hansken

description: The Hansken dataset used in this paper for digital forensic investigation.
DOI: 10.57702/22sqhdq2
title: Large-scale multi-label learning with missing labels

description: Large-scale multi-label learning with missing labels.
DOI: 10.15468/dl.t46wpg
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((89.69985 24.906783, 89.62243100000001 24.901206, 89.54694000000001 24.884614, 89.475251 24.857419, 89.40914600000001 24.820297, 89.350262 24.774171, 89.30005 24.720186, 89.259744 24.65968, 89.230327 24.594152, 89.212509 24.52522, 89.206709 24.454586, 89.213052 24.383991, 89.23136 24.315171, 89.261166 24.249817, 89.301721 24.189532, 89.352019 24.135791, 89.41081800000001 24.08991, 89.47667300000001 24.053009, 89.547972 24.025988, 89.622974 24.009508, 89.69985 24.00397, 89.776726 24.009508, 89.85172799999999 24.025988, 89.923027 24.053009, 89.988882 24.08991, 90.047681 24.135791, 90.097979 24.189532, 90.13853400000001 24.249817, 90.16834 24.315171, 90.18664800000001 24.383991, 90.19299100000001 24.454586, 90.187191 24.52522, 90.16937299999999 24.594152, 90.139956 24.65968, 90.09965 24.720186, 90.04943799999999 24.774171, 89.990554 24.820297, 89.924449 24.857419, 89.85276 24.884614, 89.777269 24.901206, 89.69985 24.906783))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0079749-251120083545085/datasets/export for details.
DOI: 10.57702/iadd53ur
title: Food Packaging Dataset

description: The dataset consists of images of retail food packaging for quality control.
DOI: 10.57702/ljua641b
title: Error Correction Code Transformer

description: Error correction code transformer for decoding linear codes
DOI: 10.57702/rk7tx0lw
title: Geospatial Data Analysis Dataset

description: The dataset used for geospatial data analysis, including remote sensing, GPS, and RFID data.