cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.j3qz4q
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Romania",
 "OccurrenceStatus is Present",
 "TaxonKey is Prunus virginiana"
 ]
}

 The dataset includes 14 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000430-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rwj4tn
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Romania",
 "OccurrenceStatus is Present",
 "TaxonKey is Heracleum sosnowskyi"
 ]
}

 The dataset includes 13 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000432-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.svd4x9
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Romania",
 "OccurrenceStatus is Present",
 "TaxonKey is Amaranthus palmeri"
 ]
}

 The dataset includes 13 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000431-250914085247600/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.30121855.v1
title: <b>Identifying Hygiene Risk Practices in Peri-Urban Communities through Sanitary Inspections: Lessons from Lagos, Nigeria</b>

description: <br>This study reports findings from sanitary inspections conducted in Agege LGA, Lagos State, Nigeria, in January 2025, under the “<i>WoleWoles in Action Against Cholera”</i> project implemented by Bread of Life Development Foundation, a Nigerian non-governmental organisation.. The objectives were to assess sanitation conditions, identify risk factors for cholera transmission, and provide evidence for targeted interventions.The Sanitary inspection, led by Environmental Health Officers (EHOs) and Voluntary Hygiene Promoters (VHPs) in Agege Local Government Area (LGA), focuses on improving sanitation and reducing cholera risks through routine inspections. These inspections cover various sectors, including households, food vendors, non-state water producers, food and street drink outlets, markets, public health facilities, and unique homes for people with disabilities. The activity aims to ensure adherence to hygiene and sanitation standards and mitigate cholera and other sanitation-related diseases. The sanitary officers also engaged in community sensitisation on water safety, proper waste management, and personal hygiene, and educated food vendors on the usage of personal protective equipment (PPE).
DOI: 10.5281/zenodo.15255270
title: Bitemporal and Dual-Polarization Synthetic Aperture Radar (SAR) for Dynamic Flood Mapping and  Socioeconomic Analysis Datasets in Bangladesh from 2015 to 2024

description: Dataset Overview:

This dataset is designed for dynamic flood mapping and socioeconomic vulnerability analysis in Bangladesh, using bitemporal and dual-polarization Synthetic Aperture Radar (SAR) imagery collected between 2015 and 2024. It is aimed at deep learning applications to assess flood impacts and societal vulnerability.

Key Components:





SAR Data for Water Increase Segmentation: The "dataset_for_DeepLearning" includes pre-post flooding event of dual-polarization Sentinel-1 SAR images (VV and VH bands) at a 30-meter resolution, each with dimensions of 256x256 pixels. The data focuses on flood-prone areas, i.e., regions that have flooded relative to pre-flood conditions. These images are paired with labels indicating areas of water increase, suitable for deep learning model training, validation, and testing.




National-scale Flood Binary Maps: The "Bangladesh_Data" component contains national-scale flood binary maps, with monthly resolution for 2015-2016 and bi-weekly resolution for 2017-2024, showing areas of flood presence and absence.




Socioeconomic Data: The dataset also includes land-use, nighttime lights, and population data, essential for analyzing the impact of flooding on socioeconomic vulnerability and assessing risk across different regions.



Version Update:The gridded population data has been updated with World Bank total population constraints.
DOI: 10.5281/zenodo.15522606
title: Bitemporal and Dual-Polarization Synthetic Aperture Radar (SAR) for Dynamic Flood Mapping and  Socioeconomic Analysis Datasets in Bangladesh from 2015 to 2024

description: Dataset Overview:

This dataset is designed for dynamic flood mapping and socioeconomic vulnerability analysis in Bangladesh, using bitemporal and dual-polarization Synthetic Aperture Radar (SAR) imagery collected between 2015 and 2024. It is aimed at deep learning applications to assess flood impacts and societal vulnerability.

Key Components:





SAR Data for Water Increase Segmentation: The "dataset_for_DeepLearning" includes pre-post flooding event of dual-polarization Sentinel-1 SAR images (VV and VH bands) at a 30-meter resolution, each with dimensions of 256x256 pixels. The data focuses on flood-prone areas, i.e., regions that have flooded relative to pre-flood conditions. These images are paired with labels indicating areas of water increase, suitable for deep learning model training, validation, and testing.




National-scale Flood Binary Maps: The "Bangladesh_Data" component contains national-scale flood binary maps, with monthly resolution for 2015-2016 and bi-weekly resolution for 2017-2024, showing areas of flood presence and absence.




Socioeconomic Data: The dataset also includes land-use, nighttime lights, and population data, essential for analyzing the impact of flooding on socioeconomic vulnerability and assessing risk across different regions.



Version Update:The gridded population data has been updated with World Bank total population constraints.
DOI: 10.5281/zenodo.15255690
title: Dual-Polarization Synthetic Aperture Radar (SAR) for Water Body Segmentation: A Dataset Collected in Bangladesh from 2015 to 2024

description: Dataset Overview

This dataset consists of 256×256 pixel SAR and optical image patches, designed to support water-body analysis before and after flooding events. It provides 30-meter resolution data from both pre-flood and post-flood periods, separated into distinct collections for easier comparison. The dataset is intended for training and testing deep learning models focused on water segmentation, specifically targeting the identification of water bodies in flood-affected regions. By including both SAR and optical data, the dataset enables enhanced futher flood mapping and monitoring, which is crucial for effective disaster management and response.




Image Size

All image patches are 256×256 pixels, ensuring consistency and allowing for straightforward application in machine learning models, particularly for convolutional neural networks (CNNs) and other image classification tasks.




File Naming Convention

The dataset follows a consistent naming convention to maintain organization and ease of use. Each file is named according to the pattern:

{LOCATION_CODE}_{ACQUISITION_START_TIME}_{ACQUISITION_END_TIME}*.tif

This structured naming facilitates quick identification and processing of the data based on the location and time of acquisition.




Mainly Band / File Types

The dataset includes various file types representing different bands and indices:





*_432.tifSentinel-2 true-color composite (bands 4, 3, 2), which is used as a visual reference for manual labeling of water bodies.




*_843.tifSentinel-2 false-color composite (bands 8, 4, 3), which helps highlight vegetation and water signatures, useful for labeling water bodies.




*_NDWI.tifNormalized Difference Water Index (NDWI), derived from Sentinel-2, which enhances water body boundaries, making it easier to delineate water regions.




*_VV.tifSentinel-1 SAR VV polarization (linear backscatter), the primary input for flood detection, capturing flood-induced changes in surface structure.




*_VH.tifSentinel-1 SAR VH polarization (cross-polarized), which provides complementary surface scattering information, enhancing flood detection accuracy.




*_VV_vis.tifVV polarization rescaled to an 8-bit unsigned integer (uint8, 0–255) for easier visualization.




*_VH_vis.tifVH polarization rescaled to an 8-bit unsigned integer (uint8, 0–255) for easier visualization.
DOI: 10.5281/zenodo.15255691
title: Dual-Polarization Synthetic Aperture Radar (SAR) for Water Body Segmentation: A Dataset Collected in Bangladesh from 2015 to 2024

description: Dataset Overview

This dataset consists of 256×256 pixel SAR and optical image patches, designed to support water-body analysis before and after flooding events. It provides 30-meter resolution data from both pre-flood and post-flood periods, separated into distinct collections for easier comparison. The dataset is intended for training and testing deep learning models focused on water segmentation, specifically targeting the identification of water bodies in flood-affected regions. By including both SAR and optical data, the dataset enables enhanced futher flood mapping and monitoring, which is crucial for effective disaster management and response.




Image Size

All image patches are 256×256 pixels, ensuring consistency and allowing for straightforward application in machine learning models, particularly for convolutional neural networks (CNNs) and other image classification tasks.




File Naming Convention

The dataset follows a consistent naming convention to maintain organization and ease of use. Each file is named according to the pattern:

{LOCATION_CODE}_{ACQUISITION_START_TIME}_{ACQUISITION_END_TIME}*.tif

This structured naming facilitates quick identification and processing of the data based on the location and time of acquisition.




Mainly Band / File Types

The dataset includes various file types representing different bands and indices:





*_432.tifSentinel-2 true-color composite (bands 4, 3, 2), which is used as a visual reference for manual labeling of water bodies.




*_843.tifSentinel-2 false-color composite (bands 8, 4, 3), which helps highlight vegetation and water signatures, useful for labeling water bodies.




*_NDWI.tifNormalized Difference Water Index (NDWI), derived from Sentinel-2, which enhances water body boundaries, making it easier to delineate water regions.




*_VV.tifSentinel-1 SAR VV polarization (linear backscatter), the primary input for flood detection, capturing flood-induced changes in surface structure.




*_VH.tifSentinel-1 SAR VH polarization (cross-polarized), which provides complementary surface scattering information, enhancing flood detection accuracy.




*_VV_vis.tifVV polarization rescaled to an 8-bit unsigned integer (uint8, 0–255) for easier visualization.




*_VH_vis.tifVH polarization rescaled to an 8-bit unsigned integer (uint8, 0–255) for easier visualization.
DOI: 10.6084/m9.figshare.30121735.v2
title: Logic 14 pdf NEW

description: Logic innovation
DOI: 10.5281/zenodo.17116524
title: Data for "Lithology dependent pathways of forming rock mass weakness plane shape regional landslide susceptibility"
DOI: 10.5281/zenodo.17116525
title: Data for "Lithology dependent pathways of forming rock mass weakness plane shape regional landslide susceptibility"
DOI: 10.34740/kaggle/dsv/13055931
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.5281/zenodo.17116503
title: Advanced Brain Sonification Documentation

description:  


This dataset contains technical and general documentation, as well as JSON summaries of brain image sonifications generated with the generate_many pipeline. The data is intended for research, artistic exploration, and reproducible science.
DOI: 10.5281/zenodo.17116504
title: Advanced Brain Sonification Documentation

description:  


This dataset contains technical and general documentation, as well as JSON summaries of brain image sonifications generated with the generate_many pipeline. The data is intended for research, artistic exploration, and reproducible science.
DOI: 10.6084/m9.figshare.30121936
title: Data for "Adsorption-Induced Internal Water Potential Amplifies Energy Barrier for Interfacial Water Flow"

description: Data for "Adsorption-Induced Internal Water Potential Amplifies Energy Barrier for Interfacial Water Flow"
DOI: 10.6084/m9.figshare.30121936.v1
title: Data for "Adsorption-Induced Internal Water Potential Amplifies Energy Barrier for Interfacial Water Flow"

description: Data for "Adsorption-Induced Internal Water Potential Amplifies Energy Barrier for Interfacial Water Flow"
DOI: 10.6084/m9.figshare.30121870
title: Code and data for: Competition-Induced Trait Variability Obscures Trait–Growth Relationships of Tree Seedlings

description: This dataset contains the analysis code and data accompanying the manuscript “Competition-Induced Trait Variability Obscures Trait–Growth Relationships of Tree Seedlings”. The workflow is implemented in a single R Markdown file; two small <i>.R </i>scripts provide core functions (model fitting and plotting). An HTML report is included for browsing results without running R.<b>Data</b><b>：</b>· <b>raw_trait_data.csv</b> — Main dataset containing measured environmental variables, competition treatments, species identity, growth rates, and PCA-derived trait axes. Columns include environmental factors (Light, Moisture, AP), treatments (Compete), species (<i>sp</i>), census year (<i>comp_year</i>), growth rates (<i>RGR</i>, <i>raw_RGR</i>), and PCA scores (<i>Above_PC1/2</i>, <i>Below_PC1/2</i>).· <b>AboveBelow_pca_results.RData</b> — R object storing PCA loadings and eigenvalues for above-ground and below-ground traits. Used to visualize trait loadings and variance explained in PCA biplots (Figures 2a–b).<b>Code:</b>· <b>Fit trait–RGR models.R</b> — core modelling functions (trait–RGR models, null models, TPD metrics).· <b>Core plotting functions.R</b> — core plotting functions used by the Rmd.· <b>Yang_code.Rmd</b> — Annotated R Markdown file containing all analysis code. This file reproduces both the main and supplementary analyses, and generates three figures reported in the manuscript.· <b>Yang_code.html</b> — rendered report from the Rmd (view-only; open in a browser).<b>session_info.txt</b> — R session information (R version, OS, and package versions).<br>
DOI: 10.6084/m9.figshare.30121870.v2
title: Code and data for: Competition-Induced Trait Variability Obscures Trait–Growth Relationships of Tree Seedlings

description: This dataset contains the analysis code and data accompanying the manuscript “Competition-Induced Trait Variability Obscures Trait–Growth Relationships of Tree Seedlings”. The workflow is implemented in a single R Markdown file; two small <i>.R </i>scripts provide core functions (model fitting and plotting). An HTML report is included for browsing results without running R.<b>Data</b><b>：</b>· <b>raw_trait_data.csv</b> — Main dataset containing measured environmental variables, competition treatments, species identity, growth rates, and PCA-derived trait axes. Columns include environmental factors (Light, Moisture, AP), treatments (Compete), species (<i>sp</i>), census year (<i>comp_year</i>), growth rates (<i>RGR</i>, <i>raw_RGR</i>), and PCA scores (<i>Above_PC1/2</i>, <i>Below_PC1/2</i>).· <b>AboveBelow_pca_results.RData</b> — R object storing PCA loadings and eigenvalues for above-ground and below-ground traits. Used to visualize trait loadings and variance explained in PCA biplots (Figures 2a–b).<b>Code:</b>· <b>Fit trait–RGR models.R</b> — core modelling functions (trait–RGR models, null models, TPD metrics).· <b>Core plotting functions.R</b> — core plotting functions used by the Rmd.· <b>Yang_code.Rmd</b> — Annotated R Markdown file containing all analysis code. This file reproduces both the main and supplementary analyses, and generates three figures reported in the manuscript.· <b>Yang_code.html</b> — rendered report from the Rmd (view-only; open in a browser).<b>session_info.txt</b> — R session information (R version, OS, and package versions).<br>
DOI: 10.6084/m9.figshare.30121870.v1
title: Code and data for: Competition-Induced Trait Variability Obscures Trait–Growth Relationships of Tree Seedlings

description: This dataset contains the analysis code and data accompanying the manuscript “Competition-Induced Trait Variability Obscures Trait–Growth Relationships of Tree Seedlings”. The workflow is implemented in a single R Markdown file; two small <i>.R </i>scripts provide core functions (model fitting and plotting). An HTML report is included for browsing results without running R.<b>Data</b><b>：</b>· <b>raw_trait_data.csv</b> — Main dataset containing measured environmental variables, competition treatments, species identity, growth rates, and PCA-derived trait axes. Columns include environmental factors (Light, Moisture, AP), treatments (Compete), species (<i>sp</i>), census year (<i>comp_year</i>), growth rates (<i>RGR</i>, <i>raw_RGR</i>), and PCA scores (<i>Above_PC1/2</i>, <i>Below_PC1/2</i>).· <b>AboveBelow_pca_results.RData</b> — R object storing PCA loadings and eigenvalues for above-ground and below-ground traits. Used to visualize trait loadings and variance explained in PCA biplots (Figures 2a–b).<b>Code:</b>· <b>Fit trait–RGR models.R</b> — core modelling functions (trait–RGR models, null models, TPD metrics).· <b>Core plotting functions.R</b> — core plotting functions used by the Rmd.· <b>Yang_code.Rmd</b> — Annotated R Markdown file containing all analysis code. This file reproduces both the main and supplementary analyses, and generates three figures reported in the manuscript.· <b>Yang_code.html</b> — rendered report from the Rmd (view-only; open in a browser).<b>session_info.txt</b> — R session information (R version, OS, and package versions).<br>
DOI: 10.5281/zenodo.17116585
title: WASTE BUSINESS: CHALLENGES AND OPPORTUNITIES
DOI: 10.5281/zenodo.17116586
title: WASTE BUSINESS: CHALLENGES AND OPPORTUNITIES
DOI: 10.7910/dvn/fr8kz6
title: Data for Probing dipolar interactions between Rydberg atoms and ultracold polar molecules

description: Experimental data for Probing dipolar interactions between Rydberg atoms and ultracold polar molecules
DOI: 10.5281/zenodo.17116575
title: Supplementary Table I Complete IFN_ differential gene expression analysis
DOI: 10.5281/zenodo.17116576
title: Supplementary Table I Complete IFN_ differential gene expression analysis
DOI: 10.5281/zenodo.17116608
title: THEORETICAL FOUNDATIONS OF USING SLIL TECHNOLOGIES IN DEVELOPING STUDENTS' LANGUAGE SKILLS
DOI: 10.5281/zenodo.17116609
title: THEORETICAL FOUNDATIONS OF USING SLIL TECHNOLOGIES IN DEVELOPING STUDENTS' LANGUAGE SKILLS
DOI: 10.5281/zenodo.16997154
title: European Lepidoptera–plant associations: a species-level interaction matrix

description: I present a large-scale matrix documenting plant–herbivore associations between 5,109 species of Lepidoptera and 3,175 species of vascular plants across Europe. The dataset is organized as a binary interaction matrix, in which each Lepidoptera species is represented by a row and each plant species by a column (1 = recorded larval association, 0 = no record). The compilation is based on an extensive review of published literature, monographs, publicly accessible online repositories, and grey literature, integrating a wide spectrum of sources. All taxonomic names were carefully curated and standardized following the most up-to-date classifications of both Lepidoptera and vascular plants. The matrix encompasses trees, shrubs, and herbaceous plants, including native, naturalized, ornamental, and invasive taxa as delimited by the European Environment Agency. The resolution of this matrix enable the examination of fundamental ecological and evolutionary questions, such as the prevalence and drivers of host specialization, the determinants of herbivore diversity, and the large-scale architecture of plant–herbivore networks. Because Lepidoptera are highly diverse and ecologically significant herbivores, the matrix provides a valuable foundation for assessing resource use and niche partitioning in relation to host traits, plant phylogeny, and biogeographic patterns. Beyond its relevance for basic ecological research, the matrix has strong applied value. It can be used to inform biodiversity conservation strategies, as identifying keystone host plants that sustain large numbers of Lepidoptera species, or by evaluating the vulnerability of herbivore communities to host loss under scenarios of habitat conversion, agricultural intensification, or climate change. Furthermore, the matrix can be integrated with environmental and trait databases to support predictive modeling, risk assessment, and long-term monitoring initiatives. The matrix is provided in a machine-readable CSV format and is accompanied by detailed documentation, facilitating reuse, reproducibility, and interoperability with other biodiversity resources. By making this resource freely available, I aim to foster broad engagement across ecological and conservation communities, enhance comparative studies of plant–herbivore interactions, and provide a lasting foundation for research on the diversity, specialization, and resilience of European herbivore assemblages.
DOI: 10.5281/zenodo.17116620
title: A REVIEW OF I. RASULOV'S SCIENTIFIC AND POPULAR SCIENTIFIC WORKS
DOI: 10.5281/zenodo.17116621
title: A REVIEW OF I. RASULOV'S SCIENTIFIC AND POPULAR SCIENTIFIC WORKS
DOI: 10.34740/kaggle/dsv/13056109
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.34973/6pyy-3x03
title: Beat gestures and prosodic prominence interactively influence language comprehension

description: Face-to-face communication is not only about ‘what’ is said but also ‘how’ it is said, both in speech and bodily signals. Beat gestures are rhythmic hand movements that typically accompany prosodic prominence in conversation. Yet, it is still unclear how beat gestures influence language comprehension. On the one hand, beat gestures may share the same functional role of focus markers as prosodic prominence. Accordingly, they would drive attention towards the concurrent speech and highlight its content. On the other hand, beat gestures may trigger inferences of high speaker confidence, generate the expectation that the sentence content is correct and thereby elicit the commitment to the truth of the statement. This study directly disentangled the two hypotheses by evaluating additive and interactive effects of prosodic prominence and beat gestures on language comprehension. Participants watched videos of a speaker uttering sentences and judged whether each sentence was true or false. Sentences sometimes contained a world knowledge violation that may go unnoticed (‘semantic illusion’). Combining beat gestures with prosodic prominence led to a higher degree of semantic illusion, making more world knowledge violations go unnoticed during language comprehension. These results challenge current theories proposing that beat gestures are visual focus markers. To the contrary, they suggest that beat gestures automatically trigger inferences of high speaker confidence and thereby elicit the commitment to the truth of the statement, in line with Grice’s cooperative principle in conversation. More broadly, our findings also highlight the influence of metacognition on language comprehension in face-to-face communication.

In this collection, you can find all the materials (experimental code and stimuli), raw data, and analysis code necessary to replicate this project's results.