cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.nnsfs5
title: Occurrence Download

description: A dataset containing 3139 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "TaxonKey is Eumaeus atala (Poey, 1832)"
 ]
}

 The dataset includes 3139 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012136-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14260537
title: BUGUNGI KUNDA IJTIMOIY TARMOQLARNING YOSHLAR RUHIY SALOMATLIGIGA TA'SIRI
DOI: 10.5281/zenodo.14260538
title: BUGUNGI KUNDA IJTIMOIY TARMOQLARNING YOSHLAR RUHIY SALOMATLIGIGA TA'SIRI
DOI: 10.15468/dl.kxsyj2
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lamna ditropis Hubbs & Follett, 1947",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012137-241126133413365/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.27940581
title: <b>Exploring </b><b><i>nagZ</i></b><b> as a Virulence Biomarker and Treatment Target in </b><b><i>Enterobacter cloacae</i></b>

description: The study underscore the crucial role of <i>nagZ</i> in <i>E. cloacae</i> pathogenesis and highlight its potential as a novel therapeutic target for treating infections caused by this pathogen.
DOI: 10.6084/m9.figshare.27940581.v1
title: <b>Exploring </b><b><i>nagZ</i></b><b> as a Virulence Biomarker and Treatment Target in </b><b><i>Enterobacter cloacae</i></b>

description: The study underscore the crucial role of <i>nagZ</i> in <i>E. cloacae</i> pathogenesis and highlight its potential as a novel therapeutic target for treating infections caused by this pathogen.
DOI: 10.15468/dl.4hn978
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cedrela odorata Blanco"
 ]
}

 The dataset includes 14 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012138-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r76kwx
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is A new species of Chrysina Kirby (Coleoptera: Scarabaeidae: Rutelinae) from the Sierra Madre Occidental of Mexico, with notes on the type locality of Chrysina adelaida (Hope, 1841)"
 ]
}

 The dataset includes 14 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012139-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25345/c52v2cn8f
title: MassIVE MSV000096569 - GNPS Shea tree_Metabolomics_Updated 12_02_2024
title: This data contains metabolomic profiles of 70 accessions of the shea tree collected in Benin in West Africa
DOI: 10.15468/dl.2bm8kz
title: Occurrence Download

description: A dataset listing the 2 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Vepris fadenii (Kokwaro) Mziray"
 ]
}

 The dataset's 2 records were derived from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012126-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.23668/psycharchives.12370
title: Data for: Measuring the effectiveness of value-framing and message valence on audience engagement across countries

description: Changing public behaviour is an essential step for successful conservation, and can be achieved through effective use of message framing. However, its use in the conservation sector is not well-studied. We first performed a content analysis to assess what types of framing styles environmental non-governmental organisations (ENGOs) often employ for their social media posts. We then ran a real-world online fundraising campaign to examine the influence of value-framing (‘Intrinsic’ and ‘Extrinsic’) and message valence (‘Positive’ and ‘Negative’) on audience engagement with the advertisements, across five countries. Altogether, ENGOs generally used ‘Positive’ framing for their posts significantly more often than ‘Negative’, but did not use one type of value-framing more than the other. For the fundraising campaign, there were significant differences between countries’ engagement with the advertisements. However, click-through rates did not significantly differ when using types of value-framing nor message valence, and no donations were received to support the campaign. These results may show that message framing alone has little influence on audience engagement, if any, at least in the context of social media. To enhance campaign success for the future, it is recommended that conservationists offer concrete information regarding fundraising outcomes, and activate social norms.
DOI: 10.5281/zenodo.14231252
title: Sen2LUCAS Dataset

description: This repository contains a curated dataset of Sentinel-2 satellite imagery aligned with the Land Use/Cover Area frame Survey (LUCAS) 2018 dataset. LUCAS, an initiative by the European Union, systematically monitors land use and land cover across Europe, providing georeferenced ground-level observations.

As part of our work on SenCLIP, a vision-language model designed to bridge the gap between satellite and ground-level perspectives for land-use/land-cover (LULC) mapping, this dataset serves as the foundation for adapting pre-trained vision-language models to remote sensing tasks. By combining Sentinel-2 imagery with LUCAS survey data, the work enables new capabilities for zero-shot LULC classification, improving flexibility and accuracy without requiring labeled satellite data. The LUCAS dataset also provides rich metadata about land use and land cover, which were invaluable for aligning ground-level observations with satellite imagery.

Data Retrieval and Processing

The data collection and preparation process involved several rigorous steps to ensure its quality and relevance:



Geolocation Matching: Sentinel-2 imagery was precisely retrieved based on geolocations provided in the LUCAS 2018 dataset, ensuring spatial alignment with ground-truth survey points.

Temporal Consistency: Images were collected from the same months and years as the LUCAS survey to maintain temporal alignment between the satellite imagery and ground-level observations.

Cloud Coverage Filtering: To ensure high image quality, scenes were filtered to include only those with less than 10-20% cloud cover, minimizing obstructions for reliable visual and spectral analysis.


Sentinel-2 Imagery Specifications



Spectral Bands: The dataset contains the Red, Green, and Blue (RGB) bands of Sentinel-2 imagery.

Spatial Resolution: Each band is provided at a 10-meter-per-pixel resolution, allowing for detailed analysis of land cover and features.

Scene Dimensions: Each image has dimensions of 100 x 100 pixels, representing a 1 km x 1 km ground area. These dimensions are consistent with the granularity of LUCAS geolocations, enabling fine-grained analysis of land-use patterns.


By integrating the LUCAS 2018 ground-level observations with high-quality Sentinel-2 imagery, this dataset supports a wide range of geospatial and remote sensing applications. Furthermore, it facilitates the development of multimodal models like SenCLIP, which unlocks new potential for scalable, accurate, and flexible Earth observation tasks.
DOI: 10.5281/zenodo.14231251
title: Sen2LUCAS Dataset

description: This repository contains a curated dataset of Sentinel-2 satellite imagery aligned with the Land Use/Cover Area frame Survey (LUCAS) 2018 dataset. LUCAS, an initiative by the European Union, systematically monitors land use and land cover across Europe, providing georeferenced ground-level observations.

As part of our work on SenCLIP, a vision-language model designed to bridge the gap between satellite and ground-level perspectives for land-use/land-cover (LULC) mapping, this dataset serves as the foundation for adapting pre-trained vision-language models to remote sensing tasks. By combining Sentinel-2 imagery with LUCAS survey data, the work enables new capabilities for zero-shot LULC classification, improving flexibility and accuracy without requiring labeled satellite data. The LUCAS dataset also provides rich metadata about land use and land cover, which were invaluable for aligning ground-level observations with satellite imagery.

Data Retrieval and Processing

The data collection and preparation process involved several rigorous steps to ensure its quality and relevance:



Geolocation Matching: Sentinel-2 imagery was precisely retrieved based on geolocations provided in the LUCAS 2018 dataset, ensuring spatial alignment with ground-truth survey points.

Temporal Consistency: Images were collected from the same months and years as the LUCAS survey to maintain temporal alignment between the satellite imagery and ground-level observations.

Cloud Coverage Filtering: To ensure high image quality, scenes were filtered to include only those with less than 10-20% cloud cover, minimizing obstructions for reliable visual and spectral analysis.


Sentinel-2 Imagery Specifications



Spectral Bands: The dataset contains the Red, Green, and Blue (RGB) bands of Sentinel-2 imagery.

Spatial Resolution: Each band is provided at a 10-meter-per-pixel resolution, allowing for detailed analysis of land cover and features.

Scene Dimensions: Each image has dimensions of 100 x 100 pixels, representing a 1 km x 1 km ground area. These dimensions are consistent with the granularity of LUCAS geolocations, enabling fine-grained analysis of land-use patterns.


By integrating the LUCAS 2018 ground-level observations with high-quality Sentinel-2 imagery, this dataset supports a wide range of geospatial and remote sensing applications. Furthermore, it facilitates the development of multimodal models like SenCLIP, which unlocks new potential for scalable, accurate, and flexible Earth observation tasks.
DOI: 10.5281/zenodo.14259723
title: Sen2LUCAS Dataset

description: This repository contains a curated dataset of Sentinel-2 satellite imagery aligned with the Land Use/Cover Area frame Survey (LUCAS) 2018 dataset. LUCAS, an initiative by the European Union, systematically monitors land use and land cover across Europe, providing georeferenced ground-level observations.

As part of our work on SenCLIP, a vision-language model designed to bridge the gap between satellite and ground-level perspectives for land-use/land-cover (LULC) mapping, this dataset serves as the foundation for adapting pre-trained vision-language models to remote sensing tasks. By combining Sentinel-2 imagery with LUCAS survey data, the work enables new capabilities for zero-shot LULC classification, improving flexibility and accuracy without requiring labeled satellite data. The LUCAS dataset also provides rich metadata about land use and land cover, which were invaluable for aligning ground-level observations with satellite imagery.

Data Retrieval and Processing

The data collection and preparation process involved several rigorous steps to ensure its quality and relevance:



Geolocation Matching: Sentinel-2 imagery was precisely retrieved based on geolocations provided in the LUCAS 2018 dataset, ensuring spatial alignment with ground-truth survey points.

Temporal Consistency: Images were collected from the same months and years as the LUCAS survey to maintain temporal alignment between the satellite imagery and ground-level observations.

Cloud Coverage Filtering: To ensure high image quality, scenes were filtered to include only those with less than 10-20% cloud cover, minimizing obstructions for reliable visual and spectral analysis.


Sentinel-2 Imagery Specifications



Spectral Bands: The dataset contains the Red, Green, and Blue (RGB) bands of Sentinel-2 imagery.

Spatial Resolution: Each band is provided at a 10-meter-per-pixel resolution, allowing for detailed analysis of land cover and features.

Scene Dimensions: Each image has dimensions of 100 x 100 pixels, representing a 1 km x 1 km ground area. These dimensions are consistent with the granularity of LUCAS geolocations, enabling fine-grained analysis of land-use patterns.


By integrating the LUCAS 2018 ground-level observations with high-quality Sentinel-2 imagery, this dataset supports a wide range of geospatial and remote sensing applications. Furthermore, it facilitates the development of multimodal models like SenCLIP, which unlocks new potential for scalable, accurate, and flexible Earth observation tasks.
DOI: 10.23668/psycharchives.12372
title: Dataset and codebook for: Effects of Strain on Boundary Management Preferences – An Experimental Vignette Study

description: Introduction: Transformations in the work–nonwork interface highlight the importance of effectively managing the boundaries between life domains. However, do the ways individuals manage the boundaries between work and nonwork life change from one day to the next? If so, which antecedents may explain these intra-individual fluctuations in boundary management? Drawing on boundary management, spillover, and resource theories, we investigate daily changes in segmentation preferences and integration enactments as a function of experiencing strain in work and nonwork life. Assuming that changes in segmentation preferences reflect an individual’s strategy to regulate negative cross-role spillover, we suppose that strain increases individuals’ segmentation preferences; at the same time, however, it could force individuals to enact more integration. Methods: We test our assumptions with data from two studies with different methodological approaches. The first study uses a daily diary research design (Study 1, 425 participants with 3,238 daily observations) in which full-time professionals rated strain in work and nonwork life, segmentation preferences, and integration enactments every evening for 10 workdays. The second study uses an experimental vignette research design (Study 2, 181 participants), where we experimentally manipulated strain in work and nonwork life and investigated causal effects on participants’ hypothetical segmentation preferences. Results: The results of multilevel modeling analyses in Study 1 show that segmentation preferences and integration enactments fluctuate from day to day as a function of strain. More specifically, strain is related to preferring more segmentation but enacting more integration. Study 2 replicates the results of Study 1, showing that strain causally affects segmentation preferences. Discussion: This two-study paper is one of the first to address daily fluctuations in segmentation preferences and integration enactments, extending our knowledge of temporal dynamics in boundary management. Furthermore, it demonstrates that strain is an antecedent of these daily fluctuations, offering starting points for practical interventions.
description: Dataset for: Mueller, N., Loeffelsend, S., Vater, E., & Kempen, R. (2023). Effects of strain on boundary management: findings from a daily diary study and an experimental vignette study. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1149969
DOI: 10.6073/pasta/286d539cac2353abbc7c993dad840a76
title: Spartina alterniflora above- and belowground biomass predictions and inundation intensity as estimated by the Belowground Ecosystem Resiliency Model for U.S. Georgia marshes from 2014 to 2023.

description: We applied the Belowground Ecosystem Resiliency Model (BERM) to estimate monthly aboveground biomass (AGB) and belowground biomass (BGB) in U.S. Georgia Spartina alterniflora marshes from 2014 to 2023 at 30 m scale. This application involved BERM version 2.0 (https://doi.org/10.5281/zenodo.13306821), which was built using data in the PLT-GCET-2308 dataset (https://dx.doi.org/10.6073/pasta/4a0b715104849d98320fcc34e7cd63a4). Data sources for BERM application included Landsat-8/9, NOAA CO-OPS Station ID: 8670870, Daymet, and USGS 3DEP 2018 DEM. Download and processing steps are described in the BERM code and in metadata methods section. Specific descriptions of data processing are available in model code: https://doi.org/10.5281/zenodo.13306821. Data provided here include model output of AGB estimates, BGB estimates, and calculated inundation intensity. See "Data reporting" method in the metadata for description of data files. 
For logisitical purposes here we present only select data from the model input and output. All model input data sources as listed in the abstract are publicly available. Model calibration data and code are published as well. Additional predictions not published here include foliar chlorophyll, foliar nitrogen, and leaf area index.
DOI: 10.15468/dl.sg9uxm
title: Occurrence Download

description: A dataset containing 834 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Puffinus griseus (Gmelin, 1789)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 834 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012140-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14260548
title: YUNUS RAJABIY NOMIDAGI ANSAMBLINING TARIXI VA BUGUNGI FAOLIYATI.
DOI: 10.5281/zenodo.14260549
title: YUNUS RAJABIY NOMIDAGI ANSAMBLINING TARIXI VA BUGUNGI FAOLIYATI.
DOI: 10.15468/dl.5kku8k
title: Occurrence Download

description: A dataset containing 2037875 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Philippines",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 2037875 records from 846 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012096-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pyfm8z
title: Occurrence Download

description: A dataset containing 37 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Eremitalpa granti granti"
 ]
}

 The dataset includes 37 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012141-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13738078
title: Processed dataset used for developing tsunami inundation emulators(Part-I Training Dataset and Model Weights)

description: This dataset contains some of the processed training datasets covering tsunami inputs and outputs parameters (for offshore waveforms, local deformation fields and inundation depths used in developing a machine learning emulator discussed in the article - Towards Using Machine Learning Emulation for Probabilistic Inundation Mapping: Multiple Earthquake Sources and Near-field Effects: Necessary for PTHA submitted to NPJ Natural Hazards journal with project repo - https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.

####Code####

Source code from the above github project :  ML4SicilyTsunami-NPJ2024.tar.gz

The post-processed numpy dat files for the two test location of Catania(CT) and Siracusa(SR) are provided for each variable retrieved from data/processed folder of the project as compressed gzip files(.gz):

####Model Checkpoints#### ML Model checkpoints from all training experiments.- outSR.tar.gz (Siracusa)- outCT.tar.gz (Catania)

####Training Datasets####- t.tar.gz: Offshore waveforms for gauge sites in a 3D numpy file- dZ.tar.gz: Local deformation fields in 2D numpy files or reduced 1D flat files on flooded grids- d.tar.gz: Maximum inundation depth in 2D numpy files or reduced 1D flat files

####Flood Mask####- Identifies floodable and non-floodable areas in the domain. - zero_mask_SR_961.npy.gz - zero_mask_CT_892.npy.gz

####Flood Indices####Links between 2D and 1D numpy files:- Indices connecting 2D (events x m x n) numpy files to flattened 1D (events x m) numpy files:  - zero_indices_SR_961.npy.gz  - zero_indices_CT_892.npy.gz- Indices connecting latitude-longitude coordinates to 1D (events x m) numpy files:  - lat_lon_idx_CT_892.npy.gz  - lat_lon_idx_SR_961.npy.gz

The filenames follow nomenclature representing {parameter}_{site}_{size}



The first var represents the parameter of the file.



d - 2 dimensional file for inundation depth (events x m x n)

dZ - 2 dimensional file for local deformation( events x m x n)

dflat - 1 dimensional flat file for inundation ( events x locations)

dZflat - 1 dimensional flat file for local deformation( events x locations)

t - 3 dimension offshore waveform for (events x gauges x timesteps)

lat_lon_idx - index file for matching lat long coordinate with location indices(locations x lat x lon)



The second var represents site of the file.



CT - Catania

SR - Siracusa



The third var represents number of events in the file or the selection size.



892 - represents the training events for the file, of 4 different sizes for Catania and Siracusa each




More information on the attached readme.
DOI: 10.57841/casdc.0004137
title: 植物多重基因组编辑技术体系SWISS的建立
DOI: 10.6084/m9.figshare.27940752
title: Processed .h5ad Anndata file from Downsampled data (to use for "Visualization")

description: Spectral flow cytometry provides greater insights into cellular heterogeneity by simultaneous measurement of up to 50 markers. However, analyzing such high-dimensional (HD) data is complex through traditional manual gating strategy. To address this gap, we developed CAFE as an open-source Python-based web application with a graphical user interface. Built with Streamlit, CAFE incorporates libraries such as Scanpy for single-cell analysis, Pandas and PyArrow for efficient data handling, and Matplotlib, Seaborn, Plotly for creating customizable figures. Its robust toolset includes density-based down-sampling, dimensionality reduction, batch correction, Leiden-based clustering, cluster merging and annotation. Using CAFE, we demonstrated analysis of a human PBMC dataset of 350,000 cells identifying 16 distinct cell clusters. CAFE can generate publication-ready figures in real time via interactive slider controls and dropdown menus, eliminating the need for coding expertise and making HD data analysis accessible
DOI: 10.15468/dl.qdwxqh
title: Occurrence Download

description: A dataset containing 2935 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ceiba speciosa (A.St.-Hil., A.Juss. & Cambess.) Ravenna"
 ]
}

 The dataset includes 2935 records from 256 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012144-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.