cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.vausk3
title: Occurrence Download

description: A dataset containing 5712649 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is France",
 "Geometry POLYGON((-1.29191 46.45863,-0.30873 46.65521,0.24153 47.68411,-0.51147 48.78283,-5.46741 49.17901,-4.40861 47.55637,-2.59947 47.14712,-1.29191 46.45863))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 5712649 records from 924 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0079741-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pn3j3w
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eragrostis rotifer",
 "Country is Belgium",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006538-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17179679
title: weecology/EvergladesWadingBird: 2.2.0

description: v2.2.0
DOI: 10.15468/dl.9kpth9
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "OccurrenceStatus is Present",
 "TaxonKey is Canavalia obidensis"
 ]
}

 The dataset includes 12 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006542-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jsp6m3
title: Occurrence Download

description: A dataset containing 22420 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Sanguisorba minor",
 "Year is less than or equal to 2000",
 "Year is greater than or equal to 1970",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 22420 records from 173 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006541-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ad2tmj
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eremopoa persica",
 "Country is France",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006544-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nqkf89
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eremopoa persica",
 "Country is Belgium",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006543-250920141307145/datasets/export for details.
DOI: 10.15468/dl.e3e9q2
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eremopoa persica",
 "Country is Netherlands",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006545-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zxrqnh
title: Occurrence Download

description: A dataset containing 483 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Sanicula crassicaulis",
 "Year is less than or equal to 2000",
 "Year is greater than or equal to 1970",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 483 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006548-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6gferz
title: Occurrence Download

description: A dataset containing 3274 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "TaxonKey is Crotalus ruber"
 ]
}

 The dataset includes 3274 records from 73 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006547-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tmdy5v
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eremopyrum bonaepartis",
 "Country is Belgium",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006549-250920141307145/datasets/export for details.
DOI: 10.15468/dl.hbjsnj
title: Occurrence Download

description: A dataset containing 42 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eremopyrum bonaepartis",
 "Country is United States of America",
 "HasCoordinate is true"
 ]
}

 The dataset includes 42 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006550-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4pg2c3
title: Occurrence Download

description: A dataset containing 167023 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is one of (Ambrosia artemisiifolia)"
 ]
}

 The dataset includes 167023 records from 1337 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006528-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u6dkgf
title: Occurrence Download

description: A dataset containing 3818 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Nepal",
 "DatasetKey is Sharing vertebrate occurrence data from camera traps in Asia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 3818 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004928-250127130748423/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.28280417.v1
title: Enhanced antimycotic activity of biomodified copper oxide nanoparticles from indigenous state flowers of Jharkhand: an <i>in vitro</i> and <i>in silico</i> approach

description: Current study investigates the medicinal applications of <i>Butea monosperma</i> (Palash), the state flower of Jharkhand, India, focusing on synthesising biomodified copper oxide nanoparticles (CuO-NPs) and its antifungal properties. Flavonoid content in the flower extract was quantified by aluminium chloride colorimetric analysis. CuO-NPs were synthesised via co-precipitation method and then modified with methanolic flower extract. Characterization confirmed the properties of these NPs including their high crystallinity, spherical structure and nanoscale size (9–90 nm size). Anti-fungal activity revealed significant inhibition of hyphal growth at concentrations of 100–1000 ppm (76.6% for <i>F. oxysporum</i> and 75.05% for <i>S. rolfsii</i>). Statistical and computational analysis confirmed the effectiveness of these NPs by showing strong interactions between the CuO-NPs and fungal proteins.
DOI: 10.18419/darus-4485
title: Mixed variational formulation and finite-element implementation of second-order poro-elasticity: Datasets

description: The datasets provided here are associated with our publication "Mixed variational formulation and finite-element implementation of second-order poro-elasticity". The main idea of the work is to develop a working model for second gradient poro-elasticity. This includes a mixed-variational formulation and finite-element formulation. The results are generated using finite-element software FEAP. The key differences between first-order and second-order models are studied using several boundary value problems. The results for each boundary value problem are provided in HDF files.
Additionally, the results for strain and change in fluid mass content are provided for Terzaghi's consolidation problem and Mandel's consolidation problem applied to layered bodies.
description: FEAP, 8.4
DOI: 10.15468/dd.5pcs4d
title: SSARP: An R Package for Easily Creating Species- and Speciation- Area Relationships Using Web Databases

description: Records used in [paper](https://doi.org/10.1101/2024.12.31.630948) (dn)
DOI: 10.15468/dl.h973ej
title: Occurrence Download

description: A dataset containing 12391 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Italy, Spain, France)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Beta L.",
 "Year 2021-2025"
 ]
}

 The dataset includes 12391 records from 102 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004929-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6ankxt
title: Occurrence Download

description: A dataset containing 7698 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Clitocybe phyllophila (Pers.) P.Kumm."
 ]
}

 The dataset includes 7698 records from 266 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004930-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fs6y48
title: Occurrence Download

description: A dataset containing 15021 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "Geometry POLYGON((-119.38735 31.81172,-109.60143 31.81172,-109.60143 38.85536,-119.38735 38.85536,-119.38735 31.81172))",
 "HasGeospatialIssue is false",
 "TaxonKey is Toxostoma lecontei Lawrence, 1851"
 ]
}

 The dataset includes 15021 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004931-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14769279
title: Measuring the Strength of Spatial Sorting

description: The zip folders contain code and data files to recreate the analysis presented in the paper "Measuring the strength of spatial sorting."

The zip file contains three folders.



The Figures folder contains R code files used to generate figures in the paper. 

The code folder contains R and Stan code files to estimate heritability, standardized sorting gradient, and resulting evolutionary responses to spatial sorting in soapberry bugs.

The data folder contains data files.



The images folder contains photos of binary wings morphology of soapberry bugs and flooded sites before and after the hurricane in the Greater Houston area

genetic_crosses_Comerford.csv file contains morphological measurements of insects crossed in the laboratory to estimate heritability of liability.

site_metadata.csv file contains information on the locations of sites where soapberry bugs were sampled during the monitoring survey from 2016 to 2020.

Spatial_Sorting_Comerford_Soapberrybugs.csv contains morphological measurements of soapberry bugs in flooded and unflooded sites from 2016 to 2020.

The Kingsolver_selection_gradients.csv file contains estimates of standardized selection gradients documented in the scientific literature (Kingsolver and Diamond 2010).
DOI: 10.15468/dl.j9gyz4
title: Occurrence Download

description: A dataset containing 16753 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Toxostoma lecontei Lawrence, 1851"
 ]
}

 The dataset includes 16753 records from 42 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004932-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10088130
title: Here comes trouble! Distinguishing GUI Component States for Blind Users using Large Language Models

description: # Data Code Repository
 
This repository contains open-source data code that provides utilities for the paper named "Here comes trouble! Distinguishing GUI Component States for Blind Users using Large Language Models". The code is designed to facilitate data-related tasks and promote reproducibility in research and data analysis projects.
 
## Features
 
- Attribute identification and extraction: Including real-time recognition and extraction of GUI components in the view type, resource-id, color, action of four attributes
- Components State Distinction: Provides the prompt needed for large language models, covering their specific design schemes and chain of thought reasoning processes as well as contextual learning content.
- Implementation: Offers specific methods to realize the process, including the setting of relevant parameters and the use of functions.
 
## Installation
 
To use the data code, you can down or clone the required code.
Notably, before using the code, make sure the necessary environment configuration is done.
 
## Dependencies
The data code has the following dependencies:
 
Python (version 3.6 or higher)
NumPy
Pandas
Seaborn
Scikit-learn
Openai
Android Studio (version 4.0)
 
Install the required dependencies using pip:
pip install numpy..
 
##License
This data code is distributed under the MIT License. See LICENSE for more information.
 
##Copyright
All copyright of the tool is owned by the author of the paper.
DOI: 10.5281/zenodo.12803370
title: Here comes trouble! Distinguishing GUI Component States for Blind Users using Large Language Models

description: # Data Code Repository
 
This repository contains open-source data code that provides utilities for the paper named "Here comes trouble! Distinguishing GUI Component States for Blind Users using Large Language Models". The code is designed to facilitate data-related tasks and promote reproducibility in research and data analysis projects.
 
## Features
 
- Attribute identification and extraction: Including real-time recognition and extraction of GUI components in the view type, resource-id, color, action of four attributes
- Components State Distinction: Provides the prompt needed for large language models, covering their specific design schemes and chain of thought reasoning processes as well as contextual learning content.
- Implementation: Offers specific methods to realize the process, including the setting of relevant parameters and the use of functions.
 
## Installation
 
To use the data code, you can down or clone the required code.
Notably, before using the code, make sure the necessary environment configuration is done.
 
## Dependencies
The data code has the following dependencies:
 
Python (version 3.6 or higher)
NumPy
Pandas
Seaborn
Scikit-learn
Openai
Android Studio (version 4.0)
 
Install the required dependencies using pip:
pip install numpy..
 
##License
This data code is distributed under the MIT License. See LICENSE for more information.
 
##Copyright
All copyright of the tool is owned by the author of the paper.
DOI: 10.5281/zenodo.14769506
title: Distinguishing GUI Component States for Blind Users using Large Language Models

description: # Data Code Repository
 
This repository contains open-source data code that provides utilities for the paper named "Here comes trouble! Distinguishing GUI Component States for Blind Users using Large Language Models". The code is designed to facilitate data-related tasks and promote reproducibility in research and data analysis projects.
 
## Features
 
- Attribute identification and extraction: Including real-time recognition and extraction of GUI components in the view type, resource-id, color, action of four attributes
- Components State Distinction: Provides the prompt needed for large language models, covering their specific design schemes and chain of thought reasoning processes as well as contextual learning content.
- Implementation: Offers specific methods to realize the process, including the setting of relevant parameters and the use of functions.
 
## Installation
 
To use the data code, you can down or clone the required code.
Notably, before using the code, make sure the necessary environment configuration is done.
 
## Dependencies
The data code has the following dependencies:
 
Python (version 3.6 or higher)
NumPy
Pandas
Seaborn
Scikit-learn
Openai
Android Studio (version 4.0)
 
Install the required dependencies using pip:
pip install numpy..
 
##License
This data code is distributed under the MIT License. See LICENSE for more information.
 
##Copyright
All copyright of the tool is owned by the author of the paper.
DOI: 10.5281/zenodo.8194883
title: Distinguishing GUI Component States for Blind Users using Large Language Models

description: # Data Code Repository
 
This repository contains open-source data code that provides utilities for the paper named "Here comes trouble! Distinguishing GUI Component States for Blind Users using Large Language Models". The code is designed to facilitate data-related tasks and promote reproducibility in research and data analysis projects.
 
## Features
 
- Attribute identification and extraction: Including real-time recognition and extraction of GUI components in the view type, resource-id, color, action of four attributes
- Components State Distinction: Provides the prompt needed for large language models, covering their specific design schemes and chain of thought reasoning processes as well as contextual learning content.
- Implementation: Offers specific methods to realize the process, including the setting of relevant parameters and the use of functions.
 
## Installation
 
To use the data code, you can down or clone the required code.
Notably, before using the code, make sure the necessary environment configuration is done.
 
## Dependencies
The data code has the following dependencies:
 
Python (version 3.6 or higher)
NumPy
Pandas
Seaborn
Scikit-learn
Openai
Android Studio (version 4.0)
 
Install the required dependencies using pip:
pip install numpy..
 
##License
This data code is distributed under the MIT License. See LICENSE for more information.
 
##Copyright
All copyright of the tool is owned by the author of the paper.
DOI: 10.5281/zenodo.8194884
title: Here comes trouble! Distinguishing GUI Component States for Blind and Low Vision Users using Large Language Models

description: <strong># Data Code Repository</strong> This repository contains open-source data code that provides utilities for the paper named “Here comes trouble! Distinguishing GUI Component States for Blind and Low Vision Users using Large Language Models”. The code is designed to facilitate data-related tasks and promote reproducibility in research and data analysis projects. <strong>## Features</strong> - Attribute identification and extraction: Including real-time recognition and extraction of GUI components in the view type, resource-id, color, action of four attributes - Components State Distinction: Provides the prompt needed for large language models, covering their specific design schemes and chain of thought reasoning processes as well as contextual learning content. - Implementation: Offers specific methods to realize the process, including the setting of relevant parameters and the use of functions. <strong>## Installation</strong> To use the data code, you can down or clone the required code. Notably, before using the code, make sure the necessary environment configuration is done. <strong>## Dependencies</strong> The data code has the following dependencies: Python (version 3.6 or higher) NumPy Pandas Seaborn Scikit-learn Openai Android Studio (version 4.0) Install the required dependencies using pip: pip install numpy.. <strong>##License</strong> This data code is distributed under the MIT License. See LICENSE for more information. <strong>##Copyright</strong> All copyright of the tool is owned by the author of the paper.
DOI: 10.5281/zenodo.14739431
title: Host specificity and host stage preference of Psyllaephagus species (Hymenoptera: Encyrtidae) towards invasive eucalypt psyllids (Hemiptera: Aphalaridae)

description: The data set captures experimental results from laboratory tests assessing the host specificity and host stage preference of Psyllaephagus species (P. blastopsyllae, P. pilosus, and P. bliteus) toward invasive eucalypt psyllids (Blastopsylla occidentalis, Ctenarytaina eucalypti, Glycaspis brimblecombei, and Spondyliaspis cf. plicatuloides). The dataset includes data from choice and no-choice tests, host stage preference trials across five nymphal stages, and parasitoid development assessments. Key variables include parasitoid emergence rates, host stage preferences, and developmental outcomes on target and non-target psyllid species.
DOI: 10.5281/zenodo.14739432
title: Host specificity and host stage preference of Psyllaephagus species (Hymenoptera: Encyrtidae) towards invasive eucalypt psyllids (Hemiptera: Aphalaridae)

description: The data set captures experimental results from laboratory tests assessing the host specificity and host stage preference of Psyllaephagus species (P. blastopsyllae, P. pilosus, and P. bliteus) toward invasive eucalypt psyllids (Blastopsylla occidentalis, Ctenarytaina eucalypti, Glycaspis brimblecombei, and Spondyliaspis cf. plicatuloides). The dataset includes data from choice and no-choice tests, host stage preference trials across five nymphal stages, and parasitoid development assessments. Key variables include parasitoid emergence rates, host stage preferences, and developmental outcomes on target and non-target psyllid species.
DOI: 10.18112/openneuro.ds005237.v1.1.0
title: Transdiagnostic Connectome Project