cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15710708
title: Gemstones Images Dataset

description: Description:









Classes: 87 distinct Gemstones Images Dataset classes for accurate classification

Context: This dataset aims to assist algorithms in distinguishing between various gemstones, such as ruby, amethyst, and emerald, by providing labeled images.








Download Dataset









Content:



Overview: The dataset comprises over 3,200 high-quality images of gemstones, organized into 87 classes. These images are pre-sorted into training and testing sets, facilitating easy implementation in machine learning projects.



Details:



Shapes Included: The images capture gemstones in various shapes including round, oval, square, rectangle, and heart.



Structure:



Train Folder (~56 MB): Contains 87 subfolders, each representing a different class, with approximately 2,800 images in .jpeg format.

Test Folder (~8 MB): Also contains 87 subfolders, with around 400 images in total, ensuring a balanced test set for evaluating model performance.







Use Cases:



Classification Tasks: Ideal for training classification models to identify gemstone types.

Computer Vision Projects: Suitable for projects requiring detailed image analysis and pattern recognition in diverse shapes and colors.

E-commerce Applications: Can be used to develop systems for automatic gemstone recognition and cataloging in online marketplaces.




Additional Information:



File Formats: All images are provided in .jpeg format, ensuring compatibility with various image processing tools.

Data Quality: Images are carefully labeled and curated to maintain high quality and relevance.


This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.15710709
title: Gemstones Images Dataset

description: Description:









Classes: 87 distinct Gemstones Images Dataset classes for accurate classification

Context: This dataset aims to assist algorithms in distinguishing between various gemstones, such as ruby, amethyst, and emerald, by providing labeled images.








Download Dataset









Content:



Overview: The dataset comprises over 3,200 high-quality images of gemstones, organized into 87 classes. These images are pre-sorted into training and testing sets, facilitating easy implementation in machine learning projects.



Details:



Shapes Included: The images capture gemstones in various shapes including round, oval, square, rectangle, and heart.



Structure:



Train Folder (~56 MB): Contains 87 subfolders, each representing a different class, with approximately 2,800 images in .jpeg format.

Test Folder (~8 MB): Also contains 87 subfolders, with around 400 images in total, ensuring a balanced test set for evaluating model performance.







Use Cases:



Classification Tasks: Ideal for training classification models to identify gemstone types.

Computer Vision Projects: Suitable for projects requiring detailed image analysis and pattern recognition in diverse shapes and colors.

E-commerce Applications: Can be used to develop systems for automatic gemstone recognition and cataloging in online marketplaces.




Additional Information:



File Formats: All images are provided in .jpeg format, ensuring compatibility with various image processing tools.

Data Quality: Images are carefully labeled and curated to maintain high quality and relevance.


This dataset is sourced from Kaggle.
DOI: 10.15468/dl.v5msgx
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Nitzschia aurariae Cholnoky, 1965",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((15.6372 -28.0032,16.2782 -28.5786,20.509 -28.5786,20.509 -24.0715,15.4933 -24.0715,15.4945 -24.1257,16.0509 -25.6631,15.6372 -28.0032))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0061634-250525065834625/datasets/export for details.
DOI: 10.15468/dl.jk6ycw
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Nitzschia aurariae Cholnoky, 1965",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((15.4784 -23.4046,15.4933 -24.0715,20.509 -24.0715,20.509 -24.0455,20.0952 -23.5072,19.9968 -23.017,19.7843 -22.5405,19.1988 -21.8785,18.5972 -21.5447,17.8859 -21.3993,17.1626 -21.4805,16.5332 -21.7596,16.1257 -22.0882,15.8234 -22.4662,15.606 -22.8993,15.4784 -23.4046))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0061635-250525065834625/datasets/export for details.
DOI: 10.15468/dl.6v6w5y
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-114.14723 25.74421,-77.85277 25.74421,-77.85277 40.25579,-114.14723 40.25579,-114.14723 25.74421))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Macrobdella diplotertia Meyer, 1975"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0061636-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15710730
title: [Supporting Information] Constraints in supply of wild fish: empirical evidence and substitution effects

description: Supporting information from the manuscript: Constraints in supply of wild fish: empirical evidence and substitution effects.

Supporting information 1: This document contains: 1. Guide to the interviews to danish Producers' Organisations carried out in this study; 2. Table with results of regressions performed on time series data; 3. Analysis of external supply of Atlantic cod to the EU

Supporting information 2: Datasets used in the study and R code used to produce plots and regressions.
DOI: 10.15468/dl.s9fxgm
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Nitzschia aurariae Cholnoky, 1965",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((25.5395 -28.5786,20.509 -28.5786,20.509 -32.9007,25.5395 -32.9007,25.5395 -28.5786))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0061637-250525065834625/datasets/export for details.
DOI: 10.6084/m9.figshare.28498422
title: Generative artificial intelligence-guided user studies: an application for air taxi services

description: User studies are crucial for meeting user needs. In user studies, real experimental scenarios and participants are constructed and recruited. However, emerging and unfamiliar studies face limitations, including safety concerns and iterative efficiency. To address these challenges, this study utilises a Generative Artificial Intelligence (GenAI) to create GenAI-generated scenarios for user experience (UX). By recruiting real users to evaluate this experience, we can collect feedback that enables rapid iteration in the early design phase. The air taxi is particularly representative of these challenges and has been chosen as the case study for this research. The key contribution was designing an Air Taxi Journey (ATJ) using Large Language Models (LLMs) and AI image and video generators. Based on the GPT-4-generated scripts, key visuals were created for the air taxi, and the ATJ was evaluated by 72 participants. Furthermore, the LLMs demonstrated the ability to identify and suggest environments that significantly improve participants' willingness toward air taxis. Education level and gender significantly influenced participants' the difference in willingness and their satisfaction with the ATJ. Satisfaction with the ATJ serves as a mediator, significantly influencing participants' willingness to take air taxis. Our study confirms the capability of GenAI to support user studies, providing a feasible approach and valuable insights for designing air taxi UX in the early design phase.
DOI: 10.6084/m9.figshare.28498422.v3
title: Generative artificial intelligence-guided user studies: an application for air taxi services

description: User studies are crucial for meeting user needs. In user studies, real experimental scenarios and participants are constructed and recruited. However, emerging and unfamiliar studies face limitations, including safety concerns and iterative efficiency. To address these challenges, this study utilises a Generative Artificial Intelligence (GenAI) to create GenAI-generated scenarios for user experience (UX). By recruiting real users to evaluate this experience, we can collect feedback that enables rapid iteration in the early design phase. The air taxi is particularly representative of these challenges and has been chosen as the case study for this research. The key contribution was designing an Air Taxi Journey (ATJ) using Large Language Models (LLMs) and AI image and video generators. Based on the GPT-4-generated scripts, key visuals were created for the air taxi, and the ATJ was evaluated by 72 participants. Furthermore, the LLMs demonstrated the ability to identify and suggest environments that significantly improve participants' willingness toward air taxis. Education level and gender significantly influenced participants' the difference in willingness and their satisfaction with the ATJ. Satisfaction with the ATJ serves as a mediator, significantly influencing participants' willingness to take air taxis. Our study confirms the capability of GenAI to support user studies, providing a feasible approach and valuable insights for designing air taxi UX in the early design phase.
DOI: 10.6084/m9.figshare.28498422.v2
title: Generative artificial intelligence-guided user studies: an application for air taxi services

description: User studies are crucial for meeting user needs. In user studies, real experimental scenarios and participants are constructed and recruited. However, emerging and unfamiliar studies face limitations, including safety concerns and iterative efficiency. To address these challenges, this study utilises a Generative Artificial Intelligence (GenAI) to create GenAI-generated scenarios for user experience (UX). By recruiting real users to evaluate this experience, we can collect feedback that enables rapid iteration in the early design phase. The air taxi is particularly representative of these challenges and has been chosen as the case study for this research. The key contribution was designing an Air Taxi Journey (ATJ) using Large Language Models (LLMs) and AI image and video generators. Based on the GPT-4-generated scripts, key visuals were created for the air taxi, and the ATJ was evaluated by 72 participants. Furthermore, the LLMs demonstrated the ability to identify and suggest environments that significantly improve participants' willingness toward air taxis. Education level and gender significantly influenced participants' the difference in willingness and their satisfaction with the ATJ. Satisfaction with the ATJ serves as a mediator, significantly influencing participants' willingness to take air taxis. Our study confirms the capability of GenAI to support user studies, providing a feasible approach and valuable insights for designing air taxi UX in the early design phase.
DOI: 10.15468/dl.amav5x
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Nitzschia aurariae Cholnoky, 1965",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((20.7576 -24.3688,20.529 -24.0715,20.509 -24.0715,20.509 -28.5786,25.5395 -28.5786,25.5395 -24.0715,25.1306 -24.0715,24.9333 -24.4571,23.8962 -24.2467,22.8065 -24.295,22.3719 -24.7207,22.2323 -25.547,22.0387 -25.7188,21.7833 -25.7784,21.5388 -25.7113,21.3553 -25.5415,20.7576 -24.3688))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0061638-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.15710755
title: Wikimedia: Wikidata Common Names DwCA

description: Wikimedia Commons is a media file repository making available public domain and freely-licensed educational media content (images, sound and video clips) to all. It acts as a common repository for the various projects of the Wikimedia Foundation, but you do not need to belong to one of those projects to use media hosted here. The repository is created and maintained not by paid-for artists but by volunteers.

https://commons.wikimedia.org/wiki/Main_Page
DOI: 10.15468/dl.vjajpk
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Nitzschia aurariae Cholnoky, 1965",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((20.529 -24.0715,20.509 -24.0455,20.509 -24.0715,20.529 -24.0715))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0061639-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.15710627
title: enteromius_guirali_spades_01.fasta.gz
DOI: 10.5281/zenodo.15710628
title: enteromius_guirali_spades_01.fasta.gz
DOI: 10.15468/dl.h7zwre
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Nitzschia aurariae Cholnoky, 1965",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((25.5395 -23.2724,25.1306 -24.0715,25.5395 -24.0715,25.5395 -23.2724))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0061640-250525065834625/datasets/export for details.
DOI: 10.5063/f1tx3ctn
title: Vegetation Data for Ellsworth Creek Preserve 2007 and 2021

description: These data were used to quantify the effects of ecological forest restoration treatments (stand thinning) from a suite of empirical data collected from two time periods. Our hypothesis was that restoration treatments accelerate the development of the following old-growth forest indicators, contained in these datasets 1) adult tree growth, 2) vertical and horizontal forest structure, 3) tree regeneration, 4) plant diversity and abundance, and 5) soil organic layer depth.
DOI: 10.5281/zenodo.11283037
title: Numerical data supporting the publication "Accurate and gate-efficient quantum Ansätze for electronic states without adaptive optimization"

description: Numerical data and plotting scripts for regenerating figures in the article "Accurate and gate-efficient quantum ansätze for electronic states without adaptive optimisation".

See README for more details.
DOI: 10.5281/zenodo.12655140
title: Data on Crop Yield, Nutrient Content of Barley (Hordeum vulgare L.) and Weather of a Vertical Agrivoltaic System in Sweden

description: The dataset location is latitude 59.55° N and longitude 16.76° E in Kärrbo Prästgård, Sweden. 

Crop data:

Raw data of barley related to yield kernels and straws (kg DM/ha), nitrogen content in kernels (%), crude protein in kernels (%), kernels yield (kg DM/ha), straws yield (kg DM/ha), starch content in kernels (%), and thousand kernel weight (%) from the harvest on September 12th, 2023, at the agrivoltaics research site in Kärrbo Prästgård, Sweden. Fifty squared samples (each 0.25 m2) distributed in 5 groups (A, B, C, D, E) were collected according to the layout presented in the corresponding publication. Groups A, B and C are based on the spatial location in the crop area between the three vertical rows of PV modules: west side (A), center side (B) and east side (C). Group R corresponds to the reference control plot conditions. Group D represent the crops that are growing in the space between the rows of the conventional ground-mounted PV system with 30° tilt.  

 

Weather data:

1-hour timeseries averaged data measurements at local time, raw data, not quality controlled from the barley growing season at Kärrbo Prästgård, Sweden from May 7th to September 12th,2023.

Temperature of air (°C), relative humidity (%), relative air pressure (hPa), wind speed (m/s), and precipitation (mm/h) are measured with a Lufft WS600-UMB Smart Weather Sensor located on-site on a 5 m height mast.

Global and diffuse horizontal irradiance (W/m2) are measured with a Delta-T SPN1 Sunshine Pyranometer.

Photosynthetically active radiation (µmol/m2/s) is measured with an Apogee PAR Quantum sensor SQ-500.
DOI: 10.5281/zenodo.8289245
title: ONFIRE Dataset: Monthly Gridded Burned Area data

description: The ONFIRE Dataset presents a 1° x 1° lat-long gridded database detailing monthly burned areas (BA) stemming from national fire data across five regions: Australia, Canada, Chile, Europe, and the United States. Each grid cell encapsulates the center's latitude and longitude, coupled with the total square meters burned within the month. The dataset spans varying periods per region, starting from 1950 in Australia, 1959 in Canada, 1985 in Chile, 1980 in Europe, and 1984 in the US, extending up to 2021.The ONFIRE DATASET is available in netCDF4, RData, and ASCII formats for accessibility and ease of integration.
description: A.G. thanks to the Ministerio de Ciencia, Innovación y Universidades of Spain for Ph.D. contract FPU19/06536. M.T. acknowledges funding by the Spanish Ministry of Science, Innovation, and Universities through the Ramón y Cajal Grant Reference RYC2019-027115-I and through the project ONFIRE, grant PID2021-123193OB-I00, funded by MCIN/AEI/ 10.13039/501100011033 and by "ERDF A way of making Europe".
DOI: 10.5286/isis.e.rb2000190
title: Investigating the different magnetic phases of a new type-II multiferroic MnBi2S4

description: Multiferroics are the rare class of materials known, where two or more ferroic order parameters coexist simultaneously. Recently, spin driven ferroelectricity was reported in the centrosymmetric monoclinic sulphide MnSb2S4, in which incommensurate spin structure induces electric polarization. Along this line, we have discovered multiferroicity in MnBi2S4, which crystallizes in the monoclinic (C2/m) structure. It exhibits magnetic phase transitions at TN1=26.5, TN2=22.7, and TN3=21.3 K, where TN2 and TN3 are associated with ferroelectricity. The distortion associated with the lone pair of Bi3+- ion in BiS5 polyhedra expected to play crucial role in the observed multiferroicity and makes this compound different from MnSb2S4. It is necessary to know the magnetic spin structure to understand the origin of multiferroicity.
DOI: 10.34740/kaggle/dsv/9770477
title: AutoXGB
DOI: 10.34740/kaggle/dsv/9770478
title: OnlineFree
DOI: 10.15468/dl.h3kedb
title: Occurrence Download

description: A dataset containing 11636 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((51.81875 47.04873, 52.00749 47.04873, 52.00749 47.17963, 51.81875 47.17963, 51.81875 47.04873))",
 "Year is greater than or equal to 2013",
 "HasGeospatialIssue is false",
 "TaxonKey is Aves",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 11636 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013609-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.2909/7eb207d6-0a62-4280-b1ca-f4ad1d9f91c3
title: European Ground Motion Service: Basic 2019-2023 (vector), Europe, yearly, Oct. 2024

description: The European Ground Motion Service (EGMS) is a component of the Copernicus Land Monitoring Service. EGMS provides consistent, regular, standardised, harmonised and reliable information regarding natural and anthropogenic ground motion phenomena over the Copernicus Participating States and across national borders, with millimetre accuracy. This set of metadata describes the first product level of EGMS: Basic. 

The EGMS Basic - provides InSAR displacement data provided in the satellite Line-of-Sight (LOS), with annotated geo-localisation and quality measures per measurement point. This product is generated from the interferometric analysis of Sentinel-1 radar images at full resolution. It contains line of sight velocity maps in ascending and descending orbits with annotated geolocalisation and quality parameters per measurement point. The Basic product is referred to a local reference point; therefore, ground motion measurements are meaningful only within a small subset of the full product. It is not possible to compare deformation from adjacent areas belonging to different processing units of the same level. 

EGMS Basic is visualised as a vector map of measurement points colour-coded by average line-of-sight velocity and distributed to users in comma-separated values format. Each point is associated with a time series of displacement, i.e. a plot with values of displacement per acquisition of the satellite. The product is generated for both ascending and descending orbits.
The processing of the dataset has taken place in the period from March 2024 to October 2024.
DOI: 10.15468/dl.yrcbus
title: Occurrence Download

description: A dataset containing 2309 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Material citation, Material sample, Human Observation)",
 "TaxonKey is Lynx pardinus (Temminck, 1827)"
 ]
}

 The dataset includes 2309 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013610-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a4zpbb
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((45.23979 31.26872, 45.31936 31.26872, 45.31936 31.36317, 45.23979 31.36317, 45.23979 31.26872))",
 "Year is greater than or equal to 2013",
 "HasGeospatialIssue is false",
 "TaxonKey is Aves",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013612-241024112534372/datasets/export for details.