cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.7e6nmb
title: Occurrence Download

description: A dataset containing 5097 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rosa stylosa Desv.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 5097 records from 135 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0291282-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5smssb
title: Occurrence Download

description: A dataset containing 27527 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pontederia crassipes Mart.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 27527 records from 431 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0183975-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/nwdzwk89zs
title: Arabian Jasmine Leaf Condition Dataset (AJLCD-2025)

description: The Arabian Jasmine Leaf Condition Dataset (AJLCD-2025) consists of 6,315 high-resolution images of Arabian Jasmine (Jasminum sambac) leaves, collected in 2025 from Sonargaon, Narayanganj, Bangladesh (GPS: 23.6303°N, 90.6028°E; altitude: 45.2 meters). The original images were captured using a Xiaomi M2102J20SG smartphone under natural daylight conditions, and contain full EXIF metadata such as F-stop (f/1.8), ISO speed (ISO-96), exposure time (1/120 sec), focal length (5 mm), metering mode (center-weighted average), and white balance set to auto. The raw images are in JPG format, primarily with dimensions of 6000×8000 pixels (some 4000×3000), 24-bit sRGB color space, and 72 dpi resolution.

To prepare the dataset for deep learning and image classification tasks, all images were preprocessed by resizing them uniformly to 1440×1080 pixels, applying background removal to isolate leaf structures, and replacing the background with a clean white canvas to enhance clarity and model performance. The dataset includes 8 different types of leaf conditions, representing various disease conditions and stages of the Arabian Jasmine plant: Anthracnose (754 images), Chlorosis (699), Dried Leaf (916), Healthy (955), Initial Stage (726), Leaf Blight (778), Leaf Spot (733), and Pest Damage (754).

This AJLCD-2025 dataset is built to support many applications like leaf disease classification using CNNs and transformer models, mobile app development for plant disease diagnosis, explainable AI (XAI) research, and smart farming solutions. It is also highly relevant for fields like Agrotechnology, where it can aid in developing advanced crop management strategies, improving plant health monitoring, and optimizing sustainable agricultural practices. The hope is that this dataset will push forward progress in precision agriculture, agro-tech innovation, and AI-driven plant pathology.
DOI: 10.21227/1nyq-gb28
title: "Acoustic Fault Dataset of Internal Combustion Engine Under Controlled Conditions for Ignition and Mechanical Anomalies"

description: "A base de dados resulta de ensaios experimentais realizados em um motor de combust\u00e3o interna (ciclo Otto, quatro tempos, igni\u00e7\u00e3o por fa\u00edsca) sob condi\u00e7\u00f5es controladas, no Laborat\u00f3rio de Motores do Departamento de Engenharia Mec\u00e2nica da Universidade Federal da Para\u00edba (UFPB). O motor, instalado em um ve\u00edculo Ford Fiesta 1.6 (modelo 2005) da frota laboratorial, teve seus sinais ac\u00fasticos capturados na regi\u00e3o do escapamento do motor, posicionando o microfone a cerca de um metro de dist\u00e2ncia e em um \u00e2ngulo de aproximadamente 45\u00b0 em rela\u00e7\u00e3o \u00e0 sa\u00edda de gases, a fim de capturar as caracter\u00edsticas sonoras representativas do funcionamento do motor. A interface USB \u00e9 utilizada para transferir dados brutos para um notebook de apoio, o software de aquisi\u00e7\u00e3o desenvolvido em linguagem C\/C++ e Python, respons\u00e1vel pelo controle de amostragem e armazenamento dos sinais em formato WAVE, com taxa de amostragem de 44,1 kHz e resolu\u00e7\u00e3o de 16 bits, garantindo fidelidade \u00e0 faixa aud\u00edvel e \u00e0s frequ\u00eancias t\u00edpicas de falhas mec\u00e2nicas e de combust\u00e3o. Foram simuladas duas categorias de falhas: (i) falhas de igni\u00e7\u00e3o, induzidas pela desconex\u00e3o intencional de cabos de velas para replicar falhas de combust\u00e3o em cilindros espec\u00edficos; e (ii) falhas mec\u00e2nicas, geradas pela remo\u00e7\u00e3o controlada de segmentos da correia trapezoidal do alternador, provocando escorregamento e perda de material. Essas interven\u00e7\u00f5es alteraram o comportamento din\u00e2mico do motor e produziram padr\u00f5es sonoros distintos, permitindo a caracteriza\u00e7\u00e3o ac\u00fastica de falhas."
DOI: 10.17632/nwdzwk89zs.1
title: Arabian Jasmine Leaf Condition Dataset (AJLCD-2025)

description: The Arabian Jasmine Leaf Condition Dataset (AJLCD-2025) consists of 6,315 high-resolution images of Arabian Jasmine (Jasminum sambac) leaves, collected in 2025 from Sonargaon, Narayanganj, Bangladesh (GPS: 23.6303°N, 90.6028°E; altitude: 45.2 meters). The original images were captured using a Xiaomi M2102J20SG smartphone under natural daylight conditions, and contain full EXIF metadata such as F-stop (f/1.8), ISO speed (ISO-96), exposure time (1/120 sec), focal length (5 mm), metering mode (center-weighted average), and white balance set to auto. The raw images are in JPG format, primarily with dimensions of 6000×8000 pixels (some 4000×3000), 24-bit sRGB color space, and 72 dpi resolution.

To prepare the dataset for deep learning and image classification tasks, all images were preprocessed by resizing them uniformly to 1440×1080 pixels, applying background removal to isolate leaf structures, and replacing the background with a clean white canvas to enhance clarity and model performance. The dataset includes 8 different types of leaf conditions, representing various disease conditions and stages of the Arabian Jasmine plant: Anthracnose (754 images), Chlorosis (699), Dried Leaf (916), Healthy (955), Initial Stage (726), Leaf Blight (778), Leaf Spot (733), and Pest Damage (754).

This AJLCD-2025 dataset is built to support many applications like leaf disease classification using CNNs and transformer models, mobile app development for plant disease diagnosis, explainable AI (XAI) research, and smart farming solutions. It is also highly relevant for fields like Agrotechnology, where it can aid in developing advanced crop management strategies, improving plant health monitoring, and optimizing sustainable agricultural practices. The hope is that this dataset will push forward progress in precision agriculture, agro-tech innovation, and AI-driven plant pathology.
DOI: 10.15468/dl.wzbrbx
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Penelope jacquacu)",
 "Country is one of (Åland Islands, Albania, Andorra, Austria, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Czechia, Denmark, Estonia, Faroe Islands, Finland, France, Germany, Gibraltar, Greece, Guernsey, Holy See, Hungary, Iceland, Ireland, Isle of Man, Italy, Jersey, Latvia, Liechtenstein, Lithuania, Luxembourg, Malta, Moldova, Republic of, Monaco, Montenegro, Netherlands, North Macedonia, Norway, Poland, Portugal, Romania, Russian Federation, San Marino, Serbia, Slovakia, Slovenia, Spain, Svalbard and Jan Mayen, Sweden, Switzerland, Ukraine, United Kingdom of Great Britain and Northern Ireland)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022698-250717081556266/datasets/export for details.
DOI: 10.15468/dl.fu7ts3
title: Occurrence Download

description: A dataset containing 115 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ipomoea purpurea",
 "Country is India",
 "HasCoordinate is true"
 ]
}

 The dataset includes 115 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022697-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/z3bv3z9k35
title: Macroeconomic Drivers and the Pricing of Uncertainty, Inflation, and Bonds

description: Replication code for "Macroeconomic Drivers and the Pricing of Uncertainty, Inflation, and Bonds" by Brandyn Bok, Thomas M. Mertens, and John C. Williams.
DOI: 10.17632/z3bv3z9k35.1
title: Macroeconomic Drivers and the Pricing of Uncertainty, Inflation, and Bonds

description: Replication code for "Macroeconomic Drivers and the Pricing of Uncertainty, Inflation, and Bonds" by Brandyn Bok, Thomas M. Mertens, and John C. Williams.
DOI: 10.5281/zenodo.16262548
title: PUDL Raw Mine Safety and Health Administration (MSHA) Mines

description: The Mine dataset lists all Coal and Metal/Non-Metal mines under MSHA's jurisdiction. It includes such information as the current status of each mine (Active, Abandoned, NonProducing, etc.), the current owner and operating company, commodity codes and physical attributes of the mine. Archived from 
https://arlweb.msha.gov/OpenGovernmentData/OGIMSHA.asp

This archive contains raw input data for the Public Utility Data Liberation (PUDL)
software developed by Catalyst Cooperative. It is
organized into Frictionless
Data Packages. For additional information about this data and PUDL, see the
following resources:


  
The PUDL Repository on GitHub
  
PUDL Documentation
  
Other Catalyst Cooperative data archives
DOI: 10.15468/dl.3apysx
title: Occurrence Download

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

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022699-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/37pys4vxt4
title: Nigerian innovation survey data

description: The pooled cross-sectional dataset contains 2643 observations including 51% manufacturing and 41% service firms.

* The dataset includes data from wave 1 (2005-2007), wave 2 (2008-2010), and wave 3 (2016-2018) of the Nigerian business innovation surveys. 
* The year variable identifies the different survey waves. Wave 1 was completed in 2008, wave 2 in 2011, and wave 3 in 2019. 
* The service variable sorts the observations broadly into manufacturing and services.
* The id variable identifies each unique firm. REPEATEDNESS WAS IGNORED BECAUSE REPEATED CASES ARE ONLY ABOUT 2.5%.
* As much as possible, variables have been matched across the three waves. 
* Due to coding changes and some inconsistencies in the survey instrument, a few variables could not be matched. 
* Any variable that could not be matched is retained in its original form. 
* Some of the variables have notes attached to them. The notes are consistent with what is in the accompanying codebook (Excel document).
* Item numbering on the questionnaire for the three waves is not consistent. Thus, rather than use question numbers for variable names as is commonly done, intuitive variable names and labels (defined in detail in the accompanying codebook) are used.
* Definitions of main concepts can be found in the accompanying codebook.
* It is strongly recommended that users thoroughly familiarize themselves with the accompanying codebook as well as the questionnaires for each of the waves before applying the dataset. This is crucial especially because of the skip patterns. While everything was done to ensure that the skip patterns were all correctly established, there can be no guarantee of perfection. 
* It is also strongly recommended that users be familiar with the nature of business innovation surveys as this will help in understanding how to treat the data for analysis. The Oslo Manual (third and fourth editions), which are freely available online, are very useful resources.

To have a feel of the sectoral distribution of the sample, type in Stata: tab service year
DOI: 10.17632/37pys4vxt4.3
title: Nigerian innovation survey data

description: The pooled cross-sectional dataset contains 2643 observations including 51% manufacturing and 41% service firms.

* The dataset includes data from wave 1 (2005-2007), wave 2 (2008-2010), and wave 3 (2016-2018) of the Nigerian business innovation surveys. 
* The year variable identifies the different survey waves. Wave 1 was completed in 2008, wave 2 in 2011, and wave 3 in 2019. 
* The service variable sorts the observations broadly into manufacturing and services.
* The id variable identifies each unique firm. REPEATEDNESS WAS IGNORED BECAUSE REPEATED CASES ARE ONLY ABOUT 2.5%.
* As much as possible, variables have been matched across the three waves. 
* Due to coding changes and some inconsistencies in the survey instrument, a few variables could not be matched. 
* Any variable that could not be matched is retained in its original form. 
* Some of the variables have notes attached to them. The notes are consistent with what is in the accompanying codebook (Excel document).
* Item numbering on the questionnaire for the three waves is not consistent. Thus, rather than use question numbers for variable names as is commonly done, intuitive variable names and labels (defined in detail in the accompanying codebook) are used.
* Definitions of main concepts can be found in the accompanying codebook.
* It is strongly recommended that users thoroughly familiarize themselves with the accompanying codebook as well as the questionnaires for each of the waves before applying the dataset. This is crucial especially because of the skip patterns. While everything was done to ensure that the skip patterns were all correctly established, there can be no guarantee of perfection. 
* It is also strongly recommended that users be familiar with the nature of business innovation surveys as this will help in understanding how to treat the data for analysis. The Oslo Manual (third and fourth editions), which are freely available online, are very useful resources.

To have a feel of the sectoral distribution of the sample, type in Stata: tab service year
DOI: 10.15468/dl.vp4xze
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Balanus improvisus",
 "Country is Iran (Islamic Republic of)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022700-250717081556266/datasets/export for details.
DOI: 10.15468/dl.g46wjq
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Penelope jacquacu)",
 "Country is one of (American Samoa, Australia, Christmas Island, Cocos (Keeling) Islands, Cook Islands, Fiji, French Polynesia, Guam, Heard Island and McDonald Islands, Kiribati, Marshall Islands, Micronesia (Federated States of), Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Papua New Guinea, Pitcairn, Samoa, Solomon Islands, Tokelau, Tonga, Tuvalu, United States Minor Outlying Islands, Vanuatu, Wallis and Futuna)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022701-250717081556266/datasets/export for details.
DOI: 10.15468/dl.2zfs4n
title: Occurrence Download

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

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022702-250717081556266/datasets/export for details.
DOI: 10.15468/dl.7uq2nj
title: Occurrence Download

description: A dataset containing 259089 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Lissotriton vulgaris"
 ]
}

 The dataset includes 259089 records from 908 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022652-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15587013
title: BLTP3A is associated with membranes of the late endocytic pathway and is an effector of CASM

description: Original data for Figure 1-7 & Figures EV 1-4. The original images associated with Figure EV4B can be found on BioImage Archive (https://doi.org/10.6019/S-BIAD2092).
DOI: 10.17632/6ynv7sr5xd
title: Attitudes and Factors Influencing the Purchase Intention of Nutrient-Enriched Edible Gel Beads

description: This dataset supports a doctoral research project under the Ph.D. Program in Technopreneurship
and Innovation Management, Chulalongkorn University.
The study investigates consumer attitudes, behavior, and purchase intentions toward
nutrient-enriched edible gel beads (e.g., boba pearls)
designed for health enhancement and food innovation.
Contents of the Dataset
1. Consent letter and Questionnaire.pdf - Includes the participant information sheet, consent form,
A full questionnaire was used for data collection.
2. Graph summary survey report_Attitudes_Factor_influencing_nutrient_enriched bead gel.pdf -
Contains graphical and tabular summaries of survey results categorized by demographic segments
and response trends.
3. IRB_Certified_Chulalongkorn University.pdf - Institutional Review Board (IRB) approval document
certifying that ethical standards were followed.
4. Raw_Data_256 response_Attitudes_Factors_Purchase Intention_Nutrient Enriched Bead Gel.xlsx
- Raw, anonymized data from 256 respondents, including all survey variables from the general
Demographics to purchase intention indicators.
Methodology Summary
- Participants: 256 respondents across various age, income, and education groups.
- Survey Sections: 
1. General Information (Gender, Age, Occupation, Health status, etc.)
2. Consumption Behavior (Frequency, purchasing patterns, etc.)
3. Attitudes & Purchase Intentions (Preferences, health claims, motivations)
The survey targeted residents in Bangkok aged 20 years and above. The appropriate sample size was calculated using the G*Power program [11], which recommended 190 participants, with an additional 2% (n = 4) added to account for potential non-responses, bringing the minimum target sample to 194. However, to ensure greater diversity and data robustness, a total of 256 valid responses were ultimately collected and analyzed.
Quota sampling was employed by predefining target characteristics and proportions of the target group. The questionnaire, including informed consent statement was distributed online via the Line application, with dissemination through acquaintances, including master’s and doctoral students, colleagues, and family members. The eligibility criteria required respondents to be aged 20 years and above. No exclusion criteria were set to ensure diversity in attitudes. Data were collected through Google Forms. All participants were informed consent in the questionnaires.
Hypotheses were tested using the Pearson Chi-Square statistical method to examine the factors influencing the purchase decision of health-enhanced pearls.
DOI: 10.17632/6ynv7sr5xd.1
title: Attitudes and Factors Influencing the Purchase Intention of Nutrient-Enriched Edible Gel Beads

description: This dataset supports a doctoral research project under the Ph.D. Program in Technopreneurship
and Innovation Management, Chulalongkorn University.
The study investigates consumer attitudes, behavior, and purchase intentions toward
nutrient-enriched edible gel beads (e.g., boba pearls)
designed for health enhancement and food innovation.
Contents of the Dataset
1. Consent letter and Questionnaire.pdf - Includes the participant information sheet, consent form,
A full questionnaire was used for data collection.
2. Graph summary survey report_Attitudes_Factor_influencing_nutrient_enriched bead gel.pdf -
Contains graphical and tabular summaries of survey results categorized by demographic segments
and response trends.
3. IRB_Certified_Chulalongkorn University.pdf - Institutional Review Board (IRB) approval document
certifying that ethical standards were followed.
4. Raw_Data_256 response_Attitudes_Factors_Purchase Intention_Nutrient Enriched Bead Gel.xlsx
- Raw, anonymized data from 256 respondents, including all survey variables from the general
Demographics to purchase intention indicators.
Methodology Summary
- Participants: 256 respondents across various age, income, and education groups.
- Survey Sections: 
1. General Information (Gender, Age, Occupation, Health status, etc.)
2. Consumption Behavior (Frequency, purchasing patterns, etc.)
3. Attitudes & Purchase Intentions (Preferences, health claims, motivations)
The survey targeted residents in Bangkok aged 20 years and above. The appropriate sample size was calculated using the G*Power program [11], which recommended 190 participants, with an additional 2% (n = 4) added to account for potential non-responses, bringing the minimum target sample to 194. However, to ensure greater diversity and data robustness, a total of 256 valid responses were ultimately collected and analyzed.
Quota sampling was employed by predefining target characteristics and proportions of the target group. The questionnaire, including informed consent statement was distributed online via the Line application, with dissemination through acquaintances, including master’s and doctoral students, colleagues, and family members. The eligibility criteria required respondents to be aged 20 years and above. No exclusion criteria were set to ensure diversity in attitudes. Data were collected through Google Forms. All participants were informed consent in the questionnaires.
Hypotheses were tested using the Pearson Chi-Square statistical method to examine the factors influencing the purchase decision of health-enhanced pearls.