cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.jwwne4
title: Occurrence Download

description: A dataset containing 234 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Coleus decurrens Gürke"
 ]
}

 The dataset includes 234 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006436-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bm1ppf
title: Occurrence Download

description: A dataset containing 731 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Country is Türkiye",
 "Country is Turkmenistan",
 "Country is Iran (Islamic Republic of)",
 "Country is Iraq",
 "Country is Azerbaijan",
 "Country is Armenia",
 "Country is Georgia"
 ]
 },
 "TaxonKey is Melanocorypha bimaculata (Ménétriés, 1832)"
 ]
}

 The dataset includes 731 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013337-181108115102211/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9gvbtk
title: Occurrence Download

description: A dataset containing 7927 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-79.12354 -4.2627,-66.07178 -4.2627,-66.07178 12.52441,-79.12354 12.52441,-79.12354 -4.2627))",
 "TaxonKey is Andigena laminirostris Gould, 1851"
 ]
}

 The dataset includes 7927 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0348555-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nrz3me
title: Occurrence Download

description: A dataset containing 720 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Veronica peduncularis M.Bieb."
 ]
}

 The dataset includes 720 records from 66 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0135976-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5eh8xs
title: Occurrence Download

description: A dataset containing 23 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Acacia dimorpha Maslin, M.D.Barrett & R.L.Barrett"
 ]
}

 The dataset includes 23 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0135980-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b7g9dn
title: Occurrence Download

description: A dataset containing 814 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aerva sanguinolenta (L.) Blume"
 ]
}

 The dataset includes 814 records from 59 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026529-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pry1aa
title: Occurrence Download

description: A dataset containing 9667 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "TaxonKey is Genista monspessulana (L.) L.A.S.Johnson"
 ]
 },
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 9667 records from 95 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013338-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r8ne3s
title: Occurrence Download

description: A dataset containing 142 species occurrences available in GBIF matching the query:
{
 "or" : [
 "TaxonKey is Darevskia parvula (Lantz & Cyrén, 1913)",
 "TaxonKey is Darevskia caucasica (Méhely, 1909)"
 ]
}

 The dataset includes 142 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0145058-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sazaf9
title: Occurrence Download

description: A dataset containing 87164 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dorosoma cepedianum (Lesueur, 1818)"
 ]
}

 The dataset includes 87164 records from 115 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026523-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ka3rxf
title: Occurrence Download

description: A dataset containing 499 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hypsiboas polytaenius (Cope, 1870)"
 ]
}

 The dataset includes 499 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013338-180824113759888/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3t98sv
title: Occurrence Download

description: A dataset containing 778 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Crypteronia paniculata Blume"
 ]
}

 The dataset includes 778 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0146448-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.khpvkf
title: Occurrence Download

description: A dataset containing 90210574 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Magnoliopsida",
 "Year 2014-2024"
 ]
}

 The dataset includes 90210574 records from 7391 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026518-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s8tefu
title: Occurrence Download

description: A dataset containing 183 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 {
 "and" : [
 "BasisOfRecord is one of (Specimen)",
 "TaxonKey is one of (Ageratina asclepiadea (L.fil.) R.M.King & H.Rob.)"
 ]
 }
 ]
}

 The dataset includes 183 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022507-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.akgwum
title: Occurrence Download

description: A dataset containing 1829898 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Arenaria interpres (Linnaeus, 1758)",
 "HasCoordinate is true",
 "Year is greater than or equal to 1970",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1829898 records from 773 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008879-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/pnmt3dt6zr.1
title: Digital Literacy

description: faktor faktor yang membentuk literasi digital
DOI: 10.17632/kfrn4phjgd.1
title: PjBLPKN

description: Data SLR Penerapan Model Pembelajaran Project Based Learning pada Mata Kuliah Pendidikan Kewarganegaraan: Suatu Tunjauan Literatur
DOI: 10.17632/vxgdnw5ppx.1
title: The impact of food enrichment on the behaviour of cownose ray (Rhinoptera bonasus) kept under human care

description: Data used to evaluate the effects of environmental enrichment items on the behavior of cownose rays
DOI: 10.15468/dl.kw3kgv
title: Occurrence Download

description: A dataset containing 6755 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Ceratopteris thalictroides (L.) Brongn.)"
 ]
}

 The dataset includes 6755 records from 315 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008883-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/jt3tb327r2.1
title: Fuzzy Intrusion Detection Method and Zero-Knowledge Authentication for Internet of Things Networks

description: This study offers a fresh security framework to solve the problems
with current algorithms in an Internet of Things setting. This study also provides an application layer security that smart environments
may use to avoid DoS attacks.
DOI: 10.17632/z4tkmgw74r.1
title: AI's place in India's Insurance Sector - Conference

description: The capacity of a computer or robot to carry out operations that are normally associated
with human intellectual processes is known as artificial intelligence (AI). It's a branch of
computer science that creates and investigates intelligent devices. The swift progress of artificial
intelligence (AI) technology has brought about a dramatic transformation in the insurance
business, as it has in many other sectors. In his 2018–19 budget speech, the Honorable Finance
Minister directed NITI Aayog to create the National Program on AI, with the goal of directing
research and development in new and emerging technologies. This was done in recognition of
AI's potential to revolutionize economies and the necessity for India to strategically approach the
subject. There are several benefits to integrating AI into the Indian insurance industry, ranging
from increased operational efficiency to better client experiences. These opportunities do,
however, come with difficulties that need for thoughtful thought and deliberate preparation.
DOI: 10.17632/3n8dkfvvwj.1
title: Computation programs and data for determining parameters in l-i AIR or l-i SEIR epidemic models

description: The attached Excel file has data and programs used for determining parameters l (average time length of latent period), i (average time length of infectious period), and α (transient incidence) in l-i AIR or l-i SEIR model equations. The detailed procedure for determining these parameters has been described in the preprint (DOI: https://doi.org/10.21203/rs.3.rs-310822/v1)
DOI: 10.17632/jppzjfjvmy.1
title: Programs Serving Individuals with Autism Spectrum Disorders (ASD) in New Jersey and Population Under 18 Years of Age

description: Autistics endure fluctuating degrees of loneliness and isolation due to a lack of acceptance by society. It is essential to ensure that a wide range of sensory-friendly extracurricular activities is available as an outlet for socialization, discipline, and responsibility. The appearance of inclusive programs is not exclusively for autistics, but is rather welcoming to all. These facilities oftentimes accommodate people with other forms of neurodivergence, like ADHD, dyslexia, and OCD, as well as neurotypical individuals. This GIS makes the public aware of regions with sufficient quantities of programs and those with a void.

The GIS provides guidance for parents, instructors, and autistics, including nonverbal individuals, to become more aware of sensory-friendly extracurricular activities in New Jersey. This visual representation also identifies and addresses regions lacking inclusive programs. This would allow future businesses to strategically plan the locations of facilities to better support the entire population.

Upon analyzing county data, 24.5% of the Ocean County population was under 18. They had twice the number of inclusive programs than Passaic County, but only a 0.8% higher population of minors. Likewise, Cumberland County had a 6.3% higher population under 18 compared to Cape May County, but Cape May had 1 program while Cumberland County had none.

After examining the census tract data, Monmouth County Tract 8015 had a population with 25.4% under 18, while the total county had 20.8% minors. A sufficient number of inclusive programs exists with 8 activities. Additionally, Somerset County, which had a 0.6% higher total population under 18 than Monmouth County, possessed 3 fewer programs.

Providing guidance to identify facilities that meet the needs of both verbal and nonverbal autistics is of utmost importance. By locating programs in New Jersey that cater to all children, inclusivity and acceptance could be achieved.

The dataset showcasing inclusive programs could be downloaded and accessed on Mendeley Data through Google Sheets, as well as PDF, and .csv files.
DOI: 10.17632/jxckfsz9vz.1
title: AI's Place in India's Insurance Sector

description: The capacity of a computer or robot to carry out operations that are normally associated
with human intellectual processes is known as artificial intelligence (AI). It's a branch of
computer science that creates and investigates intelligent devices. The swift progress of artificial
intelligence (AI) technology has brought about a dramatic transformation in the insurance
business, as it has in many other sectors. In his 2018–19 budget speech, the Honorable Finance
Minister directed NITI Aayog to create the National Program on AI, with the goal of directing
research and development in new and emerging technologies. This was done in recognition of
AI's potential to revolutionize economies and the necessity for India to strategically approach the
subject. There are several benefits to integrating AI into the Indian insurance industry, ranging
from increased operational efficiency to better client experiences. These opportunities do,
however, come with difficulties that need for thoughtful thought and deliberate preparation.
DOI: 10.17632/9bj4hnjtw6.1
title: Belief in Divine Intervention vs Psychological Resilience

description: import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

# Step 1: Simulate data
np.random.seed(42)
n_samples = 120
belief_in_divine = np.random.uniform(1, 10, n_samples)
psychological_resilience = belief_in_divine + np.random.normal(0, 1, n_samples) # Adding noise

# Create DataFrame
experiment_df = pd.DataFrame({'Belief_in_Divine': belief_in_divine,
 'Psychological_Resilience': psychological_resilience})

# Step 2: Visualize the relationship with a scatter plot
plt.figure(figsize=(8, 6))
sns.scatterplot(x='Belief_in_Divine', y='Psychological_Resilience', data=experiment_df)
plt.title('Belief in Divine Intervention vs. Psychological Resilience')
plt.xlabel('Belief in Divine Intervention')
plt.ylabel('Psychological Resilience')

# Step 3: Add regression line equation to the plot
plt.text(3, 15, r'$Y = MX + C$', fontsize=12)

# Step 4: Generate individual plots for each regression line
for i in range(5):
 # Create a new figure for each line
 plt.figure(figsize=(8, 6))
 
 # Randomly select slope and intercept values
 slope = np.random.uniform(0.5, 1.5)
 intercept = np.random.uniform(-1, 2)
 
 # Plot regression line
 x_values = np.linspace(min(belief_in_divine), max(belief_in_divine), 100)
 y_values = slope * x_values + intercept
 plt.plot(x_values, y_values, label=f'Line {i+1}: Y = {slope:.2f}X + {intercept:.2f}')
 
 # Add scatter plot on top
 sns.scatterplot(x='Belief_in_Divine', y='Psychological_Resilience', data=experiment_df)
 
 plt.title(f'Regression Line {i+1}: Belief in Divine Intervention vs. Psychological Resilience')
 plt.xlabel('Belief in Divine Intervention')
 plt.ylabel('Psychological Resilience')
 plt.legend()
 plt.grid(True)
 plt.show()

# Step 5: Correlation Analysis
correlation_coefficient = np.corrcoef(experiment_df['Belief_in_Divine'], experiment_df['Psychological_Resilience'])[0, 1]
print("Correlation Coefficient:", correlation_coefficient)

# Step 6: Perform Linear Regression
X = experiment_df['Belief_in_Divine'].values.reshape(-1, 1)
y = experiment_df['Psychological_Resilience'].values

# Calculate coefficients
X = np.column_stack((np.ones_like(X), X)) # Add intercept term
coefficients = np.linalg.lstsq(X, y, rcond=None)[0]
slope = coefficients[1]
intercept = coefficients[0]
print("Slope (m):", slope)
print("Intercept (c):", intercept)

# Step 7: Plot regression line
plt.figure(figsize=(8, 6))
plt.scatter(experiment_df['Belief_in_Divine'], experiment_df['Psychological_Resilience'])
x_values = experiment_df['Belief_in_Divine'].values # Convert to numpy array
plt.plot(x_values, slope * x_values + intercept, color='red', label='Regression Line')
plt.title('Regression Analysis: Belief in Divine Intervention vs. Psychological Resilience')
plt.xlabel('Belief in Divine Intervention')
plt.ylabel('Psychological Resilience')
plt.legend()
plt.grid(True)
plt.show()
DOI: 10.17632/673yr7wstc.1
title: KNOWLEDGE OF MEASLES AND HOW CAREGIVERS DECIDE BETWEEN MODERN AND TRADITIONAL TREATMENTS. A CASE STUDY OF CAMPBELL TOWN COMMUNITY, WATERLOO.

description: Introduction: This study presents a comprehensive exploration of caregivers' knowledge of measles and their decision-making processes regarding treatment options in the Campbell Town Community, Waterloo. It combines mixed-methods research to provide insights into the balance between modern and traditional treatments for measles.
Methods: A mixed-methods approach was employed, with a sample of 100 caregivers chosen through stratified random and convenience sampling. The research objectives assessed knowledge levels, influencing factors on treatment decisions, traditional treatments, and the effectiveness and safety of treatments. Two hypotheses regarding limited knowledge and influencing factors were tested.
Results and Discussion: The study revealed a diverse participant demographic, with 92% awareness of measles but significant knowledge gaps about symptoms and recommended treatments. Contrary to Hypostudy1, caregivers did not have limited knowledge of measles transmission. Hypostudy2 was also not supported; instead, decision-making was influenced by safety, effectiveness, cost, and availability rather than socio-cultural factors. Age, gender, and education were not significant predictors.
Conclusion: The study underscores the need for targeted interventions addressing knowledge gaps in measles symptoms and recommended treatments. Culturally sensitive approaches are essential to navigate the complex relationship between modern medical treatments and traditional remedies in measles treatment.
Recommendations: To improve measles treatment and prevention in the study community, public health initiatives should focus on increasing awareness and education, particularly related to measles symptoms and evidence-based treatments. Collaboration among healthcare providers, community leaders, and caregivers is crucial for informed decision-making. Addressing socio-cultural factors influencing treatment choices and improving healthcare accessibility are key action points for a comprehensive approach.
DOI: 10.17632/7rhnf565jz.1
title: tourism preferences of Generation Z in ancient city tourism

description: Tourism preferences of Generation Z in ancient city tourism
DOI: 10.17632/svmtnbhh25.1
title: Embracing Marian Education: Cultivating Holistic Growth in Students

description: The provided data captures information about a group of 60 respondents, each characterized by their age, name, and participation in either a standard character education program or a Marian-inspired curriculum.

Age: The ages of the respondents range between 18 and 22 years, reflecting the typical age range of college students.
Name: The names of the respondents are randomly selected from a pool of both Indian and Christian names, adding diversity to the dataset.
Standard Program Improvement (%): This column represents the improvement percentage in emotional well-being experienced by respondents who participated in the standard character education program. The improvement percentage varies within a certain range, reflecting the individual experiences of the participants.
Marian Curriculum Improvement (%): This column captures the improvement percentage in emotional well-being observed among respondents enrolled in the Marian-inspired curriculum. The improvement percentage is randomized within a range, ensuring that it is at least 30% higher than the improvement observed in the standard program group.
The dataset provides insights into the efficacy of the Marian-inspired curriculum in fostering emotional well-being among college students, offering valuable information for educational research and curriculum development.
DOI: 10.17632/tc28knsfsn.1
title: (KNQAD): Kurdish News Question answering Dataset

description: Question answering (QA) is the field of information retrieval (IR) aimed at answering questions from paragraphs in natural language processing (NLP). Essentially, IR is a technique to retrieve and rank documents based on keywords, while in the QA system, answers to questions are retrieved based on the paragraph's content. The Kurdish language belongs to the Indo-European family spoken by 30-40 million people worldwide. Almost all Kurdish people speak Sorani and Kurmanji dialects. In this dataset, the Sorani dialect is used as the first attempt to collect and create a Kurdish News Question-Answering Dataset (KNQAD). The texts are collected from numerous Kurdish news websites covering various fields such as religion, social issues, art, health, economy, politics, sports, and more. In this project, 15,002 question-answer pairs are created manually from 15,002 paragraphs. Three preprocessing steps are implemented on the raw text paragraphs: stemming, removing stop words, and removing special characters.
DOI: 10.17632/jtmp572x37.1
title: Research data

description: Research data for paper"Dataset of the effect of the number of injector holes on the heat transfer coefficient and the pressure in the combustion chamber of a hydrogen-diesel engine"
DOI: 10.17632/7k9wcm4kwb.1
title: Green Bonds Bibliometrix-Export-File-2023-12-18

description: This is downloaded from Scopus search on 'Green Bonds' research data converted by Rstudio Biblioshiny software into Bibliometrix-Export-File-2023-12-18.