cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/ht5p5pg7b7.1
title: Cat personality and predation survey

description: Dataset for the article "Pet cat personality linked to owner-reported predation frequency "
It contains survey data of French cats owners. We surveyed a large sample of cat owners living in France and estimated the personality traits of their cats using the Feline Five personality model of Litchfield et al. (2017) as well as the frequency of birds and mammals returned home by the cats as reported by their owners.  To control for potential confounding factors, we also included questions about variables previously shown to influence pet cat predation: type of environment around the home, time spent outdoors, individual characteristics, and breed.

Variable	Values	Meaning
Sexe	M/F	male/female
Age	Moinsde1/1a2/2a10/Plusde10	Less than 1 year/ 1-2 years/ 2-10 years/ More than 10 years/
Race	BEN/SBI/BRI/CHA/EUR/MCO/PER/RAG/SPH/ORI/TUV/ Autre/NSP	Bengal/ Birman/ British Shorthair/ Chartreux / European/ Maine coon / Persian/ Ragdoll/ Savannah / Sphynx/ Siamese/ Turkish angora / No breed / Other / Unknown
Nombre		Number of cats in the household
Logement	ASB/AAB/ML/MI	Apartment without balcony / Apartment with balcony or terrace/ House in a subdivision / Individual house
Zone	U/PU/R	Would you say that you live in an area that is :Urban/Periurban/rural
Ext	1/2/3/4/5	How much time does your cat spend each day outdoors? None / Limited (less than one hour)/ Moderate (1 to 5 hours)/ Long (more than 5 hours)/ All the time (come back just to eat)
Obs	1/2/3/4	How much time do you spend each day with you cat (observation, game, petting, care, ...)? None / Limited (less than one hour)/ Moderate (1 to 5 hours)/ Long (more than 5 hours)
Personality adjective : see article
Abondance	1/2/3/NSP	Around your place of residence, would you say that abundance of natural areas (tree, bushes, grass ...) is Low / Moderate/ High/ I don’t know 
PredOiseau	1/2/3/4/5	At what frequency does your cat capture birds? Never/ Rarely (1 to 5 times a year)/ Sometimes (5 to 10 times a year)/ Often (1 to 3 times a month)/ Very often (once a week or more) 
PredMamm	1/2/3/4/5	At what frequency does your cat capture small mammals (mice, field mice, shrew)? Never/ Rarely (1 to 5 times a year)/ Sometimes (5 to 10 times a year)/ Often (1 to 3 times a month)/ Very often (once a week or more)
DOI: 10.17632/7jvtwpch65.2
title: Consumer perceptions and behavior towards fast food businesses

description: Cleaned raw data in Excel csv collected on consumer perceptions and behavior towards fast food businesses in Zambia. The dataset includes a closed ended questionnaire with four sections enough to answer all the 3 research questions. The questionnaire was distributed in such a way that it included all the sections of the 2 cities of Lusaka and Kitwe in order to have a very representative sample of the population in the urban parts of the 2 cities where fast food businesses are established. 

The data gathered using a an online Google questionnaire had likert scale items that answered three main questions on perception, behavior and purchase intentions of fast foods with an overall objective of providing a descriptive analysis of the consumer perceptions, behaviors and intention to purchase fast-foods in Zambia. Needless to state that the dataset also captured some socio-economic and demographic characteristics of consumers with codes such as 1 for males and 2 for females.

Apart from the demographics section, all the other 3 sections of the questionnaire had likert scale levels that captured levels for 1=strongly disagree, 2=disagree, 3=neutral, 4=agree, 5=strongly agree. Factor analysis was then conducted using SPSS IBM version 23 in order to have a deep understanding of the role each item captured in the questionnaire played. Reliability and consistency of the data was done using Cronbach Alpha which had a significant value of 0.7.

One of the notable findings was the fact that consumption of fast food increased by 54%, post Covid-19 pandemic. This simply means that there has been an increase in the demand for fast food in Zambia after the end of Covid-19. It is therefore, viable to increase investment in fast food businesses in Zambia in order to meet up with the increased demand for fast food. 

Lastly but not the least, the dataset can be used to conduct a descriptive analysis study of the 2 major cities of Zambia (Kitwe and Lusaka) in the fast food industry. There is also potential to expand the study in order to understand more about the consumer behavior and perceptions.
DOI: 10.17632/zw4p4p7sdh.1
title: A Dataset for Voice-Based Human Identity Recognition

description: This dataset is divided into two main sub-datasets: samePhrase and differentPhrase. Each speaker has the same label in both sub-datasets. In the samePhrase sub-dataset, a speaker repeats the sentence “Machine Learning 1, 2, 3, 4, 5, 6, 7, 8, 9, 10” ten times. The length of each sample is between seven and ten seconds. For the differentPhrase sub-dataset, each speaker contributed with a phrase selected randomly from different resources such as books, songs lyrics, orone-line texts. Each speaker contributed with ten different samples, the length of each sample inthe differentPhrase sub-dataset does not exceed ten seconds
DOI: 10.17632/82pxy6yv49.1
title: QKPLIB

description: The Quadratic Knapsack Problem Library (QKPLIB)  is a set of test instances for the QKP. It has instances ranging from 100 to 2000 items (100, 200, 300, 400, 500, 750, 1000, 1500 and 2000). For each size there is a total of 36 instances. Each instance is given in a textfile. The file format  can be find as a pdf file inside the zip file containing all the instances. For each problem size there is a text file containing all the names of instances of this type. When using the data sets please cite  the book chapter 

Raka Jovanovic and Stefan Voss,Solving the Quadratic Knapsack Problem using  GRASP, in
DOI: 10.17632/jnb2xp9m4r.1
title: EnglishTense: A large scale English texts dataset categorized into three categories: Past, Present, Future tenses.

description: he EnglishTense dataset is a comprehensive collection of English sentences meticulously categorized based on their tense: Past, Present, and Future. The dataset comprises a total of 13,316 annotated sentences with three distinct tense types: 4,621 in the present tense, 3,851 in the past tense, and 4,844 in the future tense. This dataset is designed to facilitate research and development in natural language processing (NLP) and computational linguistics, particularly for English, a widely spoken language in the world. With applications spanning tense detection, text classification, language modeling, and educational tools, EnglishTense is a valuable resource for the NLP community, facilitating advancements in temporal analysis and robust NLP model development.
DOI: 10.17632/w3wsc359pc.1
title: Accelerometer and Gyroscope Sensor Readings for Writing Behavior Analysis

description: This dataset contains accelerometer and gyroscope readings of 76 undergraduate students (from different ages, different genders) during writing random sentences for 1 minute period with a mobile phone (iPhone X). We provide two datasets. One is for binary classification (one-vs-all) and the other one contains the whole data. These datasets can be used for writing behavior analysis. The columns of datasets are: ID,Gender,Age,AccX,AccY,AccZ,GyroX,GyroY,GyroZ
DOI: 10.17632/rvxcb9p2md.1
title: Polish version of the Beliefs about Medicines Questionnaire

description: This data underlie the process of validation of the Polish version of the Beliefs about Medicines Questionnaire. The data was used in a paper entitled "Adaptation and validation of the Polish version of the Beliefs about Medicines Questionnaire among cardiovascular patients and medical students".
DOI: 10.17632/xwmbk7n3c8.1
title: Supply Chain Demand Forecasting Dataset of Bangladeshi Retailer

description: The historical sales dataset for this research is obtained from a Bangladeshi retailer. The dataset covers a period of 1826 days and includes daily sales data for a particular product from 01 January 2013 to 31 December 2017. The raw sales data has 2 columns: the first column contains timestamps, while the remaining column reflects the quantity sold.
DOI: 10.17632/hr7hbxdryp.1
title: Data for: Hybrid machine learning algorithms to enhance lost-circulation prediction and management in the Marun oil field

description: The supplementary Excel file data consisting of 2820 data records for the collected data set from the Marun Field (SW Iran) is made available for readers.
DOI: 10.17632/6gdybpjnwh.1
title: Data for: Application of deep learning models to provide a generalizable approach for cloud, shadow and land cover classification in PlanetScope and Sentinel-2 imagery

description: The dataset consists of PlanetScope and Sentinel-2 derived scenes (i.e. chips) collected over the Wet Tropics of Australia between December 1, 2016 and November 1, 2017. All PlanetScope imagery contains four bands of data: red, green, blue (RGB) and near infrared (NIR), and had a ground sample distance (GSD) of 3.125 m. In contrast, Sentinel-2 imagery was trimmed to contain only RGB and NIR bands, and resampled to 3.125 m resolution to match PlanetScope imagery. Here we refer to the Wet Tropics PlanetScope- and Sentinel-2-derived data as datasets T-PS and T-S2, respectively. The T-PS and T-S2 datasets were generated by splitting satellite imagery with a grid of 128×128 pixels (i.e. 400×400 m) into image scenes, and extracting a random sample of 2.5% and 0.5% scenes per time step, respectively. This resulted in 4,943 and 4,993 image scenes in T-PS and T-S2 datasets, respectively. Datasets T-PS and T-S2 were manually labeled with 12 labels split into three groups: 1) Cloud labels (‘clear’, ‘partly cloudy’, ‘cloudy’, ‘haze’). 2) Shade labels (‘unshaded’, ‘partly shaded’, ‘shaded’), which indicated the level of shade caused by cloud cover on a specific scene. 3) Land cover labels (‘forest’, ‘bare ground’, ‘water’, ‘agriculture’, ‘habitation’). Labels belonging to cloud and shade label groups are mutually exclusive for each scene, that is, each image scene received exactly one shade label and one cloud label.
DOI: 10.17632/bh98k2rjd4.1
title: Raw data used for regression analysis  within stata format

description: This is data used for regression model in Stata format
DOI: 10.17632/y7vs4yby9y.2
title: Cardiorespiratory DB

description: The database is constituted by 50 datasets containing cardiorespiratory signals acquired from 50 healthy volunteer subjects (one dataset for each subject; 23 males and 27 females; age: 23±5 years) while performing normal breathing, deep breathing, and breath holding, and two spreadsheet files, namely the “SubjectsInfo.xlsx” and “DBInfo.xlsx” containing the metadata of subjects (including demographic data) and of acquired signals, respectively. 
Cardiorespiratory signals consisted in simultaneously recorded 12-lead electrocardiograms acquired by the clinical M12 Global Instrumentation® digital Holter ECG recorder, and single-lead electrocardiograms and respiration signals acquired by the wearable chest strap BioHarness 3.0 by Zephyr. 
The database may be useful to: (1) validate the use of wearable sensors in the acquisition of cardiorespiratory data during different respiration kinds, including apnea; (2) investigate the physiological association between cardiovascular and respiratory systems; (3) validate algorithms able to indirectly extract the respiration signal from the electrocardiogram; (4) study the fatigue level induced by a series of controlled respiration patterns; and (5) investigate the effect of COVID-19 infection on the cardiorespiratory system.
DOI: 10.17632/2vt22x9s82.1
title: Guava leaves diseases datasets Bangladesh

description: The dataset, sourced from Vimruli Guava Garden and Floating Market in Jhalakathi, Barisal, categorizes guava leaf and fruit conditions for better crop management. It includes images of healthy and diseased samples, making it a valuable resource for researchers and practitioners working on machine learning models to identify plant diseases. The dataset includes six classes for robust model training.

Dataset Summary:
 Location: Vimruli Guava Garden & Floating Market, Jhalakathi, Barisal.
 Subjects: Guava leaves and fruits.
 Purpose: Classification and detection of guava plant conditions.

Data Distribution:
 Classes:
 1. Algal Leaves Spot: 100 original, 1320 augmented, 1420 total
 2. Dry Leaves: 52 original, 676 augmented, 728 total
 3. Healthy Fruit: 50 original, 650 augmented, 700 total
 4. Healthy Leaves: 150 original, 1600 augmented, 1750 total
 5. Insects Eaten: 164 original, 1720 augmented, 1884 total
 6. Red Rust: 90 original, 1170 augmented, 1260 total

Total Samples:
 Original: 606
 Augmented: 7136
 Overall: 7742 samples

Class Details:
 1. Algal Leaves Spot: Fungal spots on leaves.
 2. Dry Leaves: Leaves dried from environmental/nutrient factors.
 3. Healthy Fruit/Leaves: Free of diseases/damage.
 4. Insects Eaten: Insect-caused damage on leaves.
 5. Red Rust: Reddish spots due to fungal infection.

This dataset is well-suited for training and evaluating machine learning models to detect and classify various conditions of guava plants, aiding in automated disease identification and better agricultural management.
DOI: 10.17632/vn4jbn3mzg.1
title: Historic dog furs unravel the origin and artificial selection of modern Nordic Lapphund and Elkhound dog breeds

description: Eight dog fur samples (around 150 years ago) collected from three Swedish Museums.
DOI: 10.17632/wgzmt67x8w
title: Numerical ability of rabbits data

description: Thirteen rabbits at The College of West Anglia, Cambridge were tested on their numerical abilities by the use of the spontaneous choice test. The first experiment explores if there is a difference between the number of times rabbits choose the larger quantity of food between 0 vs 1 pieces of carrot, 1 vs 2 pieces of carrot, 1 vs 3 pieces of carrot, 3 vs 4 pieces of carrot, 4 vs 7 pieces of carrot, and 6 vs 7 pieces of carrot. The second experiment asks if there is a difference between the number of times the rabbits choose the large food item vs the number of times that they choose the small food item. The third experiment tests if there is a difference between the number of times the rabbits choose the four large food items vs the number of times that they choose the 16 smaller food items when both options equate to equal overall amounts. 
The conditions were divided into two groups based on the ratio between the two quantities (the smaller number divided by the second number). The small ratio group with a ratio of ≤0.5 consisted of 0 vs 1 (0), 
1 vs 3 (0.33), and 1 vs 2 (0.5). The large ratio group with a ratio of &gt;0.5 consisted of 4 vs 7 (0.57), 3 vs 4 (0.75), and 6 vs 7 (0.86). 

Experiment 1:
• Condition 1: 0 v 1 pieces of carrot (4cm3) 
• Condition 2: 1 vs 2 pieces of carrot (all 4cm3) 
• Condition 3: 1 vs 3 pieces of carrot (all 4cm3) 
• Condition 4: 3 vs 4 pieces of carrot (all 4cm3) 
• Condition 5: 4 vs 7 pieces of carrot (all 4cm3) 
• Condition 6: 6 vs 7 pieces of carrot (all 4cm3) 
The experimenter cut up carrots into the quantities and volumes listed above. For each condition, they approached the first plate and dropped one of the quantities onto it sequentially from 4cm above the plate whilst the rabbit was watching. They then approached the second plate and dropped the remaining quantity of carrots from that condition onto it sequentially, from 4cm above the plate. The rabbit approached a plate and ate the contents, with the remaining plate being taken away. Each rabbit participated in every condition once, with one trial per rabbit per session, unless the rabbit appeared unmotivated or uninterested, then the trial was not included and was repeated during the next session when possible. This was repeated for all six conditions.

Experiment 2:
The same procedure outlined above was used, but with the two food choices being a whole 6cm3 baton carrot, and a 1.5cm3 (quarter) baton carrot. Therefore, one plate had a whole 6cm3 baton, and one plate had ¼ of that. Each rabbit participated in this two times, on two separate occasions

Experiment 3:
The same procedure as experiment 1 was used, but with the two food choices being four pieces of 4cm3 carrot, and 16 pieces of 1cm3 carrot, spending 10 seconds at each plate. Each rabbit participated in this two times, on two separate occasions.
DOI: 10.17632/bb5jb7t2zv.1
title: Gender Equality in Research & Innovation – 2024 Review

description: Elsevier's 2024 Gender Report tracks and analyzes data across 18 countries and 2 regions (EU 27 and the world) examining women’s performance in research and innovation across disciplines and career stages. Our Report provides new insights into interdisciplinarity, multidisciplinarity, grant recipients, patent applications, open access, UN Sustainable Development Goals (SDG) research areas, as well as alternative metrics including policy and media citations. 

More information at https://www.elsevier.com/en-au/insights/gender-and-diversity-in-research
DOI: 10.17632/mxmt3cy5dx.1
title: Health-related confidentiality and consent among minors: Data on adult perspectives from Belgium and The Netherlands

description: The data presented here provide one of the first large-scale insights on adult preferences for confidentiality and consent with regards to medical decision-making for minors. We collected data on these preferences through 12 hypothetical scenario’s that were presented, for which each participant had to indicate if they would (not) follow the minor’s preferences. Data regarding family communication, relationship quality, and sociodemographic characteristics were also collected. The data were collected through an online survey in September and October 2020, which yielded responses from 1,000 Belgian and 1,000 Dutch participants between 35 and 55 years of age. We selected this age range because it increased the chances that they had a child near the age of the fictional minor in the hypothetical cases. These data can be of interest for family researchers and/or health workers who want to explore adults’ perceptions regarding confidentiality and consent among minors.
DOI: 10.15468/dl.by2fg7
title: Occurrence Download

description: A dataset containing 1759 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Picea engelmannii Parry ex Engelm.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 1759 records from 128 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009167-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/t99ym3b7jx.3
title: Replication Code and Pseudo Dataset for "Monetary Tightening and U.S. Bank Fragility in 2023: Mark-to-Market Losses and Uninsured Depositor Runs?"

description: Replication Code and Pseudo Dataset for "Monetary Tightening and U.S. Bank Fragility in 2023: Mark-to-Market Losses and Uninsured Depositor Runs?"
DOI: 10.17632/7ky2jbjwtn.3
title: Indonesian COVID-19 Vaccination-related Tweets for Stance Detection and Aspect-based Sentiment Analysis

description: The dataset was collected using Twitter API services for specific keywords posted for ten months, starting from January 2021 to October 2021. The data has been filtered for non-Bahasa (Indonesia language), non-target-related, spam, and duplication. There are two labeling processes: stance and aspect-based sentiment. Three annotators manually labeled the sample data and used the majority vote strategy for the final class label. In our annotation strategy, for stance labeling, each annotator was asked to annotate the individual tweets as "Favor", "Against", or "Neutral" for COVID-19 vaccination programs in Indonesia. While for aspect-based sentiment labeling, each tweet has been annotated into seven predetermined aspects of the COVID-19 vaccination, namely "Services", "Implementation", "Apps", "Costs", "Participants", "Vaccine-products", and "General". Each predetermined aspect will have two possible sentiment values, between "Positive" and "Negative".
DOI: 10.17632/8rwpvrw4hw.1
title: Dataset of MOOCs' Reviews from Stepik on Russian Language

description: Dataset consists of 5721 reviews from 102 MOOCs extracted from Stepik platform on Russian language
DOI: 10.17632/tnwfs2k4np.1
title: Data for: Measurement and exponential model of ferromagnetic Hysteresis

description: Measurement and modeling of ferromagnetic hysteresis in steel is described.
DOI: 10.17632/ky8vs4xhp8.1
title: Outer Radial Glia-Like Cancer Stem Cells Contribute to Heterogeneity of Glioblastoma
DOI: 10.17632/3k5bwbccph.1
title: Synthetic Rice Leaf Disease

description: This data consists of raw uncategorized synthetic images of rice leaf disease. They contain 50,001 samples of *.png images with a uniform size of 256*256. The data can be used to improve object detection or classification of rice leaf diseases.
DOI: 10.17632/sd3rjb2b6z.1
title: Profile Permohonan dan Agihan Zakat Oktober 2021

description: Profile permohonan zakat sara diri Universiti Teknologi MARA (UiTM) Cawangan Perlis Oktober 2021
DOI: 10.17632/3wc6t92rrg.1
title: Adult Inpatient Patient Portal Dataset

description: Data for 826,843 diagnostic test reports on 40,640 unique patients. Specific data fields include: unique patient identification number (deidentified), number of inpatient admissions in 2018, whether test report was during the initial admission in 2018 or a subsequent admission, specific diagnostic test ordered, diagnostic test category, sex (as officially noted in the electronic health record), patient portal status (active or inactive), whether diagnostic test report was viewed in the patient portal, the most common self-declared race categories, simplified race category (White or non-White), age at time of testing, age category, primary/preferred language (as recorded in electronic health record), primary/preferred language (English on non-English), whether there was a legal proxy that had access to the patient’s portal account, and insurance category (private/commercial or public/uninsured).
DOI: 10.17632/khvn8nw2np.1
title: Supplementary Data for Reliability-Based Calibration of the Partial Safety Factor for Shear Design of Fibre Reinforced Concrete Members without Shear Reinforcement According to the fib Model Code 2010

description: Supplementary Data used for calibrating the partial safety factor for shear design of FRC members without shear reinforcement according to the fib Model Code 2010. Contains the model error calculation based on a database of experimental results. To be expanded in the future with further calculations.
DOI: 10.17632/vdpkty7wrf.1
title: Data for: Effect of Reservoir Characteristics on the Productivity and Production Forecasting of Montney Shale gas in Canada

description: There are geochemistry data (XRD mineralogy and Rock-eval test results) and production data for Montney wells. Correlation results between the productivity and reservoir characteristics are shown in this data file.
DOI: 10.17632/xcxspv8c2r.2
title: Physical layer data acquisition of IRIDIUM satellites broadcast messages

description: This dataset refers to a measurement campaign carried out in two periods, i.e., from Aug. 12th to Sept. 10th, 2020 and Sept. 11th to Oct. 9th, 2020. The dataset includes a total of 3,840,907 entries (IRIDIUM broadcast messages), and the associated 7,628,604,575 IQ samples.
DOI: 10.17632/vt86jmcdz3.1
title: Tamil Nadu - STEPS Survey 2020

description: A state-wide representative cross-sectional study was conducted in Tamil Nadu in 2020. We estimated the prevalence of Noncommunicable disease risk factors such as smoking, tobacco, alcohol, physical activity, overweight, abdominal obesity, hypertension, and diabetes. We also determined the predictors of hypertension and diabetes.