cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/2kyky7j3dw.1
title: Política Monetaria: introducción e instrumentos

description: La clase 1 sobre política monetaria introduce a los estudiantes al concepto y la importancia de la política monetaria en la economía. Se inicia con una presentación del curso y una justificación de su relevancia en el programa académico, destacando cómo la política monetaria afecta los mercados financieros y la macroeconomía.

Se abordan las competencias previas necesarias para el curso, como la capacidad de análisis y participación colaborativa. Se discuten los problemas que la política monetaria puede resolver, como la toma de decisiones económicas informadas. La clase también cubre la evolución del dinero y los bancos centrales, describiendo cómo estos se han desarrollado para estabilizar la economía.

Se enfatiza el papel del Banco de la República de Colombia, sus funciones principales, y su misión de garantizar la estabilidad económica y financiera del país. Además, se introduce la metodología del curso, que combina teoría y práctica a través del Aprendizaje Basado en Problemas (ABP) y el Aula Invertida. La evaluación incluye semanarios, ensayos, análisis de propuestas y casos, y el estudio de organismos multilaterales, preparando a los estudiantes para aplicar conocimientos en situaciones reales.
DOI: 10.17632/x9p5fjfdsw.1
title: Field survey data

description: This is a field survey data on migration to Jammu & Kashmir. The field survey was conducted in the year 2019. The data capture information from 507 samples. The information includes various demographic and socio-economic characteristics of the in-migrants.
DOI: 10.17632/whd9jkj66x.1
title: Opportunities to Utilize AI to Explore Knowledge and Insight For Young Entrepreneurs

description: This study shows that young entrepreneurs see AI as a powerful tool for growing their businesses. They're using AI platforms like ChatGPT and analytics tools to learn about market trends, understand their customers better, and make smarter business decisions. These young business owners find AI helpful in many ways - it saves them time, helps them spot opportunities, and solves problems faster. However, they also face some challenges when using AI, like finding it complicated at first and worrying about whether they can trust the information it gives them. To deal with this, they often check AI's suggestions against their own experience and what their customers tell them. Despite these challenges, the entrepreneurs we talked to believe AI will become even more important for running a successful business in the future. They're excited about how AI could help them learn and adapt quickly in the fast-changing business world. This research highlights both the opportunities and the hurdles young entrepreneurs face when using AI, and suggests that with the right support and strategies, AI can be a game-changer for their businesses. The findings from this study can help other young entrepreneurs, business advisors, and policymakers understand how to better use and support AI in small businesses, potentially leading to more successful startups and stronger economic growth.
DOI: 10.17632/sngg3trx69.1
title: Cheek acupuncture reduces postoperative nausea and vomiting in patients undergoing laparoscopic gynecological surgery: A randomized controlled trial

description: Cheek acupuncture, a recently established microneedle therapy, has been proven to effectively reduce pain, but its potential antiemetic effects remain unconfirmed. we carried out a study with the aim of comparing the antiemetic efficacy of cheek acupuncture with ondansetron and evaluating their cumulative effect in patients undergoing laparoscopic gynecological surgery. The original data for this study were provided here.
DOI: 10.17632/dkv4dyxdwx.1
title: Burnout Levels of Healthcare Professionals

description: This study aims to explore burnout in healthcare professionals and the specific changes and challenges brought on by the epidemic. This study is a mixed method study in which a questionnaire and semi-structured interview forms are used together to investigate burnout in healthcare workers during COVID-19. Results: Participants with low levels of burnout reported more occupational changes, while those with high and very high burnout levels experienced more psychological difficulties. High burnout levels were associated with challenges in working conditions, mask-equipment use, and psychological well-being.
DOI: 10.17632/5npjvcytg8.1
title: Weighted Overlay Analysis

description: The weighted overlay creation workflow is composed of four processes: A – Prepare the Input Dataset (Convert any input vector datasets into raster datasets); B – Classify the Raster Dataset (Raster Reclassification); C – Create the Weighted Overlay Map
DOI: 10.17632/8yvy2g2zxs.1
title: Statistical Processing of Building and Neighborhood Data Considering Energy Ratings in Dublin, Ireland

description: Central to our investigation is the Building Energy Rating (BER) dataset of Ireland, sourced from GeoDirectory data. This dataset provides a foundational resource, encompassing a comprehensive range of building-scale parameters such as detailed addresses, geocode data (latitude and longitude), area, height, number of floors, roof type, construction age, radon emission, HVAC system details, and bedroom and bathroom counts.
To enrich this dataset, we incorporated additional features from the Digital Landscape Models (DLM) Core Data from Tailte Éireann Surveying (PRIME2 Dataset). These enhancements include metrics such as nearest neighbor buildings, the density of the built environment surrounding each building, and district attributes like the ratio of green area to non-green area in the urban vicinity.
Dataset Compilation:
After carefully considering various data acquisition methods to capture building-scale and neighborhood-scale features, including both geometric and non-geometric attributes, as well as information about the surrounding built environment, we compiled a comprehensive dataset. Due to data protection policies, specific spatial details such as latitude, longitude, and addresses were omitted. However, the remaining data, aligned with our data acquisition objectives, is structured into a CSV file available in the mentioned repository.
Key Features:
1. Building-Scale Attributes:
o Identification and Location: Building ID, detailed address (excluding specific spatial details for privacy), geocode data.
o Structural Details: Area, height, number of floors, roof type, construction age.
o Energy and Environmental Data: BER rating, radon emission, HVAC system details.
o Additional Metrics: Estimated number of bedrooms and bathrooms, water heating source, space heating source.
2. Neighborhood-Scale Attributes:
o Environmental Context: Soil composition, presence of water bodies and green spaces, built-up areas.
o Urban Infrastructure: Roads, pathways, networks, and other land uses.
o Spatial Relationships: Built environment density, district green and non-green area, and land cover specification.
Acknowledgment: 
We would like to thank the funder of the project, Science Foundation Ireland (SFI) under the NexSys SFI/21/SPP/3756 programme. We also thank to CEO, Tailte Éireann (acting on behalf of the Government of Ireland) to
reproduce Tailte Éireann – Surveying maps and data for the annual copyright licence, CYAL50402364, © Tailte Éirean.n – Surveying.
DOI: 10.15468/dl.k3m39c
title: Occurrence Download

description: A dataset containing 3520 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Prunus avium (L.) L.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 3520 records from 257 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009308-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/5cxyb5fp4f.2
title: Dataset of an actual motor vehicle insurance portfolio

description: The data is formatted as a spreadsheet, encompassing the primary activities over a span of three full years (November 2015 to December 2018) concerning non-life motor insurance portfolio. This dataset comprises 105,555 rows and 30 columns. Each row signifies a policy transaction, while each column represents a distinct variable.
DOI: 10.17632/s6dhfd5m3c.1
title: Microtremor Research Data for Geothermal Manifestation MT. Telomoyo

description: This data is data from microtremor research in the Mount Telomoyo area, Indonesia.
DOI: 10.17632/sgzbgwjfkr.3
title: Brain Electrophysiological Recording during Olfactory Stimulation in Mild Cognitive Impairment and Alzheimer Disease Patients: An EEG Dataset

description: A total of 44 healthy elderly and MCI and AD patients participated in this experiment. The data of 6 participants were removed from further processing due to issues with EEG data recording, history of stroke, or traumatic brain injuries. Also, participants with any history of olfactory dysfunction were excluded from the study. The remaining 35 participants (age = 70.97±8.58, female = 57.14%) included 15 healthy (normal) individuals (age = 69.27±6.65, female = 53.33%), 7 MCI patients (age = 66.57±6.85, female = 51.14%), and 13 AD patients (age = 75.31±9.90, female = 61.54%).
An olfactory oddball perception task was performed on the participants. During this task, each participant was presented with a random sequence of two different odors. The sequence of odors was the same for all participants. One odor (lemon) was presented frequently (standard) with a probability of 0.75 and the other odor (rose) was presented non-frequently (deviant) with a probability of 0.25.

The dataset consists of four files as follows: 
1)	“AD.mat”: Contains data for Alzheimer’s disease patients.
2)	“Normal.mat”: Contains data for healthy elderly participants. 
3)	“MCI.mat”: Contains data for amnestic mild cognitive impairment patients. 
4)	“MMSE_Data”: Contains demographic and MMSE data of all participants.

The structure of the first three files is the same as described below. These files are organized as structure arrays for use in MATLAB coding. However, the data can also be used with other programming languages like python (using the scipy package).

The fourth file includes demographic data of each participant as age in a range, their diagnosed mental state, and the details of their MMSE score. The columns indicate abbreviated MMSE test components as follows: O Place: Orientation to Place, O Time: Orientation to time, Reg: Registration, Att & Calc: Attention and Calculation, Rep: Repetition, DR: Delayed Recall, VS: Visuospatial, Comm: Commands.
DOI: 10.17632/t4n9mys2sm.1
title: The effect of genetic and environmental perturbations on S. cerevisiae cell size
DOI: 10.17632/94j4ws2325.1
title: Grape Disease Dataset

description: The environment plays a crucial role in shaping crop growth and productivity. A IoT will be utilized for monitoring temperature, humidity, and leaf wetness, all of which influence grape quality and lifespan. Furthermore, a far-reaching dataset takes into consideration the investigation of illness designs, natural elements, and potential moderation systems. By leveraging weather-related variables and sensor data, grapevine producers can create an efficient and automated disease detection system. We presents a self-created weather parameter database using sensors. The database consists of 10000 records divided into 5 categories. Here, experiment have been carried out using our dataset to predict grape diseases on various machine learning algorithm.

 Data can be collected remotely or in the field. Actual results can be taken from the field at real time after regular interval. Here, experiment have been carried out using our dataset to predict grape diseases on various machine learning algorithm. The dataset is categorized into 8 distinct classes, including 3 disease categories. The disease categories cover a range of common grape diseases, such as powdery mildew, downy mildew, bacterial leaf spot. Temperature and Leaf Wetness Sensor are needed to measure weather parameters and managed by NodeMCU. Data set were analyzed to predict the instances of diseases Downey Mildew, Powdery Mildew and Bacterial Leaf Spot using the algorithm. Additionally, we may need metadata such as the type of grape variety, location, and date of observation. Integrated the collected data means combined the grape disease data with the corresponding environmental data
DOI: 10.17632/6mr577jb6b.1
title: Data From: Fecal indicator bacteria and sewage-associated marker genes are associated with nitrate and environmental properties parameters in Florida freshwater systems

description: This dataset contains measurements of fecal indicator bacteria (Escherichia coli and enterococci), microbial source tracking markers (HF183 and GFD), nutrients, and environmental parameters from two freshwater Florida streams. Water and sediment was collected over a 26-month period. Fecal indicator bacteria were cultured from water and sediment, microbial source tracking markers were assessed in water by qPCR, and nutrients were measured in water and sediment.
DOI: 10.17632/htsngg9tpc.2
title: Vehicle Type Image Dataset (Version 2): VTID2

description: After creating VTID, the researchers decided to extend the collection process to create another larger dataset to add further diversity to the dataset in order to avoid data overfitting. Finally, the new dataset, called "Vehicle Type Image Dataset 2 (VTID2)", consisted of 4,356 image samples that could be separated into five vehicle type classes as follows: 1,230 sedans, 1,240 pick-ups, 680 SUVs, 606 hatchbacks, and 600 other vehicle images.
DOI: 10.17632/vzps7jyp87.1
title: Energy to Energy to Mass Conversion

description: .The standards obtained by different text book about different Particles and Energy are different in different units. Like:- Mass value of Neutron 1.6749 x 10^-27 Kg. Proton mass 1.672623 x 10^-27 Kg, electron mass 9.109390 x 10^-31 Kg, Atomic mass 1.66050 x 10^-27 Kg in amu Unit, Neutron mass1.008664 u, Proton mass 1007276 u. electron mass 0.0005458 u. As per, e = mC^2 one energy 1.6021766 x 10^-34 Joule. Equivalent mass 1.782662 x 10^-36 Kg. There was a lot of curiosity in the quality of the same mass, same energy its answers and the strong desire to know the secrets of those numbers, digits, values. Then I went to find their relationship and saw the relationship between the values and numbers and digit obtained from all these units, and without unit. Find same value either quantity, or almost same number digit from these used Units and without Units, without power with power. For example 1.6021766 x 10^-34 Joule = 1.6021766 Unit. Energy 1.782662 x 10^-36 Kg. = 1.782662 Unit mass like this. I found the hadith of the original and prime, or root of numbers/digits in them. If the same result is found again and again and in different ways, then in the language of science it is considered as evidence. From this belief I have tried to present all those calculations in each article. It seemed my duty to tell everyone about it, Then in each article I have tried to describe each step as briefly as possible. Although I have to admit, I don't know how successful this effort has been with my little knowledge. In some cases, the change of language and the rules of writing in scientific terms have caused problems, So I have tried to make it suitable for all types of readers to understand easily. This article discusses the value of mass in each color’s light, and the value and origin of the digit and what their weight and mass might be.
DOI: 10.17632/5d7vbdp8f7.3
title: Wind Turbine Blades Fault Diagnosis based on Vibration Dataset Analysis

description: The data collection includes uniaxial vibration measurements of wind turbine induction operating at varying wind speeds. It contains three vibration datasets of wind turbines in a healthy state and three in a problematic one. In addition, the defective circumstances of wind turbines include blade cracks, blade surface degradation, and blade imbalance. There are a total of 35 wind turbine conditions datasets. The dataset was collected at a sampling rate of 1 kHz, with 500 samples per channel.
DOI: 10.17632/7md7yty9sk.2
title: NeuroBioSense Dataset

description: The dataset provided in this study serves as a helpful resource for comprehending emotional reactions within the domain of neuromarketing. Data was acquired from a sample of 58 participants, ranging in age from 18 to 70, through the implementation of a carefully planned experimental design. The participants were shown a collection of 35 branding advertisements classified into three categories: cosmetics and fashion, car and technology, and food and market. The Empatica e4 wearable sensor device was utilized to record several physiological signals, such as photoplethysmography (PPG), electrodermal activity (EDA), and body temperature. Concurrently, the process of capturing facial expressions was conducted using high-definition cameras during periods dedicated to viewing advertisements. The emotional assessments of the participants were evaluated utilizing an emotion appraisal scale, while demographic information was gathered via questionnaires.
The utilization of this multifaceted dataset allows scholars to explore the complex realm of consumer decision-making processes, taking into account variables such as age, gender, and varied cultural backgrounds. Through the integration of physiological signals and facial expressions, the dataset offers valuable insights into the underlying neurological mechanisms that drive emotional responses toward branded commercials. This can be utilized by researchers to study emotional patterns, investigate correlations between consumers and advertising, and develop customized neuromarketing methods that are beneficial for individual preferences.
DOI: 10.17632/bpt2hvf8n3.3
title: Multi-Keyboard Acoustic (MKA) Datasets

description: Our research team from the Computer Science Department at the University of Halabja has developed an innovative dataset collection named the Multi-Keyboard Acoustic (MKA) Datasets. The Multi-Keyboard Acoustic (MKA) Datasets, designed to aid in keyboard sound recognition and analysis, address the critical need for defending against acoustic-based cyber threats. With the increasing sophistication of cyberattacks, focusing on keyboard acoustics is particularly timely. The MKA Datasets encompass detailed recordings from six commonly used platforms: HP, Lenovo, MSI, Mac, Messenger, and Zoom. Each platform's dataset includes raw recordings, segmented sound files, and matrices derived from these sounds, capturing the subtle variations in typing behavior across different devices and applications.
 
We meticulously organize the MKA datasets to facilitate ease of use and thorough analysis. Each platform has a dedicated folder containing subfolders for raw data, segmented sound files, and matrices. Additionally, an aggregated folder combines data from all platforms, providing a broad spectrum for cross-platform analysis. In total, the MKA datasets consist of around 2630 files with.wav extensions for sound segments, as well as an equal number of matrix and.txt files. The number of files varies by platform, with approximately 70 files for HP, Lenovo, MSI, Zoom, and Messenger, and 61 files for Mac. Within each platform's dataset, the "Sound segments" folder stores six one-second WAV audio excerpts derived from the corresponding raw data files for each class, renamed using a convention of "class_name+1" to "class_name+6" for each platform individually and "class_name+platform_name1" to "class_name+platform_name6" for the aggregated datasets. The "Sound segment (.matrix)" folder contains feature representations, such as MFCCs, extracted from each sound segment. Additionally, the "Sound segment metadata (.txt)" folder holds detailed information for each sound segment, including recording conditions, platform information, and keystroke class labels.
 
Beyond cybersecurity, the MKA datasets have potential applications in domains such as speech recognition and natural language processing. The datasets, which provide a diverse set of sound profiles, support the development of more robust and adaptable algorithms in these fields. The versatility of the MKA datasets makes them an invaluable tool not only for advancing cybersecurity research, but also for improving the efficiency and accuracy of human-computer interaction technologies. Through our comprehensive approach, we aim to contribute significantly to both academic research and practical applications in these interconnected areas.
DOI: 10.17632/dd62d5hyzr.1
title: The key material properties of ultra-high-performance concrete (UHPC)

description: The dataset were developed to predict the compressive strength, flexural strength, workability, and porosity of Ultra-high-performance concrete (UHPC) in order to expedite the design process of UHPC. The dataset has 1,228 experimental data covering 24 mix design variables of UHPC. The mix design variables can be grouped into four categories: (1) Category 1: variables of the cementitious systems, (2) Category 2: variables of the reinforcing fibers, (3) Category 3: curing time, and (4) Category 4: dimensions of the test specimens. For more information about the development of the datasets, please refer to the accompanying research article.
DOI: 10.17632/jzy8zngkbg.4
title: HGM-4
DOI: 10.17632/dhyns2xhpy.1
title: Dataset of "A comparative analysis of mathematical methods for detecting lactate thresholds using muscle oxygenation data during a graded cycling test. "

description: Dataset of the article published in Physiological Measurement, titled "A comparative analysis of mathematical methods for detecting lactate thresholds using muscle oxygenation data during a graded cycling test. ". This dataset includes the values of muscle oxygen saturation and lactate during an incremental cycling test.
DOI: 10.17632/cp3473x7xv.1
title: LG 18650HG2 Li-ion Battery Data for FNN xEV SOC Estimator

description: The included tests were performed at McMaster University in Hamilton, Ontario, Canada by Dr. Phillip Kollmeyer (phillip.kollmeyer@gmail.com). If this data is utilized for any purpose, it should be appropriately referenced. A brand new 3Ah LG HG2 cell was tested in an 8 cu.ft. thermal chamber with a 75amp, 5 volt Digatron Firing Circuits Universal Battery Tester channel with a voltage and current accuracy of 0.1% of full scale. these data are used in the design process of an SOC estimator using a deep feedforward neural network (FNN) approach. The data also includes a description of data acquisition, data preparation, development of an FNN example script. The test data, or similar data, has been used for some publications, including: Vidal, C.,Kollmeyer, P., Naguib, M., Malysz, P.,Gross, O. and Emadi, A. (2020). Robust xEV Battery State-of-Charge Estimator Design using Deep Neural Networks. [online] Sae.org. Available at: https://www.sae.org/publications/technical-papers/content/2020-01-1181/ [Accessed 28 Jan. 2020]. C. Vidal, P. Kollmeyer, E. Chemali and A. Emadi, "Li-ion Battery State of Charge Estimation Using Long Short-Term Memory Recurrent Neural Network with Transfer Learning," 2019 IEEE Transportation Electrification Conference and Expo (ITEC), Detroit, MI, USA, 2019, pp. 1-6.