cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/87s2br3ptb.1
title: TASD-Dataset: Text-based Early Autism Spectrum Disorder Detection Dataset for Toddlers

description: The TASD-Dataset, created for early ASD detection in toddlers, comprises text sequences describing the situations of toddlers with and without ASD.It contains important ASD assessment features such as Attention Response, Word Repetition, Emotional Empathy, and more, each associated with specific toddler behaviors. These discussions reveal in-depth parental observations, shedding light on how these behaviors are understood and expressed, aiding in the identification of important ASD-related signals. Emphasizing these specific features, the dataset supports the creation of machine learning models, enabling thorough investigation of behavioral indicators essential for identifying ASD risk in early childhood.

Attention Response: Refers to a child's reaction or response to external stimuli, such as sounds, movements, or instructions. In ASD assessment, variations in attention response may indicate differences in sensory processing or responsiveness.

Change Reaction: Explores how a child adjusts to changes in their environment or routine. Children with ASD may show resistance or struggle to adapt to changes, resulting in visible reactions.

Word Repetition: Involves the repetition of words or phrases, often without contextual relevance. It can be a linguistic pattern observed in children with ASD, showcasing repetitive speech or echolalia.

Eye Contact: Indicates the ability or frequency with which a child makes eye contact during social interactions. Limited or atypical eye contact is a common trait observed in ASD, affecting social communication.

Emotional Empathy: Focuses on a child's capacity to understand and respond to others' emotions. Difficulties in emotional empathy might manifest as challenges in recognizing or responding appropriately to others' feelings.

Finger Movements: Observes any repetitive or stereotyped finger movements or hand gestures. These movements might manifest as repetitive actions or mannerisms, often seen in children with ASD.

Focused Attention: Refers to a child's ability to concentrate on a specific task or activity for an extended period. Children with ASD might display challenges in maintaining focused attention on tasks of interest or importance.

Follow Pointing: Assesses a child's ability to follow another person's pointing gesture to focus on or locate an object or event. Difficulty in following pointing gestures might indicate social communication challenges.

Repetitive Behavior: Encompasses a range of repetitive actions, behaviors, or interests that are persistent and often resist change. These behaviors might include repetitive movements, insistence on sameness, or rigid adherence to routines.

Toy Arranging: Involves a child's inclination or patterns in arranging toys or objects in a specific or repetitive manner. This behavior might relate to a preference for order or specific patterns often seen in ASD.
DOI: 10.17632/t8wrsgwgzc.1
title: Microsoft ERP Projects Dataset

description: This dataset comprises 21 past D365 projects collected from a CMMI Level 3 software development company located in Lahore, Pakistan. These projects span various domains such as footwear, apparel, cosmetics, and fragrances. 

This dataset includes values for
project code - project ID for identification purposes 
six factor points - reports points (ReP), forms points (FP), POS customizations points (PCP), integrations points (IP), workflows points (WP), and data entities points (DEP) 
six equivalent factor points - equivalent reports points (EReP), equivalent forms points (EFP), equivalent POS customizations points (EPCP), equivalent integrations points (EIP), equivalent workflows points (EWP), equivalent data entries point (EDEP)
actual Effort
estimated effort with COCOMO II
estimated effort with expert judgment.
DOI: 10.17632/yvtmpdyxtj.2
title: The Circular Economy and Entrepreneurship Nexus Global Research Trends Acumen

description: Abstract. The global shift towards sustainable development has highlighted the circular economy as a crucial approach to sustainable entrepreneurship and business. This study aims to map research trends in circular economy across various countries from 2014 to 2023. Using bibliometric analysis, we examined 237 publication documents sourced from Scopus. The research employed Biblioshiny as the analytical tool to process and visualize the data. Our findings reveal six primary research themes within the circular economy and sustainable entrepreneurship domain. The study identifies emerging research topics and clarifies the role of the circular economy in entrepreneurship. These insights provide a comprehensive overview of the field's evolution over the past decade. The bibliometric analysis highlights geographical patterns in research output, key contributors, and collaborative networks. It also uncovers the most influential publications and their impact on shaping the conversation around the circular economy in sustainable entrepreneurship. This research enhances the current understanding by presenting a methodical overview of circular economy studies within the realm of sustainable entrepreneurship. It offers crucial perspectives for scholars and business innovators aiming to grasp the present landscape and upcoming trajectories in this area of study. The findings can guide future research agendas and inform practical applications of circular economy principles in sustainable business practices. By emphasizing the importance of circular economy in promoting sustainable entrepreneurship and building resilient business models, this research underscores the need for continued exploration and practical implementation of circular economy principles. It highlights the potential of these approaches to address pressing environmental challenges and create sustainable value in the entrepreneurial ecosystem.
DOI: 10.17632/s87267tpp3.1
title: Dataset for Android intrusion detection using process control block information

description: This dataset consists of Process Control Block (PCB) data mined during the execution time of tested apps. The PCB data from 2620 malware-infested applications and 1610 benign applications were collected. The PCB data sequence was collected for 25 seconds, with an average of 18500 PCB records stored for each application. The mining method was implemented at the kernel level and synced with the process (job) context switching. The data for each program is stored in a separate CSV file and includes the PCB information for all threads running the application. The application automation testing and PCB gathering for benign and malicious applications were conducted in a closed dynamic malware analysis framework. The dataset can be used to evaluate and contrast benign and malicious Android programs' low-level (kernel) behavior. The mining approach effectively captured 99% of the context switches for the vast majority of tested applications.
DOI: 10.17632/r373x6ch4k.1
title: Replication Codes for Questioning the puzzle: fiscal policy, real exchange rate and inflation

description: Replication Codes for Questioning the puzzle: fiscal policy, real exchange rate and inflation
DOI: 10.17632/krxvfr62gx.1
title: Publication Dataset of The COVID-19 Pandemic in Indonesia 2020

description: The research and publication dataset of the COVID-19 pandemic in Indonesia, which was indexed by Scopus in 2020. The dataset contains data authors, authors ID Scopus, title, year, source title, volume, issue, article number in Scopus, DOI, link, affiliation, abstract, index keywords, references, correspondence Address, editors, publisher, conference name, conference date, conference code, ISSN, language, document type, access type, and EID.
DOI: 10.17632/v56fgdz4pv.1
title: The human brand radar in politics: content and sentiment analysis of Ecuador's 2023 election

description: Here are the files used to carry out the research: The human brand radar in politics: content and sentiment analysis of Ecuador's 2023 election. The data collected on the candidates' social media is organized, as well as the R codes used to carry out the sentiment analyses.
DOI: 10.17632/y3zxhtzd94.1
title: Research data for &lt; A complete and continuous core record of deep-water mass transport deposits &gt;

description: Supplementary Data and Log Data
DOI: 10.17632/768snffp8d.1
title: Export Performance

description: The hypothesis is to make a link between technology and export performance. Exposing the level of export performance in big manufacturing firms of the south of Brazil, that have a high level of applied technology.
DOI: 10.17632/nbs66kvx6n.1
title: ETF IoT Botnet Dataset

description: The data in this dataset represent recorded network traffic of specimens of IoT malware samples that were collected from the links found on URLHaus database website (malware.zip), in the period from 2019 to 2021, at the University of Belgrade, School of Electric Engineering. These malware samples were run on RaspberryPi devices, with restricted local network access, and the network traffic was recorded using tcpdump tool. The benign network traffic (benign.zip) represents all the network traffic recorded on a personal computer for the duration of several hours, split into two files. All local network addresses were anonymized in the process of making these pcap files. The csv file in the dataset contains description for each of the malware pcap files, consisting of: file name, UrlHaus URL, bot address, malware address, attack presence, attacked address, URLHaus tags, collection date (in the DD/MM/YYYY format), and comment.
DOI: 10.17632/442mhsrpcm.1
title: Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples: Supplementary Data 2

description: Supplementary data for the study "Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples" by Reza Mirzazadeh, Zaneta Andrusivova and Ludvig Larsson et al. 

Available data:
1. High resolution H&E images are available in the images/ folder for small intestine.
DOI: 10.17632/tjxp7njgt2.1
title: Mechanical performance and fibre counting of bending and tension SFRC specimens exposed to wet-dry cycles

description: This dataset contains the processed experimental data published in: "Marcos-Meson, V.; Fischer, G.; Solgaard, A.; Edvardsen, C.; Michel, A. Mechanical performance of steel fibre reinforced concrete exposed to wet-dry cycles of chlorides and carbon dioxide. Materials 2021 (in press)." The dataset contains the following: 3-point bending tests (results afer: 90-days (reference), 1-year, 2-years) - Mechanical - Fibre counting Uniaxial tension tests (results afer: 90-days (reference), 1-year, 2-years) - Mechanical - Fibre counting The sample reference names follow the ones shown in Table 2 of the reference: "Marcos-Meson, V.; Fischer, G.; Solgaard, A.; Edvardsen, C.; Michel, A. Mechanical performance of steel fibre reinforced concrete exposed to wet-dry cycles of chlorides and carbon dioxide. Materials 2021 (in press)."
DOI: 10.17632/j63xxtcybr.1
title: Data for: Global interpersonal income inequality decline: the role of China and India

description: Global income inequality database Global interpersonal income inequality is an important concept for a number of reasons. Indicators of income distribution, such as quantile income shares and the Gini coefficient, are available for individual countries, but from official statistical sources they are not available for the world as a whole or for various country groups. This database includes estimates for global interpersonal income inequality from 1988 to 2015 in 145 countries that account for 95 percent of global population. It also includes regional income inequality estimates within the groups of advanced countries, emerging countries, and six regional country groups: Developing Asia, Sub-Saharan Africa, Commonwealth of Independent States, European Union (EU), Latin America and Middle East/North Africa. It also includes estimates for non-EU advanced countries. The estimates are based on the two-parameter distribution method, which we found to be the most robust among four alternative methods. The database includes six versions of this method by considering three alternative distributions (the log-normal distribution, the Pareto distribution and the Weibull distribution) both in a deterministic and in a stochastic setting. We plan to update the dataset regularly.
DOI: 10.17632/y7vgr7w5xy.1
title: Forecasting COVID-19 confirmed cases in Jazan, Saudi Arabia with ARIMA prediction model using the observed dataset

description: The data collected daily for COVID-19 confirmed cases form the official website of Ministry of Health Kingdom of Saudi Arabia from 1st Sep. Till 4th Oct. 2020. It is a time-series data and a prediction model ARIMA were used to analyze and predict the near future forecast of COVID-19 for Jazan region. such type of data can be used for analysis by health administrations at any level to predict the new cases of COVID-19 and to plan before the real time situation. Even hospitals can use such type of data to predict COVID-19 cases for their care community.
DOI: 10.17632/hzmn84khmh.1
title: Anonymized Nigerian-Based Localized Fingerprint Dataset

description: This is a repository of anonymized fingerprints of 1,266 subjects of both male and female genders captured from three major ethnic groups in Nigeria (Yoruba, Igbo and Middle belt). The fingerprint images were captured using Secugen Hamster Plus Fingerprint scanner and a biometric data collector software developed using netbeans SDK.
DOI: 10.17632/xhd6m43dyd.4
title: Bursa Malaysia - Healthcare Industry

description: The dataset consists of market capitalization data for public listed companies on Bursa Malaysia in the healthcare industry.
DOI: 10.17632/hm9gcff8d9.2
title: Estimated Impact of School Closures in Chile During 2019

description: Data and do file required to reproduce our results of the paper "Estimated Impact of School Closures in Chile During 2019" In 2019 several schools in Chile had prolonged interruptions in their classes due to two unusual events. One was an extended teachers’ strike, and the other was a series of violent nationwide protests, known as the October Outbreak. Using the difference-in-differences approach, we estimated the effect these events had on student performance, attendance, and the rate of students who withdrew or were transferred from their schools that year. The study considers elementary and high-school students who are in traditional education (this is, we exclude students from adult education formats, night-shift students, and students from special-education-only schools). We created two analytical samples. The first one is at the student level and we use it to estimate the effect of closures on GPA, attendance, and the rate of students who were withdrawn or transferred from schools. The second sample is at the school level and we use it to measure the effect of closures on the results of the 8th grade SIMCE standardized test. The Rendimiento.dta and Simce.dta databases were built with public information available at: https://informacionestadistica.agenciaeducacion.cl/#/bases and https://datosabiertos.mineduc.cl/ The Ministry of Education there is information gathered by the Mineduc of schools that adhered to the stoppage during 2019, as well as the days of classes lost by each school due to the riots of that year after October 18th. This information can be crossed with the other databases we use through a unique school identifier.
DOI: 10.17632/3kzr8mt8s2.3
title: Flood Amateur Video for Semantic Segmentation Dataset

description: This dataset is flood data in the city of Parepare, South Sulawesi Province, which contains video data collected from social media Instagram. This dataset was created to develop deep learning methods for recognizing floods and surrounding objects, specializing in semantic segmentation methods. This dataset consists of three folders, namely raw video data collected from Instagram, image data resulting from splitting the video into several images, and annotation data containing images that have been color-labeled according to their objects. There are 5 object classifications based on color labels, namely: floods (blue), buildings (red), plants (green), people (gray), and vehicles (orange). This dataset has data in image (JPEG/PNG) and video (MP4) formats. This dataset is suitable for object recognition tasks with the semantic segmentation method. In addition, because this dataset contains original data in the form of videos and images, it can be developed for other purposes in the future. As a note, if you intend to use this dataset, please ensure that you comply with applicable copyright, privacy, and regulatory requirements.
DOI: 10.17632/yfcn6b52yz.1
title: conjunctions_data

description: Excel file containing data on 10,960 conjunction events occurred in 2022 between space objects in Low-Earth Orbit. The dataset is represented as a table, where each row corresponds to a unique conjunction event and each column corresponds to a different conjunction feature. The first row of the table reports the columns names. For each conjunction, the following features are provided: the two objects' names ('name_1', 'name_2'), the two objects' Norad numbers ('norad_1', 'norad_2'), a Two-Line Element (TLE) set for each object ('TLE_1', 'TLE_2'), the time difference (in days) between each TLE epoch and the Time of Closest Approach (TCA) ('prop_1', 'prop_2'), the minimum predicted distance at TCA ('min_range') and the predicted relative velocity at TCA ('rel_vel'). To propagate the objects' trajectories and reconstruct the conjunction kinematics, the only necessary conjunction features are the two TLE sets and the two propagation time-spans.
DOI: 10.17632/zj7ytsrsgj.1
title: Polarization data for 28 mL MFC reactors

description: Microbial Fuel Cell polarization data collected for 202 days. Data includes: voltage, current, current density, power and power density for 6 reactors.
DOI: 10.17632/3xm88g6m6d.1
title: A Public Domain Dataset for Real-Life Human Activity Recognition Using Smartphone Sensors

description: In recent years, human activity recognition has become a hot topic inside the scientific community. The reason to be under the spotlight is its direct application in multiple domains, like healthcare or fitness. Additionally, the current worldwide use of smartphones makes particularly easy to get this kind of data from people in a non-intrusive and cheaper way, without the need of other wearables. In this paper, we introduce our orientation-independent, placement-independent and subject-independent human activity recognition dataset. The information in this dataset is the measurements from the accelerometer, gyroscope, magnetometer, and GPS of the smartphone. Additionally, each measure is associated with one of the four possible registered activities: inactive, active, walking and driving. This work also proposes an SVM model to perform some preliminary experiments on the dataset. Considering that this dataset was taken from smartphones in their actual use, unlike other datasets, the development of a good model on such data is an open problem and a challenge for researchers. By doing so, we would be able to close the gap between the model and a real-life application. Data collection was made through an Android app, for each smartphone of every individual from the 19 that got part in the study. The data collected, by focusing such gathering on a real-life environment, does not have a fixed orientation or placement of the smartphone, leaving each individual to use their device as they would for each of the specified actions. The activities performed were four: Inactive: not carrying the mobile phone. For example, the device is on the desk while the individual performs another kind of activities. Active: carrying the mobile phone, moving, but not going to a particular place. In other words, this means that, for example, making dinner, being in a concert, buying groceries or doing the dishes count as "active" activities. Walking: Moving to a specific place. In this case, running or jogging count as a "walking" activity. Driving: Moving in a means of transport powered by an engine. This would include cars, buses, motorbikes, trucks and any similar. The data collected comes from four different sensors: accelerometer, gyroscope, magnetometer and GPS. We saved the data of the accelerometer, the gyroscope and the magnetometer with their tri-axial values. In the case of GPS, we stored the device's increments in latitude, longitude and altitude, as well as the bearing, speed and accuracy of the collected measurements. Note that these measures do not have a fixed frequency, as it is not possible to set it on Android devices. In addition, some of the people who took part in the study did not have all these sensors available on their smartphone, so there are certain sessions that do not have a gyroscope, or neither a gyroscope nor a magnetometer together. Inside each .zip file, there is a README file with details for each part.
DOI: 10.17632/crhybxpdy6.1
title: Simultaneous EEG-fMRI dataset

description: Here we present an open-access multimodal neuroimaging dataset consisting of simultaneously and independently collected electroencephalographic (EEG) and Magnetic Resonance Imaging (MRI) data from 20 healthy young male adults (mean age=26 years; SD=3.8 years). The dataset follows the BIDS standard specification and is organized into two parts: 1) EEG data collected outside the Magnetic Resonance (MR) environment, inside the MR scanner without acquiring images and during the simultaneous acquisition of functional MRI, which also includes the gradient artifact-corrected version of the EEG data (_gac suffix) and 2) the fMRI data collected during the simultaneous EEG-fMRI acquisitions, the fMRI data collected without the EEG cap, and the structural MRI data acquired with and without the EEG cap. EEG data were recorded using an MR-compatible EEG recording system (GES 400 MR, Electrical Geodesics Inc.) equipped with a 32-channel sponge-based EEG cap. 2 minutes of eyes-closed (EC) resting-state EEG data were recorded for the outside and inside scanner conditions, while 10 minutes were recorded for the EEG-fMRI condition. 2 minutes of eyes-open (EO) resting-state EEG were recorded for the 3 conditions. Subjects additionally performed an eyes open-closed (EO-EC) block-design task outside the scanner (2 minutes) and during simultaneous EEG-fMRI (4 minutes). The outside EEG data provides a baseline condition of the signal without the presence of MR-related artifacts. The inside EEG data records the contribution of the ballistocardiographic (BCG) artifact without the presence of the gradient artifact, representing an ideal condition to test and validate BCG artifact correction methods. The EEG-fMRI condition records the contribution of both the gradient and the BCG artifact. Brain images were obtained using a 3T GE MR750-Discovery MR scanner equipped with a 32-channel head coil. Whole-brain functional images were obtained using a GRE-EPI T2* weighted sequence (TR = 2000 ms, TE = 40 ms, 35 interleaved axial slices with 4 mm isometric voxels).  Structural images were acquired with a SPGR sequence (TR = 8.1 ms, TE = 3.2 ms, flip angle = 12°, 176 sagittal slices with 1 mm isometric voxels). Anatomical data were always collected after their corresponding functional acquisitions. This represents one of the largest open-access EEG-fMRI datasets available, also including independently collected EEG and structural/functional MRI data. This dataset will allow other researchers to 1) characterize the impact of the gradient and the BCG artifact on low-density EEG data, 2) use these data to assess the effectiveness of novel artifact removal approaches to reduce the artifacts contribution and preserve EEG signal properties, 3) perform hardware-setup comparison studies, 4) address the quality of structural and functional MRI data collected along with the EEG cap, and 5) implement and validate multimodal integrative analysis on the EEG-fMRI data acquired simultaneously.