cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/5vvffh53r5.2
title: Smart Meter Data-Driven Evaluation of Operational Demand Response Potential of Residential Air Conditioning Loads

description: The ground truth data used in this paper are obtained from three different areas to verify the effectiveness and robustness of the proposed methods. The detailed data information is described as follows: 1) The ground truth data from the Pecan Street dataset are collected from real households in the Muller project in Austin, TX, USA. Muller project funded by the U.S. Department of Energy and the U.S. National Science Foundation are located on the site of the Austin’s former municipal airport, close to central Austin.The selected homes in the project received monitoring equipment that captures electricity use on less than or equal to 1 min intervals for the whole home and 6 to 22 major appliances. Data over one year from August 2015 to July 2016 are analyzed, which contain the application-level and the whole-house energy consumption data. The corresponding 1-hour level temperature data are collected from the nearest Mueller weather station. We down-sample the energy consumption data to the 1-hour level to maintain consistency with the resolution of the temperature data. After data cleaning, customers without air conditioners or with missing readings are omitted, and the data of 119 residential customers are selected for accuracy analysis. 2) The ground truth data from smart home dataset are collected from real households in the Smart Home project in the Western Massachusetts, USA. The goal of this project is to optimize home energy consumption. The project involves several different types of dataset, including apartment dataset of 114 single-family, home dataset of 7 household and solar panel dataset, etc. However, the apartment dataset only contains the aggregated electrical data which can not be used to verify the accuracy of the load decomposition. Therefore, data over one year from January 2016 to December 2016 of home B and home G with individual ACLs monitor are selected for robustness analysis. 3) The ground truth data from low voltage distribution area are collected from low voltage distribution boxes in a developed city, Jiangsu province, China. Power and corresponding temperature data over one year from 2017 to 2018 are used for local DR programs. The dataset involves different distribution areas (i.e., different aggregated DR customers), including garment factory, hotel, rural neighborhoods, etc. However, the sub-meter data of all the ACLs are unavailable, thus it will only be used for aggregated DR potential analysis.
DOI: 10.17632/s26kxvspn7.2
title: Artificial Intelligence, Firm Growth, and Product Innovation

description: Replication dataset for "Artificial Intelligence, Firm Growth, and Product Innovation" published in Journal of Financial Economics.
DOI: 10.17632/p92gj2732w.2
title: Multi-mode Fault Diagnosis Datasets of Gearbox Under Variable Working Conditions

description: The gearbox is a critical component of electromechanical systems. The occurrence of multiple faults can significantly impact system accuracy and service life. The vibration signal of the gearbox is an effective indicator of its operational status and fault information. However, gearboxes in real industrial settings often operate under variable working conditions, such as varying speeds and loads. It is a significant and challenging research area to complete the gearbox fault diagnosis procedure under varying operating conditions using vibration signals. This data article presents vibration datasets collected from a gearbox exhibiting various fault degrees of severity and fault types, operating under diverse speed and load conditions. These faults are manually implanted into the gears or bearings through precise machining processes, which include health, missing teeth, wear, pitting, root cracks, and broken teeth. Several kinds of actual compound faults are also encompassed. The development of these datasets facilitates testing the effectiveness and reliability of newly developed fault diagnosis methods.
DOI: 10.17632/j32xdt2ff5.2
title: tea sickness dataset

description: This tea sickness dataset contains tea leaves showing 7 common diseases of tea: (1) Red leaf spot; (2) Algal leaf
spot; (3) Bird’s eyespot; (4) Gray blight; (5) White spot; (6) Anthracnose; (7) Brown blight. The dataset further contains a class of healthy tea leaves.  Each of the classes contains more than 100 images. The dataset was collected in Johnstone Boiyon farm, Koiwa location, Bomet county from a clone of 1510. It can be used with transfer learning for Machine learning models for predicting sickness in tea.
DOI: 10.17632/tb9yrptydn.1
title: Dataset of electric passenger cars with their specifications

description: This dataset lists all fully electric passenger cars with their attributes (properties). The collection does not contain data on plug-in hybrid cars and electric cars from the so-called “range extenders”. Hydrogen cars were also not included in the dataset due to the insufficient number of mass-produced models and different (compared to EV) specificity of the vehicle, including the different charging methods. The dataset includes cars that, as of 2 December, 2020, could be purchased in Poland as new at an authorized dealer and those available in public and general presale, but only if a publicly available price list with equipment versions and full technical parameters was available. The list does not include discontinued cars that cannot be purchased as new from an authorized dealer (also when they are not available in stock). The subject of the study is only passenger cars, the main purpose of which is to transport people (inter alia without including vans intended for professional deliveries). The dataset of electric cars includes all fully electric cars on the primary market that were obtained from official materials (technical specifications and catalogs) provided by automotive manufacturers with a license to sell cars in Poland. These materials were downloaded from their official websites. In the event that the data provided by the manufacturer were incomplete, the information was supplemented with data from the SAMAR AutoCatalog (link can be found in References section). The database consisting of 53 electric cars (each variant of a model – which differs in terms of battery capacity, engine power, etc. – is treated as separate) and 22 variables (25 variables, including make, model and “car name” merging these two previous).
DOI: 10.17632/g72y8z5987.1
title: Data for: First-Mile Accessibility to Health Services: a mHealth Model for Rural Uganda

description: data analytics
DOI: 10.17632/jh6979fg2t.4
title: Dataset of Spoilt Banknotes of India (Rupees)

description: Accurate Currency recognition and classification is one of the challenging tasks for visually impaired people. As the damaged banknotes are not accepted by vendors while doing financial transactions, thus it is necessary to classify between spoilt and unspoilt banknotes. With this objective we have created the Spoilt Indian (Rupees) bank-notes dataset. This dataset consists of total 5125 (2584 old banknotes and 2541 new banknotes) high quality images. 
Mobile phones rear camera was used to take the images of spoilt Indian banknotes. The bank-note dataset consists of 8 classes namely Spoilt New 10 Rupees, Spoilt Old 10 Rupees, Spoilt New 20 Rupees, Spoilt Old 20 Rupees, Spoilt New 50 Rupees, Spoilt Old 50 Rupees, Spoilt New 100 Rupees, and Spoilt Old 100 Rupees. The banknote images which were soiled, mutated, holed, torn were considered for dataset creation. The images were taken in dark and illuminated backgrounds as well in the cluttered background.
DOI: 10.17632/8nds4cx88k.3
title: IAFSS_2023_JWMitchell_UtilityWildfires

description: This dataset is uploaded in support of a research paper being submitted for publication: "Analysis of Utility Wildfire Risk Assessments and Mitigations in California" by Joseph W. Mitchell. A link to this paper will be provided once available. 
This work comprises analysis of California utility wildfire risk models created by Pacific Gas and Electric Company (PG&E), Southern California Edison (SCE), and San Diego Gas and Electric Company (SDG&E) in the 2021-2022 time frame. Data was obtained through participation in public proceedings at the California Public Utilities Commission (CPUC) and the California Office of Energy Infrastructure Safety (OEIS), which allows participants to issue data requests to utilities. All data has been provided without Non-Disclosure restrictions, and is suitable for public dissemination.
Files obtained from the CPUC, OEIS, and CAL FIRE websites have been uploaded for archival purposes to maintain a reference set consistent with the research paper. An identifier, file name, full title of the document, publication date, and original URL are provided.
All files are listed in the INDEX.xlsx file, on the tab marked respectively for CAL FIRE, CPUC, Data, or OEIS. The INDEX file also summarizes the analysis and methods used for each data file.  Note that the Mendeley preview does not show multiple tabs, so the file must be downloaded.
DOI: 10.17632/pg3s4hw68k.3
title: Consumer Review of Clothing Product

description: The dataset is collected on our own from various sources. This dataset comprises a comprehensive collection of reviews pertaining to clothing products and serves as a valuable resource for multilabel classification research. Each data entry is meticulously annotated with relevant labels, allowing researchers to explore various dimensions of the clothing products being reviewed. The dataset offers a rich diversity of perspectives and opinions, enabling the development and evaluation of robust classification models that can accurately predict multiple aspects of a given clothing item. With its focus on multilabel classification, this data contributes significantly to advancing the understanding and application of machine learning algorithms in the fashion industry.
DOI: 10.17632/bn7ch8tvyp.1
title: Digital images of defective and good condition tyres

description: The dataset contains 1854 digital tyres images, categorized into two classes: defective and good condition. Each image is in a digital format and represents a single tyre. The images are labelled based on their condition, i.e., whether the tyre is defective or in good condition.

This dataset can be used for various machine learning and computer vision applications, such as image classification and object detection. Researchers and practitioners in transportation, the automotive industry, and quality control can use this dataset to train and test their models to identify the condition of tyres from digital images. The dataset provides a valuable resource to develop and evaluate the performance of algorithms for the automatic detection of defective tyres.

The dataset may also help improve the tyre industry's quality control process and reduce the chances of accidents due to faulty tyres. The availability of this dataset can facilitate the development of more accurate and efficient inspection systems for tyre production.
DOI: 10.17632/wnrwpkcptf.1
title: JRCS - Dataset associated at the article journal entitled "The Challenge of Integrating “Intelligent” Technologies in Luxury Shopping Contexts: The Role of Brand Personality Appeal and Consumers’ Status Consumption Orientation"

description: JRCS - Dataset associated at the article journal entitled "The Challenge of Integrating “Intelligent” Technologies in Luxury Shopping Contexts: The Role of Brand Personality Appeal and Consumers’ Status Consumption Orientation".
Current opportunities enabled by combining the Internet of Things and the Artificial Intelligence may allow marketers and managers to completely redesign shopping environments by integrating new interactive technologies in shopping environments. However, luxury brands have been typically skeptical to the implementation of technology, because of its conceptual contrast with the ideas of craftsmanship and human touch. To advance knowledge on the potential role of advanced technologies in the context of luxury, this study investigates the effect luxury shopping environment level of intelligence (low vs. high), in the shopping context of a luxury brand, on consumers’ perceived personality appeal and, therefore, on their intention to buy. Additionally, this study also sheds light on the moderating role of consumers’ status consumption orientation, that is a relevant consumer-related characteristic in field of luxury consumption. Results of two experiments, conducted with international participants recruited online, where the level of technological intelligence is manipulated through Voice Assistants (Study 1) and Smart Mirrors (Study 2), show that the high (vs. low) luxury shopping environment’s intelligence level positively affects consumers’ willingness to buy through the effect of their perceived brand personality appeal. Moreover, this effect is moderated by consumers’ status consumption orientation. Theoretical implications are discussed, together with managerial implications useful for effectively manage advanced technologies in luxury shopping environments.
DOI: 10.17632/g4xg38mwcn.2
title: VoxHunt Expression Maps

description: 3D expression maps of the developing mouse brain of different ages derived from in situ hybridization experiments. This data was obtained from the Allen Brain Atlas and is a dependency of the VoxHunt R package.
DOI: 10.17632/8whszkhk6b.2
title: Gene Ontology functional annotation of tomato

description: Functional annotation based on Gene Ontology for tomato (Solanum lycopersicum) SL3.0 genome assembly / ITAG3.2 annotation and SL4.0 genome assembly / ITAG 4.0 annotation.
The folder "Tomato funtional annotation" contains the files "gene_association.itag3.2.nr" for the ITAG3.2 genome annotation and "gene_association.itag4.0.nr" for the ITAG4.0 genome annotation
DOI: 10.17632/xzkd2j5zdw.1
title: SENS school

description: This dataset contains information gathered as part of a study evaluating the effectiveness of the "Le sens de l’école" (The Meaning of School) program, aimed at strengthening academic perseverance and improving student engagement in the educational system. The data were collected from CM1 and CM2 students (aged 9 to 11 years) from ten schools in Île-de-France, including schools within Priority Education Networks (Réseau d’Éducation Prioritaire, REP). The dataset includes quantitative data derived from questionnaires administered before and after the workshops, measuring academic motivation, satisfaction, mindset (growth mindset), and classroom climate. Supplementary materials contain the interview guide used to collect qualitative data.
DOI: 10.17632/7m63vyct2v.1
title: The impact of online consumer reviews and their perceived credibility on consumer behavior

description: This data file contains the responses from the servey research contacted for the purposes of a MSc thesis in Management at the International Hellenic University. The servey research was contacted in November 2019 and the data were collected with the GoogleForms. For more information, please refer to the Master thesis.
DOI: 10.17632/yzhcvn7sj5.1
title: Ransomware Combined Structural Feature Dataset

description: This dataset contains several strutuctural features extracted of 2675 binary executable samples. The training and validation set consisted of 2157 samples (80%): 1023 ransomware belonging to 25 relevant families and 1134 goodware. Meanwhile, a testing set consisted of 518 samples (20%): 385 ransomware belonging to the 15 recent families and 133 goodware.

The CSV file columns are sample ID, filename, target class (GR), family ID, and numerical columns (features), as follows:
 | ID | filename | GR | family | Features |
Training Goodware | 10000 to 11133 | Their name.exe | 0 | 0 | Numerical features |
Testing Goodware | 12000 to 12132 | Their name.exe | 0 | 0 | Numerical features |
Training Ransomware | 20000 to 21022 | Their SHA-256 hash | 1 | 1-25 family IDs | Numerical features |
Testing Ransomware | 22000 to 22384 | Their SHA-256 hash | 1 | 26-40 family IDs | Numerical features |

Options:
1) The dataset is split into individual types of features without preprocessing, including headers, imported DLLs, function calls, entropy of sections, and 3, 4, and 5-grams opcode frequencies.

2) The combined datasets include headers, imported DLLs, function calls, and entropy of sections feature sets, with and without the 3-gram feature set, after the preprocessing step, according to our research paper entitled "A Comprehensive Analysis Combining Structural Features for Detection of New Ransomware Families," from the Journal of Information Security and Applications.
Note: The preprocessing step primarily involved merging similar APIs, feature selection, and normalizing the features based on their maximum and minimum values, considering only the training data. Some features exclusively exist in the test data, with zero occurrences in the training samples. For accurate testing, it's advisable to exclude these features from the training set.

Family IDs:
Avaddon 1
Babuk 2
Blackmatter 3
Conti 4
Darkside 5
Dharma 6
Doppelpaymer 7
Exorcist 8
Gandcrab 9
Lockbit 10
Makop 11
Maze 12
Mountlocker 13
Nefilim 14
Netwalker 15
Phobos 16
Pysa 17
Ragnarok 18
RansomeXX 19
Revil 20
Ryuk 21
Stop 22
Thanos 23
Wastedlocker 24
Zeppelin 25

AvosLocker 26
BianLian 27
BlackBasta 28
BlackByte 29
BlackCat 30
BlueSky 31
Clop 32
Hive 33
HolyGhost 34
Karma 35
Lorenz 36
Maui 37
Night Sky 38
PlayCrypt 39
Quantum 40
DOI: 10.17632/8gbbgf8z49.1
title: MemriSim: A theoretical framework for simulating electron transport in oxide memristors

description: We have developed a theoretical framework MemriSim for simulating the resistive switching behaviors of oxide memristors. MemriSim comprises two major parts, i) structural evolution of oxygen vacancies during conductive filament formation/rupture by kinetic Monte Carlo (kMC) algorithm, and ii) transport calculations based on the scenario of electron tunneling and thermionic emission with the kMC derived structures. As prototype probes, we have computed the current-voltage (I-V) curves of HfO2 and TaOx based memristors and compared the results with experimental measurements, which show perfect agreement. By tuning the physical parameters, MemriSim can describe resistive switching devices with different oxide layers and metal electrodes. In addition, the pulse transient current can also be simulated by considering the transient response of RLC circuit. The developed framework not only provides a general approach for understanding the fundamental mechanism of resistive switching in oxides, but also opens up new opportunities for designing and optimizing memristor-based architectures for nonvolatile memory, logic-in-memory and neuromorphic computing.
DOI: 10.17632/xw7shszjrk.1
title: Data set of evaporation from Ottawa sand with mixed wettabilities under simulated solar flux and forced convection

description: This data set contains data collected during multiple evaporation experiments of Ottawa sand. In this study, a soil-water evaporation experimental apparatus was designed and constructed to study evaporation of water from Ottawa sand, with a forced air flow convection above and through the sand layer. A simulated solar flux i.e. (112 ± 20 W/m2) was applied to the sand, while varying soil wettability. Evaporation rates were measured for three cases: 1) a 5.7-cm-thick layer of hydrophilic Ottawa sand; 2) a 5.7-cm-thick layer with 12% hydrophobic content, consisting of a 0.7-cm-layer of n-Octyltriethoxysilane coated hydrophobic sand buried 1.8 cm below the surface of hydrophilic sand; and 3) a 5.7-cm-thick layer with mixed wettabilities consisting of 12% n-Octyltriethoxysilane coated hydrophobic sand mixed into hydrophilic sand. 
The evaporation rate for each trial of the three experiments was calculated using the air flow temperature, pressure, and relative humidity recorded at the inlets and outlet to the test section. These values were used to calculate the mass flow rate of the air and the humidity ratio which were used to calculate the evaporation rate. The saturation ratio was calculated using a scale that measured the change in mass of the test section throughout experimentation. The steady-state value of these trials was determined when the temperature of the outlet air flow remained constant. This data set displays the average values recorded when the steady state air temperature was reached for each trial of each experiment. The uncertainty of each trial was calculated using the uncertainties of the instrumentation used for measurements and the uncertainty in the preparation of the sand-water mixture. The calculated uncertainties for each trial are also presented in this data set.
Evaporation from homogeneous porous media is frequently classified into three, distinct periods: constant-rate, falling-rate, and slow-rate periods. Wettability affected the observed evaporation mechanisms, including the transition from constant-rate to falling-rate periods. Evaporation entered the falling-rate period at 12%, 20%, and 24% saturations for the hydrophilic, hydrophobic layer, and hydrophobic mixture, respectively. The duration of the experiments was affected by the wettability, as the hydrophilic, hydrophobic layer, and hydrophobic mixture lasted 17, 20, and 26 trials, respectively. The temperatures measured in the sand suggest more evaporation occurring in the sand near the inlet to the test section for higher saturations. The evaporation flux was found to be 0.1-12 times higher than the vapor diffusion flux calculated, due to enhanced vapor diffusion and forced convection above and through the sand.
DOI: 10.17632/4bznd465m4.1
title: Data LBW care practice in Central Jakarta, Indonesia

description: A quasi-experiment design with pretest-posttest control group model was conducted on 159 mothers (78 in the intervention group and 81 in the control group) of LBW infants treated in the perinatology ward of three hospitals in Central Jakarta. Provision of health education to mothers of LBW infants consisted of two counseling sessions on infant care and one-on-one visits provided by primary health centre nurses. Data were collected four times consecutively over 6 weeks - 3 days after discharge, and 2, 6, and 12 weeks after discharge from the hospital. The data collected were assessed through interviews using validated questionnaires that included demographics (age, education level, employment status, income, parity), knowledge and attitudes toward LBW infant care, coping strategies, family and community health workers’ support, and information exposure. LBW infant care practices were assessed by trained primary health centre nurses who used an observation checklist. Data on infant characteristics (gestation, birth weight, weight before the intervention, length of hospitalization, and respiratory and other health problems) were obtained from the hospital.
DOI: 10.17632/4tdd6v7xk4.1
title: Usefulness of a serious game (Stigma-Stop) to raise awareness among young people in high school and at university about mental health problems

description: A total of 530 students participated in the study (231 from high school and 299 from university). Items from the Questionnaire on Students Attitudes towards Schizophrenia are included. Also scores (from 1 to 10) of the usefulness and entertainment value of the program (Stigma Stop) and whether they would recommend it or not. Number of hours that typically participants devote to playing video games (intervals of 0, 0-1 hour, 1-3 hours, 3-7 hours, 7-14 hours, 14-30 hours, or more than 30 hours) and which types of games they like most (choosing among the video game genres of action, platform, arcade, shooting, strategy, simulation, sports, racing, adventure, graphic adventures, virtual, musical, or others).
DOI: 10.17632/6849wf7xcf.1
title: Fabrication of lightweight cellular concrete using hydrolyzed keratin foaming agent from underutilized wool waste

description: Laboratory and Article data