cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/bzn593sv5d.1
title: The Motivations for Fashion Shopping in China (SPSS Dataset)

description: In this study, 403 Chinese consumers generalizable to the broader population were surveyed on their motivations to shop for fashion apparel in both high street and e-commerce environments. Statistical analysis was undertaken through multiple T-Tests and MANOVA with the assistance of SPSS and G*Power. To increase the profits of international brands, this paper presents the motivations of Chinese consumers to engage in fashion retail, building upon established theory in hedonic and utilitarian motivations. With China set to capture over 24% of the $212 billion fashion market, international brands need to understand the unique motivations of Chinese consumers in order to capitalise on the market. However, the motivations of Chinese people to engage in fashion retail are as yet undefined, limiting the ability for international fashion retailers to operate with prosperity in the Chinese market.
DOI: 10.17632/37nb83jwtd.1
title: Integrated Cryptocurrency Historical Data for a Predictive Data-Driven Decision-Making Algorithm

description: Cryptocurrency historical datasets from January 2012 (if available) to October 2021 were obtained and integrated from various sources and Application Programming Interfaces (APIs) including Yahoo Finance, Cryptodownload, CoinMarketCap, various Kaggle datasets, and multiple APIs. While these datasets used various formats of time (e.g., minutes, hours, days), in order to integrate the datasets days format was used for in this research study. The integrated cryptocurrency historical datasets for 80 cryptocurrencies including but not limited to Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Cardano (ADA), Tether (USDT), Ripple (XRP), Solana (SOL), Polkadot (DOT), USD Coin (USDC), Dogecoin (DOGE), Tron (TRX), Bitcoin Cash (BCH), Litecoin (LTC), EOS (EOS), Cosmos (ATOM), Stellar (XLM), Wrapped Bitcoin (WBTC), Uniswap (UNI), Terra (LUNA), SHIBA INU (SHIB), and 60 more cryptocurrencies were uploaded in this online Mendeley data repository. Although the primary attribute of including the mentioned cryptocurrencies was the Market Capitalization, a subject matter expert i.e., a professional trader has also guided the initial selection of the cryptocurrencies by analyzing various indicators such as Relative Strength Index (RSI), Moving Average Convergence/Divergence (MACD), MYC Signals, Bollinger Bands, Fibonacci Retracement, Stochastic Oscillator and Ichimoku Cloud. The primary features of this dataset that were used as the decision-making criteria of the CLUS-MCDA II approach are Timestamps, Open, High, Low, Closed, Volume (Currency), % Change (7 days and 24 hours), Market Cap and Weighted Price values. The available excel and CSV files in this data set are just part of the integrated data and other databases, datasets and API References that was used in this study are as follows:
[1] https://finance.yahoo.com/
[2] https://coinmarketcap.com/historical/
[3] https://cryptodatadownload.com/ 
[4] https://kaggle.com/philmohun/cryptocurrency-financial-data
[5] https://kaggle.com/deepshah16/meme-cryptocurrency-historical-data
[6] https://kaggle.com/sudalairajkumar/cryptocurrencypricehistory 
[7] https://min-api.cryptocompare.com/data/price?fsym=BTC&tsyms=USD
[8] https://min-api.cryptocompare.com/
[9] https://p.nomics.com/cryptocurrency-bitcoin-api
[10] https://www.coinapi.io/
[11] https://www.coingecko.com/en/api
[12] https://cryptowat.ch/
[13] https://www.alphavantage.co/

This dataset is part of the CLUS-MCDA (Cluster analysis for improving Multiple Criteria Decision Analysis) and CLUS-MCDAII Project: 
https://aimaghsoodi.github.io/CLUSMCDA-R-Package/
https://github.com/Aimaghsoodi/CLUS-MCDA-II 
https://github.com/azadkavian/CLUS-MCDA
DOI: 10.17632/wfn2c3437n.2
title: Vehicle trajectory data processed from the Waymo Open Dataset

description: Related paper: Processing, assessing, and enhancing the Waymo autonomous vehicle open dataset for driving behavior research (submitted to the journal Transportation research part C: emerging technologies)
Version 1 content
This dataset is processed from the Waymo Open Dataset (perception part). This dataset includes the following content:
1. Tabular format object information (Autonomous vehicle and objects detected by Lidar such as vehicles, pedestrians, cyclists)
2. Visualized object trajectory (in the format of top view videos)
3. Tabular format paired Car Following (CF) trajectory in which the CF pairs have been carefully selected for CF behavior research
Version 2 content
1. The data processing codes 
2. CF pair trajectories where large vehicles are involved are added
DOI: 10.17632/9zbsnht6j9.1
title: How Social Media Drives Travel Intentions

description: How Social Media Drives Travel Intentions: A Mixed-Methods Approach to Analysing Online Influence Using S-O-R Theory;
1,355 records.
DOI: 10.17632/jvct3269nz.1
title: Supplementary Figure 1

description: Methods 
To calculate overall power, surface EMG data were digitally bandpass filtered from 10-250Hz and then rectified. EEG data were digitally bandpass filtered between 0.5-100Hz (Kenville et al., 2020) prior to power analyses. EEG power was calculated as the squared amplitude of the signal in both the alpha (8-12Hz) and beta frequency (15-35Hz) ranges. Two-way ANOVAs with factors of Protocol and Time were run separately for females and males for alpha and beta power at the Cz electrode. For all other power analyses, data from females and males were pooled for the two-way ANOVAs. 

Results 
A Protocol by Sex interaction was found for alpha power at the Cz electrode location (F(1,14) = 9.117, p = 0.009). A main effect of Protocol was found for males (F(1,7) = 7.327, p = 0.030). Alpha power was not different between protocols at the beginning of the contractions, but, during the last minute, was higher for the matched-torque than matched-EMG contraction (t(7) = 2.650, p = 0.033). No Protocol by Time or main effect of Time was found for males. For females, alpha power did not have an interaction or main effects. 
Beta power at the Cz electrode location had a Protocol by Sex by Time interaction (F(1,14) = 11.235, p = 0.005). For males, there was a Protocol by Time interaction (F(1,7) = 17.368, p = 0.004) and a main effect of Time (F(1,7) = 6.242, p = 0.041), but not a main effect of Protocol. During the final minute of the contraction, beta power was higher for the matched-torque (t(7) = −3.934, p = 0.006), but not the matched-EMG contraction. Females did not have an interaction or main effects. 
A Time by Protocol interaction and main effects of Protocol and Time were found for biceps brachii power in both the alpha (Interaction: F(1,15) = 7.071, p = 0.018; Protocol: F(1,15) = 6.895, p = 0.019; Time: F(1,15) = 19.336, p &lt; 0.001;) and beta (Interaction: F(1,15) = 22.284, p &lt; 0.001; Protocol: F(1,15) = 9.605, p = 0.007; Time: F(1,15) = 38.660, p &lt; 0.001) frequency ranges. Power was greater at the end compared to the beginning of the matched-torque contraction for both the alpha (t(15) = −3.649, p = 0.002) and beta (t(15) = −6.341, p &lt; 0.001) ranges. In contrast, power in both the alpha and beta ranges did not increase during the matched-EMG contraction. Alpha and beta power were not different between contraction types during the first minute; however, both alpha (t(15) = 3.947, p = 0.001) and beta (t(15) = 5.405, p &lt; 0.001) power were higher for the matched-torque than matched-EMG contraction during the last minute. 
For triceps brachii, alpha power had neither an Interaction nor main effects. Beta power had a main effect of Protocol (F(1,15) = 5.785, p = 0.030), but not an interaction or main effect of Time. Beta power was not different between contractions during the first minute, but was higher during the last minute for the matched-torque compared to matched-EMG contraction (t(15) = 2.857, p = 0.012).
DOI: 10.17632/n9djzyy55x.1
title: Data for: Functionalizing graphene with titanate coupling agents as reinforcement for one-component waterborne poly(urethane-acrylate) anticorrosion coatings

description: The "original data" file include all orginal data presented in the manuscript.
DOI: 10.17632/krxz2jkyn3.1
title: Cutaneous Rosai Dofman disease

description: Rosai-Dorfman disease (RDD) is a rare non-Langerhans cell histiocytosis. RDD usually presents with massive, painless, bilateral cervical lymphadenopathy, with or without systemic symptoms. 
Although, the skin represents the most common extranodal site, clinical and dermoscopic features of cutaneous RDD (CRDD) remain insufficiently described, based mainly on single observations. A better understanding of the CRDD is required to avoid diagnostic delays and unnecessary investigations.

Records of all patients diagnosed with cutaneous histiocytosis, from 2016 to 2024, at the Department of Dermatology, Charles Nicolle Hospital, Tunis, Tunisia, were retrieved. Only patients with histopathologically confirmed CRDD were included. Dermoscopic images were independently evaluated by two dermatologists (NL and RT). 
Nine cases of CRDD were identified. 
This is the description of all cases included in the study.
DOI: 10.17632/vg77y9rtjb.3
title: Indian Spices Image Dataset

description: The Indian Spices dataset includes an extensive collection of high-quality images showcasing 19 distinct types of Indian spices. With a total of 10,991 meticulously curated images, it is designed to support various research and development endeavours, such as machine learning, image recognition, and culinary studies. This diverse assortment of spices, essential to Indian cuisine, serves as a valuable resource for both academic research and practical applications within the food industry.
DATASET FEEDBACK
We welcome user feedback, suggestions, and the sharing of use cases to improve the dataset. Please visit our feedback form at the following URL to contribute your insights and help us enhance this resource.
Dataset Feedback URL: https://forms.gle/KxuN5wKoGDbYqkKK8
GITHUB REPO CITATION
The Indian Spices Code repository, which provides sample code and resources related to the dataset, is available on Zenodo. It can be cited as follows:
Kailas, P., Sandip, T., Prawit, C., & Alfa, N. (2024). Indian Spices Code. Zenodo. https://doi.org/10.5281/zenodo.13373424
DOI: 10.17632/mz3c929xwr.1
title: Non-medical determinants of caesarean deliveries using logistic regression

description: The non-medical factors that influence expectant mothers to odecide for caesarean deliveries in Ghana were examined. Data on 395 expectant mothers across the ten regions of Ghana, located in urban, semi-rural and rural areas, and spanned a period of five years (2012 to 2016) were obtained from the Ghana Health Service. In fitting the logistic regression model, data on 355 expectant mothers (i.e. 89.9% of the data) was assigned to the analysis sample while 40 (i.e. 10.1%) was assigned to the hold-out sample. The hold-out sample together with other statistical measures of overall model fit, pseudo R^2 measures and classification accuracy were used to validate the results obtained from the analysis sample. Significance was tested at P=0.05. Determinants including, educational level of expectant mother, parity of expectant mother, baby’s birth weight, previous caesarean delivery, location of expectant mother, age of expectant mother and, period within the year of childbirth had a significant effect on caesarean delivery.
DOI: 10.17632/s4n7397jvr.3
title: Dataset for analysing the ESG-oriented technical efficiency of VNSI listed companies.

description: Financial data and ESG disclosure information are publicly available on the websites of the companies. Stock market data, reflecting the fluctuations of the companies' stocks, is stored on the Fireant website.
DOI: 10.17632/vcbh2gt8wg.1
title: Calculation of 1D and 2D densities in VMD: A flexible and easy-to-use code

description: Characterization of structural information at the atomistic level in molecular dynamics (MD) simulations is a necessary task for researchers in the fields of materials modeling and simulation. Visualization of the density distribution is typically one of the most important properties in structural characterization. Visual Molecular Dynamics (VMD) is a widely used molecular visualization package that can not only visualize complex molecular systems but also perform analysis by integrating special plugins or by running in-house generated TCL scripts. However, a density analysis is still not an in-built feature of VMD. This work presents a flexible and easy-to-use TCL code to be used in VMD, that can perform both 1D and 2D density calculations over any specified local areas of a given system. By using the built-in commands of VMD, the code can access and process trajectory files in any formats that are supported by VMD, as produced by mainstream simulation packages, i.e., LAMMPS, GROMACS, NAMD, and CHARMM, etc. This work introduces the calculation method, code, and usages in detail to provide a quick start for users in their density analysis work.
DOI: 10.17632/c3c7329tjj.1
title: Theft detection in smart grid environment

description: The dataset contains energy consumption for 16 different types of consumers. The original data includes several energy consumption measurements for several customers for one year (12 months). Measurements are taken every hour.  

Six different types of frauds are added to the original dataset. They consist of different types of thefts that some consumers can cause. The first type of theft consists of a considerable reduction of electricity consumption during the day. This reduction is calculated by multiplying the consumption by the randomly chosen value between 0.1 and 0.8. In the second type of theft, electricity consumption drops to zero at random and during an arbitrary period. The third type of theft is similar to the first type, but each consumption value (each hour) is multiplied by a random number. A random fraction of the mean consumption is generated for the fourth type of theft. The fifth type reports the mean consumption, and the last type of theft (i.e., the sixth type) reverses the order of readings. We developed a theft generator that enabled us to generate these six types of theft as described previously randomly. 

The original data is collected from the Open Energy Data Initiative (OEDI) platform. It is a centralized repository of high-value energy research datasets aggregated from the U.S. Department of Energy’s Programs, Offices, and National Laboratories. 

to use this dataset please cite this article: 
Salah Zidi, Alaeddine Mihoub, Saeed Mian Qaisar, Moez Krichen, Qasem Abu Al-Haija,
Theft detection dataset for benchmarking and machine learning based classification in a smart grid environment,
Journal of King Saud University - Computer and Information Sciences,
2022,
,
ISSN 1319-1578,
https://doi.org/10.1016/j.jksuci.2022.05.007.
(https://www.sciencedirect.com/science/article/pii/S1319157822001562)
DOI: 10.17632/w4d97jr7tb.1
title: Survey data on supply chain resilience，flexibility and reconfiguration of Chinese non-manufacturing SMEs

description: Survey data on supply chain resilience，flexibility and reconfiguration of Chinese non-manufacturing SMEs
DOI: 10.17632/ch9ktjym8k.1
title: Data for: Constraints to adopting soil fertility management practices in Malawi: a choice experiment approach

description: This data is based on a choice experiment survey conducted in the Zomba district of Malawi to understand constraints to the adoption of soil fertility management practices. The data also include farmer perceptions of climatic variability.
DOI: 10.17632/59k7d58shz
title: Quantitative-Framework-for-Green-Building-Benchmarking

description: This replication package supports the reproduction of the results presented in the study titled **"Unveiling the Impact of Green Building Benchmarking Policy: Evidence from Singapore"**. It provides access to the necessary data, scripts, and a step-by-step guide to process the data, perform the analyses, and replicate the results. Welcome to visit our GitHub repository:
https://github.com/132goodhao/Quantitative-Framework-for-Green-Building-Benchmarking/tree/main
DOI: 10.17632/b5mj79w5w9.2
title: LG 18650HG2 Li-ion Battery Data

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. The tests can be used to test Neural Network and Kalman Filter State of Charge algorithms, or to develop battery models, and are intended to be a reference so researchers can compare their algorithm and model performance for a standard data set. The test data, or similar data, has been used for some publications, including: Vidal, C., Naguib, M., Gross, O., Malysz, P., Kollmeyer P. 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.
DOI: 10.17632/6rj76z6y3n.1
title: Mexican sign language dataset

description: Mexican sign language, like other sign languages, has its own grammar rules and gestures to denote a word, then the hand gestures for the same word, even in Spanish-spoken countries, can vary. The obtention of videos of MSL signs helps to develop a methodology to translate hand gestures into words. This dataset contains 249 words of the MSL selected from the first words learned in childhood, the words can be grouped into 15 subsets: greetings, time, days of the week, months, school supplies, family, house stuff, adjectives, cuisine, clothes, body parts, vehicles, places, pronouns, verbs, professions and states of México. From the video clips, we obtain a frame sequence of the hand gesture. The environment in the videos is controlled, we use black fabric for the background and black shirts for the individuals. We use 11 individuals, these aspects help to enhance the regions of interest for the hand gesture formation, the hands, and the face, the movement of one or the two hands, the point of contact with the body, and the face gesture mainly describe the meaning of the sign. By controlling the background and the clothing, we can facilitate the segmentation of the regions of interest and obtain highly discriminating features to classify the words and follow the hand movement. The dataset includes 249 folders, from the 249 classes selected. For every class, the folder includes subfolders from the individuals. The subfolder contains the frame sequence images of the person making the hand gesture of the corresponding word, in JPG format. The number of images varies depending on the video last.
DOI: 10.17632/p838bp8pz2.1
title: Rare earth elements in water of the St. Lawrence River system 

description: To meet the strong demand for green and digital technologies, the production of rare earth elements (REEs) has increased significantly, yet the fate of REEs (Sc, Y, La, Ce, Pr, Nd, Pm, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu) in large rivers with multiple hydrological, geochemical, and anthropogenic gradients in their catchments remains poorly documented. Here we evaluated the spatial and temporal evolution of the concentrations, patterns, and speciation of REEs in surface waters along a 550 km transect of the St. Lawrence River (SLR) (Canada) and its tributaries from 2017 to 2020. Particulate and filtered (<0.45 µm) REEs concentrations increased downstream, suggesting that REEs found in the SLR predominantly originate from nearby tributaries, especially those draining the REE-rich Canadian Shield geological formation. Moreover, filtered concentrations were significantly higher in years that experienced intense spring freshets, indicating greater influence of runoff and surface soil processes on water chemistry of the SLR. Correlations with terrestrial-DOM, WHAM speciation modelling, and standardized patterns showed REEs speciation dominated by organic colloids, even in alkaline sites with low DOC and low terrestrial inputs. Strong positive correlations with Fe and Al suggested co-transport on organic matter and adsorption on oxyhydroxides. Compared to the influence of water chemistry and geology, the overall influence of anthropogenic inputs was generally low, although we observed influence of increased erosion on particulate REEs and we measured anthropogenic gadolinium (Gd) at the Montréal wastewater outlet.  Our results provide an integrative understanding of the fate of REEs through a geochemical and landscape perspective in one of the largest rivers in North America.
DOI: 10.17632/fm6xzxnf36.2
title: Triaxial Bearing Vibration Dataset of Induction Motor under Varying Load Conditions

description: The dataset includes triaxial vibration data of bearing of induction motor operated under different load conditions along the axes x, y, and z. It includes triaxial vibration datasets of motor in healthy condition with and without pulley. Moreover, the faulty conditions of bearings include inner race and outer race faults of (i) 0.7mm, (i) 0.9mm, (i) 1.1mm, (i) 1.3mm, (i) 1.5m, and (i) 1.7mm. The bearings with these fault severity levels were operated under different load conditions including 100W, 200W, and 300W. There are total 38 datasets of the bearing conditions. The data was acquired at the sampling rate of 10 kHz at the rate of 1000 samples per channel.
DOI: 10.17632/ppt5788x3x.1
title: ​The Reserve Supply Channel of Unconventional Monetary Policy

description: Replication package for "​The Reserve Supply Channel of Unconventional Monetary Policy" by William Diamond, Zhengyang Jiang, and Yiming Ma for the Journal of Financial Economics.