cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/c53rh2h435.4
title: Exploratory study of mental health among gamers

description: Gaming has increasingly become a part of life in Africa. Currently, no data on gaming disorders or their association with mental disorders exist for African countries. This exploratory study investigates (1) the prevalence of insomnia, excessive daytime sleepiness, anxiety and depression among African gamers based in Gabon and Tunisia and (2) the association between these conditions and gamer types (i.e., non-problematic, engaged, problematic and addicted). The questionnaire could only be completed once by participants with the same email address, and duplicates and incomplete forms were discarded. Responses were collected in multiple sites based in nine African countries between November 2015 and June 2017 (Rwanda, Gabon, Cameroon, Nigeria, Morocco, Tunisia, Senegal, Ivory Coast and South Africa). Because of local restrictions related to the expiration of some ethical certificates, this dataset currently provides aggregate data from Gabon and Tunisia. Data contained aggregate information describing epidemiology of self-reported measures of insomnia (with the Insomnia Severity Index), excessive daytime sleepiness (with Epworth Sleepiness Scale), anxiety (with Hospital Anxiety and Depression Scale-A), depression (Hospital and Anxiety Depression Scale-D) and gaming disorder (with game addiction scale short form) between gamers in Tunisia and Gabon. The participants who formed this convenience sample were contacted by email. The online questionnaire included a consent form on the second page, following a description of the study in French and English. Consent was required to participate in this project. The average time to answer all questions was 20 minutes. Data available are as follow: mean hours of gaming per week, period from when the participant considered him or herself a gamer, type of device used for gaming purposes, age, sex, and category of gamers. The present research is a pilot investigation which documents sleep disorders, anxiety and depression among an African sample with a focus on gamers. It should be replicated with the general population with a longitudinal cohort study to understand the global picture of gaming disorder. Similarly, more attention should be brought to the sleep health of African populations. More research on gaming addiction needs to be performed in low- and middle-income countries where little is known about internet gaming disorder.
DOI: 10.17632/hvnsh7rwz7.1
title: POLAR: Posture-level Action Recognition Dataset

description: The "POsture-Level Action Recognition" (POLAR) dataset has nine 9 categories ("bending", "jumping", "lying", "running", "sitting", "squatting", "standing", "stretching" and "walking") directly related to human pose with a total of 35,324 images and covers 99% of posture-level human actions in daily life according to authors' analysis on the VOC dataset.
DOI: 10.17632/nc5g3zs7g7.3
title: HRCTv1-COVID-19: A High Resolution Chest CT Scan Image Dataset for COVID-19 Diagnosis and Differentiation 

description: HRCTv1-COVID-19: A High Resolution Chest CT Scan Image Dataset for COVID-19 Diagnosis and Differentiation from 395 patients (197 Female, 193 Male, 5 Not Specified) including 181,106 Images with four labels of diagnosis: GGO (N = 288) , Crazy Paving (N = 57) , Air Space Consolidation (N = 27) and Negative (N = 23) diagnosis. 
*Note: Since the volume of the image dataset is about 18 GB but Mendeley Data storage capacity was 10 GB, I've uploaded the image folders on Google Drive and shared the related links in the files below.*
DOI: 10.17632/ywr9vfm9vc.1
title: Dataset on Disaster Waste post-earthquake 2016 in Manabi province, Ecuador. Recovery to recycling and reuse

description: A powerful earthquake that hit Ecuador on April 16th, 2016, generated large volumes of debris and waste. This dataset contains data on the recovered and reused disaster material. The dataset includes the following:
Recovered plastic material.
Recovered metal material.
Recovered Cardboard and paper.
Recovered glass.
Other recovered recyclable materials.
Material reused.
This dataset was created to highlight the quantity increase of disaster waste (2016), comparing it with the volume generated in conditions before the earthquake (2015) and almost four years after the earthquake (2019). Furthermore, the database shows the recycling process phases completed by each of the total medium and small-sized scrap businesses (n=62) in Manabi province and the number of months post-earthquake when the scrap dealers purchased disaster materials. Finally, the dataset includes perceptions of scrap businesses about business advantages after the earthquake. Considering the considerable volume of waste and debris generated, this dataset can provide data to analyse disaster waste management as a crucial task for post-disaster recovery.
DOI: 10.17632/y63frd6pmf.3
title: Multimodal Hinglish Tweet  Dataset for  Deep Pragmatic Analysis

description: This dataset was meticulously curated to encapsulate tweets that vividly express raw emotions, sentiments, feelings, and textual gestures related to various situations of conflict. This includes, but is not limited to, wars, crises, civil unrest, and world wars I, II, III, etc. The fundamental objective of creating this dataset is to enable the development of digital applications capable of discerning the genuine emotional pulse of digitally empowered citizens. In essence, these tools aim to empower both the general public and government authorities with real-time insights into public sentiment, enhancing their understanding and responsiveness to critical situations.
DOI: 10.17632/r9mrt9fw8y.1
title: OHCD_GIETV1

description: Odia Simple character dataset consists of Annotate Simple Odia character which consists of 1, 32,353 examples of image data. The original scanned images from Handwritten Character were sized normalized to fit in a 128x128 pixel box while preserving their aspect ratio.
Six files are available:
• Grayscale Images consists of 34,138 
• Binary Images consists of 34,138 
• Inverted Images consists of 34,138 
For research purpose the dataset is being augmented 
• Grayscale Images consists of 9,939 
• Binary Images consists of 10,000 
• Inverted Images consists of 10,000
DOI: 10.17632/97hkwn5y3x.4
title: Nairobi Securities Exchange (NSE) Kenya - All Stocks Prices 2021

description: This compilation of historical daily stock market price data relates to the Kenyan Nairobi Securities Exchange (NSE) for whole of year 2021.  This data is valuable for any machine learning algorithm that needs data (training, validation, testing).  This compilation develops on an earlier dataset (2008-2012) that was initially compiled as part of a research project to predict next day stock price, based on the previous five days, using Artificial Neural Networks (ANN). This initial research [1],[2] tested 6 stocks [3] using ANN of configuration 5:21:21:1. The data was then enhanced as a new compilation of all stocks in the period 2007-2012 [4].

Thereafter, the NSE dataset for 2013 to 2020 [5] was compiled followed by this compilation that now has the 2021 data (upto Dec.31).  The method of data compilation has remained as it was originally, being data scrapping from a publicly accessible website [6] licensed by NSE. The processing involves exporting the raw web data to spreadsheets, then cleaning up, by removal of unnecessary data elements such as HTML tags and other graphics that cannot be converted to text.

Just like the previous compilations, each stock data row has the 13 data columns (1) Date (2) Stock Code (3) Stock Name (4) 12-month Low price (5) 12-month High price (6) Day's Low price (7) Day's High price (8) Day's Final Price (9) Previous traded price (10) Change in price value (11) Change in price % (12) Volume traded (13) Adjusted price.  An additional CSV file is provided to show market sector that each stock belongs to. The 3 column headings for this additional CSV are: (1) Market sector (2) Stock Code (3) Stock Name.

With the addition of this new data compilation, researchers now have an even larger dataset (2007-2021) and even a bigger pool of stocks and their indices, since stocks keep being introduced (or even retired).  Providing the market sector information also opens up additional opportunities for data analysis and usage in machine learning research.

List of data files on this dataset: 
NSE_data_all_stocks_2021_upto_31dec2021.csv 
NSE_data_stock_market_sectors_2021.csv

References: 
[1] Wanjawa, B. W. (2014). A Neural Network Model for Predicting Stock Market Prices at the Nairobi Securities Exchange (Dissertation, University of Nairobi). 
[2] Wanjawa, B. W., & Muchemi, L. (2014). ANN model to predict stock prices at stock exchange markets. arXiv preprint arXiv:1502.06434. 
[3] Wanjawa, Barack (2020), “Nairobi Securities Exchange Prices 2008-2012 for 6 selected stocks”, Mendeley Data, v3, http://dx.doi.org/10.17632/95fb84nzcd.3 
[4] Wanjawa, Barack (2020), “Nairobi Securities Exchange All Stocks Prices 2007-2012”, Mendeley Data, v1, http://dx.doi.org/10.17632/5hk4zw32f5.1 
[5] Wanjawa, Barack (2021), “Nairobi Securities Exchange (NSE) All Stocks Prices 2013-2020”, Mendeley Data, V2, http://dx.doi.org/10.17632/73rb78pmzw.2 
[6] Synergy Systems Ltd. (2020). MyStocks. Retrieved Dec 31, 2021, from http://live.mystocks.co.ke/
DOI: 10.17632/88c5kvmgbf.3
title: Spectral data of tropical soils using dry-chemistry techniques (VNIR, XRF, and LIBS): a dataset for soil fertility prediction

description: The dataset contains spectral data and characterizations of key soil fertility attributes of 102 soil samples. These samples are from two Brazilian agricultural areas, which have soils classified as Lixisol (Field 1) and Ferralsol (Field 2). Both type of soils are commonly found in Brazil’s tropical regions. The chosen fields have different soil matrices due to considerable textural and total elemental composition contrast. Regarding the fertility attributes, they present wide ranges of the variability of fertility attributes. After soil fertility tests, the samples were scanned with the following direct analysis techniques: (i) visible and near infrared diffuse reflectance spectroscopy (VNIR), (ii) X-ray fluorescence spectroscopy (XRF), and (iii) laser-induced breakdown spectroscopy (LIBS).
DOI: 10.17632/ktc6gbfsbh.2
title: Ames Mutagenicity Dataset for Multi-Task Learning 

description: The dataset contains 5536 molecular compounds represented by their SMILES code and 1360 molecular descriptors calculated with Mordred. In addition to this data, it contains the respective labels for each compound (1: mutagenic / 0: non-mutagenic) for each of the five strains (TA98, TA100, TA102, TA1535, TA1537) and a general label (Overall) that corresponds to the ground-truth consensus label used for evaluating the final Ames mutagenicity prediction. There is a third label on the dataset: -1 (undefined). Those compounds that have an undefined label in one or more strains, but have either positive or negative labels in the remaining strains can be considered in the multi-task modeling process.
The compounds listed were originally compiled by the Istituto Superiore di Sanita’ (https://www.iss.it/isstox) and result from an exhaustive pre-processing stage, consisting of different filtering, sanitization, and canonicalization steps. 

The dataset aims to be a source for QSAR modeling of Ames mutagenicity. It provides information on the mutagenic potential for a variety of S. Typhimurium strains and the Overall label.

The .csv file contains the necessary information to be used for predictive modeling tasks. The columns "TA98", "TA100", "TA102", "TA1535", "TA1537" correspond to the labels calculated for each strain, while the column "Overall" corresponds to the consensus label used to evaluate the final prediction of mutagenicity. The "Partition" column shows three values: "Train", "Internal" and "External", which allows identifying the data partition to which each compound was assigned during our experimentation process. All remaining columns correspond to molecular descriptors calculated using Mordred.
DOI: 10.17632/kvtxc2w4sj.1
title: Computer simulation data using CST software

description: The seabed logging (SBL) data were generated using computer simulation technology (CST) software. The data consist of magnitude of electric field (E-field) and source-receiver separation distance at five different hydrocarbon resistivities (90, 180, 270, 360 and 450 Ohm.m) and five different frequencies (0.0625, 0.125, 0.25, 0.375 and 0.5 Hz).
DOI: 10.17632/z3g26tphnv.5
title: MEFAR Dataset: Neurophysiological and Biosignal Data

description: The dataset was carefully generated with the purpose of investigating mental fatigue through the analysis of physiological signals. The signals that are included are as follows: Electroencephalography (EEG) is a technique utilized to assess the electrical activity of the brain. The blood volume pulse (BVP) is a measurement technique that records fluctuations in blood volume inside the peripheral blood vessels. Electrodermal activity (EDA) is a physiological measure that monitors variations in the electrical conductivity of the skin. The heart rate (HR) is data that measures the frequency of heartbeats within a given timeframe, typically expressed as the number of beats per minute. The temperature sensor is responsible for monitoring the body temperature of the participants. The 3-axis accelerometer (ACC) data quantifies the rate of change in velocity experienced by the individuals involved. Data was gathered from a sample of 23 individuals during both morning and evening sessions. The Chalder Fatigue Scale was employed in order to assess the levels of mental weariness among the participants. The scale utilized in this study offers a numerical score to each participant, which is determined based on their responses. A score of 12 or above on this scale is indicative of a positive mental fatigue condition. The dataset includes both unprocessed/raw and processed data. The term "raw data" pertains to unaltered recorded signals, whereas "processed data" generally includes signal preprocessing methods such as filtering, artifact removal, and feature extraction. The processed data comprises data that has been processed using various sampling frequencies, namely 1 Hz, 32 Hz, and 64 Hz. The operations encompass downsampling, midsampling, and upsampling. The datasets that have undergone processing are denoted as MEFAR_DOWN, MEFAR_MID, and MEFAR_UP, representing the treated data with downsampling, midsampling, and upsampling, respectively. Furthermore, comprehensive demographic data pertaining to the participants can be found in an Excel spreadsheet named "general_info." Maintaining the identity and privacy of participants is crucial in order to adhere to ethical requirements. This dataset can be shared among researchers that are interested in analyzing mental exhaustion, enabling them to conduct more investigations, develop algorithms, and validate their findings.
DOI: 10.17632/yzzcbyvgmh.3
title: Tropical Forest Species

description: Macroscopic images were acquired in warehouses, where timber from the Pacific and Colombian Amazon region is aggregated and commercialized. Samples were taken by an expert from the environmental and forest governance department of the regional autonomous corporation of Cauca by cutting the transverse plane of the blocks. Then, each sample was moistened to increase contrast and reduce variation of physical properties. Each picture of every specie shows anatomical features that can be useful to recognize and structural studies. Besides it is a important contribution in order to use in timber system identification as a tranning data. Images resolution: Adjust to 640x480 pixels and raw images Magnification: 3.9 micrometres per pixel Area of interest: 2.5mm x1.9 mm Lighting: artificial provided by the capturing device - (Digital Microscope device)
DOI: 10.17632/7jg292f2mj.1
title: Dataset on Industry 4.0 (Big data & IOT) research trends from 2015 to 2023: A systematic literature review

description: A large set of articles was extracted from the Scopus and Web of Science databases. Applying the PRISMA methodology, which includes steps such as identification, filtering, eligibility, and inclusion, as well as the application of inclusion and exclusion criteria, the articles were scrutinized. In the end, 114 eligible papers were analyzed using SLR and bibliometric analysis.
DOI: 10.17632/p9z4h98s6j.1
title: RespiratoryDatabase@TR (COPD Severity Analysis)

description: 12-channel lung sounds for each patient Multi-channel Analysis opportunity 5 COPD severities (COPD0, COPD1, COPD2, COPD3, COPD4) Short-term recordings (At least 17s) The details: https://doi.org/10.28978/nesciences.349282 This study was supported by Scientific and Technological Research Council of Turkish (TUBITAK-116E190). The contributors express their thanks to TUBITAK for providing fully support.
DOI: 10.17632/x8cgbzttk7.1
title: Additional material for No Country is an Island by M Ferrar Minesso and M S Pagliari

description: This file contains the online appendix and the replication files to "No Country is an Island. International Cooperation and Climate Change" by M. Ferrari Minesso and M. S. Pagliari
DOI: 10.17632/9x2g6dsbtv.2
title: Bat Echolocation Call Analysis with Deep Learning Models

description: This code project contains a multitude of scripts often required for understanding bat echolocation call data. 

Basic scripts in the data preparation are provided for:
- cleaning audio data
- processing audio data into linear spectrograms or Mel-Frequency-Cepstral-Coefficients (MFCC)
- creating unique IDs for each pair of image and corresponding label

The optimization core contains the following functionalities:
- basic but highly customizable training and validation functions for neural network models
- performance metrics to evaluate models for various tasks, binary and multi-class, respectively
- logger classes to track various values and parameters 

The analysis part is divided into an unsupervised learning approach and a supervised learning approach. 
First, with the help of unsupervised methods like autoencoders and clustering algorithms like UMAP, the datasets can be analysed for general geometric properties.
The convolutional autoencoder is used to learn an efficient representation of the data and feed the UMAP clustering algorithm with highly compressed latent feature vectors for faster convergence. 
The clustering can reveal similarities between specific classes then and guide the further analysis with supervised learning methods by adjusting the composition of classes and dataset sources before deciding to train a supervised neural network model. 
Dataset sources encompass meta data like year/time of measurement and location/position of measurement devices.

The used dataset is confidential and this code project is hand-tailored to this dataset. 
Nevertheless, all concepts used in this project are applicable to most animal call datasets that provide labeled audio data. 
Thus, this code project can be a helpful template for future research in animal acoustic analysis and specifically for bat echolocation call data.
DOI: 10.17632/xbdtvf6bvj.1
title: iSTOP datasets (Billon, Bryant et al, Molecular Cell, 2017)

description: iSTOP datasets in 8 eukaryotic species (H. sapiens, M. musculus, R. norvegicus, D. rerio, C. elegans, D. melanogaster, A. thaliana and S. cerevisiae). Species names and genome assembly IDs are specified in each file name. Each row represents a targetable genomic coordinate within a gene. All ORFs were validated to have start and stop codons, an appropriate sequence length, and no internal stop codons. Columns are defined as follows: gene – a single gene name; chr – chromosome name; strand – the strand of the targeted base in the coding sequence; genome_coord – the genomic coordinate of the targeted base; codon – the codon targeted; n_isoforms – the number of isoforms considered for the gene; percent_isoforms – the percentage of a gene’s isoforms that are targeted at this coordinate; percent_NMD – the percentage of isoforms predicted to incur nonsense-mediated decay as predicted by targeting of an isoform’s coding sequence 55 bases upstream of the final exon-exon-junction; rel_pos_largest_isoform – the relative position in the largest isoform targeted at the genomic coordinate (0 = beginning of coding sequence, 1 = end of coding sequence); no_upstream_G – TRUE indicates there is no G in the 5’ position relative to the targeted C; RFLP_Loss – enzymes that uniquely cut +/- 50 bases of genomic sequence from targeted base before editing; RFLP_Gain – enzymes that uniquely cut +/- 50 bases of genomic sequence from targeted base after editing; sgNGG, sgNGA, sgNGCG, sgNGAG, sgNNGRRT, sgNNNRRT – 20 bp guide sequence for corresponding PAM (targeted C is lowercase); sgNGG_off_targets, sgNGA_off_targets, sgNGCG_off_targets, sgNGAG_off_targets, sgNNGRRT_off_targets, sgNNNRRT_off_targets – Number of off-target locations in the genome determined by searching for matching sequence with up to two mismatches allowed in the first 8 bases of the guide sequence.
DOI: 10.17632/6t6nv9z9mm.1
title: Robust regression discontinuity estimates of the causal effect of the TripAdvisor’s bubble rating on hotel popularity

description: We use detailed data on 4,599 hotels located in Rome collected from TripAdvisor, the world's largest travel platform, to examine the effects of bubble ratings (detailed to half-bubbles) on hotel popularity measured with the number of people viewing the hotel’s page. By using a regression discontinuity design, we find that bubble presentation of ratings does not create any significant jumps at cutoffs. This result is different from those obtained in previous studies of similarly designed rating systems from other industries. We provide possible explanations and implications of this result. Another finding is that web users tend to shortlist hotels with the bubble rating of at least 3. Despite that, there is no compelling evidence of review manipulation around the cutoff of 2.75 to make a transition from the 2.5-bubble rating to the 3-bubble rating. Potential uses of the number of views as a proxy of demand in hospitality research are outlined.
DOI: 10.17632/gt3f4jnbvn.3
title: Data extracted from GitHub repositories (training and test data-sets)

description: This dataset contains the SQL tables of the training and test datasets used in our experimentation. These tables contain the preprocessed textual data (in a form of tokens) extracted from each training and test project. Besides the preprocessed textual data, this dataset also contains meta-data about the projects, GitHub topics, and GitHub collections. The GitHub projects are identified by the tuple “Owner” and “Name”. The descriptions of the table fields are attached to their respective data descriptions.
DOI: 10.17632/7zhvc3knww.1
title: Hybrid Input-Output tables for Argentina at year 2012

description: All data stand for the year 2012 The IOT_Val_ARG file gives the input-output table in millions of pesos for 19 sectors The IOT_Qtities_ARG file gives the input-output table in quantities for 19 sectors. The energy sectors are in ktoe, and the others in "pseudo-quantities". The IOT_Prices_ARG file gives the unitary prices that are consistent with IOT in volume and value for intermediate consumption, final consumption, production and imports. The IOT_CO2Emis_ARG gives the energy-consumption related emissions in MtCO2 for intermediate consumption and final consumption.
DOI: 10.17632/34tztnz693.1
title: Data for: Crowdsourcing to Rescue Cultural Heritage during Disasters: A Case of Florence Flood 1966