cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/8gx2fvg2k6.4
title: DataCo SMART SUPPLY CHAIN FOR BIG DATA ANALYSIS

description: A DataSet of Supply Chains used by the company DataCo Global was used for the analysis. Dataset of Supply Chain , which allows the use of Machine Learning Algorithms and R Software. Areas of important registered activities : Provisioning , Production , Sales , Commercial Distribution.It also allows the correlation of Structured Data with Unstructured Data for knowledge generation. Type Data : Structured Data : DataCoSupplyChainDataset.csv Unstructured Data : tokenized_access_logs.csv (Clickstream) Types of Products : Clothing , Sports , and Electronic Supplies Additionally it is attached in another file called DescriptionDataCoSupplyChain.csv, the description of each of the variables of the DataCoSupplyChainDatasetc.csv.
DOI: 10.17632/ttb63krg6d.1
title: Ultrasonic guided waves data for structural health monitoring of pipelines

description: Structural Health Monitoring (SHM) techniques are intended to assess the integrity of structures, such as pipelines. They aim at detecting the initiation of damage (i.e. at early stages) to perform thereafter the maintenance of the damaged area of the structure, without any delay. Ultrasonic guided waves (UGW) technique is one among them. It relies on the use of permanently installed sensors on the structure being monitored. These sensors emit and receive signals which propagate through the medium and interact with defects. The comparison between the signal from the healthy state and the actual signal helps to determine whether the structure is damaged or not. However, the variation of the environmental and operational conditions (EOCs) where the structure operates could have a strong impact on the  acquired data. Consequently, it will be very difficult to ascertain whether the changes in the received signal are due to the presence of structural defect or just caused by the variation of the EOCs. Therefore, the aim of constructing this database was to develop a damage detection method which is sensitive to damage but robust to the variation of EOCs. 

The specimen used to build the database consists of a steel tube with 6.4 m length. It was placed in laboratory conditions where temperature fluctuates between 19°C and 26°C during the monitoring period. This variation is due to the weather changes. A 1 m portion of the tube was wrapped by a composite reparation, deposited by Prokem company with multiple number of layers. The data were collected using Wavemaker G4 system, which is commercialized by Guided Ultrasonic Ltd company and is intended for pipeline testing. This acquisition system is linked to a probe, which acts as an emitter and a receiver at the same time. This probe consists of two lines of circumferentially equidistant ultrasonic transducers, in order to master the direction of propagation of the UGW. In tubular structures, three types of propagation modes of UGW can coexist: Longitudinal, Torsional and Flexural. Each mode propagates with a specific phase and group velocities. These velocities are generally dependant of the frequency, which confer to the UGW the nature of being dispersive. The used probe allows operating with two separate guided waves modes, which are Torsional (non-dispersive mode) and Flexural (dispersive mode). A corrosion-like defect was machined by removing material from the inside of the pipe-wall in the half-axial length of the composite reparation. The distance between the probe and the created damage is 2.6 m.
DOI: 10.17632/gswj9rcv7d.1
title: Accelerated optimal design of high-performance Mg alloys through integration of Bayesian optimization with active machine learning

description: This research investigates the accelerated design of high-performance magnesium (Mg) alloys by integrating Bayesian optimization with active machine learning. The dataset encompasses various alloy compositions and processing parameters, including Zn, Y, Y, Zr, Nd, and Gd concentrations, along with solution treatment, homogenization, extrusion, and aging conditions. Mechanical properties such as Ultimate Tensile Strength (UTS), Yield Strength (YS), and Elongation (EL) are also recorded. Key findings indicate that increased Zn content generally enhances UTS and YS, though at the expense of elongation. Bayesian optimization and machine learning techniques applied to this data enable efficient identification of optimal alloy compositions and processing parameters, significantly accelerating the development of high-performance Mg alloys.
DOI: 10.17632/mc37xdzw74.1
title: Data_Mexico_COVID19

description: As spatial analysis can contribute to the understanding of COVID-19 epidemic, we compiled and georeference data for Mexico. Data were compiled from the National Population Council (CONAPO), Google, the National Institute of Statistics and Geography (INEGI), and the Secretary of Health. The data describe the cases of COVID and characteristics of the population, such as distribution, mobility, and prevalence of chronic diseases such as diabetes, hypertension, and obesity. These data were processed to be compatible and georeferenced to a common geographic framework to facilitate spatial analysis in a geographic information system (GIS). The dataset comprises GIS layers (shapefiles), tables (CSV formatted), and R scripts. A complete description will be submitted to the journal Data in Brief (https://www.journals.elsevier.com/data-in-brief/)
DOI: 10.17632/8hm59ryzb8.1
title: CLAS: A Database for Cognitive Load, Affect and Stress Recognition

description: We present the overall design and the implementation of the CLAS dataset, a multimodal resource which was purposely developed in support of research and technology development (RTD) activities oriented towards the automated recognition of some specific states of mind. Although the particular focus of our research is on the states of mind associated with negative emotions, mental strain and high cognitive effort, the CLAS dataset could offer an adequate support to research of a wider scope, such as general studies on attention assessment, cognitive load assessment, emotion recognition, as well as stress detection. The dataset consists of synchronized recordings of physiological signals, such as Electrocardiography (ECG), Plethysmography (PPG), ElectroDermal Activity (EDA), as well as accelerometer data, and metadata of 62 healthy volunteers, which were recorded while involved in three interactive tasks and two perceptive tasks. The interactive tasks aim to elicit different types of cognitive effort and included solving sequences of Math problems, Logic problems and the Stroop test. The perceptive tasks make use of images and audio-video stimuli, purposely selected to evoke emotions in the four quadrants of the arousal-valence space. The joint analysis of success rates in the interactive tasks and the information acquired through the questionnaire and the physiological recordings enables for a multifaceted evaluation of specific states of mind. These results are important for the advancement of research on efficient human-robot collaborations and general research on intelligent human-machine interaction interfaces.
DOI: 10.17632/tbjn6p2gn9.1
title: Image-based Corrosion Detection Dataset

description: http://dx.doi.org/10.17632/tbjn6p2gn9.1#file-e1592642-f948-4d1c-b10b-bf011f3e19d4
DOI: 10.17632/xf68bwh54v.1
title: Panasonic 18650PF Li-ion Battery Data and Example FNN and LSTM Neural Network SOC Estimator Training Script

description: The included example script was created by Dr. Carlos Vidal and Dr. Phillip Kollmeyer at McMaster University in Hamilton, Ontario, Canada.  If this script and the included dataset is utilized for any purpose, the following paper should be referenced along with this Mendeley dataset:

Carlos Vidal, Pawel Malysz, Mina Naguib, Ali Emadi, Phillip J. Kollmeyer, “Estimating battery state of charge using recurrent and non-recurrent neural networks,” Journal of Energy Storage, 2021 (see https://www.sciencedirect.com/ for complete citation information).

The example script is configured to train two different types of machine learning state of charge estimation algorithms - a feedforward neural network with filtered input values and a long short term memory (LSTM) recurrent neural network.  These algorithms are described in detail in the above reference.  The script trains the SOC estimator for normalized data for a Panasonic 18650PF battery dataset which can be found here: https://data.mendeley.com/datasets/wykht8y7tg/1 


%*****************************************************************************************%
-Instructions for Downloading and Running the Script:
1-Select download all files from the Mendeley Data page 
2-The files will be downloaded as a zip file.  Unzip the file to a folder, do not modify the folder structure.
3-Open and run "LSTMvsFNN_Script_Nov_2021_V2.mlx"
4-Further instructions are included in the comments and text in the script (for better experience use the "Matlab Live Code File" with extension *.mlx).


%*****************************************************************************************%
Important - Additional notes regarding the script:
When changing the type of neural network go to line 101 and follow the instructions below.

To select Neural Network Type (line 101): 

Select "1" on the drop down for LSTM
Select "2" on the drop down for FNN

Also go to line 237 and make the same selection of Neural Network Type.

%*****************************************************************************************%
Description of included files:

-All data is normalized, see "Normalization" folder for example showing how to denormalize data

-Data for -20, -10, 0, 10, and 25degC is included (in the paper, the -20degC data is not used)

-The "X" data has 7 rows, where the data in each row is as follows: { V, I, T, V_0.5mHz, I_0.5mHz, V_5mHz, I_5mHz}, where V is voltage, I is current, T is temperature, and the _0.5mHz and _5mHz data is filtered with a 1st order low pass Butterworth filter

-The "Y" data is state of charge calculated via coulomb counting

-The data is split into Train, Test, and Validation and is saved in the respective folders (see the Journal of Energy Storage paper for a description of how the data is split).  Mix 1 to 4 and US06 are used for training, LA92 -10degC for validation, and LA92, NN, and UDDS for testing

-The training data is split into ten separate files, this allows up to 10 mini batches.
DOI: 10.17632/ghkms3cgbg.1
title: Windows Registry Data Sets

description: The datasets consist of four Windows registry files collected from four different Windows PCs. Because Windows registry contains system- and applications- dependent critical information, it is useful in researching the potential system security issues and digital forensics. Because all the PCs were not currently used, the contained issues do not arise the actual security problems. All the files are extracted from Windows built-in command lines. The three files out of them are raw datasets extracted from Windows directly. The fourth file (i.e., registry 4) is transformed from the raw data in a key-value pair format for further processing, but it contains completely exact information with the others.
DOI: 10.17632/7t9vsjhtf2.1
title: Souza et al. 2023

description: The published data refer to the article “IMPORTANCE OF CONSERVATION UNITS IN THE BIOGEOCHEMISTRY OF CERRADO STREAMS”, submitted to the Journal of South American Earth Sciences.
DOI: 10.17632/8rcyzcw4yt.1
title: Raw data on effects of pH and temperature on Pangasianodon hypophthalmus juveniles

description: Raw data on effects of pH and temperature on  Pangasianodon hypophthalmus juveniles body weight, feed intake and survival rate
DOI: 10.17632/k7hcr4tfkz.1
title: Results on spatio-temporal estimation of temperature and soil moisture in La Colmena (Paraguay)

description: Data set used in the paper:  Results on spatio-temporal estimation of temperature and soil moisture in La Colmena (Paraguay)
DOI: 10.17632/mh892rksk2.4
title: AI classifier dataset

description: The dataset comprises responses to 116 questions, with contributions from both human and AI sources. The data is organized into a single folder called "AI classifier dataset," containing 100 Excel files and one JSON list file named "dataset.jsonl." Each Excel file contains three attributes: "Question", "Human", and "AI" except one file, 457c895.xlsx, which has columns "Question", "Answer," and "AI or Human."The JSON file includes four attributes for each entry: an ID, the original question, the answer, and Is_it_AI. In total, the JSON list file contains 4,231 rows of data. The source code folder contains the website design code for the question distribution and data collection website.
DOI: 10.17632/23n5xfjhyt.2
title: Reference Dataset for Text Mining Type 2 Diabetes Candidate Genes

description: The present disease-gene association data contains evidence or reference sentences which contain this disease-gene association information, which is further classified into 4 classes: Yes, No, Ambiguous and X each pertaining to Positive, Negative, Ambiguous and Not related disease-gene associations respectively. This data serves as reference data for the training text mining-based biological literature classifiers which can be used to predict classes of published literature, not just for Type 2 diabetes, but can also be expanded beyond to encompass a wide range of disease and their complications. The compilation of positively associated genes derived from these predictions can then be utilized for in-depth system-level analysis of T2D.
DOI: 10.17632/2pd9k8pz22.1
title: PKR senses nuclear and mitochondrial signals by interacting with endogenous double-stranded RNAs. Kim et al

description: Unprocessed gel and image data for "PKR senses nuclear and mitochondrial signals by interacting with endogenous double-stranded RNAs" by Kim et al.
DOI: 10.17632/cy9z6cmccm.1
title: Chakra Energy Distribution During Hare Krishna Chanting

description: Description
This code analyzes the impact of chanting the Hare Krishna mantra on the body's chakras using Python. It visualizes the cumulative energy distribution across the chakras during the chant and provides statistical analysis of the energy levels.

Imports:

numpy: For numerical operations and array handling.
matplotlib.pyplot: For plotting the heatmap and bar charts.
seaborn: For creating aesthetically pleasing statistical graphics.
Define the Mantra and Chakra Associations:

mantra_sequence: A list representing the sequence of words in the Hare Krishna mantra.
chakra_association: A dictionary mapping each word in the mantra to its associated chakras, represented as binary lists (1 for activation, 0 for no activation).
Initialize Chakra Energy Levels:

chakra_levels: A 2D array initialized with zeros to store the energy levels for each chakra throughout the mantra sequence.
Calculate Cumulative Impact on Chakras:

Iterate through each word in the mantra sequence.
For each word, update the corresponding chakra energy levels by accumulating the impact values.
Generate Heatmap for Chakra Energy Distribution:

Create a heatmap to visualize the energy distribution across the chakras for each word in the mantra sequence.
Customize the heatmap with labels for the chakras and chant sequence.
Statistical Analysis:

Compute the mean and standard deviation of the energy levels for each chakra.
Plot these statistical measures using a bar chart to compare the average energy levels and their variability across different chakras.
DOI: 10.17632/zh5jym6wwn.1
title: Efficient destriping of remote sensing images using an oriented super-Gaussian filter - dataset
DOI: 10.17632/vsn37ps3rx.4
title: KUET Bangla Emotional Speech (KBES) Dataset

description: KBES: A Dataset for Realistic Bangla Speech Emotion Recognition with Intensity Level
DOI: 10.17632/y4gywztksm.1
title: BDRice: A comprehensive image dataset of bangladeshi rice classification in computer vision applications

description: This dataset introduces a newly developed dataset named "BDRice" which includes eight different types of rice from various regions in Dhaka city such as Uttara and Mirpur in Bangladesh. The eight different rice species are formerly defined with their local names. These are Mozumder , Mozzammel-Miniket, Utsob Nazir, Mizan ,Haski ,Pyzam, Nabil ,Atash. The dataset contains a total of 2,756 original images, filtered images 920 along with an additional 3,680 augmented images. We meticulously captured each original image under natural lighting conditions with an appropriate background. This comprehensive dataset holds immense potential for researchers in utilizing various ML and DL methods to advance the healthcare sector significantly. It is a valuable resource for future investigations, laying the foundation for crucial developments in these domains.
DOI: 10.17632/2nbvx5m4hs.1
title: Tourism and Travel Reviews: Sri Lankan Destinations

description: Embark on a journey through a comprehensive collection of tourist reviews, capturing the essence of diverse travel destinations across Sri Lanka. This dataset is a treasure trove of insights into traveler preferences, sentiments, and experiences, making it an invaluable resource for researchers in the fields of tourism, hospitality, and sentiment analysis. It encompasses a rich tapestry of location details, user profiles, and review attributes, offering a multi-dimensional exploration of the traveler's world within Sri Lanka's stunning destinations.
DOI: 10.15468/dl.q4v45t
title: Occurrence Download

description: A dataset containing 33 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Phoenix sylvestris (L.) Roxb.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 33 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009162-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/n6dby9gynn.3
title: RTEII: A new high-resolution (0.1° × 0.1°) road transport emission inventory for India

description: This dataset presents gridded road transport emissions based on the new road transport emission inventory for India (RTEII) in g /year on a 0.1° × 0.1° map of India for all the species that are part of the T1-MOZART chemical mechanism and  used in chemical transport models. The  species included for the first time are 76 speciated VOCs, CH4, CO2, and CO compiled using in-situ measured emission factors for varied fuels and vehicle technologies. It also includes PM2.5 and PM10 and NH3 and CO and NOx , BC emissions using updated activity data for the year 2015.
The dataset is provided in tab-delimited and netcdf formats: Two files for all species  that are part of the T1-MOZART chemical mechanism in both tab delimited and netcdf format and two files for all NMVOCs that are not in the T1-MOZART but reported by Hakkim et al., 2021, including nitromethane.
DOI: 10.17632/t2rzh9y4mp.1
title: RAN Performance measurements for security threats

description: We are making an datasets publicly available, focusing on Intrusion Detection System (IDS) traffic classes within the RAN network. The dataset was collected from the base station deployed in the OpenIreland testbed. Additionally, we ensured a realistic testbed environment by incorporating various mobility patterns, including static, pedestrian, car, bus, and train scenarios. The availability of this dataset will contribute to advancements in the field, facilitating research and analysis related to mobile network security and performance evaluation.
DOI: 10.17632/kbbrw99vh8.5
title: Real Electronic Signal Data from Particle Accelerator Power Systems for Machine Learning Anomaly Detection

description: This work describes real-time series datasets collected from the high voltage converter modulators (HVCM) of the Spallation Neutron Source facility. HVCMs are used to power the linear accelerator klystrons, which in turn produce the high-power radio frequency to accelerate the negative hydrogen ions (H−). Waveform signals have been collected from the operation of more than 15 HVCM systems categorized into four major subsystems during the years 2020-2022. The data collection process occurred in the Spallation Neutron Source facility of Oak Ridge, Tennessee in the United States. For each of the four subsystems, there are two datasets. The first one contains the waveform signals, while the second contains the label of the waveform, whether it has a normal or faulty signal. A variety of waveforms are included in the datasets including insulated-gate bipolar transistor (IGBT) currents in three phases, magnetic flux in the three phases, modulator current and voltage, cap bank current and voltage, and time derivative change of the modulator voltage. The datasets provided are useful to test and develop machine learning and statistical algorithms for applications related to anomaly detection, system fault detection and classification, and signal processing.
DOI: 10.17632/5zrtmp7gwd.1
title: Electric Vehicle Charging Dataset

description: This electric vehicle (EV) charging dataset includes the EV connection time, charging duration, energy consumption, and day number each corresponding respectively to connectionTime_decimal, chargingDuration, kWhDelivered, dayIndicator columns in the dataset for an EV charging parking lot. The data is generated using conditional tabular generative adversarial networks (CTGAN) and kernel density estimation (KDE) from the Caltech dataset to maintain a realistic load profile, accurately model EV owner behaviors, and preserve the relationship between the columns of the dataset. This dataset includes EV data for 29,600 days while the original Caltech dataset includes data for only 185 days. This data can be useful in training machine learning algorithms, specifically, reinforcement learning algorithms. The connection time range is 0-24. The unit for charging duration is hour and energy consumption is in kWh.
DOI: 10.17632/cvkgfzp7ck.1
title: Bacteria Data for Machine Vision and Digital Biology

description: The dataset used in this work is a combination of two datasets, the datasets in [1] and the Digital Images of Bacteria Species (DIBaS) dataset, which is a publicly available dataset provided by Zieliński et al. in 2017 [2]. This dataset includes RGB images of 33 isolated bacteria species, with each species containing 20-23 images, totaling 689 images. The dataset size is relatively small for training deep learning models, which can lead to overfitting and poor generalization. Additional data were collected for this study to augment the dataset, resulting in a total of 2722 RGB images. These images were captured using a Nikon E200 microscope with a 100x objective after staining the bacteria with the Gram method. The combined dataset is not only more substantial in size but also represents multiple institutions and equipment sources, enhancing its real-world applicability.
1. M.B. Jamshidi,  S. Sargolzaei, S. Foorginezhad, and O. Moztarzadeh, Metaverse and Microorganism Digital Twins: A Deep Transfer Learning Approach
Applied Soft Computing, 2023.
2. B. Zieliński, A. Plichta, K. Misztal, P. Spurek, M. Brzychczy-Włoch, and D. Ochońska, "Deep learning approach to bacterial colony classification," PloS one, vol. 12, no. 9, p. e0184554, 2017.
DOI: 10.17632/djfc767d42.1
title: International survey of current approaches to the management of neuropathic corneal pain by experts - Raw survey results

description: International survey of current approaches to the management of neuropathic corneal pain by experts - raw survey results