cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/zhr5zsngtg.1
title: The GU-ECG Database: ECG Datasets for Detection and Classification of Acute Myocardial Ischaemia Through Machine Learning

description: Gazi University ECG (GU-ECG) database is a set of ECG data acquired from 74 coronary artery disease patients with severe stenosis (&gt;70%) in at least one coronary artery who had symptoms of chest pain and were planned to undergo elective percutaneous transluminal coronary angioplasty (PTCA) at Department of Cardiology, Faculty of Medicine, Gazi University. It is constructed as a result of a clinical research study performed to investigate morphological anomalies in ECG signals that occur during complete coronary artery occlusion caused by PTCA, which reduces myocardial blood flow and induces acute myocardial ischaemia (AMI), leading to significant changes in ST segment and T wave of ECG [1,2]. Before PTCA, 12-lead pre-inflation ECG recordings were continuously acquired prior to catheter insertion to coronary artery at cardiac catheterization laboratory. During PTCA, 12-lead inflation ECG recordings that started during balloon dilatation, then continued throughout balloon inflation period in a major coronary artery were continuously acquired from all patients. In order to achieve optimal angiographic and clinical results of PTCA, balloon was inflated for at least 60 sec. in each patient and it was deflated either due to the occurrence of chest pain, cardiac arrhythmia, hypotension, or after a maximum inflation time of 300 sec. After PTCA, 12-lead post-inflation ECG recordings were continuously acquired at least 180 sec. after balloon deflation in coronary artery at catheterization laboratory. A portable continuous 12-lead ECG device (microCOR, Infron Ltd), which amplifies, digitizes, processes and transmits data wirelessly to its software via a USB adapter in digital format, was used for data acquisition [3]. Recordings were digitized at a sampling rate of 8800 Hz with 24-bit sampling resolution and 0.1 µV amplitude resolution to produce high-resolution digital signals. Time instants related to balloon inflation and deflation during PTCA, occluded coronary artery in which PTCA is performed and patient's history of previous myocardial infarction were annotated by experienced cardiologists to facilitate the development and performance evaluation of various signal processing and artificial intelligence techniques that will utilize the GU-ECG database [4,5,6,7]. Only patients receiving elective PTCA in one of major coronary arteries were included in the database, whereas patients who had atrial fibrillation, ventricular tachycardia, paced rhythm, or myocardial infarction during data acquisition were excluded. 12-lead pre-inflation, inflation, and post-inflation ECG recordings of each patient are included in experiment data files section with file extension *.ekg, which is data format of microCOR ECG device. Computer software which receives data from ECG device, displays, saves, and exports it to different file formats is included in experiment data files section with file extension *.exe, which is executable file format of microCOR PC software.
DOI: 10.17632/ktdhwgcd66.1
title: STAD-datasets

description: The data is too large, here is a downloadable synopsis of the data, detailed data is here https://portal.gdc.cancer.gov/projects/TCGA-STAD
DOI: 10.17632/thpdf5vp8p.1
title: Raw waveform microtremor data recorded at liquefaction site of Petobo

description: These raw waveform microtremor data were acquired using (3C) Guralp CMG 6T-D broadband seismometer. These raw waveform microtremor data were collected using 100 samples per second rate.
DOI: 10.17632/pcftzv8w63.1
title: Brugge_Breast_Atlas_CyTOF_Primary_Raw

description: A Breast Atlas Integrating Single-Cell Proteomics and Transcriptomics, Developmental Cell, 2022

Raw primary breast CyTOF data (relating to Figures 1/S1 and 3/S3)
DOI: 10.17632/8hrn3bvdvk.1
title: ArSL21L: Arabic Sign Language Letter Dataset

description: We present our collected and annotated Arabic Sign Language Letters Dataset (ArSL21L) consisting of 14202 images of 32 letter signs with various back-
grounds collected from 50 people.
DOI: 10.17632/pwctygncrj.4
title: Laptop Components: Image Dataset to Classify Different Components

description: This dataset consists of two primary directories, specifically "Raw Data" and "Augmented Data". Each of these directories encompasses twenty-six sub-directories with the category names of the components. These include- 1. Battery 2. DVDRom 3. Hinge 4. Keyboard 5. Motherboard 6. DCCable 7. PowerSwitch 8. RAM 9. ScrewKit 10. USBPort 11. WiFiCard 12. Speaker 13. CMOSBattery 14. HardDiskDrive 15. WebCam 16. LCDScreen 17. Bezel 18. Heatsink 19. SSD 20. CPUFan 21. TouchPad 22. Processor 23. RAMCover 24. BasePanel 25. TopPanel 26. LVDSCable. 

Each sub-directory within the "Raw Data" directory comprises 140 high-quality images, whereas the numbers for the "Augmented Data" directories amount to 560.
DOI: 10.17632/6kmkk9bjdx.1
title: Mathematical model of power quality disturbances

description: This file includes a Matlab/Octave function (pqmodel) that implements the power quality model found in “R.Igual, C.Medrano, F.J.Arcega, G.Mantescu; Integral mathematical model of power quality disturbances". This function generates distorted signals following an integral mathematical model based on several power quality models found in the literature. In this function 29 different classes of distortions are implemented: Class 1 - Pure sinusoidal / Class 2 – Sag / Class 3 – Swell / Class 4 – Interruption / Class 5 - Transient/Impulse/Spike / Class 6 - Oscillatory transient / Class 7 – Harmonics / Class 8 - Harmonics with Sag / Class 9 - Harmonics with Swell / Class 10 - Flicker / Class 11 - Flicker with Sag / Class 12 - Flicker with Swell / Class 13 - Sag with Oscillatory transient / Class 14 - Swell with Oscillatory transient / Class 15 - Sag with Harmonics / Class 16 - Swell with Harmonics / Class 17 – Notch / Class 18 - Harmonics with Sag with Flicker / Class 19 - Harmonics with Swell with Flicker / Class 20 - Sag with Harmonics with Flicker / Class 21 - Swell with Harmonics with Flicker / Class 22 - Sag with Harmonics with Oscillatory transient / Class 23 - Swell with Harmonics with Oscillatory transient / Class 24 - Harmonics with Sag with Oscillatory transient / Class 25 - Harmonics with Swell with Oscillatory transient / Class 26 - Harmonics with Sag with Flicker with Oscillatory transient / Class 27 - Harmonics with Swell with Flicker with Oscillatory transient / Class 28 - Sag with Harmonics with Flicker with Oscillatory transient / Class 29 - Swell with Harmonics with Flicker with Oscillatory transient This function is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation.
DOI: 10.17632/wr636kbd9m.1
title: A generalization of the S-function method applied to a Duffing–Van der Pol forced oscillator

description: In Duarte et al. (2016) and Avellar et al. (2019), we have developed a method (we call it S-function method) that is successful in treating certain classes of rational second order ordinary differential equations (rational 2ODEs) that are particularly ‘resistant’ to canonical Lie methods and to Darbouxian approaches (extensions of the Prelle–Singer method). In this present paper, we generalize the S-function method making it capable of dealing with a class of elementary 2ODEs. We also present a Maple implementation of the method in a computational package – S++ – that is designed to provide a set of tools to allow the user to analyze the intermediary steps of the generalized S-function method. Finally, we apply this method to a Duffing–Van der Pol forced oscillator, obtaining an entirely new class of first integrals.
DOI: 10.17632/8gpvhrrghk.1
title: TGN dataset

description: Step-by-Step Approach
Verify Dataset and Annotations:

Double-check that your annotations.csv file contains valid entries with correct paths (image_path) and corresponding labels (label).
Load and Inspect Data:

Make sure that df_annotations is correctly loaded and contains data. Print or inspect df_annotations.head() to see the first few rows and verify its structure.
Debug and Visualize:

Implement error handling and debugging statements to pinpoint where the issue might arise. Ensure that image paths are correctly formed and that images can be loaded using matplotlib.
Display the Visualization:

Use matplotlib to display the images along with their labels, ensuring the plot dimensions are correctly set.

To implement Mask R-CNN with annotated data, we'll need to follow several steps including preparing the data, configuring the Mask R-CNN model, training it on the annotated dataset, and then using it for inference. Here’s a structured approach to accomplish this:

Step-by-Step Guide to Implement Mask R-CNN with Annotated Data
1. Prepare the Dataset
First, ensure your annotated data is structured correctly. This typically involves:

Annotations: Each image should have corresponding annotations specifying the regions of interest (ROIs) and their labels.
Image Loading: Load images and annotations into a format suitable for training Mask R-CNN.
2. Install Required Libraries
Ensure you have the necessary libraries installed:
3. Customize Mask R-CNN for Your Dataset
You might need to customize the Mask R-CNN configuration (mrcnn/config.py) for your specific dataset. This involves setting parameters such as:

Number of classes (NUM_CLASSES)
Image resizing parameters
Backbone network (default is ResNet101)
4. Prepare Data Loaders
Create data loaders to feed data into Mask R-CNN:

Dataset Class: Implement a custom dataset class (mrcnn/utils.py) that loads images and annotations.
Data Generator: Create a data generator that preprocesses images and annotations for training.
5. Configure and Train the Model
Configure and train Mask R-CNN using your annotated dataset:

Model Configuration: Set up the Mask R-CNN model (mrcnn/model.py) with your customized configuration.
Training: Train the model using the prepared data loaders and appropriate training parameters (mrcnn/train.py).
6. Evaluate and Fine-Tune
After initial training, evaluate the model's performance:

Validation: Evaluate on a validation set to assess metrics such as mAP (mean Average Precision).
Fine-Tuning: Fine-tune hyperparameters or adjust the model architecture based on validation results.
7. Inference
Use the trained Mask R-CNN model for inference on new data:

Detection and Segmentation: Apply the model to detect and segment objects in new images.
Visualization: Visualize the results of the model's predictions.
DOI: 10.17632/c8bntsrx8c.1
title: Data for: Code Smells and Refactoring: A Tertiary Systematic Review of Challenges and Observations

description: shared online replication package, containing all artifacts - documents, spreadsheets - used during the research
DOI: 10.15468/dl.rkn5sv
title: Occurrence Download

description: A dataset containing 361 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Prunus serrula Franch.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 361 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009349-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/6k4g25fhzg.1
title: A Dataset of EEG Signals from Adults with ADHD and Healthy Controls: Resting State, Cognitive function, and Sound Listening Paradigm

description: Attention deficit hyperactivity disorder (ADHD) is a condition that impacts a large number of people around the world and is characterized as a neurodevelopmental disorder. It can affect individuals of all ages, but it typically begins during childhood and can persist into adulthood. One of the diagnostic criteria of ADHD is abnormal electrical activity in the brain, as measured by Electroencephalography (EEG), particularly in frontal and central regions. We present a dataset that we collected from 79 participants, including 42 healthy adults and 37 adults with ADHD (age 20-68 years; male/female: 56/23). The EEG signals were recorded in four different states, including resting state with eyes open, eyes closed, cognitive challenge, and listening to omni harmonic. The dataset contains EEG signals recorded from five channels, including O1, F3, F4, Cz, and Fz. The sampling rate of data is 256 Hz. The participants were seated comfortably in a chair and asked to remain as calm as possible during the recordings. All participants provided informed consent, and the study protocol was approved by the ethics committee. This dataset can serve as a valuable resource for researchers working on ADHD and EEG.
DOI: 10.17632/k42j7x2kpn.1
title: CLICK-ID: A Novel Dataset for Indonesian Clickbait Headlines

description: The CLICK-ID dataset is a collection of Indonesian news headlines that was collected from 12 local online news publishers; detikNews, Fimela, Kapanlagi, Kompas, Liputan6, Okezone, Posmetro-Medan, Republika, Sindonews, Tempo, Tribunnews, and Wowkeren. This dataset is comprised of mainly two parts; (i) 46,119 raw article data, and (ii) 15,000 clickbait annotated sample headlines. Annotation was conducted with 3 annotator examining each headline. Judgment were based only on the headline. The majority then is considered as the ground truth. In the annotated sample, our annotation shows 6,290 clickbait and 8,710 non-clickbait. There are 2 folder; raw and annotated. The raw folder contains the original files that were collected from the publisher's websites. On the other hand, the annotated folder contains the 15,000 chosen sample of annotated headlines. Both folders contains three folders with names corresponding to their contents' file extensions; (.csv) and (.xlsx), plus one combined folder. The raw data files contains the attributes: title, source, date, time, category, sub-category, content, and url. While the annotated data files contains the attributes: title, label, and label_score.
DOI: 10.17632/gxjkn4zmbb
title: Questionnaire data on the architectural cultural genes of Cantonese Overseas Chinese Settlements

description: The data clearly defines the construction and application research of the Cantonese hometown cultural gene bank. Through field surveys to collect cultural genes and according to strict expert selection criteria, experts with deep professional knowledge and rich practical experience in the Cantonese hometown culture are selected, and the diversity of expert backgrounds is ensured, including representatives from different fields such as academia, industry and government departments, to ensure the comprehensiveness of opinions. After experts conducted multiple feedback surveys on the Cantonese hometown cultural genes, they sorted and analyzed the data results, and found the most convergent expert opinions to form a prediction result on the importance of the Cantonese hometown cultural genes. This method is conducive to the construction of the evaluation system of the Cantonese hometown cultural gene bank, and provides an important reference for the screening, inheritance and development of the Cantonese hometown culture.
The AHP analysis method is used to further verify the data results of the expert survey method to ensure the accuracy, effectiveness and authenticity of the indicator factors of the Cantonese hometown cultural gene bank, and finally determine the weight of each cultural gene and comprehensively rank these weights. The AHP hierarchical analysis method is conducive to determining the range of changes in cultural gene elements, and can effectively simulate the decision-making results under different indicator element values ​​for cultural genes, which is conducive to a more reasonable allocation of cultural resources and determining the future development direction of cultural genes.
According to the above data analysis, the types and importance of cultural genes can be calculated, and a detailed database can be established in the form of relevant historical documents, pictures, models and other materials, combined with two-dimensional and three-dimensional expression methods, including architectural features, styles, cultural elements, etc. The establishment of the Cantonese Overseas Chinese Hometown Cultural Gene Bank can efficiently classify, integrate and store cultural genes, which is conducive to the information sharing of cultural genes. This has important reference significance and normativeness for the study of Cantonese Overseas Chinese Hometown culture.
DOI: 10.15468/dl.cwuxpv
title: Occurrence Download

description: A dataset containing 1695666 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Occurrence evidence)",
 "Country is one of (Canada, United States of America)",
 "TaxonKey is Tringa flavipes (J.F.Gmelin, 1789)"
 ]
}

 The dataset includes 1695666 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009342-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kwdwe3
title: Occurrence Download

description: A dataset containing 2865 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Merostachys Spreng."
 ]
}

 The dataset includes 2865 records from 125 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009348-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1054692.10fu
title: SH1054692.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1054693.10fu
title: SH1054693.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1054697.10fu
title: SH1054697.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1054698.10fu
title: SH1054698.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1054700.10fu
title: SH1054700.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.