cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/xrwb9m37gd.2
title: RailEnV-PASMVS: a dataset for multi-view stereopsis training and reconstruction applications

description: A Perfectly Accurate, Synthetic dataset featuring a virtual railway EnVironment for Multi-View Stereopsis (RailEnV-PASMVS) is presented, consisting of 40 scenes and 79,800 renderings together with ground truth depth maps, extrinsic and intrinsic camera parameters and binary segmentation masks of all the track components and surrounding environment. Every scene is rendered from a set of 3 cameras, each positioned relative to the track for optimal 3D reconstruction of the rail profile. The set of cameras is translated across the 100-meter length of tangent (straight) track to yield a total of 1,995 camera views. Photorealistic lighting of each of the 40 scenes is achieved with the implementation of high-definition, high dynamic range (HDR) environmental textures. Additional variation is introduced in the form of camera focal lengths, random noise for the camera location and rotation parameters and shader modifications of the rail profile. Representative track geometry data is used to generate random and unique vertical alignment data for the rail profile for every scene. This primary, synthetic dataset is augmented by a smaller image collection consisting of 124 manually annotated photographs for improved segmentation performance. The specular rail profile represents the most challenging component for MVS reconstruction algorithms, pipelines and neural network architectures, increasing the ambiguity and complexity of the data distribution. RailEnV-PASMVS represents an application specific dataset for railway engineering, against the backdrop of existing datasets available in the field of computer vision, providing the precision required for novel research applications in the field of transportation engineering. File descriptions: + RailEnV-PASMVS.blend (227 Mb) - Blender file (developed using Blender version 2.8.1) used to generate the dataset. The Blender file packs only one of the HDR environmental textures to use as an example, along with all the other asset textures. + RailEnV-PASMVS_sample.png (28 Mb) - A visual collage of 30 scenes, illustrating the variability introduced by using different models, illumination, material properties and camera focal lengths. + geometry.zip (2 Mb) - Geometry CSV files used for all the scenes. The Bezier curve defines the geometry of the rail profile (10 mm interval). + sampleset_19.zip (2.5 Gb) - A single RailEnV-PASMVSD scene sample for easy viewing and evaluations. For the full dataset please use the link provided below. + PhysicalDataset.zip (1.8 Gb) - A smaller, secondary dataset of 124 manullay annotated photographs of railway environments; only the railway profiles are annotated. GitHub page to access the full RailEnV-PASMVS dataset (hosted on Google Drive): https://github.com/andrebroekman/RailEnV-PASMVS
DOI: 10.17632/4h7nszxcjs.1
title: Data on drying kinetics, moisture sorption isotherm, composition study of Ethiopian oyster mushroom (Pleurotus ostreatus mushroom) drying in tray dryer

description: This dataset aims to provide a comprehensive resource for researchers and industry professionals interested in optimizing the drying process of Ethiopian oyster mushrooms in tray dryers. The data compilat1ion stems from the need for in-depth understanding of drying kinetics, moisture sorption behavior, and compositional changes during the drying process. Traditionally, mushrooms are dried using open sun drying, leading to inconsistencies and potential quality deterioration. This dataset focuses on controlled drying conditions in a tray dryer with hot air, allowing for optimized drying parameters like temperature, airspeed and mass loading.
DOI: 10.17632/gbf7tjvngk.1
title: A damage tolerant bio-inspired integrated composite stiffener via AFP - Numerical parametric study for final specimen design

description: To obtain a specimen design which would achieve fracture propagation following local buckling of the skin, but prior to global buckling, I parameterised a numerical model utilising python scripting. The effect of various parameters were investigated, including: the loading length, notch length, and specimen length. Various boundary conditions were also investigated, varying the region potted at the top and bottom edge, and the use of skin stability tabs in the loading region. Here is the parametric script used to create the numerical models.
DOI: 10.17632/3nm3ctwycc.1
title: QDA-processed

description: Processed resting-state fMRI data using a new quantitative data-driven analysis (QDA) method. Reprocessing script and related mask and MNI template are also provided.
DOI: 10.17632/fpgj7vxfsn.2
title: Raw data of published papers on Transactive Memory System

description: This data set includes a representative sample of published papers indexed in the Scopus database between 1998 and 2018. The records include the bibliographic data of all the manuscripts with the term “Transactive Memory System” in the title, the abstract or the keywords. The resulting sample of 322 papers was downloaded in BibTeX format from Scopus in January 2019 and transformed as a comma-separated value format.
DOI: 10.17632/crhybxpdy6.2
title: Simultaneous EEG-fMRI dataset

description: This multimodal neuroimaging repository comprises simultaneously and independently acquired Electroencephalographic (EEG) and Magnetic Resonance Imaging (MRI) data, originally presented in our research article: “Preservation of EEG spectral power features during simultaneous EEG-fMRI”. Eyes-closed and eyes-open resting-state EEG data were recorded outside the Magnetic Resonance (MR) environment, inside the MR scanner without image acquisition and during the simultaneous acquisition of functional MRI (fMRI). Additionally, participants performed an eyes opening-eyes closure block-design task during the outside scanner and simultaneous EEG-fMRI conditions. The EEG data acquired outside the scanner provides a reference signal free of MR-related artifacts, the data acquired inside the scanner records the contribution of the ballistocardiographic (BCG) but not the gradient artifact, and the EEG recorded during fMRI acquisition captures both the gradient and the BCG artifacts. Functional MRI data were obtained with and without simultaneous EEG recording, and structural images were collected with and without the subjects wearing the EEG cap. This represents one of the largest open-access EEG-fMRI datasets available and will enable other researchers to: 1) Characterize the impact of gradient and BCG artifacts on EEG data, 2) Assess the effectiveness of novel artifact removal approaches, 3) Perform hardware-setup comparison studies, 4) Address the quality of structural and functional MRI data collected with this EEG cap, and 5) Validate multimodal integrative analysis on simultaneously acquired resting-state and task EEG-fMRI.
DOI: 10.17632/9ssyn8tff5.1
title: Speak in Colombian Sign Language: A Dynamic LSC70 Database

description: The dynamic LSC70 database consists of a collection of images of Colombian Sign Language (LSC), created to support research in artificial intelligence for developing sign language recognition applications that promote the social inclusion of individuals with hearing disabilities. This database was developed with the participation of 70 volunteers and contains 47 signs in transitions of six frames. It comprises a total of 35,208 images, including signs of the alphabet, numbers, and common words. Additionally, the dataset is divided into three sections: LSC70AN, which includes full-body signs of the alphabet and numbers with a resolution of 640x480 pixels; LSC70ANH, which similarly covers alphanumeric signs but focuses on the dominant hand of the volunteers with a resolution of 120x120 pixels; and LSC70W, which encompasses common words used in basic conversations, such as greetings and identifications, also at 640x480 pixels. Each sign was recorded under uncontrolled lighting and clothing conditions using a cellphone and was later filtered to remove duplicates and enhance the quality of the database. The images are in JPG format and organized in folders by volunteer and sign, facilitating their use in research and the development of sign language recognition models.
DOI: 10.17632/cjw5hb7y8p.1
title: Fast X-ray Synchrotron Microtomography of Solute Transport in Unsaturated Glass Bead Packing

description: X-ray imaging of solute transport in glass bead packing at the time resolution of 6 seconds and spatial resolution of 3.25 micrometers.
DOI: 10.17632/gk84n794xc.1
title: Data for: Explaining Lecture Attendance Behavior via Structural Equation Modeling: Self-Determination Theory and The Theory of Planned Behavior

description: data set including all variables used in manuscript analyses
DOI: 10.17632/3mn9843tz2.3
title: An Image Dataset of Rice Varieties

description: This extensive dataset presents a meticulously curated collection of low-resolution images showcasing 20 well-established rice varieties native to diverse regions of Bangladesh. The rice samples were carefully gathered from both rural areas and local marketplaces, ensuring a comprehensive and varied representation. Serving as a visual compendium, the dataset provides a thorough exploration of the distinct characteristics of these rice varieties, facilitating precise classification. #Dataset Composition# The dataset encompasses 18 distinct classes, encompassing Subol Lota, Bashmoti (Deshi), Ganjiya, Shampakatari, Sugandhi Katarivog, BR-28, BR-29, Paijam, Bashful, Lal Aush, BR-Jirashail, Gutisharna, Birui, Najirshail, Pahari Birui, Polao (Katari), Polao (Chinigura), Amon, Shorna-5, and Lal Binni. In total, the dataset comprises 4,730 original JPG images and 23,650 augmented images. #Image Capture and Dataset Organization# These images were captured using an iPhone 11 camera with a 5x zoom feature. Each image capturing these rice varieties was diligently taken between October 18 and November 29, 2023. To facilitate efficient data management and organization, the dataset is structured into two variants: Original images and Augmented images. Each variant is systematically categorized into 20 distinct sub-directories, each corresponding to a specific rice variety. #Original Image Dataset# The primary image set comprises 4,730 JPG images, uniformly sized at 853 × 853 pixels. Due to the initial low resolution, the file size was notably 268 MB. Employing compression through a zip program significantly optimized the dataset, resulting in a final size of 254 MB. #Augmented Image Dataset# To address the substantial image volume requirements of deep learning models for machine vision, data augmentation techniques were implemented. Transformations such as rotation (90° left, 90° right, 180° left) and flip were applied, generating an additional set of augmented images in every class, totaling 23,650 augmented images. These augmented images, also in JPG format and uniformly sized at 512 × 512 pixels, initially amounted to 781 MB. However, post-compression, the dataset was further streamlined to 699 MB. #Dataset Storage and Access# The raw and augmented datasets are stored in two distinct zip files, namely 'Original.zip' and 'Augmented.zip'. Both zip files contain 20 sub-folders representing a unique rice variety, namely 1_Subol_Lota, 2_Bashmoti, 3_Ganjiya, 4_Shampakatari, 5_Katarivog, 6_BR28, 7_BR29, 8_Paijam, 9_Bashful, 10_Lal_Aush, 11_Jirashail, 12_Gutisharna, 13_Red_Cargo,14_Najirshail, 15_Katari_Polao, 16_Lal_Biroi, 17_Chinigura_Polao, 18_Amon, 19_Shorna5, 20_Lal_Binni.
DOI: 10.17632/rmk89k4rhb.2
title: Appointment Wait Times for Primary and Specialty Care in Veterans Health Administration Facilities vs. Community Medical Centers

description: These data have been certified for public release by the VA Boston Healthcare System's Privacy Officer. Please cite the following publication when using this dataset: Y. Feyman, K.N. Griffith. (under review). Appointment wait times for primary and specialty care in Veterans Health Administration facilities vs. community medical centers. Data in Brief. These data were originally collected for the following research article: K.N. Griffith, N.J. Ndugga, S.D. Pizer. (2020). Appointment Wait Times for Specialty Care in Veterans Health Administration Facilities vs Community Medical Centers. JAMA Network Open 3(8): e2014313.
DOI: 10.17632/sd97vh79v8.1
title: Pervasive Warming Bias in CMIP6 Tropospheric Layers

description: CMIP6 and observational data for trend comparisons in the lower- and mid-troposphere
DOI: 10.17632/ggh6g39ps2.2
title: CNN for Melanoma Detection Data

description: This dataset is from Kaggle:

https://www.kaggle.com/datasets/hasnainjaved/melanoma-skin-cancer-dataset-of-10000-images

The original article that published this dataset is described below.

M. H. Javid, W. Jadoon, H. Ali and M. D. Ali, "Design and Analysis of an Improved Deep Ensemble Learning Model for Melanoma Skin Cancer Classification," 2023 4th International Conference on Advancements in Computational Sciences (ICACS), Lahore, Pakistan, 2023, pp. 1-6, doi: 10.1109/ICACS55311.2023.10089716.

Abstract: Due to global warming and the ultraviolet rays of the sun, skin diseases are spreading rapidly. If skin diseases are not treated during early stages, they can be dangerous to human life. Melanoma is a deadly type of skin cancer. Dermatologists find it difficult to diagnose melanoma skin cancer because of its complex structure. Significant human lives could be saved if melanoma cancer was diagnosed quickly and accurately. Expert dermatologists diagnose melanoma by examining the lesion's color images. It is very difficult to diagnose melanoma manually, and there is a risk of human error. Therefore, computer vision-based methods that diagnose melanoma disease correctly are offered, and there is little room for error as compared to manual diagnostic methods. In this research, we took images from multiple publicly available ISIC (International Skin Imaging Collaboration) data sets and developed a balance data set that has 10,500 images for training and testing. An ensemble of four convolution neural network (CNN) architectures (ResNet50, EfficientNet B6, InceptionV3, Xception) were utilized and trained on this dataset for classification of melanoma skin cancer. The experimental results of the proposed model show that it correctly classifies melanoma skin cancer. The proposed model gives satisfactory results as compared to other state-of-the-art methods.
URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10089716&isnumber=10089626
DOI: 10.17632/bjydv5rgpr.1
title: dog-PKCV-data

description: Pharmacokinetic and pharmacodynamic data for five reference compounds (verapamil, captopril, dofetilide, pimobendan and formoterol) 
affecting the dog cardiovascular system

These files accompany the article

'An integrative pharmacokinetic-cardiovascular physiology modelling approach based on in vivo dog studies including five reference compounds'
by Mikael Wallman, Jens Markus Borghardt, Eric Martel, Nicolas Pairet, Michael Markert, and Mats Jirstrand   
submitted for publication to the Journal of Pharmacological and Toxicological Methods

This description will be updated with a permanent link to the manuscript upon publication.

Files:

dog_all_compounds.xlsx - cardiovascular telemetry readouts (hear rate, mean arterial pressure and QT-interval) from 
inhalational (formoterol) and oral (verapamil, captopril, dofetilide, pimobendan) dosing obtained in Labrador dogs 
by Boehringer-Ingelheim Pharma & Co KG during trials with the five reference compounds. 

dog_all_compounds_PK.xlsx - digitized pharmacokinetic data from intravenous (all), intratracheal (formoterol), and 
oral (verapamil, captopril, dofetilide, pimobendan) dosing in dog, with source publications indicated in the file. 

VPC and sVPC plots.pdf - supplementary validation plots for the dog cardiovascular model described in the article above
DOI: 10.17632/txvbyxbp9t.1
title: Maps of Germany and the Czech Republic with photovoltaic and battery system sizes for electricity self-sufficient single-family houses under 18 technical and weather dependent scenarios

description: A total of 54 Geotiffs in EPSG:4326 (can easily be opened with GIS software such as ArcGIS or QGIS) is provided . These maps are the results of 18 scenarios (S01-S18) proposed to evaluate technical requirements of electricity self-sufficient single family houses in low population density areas in Germany and the Czech Republic. The non-data values inside of the territory of the countries correspond either to pixels with no population or population beyond 1,500 inhabitants per square kilometre (The classification was made using population data from the LUISA project of the Joint Research Centre of the European Commission). The file names can be interpreted in the same way as the following example: S01_Battery_min_cost_no_sc.tif where S01 is the scenario number (01 to 18 are possible), Battery is the type of technology presented in the map (there are also optimally tilted photovoltaic panels named "PV1" and photovoltaic panels with 70° inclination named "PV2"), “min” stands for minimizing and the following word stands for the minimization objective. In this case with “cost” the objective of the scenario is to minimize cost (“battery” for battery size and “pv” for photovoltaic size are also possible). Additionally, there is “no_sc” for case studies that do not consider snow cover and "sc" in case snow cover is considered. Finally some of the files include a year at the end of the file name. This stands for the year of the irradiation and temperature data sets that were used to run the scenario. All files without a year correspond to scenarios calculated with average weather data (Average hours calculated from two decades of data from the COSMO-REA6 regional reanalysis).
DOI: 10.17632/4xfh894vgv.1
title: Trove newspaper totals (historical)

description: This repository contains past harvests of the number of digitised newspaper articles available through Trove. These harvests were created between 2011 and 2022.
DOI: 10.17632/ycdcw2rdfj.1
title: St1 Deep Heat Project stimulation data

description: Hydraulic stimulation data of the St1 Deep Heat Project 2018 and 2020, Finland, deep wells OTN-3 and OTN-2.
DOI: 10.17632/wd2hy49tsp.1
title: Casanova-Salas et al; Circulating extracellular vesicles to monitor metastatic prostate cancer evolution in liquid biopsies"

description: Data associated with manuscript "Circulating extracellular vesicles to monitor metastatic prostate cancer evolution in liquid biopsies", published in Casanova-Salas et al, Cancer Cell 2024; (42)1-13. Doi: 10.1016/j.ccell.2024.06.003. Includes genomics (lpWGS) and transcriptomics (RNAseq) data for the in-vitro, in-vivo and clinical samples with codified patient ID
DOI: 10.17632/g769x5dt5r.1
title: Supplementary Material: Real-world assessment of response to anti-PD-1 therapy in advanced cutaneous squamous cell carcinoma
DOI: 10.17632/ms38bhwbtn.1
title: Data for: SYNTHESIS AND STRUCTURE OF BORON DIFLUORIDE BINUCLEAR β-DIKETONATES

description: Four binuclear diketonate complexes of boron difluoride have been prepared. Ob-tained complexes contain the following pairs of ligands connected through a sulfur atom with their central carbon atoms: acetylacetone-acetylacetone, acetylacetone-acetoacetic ester, acetylacetone-dibenzoylmethane, acetylacetone-benzoylacetone. Molecular and crystal structure have been identified for three complexes. Their structure is similar to the one of aromatic sulfides. Effect of γ-substituents on chelate cycle structure and on the substituents next to β-carbon atoms has been discussed.
DOI: 10.17632/g5d94jsbdh.1
title: Airline Schedule Disruption Scenarios

description: This dataset includes real airline operational data with various disruptions, including airport closure, aircraft-on-ground maintenance, delay of flights, and flow control. For each disruption type, there are nine scenarios which can be divided into three categories, i.e., "small", "medium", "large", based on their sizes of operations.
DOI: 10.17632/m32hfv8y9h.1
title: Journal of Iran's Economic Essays; "Jostarhaye Eghtesadiye Iran; No. 25

description: Journal of Economic Essays; an Islamic approach (JEE) is an open-access double-blind peer-reviewed academic quarterly journal that publishes original research, written with an Islamic approach in various Economic areas including Islamic economics and finance, Iran's economy, Fiqh (Islamic jurisprudence) and economics, philosophy of economics, economic theory, ethics and business, Islamic banking, economic development, and social security institution in Islam. JEE especially welcomes comparative articles that study the western and Muslim scholars' viewpoints about important economic issues to find the best answer to theoretical and applied questions and also to explore their differences and similarities with the aim of bringing them closer to each other. This journal accepts analytical articles that reread Muslim scholars' ideas according to new approaches in economics, moral philosophy, Islamic banking and finance, social security, econometrics, and Islamic economic institutions. Therefore, it aims to serve the interests of a wide range of thoughtful and academic readers, economists, philosophers, theologians, banking and finance experts and others interested in the multidisciplinary study of economics and Islam (including Fiqh "Islamic jurisprudence"). We hope that this publication creates an atmosphere of constructive dialogue on different economic topics between national and international scholars by which it could have a role in the growth of Islamic economic thought in Iran. In addition, Islamic solutions to the economic problems of Iran and the world will be examined and analyzed, and they will be presented to the policy makers to help improve the Islamic economic system.

Major Topics

 The Journal publishes papers, including the following fields:

- Islamic economics and finance;

 - Iran's economy;

 - Fiqh (Islamic jurisprudence) and economics;

 - Philosophy of economics;

 - Economic theory;

 - Ethics;

- Islamic economic institutions

 - Islamic banking;

 - Economic development;

- Social security.
DOI: 10.17632/xknjp8pxbj.1
title: Embeddings and topic vectors for MOOC lectures dataset

description: This dataset is comprised of word embeddings and document topic distribution vectors generated from transcripts of 12032 video lectures from 200 courses that were collected from Coursera learning platform. Two well-known natural language processing techniques, namely Word2Vec and Latent Dirichlet Allocation (LDA) implemented in the Gensim package in Python are used to generate word embeddings and topic vectors, respectively.