cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/m3n5sjxtjy.3
title: Dataset for Detection and Segmentation of the Radiographic Features of Pulmonary Edema

description: Objectives: This comprehensive dataset is well suited for training, evaluating, and using machine learning models to detect, segment, and analyze radiological features associated with pulmonary edema in chest X-ray images. Description: This dataset consists of a collection of chest X-rays extracted from the MIMIC database, carefully collected at the Beth Israel Deaconess Medical Center. In total, it comprises 1000 chest X-rays obtained from 741 patients with features suggestive of edema. These X-rays were carefully selected for manual annotation. The annotations are rich and detailed, covering specific radiological features commonly associated with pulmonary edema, including cephalization, Kerley lines, peribronchial cuffing, pleural effusions, bat wings, and infiltrates. Furthermore, each chest radiograph is thoughtfully assigned a severity category, categorizing it as "no edema", "vascular congestion", "interstitial edema", or "alveolar edema". Annotation Method: The annotation process was meticulously performed by a highly qualified clinician with over 10 years of radiology experience, utilizing both frontal and lateral views for each chest X-ray study. Cephalization and Kerley lines were delineated using polylines, while other features were delineated using binary masks. This methodological approach was carefully chosen to provide a comprehensive data set that would ensure accuracy in subsequent analyses and label assignments. Features and Severity Labels: The dataset includes a wide variety of radiological features, with a total of 4293 annotations. These include: 1. Cephalization: 1656 annotations 2. Kerley lines: 609 annotations 3. Peribronchial cuffings: 30 annotations 4. Pleural Effusions: 317 annotations 5. Bat wings: 1604 annotations 6. Infiltrate: 77 annotations At the same time, the dataset features a wealth of severity labels, totaling 741 cases, thoughtfully categorized as: 1. No edema: 21 cases 2. Vascular congestion: 74 cases 3. Interstitial edema: 51 cases 4. Alveolar edema: 595 cases Notably, all features are represented as bounding boxes, meticulously defined by their respective upper-left (x1; y1) and lower-right (x2; y2) corners. In addition, selected features are provided with masks encoded in base 64 format. To facilitate seamless decoding, we provide a conversion script called " mask_converter.py" that allows the transformation of encoded masks into a versatile numpy array format. This feature improves the usability of the dataset for precise analysis and deep learning applications. Datasets: 1. Source dataset: Clinician-labeled chest X-rays, consolidated into a single spreadsheet, featuring only frontal-view images. 2. Processed dataset: Emphasizes lung area analysis, excluding extraneous regions unused in clinician decision-making. 3. COCO dataset: Prepared in COCO format, ideal for training and testing, with subsets for each evaluated feature.
DOI: 10.17632/h3xzbzmf39.1
title: AARP's national survey on multi-level marketing participation
DOI: 10.17632/tyj58cxyfc.1
title: A REAL OPTION BASED MODEL FOR THE VALUATION OF PATENT PROTECTED TECHNOLOGICAL INNOVATION PROJECTS

description: This Excel spreadsheet helps to develop a patent valuation by using the Real Options Approach. You need to run it by using Crystal Ball.
DOI: 10.17632/7hkndfnr76.1
title: Effects of Mindfulness-Based Stress Reduction on Psychological Distress in Health Workers

description: The dataset includes sociodemographic, group assignment, and participation variables as well as outcome measures (total and subscale scores) for participants at each assessment point: 1. EF (enrolment) 2. T0 (baseline) 3. T1 post-intervention) 4. T2 (follow-up at 6 months/4 months post-intervention)
DOI: 10.17632/8jkw626tjs.1
title: GENDER AND FEMINIST IDEOLOGY: AN IN-DEPTH EXPLORATION OF SEX, GENDER, SOCIALIZATION, STEREOTYPES, AND INEQUALITY

description: ABSTRACT : Understanding feminist ideology requires a careful consideration of the difference between sex and gender. Gender encompasses the cultural and social constructs that differentiate individuals based on their assigned roles and expectations, whereas sex pertains to the biological dissimilarities that exist between males and females. Sex encompasses the distinct biological and physiological characteristics shown by individuals, namely men and women, which include reproductive organs, chromosomes, and hormones. Conversely, the subject of gender encompasses several intersections with societal, legal, political, and cultural issues. The topic of identity and social status is often examined in conjunction with other factors such as class, ethnicity, age, and physical ability. Gender is a sociocultural phenomenon that is shaped by the process of socialisation. Different societies may possess diverse interpretations of what constitutes "masculine" or "feminine". Gender norms are established by societies and are acquired by people via various social institutions such as family, education, and media. Gender norms are acquired and assimilated by individuals across all segments of society. Gender norms exhibit variability both temporally and cross-culturally. Gender norms are organised in a hierarchical fashion, establishing a power structure based on gender that primarily places women at a disadvantage. The presence of historical and cross-cultural diversity in the differentiation and stratification of gender roles offers compelling evidence that social factors significantly contribute to the establishment of disparities between genders. Nevertheless, it is plausible to argue that some disparities in conduct and attitudes between sexes may have a biological underpinning. The division of employment by sex has been influenced by both biological and societal factors, and this division subsequently contributes to the establishment of gender hierarchy and gender inequality. This study explores the existing body of information about sex and gender, focusing on the manner in which gender norms contribute to the formation of gender stereotypes and the imposition of social roles.
DOI: 10.17632/6grh8x4hgt.1
title: LAVA 1.0: A general-purpose python toolkit for calculation of material properties with LAMMPS and VASP

description: We introduce LAVA, a general-purpose python toolkit to provide user-friendly and high throughput calculations for material properties using both LAMMPS and VASP. It contains a set of classes as well as pre-processing and post-processing functions to prepare, execute and extract information from LAMMPS/VASP simulations. An overview of the program structure is provided. The current version contains modules to calculate elastic and mechanical properties such as lattice constant, cohesive energy, cold curve, elastic constants, bulk and shear modulus, volume conserving/non-conserving deformation path, vacancy/interstitial formation energy, surface energy, stacking fault energy, melting point, radial distribution function, and thermal expansion. These functionalities are demonstrated for different interatomic potentials and DFT calculations in Al.
DOI: 10.17632/v4knrwgj9y.2
title: Thermal imagery from composite material academic samples

description: The infrared camera recorded the thermal evolution on the surface of the inspected CFRP/GFRP sample for several seconds (approximately 16 to 17 s) at 120 and 145 frames/s sampling rate, from before applying a heat pulse, during the heat pulse and during cooling. The experiment was performed from the two faces of the specimen (providing shallow defect depths form the front face and deeper depths from the back face), and each sequence was labeled and saved into an independent file. Every .zip file contains a folder and files inside. Both are labeled as CFRP-samplename_facq-frequencyvalue_s-sideofinspection_Img-frames. This tagging scheme describes the sample name tested, the acquisition frequency used, the sample side of inspection taken, as well as the number of each frame (for .CSV files) within the sequence, and the whole number of frames from the image sequence (for the folders), respectively. We added heading to the Mendeley data files for Version 2.
DOI: 10.17632/k62fdsnwkg.1
title: Diagnosing and Predicting Clinical and Para-clinical Cutoffs for Diabetes Complications in Lur and Lak Populations of Iran: A ROC Curve Analysis to Design a Regional Guideline

description: Objectives: American diabetes association (ADA) updates its guideline every year. This guideline is obtained based on evidence-based medicine (EBM). However this guideline can be changed for different populations based on their cultural and genetic status. For this reason we intend to design a regional study in Lur and Lak of Iran, emphasizing on lipid profile cutoffs. Methods: A number of 133 diabetes mellitus (DM) patients were enrolled in this study. The collected information for each patient were sex, age, body-mass index (BMI), DM type, DM duration, fasting blood sugar (FBS), HbA1c, lipid profile, type of DM treatment, type of statin and dose, documented neuropathy, documented nephropathy, symptomatic retinopathy, peripheral vessel disease (PVD), documented cardiovascular disease (CVD), food ulcer history, dawn effect, systolic blood pressure (SBP), and diastolic blood pressure (DBP). Receiver operating characteristics (ROC) curve was used and area under curve (AUC) was reported. Results: For neuropathy, age was the most accurate diagnostic index (AUC= 79%). For nephropathy systolic SBP was the most accurate diagnostic index (AUC= 88%). For symptomatic retinopathy DM duration was the most accurate diagnostic index (AUC= 81%). For PVD HDL was the most accurate diagnostic index (reverse AUC= 100-33%). For CVD age was the most accurate diagnostic index (AUC= 81%). For foot ulcer history age was the most accurate diagnostic index (AUC= 85%). Conclusion: The final suggested guideline is like the international guidelines; however some unique points should be regarded. Blood pressure &gt;165/110mmHg is diagnostic of diabetic nephropathy. HDL &gt;48 is strongly suggested. A cohort study should be performed in our region in order to reevaluate predictive values.
DOI: 10.17632/yp8bg4pw7t.1
title: Panel data_determinants_loan growth_financing growth_conventional banks_Islamic banks_Malaysia_Over_2007_2023

description: The dataset presents panel data on 26 conventional banks and 16 Islamic banks that operates in Malaysia, focusing on the determinants of loan growth and financing growth. It includes six bank-specific factors namely, non-performing loans or impaired financing, capital, profitability, liquidity, deposit growth and home loan or home financing. Meanwhile, five macroeconomic factors are covered: gross domestic product (GDP), inflation, overnight policy rate (OPR), foreign direct investment (FDI) and government policy. The data spans from 2007 to 2023. The data sources for analysing the bank-specific factors are obtained from audited annual reports of 26 conventional banks and 16 Islamic banks, as well as from the Fitch Connect Database. Macroeconomic factors are derived from the website of World Development Indicators (WDI) and Bank Negara Malaysia (BNM). The analysis was conducted using STATA version 17. The dataset is presented in two sheets: one for conventional banks and one for Islamic banks.
DOI: 10.17632/3vyjkm9jgw.1
title: An open-source toolbox for multiphase flow in porous media

description: This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018) Abstract Multiphase flow in porous media provides a wide range of applications: from the environmental understanding (aquifer, site-pollution) to industrial process improvements (oil production, waste management). Modeling of such flows involves specific volume-averaged equations and therefore specific computational fluid dynamics (CFD) tools. In this work, we develop a toolbox for modeling multiphase flow in porous media with OpenFOAM®, an open-source platform for CFD. The underlying idea of this app... Title of program: porousMultiphaseFoam Catalogue Id: AEUR_v1_0 Nature of problem This software solves multiphase flow in porous media. Versions of this program held in the CPC repository in Mendeley Data AEUR_v1_0; porousMultiphaseFoam; 10.1016/j.cpc.2014.10.005
DOI: 10.17632/3wjvmymf68.2
title: Eastwick Compound Flood Model

description: This HEC-Ras model is developed under a NOAA funded Coastal Ocean Climate Applications and Sectoral Applications Research Programs (COCA/SARP) project titled Compound Fluvial‐Coastal Flood and Climate Adaptation: A Transferable Framework of Engagement, Modeling and Cost‐Benefit Analysis.
DOI: 10.17632/fbswb9dhz7.1
title: Early Post-Operative Water Exposure Does Not Increase Complications in Cutaneous Surgeries: A Randomized, Investigator-Blinded, Controlled Trial

description: Supplement A: Blank infection and hematoma/bleeding form 
Supplement B: Hematoma and bleeding multivariate analysis tables
DOI: 10.17632/z6dtfdpzz8.1
title: Strawberry-DS

description: - An annotated benchmark image dataset for training and validation of strawberry ripeness detection systems based on Machine learning (ML) and, Deep Learning (DL). 
- 247 Raw RGB digital images (.jpg) of strawberry fruits were taken in an orchard of the Central Laboratory for Agricultural Climate (CLAC), Agricultural Research Center, Cairo - Egypt.
-The images have been captured from the fruit top view considering different view angles using Sony Xperia Z2 LTE-A D6503 smartphone 20.7 MP camera with a CMOS sensor system and resolution of 3840 x 2160 pixels (Mpix). 
The dataset images, which contain both fully-visible strawberry fruits and partially-visible strawberry fruits concealed by leaves or by other fruits, were manually annotated, using Roboflow Annotate annotation tool. The data formats of files in Strawberry-DS dataset are RGB digital images (.jpg) and their corresponding YOLO format (.txt) annotation files.
DOI: 10.17632/5r94cfmdwj.2
title: Factors Associated with Presenting to a First Antenatal Care Appointment without a Male Partner in Northern Tanzania

description: This dataset was used in analysis for our quantitative paper on factors associated with presenting to a first antenatal care appointment without a male partner in Northern Tanzania. It is an anonymized SPSS dataset containing only variables relevant to this analysis.
DOI: 10.17632/ch25c5r9d7.1
title: Decentralization challenge and features of logistics 4.0

description: The attached files show the bibliometric data used to construct the relationship between logistics 4.0 challenge (decentralization) and logistics 4.0 features.
DOI: 10.17632/s22446k8z7.1
title: Teachers' preparedness for artificial intelligence integration

description: The study examines variables to assess teachers' preparedness for integrating AI into South African schools. The dataset on the Excel sheet consists of 42 columns. The first ten columns comprise demographic variables such as Gender, Years of Teaching Experience (TE), Age Group, Specialisation (SPE), School Type (ST), School Location (SL), School Description (SD), Level of Technology Usage for Teaching and Learning (LTUTL), Undergone Training/Workshop/Seminar on AI Integration into Teaching and Learning Before (TRAIN), and if Yes, Have You Used Any AI Tools to Teach Before (TEACHAI). Columns 11 to 42 contain constructs measuring teachers' preparedness for integrating AI into the school system. These variables are measured on a scale of 1 = strongly disagree to 6 = strongly agree.

AI Ethics (AE): This variable captures teachers' perspectives on incorporating discussions about AI ethics into the curriculum. 

Attitude Towards Using AI (AT): This variable reflects teachers' beliefs about the benefits of using AI in their teaching practices. It includes their expectations of having a positive experience with AI, improving their teaching experience, and enhancing their participation in critical discussions through AI applications.

Technology Integration (TI): This variable measures teachers' comfort in integrating AI tools and technologies into lesson plans. It also assesses their belief that AI enhances the learning experience for students, their proactive efforts to learn about new AI tools, and the importance they place on technology integration for effective AI education.

Social Influence (SI): This variable examines the impact of colleagues, administrative support, peer discussions, and parental expectations on teachers' preparedness to incorporate AI into their teaching practices.

Technological Pedagogical Content Knowledge (TPACK): This variable assesses teachers' ability to use technology to facilitate AI learning. It includes their capability to select appropriate technology for teaching specific AI content, and bring real-life examples into lessons.

AI Professional Development (AIPD): This variable evaluates the impact of professional development training on teachers' ability to teach AI effectively. It includes the adequacy of these programs, teachers' proactive pursuit of further professional development opportunities, and schools' provision of such opportunities.

AI Teaching Preparedness (AITP): This variable measures teachers' feelings of preparedness to teach AI. It includes their belief that their teaching methods are engaging, their confidence in adapting AI content for different student needs, and their proactive efforts to improve their teaching skills for AI education.

Perceived Self-Efficacy to Teaching AI (PSE): This variable captures teachers' confidence in their ability to teach AI concepts, address challenges in teaching AI, and create innovative AI-related teaching materials.
DOI: 10.17632/v66y2tgpjw.1
title: Re-examining melanoma secondary prevention and the role of skin self-examination

description: Summary of references that demonstrate associations between skin self-examination and the diagnosis of earlier stage melanomas at initial presentation. Additional references discuss the costs associated with screening for melanoma and compare the relative healthcare costs of early versus late-stage melanoma.
DOI: 10.17632/kd3n5k784m.2
title: Collective ERK/Akt dynamics in epithelia

description: Experimental single-cell time series of ERK and Akt activity from fluorescent time lapse microscopy, and R/Python scripts required to reproduce the figures from the accompanying publication: Gagliardi et al. "Collective ERK/Akt activity waves orchestrate epithelial homeostasis by driving apoptosis-induced survival". Pre-print available from here: https://doi.org/10.1101/2020.06.11.145573
DOI: 10.17632/scpvm2nbkm.4
title: KU-BdSL: Khulna University Bengali Sign Language dataset

description: The KU-BdSL refers to a Bengali sign language dataset, which includes three variants of the data. The variants are - (i) Uni-scale Sign Language Dataset (USLD), (ii) Multi-scale Sign Language Dataset (MSLD), and (iii) Annotated Multi-scale Sign Language Dataset (AMSLD). The dataset consists of images representing single-hand gestures for BdSL alphabets. Several smartphones are taken into account to capture images from 39 participants (30 males and 9 females). These 39 participants associated with the dataset creation have not offered any financial benefit. Each version includes 30 classes that resemble the 38 consonants ('shoroborno') of Bengali alphabets. There is a total of 1,500 images in jpg format in each variant. The images are captured on flat surfaces at different times of the day to vary the brightness and contrast. Class names are Unicode values corresponding to the Bengali alphabets for USLD and MSLD.

Folder Names:
2433 -> ‘Chandra Bindu’
2434 -> ‘Anusshar’
2435 -> ‘Bisharga’
2453 -> ‘Ka’
2454 -> ‘Kha’
2455 -> ‘Ga’
2456 -> ‘Gha’
2457 -> ‘Uo’
2458 -> ‘Ca’
2459 -> ‘Cha’
2460-2479 -> ‘Borgio Ja/Anta Ja’
2461 -> ‘Jha’
2462 -> ‘Yo’
2463 -> ‘Ta’
2464 -> ‘Tha’
2465 -> ‘Da’
2466 -> ‘Dha’
2467-2472 -> ‘Murdha Na/Donto Na’
2468-2510 -> ‘ta/Khanda ta’
2469 -> ‘tha’
2470 -> ‘da’
2471 -> ‘dha’
2474 -> ‘pa’
2475 -> ‘fa’
2476-2477 -> ‘Ba/Bha’
2478 -> ‘Ma’
2480-2524-2525 -> ‘Ba-y Ra/Da-y Ra/Dha-y Ra’
2482 -> ‘La’
2486-2488-2487 -> ‘Talobbo sha/Danta sa/Murdha Sha’
2489 -> ‘Ha’

USLD:
USLD has a unique size for all the images that is 512*512 pixels. The intended hand position is placed in the middle of the majority of cases in this dataset. 
MSLD:
The raw images are stored in MSLD so that researchers can make changes to the dataset. The use of various smartphones yields us a wide variety of image sizes.
AMSLD:
AMSLD has multi-scale annotated data, which is suitable for tasks like localization and classification. From many annotation formats, the YOLO DarkNet annotation has been selected. Each image has an annotation text file containing five numbers separated by white space. The initial number is an integer, and the rest are floating numbers. The first number of the file indicates the class ID corresponding to the label of that image. Class IDs are mapped in a separate text file named 'obj.names'. The second and third values are the beginning normalized coordinates, while the fourth and fifth define the bounding box's normalized width and height.

This dataset is supported by Research and Innovation Center, Khulna University, Khulna-9208, Bangladesh and all the data from this dataset is free to download, modify, and use. The previous version (Version 1) of this dataset contains the oral permission of the volunteers, and the rest versions have written consent of the participants. Therefore, we encourage researchers to use these versions (Version 2 or Version 3 or Version 4) for research objective.
DOI: 10.15468/dl.tfz4pz
title: Occurrence Download

description: A dataset containing 2912 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "Country is Brazil",
 "TaxonKey is Bombus Latreille, 1802",
 "Year 2000-2024"
 ]
}

 The dataset includes 2912 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009258-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/ktr99fc826.3
title: Loay & Maha Stamps Dataset

description: Recently, with the continuous evolution of new technology the world has faced unusual growth of multimedia data especially in document images. Huge quantities of documents are created and used in many important official organizations. The stamps assure the authenticity of the documents contents. The general objective of this dataset is enabling researchers in the field of pattern recognition to analyze, detect, localize and recognize different types of stamps. The size of the dataset is 48.9 MB. It contains a total of 1557 color stamps samples which are stored as BMP image format. The images of stamps were acquired from documents using a scanner and mobile camera. The cropping process was applied to capture only the stamp area of the whole document, using three applications which are paint, snip and sketch software. For each cropped stamp, eight stamp images are produced from rotating the original one at different angles clockwise. Rotation starts from 5 degrees and increases by 5 degrees until it reaches to 40 degrees. Note the orientation of the original stamp image is with the horizon line, which means that it is at an angle of zero. The stamps have different size, shape, complexity, position, directions and colors. For example, there are six categories of stamp shapes arranged as circle, oval, square, rectangular, triangular and some other irregular shapes. Its worth to mention that the collected dataset may be degraded in quality and resolution and the stamp can be located on a relatively complex background. The dataset was collected from various sources, including official documents from Iraqi educational institutions, documents from syndicates such as teachers, engineers, doctors, pharmacists, Iraqi hospitals, provincial councils, and most Iraqi institutions. The dataset also contains some stamps from other Arab countries.