cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/tb6bkc3g5m.1
title: Analyzed data on fiber-reinforced cementitious composite subjected to high temperature

description: Analyzed data for manuscript entitled "A multiscale investigation on the performance improvement of fiber-reinforced cementitious composites after exposure to high temperatures" in Cement and Concrete Composites, and co-submitted article entitled “Experimental dataset on the residual performance of fiber-reinforced cementitious composite subjected to high temperature” in Data in Brief.
DOI: 10.17632/tn35b9wmrm.1
title: Effects of conservation tillage on soil quality, crop production and PLFAs

description: A two-year field experiment was conducted on subtropical Ultisols continuously cultivated with maize (Zea mays L.) for over 20 years. The experiment tested three tillage types (no tillage, earthworm bio-tillage, traditional rotary tillage) along with three organic matter inputs (none, straw, composted cow manure addition). Maize yield and soil samples (0-10 cm) were collected after four growing seasons to determine the effects of conservation tillage on maize production, soil properties and microbial communities (phospholipid fatty acids, PLFAs).
DOI: 10.17632/3zzkvnj3sk.1
title: Design of a Dual Screening Tool for the Detection of Fungi and Mycotoxins in Stored Cocoa

description: This data is centered on the development of a cell-free screening method to identify mycotoxins and fungi in stored cocoa beans, addressing a critical gap in local knowledge about fungal contamination. Utilizing computational tools like Benchling for cassette design and AlphaFold for protein modeling, we identified cross-activity with Aflatoxin, specifically Gliotoxin and ABA. The constructed cassettes, featuring chimeric proteins Bichota and X19X, were validated through AlphaFold modeling, and their interactions with target molecules and natural components in healthy cacao were explored via Docking and Virtual Screening using SwissDock. The data suggests that our screening method has the potential to empower cocoa producers by providing real-time information to prevent economic losses due to fungal contamination and mycotoxin production. Furthermore, the application of this technology aligns with international market standards, ensuring compliance with maximum permissible limits of mycotoxins for cocoa exports according to European Union legislation. The success of this project could significantly contribute to strengthening the cocoa value chain, improving product quality, and expanding opportunities in global markets.
DOI: 10.17632/cb85wcs4zj.1
title: Test-retest of Comprehensive knee osteoarthritis index (CKOAI)

description: CKOAI was subjected to test- retest reliability by administrating the scale among the individuals with knee osteoarthritis on similar testing condition identical to the previous session over two separate occasions with a minimum duration of 24-hours and not more than 2 weeks by the examiner.
DOI: 10.17632/dhw9sz3tb5.1
title: Assessment of diagnostic delay, morbidity, and mortality outcomes in 302 calciphylaxis patients over a 17-year period: A retrospective cohort study

description: Supplemental figures (1-3) and tables (1-2) for manuscript entitled, "Assessment of diagnostic delay, morbidity, and mortality outcomes in 302 calciphylaxis patients over a 17-year period: A retrospective cohort study."
DOI: 10.17632/5sck77c9b9.3
title: Circulating concentrations of progestins used in contraception

description: This dataset is a compilation of published progestin levels in blood plasma or serum, representing the circulating concentrations of progestins commonly used in contraception. The dataset consists of tables supplementary to the manuscript "Pharmacokinetics, metabolism and serum concentrations of progestins used in contraception", submitted to the journal Pharmacology & Therapeutics. In part of this manuscript we discuss possible reasons for the wide inter-individual and inter-study variability in reported serum progestin concentrations. The data was sourced through extensive PubMed searches for serum or plasma concentrations of progestins used in various methods of administration e.g. intramuscular injection, subdermal implant or combined oral contraceptive.
The columns in these tables include progestin concentrations (in nanomolar[nM]/picomolar [pM] or nanograms/milliliter [ng/ml] or picograms/milliliter [pg/ml]) for several different progestins, the concentrations of endogenous estrogen or progesterone or exogenous ethinyl estradiol, the model system used (number of women, any other drugs used in combination, other demographics), the method of quantification used (typically radioimmunoassay [RIA] or liquid chromatography with mass spectrometry [LC-MS]) and the citation for the original reference (listed at the end of each table). Abbreviations are explained at the end of each table. Molecular weights used for concentration conversions between nM-ng/ml or pM-pg/ml are indicated at the end of each table. While organic solvent extraction is always used for LC-MS methods, it is sometimes used prior to RIA: where this information was available, the organic solvent used is reported. Some data may be omitted due to restricted access to the original publication. This is a non-exhaustive, manually compiled list from publications since the 1960s and as such, some mistakes and/or omissions may have unintentionally occurred.
DOI: 10.17632/6y8m379mkt.2
title: TRODO: a public vehicle odometers dataset for computer vision

description: The TRODO dataset is a collection of annotated odometer images from several vehicles operating in a real-world product delivery network. The dataset contains a total number of 2,389 raw images with proper annotations. Furthermore, TRODO also includes the ground truth information of odometer types and mileages of vehicles.

In the fields of insurance and logistics, the TRODO dataset can be utilized to build smart vision systems for mileage reading from odometer images.   It is also possible to extend the application areas of the dataset by transfer learning.  Researchers can use the dataset to train, validate, and test novel or existing supervised learning approaches.
DOI: 10.17632/8yp4jf6vn3.1
title: Data for: Complex Refractive Indices Measurements of Polymers in Infrared Bands

description: Polymers are widely used in many fields such as radiative cooling, infrared stealth, optical fibers and solar cells. In this work, the complex refractive indices of seven polymers such as polydimethylsiloxane (PDMS), polymethyl methacrylate (PMMA), polycarbonate (PC), polystyrene (PS), polyethylene terephthalate (PET), polyvinyl chloride (PVC), and polyetherimide (PEI) are measured by spectroscopic ellipsometry (SE) combined with the ray tracing method (RTM) from near-infrared to mid-infrared bands (2-20 micro). The measured results of PDMS have been proved to be accurate by comparing them with previous data in the literature. The obtained refractive indices of polymers in this paper have been compared with the results calculated by the Kramers-Kronig relations. Finally, we analyze the reasons of absorption peaks of these materials in infrared bands by the vibration of chemical bonds in molecules.
DOI: 10.17632/pywzcgyzrv.2
title: The Benefits of Transfers in Crowdsourced Pickup-and-Delivery Systems

description: Instances of the Pickup and Delivery Problem with Time Windows and Transfers solved by the ALNS heuristic proposed in the research paper The Benefits of Transfers in Crowdsourced Pickup-and-Delivery Systems. More details on the format used can be found in the README file.
DOI: 10.17632/9b262xmtm9.1
title: Multiplexed in Situ Imaging Mass Cytometry Analysis of the Human Endocrine Pancreas and Immune System in Type 1 Diabetes: Part 1

description: The interaction between the immune system and endocrine cells in the pancreas is crucial for the initiation and progression of type 1 diabetes (T1D). Imaging mass cytometry (IMC) enables multiplexed assessment of the abundance and localization of more than 30 proteins on the same tissue section at 1 µm resolution. Herein, we have developed a panel of 33 antibodies that allows for the quantification of key cell types including pancreatic exocrine cells, islet cells, immune cells, and stromal components. We employed this panel to analyze 12 pancreata obtained from donors with clinically diagnosed T1D and 6 pancreata from non-diabetic controls. In the pancreata from donors with T1D, we simultaneously visualized significant alterations in islet architecture, endocrine cell composition, and immune cell presentation. Indeed, we demonstrate the utility of IMC to investigate complex events on the cellular level that will provide new insights on the pathophysiology of T1D. The dataset here include raw data acquired from the IMC platform. FFPE sections from 18 donors were used for IMC acquisition. Image resolution is 1 μm per pixel.
DOI: 10.17632/mkpff3wkks.1
title: Perceived Parenting styles and Distress Tolerance of Adolescents with Obsessive-compulsive Disorder: Mediating Role of Anxiety Sensitivity

description: The study aimed to examine the role of anxiety sensitivity as a mediator between perceived parenting styles and distress tolerance. Using a single-group cross-sectional research design, data from 218 adolescents (104 males and 114 females) aged 12-17 years with clinically significant symptoms of OCD, recruited through purposive sampling, were collected using standardized self-report measures including the Perceived Parenting Styles Scale (PPSS), the Anxiety Sensitivity Index-3 (ASI-3) and the Distress Tolerance Scale (DTS). After obtaining the ethical approval of the Institutional Ethical Committee of Amity Institute of Behavioural (Health) and Allied Sciences, Amity University Uttar Pradesh, the data were collected via purposive sampling with the help of questionnaires hand-filled by patients, with the assistance of their legally accepted representatives (LARs) and the respective clinician as required, with clinically significant symptoms of OCD from mental healthcare settings in Delhi NCR after obtaining their requisite permissions. Assent was sought for participation from adolescents between 12 to 17 years of age, who met inclusion criteria after the required screening, as well as the informed consent form was obtained from their parents or LARs, following which data were collected through the aforementioned self-report instruments. No fee or remuneration was provided for their participation.
DOI: 10.17632/3f83gxmv57.2
title: A Citrus Fruits and Leaves Dataset for Detection and Classification of Citrus Diseases through Machine Learning

description: (1) In agriculture, plant diseases are primarily responsible for the reduction in production which causes economic losses. In plants, citrus is used as a major source of nutrients like vitamin C throughout the world. However, ‘Citrus’ diseases badly effect the production and quality of citrus fruits. (2) The computer vision and image processing techniques have been widely used for detection and classification of diseases in plants. (3) The dataset contains an image gallery of healthy and unhealthy citrus fruits and leaves that could be usable for the researchers to prevent plants from diseases using advanced computer vision techniques. The disease targeted in the data sets are the Blackspot, Canker, Scab, Greening, and Melanose. (4) The dataset contains 759 images of healthy and unhealthy images for both Citrus fruits and leaves collectively. Each image contains 256 * 25 dimensions with 72 dpi resolution. (5) All images were acquired from the Sargodha region, a tropical area of Pakistan under the supervision of Dr. Basharat ALi Saleem, Endeavour Executive Fellow Curtin University · Horticulture Research Laboratory Postharvest Australia · Bentley (6) All images were annotated manually by the domain expert Dr. Basharat ALi Saleem to represent their every class such as : For Citrus fruits (Black Spot, Canker, Greening, Scab, and healthy with total number of 150 images ), For Citrus Leaves (Black Spot, Canker, Greening, Melanose, and healthy with total number of 609 image) (6) Further details can be found in the associated publications with the dataset.
DOI: 10.17632/yxns2z5jfk.1
title: Lammps Input Files

description: This dataset contains input file and output files used in the article of "Reaxff Molecular Dynamic and Experimental Investigation of Aluminium Tri Hydroxide Decomposition Effect on nano-Silicon Carbide particles"
DOI: 10.17632/khdst33mzy.1
title: ownership structure and corporate behaviour

description: Ownership structure and Corporate Financial data
DOI: 10.17632/rz3xg97rm5.1
title: A Large Scale Tweet Dataset for Urdu Text Sentiment Analysis

description: This is a dataset of tweets in the Urdu language. There are 1,140,824 tweets in the dataset, collected from Twitter for September and October 2020. This large-scale corpus of tweets is generated by performing preprocessing, which includes removing columns containing user information, retweet’s count, followers, and duplicate tweets, removing unnecessary punctuations, links, symbols, and spaces, and finally extracting emojis if present in the tweet text. This dataset's final tweet record contains columns for tweet id, text, and emoji extracted from the text with a sentiment score. Emojis are extracted to validate Machine Learning models used for the multilingual sentiment and behavior analysis.
DOI: 10.17632/56td5w4wz2.2
title: Fruits (Banana and Guava) datasets for non-destructive quality classifications

description: This article provides fruit (banana and guava) image dataset for non-destructive quality classifications. The images were captured with a Redmi Note 10-Pro mobile camera in natural sunlight. All the images were captured at different angles and saved in JPG format. A total of 1738 images were collected. The images were classified into three different classes; Class A, Class B, and Defect Quality, according to the maturity stages of fruits. Further, the image datasets were saved into their respective folder. This dataset allows researchers to study different machine learning and deep learning algorithms for the quality classification of fruits. The dataset provides the foundation for the future study of fruit physiological behaviors underlying postharvest enzymatic browning. Because, in the later stage of the ripening the degradation of fruit peel color from yellow to brown is the major issue in the supply chain and the storage industries.
DOI: 10.17632/cp3473x7xv.2
title: LG 18650HG2 Li-ion Battery Data and Example Deep Neural Network xEV SOC Estimator Script

description: The included tests were performed at McMaster University in Hamilton, Ontario, Canada by Dr. Phillip Kollmeyer (phillip.kollmeyer@gmail.com). If this data is utilized for any purpose, it should be appropriately referenced. A brand new 3Ah LG HG2 cell was tested in an 8 cu.ft. thermal chamber with a 75amp, 5 volt Digatron Firing Circuits Universal Battery Tester channel with a voltage and current accuracy of 0.1% of full scale. these data are used in the design process of an SOC estimator using a deep feedforward neural network (FNN) approach. The data also includes a description of data acquisition, data preparation, development of an FNN example script. The test data, or similar data, has been used for some publications, including: C. Vidal, P. Kollmeyer, M. Naguib, P. Malysz, O. Gross, and A. Emadi, “Robust xEV Battery State-of-Charge Estimator Design using Deep Neural Networks,” in Proc WCX SAE World Congress Experience, Detroit, MI, Apr 2020 C. Vidal, P. Kollmeyer, E. Chemali and A. Emadi, "Li-ion Battery State of Charge Estimation Using Long Short-Term Memory Recurrent Neural Network with Transfer Learning," 2019 IEEE Transportation Electrification Conference and Expo (ITEC), Detroit, MI, USA, 2019, pp. 1-6.
DOI: 10.17632/523d6dxz4n.1
title: BdSLW-11: A Bangladeshi Sign Language Words Dataset for Recognizing 11 daily useful BdSL Words

description:  BdSLW - 11 stands for Bangladeshi Sign Language Words dataset for 11 sign words. This dataset is a collection of 1105 images of 11 classes or categories.  Each class of the dataset contains more than 77 images. The classes are labeled as the 11 BdSL daily useful common Sign Words. The classes are 'Bad', 'Beautiful',' Friend', 'Good',' House',' Me',' My', 'Request',' Skin',' Urine' and 'You'. The images are selected and 
 processed focusing on only hand gestures, clear background, and brightness.  The size of the images is 224 x 224 and the format of the images is RGB with high resolution. Images are taken from volunteer signers with full permission by smartphones.  This dataset is greatly helpful for the Deaf and Dumb community and also researchers.
DOI: 10.17632/xr3cftxcjz.1
title: modified raw data

description: Raw data from general test, EEM and FT-ICR MS.
DOI: 10.17632/9vks4xfn9x.1
title: Neuroeconomics Research Dataset (1991-2020)

description: The neuroeconomics research and publication dataset, which was indexed by Scopus from 1991 to 2020. The daraset contains data authors, authors ID Scopus, tittle, year, source tittle, volume, issue, article number in Scopus, DOI, link, affiliation, abstract, index keywords, references, Corrspondence Addressm editors, publisher, conference name, conference date, conference code, ISSN, language, document type, access type, and EID.
DOI: 10.17632/cdjyh5ytp8.2
title: Mildew detection in rice grains based on computer vision and YOLO convolutional neural network

description: data for paper Mildew detection in rice grains based on computer vision and YOLO convolutional neural network
DOI: 10.17632/52bfvs5vyn.1
title: Data for: Comparing the acute and chronic toxicity of flupyradifurone and imidacloprid to non-target aquatic arthropod species 

description: This data sets include raw data of water quality, measured water concentration, growth of tested species, survival and affected number, SSD, internal concentration and food consumption.
DOI: 10.17632/5ty2wb6gvg.1
title: RDD2020: An Image Dataset for Smartphone-based Road Damage Detection and Classification

description: The RDD2020 dataset contains 26336 road images collected from India, Japan, and the Czech Republic with more than 31000 instances of road damage. The dataset contains annotation for four damage categories: Longitudinal Cracks(D00), Transverse Cracks(D10), Alligator Cracks(D20), and Potholes(D40); and is intended for developing deep learning-based methods to detect and classify road damage automatically. The images in RDD2020 were captured using vehicle-mounted smartphones, making it useful for municipalities and road agencies to develop methods for low-cost monitoring of road pavement surface conditions. Further, the machine learning researchers can use the datasets for benchmarking the performance of different algorithms for solving other problems of the same type (classification, object detection, etc.). For instance, the Global Road Damage Detection Challenge (GRDDC'2020), organized as an IEEE Big Data Cup in 2020, utilized the dataset RDD2020 to evaluate the road damage detection models proposed by the participants. An overview of the challenge is provided in the video https://www.youtube.com/watch?v=8sh70wjn1aI. The readers may access the latest updates and the articles related to the dataset at https://www.researchgate.net/project/Global-Road-Damage-Detection.
DOI: 10.17632/6jmv43nffk.2
title: Dataset for Factors Affecting Teachers’ Burnout: A SEM Analysis in an Asian Context

description: The dataset presents the relationship between Teacher Self-Concept (TSC) and Teacher Efficacy (TE) as the predictors predicting burnout for the Indonesian context. Three components of burnout involved in this study are Emotional Exhaustion (EE), Depersonalization (DP), and Reduced Personal Accomplishment (RPA). Content Validity Index (CVI), Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), Covariance-Based Structural Equation Modeling (CB-SEM) were addressed for the model validation and relationship testing
DOI: 10.17632/rff87t7mz6.1
title: Dataset of A New product development processes for IOT-enabled home use medical devices: a systematic review

description: A systematic review study following the PRISMA-P statement in the field of IOT and home use medical device new product development process of research articles in four electronic databases: PubMed, Web of Science, ScienceDirect and IEEE Xplore, published during 2007 to 2018 in the English language.
DOI: 10.17632/wrzdhhgsn9.1
title: Anaerobic threshold and respiratory compensation point identification during CPET in chronic heart failure

description: Background. We evaluated the prognostic significance of the simple presence or absence of identifiable anaerobic threshold (AT) and respiratory compensation point (RCP) at cardiopulmonary exercise test (CPET) performed with a maximal incremental exercise protocol. Methods. In the present multicenter study, we retrospectively analyzed data of 1995 reduced-ejection-fraction heart failure (HFrEF) patients. All underwent clinical and laboratory evaluation, echocardiography, and maximal CPET at baseline. The analysis was performed according to absence of identified AT and RCP (group 1, n=292, 15%), presence of AT but absence of identified RCP (group 2, n=920, 46%), and presence of both AT and RCP (group 3, n=783, 39%). The study endpoint was the composite of cardiovascular mortality/urgent heart transplantation/left ventricular assist device implantation. Results. Median follow-up was 2.97 years (interquartile range 1.50–5.35 years). Eighty-seven (30%), 169 (18%), and 111 (14%) events were observed in group 1, 2, and 3, respectively (p= 0.025). Compared to group 3 (best survival patients), the likelihood of reaching the study endpoint increased 2.7 times when neither AT nor RCP were identified (HR 2.74), and 1.4 times when only AT was identified (HR=1.39). Moreover, adding the presence/absence of identified AT and RCP improved peak VO2 prognostic power, since a significant reclassification was obtained (3.57%, 95% CI 1.9%, 5.2%, p &lt;0.001). Conclusion. AT and RCP identification has a potential role in the prognostic stratification of HFrEF.
DOI: 10.17632/khv4y69z7g.1
title: Data for: INHERENT OCCUPATIONAL HEALTH HAZARDS IN THE PRODUCTION OF SOLAR GRADE SILICON

description: supplmentary material to manuscript INHERENT OCCUPATIONAL HEALTH HAZARDS IN THE PRODUCTION OF SOLAR GRADE SILICON
DOI: 10.17632/dk39nzc2zy.1
title: Code and Online Appendix for "Sovereign Risk and Intangible Investment"

description: Replication code for "Sovereign Risk and Intangible Investment" by Minjie Deng and Chang Liu.
Abstract: This paper measures the output and TFP losses from sovereign risk, considering firm-level intangible investment. Using Italian firm-level data, we show that firms reallocated from intangible assets to tangible assets during the 2011-2012 Italian sovereign debt crisis. This asset reallocation is more pronounced among small firms and high-leverage firms. This reallocation affects aggregate output and TFP. To explain the reallocation pattern and quantify the output and TFP losses, we build a sovereign default model incorporating firm intangible investment. In our model, sovereign risk deteriorates bank balance sheets, disrupting banks’ ability to finance firms. Firms with greater external financing needs are more exposed to sovereign risk. Facing tightening financial constraints, firms shift their resources towards tangibles because they can be used as collateral. We find that elevated sovereign risk explains 45% of the observed output losses and 31% of the TFP losses in Italy from 2011 to 2016.
Keywords: Sovereign debt crisis, intangible asset, firm investment, TFP loss