cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/3twccmpdry.1
title: NGS Patstat supplementary data

description: The datasets included in the repository concern results obtained following patent searches performed by means of Patstat online (https://www.epo.org/searching-for-patents/business/patstat.html#tab-1). The SQL based scripts aimed at the analysis performed by means of Patstat online Autumn 2019 (pls. refer to "NGS Patstat data", Priore, Riccardo (2020), “NGS Patstat data”, Mendeley Data, V1, doi: 10.17632/f45st2xmkj.1) are flanked by a collection of SQL scripts tested on the Patstat online Autumn 2020 edition. The codes of each script refer, exactly as in the former repository ("NGS Patstat data" regarding the nucleic acids' Next Generation Sequencing techniques) to the following kinds of analysis: 1) the script named 'C1' is essentially aimed at determining the amount of patent families corresponding to patent applications filed considering the Next Generation Sequencing techniques/tools. 2) The script named ‘N1’ (‘Normalization NGS’) allows to determine the number of patent applications specifically concerning the NGS matter and filed to different national patent authorities. It may be reasonably assumed that if one geographical area is much wider and characterised by a higher number of residents than a second one, in the former case a higher number of patent applications are filed than in the latter case. However, the evaluation may significantly change when dividing the number of the patent applications specifically concerning the NGS field by the number of patent applications filed to the same authority, though irrespectively of the technical item. This procedure is referred to as 'normalization'. 3) The scripts named ‘Q1 – Q5’ are the results of search criteria aimed at estimating the value of the pooled patent documents. Metrics to be used for such aim include the number of the triadic patent families, the ranking of the patent applicants based on the dimension of the patent family or on the average number of forward citations of a patent family. 4) The scripts named ‘S1 – S2’ are aimed at elucidating the collaborations occurred in a specific time-frame between one applicant and one patent attorney. Cases in which two applicants, having collaborated with the very same patent attorney, can be considered as competitors or collaborators (co-assignees according to the possibility of co-filing a patent application), could emerge by means of such search phase. Differently from the analysis performed by means of the SQL scripts stored in "Patstat NGS" repository mentioned above, the SQL scritps provided in such new repository have been slightly modified to permit the analysis, performed by means of Patstat online, of a dataset regarding the NGS technologies and produced interrogating Orbit Intelligence. A preliminary elaboration of such Orbit dataset is based on Python and has been performed to rank the most relevant technical concepts dealing with the NGS technologies.
DOI: 10.17632/y3hstv4bfn.2
title: Indonesian Javanese Dictionary Starter Kit

description: This dataset contains dictionary data of Indonesian-Javanese with three level of Javanese politeness: ngoko, krama alus, and krama inggil. The data were obtained from various free source of language data. This dataset is formatted as JSON. The data is natively used by firebase system.
DOI: 10.17632/hz6v96kcv5.3
title: Influenza Vaccination Coverage of Population and the Factors Influencing Influenza Vaccination in Mainland China: A Meta-analysis

description: The data provided was to conduct meta analysis about influenza vaccination coverage and its influencing factors . It also contained different subgroups and the meta regression.
DOI: 10.17632/bvtczszdvz.1
title: Chiropractic effects on stride parameters and heart rate during exercise in sport horses

description: Abstract
Background: Equine veterinarians performing chiropractic treatments are frequently asked to evaluate and treat sound horses to improve their performance and address pain associated with the axial skeleton. Studies describing the effects and mechanisms of chiropractic treatments in horses without overt lameness are scarce. 
Objectives: This study aimed to evaluate the effect of chiropractic treatments on stride rate, length, symmetry, heart rate, and rider-perceived quality of the ridden work in sport horses. 
Study Design: A blind randomized controlled trial with crossover design 
Methods: 38 horses ridden in the hunter-jumper discipline were enrolled. Exercise tests were recorded before and after chiropractic or sham treatment while horses were wearing a fitness tracker. Stride length, rate and symmetry, heart rate and the perceived quality of the ridden work were compared. 
Results: There was a difference in the qualitative assessment of the ridden work by riders between treatments (odds ratio 33.8, 95%CI [4.68, 458.71], p&lt; 0.01). Stride length, rate, symmetry, and heart rate were not different between treatments.
Main limitations: The quantitative outcomes measured may not be sensitive enough to detect changes that improve the ridden work. Terrain, weather, and rider were not standard across horses making small changes difficult to detect.
Conclusions: Riders participating in a blind randomized controlled trial perceived a positive effect of chiropractic treatments on the quality of the ridden work. There were no differences in stride length, stride rate, stride symmetry or heart rate. The mechanisms, indications, and potential benefits of chiropractic treatments in horses need further study.
DOI: 10.17632/3jykz7wz8d.1
title: DATASET for "Deep learning segmentation of transverse musculoskeletal ultrasound images for neuromuscular disease assessment"

description: Here we provide the entire dataset and code for the article "Deep learning segmentation of transverse musculoskeletal ultrasound images for neuromuscular disease assessment" published in Computers in Biology and Medicine (DOI: 10.1016/j.compbiomed.2021.104623). Any results obtained using the published dataset MUST cite the original research article (https://www.sciencedirect.com/science/article/abs/pii/S0010482521004170). See the ReadMe file "readme.txt" for all details. Abstract: "Ultrasound imaging is a patient-friendly and robust technique for studying physiological and pathological muscles. An automatic deep learning (DL) system for the analysis of ultrasound images could be useful to support an expert operator, allowing the study of large datasets requiring less human interaction. The purpose of this study is to present a deep learning algorithm for the cross-sectional area (CSA) segmentation in transverse musculoskeletal ultrasound images, providing a quantitative grayscale analysis which is useful for studying muscles, and to validate the results in a large dataset. The dataset included 3917 images of biceps brachii, tibialis anterior and gastrocnemius medialis acquired on 1283 subjects (mean age 50 years ± 21, 729 male). The algorithm was based on multiple deep-learning architectures, and its performance was compared to a manual expert segmentation. We compared the mean grayscale value inside the automatic and manual CSA using Bland-Altman plots and a correlation analysis. Classification in healthy and abnormal muscles between automatic and manual segmentation were compared using the grayscale value z-scores. In the test set, a Precision of 0.88 ± 0.12 and a Recall of 0.92 ± 0.09 was achieved. The network segmentation performance was slightly less in abnormal muscles, without a loss of discrimination between healthy and abnormal muscle images. Bland-Altman plots showed no clear trend in the error distribution and the two readings have a 0.99 Pearson’s correlation coefficient (p&lt;0.001, test set). The ICC(A,1) calculated between the z-score readings was 0.99. The algorithm achieves robust CSA segmentation performance and gives mean grayscale level information comparable to a manual operator. This could provide a helpful tool for clinicians in neuromuscular disease diagnosis and follow-up. The entire dataset and code are made available for the research community."
DOI: 10.17632/nn3fwy4h4f.1
title: Digital Nativity and Internet addiction data and extended data

description: The research aimed to explore the relationship between digital nativity and internet addiction among university students at Kwame Nkrumah University of Science and Technology (KNUST) in Ghana. The hypothesis was that higher levels of digital nativity, as measured by the Digital Native Assessment Scale (DNAS), would correlate with higher levels of internet addiction, measured by the Internet Addiction Test (IAT). The data from 411 undergraduate students from various academic disciplines supported this hypothesis, showing a significant positive correlation between DNAS and IAT scores (r = 0.569, p &lt; 0.001). This indicates that students with higher digital nativity are more prone to developing internet addiction behaviours.

Notable findings from the data include the influence of academic level and college affiliation on internet addiction. Students in higher academic years and those from the College of Science exhibited heightened susceptibility to internet addiction. Additionally, the data revealed that both g
DOI: 10.17632/s8tzy7pkhw
title: Supplementary data: Efficacy and safety of baricitinib in adult patients with alopecia areata: a real-world retrospective cohort study with insights of prognostic factors for hair growth

description: Supplementary data to Efficacy and safety of baricitinib in adult patients with alopecia areata: a real-world retrospective cohort study with insights of prognostic factors for hair growth
DOI: 10.17632/kjjm4z87nj.1
title: A CUDA-based GPU engine for gprMax: Open source FDTD electromagnetic simulation software

description: The Finite-Difference Time-Domain (FDTD) method is a popular numerical modelling technique in computational electromagnetics. The volumetric nature of the FDTD technique means simulations often require extensive computational resources (both processing time and memory). The simulation of Ground Penetrating Radar (GPR) is one such challenge, where the GPR transducer, subsurface/structure, and targets must all be included in the model, and must all be adequately discretised. Additionally, forward simulations of GPR can necessitate hundreds of models with different geometries (A-scans) to be executed. This is exacerbated by an order of magnitude when solving the inverse GPR problem or when using forward models to train machine learning algorithms. We have developed one of the first open source GPU-accelerated FDTD solvers specifically focussed on modelling GPR. We designed optimal kernels for GPU execution using NVIDIA’s CUDA framework. Our GPU solver achieved performance throughputs of up to 1194 Mcells/s and 3405 Mcells/s on NVIDIA Kepler and Pascal architectures, respectively. This is up to 30 times faster than the parallelised (OpenMP) CPU solver can achieve on a commonly-used desktop CPU (Intel Core i7-4790K). We found the cost-performance benefit of the NVIDIA GeForce-series Pascal-based GPUs – targeted towards the gaming market – to be especially notable, potentially allowing many individuals to benefit from this work using commodity workstations. We also note that the equivalent Tesla-series P100 GPU – targeted towards data-centre usage – demonstrates significant overall performance advantages due to its use of high-bandwidth memory. The performance benefits of our GPU-accelerated solver were demonstrated in a GPR environment by running a large-scale, realistic (including dispersive media, rough surface topography, and detailed antenna model) simulation of a buried anti-personnel landmine scenario. The previous version of this program (AFBG_v1_0) may be found at http://dx.doi.org/10.1016/j.cpc.2016.08.020.
DOI: 10.17632/djzbddbxv7.1
title: FRP shear strengthening

description: 1001个FRP抗剪加固试件统计
DOI: 10.17632/wp69xrf2c5.2
title: Network-risk framework for ArcGIS (version 2) and Bucharest road network data and results

description: INFP, CRMD and UCL have developed a framework capable of analyzing the implications of natural hazards on transportation networks, also in a time-dependent manner. This is currently embedded into an ArcGIS toolbox entitled Network-risk, which has been successfully tested for Bucharest, contributing to an insightful evaluation of emergency intervention times for ambulances and firefighters, in the case of an earthquake. The files and the user manual allow a replication of our recent analysis in Toma-Danila et al. (2022) and a download of results (such as affected roads and unaccesible areas in Bucharest), in various formats. Some of the results are also presented in an ArcGIS Online app, called "Riscul seismic al Bucurestiului" (The seismic risk of Bucharest), available at https://tinyurl.com/yt32aeyx. In the files you can find:
- the Bucharest road network used in the article;
- facilities for Bucharest and Ilfov, such as hospitals, firestations, buildings with seismic risk or tramway lines accesible by emergency vehicles
- results of the analysis: unaccesible roads and areas, service areas around facilities, closest facilities for representative points
- Excel calculator for Z elevation from OpenStreetMap data
- the user manual and a ArcGIS toolbox.

Main citation:
- Toma-Danila D., Tiganescu A., D'Ayala D., Armas I., Sun L. (2022) Time-Dependent Framework for Analyzing Emergency Intervention Travel Times and Risk Implications due to Earthquakes. Bucharest Case Study. Frontiers in Earth Science, https://doi.org/10.3389/feart.2022.834052

Previous references:
- Toma-Danila D., Armas I., Tiganescu A. (2020) Network-risk: an open GIS toolbox for estimating the implications of transportation network damage due to natural hazards, tested for Bucharest, Romania. Natural Hazards and Earth System Sciences, 20(5): 1421-1439, https://doi.org/10.5194/nhess-20-1421-2020
- Toma-Danila D. (2018) A GIS framework for evaluating the implications of urban road network failure due to earthquakes: Bucharest (Romania) case study. Natural Hazards, 93, 97-111, https://link.springer.com/article/10.1007/s11069-017-3069-y
DOI: 10.17632/cdn2vbvzgr.3
title: Dhaka EPZ air pollution: PM2.5, PM10, and CO measurements and predictions

description: This dataset provides historical and forecasted data on the concentrations of PM2.5, PM10, and CO in the air of Dhaka's export processing area. It includes measurements of these pollutants from January 2019 to December 2023 and predictions of their future levels up to December 2027. This information can be used to understand the air quality in this important industrial zone and to develop strategies for reducing air pollution. Air pollutants are forecasted using the Auto Regressive Moving Average (ARIMA) model in Rstudio, and model performance is checked. Performance results are also included in this dataset.
DOI: 10.17632/n4f5yxd66m.1
title: Penalva et al. D.cerebrum Social Preference and Reward dataset

description: Original code and data analysis supporting results presented in Penalva et al (2024):"Oxytocin-mediated social preference and socially reinforced reward learning in the miniature fish Danionella cerebrum"
DOI: 10.17632/8grb2wvhrd.1
title: Low bone mineral density among the Bangladeshi public health professionals: a cross-sectional study

description: Description: This dataset contains the data of 330 public health professionals who participated in 9th Regional Public Health Conference. Data were collected to evaluate bone mineral density (BMD) and potential demographic, behavioral and health correlates therefor. 
Systems and methods: A semi-structured questionnaire was developed to collect information on socio-demographic characteristics (age, sex, occupation, education, diet (habitual fruit consumption or vegetable intake, use of dietary supplements for vitamin D), lifestyle variables (physical activity, water intake, sleep quality, sunlight exposure and tobacco use), medical history (Hypertension, Diabetes, CVDs, Chronic Kidney disease).
Physical measurements: BMD, systolic and diastolic blood pressure (BP), height and weight.
BMD was measured by Ultrasound Bone Densitometer CM-200 Light (Furuno Electric Company, Japan) and T-scores were categorized according to the WHO criteria. Using SPSS 25.0 statistical analyses included chi-square and t-tests.
Key findings: We found that 37.9% of the respondents had low bone mass (LBM) and the prevalence increased significantly with age, especially in people aged from 46 to 65 years (45.5%). While the prevalence of LBM was slightly higher in females (38.5%) compared to males, there are significant gender-specific risks that need attention. Chronic conditions, especially chronic kidney disease (66.7%) and diabetes made up the highest LBM rate (53.3%). More respondents with a normal bone density reported managing regular physical exercise, adequate sleep, fruit and vegetable consumption, appropriate water intake and avoidance of tobacco use as compared to those with osteopenia or low BMD; however these associations were not statistically significant. While greater BMI was associated with a higher percentage of LBM, high overall BMD did not significantly correlate in this study. The LBM group had slightly higher systolic blood pressure and no significant effect on BMD was seen with respect to educational level.
Potential Uses of Dataset: This dataset offers valuable insights into bone health and associated factors within the public health workforce in Bangladesh. It can serve as a comparative basis for international studies on BMD and inform public health strategies to enhance bone health and manage related risks, particularly in aging populations.
Ethical Approval: Approval was granted by the Ethical Review Committee of Bangladesh University of Health Sciences, with informed consent obtained from all participants.
Keywords: low bone mass, Bangladesh, public health, professionals.
DOI: 10.17632/nn7y52nt46.1
title: Government debt and stock bubbles in China

description: This project provides the code for the paper titled "Government Debt and Stock Bubbles in China," which analyzes the relationship between government debt and stock bubbles using an empirical model and dynamic stochastic general equilibrium (DSGE) models. The code is divided into two sections: Empirical Model and DSGE Model.
The Empirical Model folder includes data and scripts for empirical analysis using a Vector Autoregression (VAR) model. Key datasets, such as stock bubble measurements and government debt data, are used to generate the main empirical results. The VAR.m script produces figures and table, including Figure 2, Appendix Figures A1–A4, and Appendix Table A1. 
The DSGE Model folder contains several DSGE models that explore the relationship between government debt and stock bubbles under various conditions. Different .mod files represent different model specifications. These models generate the impulse response results presented in Figures 3–12, along with additional appendix figures.
The results of this paper were obtained using MATLAB R2021a and Dynare 4.6.1.
DOI: 10.17632/s2x4d542dc.1
title: CoFFEE: Corrections For Formation Energy and Eigenvalues for charged defect simulations

description: Charged point defects in materials are widely studied using Density Functional Theory (DFT) packages with periodic boundary conditions. The formation energy and defect level computed from these simulations need to be corrected to remove the contributions from the spurious long-range interaction between the defect and its periodic images. To this effect, the CoFFEE code implements the Freysoldt-Neugebauer-Van de Walle (FNV) correction scheme. The corrections can be applied to charged defects in a complete range of material shapes and size: bulk, slab (or two-dimensional), wires and nanoribbons. The code is written in Python and features MPI parallelization and optimizations using the Cython package for slow steps.
DOI: 10.17632/4g7k9wptyd.2
title: Ready, Steady, Go AI: A Practical Tutorial on Fundamentals of Artificial Intelligence and Its Applications in Phenomics Image Analysis

description: Original PlantVillage tomato leaf images were obtained from J, Arun Pandian; Gopal, Geetharamani (2019), “Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network”, Mendeley Data, V1, doi: 10.17632/tywbtsjrjv.1. We provide: 1- A randomly split version of the dataset (80% training, 10% validation, and 10% testing). 2- 1000 annotated images with bounding boxes to train YOLOv3 on cropping leaf images. 3- 150 annotated images with segmentation masks to train SegNet on segmenting leaf images. 4- Cropped version of the split dataset. 5- Segmented version of the cropped and split dataset. 6- Balanced version of the segmented, cropped, and split dataset, balanced to1500 images per class, approximately. 7- Trained YOLO, SegNet, DCGAN, RF, DCNN, and Pretrained Densenet-161 DCNN models. For details, check our paper: "Ready, Steady, Go AI: A Practical Tutorial on Fundamentals of Artificial Intelligence and Its Applications in Phenomics Image Analysis"
DOI: 10.17632/n7nkz6jpyf.1
title: Articles on Financial literacy

description: The dataset includes bibliographic information of 1879 articles of the topic of Financial literacy in the Scopus database
DOI: 10.17632/jg7xxd524s.2
title: emergency_portugal

description: This dataset contains data about the number of response centres (hospitals, fire stations, police offices and railway stations) in every city in Portugal. It is compound of a GeoJSON file with geographic boundaries of the entire country, a GeoJSON file containing the geometry and response centres counting for each city, an OpenStreetMap (OSM) file for each city and a CSV table that summarizes all the data.
DOI: 10.17632/xrx7ddbbb4.1
title: Search queries to map scientific publications to the UN Sustainable Development Goals

description: The dataset includes search queries that can be used to identify scientific publications related to the United Nations Sustainable Development Goals (SDGs). We propose a new approach to mitigate the polysemy of terms as much as possible by targeting the most relevant subject areas for each SDG. In addition, we also used a text-mining tool to identify as many relevant phrases as possible. Publications identified through this process cannot be considered as evidence of the commitment of authors and their institutions to actions towards the targets established by the UN. However, they can be an accurate indicator of which research is relevant to the issues addressed by the SDGs, whether or not it is a direct contribution. These search queries can be used as calculated fields in Tableau Software but they can also be easily adapted to another tool by multiple search-and-replace actions.
DOI: 10.17632/tzh49mv2tg.1
title: GEE-ET

description: GEE-ET is an open-source service for implementing Surface Energy Balance Algorithm for Land (SEBAL) using Google Earth Engine (GEE).