cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/g8wrh8y3ws.1
title: Research letter: BRAF-inhibitor induced panniculitis- A systematic review Supplemental Material

description: To identify the common clinicopathological characteristics and management of BRAF-inhibitor induced panniculitis (BiP) we performed a systematic review that summarizes the current literature reporting the development of BiP. BiP appears to be a mild cutaneous adverse reaction that may benefit from symptomatic management without targeted therapy cessation. 

The following supplemental materials include a figure representing the schematic overview of our systematic review and tables of search terms and data points collected from the included reports. Additional tables of summarized patient characteristics and histopathology results, individual patient demographics and clinical findings, and the references of the studies included in this review.
DOI: 10.17632/dwjykg3xww.1
title: Refined NHANES 2007-2012 spirometry dataset for the comparison of segmented (piecewise) linear models to that of GAMLSS

description: Background
Current guidelines recommend using Generalized Additive Models for Location, Scale, and Shape (GAMLSS) to create reference equations for lung function. However, these models are complex & require additional spline tables for use. This study aimed to demonstrate that simpler methods, such as simple linear regression for the forced exhaled volume in 1 second to forced vital capacity ratio (FEV1/FVC ratio) and segmented linear regression (SLR) for forced exhaled volume in 1 second (FEV1) and forced vital capacity (FVC), can achieve similar prediction accuracies as GAMLSS in pulmonary function diagnostics.

Data
This study utilized secondary data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2007 and 2012. The dataset includes spirometry measurements from 16,596 participants (from an initial pool of 31,451) aged 6-80 years, representing diverse racial and ethnic backgrounds. Participants' weights ranged from 16.4-218.2 kg, heights from 104.6-203.8 cm, and BMI from 12.5-84.9 kg/m². The refined dataset only includes participants who met the minimum technical quality standards for spirometry maneuvers, as outlined by the American Thoracic Society (2005), specifically those performing A and B quality maneuvers. However, the data file also provides a secondary analysis of the calculated z-scores from the developed GAMLSS and piecewise regression models including whether participants had a restrictive respiratory pattern or an airway obstruction classification based on the calculated lower limit of normal. 

Methods
Reference equations FEV1, FVC, and the FEV1/FVC ratio were developed by G.Z. using different modeling techniques: simple linear regression for the FEV1/FVC ratio and segmented linear regression (SLR) for FEV1 and FVC. Initially, all races/ethnicities were grouped together as the primary hypothesis was to compare GAMLSS to SLR models and not to compare different biological ancestries. K-fold cross-validation was applied to calculate the 95% confidence interval (CI) for the root-mean-square error (RMSE), which served as an indicator of prediction accuracy. Additionally, the agreement between both modeling approaches in classifying spirometric patterns [normal, airflow obstruction, restrictive, mixed disorder, or preserved ratio impaired spirometry (PRISm)] was assessed using an unweighted kappa statistic.

Results
The RMSE values and correlation coefficients for FEV1, FVC, and the FEV1/FVC ratio were similar between the two modeling techniques. The agreement between the models in classifying spirometric patterns was also high, with kappa values ranging from 0.78 to 0.80 (95% CI).

Conclusions
Simple linear regression (FEV1/FVC ratio) and segmented linear regression (FEV1, FVC) provide prediction accuracies comparable to those of GAMLSS models. These simpler methods are more straightforward and accessible, making them a practical alternative for broader use in pulmonary function diagnostics.
DOI: 10.17632/2kpxjswz47.1
title: Theoretical framework model (TFM) for examining the professional development of teachers in the light of TALIS 2018 data

description: The aspect of this study is the editable and content-wise validation of the TFM to continue our quantitative research. We contrasted the TFM and TALIS 2018 results with Hungarian relevance, so we also got an answer to whether there is a contradiction between the central expectations towards teachers in Hungary and their belief in their professional development. First, we reconciled the dimensions and indicators of the TFM to the TALIS 2018 research tools, and then we filtered out the relevant Hungarian data of the OECD database. To show the correlations, the responses of teachers from Hungary and TALIS countries were subjected to secondary statistical analysis. Our results highlight disproportions in the priority of the TFM dimensions and shortcomings of our indicator system in career management. An additional outcome of the analysis is the exploration of the correlations underlying particular Hungary-related findings of TALIS. H.1: The career satisfaction of Hungarian teachers (working conditions; income) is significantly lower than the satisfaction of teachers in TALIS countries, and this decreases further with the increase of professional experience. H.2: The need of Hungarian teachers for professional development (learning) significantly exceeds that of teachers in TALIS countries, because compliance with the career model as an external coercive factor poses more serious challenges than ever before. H.3: The dominant form of the learning strategies of Hungarian teachers is the participation in the postgrad courses and organized professional training, as the support system in Hungary prefers these. H.4: The shortcomings of the system supporting professional development are felt more strongly among Hungarian teachers than in the TALIS countries because continuous mentoring or counselling focuses on the period of starting a career. H.5: Control has a more significant effect on the professional development of Hungarian teachers than learning motivation or interest. H.6: TFM is suitable to serve as a starting point for large-sample pedagogical research in Hungary. Its dimensions and indicator system can systematize the filtering and processing of the large dataset, and a second- analysis of TALIS 2018 thematic data confirms it. In the current research, we first performed a thematic identification of our TFM and the TALIS 2018 teacher questionnaires to fit the questions of the questionnaire into the structure of our model using content analysis and to allow filtering of the grouped quantitative data. Then, proceeding with the dimensions of the TFM, we made statistical comparisons between the answers of Hungarian teachers and the teachers of the TALIS countries to the questions of the topics on each dimension. This method enabled to supplement of the TFM indicator system and to identify early-stage effects of national education policy regulations underlying the TALIS results.
DOI: 10.17632/gdxxcb3zmw.1
title: Matlab code for automatic detection and quantifiaction of Enchytraeus crypticus

description: The available software code was developed using the Matlab Imaging Processing Toobox. The code detects and enumerates Enchytraeus crypticus from images.
DOI: 10.17632/fw5vt88xcg.1
title: Data from: Interrupting Prolonged Sitting Reduces Postprandial Glucose Concentration in Young Men with Central Obesity

description: Supplementary 1. Postprandial responses of the first meal, second meal, and overall postprandial observation period in the three experimental conditions. Data are presented as the mean ± SEM for n = 18; *P &lt; 0.05 compared with the SIT trial, **P &lt; 0.01 compared with the SIT trial, †P &lt; 0.05 compared with the 1.5-min trial, and ††P &lt; 0.01 compared with the 1.5-min trial. SIT = prolonged sitting; 3-min = 3-min of light-intensity walking every 30 min; 1.5-min = 1.5-min of moderate-intensity walking every 30 min; NEFA = non-esterified fatty acids; iAUC = incremental area under the curve.
DOI: 10.17632/j9ytfdzy27.1
title: IndoWaveSentiment: Indonesian Audio Dataset for Emotion Classification

description: IndoWaveSentiment is an audio dataset designed for classifying emotional expressions in Indonesian speech. The dataset was created using recordings from 10 actors, equally split between men and women. Recordings were made using a mono channel cardioid vocal microphone positioned no more than 10 cm from the speakers, connected to a laptop or computer. The audio was captured at a sample rate of 16 kHz with 32-bit depth. Each actor repeated the same sentence three times across five emotional categories: neutral, happy, surprised, disgusted, and disappointed. The dataset comprises 300 .wav files in total. This data was utilized to develop deep learning models as an initial study on audio classification in multimodal sentiment analysis. IndoWaveSentiment is also suitable for various signal processing applications, including voice emotion classification, and supports the development of sentiment analysis.
DOI: 10.17632/fshy54ypyh.3
title: EEG dataset of individuals with intellectual and developmental disorder and healthy controls while observing rest and music stimuli

description: This data presents a collection of EEG recordings of seven participants with Intellectual and Developmental Disorder (IDD) and seven Typically Developing Controls (TDC). The data is recorded while the participants observe a resting state and a soothing music stimuli. The data was collected using a high-resolution multi-channel dry-electrode system from EMOTIV called EPOC+. This is a 14-channel device with two reference channels and a sampling frequency of 128 Hz. The data was collected in a noise-isolated room. The participants were informed of the experimental procedure, related risks and were asked to keep their eyes closed throughout the experiment. The data is provided in two formats, (1) Raw EEG data and (2) Pre-processed and clean EEG data for both the group of participants. This data can be used to explore the functional brain connectivity of the IDD group. In addition, behavioral information like IQ, SQ, music apprehension and facial expressions (emotion) for IDD participants is provided in file “QualitativeData.xlsx".

Data Usage:
The data is arranged as follows:
1. Raw Data: 
Data/RawData/RawData_TDC/Music and Rest 
Data/RawData/RawData_IDD/Music and Rest 
2. Clean Data
Data/CleanData/CleanData_TDC/Music and Rest 
Data/CleanData/CleanData_IDD/Music and Rest 

The dataset comes along with a fully automated EEG pre-processing pipeline.  This pipeline can be used to do batch-processing of raw EEG files to obtain clean and pre-processed EEG files. Key features of this pipeline are :
(1) Bandpass filtering
(2) Linenoise removal
(3) Channel selection
(4) Independent Component Analysis (ICA) 
(5) Automatic artifact rejection
All the required files are present in the Pipeline folder.

If you use this dataset and/or the fully automated pre-processing pipeline for your research work, kindly cite these two articles linked to this dataset.
(1) Sareen, E., Singh, L., Gupta, A., Verma, R., Achary, G. K., & Varkey, B. (2020). Functional Brain Connectivity Analysis in Intellectual Developmental Disorder During Music Perception. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(11), 2420-2430. DOI:10.1109/TNSRE.2020.3024937
(2) Sareen, E., Singh, L., Varkey, B., Achary, K., Gupta, A. (2020).  EEG dataset of individuals with intellectual and developmental disorder and healthy controls under rest and music stimuli. Data in Brief, 105488, ISSN 2352-3409, DOI:https://doi.org/10.1016/j.dib.2020.105488.
(3) Sareen, E., Gupta, A., Verma, R., Achary, G. K., Varkey, B (2019). Studying functional brain networks from dry electrode EEG set during music and resting states in neurodevelopment disorder, bioRxiv 759738 [Preprint]. Available from: https://www.biorxiv.org/content/10.1101/759738v1
DOI: 10.17632/mf6fjkpw3b.2
title: Development of an Electric Vehicle (EV) Powered by Solar Energy

description: The conventional fuel run vehicle is the biggest reason for the hazardous particles emitted in our modern world. For that we are trying to shift into a renewable source of energy for running the vehicles. Fuel cell electric vehicles, hybrid electric vehicles, and electric vehicles are some of the alternatives. An electric vehicle (EV) develops with one or more electric motors for running and powered by an electrical energy source. This paper represents the usage of a solar energy system to power up the electric vehicle. Solar energy is the energy from radiant light and heat of the Sun converted to electric energy. Electric vehicles are not a new concept the first time introduced within the middle of the 19th century. In this project, a fast and power-efficient charger has been designed for a BLDC motor-driven electric vehicle where a dc battery is a feed by both conventional power source as MES and PV cells as RES. The feature components are - charge controller, rechargeable battery, power converter system, solar panel system, and Brushless DC Motor, and the circuits designed severally applying PSIM software. In this paper, we mainly focus on two things, shift into a renewable source of energy for the vehicle, and usage of solar energy to power up the electric vehicle.
DOI: 10.17632/mxpgf54czz.2
title: Python code for the estimation of missing prices in real-estate market with a dataset of house prices from Teruel city

description: This research data file contains the necessary software and the dataset for estimating the missing prices of house units. This approach combines several machine learning techniques (linear regression, support vector regression, the k-nearest neighbors and a multi-layer perceptron neural network) with several dimensionality reduction techniques (non-negative factorization, recursive feature elimination and feature selection with a variance threshold). It includes the input dataset formed with the available house prices in two neighborhoods of Teruel city (Spain) in November 13, 2017 from Idealista website. These two neighborhoods are the center of the city and “Ensanche”. This dataset supports the research of the authors in the improvement of the setup of agent-based simulations about real-estate market. The work about this dataset has been submitted for consideration for publication to a scientific journal. The open source python code is composed of all the files with the “.py” extension. The main program can be executed from the “main.py” file. The “boxplotErrors.eps” is a chart generated from the execution of the code, and compares the results of the different combinations of machine learning techniques and dimensionality reduction methods. The dataset is in the “data” folder. The input raw data of the house prices are in the “dataRaw.csv” file. These were shuffled into the “dataShuffled.csv” file. We used cross-validation to obtain the estimations of house prices. The outputted estimations alongside the real values are stored in different files of the “data” folder, in which each filename is composed by the machine learning technique abbreviation and the dimensionality reduction method abbreviation.
DOI: 10.17632/zh3wnddzxy.2
title: 10 Days DNS Network Traffic from April-May, 2016

description: Campus DNS network traffic consisting of more than 4000 active users (in peak load hours) for 10 random days in the month of April-May, 2016 is available in hourly PCAP files in the dataset. (At present only traffic for Day0(Full) and Day1(partial) could be uploaded due to 10GB data limit)
DOI: 10.17632/5939yh85bh.5
title: Data and Code for ``Return predictability with endogenous growth''

description: The code replicates the empirical analysis (folder Empirical) and the simulation from the DSGE model (folder Model) for the paper ``Return predictability with endogenous growth.'' Please see the Readme file for additional information.
DOI: 10.17632/5mh2hn3tnz.1
title: Determinants of Rural - Urban Migration and Its Impact on Environment and Health: Evidence from Cumilla City Corporation, Bangladesh

description: Availability of data and materials: The primary data were collected by field survey 2021-2022. Questionnaire and interview methods were used to collect primary data from the study areas.
DOI: 10.17632/cb8t8np9z3.1
title: Sky Images and PV power Measurements

description: Ground-based sky images related to sunny, partially cloudy, and overcast sky conditions are available in this data set with corresponding power measurements that were obtained from two rooftop PV systems. The sky camera systems were made using Raspberry Pi mini-computers (3rd Generation Model B) and using programmable high-resolution Pi-Cameras with fisheye lenses. The sky camera systems were operated remotely and were programmed to automatically capture images from 8.00 am to 4.45 pm at a rate of 10 seconds and store them in JPEG format with a resolution of 1024x768 pixels. The RPI camera was assumed to have an equidistant mapping function. To collect the data, one camera was installed near a rooftop PV system (Location 1) at the Sustainable Buildings Research Centre (SBRC) at the Innovation Campus, University of Wollongong, Australia. The second camera was installed 1.9 km away (Location 2) from the first camera and it was installed at the roof of "Building 35" at the Main Campus of the University of Wollongong, Australia. Further corresponding PV power measurements were collected from Location 1 and Locations 3 ([34.41°N, 150.88°E] rooftop PV system at "Building 28", the main campus, University of Wollongong) which is 2 km away from camera 1 (Location 1 [34.40°N, 150.90°E]). Power measurements were acquired from both locations with a 1-minute resolution. Data sets obtained from camera 1 can be used for onsite PV forecasting, whereas using both data sets obtained from camera 1 and camera 2, PV forecasts for multiple PV sites can be derived via calculating cloud base heights.
DOI: 10.17632/77ktr5jm2k.1
title: DDGS

description: Table 10.3 Fuel Ethanol Overview March 2018 Monthly Energy Review Fuel Ethanol Production (Million Gallons)
DOI: 10.17632/pwfxgzz5fj.2
title: A dataset of stem bark reflectance spectra for boreal and temperate tree species

description: The collection and preprocessing of this dataset has been reported in detail in Juola J., Hovi A., Rautiainen M., 2022. A spectral analysis of stem bark for boreal and temperate tree species. Ecology and Evolution. DOI:10.1002/ece3.8718

The dataset includes stem bark reflectance spectra of ten boreal and temperate tree species. We measured hemispherical-directional reflectance factors (HDRF) of stem bark samples. The data were measured using a Specim IQ imaging spectrometer attached to a tree mounting setup. The imaging spectrometer operates in the visible and near-infrared wavelength region (400–1000 nm). The measurements were made in the Greater Helsinki area in Finland, and in Järvselja in Estonia.  We measured 20 trees per species, accounting to a total of 200 unique stem bark samples. One sample refers to a hyperspectral reflectance image taken of a single tree stem. The 200 mean spectral signatures (presented in this dataset) were calculated for each sample by averaging the HDRF values over all pixels per wavelength. The measurements were conducted from May to August 2020. All measurements were made under natural illumination conditions and the images were taken in perpendicular view-angle to the tree stem. The dataset includes two tabular files that contain: 1) the mean reflectance spectrum for each of the ten common boreal and temperate tree species (calculated from the 20 samples per species), and 2) all 200 reflectance spectra (20 per tree species). The sampled species were: Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst), silver birch (Betula pendula Roth), grey alder (Alnus incana (L.) Moench), black alder (Alnus glutinosa (L.) Gaertn.), English oak (Quercus robur L.), European aspen (Populus tremula L.), European ash (Fraxinus excelsior L.), Norway maple (Acer platanoides L.), and littleleaf linden (Tilia cordata Mill.).

The two spreadsheets are organized as follows: first three rows that start with hashtag (#) contain comments for citing, first column "tree_species" (Pinus_sylvestris, Picea_abies, Betula_pendula...) indicates the tree species, and the subsequent columns "wl397.32"-"wl1003.58" indicate the measured HDRF value at that wavelength. The encoding of the csv file is UTF-8. 

The data in file "mean_tree_species_stem_bark_reflectance_spectra_Juola_et_al_2021.csv" is the same as what can be found in Version 1. We only changed the first row's naming to make it clearer.

If you use this dataset, please cite Juola et al. [1,2]:
[1] Juola J., Hovi A., Rautiainen M., 2022. A spectral analysis of stem bark for boreal and temperate tree species. Ecology and Evolution. DOI:10.1002/ece3.8718
[2] Juola J., Hovi A., Rautiainen M., 2022. A dataset of stem bark reflectance spectra for boreal and temperate tree species. Mendeley Data, V2, doi: 10.17632/pwfxgzz5fj.2
DOI: 10.17632/8xp3nf6d9n.1
title: Meta-data about children outcomes of divorce preventive interventions

description: This data base includes the information about the effects of 30 after-divorce intervention studies on children adjustment and the characteristics of these studies.
It includes the followinf outcome variables:
Children´s psychosocial adjustment, which involves general symptomatology (e.g., depression, behavioral problems, and negative peer relationships), personal resources (e.g., self-esteem and coping) and adaptation to divorce (e.g., divorce adjustment and negative perception of divorce); and family functioning (e.g., interparental conflict and parent-child relationship quality). 
And the following study characteristics:
publication year, sample size, active control, informant, participant, application context, country, number of intervention sessions, duration of the intervention sessions, session type, ethnicity, high education, new parents’ partner, parent’s gender, children’s gender, children’s age stage, time from divorce, custodial parent, incentive for participantion, recruitment context and the random allocation of participants.
DOI: 10.17632/2d3kr8zynf.2
title: Arbovirus clinical data, Brazil, 2013–2020

description: This data set presents clinical, sociodemographic, and laboratory information of confirmed patients of Dengue and Chikungunya, as well as patients cases discarded from these same diseases. The data were accounted for by the Health Problem and Notification Information System, from Portuguese Sistema de Informação de Agravo de Notificação (SINAN), that occurred in Brazil, from 2013 to 2020. The data set has 7,632,542 records and 56 attributes. This data set is in a single CSV named "data set.csv", and the "attributes.csv" file contains information about each attribute present in the data set.
DOI: 10.17632/v6np4jck2t.3
title: Dataset PLS-SEM Analysis Pentagon Fraud Factor of Academic Dishonesty/Fraud in The High School & Vocational High School of Jepara

description: The dataset contains an analysis of the SmartPLS 3 (PLS algorithm and Bootstrapping), filtered and translated data from Google Forms (RAW Data). The data analysis was used to determine the factors that affect academic dishonesty/fraud from the Crowe Pentagon Fraud perspective. The sample of the study was 272 high schools and/or vocational high schools in Jepara Regency, Central Java Province, Indonesia.