cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/nct6gkwhjc.1
title: OFF, Open source Finite volume Fluid dynamics code: A free, high-order solver based on parallel, modular, object-oriented Fortran API

description: This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018) Abstract OFF, an open source (free software) code for performing fluid dynamics simulations, is presented. The aim of OFF is to solve, numerically, the unsteady (and steady) compressible Navier–Stokes equations of fluid dynamics by means of finite volume techniques: the research background is mainly focused on high-order (WENO) schemes for multi-fluids, multi-phase flows over complex geometries. To this purpose a highly modular, object-oriented application program interface (API) has been developed. I... Title of program: <i>OFF</i> Catalogue Id: AESV_v1_0 Nature of problem Numerical solution of the Compressible Navier-Stokes equations for multi-fluids multi-phase flows in complex geometries Versions of this program held in the CPC repository in Mendeley Data AESV_v1_0; <i>OFF</i>; 10.1016/j.cpc.2014.04.005
DOI: 10.17632/c2gw7fy2j4.3
title: Web page phishing detection

description: The provided dataset includes 11430 URLs with 87 extracted features. The dataset are designed to be used as a a benchmark for machine learning based phishing detection systems. Features are from three different classes: 56 extracted from the structure and syntax of URLs, 24 extracted from the content of their correspondent pages and 7 are extracetd by querying external services. The datatset is balanced, it containes exactly 50% phishing and 50% legitimate URLs. Associated to the dataset, we provide Python scripts used for the extraction of the features for potential replication or extension. Datasets are constructed on May 2020. dataset_A: contains a list a URLs together with their DOM tree objects that can be used for replication and experimenting new URL and content-based features overtaking short-time living of phishing web pages. dataset_B: containes the extracted feature values that can be used directly as inupt to classifiers for examination. Note that the data in this dataset are indexed with URLs so that one need to remove the index before experimentation.
DOI: 10.17632/fnn44p4mj8.1
title: Data for: Text detection and Recognition in Raw Image Dataset of Seven Segment Digital Energy Meter Display

description: The raw dataset contains images of digital energy meters (seven segment numerals). The total number of samples collected is 169. All the samples are collected from consumer premises at Tamilnadu, india. The images captured in a way that there is no flash or reflection from the display. The labelling process has been done using the ‘labelbox’ tool for the collected images. The COCO annotation JSON file is available with the link.
DOI: 10.17632/3vcstwdbrn.1
title: Fukushima and Pollock, Supplementary Data

description: Supplementary Data for Fukushima and Pollock (2020)
DOI: 10.17632/fh9x8459h8.1
title: Data for: Modeling and validation of a DC/DC power converter for building energy simulations: Application to BIPV systems

description: The dataset contains: Validation_TH.xlsx, Validation_EL.xlsx: The results of the validation procedure where experimentally obtained measurements are compared to simulation estimates. Sensitivity_CHTC.xlsx: Sensitivity analysis of overall heat transfer coefficient from the converter housing to the exterior. Case Study.xlsx: The results of the case study, i.e. loss distribution among the components.
DOI: 10.17632/ny5rt94vn2.2
title: ASi, LPMe and dMe dataset for 'Incubation experiments characterize turbid glacier plumes as a significant source of Mn and Co to sea water'

description: This dataset is supplemented to the manuscript entitled 'Xunchi Zhu*, Mark J. Hopwood, Katja Laufer-Meiser, Eric P. Achterberg, Incubation experiments characterize turbid glacier plumes as a significant source of Mn and Co to sea water. It contains median size, amorphous silica and labile particulate (LP) Fe, Mn, Co, Cu, Ni and Cd contents of sediment samples collected from Svalbard and Greenland, and variation of dissolved inorganic phosphate (DIP), silicic acid (DSi), dissloved (d) Fe, Mn, Co, Cu, Ni and Cd concentrations observed in sediment-seawater incubation work performed under varying conditions of sediment load, incubation time, temperature and light condition. Labile particulate Fe, Mn, Co, Cu, Ni and Cd contents were processed following a 2 hour weak acid leach (Berger et al., 2008) and measured on HR-ICP-MS (Element XR). Dissolved Fe, Mn, Co, Cu, Ni and Cd samples were preconcentrated on seaFAST system (SC-4 DX, ESI) with standard addition (Mn and Co) and isotope dilution methods (Fe, Cu, Ni and Cd) exactly as per Rapp et al. (2017) and measured on HR-ICP-MS (Element XR). Amorphous silica was determined following the alkaline extraction method of DeMaster (1981). Macronutrient samples were analyzed on a Quaatro System (Seal Analytical, Germany) (Hansen and Koroleff, 1999). Grain size of particles was measured on Laser Particle Sizer (ANALYSETTE 22, FRITSCH). Seawater pH was measured with a pH sensor (Mettler Toledo).
DOI: 10.17632/2pzwhfhcfz.1
title: Patentes y registros de Universidades de Castilla y León

description: Diferentes tipologías de propiedad industrial e intelectual registradas por las universidades de Castilla y León que integran la Red TCUE (Transferencia de Conocimiento Universidad-Empresa).
DOI: 10.17632/7km7vmk4f3.2
title: Southeast Asian ecological dependency on Tibetan Plateau streamflow over the last millennium

description: The great river systems originating from the Tibetan Plateau are pivotal for the well-being of more than half the global population. Our understanding of historical ranges and future changes in water availability for much of southeast Asia is, however, limited by short observational records and complex environmental factors. Here, we present annually resolved and absolutely dated tree ring-based streamflow reconstructions for the Mekong, Salween and Yarlung Tsangpo Rivers since 1000 CE, which are supplemented by corresponding model projections until 2100 CE. We show a significant positive correlation between streamflow and dry season vegetation indices over the Indochinese Peninsula, revealing the importance of the Tibetan Water Tower for the functioning and productivity of ecological and societal systems in southeast Asia. The streamflow variability is associated with low-frequency sea-surface temperature variability in the North Atlantic and North Pacific. We find that streamflow extremes coincide with distinct shifts in local populations that occurred during medieval times, including the occupation and subsequent collapse of Angkor Wat from the 11th to the 16th century. Finally, our projections suggest that future streamflow changes will reach, or even exceed, historical ranges by the end of this century, posing unprecedented risks for inner Eurasia and southeast Asia.
DOI: 10.17632/9h5fjdxhrm.1
title: B2B Marketing Dataset

description: The authors applied a survey to 1049 sales representatives selected using the random sampling method, working full-time in the field of B2B marketing in different sectors, which are considered boundary-spanning personnel in the literature, working in different provinces of Turkey. The scales used in the study were used in previous studies and their reliability and validity were tested and they were found to be within the acceptable range. Analysis of the data of the study was carried out using the AMOS statistical analysis program.
DOI: 10.17632/kfth5g2xk3.2
title: Pothole Mix

description: This dataset for the semantic segmentation of potholes and cracks on the road surface was assembled from 5 other datasets already publicly available, plus a very small addition of segmented images on our part. To speed up the labeling operations, we started working with depth cameras to try to automate, to some extent, this extremely time-consuming phase. 

The main dataset is composed of 4340 (image,mask) pairs at different resolutions divided into training/validation/test sets with a proportion of 3340/496/504 images equal to 77/11/12 percent. This is the dataset used in the SHREC2022 competition and it is the dataset that allowed us to train the neural networks for semantic segmentation capable of obtaining the nice images and videos that you have probably already seen.

Along the main dataset we also release a set of RGB-D videos consisting of 797 RGB clips and as many clips with their disparity maps, captured with the excellent OAK-D cameras we won for being finalists at the OpenCV AI Competition 2021. In an effort to achieve (semi-)automatic labeling for these clips, we postprocessed the disparity maps using classic CV algorithms and managed to obtain 359 binary mask clips. Obviously these masks are not perfect (they cannot be by definition, otherwise the problem of automatic road damage detection would not exist), but nonetheless we believe they are an excellent starting point to create, for example, new data augmentations (creating potholes on "intact road images" belonging to other standard road datasets) or to be used as textures in the creation of 3D scenes from which to extract large amounts of images/masks for the training of neural networks. You can have a preview of what you will find in these clips by watching this video showing the overlay of images and binary masks: http://deeplearning.ge.imati.cnr.it/genova-5G/video/pothole-mix-videos/pothole-mix-rgb-d-overlay-videos-concat.html

Please take a look at the readme file inside the main dataset zipfile to have some more details about the single sub-datasets and their sources.
DOI: 10.17632/7rswnkbfyp.1
title: Middle Rio Grande random site fish data 

description: Fish assemblage data (fish counts, sampling effort) for sites in the Middle Rio Grande
DOI: 10.17632/hkph9rgzc3.1
title: Data for: dataset of Parenting Practices, Self-Control and Anti-Social Behaviors: Meta-Analytic Structural Equation Modeling

description: This dataset is used to clarify the nexus between effective parenting practices, low self-control, and anti-social behaviors in Gottfredson and Hirschi’s General Theory of Crime (GTC). In order to acquire the data, we conducted the electronic search through ProQuest, PsycINFO, Scopus, and Web of Science, and also the American Society of Criminology, National Criminal Justice Reference Service [NCJRS], Criminal Justice Abstracts. Because the GTC was introduced in 1990, the time frame of this research ranged from 1 January 1990 to 23 September 2019 for all published and non-published research.
DOI: 10.17632/k22zxz29kr.1
title: Diesel Engine Faults Features Dataset (3500-DEFault)

description: The objective of this dataset is the fault diagnosis in diesel engines to assist the predictive maintenance, through the analysis of the variation of the pressure curves inside the cylinders and the torsional vibration response of the crankshaft. Hence a fault simulation model based on a zero-dimensional thermodynamic model was developed. The adopted feature vectors were chosen from the thermodynamic model and obtained from processing signals as pressure and temperature inside the cylinder, as well as, torsional vibration of the engine’s flywheel. These vectors are used as input of the machine learning technique in order to discriminate among several machine conditions. The database is expected to emulate all operating scenarios under study. In our case, all possible diesel machine faults and system conditions variations, which correspond to severities levels containing enough information to characterize and discriminate the faults. The developed database covered the following operating conditions: Normal (without faults), Pressure reduction in the intake manifold, Compression ratio reduction in the cylinders and Reduction of amount of fuel injected into the cylinders. In all scenarios, the motor rotation frequency was set at 2500 RPM. The rotation of 2500 RPM was used, since it presented the lowest joint error rate in the estimation of the mean and maximum pressures of the burning cycle, between the experimental data (according to data supplied by the manufacturer) and the simulated data, during the validation stage of the thermodynamic and dynamic models. The entire database comprises a total of 3500 different fault scenarios for 4 distinct operational conditions. 250 of which from the normal class, 250 from ``pressure reduction in the intake manifold" class, 1500 from ``compression ratio reduction in the cylinders" class and 1500 from the ``reduction of amount of fuel injected into the cylinders" class. This database is named 3500-DEFault database.
DOI: 10.17632/8gywzhsxcp.1
title: Supplementary Appendix - Efficacy and Safety of Apremilast in Patients With Moderate to Severe Plaque Psoriasis of the Scalp: Results of a Phase 3b, Multicenter, Randomized, Placebo-Controlled, Double-Blind Study
DOI: 10.17632/s23jbjn5jg.1
title: Data for: Scaling-Up Grey-Box Models for Predicting Building Dynamics - Model Structure, Seasonal Variation, and Physical Interpretability

description: The data accompany the article Scaling-Up Grey-Box Models for Predicting Building Dynamics: Model Structure, Seasonal Variation, and Physical Interpretability, which presents an elaborate analysis of different RC model structures, different training data, different validation data, and different model selection methods. The training and validation data have been acquired by using energy modeling software TRNSYS for a building located in Zagreb. One year’s worth of data has been divided into 52 training periods, each lasting 7 days, and 182 validation periods, each lasting 2 days. Models were trained by using a combination of Latin Hypercube Sampling (LHS) and non-linear least squares. For the LHS, the number of initial parameter guesses was varied, resulting in 7 different parameter guesses densities: 2, 5, 10, 20, 50, 100, 200. Each of those guess densities was used to produce that many models for each of the training periods. That means that if the number of initial guesses is 200, there were 200 models trained for each training period, resulting in 200x52=10 400 models. 

The data with a timestep of 15 minutes used for training and validating the models can be found in the folder “0. Training and validation data”. Parameter values, RMSE for training periods and RMSE for validation periods can be found in folders “1. SE zone”, “2. SW zone”, “3. NW zone”, “4. NE zone”. The platform in which the user can interact with RC models to increase and decrease model parameters can be found in the folder “5. Platform for RC Model Parameter Analysis”. Each folder contains a description file regarding the data. 

Any additional inquiry about the data set and the RC model platform can be sent to the corresponding author, Nikola Badun (nikola.badjun@fsb.hr).
DOI: 10.17632/zmycy7t9h9.2
title: ontonotes-conll2012

description: ** Please cite the original paper and distribution but not this distribution. **

Extended version of OntoNotesV5.0 used in CoNLL2012,
includes extension version of v4 English, Arabic, Chinese (test data of 3 langs are v9), and v12 English 

This data is processed based on the official script
(v4) https://conll.cemantix.org/2012/data.html
(v12) https://cemantix.org/data/ontonotes.html
(v12 script) https://github.com/yuchenlin/OntoNotes-5.0-NER-BIO

directory structure (only top level directories showed here, refer to https://conll.cemantix.org/2012/data.html for the lower-level ones)
conll-2012
├── v12
│   └── data
│       ├── conll-2012-test
│       │   └── data
│       │       └── english
│       ├── development
│       │   └── data
│       │       └── english
│       ├── test
│       │   └── data
│       │       └── english
│       └── train
│           └── data
│               └── english
└── v4
    └── data
        ├── development
        │   └── data
        │       ├── arabic
        │       ├── chinese
        │       └── english
        ├── test
        │   └── data
        │       ├── arabic
        │       ├── chinese
        │       └── english
        └── train
            └── data
                ├── arabic
                ├── chinese
                └── english
DOI: 10.17632/v524p5dhpj.2
title: DataSet for Arabic Classification

description: The dataset is a collection of Arabic texts, which covers modern Arabic language used in newspapers articles. The text contains alphabetic, numeric and symbolic words. The existence of numeric and symbolic words in this dataset could tell the efficiency and robustness of many Arabic text classification and indexing documents. The dataset consists of 111,728 documents (cf. Table 1) and 319,254,124 words (cf. Table 2) structured in text files, and collected from 3 Arabic online newspapers: Assabah [9], Hespress [10] and Akhbarona [11] using semi-automatic web crawling process. The documents in the dataset are categorized into 5 classes: sport, politic, culture, economy and diverse. The number of documents and words for each class varies from one class to another (cf. Tables 1-2).
DOI: 10.17632/rx35j3j8nr.2
title: Multiple Pricing for Personal Assistance Services

description: Third-party payers often reimburse health care providers based on prospectively set prices. Although a key motivation of prospective payment is to contain costs, this paper shows that this aspect crucially depends on the design of the pricing scheme due to the well-known incentives of patient selection (or "dumping"). This paper provides a general theoretical framework where heterogeneous users are served by either private for-profit or public providers, each paid an hourly compensation by a third-party payer. The private, but not the public providers may select patients. It is demonstrated that this realistic feature of the model implies that total costs depends on the number of prices. The features of the model is illustrated using the Swedish system of personal assistance services as a motivating example. Numerical results show that marginal adjustments to the current uniform pricing scheme would lead to substantial savings.
DOI: 10.17632/z7sxkxg2ch.1
title: Yoga for Chronic Brain Injury
DOI: 10.17632/v9wwkvdjmm.1
title: Devanagari Handwritten CAPTCHA - Dataset of 90 K Images : A Challenge Test

description: Captcha stands for Completely Automated Public Turing Tests to Distinguish Between Humans and Computers. This test cannot be successfully completed by current computer systems; only humans can. It is applied in several contexts for machine and human identification. The most common kind found on websites are text-based CAPTCHAs.A CAPTCHA is made up of a series of alphabets or numbers that are linked together in a certain order. Random lines, blocks, grids, rotations, and other sorts of noise have been used to distort this image.It is difficult for rural residents who only speak their local tongues to pass the test because the majority of the letters in this protected CAPTCHA script are in English. Machine identification of Devanagari characters is significantly more challenging due to their higher character complexity compared to normal English characters and numeral-based CAPTCHAs. The vast majority of official Indian websites exclusively provide content in Devanagari. Regretfully, websites do not employ CAPTCHAs in Devanagari. Because of this, we have developed a brand-new text-based CAPTCHA using Devanagari writing.A canvas was created using Python. This canvas code is distributed to more than one hundred (100+) Devanagari native speakers of all ages, including both left- and right-handed computer users. Each user writes 440 characters (44 characters multiplied by 10) on the canvas and saves it on their computers. All user data is then gathered and compiled. The character on the canvas is black with a white background. No noise in the image is a benefit of using canvas. The final data set contains a total of 44,000 digitized images, 10,000 numerals, 4000 vowels, and 30,000 consonants. This dataset was published for research scholars for recognition and other applications on Mendeley (Mendeley Data, DOI: 10.17632/yb9rmfjzc2.1, dated October 5, 2022) and the IEEE data port (DOI: 10.21227/9zpv-3194, dated October 6, 2022).We have designed our own algorithm to design the Handwritten Devanagari CAPTCHA. We used the above-created handwritten character set. General CAPTCHA generation principles are used to add noise to the image using digital image processing techniques. The size of each CAPTCHA image is 250 x 90 pixels. Three (03) types of character sets are used: handwritten alphabets, handwritten digits, and handwritten alphabets and digits combined. For 09 Classes X 10,000 images , a Devanagari CAPTCHA data set of 90,0000 images was created using Python. All images are stored in CSV format for easy use to researchers. To make the CAPTCHA image less recognized or not easily broken. Passing a test identifying Devanagari alphabets is difficult. It is beneficial to researchers who are investigating captcha recognition in this area. This dataset is helpful to researchers in designing OCR to recognize Devanagari CAPTCHA and break it. If you are able to successfully bypass the CAPTCHA, please acknowledge us by sending an email to sanjayepate@gmail.com.
DOI: 10.17632/54hr8zjx8r.2
title: Code-switching in ESL classrooms of Malaysian Premier Polytechnics

description: The data was collected using a mixed method study of convergent parallel design, conducted using classroom observations, interviews and questionnaires to triangulate the data obtained from the three Premier Polytechnics in Malaysia, which involved nine lecturers and 183 students.
DOI: 10.17632/ss75fdg5dg.1
title: Synthetic FCC 3D polycrystalline microstructures with grain-wise microstructural descriptors and stress fields under uniaxial tensile deformation

description: This data set consists of 36 synthetic 3D equiaxed polycrystalline microstructures with different cubic textures, associated microstructural descriptors and the corresponding results from applying a uniaxial tensile stress through EVPFFT simulations. The dataset is organized by dividing the microstructures into 6 major texture classes.
DOI: 10.17632/5drkk544k8.1
title: Eggplant Dataset: A Comprehensive Dataset for Agricultural Research and Disease Detection

description: This dataset is a valuable resource for researchers and practitioners in agriculture, machine learning, and computer vision, focusing on classifying diseases that affect eggplant plants. The dataset comprises two main categories: the Original Dataset and the Augmented Dataset. These categories contain images depicting various conditions of eggplant leaves and fruits, encompassing diseases such as Cercospora Leaf Spot, Flea Beetles, Phytophthora Blight, Powdery Mildew, Tobacco Mosaic Virus, among others. By including augmented images generated through techniques like flip, rotation, noise addition, shift, brightness adjustment, and zoom, this dataset supports robust algorithm development and evaluation. Researchers can leverage these datasets to train and validate machine learning models for accurate disease classification and early detection in eggplant plants. The dataset aims to accelerate progress in agricultural technology, crop protection, and sustainable farming practices through innovative applications of computer vision and machine learning.

The dataset initially includes 3,116 original high-resolution images depicting various eggplant diseases, meticulously annotated for precise classification. Additionally, 10,000 augmented images were generated from these originals, expanding the dataset while maintaining an image size suitable for detailed analysis.

Folder Structure:

Augmented Dataset:
Number of datasets: 10,000
Data format: .jpg

Original Dataset:
Number of datasets: 3,116
Data format: .jpg
This comprehensive dataset provides a foundational tool for advancing research in eggplant plant pathology, facilitating the development of AI-driven solutions for disease management and crop enhancement.