cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/mzp3k6fmtz.3
title: [IDN] Ground Cover Change Dataset

description: The Global Ground Cover Change (GGCC) dataset is a reference-based raster collection of bi-annual land change maps. Data here was produced with a random forest model used to classify strata-based ground cover change trends in a disaster-affected area of Java, Indonesia. The associated research comprehends (1) data collection: ICESat-2, ATLAS08 LiDAR datasets, Land Cover datasets, and ancillary DEM-derived subsets of data for quality-based sample refinements used for model training; (2) data processing, using local temporal and static metrics to parse localized filtration ramifications in a filtering code; (3) model optimization using machine learning techniques with the sci-kit learn package; (4) preliminary classifications and accuracy assessments; (5) informed post-processing; and, (5) recovery trend evaluations on bi-annual ground change maps to inform livelihood recovery and monitor the underlying effects of relocation and resettlement in peri-urban environments. These are valuable assets to understanding how we should rebuild affected areas for humanitarian purposes where communities require restored environments that efficiently contribute to diminishing the overall human footprint on Earth.
DOI: 10.17632/2rcc8488hx.1
title: Machine Learning of MS dataset

description: Mass spectrometric profiling provides information on the protein and metabolic composition of biological samples. However, the weak efficiency of computational algorithms in correlation between tandem spectra to molecular components (proteins and metabolites) dramatically limits the use of "omics" data for the classification of nosologies. The development of machine learning methods for the intelligent analysis of raw mass spectrometric HPLC-MS/MS measurements without data preprocessing and identification seems promising. In this study, we tested the ap-plication of neural networks of two types, 1D-Residual CNN and 3D-CNN, to the combined metabolomic and proteomic HPLC-MS/MS data for the classification of three cancer phenotypes. Both neural networks are capable of classifying as gender-mixed oncological phenotypes (kidney cancer) as gender-specific phenotypes (ovarian cancer) and recognize healthy condition accuracy of 0.95 by analyzing ‘omics’ data in the ‘mgf’ data format. The neural network makes possible to determine their similarity degree (distance matrix) between submitted phenotypes, thus over-coming algorithmic barriers in identifying HPLC-MS/MS spectra. The closest distance was shown between ovarian cancer, kidney cancer, and prostate cancer/kidney cancer, whereas the healthy phenotype was the most outer from cancer phenotypes. Neural networks are versatile and can be applied to standard experimental data formats of different analytical platforms.
DOI: 10.17632/8ftshvhht6.1
title: Spherical inclusions RVE mesh models (periodic and non periodic)

description: The spherical inclusion models are generated through an iterative process with a Volume fraction ranging from 18% to 35%. The particle radius is set as a constant equal to r = 10µm. No interpenetration is permitted between each particle. The RVE width is Wp = 50µm for the periodic models. For the non-periodic, the cubical domain of each periodic model is cropped to a smaller width Wnp = 38µm < Wp − r/2 such that the periodicity constraint is annihilated. All the files are given in the .inp format from Abaqus. A classic RVE is provided with periodic models for them to be embedded. Along with non-periodic models, an enveloped RVE is provided with a thickness of 1 and a mesh density of 0,1.
DOI: 10.17632/2cymbb4gt4.1
title: Bread

description: Project Title: Good and Bad Classification of Bread (Triticum Aestivum) Using Realme 11x 5G Mobile Camera

Description:

The project, titled "Good and Bad Classification of Bread (Triticum Aestivum)," focuses on developing a machine learning system to automatically classify bread into two categories: good (fresh and high-quality) and bad (stale or low-quality). This classification task is based on a dataset of over 500 images, equally divided between good and bad bread samples. The images were captured using the Realme 11x 5G mobile camera, with bread samples photographed against a black background in daylight conditions, ensuring clarity and uniformity in the captured data.

Dataset Composition:

Good Samples (Fresh Bread): More than 250 images in the dataset depict good quality bread. These bread samples display characteristics of freshness, including an even golden-brown crust, soft and uniform texture, and a lack of defects such as burns or cracks. These images form the positive class, representing the standard of high-quality, freshly baked bread.

Bad Samples (Stale or Poor-Quality Bread): The dataset also includes over 250 images of bad quality bread. These samples may show signs of staleness (hard or dry texture), uneven baking (burns, undercooked sections), or spoilage (mold, discoloration). These bad examples form the negative class and are crucial for training the model to recognize undesirable qualities in bread.


Data Collection Setup:

The bread images were captured using a Realme 11x 5G mobile camera, which offers high-resolution imaging, ensuring that the fine details of the bread samples, such as texture, color, and crust appearance, are clearly visible. A black background was used to create contrast with the bread, making it easier to highlight the defining features of the samples. Natural daylight conditions were chosen for consistent and even lighting across all images, reducing the influence of shadows or artificial light variations.

Image Characteristics:

The dataset features a wide variety of bread types and conditions. The good and bad bread samples differ in size, shape, texture, and quality, providing a broad range of visual examples for the classification model. This variability is crucial for ensuring the model can generalize well and classify different types of bread, irrespective of their size, shape, or baking method.

Data Annotation:

Each image is labeled as either "good" or "bad" based on the visual quality of the bread. Expert observation was used to categorize the bread samples, ensuring that the labels are accurate and reliable for training the classification model. These annotations serve as the ground truth for supervised learning, enabling the model to distinguish between fresh and stale or spoiled bread effectively.

Data Preprocessing:

The dataset undergoes several preprocessing steps to ensure the machine learning model receives optimal inputs:
DOI: 10.17632/2x9df8gc53.1
title: Gestão dos resíduos sólidos madeireiros sob a perspectiva da avaliação do ciclo de vida: uma revisão de literatura

description: A revisão tem como objetivo identificar quais são os conceitos, guias, ferramentas, técnicas, modelos e práticas existentes na literatura sobre a gestão dos resíduos sólidos madeireiros dentro da perspectiva da avaliação do ciclo de vida e como se desdobram em ações para o gerenciamento dos resíduos
DOI: 10.17632/hhz46f3wgj.1
title: E-waste

description: Some data form the general proyect "Recycling of e-waste using green technologies" Experimental data of e-waste: 2018 - 2019 Zeta potential data from the non-metallic fraction and the feed at different pHs with and without collectors. Contact Angle Hysteresis with different collectors in the Feed and the non-metallic fraction X-Ray Diffraction test from feed, Non-metallic fraction and metallic fraction from PCBs. ATR FTIR data from feed, Non-metallic fraction and metallic fraction from PCBs.
DOI: 10.17632/k3zpmvhhdt.1
title: childhood diseases dataset

description: Dataset consists of age, sex, weight, height, body temperature, and any records of experiences of the 29 symptoms.
DOI: 10.15468/dl.sd7t5c
title: Occurrence Download

description: A dataset containing 515 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Prunus nigra Aiton",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 515 records from 52 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009329-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/d4ntfj4wfn.1
title: Nitrate concentration and isotopes in DA2005 core from Dome A, East Antarctica
DOI: 10.17632/vz7pzt2grf.1
title: Batik 300

description: Batik Dataset is collected by capturing 50 types of Batik cloth. Each cloth is captured to as much as six random images and then resized to 128x128 pixels size in JPEG format. Total number of images in a dataset is 300 and consists of 50 classes. In general, there are two patterns of captured batik images, geometric and non-geometric patterns.
DOI: 10.17632/brbns4k5d6.2
title: Yale Dataset (Locally Linear Embedding and fMRI feature selection for psychiatric classification)

description: ABIDE's Yale dataset. dataset intermediates are currently unavailable due to mendeley data's size restrictions.
DOI: 10.17632/xp92jxr8wn.2
title: Bangla News Dataset

description: A corpus on Bangla newspaper articles created using a custom web crawler containing 12 different topics. The total number of word tokens in this dataset is 28.5+ million. The number of unique words is around 3% of the entire vocabulary of the dataset. The Dataset is imbalanced. 20% of the dataset was separated as a held-out dataset.
DOI: 10.17632/n53xt69gnf.3
title: Data Set For Sentiment Analysis On Bengali News Comments

description: This is a data set of Sentiment Analysis On Bangla News Comments where every data was annotated by three different individuals to get three different perspectives and based on the majorities decisions the final tag was chosen. This data set contains 13802 data in total.
DOI: 10.17632/tc5mrhx9sb.1
title: WavePacket: A Matlab package for numerical quantum dynamics. I: Closed quantum systems and discrete variable representations

description: WavePacket is an open-source program package for the numerical simulation of quantum-mechanical dynamics. It can be used to solve time-independent or time-dependent linear Schrödinger and Liouville–von Neumann-equations in one or more dimensions. Also coupled equations can be treated, which allows to simulate molecular quantum dynamics beyond the Born–Oppenheimer approximation. Optionally accounting for the interaction with external electric fields within the semiclassical dipole approximation, WavePacket can be used to simulate experiments involving tailored light pulses in photo-induced physics or chemistry. The graphical capabilities allow visualization of quantum dynamics ‘on the fly’, including Wigner phase space representations. Being easy to use and highly versatile, WavePacket is well suited for the teaching of quantum mechanics as well as for research projects in atomic, molecular and optical physics or in physical or theoretical chemistry. The present Part I deals with the description of closed quantum systems in terms of Schrödinger equations. The emphasis is on discrete variable representations for spatial discretization as well as various techniques for temporal discretization. The upcoming Part II will focus on open quantum systems and dimension reduction; it also describes the codes for optimal control of quantum dynamics. The present work introduces the MATLAB version of WavePacket 5.2.1 which is hosted at the Sourceforge platform, where extensive Wiki-documentation as well as worked-out demonstration examples can be found.
DOI: 10.17632/39tj53s7cb.1
title: Specific Emitter Identification_FM

description: Due to the original data being too large (exceeding 300GB), only the data from one day out of the 15 days has been uploaded here. If the entire original dataset is needed, please contact me: liujiaxu97@foxmail.com.
DOI: 10.17632/4424s5pcwk.2
title: Leadership heuristic__Data

description: This data is generated from an experiment in lieu of the paper "Leadership heuristic"
DOI: 10.17632/fbjr5stbf2.1
title: Does Exchange Rate Volatility Affect the Impact of Appreciation and Depreciation on the Trade Balance? A Nonlinear Bivariate Approach (dataset)

description: Our study "Does Exchange Rate Volatility Affect the Impact of Appreciation and Depreciation on the Trade Balance? A Nonlinear Bivariate Approach" applies the nonlinear bivariate model of Rahman (2022) (GARCH-M [1,1] SVAR). The model allows asymmetric effects of exchange rates and volatility to be examined concurrently on the trade balance. We find that exchange rate volatility reduces the positive effects of an appreciation shock on the trade balance in developed countries in the short and long run. In developing nations, exchange rate volatility promotes the positive effects of a depreciation shock on the trade balance, both in the short and long run. Herein we provide the replication files and steps to reproduce the results. The data was obtained from the Federal Reserve Economic Data (FRED), IMF International Financial Statistics (IFS), Direction of Trade Statistics (DOTS), and Global Economic Monitor (GEM).
DOI: 10.17632/2nk84c46pj.2
title: Unmitigated Disasters JIE 2024

description: This is the Replication Package for “Unmitigated Disasters? Risk Sharing and Macroeconomic Recovery in a Large International Panel”, by G von Peter et al (2024), Journal of International Economics. 

The paper examines the patterns of macroeconomic recovery following natural disasters. In a panel with global coverage from 1960 to 2011, the study makes use of insurer-assessed losses to estimate growth responses conditional on risk transfer. The empirical results show that major disasters reduce growth by 1 to 2 percentage points on impact, and over time produce an output cost of 2% to 4% of GDP, on top of the initial damage to property and infrastructure. Akin to wars and financial crises, natural disasters have permanent effects, in the sense that output losses are not fully recovered over time. But it is the uninsured losses that drive the macroeconomic cost; insured losses are less consequential in the aggregate, and can even stimulate growth. By helping to finance the recovery, insurance mitigates the macroeconomic cost of disasters. Many countries lack the capacity to (re)insure themselves and would stand to benefit from international risk sharing.

The Stata code referenced in this replication package produced the original findings of the article cited. They mainly consist of estimated growth responses to scaled disaster impulses in a dynamic panel setting, allowing for four types of natural disasters and distinguishing between insured and uninsured losses. The code enables the exact replication of our results when using the Munich Re NatCat statistics presented in the JIE paper. For reasons of confidentiality, this replication package contains no data from Munich Re. The code can be run using public data, such as the data included in this replication package based on the EM-DAT database (publicly available at https://public.emdat.be). The datasets included in this package are sufficient for running the code, but the quantitative results do not replicate the estimates in the paper and should not be relied upon for other purposes. 

For background and detailed guidance, see the READ_ME file and other materials included in this replication package.
DOI: 10.17632/zb4zrp3b3j.1
title: Energy storage technology dataset

description: Energy storage technologies and their comparative evaluation.
DOI: 10.17632/t7sr4pr68t.1
title: Data for: The Persistence of Lying

description: The two data sets relate to each of the experiment reported in the paper. The chats collapsed data set relate to the second experiment. The do file can be used to replicate the tables in the paper.
DOI: 10.17632/f3mjnbm9b3.1
title: Indoor Laboratory Fire Dataset

description: This dataset contains time-series data of 8 controlled fire laboratory experiments in which 4 experiments were conducted using electric fire source, 2 experiments were conducted using paperboard carton source, and the other 2 experiments were conducted using clothing source. For each experiment, the sensor measurements of humidity, temperature, MQ139, TVOC, and eCO2 were recorded at the beginning when the fires were triggered to the point end when the fire alarm gets activated/sets off. Each CSV file represents time-series data for a specific fire source e.g., carton, electrical, and clothing fire.
DOI: 10.17632/chkhyxp43p.2
title: Effects of aerobic, resistance and combined training on endothelial function and arterial stiffness in older adults: a systematic review and meta-analysis

description: We examined the effects of aerobic, resistance and combined training on flow-mediated dilation and pulse wave velocity in older adults. We conducted a systematic review of randomized clinical trials evaluating the effects of aerobic, resistance and/or combined training on flow-mediated dilation and/or pulse wave velocity selected from the electronic databases PubMed, Cochrane, LILACS, EMBASE, Web of Science and the gray literature. We assessed the studies using Cochrane risk of bias (RoB2) tool and the GRADE tool. Our analysis of 23 RCTs showed that aerobic training was effective to improve flow-mediated dilation and pulse wave velocity by compared to the control group. The subgroup analyses showed no flow-mediated dilation differences following aerobic training in healthy adults when compared to those with any health condition. Combined training was effective in improving flow-mediated dilation and pulse wave velocity. But these same parameters did not show any improvement in response to resistance training. Aerobic and combined training may be effective to improve flow-mediated dilation and pulse wave velocity in older adults. Few studies evaluated resistance training alone and therefore the results for this modality should be interpreted with caution.
DOI: 10.17632/grk34bx9vf.1
title: Environments.jl

description: Collection of the Environments (in a form similar to OpenAI gym) that will be used in tests of the Monte Carlo Tree Search (MCTS) algorithm.
There are two environments available currently. One is a standard cart pole (single inverted pendulum on the cart). The second is a double inverted pendulum on the car. Both are used to evaluate the MCTS application to mechanical systems.
DOI: 10.17632/p4ft7v7ss6.1
title: GREEN MARKETING

description: the study examines the mediating roles of green awareness and green loyalty in the relationships between green marketing practices and consumer purchasing intention, focusing on the cosmetic industry of a developing economy, notably Ghana. This raw data contains all the information obtained from the consumers of green cosmetic products in Ghana in line with the research hypotheses. The data was obtained through self-administered questionnaires with the assistance of three well-trained and resourced field assistants. After data collection drastic measures were put in place to screen and clean the data to avoid high missing values. Also, the data was coded and processed using the IBM SPSS software for easy usage in the SmartPLS software.
DOI: 10.17632/wxt9m9mkr9.1
title: Overspecified Asset Pricing Models JFE

description: YO_DATA0.xlsx - Data for Tables 1 and 2. Quarterly data 1951-2001.
Sheet 1 "r25" - 25 nominal excess returns of size and book-to-market sorted portfolios.
Sheet 2 "YO" - 3 factors in Yogo model (market, nondurables, durables).

The above data were taken from Motohiro Yogo's web page

https://sites.google.com/site/motohiroyogo/


YO_DATA.xlsx - Data for Tables 1 and 2. Quarterly data 1949-2012.
Sheet 1 "r5" - 5 real excess returns of industry portfolios.
Sheet 2 "r6" - 6 real excess returns of size and book-to-market sorted portfolios.
Sheet 3 "r25" - 25 real excess returns of size and book-to-market sorted portfolios.
Sheet 4 "YO" - 3 factors in Yogo model (market, nondurables, durables).

The above data were kindly provided by Craig Burnside.


JW_DATA.xlsx - Data for Tables 1 and 2. Monthly data 1959-2012.
Sheet 1 "r5" - 5 nominal excess returns of industry portfolios.
Sheet 2 "r6" - 6 nominal excess returns of size and book-to-market sorted portfolios.
Sheet 3 "r25" - 25 nominal excess returns of size and book-to-market sorted portfolios.
Sheet 4 "JW" - 3 factors in Jagannathan-Wang model (market, labor, premium).

The above data were kindly provided by Cesare Robotti.

In all cases, the original return data were taken from Kenneth French data library

https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html
DOI: 10.17632/d2bj5c2p4s.1
title: edDIT: Technological corporations, digital educational platforms and guarantee of children's rights with a gender approach

description: The database provides valuable first-hand information about families’ perceptions of ed-tech corporations, educational digital platforms and children's rights, and the parental engagement in the use of technology by 2347 families with children in public schools in Catalonia (Spain).
DOI: 10.17632/mmr3z8j2m5.1
title: pyJac: Analytical Jacobian generator for chemical kinetics

description: Accurate simulations of combustion phenomena require the use of detailed chemical kinetics in order to capture limit phenomena such as ignition and extinction as well as predict pollutant formation. However, the chemical kinetic models for hydrocarbon fuels of practical interest typically have large numbers of species and reactions and exhibit high levels of mathematical stiffness in the governing differential equations, particularly for larger fuel molecules. In order to integrate the stiff equations governing chemical kinetics, generally reactive-flow simulations rely on implicit algorithms that require frequent Jacobian matrix evaluations. Some in situ and a posteriori computational diagnostics methods also require accurate Jacobian matrices, including computational singular perturbation and chemical explosive mode analysis. Typically, finite differences numerically approximate these, but for larger chemical kinetic models this poses significant computational demands since the number of chemical source term evaluations scales with the square of species count. Furthermore, existing analytical Jacobian tools do not optimize evaluations or support emerging SIMD processors such as GPUs. Here we introduce pyJac, a Python-based open-source program that generates analytical Jacobian matrices for use in chemical kinetics modeling and analysis. In addition to producing the necessary customized source code for evaluating reaction rates (including all modern reaction rate formulations), the chemical source terms, and the Jacobian matrix, pyJac uses an optimized evaluation order to minimize computational and memory operations. As a demonstration, we first establish the correctness of the Jacobian matrices for kinetic models of hydrogen, methane, ethylene, and isopentanol oxidation (number of species ranging 13–360) by showing agreement within 0.001% of matrices obtained via automatic differentiation. We then demonstrate the performance achievable on CPUs and GPUs using pyJac via matrix evaluation timing comparisons; the routines produced by pyJac outperformed first-order finite differences by 3–7.5 times and the existing analytical Jacobian software TChem by 1.1–2.2 times on a single-threaded basis. It is noted that TChem is not thread-safe, while pyJac is easily parallelized, and hence can greatly outperform TChem on multicore CPUs. The Jacobian matrix generator we describe here will be useful for reducing the cost of integrating chemical source terms with implicit algorithms in particular and algorithms that require an accurate Jacobian matrix in general. Furthermore, the open-source release of the program and Python-based implementation will enable wide adoption.