57 datasets found
  1. Data augmentation for Multi-Classification of Non-Functional Requirements -...

    • zenodo.org
    • investigacion.usc.gal
    • +2more
    csv
    Updated Mar 19, 2024
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    María-Isabel Limaylla-Lunarejo; María-Isabel Limaylla-Lunarejo; Nelly Condori-Fernandez; Nelly Condori-Fernandez; Miguel R. Luaces; Miguel R. Luaces (2024). Data augmentation for Multi-Classification of Non-Functional Requirements - Dataset [Dataset]. http://doi.org/10.5281/zenodo.10805331
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    csvAvailable download formats
    Dataset updated
    Mar 19, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    María-Isabel Limaylla-Lunarejo; María-Isabel Limaylla-Lunarejo; Nelly Condori-Fernandez; Nelly Condori-Fernandez; Miguel R. Luaces; Miguel R. Luaces
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    There are four datasets:

    1.Dataset_structure indicates the structure of the datasets, such as column name, type, and value.

    2. Spanish_promise_exp_nfr_train and Spanish_promise_exp_nfr_test are the non-functional requirements of the Promise_exp[1] dataset translated into the Spanish language.

    3. Blanced_promise_exp_nfr_train is the new balanced dataset of Spanish_promise_exp_nfr_train, in which the Data Augmentation technique with chatGPT was applied to increase the requirements with little data and random undersampling was used to eliminate requirements.

  2. Association between lung function and augmentation index by multivariate...

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
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    Julian G. Ayer; Elena G. Belousova; Jason A. Harmer; Brett Toelle; David S. Celermajer; Guy B. Marks (2023). Association between lung function and augmentation index by multivariate analysis. [Dataset]. http://doi.org/10.1371/journal.pone.0026303.t002
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    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Julian G. Ayer; Elena G. Belousova; Jason A. Harmer; Brett Toelle; David S. Celermajer; Guy B. Marks
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Carotid AIx75 is the dependent variable, lung function measures are the main independent (explanatory) variables, and all models include sex, height, maternal smoking status during pregnancy, ETS exposure during childhood, and randomization groups (for house dust mite avoidance and dietary interventions) as covariates. The partial R2 measures the marginal contribution of that lung function measure when the other explanatory variables are already included in the multiple linear regression model and the b-coefficient represents the change in AIx75 in % per unit change in explanatory variable.

  3. r

    Augmented Reality Map Navigation with Freehand Gestures - Transfer...

    • researchdata.edu.au
    Updated Mar 27, 2019
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    Kadek Ananta Satriadi; Kadek Ananta Satriadi (2019). Augmented Reality Map Navigation with Freehand Gestures - Transfer Function.pdf [Dataset]. http://doi.org/10.26180/5c6a56dc47411
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    Dataset updated
    Mar 27, 2019
    Dataset provided by
    Monash University
    Authors
    Kadek Ananta Satriadi; Kadek Ananta Satriadi
    Description

    The transfer function parameters used in Augmented Reality Map Navigation with Freehand Gestures study.

  4. Replication package for the paper :The Relationship Between Different Python...

    • zenodo.org
    zip
    Updated Jul 11, 2025
    + more versions
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    Anonymous Author(s); Anonymous Author(s) (2025). Replication package for the paper :The Relationship Between Different Python Argument-Passing Mechanisms and Fixes: An Empirical Study [Dataset]. http://doi.org/10.5281/zenodo.15864328
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    zipAvailable download formats
    Dataset updated
    Jul 11, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Anonymous Author(s); Anonymous Author(s)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Abstract:

    Modern programming languages, such as Python, have introduced a variety of constructs and syntactical elements to make software development more efficient and concise. Examples include lambda functions, comprehension collections, or mechanisms to facilitate the passing of arguments to a function. While many of such constructs may, in principle, be beneficial for developers, recent studies have shown that certain programming constructs may affect program understanding and even induce more fixes than other changes.
    This paper studies the effect of different Python argument-passing mechanisms to investigate their relationship with code proneness to be fixed. Specifically, we study the fix-proneness for what concerns function definitions and invocations. This is done by analyzing the evolutionary history of 200 Python projects, for a total of about 3M functions and 12M call sites. While there are varying effects for what concerns parameter declaration mechanisms, we found evidence that keyword-based argument passing is less defect-prone than positional argument passing, and this is not affected by size-related confounding factors.

  5. f

    Parameters setting.

    • figshare.com
    • plos.figshare.com
    xls
    Updated May 2, 2024
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    Meng Zhang; Yina Guo; Haidong Wang; Hong Shangguan (2024). Parameters setting. [Dataset]. http://doi.org/10.1371/journal.pone.0302124.t001
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    xlsAvailable download formats
    Dataset updated
    May 2, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Meng Zhang; Yina Guo; Haidong Wang; Hong Shangguan
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Image data augmentation plays a crucial role in data augmentation (DA) by increasing the quantity and diversity of labeled training data. However, existing methods have limitations. Notably, techniques like image manipulation, erasing, and mixing can distort images, compromising data quality. Accurate representation of objects without confusion is a challenge in methods like auto augment and feature augmentation. Preserving fine details and spatial relationships also proves difficult in certain techniques, as seen in deep generative models. To address these limitations, we propose OFIDA, an object-focused image data augmentation algorithm. OFIDA implements one-to-many enhancements that not only preserve essential target regions but also elevate the authenticity of simulating real-world settings and data distributions. Specifically, OFIDA utilizes a graph-based structure and object detection to streamline augmentation. Specifically, by leveraging graph properties like connectivity and hierarchy, it captures object essence and context for improved comprehension in real-world scenarios. Then, we introduce DynamicFocusNet, a novel object detection algorithm built on the graph framework. DynamicFocusNet merges dynamic graph convolutions and attention mechanisms to flexibly adjust receptive fields. Finally, the detected target images are extracted to facilitate one-to-many data augmentation. Experimental results validate the superiority of our OFIDA method over state-of-the-art methods across six benchmark datasets.

  6. i

    Data from: The Moderating Role of Aesthetics and Information Quality for...

    • ieee-dataport.org
    Updated Jun 26, 2021
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    Alejandro Alvarez-Marin (2021). The Moderating Role of Aesthetics and Information Quality for Acceptance of Augmented Reality [Dataset]. https://ieee-dataport.org/documents/moderating-role-aesthetics-and-information-quality-acceptance-augmented-reality
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    Dataset updated
    Jun 26, 2021
    Authors
    Alejandro Alvarez-Marin
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Database for a moderation of technological acceptance research

  7. e

    Metabolic Reprogramming Driven by Ant2 Deficiency Augments T Cell Function...

    • ebi.ac.uk
    Updated Apr 9, 2025
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    Michael Berger (2025). Metabolic Reprogramming Driven by Ant2 Deficiency Augments T Cell Function and Anti-Tumor Immunity [Dataset]. https://www.ebi.ac.uk/pride/archive/projects/PXD062646
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    Dataset updated
    Apr 9, 2025
    Authors
    Michael Berger
    Variables measured
    Proteomics
    Description

    cell activation demands a significant boost in NAD+ levels, often outpacing the capacity of oxidative phosphorylation (OXPHOS). To explore how T cells manage this metabolic stress, we created T cell-specific ADP/ATP translocase-2 knockout (Ant2-/-) mice. Ant2, which is vital for ADP/ATP exchange between the mitochondria and cytoplasm, when deleted, disrupts OXPHOS by limiting ATP synthase function and blocking NAD+ replenishment. Surprisingly, Ant2-/- naïve T cells show increased activation, proliferation, and effector functions compared to wild-type T cells. Through metabolic profiling, we find these cells adopt a metabolic state similar to that of activated T cells, with heightened mitochondrial biogenesis and anabolic processes. Inhibiting ANT pharmacologically in wild-type T cells mirrors the Ant2-/- phenotype and enhances the effectiveness of adoptive T cell therapies in cancer treatment. These results suggest that Ant2-deficient T cells bypass the usual metabolic shifts necessary for activation, leading to greater T cell function and highlighting ANT inhibition as a potential therapeutic strategy for modulating immune responses.

  8. Vocal Fold Augmentation Market Report | Global Forecast From 2025 To 2033

    • dataintelo.com
    csv, pdf, pptx
    Updated Sep 22, 2024
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    Dataintelo (2024). Vocal Fold Augmentation Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/global-vocal-fold-augmentation-market
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    pdf, csv, pptxAvailable download formats
    Dataset updated
    Sep 22, 2024
    Dataset provided by
    Authors
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Vocal Fold Augmentation Market Outlook



    The global vocal fold augmentation market is projected to grow from an estimated market size of $285 million in 2023 to approximately $495 million by 2032, reflecting a robust compound annual growth rate (CAGR) of 6.5%. This growth is driven by several factors, including advancements in medical technology, increasing prevalence of voice disorders, and the rising geriatric population who are more susceptible to such conditions.



    The rising incidence of voice disorders, often resulting from chronic diseases, surgical procedures, and aging, is a significant driver for the vocal fold augmentation market. Conditions such as vocal fold paralysis and atrophy necessitate medical interventions to restore normal voice function, creating a growing demand for vocal fold augmentation procedures. Additionally, an increasing awareness about the importance of voice health and the availability of advanced treatment options are contributing to market growth. Innovations in minimally invasive procedures and the development of more effective and safer augmentation materials are also propelling the market forward.



    Another critical growth factor is the expansion of healthcare infrastructure, particularly in emerging economies. Countries in Asia Pacific and Latin America are investing heavily in their healthcare systems, improving access to specialized treatments and advanced medical technologies. The rising disposable income in these regions is also enabling more individuals to afford elective medical procedures, including vocal fold augmentation. Furthermore, government initiatives aimed at improving healthcare quality and accessibility are expected to support market growth over the forecast period.



    The increasing geriatric population worldwide is another significant factor driving market growth. As people age, they are more likely to experience voice disorders due to the natural degeneration of vocal tissues. This demographic shift is leading to a higher demand for vocal fold augmentation procedures to improve quality of life among older adults. Additionally, the growing trend of professional voice use among various age groups, including singers, teachers, and public speakers, is likely to fuel market demand.



    Regionally, North America continues to dominate the vocal fold augmentation market due to advanced healthcare infrastructure, high healthcare expenditure, and the presence of major market players. Europe is also a significant market, driven by favorable reimbursement policies and increasing awareness about voice disorders. The Asia Pacific region is expected to witness the highest growth rate, attributed to improving healthcare facilities, rising disposable income, and growing awareness about voice health. Latin America and the Middle East & Africa regions are gradually emerging markets, spurred by economic development and improving healthcare access.



    Product Type Analysis



    The vocal fold augmentation market is segmented into various product types, including injectable fillers, implants, and others. Injectable fillers, comprising materials like hyaluronic acid and collagen, are widely used due to their minimally invasive nature and effectiveness in providing immediate results. These fillers are injected directly into the vocal folds to restore volume and improve voice quality. The rising preference for non-surgical interventions is driving the demand for injectable fillers, making this segment dominant in the market. Innovations in filler materials that offer longer-lasting effects and reduced side effects are expected to further boost this segment.



    Implants, another critical segment, are typically used in cases where more permanent solutions are needed. These are often preferred for patients with significant vocal fold atrophy or paralysis. The implants can be tailored to the patient's specific anatomical needs, providing a customized approach to vocal fold augmentation. Technological advancements in implant materials and design are enhancing their effectiveness and safety, contributing to the growth of this segment. However, the higher cost and complexity of implant procedures compared to injectable fillers might limit their widespread adoption.



    Other product types in the market include various emerging technologies and experimental treatments aimed at improving vocal fold function. These may involve the use of innovative biomaterials or novel delivery mechanisms. Research and development activities in this area are robust, driven by the need for more effective and less invasive treatment o

  9. Recurrent Expansion Framework for Deep Regression with PCA-Guided MLP...

    • zenodo.org
    zip
    Updated Jul 4, 2025
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    Berghout Tarek; Berghout Tarek (2025). Recurrent Expansion Framework for Deep Regression with PCA-Guided MLP Augmentation [Dataset]. http://doi.org/10.5281/zenodo.15808571
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    zipAvailable download formats
    Dataset updated
    Jul 4, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Berghout Tarek; Berghout Tarek
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This repository provides MATLAB implementation of a simple but extensible Recurrent Expansion (RE) framework for supervised regression tasks. The approach iteratively enhances learning by feeding prior model behavior (predictions and internal representations) back into the input. It utilizes a Multi-Layer Perceptron (MLP) with a single hidden layer and incorporates Principal Component Analysis (PCA) to compress intermediate feature representations (φ) for more efficient augmentation.

    Key components include:

    • basic_recurrent_expansion_mlp.m: Core RE function that trains an MLP over multiple rounds, using PCA-reduced hidden features and prior predictions to augment the input.

    • generate_sinusoidal.m: A helper function to generate a synthetic, noisy sinusoidal regression dataset for demonstration and testing.

    • main.m: An example script that visualizes the dataset, trains the RE model, and plots Mean Squared Error (MSE) across iterations.

    Default parameters:

    • Hidden layer size = 10

    • PCA variance threshold = 0.5 (i.e., retain 50% variance)

    • RE rounds = 100 (modifiable)

    This code can be extended to other architectures and real-world datasets. The included sinusoidal dataset and example results highlight how recurrent expansion enables progressive refinement and self-reflective learning.

  10. Fault Tree Generation and Augmentation, Phase I

    • data.nasa.gov
    application/rdfxml +5
    Updated Jun 26, 2018
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    (2018). Fault Tree Generation and Augmentation, Phase I [Dataset]. https://data.nasa.gov/dataset/Fault-Tree-Generation-and-Augmentation-Phase-I/7rfg-crqn
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    application/rssxml, json, csv, xml, tsv, application/rdfxmlAvailable download formats
    Dataset updated
    Jun 26, 2018
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    Fault Management (FM) is one of the key components of system autonomy. In order to guarantee FM effectiveness and control the cost, tools are required to automate fault-tree generation and updates based on design models specified in standardized design languages such as AADL. Accordingly, we propose a fault tree generation and augmentation environment (FTGA). Equipped by a fault class model and an FM method catalog, FTGA evaluates not only failure behavior in the application under analysis but also FM's capability and adequacy for failure mitigation. Moreover, when an inadequacy in FM is revealed during fault tree generation or analysis, the fault tree will be allowed for augmentation through FM method insertion and be followed by a quantitative evaluation for FM effectiveness validation. Therefore, unlike traditional fault tree analysis which plays a passive role in FM, the automated FTGA environment actively and explicitly influence system design and updates, enabling "fault-tree-in-the-loop" for a system's life cycle. Further, by separating its generic functions (which we collectively call "shared package") from design-language-specific functions (which we collectively call "interface package"), FTGA will be an extensible modeling environment. The anticipated results from the Phase I project will be a preliminary prototype of FTGA and a demonstration for concept validation.

  11. e

    Disruption of acetyl group balance in cardiomyocytes augments the...

    • ebi.ac.uk
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    Michael Davidson, Disruption of acetyl group balance in cardiomyocytes augments the mitochondrial acetylproteome without affecting respiratory function or heart susceptibility to pressure overload [Dataset]. https://www.ebi.ac.uk/pride/archive/projects/PXD013935
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    Authors
    Michael Davidson
    Variables measured
    Proteomics
    Description

    Circumstantial evidence links the development of heart failure to perturbations in oxidative metabolism and corresponding shifts in post-translational modifications (PTMs) of mitochondrial proteins, including lysine acetylation (Kac). Nonetheless, direct evidence that acetyl-PTMs compromise mitochondrial performance remains sparse. Here, we used a respiratory diagnostics platform and serial assessment of cardiac phenotype to evaluate functional consequences of mitochondrial hyperacetylation caused by cardiac deficiency of carnitine acetyltransferase (CrAT) and sirtuin 3 (Sirt3); enzymes that oppose Kac by buffering the acetyl CoA pool and catalyzing lysine deacetylation, respectively. Although the dual knockout (DKO) manipulation raised the cardiac acetyl-lysine landscape well beyond that observed in response to Sirt3 deficiency or pathophysiological heart remodeling, bioenergetics of DKO mitochondria were remarkably normal. Moreover, DKO hearts were not more vulnerable to pressure overload-induced dysfunction resulting from chronic transaortic constriction. The findings challenge the premise that hyperacetylation per se threatens metabolic resilience by causing broad-ranging damage to mitochondrial proteins. See Davidson et. al. 2019 for further experimental details, reagents, and references.

  12. o

    Therapeutic efficacy of keratinized mucosa augmentation for functioning...

    • osf.io
    url
    Updated Mar 20, 2023
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    Yunting Fu; Shahriar Shahami; LILIA CEPEDA; Se-Lim Oh (2023). Therapeutic efficacy of keratinized mucosa augmentation for functioning dental implants exhibiting a lack of keratinized mucosa: a scoping review [Dataset]. http://doi.org/10.17605/OSF.IO/TCH25
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    urlAvailable download formats
    Dataset updated
    Mar 20, 2023
    Dataset provided by
    Center For Open Science
    Authors
    Yunting Fu; Shahriar Shahami; LILIA CEPEDA; Se-Lim Oh
    License

    http://opensource.org/licenses/AFL-3.0http://opensource.org/licenses/AFL-3.0

    Description

    for functioning dental implants exhibiting a lack of keratinized mucosa, how effective are autogenous soft tissue grafts for improving the peri-implant soft tissue conditions.

  13. Data from: Baltimore Ecosystem Study: Increased diversity of the regional...

    • search.dataone.org
    • portal.edirepository.org
    Updated Oct 5, 2022
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    Christopher M Swan (2022). Baltimore Ecosystem Study: Increased diversity of the regional species pool via seeding augments establishment of native species in experimental vacant lot restorations [Dataset]. https://search.dataone.org/view/https%3A%2F%2Fpasta.lternet.edu%2Fpackage%2Fmetadata%2Feml%2Fknb-lter-bes%2F5021%2F1
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    Dataset updated
    Oct 5, 2022
    Dataset provided by
    Long Term Ecological Research Networkhttp://www.lternet.edu/
    Authors
    Christopher M Swan
    Time period covered
    Jan 1, 2014 - Dec 31, 2016
    Area covered
    Variables measured
    POA, ACNE, ACSP, ACVI, AGAL, AGFO, AIAL, ALPE, AMAR, AMRE, and 146 more
    Description

    The harsh geophysical template characterized by the urban environment combined with people’s choices has led ecologists to invoke environmental filtering as the main ecological phenomena explaining urban biodiversity patterns. Yet, dispersal is often overlooked as a driving factor, especially on expanding vacant land. Does overcoming dispersal limitation by seeding native species in urban environments and increasing the functional or phylogenetic diversity of the seeding pool increase native plant species diversity and abundance in urban vacant land? We took an experimental approach to learn how different dimensions of plant biodiversity within an augmented regional species pool, via seed additions, can explain variation in community structure over a 3-year period. Vacant lots were cleared and manipulated with seeding treatments of high or low phylogenetic and functional diversities from a pool of 28 native species. Establishment success, total native cover and native species richness were followed and compared to cleared, unseeded control lots as well as un-manipulated lots. Seeding increased native plant abundance and richness over uncleared plots, as well as cleared and unseeded control plots. Phylogenetically diverse seed mixtures had greater establishment success than mixtures composed of closely related species. Diversifying seed mixtures increased the likelihood of including species that are better able to establish on vacant land. However, there were no differences in varying levels of either functional or phylogenetic diversity. Augmenting the regional species pool via diverse seed mixtures can enhance native plant cover and richness under the harsh environmental conditions conferred by land abandonment.

  14. Quercetin metabolite 4-methylcatechol does not directly augment the activity...

    • data.niaid.nih.gov
    • search.dataone.org
    • +1more
    zip
    Updated Dec 16, 2024
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    Rían Manville; Hanh Nguyen; Geoffrey Abbott (2024). Quercetin metabolite 4-methylcatechol does not directly augment the activity of Kv7.4 or Kv7.5 [Dataset]. http://doi.org/10.5061/dryad.98sf7m0td
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    zipAvailable download formats
    Dataset updated
    Dec 16, 2024
    Dataset provided by
    University of California, Irvine
    Authors
    Rían Manville; Hanh Nguyen; Geoffrey Abbott
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Description

    Vascular smooth muscle voltage-gated potassium (Kv) channels, particularly Kv7.4 and Kv7.5 homomers and/or heteromers, are increasingly being recognized to play a role in regulating vascular smooth muscle cell excitability. Thus, augmenting Kv7.4 and Kv7.5 activity to induce vasorelaxation is being investigated as a mechanism for antihypertensive drug development and the underlying molecular mechanism for the antihypertensive effects of dietary components and traditional botanical medicines. Recently, Dias and colleagues wrote that “Dietary polyphenols have been associated with many beneficial cardiovascular effects. However, these effects are rather attributed to small phenolic molecules formed by the gut microbiota…4-Methylcatechol (4-MC) is one such metabolite.” Dias and colleagues demonstrate that 4-MC (15 µM) augments vasorelaxation induced by sodium nitroprusside or forskolin in rat aortic rings. The vasorelaxation was inhibited by pan voltage-gated potassium channel modulator 4-aminopyridine and to a lesser extent by Kv7 inhibitor linopirdine, but not by soluble guanylyl cyclase inhibitor ODQ. The authors concluded that “in silico reverse docking confirmed that 4-MC binds to Kv7.4 through hydrogen bonding and hydrophilic interactions” and “our findings suggested that 4-MC exerts vasorelaxation by opening Kv channels with the involvement of Kv7.4”. Here, we report that 4-MC has no direct functional effect on Kv7.4 and Kv7.5 and may be weakly inhibitory to Kv7.4/Kv7.5 heteromers at depolarized potentials. At 100 µM 4-MC has mild augmenting effects at hyperpolarized potentials on the activity of Kv1.2, Kv1.5, and Kv2.1, but not Kv1.1. In conclusion, it is critical that in silico docking predictions be experimentally validated in order to accurately draw conclusions about the identity of specific proteins as pharmacological targets. Methods cRNA prepeartion and Two-electrode voltage clamp (TEVC) cRNA transcripts encoding human Kv7.4, Kv7.5, Kv1.1, Kv1.2, Kv1.5, and Kv2.1 were generated by in vitro transcription using the mMessage mMachine kit (Thermo Fisher Scientific), after vector linearization, from cDNA sub-cloned into plasmids incorporating Xenopus laevis β-globin 5’ and 3’ UTRs flanking the coding region to enhance translation and cRNA stability. Defolliculated stage V and VI Xenopus laevis oocytes (Xenoocyte, Dexter, MI, US) were injected with KCNQ cRNAs (0.1-25 ng) and incubated at 16 oC in ND96 oocyte storage solution containing penicillin and streptomycin, with daily washing, for 1-4 days prior to two-electrode voltage-clamp (TEVC) recording.TEVC was performed at room temperature using an OC-725C amplifier (Warner Instruments, Hamden, CT) and pClamp10 software (Molecular Devices, Sunnyvale, CA) 1-4 days after cRNA injection as described in the section above. For recording, oocytes were placed in a small-volume oocyte bath (Warner) and viewed with a dissection microscope. 4-methylcatechol was sourced from Sigma and made into 250 mM stock solutions in DMSO prior to dilution in recording solution (in mM): 96 NaCl, 4 KCl, 1 MgCl2, 1 CaCl2, 10 HEPES (pH 7.6). 4-methylcatechol was introduced into the oocyte recording bath by gravity perfusion at a constant flow of 1 ml per minute for 3 minutes prior to recording. Pipettes were of 1-2 MΩ resistance when filled with 3 M KCl. Currents were recorded in response to voltage pulses between -80 mV and +40 mV at 10 mV intervals from a holding potential of -80 mV, to yield current-voltage relationships and examine activation kinetics. Data was analyzed using Clampfit (Molecular Devices) and Graphpad Prism software (GraphPad, San Diego, CA, USA), stating values as mean ± SEM. Raw or normalized tail currents were plotted versus prepulse voltage and fitted with a single Boltzmann function. Statistics and Reproducibility All values are expressed as mean ± SEM. One-way ANOVA was applied for all tests; all p values were two-sided.

  15. Hydrogel Augmentation for Rotator Cuff Repair Market Research Report 2033

    • growthmarketreports.com
    csv, pdf, pptx
    Updated Jul 5, 2025
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    Growth Market Reports (2025). Hydrogel Augmentation for Rotator Cuff Repair Market Research Report 2033 [Dataset]. https://growthmarketreports.com/report/hydrogel-augmentation-for-rotator-cuff-repair-market
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    pptx, csv, pdfAvailable download formats
    Dataset updated
    Jul 5, 2025
    Dataset authored and provided by
    Growth Market Reports
    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Hydrogel Augmentation for Rotator Cuff Repair Market Outlook



    According to our latest research, the global Hydrogel Augmentation for Rotator Cuff Repair market size reached USD 412.5 million in 2024, reflecting a robust interest in advanced orthopedic solutions. The market is currently expanding at a compound annual growth rate (CAGR) of 8.1% and is projected to achieve a value of USD 792.8 million by 2033. This substantial growth is primarily driven by the increasing prevalence of rotator cuff injuries, coupled with advancements in biomaterial technologies and the growing adoption of minimally invasive surgical techniques. As per the latest research, the integration of hydrogel-based augmentation is revolutionizing rotator cuff repair outcomes, fueling significant market expansion across both developed and emerging regions.




    The growth trajectory of the Hydrogel Augmentation for Rotator Cuff Repair market is underpinned by a rising geriatric population globally, who are more susceptible to musculoskeletal injuries, particularly rotator cuff tears. With advancing age, the incidence of degenerative tendon injuries increases, fueling a steady demand for innovative repair solutions such as hydrogel augmentation. Hydrogels, due to their biocompatibility and ability to mimic the extracellular matrix, provide an optimal environment for tendon healing and regeneration. This has led to a surge in clinical adoption, as orthopedic surgeons and healthcare providers increasingly favor hydrogel-based products over conventional suturing and grafting techniques, which often yield suboptimal healing and higher re-tear rates. The market is further buoyed by growing awareness among patients regarding the benefits of hydrogel augmentation, such as reduced post-operative pain, faster recovery, and improved functional outcomes.




    Another key driver accelerating market growth is the rapid pace of technological advancements in biomaterials science. Manufacturers are investing heavily in the development of next-generation hydrogels that offer superior mechanical strength, controlled degradation rates, and enhanced bioactivity. These innovations are addressing longstanding challenges associated with traditional repair materials, such as limited integration with host tissues and inadequate support for tendon regeneration. Additionally, the emergence of composite hydrogels that combine natural and synthetic polymers is creating new opportunities for tailored solutions in rotator cuff repair. Regulatory approvals for novel hydrogel products and the proliferation of clinical trials demonstrating their efficacy are further catalyzing market expansion. As healthcare systems worldwide prioritize value-based care, the cost-effectiveness and improved patient outcomes associated with hydrogel augmentation are positioning it as a preferred option in orthopedic surgery.




    The Hydrogel Augmentation for Rotator Cuff Repair market is also witnessing growth due to the increasing incidence of sports-related injuries and the rising participation in athletic activities across all age groups. Young adults and middle-aged individuals engaged in recreational and professional sports are prone to acute rotator cuff injuries, which require timely and effective intervention to restore shoulder function. The demand for minimally invasive procedures that facilitate faster return to activity is driving the adoption of hydrogel-based augmentation techniques in ambulatory surgical centers and specialized orthopedic clinics. Furthermore, ongoing research into the application of hydrogels in pediatric populations and complex revision surgeries is expected to expand the market's addressable patient base, reinforcing its growth prospects over the forecast period.




    From a regional perspective, North America continues to dominate the Hydrogel Augmentation for Rotator Cuff Repair market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The high prevalence of rotator cuff injuries, advanced healthcare infrastructure, and robust investment in medical research underpin the market’s leadership in North America. Europe is witnessing steady growth, driven by increasing healthcare expenditure and favorable reimbursement policies for innovative orthopedic treatments. Meanwhile, Asia Pacific is emerging as the fastest-growing region, propelled by rising healthcare awareness, expanding access to advanced surgical procedures, and a burgeoning middle-class population. Latin America and the Middle East & Africa are gradually gainin

  16. Z

    Cape Hatteras Landsat8 RGB Images and Labels for Image Segmentation using...

    • data.niaid.nih.gov
    • zenodo.org
    Updated Jul 21, 2023
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    Buscombe, Daniel (2023). Cape Hatteras Landsat8 RGB Images and Labels for Image Segmentation using the program, Segmentation Zoo [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_5895127
    Explore at:
    Dataset updated
    Jul 21, 2023
    Dataset authored and provided by
    Buscombe, Daniel
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Hatteras Island, Cape Hatteras
    Description

    Cape Hatteras Landsat8 RGB Images and Labels for Image Segmentation using the program, Segmentation Gym

    Overview

    • Test datasets and files for testing the segmentation gym program for image segmentation
    • Data set made by Daniel Buscombe, Marda Science LLC.
    • Dataset consists of a time-series of Landsat-8 images of Cape Hatteras National Seashore, courtesy of the U.S. Geological Survey.
    • Imagery spans the period February 2015 to September 2021.
    • Labels were created by Daniel Buscombe, Marda Science, using the labeling program Doodler.

    Download this file and unzip to somewhere on your machine (although not inside the segmentation_gym folder), then see the relevant page on the segmentation gym wiki for further explanation.

    This dataset and associated models were made by Dr Daniel Buscombe, Marda Science LLC, for the purposes of demonstrating the functionality of Segmentation Gym. The labels were created using Doodler.

    Previous versions:

    1.0. https://zenodo.org/record/5895128#.Y1G5s3bMIuU original release, Oct 2021, conforming to Segmentation Gym functionality on Oct 2021

    2.0 https://zenodo.org/record/7036025#.Y1G57XbMIuU, Jan 23 2022, conforming to Segmentation Gym functionality on Jan 23 2022

    This is version 4.0, created 2/25/23, and has been tested with Segmentation Gym using doodleverse-utils 0.0.26 https://pypi.org/project/doodleverse-utils/0.0.26/

    file structure

    /Users/Someone/my_segmentation_zoo_datasets
              │  ├── config
              │  |  └── *.json
              │  ├── capehatteras_data
              |  |  ├── fromDoodler
              |  |  |   ├──images
              │  |  |   └──labels
              |  |  ├──npzForModel
              │  |  └──toPredict
              │  └── modelOut
              │    └── *.png
              │  └── weights
              │    └── *.h5
    
    
    

    config

    There are 4 config files: 1. /config/hatteras_l8_resunet.json 2. /config/hatteras_l8_vanilla_unet.json 3. /config/hatteras_l8_resunet_model2.json

    1. /config/hatteras_l8_segformer.json

    The first two are for res-unet and unet models respectively. The third one differs from the first only with specification of kernel size. It is provided as an example of how to conduct model training experiments, modifying one hyperparameter at a time in the effort to create an optimal model. The last one is based on the new Segformer model architecture.

    They all contain the same essential information and differ as indicated below

    {
     "TARGET_SIZE": [768,768], # the size of the imagery you wish the model to train on. This may not be the original size
     "MODEL": "resunet", # model name. Otherwise, "unet" or "segformer"
     "NCLASSES": 4, # number of classes
     "KERNEL":9, # horizontal size of convolution kernel in pixels
     "STRIDE":2, # stride in convolution kernel
     "BATCH_SIZE": 7, # number of images/labels per batch
     "FILTERS":6, # number of filters
     "N_DATA_BANDS": 3, # number of image bands
     "DROPOUT":0.1, # amount of dropout
     "DROPOUT_CHANGE_PER_LAYER":0.0, # change in dropout per layer
     "DROPOUT_TYPE":"standard", # type of dropout. Otherwise "spatial"
     "USE_DROPOUT_ON_UPSAMPLING":false, # if true, dropout is used on upsampling as well as downsampling
     "DO_TRAIN": false, # if false, the model will not train, but you will select this config file, data directory, and the program will load the model weights and test the model on the validation subset
     if true, the model will train from scratch (warning! this will overwrite the existing weights file in h5 format)
     "LOSS":"dice", # model training loss function, otherwise "cat" for categorical cross-entropy
     "PATIENCE": 10, # number of epochs of no model improvement before training is aborted
     "MAX_EPOCHS": 100, # maximum number of training epochs
     "VALIDATION_SPLIT": 0.6, #proportion to use for validation
     "RAMPUP_EPOCHS": 20, # [LR-scheduler] rampup to maximim
     "SUSTAIN_EPOCHS": 0.0, # [LR-scheduler] sustain at maximum
     "EXP_DECAY": 0.9, # [LR-scheduler] decay rate
     "START_LR": 1e-7, # [LR-scheduler] start lr
     "MIN_LR": 1e-7, # [LR-scheduler] min lr
     "MAX_LR": 1e-4, # [LR-scheduler] max lr
     "FILTER_VALUE": 0, #if >0, the size of a median filter to apply on outputs (not recommended unless you have noisy outputs)
     "DOPLOT": true, #make plots
     "ROOT_STRING": "hatteras_l8_aug_768", #data file (npz) prefix string
     "USEMASK": false, # use the convention 'mask' in label image file names, instead of the preferred 'label'
     "AUG_ROT": 5, # [augmentation] amount of rotation in degrees
     "AUG_ZOOM": 0.05, # [augmentation] amount of zoom as a proportion
     "AUG_WIDTHSHIFT": 0.05, # [augmentation] amount of random width shift as a proportion
     "AUG_HEIGHTSHIFT": 0.05,# [augmentation] amount of random width shift as a proportion
     "AUG_HFLIP": true, # [augmentation] if true, randomly apply horizontal flips
     "AUG_VFLIP": false, # [augmentation] if true, randomly apply vertical flips
     "AUG_LOOPS": 10, #[augmentation] number of portions to split the data into (recommended > 2 to save memory)
     "AUG_COPIES": 5 #[augmentation] number iof augmented copies to make
     "SET_GPU": "0" #which GPU to use. If multiple, list separated by a comma, e.g. '0,1,2'. If CPU is requested, use "-1"
     "WRITE_MODELMETADATA": false, #if true, the prompts `seg_images_in_folder.py` to write detailed metadata for each sample file
     "DO_CRF": true #if true, apply CRF post-processing to outputs
    
    
     "LOSS_WEIGHTS": false, #if true, apply per-class weights to loss function
    
    
     "MODE": "all", #'all' means use both non-augmented and augmented files, "noaug" means use non-augmented only, "aug" uses augmented only
    
    
     "SET_PCI_BUS_ID": true, #if true, make keras aware of the PCI BUS ID (advanced or nonstandard GPU usage)
    
    
     "TESTTIMEAUG": true, #if true, apply test-time augmentation when model in inference mode
    
    
     "WRITE_MODELMETADATA": true,# if true, write model metadata per image when model in inference mode
    
    
     "OTSU_THRESHOLD": true# if true, and NCLASSES=2 only, use per-image Otsu threshold rather than decision boundary of 0.5 on softmax scores
    
    
    }
    

    capehatteras_data

    Folder containing all the model input data

              │  ├── capehatteras_data: folder containing all the model input data
              |  |  ├── fromDoodler: folder containing images and labels exported from Doodler using [this program](https://github.com/dbuscombe-usgs/dash_doodler/blob/main/utils/gen_images_and_labels_4_zoo.py)
              |  |  |   ├──images: jpg format files, one per label image
              │  |  |   └──labels: jpg format files, one per image
              |  |  ├──npzForModel: npz format files for model training using [this program](https://github.com/dbuscombe-usgs/segmentation_zoo/blob/main/train_model.py) that have been created following the workflow [documented here](https://github.com/dbuscombe-usgs/segmentation_zoo/wiki/Create-a-model-ready-dataset) using [this program](https://github.com/dbuscombe-usgs/segmentation_zoo/blob/main/make_nd_dataset.py)
              │  |  └──toPredict: a folder of images to test model prediction using [this program](https://github.com/dbuscombe-usgs/segmentation_zoo/blob/main/seg_images_in_folder.py)
    

    modelOut

    PNG format files containing example model outputs from the train ('_train_' in filename) and validation ('_val_' in filename) subsets as well as an image showing training loss and accuracy curves with trainhist in the filename. There are two sets of these files, those associated with the residual unet trained with dice loss contain resunet in their name, and those from the UNet are named with vanilla_unet.

    weights

    There are model weights files associated with each config files.

  17. t

    BIOGRID CURATED DATA FOR PUBLICATION: Tyrosine kinase 2 interacts with the...

    • thebiogrid.org
    zip
    Updated Aug 25, 2010
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    BioGRID Project (2010). BIOGRID CURATED DATA FOR PUBLICATION: Tyrosine kinase 2 interacts with the proapoptotic protein Siva-1 and augments its apoptotic functions. [Dataset]. https://thebiogrid.org/170183/publication/tyrosine-kinase-2-interacts-with-the-proapoptotic-protein-siva-1-and-augments-its-apoptotic-functions.html
    Explore at:
    zipAvailable download formats
    Dataset updated
    Aug 25, 2010
    Dataset authored and provided by
    BioGRID Project
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    Protein-Protein, Genetic, and Chemical Interactions for Shimoda HK (2010):Tyrosine kinase 2 interacts with the proapoptotic protein Siva-1 and augments its apoptotic functions. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Siva-1 is a molecule that has the potential to induce both extrinsic (receptor-mediated) and intrinsic (non-receptor-mediated) apoptosis. Siva-1 binds to CD27, a member of the tumor necrosis factor receptor (TNFR) family, Abl-related gene (ARG), and BCL-X(L), and these partner molecules reportedly enhance the apoptotic properties of Siva-1. In this study, we show that Siva-1 also interacts with a member of the Jak family protein kinases, tyrosine kinase 2 (Tyk2). Siva-1 bound to Tyk2 via its N-terminal region, and Tyk2 phosphorylated Siva-1 at tyrosines 53 and 162. In murine pro-B cells, Ba/F3 cells, expression of Tyk2 augmented Siva-1-induced apoptosis. This augmentation of Siva-1-induced apoptosis was retained regardless of the phosphorylation of Siva-1, but was almost completely prevented by the abrogation of the Tyk2-Siva-1 association. These findings indicate that the interaction between Siva-1 and Tyk2 directly augments the apoptotic activity of Siva-1. Our novel observations suggest that Siva-1 forms a functional complex with Tyk2 and participates in the transduction of signals that inhibit B lymphocyte growth.

  18. Data augmentation with Generative AI for DoW attack detection in serverless...

    • zenodo.org
    zip
    Updated Sep 14, 2024
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    Higinio Mora; Higinio Mora (2024). Data augmentation with Generative AI for DoW attack detection in serverless architectures [Dataset]. http://doi.org/10.5281/zenodo.13758901
    Explore at:
    zipAvailable download formats
    Dataset updated
    Sep 14, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Higinio Mora; Higinio Mora
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Serverless computing is one of the latest paradigms in cloud computing. It offers a framework for the development of event-driven applications whose functions are executed in a scalable environment provided by the corresponding cloud platform. In this way, resources are obtained on demand, paying only for the time the function is running. This new model has new vulnerabilities and, therefore, new types of cybersecurity attacks. However, there are still not enough transaction datasets for serverless systems with a sufficient amount of data to develop advanced detection methods for this type of threat. Therefore, we present this dataset that has been built with generative AI to advance the development of models that can effectively deal with these threats.

  19. f

    Pseudomonas aeruginosa ExoU augments neutrophil transepithelial migration

    • figshare.com
    tiff
    Updated Jun 1, 2023
    + more versions
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    Michael A. Pazos; Bernard B. Lanter; Lael M. Yonker; Alex D. Eaton; Waheed Pirzai; Karsten Gronert; Joseph V. Bonventre; Bryan P. Hurley (2023). Pseudomonas aeruginosa ExoU augments neutrophil transepithelial migration [Dataset]. http://doi.org/10.1371/journal.ppat.1006548
    Explore at:
    tiffAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOS Pathogens
    Authors
    Michael A. Pazos; Bernard B. Lanter; Lael M. Yonker; Alex D. Eaton; Waheed Pirzai; Karsten Gronert; Joseph V. Bonventre; Bryan P. Hurley
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Excessive neutrophil infiltration of the lungs is a common contributor to immune-related pathology in many pulmonary disease states. In response to pathogenic infection, airway epithelial cells produce hepoxilin A3 (HXA3), initiating neutrophil transepithelial migration. Migrated neutrophils amplify this recruitment by producing a secondary gradient of leukotriene B4 (LTB4). We sought to determine whether this two-step eicosanoid chemoattractant mechanism could be exploited by the pathogen Pseudomonas aeruginosa. ExoU, a P. aeruginosa cytotoxin, exhibits phospholipase A2 (PLA2) activity in eukaryotic hosts, an enzyme critical for generation of certain eicosanoids. Using in vitro and in vivo models of neutrophil transepithelial migration, we evaluated the impact of ExoU expression on eicosanoid generation and function. We conclude that ExoU, by virtue of its PLA2 activity, augments and compensates for endogenous host neutrophil cPLA2α function, leading to enhanced transepithelial migration. This suggests that ExoU expression in P. aeruginosa can circumvent immune regulation at key signaling checkpoints in the neutrophil, resulting in exacerbated neutrophil recruitment.

  20. f

    S1 Data -

    • plos.figshare.com
    zip
    Updated Feb 20, 2025
    + more versions
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    Wenbing Shi; Ji Huang; Gaoming Yang; Shuzhi Su; Shexiang Jiang (2025). S1 Data - [Dataset]. http://doi.org/10.1371/journal.pone.0317461.s001
    Explore at:
    zipAvailable download formats
    Dataset updated
    Feb 20, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Wenbing Shi; Ji Huang; Gaoming Yang; Shuzhi Su; Shexiang Jiang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Coal and gas outburst (CGO) is a complicated natural disaster in underground coal mine production. In constructing smart mines, predicting CGO risks efficiently and accurately is necessary. This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. First, sample features are applied to the transfer function using a pointwise intensity transformation to obtain new feature samples. It solves the problems of imbalanced data samples and insufficient diversity. Second, the feature importance score sorting and Sparse PCA dimensionality reduction are performed on the data-augmented samples. It provides the initial genome code for the evolutionary neural network. Finally, an evolutionary neural network for CGO prediction is constructed through population initialization, fitness evaluation, species differentiation, genome mutation, and recombination. The optimal phenotype is obtained in the evolutionary generations. In the experiment, we verify the effectiveness of ANEAT from multiple aspects such as data augmentation effectiveness analysis, deep learning model comparison, swarm intelligence optimization algorithm comparison, and other method comparisons. The results show that the MAE, RMSE, and EVAR indexes of ANEAT on the test set are 0.0816, 0.1322, and 0.8972, respectively. It has the optimal CGO prediction effect. ANEAT realizes the high-precision mapping of feature parameters and outburst risk with a lightweight network architecture, which can be well applied to CGO prediction.

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María-Isabel Limaylla-Lunarejo; María-Isabel Limaylla-Lunarejo; Nelly Condori-Fernandez; Nelly Condori-Fernandez; Miguel R. Luaces; Miguel R. Luaces (2024). Data augmentation for Multi-Classification of Non-Functional Requirements - Dataset [Dataset]. http://doi.org/10.5281/zenodo.10805331
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Data augmentation for Multi-Classification of Non-Functional Requirements - Dataset

Explore at:
csvAvailable download formats
Dataset updated
Mar 19, 2024
Dataset provided by
Zenodohttp://zenodo.org/
Authors
María-Isabel Limaylla-Lunarejo; María-Isabel Limaylla-Lunarejo; Nelly Condori-Fernandez; Nelly Condori-Fernandez; Miguel R. Luaces; Miguel R. Luaces
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

There are four datasets:

1.Dataset_structure indicates the structure of the datasets, such as column name, type, and value.

2. Spanish_promise_exp_nfr_train and Spanish_promise_exp_nfr_test are the non-functional requirements of the Promise_exp[1] dataset translated into the Spanish language.

3. Blanced_promise_exp_nfr_train is the new balanced dataset of Spanish_promise_exp_nfr_train, in which the Data Augmentation technique with chatGPT was applied to increase the requirements with little data and random undersampling was used to eliminate requirements.

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