cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15151/esrf-es-799266138
title: μFTIR and nXRF study of the effect of DG4-His-Mal dendrimer encapsulated in liposomes in an Alzheimer Disease transgenic mice model: amyloid

description: Alzheimer Disease (AD) is a neurodegenerative process distinct from normal ageing by the presence of Senile Plaques composed of Abeta peptide. Dendrimers have been shown to possess properties as antiamyloidogenic agent. Maltose modified dendrimers decorated with histidine’s (DG4-His-Mal) are nanoparticles that prevent APP/PS1 transgenic mice from developing cognitive deficit and μFTIR studies have shown that DG4-His-Mal reduce the formation of early aggregates and fibrils in the cortex. To improve this effect, we have administered DG4-His-Mal encapsulated in liposomes to 5xFAD transgenic mice (model for AD). The experiment is still on going, however, the midterm cognitive test show that DG4-His-Mal and liposomes alone improve the performance in cognitive test and have a synergic effect when administered together in AD transgenic mice. To understand the effects of these treatments we will combine histochemical analysis, with μFTIR (amyloid structure) and nXRF f(metal distribution).
DOI: 10.15151/esrf-es-776857198
title: Next-generation DFXM
DOI: 10.6084/m9.figshare.20058914
title: Supplementary Material for: Efficacy and Safety of Multiple Dupilumab Dose Regimens in Patients with Moderate-To-Severe Atopic Dermatitis: A Systematic Review and Network Meta-Analysis of Randomized Controlled Trials

description: <b><i>Background:</i></b> Dupilumab ameliorates the signs and symptoms of atopic dermatitis (AD) and improves the patient’s quality of life. Multiple-dose regimens of dupilumab have been applied by clinicians, but the efficacy of some regimens remains unclear. <b><i>Objectives:</i></b> The aim of the study was to systematically evaluate the efficacy and safety of multiple dupilumab dose regimens in patients with moderate-to-severe AD in terms of comprehensive outcomes. <b><i>Methods:</i></b> We searched electronic databases and subjected the selected studies to risk-of-bias assessment and network meta-analysis (NMA). Efficacy and safety outcomes were compared using a random-effects NMA to estimate pooled relative risk ratio (RR) of direct and indirect comparisons among multiple dupilumab dose regimens. The Eczema Area Severity Index, Investigator’s Global Assessment, and pruritus numerical rating scale were analyzed to assess the efficacy, while adverse events (AEs) and serious adverse events to represent the safety. <b><i>Results:</i></b> Eight randomized controlled trials involving 3,679 patients were identified. Most patients received therapy for 16 weeks. Multiple dupilumab dose regimens, including 300 mg weekly (QW), 300 mg every 2 weeks (Q2W), 200 mg Q2W, 300 mg monthly (QM), 300 mg every 2 months (Q2M), and 100 mg QM were analyzed. All regimens, except 100 mg QM, had significantly better efficacy than placebo. 300 mg QW and 300 mg Q2W appeared to have similar efficacy. Notably, both 300 mg QW and 300 mg Q2W had no significantly better efficacy than 300 mg QM. As for 300 mg Q2M, significantly reduced efficacy was noted in only one efficacy outcome when compared to 300 mg QW and 300 mg Q2W. In terms of safety outcomes, AEs occurring with any of the regimens were comparable with the placebo. No significant inconsistency was noted within the network in all efficacy outcomes. <b><i>Conclusions:</i></b> Our NMA indicated that the administration of the following dupilumab regimens was effective for patients with moderate-to-severe AD: 300 mg QW, 300 mg Q2W, 200 mg Q2W, 300 mg QM, and 300 mg Q2M. Our data can improve the understanding of the relative efficacy and safety of multiple dupilumab dose regimens, which will help in shared decision-making between clinicians and patients.
DOI: 10.6084/m9.figshare.20058917
title: data.csv

description: Fetal presentation was recorded in each maternal visit. In ultimate presentation and Xth week columns; 0: cephalic 1: breech 2: transverse x: missing NaN: no visit
DOI: 10.6084/m9.figshare.20058917.v1
title: data.csv

description: Fetal presentation was recorded in each maternal visit. In ultimate presentation and Xth week columns; 0: cephalic 1: breech 2: transverse x: missing NaN: no visit
DOI: 10.6084/m9.figshare.20058938
title: Supplementary Material for: Antioxidants for Treatment of Duchenne Muscular Dystrophy: A Systematic Review and Meta-Analysis

description: <b><i>Introduction:</i></b> Increasing evidence has shown that oxidative stress is involved in the pathogenesis of Duchenne muscular dystrophy (DMD). Oxidative stress impairs muscle function, reduces regenerative capacity, and leads to atrophy and muscle weakness. The present study aimed to evaluate the effectiveness and safety of antioxidants in treatment of DMD patients. <b><i>Methods:</i></b> Medline, Embase, EBSCOhost, and Cochrane Library databases were searched using relevant keywords regarding DMD and antioxidants. The risk of bias for all included studies was assessed using the Cochrane risk of bias tool. The effectiveness of antioxidants in improving pulmonary function and muscle strength in DMD patients and their rate of adverse events was evaluated by meta-analysis. <b><i>Results:</i></b> A total of nine eligible studies were identified. Among these, two studies involving 85 patients compared idebenone with placebo. Pooled data showed a significant improvement in pulmonary function after idebenone treatment. Flavonoids- and omega 3-based compounds (FLAVOMEGA) significantly improved muscle strength. Two studies evaluated coenzyme Q<sub>10</sub> (CoQ<sub>10</sub>) and reported clinical improvement in physical activity. The remaining four studies evaluated pentoxifylline, superoxide dismutase, vitamin E combination with penicillamine and penicillamine alone, respectively, and found no significant differences between the intervention and placebo groups, measured by pulmonary function, muscle strength, movement function, or quality of life. Most adverse events were mild, while the rates of dropout and serious adverse events were low with respect to antioxidants. <b><i>Conclusions:</i></b> Idebenone appeared to be safe and effective in improving pulmonary function in DMD patients, while pentoxifylline, superoxide dismutase, penicillamine, or a combination of vitamin E with penicillamine did not show a significant therapeutic effect. CoQ<sub>10</sub> and FLAVOMEGA might be beneficial in improving muscle strength or physical activity in DMD patients. However, additional trials with more participants are warranted in the future.
DOI: 10.5281/zenodo.5824433
title: A dataset of environmental audio recordings containing chainsaw events

description: This audio dataset contains audio recordings (.wav format, 8kHz sampling rate) of various durations acquired using eight Cornel University SWIFT Autonomous Recording Units (ARUs) and corresponding metadata (.textgrid files). The recordings were collected within the Rodopi Mountain-Range National Park, Greece, at different seasons between 2018 and 2019. They are part of longer audio recordings. This audio content was used for the evaluation of a chainsaw sound detection system that was developed for the needs of a research project funded via a Single RTDI State Aid Action ”Research – Create – Innovate” grant (T1EDK-04488), which is co-financed by Greece and the European Union (European Regional Development Fund) as part of the Operational Program ”Competitiveness, Entrepreneurship and Innovation” of the National Strategic Reference Framework (NSRF) 2014-2020. Portions of each uploaded recording contain chainsaw events. The temporal location of these events, originally located manually by human listeners, are marked within the corresponding .textgrid files. Each audio recording with the corresponding textgrid file can be opened using the Praat program (https://www.fon.hum.uva.nl/praat/). If you find this dataset useful please cite the following paper: N. Stefanakis, K. Psaroulakis, N. Simou and C. Astaras, "An open-access system for long-range chainsaw sound detection", in Proceedings of EUSIPCO (2022). The chainsaw sound detection algorithm that was developed is also open access and is available in the form of python code from https://github.com/spl-icsforth/An-open-access-system-for-long-range-chainsaw-sound-detection
DOI: 10.5281/zenodo.5824432
title: A dataset of environmental audio recordings containing chainsaw events

description: This audio dataset contains audio recordings (.wav format, 8kHz sampling rate) of various durations acquired using eight Cornel University SWIFT Autonomous Recording Units (ARUs) and corresponding metadata (.textgrid files). The recordings were collected within the Rodopi Mountain-Range National Park, Greece, at different seasons between 2018 and 2019. They are part of longer audio recordings. This audio content was used for the evaluation of a chainsaw sound detection system that was developed for the needs of a research project funded via a Single RTDI State Aid Action ”Research – Create – Innovate” grant (T1EDK-04488), which is co-financed by Greece and the European Union (European Regional Development Fund) as part of the Operational Program ”Competitiveness, Entrepreneurship and Innovation” of the National Strategic Reference Framework (NSRF) 2014-2020. Portions of each uploaded recording contain chainsaw events. The temporal location of these events, originally located manually by human listeners, are marked within the corresponding .textgrid files. Each audio recording with the corresponding textgrid file can be opened using the Praat program (https://www.fon.hum.uva.nl/praat/). If you find this dataset useful please cite the following paper: N. Stefanakis, K. Psaroulakis, N. Simou and C. Astaras, "An open-access system for long-range chainsaw sound detection", in Proceedings of EUSIPCO (2022). The chainsaw sound detection algorithm that was developed is also open access and is available in the form of python code from https://github.com/spl-icsforth/An-open-access-system-for-long-range-chainsaw-sound-detection
DOI: 10.34725/dvn/5rhdqc/lfjonr
title: Aynekulu et al. 2021.pdf

description: Publication
DOI: 10.34725/dvn/5rhdqc/pczqdj
title: Burkina_soil_data_2021_08_16.tab

description: Soil data
DOI: 10.34725/dvn/5rhdqc/kndppk
title: 0Variable description-Burkina_fire_grazing_study.tab

description: Variable descriptions
DOI: 10.34725/dvn/5rhdqc
title: No changes in soil organic carbon and nitrogen following long term prescribed burning and livestock exclusion in the Sudan savanna woodlands of Burkina Faso

description: This study was done to study the impact of grazing and fire on soil Carbon stocks. It was part of a long-term split-plot experiment established in 1992 to explore the ecological impacts of prescribed fire and grazing intensity. It analyzed the effects of 19 years of prescribed annual burning and livestock exclusion on tree density, SOC and TN concentrations in the Sudanian savanna ecoregion at two sites (Tiogo and Laba) in Burkina Faso. The study was conducted in partnership with Environmental Institute for Agricultural Research (INERA) and International Livestock Research Institute (ILRI). It was supported by the GIZ/BEAF International Agricultural Research Grants Programme; and Water Land and Ecosystems program of the CGIAR. The study contributed to the Carbon sequestration options in pastoral agro-pastoral systems in Africa project.
DOI: 10.57702/hbbmkjx4
title: Example Video Visualizations

description: Video about auto combustion reactions of STF50 with EDTA+CA: variying phi. Video about boundary value problem of a push rod. The video was published by Leibniz University Hannover. Video about boundary value problem of a spring. The video was published by Leibniz University Hannover.
DOI: 10.5281/zenodo.6631784
title: Model Zoo: A Dataset of Diverse Populations of Neural Network Models - STL10 - Raw Datasets

description: <strong>Abstract</strong> In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before. <strong>Dataset</strong> This dataset is part of a larger collection of model zoos and contains the zoos trained on the labelled samples from STL10. All zoos with extensive information and code can be found at www.modelzoos.cc. This repository contains the raw model zoos as collections of models (file names beginning with "cifar_"). Zoos are trained with small and large CNN models, in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). Due to the large filesize, the preprocessed datasets are hosted in a separate repository. The index_dict.json files contain information on how to read the vectorized models. For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.
description: This dataset is part of a larger collection of model zoo datasets. Further information and references to all zoos can be found on www.modelzoos.cc
DOI: 10.5281/zenodo.6631783
title: Model Zoo: A Dataset of Diverse Populations of Neural Network Models - STL10 - Raw Datasets

description: <strong>Abstract</strong> In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before. <strong>Dataset</strong> This dataset is part of a larger collection of model zoos and contains the zoos trained on the labelled samples from STL10. All zoos with extensive information and code can be found at www.modelzoos.cc. This repository contains the raw model zoos as collections of models (file names beginning with "cifar_"). Zoos are trained with small and large CNN models, in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). Due to the large filesize, the preprocessed datasets are hosted in a separate repository. The index_dict.json files contain information on how to read the vectorized models. For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.
description: This dataset is part of a larger collection of model zoo datasets. Further information and references to all zoos can be found on www.modelzoos.cc
DOI: 10.5281/zenodo.6632121
title: Model Zoo: A Dataset of Diverse Populations of Neural Network Models - SVHN

description: <strong>Abstract</strong> In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before. <strong>Dataset</strong> This dataset is part of a larger collection of model zoos and contains the zoos trained on the labelled samples from SVHN. All zoos with extensive information and code can be found at www.modelzoos.cc. This repository contains two types of files: the raw model zoos as collections of models (file names beginning with "svhn_"), as well as preprocessed model zoos wrapped in a custom pytorch dataset class (filenames beginning with "dataset"). Zoos are trained in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). The index_dict.json files contain information on how to read the vectorized models. For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.
description: This dataset is part of a larger collection of model zoo datasets. Further information and references to all zoos can be found on www.modelzoos.cc
DOI: 10.5281/zenodo.6633627
title: Model Zoo: A Dataset of Diverse Populations of Neural Network Models - USPS

description: <strong>Abstract</strong> In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before. <strong>Dataset</strong> This dataset is part of a larger collection of model zoos and contains the zoos trained on the labelled samples from USPS. All zoos with extensive information and code can be found at www.modelzoos.cc. This repository contains two types of files: the raw model zoos as collections of models (file names beginning with "usps_"), as well as preprocessed model zoos wrapped in a custom pytorch dataset class (filenames beginning with "dataset"). Zoos are trained in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). The index_dict.json files contain information on how to read the vectorized models. For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.
description: This dataset is part of a larger collection of model zoo datasets. Further information and references to all zoos can be found on www.modelzoos.cc