cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24529384.v5
title: <b>Scope and limitations of library metrics for the assessment of ebook usage: COUNTER R5 and link resolver. </b>

description: <b>Scope and limitations of library metrics for the assessment of ebook usage: COUNTER R5 and link resolver. Datasets</b>(a) The CAMBRIDGE CORE EBOOKS 2019-2021 dataset includes data comparing the metrics of matched e-book titles between COUNTER R5 and the link resolver. The data was collected in January 2022 from the Library Discovery of Autonomous University of Madrid in Spain, covering the years 2019 to 2021.(b) The EBSCOhost EBOOKS 2019-2021 dataset includes data comparing the metrics of matched e-book titles between COUNTER R5 and the link resolver. The data was collected in January 2022 from the Library Discovery of Autonomous University of Madrid in Spain, covering the years 2019 to 2021. (c) The SCIENCEDIRECT EBOOKS 2019-2021 dataset includes data comparing the metrics of matched e-book titles between COUNTER R5 and the link resolver. The data was collected in January 2022 from the Library Discovery of Autonomous University of Madrid in Spain, covering the years 2019 to 2021.<br>
DOI: 10.6084/m9.figshare.23244213
title: Association Between Higher-Level Functional Capacity and Subjective Well-Being in Older Adults with Chronic Conditions: A Cross-Sectional Study

description: The aim of the present study was to examine the associations between higher-level functional capacity and subjective well-being (SWB) in older adults with chronic conditions. We used data from a self-administered questionnaire survey conducted in two day-service facilities and one home-visit nursing station. We performed multiple linear regression analyses with SWB measured by the revised Philadelphia Geriatric Center Morale Scale and higher-level functional capacity including instrumental self-maintenance, intellectual activity, and social role. 140 persons were included in the analyses. We did not find an association between instrumental self-maintenance and SWB (<i>B</i> = 0.30, 95% confidence interval [CI]: −0.15, 0.75). On the other hand, intellectual activity (<i>B</i> = 0.77, 95%CI: 0.19, 1.34) and social role (<i>B</i> = 0.88, 95% CI: 0.34, 1.43) were associated with SWB. Being intellectually active and performing social role may be important to increase SWB in older adults with chronic conditions.
DOI: 10.6084/m9.figshare.23244213.v1
title: Association Between Higher-Level Functional Capacity and Subjective Well-Being in Older Adults with Chronic Conditions: A Cross-Sectional Study

description: The aim of the present study was to examine the associations between higher-level functional capacity and subjective well-being (SWB) in older adults with chronic conditions. We used data from a self-administered questionnaire survey conducted in two day-service facilities and one home-visit nursing station. We performed multiple linear regression analyses with SWB measured by the revised Philadelphia Geriatric Center Morale Scale and higher-level functional capacity including instrumental self-maintenance, intellectual activity, and social role. 140 persons were included in the analyses. We did not find an association between instrumental self-maintenance and SWB (<i>B</i> = 0.30, 95% confidence interval [CI]: −0.15, 0.75). On the other hand, intellectual activity (<i>B</i> = 0.77, 95%CI: 0.19, 1.34) and social role (<i>B</i> = 0.88, 95% CI: 0.34, 1.43) were associated with SWB. Being intellectually active and performing social role may be important to increase SWB in older adults with chronic conditions.
DOI: 10.6084/m9.figshare.24089831.v1
title: A study of neural artistic style transfer models and architectures for Indian art styles

description: Neural Style Transfer (NST) has been a widely researched topic as of late enabling new forms of image manipulation. Here we perform an extensive study on NST algorithms and extend the existing methods with custom modifications for application to Indian art styles. In this paper, we aim to provide a comprehensive analysis of various methods ranging from the seminal work of Gatys et al which demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style, to the state of the art image-to-image translation models which use Generative Adversarial Networks (GANs) to learn the mapping between two domain of images. We observe and infer based on the results produced by the models on which one could be a more suitable approach for Indian art styles, especially Tanjore paintings which are unique compared to the Western art styles. We then propose the method which is more suitable for the domain of Indian Art style along with custom architecture which includes an enhancement and evaluation module. We then present evaluation methods, both qualitative and quantitative which includes our proposed metric, to evaluate the results produced by the model.
DOI: 10.6084/m9.figshare.24486526
title: Optimization of data pre-processing methods for time-series classification of electroencephalography data

description: The performance of time-series classification of electroencephalographic data varies strongly across experimental paradigms and study participants. Reasons are task-dependent differences in neuronal processing and seemingly random variations between subjects, amongst others. The effect of data pre-processing techniques to ameliorate these challenges is relatively little studied. Here, the influence of spatial filter optimization methods and non-linear data transformation on time-series classification performance is analyzed by the example of high-frequency somatosensory evoked responses. This is a model paradigm for the analysis of high-frequency electroencephalography data at a very low signal-to-noise ratio, which emphasizes the differences of the explored methods. For the utilized data, it was found that the individual signal-to-noise ratio explained up to 74% of the performance differences between subjects. While data pre-processing was shown to increase average time-series classification performance, it could not fully compensate the signal-to-noise ratio differences between the subjects. This study proposes an algorithm to prototype and benchmark pre-processing pipelines for a paradigm and data set at hand. Extreme learning machines, Random Forest, and Logistic Regression can be used quickly to compare a set of potentially suitable pipelines. For subsequent classification, however, machine learning models were shown to provide better accuracy.
DOI: 10.6084/m9.figshare.24486526.v1
title: Optimization of data pre-processing methods for time-series classification of electroencephalography data

description: The performance of time-series classification of electroencephalographic data varies strongly across experimental paradigms and study participants. Reasons are task-dependent differences in neuronal processing and seemingly random variations between subjects, amongst others. The effect of data pre-processing techniques to ameliorate these challenges is relatively little studied. Here, the influence of spatial filter optimization methods and non-linear data transformation on time-series classification performance is analyzed by the example of high-frequency somatosensory evoked responses. This is a model paradigm for the analysis of high-frequency electroencephalography data at a very low signal-to-noise ratio, which emphasizes the differences of the explored methods. For the utilized data, it was found that the individual signal-to-noise ratio explained up to 74% of the performance differences between subjects. While data pre-processing was shown to increase average time-series classification performance, it could not fully compensate the signal-to-noise ratio differences between the subjects. This study proposes an algorithm to prototype and benchmark pre-processing pipelines for a paradigm and data set at hand. Extreme learning machines, Random Forest, and Logistic Regression can be used quickly to compare a set of potentially suitable pipelines. For subsequent classification, however, machine learning models were shown to provide better accuracy.
DOI: 10.6084/m9.figshare.24089831
title: A study of neural artistic style transfer models and architectures for Indian art styles

description: Neural Style Transfer (NST) has been a widely researched topic as of late enabling new forms of image manipulation. Here we perform an extensive study on NST algorithms and extend the existing methods with custom modifications for application to Indian art styles. In this paper, we aim to provide a comprehensive analysis of various methods ranging from the seminal work of Gatys et al which demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style, to the state of the art image-to-image translation models which use Generative Adversarial Networks (GANs) to learn the mapping between two domain of images. We observe and infer based on the results produced by the models on which one could be a more suitable approach for Indian art styles, especially Tanjore paintings which are unique compared to the Western art styles. We then propose the method which is more suitable for the domain of Indian Art style along with custom architecture which includes an enhancement and evaluation module. We then present evaluation methods, both qualitative and quantitative which includes our proposed metric, to evaluate the results produced by the model.
DOI: 10.18454/rulb.2023.47.22.1
title: Композиция судебного решения

description: Russian Linguistic Bulletin, Выпуск 11 (47) 2023
DOI: 10.18454/rulb.2023.47.15.1
title: Этапы развития персонажа

description: Russian Linguistic Bulletin, Выпуск 11 (47) 2023
DOI: 10.18454/rulb.2023.47.24.5
title: Ключевые слова в речи В.В. Путина, выделенные при помощи программного обеспечения InfraNodus

description: Russian Linguistic Bulletin, Выпуск 11 (47) 2023
DOI: 10.18454/rulb.2023.47.4.1
title: The values of Spirman’s rank correlation (rs) in the experimental texts

description: Russian Linguistic Bulletin, Выпуск 11 (47) 2023
DOI: 10.25416/edgehill.24511390
title: Fear of Falling in Adults Under 50 who are Overweight or Obese

description: Coded data from 12 interviews exploring fear falling in the overweight and obese in a population who are under the age of 50. This study sheds light on how fear of falling is influenced by both mental and environmental elements in individuals under 50 who are overweight or obese. It uncovers several factors such as worry, social shame, and physical obstacles in the environment, which contribute to the understanding of this group's experiences. The research suggests that these individuals tend to have a pattern of initially paying attention to what they see as threats (like uneven sidewalks), but then they distract themselves, which might lead to falls. This reaction is more evident in situations where they might lose their balance, highlighting how psychological aspects can affect the likelihood of falling. Anxiety, for instance, may overwhelm their attention, making it difficult to notice all the potential hazards around them, leading to a higher chance of falling. These findings emphasize the complex link between emotions like anxiety and the actual risk of falling in certain groups of people. The insights gained from this research could pave the way for future studies and help create specific ways to help these individuals.
DOI: 10.25416/edgehill.24511390.v1
title: Fear of Falling in Adults Under 50 who are Overweight or Obese

description: Coded data from 12 interviews exploring fear falling in the overweight and obese in a population who are under the age of 50. This study sheds light on how fear of falling is influenced by both mental and environmental elements in individuals under 50 who are overweight or obese. It uncovers several factors such as worry, social shame, and physical obstacles in the environment, which contribute to the understanding of this group's experiences. The research suggests that these individuals tend to have a pattern of initially paying attention to what they see as threats (like uneven sidewalks), but then they distract themselves, which might lead to falls. This reaction is more evident in situations where they might lose their balance, highlighting how psychological aspects can affect the likelihood of falling. Anxiety, for instance, may overwhelm their attention, making it difficult to notice all the potential hazards around them, leading to a higher chance of falling. These findings emphasize the complex link between emotions like anxiety and the actual risk of falling in certain groups of people. The insights gained from this research could pave the way for future studies and help create specific ways to help these individuals.
DOI: 10.5281/zenodo.10089520
title: ESTRAM: data documentation

description: This documentation offers an overview of data employed in energy system optimization models built with the ESTRAM framework by a research group of the Leibniz University Hannover and the Institute for Solar Energy Research Hamelin (ISFH). It is important to note that specific models may utilize distinct data as indicated in their respective studies.
DOI: 10.5281/zenodo.10089521
title: ESTRAM: data documentation

description: This documentation offers an overview of data employed in energy system optimization models built with the ESTRAM framework by a research group of the Leibniz University Hannover and the Institute for Solar Energy Research Hamelin (ISFH). It is important to note that specific models may utilize distinct data as indicated in their respective studies.
DOI: 10.23729/cf8e815e-83e4-4738-9f95-f67d83d7dc9c
title: FERMAID Part2 preview

description: Second batch of samples for the FERMAID project, collective preview entry found at https://doi.org/10.23729/c9377f8c-9978-48d2-8dec-1f0daf2b0eb0 Preview of PAS dataset https://doi.org/10.23729/4dca6319-377b-470a-aff1-bca8a17c70f6
DOI: 10.3886/e195022
title: Promoting Creativity in Early Childhood Education

description: This study aims to find out the opinions and experience of teachers and teacher candidates on promoting creativity and creative thinking in the early childhood stage within the scope of the current preschool educational program. The method of the study is the basic qualitative research design. The study group consists of 25 preschool teachers employed in the province of Alanya in the city of Antalya, and 25 preschool teacher candidates who were students in their 3rd and 4th year at Akdeniz University, Faculty of Education, Department of Preschool Education. Open-ended questionnaire form was used for getting the opinions of teachers and teacher candidates. The data was analyzed within the principles of content analysis. According to the results of the study, statements on the prominence of creative thinking mainly emphasized the child being able to express her/his emotions and thoughts effectively, developing the child’s problem-solving skills, forming cause- effect relationships, and being able to create a different point of view towards events and situations. As for developing creative thinking, the opinions that come to the forefront are going on trips with the children, conducting art activities, focusing on drama activities, conducting science and maths activities based on research, and motivating children to create authentic products with different materials. On the whole, teachers and teacher candidates expressed that the present preschool educational program has positive contributions to promoting creative thinking in children.
description: Preschool teachers living in Antalya.
DOI: 10.3886/e195022v1
title: Promoting Creativity in Early Childhood Education

description: This study aims to find out the opinions and experience of teachers and teacher candidates on promoting creativity and creative thinking in the early childhood stage within the scope of the current preschool educational program. The method of the study is the basic qualitative research design. The study group consists of 25 preschool teachers employed in the province of Alanya in the city of Antalya, and 25 preschool teacher candidates who were students in their 3rd and 4th year at Akdeniz University, Faculty of Education, Department of Preschool Education. Open-ended questionnaire form was used for getting the opinions of teachers and teacher candidates. The data was analyzed within the principles of content analysis. According to the results of the study, statements on the prominence of creative thinking mainly emphasized the child being able to express her/his emotions and thoughts effectively, developing the child’s problem-solving skills, forming cause- effect relationships, and being able to create a different point of view towards events and situations. As for developing creative thinking, the opinions that come to the forefront are going on trips with the children, conducting art activities, focusing on drama activities, conducting science and maths activities based on research, and motivating children to create authentic products with different materials. On the whole, teachers and teacher candidates expressed that the present preschool educational program has positive contributions to promoting creative thinking in children.
description: Preschool teachers living in Antalya.
DOI: 10.34740/kaggle/dsv/6928624
title: Dark Triad vs Machiavellianism
DOI: 10.5281/zenodo.10078142
title: Benchmark Data for Chemprop

description: Datasets and splits of the manuscript "Chemprop: Machine Learning Package for Chemical Property Prediction." Train, validation and test splits are located within each folder, as well as additional data necessary for some of the benchmarks. To train Chemprop models, refer to our code repository to obtain ready-to-use scripts to train machine learning models for each of the systems.
Available benchmarking systems:

`hiv` HIV replication inhibition from MoleculeNet and OGB with scaffold splits
`pcba_random` Biological activities from MoleculeNet with random splits  (with missing targets filled in with zeros as provided by MoleculeNet)
`pcba_random_nans` Biological activities from MoleculeNet with random splits and data format to match OGB (with missing targets not filled in with zeros)
`pcba_scaffold` Biological activities from OGB with scaffold splits
`qm9_multitask` DFT calculated properties from MoleculeNet and OGB, trained as a multi-task model
`qm9_u0` DFT calculated properties from MoleculeNet and OGB, trained as a single-task model on the target U0 only
`qm9_gap` DFT calculated properties from MoleculeNet and OGB, trained as a single-task model on the target gap only
`sampl` Water-octanol partition coefficients, used to predict molecules from the SAMPL6, 7 and 9 challenges
`atom_bond_137k` Quantum-mechanical atom and bond descriptors
`bde` Bond dissociation enthalpies trained as single-task model
`bde_charges` Bond dissociation enthalpies trained as multi-task model together with atomic partial charges
`charges_eps_4` Partial charges at a dielectric constant of 4 (in protein)
`charges_eps_78` Partial charges at a dielectric constant of 78 (in water)
`barriers_e2` Reaction barrier heights of E2 reactions
`barriers_sn2` Reaction barrier heights of SN2 reactions
`barriers_cycloadd` Reaction barrier heights of cycloaddition reactions
`barriers_rdb7` Reaction barrier heights in the RDB7 dataset
`barriers_rgd1` Reaction barrier heights in the RGD1-CNHO dataset
`multi_molecule` UV/Vis peak absorption wavelengths in different solvents
`ir` IR Spectra
`pcqm4mv2` HOMO-LUMO gaps of the PCQM4Mv2 dataset
`uncertainty_ensemble` Uncertainty estimation using an ensemble using the QM9 gap dataset
`uncertainty_evidential` Uncertainty estimation using evidential learning using the QM9 gap dataset
`uncertainty_mve` Uncertainty estimation using mean-variance estimation using the QM9 gap dataset
`timing` Timing benchmark using subsets of QM9 gap
Version: This version of the dataset (Version 2) is compatible with all versions of Chemprop (supporting the respective functionality). Version 1 of this dataset is compatible with all versions except Chemprop v.1.6.1, which cannot process the `charges_eps_4`  and `charges_eps_78` datasets (all other benchmarks work as expected). We therefore recommend to always use Version 2 of the dataset (with reformatted `charges_eps_4`  and `charges_eps_78`  datasets), since it is compatible with all versions of Chemprop. For use with any other ML software, you can use any version.
DOI: 10.5281/zenodo.8174267
title: Benchmark Data for Chemprop

description: Datasets and splits of the manuscript "Chemprop: Machine Learning Package for Chemical Property Prediction." Train, validation and test splits are located within each folder, as well as additional data necessary for some of the benchmarks. To train Chemprop models, refer to our code repository to obtain ready-to-use scripts to train machine learning models for each of the systems.
Available benchmarking systems:

`hiv` HIV replication inhibition from MoleculeNet and OGB with scaffold splits
`pcba_random` Biological activities from MoleculeNet with random splits  (with missing targets filled in with zeros as provided by MoleculeNet)
`pcba_random_nans` Biological activities from MoleculeNet with random splits and data format to match OGB (with missing targets not filled in with zeros)
`pcba_scaffold` Biological activities from OGB with scaffold splits
`qm9_multitask` DFT calculated properties from MoleculeNet and OGB, trained as a multi-task model
`qm9_u0` DFT calculated properties from MoleculeNet and OGB, trained as a single-task model on the target U0 only
`qm9_gap` DFT calculated properties from MoleculeNet and OGB, trained as a single-task model on the target gap only
`sampl` Water-octanol partition coefficients, used to predict molecules from the SAMPL6, 7 and 9 challenges
`atom_bond_137k` Quantum-mechanical atom and bond descriptors
`bde` Bond dissociation enthalpies trained as single-task model
`bde_charges` Bond dissociation enthalpies trained as multi-task model together with atomic partial charges
`charges_eps_4` Partial charges at a dielectric constant of 4 (in protein)
`charges_eps_78` Partial charges at a dielectric constant of 78 (in water)
`barriers_e2` Reaction barrier heights of E2 reactions
`barriers_sn2` Reaction barrier heights of SN2 reactions
`barriers_cycloadd` Reaction barrier heights of cycloaddition reactions
`barriers_rdb7` Reaction barrier heights in the RDB7 dataset
`barriers_rgd1` Reaction barrier heights in the RGD1-CNHO dataset
`multi_molecule` UV/Vis peak absorption wavelengths in different solvents
`ir` IR Spectra
`pcqm4mv2` HOMO-LUMO gaps of the PCQM4Mv2 dataset
`uncertainty_ensemble` Uncertainty estimation using an ensemble using the QM9 gap dataset
`uncertainty_evidential` Uncertainty estimation using evidential learning using the QM9 gap dataset
`uncertainty_mve` Uncertainty estimation using mean-variance estimation using the QM9 gap dataset
`timing` Timing benchmark using subsets of QM9 gap
Version: This version of the dataset (Version 2) is compatible with all versions of Chemprop (supporting the respective functionality). Version 1 of this dataset is compatible with all versions except Chemprop v.1.6.1, which cannot process the `charges_eps_4`  and `charges_eps_78` datasets (all other benchmarks work as expected). We therefore recommend to always use Version 2 of the dataset (with reformatted `charges_eps_4`  and `charges_eps_78`  datasets), since it is compatible with all versions of Chemprop. For use with any other ML software, you can use any version.