cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19775384
title: Outcomes of a novel single case study incorporating Rapid Syllable Transition treatment, AAC and blended intervention in children with cerebral palsy: a pilot study

description: Motor speech and augmentative and alternative communication (AAC) interventions are commonly used with children with cerebral palsy (CP) but there is limited literature comparing the effectiveness of these interventions. The purpose of this study was to investigate the effectiveness of intensive AAC, Rapid Syllable Transition Treatment (ReST), and blended intervention, a combination of motor-speech and AAC, on speech accuracy and sentence length and complexity. A single case experimental design across participants with repeated measurements across phases (ABACADA design) was used. Four participants aged 8–14 years with CP who used a speech-generating device (SGD) received three 6-week intervention blocks that included ReST, AAC, and blended intervention. Measures were taken during intervention and baseline phases and at maintenance two and four weeks after the last intervention phase. Participants were randomized to starting with either ReST or AAC with the blended intervention delivered last. All participants improved their speech accuracy and sentence length and complexity in speech and with their SGD in all three interventions. The data demonstrated overall immediacy of the effect with all interventions and retention of gains across the whole sequence of phases. The order of AAC or ReST interventions or the severity of CP did not impact the intervention gains. This study suggests that intensive AAC, a multi-modal approach, and ReST improve speech accuracy and sentence length and complexity in children with moderate CP, but all require further investigation.Implications for rehabilitationIn this paper intensive AAC intervention with a speech generating device (SGD), ReST treatment and multimodal blended intervention were effective in improving speech accuracy and sentence length and complexity in both verbal speech and on communication with the SGD.ReST treatment has not been used with children with CP before. This study established a proof of concept for its effectiveness in children with CP. Further research is warranted. In this paper intensive AAC intervention with a speech generating device (SGD), ReST treatment and multimodal blended intervention were effective in improving speech accuracy and sentence length and complexity in both verbal speech and on communication with the SGD. ReST treatment has not been used with children with CP before. This study established a proof of concept for its effectiveness in children with CP. Further research is warranted.
DOI: 10.6084/m9.figshare.19561254.v1
title: Application of the extended technology acceptance model to explore clinician likelihood to use robotics in rehabilitation

description: Evidence suggests that patients with upper limb impairment following a stroke do not receive recommended amounts of motor practice. Robotics provide a potential solution to address this gap, but clinical adoption is low. The aim of this study was to utilize the technology acceptance model as a framework to identify factors influencing clinician adoption of robotic devices into practice. Mixed methods including survey data and focus group discussions with allied health clinicians whose primary caseload was rehabilitation of the neurologically impaired upper limb. Surveys based on the technology acceptance measure were completed pre/post exposure to and use of a robotic device. Focus groups discussions based on the theory of planned behaviour were conducted at the conclusion of the study. A total of 34 rehabilitation clinicians completed the surveys with pre-implementation data indicating that rehabilitation clinicians perceive robotic devices as complex to use, which influenced intention to use such devices in practice. The focus groups found that lack of experience and time to learn influenced confidence to implement robotic devices into practice. This study found that perceived usefulness and perceived ease of use of a robotic device in clinical rehabilitation can be improved through experience, training and embedded technological support. However, training and embedded support are not routinely offered, suggesting there is a discordance between current implementation and the learning needs of rehabilitation clinicians.IMPLICATIONS FOR REHABILITATIONPatients do not receive adequate amounts of upper limb motor practice following a stroke, and although robotic devices have the potential to address this gap, clinical adoption is low.The technology acceptance model identified that clinicians perceive robotic devices to be complex to use with current implementation efforts failing to consider their training needs.Implementation adoption of robotic devices in rehabilitation should be supported with adequate training and technological support if sustainable practice change is to be achieved. Patients do not receive adequate amounts of upper limb motor practice following a stroke, and although robotic devices have the potential to address this gap, clinical adoption is low. The technology acceptance model identified that clinicians perceive robotic devices to be complex to use with current implementation efforts failing to consider their training needs. Implementation adoption of robotic devices in rehabilitation should be supported with adequate training and technological support if sustainable practice change is to be achieved.
DOI: 10.6084/m9.figshare.19775384.v1
title: Outcomes of a novel single case study incorporating Rapid Syllable Transition treatment, AAC and blended intervention in children with cerebral palsy: a pilot study

description: Motor speech and augmentative and alternative communication (AAC) interventions are commonly used with children with cerebral palsy (CP) but there is limited literature comparing the effectiveness of these interventions. The purpose of this study was to investigate the effectiveness of intensive AAC, Rapid Syllable Transition Treatment (ReST), and blended intervention, a combination of motor-speech and AAC, on speech accuracy and sentence length and complexity. A single case experimental design across participants with repeated measurements across phases (ABACADA design) was used. Four participants aged 8–14 years with CP who used a speech-generating device (SGD) received three 6-week intervention blocks that included ReST, AAC, and blended intervention. Measures were taken during intervention and baseline phases and at maintenance two and four weeks after the last intervention phase. Participants were randomized to starting with either ReST or AAC with the blended intervention delivered last. All participants improved their speech accuracy and sentence length and complexity in speech and with their SGD in all three interventions. The data demonstrated overall immediacy of the effect with all interventions and retention of gains across the whole sequence of phases. The order of AAC or ReST interventions or the severity of CP did not impact the intervention gains. This study suggests that intensive AAC, a multi-modal approach, and ReST improve speech accuracy and sentence length and complexity in children with moderate CP, but all require further investigation.Implications for rehabilitationIn this paper intensive AAC intervention with a speech generating device (SGD), ReST treatment and multimodal blended intervention were effective in improving speech accuracy and sentence length and complexity in both verbal speech and on communication with the SGD.ReST treatment has not been used with children with CP before. This study established a proof of concept for its effectiveness in children with CP. Further research is warranted. In this paper intensive AAC intervention with a speech generating device (SGD), ReST treatment and multimodal blended intervention were effective in improving speech accuracy and sentence length and complexity in both verbal speech and on communication with the SGD. ReST treatment has not been used with children with CP before. This study established a proof of concept for its effectiveness in children with CP. Further research is warranted.
DOI: 10.34740/kaggle/dsv/7326272
title: Trending Youtube Video Statistics (113 Countries)
DOI: 10.34740/kaggle/dsv/7326275
title: Full TMDB Movies Dataset 2023 (930K Movies)
DOI: 10.5281/zenodo.10452623
title: RSS-NET annotation files at Sherlock (Part 2)
DOI: 10.5281/zenodo.10452624
title: RSS-NET annotation files at Sherlock (Part 2)
DOI: 10.5281/zenodo.8266241
title: Labeled 17 Hardwood Species and 55 Genotypes of Populus Stomatal Images Datasets

description: Research has indicated the potential of using machine learning algorithms to detect and measure stomata automatically. However, the current limitation for further improving and fine-tuning machine learning-based stomatal study methods is due to the small, inconsistent, and monotypic nature of stomatal datasets, which are also not easily accessible. To address this issue, our collection comprises about 11,000 unique images of hardwood leaf stomata gathered from projects conducted between 2015 and 2020-2022. The dataset includes over 7,000 images of 17 frequently encountered hardwood species, including oak, maple, ash, elm, and hickory, as well as over 3,000 images of 55 genotypes from seven Populus taxa (as detailed in Table 1). Each image has been labeled as either stomata (stomatal aperture only) or whole_stomata (stomatal aperture and guard cells) and has a corresponding YOLO label file that can be transformed to other annotation formats. These images and labels are publicly available, making it easier to train machine-learning models and examine leaf stomatal traits. By utilizing our dataset, users can (1) use state-of-the-art machine learning models to identify, count, and quantify leaf stomata; (2) investigate the diverse range of stomatal characteristics across different types of hardwood trees; and (3) create new indices for measuring stomata.
DOI: 10.5281/zenodo.8266240
title: Labeled 17 Hardwood Species and 55 Genotypes of Populus Stomatal Images Datasets

description: Research has indicated the potential of using machine learning algorithms to detect and measure stomata automatically. However, the current limitation for further improving and fine-tuning machine learning-based stomatal study methods is due to the small, inconsistent, and monotypic nature of stomatal datasets, which are also not easily accessible. To address this issue, our collection comprises about 11,000 unique images of hardwood leaf stomata gathered from projects conducted between 2015 and 2020-2022. The dataset includes over 7,000 images of 17 frequently encountered hardwood species, including oak, maple, ash, elm, and hickory, as well as over 3,000 images of 55 genotypes from seven Populus taxa (as detailed in Table 1). Each image has been labeled as either inner guard cell walls or whole_stomata (stomatal aperture and guard cells) and has a corresponding YOLO label file that can be transformed to other annotation formats. These images and labels are publicly available, making it easier to train machine-learning models and examine leaf stomatal traits. By utilizing our dataset, users can (1) use state-of-the-art machine learning models to identify, count, and quantify leaf stomata; (2) investigate the diverse range of stomatal characteristics across different types of hardwood trees; and (3) create new indices for measuring stomata.
DOI: 10.5281/zenodo.8271253
title: Labeled 17 Hardwood Species and 55 Genotypes of Populus Stomatal Images Datasets

description: Research has indicated the potential of using machine learning algorithms to detect and measure stomata automatically. However, the current limitation for further improving and fine-tuning machine learning-based stomatal study methods is due to the small, inconsistent, and monotypic nature of stomatal datasets, which are also not easily accessible. To address this issue, our collection comprises about 11,000 unique images of hardwood leaf stomata gathered from projects conducted between 2015 and 2020-2022. The dataset includes over 7,000 images of 17 frequently encountered hardwood species, including oak, maple, ash, elm, and hickory, as well as over 3,000 images of 55 genotypes from seven Populus taxa (as detailed in Table 1). Each image has been labeled as either inner guard cell walls or whole_stomata (stomatal aperture and guard cells) and has a corresponding YOLO label file that can be transformed to other annotation formats. These images and labels are publicly available, making it easier to train machine-learning models and examine leaf stomatal traits. By utilizing our dataset, users can (1) use state-of-the-art machine learning models to identify, count, and quantify leaf stomata; (2) investigate the diverse range of stomatal characteristics across different types of hardwood trees; and (3) create new indices for measuring stomata.
DOI: 10.25919/9t87-3f07
title: Parkes observations for project P1189 semester 2023OCTS_01

description: The solar wind and space weather events have a crucial influence on the solar system, and can present a hazard to space and Earth-based technologies. Understanding the origins of the solar wind and space weather is therefore crucial, so that we can better understand how these events are driven, and to improve space weather prediction efforts. In particular, magnetic structures such as coronal holes, helmet streamers, and prominences in the lower corona are thought to be crucial drivers of the winds and coronal mass ejections (CMEs). However, there are very few observational constraints on the solar wind and space weather events at low solar altitudes (&lt; 20 solar radii), as this region is inaccessible to space probes. In this proposal, we seek to monitor a set of pulsars as they pass within 5.7 degrees of the Sun. We will use the UWL receiver on the Parkes Radio Telescope to make precision measurements of the dispersive group delay and Faraday rotation of the pulses. This will enable us to probe the densities and magnetic fields of the coronal structures driving the wind, and probe $6 \pm 4$ CMEs as they pass by the pulsars serendipitously. We will tie this information together with complementary efforts to probe the solar wind via interplanetary scintillation, constraints from space probes, and simulations. This will give us an exciting opportunity to form a holistic picture of the solar wind and space weather.
DOI: 10.5281/zenodo.10452708
title: Data for: What are the Most Important Items to Inspect on an MSE Wall?

description: Geotechnical asset management demonstrates stewardship, provides data and processes for making informed decisions, and can enable financial savings throughout the geotechnical life cycle. For a geotechnical asset management program to be effective, a well-defined inspection system for evaluating the current condition of assets is required. If the process contains a substantial number of inspection items, individual items need to be weighted based on relative importance. This dataset includes the criteria comparison matrices of interviews from 30 experts in geotechnical engineering using the analytical heirarchy process. Criteria comparison matrices are also provided for aggregate data, analyzing based on career type and geographic location.
DOI: 10.5281/zenodo.10452709
title: Data for: What are the Most Important Items to Inspect on an MSE Wall?

description: Geotechnical asset management demonstrates stewardship, provides data and processes for making informed decisions, and can enable financial savings throughout the geotechnical life cycle. For a geotechnical asset management program to be effective, a well-defined inspection system for evaluating the current condition of assets is required. If the process contains a substantial number of inspection items, individual items need to be weighted based on relative importance. This dataset includes the criteria comparison matrices of interviews from 30 experts in geotechnical engineering using the analytical heirarchy process. Criteria comparison matrices are also provided for aggregate data, analyzing based on career type and geographic location.
DOI: 10.6073/pasta/ebc0247af95c9dc4212bf241b7b73925
title: Weather station data from three locations at Lake Sunapee (NH, USA), July 2019 – December 2023

description: Davis weather stations, owned and operated by the Lake Sunapee Protective Association (LSPA), were installed at three locations near the shoreline of Lake Sunapee (NH, USA) in July 2019. These stations collect data at 30-minute intervals for a number of weather and meteorological variables continuously throughout the year. In addition to the measured variables, a number of derived variables are also recorded at the same time intervals. Data are manually downloaded from Davis’s WeatherLink website every quarter, then collated and QAQC’d to remove any obvious outliers or recording errors in the R programming language.
DOI: 10.5281/zenodo.10452703
title: RSS-NET annotation files at Sherlock (Part 3)
DOI: 10.5281/zenodo.10452704
title: RSS-NET annotation files at Sherlock (Part 3)
DOI: 10.34740/kaggle/dsv/7326431
title: Daily Public Opinion on Israel-Palestine War
DOI: 10.34740/kaggle/dsv/7326453
title: Public Opinion on Climate Change (Updated Daily)
DOI: 10.5281/zenodo.10452770
title: whole genome sequences for two African elephants
DOI: 10.5281/zenodo.10452771
title: whole genome sequences for two African elephants
DOI: 10.5281/zenodo.10016133
title: Forecasts, score summary files, and target observational data to accompany the manuscript "Forecast performance of near-term, iterative water temperature and dissolved oxygen forecasts varies across time and depth"

description: This data publication includes raw ensemble forecast output (forecasts.zip), as well as summary score files (scores.zip) for process-based forecasts produced with the Forecasting Lake and Reservoir Ecosystems (FLARE) framework. In addition, it includes scores for climatology (climatology_scores.csv) and random walk (RW_scores.csv) forecasts, as well as formatted observational data of target variables (sunp-targets-insitu.csv) required for analysis to accompany the manuscript "Forecast performance of near-term, iterative water temperature and dissolved oxygen forecasts varies across time and depth". Forecasts were made of water temperature and dissolved oxygen at Lake Sunapee, NH in 2021 and 2022.
DOI: 10.5281/zenodo.10127798
title: Process-based forecasts of lake water temperature and dissolved oxygen outperform null models, with variability over time and depth

description: This data publication includes raw ensemble forecast output (forecasts.zip), as well as summary score files (scores.zip) for process-based forecasts produced with the Forecasting Lake and Reservoir Ecosystems (FLARE) framework. In addition, it includes scores for climatology (climatology_scores.csv) and random walk (RW_scores.csv) null forecasts, formatted observational data of target variables (sunp-targets-insitu.csv), and meteorological driver files required for analysis to accompany the manuscript "Process-based forecasts of lake water temperature and dissolved oxygen outperform null models, with variability over time and depth". Forecasts were made of water temperature and dissolved oxygen at Lake Sunapee, NH in 2021 and 2022.
DOI: 10.5281/zenodo.10223812
title: Forecasts, score summary files, target observational data and meteorological driver files to accompany the manuscript "Skill of process-based forecasts relative to multiple null models varies across time and depth for water temperature and dissolved oxygen"

description: This data publication includes raw ensemble forecast output (forecasts.zip), as well as summary score files (scores.zip) for process-based forecasts produced with the Forecasting Lake and Reservoir Ecosystems (FLARE) framework. In addition, it includes scores for climatology (climatology_scores.csv) and random walk (RW_scores.csv) null forecasts, formatted observational data of target variables (sunp-targets-insitu.csv), and meteorological driver files required for analysis to accompany the manuscript "Skill of process-based forecasts relative to multiple null models varies across time and depth for water temperature and dissolved oxygen". Forecasts were made of water temperature and dissolved oxygen at Lake Sunapee, NH in 2021 and 2022.
DOI: 10.5281/zenodo.8212703
title: Forecasts and score summary files to accompany the manuscript "Forecast performance of near-term, iterative water temperature and dissolved oxygen forecasts varies across time and depth"

description: This data publication includes raw ensemble forecast output (forecasts.zip), as well as summary score files (scores.zip) required for analysis to accompany the manuscript "Forecast performance of near-term, iterative water temperature and dissolved oxygen forecasts varies across time and depth". Forecasts were made of water temperature and dissolved oxygen at Lake Sunapee, NH in 2021 and 2022.
DOI: 10.5281/zenodo.10223780
title: Forecasts, score summary files, target observational data and meteorological driver files to accompany the manuscript "Skill of process-based forecasts relative to multiple null models varies across time and depth for water temperature and dissolved oxygen"

description: This data publication includes raw ensemble forecast output (forecasts.zip), as well as summary score files (scores.zip) for process-based forecasts produced with the Forecasting Lake and Reservoir Ecosystems (FLARE) framework. In addition, it includes scores for climatology (climatology_scores.csv) and random walk (RW_scores.csv) null forecasts, formatted observational data of target variables (sunp-targets-insitu.csv), and meteorological driver files required for analysis to accompany the manuscript "Skill of process-based forecasts relative to multiple null models varies across time and depth for water temperature and dissolved oxygen". Forecasts were made of water temperature and dissolved oxygen at Lake Sunapee, NH in 2021 and 2022.
DOI: 10.5281/zenodo.8212702
title: Forecasts, score summary files, target observational data and meteorological driver files to accompany the manuscript "Skill of process-based forecasts relative to multiple null models varies across time and depth for water temperature and dissolved oxygen"

description: This data publication includes raw ensemble forecast output (forecasts.zip), as well as summary score files (scores.zip) for process-based forecasts produced with the Forecasting Lake and Reservoir Ecosystems (FLARE) framework. In addition, it includes scores for climatology (climatology_scores.csv) and random walk (RW_scores.csv) null forecasts, formatted observational data of target variables (sunp-targets-insitu.csv), and meteorological driver files required for analysis to accompany the manuscript "Skill of process-based forecasts relative to multiple null models varies across time and depth for water temperature and dissolved oxygen". Forecasts were made of water temperature and dissolved oxygen at Lake Sunapee, NH in 2021 and 2022.