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36 results for “Cloud computing”
The Software Sustainability Institute's Collaborations Workshop 2015 (CW15) attendees computational tools word-cloud
<p>Word cloud representing the computational tools used by those attending the Software Sustainability Institute's Collaborations Workshop 2015 (CW15).</p> <p>For more information see www.software.ac.uk/cw15</p> <p>Please note there is an ERROR in the diagram for some reason wordle.net did not pickup 'R' in the dataset - http://dx.doi.org/10.5281/zenodo.19828 - i.e. the usage of R in research software in the people who attended CW15 is not represented in this diagram.</p>
Applying innovative cloud computing technology for the effective management of Groundwater resources to promote SUStainable food security within the Sokoto Basin, Nigeria (AGSUS)
Open the record for dataset details and reuse information.
Survey on Cloud Computing usage in Montenegrin SMEs, 2017-2023
<p>Data collected among 100 SMEs in Montenegro related to their persepctvies on usage of cloud computing services in their businesses. Comprehesive questionnaire prepared on the bases of European Union Agency for Cybersecurity: Cloud Computing - SME Survey, conducted in 2017 and 2023</p>
Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.
<p>Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.</p>
Nanopore sequencing data analysis using Microsoft Azure cloud computing service
<p>Genetic information provides insights into the exome, genome, epigenetics and structural organisation of the organism. Given the enormous amount of genetic information, scientists are able to perform mammoth tasks to improve the standard of health care such as determining genetic influences on outcome of allogeneic transplantation. Cloud-based computing has increasingly become a key choice for many scientists, engineers and institutions as it offers on-demand network access and users can conveniently rent rather than buy all required computing resources. With the positive advancements of cloud computing and nanopore sequencing data output, we were motivated to develop an automated and scalable analysis pipeline utilizing cloud infrastructure in Microsoft Azure to accelerate HLA genotyping service and improve the efficiency of the workflow at lower cost. In this study, we describe (i) the selection process for suitable virtual machine sizes for computing resources to balance between the best performance versus cost-effectiveness; (ii) the building of Docker containers to include all tools in the cloud computational environment; (iii) the comparison of HLA genotype concordance between the in-house manual method and the automated cloud-based pipeline to assess data accuracy. In conclusion, the Microsoft Azure cloud-based data analysis pipeline was shown to meet all the key imperatives for performance, cost, usability, simplicity and accuracy. Importantly, the pipeline allows for the ongoing maintenance and testing of version changes before implementation. This pipeline is suitable for data analysis from MinION sequencing platforms and could be adopted for other data analysis application processes.</p>
Cost Management in Cloud Computing: a study on financial waste and correction strategies
<p>This project explores the identification and mitigation of financial wastage in Cloud Computing (CC) operations by professionals in the field, examining the alignment of their strategies with FinOps guidelines. Through a survey distributed via social media and messaging apps to CC professionals, the study engaged 28 participants from various companies, sizes, and sectors. It revealed that these professionals have encountered cost-related issues in cloud applications, particularly financial wastage. The investigation aimed to understand the prevalence of financial wastage in CC and evaluate the effectiveness of the adopted practices in addressing these inefficiencies within the framework of FinOps principles.</p>
Cloud Computing Badge
<p>The Cloud Computing Badge can be used for presentations, courses, ...</p>
Deep Gradient Reinforcement learning for Music Improvisation in cloud computing framework
<p><span>The improvised music is further rendered in the MIDI format. The Bach Chorales dataset with six different attributes relevant to musical compositions is employed in implementing the present research. The model was set up in a containerised cloud environment and controlled for smooth load distribution. Five different parameters, such as pitch frequency (PF), standard pitch delay (SPD), average distance between peaks (ADP), note duration gradient (NDG) and pitch class gradient (PCG) are leveraged to assess the quality of the improvised music.</span></p>
Nanopore sequencing data analysis using Microsoft Azure cloud computing service
Open the record for dataset details and reuse information.
Banking dataset used for cloud computing and comparison with Human
<p>Banking dataset used for cloud computing </p>
EVALUACIÓN DE LOS SERVICIOS DE CLOUD COMPUTING EN INSTITUTOS DE EDUCACIÓN SUPERIOR EN MEDIOS RURALES
<p>Datos recolectados para la tesis de maestria titulada: EVALUACIÓN DE LOS SERVICIOS DE CLOUD COMPUTING EN INSTITUTOS<br>DE EDUCACIÓN SUPERIOR EN MEDIOS RURALES</p>
Cloudsim Cloud Computing Simulation Platform and data set Creators
<p>In this uploaded file, Cloudsim-AHP is the cloud computing simulation platform, in which the VM placement method has been modified to the VM placement strategy based on AHP multidimensional decision making method proposed in this paper. The "data set" file is the inbuilt data set in the above mentioned cloud simulation platform.</p>
Development of an Screening System in Children With Congenital Heart Disease Based on Cloud Computing and Big Data
ClinicalTrials.gov study NCT03929133. IPD Sharing: NO. Countries: 1. Publications: 5.
Supplement to the manuscript "Overcoming computational challenges to realize meter-to-submeter-scale resolution in cloud simulations using super-droplet method" (Matsushima et al., 2023)
<p>Supplemental codes, figures, movies, and datasets to the manuscript "Overcoming computational challenges to realize meter-to-submeter-scale resolution in cloud simulations using super-droplet method" (Matsushima et al., 2023)</p> <p>See README.md for more details.</p>
A geospatial big data cloud computing-based method for constructing spatiotemporal dataset of dengue influencing factors in Brazil
<p>This datasets includes 12 dengue-associated factors of 418 epi weeks from 2013 to 2020 in microregion-level in Brazil.</p>
Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing
<p>This dataset contains continental (Africa) land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources. The dataset contains seven classes, prepared annually from 2000 to 2015, using high‐resolution Landsat 7 images (ETM+) and analyzed by Google Earth Engine cloud computing method. The model that generated the LULC classification was evaluated for predictive accuracy across classes as well as overall accuracy. The model achieved an overall accuracy of 88% with class-specific user’s and producer’s accuracies ranged from 84-94% and 79-96% respectively (Midekisa et al., 2017).</p> <p> </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.