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8 results for “Technology usage”
Great Britain (GB) Domestic Electricity Usage by Low Carbon Technology by Season
<p><strong>Important</strong>: As an research not-for-profit organisation, if you found this dataset useful we would appreciate your time in filling out <a href="https://docs.google.com/forms/d/e/1FAIpQLSfqCAoQt4AzuGH8Th5tJjnkGP956Fgc6O8T6wJaM7Nhd_nRdg/viewform?usp=pp_url&entry.1276408097=10.5281/zenodo.6576108">this short survey</a>.</p> <p> </p> <p>This dataset contains 3 aggregate datasets from the electricity smart meter data of over 25,000 customers in Great Britain (GB) from March 2021 - March 2022.</p> <p>For each consumer, we know (via a survey) what low carbon technologies (LCTs) they own. The potential LCT options are: Solar PV, Heat Pump (Air Source, or Ground Source), Electric Vehicle, Battery, Electric Storage Heaters.</p> <p>For simplicity, this dataset contains only customers with one type of LCT (with the exception of Solar PV, where we include Solar PV + Battery customers as is common in GB). We do not include customers with multiple LCTs (for example home battery + EV)</p> <p>We include quantiles of usage for each half hour (the "profile") for each type of LCT ownership "archetype", both overall (when season=None) and by season. As is common in the literature, we normalise by the square meterage of the house using open EPC data in GB (https://epc.opendatacommunities.org/) to get the watt hours per square meter. You can also find the raw, unnormalised, kwh values by quantile in this release. These two datasets have the quantiles for each half hour period. In addition, we release the daily quantiles of electricity consumption, in kwh per square meterage, by LCT type.</p> <p>In summary the data we are releasing, aggregated over 25,000 customers over 1 year of usage from March 2021 - March 2020 is:</p> <ul> <li>daily_elec_consumption_quantiles_by_lct_ownership.csv - The daily quantiles of usage [kWh/m2] by LCT</li> <li>lct_elec_consumption_profiles.csv - The half hourly quantiles of usage [Wh/m2] by LCT by season</li> <li>lct_elec_consumption_profiles_kwh.csv - The half hourly quantiles of usage [kWh] by LCT by season</li> </ul> <p>We believe this data will be useful for modelling efforts, as customers with different types of LCTs use energy at different times of the day, and by different amounts daily. By releasing this data openly, we hope forecasting scenarios for the future energy system are more accurate. We have a supporting blog post on our website at https://www.centrefornetzero.org/res/lessons-from-early-adopters-electricity-consumption-profiles/.</p>
Stable Modeling on Resource Usage Parameters of MapReduce Application-Department of Networked Systems and Services, Budapest University of Technology and Economics, Budapest, Hungary
<p>In Figure 5, the positive dependency of different strength between each resource usage parameter and the corresponding previous usage parameter is exhibited for all MapReduce applications. It indicates that all current resource usage parameters are positively dependent on the previous values to some extent degree. Except for these common dependencies, there exist some special dependencies for different applications. On the top-left panel of Figure 5, CPU usage of Pi application shows the strongest positive dependency to lagged CPU usage, the Teragen application had the weakest positive dependency, and others exhibit the moderate positive dependency. </p>
Anonymised dataset about Finnish teachers' digital technology usage in teaching
<p>Data have been collected in Finland in 2017–2019 from basic education teachers.The dataset is fully anonymised to make it suitable for public opening to support the reported results submitted for publication.</p> <p>Dataset contains the following variables:</p> <ol> <li>Gender (0=female, 1 = male)</li> <li>Municipality (unique numeric identifier)</li> <li>School (unique numeric identifier)</li> <li>Administrative_district (unique numeric identifier)</li> <li>Usage_of_computers_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_tablets_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_smartphones_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_digital_learning_environments_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_online_learning_materials_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_games_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_internet_for_information_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_videa_sharing_services_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_blogs_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_networking_services_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_digital_assessment_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_mobileapps_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_email_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_office_suite_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Total_scores_in_ICTskilltest (range 0-30)</li> <li>Age (years in numbers)</li> <li>Digital_self_efficacy (perceived level of competence on a scale of 0-100% in relation to one's own work)</li> <li>In_service_training (perceived adequacy level on a scale of 0 to 100% in relation to one's own work)</li> <li>STEM_teacher (in the case of a teacher of STEM subjects = 1, otherwise = 0)</li> <li>Humanities_social_science_teacher (in the case of a teacher of humanities or social science subjects = 1, otherwise = 0)</li> <li>Arts_skills_teacher (in the case of a teacher of arts and skills subjects = 1, otherwise = 0)</li> <li>Use_of_devices_for_teaching (the sum variable of the use of digital devices, i.e., the maximum use of any type of device)</li> <li>Versatility_of_usage (the sum variable for regular (= at least weekly) use of different applications)</li> <li>Teacher_type (0 = classroom teacher, 1 = subject teacher, missing value = other teaching staff)</li> <li>Classroom_teacher in the case of a classroom teacher = 1, otherwise = 0</li> </ol>
Exploration of Women's Experiences and Technology Usage Before, During, and After Pregnancy in Singapore
ClinicalTrials.gov study NCT05099900. IPD Sharing: NO. Countries: 1. Publications: 14.
Awareness Development and Usage of mHealth Technology Among Hypertensive Patients in a Rural Community of Bangladesh
ClinicalTrials.gov study NCT03614104. IPD Sharing: NO. Countries: 1. Publications: 2.
TEACHING MATHEMATICS IN THE EDUCATIONAL SYSTEM FROM MODERN INFORMATION TECHNOLOGIES USAGE
Open the record for dataset details and reuse information.
A Study to Understand the Usage of an Artificial Intelligence Technology Called DARWEN for Eye Disorders and to Find Out the Factors Influencing Treatment Frequencies in People With nAMD and RVO
ClinicalTrials.gov study NCT06495203. IPD Sharing: NO. Countries: 1. Publications: 0.
Usage of Health Information Exchange (HIE) Technologies
ClinicalTrials.gov study NCT01250288. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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OpenNeuro
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