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334 results for “household”
Plasmodium falciparum genomic surveillance reveals spatial and temporal trends, association of genetic and physical distance, and household clustering
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Atmospheric pollutant concentrations and leaf chemistry variables collected along gradients of median household income and traffic density in Salt Lake Valley, UT
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Household Active Power Consumption Dataset
<p>This dataset is part of the Monash, UEA & UCR time series regression repository. <a href="http://tseregression.org/">http://tseregression.org/</a></p> <p>The goal of this dataset is to predict total active power consumption in a household. This dataset contains 1440 time series obtained from the Individual household electric power consumption dataset from the UCI repository. The time series has 5 dimensions. This includes measurements for voltage, current annd 3 sub-metering energy usage.</p> <p> <br> Please refer to <a href="https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption">https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption</a> for more details<br> <br> Source<br> Georges Hebrail (georges.hebrail '@' edf.fr), Senior Researcher, EDF R&D, Clamart, France<br> Alice Berard, TELECOM ParisTech Master of Engineering Internship at EDF R&D, Clamart, France</p>
Household Reactive Power Consumption Dataset
<p>This dataset is part of the Monash, UEA & UCR time series regression repository. <a href="http://tseregression.org/">http://tseregression.org/</a></p> <p>The goal of this dataset is to predict total reactive power consumption in a household. This dataset contains 1440 time series obtained from the Individual household electric power consumption dataset from the UCI repository. The time series has 5 dimensions. This includes measurements for voltage, current annd 3 sub-metering energy usage.</p> <p> <br> Please refer to <a href="https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption">https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption</a> for more details<br> <br> Source<br> Georges Hebrail (georges.hebrail '@' edf.fr), Senior Researcher, EDF R&D, Clamart, France<br> Alice Berard, TELECOM ParisTech Master of Engineering Internship at EDF R&D, Clamart, France</p>
Data from: Individual correlates of infectivity of influenza A virus infections in households
Background: Identifying individual correlates of infectivity of influenza virus is important for disease control and prevention. Viral shedding is used as a proxy measure of infectivity in many studies. However, the evidence for this is limited. Methods: In a detailed study of influenza virus transmission within households in 2008–12, we recruited index cases with confirmed influenza infection from outpatient clinics, and followed up their household contacts for 7–10 days to identify secondary infections. We used individual-based hazard models to characterize the relationship between individual viral shedding and individual infectivity. Results: We analyzed 386 households with 1147 household contacts. Index cases were separated into 3 groups according to their estimated level of viral shedding at symptom onset. We did not find a statistically significant association of virus shedding with transmission. Index cases in medium and higher viral shedding groups were estimated to have 21% (95% CI: -29%, 113%) and 44% (CI: -16%, 167%) higher infectivity, compared with those in the lower viral shedding group. Conclusions: Individual viral load measured by RT-PCR in the nose and throat was at most weakly correlated with individual infectivity in households. Other correlates of infectivity should be examined in future studies.
500 Hourly Synthetic Single-Family Household Heat Pump Load Profiles for Karlsruhe, Germany (2021)
<p>We created a synthetic dataset of 500 hourly single-family household water-to-water heat pump load profiles based on the weather profile of Karlsruhe, Germany in 2021. We have applied the open-source methodology published in [1], which applies a k-means clustering process to match daily weather profiles with randomly drawn empirical observations from the high-quality heat pump load profile dataset published in [2]. We have selected a number of 5 clusters, for a good balance between variance of profiles and accuracy, as discussed in [1]. The dataset can be used for modeling large numbers of heat pumps in grid sections or energy communities. </p> <p>The unit of the measurement is Wh. Through the "SFH" identifier, the underlying, randomly drawn households from [2] can be identified. </p> <p>[1] Semmelmann, L., Jaquart, P., & Weinhardt, C. (2023). Generating synthetic load profiles of residential heat pumps: a k-means clustering approach. <em>Energy Informatics</em>, <em>6</em>(Suppl 1), 37.</p> <p>[2] Schlemminger, M., Ohrdes, T., Schneider, E., & Knoop, M. (2022). Dataset on electrical single-family house and heat pump load profiles in Germany. <em>Scientific data</em>, <em>9</em>(1), 56.</p>
B-MICS Household Survey Data
<p>You can download the complete dataset by following this link: <strong>https://mics.unicef.org/surveys</strong></p>
Households with Variable-Rate Mortgages: Factors Contributing to Arrears Under Rising Market Rates
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Lwala household survey 2018-2019: Demographic, depression, and economic variables
<p>This dataset is a subset of data collected during the Lwala Household Survey in 2018-2019. This was a large household survey in Migori County, Kenya undertaken by the Lwala Community Alliance to understand health metrics in the region. This subset of the overall survey data was used for a manuscript published in PLOS ONE to analyze the relationship between poverty and depression. It includes demographic, economic, and mental health variables. Each row represents a different respondent. All respondents live in separate households.</p>
Household Survey - Impacts of large-scale land acquisitions on smallholder agriculture and livelihoods in Tanzania
<p><strong>** Article & Dataset Currently Under Review **</strong></p> <p><strong>Dataset Overview</strong></p> <p>Our household dataset is associated with a pre-print article "Impacts of large-scale land acquisitions on smallholder agriculture and livelihoods in Tanzania". The household survey is designed for the purposes of policy evaluation with selection of households based on proximity to large-scale land acquisitions (treatment) and a set of households in similar socio-ecological contexts with no association to large-scale land acquisitions (control). Households were selected as a random sample in 35 villages surrounding LSLAs who provided responses to a questionnaire covering household income, assets, farming practices, health, food-security, and energy-use. </p> <p>Two datasets are provided. First, the "hh_dataset_rep.csv" providing household responses for variables used in this study. Second, the "hh_crops_rep.csv" provides detail on crops cultivated by each household, self-reported yields and farm-gate prices. Each variable is described in the "variable_descriptoin.xlsx". In addition to the datasets, we provide replication code for this study "lsla_mechanisms_rep.Rmd" as an R-Markdown file.</p> <p><strong>Article Abstract</strong></p> <p>Improving agricultural productivity is a major sustainability challenge of the 21<sup>st</sup> century. Large-scale land acquisitions (LSLAs) have important effects on both well-being and the environment in the Global South, but their impacts on agricultural productivity and subsequent effects on farm incomes or food-security are under-investigated. Prior studies lack data or methods to investigate the mechanistic nature of household change in agricultural practices that may vary due to LSLA conditions. To overcome this challenge, we use a novel household dataset and a quasi-experimental design to estimate household level changes in agricultural value driven by LSLAs in Tanzania. In addition, we use a causal mediation analysis to assess how contract farming arrangements, land loss, and adoption of new farming technologies around LSLAs influence agricultural productivity. We find that households near LSLAs produced 19.2% (95% CI: 3.5 – 37.2%) higher agricultural value, primarily due to increased crop prices and farmer selection of high-value crops. Importantly, effect sizes are positively and negatively mediated by different mechanisms. The presence of contract farming explains 18.1% (95% CI: 0.56%, 47%) of the effect size in agricultural value, whereas land loss reduces agricultural value by 26.8% (95% CI: -71.3%, -4.0%). We also estimate whether improvements in food-security and household incomes occur in proximity to LSLAs, as anticipated with higher agricultural value. However, we do not find increases in agricultural income and food security, which may be due to higher crop prices in proximity to LSLAs. Our results stand in contrast to assumptions that technological spillovers occur through LSLAs and are principal drivers of agrarian change, holding important implications for agricultural transformations. Instead access to output markets through contract farming enables greater agricultural value whereas land loss negatively affects the agricultural value of households. Governance strategies should focus on limiting negative impacts related to the loss of smallholder land rights enabling greater access to contract farming.</p> <p> </p>
2019 TenPercent_ Population and Household census data from KNBS
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SOCIAL AND HOUSEHOLD FORMS OF EPIDEICTIC SPEECH GENRES IN THE UZBEK LANGUAGE (IN THE EXAMPLE OF COLLECTIVE PRAYERS)
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Micro-Loans, Macro-Impacts: Examining the Reverberating Gains for Habru Woreda's Small-Scale Agrarian Households.
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Wealth at death of black and white households in the United States, 1989-2019
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THE EFFECT OF GENDER DISPARITY ON HOUSEHOLD'S INCOME IN ETHIOPIA: THE CASE OF WOLDIA CITY ADMINISTRATION
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#StayHome: Early Hydroxychloroquine to Reduce Secondary Hospitalisation and Household Transmission in COVID-19
ClinicalTrials.gov study NCT04385264. IPD Sharing: YES. Countries: 1. Publications: 0.
Food FARMacia: Reducing Childhood Obesity in Households With Food Insecurity
ClinicalTrials.gov study NCT06051591. IPD Sharing: YES. Countries: 1. Publications: 0.
Antenatal and Postnatal Care Research Collective - Household Survey (ARCH)
ClinicalTrials.gov study NCT05154331. IPD Sharing: YES. Countries: 1. Publications: 0.
Protecting Households On Exposure to Newly Diagnosed Index Multidrug-Resistant Tuberculosis Patients
ClinicalTrials.gov study NCT03568383. IPD Sharing: YES. Countries: 13. Publications: 0.
A Study to Evaluate AAV9 Neutralizing Antibody Seroconversion in Household Contacts.
ClinicalTrials.gov study NCT04543357. IPD Sharing: NO. Countries: 1. Publications: 0.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.