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334 results for “household”
Household Study of COVID-19, Influenza and RSV Burden, Transmission Dynamics and Viral Interaction in South Africa
ClinicalTrials.gov study NCT05277298. IPD Sharing: YES. Countries: 1. Publications: 0.
Salutogenic Healthy Aging Program Embracement (SHAPE) for Elderly-only Households
ClinicalTrials.gov study NCT03147625. IPD Sharing: NO. Countries: 0. Publications: 2.
COVID-19 Household Transmission Study
ClinicalTrials.gov study NCT04445233. IPD Sharing: YES. Countries: 1. Publications: 0.
Staph Household Intervention for Eradication (SHINE)
ClinicalTrials.gov study NCT02572791. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Epidemiology and Household Transmission of Streptococcus Pneumoniae and Respiratory Syncytial Virus
ClinicalTrials.gov study NCT05017519. IPD Sharing: NO. Countries: 0. Publications: 40.
Data from: Individual correlates of infectivity of influenza A virus infections in households
Open the record for dataset details and reuse information.
Data from: Microbiome sharing between children, livestock and household surfaces in western Kenya
Open the record for dataset details and reuse information.
Lwala household survey 2018-2019: Demographic, depression, and economic variables
Open the record for dataset details and reuse information.
LBA-ECO LC-01 Northern Ecuadorian Amazon Household Surveys, Summary Results: 1999
This data set reports summary statistics from socioeconomic and demographic surveys administered to the male and female heads of household on 767 farm plots. The surveys were performed in the provinces of Sucumbios and Napo/Orellana, in the northern Ecuadorian Amazon colonist settlements (Oriente) in 1999 (Pan and Bilsborrow, 2005). In addition, perception of, and opinions about local climate, soil quality, and environmental contamination were assessed for both the male and female heads of household. There are two comma-delimited (csv) ASCII data files. One file provides summary data from male respondents; the other data file provides summary responses from the female household survey (generally the spousal respondent). The original questionnaire forms are included as companion files (PDF format).
A blood RNA signature for tuberculosis disease risk in household contact study - GC6 cohort.
GEO Series GSE94438. Homo sapiens. 434 samples. Type: Expression profiling by high throughput sequencing.
Cell-specific methylation patterns in TB patients and household contacts
GEO Series GSE72338. Homo sapiens. 38 samples. Type: Methylation profiling by genome tiling array.
Cell-specific gene expression patterns in TB patients and household contacts
GEO Series GSE70476. Homo sapiens. 32 samples. Type: Expression profiling by array.
Household laundry detergents disrupt barrier integrity and induce inflammation in mouse and human skin
GEO Series GSE234841. Mus musculus. 18 samples. Type: Expression profiling by high throughput sequencing.
Cell-specific microRNA expression patterns in TB patients and household contacts
GEO Series GSE70425. Homo sapiens. 32 samples. Type: Non-coding RNA profiling by array.
Gene expression profiles in individuals with asymptomatic infections against cognate household members with dengue fever
GEO Series GSE50634. Homo sapiens. 29 samples. Type: Expression profiling by array.
Data from: Utilization of health services in a resource-limited rural area in Kenya: prevalence and associated household-level factors
Introduction Knowledge of utilization of health services and associated factors is important in planning and delivery of interventions to improve health services coverage. We determined the prevalence and factors associated with health services utilization in a rural area of Kenya. Our findings inform the local health management in development of appropriately targeted interventions. Methods and Results We used a cluster sample survey design and interviewed household key informants on history of illness for household members and health services utilization in the preceding month. We estimated prevalence and performed random effects logistic regression to determine the influence of individual and household level factors on decisions to utilize health services. 1230/6,440 (19.1%, 95% CI: 18.3%-20.2%) household members reported an illness. Of these, 76.7% (95% CI: 74.2%-79.0%) sought healthcare in a health facility. The majority (94%) of the respondents visited dispensary-level facilities and only 60.1% attended facilities within the study sub-counties. Of those that did not seek health services, 43% self-medicated by buying non-prescription drugs, 20% thought health services were too costly, and 10% indicated that the sickness was not serious enough to necessitate visiting a health facility. In the multivariate analyses, relationship to head of household was associated with utilization of health services. Relatives other than the nuclear family of the head of household were five times less likely to seek medical help (Odds Ratio 0.21 (95% CI: 0.05-0.87). Conclusion Dispensary level health facilities are the most commonly used by members of this community, and relations at the level of the household influence utilization of health services during an illness. These data enrich the perspective of the local health management to better plan the allocation of healthcare resources according to need and demand. The findings will also contribute in the development of community-level health coverage interventions that target the disadvantaged household groups.
WEEE-dismantling trial: Investigation on increasing resource efficiency and environmental protection for the recycling of small household electrical and electronic devices
<p>In the frame of the FORCE-project, Aurubis AG and Stadtreinigung Hamburg (the city of Hamburg’s municipal waste management service) carried out a trial to investigate the recycling advantages of manually pre-dismantling small electrical and electronic devices compared to non-dismantled devices. For this purpose, Stadtreinigung Hamburg dismantled a 10 t test charge of waste electrical and electronic devices and separated plastic, iron, nonferrous metal (NF metal), and aluminum to the greatest possible extent. The fractions were sampled, evaluated, and compared with another 10 t test charge of unhandled (mechanically shredded) devices.<br> Based on the results, the benefits for environmental protection and resource conservation were investigated through an ecological assessment, and an economic efficiency analysis of manual predismantling was carried out as well. Because manual pre-dismantling has proven to be economically inefficient under the current conditions, suggestions for future device design were developed – based on examples of individual product groups – to improve the economic efficiency of manual dismantling<br> and also possibly enable additional metals to be recovered in a cost-efficient manner in the future.</p> <p>Overview of the results<br> - Device-specific – small electrical and electronic devices from collection group 5 (CG 5):<br> o Breakdown of the different device types<br> o Manual dismantling process (dismantling time)<br> o Pollutant content in the electrical and electronic devices<br> o Assessment of difficulties that arose during manual dismantling<br> o Non-ferrous metal percentages for the device type at hand</p> <p>- Charge-specific (manual dismantling vs. mechanical shredding):<br> o Percentages of the material fractions plastic, iron, NF metal, aluminum, and residual material<br> (wood, fabric) for both charges (manually dismantled and mechanically shredded)<br> o Economic efficiency analysis of manual dismantling<br> o Ecological comparison of manual dismantling and mechanical shredding<br> o Suggestions for Design for Recycling</p>
A subsection of England and Wales EPC households, joined with PPD data, used for simulation modelling
<p>If you want to give feedback on this dataset, or wish to request it in another form (e.g csv), please fill out this survey <a href="https://docs.google.com/forms/d/e/1FAIpQLSfqCAoQt4AzuGH8Th5tJjnkGP956Fgc6O8T6wJaM7Nhd_nRdg/viewform?usp=pp_url&entry.1276408097=10.5281/zenodo.7322967">here</a>. We are a not-for-profit research organisation keen to see how others use our open models and tools, so all feedback is appreciated! It's a short form that takes 5 minutes to complete. </p> <p><strong>Important Note: Before downloading this dataset, please read the License and Software Attribution section at the bottom.</strong></p> <p>This dataset aligns with the work published in Centre for Net Zero's report "Hitting the Target". In this work, we simulate a range of interventions to model the situations in which we believe the UK will meet its 600,000 heat pump installation per year target by 2028. For full modelling assumptions and findings, read our <a href="https://www.centrefornetzero.org/res/hitting-the-target/">report on our website</a>.</p> <p>The code for running our simulation is open source <a href="https://github.com/centrefornetzero/domestic-heating-abm">here</a>.</p> <p>This dataset contains over 9 million households that have been address matched between Energy Performance Certificates (EPC) data and Price Paid Data (PPD). The code for our address matching is <a href="https://github.com/centrefornetzero/epc-ppd-address-matching">here</a>. Since these datasets are Open Government License (OGL), this dataset is too. We basically model specific columns from various datasets, as set out in our methodology section in our report, to simplify and clean up this dataset for academic use. License information is also available in the appendix of our report above.</p> <p>The EPC data loaders can be found <a href="https://github.com/centrefornetzero/epc-england-wales-parquet">here</a> (the data is <a href="https://epc.opendatacommunities.org/">here</a>) and the rest of the schemas and data download locations can be found <a href="https://github.com/centrefornetzero/bigquery-schemas">here</a>.</p> <p>Note that this dataset is not regularly maintained or updated. It is correct as of January 2022. The data was curated and tested using dbt via <a href="https://github.com/centrefornetzero/domestic-heating-data">this Github repository</a> and would be simple to rerun on the latest data.</p> <p>The schema / data dictionary for this data can be found <a href="https://github.com/centrefornetzero/domestic-heating-data/blob/main/cnz/models/marts/domestic_heating/domestic_heating.yml#L5">here</a>.</p> <p>Our recommended way of loading this data is in Python. After downloading all "parts" of the dataset to a folder. You can run:</p> <p>```</p> <p>import pandas as pd</p> <p>data = pd.read_parquet("path/to/data/folder/")</p> <p>```</p> <p> </p> <p><strong>Licenses and software attribution</strong>:</p> <p><em>For EPC, PPD and UK House Price Index data</em>:</p> <p>For the EPC data, we are permitted to republish this providing we mention that all researchers who download this dataset follow <a href="https://epc.opendatacommunities.org/docs/copyright">these copyright restrictions</a>. We <strong>do not explicitly release any Royal Mail address data</strong>, instead we use these fields to generate a pseudonymised "address_cluster_id" which reflects a unique combination of the address lines and postcodes, as well as other metadata. When viewing <a href="https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-protection-regulation-gdpr/what-is-personal-data/what-is-personal-data/">ICO and GDPR guidelines</a>, this still counts as personal data, but we have gone to measures to pseudonymise as much as possible to fulfil our obligations as a data processor. You <strong>must read this carefully before downloading the data</strong>, and ensure that you are using it for the research purposes as determined by this copyright notice.</p> <p>Contains HM Land Registry data © Crown copyright and database right 2021. This data is licensed under the Open Government Licence v3.0.</p> <p>Contains OS data © Crown copyright and database right 2022.</p> <p>Contains Office for National Statistics data licensed under the Open Government Licence v.3.0.</p> <p>The OGL v3.0 license states that we are free to:</p> <ul> <li>copy, publish, distribute and transmit the Information;</li> <li>adapt the Information;</li> <li>exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application.</li> </ul> <p>However we must (where we do any of the above):</p> <ul> <li>acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence;</li> </ul> <p>You can see more information <a href="https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">here</a>.</p> <p><em>For XOServe Off Gas Postcodes</em>:</p> <p>This dataset has been released openly for all uses <a href="https://www.cse.org.uk/projects/view/1259#GB_postcodes_off_the_mains_gas_grid">here</a>.</p> <p><em>For the address matching:</em></p> <p>GNU Parallel: O. Tange (2018): GNU Parallel 2018, March 2018, https://doi.org/10.5281/zenodo.1146014</p>
Household satisfaction with health services and choice of response strategies to malaria occurrence: The case of mountain communities of Elgon in Uganda
<p>This upload includes our dataset and code for analysis. We undertook a cross-sectional survey in Elgon region of Uganda exploring household satisfaction with health services. During the same survey, we also explored several aspects including interventions to address malaria in the changing climate.</p>
Non-communicable diseases household survey
<p><strong> </strong></p><p><strong>*Corresponding Author:</strong></p><p>Ghamdan Gamal Alkholidy (ghamdangamal@gmail.com)</p><p> </p><p><strong>Abstract</strong></p><p>Non-communicable diseases (NCDs) claim 41 million lives annually, accounting for 71% of global mortality. The Middle East sees a rapid increase in NCD cases, yet Yemen remains underexplored in this context. This study aims to investigate NCD epidemiology in Sana'a City, Yemen, for 2017. Raw data from a 2017 house-to-house survey conducted by the Ministry of Public Health and Population was analyzed. Household heads reported any of the following five NCDs in the household: hypertension (HTN), diabetes mellitus (DM), bronchial asthma (BA), mental disorders (MD), and epilepsy. Data were entered and analyzed using Epi info 7.2, using 2017 projections from the 2004 census. The study encompassed 241,310 households, housing 1,592,646 individuals. Among these, 59,061 households (24.48%) included 70,178 individuals with at least one NCD. The overall NCD prevalence was 4.4%, with specific prevalence: HTN 2.3%, DM 2.2%, BA 0.4%, MD 0.27, and epilepsy 0.19%. NCD prevalence was significantly higher among females than males (5.1% vs. 3.8%; odds ratio [OR] 1.35, 95% confidence intervals [CI] 1.33–1.35), a trend mirrored in HTN (3.1% vs 1.6%; OR 1.94, 95% CI 1.90–1.98), DM (2.3% vs 2.1%; OR 1.11, 95% CI 1.09–1.13), and BA (0.5% vs 0.3%; OR 1.56, 95% CI 1.49–1.65). Conversely, MD was more prevalent among males than females (0.35% vs. 0.16%; OR 2.2, 95% CI 2.06–2.31). NCD prevalence increased with age, with nearly 18% of patients having more than one NCD, including 35.2% of HTN patients also having DM. One-quarter of surveyed households had at least one member with one or more of the five NCDs, emphasizing an overall NCD prevalence of 4.4%. These findings rely only on self-reported diagnosed cases, lacking standardized measures. In light of these findings, it is crucial to increase focus on NCDs, enhance healthcare provision, improve data collection, implement an NCDs stepwise survey, and establish an NCD surveillance system.</p><p> </p><p><i><strong>Keywords:</strong></i></p><p> Non-communicable diseases; Hypertension; Diabetes; Bronchial asthma</p>
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
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.