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200 results for “health research”
Data relating to Clyne et al. Quality, scope and reporting standards of randomised controlled trials in Irish Health Research: an observational study
<p>Data relating to the study reported in the paper "Quality, scope and reporting standards of randomised controlled trials in Irish Health Research: an observational study".</p>
Dataset from: Reporting of patient involvement: A mixed-methods analysis of current practice in health research publications
<p>This record includes the data associated with the study "Reporting of patient involvement: A mixed-methods analysis of current practice in health research publications". In this study, we evaluated the extent and quality of patient involvement reporting in examples of current practice in health research. We used a targeted search strategy to identify publications that report on patient involvement using the following three samples:</p> <ul> <li>Publications published in 2019 in <em>The BMJ, </em>which requires reporting on patient involvement in research articles</li> <li>Publications listed in the PCORI database. We filtered for topic: example of engagement in health research; stakeholder involvement: patients; year: 2019</li> <li>Publications citing the <a href="https://www.bmj.com/content/358/bmj.j3453">GRIPP2 reporting checklist</a> or a <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/j.1369-7625.2010.00607.x">critical appraisal guideline to assess the quality and impact of user involvement in research</a></li> </ul> <p>After applying our inclusion and exclusion criteria, the final sample consisted of 87 publications that reported on patient involvement. Included publications were coded according to three coding schemes.</p> <p>This deposit includes the following:</p> <ul> <li>Overview of publications that did not meet our inclusion criteria across all 3 samples (BMJ, PCORI, and citation)</li> <li>Overview of publications and additional documents (if applicable) that met our inclusion criteria across all 3 samples (BMJ, PCORI, and citation)</li> <li>Coded segments and analysis for the coding scheme "Phase of involvement" across all 3 samples (BMJ, PCORI, and citation). This includes the count of each sub-code across publications and the final results table. </li> <li>Coded segments and analysis for the coding scheme "GRIPP2-SF reporting guidelines according to Staniszewska et al., 2017" across all 3 samples (BMJ, PCORI, and citation). This includes the count of each sub-code across publications and the final results table.</li> <li>Coded segments and analysis for the coding scheme "Critical appraisal tool according to Wright et al., (2010)" across all 3 samples (BMJ, PCORI, and citation). This includes the count of each sub-code across publications and the final results table.</li> </ul>
Research Beyond the Lab, Spring Term 2022, Global Health Engineering, ETH Zurich. Raw data and analysis-ready derived data on waste management in public spaces in Zurich, Switzerland.
<p>This repository contains all raw and derived data produced as part of the <a href="https://rbtl-fs22.github.io/website/">ETH Zurich course "Research Beyond the Lab: Open Science and Research Methods for a Global Engineer" (151-8102-00L)</a> offered in spring term 2022.</p> <p>Students were assigned teams of four to conduct a collaborative research project broadly addressing the theme of “Trash in the Public Spaces of Zurich” in collaboration with <a href="https://www.stadt-zuerich.ch/ted/de/index/entsorgung_recycling.html">Entsorgung & Recycling Zürich (ERZ)</a>, the waste management department at Stadt Zürich.</p> <p>Research methods and design are taught in the first half of the course. Surveys and a waste characterisation study are then designed based on the research questions students have developed in their respective teams. The collected raw data is used in the course to teach principles of research data management, tidy data structures, reproducible research with R & RStudio, and collaboration and version control with Git & GitHub.</p>
Aventa AV-7 ETH Zurich Research Wind Turbine SCADA and high frequency Structural Health Monitoring (SHM) data
<p><strong>General description of wind turbine: </strong>The ETH owned wind turbine is Aventa AV-7, manufactured by Aventa AG in Switzerland and was commissioned in December 2002. The turbine is operated via a belt-driven generator and a frequency converter with a variable speed drive. The rated power of the Aventa AV-7 is 7 kW, beginning production at a wind speed of 2 m/s and having a cut-off speed of 14 m/s. The rotor diameter is 12.8 m with 3 rotor blades, and a hub height is 18m. The maximum rotational speed of the turbine is 63 rpm. The tower is a tubular steel-reinforced concrete structure, supported on concrete foundation, while the blades are made of glassfiber with a tubular steel main-spar. The turbine is regulated via a variable-speed and variable pitch control system.</p> <p><strong>Location of site: </strong>The wind turbine is located in Taggenberg, about 5 km from the city centre of Winterthur, Switzerland. This site is easily accessible by public transport and on foot with direct road access right next to the turbine. This prime location reduces the cost of site visits and allows for frequent personal monitoring of the site when test equipment is installed. The coordinates of the site are: 47°31'12.2"N 8°40'55.7"E.</p> <p><strong>Control and measurement systems and signals: </strong>The turbine is regulated via a variable-speed and collective variable pitch control system.</p> <p><strong>SHM Motivation: </strong>Designed and commissioned in 2002, the Aventa wind turbine in Winterthur is soon reaching its end of design lifetime. In order to assess the various techniques of predicting the remaining useful lifetime, a Structural Health Monitoring (SHM) campaign was implemented by ETH Zurich. The monitoring campaign started in 2020, and is still ongoing. In addition, the setup is used as a research platform on topics such as system identification, operational modal analysis, faults/damage detection and classification. We analyze the influence of operational and environmental conditions on the modal parameters and to further infer Performance Indicators (PIs) for assessing structural behavior in terms of deterioration processes.</p> <p><strong>Data Description: </strong>The tower and nacelle have been instrumented with 11 accelerometers distributed along the length of the tower, nacelle main frame, main bearing and generator. Two full bridge strain gauges are installed on the concrete tower based measuring fore-aft and side-side strain (and can be converted to bending moments) – all acceleration and strain signals sampled at 200Hz. Temperature and humidity are measured at the tower base – 1Hz data. In additional we are collecting operational performance data (SCADA), namely: wind speed, nacelle yaw orientation, rotor RPM, power output and turbine status – SCADA signals are sampled at 10Hz. See appendix for further details of the sensors layout.</p> <p>The measurements/instrumentation setup, type and layout is provided in the pdf files.</p> <p><strong>The data:</strong> the data is provided in zip files corresponding to four use-cases as follows:</p> <ul> <li>Normal operation data for system identification</li> <li>Aerodynamic imbalance on one blade</li> <li>Rotor icing event</li> <li>Failure of the flexible coupling of the linear drive of the collective pitch system</li> </ul> <p>The data for each of the four uses-cases is organized in zip files. The content of each zip file is as follows:</p> <ul> <li>Time-series data in HDF5 format</li> <li>Metadata: <ul> <li>Turbine specification (Aventa-AV-7.json and Aventa-AV-7.yaml)</li> <li>Sensor specification (Aventa_sensors.json )</li> <li>Unstructured description of the Aventa Turbine and the installed sensors (Aventa_Sensors_Specs.xlsx)</li> </ul> </li> <li>Semantic artifacts: <ul> <li>WindIO Wind Turbine YAML schema describing turbine specifications (IEAontology_schema.yaml)</li> <li>Sensor specification JSON schema (sensors_schema.json)</li> </ul> </li> <li>Media: Pictures of leading edge roughness and a clip of wind turbine operation</li> <li>Code: Jupyter notebook containing example code to load metadata from JSON and data from HDF5 files (example.ipynb)</li> </ul> <p>Additional data is available upon request, please contact:</p> <ul> <li>Prof. Dr. Eleni Chatzi (chatzi@ibk.baug.ethz.ch)</li> <li>Dr. Imad Abdallah (ai@rtdt.ai , abdallah@ibk.baug.ethz.ch)</li> </ul> <p>For further details or questions, please contact:</p> <p>Prof. Dr. Eleni Chatzi<br> Chair of Structural Mechanics & Monitoring</p> <p>ETH Zürich<br> <a href="http://www.chatzi.ibk.ethz.ch/">http://www.chatzi.ibk.ethz.ch/</a></p>
Dataset: Knowledge, information needs and behavior regarding HIV and sexually transmitted infections among migrants from sub-Saharan Africa living in Germany: Results of a participatory health research survey.
<p>Dataset for: Koschollek C, Kuehne A, Müllerschön J, Amoah S, Batemona-Abeke H, Dela Bursi T, Mayamba P, Thorlie A, Mputu Tshibadi C, Wangare Greiner V, Bremer V, Santos-Hövener C: Knowledge, information needs and behavior regarding HIV and sexually transmitted infections among migrants from sub-Saharan Africa living in Germany: Results of a participatory health research survey.</p> <p>This dataset has been described in a PLoS One paper and contains all data necessary to replicate the results presented within this paper (10.1371/journal.pone.0227178). Please cite both the paper as well as the DOI of this dataset if you make use of the data.</p>
H2M survey data on commercialisation training needs of Health Researchers
<p>Health-2-Market was a 3-year long Coordination and Support Action, funded by the European Union’s Seventh Framework Programme for research, technological development and demonstration (Grant Agreement No 305532). H2M aimed at providing training and individual support to Health / Life Sciences researchers in the process of translating their research results into successful new business ideas.</p> <p>With a view to properly adapting the training offer of the project to the needs of Health / Life Sciences researchers in terms of entrepreneurship and business skill development a Training Needs Analysis (TNA) was conducted. In this context, H2M launched an online survey targeted at Health / Life Sciences researchers who have been involved in EU health projects. In particular, the objectives of the survey were:</p> <ul> <li>To formulate a descriptive understanding of various aspects of commercialisation and training needs of the main target group of the project;</li> <li>To divide this target group into homogeneous sub-groups (clusters) along a number of key characteristics such as demographics, commercialisation attitudes and needs;</li> <li>To understand preferences and importance of different aspects and needs through the analysis of: <ul> <li>Knowledge areas that can influence commercialisation behaviour;</li> <li>Training modalities that have an effect on the intention to participate and /or on the perception of the usefulness of a commercialisation training;</li> <li>Variations identified over different sub-groups.</li> </ul> </li> </ul> <p>The survey was dispatched to a database composed of 7,991 unique contacts of participants in previous health projects, accessed through the Directorate General for Health and Food Safety of the European Commission. The initial aim of at least 50 complete responses was overwhelmingly surpassed: 637 respondents completed the survey in full.</p> <p>The “H2M survey data on commercialisation training needs of Health Researchers” dataset contains the raw, anonymised data that were collected from these respondents, along with the questionnaire items that were utilised.</p>
Research Data Management in Selected Health Research Institutions in Uganda
<p>This data set was collected from Researchers in three purposively selected health reseach Institutions in Uganda. The purpose of the study was to explore compliance to FAIR data princiles and Open science initiative given the increasing dependence on donor funding and need to fulfill the requirement for good research practices. </p>
DailySense: a Daily Self-reports and Physiological Signals Sensing Dataset for Subjective Health Research in the Wild
<p><strong>Description:<br></strong>This is a daily self-reports and physiological signals sensing dataset for subjective health research in the wild (<strong><em>DailySense</em></strong>). This is a dataset from a consecutive 14-day experiment in real-life settings composed of smartphone-based subjective psychological evaluation and physiological sensing. A Total of 36 healthy Japanese adult remote workers (mean±SD, 35.8±7.5; range, 27–58 years; 21 male and 15 female participants) participated in the experiment through two terms (1st term [1], 18 participants from a Japanese company without rewards; 2nd term, 18 participants from a participant's pool with rewards).</p> <p>The study protocol was approved by the internal review board of Research & Development Group, Hitachi, Ltd., and was conducted in accordance with the Declaration of Helsinki. All participants provided informed consent prior to enrollment in this study. The permission to share the raw data with participants' anonymization was included in this approval and explicitly obtained in that informed consent.</p> <p>This dataset contains the following data:</p> <p> - <strong>pre- and post-term data of<br></strong> - responses to self-reporting questionnaires (i.e., the Japanese versions of NEO-FFI, STAI, CES-D, CFS, PSQI, WHO-QOL, and SF-36v2<sup>©</sup>).<br> - demographics<br> - survey regarding this experiment</p> <p>- <strong>mid-term data of<br></strong> - responses to emotional self-reports (i.e., Affective Slider and I-PANAS-SF) and their behavior in ESM 6 times/day at maximum<br> - responses to subjective health (i.e., degree of fatigue, stress, anxiety, depression, and sleeplessness), wake-up/in-bed times, and work style 1time/day<br> - continuously monitored physiological data (i.e., EDA, PPG, Acc) and event tags obtained by a wristband sensor (E4 wristband, Empatica Inc.) during their waking hours<br> - response profile data estimated using the proposed method<br> - log data of an experience sampling support system (exkuma, Japan Experience Sampling Method Association)</p> <p>Details of data are mentioned in an xlsx file of the root directory. Due to the limitation of questionnaires, descriptions of original instructions of items in each questionnaire are omitted.</p> <p>The details of the experiment are described in [1][2]. Note that, in [1], participant #29 was excluded due to insufficient physiological data quality. In addition, in [2], participant #47 was excluded since he did not complete a personality questionnaire, but #29 was included since he completed responses to all the questionnaires.</p> <p> </p> <p><strong>License:<br></strong>This dataset is made available by <strong>Hitachi, Ltd.</strong> under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.</p> <p> </p> <p><strong>References:<br></strong>If you use this dataset, please cite the following papers:</p> <p>[1] Shunsuke Minusa, Chihiro Yoshimura, and Hiroyuki Mizuno "Emodiversity evaluation of remote workers through health monitoring based on intra-day emotion sampling," Front. Public Heal., vol. 11, no. August, pp. 1–12, 2023, doi: <a href="https://doi.org/10.3389/fpubh.2023.1196539" target="_blank" rel="noopener">10.3389/fpubh.2023.1196539</a></p> <p>[2] Shunsuke Minusa, Tadayuki Matsumura, Kanako Esaki, Yang Shao, Chihiro Yoshimura, and Hiroyuki Mizuno, "Response Style Characterization for Repeated Measures Using the Visual Analogue Scale," arXiv preprint <a href="https://arxiv.org/abs/2403.10136" target="_blank" rel="noopener">arXiv:2403.10136</a>, 2024.</p> <p> </p> <p><strong>Contact:<br></strong>If there is any problem, please contact us:</p> <ul> <li>Shunsuke Minusa, <a href="mailto:shunsuke.minusa.hd@hitachi.com" target="_blank" rel="noopener">shunsuke.minusa.hd@hitachi.com</a></li> </ul>
Using the Socialise app to collect smartphone sensor data for mental health research: A feasibility study
<p>To investigate the feasibility of collecting smartphone sensor data for mental health research, we tested the Socialise app that was developed at the Black Dog Institute in a group of people with a lived experience of mental health challenges (n=32). Bluetooth, GPS and battery status data were collected at regular intervals (3, 4, 5 or 8 minutes) for 4 weeks. In addition, survey data was collected using the app to investigate the views of participants on user experience and the acceptability of passive data collection for mental health research. No mental health data was collected as part of the feasibility study.</p>
Data and Charting information Animus Prime Health Research
<p>Data and Charting 3 information. More HealthCare Veteran Data.For utilization for Education and Research purposes for Animus Prime Research.</p>
The Co-Researchers Journey in CoAct for Mental Health
<p>This graphic document is a testimony of the Co-Researchers’ contribution to CoAct for Mental Health over a long and still unfinished journey, from 2020 to 2022. Co-Researchers, people with mental health problems and their families, have been the main actors of this research, as in-the-field competent experts. The research involved launching a chatbot in Telegram where anyone can listen to their lived experiences and react to them. Co-Researchers have been involved in the interpretation of the data collected and drew conclusions to make political recommendations and support specific demands.CoAct for Mental Health is part of CoAct (Co-designing Citizen Social Science for Collective Action), a project funded by the European Union’s Horizon 2020 research and innovation programme. CoAct understands Citizen Social Science as participatory research co-designed and directly driven by citizen groups sharing a social concern. We expect to upscale this project at a more global level and to replicate it to other social pressing issues.</p>
Antimicrobial Resistance Microbiological Dataset (ARMD-UTSW): A deidentified collection of electronic health records, from a quaternary, academic medical center, for antimicrobial resistance research
Open the record for dataset details and reuse information.
Antimicrobial Resistance Microbiological Dataset (ARMD-ECUH): A deidentified collection of electronic health records from a rural academic health system for antimicrobial resistance research
Open the record for dataset details and reuse information.
Figure 2. from: Unicorn–Open science for assessing environmental state, human health and regional economy - Research Ideas and Outcomes 2: e9232 (16 May 2016) https://doi.org/10.3897/rio.2.e9232
Figure 2. - Time line of the tasks in UNICORN-project
Figure 1. from: Unicorn–Open science for assessing environmental state, human health and regional economy - Research Ideas and Outcomes 2: e9232 (16 May 2016) https://doi.org/10.3897/rio.2.e9232
Figure 1. - Links and interactions between the work packages
Open data for spatial public health research
<p><strong>Background</strong></p> <p>Preventive and health-promoting policies can guide (place and space-specific) factors influencing human health, such as the physical and social environment. Required is data that can lead to a more nuanced decision-making process and identify both, existing and future challenges. Along with the rise of new technologies, and thus the multiple opportunities to use and process data, new options have emerged to measure and monitor factors that affect health. Thus, in recent years, several gateways for open data (including governmental and geospatial data) became available. At present, an increasing number of research institutions as well as (state and private) companies and citizens' initiatives provide data. However, there is a lack of overviews covering the range of such offerings regarding health. In particular, for geographically differentiated analyses, there are challenges related to data availability at different spatial levels and the growing number of data providers.</p> <p><strong>Objectives</strong></p> <p>To provide an overview of open data resources available in the context of space and health to date. It also describes the technical and legal conditions for using open data</p> <p><strong>Results</strong></p> <p>An up-to-date summary of results including information on relevant data access and terms of use is provided along with a web visualization. All data is available for further use under an open license.</p>
Open Access Publication of Public Health Research in African Journals: Dataset
<p>This study explored the open publishing practices of African journals expected to publish articles on Public Health.</p>
Researchers and Their Experimental Models: A Pilot Survey in the Context of the European Union Health and Life Science Research
<p>A significant debate is ongoing on the effectiveness of animal experimentation, due to the increasing reports of failure in the translation of results from preclinical animal experiments to human patients. Scientific, ethical, social and economic considerations linked to the use of animals raise concerns in a variety of societal contributors (regulators, policy makers, non-governmental organisations, industry, etc.). The aim of this study was to record researchers’ voices about their vision on this science evolution, to reconstruct as truthful as possible an image of the reality of health and life science research, by using a key instrument in the hands of the researcher: the experimental models. Hence, we surveyed European-based health and life sciences researchers, to reconstruct and decipher the varying orientations and opinions of this community over these large transformations. In the interest of advancing the public debate and more accurately guide the policy of research, it is important that policy makers, society, scientists and all stakeholders (1) mature as comprehensive as possible an understanding of the researchers’ perspectives on the selection and establishment of the experimental models, and (2) that researchers publicly share the research community opinions regarding the external factors influencing their professional work. Our results highlighted a general homogeneity of answers from the 117 respondents. However, some discrepancies on specific key issues and topics were registered in the subgroups. These recorded divergent views might prove useful to policy makers and regulators to calibrate their agenda and shape the future of the European health and life science research. Overall, the results of this pilot study highlight the need of a continuous, open and broad discussion between researchers and science policy stakeholders.</p>
Research data on health facility-level factors that contribute to delayed diagnosis of cervical cancer
<p>In Kenya, cervical cancer is the 2nd commonly diagnosed type of cancer and the top cause of cancer-related deaths among women. Globally, over 50% of cervical cancer diagnoses are made late, with this proportion rising to 80% in developing countries. Poor Health systems can cause delays in diagnosis, thus, this study focused on determining the health facility-level factors that contribute to delayed diagnosis among cervical cancer patients at the Kenyatta National Hospital (KNH). An analytical cross-sectional mixed method study was adopted to collect data on hospital and referral experiences from 139 cervical cancer patients systematically sampled at KNH, using a semi-structured questionnaire. Associations between the stage at diagnosis and hospital and referral experiences were tested using a logistic regression model at 95% Confidence Interval. 86 (61.9%) were diagnosed at advanced stages III and IV. The potential predictors for delayed diagnosis were; More number of hospital referral times (p-value=0.000), Facing referral challenges (p-value=0.041), Longer time taken for diagnosis appointment (p-value=0.059), and Longer time taken for diagnostic results (p-value=0.007) in the bivariate analysis. More number of hospital referral times (p-value=0.001) and longer time taken for diagnostic results (p-value=0.025), were significantly associated with delayed diagnosis of cervical cancer in the multivariate logistic regression test model. Referral challenges included misdiagnosis, cost of diagnosis, and prolonged diagnosis appointments. The study concluded that the cause of delays in diagnosis for most patients is due to poor health and referral systems and inadequate medical personnel and diagnosis equipment. This study recommends improving referral systems and encouraging partnerships to decentralize diagnostic centers and equipment and train more expertise on cervical cancer.</p>
Research funding for male reproductive health and infertility in the UK and USA [2016 – 2019]
<p>There is a paucity of data on research funding levels for male reproductive health (MRH). We investigated the research funding for MRH and infertility by examining publicly accessible webdatabases from the UK and USA government funding agencies. Information on the funding was collected from the UKRI-GTR, the NIHR's Open Data Summary, and the USA's NIH RePORT webdatabases. Funded projects between January 2016 and December 2019 were recorded and funding support was divided into three research categories: (i) male-based; (ii) female-based; and (iii) not-specified. Between January 2016 and December 2019, UK agencies awarded a total of £11,767,190 to 18 projects for male-based research and £29,850,945 to 40 projects for female-based research. There was no statistically significant difference in the median funding grant awarded within the male-based and female-based categories (p=0.56, W=392). The USA NIH funded 76 projects totalling $59,257,746 for male-based research and 99 projects totalling $83,272,898 for female-based research Again, there was no statistically significant difference in the median funding grant awarded between the two research categories (p=0.83, W=3834). This is the first study examining funding granted by main government research agencies from the UK and USA for MRH. These results should stimulate further discussion of the challenges of tackling male infertility and reproductive health disorders and formulating appropriate investment strategies.</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.