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12,574 results for “Health”
Red-backed Salamanders as Indicators of Hemlock Forest Health at Harvard Forest 2014
Species that react quickly to ecological changes are useful indicators for assessing forest health. Red-backed salamanders (Plethodontid cinereus) may be used as an indicator species in declining hemlock forests, allowing for assessment of soil quality and forest health. We hypothesized that decline of hemlock stands due to the effect of hemlock woolly adelgid would negatively impact the abundance of red-backed salamanders by altering soil quality. In the Hemlock Removal Experiment at Harvard Forest, eight-0.8ha plots were treated either with girdling to simulate adelgid invasion, logging to simulate preemptive logging strategies, no treatment in hemlock areas, or no treatment in hardwood areas. Within these treatments in June and July of 2014, P. cinereus relative abundance was determined from coverboard surveys. Soil quality was determined from core samples. Bulk density, pH, carbon-nitrogen ratio, moisture, soil temperature, and litter depth were measured, and ordination was used to create an index of soil quality from these. There was a significant correlation between plot treatment and soil quality (p less than 0.05). Temperature, CN ratio, and pH influenced a strong significant negative correlation between soil quality and salamander abundance (pless than 0.05). Soil quality affected by hemlock decline influenced salamander abundance. These data suggest that we could gain an understanding of forest health and soil quality by examining salamander abundance. Assessing adelgid invasion can be difficult and objective, whereas salamander abundances are easily measured; therefore, we suggest that salamander abundances could be useful in assessing the health of hemlock forest ecosystems.
MethylDetectR - A Translational Tool for Methylation-Based Health Profiling
<p><strong>** CORRECTION (2025-05-29): Please note that the script <a href="https://zenodo.org/api/records/15548022/draft/files/Script_For_User_To_Generate_Scores.R/content" target="_blank" rel="noopener noreferrer">Script_For_User_To_Generate_Scores.R</a> had an error whereby single-CpG EpiScores were producing the same score for all samples. This has been corrected in the newest version. </strong><br><br>This dataset includes reproducible code for the two applications related to the 'MethylDetectR' software. These .R files are included as 'MethylDetectR - Calculate Your Scores.R' and 'MethylDetectR.R'. An example DNAm file and SexAgeinfo file for upload to 'MethylDetectR - Calculate Your Scores' are included. These are 'DNAm_File_Example.rds' and 'SexAgeinfo_example.csv' respectively. An example output file from this application/for upload to 'MethylDetectR' is included as 'MethylDetectR - Test For Upload.csv'. An example and optional input file for case/control data is also available as 'MethylDetectR_Case_Control_Example.csv'. </p> <p>Furthermore, a script for the user to generate their own DNAm-based estimated values for human traits is included as 'Script_For_User_To_Generate_Scores.R'. A necessary associated file as 'Predictors_Shiny_By_Groups.csv' is also present for the script to run. We have also included separate necessary files to generate the chronological age predictor from Bernabeu <em>et al.</em> in our most recent versions of MethylDetectR. </p> <p>Lastly, an additional file called 'Truncate_to_these_CpGs.csv' is available which allows users to subset their methylation file to those CpG sites used in the 'MethylDetectR - Calculate Your Scores' application. This may substantially reduce the size of the methylation file for upload as well as its upload time. </p>
OHHR – The Oldenburg Hearing Health Record [Dataset]
<p><strong>Description of the dataset</strong></p> <p>The Oldenburg Hearing Health Record (OHHR) provides a publicly accessible dataset that can be used to advance hearing health research. It includes a constellation of data collected from 581 participants (aged 18–86 years<em>; </em>255 female; <em>Mean age = 67.31 years; SD = 11.93</em>) between 2013 and 2015 at the Hörzentrum Oldenburg in collaboration with the Cluster of Excellence "Hearing4all". The data was anonymized in accordance with the General Data Protection Regulation (GDPR; Regulation (EU) 2016/679). Each participant was assigned a unique identifier to maintain anonymity while enabling multivariate individualized analyses. </p> <p>All the different data types are listed below:<br><br><strong>Subjective Measures</strong></p> <ul> <li>Home Questionnaire</li> <li>SF-12 Health Survey</li> <li>Technology Readiness Questionnaire</li> <li>Anamnesis</li> </ul> <p><strong>Audiological Tests</strong></p> <ul> <li>Pure Tone Audiometry</li> <li>Adaptive Categorical Loudness Scaling</li> <li>Digit Triplet Test (Speech Reception Threshold in Noise: Screening)</li> <li>Göttingen Sentence Test (Speech Reception Threshold in Noise)</li> </ul> <p><strong>Cognitive Measures</strong></p> <ul> <li>DemTect</li> <li>WortSchatz</li> </ul> <p><strong>Demographic Information</strong></p> <ul> <li>Socio-economic data</li> <li>Scheuch-Winkler Index (calculated)</li> </ul> <p><strong>Supporting Documentation</strong></p> <p><strong>MethodsDescription.rtf/.pdf:</strong> Provides detailed explanations of data type and collection procedures.<br><strong>data.zip/metadata</strong><strong>: </strong>Includes schema and description files for all data tables.</p> <p>A supporting paper was published on Scientific Data:</p> <div> <div> <p>Jafri, S., Berg, D., Buhl, M. <em>et al.</em> The Oldenburg Hearing Health Record (OHHR). <em>Sci Data</em> <strong>12</strong>, 1546 (2025). https://doi.org/10.1038/s41597-025-05884-y</p> </div> </div>
Hochfrequente Mental Health Surveillance
<p>Im Rahmen der Mental Health Surveillance (MHS) am Robert Koch-Institut (RKI) werden für eine Auswahl an Indikatoren der psychischen Gesundheit von Erwachsenen basierend auf Surveydaten Zeitreihen bestehend aus gleitenden Drei-Monats-Schätzern und Glättungskurven berechnet. Dadurch sollen Entwicklungen in der psychischen Gesundheit der erwachsenen Bevölkerung in Deutschland mit möglichst geringem Zeitverzug beobachtet und insbesondere negative Entwicklungen frühzeitig erkannt werden. Diese hochfrequente Surveillance wurde ursprünglich vor dem Hintergrund neuer Informationsbedarfe zur Entwicklung der psychischen Gesundheit der Bevölkerung in der COVID-19-Pandemie entwickelt.</p>
Data from: mPRIME Study - Interaction of Insulin Resistance with Cognition, Lifestyle, and Mental Health
<p>The presented datasets were collected within the <em>m</em>PRIME study, a prospective, observational study of the H2020 project Prevention and Remediation of Insulin Multimorbidity in Europe (PRIME) (grant No. 847879). The study investigates the interaction of insulin resistance with cognition, lifestyle, and mental health by combining traditional methods with ambulatory assessment and sensor-based data collection. Recruitment took place between March 2021 and March 2023 at the University Hospital Frankfurt, Germany.</p> <p>The eligibility criteria for the study were as follows: Age above 18 years, no intake of antidiabetic medication, insulin or glucocorticoids, no existing type 1 diabetes mellitus or gestational diabetes, no diagnoses of bipolar I disorder, schizophrenia, organically caused mental disorders and substance dependence, no severe neurological disorders, no current pregnancy or breastfeeding, no non-correctable visual impairments, no participation in medication-related studies within the last 6 months, no use of weight-reducing medications or a diet within the last 3 months, sufficient proficiency in German to complete questionnaires and neuropsychological tests.</p> <p>All participants in the <em>m</em>PRIME study provided written informed consent. The study protocol and procedures were approved by the local ethics committee.</p> <p><strong>Study Design</strong></p> <p>Individuals completed a baseline assessment and a one-week ambulatory assessment. The baseline assessment included: socio-demographic information, blood samples, anthropometric measures, neuropsychological tests, and several questionnaires. In addition, individuals were introduced to smartphone-based ecological momentary assessment (EMA), food protocols, and the use of sensors (continuous glucose monitor, accelerometer). Food protocols and EMA were conducted on three consecutive days, including two weekdays and one weekend day (Thursday to Saturday or Sunday to Tuesday). Several times a day, individuals were prompted via their smartphone to complete a working memory task and answer questions about stress, affect, and food intake. The continuous glucose monitor and accelerometer were worn continuously for 1 week.</p> <p> </p>
Physical Health of Adults during Covid-19
<h1>Background</h1> <p>This dataset is one of the studies of the <a href="https://www.corona-health.net/en/">Corona Health project</a>. It addresses how the physical health and habits of adults during the global pandemic changed over time. It consists of two questionnaires, a baseline questionnaire and a bi-weekly follow up questionnaire to track the behaviour in the last 14 days. The whole datasets contains more than <strong>1800 users</strong> (98 % of them German) and a total of <strong>7000 questionnaires</strong>. For some users, it also has GPS and app usage data.</p> <h1>Files</h1> <ul> <li>rki_heart_baseline.csv -> The baseline questionnaire, containing demographic data as well</li> <li>rki_heart_followup.csv -> The follow-up questionnaire</li> <li>answersheets.csv -> The unprocessed answersheets from both, baseline and follow-up questionnaires in a raw format.</li> <li>codebook.xlsx -> The Codebook that describes the three files in detail</li> </ul> <h1>More information</h1> <ul> <li> <p><strong>Key Facts</strong></p> <ul> <li>No. of questionnaires: 1805 Baseline + 5895 Follow-up</li> <li>n Tracking Consent GPS (ratio): 1366 (75%)</li> <li>n Tracking Consent App Usage (ratio): 101 (5.6%)</li> </ul> </li> <li> <p><strong>Sociodemographics</strong></p> <ul> <li>Age, mean (SD): 41.7 (15.1)</li> <li>Gender Ratio: <ul> <li>Male: 36%</li> <li>Female: 64%</li> <li>Diverse: 0%</li> </ul> </li> <li>Body Mass Index, mean (SD): 26.7 (6.23)</li> <li>Users located in Germany (ratio): 98.7%</li> </ul> </li> <li> <p><strong>Lifestyle habits at baseline</strong></p> <ul> <li>Daily Smokers (ratio): 292 (16.2%)</li> <li>Daily fruit consumers before lockdown (ratio): 533 (29.5%)</li> <li>Daily fruit consumers after lockdown (ratio): 538 (29.8%)</li> <li>Daily vegetable consumers before lockdown (ratio): 562 (31.1%)</li> <li>Daily vegetable consumers after lockdown (ratio): 559 (31.0%)</li> </ul> </li> <li> <p><strong>Cardiovascular Health at baseline</strong></p> <ul> <li>History of hypertension (ratio): 454 (25.2%)</li> <li>History of diabetes mellitus (ratio): 113 (6.3%)</li> <li>History of hyperlipidemia (ratio): 461 (25.5%)</li> </ul> </li> </ul> <p>For a more detailed description of the dataset, please go on <a href="https://github.com/joa24jm/CH-Heart" target="_blank" rel="noopener">GitHub/joa24jm/ch-heart.</a> There, you can also find a link to our publication.</p>
FULFILL dataset - diet policy acceptability - health information provision France
<p>This dataset represents survey data on sufficiency-oriented policy acceptability in regard to dietary consumption. The study was part of the second round surveys in France in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.</p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from three countries: France, Italy, and Latvia, with representative sampling (age, income, gender, current region). In this survey on the acceptability of sufficiency-oriented diet policies we recruited a representative sample with approximately 800 participants from France, taking into account primarily the individual perspective, added by some questions on the household level.</p> <p>The central part of the survey includes the randomised provision of information on the health-risks associated with meat consumption. We were interested in peoples' acceptability on three majorly discussed and sufficiency-relevant policies, i.e. meat tax, carbon label or meat-free day at public canteens. We investigated if the information provision impacted people's acceptability (overall, self vs. others perspective). We measured several control variables (socio-economics such as age, gender, income, education, household size, life stage, ideological measures such as political orientation or attitudinal measures such as sufficiency orientation and climate change denial). A quantitative assessment of the carbon footprint in the food consumption domain was also included.</p>
FULFILL dataset - diet policy acceptability - health information provision Latvia
<p>This dataset represents survey data on sufficiency-oriented policy acceptability in regard to dietary consumption. The study was part of the second round surveys in Latvia in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.</p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from two countries: France, Italy, and Latvia, with representative sampling (age, income, gender, current region). In this survey on the acceptability of sufficiency-oriented diet policies we recruited a representative sample with approximately 500 participants from Latvia, taking into account primarily the individual perspective, added by some questions on the household level.</p> <p>The central part of the survey includes the randomised provision of information on the health-risks associated with meat consumption. We were interested in peoples' acceptability on three majorly discussed and sufficiency-relevant policies, i.e. meat tax, carbon label or meat-free day at public canteens. We investigated if the information provision impacted people's acceptability (overall, self vs. others perspective). We measured several control variables (socio-economics such as age, gender, income, education, household size, life stage, ideological measures such as political orientation or attitudinal measures such as sufficiency orientation and climate change denial). A quantitative assessment of the carbon footprint in the food consumption domain was also included.</p>
FULFILL dataset - diet policy acceptability - health information provision Italy
<p>This dataset represents survey data on sufficiency-oriented policy acceptability in regard to dietary consumption. The study was part of the second round surveys in Italy in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.</p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from three countries: France, Italy, and Latvia, with representative sampling (age, income, gender, current region). In this survey on the acceptability of sufficiency-oriented diet policies we recruited a representative sample with approximately 800 participants from each country, taking into account primarily the individual perspective, added by some questions on the household level.</p> <p>The central part of the survey includes the randomised provision of information on the health-risks associated with meat consumption. We were interested in peoples' acceptability on three majorly discussed and sufficiency-relevant policies, i.e. meat tax, carbon label or meat-free day at public canteens. We investigated if the information provision impacted people's acceptability (overall, self vs. others perspective). We measured several control variables (socio-economics such as age, gender, income, education, household size, life stage, ideological measures such as political orientation or attitudinal measures such as sufficiency orientation and climate change denial). A quantitative assessment of the carbon footprint in the food consumption domain was also included.</p>
Dataset of The distinct influence of different maternal mental health symptom profiles on infant sleep during the first year postpartum: a cross-sectional survey
<p>The distinct influence of different, but comorbid, maternal mental health difficulties, such as postpartum depression, anxiety, or childbirth-related posttraumatic stress disorder (CB-PTSD) on infant sleep is unknown, although maternal mental health was reported to be associated with infant sleep. This paper first aimed to associations between maternal mental health symptoms and infant sleep. Second, it aimed to exploratory obtain maternal mental health symptom profiles from maternal mental health symptoms. Finally, it aimed to investigate the distinct influence of these maternal mental health symptom profiles on infant sleep, when including mediators (i.e., maternal perception of infant temperament and method to fall asleep) and moderators (maternal educational level and infant age).</p> <p>This dataset contains data on the mental health (i.e., CB-PTSD, depression, anxiety) of 410 mothers with an infant aged between 3 to 12 months old. Information on infant sleep and temperament (negative emotionality) was collected via standardised maternal-report questionnaires (City BiTS, EPDS, HADS, BISQ, and IBQ-R very short form). Sociodemographic data such as maternal age, marital status, educational level, infant age, and week of gestation are reported.</p> <p>This dataset is related to: Sandoz, V.; Lacroix, A.; Stuijfzand, S.; Bickle Graz, M.; Horsch, A. Maternal Mental Health Symptom Profiles and Infant Sleep: A Cross-Sectional Survey. <em>Diagnostics</em> <strong>2022</strong>, <em>12</em>, 1625. https://doi.org/10.3390/diagnostics12071625. </p>
Mohonk Preserve Forest Health Monitoring Data 2018-2021
In 2018, the Mohonk Preserve’s Daniel Smiley Research Center implemented a long-term research project aimed at inventorying forest vegetation and monitoring forest health. The protocol was adapted from the National Park Service’s Northeast Temperate Network (https://www.nps.gov/im/netn/forest-health.htm). This project monitors the composition and structure of the Mohonk Preserve forests, and collects data for assessing forest soil condition, impacts of white-tailed deer herbivory, and land cover. In 2018, 24 plots were established in four habitat types: Eastern hemlock forest (n = 6), white ash forest (n = 6), historic prescribed burn forest (n = 6), and randomly selected forest (n = 6). In 2021, an additional 14 plots were established in two historic Breeding Bird Survey research areas: Eastern hemlock forest (n = 8) and pitch pine forest (n = 6). All data collection occurred between the months of June through August. Plots are scheduled to be resampled every four years.
Tree Health Conditions (mortality, damage, disease, bark beetles) in Fuel Reduction Treatments Located Near Communities in Interior Alaska and the Cook Inlet Region of Alaska - Observations from July-August 2023
This dataset contains tree-, transect-, and site-level observations of forest stands at sites that received a fuel reduction treatment. Tree-level observations include species, diameter, living status, damage, disease, and bark beetle presence. Transect-level observations include level of coarse woody debris and bark beetle presence. Sites are categorized by region (recent/ongoing spruce beetle oubreak or endemic spruce beetle population levels) and treatment type (hand-thinned or mechanincally felled and masticated). These observations are from July-August 2023. Sites are located near communities in Interior Alaska and the Cook Inlet Region.
Dataset of "Advanced machine learning techniques for State-of-Health estimation in lithium-ion batteries: A comparative study"
This research focuses on State-of-Health (SOH) estimation of lithium-ion (Li-ion) batteries to enhance lifespan and reliability. Using Samsung INR18650-35E cells, 600 cycles were analyzed with machine learning (ML) techniques, including Gaussian Process Regression (GPR), Support Vector Regression (SVR), Feed-Forward Neural Network (FFNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). Input features from charging and discharging cycles were selected with Pearson Correlation Analysis (PCA) and Exhaustive Search (ES) to optimize inputs for each ML method. Models were tested on datasets of varying sizes to evaluate performance and overfitting, including an experiment where SOH estimation of one battery was performed using training data from another. The findings highlight each model's strengths and limitations, guiding their application in battery health prediction.
Dataset - Terminology of e-Oral Health: Consensus Report of the IADR's e-Oral Health Network Terminology Task Force.
<p>README<br>====================<br>This repository contains the data and documentation for a research project. It includes the dataset,<br>which is provided in CSV format and the original PDF with the survey answers.</p> <p>Research Information<br>====================<br>Terminology of e-Oral Health: Consensus Report of the IADR’s e-Oral Health Network Terminology<br>Task Force. Authors reported multiple definitions of e-oral health and related terms, and used several definitions<br>interchangeably, like mhealth, teledentistry, teleoral medicine and telehealth. The International<br>Association of Dental Research e-Oral Health Network (e-OHN) aimed to establish a consensus on<br>terminology related to digital technologies used in oral healthcare.</p> <p>This dataset contains data from a survey about digital oral health. The survey asked participants to provide their definition of various terms related to digital oral health, as well as their agreement with the provided definitions. The dataset also includes three figures that the participants were asked to review.</p> <p>The purpose of this dataset is to collect data on the public's understanding of digital oral health terms and to identify areas where there may be confusion or misinterpretation. The data from this dataset could be used to develop educational materials or to improve the way that digital oral health information is communicated to the public.</p> <p>Additional notes<br>====================<br>The data is not currently cleaned or preprocessed.</p> <p>Dataset<br>====================<br>The dataset file, named "dataset.csv," is in this repository. It contains the raw anonymized data<br>collected from the participants in a structured format. Each row represents a respondent, and the<br>columns correspond to different variables.</p> <p>Codebook<br>====================<br>The codebook file, named "codebook.pdf," is also included in this repository. It provides a<br>comprehensive description of the variables present in the dataset. The codebook outlines each<br>variable's meaning, type, and possible values, allowing users to understand and analyze the data<br>effectively.</p> <p>Metadata<br>====================<br>No metadata is provided</p> <p>Files<br>====================<br>01_readme.txt this readme file<br>02_codebook.pdf The codebook of the dataset<br>03_dataset.csv The dataset in csv format<br>04_e-OHN Delphi (2023-02-03).pdf The output from the survey</p> <p>Usage<br>====================<br>To work with the dataset, you can download the "dataset.csv" file and import it into your preferred<br>software or programming language for analysis. The codebook provides valuable information about<br>the variables, allowing you to understand the data structure and make informed decisions during your<br>analysis.<br>Please note that while every effort has been made to ensure the accuracy and quality of the data, it is<br>important to review the codebook and understand the context of the research before concluding the<br>dataset.</p> <p>License<br>====================<br>The data and documentation in this repository are provided under the CC BY-SA.<br>This license enables reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. If you remix, adapt, or build upon the material, you must license the modified material under identical terms. CC BY-SA includes the following elements:</p> <p> BY: credit must be given to the creator.<br> SA: Adaptations must be shared under the same terms.<br> <br>Please refer to the license file for further details on how the data can be used and shared.</p> <p>Contact Information<br>====================<br>For any questions, clarifications, or inquiries related to the dataset or research project, please contact<br>Assoc Prof Dr Sergio Uribe, sergio.uribe@rsu.lv</p>
Inter-Chemical Correlation results for the study: HHEARx2018-2512 (The Role of Environmental Endocrine Disruptors on the Health of Inner City Children)
Title: The Role of Environmental Endocrine Disruptors on the Health of Inner City Children <br>Species: Homo sapiens <br>Number of samples: 651 <br>Number of named analytes: 26 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=62 <br>
Inter-Chemical Correlation results for the study: HHEARx2018-2532 (Environmental Toxins in Early Life: Shaping Health and Disease in Childhood)
Title: Environmental Toxins in Early Life: Shaping Health and Disease in Childhood <br>Species: Homo sapiens <br>Number of samples: 1147 <br>Number of named analytes: 48 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=69 <br>
Dataset Musculoskeletal pain in health professionals at the end of their studies and 1 year after entry into the profession: A multi-center longitudinal study.
<p>Dataset related to the paper "Musculoskeletal pain in health professionals at the end of their studies and 1 year after entry into the profession: A multi-center longitudinal study."</p> <p><strong>Abstract</strong></p> <p>Background: Musculoskeletal pain, especially back pain, is common among health professionals (HPs). They reduce work productivity and cause high costs. This follow up study investigates the prevalence and individual course of Musculoskeletal pain among HP students at the end of their studies and one year after entering the health care workforce. Participants were asked whether their Musculoskeletal pain was related to study or work conditions.</p> <p>Method: Self reported one year prevalence for lower back pain, neck and shoulder pain, pain in arms or hands, and pain in legs or feet was collected by an online questionnaire at two timepoints from 1046 participating HPs. Generalized estimating equation (GEE) models of the binomial family with log link were employed to estimate adjusted prevalence and corresponding normal based 95% confidence intervals were derived using the bootstrap method with 1,000 replications.</p> <p>Results: Prevalence of lower back pain as well as neck and shoulder pain was very high at baseline and follow up in all students and later HPs. Prevalence for pain in arms or hands, legs or feet was lower and there were significant differences between the professions. HP associated their lower back pain and neck and shoulder pain clearly with study and work conditions; HPs linked pain in arms or hands, legs or feet strongly with work conditions only.</p> <p>Conclusion: The prevention of lower back pain and neck and shoulder pain must be included in the curricula of all health professions at universities. As best practice example, they should incorporate ergonomic measures and exercises as a daily routine of the formation of health professionals. The impact of physically demanding professional tasks on upper and lower extremities needs to be investigated in further studies in order to take preventive measures.</p>
Data for the article "Professionalism, emotional wellbeing, and dropout intention in health professions students during the pandemic"
<p>Dataset from a study of attitudes and perceptions of medicine and nursing students in Peru during the COVID-19 pandemic. Survey was applied from 2020-07-24 to 2021-04-16.</p> <p>This dataset is described in the article: </p> <p>Castagnetto, J.M., Hancco-Monrroy, D.E., Caballero-Apaza, L.M. <em>et al.</em> Professionalism, emotional wellbeing, and dropout intention in health professions students during the pandemic. <em>Sci Data</em> <strong>12</strong>, 1259 (2025). <a href="https://doi.org/10.1038/s41597-025-05508-5">https://doi.org/10.1038/s41597-025-05508-5</a> (<a href="https://www.nature.com/articles/s41597-025-05508-5">https://www.nature.com/articles/s41597-025-05508-5</a>)</p>
Supporting Material for "Enhancing Mental Health and Cognitive Function in Older Adults: A Swiss Perspective on Public Health Interventions and Stigma Mitigation Strategies Informed by a Desk Review"
<p>This dataset contains all supporting material for the paper "Enhancing Mental Health and Cognitive Function in Older Adults: A Swiss Perspective on Public Health Interventions and Stigma Mitigation Strategies Informed by a Desk Review", published in the journal Swiss Psychology Open:</p> <p><em>Mack, M., Scarampi, C., Joly-Burra, E., Zuber, S., de Freitas, C., Teixeira, R. and Kliegel, M. (2025) ‘Enhancing Mental Health and Cognitive Function in Older Adults: A Swiss Perspective on Public Health Interventions and Stigma Mitigation Strategies Informed by a Desk Review’, Swiss Psychology Open, 5(1), p. 2. Available at: <a href="https://doi.org/10.5334/spo.81.">https://doi.org/10.5334/spo.81</a>.</em></p> <p>It includes the following documents and files:</p> <p><strong>S1. Protocol:</strong> ADVANCE Protocol for desk reviews</p> <p><strong>S2. Search strategy</strong></p> <p><strong>S3. Guidelines for title and abstract screening:</strong> Guidelines for the selection of articles included in the desk review</p> <p><strong>S4. Guidelines full-text screening:</strong> Guidelines for the selection of articles included in the desk review</p> <p><strong>S5. Guidelines data extraction:</strong> ADVANCE Guidelines/codebook data extraction</p> <p><strong>data extraction_desk review_switzerland.xlsx</strong></p> <p>This desk review was conducted as part of the ADVANCE project, which aims to enhance our understanding of mental health promotion and prevention. This desk review evaluates the current state of interventions for mental health and cognitive functioning among older adults in Switzerland focusing on the features of these interventions as well as on Swiss-specific contextual factors that contribute to vulnerability and stigma. This results of the desk review has been submitted for publication to 'LIVES Working Papers' and 'Swiss Psychology Open' . The two versions of the desk review differ slightly. The version for LIVES Working Papers, included the results of the Delphi survey and the resulting intervention scenarios. The version for Swiss Psychology Open, did not include the Delphi survey results and the resulting intervention scenarios, but included a more detailed discussion of the review results.</p>
Data for manuscript Marmet, Studer, Lemoine, Grazioli, Bertholet & Gmel (2019). Reconsidering the associations between self-reported alcohol use disorder and mental health problems in the light of co-occurring addictions in young Swiss men. Plos One. DOI: 10.1371/journal.pone.0222806.
<p>Dataset for the manuscript Marmet, Studer, Lemoine, Grazioli, Bertholet & Gmel (2019). Reconsidering the associations between self-reported alcohol use disorder and mental health problems in the light of co-occurring addictions in young Swiss men. Plos One. DOI: 10.1371/journal.pone.0222806.</p> <p>The dataset contains all data needed to reproduce the results in the above cited manuscript. Variable description and labels can be found in the codebook. For further information on the instruments used please refer to the manuscript.</p> <p>The data was collected between April 2016 and March 2018 in Switzerland by the C-SURF study (<a href="http://www.c-surf.ch">www.c-surf.ch</a>). Participants were on average 25 years old when they answered the questionnaires. The final sample size used in the manuscript is 5516. Please note that the dataset contains 25 datasets created with multiple imputation, therefore there are no missing values in the dataset.</p> <p>The research protocol for this study was approved by the Human Research Ethics Committee of the Canton Vaud (Protocol No. 15/07). Data collection was funded by the Swiss National Science Foundation (FN 33CSC0-122679, FN 33CS30_139467, FN 33CS30_148493)</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.