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227 results for “population health”
Population-level health and economic impacts of introducing Vaccae vaccination in China: A modeling study
<p>Supplementary to "Population-level health and economic impacts of introducing Vaccae vaccination in China: A modeling study"</p>
Extended 1.0 Dataset of "Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary"
<p><strong>Introduction</strong></p> <p>We are enclosing the database used in our research titled "Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary", along with our statistical calculations. For the sake of reproducibility, further information can be found in the file <em>Short_Description_of_Data_Analysis.pdf </em>and <em>Statistical_formulas.pdf </em></p> <p>The sharing of data is part of our aim to strengthen the base of our scientific research. As of March 7, 2024, the detailed submission and analysis of our research findings to a scientific journal has not yet been completed.</p> <p><em>The dataset was expanded on <strong>23rd September 2024</strong> to include SPSS statistical analysis data, a heatmap, and buffer zone analysis around the Health Development Offices (HDOs) created in QGIS software.</em></p> <p><strong>Short Description of Data Analysis and Attached Files (datasets):</strong></p> <p>Our research utilised data from 2022, serving as the basis for statistical standardisation. The 2022 Hungarian census provided an objective basis for our analysis, with age group data available at the county level from the Hungarian Central Statistical Office (KSH) website. The 2022 demographic data provided an accurate picture compared to the data available from the 2023 microcensus. The used calculation is based on our standardisation of the 2022 data. For xlsx files, we used MS Excel 2019 (version: 1808, build: 10406.20006) with the SOLVER add-in.</p> <p>Hungarian Central Statistical Office served as the data source for population by age group, county, and regions: <a href="https://www.ksh.hu/stadat_files/nep/hu/nep0035.html">https://www.ksh.hu/stadat_files/nep/hu/nep0035.html</a>, (accessed 04 Jan. 2024.) with data recorded in MS Excel in the <em>Data_of_demography.xlsx</em> file.</p> <p>In 2022, 108 Health Development Offices (HDOs) were operational, and it's noteworthy that no developments have occurred in this area since 2022. The availability of these offices and the demographic data from the Central Statistical Office in Hungary are considered public interest data, freely usable for research purposes without requiring permission.</p> <p>The contact details for the Health Development Offices were sourced from the following page (Hungarian National Population Centre (NNK)): <a href="https://www.nnk.gov.hu/index.php/efi">https://www.nnk.gov.hu/index.php/efi</a> (n=107). The Semmelweis University Health Development Centre was not listed by NNK, hence it was separately recorded as the 108th HDO. More information about the office can be found here: <a href="https://semmelweis.hu/egeszsegfejlesztes/en/">https://semmelweis.hu/egeszsegfejlesztes/en/</a> (n=1). (accessed 05 Dec. 2023.)</p> <p>Geocoordinates were determined using Google Maps (N=108): <a href="https://www.google.com/maps">https://www.google.com/maps</a>. (accessed 02 Jan. 2024.) Recording of geocoordinates (latitude and longitude according to WGS 84 standard), address data (postal code, town name, street, and house number), and the name of each HDO was carried out in the: <em>Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv</em> file.</p> <p>The foundational software for geospatial modelling and display (QGIS 3.34), an open-source software, can be downloaded from:</p> <p><a href="https://qgis.org/en/site/forusers/download.html">https://qgis.org/en/site/forusers/download.html</a>. (accessed 04 Jan. 2024.)</p> <p>The HDOs_GeoCoordinates.gpkg QGIS project file contains Hungary's administrative map and the recorded addresses of the HDOs from the</p> <p><em>Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv</em> file,</p> <p>imported via .csv file.</p> <p>The OpenStreetMap tileset is directly accessible from <a href="http://www.openstreetmap.org">www.openstreetmap.org</a> in QGIS. (accessed 04 Jan. 2024.)</p> <p>The Hungarian county administrative boundaries were downloaded from the following website: <a href="https://data2.openstreetmap.hu/hatarok/index.php?admin=6" target="_new">https://data2.openstreetmap.hu/hatarok/index.php?admin=6</a> (accessed 04 Jan. 2024.)</p> <p>HDO_Buffers.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding buffer zones with a radius of 7.5 km.</p> <p>Heatmap.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding heatmap (Kernel Density Estimation).</p> <p>A brief description of the statistical formulas applied is included in the <em>Statistical_formulas.pdf.</em></p> <p>Recording of our base data for statistical concentration and diversification measurement was done using MS Excel 2019 (version: 1808, build: 10406.20006) in .xlsx format.</p> <ul> <li>Aggregated number of HDOs by county: <em>Number_of_HDOs.xlsx</em></li> <li>Standardised data (Number of HDOs per 100,000 residents): <em>Standardized_data.xlsx</em></li> <li>Calculation of the Lorenz curve: <em>Lorenz_curve.xlsx</em></li> <li>Calculation of the Gini index: <em>Gini_Index.xlsx</em></li> <li>Calculation of the LQ index: <em>LQ_Index.xlsx</em></li> <li>Calculation of the Herfindahl-Hirschman Index: <em>Herfindahl_Hirschman_Index.xlsx</em></li> <li>Calculation of the Entropy index: <em>Entropy_Index.xlsx</em></li> <li>Regression and correlation analysis calculation: <em>Regression_correlation.xlsx</em></li> </ul> <p>Using the SPSS 29.0.1.0 program, we performed the following statistical calculations with the databases Data_HDOs_population_without_outliers.sav and Data_HDOs_population.sav:</p> <ul> <li>Regression curve estimation with elderly population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_elderly_without_outlier.spv</li> <li>Pearson correlation table between the total population, elderly population, and number of HDOs per county, excluding outlier values such as Budapest and Pest County: Pearson_Correlation_populations_HDOs_number_without_outliers.spv.</li> <li>Dot diagram including total population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_total_population_without_outliers.spv.</li> <li>Dot diagram including elderly (64<) population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_elderly_population_without_outliers.spv</li> <li>Regression curve estimation with total population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_without_outlier.spv</li> <li>Dot diagram including elderly (64<) population and number of HDOs per county: Dot_HDO_elderly_population.spv</li> <li>Dot diagram including total population and number of HDOs per county: Dot_HDO_total_population.spv</li> <li>Pearson correlation table between the total population, elderly population, and number of HDOs per county: Pearson_Correlation_populations_HDOs_number.spv</li> <li>Regression curve estimation with total population and number of HDOs, (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_total_population.spv</li> </ul> <p>For easier readability, the files have been provided in both SPV and PDF formats.</p> <p>The translation of these supplementary files into English was completed on 23rd Sept. 2024.</p> <p> </p> <p><em>If you have any further questions regarding the dataset, please contact the corresponding author: <a target="_new">domjan.peter@phd.semmelweis.hu</a></em></p> <p> </p>
Data from: Genetic wealth, population health: major histocompatibility complex variation in captive and wild ring-tailed lemurs (Lemur catta)
Across species, diversity at the major histocompatibility complex (MHC) is critical to individual disease resistance and, hence, to population health; however, MHC diversity can be reduced in small, fragmented, or isolated populations. Given the need for comparative studies of functional genetic diversity, we investigated whether MHC diversity differs between populations which are open, that is experiencing gene flow, versus populations which are closed, that is isolated from other populations. Using the endangered ring-tailed lemur (Lemur catta) as a model, we compared two populations under long-term study: a relatively "open," wild population (n = 180) derived from Bezà Mahafaly Special Reserve, Madagascar (2003–2013) and a "closed," captive population (n = 121) derived from the Duke Lemur Center (DLC, 1980–2013) and from the Indianapolis and Cincinnati Zoos (2012). For all animals, we assessed MHC-DRB diversity and, across populations, we compared the number of unique MHC-DRB alleles and their distributions. Wild individuals possessed more MHC-DRB alleles than did captive individuals, and overall, the wild population had more unique MHC-DRB alleles that were more evenly distributed than did the captive population. Despite management efforts to maintain or increase genetic diversity in the DLC population, MHC diversity remained static from 1980 to 2010. Since 2010, however, captive-breeding efforts resulted in the MHC diversity of offspring increasing to a level commensurate with that found in wild individuals. Therefore, loss of genetic diversity in lemurs, owing to small founder populations or reduced gene flow, can be mitigated by managed breeding efforts. Quantifying MHC diversity within individuals and between populations is the necessary first step to identifying potential improvements to captive management and conservation plans.
Data from: Impact of involvement of non-formal health providers on TB case notification among migrant slum-dwelling populations in Odisha, India
Background: Migrant labourers living in the slums of urban and industrial patches across India make up a key sub-population so far controlling Tuberculosis (TB) in the country is concerned. This is because many TB patients from these communities remain under reached by the Revised National Tuberculosis Control Programme (RNTCP) of India. This marginalized community usually seeks early-stage healthcare from "friendly neighbourhood" non-formal health providers (NFHPs). Because, RNTCP has limited capacity to involve the NFHPs, an implementation research project was conceived, whereby an external partner would engage with the NFHPs to enable them to identify early TB symptomatics from this key sub-population who would be then tested using Xpert MTB/RIF technology. Diagnosed TB cases among them would be referred promptly to RNTCP for treatment. This paper aimed to describe the project and its impact. Methods: Adopting a quasi-experimental before-after design, four RNTCP units from two major urban-industrial areas of Odisha were selected for intervention, which spanned five quarters and covered 151,400 people, of which 30% were slum-dwelling migrants. Two similar units comprised the control population. The hypothesis was, reaching the under reached in the intervention area through NFHPs would increase TB notification from these traditionally under-notifying units. RNTCP notification data during intervention was compared with pre-intervention era, adjusted for contemporaneous changes in control population. Results: The project detected 488 Xpert+ TB cases, of whom 466 were administered RNTCP treatment. This translated into notification of additional 198 new bacteriologically positive cases to RNTCP, a 30% notification surge, after adjustment for 2% decline in control. This meant an average quarterly increase in notification of 41.20(20.08, 62.31; p<0.001) cases. The increase was immediate, evident from the rise in level in the time series analysis by 50.42(10.28, 90.55; p=0.02) cases. Conclusion: Engagement with NFHPs contributed to an increase in TB notification to RNTCP from key under reached, slum-dwelling migrant populations.
Impacts of habitat disturbance on population health of Bornean frogs
<b>Description: </b><p>The raw data required for estimates of size, body condition and fluctuating asymmetry of riparian anurans across a series of streams within the SAFE landscape, providing insight into the population health of frog populations at these sites. Streams were selected to provide a habitat disturbance gradient enabling identification of any effect of habitat disturbance on the population health indices. Stream characteristics were measured to provide estimates of habitat disturbance sorrounding the streams (canopy cover) and enable the impact of any potentially confounding variables (stream width and slope) to be eliminated. </p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/205"><b>The impact of habitat modification on feeding interactions between riparian anuran communities and their arthropod prey </b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3485086">here</a></p><p><b>Files: </b>This consists of 1 file: Fluctuating_Asymmetry_and_Body_Condition_of_Anurans_in_the_SAFE_Project.xlsx</p><p><b>Fluctuating_Asymmetry_and_Body_Condition_of_Anurans_in_the_SAFE_Project.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>Fluctuating asymmetry and body condition measurements of Bornean riparian anurans</b> (described in worksheet Frogs)</p><p>Description: Species, snout-vent length, mass and limb measurements of anurans sampled across a tropical forest disturbance gradient and within oil palm plantations. </p><p>Number of fields: 37</p><p>Number of data rows: 240</p><p>Fields: </p><ul><li><b>site</b>: SAFE identification code for the location where sampling was conducted (Field type: location)</li><li><b>date</b>: Date of sampling (Field type: date)</li><li><b>identification_code</b>: Code for the distinction of individual sampled frogs (Site of capture followed by the number of the individual within the overall sample taken within the site) (Field type: id)</li><li><b>species</b>: Identification of sampled frog to species level (Field type: taxa)</li><li><b>sex</b>: Sex of the sampled frog (male, female or unknown) (Field type: categorical trait)</li><li><b>svl</b>: Length of the frog from its snout to its vent (Field type: numeric trait)</li><li><b>mass</b>: Mass of the frog (Field type: numeric trait)</li><li><b>left_ru_1</b>: First measurement of the left radio-ulna of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>left_ru_2</b>: Second measurement of the left radio-ulna of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>left_ru_3</b>: Third measurement of the left radio-ulna of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>left_ru_mean</b>: Average of the three measurements of the left radio-ulna of the frog (NA indicates that frog escaped before all measurements could be completed) (Field type: numeric trait)</li><li><b>right_ru_1</b>: First measurement of the right radio-ulna of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>right_ru_2</b>: Second measurement of the right radio-ulna of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>right_ru_3</b>: Third measurement of the right radio-ulna of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>right_ru_mean</b>: Mean of the three measurements of the right radio-ulna of the frog (NA indicates that frog escaped before all measurements could be completed) (Field type: numeric trait)</li><li><b>mean_ru_size</b>: Average length of both radio-ulnas (NA indicates that frog escaped before all measurements could be completed) (Field type: numeric trait)</li><li><b>ru_diff</b>: Difference between the mean length of the right radio-ulna from all three measurements and that of the left (Field type: numeric trait)</li><li><b>left_thigh_1</b>: First measurement of the left thigh of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>left_thigh_2</b>: Second measurement of the left thigh of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>left_thigh_3</b>: Third measurement of the left thigh of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>left_thigh_mean</b>: Average of the three measurements of the left thigh of the frog (NA indicates that frog escaped before all measurements could be completed) (Field type: numeric trait)</li><li><b>right_thigh_1</b>: First measurement of the right thigh of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>right_thigh_2</b>: Second measurement of the right thigh of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>right_thigh_3</b>: Third measurement of the right thigh of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>right_thigh_mean</b>: Mean of the three measurements of the right thigh of the frog (NA indicates that frog escaped before all measurements could be completed) (Field type: numeric trait)</li><li><b>mean_thigh_size</b>: Average length of both thighs (NA indicates that frog escaped before all measurements could be completed) (Field type: numeric trait)</li><li><b>thigh_diff</b>: Difference between the mean length of the right thigh from all three measurements and that of the left (NA indicates that frog escaped before all measurements could be completed) (Field type: numeric trait)</li><li><b>left_tf_1</b>: First measurement of the left tibio-fibula of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>left_tf_2</b>: Second measurement of the left tibio-fibula of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>left_tf_3</b>: Third measurement of the left tibio-fibula of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>left_tf_mean</b>: Mean of the three measurements of the left tibio-fibula of the frog (NA indicates that frog escaped before all measurements could be completed) (Field type: numeric trait)</li><li><b>right_tf_1</b>: First measurement of the right tibio-fibula of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>right_tf_2</b>: Second measurement of the right tibio-fibula of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>right_tf_</b>: Third measurement of the right tibio-fibula of the frog (NA indicates that frog escaped before measurement could be completed) (Field type: numeric trait)</li><li><b>right_tf_mean</b>: Mean of the three measurements of the right tibio-fibula of the frog (NA indicates that frog escaped before all measurements could be completed) (Field type: numeric trait)</li><li><b>mean_tf_length</b>: Average length of both tibio-fibulas (NA indicates that frog escaped before all measurements could be completed) (Field type: numeric trait)</li><li><b>tf_diff</b>: Difference between the mean length of the right tibio-fibula from all three measurements and that of the left (NA indicates that frog escaped before all measurements could be completed) (Field type: numeric trait)</li></ul></li><li><p><b>Stream morphometrics and riparian canopy cover</b> (described in worksheet Streams)</p><p>Description: The width and slope of, as well as the canopy cover above, the streams along which anurans were sampled.</p><p>Number of fields: 10</p><p>Number of data rows: 260</p><p>Fields: </p><ul><li><b>site</b>: SAFE identification code for the location where sampling was conducted (Field type: location)</li><li><b>date</b>: Date of sampling (Field type: date)</li><li><b>point</b>: 20m interval point along 500m in-stream transect at which measurements were taken (Field type: id)</li><li><b>upstream_%_canopy_cover</b>: Percentage canopy coverage when facing upstream from the centre of the stream at the given sampling point (Field type: numeric)</li><li><b>right_%_canopy</b>: Percentage canopy coverage when facing toward the right bank from the centre of the stream at the given sampling point (Field type: numeric)</li><li><b>downstream_%_canopy_cover</b>: Percentage canopy coverage when facing downstream from the centre of the stream at the given sampling point (Field type: numeric)</li><li><b>left_%_canopy_cover</b>: Percentage canopy coverage when facing toward the left bank from the centre of the stream at the given sampling point (Field type: numeric)</li><li><b>average_cover</b>: Average of the four canopy cover measurements (Field type: numeric)</li><li><b>stream_width</b>: Wetted width of the stream (Field type: numeric)</li><li><b>stream_slope</b>: Slope of the stream (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2019-03-23 to 2019-05-16</p><p><b>Latitudinal extent: </b>4.6025 to 4.7317</p><p><b>Longitudinal extent: </b>117.4556 to 117.6414</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Amphibia <br> -  -  -  -  Anura <br> -  -  -  -  -  Bufonidae <br> -  -  -  -  -  -  <i>Phrynoidis</i> <br> -  -  -  -  -  -  -  <i>Phrynoidis juxtaspera</i> <br> -  -  -  -  -  Dicroglossidae <br> -  -  -  -  -  -  <i>Limnonectes</i> <br> -  -  -  -  -  -  -  <i>Limnonectes finchi</i> <br> -  -  -  -  -  -  -  <i>Limnonectes kuhlii</i> <br> -  -  -  -  -  -  -  <i>Limnonectes leporinus</i> <br> -  -  -  -  -  Rhacophoridae <br> -  -  -  -  -  -  <i>Polypedates</i> <br> -  -  -  -  -  -  -  <i>Polypedates otilophus</i> <br> -  -  -  -  -  Ranidae <br> -  -  -  -  -  -  <i>Chalcorana</i> <br> -  -  -  -  -  -  -  [Chalcorana raniceps] <br> -  -  -  -  -  -  <i>Meristogenys</i> <br> -  -  -  -  -  -  -  [Meristogenys orphnocnemus] <br> -  -  -  -  -  -  <i>Staurois</i> <br> -  -  -  -  -  -  -  <i>Staurois guttatus</i> <br> -  -  -  -  -  -  -  <i>Staurois latopalmatus</i> <br> -  -  -  -  -  Megophryidae <br> -  -  -  -  -  -  <i>Leptolalax</i> <br> -  -  -  -  -  -  -  [Leptolalax friteniens] <br> -  -  -  -  -  -  <i>Leptobrachium</i> <br> -  -  -  -  -  -  -  [Leptobrachium abboti] <br></div><p></p>
Prevalence and Factors Associated with the Use of Traditional Medicine in Individuals with Hypercholesterolemia, Hyperglycaemia, and Arterial Hypertension in Ecuador: Results from a Population-based Study in Two Health Districts
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Animal population data complementing the European Union One Health 2021 Zoonoses Report
<p>This dataset includes animal population aggregated data under the framework of Directive 2003/99/EC.</p>
I Am Able: Population Based Rehabilitation in a Family Health Team
ClinicalTrials.gov study NCT00859638. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Evaluating Public Health Interventions to Improve COVID-19 Testing Among Underserved Populations
ClinicalTrials.gov study NCT05270694. IPD Sharing: Not stated. Countries: 1. Publications: 7.
Addressing Social Determinants of Health & Diabetes Self-Management in Vulnerable Populations
ClinicalTrials.gov study NCT03802825. IPD Sharing: NO. Countries: 1. Publications: 2.
Medication Optimization Using Pharmacogenetic Testing and the G-DIG to Reduce Polypharmacy in a Mental Health Population
ClinicalTrials.gov study NCT03468309. IPD Sharing: NO. Countries: 1. Publications: 1.
Can Mental Health Chatbots Help Chronic Disease Populations?
ClinicalTrials.gov study NCT04620668. IPD Sharing: NO. Countries: 1. Publications: 6.
Evaluation of the Benefits for Overall Health Following Cochlear Implant Treatment in the Elderly Population
ClinicalTrials.gov study NCT03072862. IPD Sharing: NO. Countries: 4. Publications: 2.
Harnessing Mobile Technology to Reduce Mental Health Disorders in College Populations
ClinicalTrials.gov study NCT04162847. IPD Sharing: YES. Countries: 1. Publications: 2.
Data from: Mother's social status is associated with child health in a horticulturalist population
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Data from: A study of impulsivity and adverse childhood experiences in a population health setting
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Data from: Genetic wealth, population health: major histocompatibility complex variation in captive and wild ring-tailed lemurs (Lemur catta)
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Negative associations between parasite avoidance, resistance and tolerance predict host health in salmonid fish populations
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Data from: Impact of involvement of non-formal health providers on TB case notification among migrant slum-dwelling populations in Odisha, India
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Data from: Different ornaments signal male health and MHC variation in two populations of a warbler
Male traits that signal health and vigour are used by females to choose better quality mates, but in some cases the male trait selected by females differs among populations. Multiple male traits can be maintained through female mate choice if both traits are equally honest indicators of male quality, but tests of this prediction are rare. By choosing males based on such traits, females could gain direct benefits from males (assistance with parental care), but when females choose extra-pair mates based on these traits, females gain only male sperm, and potentially indirect genetic benefits for their offspring. In common yellowthroats (Geothylpis trichas), female choice of extra-pair mates targets two different plumage ornaments: the black mask in a Wisconsin population and the yellow bib in a New York population. Previously, we found that the black mask in Wisconsin is related to greater major histocompatibility complex (MHC) class II variation, which in turn signals better survival and disease resistance. In this study, we examined the signalling function of the yellow bib in New York to test whether it signals the same aspects of male quality as the black mask in Wisconsin. As predicted, we found that the yellow bib in New York is most closely associated with MHC variation, which also signals survival and resistance to blood parasites. Thus, the ornament preferred by females differs between the two populations, but the different ornaments signal similar aspects of male health and genetic quality, specifically information regarding MHC variation and potential indirect genetic benefits to females.
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.