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2,667 results for “Prevalence”

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dryad36/100

Data from: Genetic diversity, infection prevalence, and possible transmission routes of Bartonella spp. in vampire bats

Bartonella spp. are globally distributed bacteria that cause endocarditis in humans and domestic animals. Recent work has suggested bats as zoonotic reservoirs of some human Bartonella infections; however, the ecological and spatiotemporal patterns of infection in bats remain largely unknown. Here we studied the genetic diversity, prevalence of infection across seasons and years, individual risk factors, and possible transmission routes of Bartonella in populations of common vampire bats (Desmodus rotundus) in Peru and Belize, for which high infection prevalence has previously been reported. Phylogenetic analysis of the gltA gene for a subset of PCR-positive blood samples revealed sequences that were related to Bartonella described from vampire bats from Mexico, other Neotropical bat species, and streblid bat flies. Sequences associated with vampire bats clustered significantly by country but commonly spanned Central and South America, implying limited spatial structure. Stable and nonzero Bartonella prevalence between years supported endemic transmission in all sites. The odds of Bartonella infection for individual bats was unrelated to the intensity of bat flies ectoparasitism, but nearly all infected bats were infested, which precluded conclusive assessment of support for vector-borne transmission. While metagenomic sequencing found no strong evidence of Bartonella DNA in pooled bat saliva and fecal samples, we detected PCR positivity in individual saliva and feces, suggesting the potential for bacterial transmission through both direct contact (i.e., biting) and environmental (i.e., fecal) exposures. Further investigating the relative contributions of direct contact, environmental, and vector-borne transmission for bat Bartonella is an important next step to predict infection dynamics within bats and the risks of human and livestock exposures.

opencc-zeroDec 2017View details →
zenodo36/100

Supporting data: Prevalence of sexual dimorphism in mammalian phenotypic traits

<p>Supporting material for the manuscript <strong>“</strong><strong>Prevalence of sexual dimorphism in mammalian phenotypic traits”</strong> .  This manuscript was published in Nature Communication 26th January 2017</p>

opencc-by-4.0Oct 2016View details →
zenodo36/100

Custom database for coronavirus prevalence analysis

<p>These coronavirus genomes in this dataset were obtained from the NCBI Refseq and are contained in the custom database utilised for both pan-coronavirus primer design, and homology search resulting in Figure 4c of the associated manuscript.</p>

opencc-by-4.0Nov 2023View details →
dryad36/100

A meta-analysis exploring associations between habitat degradation and Neotropical bat virus prevalence and seroprevalence

<p>Habitat degradation can increase zoonotic disease risks by altering infection dynamics in wildlife and increasing wildlife–human interactions. Bats are an important taxonomic group to consider these effects, because they harbour many relevant zoonotic viruses and have species- and context-dependent responses to degradation that could affect zoonotic virus dynamics. Yet our understanding of the associations between habitat degradation and bat virus prevalence and seroprevalence are limited to a small number of studies, which often differ in the bats or viruses sampled, the study region, and methodology. To develop a broad understanding of the associations between bat viruses and habitat degradation, we conducted an initial phylogenetic meta-analysis that combines published prevalence and seroprevalence ("(sero)prevalence") with remote-sensing habitat degradation data. Our dataset includes 588 unique records of (sero)prevalence across 16 studies, 64 bat species, and five virus families. We quantified the overall strength and direction of the relationship between habitat degradation and bat virus outcomes and tested how this relationship is moderated by the time between habitat degradation and bat sampling and by ecological traits of bat hosts while controlling for phylogenetic nonindependence among bat species. We found no effect of degradation on prevalence overall, although a weak effect may exist when forest loss occurs the year prior to bat sampling. In contrast, we detected a negative but weak association between degradation and seroprevalence overall that was strengthened when forest loss occurred the year prior to bat sampling. No bat traits that we investigated interacted with habitat degradation to impact virus outcomes, suggesting observed trends are independent of these traits. Biases in our initial dataset highlight opportunities for future work; prevalence was highly zero-inflated, and seroprevalence was dominated by <em>Desmodus rotundus</em> and rabies virus. These findings and subsequent analyses will improve our understanding of how global change affects host–pathogen dynamics.</p>

opencc-zeroDec 2023View details →
zenodo36/100

Sample based prevalence data complementing the European Union One Health 2022 Zoonoses Report - the United Kingdom (Northern Ireland)

<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>

opencc-by-4.0Dec 2023View details →
zenodo36/100

Sample based prevalence data complementing the European Union One Health 2022 Zoonoses Report - Croatia

<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011,&nbsp;Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>

opencc-by-4.0Dec 2023View details →
zenodo36/100

Sample based prevalence data complementing the European Union One Health 2022 Zoonoses Report - Ireland

<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>

opencc-by-4.0Dec 2023View details →
zenodo36/100

Sample based prevalence data complementing the European Union One Health 2022 Zoonoses Report - Sweden

<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>

opencc-by-4.0Dec 2023View details →
zenodo36/100

Sample based prevalence data complementing the European Union One Health 2022 Zoonoses Report - Norway

<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>

opencc-by-4.0Dec 2023View details →
zenodo36/100

Sample based prevalence data complementing the European Union One Health 2022 Zoonoses Report - Luxembourg

<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>

opencc-by-4.0Dec 2023View details →
zenodo36/100

Sample based prevalence data complementing the European Union One Health 2022 Zoonoses Report - Finland

<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>

opencc-by-4.0Dec 2023View details →
zenodo36/100

Prevalence data complementing the European Union One Health 2022 Zoonoses Report

<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation is: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>

opencc-by-4.0Dec 2023View details →
dryad36/100

Prevalence and characteristics of long COVID-19 in Jordan: A cross sectional survey

<p>Early in the pandemic, the spread of the emerging virus SARS-CoV-2 was causing mild illness lasting less than two weeks for most people, with a small proportion of people developing serious illness or death. However, as the pandemic progressed, many people reported suffering from symptoms for weeks or months after their initial infection. Persistence of COVID-19 symptoms beyond one month, or what is known as long COVID-19, is recognized as a risk of acute infection. Up to date, information on long COVID-19 among Jordanian patients has not been reported. Therefore, we sought to conduct this cross-sectional study utilizing a self-administered survey. The survey asks a series of questions regarding participant demographics, long COVID-19 symptoms, information about pre-existing medical history, supplements, vaccination history, and symptoms recorded after vaccination.  Chi square analysis was conducted on 990 responders, and the results showed a significant correlation (P&lt;0.05) between long COVID-19 syndrome and age, obesity, chronic illness, vitamin D intake, number of times infected by COVID-19, number of COVID-19 symptoms and whether the infection was pre or post vaccination. The long-term symptoms most enriched in those with long COVID-19 were tinnitus (73.4%), concentration problems (68.6%) and muscle and joint ache (68.3%). A binomial logistic regression analysis was done to explore the predictors of long COVID-19 and found that age 18-45, marital status, vitamin D, number of COVID-19 symptoms and signs after vaccination are positive predictors of long COVID-19, while zinc intake is a negative predictor. Although further studies on long-term persistence of symptoms are needed, the present study provides a baseline that allows us to understand the frequency and nature of long COVID-19 among Jordanians.</p>

opencc-zeroDec 2023View details →
zenodo36/100

Temporal increase in ticks and pathogen prevalence in the small mammal part of the Lyme disease cycle in northern Europe

<p>Data and Scripts for our research article "Temporal increase in ticks and pathogen prevalence in the small mammal part of the enzootic Lyme disease cycle in northern Europe"</p>

opencc-by-4.0Feb 2024View details →
zenodo36/100

Prevalence of Listeria monocytogenes and other Listeria species in fish, fish products and fish processing environment: A systematic review and meta-analysis

<p>Dane wykorzytsane w publikajci "Prevalence of <em>Listeria monocytogenes</em> and other <em>Listeria</em> species in fish, fish products and fish processing environment: A systematic review and meta-analysis" . Dane obejmują wyniki surowe z baz artykuł&oacute;w, dane wykorzystane do wykonania metaanalizy oraz wyniki surowe uzyskane po przeprowadzaniu metaanalizy</p>

opencc-by-4.0Jan 2024View details →
zenodo36/100

Figure 1. A in Prevalence and density of Demodex mites (Acari: Demodecidae) in patients with seborrheic dermatitis

Figure 1. A. Demodex spp. (at 40x magnification), B. D. folliculorum (at 40x magnification.

opencc-by-4.0Jul 2022View details →
zenodo36/100

Lack of shared neoantigens in prevalent mutations in cancer

<p><span>Tumors are mostly characterized by genetic instability, as result of mutations in surveillance mechanisms, such as DNA damage checkpoint, DNA repair machinery and mitotic checkpoint. Defect in one or more of these mechanisms causes additive accumulation of mutations. Some of these mutations are drivers of transformation and are positively selected during the evolution of the cancer, giving a growth advantage on the cancer cells. If such mutations would result in mutated neoantigens, these could be actionable targets for cancer vaccines and/or adoptive cell therapies. However, the results of the present analysis show, for the first time, that the most prevalent mutations identified in human cancers do not express mutated neoantigens. The hypothesis is that this is the result of the selection operated by the immune system in the very early stages of tumor development. At that stage, the tumor cells characterized by mutations giving rise to highly antigenic non-self mutated neoantigens would be efficiently targeted and eliminated. Consequently, the outgrowing tumor cells cannot be controlled by the immune system, with an ultimate growth advantage to form large tumors embedded in an immunosuppressive tumor microenvironment (TME). The outcome of such a negative selection operated by the immune system is that the development of off-the-shelf vaccines, based on shared mutated neoantigens, does not seem to be at hand.</span></p>

opencc-by-4.0Dec 2023View details →
dryad36/100

Data from: Effects of microclimate on disease prevalence across an urbanization gradient

<p>Increased temperatures associated with urbanization (the "urban heat island" effect) have been shown to impact a wide range of traits across diverse taxa. At the same time, climatic conditions vary at fine spatial scales within habitats due to factors including shade from shrubs, trees, and built structures. Patches of shade may function as microclimate refugia that allow species to occur in habitats where high temperatures and/or exposure to ultraviolet radiation would otherwise be prohibitive. However, the importance of shaded microhabitats for interactions between species across urbanized landscapes remains poorly understood. Weedy plants and their foliar pathogens are a tractable system for studying how multiple scales of climatic variation influence infection prevalence. Powdery mildew pathogens are particularly well suited to this work, as these fungi can be visibly diagnosed on leaf surfaces. We studied the effects of shaded microclimates on rates of powdery mildew infection on <em>Plantago</em> host species in (1) "pandemic pivot" surveys in which undergraduate students recorded shade and infection status of thousands of plants along road verges in urban and suburban residential neighborhoods, (2) monthly surveys of plant populations in 22 parks along an urbanization gradient, and (3) a manipulative field experiment directly testing effects of shade on growth and transmission of powdery mildew. Together, our field survey results show strong positive effects of shade on mildew infection in wild <em>Plantago</em> populations across urban, suburban, and rural habitats. Our experiment suggests that this relationship is causal, where microclimate conditions associated with shade promote pathogen growth. Overall, infection prevalence increased with urbanization despite a negative association between urbanization and tree cover at the landscape scale. These findings highlight the importance of taking microclimate heterogeneity into account when establishing links between macroclimate or land use context and the prevalence of disease.</p>

opencc-zeroMar 2024View details →
dryad36/100

Data from: Variation in prevalence and intensity of macroparasites in moose and their interactions with winter tick load in eastern Canada

<p>Wild animals are infected with a large diversity and abundance of parasites that can affect their behavior, growth, body condition, and ultimately their survival. Although the adverse effects of parasites and the mechanisms involved in the interactions between a host and its parasites are generally well studied, much less is known about the additive or synergistic effects of multiple parasite species on a host. Moose populations in eastern Canada are infected by several species of endoparasites. In the last decades, the intensity of infestations by winter ticks, an ectoparasite, on moose have increased as a result of increased moose densities and favorable weather conditions that benefit winter tick survival. We aimed to document the diversity, intensity, prevalence, and distribution of different parasite species of moose in southern Quebec, Canada. We then evaluated the potential interaction between winter tick and endoparasites of moose, and we evaluated the effect of the simultaneous presence of ticks and endoparasites on moose body condition. To do so, we collected organs to identify and count endoparasite species, estimate winter tick abundance, and measure subcutaneous fat thickness from 174 hunted moose in fall 2019 in 8 regions of Quebec. Our results showed that the prevalence and intensity of winter tick and gastrointestinal parasites differed among regions, as well as the prevalence of the heart parasite <em>Taenia krabbei</em> and the intensity of lung parasite <em>Echinoccocus granulosus</em>. Moose body condition, however, was not influenced by the simultaneous presence of winter tick and endoparasites. The documentation of the interactive effects of multiple parasite species on a host is fundamental given that future environmental conditions in temperate climate will favor the reproduction, development, and survival of several parasite species, which could affect parasite diversity and abundance in the environment and modify host-parasite dynamics.</p>

opencc-zeroMar 2024View details →
zenodo36/100

Data from: "Cross-realm transferability of species distribution models – species characteristics and prevalence matter more than modelling methods applied"

<h2>Abstract</h2> <p>This data contains occurrence observations (presence-absence) of 11 aquatic macrophytes from Bothnian Sea and Lake Puruvesi, and environmental covariates used to build species distribution models (SDMs) in &nbsp;paper "Cross-realm transferability of species distribution models &ndash; species characteristics and prevalence matter more than modelling methods applied" in Ecological Modelling.</p> <p>In addition to data files, also R code for fitting the SDMs is supplied, as is the R code to replicate the analysis conducted in the paper. The data is stored in rdata format (point data), without coordinate information due to data policy restrictions. The species in the data are <em>Iso&euml;tes lacustris, Iso&euml;tes echinospora, Ranunculus reptans, Ranunculus schmalhausenii, Potamogeton berchtoldii, Potamogeton perfoliatus, Potamogeton gramineus, Myriophyllum alterniflorum,&nbsp;Equisetum fluviatile, Eleocharis acicularis and Elodea canadensis.&nbsp;</em>The environmental covariates are bottom water salinity, turbidity, sandy substrate occurrence, colored dissolved organic matter (CDOM), surface fetch, sampling depth, total nitrogen and total phosphorus, and distance to closest&nbsp;<em>Phragmites australis </em>reed bed.&nbsp;</p> <h2>Objective of the study&nbsp;</h2> <p>The modelling objective of the paper was species distribution model (SDM) transferability assesment. Transferability was assessed using models built in marine areas in projecting the distributions of the target species in Lake Puruvesi, Saimaa, Eastern Finland. Macrophyte mapping data from Lake Puruvesi was used as independent test data, against which transferability of the models was assessed.</p> <h2>Location</h2> <p>The species data was collected from two geographic areas: Bothnian Bay (Baltic Sea) and Lake Puruvesi (Eastern Finland). The marine observations from Bothnian Bay were split into three overlapping areas (areas 1-3), to test the effect of input data gradient length to SDM transferability. The largest marine sampling area (Area 3) ranged from 62.95, 65.91 latitude and 19.14, 27.99 longitude. Area 2 ranged from 63.95, 65.91 latitude and 21.53, 27.99 longitude.&nbsp;Area 1 ranged from 64.91, 65.91 latitude and 23.82, 27.99 longitude. The Hummonselk&auml; subbasin of Lake Puruvesi, where the macrophyte test data was collected, is located at 61.89, 62.05 latitude and 29.58, 29.78 longitude.</p> <h2>Species data&nbsp;</h2> <p>The species observations were collected using diving transects placed in the floor of the sea or lake, and species observations were recorded in 2 m22 grid cells separated by 10 meters along the transect or 1 meters depth, depending which criteria was met first. The species data was collected in 2010 - 2020 from marine area, and 2017 from Lake Puruvesi. All macrophytes in 2 x 1 m frames were identified to species level by the diver, and the data contained information on species presence or absence in each grid cell. The diving transects were conducted using systematic survey protocol used in the underwater inventories of the Finnish Underwater Biodiversity Survey Program (VELMU) (Frosblom &amp; Virtanen et al. 2024). The locations of the diving transects were not randomly distributed, but were placed using expert judgement. As all our study species are macroscopic and relatively easily identifiable in the field (with the exception of possibility of mixing <em>I. echinospora</em>&nbsp;and&nbsp;<em>I. lacustris</em>), we consider the absences in our observation data to indicate true absences. That said, as the observation area is rather small (2 m2), it is possible that a species may be found in the site of investigation (e.g. a small lagoon) but be located outside the vegetation sampling grid.</p> <h2>Environmental data</h2> <h3>Bottom water salinity</h3> <p>The seasonal mean bottom water salinity was modeled using a generalized additive model with mean salinity as response, with log-link and gamma distribution for the errors. This was necessary to keep the resulting predictions positive. Bottom depth, CDOM, river influence and spatial location were used as predictors. Data from 448 locations were used and each location had a minimum of three observations. The model was validated using 30 % of the data left outside of the model fitting. The explained deviance of the model was 0.94 and the correlation between raw data and predicted values was 0.95 with few outliers.</p> <h3>Turbidity</h3> <p>Maps of turbidity (in FNU, Formazin Nephelometric Unit) were generated from Sentinel-2 Multi-Spectral Imager (MSI) observations using the Case-2 Regional Coast Colour (C2RCC) bio-optical inversion model, containing separate atmospheric correction and water quality parts. Before computing C2RCC, the original 10-meter input data was downsampled to 60 meters. The output variable of the C2RCC processor correlative to turbidity is the backscattering of total suspended sediments at 443 nm, which was further calibrated into turbidity (FNU) values using SYKE's empirical equations for coastal waters and clear lakes (for a similar approach, see&nbsp;Attila et al. (2013)&nbsp;and&nbsp;Sagerman, Hansen, and Wikstr&ouml;m (2020)). Monthly observations of turbidity were aggregated into median composites to reduce the effects of cloud cover and other disturbances. Due to low solar elevation and ice cover in winter, the turbidity distribution maps are generated only for the summer months (May to September). The current processing covers years 2017 to 2021. An average raster layer was created from monthly observations as input for SDM building.</p> <h3>Probability of sandy substrate&nbsp;</h3> <p>Random forest model was used to classify sandy bottoms from Sentinel 2 MSI satellite images in shallow water areas. Identifying sandy substrate is based on the higher reflectance compared to other substrates. The model was trained and validated using diver recorded field observations in the Baltic area, and diver recorded and echo sounding observations in the freshwater area. For full coverage including areas beyond the shallow water, the satellite image classification was combined with boosted regression tree modelling result in the Baltic, and echo sounding based product in the freshwater region. The resulting layers were probabilities of sandy substrate with 10-meter cell resolution.</p> <h3>CDOM</h3> <p>We used different methods to estimate the CDOM levels in Bothnian Bay and Lake Puruvesi, based on biogeochemical model data and satellite images. For Lake Puruvesi, we applied the Finnish Environment Institute's (Syke) in-house CDOM algorithm to the Sentinel-2 MSI images processed by the C2RCC bio-optical processor&nbsp;(Brockmann et al. 2016). The observations in 10 m resolution were aggregated as monthly averages for each month of the summer season (May to October) from 2017 to 2021. For Bothnian Bay, we used Syke's in-house Sentinel-2 MSI CDOM layers (resolution: 60 m) aggregated as seasonal averages (1 Jul to 7 Sep). CDOM values are given as absorption coefficient of CDOM at 400 nm [m⁻&sup1;].</p> <h3>Surface fetch</h3> <p>A surface fetch raster was produced to the Puruvesi and Bothnian Bay. The analysis required a feature layer of shorelines from Puruvesi and Baltic Sea. First, we created polyline from north to south spanning over the whole area of interest with a gap of 20 meters which is also the resolution of the output raster. These lines were then cut each time they hit the shoreline and the part of the line that was overlapping land was removed. The distance of the remaining lines was then calculated and a point with the distance value was created every 20 meters. Each time the line was cut when hitting an island for example and starting again from the other side of the island, the distance calculation started from 0. This created a point dataset with a distance value in each point. We repeated the procedure for 15 times for different compass directions with 22.5 degree intervals and calculated average fetch for each point location on 20 meters grid from these 15 point layers.</p> <h3>Depth</h3> <p>Depth was measured by a diver using a dive computer while surveying each vegetation grid cell, and measured depth was used when projecting model results to Puruvesi (transferability performance). In addition, a depth model for the freshwater region was created from Sentinel 2 MSI satellite image using the logarithmic band ratio model of blue and red band. The model was calibrated using diver recorded field observations and validated against echo sounding measurements. For more complete coverage and to include deep areas, echo soundings from multiple sources were combined with the satellite derived bathymetry. The cell resolution of the resulting depth layer was 10 meters.</p> <h3>Total nitrogen and phosphorus</h3> <p>Mean total nitrogen and phosphorus layers for marine area were produced using ArcGIS "splines with barriers" tool for the EEZ of Finland with 20 meters spatial resolution&nbsp;(Virtanen et al. 2018). Summer (July - September) nutrient measurements from 0 to 10 meters depth between 2010 and 2020, obtained from the VESLA database, were used as input data for the interpolation.</p> <p>Nitrogen and phosphorus measurements in Puruvesi between 2010 and 2020 was gathered from the VESLA database. Data from July to September was selected to represent the growing season. A mean value of NTOT and PTOT was then calculated for each location. Spline with Barriers (SwB) tool was used to interpolate the values (Arcmap 10.7.1). The tool uses a feature layer as barrier to create the raster representing only the area of interest. For the barrier and the extent of the interpolated raster we used a shapefile representing Lake Puruvesi shoreline. The resolution was set to 5x5 meters. SwB tool created an "extent box" around the area of interest which was removed with Extract by Mask tool using the shoreline feature layer. After the interpolation we noticed that either one of the locations was situated on land or the polygon used as barrier was "leaking". SwB doesn&acute;t interpolate areas that doesn't have locations with values or aren&acute;t connected to the main body of water. To fix this, the raster was extended outwards based on the values of nearby cells and after that the raster was masked again to remove any cells on land. The phosphorus interpolation provided negative values in southern parts on Enanlahti in Kontiolahti and Muholanlahti. These negative values were caused by considerably larger phosphorus values in Enanlahti Lamminniemi (9m) Enanlahti Lamminniemi (4m) locations when compared with the nearby Puruvesi Enanlahti location. The interpolation apparently continued to decrease the values according to the trend set by the difference between these locations and caused it to reach negative values. The southern parts of the bay, about 750 meters, was removed and new values were calculated based on the surrounding cells with Focal Statistics tool. The interpolations were validated by removing 20 % of the locations and reproducing the interpolation. The removed locations and their values were then compared to the interpolated raster. R&lowast;2&lowast;2 value from phosphorus interpolation model was 0.91 after removing two outliers and R22 value from nitrogen interpolation model was 0.715 after removing one outlier.</p> <h3>Distance to closest reed&nbsp;</h3> <p>The aquatic vegetation (<em>Phragmites australis</em>&nbsp;reeds) presence/absence maps were also generated from Sentinel-2 MSI data. The processing included extracting one month of data (July 2019) from green and near-infra-red bands from Sentinel-2 Global Mosaic (S2GM) service and transforming those to normalized-difference vegetation indices (NDVIs). After that, Bayesian statistics were used to predict the posterior probability of vegetation occurrence when distance from shore and NDVI were used as predictor variables. The posterior variable was thresholded and the resulting vegetation presence areas were sieved so that both too small vegetation areas (fewer than 5 pixels) or areas that were not directly attached to shoreline were removed. The resulting map has 10 m pixel size and tentatively represents the locations of reed belts or other shoreline-attached vegetation. This EO-based layer could also be referred to as helophytes or helophytic macrophytes, as it denotes a specific zone of vegetation with emergent aquatic plants containing leaf-green, particularly those that grow densely and have horizontally oriented leaves. In some lakes, this layer can represent, for example, thick stands of&nbsp;<em>Equisetum fluviatile</em>, although in most cases, it is associated with common reed belts. The approach is described in more detail in&nbsp;Koponen et al. (2022).</p> <h2>Data partitioning&nbsp;</h2> <p>Data was partitioned with 70/30 splitting into training and test (interpolation accuracy) data. The splitting was repeated 100 times for each species by randomly selecting 70 % of observations which were used to build each of the SDMs (GLM, GAM, BRT and BART). The partitioning was repeated for each of the three input data areas and 11 species. The input data indexes for replicating the split are supplied in the data files.&nbsp;</p> <h2>R code&nbsp;</h2> <p>Code files contain scripts for fitting the SDM models described in the paper using the data. Also code for calculating calidation statistics (modelling results) and code for statistical analyses for making inferences in the paper, are supplied.&nbsp;</p> <h2>Additional info</h2> <p>More details on modelling protocol may be found in the original paper in Ecological Modelling, and in the Supplementary Information file of the original paper, which follows the model reporting template "ODMAP" by Zurell et al. (2020).</p> <h2>References&nbsp;</h2> <p><span>Attila, J., et al. 2013. MERIS Case II water processor comparison on coastal sites of the northern Baltic Sea. - Remote Sensing of Environment 128: 138-149.</span></p> <p><span>Brockmann, C., et al. 2016. Evolution of the C2RCC neural network for Sentinel 2 and 3 for the retrieval of ocean colour products in normal and extreme optically complex waters. - In: Living Planet Symposium. p. 54.</span></p> <p><span>Forsblom, L., et al. 2024. Finnish inventory data of underwater marine biodiversity<span>&nbsp;&nbsp; </span>-Scientic data</span></p> <p><span>Koponen, S., et al. 2022. Blue Carbon Habitats: &ndash; a comprehensive mapping of Nordic salt marshes for estimating Blue Carbon storage potential. - Nordisk Ministerr&aring;d.</span></p> <p><span>Sagerman, J., et al. 2020. Effects of boat traffic and mooring infrastructure on aquatic vegetation: A systematic review and meta-analysis. - Ambio 49: 517-530.</span></p> <p><span>Zurell, D., et al. 2020. A standard protocol for reporting species distribution models. - Ecography 43: 1261-1277.</span></p> <p></p> <p>&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p>

opencc-by-4.0Nov 2024View details →

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record