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4,243 results for “seasonality”
Fig. 5 in Seasonal and longitudinal variation in fish assemblage structure along an unregulated stretch of the Middle Uruguay River
Fig. 5. Detrended Correspondence Analysis (DCA) applied to ordinate samples according to variations in fish composition and abundance along the Uruguay River. Rectangles depict groups confirmed by a Multiple Response Permutation Procedure (Tab. 2). Sites: S1 = upstream; S6 = downstream. Seasons: Au= Autumn; Sp= Spring; Su= Summer and Wi= Winter.
Fig. 2 in Seasonal and longitudinal variation in fish assemblage structure along an unregulated stretch of the Middle Uruguay River
Fig. 2. Variation (mean ±standard deviation) in species richness and biomass (CPUEb/100m2) along the river channel (A and C) and among seasons (B and D), in the Middle Uruguay River. Sites: S1 = upstream; S6 = downstream. Different letters indicate statistical difference (p <0.05).
GGCMI Phase 2 masks and growing season input data
<p>Growing season data for crops as supplied to modelers in the GGCMI Phase 2 experiment (Franke et al. 2020). Other than for wheat, which is split in spring wheat and winter wheat in Phase 2, the growing season input data is the same as in Phase 1 (Elliott et al. 2015).</p> <p>A boolean mask on what regions can be excluded from the simulations, modeling all crops and irrigation systems everywhere otherwise.</p> <p>A mask assigning harvested wheat areas to winter or spring wheat.</p> <p> </p> <p>References:</p> <p>Franke J, Müller C, Elliott J, Ruane AC, Jagermeyr J, Balkovic J, Ciais P, Dury M, Falloon P, Folberth C, Francois L, Hank T, Hoffmann M, Izaurralde RC, Jacquemin I, Jones C, Khabarov N, Koch M, Li M, Liu W, Olin S, Phillips M, Pugh TAM, Reddy A, Wang X, Williams K, Zabel F, and Moyer E. 2020, The GGCMI Phase II experiment: global gridded crop model simulations under uniform changes in CO2, temperature, water, and nitrogen levels (protocol version 1.0), Geosci. Model Dev. Discuss., 2019, 1-30, doi: <a href="http://dx.doi.org/10.5194/gmd-2019-237">10.5194/gmd-2019-237</a></p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:<a href="http://dx.doi.org/10.5194/gmd-8-261-2015">10.5194/gmd-8-261-2015</a>.</p>
Figure 3 in Seasonal changes in the population structure of dominant planktonic copepods collected using a sediment trap moored in the western Arctic Ocean
Figure 3. Seasonal changes in sea ice concentration, surface chl. a (from satellite) and total mass flux (a), and daylight hours (b) at St. NAPt from October 2010 to September 2012.
Figure 8 in Seasonal changes in the population structure of dominant planktonic copepods collected using a sediment trap moored in the western Arctic Ocean
Figure 8. Seasonal changes in sea ice concentration, daylight hours, chl. a, and total mass flux from January to December (upper panel). The ecological characteristics of the five dominant copepods (lower panel). The open and solid bars indicate the high abundance and reproductive periods for each species, respectively.
Figure 2. Summer core area delineation. The straight line with a in Demographic characteristics, seasonal range and habitat topography of Balkan chamois population in its southernmost limit of its distribution (Giona mountain, Greece)
Figure 2. Summer core area delineation. The straight line with a slope of –1 represents the random use of space within the population seasonal range. The curve that sags below the line of random use represents the clumped use of space. The summer core area can be defined at the point whose tangent has slope –1, e.g. 85%, that is, whose tangent is parallel to the line of random use. This is also the point of the curve that is furthest from the line of random use.
Landsat-based maps of irrigated dry season cropping in Southeastern Anatolia, Turkey
<p><strong>Landsat-based maps of irrigated dry-season cropping in Southeastern Anatolia, Turkey</strong></p> <p>Long-term monitoring of the extent and intensity of irrigation systems is needed to track crop water consumption and to optimize land use in a changing climate. We mapped the expansion and land use intensity of irrigated dry season cropping in Turkey´s Southeastern Anatolia Project annually from 1990 to 2018 using Landsat time series and Google Earth Engine.</p> <p>This dataset includes multiple maps documenting the expansion and land use intensity of irrigated dry season cropping in Turkey´s largest irrigation scheme. We aggregated all Landsat imagery acquired during the July through September for the period 1990 to 2018 into spectral-temporal metrics and predicted dry season cropping annually using a machine learning classifier. We performed several post-processing steps to derive multiple map products for all areas with at least two dry season cropping cycles in the study period. The dataset comes in .zip format and includes the following map products:</p> <ul> <li>gap_dsc_fst.tif: first year of dry season cropping</li> <li>gap_dsc_yrs.tif: number of years with dry season cropping</li> <li>gap_dsc_frq.tif: dry season cropping frequency (% of years since first dry season cultivation):</li> <li>gap_dsc_trd.tif: five-year dry season cropping frequency trend magnitude</li> <li>gap_dsc_pvl.tif: significance level (p-values)</li> </ul> <p><strong>Spatial coverage</strong><br> The maps come in 30m spatial resolution and cover the Southeastern Anatolia Project (Güneydoğu Anadolu Projesi, GAP) region. The region consists of nine provinces which account for approximately 10% of the Turkish land area. </p> <p><strong>Temporal coverage</strong><br> The analyses cover the period 1990-2018. The first year of dry season cropping and the number of years with dry season cropping represent the time period 1990-2017. The temporal coverage of dry season cropping frequency varies on a pixel level, depending on the initial year of dry-season cultivation. The temporal coverage of the trend indicators also vary on a pixel level and furthermore have a constrained maximum temporal coverage of 1992-2012 due to the post-processing steps involved.</p> <p><strong>Data format</strong><br> The data are delivered as 16bit single layer GeoTIFFs in EPSG:3035 projection. The images are LZW compressed, and have NoData value 0.</p> <p><strong>Publication & further information</strong><br> Please see the publication for further information on the methodology and accuracy of the map products:</p> <p>Rufin, P.; Müller, D.; Schwieder, M.; Pflugmacher, D.; Hostert, P. (2020): Landsat time series reveal simultaneous expansion and intensification of irrigated dry season cropping in Southeastern Turkey. <em>Journal of Land Use Science. </em>DOI: http://dx.doi.org/10.1080/1747423X.2020.1858198</p> <p><strong>Acknowledgments</strong><br> This research contributes to the Landsat Science Team 2018-2023 (http://www.usgs.gov/land-resources/nli/landsat/landsat-science-teams) and the Global Land Programme (https://glp.earth/). We gratefully acknowledge the open cloud processing platform provided by Google. </p>
Seasonal Effects in Gastrointestinal Parasite Prevalence, Richness and Intensity in Vervet Monkeys Living in a Semi-Arid Environment
<p>Data and R Notebook for Seasonal Effects in Gastrointestinal Parasite Prevalence, Richness and Intensity in Vervet Monkeys Living in a Semi-Arid Environment</p>
Dataset for "Topography-based statistical modelling reveals high spatial variability and seasonal emission patches in forest floor methane flux"
<p>This dataset provides measured and upscaled forest floor methane (CH4) fluxes and soil moisture.</p> <p>This dataset is related to the following manuscript:</p> <p>Vainio et al., Topography-based statistical modelling reveals high spatial variability and seasonal emission patches in forest floor methane flux, Biogeosciences, in review. (The discussion preprint is available at https://doi.org/10.5194/bg-2020-263.)</p>
Electron density and altitude of the main ionospheric peak of Mars as observed by Mars Express instruments. Archived data for the paper "Seasonal and geographical variability of the Martian ionosphere from Mars Express observations", submitted to JGR-Planets
<p>This repository contains archived data for the manuscript "Seasonal and geographical variability of the Martian ionosphere from Mars Express observations", published in Journal of Geophysical Research-Planets. Details about the methods to generate the data can be found in the paper.</p> <p>5 data files plus 2 readme text files are included.</p> <p>The file MEx_ionpeak.dat (described in the readme file README_ionpeak.txt) contains the peak electron densities and peak altitudes resulting from 34539 observations. Each record includes 14 columns. The content of each column is:</p> <p>Column 1: Instrument providing the observation (MARSIS or MaRS)<br> Column 2: Mars Year at which the observation was obtained (from MY27 to MY33)<br> Column 3: Solar Longitude (Ls) of the observation (unit: degrees)<br> Column 4: Latitude of the observation (unit: degrees)<br> Column 5: Longitude of the observation (unit: degrees)<br> Column 6: Solar Zenith Angle (SZA) of the observation (unit: degrees)<br> Column 7: F10.7 solar proxy index at 1 Astronomic Unit (unit: solar flux units)<br> Column 8: Peak electron density measured by the instrument (unit: cm-3)<br> Column 9: Peak electron density at the subsolar point, i.e., corrected for the SZA variation (unit: cm-3)<br> Column 10: Peak electron density at the subsolar point and at F10.7 (1AU)=100, i.e., corrected for the SZA and the solar radiation output variations (unit: cm-3)<br> Column 11: Peak electron density at the subsolar point, at F10.7 (1AU)=100 and corrected for the seasonal variation (unit: cm-3)<br> Column 12: Peak altitude measured by the instrument (unit: km)<br> Column 13: Peak altitude at the subsolar point, i.e. corrected for the SZA variation (unit: km)<br> Column 14: Peak altitude at the subsolar point and corrected for the seasonal variation (unit: km)</p> <p> </p> <p>The files eprofiles_MaRS.dat, eprofiles_MARSIS_prof1.dat, eprofiles_MARSIS_prof2.dat and eprofiles_MARSIS_prof3.dat contain 4 electron density profiles. They are described in the file README_eprofiles.txt. Each file includes 2 columns, the first one being the altitude (unit: km) and the second one the electron density (unit: cm-3).</p> <p> </p> <p>Contact: Francisco Gonzalez-Galindo, ggalindo@iaa.es<br> </p>
Seasonal Precipitation and Temperature Data in Canberra, Australia
<p>This dataset contains the precipitation, mean maximum temperature and mean minimum temperature data used in the study Application of Machine Learning to Attribution and Prediction of Seasonal Precipitation and Temperature Trends in Canberra, Australia. This data was originally from the Australian Bureau of Meteorology Climate Data Online (http://www.bom.gov.au/climate/data/index.shtml), but has been updated to have missing values (1% of data) filled using a moving average centred on the year for which the data is missing. <br> <br> Below is the abstract for the paper.</p> <p>Southeast Australia is frequently impacted by drought, requiring monitoring of how the various factors influencing drought change over time. Precipitation and temperature trends were analysed for Canberra, Australia, revealing decreasing autumn precipitation. However, annual precipitation remains stable as summer precipitation increased and the other seasons show no trend. Further, mean temperature increases in all seasons. These results suggest that Canberra is increasingly vulnerable to drought. Wavelet analysis suggests that the El-Niño Southern Oscillation (ENSO) influences precipitation and temperature in Canberra, although its impact on precipitation has decreased since the 2000s. Linear regression (LR) and support vector regression (SVR) were applied to attribute climate drivers of annual precipitation and mean maximum temperature (TMax). Important attributes of precipitation include ENSO, the southern annular mode (SAM), Indian Ocean Dipole (DMI) and Tasman Sea SST anomalies. Drivers of TMax included DMI and global warming attributes. The SVR models achieved high correlations of 0.737 and 0.531 on prediction of precipitation and TMax, respectively, outperforming the LR models which obtained correlations of 0.516 and 0.415 for prediction of precipitation and TMax on the testing data. This highlights the importance of continued research utilising machine learning methods for prediction of atmospheric variables and weather pattens on multiple time scales.</p>
IMPACT OF US BROWN SWISS GENETICS ON MILK QUALITY FROM LOW-INPUT HERDS IN SWITZERLAND: INTERACTIONS WITH SEASON
<p>This study aimed to investigate the effect of, and interactions between, US Brown Swiss genetics and season on milk yield, basic composition and fatty acid profiles, from cows on low-input farms in Switzerland. Milk samples (n=1,976) were collected from 1,220 crossbreed cows with differing proportions of BS, Braunvieh and Original Braunvieh genetics on 40 farms during winter-indoor and summer-grazing seasons. Cows with more Brown Swiss genetics produced more milk in winter but not in summer, possibly because of underfeeding high-yielding cows on low-input pasture-based diets. Cows with more Original Braunvieh genetics produced milk with higher concentrations of (i) nutritionally desirable <em>trans</em>-9 palmitoleic, eicosapentaenoic and docosapentaenoic acids, throughout the year, and (ii) vaccenic and α-linolenic acids, total omega-3 fatty acids concentrations and a higher omega-3/omega-6 ratio during the summer-grazing period only. This suggests that overall milk quality could be improved by re-focusing breeding strategies on the cows’ ability to respond to local dietary environments and seasonal changes in feeding regimes.</p>
Honey bee Seasonal mortality 2012-2014 - Epilobee analysis
<p>EPILOBEE was the first active epidemiological surveillance program implemented in 17 EU Member States, over 2 consecutive years (from autumn 2012 to summer 2014), following a harmonised protocol based on the EU reference laboratory guidelines. EFSA requested a statistical analysis on the EPILOBEE dataset to establish associations between colony mortalities and some factors including disease prevalence, the context of beekeeping and the apiary geographical distribution.The data set published is the result of the data cleaning and categorization performed on the EPILOBEE original dataset regarding seasonal mortality. The dataset comprises 4758 observations from apiaries across Europe.</p>
Supplementary material 5: Bombus spp trapped in Palmer Alaska, 2009 from: Bumble Bees (Hymenoptera: Apidae: Bombus spp.) of Interior Alaska: Species Composition, Distribution, Seasonal Biology, and Parasites - Biodiversity Data Journal 3: e5085 (08 May 2015) https://doi.org/10.3897/BDJ.3.e5085
1040 specimens of fourteen species trapped using Blue Vane pollinator traps with counts of queens, workers, and males by date.
Supplementary material 3: Bombus spp trapped in Fairbanks Alaska, 2009 from: Bumble Bees (Hymenoptera: Apidae: Bombus spp.) of Interior Alaska: Species Composition, Distribution, Seasonal Biology, and Parasites - Biodiversity Data Journal 3: e5085 (08 May 2015) https://doi.org/10.3897/BDJ.3.e5085
2,131 specimens of fifteen species trapped using Blue Vane pollinator traps with counts of queens, workers, and males by date.
Supplementary material 2: Bombus spp trapped in Delta Junction Alaska, 2010 from: Bumble Bees (Hymenoptera: Apidae: Bombus spp.) of Interior Alaska: Species Composition, Distribution, Seasonal Biology, and Parasites - Biodiversity Data Journal 3: e5085 (08 May 2015) https://doi.org/10.3897/BDJ.3.e5085
1812 specimens of sixteen species trapped using Blue Vane pollinator traps with counts of queens, workers, and males by date.
Supplementary material 1: Bombus spp trapped in Delta Junction Alaska, 2009 from: Bumble Bees (Hymenoptera: Apidae: Bombus spp.) of Interior Alaska: Species Composition, Distribution, Seasonal Biology, and Parasites - Biodiversity Data Journal 3: e5085 (08 May 2015) https://doi.org/10.3897/BDJ.3.e5085
2,446 specimens of sixteen species trapped using Blue Vane pollinator traps with counts of queens, workers, and males by date.
Supplementary material 4: Bombus spp trapped in Fairbanks Alaska, 2010 from: Bumble Bees (Hymenoptera: Apidae: Bombus spp.) of Interior Alaska: Species Composition, Distribution, Seasonal Biology, and Parasites - Biodiversity Data Journal 3: e5085 (08 May 2015) https://doi.org/10.3897/BDJ.3.e5085
57 specimens of seven species trapped using Blue Vane pollinator traps with counts of queens, workers, and males by date.
Figure 3. from: Bumble Bees (Hymenoptera: Apidae: Bombus spp.) of Interior Alaska: Species Composition, Distribution, Seasonal Biology, and Parasites - Biodiversity Data Journal 3: e5085 (08 May 2015) https://doi.org/10.3897/BDJ.3.e5085
Figure 3. - Mean number and standard errors of B.centralis, B.flavifrons, and B.occidentalis per trap per 7 day sampling period collected with blue vane traps near Palmer, Alaska 2009 and 2010 (see Suppl. materials 5, 6).
Figure 2. from: Bumble Bees (Hymenoptera: Apidae: Bombus spp.) of Interior Alaska: Species Composition, Distribution, Seasonal Biology, and Parasites - Biodiversity Data Journal 3: e5085 (08 May 2015) https://doi.org/10.3897/BDJ.3.e5085
Figure 2. - Mean number and standard errors of B.centralis, B.jonellus, B.occidentalis, and B.perplexus per trap per 7 day sampling period collected with blue vane traps near Fairbanks, Alaska 2009 and 2010. (see Suppl. materials 3, 4).
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.