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1,436 results for “Oaks”
Tree survey:Effects of Long Term Fertilization and Oak Canopy Cover on Plant Communities and Ecosystem Processes
In 1996 E142 was established in field D on top of the E004 macroplots. E004 was conducted in fields A, B, C and D by Dave Tilman. The purpose of E004 was to see what effect NH4NO3 addition has on large areas over a longer period of time with exposure to naturally-occurring levels of herbivory. The nutrient addition treatments in E004, E142 plots have been applied annually since 1982. These experiments, along with others at Cedar Creek, examine the community and ecosystem consequences of chronic nutrient loading.
Canopy litter biomass:Effects of Long Term Fertilization and Oak Canopy Cover on Plant Communities and Ecosystem Processes
In 1996 E142 was established in field D on top of the E004 macroplots. E004 was conducted in fields A, B, C and D by Dave Tilman. The purpose of E004 was to see what effect NH4NO3 addition has on large areas over a longer period of time with exposure to naturally-occurring levels of herbivory. The nutrient addition treatments in E004, E142 plots have been applied annually since 1982. These experiments, along with others at Cedar Creek, examine the community and ecosystem consequences of chronic nutrient loading.
Herbaceous Vegetation Survey:Effects of Long Term Fertilization and Oak Canopy Cover on Plant Communities and Ecosystem Processes
In 1996 E142 was established in field D on top of the E004 macroplots. E004 was conducted in fields A, B, C and D by Dave Tilman. The purpose of E004 was to see what effect NH4NO3 addition has on large areas over a longer period of time with exposure to naturally-occurring levels of herbivory. The nutrient addition treatments in E004, E142 plots have been applied annually since 1982. These experiments, along with others at Cedar Creek, examine the community and ecosystem consequences of chronic nutrient loading.
Hubbard Brook Experimental Forest: Annual measurements on marked northern red oak seedlings, 2014-ongoing
Marked individual northern red oak seedlings were individually marked and mapped at 20 valleywide transects starting in summer 2011. This data set includes detailed seedling measures starting in summer 2014. The data were used to examine the impact of previous year seedling condition on whether the seedling survived in the next season. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Sudden_Oak_Death_in_Oregon_Forests: Spatial and temporal population dynamics of the sudden oak death epidemic in Oregon Forests
<p>Release of code associated with the submitted manuscript</p> <p><strong>Authors</strong></p> <p>ZN Kamvar, MM Larsen, AM Kanaskie, EM Hansen, and NJ Grünwald.</p> <p><strong>Title</strong></p> <p>Spatial and temporal population dynamics of the sudden oak death epidemic in Oregon Forests.</p>
Aoife Daly & Ian Tyers 2022. The sources of Baltic oak. Journal of Archaeological Science 139, 105550, https://doi.org/10.1016/j.jas.2022.105550
<p>Past trade in oak from the lands to the east and south of the Baltic Sea was extraordinary in that it dominated the Northern European market for specialized oak products for many centuries. Equally extraordinary is the fact that since the 1980s, when a large corpus of dendrochronological data from artworks was demonstrated to represent the material evidence for this past trade, we, until now, have not been able to pinpoint where, in this large region, the trees for this trade grew. Through our analysis we can now present the likely sources of three major timber groups in this material, and significantly, the new chronologies that we make available here, will be the key foundation for dating and identifying provenance of Baltic oak, for tree-ring research long into the future.</p>
Potential and realized distribution at 30m for Cork oak (Quercus suber) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the cork oak (<em>Quercus suber, L.</em>) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods:</p> <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> <p>Files are named according to the following naming convention, e.g:</p> <ul> <li>veg_quercus.suber_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>quercus.suber</strong>,</li> <li>species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation),</li> <li>species estimation method: e.g. <strong>eml</strong>,</li> <li>species estimation type: e.g. <strong>md</strong> ( = model deviation),</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>reference period begin end: e.g. <strong>2000..2002</strong>,</li> <li>reference area: e.g. <strong>eumap</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v0.3</strong>.</li> </ul> <p>For each species is then easy to identify probability and uncertainty distribution maps:</p> <ul> <li>veg_quercus.suber_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_quercus.suber_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_quercus.suber_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_quercus.suber_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution</li> </ul> <p>Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format.</p> <p>If you would like to know more about the creation of the maps and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li><strong>access </strong>the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li><strong>read </strong>the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a></p>
Potential and realized distribution at 30m for Turkey oak (Quercus cerris) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the Turkey oak (<em>Quercus cerris, L.</em>) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods:</p> <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> <p>Files are named according to the following naming convention, e.g:</p> <ul> <li>veg_quercus.cerris_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>quercus.cerris</strong>,</li> <li>species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation),</li> <li>species estimation method: e.g. <strong>eml</strong>,</li> <li>species estimation type: e.g. <strong>md</strong> ( = model deviation),</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>reference period begin end: e.g. <strong>2000..2002</strong>,</li> <li>reference area: e.g. <strong>eumap</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v0.3</strong>.</li> </ul> <p>For each species is then easy to identify probability and uncertainty distribution maps:</p> <ul> <li>veg_quercus.cerris_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_quercus.cerris_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_quercus.cerris_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_quercus.cerris_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution</li> </ul> <p>Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format.</p> <p>If you would like to know more about the creation of the maps and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li><strong>access </strong>the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li><strong>read </strong>the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> <p> </p> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.</p>
Potential and realized distribution at 30m for pedunculate oak (Quercus robur) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the pedunculate oak (<em>Quercus robur, L.</em>) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods:</p> <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> <p>Files are named according to the following naming convention, e.g:</p> <ul> <li>veg_quercus.robur_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>quercus.robur</strong>,</li> <li>species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation),</li> <li>species estimation method: e.g. <strong>eml</strong>,</li> <li>species estimation type: e.g. <strong>md</strong> ( = model deviation),</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>reference period begin end: e.g. <strong>2000..2002</strong>,</li> <li>reference area: e.g. <strong>eumap</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v0.3</strong>.</li> </ul> <p>For each species is then easy to identify probability and uncertainty distribution maps:</p> <ul> <li>veg_quercus.robur_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_quercus.robur_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_quercus.robur_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_quercus.robur_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution</li> </ul> <p>Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format.</p> <p>If you would like to know more about the creation of the maps and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li><strong>access </strong>the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li><strong>read </strong>the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> <p> </p> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a></p>
OAK-D Synthethic Pose Dataset
<p>"OAK-D Synthethic Pose Dataset" is a synthetic image dataset of humans, annotated with 2d and 3d keypoints for a OAK-D like camera configuration.</p> <p>The dataset is part of the "3D Stereo Body Pose Estimation" project, originated in the context of the activities of the postgraduate course IA904 - Model Project in Visual Computing, offered in the first semester of 2024, at Unicamp, under the supervision of Prof. Dr. Leticia Rittner and Prof. Paula D. Paro Costa, both from the Department of Computer and Automation Engineering (DCA) of the Faculty of Electrical and Computer Engineering (FEEC).</p> <p>The <a href="https://github.com/Disciplinas-FEEC/IA904-2024S1/tree/main/projetos/3DStereoBodyPoseEstimation">project page</a> have a full description of the project (portuguese).</p>
SSR genotypes of oak parents and progeny in Pentalofos Forest Greece
<p>Two files (datasets) with multi-locus genotypes at 17 nuclear microsatellites; one containing the genotypes of all 59 adult trees within a mixed oak forest stand in the Pentalofod Forest in Evros County in Greece and the other the genotypes of 110 juvenile trees in the same stand. A comparison can be performed for parentage assignement and studies of hybridization. Oak species: <em>Quercus frainetto, Q. petraea, Q pubescens.</em></p>
Variations in Solar Activity Across the Sporer Minimum Based on Radiocarbon in Danish Oak (dataset)
<p>This dataset (AARAMS_Radiocarbon_1432_1578_ver2) provides radiocarbon ages of LW tree rings from Danish oak from the period AD 1432 to 1578. The measurements were conducted at the Aarhus AMS Centre (AARAMS) at Aarhus University, Denmark. Ring widths of the two wood samples (SchB and Gr02) used for the measurements are found in the two files: TableS1_SchB_Ringwidths and TableS2_Gr02_Ringwidths. These data are presented in a paper submitted to Geophysical Research Letters with the title: Variations in Solar Activity Across the Sporer Minimum Based on Radiocarbon in Danish Oak.</p>
Data and R code for “Individual-level variation in reproductive effort in chestnut oak (Quercus montana Willd.) and black oak (Q. velutina Lam.)”, Forest Ecology and Management, 2022
Masting is a population-level reproductive strategy, where individuals synchronize large but intermittent seed production. Despite the high degree of synchrony at the population level, there can be considerable variation in reproduction among individuals (intraspecific variation). Here, we use 18 years of acorn production data from individual chestnut oak and black oak from control and thinned stands, to understand what factors influence individual differences in reproductive effort and variability. We included a variety of tree-level measurements, environmental characteristics, and measurements from tree cores to determine if certain characteristics were associated variations in reproduction. We considered both mean annual acorn production per m2 crown and interannual variation in acorn production (CV) as response variables. We also classified individuals as super producers (i.e., those that consistently produce more acorns than others), good, fair and poor producers (i.e., those that consistently produce less or have a higher number of failure years). In chestnut oak, 14% of the individuals were classified as super producers and contributed 34% of the total acorns, while poor producers made up 35% of the trees and contributed only 16% to total acorn production. In black oak, super producers (14% of the individuals) contributed 31% of total acorns and poor producers (24% of the individuals) contributed only 9% of the acorns. Diameter at breast height (DBH) was the most consistent variable for explaining intraspecific variation in reproductive effort and variability (i.e., larger individuals had higher mean acorn production for both chestnut oak and black oak, and lower CV for black oak). Other variables that influenced reproduction and variation included elevation and clay content for chestnut oak, and slope for black oak. We found no significant effect from the thinning treatment on acorn production. Our results illustrate how tree-level and environmental characte
California Blue Oak reproduction and recruitment, Sierra Foothills, 2005-2010
This data archive contributes to the study of blue oak conservation by quantifying reproductive success (numbers of new seedlings and their sizes) and survival and growth of the younger life stages (seedlings and saplings), and by exploring how the timing of grazing, topographical variables, and rainfall limit reproduction, growth, and survival for these life stages, helping to identify potential management and restoration strategies to enhance the population viability of blue oak. We followed a population of blue oak (Quercus douglasii) seedlings and saplings in the Sierra Nevada foothills, measuring adult seed production and initial seedling recruitment (number and size of new seedlings), seedling growth and survival, seedling recruitment to saplings (growing greater than 10 cm in height), and sapling growth and survival. We tested the impacts on these demographic processes of timing and intensity of grazing, light availability, water availability, and individual size. We published our analysis of these data as part of the 8th Oak Woodland Symposium. This data archive contains the underlying data for our analysis plus some data we did not include in our analysis (residual dry matter, photosynthetically active radiation, percent exotic/native cover), including raw data and R and Perl scripts used to format them for analysis, and additional information not included in the conference proceedings.
Decomposition of Microstegium vimineum litter, plants grew through the Big Oaks National Wildlife Refuge in 2019. Litter used in this experiment naturally senesced in the fall 2019, decomposition data collected through 2020. Plants were infected or not-infected with the foliar fungal pathogen Bipolaris gigantea during the 2019 growing season.
Decomposition of plant litter, facilitated primarily by microbial decomposers, plays a critical role in biogeochemical cycling and ecosystem function. Emerging pathogens have the potential to impact litter decomposition by altering the chemical composition and associated microbial community of host tissue. Here, we compared litter decomposition of the invasive grass Microstegium vimineum collected from sites with Bipolaris leaf spot symptoms and sites with no apparent disease symptoms in a common garden experiment. Our results revealed that leaf tissue from litter from non-infected sites decomposed more rapidly through the spring than litter from infected sites. Differences in fungal composition between infected and non-infected litter at the start of the experiment largely persisted through the summer. Our work demonstrates that pathogen colonization may facilitate the persistence of infected host litter, potentially slowing the return of nutrients to the environmental pool while also promoting the survival and dispersal of primary inoculum the following season.
Tree species identity, diameter and qualitative canopy health measurements (full, partial or dead) from 2005 to 2023 on 12 experimental oak loss plots in Black Rock Forest, NY.
Black Rock Forest established a series of 12, 0.56 ha plots in 2005 to assess impacts of the loss of tree in the genus Quercus on the forest ecosystem (entitled the Future of Oak Forests experiment). Three trunk girdling treatments, with control plots were instituted in 2008. Each plot also contained an ~10m by ~15m deer exclosure to assess the impact of herbivory post-disturbance. Trees were measured twice per year from 2008 to 2013 (except 2009 when trees were measured once) and once per year from 2014 to 2023. Data include tree species identity, diameter at breast height (DBH), canopy health (a qualitative assessment of approximate cover as full, partial or dead), presence/absence of sprouts, and location within the plot. All live trees equal to or larger than 2.5 cm DBH are included in the dataset.
Data from: Evolutionary and demographic history of the Californian scrub white oak species complex: an integrative approach
<p>Understanding the factors promoting species formation is a major task in evolutionary research. Here, we employ an integrative approach to study the evolutionary history of the Californian scrub white oak species complex (genus <em>Quercus</em>). To infer the relative importance of geographical isolation and ecological divergence in driving the speciation process, we (i) analyzed inter- and intra-specific patterns of genetic differentiation and employed an approximate Bayesian computation (ABC) framework to evaluate different plausible scenarios of species divergence. In a second step, we (ii) linked the inferred divergence pathways with current and past species distribution models, and (iii) tested for niche differentiation and phylogenetic niche conservatism across taxa. ABC analyses showed that the most plausible scenario is the one considering the divergence of two main lineages followed by a more recent pulse of speciation. Genotypic data in conjunction with species distribution models and niche differentiation analyses support that different factors (geography vs. environment) and modes of speciation (parapatry, allopatry and maybe sympatry) have played a role in the divergence process within this complex. We found no significant relationship between genetic differentiation and niche overlap, which probably reflects niche lability and/or that multiple factors have contributed to speciation. Our study shows that different mechanisms can drive divergence even among closely related taxa representing early stages of species formation and exemplifies the importance of adopting integrative approaches to get a better understanding of the speciation process.</p>
Data for: Speciation in kleptoparasites of oak gall wasps often correlates with shifts into new tree habitats, tree organs, or gall morphospace
<p><span>Host shifts to new plants can drive speciation for plant-feeding insects, but how commonly do host shifts also drive diversification for the parasites of those same insects? Oak gall wasps induce galls on oak trees, and shifts to novel tree hosts and new tree organs have been implicated as drivers of oak gall wasp speciation. Gall wasps are themselves attacked by many insect parasites, which must find their hosts on the correct tree species and organ, but which also must navigate the morphologically variable galls with which they interact. Thus, we ask whether host shifts to new trees, organs, or gall morphologies correlate with gall parasite diversification. We delimit species and infer phylogenies for two genera of gall kleptoparasites, <em>Synergus</em> and <em>Ceroptres</em>, reared from a variety of North American oak galls. We find that most species were reared from galls induced by just one gall wasp species, and no parasite species was reared from galls of more than four species. Most kleptoparasite divergence events correlate with shifts to non-ancestral galls. These shifts often involved changes in tree habitat, gall location, and gall morphology. Host shifts are thus implicated in driving diversification for both oak gall wasps and their kleptoparasitic associates.</span></p>
Fig. 11 in New Genera and Species of Oak-Associated Phylini (Heteroptera: Miridae: Phylinae) from Western North America
Fig. 11. Female genitalic structures, comprising bursa copulatrix and seminal depository in dorsal view, vestibulum in anterior view, and—if sclerotizations are present—posterior wall.
Fig. 9 in New Genera and Species of Oak-Associated Phylini (Heteroptera: Miridae: Phylinae) from Western North America
Fig. 9. Male genitalic structures, scanning micrographs, scale bar measurements in Mm. A, B. Crassomiris fatisco. A. left: posterior view of the left paramere, right: apex of the posterior process of the left paramere. B. posterolateral view of phallotheca and right paramere from the right side. C–E. Phallospinophylus setosus. C. posterolateral view of pygophore, showing patch of stout setae. D. patch of stout setae on the pygophore. E. phallotheca. F–H. Pygovepres vaccinicola. F. anterolateral view of pygophore, showing spinous process phallotheca and left paramere. G. left: spinous process, right: close up of one of the spines. H. apex of the phallotheca. I. Rubeospineus truncatus, phallotheca and left paramere from the left side.
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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.
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.