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7,883 results for “Tropic”
Data from: Local and landscape scale woodland cover and diversification of agroecological practices shape butterfly communities in tropical smallholder landscapes
<p>The conversion of biodiversity-rich woodland to farmland and subsequent management has strong, often negative, impacts on biodiversity. In tropical smallholder agricultural landscapes, the impacts of agriculture on insect communities, both through habitat change and subsequent farmland management, is understudied. The use of agroecological practices has social and agronomic benefits for smallholders. Although ecological co-benefits of agroecological practices are assumed, systematic empirical assessments of biodiversity effects of agroecological practices are missing, particularly in Africa.</p> <p>In Malawi, we assessed butterfly abundance, species richness, species assemblages and community life-history traits on 24 paired woodland and smallholder-managed farmland sites located across a gradient of woodland cover within a 1 km radius. We tested whether habitat type (woodland vs. farmland) and woodland cover at the landscape scale interactively shaped butterfly communities. Farms varied in the implementation of agroecological pest and soil management practices and flowering plant species richness.</p> <p>Farmland had lower butterfly abundances and approximately half the species richness than woodland. Farmland butterfly communities had, on average, a larger wingspan than woodland site communities. Surprisingly, higher woodland cover in the landscape had no effect on butterfly abundance in both habitats. In contrast, species richness was higher with higher woodland cover. Butterfly species assemblages were distinct between wood- and farmland and shifted across the woodland cover gradient.</p> <p>Farmland butterfly abundance, but not species richness, was higher with higher flowering plant species richness on farms. Farms with a higher number of agroecological pest management practices had a lower abundance of the dominant butterfly species, but not of rarer species. However, a larger number of agroecological soil management practices was associated with a higher abundance of rarer species. </p> <p><em>Synthesis and applications</em>: We show that diversified agroecological soil practices and flowering plant richness enhanced butterfly abundance on farms. However, our results suggest that on-farm measures cannot compensate for the negative effects of continued woodland conversion. Therefore, we call for more active protection of remaining African woodlands in tandem with promoting agroecological soil management practices and on-farm flowering plant richness to conserve butterflies whilst benefiting smallholders.</p>
Tropical forests are approaching critical temperature thresholds
<p class="MsoNormal"><span>The critical temperature beyond which<strong> </strong>photosynthetic machinery in tropical trees begins to fail averages ~<span>46.7°C </span>(T<sub>crit</sub>) <sup>1</sup>. However, it remains unclear whether leaf temperatures experienced by tropical vegetation approach this threshold or soon will under climate change. We found that pantropical canopy temperatures independently triangulated from individual leaf thermocouples, pyrgeometers, and remote sensing (ECOSTRESS) have midday-peak temperatures of ~34°C during dry periods, with a long high-temperature tail that can exceed 40°C. Leaf thermocouple data from multiple sites across the tropics suggest that even within pixels of moderate temperatures, upper-canopy leaves exceed T<sub>crit</sub> 0.01% of the time. Further, upper-canopy leaf warming experiments (+2, 3, and 4°C in Brazil, Puerto Rico, and Australia) increased leaf temperatures non-linearly with peak leaf temperatures exceeding T<sub>crit</sub> 1.3% of the time (11% >43.5°C, 0.3% >49.9°C). Using an empirical model incorporating these dynamics (validated with warming experiment data), we found that tropical forests can withstand up to a 3.9 ± 0.5 °C increase in air temperatures before a potential collapse in metabolic function, but the remaining uncertainty in our understanding of T<sub>crit</sub> could reduce this to 2.6 ± 0.6°C. The 4.0°C estimate is within the "worst case scenario" (RCP-8.5) of climate change predictions<sup>2</sup> for tropical forests and therefore it is still within our power to decide (e.g., by not taking the RCP 8.5 route) the fate of these critical realms of carbon, water, and biodiversity <sup>3,4</sup>.</span></p>
Gridded and aggregated tropical cyclone disaster datasets in China (1990-2015)
<p><strong>Gridded_Datasets:</strong> The geotransformation from image coordinate space to georeferenced coordinate space for each tiff file is (97.0, 0.1, 0.0, 54.0, 0.0, -0.1).</p> <p><strong>Aggregated_Datasets: </strong></p> <p>Including variables aggregated to TC-event scale and province scale:</p> <ul> <li>year (year of the tropical cyclone)</li> <li>cnid (Chinese tropical cyclone ID)</li> <li>DEL (Direct Economic Loss, CNY)</li> <li>W (maximum of gridded sustained wind speed, m/s)</li> <li>P (maximum of gridded daily precipitation, mm)</li> <li>K (asset value exposure, CNY)</li> <li>I (GDP per capita, CNY)</li> <li>H (proportion of nonsteel-concrete residential buildings)</li> </ul> <p> </p>
KScale Tropical Channel 2.2 km resolution Precip
<p>KScale Tropical channel 2.2 km resolution precipitation data. Model run initialised 20171230T0600Z and running from T+0 to T+288 (12 days)</p>
Drivers of soil organic carbon stock during tropical forest succession
<p>Soil organic matter contributes to productivity in terrestrial ecosystems and contains more carbon than is found in the atmosphere. Yet, there is little understanding of soil organic carbon (SOC) sequestration processes during tropical forest succession, particularly after land abandonment from agriculture practices.</p> <p>Here we used vegetation and environmental data from two large-scale surveys covering a total landscape area of 20,000 ha in Southeast Asia to investigate the effects of plant species diversity, functional trait diversity, phylogenetic diversity, aboveground biomass, and environmental factors on SOC sequestration during forest succession.</p> <p>We found that functional trait diversity plays an important role in determining SOC sequestration across successional trajectories. Increases in SOC carbon storage were associated with indirect positive effects of species diversity and succession age <em>via</em> functional trait diversity, but phylogenetic diversity and aboveground biomass showed no significant relationship with SOC stock. Furthermore, the effects of soil properties and functional trait diversity on SOC carbon storage shift across elevation.</p> <p>Synthesis: Our results suggest that reforestation and restoration management practices that implement a trait-based approach by combining long-lived and short-lived species (conservative and acquisitive traits) to increase plant functional diversity could enhance SOC sequestration for climate change mitigation and adaptation efforts, as well as accelerate recovery of healthy soils.</p>
Tropical deforestation drivers 2008-2019, 100m, IIASA - accompanying file to https://doi.org/10.3389/fcosc.2022.830248
<p>The zip file contains the map used for the statistics calculated in the publication 'A Continental Assessment of the Drivers of Tropical Deforestation With a Focus on Protected Areas', available at <a href="https://doi.org/10.3389/fcosc.2022.830248">https://doi.org/10.3389/fcosc.2022.830248</a>. </p> <p>This is a first version of the drivers of forest loss map, It is an allocation of crowdsourced data on predominant drivers of forest loss (data available here <a href="https://doi.org/10.1038/s41597-022-01227-3">https://doi.org/10.1038/s41597-022-01227-3</a>) to the Hansen et al (2013) dataset of global tree loss (v 1.7), set to a resolution of 100x100 m.</p> <p>Furthermore, the classes 'Other subsistence agriculture' and 'Shifting cultivation' are parts of the 'Subsistence agriculture' class on the original crowdsourced data, separated by extracting shifting agriculture according to Heinimann et al (2017).</p> <p>The map is in Goode Homolosine projection.</p>
Spring arrival of the common cuckoo at breeding grounds is strongly determined by environmental conditions in tropical Africa
<p>Failure to adapt migration timing to changes in environmental conditions along migration routes and at breeding locations can result in mismatches across trophic levels, as occurs between the brood parasitic common cuckoo <em>Cuculus</em> <em>canorus</em> and its hosts. Using satellite tracking data from 87 male cuckoos across 11 years, we evaluate why the cuckoo has not advanced its arrival to the UK. Across years, breeding ground arrival was primarily determined by timing of departure from stopover in West Africa before northward crossing of the Sahara. Together with high population synchrony and low apparent endogenous control of this event, this suggests that a seasonal ecological constraint operating here limits overall variation in breeding grounds arrival, although this event was itself influenced by carry-over from timing of arrival into tropical Africa. Between-year variation within individuals was, in contrast, mostly determined by northward migration through Europe, likely due to weather conditions. We find evidence of increased mortality risk for a) early birds following migration periods positively impacting breeding grounds arrival, and b) late birds, possibly suffering energy limitation, after departure from the breeding grounds. These results help identify areas where demands of responding to global change can potentially be alleviated by improving stopover quality.</p>
Throughfall exclusion and fertilization effects on tropical dry forest tree plantations, a large-scale experiment
<p>Across tropical ecosystems, global environmental change is causing drier climatic conditions and increased nutrient deposition. Such changes represent large uncertainties due to unknown interactions between drought and nutrient availability in controlling ecosystem net primary productivity (NPP). Using a large-scale manipulative experiment, we studied for 4 years whether nutrient availability affects the individual and integrated responses of aboveground and belowground ecosystem processes to throughfall exclusion in 30-year-old mixed plantations of tropical dry forest tree species in Guanacaste, Costa Rica. We used a factorial design with four treatments: control, fertilization (F), drought (D), and drought + fertilization (D + F). While we found that a 13 %–15 % reduction in soil moisture only led to weak effects in the studied ecosystem processes, NPP increased as a function of F and D + F. The relative contribution of each biomass flux to NPP varied depending on the treatment, with woody biomass being more important for F and root biomass for D + F and D. Moreover, the F treatment showed modest increases in maximum canopy cover. Plant functional type (i.e., N fixation or deciduousness) and not the experimental manipulations was the main source of variation in tree growth. Belowground processes also responded to experimental treatments, as we found a decrease in nodulation for F plots and an increase in microbial carbon use efficiency 25 for F and D plots. Our results emphasize that nutrient availability, more so than modest reductions in soil moisture, limits ecosystem processes in tropical dry forests and that soil fertility interactions with other aspects of drought intensity (e.g., vapor pressure deficit) are yet to be explored.</p>
Figure 1 in Revisiting the ideas of trees as templates and the competition paradigm in pairwise analyses of ground-dwelling ant species occurrences in a tropical forest
Figure 1 Location of REGUA in the State of Rio de Janeiro, Brazil.
Data from: Hydraulic vulnerability of tropical forests is largely independent of water availability
<p>Tropical rainforest woody plants have been thought to have uniformly low resistance to hydraulic failure and to function near the edge of their hydraulic safety margin, making these ecosystems vulnerable to drought; however, this may not be the case. Using data collected at 30 tropical forest sites for three key traits associated with drought tolerance, we show that site-level hydraulic diversity of leaf turgor loss point, resistance to embolism (P<sub>50</sub>), and hydraulic safety margins (HSMs) is high across tropical forests and largely independent of water availability. Species with high HSMs (>1 MPa) and low P<sub>50</sub> values (<-2 MPa) are common across the wet and dry tropics. This high site-level hydraulic diversity, largely decoupled from water stress, could influence which species are favored and become dominant under a drying climate. High hydraulic diversity could also make these ecosystems more resilient to variable rainfall regimes.</p>
Tropical cyclone variables from CESM2 experiments
<p>There are three files: control, EP and noEP. Each file contains dimensions latitude, longitude and year. Each file contains variables absolute vorticity (avor), genesis potential index (gpi), relative humidity (rh), vertical stratification (tz), potential intensity (vpot), and vertical wind shear (vws). Contact the corresponding author (kristopher.karnauskas@colorado.edu) for additional information.</p>
CESM experiments for diverse impacts of ENSO on summertime synoptic-scale disturbance intensity over the tropical western North Pacific
<p>Experiments are conducted using Community Atmospheric Model version 5.2 (CAM-5.2; <a href="https://www.cesm.ucar.edu/models/cesm1.1/cam/">https://www.cesm.ucar.edu/models/cesm1.1/cam/</a>) which is the atmosphere component of Community Earth System Model (CESM) of the National Center for Atmospheric Research (NCAR). The model resolution is f19_g16, with f19 finite volume grid (1.9° latitude × 2.5° longitude) for the atmosphere and land components combined with the gx1v6 displaced pole grid (approximately 1°) for the ocean and ice components.</p> <p> Five sets of model experiments are designed in this study:</p> <ol> <li>Climatological SST forcing simulation (CLM-SST run): The CAM is forced by global climatological mean monthly SST that is obtained from OI version 2 SST data for the period from January 1982 to December 2020 and integrated for 30 years.</li> <li>Full SST forcing simulation (Full-SST run): The CAM is forced by global monthly OI version 2 SST and integrated from January 1982 to December 2020.</li> <li>Tropical central-eastern Pacific SST forcing simulation (CEP-SST run): The CAM is forced by total monthly SST in the domain of 15°S–15°N and 160°E–80°W and climatological mean monthly SST in the other regions and integrated from January 1982 to December 2020.</li> <li>Sensitivity experiments of tropical central-eastern Pacific SST anomalies: The CAM is forced by global climatological mean monthly SST from January to December plus composite JJA mean SST anomalies in El Niño and La Niña events in the tropical central-eastern Pacific (15°S–15°N, 160°E–80°W) and the above SST anomalies multiplied by -1 from May to December and integrated for 30 years. These four sensitivity experiments are referred to as CEP-EN, CEP-LN, CEP-ENrev, and CEP-LNrev, respectively.</li> <li>Double tropical central-eastern Pacific SST anomalies: The CAM is forced by global climatological mean monthly SST from January to December plus composite JJA mean SST anomalies during El Niño and La Niña events over the tropical central-eastern Pacific (15°S–15°N, 160°E–80°W) multiplied by 2 from May to December and integrated for 30 years. The simulations with composite SST anomalies derived from El Niño (La Niña) are referred to as CEP-2EN (CEP-2LN) runs.</li> </ol>
Tropical forests are mainly unstratified especially in Amazonia and regions with lower fertility or higher temperatures
<p>The stratified nature of tropical forest structure had been noted by early explorers, but until recent use of satellite-based LiDAR (GEDI, or Global Ecosystems Dynamics Investigation LiDAR), it was not possible to quantify stratification across all tropical forests. Understanding stratification is important because by some estimates, a majority of the world's species inhabit tropical forest canopies. Stratification can modify vertical microenvironment, and thus can affect a species' susceptibility to anthropogenic climate change. Here we find that, based on analyzing each GEDI 25m diameter footprint in tropical forests (after screening for human impact), most footprints (60-90%) do not have multiple layers of vegetation. The most common forest structure has a minimum plant area index (PAI) at ~40m followed by an increase in PAI until ~15m followed by a decline in PAI to the ground layer (described hereafter as a one peak footprint). There are large geographic patterns to forest structure within the Amazon basin (ranging between 60–90% one peak) and between the Amazon (79 ± 9 % sd) and SE Asia or Africa (72 ± 14 % v 73 ±11 %). The number of canopy layers is significantly correlated with tree height (r<sup>2</sup>=0.12) and forest biomass (r<sup>2</sup>=0.14). Environmental variables such as maximum temperature (Tmax) (r<sup>2</sup>=0.05), vapor pressure deficit (VPD) (r<sup>2</sup>=0.03) and soil fertility proxies (e.g. total cation exchange capacity - r<sup>2</sup>=0.01) were also statistically significant but less strongly correlated given the complex and heterogeneous local structural to regional climatic interactions. Certain boundaries, like the Pebas Formation and Ecoregions, clearly delineate continental scale structural changes. More broadly, deviation from more ideal conditions (e.g. lower fertility or higher temperatures) leads to shorter, less stratified forests with lower biomass.</p>
The potential for natural forest regeneration in tropical regions
<p>Extensive forest restoration is a key strategy to meet nature-based sustainable development goals and provide multiple social and environmental benefits. Yet achieving forest restoration at scale requires cost-effective methods. Tree planting in degraded landscapes is a popular but costly forest restoration method, which often results in less biodiverse forests when compared to natural regeneration techniques under similar conditions. Here, we assess the current spatial distribution of pantropical natural forest (from 2000-2016) and use this information to present the first model of the potential for natural regeneration across tropical forested countries and biomes at 30-meter spatial resolution. We estimate that 215 million hectares - an area greater than the entire country of Mexico - have potential for natural forest regeneration, representing an above-ground carbon sequestration potential of 23.4 Gt CO2 (range 21.1-25.7 Gt) over 30 years. Five countries (Brazil, Indonesia, China, Mexico, and Colombia) account for 52% of this estimated potential, showcasing the need for targeting restoration initiatives that leverage natural regeneration potential. Our results facilitate broader equitable decision-making processes that capitalise on the widespread opportunity for natural regeneration to help achieve national and global environmental agendas. </p> <p><em>File descriptions:</em></p> <p><em>pnv_pct_30m - files named with this prefix are the 30m continuous probability predictions of the potential for natural regeneration, stored as integers representing percentages to minimise file sizes </em></p> <p><em>tile_index_number_map.jpg – corresponding locations of the ‘pnv_pct_30m’ tiles </em></p> <p><em>prop pnv v1 1km.tif - for display purposes only (not used in the Williams et al. analysis) the approximately 1km resolution overview raster representing the proportion of the area of each pixel that has the potential for natural regeneration </em></p> <p><em>pnv_bin_30m.zip - this .zip folder contains the 30m resolution binary predictions (where a value of >0.5 is allocated a value of 1, acknowledging users may prefer to use a different threshold for their context) of areas suitable for natural regeneration </em></p> <p><em>The 30m resolution datasets have been tiled into 10 degree latitude/longitude tiles</em></p>
Tropical Andes Land Cover Dataset (TALANDCOVER)
<p>The Tropical Andes Land Cover Dataset (TALANDCOVER) was built for the department of Antioquia Colombia and consists of three folders for the three types of sampling.</p><ul><li>Random: 5000 images</li><li>Balanced of minimum 50% coverage per class: 1389 images</li><li>Balanced of minimum 70% coverage per class:731 images</li></ul><p>The coordinate system of each dataset is EPSG:3857 - WGS 84 / Pseudo-Mercator with spatial resolution of 4.77 meters and 128*128px.</p><p>Example of image and corresponding label name:</p><ul><li>image_PNICFI_D2019-05_T586-1068_C1_N100.tif</li><li>label_PNICFI_D2019-05_T586-1068_C1_N100.tif</li></ul><p>Pixel values for label are:</p><p>0 Bare-degraded lands<br>1 Grasslands<br>2 Heterogeneous agricultural areas<br>3 Dense forest<br>4 Water bodies<br>5 Built-up areas</p><p>There are multiple keywords intentionally inserted in each image name or label name that enable the split of the name in pieces of information about each image and label metadata.</p><p>Spliting the image or label name by the keywords, would get 6 items:</p><ol><li>The item (image/label) describe if the file correspond to a patch image or a patch label.</li><li>The second item (Keyword "_P") gives the name of the product NICFI (<a href="https://assets.planet.com/docs/NICFI_User_Guide_v4_EN.pdf">https://assets.planet.com/docs/NICFI_User_Guide_v4_EN.pdf</a>)</li><li>The third item (keyword "_D") gives a date for the composite</li><li>The fourth (keyword "_T") gives the tile number, as stated in the planet scope visual base map documentation: "The name of each basemap quad within the Basemaps API is designed to represent the x and y position of the quad within the two dimensional grid which makes up the basemap. It is generally {X}-{Y}, where X and Y are the x and y position of the quad in the grid". <a href="https://developers.planet.com/docs/data/visual-basemaps/">https://developers.planet.com/docs/data/visual-basemaps/</a></li><li>The fifth (keyword "_C") when available gives the cover class used by the slidding windows to extract the patch, resulting in at least a minimum of 50% or 70% of the pixels within the image correspond to that specific cover class, depending on the selected sample dataset.</li></ol><p>Take into account that random samples dont have this keyword since the patches where collected at random from a 2d grid without regard of the cover classes present</p><p> </p><p>Article : "Land Cover Classification in the Antioquia Region of the Tropical Andes Using NICFI Satellite Data Program Imagery and Semantic Segmentation Techniques". </p>
Data from: Trait interactions effects on tropical tree demography depend on the environmental context
<p><span class="fontstyle0">Although functional traits are defined based on their impact on demographic parameters, trait-demography relationships are often reported as weak. These weak relationships might be due to disregarding trait interactions and environmental contexts, which should modulate species trait-demography relationships. We applied different models, including boosted regression tree (BRT) models, to investigate changes in the relationship between traits and demographic rates of tropical tree species in plots along an elevational gradient and among time intervals between censuses, analyzing the effect of a strong drought event. Based on a large dataset of 18,000 tree individuals from 133 common species, distributed among twelve 1-ha plots (habitats) in the Atlantic Forest (Brazil), we evaluated how trait interactions and the environmental context influence the demographic rates (growth, mortality, and recruitment). Functional traits, trait-trait, and trait-habitat interactions predicted demography with a good fit through either BRTs or linear mixed-models. Changes in growth rates were best related to size (diameter), and mortality rates to habitats, while changes in recruitment rates were best related to the specific leaf area. Moreover, the influence of traits differed among time intervals, and for demographic parameters, habitat affected growth and mortality by interacting with diameter. Here, we provide evidence that trait-demography relationships can be improved when considering the environmental context (space and time) and trait interactions to cope with the complexity of changes in the demography of tropical tree communities. Thus, to expand predictions of demography based on functional traits, we show that it is useful to fully incorporate the concept of multiple trait-fitness optima, resulting from trait interactions in different habitats and growth conditions.</span> </p>
Data from: The effect of initial vortex asymmetric structure on tropical cyclone intensity change in response to an imposed environmental vertical wind shear
<p>Previous studies have investigated how the environmental vertical wind shear (VWS) may trigger the asymmetric structure in an initially axisymmetric tropical cyclone (TC) vortex and how TC intensity changes in response. In this study, the possible effect of the initial vortex asymmetric structure on the TC intensity change in response to an imposed environmental VWS is investigated based on idealized full-physics model simulations. Results show that the effect of the asymmetric structure in the initial TC vortex can either enhance or suppress the initial weakening of the TC in response to the imposed environmental VWS. When the initial asymmetric structure is in phase of the VWS-induced asymmetric structure, the TC weakening will be enhanced and vice versa. Our finding calls for realistic representation of initial TC asymmetric structure in numerical weather prediction models and observations to better resolve the asymmetric structure in TCs.</p>
Demographic rates of 165 tree species in a moist tropical forest
<p>Mortality, recruitment and growth rates of 165 tree species on Barro Colorado Island from 1990 to 2014. The demographic rates are calculated based on the BCI (Barro Colorado Island) plot data. For more information on the BCI plot data, please see https://doi.org/10.15146/5xcp-0d46 (Condit et al., 2019). Reference: Condit, R. et al. (2019). Complete data from the Barro Colorado 50-ha plot: 423617 trees, 35 years, Dryad, Dataset, https://doi.org/10.15146/5xcp-0d46. For more information on the calculation of demographic rates, please see https://doi.org/10.1111/1365-2435.14424.</p>
Composition of non-volant small mammals inhabiting a degradation gradient in a lowland tropical forest in Uganda
<p><span>A study aimed at assessing the structure of rodent and shrew assemblages inhabiting a degradation gradient while considering rainfall patterns was conducted in one of the few remaining lowland tropical forests in Eastern Africa. We collected a unique dataset of 1411 rodents and shrews, representing 24 species (19 rodents, 5 shrews). The most abundant species alternated in dominance as species abundance significantly fluctuated across the study period following a degradation gradient, </span><span>While only generalist species were observed near the degraded forest edge, habitat specialists such as <em>Deomys ferrugineus, Malacomys longipes</em> and <em>Scutisorex congicus</em>, were observed in the primary forest interior suggesting<span class="gnkrckgcgsb"><span> a significant</span></span></span><span class="gnkrckgcgsb"><span> association between species and their associated habitats and habitat attributes</span></span><span>. There was also an observed correlation between rainfall patterns and species abundance. Capturing more species in adjacent fallows and along the degraded forest edge suggests that many species are able to live in degraded habitats that offer a variety of food resources. The continued pressure on forest resources, however, may lead to changes in habitat structure. This, coupled with the dependence of forest ecological functions on rainfall, which is typically not the case, may ultimately cause the local extinction of highly specialized but less adaptable species.</span></p>
Tapajos K67 tropical forest seasonal flux tower data
<p>Seasonal (16-day) eddy covariance measurements that include water, carbon, and energy flux, radiation, canopy temperature, humidity, and ancillary calculations such as evaporation, transpiration, and leaf area index. Data was collected at the Tapajós National Forest, Santarem K67 (BR-Sa1), a tropical primary forest (2.857°S, 54.959°W). Measurements correspond to the periods: April 2001 - January 2006, July 2008 - July 2012, and January 2015 - July 2020. The 16-day fluxes and meteorological data were obtained from an average daily cycle based on hourly observations. As a result, we refrain from assigning greater significance to specific hours when the fluxes tend to be complete, and vice versa (for instance, in cases of missing flux values due to afternoon rainfall events). Daytime values were selected as above a 5 W m<sup>-2</sup> top of the atmosphere radiation (TOA; W m<sup>-2</sup>) and labeled as LE<sub>daytime</sub> in the case of latent heat flux (LE), H<sub>daytime</sub> for sensible heat flux (H), and so on.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.