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Figure 1 in Niche separation between the cosmopolitan species Drosophila melanogaster and the tropical Asian species Drosophila ananassae based on larval host-plant species
Figure 1. Number of male flies eclosing from cucumberfruit (Averrhoa bilimbi) vs. banana (Musa species), pooled across Drosophila Species.
Figs. 2–4 in Myrmecofauna (Hymenoptera: Formicidae) response to habitat characteristics of tropical montane cloud forests in central Veracruz, Mexico
Figs. 2–4. Species richness, diversity profiles, and rank–abundance curves. Fig. 2. Comparison of the richness of woody plants at a sampling coverage of 90% and of ants at 85% coverage, among 5 fragments of tropical montane cloud forest in central Veracruz, Mexico. Statistical differences are considered when 95% confidence intervals do not overlap, whereas no differences are assumed when they do overlap, with an α = 0.05. Fig. 3. Diversity profiles of the ant assemblages of F1–F5 based on the equivalent species number. Statistical differences are considered when 95% confidence intervals do not overlap, whereas no differences are assumed when they do overlap, with an α = 0.05. Fig. 4. Rank–abundance curves of the ant assemblages of F1–F5. Total number of ant incidences in each fragment is 60 traps. Only those species with a relative abundance equal to or higher than 10% in a given fragment are shown. Ant species are numbered in accordance with Table 2.
Figs. 5 and 6. Results from cluster and linkage tree analyses. Fig. 5 in Myrmecofauna (Hymenoptera: Formicidae) response to habitat characteristics of tropical montane cloud forests in central Veracruz, Mexico
Figs. 5 and 6. Results from cluster and linkage tree analyses. Fig. 5. Dendrogram of hierarchical standardized clustering based on the SØrensen similarity index of the studied fragments. The cophenetic correlation coefficient of the cluster is 0.89. The dendrogram displays with continuous lines the divisions for which the SIMPROF test rejects the null hypothesis (where assemblages in that group have no further structure to explore) and with dashed lines the groups of assemblages not separated (at P <0.05) by SIMPROF. Fig. 6. Linkage tree analysis (LINKTREE) showing divisive clustering of fragments (F1–F5) from species compositions constrained by inequalities on one or more environmental variables. Only binary partitions of uncorrelated environmental variables are shown in the cluster. The dendrogram displays with continuous lines the divisions for which the SIMPROF test rejects the null hypothesis (where assemblages in that group have no further structure to explore) and with dashed lines the groups of assemblages not separated (at P <0.05) by SIMPROF.
Fig. 1 in Myrmecofauna (Hymenoptera: Formicidae) response to habitat characteristics of tropical montane cloud forests in central Veracruz, Mexico
Fig. 1. Location of the study area in central Veracruz, Mexico. The black polygons indicate the selected fragments (F1–F5) of tropical montane cloud forest.
Fig. 1 in Feeding ecology of three juvenile mojarras (Gerreidae) in a tropical estuary of northeastern Brazil
Fig. 1. Map of the study area with locations of the sampling sites in the rio Mamanguape Estuary, Brazil: 1- Mud flat; 2- Tidal Creek 1; 3- Tidal Creek 2; 4- Tidal Creek 3.
Fig. 4 in Feeding ecology of three juvenile mojarras (Gerreidae) in a tropical estuary of northeastern Brazil
Fig. 4. Non-metric multidimensional scaling plots (nMDS) coded by habitat for Diapterus rhombeus, Eucinostomus melanopterus and Eugerres brasilianus in the rio Mamanguape estuary, Brazil. Filled symbols (gray= D. rhombeus and black= E. melanopterus) and open symbols (E. brasilianus).
Fig. 3 in Feeding ecology of three juvenile mojarras (Gerreidae) in a tropical estuary of northeastern Brazil
Fig. 3. Spatial variation of numerical abundance of the three species (Diapterus rhombeus, Eucinostomus melanopterus and Eugerres brasilianus) in the rio Mamanguape estuary, Brazil.
Fig. 2 in Feeding ecology of three juvenile mojarras (Gerreidae) in a tropical estuary of northeastern Brazil
Fig. 2. Ordination Diagram for principal components analysis on environmental parameters (A= PC1 vs. PC2; B= PC1 vs. PC3), coded by site from rio Mamanguape estuary. Sites: Mud flat (Diamond); Tidal Creek 1 (Square); Tidal Creek 2 (Black triangle); Tidal Creek 3 (Black dot).
Fig. 1 in Environmental constraints structuring fish assemblages in riffles: evidences from a tropical stream
Fig. 1. Biplot of the Redundancy Analysis (RDA) of the reduced environmental model with species abundance (a) and trophic composition (b) as response variable. The first two axes of the RDA explained 92.4% of the speciesenvironment relationship and 98.1% of the trait-environment relationship. Species: Aspsp - Aspidoras sp.; Cetihe - Cetopsorhamdia cf. iheringi; Impsch - Imparfinis schubarti; Phesp - Phenacorhamdia sp.; Hypsp - Hypostomus sp.; Harpun - Harttia punctata; Apamac - Apareiodon machrisi; Chazeb - Characidium zebra; Crebri - Creagrutus britskii; Knocha - Knodus cf. chapadae.
Fig. 1 in Fishers' ecological knowledge of smalleye hammerhead, Sphyrna tudes , in a tropical estuary
Fig. 1. Location of Cassurubá Extractive Reserve, Abrolhos Bank, eastern Brazil. Numbers represent fishing villages where fishers were interviewed. Caravelas city: 1- Barra (n= 16), 2- Massangano (n = 8), 3- Calabouço (n = 6), 4- Tapera (n = 8), 5- Caribê (n = 12). Nova Viçosa city: 6- Peróba (n = 12), 7- Barra Velha (n= 11) and 8- Tranqueira (n= 6).
Fig. 3 in Fishers' ecological knowledge of smalleye hammerhead, Sphyrna tudes , in a tropical estuary
Fig. 3. (a) Number of fishers that had already caught Sphyrna tudes, distributed over fishers' generations on Abrolhos Bank; (b) Mean weight (+95% CI) of Sphyrna tudes caught over three generations of fishers. Different letters above bars indicate significant differences (p <0.05).
Fig. 4 in Fishers' ecological knowledge of smalleye hammerhead, Sphyrna tudes , in a tropical estuary
Fig. 4. Food items consumed by Sphyrna tudes according to fishers´ local ecological knowledge of Cassurubá Extractive Reserve, eastern Brazil.
Fig.1 in Parasites of two coexisting invasive sailfin catfishes (Siluriformes: Loricariidae) in a tropical region of Mexico
Fig.1. Intra-annual variations in: a) prevalence, b) intensity of infection and c) abundance of parasites in the invasive loricarids Pterygoplichthys disjunctivus and P. pardalis from a freshwater ecosystem of southeastern Mexico.
Fish community composition in the tropical archipelago of São Tomé and Príncipe
<p>Understanding species distribution across habitats and environmental variables is important to inform area-based management. However, observational data are often lacking, particularly from developing countries, hindering effective conservation design. One such data-poor area is the Gulf of Guinea, an understudied and biodiverse region whose waters play a critical role in coastal livelihoods. To fill this gap, we conducted an extensive, national-scale Baited Remote Underwater Video Systems (BRUVS) survey in the coastal waters of the São Tomé and Príncipe archipelago (STP). From 2018 to 2020, we successfully deployed 417 benthic BRUVS, aiming to understand the effects of several environmental variables on fish community composition and diversity. Species richness and relative abundance were higher in deeper waters, on steeper slopes, and in rocky reef habitats. Nevertheless, maerl and sand habitats also hosted unique, and economically important species. Our results potentially indicate historical impacts of fishing in the archipelago, especially in São Tomé Island, where observed fishing effort is higher. Indeed, abundance of large predatory fish was low in both islands and abundance of species targeted by artisanal fisheries was lower in São Tomé than in Príncipe. Our results provide crucial information supporting the designation and future monitoring of marine protected areas in STP.</p>
Fig 4. Capsicum longifolium Barboza & S in Four new species of Capsicum (Solanaceae) from the tropical Andes and an update on the phylogeny of the genus
Fig 4. Capsicum longifolium Barboza & S. Leiva. (A) Plant. (B) Internode with lenticels. (C, D) Flower buds. (E) Flower, longitudinal section. (F) Flowers showing corolla yellow with brownish center. (G) Same flower as in F, lateral view. (H, I) Flowers with completely yellow corollas, upper and lateral view, respectively. (J, K) Flowers with yellow corollas with red-brown edges, upper and lateral view, respectively. (L, M) Immature fruits. (N) Mature fruit. Photos by S. Leiva González and G. E. Barboza.
Fig 3. Capsicum longifolium Barboza & S in Four new species of Capsicum (Solanaceae) from the tropical Andes and an update on the phylogeny of the genus
Fig 3. Capsicum longifolium Barboza & S. Leiva. (A) Flowering and fruiting branch. (B) Flower. (C) Opened corolla. (D, E, F) Anther, ventral, lateral and dorsal view, respectively. (G) Gynoecium. (H) Ovary in cross section. (I) Fruit. (J) Seed. Drawn by S. Leiva González.
Fig 1 in Four new species of Capsicum (Solanaceae) from the tropical Andes and an update on the phylogeny of the genus
Fig 1. Capsicum benoistii Hunz. ex Barboza. (A) Flowering branch. (B) Flower. (C) Calyx. (D) Section of the calyx showing venation in tube and appendages. (E) Trichome of the calyx. (F) Opened corolla. (G, H) Gynoecium, with long and short style, respectively. Drawn by N. de Flury.
Fig 7. Capsicum piuranum Barboza & S in Four new species of Capsicum (Solanaceae) from the tropical Andes and an update on the phylogeny of the genus
Fig 7. Capsicum piuranum Barboza & S. Leiva. (A) Flowering branch. (B) Calyx. (C) Flower. (D) Opened corolla. (E, F, G). Anther, ventral, dorsal and lateral view, respectively. (H) Gynoecium. (I) Fruit. (J) Seed. Drawn by S. Leiva González.
MOM6-COBALT2 model results for asymmetrical Ocean Carbon Responses to La Niña and El Niño in the Tropical Pacific Ocean
<p>1. MOM6 COBALT2 model results</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_fco2_decomposition_1990_2021.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_fco2_decomposition_1990_2021.nc</a></p> <p>(air-sea carbon flux decomposition)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_dic_stf_gas_1980_2023_detrend_deseason_matlab_tp.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_dic_stf_gas_1980_2023_detrend_deseason_matlab_tp.nc</a></p> <p> (detrended and deseasonalized air-sea CO2 flux, positive to ocean)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_sfc_chl_1980_2023.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_sfc_chl_1980_2023.nc</a></p> <p> (sea surface chlorophyll)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_SSH_1990_2021_detrend_deseason_matlab.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_SSH_1990_2021_detrend_deseason_matlab.nc</a></p> <p> (detrended and deseasonalized sea surface height)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_sfc_no3_1980_2023.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_sfc_no3_1980_2023.nc</a></p> <p>(sea surface nitrate, NO3)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_SST_1980-2023.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_SST_1980-2023.nc</a></p> <p> (sea surface temperature)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_sfc_po4_1980_2023.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_sfc_po4_1980_2023.nc</a></p> <p> (sea surface phosphate, PO4)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_SSS_1980-2023.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_SSS_1980-2023.nc</a></p> <p> (sea surface salinity)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_SSS_1990-2023_detrend_deseason_matlab.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_SSS_1990-2023_detrend_deseason_matlab.nc</a></p> <p>(detrended and deseasonalized sea surface salinity)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_SST_1990-2023_detrend_deseason_matlab.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_SST_1990-2023_detrend_deseason_matlab.nc</a></p> <p>(detrended and deseasonalized sea surface temperature)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_u_1990-2021_detrend_deseason_matlab.nc.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_u_1990-2021_detrend_deseason_matlab.nc.nc</a></p> <p>(detrended and deseasonalized sea surface zonal velocity)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_MLD_003_1980-2023.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_MLD_003_1980-2023.nc</a></p> <p>(Mixing layer depth with a density criterial of 0.03 kg/m3)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_v_1990-2021_detrend_deseason_matlab.nc.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_v_1990-2021_detrend_deseason_matlab.nc.nc</a></p> <p>(detrended and deseasonalized sea surface meridional velocity)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_pco2surf_1980_2023_detrend_deseason_matlab_tp.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_pco2surf_1980_2023_detrend_deseason_matlab_tp.nc</a></p> <p> (detrended and deseasonalized ocean pCO2 in the ocean surface)</p> <p><a href="../api/records/13325632/draft/files/hist_gcb_simA_ocean_static.nc/content" target="_blank" rel="noopener noreferrer">hist_gcb_simA_ocean_static.nc</a></p> <p>(grid of model)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_budget_mld001_pco2surf_1990_2021_detrend_deseason_matlab.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_budget_mld001_pco2surf_1990_2021_detrend_deseason_matlab.nc</a></p> <p> (budget terms used for ocean pCO2 budget analysis, vertical mean in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br>pco2_hadv_hdif: horizontal transport term, H_circ;<br>dpco2_vadv_vdif: vertical transport term,V_circ;<br>dpco2_bio: biological term<br>dpco2_rain: surface freshwater term<br>dpco2_sst: thermal term<br>dpco2_dt: pco2 response term<br>dpco2_flux: CO2 flux response term</p> <p>2. dataset</p> <p><a href="../api/records/13325632/draft/files/co2_products_Rodenbeck_v2022_monthly_all_detrend_deseason_by_filter.nc/content" target="_blank" rel="noopener noreferrer">co2_products_Rodenbeck_v2022_monthly_all_detrend_deseason_by_filter.nc</a></p> <p>(CO2 based MLS: detrended and deseasonalized ocean pCO2 and air-sea carbon flux in the ocean surface)</p> <p><a href="../api/records/13325632/draft/files/co2_products_Rodenbeck_v2022_monthly_area.nc/content" target="_blank" rel="noopener noreferrer">co2_products_Rodenbeck_v2022_monthly_area.nc</a></p> <div>(the grid of MLS)</div> <p><a href="../api/records/13325632/draft/files/co2_products_Landschutzer_v2022_MPI_SOM_FFN_2022_NCEI_OCADS_detrend_deseason_matlab.nc/content" target="_blank" rel="noopener noreferrer">co2_products_Landschutzer_v2022_MPI_SOM_FFN_2022_NCEI_OCADS_detrend_deseason_matlab.nc</a></p> <p>(CO2 based SOM-FFN: detrended and deseasonalized ocean pCO2 and air-sea carbon flux in the ocean surface)</p> <p><a href="../api/records/13325632/draft/files/co2_products_Landschutzer_v2022_MPI_SOM_FFN_2022_NCEI_OCADS_area.nc/content" target="_blank" rel="noopener noreferrer">co2_products_Landschutzer_v2022_MPI_SOM_FFN_2022_NCEI_OCADS_area.nc</a></p> <p>(the grid of SOM-FFN)</p> <p><a href="../api/records/13325632/draft/files/precipitation_JRA55-do-v1.5merge_res_all_monthly_prra.JRA.1.5.monthly_detrend_deseason.nc/content" target="_blank" rel="noopener noreferrer">precipitation_JRA55-do-v1.5merge_res_all_monthly_prra.JRA.1.5.monthly_detrend_deseason.nc</a></p> <p>(JRA: detrended and deseasonalized precipitation)</p> <p><a href="../api/records/13325632/draft/files/phosphate_all_woa18_all_p_monthly.nc/content" target="_blank" rel="noopener noreferrer">phosphate_all_woa18_all_p_monthly.nc</a></p> <p>(WOA: sea surface phosphate, PO4)</p> <p><a href="../api/records/13325632/draft/files/ssh_AVISO_all_sla_tp_twosat_phy_l4_vDT2018_monthly_1994_2020_03_01_detrend_deseason.nc/content" target="_blank" rel="noopener noreferrer">ssh_AVISO_all_sla_tp_twosat_phy_l4_vDT2018_monthly_1994_2020_03_01_detrend_deseason.nc</a></p> <p>(AVISO: detrended and deseasonalized sea surface height)</p> <p><a href="../api/records/13325632/draft/files/sss_satellite_all_oisss_v1_sss_201109-202203_monthly.nc/content" target="_blank" rel="noopener noreferrer">sss_satellite_all_oisss_v1_sss_201109-202203_monthly.nc</a></p> <p>(OISSS: sea surface salinity)</p> <p><a href="../api/records/13325632/draft/files/sss_satellite_all_oisss_v1_sss_201109-202203_monthly_detrend_deseason.nc/content" target="_blank" rel="noopener noreferrer">sss_satellite_all_oisss_v1_sss_201109-202203_monthly_detrend_deseason.nc</a></p> <div>(OISSS: detrended and deseasonalized sea surface salinity)</div> <p><a href="../api/records/13325632/draft/files/co2_SOCAT_SOCATv2022_tracks_gridded_monthly.nc/content" target="_blank" rel="noopener noreferrer">co2_SOCAT_SOCATv2022_tracks_gridded_monthly.nc</a></p> <p>(SOCAT: ocean pCO2)</p> <p><a href="../api/records/13325632/draft/files/mld_obs_mld_DR003_c1m_reg2.0_rewrite_nanvalue.nc/content" target="_blank" rel="noopener noreferrer">mld_obs_mld_DR003_c1m_reg2.0_rewrite_nanvalue.nc</a></p> <p>(Mixing layer depth with a density criterial of 0.03 kg/m3)</p> <p><a href="../api/records/13325632/draft/files/nitrate_all_woa18_all_n_monthly.nc/content" target="_blank" rel="noopener noreferrer">nitrate_all_woa18_all_n_monthly.nc</a></p> <p>(WOA: sea surface nitrate, NO3)</p> <p><a href="../api/records/13325632/draft/files/sst_OISST_allsst.avhrr-only-v2.1_1990_2021_monthly.nc/content" target="_blank" rel="noopener noreferrer">sst_OISST_allsst.avhrr-only-v2.1_1990_2021_monthly.nc</a></p> <p> (OISST: sea surface temperature)</p> <p><a href="../api/records/13325632/draft/files/sst_OISST_allsst.avhrr-only-v2.1_1990_2021_monthly_detrend_deseason.nc/content" target="_blank" rel="noopener noreferrer">sst_OISST_allsst.avhrr-only-v2.1_1990_2021_monthly_detrend_deseason.nc</a></p> <p>(OISST: detrended and deseasonalized sea surface temperature)</p> <p><a href="../api/records/13325632/draft/files/chl_OC_CCI_all_OC_CCI_momthly_chlor_a_4km_fv5.0_199709_202112_clim.nc/content" target="_blank" rel="noopener noreferrer">chl_OC_CCI_all_OC_CCI_momthly_chlor_a_4km_fv5.0_199709_202112_clim.nc</a></p> <p>(GlobColour: sea surface chlorophyll)</p> <p><a href="../api/records/13325632/draft/files/uwind_JRA55-do-v1.5merge_res_all_monthly_uas.JRA.1.5.monthly_detrend_deseason.nc/content" target="_blank" rel="noopener noreferrer">uwind_JRA55-do-v1.5merge_res_all_monthly_uas.JRA.1.5.monthly_detrend_deseason.nc</a></p> <p>(JAR: detrended and deseasonalized zonal wind velocity)</p> <p><a href="../api/records/13325632/draft/files/TAO_CO2_tao_pco2_13stations_monthly_deseason_1997_2017.nc/content" target="_blank" rel="noopener noreferrer">TAO_CO2_tao_pco2_13stations_monthly_deseason_1997_2017.nc</a></p> <p>(TAO: sea surface temperature, sea surface salinity, and ocean pco2)</p> <p>3. Figure</p> <p>Figure1-4, FigureS1-S11 are data analysis files used python</p> <p> </p>
Soil microbial communities from tropical forest and oil palm
<div> <h1>Description</h1> <p>A study examining the impact of selective logging and forest conversion to oil palm on soil microbial community composition. Soil samples were collected from old growth forest, selectively logged forest and oil palm plantations. Soil bacterial, protistan and fungal community composition were measured and summarised by calculating richness. </p> <h1>Projects</h1> <p> This dataset was collected as part of the following projects: </p><ul> <li><a href="https://safeproject.net/projects/project_view/124">https://safeproject.net/projects/project_view/124</a> </li> </ul> <p></p> <h1>Funding</h1> <p> These data were collected as part of research funded by: </p> <ul> <li>UK NERC-funded Biodiversity And Land-use Impacts on Tropical Ecosystem Function (BALI) consortium (Standard grant , NE/K016377/1 ) </li> </ul> <p></p> <p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p> <h1>Permits</h1> <p>These data were collected under permit from the following authorities:</p> <ul> <li>Sabah Biodiversity Centre ( Research licence JKM/MBS.1000-2/2 JLD.5 (20))</li> <li>Sabah Biodiversity Centre ( Export licence JKM/MBS.1000-2/3 JLD.2 (70))</li> </ul> <p></p> <h1>Files</h1> <p>This dataset consists of 1 file: SAFE_Dataset_Richness.xlsx</p> <h2>SAFE_Dataset_Richness.xlsx</h2> <p>This file contains dataset metadata and 1 data tables:</p> <h3>Soil_Microbial_Communities</h3> <ul> <li>Worksheet: Soil_Microbial_Communities</li> <li>Description: Summary richness statistics from bacterial 16S, protistan 18S and fungal ITS biomarker microbial sequencing from DNA extracted from soils</li> <li>Number of fields: 10</li> <li>Number of data rows: 225</li> <ul> <li>PlotName: Plot name corresponding to the GEM Carbon plot where soils were sampled (type: id)</li> <li>ForestType: Old-growth, Logged or Oil palm (type: categorical)</li> <li>ForestPlotsCode: Plot name as listed in ForestPlots database (type: id)</li> <li>Replicate: replicate identifier for which soil core taken from each subplot (type: replicate)</li> <li>location_name: Name of subplot where soils were collected (type: location)</li> <li>Bacteria_Richness: Number of observed bacterial taxa from sequencing of 16S marker genes from soil samples (type: numeric)</li> <li>Protist_Richness: Number of observed protistan taxa from sequencing of 18S marker genes from soil samples (type: numeric)</li> <li>Fungal_Richness: Number of observed fungal taxa from sequencing of 16S marker genes from soil samples (type: numeric)</li> <li>EcM_Fungal_Richness: Number of observed Ectomycorrhizal fungal taxa from sequencing of ITS marker genes from soil samples (type: numeric)</li> <li>AMF_Fungal_Richness: Number of observed Arbuscular Mycorrhizal fungal taxa from sequencing of ITS marker genes from soil samples (type: numeric)</li> </ul> </ul> <h1>Extents</h1> <ul> <li>Date range: 2014-10-01 to 2018-09-01</li> <li>Latitudinal extent: 4.64° to 4.954°</li> <li>Longitudinal extent: 116.95° to 117.796°</li> </ul> </div>
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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.