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FIGURE 1 in Dispersion of hooks on the anal fins of primary and secondary males in Brycon orbignyanus (Characiformes: Bryconidae): a secondary sexual trait for breeder selection
FIGURE 1 | Anal fins in Brycon orbignyanus. A. Specimen of B. orbignyanus. B. Anal fin regions. C. Rays (r). D. Anal fin rays. af: anal fin. bi: bifurcation of rays. ca: caudal region. cr: cranial region. fr: first ray. im: interradial membrane. me: medial region. sg: radius segment. Scales: A. 5 cm; B and D. 1 cm; C. 200 µm.
FIGURE 2 in Dispersion of hooks on the anal fins of primary and secondary males in Brycon orbignyanus (Characiformes: Bryconidae): a secondary sexual trait for breeder selection
FIGURE 2 | Details regarding the fins of Brycon orbignyanus. A, C and E. Rays without hooks. B, D and F. Rays with hooks. b: base. fb: first fork. r: rays. rs: rays with hooks. s: hooks. sg: rays segment. st: hooks cusp. tb: terminal bifurcation. Scales: A and B. 1 cm; C and D. 200 µm; E. 100 µm; F. 50 µm.
FIGURE 2 in Urban market amplifies strong species selectivity in Amazonian artisanal fisheries
FIGURE 2 | The most important 20 fish species by total biomass caught, in descending order, split by season. These species represent 90% of the total biomass caught.
FIGURE 5 in Urban market amplifies strong species selectivity in Amazonian artisanal fisheries
FIGURE 5 | Fish species diversity caught by rural artisanal fishers on the Rio Purus. Fishing catches to be sold were less diverse than those to be consumed. Trips were classified based on whether the stated intention by the fisher was to sell, for household consumption or both. The Shannon's diversity index, calculated per fishing trip, is shown.
FIGURE 4 in Urban market amplifies strong species selectivity in Amazonian artisanal fisheries
FIGURE 4 | Fish species rank curve (by percentage biomass), during the (A) high-water and (B) low-water seasons. (A) Mylossoma albiscopum and Colossoma macropomum dominate high water catches, both for sale only (red) and for both sale and consumption (black). These species are also both important in catches for consumption only (green), but Pimelodus blochii is the most important species in this category. (B) Arapaima gigas dominates low-water season commercial fish catches (red), Osteoglossum bicirrhosum in catches for both consumption and sale (black), and Mylossoma albiscopum (=M. duriventre) and Triportheus angulatus in catches for consumption only.
FIGURE 3 in Urban market amplifies strong species selectivity in Amazonian artisanal fisheries
FIGURE 3 | Fish assemblages vary markedly by (A) hydrological season and (B) geographical remoteness, shown by NMDS (Non-metric Multidimensional Scaling) graphs. Based on similarity analysis of seasonal fish assemblages. Less-remote communities are those <600 km fluvial travel distance from Manaus, which receive visits by Manaus-based boats that purchase fish and deposit ice at least weekly, while more-remote communities are those> 600 km fluvial travel distance from Manaus that do not.
FIGURE 1. Selected lizard and amphisbaenian material from studied localities. 1-2 in First early Eocene lizards from Spain and a study of the compositional changes between late Mesozoic and early Cenozoic Iberian lizard assemblages
FIGURE 1. Selected lizard and amphisbaenian material from studied localities. 1-2, Geiseltaliellus sp.: 1, left dentary (IPS 49740); 2, maxilla (IPS 83552); 3-4, Iguanidae indet.: 3, fragment of dentary (IPS 83535) with one preserved tooth, 4, fragment of?maxilla with four preserved teeth (IPS 49756); 5-6, Agamidae indet.: 5, Fragment of toothbearing bone preserving one tooth (IPS 83546), 6, fragment of dentary preserving two teeth (IPS 83543). 7-8, Gekkota indet.: 7, posterior portion of left dentary (IPS 59559), 8, anterior portion of left dentary (IPS 83520); 9, Scincoidea (?Scincidae) indet., fragment of right dentary (IPS 49752); 10,?Lacertidae indet., fragment of tooth-bearing bone perserving two teeth (IPS 49762); 11, Amphisbaenia indet., vertebra (IPS 59529); 12, cf. Placosaurus sp., partial parietal with fused osteoderms (IPS 59567); 13, Glyptosaurini indet., skull osteoderm (IPS 83532); 14, Glyptosaurinae indet., body osteoderm (IPS 83533); 15-18, Anguinae indet.: 15, keeled body osteoderm (IPS 83540), 16, unkeeled body osteoderm (IPS 83533), 17, partial parietal (IPS 83557), 18, vertebra (IPS 59538); 19-20, "Necrosauridae" indet.: 19, partial left dentary (IPS 83545), 20, osteoderm (IPS 49741). 1, 2, 5, 6, 11, 15 and 17-20 from Masia de l'Hereuet (MP8+9); 3, 4, 7, 8, 10 and 14 from La Morera (MP10); 12 from Escarlà (MP10); 13 and 16 from Font del Torricó. 1-10 and 19 in labial view; 11-12, 17 in dorsal view; 13-16 and 20 in external view; 18 in ventral view.
Dataset: Stimulus selection drives value-modulated somatosensory processing in superior colliculus
<p>Pluta Lab</p> <p>.mat data files used in the following paper</p> <p>Stimulus selection drives value-modulated somatosensory processing in superior colliculus </p> <p>Details can be found in readme.txt</p>
Interactions between enrichment planted seedlings and mature trees in selectively logged lowland dipterocarp forest - dataset
<div> <h2>Description</h2> <p>Old-growth forests in Southeast Asia are dominated by trees of the Dipterocarpaceae family which are targeted by selective logging. Their traits (supra-annual mast fruiting, limited dispersal, and recalcitrant seeds that form no seed bank) mean they can have poor natural regeneration rates in some selectively logged forests. Enrichment planting is commonly used to overcome recruitment limitation and increase restoration success. However, it is still unclear what factors influence the success rate of planted seedlings, including the characteristics of the surrounding tree matrix and local neighbourhood. <br>Here, we examine the growth and survival of 721 enrichment line-planted seedlings within 24 plots of the selectively logged forest of the Sabah Biodiversity Experiment, in Malaysian Borneo, in relation to their species identity and local neighbourhoods. We mapped the spatial location, size, and identity of nearly 5,000 surrounding matrix trees (≥ 10 cm diameter at breast height ) within a 10 m radius of focal planted seedlings in 2012 and 2015. We analysed levels of tree density-dependence (in terms of canopy openness and total basal area of surrounding matrix trees), asymmetric competition with naturally occurring trees (the proportion of total basal area within a 10 m radius belonging to the largest tree), and confamilial density-dependence (the proportion of total basal area within a 10 m radius belonging to dipterocarps) for each seedling. The following research questions were addressed: how do changes in (a) light availability, (b) local competition, (c) asymmetric competition, and (d) confamilial density-dependence affect the (1) growth and (2) survival of enrichment planted dipterocarp seedlings? To answer these questions, we mapped the tree size, identity (dipterocarp or non-dipterocarp), and spatial location of surrounding matrix trees with DBH ≥ 10 cm within a 10 m radius of each planted seedling. Column descriptions:<br>tree.id: The ID of the tree in question. This takes the format [plot].[line].[number] with cohort (Old [O] or new [N]) at the end.<br>IDPlots: The plot number the tree in question is located in.<br>Line_number: The specific line within a plot that the tree in question is located in.<br>sp.plot: Column specifying both the species and the plot number of the tree in question, separated by a period.<br>sp.cohort: Column specifying both the species and the cohort of the tree in question, separated by a comma.<br>richness: If the plot the tree is located in is a plot enrichment-planted with one (mono) or 16 species (sixteen).<br>X_m: Relative x coordinate - the first position of Field-Map had x,y,z-coordinates (0,0,0).<br>Y_m: Relative y coordinate.<br>Z_m: Relative z coordinate.<br>genus: The genus-level identity of the seedling.<br>species: The species-level identity of the seedling<br>survival: If the seedling was alive by 2015 (1 = alive, 0 = dead).<br>planting.date: The date of planting of the seedling.<br>survey_2002: the date of the survey for the 2002 census<br>survey_2011: the date of the survey for the 2011 census.<br>survey_2012: the date of the survey for the 2012 census.<br>survey_2015: the date of the survey for the 2015 census.<br>cohort: if this seedling was part of the initial planted cohort planted in 2002 (cohort 1) or the second cohort (cohort 2) which was planted later to replace those that had died.<br>age_2002: the age (in days) of seedlings at the time of their survey in 2002<br>age_2011: the age (in days) of seedlings at the time of their survey in 2011<br>age_2012: the age (in days) of seedlings at the time of their survey in 2012<br>age_2015: the age (in days) of seedlings at the time of their survey in 2015<br>diam_2002: Basal diameter of seedling in 2002, measured in mm.<br>diam_2011: Basal diameter of seedling in 2011, measured in mm.<br>diam_2012: Basal diameter of seedling in 2012, measured in mm.<br>diam_2015: Basal diameter of seedling in 2015, measured in mm.<br>DBH_2002: DBH of seedling in 2002, measured in mm.<br>DBH_2011: DBH of seedling in 2011, measured in mm.<br>DBH_2012: DBH of seedling in 2012, measured in mm.<br>DBH_2015: DBH of seedling in 2015, measured in mm.<br>rgr_2002: the relative growth rate of seedling, calculated as (ln(diameter in 2015)-ln(diameter in 2002) / (age in days in 2015 - age in days in 2002).<br>rgr_2011: the relative growth rate of seedling, calculated as (ln(diameter in 2015)-ln(diameter in 2011) / (age in days in 2015 - age in days in 2011).<br>rgr_2012: the relative growth rate of seedling, calculated as (ln(diameter in 2015)-ln(diameter in 2012) / (age in days in 2015 - age in days in 2012).<br>openness: The average canopy openness score across 4 cardinal directions, taken during the second census.<br>ba_total_5: the total basal area of surrounding trees within 5 m of the focal seedling <br>ba_total_10: the total basal area of surrounding trees between 5 and 10 m of the focal seedling <br>ba_total: the total basal area of surrounding trees within 10 m of the focal seedling <br>ba_mean_5: the mean basal area of surrounding trees within 5 m of the focal seedling <br>ba_mean_10: the mean basal area of surrounding trees between 5 and 10 m of the focal seedling <br>ba_mean: the mean basal area of surrounding trees within 10 m of the focal seedling <br>dbh_max_5: The basal area of the largest tree within 5 m of the focal seedling <br>dbh_max_10: The basal area of the largest tree between 5 and 10 m of the focal seedling <br>dbh_max: The basal area of the largest tree within 10 m of the focal seedling <br>no_trees_5: The number of trees within 5 m of the focal seedling<br>no_trees_10: The number of trees between 5 and 10 m of the focal seedling<br>no_trees: The number of trees between 5 and 10 m of the focal seedling<br>ba_total_5_nondip: the total basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding non-dipterocarps<br>ba_total_10_nondip: the total basal area of surrounding trees between 5 and 10 m of the focal seedling , considering only surrounding non-dipterocarps<br>ba_total_nondip: the total basal area of surrounding trees within 10 m of the focal seedling , considering only surrounding non-dipterocarps<br>ba_mean_5_nondip: the mean basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding non-dipterocarps<br>ba_mean_10_nondip: the mean basal area of surrounding trees between 5 and 10 m of the focal seedling, considering only surrounding non-dipterocarps<br>ba_mean_nondip: the mean basal area of surrounding trees within 10 m of the focal seedling, considering only surrounding non-dipterocarps<br>dbh_max_5_nondip: The basal area of the largest tree within 5 m of the focal seedling, considering only surrounding non-dipterocarps<br>dbh_max_10_nondip: The basal area of the largest tree between 5 and 10 m of the focal seedling, considering only surrounding non-dipterocarps<br>dbh_max_nondip: The basal area of the largest tree within 10 m of the focal seedling, considering only surrounding non-dipterocarps<br>no_trees_5_nondip: The number of trees within 5 m of the focal seedling, considering only surrounding non-dipterocarps<br>no_trees_10_nondip: The number of trees between 5 and 10 m of the focal seedling, considering only surrounding non-dipterocarps<br>no_trees_nondip: The number of trees within 10 m of the focal seedling, considering only surrounding non-dipterocarps<br>ba_total_5_dip: the total basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding dipterocarps<br>ba_total_10_dip: the total basal area of surrounding trees between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps<br>ba_total_dip: the total basal area of surrounding trees within 10 m of the focal seedling , considering only surrounding dipterocarps<br>ba_mean_5_dip: the mean basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding dipterocarps<br>ba_mean_10_dip: the mean basal area of surrounding trees between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps<br>ba_mean_dip: the mean basal area of surrounding trees within 10 m of the focal seedling, considering only surrounding dipterocarps<br>dbh_max_5_dip: The basal area of the largest tree within 5 m of the focal seedling, considering only surrounding dipterocarps<br>dbh_max_10_dip: The basal area of the largest tree between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps<br>dbh_max_dip: The basal area of the largest tree within 10 m of the focal seedling, considering only surrounding dipterocarps<br>no_trees_5_dip: The number of trees within 5 m of the focal seedling, considering only surrounding dipterocarps<br>no_trees_10_dip: The number of trees between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps<br>no_trees_dip: The number of trees within 10 m of the focal seedling, considering only surrounding dipterocarps<br>openness_log: the openness column now expressed on a natural log scale.<br>ba_total_log: The ba_total column expressed on a natural log scale.<br>ba_total_log_scaled: The ba_total_log column scaled using the scale() function (center = TRUE, scale = FALSE).<br>prop_dip: The proportion of basal area of surrounding trees that belong to dipterocarps<br>ba_max: The basal area of the largest tree, calculated as pi * ((dbh_max / 10) ^ 2) / 40000.<br>prop_ba_max: the proportion of the basal area that belongs to the largest single tree</p> <h2>Contact Person</h2> <p>Ryan Veryard (ryan@veryard.co.uk)</p> <h2>Funding</h2> <p>These data were collected as part of research funded by:</p> <ul> <li>National Environmental Research Council (Standard grant , NE/S007474/1 , <a href="https://www.environmental-research.ox.ac.uk/">https://www.environmental-research.ox.ac.uk/</a> )</li> <li>Agencia Estatal de Investigación de España (Standard grant , RYC2021-032049-I , <a href="https://www.aei.gob.es/">https://www.aei.gob.es/</a> )</li> <li>Ministry of Education, Youth and Sports of the Czech Republic (Standard grant , INTER-TRANSFER LTT17017 , <a href="https://msmt.gov.cz/">https://msmt.gov.cz/</a> )</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> <h2>Files</h2> <p>This dataset consists of 1 file: fieldmap_dataset.xlsx</p> <h3>fieldmap_dataset.xlsx</h3> <p>This file contains dataset metadata and 1 data tables:</p> <h3>Interactions between enrichment planted seedlings and mature trees in selectively logged lowland dipterocarp forest - dataset</h3> <ul> <li>Worksheet: Fieldmap_data</li> <li>Description: Dataset for the published paper "Interactions between enrichment planted seedlings and mature trees in selectively logged lowland dipterocarp forest"</li> <li>Number of fields: 74</li> <li>Number of data rows: 721</li> <ul> <li>tree.id: The ID of the tree in question. This takes the format [plot].[line].[number] with cohort (Old [O] or new [N]) at the end. (type: replicate)</li> <li>IDPlots: The plot number the tree in question is located in. (type: location)</li> <li>Line_number: The specific line within a plot that the tree in question is located in. (type: numeric)</li> <li>richness: If the plot the tree is located in is a plot enrichment-planted with one (mono) or 16 species (sixteen). (type: ordered categorical)</li> <li>X_m: Relative x coordinate - the first position of Field-Map had x,y,z-coordinates (0,0,0). (type: numeric)</li> <li>Y_m: Relative y coordinate. (type: numeric)</li> <li>Z_m: Relative z coordinate. (type: numeric)</li> <li>genus: The genus-level identity of the seedling. (type: categorical)</li> <li>species: The species-level identity of the seedling (type: taxa)</li> <li>survival: If the seedling was alive by 2015 (1 = alive, 0 = dead). (type: numeric trait)</li> <li>planting.date: The date of planting of the seedling. (type: date)</li> <li>survey_2002: the date of the survey for the 2002 census (type: date)</li> <li>survey_2011: the date of the survey for the 2011 census. (type: date)</li> <li>survey_2012: the date of the survey for the 2012 census. (type: date)</li> <li>survey_2015: the date of the survey for the 2015 census. (type: date)</li> <li>cohort: if this seedling was part of the initial planted cohort planted in 2002 (cohort 1) or the second cohort (cohort 2) which was planted later to replace those that had died. (type: ordered categorical)</li> <li>age_2002: the age (in days) of seedlings at the time of their survey in 2002 (type: numeric trait)</li> <li>age_2011: the age (in days) of seedlings at the time of their survey in 2011 (type: numeric trait)</li> <li>age_2012: the age (in days) of seedlings at the time of their survey in 2012 (type: numeric trait)</li> <li>age_2015: the age (in days) of seedlings at the time of their survey in 2015 (type: numeric trait)</li> <li>diam_2002: Basal diameter of seedling in 2002, measured in mm. (type: numeric trait)</li> <li>diam_2011: Basal diameter of seedling in 2011, measured in mm. (type: numeric trait)</li> <li>diam_2012: Basal diameter of seedling in 2012, measured in mm. (type: numeric trait)</li> <li>diam_2015: Basal diameter of seedling in 2015, measured in mm. (type: numeric trait)</li> <li>DBH_2002: DBH of seedling in 2002, measured in mm. (type: numeric trait)</li> <li>DBH_2011: DBH of seedling in 2011, measured in mm. (type: numeric trait)</li> <li>DBH_2012: DBH of seedling in 2012, measured in mm. (type: numeric trait)</li> <li>DBH_2015: DBH of seedling in 2015, measured in mm. (type: numeric trait)</li> <li>rgr_2002: the relative growth rate of seedling, calculated as (ln(diameter in 2015)-ln(diameter in 2002) / (age in days in 2015 - age in days in 2002). (type: numeric trait)</li> <li>rgr_2011: the relative growth rate of seedling, calculated as (ln(diameter in 2015)-ln(diameter in 2011) / (age in days in 2015 - age in days in 2011). (type: numeric trait)</li> <li>rgr_2012: the relative growth rate of seedling, calculated as (ln(diameter in 2015)-ln(diameter in 2012) / (age in days in 2015 - age in days in 2012). (type: numeric trait)</li> <li>openness: The average canopy openness score across 4 cardinal directions, taken during the second census. (type: numeric)</li> <li>ba_total_5: the total basal area of surrounding trees within 5 m of the focal seedling (type: numeric)</li> <li>ba_total_10: the total basal area of surrounding trees between 5 and 10 m of the focal seedling (type: numeric)</li> <li>ba_total: the total basal area of surrounding trees within 10 m of the focal seedling (type: numeric)</li> <li>ba_mean_5: the mean basal area of surrounding trees within 5 m of the focal seedling (type: numeric)</li> <li>ba_mean_10: the mean basal area of surrounding trees between 5 and 10 m of the focal seedling (type: numeric)</li> <li>ba_mean: the mean basal area of surrounding trees within 10 m of the focal seedling (type: numeric)</li> <li>dbh_max_5: The basal area of the largest tree within 5 m of the focal seedling (type: numeric)</li> <li>dbh_max_10: The basal area of the largest tree between 5 and 10 m of the focal seedling (type: numeric)</li> <li>dbh_max: The basal area of the largest tree within 10 m of the focal seedling (type: numeric)</li> <li>no_trees_5: The number of trees within 5 m of the focal seedling (type: numeric)</li> <li>no_trees_10: The number of trees between 5 and 10 m of the focal seedling (type: numeric)</li> <li>no_trees: The number of trees between 5 and 10 m of the focal seedling (type: numeric)</li> <li>ba_total_5_nondip: the total basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>ba_total_10_nondip: the total basal area of surrounding trees between 5 and 10 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>ba_total_nondip: the total basal area of surrounding trees within 10 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>ba_mean_5_nondip: the mean basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>ba_mean_10_nondip: the mean basal area of surrounding trees between 5 and 10 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>ba_mean_nondip: the mean basal area of surrounding trees within 10 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>dbh_max_5_nondip: The basal area of the largest tree within 5 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>dbh_max_10_nondip: The basal area of the largest tree between 5 and 10 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>dbh_max_nondip: The basal area of the largest tree within 10 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>no_trees_5_nondip: The number of trees within 5 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>no_trees_10_nondip: The number of trees between 5 and 10 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>no_trees_nondip: The number of trees within 10 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>ba_total_5_dip: the total basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>ba_total_10_dip: the total basal area of surrounding trees between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>ba_total_dip: the total basal area of surrounding trees within 10 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>ba_mean_5_dip: the mean basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>ba_mean_10_dip: the mean basal area of surrounding trees between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>ba_mean_dip: the mean basal area of surrounding trees within 10 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>dbh_max_5_dip: The basal area of the largest tree within 5 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>dbh_max_10_dip: The basal area of the largest tree between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>dbh_max_dip: The basal area of the largest tree within 10 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>no_trees_5_dip: The number of trees within 5 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>no_trees_10_dip: The number of trees between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>no_trees_dip: The number of trees within 10 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>openness_log: the openness column now expressed on a natural log scale. (type: numeric)</li> <li>ba_total_log: The ba_total column expressed on a natural log scale. (type: numeric)</li> <li>ba_total_log_scaled: The ba_total_log column scaled using the scale() function (center = TRUE, scale = FALSE). (type: numeric)</li> <li>prop_dip: The proportion of basal area of surrounding trees that belong to dipterocarps (type: numeric)</li> <li>ba_max: The basal area of the largest tree, calculated as pi * ((dbh_max / 10) ^ 2) / 40000. (type: numeric)</li> <li>prop_ba_max: the proportion of the basal area that belongs to the largest single tree (type: numeric)</li> </ul> </ul> <h2>Extents</h2> <ul> <li>Date range: 2002-07-01 to 2015-05-28</li> <li>Latitudinal extent: 5.07° to 5.1°</li> <li>Longitudinal extent: 117.64° to 117.67°</li> </ul> <h2>Taxonomic coverage</h2> <p>This dataset contains data associated with taxa and these have been validated against appropriate taxonomic authority databases.</p> <h3>GBIF taxa details</h3> <p>The following taxa were validated against the GBIF backbone dataset (version 2023-08-28). If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p> <div> - Plantae<br> -  - Tracheophyta<br> -  -  - Magnoliopsida<br> -  -  -  - Malvales<br> -  -  -  -  - Dipterocarpaceae<br> -  -  -  -  -  - <em>Dipterocarpus</em><br> -  -  -  -  -  -  - <em>Dipterocarpus conformis</em><br> -  -  -  -  -  - <em>Dryobalanops</em><br> -  -  -  -  -  -  - <em>Dryobalanops lanceolata</em><br> -  -  -  -  -  - <em>Hopea</em><br> -  -  -  -  -  -  - <em>Hopea ferruginea</em><br> -  -  -  -  -  -  - <em>Hopea sangal</em><br> -  -  -  -  -  - <em>Parashorea</em><br> -  -  -  -  -  -  - <em>Parashorea malaanonan</em><br> -  -  -  -  -  -  - <em>Parashorea warburgii</em> (as synonym: <em>Parashorea tomentella</em>)<br> -  -  -  -  -  - <em>Shorea</em><br> -  -  -  -  -  -  - <em>Shorea argentifolia</em><br> -  -  -  -  -  -  - <em>Shorea beccariana</em><br> -  -  -  -  -  -  - <em>Shorea faguetiana</em><br> -  -  -  -  -  -  - <em>Shorea gibbosa</em><br> -  -  -  -  -  -  - <em>Shorea johorensis</em><br> -  -  -  -  -  -  - <em>Shorea leprosula</em><br> -  -  -  -  -  -  - <em>Shorea macrophylla</em><br> -  -  -  -  -  -  - <em>Shorea macroptera</em><br> -  -  -  -  -  -  - <em>Shorea ovalis</em><br> -  -  -  -  -  -  - <em>Shorea parvifolia</em></div> </div>
FIGURE 2 in Prey selectivity of the invasive largemouth bass towards native and non-native prey: an experimental approach
FIGURE 2 | Relationship between the Manly-Chesson selectivity and prey availability for Micropterus salmoides. Higher values indicate preference for non-native species. Shading represents 95% confidence intervals. Note that because the index fluctuates between 0 and 1, with 2 types of prey and equal availability of prey for both types, the result of the index for one prey is exactly the opposite of the other. For this reason, the graph only shows the results of the index for the non-native species. The graph for the other type of prey would be the spectral image of this one.
FIGURE 1 in Prey selectivity of the invasive largemouth bass towards native and non-native prey: an experimental approach
FIGURE 1 | Relative consumption of non-native (Oreochromis niloticus and Coptodon rendalli) and native (Geophagus iporangensis) prey, considering different prey availability for Micropterus salmoides.
The Neural Basis of Attentional Selection in Goal-Directed Memory Retrieval
<p>The provided behavioral and EEG data belongs to the publication entitled: "Neural Basis of Attentional Selection in Goal-Directed Memory Retrieval" published in the journal Scientific Reports (article DOI: 10.1038/s41598-024-71691-x). </p> <p><strong>Abstract</strong></p> <p>Goal-directed memory reactivation involves retrieving the most relevant information for the current behavioral goal. Previous research has linked this process to activations in the fronto-parietal network, but the underlying neurocognitive mechanism remains poorly understood. The current electroencephalogram (EEG) study explores attentional selection as a possible mechanism supporting goal-directed retrieval. We designed a long-term memory experiment containing three phases. First, participants learned associations between objects and two screen locations. In a following phase, we changed the relevance of some locations (selective cue condition) to simulate goal-directed retrieval. We also introduced a control condition, in which the original associations remained unchanged (neutral cue condition). Behavior performance measured during the final retrieval phase revealed faster and more confident responses in the selective vs. neutral condition. At the EEG level, we found significant differences in decoding accuracy, with above-chance effects in the selective cue condition but not in the neutral cue condition. Additionally, we observed a stronger posterior contralateral negativity and lateralized alpha power in the selective cue condition. Overall, these results suggest that attentional selection enhances task-relevant information accessibility, emphasizing its role in goal-directed memory retrieval.</p>
Data described in the article "Diaminocyclopentane – l-Lysine Adducts: Potent and selective inhibitors of human O-GlcNAcase"
<p>The dataset includes supplementary data: Synthesis, kinetic assays, molecular modeling, NMR spectra of the study titled "Diaminocyclopentane – l-Lysine Adducts: Potent and selective inhibitors of human O-GlcNAcase" available here: https://doi.org/10.1016/j.bioorg.2024.107452</p>
Selected Simple Natural Antimicrobial Terpenoids as Additives to Control Biodegradation of Polyhydroxy Butyrate
<p><strong>Abstract</strong></p> <div>In this experimental research, different types of essential oils (EOs) were blended with polyhydroxybutyrate (PHB) to study the influence of these additives on PHB degradation. The blends were developed by incorporating three terpenoids at two concentrations (1 and 3%). The mineralization rate obtained from CO<sub>2</sub> released from each sample was the factor that defined biodegradation. Furthermore, scanning electron microscope (SEM), differential scanning calorimetry (DSC), and dynamic mechanical analysis (DMA) were used in this research. The biodegradation percentages of PHB blended with 3% of eucalyptol, limonene, and thymol after 226 days were reached 66.4%, 73.3%, and 76.9%, respectively, while the rate for pure PHB was 100% after 198 days, and SEM images proved these results. Mechanical analysis of the samples showed that eucalyptol had the highest resistance level, even before the burial test. The other additives showed excellent mechanical properties although they had less mechanical strength than pure PHB after extrusion. The samples’ mechanical properties improved due to their crystallinity and decreased glass transition temperature (Tg). DSC results showed that blending terpenoids caused a reduction in Tg, which is evident in the DMA results, and a negligible reduction in melting point (Tm).</div> <p> </p> <p><strong>Open access data</strong></p> <p>The datasets for this publication can be accessed using the DOI: 10.5281/zenodo.13829790 or via the zip folder below.</p>
Unmet health-related needs for three selected health conditions in Belgium
<p><strong>NEED estimates for the appraisal of unmet health-related needs for three causes in Belgium.</strong></p> <p>The identification of unmet health-related needs is crucial for the development of a needs-based healthcare policy and innovation. Meeting unmet needs is a common objective of many stakeholders in the health system. Although there is no single definition of unmet health-related needs, there is a consensus that these can be approached both from the perspective of the individual patient and from the societal perspective.</p> <p>To address this need, the <strong>KCE</strong> and <strong>Sciensano</strong> are conducting a national <strong>unmet health-related needs study</strong>. In this study, evidence related to unmet health-related needs are collected via a literature review, a patient interview, and a patient survey.</p> <p>For more info, please visit the <a href="https://www.unmet-needs.eu/" target="_blank" rel="noopener">NEED project page</a></p> <p>The data can be interactively explored via the <a href="https://healthinformation.sciensano.be/shiny/NEED">NEED app</a></p>
Data for Publication - Farmer Preferences and Selection Criteria for Shade Trees in Robusta Coffee Agroforestry Systems in Tshopo Province, DRC
<p>Data used for publication - "Farmer Preferences and Selection Criteria for Shade Trees in Robusta Coffee Agroforestry Systems in Tshopo Province, DRC"</p>
CLIP Features and Selected Relevance Judgments Subset for TRECVID Ad-hoc Search (2019-2023)
<div> <div> <div> <div> <div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <p>This repository contains CLIP features and annotations for a subset of V3C images, based on their relevance to selected queries from the TREC Video Retrieval Evaluation (TRECVID) Ad-hoc Video Search (AVS) task. The data includes annotations for AVS queries and judgments conducted in TRECVID from 2019 to 2023 [1], using the V3C1 and V3C2 collections [2]. Specifically, the TRECVID-AVS collection covers 89 queries, with video shots manually labeled as relevant (1), non-relevant (0), or not annotated (-1).</p> <p>We used approximately 2.6 million keyframes extracted from these video shots, mapping the annotations to the corresponding keyframes (note that there may not be a one-to-one correspondence between TRECVID shotID since multiple frames might be extracted from a single shot). Image representations are based on CLIP ViT-H/14 - LAION-2B features [3]. The timestamps of the keyframes and their CLIP features are sourced from the VISIONE repository [4].</p> <p>Given the incomplete nature of the TRECVID ground truth (where only a subset of video segments were judged per query), we focused on queries with at least 200 positive and 1400 negative annotations. This resulted in 80 datasets, each containing 1500 images—10% labeled as relevant and 90% as non-relevant.</p> <h3>Contents of the Repository:</h3> <ol> <li> <p><strong>Query-specific CSV Files:</strong> For each of the 80 selected AVS query (e.g., <code>1591</code>), the corresponding CSV file (e.g., <code>1591.csv</code>) contains a column for each image, where:</p> <ul> <li><strong>VISIONE image ID</strong> is in the first row.</li> <li><strong>CLIP features</strong> are in the subsequent rows.</li> <li><strong>Relevance annotations</strong> are in the last row: <code>1</code> for relevant, <code>0</code> for non-relevant.</li> </ul> </li> <li> <p><strong>Post-processed Datasets:</strong></p> <ul> <li><code>dataset_normalized.zip</code>: L2-normalized CLIP features.</li> <li><code>dataset_softmax.zip</code>: CLIP features converted into probabilities using a softmax function.</li> <li><code>dataset_logistic.zip</code>: CLIP features converted into probabilities using a logistic function followed by L1 normalization.</li> </ul> </li> <li> <p><strong>Text Feature Data:</strong> <code>clip_laion_text_features.csv</code> contains additional details for each query, including the query ID, query text, and L2 normalized CLIP features extracted from the query text.</p> </li> </ol> <h3>Citation and Usage:</h3> <p>This data was used in the experiments described in:</p> <p>Lucia Vadicamo, Francesca Scotti, Alan Dearle, Richard Connor, <em>Comparative Analysis of Relevance Feedback Techniques for Image Retrieval</em>, in Proceedings of the 31st International Conference on Multimedia Modeling (MMM 2025).</p> <p>The data is released under a Creative Commons Attribution license. If you use it in your research, please cite the above work. </p> <h3>References:</h3> <p>[1]TRECVID Data: <a href="https://www-nlpir.nist.gov/projects/trecvid/trecvid.data.html">https://www-nlpir.nist.gov/projects/trecvid/trecvid.data.html</a><br>[2] Rossetto, L., Schuldt, H., Awad, G., Butt, A.A.: V3C - A research video collection. <em>In: International Conference on Multimedia Modeling</em>, pp. 349–360. Springer (2019).<br>[3] https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K<br>[4] VISIONE Repository: <a href="https://zenodo.org/records/8188570">https://zenodo.org/records/8188570</a></p> </div> </div> </div> </div> </div> </div> <p> </p> <p> </p> <p> </p>
Linked collectors and determiners for: Flora of Sumatra: Vascular plant collection of selected families deposited at Herbarium of Andalas University (ANDA).
Natural history specimen data linked to collectors and determiners held within, "Flora of Sumatra: Vascular plant collection of selected families deposited at Herbarium of Andalas University (ANDA)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/3e0987c4-375f-4d68-b2ac-5e4e3a6d3d6d">https://bionomia.net/dataset/3e0987c4-375f-4d68-b2ac-5e4e3a6d3d6d</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/3e0987c4-375f-4d68-b2ac-5e4e3a6d3d6d">https://gbif.org/dataset/3e0987c4-375f-4d68-b2ac-5e4e3a6d3d6d</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Vascular plants of the Amur River Basin, Russia: specimen based occurrence dataset of 100 selected species.
Natural history specimen data linked to collectors and determiners held within, "Vascular plants of the Amur River Basin, Russia: specimen based occurrence dataset of 100 selected species". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/0c7bd9e3-ded7-4de4-99ec-d5145361ff48">https://bionomia.net/dataset/0c7bd9e3-ded7-4de4-99ec-d5145361ff48</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/0c7bd9e3-ded7-4de4-99ec-d5145361ff48">https://gbif.org/dataset/0c7bd9e3-ded7-4de4-99ec-d5145361ff48</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Occurrence of Heilipus squamosus from selected collections in the United States.
Natural history specimen data linked to collectors and determiners held within, "Occurrence of Heilipus squamosus from selected collections in the United States". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/17909377-6d89-41b5-bf65-807665c68a3b">https://bionomia.net/dataset/17909377-6d89-41b5-bf65-807665c68a3b</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/17909377-6d89-41b5-bf65-807665c68a3b">https://gbif.org/dataset/17909377-6d89-41b5-bf65-807665c68a3b</a>. Formatted as a Frictionless Data package.
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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.