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2,609 results for “Web”
Figure 8 in The web amount and quality of web spiders (Agelenidae, Pholcidae), Agelena labyrinthica (Clerck, 1757) and Holocnemus pluchei (Scopoli, 1763), depending on food and temperature
Figure 8. Food amounts of Holocnemus pluchei at 20–25 °C (1: Drosophila melanogaster; 2: food caught with insect net).
Figure 7 in The web amount and quality of web spiders (Agelenidae, Pholcidae), Agelena labyrinthica (Clerck, 1757) and Holocnemus pluchei (Scopoli, 1763), depending on food and temperature
Figure 7. Food amounts of Agelena labyrinthica at 25–30°C (1: Drosophila melanogaster; 2: food caught with insect net).
Figure 6 in The web amount and quality of web spiders (Agelenidae, Pholcidae), Agelena labyrinthica (Clerck, 1757) and Holocnemus pluchei (Scopoli, 1763), depending on food and temperature
Figure 6. Food amounts of Agelena labyrinthica at 20–25°C (1:Drosophila melanogaster; 2: food caught with insect net). Table 4. The Mann–Whitney U test results of the amounts of food consumed under different conditions.
Fig. 3 in The role of cladocerans in green and brown food web coupling
Fig. 3. The proportion of the contribution of POC, phytoplankton, and biofilm to cladoceran biomass in the three PIAP lagoons. Boxplots show the quartiles of 97.5%, 75%, 50%, 25%, and 2.5%. The lower and upper boxes indicate the 25% and 50% quartiles. Likewise, the lower and upper vertical lines indicate the 97.5% and 2.5% quartiles and the horizontal lines show the mode of contribution.
Figure 5 in Predator exposure and size-related variation in web-building and web-decorating behavior in Argiope appensa
Figure 5. Frequency distribution of cruciate-patterned web decorations from spiders in Rota and Guam. Stacks represent size class of spiders.
Figure 1 in Predator exposure and size-related variation in web-building and web-decorating behavior in Argiope appensa
Figure 1. Web decoration in A. appensa: (A) close-up of web decoration; (B) linear/ diagonal pattern; (C) cruciate pattern.
Figure 3 in Predator exposure and size-related variation in web-building and web-decorating behavior in Argiope appensa
Figure 3. Variation in web-building behavior in A. appensa between locations and between spider size class categories, (A) web diameter, SNK: small <medium <large; and (B) web decoration length, SNK: small = medium <large. Dashed lines represent the mean for each location with all the size classes pooled.
On the Role of Font Formats in Building Efficient Web Applications - Replication Package
<p>Replication Package for the paper - On the Role of Font Formats in Building Efficient Web Applications. It is the final version of the replication package for the PROFES 2023 conference (<a href="https://conf.researchr.org/details/profes-2023/profes-2023-papers/18/On-the-Role-of-Font-Formats-in-Building-Efficient-Web-Applications">https://conf.researchr.org/details/profes-2023/profes-2023-papers/18/On-the-Role-of-Font-Formats-in-Building-Efficient-Web-Applications</a>). </p>
The mOTUs online database provides web-accessible genomic context to taxonomic profiling of microbial communities - Supplementary Tables
<p><strong>Supplementary Table 1:</strong></p> <p>A map between each of the genomes in mOTUs-db (3’747’151), the associated study and its metagenomic sample (in case of MAGs).</p> <p>Columns:</p> <p><code> GENOME → Unique mOTUs-db name of the genome</code><br><code> STUDY → Unique mOTUs-db name of the study</code><br><code> IS_MAG → True if genome is a MAG, otherwise False </code><br><code> METAGENOMIC_SAMPLE → Unique name of the metagenomic sample or NA in case of non-MAG genome</code></p> <p>Example:</p> <p><code> GENOME STUDY IS_MAG METAGENOMIC_SAMPLE</code><br><code> ---------------------------------------------------------------------------------------------</code><br><code> ACIN21-1_SAMN05421555_MAG_00000001 ACIN21-1 True ACIN21-1_SAMN05421555_METAG</code><br><code> RSGB23-1_GCA-006096615-V1_GENO_10000001 RSGB23-1 False NA</code></p> <p><strong>Supplementary Table 2:</strong></p> <p>A map between all non-MAG genomes (919’090) and their source (e.g. Refseq or JGI).</p> <p>Columns:</p> <p><code> GENOME → Unique mOTUs-db name of the genome</code><br><code> SOURCE_SAMPLE_LINK → Link to the original location of this genome</code></p> <p>Example:</p> <p><code> #GENOME SOURCE_SAMPLE_LINK</code><br><code> --------------------------------------------------------------------------------------------------------</code><br><code> JGIG23-1_GA0055041_GENO_10000001 https://gold.jgi.doe.gov/analysis_project?id=Ga0055041</code><br><code> RSGB23-1_GCA-006717865-V1_GENO_10000001 https://www.ncbi.nlm.nih.gov/datasets/genome/GCA_006717865.1</code></p> <p><strong>Supplementary Table 3:</strong></p> <p>A list of all metagenomic studies processed for the mOTUs-db, their number of samples, the number of reconstructed MAGs and the associated publication.</p> <p>Columns:</p> <p><code> STUDY --> Unique mOTUs-db study identifier</code><br><code> BIOPROJECT --> Public identifier (NCBI/JGI) of metagenomic sequencing project</code><br><code> SAMPLES --> Number of metagenomic samples</code><br><code> MAGs --> Number of reconstructed MAGs</code><br><code> PUBLICATION --> Link to publication</code></p> <p>Example:</p> <p><code> STUDY BIOPROJECT SAMPLES MAGs PUBLICATION</code><br><code> -------------------------------------------------------------------------------------------------</code><br><code> ACIN21-1 PRJEB44456 58 1,110 https://www.nature.com/articles/s42003-021-02112-2</code></p> <p><strong>Supplementary Table 4:</strong></p> <p>Mapping between mOTUs-db sample identifier, the associated biosample and the environment.</p> <p>Columns:</p> <p><code> SAMPLE --> Unique mOTUS-db sample identifier</code><br><code> BIOSAMPLE --> Public identifier (NCBI/JGI) of metagenomic sample</code><br><code> STUDY --> Unique mOTUs-db study identifier</code><br><code> ENVIRONMENT --> Environment of metagenomic sample</code><br><code> SOURCE_SAMPLE_LINK --> Link to the original location of this sample</code></p> <p>Example:</p> <p><code> #SAMPLE BIOSAMPLE STUDY ENVIRONMENT SOURCE_SAMPLE_LINK</code><br><code> ---------------------------------------------------------------------------------------------------------------------</code><br><code> ACIN21-1_SAMN05421555_METAG SAMN05421555 ACIN21-1 marine https://www.ncbi.nlm.nih.gov/biosample/SAMN05421555/</code></p> <p><strong>Supplementary Table 5:</strong></p> <p>A list of environments covered in the mOTUs-db mapped to the respective NCBI taxonomy (if possible)</p> <p>Columns:</p> <p><code> TERM --> Unique environment name</code><br><code> NCBI TAXONOMY ID --> Link to the NCBI taxonomy</code></p> <p>Example:</p> <p><code> TERM NCBI TAXONOMY ID</code><br><code> ----------------------------------------------</code><br><code> activated sludge metagenome NCBI:txid942017</code><br><code> air metagenome NCBI:txid655179</code></p>
FIGURE 7 in The cost of gold: Mercury contamination of fishes in a Neotropical river food web
FIGURE 7 | Relationships between body size (standard length, mm) and total Hg of fishes commonly used for consumption. Top panel (non-mined) contains three species that were common at mined sites (bottom panel). Relationships were estimated for five species that are commonly found and consumed, and for what a class structure was observed. Symbols represent individual fish. Clear diamonds = frugivores [1 sp.: Myloplus asterias (n = 19)], dark triangles = carnivores [1 sp.: Serrasalmus eigenmanni (n = 14)], green circles = piscivores [(3 spp.: S. rhombeus (n = 24), Ageneiosus ucayalensis (n = 2), Hoplias malabaricus (n = 1)].
FIGURE 6 in The cost of gold: Mercury contamination of fishes in a Neotropical river food web
FIGURE 6 | Mean Hg concentrations (dry weight) in sediments and instream biota separated by trophic groups (producers and consumers) between mined and non-mined sites in the Mazaruni River, Guyana. Species were grouped together based on their trophic guild. Polynomial trendline: y = 0.05x2 - 0.22x + 0.28, coefficient of determination R² = 0.98.
FIGURE 2 in The cost of gold: Mercury contamination of fishes in a Neotropical river food web
FIGURE 2 | Locations of sampling sites in the middle Mazaruni River drainage in A. Guyana within B. South America. Sampling sites included location in the C. Main channel of the Mazaruni River (mined) and three main tributaries including Eping River (non-mined), Puterang and Kurupung rivers (mined).
FIGURE 5 in The cost of gold: Mercury contamination of fishes in a Neotropical river food web
FIGURE 5 | Biomagnification of Hg in the Mazaruni River, Guyana, represented as the log total Hg concentrations plotted against nitrogen (δ15N) isotope ratios. Species were grouped together based on their trophic guild. Each symbol represents the average of all species within each trophic guild. The dashed horizontal line indicates the reference limit of 0.5 µg/g by WHO guideline for Hg in fish consumed by humans.
FIGURE 1 in The cost of gold: Mercury contamination of fishes in a Neotropical river food web
FIGURE 1 | Conceptual model of how Hg enters riverine food webs of the Mazaruni River, Guyana. First, artisanal gold mining operations installed in the river use a suction dredge to reach gold contained in bottom sediments. These mining operations use mercury for gold amalgamation of which the majority is lost to the atmosphere or river. Once in the river, Hg can be methylated by microbes into the toxic Methylmercury (MeHg) and be assimilated rapidly by aquatic biota. Hg concentration bioaccumulates and biomagnifies in the trophic food chain. Fishes at higher trophic levels [piscivores and carnivores (e.g., piranhas, aimaras, and catfishes)] are important items in the diet of local communities and can become a direct source of Hg uptake via direct consumption.
FIGURE 4 in The cost of gold: Mercury contamination of fishes in a Neotropical river food web
FIGURE 4 | Mean nitrogen (δ15N) and carbon (δ13C) isotope ratios of fish species collected from mined (right panel) and non-mined (left panel) sites in the Mazaruni River, Guyana. Species were grouped together based on their trophic guild. Each symbol represents the average of all species within each trophic guild. Numbers represent the basal resources collected at surveyed sites (1 = bryophyte, 2 = benthic algae, 3 = aquatic macrophytes). The abbreviations for the fish trophic guilds: Algivore/ Detritivore (Alg/Det), Insectivore (Insec), Herbivore (Herb), Omnivore (Omni), Carnivore (Carn), Piscivore (Pisc), as well as one shrimp (Macrobrachium).
FIGURE 3 in The cost of gold: Mercury contamination of fishes in a Neotropical river food web
FIGURE 3 | Sampling locations on the middle Mazaruni River: A. Main channel of the upper Eping River a small, black water tributary of the Mazaruni River. This river appeared more pristine and less impacted by gold mining activities; B. Sandy, shallow habitat sampled in middle channel of the upper Eping River; C. A gold dredge in the middle of the Kurupung River, on the right side is evidence of soil removal that resulted from mining activities; D. Deforestation (to establish mining stations) along the Kurupung River upstream, left bank from the confluence with the Mazaruni River; E-F. Main channel of the Mazaruni River at Olive Creek. At this location, the main channel of Mazaruni River is highly impacted by gold mining activities and gold dredges and 'tailing' beaches are commonly observed; G-H. The main channel of the lower Puterang River before the confluence with the Mazaruni River. At these locations, the Puterang River carries down heavily silted sediments that are spilled into the Mazaruni River.
Figures 14–23. 14–21 Theridion manjithar Tikader, 1970 in A new species of the cob web spider genus Theridion from India (Araneae: Theridiidae)
Figures 14–23. 14–21 Theridion manjithar Tikader, 1970, holotype (NZC-ZSI-3275/18).14, abdomen, dorsal view. 15, same, anterior view. 16, same, posterior view. 17, same, ventral view. 18, carapace, dorsal view. 19, same, lateral view. 20, epigyne, dorsal view. 21, vulva, dorsal view. Scale bars 14-17, 0.5mm; 18-19, 0.2mm; 20-21, 1mm. 22–23 Theridion melanostictum O. Pickard-Cambridge, 1876. 22- internal epigyne, dorsal view. 23- vulva, dorsal view. Scale bars, 1mm.
Figures 1–6 in A new species of the cob web spider genus Theridion from India (Araneae: Theridiidae)
Figures 1–6. Theridion odisha sp.n. 1, habitus, dorsal view. 2, same, ventral view. 3, epigyne, ventral view. 4, vulva, dorsal view. 5, epigyne, ventral view. 6, vulva, dorsal view. Scale bars 1–2, 1mm; 3–6, 0.2 mm
Figures 7–13 in A new species of the cob web spider genus Theridion from India (Araneae: Theridiidae)
Figures 7–13. Nihonhimea indicum (Tikader, 1977) comb.n., type material (NZC-ZSI-5352/18). 7, female holotype, habitus, dorsal view. 8, same, ventral view. 9, same, lateral view. 10-11, epigyne, dorsal view. 12-13, vulva, dorsal view. Scale bars, 7-9, 1mm; 10-11, 0.2mm; 12-13, 1mm.
Hydrology and trophic flexibility structure alpine stream food webs in the Teton Range, Wyoming, USA
<p>Data and code necessary to replicate the findings from the manuscript titled "Hydrology and trophic flexibility structure alpine stream food webs in the Teton Range, Wyoming, USA".</p> <p><span>Abstract:</span><strong><span> </span></strong><span>Understanding biotic interactions and how they vary across habitats is important for assessing the vulnerability of communities to climate change. Receding glaciers in high mountain areas can lead to the hydrologic homogenization of streams and reduce habitat heterogeneity, which are predicted to drive declines in regional diversity and imperil endemic species. However, little is known about food web structure in alpine stream habitats, particularly among streams fed by different hydrologic sources (e.g., glaciers or snowfields). We used gut content and stable isotope analyses to characterize food web structure of alpine macroinvertebrate communities in streams fed by glaciers, subterranean ice, and seasonal snowpack in the Teton Range, Wyoming, USA. Specifically, we sought to: (1) assess community resource use among streams fed by different hydrologic sources; (2) explore how variability in resource use relates to feeding strategies; and (3) identify which environmental variables influenced resource use within communities. Average taxa diet differed among all hydrologic sources, and food webs in subterranean ice-fed streams were largely supported by the gold alga <em>Hydrurus</em>. This finding bolsters a hypothesis that streams fed by subterranean ice may provide key habitat for cold-water species under climate change by maintaining a longer growing season for this high-quality food resource. While a range of environmental variables associated with hydrologic source (e.g., stream temperature) were related to diet composition, hydrologic source categories explained the most variation in diet composition models. Less variable diets within versus among streams suggests high trophic flexibility, which was further supported by high levels of omnivory. This inherent trophic flexibility may bolster alpine stream communities against future changes in resource availability as the mountain cryosphere fades. Ultimately, our results expand understanding of the habitat requirements for imperiled alpine taxa while empowering predictions of their vulnerability under climate change.</span></p>
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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.