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1,076 results for “Metabarcoding”
A national scale BioBlitz using citizen science and eDNA metabarcoding for monitoring coastal marine fish
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Boardman River 2019 eDNA metabarcoding water sample data
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Data from: Metabarcoding under Brine: Microbial ecology of five hyper-saline lakes at Rottnest Island (WA, Australia)
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Data from: Environmental DNA metabarcoding reliably recovers arthropod interactions which are frequently observed by video recordings of flowers
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The applicability of eDNA metabarcoding approaches for sessile benthic surveying in the Kimberley region, north-western Australia
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Relationship between eDNA concentration from metabarcoding method and stream fish density under field conditions
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Efficacy of metabarcoding for identification of fish eggs evaluated with mock communities
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Supplementary material 1 from: Schenk J, Geisen S, Kleinboelting N, Traunspurger W (2019) Metabarcoding data allow for reliable biomass estimates in the most abundant animals on earth. Metabarcoding and Metagenomics 3: e46704. https://doi.org/10.3897/mbmg.3.46704
: Data type: species data
Supplementary material 1 from: Schenk J, Geisen S, Kleinboelting N, Traunspurger W (2019) Metabarcoding data allow for reliable biomass estimates in the most abundant animals on earth. Metabarcoding and Metagenomics 3: e46704. https://doi.org/10.3897/mbmg.3.46704
: Data type: species data
Supplementary material 8 from: Garrido-Sanz L, Senar MÀ, Piñol J (2020) Estimation of the relative abundance of species in artificial mixtures of insects using low-coverage shotgun metagenomics. Metabarcoding and Metagenomics 4: e48281. https://doi.org/10.3897/mbmg.4.48281
: Data type: Excel table
Supplementary material 5 from: Garrido-Sanz L, Senar MÀ, Piñol J (2020) Estimation of the relative abundance of species in artificial mixtures of insects using low-coverage shotgun metagenomics. Metabarcoding and Metagenomics 4: e48281. https://doi.org/10.3897/mbmg.4.48281
: Data type: Excel table
Supplementary material 4 from: Garrido-Sanz L, Senar MÀ, Piñol J (2020) Estimation of the relative abundance of species in artificial mixtures of insects using low-coverage shotgun metagenomics. Metabarcoding and Metagenomics 4: e48281. https://doi.org/10.3897/mbmg.4.48281
: Data type: Excel table
Supplementary material 3 from: Garrido-Sanz L, Senar MÀ, Piñol J (2020) Estimation of the relative abundance of species in artificial mixtures of insects using low-coverage shotgun metagenomics. Metabarcoding and Metagenomics 4: e48281. https://doi.org/10.3897/mbmg.4.48281
: Data type: Excel table
Supplementary material 6 from: Garrido-Sanz L, Senar MÀ, Piñol J (2020) Estimation of the relative abundance of species in artificial mixtures of insects using low-coverage shotgun metagenomics. Metabarcoding and Metagenomics 4: e48281. https://doi.org/10.3897/mbmg.4.48281
: Data type: Excel table
Supplementary material 7 from: Garrido-Sanz L, Senar MÀ, Piñol J (2020) Estimation of the relative abundance of species in artificial mixtures of insects using low-coverage shotgun metagenomics. Metabarcoding and Metagenomics 4: e48281. https://doi.org/10.3897/mbmg.4.48281
: Data type: Excel table
Supplementary material 2 from: Garrido-Sanz L, Senar MÀ, Piñol J (2020) Estimation of the relative abundance of species in artificial mixtures of insects using low-coverage shotgun metagenomics. Metabarcoding and Metagenomics 4: e48281. https://doi.org/10.3897/mbmg.4.48281
: Data type: Excel table
Supplementary material 1 from: Garrido-Sanz L, Senar MÀ, Piñol J (2020) Estimation of the relative abundance of species in artificial mixtures of insects using low-coverage shotgun metagenomics. Metabarcoding and Metagenomics 4: e48281. https://doi.org/10.3897/mbmg.4.48281
: Data type: Boxplot
Data from: DNA metabarcoding as a tool for disentangling food webs in agroecosystems
<p>Better knowledge of food webs and related ecological processes is fundamental to understanding the functional role of biodiversity in ecosystems. This is particularly true for pest regulation by natural enemies in agroecosystems. However, it is generally difficult to decipher the impact of predators as they often leave no direct evidence of their activity. Metabarcoding via high throughput sequencing (HTS) offers new opportunities for unraveling trophic linkages between generalist predators and their prey, and ultimately identifying key ecological drivers of natural pest regulation. Here, this approach proved effective in deciphering the diet composition of key predatory arthropods (nine species of spiders, carabid beetles, ants, etc. – 27 prey taxa), insectivorous birds (one species, <em>Ploceus cucullatus</em>- 13 prey taxa) and bats (one species, <em>Taphozous mauritianus</em> – 103 prey taxa) sampled in a millet-based agroecosystem in Senegal. Such information enabled to inform the diet breadth or preference of predators (e.g., mainly moths for bats), to design a qualitative trophic network and to identify patterns of intraguild predation across arthropod predators, insectivorous vertebrates and parasitoids. Appropriateness and limitations of the proposed molecular-based approach for assessing the diet of crop pest predators and trophic linkages are discussed.</p>
Raw data for comparison of bioinformatics pipelines for Diatom DNA metabarcoding for ecological assessment
<p>This archive contains the raw .fatsq files for 29 samples from water bodies (lakes and rivers) located in Nordic countries (Sweden, Finland, Norway) sequenced on Illumina MiSeq, with the 18S-V4 marker and with the <em>rbc</em>L marker. For both marker two separate datasets are provided, containing the F and R fragments ( R1 and R2). The samples tags and primer sequences are also provided.</p> <p>The archive also contains the custom curated reference database used for the taxonomic identification using bioinformatics pipeline. For both marker, two files are provided: an .rarl file with the sequences and sequence ID and a .tax file with the taxonomic information associated. </p>
Supplementary material 8 from: Zizka VMA, Weiss M, Leese F (2020) Can metabarcoding resolve intraspecific genetic diversity changes to environmental stressors? A test case using river macrozoobenthos. Metabarcoding and Metagenomics 4: e51925. https://doi.org/10.3897/mbmg.4.51925
Table S1. Number of macroinvertebrate individuals per sample and season
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Allen Brain Atlas
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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.
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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.
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