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206 results for “Sampling & Detection”
Table S27: Target and identified unknown organic micropollutants detected in surface water samples taken during heavy rain events
<p>In the following table, peak intensities of detected organic micropollutants in water samples are displayed.</p> <p>This data table is part of the appendix of Chapter 4 of the PhD thesis “Novel approaches to identify drivers of chemical stress in small rivers” by Liza-Marie Beckers prepared at RWTH Aachen University and at the Helmholtz Centre for Environmental Research-UFZ. In Chapter 4, precipitation-related pollutant patterns and indicator compounds during heavy rain events were identified in the Holtemme River by nontarget screening and cluster analysis. The table contains peak heights of organic micropollutants detected in water samples taken during heavy rain events in the Holtemme River (Saxony – Anhalt, Germany). The table is structured into the following columns: Compound name, use class of compound (e.g., pharmaceutical or pesticide), distinction between target or identified unknown compounds, mass-to-charge ratio (m/z), retention time (RT), assignment to a pattern identified by cluster analysis (i.e., “Base” or “Quick”), the probability of belonging to the assigned pattern as number between 0 and 1 as well as the peak height of the compound in each sample. The samples are indicated by "B" for "bottle" and a number from 1-16. The use class “NA” indicates that now major use class for this compound could be identified.</p> <p>The sampling was triggered by combined sewer overflow at a wastewater treatment plant upstream of the sampling point. Samples were taken by an automated sampler in 30-min composite samples for 8 hours resulting in 16 samples per rain event. In total, 6 heavy rain events from May to September 2016 were sampled during this study. The table is divided into 6 subtables (i.e., Table S27 A-F). Each subtable displays compounds and their peak heights detected in samples from one heavy rain event. The different rain events are abbreviated by the sampling date:</p> <p>Table S27A displays results from the rain event samples May 29<sup>th</sup> 2016 : E2905</p> <p>Table S27B displays results from the rain event samples June 01<sup>st</sup> 2016 : E0106</p> <p>Table S27C displays results from the rain event samples June 24<sup>th</sup> 2016 : E1306</p> <p>Table S27D displays results from the rain event samples June 13<sup>th</sup> 2016 : E2406</p> <p>Table S27E displays results from the rain event samples July 13<sup>th</sup> 2016 : E1307</p> <p>Table S27F displays results from the rain event samples September 17<sup>th</sup> 2016 : E1709</p> <p>Chemical analysis of the water samples was performed by liquid chromatography (UltiMate 3000 LC system (Thermo Scientific)) coupled to high resolution mass spectrometry (Q Exactive Plus, Thermo Scientific) with a heated electrospray ionization (HESI) source. Nontarget screening was performed as it allows for a comprehensive characterization of the chemical exposure during heavy rain events. However, only annotated target compounds and unknown compounds identified by structure elucidation are presented in the table. Details on data evaluation methods are described in Chapter 4 of the PhD thesis.</p> <p>Beckers, L.M. (2019): Novel approaches to identify drivers of chemical stress in small rivers. RWTH Aachen University, Aachen.</p>
Data for "Detection of metabolite-protein interactions in complex biological samples by high-resolution relaxometry: towards interactomics by NMR"
<p>Raw NMR data for relaxometry experiments, divided by donor sample. For every donor sample 2 or 3 different samples were used in order to record data at 19 different magnetic fields.</p> <p>Data from fast field-cycling relaxometry. All the data is in one xlsx file, divided by donor sample.</p> <p>Relaxometry results for alanine, lactate, creatinine and glutamine, obtained from the fitting of their relaxation decays recorded at 19 different fields, divided by donor sample.</p>
Human papillomavirus (HPV) detection in vaginal self-samples: evaluation of eNat® as an alternative suspension medium to ThinPrep®PreservCyt® for vaginal swabs
<p>Dataset 1: HPV positivity on cervical and vaginal self-samples with different HPV real-time PCR assays</p> <p>Dataset 2: hrHPV viral load in cervical and vaginal self-samples</p> <p>Datakey 1: HPV positivity on cervical and vaginal self-samples with different HPV real-time PCR assays</p> <p>Datakey 2: hrHPV viral load in cervical and vaginal self-samples</p>
CLDF Dataset derived from List's "Sample Size and Cognate Detection" from 2014
<p>Cite the source of the dataset as:</p> <blockquote> <p>List, Johann-Mattis (2014): Investigating the impact of sample size on cognate detection. Journal of Language Relationship. 11. 91-102. DOI: https://doi.org/10.31826/jlr-2014-110111</p> </blockquote>
Fig. 1 in Evidence for intercontinental parasite exchange through molecular detection and characterization of haematozoa in northern pintails (Anas acuta) sampled throughout the North Pacific Basin
Fig. 1. Approximate locations in North America and East Asia at which northern pintail tissue samples were collected during 2011–2012 to test for haemosporidian infection. Regions (i.e. Alaska, California, and Japan) and sub-regions (Koyukuk-Nowitna NWR, Yukon-Kuskokwim Delta NWR, Izembek NWR, Sacramento Valley, San Joaquin Valley) for sampling locations are indicated (NWR = National Wildlife Refuge). The number of tissue samples per location is indicated in parentheses. Sample tissue was whole blood unless indicated by an asterisk (signifying wing muscle tissue).
Data from: eDNA metabarcoding of log hollow sediments and soils highlights the importance of substrate type, frequency of sampling and animal size, for vertebrate species detection
<p>Fauna monitoring often relies on visual monitoring techniques such as camera trappings, which have biases leading to underestimates of vertebrate species diversity. Environmental DNA (eDNA) has emerged as a new source of biodiversity data that may improve biomonitoring; however, eDNA based assessments of species richness remain relatively untested in terrestrial environments. We investigated the suitability of fallen log hollow sediment as a source of vertebrate eDNA, across two sites in south-western Australia - one with a Mediterranean climate and the other semi-arid. We compared two different approaches (camera trapping and eDNA metabarcoding) for monitoring of vertebrate species, and investigated the effect of other factors (frequency of species, timing of visits, frequency of sampling, body size) on vertebrate species detectability. Metabarcoding of hollow sediments resulted in the detection of higher species richness in comparison Hollow sediment detected higher species richness (29 taxa: six birds, three reptiles and 20 mammals) to metabarcoding of soil at the entrance of the hollow (13 taxa: three birds, two reptiles and eight mammals). We detected 31 taxa in total with eDNA metabarcoding and 47 with camera traps, with 14 taxa detected by both (12 mammals and two birds). By comparing camera trap data with eDNA read abundance, we were able to detect vertebrates through eDNA metabarcoding that had visited the area up to two months prior to sample collection. Larger animals were more likely to be detected, and so were vertebrates that were identified multiple times in the camera traps. These findings demonstrate the importance of substrate selection, frequency of sampling, and animal size, on eDNA based monitoring. Future eDNA experimental design should consider all these factors as they affect detection of target taxa. </p>
Рис. 4. Распределение станций отбора проб по глубине и типу грунта (круЖком обведены станции, на которых макробентос не обнаруЖен; БО – биогенные остатки, ГМ – галька мелкаЯ, Гр – гравий, И – ил, П – песок). Fig. 4. Distribution of sampling stations by depth and type of bottom sediments (circles are around the stations where no macrobenthos was detected; БО – biogenic residues, ГМ – pebbles, Гр – gravel, И – silt, П – sand). in Species composition and distribution of bivalve mollusks in plankton and benthos in Nevelsky Strait in summer
Рис. 4. Распределение станций отбора проб по глубине и типу грунта (круЖком обведены станции, на которых макробентос не обнаруЖен; БО – биогенные остатки, ГМ – галька мелкаЯ, Гр – гравий, И – ил, П – песок). Fig. 4. Distribution of sampling stations by depth and type of bottom sediments (circles are around the stations where no macrobenthos was detected; БО – biogenic residues, ГМ – pebbles, Гр – gravel, И – silt, П – sand).
Fig. 1 in Detection of Escherichia fergusonii - an emerging pathogen harbouring drug resistant genes from seafood samples of Tamil Nadu, India
Fig. 1 — Gene specific PCR amplification of Escherichia fergusonii (lane 1 – 100 bp DNA ladder, lane 2 – positive control (clinical E. fergusonii), lane 3 – negative control, lane 4 – E011, lane 5 – E060)
Figure 3. Chromatogram from the sample fairs MAC 02 and ARA 0 in Detection of enteropathogens and research of pesticide residues in Lactuca sativa from traditional and agroecological fairs
Figure 3. Chromatogram from the sample fairs MAC 02 and ARA 0, with the peaks of Diphenoconazole compared with the pattern.
Fig. 5 in Sampling mammalian carnivores in western Thailand: Issues of rarity and detectability
Fig. 5. Model averaged estimates of detection probability for 15 carnivore species by camera trapping plotted as a function of log10(weight in kg). Error bars represent 95% confidence intervals. Fitted line is derived from a second-order polynomial which described the best fit of the data. R2 is the coefficient of determination of the fitted line. The six species with lg(weight) greater than 1.0 are Asiatic golden cat, Asiatic jackal, dhole, sun bear, Asiatic black bear, and tiger, from left to right.
Fig. 4 in Sampling mammalian carnivores in western Thailand: Issues of rarity and detectability
Fig. 4. Model averaged estimates of detection probability across the models shown in Tables 2B and 2C for species functional groups (TRAIT; large vs small and terrestrial vs semi-arboreal behavior). Estimated detection probabilities of each species are also shown for TERRESTRIAL and SEMI-ARBOREAL species. Error bars represent 95% confidence intervals.
Fig. 3 in Sampling mammalian carnivores in western Thailand: Issues of rarity and detectability
Fig. 3. Model averaged estimates of detection probability of four functional groups of carnivores (top row) and for each of 15 species separately. Detection probabilities are estimated as functions of camera placement categories (road [no. cameras = 1], streams [5], trails [14], & trails by streams [tbs; 33]). Error bars represent 95% confidence intervals. Estimates were presented without error bars when standard errors could not be estimated. Estimates with 95% confidence intervals less than zero and/or greater than one indicate a lack of model convergence. n refers to the number of independent photographs used to estimate detection probabilities.
Fig. 2 in Sampling mammalian carnivores in western Thailand: Issues of rarity and detectability
Fig. 2. Species discovery curves generated from rarefaction for mammalian carnivores based on camera trap surveys at four study sites within mosaic forest types of Thung Yai between November 2007 and August 2008. Bars represent 95% confidence intervals of the estimates scaled by survey effort (camera trap nights) combined across all active camera locations (n = 53). Curves were estimated using package BiodiversityR in program R.
Fig. 1 in Sampling mammalian carnivores in western Thailand: Issues of rarity and detectability
Fig. 1. Map of Thung Yai Naresuan Wildlife Sanctuary showing camera trap polygons in four study sites, from south to north, Headquarters (HQ), Tikong (TK), Sesawo (SSW), and Mae Gatha (MGT) between November 2007 and August 2008.
Fig. 1 in Camera traps and genetic identification of faecal samples for detection and monitoring of an endangered ungulate
Fig. 1. Distribution of deployed camera traps showing presence (black circles) and non-detection (purple circles) and genetic sampling locations showing presence (black triangles) and non-detection (purple triangles) of Eld's deer. Inset map shows the location of Chhaeb Wildlife Sanctuary in Cambodia (black rectangle). Background shows proportion of tree cover from WorldCover land cover map (© ESA WorldCover project 2020 / Contains modified Copernicus Sentinel data (2020) processed by ESA WorldCover consortium).
Data from the manuscript 'Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context'
<p>Data sets from the manuscript 'Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context'. These include the test statistics from analyses of real and simulated data, and the data used to generate the figures relating to the goodness-of-fit of the generalised extreme value distribution to the GPS test statistics under the null. Please see the enclosed README for more details.</p>
Fluxes of the protonated masses from the soil samples collected from two temperate ecosystems detected by PTR-ToF-MS
<p>Volatile organic compounds (VOCs) are reactive gaseous compounds with significant impacts on air quality and the Earth's radiative balance. While natural ecosystems are known to be major sources of VOCs, primarily due to vegetation, soils, an important component of these ecosystems, have received relatively less attention as potential sources and sinks of VOCs.</p> <p>In this study, soil samples were collected from two temperate ecosystems: a beech forest and a heather heath, and then sieved, homogenized, and incubated under various controlled conditions such as different temperatures, oxic <em>vs</em>. anoxic conditions, and different ambient VOC levels. A dynamic flow-through system coupled to a proton transfer reaction-time of flight-mass spectrometry (PTR-ToF-MS) was used to measure production and/or uptake rates of selected VOCs, aiming to explore the processes and their controlling mechanisms.</p> <p>This dataset therefore is collected from these experiments. It includes the raw flux data and figure source data associated with a peer-reviewed publication in Soil Biology & Biochemistry at <a href="https://doi.org/10.1016/j.soilbio.2023.109153">https://doi.org/10.1016/j.soilbio.2023.109153</a>.</p> <p>Overall, our results showed that these soils were natural sources of a variety of VOCs, and the strength and profile of these emissions were influenced by soil biogeochemical properties (e.g. moisture, soil organic matter), oxic/anoxic conditions, and temperature. The soils also acted as sinks for most VOCs when VOC substrates at parts per billions levels (ranging between 0.18-68.65 ppb) were supplied to the headspace of the enclosed soils, and the size of the sink corresponded to the amount of VOCs available in the ambient air. Temperature-controlled incubations and glass bead simulations indicated that the uptake of VOCs by soils was likely driven by microbial metabolism, with a minor contribution from physical adsorption to soil particles. In conclusion, our study suggests that soil uptake of VOCs can mitigate the impact of other significant VOC sources in the near-surface environment and potentially regulate the net exchange of these trace gases in ecosystems.</p> <p>Should you have any questions regarding the dataset, please free feel to contact Yi jiao at yi.jiao@bio.ku.dk or Prof. Rinnan at riikkar@bio.ku.dk</p>
Data from: Large-scale eDNA sampling and hierarchical modeling elucidates the importance of stream habitat for eastern hellbender (<em>Cryptobranchus a. alleganiensis</em>) occupancy and eDNA detection
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How, what, and where you sample environmental DNA affects diversity estimates and species detection
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Data from: eDNA metabarcoding of log hollow sediments and soils highlights the importance of substrate type, frequency of sampling and animal size, for vertebrate species detection
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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)
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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.
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.