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220 results for “sampling method”
The role of injection method on residual trapping at the pore-scale in continuum-scale samples: segmented data
<p>The experiments in this work explore the role of a variable injection rate on gas saturation and residual trapping. There are 2 experiments in this work H2L (high to low injection rate) and L2H (low to high injection rate). The workflow for processing the micro-CT images to get the segmented images is described in [1]. </p><p>The following scans are included in this repository NB. all data for this repository is segmented micro-CT data.: </p><ol><li>Dry scan prior to experiment = merged_binning_2_38_1927</li><li>H2L during high flow = merged_segmented_flow_09_h2lh_merged</li><li>H2L during low flow = merged_segmented_flow_11_h2ll_2_merged</li><li>H2L at the end of drainage (no flow) =merged_segmented_flow_16_dra1_pd5_merged</li><li>H2L at the end of imbibition (no flow) =merged_segmented_flow_21_imb1_pi1_merged</li><li>L2H during low flow = merged_segmented_flow_29_2_l2hl_merged</li><li>L2H during high flow = merged_segmented_flow_30_l2hh_merged</li><li>L2H at the end of drainage (no flow) =merged_segmented_flow_31_dra2_pd1_merged</li><li>L2H at the end of imbibition (no flow) =merged_segmented_flow_33_imb2_pi1_merged</li></ol>
Dataset from "A user-friendly method to get automated pollen analysis from environmental samples". New Phytologist.
<p>Dataset used in publication "A user-friendly method to get automated pollen analysis from environmental samples". New Phytologist.</p> <p><br>This repository contains images from annual pollen trap samples mounted on slides and scanned under light microscopy; image annotation metadata; and the weights of the trained models from the YOLOv5 algorithm, saved after the last training epoch.</p> <p>More details can be found in the README file.</p>
Unraveling the secrets of plant roots: Simplified method for large scale root exudate sampling and analysis in Arabidopsis thaliana
<p>Plants exude a plethora of compounds, both above- and below ground, to communicate with their environment. Although much is known about this communication above ground, we are only beginning to fathom the identity, level of variation, and role of below-ground chemical signals in a plant’s life. There have been many challenges associated with establishing a standardized methodology for studying root-exuded compounds, thus their role in plant-environment communication is still not well described.</p> <p>Here, we develop an interdisciplinary workflow to explore the natural variation in root exudate chemical composition of the model plant <em>Arabidopsis thaliana</em>. We highlight key challenges associated with sampling strategies and develop a framework for analyzing narrow and broad-scale patterns in root exudate composition of a large sample set of natural <em>A. thaliana</em> accessions.</p> <p>Our method consists of <em>in vitro</em> cultivation of individual seedlings inside a plastic mesh followed by a short hydroponic sampling period in micro quantities of ultrapure water. The mesh eases handling individual plants of varying sizes, thus making this setup advantageous for large-scale characterization of root exudates of individual plants in axenic conditions, and can be easily extended for prolonged temporal exudate collection experiments. Furthermore, a short sampling duration minimizes the experiment duration from days to mere hours, and is validated by yielding sufficient signal even with the small volume of sampling solution. An untargeted metabolic profiling analytical approach using ultra-high performance liquid chromatography coupled with mass spectrometry (UHPLC-QTOF-MS), followed by compound identification using open access software MZMine3 and SIRIUS 5, was used to capture a broad picture of the root exudate composition of <em>A. thaliana</em> accessions. This methodology can be broadly applied for investigating the role of root exudates as signals involved in plant belowground interactions. We report the first findings from the analysis here with results from Columbia genotype.</p> <p>We include here</p> <p> </p> <p>In <strong>Raw datafiles</strong>: The raw datasets obtained from MZmine 3 analysis, which contains aligned features of Columbia genotypes (Sheet1) as well as control samples (Sheet2). The dataset consists of <em>feature (row)ID, average (mass-to-charge ratios) m/z</em> and <em>retention times (RT)</em> across samples for individual features. It also includes sample-specific information including <em>feature status, name, m/z, RT, feature peak height,</em> and <em>area</em>. We also include a filtered datasheet excluding the features obtained in control as well as samples (Sheet3). Sheet 4 contains the phenotypic data on the number of leaves and rosette size of the 28 replicates at the time of sampling, along with the total peak area for each sample from MZmine data.</p> <p>In <strong>Supplementary tables</strong>: The 354 metabolites obtained after filtering out control features are listed with their <em>mass-to-charge ratios</em>, <em>retention times, and the mean, variance and coefficient of variation of the peak areas </em>(Supplementary Table 1). Supplementary Table 2 details the features identified by SIRIUS 5 with their <em>mass-to-charge ratios, retention times, chemical formula, chemical annotations,</em> and corresponding <em>probabilities scored by SIRIUS</em></p> <p>In <strong>Extended data analysis:</strong> Contains supporting data analysis for reproducibility and validity of our method for root exudate collection and analysis in <em>Arabidopsis thaliana.</em> These extended data analyses enhance the understanding of the relationship between plant phenotypic traits, peak area, and variation in compound abundance</p>
Optimizing sampling across methods improves the power of ecological monitoring data
Transect-based monitoring has long been a valuable tool in ecosystem monitoring. These transects are often used to measure multiple ecosystem attributes. The line-point intercept (LPI), vegetation height, and canopy gap intercept methods comprise a set of core methods, which provide indicators of ecosystem condition. However, users struggle to design a sampling strategy that optimizes the ability to detect ecological change using transect-based methods. We assessed the sensitivity of these core methods on a one-hectare plot to transect length, number, and sampling interval to determine: 1) minimum sampling required to describe ecosystem characteristics and detect change for each method and 2) optimal transect length and number for all three methods to make recommendations for future analyses and monitoring efforts. We used data from 13 National Wind Erosion Research Network locations spanning the western US, which included 151 measurements over time across five biomes. We found that longer and increased numbers of transects were more important for reducing sampling error than increased sample intensity along transects. For all methods and indicators across plots, three 100-m transects reduced sampling error so that indicator estimates fall within an 95% confidence interval of +/- 5% for canopy gap intercept and LPI-total foliar cover, +/- 5 cm for height and +/- two species for LPI-species counts. For the same criteria at 80% confidence intervals, two 100-m transects are needed. Site-scale inference was strongly affected by sample design, consequently our understanding of ecological dynamics may be influenced by sampling decisions.
The Set Increment with Limited Views Encoding Ratio (SILVER) Method for Optimizing Radial Sampling of Dynamic MRI: Supporting Data
<p>This data was created to perform the first experiments with the SILVER method of optimizing radial MRI acquisition. The files are mainly .mat files containing the numerical data used to assess the SILVER method using MATLAB (Version R2018b). Instructions on how to use the data and how to reproduce the experiments are available on https://github.com/SophieSchau/SILVER</p>
Data from: Effects of taxon sampling and tree reconstruction methods on phylodiversity metrics
1. The amount and patterns of phylodiversity in a community are often used to draw inferences about the local and historical factors affecting community assembly and can be used to prioritize communities and locations for conservation. Because measures of phylodiversity are based on the topology and branch lengths of phylogenetic trees, which are affected by the number and diversity of taxa in the tree, these analyses may be sensitive to changes in taxon sampling and tree reconstruction methods. 2. To investigate the effects of taxon sampling and tree reconstruction methods on measures of phylodiversity, we investigated the community phylogenetics of the Ordway-Swisher Biological Station (Florida), which is home to over 600 species of vascular plants. We studied the effects of 1) the number of taxa included in the regional phylogeny; 2) random vs. targeted sampling of species to assemble the regional species pool; 3) including only species from specific clades rather than broad sampling; 4) using trees reconstructed directly for the taxa under study compared to trees pruned from a larger reconstructed tree; and 5) using phylograms compared to chronograms. 3. We found that including more taxa in a study increases the likelihood of observing significantly non-random phylogenetic patterns. However, there were no consistent trends in the phylodiversity patterns based on random taxon sampling compared to targeted sampling, or within individual clades compared to the complete dataset. Using pruned and reconstructed phylogenies resulted in similar patterns of phylodiversity, while chronograms in some cases led to significantly different results from phylograms. 4. The methods commonly used in community phylogenetic studies can significantly impact the results, potentially influencing both inferences of community assembly and conservation decisions. We highlight the need for both careful selection of methods in community phylogenetic studies and appropriate interpretation of results, depending on the specific questions to be addressed.
Application of contact-resonance AFM methods to polymer samples (Raw Data)
<p>Raw data and figures of the article "Application of contact-resonance AFM methods to polymer samples", published in Beilstein Journal of Nanotechnology on 12 Nov 2020</p> <p> </p> <p>The raw data can be opened with the software "Igor Pro"</p>
Piecewise continuous sampling: a method for minimizing bias and sampling effort for estimated metrics of animal behavior
<p>Capturing qualitative features of animal behavior requires recording occurrences of behavior over time. Continuous sampling is best for capturing brief behaviors, but can be very time consuming. Instantaneous sampling can reduce the amount of labor required, but can miss short-duration behaviors. We therefore synthesized these techniques by continuously sampling during randomly scattered time intervals; a technique we call piecewise continuous sampling. To optimize and test the efficacy of this technique, we collected a continuous behavioral dataset of harvester ant workers, and then we developed a protocol to estimate the amount of sampling time necessary to reconstruct the proportion of time animals spend in different behavioral states. This protocol finds the sample size needed for the variance of the sample to converge on the variation of the population. We then divided this estimated time into equal-duration intervals that were randomly distributed across the entire continuous dataset. Finally, we calculated both time-dependent and time-independent error from this sample. We found that 4 to 16 sampling intervals minimize both types of error simultaneously. This finding was robust to differences in underlying behavior and was validated with simulations, implying that this method could be used for many types of organisms.</p>
Challenges of sampling and how phylogenetic comparative methods help: Supplementary data
<p>Supplementary data and results files for the paper:</p> <p>Macklin-Cordes, Jayden L. & Erich R. Round (2022). Challenges of sampling and how phylogenetic comparative methods help: With a case study of the Pama-Nyungan laminal contrast. <em>Linguistic Typology</em> (advance online publication). <a href="https://doi.org/10.1515/lingty-2021-0025">https://doi.org/10.1515/lingty-2021-0025</a></p>
A GC-IRMS method for measuring sulfur isotope ratios of carbonyl sulfide from small air samples
<p>This dataset contains supporting files, figures and datasets for the method of sulfur isotope measurements of carbonyl sulfide and ambient air measurements in the Netherlands during 2020 and 2021.</p>
Enzymatic digestion method development for long-term stored chitinaceous planktonic samples - Data
<table> <tbody> <tr> <td>Data for Carrillo-Barragán, Priscilla, Heather Sugden, Catherine Scott, and Clare Fitzsimmons. 2022.<br> “Enzymatic Digestion Method Development for Long-Term Stored Chitinaceous Planktonic Samples.”<br> Marine Pollution Bulletin. </td> </tr> </tbody> </table>
Sample 3D image data from RIMS method for image analysis code demo
<p>Sample 3D image data from RIMS method applied to mechanical test on hydrogel sphere packings, to be used in image analysis code demo as demonstrated in the ALERT Geomechanics doctoral school 2022. The data is a small subset from a larger set of data as found on Dryad via 10.5061/dryad.6djh9w0x8 and is separated here on Zenodo to make the subset more machine-readable.</p>
A comparative study of commercially available, minimally invasive, sampling methods on Early Neolithic humeri analysed via palaeoproteomics
Open the record for dataset details and reuse information.
Figure 1 in Comparing the effectiveness of pitfall traps and active sampling methods for ants and spiders in a Chromolaena odorata invaded site
Figure 1. Individual-based rarefaction/extrapolation sampling curves representing ant (A) and spider (B) sampling size collected by both active and passive sampling strategies used at Buffelsdraai Conservancy [AHC = aerial hand collection above the knee; AHCCRYPTIC = aerial hand collection below the knee cryptic; AHCOBV = aerial hand collection below the knee noticeable or non-cryptic; BB = vegetation beating].
Figure 4 in Comparing the effectiveness of pitfall traps and active sampling methods for ants and spiders in a Chromolaena odorata invaded site
Figure 4. Non-metric Multi-Dimensional Scaling (NMDS) representing the similarity of ant (A) and spider (B) species sampled by active and passive sampling techniques. The count abundance was transformed using square root and the data was analysed using Bray-Curtis similarity to produce a two-dimensional plot with a stress level = 0.07 and = 0.01, respectively [AHC CRYPTIC = aerial hand collection below the knee cryptic; AHC OBV = aerial hand collection below the knee noticeable or non-cryptic; AHC = aerial hand collection above the knee; BB = vegetation beating].
Figure 3 in Comparing the effectiveness of pitfall traps and active sampling methods for ants and spiders in a Chromolaena odorata invaded site
Figure 3. Spider species richness collected using active and passive sampling techniques in Buffelsdraai Conservancy [AHC = aerial hand collection above the knee; AHC CRYPTIC = aerial hand collection below the knee cryptic; AHC OBV = aerial hand collection below the knee noticeable or non-cryptic; BB = vegetation beating].
Fig. 2 in Endo- and ectoparasites of large whales (Cetartiodactyla: Balaenopteridae, Physeteridae): Overcoming difficulties in obtaining appropriate samples by non- and minimally-invasive methods
Fig. 2. Minimally-invasive sampling method. Dart-based skin biopsy sampling using a crossbow from a free-ranging sperm whale (Physeter macrocephalus) in the North Atlantic Ocean, Portugal.
Fig. 1 in Endo- and ectoparasites of large whales (Cetartiodactyla: Balaenopteridae, Physeteridae): Overcoming difficulties in obtaining appropriate samples by non- and minimally-invasive methods
Fig. 1. Non-invasive sampling method. Faecal sample collection from free-ranging whales in the North Atlantic Ocean, Portugal: (A) blue whale (Balaenoptera musculus) defecating, (B) floating blue whale (B. musculus) faeces, (C) telescope fixed net with collected blue whale faeces.
Рис. 1. ФиΛогенетические Αеревья хантавируса AMRV и его прироΑного носитеΛя восточноазиатской мыши Apodemus peninsulae Thomas, 1906. А. ФиΛогенетическое Αерево восточноазиатской мыши Apodemus peninsulae, построенное метоΑом «максимаΛьного правΑопоΑобия» (ML) и поΛученное на основе анаΛиза участка гена цитохрома b мтΔНК (744 п.н.). В узΛах ветвΛения указаны бутстреп-поΑΑержки, рассчитанные ΑΛя 1000 повторов. Цветными Λиниями обозначены фиΛогенетические Λинии: Αве Китайские (зеΛеный), Корейская «Korea» (синий), Амурская «Amur» (красный). ПоΛужирным шрифтом выΑеΛены собственные образцы. Названия образцов из GenBank/NCBI быΛи сокращены; B. ФиΛогенетическое Αерево из работы Α. Н. Яшиной с ΑопоΛнениями, построенное метоΑом «бΛижайшего сосеΑа» (NJ) на основе посΛеΑоватеΛьностей фрагмента М-сегмента (2737–2980 н.п.) генома хантавирусов. В узΛах ветвΛения указаны бутстреппоΑΑержки, рассчитанные ΑΛя 1000 повторов. Жирным выΑеΛены иссΛеΑованные РНК изоΛяты (Яшина 2012; Яшина и Αр. 2019) Fig. 1. Phylogenetic trees of AMRV and its natural reservoir host — the Korean field mouse Apodemus peninsulae Thomas, 1906. A. Phylogenetic tree of the Korean field mouse Apodemus peninsulae constructed by the "maximum likelihood" method (ML). The data are obtained from the analysis of the cytochrome b mtDNA gene fragments (744 bp). Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. Colored lines indicate phylogenetic lines: two Chinese (green), Korea (blue), and Amur (red). Own samples are highlighted in bold. The names of the samples from GenBank/NCBI have been shortened; B. Phylogenetic tree from L. N. Yashina's work with additions constructed by the neighbour joining method (NJ). It is based on the sequences of an M-segment fragment (2737–2980 bp) of the hantavirus genome. Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. The researched RNA isolates are highlighted in bold (Yashina 2012; Yashina et al. 2019) in Variability of the gene cyt b in the Korean field mouse Apodemus peninsulae Thomas, 1906 - a reservoir host of AMRV in the Khasansky District of Primorsky Krai
Рис. 1. ФиΛогенетические Αеревья хантавируса AMRV и его прироΑного носитеΛя восточноазиатской мыши Apodemus peninsulae Thomas, 1906. А. ФиΛогенетическое Αерево восточноазиатской мыши Apodemus peninsulae, построенное метоΑом «максимаΛьного правΑопоΑобия» (ML) и поΛученное на основе анаΛиза участка гена цитохрома b мтΔНК (744 п.н.). В узΛах ветвΛения указаны бутстреп-поΑΑержки, рассчитанные ΑΛя 1000 повторов. Цветными Λиниями обозначены фиΛогенетические Λинии: Αве Китайские (зеΛеный), Корейская «Korea» (синий), Амурская «Amur» (красный). ПоΛужирным шрифтом выΑеΛены собственные образцы. Названия образцов из GenBank/NCBI быΛи сокращены; B. ФиΛогенетическое Αерево из работы Α. Н. Яшиной с ΑопоΛнениями, построенное метоΑом «бΛижайшего сосеΑа» (NJ) на основе посΛеΑоватеΛьностей фрагмента М-сегмента (2737–2980 н.п.) генома хантавирусов. В узΛах ветвΛения указаны бутстреппоΑΑержки, рассчитанные ΑΛя 1000 повторов. Жирным выΑеΛены иссΛеΑованные РНК изоΛяты (Яшина 2012; Яшина и Αр. 2019) Fig. 1. Phylogenetic trees of AMRV and its natural reservoir host — the Korean field mouse Apodemus peninsulae Thomas, 1906. A. Phylogenetic tree of the Korean field mouse Apodemus peninsulae constructed by the "maximum likelihood" method (ML). The data are obtained from the analysis of the cytochrome b mtDNA gene fragments (744 bp). Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. Colored lines indicate phylogenetic lines: two Chinese (green), Korea (blue), and Amur (red). Own samples are highlighted in bold. The names of the samples from GenBank/NCBI have been shortened; B. Phylogenetic tree from L. N. Yashina's work with additions constructed by the neighbour joining method (NJ). It is based on the sequences of an M-segment fragment (2737–2980 bp) of the hantavirus genome. Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. The researched RNA isolates are highlighted in bold (Yashina 2012; Yashina et al. 2019)
Supporting data for "Snap happy: camera traps are an effective sampling tool when compared to alternative methods"
<p>Author recommendations and response ratios extracted from studies comparing camera traps to another survey method. These data underlie the analyses in a the journal article 'Snap happy: camera traps are an effective sampling tool when compared to alternative methods', published in the journal Royal Society Open Science (https://doi.org/10.1098/rsos.181748). </p>
ScienceDex guides
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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.