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Raw data supporting metabarcoding unsorted kick-samples research
<p>Raw data supporting: Metabarcoding unsorted kick-samples facilitates macroinvertebrate-based bioassessment with increased taxonomic resolution, while outperforming environmental DNA.</p> <p>Two-step PCRs were performed on each sample replicate using the Qiagen Multiplex PCR Plus Kit.</p> <p>The pool was loaded onto an Illumina MiSeq at 9pM, with 5% Phi-X, using a 600 cycle V3 kit with 300 bp paired end sequencing (index read steps skipped).<br> <br> Also available in the NCBI SRA: <a href="https://www.ncbi.nlm.nih.gov/sra/PRJNA629361">https://www.ncbi.nlm.nih.gov/sra/PRJNA629361</a></p>
Supplementary information for samples and data used in Salazar et al. (2019)
<p>Supplementary information for samples and data used in Salazar et al. (2019). <em>Gene expression changes and community turnover differentially shape the global ocean metatranscriptome</em>. https://doi.org/10.1016/j.cell.2019.10.014</p>
Figure. Sampling sites of blood sucking lice (Phthiraptera: Anoplura) in Croatia. in The blood sucking lice (Phthiraptera: Anoplura) of Croatia: review and new data
Figure. Sampling sites of blood sucking lice (Phthiraptera: Anoplura) in Croatia.
Supporting data (Sample location and age data)
<p>Supporting data for the manuscript entitled "The Early Cretaceous to Miocene tectonic evolution of the NW Cyclades based on 40Ar/39Ar multiple single crystal dating of white mica from Andros", submitted to Tectonics.</p>
Comparison of Fixed Single Cell RNA-seq Methods to Enable Transcriptome Profiling of Neutrophils in Clinical Samples - Time course data
<p>Monitoring neutrophil gene expression is a powerful tool for understanding disease mechanisms, developing new diagnostics, therapies and optimizing clinical trials. Neutrophils are sensitive to the processing, storage and transportation steps that are involved in clinical sample analysis. This study is the first to evaluate the capabilities of technologies from 10X Genomics, PARSE Biosciences, and HIVE (Honeycomb Biotechnologies) to generate high-quality RNA data from human blood-derived neutrophils. Our comparative analysis shows that all methods produced high quality data, importantly capturing the transcriptomes of neutrophils. 10X FLEX cell populations in particular showed a close concordance with the flow cytometry data. Here, we establish a reliable single-cell RNA sequencing workflow for neutrophils in clinical trials: we offer guidelines on sample collection to preserve RNA quality and demonstrate how each method performs in capturing sensitive cell populations in clinical practice.</p> <p><strong>This dataset includes only the 10X Flex time course data and analysis.</strong></p>
Supporting Data for "Why Half‐Cell Samples Provide Limited Insight Into the Aging Mechanisms of Potassium Batteries"
<p>This dataset provides the raw data to the manuscript</p> <p><strong>"Why Half‐Cell Samples Provide Limited Insight Into the Aging Mechanisms of Potassium Batteries"</strong></p> <p>published in Advanced Energy Materials, <strong>2024</strong>, DOI 10.1002/aenm.202403811</p> <p><a href="https://doi.org/10.1002/aenm.202403811">Link to Publisher</a></p> <p> </p> <p>Comments:</p> <ul> <li>HAXPES synchrotron data is provided as IGOR file (IGOR Pro (v6.37, WaveMaterics Inc.). The file contains the original 2D data and the converted 1D datasets used for analysis</li> <li>In-House XPS data is provided with corresponding peak fits as excel spreadsheet</li> <li>peak data (binding energy (after referencing), intensity, area, FWHM and at.%) is provided in separate excel spreadsheet.</li> </ul> <p> </p>
Sample WebAssembly Data Files for Reproducible Analysis and Visualization of iEEG (RAVE)
<p>The data was derived from the following work and packaged into WebAssemply via Emscripten. The modification includes removing large data files and only keep up with the minimal requirements.</p> <blockquote> <p>Magnotti, J. F., Wang, Z., & Beauchamp, M. S. (2020). RAVE: Comprehensive open-source software for reproducible analysis and visualization of intracranial EEG data. <em>NeuroImage</em>, <em>223</em>, 117341.</p> </blockquote> <p> </p>
Rock Magnetic Data for Samples from IODP Expedition 385 Sites U1549 and U1552
<p>This dataset includes rock magnetic data for Samples from IODP Expedition 385 Sites U1549 and U1552 associated with the publication of a data report. The reader is referred to the READ ME file for more information.</p>
Phototaxis raw data sample
<p>This is an example of data from one clutch over 4 days 7-10 dpf. </p> <p>If you need the other datasets, please contact the authors</p>
Sample data - 2hs
<p>If you use this data for publication, please cite:<br>Sainsbury Wellcome Centre Foraging Behaviour Working Group. (2023). Aeon: An open-source platform to study the neural basis of ethological behaviours over naturalistic timescales [Computer software]. <span><span><a title="https://doi.org/10.5281/zenodo.8411157" href="https://doi.org/10.5281/zenodo.8411157" target="_blank" rel="noreferrer noopener">10.5281/zenodo.8411157</a> </span></span></p>
CellEnrich sample data
<p>Sample datasets for testing CellEnrich package.</p>
Table 5. Analytical data for miscellaneously attributed samples from Broken Hill, including the Broken Hill Consols Mine. a in Compositions of silver halides from the Broken Hill district, New South Wales
<p><b>Table</b> 5. Analytical data for miscellaneously attributed samples from Broken Hill, including the Broken Hill Consols Mine.a</p><table><tbody><tr><th>No.</th><th><b>Analyses</b></th></tr></tbody><tbody><tr><th>D46338b</th><td>Cl</td><td>60</td><td>57</td><td>57</td><td>53</td><td>56</td><td>57</td><td>58</td><td>68</td><td>56</td><td>66</td><td></td></tr><tr><th></th><td>Br</td><td>32</td><td>35</td><td>34</td><td>37</td><td>35</td><td>34</td><td>32</td><td>31</td><td>35</td><td>33</td><td></td></tr><tr><th></th><td>I</td><td>8</td><td>8</td><td>9</td><td>10</td><td>9</td><td>9</td><td>10</td><td>1</td><td>9</td><td>1</td><td></td></tr><tr><th>D26168c</th><td>Cl</td><td>72</td><td>76</td><td>63</td><td>65</td><td>60</td><td>77</td><td>77</td><td>72</td><td>66</td><td>65</td><td></td></tr><tr><th></th><td>Br</td><td>28</td><td>24</td><td>37</td><td>35</td><td>40</td><td>22</td><td>23</td><td>28</td><td>34</td><td>35</td><td></td></tr><tr><th>D28184d</th><td>I Cl</td><td>75</td><td>68</td><td>86</td><td>70</td><td>70</td><td>1 70</td><td>78</td><td>70</td><td>84</td><td></td><td></td></tr><tr><th></th><td>Br</td><td>25</td><td>30</td><td>14</td><td>28</td><td>30</td><td>29</td><td>21</td><td>30</td><td>16</td><td></td><td></td></tr><tr><th>D28185d</th><td>I Cl</td><td>55</td><td>2 56</td><td>55</td><td>2 56</td><td>55</td><td>155</td><td>1 60</td><td>50</td><td>45</td><td>50</td><td>56</td></tr><tr><th></th><td>Br</td><td>43</td><td>43</td><td>44</td><td>43</td><td>44</td><td>43</td><td>40</td><td>46</td><td>45</td><td>45</td><td>41</td></tr><tr><th></th><td>I</td><td>2</td><td>1</td><td>1</td><td>1</td><td>1</td><td>2</td><td></td><td>4</td><td>10</td><td>5</td><td>3</td></tr></tbody></table><p>a Analyses reported as in Table I. b Trace S detected. C Traces Pb, Cu, S, As detected. d Consols Mine; traces Pb, As, S, Fe detected.</p>
Occupant Simulation Data based on Honda Accord 2024 Simplified Passenger Model and Full-factorial Sampling with 3,125 samples and HIII05F, HIII50M, HIII95M
<p>Database and FE-models with 9,375 Honda Accord 2014 passenger occupant simulations. </p> <p> </p>
Physical features for small sample of data (1000 events per class)
<p>These repository contains physical features extracted from four classes - <br>1) Earthquake, 2) Explosions, 3) Noise, 4) Surface events. <br><br>Each waveform is of 40s (P-10, P+30). <br>tapered 10%<br>bandpass filtered (0.5 - 15 Hz)<br>normalized by max. </p>
Data from: Sporadic sampling not climatic forcing drives observed early hominin diversity
The role of climate change in the origin and diversification of early hominins is hotly debated. Most accounts of early hominin evolution link observed fluctuations in species diversity to directional shifts in climate or periods of intense climatic instability. None of these hypotheses, however, have tested whether observed diversity patterns are distorted by variation in the quality of the hominin fossil record. Here, we present a detailed examination of early hominin diversity dynamics, including both taxic and phylogenetically corrected diversity estimates. Unlike past studies, we compare these estimates to sampling metrics for rock availability (hominin-, primate-, and mammal-bearing formations) and collection effort, in order to assess the geological and anthropogenic controls on the sampling of the early hominin fossil record. Taxic diversity, primate-bearing formations, and collection effort show strong positive correlations, demonstrating that observed patterns of early hominin taxic diversity can be explained by temporal heterogeneity in fossil sampling rather than genuine evolutionary processes. Peak taxic diversity at 1.9 million years ago (Ma) is a sampling artefact, reflecting merely maximal rock availability and collection effort. In contrast, phylogenetic diversity estimates imply peak diversity at 2.4 Ma and show little relation to sampling metrics. We find that apparent relationships between early hominin diversity and indicators of climatic instability are, in fact, driven largely by variation in suitable rock exposure and collection effort. Our results suggest that significant improvements in the quality of the fossil record are required before the role of climate in hominin evolution can be reliably determined.
Data from: Effects of sampling effort on biodiversity patterns estimated from environmental DNA metabarcoding surveys
Environmental DNA (eDNA) metabarcoding can greatly enhance our understanding of global biodiversity and our ability to detect rare or cryptic species. However, sampling effort must be considered when interpreting results from these surveys. We explored how sampling effort influenced biodiversity patterns and nonindigenous species (NIS) detection in an eDNA metabarcoding survey of four commercial ports. Overall, we captured sequences from 18 metazoan phyla with minimal differences in taxonomic coverage between 18 S and COI primer sets. While community dissimilarity patterns were consistent across primers and sampling effort, richness patterns were not, suggesting that richness estimates are extremely sensitive to primer choice and sampling effort. The survey detected 64 potential NIS, with COI identifying more known NIS from port checklists but 18 S identifying more operational taxonomic units shared between three or more ports that represent un-recorded potential NIS. Overall, we conclude that eDNA metabarcoding surveys can reveal global similarity patterns among ports across a broad array of taxa and can also detect potential NIS in these key habitats. However, richness estimates and species assignments require caution. Based on results of this study, we make several recommendations for port eDNA sampling design and suggest several areas for future research.
Raw data for NMR-POISE: On-the-fly, Sample-tailored Optimisation of NMR Experiments
<p>The NMR-POISE paper can be found at: <em>Anal. Chem.</em> <strong>2021,</strong> <em>93</em> (31), 10735–10739 (DOI: <a href="https://doi.org/10.1021/acs.analchem.1c01767">10.1021/acs.analchem.1c01767</a>).</p> <p>The majority of one- and multi-dimensional NMR experiments, indispensable to chemists in many areas of research, are often run with generic or "compromise" parameter values that are not optimised. This is particularly problematic when robust, automated acquisition on a variety of samples is desired. Here we present a Python package, NMR-POISE (Parameter Optimisation by Iterative Spectral Evaluation), with full integration into Bruker’s TopSpin software, that utilises feedback control for on-the-fly, sample-tailored optimisation of NMR experiments. POISE provides a highly extensible and user-friendly framework which allows its core optimisation algorithms to be implemented in a wide variety of scenarios.</p> <p>The data attached herein provide examples of optimisation procedures where POISE can be used to great effect. The raw NMR data is attached here, together with all of the scripts used for processing and plotting this data (which can be used to directly regenerate the figures in the manuscript).</p> <p>The raw NMR data is in the "datasets" directory, and the processing scripts in the "figures" directory. The scripts can be run as long as this directory structure is maintained, but require v0.4.1 of the "penguins" Python package: this can be installed using the command "pip install penguins=0.4.1" (without quotes). Please refer to the Supporting Information of the POISE paper for more details, including a full description of the individual datasets.</p> <p><strong>Changelog</strong></p> <p>v1.1.0 of this dataset contains extra raw data and figures added during revision of the manuscript.</p>
Sample Raw (PPG, Accel, and Gyro) and Processed (steps, calories, sleep, HR, HRV, SPO2, Respiratory Rate, R-R) data over 24 hours
<p>Over the course of 24 hours, we collected raw (Photoplethysmography (PPG), Acceleration, and Gyro) and processed (steps, calories, sleep, HR, HRV, SPO2, Respiratory Rate, R-R) data samples. Biostrap approaches health insights from a data-driven perspective. Our clinical-grade hardware enables users to accurately track SpO2, HRV, RHR, and a variety of other biometrics with confidence.</p>
Data from: Detection of the endangered European weather loach (Misgurnus fossilis) via water and sediment samples: testing multiple eDNA workflows.
<p>The European weather loach (<i>Misgurnus fossilis</i>) is classified as highly endangered in several countries of Central Europe. Populations of <i>M. fossilis</i> are predominantly found in ditches with low water levels and thick sludge layers and are thus hard to detect using conventional fishing methods. Therefore, environmental DNA (eDNA) monitoring appears particularly relevant for this species. In previous studies, <i>M. fossilis</i> was surveyed following eDNA water sampling protocols, which were not optimized for this species. Therefore, we created two full factorial study designs to test six different eDNA workflows for sediment samples and twelve different workflows for water samples. We used qPCR to compare the Threshold cycle (Ct) values of the different workflows, which indicate the target DNA amount in the sample, and spectrophotometry to quantify and compare the total DNA amount inside the samples. We analyzed 96 water samples and 48 sediment samples from a pond with a known population of <i>M. fossilis</i>. We tested several method combinations for long-term sample preservation, DNA capture and DNA extraction. Additionally, we analyzed the DNA yield of samples from a ditch with a natural <i>M. fossilis</i> population monthly over one year to determine the optimal sampling period. Our results showed that the long-term water preservation method commonly used for eDNA surveys of <i>M. fossilis </i>did not lead to optimal DNA yields, and we present a valid long-term sample preservation alternative. A cost-efficient high salt DNA extraction led to the highest target DNA yields and can be used for sediment and water samples. Furthermore, we were able to show that in a natural habitat of <i>M. fossilis</i>, total and target eDNA were higher between June and September, which implies that this period is favorable for eDNA sampling. Our results will help to improve the reliability of future eDNA surveys of <i>M. fossilis</i>.</p>
Data from: Low frequency sampling rates are effective to record bottlenose dolphins
<p>Acoustic monitoring in cetacean studies is an effective but expensive approach. This is partly because of the high sampling rate required by acoustic devices when recording high-frequency echolocation clicks. However, the proportion of recording echolocation clicks at different frequencies is unknown for many species, including bottlenose dolphins. Here, we investigated the echolocation clicks for two subspecies of bottlenose dolphins in the western South Atlantic Ocean. The possibility of record echolocation clicks at 24 and 48 kHz was assessed by two approaches. First, we considered the clicks in the frequency range up to 96 kHz. We found a loss of 0.95-13.90% of echolocation clicks in the frequency range below 24 kHz, and 0.01-0.42% below 48 kHz, to each subspecies. Then, we evaluated these recordings downsampled at 48 and 96 kHz and confirmed that echolocation clicks are recorded at these lower frequencies, with some loss. Therefore, despite reaching high frequencies, the clicks can also be recorded at lower frequencies because echolocation clicks from bottlenose dolphins are broadband. We concluded that ecological studies based on presence-absence data are still effective for bottlenose dolphins when acoustic devices with a limited sampling rate are used.</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.