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424 results for “drift”
3D mesh model and raw images of a drifting iceberg in Nuup Kangerlua (Godthåbsfjord), SW Greenland on 17 August 2017
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Nuup Kangerlua (Godthåbsfjord) in southwest Greenland on 17 August 2017. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_528, 530, 531-533 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p>
3D mesh model and raw images of a drifting iceberg in Nuup Kangerlua (Godthåbsfjord), SW Greenland on 11 August 2017
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Nuup Kangerlua (Godthåbsfjord) in southwest Greenland on 11 August 2017. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_330, 332, 334, 335, 336, 337, 338, 339 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p>
Data from: Drift happens: molecular genetic diversity and differentiation among populations of jewelweed (Impatiens capensis Meerb.) reflect fragmentation of floodplain forests
Landscape features often shape patterns of gene flow and genetic differentiation in plant species. Populations that are small and isolated enough also become subject to genetic drift. We examined patterns of gene flow and differentiation among 12 floodplain populations of the selfing annual jewelweed (Impatiens capensis Meerb.) nested within four river systems and two major watersheds in Wisconsin, USA. Floodplain forests and marshes provide a model system for assessing the effects of habitat fragmentation within agricultural/urban landscapes and for testing whether rivers act to genetically connect dispersed populations. We generated a panel of 12,856 single nucleotide polymorphisms and assessed genetic diversity, differentiation, gene flow, and drift. Clustering methods revealed strong population genetic structure with limited admixture and highly differentiated populations (mean multilocus FST = 0.32, FST' = 0.33). No signals of isolation by geographic distance or environment emerged, but alleles may flow along rivers given that genetic differentiation increased with river distance. Differentiation also increased in populations with fewer private alleles (R2 = 0.51) and higher local inbreeding (R2 = 0.22). Populations varied greatly in levels of local inbreeding (FIS = 0.2 to 0.9) and FIS declined in smaller, more isolated populations. These results suggest that genetic drift dominates other forces in structuring these Impatiens populations. In rapidly changing environments, species must migrate or genetically adapt. Habitat fragmentation limits both processes, potentially compromising the ability of species to persist in fragmented landscapes.
Data from "Multi-wavelength continuum sizes of protoplanetary discs: scaling relations and implications for grain growth and radial drift"
<p>Table 1, Table 2, and Table 3 from Tazzari et al., 2021, "Multi-wavelength continuum sizes of protoplanetary discs: scaling relations and implications for grain growth and radial drift", Monthly Notices of the Royal Astronomical Society, arXiv:2010.02249</p> <p>Both tables are available in IPAC format, which is in human- and machine-readable:</p> <pre><code class="language-python">from astropy.io import ascii tb = ascii.read('Table1.txt', format='ipac')</code></pre> <p>Table comments (stored at the beginning of the ASCII file as lines starting with "/") can be read as:</p> <pre><code class="language-python">tb.meta['comments'] </code></pre>
Plasma density turbulence obtained from a Hasegawa-Wakatani drift-wave turbulence model within the BOUT++ framework
<p>A Hasegawa-Wakatani drift-wave turbulence model [1] within the BOUT++ framework [2] was used to generate a set of turbulent plasma density profiles. Drift-wave turbulence is thought to be the dominant mechanism responsible for the anomalous transport observed in the edge of tokamak plasmas.</p> <p>The motivation to generate this rather large set of density profiles was to properly study the influence of plasma density fluctuations on traversing microwaves which requires ensemble-averaging to get statistically relevant results.</p> <p>The density data is given in normalized units (1.0 would correspond to 100 % fluctuation degree) on a mean-free background and the spatial coordinates are given in units of the ion Larmor radius.</p> <p>The form of equations used was the un-modified Hasegawa-Wakatani model [3]. Input parameters were kappa = 1.0 (normalised background density gradient), alpha = 0.5 (adiabaticity parameter), and diffusion constant D=1e-2.</p> <ol> <li>Wakatani, M., Hasegawa, A. (1984). <em>A collisional drift wave description of plasma edge turbulence.</em> Phys. Fluids <strong>27</strong>(3), 611. doi:10.1063/1.864660</li> <li>Dudson, B. <em>et al.</em> (2009). <em>BOUT++: A framework for parallel plasma fluid simulations</em>. Comp. Phys. Comm. <strong>180</strong>(9), 1467. doi:10.1016/j.cpc.2009.03.008</li> <li>Numata, R., Ball, R., & Dewar, R. L. (2007). <em>Bifurcation in electrostatic resistive drift wave turbulence</em>. Phys. Plasmas <strong>14</strong>(10), 102312. doi:10.1063/1.2796106</li> </ol>
Simulated Sea Ice and Snow Thickness along the MOSAIC drift trajectory, from AWI-CM-1 and AWI-CM-3 nudged simulations.
<p>Sea ice thickness and snow (on sea ice) thickness from nudged simulations performed using the coupled climate models AWI-CM-1 (zonal wavenumber truncated at 20) and AWI-CM-3 (T20 truncation; Pithan et al., 2023) with a 1h relaxation time. The model data is collocated to the drift trajectory of the Multidisciplinary Drifting Observatory for the Study of the Arctic Climate (MOSAIC) across the Arctic Ocean, from 01 September 2019 until 31 August 2020. The collocation is done daily, by finding all model grid cells within the area covered by the distributed network of snow and sea ice measuring instruments deployed and maintained during MOSAIC. The sea ice and snow thickness is then spatially averaged for each day. </p><p>Data is provided in three .nc files for each model and variable (m_ice for sea ice thickness, m_snow for snow thickness) representing ensemble members 1 to 3.</p>
Machine learning reveals that climate, geography, and cultural drift all predict bird song variation in coastal Zonotrichia leucophrys
<p>Previous work has demonstrated that there is extensive variation in the songs of White-crowned Sparrow (<em>Zonotrichia leucophrys</em>) throughout the species range, including between neighboring (and genetically distinct) subspecies <em>Z. l. nuttalli </em>and <em>Z. l. pugetensis</em>. Using a machine learning approach to bioacoustic analysis, we demonstrate that variation in song is correlated with year of recording (representing cultural drift), geographic distance, and climatic differences, but the response is subspecies- and season-specific. Automated machine learning methods of bird song annotation can process large datasets more efficiently, allowing us to examine 1,913 recordings across ~60 years. We utilize a recently published artificial neural network to automatically annotate White-crowned Sparrow vocalizations. By analyzing differences in syllable usage and composition, we recapitulate the known pattern where <em>Z. l. nuttalli </em>and <em>Z. l. pugetensis </em>have significantly different songs. Our results are consistent with the interpretation that these differences are caused by the changes in characteristics of syllables in the White-crowned Sparrow repertoire. This supports the hypothesis that the evolution of vocalization behavior is affected by the environment, in addition to population structure.</p>
Data for: Harvest and decimation affect genetic drift and the effective population size in wild reindeer
<p>Harvesting and culling are methods used to monitor and manage wildlife diseases. An important consequence of these practices is a change in the genetic dynamics of affected populations that may threaten their long-term viability. The effective population size (N<sub>e</sub>) is a fundamental parameter for describing such changes as it determines the amount of genetic drift in a population. Here, we estimate N<sub>e</sub> of a harvested wild reindeer population in Norway. Then we use simulations to investigate the genetic consequences of management efforts for handling a recent spread of chronic wasting disease, including increased adult male harvest and population decimation. The N<sub>e</sub>/N ratio in this population was found to be 0.124 at the end of the study period, compared to 0.239 in the preceding 14-year period. The difference was caused by increased harvest rates with a high proportion of adult males (older than 2.5 years) being shot (15.2 % in 2005-2018 and 44.8 % in 2021). Increased harvest rates decreased N<sub>e</sub> in the simulations, but less sex-biased harvest strategies had a lower negative impact. For harvest strategies that yield stable population dynamics, shifting the harvest from calves to adult males and females increased N<sub>e</sub>. Population decimation always resulted in decreased genetic variation in the population, with higher loss of heterozygosity and rare alleles with more severe decimation or longer periods of low population size. A very high proportion of males in the harvest had the most severe consequences for the loss of genetic variation. This study clearly shows how the effects of harvest strategies and changes in population size interact to determine the genetic drift of a managed population. The long-term genetic viability of wildlife populations subject to disease will also depend on the population impacts of the disease and how these interact with management actions.</p>
An Evaluation of Equatorial Perturbation Electric Fields Using Empirical Vertical Drift Models
<p>Dataset for the article titled "An Evaluation of Equatorial Perturbation Electric Fields Using Empirical Vertical Drift Models", submitted to <em>Space Weather</em>. The dataset includes the outputs from the four empirical vertical drift models used in the article: Fejer and Scherliess (1997), Kelley and Retterer (2008), Manoj and Maus (2012), and Scherliess and Fejer (1999).</p>
The spreading of magnetic reconnection X-line in particle-in-cell simulations– mechanism and the effect of drift-kink instability
<p>This dataset contains data and Python scripts in "The spreading of magnetic reconnection X-line in particle-in-cell simulations– mechanism and the effect of drift-kink instability" prepared to submit to the Journal of Geophysical Research. </p>
Supporting Data for Drift Phase Structure Implications for Radiation Belt Transport by T.P. O'Brien et al. submitted to J. Geophysical Res.
<p>Datasets used in Drift Phase Structure Implications for Radiation Belt Transport by T.P. O'Brien et al. submitted to J. Geophysical Res.</p>
Supplementary material for: Phylogeny and biogeography of the ancient spider family Filistatidae (Araneae) is consistent both with long-distance dispersal and vicariance following continental drift
<p>Raw data and input files for phylogenetic and biogeographic analysis of the article "<strong>Phylogeny and biogeography of the ancient spider family Filistatidae (Araneae) is consistent both with long-distance dispersal and vicariance following continental drift</strong>".</p> <p><strong>Supplementary material S1. </strong>Matrix of phenotypic characters in .ss format.</p> <p><strong>Supplementary material S2. </strong>Alignment of COI sequences in fasta format..</p> <p><strong>Supplementary material S3. </strong>Alignment of H3 sequences in fasta format.</p> <p><strong>Supplementary material S4. </strong>Alignment of 16S sequences in fasta format before trimming with gblocks.</p> <p><strong>Supplementary material S5. </strong>Alignment of 28S sequences in fasta format before trimming with gblocks.</p> <p><strong>Supplementary material S6. </strong>Input for running parsimony analysis using TNT (phenotypic data only).</p> <p><strong>Supplementary material S7. </strong>Input for running Bayesian inference using MrBayes (phenotypic data only).</p> <p><strong>Supplementary material S8. </strong>Input for running parsimony analysis using TNT (sequence data only).</p> <p><strong>Supplementary material S9. </strong>Input for running Bayesian inference using MrBayes (sequence data only).</p> <p><strong>Supplementary material S10. </strong>Input for running parsimony analysis using TNT (total evidence).</p> <p><strong>Supplementary material S11. </strong>Input for running Bayesian inference using MrBayes (total evidence).</p> <p><strong>Supplementary material S12. </strong>Input for running parsimony analysis using TNT (total evidence, dataset with reduced number of terminals).</p> <p><strong>Supplementary material S13. </strong>Input for running Bayesian inference using MrBayes (total evidence, dataset with reduced number of terminals).</p> <p><strong>Supplementary material S14. </strong>Input for running Bayesian inference using MrBayes (total evidence) and estimating node ages using tip-dating.</p> <p><strong>Supplementary material S15. </strong>Input for running Bayesian inference using Beast (sequence data only) and estimating node ages using node-dating.</p> <p><strong>Supplementary material S16. </strong>Raw geographic distances among areas in each time slice and dispersal probability matrices for each biogeographic model.</p> <p><strong>Supplementary material S17. </strong>Inputs for estimating ancestral ranges and performing biogeographic stochastic maps for our dataset.</p> <p><strong>Supplementary material S18. </strong>Consensus tree found with parsimony analysis using TNT (phenotypic data only).</p> <p><strong>Supplementary material S19. </strong>Consensus tree found with Bayesian inference using MrBayes (phenotypic data only).</p> <p><strong>Supplementary material S20. </strong>Consensus tree found with parsimony analysis using TNT (sequence data only).</p> <p><strong>Supplementary material S21. </strong>Consensus tree found with Bayesian inference using MrBayes (sequence data only).</p> <p><strong>Supplementary material S22. </strong>Consensus tree found with parsimony analysis using TNT (total evidence).</p> <p><strong>Supplementary material S23. </strong>Consensus tree found with Bayesian inference using MrBayes (total evidence).</p> <p><strong>Supplementary material S24. </strong>Consensus tree found with parsimony analysis using TNT (total evidence, dataset with reduced number of terminals).</p> <p><strong>Supplementary material S25. </strong>Consensus tree found with Bayesian inference using MrBayes (total evidence, dataset with reduced number of terminals).</p> <p><strong>Supplementary material S26. </strong>Consensus tree found with Bayesian inference using MrBayes (total evidence) and with node ages estimated using tip-dating.</p> <p><strong>Supplementary material S27. </strong>Maximum clade credibility tree found with Bayesian inference using Beast (sequence data only) and with node ages estimated using node-dating.</p>
Apertif drift scan based compound beam maps (1220-1520 MHz)
<p>Apertif (APERture Tile In Focus) is a Phased Array Feed (PAF) system on the Westerbork Synthesis Radio Telescope (WSRT) that conducted the Apertif legacy surveys between the 1st of July 2019 and the 28th of February 2022. Apertif carried out a two tiered imaging survey, with a shallow, wide-area and a medium-deep, small-area component, and a time domain survey. In the standard observing mode for imaging 40 partially overlapping compound beams (CBs) are formed simultaneously in a rectangular configuration. Each of these 40 CBs have a unique shape that differs substantially from the "old" WSRT beam shape.</p> <p>This data set contains Apertif drift scan based CB maps measured at the second Apertif imaging survey frequency setting (1220-1520 MHz). Apertif imaging observations were take at these frequencies between January 2021 and February 2022. The CB maps are generated from drift scan observations using the AperPB scrips (DOI: https://doi.org/10.5281/zenodo.6544109). These CB maps can be used to primary beam correct or mosaic images or data cubes from the Apertif imaging surveys.</p> <p>The dataset contains two types of fits files:</p> <p>1) Reconstructed CB maps from the drift scans. These maps are within directories called "drift_maps" and have the following naming scheme: <source>_<CB map ID>_<CB number>_<polarisation>.fits. The source refers to the continuum source used for the drift scan observations, this can be either CygA or CasA. The CB map ID is the identification of a set of CB maps based on the observing date in the format of yymmdd e.g. 190628. The CB number refers to which of the 40 Apertif CBs the map presents (numbering from 00 to 39). Polarisation denotes which polarisation the map shows, these can be xx, yy, I and diff, where diff refers to xx-yy. These maps are 3.36 × 2.3 deg in size and are oriented upside down compared to the sky.</p> <p>2) Spline fitted CB maps. These are within directories called "beam_models" and have the following naming: <CB map ID>_<CB number>_<polarisation>_model.fits. The CB map ID is the identification of a set of CB maps based on the observing date e.g. 190628. The CB number refers to which of the 40 Apertif CBs the map presents. Polarisation denotes which polarisation the map shows, these can be xx, yy, I. These maps are 1.1 × 1.1 deg in size and are subdivided into 18 frequency bins in directories called "channel_<num>". These maps are suitable for primary beam correcting or mosaicing Apertif images and data cubes. Since the CB shapes of Apertif change over time, we recommend to use the closest CB data set in time to the observation that the primary beam correction or mosaicing is going to be applied to. </p> <p>All CB data sets also contain per antenna CB maps and spline fitted CB maps within the directory "per_antenna_maps". The naming and organisation of the per antenna maps is the same as described above, with the exception that the spline fitted CB maps are not subdivided by frequency bins into separate directories, but are contained within a single fits file. The Apertif system uses 12 of the 14 WSRT antennas. In some cases one or two antennas were malfunctioning during the observations and there are only 10 or 11 per antenna CB map sets. </p> <table> <caption>Notes on the individual CB data sets</caption> <tbody> <tr> <td>CB map ID</td> <td>continuum source</td> <td>notes</td> </tr> <tr> <td> <p>210205</p> </td> <td>Cyg A</td> <td> <p>RFI in channels 13-15 (1.46-1.48 GHz)</p> </td> </tr> <tr> <td> <p>210402</p> </td> <td>Cas A</td> <td> <p>RFI in channels 13-15 (1.46-1.48 GHz), no RTC and RTD, spline fit for CB01 failed for the per antenna maps</p> </td> </tr> <tr> <td> <p>211103</p> </td> <td>Cas A</td> <td> <p>RFI in channels 13-15 (1.46-1.48 GHz), spline fit for CB01 failed for the per antenna maps</p> </td> </tr> <tr> <td> <p>211118</p> </td> <td>Cyg A</td> <td> <p>RFI in channels 13-15 (1.46-1.48 GHz), spline fit for CB01 failed for the per antenna maps</p> </td> </tr> </tbody> </table> <p>For a detailed description of the characteristics of the Apertif CBs and how the CB maps are produced see the upcoming publication Dénes et al. 2022 submitted to A&A (ArXiv: https://arxiv.org/abs/2205.09662).<br> <br> For more information on the Apertif system see: van Cappellen et al. 2022, A&A, 658, 146 DOI: https://doi.org/10.1051/0004-6361/202141739</p>
Apertif drift scan based compound beam maps (1130-1430 MHz)
<p>Apertif (APERture Tile In Focus) is a Phased Array Feed (PAF) system on the Westerbork Synthesis Radio Telescope (WSRT) that conducted the Apertif legacy surveys between the 1st of July 2019 and the 28th of February 2022. Apertif carried out a two tiered imaging survey, with a shallow, wide-area and a medium-deep, small-area component, and a time domain survey. In the standard observing mode for imaging 40 partially overlapping compound beams (CBs) are formed simultaneously in a rectangular configuration. Each of these 40 CBs have a unique shape that differs substantially from the "old" WSRT beam shape.</p> <p>This data set contains Apertif drift scan based CB maps measured at the first Apertif imaging survey frequency setting (1130-1430 MHz). Apertif imaging observations were take at these frequencies between July 2019 and January 2021. The CB maps are generated from drift scan observations using the AperPB scrips (DOI: https://doi.org/10.5281/zenodo.6544109). These CB maps can be used to primary beam correct or mosaic images or data cubes from the Apertif imaging surveys.</p> <p>The dataset contains two types of fits files:</p> <p>1) Reconstructed CB maps from the drift scans. These maps are within directories called "drift_maps" and have the following naming scheme: <source>_<CB map ID>_<CB number>_<polarisation>.fits. The source refers to the continuum source used for the drift scan observations, this can be either CygA or CasA. The CB map ID is the identification of a set of CB maps based on the observing date in the format of yymmdd e.g. 190628. The CB number refers to which of the 40 Apertif CBs the map presents (numbering from 00 to 39). Polarisation denotes which polarisation the map shows, these can be xx, yy, I and diff, where diff refers to xx-yy. These maps are 3.36 × 2.3 deg in size and are oriented upside down compared to the sky.</p> <p>2) Spline fitted CB maps. These are within directories called "beam_models" and have the following naming: <CB map ID>_<CB number>_<polarisation>_model.fits. The CB map ID is the identification of a set of CB maps based on the observing date e.g. 190628. The CB number refers to which of the 40 Apertif CBs the map presents. Polarisation denotes which polarisation the map shows, these can be xx, yy, I. These maps are 1.1 × 1.1 deg in size and are subdivided into 18 frequency bins in directories called "channel_<num>". These maps are suitable for primary beam correcting or mosaicing Apertif images and data cubes. Since the CB shapes of Apertif change over time, we recommend to use the closest CB data set in time to the observation that the primary beam correction or mosaicing is going to be applied to. </p> <p>Wit the exception of two datasets (191008 and 191023), all CB data sets also contain per antenna CB maps and spline fitted CB maps within the directory "per_antenna_maps". The naming and organisation of the per antenna maps is the same as described above, with the exception that the spline fitted CB maps are not subdivided by frequency bins into separate directories, but are contained within a single fits file. The Apertif system uses 12 of the 14 WSRT antennas. In some cases one or two antennas were malfunctioning during the observations and there are only 10 or 11 per antenna CB map sets. </p> <p>Notes on the individual CB data sets:</p> <table> <tbody> <tr> <td>CB map ID</td> <td>continuum source</td> <td>notes</td> </tr> <tr> <td>190628</td> <td>Cyg A</td> <td>issue in channel 2 (1.31 GHz) with CBs 15-16,<br> issue in channels 5-6 (1.36 - 1.37 Ghz) and 8-9 (1.4 - 1.41 GHz), spline fit for CBs 01-07 failed for the per antenna maps</td> </tr> <tr> <td>190722</td> <td>Cyg A</td> <td>RFI in chan 6 (1.37 GHz) for CB 1</td> </tr> <tr> <td>190821</td> <td>Cyg A</td> <td> </td> </tr> <tr> <td>190826</td> <td>Cyg A</td> <td> </td> </tr> <tr> <td>190912</td> <td>Cyg A</td> <td> </td> </tr> <tr> <td>190916</td> <td>Cyg A</td> <td> </td> </tr> <tr> <td>191008</td> <td>Cyg A</td> <td>CBs 14, 19, 20 and 26 are distorted, no per antenna maps</td> </tr> <tr> <td>191023</td> <td>Cyg A</td> <td>RFI in channel 6 ( 1.37 GHz) for CBs 0, 3-4, 9-10, 15-17, 22, no per antenna maps</td> </tr> <tr> <td>191120</td> <td>Cyg A</td> <td>RFI in channels 8-9 (1.4 - 1.41 GHz)</td> </tr> <tr> <td>200130</td> <td>Cyg A</td> <td>no RT5, RFI in channel 6 (1.37 GHz) for CBs 1, 6-8, 14, 21, 33</td> </tr> <tr> <td>200430</td> <td>Cyg A</td> <td>no RTC and RTD</td> </tr> <tr> <td>200710</td> <td>Cyg A</td> <td>no RTB, CBs 0, 20-39 are affected by RFI</td> </tr> <tr> <td>200819</td> <td>Cyg A</td> <td>issue with CBs 37-39, RFI in channel 6 (1.37 GHz), spline fit for CB01 failed for the per antenna maps</td> </tr> <tr> <td>201009</td> <td>Cyg A</td> <td>no RTB, antenna 9 (RTB) files are empty in the antenna based maps</td> </tr> <tr> <td>201028</td> <td>Cas A</td> <td>spline fit for CB01 failed for the per antenna maps</td> </tr> <tr> <td>201218</td> <td>Cyg A</td> <td> </td> </tr> </tbody> </table> <p>For a detailed description of the characteristics of the Apertif CBs and how the CB maps are produced see the upcoming publication Dénes et al. 2022 submitted to A&A (ArXiv: https://arxiv.org/abs/2205.09662).<br> <br> For more information on the Apertif system see: van Cappellen et al. 2022, A&A, 658, 146 DOI: https://doi.org/10.1051/0004-6361/202141739</p>
Data about the aquatic insects drift in Xingu River
<p>The drift movement consists of the displacement of the organisms inside the water which allows its locomotion. This movement will result in a variation of the communities of organisms along the river, generating patterns. Based on this, we tested the hypotheses a) the movement of organisms along an upstream-downstream gradient will result in a pattern of nesteness distribution of organisms in aquatic insect communities; b) there will be an increase in the number of individuals and genera as we approach the most downstream point. The present study was carried out in seven sampling points distributed along the Xingu River. The sampling occurred at night in the central area of the river. The distribution of genera along the river remained constant. A nesteness distribution of the communities in the upstream-downstream gradient was not observed. Based on the results it is possible to visualize that the dispersion movement generates a nestedness of the genera downstream of the river, occurring a confluence for the last sampling point. The organisms are carried by the flow of the water stream and are influenced by the characteristics of the water body adapting to the type of environment in which they are located.</p>
Synthetic tetrode recording dataset with spike-waveform drift
<p><strong>Introduction</strong></p> <p>This synthetic ground-truth dataset accurately models long-term, continuous extracellular tetrode recordings from the rodent brain over a time-period of 256 hours. Each "recording" comprises spiking of 8 distinct single-units with firing rates ranging from 0.1 - 6 Hz, superimposed on background multi-unit spiking activity at 20 Hz. The recording sampling rate is 30 kHz. Single-unit spike amplitudes drift over a range of 100 to 400 <span class="math-tex">\(\mu V\)</span> based on the drift we observe in our own long-term recordings from the rodent motor cortex and striatum. For more details, please see our paper "Automated long-term recording and analysis of neural activity in behaving animals" ( https://doi.org/10.1101/033266).</p> <p>These recordings can be used to test the accuracy of spike-sorting algorithms when clustering non-stationary spike waveform data, such as our own Fast Automated Spike Tracker (FAST) outlined in our paper and available at https://github.com/Olveczky-Lab/FAST.</p> <p> </p> <p><strong>Dataset</strong></p> <p>Due to size restrictions, we provide here 1 sample tetrode of the full 6 tetrode dataset. Please contact us (https://olveczkylab.oeb.harvard.edu/about) if you require access to the other 5 synthetic tetrode recordings.</p> <p> </p> <p><strong>Instructions</strong></p> <p>The dataset comprises spike times and spike waveform snippets extracted a continuous synthetic tetrode recording. Provided are...</p> <ul> <li>A <strong>SpikeTimes</strong> file with a list of sample numbers for detected events (spikes) at <em>uint64</em> precision.</li> <li>A <strong>Spikes</strong> file with the waveforms of the detected events in <em>int16</em> precision. Each event waveform comprises <em>4 channels X 64 samples</em> 16-bit words arranged in the order [Ch0-Sample0, Ch1-Sample0, Ch2-Sample0, Ch3-Sample0, Ch0-Sample1, etc.]. To convert to units of voltage, change type to double precision and multiply by 1.95e-7.</li> <li>A <strong>SnippeterSettings.xml</strong> file with snippeting parameters (this is auto-generated by the FAST snippeting algorithm).</li> <li>A <strong>dataset_params.mat</strong> MATLAB data file containing the simulation parameters. The most important variables in the mat file are <em>sp</em> which contains a list of true spike-times (in samples @ 30 kHz) for all single-units in the dataset, and <em>sp_u</em> which specifies which unit (1-8) each spike originates from. Spike-times are generated by a homogenous Poisson process with firing rate specified for each unit by the variable <em>uFRs</em> and an absolute refractory period of 2 ms. The variable <em>d_Amps</em> specifies the amplitude of each single-unit spikes. The basic spike-waveform shape of each unit is provided in the variable <em>uWVs</em>. The spike-times and identity of background (multi-unit) spikes are specified in <em>b_sp</em> and <em>b_sp_u</em>. </li> </ul>
Data from: Towards drift-free high-throughput nanoscopy through adaptive intersection maximization
<p>Single-molecule localization microscopy (SMLM) often suffers from suboptimal resolution due to imperfect drift correction. Existing marker-free drift-correction algorithms often struggle to reliably track high-frequency drift and lack the computational efficiency to manage large, high-throughput localization datasets. We present an adaptive intersection maximization-based method (AIM) that leverages the entire dataset's information content to minimize drift correction errors, particularly addressing high-frequency drift, thereby enhancing the resolution of existing SMLM systems. We demonstrate that AIM can robustly and efficiently achieve an angstrom-level tracking precision for high-throughput SMLM datasets under various imaging conditions, resulting in an optimal resolution in simulated and biological experimental datasets. We offer AIM as simple and model-free software for instant resolution enhancement with standard CPU devices.</p>
Dataset for Securing the Sky: Detecting Aircraft Location Drifting through Cross-Checking Receiver-Based Estimated and Received ADS-B Trajectories
<p>Dataset utilized for our tested data in our accepted paper "Securing the Sky: Detecting Aircraft Location Drifting through Cross-Checking Receiver-Based Estimated and Received ADS-B Trajectories"</p>
Data and Analysis Scripts for "Drift in Individual Behavioral Phenotype as a Strategy for Unpredictable Worlds"
<p>This document contains the raw data and analysis scripts for the paper "Drift in Individual Behavioral Phenotype as a Strategy for Unpredictable Worlds", including <em>Drosophila melanogaster</em> circling and handedness behavior at multiple timepoints and across genotypes and experimental conditions manipulating serotonin. It also contains code used to run ecological simulations in the paper and the results of those simulations, as well as code to generate figures for the paper.</p>
E-nose drift analysis dataset
<p>In this file, there are 2 E-nose datasets including board drift and time drift. Take time drift as an example,</p> <p>after loading data_lab_after.mat, the sensor array response and the correspongding label could be possessed</p> <p>This dataset is supplied by School of Microelectronics and Communication Engineering, Chongqing University and Chongqing Key Laboratory of Bio-perception Intelligent Information Processing.</p> <p> </p>
ScienceDex guides
Understand access before you commit
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.