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11,506 results for “Dynamics”
Live cribwall + slope grating + fascines drainage system - soil-water dynamics
<p>Dataset containing raw time series (August 2022) for soil-water dynamics -i.e., volumetric soil moisture, matric suction, soil-pore water pressure, and soil temperature - retrieved from a live, vegetated cribwall+slope grating+fascines drainage system built in Catterline, Scotland. NBS intervention built to restore a landslide taking place in February 2021. </p>
Replication package for "Motivation in the Dynamics of European Youth Migration"
<p>Replication package for the paper "Motivation in the Dynamics of European Youth Migration". The package contains the data and the SPSS and Stata code for the the analyses presented in the paper. The original data from which the variables are extracted was collected within the EU Horizon 2020 project YMOBILITY (2015-2018).</p>
Dataset for: Water column dynamics control nitrite-dependent anaerobic methane oxidation by Candidatus 'Methylomirabilis' in stratified lake basins
<p>Dataset containing treated 16S rRNA amplicon sequence data, accompanying the manuscript "Water column dynamics control nitrite-dependent anaerobic methane oxidation by Candidatus ‘Methylomirabilis’ in stratified lake basins" </p> <p>Files: </p> <p>- Mapping file</p> <p>- ASV table</p> <p>- Refseq file</p> <p>-Tree file</p> <p>- Relative abundances of dominant methanotrophs in the water column of Lake Lugano North Basin (used to create Fig. 6c)</p> <p>- Multi annual dataset of water chemistry data of the Lake Lugano North Basin</p> <p> </p> <p> </p> <p> </p>
Compound I Formation and Reactivity in Dimeric Chlorite Dismutase: Impact of pH and the Dynamics of the Catalytic Arginine
<ul> <li><strong>Data type</strong>: spectroscopic measurements (UV-visible, ECD, EPR), enzyme activity measurements, mass spectrometry, spectroelectrochemistry and data analysis.</li> <li>Files are in<strong> .DTA, .DSC, .xlsx, .m, .mat, .BSW, .csv, .txt, .dsx, .pdf, .uds </strong>formats</li> <li>Information on <strong>origin of the data</strong>: <ul> <li>EPR spectroscopic measurements in <strong>DTA </strong>and<strong> DSC</strong> formats</li> <li>EPR spectroscopic simulation and analyses in <strong>m </strong>and<strong> mat</strong> format</li> <li>Analysis of Rapid Freeze-Quench calibration curve is in <strong>xlsx</strong> format</li> <li>UV-vis spectroscopic measurements in <strong>csv, xlsx, txt, uds and dsx</strong> format</li> <li>ECD measurements in <strong>csv, xlsx and dsx</strong> format</li> <li>Enzyme activity data in <strong>csv and xlsx</strong> format</li> <li>Mass spectrometry data in <strong>pdf and xlsx</strong> format</li> <li>Spectroelectrochemistry data in <strong>BSW</strong> and <strong>xlsx</strong> format</li> </ul> </li> <li>The data are <strong>generated</strong> by: <ul> <li>UV−vis spectra were recorded using a Cary 60 UV–vis spectrophotometer (Agilent) and a U-3900 spectrophotometer (Hitachi, Mannheim, Germany).</li> <li>Electronic circular dichroism spectroscopy was performed using Chirascan (Applied Photophysics, Leatherhead, UK).</li> <li>Rapid Freeze-Quench of EPR sample was performed with the use of a device from BioLogic (Grenoble, France), consisting of an SFM-2000 stopped-flow unit and an MPS-70 controller unit, combined with a freeze-quench sample collector adapted for EPR tubes. The ejected volumes and flow rate were controlled by the BioLogic BIOKINE software, v. 4.72.</li> <li>X-Band CW-EPR experiments were performed on X-band ELEXSYS E580 spectrometer (Bruker BioSpin GmbH) operating at a microwave frequency of ∼9.4 GHz and equipped with a standard TE102 cavity and a liquid He cryostat (Oxford Inc.)</li> <li>Enzyme activity was measured polarographically following the release of O<sub>2</sub> by using a Clark-type oxygen electrode (Oxygraph Plus; Hansatech Instruments, Norfolk, UK).</li> <li>Stopped-flow spectroscopy measurements were performed with a SX-18MV or Pi-star from Applied Photophysics using either a diode array detector or a monochromator and photomultiplier detector.</li> <li>Mass spectrometry analysis was performed with an ion-trap mass spectrometer (amaZon speed ETD, Bruker) equipped with the standard ESI source in positive ion, DDA mode (i.e., switching to MSMS mode for eluting peaks).</li> <li>All spectroelectrochemistry experiments were conducted in a homemade OTTLE (optical transparent thin-layer spectroelectrochemical) cell. In detail, the three-electrode configuration consisted of a gold minigrid working electrode (Buckbee-Mears, Chicago, IL), a homemade Ag/AgCl/KCl<sub>sat</sub> microreference electrode separated from the working solution by a Vycor set, and a platinum wire as the counter electrode. UV−vis spectra were recorded using a Varian Cary C50 spectrophotometer.</li> </ul> </li> </ul> <p> </p> <p> </p> <ul> <li>If the dataset includes multiple files that relate to each other: <ul> <li>Files in <strong>PARACAT_WP3_20230302_01_EPR </strong>folder includes EPR spectroscopic measurements and computer simulations/analyses, original data are in <strong>DTA/DSC</strong> formats; files in <strong>m</strong> format were used to process the data.</li> <li>Files in <strong>PARACAT_WP3_20230302_02_UVVIS </strong>folder includes sequential and non-sequential stopped flow data, measured with detection by photodiode array or monochromator (time traces): original data is in <strong>dsx and csv </strong>format<strong>, xlsx </strong>format contains processed data; conventional photometric data is originally in <strong>txt and uds </strong>format, <strong>xlsx </strong>format contains processed data</li> <li>Files in <strong>PARACAT_WP3_20230302_03_ECD</strong> folder contain ECD spectra: original data is in <strong>dsx and csv </strong>format<strong>, xlsx </strong>format contains processed data</li> <li>Files in <strong>PARACAT_WP3_20230302_04_Enzyme activity </strong>folder contain polarographically detected changes in dioxygen concentration, due to enzyme activity: original data is in <strong>csv </strong>format<strong>, xlsx </strong>format contains processed data</li> <li>Files in <strong>PARACAT_WP3_20230302_05_MassSpec </strong>folder contain mass spectrometry data for the MNP assay: original data is in <strong>pdf </strong>format<strong>, xlsx </strong>format contains processed data</li> <li>Files in <strong>PARACAT_WP3_20230302_06_Spectroelectrochemistry </strong>folder includes spectroelectrochemistry measurements and computer analyses, original data are in <strong>BSW</strong> formats; files in <strong>xlsx</strong> format were used to process the data.</li> </ul> </li> </ul> <p> </p> <p>NB. See the “READ ME” text file in each subfolder for more detailed information on files organization.</p> <p> </p> <ul> <li>Information on: <ul> <li><strong>Abbreviations:</strong> <ul> <li>, chlorite dismutase; <strong><em>C</em>Cld</strong>, chlorite dismutase from Cyanothece sp. PCC7425; <strong>CcP</strong>, cytochrome c peroxidase;<strong> DaCld</strong>, chlorite dismutase from <em>Dechloromonas aromatica</em>; <strong>E°′</strong>, standard reduction potential; <strong>ECD</strong>, electronic circular dichroism; <strong>EPR</strong>, electron paramagnetic resonance; <strong>HRP</strong>, horseradish peroxidase; <strong>LPO</strong>, lactoperoxidase; <strong>MNP</strong>, 2-methyl-2-nitrosopropane; <strong>MPO</strong>, myeloperoxidase; <strong>PAA</strong>, peracetic acid; <strong>RFQ</strong>, rapid freeze-quench.</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Units of measurement</strong>: <ul> <li>Concentration: <strong>mM</strong> (millimolar), <strong>µM</strong> (micromolar), <strong>nM</strong> (nanomolar), <strong>mg/mL</strong> (milligrams per milliliter)</li> <li>Molecular mass: <strong>Da</strong> (Dalton)</li> <li>Absorptivity: <strong>M<sup>-1</sup></strong> <strong>cm<sup>-1</sup></strong></li> <li>Volume: <strong>mL</strong> (milliliters), <strong>µL </strong>(microliters), <strong>nL</strong> (nanoliters)</li> <li>Wavelength:<strong> nm</strong> (nanometers)</li> <li>Temperature:<strong> °C</strong> (Celsius degrees), <strong>K</strong> (Kelvin degrees)</li> <li>Time:<strong> ms</strong> (milliseconds), <strong>s</strong> (seconds), <strong>min</strong> (minutes), <strong>h</strong> (hours),</li> <li>Ellipticity: millidegrees</li> <li>Frequency: <strong>GHz</strong> (gigahertz), <strong>kHz</strong> (kilohertz)</li> <li>Power: <strong>mW</strong> (milliwatt)</li> <li>Magnetic field: <strong>mT</strong> (milliTesla)</li> <li>Reduction potential: <strong>mV </strong>(milliVolts)</li> </ul> </li> </ul>
Land Productivity Dynamics (LPD) maps based on approaches recommended by the United Nations Convention to Combat Desertification (UNCCD) for the Brazilian Semiarid Region (BSR).
<p>Maps of the land productivity dynamic (LPD) based on approaches recommended by the United Nations Convention to Combat Desertification (UNCCD) for the Brazilian Semiarid Region for the period 2001-2015. LPD from Trends.Earth (TE), LPD from the Joint Research Center (JRC-LPD), and LPD from the Food and Agriculture Organization – World Overview of Conservation Approaches and Technologies (FAO-WOCAT LPD). In addition, TE-based LPD with climate correction based on Rain Use Efficiency (RUE), Residual Trend Analysis (RESTREND), and Water Use Efficiency (WUE) calculated using the procedures described in the second version of the Good Practice Guidance for SDG Indicator 15.3.1. The annual LCLU maps from the MapBiomas project at 30 m spatial resolution for 2001 and 2015, and the 16-day MOD13Q1 NDVI dataset from 2001 to 2015 were used as inputs.</p> <p>**********************</p> <p>A total of seven GeoTIFF files in Geographic Tagged Image File Format (GeoTIFF) format are provided at 250 m spatial resolution.</p> <p>Coding for the LPDs.</p> <p>Value Meaning</p> <p>-32768 No data</p> <p>1 Declining</p> <p>2 Moderate decline</p> <p>3 Stressed</p> <p>4 Stable</p> <p>5 Increasing</p> <p>Funding: this study was undertaken as part of the Satellite Desertification Monitoring Program in the Brazilian Semiarid Region [Grant Number 403223/2021-0] supported by the CNPq. It also had the support of Capes, through Notice no. 28/2022 – PDPG Social Vulnerability & Human Rights [Grant Number 88881.705050/2022-01].</p>
Advection datasets from "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"
<p>Advection datasets from the paper:<br> Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics (https://doi.org/10.1063/5.0097679)</p> <p>The datasets are:<br> - AdvBox<br> - AdvInBox<br> - AdvTaylor<br> - AdvCircle<br> - AdvCircleAng<br> - AdvSquare<br> - AdvEllipseH<br> - AdvEllipseV<br> - AdvSpline<br> - AdvSquareAndCircle<br> - Adv3Circles</p> <p>Check the "README.txt" file for information on how the simulations are organised. The features of each dataset and how they were generated are explained in the journal publication.</p> <p> </p> <p>To cite these datasets, use the following reference:</p> <p>Mario Lino, Stathi Fotiadis, Anil A. Bharath, and Chris Cantwell. "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics". Physics of Fluids, 34 (2022).</p> <pre><code>@article{lino2022multi, author = {Lino, Mario and Fotiadis, Stathi and Bharath, Anil A. and Cantwell, Chris}, title = {{Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics}}, journal = {Physics of Fluids}, volume = {34}, year = {2022}, url = {https://doi.org/10.1063/5.0097679}, }</code></pre> <p><br> </p>
Dataset for "Light Scalar Meson and Decay Constant in SU(3) Gauge Theory with Eight Dynamical Flavors"
<p><strong>Decoding File Names</strong>: Consider the file name f8l24t48b48m00889_S0.csv. We will break down the meaning of the various pieces of the filename</p> <ul> <li>"f8" means 8 Dirac flavors.</li> <li>"l24t48" means 24<sup>3</sup>×48 lattice.</li> <li>"b48" means beta=4.8, related to the inverse bare gauge coupling.</li> <li>"m00889" means fermion mass m=0.00889.</li> <li>"S" means flavor-singlet scalar meson. Other options are "P" for flavor non-singlet pseudoscalar meson and "C" for flavor non-singlet scalar meson.</li> <li>"0" an integer from 0 to 4 proportional to the squared length of the spatial momentum vector of the correlation function.</li> </ul> <p><strong>Columns of the CSV files</strong>: Each line of the CSV file should contain 41 entries, separated by commas. Refer to the Eq. (8) which defines model A in the accompanying paper to understand the physical interpretation of these parameters.</p> <ol> <li>Model number: 1 is model A, 2 is model B, 3 is model C.</li> <li>n<sub>max</sub>: the number of non-oscillating states in the fit.</li> <li>j<sub>max</sub>: the number of oscillating states in the fit.</li> <li>t<sub>min</sub>: the minimum t value used in the fit.</li> <li>t<sub>max</sub>: the maximum t value used in the fit.</li> <li>𝜒<sup>2</sup> of the fit.</li> <li><span class="math-tex">\(\log\ p\left(\left.M\right|D\right)\)</span>: log of model probability used in Bayesian model averaging.</li> <li>fit value for c<sub>0</sub> (model A) or <span class="math-tex">\(\overline{c}_0\)</span> (model B).</li> <li>fit error for c<sub>0</sub> (model A) or <span class="math-tex">\(\overline{c}_0\)</span> (model B).</li> <li>fit value for c<sub>1</sub>.</li> <li>fit error for c<sub>1</sub>.</li> <li>fit value for c<sub>2</sub>.</li> <li>fit error for c<sub>2</sub>.</li> <li>fit value for c<sub>3</sub>.</li> <li>fit error for c<sub>3</sub>.</li> <li>fit value for c<sub>4</sub>.</li> <li>fit error for c<sub>4</sub>.</li> <li>fit value for <span class="math-tex">\(c_1^\prime\)</span>.</li> <li>fit error for <span class="math-tex">\(c_1^\prime\)</span></li> <li>fit value for <span class="math-tex">\(c_2^\prime\)</span>.</li> <li>fit error for <span class="math-tex">\(c_2^\prime\)</span>.</li> <li>fit value for <span class="math-tex">\(c_3^\prime\)</span>.</li> <li>fit error for <span class="math-tex">\(c_3^\prime\)</span>.</li> <li>fit value for <span class="math-tex">\(c_4^\prime\)</span>.</li> <li>fit error for <span class="math-tex">\(c_4^\prime\)</span>.</li> <li>fit value for <span class="math-tex">\(\log(E_2 - E_1)\)</span>.</li> <li>fit error for <span class="math-tex">\(\log(E_2-E_1)\)</span>.</li> <li>fit value for <span class="math-tex">\(\log(E_3-E_2)\)</span>.</li> <li>fit error for <span class="math-tex">\(\log(E_3-E_2)\)</span>.</li> <li>fit value for <span class="math-tex">\(\log(E_4-E_3)\)</span>.</li> <li>fit error for <span class="math-tex">\(\log(E_4-E_3)\)</span>.</li> <li>fit value for <span class="math-tex">\(\log(E_2^\prime - E_1^\prime)\)</span>.</li> <li>fit error for <span class="math-tex">\(\log(E_2^\prime - E_1^\prime)\)</span>.</li> <li>fit value for <span class="math-tex">\(\log(E_3^\prime - E_2^\prime)\)</span>.</li> <li>fit error for <span class="math-tex">\(\log(E_3^\prime - E_2^\prime)\)</span>.</li> <li>fit value for <span class="math-tex">\(\log(E_4^\prime - E_3^\prime)\)</span>.</li> <li>fit error for <span class="math-tex">\(\log(E_4^\prime - E_3^\prime)\)</span>.</li> <li>fit value for <span class="math-tex">\(\log(E_1)\)</span>.</li> <li>fit error for <span class="math-tex">\(\log(E_1)\)</span>.</li> <li>fit value for <span class="math-tex">\(\log(E_1^\prime)\)</span>.</li> <li>fit error for <span class="math-tex">\(\log(E_1^\prime)\)</span>.</li> </ol>
STEMPO - dynamic X-ray tomography phantom
<p>The Spatio-TEmporal Motor-Powered (<strong>STEMPO</strong>) phantom is a physical phantom designed for collecting dynamic X-ray tomography data. The dynamic part of the phantom is computer controlled allowing for wide variety of different measurements and sampling setups to be used. The primary goal is to help mathematical community test and validate novel dynamic tomography reconstruction methods.</p> <p>Detailed documentation of the phantom, the included data (volume 1 only) and some examples can be found on the related publication: <a href="https://doi.org/10.1007/978-981-97-6769-4_1">https://doi.org/10.1007/978-981-97-6769-4_1</a> (available as an arXiv preprint: <a href="http://arxiv.org/abs/2209.12471">http://arxiv.org/abs/2209.12471</a>).</p> <p>This data set can be appended with new data in the future. Current version (<strong>1.2</strong>) includes<br> <strong>Data - vol.1 (v1.0)</strong></p> <ul> <li>stempo_static_2d_b*.mat</li> <li>stempo_static_3d_b*.mat</li> <li>stempo_cont360_2d_b*.mat</li> <li>stempo_cont360_3d_b*.mat</li> <li>stempo_seq8x45_2d_b*.mat</li> <li>stempo_seq8x45_3d_b*.mat</li> <li>stempo_data_geometries.csv</li> </ul> <p> <strong>Data - vol.2 (added in v1.2)</strong></p> <ul> <li>stempo_seq8x180_2d_b*.mat</li> <li>stempo_seq8x180_3d_b*.mat</li> </ul> <p>where b* denotes downsampling or binning of the data by a factor of (4, 8, 16 or 32). These are 2D and 3D data collected from a <em>static</em> object for reference, or from a dynamic target in a <em>continuous</em> 360 projection scan or <em>sequence</em> of 8 rotations, each consisting of 45 or 180 projections (with <strong>seq8x45</strong> and <strong>seq8x180</strong> data respectively). Finally stempo_data_geometries.csv is a simple table containing the key parameters of the measurement geometry in text format. Note that the height of the phantom for volume 2 data is slightly different compared to volume 1, including the static scan (mostly relevant for comparing 3D reconstructions).</p> <p>In addition the data set contains</p> <p> <strong>Additional files</strong></p> <ul> <li>stempo_ground_truth_2d_b4.mat</li> </ul> <p>which is an approximation of the true motion obtained from a single static FBP reconstruction which has been interpolated to match the location of the moving block during the <em>cont360</em> and <em>seq8x45</em> scans. Finally there are</p> <p> <strong>Example algorithms</strong></p> <ul> <li>stempo_fbp_example.m</li> <li>stempo_fdk_example.m</li> <li>stempo_pdfp_wavelet_2d_example.m</li> <li>stempo_LplusS_2d_example.m</li> </ul> <p>which are short example algorithms of well know analytic (<em>FBP</em> and <em>FDK</em>) and iterative methods. <em>stempo_pdfp_wavelet_2d.m<strong> </strong></em>uses variational regularization and wavelet transform of the 2D + time object to reach a suitable solution. The codes are adapted from [<a href="https://doi.org/10.1088/1361-6501/aa9260">1</a>,<a href="https://doi.org/10.1088/1361-6420/ab9c15">2</a>]. <em>stempo_LplusS_2d_example.m</em> attempts to split the reconstruction into low-rank component <em>L</em> and a sparse dynamic component <em>S</em>. This code is adapted from [<a href="https://doi.org/10.1002/mrm.25240">3</a>]. These are meant to give users ideas how the data can be used in different applications to match the requirements of different methods.</p> <p>Easiest way to utilize the data is with the <a href="http://www.astra-toolbox.com/">ASTRA Toolbox</a> and the <a href="https://github.com/Diagonalizable/HelTomo">HelTomo Toolbox</a>. Some of the example codes also require <a href="https://www.cs.ubc.ca/labs/scl/spot/">Spot Linear Operator Toolbox</a> (highly recommended) and the Wavelet Toolbox. However none of these are mandatory and any method (including programming languages other than MATLAB) are fine as long as the measurement geometry is respected.</p> <p><br>The author is supported by the Emil Aaltonen Foundation junior researcher grant no. 200029 and the Vilho, Yrjö and Kalle Väisälä Foundation of the Finnish Academy of Science and Letters. The author also acknowledges the support of Academy of Finland through the Finnish Centre of Excellence in Inverse Modelling and Imaging 2018–2025, decision number 312339. Finally the author would like to thank E. Heikkilä, T. Heikkilä, A. Meaney and F.S. Moura for all their technical expertise and help in developing, building and imaging the mechanism.</p> <p>The author also thanks O. Tapaninen for helping measure the data for <strong>vol.2</strong>.</p>
Data for: A tool based on the Industry Foundation Classes standard for dynamic data collection and automatic generation of Building Automation Control Networks
<p>This dataset shows the results obtained for a case study at TRL4 for the research paper title <em><strong>A tool based on the Industry Foundation Classes standard for dynamic data collection and automatic generation of Building Automation Control Networks</strong></em>, with DOI: https://doi.org/10.1016/j.jobe.2023.107625</p> <p>This dataset is an enhanced IFC (Industry Foundation Classes) file with the creation of the BACN (Building Automation Control Network). This IFC file includes the devices created automatically by the BACN2BIM tool (developed by CARTIF Technology Centre) for the case study validated at TRL4. The original IFC was obtained from the Institute for Automation and Applied Informatics (IAI) / Karlsruhe Institute of Technology (KIT) https://www.ifcwiki.org/images/e/e3/AC20-FZK-Haus.ifc, under an unrestricted license, as served as one of the case studies for this research.</p> <p>*Depending on the IFC viewer used, the included sensors may not be represented correctly. In this case, it is recommended to try with another IFC viewer, for example xBIM explorer https://docs.xbim.net/downloads/xbimxplorer.html or BimCollab Zoom Free https://www.bimcollab.com/en/support/downloads/</p>
NMR data for "Rapid and simple 13C-hyperpolarization by 1H dissolution dynamic nuclear polarization followed by an in-line magnetic field inversion"
<p>Liquid-state and solid-state NMR data for "Rapid and simple 13C-hyperpolarization by 1H dissolution dynamic nuclear polarization followed by an in-line magnetic field inversion".</p> <p>The data enclosed are NMR data generated by the software Topspin by Burker Biospin. The experiments are dDNP runs that come in two parts: a solid-state and a liquid-state part.</p> <ul> <li>Experiments from 1 to 9 are reference experiments used to quantify polarization in other experiments</li> <li>Experiments 11-19, 21-29, 31-39, ... 61-69 correspond to 6 dDNP runs performed a different samples from the same batch. The numbers correspond between solid and liquid-state datasets</li> </ul> <p>The codes used to analyze the data are available at in a next upload.</p> <p>Refer to the main text of the paper and its supplementary material at 10.26434/chemrxiv-2023-6gd0l for more information.</p>
Data from "Evaluating top-down, bottom-up, and environmental drivers of pelagic food web dynamics along an estuarine gradient"
Synthesized fish, benthic invertebrate, and water quality dataset used for analysis in: Rogers, T., S. Bashevkin, C. Burdi, D. Colombano, P. Dudley, B. Mahardja, L. Mitchell, S. Perry, and P. Saffarinia. 2022. Evaluating top-down, bottom-up, and environmental drivers of pelagic food web dynamics along an estuarine gradient. preprint, EcoEvoRxiv. https://doi.org/10.32942/X2MK5Z
Invasion dynamics of quagga mussels within a Southern California reservoir and its spatially intermittent watershed
Since its discovery in Lake Mead, Nevada in 2007, the invasive quagga mussel (Dreissena rostriformis bugensis) spread throughout the lower Colorado River drainage and into connected Southern California water systems. In December 2013, quagga mussels were found in Lake Piru, California, a reservoir with no connection to the Colorado River drainage. An initial “boom” period occurred in the first year after colonization. High densities and settlement rates continued for three years while lake water levels were low and relatively stable, despite periodic removals of mussels from lake infrastructure. Mussels were initially restricted to hard substrates but were regularly found on soft sediments within two years of colonization. Storms in 2017 dramatically increased the lake level and deposited substantial sediment, which eliminated mussels on soft sediments and reduced the overall mussel population. Reproduction and juvenile settlement rebounded within 6 months, despite the low population of adult mussels in the lake. Environmental conditions, particularly fill status and water temperature, rather than adult density, appear to be the primary driver of veliger abundance in this system, while recruitment was primarily explained by veliger abundance. Elevated water releases from the reservoir increased the flux of veligers downstream and led to mussel recruitment >15 km downstream. Sustained establishment of quagga mussels downstream has not occurred in the Santa Clara River and seems unlikely due to the unstable habitat conditions. However, periodic downstream colonization increases the likelihood for the infestation to spread and impact agricultural and municipal water systems that receive water from the river.
Long-term (1993-2019) dynamics of tree populations on a mapped 3-ha permanent plot in old-growth northern hardwood forest, Huron Mts., Marquette Co., MI, USA
This data-set includes multiple remeasurements, over 25 years, of all woody stems >2 cm diameter (total of 2125 stems) on a 2.72-ha stem-mapped plot in old-growth northern hardwood forest in the Huron Mountains region of northern Marquette County, MI. The plot and surrounding forest is dominated by sugar maple (Acer saccharum) and eastern hemlock (Tsuga canadensis). Among secondary species, yellow birch (Betula alleghaniensis) and basswood (Tilia americana) are most common. Soils (identified as Kalkaska series) are developed on deep sandy glacial outwash. The plot is within a much larger region of old-growth forest, protected since ca. 1880, with only minimal disturbance associated with access tracks and trails. Numerous other forest community and dendrochronological studies support the interpretation that the area around the study plot has not experienced stand-initiating disturbance for at least 400 years. Initial mapping and measurements (1993-1995 for 2.52 ha; an additional 0.2 ha added in 1999) used a 20x20 m grid established in a near-level area of uniform substrate. All stems were identified to species, mapped on polar coordinates from the center of each grid cell (including, at first measurement, identifiable dead trees, standing and down), and diameter at breast height (dbh) measured to nearest 0.1 cm. All stems were remeasured on a five-year cycle 1999-2019, and new mortality was recorded at each remeasurement. New recruits > 2 cm dbh were added at each remeasurement.
Virgin Islands National Park: Coral Reef: Population Dynamics: Scleractinian corals
These data are evidence of the the long-term dynamics of shallow coral reefs along the south coast of St. John from as early as 1987. These data describe coral reef community structure as percent cover based on the analysis of color photographs. All of these data originate from color images of photoquadrats recorded annually (usually in the summer) from as early as 1987. The data falls into three groups. The two groups that are contained in this data package are (1) Tektite & Yawzi and (2) Random sites. The juvenile coral density is packaged separately. Tektite – this is at 14 m depth on the eastern side of Great Lameshur Bay and is the original site of the Tektite man-in-the sea project in 1969; this project marked the birth of the Virgin Islands Ecological Research Station (later the Virgin Islands Environmental Resource Station) that hosts the field component of the project. The reef in this location consists of a single buttress that has remained dominated by Montastraea anularis since the start of the research (1987). These surveys consist of 30 photoquadrats (1 x 1 m) distributed along three, 10 m transects. Yawzi – this is at 9 m depth and is on the western side of Great Lameshur Bay and has been recorded photographically since 1987. This reef also started the study period dominated by Montastraea annularis, but has degraded much more rapidly that the Tektite site. These surveys consist of 30 photoquadrats (1 x 1 m) distributed along three, 10 m transects. Random sites – were added in 1992 to address the concern that the original sites (Yawzi and Tektite) were selected on “good” areas of reef and, therefore, could only decline in condition. The Random sites were selected using random coordinates in 1992, and consist of 6 sites (at 7-9 m depth) scattered between Cabritte Point and White Point. All lie a little shoreward of Yawzi and Tektite, and have always been characterized by low coral cover (< 10% cover). The surveys consist of 18-40 photoquadrats (0.5 x 0
Virgin Islands National Park: Coral Reef: Population Dynamics: Diadema antillarum
A potential consequence of individuals compensating for density-dependent processes is that rare or infrequent events can produce profound and long-term shifts in species abundance. In 1983–1984 a mass mortality event reduced the numbers of the abundant sea urchin Diadema antillarum by 95–99% throughout the caribbean and western atlantic. Following this event, the abundance of macroalgae increased and the few surviving D. antillarum responded by increasing in body size and fecundity. these initial observations suggested that populations of D. antillarum could recover rapidly following release from food limitation. In contrast, published studies of field manipulations indicate that this species had traits making it resistant to density-dependent effects on offspring production and adult mortality; this evidence raises the possibility that density-independent processes might keep populations at a diminished level. Decadal scale (1983–2011) monitoring of recruitment, mortality, population density and size structure of D. antillarum from st John, us Virgin Islands, indicates that population density has remained relatively stable and more than an order of magnitude lower than that before the mortality event of 1983–1984. We detected no evidence of density-dependent mortality or recruitment since this mortality event. In this location, model estimates of equilibrium population density, assuming density-independent processes and based on parameters generated over the first decade following the mortality event, accurately predict the low population density 20 years later (2011). We find no evidence to support the notion that this historically dominant species will rebound from this temporally brief, but spatially widespread, perturbation.
Rabbit Population Dynamics in Chihuahuan Desert Grasslands and Shrublands at the Sevilleta National Wildlife Refuge, New Mexico (1992-present) (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sev/23/121705. The abstract below was extracted from the Level 0 data package and is included for context: This study explores the population dynamics of black-tail jackrabbits (Lepus californicus) and desert cottontail rabbits (Sylvilagus auduboni) in the grasslands and creosote shrublands of McKenzie Flats, Sevilleta National Wildlife Refuge. The study was initiated in January 1992, and continues quarterly each year. Rabbits are sampled via night-time spotlight transect sampling along the roads of McKenzie Flats once during winter, spring, summer, and fall. The route is 21.5 miles long. Measurements of perpendicular distance of each rabbit from the center of the road are used to estimate densities (number of rabbits per square kilometer) via Program DISTANCE. Results from January 1992 to May 2004 indicated that spring was the period of peak density period, with generally steady declines through the rest of the year until the following spring. Evidence of a long-term "cycle" (e.g., the 11-year-cycle reported for rabbits in the Great Basin Desert) does not appear in the Sevilleta rabbit populations.
Virgin Islands National Park: Coral Reef: Population Dynamics: Scleractinian corals (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/291/2. The abstract below was extracted from the Level 0 data package and is included for context: These data are evidence of the the long-term dynamics of shallow coral reefs along the south coast of St. John from as early as 1987. These data describe coral reef community structure as percent cover based on the analysis of color photographs. All of these data originate from color images of photoquadrats recorded annually (usually in the summer) from as early as 1987. The data falls into three groups. The two groups that are contained in this data package are (1) Tektite & Yawzi and (2) Random sites. The juvenile coral density is packaged separately. Tektite – this is at 14 m depth on the eastern side of Great Lameshur Bay and is the original site of the Tektite man-in-the sea project in 1969; this project marked the birth of the Virgin Islands Ecological Research Station (later the Virgin Islands Environmental Resource Station) that hosts the field component of the project. The reef in this location consists of a single buttress that has remained dominated by Montastraea anularis since the start of the research (1987). These surveys consist of 30 photoquadrats (1 x 1 m) distributed along three, 10 m transects. Yawzi – this is at 9 m depth and is on the western side of Great Lameshur Bay and has been recorded photographically since 1987. This reef also started the study period dominated by Montastraea annularis, but has degraded much more rapidly that the Tektite site. These surveys consist of 30 photoquadrats (1 x 1 m) distributed along three, 10 m transects. Random sites – were added in 1992 to address the concern that the original sites (Yawzi and Tektite) were selected on “good” a
Simulated forest dynamics (2016-2100) for six future climate-fire scenarios and five representative landscapes in Greater Yellowstone, USA
We simulated fire (incorporating fuels feedbacks) and forest dynamics on five landscapes spanning the Greater Yellowstone Ecosystem (GYE) to ask: (1) How and where are forest landscapes likely to change with 21st-century warming and fire activity? (2) Are future forest changes gradual or abrupt, and do forest attributes change synchronously or sequentially? (3) Can forest declines be averted by mid-21st-century stabilization of atmospheric greenhouse gas (GHG) concentrations? We used the spatially explicit individual-based forest model iLand to track multiple attributes (forest extent, stand age, tree density, basal area, aboveground carbon stocks, dominant forest types, species occupancy) through 2100 for six climate scenarios. The five study landscapes are representative of dominant forest types and environmental gradients of the Northern Rockies; collectively, they encompass nearly 300,000 ha, of which 279,488 ha are potentially stockable with trees. This data set contains annual landscape-level output data for simulations to 2100 with 6 climate scenarios (3 general circulation models x 2 representative concentration pathways) x 5 landscapes x 20 iterations of simulated fires. We include the data and R scripts used for the analyses of abrupt change in the publication associated with these data; all other analyses used standard functions in R.
SBC LTER: Reef: Kelp Forest Community Dynamics: Fish abundance (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/189/2, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sbc/17/35. The abstract below was extracted from the Level 0 data package and is included for context: These data describe the abundance and size of fish species as part of SBCLTER's kelp forest monitoring program to track long-term patterns in species abundance and diversity. This study began in 2000 in the Santa Barbara Channel, California, USA. The abundance and size of all taxa of resident kelp forest fish encountered along permanent transects are recorded at nine reef sites located along the mainland coast of the Santa Barbara Channel and at two sites on the north side of Santa Cruz Island. These sites reflect several oceanographic regimes in the channel and vary in distance from sources of terrestrial runoff. In these surveys, fish were counted in either a 40x2m benthic quadrat, or in the water parcel 0-2m off the bottom over the same area. The two tables in this data package include: 1) The annual benthic fish community survey which was conducted on 11 reefs once a year around late July or early August; and 2) The monthly fish survey which was conducted once a month at a subset of the sites (3 of the annual sites) The time period of data collection for the annual benthic fish community survey varied among the 11 kelp forest sites. Sampling at BULL, CARP, and NAPL began in 2000, sampling at the other 6 mainland sites (AHND, AQUE, IVEE, GOLB, ABUR, MOHK) began in 2001 (transects 3, 5, 6, 7, 8 at IVEE were added in 2011). Data collection at the two Santa Cruz Island sites (SCTW and SCDI) began in 2004. The monthly fish survey at ABUR (Transect 1, 2, and 3), AQUE
Seasonal N dynamics and fluxes of nitrogen in leachate and runoff from experimental rainfalls on fertilized and unfertilized lawns in Baltimore County, Maryland
The aim of this research was to examine the spatial and temporal variation in export control points of nitrogen on residential lawns (locations prone to mobilizing nitrogen during a rain event) and to examine if previously measured hydrobiogeochemical properties were predictive of N mobilization in lawns. This data set contains measurements of saturated infiltration rates, sorptivity, soil moisture, soil organic matter, bulk density, pH, soil nitrate, soil ammonium, N2O, N2 and CO2 fluxes from soil cores, nitrogen mineralization rates and fluxes of N in runoff and leachate from fertilized and unfertilized residential and institutional lawns. Study lawns were located at homes of people who agreed to volunteer their lawn for the study from a door knocking campaign. Four sampling houses were located in an exurban neighborhood in Baisman Run. Five sampling houses were located in a suburban neighborhood in Dead Run. Two sampling locations on institutional lawns were located at University of Maryland Baltimore County. At the exurban study houses and institutional lawns sites, we identified one hillslope to conduct sampling on. At the Dead Run houses we identified one hillslope on the front yard and one in the backyard as there were distinct locations that were not present in the exurban neighborhood. Locations within the yards for sampling were selected based on sampling conducted in October 2017. Locations were grouped into four categories based on have either high or low potential denitrification rates and high or low saturated infiltration rates (n=48). These locations were also distributed across yard types (exurban, suburban or institutional), fertilizer treatments, and hillslope location (top or bottom of hillslope). At each sampling location we ran a Cornell Sprinkle Infiltrometer to generate an experimental rainfall during which we collected runoff and leachate to quantity N flux. We also measure sorptivity and saturated infiltration rates. Volumetric water conten
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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)
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.