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453 results for “Dynamic dataset”
Dataset for article - Dislocation dynamics in Ni: Parameterising dislocation trajectories from atomistic simulations
<p>Data supporting findings in "Dislocation dynamics in Ni: Parameterising dislocation trajectories from atomistic simulations". In this work, 8 identical Molecular Dynamics (MD) simulations of edge dislocations in pure Nickel were run and analysed, where dislocation positions were extracted and each dislocation is tracked for further analysis. Each simulation only differs in the random seed used for setting the initial atom velocities. Files needed to reproduce a specific simulation can be found in the directories provided in <em>data.zip</em>:</p> <ul> <li>fr_18430</li> <li>fr_27743</li> <li>fr_44345</li> <li>fr_46165</li> <li>fr_54382</li> <li>fr_54992</li> <li>fr_97181</li> <li>fr_98232</li> </ul> <p>The random seed needed to set the initial velocities of the atoms are set in the LAMMPS script. The log files generated by LAMMPS are provided for the different stages of a given simulation in the <em>LAMMPS_logs</em> sub-directories. The corresponding output files from the OVITO DXA are also included in the sub-directory <em>Ni_disloc_const</em>. The files <em>disloc_data_*.ca</em> contain the raw output from the OVITO DXA and can be opened directly in OVITO. The <em>disloc_data_*.txt </em>files contain information extracted from the OVITO DXA in a format that can be read in and processed by the dislocation tracking code (<a href="https://github.com/geraldineanis/DislocCode/tree/v1.0.0">https://github.com/geraldineanis/DislocCode/tree/v1.0.0</a>). The wildcard character "*" is replaced with the simulation timestep. In each directory, the dislocation position vs. time data is included as a text file<em> </em>named<em> </em><em>perfect_pos_<seed>.txt</em>. The Mishin 2004 EAM interatomic potential file <em>NiAl_Mishin_2004.eam.alloy</em> used to generate the data is also provided.</p> <p>Additional simulations were carried out at a range of applied shear stresses (20 MPa - 50 MPa). Files needed to reproduce theses simulations are provided in <em>data_stress.zip </em>and follow the same format described above.</p> <p>The data provided here was generated with the <code>LAMMPS/29Sep2021-kokkos</code><em> </em>module on the SULIS Tier 2 HPC platform, which has been built with the OpenMP backend of the <code>kokkos</code> package and uses the <code>foss-2021b</code> toolchain (<a href="https://docs.easybuild.io/common-toolchains/">https://docs.easybuild.io/common-toolchains/</a>). For further information, please refer to <a href="https://sulis-hpc.github.io/appnotes/lammps.html">https://sulis-hpc.github.io/appnotes/lammps.html</a>.</p> <p>For more details on the calculations, please refer to the publication (in preparation). Please refer to the GitHub repository at <a href="https://github.com/geraldineanis/DislocCode/tree/v1.0.0">https://github.com/geraldineanis/DislocCode/tree/v1.0.0</a> for the analysis tools developed and for detailed instructuctions for their use.</p>
Dataset related to the publication "Temporal dynamics and environmental controls of carbon dioxide and methane fluxes measured by the eddy covariance method over a boreal river"
<h2>Summary</h2> <p>Dataset related to the publication "Temporal dynamics and environmental controls of carbon dioxide and methane fluxes measured by the eddy covariance method over a boreal river" by Aki Vähä, Timo Vesala, Sofya Guseva, Anders Lindroth, Andreas Lorke, Sally MacIntyre, and Ivan Mammarella (2024), published in Biogeosciences.</p> <h2>Materials and Methods</h2> <h3>Measurement site</h3> <p>The experiment was conducted on a floating platform on the River Kitinen in northern Finland. The measurements took place from 1 June to 2 October, 2018.</p> <p>The River Kitinen is 235 km long and has a catchment area of 7672 km2. The catchment area consists mostly of managed boreal forest with Scots pine (Pinus sylvestris) and Norway spruce (Picea abies) as the main tree species, wetlands of which a large portion is drained, small streams and rivers, some low mountains and a few small settlements. The experiment site (67.37◦ N, 26.62◦ E, 173 m above sea level) was located next to the Finnish Meteorological Institute’s research and weather station in Tähtelä. At the experiment location the river is 180 m wide and forms a straight section extending approximately 600 m upstream and 1000 m downstream from the site. The direction of the river at the site is roughly north-northwest–south-southeast and it flows towards the south. The mean annual discharge, measured at the closest power plant downstream, is 103 m3 s−1. The maximum depth at the site is 7 m. The River Kitinen’s Strahler stream order at the site is 5. The floating platform was located about 70 m from the eastern river bank where the water depth was 4.5 m.</p> <h3>Eddy covariance</h3> <p>The eddy covariance system measuring water-atmosphere turbulent fluxes was mounted on a mast on the southern side of the platform. This installation consisted of an ultrasonic anemometer (uSonic-3 Scientific, METEK Meteorologische Messtechnik GmbH, Elmshorn, Germany) for measuring the wind speed in three Cartesian coordinates and the sonic temperature, an enclosed-path gas analyser (LI-7200RS, LI-COR Biosciences, Inc., Lincoln, Nebraska, USA) for measuring carbon dioxide and water vapour mole fractions, and a closed-path gas analyser (G1301-f, Picarro, Inc., Santa Clara, California, USA) for measuring methane and water vapour mole fractions. The centre of the sonic anemometer was 1.82 m above the water surface. An inclinometer (DOG2 micro-electro-mechanical system, Measurement Specialties, Inc., Hampton, Virginia, USA) was used for measuring the pitch and roll of the platform. Eddy covariance fluxes were calculated using the EddyUH software (Mammarella et al. 2016), following the state of art methodologies (Sabbatini et al. 2018, Nemitz et al. 2018).</p> <h3>Auxiliary measurements</h3> <p>Ambient air temperature and relative humidity were measured with a Rotronic HC2-S3C03 probe (Rotronic AG, Bassersdorf, Germany), mounted inside a Young model 41003 (R. M. Young Company, Traverse City, Michigan, USA) multi-plate radiation shield on the platform’s north-eastern corner. Air temperature and relative humidity were available only after 15th of June. Before that, the sonic temperature and humidity calculated from χH2O, measured with the LI-7200RS, were used instead. Atmospheric pressure and precipitation were measured at the Tähtelä weather station. Photosynthetically active radiation (PAR) in water was measured with two LI-192 sensors (LI-COR Biosciences, Inc., Lincoln, Nebraska, USA) and one LI-193 sensor (LI-COR). The sensors were hanging from wires at 0.3 m, 0.65 m and 1.0 m depths on a beam on the southern side of the platform. Measurements of water side CO2 partial pressure (pCO2) were done by using an off-axis integrated cavity output spectrometer (Ultraportable Greenhouse Gas Analyzer – UGGA), Los Gatos Research, Inc., Santa Clara, California, USA) that was connected to the headspace of an equilibrator consisting of a floating Plexiglas chamber.</p> <p>A water temperature chain was set up 100 m upstream of the platform. It consisted of five temperature loggers of the type RBR Solo (RBR Ltd. Ottawa, Ontario, Canada). The loggers were placed on a taut line mooring at depths of 0.35 m, 1.35 m, 2.35 m, 3.35 m and 4.35 m (6 June to 17 June) and 0.07 m, 1.05 m, 2.05 m, 3.05 m and 4.05 m (17 June onwards). The topmost measurement was used as the surface temperature. The water flow velocity was measured with a acoustic Doppler velocimeter (Nortek Vector, Nortek AS, Rud, Norway) which was installed on a beam on the north-western corner of the platform, facing down (Guseva et al., 2021). The depth of the measurements was 0.4 m below the surface.</p>
The DR-Train dataset: dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh
<p><strong>Note: Downloading the large data file could have a timeout issue. If you cannot directly download it here, please use the following link as a complementary method for getting the data. </strong></p> <p><a href="https://drive.google.com/drive/folders/1oKn7IN7zznQuhwjDCDdjq8r9wHJYBEhj?usp=sharing">https://drive.google.com/drive/folders/1oKn7IN7zznQuhwjDCDdjq8r9wHJYBEhj?usp=sharing</a></p> <p> </p> <p>This dataset contains the dynamic responses (acceleration records) of two passenger trains with corresponding GPS positions, environmental conditions and track maintenance schedules for a light rail network in the city of Pittsburgh, Pennsylvania in the United States of America.</p> <p>In particular, two light rail vehicles were instrumented (identified as LRV4306 and LRV4313): <br> LRV 4306 has 5 acceleration channels, corresponding to the two uni-axial accelerometers inside the train and the three channels of the tri-axial accelerometer on the wheel truck.</p> <p><em>- The last digit of each acceleration file: 1, 2, 3, 4, 5<br> - Corresponding sensor channels: tri-axial x, tri-axial y, tri-axial z, front cabinet uni-axial, back cabinet uni-axial</em></p> <p><br> LRV 4313 has 8 acceleration channels, corresponding to the two uni-axial accelerometer and the two tri-axial accelerometers inside the train.</p> <p><em>- The last digit of each acceleration file: 1, 2, 3, 4, 5, 6, 7, 8<br> - Corresponding sensor channels: front cabinet uni-axial, back cabinet uni-axial, front tri-axial x, front tri-axial y, front tri-axial z, back tri-axial x, back tri-axial y, back tri-axial z.<br> - x longitudinal (vehicle moving direction); y-axis, transverse; z-axis, vertical.</em></p> <p>The dataset contained in this repository is a condensed version of the original raw data. While the accelerometers on the train were sampled continuously, this dataset contains only those measurements for when the train was actually moving along the track (i.e. not idling at a terminal).</p> <p>The data is stored in binary MAT-files (a MATLAB/Octave data format). These files contain MATLAB objects of the class "pass", which is defined in the file pass.m that can be found in the "code" folder. Specifically, two MAT-files named "obj_dic.mat", and found in the "LRV4306" and "LRV4313" folders, contain the "pass" objects of the two trains, respectively.</p> <p>Each category is described in detail. For more detail on the regions of the track, refer to the 'region.fig' file in this folder. The track was divided into distinct regions so that the data over specific sections of track could be compared. These regions were chosen for two reasons: <br> (1) within a region, the train always followed the same track and <br> (2) there are no tunnels in them so the GPS data is relatively consistent. </p> <p>To get started, using MATLAB or Octave try running "main_script.m" in the "code" folder.</p> <p>A data descriptor paper with details of the data collection process was published.</p> <p>Please cite as</p> <p><strong>Liu, J., Chen, S., Lederman, G., Kramer, D. B., Noh, H. Y., Bielak, J., Garrett, J. H., Kovačević, J., & Berges, M. Dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh. Scientific Data, 6, 146. <a href="https://doi.org/10.1038/s41597-019-0148-9">https://doi.org/10.1038/s41597-019-0148-9</a>(2019)</strong></p> <p><strong>Liu, J., Chen, S., Lederman, G., Kramer, D. B., Noh, H. Y., Bielak, J., Garrett, J. H., Kovačević, J., & Berges, M. The DR-Train dataset: dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh. Zenodo, <a href="https://doi.org/10.5281/zenodo.1432702">https://doi.org/10.5281/zenodo.1432702</a>(2018).</strong></p> <p>For questions or suggestions please e-mail Jingxiao Liu <liujx@stanford.edu></p>
Datasets for publication titled "Chiral control of spin-crossover dynamics in Fe(II) complexes"
<p>Transient absorption (TA), transient absorption anisotropy (TAA), and time-resolved circular dichroism (TRCD) datasets analyzed and interpreted in the publication titled "Chiral control of spin-crossover dynamics in Fe(II) complexes" published in Nature Chemistry under the DOI 10.1038/s41557-022-00933-0.</p>
Dataset: Human-vegetation dynamics in Holocene South-Eastern Norway based on radiocarbon-dated charcoal from archaeological excavations
<p>The repository contains radiocarbon data used in the paper <em>Human-vegetation dynamics in Holocene South-Eastern Norway based on radiocarbon-dated charcoal from archaeological excavations</em>, written by By Axel Mjærum, Kjetil Loftsgarden, and Steinar Solheim (University of Oslo).</p> <p>The paper is accepted for publication in The Holocene.</p> <p>ABSTRACT: Charcoal from archaeological contexts differs from off-site pollen samples as it is mainly a product of intentional human action. As such, analysis of charcoal from excavations is a valuable addition to studies of past vegetation and the interaction between humans and the environment. In this paper, we use a dataset consisting of 6,186 dated tree species samples from 1,239 archaeological sites as a proxy to explore parts of the Holocene forest development and human-vegetation dynamics in South-Eastern Norway.</p> <p>From the middle of the Late Neolithic (from <em>c</em>. 2000 BC) throughout the Early Iron Age (to <em>c</em>. AD 550) the region’s agriculture is characterized by fields, pastures, and fallow. Based on our data, we argue that these practices, combined with forest management, clearly altered the natural distribution of trees, and favoured some species of broadleaved trees. The past distribution of hazel (<em>Corylus avellana</em>) is an example of human impact on the vegetation. Today, hazel is not even among the 15 most common tree species, while it is one of the most prevalent species in the archaeological record before AD 550. The data indicate that this species was favoured already by the region’s Mesolithic hunter-fisher-gatherers, and that it was among the species that thrived extremely well in the early farming landscape. Secondly, our analysis also indicates that spruce (<em>Picea abies</em>) first formed large stands in the south-eastern parts of Norway <em>c</em>. 500 BC, centuries earlier than previously assumed. It is argued that this event, and a further westward expansion of spruce, was partly a consequence of a specific historical event – the first millennium BC farming expansion.</p>
Dataset for Dynamics of Solid-Electrolyte Interphase Formation on Silicon Electrodes Revealed by Combinatorial Electrochemical Screening
<p>This dataset provides the raw data to the manuscript</p> <p>"<strong>Dynamics of Solid-Electrolyte Interphase Formation on Silicon Electrodes Revealed by Combinatorial Electrochemical Screening"</strong></p> <p>published in Angewandte Chemie International Edition (2022): <a href="https://doi.org/10.1002/anie.202207184">https://doi.org/10.1002/anie.202207184</a></p> <p>Specifically, the following measurements are provided:</p> <ul> <li>Electrochemical measurements for combinatorial preparation of solid-electrolyte layers under different conditions and repetitions ("SECCM/")</li> <li>Raman spectra obtained using SHINERS for all the prepared conditions and repetitions ("SHINERS/")</li> <li>Atomic force microscopy data for each SEI layer ("AFM/")</li> <li>Energy-dispersive X-ray spectroscopy data ("EDX/")</li> </ul>
Stochastic Occupancy Grid Map Prediction in Dynamic Scenes: Dataset
<p>Three occupancy grid map (OGM) datasets for the paper titled "Stochastic Occupancy Grid Map Prediction in Dynamic Scenes" by Zhanteng Xie and Philip Dames</p> <p>1. OGM-Turtlebot2: collected by a simulated Turtlebot2 with a maximum speed of 0.8 m/s navigates around a lobby Gazebo environment with 34 moving pedestrians using random start points and goal points</p> <p>2. OGM-Jackal: extracted from two sub-datasets of the socially compliant navigation dataset (SCAND), which was collected by the Jackal robot with a maximum speed of 2.0 m/s at the outdoor environment of the UT Austin</p> <p>3. OGM-Spot: extracted from two sub-datasets of the socially compliant navigation dataset (SCAND), which was collected by the Spot robot with a maximum speed of 1.6 m/s at the Union Building of the UT Austin</p> <p>The relevant code is available at: <br> OGM prediction: https://github.com/TempleRAIL/SOGMP<br> OGM mapping with GPU: https://github.com/TempleRAIL/occupancy_grid_mapping_torch</p>
Dataset associated with Banks et al.: "Dust aerosol from the Aralkum Desert influences the radiation budget and atmospheric dynamics of Central Asia"
<p>This dataset contains the COSMO-MUSCAT simulation output for the 'Dustbelt' (DUBLT) scenarios of Central Asian dust aerosol and associated radiative effects described by the paper "Radiative cooling and atmospheric perturbation effects of dust aerosol from the Aralkum Desert in Central Asia", written by Banks et al. and submitted to ACP in 2023. The paper was renamed "Dust aerosol from the Aralkum Desert influences the radiation budget and atmospheric dynamics of Central Asia" in 2024.</p>
Assessment of mutation probabilities of KRAS G12 missense mutants and their long-time scale dynamics by atomistic molecular simulations and Markov state modeling: Datasets.
<p>Datasets related to the publication [1].<br> Including:</p> <ul> <li>KRAS G12X mutations derived from COSMIC v.79 [http://cancer.sanger.ac.uk/cosmic/] (KRAS_G12X_mut_COSMICv79..xlsx)</li> <li>RMSFs (300-2000ns) of GDP-systems (300_2000rmsf_GDP_systems_RAW_AVG_SE.xlsx)</li> <li>RMSFs (300-2000ns) of GTP-systems (300_2000RMSF_GTP_systems_RAW_AVG_SE.xlsx)</li> <li>PyInteraph analysis data for salt-bridges and hydrophobic clusters (.dat files for each system in the PyInteraph_data.zip-file)</li> <li>Backbone trajectories for each system (residues 4-164; frames for every 1ns). Last number (e.g. _1) refers to the replica of the simulated system.</li> <li>backbone_4-164.gro/.pdb/.tpr -files (resid 4-164) </li> </ul> <p><br> [1] Pantsar T et al. Assessment of mutation probabilities of KRAS G12 missense mutants and their long-time scale dynamics by atomistic molecular simulations and Markov state modeling. <em>PLoS Comput Biol Submitted</em> (2018)</p>
Global monthly discharge dataset, derived from dynamical 1-D water-energy routing model (DynWat) at 10 km spatial resolution
<pre>Global 10km spatial resolution discharge dataset at the global scale for all major rivers, lakes and reservoirs. Data are provided at a monthly temporal resolution.</pre>
Global monthly water temperature dataset, derived from dynamical 1-D water-energy routing model (DynWat) at 10 km spatial resolution
<pre>Global 10km spatial resolution water temperature dataset at the global scale for all major rivers, lakes and reservoirs. Data are provided at a monthly temporal resolution.</pre> <p>V1.1 update includes a improved version of the model removing some initial spikes related to rapid ice melt and streams that fall dry. The record has been reduced from 1981 tot 2014 to remove potential spinup impacts.</p> <p>The 1960-2010 data from v1.0 can be used for the earlier years.</p> <p>Consistent forcing is used for both time periods to remove potential biases that might occur otherwise.</p>
Experimental datasets of networks of nonlinear oscillators: Structure and dynamics during the path to synchronization
<p>The analysis of the interplay between structural and functional networks require experiments where both the specific structure of the connections between nodes and the time series of the underlying dynamical units are known at the same time. However, real datasets typically contain only one of the two ways (structural or functional) a network can be observed. Here, we provide experimental recordings of the dynamics of 28 nonlinear electronic circuits coupled in 20 different network configurations. For each network, we modify the coupling strength between circuits, going from an incoherent state of the system to a complete synchronization scenario. Time series containing 30000 points are recorded using a data-acquisition card capturing the analogic output of each circuit. The experiment is repeated three times for each network structure allowing to track the path to the synchronized state both at the level of the nodes (with its direct neighbors) and at the whole network. These datasets can be useful to test new metrics to evaluate the coordination between dynamical systems and to investigate to what extent the coupling strength is related to the correlation between functional and structural networks.</p> <p>We provide the times series of N=28 Rössler electronic oscillators for 20 different network configurations (compressed file with tag R1 to R20). For each network structure, we recorded the times series for 101 different coupling strengths between oscillators. Each one of the 101 corresponding files is labeled as ST_X_Y.dat where X is a value between X=0 and X=100 that corresponds, respectively, to the minimum and maximum coupling strength. The value of Y corresponds to the repetition number, which can be 1, 2 of 3 (i.e., we repeated the same experiment three times). Data files contain the second variable of the 28 nodes arranged in columns with a length of 30000 points. In a second file named Structure.zip, all the network structures are given, each file having a name Net_R.dat, where R=1, 2… 20. The degree of each node (i.e., number of output connections) is the same for all network configurations, where the specific neighbors of each node are re-arranged randomly.</p>
Example dataset for openPMD-conform molecular dynamics data (MD domain extension)
<p>This dataset results from the molecular dynamics (MD) simulation of the photon-sample interaction. The photons are propagated through the SASE1 beamline and the SPB-SFX instrument at European XFEL, with an initial energy of 5 keV. The sample is the two-nitrogenase iron protein (2nip) with 4348 atoms. The simulation is performed with a demo version of XMDYN. The datasets were rewritten from the original XMDYN output into an hdf5 format that complies with the openPMD metadata standard for particle and mesh data and the proposed domain extension of this standard for MD data. The dataset "pure_2nip_pmi_out.opmd.h5" conforms the openPMD metadata MD domain extension strictly, while the dataset "pure_2nip_pmi_out.opmd.ff.h5" stores form factor results additionally for SingFEL diffraction simulation.</p> <p>This dataset is part of the Deliverable D5.1 in Workpackage 5 (Virtual Neutron and X-ray Laboratory) of the Photon and Neutron Open Science Cloud (PaNOSC).</p> <p>This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 823852.<br> </p>
Active Foam Dynamics of Tissue Spheroid Fusion (Datasets and scripts)
<p>The file attached supporting information for the paper "Active Foam Dynamics of Tissue Spheroid Fusion". Included are data from experiments, data from simulations, and a singularity image to execute a demo simulation similarly to the ones of the paper. </p>
Experimental results dataset reported in the paper: "Temporal teleportation with pseudo-density operators: how dynamics emerges from temporal entanglement."
<p><strong>Temporal teleportation with pseudo-density operators: how dynamics emerges from temporal entanglement.</strong></p> <p>Experimental results data-set (as reported in Figure 2 of the main paper).</p> <p>Caption of the Figure: Classical bound violation ∆S<em><sup>(l)</sup></em>(n) =S<em><sup>(l)</sup></em>(2n)−(2n−2) (<em>l=T,H,S</em>) for the multi-parameter <em>CHSH</em> inequalities in the temporal (red), spatial (grey) and “hybrid” (blue) domain. The dots represent the experimental results, with the uncertainty bars evaluated as statistical fluctuations among repeated measurement sets, while the solid curves show the theoretically-expected values (for the correlations belonging to the spatial domain, deviations from the ideal case due to the V<em><sub>s</sub></em>= 0.982 estimated visibility of the generated |<em>ψ<sub>−</sub></em>〉state were considered)</p> <p>Data-set description:</p> <p>Column 1 = X-coordinate: n (Number of Settings)</p> <p>Column 2 = Y-coordinate: ∆S<em><sup>(S)</sup></em>(n), Space-like (S) CHSH Violation [Grey color in the figure on the paper]</p> <p>Column 3 = Uncertainty on ∆S<em><sup>(S)</sup></em>(n) (Space-like, S, Grey in the figure on the paper), </p> <p>Column 4 = Y-coordinate: ∆S<em><sup>(H)</sup></em>(n), Hybrid (H) CHSH Violation [Blue color in the figure on the paper]</p> <p>Column 5 = Uncertainty on ∆S<em><sup>(H)</sup></em>(n) (Hybrid, H, Blue in the figure on the paper)</p> <p>Column 6 = Y-coordinate: ∆S<em><sup>(T)</sup></em>(n), Time-like (T) CHSH Violation [Red color in the figure on the paper]</p> <p>Column 7 = Uncertainty on ∆S<em><sup>(T)</sup></em>(n) (Time-like, T, Red in the figure on the paper)</p>
Stand structure and tree population dynamic attribute dataset of long abandoned strict forest reserves
<p>We provide an integrated dataset of two consecutive forest inventories, both containing plot-level, and individual tree-level data. The first provides the descriptions and measuring units (or categories) of plot-level variables (Table 1). The plot level table contains 233 records (rows), one for each selected permanent plot of six strict forest reserves located in Hungary. This dataset is georeferenced and contains information on inventories and basic stand structure attributes (Table_1_Plots ESRI shape format). </p> <p>The individual tree-level datasets were acquired by the sampling procedure, detailed in section 2.2. Species, dendrometric attributes, relative crown position, health, and decay status were documented for each tree belonging to the samples. Table 2 provides the descriptions and measuring units (or categories) of tree-level datasets in detail. Furthermore, it provides a tree history classification based on the interpretation of tree status changes. According to a simple scheme of the life and dead history of a tree, it could be classified into four main phases: establishment/regeneration phase; developmental phase; death and gradual decay of the tree trunk; terminated in decomposed/disintegrated state. The main events along these phases are ingrowth regeneration; death of the tree (mortality); disaggregation and decomposition of deadwood. We classify each sampled tree individuals into tree history categories (events and phases, Table 3) that can provide population dynamic aspects at stand level by appropriate tree aggregation functions.</p> <p>Relational link can be set between the plot-level and tree-level datasets based on the unique identification code of the site and sampling plots.</p>
Dataset of traffic dynamics during the 2019 Kincade Wildfire Evacuation
<p>This dataset has been sourced from the Performance Measurement System of the California Department of Transportation. The data has been processed, analysed, presented and summarized in the paper: <em>Rohaert et al., ‘Traffic dynamics during the 2019 Kincade wildfire evacuation’, [Submitted for peer-review to an international journal.], 2022.</em></p> <p><strong>CRediT author statement</strong></p> <p><strong>Arthur Rohaert: </strong>Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing - original draft, Writing - review & editing. <strong>Erica D. Kuligowski: </strong>Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Writing - review & editing. <strong>Adam Ardinge: </strong>Conceptualization, Formal analysis, Investigation, Methodology, Resources, Validation, Writing - review & editing. <strong>Jonathan Wahlqvist:</strong> Conceptualization, Formal analysis, Investigation, Methodology, Resources, Supervision, Validation, Writing - review & editing. <strong>Steven M.V. Gwynne: </strong>Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Writing - review & editing. <strong>Amanda Kimball: </strong>Conceptualization, Funding acquisition, Investigation, Methodology, Project Administration, Resources, Supervision, Validation, Writing - review & editing. <strong>Noureddine Bénichou: </strong>Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Writing - review & editing. <strong>Enrico Ronchi: </strong>Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Writing - original draft, Writing - review & editing</p> <p><strong>Acknowledgements</strong></p> <p>This work has been funded under award 60NANB20D191 from the National Institute of Standards and Technology (NIST), U.S. Department of Commerce. The authors would like to thank the WUI-NITY team (Guillermo Rein, Nikolaos Kalogeropoulos, Harry Mitchell, Max Kinateder, Maxime Berthiaume). The authors also acknowledge the technical panel of the project for their support and guidance: Carole Adam, Amy Christianson, Tom Cova, Lauren Folk, Abishek Gaur, Paolo Intini, Justice Jones, Bryan Klein, Chris Lautenberger, Ruggiero Lovreglio, Jerry McAdams, Ruddy Mell, Elise Miller-Hooks, Cathy Stephens, Steve Taylor, Sandra Vaiciulyte, Xilei Zhao, Rita Fahy, Lucian Deaton, and Michele Steinberg. </p> <p> </p>
3D Charged Particles Dataset - Roto-translated Local Coordinate Frames for Interacting Dynamical Systems
<p>This repository contains the "<strong>3D charged particles</strong>" dataset from the paper</p> <blockquote> <p><strong>Roto-translated Local Coordinate Frames for Interacting Dynamical Systems</strong><br> <a href="https://mkofinas.github.io/">Miltiadis Kofinas</a>, <a href="https://menaveenshankar.github.io/">Naveen Shankar Nagaraja</a>, <a href="https://egavves.com/">Efstratios Gavves</a><br> NeurIPS 2021<br> <a href="https://arxiv.org/abs/2110.14961">https://arxiv.org/abs/2110.14961</a><br> <a href="https://github.com/mkofinas/locs">https://github.com/mkofinas/locs</a></p> </blockquote> <p>It contains simulations of trajectories of 5 charged particles in 3 dimensions, interacting via Coulomb forces.</p> <p>There are 30,000 simulations for training, 5,000 for validation, and 5,000 for testing.</p> <p>Train and validation simulations last for 99 timesteps, while test simulations last for 99 timesteps.</p> <p>The features comprise positions and velocities of particles, while edges describe the product of pairwise charges.</p>
Controllable temporal dynamics of titanium oxide memristor for analog time-based neuromorphic computing: Dataset
<p>Dataset used to produce graphs related to the temporal behavior of the Pt/TiO/Au memristors.</p>
Spatiotemporal dynamics in freshwater amphipod assemblages are associated with surrounding terrestrial land use type - Dataset
<p>Biological assemblages are the result of dynamic processes that have explicit temporal and spatial dimensions. While biodiversity patterns can be directly inferred from the structure of these assemblages, an assessment of changes through time and space is needed to understand how organisms initially assembled and how they are responding to local environmental and biotic factors. Small freshwater streams are particularly affected by contemporary anthropogenic activities and biological invasions, yet are commonly less studied, as studies often focus on lakes and large streams. Here, we conducted a spatially explicit analysis of keystone shredder assemblages across eight years in twelve replicated small tributary streams. In each stream, we monitored multiple sites per km stream length. By assessing temporal beta diversity dynamics, defined by the gain or loss of species or abundance-per-species at individual sites, we show that changes in amphipod assemblages occur within the context of the surrounding terrestrial matrix and reflect recent amphipod colonization history. While amphipod composition was mostly constant in streams located in forested catchments, streams embedded in catchments with more extensive agricultural land use displayed more pronounced temporal changes, either driven by colonization of unoccupied upstream locations, or by more pronounced but undirected fluctuations in gains and losses of species or abundance-per-species. Our study thus suggests that agricultural landscapes might destabilize aquatic amphipod assemblages, causing higher temporal changes in community structures, and highlighting the vulnerability of aquatic ecosystems to terrestrial land use drivers.</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.