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opencc-by-4.0Nov 2024View details →

Double-Versus Triple-Potential Well Energy Harvesters: Dynamics and Power Output

<div>The present datasets and figures focus on the analysis of BEH and TEH systems where the corresponding depth of the potential well and the width of their characteristics are the same. The efficiency of energy harvesting for TEH and BEH systems assuming similar potential parameters is provided. The basic types of multistable energy harvesters are bistable energy harvesting systems (BEH) and tristable energy harvesting systems (TEH). Providing such parameters allows for reliable formulation of conclusions about the efficiency in both types of systems. These energy harvesting systems are based on permanent magnets and a cantilever beam designed to obtain energy from vibrations. Starting from the bond graphs, we derived the nonlinear equations of motion. Then we followed the bifurcations along the increasing frequency for both configurations. To identify the character of particular solutions, we estimated their corresponding phase portraits, Poincare sections, and Lyapunov exponents. The selected solutions are associated with their voltage output. The results in this numerical study show clearly that the bistable potential is more efficient for energy harvesting provided the corresponding excitation amplitude is large enough. However, the tristable one could work better in the limits of low-level and low-frequency excitations. </div> <div> <h2>Series information</h2> Potential characteristics of energy harvesting systems caused by magnetic field of distributed permanent magnets: <ul> <li>Fig4a_b_V_y1.txt</li> <li>Fig4a_r_V_y1.txt</li> </ul> Potential characteristics of energy harvesting systems caused by magnetic field effect as in the previous case and an additional change in the stiffness of the flexible cantilever beam: <ul> <li>Fig4b_b_V_y1.txt</li> <li>Fig4b_r_V_y1.txt</li> </ul> Series of steady states of the system against frequency for two potential wells.  <ul> <li>Fig6a_p005.txt</li> <li>Fig6c_p025.txt</li> <li>Fig6e_p05.txt</li> <li>Fig6g_p085.txt</li> </ul> Series of steady states of the system against frequency for three potential wells <ul> <li>Fig6b_p005.txt</li> <li>Fig6d_p025.txt</li> <li>Fig6f_p05.txt</li> <li>Fig6h_p085.txt</li> </ul> The excitation amplitude increases downwards from 0.05 to 0.85 and its values are listed in the corresponding description. The results were obtained for zero initial conditions. ω and x are dimensionless. </div> <div>Figures:</div> <div> <a href="https://zenodo.org/api/records/14176152/draft/files/Fig%202.jpg/content" target="_blank" rel="noopener"> ig 2.jpg</a> - A graph of bonds representing the dynamics of the tested design solutions of energy harvesting systems. <a href="https://zenodo.org/api/records/14176152/draft/files/Fig%203.jpg/content" target="_blank" rel="noopener">Fig 3.jpg</a> - A Lagrangian bond graph, with causality conflicts intentionally introduced. <a href="https://zenodo.org/api/records/14176152/draft/files/Fig%204.jpg/content" target="_blank" rel="noopener">Fig 4.jpg</a> - Potential characteristics of energy harvesting systems caused by: (a) magnetic field of distributed permanent magnets (Fig. 1); (b) magnetic field effect as in previous case and an additional change in stiffness of the flexible cantilever beam (to satisfy equal potential barriers V2 = V3 ) used in further calculations. <a href="https://zenodo.org/api/records/14176152/draft/files/Fig%206.jpg/content" target="_blank" rel="noopener">Fig 6.jpg</a> - Bifurcation diagrams (stroboscopic) of steady states of the system against frequency for: (a) Two potential wells; (b) three potential wells. The excitation amplitude increases downwards from 0.05 to 0.85 and its values are listed in the corresponding sub-figures. The results were obtained for zero initial conditions. ω and x are dimensionless. <a href="https://zenodo.org/api/records/14176152/draft/files/Fig%207.jpg/content" target="_blank" rel="noopener">Fig 7.jpg</a> - Exemplary solutions showing the geometrical structures of chaotic phase flows and the corresponding Poincaré cross-sections of a BEH. Dc denotes the corresponding correlation dimension. ω, p, x, and x' are dimensionless. <a href="https://zenodo.org/api/records/14176152/draft/files/Fig%208.jpg/content" target="_blank" rel="noopener">Fig 8.jpg</a> - Examples of periodic responses of a BEH system identified for dimensionless mechanical vibration amplitudes: (a) p = 0.05; (b) p = 0.25; (c) p = 0.5; (d) p = 0.85. ω, p, x, and x' are dimensionless. <a href="https://zenodo.org/api/records/14176152/draft/files/Fig%209.jpg/content" target="_blank" rel="noopener">Fig 9.jpg</a> - Example solutions showing geometric structures of chaotic phase flows and corresponding Poincaré cross-sections, which were identified for a system with three potential wells (TEH). Dc denotes the corresponding correlation dimension. ω, p, x, and x' are dimensionless. Fig 10.jpg – Influence of external load characteristics on periodic induced solutions in a TEH. Trajectory shapes are plotted for selected frequencies ω. ω, p, x, and x' are dimensionless. <a href="https://zenodo.org/api/records/14176152/draft/files/Fig%2011.jpg/content" target="_blank" rel="noopener">Fig 11.jpg</a> - Multicolored maps of the values of effective energy harvesting systems (RMS voltage outputs) with the potential: (a) two-well (BEH); (b) three-well (TEH) for zero initial conditions. ω and p are dimensionless, while URMS is expressed in Volts. <a href="https://zenodo.org/api/records/14176152/draft/files/Fig%2012.jpg/content" target="_blank" rel="noopener">Fig 12.jpg</a> - Comparison of RMS voltage outputs for two and three wells potential systems versus amplitude and frequency. ω is dimensionless while URMS is expressed in Volts. </div>

opencc-by-4.0Nov 2024View details →

Time-lapse 3D confocal microscopy videos of mitochondrial dynamics in human alveolar epithelial cells (A549-DsRed) infected with Mycobacterium marinum (Mmar) strains

<div>The dataset consists of time-lapse, 3D confocal images of mitochondrial dynamics in human alveolar epithelial cells (A549-DsRed) infected with Mycobacterium marinum (Mmar) strains. Images were captured at 60X magnification in an environmental chamber at 35°C for live-cell imaging. Host cell mitochondria were labeled with red fluorescent protein (RFP) and infected with both wildtype (wt) and ESAT-6 operon knockout mutant labeled with green fluorescent protein (GFP) at MOI of 100 for 24 hours at 35°C. Infected cells were identified and analyzed to explore the effect of pathogenic mycobacteria on mitochondrial morphology over time.</div> <div> </div> <div>More details available in this preprint: <a href="https://doi.org/10.48550/arXiv.2411.06035">https://doi.org/10.48550/arXiv.2411.06035</a></div>

opencc-by-4.0Dec 2024View details →

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> 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. <h2>Materials and Methods</h2> <h3>Measurement site</h3> 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. 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. <h3>Eddy covariance</h3> 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). <h3>Auxiliary measurements</h3> 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. 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.

opencc-by-4.0Mar 2024View details →

Supplement to "Proof of concept for Bayesian inference of dynamic rating curve uncertainty" (v3)

<div>This deposit contains part of the updated supplement to “Proof of concept for Bayesian inference of dynamic rating curve uncertainty” (<a href="https://www.tandfonline.com/doi/full/10.1080/02626667.2024.2401094" target="_blank" rel="noopener">Cornelio et al. 2024, HSJ</a>). This version, in particular, contains two files in which the following changes were made from the earlier version (v2.0.1):</div> <div> <ul> <li>250117_Lbn_RC_new.R is the updated R code. The argument for the random number generator (RNG) kind is defined for the set.seed() functions used in the script. </li> <li>Lbn-DMs-csv0.csv is the updated input file containing the stage-discharge gaugings. The column for the stage values has been renamed to "H_rec" (instead of "H_m" as in the original CSV) to be consistent with the attribute name used throughout the R code.</li> </ul> Except for the above files, all the input and output files in <a href="https://zenodo.org/records/12792513" target="_blank" rel="noopener">v2.0.1</a> remain unchanged.  </div>

opencc-by-4.0Jul 2024View details →

Data and Supplementary Information : timber ressource dynamics in West African cocoa agroforestry systems

Here we present three datasets (growthDB.csv, heightDB.csv and volumeDB.csv) describing 59 tree species used for wood production in cocoa Agroforestry System (AFS) in Côte d'Ivoire (West Afica). Our dataset includes a total of 4,634 trees: 2530 spontaneous and 2104 (trans)planted.  We provide three table (per datasets in .csv format) with the following column per tree: - gensp: species latin names - dbh : tree diameter at breast height (cm) - age: tree age (years) - origin: tree origin (spontaneous or (trans)planted) - WDmean : tree wood density (g.cm³) - clus: study sites - indsp : species index - indclus : sites index - hlog: tree bole height (m) - vol : tree bole volume (m³) We also provide, the STAN code (models.stan) to assess the wood production potential of trees in cocoa AFS: (i) a model describing the diameter growth trajectories of trees as a function of their age, (ii) a model evaluating the relationship between tree diameter and bole height, and (iii) a model assessing the commercial volume of trees as a function of their diameter and bole height. Finally, we present additional tables and figures in Supplementary Information.pdf.

The impact of Syrian Civil War on cultivated cropland dynamics

The data needed to replicate paper --'The impact of Syrian Civil War on cultivated cropland dynamics'

Dynamics of CTCF and cohesin mediated chromatin looping revealed by live-cell imaging

Overview This repository contains all the raw and processed trajectory data associated with “paper title”. In this ReadMe file we provide the following information: <ul> <li>The cell lines and conditions used in this study</li> <li>A summary of how the data was collected</li> <li>The structure of the chromosome locus tracking data</li> </ul> Cell lines and conditions In total, the dataset covers 12 experimental conditions representing the following cell lines and treatment conditions: <ul> <li>C36</li> <li>C65</li> <li>C27</li> <li>CTCF-AID (untreated)</li> <li>CTCF-AID (2 hours AID)</li> <li>CTCF-AID (4 hours AID)</li> <li>RAD21-AID (untreated)</li> <li>RAD21-AID (2 hours AID)</li> <li>RAD21-AID (4 hours AID)</li> <li>WAPL-AID (untreated)</li> <li>WAPL-AID (4 hours AID)</li> <li>WAPL-AID (6 hours AID)</li> </ul>   Data and data processing Trajectories were obtained from 3D timeseries of mouse embryonic stem cell colonies in the conditions listed above using a LSM900 Airyscan 2 Zeiss microscope. For each movie we recorded 365 frames of 49.69 µm x 49.69 µm (584 x 584 pixels, pixel size: 0.085 µm by 0.085 µm), separated by an interval of 20 seconds for a total of just over 2 hours. 3D images were composed of 30 z-stacks separated by 0.25 µm, for a total height of 7.25 µm. Imaging was performed in two colors allowing the tracking of two arrays of fluorophores on Chromosome 18 near the Fbn2 gene. In all conditions, the fluorophore arrays were separated by 515 kb (except the C27 clone where separation was 10 kb). The 3D image time series were processed using ConnectTheDots: <a href="https://github.com/ahansenlab/connect_the_dots">https://github.com/ahansenlab/connect_the_dots</a> to obtain paired trajectories of chromosome loci over time. The trajectories have been corrected for chromatic shifts and aberrations. Data are provided in an “unfiltered” format (meaning that individual dot localizations were not quality control filtered) , or a filtered format (the same data set, but having undergone quality control). The filtered (quality controlled) trajectory data was used for all the quantitative analyses in the article “”. File names are formatted follows. <ul> <li>Quality controlled data have the structure: {Clone_and_condition_name}.tagged_set.tsv</li> <li>Unfiltered data have the structure: {Clone_and_condition_name}.unfiltered.tagged_set.tsv</li> </ul> For example, for RAD21-AID tagged clone, for imaging performed after two hours of protein degradation, the quality-controlled file name is: RAD21_2_hr.tagged_set.tsv. Please note that for all no-treatment conditions, we used “0 hours” as the tag. Thus, the RAD21 (untreated) becomes RAD21_0_hr.tagged_set.tsv.   Structure of Data The trajectory data are provided as tab-separated text files consisting of 10 columns. The column headers are: <ul> <li>id: a unique dot pair index</li> <li>t: the frame in which the dots were localized</li> <li>x: x-coordinate of the dot in the EGFP channel (units in µm)</li> <li>y: y-coordinate of the dot in the EGFP channel (units in µm)</li> <li>z: z-coordinate of the dot in the EGFP channel (units in µm)</li> <li>x2: x-coordinate of the dot in the mScarlet channel (units in µm)</li> <li>y2: y-coordinate of the dot in the mScarlet channel (units in µm)</li> <li>z2: z-coordinate of the dot in the mScarlet channel (units in µm)</li> <li>dist: 3D distance between the dots across channels (units in µm)</li> <li>movie_index: an identifier used to link the dot pair back to the raw image timeseries.</li> </ul>

Dry trajectories of SARS-CoV-2 RBD from accelerated molecular dynamics simulation

These are supplementary files to the preprint/paper "SARS-CoV-2 spike protein unlikely to bind to integrins via the Arg-Gly-Asp (RGD) motif of the Receptor Binding Domain: evidence from structural analysis and microscale accelerated molecular dynamics" (http://dx.doi.org/10.1101/2021.05.24.445335). The attached code in Jupyter notebook can be run after installing the virtual environment using the `environment.yml ` The file `data.zip` needs to be extracted to the same path where the notebook is run from

Can Artificial Intelligence help in the study of vegetative growth dynamics from herbarium collections? An evaluation of the tropical flora of the French Guiana forest

Dataset was used for the article "Can Artificial Intelligence help in the study of vegetative growth dynamics from herbarium collections? An evaluation of the tropical flora of the French Guiana forest". The related work proposes to study to what extent the use of automated visual analysis techniques, based on deep learning, can help not only to detect relatively rare vegetative structures in herbarium collections but also to automatically classify them by type of growing shoot (continuous or rhythmic). Abstract of the paper: A better knowledge of tree vegetative growth patterns and their relationship to environmental variables is crucial in understanding forest growth dynamics and how climate change may affect them. Generally less studied than reproductive structures, the phenology of tree vegetative growth mainly focuses on the analysis of growing shoots, from vegetative buds development to leaf fall. This growth process usually strongly differs between temperate and tropical regions. In temperate regions, this pattern is quite well known. Low winter temperatures impose a stop of the vegetative growth shoots and lead to the typical expression of an annual growth cycle for the vast majority of tree species. In moist tropical regions, on the other hand, the seasonality is much less marked. In addition, these regions contain a much wider variety of tree species. These two aspects lead to a tremendous diversity of phenological patterns that are still poorly known and understood. In particular, not much is known on the periodicity and timing of growth at individual trees, population, or community levels. The work carried out in this study aims to advance knowledge in this area, focusing more particularly on herbarium scans, as herbarium collections offer the promise of monitoring plant phenology over long time periods. However, such a study requires the ability to detect a sufficiently large number of growing shoots in herbarium collections to draw statistically relevant conclusions, which can be very costly if the work is done manually. Furthermore, herbarium collections traditionally focus on reproductive organs, and herbarium specimens showing growing shoots are pretty rare. We propose in this paper to study to what extent the use of automated visual analysis techniques, based on deep learning, can help not only to detect these relatively rare vegetative structures in herbarium collections but also to automatically classify them by type of growing shoot (continuous or rhythmic). Our results show the relevance of using herbarium data for vegetative phenology research, as well as the potential of deep learning approaches for growth shoot detection.

opencc-by-4.0Aug 2021View details →

Molecular Dynamics simulations suggest possible activation and deactivation pathways in hERG channel

<ol> <li>equil_gating_4_assembly_xleap.prmtop: file topology of the hERG closed state with gating charge 4 equilibration trajectory</li> <li>equil_gating_6_assembly_xleap.prmtop: file topology of the hERG closed state with gating charge 6 equilibration trajectory</li> <li>equil_gating_8_assembly_xleap.prmtop: file topology of the hERG closed state with gating charge 8 equilibration trajectory</li> <li>equil_gating_4.dcd: 100 ns NPT trajectory of the hERG closed state with gating charge 4</li> <li>equil_gating_6.dcd: 100 ns NPT trajectory of the hERG closed state with gating charge 6</li> <li>equil_gating_8.dcd: 100 ns NPT trajectory of the hERG closed state with gating charge 8</li> <li>equil_open_assembly_xleap.prmtop: file topology of the hERG open state equilibration trajectory</li> <li>equil_open.dcd: 100 ns NPT trajectory of the hERG open state</li> <li>herg_closed_gating_4.pdb: PDB file of hERG closed state with gating charge 4 after Steered MD simulations</li> <li>herg_closed_gating_6.pdb: PDB file of hERG closed state with gating charge 6 after Steered MD simulations</li> <li>herg_closed_gating_8.pdb: PDB file of hERG closed state with gating charge 8 after Steered MD simulations</li> <li>TMD_O-C_closed_gating_8_assembly_xleap.prmtop: file topology of the hERG closed state with gating charge 8 TMD trajectory</li> <li>TMD_O-C_closed_gating_6_assembly_xleap.prmtop: file topology of the hERG closed state with gating charge 6 TMD trajectory</li> <li>TMD_O-C_closed_gating_4_assembly_xleap.prmtop: file topology of the hERG closed state with gating charge 4 TMD trajectory</li> <li>TMD_O-C_closed_gating_8.dcd: TMD trajectory of the hERG closed state with gating charge 8</li> <li>TMD_O-C_closed_gating_6.dcd: TMD trajectory of the hERG closed state with gating charge 6</li> <li>TMD_O-C_closed_gating_4.dcd: TMD trajectory of the hERG closed state with gating charge 4</li> </ol> MD trajectories (equilibration and Targeted MD trajectories) in dcd format can be visualized using visualization tools such as VMD or PyMol after uploading the topology file. The directory data_supplementary-note-4.tar.bz2 contains the files related to the Supplementary Notes 4: "A practical example of pathway calculation".

opencc-by-4.0Oct 2018View details →

The DR-Train dataset: dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh

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.  <a href="https://drive.google.com/drive/folders/1oKn7IN7zznQuhwjDCDdjq8r9wHJYBEhj?usp=sharing">https://drive.google.com/drive/folders/1oKn7IN7zznQuhwjDCDdjq8r9wHJYBEhj?usp=sharing</a>   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. In particular, two light rail vehicles were instrumented (identified as LRV4306 and LRV4313):  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. - The last digit of each acceleration file: 1, 2, 3, 4, 5 - Corresponding sensor channels: tri-axial x, tri-axial y, tri-axial z, front cabinet uni-axial, back cabinet uni-axial LRV 4313 has 8 acceleration channels, corresponding to the two uni-axial accelerometer and the two tri-axial accelerometers inside the train. - The last digit of each acceleration file: 1, 2, 3, 4, 5, 6, 7, 8 - 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. - x longitudinal (vehicle moving direction); y-axis, transverse; z-axis, vertical. 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). 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. 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:  (1) within a region, the train always followed the same track and  (2) there are no tunnels in them so the GPS data is relatively consistent.  To get started, using MATLAB or Octave try running "main_script.m" in the "code" folder. A data descriptor paper with details of the data collection process was published. Please cite as 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) 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). For questions or suggestions please e-mail Jingxiao Liu <liujx@stanford.edu>

opencc-by-4.0Jan 2022View details →

A workflow for exploring ligand dissociation from a macromolecule: Efficient random acceleration molecular dynamics simulation and interaction fingerprint analysis of ligand trajectories

Containes input data   for MD simulations of 3 HSP90- small compound complexes from the paper A workflow for exploring ligand dissociation from a macromolecule: Efficient random acceleration molecular dynamics simulation and interaction fingerprint analysis of ligand trajectories" from Daria B. Kokh, Bernd Doser , Stefan Richter , Fabian Ormersbach , Xingyi Cheng, Rebecca C. Wade, publishe in J. Chem. Phys. 153, 125102 (2020); <a href="https://doi.org/10.1063/5.0019088">https://doi.org/10.1063/5.0019088</a> <ul> <li>ref.pdb - structure of the complex in PDB format</li> <li>ref.prmtop - topology file in AMBER</li> <li>ref-equal-NTP.pdb  - structure  after NTP equilibration </li> <li>ref-equal-NTP.rst7  - coordinates  after NTP equilibration</li> <li>ref-equal-NTP.crd  - coordinates  after NTP equilibration </li> <li>gromacs.gro - coordinates in Gromacs format (after NTP equalibration)</li> <li>gromacs.top - Gromacs topology </li> </ul>

opencc-by-4.0Jan 2022View details →

Vegetation Dynamics on Crete

This dataset contains long-term vegetation dynamics assessments for the Island of Crete, Greece. 10+ years ago, Landsat time series analyses were inevitably limited to few expensive images from carefully selected acquisition dates. Yet, such a static selection may have introduced uncertainties when spatial or inter-annual variability in seasonal vegetation growth were large. As seminal pre-open-data-era papers are still heavily cited, variations of their workflows are still widely used, too. Thus, we here quantitatively assessed the level of agreement between an approach using carefully selected images, and a state-of-the-art analysis that uses all available images. Further details can be found in the corresponding paper. Temporal Extent: 1984-2006. Spatial Extent: Crete, Greece Data format: The data come in tiles of 30x30km. The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There are mosaics in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems.  Sub-directories: <ul> <li>cso: Number of Clear-Sky Observations available for this study</li> <li>dem: Digital Elevation Model (SRTM filled with ASTER)</li> <li>vd-1.0: Vegetation Dynamics 1.0 (see paper) <ul> <li>bap: Best Available Pixel composites</li> <li>trend: monotonic trends fitted on BAPs</li> </ul> </li> <li>vd-2.0: Vegetation Dynamics 2.0 (see paper) <ul> <li>trend <ul> <li>monotonic trends fitted on value of peak of season phenometrics (VPS)</li> <li>monotonic trends fitted on value of seasonal amplitude phenometrics (VSA)</li> <li>change and piecewise monotonic trends fitted on value of base level phenometrics (VBL)</li> </ul> </li> <li>syndromes <ul> <li>syndrome classification for woody vegetation</li> <li>syndrome classification for herbaceous vegetation</li> <li>net cover change for woody vegetation</li> <li>net cover change for herbaceous vegetation</li> </ul> </li> </ul> </li> </ul> Further information: For further information, please see the publication. A web-visualization of this dataset is available <a href="https://ows.geo.hu-berlin.de/webviewer/crete/">here</a>. Publication: Frantz, D., Hostert, P., Rufin, P., Ernst, S., Röder, A., van der Linden, S. (2022): Revisiting the past: Replicability of a historic long-term vegetation dynamics assessment in the era of big data analytics. Remote Sensing 14(3), 597; https://doi.org/10.3390/rs14030597 Funding:  This publication was financially supported by Geo.X, the Research Network for Geosciences in Berlin and Potsdam under grant number SO_087_GeoX, and by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project-ID 414984028 - SFB 1404.

Morpho-sedimentary outlines displayed in Figures 1, S1, and S6-S15 of the article "Source-to-sink aeolian fluxes from arid landscape dynamics in the Lut Desert"

Morpho-sedimentary outlines of the aeolian landforms in the Lut Desert.

Datasets for publication titled "Chiral control of spin-crossover dynamics in Fe(II) complexes"

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.

Mimicking CA3 Temporal Dynamics Controls Limbic Ictogenesis

This dataset includes the microelectrode array recordings from the paper Mimicking CA3 Temporal Dynamics Controls Limbic Ictogenesis.

Simulation Data for "Community-Driven Code Comparisons for Three-Dimensional Dynamic Modeling of Sequences of Earthquakes and Aseismic Slip"

Simulation data from Jiang et al. (2022), "Community-Driven Code Comparisons for Three-Dimensional Dynamic Modeling of Sequences of Earthquakes and Aseismic Slip," Journal of Geophysical Research: Solid Earth. The archive includes simulation data for 3D SEAS benchmarks BP4-QD and BP5-QD that are analyzed in our paper (descriptions in NOTES.txt)  BP4-QD Benchmark Simulations: 1000 m:  jiang.5, lambert.8, barbot.3, barbot.2, dliu.2, li.4 500 m:  jiang.3, lambert.3, barbot.5, barbot.7, ozawa BP5-QD Benchmark Simulations: 2000 m:  jiang.6, lambert.8,  liu.4, cattania.5,          dli.7, barbot.3, dliu.10, li.3 1000 m:  jiang.2, lambert.7,  liu.5, cattania.3, ozawa,   dli.5, barbot,   dliu.6,  li.2 500 m:  jiang.4, lambert.9,  liu.6, cattania.4, ozawa.2, dli.6, barbot.2, dliu.8 250 m:  lambert.10, liu.7 BP5-QD with Off-Fault Data: 1000 m:  lambert.7, dli.5, barbot,   dliu.6, li.2 500 m:  lambert.9, dli.6, barbot.2, dliu.8 Tables 2–4 in our paper summarizes details of numerical codes and selected simulations. The benchmark descriptions and the full suite of simulation data are available at SEAS online platform https://strike.scec.org/cvws/seas/.

opencc-by-4.0Aug 2021View details →

Supplementary data - Modelling the role of dynamic topography and eustasy in the evolution of the Great Artesian Basin

This data repository contains the supplementary data for the paper: Modelling the role of dynamic topography and eustasy in the evolution of the Great Artesian Basin. Carmen Braz1, Sabin Zahirovic1, Tristan Salles1, Nicolas Flament2, Lauren Harrington1, R. Dietmar Müller1 1 EarthByte Group, School of Geosciences, The University of Sydney, Sydney, Australia 2 GeoQuEST Research Centre, School of Earth and Environmental Sciences, University of Wollongong, Wollongong, NSW, Australia Basin Research, https://doi.org/10.1111/bre.12606 Included in this supplement are: <ul> <li>    All input files required for running Badlands models M1-M4</li> <li>    Badlands digital output for preferred model M4</li> <li>    Animations of topography and erosion-deposition through time for all four models presented in the paper </li> <li>    Sediment layers for all time steps for preferred model M4 provided as netcdf grids.</li> </ul>

opencc-by-4.0Mar 2022View details →

Intrinsic and apparent slip at gas-enriched liquid-liquid interfaces: a molecular dynamics study

- "sl1.dat" : text file with data slip length vs number of gas atoms for k_gas = 1.0 - "sl5.dat" : text file with data slip length vs number of gas atoms for k_gas = 0.5 - "sl25.dat" : text file with data slip length vs number of gas atoms for k_gas = 0.25 - "sl125.dat" : text file with data slip length vs number of gas atoms for k_gas = 0.125 - "plotsl.plt": gnuplot script to plot slip lengths data and obtain figure 5a of the article - "dg.dat": data for solubilities in kbT units from figure 3 of the article - "3600gask0125.xyz": trajectory file in xyz format for the system with k_gas= 0.125 and 3600 gas atoms - "3600.data": starting configuration for k_gas = 0.125 and 3600 gas atoms in restart.data format for lammps - "in.shear": lammps input script to run the shear simulation for the system with k_gas = 0.125 and 3600 gas atoms starting from configuration store in "3600.data" file