Find research datasets worth reusing

opencc-by-4.0Dec 2021View details →

Coded data and R scripts for the article-Toward a dynamic behavioral profile of the Mandarin Chinese temperature term re

These are the coded dataset and R scripts for the article "Towards a dynamic behavioral profile of Mandarin Chinese temperature term re: A diachronic semasiological approach".

Choline acetyltransferase (ChAT) - Amyloid beta peptides complex Molecular dynamics Trajectories

In silico molecular dynamics study was performed for the choline acetyltransferase (ChAT) - Abeta peptides complex. The molecular docking of Aβ40 and Aβ42 on ChAT suggested three most probable binding clusters for both the Aβ peptides. Thus generating ChAT-Aβ40 Cluster-0, ChAT-Aβ40 Cluster-1, ChAT-Aβ40 Cluster-2 for Aβ40 peptide on ChAT, likewise ChAT-Aβ42 Cluster-0, ChAT-Aβ42 Cluster-1, ChAT-Aβ42 Cluster-2 were generated for Aβ42 peptide on ChAT. Each of the folders contains the topology file with a ‘.gro’ extension and a trajectory file with ‘.xtc’ extension generated from the 100 ns molecular dynamics performed for each of the clusters mentioned above that were generated from the molecular docking. The folders are named as follows: <ol> <li>ChAT_AB40_Cluster_0: Containing the topology file (ab40_0.gro) and the trajectory file (ab40_0.xtc)</li> <li>ChAT_AB40_Cluster_1: Containing the topology file (ab40_1.gro) and the trajectory file (ab40_1.xtc)</li> <li>ChAT_AB40_Cluster_2: Containing the topology file (ab40_2.gro) and the trajectory file (ab40_2.xtc)</li> <li>ChAT_AB42_Cluster_0: Containing the topology file (ab42_0.gro) and the trajectory file (ab42_0.xtc)</li> <li>ChAT_AB42_Cluster_1: Containing the topology file (ab42_1.gro) and the trajectory file (ab42_1.xtc)</li> <li>ChAT_AB42_Cluster_2: Containing the topology file (ab42_2.gro) and the trajectory file (ab42_2.xtc</li> </ol>

SeisSol input files for the dynamic rupture scenarios based on the 2004 Sumatra-Andaman earthquake published in Madden et al. (2022) "The state of pore fluid pressure and 3D megathrust earthquake dynamics" JGR-Solid Earth

This dataset contains the input files of the dynamic rupture scenarios from Madden, E. H., T. Ulrich and A.-A. Gabriel (2022), The State of Pore Fluid Pressure and 3-D Megathrust Earthquake Dynamics, Journal of Geophysical Research-Solid Earth, <a href="https://doi.org/10.1029/2021JB023382">https://doi.org/10.1029/2021JB023382</a>. (Earlier preprint available at: <a href="https://doi.org/10.1002/essoar.10508297.1">https://doi.org/10.1002/essoar.10508297.2</a>) easi/yaml parameter files for the 6 scenarios studied:  PAR_Sumatra_scen1new_gen.par, PAR_Sumatra_scen2new_gen.par, PAR_Sumatra_scen3new_gen.par, PAR_Sumatra_scen4new_gen.par, PAR_Sumatra_scen5new_gen.par, PAR_Sumatra_scen6new_gen.par easi/yaml files setting initial on-fault friction, stress and pore fluid pressure conditions for the 6 scenarios studied: iniStress_Sumatra_scen1new.yaml, iniStress_Sumatra_scen2new.yaml, iniStress_Sumatra_scen3new.yaml, iniStress_Sumatra_scen4new.yaml, iniStress_Sumatra_scen5new.yaml, iniStress_Sumatra_scen6new.yaml easi/yaml file describing the rock elastic properties in all 6 scenarios:  matprops_Sumatra_2019_LVZ.yaml mesh file:  topo4_splays_fix9-14.1e6-28m.dtc1-v2-suma

Dynamics of an interfacial bubble controls adhesion mechanics in a van der Waals heterostructure

These are the COMSOL Multiphysics for our FEM calculations of frequency dispersion with gate voltage ($V_g^{dc}$) and stress at different values of $V_g^{dc}$ for different conditions of graphene-hBN heterostructure interface. For the bubble growth rate equation, we considered linear gate-voltage dependence. Bubble growth will be restricted as the bubble diameter reaches the diameter of the drum. So the growth is conditional, it increases as the gate voltage increases with a constant slope till its radius reaches the drum radius ($R_f $); its governing equation is $R_{\textrm{bub}}(V_g^{\textrm{dc}})=R_i+mV_g^{\textrm{dc}} for R_{bub}\leq R_f$ . Bubble growth stops at $R_{bub} ≈ R_f $, where $R_i $, $R_f $are the initial and final radii of the bubble and m is the increase in bubble radius per unit change in gate voltage ($µm/V$).

Experimental results for Dynamics of an interfacial bubble controls adhesion mechanics in a van der Waals heterostructure

These are the COMSOL Multiphysics file used for our FEM calculations of frequency dispersion with gate voltage ($V_g^{dc}$) and stress at different values of $V_g^{dc}$ for different conditions of graphene-hBN heterostructure interface. For the bubble growth rate equation, we considered linear gate-voltage dependence. Bubble growth will be restricted as the bubble diameter reaches the diameter of the drum. So the growth is conditional, it increases as the gate voltage increases with a constant slope till its radius reaches the drum radius ($R_f $); its governing equation is $R_{\textrm{bub}}(V_g^{\textrm{dc}})=R_i+mV_g^{\textrm{dc}} for R_{bub}\leq R_f$ . Bubble growth stops at $R_{bub} ≈ R_f $, where $R_i $, $R_f $are the initial and final radii of the bubble and m is the increase in bubble radius per unit change in gate voltage ($µm/V$).

Immune disease risk variants regulate gene expression dynamics during CD4+ T cell activation

During activation, T cells undergo extensive changes in gene expression which shape the properties of cells to exert their effector function. Therefore, understanding the genetic regulation of gene expression during T cell activation provides essential insights into how genetic variants influence the response to infections and immune diseases. We generated a single-cell map of expression quantitative trait loci (eQTL) across a T cell activation time-course. We profiled 655,349 CD4+ naive and memory T cells, capturing transcriptional states of unstimulated cells and three time points of cell activation in 119 healthy individuals. We identified 38 cell clusters, including stable clusters such as central and effector memory T cells and transient clusters that were only present at individual time points of activation, such as interferon-responding cells. We mapped eQTLs using a T cell activation trajectory and identified 6,407 eQTL genes, of which a third (2,265 genes) were dynamically regulated during T cell activation. We integrated this information with GWAS variants for immune-mediated diseases and observed 127 colocalizations, with significant enrichment in dynamic eQTLs. Immune disease loci colocalized with genes that are involved in the regulation of T cell activation, and genes with similar functions tended to be perturbed in the same direction by disease risk alleles. Our results emphasize the importance of mapping context-specific gene expression regulation, provide insights into the mechanisms of genetic susceptibility of immune diseases, and help prioritize new therapeutic targets. This dataset comprises of summary stats for eQTLs identified in the study (parquet files) and the ones which passed significance threshold  (tensor_out.tar.gz archive). Files are described by cell subset (CD4 Naive, CD 4 Memory, TEMRA, TCM, etc.), time since activation (16h, 4h, 5days)  as described in the publication (preprint https://doi.org/10.1101/2021.12.06.470953)

opencc-by-4.0Jun 2021View details →

Dataset of Dynamic Strength training and Dynamic Endurance training

Here is the dataset for Numeric pain rating scale and Neck disability index for Dynamic strength training and dynamic endurance training groups in patients with mechanical neck pain before and after treatment. The objective was to compare effectiveness of two different exercise regimen in individuals with mechanical neck pain.

Molecular Dynamics of Omicron-RBD and hACE2 performed with NAMD at 37 degrees Celsius

Molecular Dynamics of Omicron-RBD and hACE2 performed with NAMD at 37 degrees Celsius. The PDB used is 7T9K for an MD of 9 nanoseconds. Amino acids side chains are in yellow which are the 11 amino acids specific to the Omicron variant mapped to the interface with hACE2 receptor. The mutations in Omicron make this variant to bind better to the hACE2 receptor and to evade most previous immunity including vaccines. Visualization in UCSF Chimera.

opencc-zeroFeb 2022View details →

dryad36/100

Data from: A millennium of climatic and floristic dynamics in the Eastern Cordillera of the Colombian Andes

The transition from the Medieval Climate Anomaly (MCA, 950-1250 CE) to the Little Ice Age (LIA, 1350 to 1800 CE) is the largest pre-industrial climate shift within the last two millennia, offering an opportunity to study how vegetation responds to rapid climate change. We analyzed a sedimentary record from the Colombian Andes to reconstruct regional vegetation dynamics during this time interval, identify the modern environmental distribution of taxa present in the fossil record, and provide a reference for modern regional temperature change. A sediment core was analyzed for organic geochemistry and pollen to reconstruct environmental and vegetation variability between 800 and 1800 CE. Modern occurrences of individuals of the genera found in the fossil record were used to model the distribution of taxa across gradients of mean annual temperature and precipitation. These models were used to reconstruct temperature and precipitation across time and evaluate the probability of occurrence of taxa from the fossil record across the contemporary landscape. Reconstructed regional vegetation dynamics were highly coupled with global temperature patterns. Reconstructed climate showed that conditions were warm and dry during the MCA, and cold and relatively wet during the LIA, although quantitative estimations might have been amplified by human disturbances. Rates of temperature change during the MCA-LIA transition were substantially lower than those calculated for the last two decades. Vegetation turnover between 800 and 1800 CE resembled those expressed through a transect extending ~30 km southeast from the lake. Around lake Tota, modern rates of temperature change have no precedent within the last two millennia. The modern environmental affinities of taxa involved in vegetation turnover during the MCA-LIA transition point to the importance of landscape diversity and connectivity for rapid vegetation response to climate change.

dryad36/100

Understanding complex spatial dynamics from mechanistic models through spatio-temporal point processes

Landscape heterogeneity affects population dynamics, which determine species persistence, diversity and interactions. These relationships can be accurately represented by advanced spatially-explicit models (SEMs) allowing for high levels of detail and precision. However, such approaches are characterised by high computational complexity, high amount of data and memory requirements, and spatio-temporal outputs may be difficult to analyse. A possibility to deal with this complexity is to aggregate outputs over time or space, but then interesting information may be masked and lost, such as local spatio-temporal relationships or patterns. An alternative solution is given by meta-models and meta-analysis, where simplified mathematical relationships are used to structure and summarise the complex transformations from inputs to outputs. Here, we propose an original approach to analyse SEM outputs. By developing a meta-modelling approach based on spatio-temporal point processes (STPPs), we characterise spatio-temporal population dynamics and landscape heterogeneity relationships in agricultural contexts. A landscape generator and a spatially-explicit population model simulate hierarchically the pest-predator dynamics of codling moth and ground beetles in apple orchards over heterogeneous agricultural landscapes. Spatio-temporally explicit outputs are simplified to marked point patterns of key events, such as local proliferation or introduction events. Then, we construct and estimate regression equations for multi-type STPPs composed of event occurrence intensity and magnitudes. Results provide local insights into spatio-temporal dynamics of pest-predator systems. We are able to differentiate the contributions of different driver categories ( i.e., spatio-temporal, spatial, population dynamics). We highlight changes in the effects on occurrence intensity and magnitude when considering drivers at global or local scale. This approach leads to novel findings in agroecology where, for example, we show that the organisation of cultivated patches and semi-natural elements play different roles for pest regulation depending on the scale considered. It aids to formulate guidelines for biological control strategies at global and local scale.

opencc-zeroFeb 2022View details →

opencc-by-4.0Apr 2021View details →

A 24-hour dynamic population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland

Related article: Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. <a href="https://www.nature.com/articles/s41597-021-01113-4">Scientific Data 9, 39</a>.   In this dataset: We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning.  Please cite this dataset as: Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4   Organization of data The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files: <ol> <li> HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.</li> <li> HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.</li> <li> HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.</li> <li>target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.</li> </ol> Column names <ol> <li>YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.</li> <li>H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59.  The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)</li> </ol> In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets. License Creative Commons Attribution 4.0 International. Related datasets <ul> <li>Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612</li> <li>Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564</li> </ul>

Molecular Dynamics of hACE2 Receptor and SARS-CoV-2 Omicron-RBD (Receptor Binding Domain) in Electrostatics View

Molecular Dynamics of hACE2 Receptor and SARS-CoV-2 Omicron-RBD (Receptor Binding Domain) in Electrostatics View. Molecular Dynamics performed with NAMD in Frontera supercomputer for 8 nanoseconds at 37 degrees Celsius. Electrostatics is visualized with ChimeraX (red is negative and blue is positive). By Victor Padilla-Sanchez, PhD; Texas Advanced Computing Center.

opencc-by-4.0Dec 2021View details →

Dataset from paper "Dynamic Concentration Factor: A Novel Parameter For The Rigorous Evaluation Of Solar Compound Parabolic Collectors"

<ul> <li>Dynamic Concentration Factor as a function of the beam angle (α) for CPC with different acceptance angles (θ), reflectivity (r) of the collector surface ranging from 0.0 to 1.0, and optical density ranging from 0.0 to 10.0</li> <li>Diffuse Dynamic Concentration factor for CPC with different acceptance angles (θ), reflectivity (r) of the collector surface ranging from 0.0 to 1.0, and optical density ranging from 0.0 to 10.0</li> <li>Experimental validation of the Dynamic Concentration Factor using actinometry results</li> <li>Independence test of Dynamic Concentration results of the number of rays cast in the software tool.</li> <li>Full-precision data of the Dynamic Concentration Factor as a function of the beam angle (α) for CPC with different acceptance angles (θ), reflectivity (r) of the collector surface ranging from 0.0 to 1.0, and optical density ranging from 0.0 to 10.0</li> </ul>

Figure data for "RF-induced heating dynamics of non-crystallized trapped ions"

Figure data of the paper RF-induced heating dynamics of non-crystallized trapped ions.

Single cell imaging of ERK and Akt activation dynamics and heterogeneity induced by G protein-coupled receptors - Scripts & Source data

Source data and scripts to reproduce the figures that are part of the publication "Single cell imaging of ERK and Akt activation dynamics and heterogeneity induced by G protein-coupled receptors". Journal of Cell Science (2022) 135, jcs259685, DOI: 10.1242/jcs.259685   An earlier version of this work is published as a preprint: "Heterogeneity and dynamics of ERK and Akt activation by G protein-coupled receptors depend on the activated heterotrimeric G proteins", DOI: <a href="https://doi.org/10.1101/2021.07.27.453948">10.1101/2021.07.27.453948</a>

Project files provided as supporting information to the manuscript "Coarse-grained Mori-Zwanzig dynamics in a time-non-local stationary-action framework"

Project files provided as supporting information to the manuscript "Coarse-grained Mori-Zwanzig dynamics in a time-non-local stationary-action framework" GLE Optimization: Optimizator of GLE parameters. Uses Matlab MD_GLE:           CG GLE and LE simulator. Uses Matlab Water_simulation: Folders for atomistic water system simulation with GROMACS. It requires to be run on Linux with GROMACS                    and VOTCA packages installed.

100 Hz ROCS microscopy correlated with fluorescence reveals cellular dynamics on different spatiotemporal scales

Image Datasets to 100 Hz ROCS microscopy correlated with fluorescence reveals cellular dynamics on different spatiotemporal scales

Inputdata for CESM2.3x compset F2000climo with spectral-element dynamical core.and refined grid over Greenland

Input data necessary to run a CESM2.3x case for the F2000 compset and a variable resolution mesh with a 1 degree global horizontal resolution grid and regional refinement of 1/4 degree resolution over the broader Arctic region plus an additionally 1/8 degree refinement over Greenland

On the scale dependence in the dynamics of frictional rupture: constant fracture energy versus size-dependent breakdown work

<pre>Potential energy stored during the inter-seismic period by tectonic loading around faults is released during earthquakes as radiated energy, frictional dissipation and fracture energy. The latter is of first importance since it is expected to control the nucleation, the propagation and the arrest of the seismic rupture. On one side, the seismological fracture energy estimated for natural earthquakes (commonly called breakdown work) ranges between 1 $\mathrm{J/m^2}$ and tens of $ \mathrm{MJ/m^2} $ for the largest events, and shows a clear slip dependence. On the other side, recent experimental studies highlighted that, concerning rupture experiments, fracture energy is a material property (energy required to break the fault interface) independently of the size of the event, i.e. of the seismic slip. </pre> <pre>To reconcile these contradictory observations and definitions, we performed stick-slip experiments, as analog for earthquakes, in a bi-axial shear configuration. We estimated fracture energy through both Linear Elastic Fracture Mechanics (LEFM) and a Cohesive Zone Model (CZM) and through the integration of the near-fault stress-slip evolution. We show that, at the scale of our experiments, fault weakening is divided into a near-tip weakening, corresponding to an energy of few $ \mathrm{J/m^2} $, consistent with the one estimated through LEFM and CZM, and a long-tailed weakening corresponding to a larger energy not localized at the rupture tip, increasing with slip.</pre> <pre>Through numerical simulations, we demonstrate that only near-tip weakening controls the rupture initiation and that long-tailed weakening can enhance slip during rupture propagation and allow the rupture to overcome stress heterogeneity along the fault. We conclude that the origin of the seismological estimates of breakdown work could be related to the energy dissipated in the long-tailed weakening rather than to the one dissipated near the tip.</pre>