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54 results for “Inertia”
Raw data for: Pressure and inertia sensing drifters for glacial hydrology flow path measurements
<p>Raw data for paper</p> <p>Title: Pressure and inertia sensing drifters for glacial hydrology flow path measurements</p> <p>Authors: A.Alexander, M.Kruusmaa, J.A. Tuhtan, A.J. Hodson, T.V. Schuler, A. Kääb</p> <p>Journal: The Cryosphere</p> <p>Year, 2020</p>
Supplementary Material for 'Benchmarking Explanatory Models for Inertia Forecasting using Public Data of the Nordic Area'
<p>This data set is supplementary material for the paper 'Benchmarking Explanatory Models for Inertia Forecasting using Public Data of the Nordic Area' by Jemima Sophie Graham, Evelyn Heylen, and Fei Teng.</p> <p>This data set is intended for day-ahead inertia forecasting in the Nordic (Eastern Denmark, Finland, Norway, Sweden). It contains hourly data for the inertial energy (MVAs), day-ahead national demand forecast (MW), day-ahead wind power forecast (MW), day-ahead solar power forecast (MW), and interconnection flow (MW) in the Nordic between January 2016 and August 2020. </p>
Maps of thermal inertia, dielectric constant and brightness temperature of asteroid (16) Psyche derived from ALMA data
<p>These data and results are in support of the findings by Cambioni, S., de Kleer, K. and Shepard, M. in their paper "The Heterogeneous Surface of Asteroid (16) Psyche", Journal of Geophysical Research: Planets, link: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2021JE007091. </p> <p>If using any of this material, please cite the above article as doi: 10.1029/2021JE007091</p> <p> </p>
Rock thermal conductivity and thermal inertia measurements under martian atmospheric pressures - Data
<p>Dataset accompanying the paper "Rock thermal conductivity and thermal inertia measurements under martian atmospheric pressures" published in Icarus in September 2024. </p> <p>Files included: </p> <p>Individual photos of the rocks in this study's sample suite; photos of laboratory equipment and setup; raw emissivity spectral data, emissivity specta plotted; thermal inertia data collected with the MTPS sensor; thermal conductivity data collected with the TPS sensor; itemized calculations of thermophysical data error; sample mineral abundance data; sample major/trace element measurement data; and detailed sample porosity data. </p> <p> </p>
Scaling between macro- to microscale climatic data reveals strong phylogenetic inertia in niche evolution in plethodontid salamanders
<p>Macroclimatic niches are indirect and potentially inadequate predictors of the realized environmental conditions that many species experience. Consequently, analyses of niche evolution based on macroclimatic data alone may incompletely represent the evolutionary dynamics of species niches. Yet, understanding how an organisms' climatic (Grinnellian) niche responds to changing macroclimatic conditions is of vital importance for predicting their potential response to global change. In this study, we integrate microclimatic and macroclimatic data across 26 species of plethodontid salamanders to portray the relationship between microclimatic niche evolution in response to changing macroclimate. We demonstrate stronger phylogenetic signal in microclimatic niche variables than at the macroclimatic scale. Even so, we find that the microclimatic niche tracks climatic changes at the macroscale, but with a phylogenetic lag at million-year timescales. We hypothesize that behavioral tracking of the microclimatic niche over space and phenology generates the lag: salamanders preferentially select microclimates similar to their ancestral conditions rather than adapting with changes in physiology. We demonstrate that macroclimatic variables are weak predictors of niche evolution and that incorporating spatial scale into analyses of niche evolution is critical for predicting responses to climate change.</p>
Self-similarity of solitary waves on inertia-dominated falling liquid films (Supporting data)
<p>This data accompanies the paper "Self-similarity of solitary waves on inertia-dominated falling liquid films", published in Physical Review E 93 (2016), 033121, DOI: 10.1103/PhysRevE.93.033121</p>
A Novel Surface Energy Balance Method for Thermal Inertia Studies of Terrestrial Analogs
<p>Thermophysical data collected from Woodhouse Mesa, AZ, USA in May 2021 and Sept 2022</p>
Spatial data for creating a thermal inertia index and incorporating it for conservation applications
<p>This repository contains supporting material for a journal article being submitted to one of the journals published by the American Geophysical Union, titled Earth's Future. The repository contains the following items:</p> <p>1. README file of what is in the repository including methods associated with the geodatabase</p> <p>2. File Geodatabase</p> <p><strong>1. README file</strong></p> <p>The files collected here relate to a study being submitted to the American Geophysical Union's journal, Earth's Future. The title of the paper being submitted is, "The contribution of Microrefugia to landscape thermal inertia for climate-adaptive conservation and adaptation strategies."</p> <p>The study was conducted across 40,250 km<sup>2</sup> of complex mountainous terrain in Northern California. The objective of the study was to consider whether it was possible to identify the relative strength of microrefugia systematically in order to provide conservation and climate-adaptation strategies with information that could help with prioritizing actions. We selected an operational scale of 10 ha (25 acres) as a scale that is suitable for various types of landscape planning exercises, and created a hexagon grid for the region. We calculated the mean value for multiple variables and appended them into the hexagons. For thermal inertia, we calculated the mean elevation per hexagon and then its coolest (highest) point using an environmental lapse rate. We also calculated solar energy loading, calculated the mean solar load per hexagon, and calculated its effect on air temperature. We combined these two temperature metrics to identify how much thermal buffering capacity each hexagon contains, as measured by how much warming it could experience before the mean temperature, as determined from a baseline time period, is no longer found anywhere within the hexagon. We tied the mean annual temperature from 1981–2010 to the mean elevation in each hexagon, as well as a temperature from an earlier period, and from several future periods, based on global circulation models.</p> <p>The study shows how long current (baseline) climate conditions found in each hexagon may persist and shows how the resulting map of landscape thermal inertia can be used when considering natural vegetation types for conservation, identifying which parts of high-priority wildlife corridors have the greatest capacity to retain their current climate conditions, and what the potential for retaining baseline climate conditions is for areas with late-seral forest conditions as represented by forest canopy height.</p> <p class="MsoNormal">The methods section below describes the data used in the study to create the data in the geodatabase that is posted here. The Geodatabase itself provides all the data needed to replicate the various results presented in the paper. Further information can be found in Thorne et al. 2020. That report is more extensive than the results in our associated paper, but it contains more information on the calculation of various metrics associated with and was the foundation from which we developed this study. The report is provided here in order to keep all the relevant materials compiled for potential use by others. </p> <p><strong>2. File Geodatabase</strong></p> <p>The geodatabase is provided as a separate file.</p> <p>Name: ThermalInertiaIndex.gdb</p> <p>Contents:</p> <ul> <li>AllHexagons <ul> <li>A feature class containing all 408,948 hexagon grids used in this study</li> <li>Fields within the feature class:</li> </ul> </li> </ul> <div> <table> <tbody> <tr> <td> <p>Id</p> </td> <td> <p>A unique ID for each hexagon</p> </td> </tr> <tr> <td> <p>Watershed</p> </td> <td> <p>Watershed the hexagon falls within</p> </td> </tr> <tr> <td> <p>DomWHR</p> </td> <td> <p>Habitat type (WHR) that had the majority coverage within the hexagon</p> </td> </tr> <tr> <td> <p>WHR_Name</p> </td> <td> <p>Descriptive name of the habitat type</p> </td> </tr> <tr> <td> <p>WHR_GroupName</p> </td> <td> <p>Major vegetation type</p> </td> </tr> <tr> <td> <p>CanopyHt_Score</p> </td> <td> <p>Canopy Height Score ranging from 1 (under 1m) to 5 (over 25m)</p> </td> </tr> <tr> <td> <p>CanopyHt_m</p> </td> <td> <p>Average canopy height within the hexagon (m)</p> </td> </tr> <tr> <td> <p>Conn_Score</p> </td> <td> <p>Connectivity Score ranging from 1 (low) to 5 (high)</p> </td> </tr> <tr> <td> <p>dem10m</p> </td> <td> <p>Average elevation within the hexagon (m)</p> </td> </tr> <tr> <td> <p>dem10m_min</p> </td> <td> <p>Minimum elevation within the hexagon (m)</p> </td> </tr> <tr> <td> <p>dem10m_max</p> </td> <td> <p>Maximum elevation within the hexagon (m)</p> </td> </tr> <tr> <td> <p>SRtemp_min</p> </td> <td> <p>The lowest Solar Radiation load within the hexagon (degree C)</p> </td> </tr> <tr> <td> <p>ElevLR_NegEff2</p> </td> <td> <p>Effect of elevation on air temperature (degree C)</p> </td> </tr> <tr> <td> <p>Thermal_Inertia</p> </td> <td> <p>Hexagon buffering capacity (degree C)</p> </td> </tr> <tr> <td> <p>tave_5180</p> </td> <td> <p>Average temperature 1951-1980</p> </td> </tr> <tr> <td> <p>tave_8110</p> </td> <td> <p>Average temperature 1981-2010</p> </td> </tr> <tr> <td> <p>tave_1039mi8</p> </td> <td> <p>Average temperature 2010-2039 (MIROC-ESM RCP 8.5)</p> </td> </tr> <tr> <td> <p>tave_4069mi8</p> </td> <td> <p>Average temperature 2040-2069 (MIROC-ESM RCP 8.5)</p> </td> </tr> <tr> <td> <p>tave_7099mi8</p> </td> <td> <p>Average temperature 2070-2099 (MIROC-ESM RCP 8.5)</p> </td> </tr> <tr> <td> <p>tave_1039cn8</p> </td> <td> <p>Average temperature 2010-2039 (CNRM-CM5 RCP 8.5)</p> </td> </tr> <tr> <td> <p>tave_4069cn8</p> </td> <td> <p>Average temperature 2040-2069 (CNRM-CM5 RCP 8.5)</p> </td> </tr> <tr> <td> <p>tave_7099cn8</p> </td> <td> <p>Average temperature 2070-2099 (CNRM-CM5 RCP 8.5)</p> </td> </tr> </tbody> </table> </div> <p> </p> <ul> <li>Connectivity_Scores <ul> <li>90m raster containing all 3 connectivity scores</li> <li>Fields within the raster:</li> </ul> </li> </ul> <div> <table> <tbody> <tr> <td> <p>TNC_Conn_Score</p> </td> <td> <p>Connectivity Score from reclassed TNC/Omniscape</p> </td> </tr> <tr> <td> <p>CEHC_Score</p> </td> <td> <p>Connectivity Score from reclassed California Essential Habitat Connectivity</p> </td> </tr> <tr> <td> <p>Combined_Score</p> </td> <td> <p>Overall Connectivity Score</p> </td> </tr> </tbody> </table> </div> <p> </p>
Chaotic microcomb inertia-free parallel ranging dataset and code
<p>Available data and code for manuscript: "Chaotic microcomb inertia-free parallel ranging"</p> <p>Arxiv version: https://arxiv.org/abs/2212.14275</p> <p>Execution tested with Matlab 2020b or newer on Windows. Unzip folder to access files.</p> <p>Matlab figures are stored in matlab_figures folder.</p> <p>For figure 2 execute feed_forward_tuning_analysis.m file<br> Change tuning rate (row 23) for appropriate values (25, 50, 100 Hz).<br> For figure 3 execute sequential_analysis_findpeaks.m and vipa_point_cloud.m files. Raw data are stored in raw_data folder.<br> <br> Contact anton.lukashchuk@epfl.ch if problems with matlab code arise. <br> All matlab code remains under copyright by the authors: Anton Lukashchuk. The code is provided solely to be used to reproduce the figures of the aforementioned paper.</p>
Reducing Clinical Inertia in Hypertension Treatment: A Pragmatic Trial
ClinicalTrials.gov study NCT01145391. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Personalizing Intervention to Reduce Clinical Inertia in the Treatment of Hypertension
ClinicalTrials.gov study NCT04603560. IPD Sharing: NO. Countries: 1. Publications: 1.
Spatial data for creating a thermal inertia index and incorporating it for conservation applications
Open the record for dataset details and reuse information.
Data from: Climate change, wildfire, and vegetation shifts in a high-inertia forest landscape: Western Washington, U.S.A.
Open the record for dataset details and reuse information.
Scaling between macro- to microscale climatic data reveals strong phylogenetic inertia in niche evolution in plethodontid salamanders
Open the record for dataset details and reuse information.
Figure 2 in Tyrannosaurus en pointe: allometry minimized rotational inertia of large carnivorous dinosaurs
Figure 2. RI versus body mass in carnivorous archosaurs. Grey bands indicate the range of changes in RI magnitudes when computed with body widths that were 110% and 90% of the original models.
Surface-wave instability without inertia in shear-thickening suspensions
<p>All data plotted in the figures of the article "Surface-wave instability without inertia in shear-thickening suspensions".</p> <p> </p>
Data from: "Evidence of superfluidity in a dipolar supersolid from non-classical rotational inertia"
<p>Here we store the data shown in the main text and in the supplementary material of the manuscritpt: "Evidence of superfluidity in a dipolar supersolid from non-classical rotational inertia", and the codes used to analyze the experimental data. All programs are Mathematica codes.</p>
Pairing in Fission: Mean-Field and Collective Inertias Study
<p>Related to the work: "Pairing in Fission: Mean-Field and Collective Inertias Study", by: A. Zdeb, M. Warda, L. M. Robledo, S.A. Giuliani</p> <p>There are 3 main directories containing the data collected during the studies of the impact on fission description 3 microscopic quantities related to pairing correlations. The delta (pairing gap) - delta directory. Particle number fluctuation - dn2 directory, quenching factor - qf directory. The directories contain the sub-directories which name indicates a specific studied isotope. Inside the subdirectories on can find text files containing the data related to the collective inertias and the HFB energy obtained within the D1S parametrization. The content of each column in the text files is explained in the headers. </p> <p>The detailed explanation of the "qf" directory: <br>The qf directory contains 5 sub-directories containing the data obtained with the various value of the quenching factor: "qf0_9": qf=0.9, "qf0_95": qf=0.95, "qf1_0": qf=1, "qf1_05": qf=1.05, "qf1_10": qf=1.1. Each of these sub-directories contains the data obtained during the studies of the isotopes which is indicated in the name of the file: "isotope.txt". The first line of the file contains the ground state energy and the ground state q_20 value, q_20 value of the exit point and the total number of points. The 3 columns of data represent: q_20, E_HFB+ZPE, B_22 along the least-energy path.</p> <p>The directory "inertias" contain the data collected when comparing the the effective inertias with delta and particle number fluctuations as constraints. The name of the file corresponds to the fixed value of delta as a collective coordinate. The content of the columns is given in the headers.<br> <br>The research work of A.Z. and M.W. is a part of the project No.2021/43/P/ST2/03036 co-funded by the National Science Centre and the European Union Framework Programme for Research and Innovation Horizon 2020 under the Marie Skłodowska-Curie grant agreement no. 945339.</p>
Data for Optimal Estimation of Under-Frequency Load Shedding Scheme Parameters by Considering Virtual Inertia Injection
<p> <span>The data presented are related to the paper entitled</span> <span>Optimal Estimation of </span><span>Under-Frequency Load Shedding Scheme Parameters by Considering Virtual Inertia Injection</span><span>, available in </span><span>Energies journal. Here, data are included to show the results of an Under Frequency Load Shedding</span> <span>(UFLS) scheme that considers the injection of virtual inertia by a VSC-HVDC link. The data obtained</span> <span>in six cases that were considered and analyzed are shown. In this case, each case represents a different</span> <span>frequency response configuration in the event of generation loss, taking into account the presence or</span> <span>absence of a VSC-HVDC link, traditional and optimized UFLS schemes, as well as the injection of</span> <span>virtual inertia by the VSC-HVDC link. Data for each example contains: state of the relay, threshold,</span> <span>position in every delay, load shed, and relay configuration parameters. Data were obtained through</span> <span>Digsilent Power Factory and Python simulations. The purpose of this dataset is that other researchers</span> <span>can reproduce the results reported in our paper</span></p>
"Wing Inertia Influences the Phase and Amplitude Relationships Between Thorax Deformation and Flapping Angle in Bumblebees"-Time Series Data
<p>This file contains all supporting data for the study titled "Wing Inertia Influences the Phase and Amplitude Relationships<br>Between Thorax Deformation and Flapping Angle in Bumblebees" By Braden Cote, Cailin Casey, and Mark Jankauski</p>
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International Brain Laboratory public data
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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.