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1,444 results for “mitigation”
Dataset for paper "Mitigating the effect of errors in source parameters on seismic (waveform) inversion"
<p>Dataset corresponding to the journal article "Mitigating the effect of errors in source parameters on seismic (waveform) inversion" by Blom, Hardalupas and Rawlinson, accepted for publication in Geophysical Journal International. In this paper, we demonstrate the effect or errors in source parameters on seismic tomography, with a particular focus on (full) waveform tomography. We study effect both on forward modelling (i.e. comparing waveforms and measurements resulting from a perturbed vs. unperturbed source) and on seismic inversion (i.e. using a source which contains an (erroneous) perturbation to invert for Earth structure. These data were obtained using Salvus, a state-of-the-art (though proprietary) 3-D solver that can be used for wave propagation simulations (Afanasiev et al., GJI 2018).</p> <p>This dataset contains:</p> <ul> <li>The entire Salvus project. This project was prepared using Salvus version 0.11.x and 0.12.2 and should be fully compatible with the latter.</li> <li>A number of Jupyter notebooks used to create all the figures, set up the project and do the data processing.</li> <li>A number of Python scripts that are used in above notebooks.</li> <li>two conda environment .yml files: one with the complete environment as used to produce this dataset, and one with the environment as supplied by Mondaic (the Salvus developers), on top of which I installed basemap and cartopy.</li> <li>An overview of the inversion configurations used for each inversion experiment and the name of hte corresponding figures: inversion_runs_overview.ods / .csv .</li> <li>Datasets corresponding to the different figures. <ul> <li>One dataset for Figure 1, showing the effect of a source perturbation in a real-world setting, as previously used by Blom et al., Solid Earth 2020</li> <li>One dataset for Figure 2, showing how different methodologies and assumptions can lead to significantly different source parameters, notably including systematic shifts. This dataset was kindly supplied by Tim Craig (Craig, 2019).</li> <li>A number of datasets (stored as pickled Pandas dataframes) derived from the Salvus project. We have computed: <ul> <li>travel-time arrival predictions from every source to all stations (df_stations...pkl)</li> <li>misfits for different metrics for both P-wave centered and S-wave centered windows for all components on all stations, comparing every time waveforms from a reference source against waveforms from a perturbed source (df_misfits_cc.28s.pkl)</li> <li>addition of synthetic waveforms for different (perturbed) moment tenors. All waveforms are stored in HDF5 (.h5) files of the ASDF (adaptable seismic data format) type</li> </ul> </li> </ul> </li> </ul> <p>How to use this dataset:</p> <ul> <li>To set up the conda environment: <ol> <li>make sure you have anaconda/miniconda</li> <li>make sure you have access to Salvus functionality. This is not absolutely necessary, but most of the functionality within this dataset relies on salvus. You can do the analyses and create the figures without, but you'll have to hack around in the scripts to build workarounds.</li> <li>Set up Salvus / create a conda environment. This is best done following the instructions on the Mondaic website. Check the changelog for breaking changes, in that case download an older salvus version.</li> <li>Additionally in your conda env, install basemap and cartopy: <pre><code class="language-bash">conda-env create -n salvus_0_12 -f environment.yml conda install -c conda-forge basemap conda install -c conda-forge cartopy</code></pre> </li> <li> <p>Install LASIF (https://github.com/dirkphilip/LASIF_2.0) and test. The project uses some lasif functionality.</p> </li> <li> <p> </p> </li> <li> <p> </p> </li> </ol> </li> <li>To recreate the figures: This is extremely straightforward. Every figure has a corresponding Jupyter Notebook. Suffices to run the notebook in its entirety. <ul> <li>Figure 1: separate notebook, Fig1_event_98.py</li> <li>Figure 2: separate notebook, Fig2_TimCraig_Andes_analysis.py</li> <li>Figures 3-7: Figures_perturbation_study.py</li> <li>Figures 8-10: Figures_toy_inversions.py</li> </ul> </li> <li>To recreate the dataframes in DATA: This can be done using the example notebook Create_perturbed_thrust_data_by_MT_addition.py and Misfits_moment_tensor_components.M66_M12.py . The same can easily be extended to the position shift and other perturbations you might want to investigate.</li> <li>To recreate the complete Salvus project: This can be done using: <ul> <li>the notebook Prepare_project_Phil_28s_absb_M66.py (setting up project and running simulations)</li> <li>the notebooks Moment_tensor_perturbations.py and Moment_tensor_perturbation_for_NS_thrust.py</li> <li>For the inversions: using the notebook Inversion_SS_dip.M66.28s.py as an example. See the overview table inversion_runs_overview.ods (or .csv) as to naming conventions.</li> </ul> </li> </ul> <p> </p> <p>References:</p> <ul> <li>Michael Afanasiev, Christian Boehm, Martin van Driel, Lion Krischer, Max Rietmann, Dave A May, Matthew G Knepley, Andreas Fichtner, Modular and flexible spectral-element waveform modelling in two and three dimensions, <em>Geophysical Journal International</em>, Volume 216, Issue 3, March 2019, Pages 1675–1692, <a href="https://doi.org/10.1093/gji/ggy469">https://doi.org/10.1093/gji/ggy469</a></li> <li>Nienke Blom, Alexey Gokhberg, and Andreas Fichtner, Seismic waveform tomography of the central and eastern Mediterranean upper mantle, <em>Solid Earth</em>, Volume 11, Issue 2, 2020, Pages 669–690, 2020, <a href="https://doi.org/10.5194/se-11-669-2020">https://doi.org/10.5194/se-11-669-2020</a></li> <li>Tim J. Craig, Accurate depth determination for moderate-magnitude earthquakes using global teleseismic data. <em>Journal of Geophysical Research: Solid Earth</em>, 124, 2019, Pages 1759– 1780. <a href="https://doi.org/10.1029/2018JB016902">https://doi.org/10.1029/2018JB016902</a></li> </ul> <p> </p>
IDENTIFYING AND MITIGATING CONGESTION ONSET (Project J3)
<p>These data were used in the Sacramento and Tampa case studies as described in the final report of the STRIDE J3 project.</p>
Estimating household preferences for coastal flood risk mitigation policies under ambiguity
<p>Risk mitigation policies (like dike rising) are essential to address increasing coastal flood risks due to global warming. Furthermore, the optimal level of risk mitigation policy should be determined by public preferences for risk reduction. However, it is difficult to reveal public preferences for coastal flood risk reduction because projections of coastal flood risks inevitably involve uncertainty. This study aims to estimate household preference for coastal flood reduction under ambiguity and multiple projections of coastal flood risks. By coupling storm surge inundation simulations and stated preference experiments with decision models, we estimate the expected loss reduction, risk premium, and ambiguity premium for coastal flood risk mitigation policies. Results of the study show that ignoring the ambiguity premium causes significant undervaluation of coastal flood risk mitigation, and the ambiguity premium stems from households' over-concern about the worst projection, which may lead to an over-allocation of resources to prevent inundation damage caused from the worst-case flood before a disaster. The study concludes that a risk mitigation policy combining public insurance for the worst projection and pre-disaster prevention measures can be effective and efficient.</p>
Ungulates mitigate the effects of drought and shrub encroachment on the fire hazard of Mediterranean oak woodlands
<p>Dataset included: Shrub density; Shrub biomass; Fuel load of <em>Cistus ladanifer</em>; Fuel load herbs; Fuel load litter</p>
Dataset for paper "Marked impacts of pollution mitigation on crop yields in China"
<p>"corn.csv", "winterwheat.csv" and "midlatepaddy.csv" report historical crop yield (unit: kg/hectare)of corn, winter wheat and single cropping rice in China. <br> "regression.ipynb" contains python code to (i) derive the sensitivity of crop yield to climate and pollution variables (ii) compare predicted yield vs. observed yield (iii) calculate the relative contribution of individual climate and pollution variables to inter-annual variations of crop yield. Here we provide the complete datasets for corn in file “organized_data_corn.csv”, which includes air temperature at 2m (t2m), precipitation (tp), aerosol optical depth (aod) and surface ozone (o3) for plant season (plant), growing season (mid) and harvest season (harvest). Please contact us if you are interested in datasets of other two crop species.</p>
Data for publication "Committed sea-level rise under the Paris Agreement and the legacy of delayed mitigation action".
<p>Data underlying the publication "Committed sea-level rise under the Paris Agreement and the legacy of delayed mitigation action".</p> <p>Journal: Nature Communications</p> <p>Authors: <em>Matthias Mengel<sup>1*</sup></em><em>, Alexander Nauels</em><sup><em>2</em></sup><em>, Joeri Rogelj</em><sup><em>3,4</em></sup><em>, Carl-Friedrich Schleussner<sup>1,5</sup></em></p> <p>(1) Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, D-14412 Potsdam, Germany</p> <p>(2) Australian-German College of Climate & Energy Transitions, The University of Melbourne, Parkville, Victoria 3010, Australia</p> <p>(3) ENE Program, International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, Laxenburg A-2361, Austria</p> <p>(4) Institute for Atmospheric and Climate Science, ETH Zurich, Universitätstrasse 16, Zurich 8006, Switzerland</p> <p>(5) Climate Analytics, Ritterstr. 3, 10969 Berlin, Germany</p> <p>(*) email matthias.mengel@pik-potsdam.de</p> <p>Abstract:</p> <p>Sea-level rise is a major consequence of climate change that will continue long after emissions of greenhouse gases have stopped. The 2015 Paris Agreement aims at reducing climate-related risks by reducing greenhouse gas emissions to net zero and limiting global-mean temperature increase. Here we quantify the effect of these constraints on global sea-level rise until 2300 including Antarctic ice-sheet instabilities. We estimate median sea-level rise between 0.7 and 1.2m if net zero greenhouse gas emissions are sustained until 2300, varying with the pathway of emissions during this century. Temperature stabilization below 2°C is insufficient to hold median sea-level rise until 2300 below 1.5m. We find that each 5-year delay in near-term peaking of CO2 emissions increases median year-2300 sea-level rise estimates by ca. 0.2m, and extreme sea-level rise estimates at the 95th percentile by up to 1m. Our results underline the importance of near-term mitigation action for limiting long-term sea-level rise risks.</p> <p> </p> <p>Large zip files provides data. Small zip file python code for plotting and writing supplementary data.</p>
Data supporting the manuscript entitled: 'Intermittent soil water stress history favors microbial traits that better mitigate wheat biomass losses during subsequent water stress.'
<p>Data living in this data repository supports the scientific article entitled: Intermittent soil water stress history favors microbial traits that better mitigate wheat biomass losses during subsequent water stress.</p> <p> </p> <p> </p> <p> </p>
Dataset: How do news about a heatwave affect public prioritization of climate change adaptation and mitigation behaviors?
<p><span>These datasets contain survey data that was used to evaluate the effect of the exposure to heatwave news texts on people’s preference for climate mitigation and adaptation actions, as presented in the manuscript titled “<em>How do news about a heatwave affect public prioritization of climate change adaptation and mitigation behaviors?</em>”. Three versions of the dataset are available:</span></p> <ol> <li><strong>Original dataset</strong>: This version contains choice text as data points and includes all finished survey responses that passed the attention check questions (n=1209).</li> <li><strong>Original recoded dataset</strong>: This version was generated by recoding choice text into numerical values. The 'Income' variable, representing household income levels for both Canadian and US residents, was added by converting reported income ranges to a unified scale based on exchange rate equivalencies. The "Income_Canadians" and "Income_US" columns were subsequently removed to avoid repetitions. </li> <li><strong>Final dataset</strong>: This version excludes observations from participants who completed the survey in under four minutes and those who selected the same response for every item within each matrix-style question (also known as straight-lining). Additionally, responses with missing values in questions regarding political views, gender, and household income, as well as responses where participants identified as non-binary or indicated that their gender was not listed, were omitted (see “Methods” for more details). Dependent variables have been added based on the original responses, including personal-level mitigation and adaptation likelihoods, personal-level mitigation preference, and both non-weighted and weighted collective-level mitigation preference. Furthermore, the dataset includes a 'Climate Change Concern' variable, derived through principal component analysis of thirteen variables expressing participants’ climate change attitudes and efficacy beliefs concerning climate actions. Variables not used in the subsequent data analysis were removed. Age, political views, education, and income columns were standardized. The final dataset was used for the data analysis presented in the manuscript.</li> </ol> <p>The following variables/columns can be found across the three versions of the dataset:</p> <ul> <li>Dependent variables: <ul> <li>Starting with “<em>Personal_Mitigation</em>”: participant’s self-reported likelihood of taking selected personal-level climate change mitigation actions</li> <li>Starting with “<em>Personal_Adaptation</em>”: participant’s self-reported likelihood of taking selected personal-level climate change adaptation actions</li> <li>Starting with “<em>Collective_Mitigation</em>”: participant’s ranking of the collective-level climate change mitigation initiatives</li> <li>Starting with “<em>Collective_Adaptation</em>”: participant’s ranking of the collective-level climate change adaptation initiatives</li> <li><em>Personal_Mitigation_Likelihood</em>: personal-level mitigation likelihood (present only in the final dataset)</li> <li><em>Personal_Adaptation_Likelihood</em>: personal-level adaptation likelihood (present only in the final dataset)</li> <li><em>Personal_Preference</em>: personal-level mitigation preference (present only in the final dataset)</li> <li><em>Collective_Preference_Unweighted</em>: non-weighted collective-level mitigation preference (present only in the final dataset)</li> <li><em>Collective_Preference_Weighted</em>: weighted collective-level mitigation preference (present only in the final dataset)</li> </ul> </li> <li>Independent variables: <ul> <li><em>Group</em>: group that the participant was assigned to as part of the experimental intervention</li> <li><em>Distance</em>: indicates whether the participant was assigned to read about a heatwave occurring in their community or a city 6,000 km away (for experimental groups only)</li> <li><em>Severity</em>: indicates whether the participant was prompted to read about a heatwave without or with the mention of associated causalities (for experimental groups only)</li> </ul> </li> <li>Covariates and supporting variables: <ul> <li><em>Gender</em>: gender identity</li> <li><em>Identity</em>: ethnic and/or racial identity</li> <li><em>Age</em>: age</li> <li><em>Political_Views</em>: position on the liberal-conservative continuum</li> <li><em>Education</em>: highest level of education</li> <li><em>Country</em>: country of residence</li> <li><em>Canada_Province</em>: province or territory of residence (for Canadian participants only)</li> <li><em>US_State</em>: state of residence (for US participants only)</li> <li><em>Duration_Residence</em>: duration of residence in the current community</li> <li><em>Income_Canadians</em>: annual household income in Canadian dollars (for Canadian participants only)</li> <li><em>Income_US</em>: annual household income in US dollars (for US participants only)</li> <li><em>Income</em>: annual household income for both Canadian and US residents derived by converting reported income ranges to a unified scale based on exchange rate equivalencies</li> <li><em>Efficacy_Mitigation_Personal</em>: belief regarding the response efficacy of personal-level climate change mitigation actions</li> <li><em>Efficacy_Mitigation_Collective</em>: belief regarding the response efficacy of collective-level climate change mitigation actions</li> <li><em>Efficacy_Adaptation_Personal</em>: belief regarding the response efficacy of personal-level climate change adaptation actions</li> <li><em>Efficacy_Adaptation_Collective</em>: belief regarding the response efficacy of collective-level climate change adaptation</li> <li><em>Climate_Change_Importance:</em> perception of climate change as a personally important issue</li> <li>Climate_Change_Worry: level of worry about climate change</li> <li>Starting with “<em>Climate_Risk</em>”: beliefs regarding the degree of harm that climate change will cause to plants and animal species (Climate_Risk_Animals_Plants), future generations of people (Climate_Risk_Future_Generations), people in developing countries (Climate_Risk_Developing_Countries), people in participant’s country (Climate_Risk_Country), people in participant’s community (Climate_Risk_Community), and the participant personally (Climate_Risk_Personal)</li> <li>Climate_Change_Onset_Time: belief regarding when climate change will start harming people in their community</li> <li><em>Six_Americas_Segment</em>: the Global Warming's Six Americas segment participant aligns with derived based on the Six Americas Short SurveY (SASSY) Group Scoring Tool</li> <li><em>Climate_Change_Concern</em>: variable derived through PCA of thirteen variables expressing participants' climate change attitudes and efficacy beliefs pertaining to climate actions (present only in the final dataset)</li> <li><em>Survey_Duration_Seconds</em>: The amount of time it took the respondent to complete the survey</li> </ul> </li> </ul>
Figure 7 in Mitigation of the effects of salt stress in cowpea bean through the exogenous aplication of brassinosteroid
Figure 7. Effect of 24-epibrasinolide in the activity of the enzyme nitrate reductase of cowpea roots under salt stress. Capital letters indicate statistical differences between EBL treatments (p <0.05) based on upon a Tukey's test; small letters indicate statistical differences between salt treatments (p <0.05) based on upon a Tukey's test.
Figure 6 in Mitigation of the effects of salt stress in cowpea bean through the exogenous aplication of brassinosteroid
Figure 6. Effect of 24-epibrasinolide in the activity of the enzyme nitrate reductase of cowpea leaves under salt stress. Capital letters indicate statistical differences between EBL treatments (p <0.05) based on upon a Tukey's test; small letters indicate statistical differences between salt treatments (p <0.05) based on upon a Tukey's test.
Figure 3 in Mitigation of the effects of salt stress in cowpea bean through the exogenous aplication of brassinosteroid
Figure 3. Effect of 24-epibrasinolide in the stem diameter of cowpea plants under salt stress. Capital letters indicate statistical differences between EBL treatments (p <0.05) based on upon a Tukey's test; small letters indicate statistical differences between salt treatments (p <0.05) based on upon a Tukey's test.
Figure 6 in Salicylic acid does not mitigate salt stress on the morphophysiology and production of hydroponic melon
Figure 6. Equatorial diameter - ED (A) and polar diameter - PD (B) of fruits of 'Gaúcho' melon cultivated in a hydroponic system with different levels of electrical conductivity of the nutrient solution - ECns and exogenous application of salicylic acid. ** represents significance at 0.01 probability level.
Figure 5 in Salicylic acid does not mitigate salt stress on the morphophysiology and production of hydroponic melon
Figure 5. Fresh fruit weight - FFW (A) and soluble solids content - SS (B) of 'Gaúcho' melon fruits, as a function of the interaction between the levels of electrical conductivity of the nutrient solution - ECns and foliar application of salicylic acid. X and Y correspond to ECns and salicylic acid concentrations, respectively. * and ** represent significance at 0.05 and 0.01 probability levels, respectively.
Figure 3 in Salicylic acid does not mitigate salt stress on the morphophysiology and production of hydroponic melon
Figure 3. Internal CO 2 concentration - Ci (A) and CO2 assimilation rate - A (B) of 'Gaúcho' melon, as a function of the interaction between the levels of electrical conductivity of the nutrient solution - ECns and foliar application of salicylic acid, 56 days after transplanting. X and Y correspond to ECns and salicylic acid concentrations, respectively. * and ** represent significance at 0.05 and 0.01 probability levels, respectively.
Figure 4 in Salicylic acid does not mitigate salt stress on the morphophysiology and production of hydroponic melon
Figure 4. Intercellular electrolyte leakage - IEL (A), shoot dry biomass - SDB (B), and total dry biomass - TDB (C) of 'Gaúcho' melon as a function of the levels of electrical conductivity of the nutrient solution - ECns, 74 days after transplanting. X and Y correspond to ECns and salicylic acid concentrations, respectively. ** represent significance at 0.01 probability levels.
Figure 1 in Salicylic acid does not mitigate salt stress on the morphophysiology and production of hydroponic melon
Figure 1. Air temperature (maximum and minimum) and mean relative air humidity inside the greenhouse during the experimental period.
Figure 2 in Salicylic acid does not mitigate salt stress on the morphophysiology and production of hydroponic melon
Figure 2. Stomatal conductance - gs (A) and transpiration - E (B) of 'Gaúcho' melon, as a function of the interaction between the levels of electrical conductivity of the nutrient solution - ECns and foliar application of salicylic acid, 56 days after transplanting. X and Y correspond to ECns and salicylic acid concentrations, respectively. * and ** represent significance at 0.05 and 0.01 probability levels, respectively.
Script and Data Repository - "Future food prices will become less sensitive to agricultural market prices and mitigation costs" Chen et al.
<p>MAgPIE Model outputs and scripts for analysis of markups, based on MarkupsChen package version 1.2 available here: https://github.com/caviddhen/MarkupsChen/releases/tag/v1.2</p> <p> </p> <p> </p>
Inputs for the publication "A new solution to mitigate hydropeaking? Batteries versus re-regulation reservoirs"
<p>This file contains the main inputs for the publication "A new solution to mitigate hydropeaking? Batteries versus re-regulation reservoirs".</p>
Supplementary material for research article "Quantifying the risk mitigation efficiency of changing silvicultural systems under storm risk throughout history"
<p>This public repository contains mainly datasets generated and analyzed during the current study, closely linked to the research article "Quantifying the risk mitigation efficiency of changing silvicultural systems under storm risk throughout history". Furthermore, the repository contains additional figures and deep dives on the methodological background the research article was built on.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.