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1,444 results for “Mitigation”
Gallic Acid Pretreatment Mitigates Parathyroid Ischemia-Reperfusion Injury through Signaling Pathway Modulation
GEO Series GSE240162. Oryctolagus cuniculus. 9 samples. Type: Expression profiling by high throughput sequencing.
Targeting AEBP1 to mitigate pro-tumor activity of cancer-associated fibroblasts and increase therapeutic efficacy to anti-PD-1
GEO Series GSE301078. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Data from paper: Large carbon sink potential of Secondary Forests in Brazilian Amazon to mitigate climate change
<p><strong>Title</strong>: Large carbon sink potential of Secondary Forests in the Brazilian Amazon to mitigate climate change</p> <p><strong>Contact:</strong> Viola Heinrich (viola.heinrich@bristol.ac.uk)</p> <p><strong>This repository contains</strong>:</p> <ol> <li>Zipped folder:<strong> Fig1_data_input.zip</strong> - all the files needed to produce Figure 1a-e of the main paper. Set the working directory to folder containing the file and use the script "Fig1_analysis_all_variables_asAGC.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 1 - these files are in the format "<strong><driver>_assessment_v2.csv</strong>". The columns in the files are: A: age of secondary forest; B: 50th percentile (median) of the modal Aboveground Biomass (AGB) value for the given age (note, units are in biomass not carbon: Mg/ha/yr); C: The bias-corrected AGB value, calculated by subtracting the lowest AGB value in column B such that the AGB data starts at or near 0Mg/ha/yr at age 1. D: the number of secondary forest pixels observed to have the given age, E: "Threshold" : the threshold limits of the given driver e.g. 0 Fires in fire_assessmentv2.csv implies the corresponding secondary forest pixels experienced 0 fires throughout the analysis period. </li> <li>Zipped folder:<strong> Fig1_confidence_intervals.zip</strong> - all the files need to produce the confidence intervals seen in Figure 1a-e of the main paper: units are in MgC/ha/yr as they appear in the Figure. column A: lower limit; B: upper limit</li> <li>Zipped folder:<strong> Fig2_regions_outline.zip</strong> - contains the boundaries of the 4 regions identified in Figure 2a of the main paper in a shapefile (.shp) format and the corresponding file formats needed to produce and load a shapefile. </li> <li>Zipped folder: <strong>Fig1g_2b_e_variable_importance.zip</strong> - contains the output files of the random forest analysis assessing the variable importance for the whole Amazon ("whole_Amazon" subfolder) and for the different regions identified in Figure2a. Files are given as .RDS files that can be loaded in R and the corresponding figures produced using the script "Fig1g_2b_e_variable_importance.R". Files start with the region of interest e.g. "whole_Amazon" or "NE_sector". Middle part of the filename - importance_conditionalTrue/False - this determines whether the importance was calculated using the conditional permutation (True) or not (False). The end of the file name - seed<NUM> - denotes the number of the random seed that was set to extract the sample data. e.g. whole_Amazon_2500_cforest_important_conditionalTrue_seed200.RDS - shows the conditional permutation importance assessment using a sample size of 2500 when the setseed parameter was set to 200 to extract a random sample representing the whole Amazon. The remaining files include the sample data used to build the random forest model at each iteration - as a .csv file and the random forest output - as .RDS file. Please note the code to produce the random forest model and the importance assessment has not been included here - this code takes multiple days to run, so only the input and outputs have been included here. Please contact the corresponding author (see end) for more information on this. </li> <li>Zipped folder: <strong>Fig3_data_input.zip</strong> - all the files needed to produce Figure 3a-d of the main paper. Set the working directory to folder containing the file and use the script "Fig3_analysis_byAllRegions_asAGC.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 3 - these files are in the format "<strong><REGION>-Group.csv</strong>". See bullet point 1 for explanations for the columns in the file. Again column E -"threshold" denotes the code used to identify the the 4 subclasses of regrowth seen in the Figure. Where 11 = No disturbance; 12 = Only burning; 21 = Only (multiple) deforestations; 22 = Both burning and multiple deforestations as disturbance. The folder also contains another set of files "<strong><REGION>_whole_class.csv" </strong>these files do not distinguish disturbance and can be used to model the regrowth for the whole region (this is not shown in any of the Figures). The code takes data in AGB and converts to AGC.</li> <li>Zipped folder:<strong> Fig3_confidence_intervals.zip</strong> - all the files needed to produce the confidence intervals seen in Figure 3a-d of the main paper. These filenames are in the format <region>_number of the region_<number referring to the disturbance combination>_confidence_interval_asAGC.csv. Where the number of the region: 1 - SW; 2 - SE; 3 - NW; 4 - NE. Where the disturbance combination: 1 - No disturbance; 2 - Only fire disturbance; 3 - Only deforestation disturbance; 4 - Both disturbances. so the file NE_4_1_confidence_interval_as_AGC.csv, contains the confidence intervals of the regrowth model in the NE sector of the Amazon under No disturbance. " Units are in MgC/ha/yr as they appear in the Figure. column A: lower limit; B: upper limit. </li> <li> Zipped folder: <strong>Fig4_5_carbon_sink_2017.zip </strong>- Contains two subfolders: a) <strong>Map_aggre_0.1deg</strong> -this folder contains .tiff files (and associated files) of the losses, gains and net change in AGC between 2016 - 2017 in secondary forests in Amazonia - this has been aggregated to 0.1 degree grid cells so each cell contains the total sum of the losses/gains experienced by secondary forests in that 0.1degree grid cell. b) <strong>secondary_forest_by_region_and_disturbance </strong>- this folder contains .tiff files (and associated files) of the secondary forest data at the original resolution (30m) for 2016 and 2017 split up according to the regions identified in Figure 2, and the type of disturbance (if any). The associated files include a .dbf file which includes additional data [read "README.txt" file in folder] - upon loading the data in a GIS software - the age of the secondary forest pixel will be displayed - open the attribute table to see more data associated with that given pixel e.g. modelled associated AGB for a given pixel. Files in this folder can be used to make Figure 4d and Figure 5 - see script "Fig4_Fig5_analysis.R" in the code repository (see below). </li> </ol> <p><strong>Code: </strong>The corresponding code mentioned here can be access here: <a href="https://github.com/heinrichTrees/secondary-forest-amazonia-regrowth">heinrichTrees/secondary-forest-amazonia-regrowth: This repository contains the code used to produce data shown in Heinrich et al. (github.com)</a> </p> <p><strong>Data usage: </strong>When using any code or data in this repository or another related to this study please cite Heinrich et al.2021 and the original paper. </p> <p>If you need anything else, please contact the corresponding author: Viola Heinrich (viola.heinrich@bristol.ac.uk)</p>
Capacity of % reduction conductivity from halophytes to mitigate salinity intrusion
<p>Results from the experiment carried out in an-open air laboratory in Barcelona, Spain (Urban River Lab) in 12 channels where an halophyte specie (<em>Atriplex portulacoides</em>) was analyzed for their capacity to uptake salt in different salinity conditions. </p>
Code and data used in "Designing marine reserve networks to mitigate larval dispersal volatility with the connectivity portfolio effect"
<p>This repository contains the code and data used in "Designing marine reserve networks to mitigate larval dispersal volatility with the connectivity portfolio effect".</p> <p>The files have been placed inside a .rar to maintain the file structure necessary to run Marxan correctly.</p> <p>The main file to run the analyses is "Comparing spatial conservation prioritisation run.R" which uses functions from "Comparing spatial conservation prioritisation functions.R".</p> <p>The main input files are located in data01.Rdata, data02.Rdata, and data03.Rdata, which contain 1. a data frame of planning unit centroids, sizes, and amount of reef habitat, 2. a subset of planning unit data as a matrix, 3-14. migration, probability, and flow larval dispersal matrices, 15-18. larval release dates, 19-34 indices of Oceanic nino index (ONI), El Nino Modoki index (MOD), Dipole Mode Index (DMI), and Pacific Decadal Oscillation (PDO) of coral trout, snapper, rabbitfish, and octopus.</p> <p> </p>
Data for climate mitigation scenarios with persistent COVID-19 related energy demand changes
<p>This repository contains data for the main text figures plus some supplementary figures in the article:<br> Kikstra et al 2021 Nat. Energy. DOI: <a href="https://doi.org/10.1038/s41560-021-00904-8">10.1038/s41560-021-00904-8</a></p> <p>This dataset should be cited as: Kikstra et al. (2021). Data for climate mitigation scenarios with persistent COVID-19 related energy demand changes. DOI: <a href="https://doi.org/10.5281/zenodo.5211169">10.5281/zenodo.5211169</a></p> <p>In order to reproduce the figures, one needs to use the script that is available on GitHub at:<br> <a href="https://github.com/iiasa/covid-energy-demand-scenarios">https://github.com/iiasa/covid-energy-demand-scenarios</a></p> <p>The most accessible way of exploring the scenario data behind this article would be to go to <a href="https://data.ece.iiasa.ac.at/engage/#/workspaces/60">https://data.ece.iiasa.ac.at/engage/#/workspaces/60</a>.<br> This goes to a web tool hosted by the International Institute of Applied Systems Analysis (IIASA) which provides access to a database of these and more variables of interest, defined for each scenario on the detail of MESSAGE regions, with a few example workspaces available within the ENGAGE Scenario Explorer.<br> The Scenario Explorer is a versatile open access tool to browse, visualize and download data and results. Users can freely create a private workspace where customized plots can be saved and shared.<br> For tutorials on how to use the Scenario Explorer, please visit <a href="https://software.ece.iiasa.ac.at/ixmp-server/tutorials.html">https://software.ece.iiasa.ac.at/ixmp-server/tutorials.html</a>.</p> <p>The scenarios that were used for the IPCC Special Report on 1.5C warming (SR1.5) have been made available at <a href="https://data.ece.iiasa.ac.at/iamc-1.5c-explorer/">https://data.ece.iiasa.ac.at/iamc-1.5c-explorer/</a>.</p> <p>The data is available for download at the <a href="https://data.ece.iiasa.ac.at/engage/">ENGAGE Scenario Explorer</a>. The license permits use of the scenario ensemble for scientific research and science communication, but restricts redistribution of substantial parts of the data. Please refer to the FAQ and <a href="https://data.ece.iiasa.ac.at/engage/#/license">legal code</a> for more information.</p>
Screening tests for N sorption allow to select and engineer biochars for N mitigation during biomass processing
<p>Csv and xlsx files:</p> <ul> <li>Biochar_characteristics: Biochar characteristics including chemical analyses, NH3 and NH4 sorption. </li> <li>Ammonium_sorption_treatments: Effect of treatment on NH4 sorption + difference in NH4 sorption of feedstock and biochars made from it</li> <li>Ammonia_sorption_treatments: Effect of treatment on NH3 sorption</li> </ul> <p>Metadata can be found in the corresponding txt. files. </p> <p>R-scripts:</p> <ul> <li>Gasmetingen_purebiochars_2021_NH3: calculation of the NH3 sorption</li> <li>NH3_treatments: Effect of treatment on NH3 sorption</li> <li>ARF_treatments: Effect of treatment on NH4 sorption</li> <li>ARF: First data analysis (correlation)</li> <li>ARF en NH3 sorptie ggplot: figures linear relationship between NH3 or NH4 sorption with chemical parameters</li> </ul>
DRP Y1 Previous Permafrost Mitigations Shapefile and Data
<p>Shapefile and associated csv of the Y1 deliverable mapping the locations of previous attempts to mitigate permafrost in Alaska</p>
Prometra's Utilization in Mitigating Pain II
ClinicalTrials.gov study NCT00866164. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Melatonin Enhances Vitrified Mouse Blastocyst Development by Mitigating Oxidative Stress and Preserving Mitochondrial Function
GEO Series GSE306812. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Gut Commensal Parabacteroides goldsteinii mitigates LRRK2 Parkinsonism through reducing neuroinflammation and multifaceted effects on gut homeostasis [ST]
GEO Series GSE283669. Mus musculus. 4 samples. Type: Other.
Inhibition of transglutaminase 2 mitigates transcriptional dysregulation in models of Huntington disease
GEO Series GSE21237. Mus musculus. 23 samples. Type: Expression profiling by array.
Sirtuin 6 mitigates thoracic aortic aneurysm progression via maintenance of mitochondria homeostasis in vascular smooth muscle cells
GEO Series GSE290902. Mus musculus. 19 samples. Type: Expression profiling by high throughput sequencing.
Neuronal CD47 Induces Microglial Phagocytosis Defects and Autism-like Behaviors While Mitigating Aβ-Induced Synaptic Loss
GEO Series GSE263398. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
IFIT1 Knockdown Mitigates Translational Suppression, Enhancing Myogenic Differentiation and Revitalizing Mesenchymal Stem Cell Therapy for Myocardial Infarction [II]
GEO Series GSE286132. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
A ketogenic diet can mitigate SARS-CoV-2 induced systemic reprogramming and inflammation
GEO Series GSE243426. Mus musculus. 54 samples. Type: Expression profiling by high throughput sequencing.
Thyroid Hormone T4 Mitigates Traumatic Brain Injury by Dynamically Remodeling Cell Type Specific Genes, Pathways, and Networks in Hippocampus and Frontal Cortex
GEO Series GSE247339. Mus musculus. 41 samples. Type: Expression profiling by high throughput sequencing.
Reversing lysosome-mTORC1-ribosome axis dysregulations to mitigate C9FTD/ALS neurodegeneration
GEO Series GSE206666. Mus musculus. 13 samples. Type: Expression profiling by high throughput sequencing.
Solithromycin mitigates Prevotella intermedia–induced exacerbation of MRSA ventilator-associated pneumonia by modulating hyperinflammatory responses and enhancing alveolar macrophage function
GEO Series GSE307943. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.
miR-17/20a mitigates interstitial fibrosis by repressing Frmd6 expression in renal epithelia [PAR-CLIP]
GEO Series GSE254785. Homo sapiens. 6 samples. Type: Other.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.