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Data and code for "Meeting U.S. Greenhouse Gas Emissions Goals with the International Air Pollution Provision of the Clean Air Act"
<p>For the files and data associated with the Yuan et al. 2022 "Meeting U.S. Greenhouse Gas Emissions Goals with the International Air Pollution Provision of the Clean Air Act"</p> <p>Description: Data/code used in energy-economic impacts and health impacts analysis.</p> <p>Directory contents:</p> <p><strong>Energy Economic Impacts</strong></p> <ul> <li><strong>Code </strong>used for producing figures and data tables <ul> <li>'paperFigs_March2022.Rmd' contains the R code used for data analysis and visualization in the paper. (<em>The code runs with R v4.0.0, RStudio v1.4.1106, and the following packages: scales_1.1.1, ggpubr_0.4.0, cowplot_1.1.0, readxl_1.3.1, here_0.1, forcats_0.5.0, stringr_1.4.0, dplyr_1.0.4, purrr_0.3.4, readr_1.3.1, tidyr_1.1.0, tibble_3.0.6, ggplot2_3.3.4, and tidyverse_1.3.0.</em>)</li> <li>'ERL_Figure4.py' contains the Python code used for generating Figure 4 in the paper</li> </ul> </li> <li><strong>Table</strong>: data tables for figures in the paper and supplementary materials</li> <li><strong>Figure</strong>: figures in the paper and supplementary materials</li> <li><strong>Data</strong>: USREP-ReEDS results and data from other sources <ul> <li>'rrpt_subset.csv' contains the portions of the ReEDS output from February 26, 2021 that are necessary to create the figures in the paper.</li> <li>'urpt_subset.csv' contains the portions of the USREP output from February 26, 2021 that are necessary to create the figures in the paper.</li> <li>'urpt_welfare_subset.csv' contains more detailed USREP welfare output from February 26, 2021.</li> <li>'cooper_pop_proj.csv' contains U.S. population projections from the University of Virginia Weldon Cooper Center for Public Service published in 2018.</li> <li>'carbon_price_comparison.csv' contains data from other recent carbon pricing studies, as described in supplementary materials G.</li> </ul> </li> </ul> <p><strong>Health Impacts</strong></p> <ul> <li><strong>analysis</strong>: <ul> <li><strong>lib</strong>: annotated code library, which loads raw data from the root data folder and conducts health impacts analysis</li> <li><strong>data</strong>: outputs <ul> <li><strong>inmap</strong>: spatial inputs/outputs for inmap</li> <li><strong>working</strong>: intermediate procssed output files</li> <li><strong>final</strong>: final health impacts results</li> </ul> </li> </ul> </li> <li><strong>data</strong>: raw data used in analysis <ul> <li><strong>working</strong>: processed intermediate raw data for faster loading in R</li> </ul> </li> </ul>
Impacts of wind power on air quality, premature mortality, and exposure disparities in the United States
<p>This repo includes supporting material for the publication:</p> <p>Qiu, M., Zigler, C. M., & Selin, N. E. (2022). Impacts of wind power on air quality, premature mortality, and exposure disparities in the United States. <em>Science Advances</em>, <em>8</em>(48), eabn8762.</p> <p>Please download and unzip the file "<a href="https://zenodo.org/api/files/bef77248-ca65-4ae6-9a92-ad00fc068665/mhqiu/wind_pollution_EJ-v1.0.zip?versionId=a9b5b8e9-273c-4431-a101-4f9a4c331675">mhqiu/wind_pollution_EJ-v1.0.zip</a>". Please see README for a full description of the sample data included in this repo. </p> <p><strong>README</strong></p> <p><strong>1. Regression results: </strong><br> <em>EGU_regression_scenario_results.xlsx </em><br> It includes regression results for each EGU in our sample (1264 EGUs in total). </p> <p><strong>2. Air quality simulation results:</strong><br> <strong>2.1 GEOS-Chem simulation</strong><br> <em>GC_daily_pm25_o3_scenarios.nc </em><br> It contains surface level annual mean PM2.5 and MDA8 O3 concentration under different emission scenarios. We include four scenarios in total:<br> - baseline scenario: air quality **without** the amount of wind power associated with 2014 RPS targets <br> - expost scenario: air quality with the wind power associated with 2014 RPS targets under the current dispatch decisions<br> - health damage minimizing scenario: air quality with the wind power associated with 2014 RPS targets under a hypothetical dispatch scenario that minimizes the health damage<br> - CO2 minimizing scenario: air quality with the wind power associated with 2014 RPS targets under a hypothetical dispatch scenario that minimizes the CO2 emissions</p> <p>Therefore, to calculate the air quality impacts of wind power under different dispatch decisions: <br> current (ex post) = ex post - baseline. <br> health damage minimizing = health damage minimizing - baseline. <br> CO2 minimizing scenario = CO2 minimizing - baseline. </p> <p><strong>2.2 InMAP simulations</strong><br> <em>InMAP/xx.shp </em><br> The shapefiles contain annual mean PM2.5 concentration simulated with InMAP under different emission scenarios. <br> baseline.shp: baseline scenario <br> ex post.shp: ex post scenario <br> health_damage_minimizing.shp: health damage minimizing scenario <br> co2_minimizing.shp: CO2 minimizing scenario</p> <p>Descriptions of the four scenarios are the same as above for GEOS-Chem.</p> <p><strong>3. County-level air quality change for different demographic groups (GEOS-Chem)</strong><br> <em>county_pm_o3_changes_by_groups_GEOS_CHEM.xlsx </em><br> This file contains changes in county-level simulated PM2.5 and O3 concentrations due to wind power under different scenarios. It also includes the total population at the county level and the population for each subgroup. This data can be used to calculate the distributional effects of air quality benefits across different population groups.</p> <p> </p> <p> </p>
Fig. 4 in Landuse Patterns, Air Quality And Bird Diversity In Urban Landscapes Of Delhi
Fig. 4. Variation in abundance of foraging guilds (%) of birds among different sampling sites.
Fig. 1 in Landuse Patterns, Air Quality And Bird Diversity In Urban Landscapes Of Delhi
Fig. 1. Map of Delhi showing location of sampling sites.
Phylogenomic resolution of the root of Panpulmonata, a hyperdiverse radiation of gastropods: new insight into the evolution of air breathing
<p class="MsoBodyText">Transitions to terrestriality have been associated with major animal radiations including land snails and slugs in Stylommatophora (>20,000 described species), the most successful lineage of 'pulmonates' (a non-monophyletic assemblage of air-breathing gastropods). However, phylogenomic studies have failed to robustly resolve relationships among traditional pulmonates and affiliated marine lineages that comprise clade Panpulmonata (Mollusca, Gastropoda), especially two key taxa: Sacoglossa, a group including photosynthetic sea slugs; and Siphonarioidea, intertidal limpet-like snails with a non-contractile pneumostome (narrow opening to a vascularized pallial cavity). To clarify the evolutionary history of the panpulmonate radiation, we performed phylogenomic analyses on datasets of up to 1,160 nuclear protein-coding genes for 110 gastropods, including 40 new transcriptomes for Sacoglossa and Siphonarioidea. All 18 analyses recovered Sacoglossa as the sister group to a clade we named Pneumopulmonata, within which Siphonarioidea was sister to the remaining lineages in most analyses. Comparative modeling indicated shifts to marginal habitat (estuarine, mangrove and intertidal zones) preceded and accelerated the evolution of a pneumostome, present in the pneumopulmonate ancestor along with a one-sided plicate gill. These findings highlight key intermediate stages in the evolution of air-breathing snails, supporting the hypothesis that adaptation to marginal zones played an important role in major sea-to-land transitions.</p>
The gridded 2-m air temperature data produced by Yao et al. (2021),the depth of major lakes in Wuhan, and and the land use/land cover data in 2000, 2010, and 2020
<p>The gridded 2-m air temperature data produced by Yao et al. (2021), the depth of major lakes in Wuhan, and the land use/land cover data in 2000, 2010, and 2020 used in the manuscript </p>
Madrid datasets (air quality, meteorological and traffic data)
<p>The datasets consist of air quality, meteorological, and traffic data from January to June 2019 and from January to June 2020. The following are the features of the datasets: Nitrogen dioxide, Wind speed, Wind direction (u component, v component, north, northeast, east, southeast, south, southwest, west, northwest), Pressure, Temperature, Humidity, Solar irradiance, Intensity, Occupancy time, Load, Average traffic speed.</p>
On-Glacier Air Temperatures for Tsanteleina Glacier, 2015
<p>Tsanteleina_Glacier_Air_Temperature_Data_2015.xlsx<br> %------------------------------------------%<br> Data Generated on 13th May 2022</p> <p>Data Curator: Dr. Thomas Shaw (Swiss Federal Institute, WSL, Switzerland)</p> <p>Data Provider(s): Dr. Thomas Shaw (Swiss Federal Institute, WSL, Switzerland) thomas.shaw@wsl.ch<br> Prof. Benjamin Brock (Northumbria University, Newcastle, UK) benjamin.brock@northumbria.ac.uk</p> <p>Data period: 19th June - 15th September, 2015</p> <p><br> Details:<br> Hourly data are generated for air temperature stations ('T-Loggers') distributed across Tsanteleina Glacier, Italy (45.4812°N, 7.0618°E).<br> Air temperatures (°C) were measured using Tinytag thermistors (accuracy +/- 0.2-0.35°C) housed in naturally ventilated Campbell MET20 / MET21 radiation shields.</p> <p>Some stations fell over at times during the summer season due to differential ablation and tripod stability.<br> Filtering of this data therefore leaves gaps (NaNs) for various stations at different times of the observation period. </p> <p>Off-Glacier air temperatures in the region can be accessed from the platform of the Regione Autonoma Valle d'Aosta:<br> https://presidi2.regione.vda.it/str_dataview_station/3060. </p> <p>Additional details can be found in the article: <br> Shaw, T. E., Brock, B. W., Ayala, A., Rutter, N., & Pellicciotti, F. (2017). <br> Centreline and cross-glacier air temperature variability on an Alpine glacier: assessing temperature distribution methods and their influence on melt model calculations. <br> Journal of Glaciology, 1–16. https://doi.org/10.1017/jog.2017.65</p> <p>Please cite the above article for any usage of the dataset.</p> <p>Data are shared and compiled as part of a wider project to estimate on-glacier air temperatures from off-glacier data<br> For more details on the 'TEMPEST' project, visit: https://tempestglacier.com/</p> <p><br> </p>
Data of the Air-Sea Momentum Flux of the Coastal Marine Boundary Layer During Typhoons
<p>Contains data on the disturbance intensity of windspeed, friction velocity, wind speed and wind direction of Typhoon Chanthu, Hato and Koppu.</p>
Data accompanying: Performance characterization of low-cost air sensors for off-grid deployment in rural Malawi
<p>Low-cost gas and particulate sensor packages offer a compact, lightweight, and easily transportable solution to address global gaps in air quality (AQ) observations. However, regions that would benefit most from widespread deployment of low-cost AQ monitors often lack the reference grade equipment required to reliably calibrate and validate them. In this study, we explore approaches to calibrating and validating three integrated sensor packages before a one year deployment to rural Malawi using collocation data collected at a regulatory site in North Carolina, USA. We compare the performance of five computational modelling approaches to calibrate the electrochemical gas sensors: k-Nearest Neighbor (kNN) hybrid, random forest (RF) hybrid, high-dimensional model representation (HDMR), multilinear regression (MLR), and quadratic regression (QR). For the CO, O<sub>x</sub>, NO, and NO2 sensors, we found that kNN hybrid models returned the highest coefficients of determination and lowest error metrics when validated. Hybrid models also were the most transferable approach when applied to deployment data collected in Malawi. We compared kNN-hybrid calibrated CO observations from two regions in Malawi to remote sensing data and found qualitative agreement in spatial and annual trends. However, ARISense monthly mean surface observations were 2 to 4 times higher than the remote sensing data, due to proximity to residential biomass combustion activity not resolved by satellite imaging. We also compared the performance of the integrated Alphasense OPC-N2 optical particle counter to a filter-corrected nephelometer using collocation data collected at one of our deployment sites in Malawi. We found the performance of the OPC-N2 varied widely with environmental conditions, with the worst performance associated with high relative humidity (RH > 70%) conditions and influence from emissions from nearby residential biomass combustion. We did not find obvious evidence of systematic sensor performance decay after the one year deployment to Malawi. Data recovery (30-80%) varied by sensor and season and was limited by insufficient power and access to resources at the remote deployment sites. Future low-cost sensor deployments to rural Sub-Saharan Africa would benefit from adaptable power systems, standardized sensor calibration methodologies, and increased regional regulatory grade monitoring infrastructure. </p>
Ground obeservation data (meteorological factors and air pollution) in Greater Bay Area, 2015 - 2021
<p>Ground observation data used in the paper <em>Development of an LSTM-Broadcasting deep-learning framework for regional air pollution forecast improvement</em>.</p>
CeA Psilocin fiber photometry study: Data and custom code from experiments looking at changes in calcium dynamics in response to an air puff events
<p>Psilocybin, and its active metabolite psilocin, have been shown to elicit rapid and long-lasting symptom improvements in a variety of affective psychiatric illnesses. However, the specific mechanisms behind these therapeutic effects remain relatively unknown. The central amygdala (CeA) is a primary output region within the extended amygdala that is heavily dysregulated in affective psychiatric disorders. Here, we utilized fiber photometry to measure changes in CeA reactivity to an aversive air puff stimulus after psilocin administration, both acutely and at varying prolonged time points. We found that administration of psilocin increased reactivity acutely in females, but not males. Additionally, we show that one dose of psilocin produced decreases in reactivity in males, but not females, 28-days post administration, with decreases seen as early as 2-days post administration. We further report stimulus-specific changes in CeA reactivity after psilocin with differential responsivity to an aversive air puff stimulus and an auditory stimulus. We also measured changes in behavioral response to the air puff stimulus and report sex differences in exploratory behavior but not threat responding or general locomotion. This study provides evidence that a single dose of psilocin can elicit sex-specific, dynamic, and enduring changes in CeA reactivity to an aversive stimulus. These data are an important step towards dissecting circuit-based mechanisms underlying the effects of psychedelics.</p>
Respirometry protocols for avian thermoregulation at high air temperatures: stepped and steady-state profiles yield similar results
<p>Relationships between air temperature (Tair) and avian body temperature (Tb), resting metabolic rate (RMR) and evaporative water loss (EWL) during acute heat exposure can be quantified through respirometry using several approaches. One involves birds exposed to a stepped series of progressively increasing Tair setpoints for short periods (< 20-30 min), whereas a second seeks to achieve steady-state conditions by exposing birds to a single Tair for longer periods (> 1-2 h). To compare these two approaches, we measured Tb, RMR and EWL over Tair = 28 C to 44 C in the dark-capped bulbul (Pycnonotus tricolor). The two protocols yielded indistinguishable values of Tb, RMR and EWL and related variables at most Tair values, revealing that both are appropriate for quantifying avian thermal physiology during heat exposure over the range of Tair in the present study. The stepped protocol, however, has several ethical and practical advantages. </p>
Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions
<p>This repo includes the GEOS-Chem simulations and R scripts that are needed to replicate and evaluate the conclusions from Qiu, Zigler, and Selin, ACP, 2022 "Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions".</p> <p><strong>The GEOS-Chem simulations</strong></p> <ul> <li>For the US (2011-2017): <ul> <li><em>observational_o3_pm_2011_2017_us.rds:</em> the simulated daily PM2.5 and O3 concentrations, and MERRA-2 meteorological features in the observational scenarios (<strong>changing</strong> meteorology, <strong>changing </strong>emissions).</li> <li><em>counterfactual_o3_pm_2011_2017_us.rds</em><em>:</em> the simulated daily PM2.5 and O3 concentrations, and MERRA-2 meteorological features in the counterfactual scenarios (<strong>constant</strong> meteorology, <strong>changing</strong> emissions).</li> <li><em>constant_emis_o3_pm_2012_2017_us.rds: </em>the simulated daily PM2.5 and O3 concentrations in the constant-emission scenarios (<strong>constant</strong> meteorology, <strong>constant</strong> emissions).</li> <li><em>regional_features_2011_2017_4x5_us.rds: </em>the MERRA-2 meteorological features in the observational scenarios (aggregated to 4x5 degrees), inputs for the "RF-regional" model.</li> </ul> </li> <li>For China (2013-2017): <ul> <li><em>observational_o3_pm_2013_2017_china.rds:</em> the simulated daily PM2.5 and O3 concentrations, and MERRA-2 meteorological features in the observational scenarios (<strong>changing</strong> meteorology, <strong>changing </strong>emissions).</li> <li><em>counterfactual_o3_pm_2013_2017_china.rds:</em> the simulated daily PM2.5 and O3 concentrations, and MERRA-2 meteorological features in the counterfactual scenarios (<strong>constant</strong> meteorology, <strong>changing</strong> emissions).</li> <li><em>constant_emis_o3_pm_2014_2017_china.rds</em><em>: </em>the simulated daily PM2.5 and O3 concentrations in the constant-emission scenarios (<strong>constant</strong> meteorology, <strong>constant</strong> emissions).</li> <li><em>regional_features_2013_2017_4x5_china.rds: </em>the MERRA-2 meteorological features in the observational scenarios (aggregated to 4x5 degrees), inputs for the "RF-regional" model.</li> </ul> </li> </ul> <p><strong>R scripts:</strong></p> <ul> <li><a href="https://zenodo.org/api/files/065be469-ef8d-4c9b-9bd8-a6a808275237/main.r">main.r</a>: the main script to perform statistical correction of meteorological variability.</li> <li>main.r uses functions from the other R script files (see below) which perform different statistical correction methods, respectively. </li> <li><a href="https://zenodo.org/api/files/065be469-ef8d-4c9b-9bd8-a6a808275237/parametric_regression_methods.r">parametric_regression_methods.r</a>: performs meteorological correction with parametric regression methods (MLR, polynomial, spline, GAM)</li> <li><a href="https://zenodo.org/api/files/065be469-ef8d-4c9b-9bd8-a6a808275237/tune_RF_regional.r">tune_RF_regional.r</a> and <a href="https://zenodo.org/api/files/065be469-ef8d-4c9b-9bd8-a6a808275237/RF_regional.r">RF_regional.r</a>: perform the meteorological correction with the "RF-regional" model</li> <li><a href="https://zenodo.org/api/files/065be469-ef8d-4c9b-9bd8-a6a808275237/GEOS_Chem_constant_emis.r">GEOS_Chem_constant_emis.r</a>: performs the meteorological correction using the simulations from the constant emission scenarios from the GEOS-Chem model</li> </ul> <p> </p>
FOD CT Data: air pockets in avocado and stone in modelling clay
<p><strong>Summary</strong></p> <p>This submission contains X-ray CT data of avocado fruits and pieces of modelling clay containing pebble stones.<br> Data for every object include binned pre-processed projections and volume segmentations.<br> These datasets can be used for training and testing deep learning methods for foreign object detection.</p> <p>The data is made available as a part of the paper "CT-based data generation for foreign object detection on a single X-ray projection".</p> <p><strong>Data acquisition</strong></p> <p>A majority of raw data for modeling clay (excluding 10 samples without pebble stones in the Test subset) is taken from the dataset<br> "A collection of 131 CT datasets of pieces of modeling clay containing stones"<br> [](https://doi.org/10.5281/zenodo.5866228)</p> <p>The remaining pieces of modeling clay and all avocado fruits were scanned at the FleX-ray laboratory<br> of the Centrum Wiskunde & Informatica (CWI) in Amsterdam, the Netherlands (details can be found in [Coban 2020]).<br> For every fruit, we made scans with significantly different amounts of air pockets by waiting for a few days between experimental acquisitions.<br> The measurements were performed with the voltage of 90 kV, power of 45 W, exposure time of 300 ms per projection, and magnification factor of 1.3.<br> The original X-ray image size was 1912 px x 1520 px with a pixel size of 75 μm, 1440 images were acquired for every sample.<br> For faster deep learning model training, images and reconstructions were downsampled with a factor of 4, leading to the effective pixel size of 300 μm and voxel size of 230 μm.<br> Additional scans of the pieces of modeling clay were acquired with settings similar to the main collection.</p> <p><strong>Data Description</strong></p> <p>The submission is split into "Avocado" and "Playdoh" (pieces of modeling clay) datasets. Each dataset is further split into Training and Test subsets.</p> <p>The folder for every scanned object contains<br> - ./log/ - subfolder with logarithmed X-ray projections after darkfield and flatfield correction.<br> - ./segm/ - subfolder with slices of the segmented volume.<br> - ./scan settings.txt - a file with scanner metadata containing scan geometry<br> - ./volume_info.csv - a file with a voxel count for every class in the segmentation.</p> <p>For playdoh objects, the segmentation classes are modeling clay (Class 1) and pebble stone (Class 2). In this case, pebble stones are foreign objects.</p> <p>For avocado objects, the segmentation classes are peel (Class 1), avocado meat (Class 2), seed (Class 3) and air pockets (Class 4). Air pockets are considered a foreign object.</p> <p><strong>Additional Links</strong></p> <p>These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI). For any relevant Python/MATLAB scripts for the FleX-ray datasets, we refer the reader to our group's GitHub page.</p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please get in touch with<br> - vladyslav.andriiashen [at] cwi.nl</p> <p><strong>References</strong></p> <p>[Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p>
PM10, SO2, and NO2 Ambient Air Quality Monitoring Data from India's National Ambient Monitoring Program (NAMP) 2011-2015
<p>India's Central Pollution Control Board (CPCB) operates and maintains the National Ambient Monitoring Program (<a href="https://cpcb.nic.in/about-namp/">NAMP</a>) which includes both continuous and manual ambient monitoring stations. This dataset is a collation of manual monitoring data by day for years 2011, 2012, 2013, 2014, and 2015 for PM10, SO2, and NO2. These stations collect for a maximum of 104 days in a year. This cleaned dataset was utilized for understanding trends and conducting comparisons with modeled concentrations under the APnA city program, published <a href="https://doi.org/10.1016/j.uclim.2018.11.005">here</a> (<a href="https://doi.org/10.1016/j.uclim.2018.11.005">Urban Climate, 2019</a>).<br> <br> Data format - year, month, day, SO2, NO2, PM10, Stn Code, State, City<br> All units - micro-gm/m3 (ug/m3)</p> <p>Official annual summary reports (PDFs) are available <a href="https://cpcb.nic.in/namp-data/">here</a>.</p> <p>For guidelines for ambient and emissions monitoring, summaries of available data, and other resources on monitoring in India, visit <a href="https://urbanemissions.info/resources-energy-emissions-analysis-in-india/#monitoring">https://urbanemissions.info/resources-energy-emissions-analysis-in-india</a></p>
GNSS and Digisonde sporadic-E maps produced by Air Force Institute of Technology
<p>Spatial maps of sporadic-E are produced by combining COSMIC-2 RO data with Digisonde measurements, using the <em>S<sub>4</sub></em>-based approach described by Carmona et al. (2022). Images from the TEC-based approach used in other studies are also included for reference. </p> <p> </p> <p>Carmona, R. A., Nava, O. A., Dao, E. V., & Emmons, D. J. (2022). A Comparison of Sporadic-E Occurrence Rates Using GPS Radio Occultation and Ionosonde Measurements. <em>Remote Sensing</em>, <em>14</em>(3), 581.</p> <p> </p>
Effects of Reanalysis Forcing Fields on Ozone Trends and Age of Air from a Chemical Transport Model
<p>This dataset is based on the global off-line 3-D chenmical transport model (TOMCAT/SLIMCAT) forced with ECMWF reanalyses (ERA-Interim and ERA5) to compare the performance of the stratospheric ozone simulations. Each field is separately saved as NETCDF file. Each field is show on geographic coordinates, which can be longitude, latitude, vertical hybrid-pressure level (for zonal mean fields, such as ozone, temperature and age-of-air).</p> <p>The dimensions in each field are:</p> <p>lat --> latitude</p> <p>lon --> longitude</p> <p>lev --> hydrid pressure level</p> <p>time --> months of the simulation</p> <p>The output of the total column ozone from the TOMCAT/SLIMCAT simulations forced with ERA-Interim and ERA5 for Figures 1-4 and Figure S2 in the supplement are in files:</p> <p>toz_A_ERAI.nc</p> <p>toz_B_ERA5.nc</p> <p>The output of the stratospheric column ozone (SCO) in Figure S1 in the supplement are in the file (levels1-3 are SWOOSH, B_ERA5 and A_ERAI SCO data, respectively):</p> <p>sco_SWOOSH_A_ERAI_B_ERA5.nc</p> <p>The output of zonal mean ozone profiles from the TOMCAT/SLIMCAT simulations forced with ERA-Interim and ERA5 for Figures 5-7, 9 and Figures S3-4 are in files:</p> <p>O3_mm_A_ERAI.nc</p> <p>O3_mm_B_ERA5.nc</p> <p>The output of zonal mean temperature from the TOMCAT/SLIMCAT simulations forced with ERA-Interim and ERA5 for Figure 8 are in files:</p> <p>te_mm_A_ERAI.nc</p> <p>te_mm_B_ERA5.nc</p> <p>The output of zonal mean age-of-air from the TOMCAT/SLIMCAT simulations forced with ERA-Interim and ERA5 for Figures 10-12 are in files:</p> <p>Age_mm_A_ERAI.nc</p> <p>Age_mm_B_ERA5.nc</p> <p>The output of the zonal mean ozone, temperature and age-of-air from the ERA5.1 reanalysis corrected simulations during the period from 2000 to 2006 in all Figures above using ERA5 are in files:</p> <p>ERA5_1_O3_2000_18.nc</p> <p>ERA5_1_te_2000_18.nc</p> <p>ERA5_1_Age_2000_18.nc</p> <p> </p>
Dataset of the air CO2 surveys performed at Vulcano from August 2020 to November 2021
<p>The dataset includes the stable isotope compositions of the air CO2 and the CO2 concentration in the air at Vulcano, Aeolian Island, Italy, from August 2020 to November 2021.</p> <p>Author Contributions: Conceptualization, Roberto M.R. Di Martino; methodology, Roberto M.R. Di Martino; investigation, Roberto M.R. Di Martino, and Sergio Gurrieri; resources, Roberto M.R. Di Martino, and Sergio Gurrieri; data curation, Roberto M.R. Di Martino, and Sergio Gurrieri</p>
Importance of high-latitude sources for ice-nucleating particles in cold-air outbreaks and the implications for cloud-phase feedback
<p>This dataset contains FLEXPART results and codes for data analysis and visualization used in the MRes research project. </p> <p>Numpy arrays for FLEXPART data are in "data_flexpart.zip" with longitude and latitude coordination files.</p> <p>Codes for data analysis and visualization are in "ipynb.zip". Codes were written in Python language. Codes are categorized by figures in the article. </p>
ScienceDex guides
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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.