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opencc-by-4.0May 2024View details →

A catalogue of impact craters with diameters larger than 200 m in the Chang'e-6 landing area

Chang'e-6 (CE-6) is the first sample-return mission from the lunar farside and will be launched in May of 2024. The landing area is in the south of Apollo basin inside the South Pole Aitken basin. Statistics and analyses of impact craters in the landing area are essential to support safe landing and geologic studies. This dataset is craters with diamters larger than 200m in the 134 km × 246 km landing area. The craters are extracted by an automated method and checked manually.

opencc-by-4.0May 2024View details →

Combined data file for Jokinen et al. "Terrestrial organic matter input drives sedimentary trace metal sequestration in a human-impacted boreal estuary", Science of the Total Environment 717, 2020

The datafile contains all the new raw data presented in the figures in the publication.

Constructing visualization tools and training resources to assess climate impacts on the channel islands national marine sanctuary NetCDF files

The Channel Islands Marine Sanctuary (CINMS) comprises 1,470 square miles surrounding the Northern Channel Islands: Anacapa, Santa Cruz, Santa Rosa, San Miguel, and Santa Barbara, protecting various species and habitats. However, these sensitive habitats are highly susceptible to climate-driven 'shock' events which are associated with extreme values of temperature, pH, or ocean nutrient levels. A particularly devastating example was seen in 2014-16, when extreme temperatures and changes in nutrient conditions off the California coast led to large-scale die-offs of marine organisms. Global climate models are the best tool available to predict how these shocks may respond to climate change. To better understand the drivers and statistics of climate-driven ecosystem shocks, a 'large ensemble' of simulations run with multiple climate models will be used. The objective of this project is to develop a Python-based web application to visualize ecologically significant climate variables near the CINMS. The web application will be used by researchers from the University of California, Santa Barbara (UCSB) to analyze climate model output, and by CINMS staff to develop new indicators of shocks to marine ecosystems.

opencc-by-4.0Feb 2024View details →

Replication Package for Understanding the Impact of APIs Behavioral Breaking Changes on Client Applications

This repository contains the replication package for the paper Understanding the Impact of APIs Behavioral Breaking Changes on Client Applications. This paper will be published in the Proceedings of the ACM on Software Engineering journal. The replication package includes the scripts and data we extracted, leading us to our findings.

opencc-by-4.0Sep 2021View details →

Data and Code for "Climate impacts and adaptation in US dairy systems 1981-2018"

This data and code archive provides all the files that are necessary to replicate the empirical analyses that are presented in the paper "Climate impacts and adaptation in US dairy systems 1981-2018" authored by Maria Gisbert-Queral, Arne Henningsen, Bo Markussen, Meredith T. Niles, Ermias Kebreab, Angela J. Rigden, and Nathaniel D. Mueller and published in 'Nature Food' (2021, DOI: <a href="https://doi.org/10.1038/s43016-021-00372-z">10.1038/s43016-021-00372-z</a>). The empirical analyses are entirely conducted with the "R" statistical software using the add-on packages "car", "data.table", "dplyr", "ggplot2", "grid", "gridExtra", "lmtest", "lubridate", "magrittr", "nlme", "OneR", "plyr", "pracma", "quadprog", "readxl", "sandwich", "tidyr", "usfertilizer", and "usmap". The R code was written by Maria Gisbert-Queral and Arne Henningsen with assistance from Bo Markussen. Some parts of the data preparation and the analyses require substantial amounts of memory (RAM) and computational power (CPU). Running the entire analysis (all R scripts consecutively) on a laptop computer with 32 GB physical memory (RAM), 16 GB swap memory, an 8-core Intel Xeon CPU E3-1505M @ 3.00 GHz, and a GNU/Linux/Ubuntu operating system takes around 11 hours. Running some parts in parallel can speed up the computations but bears the risk that the computations terminate when two or more memory-demanding computations are executed at the same time. This data and code archive contains the following files and folders: * README Description: text file with this description * flowchart.pdf Description: a PDF file with a flow chart that illustrates how R scripts transform the raw data files to files that contain generated data sets and intermediate results and, finally, to the tables and figures that are presented in the paper. * runAll.sh Description: a (bash) shell script that runs all R scripts in this data and code archive sequentially and in a suitable order (on computers with a "bash" shell such as most computers with MacOS, GNU/Linux, or Unix operating systems) * Folder "DataRaw" Description: folder for raw data files This folder contains the following files: - DataRaw/COWS.xlsx Description: MS-Excel file with the number of cows per county Source: USDA NASS Quickstats Observations: All available counties and years from 2002 to 2012 - DataRaw/milk_state.xlsx Description: MS-Excel file with average monthly milk yields per cow Source: USDA NASS Quickstats Observations: All available states from 1981 to 2018 - DataRaw/TMAX.csv Description: CSV file with daily maximum temperatures Source: PRISM Climate Group (spatially averaged) Observations: All counties from 1981 to 2018 - DataRaw/VPD.csv Description: CSV file with daily maximum vapor pressure deficits Source: PRISM Climate Group (spatially averaged) Observations: All counties from 1981 to 2018 - DataRaw/countynamesandID.csv Description: CSV file with county names, state FIPS codes, and county FIPS codes Source: US Census Bureau Observations: All counties - DataRaw/statecentroids.csv Descriptions: CSV file with latitudes and longitudes of state centroids Source: Generated by Nathan Mueller from Matlab state shapefiles using the Matlab "centroid" function Observations: All states * Folder "DataGenerated" Description: folder for data sets that are generated by the R scripts in this data and code archive. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these generated data files so that parts of the analysis can be replicated (e.g., on computers with insufficient memory to run all parts of the analysis). * Folder "Results" Description: folder for intermediate results that are generated by the R scripts in this data and code archive. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these intermediate results so that parts of the analysis can be replicated (e.g., on computers with insufficient memory to run all parts of the analysis). * Folder "Figures" Description: folder for the figures that are generated by the R scripts in this data and code archive and that are presented in our paper. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these figures so that people who replicate our analysis can more easily compare the figures that they get with the figures that are presented in our paper. Additionally, this folder contains CSV files with the data that are required to reproduce the figures. * Folder "Tables" Description: folder for the tables that are generated by the R scripts in this data and code archive and that are presented in our paper. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these tables so that people who replicate our analysis can more easily compare the tables that they get with the tables that are presented in our paper. * Folder "logFiles" Description: the shell script runAll.sh writes the output of each R script that it runs into this folder. We provide these log files so that people who replicate our analysis can more easily compare the R output that they get with the R output that we got. * PrepareCowsData.R Description: R script that imports the raw data set COWS.xlsx and prepares it for the further analyses * PrepareWeatherData.R Description: R script that imports the raw data sets TMAX.csv, VPD.csv, and countynamesandID.csv, merges these three data sets, and prepares the data for the further analyses * PrepareMilkData.R Description: R script that imports the raw data set milk_state.xlsx and prepares it for the further analyses * CalcFrequenciesTHI_Temp.R Description: R script that calculates the frequencies of days with the different THI bins and the different temperature bins in each month for each state * CalcAvgTHI.R Description: R script that calculates the average THI in each state * PreparePanelTHI.R Description: R script that creates a state-month panel/longitudinal data set with exposure to the different THI bins * PreparePanelTemp.R Description: R script that creates a state-month panel/longitudinal data set with exposure to the different temperature bins * PreparePanelFinal.R Description: R script that creates the state-month panel/longitudinal data set with all variables (e.g., THI bins, temperature bins, milk yield) that are used in our statistical analyses * EstimateTrendsTHI.R Description: R script that estimates the trends of the frequencies of the different THI bins within our sampling period for each state in our data set * EstimateModels.R Description: R script that estimates all model specifications that are used for generating results that are presented in the paper or for comparing or testing different model specifications * CalcCoefStateYear.R Description: R script that calculates the effects of each THI bin on the milk yield for all combinations of states and years based on our 'final' model specification * SearchWeightMonths.R Description: R script that estimates our 'final' model specification with different values of the weight of the temporal component relative to the weight of the spatial component in the temporally and spatially correlated error term * TestModelSpec.R Description: R script that applies Wald tests and Likelihood-Ratio tests to compare different model specifications and creates Table S10 * CreateFigure1a.R Description: R script that creates subfigure a of Figure 1 * CreateFigure1b.R Description: R script that creates subfigure b of Figure 1 * CreateFigure2a.R Description: R script that creates subfigure a of Figure 2 * CreateFigure2b.R Description: R script that creates subfigure b of Figure 2 * CreateFigure2c.R Description: R script that creates subfigure c of Figure 2 * CreateFigure3.R Description: R script that creates the subfigures of Figure 3 * CreateFigure4.R Description: R script that creates the subfigures of Figure 4 * CreateFigure5_TableS6.R Description: R script that creates the subfigures of Figure 5 and Table S6 * CreateFigureS1.R Description: R script that creates Figure S1 * CreateFigureS2.R Description: R script that creates Figure S2 * CreateTableS2_S3_S7.R Description: R script that creates Tables S2, S3, and S7 * CreateTableS4_S5.R Description: R script that creates Tables S4 and S5 * CreateTableS8.R Description: R script that creates Table S8 * CreateTableS9.R Description: R script that creates Table S9

Data from: Severity of topsoil compaction controls the impact of skid trails on soil ecological processes

Skid trails are a major management-induced disturbance in temperate forest ecosystems with considerable impact on soil ecological processes that are so far poorly understood. In German forests, skid trails comprise 10 – 20 % of the forest area that is potentially affected by soil compaction through heavy machinery. We systematically investigated the influence of skid trails on physical, chemical, and microbiological soil parameters at 84 paired plots across four Central European forest types. In low mountain forests with steeper topography, skid trails had more drastic effects than in lowland forests. Skid trails in low mountain areas showed a decrease in the C to N ratio of microbial biomass (MBC/MBN), as well as increased microbial (MBC/SOC) and enzyme activities leading to faster carbon turnover (lower C/N, EOC/EN) and increased CO2 losses (CO2/SOC) from the soil. The overall effects of the skid trails in lowland forests were small. On base-poor soils, we found an increase in the MBC/MBN ratio, while skid trails in base-rich lowland soils showed a reduction in CO2/SOC, suggesting a proportional increase in soil carbon storage. Regardless of region-specific effects, the relative increase in the bulk density of the fine soil was identified as a 'golden trait' that determined the effects of skid trails on many soil parameters, as shown by negative correlations with SOC, N, MBC, MBN, MBP, MBC/SOC and CO2/SOC and positive ones with the activities of certain hydrolytic enzymes. Synthesis and Applications: Our data clearly showed that carbon conversion processes and soil respiration leading to significant carbon and nutrient losses increased significantly on skid trails in low mountain regions with relatively steep slopes, which was in sharp contrast to lowland sites. The strong context dependence of our findings suggests that the mapping of soil conditions in terms of slope, substrate and moisture with high spatial resolution is mandatory to assess the vulnerability of sites to soil compaction by heavy machinery. Based on such vulnerability analysis, negative impacts can be minimized through the designation of permanently fixed skid trails, the technical adaptation of vehicles (e.g., wide base tyres) as well as careful planning and timing of management operations that should be restricted to dry weather and soil moisture conditions or periods of frost.

Assessing the Impact of Supply-Side Policies on Oil Extraction, Health, and Employment in California

California's ambitious goal to slash GHG emissions by 90% by 2045 marks a significant shift towards sustainability. Supply-side policies, such as Senate Bill 1137, which bans new oil and gas wells within 3,200 feet of sensitive areas, signal a commitment to environmental and public health protection. To gauge SB 1137's impact accurately, the existing model must be adapted to incorporate this setback distance. This capstone project aims to bridge this gap by updating the model and creating accessible educational materials for Californians. Objectives include updating the model, predicting well locations and oil extraction using machine learning, and developing a public online app with R Shiny. The MEDS capstone group will investigate the effects of the 3,200-foot setback distance on emissions, employment, and health, contributing to the evidence supporting SB 1137.

Coupled PPE model output - land parameter impacts on the mean climate state

Terrestrial processes influence the atmosphere by controlling land-to-atmosphere fluxes of energy, water, and carbon. Prior research has demonstrated that parameter uncertainty drives uncertainty in land surface fluxes. However, the influence of land process uncertainty on the climate system remains underexplored. Here, we quantify how assumptions about land processes impact climate using a perturbed parameter ensemble for 18 land parameters in the Community Earth System Model (CESM2) under preindustrial conditions. We find that an observationally-informed range of land parameters generate biogeophysical feedbacks that significantly influence the mean climate state, largely by modifying evapotranspiration. Global mean land surface temperature ranges by 2.2°C across our ensemble (standard deviation = 0.5°C) and precipitation changes were significant and spatially variable. Our analysis demonstrates that the impacts of land parameter uncertainty on surface fluxes propagates to the entire Earth system, and provides insights into where and how land process uncertainty influences climate.

Impact of low pH on glutamine incorporation in CD8+ T cells

OT-I CTLs were cultured for 4 hours with 13C-glutamine in the presence, or absence, of IL-2 at pH7.4 or pH6.6. Metabolites that incorporated isotopic glutamine were identified by HILIC-HRMS.

Fig. 4 in Very severe cyclonic storm "Gaja" and its impact off the Tamil Nadu coastline

Fig. 4 — Extent of damages documented off the Nagapattinam coast, Tamil Nadu

Fig. 3 in Very severe cyclonic storm "Gaja" and its impact off the Tamil Nadu coastline

Fig. 3 — Cyclone shelters in the villages showing distance to access

Fig. 6 in Very severe cyclonic storm "Gaja" and its impact off the Tamil Nadu coastline

Fig. 6 — Periodicity and usage frequency of the weather forecast

Fig. 5 in Very severe cyclonic storm "Gaja" and its impact off the Tamil Nadu coastline

Fig. 5 — Percentage variations in respondents using different source of forecast

Fig. 1 in Very severe cyclonic storm "Gaja" and its impact off the Tamil Nadu coastline

Fig. 1 — Study area – Nagapattinam coast. Source: Central ground water board, Tamil Nadu

Fig. 2 — a in Very severe cyclonic storm "Gaja" and its impact off the Tamil Nadu coastline

Fig. 2 — a) Percentage variability of total fishing population; and b) affected individuals

Output data for: Flammable Futures – Storylines of climatic impacts on wildfire events and palm oil plantations in Indonesia

This repository contains the output data associated with the publication "Flammable Futures – Storylines of climatic impacts on wildfire events and palm oil plantations in Indonesia". It contains the FLAM modeled burned area and the GLOBIOM output, as well as the a downscaling grid. Descriptions of the results can be found in the publication (DOI will follow).

Impact of Freeze-thaw Cycles on Coatings Applied to the Surface of Alkali-activated Materials

This paper focuses on the possibility of applying selected types of coatings (synthetic and epoxy) on the surface of alkali-activated materials based on finely ground granulated blast furnace slag with an admixture of cement by-pass dust and silica fly ash. Admixtures represent 30% of the binder component (15% fly ash, 15% cement by-pass dust). The mixture is activated with anhydrous disodium metasilicate. The samples were categorized into two series samples stored in water and samples wrapped in foil. Following this, the surface of these two series was modified by two methods, namely brushing with a steel brush and roughening with a diamond wheel. The properties related to the adhesion of the coating to the surface were mainly investigated before and after 100 freeze-thaw cycles. The cross-cut method and pull-off test for adhesion determined the adhesion. The cross-cut test found that the synthetic coating was less susceptible to surface modification than the epoxy coating. The samples were exposed to 100 freeze-thaw cycles. Then, the cross-cut method was applied to the coated roughened surface and the results of this test were classified into category 3. During the determination of the adhesion of the coatings by the pull-off test on the roughened surface for both methods of sample storage, the character of breakage for the synthetic coating was of the cohesive type, thus it was the tensile strength of the materials, while for the epoxy coating, it was the adhesive breakage, thus it was the adhesion between the surface and the coating.

Infection of mice by the enteroaggregative E. coli strain 042 and two mutant derivatives overexpressing virulence factors: impact on disease markers, gut microbiota and concentration of SCFAs in feces

This dataset contains raw sequencing data from the microbiota analysis conducted in the enteroaggregative E. coli strain 042 study. The data includes FASTQ files generated from Illumina sequencing, along with metadata describing the sample collection and processing methods.

Impact of Ambient Temperature on Light-duty Gasoline Vehicle Fuel Consumption under Real-World Driving Conditions

This dataset includes the vehicle operating (speed, acceleration), fuel consumption rate, and weather condition (temperature, pressure, humidity, heat index) data of Beijing, China. See our published paper for more information:  <div> <div>Fan, P., Song, G., Lu, H., Yin, H., Zhai, Z., Wu, Y., Yu, L., 2024. Impact of ambient temperature on light-duty gasoline vehicle fuel consumption under real-world driving conditions. International Journal of Sustainable Transportation 1–16. <a href="https://doi.org/10.1080/15568318.2024.2385635">https://doi.org/10.1080/15568318.2024.2385635</a></div> </div>

The Interconnected Nature of Multiple Threats are Impacting Freshwater Biodiversity

Freshwater biodiversity is in crisis across the globe: the significant extraction, modification, and pollution of freshwater resources puts these communities and systems at great risk. Here, using probabilistic network analysis, we show that globally and across all taxonomic groups and geographic regions, threats to freshwater species are interconnected and do not occur in isolation. However, we also find that species in higher risk categories are more acutely threatened by single, dominant threats as compared to species at lower risk of global extinction. Determining when and which species are threatened by isolated threats or a suite of co-occurring threats provides important information for designing effective freshwater conservation strategies.