Dataset Search Results

Trajectory data for Nature Nanotechnology manuscript "DNA double helix, a tiny electromotor" that demonstrates how an electric field applied along the helical axis of a DNA or RNA molecule will generate an electroosmotic flow that causes the duplex to spin about that axis, much like a turbine.

This dataset contains the historical and future (SSP3 and RCP7.0) CESM climate simulations used in the article "Large humidity effects on urban heat exposure and cooling challenges under climate change" (upcoming). Further details about these simulations can be found in the article. This dataset documents the monthly mean projections of air temperature, wet-bulb temperature, precipitation, relative humidity, and numerous other climatic variables for 2000-2009 (for the historical run) and for 2015-2100 (for the future projection under SSP3-RCP7). This dataset may be useful for urban planners, climate scientists, and decision-makers interested in changes in urban and rural climate under climate change.

This dataset contains the emulated global multi-model urban climate projections under RCP 8.5 and RCP 4.5 used in the article "Global multi-model projections of local urban climates" (https://www.nature.com/articles/s41558-020-00958-8). Details about this dataset and the local urban climate emulator are described in the article. This dataset documents the monthly mean projections of urban temperatures and urban relative humidity of 26 CMIP5 Earth system models (ESMs) from 2006 to 2100 across the globe. This dataset may be useful for multiple communities regarding urban climate change, impacts, vulnerability, risks, and adaptation applications.

This dataset consists of all the files and codes that are part of the manuscript (main text and supplement) titled "Spin-selective tunneling from nanowires of the candidate topological Kondo insulator SmB6". For detailed information on the individual files refer to the specific readme files.

This dataset contains best estimates of the particle size distribution and measurements of the radar reflectivity factor and total water content for instances where ground-based radar and airborne microphysical observations were considered collocated with each other.

This datasets provide basis of our analysis in the paper - Potential Impacts of Supersonic Aircraft on Stratospheric Ozone and Climate. All datasets here can be categorized into emission data and model output data (WACCM). All the model simulations (background and perturbation) were run to steady-state and only the datasets used in analysis are archived here.

Global assessments of climate extremes typically do not account for the unique characteristics of individual crops. A consistent definition of the exposure of specific crops to extreme weather would enable agriculturally-relevant hazard quantification. We introduce the Agriculturally-Relevant Exposure to Shocks (ARES) model, a novel database of both the temperature and moisture extremes facing individual crops by explicitly accounting for crop characteristics. Specifically, we estimate crop-specific temperature and moisture shocks during the growing season for a 0.25-degree spatial grid and daily time scale from 1961-2014 globally for 17 crops. The resulting database presented here provides annual crop- and event-specific exposure rates. Both gridded and country-level exposure rates are provided for each of the 17 crops. Our results provide new insights into the changes in the magnitude as well as spatial and temporal distribution of extreme events that impact crops over the past half-century. For additional information, please see the related paper by Jackson et al. (2021) in Environmental Research Letters.

Conjugate photoelectron energy spectra derived from coincident FUV and radio measurements. These are outputs of simulations from the semi-empirical SAMI2-PE (Varney et al. 2012) for the night of January 4, 2020.

This dataset is provided to support the statements in Kim, H., and R.Y. Makhnenko. 2022. "Evaluation of CO2 sealing potential of heterogeneous Eau Claire shale". Journal of the Geological Society. In geologic carbon dioxide (CO2) storage in deep saline aquifers, buoyant CO2 tends to float upwards in the reservoirs overlaid by low permeable formations called caprocks. Caprocks should serve as barriers to potential CO2 leakage that can happen through a diffusion loss and permeation through faults, fractures, or pore spaces. The leakage through intact caprock would mainly depend on its permeability and CO2 breakthrough pressure, and is affected by the heterogeneities in the material. Here, we study the sealing potential of a caprock from Illinois Basin - Eau Claire shale, with sandy and shaly fractions distinguished via electron microscopy and grain/pore size and surface area characterization. The direct measurements of permeability of sandy shale provides the values ~ 10-15 m2, while clayey specimens are three orders of magnitude less permeable. The CO2 breakthrough pressure under in-situ stress conditions is 0.1 MPa for the sandy shale and 0.4 MPa for the clayey counterpart – these values are higher than those predicted by the porosimetry methods performed on the unconfined specimens. Sandy Eau Claire shale would allow penetration of large CO2 volumes at low overpressures, while the clayey formation can serve as a caprock in the absence of faults and fractures in it.

This dataset contains all the necessary information to recreate the study presented in the paper entitled "Learning coagulation processes with combinatorially-invariant neural networks". This consists of (1) the aggregated output files used for machine learning, (2) the machine learning codes used to learn the presented models, (3) the PartMC model source code that was used to generate the simulation data and (4) the Python scripts used construct the scenario library for training and testing simulations. This data was used to investigate a method (combinatorally-invariant neural network) for learning the aerosol process of coagulation. This data may be useful for application of other methods.

The data files are for the paper entitled: Long-lifetime spin excitations near domain walls in 1T-TaS2 to be published in PNAS. The data was obtained on a 300 mK custom designed Unisoku scanning tunneling microscope using the Nanonis module. All the data files have been named based on the Figure numbers that they represent.

This dataset contains all the data for the results section in the study presented in the paper entitled "Chemistry Across Multiple Phases (CAMP) version 1.0: An integrated multi-phase chemistry mode" submitted to Geoscientific Model Development (GMD). In this paper, two sets of simulations were run to test CAMP with this results included here. This consists of (1) box model inputs and outputs presented in Section 4.2 for modal, binned and particle-resolved simulations to compare the application of identical chemical mechanisms to different aerosol representations and (2) the 3D Eulerian output presented in Section 4.3.

This dataset provides estimates of agricultural and food commodity flows [kg] between all county pairs within the United States for the years 2007, 2012, and 2017. The database provides 206.3 million data points, since pairwise information is provided between 3134 counties, for 7 commodity categories, and 3 time periods. The commodity categories correspond to the Standardized Classification of Transported Goods and are: - SCTG 1: Iive animals and fish - SCTG 2: cereal grains - SCTG 3: agricultural products (except for animal feed, cereal grains, and forage products) - SCTG 4: animal feed, eggs, honey, and other products of animal origin - SCTG 5: meat, poultry, fish, seafood, and their preparations - SCTG 6: milled grain products and preparations, and bakery products - SCTG 7: other prepared foodstuffs, fats and oils For additional information, please see the related paper by Karakoc et al. (2022) in Environmental Research Letters.

Simulation trajectory data and scripts for Nature Nanotechnology manuscript "A DNA turbine powered by a transmembrane potential across a nanopore" that demonstrates a rationally designed nanoscale DNA-origami turbine with three chiral blades that uses a transmembrane electrochemical potential across a nanopore to drive a DNA bundle into sustained unidirectional rotations of up to 10 revolutions/s. Driven by the asymmetric mobility of a DNA duplex, the rotation direction of the turbine is set by its designed chirality and the salinity of the solvent.

This dataset contains the PartMC-MOSAIC simulations used in the article "Quantifying Errors in the Aerosol Mixing-State Index Based on Limited Particle Sample Size". The 1000 simulations of output data is organized into a series of archived folders, each containing 100 scenarios. Within each scenario directory are 25 NetCDF files, which are the hourly output of a PartMC-MOSAIC simulation containing all information regarding the environment, particle and gas state. This dataset was used to investigate the impact of sample size on determining aerosol mixing state. This data may be useful as a data set for applying different types of estimators.

This is the experimental data of isolated nanomagnet islands with or without the presence of large nanomagnet islands. The small islands are made of Permalloy materials with size of 170 nm by 470 nm by 2.5 nm. The systems are measured at a temperature where the small islands are fluctuating around room temperature. The data is recorded as photoemission electron microscopy intensity. More details about the data can be found in the note.txt and Spe_2016.xlsx file. Note: The raw data folders are stored in five volumes during the compression. All five volumes are needed in order to recover the original folder.

This dataset consists of microclimatic temperature and vegetation structure maps at a 3-meter spatial resolution across the Great Smoky Mountains National Park. Included are raster models for sub-canopy, near-surface, minimum and maximum temperature averaged across the study period, season, and month during the growing season months of March through November from 2006-2010. Also available are the topographic and vegetation inputs developed for the microclimate models, including LiDAR-derived vegetation height, LiDAR-derived vegetation structure within four height strata, solar insolation, distance-to-stream, and topographic convergence index (TCI).

This data set contains mass spectrometry data used for the publication "mspack: efficient lossless and lossy mass spectrometry data compression".

This dataset contains results from WRF simulations over northern South America. The Orinoco Low-Level Jet (OLLJ) and the Cross-Equatorial Moisture Transport are important circulation structures of the climate of tropical South America. We explore the sensitivity of the OLLJ and cross-equatorial transport to the representation of surface fluxes and turbulence by using two different Land Surface Model (LSM) schemes (Noah and CLM) and three Planetary Boundary Layer (PBL) schemes (YSU, QNSE and MYNN).

These datasets provide the basis of our analysis in the paper - The Potential Impact of a Clean Energy Society On Air Quality. All datasets here are from the model output (CAM4-chem). All the simulations were run to steady-state and only the outputs used in the analysis are archived here.

Example scripts and configuration files needed to perform select simulations described in the manuscript "Percolation transition prescribes protein size-specific barrier to passive transport through the nuclear pore complex."

Drainage network analysis is fundamental to understanding the characteristics of surface hydrology. Based on elevation data, drainage network analysis is often used to extract key hydrological features like drainage networks and streamlines. Limited by raster-based data models, conventional drainage network algorithms typically allow water to flow in 4 or 8 directions (surrounding grids) from a raster grid. To resolve this limitation, this paper describes a new vector-based method for drainage network analysis that allows water to flow in any direction around each location. The method is enabled by rapid advances in Light Detection and Ranging (LiDAR) remote sensing and high-performance computing. The drainage network analysis is conducted using a high-density point cloud instead of Digital Elevation Models (DEMs) at coarse resolutions. Our computational experiments show that the vector-based method can better capture water flows without limiting the number of directions due to imprecise DEMs. Our case study applies the method to Rowan County watershed, North Carolina in the US. After comparing the drainage networks and streamlines detected with corresponding reference data from US Geological Survey generated from the Geonet software, we find that the new method performs well in capturing the characteristics of water flows on landscape surfaces in order to form an accurate drainage network. This dataset contains all the code, notebooks, datasets used in the study conducted for the research publication titled " A Vector-Based Method for Drainage Network Analysis Based on LiDAR Data ". ## What's Inside A quick explanation of the components * `A Vector Approach to Drainage Network Analysis Based on LiDAR Data.ipynb` is a notebook for finding the drainage network based on LiDAR data *`Picture1.png` is a picture representing the pseudocode of our new algorithm * HPC` folder contains codes for running the algorithm with sbatch in HPC ** `execute.sh` is a bash script file that use sbatch to conduct large scale analysis for the algorithm ** `run.sh` is a bash script file that calls the script file `execute.sh` for large scale calculation for the algorithm ** `run.py` includes the codes implemented for the algorithm * `Rowan Creek Data` includes data that are used in the study ** `3_1.las` and `3_2.las ` are the LiDAR data files that is used in our analysis presented in the paper. Users may use this data file to reproduce our results and may replace it with their own LiDAR file to run this method over different areas ** `reference` folder includes reference data from USGS *** `reference_3_1.tif` and `reference_3_2.tif` are reference data for the drainage system analysis retrieved from USGS.

Ground based radar data sets collected during the 2013 NASA EVEX Campaign conducted in Roi-Namur island of the Kwajalein Atoll in the Republic of Marshall Islands are deposited in this databank. Radar data were collected with IRIS VHF and ALTAIR VHF/UHF systems.

This dataset helps to investigate the Spatial Accessibility to HIV Testing, Treatment, and Prevention Services in Illinois and Chicago, USA. The main components are: population data, healthcare data, GTFS feeds, and road network data. The core components are: 1) `GTFS` which contains GTFS (<a href="https://gtfs.org/">General Transit Feed Specification</a>) data which is provided by Chicago Transit Authority (CTA) from <a href="https://developers.google.com/transit/gtfs">Google's GTFS feeds</a>. Documentation defines the format and structure of the files that comprise a GTFS dataset: <a href="https://developers.google.com/transit/gtfs/reference?csw=1">https://developers.google.com/transit/gtfs/reference?csw=1</a>. 2) `HealthCare` contains shapefiles describing HIV healthcare providers in Chicago and Illinois respectively. The services come from <a href="https://locator.hiv.gov/">Locator.HIV.gov</a>. 3) `PopData` contains population data for Chicago and Illinois respectively. Data come from The American Community Survey and <a href="https://map.aidsvu.org/map">AIDSVu</a>. AIDSVu (https://map.aidsvu.org/map) provides data on PLWH in Chicago at the census tract level for the year 2017 and in the State of Illinois at the county level for the year 2016. The American Community Survey (ACS) provided the number of people aged 15 to 64 at the census tract level for the year 2017 and at the county level for the year 2016. The ACS provides annually updated information on demographic and socio economic characteristics of people and housing in the U.S. 4) `RoadNetwork` contains the road networks for Chicago and Illinois respectively from <a href="https://www.openstreetmap.org/copyright">OpenStreetMap</a> using the Python <a href="https://osmnx.readthedocs.io/en/stable/">osmnx</a> package. <b>The abstract for our paper is:</b> Accomplishing the goals outlined in “Ending the HIV (Human Immunodeficiency Virus) Epidemic: A Plan for America Initiative” will require properly estimating and increasing access to HIV testing, treatment, and prevention services. In this research, a computational spatial method for estimating access was applied to measure distance to services from all points of a city or state while considering the size of the population in need for services as well as both driving and public transportation. Specifically, this study employed the enhanced two-step floating catchment area (E2SFCA) method to measure spatial accessibility to HIV testing, treatment (i.e., Ryan White HIV/AIDS program), and prevention (i.e., Pre-Exposure Prophylaxis [PrEP]) services. The method considered the spatial location of MSM (Men Who have Sex with Men), PLWH (People Living with HIV), and the general adult population 15-64 depending on what HIV services the U.S. Centers for Disease Control (CDC) recommends for each group. The study delineated service- and population-specific accessibility maps, demonstrating the method’s utility by analyzing data corresponding to the city of Chicago and the state of Illinois. Findings indicated health disparities in the south and the northwest of Chicago and particular areas in Illinois, as well as unique health disparities for public transportation compared to driving. The methodology details and computer code are shared for use in research and public policy.