Find research datasets worth reusing

Data for the manuscript "Direct visualization of colloid transport over natural heterogeneous and artificial smooth rock surfaces"

The files contain the data used to produce the figures in the manuscript "Direct visualization of colloid transport over natural heterogeneous and artificial smooth rock surfaces" by Borgman, Be'er, and Weisbrod. Included in the data set: <ol> <li>ImageAnalysesAndPlots.m: A MATLAB script to generate the figures from the included images and data files.</li> <li>myCmap.mat: A custom set of colors for the images.</li> <li>LH_btc.csv, LH_b_btc.csv, HH_btc.csv, HH_b_btc.csv: Data for the breakthrough curves.</li> <li>LH_t=360min.czi, HH_t=300min.czi: Final images from the experiments, from which the residual surface fluorescence is calculated.</li> <li>LHSurfTopo.csv, HHSurfTopo.csv: Tables containing the profilometer scan data.</li> <li>LH, HH: Folders containing the images for the colloid displacement front</li> <li>Exp01-Exp04: Images for calculating the dispersion coefficient </li> <li>PlotVelocities2.m: A script for plotting the calculated velocity fields</li> <li>velocity_magnitude_HH_flux_boundary.txt/velocity_magnitude_LH_flux_boundary.txt: The data files for the velocity fields.</li> </ol> For the .czi files, it's necessary to use the Bio-Formats for MATLAB <a href="https://docs.openmicroscopy.org/bio-formats/6.1.0/users/matlab/index.html">package</a>.

Uncertainty-aware molecular dynamics from Bayesian active learning: Phase Transformations and Thermal Transport in SiC

Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomic level processes. Active learning methods have been recently developed to train force fields efficiently and automatically. Among them, Bayesian active learning utilizes principled uncertainty quantification to make data acquisition decisions. In this work, we present an efficient Bayesian active learning workflow, where the force field is constructed from a sparse Gaussian process regression model based on atomic cluster expansion descriptors. To circumvent the high computational cost of the sparse Gaussian process uncertainty calculation, we formulate a high-performance approximate mapping of the uncertainty and demonstrate a speedup of several orders of magnitude. As an application, we train a model for silicon carbide (SiC), a wide-gap semiconductor with complex polymorphic structure and diverse technological applications in power electronics, nuclear physics and astronomy. We show that the high pressure phase transformation is accurately captured by the autonomous active learning workflow. The trained force field shows excellent agreement with both \textit{ab initio} calculations and experimental measurements, and outperforms existing empirical models on vibrational and thermal properties. The active learning workflow is readily generalized to a wide range of systems, accelerates computational understanding and design.

Risk perception and travel satisfaction associated with the use of public transport in the time of COVID-19. The case of Turin, Italy

Dataset created from the data of a survey about risk perception and travel satisfaction associated with the use of PT in the time of Covid-19 in Turin metropolitan area (Italy).

In silico dataset of features for multidrug resistant efflux transporter protein familes

This dataset constitutes in silico features as amino acids fractions, dipeptide count, and other basic features information for efflux transporter family proteins.

Impacts of transboundary transport on coastal air quality of south China

observation data for air pollutants from China’s Ministry of Ecology and Environment

Data for: "Exciton transport in molecular organic semiconductors boosted by transient quantum delocalization"

Data: Figure 2 (all panels), Figure 3 (all panels), Figure 4 (all panels), Figure 5 (all panels)

opencc-by-4.0Apr 2022View details →

Measurement report: On the contribution of long-distance transport to the secondary aerosol formation and aging

(1) Chemical composition of PM2.5 in four transport sectors. (2) Time series of organic aerosol factors resolved by PMF&ME-2 in four transport sectors. (3) Meteorological data corresponded to the ACSM and PMF&ME-2 data. (4) f44 and f43 ratio in four transport sectors.

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Intermediate results for: Large differences in carbohydrate degradation and transport potential among lichen fungal symbionts

Lichen symbioses are thought to be stabilized by the transfer of fixed carbon from a photosynthesizing symbiont to a fungus. In other fungal symbioses, carbohydrate subsidies correlate with reductions in plant cell wall-degrading enzymes, but whether this is true of lichen fungal symbionts (LFSs) is unknown. We predicted genes encoding carbohydrate-active enzymes (CAZymes) and sugar transporters in 46 genomes from the Lecanoromycetes, the largest extant clade of LFSs. All LFSs possess a robust CAZyme arsenal including enzymes acting on cellulose and hemicellulose, confirmed by experimental assays. However, the number of genes and predicted functions of CAZymes vary widely, with some fungal symbionts possessing arsenals on par with well-known saprotrophic fungi. These results suggest that stable fungal association with a phototroph does not in itself result in fungal CAZyme loss, and lends support to long-standing hypotheses that some lichens may augment fixed CO2 with carbon from external sources.

opencc-zeroApr 2022View details →

dryad32/100

Absorptive roots drive a larger microbial carbon pump efficacy than transport roots in alpine coniferous forests

Root activity creates a unique microbial hotspot in the rhizosphere and profoundly regulates soil carbon (C) dynamics, but empirical assessments of the soil microbial carbon pump (MCP, the iterative accumulation of necromass after microbial anabolism) and associated ecological consequences on soil C storage based on insight of the rhizosphere are still neglected, especially for different root functional modules. We assessed the soil MCP efficacy (i.e., the contribution of microbial necromass to SOC) by investigating the divergent contribution of microbial necromass based on amino sugar extrapolations to soil organic C (SOC) in the rhizosphere of two root functional modules (i.e., absorptive roots and transport roots) and the bulk soil in an alpine coniferous forest. The results showed that the MCP efficacy in both rhizosphere and bulk soil was more than 50%, suggesting that microbial necromass plays a key role in SOC formation. More importantly, absorptive roots drove a greater MCP efficacy (56%) in the rhizosphere than transport roots (51%). Synthesis. These observations suggest that the microbial necromass is a major contributor to SOC storage in both rhizosphere and bulk soil in alpine coniferous forests. The magnitude of the contribution of microbial necromass to rhizosphere SOC depends on root functional differentiation. Our study provides novel and direct empirical evidence for the active soil MCP functions in SOC sequestration from the perspective of the rhizosphere.

opencc-zeroApr 2022View details →

opencc-by-4.0Apr 2022View details →

5.0 EVALUATING CHANGES IN TRANSIT ACCESSIBILITY FOR TRANSPORTATION-DISADVANTAGED POPULATIONS IN THE CITY OF GAINESVILLE

This dataset contains four parts: 1. A geodatabase of street network in Alachua County ; 2. Shapefiles of places (hospitals, parks, schools, etc.) in Florida that were defined as "trip destinations" in our research; 3. GTFS (General Transit Feed Specification) data used to developed different transit service scenarios; 4. Data of the transit service scenarios that we developed.

Data of event list of cold and hot patches for manuscript "The Dependence of Cold and Hot Patches on Local Plasma Transport and Particle Precipitation in Northern Hemisphere Winter"

A database of 4,634 cold patches (high density and low electron temperature) and 4,700 hot patches (high density and high electron temperature) from Defense Meteorological Satellite Program F16 in 2005-2018 winter months.

opencc-by-3.0-usMay 2022View details →

Network-Nanostructured ZIF-8 too Enable Percolation for Enhanced Gas Transport (Data Repository)

Data repository

contribution of atmospheric transport to phosphorus in the East China Sea in summer

For the analysis of the main sources of phosphate in the East China Sea during summer, we choose the area between 120.93 ˚ E-125.9 ˚ E and 26.08˚ N-32.35 ˚ N as research sites . The samples were all from the voyage of the research ship (Xiangyanghong 18). Surface seawater was collected at 49 stations for δ18Op analysis by a conductivity-temperature-depth profiler. Then we utilized δ18Op to trace the source of phosphate in the East China Sea.  The δ18Op values in seawater are affected by water masses with different phosphate concentrations.We need the two-component mixing model for the analysis of water masses that control phosphate.In this research, atmospheric deposition and adjacent sea transportation were used as terrestrial and seawater end-members respectively to construct the δ18Op end-member mixing model. In order to quantify the source of phosphate in the sea, a Bayesian isotope mixing model run in the R software package (Stable Isotope Analysis in R, SIAR) was selected for analysis.

Mechanism underlying the millennial scale variation of offshore sediment transport in the North Yellow Sea

Sediment cores YC10 (length: 548 cm, water depth: 54 m) and YC26 (length: 435 cm, water depth: 15 m) were collected by hammer drilling on a fishing vessel from the NYS in July 2016. The sediment cores were split lengthwise, described, photographed, and subsampled in the lab. They were sliced into thin sections for trace element analyses at intervals of 4–10 cm according to the age model. The samples were freeze-dried under vacuum, and the fine fraction (< 63 μm) was sieved from the bulk samples for further geochemical analysis. The number of samples for REEs in cores YC10 and YC26 was 71 and 80, respectively, while those for Sr-Nd isotopes were 12 and 14, respectively.

Experiments on a single large particle segregating in bedload transport

This depository contains all the data presented in "Experiments on a single large particle segregating in bedload transport" from H. Rousseau, J. Chauchat and P. Frey in Physical Review Fluids, as well as the code to read these data. There are 10 folders that each correspond to a configuration (i.e. size ratio and Shields number). These folders contain subfolders that correspond to the different repetitions we made for each configuration. In a subfolder, one can find: - The first image of the experiment (t=0s). - An hdf5 file called "bedAndWaterLines.h5" which contains the data for the waterline positions and the bedline positions with time. - An hdf5 file called "frame_0_to_3000_with_step_1_and_shift_1.hdf5" which contains the granular bed velocity fields Ux and Uy interpolated over the time. These velocities have been obtained using the OpyFlow toolbox (https://github.com/groussea/opyflow.git). - An hdf5 file called "DataTracked.h5" which contains the results from the detection of the intruder. Inside "DataTracked.h5", one can find one folder by timestep that includes the coordinates of the intruder. The total number of frame, the acquisition rate and the scale are also saved as datasets in "DataTracked.h5". The code "plotData.py" has been coded in python3 and allows one to read the data from the hdf5 files (make sure you installed the h5py package for python before). "plotData.py" is annotated and thus, it contains all the instructions to plot the data of a given repetition. It is based on the following classes: - "LoadResult" that reads "DataTracked.h5" - "loadWaterAndBed" that reads "bedAndWaterLines.h5" - "readOpyf" that reads "frame_0_to_3000_with_step_1_and_shift_1.hdf5"   The file "listRepetitions.ods" is also provided. It allows one to match a given experiment in the paper to its name in this depository. Do not hesitate to contact us if you need more info.

PM2.5 data for "Contribution of fire emissions to PM2.5 and its transport mechanism over the Yungui Plateau, China during 2015–2019"

PM2.5 data for "Contribution of fire emissions to PM2.5 and its transport mechanism over the Yungui Plateau, China during 2015–2019"

opencc-by-4.0Feb 2022View details →

FEOTS Argentine Basin Transport Operators (5-day Average)

The Fast Equilibration of Ocean Tracers Software (FEOTS; https://github.com/lanl/feots) contains utilities for diagnosing transport operators consistent with the numerical discretizations in the Parallel Ocean Program (POP) and applying them in an offline setting to model passive tracers. Additionally, FEOTS is capable of extracting operator subsets for regional tracer modeling. This data set includes the parent model mesh as well as regional mapping files and 5 years of 5-day averaged operators for modeling tracer advection and diffusion around the Argentine Basin. The associated parent model is the E3SMV0-HiLAT Climate model. The Argentine Basin region is defined as the geographic region bounded by [52.18S,28.06S]×[70.25W,24.90W].

opencc-by-4.0Jun 2022View details →

Data related to "Near-bed sediment transport processes during onshore bar migration in large-scale experiments. Comparison with offshore bar migration."

Abstract: This paper presents novel insights into nearshore sediment transport processes during bar migration on the basis of large-scale laboratory experiments with bichromatic wave groups on a relatively steep initial beach slope (1:15). Insights are based on detailed measurements of velocity and sand concentration near the bed from shoaling up to the outer breaking zone including suspended sediment and sheet flow transport. The analysis focuses on onshore migration under an accretive wave condition but comparison to an erosive condition highlights important differences. Decomposition shows that total net transport mainly results from a balance of short wave-related, bedload net onshore transport and current-related, suspended net offshore transport. When comparing the accretive to the more energetic erosive condition, the balance shifts towards net onshore transport, and onshore migration, because the short wave-related transport does not decrease as much as the current-related transport. This is related to the effects of skewness and asymmetry combined with larger sediment entrainment and undertow magnitude under the erosive condition. Net transports from streaming in the wave boundary layer and from infragravity waves are noticeable but only play a subordinate role. Identified priorities for numerical model development include parametrization of wave nonlinearity effects and better description of wave breaking and its influences on sediment suspension. The present data, unique in their combination of high measurement detail with fully-evolving accretive beach profiles, help to improve numerical modeling of long-term morphological evolution. About the data: The folder “Beach Profiles” contains the measurements from the mechanical profiler before and after each test. To save time, only the morphologically active section of the profiles was measured. Additionally, the folder contains the initial profiles at the start of each sequence (after application of the benchmark waves). Here the full profile was measured. The structure “MobFrame” contains the absolute cross-shore position of the mobile frame (from which detailed measurements were taken) in the considered tests. The folder “ACVP” contains structures with ensemble-averaged velocity and concentration measurements in vertical reference to the undisturbed bed level or a few bins below it (zeta0-coordinate system as described in the paper). For better interpretation of the measurements, it also features the ensemble-averaged intrawave instantaneous bed elevation (erosion depth) and the upper limit of the sheet flow layer. The folder “ADV” contains structures with the ensemble-averaged ADV data of each test. Apart from the velocity components of each ADV they contain the vertical elevation of each ADV with respect to the ACVP transceiver. The ADV measurements were not subject to the same vertical referencing procedure that was described in the paper for the near-bed ACVP measurements. The folder “OBS” contains structures with the ensemble-averaged OBS data of each test. Apart from the concentration measurements in each OBS sensor they contain the vertical elevation of each OBS with respect to the ACVP transceiver. The folder “ETA” contains structures with the ensemble-averaged surface elevation data of each test (from different instruments as described in the paper). The location of each instrument is given in absolute cross-shore coordinates x.   For visualizing the near-bed concentration data, which may not be as trivial as visualizing the rest of the data, an example of MATLAB code is given: %S=ACVP_xx; %to choose which ACVP file you want to look into con=S.c; con(con<1)=1; %to cater for the cells where the logarithm is not defined xphase=linspace(0,1,length(S.solbed)).*ones(size(S.c,2),size(S.c,1)); figure; hold on; box on; [C,h]=contourf(xphase,S.z,log10(transpose(con)),[0:0.1:3]); cbh=colorbar; caxis([0 3]); set(h,'edgecolor','none'); tt=get(cbh,'Title'); set(tt,'String','$log_{10}(c)$ $[kg/m^3]$','Interpreter','Latex'); plot(xphase(1,:),S.solbed,'k','Linewidth',1.5); plot(xphase(1,:),S.solflo,'r','Linewidth',1.5); xlabel('$t/T_r$','Interpreter','Latex') ylabel('$\zeta_0$ $[m]$','Interpreter','Latex') set(gca,'Fontsize',18)