Find research datasets worth reusing

opencc-by-4.0Dec 2022View details →

PM100: A Job Power Consumption Dataset of a Large-Scale HPC System

The dataset is a collection of jobs extracted from the job_table data of M100 (<a href="https://doi.org/10.5281/zenodo.7588815">https://doi.org/10.5281/zenodo.7588815</a>), a collection of data extracted from a Tier-0 supercomputer hosted at CINECA (Marconi100, <a href="https://www.hpc.cineca.it/hardware/marconi100">https://www.hpc.cineca.it/hardware/marconi100</a>).  The original job data present in M100 are filtered out by considering only the jobs running exclusively on the resources. Each job entry included in PM100 contains the power consumption of the job recorded at Node level, CPU level and Memory level. The final dataset contains 231116 jobs, executed on Marconi100 between May and October 2020. The dataset is stored as a parquet file, where each entry contains the information on a job execution. The structure of the data, as well as the code to generate them, is contained in the official GitHub repository of the project: <a href="https://github.com/francescoantici/PM100-data/">https://github.com/francescoantici/PM100-data/</a>.

A large-scale artificial forest tree population for sampling and estimation methods simulations

The dataset utilized in the Simulation Study section of the article is generated for a 10 km x 10 km area. This artificial dataset is constructed by populating polygons with trees sourced from the NFI database, representing the stands of the associated Vosges polygon. The aim is to create a sizable dataset that closely resembles a real forest by incorporating authentic information. As the Vosges dataset consists of a set of polygons, the artificial dataset is formed using voronoi polygons, each randomly linked to a Vosges dataset polygon. The Vosges dataset contributes basal area information to the voronoi polygons. These voronoi polygons are then filled with trees from the NFI database, corresponding to the stands of the Vosges polygon. The dataset comprises 1,174,227 trees, each with specified attributes such as circumference at a height of 130cm, top height, volume, species, and status (dead/alive).

A tunable family of CAAC-ruthenium olefin metathesis catalysts modularly derived from a large-scale produced ibuprofen intermediate

Data confirming the structure of the new compounds obtained within the project, published in  Chem. Sci., 2023,14, 10744-10755The research was supported by the National Science Centre, Poland (OPUS grant DEC-2017/27/B/ST5/02563).

Large-scale green grabbing for wind and solar PV development in Brazil

<h2>Large-scale green grabbing for wind and solar PV development in Brazil</h2>This repository contains the R code and parts of the data used for the analysis in the paper "Large-scale green grabbing for wind and solar PV development in Brazil" by Michael Klingler, Nadia Amelie, Jamie Rickman, and Johannes Schmidt, available as <a href="https://eartharxiv.org/repository/view/5824/">pre-print</a>.Due to data sharing limitations, we cannot provide all data in the repository. Partly this data is not available publically at all (i.e. Bloomberg data, data by the instituto socio ambiental), partly the data has to be downloaded manually (CAR).We still provide a repository which at least allows to understand the procedures we used during the analysis.<h3>Land tenure data set</h3>The procedures used to form our final land tenure data set can be found in land-tenure-data/processing.txt It is a mix of analyses in Python and in QGis.<h3>Analysis of land tenure data and park ownership/investment information</h3>The R-code to analyze the owernship relationships between windpark areas and investors/owners can be found in src/. All required libraries will install automatically.The first two scripts cannot be executed due to data limitations. They create the sankey diagrams linking park areas to onwers and investors:- 1.1-figures-results-1-wind.R- 1.2-figures-results-1-solar.RThese three scripts are used to analyze the land tenure types prevailing on parks and comparing them to random areas. They should run with the provided data sets:- 2-random-sampling-areas.R- 3-intersection-parks-land-tenure.R- 4-figures-land-tenure.RThis script validates our data against an independent data source. However, it cannot be run as it needs the proprietary Bloomberg database:- 5-validation.R

openOct 2023View details →

Tactician's Web of Large-Scale Formal Knowledge

This dataset contains machine-readable data extracted by <a href="https://coq-tactician.github.io">Tactician</a> from formal knowledge encoded in the <a href="https://coq.inria.fr">Coq Proof Assistant</a>. It includes definitions, theorems, proof terms and tactical proofs presented both as Coq's default textual format and a graph format. The entire known universe of formal knowledge in Coq is encoded as a single interconnected graph of mathematical concepts where all knowledge is faithfully and unambiguously encoded. The dataset is intended for machine learning, analysis and general proof engineering purposes. In addition to exploring this offline dataset, it is also possible to interact directly with Coq in the same data format through <a href="https://coq-tactician.github.io/api">Tactician's API</a>.See the README.md file for more information.

Data for the paper Large-Scale Tropical Circulation Intensification by Aerosol Effect on Clouds

Data for the paper Large-Scale Tropical Circulation Intensification by Aerosol Effect on Clouds.   Please see the README for more information.

TinyWT: A Large-Scale Wind Turbine Dataset of Satellite Images for Tiny Object Detection

This dataset is from the paper "TinyWT: A Large-Scale Wind Turbine Dataset of Satellite Images for Tiny Object Detection", which has been accepted by the WACV 2024 CV4EO Workshop.

Experimental measurements of the effects of surface roughness on large-scale downburst-like impinging jets at the WindEEE Dome laboratory

Thunderstorm downbursts originate as negatively buoyant currents of cold air descending from cumulonimbus clouds. Upon impacting the ground, a strong radial outflow develops with maximum wind velocities occurring at the near-ground level. These types of flows pose serious hazard to the natural and built environment. Their restricted time and spatial extent as well as their intermittent and non-Gaussian fluctuating nature make them extremely challenging to be recorded and analyzed through classic full-scale measurements in nature. Alongside synoptic-scale extra tropical cyclones, downbursts govern the wind climate at the mid-latitude areas around the globe. Recent trends in climate change studies suggest both an increased intensity as well as frequency of occurrence of these events. Therefore, their scientific comprehension urges serious consideration. In the context of the project THUNDERR “Detection, simulation, modelling and loading of thunderstorm outflows to design wind-safer and cost-efficient structures”, financed by the European Research Council (ERC) Advanced Grant 2016 (grant No. 741273, P.I. Prof. Giovanni Solari, University of Genoa), an extensive experimental campaign was recently conducted at the WindEEE Dome wind chamber. This campaign focused on measuring downburst-like flows (DLFs) generated by large-scale impinging jets. The dataset presented here encompasses a portion of the measurements collected during this comprehensive experimental initiative. Specifically, this series of tests aimed to unravel the role of surface roughness in potentially altering the configuration and dynamics of the overall radial outflow. The term "surface roughness" pertains to the ground patch under examination, encompassing both the natural features of the terrain and any obstacles, such as buildings, present on the ground. While surface roughness plays a decisive role in changing the shape and magnitudes of the wind speed vertical profiles for extra-tropical cyclones, the scientific literature has not yet thoroughly addressed its impact on downburst winds, which are different being dominated by intense vortex dynamics. Impinging jets, considered representative for the simulation of downburst like flow (DLF), are here simulated as transient phenomena through the opening and closing of the bell-mouth that connects the test chamber and the upper plenum of the dome, the latter being pressurized before releasing the jet. As a result, the velocity records exhibit a distinct pattern, featuring a sudden ramp-up of velocity, followed by a velocity peak, a statistically-stationary phase, and ultimately, a gradual velocity deceleration—mirroring the behavior observed in real-world scenarios. The database consists of six ASCII tab-delimited text files, denoted as ‘windspeedDB89z0eq007.txt’, ‘windspeedDB89z0eq020.txt’, ‘windspeedDB89z0eq320.txt’, ‘windspeedDB124z0eq007.txt’, ‘windspeedDB124z0eq020.txt’, and ‘windspeedDB124z0eq320.txt’, aligning with the two jet intensities and three rough surfaces employed in the experiments. These filenames correspond to: (i) centerline jet velocities at the nozzle outlet section, with values of Wjet = 8.9 and 12.4 m/s (indicated as “Wjet” in the database files); (ii) equivalent full-scale roughness lengths z0eq = 0.007, 0.020, 0.32 m (“z0eq” in the database files, see details below). Each file encompasses wind speed timeseries, detailed as follows: The three-component velocity measurements were recorded by means of 11 Cobra probes (sampling frequency 2,500 Hz) mounted on a stiff mast. The heights (z) of the probes were z = 0.040, 0.070, 0.100, 0.125, 0.150, 0.200, 0.300, 0.400, 0.500, 0.700, 1.000 m above the surface. Within the database files, the wind speed linked to various heights is labeled as “v_zXXXXmm”. In this notation, 'v' designates the velocity component: longitudinal ‘U’ (along the horizontal axis of the probe), corresponding to the radial outflow of the downburst, with a positive value when the flow is directed toward the probe. Transversal, ‘V’, represents the velocity component transverse to the probe's centerline axis, having a positive value when the flow is directed right-to-left concerning an observer facing the probe's head. The vertical component is denoted as ‘W’ with a positive value indicating an upward direction. The term “XXXX” signifies the height of the probe, specified in millimeters (mm). The mast with the Cobra probes was subsequently positioned at ten radial r distances with respect to the jet impingement position in the range r/D (D = 3.2 m is the jet diameter) between 0.2–2.0 with an increment of 0.2. Note that the position r/D = 0.8 was adjusted to r/D = 0.75. This modification was necessary due to irregularities on the chamber floor at r/D = 0.8, which could have otherwise introduced bias into the measurements. The radial distance is identified with “r/D_distance” in the dataset files. The ceiling height of the testing chamber is H = 3.75 m, which leads to H/D > 1 allowing for a full vertical development of the downburst radial outflow. For every r/D position, each experiment with the same initial condition (i.e., Wjet) was repeated 10 times (“repetition#” in the database files) to inspect the repeatability of the tests and their variance. Each velocity record lasted 12 s (12 × 2,500 = 30,000 samples) and the duration of the downburst-like part of the record varied between 3–5 s. Overall, 6,600 total time series (2 Wjet × 3 rough surface × 10 repetitions × 10 r/D positions × 11 heights z) of downburst-like outflows were recorded during this set of experimental tests. The reported accuracy of Cobra probes from the manufacturer is +/- 0.5 m/s and +/- 1° for velocity and yaw/pitch angles respectively, up to approximately 30% of turbulence intensity. All velocity magnitudes below 1 m/s were removed and converted to NaN (Not a Number) in the database due to the poor accuracy of Cobra probes for velocities below this threshold. In addition, some velocity values were reported as null in the instrument readings due to the incoming flow being outside the probe's spatial cone of measurement (+/- 45° in respect to the probe horizontal axis). These values are flagged as NULL values in the database. This notation aligns with that utilized in a preceding database of measurements collected within the same experimental campaign at the WindEEE Dome (Canepa et al., 2021; <a href="https://doi.org/10.1594/PANGAEA.931205">https://doi.org/10.1594/PANGAEA.931205</a>). DLFs were tested on three different surfaces: (i) WindEEE Dome bare floor; (ii) Carpet; (iii) Artificial grass. A 1 m × 8 m rectangular section was selected from each of the three surfaces for testing purposes. Each surface was positioned with a 1 m offset relative to the geometric location of the jet impingement. It was identified by an equivalent full-scale roughness length, “z0eq”based on matching atmospheric boundary layer profiles measured in WindEEE in boundary layer mode with standard ESDU (Engineering Science Data Unit) profiles. A total of 15 different Atmospheric Boundary Layer (ABL)-like profiles were tested inside the chamber by varying the rotation-per-minute (rpm) of the fans across the 4 rows of the 60-fan wall—a peripheral wall of the hexagonal WindEEE Dome chamber comprising a matrix of 4 × 15 (rows × columns) fans that is used to produce ABL -like flows. A specific configuration of the 60-fan wall and a length scale of 1:200 were chosen based on correlation analysis between physically reproduced ABL profiles and curve fitting through Eq. A1.8 of the ESDU 82026. This scale is deemed suitable for both ABL and downburst winds produced at the laboratory. Through a linear fitting of the measured data on the U – ln(z) chart, employing the logarithmic law-of-the-wall (dependent on roughness length z0 and friction velocity u*), the equivalent roughness lengths for the three surfaces were determined: z0eq = 0.007, 0.020, 0.320 m for the WindEEE Dome bare floor, carpet, and artificial grass, respectively. In summary, each experimental velocity time series consists of 30,000 rows, with 33 columns detailing the three velocity components (U, V, W) across the 11 Cobra probe heights. Columns 34 to 37 provide information on the repetition number, radial position of measurement, equivalent full-scale roughness length, and jet intensity. The subsequent timeseries within the dataset refer to the parameters in columns 34 to 37, each one spanning its entire range in the specified order. Researchers can leverage this database to validate and calibrate numerical and analytical models of thunderstorm winds, in addition to interpreting full-scale measurements of the phenomenon. It also serves as a valuable resource for the fluid dynamics community, particularly those interested in the physical comprehension of downscaled flows or the surface flow dynamics of large Reynolds number impinging jets.

opencc-zeroMar 2024View details →

Using mobile device built-in microphones to monitor bats: A new opportunity for large-scale participatory science initiatives

Here we present audio data collected in the study titled "Using mobile device built-in microphones to monitor bats: a new opportunity for large-scale participatory science initiatives", now accepted for publication in Biodiversity and Conservation (DOI: 10.1007/s10531-024-02818-9). Citizen science has become a crucial tool in biodiversity monitoring, often facilitated by the diffusion of mobile devices, such as smartphones and tablets. High costs of professional equipment often limit large-scale monitoring, particularly in bat monitoring programmes based on acoustic surveys. Here we present the potential of using mobile devices for bat monitoring, allowing for large-scale, volunteer-based monitoring programmes. We initially compared mobile devices' performance with a professional bat detector for recording low-frequency bat calls. We then conducted a citizen science pilot study to test the method's feasibility in a real-world setting, recording echolocation and social calls of nine European bat species. We found high similarity in spectrogram quality between calls recorded by mobile devices and professional bat detectors. However, differences in sound quality and effectiveness among mobile device brands and models were found. The citizen science pilot study tested 35 mobile device models, all of which effectively recorded bats. This study suggests that mobile devices could be an accessible, no-cost tool for large-scale bat monitoring. Incorporating mobile devices into existing monitoring networks or creating new dedicated programmes could not only enhance data collection, but also boost public knowledge and awareness about bats, ultimately promoting informed decision-making and better conservation strategies. To facilitate a comprehensive evaluation of the acoustic data quality achievable through the described method, we are providing access to all bat recordings collected via mobile devices during the study. These have been compiled into the compressed file named "mobile_device_recs.zip". For those seeking a quicker review, we recommend downloading the "Appendix_S6_recs.zip" file, which contains selected examples of echolocation or social calls from the nine bat species recorded during our research. Spectrograms of these example recordings are displayed in Appendix S6 of the published study.

opencc-by-4.0Nov 2023View details →

Animation, MESA inlists, and post-processing package for "Is Betelgeuse really rotating? Synthetic ALMA observations of large-scale convection in 3D simulations of Red Supergiants"

Videos, post-processing package for mock ALMA observations, MESA (r23.05.1 with mesasdk-x86_64-linux-23.7.3) inlists and history files, Jupyter notebook and data to reproduce figures of the paper "Is Betelgeuse really rotating? Synthetic ALMA observations of large-scale convection in 3D simulations of Red Supergiants".

opencc-by-4.0Apr 2024View details →

Large-scale simulation of thermal conductivity in CaSiO3 perovskite with neuroevolution potential

The dataset contains the thermal conductivity of CaSiO3 perovskit, MgSiO3 perovskite, periclase, as well as the heat flux across the core-mantle boundary.

opencc-by-4.0Apr 2024View details →

Processed KuaiRand-1K dataset for the paper: Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework

The original public dataset is published in https://zenodo.org/records/10439422, we processed the KuaiRand-1K dataset for the paper: Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized  Integration Framework.

opencc-by-4.0Nov 2024View details →

Source data of the MultiSTAAR manuscript "A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies".

This dataset serves as the source data for Figures 2-3 and Extended Data Figures 1-2 of the MultiSTAAR manuscript titled "A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies". MultiSTAAR is a statistical framework and computationally-scalable analytical pipeline for functionally-informed multi-trait rare variant analysis in large-scale WGS studies. Figure 2. Manhattan plots and Q-Q plots for unconditional gene-centric coding, noncoding and ncRNA multi-trait analysis of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglycerids (TG) using TOPMed data (n = 61,838). Figure 3. TOPMed genetic region (2-kb sliding window) unconditional multi-trait analysis results of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG) using TOPMed data (n = 61,838). Extended Data Figure 1. Manhattan plots and Q-Q plots for unconditional gene-centric coding, noncoding and genetic region (2-kb sliding window) multi-trait analysis of fasting glucose (FG) and fasting insulin (FI) using TOPMed data (n = 21,731). Extended Data Figure 2. Manhattan plots and Q-Q plots for unconditional gene-centric coding, noncoding and genetic region (2-kb sliding window) multi-trait analysis of C-reactive protein (CRP), interleukin 6 (IL-6), lipoprotein-associated phospholipase A2 (Lp-PLA2) activity, and lipoprotein-associated phospholipase A2 (Lp-PLA2) mass using TOPMed data (n = 9,380).

opencc-by-4.0Aug 2024View details →

SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part II)

This page only provides the drone-view image dataset. <ul> <li>For the ground-level image dataset, please visit <a href="https://zenodo.org/records/13570934" target="_blank" rel="noopener">SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part I)</a>.</li> <li>For the point clouds, please visit <a href="https://zenodo.org/records/14228467" target="_blank" rel="noopener">SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part III)</a>.</li> </ul> The dataset contains drone-view RGB images, depth maps and instance segmentation labels collected from different scenes. Data from each scene is stored in a separate .7z file, along with a <code>color_palette.xlsx</code> file, which contains the RGB_id and corresponding RGB values. All files follow the naming convention: <code>{central_tree_id}_{timestamp}</code>, where <code>{central_tree_id}</code> represents the ID of the tree centered in the image, which is typically in a prominent position, and <code>timestamp</code> indicates the time when the data was collected. Specifically, each 7z file includes the following folders: <ul> <li> rgb: This folder contains the RGB images (PNG) of the scenes and their metadata (TXT). The metadata describes the weather conditions and the world time when the image was captured. An example metadata entry is: <code>Weather:Snow_Blizzard,Hour:10,Minute:56,Second:36</code>. </li> <li> depth_pfm: This folder contains absolute depth information of the scenes, which can be used to reconstruct the point cloud of the scene through reprojection. </li> <li> instance_segmentation: This folder stores instance segmentation labels (PNG) for each tree in the scene, along with metadata (TXT) that maps <code>tree_id</code> to <code>RGB_id</code>. The <code>tree_id</code> can be used to look up detailed information about each tree in <code>obj_info_final.xlsx</code>, while the <code>RGB_id</code> can be matched to the corresponding RGB values in <code>color_palette.xlsx</code>. This mapping allows for identifying which tree corresponds to a specific color in the segmentation image. </li> <li> obj_info_final.xlsx: This file contains detailed information about each tree in the scene, such as position, scale, species, and various parameters, including trunk diameter (in cm), tree height (in cm), and canopy diameter (in cm). </li> <li> landscape_info.txt: This file contains the ground location information within the scene, sampled every 0.5 meters. </li> </ul> For birch_forest, broadleaf_forest, redwood_forest and rainforest, we also provided COCO-format annotation files (.json). Two such files can be found in these datasets: <ul> <li>{name}_coco.json: This file contains the annotation of each tree in the scene.</li> <li>{name}_filtered.json: This file is derived from the previous one, but filtering is applied to rule out overlapping instances.</li> </ul> ⚠️: 7z files that begin with "!" indicate that the RGB values in the images within the <code>instance_segmentation</code> folder cannot be found in <code>color_palette.xlsx</code>. Consequently, this prevents matching the trees in the segmentation images to their corresponding tree information, which may hinder the application of the dataset to certain tasks. This issue is related to a bug in Colossium/AirSim, which has been reported in <a href="https://github.com/microsoft/AirSim/issues/3423" target="_blank" rel="noopener">link1</a> and <a href="https://github.com/microsoft/AirSim/issues/1852" target="_blank" rel="noopener">link2</a>.

opencc-zeroNov 2021View details →

Data for: Concordant phylogeographic responses to large-scale coastal disturbance in intertidal macroalgae and their epibiota

Major ecological disturbance events can provide opportunities to assess multispecies responses to upheaval. In particular, catastrophic disturbances that regionally extirpate habitat-forming species can potentially influence the genetic diversity of large numbers of co-distributed taxa. However, due to the rarity of such disturbance events over ecological timeframes, the genetic dynamics of multispecies recolonization processes have remained little understood. Here we use single nucleotide polymorphism (SNP) data from multiple coastal species to track the dynamics of co-colonization events in response to ancient earthquake disturbance in southern New Zealand. Specifically, we use a comparative phylogeographic approach to understand the extent to which epifauna (with varying ecological associations with their macroalgal hosts) share comparable spatial and temporal recolonization patterns. Our study reveals concordant disturbance-related phylogeographic breaks in two intertidal macroalgal species along with two associated epibiotic species (a chiton and an isopod). By contrast, two co-distributed species, one of which is an epibiotic amphipod and the other a subtidal macroalga, show few if any genetic effects of palaeoseismic coastal uplift. Phylogeographic model selection reveals similar post-uplift recolonization routes for the epibiotic chiton and isopod and their macroalgal hosts. Additionally, co-demographic analyses support synchronous population expansions of these four phylogeographically similar taxa. Our findings indicate that coastal paleoseismic activity has driven concordant impacts on multiple codistributed species, with concerted recolonization events likely facilitated by macroalgal rafting. These results highlight that high-resolution comparative genomic data can help reconstruct concerted multispecies responses to recent ecological disturbance.

opencc-zeroNov 2021View details →

Impacts of growth form and phylogenetic relatedness on seed germination: a large-scale analysis of a subtropical regional flora

Plant regeneration strategy plays a critical role in species survival and can be used as a proxy for the evolutionary response of species to climate change. However, information on the effects of key plant traits and phylogenetic relatedness on seed germination is limited at large regional scales that vary in climate. To test the hypotheses that phylogenetic niche conservatism plays a critical force in shaping seed ecophysiological traits across species, and also drives their response to climatic fluctuation, we conducted a controlled experiment on seed germination and determined the percentage and rate of germination for 249 species in subtropical China under two temperature regimes (i.e., daily 25ºC; daily alternating 25/15ºC for each 12 h). Germination was low with a skewed distribution (mean = 38.9% at 25ºC, and 43.3% at 25/15ºC). One fifth of the species had low (<10%) and slow (4–30d) germination, and only a few (8%) species had a high (>80%) and rapid (1.2–6.6d) germination. All studied plant traits (including germination responses) showed a significant phylogenetic signal, with an exception of seed germination percentage under the alternating temperature scenario. Generalized linear models (GLMs) and phylogenetic generalized estimation equations (GEEs) demonstrated that growth form and seed dispersal mode were strong drivers of germination. Our experimental study highlights that integrating plant key traits and phylogeny is critical to predicting seed germination response to future climate change.

opencc-by-4.0Nov 2021View details →

A Framework for Automated Construction of Heterogeneous Large-Scale Biomedical Knowledge Graphs (Recorded Talk)

This entry contains the recording of the presentation that was presented at the 2020 Intelligent Systems for Molecular Biology as part of the Bio-Ontologies COSI (https://www.iscb.org/ismb2020).

opencc-by-4.0Nov 2021View details →

Data related to "Near-bed sediment transport during offshore bar migration in large-scale experiments"

Abstract:  This paper presents novel insights into hydrodynamics and sediment fluxes in large-scale laboratory experiments with bi-chromatic wave groups on a relatively steep initial slope (1:15). An Acoustic Concentration and Velocity Profiler provided detailed information of velocity and sand concentration near the bed from shoaling up to the outer breaking zone including suspended sediment and sheet flow transport. The morphological evolution was characterized by offshore migration of the outer breaker bar. Decomposition of the total net transport revealed a balance of onshore-directed, short wave-related and offshore-directed, current-related net transport. The short wave-related transport mainly occurred as bedload over small vertical extents. It was linked to characteristic intrawave sheet flow layer expansions during short wave crests. The current-related transport rate featured lower maximum flux magnitudes but occurred over larger vertical extents. As a result, it was larger than the short wave-related transport rate in all but one cross-shore position, driving the bar's offshore migration. Net flux magnitudes of the infragravity component were comparatively low but played a non-negligible role for total net transport rate in certain cross-shore positions. Net infragravity flux profiles sometimes featured opposing directions over the vertical. The fluxes were linked to a standing infragravity wave pattern and to the correlation of the short wave envelope, controlling suspension, with the infragravity wave velocity. About the data: The data on beach profile (from mechanical profiler), velocity (from ACVP and ADV), sand concentration (from ACVP and OBS) and water surface elevation (from RWG, AWG and PT) measurements is given in .mat (MATLAB) files. 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 a 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) sampled at 50.5051Hz. For better interpretation of the measurements, it also features the ensemble-averaged intrawave instantaneous erosion depth (bed elevation) and the upper limit of the sheet flow layer. The folder “ADV” contains structures with the ensemble-averaged ADV data of each test sampled at 100Hz. Apart from the velocity components of each ADV it contains 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 and a more or less constant distance to the bed was assumed. The folder “OBS” contains structures with the ensemble-averaged OBS data of each test sampled at 40Hz. Apart from the concentration measurements it contains the vertical elevation of the OBSs with respect to the ACVP transceiver. The folder “ETA” contains structures with the ensemble-averaged water surface elevation measurements in many different absolute cross-shore locations in the flume sampled at 40Hz. 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','$^{10}log(c)$ $[kg/m^3]$','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)

opencc-zeroNov 2021View details →

Large-scale homogenization of soil bacterial communities in response to agricultural practices in paddy fields, China

<div>This dataset contains data from 257 sites in four typical rice-growing regions across a 4,000-km transect in China, including geographic location data and environment factors data.</div> <div> The study was conducted across a 4,000-km transect of China's rice-growing areas, from Heilong Jiang province to Yunnan province (100°55′ E to 134°08′° E, 22°46′ N to 48°02′ N, Table S1). Four typical rice-growing regions were selected along this transect: Sanjiang Plain (modern mechanical farming), Taihu Plain (mechanical plus minor manual farming), Lianghu Plain (manual plus minor mechanical farming), and Hani Terrace (traditional manual farming).</div> <div> Field sampling was conducted during July and August in 2014 and 2015. Soil samples were collected from 178 flooded paddy fields and 79 surrounding non-paddy areas across four typical rice-growing regions of China. At each site, one homogenized sample was obtained, which was then separated into two parts. One part, which was obtained for DNA extraction, was placed into a sterile plastic tube then immediately placed in liquid nitrogen for short-term transportation. After shipping to the laboratory, these tubes were stored at −80 °C. The second part of the soil sample was placed into a plastic bag and stored at 4.0 °C for determining the soil physicochemical properties.</div> <div> Main results of the experiments are that: (1) Distance–decay patterns of bacterial communities in paddy fields revealed reduced β-diversity compared to surrounding natural habitats. (2) Modern rice farming practices (plowing with machines) caused stronger homogenization of soil bacterial communities than traditional farming (plowing by hand). Among the four paddy regions, plowing by hand retained the highest soil bacterial β-diversity. (3) Moreover, a significant inverse correlation was observed between bacterial β-diversity and the agricultural mechanization level. (4) Among multiple environmental factors, dramatic spatial homogenization of soil physicochemical properties, particularly soil nutrient contents, and reduced dispersal limitation caused by modern farming activities both strongly predict a reduction of bacterial β-diversity in modern paddy fields.</div>