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1,891 results for “Field data”
Ab initio data to generates machine-learned force fields of ions in aqueous medium in VASP format.
<p>ML_AB_H: Datasets of 64 water molecules and a single proton with 64 water molecules.</p> <p>ML_AB_VP2: Datasets of a single V^2+ ion with 64 water molecules.</p> <p>ML_AB_VP3: Datasets of a single V^3+ ion with 64 water molecules.</p> <p>ML_AB_FeP2: Datasets of a single Fe^2+ ion with 64 water molecules.</p> <p>ML_AB_FeP3: Datasets of a single Fe^3+ ion with 64 water molecules.</p> <p>ML_AB_CuP1: Datasets of a single Cu^+ ion with 64 water molecules.</p> <p>ML_AB_CuP2: Datasets of a single Cu^2+ ion with 64 water molecules.</p> <p>ML_AB_RuP2: Datasets of a single Ru^2+ ion with 64 water molecules.</p> <p>ML_AB_RuP3: Datasets of a single Ru^3+ ion with 64 water molecules.</p> <p>ML_AB_AgP1: Datasets of a single Ag^+ ion with 64 water molecules.</p> <p>ML_AB_AgP2: Datasets of a single Ag^2+ ion with 64 water molecules.</p> <p>ML_AB_O2: Datasets of a single O2 ion with 64 water molecules.</p> <p>ML_AB_O2N1: Datasets of a single O2^- ion with 64 water molecules.</p> <p>ML_AB_water: Datasets of 64 water molecules presenting bulk water and 64 water molecules representing water slab.</p> <p>All datasets were generated by VASP using PAW, plane wave basis sets with cutoff energy of 520 eV and RPBE+D3 exchange-correlation functional with zero-damping. All ab initio calculations were done on extended systems with periodic boundary conditions. See details in <a href="https://doi.org/10.48550/arXiv.2409.11000">https://doi.org/10.48550/arXiv.2409.11000</a>.</p>
Field investigations of salt partitioning and aqueous chemistry of freezing closed-basin lakes in Mongolia as terrestrial analogs of subsurface brine reservoirs on icy bodies [Data set]
<p>All measurement and calculation data</p> <p>Measurement and calculation data in Version 4 were uploaded in 2021-11-2</p>
Data for the paper "Magnetic fields with precise quasisymmetry"
<p>This archive contains data and source code used for the paper "Magnetic fields with precise quasisymmetry".</p>
Data related to publication "Coherent phase transfer for real-world twin-field quantum key distribution"
<p>These datasets have been used to produce Figure 3, 4 and 5 of manuscript: "Coherent phase transfer for real-world twin-field quantum key distribution". The files contain two arrays: time in seconds and normalised intensity.</p> <p>Explanation for "Data_fig3_XXX.txt": the files contains the raw data used to produce Fig. 3. The following timespans have been used:</p> <p>Data_fig3_stabilised.txt: t_start= 0.29 s, t_stop=0.292 s</p> <p>Data_fig3_unstabilised.txt: t_start=0.00225 s, t_stop=0.00424 s</p> <p>Explanation for "Data_fig4_XXX.txt" files: the procedure to obtain the phase deviation from the normalised interference is detailed in the text (Methods section). Datasets with different sampling rate have been combined to obtain the phase deviation on the long and short term.</p> <p>Explanation for "Data_fig5.txt": the files contains the raw data used to produce Fig. 5.</p>
Data from: N-mixture models estimate abundance reliably: a field test on Marsh Tit using time-for-space substitution
<p>Imperfect detection in field studies on animal abundance, including birds, is common and can be corrected for in various ways. The binomial N-mixture (hereafter binmix) model developed for this task is widely used in ecological studies owing to its simplicity: it requires replicated count results as the input. However, it may overestimate abundance and be sensitive to even small violations of its assumptions. We used a 33-year dataset on the Marsh Tit, Poecile palustris, a sedentary forest passerine, from Białowieża Forest, Poland to validate inference from binmix models by comparing model-estimated abundances to the true number of breeding pairs within the plots, determined by exhaustive population study. The abundance estimates, derived from six springtime (April-May) counts of males on each plot in each year, were highly reliable: 116 out of 132 year-plot estimates (88%) included the true number of pairs within the 95% confidence intervals. Over- and underestimations were thus rare and similarly frequent (9 and 12 cases, respectively), with a tendency to overestimate at low densities and underestimate at high densities. Marsh Tits sing rarely but the frequency of countersinging increases with abundance, leading to non-independence in detections. When accounted for in a submodel for detection, the per-survey number of countersinging events positively affected detection probability but only weakly affected abundance estimates. Simulations further demonstrate that this property, overestimation at low densities and underestimation at high densities, may be a systematic bias of binmix model even if density-dependent detection is absent. While the behaviour of binmix models in specific situations requires more study, we conclude that these models are a valid tool to estimate abundance reliably when intensive population monitoring is not feasible.</p>
Data on measuring splash dispersal of a major wheat pathogen in the field
<p>Capacity for dispersal is a fundamental fitness component of plant pathogens. Characterization of plant pathogen dispersal is important for understanding how pathogen populations change in time and space. We devised a systematic approach to measure and analyze rain splash-driven dispersal of plant pathogens in field conditions, using the major fungal wheat pathogen Zymoseptoria tritici as a case study.We inoculated field plots of wheat (Triticum aestivum) with two distinct Z. tritici strains. Next, we measured disease intensity as counts of fruiting bodies (pycnidia) using automated image analysis. These measurements characterized primary disease gradients, which we used to estimate effective dispersal of the pathogen population. Genotyping of reisolated pathogen strains with strain-specific PCR confirmed the conclusions drawn from phenotypic data. Consistently with controlled environment studies,we found that the characteristic scale of dispersal is tens of centimeters.We analyzed the data using a spatially explicit mathematical model that incorporates the spatial extent of the source, rather than assuming a point source,which allows for a more accurate estimation of dispersal kernels.We employed bootstrapping methods for statistical testing and adopted a two-dimensional hypotheses test based on kernel density estimation, enabling more robust inference compared with standard methods. We report the first estimates of dispersal kernels of the pathogen in field conditions. However, repeating the experiment in other environments would lead to more robust conclusions.We put forward advanced methodology that paves the way for further measurements of plant pathogen dispersal in field conditions, which can inform spatially targeted plant disease management. </p>
Field-comprehensive-data-from-a-representative-groundwater-depth-transect-in-Central-Asia
<p>Data and Code Availability for paper--Anthropogenic drought accelerates catastrophic regime shifts of groundwater-dependent ecosystems in drylands</p>
Reanalysed JLA Type Ia Supernovae Data with 2M++ Density Field
<p>These are the data products to supplement Rahman et al. 2021 here https://arxiv.org/abs/2108.12497</p> <p>The covariance file statssys_JLA_A2_no_colour_off_diag.gz contains the new peculiar velocity corrections and removes the off-digonal colour correlations to be consistent with the selection effects methodlogy applied in Rahman et al. 2021</p> <p>If you want the color covariances to be included because you want to ignore the selection effects analysis in the paper or apply your own, this version of the covariance matrix is called statssys_JLA_A2.gz</p> <p> </p> <p> </p> <p>nb: for each 3 x 3 sub matrix, the order of the covariance matrix is color, x1, mb on the diagonal.</p> <p> </p>
LSPSW_field_data
<p>The zip file contains the field data used in the paper "<em>Extraction of surface wave phase velocities from ambient noises in the presence of local noise sources based on matched-field processing</em>" submitted to GJI. Data is saved as .sac file Details can be found in <em>readme.txt</em> in the zip file.</p>
Unwrapping reworked crust at the Columbia supercontinent margin within Amazonian Craton using satellite potential field data synergy
<p>This file contains the geochronology data inventory (Supplementary Material Table 1) as a MS Excel (*.xls) table used for interpretation in the the original publication of 'Unwrapping reworked crust at the Columbia supercontinent margin within Amazonian Craton using satellite potential field data synergy'. Paper published in Geoscience Frontiers in early 2022.</p>
River microplastic field data locations associated with publication
<p>Field data locations associated with Piehl et al (2020) Can Water Constituents Be Used as Proxy to Map Microplastic Dispersal Within Transitional and Coastal Waters? doi: <a href="https://www.frontiersin.org/articles/10.3389/fenvs.2020.00092/full">10.3389/fenvs.2020.00092</a>. Full dataset table, including water quality measurements and sampled microplastic concentrations, can be found in the Supplemental Material of that publication. </p>
Data supporting "Can Long-Term Experiments Predict Real Field N and P Balance and System Sustainability? Results from Maize, Winter Wheat, and Soybean Trials Using Mineral and Organic Fertilisers"
<p>Data supporting "Can Long-Term Experiments Predict Real Field N and P Balance and System Sustainability? Results from Maize, Winter Wheat, and Soybean Trials Using Mineral and Organic Fertilisers" by Piccoli et al. (2021) Agronomy 2021, 11, 1472. https://doi.org/10.3390/agronomy11081472</p>
Data from: Comparing the efficacy of control strategies for infectious disease outbreaks using field and simulation studies
<p>Diseases characterized by long distance inoculum dispersal (LDD) are among the fastest spreading epidemics in both natural and managed landscapes. Management of such epidemics is extremely challenging because of asymptomatic infection extending at large spatial scales and frequent escape from the newly established disease sources. We compared the efficacy of area- and timing-based disease management strategies in artificially initiated field epidemics of wheat stripe rust and complemented with simulations from an updated version of the spatially explicit model EPIMUL, using model parameters relevant to field epidemics. The model was further used to expand the number of epidemic mitigations beyond that feasible to incorporate in the field. The field experiment was conducted for two years in two locations having different climatic conditions. Culling and protection treatments were applied at different times after epidemic initiation and to different spatial extents surrounding the outbreaks. In each experiment, treatments were replicated four times in plots 33.5 m long and 1.52 m wide with a 0.76 x 0.76 m inoculated focus centered within each plot. Disease gradients were assessed along the center lines of the plots at 1.52 m intervals both upwind and downwind from the focus. Both field and simulation results indicated that control measures applied over the entire population were highly effective in suppressing the epidemics by more than 99% but may not always be logistically and economically feasible at large spatial scales. Comparison between the variable sized treatment areas and application timings suggested that implementing contiguous premises (CP) cull at 1 day after first sporulation in the outbreak focus reduced rust by 52 and 60% in Corvallis and Madras, respectively. However, altering the cull size did not significantly affect the disease epidemic development, which suggested that early timing had a greater influence in suppressing the epidemics than did increased area of application. However, sufficiently large, treated areas may compensate for a delay in application timing to some extent. Results from these replicated treatments may help to devise appropriate management strategies for other LDD pathogens. </p>
Data for RSTA-2021-0255 - Modelling attenuation of irregular wave fields by artificial ice floes in the laboratory
<p>Data are from two independent experimental campaigns in 3D wave basins, which were conducted to investigate the interaction between surface waves and thin floating plates, mimicking the interaction between ocean waves and sea ice floes. </p> <p>One experiment tested irregular waves, including directional spreading, with two peak steepness values comparable to storm and polar cyclone conditions, interacting with a single square polypropylene floe, with length close to the dominant wavelength (scattering regime) -- data file Single_Plate.zip.</p> <p>The second experiment used arrays of wooden floes with low- and high-concentrations to test wave-floe interaction at a range of peak periods and peak wave steepness spanning from gentle to storm-like conditions -- data file MultipleDisks.zip.</p> <p>Both data files contains raw data, experimental matrix and experimental set.</p> <p>Data are fully described and analysed in a contribution to "Theory, modelling and observations of marginal ice zone dynamics: Multidisciplinary perspectives and outlooks" a special theme issue for Philosophical Transactions A: </p> <p>Toffoli A., Pitt J. P. A., Alberello A., Bennetts L.G., 2022. Modelling attenuation of irregular wave fields by artificial ice floes in the laboratory. Philos. T. Roy. Soc. A (submitted)</p> <p>Additional information on the data sets can be found in </p> <p>Bennetts LG, Alberello A, Meylan MH, Cavaliere C, Babanin AV, Toffoli A. 2015 An idealised experimental model of ocean surface wave transmission by an ice floe, Ocean Model. 96, 85–92 </p> <p>Bennetts LG, Williams TD. 2015 Water wave transmission by an array of floating discs, Proc. Roy. Soc. A 471, 20140698</p>
Gravity data collected during volcanic unrest period of the Svartsengi geothermal field in Iceland
<p>The file contains free-air corrected data collected during the one-year unrest at the Svartsengi geothermal field in Iceland as a precursor the Fagradalsfjall eruption in 2021.</p> <p>Column 1: Name of the measurement site</p> <p>Column 2 and 3: Geographical coordinates of measurement sites</p> <p>Column 4-6. Change in elevation, free-air correction and free air gravity change from January 28-29th to April 22-28th 2020</p> <p>Column 7-9. Change in elevation, free-air correction and free air gravity change from April 22-28th to October 5-6th 2020</p> <p>Column 10-12. Change in elevation, free-air correction and free air gravity change from October 5-6th 2020 to February 17-18th 2021</p>
Long-wave infrared super-resolution wide-field microscopy by sum-frequency generation - experimental data
<p>Experimental Data for "Long-wave infrared super-resolution wide-field microscopy by sum-frequency generation", under consideration at APL, preprint: <a href="https://doi.org/10.48550/arXiv.2112.08112">https://doi.org/10.48550/arXiv.2112.08112</a></p> <p>Files:</p> <p>readme.txt: explanation of the content<br> SFGmicroscope.h5: microscope data<br> APL_test_script.m: matlab test script generating the relevant figures from the data</p> <p>For more information, please contact Richarda Niemann (niemann@fhi-berlin.mpg.de) or Alex Paarmann (alexander.paarmann@fhi-berlin.mpg.de).</p>
Labeled data for citation field extraction
<p>Citations are an important part of scientific papers, and the proper handling of them is indispensable for the science of science. Citation field extraction is the task of parsing citations: given a citation string, extract authors, title, venue, doi etc. Since the number of citations is counted by hundreds millions, efficient computer based methods for this task are very important.</p> <p>The development of machine learning methods for citation field extraction requires ground truth: a large corpus of labeled citations. This dataset provides a very large (41M) corpus of labeled data obtained by the reverse process: we took structured citation lists and used BibTeX to generate labeled citation strings.</p>
Dataset of "Pressure from data-driven estimation of velocity fields using snapshot PIV and fast probes"
<p>Dataset of the article <em>Pressure from data-driven estimation of velocity fields using snapshot PIV and fast probes </em>(<a href="https://doi.org/10.1016/j.expthermflusci.2022.110647">https://doi.org/10.1016/j.expthermflusci.2022.110647</a>). A data-driven method is applied to combine non-time-resolved velocity field and fast probe data, and achieve time-resolved velocity and pressure field.</p> <p>The codes processing data here are on https://github.com/erc-nextflow/Data-driven-pressure-estimation-with-EPOD.</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 949085, NEXTFLOW).</p>
Database of non-target invertebrates recorded in field experiments of genetically engineered Bt maize and corresponding non-Bt maize: data files
<p><span>This database represents a comprehensive collection of experimental field data from all over the world on non-target invertebrates recorded in genetically engineered/modified Bt and non-Bt maize. The three data files deposited here are described by Meissle et al. (2022), BMC Research Notes, <span><a href="https://doi.org/10.1186/s13104-022-06021-3"><span>https://doi.org/10.1186/s13104-022-06021-3</span></a></span><span> </span>.</span></p> <p><span>The database was created for a systematic review with the question if growing Bt maize changes abundance or ecological function of non-target animals compared to growing of non-GM maize, published by Meissle et al. (2022), Environmental Evidence, <a href="https://doi.org/10.1186/s13750-022-00272-0"><span>https://doi.org/10.1186/s13750-022-00272-0</span></a></span>.</p> <p>Data file 1 contains the database with 7279 records of non-target invertebrate abundance, activity density, or predation or parasitism in Bt and non-Bt maize, extracted from 120 publications. Data file 2 includes the list and definitions of variables in the database, and data file 3 represents the critical appraisal questions and answer options.</p>
Oscillatory Flow Testing Data Collected at Field Site for Research in Fractured Sedimentary Rock (FSR)^2
<p>This dataset contains raw and processed pressure data collected in 2019 during oscillatory flow testing experiments at the Field Site for Research in Fractured Sedimentary Rock (FSR)^2 near Madison, WI. The included ReadMe file describes the data and code used in data processing. The companion processing and analysis code is included as a separate upload (doi:10.5281/zenodo.6584777)</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.