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1,891 results for “Field data”
'Danum Revised' forest field plot data for twenty 0.28 hectare field plots, measured in 2017
<p>Permanent forest plot data from Sabah, Malaysia. Danum Valley Conservation Area. Here we provide a brief description of the permanent plot data collected following the same protocols utilized within the Kuamut forest reserve.</p> <p>Twenty circular field plots with a 30 m radius that were established across the protected area as part of a collaboration between SEARRP and the Carnegie Airborne Observatory (CAO) in 2017. These plots were surveyed following the same protocols as those described by the Kuamut conservation project. The field protocol is provided. Field plot locations where selected using airborne LiDAR to specifically target both high Aboveground Carbon Density (ACD) areas, and also areas with different ACD predictions based on a draft set of carbon mapping models. See Jucker et al. (2018) and Asner et al. (2017) for further details. Note subsequent measurements (in 2020) include more plots. Subsequent measurements allowed further data checking and corrections, so some minor updates to the 2017 dataset have been made since the previous publications. Please see additional Zenodo data entries for the 2020 measure.</p> <p>Use of these data require citation of this dataset and citation of the two original journal articles that delivered and explain the work, and that you inform us of the use of the data. We would also appreciate the opportunity to be involved in work making use of this data. The required citations are as follows:</p> <p>Asner, Gregory P., Philip G. Brodrick, Christopher Philipson, Nicolas R. Vaughn, Roberta E. Martin, David E. Knapp, Joseph Heckler, et al. 2017. “Mapped Aboveground Carbon Stocks to Advance Forest Conservation and Recovery in Malaysian Borneo.” <em>Biological Conservation</em> 217 (June 2017): 289–310. <a href="https://doi.org/10.1016/j.biocon.2017.10.020">https://doi.org/10.1016/j.biocon.2017.10.020</a></p> <p>Jucker, Tommaso, Gregory P. Asner, Michele Dalponte, Philip G. Brodrick, Christopher D. Philipson, Nicholas R. Vaughn, Yit Arn Teh, et al. 2018. “Estimating Aboveground Carbon Density and Its Uncertainty in Borneo’s Structurally Complex Tropical Forests Using Airborne Laser Scanning.” <em>Biogeosciences</em> 15 (12): 3811–30. <a href="https://doi.org/10.5194/bg-15-3811-2018">https://doi.org/10.5194/bg-15-3811-2018</a></p> <p>This work would not be possible without the incredibly hard work and dedication of the entire SEARRP field team, including Philip Ak Ulok; Hii Siew Yee; Remmy Bin Murus; Alexander Karolus; Andy Brian Karolus; Frederica Karolus; Zidey Fulgentius; Welday Bin Girang; Mohamad Taufiq Bin Sumin, Mohd Fadil Bin Abd Karim, Joulu Rasion, and Japin Bin Rasion. We are very grateful to the help of all the field staff not specifically mentioned here.­</p>
Data of "A micromechanical Mean-Field Homogenization surrogate for the stochastic multiscale analysis of composite materials failure"
<p><strong>Id</strong><br>title = "A micromechanical Mean-Field Homogenization surrogate for the stochastic multiscale analysis of composite materials failure"<br>journal = International Journal for Numerical Methods in Engineering<br>year = 2023<br>volume = 124<br>pages = 5200-5262<br>doi = 10.1002/nme.7344<br>authors = "Calleja, Juan Manuel and Wu Ling, and Nguyen, Van-Dung and Noels, Ludovic"</p> <p>If you use these data or model, we would be grateful if you could cite this above paper</p> <p><strong>Software</strong><br>Requires GMSH and Python 3 with packages numpy, matplotlib, sklearn (scikit-learn), os, pickle, scipy, pandas, cvs, math, seaborn.<br>Each folder contains readme that will help the user to navigate through the data.</p> <p>To run the model you need the open source code <a href="http://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries </a>but you need to request access to cm3MFH as well</p> <p><strong>Directories</strong></p> <ol> <li>Main: Contains fast and easy access to the plots presented in the paper. The readme contained in this plot specifies the plots that are run with each code.</li> <li>1_SVE_Generator:Contains the files needed for the generation of the SVE, the statistical properties of the microstructure, and PLY samples for the full-field simulations, as well as the used samples</li> <li>2_Full_Field: contains the extracted data from the FF composite realizations, as well as the used random SVE geometries.</li> <li>3_Identification: Contains the identification code to find the effective parameters for each SVE realization as well as the obtained identification results.</li> <li>4_Generator: Contains the generated set of parameters for the 25 and 45 micrometer squared SVEs as well as the codes for the new data generation, the file with the generated data and the plots related with the MF-ROM random parameters and their cross-relations shown in Sections 2.5.2, 3.2.3 and 4.</li> <li>5_Tests: Contains all the information concerning the tests used for the verification of the MF-ROM and the ply and experimental compression results.</li> <li>MFH_vs_FF: Allows to easily test the inverse identification process through the use of random SVEs and verify the performance of the identified MFH parameters against its full-field counterpart.</li> </ol> <p><strong>Plot of figures</strong></p> <p>Figure 9 : Run "python plot_Gc.py" which can be found in folder Main/Full_Field_Energy<br>Figure 10: Run "python3 PDF_HIST_Gc.py", which can be found in folder Main/Histograms<br>Figure 23: Run "python3 plot.py" which can be found in folder Main/MFH_FF_Comparison<br>Figure 24: Run "python3 plot.py" which can be found in folder Main/MFH_FF_Comparison<br>Figure 27: Run "python3 Correlation_Graphs_25.py contained in folder Main/Distributions_25_Micrometer_SVE<br>Figure 29: Run "python3 PDF_HIST.py" which can be found in folder Main/Histograms<br>Figure 30: To obtain the data used in this figure, run "python3 DistanceCorrelation_25.py" which can be found in folder /4_Generator<br>Figure 31: To obtain the data used in this figure, run "python3 DistanceCorrelation_45.py" which can be found in folder /4_Generator<br>Figure 32: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 33: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 34: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 36: Run "python3 plot_New.py" which can be found in folder Main/PlyTests<br>Figure 46: Run "python3 plot_Test.py" which can be found in folder Main/CompressionExperiment<br>Figure B3: Run "python3 MicroStrAna.py" which can be found in folder Main/MicroStructStatistics<br>Figure B4: Run "python3 MicroStrAna.py" which can be found in folder Main/MicroStructStatistics<br>Figure D5: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D6: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D7: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D8: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D9: Run "python3 PDF_HIST_B.py" which can be found n folder Main/Histograms<br>Figure D10: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D11: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D12: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D13: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D14: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure E15: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure E16: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure E17: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure F18: Run "python3 plot_Convergence_25.py" which can be found in folder Main/Convergence<br>Figure F19: Run "python3 plot_Convergence_45.py" which can be found in folder Main/Convergence<br> </p> <p> </p> <p> </p> <p> </p>
Data for: Magnetic-field-assisted molecular beam epitaxy: Engineering of Fe3O4 ultrathin films on MgO(111)
<p>Molecular beam epitaxy is widely used for engineering low-dimensional materials. Here, we present a novel extension of the capabilities of this method by assisting epitaxial growth with the presence of an external magnetic field (MF). MF-assisted epitaxial growth was implemented under ultra-high vacuum conditions thanks to specialized sample holders for generating in-plane or out-of-plane MF and dedicated manipulator stations with heating and cooling options. The significant impact of MF on the magnetic properties was shown for ultra-thin epitaxial magnetite films grown on MgO(111). Using in situ and ex situ characterization methods, scanning tunneling microscopy, conversion electron Mössbauer spectroscopy, and the magneto-optic Kerr effect, we showed that the in-plane MF applied during the reactive deposition of 10 nm Fe3O4(111)/MgO(111) heterostructures influenced the growth morphology of the magnetite films, which affects both in-plane and out-of-plane characteristics of the magnetization process. The observed changes are explained in terms of modification of the effective magnetic anisotropy.</p>
Supplemental networks of cowords of the paper Measuring the impact of Big Data in the scientific research in Agriculture and allied fields
<p>Supplemental networks of cowords of the paper Measuring the impact of Big Data in the scientific research in Agriculture and allied fields.</p>
Raw video and pose estimation data of top view open field mouse behavior recordings of acute and chronic stress models
<p>This repository contains raw data for 411 different open field recordings of mice. these include top view raw video .mp4 files (Videos.zip) and the corresponding .csv pose estimation data (data.zip) obtained with DeepLabCut. The data is from multiple different experiments. The METADATA.csv or METADATA.xlsx files contain all grouping variables and help linking the pose estimation files (located in multiple subfolders of /data) to the video files. Visit https://github.com/ETHZ-INS/BehaviorFlow to find out more about how this data has be used by us.</p>
Raw video and pose estimation data of top view open field mouse behavior recordings after yohimbine injections
<p>This repository contains raw data for 32 different open field recordings of mice. these include top view raw video .mp4 files (Videos.zip) and the corresponding .csv pose estimation data (data.zip) obtained with DeepLabCut. The data is from a single yohimbine injection experiment at Roche, where animals were injected with saline, 1mg/kg,3mg/kg or 6mg/kg of yohimbine. The METADATA.csv or METADATA.xlsx files contain all grouping variables and help linking the pose estimation files (located in data/Yohimbine_Roche) to the video files. Visit https://github.com/ETHZ-INS/BehaviorFlow to find out more about how we have used this data</p>
Data for reconstruction of planar flow field
<p>The zip file consists of four subfolders. Refer to "Synopsis.pdf" for detailed explanations on contents in each subfolder.</p>
Data from: Zero- to ultralow-field relaxometry of chemical and biological fluids
<p>NMR relaxometry is an analytical technique that provides information about the molecular environment by analyzing the properties of NMR signals versus the magnitude of the longitudinal field. In this work, we present NMR relaxometry at zero and ultra-low magnetic fields (ZULFs). Operation under ZULFs allows us to investigate many slow (bio)chemical processes, whose timescale (milliseconds-seconds) coincides with a timescale of spin evolution. In contrast to their high-field NMR, ZULF NMR measurements can be performed with atomic magnetometers. Here, we share the dataset obtained from ZULF NMR relaxometry measurements of (bio)chemical compounds containing 1H 13C, 1H-15N, and 1H-31P spin pairs, human whole-blood and blood-plasma.</p>
Data for Geographic variation in phenotypic divergence between two hybridizing field cricket species
<p><span>Patterns of morphological divergence across species' ranges can provide insight into local adaptation and speciation. In this study, we compare phenotypic divergence among 4,221 crickets from 337 populations of two closely related species of field cricket, <em>Gryllus firmus</em> and <em>G. pennsylvanicus </em>and their hybrids. We find that these species differ across their geographic range in key morphological traits, such as body size and ovipositor length, and we directly compare phenotype with genotype for a subset of crickets to demonstrate nuclear genetic introgression, phenotypic intermediacy of hybrids, and essentially unidirectional mitochondrial introgression. We discuss how these morphological traits relate to life history differences between these two species. Our comparisons across geographic areas support prior research that suggested that cryptic variation within <em>G. firmus</em> may represent different species. Overall, our study highlights how variable morphology can be across wide ranging species, and the importance of studying reproductive barriers in more than one or two transects of a hybrid zone.</span></p>
Data set for "Electrical detection of the flat band dispersion in van der Waals field-effect structures"
<p>Data set for "Electrical detection of the flat band dispersion in van der Waals field-effect structures" paper published in doi:10.1038/s41565-023-01489-x</p>
Simulation data for "Connection between Chorus Wave Amplitude and Background Magnetic Field Inhomogeneity: A Parametric Study" which will be submitted to Geophysical Research Letters
<p>Simulation data for "Connection between Chorus Wave Amplitude and Background Magnetic Field Inhomogeneity: A Parametric Study" which will be submitted to Geophysical Research Letters.</p> <p>Including the simulation input parameter file and the necessary output data for analysis described in the article. The output data consists of waveform data at certain locations (|MLAT| = 3 deg).</p>
Field data of seed dormancy and pod dehiscence in Hairy Vetch (V. villosa)
<p>Hairy vetch (Vicia villosa Roth), a winter hardy annual legume, is a promising cover crop. To fully leverage its potential, seed production and field performance of V. villosa must be improved to facilitate producer adoption. Two classic domestication traits, seed dormancy (hard seed) and dehiscence (pod shatter), are selection targets in an ongoing breeding program. This study reports a genome-wide association study of 1,019 V. villosa individuals evaluated at two sites (Knox City, Texas and Corvallis, Oregon) for proportion of dormant seed, visual pod dehiscence scores, and two dehiscence surrogate measures (force to dehiscence and pod spiraling score).Trait performance varied between sites, but reliability (equivalent to heritability) across sites was strong (dormant seed proportion: 0.68; dehiscence score: 0.61; spiraling score: 0.42; force to dehiscence: 0.41). A major locus controlling seed dormancy was found (q-value: 1.29 x 10 -5 ; Chromosome 1: Position: 63611165), which can be used by breeding programs to rapidly reduce dormancy in breeding populations. No significant dehiscence score QTL were found, primarily due to the high dehiscence rates in Corvallis, Oregon. Since Oregon is a potentially major V. villosa seed production region, further dehiscence resistance screening is necessary.</p>
Phase field modelling combined with data-driven approach to unravel the orientation influenced growth of interfacial Cu6Sn5 intermetallics under electric current stressing
<p><strong>Description:</strong></p> <p>The datasets are constituted by two folders, namely, (A) data_features_and_metric.zip and (B) grain_area_prediction.zip. </p> <p><strong>(A) data_features_and_metric.zip:</strong></p> <p>The following are the contents of this folder</p> <p>(i) <em>grainTheta.csv file</em> : The "grainTheta.csv" file consists the datasets generated from multiple phase field simulations. Name of the columns in the csv file are:</p> <p> <strong>gnid </strong>= grain id number "n", <strong>ntheta = </strong>orientation angle of n<sup>th</sup> grain (<sup>o</sup>); <strong>nltheta </strong>= orientation angle of grain to the left of n<sup>th</sup> grain (<sup>o</sup>); <strong>nrtheta </strong>= orientation angle of grain to the right of n<sup>th</sup> grain (<sup>o</sup>); <strong>j </strong>= current density (A/m<sup>2</sup>);<strong> t =</strong> time (s); <strong>area</strong> = area of n<sup>th</sup> grain (m<sup>2</sup>);<strong> tl</strong> = horizontal length of the top edge of grain "n" (m) ; <strong>bl </strong>= horizontal length of the bottom edge of grain "n" (m) </p> <p>The features gnid, ntheta, nltheta and nrtheta for a given observation are determined during the design of initial conditions of the corresponding phase field simulation. The value of "j" for the observation is determined via the boundary condition in the same numerical simulation. The result from the finite element method based phase field simulation has provided the numerical quantities for t, area, tl and bl attributes. The multiple observations in the data file have been obtained from multiple phase field simulations. </p> <p>(ii) <em>imc_theta.ipynb, imc_theta.py and imc_theta.html files</em>: These files contain the code to build the Pearson's Correlation Coefficient (PCC) heatmap analysis of the data contained in grainTheta.csv file. </p> <p>(iii) <em>comparison_mse.csv</em>: This data file includes the information about mean square error for training data (tmse) and mean square error for validation data (vmse) at Epoch = 199 resulting from 10 different artificial neural network (ANN) models distinguished by 10 different values of learning rates (lr) . Thus, the name of the columns in this csv file are <strong>modelno</strong>, <strong>lr</strong>, <strong>tmse</strong> and <strong>vmse</strong>. </p> <p>(iv) <em>mse_comparison.gnu</em>: This file consists the codes required to output a png image from the data provided in comparison_mse.csv<em>. </em></p> <p>(v) train_loss.csv and val_loss.csv: These files consist of the data of tmse and vmse at all points of Epochs for the ANN model with lr = 2.5E-4 . Thus, the first column in train_loss.csv file corresponds to tmse whereas the second column is Epochs number. Similarly, vmse and Epochs represent the two columns in val_loss.csv file. </p> <p> </p> <p>(vi) <em>mse_lr2p5e-4.gnu</em> : This file consists the codes required to output a png image from the data provided in train_loss.csv and val_loss.csv<em>. </em></p> <p><strong>(B) grain_area_prediction.zip:</strong></p> <p>Inside this folder, there is a folder named "prediction_of_grain_area" consisting of the following files:</p> <p><em>initial_area.csv file</em>: This file consists the value of the initial grain area of grain 4. It is a constant at all orientation angle.</p> <p><em>predicted_result_00_5e4.csv</em>: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 0<sup>o</sup> and 0<sup>o </sup>respectively, and for applied current density of 5.0E+4 J/m<sup>2</sup> .</p> <p><em>predicted_result_00_5e5.csv</em>: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 0<sup>o</sup> and 0<sup>o </sup>respectively, and for applied current density of 5.0E+5 J/m<sup>2</sup> .</p> <p><em>predicted_result_9090_5e4.csv</em>: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 90<sup>o</sup> and 90<sup>o </sup>respectively, and for applied current density of 5.0E+4 J/m<sup>2</sup> .</p> <p><em>predicted_result_9090_5e5.csv</em>: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 90<sup>o</sup> and 90<sup>o </sup>respectively, and for applied current density of 5.0E+5 J/m<sup>2</sup> .</p> <p><em>area_00_adj.gnu</em> : This gnu file contains the code to produce the png image from the data contained in <em>predicted_result_00_5e4.csv </em>and<em> predicted_result_00_5e5.csv </em>. The information about the initial area of grain 4 is obtained from <em>initial_area.csv</em> file by the code.</p> <p><em>area_9090_adj.gnu</em> : This gnu file contains the code to produce the png image from the data contained in <em>predicted_result_9090_5e4.csv </em>and<em> predicted_result_9090_5e5.csv </em>. The information about the initial area of grain 4 is obtained from <em>initial_area.csv</em> file by the code.</p> <p> </p>
Data from: The Phoridae of NE-Westphalia: A field study over five years, Part I. Genera except Megaselia Rondani
<p class="western">Contributing reliable data sets to support conservation efforts is currently the most important target of taxonomy and faunistics, yet the lack of statistically significant data makes unambiguous recommendations based on actual biodiversity mostly elusive. In particular, the abundance distribution of large and ecologically diverse groups as a prerequisite to assess regional differences is in general not available. In this study, a comprehensive faunistic record of Phoridae comprising about 290 species and more than 62000 specimens from a single location in NE-Westphalia caught over five years in a Malaise trap and occasionally by further methods is presented. The study will be distributed among two titles: I) Genera except <em>Megaselia</em>. II) <em>Megaselia</em>.</p> <p class="western">This first part is devoted to 71 species of Phoridae except <em>Megaselia</em>: their sex ratio, abundance distribution, rarefaction and a thorough presentation of rare species and species new to the area of investigation including annotations concerning morphology, ecology, distribution and used literature. Where feasible, a photo showing important characters has been added.</p>
Raw Data From: "Investigating the crystallinity of hard candies prepared and stored at different temperatures with low field-NMR relaxometry"
<p><strong>Background: </strong>In this study, hard candies were produced by using sucrose, glucose syrup and water. They were cooked at different temperatures, changing from 135 to 145 °C with an interval of 2.5 °C. They were stored at different storage temperatures, which were 25, 4, -18 and -80 °C. Hard candies placed at room temperature were stored for 2 months. In order to understand the crystallization characteristics of the hard candies, time domain (TD) proton nuclear magnetic resonance (<sup>1</sup>H-NMR) parameters of longitudinal relaxation time (T<sub>1</sub>) and second moment (M<sub>2</sub>) measurements were conducted. Moisture contents of the hard candies were determined by Karl-Fischer titration. X-ray diffraction experiments were also conducted as the complementary analysis.</p> <p><strong>Results: </strong>Increasing cooking temperature increased the crystallinity and decreased the moisture content of the hard candies significantly (P ≤0.05). Furthermore, storage temperature and storage time had significant effects on the crystallinity of the hard candies (P ≤0.05). The results of T<sub>1</sub> and M<sub>2</sub> correlated with each other (r > 0.8, P ≤ 0.5) and both produced the highest value at the cooking temperature of 145 °C and storage temperature of 4 °C (P ≤ 0.05). The values of T<sub>1</sub> and M<sub>2</sub> were obtained as 245.9 ms and 13.0 × 10<sup>-8</sup> Hz<sup>2</sup>, respectively, for the cooking temperature of 145 °C and storage temperature of 4 °C.</p> <p><strong>Conclusion: </strong>This study demonstrated that the crystallinity of hard candies can be observed and examined by TD-NMR relaxometry, as an alternative to commonly used methods. © 2024 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.</p>
Data from: Time and magnetic field dependent spectra of NODIPS-Tetracene
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Data from: Field-realistic antidepressant exposure disrupts group foraging dynamics in mosquitofish
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Data from: Interacting effects of genetic variation for seed dormancy and flowering time on phenology, life history, and fitness of experimental Arabidopsis thaliana populations over multiple generations in the field
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Data from: Molecular mapping and identification of quantitative trait loci for domestication traits in field cress (Lepidium campestre L.) genome
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Data for: Foraging speed and precision of arbuscular mycorrhizal fungi under field conditions: An experimental approach
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ScienceDex guides
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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.