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26 results for “grain set”
Data set for: Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models
<p>This data set contains the simulations and data analysis files used in the publication: "<em>Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models</em>", by D. Cortés-Ortuño, K. Fabian and L. V. de Groot.</p> <p>The data set includes:</p> <ul> <li>Scripts and output files from MERRILL simulations</li> <li>Jupyter notebooks with data analysis</li> <li>Figures</li> </ul> <p>A preprint of this work can be found in:</p> <p>David Cortés-Ortuño, Karl Fabian and Lennart V. de Groot. <em>Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models.</em> DOI: 10.1002/essoar.10510574.1. Earth and Space Science Open Archive. <a href="https://doi.org/10.1002/essoar.10510574.1">https://doi.org/10.1002/essoar.10510574.1</a></p> <p>The README file in this dataset (in markdown format) contains full details about the simulations. The dataset also contains pre-computed data files to calculate the inversions and produce the figures and analyze the inversion data without processing the vbox files.</p> <p>To cite this dataset you can use the following bibtex entry:</p> <pre><code>@Misc{Cortes2022, author = {Cortés-Ortuño, David and Fabian, Karl and de Groot, Lennart V.}, title = {{Data set for: Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models}}, publisher = {Zenodo}, year = {2022}, doi = {10.5281/zenodo.6501818}, url = {https://doi.org/10.5281/zenodo.6501818}, } </code></pre> <p> </p>
Supporting data: Grain-dependent responses of mammalian diversity to land-use and the implications for conservation set-aside
<p>Camera trap and live trap datasets underlying the analyses in an <em>Ecological Applications </em>paper (http://onlinelibrary.wiley.com/doi/10.1890/15-1363/abstract), provided in .csv format. Each row consists of a single trap night at a given location, with species in different columns. Old-growth forest, logged forest and oil palm plantation locations have the prefixes "Old", "Log" and "Palm", respectively. Values in each cell are the number of independent captures, as defined in the paper. </p>
Strong Grain Neighbour Effects in Polycrystals- Data set
<p>3D-XRD measurements are for a commercially pure zirconium (CPZr) sample deformed in-situ to 1.2% strain.</p> <p>HR-EBSD measurements are for the same deformed sample after unload.</p> <p>Crystal Plasticity Finite Element (CPFE) simulation was done on the measured micro-structure and results of the NHS model are provided.</p> <p>HR-EBSD and CPFE results are generated using in-house codes.</p> <p>Details of the 3D-XRD codes are provided in the following link:</p> <p>https://sourceforge.net/p/fable/wiki/Home/</p> <p> </p> <p>The work and further analysis of the results are described in:</p> <p>"Strong Grain Neighbour Effects in Polycrystal" Published in Nature Communications, DOI: <strong>10.1038/s41467-017-02213-9</strong></p> <p> </p> <p>Other relevant papers:</p> <p>Abdolvand, H., Majkut, M., Oddershede, J., Wright, J., Daymond, M. R., “Study of 3-D Stress Development in Parent and Twin Pairs of a Hexagonal Close-Packed Polycrystal: Part I- In situ Three-Dimensional X-ray Diffraction Measurement”, Acta Materialia, July 2015, Vol 93, Page 246-255.</p> <p> </p> <p>Abdolvand, H., Majkut, M., Oddershede, J., Wright, J., Daymond, M. R., “Study of 3-D Stress Development in Parent and Twin Pairs of a Hexagonal Close-Packed Polycrystal: Part II- Crystal Plasticity Finite Element Modeling”, Acta Materialia, July 2015, Vol 93, Page 235-245.</p> <p> </p> <p>Abdolvand, H., Majkut, M., Oddershede, J., Schmidt, S., Lienert, U., Diak, B., Withers, P. J., Daymond, M. R., “On the Deformation Twinning of MgAZ31B: a Three-Dimensional X-ray Diffraction Experiment and Crystal Plasticity Finite Element Model”, International Journal of Plasticity, July 2015, Vol 70, Page 77-97.</p> <p> </p> <p>Gong, J., Britton, B. T., Cuddihy, M. A., Dunne, F. P. E. & Wilkinson, A. J. <a> Prismatic, <a> basal, and <c+a> slip strengths of commercially pure Zr by micro-cantilever tests. Acta Mater. 96, 249–257 (2015).</p> <p> </p> <p>Poulsen, H. F. An introduction to three-dimensional X-ray diffraction microscopy. J. Appl. Crystallogr. 45, 1084–1097 (2012)</p> <p> </p> <p>Wilkinson, A. J., Meaden, G. & Dingley, D. J. High-resolution elastic strain measurement from electron backscatter diffraction patterns: New levels of sensitivity. Ultramicroscopy 106, 307–313 (2006)</p> <p> </p>
Text-fig. 6. Geological plan of Malo-Mikhaylovka. 1 – andesito-dacite; 2 – coarse-grained tuff; 3 – argillitic tuffite; 4 – tuffitic sandstone; 5 – lignite, coal clay; 6 – lenses of tuffitic conglomerate; 7 – acidic tuff; 8 – dacite; 9 – andesito-basalt; 10 – basalt; 11 – sandstone; 12 – andesite; 13 – break; 14 – inclination/direction of beds; 15 – plant-bearing levels; 16 – talus. in Mid-Latitude Palaeogene Floras Of Eurasia Bound To Volcanic Settings And Palaeoclimatic Events - Experience Obtained From The Far East Of Russia (Sikhote-Alin') And Central Europe (Bohemian Massif)
Text-fig. 6. Geological plan of Malo-Mikhaylovka. 1 – andesito-dacite; 2 – coarse-grained tuff; 3 – argillitic tuffite; 4 – tuffitic sandstone; 5 – lignite, coal clay; 6 – lenses of tuffitic conglomerate; 7 – acidic tuff; 8 – dacite; 9 – andesito-basalt; 10 – basalt; 11 – sandstone; 12 – andesite; 13 – break; 14 – inclination/direction of beds; 15 – plant-bearing levels; 16 – talus.
Text-fig. 3. Distribution of main types of volcanoes in the NearShore Volcanic Belt of Eastern Sikhote-Alin' (Eocene–Neogene). 1 – Central volcanoes (partly preserved); 2 – Central volcanoes (destructed); 3 – Shield and gentle sloping volcanoes with a dolerite or trachy-basaltic neck on the top; 4 – Lava and scoria cones; 5 – Pyroclastic, tuffaceous coarse- and fine-grained terrigenous sedimentary rocks, partly with plant-bearing levels; 6 – Eruption centers of plateau-basalts and the direction of lava flows; 7 – Main Late Cenozoic basaltic plateaus; 8 – Fumarol fields; 9 – Hot springs. in Mid-Latitude Palaeogene Floras Of Eurasia Bound To Volcanic Settings And Palaeoclimatic Events - Experience Obtained From The Far East Of Russia (Sikhote-Alin') And Central Europe (Bohemian Massif)
Text-fig. 3. Distribution of main types of volcanoes in the NearShore Volcanic Belt of Eastern Sikhote-Alin' (Eocene–Neogene). 1 – Central volcanoes (partly preserved); 2 – Central volcanoes (destructed); 3 – Shield and gentle sloping volcanoes with a dolerite or trachy-basaltic neck on the top; 4 – Lava and scoria cones; 5 – Pyroclastic, tuffaceous coarse- and fine-grained terrigenous sedimentary rocks, partly with plant-bearing levels; 6 – Eruption centers of plateau-basalts and the direction of lava flows; 7 – Main Late Cenozoic basaltic plateaus; 8 – Fumarol fields; 9 – Hot springs.
Text-fig. 12. Geological plan of the Velikaya Kema plant-bearing locality (4 km north of Velikaya Kema village). 1 – basalt with flaggy flows; 2 – massive basalt; 3 – andesite with flaggy flows; 4 – andesite-basalt; 5 – trachyte, 6 – felsite; 7 – agglomerate, basalt and andesite; 8 – tuff coarse-grained; 9 – conglomerate; 10 – thin layers of andesitic tuff; 11 – tuffite, tuffaceous argillite, diatomite; 12 – plant bearing levels. in Mid-Latitude Palaeogene Floras Of Eurasia Bound To Volcanic Settings And Palaeoclimatic Events - Experience Obtained From The Far East Of Russia (Sikhote-Alin') And Central Europe (Bohemian Massif)
Text-fig. 12. Geological plan of the Velikaya Kema plant-bearing locality (4 km north of Velikaya Kema village). 1 – basalt with flaggy flows; 2 – massive basalt; 3 – andesite with flaggy flows; 4 – andesite-basalt; 5 – trachyte, 6 – felsite; 7 – agglomerate, basalt and andesite; 8 – tuff coarse-grained; 9 – conglomerate; 10 – thin layers of andesitic tuff; 11 – tuffite, tuffaceous argillite, diatomite; 12 – plant bearing levels.
Short communication: Synchrotron-based elemental mapping of single grains to investigate variable infrared-radiofluorescence emissions [Data set]
<p>This dataset accompanies a research paper in the journal Geochronology (<span><a href="https://doi.org/10.5194/gchron-6-77-2024"><span>https://doi.org/10.5194/gchron-6-77-2024</span></a></span>). It contains the raw output of micro-XRF measurements carried out at the 5-ID SRX beamline at the National Synchrotron Light Source II (NSLS-II) at Brookhaven National Laboratory, USA, on coarse K-feldspar grains. Additionally, the XRF intensity attributed to each element after spectral fitting is provided for elemental mapping. The dataset also includes the output from scanning electron microscope energy-dispersive X-ray spectroscopy (SEM-EDS) measurements on coarse K-feldspar grains of two samples taken at Archéosciences Bordeaux, France.</p>
Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites: data set
<p>Abstract:<br> from [1]</p> <blockquote> <p>Polymer nanocomposites are an important class of materials for engineering applications due to their high versatility and good mechanical properties combined with low density. By directly attaching the polymer chains to the nanofillers, the so-called grafting, a better load transfer between matrix and filler is achieved, and, in addition, a better dispersion of the fillers is obtained. Both result in enhanced mechanical properties. Since experimental investigations on the nanoscale are extremely challenging, complementary numerical studies are needed to unravel the mechanical behavior of polymer nanocomposites. To this end, molecular dynamics is ideally suited since it captures the microstructure, but is also numerically expensive. Therefore, this contribution presents a fast coarse-grained molecular dynamics model for the investigation of the mechanical behavior of grafted polymer nanocomposites. For this purpose, we extend an existing model by grafting bonds, which allows us to compare the effect of untreated and grafted fillers directly. In particular, we investigate the influence of filler content, grafting degree, and filler size on the stiffness and strength of the polymer (grafted) nanocomposites. We conclude that the grafting bonds have little effect on the stiffness, while the strength is significantly improved compared to the untreated fillers, which is in agreement with the literature. The presented molecular dynamics model for polymer grafted nanocomposites provides the basis for further investigations, particularly of the crucial matrix-filler interphase. In addition, this contribution translates molecular dynamics insights into mechanical properties, which bridges the gap to the engineering scale and thus represents a step towards exploiting the full potential of polymer (grafted) nanocomposites.</p> </blockquote> <p> </p> <p><strong>Contact:</strong></p> <p>Maximilian Ries<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Egerlandstr. 5<br> 91058 Erlangen</p> <p><strong>Software:</strong></p> <p>All MD simulations were performed with LAMMPS [2,3], version: 29 Oct 2020 / 20201029</p> <p>Compiled with<br> Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled<br> C++ standard: C++11</p> <p>Active compile time flags:<br> -DLAMMPS_GZIP<br> -DLAMMPS_SMALLBIG</p> <p>Installed packages:<br> CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD</p> <p>Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [4]</p> <p>Post-processing Matlab R2019b</p> <p><strong>License:</strong></p> <p>Creative Commons Attribution 4.0 International</p> <p><strong>Context:</strong></p> <p>Data set supplementing journal paper:</p> <p>[1] M. Ries, S. Reber, P. Steinmann, & S. Pfaller, “Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites,” <em>Forces in Mechanics</em>, vol. 12, p. 100 207, <strong>2023</strong>.</p> <p><strong>Content:</strong></p> <p>structure of data set:</p> <ul> <li>04_Equilibration<br> folders containing the sample equilibration used in the presented parameter study <ul> <li>01_filler_content<br> variation of filler content</li> <li>02_grafting_density<br> variation of grafting density</li> <li>03_grafting_potential<br> variation of grafting potential</li> <li>04_filler_size<br> variation of filler size</li> <li>05_reference<br> reference samples without grafting</li> </ul> </li> <li>05_UT<br> folders containing the uniaxial tension simulations used in the presented parameter study <ul> <li>01_filler_content<br> variation of filler content</li> <li>02_grafting_density<br> variation of grafting density</li> <li>03_grafting_potential<br> variation of grafting potential</li> <li>04_filler_size<br> variation of filler size</li> <li>05_reference<br> reference samples without grafting</li> </ul> </li> </ul> <p>Each simulation directory contains:</p> <ul> <li> <p>lammps input file (*.in) of the specific simulation</p> </li> <li> <p>data file (*.data) containing the initial sample configuration</p> </li> <li> <p>input.prm: input parameters of the specific simulation (read by the input file)</p> </li> <li> <p>meta.info: meta data of the specific simulation run</p> </li> <li> <p>LAMMPS_out:<br> simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below</p> <ul> <li> <p>thermo_out.Dat: raw output </p> </li> <li> <p>thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</p> </li> <li> <p>thermo_out_STD.Dat: standard deviation of raw output</p> </li> </ul> </li> </ul> <p>Output quantities (columns of *.Dat files):<br> Please note that the normalized Lennard-Jones unit set is used, so all quantities are normalized to fundamental mass, length, energy, time and the Boltzmann constant. Thus all entries are unitless [1].</p> <ul> <li> <p>Step: time step </p> </li> <li> <p>Time: time </p> </li> <li> <p>TotEng: total energy </p> </li> <li> <p>PotEng: potential energy</p> </li> <li> <p>KinEng: kinetic energy </p> </li> <li> <p>E_pair: pair energy </p> </li> <li> <p>E_bond: bond energy </p> </li> <li> <p>E_angle: angle energy </p> </li> <li> <p>E_dihed: dihedral energy </p> </li> <li> <p>Temp: temperature</p> </li> <li> <p>Press: hydrostatic pressure</p> </li> <li> <p>Pxx: xx component of pressure tensor </p> </li> <li> <p>Pyy: yy component of pressure tensor </p> </li> <li> <p>Pzz: zz component of pressure tensor </p> </li> <li> <p>Pxy: xy component of pressure tensor</p> </li> <li> <p>Pxz: xz component of pressure tensor</p> </li> <li> <p>Pyz: yz component of pressure tensor</p> </li> <li> <p>Volume: volume of simulation box </p> </li> <li> <p>Lx: box length in x direction </p> </li> <li> <p>Ly: box length in y direction </p> </li> <li> <p>Lz: box length in z direction </p> </li> <li> <p>Density: density </p> </li> <li> <p>c_RG: radius of gyration scalar </p> </li> <li> <p>c_RG[1]: squared radius of gyration tensor (xx component) </p> </li> <li> <p>c_RG[2]: squared radius of gyration tensor (yy component) </p> </li> <li> <p>c_RG[3]: squared radius of gyration tensor (zz component) </p> </li> <li> <p>c_RG[4]: squared radius of gyration tensor (xy component) </p> </li> <li> <p>c_RG[5]: squared radius of gyration tensor (xz component) </p> </li> <li> <p>c_RG[6]: squared radius of gyration tensor (yz component) </p> </li> <li> <p>c_bondave[1]: bond energy averaged over all atoms </p> </li> <li> <p>c_bondave[2]: bond distance averaged over all atoms </p> </li> <li> <p>c_bondave[3]: squared bond distance averaged over all atoms </p> </li> <li> <p>c_angleave[1]: angle energy averaged over all atoms </p> </li> <li> <p>c_angleave[2]: angle averaged over all atoms degree</p> </li> <li> <p>c_angleave[3]: cosine of angle </p> </li> <li> <p>c_angleave[4]: squared cosine of angle </p> </li> <li> <p>c_MSD[1]: mean squared displacement x-direction </p> </li> <li> <p>c_MSD[2]: mean squared displacement y-direction </p> </li> <li> <p>c_MSD[3]: mean squared displacement z-direction </p> </li> <li> <p>c_MSD[4]: total mean squared displacement </p> </li> <li> <p>c_COM[1]: x coordinate of center of mass </p> </li> <li> <p>c_COM[2]: y coordinate of center of mass </p> </li> <li> <p>c_COM[3]: z coordinate of center of mass </p> </li> <li> <p>v_strain_xx: xx component of engineering strain tensor </p> </li> <li> <p>v_strain_yy: yy component of engineering strain tensor </p> </li> <li> <p>v_strain_zz: zz component of engineering strain tensor </p> </li> <li> <p>v_vMisesequivstress: von Mises equivalent stress </p> </li> <li> <p>v_Cauchy_xx: xx component of stress tensor </p> </li> <li> <p>v_Cauchy_yy: yy component of stress tensor</p> </li> <li> <p>v_Cauchy_zz: zz component of stress tensor</p> </li> <li> <p>v_Cauchy_xy: xy component of stress tensor </p> </li> <li> <p>v_Cauchy_xz: xz component of stress tensor </p> </li> <li> <p>v_Cauchy_yz: yz component of stress tensor </p> </li> <li> <p>v_strain_xy: xy component of engineering strain tensor </p> </li> <li> <p>v_strain_xz: xz component of engineering strain tensor </p> </li> <li> <p>v_strain_yz: yz component of engineering strain tensor </p> </li> </ul> <p><strong>References</strong>:</p> <p>[1] M. Ries et al., “Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites,” <em>Forces in Mechanics</em>, vol. 12, p. 100 207, <strong>2023</strong>.</p> <p>[2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” <em>Journal of computational physics</em>, <strong>1995</strong>, 117, 1-19.</p> <p>[3] A. P. Thompson et al., “LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,” <em>Computer Physics Communications</em>, vol. 271, p. 108171, <strong>2022</strong>.</p> <p>[4] M. Ries, V. Dötschel, J. Seibert, S. Pfaller. “A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, <em>Zenodo</em>, 2022. <a href="https://doi.org/10.5281/zenodo.6245699">https://doi.org/10.5281/zenodo.6245699</a></p>
Data set for: Genetic dissection of marker trait associations for grain micro-nutrients and thousand grain weight under heat and moisture deficit stress conditions in wheat
<p>The study material in the GWAS panel with 193 bread wheat genotypes from Indian and exotic collections was selected to map the genomic regions responsible for grain iron and Zinc content under drought and heat stress treatments.</p> <p>Phenotypic data:</p> <p>The GWAS panel was evaluated at IARI, New Delhi - DL (28.6550° N, 77.1888° E, MSL 228.61 m) under Irrigated (IR), Restricted Irrigated (RI) and Late sown (LS) treatment conditions over 2 years i.e. 2020 and 2021 with augmented RCBD design. Data was collected on Grain Iron and Grain zinc content along with thousand-grain weight. Around 20 g of grain sample from each of 282 genotypes from the GWAS panel under all three conditions were used for phenotyping GFeC and GZnC through high-throughput Energy Dispersive X-ray Fluorescence (ED-XRF) machine (model X-Supreme 8000; Oxford Instruments plc, Abingdon, United Kingdom) calibrated with glass beads-based values. To record TGW, manual counting of grains was followed and the weight of the grains was recorded in grams with an electronic balance.</p> <p>Genotypic data:</p> <p>Genomic DNA of the GWAS panel was extracted from the leaves of seedlings by Cetyl Trimethyl Ammonium Bromide (CTAB) method. The panel was genotyped using Axiom Wheat Breeder's Genotyping Array (Affymetrix, Santa Clara, CA, United States) having 35,143 genome-wide SNPs. The monomorphic, markers with minor allele frequency (MAF) of <5%, missing data of >10%, and heterozygote frequency >50% were removed from the analysis. The remaining set of 13,947 high-quality SNPs was used in GWAS analysis.</p>
Data set for: Genetic dissection of marker trait associations for grain micro-nutrients and thousand grain weight under heat and moisture deficit stress conditions in wheat
Open the record for dataset details and reuse information.
Data set: Grain Reynolds number scale effects in dry granular slides
<p>Scale series of velocity, flow depth and run out data for dry granular materials flowing down a slope with side walls. </p>
Data Set S1. Microplastic properties and Data Set S2. Sediment grain size
<p>The data for microplastic properties (including abundance, shape, colour, polymer type and size) and sediment grain size (including the grain size and the content of clay, silt and sand) in Core CCYY1.</p>
Data sets for "Bridgmanite grain size variation accounts for the mid-mantle viscosity jump" by H. Fei et al.
<p>The date sets contain the grain size data and EPMA data for the article "Bridgmanite grain size variation accounts for the mid-mantle viscosity jump" by H. Fei et al.</p>
Coarse-grained simulations of a phase-separated membrane with different LINCS settings (part 2/3)
<p>systems with lincs_iter = 2. To be written.</p>
Coarse-grained simulations of a phase-separated membrane with different LINCS settings (part 1/3)
<p>systems with lincs_iter = 1. To be written.</p>
Coarse-grained simulations of a phase-separated membrane with different LINCS settings (part 3/3)
<p>Additional simulations with lincs_order 10 & 12</p>
Fine-grained Vocal Imitation Set
<p>This dataset includes 763 vocal imitations of 108 sound events. The sound event recordings were taken from a subset of Vocal Imitation Set (<a href="http://zenodo.org/record/1340763">zenodo.org/record/1340763</a>). While the original VocalImitationSet only contains vocal imitations of a single reference recording per class, this new dataset contains vocal imitations of multiple reference recordings per class. Class names and filenames in this dataset are matched with the VocalImitationSet. Read the following paper to get more detailed information about VocalImitationSet.</p> <p>[<a href="https://interactiveaudiolab.github.io/assets/papers/DCASE2018_Kim.pdf">pdf</a>] Bongjun Kim, Madhav Ghei, Bryan Pardo, and Zhiyao Duan, "Vocal Imitation Set: a dataset of vocally imitated sound events using the AudioSet ontology," *Proceedings of the Detection and Classification of Acoustic Scenes and Events 2018 Workshop (DCASE2018)*, Nov. 2018.</p> <p>Contact Info:</p> <p>- Interactive Audio Lab: <a href="http://music.eecs.northwestern.edu/">http://music.eecs.northwestern.edu</a></p> <p>- Bongjun Kim <a href="mailto:bongjun@u.northwestern.edu">bongjun@u.northwestern.edu</a> | <a href="http://www.bongjunkim.com/">http://www.bongjunkim.com</a></p> <p>- Bryan Pardo <a href="mailto:pardo@northwestern.edu">pardo@northwestern.edu</a> | <a href="http://www.bryanpardo.com/">http://www.bryanpardo.com</a></p>
TAMU - ARPA-E SMARTFARM Grain Sorghum 2022 Texas site comprehensive sensor modalities data set.
<p>Comprehensive Year 2 data of ARPA-E SMARTFARM Grain Sorghum project titled "Establishing Validation Sites for Field-Level Emissions Quantification from Grain Sorghum in Southern Great Plains". Data sets includes Eddy Caovariance measurements of GHGs (CO2, CH4 and N2O) along with sub acre level soil moisture, soil temperarature, soil N and carbon, plant biomass and yield. This data is from the Texas site of the project. </p>
UF/OSU - ARPA-E SMARTFARM Grain Sorghum 2022 Oklahoma site comprehensive sensor modalities data set.
<p>Comprehensive Year 2 data of ARPA-E SMARTFARM Grain Sorghum project titled "Establishing Validation Sites for Field-Level Emissions Quantification from Grain Sorghum in Southern Great Plains". Data sets includes Eddy Caovariance measurements of GHGs (CO2, CH4 and N2O) along with sub acre level soil moisture, soil temperarature, soil N and carbon, plant biomass and yield. This data is from the Oklahoma site of the project. </p>
TAMU - ARPA-E SMARTFARM Grain Sorghum 2021 Texas site comprehensive sensor modalities data set.
<p>Comprehensive Year 1 data of ARPA-E SMARTFARM Grain Sorghum project titled "Establishing Validation Sites for Field-Level Emissions Quantification from Grain Sorghum in Southern Great Plains". Data sets includes Eddy Caovariance measurements of GHGs (CO2, CH4 and N2O) along with sub acre level soil moisture, soil temperarature, soil N and carbon, plant biomass and yield. This data is from the Texas site of the project. </p>
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.