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3,481 results for “data set”
Social learning data in a foraging setting for Heliconius erato
<p><span>Insects may acquire social information by active communication and through inadvertent social cues. In a foraging setting, the latter may indicate the presence and quality of resources. Although social learning in foraging contexts is prevalent in eusocial species, this behaviour has been hypothesised to also exist between conspecifics in non-social species with sophisticated behaviours, including </span><span><em>Heliconius</em> </span><span>butterflies</span><span>. </span><span><em>Heliconius</em> </span><span>are the</span><span> only butterfly genus with active pollen feeding, a dietary innovation </span><span>associated with a specialised, spatially faithful foraging behaviour known as trap-lining. Long-standing hypotheses suggest that <em>Heliconius</em> may acquire trap-line information by following experienced individuals. Indeed, <em>Heliconius</em></span> <span>often aggregate in social roosts, which could act as 'information centres', and present conspecific following behaviour, enhancing opportunities for social learning. Here, we provide a direct test of social learning ability in <em>Heliconius</em> using an associative learning task in which naïve individuals completed a colour preference test in the presence of demonstrators trained to feed randomly or with a strong colour preference. We found no evidence that </span><span><em>Heliconius</em> <em>erato</em></span><span>, which roost socially, used social information in this task</span><span>. Combined with existing field studies our results add to data which contradict the hypothesised role of social learning in <em>Heliconius</em> foraging behaviour.</span></p>
Supporting data sets for "Estimating Carbon Fixation of Plant Organs for Afforestation Monitoring using a Process-based Ecosystem Model and Ecophysiological Parameter Optimization". (the survey of tree breast diameter and tree height in 11-year old Eucommia ulmoides plantation, values of simulation results used in figures and tables.)
<p>Supporting data sets for Miyauchi et al., Ecology and Evolution, 2019 (accepted).</p> <p>The files store: </p> <p>(1) The survey of tree breast diameter and tree height in <em>Eucommia ulmoides</em> plantation<em>.</em> The ring and stem analysis and dry weight of seven harvested sample trees in the plantation.</p> <p>(2) Values of optimization result used fig.7.</p> <p>(3) Values of prediction result used fig.8. and table 4.</p> <p>(4) Values of optimized parameters by optimization methods, parameter range and constrain.</p>
Proteomic data sets after selecting mitochondrial proteins from the scaffold software for Ingenuity Pathway analysis (IPA Qiagen)
<p>List of fold change proteomic data sets of dFCM- 39 vs. 12Day and105 vs. 12Day, cFCM- 40 vs. 12Day and115 vs. 12Day , mouse heart 90 vs. 1 day after selecting mitochondrial proteins from the scaffold software for Ingenuity Pathway Analysis (IPA Qiagen)</p>
The RE-Europe data set
<p>This data set models the continental European electricity system, including demand and renewable energy inflows for the period 2012-2014.</p> <p>The main features of the data set are:</p> <ul> <li>High resolution (~50km, 1 hour) and large extent (Mainland Europe, 3 years)</li> <li>Technical & economic characteristics of generators from real-world data and best available estimates</li> <li>Synthetic wind and solar observations and forecasts from numerical weather prediction models, describing the full spatio-temporal structure of potential wind and solar production</li> </ul> <p>The transmission system comprises 1494 buses and 2156 lines, and is fitted based on [1].<br> The location, capacity and fuel type for 969 real-world generators are given based on the information in [2], and these are supplied with full cost specifications estimated based on fuel type [3].<br> For each bus, signals for load [4, 5], wind and solar production is given for each hour of the three years, with the wind and solar signals based on meteorological weather data from [6,7].<br> Further, at hour 00 and 12, forecasts for the solar and wind production are given for the following 91 hours, based on weather data from [6].<br> All spatially-distributed data is aggregated to the nodal domain by summation/averaging over the area closest to each node.</p> <p>Wind and solar signals and forecast are given as capacity factors, i.e. production relative to rated power. To use the renewable signals, a capacity layout must be specified, which assigns an installed solar and wind capacity to each node.<br> We supply two sets of capacity layouts, both scaled so the mean yearly production of (solar, wind) is equal the mean yearly load across EU. <br> The Uniform layout is scaled to make the capacity in each node proportional to the area aggregated by that node - i.e. capacity is distributed uniformly across EU.<br> The Proportional layout is scaled to make the capacity in each node proportional to the area aggregated by that node times the mean yearly capacity factor of the resource at that node - i.e. capacity is installed preferentially in nodes with high capacity factors.<br> <br> The data is intended for use in, e.g:</p> <ul> <li>Operational studies on markets</li> <li>Investment studies (generation capacity and transmission)</li> <li>Evaluation of future energy scenarios</li> </ul> <p>The source code used to generate this data is available at [9].</p> <p>Version History:</p> <p>V1.2:</p> <ul> <li>Line information extended with data on the number of parallel lines in each connection.</li> <li>Fixed generator capacity typos.</li> </ul> <p>V1.1: License relaxed to CC-BY</p> <p>V1.0: Initial Release</p>
CheckMyBlob evaluation data set (CL)
<p>A data set of ligands used to evaluate the CheckMyBlob method, described in the Kowiel et al. paper "<em>Automatic recognition of ligands in electron density by machine learning methods</em>".</p> <p>This data set repeats the setup used in the study of Carolan & Lamzin titled "<em>Automated identification of crystallographic ligands using sparse-density representations</em>". It consists of ligands from X-ray diffraction experiments with 1.0–2.5 Å resolution. Adjacent PDB ligands were not connected. Ligands were labeled according to the PDB naming convention. The data set was limited to the 82 ligand types listed by Carolan & Lamzin. The resulting data set consists of 121,360 examples with ligand counts ranging from 42,622 examples for SO4 to 16 for SPO (spheroidene).</p> <p>For machine learning (classification) purposes, the target attribute is: <strong>res_name</strong>.</p>
CheckMyBlob evaluation data set (TAMC)
<p>A data set of ligands used to evaluate the CheckMyBlob method, described in the Kowiel et al. paper "<em>Automatic recognition of ligands in electron density by machine learning methods</em>".</p> <p>This data set attempts to repeat the experimental setup from Terwilliger <em>et al.</em> described in "<em>Ligand identification using electron-density map correlations</em>". It consists of ligands from X-ray diffraction experiments with 6–150 non-H atoms. Connected PDB ligands were labeled as single alphabetically ordered strings of hetero-compound codes, whereas unknown species, water molecules, standard amino acids, and nucleotides were excluded. Finally, the data set was limited to 200 most popular ligands. The resulting data set consisted of 161,758 examples with individual ligand counts ranging from 36,535 examples for GOL (glycerol) to 114 for LMG (1,2-distearoyl-monogalactosyl-diglyceride).</p> <p>For machine learning (classification) purposes, the target attribute is: <strong>res_name</strong>.</p>
Data sets for the Simulated AMPI (SAMPI) load balancing simulation workflow and Ondes3D performance analysis (Companion to CCPE - Euro-Par 2017 special issue)
<p>This package contains data sets and scripts (in an Org-mode file) related to our submission to the special Euro-Par 2017 issue of the journal "Concurrency and Computation: Practice and Experience", under the title "Performance Modeling of a Geophysics Application to Accelerate Over-decomposition Parameter Tuning through Simulation".</p>
Data from SwAsp collection - environmental PCAs and bud set BLUPs
<p>Environmental variables at place of origin for individuals from the SwAsp population as well as the three first environmental PCAs calculated from this data. </p> <p>Best linear unbiased prediction of clone genetic values for bud set for clones form the SwAsp collection BLUPs are based on data from two common gardens (Sävar (63.89N, 20.54E) and Ekebo (55.95N, 13.12E)) and three years (2005, 2006 and 2007). More details about the common gardens can be found in Luquez, V., Hall, D., Albrectsen, B. R., Karlsson, J., Ingvarsson, P. K., & Jansson, S. (2008). Natural phenological variation in aspen (Populus tremula): the SwAsp collection. Tree Genetics & Genomes, 4(2), 279–292.</p>
Probabilistic Shaping: Experimental Optical Back-to-Back Data Set
<p>Data set containing experimental results on B2B operation of single-carrier probabilistic shaping optical signals spanning from net bit-rates of 200G to 250G.</p>
Single-Carrier: Experimental Optical Back-to-Back Data Set
<p>Data set containing experimental results on B2B operation of single-carrier optical signals spanning from net bit-rates of 200G (PM-16QAM) to 250G (PM-32QAM).</p>
FDHMF: Experimental Optical Back-to-Back Data Set
<p>Data set containing experimental results on B2B operation of FDHMF optical signals spanning from net bit-rates of 200G to 250G.</p>
FDHMF: Experimental Loop Recirculation Data Set - 200G
<p>Data set containing experimental results on the propagation of frequency-domain hybrid modulation formats (FDHMF) optical signals over pure silica core fiber (PSCF) inside a recirculating loop. The net bit-rate is fixed at 200G.</p>
Single-Carrier: Experimental Loop Recirculation Data Set - 250G
<p>Data set containing experimental results on the propagation of single-carrier optical signals over pure silica core fiber (PSCF) inside a recirculating loop. The net bit-rate is fixed at 250G (PM-16QAM).</p>
Single-Carrier: Experimental Loop Recirculation Data Set - 200G
<p>Data set containing experimental results on the propagation of single-carrier optical signals over pure silica core fiber (PSCF) inside a recirculating loop. The net bit-rate is fixed at 200G (PM-16QAM).</p>
Probabilistic Shaping: Experimental Loop Recirculation Data Set - 250G
<p>Data set containing experimental results on the propagation of probabilistic shaping optical signals over pure silica core fiber (PSCF) inside a recirculating loop. The net bit-rate is fixed at 250G.</p>
FDHMF: Experimental Loop Recirculation Data Set - 225G
<p>Data set containing experimental results on the propagation of frequency-domain hybrid modulation formats (FDHMF) optical signals over pure silica core fiber (PSCF) inside a recirculating loop. The net bit-rate is fixed at 225G.</p>
Probabilistic Shaping: Experimental Loop Recirculation Data Set - 225G
<p>Data set containing experimental results on the propagation of probabilistic shaping optical signals over pure silica core fiber (PSCF) inside a recirculating loop. The net bit-rate is fixed at 225G.</p>
Probabilistic Shaping: Experimental Loop Recirculation Data Set - 200G
<p>Data set containing experimental results on the propagation of probabilistic shaping optical signals over pure silica core fiber (PSCF) inside a recirculating loop. The net bit-rate is fixed at 200G.</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>
Data set on soil physicochemical parameters, biomass accumulation and carbon credit generation in different management systems in Rio Verde, GO, Brazil
<h1>Description</h1> <p>This repository contains a comprehensive dataset focused on soil organic carbon and its role in mitigating climate change through carbon sequestration on agricultural lands in Rio Verde, GO, Brazil. With the global imperative to reduce anthropogenic CO2 emissions, our data highlights the effectiveness of no-till agricultural practices in both improving soil quality and enhancing carbon storage. This collection represents extensive soil and biomass sampling from five distinct areas within the Cerrado region, utilizing three priority management systems:</p> <p>No-till with soybean and maize in sequence under rainfed conditions. No-till with soybean and maize in sequence with central pivot irrigation. First and second cuts of sugarcane. The samples were meticulously collected post-harvest and used to estimate both soil biomass accumulation and carbon stock indices. A thorough analysis of the soil's physicochemical parameters was conducted for the 0-20 cm soil profile in each area. This dataset not only provides a valuable resource for studying the impact of different no-till practices on carbon sequestration but also serves as a critical input for modeling future contributions of conservation management systems to carbon trading markets.</p> <div> <div> </div> <div> <h2>Data Contents</h2> </div> <p>Soil organic carbon measurements for various no-till systems. Biomass accumulation data post-harvest. Carbon stock indices derived from biomass samples. Detailed physicochemical profiles of soil samples.</p> <div> <h2>Significance</h2> </div> <p>This dataset is pivotal for researchers and policymakers focusing on the potentials of agricultural carbon sequestration and its implications for carbon trading schemes. It offers insights into the current contributions of no-till conservation management systems and aids in the development of future strategies to enhance carbon</p> <h1>Metadata Description and Script</h1> </div> <p>This repository contains two key data files that encapsulate diverse aspects of soil physicochemical parameters, biomass accumulation, and carbon credit generation across different management systems in Rio Verde, GO, Brazil. Below are descriptions of each file's contents and structure.</p> <div> <h2>all.txt</h2> </div> <p>This text file presents aggregated data from various sites under different agricultural management systems. Each row in the dataset represents measurements from distinct sample plots, with the following fields:</p> <ul> <li><code>Sites</code> - Identifier for the plot location.</li> <li><code>SB</code> - Soil bulk density (g/cm³).</li> <li><code>SOC</code> - Soil organic carbon (%).</li> <li><code>Stock</code> - Carbon stock (ton/ha).</li> <li><code>Biomass</code> - Biomass accumulation (ton/ha).</li> <li><code>Credits</code> - Estimated carbon credits (ton CO2 equivalent/ha).</li> </ul> <div> <h2>Quimica.xlsx</h2> </div> <p>This Excel file provides detailed physicochemical analyses of soil samples from different management zones in the study area. The data is structured to support in-depth analysis of soil characteristics influencing carbon sequestration capabilities. Each sheet in the workbook corresponds to a specific area, with columns typically representing:</p> <ul> <li><code>pH</code> - Soil pH, indicating the acidity or alkalinity.</li> <li><code>EC</code> - Electrical conductivity (dS/m).</li> <li><code>Cation Exchange Capacity (CEC):</code> - (meq/100g).</li> <li><code>Organipont c Matter:</code> - (%).</li> <li><code>NPK levels</code> - Concentrations of Nitrogen (N), Phosphorus (P), and Potassium (K).</li> </ul>
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.