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4,276 results for “Estimation”
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>
On the use of envelope following responses to estimate peripheral level compression in the auditory system
<p>Dataset containing the envelope following responses (EFR) recordings related to the manuscript "Can envelope following responses be used to estimate level compression in the auditory system?".</p> <p>Files content and structure:</p> <p><strong>EFR:</strong></p> <ul> <li><em>1_data__efr_nh.csv</em>: EFR recordings from the normal-hearing (NH) listeners</li> <li><em>2_data__efr_hi.csv</em>: EFR recordings from the hearing-impaired (HI) listeners</li> <li><em>3_data__efr_nh_repeat.csv</em>: Repeated EFR recordings from the NH listeners</li> </ul> <p>The files regarding the EFR recordings contain:</p> <ul> <li><em>subject</em>: Listener id</li> <li><em>lvl_vect</em>: Stimulation level</li> <li><em>efr_magn_xxxx_hz: </em>EFR magnitude (dB re to 1 µV) for each of the simultaneously recorded frequencies (0.5, 1, 2 and 4 kHz).</li> <li><em>efr_bkg_xxxx_hz: </em>Magnitude (dB re to 1 µV) of an estimate of the background noise floor of the recording for each frequency. </li> <li><em>ftest_xxxx_hz: </em>Ftest (0 or 1) value for each frequency.</li> </ul> <p> </p> <p>Distortion-product otoacoustic emissions (DPOAE) were also recorded in the same listeners although the data was not included in the final manuscript. Details of the methodology used can be given on request.</p> <p><strong>DPOAE:</strong></p> <ul> <li><em>4_data__dpoae_nh.csv</em>: DPOAE recordings from the NH listeners.</li> </ul> <p>The files regarding the DPOAE recordings contain:</p> <ul> <li><em>subject</em>: Listener id</li> <li><em>lvl_vect</em>: Stimulation level</li> <li><em>dpoae_magn_xxxx_hz: </em>DPOAE magnitude (dB SPL) for each of the simultaneously recorded frequencies (0.5, 1, 2 and 4 kHz).</li> <li><em>dpoae_bkg_xxxx_hz: </em>Magnitude (dB SPL) of an estimate of the background noise floor of the recording for each frequency. </li> <li><em>signif_xxxx_hz: </em>Satisfaction of the significance criterion (0 or 1) for each frequency.</li> </ul> <p> </p>
Re-projection alignment for trajectory perturbation estimation in micro-tomography: simulation data
<p>Simulation cone-beam-tomography data sets including:</p> <p>a) Synthetic phantom tomogram (3 spheres + random ellipsoids)</p> <p>b) Forward projection of phantom using low-pitch-helix (LPH) trajectory</p> <p>c) Forward projection of phantom using space-filling (SF) trajectory</p> <p>d) Synthetic per-projection perturbations</p>
Numerosity estimation benefits from transsaccadic information integration
<p>Dataset from the following publication:</p> <p>Hübner, C., & Schütz, A. C. (2017). Numerosity estimation benefits from transsaccadic information integration. Journal<br> of Vision, 17(13):12, 1–16, doi:10.1167/17.13.12.</p>
Figure 4: The estimated impedance within the measured frequency range for the symmetric (*) and the asymmetric (o) case against averaged data from healthy subjects-THE RESPIRATORY IMPEDANCE IN AN ASYMMETRIC MODEL OF THE LUNG STRUCTURE
<p>It is significant to observe that in the frequency interval of clinical interest,<br> ! 2 [25; 300] rad/s, the two impedances tend to behave similarly. For the<br> asymmetric case, we have a decrease of about -10dB/dec and a phase of ap-<br> proximately ¡50o, resulting in a fractional order of n »=</p> <p>This observation suggests that a combined efect of more than one fractal order is present in the<br> lungs and that it leads naturally to values closer to measured data in the low<br> frequency range. In other words, the symmetric tree representation does not<br> suffice to obtain a good fit between the model and the measured impedance<br> data. Another observation is that the constant-phase behavior is emphasized<br> at frequencies below those evaluated standardly in clinical practice, i.e. below<br> 5Hz. However, in the standard clinical range of frequencies for the forced oscil-<br> lation technique, namely 4-48Hz, both symmetric and asymmetric tree models<br> give similar results, as depicted in ¯gure 4</p>
Figure 4: The estimated impedance within the measured frequency range for the symmetric (*) and the asymmetric (o) case against averaged data from healthy subjects
<p>Another observation is that the constant-phase behavior is emphasized<br> at frequencies below those evaluated standardly in clinical practice, i.e. below<br> 5Hz. However, in the standard clinical range of frequencies for the forced oscil-<br> lation technique, namely 4-48Hz, both symmetric and asymmetric tree models<br> give similar results, as depicted in ¯gure 4 and ¯gure 5.</p>
Figure 5: The estimated impedance within the measured frequency range for the symmetric (*) and the asymmetric (o) case, polar plot representation.
<p>In other words, the symmetric tree representation does not<br> su±ce to obtain a good ¯t between the model and the measured impedance<br> data. Another observation is that the constant-phase behavior is emphasized<br> at frequencies below those evaluated standardly in clinical practice, i.e. below<br> 5Hz. However, in the standard clinical range of frequencies for the forced oscil-<br> lation technique, namely 4-48Hz, both symmetric and asymmetric tree models<br> give similar results, as depicted in ¯gure 4 and ¯gure 5.</p>
Phylogeny and divergence time estimation of Io moths and relatives (Lepidoptera: Saturniidae: Automeris)
<p>The saturniid moth genus <em>Automeris</em> includes 145 described species. Their geographic distribution ranges from the eastern half of North America to as far south as Peru. <em>Automeri</em>s moths are cryptically colored and their forewings resemble dead leaves, with conspicuously colored, elaborate eyespots hidden on their hindwings. Despite their charismatic nature, the evolutionary history and relationships within <em>Automeris</em> and between closely related genera, remain poorly understood. In this study, we present the most comprehensive phylogeny of <em>Automeris</em> to date, including 80 of the 145 described species. We also incorporate two morphologically similar hemileucine genera, <em>Pseudautomeris</em> and <em>Leucanella</em>, as well as a morphologically distinct genus, <em>Molippa</em>. We obtained DNA data from both dry-pinned and ethanol-stored museum specimens and conducted Anchored Hybrid Enrichment (AHE) sequencing to reconstruct a high-quality dataset for phylogenetic analysis. The resulting phylogeny supports <em>Automeris</em> as a paraphyletic genus, with <em>Leucanella</em> and <em>Pseudautomeris</em> nested within, with the most recent common ancestor dating back to 21 mya. This study lays the foundation for future research on various aspects of <em>Automeris</em> biology, including anti-predator defense mechanisms, ecological adaptations, geographical distribution patterns, and potential drivers of speciation.</p>
Spectral and trait data for Rapid estimates of leaf litter chemistry using reflectance spectroscopy
<p>Measuring the chemical traits of leaf litter is important for understanding plants' roles in nutrient cycles, including through nutrient resorption and litter decomposition, but conventional leaf trait measurements are often destructive and labor-intensive. Here, we develop and evaluate the performance of partial least-squares regression (PLSR) models that use reflectance spectra of intact or ground leaves to estimate leaf litter traits, including carbon and nitrogen concentration, carbon fractions, and leaf mass per area (LMA). Our analyses included more than 300 samples of senesced foliage from 11 species of temperate trees, including needleleaf and broadleaf species. Across all samples, we could predict each trait with moderate-to-high accuracy from both intact-leaf litter spectra (validation <em>R<sup>2</sup></em> = 0.543-0.941; %RMSE = 7.49-18.5) and ground-leaf litter spectra (validation <em>R<sup>2</sup></em> = 0.491-0.946; %RMSE = 7.00-19.5). Notably, intact-leaf spectra yielded better predictions of LMA. Our results support the feasibility of building models to estimate multiple chemical traits from leaf litter of a range of species. In particular, the success of intact-leaf spectral models allows non-destructive trait estimation in a matter of seconds, which could enable researchers to measure the same leaves over time in studies of nutrient resorption.</p>
Рис. 6. Графики Зависимости оценок варианс (S2) от средней плотности (D) популЯций наЗемных моллюсков B. cylindrica (А) и M. cartusiana (В): 1 – участок № 1, 2010 г.; 2 – участок № 2, 2011 г.; 3 – участок № 4, 2012 г.; 4 – участок № 5, 2012 г. Fig. 6. Variance estimation (S2) and average density (D) of the land snail B. cylindrica (А) and M. cartusiana (В) population scatter plots: 1 – site 1, 2010; 2 – site 2, 2011; 3 – site 4, 2012; 4 – site 5, 2012. in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 6. Графики Зависимости оценок варианс (S2) от средней плотности (D) популЯций наЗемных моллюсков B. cylindrica (А) и M. cartusiana (В): 1 – участок № 1, 2010 г.; 2 – участок № 2, 2011 г.; 3 – участок № 4, 2012 г.; 4 – участок № 5, 2012 г. Fig. 6. Variance estimation (S2) and average density (D) of the land snail B. cylindrica (А) and M. cartusiana (В) population scatter plots: 1 – site 1, 2010; 2 – site 2, 2011; 3 – site 4, 2012; 4 – site 5, 2012.
Supplement code and data for "Accounting for trade in derived products when estimating European Union's role in driving deforestation"
<p>This repository includes the code to reproduce the results and visualizations as well as the complete output underlying the publication:</p> <div> <div>Laroche, P.C.S.J., Gómez-Suárez, M., Persson, U.M., Pendrill, F., Schwarzmueller, F., Schulp, C.J.E., Kastner, T., 2024. Accounting for trade in derived products when estimating European Union’s role in driving deforestation. Ecological Economics 224, 108288. <a href="https://doi.org/10.1016/j.ecolecon.2024.108288">https://doi.org/10.1016/j.ecolecon.2024.108288</a></div> </div> <p> </p> <p>The file "01_calculations.R" calculates the files included in the "output" folder. These files can be used to visualize the results using the file "02_visualization.R".<br>The folder input_data contains some of the files necessary to run the code in "01_calculations.R". Note that additional data has to be downloaded to run the calculations, as specified in the R file. The folder "results" includes an xlsx file with the complete output of the calculation using the "mean" conversion factors.</p>
Key dataset used in the paper of "Emergent constraints reveal lower estimates of global river flow"
<p>This dataset includes key data used in the emergent constraint approach for refined partitioning of global water cycle components. </p>
The SEERA Software Cost Estimation Dataset
<p>The <strong>SEERA</strong> (<strong>S</strong>oftware engin<strong>EER</strong>ing in Sud<strong>A</strong>n) cost estimation dataset: is a dataset for technically and economically constrained environments. It is the result of the collection of 120 software development project data from 42 organizations in Sudan. The SEERA dataset contains 76 attributes and, unlike current cost estimation datasets, is augmented with metadata and sub-attributes containing the raw data before coding. This dataset fills the current gap in providing data more relevant to developing countries and software industries in constrained environments.</p> <p>The SEERA dataset is described in the paper:</p> <p>Emtinan I. Mustafa and Rasha Osman. 2020. SEERA: A software cost estimation dataset for constrained environments. In <em>Proceedings of the 16<sup>th </sup>International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE’20</em><em>).</em></p> <p>When referring to the dataset please cite the paper above.</p>
Goat-CNN: A Lightweight Convolutional Neural Network for Pose-Independent Body Condition Score Estimation in Goats
<p>Here we introduce the dataset utilized in our published paper entitled "<a href="https://www.sciencedirect.com/science/article/pii/S2666154324002114">Goat-CNN: A Lightweight Convolutional Neural Network for Pose-Independent Body Condition Score Estimation in Goats</a>".</p> <p>Contained within the "bcs" folder are all the videos collected for this study. Each video file is named with a format denoting its respective details. The first number signifies the sequence of collection, the second denotes the ear tag, and the final figure represents the body condition score (BCS) value.</p> <p>For example: "1_158734_2.50" indicates the first sampling of an animal with the ear tag "158734" and a BCS value of "2.50".</p> <p>Additionally, we provide two Python scripts in this repository. The first script, "Video2Frame.py", facilitates the splitting of videos into individual frames. The second script, "Frames2npy.py", converts these frames into two numpy-friendly files with the extension ".npy". These files contain both the images ("X_train_bcs300.npy") and their corresponding labels ("Y_train_bcs300.npy").</p> <p>Furthermore, for the convenience of swift experimentation, we have included the desired .npy files within the repository.</p> <p>To load these files into your Python environment, you can use the following code snippet:</p> <div> <div>th4figs = '/content/drive/MyDrive/compag_2023/'</div> <br> <div>path4images = "/content/drive/MyDrive/CodeRefarm/datasets/BCS/X_train_bcs300.npy"</div> <div>Xtrain = np.load(path4images)</div> <br> <div>path4labels = "/content/drive/MyDrive/CodeRefarm/datasets/BCS/Y_train_bcs300.npy"</div> <div>Ytrain = np.load(path4labels).astype(float)</div> <br> <div>print("X train : ", Xtrain.shape)</div> <div>print("Y train : ", Ytrain.shape)</div> <div> <div> <div> <div> <div> <div> <div> </div> </div> <div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <div> <div> <pre>X train : (5332, 300, 300, 3) Y train : (5332,)<br> </pre> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
Toward a semi-supervised learning approach to phylogenetic estimation
<p>Models have always been central to inferring molecular evolution and to reconstructing phylogenetic trees. Their use typically involves the development of a mechanistic framework reflecting our understanding of the underlying biological processes, such as nucleotide substitutions, and the estimation of model parameters by maximum likelihood or Bayesian inference. However, deriving and optimizing the likelihood of the data is not always possible under complex evolutionary scenarios or even tractable for large datasets, often leading to unrealistic simplifying assumptions in the fitted models. To overcome this issue, we coupled stochastic simulations of genome evolution with a new supervised deep learning model to infer key parameters of molecular evolution. Our model is designed to directly analyze multiple sequence alignments and estimate per-site evolutionary rates and divergence, without requiring a known phylogenetic tree. The accuracy of our predictions matched that of likelihood-based phylogenetic inference, when rate heterogeneity followed a simple gamma distribution, but it strongly exceeded it under more complex patterns of rate variation, such as codon models. Our approach is highly scalable and can be efficiently applied to genomic data, as we showed on a dataset of 26 million nucleotides from the clownfish clade. Our simulations also showed that the integration of per-site rates obtained by deep learning within a Bayesian framework led to significantly more accurate phylogenetic inference, particularly with respect to the estimated branch lengths. We thus propose that future advancements in phylogenetic analysis will benefit from a semi-supervised learning approach that combines deep-learning estimation of substitution rates, which allows for more flexible models of rate variation, and probabilistic inference of the phylogenetic tree, which guarantees interpretability and a rigorous assessment of statistical support.</p>
A sensitivity dataset for estimating carbon dioxide's marginal abatement cost (MAC) curves
<p>This dataset was created using AIM/Hub V2.2 with the carbon price pathway as constraints. It is used to calculate marginal abatement costs (MAC), which are then employed in the cost-benefit Integrated Assessment Model (CB-IAM) (https://github.com/sooxm/scm4eco).</p> <p> </p> <p>References</p> <p>Fujimori, Shinichiro, Tomoko Hasegawa, Toshihiko Masui, Kiyoshi Takahashi, Diego Silva Herran, Hancheng Dai, Yasuaki Hijioka, and Mikiko Kainuma. 2017. “SSP3: AIM Implementation of Shared Socioeconomic Pathways.” Global Environmental Change - Human and Policy Dimensions 42: 268–83. https://doi.org/10.1016/j.gloenvcha.2016.06.009.</p> <p>Fujimori, Shinichiro, Toshihiko Masui, and Yuzuru Matsuoka. 2017. “AIM/CGE V2.0 Model Formula.” In Post-2020 Climate Action: Global and Asian Perspectives, edited by Shinichiro Fujimori, Mikiko Kainuma, and Toshihiko Masui, 201–303. Singapore: Springer Singapore. https://doi.org/10.1007/978-981-10-3869-3_12.</p> <p>Fujimori, Shinichiro, Ken Oshiro, Tomoko Hasegawa, Junya Takakura, and Kayo Ueda. 2023. “Climate change mitigation costs reduction caused by socioeconomic-technological transitions.” Npj Climate Action 2 (1): 9. https://doi.org/10.1038/s44168-023-00041-w.</p> <p>Su, X., Takahashi, K., Yokohata, T., Tanaka, K., Fujimori, S., Takakura, J., Yamaguchi, R., and Xiong, W.: Developing an integrated assessment model to explore optimal cost-benefit paths for Shared Socioeconomic Pathways scenarios, EGUsphere [preprint], <a href="https://doi.org/10.5194/egusphere-2024-1640" rel="nofollow">https://doi.org/10.5194/egusphere-2024-1640</a>, 2024.</p>
Pose Selector Workflow - Docking Poses, Absolute Binding Free Energy Estimates and Structure Input Files for Machine Learning
<p>The Pose Selector (PS) workflow calculates absolute binding free energies (ABFEs) for binding poses of protein-ligand complexes. First, it converts the binding poses (both docking poses as well as experimentally observed ligand binding poses), which are provided as a combination of protein PDB file and ligand MOL2 file, into input files for molecular dynamics (MD) simulations with GROMACS after they have passed extensive quality checks and repair steps. Next, the PS workflow post-processes and analyses the last frame of the resulting eight 100 ps trajectories per binding pose with the Generalised Born model of implicit solvation as implemented in gmx_MMPBSA to obtain the ABFE estimates. The workflow was designed for soluble proteins without post-translational modifications, co-factors and non-standard amino acids, and it has limited support for coordinated ions.</p> <p>For the dataset published here, the PS workflow was run on docking poses generated for the PDBbind 2020 dataset (http://www.pdbbind.org.cn/index.php), shared in dockingPosesPDBBind2020.tar.gz. This entry and its partner entry 10.5281/zenodo.11397486 also share the intial coordinates used in the MD simulations of >800,000 docking poses of 4022 protein-ligand complexes (structureFiles_dockingPoses1.tar.gz in this entry and structureFiles_dockingPoses2.tar.gz in 10.5281/zenodo.11397486) and of the experimental ligand binding pose of 4549 complexes (structureFiles_experimentalStructures.tar.gz) as well as the corresponding ABFE estimates (absoluteBindingFreeEnergyEstimates.tar.gz). The MD simulations were run on the LUMI and MeluXina supercomputers while the implicit-solvent calculations were carried out on Galileo (Cineca).</p> <p>The README file describes the structure of the shared data in more detail and points out how to reproduce the MD trajectories and the subsequent implicit-solvent calculations yielding the free-energy estimates as well as how to use the data provided in this entry to train a machine-learning model predicting the ABFE of binding poses of protein-ligand complexes. The workflow scripts can be downloaded from GitHub (https://github.com/LigateProject/Pose-Selector-workflow). The MD simulations were run with GROMACS 2023.2 (https://manual.gromacs.org/2023.2/index.html), and the implicit-solvent calculations were carried out with gmx_MMPBSA 1.6.1 (https://valdes-tresanco-ms.github.io/gmx_MMPBSA/v1.6.1/).</p>
DATASET Estimating the Use of ChatGPT in Dental Research Publications
<p>These files contain the data and the research script for analyzing the use of ChatGPT in dental research writing from 2018 to 2024. The dataset includes various CSV files with publication details and analysis scripts to investigate trends and patterns in the usage of specific signaling words associated with ChatGPT.</p>
Auxiliary data files for replication of "Augmenting the availability of historical GDP per capita estimates through machine learning"
<p>This repository holds auxiliary data files needed for the replication "Augmenting the availability of historical GDP per capita estimates through machine learning". All further information and data is provided in the <a href="https://github.com/philmkoch/historicalGDPpc" target="_blank" rel="noopener">GitHub repository</a>.</p> <p>The data included in this auxiliary folder is based on the work by Laouenan et al. (https://www.nature.com/articles/s41597-022-01369-4). </p>
Biomass estimation of oilseed rape using the light use efficiency model in Bavaria (Atlas) in LandKlif project
<p>This dataset shows the predicted biomass (g/m2) of oilseed rape (OSR) using the light use efficiency (LUE) model for Bavaria in 2019. The LUE model uses satellite data (Landsat-8, MODIS) and climate data (temperature and solar radiation) to calculate the plant biomass. The crop yield is predicted and validated at district level using LfStat data. The validation results show an R2 of 0.79 with an RMSE of 2.21 dt/ha. </p> <p>This dataset is conducted under LandKlif project. LandKlif is funded by the <a href="https://www.stmwk.bayern.de/englisch.html"><strong>Bavarian State Ministry of Science and the Arts</strong></a> within the <a href="https://www.bayklif.de/"><strong>Bavarian Climate Research Network (bayklif)</strong></a><strong>. </strong> Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. <strong>LandKliF</strong>, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</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.