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4,276 results for “Estimation”
Global continental discharge estimates from ocean mass balance
<p>These files include a time series of global continental discharge estimated from ocean mass balance following Chandanpurkar et al., 2017. </p> <p>The ocean mass balance is obtained from these components:</p> <p>dM/dt (change in ocean mass): From altimetry and from GRACE/FO. When derived from altimetry, steric level change is subtracted from the GMSL using EN4.2.2 temperature and salinity data. </p> <p>E-P: Here, two methods are used:</p> <p>1. Directly, using estimates of ocean E and P, using OAFlux for E and GPCP and CMAP separately for P</p> <p>2. Indirectly, using atmospheric moisture balance using vertically integrated horizontal moisture flux divergence, and change in the total column water vapor. These are obtained using ERA5 and MERRA-2 reanalyses products.</p> <p>The eight discharge estimates are combinations of the above, and the exact combination is mentioned in the filename. </p>
Data from: Narwhal (Monodon monoceros) echolocation click rates to support cue counting passive acoustic density estimation
<p class="MsoNormal"><span>The datasets correspond to the data used to obtain the results shown in the manuscript "Narwhal (<em>Monodon monoceros</em>) echolocation click rates to support cue counting passive acoustic density estimation".</span></p> <p class="MsoNormal"><span>When the manuscript is accepted we will also edit and add here the full reference including the DOI of the publication.</span></p>
A Bayesian Estimation of the Milky Way's Circular Velocity Curve using Gaia DR3
<p>The derived input dataset of approximately 0.6 million RGB stars used in the work "A Bayesian Estimation of the Milky Way’s Circular Velocity Curve using Gaia DR3" . The authors kindly ask to cite the original work described in (<a href="https://doi.org/10.1051/0004-6361/202346474">https://doi.org/10.1051/0004-6361/202346474</a>) should one make use of this catalogue.</p> <p> </p> <p> </p>
INCYDE: A large scale cyclone detection and intensity estimation dataset using satellite infrared imagery
<p>INCYDE (INSAT-based Cyclone Detection and Intensity Estimation) is a cyclone detection and intensity estimation dataset. The cyclone images in the dataset are captured from INSAT 3D/3DR satellites over the Indian Ocean. The proposed INCYDE dataset contains over 100k cyclone images with augmentations taken from cyclones over the Indian Ocean from the year 2013 to 2021. The dataset pertains to two specific tasks: cyclone detection as an object detection task, and intensity estimation as a regression task. In addition to the dataset, this study introduces baseline models that were trained on the newly presented dataset</p>
bias_estimation_data
<p>Radio occultation data used in the project "Estimating the refractivity bias of Formosat-7/COSMIC-II GNSS Radio Occultation in the planetary boundary layer".<br> The data has been filtered from profiles to a compact netcdf4 file.</p>
Dataset for the article: Evaluating the Predictive Performance of Quick Methods for Estimating Task Difficulty and Student Ability in Automated Computer Programming Assessment
<p>This open access repository houses the dataset utilized in the research article:</p><p>Pankiewicz, M. (2023). Evaluating the Predictive Performance of Quick Methods for Estimating Task Difficulty and Student Ability in Automated Computer Programming Assessment. In T. Bastiaens (Ed.), Proceedings of EdMedia + Innovate Learning (pp. 1413-1418). Vienna, Austria: Association for the Advancement of Computing in Education (AACE). Retrieved from https://www.learntechlib.org/primary/p/222666</p><p>The repository includes these files:</p><p>"submissions.csv": This data file captures the evaluation results of programming assignments. It is organized by the following columns:</p><p> </p><ul><li>"user_id": The unique identifier for each student who submitted the assignment.</li><li>"task_id": The unique identifier for each task that received submissions.</li><li>"submission_seconds": The number of seconds since the first user accessed the initial task's description within the system.</li><li>"correct": The outcome of the evaluation (1 denotes correct; 0 denotes incorrect).</li><li>"subject": The specific subject matter that the task addresses.</li></ul><p> </p><p>"subjects.csv": This data file comprises the roster of subjects for which tasks have been assigned within the system. It includes these columns:</p><p> </p><ul><li>"subject_id": The unique identifier for each subject.</li><li>"subject": The actual name of the subject.</li></ul><p> </p>
Estimates of ecosystem metabolism for 59 rivers in North America, 2008-2021
<p>This dataset is companion with the github repository https://github.com/nmarzolf91/usgs_metabolism_project that accesses USGS water quality and hydrologic data to estimate ecosystem metabolism in 59 rivers across the US from 2007-2021. Contained in this data citation are information about sites, raw time-series data at sub-daily intervals, prepared time series for modeling metabolism, metabolism outputs in raw form, processed and QAQC'd metabolism estimates, and supplementary information for the associated manuscript(s).</p>
Biochemical networks with simulation-based estimations of dynamical properties
<p>This datasets collection was first introduced in the article: </p> <p><a href="https://academic.oup.com/bioinformatics/article/39/11/btad678/7407341" target="_blank" rel="noopener">Exploiting the structure of biochemical pathways to investigate dynamical properties with neural networks for graphs</a>. (Bioinformatics 2023)</p> <p> </p> <p>The collection contains three datasets that contain information about three dynamical properties computed on a set of 483 biochemical pathways downloaded from the BioModels database. The three dynamical properties are:</p> <ul> <li>robustness</li> <li>sensitivity</li> <li>monotonicity</li> </ul> <p>The files are organized as follows:</p> <ol> <li>The `pathways` directory contains 483 files in .dot format for each biochemical pathway downloaded from the BioModels database (May 2021), represented in Petri net format (see <a href="https://doi.org/10.5220/0008964700320043">this article</a> for the exact definition). The file name is the ID of the pathway in the BioModels database.</li> <li>The other folders contain one .csv file for each property. A single .csv file contains 4 columns: <ol> <li>`PathwayID`: the ID of the Pathway in the BioModels database</li> <li>`Input`: the input molecular species on which the property has been assessed</li> <li>`Output`: the output molecular species on which the property has been assessed</li> <li>`Property`: the value of the property assessed with numerical simulations on the pathway for that particular input/output species pair.</li> </ol> </li> <li>The `loader.py` file is an optional script that allows to use the data in python. The script requires that the libraries `networkx`, `pandas`, and `pydot` are installed in the target machine.</li> </ol>
China's fossil fuel CO2 emissions estimated using surface observations of co-emitted NO2
<p>We employed an EnKF-based Regional Multi-Air Pollutant Assimilation System (RAPAS) to assimilate <em>in-situ</em> NO<sub>2</sub> observations, allowing us to combine observation-constrained NO<em><sub>x</sub></em> emissions co-emitted with FFCO<sub>2</sub> and grid-specific CO<sub>2</sub>-to-NO<em><sub>x</sub></em> emission ratios for inferring daily <strong>FFCO<sub>2</sub> emissions</strong> over China.</p> <p><strong>cnemc_obs.nc </strong>includes assimilated and verified observations.</p> <p><strong>emission.tar.gz</strong> includes inferred daily posterior NO<em><sub>x</sub></em> and FFCO<sub>2</sub> emissions for the year 2016.</p>
Hidden and total fishing activity hotspots in the Mediterranean Sea between 2017 and 2022 estimated from AIS data
<p>Hidden and total fishing activity hotspots in the Mediterranean Sea between 2017 and 2022 estimated from AIS data</p>
First Atmospheric Measurements and Emission Estimates of HFO-1336mzz(Z)
<p>Atmospheric measurement data (mole fractions) for HFO-1336mzz(Z) (((<em>Z</em>)-1,1,1,4,4,4-hexafluoro-2-butene, <em>cis</em>-CF<sub>3</sub>CH=CHCF<sub>3</sub>). The data are related to article in ES&T (<a href="https://doi.org/10.1021/acs.est.3c01826">https://doi.org/10.1021/acs.est.3c01826</a>). Observations were made at the sites Beromünster (CH), Sottens (CH), Dübendorf (CH), Jungfraujoch (CH), and Cabauw (NL). Measurements were conducted using Medusa pre-concentration units coupled to gas chromatography and mass spectrometry (GC-MS), as is used within the global AGAGE network.</p>
Estimates of the Wavenumber Wavelet Power Spectrum of Magnetic Fluctuations during Magnetic Reconnection Figure Data
<p>This is data for the publication, "Estimates of the Wavenumber Wavelet Power Spectrum of Magnetic Fluctuations during Magnetic Reconnection".</p>
Figure 1 in Comparison of Sampling Intensity to Estimate Infestations of Coffee Berry Borer on Hawaii
Figure 1. Mean proportion of infested berries ± SEM estimated by counting berries using three sampling intensities. The data are averages from four farms on Hawaii island.
Source code and data for AIMMD-TPS and PE estimate
<p>This repository contains the source code, simulation data, and analysis from the study by Lazzeri, Jung, Bolhuis, and Covino (paper submitted in 2023). The code, adaptable for any two-state molecular dynamics system, is paired with annotations to promote understanding. The dataset allows full replication of the paper's results and figures.</p>
Dataset supporting the paper "Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities"
<p>Datasets involved in the construction and benchmarking of the CollecTRI-derived regulons as presented in the paper "Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities".</p>
Reliable imputation of spatial transcriptome with uncertainty estimation and spatial regularization
<p>Imputation of missing features in spatial transcriptomics is urgently demanded due to technology limitations, while most existing computational methods suffer from moderate accuracy and cannot estimate the reliability of the imputation. <br> To fill the research gaps, we introduce a computational model, TransImp, that imputes the missing feature modality in spatial transcriptomics by mapping it from single-cell reference. Uniquely, we derived a set of attributes that can accurately predict imputation uncertainty, hence enabling us to select reliably imputed genes. Also, we introduced a spatial auto-correlation metric as a regularization to avoid overestimating spatial patterns. Multiple datasets from various platforms have demonstrated that our approach significantly improves the reliability of downstream analyses in detecting spatial variable genes and interacting ligand-receptor pairs. Therefore, TransImp offers a way towards a reliable spatial analysis of missing features for both matched and unseen modalities, e.g., nascent RNAs.</p>
There and back to the present: a model-based framework to estimate phylogenetically constrained alpha diversity gradients
<p>The imprint left by niche evolution on the variation of biological diversity across spatial and environmental gradients is still debated among ecologists. Furthermore, understanding to what extent dispersal limitation may reinforce or blur such an imprint is still a gap in our ecological knowledge. In this article we introduce a simulation approach coupled to Approximate Bayesian Computation (ABC) that parameterizes both the adaptation rate of species' niche positions over the evolution of a monophyletic lineage and the intensity of dispersal limitation associated with the variation of species alpha diversity among assemblages distributed across spatial and environmental gradients. The analytical tool was implemented in the R package <em>mcfly</em>. We evaluated the statistical performance of the analytical framework using simulated datasets, which confirmed the suitability of the analysis to estimate the adaptation rate parameter but showed to be less precise in relation to the dispersal limitation parameter. Also, we found that increased dispersal limitation levels improved the parameterization of the adaptation rate of species' niche positions in simulated datasets. Further, we evaluated the role played by niche evolution and dispersal limitation on species alpha diversity variation of Phyllostomidae bats across the Neotropics. The framework proposed here sheds light on the links between niche evolution, dispersal limitation and gradients of biological diversity, and thereby improved our understanding of evolutionary imprints on current biological diversity patterns.</p>
Replication Package for "A Catch-22--the Test-Retest Method of Reliability Estimation"
<p>Replication package for the paper "A Catch-22--the Test-Retest Method of Reliability Estimation".</p><p>Files included in the replication package and purpose of each file:</p><p>[1]Datafiles containing variables used in the analysis: question content, stability and reliability estimates (in SPSS and Stata format):<br>01_GSS_gammaV26_April2022_extract.sav<br>01_GSS_gammaV26_April2022_extract.dta<br>01_GSS_gammaV26_April2022_long_extract.dta</p><p>[2]SPSS Syntax file for replicating Tables 2,3 and Appendix Table: <br>02_SPSS_syntax_Catch22.sps</p><p>[3]Stata .do file containing the code for the regression models (Table 4):<br>03_Stata_code_Catch22.do</p><p>[4]List of GSS variables, wordings, and responses for variables included in the analysis (Excel file)<br>04_GSS variable wordings.xlsx </p>
Swarm smart meta-estimator for 2D/2D heterostructure design
<p>Please see ref:</p><p>Botella, R.; Kistanov, A., A.; Cao, W. <i>J. Chem. Inf. Model.</i> <strong>2023</strong>, 63, 20, 6212-6223</p><p>For more information about the dataset.</p>
Scripts and datas for "Global Estimation of the Eddy Kinetic Energy Dissipation from a Diagnostic Energy Balance"
<p>Input and output datasets used for a global reconstruction of the eddy kinetic energy (EKE) dissipation rate in relation to a submitted work :</p> <p><strong>R. Torres, R. Waldman, J. Mak and R. Séférian </strong>: <em>Global Estimation of the Eddy Kinetic Energy Dissipation from a Diagnostic Energy Balance</em>.</p> <p>Inputs datas include a merge of 2 datasets from the World Ocean Atlas 2018 (WOA18, Garcia et al., 2019) and cover the 1995-2017 (95B7) period. Folder structure for the surface altimetry L4 datasets from the EU-Copernicus Marine Services (2021) is kept empty in order to limit the archive size. Datas can be download <a href="https://data.marine.copernicus.eu/product/SEALEVEL_GLO_PHY_L4_MY_008_047/services">here</a>.</p> <p>Optional datasets include CMEMS MDT product (<em>CMEMS/SEALEVEL_GLO_PHY_MDT_008_063/P20Y</em>) downloaded <a href="https://data.marine.copernicus.eu/product/SEALEVEL_GLO_PHY_MDT_008_063/">here</a> and ocean masks (<em>misc/basins/doi_10.5281</em>) from Martinez-Moreno et al. (2021).</p> <p>In addition, simulation outputs from the NEMO-OMIP2 model runned with the GEOMETRIC parameterization are processed (mainly time-averaged) and stored in <em>CNRM/runs/omip2_LR.Geom_Emin0-alpha01_1cyc-trd/post</em>. These files are used in the uncertainties and errors quantification.</p> <p>Outputs and published results are stored in each individual product post-processing folder while final EKE dissipation computation are located in the <em>EKE_dissipation_rate</em> folder since it results from a combination of multiple products.</p> <p>IPython notebooks for computing and plotting global maps are also provided :</p> <ul> <li><em>1-post_process_climato.ipynb</em> : compute from the climatology (e.g. WOA18 datas) the EKE dissipation timescales (units in days) and the surface modes with rough topography (LaCasce and Groeskamp, 2020).</li> <li><em>2-post_process_altimetry.ipynb</em> : compute from altimetry (CMEMS) datasets the EKE at surface and eventually coarsen the grid from 0.25 to 1 degree in order to match the climatology grid.</li> <li><em>3-compute_global_eke_dissipation.ipynb</em> : combine both outputs from the two above scripts to compute the global EKE dissipation. The script also plots new maps.</li> <li><em>0-plot_global_maps.ipynb</em> : plot the global maps for climatology and altimetry products.</li> <li><em>0-plot_lbekedis_ogcm.ipynb</em> : plot and analyse EKE timescale errors from the NEMO-OMIP2 simulation outputs.</li> </ul> <p>Note however that these scripts use the author python library XOCE availbale on GitHub: https://github.com/torresr-cnrm/xoce. All the scripts have been runned using the version 0.2 of XOCE. Feel free to contact (romain.torres@meteo.fr) for any help in installing and using this library.</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.