Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
104
datasets available to search
ShareScore release 0.9.0
Dataset results
104 results for “State estimation”
Arctic Ocean state estimates for 2010 using the GECCO model
<p>The dataset contains the 2010 data of a 10-year ocean synthesis (2007-2016) obtained by assimilating available observations of sea ice and ocean parameters into the GECCO model. Data from, among others, several satellite programs such as AMSRE, SSMI, AMSR2, Envisat, Jason, Cryosat., AVHRR, and SMOS, and available moorings in the Davis Strait, the Bering Strait, the Fram Strait, the Barents Sea Opening, and by the Nansen and Amundsen Basins Observational System (NABOS), the North Pole Environmental Observatory (NPEO), and the Beaufort Gyre Exploration Project (BGEP) project. A detailed description can be found in Lyu et al., 2020.</p> <p>Guokun Lyu, Nuna Serra, Armin Koehl and Detlef Stammer, 2020. INTAROS Deliverable 6.4 Ice-ocean statistics and state estimation V1. https://intaros.nersc.no/sites/intaros.nersc.no/files/D6.4_INTAROS_Data_assimilation_v1.3.pdf </p>
Arctic Ocean state estimates for 2015 using the GECCO model
<p>The dataset contains the 2015 data of a 10-year ocean synthesis (2007-2016) obtained by assimilating available observations of sea ice and ocean parameters into the GECCO model. Data from, among others, several satellite programs such as AMSRE, SSMI, AMSR2, Envisat, Jason, Cryosat., AVHRR, and SMOS, and available moorings in the Davis Strait, the Bering Strait, the Fram Strait, the Barents Sea Opening, and by the Nansen and Amundsen Basins Observational System (NABOS), the North Pole Environmental Observatory (NPEO), and the Beaufort Gyre Exploration Project (BGEP) project. A detailed description can be found in Lyu et al., 2020.</p> <p>Guokun Lyu, Nuna Serra, Armin Koehl and Detlef Stammer, 2020. INTAROS Deliverable 6.4 Ice-ocean statistics and state estimation V1. https://intaros.nersc.no/sites/intaros.nersc.no/files/D6.4_INTAROS_Data_assimilation_v1.3.pdf </p>
Arctic Ocean state estimates for 2008 using the GECCO model
<p>The dataset contains the 2008 data of a 10-year ocean synthesis (2007-2016) obtained by assimilating available observations of sea ice and ocean parameters into the GECCO model. Data from, among others, several satellite programs such as AMSRE, SSMI, AMSR2, Envisat, Jason, Cryosat., AVHRR, and SMOS, and available moorings in the Davis Strait, the Bering Strait, the Fram Strait, the Barents Sea Opening, and by the Nansen and Amundsen Basins Observational System (NABOS), the North Pole Environmental Observatory (NPEO), and the Beaufort Gyre Exploration Project (BGEP) project. A detailed description can be found in Lyu et al., 2020.</p> <p>Guokun Lyu, Nuna Serra, Armin Koehl and Detlef Stammer, 2020. INTAROS Deliverable 6.4 Ice-ocean statistics and state estimation V1. https://intaros.nersc.no/sites/intaros.nersc.no/files/D6.4_INTAROS_Data_assimilation_v1.3.pdf </p>
Arctic Ocean state estimates for 2007 using the GECCO model
<p>The dataset contains the 2007 data of a 10-year ocean synthesis (2007-2016) obtained by assimilating available observations of sea ice and ocean parameters into the GECCO model. Data from, among others, several satellite programs such as AMSRE, SSMI, AMSR2, Envisat, Jason, Cryosat., AVHRR, and SMOS, and available moorings in the Davis Strait, the Bering Strait, the Fram Strait, the Barents Sea Opening, and by the Nansen and Amundsen Basins Observational System (NABOS), the North Pole Environmental Observatory (NPEO), and the Beaufort Gyre Exploration Project (BGEP) project. A detailed description can be found in Lyu et al., 2020.</p> <p>Guokun Lyu, Nuna Serra, Armin Koehl and Detlef Stammer, 2020. INTAROS Deliverable 6.4 Ice-ocean statistics and state estimation V1. https://intaros.nersc.no/sites/intaros.nersc.no/files/D6.4_INTAROS_Data_assimilation_v1.3.pdf </p>
Arctic Ocean state estimates for 2011 using the GECCO model
<p>The dataset contains the 2011 data of a 10-year ocean synthesis (2007-2016) obtained by assimilating available observations of sea ice and ocean parameters into the GECCO model. Data from, among others, several satellite programs such as AMSRE, SSMI, AMSR2, Envisat, Jason, Cryosat., AVHRR, and SMOS, and available moorings in the Davis Strait, the Bering Strait, the Fram Strait, the Barents Sea Opening, and by the Nansen and Amundsen Basins Observational System (NABOS), the North Pole Environmental Observatory (NPEO), and the Beaufort Gyre Exploration Project (BGEP) project. A detailed description can be found in Lyu et al., 2020.</p> <p>Guokun Lyu, Nuna Serra, Armin Koehl and Detlef Stammer, 2020. INTAROS Deliverable 6.4 Ice-ocean statistics and state estimation V1. https://intaros.nersc.no/sites/intaros.nersc.no/files/D6.4_INTAROS_Data_assimilation_v1.3.pdf </p>
Arctic Ocean state estimates for 2016 using the GECCO model
<p>The dataset contains the 2016 data of a 10-year ocean synthesis (2007-2016) obtained by assimilating available observations of sea ice and ocean parameters into the GECCO model. Data from, among others, several satellite programs such as AMSRE, SSMI, AMSR2, Envisat, Jason, Cryosat., AVHRR, and SMOS, and available moorings in the Davis Strait, the Bering Strait, the Fram Strait, the Barents Sea Opening, and by the Nansen and Amundsen Basins Observational System (NABOS), the North Pole Environmental Observatory (NPEO), and the Beaufort Gyre Exploration Project (BGEP) project. A detailed description can be found in Lyu et al., 2020.</p> <p>Guokun Lyu, Nuna Serra, Armin Koehl and Detlef Stammer, 2020. INTAROS Deliverable 6.4 Ice-ocean statistics and state estimation V1. https://intaros.nersc.no/sites/intaros.nersc.no/files/D6.4_INTAROS_Data_assimilation_v1.3.pdf </p>
Arctic Ocean state estimates for 2014 using the GECCO model
<p>The dataset contains the 2014 data of a 10-year ocean synthesis (2007-2016) obtained by assimilating available observations of sea ice and ocean parameters into the GECCO model. Data from, among others, several satellite programs such as AMSRE, SSMI, AMSR2, Envisat, Jason, Cryosat., AVHRR, and SMOS, and available moorings in the Davis Strait, the Bering Strait, the Fram Strait, the Barents Sea Opening, and by the Nansen and Amundsen Basins Observational System (NABOS), the North Pole Environmental Observatory (NPEO), and the Beaufort Gyre Exploration Project (BGEP) project. A detailed description can be found in Lyu et al., 2020.</p> <p>Guokun Lyu, Nuna Serra, Armin Koehl and Detlef Stammer, 2020. INTAROS Deliverable 6.4 Ice-ocean statistics and state estimation V1. https://intaros.nersc.no/sites/intaros.nersc.no/files/D6.4_INTAROS_Data_assimilation_v1.3.pdf </p>
Arctic Ocean state estimates for 2007-2016 using the GECCO model
<p>The dataset contains a 10-year ocean synthesis (2007-2016) obtained by assimilating available observations of sea ice and ocean parameters into the GECCO model. Data from, among others, several satellite programs such as AMSRE, SSMI, AMSR2, Envisat, Jason, Cryosat., AVHRR, and SMOS, and available moorings in the Davis Strait, the Bering Strait, the Fram Strait, the Barents Sea Opening, and by the Nansen and Amundsen Basins Observational System (NABOS), the North Pole Environmental Observatory (NPEO), and the Beaufort Gyre Exploration Project (BGEP) project. A detailed description can be found in Lyu et al., 2020.</p> <p>Guokun Lyu, Nuna Serra, Armin Koehl and Detlef Stammer, 2020. INTAROS Deliverable 6.4 Ice-ocean statistics and state estimation V1. https://intaros.nersc.no/sites/intaros.nersc.no/files/D6.4_INTAROS_Data_assimilation_v1.3.pdf </p>
A multi-state occupancy modeling framework for robust estimation of disease prevalence in multi-tissue disease systems
<p>1. Given the public health, economic, and conservation implications of zoonotic diseases, their effective surveillance is of paramount importance. The traditional approach to estimating pathogen prevalence as the proportion of infected individuals in the population is biased because it fails to account for imperfect detection. A statistically robust way to reduce bias in prevalence estimates is to obtain repeated samples (or sample many tissues in multi-tissue disease systems) and to apply statistical methods that account for imperfect detection and permit the interdependence of the infection process across multiple tissues.</p> <p>2. We developed a multi-state occupancy modeling framework which considers two scenarios about the infection process, one where no assumptions about the dependencies among the tissues are made (general), and another where dependence among tissues is not permitted (constrained).</p> <p>3. We applied this model to pseudorabies virus (PrV) DNA detection data obtained from whole blood; and oral, nasal, and genital mucosa of 510 feral swine (Sus scrofa) during the years 2014-2016 in Florida, USA.</p> <p>4. The constrained model was better supported by data. Estimated PrV prevalence varied among tissues, ranging from to 0.06 (CI: 0.02-0.14) in genital to 0.54 (CI: 0.14-0.82) in nasal tissue. Probability of PrV detection ranged from 0.11 (CI: 0.06-0.18) in nasal to 0.51 (CI: 0.21-0.81) in genital tissue. Estimates of PrV prevalence after accounting for imperfect detection were higher than the naïve estimates for all four tissues.</p> <p>5. PrV prevalence was not affected by the age or sex of the animal or the year of sampling, but prevalence increased as drought severity increased.</p> <p>6. The conditional probability of detecting PrV given infection in at least one tissue type within an individual was highest for nasal tissue, suggesting that nasal is the best tissue to sample for PrV surveillance if only one tissue can be sampled, at least for systems with tissue-specific prevalence and detection probabilities similar to ours.</p> <p>7. We found that pathogen prevalence in multi-tissue disease systems can vary across tissues. Our results emphasize the importance of sampling multiple tissues, and the application of robust statistical models to account for imperfect detection in the surveillance of systemic diseases. The multi-state modeling framework is broadly applicable to the surveillance of pathogens that infect multiple tissues and where the infection status or detection of the pathogen in one tissue may depend on the infection status of the pathogen in other tissues). 29-Jul-2020</p>
Dataset for "Recursive Input and State Estimation: A General Framework for Learning from Time Series with Missing Data"
<p>Dataset for "Recursive Input and State Estimation: A General Framework for Learning from Time Series with Missing Data"</p> <p> </p> <p>Missing values in the blood glucose datasets are represented with -2.</p>
Data from: Joint estimation of survival and breeding probability in female dolphins and calves with uncertainty in state assignment
While the population growth rate in long-lived species is highly sensitive to adult survival, reproduction can also significantly drive population dynamics. Reproductive parameters can be challenging to estimate as breeders and non-breeders may vary in resighting probability and reproductive status may be difficult to assess. We extended capture–recapture (CR) models previously fitted for data on other long-lived marine mammals to estimate demographic parameters while accounting for detection heterogeneity between individuals and state uncertainty regarding reproductive status. We applied this model to data on 106 adult female bottlenose dolphins observed over 13 years. The detection probability differed depending on breeding status. Concerning state uncertainty, offspring were not always sighted with their mother, and older calves were easier to detect than young-of-the-year (YOY), respectively 0.79 (95% CI 0.59–0.90) and 0.58 (95% CI 0.46–0.68). This possibly led to inaccurate reproductive status assignment of females. Adult female survival probability was high (0.97 CI 95% 0.96–0.98) and did not differ according to breeding status. Young-of-the-year and 1-year-old calves had a significantly higher survival rate than 2-year-old (respectively 0.66 CI 95% 0.50-0.78 and 0.45 CI 95% 0.29–0.61). This reduced survival is probably related to weaning, a period during which young are exposed to more risks since they lose protection and feeding from the mother. The probability of having a new YOY was high for breeding females that had raised a calf to the age of 3 or lost a 2-year-old calf (0.71, CI 95% 0.45– 0.88). Yet this probability was much lower for non-breeding females and breeding females that had lost a YOY or a 1-year-old calf (0.33, 95% CI 0.26–0.42). The multievent CR framework we used is highly flexible and could be easily modified for other study questions or taxa (marine or terrestrial) aimed at modelling reproductive parameters.
A Machine Learning Approach for Real-time Cortical State Estimation
<p>Data and code accompanying the following publication: Weiss, D. A., Borsa, A. M., Pala, A., Sederberg, A. J., & Stanley, G. B. (2024). A machine learning approach for real-time cortical state estimation. <em>Journal of neural engineering</em>, <em>21</em>(1), 10.1088/1741-2552/ad1f7b. https://doi.org/10.1088/1741-2552/ad1f7b</p>
State of Health Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy and Backpropagation Neural Network
Open the record for dataset details and reuse information.
Development of equivalent circuit model for state of power estimation of NMC-based Li-ion cell
Open the record for dataset details and reuse information.
Model Development for State-of-Power Estimation of Large-Capacity Nickel-Manganese-Cobalt Oxide-Based Lithium-Ion Cell Validated Using a Real-Life Profile
Open the record for dataset details and reuse information.
Dataset: Modular Piezoresistive Smart Textile for State Estimation of Cloths
<p>This set contains data obtained using the smart textile featured in "Modular Piezoresistive Smart Textile for State Estimation of Cloths", R. Proesmans et al., MDPI Sensors.<br> </p>
A Data Set for State and Parameter Estimation in Power Systems
<p>This data set consists of data from three power system models of different scales (IEEE 14, IEEE 118 and <a href="https://doi.org/10.5281/zenodo.2642175">PanTaGruEl</a>). For each of these systems, 5 different cases are provided, they are sorted from the least to the most "advanced" system operations.</p> <p>Data are stored in <a href="https://en.wikipedia.org/wiki/Hierarchical_Data_Format">HDF5</a> format (as H5T_NATIVE_FLOAT) which can be read by (mostly) any language (e.g. Python, Matlab or Julia).</p> <p><strong>Description of the different cases:</strong></p> <ul> <li><em>Case 1#</em> consists of 2000 samples. Each sample is obtained by: firstly, defining total active and reactive loads in the system which are then distributing to the buses and, secondly, dispatching generation (this is performed by running an OPF (Optimal Power Flow) with <a href="https://matpower.org/">Matpower</a>). The same distribution factors were used for every samples.</li> <li><em>Case 2#</em> is similar to <em>case 1#</em> with the addition of independent white noises to each bus load.</li> <li><em>Case 3#</em> differs from <em>case 1#</em> in that independent active and reactive bus loads are randomly drawn.</li> <li><em>Case 4# </em>is similar to <em>case 3#</em>, but some generators are randomly drawn to be in maintenance. This set of generators is independently generated for each sample.</li> <li><em>Case 5#</em> is similar to <em>case 4#</em>, plus the generation cost of each generator is randomly drawn from a predefined range. Costs are independently generated for each sample.</li> </ul> <p><strong>General Description:</strong></p> <p>Each data set case file contains the following elements:</p> <ul> <li>V (<span class="math-tex">\(N_{\rm bus} \times N_{\rm sample}\)</span> matrix): Voltage magnitudes,</li> <li>theta (<span class="math-tex">\(N_{\rm bus} \times N_{\rm sample}\)</span> matrix): Voltage phases,</li> <li>P (<span class="math-tex">\(N_{\rm bus} \times N_{\rm sample}\)</span> matrix): <a href="https://en.wikipedia.org/wiki/AC_power">Active</a> power injections (i.e. = generation - load),</li> <li>Q (<span class="math-tex">\(N_{\rm bus} \times N_{\rm sample}\)</span> matrix): <a href="https://en.wikipedia.org/wiki/AC_power">Reactive</a> power injections,</li> <li>idgen (<span class="math-tex">\(N_{\rm gen}\)</span> vector): index of generator buses,</li> <li>id_slack: index of the bus used as <a href="https://en.wikipedia.org/wiki/Slack_bus">slack bus</a>,</li> <li>epsilon (<span class="math-tex">\(N_{\rm line} \times 2\)</span> matrix): list of the lines in the system (Each row corresponds to a line. Entries are buses’ indices.),</li> <li>b (<span class="math-tex">\(N_{\rm line}\)</span> vector): line <a href="https://en.wikipedia.org/wiki/Admittance">susceptances</a>,</li> <li>g (<span class="math-tex">\(N_{\rm line}\)</span> vector): line <a href="https://en.wikipedia.org/wiki/Admittance">conductances</a>,</li> <li>bsh (<span class="math-tex">\(N_{\rm bus}\)</span> vector): shunt susceptances,</li> <li>gsh (<span class="math-tex">\(N_{\rm bus}\)</span> vector): shunt conductances.</li> </ul> <p><strong>Visualization:</strong></p> <p>The data set also includes bus coordinates.</p> <p><strong>Some theory:</strong></p> <p>The <a href="https://en.wikipedia.org/wiki/Incidence_matrix">incidence matrix</a> B is defined as</p> <p><span class="math-tex">\(B_{ij} = \left\{\begin{array}{l}-1,\; \text{if line $j$ starts at bus $i$,}\\1,\; \text{if line $j$ ends at bus $i$,}\\ 0,\; \text{otherwise.} \end{array}\right.\)</span></p> <p>(“Ends” and “starts” are purely conventional, but they have to be assigned to account for the direction power flows in the system. We use the first column of epsilon as "starts" and the second one as "ends".)</p> <p>The <a href="https://en.wikipedia.org/wiki/Nodal_admittance_matrix">admittance matrix</a> Y is obtained by</p> <p><span class="math-tex">\(y = g + ib,\\ y_{\rm sh} = g_{\rm sh} + ib_{\rm sh},\\ Y = B\,{\rm diag}(y)\,B^\top + {\rm diag}(y_{\rm sh}) .\)</span></p> <p>Defining the <a href="https://en.wikipedia.org/wiki/AC_power">complex</a> power injections and voltages, respectively, as</p> <p><span class="math-tex">\(S = P + iQ,\\ \underline{V} = V \cdot e^{i \theta}, \)</span></p> <p>where <span class="math-tex">\(\cdot\)</span> denotes the element-wise product. One has the following relation</p> <p><span class="math-tex">\(S = \underline{V} \cdot {\rm conj}(Y\, \underline{V}).\)</span></p> <p>This relation is equivalent to the <a href="https://en.wikipedia.org/wiki/Power-flow_study">power flow equations</a>.</p> <ul> </ul> <p> </p>
Tracer and Observationally Derived Constraints on Diapycnal Diffusivities in an Ocean State Estimate
<p>Data used to generate figures in Trossman et al. (2022) in Ocean Science</p>
Electrical ocean conductivity variability from observations and its budget from an ocean state estimate
<p>These are the data used to generate the figures in Trossman and Tyler (submitted to GRL, 2022).</p>
Standard analysis of the OCCA2 ocean state estimate (1980-2023)
<p>OCCA2 is an estimate of the time-variable ocean state over the 1980-2023 period. This repository contains standard views of the OCCA2 estimate, generated by the <a href="https://github.com/JuliaOcean/OceanStateEstimation.jl">OceanStateEstimation.jl</a> Julia pacakge.</p> <p>Reference : Gael Forget. Energy Imbalance in the Sunlit Ocean Layer, 11 April 2024, PREPRINT (Version 1), <a href="https://doi.org/10.21203/rs.3.rs-3979671/v1">https://doi.org/10.21203/rs.3.rs-3979671/v1</a></p> <p> </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.