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.
37
datasets available to search
ShareScore release 0.9.0
Dataset results
37 results for “Model Error”
Representation of model error in convective-scale data assimilation: additive noise, relaxation methods and combinations
<p>ModelError_1_dpsdt.tar.xz for Fig. 11<br> <br> ModelError_1_innovstat.tar.xz for Fig. 4, 10 and 13<br> <br> ModelError_1_KESpectrum.tar.xz for Fig. 1<br> <br> ModelError_1_pre_veri.tar.xz for Fig. 8 and 9<br> <br> ModelError_1_precipobs_tau.tar.xz for Fig. 2<br> <br> ModelError_1_refl_veri.tar.xz for Fig. 6, 12 and 14</p> <p>For plotting, MATLAB (R2017b) and python are used</p> <p> </p>
SEER: Estimating the sampling size in performance models considering errors
<p>Generating a performance model to optimize or to reason about variability-intensive systems is costly and the user never knows (unless she or he is a seer) when to stop sampling. The performance measurement can take from several seconds to several days, depending on the measurement and the noise, so it is very important to know if new measurements are profit. Here is where SEER can support users, by estimating the sampling size to generate performance models below a relative prediction error.</p> <p>There are three folders their files that can help you to start using SEER. The folder CODES include all the source code needed to use it. SEER has been validated with ten configurable systems. Folders Ground Truth and Results include the measurements done to validate SEER.</p>
Experimental Results for the PRICAI 2023 Paper ``Detecting AI Planning Modelling Mistakes -- Potential Errors and Benchmark Domains''
<p>This repository contains data related to the following paper:</p> <p>An overview of which domains have been tested on which software, refer to the following paper:<br> @InProceedings{Sleath2023PossibleModelingErrors,<br> author = {Kayleigh Sleath and Pascal Bercher},<br> title = {Detecting AI Planning Modelling Mistakes -- Potential Errors and Benchmark Domains},<br> booktitle = {Proceedings of the 20th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2023)},<br> year = {2023},<br> publisher = {Springer}<br> }</p> <p>Specifically, it contains:</p> <p>- the benchmarks used in the paper.<br> - screenshots of the output of call commands to the respective software artifacts<br> (the actual call commands are only contained in some of them)<br> - excel tables collecting the results</p> <p>Please note:</p> <p>(1) These benchmark problems are just copied in here for the sake of completeness and transparency. But if you are actually interested in using them, please use the newest version, which might have corrections and additional test cases. You find it here: https://github.com/ProfDrChaos/flawedPlanningModels</p> <p>(2) Please also note that the screenshots of our tests might not perfectly fit the folder structure of our benchmarks because we made some minor restructurings after the paper submission. Specifically, some test cases that we classified as syntactical in the submission were then changed into a semantic ones for the camera-ready version. (Hence the paths in the screenshots or tables might be slightly different.)</p> <p>(3) Also note that by the time you read this, several errors undetected by these systems at the time of the evaluation might be resolved by now. (We included the version numbers of the tested software.)</p>
Effect and Associated Factors of the Clinical Pharmacy Model in the Incidence of Medical Errors
ClinicalTrials.gov study NCT03338725. IPD Sharing: NO. Countries: 1. Publications: 2.
Data from: Detection error influences both temporal seroprevalence predictions and risk factors associations in wildlife disease models
Open the record for dataset details and reuse information.
Data from: Correction of location errors for presence-only species distribution models
Open the record for dataset details and reuse information.
Data from: Avoidable errors in the modeling of outbreaks of emerging pathogens, with special reference to Ebola
Open the record for dataset details and reuse information.
Data from: Occupancy models for data with false positive and false negative errors and heterogeneity across sites and surveys
Open the record for dataset details and reuse information.
Supporting data for "Reducing uncertainties in urban drainage models by explicitly accounting for timing errors in objective functions"
<p>Supporting data for "Reducing uncertainties in urban drainage models by explicitly accounting for timing errors in objective functions" submitted to Water Resources Research.</p> <p>Contains:</p> <ul> <li>SWMM template files.</li> <li>Objective function values for all model runs.</li> <li>Jupyter Notebooks used to create figures and tables for the article.</li> <li>Copy of rainfall runoff data available from <a href="https://doi.org/10.5281/zenodo.3931582">https://doi.org/10.5281/zenodo.3931582</a></li> </ul> <p>For python implementations of the Hydrograph Matching Algorithm (Ewen 2011) see <a href="https://doi.org/10.5281/zenodo.3923792">https://doi.org/10.5281/zenodo.3923792</a></p> <p>Ewen, John. “Hydrograph Matching Method for Measuring Model Performance.” <em>Journal of Hydrology</em> 408, no. 1–2 (September 2011): 178–87. <a href="https://doi.org/10.1016/j.jhydrol.2011.07.038">https://doi.org/10.1016/j.jhydrol.2011.07.038</a>.</p>
Data from: SNP discovery in non-model organisms: strand-bias and base-substitution errors reduce conversion rates
Single nucleotide polymorphisms (SNPs) have become the marker of choice for genetic studies in organisms of conservation, commercial or biological interest. Most SNP discovery projects in nonmodel organisms apply a strategy for identifying putative SNPs based on filtering rules that account for random sequencing errors. Here, we analyse data used to develop 4723 novel SNPs for the commercially important deep-sea fish, orange roughy (Hoplostethus atlanticus), to assess the impact of not accounting for systematic sequencing errors when filtering identified polymorphisms when discovering SNPs. We used SAMtools to identify polymorphisms in a velvet assembly of genomic DNA sequence data from seven individuals. The resulting set of polymorphisms were filtered to minimize 'bycatch'—polymorphisms caused by sequencing or assembly error. An Illumina Infinium SNP chip was used to genotype a final set of 7714 polymorphisms across 1734 individuals. Five predictors were examined for their effect on the probability of obtaining an assayable SNP: depth of coverage, number of reads that support a variant, polymorphism type (e.g. A/C), strand-bias and Illumina SNP probe design score. Our results indicate that filtering out systematic sequencing errors could substantially improve the efficiency of SNP discovery. We show that BLASTX can be used as an efficient tool to identify single-copy genomic regions in the absence of a reference genome. The results have implications for research aiming to identify assayable SNPs and build SNP genotyping assays for nonmodel organisms.
Modeling Technical Error and Adverse Outcomes
ClinicalTrials.gov study NCT04183439. IPD Sharing: UNDECIDED. Countries: 0. Publications: 1.
Data from: SNP discovery in non-model organisms: strand-bias and base-substitution errors reduce conversion rates
Open the record for dataset details and reuse information.
Error Metrics from ML models trained in Desai et al.
<p>Error metrics generated from training different machine learning models with varying levels of added gaussian noise.</p>
Reduce Medication Errors by Translating AESOP Model Into CPOE Systems
ClinicalTrials.gov study NCT03484793. IPD Sharing: Not stated. Countries: 1. Publications: 0.
AVISO Level 4 Absolute Dynamic Topography for Climate Model Comparison Standard Error
These data are the standard error calculated from the AVISO Level 4 Absolute Dynamic Topography for Climate Model Comparison Number of Observations data set ( in PO.DAAC Drive at https://podaac-tools.jpl.nasa.gov/drive/files/allData/aviso/L4/abs_dynamic_topo ). This data set is not meant to be used alone, but with the absolute dynamic topography data. These data were generated to help support the CMIP5 (Coupled Model Intercomparison Project Phase 5) portion of PCMDI (Program for Climate Model Diagnosis and Intercomparison). The dynamic topograhy are from sea surface height measured by several satellites, Envisat, TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 and referenced to the geoid. These data were provided by AVISO (French space agency data provider), which are based on a similar dynamic topography data set they already produce( http://www.aviso.oceanobs.com/index.php?id=1271 ).
Dataset of the paper "Minimal detectable change of gait and balance measures in older neurological patients: estimating the standard error of the measurement from before-after rehabilitation data thanks to the linear mixed-effects models"
<p>The Excel file contains the dataset whose analysis has been presented in the manuscript entitled <em>"Minimal detectable change of gait and balance measures in older neurological patients: estimating the standard error of the measurement from before-after rehabilitation data thanks to the linear mixed-effects models" </em>and published in J Neuroeng Rehabil (doi: 10.1186/s12984-024-01339-4; PMID: 38566189).</p> <p>This data set cannot be publicly available because of sensitive information. Please send your request to: a.caronni@auxologico.it.</p>
Representation error in global model CO2 simulations over India
<p>The data provides the possible representation error in the model simulations of CO<sub>2 </sub>concentrations over India, with a spatial resolution of one degree. Representation error is is estimated following Pillai et al. (2010) using WRF-Chem-GHG simulations at 10 km x 10 km spatial resolution.</p> <p>Reference.</p> <p>Pillai, D., Gerbig, C., Marshall, J., Ahmadov, R., Kretschmer, R., Koch, T., and Karstens, U.: High resolution modeling of CO2 over Europe: implications for representation errors of satellite retrievals, Atmospheric Chemistry and Physics, 10, 83–94, https://doi.org/10.5194/acp-10-83-2010, 2010.</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.