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.
17
datasets available to search
ShareScore release 0.9.0
Dataset results
17 results for “Parameter Identifiability”
Calculated Parameters of Thyroid Homeostasis: de-identified Data
<p>De-identified data of patients with different thyroid conditions. TI: Thyrotropic insufficiency; TT: Thyrotoxicosis; PH: Primary hypothyroidism; ST: Secondary hyperthyroidism. Parameters are reported with units of measurement and reference interval.</p>
Dataset for: "Parameter identifiability and model selection for partial differential equation models of cell invasion"
<p>This is the dataset accompanying the paper "Parameter identifiability and model selection for partial differential equation models of cell invasion" (https://arxiv.org/abs/2309.01476). It consists of a series of images taken of a barrier assay experiment to study tissue expansion of MDCK cells, along with cell density data in MATLAB format.</p> <p>File structure: the contents of the four zip files should be combined (they were split into four files for practical reasons regarding file size). The data corresponds to eight experiments, four with circular initial conditions, and four with triangular initial conditions, the associated data are located in 04-05-22 exp1/Circle and 04-05-22 exp1/Triangle respectively, each labeled "xy<n>", where <n> from 1 to 8 is an identifier for the experiment. The images under the xy<n>_Phase folders are the raw images taken of the experiment, those under the xy<n>_mask folder are processed images indicating the extend of the spread of the cell population. The DensityCellcyleFraction folder contain data files in MATLAB format. The most relevant is the "density" variable, which is a rank-3 tensor of size 150x150x77 such that density(i,j,k) corresponds to the cell density at location (x_i,y_j) and time t_k. The process for calculating the cell density is described in the paper.</p> <p>Alternatively, the density data is also provided in csv format. In the csv_data folder, xy<n>/t<k>.csv encodes a matrix representing cell density for experiment <n> at time t_k.</p> <p>The code for processing and analysing these data are provided in the "code" folder. It is also available at https://github.com/liuyue002/woundhealing .</p> <p>Abstract of the paper:</p> <p>When employing a mechanistic model to study biological systems, practical parameter identifiability is important for making predictions in a wide range of scenarios, as well as for understanding the mechanisms driving the system behaviour. We argue that parameter identifiability should be considered alongside goodness-of-fit and model complexity as criteria for model selection. To demonstrate, we use a profile likelihood approach to investigate parameter identifiability for four extensions of the Fisher--KPP model, given experimental data from a cell invasion assay. We show that more complicated models tend to be less identifiable, with parameter estimates being more sensitive to subtle differences in experimental procedures, and require more data to be practically identifiable. The results from identifiability analysis can inform model selection, as well as data collection and experimental design.</p>
Identifying Episodes of Hypovigilance in Intensive Care Units Using Routine Physiological Parameters and Artificial Intelligence: a Derivation Study. Open Code and Dataset
<p>The purpose of this project is to detect hypogilance using the EVEILS database.</p> <p>Database is ICU data from Hôtel-Dieu De Lévis , Québec, Canada. Please cite us if you use either the data or code. </p> <p>This code was written during Raphaëlle Giguère Msc in Computer Science. The goal of her project is to detect hypovigilance using machine learning in the ICU. In this repository, you have the data set before preprocessing:</p> <ul> <li>df_hypovigilance : Contains the hours, date and value of the vigilance level, using either the RASS or Ramsay and already converted using the thresholds shown in the paper.</li> <li>raw_df : Contains the raw values from the gateway for each participant. All of the identifying values have been removed.</li> </ul> <p>At the end of the preprocessing_anonymous script, you should generate a new dataset called "df_final". This dataset is used for the training_model script.</p> <p>The cross validation employs groups of random size meaning the results might differ from time to time but should stay consistent.</p> <p> </p>
Parameter estimation and identifiability analysis for a bivalent analyte model of monoclonal antibody-antigen binding
<p>Data for non-regenerative bivalent analyte binding interaction between CH505 transmitted founder gp120 and CH31 monoclonal antibody, an HIV-1 mAb with CD4-binding site specificity.</p> <p>This data set is submitted as part of the manuscript titled "Parameter estimation and identifiability analysis for a bivalent analyte model of monoclonal antibody-antigen binding"</p>
Parameter estimation data release for paper "Waveform systematics in identifying gravitationally lensed gravitational waves: Posterior overlap method"
<p>This is a second data release for the paper "Waveform systematics in identifying gravitationally lensed gravitational waves: Posterior overlap method", which is available on <a href="https://arxiv.org/abs/2306.12908">https://arxiv.org/abs/2306.12908</a>.</p> <p>This data release contains posterior samples and configuration files for parameter estimation runs performed for the paper. For the lensing hypothesis tests results see the other <a href="https://doi.org/10.5281/zenodo.8409635">data release</a> for the same paper.</p> <p>For each event that we have run parameter estimation for, we provide a tar.gz file that includes the configurations, the priors and the results for each run, typically with several different waveforms. All runs were performed with <a href="https://lscsoft.docs.ligo.org/parallel_bilby/">parallel bilby</a> with different versions as described in section 6.1 of the paper. The input data is available from <a href="https://gwosc.org/">GWOSC</a>.</p> <p>The results for IMRPhenom* waveforms (runs with parallel bilby version 1.0.1) are in json format, while for SEOBNRv5PHM and NRSur7dq4 (run with parallel bilby version 2.0.2) some of the results are in hdf5 and others in json, depending on whether multiple runs were merged together. These should be readable with bilby or <a href="https://lscsoft.docs.ligo.org/pesummary/">pesummary</a>.</p> <p>We provide the "complete" configuration files processed by bilby. These can be used for reproducing the runs, but the prior file information needs to be added with a syntax like the following `prior-file=ProdF4.prior`. Generally most runs use the same prior file, with the labels `Prod*.prior`, taken from the LVK analyses. The priors used for the aligned-spin waveforms have the label `_AS`. For the NRSur7dq4 waveform we use a restricted prior with the label `_NR_Sur_constrMtot`. For GW190527 we also use a different prior for IMRPhenomXP and IMRPhenomTPHM, which has the label `_restricted`.</p> <p>Also note that for the result file GW190527_NRSur7dq4_N4096_nact50_fmin0_nparallel3_merged_result.hdf5 this was merged from the results obtained with the config GW190527_NRSur7dq4_N4096_nact50_fmin0_nparallel3_config_complete.ini together with the fourth chain of identical configuration.</p>
Datasets associated with "Improving Small Molecule Force Fields by Identifying and Characterizing Small Molecules with Inconsistent Parameters"
<p>This provides associated datasets for a paper/preprint forthcoming soon, by Ehrman et al., "Improving small molecule force fields by identifying and characterizing small molecules with inconsistent parameters". A poster describing an earlier portion of the work is available at https://doi.org/10.5281/zenodo.3385278. The AllMolsAllData.csv.gz file provides a Pandas dataframe for the full set of molecules considered, as described in the paper, whereas the 25andunderarchive.tar.gz provides optimized geometries for 265,847 molecules with four or more difference flags.</p>
Identifying an Ideal Cardiopulmonary Exercise Test Parameter
ClinicalTrials.gov study NCT01162083. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Validation of a Biomathematical Model to Help Identify the Parameters of Flexible Insulin Therapy
ClinicalTrials.gov study NCT04572009. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Simultaneous measurement of excitation-contraction coupling parameters identifies mechanisms underlying contractile responses of hiPSC-derived cardiomyocytes
Cardiomyocytes from human induced pluripotent stem cells (hiPSC-CMs) are increasingly recognized as valuable for determining the effects of drugs on ion channels but they do not always accurately predict contractile responses of the human heart. This is in part attributable to their immaturity but the sensitivity of measurement tools may also be limiting. Measuring action potential, calcium flux or contraction individually misses critical information that is captured when interrogating the complete excitation-contraction coupling cascade simultaneously. Here, we develop an hypothesis-based statistical algorithm that identifies mechanisms of action. We design and build a high-speed optical system to measure action potential, cytosolic calcium and contraction simultaneously using fluorescent sensors. These measurements are automatically processed, quantified and then assessed by the algorithm. Multiplexing these three critical physical features of hiPSC-CMs allows identification of all major drug classes affecting contractility with detection sensitivities higher than individual measurement of action potential, cytosolic calcium or contraction.
Data from: Assessing parameter identifiability in phylogenetic models using Data Cloning
The success of model-based methods in phylogenetics has motivated much research aimed at generating new, biologically informative models. This new computer-intensive approaches to phylogenetics demands validation studies and sound measures of performance. To date such work has consisted only of simulation studies, estimation of known phylogenies and difficult mathematical analyses assessing the estimability of parameters. Little practical guidance has been available to practitioners and theoreticians alike as to when and why the parameters in a particular model can be identified reliably. Here, we illustrate how Data Cloning (DC), a recently developed methodology to compute the Maximum Likelihood estimates along with their asymptotic variance, can be used to diagnose structural parameter non-identifiability (NI) and distinguish it from other parameter estimability problems including the case where parameters are structurally identifiable, but are not estimable in given data set (INE), and the case where parameters are identifiable, and estimable, but only weakly so (WE). The application of the DC theorem uses well-known and widely used Bayesian computational techniques. With the DC approach, practitioners can use any Bayesian phylogenetics software to be able to diagnose non-identifiability. Theoreticians and practitioners alike now have a powerful tool to detect non-identifiability while investigating complex modeling scenarios, where getting closed-form expressions in a probabilistic study is complicated. Furthermore, here we also show how DC can be used as a tool to examine and eliminate the influence of the priors, in particular if the process of prior elicitation is not straightforward. Finally, when applied to phylogenetic inference, DC can be used to study at least two important statistical questions: assessing identifiability of discrete parameters, like the tree topology, and developing efficient sampling methods for computationally expensive posterior densities.
Table ¹: Model performance in relation to predictor variables combinations and MaxEnt parameters for the two best models identified in our analysis. in Understanding habitat suitability and road mortality for the conservation of the striped hyaena (Hyaena hyaena) in Batna (East Algeria)
<p><b>Table ¹:</b> Model performance in relation to predictor variables combinations and MaxEnt parameters for the two best models identified in our analysis.</p><table><tbody><tr><th><b>Prediction set</b></th><th><b>FC</b></th><th><b>RM</b></th><th><b>P</b> <b>ROC</b></th><th><b>OR</b></th><th><b>AICc</b></th><th><b>ΔAICc</b></th><th><b>WAICc</b></th><th><b>Number of parameters**</b></th></tr></tbody><tbody><tr><th>313</th><td>lqp</td><td>0.7</td><td>0</td><td>0</td><td>752.875</td><td>1.226</td><td>0.0337</td><td>10</td></tr><tr><th>314*</th><td>lqp</td><td>1</td><td>0</td><td>0</td><td>751.649</td><td>0.000</td><td>0.057</td><td>7</td></tr></tbody></table><p>The selected model set (*) met the statistical significance and omission rate criteria during evaluation with train and test data.**Number of environmental variables. FC, feature class; RM, regularization multiplier; OR = omission rate; ΔAICc, Akaike information criterion corrected for small simple sizes; WAICc, weighted AICc.</p>
Identifying Decision Making Parameters in Healthy Volunteers and Anxiety Patients
ClinicalTrials.gov study NCT05092971. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Simultaneous measurement of excitation-contraction coupling parameters identifies mechanisms underlying contractile responses of hiPSC-derived cardiomyocytes
Open the record for dataset details and reuse information.
Data from: Assessing parameter identifiability in phylogenetic models using Data Cloning
Open the record for dataset details and reuse information.
Morphological Parameters of In-stent RESTenosis Assessed and Identified by OCT
ClinicalTrials.gov study NCT04268875. IPD Sharing: Not stated. Countries: 3. Publications: 0.
A Prospective Observational Study to Determine the Predictive Accuracy of Snakebite Severity Score (SSS) in Identifying Syndrome-Specific Core Outcome Parameters Amongst Envenomated Patients Presentin
ClinicalTrials.gov study NCT07079137. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Critical quality attributes identify MSCs with potent fitness ranges in response to culture process parameters
GEO Series GSE212368. Homo sapiens. 29 samples. Type: Expression profiling by array.
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.