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5,805 results for “Data model”
Data from: Evolution of social versus individual learning in an infinite island model
We model the evolution of learning in a population composed of infinitely many, finite-sized islands connected by migration. We assume that there are two discrete strategies, social and individual learning, and that the environment is spatially homogeneous but varies temporally in a periodic or stochastic manner. Using a population-genetic approximation technique, we derive a mathematical condition for the two strategies to coexist stably and the equilibrium frequency of social learners under stable coexistence. Analytical and numerical results both reveal that social learners are favored when island size is large or migration rate between islands is high, suggesting that spatial subdivision disfavors social learners. We also show that the average fecundity of the population under stable coexistence of the two strategies is in general lower than that in the absence of social learners and is minimized at an intermediate migration rate.
Data from: Quantifying habitat use of migratory fish across riverscapes using space-time isotope models
1.Migratory animals pose difficult challenges to conservation and management because identifying critical habitats used throughout their lives is rarely possible. Endogenous tracers (e.g., isotope ratios) recorded in sequentially growing biogenic tissues, however, represent a potential source of unique insights at the more elusive temporal and spatial scales central to understanding the ecology of mobile species. To this end, a general probabilistic framework has emerged that quantitatively compares predictive models of isotopic variation across landscapes (called isoscapes) to the isotopic composition recorded in a biological tissue to determine the provenance and movements of animals throughout their lives. 2.Although this spatially continuous approach to isotope‐based geographic assignment is becoming more common across taxa and ecosystems, adopting this framework to take advantage of serial isotope records stored within sequentially growing biogenic tissues (e.g., teeth or otoliths) is less common. 3.Here, we construct a novel space‐time isotope model of provenance (STIMP) that determines the habitat use through time of migratory fish across river basins. To do so, this model integrates: strontium isotope (87Sr/86Sr) ratios across a riverscape, the serial records of 87Sr/86Sr within otoliths, habitat geomorphology, and the directional movement patterns of fish through river networks. 4.To illustrate an application of the model, we applied it to a published dataset from Chinook salmon (Oncorhynchus tshawytscha) harvested in 2011 during a coastal fishery in Bristol Bay, Alaska, U.S.A. Using this model, we show how individuals exploit an array of habitat types to achieve their juvenile growth prior to ocean migration, and that the intensity of habitat use among habitat types across the basin shifts spatially over the course of freshwater residence (e.g., from headwaters to migration corridors). The STIMP presented here integrates diverse information sources to reveal the cryptic juvenile movement patterns of a highly migratory species, providing new insights critical to their conservation. This general framework is applicable to any migratory taxa that use isotopically heterogeneous landscapes during their lives and record such variation in sequentially growing biogenic tissues.
Data from: Oral administration of Polymyxin B modulates the activity of Lipooligosaccharide E. coli B against lung metastases in murine tumor models
Introduction: Polymyxin B (PmB) belongs to the group of cyclic peptide antibiotics, which neutralize the activity of LPS by binding to lipid A. The aim of this study was to analyze the effect of PmB on the biological activity of lipooligosaccharide (LOS E. coli B,rough form of LPS) in vitro and in experimental metastasis models. Results: Cultures of murine macrophage J774A.1 cells and murine bone marrow-derived dendritic cells (BM-DC) stimulated in vitro with LOS and supplemented with PmB demonstrated a decrease in inflammatory cytokine production (IL-6, IL-10, TNF-α) and down-regulation of CD40, CD80, CD86 and MHC class II molecule expression. Additionally, PmB suspended in drinking water was given to the C57BL/6 mice seven or five days prior to the intravenous injection of B16 or LLC cells and intraperitoneal application of LOS. This strategy of PmB administration was continued throughout the duration of the experiments (29 or 21 days). In B16 model, statistically significant decrease in the number of metastases in mice treated with PmB and LOS (p<0.01) was found on the 14th day of the experiments, whereas the most intensive changes in surface-antigen expression and ex vivo production of IL-6, IL-1β and TNF-α by peritoneal cells were observed 7 days earlier. By contrast, antigen expression and ex vivo production of IL-6, IL-10, IFN-γ by splenocytes remained relatively high and stable. Statistically significant decrease in LLC metastases number was observed after the application of LOS (p<0.01) and in the group of mice preconditioned by PmB and subsequently treated with LOS (LOS + PmB, p<0.01). Conclusions: In conclusion, prolonged in vivo application of PmB was not able to neutralize the LOS-induced immune cell activity but its presence in the organism of treated mice was important in modulation of the LOS-mediated response against the development of metastases.
Data from: Evaluation of the innate immunostimulatory potential of originator and non-originator copies of insulin glargine in an in vitro human immune model
Background: The manufacture of insulin analogs requires sophisticated production procedures which can lead to differences in the structure, purity, and/or other physiochemical properties of resultant products that can affect their biologic activity. Here, we sought to compare originator and non-originator copies of insulin glargine for innate immune activity and mechanisms leading to differences in these response profiles in an in vitro model of human immunity. Methods: An endothelial/dendritic cell-based innate immune model was used to study antigen-presenting cell activation, cytokine secretion, and insulin receptor signalling pathways induced by originator and non-originator insulin glargine products. Mechanistic studies included signalling pathway blockade with specific inhibitors, analysis of the products in a Toll-like receptor reporter cell line assay, and insulin removal from the products by immunopurification. Findings: All insulin glargine products elicited at least a minor innate immune response comparable to human insulin, but some lots of a non-originator copy product induced the elevated secretion of the cytokines, IL-8 and IL-6. In studies aimed at addressing the mechanisms leading to differential cytokine production by these products, we found (1) the inflammatory response was not mediated by bacterial contaminants, (2) the innate response was driven by the insulin receptor through the MAPK pathway, and (3) the removal of insulin significantly reduced their capacity to induce innate activity. No evidence of product aggregates was detected, though the presence of some high molecular weight proteins argues for the presence of insulin dimers or others contaminants in these products. Conclusion: The data presented here suggests some non-originator insulin glargine product lots drive heightened in vitro human innate activity and provides preliminary evidence that changes in their biochemical composition (dimers, impurities) might be responsible for their greater immunostimulatory potential.
Data from: Cost-minimization model of a multidisciplinary antibiotic stewardship team based on a successful implementation on a urology ward of an academic hospital
Background: In order to stimulate appropriate antimicrobial use and thereby lower the chances of resistance development, an Antibiotic Stewardship Team (A-Team) has been implemented at the University Medical Center Groningen, the Netherlands. Focus of the A-Team was a pro-active day 2 case-audit, which was financially evaluated here to calculate the return on investment from a hospital perspective. Methods: Effects were evaluated by comparing audited patients with a historic cohort with the same diagnosis-related groups. Based upon this evaluation a cost-minimization model was created that can be used to predict the financial effects of a day 2 case-audit. Sensitivity analyses were performed to deal with uncertainties. Finally, the model was used to financially evaluate the A-Team. Results: One whole year including 114 patients was evaluated. Implementation costs were calculated to be €17,732, which represent total costs spent to implement this A-Team. For this specific patient group admitted to a urology ward and consulted on day 2 by the A-Team, the model estimated total savings of €60,306 after one year for this single department, leading to a return on investment of 5.9. Conclusions: The implemented multi-disciplinary A-Team performing a day 2 case-audit in the hospital had a positive return on investment caused by a reduced length of stay due to a more appropriate antibiotic therapy. Based on the extensive data analysis, a model of this intervention could be constructed. This model could be used by other institutions, using their own data to estimate the effects of a day 2 case-audit in their hospital.
Data from: A worldwide model for boundaries of urban settlements
The shape of urban settlements plays a fundamental role in their sustainable planning. Properly defining the boundaries of cities is challenging and remains an open problem in the science of cities. Here, we propose a worldwide model to define urban settlements beyond their administrative boundaries through a bottom-up approach that takes into account geographical biases intrinsically associated with most societies around the world, and reflected in their different regional growing dynamics. The generality of the model allows one to study the scaling laws of cities at all geographical levels: countries, continents and the entire world. Our definition of cities is robust and holds to one of the most famous results in social sciences: Zipf's law. According to our results, the largest cities in the world are not in line with what was recently reported by the United Nations. For example, we find that the largest city in the world is an agglomeration of several small settlements close to each other, connecting three large settlements: Alexandria, Cairo and Luxor. Our definition of cities opens the doors to the study of the economy of cities in a systematic way independently of arbitrary definitions that employ administrative boundaries.
Data from: Studying phenology by flexible modeling of seasonal detectability peaks
1.Many animals and plant species have advanced spring phenology in response to climate warming. The majority of avian phenological studies are based on arrival dates. Consequently knowledge on bird phenology is mainly based on migratory species. In addition, arrival dates of migratory birds may be substantially affected by en-route climate conditions; thus failing to provide good indicators for spring phenology on the breeding grounds. Correlating arrival dates with other phenological data or with environmental covariates may be meaningless in these cases. 2.We propose the date of highest singing activity, quantified by detection probability, as a powerful proxy for breeding phenology that is applicable to migratory and sedentary bird species alike. In contrast to arrival dates, breeding phenology is mainly (non-migrants) or at least partially (migrants) influenced by conditions experienced within the breeding area. 3.We developed a new method for flexible estimation of peak detectability date in spring by combining multi-season site-occupancy with semi-parametric regression modeling (thin-plate splines). We applied our approach to opportunistic observations of 27 bird species (mostly passerines) in Switzerland. 4.We found substantial differences among species in the date of spring peak detectability: late February to mid-April in sedentary and short-distance migratory species and mid-April to late May in long-distance migrants. Among 10 species with data for >9 years, five showed a trend in detectability peaks towards an earlier spring phenology by nine to 17 days within 10 years. The mean shift over all species was ~3.5 days per 10 years. 5.Our approach is widely applicable, especially for temporally and spatially large-scale data from monitoring or citizen-science programs. Besides using the detectability peak as measure of phenology, the estimated seasonal pattern in detectability can help designing monitoring programs for improved efficiency. Our approach may be applied to any species with pronounced acoustic displays or other behavioral traits strongly influencing detectability during the breeding period. We believe that it can contribute substantially to unraveling how species and communities respond to environmental change.
Data from: Factors affecting GEBV accuracy with single-step Bayesian models
A single-step approach to obtain genomic prediction was firstly proposed in 2009. Many studies have investigated the components of GEBV accuracy in genomic selection. However, it is still unclear how the population structure and the relationships between training and validation populations influence GEBV accuracy in term of single-step analysis. Here, we explored the components of GEBV accuracy in single-step Bayesian analysis with a simulation study. Three scenarios with various numbers of QTL (5, 50 and 500) were simulated. Three models were implemented to analyze the simulated data: single-step GBLUP (SSGBLUP), single-step BayesA (SS-BayesA) and single-step BayesB (SS-BayesB). According to our results, GEBV accuracy was influenced by the relationships between the training and validation populations more significantly for ungenotyped animals than that for genotyped animals. SS-BayesA/BayesB showed an obvious advantage over SSGBLUP with the scenarios of 5 and 50 QT L. SS-BayesB model obtained the lowest accuracy with the 500 QTL in the simulation. SS-BayesA model was the most efficient and robust considering all QTL scenarios. Generally, both the relationships between training and validation populations and LD between markers and QTL contributed to GEBV accuracy in the single-step analysis, and the advantages of single-step Bayesian models were more apparent when the trait is controlled by fewer QTL.
Data from: A simple biophysical model emulates budding yeast chromosome condensation
Mitotic chromosomes were one of the first cell biological structures to be described, yet their molecular architecture remains poorly understood. We have devised a simple biophysical model of a 300 kb-long nucleosome chain, the size of a budding yeast chromosome, constrained by interactions between binding sites of the chromosomal condensin complex, a key component of interphase and mitotic chromosomes. Comparisons of computational and experimental (4C) interaction maps, and other biophysical features, allow us to predict a mode of condensin action. Stochastic condensin-mediated pairwise interactions along the nucleosome chain generate native-like chromosome features and recapitulate chromosome compaction and individualization during mitotic condensation. Higher order interactions between condensin binding sites explain the data less well. Our results suggest that basic assumptions about chromatin behavior go a long way to explain chromosome architecture and are able to generate a molecular model of what the inside of a chromosome is likely to look like.
Data and R script for publication: The LOKI underwater imaging system and an automatic identification model for the detection of zooplankton taxa in the Arctic Ocean
<p>This is a www.zenodo.org data and R script upload for publication:</p> <p> </p> <p>Schmid, M.S. et al., The LOKI underwater imaging system and an automatic identification model for the detection of zooplankton taxa in the Arctic Ocean. Methods in Oceanography (2016), http://dx.doi.org/10.1016/j.mio.2016.03.003</p> <p> </p> <p>Downloadable script: Script_Schmid_Mio_2016_data_upload.R</p> <p>Downloadable data: Schmid_2016_MIO_CGlac.csv</p>
Model data for Sterzinger and Igel (2023) "Simulated Idealized Arctic Cloud Sensitivity to Above Cloud CCN Concentrations"
<p>Data for Sterzinger and Igel (2023) "Simulated Idealized Arctic Cloud Sensitivity to Above Cloud CCN Concentrations"</p> <p> </p> <p>all_data.tar.gz contains the processed horizontally-averaged data used to plot the figures in the paper.</p> <p> </p> <p>data_processing.tar.gz contains the scripts to process the 4-D data output from the model and namelists in https://doi.org/10.5281/zenodo.7991355</p>
The simulation data for the paper: Modeling the inner part of the jet in M87: confronting jet morphology with theory
<p>"mad98.prim.02740.athdf" is the simulation data of the fiducial model MAD98, "mad98low.prim.03900.athdf" is the simulation of the low resolution of MAD98.</p>
Data used in generation of results in 'Bias Correction of Climate Models using a Bayesian Hierarchical Model' J.Carter et. al.
<p>The data used in generation of results in 'Bias Correction of Climate Models using a Bayesian Hierarchical Model' J.Carter et. al. The datasets are dictionaries and are saved with .npy extensions. The datasets can be loaded in Python with expressions like: 'scenario_base = np.load(f"{filepath}scenario_base_hierarchical.npy",allow_pickle="TRUE").item()'.</p>
Data associated with Cell Reports publication: Dura-Bernal, Griffith, et al. 2023, "Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics" (4/4)
<p>This dataset includes experimental data used to constrain and validate the model, and model simulation output data for the following Cell Reports publication: <a href="https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6">https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6</a></p><p>The source code for the associated A1 model and data analysis can be found here: <a href="https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data">https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data</a>.</p><p>All zip files should unzipped into a parent folder called /data inside the Github repository above.</p><p><strong>Important:</strong> Due to the Zenodo size limit, this dataset is split among 4 Zenodo uploads. This is upload <strong>4 out of 4</strong>. The other 3 uploads can be found at: </p><p>Upload 1/4: <a href="http://doi.org/10.5281/zenodo.10066993">http://doi.org/10.5281/zenodo.10066993</a> (https://zenodo.org/uploads/10066993)</p><p>Upload 2/4: <a href="http://doi.org/10.5281/zenodo.10069553">http://doi.org/10.5281/zenodo.10069553</a> (https://zenodo.org/uploads/10069553)</p><p>Upload 3/4: <a href="http://doi.org/10.5281/zenodo.10071726">http://doi.org/10.5281/zenodo.10071726</a> (https://zenodo.org/uploads/10071726)</p><p>For more information please contact: salvador.dura-bernal@downstate.edu </p>
Data of "An enhanced lattice beam element model for the numerical simulation of rate-dependent self-healing in cementitious materials"
Open the record for dataset details and reuse information.
Data and R-Code from: How to account for behavioral states in step-selection analysis: a model comparison
<p>This repository provides the R-code and data used for the simulation and case study of the research paper: "How to account for behavioral states in step-selection analysis: a model comparison".</p><p>The folder "<strong>Pohle_et_al_2023_BehavioralStates_iSSA_Data</strong>" contains the landscape rasters used for data generation in the simulation study, and the bank vole (<i>Myodes glareolus</i>) movement data used in the case study on bank vole interactions:</p><ul><li>landscape10.RData and landscape50.RData: Landscape rasters for the simulation study.</li><li>Vole_case_control.rds: Case-control bank vole data for the case study.</li><li>Info_replicates.rds: Information about bank vole indiviuals and corresponding replicates for the case study.</li><li>Codebook_case_study.xlsx: Codebook for the case study data sets.</li><li>Read_me.txt</li></ul><p>The folder "<strong>Pohle_et_al_2023_BehavioralStates_iSSA_RCode</strong>" contains the R-scripts for the simulation and case study:</p><ul><li>Functions.R: Functions to apply HMMs, TS-iSSAs, and HMM-iSSAs to movement data; used for the simulation and case study.</li><li>Simulation_study.R: R-Code to run the simulation study. Parallel computation is used.</li><li>Results_simulation_study.R: R-Code to create the result figures and tables for the simulation study.</li><li>Case_study.R: R-Code to run the bank vole interaction case study. Parallel computation is used.</li><li>Results_case_study.R: R-Code to create the result figures and tables for the case study.</li><li>Read_me.txt</li></ul><p>Besides the simulation and case study from the paper, the included functions (<i>Functions.R</i>) can generally be used to perform an HMM-iSSA analysis.</p><p>For the bank vole movement data without control locations, see: Schlägel, U.E. et al. (2019). Data from: Estimating interactions between individuals from concurrent animal movements [Dataset]. Dryad. <a href="https://doi.org/10.5061/dryad.rt535m8">https://doi.org/10.5061/dryad.rt535m8</a>.</p><p><strong>Acknowledgements</strong></p><p>We thank Sophie Eden, Angela Puschmann and Pauline Lange for help with the bank vole data collection and maintenance of the outdoor enclosures.</p><p> </p><p> </p>
The best performing landslide susceptibility maps using ensemble machine learning models and precipitation data on basin and regional level in Lombardy, Italy
<p>A selection of landslide susceptibility maps computed through ensemble machine learning models with included precipitation data for the basin of Valchiavenna, and the Lombardy region in Italy.</p> <p>A list of the used base machine learning methods:</p> <ul> <li>Neural Networks.</li> </ul> <p>A list of the precipitation data included in the models:</p> <ul> <li>Average hourly precipitation for the year of 2020,</li> <li>90<sup>th</sup> percentile for the hourly precipitation for the year of 2020 ,</li> <li>Averaged + 90<sup>th</sup> percentile for the hourly precipitation for the year of 2020.</li> </ul> <p>A full list of the model combinations can be found in the "Case Studies" document.</p> <p>The maps are in WGS 84/ UTM zone 32N (EPSG:32632).</p> <p>The map production process details are discussed in Xu et al. 2024. If you use the dataset, please, cite also the paper:</p> <p><em>Qiongjie Xu, Vasil Yordanov, Lorenzo Amici & Maria Antonia Brovelli (2024) Landslide susceptibility mapping using ensemble machine learning methods: a case</em><br><em>study in Lombardy, Northern Italy, International Journal of Digital Earth, 17:1, 2346263, DOI:10.1080/17538947.2024.2346263</em></p> <p>The maps are produced as part of the "Geoinformatics and Earth Observation for Landslide Monitoring" Italy-Vietnam.</p> <p>The work is partially funded by the Italian Ministry of Foreign Affairs and International Cooperation within the project “Geoinformatics and Earth Observation for Landslide Monitoring” CUP D19C21000480001.</p> <p> </p>
Data for "Impact-induced initiation of Snowball Earth: A model study"
<p>Data and post-processing scripts for "Impact-induced initiation of Snowball Earth: A model study"<br>Submitted Nov 2023</p><p>Minmin Fu, Dorian S. Abbot, Christian Koeberl, and Alexey Fedorov</p><p>minmin.fu@yale.edu<br>Yale University</p>
Finite Volume Community Ocean Model-based Arctic Ocean Forecast System: A Comprehensive Assessment of Sea Ice Forecast Results without Data Assimilation
<p>The dataset contains sea ice forecast results from the Finite Volume Community Ocean Model-based Arctic Ocean Forecast System (FVCOM-AOFS) for the period 2019-2020. The output result of the system is presented as daily mean values.</p><p>Each netCDF file within the dataset includes the following variables: lon, lat, lonc, latc, aice, vice, uuice, and vvice. Specifically, the variables of lon and lat represent the longitude and latitude of the unstructured triangular grid. The variables of lonc and latc represent the longitude and latitude of the unstructured triangular cell. The variables of aice and vice indicate sea ice concentration and sea ice thickness. The variables of uuice and vvice indicate eastward and northward sea ice drift velocity. The scalars including aice and vice use lon and lat coordinates, and the vectors including uuice and vvice use lonc and latc coordinates. </p>
Supplementary Data: Measured values of 13 flood events in the upper watershed of Qingshan Hydrological Station and their simulation results by two models
<p>The files in this record contain measured data of 13 flood events and simulated flood processes from the XAJ and CM-XAJ models considered for publication in Water Resources Research.</p><p> </p><p>The files consist of:</p><p> </p><p>Measured values for 13 flood events of Qingshan Hydrological Station;</p><p>Source code (incomplete) and results of the XAJ Model;</p><p>Source code (incomplete) and results of the CM-XAJ Model;</p><p>Source code of Genetic Algorithm with recourse model.</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.