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44 results for “world modeling”
Ensemble Digital Terrain Model (EDTM) of the world
<p>Layers include: Ensemble Digital Terrain Model (EDTM) in 250-m resolution. Unit is in metre(m) and precision is in decimetre (dm). Maps are downscaled from 30-m resolution to 250-m in order to fit the size limit. We provide 30-m EDTM and its standard deviation as links:</p> <ul> <li><strong>30-m EDTM</strong></li> </ul> <p><a href="https://s3.eu-central-1.wasabisys.com/openlandmap/dtm/dtm.bareearth_ensemble_p10_30m_s_2018_go_epsg4326_v20230221.tif">https://s3.eu-central-1.wasabisys.com/openlandmap/dtm/dtm.bareearth_ensemble_p10_30m_s_2018_go_epsg4326_v20230221.tif</a></p> <ul> <li><strong>Standard deviation</strong></li> </ul> <p><a href="https://s3.eu-central-1.wasabisys.com/openlandmap/dtm/dtm.bareearth_ensemble_std_30m_s_2018_go_epsg4326_v20230221.tif"><strong>https://s3.eu-central-1.wasabisys.com/openlandmap/dtm/dtm.bareearth_ensemble_std_30m_s_2018_go_epsg4326_v20230221.tif </strong></a></p> <p>Derived using <a href="https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm">ALOS AW3D</a>, <a href="https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model">GLO-30</a>, <a href="http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/">MERITDEM</a>, and national DTMs. We derived a lower 10% quantile from all maps. In order to create bare earth data, we used <a href="https://glad.umd.edu/dataset/gedi/">canopy height</a> (canopy height > 2m) and standard deviation (sd > 6m) to mask building and forest in AW3D and GLO-30. Practical processing is written <a href="https://gitlab.opengeohub.org/yu-feng.ho/faen-artifact/-/blob/main/ensemble_dtm.ipynb">here</a> in Python.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">https://gitlab.com/openlandmap/global-layers/-/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL in Cloud Optimised GeoTiff (COG). File naming convention:</p> <ul> <li>dtm.bareearth = variable: digital terrain model (m), bare earth</li> <li>ensemble = determination method: ensemble of mutli-source dsm and dtm</li> <li>p10/std = aggregation/statistics method: 10th percentile / standard deviation</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>s = vertical reference: at surface,</li> <li>go = bounding box: global land without Antarctica</li> <li>epsg.4326 = ESPG code: epsg.4326</li> <li>v20230221 = version code: creation date 20230221</li> </ul>
Smart Analyser of Variability Requirements of Unknown Spaces (SAVRUS) Dataset of a study with 5 real-world large numerical variability models.
<p>The publications and research associated to cite is in:</p><p><a href="https://doi.org/10.1016/j.knosys.2023.110558">https://doi.org/10.1016/j.knosys.2023.110558</a></p><p>In that research we detail the Smart Analyser of Variability Requirements of Unknown Spaces (SAVRUS) approach, and provide a web-tool prototype in <a href="https://hadas.caosd.lcc.uma.es/savrus">https://hadas.caosd.lcc.uma.es/savrus</a></p><p>In the study, we model 5 different real-world software product lines to then analysed them with SAVRUS:</p><p>Detailed real-world variability models ordered by their search space size, of which GEC QA is incompletely measured NVM Description #Booleans #Numericals Space QA #Measurements </p><p>Dune1</p><p> </p><p>Multi-grid solver</p><p> </p><p>11</p><p> </p><p>3</p><p> </p><p>2,304</p><p> </p><p>Complex..</p><p> </p><p>2,304</p><p> </p><p>HSMGP1</p><p> </p><p>Stencil-grid solver</p><p> </p><p>14</p><p> </p><p>3</p><p> </p><p>3,456</p><p> </p><p>..equation..</p><p> </p><p>3,456</p><p> </p><p>HiPAcc1</p><p> </p><p>Image processing framework</p><p> </p><p>33</p><p> </p><p>2</p><p> </p><p>13,485</p><p> </p><p>..solving..</p><p> </p><p>13,485</p><p> </p><p>Trimesh2</p><p> </p><p>Triangle mesh library</p><p> </p><p>13</p><p> </p><p>4</p><p> </p><p>239,360</p><p> </p><p>..time</p><p> </p><p>239,360</p><p> </p><p>GEC</p><p> </p><p>Generic edge computing</p><p> </p><p>552</p><p> </p><p>2</p><p> </p><p>~5.3*108</p><p> </p><p>Energy Consumption</p><p> </p><p>132500</p><p> </p><p>The dataset zip file contains:</p><ul><li>5 numerical variability models in Clafer format (.txt) for each software product line.</li><li>5 CSV files with the respective quality attribute measurements</li><li>An .xlsx file containing SAVRUS scalability results divided in different tabs.</li></ul><p>References:</p><p>[1] N. Siegmund, A. Grebhahn, S. Apel, C. Kastner, Performance-influence models for highly configurable systems, in: Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, ESEC/FSE 2015, Association for Computing Machinery, New York, NY, USA, 2015, p.284–294. doi:10.1145/2786805.2786845.</p><p>[2] M. Bauer, A comparison of six constraint solvers for variability analysis, Tech. rep., University of Passau (2019).</p>
Quantifying the basic reproduction number and the under-estimated fraction of mpox cases around the world at the onset of the outbreak: a mathematical modeling and machine learning- based study
<p><span>In 2022, there was a global resurgence of mpox, with different clinico-epidemiological features compared with previous</span><br><span>outbreaks. During this resurgence, sexual contact was hypothesized as the primary transmission route, with the community</span><br><span>of men having sex with men (MSM) being disproportionately affected. Because of the stigma associated with sexually</span><br><span>transmitted infections, especially those impacting MSM, the real burden of mpox could be masked.</span><br><span>We quantified the basic reproduction number (R</span><span>0</span><span>) and the under-estimated fraction of mpox cases in 16 countries, from the</span><br><span>onset of the outbreak until early September 2022, using Bayesian inference and a compartmentalized, risk-structured (high-</span><br><span>and low-risk populations), two-route (sexual and non-sexual transmission) mathematical model. Machine learning (ML) was</span><br><span>leveraged to identify under-estimation determinants.</span><br><span>Estimated R</span><span>0</span><span> </span><span>ranged between 1·37 (Canada) and 3·68 (Germany). The under-estimation rates for the high- and low-risk</span><br><span>populations varied between 25-93% and 65-85%, respectively. The estimated total number of mpox cases, relative to the</span><br><span>reported cases, is highest in Colombia (3·60) and lowest in Canada (1·08). In the ML analysis, two clusters of countries could</span><br><span>be identified, differing in terms of attitudes towards the 2SLGBTQIAP+ community and importance of religion.</span><br><span>Given the substantial mpox under-estimation, surveillance should be enhanced and campaigns against the stigmatization of</span><br><span>MSM should be organized. Countries have different social characteristics, potentially explaining the various degrees of under-</span><br><span>reporting in mpox cases, which should be considered by studies assessing the effectiveness of community-based</span><br><span>interventions.</span></p>
Datasets for the paper "Group formation on a small-world: experiment and modelling"
<p>Datasets required for reproducing all the results presented in the article "Group formation on a small-world: experiment and modelling" to be published in the Journal of the Royal Society Interface.</p> <p>Take a look into the included README for a description about the data format.</p>
Processed Synthetic Real-World Data for tristate modelling
<p>This model learning dataset is created out of the <a href="https://zenodo.org/record/7409763">Raw Synthetic RWD</a> raw dataset, including some of the original attributes. It is distributed in JOBLIB files, where .joblib files contain the vectors and _ids.joblib contain the ID of the person from which each vector is extracted.</p> <p>This is useful in case it is needed to map the vectors to metadata about the people that are found in the original raw dataset. Note that corresponds to , or , depending on the dataset.</p> <p>The split is roughly 60% of the people are in the training dataset, and 20% in each of the validation and the testing datasets. The input attributes are the age, the short-term averages and the trends of the current week’s BMI, steps walked, calories burned, sleep quality, mood and water consumption, as well as the previous week’s short-term average and trend of the answer to the health self-assessment question.</p> <p>The outcome to be predicted is a tristate quantized version of the health self-assessment answer to be given in the current week. The dataset is normalized based on the training set. The means and standard deviations used can be found in the train_statistics.joblib file. Finally, the output_descriptions.joblib file contains descriptions of the outcomes to be predicted (not actually needed, since included here).</p>
Processed Synthetic Real-World Data for binary modelling
<p>This model learning dataset is created out of the <a href="https://zenodo.org/record/7409763">Raw Synthetic RWD</a> raw dataset, including some of the original attributes. It is distributed in JOBLIB files, where .joblib files contain the vectors and _ids.joblib contain the ID of the person from which each vector is extracted.</p> <p>This is useful in case it is needed to map the vectors to metadata about the people that are found in the original raw dataset. Note that corresponds to , or , depending on the dataset. The split is roughly 60% of the people are in the training dataset, and 20% in each of the validation and the testing datasets. The input attributes are the age, the short-term averages and the trends of the current week’s BMI, steps walked, calories burned, sleep quality, mood and water consumption, as well as the previous week’s short-term average and trend of the answer to the health self-assessment question.</p> <p>The outcome to be predicted is the binary quantized health self-assessment answer to be given in the current week. The dataset is normalized based on the training set. The means and standard deviations used can be found in the train_statistics.joblib file. Finally, the output_descriptions.joblib file contains descriptions of the outcomes to be predicted (not actually needed, since included here).</p>
Optimally interpolated dissolved oxygen based on the World Ocean Database 2018 and CMIP6 models
<p>Optimal interpolation of observed and modeled dissolved oxygen data from WOD18 and CMIP6. Technical details are provided in the publication (Ito et al., 2023). </p><p>Ito, T., Garcia, H. E., Wang, Z., Minobe, S., Long, M. C., Cebrian, J., Reagan, J., Boyer, T., Paver, C., Bouchard, C., Takano, Y., Bushinsky, S., Cervania, A., and Deutsch, C. A.: Underestimation of global O2 loss in optimally interpolated historical ocean observations, Biogeosciences Discuss. [preprint], https://doi.org/10.5194/bg-2023-72, in review, 2023.</p>
A comparison of the World Health Organisation's HEAT model results using a non-linear physical activity dose response function with results from the existing tool
<p>Datasets relating to the Wellcome Open Research publication of the same name.</p>
Figure 7. World model construction and artificial intelligent structure running on the PDA
<p>In this section the AI part of the software is briefly introduced. There are three distinct<br> layers: AI Core, Role Engine and Behavior Engine. AI Core receives the computed field data from<br> World Map Modeling unit and determines the play state according to the ball, opponents and our<br> robots positions.<br> Considering the current game strategy, determination of the play state is done by fuzzy<br> decision-making to avoid undesirable and sudden changes of roles or behaviors.[3-4]</p>
Dataset of Last Interglacial climate from publication "Modeled storm surge changes in a warmer world: the Last Interglacial" by P. Scussolini et al.
<p>Results from the simulation of Last Interglacial (Eemian) climate with climate model CESM1.2. Variables are: sea-level pressure (PSL); meridional wind (V), and zonal wind (U). Time step is 6-hourly.</p> <p>Detailed description of the methods are in the original publication:</p> <p>Scussolini, P., Dullaart, J., Muis, S., Rovere, A., Bakker, P., Coumou, D., Renssen, H., Ward, P. J., and Aerts, J. C. J. H.: Modelled storm surge changes in a warmer world: the Last Interglacial, EGUsphere, 2022, 1-20, 10.5194/egusphere-2022-101, 2022.</p>
Dataset of pre-industrial climate from publication "Modeled storm surge changes in a warmer world: the Last Interglacial" by P. Scussolini et al.
<p>Results from the simulation of pre-industrial climate with climate model CESM1.2. Variables are: sea-level pressure (PSL); meridional wind (V), and zonal wind (U). Time step is 6-hourly.</p> <p>Detailed description of the methods are in the original publication:</p> <p>Scussolini, P., Dullaart, J., Muis, S., Rovere, A., Bakker, P., Coumou, D., Renssen, H., Ward, P. J., and Aerts, J. C. J. H.: Modelled storm surge changes in a warmer world: the Last Interglacial, EGUsphere, 2022, 1-20, 10.5194/egusphere-2022-101, 2022.</p>
The past, present, and future of predator-prey interactions in a warming world: using species distribution modeling to forecast ectotherm-endotherm niche overlap
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RadCases evaluation results: Evaluating acute image ordering for real-world patient cases via language model alignment with radiological guidelines
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Analysing the distribution of SARS-CoV-2 infections in schools: integrating model predictions with real world observations
<p>Dataset and analysis for:</p> <p>Analysing the distribution of SARS-CoV-2 infections in schools: integrating model predictions with real world observations.<br>Arnab Mukherjee, Sharmistha Mishra, Vijaya Kumar Murty, Swetaprovo Chaudhuri<br> </p> <p>For any questions please contact the first author at: arnab.mukherjee@mail.utoronto.ca</p> <p><strong>Contents:</strong></p> <ol> <li><strong>school_active_cases_ON.zip:</strong> Contains datasets for number of COVID-19 infections reported by public schools in Ontario on ten different dates. The data files have been created based on the raw data in the file named 'covidtesting.csv' that has also been shared.</li> <li><strong>school_active_cases_pdf.m:</strong> Matlab code to obtain PDF of secondary infections in schools for a particular date based on the datasets in 'school_active_cases_ON.zip'. To obtain PDF for different dates, the appropriate dataset needs to be loaded. Created in MATLAB R2021b.</li> <li><strong>U_jet2.m:</strong><em> </em>User-defined Matlab function that is required to run the code 'gZ_code.m'. The function simulates the evolution of a simple jet/puff. Created in MATLAB R2021b.</li> <li><strong>gZ_code.m:</strong> Matlab code to obtain the analytical PDF of secondary infections due to long-range transmission, near-field transmission, or both. Created in MATLAB R2021b.</li> <li><strong>covidtesting.zip: </strong>Contains the data file 'covidtesting.csv' that reports the breakdown of COVID-19 infections in different public schools in Ontario on a daily basis. Data obtained from 'https://data.ontario.ca/dataset/summary-of-cases-in-schools/resource/dc5c8788-792f-4f91-a400-036cdf28cfe8'. Contains information licensed under the Open Government License – Ontario.</li> <li><strong>schoolrecentcovid2021_2022.zip:</strong> Contains the data file 'schoolrecentcovid2021_2022.csv<strong>' </strong>that reports the status of COVID-19 cases in Ontario, obtained from 'https://data.ontario.ca/en/dataset/status-of-covid-19-cases-in-ontario/resource/ed270bb8-340b-41f9-a7c6-e8ef587e6d11'. Contains information licensed under the Open Government License – Ontario.</li> </ol> <p> </p> <p> </p> <p> </p>
Evolution towards increasing complexity through functional diversification in a protocell model of the RNA world
<p>The encapsulation of genetic material inside compartments together with the creation and sustenance of functionally diverse internal components are likely to have been key steps in the formation of 'live', replicating protocells in an RNA world. Several experiments have shown that RNA encapsulated inside lipid vesicles can lead to vesicular growth and division through physical processes alone. Replication of RNA inside such vesicles can produce a large number of RNA strands. Yet, the impact of such replication processes on the emergence of the first ribozymes inside such protocells and on the subsequent evolution of the protocell population remains an open question. In this paper, we present a model for the evolution of protocells with functionally diverse ribozymes. Distinct ribozymes can be created with small probabilities during the error-prone RNA replication process via the rolling circle mechanism. We identify the conditions that can synergistically enhance the number of different ribozymes inside a protocell and allow functionally diverse protocells containing multiple ribozymes to dominate the population. Our work demonstrates the existence of an effective pathway towards increasing complexity of protocells that might have eventually led to the origin of life in an RNA world.</p>
Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.
<p>Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.</p>
Fully synthetic longitudinal real-world data from hearing aid wearers for public health policy modeling
<p>Real-world data from hearing aids and Bluetooth connected smartphones. The associated data report can be found here: <a href="https://doi.org/10.3389/fnins.2019.00850">https://doi.org/10.3389/fnins.2019.00850</a></p>
Experimental Validation of Cryobot Thermal Models for the Exploration of Ocean Worlds
<p>The tables in this repository represent the data used in the figures and analyses of the paper "Experimental Validation of Cryobot Thermal Models for the Exploration of Ocean Worlds", published in the Planetary Science Journal. The provided data was collected between 2020 and 2022.</p> <ul> <li>AllResults.xlsx: compilation of tables 4, 5, 6, and 7 on the paper.</li> <li>WarmA1.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Total Power [W], H6 Power [W], H5 Power [W], H4 Power [W], H3 Power [W], H2 Power [W], H1 Power [W]".</li> <li>WarmA2.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Total Power [W], H6 Power [W], H5 Power [W], H4 Power [W], H3 Power [W], H2 Power [W], H1 Power [W]".</li> <li>CryoA1.xlsx: data presented in tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth Estimation [m], Power [W]".</li> <li>CryoB1.xlsx: data presented in figures 9 and 10, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoB2.xlsx: data presented in figures 9, 10, 11, and 12, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoB3.xlsx: data presented in figures 6, 9, 10, 11, and 12, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoC1.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoC2.xlsx: data presented in figures 9, 10, 11, and 12, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoC3.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Truncated Coarse Depth [m], Power [W]".</li> </ul> <p> </p>
The best of two worlds: using stacked generalisation for integrating expert range maps in species distribution models
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Evolution towards increasing complexity through functional diversification in a protocell model of the RNA world
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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.