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5,805 results for “Data model”
Data Set for Enhanced Performance Prediction of ATL Model Transformations
<p>Model transformation languages are domain-specific languages, which are designed to comfortably define transformations. With the increasing use of transformations in various domains, the complexity and size of input models are also increasing. However, developers often lack suitable models for performance testing. We have therefore conducted experiments in which we predict the performance of model transformations based on characteristics of input models using machine learning approaches. In particular, we focused on how to predict the performance of transformations that also transform attributes whose values can have arbitrary size. This dataset contains our raw and processed input data, the scripts necessary to repeat our experiments, and the results we obtained.</p> <p>Our input data consists of the time measurements for six different transformations defined in the Atlas Transformation Language (ATL), as well as the collected characteristics of the real-world input models we used. In this data set, we provide the script that implements our experiments. We predict the execution time of ATL transformations using the machine learning approaches linear regression, random forests and support vector regression using a radial basis function kernel. We also investigate different sets of characteristics of input models as input for the machine learning approaches. These are described in detail in the provided documentation.pdf. The results of the experiments are provided as raw data in individual cvs files. Furthermore, we provide our Eclipse plugin, which collects the characteristics for a set of given models.</p> <p>A detailed documentation is available in documentaion.pdf.</p>
Data for paper on inductive reasoning in large language models
<p>This dataset contains all data that was used for:</p> <p>Han, S. J., Ransom, K. J., Perfors, A. & Kemp, C. (2023). Inductive reasoning in humans and large language models. <em>Cognitive Systems Research</em>.</p> <p>The code for this project can be found <a href="https://github.com/S-J-HAN/InductiveReasoningInLargeLanguageModels">here</a>.</p> <p> </p>
A Data-facilitated Numerical Method for Richards Equation to Model Water Flow Dynamics in Soil Dataset
<p>This dataset contains the reference solutions used for training the two neural networks in 1-, 2- and 3-D cases for the article:"A Data-facilitated Numerical Method for Richards Equation to Model Water Flow Dynamics in Soil" by Zeyuan Song and Zheyu Jiang, submitted to the journal Water Resources Research. </p> <p>This dataset which describes the relationship between the pressure head and number of particles used to train two MLPs in D-GRW based solvers consists of three files, i.e., 1-, 2- and 3-D case study. There are two parts, original reference solutions and reference solutions, corresponding to the original solutions generated by coarse mesh solvers and solutions after data augmentation process, respectively.The dataset is generated by GRW based solvers and simulation results (e.g., Celia's finite difference method). Original reference solutions admit GRW proportionality assumption. We initialize the number of particles by multiplying the initial condition and 1E10. </p>
Model output data to "Land surface modeling in the Himalayas: on the importance of evaporative fluxes for the water balance of a high elevation catchment"
<p>We provide i) gridded initial conditions (.tif), ii) modeled gridded monthly outputs (.tif), and iii) modeled hourly outputs at the station locations (.txt) for the hydrological year 2019. Information about the variables and units can be found in the figures (.png) associated to each dataset. Details about the datasets can be found in the original publication by Buri and others (2023).</p><p> </p><p>Buri, P., Fatichi, S., Shaw, T. E., Miles, E. S., McCarthy, M. J., Fyffe, C. L., ... & Pellicciotti, F. (2023). Land Surface Modeling in the Himalayas: On the Importance of Evaporative Fluxes for the Water Balance of a High‐Elevation Catchment. <i>Water Resources Research</i>, <i>59</i>(10), e2022WR033841. DOI: <a href="https://doi.org/10.1029/2022WR033841"><strong>10.1029/2022WR033841</strong></a></p>
Data from Genome scale metabolic network modelling for metabolic profile predictions
<p>Data used to produce figures 4, 5 and 6 in the paper Genome scale metabolic network modelling for metabolic profile predictions.</p>
Simulated UV Data Models
<p>This archive contains the simulated UV data files described in our paper "Residual Entropy as a Diagnostic and Stopping Metric for CLEAN" by Homan, Roth, and Pushkarev</p>
Data sets and machine learning models for: Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates
<p>The datasets and final machine learning model files for the manuscript "Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates". Citation should refer directly to the manuscript:</p> <ul> <li>Chung, Y.; Green, W. H. Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates. <em>Chemical Science </em><strong>2024,</strong> doi: <a href="https://doi.org/10.1039/D3SC05353A">10.1039/D3SC05353A</a></li> </ul> <p>To use the machine learning models, please refer to the sample files and instructions on <a href="https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML">https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML</a>. </p> <p>Detailed information can be found in README.md file.</p> <p><br><strong>Details on the files</strong></p> <p>In the pretraining and finetuning set csv files, each column represents:</p> <ol> <li>rxn_smiles: atom-mapped reaction SMILES</li> <li>solvent_smiles: solvent SMILES</li> <li>ddGsolv: solvation free energy of activation of a reaction-solvent pair at 298K in kcal/mol (main prediction target)</li> <li>ddHsolv: solvation enthalpy of activation of a reaction-solvent pair at 298K in kcal/mol (main prediction target)</li> <li>dGsolv_reactant: solvation free energy of reactant(s) at 298K in kcal/mol (additional feature)</li> <li>dGsolv_product: solvation free energy of product(s) at 298K in kcal/mol (additional feature)</li> <li>dHsolv_reactant: solvation enthalpy of reactant(s) at 298K in kcal/mol (additional feature)</li> <li>dHsolv_product: solvation enthalpy of product(s) at 298K in kcal/mol (additional feature)</li> </ol> <p><strong>Data sets under 'RxnSolvKSE_dataset_v1.1.zip'</strong></p> <ul> <li>pretraining_set: contains the dataset used for pre-training <ul> <li>all_data: contains all calculated data <ul> <li>pretraining_rxn_solvent_ddGsolv_ddHsolv_with_features_all.csv: contains both main prediction targets and additional feature for reaction-solvent pairs</li> <li>pretraining_solvent_info.csv: list of all solvents</li> <li>pretraining_unique_rxn.csv: list of all reactions, both forward and reverse directions</li> </ul> </li> <li>chosen_500k_data: contains the chosen 500k data <ul> <li>pretraining_rxn_solvent_ddGsolv_ddHsolv_500k.csv: contains main prediction targets for reaction-solvent pairs</li> <li>pretraining_features_react_prod_dGsolv_dHsolv_500k.csv: contains additional features for reaction-solvent pairs</li> <li>train_test_split: contains the 5-fold random split training and test sets.</li> </ul> </li> </ul> </li> <li>finetuning_set: contains the dataset used for fine-tuning <ul> <li>all_data: contains all calculated data <ul> <li>finetuning_rxn_solvent_ddGsolv_ddHsolv_with_features_all.csv: constains both main prediction targets and additional features for reaction-solvent pairs. The rxn_key column indicates whether the reaction is bimolecular hydrogen abstraction (bihabs), unimolecular hydrogen migration (intrahabs), or radical addition to a multiple bond (raddition). The 'fwd' and 'rev' each indicate forward and reverse reactions.</li> <li>finetuning_solvent_info.csv: list of all solvents</li> <li>finetuning_unique_rxn.csv: list of all reactions, both forward and reverse directions</li> </ul> </li> <li>chosen_data: contains chosen data <ul> <li>finetuning_rxn_solvent_ddGsolv_ddHsolv_chosen.csv: contains main prediction targets for reaction-solvent pairs</li> <li>finetuning_features_react_prod_dGsolv_dHsolv_chosen.csv: contains additional features for reaction-solvent pairs</li> </ul> </li> </ul> </li> <li>experimental_set: contains the experimental rate constant data used to test the model. The original experimental data can be found at <a href="../record/7747557">https://zenodo.org/record/7747557</a>. <ul> <li> expt_rxn_atom_mapped_smiles.csv: contains the atom-mapped reaction SMILES used for the experimental data.</li> <li>expt_data_collected.xlsx: contains all experimental data and detailed information</li> <li>expt_rxn_solv_smiles_with_features_all.csv: contains the computed additional features for the experimental reaction-solvent pairs.</li> </ul> </li> </ul> <p><strong>Machine learning model files under 'RxnSolvKSE_ML_model_files.zip'</strong></p> <ul> <li>Contains the Chemprop machine learning model files for predicting ddGsolv and ddHsolv for a reaction-solvent pair. It takes atom-mapped reaction SMILES and solvent SMILES as inputs.</li> <li>To use these ML models, please refer to the sample files and instructions on <a href="https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML">https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML</a></li> </ul>
CM2.1 Model Data
<p>GFDL-CM2.1 Model Volcanic and non-volcanic (i.e., Control) simulation data:</p> <p>This data is for the positive ENSO phase (PENSO, 8 realizations), negative ENSO (NENSO, 8 realizations), and Neutral (NEUTRAL, 8 realizations) cases. There is also one corresponding control each for PENSO, NENSO, and Neutral case. A long control run used to initialize the volcanic experiments with different ENSO phases (PENSO, NENSO, and NEUTRAL) is also included. The model output files are explained below.</p> <p>1. Volcanic simulation data files produced by initializing the model with a positive ENSO phase (PENSO) are named as follows:</p> <p><a href="https://zenodo.org/api/files/46b939f5-6351-4b47-88f2-e472d4f2054e/t_surf_VOL_R8_PENSO_REALIZATION1.nc">t_surf_ps_VOL_R8_PENSO_REALIZATION1.nc </a>, <a href="https://zenodo.org/api/files/46b939f5-6351-4b47-88f2-e472d4f2054e/t_surf_VOL_RR1_PENSO_REALIZATION2.nc">t_surf_ps_VOL_RR1_PENSO_REALIZATION2.nc t_surf_ps_VOL_RR2_PENSO_REALIZATION3.nc </a>, <a href="https://zenodo.org/api/files/46b939f5-6351-4b47-88f2-e472d4f2054e/t_surf_VOL_RR4_PENSO_REALIZATION4.nc">t_surf_ps_VOL_RR4_PENSO_REALIZATION4.nc</a> <a href="https://zenodo.org/api/files/46b939f5-6351-4b47-88f2-e472d4f2054e/t_surf_VOL_RR4_PENSO_REALIZATION4.nc">t_surf_ps_VOL_RR5_PENSO_REALIZATION5.nc </a>, <a href="https://zenodo.org/api/files/46b939f5-6351-4b47-88f2-e472d4f2054e/t_surf_VOL_RR6_PENSO_REALIZATION6.nc">t_surf_ps_VOL_RR6_PENSO_REALIZATION6.nc </a><a href="https://zenodo.org/api/files/46b939f5-6351-4b47-88f2-e472d4f2054e/t_surf_VOL_RR7_PENSO_REALIZATION7.nc">t_surf_ps_VOL_RR7_PENSO_REALIZATION7.nc </a>, <a href="https://zenodo.org/api/files/46b939f5-6351-4b47-88f2-e472d4f2054e/t_surf_VOL_RR9_PENSO_REALIZATION8.nc">t_surf_ps_VOL_RR9_PENSO_REALIZATION8.nc </a></p> <p>2. Volcanic simulation data files produced by initializing the model with the NEUTRAL phase are named as follows:</p> <p><a href="https://zenodo.org/api/files/46b939f5-6351-4b47-88f2-e472d4f2054e/t_surf_VOL_NNN1_NEUTRAL_REALIZATION1.nc">t_surf_ps_VOL_NNN1_NEUTRAL_REALIZATION1.nc </a>, <a href="https://zenodo.org/api/files/46b939f5-6351-4b47-88f2-e472d4f2054e/t_surf_VOL_NNN1_1_NEUTRAL_REALIZATION2.nc">t_surf_ps_VOL_NNN1_1_NEUTRAL_REALIZATION2.nc </a> <a href="https://zenodo.org/api/files/46b939f5-6351-4b47-88f2-e472d4f2054e/t_surf_VOL_NNN1_2_NEUTRAL_REALIZATION3.nc">t_surf_ps_VOL_NNN1_2_NEUTRAL_REALIZATION3.nc</a>, <a href="https://zenodo.org/api/files/46b939f5-6351-4b47-88f2-e472d4f2054e/t_surf_VOL_NNN1_2_NEUTRAL_REALIZATION3.nc">t_surf_ps_VOL_NNN1_3_NEUTRAL_REALIZATION4.nc</a> <a href="https://zenodo.org/api/files/46b939f5-6351-4b47-88f2-e472d4f2054e/t_surf_VOL_NNN1_4_NEUTRAL_REALIZATION5.nc">t_surf_ps_VOL_NNN1_4_NEUTRAL_REALIZATION5.nc </a>, <a href="https://zenodo.org/api/files/46b939f5-6351-4b47-88f2-e472d4f2054e/t_surf_VOL_NNN1_5_NEUTRAL_REALIZATION6.nc">t_surf_ps_VOL_NNN1_5_NEUTRAL_REALIZATION6.nc </a><a href="https://zenodo.org/api/files/46b939f5-6351-4b47-88f2-e472d4f2054e/t_surf_VOL_NNN1_7_NEUTRAL_REALIZATION7.nc">t_surf_ps_VOL_NNN1_7_NEUTRAL_REALIZATION7.nc </a>, <a href="https://zenodo.org/api/files/46b939f5-6351-4b47-88f2-e472d4f2054e/t_surf_VOL_NNN1_8_NEUTRAL_REALIZATION8.nc">t_surf_ps_VOL_NNN1_8_NEUTRAL_REALIZATION8.nc </a></p> <p>3. Volcanic simulation data files produced by initializing the model with a negative ENSO phase (NENSO) are named as follows:</p> <p><a href="https://zenodo.org/api/files/46b939f5-6351-4b47-88f2-e472d4f2054e/t_surf_VOL_RR_TEST_RR1_NENSO_REALIZATION1.nc">t_surf_ps_VOL_RR_TEST_RR1_NENSO_REALIZATION1.nc,</a> <a href="https://zenodo.org/api/files/46b939f5-6351-4b47-88f2-e472d4f2054e/t_surf_VOL_RR_TEST_RR1_NENSO_REALIZATION1.nc">t_surf_ps_VOL_RR_TEST_RR2_NENSO_REALIZATION2.nc </a></p> <p><a href="https://zenodo.org/api/files/46b939f5-6351-4b47-88f2-e472d4f2054e/t_surf_VOL_RR_TEST_RR3_NENSO_REALIZATION3.nc">t_surf_ps_VOL_RR_TEST_RR3_NENSO_REALIZATION3.nc</a>, <a href="https://zenodo.org/api/files/46b939f5-6351-4b47-88f2-e472d4f2054e/t_surf_VOL_RR_TEST_RR4_NENSO_REALIZATION4.nc">t_surf_ps_VOL_RR_TEST_RR4_NENSO_REALIZATION4.nc </a></p> <p><a href="https://zenodo.org/api/files/46b939f5-6351-4b47-88f2-e472d4f2054e/t_surf_VOL_RR_TEST_RR5_NENSO_REALIZATION5.nc">t_surf_ps_VOL_RR_TEST_RR5_NENSO_REALIZATION5.nc</a>, <a href="https://zenodo.org/api/files/46b939f5-6351-4b47-88f2-e472d4f2054e/t_surf_VOL_RR_TEST_RR5_NENSO_REALIZATION5.nc">t_surf_ps_VOL_RR_TEST_RR6_NENSO_REALIZATION6.nc </a></p> <p><a href="https://zenodo.org/api/files/46b939f5-6351-4b47-88f2-e472d4f2054e/t_surf_VOL_RR_TEST_RR7_NENSO_REALIZATION7.nc">t_surf_ps_VOL_RR_TEST_RR7_NENSO_REALIZATION7.nc</a>, <a href="https://zenodo.org/api/files/46b939f5-6351-4b47-88f2-e472d4f2054e/t_surf_VOL_RR_TEST_RR9_NENSO_REALIZATION8.nc">t_surf_ps_VOL_RR_TEST_RR9_NENSO_REALIZATION8.nc </a></p> <p>4. Each experiment (PENSO, NEUTRAL, and NENSO) has its corresponding control runs which are named as follows:</p> <p><a href="https://zenodo.org/api/files/46b939f5-6351-4b47-88f2-e472d4f2054e/t_surf_NVOL_RR9_PENSO_CONTROL.nc">t_surf_ps_NVOL_RR9_PENSO_CONTROL.nc </a>(PENSO Control)<br> <a href="https://zenodo.org/api/files/46b939f5-6351-4b47-88f2-e472d4f2054e/t_surf_NVOL_NNN1_NEUTRAL_CONTROL.nc">t_surf_ps_NVOL_NNN1_NEUTRAL_CONTROL.nc </a>(NEUTRAL Control)<br> <a href="https://zenodo.org/api/files/46b939f5-6351-4b47-88f2-e472d4f2054e/t_surf_NVOL_RR_TEST_RR1_NENSO_CONTROL.nc">t_surf_ps_NVOL_RR_TEST_RR1_NENSO_CONTROL.nc </a>(NENSO control)</p> <p>5. The long control run (used to initialize the aforementioned volcanic with three ENSO cases) is named as follows:</p> <p><a href="https://zenodo.org/api/files/46b939f5-6351-4b47-88f2-e472d4f2054e/t_surf_LONG_CONTROL.nc">t_surf_LONG_CONTROL.nc </a></p>
A global hybrid tropical cyclone risk model based upon statistical and coupled climate models - Supporting figures and data
<p><strong>Introduction</strong></p><p>This contribution consists of 1) supporting figures and 2) supporting data for the submitted manuscript "A Global Hybrid Tropical Cyclone Risk Model based upon Statistical and Coupled Climate Models." Supporting figures are presented in two interactive HTML documents. The supporting datasets contain tropical cyclone event sets, catalogs, and an example analysis that plots summaries of the simulated catalogs and compares them to historical observations. All files are provided for the 400 ensemble members (event sets) that represent climate model years 1981-2020 (40 years) from the first 10 CESM-LE members.</p><p><strong>Contents</strong></p><p>./Catalog/sim_650</p><p>Tropical cyclone annual catalog for each basin based on the CESM-LE distribution of ENSO phases. 650 simulations are provided for each of 400 event sets. Each file contains the catalog for a single basin and is written as catalog_tc_(BASIN)_sim650_my400_nbinom_condmeanensojma.csv., where BASIN can be NA, EP, WP, NI, SI, SP.</p><p>./Documentation</p><p>TCMODEL_UQAM_EXAMPLE.html: Analysis script showing example of use of the tropical cyclone catalogs and comparison to historical observations.</p><p>UQAM_TC_Model_Data_Supplement_Dictionary.xlsx: Data dictionary of all data supplement file contents.</p><p>./IBTRACS</p><p>Summary of IBTrACS data required in the analysis script.</p><p>./TrajectoryBanks</p><p>Contains subdirectories for each basin (EP, NAT, NI, SI, SP, WP)</p><p>Each subdirectory contains several files summarizing the event sets, or banks, of tropical cyclone trajectories.</p><p><strong>Versions</strong></p><p>Version 1.0.1: Updated TCMODEL_UQAM_SUPPORTING_FIGURES.zip for revisions to submitted manuscript.</p><p>Version 1.0.0: Original version.</p><p> </p>
Data and model code from: Tracing growth patterns in cod (Gadus morhua L.) using bioenergetic modelling
<p><span>Understanding individual growth in commercially exploited fish populations is key to successful stock assessment and informed ecosystem-based fisheries management. Traditionally, growth rates in marine fish are estimated using otolith age-reading in combination with age-length relationships from field samples, or tag-recapture field experiments. However, for some species, otolith-based approaches have been proven unreliable, and tag-recapture experiments suffer from high working effort and costs as well as low recapture rates. An important alternative approach for estimating fish growth is represented by bioenergetic modelling, which, in addition to pure growth estimation, can provide valuable insights into the processes leading to temporal growth changes resulting from environmental and related behavioural changes. We here developed an individual-based bioenergetic model for Western Baltic cod (<em>Gadus</em> <em>morhua</em>), traditionally a commercially important fish species that however collapsed recently and likely suffers from climate change effects. Western Baltic cod is an ideal case study for bioenergetic modelling because of recently gained in-situ process knowledge on spatial distribution and feeding behaviour based on highly resolved data on stomachs and fish distribution. Additionally, physiological processes such as gastric evacuation, consumption, net-conversion efficiency, and metabolic rates have been well studied for cod in laboratory experiments. Our model reliably reproduced seasonal growth patterns observed in the field. </span><span>Importantly, our bioenergetic modelling approach implementing depth-use patterns and food intake allowed us to explain the potentially detrimental effect summer heat periods have on growth of Western Baltic cod that likely will increasingly occur in the future. Hence our model simulations highlighted a potential mechanism of how warming due to climate change affects the growth of a key species that may apply for similar environments elsewhere. </span></p> <p><span>Here we provide access to the individual-based bioenergetic growth model which is set up to model the growth of cod in ages 2 to 4 (<em>Gadus</em> <em>morhua</em> L.) in the Belt Sea (western part of the Western Baltic Sea) on a daily basis within one year. The model incorporates contemporary in-situ process knowledge on food intake and seasonal- and temperate-related spatial distribution of cod and allows us to identify seasonal growth patterns. The model is written in the statistical and programming environment R.<br></span></p>
Spatial Modeling of Groundwater Potential in the North of Minas Gerais, Brazil: An Integrated Approach Using Machine Learning and Environmental Data
<p>This database is associated with the article published in the Revista Brasileira de Cartografia (RBC), entitled: Spatial Modeling of Groundwater Potential in the North of Minas Gerais, Brazil: An Integrated Approach Using Machine Learning and Environmental Data. This database contains the Groundwater flow rasters and the covariates used in spatial modeling. This database is associated with the article published in the Revista Brasileira de Cartografia (RBC), entitled: Spatial Modeling of Groundwater Potential in the North of Minas Gerais, Brazil: An Integrated Approach Using Machine Learning and Environmental Data. This database contains the Groundwater flow rasters and the covariates used in spatial modeling.</p>
data collection for NEMO BGC assessment in idealised models (GM in non-eddying models)
<p>Edit in ver 3, 17 Oct 2023:</p><ul><li>(<strong>important</strong>) there is a bug NEMO GEOMETRIC code (advection of parameterised eddy energy; see ldfeke.F90), fixed in the present version</li><li>new data files updated (everything is included for completeness, although only the ones using GEOMETRIC have been updated)</li><li>data files and software files have been split out into separate zip files to enable easier downloads</li></ul><p>=========================</p><p>Edit in ver 2, 06 Feb 2023:</p><ul><li>added calculation files with Treguier et al variant of GM, supplement data not explicitly leading to figure in paper;</li><li>fixes of masking for boundary values when computing averages (no change to figures, minor changes to numerical values);</li><li>fixing a bug in the calculation of surface vorticity (raw numerical value in units of s-1 correct, but incorrect in units of f_0 because of a missing sin(latitude) factor)</li></ul><p>=========================</p><p>Data archive for "Combined physical and biogeochemical assessment of mesoscale eddy parameterisations in ocean models: eddy induced advection at non-eddying resolutions". Provided are:</p><p>1) modification and configuration files for the GYRE_PISCES configuration in NEMO 4.0.5 (r14538), with sample restart and output files</p><ul><li>CONST restart file at year 2000 (end of spin up, denoted year -300 in the paper)</li><li>CONST, GEOM, R12 restart file at year 2300 (beginning of control/climate change split, denoted year 0 in the paper)</li><li>CONST, GEOM, R12 sample output files at year 2366 to 2370 (denoted year 66 to 70 in paper)</li></ul><p>2) processed time-averaged data for regenerating figures from the article<br>3) python scripts and notebooks for analysing the data</p>
Dataset for "Enabling Machine Learning Models in Alarm Fatigue Research: Creation of a Large Relevance-annotated Oxygen Saturation Alarm Data Set"
<p>Chromik and Flint et al. (2024) (under review) propose an algorithm that uses clinical alarm logs, an annotation guideline (Klopfenstein et al. 2023), and routinely collected intensive care data to create a data set of relevance-annotated oxygen saturation alarms. We provide the algorithm's source code and data set of annotated oxygen saturation alarms as supplementary material to the publication.</p> <ul> <li>The algorithm's implementation is open-source and can be re-used on similar data sets.</li> <li>Our implementation used airway management data mappings to identify airway devices (AD), ventilation devices (VD), and ventilation modes (VM). These mappings can be found here: <a href="../doi/10.5281/zenodo.7511031">https://zenodo.org/doi/10.5281/zenodo.7511031</a></li> <li>The data set suggests that the majority of oxygen saturation alarms in the intensive care unit is non-actionable.</li> <li>We are the first to provide such an extensive data set of annotated oxygen saturation alarms.</li> </ul>
Model version, input data, results, and processing scripts for Speizer et al., "Rapid implementation of mitigation measures can facilitate decarbonization of the global steel sector in 1.5°C-consistent pathways"
<p>Includes the files needed to run the scenarios, analyze the outputs, and produce the figures for Speizer et al., "Rapid implementation of mitigation measures can facilitate decarbonization of the global steel sector in 1.5°C-consistent pathways." </p>
Calibration of a pedestrian ingress model based on CCTV surveillance data using machine learning methods: data and code
<p>The package includes a dataset of trajectories obtained from a real-time pedestrian traffic detector at the vaccination centre and the code needed to validate the analyses described in the paper entitled Calibration of a pedestrian ingress model based on CCTV surveillance data using machine learning methods. Version 1.0.1 is a patch that corrects path to data.</p>
Case study result data set for the submitted article "Implications of hydrogen import prices for the German energy system in a model-comparison experiment"
<p>The data set contains result data for the German energy system in a long term scenario (scenario year 2045) as described in the publication "Implications of hydrogen import prices for the German energy system in a model-comparison experiment". The results have been generated with the models REMod of Fraunhofer Institute for Solar Energy Systems ISE, Enertile of Fraunhofer Institute for Systems and Innovation Research ISI, and SCOPE SD of Fraunhofer Institute for Energy Economics and Energy System Technology IEE.</p><p><strong>Abbreviations:</strong></p><ul><li>BEV - Battery Electric Vehicles</li><li>CC - Combined Cycle</li><li>CCGT - Combined Cycle Gas Turbine</li><li>CHP - Combined heat and power</li><li>CO2 - Carbon dioxide</li><li>con - consumption</li><li>FC - Fuel Cell</li><li>FCEV - Fuel Cell Electric Vehicle</li><li>gen - generation</li><li>H2 - Hydrogen</li><li>HT - High temperature</li><li>ICE - Internal Combustion Engine</li><li>LDV - Light-Duty Vehicle</li><li>LT - Low temperature</li><li>med - medium</li><li>OC - Open Cycle</li><li>OCGT - Open Cycle Gas Turbine</li><li>PHEV - Plug-In Hybrid Vehicles</li><li>PS - Pumped Storage</li><li>PV - Photovoltaics</li><li>ST - Steam turbine</li><li>SynFuel - Synthetic fuel</li><li>w/ - with</li><li>w/o - without</li><li>yr - year</li></ul>
K-model and H-gradient model ensemble averaged data (Version 1)
<p>It is the ensemble averaged data used in the creation of the manuscript "The impact of subgrid-scale turbulence model on tropical cyclone dynamics in convection-permitting simulations"</p>
Development and Validation of a Deep Learning-Based Survival Prediction Model for Pediatric Glioma Patients: A Retrospective Study Using the SEER Database and Chinese Data
ClinicalTrials.gov study NCT06199388. IPD Sharing: NO. Countries: 1. Publications: 2.
Development of a Dietary Intervention Model Based on Genetic Data as an Implementation of a Healthy Lifestyle in the Management of Systemic Lupus Erythematosus Patients
ClinicalTrials.gov study NCT07183007. IPD Sharing: NO. Countries: 1. Publications: 1.
Flow virometry for water-quality assessment: Protocol optimization for a model virus and automation of data analysis
Open the record for dataset details and reuse information.
ScienceDex guides
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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.