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266 results for “experimental models”
Experimental data set for the article entitled "Mathematical Model of Steam Reforming in the Anode Channel of a Molten Carbonate Fuel Cell"
<p>Experimental data for the article: Szablowski, L.; Dybinski, O.; Szczesniak, A.; Milewski, J. Mathematical Model of Steam Reforming in the Anode Channel of a Molten Carbonate Fuel Cell. Energies 2022, 15, 608. The experiments were performed by the first two authors.<br> These data set refer to experiments carried out on a stand used to test high-temperature fuel cells. The subject of the study was a molten carbonate fuel cell fueled with a mixture of methane and steam with steam to carbon ratio of 2.0, 2.5, 3.0 and 3.5 and at the cell operating temperature of 550°C and 650°C. Additionally, in the anode channel of the cell, there was a catalyst in the amount of 2 g. The active area of the cell was 20.25 cm<sup>2</sup>. The article that uses these research results is published in an open access journal with a CC-BY license. This research was funded by the National Science Center, Poland (Grant number 2020/39/D/ST8/02021).</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>
Impact of Slavin's Effective Teaching Model on Procrastination in Nursing Students: a Quasi-Experimental Study
ClinicalTrials.gov study NCT06675838. IPD Sharing: NO. Countries: 1. Publications: 0.
Neurophysiology of Postpartum Depression in an Experimental Model of Pregnancy and Parturition
ClinicalTrials.gov study NCT01762943. IPD Sharing: NO. Countries: 1. Publications: 3.
A Clinical Study to Evaluate Experimental Children's Toothpastes in an In-Situ Caries Model
ClinicalTrials.gov study NCT01607411. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Study of the Anti-Inflammatory Effects of Colgate Total® During an Experimental Gingivitis Model
ClinicalTrials.gov study NCT01799226. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Intracranial Pressure in Experimental Models of Headache
ClinicalTrials.gov study NCT01288781. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Experimental data used in mathematical modeling
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Modelling data for: Short-course combination treatment for experimental chronic Chagas disease
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Data from: A proof-of-concept experimental-theoretical model to predict pesticide resistance evolution
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Data from: Experimental heatwaves reduce the effectiveness of ejaculates at occupying female reproductive tracts in a model insect
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Data from: Warming speeds up range expansion in an experimental model system
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Experimental evolution reveals that males evolving within warmer thermal regimes improve reproductive performance under heatwave conditions in a model insect
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Experimental realization of the 1D random field Ising model
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A model of digestive tooth corrosion in lizards: experimental tests and taphonomic implications
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An escape theory model for directionally moving prey and an experimental test in juvenile Chinook salmon
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TesCaV: An Approach for Learning Model-based Testing and Coverage in Practice, Experimental Data
<p>The data in this sheet provides the result of the exploratory experiment presented in the following paper:</p> <p>Beatriz Marín, Sofía Alarcón, Giovanni Giachetti, and Monique Snoeck. (2020) TesCaV: An Approach for Learning Model-based Testing and Coverage in Practice, in Fabiano Dalpiaz, Jelena Zdravkovic, Pericles Loucopoulos (eds), Proceedings of the 14th International Conference on Research Challenges in Information Science, LNCS, Springer.</p> <p> </p>
Impact of horizontal resolution on global ocean-sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2)
<p>Datasets for the Geoscientific Model Development publication: "Impact of horizontal resolution on global ocean-sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2)"</p> <p>Abstract: This paper presents global comparisons of fundamental global climate variables from a suite of four pairs of matched low- and high-resolution ocean and sea-ice simulations that are obtained following the OMIP-2 protocol (Griffies et al., 2016) and integrated for one cycle (1958-2018) of the JRA55-do atmospheric state and runoff dataset (Tsujino et al., 2018). Our goal is to assess the robustness of climate-relevant improvements in ocean simulations (mean and variability) associated with moving from coarse (~1º) to eddy-resolving (~0.1º) horizontal resolutions. The models are diverse in their numerics and parameterizations, but each low-resolution and high-resolution pair of models is matched so as to isolate, to the 20 extent possible, the effects of horizontal resolution. A variety of observational datasets are used to assess the fidelity of simulated temperature and salinity, sea surface height, kinetic energy, heat and volume transports, and sea ice distribution. This paper provides a crucial benchmark for future studies comparing and improving different schemes in any of the models used in this study or similar ones. The biases in the low-resolution simulations are familiar and their gross features – position, strength, and variability of western boundary currents, equatorial currents, and Antarctic Circumpolar Current – are 25 significantly improved in the high-resolution models. However, despite the fact that the high-resolution models “resolve’’ most of these features, the improvements in temperature or salinity are inconsistent among the different model families and some regions show increased bias over their low-resolution counterparts. Greatly enhanced horizontal resolution does not deliver unambiguous bias improvement in all regions for all models.</p>
Experimental evidence of warming-induced disease emergence and its prediction by a trait-based mechanistic model
<p>Predicting the effects of seasonality and climate change on the emergence and spread of infectious disease remains difficult, in part because of poorly understood connections between warming and the mechanisms driving disease. Trait-based mechanistic models combined with thermal performance curves arising from the Metabolic Theory of Ecology (MTE) have been highlighted as a promising approach going forward; however, this framework has not been tested under controlled experimental conditions that isolate the role of gradual temporal warming on disease dynamics and emergence. Here, we provide experimental evidence that a slowly warming host – parasite system can be pushed through a critical transition into an epidemic state. We then show that a trait-based mechanistic model with MTE functional forms can predict the critical temperature for disease emergence, subsequent disease dynamics through time, and final infection prevalence in an experimentally warmed system of <i>Daphnia </i>and a microsporidian parasite. Our results serve as a proof of principle that trait-based mechanistic models using MTE sub-functions can predict warming-induced disease emergence in data-rich systems – a critical step towards generalizing the approach to other systems.</p>
Data from: How do foragers decide when to leave a patch? A test of alternative models under natural and experimental conditions
1. A forager's optimal patch-departure time can be predicted by the prescient marginal value theorem (pMVT), which assumes they have perfect knowledge of the environment, or by approaches such as Bayesian-updating and learning rules, which avoid this assumption by allowing foragers to use recent experiences to inform their decisions. 2. In understanding and predicting broader scale ecological patterns, individual-level mechanisms, such as patch-departure decisions, need to be fully elucidated. Unfortunately, there are few empirical studies that compare the performance of patch-departure models that assume perfect knowledge with those that do not, resulting in a limited understanding of how foragers decide when to leave a patch. 3. We tested the patch-departure rules predicted by fixed-rule, pMVT, Bayesian-updating and learning models against one another, using patch residency times recorded from 54 chacma baboons (Papio ursinus) across two groups in natural (n = 6,594 patch visits) and field-experimental (n = 8,569) conditions. 4. We found greater support in the experiment for the model based on Bayesian-updating rules, but greater support for the model based on the pMVT in natural foraging conditions. This suggests that foragers may place more importance on recent experiences in predictable environments, like our experiment, where these experiences provide more reliable information about future opportunities.5. Furthermore, the effect of a single recent foraging experience on patch residency times was uniformly weak across both conditions. This suggests that foragers' perception of their environment may incorporate many previous experiences, thus approximating the perfect knowledge assumed by the pMVT. Foragers may, therefore, optimise their patch-departure decisions in line with the pMVT through the adoption of rules similar to those predicted by Bayesian-updating.
ScienceDex guides
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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.