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870 results for “Model development”
Ensemble Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European Marine Species based on Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Ensemble Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European marine species based on Ecological Niche Models developed with (i) Artificial Neural Networks, (ii) Maximum Entropy, (iii) Support Vector Machines, and (iv) AquaMaps at 0.5° Resolution. The data report, for each 0.5° cell, how many models (from 0 to 4) overcome a model-specific decision threshold to assess species presence in the cell.</p>
Development of a Predictive Model for In-Hospital Mortality in COVID-19 Patients Using CAR, IL-6, IL-6/LY, and NLR: A Single-Center Study in Indonesia
<p>Table 1. Demographic data of the subject</p> <p><sup>*</sup>mean (+SD); <sup>#</sup>median (Q1-Q3)</p> <p> </p> <p>Table 2. CAR, IL-6, IL-6/LY, NLR cut-off values and performance in determining mortality</p>
Developing a Physics-informed Deep Learning Model to Simulate Runoff Response to Climate Change in Alpine Catchments
<p>This data archive includes the source code of EXP-HYDRO, standard DL, hybrid-J, and hybrid-Z models, as well as simulated daily runoff (mm/d) of all five models in the paper at the three subbasins in the source region of the Yellow River. For more details please see the publication.</p> <p>Please cite the paper as follows:</p> <p>Zhong, L., Lei, H., & Gao, B. (2023). Developing a physics-informed deep learning model to simulate runoff response to climate change in Alpine catchments. Water Resources Research, 59, e2022WR034118. https://doi. org/10.1029/2022WR034118</p> <p> </p>
Analysis datasets for NEMO_validation workflow Byrne et al 2023 GMD. "Using the COAsT Python package to develop a standardised validation workflow for ocean physics models"
<p>Analysis datasets in support of Byrne et al. (2023) "Using the COAsT Python package to develop a standardised validation workflow for ocean physics models", <em>Geoscientific Model Development</em>.</p> <p> </p> <p>The datasets are from a comparative analysis of two versions of the European shelf sea AMM15 (Atlantic Margin Model at 1.5km horizontal resolution) configuration. These are NEMO ocean model configurations with different code base versions. The configurations are CO7, which is based on NEMOv3.6, and CO9p0 (also referred to as P0.0), which is based on NEMOv4.0.4.</p>
Aspen Plus v12 models for design a project-based learning on developing a novel technology for slurry management
<p>Preliminary investigation on the likelihood of trapping CO<sub>2</sub> and NH<sub>3</sub>, which are emitted at a rate of 132 mg/h and up to 5.1 – 510 mg/h during the storage of manure at 25 °C, in brines of NaCl and CaCl<sub>2</sub> was conducted in Aspen Plus® v12. The supersaturated solution of 0.5 kg H<sub>2</sub>O and 10 kg salt was considered to account for any possible absorption in the liquid film that is generated during the deliquescence phenomenon. Only in the case of using the CaCl<sub>2</sub> as dehydrating agent (anhydrous salt) in the prototype, the simulation in the commercial package predicted the absorption of CO<sub>2</sub> and NH<sub>3</sub> and formation of CaCO<sub>3</sub> and NH<sub>4</sub>Cl. On the other hand, the extents of formation of NaHCO<sub>3</sub>, and even NH<sub>4</sub>HCO<sub>3</sub> or NH<sub>4</sub>COONH<sub>2</sub> were found to be negligible under the conditions that the prototype operates. Prediction of the solubilities of NaCl, NH<sub>4</sub>Cl, NaHCO<sub>3</sub>, CaCO<sub>3</sub>, Ca(OH)<sub>2</sub>, CaCl<sub>2</sub>, CaCl<sub>2</sub>·H<sub>2</sub>O, CaCl<sub>2</sub>·2H<sub>2</sub>O, CaCl<sub>2</sub>·4H<sub>2</sub>O, and CaCl<sub>2</sub>·6H<sub>2</sub>O in the temperature range 0 – 100 °C at 1 atm.</p>
Developing a Machine Learning Model to Predict Pleural Adhesion Preoperatively Using Pleural Ultrasound
ClinicalTrials.gov study NCT06423066. IPD Sharing: NO. Countries: 1. Publications: 2.
Development and Validation of a Prediction Model for AKI Following Cisplatin-Based HIPEC in Patients With Ovarian Cancer
ClinicalTrials.gov study NCT06697613. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Development and Validation of DM and Pre-DM Risk Prediction Model
ClinicalTrials.gov study NCT04881383. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Development of a Prediction Model for Intraoperative Blood Pressure Variability
ClinicalTrials.gov study NCT05698433. IPD Sharing: NO. Countries: 1. Publications: 0.
Study to Develop a Kinetic Model for FDG and Me4FDG in Kidneys of Type 2 Diabetic Patients
ClinicalTrials.gov study NCT03557138. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Development of a Predictive Model for Sexually Transmitted Infections in Individuals Using Pre-Exposure Prophylaxis for HIV in Spain
ClinicalTrials.gov study NCT06860945. IPD Sharing: UNDECIDED. Countries: 1. Publications: 13.
Development and Implementation Model of Home Based Cardiac Rehabilitation With Family Empowerment Approach in Patient With ACS (HBCR in ACS)
ClinicalTrials.gov study NCT06625463. IPD Sharing: NO. Countries: 1. Publications: 3.
Implementation of Up-front ctDNA Into Lung Cancer Care and Development of Liquid Biopsy-based Decision Support Models - LM² Study
ClinicalTrials.gov study NCT06105177. IPD Sharing: UNDECIDED. Countries: 1. Publications: 34.
Develop a Risk Prediction Model for Phthalate-ester-induced Diseases
ClinicalTrials.gov study NCT05892029. IPD Sharing: NO. Countries: 1. Publications: 1.
Development of a Coordinated, Community-Based Medication Management Model for Home-Dwelling Aged in Primary Care
ClinicalTrials.gov study NCT02545257. IPD Sharing: Not stated. Countries: 1. Publications: 16.
Comprehensive Assessment of Atherosclerotic Ischemic Stroke Risk and Development of a Prediction Model
ClinicalTrials.gov study NCT06315153. IPD Sharing: NO. Countries: 1. Publications: 4.
Development and Validation of The Post-RT LARS Prediction Model (PORTLARS)
ClinicalTrials.gov study NCT05129215. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Development of pk/pd Model of Propofol in Patients With Severe Burns
ClinicalTrials.gov study NCT03704285. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Development and Validation of Models to Predict Functional Cure in Patients With CHB After Peg-IFN Based Therapy
ClinicalTrials.gov study NCT06196632. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Development of Health Management Model for Chinese Medical Preventive Medicine
ClinicalTrials.gov study NCT03640169. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.