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481 results for “network modeling”

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zenodo28/100

Network Model with Internal Complexity Bridges Artificial Intelligence and Neuroscience

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opencc-by-4.0Jun 2024View details →
zenodo28/100

Precision of Radiation Chemistry Networks: Playing Jenga with Kinetic Models for Liquid-Phase Electron Microscopy

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opencc-by-4.0Dec 2024View details →
zenodo28/100

ChEMBL Data for 'Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models'

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opencc-by-4.0Jul 2024View details →
zenodo28/100

CIANNA V1.0 release example network models

<p>Set of pre-trained artificial network models to be used with CIANNA examples associated with the V-1.0 release of the framework.<br><br>See related works for more information, or go to https://github.com/Deyht/CIANNA for the latest version of the related framework.<br><br>The provided files are in binary format and are only useful for loading with CIANNA.</p>

opencc-by-4.0Jul 2024View details →
zenodo28/100

Extending the gene regulatory network model for B cell differentiation

<p>Scripts used for modelling the gene regulatory network of B cell differentiation</p>

opencc-by-4.0Jun 2018View details →
zenodo28/100

Training and test datasets for dual-layer network model

<p>Training and test datasets for dual-layer network model</p>

opencc-by-4.0Apr 2019View details →
zenodo28/100

The "lastfm" data set used in the article "A comparative study of social network models: Network evolution models and nodal attribute models"

<p>This is the &quot;lastfm&quot; network used in the article:</p> <p>Toivonen, R., Kovanen, L., Kivel&auml;, M., Onnela, J. P., Saram&auml;ki, J., &amp; Kaski, K. (2009). A comparative study of social network models: Network evolution models and nodal attribute models. Social networks, 31(4), 240-254.</p> <p>doi:10.1016/j.socnet.2009.06.004</p> <p>The data set is described in the article. Please cite the original article when using this data set.</p> <p>Format of the data set is an edge list, where row in the file is an edge connecting the two nodes indicated by the two numbers separated by a whitespace. Each node number corresponds to a single account in the website.</p> <p>The original data in which this network is based on was licensed under the &quot;Creative Commons Attribution-NonCommercial-ShareAlike 2.0 UK: England &amp; Wales&quot; licese, and accordinly this data set uses the same license. License available at&nbsp;https://creativecommons.org/licenses/by-nc/2.0/uk/</p>

openother-ncSep 2019View details →
zenodo28/100

Building Resilient Business For SMES: The Role of Financial Management Skill, Networking Capabilities, and Business Model Innovation

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opencc-by-4.0Aug 2024View details →
zenodo28/100

HiPHD: Hierarchical Classification for Protein Remote Homology Detection using Graph Neural Networks and Language Models

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opencc-by-4.0Sep 2024View details →
dryad28/100

Dendritic prioritization through spatial stream network modeling informs targeted management of Himalayan riverscapes under brown trout invasion

<p>With the concept of 'riverscapes' long pending to be acknowledged in the 'landscape-centric' legislative framework of Himalayan nations, conservation of native riverine species stays practically unheeded. This necessitates urgent prioritization of stream networks to conserve the lotic taxa under invasion pressures. Himalayan riverscapes are pervaded with the invasive-exotic brown trout <i>Salmo trutta</i>,<i> </i>posing serious threats to the co-occurring native, the snow trout <i>Schizothorax richardsonii</i>. Using intensive surveys (218.7km) and geostatistical stream network models (n=537), we contrasted snow trout in two stream networks with and without invasives, for assessing differences in their spatial distribution. Our models indicate invasion-induced relegations of natives from the river mainstem into headwaters, with large sections of mainstem occupied by invasives. Furthermore, a concerningly small percentage of potential habitat left for natives to occupy in the mainstem is threatened, where a 100% overlap of native and invasive trout distributions is predicted. With a higher presence probability for the natives in headwaters of invaded watershed as compared to the non-invaded watershed, we highlight the headwater streams as<b> </b>potential refugia for the natives under invasion.</p> <p><i>Synthesis and Applications: </i>Our approach of basin-scale dendritic prioritization provides immediate management solutions to tackle brown trout invasion threats in Himalaya. We inform decisions on delineation of headwaters as invasion refugia for native fish, with assisted recovery of their fragmented populations in the river mainstems through targeted management of invasives</p>

opencc-zeroJul 2021View details →
zenodo28/100

Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modeling

<p><strong>Abstract:</strong><br> To simultaneously address the rising need of expressing uncertainties in deep learning models along with producing model outputs which are consistent with the known scientific knowledge, we propose a novel physics-guided architecture (PGA) of neural networks in the context of lake temperature modeling where the physical constraints are hard coded in the neural network architecture. This allows us to integrate such models with state of the art uncertainty estimation approaches such as Monte Carlo (MC) Dropout without sacrificing the physical consistency of our results. We demonstrate the effectiveness of our approach in ensuring better generalizability as well as physical consistency in MC estimates over data collected from Lake Mendota in Wisconsin and Falling Creek Reservoir in Virginia, even with limited training data. We further show that our MC estimates correctly match the distribution of ground-truth observations, thus making the PGA paradigm amenable to physically grounded uncertainty quantification.</p>

opencc-by-4.0May 2020View details →
zenodo28/100

Are neural network potentials trained on liquid states transferable to crystal nucleation? A test on ice nucleation in the mW water model

<p>Dataset used to train the neural network potential for reproducing&nbsp;mW nucleation.</p>

opencc-by-4.0Apr 2023View details →
zenodo28/100

Study of terminological subsystems of modern school textbooks in Russian with the help of word embedding models Word2Vec and neural networks

<p>The reported study was funded by RFBR, project number 19-29-14032 mk.</p>

opencc-by-4.0Oct 2020View details →
zenodo28/100

Data Repository for "Integrating Water Quality Data with a Bayesian Network Model to Improve Spatial and Temporal Phosphorus Attribution: Application to the Maumee River Basin"

<p>Data for &quot;Integrating Water Quality Data with a Bayesian Network Model to Improve Spatial and Temporal Phosphorus Attribution: Application to the Maumee River Basin&quot;. This repository contains all the processed data used in the simulation (in &quot;processed&quot; folder),&nbsp;part of the raw data (in &quot;raw&quot; folder), and the SWAT simulation results (in &quot;SWAT&quot; folder). The code for processing the raw data, which are either provided here or publicly available online, is provided in the <a href="https://doi.org/10.5281/zenodo.8132662">code repository</a>. The links to the publicly available raw data&nbsp;are also provided in the code repository.</p>

openNov 2022View details →
zenodo28/100

MODELING OF TRANSPORT SYSTEMS. STREAMS IN AD NETWORKS

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opencc-by-4.0Oct 2023View details →
dryad28/100

Data from: System-level insights into the cellular interactome of a non-model organism: inferring, modelling and analysing functional gene network of Soybean (Glycine max)

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publicOct 2015View details →
dryad28/100

Data from: A model to identify urban traffic congestion hotspots in complex networks

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publicSep 2016View details →
dryad28/100

Data from: Trait-based modeling of multi-host pathogen transmission: plant-pollinator networks

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publicDec 2018View details →
dryad28/100

Data from: Robustness to noise in gene expression evolves despite epistatic constraints in a model of gene networks

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publicJul 2015View details →
dryad28/100

The performance of permutations and exponential random graph models when analysing animal networks (R code and data)

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publicAug 2020View details →

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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record