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5 results for “Neural Science”
Using convolutional neural networks to efficiently extract immense phenological data from community science images
<p>Community science image libraries offer a massive, but largely untapped, source of observational data for phenological research. The iNaturalist platform offers a particularly rich archive, containing more than 49 million verifiable, georeferenced, open access images, encompassing seven continents and over 278,000 species. A critical limitation preventing scientists from taking full advantage of this rich data source is labor. Each image must be manually inspected and categorized by phenophase, which is both time-intensive and costly. Consequently, researchers may only be able to use a subset of the total number of images available in the database. While iNaturalist has the potential to yield enough data for high-resolution and spatially extensive studies, it requires more efficient tools for phenological data extraction. A promising solution is automation of the image annotation process using deep learning. Recent innovations in deep learning have made these open-source tools accessible to a general research audience. However, it is unknown whether deep learning tools can accurately and efficiently annotate phenophases in community science images. Here, we train a convolutional neural network (CNN) to annotate images of Alliaria petiolata into distinct phenophases from iNaturalist and compare the performance of the model with non-expert human annotators. We demonstrate that researchers can successfully employ deep learning techniques to extract phenological information from community science images. A CNN classified two-stage phenology (flowering and non-flowering) with 95.9% accuracy and classified four-stage phenology (vegetative, budding, flowering, and fruiting) with 86.4% accuracy. The overall accuracy of the CNN did not differ from humans (p = 0.383), although performance varied across phenophases. We found that a primary challenge of using deep learning for image annotation was not related to the model itself, but instead in the quality of the community science images. Up to 4% of A. petiolata images in iNaturalist were taken from an improper distance, were physically manipulated, or were digitally altered, which limited both human and machine annotators in accurately classifying phenology. Thus, we provide a list of photography guidelines that could be included in community science platforms to inform community scientists in the best practices for creating images that facilitate phenological analysis.</p>
Datasets used in "Assesing the quality of random number generators through neural networks", Machine Learning: Science and Technology 5 (2024) 025072
<p>Datasets corresponding to the bits generated by different random number generators used in J. L. Crespo et al, Machine Learning: Science and Technology 5 (2024) 025072.</p> <p>VCSEL_QRNG_postprocessed_bits.txt: postprocessed bits from the random generator based on gain-switching of VCSELs </p> <p>EC_LCG_bits.txt: bits from the linear congruential generator on elliptic curves</p> <p>LCG_32_bits.txt:: bits from the linear congruential generator with 32 bits</p> <p>VCSEL_QRNG_raw_bits.txt: raw bits from the random generator based on gain-switching of VCSELs</p> <p> </p>
Using convolutional neural networks to efficiently extract immense phenological data from community science images
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Machine learning for Material Science 2022 - Neural Networks Assignment Dataset
<p>Data for the neural network assignment</p> <p> </p>
Data from: Using artificial neural networks and citizen science data to assess jellyfish presence along coastal areas
<p><strong><span>General Information</span></strong></p> <p><span>This dataset was used in the study titled "Using artificial neural networks and citizen science data to assess jellyfish presence along coastal areas". The study employs citizen science data collected from the Infomedusa application to assess the presence of jellyfish on beaches along the Andalusian coast, along with environmental data to analyze the factors influencing jellyfish distribution. The study aims to employ machine learning techniques, specifically a Multi-Layer Perceptron (MLP) neural network, to classify user comments on the presence or absence of jellyfish and analyze how environmental factors such as sea surface temperature, wind direction, and wind speed influence jellyfish distribution.</span></p> <p><strong><span>Dataset Columns</span></strong></p> <ul> <li><strong><span>Fecha</span></strong><span>: Timestamp of the comment made by the user in the Infomedusa application.</span></li> <li><strong><span>Municipio</span></strong><span>: Name of the municipality where the beach mentioned in the comment is located.</span></li> <li><strong><span>Jellyfish</span></strong><span>: Binary variable indicating the presence (1) or absence (0) of jellyfish according to the user’s comment.</span></li> <li><strong><span>Comunidad</span></strong><span>: Autonomous community to which the municipality belongs.</span></li> <li><strong><span>Provincia</span></strong><span>: Province to which the municipality belongs.</span></li> <li><strong><span>Latitud</span></strong><span>: Geographical latitude of the municipality where the comment was made.</span></li> <li><strong><span>Longitud</span></strong><span>: Geographical longitude of the municipality where the comment was made.</span></li> <li><strong><span>Set</span></strong><span>: Set of grouped beaches for geographical analysis. Each set includes beaches close to each other and the nearest weather station.</span></li> <li><strong><span>Month</span></strong><span>: Month when the comment was made.</span></li> <li><strong><span>Longitud_sea</span></strong><span>: Longitude of the nearest point in the sea for which environmental data was available.</span></li> <li><strong><span>Latitud_sea</span></strong><span>: Latitude of the nearest point in the sea for which environmental data was available.</span></li> <li><strong><span>SST</span></strong><span>: Sea Surface Temperature at the nearest point in the sea to the municipality, obtained from the Copernicus Marine Environment Monitoring Service.</span></li> <li><strong><span>Wind_dir</span></strong><span>: Wind direction measured at the weather station closest to the municipality, provided by the Spanish Meteorological Agency (AEMET).</span></li> <li><strong><span>Wind_speed</span></strong><span>: Wind speed measured at the weather station closest to the municipality, provided by the AEMET.</span></li> </ul> <p><strong><span>Data Sources</span></strong></p> <ul> <li><strong><span>Infomedusa APP</span></strong><span>: Application developed by the Provincial Council of Malaga and Aula del Mar of Malaga to monitor the presence of jellyfish through citizen participation.</span></li> <li><strong><span>Copernicus Marine Environment Monitoring Service (CMEMS)</span></strong><span>: Provides data on sea surface temperature with an hourly temporal resolution and a spatial resolution of 0.0625° x 0.0625°.</span></li> <li><strong><span>Agencia Estatal de Meteorología (AEMET)</span></strong><span>: Provides daily data on wind direction and speed.</span></li> </ul>
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.