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2,747 results for “artificial”
CVPIA Predation Contact Point Study - 2019: Impacts of Artificial Light At Night in the Sacramento – San Joaquin Delta
The Central Valley Project Improvement Act (CVPIA) has led to the implementation of a Decision Support Model (DSM) to assist in the prioritization of CVPIA restoration actions. The fall-run Chinook salmon DSM depends on a coarse-resolution salmon life-cycle model to predict the population benefits of different restoration actions and scenarios. One critical element of the life-cycle model is how to incorporate predation mortality during the juvenile rearing and outmigration portion of the salmon life-cycle in the Sacramento-San Joaquin Delta. Of particular importance to potential restoration activities, is the predation mortality that occurs in proximity to, and as a result of contact points between predator and prey fishes. A recent Literature review and meta-analysis of potential contact points in the Sacramento-San Joaquin Delta identified artificial lighting at night (ALAN) and submerged aquatic vegetation (SAV) as two contact points that have been found to influence predation elsewhere and warrant further study in this river delta (Lehman et al. 2019). Other contact points identified in this review that may affect predation of fall-run Chinook salmon juveniles included water diversions, docks, piers, scour holes, and rip rap; however, the literature on predator prey interactions associated with these contact points is lacking (Lehman et al. 2019). These datasets cover two different experiments in the Sacramento-San Joaquin Delta during spring 2019 from April - June. One experiment focused on artificial illumination and was a paired control impact study where new artificial illumination sources were introduced into the ecosystem. The other experiment relied on existing physical contact points (SAV, docks, pilings, bridges, and diversions) and assessed predation risk as a function of proximity to these points. Both experiments used predation event recorders to quantify relative predation risk and the ALAN experiment used Adaptive Resolution Imaging Sonar (ARIS) t
Improving Artificial Teachers by Considering How People Learn and Forget: Dataset
<p>This dataset contains the results of the experiment described in <a href="https://dl.acm.org/doi/10.1145/3397481.3450696">Nioche et al. (2021)</a>. </p> <p>This dataset contains 4 data files:</p> <ul> <li><em>data.csv</em>: the main data file.</li> <li><em>stimuli.csv:</em> the description/listing of the stimuli.</li> <li><em>demographic_info.csv</em>: the demographic information about the users.</li> <li><em>data_incl_preliminary_exp.csv</em>: an additional data file that includes the user of the preliminary experiments</li> </ul> <p>The main data file contains the logs of 53 different users using a self-teaching application for one week. The goal of the users was to learn the English meaning of Japanese kanji. Each user completed between 1370 trials and 1608 trials. Each user saw between 85 and 204 characters. </p> <p>Two additional files are also joint to the data files:</p> <ul> <li><em>info.ipynb</em>: A Jupyter notebook that provides information about each data file, a few descriptive plots, and an example of data manipulation.</li> <li><em>info.pdf: </em>A pdf rendering of the notebook.</li> </ul> <p>If you use this dataset, please refer to it by citing <a href="https://dl.acm.org/doi/10.1145/3397481.3450696">Nioche et al. (2021)</a>.</p>
The interactive effects of nitrate and road salt on benthic algal assemblages in an artificial stream experiment
To investigate and quantify the multi-tiered responses of benthic algal assemblages to the impacts of road salt and nitrate, we created artificial flow-through streams with terracotta vessels with nutrient diffusing substrates (NDS) containing varying concentrations of both salt (0-7500 mg/L) and nitrate (0-5.9 mg/L) and incubated for 56 days during the summer. This work was done at the University of Michigan Biological Station's stream research facility. The streams and algae were sampled on day 7, 14, 28, and 56. The algae pigments were quantified via a fluoroprobe and diatoms were quantified with counts on slides. Finally, both 13s and 16s DNA sequencing was performed on all of the samples.
Ten years (2013-2023) of fish assemblage data collected seasonally with underwater visual surveys on paired artificial and natural reefs
<p>The study of assembly patterns and dynamics of organisms has long remained a foundational theme in ecology. Further, the relationship between assemblages and different habitats can provide important insight on ecological processes and guide management and conservation efforts (e.g., restoration, protected areas). We conducted underwater visual surveys of reef fish assemblages at 14 sites in the eastern Gulf of Mexico, including eight that were paired artificial and natural reefs. By using a paired design, we controlled biotic (e.g., larval supply), abiotic (e.g., depth), and socio variables (e.g., fishing access) to isolate the effect of reef type. Trained scientific SCUBA divers with extensive experience with reef fishes from the broader tropical western Atlantic region conducted two to four 10-minute stationary surveys on the paired reefs each season (i.e., calendar quarters) for 10 years from spring 2013 to spring 2023. We also surveyed six additional artificial reefs from winter 2020 to spring 2023 that lacked natural reef pairs. During each survey, the divers identified and estimated the total lengths of all taxa<strong> </strong>observed within an imaginary cylinder around them. The imaginary cylinders had a radius up to 7.5 meters (depending on horizontal visibility) and extended from the seafloor to the highest visible water above the diver. During the period of study, we conducted a total of 1,349 surveys and counted 544,736 fish that represented 171 taxa (most at the species level). Analyses of these data have revealed habitat-specific heterogeneity of the fish assemblages at both taxonomic and functional trait levels, the importance of herbivory in structuring the benthos, and socio-ecological interactions in the system, among other findings. These data may be useful for other researchers interested in patterns and dynamics of populations and communities, functional traits, taxa-habitat relationships, and for parameterizing statistical, joint distribution, metacommunity, and ecosystem models. In addition, because many of the observed taxa<strong> </strong>are of management concern, they may be useful for researchers interested in fisheries science. The data are free to use, are not copyright restricted, and we ask users to cite this data paper.</p>
Flume Experiment Testing the Impact of Artificial Streambank Roots on Velocity, Reynold's Shear Stress, and Turbulent Kinetic Energy using an Acoustic Doppler Profiler
The data published here is expected to accompany one publicly available dissertation (Chapter 4 of dissertation) and one separate journal publication. Once published and available online, the metadata will be updated with the relevant article information. The journal article/dissertation will have additional information regarding the published datasets and the methods used to collect the data. All data collected from these studies, and the accompanying Acoustic Doppler Profiler MATLAB files, are presented here. Journal Article title: Impact of Flexible and Rigid Artificial Roots on Stream Hydrodynamics
California's Central Valley Project Improvement Act Predation Contact Point Study - 2022: Predator-prey interactions under low artificial lighting in a laboratory setting
The highest rates of piscivorous predation in the field have been recorded during crepuscular light levels associated with sunrise and sunset or artificial lighting at night (ALAN). We conducted a laboratory study where groups of predator-naïve, hatchery-raised juvenile rainbow trout (Oncorhynchus mykiss) were exposed to natural-origin piscivorous largemouth bass (Micropterus salmoides) under three light treatments representative of brighter crepuscular periods or direct ALAN illumination (“high” treatment), dimmer crepuscular periods or sky glow from ALAN (“medium” treatment), and night or no ALAN (“low” treatment). We then statistically evaluated potential associations between light treatment, prey group cohesion, and predator activity.
Satellite-derived estimate of artificial lakes in the Phoenix, Arizona, USA Metropolitan area (2000)
Artificial lakes are a prominent example of a local hydrogeomorphic modification in the Phoenix, Arizona metropolitan area. Although lakes are not a natural feature of Sonoran desert ecosystems, numerous artificial lakes are evident in the region. Here we attempt to quantify the heretofore unknown number and extent of artificial lakes. As official GIS layers from local and state agencies had significant misclassifications and omissions, we used two published land cover datasets to estimate the number and areal extent of artificial lakes. We discovered that there are 908-1,390 lakes in the Phoenix area, with the number varying according to level of aggregation. Accurate data on the extent and distribution of these designed ecosystems is vital for water-resources planning and stormwater management. Results, discussion, and additional details are available in document DOI 10.1007/s11252-011-0208-1.
Data and code from Artificial light at night increases top-down pressure on caterpillars: experimental evidence from a light-naive forest - 2021-2022
This dataset has been prepared in support of a paper to be published in Proceedings of the Royal Society B: Biological Sciences. It includes both data files and R scripts used for the analysis in this publication: Deitch, J.F. and S.A. Kaiser. 2023. Artificial light at night increases top-down pressure on caterpillars: experimental evidence from a light-naive forest. Proceedings of the Royal Society B: Biological Sciences. (https://doi.org/10.1098/rspb.2023.0153) Artificial light at night (ALAN) is a globally widespread and expanding form of anthropogenic change that impacts arthropod biodiversity. ALAN alters interspecific interactions between arthropods, including predation and parasitism. Despite their ecological importance as prey and hosts, the impact of ALAN on larval arthropod stages, such as caterpillars, is poorly understood. We examined the hypothesis that ALAN increases top-down pressure on caterpillars from arthropod predators and parasitoids. We experimentally illuminated study plots with moderate levels (10-15 lux) of LED lighting at light-naive Hubbard Brook Experimental Forest, New Hampshire. We measured and compared between experimental and control plots: 1) predation on clay caterpillars and 2) abundance of arthropod predators and parasitoids. We found that predation rates on clay caterpillars and abundance of arthropod predators and parasitoids were significantly higher on ALAN treatment plots relative to control plots. These results suggest that moderate levels of ALAN increases top-down pressure on caterpillars. We did not test mechanisms, but sampling data indicates that increased abundance of predators near lights may play a role. This study highlights the importance of examining the effects of ALAN on both adult and larval life stages and suggests potential consequences of ALAN on arthropod populations and communities. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubba
A Spatially Variable Time Series of Sea Level Change Due to Artificial Water Impoundment
<p>This database contains a series of gravitational, rotational, and deformational (GRD) "fingerprints"—the spatial response of sea level—corresponding to redistribution of water mass because of impoundment of water in artificial reservoirs, as reported in Hawley <em>et al</em>. (2020). Fingerprints for the GRanD database (Lehner <em>et al</em>.; 2011) are for individual years, noted in the file name.</p> <p>Three additional files come from the dataset provided by Zarfl <em>et al</em>. (2015), as described in Hawley <em>et al.</em> (2020). "Const" includes the fingerprint for all reservoirs under construction in their database; "Plan" includes the fingerprint for all reservoirs in the planning phase. "Zarfl" includes the fingerprint for all reservoirs in "Const," with 15 years of seepage, as well as all reservoirs for "Plan" with 5 years of seepage, as described in Hawley <em>et al</em>. (2020).</p> <p>Each fingerprint has 525,825 points, which fill out a global grid of 513 x 1025 [lat x lon] points. Each node in latitude and longitude is evenly spaced. The first point represents the northernmost point at 0 [deg] longitude, and increase first to the east, then to the south.</p>
Data files for figures in "Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability" by Bird et al.
<p>The data files for figures in <i>Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability</i> by Bird, Bodeker and Clem. The files required to create each figure in the paper and in the supplementary material are described in a readme.txt file which is also provided below:</p><p><strong>Figure 1</strong></p><p>The background image was obtained from the 'NaturalEarthFeature' function of the python Cartopy library (Figure1_background.png). The data required to generate the plots shown in Figure 1 are provided in the Figure1.nc file:</p><ul><li>The latitudes and longitudes for the 10,000 training sites are provided in Training_location_latitudes and Training_location_longitudes variables. </li><li>The latitudes and longitudes for the 8 sites used to demonstrate the ability of the CNN to generalise spatially are provided in the Validation_location_latitudes and Validation_location_longitudes variables. </li><li>The block maxima at each of the 8 sites are provided in the Location_1year_block_maxima variable.</li><li>The GEV fits at 0°C are provided in the GEV_fit_at_0.0C variable.</li><li>The GEV fits at 1.5°C are provided in the GEV_fit_at_1.5C variable.</li></ul><p><strong>Figure 2</strong></p><ul><li>The 1-in-100 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named Figure2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-100 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named Figure2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul><p><strong>Figure 3</strong></p><ul><li>The cumulative distribution functions (CDFs) shown in the lower four panels are provided as text files listing the ARI in years and the daily total precipitation depth in mm. These files are named CDF_<lat>_<long>.dat where <lat> is the latitude and <long> is the longitude. Files for each region are zipped into .7z files named Figure3_<region>.7z where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The latitudes and longitudes for the upper panels can be inferred from the file names for each region.</li></ul><p><strong>Figure 4</strong></p><p>The data for each panel are provided in a netCDF file named Figure4_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</p><p><strong>Figure 5</strong></p><p>The data are provided as text files named Figure5_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the sensitivities at ARIs of 10, 20, 50, 100, and 200 years.</p><p><strong>Figure 6</strong></p><p>The data are provided as text files named Figure6_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the precipitation depths and the sensitivities at ARIs of 10, 20, 50, 100, and 200 years. </p><p><strong>Figure 7</strong></p><p>The data for each panel are provided in a netCDF file named Figure7_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</p><p><strong>Figure 8</strong></p><p>The data are provided as text files named Figure8_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the global surface temperature anomaly (°C) and the average negative log likelihood.</p><p><strong>Figure S1 and Figure S3</strong></p><p>The data are provided as text files named Figure_S1_and_S3_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes its contents.</p><p><strong>Figure S2</strong></p><ul><li>The 1-in-20 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named FigureS2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-20 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named FigureS2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul><p><strong>Figure S4</strong></p><ul><li>The block maxima for each site are provided in text files named FigureS4_blockmaxima_siteA.txt and FigureS4_blockmaxima_siteB.txt. A header at the top of each column described the column contents. </li><li>The GEV-derived curves for each site are provided in text files named FigureS4_gevcurves_siteA.txt and FigureS4_gevcurves_siteB.txt. A header at the top of each column described the column contents. </li></ul><p><strong>Figure S5</strong></p><p>There are no data associated with this figure. This figure was made using Microsoft Powerpoint.</p><p><strong>Figure S6</strong></p><p>The data are provided as text files named FigureS6_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes the files contents.</p>
Effects of intercropping on the herbage production of a binary grass-legume mixture (Hedisarum coronarium L. and Lolium multiflorum Lam.) under artificial shade in Mediterranean rainfed conditions
<p>This dataset refers to the experimental raw data (csv version) collected within the trial reported in the concerned article on the following parameters:</p> <p>1. crop aboveground biomass, splitted per field, mowing, crop, treatment and replicate (crop aboveground biomass.csv)</p> <p>2. cumulated crop aboveground biomass, splitted per field, year, crop, treatment and replicate (cumulated crop aboveground biomass_year.csv)</p> <p>3. cumulated crop aboveground biomass for the two years of the growing cycle, splitted per field, crop, treatment and replicate (cumulated crop aboveground biomass_2years.csv)</p> <p>4. partial and total RYT splitted per year, field and treatment (RYT_year)</p> <p>5. partial and total RYT for the two years of the growing cycle, splitted per field and treatment (RYT_2years)</p> <p> </p> <p><br> </p>
Artificial Intelligence for Quality Control of manufacturing operations: Macro-mechanical milling in the Pilot Line GAMHE 5.0.
<p>Quality is defined as the extent to which a product conforms to the design specifications and how it complies with the requirements of component functionality. For some industries, such as automotive and aeronautical, the quality of their parts is very important given the high requirements to which they are subject. However, difficulties arise from the fact that a measure of quality can only be evaluated ‘‘out-of-process”, resulting in losses because there is no alternative to removing defective parts from the production line. Therefore, it is necessary to apply Artificial Intelligence-based kits/solutions that provide in-process estimation to predict quality from some measured variables. </p> <p>The main goal of these datasets is to monitor the final quality of the manufactured components or parts by estimating surface roughness from vibration signals and cutting parameters information using Artificial Intelligence-based solutions. Surface roughness is an essential feature in quality control defined by the deviation in the direction of the normal vector of a real surface from its ideal form. Because the roughness measurement is an offline and post process procedure, being able to estimate this value online brings a series of benefits in terms of time and cost reduction in manufacturing lines, energy efficiency, unnecessary wear of tools and machines, etc. Once a part has been detected with a surface quality below what is desired, a series of corrective measures can be applied for the following operations, such as: reducing the feed rate percentage, increasing the percentage of spindle speed or reducing the axial depth per pass, etc.</p>
A living catalogue of artificial intelligence datasets and benchmarks for medical decision making
<p>We provide a comprehensive curated catalogue of <strong>artificial intelligence datasets</strong> and <strong>benchmarks for medical decision making</strong>. At the time of first release (April 2021), the dataset contains more than 400 biomedical and clinical datasets of which 252 are publicly available or available upon request.</p> <p>The dataset was compiled based on a systematic literature review covering both biomedical and computer science literature and grey literature data sources. All datasets were manually systematized and annotated for meta-information, such as:</p> <ul> <li>Availability and licensing information</li> <li>Type of source data</li> <li>Links to source publications, main references or dataset repositories</li> </ul> <p>Benchmark dataset were additionally annotated for the following information:</p> <ul> <li>Associated task</li> <li>Performance metrics commonly used for evaluation</li> <li>Clinical relevance</li> <li>The availability of data splits</li> </ul> <p>In addition to the versioned TSV file on Zenodo, the dataset can also be explored live via <a href="https://docs.google.com/spreadsheets/d/1QjUxxnZ3tuyW5dj6nkt_o5yJcWUZec4ttfJxO8Zlty4/edit?usp=sharing">this Google Spreadsheet</a>. The dataset is intended as a living, extendable resource. Edit suggestions and additions are encouraged and can be submitted via the comment function of the Google sheet.</p> <p> </p> <p><strong>File descriptions</strong></p> <p><em>annotated-datasets.tsv</em> -- contains the annotated datasets</p> <p><em>arXiv-literature-export.tsv</em> -- contains the original literature record export from arXiv</p> <p><em>pubmed-literature-export.tsv</em> -- contains the original literature record export from PubMed</p> <p><em>README.md</em> -- contains a detailed description of all annotation fields</p>
Artificial Intelligence for quality control in manufacturing operations: Micro-mechanical milling in the Pilot Line GAMHE 5.0
<p>Quality is defined as the extent to which a product conforms to the design specifications and how it complies with the requirements of component functionality. For some industries, such as automotive and aeronautical, the quality of of manufactured parts is very important due to the high requirements. However, difficulties arise from the fact that a measure of quality can only be evaluated ‘‘out-of-process”, resulting in losses because there is no alternative to removing defective parts from the production line. Therefore, it is necessary to incorporate AI-based kits/solutions that provide in-process estimation to predict quality from some measured variables.</p> <p>The main goal of these datasets is to enable monitoring of final quality of the manufactured components or parts by estimating surface roughness from vibration signals and cutting parameters information. Surface roughness is an essential feature in quality control defined by the deviation in the direction of the normal vector of a real surface from its ideal form. Because the roughness measurement is an offline and post process procedure, being able to estimate this value online brings a series of benefits in terms of time and cost reduction in manufacturing lines, energy efficiency, unnecessary wear of tools and machines, etc. Once a part has been detected with a surface quality below what is desired, a series of corrective measures can be applied for the following operations, such as: reducing the feed rate percentage, increasing the percentage of spindle speed or reducing the axial depth per pass, etc.</p> <p>Workstation 4 (WS4) of the GAMHE 5.0 pilot line is a Kern Evo high-precision machining centre, with a maximum spindle speed of 50 000 rpm and Blum laser system and is used to run micro-milling and micro-drilling operations. In this experimental dataset, five cutting parameters were considered in the processes: spindle speed, <em>n</em>; feed rate, <em>f</em>; and axial depth of cut, <em>a<sub>P</sub></em>. The radial depth of cut, <em>a<sub>e</sub></em>; was equal to the mill tool radius, <em>r</em>, in all of the slots.</p> <p>These experiments were micro-milling operations with 0.3 mm, 0.5 mm, 0.8 mm and 1 mm-diameter mills on a sintered tungsten-copper alloy (W78Cu22). The data collected for each micro milling operation was the rms and peak value of the vibrations in the three-machine axis. In addition, five cutting parameters were also collected: position in <em>X</em> of the last point of the sample, feed rate, spindle speed, tool radius and axial depth.</p>
Indicative distribution map for Ecosystem Functional Group MT3.1 Artificial shorelines
<p>This archive contains indicative distribution maps and profiles for <strong>MT3.1 Artificial shorelines</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Indicative distribution map for Ecosystem Functional Group M4.1 Submerged artificial structures
<p>This archive contains indicative distribution maps and profiles for <strong>M4.1 Submerged artificial structures</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
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, developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Native ecological niche models of 1508 European species (894 fish and 614 non fish) developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 and under RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, at 0.5° spatial resolution.</p>
Biodiversity Index, in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European Marine Species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Biodiversity Index in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European marine species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution. The Index counts the number of species (among the 1508) potentially present in each 0.5° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
Ecological Niche Models of 96 European Marine Species, for 2019, developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines at 0.1° Resolution
<p>Native ecological niche models of 96 European marine species of particular commercial and conservation interest developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 at 0.1° spatial resolution.</p>
Ensemble Ecological Niche Models and Biodiversity Index for 2019 of 96 European Marine Species based on Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, AquaMaps, and Support Vector Machines at 0.1° Resolution
<p>Ensemble Ecological Niche Models for 2019 of 96 European marine species of particular commercial and conservation interest, based on Ecological Niche Models developed with (i) Artificial Neural Networks, (ii) Maximum Entropy, (iii) Support Vector Machines, and (iv) AquaMaps at 0.1° Resolution. The data report, for each 0.1° cell, how many models (from 0 to 4) overcome a model-specific decision threshold to assess species presence in the cell. A Biodiversity Index is also provided as the count of the number of species (among the 96) potentially present in each 0.1° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
ScienceDex guides
Understand access before you commit
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.