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2,747 results for “artificial”
CVPIA Predation Contact Point Study - 2020: Impacts of Artificial Light At Night in the Upper Sacramento River
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. Sacramento River winter-run Chinook salmon (Oncorhynchus tshawytscha) are a genetically distinct Evolutionary Significant Unit (ESU) with a unique life history and are listed as endangered at both state and federal levels. Predation of juvenile winter-run by piscivorous fishes is considered to be an important stressor that may reduce the population size of this ESU. Notably, the presence of artificial illumination at night (ALAN) has been shown to aggregate and slow out-migrating salmonids and increase predation by piscivores, and may be an important contact point affecting winter-run Chinook salmon, especially given the vast majority of winter-run Chinook salmon are born and rear within the city limits of Redding, CA. Perhaps the most significant source of ALAN in this region of the Sacramento River is the iconic Sundial Bridge. This bridge is illuminated at night and regional biologists have long been concerned on its potential impacts on winter-run Chinook salmon, as mediated through predation by Rainbow Trout. We therefore performed a field-based experiment to better inform this management concern. To assess the impacts of Sundial Bridge ALAN on fishes, our first objective was to determine whether variable ALAN intensities altered the relati
Artificial Communities Experiment: Seedling growth
This study considers the relative role of competition and facilitation in primary succession of the boreal forest floodplain of Bonanza Creek Experimental Forest. Alder (Alnus tenuifolia Nutt.) is an early colonist of young floodplain sites, where it contributes greatly to soil development through the addition of nitrogen to the soil. White spruce (Picea glauca (Moench.) Voss) typically colonizes such sites later; it becomes established beneath a canopy of alder and willow (Salix spp.) which is sometimes overtopped by young balsam poplar (Populus balsamifera L.). The alder may potentially influence white spruce both positively and negatively. Possible facilitative effects include protection of small seedlings from excess heat or heavy siltation accompanying flooding and addition of soil nitrogen, an effect that may extend for many years. Competition for light (and other resources) before the white spruce has overtopped the alder canopy may prove to be a major negative effects. This study seeks to determine the net balance of these effects on the floodplain successional sequence.
Artificial Communities Experiment: Predictor Variables
This study considers the relative role of competition and facilitation in primary succession of the boreal forest floodplain of Bonanza Creek Experimental Forest. Alder (Alnus tenuifolia Nutt.) is an early colonist of young floodplain sites, where it contributes greatly to soil development through the addition of nitrogen to the soil. White spruce (Picea glauca (Moench.) Voss) typically colonizes such sites later; it becomes established beneath a canopy of alder and willow (Salix spp.) which is sometimes overtopped by young balsam poplar (Populus balsamifera L.). The alder may potentially influence white spruce both positively and negatively. Possible facilitative effects include protection of small seedlings from excess heat or heavy siltation accompanying flooding and addition of soil nitrogen, an effect that may extend for many years. Competition for light (and other resources) before the white spruce has overtopped the alder canopy may prove to be a major negative effects. This study seeks to determine the net balance of these effects on the floodplain successional sequence.
Artificial Communities Experiment: Alder Seedlings
This study considers the relative role of competition and facilitation in primary succession of the boreal forest floodplain of Bonanza Creek Experimental Forest. Alder (Alnus tenuifolia Nutt.) is an early colonist of young floodplain sites, where it contributes greatly to soil development through the addition of nitrogen to the soil. White spruce (Picea glauca (Moench.) Voss) typically colonizes such sites later; it becomes established beneath a canopy of alder and willow (Salix spp.) which is sometimes overtopped by young balsam poplar (Populus balsamifera L.). The alder may potentially influence white spruce both positively and negatively. Possible facilitative effects include protection of small seedlings from excess heat or heavy siltation accompanying flooding and addition of soil nitrogen, an effect that may extend for many years. Competition for light (and other resources) before the white spruce has overtopped the alder canopy may prove to be a major negative effects. This study seeks to determine the net balance of these effects on the floodplain successional sequence.
Artificial Communities Experiment: Species Data for Ordination
This study considers the relative role of competition and facilitation in primary succession of the boreal forest floodplain of Bonanza Creek Experimental Forest. Alder (Alnus tenuifolia Nutt.) is an early colonist of young floodplain sites, where it contributes greatly to soil development through the addition of nitrogen to the soil. White spruce (Picea glauca (Moench.) Voss) typically colonizes such sites later; it becomes established beneath a canopy of alder and willow (Salix spp.) which is sometimes overtopped by young balsam poplar (Populus balsamifera L.). The alder may potentially influence white spruce both positively and negatively. Possible facilitative effects include protection of small seedlings from excess heat or heavy siltation accompanying flooding and addition of soil nitrogen, an effect that may extend for many years. Competition for light (and other resources) before the white spruce has overtopped the alder canopy may prove to be a major negative effects. This study seeks to determine the net balance of these effects on the floodplain successional sequence.
RECAP Artificial Data Traces
<p>The objective of the work package "Data Collection, Visualization and Analysis" of RECAP is to provide the necessary tools for managing and refining the data needed for the rest of the work packages. This includes the collection as well as the generation of data.</p> <p>Within this work package, the task of Artificial Workload Generation is responsible for the generation of a collection of datasets with artificial workloads, that complement the real data traces collected from industrial partners. Moreover, because publicly available workload data is scarce we provide the data as public data sets.</p> <p>This document is a companion report to deliverable which is of type “dataset”. The aim of the report is to describe the collection of datasets that constitute D5.3 and the mathematical techniques (structural time series models, generative adversarial networks, and workload based on traffic propagation) by which one can artificially generate and/or augment such datasets.</p> <p>The datasets described include real data traces collected by industrial partners and artificial data traces generated by the use of statistical models and neural networks. Each published data set can be used by the scientific and industrial community as a starting point for the modelling and experimental validation of distributed edge and cloud applications, facilitating the repeatability of the results.</p> <p> </p>
Artificial Intelligence for EU Decision-Making: Effects on Citizens Perceptions of Input, Throughput and Output Legitimacy
<p>The uploaded dataset was used for the statistical analysis of the pre-print "Artificial Intelligence for EU Decision-Making: Effects on Citizens’ Perceptions of Input, Throughput and Output Legitimacy" (Permanent identifier: <a href="https://arxiv.org/abs/2003.11320">arXiv:2003.11320</a>)</p> <p>A lack of political legitimacy undermines the ability of the European Union (EU) to resolve major crises and threatens the stability of the system as a whole. By integrating digital data into political processes, the EU seeks to base decision-making increasingly on sound empirical evidence. In particular, artificial intelligence (AI) systems have the potential to increase political legitimacy by identifying pressing societal issues, forecasting potential policy outcomes, informing the policy process, and evaluating policy effectiveness. This paper investigates how citizens’ perceptions of EU input, throughput, and output legitimacy are influenced by three distinct decision-making arrangements: (1) independent human decision-making (HDM); (2) independent algorithmic decision-making (ADM) by AI-based systems; and (3) hybrid decision-making by EU politicians and AI-based systems together. The results of a pre-registered online experiment (n = 572) suggest that existing EU decision-making arrangements are still perceived as the most democratic (input legitimacy). However, regarding the decision-making process itself (throughput legitimacy) and its policy outcomes (output legitimacy), no difference was observed between the status quo and hybrid decision-making involving both ADM and democratically elected EU institutions. Where ADM systems are the sole decision-maker, respondents tend to perceive these as illegitimate. The paper discusses the implications of these findings for (a) EU legitimacy and (b) data-driven policy-making.</p>
Artificial Primary Vertexing Data
<p>This data set contains the positions of artificial vertex positions and the associated particle tracks within the Compact Muon Solenoid (CMS) particle detector located at the Large Hadron Collider (LHC). A Gaussian with a width of 35 mm is used to approximate the vertex position distribution. The z<sub>0</sub> parameter of the artificial particle tracks is also sampled from a Gaussian centered around the sampled vertex position with a width determined from track resolutions reported by the CMS Collaboration in "Description and performance of track and primary-vertex reconstruction with the CMS tracker" (2014). The widths of the z<sub>0</sub> Gaussian distribution are dependent on the momentum of the particle tracks, which are also sampled from the reported distribution in the aforementioned paper.</p> <p> </p> <p>The zipped data set contains 100 samples for a given number of primary vertices and particle tracks. Seven different configurations for a given number of primary vertices and particle tracks are included, giving a total number of 700 samples to analyze. Each sample is a JSON file containing the primary vertex location and the associated tracks with their positions and uncertainties. Below is an example code in Python to load the data set:</p> <pre><code class="language-python">inputFile = open(filename) d_vertextracks = json.load(inputFile) zT_i = [] zT_unc_i = [] for vertexTracks in d_vertextracks: primary_vertex = vertexTracks[0] tracks = vertexTracks[1] for i in tracks: zT_i.append(i[0]) zT_unc_i.append(i[1]) print(str(i[0])+" "+str(i[1]))</code></pre> <p> </p>
Patient-derived and artificial ascites have minor effects on MeT-5A mesothelial cells and do not facilitate ovarian cancer cell adhesion
<p>Raw data of "Patient-derived and artificial ascites have minor effects on MeT-5A mesothelial cells and do not facilitate ovarian cancer cell adhesion".</p>
Supplementary files for "Influence of the Artificial Nanostructure on the LiF Formation at the Solid−Electrolyte Interphase of Carbon-Based Anodes"
<p>Databases containing DFT optimized structures used for the paper: ''Influence of the Artificial Nanostructure on the LiF Formation at the Solid−Electrolyte Interphase of Carbon-Based Anodes". For further details of the computational setup we refer to this paper.</p> <p>Each database contains structures for one carbon substrate. The structures can be retrieved using the Atomic Simulation Environment (ASE).</p>
1QIsaa data collection (binarized images, feature files, and plotting scripts) for writer identification test using artificial intelligence and image-based pattern recognition techniques
<p><strong>The Great Isaiah Scroll (1QIsa<sup>a</sup>) data set for writer identification</strong></p> <p>This data set is collected for the ERC project:<br> The Hands that Wrote the Bible: Digital Palaeography and Scribal Culture of the Dead Sea Scrolls<br> PI: Mladen Popović<br> Grant agreement ID: 640497</p> <p>Project website: <a href="https://cordis.europa.eu/project/id/640497">https://cordis.europa.eu/project/id/640497</a><br> <br> <strong>Copyright (c) </strong> University of Groningen, 2021. All rights reserved.<br> <strong>Disclaimer and copyright notice for all data contained on this .tar.gz file:</strong></p> <p><strong>1)</strong> permission is hereby granted to use the data for research purposes. It is not allowed to distribute this data for commercial purposes.</p> <p><strong>2) </strong>provider gives no express or implied warranty of any kind, and any implied warranties of merchantability and fitness for purpose are disclaimed.</p> <p><strong>3) </strong>provider shall not be liable for any direct, indirect, special, incidental, or consequential damages arising out of any use of this data.</p> <p><strong>4) </strong>the user should refer to the first public article on this data set:<br> <br> <em>Popović, M., Dhali, M. A., & Schomaker, L. (2020). Artificial intelligence-based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsa<sup>a</sup>). arXiv preprint arXiv:2010.14476.</em><br> <br> BibTeX:</p> <pre>@article{popovic2020artificial, title={Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa)}, author={Popovi{\'c}, Mladen and Dhali, Maruf A and Schomaker, Lambert}, journal={arXiv preprint arXiv:2010.14476}, year={2020} }</pre> <p><strong>5) </strong>the recipient should refrain from proliferating the data set to third parties external to his/her local research group. Please refer interested researchers to this site for obtaining their own copy.</p> <p><strong>Organisation of the data:</strong></p> <p>The .tar.gz file contains three directories: images, features, and plots. The included 'README' file contains all the instructions.</p> <p>The 'images' directory contains NetPBM images of the columns of 1QIsa<sup>a</sup>. The NetPBM format is chosen because of its simplicity. Additionally, there is no doubt about lossy compression in the processing chain. There are two images for each of the Great Isaiah Scroll columns: one is the direct binarized output from the BiNet (<em>arxiv.org/abs/1911.07930</em>) system, and the other one is the manually cleaned version of the binarized output. The file names for the direct binarized output are of the format '1QIsaa_col<columnnr>.pbm', for example, '1QIsaa_col15.pbm'. And, for the cleaned version, the format is '1QIsaa_col<columnnr>_cleaned.pbm', for example, '1QIsaa_col15_cleaned.pbm'. Note: the image files are not in a separate directory; they will be extracted in the same place. However, due to the unique naming, there is no problem extracting them in one single directory.</p> <p>The 'features' directory contains feature files computed for each of the column images. There are two types of feature files: Hinge and Adjoined. They are distinguishable by their extension, for example, '1QIsaa_col15_cleaned.hinge' and '1QIsaa_col15_cleaned.adjoined'. They are also arranged in separate directories for ease of use.</p> <p>The 'plots' directory contains a simple python script to perform PCA on the feature files and then visualize them in a 3D plot. The file takes the location of feature files as an input. The 'README_plot' file contains examples of how-to-run in the terminal.</p> <p><strong>Brief description:</strong><br> According to ImageMagick's' identify' tool, the original images are in grayscale (.jpg) from Brill collection, in '8-bit Gray 256c'. These images pass through multiple preprocessing measures to become suitable for pattern recognition-based techniques. The first step in preprocessing is the image-binarization technique. In order to prevent any classification of the text-column images based on irrelevant background patterns, a specific binarization technique (BiNet) was applied, keeping the original ink traces intact. After performing the binarization, the images were cleaned further by removing the adjacent columns that partially appear on the target columns' images. Finally, few minor affine transformations and stretching corrections were performed in a restrictive manner. These corrections are also targeted for aligning the texts where the text lines get twisted due to the leather writing surface's degradation. Hence, the clean images are there in the directory along with the direct binarized images. No effort has been made to obtain a balanced set in any way.</p> <p><strong>Tools:</strong><br> <strong>Binarization:</strong><br> The BiNet tool is available for scientific use upon request (m.a.dhal(at)rug.nl)</p> <p><strong>Image Morphing:</strong><br> In the original article, data augmentation was performed using image morphing. The tool is available on GitHub:<br> https://github.com/GrHound/imagemorph.c</p> <p><strong>Features for writer identification:</strong><br> Lambert Schomaker<br> http://www.ai.rug.nl/~lambert/allographic-fraglet-codebooks/allographic-fraglet-codebooks.html<br> http://www.ai.rug.nl/~lambert/hinge/hinge-transform.html<br> <em><strong>1. </strong>L. Schomaker & M. Bulacu (2004). Automatic writer identification using connected-component contours and edge-based features of upper-case Western script. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 26(6), June 2004, pp. 787 - 798.<br> <strong>2. </strong>Bulacu, M. & Schomaker, L.R.B. (2007). Text-independent Writer Identification and Verification Using Textural and Allographic Features, IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), Special Issue - Biometrics: Progress and Directions, April, 29(4), p. 701-717.</em><br> <br> The features (hinge, fraglets) have been combined in a single MS Windows application, GIWIS, which is available for scientific use upon request (l.r.b.schomaker(at)rug.nl)</p> <p><strong>If you have any question, please contact us:</strong><br> Maruf A. Dhali <m.a.dhali(at)rug.nl><br> Lambert Schomaker <l.r.b.schomaker(at)rug.nl><br> Mladen Popović <m.popovic(at)rug.nl></p> <p><strong>Please cite our papers if you use this data set:</strong><br> <em><strong>1.</strong> Popović, M., Dhali, M. A., & Schomaker, L. (2020). Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsa<sup>a</sup>). arXiv preprint arXiv:2010.14476.<br> <strong>2. </strong>Dhali, M. A., de Wit, J. W., & Schomaker, L. (2019). Binet: Degraded-manuscript binarization in diverse document textures and layouts using deep encoder-decoder networks. arXiv preprint arXiv:1911.07930.</em></p>
Artificial COVID-19 Cases in Paris and Geographic Data Useful for Geomasking
<p>Artificial dataset of addresses of COVID-19 cases in Paris. The dataset was created to test geomasking techniques to be used on the real data collected by the French health administration. The dataset was used in the paper "Geographically Masking Addresses to Study COVID-19 Clusters" by Walid Houfaf-Khoufaf and Guillaume Touya. The dataset contains the following files:</p> <ul> <li>roads_paris_IGN.shp contains the road lines from IGN France in Paris;</li> <li>buildings_paris_IGN.shp contains the building polygons from IGN France in Paris (useful to aggregate points to building groups);</li> <li>faces.shp contains the blocks built from the roads (useful to aggregate points to blocks);</li> <li>ban_75.shp contains all the address points in Paris from the open BAN database.</li> <li>artificial_COVID_cases.csv contains the artificial COVID cases generated from 3 months in 2020 in the Paris area.</li> </ul> <p> </p>
Data release for "OrchID: a Generalized Framework for Taxonomic Classification of Images Using Evolved Artificial Neural Networks"
<p><strong>Abstract</strong></p> <p>Taxonomic expertise for the identification of species is rare and costly. On-going advances in computer vision and machine learning have led to the development of numerous semi- and fully automated species identification systems. However, these systems are rarely agnostic to specific morphology, rarely can perform taxonomic “approximation” (by which we mean partial identification at least to higher taxonomic level if not to species), and frequently rely on costly scientific imaging technologies.</p> <p>We present a generic, hierarchical identification system for automated taxonomic approximation of organisms from images. We assessed the effectiveness of this system using photographs of slipper orchids (Cypripedioideae), for which we implemented image pre-processing, segmentation, and colour and shape feature extraction algorithms to obtain digital phenotypes for 116 species. The identification system trained on these digital phenotypes uses a nested hierarchy of artificial neural networks for pattern recognition and automated classification that mirrors the Linnean taxonomy, such that user-submitted photos can be assigned a genus, section, and species classification by traversing this hierarchy.</p> <p>Performance of the identification system varied depending on photo quality, number of species included for training, and desired taxonomic level for identification. High quality photos were scarce for some taxa and were under-represented in the training set, resulting in imbalanced network training. The image features used for training were sufficient to reliably identify photos to the correct genus but less so to the correct section and species.</p> <p>The outcomes of this project include a library of feature extraction algorithms called <em>ImgPheno</em>, a collection of scripts for neural network training called <em>NBClassify</em>, a library for evolutionary optimization of artificial neural network construction called <em>AI::FANN::Evolving</em> and a planned web application called <em>OrchID</em> for identification of user-submitted images. All project outcomes are open source and freely available.</p> <p><strong>About this release</strong></p> <p>This release corresponds belongs with our response to the reviewers of PLoS One. At this stage of the review cycle the manuscript is assessed as 'minor revision'. Consequently, we don't anticipate making more releases until publication.</p>
Artificial Intelligence Identifies Individuals with Prediabetes from Single-Lead Electrocardiograms
<h2>Contents</h2> <ul> <li><strong>codes.zip</strong> <ul> <li>For ECG feature extraction (this will need original ECG signal data) <ul> <li>ecg_feature_extraction.sh</li> <li>ecg_feature_extraction.py</li> <li>feature_extractor.py</li> </ul> </li> <li>For training with hyperparameter optimization <ul> <li>train.sh</li> <li>train.py</li> </ul> </li> <li>For prediction of prediabetes/diabetes from ECG feature <ul> <li>test.sh</li> <li>test.py</li> </ul> </li> </ul> </li> <li><strong>raw_ecg_data.zip</strong>: 16,766 ECG records used in our analyses. Each record is a 5,000 x 12 matrix in a CSV file. (In this dataset, value 1 represents 4.88 µV.)</li> <li><strong>external_ecg_data.zip</strong>: 2,456 ECG records used in our external validation. Each record is a 5,000 x 12 matrix in a CSV file. (In this dataset, value 1 represents 1 µV.)</li> <li><strong>participant_characteristics.csv</strong>: Health check records of 16,766 participants where the information below are stored. <ul> <li>participant_id: IDs for participant. Some IDs are duplicated because the dataset contains multiple records from some of the participants.</li> <li>ecg_id: IDs for ECG records, all of which are unique</li> <li>age: The age of each participant at the time of the health checkup</li> <li>male_sex: If the participant is male, "True" is recorded</li> <li>smoking: if the participant smokes, "True" is recorded</li> <li>drinking: 1 for "rarely", 2 for "occasionally" and 3 for "regularly" is recorded according to the frequency of drinking</li> <li>height: participant's height in centimeters (cm)</li> <li>weight: participant's body weight in kilograms (kg)</li> <li>BMI: body mass index, calculated using the formula: weight (kg) / [height (m)]^2</li> <li>pulse_rate: pulse rate in pulse per minute (/min) </li> <li>sBP: systolic blood pressure in mmHg</li> <li>dBP: diastolic blood pressure in mmHg</li> <li>FPG: fasting plasma glucose levels measured in milligrams per deciliter (mg/dL)</li> <li>HbA1c: hemoglobin A1c levels in %</li> <li>dm_under_treatment: if the participant was undergoing treatment for known diabetes, "True" is recorded</li> <li>prediabetes_diabetes: classification label which is "True" if a participant meet either of the following criteria <ul> <li>FPG ≥ 110 mg/dL</li> <li>HbA1c ≥ 6.0%</li> <li>Undergoing treatment for diabetes</li> </ul> </li> <li>development_data: "True" in records used as development data in our study</li> </ul> </li> <li><strong>external_cohort_characteristics.csv</strong>: Health check records of 2,456 participants where the information below are stored. <ul> <li>ecg_id: IDs for ECG records, all of which are unique</li> <li>prediabetes_diabetes: classification label which is "True" if a participant meet either of the following criteria <ul> <li>FPG ≥ 110 mg/dL</li> <li>HbA1c ≥ 6.0%</li> <li>Undergoing treatment for diabetes</li> </ul> </li> <li>FPG: fasting plasma glucose levels measured in milligrams per deciliter (mg/dL)</li> <li>HbA1c: hemoglobin A1c levels in %</li> <li>dm_under_treatment: if the participant was undergoing treatment for known diabetes, "True" is recorded</li> </ul> </li> <li><strong>ecg_feature_data.zip</strong>: extracted ECG features (unprocessed), for 12-lead and 1-lead ECG <ul> <li>ecg_features_1-lead.csv [Single-lead (lead I) ECG]</li> <li>ecg_features_12-lead.csv [12-lead ECG]</li> <li>ecg_features_12-leads_external_cohort.csv [12-lead ECG of external cohort]</li> </ul> </li> <li><strong>feature_list.zip</strong>: List of ECG features used (to be used for ECG extraction for original data) <ul> <li>feature_list_269_12-lead.csv [269 features for 12-lead ECG analysis]</li> <li>feature_list_28_1-lead.csv   [28 features for single-lead (lead I) analysis]</li> </ul> </li> <li><strong>model_12-lead.zip, model_1-lead.zip</strong>: model trained with our 12-lead or single-lead (lead I) ECG data, and the classification thresholds, used for test<br> <ul> <li>model_fold_1.pkl - model_fold_10.pkl : model for each of 10-fold cross validation</li> <li>average_threshold.pkl : classification threshold, which is the average of 10-fold</li> </ul> </li> </ul> <p>Codes and data for demo are also available in (https://github.com/dkoga4116/diabetes_detector)</p>
Numerically predicted permeability of over 6500 artificially generated fibrous microstructures
<p>This data set was generated in the project "ML4ProcessSimulation - Machine Learning for Simulation Intelligence in Composite Process Design" (Leibniz Collaborative Excellence funding program: K377/2021), at Leibniz-Institut für Verbundwerkstoffe GmbH. The goals were to create a comprehensive data set for training different neural networks and to gain insight into the influence of fiber structure on permeability. The models represent the fiber structure within fiber bundles in fiber-reinforced plastic composites (FRPC). Over 6500 structure models were generated in the software GeoDict® [1] and the permeability tensor of these models was numerically calculated in the GeoDict® module FlowDict [2]. The zip files contain the structure file (<i>gdt</i>), the model generation result file (<i>FiberGeo_[...].gdr</i>) and the flow simulation result file (<i>LIRStokesResult_[...].gdr</i>). For each zip file is a JSON meta data file available and in addition the gdr files contain all input and output data of the model generation and the flow simulation. The file <i>Table_of_Parameter_studies_and_model_pictures.jpg</i> gives an overview of the parameter studies and exemplarily shows two models each.The data set is divided into three parameter studies: </p><ul><li>1_Parameter_study_round_fibers with round fibers by varying the fiber volume content (fvc), fiber diameter (fdia) and fiber orientation (fdir). For each modeling parameter, 5 - 100 models (random seed or RS) were generated, all differing due to the randomized fiber positioning during model generation.</li><li>2_Parameter_study_elliptical_fibers with elliptical fibers that was varied based on different aspect ratios (asp1, asp2, asp3). In addition, fdia and fvc were varied and 5 models (RS) were calculated. </li><li>3_Parameter_study_undulation with elliptical fibers, whose undulation was varied. In addition, fdia and fvc were varied and 12 models (RS) were calculated.</li></ul><p><i>[1] J. Hilden, S. Rief, and B. Planas, GeoDict 2023 User Guide. FiberGeo handbook. DE: Math2Market GmbH, 2023. Accessed: Oct. 26, 2023. [Online]. Available: https://doi.org/10.30423/userguide.geodict</i></p><p><i>[2] J. Hilden, S. Linden, and B. Planas, "GeoDict 2023 User Guide. FlowDict handbook." Math2Market GmbH, 2023. Accessed: Jul. 31, 2023. [Online]. Available: https://doi.org/10.30423/userguide.geodict</i></p>
Artificial Neural Networks-generated Dataset: pH, Total Alkalinity, and Hydrogen Ion Concentration in Ría de Vigo (NW Spain), 1995–2020
<p>This dataset comprises input data from INTECMAR and the predicted outcomes. The variables and their units are as follows:</p> <p>station: 'Station ID [1-6]'</p> <p>year: 'Year [1995-2020]'</p> <p>month: 'Month [1-12]'</p> <p>day: 'Day'</p> <p>latitude: 'Latitude (decimal degrees)'</p> <p>longitude: 'Longitude (decimal degrees)'</p> <p>depth: 'Depth (meters)'</p> <p>temperature: 'Temperature (degrees Celsius)'</p> <p>salinity: 'Salinity (psu)'</p> <p>phosphate: 'Phosphate (umol/kg)'</p> <p>nitrate: 'Nitrate (umol/kg)'</p> <p>silicate: 'Silicate (umol/kg)'</p> <p>cweek: 'Cosine week'</p> <p>sweek: 'Sine week'</p> <p>TA: 'Total Alkalinity predicted (umol/kg)'</p> <p>NTA: 'Normalized Total Alkalinity (umol/kg)'</p> <p>NAT_st: 'Normalized per station Total Alkalinity (umol/kg)'</p> <p>NTA_gl: 'Normalized globally Total Alkalinity (umol/kg)'</p> <p>pHTS_insitu: 'pH insitu (pH units)'</p> <p>HT: 'Hydrogen ion concentration predicted (nmol/kg)'</p> <p> </p> <p>The authors gratefully acknowledge the financial support by the Programa de axudas á etapa predoutoral da Xunta de Galicia (Axencia Galega de Innovación) (Grant nº IN606A-2022/025). F.F.P. and A.V. were supported by REDEIRA (TED2021-132188B-I00) project, funded by MCIN/AEI/10.13039/501100011033. The authors also express their gratitude to the Instituto Tecnolóxico para o Control do Medio Mariño de Galicia (INTECMAR), for the analyses and production of the database used to make predictions.</p>
Artificial Intelligence Enables Precision Diagnosis of Cervical Cytology Grades and Cervical Cancer
<p>This repository includes source data used to genrtate all tables and figures for published stduy "Artificial Intelligence Enables Precision Diagnosis of Cervical Cytology Grades and Cervical Cancer". Besides, a small set of digital images for different class of cervical smear samples are included.</p>
Artificial fingerprints engraved through block-copolymers as nanoscale physical unclonable functions for authentication and identification - Dataset
<p>This is the dataset of "Artificial fingerprints engraved through block-copolymers as nanoscale physical unclonable functions for authentication and identification" by Irdi Murataj, Chiara Magosso, Stefano Carignano, Matteo Fretto, Federico Ferrarese Lupi, and Gianluca Milano, Nature Communications (2024), DOI: 10.1038/s41467-024-54492-8</p> <p>Part of this was funded by the project MEMQuD, code 20FUN06. The project has received funding from the EMPIR program co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation program.</p> <p>Part of this work was supported by the European project OpMetBat, code 21GRD01. The project has received funding from the European Partnership on Metrology, cofinanced from the the European Union's Horizon Europe Research and Innovation Programme, and by Participating States.</p> <p>Part of this work was supported by the European Union - Next Generation EU under the National Recovery and Resilience Plan (NRRP), Mission 04 Component 2 Investment 3.1 | Project Code: IR0000027 - CUP: B33C22000710006 - iENTRANCE@ENL: Infrastructure for Energy TRAnsition aNd Circular Economy @EuroNanoLab.</p>
Data for publication 'Detection of Artificial Seed-like Objects from UAV Imagery'
<p>This resource contains the datasets supporting the model development as published in the article 'Detection of Artificial Seed-like Objects from UAV Imagery' (https://doi.org/10.3390/rs15061637).</p> <p>In the last two decades, unmanned aerial vehicle (UAV) technology has been widely utilized as an aerial survey method. Recently, a unique system of self-deployable and biodegradable microrobots akin to winged achene seeds was introduced to monitor environmental parameters in the air above the soil interface, which requires geo-localization. This research focuses on detecting these artificial seed-like objects from UAV RGB images in real-time scenarios, employing the object detection algorithm YOLO (You Only Look Once). Three environmental parameters, namely, daylight condition, background type, and flying altitude, were investigated to encompass varying data acquisition situations and their influence on detection accuracy. Artificial seeds were detected using four variants of the YOLO version 5 (YOLOv5) algorithm, which were compared in terms of accuracy and speed. The most accurate model variant was used in combination with slice-aided hyper inference (SAHI) on full resolution images to evaluate the model’s performance. It was found that the YOLOv5n variant had the highest accuracy and fastest inference speed. After model training, the best conditions for detecting artificial seed-like objects were found at a flight altitude of 4 m, on an overcast day, and against a concrete background, obtaining accuracies of 0.91, 0.90, and 0.99, respectively. YOLOv5n outperformed the other models by achieving a mAP0.5 score of 84.6% on the validation set and 83.2% on the test set. This study can be used as a baseline for detecting seed-like objects under the tested conditions in future studies.</p>
Dataset for: Pre-pandemic artificial MERS analog of polyfunctional SARS-CoV-2 S1/S2 furin cleavage site domain is unique among spike proteins of genus Betacoronavirus
<table> <tbody> <tr> <th> </th> <td> <div> <h3><strong>Data File Descriptions and Methods</strong></h3> <ol> <li><strong>Data file 1 [betacov_matching_IPR042578.fasta]</strong>: Representative set of 2,465 betacoronavirus S protein overlapping homologous superfamily sequences retrieved in fasta format on 4 December 2022 from the InterPro repository at https://www.ebi.ac.uk/interpro/entry/InterPro/IPR042578/.<br><br></li> <li><strong>Data File 2 [betacov_matching_IPR042578_motif.fasta]</strong>: With Data File 1 as input, extracted 98,122 furin cleavage site (FCS) output motifs of 20 amino acids length, including overlapping and redundant sequences, produced with the FindFur algorithm with preset parameters as described by (Gu, 2020). FindFur as used was deposited on 15 December 2020 at the GitHub software repository at https://github.com/chwisteeng/FindFur.<br><br></li> <li><strong>Data File 3 [table_s1s2_hits_betacov_polyf.pdf]</strong>: Compiled summary table of sequence hits (PDF) of spike S1/S2 domains across genus <em>Betacoronavirus. </em>The compiled table of hits removed from Data File 2 sequences corresponding to spike protein fragments (incomplete length spike proteins as deposited at GenBank) and duplicates (redundant parts identically overlapping within the 20 amino acids motif windows), and then selected one sequence representative for multiple but identical sequences.<em> </em>Collection dates and geographical locations were retrieved from the NCBI Genbank protein database at https://www.ncbi.nlm.nih.gov/protein/. For SARS-CoV-2 spike variants, these data were also cross-validated with the SARS-CoV-2 lineage mutation tracker (Gangavarapu, 2023) available at https://outbreak.info which was based on extensive sequencing data from the global GISAID initiative (https://gisaid.org/). Solid lines (-) depict pat7 NLS, asterisks (*) O-glycosites, and circumflex (^) symbols FCS.<br><br></li> <li> <p><strong>Data File 4 [table_s1s2_hits_betacov_polyf.xlsx]</strong>: Compiled summary table of sequence hits (MS Excel) of spike S1/S2 domains across genus <em>Betacoronavirus. </em>The compiled table of hits removed from Data File 2 sequences corresponding to spike protein fragments (incomplete length spike proteins as deposited at GenBank) and duplicates (redundant parts identically overlapping within the 20 amino acids motif windows), and then selected one sequence representative for multiple but identical sequences.<em> </em>Collection dates and geographical locations were retrieved from the NCBI Genbank protein database at https://www.ncbi.nlm.nih.gov/protein/. For SARS-CoV-2 spike variants, these data were also cross-validated with the SARS-CoV-2 lineage mutation tracker (Gangavarapu, 2023) available at https://outbreak.info which was based on extensive sequencing data from the global GISAID initiative (https://gisaid.org/). Solid lines (-) depict pat7 NLS, asterisks (*) O-glycosites, and circumflex (^) symbols FCS.<br><br></p> </li> <li> <p><strong>Data File 5 [betacov_s1s2_nls_pat7_furin_psort.txt]: </strong>Nuclear localization signal (NLS) detection output for 5 representative betacoronavirus spike sequence domains, including the positive hits for pat7 in SARS-CoV-2 and for MERS-MA30 CoV. NLS predictions used the PSORT algorithm available as a webservice at https://wolfpsort.hgc.jp/ which is based on the work of Nakai and Horton (Nakai and Horton, 1999). Numbering refers to Data File 3 and Data File 4.<br><br></p> </li> <li> <p><strong>Data File 6 [betacov_s1s2_oglyc_netogly.txt]: </strong>Detection output for 5 representative betacoronavirus spike sequence domains tested for Thr/Ser O-glycosite residue pairs with the standard prediction software NetOGlyc4.0 (Steentoft et al., 2013) as available at https://services.healthtech.dtu.dk/services/NetOGlyc-4.0/. Positive hits have scores above 0.5. Numbering refers to Data File 3 and Data File 4.<br><br></p> </li> <li> <p><strong>Data File 7 [betacov_s1s2_nls_pat7_furin_blastp.txt]</strong>: Comprehensive sequence database searches using were performed using the NCBI protein BLAST (blastp) algorithm with webservice available at https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE=Proteins. The following blastp search parameters and settings were used: Word size=2; Expect value=200000; Hitlist size=500; Gapcosts=9,1; Matrix=PAM30; Filter string=F; Genetic Code=1;Window Size=40; Threshold=11; Composition-based stats=0; Database Posted date=Jan 19, 2023 2:59 AM; Number of letters=17,117,563; Number of sequences=10,766; Entrez query: Includes: Betacoronavirus (taxid:694002); Excludes: SARS-CoV-2 (taxid:2697049). The six polyfunctional input query consensus motif sequences were TXXPR(K/H/R)XRSX and TXXPRX(K/H/R)RSX.</p> </li> </ol> <h3><strong>References</strong></h3> <p>Gu, C., 2020. FindFur: A Tool for Predicting Furin Cleavage Sites of Viral Envelope Substrates. Master’s Thesis, San Jose State University, CA, USA. doi: <a href="https://doi.org/10.31979/etd.4ahv-9jya">10.31979/etd.4ahv-9jya</a> </p> <p>Gangavarapu K, Latif AA, Mullen JL, Alkuzweny M, Hufbauer E, Tsueng G, Haag E, Zeller M, Aceves CM, Zaiets K, Cano M, Zhou X, Qian Z, Sattler R, Matteson NL, Levy JI, Lee RTC, Freitas L, Maurer-Stroh S; GISAID Core and Curation Team; Suchard MA, Wu C, Su AI, Andersen KG, Hughes LD. Outbreak.info genomic reports: scalable and dynamic surveillance of SARS-CoV-2 variants and mutations. Nat Methods. 2023. 20(4):512-522. doi: <a href="https://doi.org/10.1038/s41592-023-01769-3">10.1038/s41592-023-01769-3</a>.</p> <p>Nakai, K., Horton, P., 1999. PSORT: a program for detecting sorting signals in proteins and predicting their subcellular localization. Trends Biochem Sci 24, 34–36. doi: <a href="https://doi.org/10.1016/s0968-0004(98)01336-x">10.1016/s0968-0004(98)01336-x</a></p> <p>Steentoft, C., Vakhrushev, S.Y., Joshi, H.J., Kong, Y., Vester-Christensen, M.B., Schjoldager, K.T.-B.G., Lavrsen, K., Dabelsteen, S., Pedersen, N.B., Marcos-Silva, L., Gupta, R., Bennett, E.P., Mandel, U., Brunak, S., Wandall, H.H., Levery, S.B., Clausen, H., 2013. Precision mapping of the human O-GalNAc glycoproteome through SimpleCell technology. EMBO J 32, 1478–1488. doi: <a href="https://doi.org/10.1038/emboj.2013.79">10.1038/emboj.2013.79</a></p> </div> </td> </tr> </tbody> </table>
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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.