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2,747 results for “artificial”
Data base of the complexity Indexes to compute MFA and MCI from the paper "Unleashing The Potential Of Artificial Reefs Design"
<p>This data base compiles the Complexity indexes used to compute the MFA and extract the MCI from the paper "Unleashing The Potential Of Artificial Reefs Design: A Purpose-Driven Evaluation Of Structural Complexity" (https://doi.org/10.32942/X2G300)</p>
Seedling recruitment in artificial gaps
We examined the role of both intermediate-sized gaps and a dense shrub layer on subsequent tree seedling recruitment in a southern Appalachian deciduous forest. We monitored the density of tree seedlings in twelve artificial gaps and adjacent canopy controls under two contrasting understory conditions. Half of the experimental gaps were dominated by the dense, shade-producing shrub, Rhododendron maximum, while the remaining gaps were relatively open. Canopy gaps 20 m in diameter were created by girdling canopy trees, leaving standing dead trees with minimal disturbance to the understory. Density of first-year and first-year seedlings was monitored in transects extending from adjacent undisturbed forest through the experimental gaps.
Manual soil moisture measurements from ten artificial forest gaps at the Coweeta Hydrologic Laboratory, North Carolina, 2000-2018
Ten artificial forest gaps were created in March 2002 at Coweeta, following two years of pretreatment data collection. Experimental gaps were created by pulling canopy trees with a winch until they were down. Trees, saplings, and seedlings were censused and tracked as part of a demography study. Soil moisture data was collected during the growing season as an explanatory variable for tree survivorship and mortality.
Què es la fotosíntesi artificial? - Entrevista amb l'Elisabet Romero i el Julio Lloret-Fillol
<p>Radio interview with Elisabet Romero and Julio Lloret-Fillol from ICIQ, consortium member.</p> <p>El 8 d'octubre de 2019, els Dr. Elisabet Romero i Julio Lloret-Fillol van ser entrevistats en el programa de La Xarxa "Cinq cèntims" per explicar en què consisteix el fenomen de la fotosíntesi artificial. Tots els dos són investigadors del Institut Català d'Investigació Química de Tarragona que forma part del projecte europeu SUNRISE. No et perdis la seva entrevista!</p>
¿Qué es la fotosíntesis artificial? - Entrevista con Víctor A. de la Peña O'Shea
<p>Radio interview in Spanish to the consortium member Víctor de la Peña O'Shea from IMDEA Energía.</p> <p>El 4 de julio de 2019, el Dr. Víctor A. de la Peña fue entrevistado en el programa de Radio Internacional "Quien exporta... importa" para explicar en qué consiste el fenómeno de la fotosíntesis artificial. Dr. de la Peña es un investigador del Instituto IMDEA Energía de Madrid que forma parte del proyecto europeo SUNRISE. ¡No te pierdas su entrevista! Para ver otros vídeos relacionados con el tema y la iniciativa SUNRISE. </p>
SUNRISE: haciendo realidad la fotosíntesis artificial
<p>Spanish version of the SUNRISE presentation video.</p> <p>Mejor que cualquier página de “quiénes somos”, este vídeo aporta una visión general de esta iniciativa de investigación internacional que trabaja con el fin de hacer de la fotosíntesis artificial una realidad para un futuro sostenible.</p>
SUNRISE - Making Artificial Photosynthesis a Reality
<p>Presentation video of the SUNRISE action.</p> <p>Better than any “who we are”- page, this video brings you the big picture of this international research initiative working to make artificial photosynthesis a reality for a sustainable future.</p>
Huub de Groot - Breaking down the Artificial Photosynthesis
<p>Who was the first person that came up with the idea of artificial photosynthesis? What impact could it have on our environment? Find out more about this innovative research field & don't miss our interview with Huub de Groot, professor at Leiden University and SUNRISE coordinator.</p>
Magnon Modes of Microstates and Microwave-Induced Avalanche in Kagome Artificial Spin Ice with Topological Defects
<p>The attached folder contains the dataset for the manuscript entitled "Magnon Modes of Microstates and Microwave-Induced Avalanche in Kagome Artificial Spin Ice with Topological Defects".</p>
Artificial viscosity model to mitigate numerical artefacts at fluid interfaces with surface tension (Supporting data)
<p>This data accompanies the paper "Artificial viscosity model to mitigate numerical artefacts at fluid interfaces with surface tension", published in Computers & Fluids.</p>
Generative artificial intelligence predicts human performance
<p>Research data for a study that used generative artificial intelligence (i.e., ChatGPT with the GPT-4 and the Google Gemini 2.0 Flash models) to predict human performance in a language-based memory task. In particular, we studied the effects of context on the relatedness and memorability of garden-path sentences. </p>
Data from: Artificial intelligence enabled multi-purpose smart detection in active-matrix digital microfluidics
<p>Active-matrix digital microfluidics (AM-DMF), integrated with hundreds of thousands of active electrodes, can simultaneously realize multiple on-chip bio-chemical reactions at the single-cell level. An intelligent detection system is critical for fully automating manipulations of thousands of digitalized bio-samples and programming the subsequent experiments in real time. In this work, we developed a series of deep learning algorithms based on an AM-DMF system for sample detections. We used the U-net model to quantitatively evaluate different splitting methods on sample droplet generation uniformity. The results revealed that droplets generated using the "one-to-two" strategy exhibits optimal uniformity. We used the YOLOv5 model to monitor the droplet splitting success rates over 18 different AM-DMF chips, and a 97.7% splitting success rate was observed. The results indicated that the model precision was 99.980% and the model recall was 99.976% through manual verification. In addition, we used an improved YOLOv8 model to detect single cells in nanoliter droplets effectively. In comparison with manual verification, the results showed that the model achieved a precision of 99.260% and a recall of 99.193%. By leveraging an artificial intelligence enabled smart detection system, AM-DMF has shown great potential as a ubiquitous platform for true lab-on-a-chip.</p>
Data from: Supervised classification of plant communities with artificial neural networks
<p>This dataset was used to test the performance of artificial neural networks for supervised classification of plant communities, published in:</p><p>Černá L. & Chytrý M. (2005) Supervised classification of plant communities with artificial neural networks. <i>Journal of Vegetation Science</i> 16, 407-414. https://doi.org/10.1111/j.1654-1103.2005.tb02380.x</p><p>The meaning of the individual columns (separated by semicolons) in the file is as follows (for details see the above-mentioned article):</p><ul><li>Plot no - unique number of the vegetation plot</li><li>Group expert - plot membership in classes 1-11 of the expert classification</li><li>Subset expert random B - assignment of the plot to the training, selection, test or ignored data subset, using the random selection of the training (and selection) subset, for the expert classification</li><li>Subset expert dg species B - assignment of the plot to the training, selection, test or ignored data subset, using the selection of the training (and selection) subset by diagnostic species, for the expert classification</li><li>Assignment expert random - a class assignment of the plot by the MLP classifier, when trained with the randomly selected training (and selection) subset, for expert classification</li><li>Assignment expert dg-sp - a class assignment of the plot by the MLP classifier, when trained with the plots rich in diagnostic species contained in the training (and selection) subset, for expert classification</li><li>Group cluster - plot membership in classes 1-11 of the numerical classification</li><li>Subset cluster random - assignment of the plot to the training, selection, test or ignored data subset, using the random selection of the training (and selection) subset, for numerical classification</li><li>Subset cluster dg species - assignment of the plot to the training, selection, test or ignored data subset, using the selection of the training (and selection) subset by diagnostic species, for expert classification, for numerical classification</li><li>Assignment cluster random - class assignment of the plot by the MLP classifier, when trained with the randomly selected training (and selection) subset, for expert classification, for numerical classification</li><li>Assignment cluster dg-sp - class assignment of the plot by the MLP classifier, when trained with the plots rich in diagnostic species contained in the training (and selection) subset, for expert classification, for numerical classification </li><li>598 species, with cover/abundance estimates on an ordinal scale of 1-9</li></ul>
Quantitative results of the analysis of relevant components of artificial bilayered substitutes developed by tissue engineering
<p>This dataset corresponds to the quantification results carried out for artificial bilayered substitutes developed by tissue engineering and control tissues analyzed in the manuscript entitled "<span>Spatiotemporal characterization of extracellular matrix maturation in human artificial stromal-epithelial tissue substitutes</span>". Tissue engineering techniques offer new strategies to understand complex processes in a controlled and reproducible system. In this study, we generated bilayered human tissue substitutes consisting of a cellular connective tissue with a suprajacent epithelium (full-thickness stromal-epithelial substitutes or SESS), and human tissue substitutes with an epithelial layer generated on top of an acellular biomaterial (epithelial substitutes or ESS). Both types of artificial tissues were studied at sequential time periods to analyze the maturation process of the extracellular matrix (ECM) using histochemical and immunohistochemical techniques. Results showed that both models were able to exhibit a partial development of the epithelial layer. ESS cells showed active proliferation, positive expression of KRT5 and low expression of differentiation markers, whereas SESS epithelium showed higher differentiation levels, with a progressive positive expression of KRT10 and claudin, although the differentiation levels of control native tissues were not reached. Despite the typical rete-ridges and papillae of native tissues were not found, stromal cells in SESS tended to accumulate and actively synthetize ECM components such as collagens and proteoglycans in the stromal area in direct contact with the epithelium (Z1 zone), whereas these components were very scarce in ESS. Regarding the basement membrane (BM), ESS showed a partially-differentiated structure containing fibronectin-1 (FN1) and perlecan (HSPG2), although the PAS staining signal was significantly lower than control native tissues. However, SESS showed higher BM differentiation, with positive expression of FN1, HSPG2, nidogen 1 (NID1), chondroitin-6-sulfate proteoglycans (CH6S), agrin (AGRN), and collagens types IV (COL-IV) and VII (COL-VII), although this structure was negative for lumican (LUM). These results confirm the relevance of epithelial-stromal interaction for ECM development and differentiation, especially regarding BM components, and suggest the usefulness of bilayered artificial tissue substitutes to reproduce ex vivo the ECM maturation and development process of human tissues. The original data obtained for the quantitative analyses of each component are shown in this dataset.</p> <p> </p>
Inteligencia artificial y enfermedades respiratorias
<p>Project "Artificial intelligence, smart sensing, and new physiological and environmental predictors for enhancing COPD management” (CICERONE)</p> <p>The dissemination activity is part of the project PID2021-126810OB-I00, funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way of making Europe”.</p> <p>Este video es parte del proyecto de I+D+i PID2021-126810OB-I00, financiado por MCIN/ AEI/10.13039/501100011033/ y por “FEDER Una manera de hacer Europa”.</p>
Data from: Artificial light at night and warming impact grazing rates and gonad index of the sea urchin Centrostephanus rodgersii
<p>Artificial light at night (ALAN) is a growing threat to coastal habitats, and is likely to exacerbate the impacts of other stressors. Kelp forests are dominant habitats on temperate reefs but are declining due to ocean warming and overgrazing. We tested the independent and interactive effects of ALAN (dark vs ALAN) and warming (ambient vs warm) on grazing rates and gonad index of the sea urchin <em>Centrostephanus rodgersii. </em>Within these treatments, urchins were fed either 'fresh' kelp or 'treated' kelp. Treated kelp (<em>Ecklonia radiata</em>) was exposed to the same light and temperature combinations as urchins. We assessed photosynthetic yield, carbon and nitrogen content and C:N ratio of treated kelp to help identify potential drivers behind any effects on urchins. Grazing increased with warming and ALAN for urchins fed fresh kelp, and increased with warming for urchins fed treated kelp. Gonad index was higher in ALAN/ambient and dark/warm treatments compared to dark/ambient treatments for urchins fed fresh kelp. Kelp carbon content was higher in ALAN/ambient treatments than ALAN/warm treatments at one time point. This indicates ocean warming and ALAN may increase urchin grazing pressure on rocky reefs, an important finding for management strategies.</p>
Canada's national artificial intelligence governance system: Dataset from interviews with 20 government leaders & subject matter experts
<p><strong>Summary</strong></p> <p>Anonymized aggregate data from interviews with 20 government leaders and subject matter experts. The data was collected as part of a study of Canada's national system of artificial intelligence governance. The data was collected from February 2023 to July 2023. The dataset contains 610 topics that emerged from thematic analysis of interview transcripts from July 2023 to October 2023. The contexts, actors, resources, networks, evaluations, logics, functional bounds, rules, ecosystem-level dynamics, opportunities for improvement, and other topics contained in the dataset collectively represent the most significant components of Canada's national AI governance system that emerged over the course of the interviews with the 20 participants.</p> <p> </p> <p><strong>Notes for interpreting this dataset</strong></p> <p>Topics in analytical dimensions 1, 3, and 6-11 contain counts of the frequency with which aggregate topics emerged across each of the interviews with the 20 participants. Topics in analytical dimensions 2, 4, and 5 contain categories instead of frequency counts: the topics in these dimensions represent every unique actor, resource, and network that emerged over the course of the interviews instead of aggregate topics. </p> <p>Column titles contain the following abbreviations:<br>LEAD: Interviews with leaders of public sector AI governance initiatives.<br>SME-PS: Interviews with subject matter experts employed in the private sector.<br>SME-CS: Interviews with subject matter experts employed in the academic or civil sectors.</p> <p> </p> <p><strong>Full report</strong></p> <p>A report containing more information about this dataset and about the findings of our study can be found on SSRN: <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4783525">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4783525</a></p> <p> </p>
Archival Datasets for SuperNova Artificial Inference by Lstm neural networks (SNAIL)
<p>The spectral-observation dataset (enclosed in the file archival_spec_observations.tar.gz) is comprised of 3091 observed spectra from 361 SNe Ia, largely contributed from CfA (Blondin et al. 2012), BSNIP (Silverman et al. 2012), CSP (Folatelli et al. 2013) and Supernova Polarimetry Program (Wang & Wheeler 2008; Cikota et al. 2019a; Yang et al. 2020).</p> <p>The spectral-template dataset (enclosed in the file archival_spec_templates.tar.gz) includes 361 spectral templates, each of them (covering -15 to +33d with wavelength from 3800 to 7200 A) was generated from the available spectroscopic observations of an individual SN via a LSTM neural network model.</p> <p>The auxiliary photometry dataset (enclosed in the file archival_phot_observations.tar.gz) provides the B & V light curves of these SNe (in total, 196 available SNe Ia), that were used to calibrate the synthetic B-V color of the observed spectra.</p> <p>In additional, the two master catalogs give the detailed information about the 361 SNe and their spectroscopic observations, respectively. </p> <p>These datasets are associated to the paper "Spectroscopic Studies of Type Ia Supernovae Using LSTM Neural Networks" (Hu et al. 2022, ApJ, accepted).</p>
DeepBacs – Artificial labeling of E. coli membranes dataset and fnet/CARE models
<p>Training and test images of <em>E. coli </em>cells for artificial labeling of membranes in brightfield images using fnet or CARE, as well as trained models for prediction of super-resolution membranes.</p> <p>Additional information can be found on this <a href="https://github.com/HenriquesLab/DeepBacs/wiki">github wiki</a>.</p> <p>Example image shows an <em>E. coli</em> bright field image and PAINT membrane image predicted by the neural network (scale bar is 1 µm).</p> <p> </p> <p><strong>Training and testing dataset</strong></p> <p><strong>Data type</strong>: Paired bright field and super-resolution images</p> <p><strong>Microscopy data type</strong>: Bright field and fluorescence microscopy (widefield and point accumulation for imaging in nanoscale topography (PAINT) images)</p> <p><strong>Microscope</strong>: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective</p> <p><strong>Cell type</strong>: <em>E. coli </em>K12 strain derivatives</p> <p><strong>File format</strong>: .tif (8-bit)</p> <p><strong>Image size</strong>: 512x512 px<sup>2</sup> with different pixel sizes:</p> <p>1x tube lens: 158 nm (raw) and 19.75 nm (8x upscaled for PAINT images)</p> <p>1.5x tube lens: 106 nm (raw) (widefield fluorescence only)</p> <p> </p> <p><strong>fnet model (PAINT membrane images)</strong></p> <p>The fnet 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained for 200,000 steps on 33 paired images (image dimensions: (512 x 512 px²), patch size: (128 x 128 px²)) with a batch size of 4, a learning rate of 0.0004, 10% validation split and 4x data augmentation (flipping and rotation).</p> <p>Model weights can be used with the ZeroCostDL4Mic fnet 2D notebook.</p> <p> </p> <p><strong>CARE model (PAINT membrane images):</strong></p> <p>The CARE 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained for 300 epochs (100 steps/epoch) on 33 paired images (image dimensions: 512 x 512 px², patch size: 256 x 256 px²) with a batch size of 4, a learning rate of 0.0004, 90/10% train/validation split and 4x data augmentation (flipping and rotation).</p> <p>Model weights can be used with the ZeroCostDL4Mic CARE 2D notebook or the CSBDeep Fiji plugin.</p> <p><br> <strong>Author(s)</strong>: Christoph Spahn<sup>1,2</sup>, Mike Heilemann<sup>1,3</sup></p> <p><strong>Contact email</strong>: christoph.spahn@mpi-marburg.mpg.de</p> <p> </p> <p><strong>Affiliation(s)</strong>: </p> <p>1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany</p> <p>2) ORCID: 0000-0001-9886-2263 </p> <p>3) ORCID: 0000-0002-9821-3578</p>
Fig. 4 in Microcrustacean (Cladocera, Copepoda) Communities In Artificial Lakes In The Region Of The North Hungarian Mountains, With Special Reference To The Adventive Species
Fig. 4. Occurrences of the non-indigenous microcrustacean species in the investigated artificial lakes of north-eastern Hungary. Abbrev.: Eurytemora velox: +, Daphnia ambigua: ▲, Pleuroxus denticu-
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.