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3,688 results for “Computer”
Supporting Information for "Where does the energy go during the interstellar NH3 formation on water ice? A computational study"
<p>Supplementary Material consisting of:</p> <ol> <li> The energetics of the gas-phase reactions for both the H-additions and H-abstractions,</li> <li> The evolution with time of the total, potential and kinetic energies of the studied processes</li> <li> Results of the NVE AIMD simulations for the NH<sub>3</sub> formation from Pos2</li> </ol>
The supplementary (hourly output data & computational performance log) for OMARE of the Ocean Dynamics manuscript
<p>The supplementary (hourly output data & computational performance log) for OMARE of the Ocean Dynamics manuscript with the title: Computational performance and scalability Analysis for the flexible ocean Modeling framework of OMARE (Ocean Modeling with Adaptive REsolution).</p> <p>amr_wbc.tar.gz: The hourly output data of core prognostics (u, v, ssh, ts) of experiments X-AMR, X, S for OMARE in TianHe platform, including the picture results (png format).</p> <p>Computational_Performance.zip: The computational performance logs of the M, S, X, L-M-50, M-S-30, M-S-100, S-X-10, for OMARE in ThianHe and IntelCluster platform. The computational performance logs of X-AMR and I/O in TianHe platform.</p>
Mental comparison of 3D objects is based on 2D optical flow computations: human data
<p>Human behavioural data for manuscript:</p> <p><strong>Mental comparison of 3D objects is based on 2D optical flow computations.</strong></p>
Experimental and Computational data related to research on ``Investigating the Electronic Structure of Prospective Water-splitting Oxide BaCe0.25Mn0.75O3−δ Before and After Thermal Reduction''
<p>Data files and gnuplot scripts for the figures included in the submission titled ``<strong>Investigating the Electronic Structure of Prospective Water-splitting Oxide BaCe<sub>0.25</sub>Mn<sub>0.75</sub>O<sub>3−δ </sub>Before and After Thermal Reduction''</strong></p>
Data Table - Digital Scholarship, PhD project "Bridging Data Science and Intellectual History: Computing the Nodes and Edges in the Old University of Louvain (1425-1797)"
<p>How were academic networks configured in the premodern world, and how did they change? This doctoral project seeks to offer a data-driven answer by computing networks at and around the Old University of Louvain (1425-1797), a crucial hub for the transfer of knowledge in late medieval and early modern Europe. Drawing upon datasets under construction from the teams of the PIs at KU Leuven and UCLouvain, this project sets out to plot and visualize networks of students, scholars and their ‘books’ over almost four centuries, thus integrating data from demographic, prosopographical and book historical datasets. This will lead to a better understanding of how academic communities evolved in the past, and it will help to assess how their organization and structure promoted or hindered the creation and transfer of knowledge in premodern Europe. Hence, the doctoral research project offers an innovative test case to develop novel understandings of networks (e.g.. new tested ways to define nodes and edges) in datasets on scholars and ‘literati’, and it will be able to compare these new results to interpretations of human capital indices in the past. As such, the project creates a pioneering pilot for integrating data science into the field of intellectual and early modern history. This enhanced collaboration between KU Leuven and UCLouvain on a theme related to their common past is timely in view of 600 years Leuven/Louvain in 2025.</p>
Intermediary and supplemental data for publication "Heterogenous circulating miRNA changes in ME/CFS converge on a unified cluster of target genes and may be a result of modulation by latent herpesviruses: A computational analysis"
<p>Intermediary and supplemental data for publication "Heterogenous circulating miRNA changes in ME/CFS converge on a unified cluster of target genes and may be a result of modulation by latent herpesviruses: A computational analysis"</p>
Data for "Cubic or not Cubic? Combined Experimental and Computational Investigation of the Short-Range Order of Tin Halide Perovskites"
<p>The dataset contains various structural models of tin halide perovskites coming from fitting PDF measurements and from molecular dynamics.</p>
Microfarms and computational agriculture: a future of farming?
<p><strong>The following video introduces the Robots for Microfarms (ROMI) project and describes the concept of Micro Farms and Computational Agriculture. Funded by EU Grant 773875.</strong></p> <p><em>Videos are available in:</em></p> <ul> <li>Hi-res (1080p Apple ProRes)</li> <li>Mid-res (1080p H265)</li> </ul> <p><strong>Video script:</strong></p> <p>(MINCHIN) Micro-Farms are farms which are smaller than five hectares and that is a European commission designation of that type of farm which are very common and proliferate all across Europe and are growing in number.<br> <br> (HANAPPE) Micro-farms the way I see it now would be a small farm 1000 square meters maybe a bit more using poly-cropping to really optimise the space and situated next to the city, with the direct link to the city.<br> <br> (MINCHIN) But the Farms themselves are very interesting because being a small scale they cannot compete with a conventional agriculture and for that reason often turn to more diverse crops, polyculture systems and diversity as a solution for their markets. difference information<br> <br> (SERRA CANTí) An organic farm is a space where the coal is to cultivate the entire ecosystem, and to do so we focus on different crops that help and complement between them, and are not monocultures. Rotating crop beds, adding organic matter, structuring the soil and taking care of the life in the soil such as microorganisms, bacteria, fungi. All of this ultimately ensure that the plant is healthy and does not have so many pests and diseases. A conventional field can have many hectares with the same crop, they are monocultures. That means that if one disease comes, or one insect it can reproduce exponentially. But instead if you have different crops, this stops because the food that it has is limited.<br> <br> (MINCHIN) When we work with Organic agriculture we're really looking to a tradition of companion planting and polyculture which is much more complex than the conventional agriculture of today. So what we're seeing here is planting of tomatoes together with lettuce and together with a basil and the Basils give a natural defence against predators for the tomatoes but we can also use an intercrop lettuces in the gaps. But dealing with that polyculture becomes rather complex.<br> <br> (HANAPPE) And managing its complexity is difficult of course that's one of the reasons why we went to monoculture in the first place. But now with the digital tools that we have we can reintroduce some of this complexity and handle AI and Robotics to assist us in managing this complexity.<br> <br> (MINCHIN) Farmers working on a small scale with a large diversity of crop finds that the work is often very very labor intensive.<br> <br> (HANAPPE) For two months I've experienced myself at the Chatelain farm and in it it's quite taxing on the knees and the back. Ninety Five percent of the disorders in agriculture are muscular skeletal problems.<br> <br> (MINCHIN) The statistics show that after four years of starting a new Farm, many of these young Farmers actually end up with back problems and really struggle to manage the intensity of that biodiversity, of that diversity of crop. So for ROMI our challenge really is to try and support those young Farmers to deal with the complexity and to manage some of the menial tasks that they face day to day.<br> <br> (BAORI) If we can help a small organic micro-farm stay in business, because we're helping to replace a little bit of the manual labor and the the kind of hardship of that keep their costs down it means that we're encouraging smaller Farms to not use chemicals they can use a Rover for weeding, and it's better for for the land it's better for the farmers it's better for us all really in the end so it's a very worthwhile project.<br> <br> (CAMPRODON) Agricultural itself is technology right it's not lets say we don't wake up one day and suddenly the world looked like it is, no, we built it for thousands of years. And so for me I don't know if there's such a big disconnection or we should think as a big disconnection between traditional farming and this new farming. Things change but I like to see it more as a natural evolution of the way that humans use technology. Basically they develop this cultural practice, that helps them to modify their environment and they apply that to farming.<br> <br> (HANAPPE) What can we do with new technology to help these farms? And then it was at some point you know just sort of well, let's do something very provocative, let's do robotics for these farms. Which was a bit of a clash because many of these farmers actually started farming because they sort of wanted to go back to to Nature. And then saying to them look well we can introduce robotics for you, is a bit of a sort of edgy topic.<br> <br> (MINCHIN) And that really means that we might be able to take the best of the past and combine it with the tools of the future to forge a ‘computational agriculture’ which feeds the land, feeds the ecology and also feeds ourselves. The ROMI tools are really set up to be able to help farmers with the support of biologists and with the support of computation, but it's not there yet. That's why we have open sourced these tools to allow a future development towards those objectives.</p>
Botnet Data - Computer Networks Lab 2022/2
<p>Dataset created for the Computer Network Laboratory class at UFES.</p> <p>Reproduction in a different experimental setup of the botnet attack presented in:</p> <p>"An empirical comparison of botnet detection methods" Sebastian Garcia, Martin Grill, Jan Stiborek and Alejandro Zunino. Computers and Security Journal, Elsevier. 2014. Vol 45, pp 100-123. http://dx.doi.org/10.1016/j.cose.2014.05.011</p> <p>More specifically, the scenario https://mcfp.felk.cvut.cz/publicDatasets/CTU-Malware-Capture-Botnet-48/</p> <p>For more information, https://github.com/VitorSpa/LabRedes-2023</p> <p> </p>
Input data for "Characterisation of the mechanism of Bile Salt Hydrolase substrate specificity by experimental and computational analyses"
<p>Topologies, coordinates, input and analysis scripts for Amber20 simulations performed in "Characterisation of the mechanism of Bile Salt Hydrolase substrate specificity by experimental and computational analyses", Structure, 2023</p>
Computer vision: algorithms to make sense of the world
<p><strong>The following video describes how computer vision is used by the ROMI platform for object and species detection in both 2D and 3D, and how it is integral to the weeding tool. Funded by EU Grant 773875.</strong></p> <p><em>Videos are available in:</em></p> <ul> <li>Hi-res (1080p Apple ProRes)</li> <li>Mid-res (1080p H265)</li> </ul> <p><strong>Video script:</strong></p> <p>(CAMPRODON) What's computer vision? Mmm … computer vision for me is making sense of pixels. I think computer vision has a profound effect in the way that we understand the world because as humans vision is so centric right. If dogs would be making computers, maybe they wouldn't talk that much about vision. But for us it's so centric in the way that we perceive the world and the way we learn about the world, that actually I think it's easier for that of course to program and think useful ways machines could get information you know through vision. And also especially because vision is one of the much more complex senses that we have.<br> <br> (SOLLAZZO) Image and videos that represent nowadays the 80 percent of the data that we produce and the introduction of computer vision and machine learning becomes necessary to start extrapolating information out of this new source of data.<br> <br> (COLLIAUX) So just a point of clarification because we often talk about AI and so just to be a bit more precise about what we do in ROMI. Because AI is quite a vague term, and so what we do mainly is robotics and computer vision.<br> <br> (SOLLAZZO) Computer vision is at the end a limited set of tools and systems that are basically based on mathematical representation and description of the pixel that represent the image, they are part of the image, and machine learning is based on a different approach of interpretation of those pixels.<br> <br> (COLLIAUX) So the rover is for weeding and to remove the weeds you need to detect the weeds first and so we use a computer vision algorithm to detect where are the weeds where are the salads.<br> <br> (SOLLAZZO) So let's see one by one which are the methods that we implemented, in our algorithm, in our system. So we start with the feature extraction in order to do that in fact we go one by one over the images and we understand which are the pixels in common between one and the other. From these method in fact it's possible to recreate an orthomosaic view, an orthomosaic image, but afterwards we need to align it to all the previous images that we've been creating in the previous analysis. So after the generation of the orthomosaic view, what we do is that we start to cut the main image into a portion into a series of smaller portions. This facilitates the execution of the machine learning algorithm and the possibility to recognise the presence or not, of lettuce in the scene. After the recognition has been performed we put together the images once again and we can reconstruct an orthomosaic view with a detected position of the different lettuce. This is necessary to understand not only the position but also the area of growth that these different lettuce are occupying over time. From the geolocation of every single plant we start to analyse the growing curve over time. This is possible thanks to the implementation of ‘Mask RCNN’. So thanks to the generation of all these different areas that during time, will tell us the growing pattern of every single lettuce, and this will be extremely useful to understand when is the moment to harvest the plant when the plant is in fact bolting, more or less this is ok.<br> <br> (COLLIAUX) So we do what I showed was about 2d computer vision, but we do a lot of 3d computer vision also in the project and so let me show you a bit what we do with a plant scanner. So it is uh used by biologists to study the geometry of the plants so they want to reconstruct the pre-architecture of a plant and study that architecture. So for this we take many images of a plant by turning a camera in a circle around the plant, we generate a mask but again a segmentation algorithm to detect where where the plant is and where the background is, and then we can generate a point cloud by an algorithm called ‘space carving’ or ‘shape from silhouette’ which based on the many silhouettes you collected it looks for it it carves the space for the shape which is the most compatible with all the projection of the shape.<br> <br> (CAMPRODON) So what we're doing in ROMI at the end, I would say in a way we hack existing technologies, we take advantage of the low cost cameras that exist in phones right we don't need to rely anymore in high-end industrial cameras, we take advantage of the low-cost computational power, computing cheaper than ever. So these images that we take we can process them with software in ways that was not possible before, we take advantage of software, of especially of open source software and free software and then we build the training models right, so this software is capable to detect on top of that images insights, to go from data to information.</p>
Experimental and Computational Dataset for ""Direct monitoring of the thermodynamics and kinetics of DNA and RNA dinucleotide hybridization onto gaps and overhangs"
<p>Binding of short nucleic acid segments (<4 nucleotides) to single-strand templates occurs as a critical intermediate in processes such as non-enzymatic nucleic acid replication and toehold-mediated strand displacement. The templates in these reactions often contain adjacent duplex segments that stabilize base pair formation to single-strand gaps or overhangs, but the thermodynamics and kinetics of hybridization in such contexts are poorly understood due to the<br> experimental challenges of probing weak binding and rapid structural dynamics. Here we develop an approach to directly measure the thermodynamics and kinetics of DNA and RNA dinucleotide<br> hybridization using steady-state and temperature-jump infrared spectroscopy. Thermodynamic results indicate that duplex segments adjacent to overhangs and gaps stabilize dinucleotide binding<br> through a combination of coaxial stacking interactions and additional factors that remain unclear. All-atom molecular dynamics simulations indicate potential structural differences between the<br> dinucleotide-gap complexes and canonical duplexes that may contribute to binding stability. We demonstrate time-resolved measurements of dinucleotide dehybridization and observe timescales ranging from 200 ns to 40 µs depending on the template and temperature. Together, our work provides an initial step for predicting the binding stability and kinetics of short DNA and RNA duplex segments onto various type of nucleic acid templates</p>
Data for Computer Vison final project
<ul> <li>dataset.zip contains images used for our colorization project. We collected those images by filtering the places dataset. Statistically, we obtain 36500 images for training, 18250 images for validation and 18250 images for testing. </li> <li>colorized_images.zip contains the test images inferred by the SOTA approaches we tried.</li> </ul>
Computational Philosophy: Reflections on the PolyGraphs Project
<p>Talk at the <a href="https://www.digital-philosophy.org/">Philosophy [in:of:for:and] Digital Knowledge Infrastructures</a> online workshop (08/09/2022).</p>
Canine mammary tumors histopathological image classification by computer-aided pathology_ supplementary files
<p>Supplementary files</p>
Figure 12. Maximum likelihood phylogram computed for 18S in Litthabitellidae: a new family of the Truncatelloidea (Mollusca: Caenogastropoda)
Figure 12. Maximum likelihood phylogram computed for 18S sequences; bootstrap support and Bayesian posterior probabilities are shown when bootstrap supports>65%.
Figure 13. Maximum likelihood phylogram computed for H3 in Litthabitellidae: a new family of the Truncatelloidea (Mollusca: Caenogastropoda)
Figure 13. Maximum likelihood phylogram computed for H3 sequences; bootstrap support and Bayesian posterior probabilities are shown when bootstrap supports>65%.
Dataset for computational study of the ALS mutation G335D modulating the dimerization of TDP-43 amyloidogenic core peptide
<p>This dataset consist the following contents:</p> <p>1. The data used to generate MD/RSET2 trajectories for WT and G335D systems, including initial structures, parameter files and topology files;</p> <p>2. The final structures and representative conformations of intermediates for WT and G335D systems in MD simulations;</p> <p>3. Representative conformations of the top eight clusters for WT and G335D systems in REST2 simulations.</p>
Multidimensional cerebellar computations for flexible kinematic control of movements
<p>Data and figures for the publication "Multidimensional cerebellar computations for flexible kinematic control of movements"</p>
Replication package for: Optimal Long-Term Health Insurance Contracts: Characterization, Computation, and Welfare Effects
<p>This is a replication package for: </p> <p>Ghili, S., Handel, B., Hendel, I. and Whinston, M. "Optimal Long-Term Health Insurance Contracts: Characterization, Computation, and Welfare Effects." </p> <p>The replication package .zip file contains a ReadMe, which provides instructions, and data matrices and code for the paper. </p>
ScienceDex guides
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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.