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3,690 results for “Computing”
Figure 3 from: Conte M, Flynn AJ, Barrison P, Boisvert P, Landis-Lewis Z, Friedman C (2023) Digital objects to make computable biomedical knowledge FAIR: an infrastructural approach to knowledge representation, dissemination and implementation. Research Ideas and Outcomes 9: e109307. https://doi.org/10.3897/rio.9.e109307
Figure 3 This figure illustrates the dual nature of Knowledge Objects: knowledge-as-resource and knowledge-as-service. A KO can be curated and maintained in a repository, pass metadata to a knowledge graph or deployed into applications. Different to other digital objects, the methods to deploy the KO to applications via custom or generic runtimes called by microservices are built into the KO.
Figure 2 from: Conte M, Flynn AJ, Barrison P, Boisvert P, Landis-Lewis Z, Friedman C (2023) Digital objects to make computable biomedical knowledge FAIR: an infrastructural approach to knowledge representation, dissemination and implementation. Research Ideas and Outcomes 9: e109307. https://doi.org/10.3897/rio.9.e109307
Figure 2 (L) Sample KO as viewed from the KGrid Library, from which the KO can be implemented in a hosted runtime environment or downloaded. (R) Sample output results from deploying the KO.
Fig. 1 in Computational insight into the chemical space of plant growth regulators
Fig. 1. Representative examples of phytohormones.
Fig. 5 in Computational insight into the chemical space of plant growth regulators
Fig. 5. The representative examples of the reference scaffolds.
Fig. 2 in Computational insight into the chemical space of plant growth regulators
Fig. 2. The mechanisms of action for herbicides (examples).
the computed composition of hetero-oligomeric ferritins
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DFT computational output files _ Single Point Calculation
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Computational Data for "A conserved peptide binding pocket in HyNaC/ASIC ion channels"
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Datasets for Computational Fluid Dynamics
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National-Level Webinar on BLOCKCHAIN held on July 17, 2020 organised by Computer Engineering Department during National-Level Webinar Series on EMERGING AREAS OF TECHNOLOGY
<p>National-Level Webinar on<br>BLOCKCHAIN<br>held on July 17, 2020<br>organised by<br>Computer Engineering Department<br>during National-Level Webinar Series on<br>EMERGING AREAS OF TECHNOLOGY</p>
A GUIDE TOWARDS FULL STACK DEVELOPMENT which was presented by Er.T.Nageswari, Technology Lead, Infosys Ltd, Trivandrum., organized by Department of Computer Applications in association with FX Alumni Association of Francis Xavier Engineering College, Tirunelveli, Tamil Nadu on 06-06-2020.
<p>A GUIDE TOWARDS FULL STACK DEVELOPMENT which was presented<br>by Er.T.Nageswari, Technology Lead, Infosys Ltd, Trivandrum., organized by Department<br>of Computer Applications in association with FX Alumni Association of Francis Xavier<br>Engineering College, Tirunelveli, Tamil Nadu on 06-06-2020.</p>
Scalable Non-Volatile Tuning of Photonic Computational Memories by Automated Silicon Ion Implantation
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Accelerating the Conjugate Gradient Method on Distributed-Memory Computers: Software and Experiment Data
<p>This Zenodo record consists of supplementary material for journal article 'Accelerating the Conjugate Gradient Method on Distributed-Memory Computers' by Kozický, Šimeček and Rúra. Archive 'software.zip' contains the source code of the software we used to perform the experiments in our article, archive 'data-experiments.zip' contains the raw data from our experiments and archive 'data-manuscript.zip' contains the result data presented in our article.</p>
(DATA) Integrated Computational Biophysics Approach for Drug Discovery against Nipah Virus
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Data for the publication "Ejection of marine microplastics by raindrops: a computational and experimental study"
<p>This archive contains all simulation data generated for the publication "Ejection of marine microplastics by raindrops: a computational and experimental study". The full FluidX3D simulation source code is available from the corresponding author upon reasonable request.</p>
A Code Token Type Taxonomy-enhanced dataset with pre-computed token types for Python150k
<p>Code Token Type Taxonomy (CT3) is a methodology for refined evaluation of ML-based code completion approaches.</p> <p>We published the CT3-enhanced dataset with pre-computed token types for each token in the <a href="https://www.sri.inf.ethz.ch/py150">Python150k dataset</a>.</p> <p>The dataset was obtained from an empirical study of the below paper:</p> <p>Kim Tuyen Le, Gabriel Rashidi, and Artur Andrzejak. A Methodology for Refined Evaluation of ML-based Code Completion Approaches. In <em>Special Issue on Programming Language Processing, Data Mining and Knowledge Discovery</em>.</p> <p>Please read the README.txt file for detailed information of structuring the enhanced dataset.</p>
The Number of Messages for Graph Computation
<p>The number of messages for graph computation</p>
Data from: Investigating human repeatability of a computer vision based task to identify meristems on a potato plant (Solanum tuberosum)
<p>Labelled training data in artificial intelligence (AI) is used to teach so-called 'supervised learning models'. However, such data may contain error or bias, which can impact model prediction accuracy. Thus, obtaining accurate training data is of high importance. In applications of AI, such as in classification and detection problems, raw training data is not always made available in published research. Likewise, the process of obtaining labelled data is not always documented well enough to enable reproducibility. This training data set captures a repeatability exercise in AI training data collection for a task that is difficult for humans to perform, delineating a bounding box in a two-dimensional image of a growing apical meristem in potato plants.</p>
An AAV Gene Therapy Computes Over Multiple Cellular Inputs to Enable Precise Targeting of Multifocal Hepatocellular Carcinoma in Mice
<p>Data underlying the figures in the publication “An AAV gene therapy computes over multiple cellular inputs to enable precise targeting of multifocal hepatocellular carcinoma in mice”, published in <em>Sci. Transl. Med.</em>, <strong>2021</strong>, 13, eabh4456.</p> <p><a href="https://doi.org/10.1126/scitranslmed.abh4456">DOI: 10.1126/scitranslmed.abh4456</a></p> <p>Table of contents:</p> <p><strong>1. Supplementary Material</strong>: File containing Materials and Methods, <em>Fig S1-S8</em>, <em>Table S1</em>, Legend for <em>Table S2</em>, Legends for data files <em>S1</em> and <em>S2</em> and References (63-65).</p> <p><strong>2. Dataset 1.xlsx</strong>: Raw data points used to create the main figures of the paper (<em>1C, 1D, 2B, 2C, 2E, 2G, 2H, 2I, 2K, 3A, 3B, 3C, 4B, 4C, 51, 6A, 7A, 7B, 8B, 8C </em>and<em> 8F</em>) arranged in worksheets panel by panel. The processing steps are described in the methods section of the manuscript and in the supplementary material.</p> <p><strong>3. Dataset 2.xlsx:</strong> Raw data points used to create the supplementary figures of the paper (<em>S1A, S1B, S1C, S3D, S3E, S4A, S4B, S5B, S8A, S8B </em>and <em>S8C</em>) arranged in worksheets panel by panel. The processing steps are described in the methods section of the manuscript and in the supplementary material.</p> <p><strong>4. Table S2</strong>: Experimental data for <em>Table S2.</em></p>
Data representing logical computations along four stages in the brain
<p>A key challenge in neuroimaging remains to understand where, when and now particularly how human brain networks compute over sensory inputs to achieve behavior. To study such dynamic algorithms from mass neural signals, we recorded the magnetoencephalographic (MEG) activity of participants who resolved the classic XOR, OR and AND functions as overt behavioral tasks (N = 10 participants/task, N-of-1 replications). Each function requires a different computation over the same inputs to produce the task- specific behavioral outputs. In each task, we found that source-localized MEG activity progresses through four computational stages identified within individual participants: (1) initial contra-lateral representation of each visual input in occipital cortex, (2) a joint linearly combined representation of both inputs in midline occipital cortex and right fusiform gyrus, followed by (3) nonlinear task-dependent input integration in temporal-parietal cortex and finally (4) behavioral response representation in post-central gyrus. We demonstrate the specific dynamics of each computation at the level of individual sources. The spatio-temporal patterns of the first two computations are similar across the three tasks; the last two computations are task specific. Our results therefore reveal where, when and how dynamic network algorithms perform different computations over the same inputs to produce different behaviors.</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.