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2,373 results for “Platform”
PHARAOH: A Collaborative Crowdsourcing Platform for PHenotyping And Regional Analysis Of Histology: KIRK Validation Cohort Grades 3 and 4
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Green AI in the Cloud: Energy-Efficient Architectural Tactics for ML-Enabled Systems on Public Platforms
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PHARAOH: A Collaborative Crowdsourcing Platform for PHenotyping And Regional Analysis Of Histology: KIRK Validation Cohort Grades 1 and 2
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PHARAOH: A Collaborative Crowdsourcing Platform for PHenotyping And Regional Analysis Of Histology: CCRCC Validation Cohort Grades 3 and 4
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PHARAOH: A Collaborative Crowdsourcing Platform for PHenotyping And Regional Analysis Of Histology: CCRCC Validation Cohort Grades 1 and 2
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Seq2scFv: a toolkit for the comprehensive analysis of display libraries from long-read sequencing platforms
<p>Annotation of full-length scFvs sequenced with PacBio using Seq2scFv (https://github.com/ngs-ai-org/seq2scfv). </p> <p>Raw files were obtained from publicly available dataset generated by Nannini and colleagues in "Combining phage display with SMRTbell next-generation sequencing for the rapid discovery of functional scFv fragments." <em>MAbs</em>. Vol. 13. No. 1. Taylor & Francis, 2021.</p> <p>The results of the annotation are organized in directories. These correspond to the different steps of the tutorial presented in the Seq2scFv repository. </p> <p>├── 1.Preprocessed<br>├── 2.Catalogued<br>├── 3.vscan<br>├── 4.scFv_split<br>├── 5.Linkers<br>├── 6.Flags<br>├── 7.Counts<br>└── focus_libs.txt</p> <p>This entry also contains the code provided in the GitHub repository.</p>
The Effect of Social Commerce on Purchasing Decisions on E-Commerce Platforms
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Analysis and Optimizing Solar Panels for Offshore Remote Wellhead Platforms as a Sustainable and Renewable Energy Source
<p>Data set</p>
Instrumental Platform at CEA/DRF/IRAMIS/NIMBE/LIONS
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Research data - design patterns for platform revenue models
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Data from: Incubator-independent cell-culture perfusion platform for continuous long-term microelectrode array electrophysiology and time-lapse imaging
Most in vitro electrophysiology studies extract information and draw conclusions from representative, temporally limited snapshot experiments. This approach bears the risk of missing decisive moments that may make a difference in our understanding of physiological events. This feasibility study presents a simple benchtop cell-culture perfusion system adapted to commercial microelectrode arrays (MEAs), multichannel electrophysiology equipment and common inverted microscopy stages for simultaneous and uninterrupted extracellular electrophysiology and time-lapse imaging at ambient CO2 levels. The concept relies on a transparent, replica-casted polydimethylsiloxane perfusion cap, gravity- or syringe-pump-driven perfusion and preconditioning of pH-buffered serum-free cell-culture medium to ambient CO2 levels at physiological temperatures. The low-cost microfluidic in vitro enabling platform, which allows us to image cultures immediately after cell plating, is easy to reproduce and is adaptable to the geometries of different cell-culture containers. It permits the continuous and simultaneous multimodal long-term acquisition or manipulation of optical and electrophysiological parameter sets, thereby considerably widening the range of experimental possibilities. Two exemplary proof-of-concept long-term MEA studies on hippocampal networks illustrate system performance. Continuous extracellular recordings over a period of up to 70 days revealed details on both sudden and gradual neural activity changes in maturing cell ensembles with large intra-day fluctuations. Correlated time-lapse imaging unveiled rather static macroscopic network architectures with previously unreported local morphological oscillations on the timescale of minutes.
Data from: GLO-Roots: an imaging platform enabling multidimensional characterization of soil-grown roots systems
Root systems develop different root types that individually sense cues from their local environment and integrate this information with systemic signals. This complex multi-dimensional amalgam of inputs enables continuous adjustment of root growth rates, direction and metabolic activity that define a dynamic physical network. Current methods for analyzing root biology balance physiological relevance with imaging capability. To bridge this divide, we developed an integrated imaging system called Growth and Luminescence Observatory for Roots (GLO-Roots) that uses luminescence-based reporters to enable studies of root architecture and gene expression patterns in soil-grown, light-shielded roots. We have developed image analysis algorithms that allow the spatial integration of soil properties, gene expression and root system architecture traits. We propose GLO-Roots as a system that has great utility in presenting environmental stimuli to roots in ways that evoke natural adaptive responses and in providing tools for studying the multi-dimensional nature of such processes.
Polymer–Lipid Hybrid Membranes as a Model Platform to Drive Membrane–Cytochrome c Interaction and Peroxidase-like Activity
<p>Data underlying the figures in the publication “Polymer–Lipid Hybrid Membranes as a Model Platform to Drive Membrane–Cytochrome c Interaction and Peroxidase-like Activity”, published in <em>J. Phys. Chem. B.,</em> <strong>2020</strong>, 124, 22, 4454–4465. <a href="https://pubs.acs.org/doi/10.1021/acs.jpcb.0c02727">https://pubs.acs.org/doi/10.1021/acs.jpcb.0c02727</a></p> <p>Table of contents:</p> <p><strong>1. Figure 1</strong>; Zip file containing the BAM images (<em>Figure 1b)</em> and excel files with the numerical data for the Langmuir isotherm graphs (<em>Figure 1a).</em></p> <p><strong>2. Figure 2, 5</strong>: Zip file containing the AFM images for the characterization of the solid-supported bilayers (<em>Figure 2</em>) and of the different membranes after protein insertion (<em>Figure</em> <em>5).</em> </p> <p><strong>3. Figure 4</strong>: Zip file containing the numerical data for <em>Figure 4,</em> showing the QCM-D plots of protein combination with supported membranes.</p> <p><strong>4. Figure 6</strong>: Zip file containing the numerical data for <em>Figure 6,</em> showing the qualitative comparison of protein activity by fluorimetric studies.</p>
Segregated Nanocompartments Containing Therapeutic Enzymes and Imaging Compounds within DNA-Zipped Polymersome Clusters for Advanced Nanotheranostic Platform
<p>Data underlying the figures in the publication “Segregated Nanocompartments Containing Therapeutic Enzymes and Imaging Compounds within DNA-Zipped Polymersome Clusters for Advanced Nanotheranostic Platform”, published in <em>Small,</em> <strong>2020</strong>, 16, 1906492.</p> <p><a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/smll.201906492">https://onlinelibrary.wiley.com/doi/abs/10.1002/smll.201906492</a></p> <p>Table of contents:</p> <p><strong>1. Figure 2</strong>; Zip file containing the numerical data for the graphs of <em>Figure 2 (2c, 2e, 2g & 2i)</em>.</p> <p><strong>2. Figure 3</strong>; Zip file containing the numerical data for the graphs of <em>Figure 3 (3a-d)</em>.</p> <p><strong>3. Figure 4</strong>; Zip file containing the numerical data for the graphs of <em>Figure 4 (4a-d)</em>.</p> <p><strong>4. Figure 5</strong>; Zip file containing the CLSM microscopic images from four different locations of 488-633-Ncomp clusters attached to the surface (<em>Figure 5a</em>) and images of the merged DY-633 and Atto-488 channels of the four locations with colocalized regions appearing in white (<em>Figure 5b</em>).</p> <p>The dataset also includes the numerical data/images for the <em>Figures S2, S3, S5, S7, S9, S10, S11, S13, S14</em> and <em>S16-S22</em>.</p>
Data from: Use of a microfluidic platform to uncover basic features of energy and environmental stress responses in individual cells of Bacillus subtilis
Bacteria use a variety of stress-sensing systems to sense and respond to diverse stressors and to ensure their survival under adverse conditions. The gram-positive bacterium Bacillus subtilis responds to energy stress (ATP depletion) and to environmental stressors using two distinct stress-sensing pathways that converge on the alternative sigma factor σB to provoke a general stress response. Past efforts to study the σB stress response in bulk culture and on agarose pads were unable to visualize the responses of individual cells under tightly controlled conditions for extended periods of time. Here we use a microfluidics-based strategy to discern the basic features of σB activation in single cells in response to energy and environmental stress, both immediately upon stressor exposure and for tens of generations thereafter. Upon energy stress at various levels of stressor, cells exhibited fast, transient, and amplitude-modulated responses but not frequency modulation as previously reported. Upon environmental stress, which is mediated by the stressosome complex, wild-type cells primarily exhibited a transient and amplitude-modulated response. However, mutant cells producing only one of the four paralogous RsbR stressosome proteins showed striking and previously unseen differences. Whereas RsbRA-only cells mimicked the wild type, RsbRC-only cells displayed a slower but sustained overall response composed of repeated activation events in single cells.
HeteroFuzz: Fuzz Testing to Detect Platform Dependent Divergence for Heterogeneous Applications
<p>HeteroFuzz supplementary material.</p>
Dataset supporting publication: "Geofit Project Creating the Opportunity of Geographical – BIM (GEOBIM) Platform to Manage Geothermal Systems"
<p>Dataset supporting publication: “Geofit Project Creating the Opportunity of Geographical – BIM (GEOBIM) Platform to Manage Geothermal Systems” (publication available in <a href="https://zenodo.org/record/7274056#.Y2Jml3bMJPY">GEOFIT Zenodo</a>)</p> <p>Within the GEOFIT project (Smart Geothermal Systems1), BIM environment has been defined as GeoBIM platform. This term refers to those specific geothermal applications which are included in a tailormade BIM platform to manage the geothermal systems, building, site and assets information from models, sensors installed and simulations. In GEOFIT project, the demo-sites location is enriched up to the holistic view of the retrofitted buildings with all the geothermal facilities designed, simulated, installed, commissioned, and monitored, from inception onward, during the lifecycle of a facility and includes all stakeholders who need facility information – from the designers to the occupants with the building in operation. This holistic view includes the execution control and the permanent geographical reference because the simulation, monitoring and design processes happen in a specific geographical context. The definition and implementation of a GEOBIM platform is paramount for the project and it is one of the main outcomes of<br> GEOFIT project. While BIM implementation is ubiquitous in the architectural issues of the project, relying mostly on CAD designs, geographical information has a limited role particularly in construction projects, it is often restricted to some specific tasks or seen as a potential redundancy to BIM. Considering the geographical dependent tasks in GEOFIT, GIS can bring a valuable complementary contribution to the BIM process by providing spatial input and geospatial visualization, adding information on the retrofitting demo-site’s surrounding environment and underground thermal information that is essential for design decisions and the approval processes regarding building integrity and geothermal energy availability. In this paper, an interdisciplinary cooperation, data exchange, and data transfer occurs among the different professionals and disciplines involved for the successful retrofitting project planning and energy efficiency demonstration throughout the GEOBIM platform. This is implemented to assemble this set of powerful assessment, inspection and ground research, testing, and real time monitoring tools.</p>
RGB-LWIR Labeled Dataset for Ground-based platforms
<p>Most object detection models deployed on unmanned aerial systems (UAS) focus on identifying objects in the visible spectrum, also known as Red-Green-Blue (RGB) imagery, due to availability and cost. Fusing long wave infrared (LWIR) images with RGB imagery can increase the resiliency and overall performance of a machine learning (ML) object detection model in changing environments. Because LWIR based ML models are not commonly utilized or studied there exists a gap in the literature that discusses performance metrics between LWIR, RGB and LWIR-RGB fused ML object detection models from various ground and air collection platforms. Understanding multispectral ML object detection performance from UAS is highly meaningful because of the increasing convergence of ML and UAS technologies. Therefore, the need to establish baseline performance metrics for how certain ML models perform on various ground and air platforms is necessary for effective implementation and deployment of these two technologies. Using object detection results from both ground and air platforms, this research provided a baseline RGB ML model mean average precision (mAP) of 95.1%, a LWIR ML model mAP of 94.5%, and a blended RGB-LWIR ML model mAP of 92.6%. This research contributes to the literature a labelled training dataset of 12,600 images for ground-based and air-based RGB, LWIR, and RGB-LWIR fused imagery, to encourage further multispectral machine-driven object detection research from both air and ground platforms. </p>
RGB-LWIR Inference Videos from Air-Based and Ground-Based Platforms
<p>When watching the inference videos, the top three videos are models without IP, while the bottom three videos are models with IP. In the inference at elevation video, only IP models were used.</p> <p> </p> <p>Most object detection models deployed on unmanned aerial systems (UAS) focus on identifying objects in the visible spectrum, also known as Red-Green-Blue (RGB) imagery, due to availability and cost. Fusing long wave infrared (LWIR) images with RGB imagery can increase the resiliency and overall performance of a machine learning (ML) object detection model in changing environments. Because LWIR based ML models are not commonly utilized or studied there exists a gap in the literature that discusses performance metrics between LWIR, RGB and LWIR-RGB fused ML object detection models from various ground and air collection platforms. Understanding multispectral ML object detection performance from UAS is highly meaningful because of the increasing convergence of ML and UAS technologies. Therefore, the need to establish baseline performance metrics for how certain ML models perform on various ground and air platforms is necessary for effective implementation and deployment of these two technologies. Using object detection results from both ground and air platforms, this research provided a baseline RGB ML model mean average precision (mAP) of 95.1%, a LWIR ML model mAP of 94.5%, and a blended RGB-LWIR ML model mAP of 92.6%. This research contributes to the literature a labelled training dataset of 12,600 images for ground-based and air-based RGB, LWIR, and RGB-LWIR fused imagery, to encourage further multispectral machine-driven object detection research from both air and ground platforms. </p>
Offshore platforms as novel ecosystems: a case study from Australia's Northwest Shelf
<p>The decommissioning of offshore oil and gas platforms typically involves removing some or all of the associated infrastructure and the consequent destruction of the associated marine ecosystem that has developed over decades. There is increasing evidence of the important ecological role played by offshore platforms. Concepts such as novel ecosystems allow stakeholders to consider the ecological role played by each platform in the decommissioning process. This study focused on the Wandoo field in Northwest Australia as a case study for the application of the novel ecosystem concept to the decommissioning of offshore platforms. Stereo-baited remote underwater video systems were used to assess the habitat composition and fish communities at Wandoo, as well as two control sites: a sandy one that resembled the Wandoo site pre-installation, and one characterised by a natural reef as a control for natural hard substrate and vertical relief. We found denser macrobenthos habitat at the Wandoo site than at either of the control sites, which we attributed to the exclusion of seabed trawling around the Wandoo infrastructure. We also found that the demersal and pelagic taxonomic assemblages at Wandoo more closely resemble those at a natural reef than those which would likely have been present pre-installation, but these assemblages are still unique in a regional context. The demersal assemblage is characterised by reef-associated species with higher diversity than those at the sand control and natural reef control sites, with the pelagic community characterised by species associated with oil platforms in other regions. These findings suggest that a novel ecosystem has emerged in the Wandoo field. It is likely that many of the novel qualities of this ecosystem would be lost under decommissioning scenarios that involve partial or complete removal. This study provides an example for classifying offshore platforms as novel ecosystems.</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.