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1,271 results for “Data Flow”
Data and supplementary files for: Directing Min protein patterns with advective bulk flow
<p>Simulation files as well as experiment data and analysis for the paper "Directing Min protein patterns with advective bulk flow".</p>
Aurora A and cortical flows promote polarization and cytokinesis by inducing asymmetric ECT-2 accumulation - Data Archive
<p>This archive contains all the images and raw data generated for: Katrina M Longhini & Michael Glotzer, “Aurora A and cortical flows promote polarization and cytokinesis by inducing asymmetric ECT-2 accumulation”</p> <p>The data is organized by figure then experiment. Each embryo folder contains formatted embryos, which are rotated, cropped, and photobleach corrected. Each recording starts at time 0 on the respective graph.</p> <p>The “AccumulationDataNew” files contain all the data generated from the “3pixel_membranefinder_w_dir.ijm” and “membrane_ROI_TimeSeries_max_scaled_dirs.ijm” scripts organized in a Tidy format generated in R using the first portion of the script “Edited_Membrane_Accumulation_Line_Script.Rmd”. These Fiji and R Markdown scripts are located in the “Analysis Scripts“ folder.</p> <p>All data visualization scripts are included in the “Analysis Scripts” folder.</p> <p>Sequences of plasmids generated for this paper are located in the “Plasmid Sequences” folder.</p>
Data from laboratory granular-flow experiments with acoustic sensors
<p>Experimental data of dynamic pressures generated by dry granular flows moving down and impacting on a plate embedded in an inclined chute facility. The data consists of basal impact pressures measured with a pressure sensor for variable slope angle ranging from 30° to 38° with an initial mass of 100 kg.</p>
Data for: Amazonian birds in more dynamic habitats have less population genetic structure and higher gene flow
<p>Understanding the factors that govern variation in genetic structure across species is key to the study of speciation and population genetics. Genetic structure has been linked to several aspects of life history, such as foraging strategy, habitat association, migration distance, and dispersal ability, all of which might influence dispersal and gene flow. Comparative studies of population genetic data from species with differing life histories provide opportunities to tease apart the role of dispersal in shaping gene flow and population genetic structure. Here, we examine population genetic data from sets of bird species specialized on a series of Amazonian habitat types hypothesized to filter for species with dramatically different dispersal abilities: stable upland forest, dynamic floodplain forest, and highly dynamic riverine islands. Using genome-wide markers, we show that habitat type has a significant effect on population genetic structure, with species in upland forest, floodplain forest, and riverine islands exhibiting progressively lower levels of structure. Although morphological traits used as proxies for individual-level dispersal ability did not explain this pattern, population genetic measures of gene flow are elevated in species from more dynamic riverine habitats. Our results suggest that the habitat in which a species occurs drives the degree of population genetic structuring via its impact on long-term fluctuations in levels of gene flow, with species in highly dynamic habitats having particularly elevated gene flow. These differences in genetic variation across taxa specialized in distinct habitats may lead to disparate responses to environmental change or habitat-specific diversification dynamics over evolutionary time scales.</p>
Supplementary data for: "Towards automatic generation of control structures for Process Flow Diagrams (PFDs) with Artificial Intelligence"
<p>Uploaded on 20. February 2023</p> <p>This is the supplementary data for the publication</p> <p>"Towards automatic generation of control structures for Process Flow Diagrams (PFDs) with Artificial Intelligence" (2023) by Edwin Hirtreiter, Lukas Schulze Balhorn, Artur M. Schweidtmann</p> <p>Corresponding author: A. M. Schweidtmann, E-mail: a.schweidtmann@tudelft.nl<br> Delft University of Technology, Department of Chemical Engineering, Process Intelligence Group, Van der Maasweg 9, 2629 HZ Delft, The Netherlands</p> <p>The folder contains json files with the training (train), test (test), and augmented training (train_augm) data files. The json files contain syntetically generated SFILES. </p> <p>The pre-print of the manuscript is accessible at https://doi.org/10.48550/arXiv.2211.05583</p>
Data for: Herbivores disrupt the flow of food resources to termites in dryland ecosystems
<p><span>Irruption of herbivore populations due to the extirpation of predators has led to dramatic changes in ecosystem functioning worldwide. Herbivores compete with other species for their primary source of nutrition, plant biomass. Such competition is typically considered to occur between species in closely related clades and functional groups but could also occur with detritivores that consume senescent plant biomass. Here, we test predictions that herbivores' indirect impacts on dead vegetation increase with primary productivity and extend to termites which feed on senescent vegetation. We compared dead vegetation cover and termite activity in herbivore exclosures and associated grazed plots at 3 locations situated along a rainfall gradient in arid Australia where kangaroo populations have irrupted. Dead vegetation cover and termite activity increased with rainfall in ungrazed plots but showed a negligible response to rainfall in grazed plots. Our results suggest that grazing can disrupt the flow of energy to detritivores and decouple the relationship between termite activity and primary productivity. Such disruption could have far-reaching impacts on arid ecosystems because many organisms sit within "brown food webs" that are sustained by energy derived from decomposition of senescent plant-tissues. </span></p>
Data accompanying the paper "The electron flow in marine sediments experiencing microbial long-distance electron transport"
<p>Data accompanying the paper "The electron flow in marine sediments experiencing microbial long-distance electron transport"</p>
Merging and imputation of flow cytometry data: a critical assessment
<p>This dataset contains the data necessary to reproduce the analyses by Mocking <em>et al</em>.</p> <p>Scripts for reproducing our analyses are available at:</p> <p>https://github.com/AUMC-HEMA/imputation-manuscript</p>
Data for: "The effects of DEM resolution on the PyFLOWGO thermorheological lava flow model" paper
<p>PyFLOWGO results for a topographic sensitivity analysis. </p> <p>These are the 5m step size results presented in the publication, additional results upon request. </p>
Flow cytometry data for "Vitamin B12 conveys a protective advantage to phycosphere-associated bacteria at high temperatures"
<p>More details—including the analysis pipeline—are available in the GitHub repository: <a href="http://github.com/maggimars/bactB12">https://github.com/maggimars/bactB12</a>. </p> <p>Direct link to analysis pipeline interactive document: <a href="https://maggimars.github.io/bactB12/Flow_Cytometry_Analysis.html">https://maggimars.github.io/bactB12/Flow_Cytometry_Analysis.html</a></p> <p> </p> <p> </p>
Data for figures in "Next-generation ice nucleating particle sampling on aircraft: Characterization of the High-volume flow aERosol particle filter sAmpler (HERA)"
<p>Atmospheric ice nucleating particle (INP) concentration data from the free troposphere are sparse, but urgently needed to understand vertical transport processes of INPs and their influence on cloud formation and properties. Here, we introduce the new High-volume flow aERosol particle filter sAmpler (HERA) which was specially developed for installation on research aircraft and subsequent offline INP analysis. HERA is a modular system constisting of a sampling unit and a powerful pump unit and has several features which were integrated specifically for INP sampling. Firstly, the pump unit enables sampling at flow rates exceeding 100 L min<sup>−1</sup>, which is well above typical flow rates of aircraft INP sampling systems described in the literature (~10 L min<sup>−1</sup>). Consequently, required sampling times to capture rare, high-temperature INPs (≥-15 °C) are reduced in comparison to other systems and potential source regions of INPs can be confined more precisely. Secondly, the sampling unit is designed as a seven-way valve, enabling switching between six filter holders and a bypass with one filter being sampled at a time. In contrast to other aircraft INP sampling systems, the valve position is controlled remotely via software so that manual filter changes in-flight are eliminated and the potential for sample contamination is decreased. This design is compatible with a high degree of automation, i.e., triggering filter changes depending on parameters like flight altitude, geographical location, temperature, or time. In addition to the design and principle of operation of HERA, this paper presents laboratory characterization experiments with size-selected test substances, i.e., SNOMAX® and Arizona Test Dust. The particles were sampled on filters with HERA, varying either particle diameter (300 nm to 800 nm) or flow rate (10 L min<sup>−1</sup> to 100 L min<sup>−1</sup>) between experiments. The subsequent offline INP analysis showed good agreement with literature data and comparable sampling efficiencies for all investigated particle sizes and flow rates. Furthermore, the deposition efficiency of atmospheric INPs in HERA was compared to a straightforward filter sampler and good agreement was found. Finally, results from the first campaign of HERA on the High Altitude and LOng range research aircraft (HALO) demonstrate the functionality of the new system in the context of aircraft application.</p> <p>The given csv files contain the data for reproducing the figures in the publication. The data structure of the csv files is explained in the README file.</p>
FarmConners Wind Farm Flow Control Benchmark: Blind Test with CL-WINDCON Wind Tunnel Data
<p>This is the dataset used for running the fourth Blind Test of the FarmConners Wind Farm Flow Control Benchmark. The Blind Test was performed with an extensive dataset gathered while testing a cluster of three scaled wind turbines within a large boundary layer wind tunnel. The experimental dataset has been compared against the predictions provided by 5 different control-oriented wind farm flow models. The resulting comparison is described in the paper "FarmConners Wind Farm Flow Control Benchmark: Blind Test Results, Part 2", by Campagnolo et al, (2023). The dataset consists of:</p> <ol> <li>measurements of the flow within the wake shed by one or two machines, as well as measurements of the power, loads (on the rotating shaft and at tower base), and pitch/yaw/torque actuators states of the three scaled machines. The measurements have been performed under a wide range of inflow and machines operating conditions. The time series of the measured data are provided in the format of Matlab structures saved in .mat files.</li> <li>Predictions provided by the models used by the Blind Test participants</li> <li>Matlab scripts used for comparing the experimental dataset and the numerical predictions provided by the Blind Test participants</li> <li>Additional data provided to the Blind Test participants. This includes a FAST model of the scaled wind turbine and the mapping of the inflow of the empty wind tunnel. </li> </ol>
Simulated datasets for detector and particle flow reconstruction: CLIC detector, hit-based data, machine learning format
<p>Derived from https://zenodo.org/record/8260741, prepared in a machine-learning friendly TFDS format, ready to be used with https://zenodo.org/record/8397954.</p> <ul> <li>clic_edm_ttbar_hits_pf10k.tar: ee -> ttbar, center of mass energy at 380 GeV, 10k events</li> <li>clic_edm_qq_hits_pf10k.tar: ee -> Z* -> qqbar, center of mass energy at 380 GeV, 10k events</li> </ul> <p><strong>Contents</strong></p> <p>Each .tar file contains the dataset in the <a href="https://github.com/tensorflow/datasets">tensorflow-datasets</a> (minimum version v4.9.1), <a href="https://github.com/google/array_record">array_record</a> format.</p> <p><strong>Dataset semantics</strong></p> <p>Each dataset consists of events that can be iterated over using the tensorflow-datasets library in either tensorflow or pytorch. Each event has the following information available:</p> <ul> <li>X: the reconstruction input features, i.e. tracks and calorimeter hits</li> <li>ygen: the ground truth particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle</li> <li>ycand: the baseline Pandora PF particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle</li> </ul> <p>The full semantics, including the list of features for X, are available at https://github.com/jpata/particleflow/blob/v1.6/mlpf/heptfds/clic_pf_edm4hep_hits/utils_edm.py.</p>
Higher-order Mobility Flow Data
<p>This dataset is a collection of higher-order mobility datasets, primarily aimed at trajectory data mining applications. These datasets have been created using the Point2Hex tool, allowing us to transform traditional GPS-based geolocations and check-in data into sequences of higher-order geometric elements, particularly hexagons. This transformation has various advantages, including reduced sparsity, analysis at different levels of granularity, improved compatibility with common machine learning architectures, enhanced generalization and overfitting reduction, and efficient visualization.</p> <p>Seven popular mobility datasets, typically utilized in various trajectory-related tasks and technical problems, were subjected to this transformation process. These include applications like trajectory prediction, classification, clustering, imputation, and anomaly detection, among others.</p> <p>To foster the culture of reusability and reproducibility, we are providing not only the transformed higher-order mobility flow datasets but also the source code for the Point2Hex tool and comprehensive documentation. This offering aims to streamline the generation process, ensuring that users have clear guidance on how to reproduce curated or customized versions of these datasets. The material is stored in publicly accessible repositories, ensuring its widespread accessibility.</p>
Data and scripts for the colour analysis from: Gene flow throughout the evolutionary history of a colour polymorphic and generalist clownfish
<p>Even seemingly homogeneous on the surface, the oceans display high environmental heterogeneity across space and time. Indeed, different soft barriers structure the marine environment, which offers an appealing opportunity to study various evolutionary processes such as population differentiation and speciation. Here, we focus on <em>Amphiprion clarkii </em>(Actinopterygii; Perciformes), the most widespread of clownfishes that exhibits the highest colour polymorphism. Clownfishes can only disperse during a short pelagic larval phase before their sedentary adult lifestyle, which might limit connectivity among populations, thus facilitating speciation events. Consequently, the taxonomic status of <em>A. clarkii</em> has been under debate. We used whole-genome resequencing data of 67 <em>A. clarkii</em> specimens spread across the Indian and Pacific Oceans to characterise the species' population structure, demographic history, and colour polymorphism. We found that <em>A. clarkii</em> spread from the Indo-Pacific Ocean to the Pacific and Indian Oceans following a stepping-stone dispersal and that gene flow was pervasive throughout its demographic history. Interestingly, colour patterns differed noticeably among the Indonesian populations and the two populations at the extreme of the sampling distribution (i.e. Maldives and New Caledonia), which exhibited more comparable colour patterns despite their geographic and genetic distances. Our study emphasises how whole-genome studies can uncover the intricate evolutionary past of wide-ranging species with diverse phenotypes, shedding light on the complex nature of the species concept paradigm.</p>
Data for: Mimicking functional elements of the natural flow regime promotes native fish recovery in a regulated river
Open the record for dataset details and reuse information.
Supplementary data for: Comparison of optical flow derivation techniques for retrieving tropospheric winds from satellite image sequences
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Data from: Performance characteristics and bluff-body modeling of high-blockage cross-flow turbine arrays with varying rotor geometry
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Data from: Energy harvesting in a flow-induced vibrating flapper with biomimetic gaits
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Data from: Broad-scale meta-analysis of drivers mediating adverse impacts of flow regulation on riparian vegetation
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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.