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115 results for “Time integration”
CCE LTER process cruise, in the California Current region, event log records including date, time, position and activity for use in post-cruise data integration based on co-sampling indexes. From 2006 to 2019 CCE LTER used a locally developed event logging system. During P2107, CCE LTER started to utilize the R2R Event Logger on UNOL ships, 2006 - 2024 (ongoing).
The event logger program developed and maintained by the California Cooperative Oceanic Fisheries Investigations, SIO, program is used aboard CCE LTER process cruises to create indexes with temporal, spatial and activity information for post-cruise data integration. The event log is configured aboard the ship for the recording of sampling events by both ship crew personnel on the bridge, and research personnel in the lab. The event log is processed post-cruise to correct for various errors.
Dataset: "Probabilistic Framework for Integration of Mass Spectrum and Retention Time Information in Small Molecule Identification"
<p>The SQLite database contains the pre-computed tandem mass spectra (MS2) and retention order scores used for the experiments in the publication: "<a href="https://doi.org/10.1093/bioinformatics/btaa998">Probabilistic Framework for Integration of Mass Spectrum and Retention Time Information in Small Molecule Identification</a>" by Bach et al. (2020).</p> <p>A detailed description of the database structure is given in the 'README.md' and can also be found in the <a href="https://github.com/aalto-ics-kepaco/msms_rt_score_integration/tree/master/data">code-repository associated with the publication</a>. The database layout is illustrated in the 'db_layout.png' file.</p> <p>The SQLite file 'ms_and_rt_score_DB_bach_etal_2020.db.gz' is compressed using <a href="https://en.wikipedia.org/wiki/Gzip">gzip</a>.</p>
Real Time Integration Center of Mass (riCOM) Reconstruction for 4D-STEM
<p>The datasets are a part of the publication: <strong>Real Time Integration Center of Mass (riCOM) Reconstruction for 4D-STEM</strong></p>
FIGURE 18 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 18. Ancestral state reconstruction of prey capture strategies and prey type preference in stem and crown Mysticeti. Topology follows Gatesy et al. (2013) and Fordyce and Marx (2018). Details on the tree can be found in Appendix 1. All data matrices and complete trees with branch lengths can be found in the Supplementary Information (https://doi.org/10.6086/d14671).
FIGURE 3 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 3. Anatomical features associated with suction feeding in walrus (Odobenus rosmarus skull, from Jefferson et al., 2015) and North Sea beaked whale (Mesoplodon bidens skull, authors' work).
FIGURE 11 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 11. Sirenia stem and familial level diversity through time. "Protosirenidae / Prorastomidae" includes all taxa that are not included in the two extant groups. Dashed vertical lines: black, epoch boundaries; gray, age boundaries.
FIGURE 7 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 7. Pinnipedimorpha stem taxa and familial level diversity through time. "Stem-Pinnipedimorpha" includes all stem taxa that are not included in Desmatophocidae and the three extant groups. Dashed vertical lines: black, epoch boundaries; gray, age boundaries.
FIGURE 5 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 5. Anatomical features associated with grazing in African manatee (Trichechus senegalensis skull, from Werth, 2000), Desmostylia (Paleoparadoxia skull, public domain), and aquatic sloth (Thalassocnus sp. skull, modified from de Muizon et al., 2004).
FIGURE 8 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 8. Cetacean stem and Neoceti taxa diversity through time. "Archaeoceti" includes all stem taxa that are not included in the two extant groups. Dashed vertical lines: black, epoch boundaries; gray, age boundaries.
FIGURE 16 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 16. Ancestral state reconstruction of prey capture strategies and tooth pattern and cusp shape in Odobenidae. Topology follows Berta et al. (2018). Details on the tree can be found in Appendix 1. All data matrices and complete trees with branch lengths can be found in the Supplementary Information uploaded to Dryad repository (https:// doi.org/10.6086/d14671).
FIGURE 17 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 17. Ancestral state reconstruction of prey type preference in stem and crown Pinnipedimorpha. Topology follows Rybczynski et al. (2009), Dewaele et al. (2017), and Berta et al. (2018). Details on the tree can be found in Appendix 1. All data matrices and complete trees with branch can be found in the Supplementary Information (https:// doi.org/10.6086/d14671).
FIGURE 19 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 19. Ancestral state reconstruction of prey capture strategies and tooth pattern and cusp shape in stem Odontoceti. Topology follows Gatesy et al. (2013) and Boessenecker et al. (2017). Details on the tree can be found in Appendix 1. All data matrices and complete trees with branch lengths can be found in the Supplementary Information (https://doi.org/10.6086/d14671).
FIGURE 20 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 20. Ancestral state reconstruction of prey capture strategies and tooth pattern and cusp shape in crown Odontoceti. Topology follows McGowen et al. (2009) and Gatesy et al. (2013). Details on the tree can be found in Appendix 1. All data matrices and complete trees with branch lengths can be found in the Supplementary Information (https://doi.org/10.6086/d14671).
FIGURE 1 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 1. Feeding strategies of extant marine mammal predators (from Kienle et al., 2017) with the addition of grazing (sirenians, authors' work).
Vertical profiles and integrated time series of bird density and flight speed vector (19.09.2016-10.10.2016)
<p><strong>Description</strong></p> <p>This dataset contains the vertical profiles and integrated time series of bird density and flight speed (NS and EW) used in Nussbaumer (2019) [open access: <a href="https://www.mdpi.com/2072-4292/11/19/2233">https://www.mdpi.com/2072-4292/11/19/2233</a>]. Data are stored in a JavaScript Object Notation (JSON) file for each radar, with the following structure:</p> <pre><code>{ "name" : "bejab", //code name of the radar (http://eumetnet.eu/wp-content/themes/aeron-child/observations-programme/current-activities/opera/database/OPERA_Database/index.html) "lat" : 51.1917, //Latitude "lon" : 3.0642, //Longitude "height" : 50, //Height of the radar antenna [m] a.s.l. "maxrange" : 25, //Maximum range [km] used for profile "alt" : [100, 300,...], "time" : ["19-Sep-2016 00:00:00", "19-Sep-2016 00:05:00",...], "dens" : [[...],...], //Vertical profile of bird density [1/km3] "u" : [[...],...], //Vertical profile of bird flight speed in East(+)/West(-) [m/s] "v" : [[...],...], //Vertical profile of bird flight speed in North(+)/South(-) [m/s] "denss" : [...], //Integrated profile of bird density [1/km2] "us" : [...], //Integrated profile of bird flight speed in East(+)/West(-) [m/s] "vs" : [...], //Integrated profile of bird flight speed in North(+)/South(-) [m/s] }</code></pre> <p> </p> <p><strong>Procedure</strong></p> <p>The raw data are downloaded on the <a href="http://enram.github.io/data-repository/">ENRAM repository</a>,( see Dokter (2011) and (2019) for more details) and processed according to the procedure described below.</p> <ol> <li>Of the 84 radars contributing data during the study period, 11 radars are discarded because of their poor quality due to S-band radar type, poor processing or large gaps (temporal or altitude cut). The same radars were removed in Nilsson et al. (2019).In addition, the 4 radars from Bulgaria and Portugal were excluded because of their geographic isolation.</li> <li>The full vertical profile was discarded when rain was present at any altitude bin. A dedicated MATLAB GUI was used to visualise the data and manually set bird densities to “not-a-number” in such cases. </li> <li>Zones of high bird densities can sometimes be incorrectly eliminated in the raw data. To address this, Nilsson et al. (2019) excluded problematic time or height ranges from the data. Here, in order to keep as much data as possible, the data was manually edited to replace erroneous data either with “not-a-number”, or by cubic interpolation using the dedicated MATLAB GUI.</li> <li>Due to ground scattering,the lower altitude layers are sometimes contaminated by errors or excluded in the raw data. We vertically interpolated bird density by copying the first layer without error into to the lower ones. This approach is relatively conservative as bird migration intensity usually decreases with height in the absence of obstacles, and more so in autumn (Bruderer, 2018)</li> <li>The vertical profiles are vertically integrated from the radar altitude and up to 5000 m asl.</li> <li>The data recorded during daytime are excluded. Daytime is defined at each radar by the civil dawn and dusk (6° below horizon).</li> <li>Finally, the data of 10 radars with high temporal resolution (5-10minutes) was down-sampled to 15 minutes to preserve a balanced representation of each radar.</li> </ol> <p>The resulting cleaned vertical-integrated time series of nocturnal bird density can be viewed in vp_corrected.zip.</p> <p>More details and illustrations are available in Nussbaumer (2019) [open access: <a href="https://www.mdpi.com/2072-4292/11/19/2233">https://www.mdpi.com/2072-4292/11/19/2233</a>], </p> <p><strong>Acknowledgement</strong></p> <p>We acknowledge the <a href="http://eumetnet.eu/activities/observations-programme/current-activities/opera/">European Operational Program for Exchange of Weather Radar Information (EUMETNET/OPERA)</a> for providing access to European radar data, faciliated through a research-only license agreement between EUMETNET/OPERA members and <a href="http://enram.eu/">ENRAM</a>.</p> <p> </p> <p><strong>References</strong></p> <p>Bruderer, B.; Liechti, F. Variation in density and height distribution of nocturnal migration in the south of israel. <em>Israel Journal of Zoology</em> <strong>1995</strong>, <em>41</em>, 477–487. <a href="http://doi.org/10.1080/00212210.1995.10688815">doi:10.1080/00212210.1995.10688815</a>.</p> <p>Dokter A. M. , F. Liechti, H. Stark, L. Delobbe, P. Tabary, and I. Holleman, “Bird migration flight altitudes studied by a network of operational weather radars,” <em>J. R. Soc. Interface</em>, vol. 8, no. 54, pp. 30–43, Jan. <strong>2011</strong>. <a href="http://doi.org/10.1098/rsif.2010.0116">doi:10.1098/rsif.2010.0116</a></p> <p>Dokter A. M. , P. Desmet, J. H. Spaaks, S. van Hoey, L. Veen, L. Verlinden, C. Nilsson, G. Haase, H. Leijnse, A. Farnsworth, W. Bouten, and J. Shamoun‐Baranes, “bioRad: biological analysis and visualization of weather radar data,” <em>Ecography </em>(Cop.)., vol. 42, no. 5, pp. 852–860, May <strong>2019</strong>. <a href="http://doi.org/10.1111/ecog.04028">doi: 10.1111/ecog.04028</a></p> <p>Nilsson, C.; Dokter, A.M.; Verlinden, L.; Shamoun-Baranes, J.; Schmid, B.; Desmet, P.; Bauer, S.; Chapman, J.; Alves, J.A.; Stepanian, P.M.; Sapir, N.;Wainwright, C.; Boos, M.; Górska, A.; Menz, M.H.M.; Rodrigues, P.; Leijnse, H.; Zehtindjiev, P.; Brabant, R.; Haase, G.; Weisshaupt, N.; Ciach, M.; Liechti, F. Revealing patterns of nocturnal migration using the European weather radar network. <em>Ecography </em><strong>2019</strong>, <em>42</em>, 876–886. <a href="http://doi.org/10.1111/ecog.04003">doi:10.1111/ecog.04003</a>.</p> <p>Nussbaumer R., L. Benoit, G. Mariethoz, F. Liechti, S. Bauer, and B. Schmid, “A Geostatistical Approach to Estimate High Resolution Nocturnal Bird Migration Densities from a Weather Radar Network,” <em>Remote Sens</em>., vol. 11, no. 19, p. 2233, Sep. <strong>2019</strong>. <a href="https://www.mdpi.com/2072-4292/11/19/2233">doi: 10.3390/rs11192233</a></p> <p> </p> <p> </p>
Real-time monitoring of a 3D blood-brain barrier model maturation and integrity with a sensorized microfluidic device
<p><span>A significant challenge in the treatment of central nervous system (CNS) disorders is represented by the presence of the blood-brain barrier (BBB), a highly selective membrane that regulates molecular transport and restricts the passage of pathogens and therapeutic compounds. Traditional <em>in vivo</em> models are constrained by high costs, lengthy experimental timelines, ethical concerns, and interspecies variations. <em>In vitro</em> models, particularly microfluidic BBB-on-a-chip devices, have been developed to address these limitations. These advanced models aim to more accurately replicate human BBB conditions by incorporating human cells and physiological flow dynamics. In this framework, here we developed an innovative microfluidic system that integrates thin-film electrodes for non-invasive, real-time monitoring of BBB integrity using electrochemical impedance spectroscopy (EIS). EIS measurements showed frequency-dependent impedance changes, indicating BBB integrity and distinguishing well-formed from non-mature barriers. The data from EIS monitoring was confirmed by permeability assays performed with a fluorescence tracer. The model incorporates human endothelial cells in a vessel-like arrangement to mimic the vascular component and three-dimensional cell distribution of human astrocytes and microglia to simulate the parenchymal compartment. By modeling the BBB-on-a-chip with an equivalent circuit, a more accurate trans-endothelial electrical resistance (TEER) value was extracted. The device demonstrated successful BBB formation and maturation, confirmed through live/dead assays, immunofluorescence and permeability assays. Computational fluid dynamics (CFD) simulations confirmed that the device mimics <em>in vivo</em> shear stress conditions. Drug crossing assessment was performed with two chemotherapy drugs: doxorubicin, with a known poor BBB penetration, and temozolomide, conversely specific drug for CNS disorders and able to cross the BBB, to validate the model predictive capability for drug crossing behavior. The proposed sensorized microfluidic device represents a significant advancement in BBB modeling, offering a versatile platform for CNS drug development, disease modeling, and personalized medicine.</span></p>
Integrating floral trait and flowering time distribution patterns help reveal a more dynamic nature of co-flowering community assembly processes
<p>Species' floral traits and flowering times are known to be the major drivers of pollinator-mediated plant-plant interactions in diverse co-flowering communities. However, their simultaneous role in mediating plant community assembly and plant-pollinator interactions is still poorly understood. Since not all species flower at the same time, inference of facilitative and competitive interactions based on floral trait distribution patterns should account for fine phenological structure (intensity of flowering overlap) within co-flowering communities. Such an approach may also help reveal the simultaneous action of competitive and facilitative interactions in structuring co-flowering communities.</p> <p>Here we used modularity within a co-flowering network context, as a novel approach to detect convergent and/or over-dispersed patterns in floral trait distribution and pollinator sharing. Specifically, we evaluate differences in floral trait and pollinator distribution patterns within (high temporal flowering overlap) and among co-flowering modules (low temporal flowering overlap). We further evaluate the consistency of observed floral trait and pollinator sharing distribution patterns across space (three geographic regions) and time (dry and rainy seasons).</p> <p>We found that floral trait similarity was significantly higher in plant species within co-flowering modules than in species among them. This suggests pollinator facilitation may lead to floral trait convergence, but only within co-flowering modules. However, our results also revealed seasonal and spatial shifts in the underlying interactions (facilitation or competition) driving co-flowering assembly, suggesting that the prevalent dominant interactions are not static.</p> <p>Synthesis: Overall, we provide strong evidence showing that the use of flowering time and floral trait distribution alone may be insufficient to fully uncover the role of pollinator-mediated interactions in community assembly. Integrating this information along with patterns of pollinator sharing will greatly help reveal the simultaneous action of facilitative and competitive pollinator-mediated interactions in co-flowering communities. The spatial and temporal variation in flowering and trait distribution patterns observed further emphasize the importance of adopting a more dynamic view of community assembly processes.</p>
[Data] Real-time monitoring and quality assurance for laser-based directed energy deposition: integrating co-axial imaging and self-supervised deep learning framework
<p>The experimental setup utilized a co-axial color Charged Couple Device (CCD) camera, integrated into the laser deposition head. This camera operates at a frame rate of 30 frames per second and captures the morphology of the process area. The captured images consist of three RGB channels with a 640 × 480 pixels resolution. To enable the camera to capture the radiation from the process zone, a beam splitter is installed on Precitec's laser applicator head. An optical notch filter within the 650–675 nm range also blocks the laser wavelengths.</p> <p>The dataset consists of four categories that covers the process map of DED process [.rar file].<br>The dataset consist of around 48,000 images that are labelled into 4 categories [P1-P2-P3-P4]. The images correspond to DED process zone captured co-axially<br>The categories are function of linear laser energy deposited. The folder is already split into Train and Test.</p>
A bipartite function of ESRRB can integrate signalling over time to balance self - renewal and differentiation
<p>Cooperative DNA binding of transcription factors (TFs) integrates the cellular contextto support cell specification during development. Naïvemouse embryonic stem cells are derived from early development and can sustain the pluripotent identity indefinitely. Here we ask whether TFs associated with pluripotency evolved to directly support this state, or if the state emerges from their combinatorial action. NANOG and ESRRB are key pluripotency factors that co-bind DNA. We find that when both factors are expressed, ESRRB supports pluripotency. However, when NANOG is absent, ESRRB supports a bistable culture of cells with an embryo-like primitive endoderm identity ancillary to pluripotency. The stoichiometry between NANOG and ESRRB allows quantitative titration of this differentiation, and in silico modelling of bipartite ESRRBactivity suggests itsafeguards plasticity in differentiation. Thus, the concerted activity of cooperative TFs can transform their effect to sustain intermediate cell identities and allow ex vivo expansion of immortalstem cells. A record of this paper’s Transparent Peer Review process is included in the Supplemental Information.</p>
Morphological integration, canalization, and plasticity in response to emergence time in Abutilon theophrasti
<p>The relationships between trait plasticity and canalization, and between phenotypic integration and plasticity, have been under debate, largely because direct evidence is still scarce for their associations, especially in response to environments. To investigate the relationships between canalization, integration and phenotypic plasticity in response to emergence time, we conducted a field experiment with an annual herbaceous species of <em>Abutilon theophrasti</em>, by subjecting plants to four treatments of emergence time (spring, late spring, summer and late summer), to measure a number of morphological traits and analyze correlations of plasticity with canalization and integration in these traits, at two stages of plant growth. Results showed plants with delayed emergence had higher phenotypic integration and more positive correlations between integration and plasticity, but less negative correlations between decreased canalization and plasticity, compared to those emerged in spring. Results suggested significant environmental changes that induce plastic responses, rather than environmental stress, can result in greater phenotypic integration in plants. Negative correlations between decreased canalization and plasticity occurred more frequently in plants emerging in spring and the least frequently in those emerging in summer, suggesting their relationship depends on specific environmental conditions and the degree of plasticity. Both increased phenotypic integration and decreased canalization might merely be the outcome of plastic responses, rather than mechanisms constraining or facilitating plasticity.</p>
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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.