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914 results for “filtered”
Data from: Soil-mediated filtering organizes tree assemblages in regenerating tropical forests
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Data-Two-dimensional numerical study on particle motion trajectories and deposition in a channel of partial diesel particulate filter
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Data from: Environmental filtering explains variation in plant diversity along resource gradients
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The role of environmental vs. biotic filtering in the structure of European ant communities: a matter of trait type and spatial scale
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Data from: Joint effects of environmental filtering and dispersal limitation on species assemblage of the Tibetan Plateau
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Data from: Habitat diversity associated with island size and environmental filtering control the species richness of rock-savanna plants in neotropical inselbergs
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Obovaria olivaria maf filtered vcf file from: RAD-tag and mitochondrial DNA sequencing reveal the genetic structure of a widespread and regionally imperiled freshwater mussel, Obovaria olivaria (Bivalvia: Unionidae)
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Filtered phytoplankton movement data
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Aggregated filter-feeders govern the flux and stoichiometry of locally available energy and nutrients in rivers
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Mammals on mountainsides revisited: trait-based tests of assembly reveal the importance of abiotic filters
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Temporal and sociocultural effects of human colonisation on native biodiversity: Filtering and rates of adaptation
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PIE LTER 2005 Digital elevation model for the Plum Island Sound estuary, Massachusetts, filtered_grd, last filtered grid - Raster
On April 18 and 19, 2005 LIDAR (Light Detection and Radar) flights occured in the lower portion of the Plum Island Sound, Massachusetts estuary and were timed to correspond to low tides to minimize higher tide influences on pulsed returns. The LIDAR survey was conducted by the National Center for Airborne Laser Mapping (NCALM). This data set consists of a one meter Digital Elevation Model (DEM) in ESRI GRID file format based upon last filtered grid (bare-earth) data from the LIDAR flights.
Filtered Data from the Retrospective Analysis of Antarctic Tracking Data Project from the Scientific Committee on Antarctic Research
<p>The Retrospective Analysis of Antarctic Tracking Data (RAATD) is a Scientific Committee for Antarctic Research (SCAR) project led jointly by the Expert Groups on Birds and Marine Mammals and Antarctic Biodiversity Informatics, and endorsed by the Commission for the Conservation of Antarctic Marine Living Resources. The RAATD project team consolidated tracking data for multiple species of Antarctic meso- and top-predators to identify Areas of Ecological Significance. These datasets constitute the compiled tracking data from a large number of research groups that have worked in the Antarctic since the 1990s.<br> <br> This metadata record pertains to the "filtered" version of the data files. These files contain position estimates that have been processed using a state-space model in order to estimate locations at regular time intervals. For technical details of the filtering process, consult the data paper. The filtering code can be found in the https://github.com/SCAR/RAATD repository.<br> <br> This data set comprises one metadata csv file that describes all deployments, along with data files (3 files for each of 17 species). For each species there is:<br> - an RDS file that contains the fitted TMB filter model object and model predictions (this file is RDS format that can be read by the R statistical software package)<br> - a PDF file that shows the quality control results for each individual model<br> - a CSV file containing the interpolated position estimates<br> <br> For details of the file contents and formats, consult the data paper.</p> <p>The original copy of these data are available through the Australian Antarctic Data Centre (https://data.aad.gov.au/metadata/records/SCAR_EGBAMM_RAATD_2018_Filtered)</p> <p><br> The data are also available in a standardized version (see https://data.aad.gov.au/metadata/records/SCAR_EGBAMM_RAATD_2018_Standardised) that contain position estimates as provided by the original data collectors (generally, raw Argos or GPS locations, or estimated GLS locations) without state-space filtering.</p>
Insights into the photoprotection mechanism of the UV filter homosalate
<p>Homosalate (HMS) is a salicylate molecule that is commonly included within commercial sunscreen formulations to provide protection from the adverse effects of ultraviolet (UV) radiation exposure. In the present work, the mechanisms by which HMS provides UV photoprotection are unravelled, using a multi-pronged approach involving a combination of time-resolved ultrafast laser spectroscopy in the gas-phase and in solution, laser-induced fluorescence, steady-state absorption spectroscopy, and computational methods. The unique combination of these techniques allow us to show that the <em>enol</em> tautomer of HMS undergoes ultrafast excited state intramolecular proton transfer (ESIPT) upon photoexcitation in the UVB (290–320 nm) region; once in the <em>keto</em> tautomer, the excess energy is predominantly dissipated non-radiatively. Sharp transitions are observed in the LIF spectrum at close-to-origin excitation energies, which points towards the potential presence of a second conformer that does not undergo ESIPT. These studies demonstrate that, overall, HMS exhibits mostly favourable photophysical characteristics of a UV filter for inclusion in sunscreen formulations.</p>
Use of GIMP Retinex filter to enhance the visibility of craquelure: example n. 2
<p>In the image, A represents the original image. It is a detail of the Self-Portrait (Madrid), by Albrecht Dürer, (highest resolution), courtesy Wikipedia. B is the grey-tone corresponding image. C is the image that we can obtain using the GIMP thresholding (threshold tone T = 65), applied to B. D is the image that we can have, applying GIMP Retinex filter on B (level=low, scale=250, scale div.=8, dynamics=4,0). E is the result of applying Retinex once more (the same parameters used to obtain D) on D. The iteration continues obtaining E from D, F from E, and G from F, using the same parameters. H is the result of thresholding the G image (T = 127), and I is the craquelure as it appears, after applying the generic filter Erode of GIMP. The example shows that the use of a thresholding on a Retinex filtered image gives much better results than the simple thresholding. The iteration of the Retinex filter is able to enhance the visibility of the craquelure in the dark areas. This approach, that is the use of Retinex filtering, represents a fast manner for enhancing the visibility of craquelure. Another example is given at http://doi.org/10.5281/zenodo.3956124 .</p>
Use of GIMP Retinex filter to enhance the visibility of craquelure: example n. 4
<p>In the image, A represents the original image. It is a detail of Woman with a Water Jug, a painting finished between 1660–1662 by the Dutch painter Johannes Vermeer (highest resolution), courtesy Wikipedia. B is the grey-tone corresponding image. C is the image that we can obtain using the GIMP thresholding on B, adjusting the value of the threshold to have the best possible visibility of the craquelure. D is the image that we can have, applying GIMP Retinex filter on B (level=low, scale=250, scale div.=8, dynamics=4,0). E is the result of the GIMP generic Erode filter applied to D. F is the result of the GIMP edge detection, obtained by using the so-called “Difference of Gaussians” algorithm, applied to the image E. G is the result of applying Retinex once more (the same parameters used to obtain D) to image D. H is the result of the use of the generic Erode filter on G. I is the result of the GIMP edge detection, “Difference of Gaussians” algorithm. The example shows that the use of an edge detection (F,I) on a Retinex filtered image, enhanced by means of Erode (E,H), gives much better results than the simple thresholding. This approach, that is the use of Retinex filtering, accompanied by other GIMP filters, represents a fast manner for enhancing the visibility of craquelure. Other examples are given at http://doi.org/10.5281/zenodo.3956124 http://doi.org/10.5281/zenodo.3956204 http://doi.org/10.5281/zenodo.3957439</p>
Use of GIMP Retinex filter to enhance the visibility of craquelure: example n. 3
<p>In the image, A represents the original image. It is a detail of the Gioconda, by Leonardo da Vinci, (highest resolution), courtesy Wikipedia. B is the grey-tone corresponding image. C is the image that we can obtain using the GIMP thresholding (threshold tone T = 127), applied to B. D is the image that we can have, applying GIMP Retinex filter on B (level=low, scale=250, scale div.=8, dynamics=4,0). E is the result of applying Retinex once more (the same parameters used to obtain D) on D. The iteration continues obtaining E from D, and F from E, using the same parameters. G, H and I are the result of thresholding of the D, E and F images respectively (T = 127). The example shows that the use of a thresholding on a Retinex filtered image gives much better results than the simple thresholding. The iteration of the Retinex filter is able of enhance the visibility of the craquelure in the dark areas. This approach, that is the use of Retinex filtering, represents a fast manner for enhancing the visibility of craquelure. Other examples are given at http://doi.org/10.5281/zenodo.3956124 and http://doi.org/10.5281/zenodo.3956204</p>
Daily cross-correlation functions for "Time-lapse monitoring of seismic velocity associated with 2011 Shinmoe-dake eruption using seismic interferometry: an extended Kalman filter approach"
<p>The daily cross-correlation functions used in Nishida et al. 2020. We used three-component seismograms recorded at eight stations (six broadband sensors and two short-period sensors with a natural frequency of 1 Hz) from May 1st, 2010 to April 30th, 2018. Five stations were deployed by the Earthquake Research Institute, the University of Tokyo, and the other three were deployed by the National Research Institute for Earth Science and Disaster Prevention (NIED). The data can be found in the HDF5 file. You can also find a python code of an implementation of an extended Kalman filter/smoother for time-lapse monitoring of seismic velocity at GitHub (https://github.com/qnishida/eKlfS). The code estimates the temporal change in seismic velocities using this data set. </p>
Environmental filtering and spatial processes shape the beta diversity of liana communities in a valley savanna in Southwest China
<p><b><span>Questions:</span></b> Lianas contribute substantially to diversity and function of ecosystems. What is the relative importance of environmental filtering and spatial processes on structuring liana beta diversity at taxonomic, functional and phylogenetic levels? Is there any synergy between these drivers (environmental factors and spatial distance) on shaping three dimensions of beta diversity in a savanna liana community?</p> <p><b><span>Location:</span></b> A dry-hot valley savanna in Yunnan Province, southwest China.</p> <p><b><span>Methods: </span></b>We established 30 20×20 m plots in the savanna to collect data on the distribution of 22 liana species, 19 functional traits, and plot-level soil nutrients, elevation, and slope. The relative contributions of these environmental factors and spatial distance to liana taxonomic, functional and phylogenetic dissimilarity were analyzed using multiple regression on distance matrices. We also tested which environmental factors influence the beta diversity of liana community using permutational multivariate analysis of variance.</p> <p><b><span>Results: </span></b>Both environmental and spatial distances were significantly correlated with taxonomic, functional and phylogenetic dissimilarity. Spatial distance explained more variation in taxonomic beta diversity than environmental factors. But for both nearest neighbor functional and phylogenetic distance <i><span>D</span></i><i><sub><span><span>nn</span></span></sub></i><sub><span>'</span></sub>, environment explained a relatively higher variation than space did. Moreover, the proportion explained by environmental variables was ranked in decreasing order as follows: functional <i><span>D</span></i><i><sub><span><span>nn</span></span></sub></i><sub><span>'</span></sub>, phylogenetic <i><span>D</span></i><i><sub><span><span>nn</span></span></sub></i><sub><span>'</span></sub>, and taxonomic beta diversity. We found soil pH had the highest contribution to taxonomic and functional beta diversity, while soil total nitrogen contributed most to phylogenetic beta diversity.</p> <p><b><span>Conclusions: </span></b>This study revealed that liana taxonomic, functional and phylogenetic beta diversity in the study hot-dry savanna ecosystem are affected and maintained by both environmental filtering and spatial processes. Moreover, the functional and phylogenetic diversities were more strongly subject to environmental filtering. Our study provides the information on mechanisms underlying liana diversity maintenance in savanna, which are necessary to inform conservation management in this vulnerable ecosystem. </p>
Lorentzian filter correction of turbulence measurements on oscillating floating platforms: impact on wind spectra and eddy covariance fluxes
<p class="MsoTitle">Turbulence and eddy covariance measurements on a floating platform over water surfaces can be contaminated by platform oscillations, which may affect the calculated air–water exchange. The conventional method for decontamination of the platform oscillations from the wind velocity measurements requires the installation of an additional sensitive, and often costly, motion sensor. This paper examines a new mathematical decontamination method, termed Lorentzian filter, which avoids the need for such an instrument. The method, based on the Lorentzian function, capitalizes on the pseudo-harmonic behavior of the platform oscillations and reduces the amplitude of turbulent wind velocity data detected as artifacts at the specific natural frequencies of the platform. The Lorentzian filter was applied to wind velocity data measured by sonic anemometer and eddy covariance system over the Dead Sea, Israel, for 30 days. We examined three approaches of dealing with motion contamination: Lorentzian filter decontamination, motion sensor decontamination, and non-filtered raw wind velocity. Using the 3D wind velocity series, we examined the wind spectra, the co-spectra of water vapor concentration and horizontal wind speed with vertical wind speed and H2O and momentum fluxes. The Lorentzian filter performed very well in decontaminating the wind spectrum, meaning that it efficiently identified the contamination in the natural oscillation frequency and returned a decontaminated wind velocity time series. The co-spectra and fluxes were less prone to the contamination of platform oscillations, presumably due to low correlations between the spurious wind velocity components and other measured scalars, such as water vapor.</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.