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914 results for “filter”
Filtered GBIF dataset of occurrences for food species in the brown bear (Ursus arctos) trophic database
<p>We reviewed 47 studies of brown bear diet in Europe by searching in SCI Journals, master’s and PhD theses, and grey literature. We obtained a list of 276 species in the brown bear diet in Europe and Turkey. We used the R package rgbif to download occurrences of each food species from the Global Biodiversity Information Facility (GBIF). We selected occurrences of food species, with an uncertainty of <1 km2, in Europe, North Africa and the Middle East for the period 1989–2018.</p>
Inner filter effect correction for fluorescence measurements in microplates (AddAbs) - experimental data
<p>Experimental data for the paper entitled <em>Reducing the Inner Filter Effect in Microplates by Increasing Absorbance? Linear Fluorescence in Highly Concentrated Fluorophore Solutions in the Presence of an Added Absorber</em> (<a href="https://doi.org/10.1021/acs.analchem.3c01295">https://doi.org/10.1021/acs.analchem.3c01295</a>).</p> <p>Separate worksheets are provided for the following:</p> <p>1. Fluorescence measurements - raw values in triplicate; 2 worksheets for transparent (T) and nontransparent (NT) microplates, each with 15 titrations: L<sub>1</sub>-L<sub>12</sub> and H<sub>1</sub>-H<sub>3</sub>,</p> <p>2. Absorbance measurements - raw values in triplicate (<em>λ</em><sub>ex</sub> = 345 nm, <em>λ</em><sub>em</sub> = 390 nm); 1 worksheet for T microplates only, with 4 titrations: L<sub>1</sub>-L<sub>4</sub>,</p> <p>3. Fluorescence measurements - averaged, baseline-corrected averaged and normalized baseline-corrected averaged triplicates; 2 worksheets for T and NT microplates, each with 15 titrations: L<sub>1</sub>-L<sub>12</sub> and H<sub>1</sub>-H<sub>3</sub>,</p> <p>4. ZINFE/NINFE-corrected fluorescence in T microplates; 12 worksheets for 12 titrations: L<sub>1</sub>-L<sub>9</sub> and H<sub>1</sub>-H<sub>3</sub>,</p> <p>5. ZINFE/NINFE-corrected fluorescence in NT microplates; 12 worksheets for 12 titrations: L<sub>1</sub>-L<sub>9</sub> and H<sub>1</sub>-H<sub>3</sub>.</p> <p>The results of the ZINFE and NINFE correction methods obtained using the online calculator service written in Javascript: https://ninfe.science (version 09.5.2022.)</p> <p>For additional details please visit: https://glymech.pharma.hr//GlyMech.html.</p>
Mapping User Attention: Filtering and Visualizing Relevant UI Components in Screenshots based on Gaze Fixations
<p>These data correspond to the set of problems used for the evaluation of the proposal <em>What Are You Gazing At? An Approach to Use Eye-tracking for Robotic Process Automation.</em></p> <p>Each problem consists of a set of 10 screenshots with the same <em>look and feel</em> but different data values for those values that can be entered/modify by the user. Each problem has its associated gaze fixation data. In each of the problems there is a <em>key UI element</em> that primarily attracts the attention of the user.</p> <p>The evaluation is based on a set of images which resemble realistic screenshots of activities in the administrative domain. More precisely, 5 different set of screenshots (<em>S</em>) are generated, each of them with a different level of complexity. Complexity is measured in terms of the number of UI elements per screenshot. The sets are:</p> <ul> <li><em>S1 Mockup-based email view</em>. Represents the activity of viewing an email to check if it contains an attachment. In this case, the <em>key UI element</em> that receives the attention is the attachment inside the email.</li> <li><em>S2 Mockup-based CRM user details</em>. Represents a user's detail viewing activity within a Client Relationship Management (CRM) platform. The key UI element is the checkbox that indicates if the user has all his invoices paid.</li> <li><em>S3 Real screenshot email view. </em>Analogous to <em>S1</em> but with real screenshots. It represents the activity of viewing an e-mail to check if it contains an attachment. In this case, the key UI element to which attention is paid is the attachment contained in the e-mail.</li> <li><em>S4 Real screenshot CRM user details. </em>Analogous to <em>S2</em> but with real screenshots. It represents a user's detail viewing activity within a CRM platform. The key UI element is the checkbox indicating whether the user has all their invoices paid.</li> <li><em>S5 Real screenshot CRM user details. </em>Represents the split-screen display of two applications. On the left side a pdf viewer, showing a covid vaccination certificate. And on the right side a human resources management system (basic recreation of real system for privacy reasons). In this one the detail of the employee to whom the certificate of the left side corresponds is visualized. These screenshots, having two applications, have two key UI elements. In the pdf viewer it is the name of the certificate holder and in the human resources management system it is the name of the employee whose detail view is being displayed. The activity being carried out is the verification that the covid certificate received corresponds to that of an employee.</li> </ul> <p>Two types of filters based on the gaze fixation data are applied to these sets of screenshots: <em>Pre-filtering</em> and <em>Post-filtering</em>, corresponding to applying the filtering before and after detecting UI components in the screenshots, respectively. The structure of the data packages is divided in two folders <em>input </em>and <em>output</em>. The <em>input </em>folder is organized as follows:</p> <p><strong>input/</strong></p> <ul> <li><strong>screenshots/</strong>: corresponds to the screenshots. The sets of screenshots are easily identifiable, they are named following the pattern: <em>SX_screenshot_DDDD.jpeg</em>. Where <em>X </em>indicates to which of the set of screenshots described in the previous list it belongs, and <em>DDDD</em> represents a unique identifier for each screenshot. Each group consists of 10 screenshots, being 50 in total.</li> <li><strong>fixation.json</strong>: It is a JSON file that contains a <em>key </em>associated with each of the screenshots. For each screenshot, it contains a "fixation_points" key where information about the fixations that have occurred on the screenshot is stored. Here's an example: <pre><code> "S5_screenshot_0050.jpeg": { "fixation_points": { "334.25#497.166666666667": { "#events": 6, "start_index": 33224, "ms_start": 553962.1467, "ms_end": 554061.9899, "duration": 99.8432000001194, "imotions_dispersion": 0.300325967868111, "last_index": 33229, "dispersion": 14.044275227531914 }, "1258.80769230769#507.576923076923": { "#events": 13, "start_index": 33234, "ms_start": 554128.5427, "ms_end": 554345.3595, ...</code></pre> </li> </ul> <p>The <em>output </em>folder is organized in three subfolders, the first one containing the information of the <em>non-filtered</em> screenshots (i.e. without having applied to them any filtering or processing), and the next two with the information resulting from <em>pre-filtering</em> and <em>post-filtering</em>.</p> <p><strong>output/</strong></p> <ul> <li><strong>non-filter/</strong> <ul> <li><strong>borders/</strong>: screenshots with highlighted borders of <strong>all </strong>UI components detected in it.</li> <li><strong>components_json/</strong>: a collection of JSON files with the same name as the screenshot, containing the "img_shape" key with a list of the screen resolution and the number of layers the image has: [1080, 1920, 3], and the "compos" key with a list of <strong>all</strong> UI components representing the Screen Object Model.<br> </li> </ul> </li> <li><strong>pre-filter/</strong> and <strong>post-filter/</strong> <ul> <li><strong>borders/</strong>: screenshots with the borders of the <strong>relevant </strong>UI components. In the case of prefiltering, the detection of components is only performed on the parts of the screenshot that have received attention. In postfiltering, the complete screenshot is shown, with only the borders of the relevant UI components highlighted.</li> <li><strong>components_json/</strong>: a collection of JSON files with the same name as the screenshot is included, containing the following keys: <ul> <li>"img_shape": A list representing the screen resolution and the number of layers in the image, e.g., [1080, 1920, 3].</li> <li>"compos": A list of all UI components representing the Screen Object Model (SOM). During post-filtering, each UI component is augmented with an additional property called "relevant." If this property is set to <em>true</em>, it indicates that the respective UI component has received attention.</li> </ul> </li> <li><strong>(pre)/(post)filter_attention_maps/</strong>: represent the attention maps. In the case of prefiltering, any surface of the screen that has not received attention will be shown in black. In the case of postfiltering, the areas of attention will be shown as red circles, and the UI components whose area intersects with the areas of attention by more than 25% will be shown in yellow.</li> </ul> </li> </ul> <p>In conclusion, the described data package consists of sets of screenshots, accompanied by prefiltering and postfiltering filters using gaze fixation data, enabling the identification of relevant UI components. The organized data packages include input and output folders, where the output folder offers processed screenshots, UI component information, and attention maps. This resource provides valuable insights into user attention and interaction with UI elements on different types of scenarios.</p>
Additional ASAS-SN 100 Million Variable Star Database Python Filter CSV files
<p>Additional ASAS-SN 100 Million Variable Star Database Python Filter CSV files</p>
Genomic datasets of Laminaria digitata: Paired-end reads from dd-RADseq, reference genome assembly and filtered VCF
<p>The long-term persistence of species in the face of climate change can be evaluated by examining the interplay between selection and genetic drift in the contemporary evolution of populations. In this study, we focused on spatial and temporal genetic variation in four populations of the cold-water kelp Laminaria digitata using thousands of SNPs (ddRAD-seq). These populations were sampled from the center to the south margin in the North Atlantic at two different time points, spanning at least two generations. By conducting genome scans for local adaptation from a single time point, we successfully identified candidate loci that exhibited clinal variation, closely aligned with the latitudinal changes in temperature. This finding suggests that temperature may drive the adaptive response of kelp populations, although other factors, such as the species' demographic history should be considered. Furthermore, we provided compelling evidence of selection through the examination of allele frequency changes over time, by taking into the impact of genetic drift. Specifically, we detected candidate loci exhibiting temporal differentiation that surpassed the levels typically attributed to genetic drift at the south margin, confirmed through simulations. This finding was in sharp contrast with the lack of detection of outlier loci based on temporal differentiation in a population from the North Sea, exhibiting low and decreasing levels of genetic diversity. These contrasting evolutionary scenarios among populations can be primarily attributed to the differential prevalence of selection relative to genetic drift. In conclusion, our study highlights the potential of temporal genomics to gain deeper insights into the contemporary evolution of marine foundation species in response to rapid environmental changes.</p>
Alkali metal cations modulate the geometry of different binding sites in HCN4 selectivity filter for permeation or block
<p>Dataset underlying findings published in:<br> "Alkali metal cations modulate the geometry of different binding sites in HCN4 selectivity filter for permeation or block"</p>
IUCLID for pesticides filtering rule proposal for April 2024
<p>These are the draft filtering rules for pesticides dossiers in IUCLID proposed for implementation as from April 2024. The proposal is made by EFSA with the endorsement of the IUCLID PSN sub-group. These rules are up for public consultation until 20 September and any feedback can be provided through EU Survey where more details on the proposal can also be found: <a href="https://ec.europa.eu/eusurvey/runner/bae82a4d-e924-67e4-59fc-b1f3bcfdedb5">https://ec.europa.eu/eusurvey/runner/bae82a4d-e924-67e4-59fc-b1f3bcfdedb5</a></p>
Baroclinic and barotropic tidal data for the pacific basin, filtered at the M2 frequency
<p>Sea surface height, vertically averaged pressure anomalies and pressure gradients, filtered at the M2 frequencies (period: 12.4206 hours)</p> <p>This data was used to produce the figures in: Baroclinic Sea-Level, by McWilliams, Molemaker, and Damien.</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>
Functional traits and metacommunity theory reveal that habitat filtering and competition maintain bird diversity in a human shared landscape
Open the record for dataset details and reuse information.
Environmental vs. biotic filtering: what if species traits contribute to both?
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Reference genome choice and filtering thresholds jointly influence phylogenomic analyses
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Data from: Bryophyte community assembly on young land uplift islands – dispersal and habitat filtering assessed using species traits
Open the record for dataset details and reuse information.
Data from: Floral organs act as environmental filters and interact with pollinators to structure the yellow monkeyflower (Mimulus guttatus) floral microbiome
Open the record for dataset details and reuse information.
Genomic datasets of Laminaria digitata: Paired-end reads from dd-RADseq, reference genome assembly and filtered VCF
Open the record for dataset details and reuse information.
Trait coordination and environmental filters shape functional trait distributions of forest understory herbs
We test the importance of environmental filtering and trait covariance for structuring the functional traits of understory herbaceous communities.
Detrended and filtered DEMs
<p>Detrended and filtered DEMs for channel C2 and runs S, SLW, SVLWa, SVLWb, SVLWc, SVLWd (see Table 1 for details). Lowest 5%, 10%, 15% points are highlighted in magenta, red and pink, respectively. Flow is from bottom to top.</p>
Wikidata Dump Instantiation, specialization (filtered)
<p> RDF dump of wikidata produced with <a href="https://tools.wmflabs.org/wdumps/">wdumps</a>. </p> <p> <br> <a href="https://tools.wmflabs.org/wdumps/dump/265">View on wdumper</a> </p> <p> <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0 </p>
Data set for 'Lunge filter feeding biomechanics constrain rorqual foraging ecology across scale'...
<p>Fundamental scaling relationships influence the physiology of vital rates, which in turn shape the ecology and evolution of organisms. For diving mammals, benefits conferred by large body size include reduced transport costs and enhanced breath-holding capacity, thereby increasing overall foraging efficiency. Rorqual whales feed by engulfing a large mass of prey-laden water at high speed and filter it through baleen plates. However, as engulfment capacity increases with body length across species (Engulfment Volume ∝ Body Length <sup>3.57</sup>), the surface area of the baleen filter does not increase proportionally (Baleen Area ∝ Body Length<sup>1.82</sup>), and thus the filtration time of larger rorquals predictably increases because the baleen surface area must filter a disproportionally large amount of water. We predicted that filtration time should scale with body length to the power of 1.75 (Filter Time ∝ Body Length<sup>1.75</sup><i>)</i>. We tested this hypothesis on four rorqual species using multi-sensor tags with corresponding unoccupied aerial systems (UAS) -based body length estimates. We found that filter time scales with body length to the power of 1.79 (95% CI: 1.61 - 1.97). This result highlights a scale-dependent trade-off between engulfment capacity and baleen area that creates a biomechanical constraint to foraging through increased filtration time. Consequently, larger whales must target high density prey patches commensurate to the gulp size to meet their increased energetic demands. If these optimal patches are absent, larger rorquals may experience reduced foraging efficiency compared to smaller whales if they do not match engulfment capacity to the size of targeted prey aggregations.</p>
Data and code from "A dimmer shade of pale: revealing the faint signature of local assembly processes on the structure of strongly filtered plant communities"
<p>Trait-based ecology suggests that abiotic filtering is the main mechanism structuring the regional species pool in different subsets of habitat-specific species. At more local spatial scales, other ecological processes may add on giving rise to complex patterns of functional diversity (FD). Understanding how assembly processes operating on the habitat-specific species pools produce the locally observed plant assemblages is an ongoing challenge. Here, we evaluated the importance of different processes to community assembly in an alpine fellfield, assessing its effects on local plant trait FD. Using classical randomization tests and linear mixed models, we compared the observed FD with expectations from three null models that hierarchically incorporate additional assembly constraints: stochastic null models (random assembly), independence null models (each species responding individual and independently to abiotic environment), and co-occurrence null models (species responding to environmental variation and to the presence of other species). We sampled species composition in 115 quadrats across 24 locations in the central Pyrenees (Spain) that differed in soil conditions, solar radiation and elevation. Overall, the classical randomization tests were unable to find differences between the observed and expected functional patterns, suggesting that the strong abiotic filters that sort out the flora of extreme regional environments blur any signal of other local processes. However, our approach based on linear mixed models revealed the signature of different ecological processes. In the case of seed mass and leaf thickness, observed FD significantly deviated from the expectations of the stochastic model, suggesting that fine-scale abiotic filtering and facilitation can be behind these patterns. Our study highlights how the hierarchical incorporation of ecological additional constraints may shed light on the dim signal left by local assembly processes in alpine environments.</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.