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635 results for “Attributes”
Fig. 1 in The Importance Of Artificial Wetlands In The Conservation Of Wetland Birds And The Impact Of Land Use Attributes Around The Wetlands: A Study From The Ajara Conservation Reserve, Western Ghats, India
Fig. 1. Map of study sites: A — Gavase wetland, B — Dhangarmola wetland, C — Khanapur wetland, D — Erandol wetland, E — Ningudage wetland. Adopted from Patil & Choudaj (2022).
Fig. 4 in The Importance Of Artificial Wetlands In The Conservation Of Wetland Birds And The Impact Of Land Use Attributes Around The Wetlands: A Study From The Ajara Conservation Reserve, Western Ghats, India
Fig. 4. Photographs of some of the wetland birds: a — Ruddy Shelduck Tadorna ferruginea; b — Little Ringed Plover Charadrius dubius; c — Small Pratincole Glareola lacteal; d — Painted Stork Mycteria leucocephala; e — Black-headed Ibis Threskiornis melanocephalus; f — Asian Openbill Anastomus oscitans; g — Eurasian Spoonbill Platalea leucorodia; h — Black-winged Stilt Himantopus himantopus; i — River Tern Sterna aurantia.
Fig. 2 in The Importance Of Artificial Wetlands In The Conservation Of Wetland Birds And The Impact Of Land Use Attributes Around The Wetlands: A Study From The Ajara Conservation Reserve, Western Ghats, India
Fig. 2. Total number of wetland and wetland associated birds recorded at five artificial wetlands during 2011– 2015: A —Gavase wetland; B — Dhangarmola wetland; C — Khanapur wetland; D — Erandol wetland; E — Ningudage wetland.
Quality Attributes Assessment in Self-Adaptive Systems: An Empirical Evaluation
<p>Self-adaptive Systems (SAS) can monitor themselves and their context. They can detect changes and react to unexpected conditions with minimal human supervision during their execution. One of the challenges behind developing SAS is dealing with the decision-making process while analyzing the tradeoff points among the multiple quality attributes (QA). In Software Engineering, a widely accepted method of evaluating QA goals in software projects is the Architecture Tradeoff Analysis Method (ATAM). However, despite its importance and wide acceptance, there are few reports of empirical studies on analyzing QA tradeoffs in SAS. In this sense, the present investigation proposes an adapted version of ATAM called ATAM-4SAS to deal with the particularities of SAS. To achieve the research goal, we employed the UPPAAL SMC (statistical verification model) to analyze a set of QA. To evaluate the feasibility of the proposed method, we performed an empirical study on the execution of the ATAM-4SAS in a SAS developed according to the MAPE-K model. This model encompasses the Monitoring, Analysis, Planning, and Execution phases. Such steps share a knowledge base (K), which is fundamental in supporting decision-making. We complemented the empirical evaluation by conducting a focus group, which sought to assess the perceived ease of use and the perceived usefulness of the ATAM-4SAS to support the strategic choice of QA in a SAS. As a result, we observed that most participants agreed that ATAM-4SAS provides adequate support for the strategic choice of QA in SAS.</p>
Air quality source attribution and scenario analysis in the UNECE region
<p>The dataset contains the metrics of PM2.5 and ozone exposure in the UNECE region attributed to 13 activity sectors in three different ECLIPSE v6b emission scenarios (CLE BASE, MFR-BASE and SDS-MFR) used by the authors in the publication "Air quality and related health impact in the UNECE region: source attribution and scenario analysis" submitted to the Journal Atmospheric Chemistry and Physics (https://doi.org/10.5194/acp-2022-776).</p>
Anthropogenic attribution of the increasing seasonal amplitude in surface ocean pCO2: data to prepare figures
<p>The file contains the data to plot the graphics displayed in Joos et al., Anthropogenic attribution of the increasing seasonal amplitude in surface ocean pCO2, Geophys. Res. Letters, in press, June 2023.</p>
APPENDIX 12 in Detangling the effects of patch attributes on bryophyte diversity in fragmented subtropical secondary forests - a case study of land-bridge islands
APPENDIX 12. — Relationships of accumulative species number with accumulative sampling efforts for eight largest islands.
FIG. 2 in Detangling the effects of patch attributes on bryophyte diversity in fragmented subtropical secondary forests - a case study of land-bridge islands
FIG. 2. — Relationships of species richness with number of habitat types, area, elevation, shape irregularity, vegetative cover, and ISW for five bryophyte categories in 168 forest fragments of the Thousand Island Lake, China. The regression equations are derived from GLMMs. Note: ISW, the relative proportion of water within a circle of a diameter of 1000 m centered on a given island.
Appendix of the manuscript: The burden of disease attributable to high body mass index in Belgium
<p>These datasets are part of the Appendix of the manuscript: <em>The burden of disease attributable to high body mass index in Belgium </em>from Gorasso et al.</p> <p>Appendix 3 includes the relative risks by age, sex and disease extracted from GBD 2019 used in the manuscript;</p> <p>Appendix 4 includes the results of the population attributable fractions of high body mass index by age, sex and disease derived in the manuscript.</p>
Attributing European forest disturbances to storm and fire
<p>This repository contains maps attributing each disturbance patch of the <a href="https://zenodo.org/record/4570157#.YFB27i337OQ">European Forest Disturbance Map</a> (version 1.1.4) to bark beetle/wind, fire or other disturbances (mostly harvest). The dataset is based on methods described in following paper, but have been updated with new reference data covering now also bark beetle disturbances: </p> <p>Senf, C. and Seidl, R. (2021) Storm and fire disturbance in Europe: Distribution and trends. <strong>Global Change Biology</strong>. <a href="https://doi.org/10.1111/gcb.15679">https://doi.org/10.1111/gcb.15679</a></p> <p>To get the year of disturbance, please see the underlaying disturbance maps (version 1.1.4.; link given above).</p> <p><strong>Map classes:</strong></p> <p>NA = no disturbance<br> 1 = bark beetle or wind disturbances (both classes had to be grouped due to technical reasons)<br> 2 = fire disturbances<br> 3 = other disturbances, mostly harvest but might include salvage logging go small-scale natural disturbances and infrequent other natural agents (e.g., defoliation, avalanches, etc.)</p> <p><strong>Reference system:</strong></p> <p>The spatial reference system is EPSG 3035 (ETRS89 / LAEA Europe).</p> <p><strong>Word of caution:</strong></p> <p>Remote sensing-based maps, while fascinating to look at, contain errors. If you intent to use the map for your research, please carefully read the discussion on limitations in the paper accompanying the dataset. There will be many instances where the attribution (or even disturbance detection) is wrong. The maps are intended to give a broad, continental-scale overview on the distribution of disturbance agents.</p>
Outputs of the Jupyter Notebook - Deep learning and variational inversion to quantify and attribute climate change (CIRC23)
<p>The dataset contains the outputs of the notebook "Deep learning and variational inversion to quantify and attribute climate change (CIRC23)" published in The Environmental Data Science Book.</p>
Data from: Frugivore traits predict plant-frugivore interactions using generalized joint attribute modeling
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Masting is shaped by tree-level attributes and stand structure, more than climate, in a Rocky Mountain conifer species
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Data from: A tale of two studies: detection and attribution of the impacts of invasive plants in observational surveys
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Simple attributes predict the value of plants as hosts to fungal and arthropod communities
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Data from: The mechanism of promoting rhizosphere nutrient turnover for arbuscular mycorrhizal fungi attribute to recruited functional bacterial assembly
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Physical Attributes of the Hubbard Brook Valley Plots, 1995 - 1998 Survey Data
The valley-wide plots are a grid of 431 sites along fifteen N–S transects established at 500-m intervals spanning the entire Hubbard Brook Valley. Multiple above- and below- ground attributes were measured between 1995 and 1998. This dataset includes physical attribute data; tree inventory, soil data and other measurements are presented in separate datasets. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Hubbard Brook Experimental Forest: soil, litter, plant and microbial attributes on mycorrhizae litter decomposition plots
Studies show mycorrhizal fungi can influence leaf litter decomposition in a variety of ways, but the effects of arbuscular mycorrhizal (AM) fungi and ectomycorrhizal (ECM) fungi on litter decay in forests vary widely across published reports. We experimentally reduced the presence of fine roots and their associated mycorrhizal fungi by soil trenching within a series of plots spanning a gradient of mycorrhizal dominance containing from 96% AM to 100% ECM-associated trees at Hubbard Brook Experimental Forest in Woodstock, NH. We incubated four species of leaf litter in mesh decomposition bags in areas with reduced access to roots and mycorrhizal fungi and in adjacent areas with intact roots and mycorrhizal fungi. After 608 days of decomposition (November 2017 through July 2019), we found that litter decayed more rapidly in the presence of fine roots and mycorrhizal hyphae in all plots, regardless of dominant tree mycorrhizal type. Root and mycorrhizal exclusion did not affect enzyme activities on decomposing litter or soil microbial community composition. Despite reports that both AM and ECM fungi may reduce litter decay rate, our results indicate that AM and ECM-associated fine roots stimulate litter decomposition.
Sample Attributes
<p>This file contains the following:</p> <p>Sample Name, Sample Title, Bioproject_accession, Organism, Host, Isolation_source, Collection_date, Geo_loc_name, Lat_lon, Ref_biomaterial, rel_to_oxygen, samp_collect_device, samp_mat_process, samp_size, source_material_id, description, sequencing_replicate, concentration_after_ampl (ng/ul), ng_sequenced, read_count</p>
Attributes of Nuclear Waste Disposal Systems through collapsible tree diagram
<p>Code for https://doi.org/10.3390/su10124390.</p> <p>Code produced by François Diaz-Maurin.</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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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.