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58 results for “resource quality”
Quality controlled observations of hourly incoming shortwave radiation data at the surface for solar resource mapping in Norway (2016-2020).
<p>Observed hourly incoming shortwave radiation data at the surface of Norway for the years 2016-2020 along with quality control flags, visualization plots and a descriptive report. The data has been collected, visually inspected and quality controlled within the SunPoint project (SUn in Norway - POtential and INTegration of the solar energy resource, Norwegian Research Council project 320750). The main data source is frost.met.no but some gaps were filled with data directly obtained by the station holders.</p> <p>There are three NetDCF files for 47 stations selected after quality control:</p> <ul> <li>rsds_1hr_selection_v5_2016-2020.nc: Raw data</li> <li>rsds_flagged_1hr_selection_v5_2016-2020.nc: Raw data with flags</li> <li>rsds_cleaned_1hr_selection_v5_2016-2020.nc: Filtered data (i.e. all flagged data has been removed)</li> </ul> <p>and one NetCDF file for all available stations (106 stations)</p> <ul> <li>rsds_1hr_frost_and_more_2016-2020.nc</li> </ul> <p>Version 3.5 of the McClear clear-sky model is used for flagging which reduces the bias to ground measurements compared to earlier versions. </p> <p>The visualization and automated quality control routines are available in the Scripts.zip file (python).</p>
Four Reference Quality Genome Assemblies of Pyrenophora teres f. maculata: A Resource for Studying the Barley Spot Form Net Blotch Interaction
<p>Updated draft genome assembly (FASTA) and annotation (GFF) for the <em>P. teres </em>f.<em> maculata </em>isolate FGOB10Ptm-1. </p>
Four Reference Quality Genome Assemblies of Pyrenophora teres f. maculata: A Resource for Studying the Barley Spot Form Net Blotch Interaction
<p>Updated draft genome assembly (FASTA) and annotation (GFF) for the <em>P. teres </em>f.<em> maculata </em>isolate P-A14. </p>
Dataset linking to the publication "An assessment of data sources, data quality and changes in national forest monitoring capacities in the Global Forest Resources Assessment 2005–2020"
<p>This dataset links to the study “An assessment of data sources, data quality and changes in national forest monitoring capacities in the Global Forest Resources Assessment 2005–2020”. This study is published in the journal “Environmental Research Letters” which can be found at <a href="https://iopscience.iop.org/article/10.1088/1748-9326/abd81b">https://iopscience.iop.org/article/10.1088/1748-9326/abd81b</a>. The dataset contains two files, one csv file, and one shape file. The two files contain the same data to meet the different users' needs. The dataset contains variables for assessing national forest monitoring data sources i.e., RS and/or NFI. Separate indicators namely 'Use of RS', and 'Use of NFI' were used to analyze the two data sources (RS and NFI). The description of each variable for these two indicators contained in the dataset is given in the Table below.</p> <table> <caption><strong>The description of the variables in the datase</strong>t <strong>for country capacity assessment</strong></caption> <tbody> <tr> <td><strong>Variables Name</strong></td> <td><strong>Description of the variables</strong></td> </tr> <tr> <td>Country</td> <td>Country</td> </tr> <tr> <td>ISO_A3_CODE</td> <td>ISO A3 Code for country</td> </tr> <tr> <td>ADM0_CODE</td> <td>ADMO Code for country</td> </tr> <tr> <td>CONTINENT</td> <td>Continent</td> </tr> <tr> <td>Region</td> <td>Region</td> </tr> <tr> <td>RSInd_05</td> <td>Use of remote sensing (RS) for forest area (change) monitoring 2005 Indicator</td> </tr> <tr> <td>RSSc_05</td> <td>Use of RS for forest area (change) monitoring 2005 Score</td> </tr> <tr> <td>RSInd _10</td> <td>Use of RS for forest area (change) monitoring 2010 Indicator</td> </tr> <tr> <td>RSSc _10</td> <td>Use of RS for forest area (change) monitoring 2010 Score</td> </tr> <tr> <td>RSInd_15</td> <td>Use of RS for forest area (change) monitoring 2015 Indicator</td> </tr> <tr> <td>RSSc _15</td> <td>Use of RS for forest area (change) monitoring 2015 Score</td> </tr> <tr> <td>RSInd_20</td> <td>Use of RS for forest area (change) monitoring 2020 Indicator</td> </tr> <tr> <td>RSSc _20</td> <td>Use of RS for forest area (change) monitoring 2020 Score</td> </tr> <tr> <td>DRS05_20</td> <td>Difference ‘use of RS’ 2005-2020</td> </tr> <tr> <td>NFIInd_05</td> <td>Use of national forest inventories (NFI) for forest monitoring 2005 Indicator</td> </tr> <tr> <td>NFISc_05</td> <td>Use of NFI for forest monitoring 2005 Score</td> </tr> <tr> <td>NFIInd _10</td> <td>Use of NFI for forest monitoring 2010 Indicator</td> </tr> <tr> <td>NFISc _10</td> <td>Use of NFI for forest monitoring 2010 Score</td> </tr> <tr> <td>NFIInd_15</td> <td>Use of NFI for forest monitoring 2015 Indicator</td> </tr> <tr> <td>NFISc _15</td> <td>Use of NFI for forest monitoring 2015 Score</td> </tr> <tr> <td>NFIInd_20</td> <td>Use of NFI for forest monitoring 2020 Indicator</td> </tr> <tr> <td>NFISc _20</td> <td>Use of NFI for forest monitoring 2020 Score</td> </tr> <tr> <td>DNFI05_20</td> <td>Difference ‘Use of NFI’ 2005-2020</td> </tr> </tbody> </table> <p>Indicators and Scores in the above Table for showing the use of RS and NFI data for forest monitoring in Figure 1 (1a, 1b, and 2a, 2b) are related in the following way.</p> <table> <caption><strong>The indicator values and scores of the country capacity assessment</strong></caption> <tbody> <tr> <td><strong>Indicator</strong></td> <td><strong>Score</strong></td> </tr> <tr> <td>Low</td> <td>0</td> </tr> <tr> <td>Limited</td> <td>1</td> </tr> <tr> <td>Intermediate</td> <td>2</td> </tr> <tr> <td>Good</td> <td>3</td> </tr> <tr> <td>Very Good</td> <td>4</td> </tr> </tbody> </table> <p>The capacity changes from 2005 to 2020 in Figure 1 (1c & 2c) are related in the following way.</p> <table> <caption><strong>The indicator values and levels for country capacity changes</strong></caption> <tbody> <tr> <td><strong>Capacity change values</strong></td> <td><strong>Capacity change levels</strong></td> </tr> <tr> <td>1,2,3,4</td> <td>Increase</td> </tr> <tr> <td>0</td> <td>No change</td> </tr> <tr> <td>-1,-2,-3,-4</td> <td>Decrease</td> </tr> </tbody> </table> <p> </p>
A Galaxy-based training resource for single-cell RNA-seq quality control and analyses
<p>This is the tutorial data for the 'Single-cell quality control with scater' tutorial on the Galaxy Training Network. The data is the same dataset that is used as the inbuilt example dataset within scater, but has been implemented as individual files.</p>
Resource quantity and quality differentially control stream invertebrate biodiversity across spatial scales
<p class="MsoNormal"><span>Resource quantity controls biodiversity across spatial scales, however the importance of resource quality to cross-scale patterns in species richness has seldom been explored. We evaluated the relationship between stream basal resource quantity (periphyton chlorophyll-<em>a</em>) and invertebrate richness and compared this to the relationship of resource quality (periphyton stoichiometry) and richness at local and regional scales across 27 North American streams. At the local scale, invertebrate richness peaked at intermediate levels of chlorophyll-<em>a</em>, but had a shallow negative relationship with periphyton C:P and N:P. However, at the regional scale richness had a strong negative relationship with both chlorophyll-<em>a</em> and periphyton C:P and N:P. The divergent effects of periphyton chl-<em>a</em> and stoichiometry on invertebrate richness suggest that basal resource quantity limits diversity more than resource quality, consistent with patterns of eutrophication. Collectively, we demonstrate that resource quantity and quality play important, yet differing roles in shaping freshwater biodiversity across spatial scale.</span></p>
Data from: Home is where the high-quality resources are: nursery characteristics and territory distribution suggest reproductive resource defense in golden rocket frogs
<p><span>For externally fertilizing animals, the early stages of development are often the most precarious. In the face of multiple abiotic and biotic stressors, parents must assess and select rearing sites that maximize the probability of offspring survival. This is particularly true for Neotropical poison frogs, many of which transport tadpoles to small pools of water called phytotelmata that serve as larval-rearing sites. In these systems, pool choice can have a large effect on offspring growth and survival. Here, we studied the golden rocket frog (</span><em>Anomaloglossus</em> <em>beebei</em>, Aromobatidae), a territorial phytotelm-breeding frog that lives exclusively in giant tank bromeliads (<em>Brocchinia</em> <em>micrantha</em>), to examine phytotelm selection and reproductive resource defense. We first quantified the characteristics of phytotelmata and found that tadpoles were more likely to occur in pools with low levels of mucilage and in leaves at intermediate heights on the plant. We additionally found that low mucilage pools have significantly clearer water, have higher concentrations of dissolved oxygen, and are exposed to lower levels of photosynthetically active radiation. We then mapped the spatial distribution of pools with low levels of mucilage in relation to male territories and found that these "clear" pools are (1) more likely to be within male territories than outside of them, and (2) territory centroids are closer to clear pools than are random locations. Overall, our results show that male golden rocket frogs defend territories that include preferred tadpole deposition sites, suggesting a direct relationship between high-quality reproductive resources and territory defense.</p>
Data from: Home is where the high-quality resources are: nursery characteristics and territory distribution suggest reproductive resource defense in golden rocket frogs
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Data and code for: Moisture and competition constrain ephemeral resource quality for burying beetle reproduction
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Resource quantity and quality differentially control stream invertebrate biodiversity across spatial scales
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Data and R code from: Resource quantity and quality co-limit consumer production in forest streams
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Resources for "BMF CP 91: Socio-demographic factors, illness experience, severity perception, and sensitivity to air quality index"
<p><span>The current study is conducted to examine the following research questions:</span></p> <ul> <li><span>What are the factors associated with the sensitivity towards the air quality rating index to reduce outdoor activities?</span></li> </ul>
Full list of publications dealing with resources nutritional quality in ecology
<p><span>Our understanding of ecosystem functioning is strongly linked to the study of predator-prey relationships and food web structures. However, trophic ecology has often focused on identifying taxonomic relationships and quantifying the biomass or energy ingested by consumers, but has often failed to integrate the importance of the nutritional quality of resources in ecological dynamics. Underlying this gap is the multi-dimensional nature of resource quality which has hampered any consensus on the definition of resource nutritional quality. In this special issue, we aimed at gathering a subset of articles exemplifying the diversity of variables by which resources quality is quantified, the diversity of research topics that can be tackled in ecology - from physiological or evolutionary aspects to ecosystem processes - and propose some perspectives on the integration of nutritional quality within broader ecological concepts. Using a semi-automated literature analysis, we map the current landscape of the "resources nutritional quality" research of the last 30 years. We depict how it has been quantified through physical, biological or chemical indicators, the use of these parameters being largely dependent on the type of ecosystem studied and on the investigated ecological process. We then position t</span><span>he articles published in this special issue of Oikos within this landscape, showing they cover a small but relatively well representative subset of the domains of resources quality-related issues. Articles in this special issue browse a range of individual and population-level approaches (embracing evolutionary questions) to community related questions, include methodological issues and ecosystem-wide approaches using trophic quality indicators as tracers of resources origin. Based on these studies and on the literature review, we identify a non-exhaustive list of challenges and perspectives of research that we consider of highest priority in the large topic of trophic ecology.</span></p>
Experiment resources for "Quality of Binaural Rendering From Baffled Microphone Arrays Evaluated Without an Explicit Reference"
<div>This data set contains the following resources to reproduce the listening experiment and statistical analysis of the referenced manuscript:</div> <div> <ul> <li>The <em>binaural room impulse responses</em> (<strong>BRIR</strong>s) of all listening conditions presented in the perceptual experiment.</li> <li>The tools to create the listening test infrastructure, including <em>Pure Data</em> (<strong>Pd</strong>) patches and configuration files for the <em>SoundScape Renderer</em> (<strong>SSR</strong>) and <em>graphical user interface</em> (<strong>GUI</strong>).</li> <li>The raw response data as gathered from the experiment subjects.</li> <li>The R and Stan scripts to perform the statistical analysis and generate the resulting plots and tables.</li> </ul> </div> <div> <p> </p> <p>The archive contains the following components described below.</p> <p>Directory "dependencies/":</p> <ul> <li>Matlab, R, Stan, and Pd functions that are utilized in the code and experimental setup</li> <li>Additional dependencies of available open-source projects may be required for certain code functions. If so, the source and setup process for the necessary dependencies are documented in the file header.</li> </ul> </div> <div> <p>Directory "plots/4_Equalization/":</p> <ul> <li>Plots of all available headphone equalization filters as generated by the following Matlab scripts.</li> </ul> <p>Directory "plots/8_User_study/":</p> <ul> <li>Plots of the raw and analyzed experimental results as generated by the following Matlab and R scripts.</li> </ul> </div> <div> <p>Directory "resources/BRIR_auralization/":</p> <ul> <li>Audio files with static binaural auralizations of all listening conditions presented in the perceptual experiment as generated by the following Matlab scripts.</li> <li>The files in "Kemar_HRTF_sofa_N44_adjusted" do not include a headphone equalization.</li> <li>The files in "Kemar_HRTF_sofa_N44_adjusted+Sennheiser_HD650_lin" include the equalization for the <em>Sennheiser HD650</em> headphones employed in the listening experiment. The files are identical to the annotated <a href="http://www.ta.chalmers.se/research/audio-technology-group/audio-examples/jaes-2024a/" target="_blank" rel="noopener">listening examples</a> published for the manuscript.</li> </ul> <p>Directory "resources/BRIR_rendered/":</p> <ul> <li>BRIRs and rendering parameters of all listening conditions presented in the perceptual experiment as generated from the associated <a href="../doi/10.5281/zenodo.8206570" target="_blank" rel="noopener">data set</a> and <a href="https://github.com/HaHeho/baffled-arrays-to-binaural/releases/tag/v2024.JAES" target="_blank" rel="noopener">rendering code</a>.</li> <li>Scene configuration files for the SSR with all listening conditions presented in the perceptual experiment as generated from the following Matlab scripts.</li> </ul> <p>Directory "resources/HPCF_KEMAR/":</p> <ul> <li>Impulse responses of equalization filters for various headphones on the G.R.A.S KEMAR acoustic dummy head as measured for this experiment.</li> </ul> <p>Directory "resources/User_study/":</p> <ul> <li>Various resources for the listening experiment.</li> <li>The files in "Exp1_analysis" include intermediate and final statistical analysis results as generated from the following R scripts.</li> <li>"Exp1_config.json" contains the configuration of the study GUI with conditions presented in the listening experiment.</li> <li>"Exp1_Introduction.pdf" contains the instructions presented to the subjects at the start of the listening experiment.</li> <li>"Exp1_Part2_data_strings.xls" contains all subjects' raw perceptual response data gathered from the listening experiment.</li> <li>"Questionnaire.pdf" contains the questionnaire given to the subjects at the end of the listening experiment.</li> </ul> <p>Matlab script "x4_Gather_Headphone_Compensations.m":</p> <ul> <li>Generate plots of measured headphone compensation filters. Furthermore, the generated minimum phase filters and filters to yield a linear phase response from the headphones are extracted as separate WAV files.</li> </ul> </div> <div> <p>Matlab script "x6_Gather_SSR_Configurations.m":</p> <ul> <li>Collect several specified pre-rendered binaural room impulse response sets into an ASD file. The SSR can load this scene to present all gathered configurations in direct comparison with head tracking.</li> </ul> </div> <div> <p>Readme file "x6a_Normalize_SSR_Loudnesses.txt":</p> </div> <div> <div> <div> <ul> <li>Ideally, the rendering script would implement a measure to provide a reliable estimation of the binaural loudness of the rendered configuration. This could be used to normalize all stimuli levels. However, such a measure is currently not available or implemented.</li> <li>Therefore, tuning the stimuli loudness for the user study was performed beforehand by ear. The adjusted playback levels are set in a modified SSR configuration file for the listening experiment.</li> </ul> </div> <div> <p>Matlab script "x6_Gather_SSR_Configurations.m":</p> <ul> <li>Perform convolution of (rendered) binaural room impulse responses with a source audio signal. This is done for a specified selection of static head orientations and a continuous rotation over all horizontal head orientations.</li> <li>The resulting auralizations are published as <a href="http://www.ta.chalmers.se/research/audio-technology-group/audio-examples/jaes-2024a/">supplementary materials</a> to the manuscript.</li> </ul> <p>Shell script "x7_Start_Study_GUI.sh":</p> <ul> <li>Initialize all required components to perform the perceptual user study, including: <ul> <li>SSR to perform the real-time rendering of the BRIRs with head tracking</li> <li>SSR to extract head-tracking data (in case a Polhemus tracker is used)</li> <li>Pd to extract head-tracking data (in case a Supperware tracker is used)</li> <li>Pd to perform real-time convolution to apply headphone compensation</li> <li>Pd to receive OSC messages from the study GUI</li> <li>Pd to trigger audio file playback from received OSC messages</li> <li>Pd to translate OSC messages into FUDI messages for the SSR</li> <li>The GUI to be used by the participants and implement the study procedure</li> </ul> </li> <li>Some static configuration variables can be adjusted, whereas other parameters are chosen during script execution.</li> </ul> <p>Matlab script "x8_Gather_Study_Data.m":</p> <ul> <li>Transform the raw result data from the questionnaire (*.xls) and the study GUI (*.json) into a compact format (*.xls) that can be loaded to plot the raw data and imported by software for the subsequent statistical analysis.</li> <li>Note that this contains the responses from all subjects, whereas responses from the investigators must be excluded from the statistical analysis (which is implemented in the analysis scripts).</li> </ul> <p>Matlab script "x8a_Plot_Study_Data.m":</p> <ul> <li>Generate a set of violin plots to visualize the initial distribution of the raw perceptual data. The data is split by specified attributes and plotted separately for visual inspection.</li> <li>The data may also be transformed into ranks for a first distribution inspection. Note that implementing the ranking method, notably how ties are resolved, may differ from the technique employed in the statistical analysis.</li> <li>Note that this contains the responses from all subjects, whereas responses from the investigators must be excluded from the statistical analysis (which is implemented in the analysis scripts).</li> </ul> <p>R script "x8b_Analyze_Exp1_Data.R":</p> <ul> <li>Perform the statistical analysis by transforming the observed subject ratings into a predicted distribution of ranks using a hierarchical generalized linear regression model.</li> <li>Executing the statistical model may take some time due to the Bayesian framework employing Markov-chain Monte Carlo simulations.</li> <li>Data is exported at various intermediate steps to be loaded and visualized by the following R script.</li> </ul> <p>R markdown script "x8c_Plot_Exp1_Results.Rmd":</p> <ul> <li>Generate various plots and data tables of the observed data and the predicted results to visualize the distribution and influence of different analysis parameters.</li> <li>Some of the resulting plots were used in the manuscript.</li> <li>"x8c_Plot_Exp1_Results.html" conveniently summarizes all plots and data tables generated by "knitting" the R markdown script.</li> </ul> </div> </div> </div>
Resources for "BMF CP 92: Who had no wildlife smoke notifications and knowledge of the air quality rating system?"
<p>The current study is conducted to examine the following research questions:</p> <ul> <li>Who were the people who had no access to wildfire smoke notifications during the smoke event in the summer of 2018 in the Boise Metropolitan Area in Idaho?</li> <li>Who were the people who were not familiar with the air quality rating system?</li> </ul>
Data from: An experimental test of the effects of temperature and resource quality on carrying capacity
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Full list of publications dealing with resources nutritional quality in ecology
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Data set: Resource footprints, quality of life and economic development
<p>This data set accompanies the publication "Towards a comprehensive framework of the relationships between resource footprints, quality of life and economic development" by Stefan Cibulka and Stefan Giljum from the Institute for Ecological Economics at the Vienna University of Economics and Business (WU).</p> <p>The file provides data on resource footprints (carbon footprint, material footprint), Human Development Index, Happiness Index as well as GDP for countries world-wide in the time span from 1990 to 2015. In addition, it provides all specifications of the regression analyses undertaken in the course of this study and the detailed regression results.</p>
Data from: Predation risk, resource quality, and reef structural complexity shape territoriality in a coral reef herbivore
For many species securing territories is important for feeding and reproduction. Factors such as competition, habitat availability, and male characteristics can influence an individual's ability to establish and maintain a territory. The risk of predation can have an important influence on feeding and reproduction; however, few have studied its effect on territoriality. We investigated territoriality in a haremic, polygynous species of coral reef herbivore, Sparisoma aurofrenatum (redband parrotfish), across eight reefs in the Florida Keys National Marine Sanctuary that were either protected or unprotected from fishing of piscivorous fishes. We examined how territory size and quality varied with reef protection status, competition, predation risk, and male size. We then determined how territory size and quality influenced harem size and female size to understand the effect of territoriality on reproductive potential. We found that protected reefs trended towards having more large predatory fishes and that territories there were smaller but had greater algal nutritional quality relative to unprotected reefs. Our data suggest that even though males in protected sites have smaller territories, which support fewer females, they may improve their reproductive potential by choosing nutritionally rich areas, which support larger females. Thus, reef protection appears to shape the trade-off that herbivorous fishes make between territory size and quality. Furthermore, we provide evidence that males in unprotected sites, which are generally less complex than protected sites, choose territories with higher structural complexity, suggesting the importance of this type of habitat for feeding and reproduction in S. aurofrenatum. Our work argues that the loss of corals and the resulting decline in structural complexity, as well as management efforts to protect reefs, could alter the territory dynamics and reproductive potential of important herbivorous fish species.
Data from: Argentine ants (Linepithema humile) use adaptable transportation networks to track changes in resource quality
Transportation networks play a crucial role in human and animal societies. For a transportation network to be efficient, it must have adequate capacity to meet traffic demand. Network design becomes increasingly difficult in situations where traffic demand can change unexpectedly. In humans, network design is often constrained by path dependency because it is difficult to move a road once it is built. A similar issue theoretically faces pheromone-trail-laying social insects; once a trail has been laid, positive feedback makes re-routing difficult because new trails cannot compete with continually reinforced pre-existing trails. In the present study, we examined the response of Argentine ant colonies and their trail networks to variable environments where resources differ in quality and change unexpectedly. We found that Argentine ant colonies effectively tracked changes in food quality such that colonies allocated the highest proportion of foragers to the most rewarding feeder. Ant colonies maximised access to high concentration feeders by building additional trails and routes connecting the nest to the feeder. Trail networks appeared to form via a pruning process in which lower traffic trails were gradually removed from the network. At the same time, we observed several instances where new trails appear to have been built to accommodate a surge in demand. The combination of trail building when traffic demand is high and trail pruning when traffic demand is low results in a demand-driven network formation system that allows ants to monopolise multiple dynamic resources.
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.