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1,456 results for “residues”
Dataset: Nutrient Removal in Roadside Stormwater Bioretention Cells Amended with Drinking Water Treatment Residuals
<p>This dataset contains storm volumes and nitrogen and phosphorus concentrations measured at the inflow and outflow of four roadside, lined bioretention cells in a field study conducted in Burlington, Vermont</p> <p>Phosphorus data are published in: Ament, M. R., Roy, E. D., Yuan, Y., & Hurley, S. E. (2022). Phosphorus removal, metals dynamics, and hydraulics in stormwater bioretention systems amended with drinking water treatment residuals. <em>Journal of Sustainable Water in the Built Environment</em>, <em>8</em>(3), 04022003.</p> <p>Nitrogen data are included in a manuscript currently under review.</p> <p> </p> <p> </p>
Overview of key residues for mAMCase activity.
<p>This directory contains all files to analyze the aromatic, polar, catalytic, and ligand-binding residues as well as overall protein structure as presented in <strong>Supplemental Figure 5</strong> in the manuscript <a href="https://www.biorxiv.org/content/10.1101/2023.06.03.542675">Díaz et al.<em> </em>(2023)</a>.</p> <p>Structure models were analyzed in PyMOL. Figures were compiled using Adobe Illustrator.</p> <p> </p> <p>Contact:</p> <p>Roberto Efraín Díaz, robertoefrain.diaz@ucsf.edu</p> <p>James Fraser, jfraser@fraserlab.com</p>
District-level Agricultural Residue Burning Emissions (DARBE) in India 2011-2020 v1.3
<p>The data provides satellite-based estimates of agricultural burned area, crop residue dry matter burned and agricultural residue burning emissions across India from 2011-2020. It also includes visualisation tools used in the emission estimation and visualisation of the data for the reader's reference.</p> <p>References: </p> <p>1. IPCC 1996, Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories: Reference Manual (Volume 3) https://www.ipcc-nggip.iges.or.jp/public/gl/invs6c.html (accessed May 04, 2023)</p> <p>2. Jain M, Mondal P, DeFries RS, Small C, Galford GL. Mapping cropping intensity of smallholder farms: A comparison of methods using multiple sensors. Remote Sens Environ 2013;134:210–23. https://doi.org/10.1016/j.rse.2013.02.029.</p> <p>3. Deshpande MV, Pillai D, Jain M. Detecting and quantifying residue burning in smallholder systems: An integrated approach using Sentinel-2 data. Int J Appl Earth Obs Geoinf 2022a;108:1–10. https://doi.org/10.1016/j.jag.2022.102761.</p> <p>4. Deshpande MV, Pillai D, Jain M. Agricultural burned area detection using an integrated approach utilizing multi spectral instrument based fire and vegetation indices from Sentinel-2 satellite. MethodsX 2022b;9. https://doi.org/10.1016/j.mex.2022.101741.</p> <p>5. Sahu SK, Mangaraj P, Beig G, Samal A, Chinmay Pradhan, Dash S, et al. Quantifying the high resolution seasonal emission of air pollutants from crop residue burning in India. Environ Pollut 2021;286:117165. https://doi.org/10.1016/j.envpol.2021.117165.</p>
Dataset for "Cover crop inclusion and residue retention improves soybean production and physiology in drought conditions"
<p>Data and code for "Cover crop inclusion and residue retention improves soybean production and physiology in drought conditions"</p> <p><strong>CONTEXT: </strong>Soybean (<em>Glycine max</em> (L.) Merr.) planting has increased in central and western North Dakota despite frequent drought occurrences that limit productivity. Soybean plants need high photosynthetic and transpiration rates to be productive, but they also need high water use efficiency when water is limited. Retaining crop residues and including cover crops in crop rotations are management strategies that could improve soybean drought resilience in the northern Great Plains. </p> <p><strong>OBJECTIVE</strong>: We aimed to examine how a management practice that included cover crops and residue retention impacts agronomic, ecosystem water and carbon dioxide flux, and canopy-scale physiological attributes of soybeans in the northern Great Plains under drought conditions. </p> <p><strong>METHODS</strong>: We compared two soybean fields over two years with business-as-usual and aspirational management that included residue retention and cover crops during a drought year. This comparison was based on yield, aboveground biomass, Phenocam images, and fluxes from eddy covariance and ancillary measurements. These measurements were used to derive meteorological, physical, and physiological attributes with the ‘big leaf’ framework. </p> <p><strong>RESULTS: </strong>Soybean yields were 29% higher under drought conditions in the field managed in a system that included cover crops and residue retention. This yield increase was caused by extending the maturity phenophase by 5 days, increasing agronomic and intrinsic water use efficiency by 27% and 33%, respectively, increasing water uptake, and increasing the rubisco-limited photosynthetic capacity (V<sub>cmax25</sub>) by 42%.</p> <p><strong>CONCLUSIONS:</strong> The inclusion of cover crops and residue retention into a cropping system improved soybean productivity because of differences in water use, phenology timing, and photosynthetic capacity.</p> <p><strong>IMPLICATIONS:</strong> These results suggest that farmers can improve soybean productivity and yield stability by incorporating cover crops and residue retention into their management practices because these practices allow soybean plants to shift to a more aggressive water uptake strategy.</p> <p><strong>Code:</strong></p> <p><strong>1_phenocam.rmd: </strong>Code to download Phenocam data and identify phenophase transition dates.</p> <p><strong>2_Daily_CO2_Water_Fluxes.Rmd: </strong>Code to analyze daily carbon and water fluxes (Figure 1, 2 3 and Table 2).</p> <p><strong>3_Inferring_LAI_and_Height.Rmd: </strong>Code to calculate the predicted LAI and height for each day. The output is used in the big-leaf framework.</p> <p><strong>4_Big_Leaf.Rmd: </strong>Code for the big-leaf ecophysiology estimates (Figure 4, 5 and 6; Table 3 and 4).</p> <p><strong>4_Data_Dictionary_Vairables:</strong> Code to identify the data dictionary variables. </p> <p> </p>
Verzehrsstudie zur Ermittlung der Lebensmittelaufnahme von Säuglingen und Kleinkindern für die Abschätzung eines akuten Toxizitätsrisikos durch Rückstände von Pflanzenschutzmitteln (VELS) [Consumption study to determine the food intake of infants and young children for the estimation of an acute toxicity risk through residues of plant protection products]
<p>The VELS study (<strong>V</strong>erzehrsstudie zur <strong>E</strong>rmittlung der Lebensmittelaufnahme von <strong>S</strong>äuglingen und Kleinkindern für die Abschätzung eines akuten Toxizitätsrisikos durch Rückstände von Pflanzenschutzmitteln; VELS) was conducted in 2001/2002. Data were collected from 804 infants, toddlers and children (age group 0.5 to <5 years) in Germany. The food consumed was recorded by the parents as "food as eaten" over a total of 6 days (2x3-day food record with an interval of 3-6 months for toddlers and 4-8 weeks for infants). Anthropometric data are also included.</p> <p>Foods are coded according to the German food coding system (BLS; Bundeslebensmittelschlüssel) and food names and additional information is available in German only.</p> <p>Dataset owner: German Federal Institute for Risk Assessment (BfR)</p> <p>Data collection was carried out by the University of Paderborn on behalf of the Federal Ministry of Food and Agriculture [Bundesministerium für Ernährung und Landwirtschaft; BMEL] – formerly Federal Ministry for Food, Agriculture and Consumer Protection [Bundesministerium für Verbraucherschutz, Ernährung und Landwirtschaft; BMVEL]</p>
Figure 4. 1H in General Protocol to Obtain D-Glucosamine from Biomass Residues: Shrimp Shells, Cicada Sloughs and Cockroaches
Figure 4. 1H NMR data (400 MHz, 298 K) of glucosamine hydrochloride in D O: a) 1H NMR spectrum with anomeric distribution at equilibrium; 2 b) mutarotation mechanism of glucosamine; c) anomeric region of the 1H NMR spectra recorded at different elapsed times; and d) relative amount of each anomer as a function of time.
Figure 2 in General Protocol to Obtain D-Glucosamine from Biomass Residues: Shrimp Shells, Cicada Sloughs and Cockroaches
Figure 2. Schematic representation of the general procedure to obtain D-glucosamine from shrimp shells, cicada sloughs, and cockroaches.
Figure 3. a in General Protocol to Obtain D-Glucosamine from Biomass Residues: Shrimp Shells, Cicada Sloughs and Cockroaches
Figure 3. a) IR and b) mass spectra of D-glucosamine obtained from shrimp shells; similar spectra were obtained for the glucosamine isolated from cicada sloughs and cockroaches.
Study of Efficacy and Safety of Twice Daily Oral LNP023 in Adult PNH Patients With Residual Anemia Despite Anti-C5 Antibody Treatment
ClinicalTrials.gov study NCT04558918. IPD Sharing: YES. Countries: 12. Publications: 3.
Data from: Residues neighboring an SH3-binding motif participate in the interaction in vivo
Open the record for dataset details and reuse information.
Data for: Residual trapping of H2
Open the record for dataset details and reuse information.
Enhancing accuracy of air quality and temperature forecasts during paddy crop-residue burning season in Delhi via chemical data assimilation
<p>This paper examines the accuracy of Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) generated 72 h fine particulate matter (PM<sub>2.5</sub>) forecasts in Delhi during the crop residue burning season of Oct-Nov 2017 with respect to assimilation of the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals, persistent fire emission assumption, and aerosol-radiation interactions. The assimilation significantly pushes the model AOD and PM<sub>2.5</sub> towards the observations with the largest changes below 5 km altitude in the fire source regions (northeastern Pakistan, Punjab, and Haryana) as well as the receptor New Delhi. WRF-Chem forecast with MODIS AOD assimilation, aerosol-radiation feedback turned on, and real-time fire emissions reduce the mean bias by 88-195 µg/m<sup>3</sup> (70-86%) with the largest improvement during the peak air pollution episode of 6-13 November 2017. Aerosol-radiation feedback contributes ~21%, ~25%, and ~24% to reduction in mean bias of the first, second, and third day of PM<sub>2.5 </sub>forecast. Persistence fire emission assumption is found to work really well, as the accuracy of PM<sub>2.5</sub> forecasts driven by persistent fire emissions was only 6% lower compared to those driven by real fire emissions. Aerosol-radiation feedback extends the benefits of assimilating satellite AOD beyond PM<sub>2.5</sub> forecasts to surface temperature forecast with a reduction in the mean bias of 0.9<sup>o</sup>C - 1.5<sup>o</sup>C (17-30%). These results demonstrate that air quality forecasting can benefit substantially from satellite AOD observations particularly in developing countries that lack resources to rapidly build dense air quality monitoring networks.</p>
Aerosol particle formation in the upper residual layer: dataset
<p>This dataset contains the following data:</p> <p>-Doppler lidar measurements from Hyytiälä, Finland (May 1-2, 2017)<br> -Ion spectrometer data from Hyytiälä, Finland (May 2, 2017)<br> -Airborne measurement data from southern Finland using an instrumented Cessna 172 (several campaigns between 2011-2018)<br> -New particle formation event classifications from Hyytiälä, Finland</p>
Age predicts risky investment better than residual reproductive value
Life-history theory predicts that investment into reproduction should increase as future reproductive opportunities (i.e., residual reproductive value, RRV) decrease. Researchers have thus intuitively used age as a proxy for RRV and assume RRV decreases with age when interpreting age-specific investment. Yet, age is an imperfect proxy for RRV and may even be a poor correlate in some systems. We used a 30-year study of the nesting ecology of painted turtles ( Chrysemys picta ) to assess how age and RRV compare in explaining variation in a risky investment behavior. We predicted that RRV would be a better predictor of risky investment than age because RRV accounts for variation in future reproductive potential across life. We found that RRV was high in early life, slowly decreased until midlife, and then steadily decreased to terminal reproduction. However, age predicted risky behavior better than RRV. This finding suggests stronger correlates of age (e.g., size) may be more responsible for this behavior in turtles. This study highlights that researchers should not assume that age-specific investment is driven by RRV and that future work should quantify RRV to more directly test this key element of life-history theory.
Towards resolving the complex paramagnetic NMR spectrum of small laccase: Assignments of resonances to residue specific nuclei
<p>NMR dataset as TopSpin (Bruker Corporation) files for the article titled "Towards resolving the complex paramagnetic NMR spectrum of small laccase: Assignments of resonances to residue specific nuclei" (https://doi.org/10.5194/mr-2020-31).</p>
Molecular dynamics derived side chain order parameters for Asp, Glu, Asn, and Gln residues in ribonucleases H, and molecular dynamics trajectories for E. coli ribonuclease H
<p>This dataset contains tab-delimited text files reporting the complete calculated S2 side chain order parameters for carboxyl- and carbonyl-containing residues (Asp, Glu, Asn, Gln) in all RNase H homologs studied, as well as complete simulation trajectory data for the E. coli apo, site A, and site B simulations described in the text. We hope that easy access to the complete trajectory data for the experimentally best-characterized homolog will facilitate future comparison between simulation and experiments.</p> <p>order_parameters_magnesium.dat contains the calculated S2 side chain order parameters for carboxyl- and carbonyl-containing residues in each of the three E. coli trajectories supplied in this dataset. These data are sufficient to reproduce Figure 6.</p> <p>2RN2.apo.pdb, 2RN2.MG.siteA.pdb, and 2RN2.MG.siteB.pdb define the equilibrated structure from which the E. coli apo, site A, and site B trajectories were initiated. All protein atoms, including hydrogens, are included, as well as Mg ions. Explicit waters and chloride counterions have been removed.</p> <p>2RN2.apo.dcd, 2RN2.MG.siteA.dcd, and 2RN2.MG.siteB.dcd contain 100ns trajectories initiated from the starting structures in the corresponding pdb files. Frames were written every 4.5ps. Trajectories have been aligned to the initial structure of the apo trajectory to facilitate visualization.</p> <p>The remaining *.S2.CO.dat files contain the calculated S2 side chain order parameters for all apo RNases H simulated in this study. Each file is named for the PDB ID of its corresponding structure (see Table 1). These data are sufficient to reproduce Figure 8 (which reports on only the conserved active-site residues).</p>
Results for paper "Energy dependent mesh adaptivity of discontinuous isogeometric discrete ordinate methods with dual weighted residual error estimators"
<p>This spreadsheet contains the results used to generate the plots in the paper "Energy dependent mesh adaptivity of discontinuous isogeometric discrete ordinate methods with dual weighted residual error estimators".</p>
Data providers package for reporting monitoring results for veterinary medicinal product residues (2017 test phase)
<p>This data providers package provides the data collection configuration and supporting materials for reporting veterinary medicinal product residues (VMPR) results according to Council Directive 96/23/EC of 29 April 1996 on measures to monitor certain substances and residues thereof in live animals and animal products and repealing Directives 85/358/EEC and 86/469/EEC and Decisions 89/187/EEC and 91/664/EEC. These are to be used for the 2017 data reporting test phase.</p> <p>The package includes;</p> <p>Advice on the values to be reported for the mandatory fields specified for this data collection</p> <p>The Standard Sample Description Version 2 XML schema definition for VMPR reporting</p> <p>The STX transformation file which automatically assigns sampEventId and sampAnId when this information is not provided</p> <p>The general and VMPR specific business rules applied for the automatic validation of the submitted datasets</p> <p>The VMPR specific terminologies to be used for reporting analytical methods and the residues included in the scope of the analytical methods</p> <p>An excel tool which can support users in creating the required XML file for submission where automated data collation tools are not available and a manual for the tool</p> <p> </p>
The role of injection method on residual trapping at the pore-scale in continuum-scale samples: raw micro-CT dataset
<p>The experiments in this work explore the role of a variable injection rate on gas saturation and residual trapping. There are 2 experiments in this work H2L (high to low injection rate) and L2H (low to high injection rate). The workflow for processing the micro-CT images to get the segmented images is described in [1]. </p><p>The following scans are included in this repository NB. all data for this repository is segmented micro-CT data.: </p><ol><li>Dry scan prior to experiment = <a href="https://zenodo.org/api/records/10145102/draft/files/05_dry_bin2_merged.tif/content">05_dry_bin2_merged.tif</a></li><li>Sample fully saturated with brine = <a href="https://zenodo.org/api/records/10145102/draft/files/07_wet_bin2_merged.tif/content">07_wet_bin2_merged.tif</a></li><li>H2L during high flow = <a href="https://zenodo.org/api/records/10145102/draft/files/09_h2lh_merged.tif/content">09_h2lh_merged.tif</a></li><li>H2L during low flow = <a href="https://zenodo.org/api/records/10145102/draft/files/11_h2ll_2_merged.tif/content">11_h2ll_2_merged.tif</a></li><li>H2L at the end of drainage (no flow) = <a href="https://zenodo.org/api/records/10145102/draft/files/16_dra1_pd5_merged.tif/content">16_dra1_pd5_merged.tif</a></li><li>H2L at the end of imbibition (no flow) = <a href="https://zenodo.org/api/records/10145102/draft/files/21_imb1_pi1_merged.tif/content">21_imb1_pi1_merged.tif</a></li><li>Sample fully resaturated with brine = <a href="https://zenodo.org/api/records/10145102/draft/files/28_wet2_bin2_merged.tif/content">28_wet2_bin2_merged.tif</a></li><li>L2H during low flow = <a href="https://zenodo.org/api/records/10145102/draft/files/29_2_l2hl_merged.tif/content">29_2_l2hl_merged.tif</a></li><li>L2H during high flow = <a href="https://zenodo.org/api/records/10145102/draft/files/30_l2hh_merged.tif/content">30_l2hh_merged.tif</a></li><li>L2H at the end of drainage (no flow) =<a href="https://zenodo.org/api/records/10145102/draft/files/31_dra2_pd1_merged.tif/content">31_dra2_pd1_merged.tif</a></li><li>L2H at the end of imbibition (no flow) =<a href="https://zenodo.org/api/records/10145102/draft/files/33_imb2_pi1_merged.tif/content">33_imb2_pi1_merged.tif</a></li></ol>
Residual force depression data
<p>Raw data used to create the results text and figures presented in the manuscript entitled "Residual force depression is not related to positive muscle fascicle work during submaximal voluntary dorsiflexion contractions in humans".</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.