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3,494 results for “modified”
Monthly aerosol emissions and GHG concentration projections from 2020-2025: modified SSP2-4.5 to account for COVID-19 impacts on sector activity
<p>This repository holds the netcdf files for emissions and concentrations projected by the scenario SSP2-4.5, from the Scenario4MIPs database (<a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown, projected out for 5 years after 2020. The details of these activity estimates are available from <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a>.</p> <p>The methodology behind these calculations is based on <a href="https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/">https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/tree/endof2020</a>, a slight modification of the approach used in <a href="https://zenodo.org/record/3947917#.XxR_qyhKhPZ">https://zenodo.org/record/3947917#.XxR_qyhKhPZ</a> to have a different timeframe. </p> <p>Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a> </p>
Four-year blip emissions changes due to COVID-19: modified SSP2-4.5 to account for sector activity level
<p>This repository holds the netcdf files for aerosol emissions projected by the scenario SSP2-4.5, from the Scenario4MIPs database ( <a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown, projected out for 5 years after 2020 before returning to baseline. The details of these activity estimates runs in parallel to those described in <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a>, except instead of a 2-year blip, we have done a 4-year blip. Note that it is one year after the blip has finished before things return to baseline.</p> <p>The methodology behind these calculations is based on <a href="https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/">https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/</a>, a slight modification of the approach used in <a href="https://zenodo.org/record/3947917#.XxR_qyhKhPZ">https://zenodo.org/record/3947917#.XxR_qyhKhPZ</a> for aerosols emissions. We present only a single scenario (called 4-year blip, featuring a one year recovery after the end of the 4 years) compared to the baseline.</p> <p>Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a> </p>
Dataset of "Asparagine-Modified Magnetic Graphene Oxide: An Efficient and Green Nanocatalyst for Synthesis of 5-oxodihydropyrano[3,2-c]chromenes and dihydropyrano[2,3- c]pyrazole derivatives and the Density functional theory calculation".
<p>The primary focus of this study involved the fabrication of a novel nanocatalyst Fe3O4-supported asparagine functionalized graphene oxide (Fe3O4@GO-N-(Asparagine)). The catalyst was synthesized through a four-step procedure.</p>
NEON distributed initial soil characterization dataset (DP1.10047.001) modified for statistical analysis of organic carbon and extractable metals in Hall and Thompson (2021)
We compiled National Ecological Observatory Network (NEON) datasets related to the initial distributed soil sampling effort and subsetted them (removed samples with missing values for certain variables, and several samples with extreme values) for use in statistical analyses to describe relationships between soil organic carbon (SOC) and metals measured in several soil chemical extractions. The NEON provisional data products we used were DP1.10047.001 and DP1.10008.001, which were subsequently combined by NEON as a single data product DP1.10047.001, “Soil physical and chemical properties, distributed initial characterization”. These datasets were used for the analyses reported in a manuscript by Hall and Thompson (2021) in the Soil Science Society of America Journal.
Long-term composited Modified Normalized Difference Water Index (MNDWI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2023
Abstract ======== This data package consists of multiple decades of modified normalized difference water index (MNDWI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona (USA), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). By providing a metric by which to reliably identify bodies of open water, these MNDWI data are intended to facilitate analyses of land-based environmental variables (e.g., urbanization, vegetation, land surface temperature) and can also be used to track long-term and seasonal change in the coarse extent of open water as a land-cover type. MNDWI was derived, following the methods of Xu (2006), from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see \'Methods and Protocols\') and accompanying Javascript code. **Citations:** - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18--27. <https://doi.org/10.1016/j.rse.2017.06.031> - Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. *International Journal of Remote Sensing*, *27*(14), 3025--3033. <https://doi.org/10.1080/01431160600589179>
Monthly CO2 emissions projections from 2015-2025: modified SSP2-4.5 to account for COVID-19 impacts on sector activity
<p>Monthly CO2 emissions projections 2015-2025, modified by country-specific impacts of COVID-19 lockdown in 2020-2023, with 4 different projections for the period 2024-2025. </p> <p>This repository holds the netcdf files for CO2 emissions from ground-level and aviation sources from the MESSAGE_GLOBIOM scenario SSP2-4.5, from the Scenario4MIPs database (<a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown for 2020. Sector activity level in 2020 is based on data up until June, and a fixed estimate is used thereafter. This is the monthly equivalent of <a href="https://zenodo.org/record/3951601#.XxYBsihKhPY">https://zenodo.org/record/3951601#.XxYBsihKhPY</a> for this time period.</p> <p>Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a> </p> <p>see <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a> for more details.</p>
Data for: Temperature-controlled Molecular Bonding Hysteresis: Interphase Dynamics of a Nanoparticle-modified Polymer Network
<p>The data is supplementary to the publication "Temperature-controlled Molecular Bonding Hysteresis: Interphase Dynamics of a Nanoparticle-modified Polymer Network", DOI: <a title="DOI URL" href="https://doi.org/10.1021/acs.jpclett.4c00406">10.1021/acs.jpclett.4c00406</a></p> <p>Key words: Thermal volume expansion, Interphase dynamics, Temperature-modulated optical refractometry, Nanoparticles, Optical Remanence, Hysteresis, Refractive index</p> <p>The data sets contain measured and processed data on the interphase dynamics of a nanoparticle modified epoxy resin collected via Temperature-modulated optical refractometry (TMOR).</p> <p>Material details:</p> <ul> <li>Cycloaliphatic epoxy resin + Anhydride curing agent + 1-methylimidazole</li> <li>Core-shell rubber nanoparticles, 100 nm, dispersed in a cycloaliphatic epoxy carrier resin</li> </ul> <p>Funding received from:</p> <ul> <li>German Research Foundation (DFG), project number: 521902629.</li> </ul>
Modified WRF/Chem source code, output data, and post-processing scripts for the GMD manuscript "Evaluation of WRF/Chem model (v3.9.1.1) real-time air quality forecasts over the Eastern Mediterranean"
<p>Here you will find the modified WRF/Chem code used in the simulations, the scripts used for post-processing and the model output data used in the manuscript. </p> <p>Two modifications have been made in module_aerosols_soa_vbs.F:</p> <ol> <li>ch_dust is set to1.0D-9*0.36</li> <li>The model is set not to initialize during restarts</li> </ol> <p>The model data directory includes:</p> <ol> <li>Two csv files (Winter and Summer) with the hourly concentrations of atmospheric pollutants at the locations of the ground stations. These data were used to produce Figures 4-8 in the manuscript as well as all the metrics.</li> <li>Two netcdf files (Winter and Summer) with the average ground concentrations of atmospheric pollutants over Cyprus. These data were use to produce Figure 3 in the manuscript. </li> </ol>
Data for "Modelling soil carbon stocks following reduced tillage intensity: a framework to estimate decomposition rate constant modifiers for RothC-26.3, demonstrated in north-west Europe"
<p>Dataset of paired observations of conventional tillage (CT) with no tillage (NT) and reduced tillage (RT) from studies in temperate oceanic regions of Western Europe, extracted from a recent systematic review (Jordon et al. preprint, see DOI below).</p> <p>R code of modelling framework to estimate tillage rate modifiers (TRM) for simulating adoption of RT and NT using RothC-26.3, and meta-estimates of TRM across studies.</p>
Data and script for Van Berkel et al: Can starlings use a reliable cue of future food deprivation to adaptively modify foraging and fat reserves?
<p>Supporting materials for:</p> <p><strong>Can starlings use a reliable cue of future food deprivation to adaptively modify foraging and fat reserves?</strong></p> <p>Menno van Berkel<sup>a</sup>, Melissa Bateson<sup>a</sup>, Daniel Nettle<sup>a</sup> and Jonathon Dunn<sup>a</sup>*</p> <p><sup>a</sup>Centre for Behaviour and Evolution & Institute of Neuroscience, Newcastle University, Newcastle, UK</p> <p>*Author for correspondence (email: jonathon.dunn@newcastle.ac.uk; telephone: (+44)7730015855; postal address: Institute of Neuroscience, Henry Wellcome Building, The Medical School, Framlington Place, Newcastle University, Newcastle upon Tyne, UK, NE2 4HH).</p> <p>R script and 3 .csv files.</p>
Functional redundancy of non-volant small mammals increases in human-modified habitats
<p>This repository hosts all R codes, data and output supporting the findings of the study "Functional redundancy of non-volant small mammals increases in human-modified habitats", by André L. Luza (UFRGS, BR), Catherine H. Graham (WSL, CH), Sandra M. Hartz (UFRGS, BR), and Dirk, N. Karger (WSL, CH).</p> <p>The only data that are not here are the Ecoregions of WWF. These data can be found in the webpage of WWF.</p>
Climate warming and drought modify galling effects on tall goldenrod
These data and R scripts are from a study of climate change impacts on galling in goldenrod (Solidago altissima), at Kellogg Biological Station Long-Term Ecological Research (KBS LTER) site, Hickory Corners, Michigan, USA during the summers of 2021-2022 (REX 2024). This study set is part of the KBS LTER Rainfall Exclusion eXperiment (REX). Goldenrod plants with and without galls caused by Rhopalomyia solidaginis were exposed to warmed, drought, and warmed x drought treatments, and ambient (no treatment) and irrigated control conditions. Warming was achieved by use of open-top chambers for tall-stature plant communities (Welshofer et al. 2018 MEE) and a 6-week drought was implemented by use of rain-out shelters (Kahmark et al. 2024 Zenodo). L0 data are available upon request; they include the raw data from the KBS LTER REX project. The scripts that are used to clean L0 data and produce L1 data are also available upon request. The L1 data are the result of merged L0 data and are cleaned for typos and use standardized names. L1 data contain plant and gall traits from all treatments. The L2 scripts use the L1 data for statistical analyses and to create figures. Literature cited: Kahmark, K., Jones, M., Bohm, S., Baker, N., & Robertson, G. P. (2024). Rainfall manipulation shelters for agricultural research. Zenodo. https://doi.org/10.5281/zenodo.10607631. Rain Exclusion eXperiment (REX). (2024). https://lter.kbs.msu.edu/research/rainfall-exclusion-experiment/. https://lter.kbs.msu.edu/research/rainfall-exclusion-experiment/. Welshofer KB, Zarnetske PL, Lany NK, Thompson LAE (2018) Open-top chambers for temperature manipulation in taller-stature plant communities. Methods Ecol Evol 9:254–259. https://doi.org/10.1111/2041-210X.12863.
Multi-omic approach to identify phenotypic modifiers underlying cerebral demyelination in X-linked adrenoleukodystrophy
<p>These are the data tables used to produce results in the publication:</p> <p>"Multi-omic approach to identify phenotypic modifiers underlying cerebral demyelination in X-linked adrenoleukodystrophy."<br> Phillip A. Richmond & Frans van der Kloet et al.</p> <p>Submitting to Frontiers in Cellular and Developmental Biology, 2020, Peroxisomal Special Issue. </p> <p>These tables include normalized measurements from four omics technologies, with no identifying information included. For details on processing, see the manuscript or contact:</p> <p>prichmond (at) cmmt (dot) ubc (dot) ca. </p> <p>Description of Files</p> <ul> <li>Sample mapping <ul> <li>20180314_sib_pairs.xlsx <ul> <li>Excel sheet describing family numbering, etc. used as a mapping table within the sheets below. </li> </ul> </li> </ul> </li> <li>Methylation: <ul> <li>DMRs_5_Families_ALL_0.10DB_Dec2019.csv <ul> <li>Significant methylated regions with delta beta at least 10 percent when a single family is left out</li> </ul> </li> <li>ALD_Deconvoluted_Betas_Dec2019.csv <ul> <li>All fitted betas for every subject (single CpG)</li> </ul> </li> <li>ALD_Limma_Final_Dec2019_CHR.csv <ul> <li>All fitted effects using limma modeling per CpG </li> </ul> </li> </ul> </li> <li>RNA: <ul> <li>Count_data.txt <ul> <li>The raw count table summed at the gene level using featureCounts.</li> </ul> </li> <li>Pvalues_all_23_01_2019.csv <ul> <li>All pvalues and log fold changes for the genes included in the modeling process (also with family left out)</li> </ul> </li> <li>Tmm_norm_counts_5_2_2020.csv <ul> <li>Tmm normalized RNA count data</li> </ul> </li> </ul> </li> <li>Proteomic <ul> <li>Report_Precursor_Peptides.xls <ul> <li>The proteomic data as an excel spreadsheet</li> </ul> </li> </ul> </li> <li>Pvalues_prot_13_3_2019.xlsx <ul> <li>The pvalues and log fold changes (also with family left out)</li> </ul> </li> <li>Lipids: <ul> <li>Lipid_data.csv <ul> <li>The lipid data (metabolites with missings are removed)</li> </ul> </li> <li>Pvalues_lipids.csv <ul> <li>Pvalues for the lipid data (also with family left out)</li> </ul> </li> </ul> </li> </ul> <p><br> NOTE: For use of these data files for processing and reproducing results of the manuscript, please see https://github.com/Phillip-a-richmond/ALD_Modifier_Project. </p> <p> </p>
Modified Swadesh-100 list of 23 Cariban languages
<p>Cite the source of the dataset as:</p> <blockquote> <p>Matter, Florian. 2020. Comparative Cariban Database. Leipzig: Max Planck Institute for Evolutionary Anthropology. (Available online at https://cariban.clld.org/)</p> </blockquote>
Eectrochemical immunosensor for the quantification of S100B at clinically relevant levels using a cysteamine modified surface
<p>Datasets analyzed during the work titled "An electrochemical immunosensor for the quantification of S100B at clinically relevant levels using a cysteamine modified surface".</p>
Analyzing marine biofilms developed on carbon nanotube-modified surfaces by 3D OCT approach
<p>Glass, epoxy resin, and carbon nanotubes (CNT) composite were analyzed regarding wettability by water contact angle measurement, and roughness by atomic force microscopy. Cyanobacterial biofilms formed by Nodosilinea cf. nodulosa LEGE 10377 were developed on these surfaces for seven weeks and under controlled hydrodynamic conditions. Biofilm wet weight and structural parameters such as biofilm thickness, contour coefficient, biovolume, porosity, and average size of non-connected pores obtained from Optical Coherence Tomography (OCT) were assessed.</p>
Collective Strong Coupling Modifies Aggregation and Solvation - Dataset
<p>Dataset to complement "Collective Strong Coupling Modifies Aggregation and Solvation" - includes output and cube files obtained using the <a href="https://etprogram.org/">eT program</a>, an open source electronic (and molecular-polaritonic) structure program.</p> <p>See the paper at <a title="DOI URL" href="https://doi.org/10.1021/acs.jpclett.3c03506">https://doi.org/10.1021/acs.jpclett.3c03506</a></p>
Ultra-high-resolution modified RGB UAV-imaging of Alternaria solani
<p>This dataset is collected from both symptomatic and non-symptomatic plants during the growing seasons of 2019 and 2022, on 40x20 m experimental fields in Lemberge (Merelbeke), Belgium (50.986544°N, 3.774066°E) using a DJI M600 PRO unmanned aerial vehicle equiped with a modified Sony Alpha 7III camera with 135 mm lens. The field trial is conducted in analogy to the method described by Van De Vijver et al. (2020, 2022), using two different cultivers, Spunta (2019) and Fontane (2022) respectively. The dataset of 2019 comprises data from three different flights (3, 6 and 9 days after inoculation) and the dataset of 2022 from four different flights (5,7, 9 and 13 days after inoculation). </p> <p>This dataset consists out of 7660 patches of 256x256 pixels, cropped out of the original images, labeled and sorted in two categories (1: Alternaria, 0: no Alternaria), accompagned by a csv file containing the following information:</p> <ul> <li>Original patch name</li> <li>Random patch name (used during the labeling process)</li> <li>Row patch number</li> <li>Column patch number</li> <li>Block number, column block number and row block number</li> <li>Original mage name</li> <li>Coordinates of original image: latitude, longitude, altitude</li> <li>Date of flight</li> <li>Label (0: no Alternaria, 1: Alternaria)</li> </ul> <p>More detailed information about this dataset (both the collection and the preprocessing) can be found in the corresponding article 'Ultra-high-resolution UAV-Imaging and Supervised Deep Learning for Accurate Detection of Alternaria Solani in Potato Fields.' </p> <p> </p> <p>If you use this dataset, please refer to the related journal paper as follows: "Wieme J, Leroux S, Cool SR, Van Beek J, Pieters JG and Maes WH (2024) Ultra-highresolution UAV-imaging and supervised deep learning for accurate detection of Alternaria solani in potato fields. Front. Plant Sci. 15:1206998. doi: 10.3389/fpls.2024.1206998"</p> <p> </p> <p>This dataset was gathered within the Proeftuin Smart Farming 4.0 project (180503) within the Industry 4.0 Living Labs with funding from Flanders innovation & entrepreneurship (VLAIO, Belgium) and in the Horizon 2020 project SmartAgriHubs - Connecting the dots to unleash the innovation potential for digital transformation of the European agrifood sector with funding from the European Union under grant agreement No. 818182. Jana Wieme is funded by grant 1SE3921N of Research Foundation Flanders (FWO).</p> <p> </p>
Investigating the ageing process of polymer modified bitumen using a modified Thin-Film Oven Test in the aspect of recycling purpose within Weave-UNISONO 2021 project, NCN project No 2021/03/Y/ST8/00079
<div><strong>Summary:</strong></div> <div>One polymer-modified bitumen PMB 25/55-60 was tested in two stages: original and after the modified Thin-Film Oven Test (TFOT). The time ranges from 1h-5h, and temperatures from 120°C-200°C were used. The Fourier-Transform Infrared (FTIR) Spectroscopy and Dynamic Shear Rheometer (DSR) with parallel plates were conducted. Test temperatures range from 30–70°C for a 25 mm diameter plate and 0–30°C for an 8 mm plate with 10°C intervals and angular frequency range of 0.1, 1.0, and 10 Hz.</div> <div> </div> <div> </div> <div><strong>The dataset includes:</strong></div> <div>Basic characteristics of bituminous binder (R&B Temperatur, Penetration), CSV raw data:</div> <div> <ul> <li>01 - SP Pen.csv</li> </ul> </div> <div> </div> <div>Dynamic shear rheometer (Temperatures 0-70 °C, Angular Frequency 0.1Hz, 1.0Hz, 10 Hz, Complex Shear Modulus, Phase Angle):</div> <ul> <li>02.1 - DSR Rheology_Unaged.csv</li> <li>02.2 - DSR Rheology_2h_140C.csv</li> <li>02.3 - DSR Rheology_2h_200C.csv</li> <li>02.4 - DSR Rheology_5h_140C.csv</li> <li>02.5 - DSR Rheology_5h_200C.csv</li> </ul> <div> </div> <div>FTIR - Fourier-Transform Infrared Spectroscopy </div> <div> <ul> <li>OPUS Spectroscopy files.zip</li> </ul> </div> <div> </div> <div>--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---</div> <div>to open the OPUS files, please go to the © Bruker webpage and download the free OPUS Viewer.</div> <div>https://www.bruker.com/en/products-and-solutions/infrared-and-raman/opus-spectroscopy-software/downloads.html</div> <div>--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---</div> <div> </div>
Perception and evaluation of (modified) wood by older adults from Slovenia and Norway (Datasets, R analysis code, and supplementary tables)
<p>This entry contains datasets, R analysis code, and supplementary tables for the article <em>Perception and evaluation of (modified) wood by older adults from Slovenia and Norway.</em></p> <p>The article investigates human perception and evaluation of handrails made of different materials. Our goal was to identify how older adults perceive handrails made of unmodified wood, modified wood, and steel. We examined if certain materials are more preferred than others, which material properties might be associated with differences in human preference, and what are the roles of tactile and tactile-visual domains in material perception. Our analysis is based on the results from an 11-item rating scale and a ranking task.</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.