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18,658 results for “impacts”
Graph Data: Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios
<p>Data used for creating the figures in the paper: Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios.</p> <p>It contains the flow exceedances (as mm day<sup>-1</sup>), flow duration slope, median elasticity and runoff ratio for the different afforestation scenarios. Also included is the information on the changes of broadleaf afforestation. </p> <p>If you have any questions, please email marcus.buechel@ouce.ox.ac.uk.</p>
Martian crater ages and crater counting - Does the impact flux of small and large asteroids varied through time on Mars, the Earth and the Moon?
<ul> <li>The SM_mars_crater_dating.xlsx table contains all the information used to date the 49 martian impact craters considered in this study (< 600 Ma). </li> </ul> <ol> <li>CRATER ID </li> <li>CRATER NAME</li> <li>DIAM KM </li> <li>LAT </li> <li>LONG </li> <li>DEPTH RIM KM </li> <li>DEPTH SURF KM </li> <li>DEPTH FLOOR KM </li> <li>NUMBER LAYER</li> <li>MORPHO EJECTA </li> <li>PRESERVATION </li> <li>COUNT AREA KM2: counting area from ejecta banket mapping </li> <li>COUNT AREA ASCI* KM2: counting area after removal of surfaces contaminated by secondary craters </li> <li>THRESHOLD AREA KM2: minimum size of Voronoi polygon area below which all associated detected craters are considered of secondary origin</li> <li>NB SEC: number of secondary craters dentified by ASCI </li> <li>PERCENT SEC</li> <li>NB CRAT 100M: total number of craters > 100 m detected by the CDA** on the CTX global mosaic*** over the counting area</li> <li>NB PRIM 100M: number of craters identified as primaries by ASCI</li> <li>TURNOFF DIAM KM: minimum crater diameter used to fit the crater-size frequency distribution (CSFD) with an isochron</li> <li>NB CRAT FIT: number of craters used to fit the CSFD with an isochron</li> <li>AGE GA: model age based on Hartmann (2005) chronology model**** and Michael et al. (2016) fitting technique*****</li> <li>AGE MAX GA</li> <li>AGE MIN GA</li> <li>N(1): equivalent number of accumulated craters >1km per km2</li> <li>N(1) MAX</li> <li>N(1) MIN</li> </ol> <p>*ASCI: Automatic Secondary Crater Identification: A. Lagain, K. Servis, G. K. Benedix, C. Norman, S. Anderson, P. A. Bland, Model Age Derivation of Large Martian Impact Craters, Using Automatic Crater Counting Methods, Earth and Space Science 8 (2) (2021). doi:10.1029/2020EA001598.</p> <p>**CDA: Crater Detection Algorithm: G. K. Benedix, A. Lagain, K. Chai, S. Meka, S. Anderson, C. Norman, P. A. Bland, J. Paxman, M. C. Towner, T. Tan, Deriving Surface Ages on Mars Using Automated Crater Counting, Earth and Space Science 7 (3) (2020). doi:10.1029/2019EA001005.</p> <p>*** CTX global mosaic: Context Camera global mosaic: J. L. Dickson, L. A. Kerber, C. I. Fassett, B. L. Ehlmann, A Global, Blended CTX Mosaic of Mars with Vectorized Seam Mapping: A New Mosaicking Pipeline Using Principles of Non-Destructive Image Editing, in: Lunar and Planetary Science Conference (2018), p. 2480.</p> <p>**** W. K. Hartmann, Martian cratering 8: Isochron refinement and the chronology of Mars, Icarus 174 (2) (2005) 294–320. doi:10.1016/j.icarus.2004.11.023.</p> <p>***** G. G. Michael, T. Kneissl, A. Neesemann, Planetary surface dating from crater size-frequency distribution measurements: Poisson timing analysis, Icarus 277 (2016) 279–285. doi:10.1016/j.icarus.2016.05.019.</p> <ul> <li>The crater_counting.csv table contains the location and size of impact craters used to derive the ages of the 49 craters younger than 600 Ma old presented in this study. </li> </ul>
Citation data of arXiv eprints and the associated quantitatively-and-temporally normalised impact metrics
<p><strong>Data collection</strong></p> <p>This dataset contains information on the eprints posted on arXiv from its launch in 1991 until the end of 2019 (1,589,006 unique eprints), plus the data on their citations and the associated impact metrics. Here, eprints include preprints, conference proceedings, book chapters, data sets and commentary, i.e. every electronic material that has been posted on arXiv. </p> <p>The content and metadata of the arXiv eprints were retrieved from the arXiv API (https://arxiv.org/help/api/) as of 21st January 2020, where the metadata included data of the eprint’s title, author, abstract, subject category and the arXiv ID (the arXiv’s original eprint identifier). In addition, the associated citation data were derived from the Semantic Scholar API (https://api.semanticscholar.org/) from 24th January 2020 to 7th February 2020, containing the citation information in and out of the arXiv eprints and their published versions (if applicable). Here, whether an eprint has been published in a journal or other means is assumed to be inferrable, albeit indirectly, from the status of the digital object identifier (DOI) assignment. It is also assumed that if an arXiv eprint received <em>c</em><sub>pre</sub> and <em>c</em><sub>pub</sub> citations until the data retrieval date (7th February 2020) before and after it is assigned a DOI, respectively, then the citation count of this eprint is recorded in the Semantic Scholar dataset as <em>c</em><sub>pre</sub> + <em>c</em><sub>pub</sub>. Both the arXiv API and the Semantic Scholar datasets contained the arXiv ID as metadata, which served as a key variable to merge the two datasets.</p> <p>The classification of research disciplines is based on that described in the arXiv.org website (https://arxiv.org/help/stats/2020_by_area/). There, the arXiv subject categories are aggregated into several disciplines, of which we restrict our attention to the following six disciplines: Astrophysics (‘astro-ph’), Computer Science (‘comp-sci’), Condensed Matter Physics (‘cond-mat’), High Energy Physics (‘hep’), Mathematics (‘math’) and Other Physics (‘oth-phys’), which collectively accounted for 98% of all the eprints. Those eprints tagged to multiple arXiv disciplines were counted independently for each discipline. Due to this overlapping feature, the current dataset contains a cumulative total of 2,011,216 eprints. </p> <p>Some general statistics and visualisations per research discipline are provided in the original article (Okamura, 2022), where the validity and limitations associated with the dataset are also discussed.</p> <p> </p> <p><strong>Description of columns (variables)</strong></p> <ul> <li><strong>arxiv_id</strong> : arXiv ID</li> <li><strong>category</strong> : Research discipline</li> <li><strong>pre_year</strong> : Year of posting v1 on arXiv</li> <li><strong>pub_year</strong> : Year of DOI acquisition</li> <li><strong>c_tot</strong> : No. of citations acquired during 1991–2019</li> <li><strong>c_pre</strong> : No. of citations acquired before and including the year of DOI acquisition</li> <li><strong>c_pub</strong> : No. of citations acquired after the year of DOI acquisition</li> <li><strong>c_<em>yyyy</em></strong> (<em>yyyy</em> = 1991, …, 2019) : No. of citations acquired in the year <em>yyyy</em> (with ‘<em>yyyy</em>’ running from 1991 to 2019)</li> <li><strong>gamma</strong> : The quantitatively-and-temporally normalised citation index</li> <li><strong>gamma_star</strong> : The quantitatively-and-temporally standardised citation index</li> </ul> <p><em>Note:</em> The definition of the quantitatively-and-temporally normalised citation index (γ; ‘gamma’) and that of the standardised citation index (γ*; ‘gamma_star’) are provided in the original article (Okamura, 2022). Both indices can be used to compare the citational impact of papers/eprints published in different research disciplines at different times. </p> <p> </p> <p><strong>Data files</strong></p> <p>A comma-separated values file (‘<strong>arXiv_impact.csv</strong>’) and a Stata file (‘<strong>arXiv_impact.dta</strong>’) are provided, both containing the same information.</p> <p> </p>
Impact of the dynamics of the catalytic arginine on nitrite and chlorite binding by dimeric chlorite dismutase
<ul> <li><strong>Data type</strong>: spectroscopic measurements (UV-visible, ECD, EPR), DSC measurements, X-ray crystallography datasets, kinetic measurements, Molecular Dynamics simulations and data analysis.</li> <li>Files are in <strong>spc, par, DTA, DSC, dsx, csv, mtz </strong>formats</li> <li>Information on <strong>origin of the data</strong>: <ul> <li>EPR spectroscopic measurements in <strong>spc</strong>,<strong> par</strong>,<strong> DTA </strong>and<strong> DSC</strong> formats</li> <li>EPR spectroscopic simulation and analyses in <strong>m </strong>and<strong> mat</strong> format</li> <li>UV-vis spectroscopic measurements in <strong>csv</strong> format</li> <li>ECD measurements in <strong>dsx</strong> format</li> <li>Enzyme activity data in <strong>csv</strong> format</li> <li>DSC measurements in <strong>csv </strong>format</li> <li>X-ray data in <strong>mtz</strong> format</li> <li>MD simulations data are in<strong> xlsx</strong> format</li> </ul> </li> <li>The data are <strong>generated</strong> by: <ul> <li>UV−vis spectra were recorded using a Cary 60 UV−vis spectrophotometer (Agilent).</li> <li>Electronic circular dichroism spectroscopy was performed using Chirascan (Applied Photophysics, Leatherhead, U.K.).</li> <li>X-Band CW-EPR experiments were performed on A) a Bruker ESP300E spectrometer equipped with a liquid helium cryostat (Oxford Inc.); B) a Bruker ELEXSYS E580 X-band spectrometer equipped with an Oxford ESR 900 continuous-flow helium cryostat and a Bruker ER 4122 SHQ resonator.</li> <li>Enzyme activity was measured polarographically following the release of O2 by using a Clark-type oxygen electrode (Oxygraph Plus; Hansatech Instruments, Norfolk, U.K.).</li> <li>Differential scanning calorimetry experiments were performed on a Micro-Cal PEAQ-DSC Automated instrument (Malvern Panalytical Ltd., Malvern, U.K.) equipped with an autosampler for 96-well plates and controlled by the MicroCal PEAQ-DSC software.</li> <li>Crystallization experiments were performed using the sitting drop vapor diffusion method in SWISSCI MRC three-well crystallization plates (Molecular Dimensions, Newmarket, U.K.). Crystallization drops were set up using a mosquito crystallization robot (TTP Labtech). Commercially available crystallization screens were used for further screening. Crystallization plates were stored in a Formulatrix RI-1000 imaging device at 22 °C. Data were collected at 100 K using an Eiger2 XE 16 M detector at the beamline i04 at the Diamond Light Source (DLS, Didcot, United Kingdom). Further data was collected at 100 K at beamline ID30A-3 using a Eiger X 4 M detector, at beamline ID-23-1 using a Pilatus 6 M detector and at beamline ID23-2 using a PILATUS3 X 2 M detector at the of European Synchrotron Radiation Facility (ESRF, Grenoble, France). Data sets were processed with XDS, and symmetry equivalent reflections merged with XDSCONV.</li> <li>Molecular dynamics simulations were performed using the GROMOS11 molecular simulation package and GROMOS force field 54A8. Analyses of the coordinate trajectories was done with Gromos++ programs hbond, rdf and mdf. Coordinates at specific time point were generated using Gromos++ program frameout and analysed using PyMOL Molecular Graphics System.</li> <li> <ul> <li>Files in <strong>PARACAT_WP3_20211216_01_EPR </strong>folder includes EPR spectroscopic measurements and computer simulations/analyses, original data are in <strong>spc</strong>/<strong>par </strong>or<strong> DTA/DSC</strong> formats; files in <strong>m</strong> format were used to process the data.</li> <li>Files in <strong>PARACAT_WP3_20211216_02_UV-vis </strong>folder includes UV-Vis spectroscopic measurements of pH-titration in <strong>csv</strong> format.</li> <li>Files in <strong>PARACAT_WP3_20211216_03_ECD </strong>folder includes ECD measurements of the far UV (180–260 nm) and visible (260–500 nm) area, as well as unfolding curves in <strong>dsx</strong> format.</li> <li>Files in <strong>PARACAT_WP3_20211216_04_activity </strong>folder includes Clark electrode/activity measurements in<strong> csv</strong> format.</li> <li>Files in <strong>PARACAT_WP3_20211216_05_DSC </strong>folder includes DSC measurements in <strong>csv </strong>format.</li> <li>Files in <strong>PARACAT_WP3_20211216_06_Xray </strong>folder includes pre-processed (from the beamline pipeline) data sets in <strong>mtz</strong> format.</li> <li>Files in <strong>PARACAT_WP3_20211216_MD-Simulations </strong>folder includes pre-processed molecular dynamics data in<strong> xlsx</strong> format.</li> </ul> </li> </ul> </li> </ul> <p>NB. See the “READ ME” text file in each subfolder for more detailed information on files organization.</p> <ul> <li><strong>Information on</strong>: <ul> <li>Abbreviations:<strong> Cld</strong>, chlorite dismutase; <strong><em>C</em>Cld</strong>, chlorite dismutase from Cyanothece sp. PCC7425; <strong>CW</strong>, continuous wave; <strong><em>D</em></strong>, tetragonal zero-field splitting; <strong>DSC</strong>, differential circular calorimetry; <strong><em>E</em></strong>, rhombic zero-field splitting; <strong>ECD</strong>, electronic circular dichroism; <strong>EPR</strong>, electron paramagnetic resonance; <strong>HS</strong>, high-spin; <strong>LS</strong>, lowspin; <strong><em>Nd</em>Cld</strong>, chlorite dismutase from “Candidatus Nitrospira defluvii”; <strong>WT</strong>, wild type; <strong>ZFS</strong>, zero-field splitting.</li> </ul> </li> </ul> <p> </p> <ul> <li>Units of measurement: <ul> <li>Concentration: <strong>mM</strong> (millimolar), <strong>µM</strong> (micromolar), <strong>mg/mL</strong> (milligrams per milliliter), <strong>w/v %</strong> (weigth/volume) <strong>v/v %</strong> (volume/volume)</li> <li>Absorptivity: <strong>M<sup>-1</sup></strong> <strong>cm<sup>-1</sup></strong></li> <li>Volume: <strong>mL</strong> (milliliters), <strong>µL </strong>(microliters), <strong>nL</strong> (nanoliters)</li> <li>Wavelength:<strong> nm</strong> (nanometers)</li> <li>Temperature:<strong> °C</strong> (Celsius degrees), <strong>K</strong> (Kelvin degrees)</li> <li>Time:<strong> min</strong> (minutes), <strong>h</strong> (hours), <strong>ns</strong> (nanoseconds)</li> <li>Ellipticity: millidegrees</li> <li>Frequency: <strong>GHz</strong> (gigahertz), <strong>kHz</strong> (kilohertz)</li> <li>Power: <strong>mW</strong> (milliwatt)</li> <li>Pressure: <strong>atm</strong> (atmosphere)</li> </ul> </li> </ul>
Supporting Data for: McKenna et al. (2018), Arctic sea-ice loss in different regions leads to contrasting Northern Hemisphere impacts
<p>This is a dataset of output from version 4 of the Reading Intermediate Global Circulation Model (IGCM4) that was used in the article: </p> <p>McKenna, C. M., Bracegirdle, T. J., Shuckburgh, E. F., Haynes, P. H., & Joshi, M. M. (2018). Arctic sea ice loss in different regions leads to contrasting Northern Hemisphere impacts. <em>Geophysical Research Letters</em>, 45, 945-954. <a href="https://doi.org/10.1002/2017GL076433">https://doi.org/10.1002/2017GL076433</a></p> <p> </p> <p>Files required to setup the IGCM4 simulations are given in the directory 'IGCM4_setup'.</p> <p>All other directories contain netcdf files of timeseries of various monthly mean fields for each IGCM4 simulation (see paper for details on these simulations). The available variables are:</p> <ul> <li>ua: zonal winds</li> <li>zg: geopotential height</li> <li>ts: surface temperature</li> <li>hfls, hfss, rlds, rlus: surface heatfluxes</li> <li>Flat, Fz, divF: Eliassen-Palm flux vectors and their divergence (only for months November-February)</li> </ul> <p>The ua and zg variables are given for different pressure levels indicated in the filenames (e.g., ua500 is ua at 500 hPa). ua is additionally given in terms of the zonal mean with latitude and pressure. zg is additionally given in terms of longitude and pressure, averaged over latitudes between 60N-80N. All files follow CF conventions in terms of metadata, variable names, etc. </p> <p>Note that the CTL, ATL, PAC, and ATLandPAC simulations were all run continuously in time (i.e., every year starts from the end of the previous year). The 0.5ATL and 0.5PAC simulations, however, were run for 300 years in three separate 100-year chunks (i.e., the initial conditions used to start each 100-year chunk were different). The three 100-year chunks have been appended together in the netcdf files. </p>
Data for: Impact of SO2 injection profiles on simulated volcanic forcing for the Sarychev 2009 eruptions - investigating the importance of using high vertical resolution methods when compiling SO2 data
<p>The files are data assosicated with the study High-resolution stratospheric volcanic SO2 injections in WACCM. The files are associated with four differnt simulaions described in the paper: M16, S21-1D, S21-3D and No-Volc. The files with "input" in the name are the SO2 input files used in the WACCM (Whole Atmosphere Community Climate Model) simulations in the paper. The files with "monthly_averages" in the filenames are monthly averages of model output data the variables used in the paper. </p> <p>The CALIOP_monthly_averages.nc file is monthly average of the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) satellite data used in the study to evaluate the WACCM simulations. </p> <p> </p>
Life cycle inventories for the article: Circular Battery Production in the EU: Insights from integrating Life Cycle Assessment into System Dynamics Modeling on Recycled Content and Environmental Impacts
<p>This repository provides the unregionalized life cycle inventories to the paper "<span>Ginster, R.</span>, <span>Blömeke, S.</span>, <span>Popien, J. L.</span>, <span>Scheller, C.</span>, <span>Cerdas, F.</span>, <span>Herrmann, C.</span>, & <span>Spengler, T. S.</span> (<span>2024</span>). <span>Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts</span>. <em>Journal of Industrial Ecology</em>, <span>1</span>–<span>18</span>. <a href="https://doi.org/10.1111/jiec.13527">https://doi.org/10.1111/jiec.13527</a>".</p> <h2>Contents</h2> <p>The repository is split into 2 parts and comprises the following files:</p> <p><strong>01_production: </strong>contains the necessary life cycle inventories for battery production.</p> <ul> <li><strong>01_primary</strong>: contains the life cycle inventories for battery production from primary materials.</li> <li><strong>02_secondary</strong>: contains the life cycle inventories for battery production from secondary materials.</li> <li><strong>03_active_material</strong>: contains the life cycle inventories for the active battery materials from primary materials.</li> <li><strong>04_active_material</strong>: contains the life cycle inventories for the active battery materials from secondary materials.</li> </ul> <p> </p> <p><strong>02_recycling: </strong>contains the necessary inventories for battery recycling.</p> <ul> <li><strong>01_process</strong>: contains the life cycle inventories for battery recycling.</li> <li><strong>02_intermediate</strong>: contains the life cycle inventories for the intermediate system for battery recycling.</li> <li><strong>03_output</strong>: contains the life cycle inventories for the resulting substances from battery recycling.</li> </ul> <h2>Summary</h2> <p>These files allow to reproduce the results of our study. Each file contains the life cycle inventory of one distinct battery capacity (20, 45, 68, 85, 95, 100 kWh) with a specific cell chemistry (LFP, NCA, NMC333, NMC532, NMC622, NMC811, NMC955) for battery production (based on Knehr et al. 2022) or for battery recycling (based on Blömeke et al. 2023).</p> <h2>Related publication</h2> <p>More details on the scientific context is provided in the publication itself:</p> <p><span>Ginster, R.</span>, <span>Blömeke, S.</span>, <span>Popien, J. L.</span>, <span>Scheller, C.</span>, <span>Cerdas, F.</span>, <span>Herrmann, C.</span>, & <span>Spengler, T. S.</span> (<span>2024</span>). <span>Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts</span>. <em>Journal of Industrial Ecology</em>, <span>1</span>–<span>18</span>. <a href="https://doi.org/10.1111/jiec.13527">https://doi.org/10.1111/jiec.13527</a></p> <h2>Funding</h2> <p>This publication (Raphael Ginster and Steffen Blömeke) was created within the Research Training Group CircularLIB, supported by the Ministry of Science and Culture of Lower Saxony with funds from the program zukunft.niedersachsen of the Volkswagen Foundation (MWK | ZN3678).</p> <p>The publication on which this dataset is based were funded by the German Federal Ministry of Education and Research within the Competence Cluster Recycling & Green Battery (greenBatt) under the grant numbers 03XP0302A (Christian Scheller) and 03XP0331A (Jan-Linus Popien). The authors are responsible for the contents of this publication.</p>
Can household storage conditions reduce food waste and environmental impact ? A broccoli case study
<p>The consumption stage has been identified as the largest producer of food waste (FW) across the food supply chain (FSC), with fruit and vegetables being the most affected product category. The present study aims to determine the op- timal storage scenario at household level to avoid food waste and which has the lowest environmental footprint. Broc- coli was stored under different storage conditions: unbagged or bagged (periodically opened) in bioplastic bags inside a domestic refrigerator at 5 or 7 °C for 34 days and then analysed for relative humidity (RH), sensory properties and bioactive compounds. A life cycle assessment (LCA) was conducted to evaluate the environmental profile of 1 kg of broccoli purchased by the consumer (cradle-to-grave). At day 0 (base scenario) the carbon footprint was 0.81 kg CO2 eq/kg, with the vegetable farming being the main contributor to this environmental impact, mostly driven by fertiliser (production and its emissions to air and water) and irrigation (due to electricity consumption for water pumping). Quality and food waste depended on time and storage conditions: For short storage times, within three days, the best quality combined with the lowest environmental footprint was for unbagged broccoli at 7 °C and no household food waste. However, this scenario had the highest food waste level from day 3 onwards, with increased resource loss and overall environmental footprint. For long-term storage, using a bag and storing at 5 °C helped to re- duce food waste with the lowest environmental footprint. For example, at 16 days, this scenario (bagged at 5 °C) could save 4.63 kg/FU ofbroccoli and 3.16 kg CO2 eq/FU compared to the worst scenario (unbagged at 7 °C). Consumers are the key to reducing household food waste and this research provides the knowledge for improvement.</p>
Triangle of Biomedicine Framework to Analyze the Citations' Impact on Categories Dissemination in the PubMed Database
<p>This is the data and the most relevant script of the paper 'Triangle of Biomedicine Framework to Analyze the Citations’ Impact on Categories Dissemination in the PubMed Database'.</p>
Cardelino: Integrating whole exomes and single-cell transcriptomes to reveal phenotypic impact of somatic variants
<p>This dataset consists of the reference data files, metadata and processed results files for the paper "Cardelino: Integrating whole exomes and single-cell transcriptomes to reveal phenotypic impact of somatic variants," which investigates clonality in normal human dermal fibroblast cell populations in 32 cell lines from distinct donors, using bulk whole-exome sequencing and single-cell RNA-sequencing data.</p> <p>This dataset contains everything required to reproduce the results presented in the paper from processed data and results of our data processing workflows. Our analyses can be reproduced using the <a href="https://github.com/davismcc/fibroblast-clonality">source code</a> and instructions available at our <a href="https://davismcc.github.io/fibroblast-clonality/">project website</a>.</p> <p>The <em>entire</em> analysis workflow from raw data to final results is also reproducible but is substantially more complicated and computationally intensive. It also requires large datasets to be obtained from other repositories. Specifically, single-cell RNA-seq data have been deposited in the ArrayExpress database at EMBL-EBI under accession number E-MTAB-7167. Whole-exome sequencing data is available through the HipSci portal (www.hipsci.org). Combined with the dataset in this repository and following the instructions on the project website, it is possible to run our entire analysis pipeline.</p> <p> </p>
Supplementary data: Impact of a global temperature rise of 1.5 degrees Celsius on Asia's glaciers
<p>Supplementary data to <a href="http://doi.org/10.1038/nature23878"><em>Kraaijenbrink, Bierkens, Lutz and Immerzeel, 2017. Impact of a global temperature rise of 1.5 degrees Celsius on Asia’s glaciers, Nature.</em></a> Model code can be found <a href="https://doi.org/10.5281/zenodo.2548689">here</a>.</p> <p>Please note that all data is provided in 7z-archives. To extract the data use the open source software <a href="http://www.7-zip.org/">7zip</a>.</p> <p> </p> <p><strong>Model input: </strong>Raster data</p> <p>The raster data that is required to run the model is available for the entire High Mountain Asia (<em>complete-hma.7z</em>) and for each <a href="https://www.glims.org/RGI/">RGI v5.0</a> sub-region (<<em>region-name>.7z</em>). The 7z-archives hold separate folders for each glacier, which are named by RGI glacier ID. The rasters for each glacier are in GeoTIFF format, have a 30 m resolution, are in local UTM projection (WGS84 datum), and are clipped to the RGI glacier extent.</p> <p>Rasters present for each glacier are:</p> <pre>classification.tif The debris classification made in google earth engine. debris-thickness-50cm.tif Debris thickness estimation based on Landsat 8 surface temperature. ice-thickness.tif Ice thickness determined using the Glabtop2 model ls8-composite-b456.tif Landsat 8 warmest-pixel optical composite (bands RED, NIR, SWIR1) ls8-composite-tsurf.tif Landsat 8 warmest-pixel surface temperature composite srtm-elevation.tif SRTM 1 arc second elevation data srtm-slope.tif Slope of the SRTM 1 arc second data</pre> <p> </p> <p><strong>Model input: </strong>RDS data</p> <p>The general model input data (<em>mbg-model-rds-data.7z)</em> is stored in R’s binary RDS format and <em><a href="https://www.r-project.org/">R</a></em> is required to open and read the data.</p> <p>Files present in the 7z-archive are:</p> <pre>dP_factors_2006-2100.rds Precipitation changes (delta factors) up to 2100 dT_degrees_2006-2100.rds Temperature changes (Kelvin) up to 2100 glacier-data.rds Glacier centroids with current climate and mass balance input ostrem_meancurve.rds The Östrem curve used by the model rgi-subregions.rds RGI sub-region polygons for Asia</pre> <p> </p> <p><strong>Output data</strong></p> <p>Region-aggregated output is available in ESRI Shapefile format for the RGI sub-regions, major river basins, and for a 1×1 degree grid (<em>output-shapefiles.7z</em>). The attribute tables of all shapefiles hold data on the occurrence of debris as well as current glacier area and volume, and volume projections for the end of century.</p> <p>The available shapefile attributes are:</p> <pre>count number of glaciers a_total total glacier area (m2) a_debris glacier area covered by debris (m2) a_ela glacier area below modelled ELA (m2) a_ela_deb glacier area below modelled ELA covered by debris (m2) v_total total glacier volume (m3) v_debris glacier volume covered by debris (m3) v_ela glacier volume below modelled ELA (m3) v_ela_deb glacier volume below modelled ELA covered by debris (m3) m_total_gt total glacier mass (gigaton) volST_EOC volume remaining in end of century under a stable current temperature vol15_EOC volume remaining in end of century under 1.5 degree scenario vol26_EOC volume remaining in end of century for the RCP2.6 model ensemble vol45_EOC volume remaining in end of century for the RCP4.5 model ensemble vol60_EOC volume remaining in end of century for the RCP6.0 model ensemble vol85_EOC volume remaining in end of century for the RCP8.5 model ensemble</pre>
Exploring the Impact of Physiotherapy on Health Outcomes in Elderly Patients with Chronic Diseases: A Cross-Sectional Analysis
<p>In this cross-sectional analysis, we investigate the transformative impact of physiotherapy on health outcomes among elderly patients grappling with chronic diseases. Physiotherapy emerges as a pivotal intervention, offering multifaceted benefits that extend beyond mere symptom management. Through tailored exercises, mobility enhancements, and targeted pain management strategies, physiotherapy not only mitigates physical limitations but also fosters greater independence and quality of life. By examining a diverse cohort of elderly individuals diagnosed with chronic conditions such as osteoarthritis and cardiovascular diseases, this study underscores the profound role of physiotherapy in promoting functional mobility, reducing healthcare burdens, and enhancing overall well-being among this vulnerable population."</p>
Impact of medical radionuclide discharges on people and the environment: scenario data used in the non-human biota impact assessment
<p>This dataset contains the input data for the D-DAT model: activity concentrations in water for the simulated Molse Nete scenario. It also contains the dynamic model-calculated activity concentrations in sediment and the non-human biota. These are the primary data upon which the dose calculations werte performed, and they can be used to reproduce these calculations. The related preprint article is also given in this repository: https://zenodo.org/records/10488393.</p> <p> </p> <p> </p> <p> </p>
Empirical datasets for "Evaluating the impact of lifestyle changes: A scenario-based analysis for Europe's residential buildings sector"
<p>This dataset includes the empirical datasets for the manuscript: Andreas Andreou, Panagiotis Fragkos, Faidra Filippidou, Eleftheria Zisarou, Georgios Avgerinopoulos, Robert Pietzcker, Robin Hasse, Ricarda Rosemann, Evaluating the impact of lifestyle changes: A scenario-based analysis for Europe’s residential buildings sector (under review in Environmetal Research Letters). The dataset contains one CSV file with detailed modelling results for the scenarios presented in the manuscript.</p>
Impact Areas and Dynamical Features associated with Mediterranean Cyclones (1980-2019)
<p>The dataset includes NetCDF files of Impact Areas and Dynamical Features associated with Mediterranean Cyclones (henceforth MedCyclones).</p> <p>MedCyclone tracks correspond to confidence-level 5 tracks from Flaounas et al. (2023), <a href="https://doi.org/10.5194/wcd-4-639-2023">https://doi.org/10.5194/wcd-4-639-2023</a>.</p> <p>Temporal frequency: 6h (00, 06, 12, 18 UTC)<br>Years: 1980 – 2019<br>Spatial resolution: 0.5 deg<br>Grid extension: 0-70N, 40W-65E</p> <p> </p> <h2>Dynamical Features</h2> <p>Files "dynfeats_bool_rmax2000_YYYY.nc" include the following list of variables, describing connected boolean objects:</p> <ul> <li><strong>r_500</strong>, <strong>r_1000</strong>: central areas of fixed 500 or 1000 km radius around MedCyclone centres;</li> <li><strong>WCB</strong>: warm conveyor belts related to MedCyclones (i.e., overlapping with r_500 in at least one grid point). Each <strong>WCB</strong> is eventually separated into inflow (<strong>WCBin</strong>, up to 800 hPa) and ascent (<strong>WCBout</strong>, between 800 and 400 hPa) regions. Ref. at <a href="https://doi.org/10.1175/JCLI-D-12-00720.1">https://doi.org/10.1175/JCLI-D-12-00720.1</a>, <a href="https://doi.org/10.5194/wcd-5-537-2024">https://doi.org/10.5194/wcd-5-537-2024</a>;</li> <li><strong>fronts</strong>: cold fronts related to MedCyclones (i.e., overlapping with r_500 in at least one grid point). Ref. at <a href="https://doi.org/10.5194/gmd-17-6137-2024">https://doi.org/10.5194/gmd-17-6137-2024</a>;</li> <li><strong>DI</strong>: dry instrusions related to MedCyclones (i.e., overlapping with r_1000 in at least one grid point). Ref. at <a href="https://doi.org/10.1175/JCLI-D-16-0782.1">https://doi.org/10.1175/JCLI-D-16-0782.1</a>;</li> <li><strong>r_1000_Nodynfeat</strong>: the central 1000 km area excluding regions of MedCyclone WCB, fronts and DI objects.</li> </ul> <p>The criteria for the identification of WCB, fronts and DI objects are described in Section 2.3 of Portal et al. (2024), <a href="https://doi.org/10.5194/wcd-5-1043-2024">https://doi.org/10.5194/wcd-5-1043-2024</a>.</p> <p>Additionally, we note that :<br>i. a weaker overlap constraint was used to associate DI objects to MedCyclones (r_1000 compared to r_500 for WCB and fronts objects) because of the relatively large distance of the DI airstream from the cyclone centre;<br>ii. in this dataset, all connected objects related to MedCyclones are cropped within a 2000 km area circle from the cyclone centre for two reasons. Firstly, the dynamical-feature related surface impacts usually weaken with the distance from the cyclone centre. Secondly, to cut connected objects composed by multiple overlapping features of the same kind - this often happens for fronts in summer because of their high detection density. Far from the cyclone centre, these objects are usually unrelated with the MedCyclone circulation.</p> <p> </p> <h2>Impact Areas</h2> <p>Files "IAs_bool_rmax2000_YYYY.nc" include boolean impact areas, combining a central area (r_1000 or r_500) and cyclone-related WCB, CF and DI objects. The three types of impact area are described in the following :</p> <ol> <li><strong>IA01</strong> is composed by a 1000 km radius circle around the cyclone centre (r_1000) extended by cyclone-related WCB, fronts and DI;</li> <li><strong>IA02</strong> is composed by a 500 km radius circle around the cyclone centre (r_500) extended by cyclone-related WCB, fronts and DI</li> <li><strong>IA03</strong> is composed by a 500 km radius circle around the cyclone centre (r_500) extended by cyclone-related WCB and fronts (DI is neglected).</li> </ol> <p>As discussed in Section 3.1 and Appendix A of Portal et al. (2024) (<a href="https://doi.org/10.5194/wcd-5-1043-2024">https://doi.org/10.5194/wcd-5-1043-2024</a>), IA01, composed by a central area of 1000 km, is adequate for intercepting long-range wind impacts associated with MedCyclones. IA02 and IA03, on the contrary, are better devised for detecting impacts expected at shorter distances from the cyclone centre, such as rainfall, thunderstorm and storm surges. In particular, IA03 neglects the DI region, which is normally of little interest for cyclone-related moist processes, involved in producing precipitation. Noetheless, DI remains relevant for the identification of strong cyclone-related winds.</p> <p> </p> <h3>Case Studies</h3> <p>A pdf file providing the visualisation and description of impact areas and dynamical features of all MedCyclones occurring in 1980 is available at the link <a href="https://boris.unibe.ch/192315/">https://boris.unibe.ch/192315/</a>. Note that in the examples the dynamical features are not cropped at 2000 km from the cyclone centres, as for the present dataset. </p> <p>Note that many of the "Annotations and Limitations" listed below derive from the attentive analysis of these study cases.</p> <h3>Annotations and limitations</h3> <ul> <li>In the case of more than one MedCyclone centre per timestep, the dasaset does not distinguish the impact areas / dynamical features associated with each centre.</li> <li>Because of the automated criteria for associating WCB, fronts and DI objects to MedCyclones, at times objects close to the centre but unrelated to the MedCyclone's circulation, are considered to be cyclone-related and included in the impact area.</li> <li>Elaborating on the point above, at times fronts responsible for Mediterranean cyclogenesis (and not produced by the cyclonic circulation itself) are included in the MedCyclone impact area.</li> <li>When computing statistics over a long time interval (e.g., climatology), the effects of erroneous associations of dynamical features to MedCyclone impact areas are attenuated by the aggregation of large quantity of data. </li> <li>Over a long time interval (e.g., climatology) the choice of a 1000 km fixed-radius impact area provides similar statistics to IA01, although in the first case it is not possible to isolate the role played by the different features composing the MedCyclones.</li> </ul>
Energy and Emissions Impacts of Atlanta's Reversible Express Toll Lanes and High-Occupancy Toll Lanes
<p>This dataset is the MOVES-Matrix emission rates for the NCST project of Energy and Emissions Impacts of Atlanta’s Reversible Express Toll Lanes and High-Occupancy Toll Lanes, developed by our research team at Georgia Institute of Technology.</p> <p> </p> <p>The abstract of the project is as follows.</p> <p>This report summarizes the impact on corridor-level energy use and emissions associated with the 2018 opening of the I-75 Northwest Corridor (NWC) and I-85 Express Lanes in Atlanta, GA. The research team tracked changes in vehicle throughput on the managed lane corridors (extracted from GDOT’s Georgia NaviGAtor machine vision system after comprehensive QA/QC) and performed a difference-in-difference analysis to exclude regional changes, pairing test sites vs. control sites not influenced by the openings. The results show a large increase in overall peak-period vehicle throughput on the NWC, especially on I-575, due to the congestion decrease (20 mph speed increases at some locations). The increase in corridor-level energy use and emissions was smaller than vehicle throughput, but still significant. Predicted downwind maximum CO concentrations only increased from 1.81 ppm to 1.93 ppm(which remains extremely low). The increase in morning peak activity on the corridor likely resulted from diversion of some traffic into the peak from the shoulder periods, diversion of some traffic from other nearby freeway corridors, and diversion of local road traffic into the corridor. Unfortunately, without overall control volume totals and/or pre-and-post travel behavior surveys for the alternative commute routes, it is not possible to quantify the likely reductions in traffic flow and emissions that occurred along the other corridors that likely resulted from morning commute shifts. Hence, the team cannot draw reliable conclusions related to net regional or sub-regional impacts associated with the new managed lane corridors. The impact observed on the I-85 corridor was much smaller than on the NWC, especially at Indian Trail/Lilburn Road (far from the Express Lane Extension). After the Express Lanes opened, energy use and emission rates at Old Peachtree Road increased slightly (as uncongested vehicle speeds increased), but this increase may be short-lived as traffic on the corridor changes over time.</p> <p> </p>
The impact of beech deadwood on soil properties and microbial diversity
<p><span><span>Our research is an attempt to determine the role of decaying wood in shaping the properties of forest soils in mountain ecosystems.</span></span><span><span> </span></span></p>
Inter-Chemical Correlation results for the study: HHEARx2017-1839 (Zika Virus Congenital Health Outcomes and the Impact of Maternal Environmental Exposures)
Title: Zika Virus Congenital Health Outcomes and the Impact of Maternal Environmental Exposures <br>Species: Homo sapiens <br>Number of samples: 2705 <br>Number of named analytes: 10 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=61 <br>
Data for manuscript titled 'Impact of urbanization and drought on river water quality, case study of nutrient levels in Cuenca and Giron (Azuay, Ecuador)'
<p>The uploaded zip-file entails the data obtained through four field campaigns performed in the province of Azuay (Ecuador) in the period July 2023 - May 2024, which is used as a basis for the manuscript titled 'Impact of urbanization and drought on river water quality, case study of nutrient levels in Cuenca and Giron (Azuay, Ecuador)' that was submitted to a Special Issue in the journal Water in 2024. The study aimed at illustrating the impact of urbanisation and drought on the abiotic water conditions of the rivers passing through the studied urban areas.</p> <p>The data includes a subfolder with data obtained from an external website (https://generacioncsr.celec.gob.ec/graficasproduccion/) and aligns with the folder structure of the GitHub-repository that contains the analysis scripts (to be added when the manuscript is accepted). The data file only contains the baseline data, while results can be obtained through running the R-scripts in the GitHub-repository. Additional comments on the analyses are also provided in the analysis scripts.</p> <p><strong>DATA COLLECTION</strong></p> <p>Information on the locations was collected prior to the first field campaign (July 2023) and confirmed in the field (and corrected when necessary). The following variables were registered: Date & Time, Coordinates (latitude and longitude, in WGS84 format), Altitude (in meters above mean sea level), Distance (to a fixed location downstream; being the province border), and Category (River or Stream).</p> <p>Information on the physicochemical conditions was collected directly in the field with a <strong>Horiba U-52</strong> multiprobe. The following variables were registered: Temperature, pH, Electrical conductivity (reference at 25 °C), Oxygen level (as concentration), and Turbidity (in NTU).</p> <p>At each site, a bucket was rinsed thrice with prevailing surface water and subsequently filled with a water sample of the top of the water column. The multiprobe was rinsed with this sample water and then submerged in the bucket, followed by continuous stirring (to avoid a decrease of the oxygen levels) until the readings stabilised. After stabilisation, readings were recorded on a separate data sheet prior to being digitalised.</p> <p>Information on the nutrient levels was obtained through the collection of water samples in the field and the subsequent analysis in the laboratory. The following nutrients were selected: ammonium, nitrate, nitrite, and orthophosphate. For the analyses, <strong>Merck test kits</strong> (equivalent to USEPA analyses) were used in combination with a Genesys UV-VIS spectrophotometer (Thermofisher).</p> <p><strong>In the field</strong>, a bucket was rinsed thrice with prevailing surface water and subsequently filled with a water sample of the top of the water column. A polyethylene syringe was rinsed thrice with sample water and subsequently filled prior to being fitted with a 0.45 µm PES filter. About 100 mL of sampled water was filtered and collected in a 250 mL polyethylene bottle that was rinsed with the first 5 mL of filtered water. The bottle was stored in a cooling box and transported to the laboratory.</p> <p><strong>In the laboratory</strong>, the 250 mL bottle was stored at 4 °C until analysis. Within 36 hours, concentrations of ammonium, nitrate, nitrite, and orthophosphate were determined <strong>in triplicate</strong>. More specifically, the following test kits were used to determine said nitrogen and phosphorus concentrations (with quantification range between brackets):</p> <ul> <li>Ammonium: 1.14752.0001 (0.05-3.00 mgN/L)</li> <li>Nitrate: 1.14773.0001 (2-20 mgN/L)</li> <li>Nitrite: 1.14776.0001 (0.02-1.00 mgN/L)</li> <li>Orthophosphate: 1.14848.0001 (0.05-5.00 mgP/L)</li> </ul> <p>Regarding the <strong>spectrophotometric determination</strong>, all analyses were complemented with a blank and a standard with a known concentration of each individual nutrient component. For each nutrient, a specific wavelength was used and the resulting absorbance was converted to the associated nutrient concentration through known factors (similar to the use of calibration curves), after setting the absorbance of the blank as reference absorbance (i.e. a concentration of 0 mg/L). All of the analyses were performed with plastic 1-cm cuvettes during the first campaign, while 5-cm cuvettes were used in the remaining three campaigns due to low nutrient levels (except for nitrate, for which an analysis through 5-cm cuvettes is not supported by the used test kits).</p>
Dataset: The impact of rose-waste compost on commercial cut rose cultivation in Kenya
<p>This dataset and these scripts supports the manuscript 'From waste to fertilizer: The impact of rose-waste compost on commercial cut rose cultivation in Kenya' as submitted to Cleaner Waste Systems. </p> <p>Roses are an important crop for the floricultural sector of Kenya and roses are a perennial crop and under continuous production for six to ten years. The cultivation produces large quantities of green waste, up to 50 kg per hectare per day. In this experiment we evaluated the impact of compost amendment on the yield and quality of cut roses cultivation in a large-scale commercial setting with over 7,500 rose plants. It was conducted between August 2022 and February 2024 near Lake Naivasha in Kenya. Additionally we evaluated the potential of compost to partially substitute mineral fertilizer. Yields were recorded daily and cut rose quality and physicochemical soil parameters were assessed every three to six months to comprehensively evaluate the impact of compost incorporation on cut rose production.</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.