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18,658 results for “impacts”
Supplementary Data from, "Causal health impacts of power plant emission controls under modeled and uncertain physical process interference."
<p>These data are used to conduct the analysis in, "<a href="https://arxiv.org/abs/2306.05665">Causal health impacts of power plant emission controls under modeled and uncertain physical process interference</a>," by Wikle and Zigler (2024), to appear in <em>Annals of Applied</em> Statistics. This is purely for archival purposes to facilitate access to and replication of the aforementioned analysis. Data were obtained from the following sources:</p> <ol> <li> U.S. Emissions Data [<a href="https://ampd.epa.gov/ampd">U.S. EPA, Air markets program data (AMPD)</a>] <ul> <li>AMPD_Unit_with_Sulfur_Content_and_Regulations_with_Facility_Attributes.csv</li> </ul> </li> <li> US Census 2016 American Community Survey [<a href="https://www.census.gov/programs-surveys/acs">US Census Bureau ACS</a>] <ul> <li>Census_2016_TxZCTA.RDS</li> <li><em>Note: data were obtained using the r package ‘<a href="https://walker-data.com/tidycensus/">tidycensus</a>’.</em></li> </ul> </li> <li> Daymet Annual Climate Summaries [<a href="https://daac.ornl.gov/DAYMET/guides/Daymet_V4_Annual_Climatology.html">Daymet Version 4</a>] <ul> <li>daymet_v4_prcp_annttl_na_2016.nc</li> <li>daymet_v4_tmax_annavg_na_2016.nc</li> <li>daymet_v4_tmin_annavg_na_2016.nc</li> <li>daymet_v4_vp_annavg_na_2016.nc</li> </ul> </li> <li> SO<sub>4</sub> and Black Carbon Concentrations [<a href="https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V4.NA.03">Randall Martin Atmospheric Composition Analysis Group, North American Regional Estimates, version V4.NA.02</a>] <ul> <li>GWRwSPEC_BC_NA_201601_201612.nc</li> <li>GWRwSPEC_SO4_NA_201601_201612.nc</li> </ul> </li> <li> HyADS Coal-Attributed PM2.5 Concentrations [<a href="https://doi.org/10.1097/EDE.0000000000001024">Henneman et al. (2019)</a>] <ul> <li>HyADS_grids_pm25_byunit_2016.fst</li> <li>HyADS_grids_pm25_total_2016.fst</li> </ul> </li> <li> Mexico Emissions Data [<a href="https://www.epa.gov/air-emissions-modeling/2014-2016-version-7-air-emissions-modeling-platforms">National Emissions Inventory Collaborative, 2016v1 emissions modeling platform</a>] <ul> <li>Mexico_2016_point_interpolated_02mar2018_v0.csv</li> </ul> </li> <li> North American Regional Reanalysis Meteorological Data [<a href="https://psl.noaa.gov/data/gridded/data.narr.monolevel.html">NOAA</a>] <ul> <li>rhum.2m.mon.mean.nc</li> <li>uwnd.10m.mon.mean.nc</li> <li>vwnd.10m.mon.mean.nc</li> </ul> </li> <li> Cigarette smoking data [<a href="https://doi.org/10.1186/1478-7954-12-5">Dwyer-Lindgren et al. (2014)</a>] <ul> <li>smokedatwithfips_1996-2012.csv</li> </ul> </li> <li> Synthetic pediatric asthma data [<em>Note:<strong> synthetic data!</strong> Simulated to match the format, but not the observations, from the <a href="https://www.dshs.texas.gov/texas-health-care-information-collection">Texas Health Care Information Collection (THCIC), Texas DSHS</a></em>] <ul> <li>synth-ped-asthma-data.csv</li> </ul> </li> <li> Texas state shape file [<a href="https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html">US Census</a>] <ul> <li>texas-state-sf.RDS</li> </ul> </li> <li> US ZIPcode-to-county data crosswalk [<a href="https://mcdc.missouri.edu/applications/geocorr2014.html">Missouri Census Data Center</a>] <ul> <li>tx-zip-to-county.csv</li> </ul> </li> </ol> <p>Code and supplementary material from this analysis, as well as more detailed data descriptions, are available at: <a href="https://github.com/nbwikle/estimating-interference">https://github.com/nbwikle/estimating-interference</a></p>
Data for a publication "Impact disruption of Bjurböle porous chondritic projectile"
<p>This archive contains raw and processed research data and photographic documentation for a publication "<em><strong>Impact disruption of Bjurböle porous chondritic projectile</strong></em>" by Kohout et al. The sample identifiers are consistent with the one used in the publication.</p> <p>Content:</p> <ol> <li><strong>Bjurböle photos.zip</strong>, <strong>Bjurböle photos II.zip</strong>, and <strong>Bjurböle photos III.zip</strong> - Photographs of studied Bjurböle meteorites from Finnish Museum of Natural History as well as from worldwide collections. Unless specified otherwise in the photographs (worldwide collections) the photo credits are Tomas Kohout, Assi-Johanna Soini, Arto Luttinen, University of Helsinki.</li> <li><strong>Meteoriteidentifier.ply</strong> - 3D shape reconstructions of studied Bjurböle and Chelyabinsk meteorites. By default a 3D mesh reconstruction with relaxed fitting was applied to partial aligned laser scans. In certain cases where multiple reconstruction techniques were applied (e.g. volume merge) this is indicated in file name.</li> <li><strong>All tables.xlsx</strong> - Spreadsheet file containing the tables presented in the publication</li> </ol>
Evaluation of transcription factor knockout impact on paclitaxel response for Triple Negative Breast Cancer
<div>Data and code related to Zenodo repository: 10.5281/zenodo.11238552</div> <div> </div> <div>Two experimental formats included:</div> <div>'fixed' prefix: data from terminal time point of siRNA screen applied to HCC1143, HCC1806, and MDA-MB-468 Triple Negative Breast Cancer cell lines.</div> <div>'live' prefix: data from live-cell imaging of cell cycle reporter (HDHB-mClover/NLS-mCherry) HCC1143 Triple Negative Breast Cancer cell line.</div> <div>Note: 'live' level 1 data is available upon request (heiserl@ohsu.edu, calistri@ohsu.edu).</div> <div> </div> <div>Experimental goal:</div> <div>Evaluate whether siRNA knockdown of transcription factors elevated during paclitaxel response impact cell count, cell morphology or cycling dynamics.</div> <div> </div> <div>Methods:</div> <div>siRNA Knockdown: Cells were plated in 90ul of serum free media per well of a 96 well plate. 24 hours later, siRNA knockdown mixture was prepared using a cell-line optimized concentration of Lipofectamine RNAiMAX (cat 13778075-075, Invitrogen) and siRNA (Horizon Discovery ON-TARGETplus) following RNAiMAX recommended protocol. The final concentration of siRNA per well was 1pmol and the final volume of RNAiMAX per well was 75nL for HCC1143, and 37.5nL for HCC1806 or MDA-MB-468 in 100uL of cell containing volume. 24 hours after siRNA transfection cells were treated with an addition of 100uL complete media containing either DMSO vehicle control or paclitaxel. </div> <div> </div> <div>Fixed-cell assays: Cells were plated at 3000 cells in 100ul of complete media per well in a 96 well plate (#08-772-225, FisherScientific). After 24 hours, an additional 100ul of either vehicle (0.1% DMSO) or paclitaxel containing complete media was added. After 72 hours cells were fixed with 4% Formaldehyde (#28908, ThermoFisher Scientific) for 15 minutes at room temperature, then permeabilized with 0.3% Triton X-100 (#X100-100ML, Sigma Aldrich) for 10 minutes at room temperature, then washed twice with PBS. Fixed cells were then stained with 0.5ug/mL DAPI (4083S, Cell Signaling Technology) in PBS for 15 minutes at room temperature. Following DAPI staining, wells were washed once with PBS, then stained with 1:20,000 HCS CellMask Green in PBS (H32714, Invitrogen) for 15 minutes at room temperature. Wells were washed twice with room temperature PBS and then 4 fields of view per well imaged on an InCell 6000 (GE Healthcare). Images were segmented with two custom Cellpose models to segment the nucleus (using parameters: diameter = 50, chan = DAPI, chan2 = Cellmask Orange) and cytoplasm (using parameters: diameter = 90, chan = Cellmask Orange, chan2 = DAPI). Image quantification was performed in R (v4.3.1) using EBImage (v4.42.0), and cells were annotated based on the number of distinct nuclei segmented within each cytoplasmic mask. </div> <div> </div> <div>HDHB reporter live-cell assays: siRNA knockdown and drug treatment was performed as described above, and then the plate was loaded on an Incucyte S3 (Sartorious) and cells imaged every 15 minutes for 72 hours post drug treatment. At each timepoint 4 fields of view were captured at 20x magnificantion in each well using the phase, red and green channels. A cytoplasmic mask was computed from the mean of normalized red/green channel (cellpose parameters: diameter = 57, chan = mean(normalized(red), normalized(green)), and a nuclear mask was computed from the red channel (cellpose parameters: diameter = 30, chan = DAPI) using custom trained Cellpose models. Image quantification was performed in R (v4.3.1) using EBImage (v4.42.0). An additional perinuclear ring mask was computed as the 11 pixel dilation from the nuclear mask, but still bound by the cytoplasmic mask. To determine mClover localization thresholds for cell cycle assignment, 250 cell images were randomly selected and manually assigned to the G1, S/G2 or M cell cycle state based on mClover localization. The mClover intensity ratios were then used to determine thresholds for automated cell cycle phase calling which was applied to the rest of the data set (Supplemental Figure 5A). Mononuclear cells with a Perinuclear:Nuclear mean intensity ratio greater than 0.8 and Nuclear:Cytoplasmic total intensity less than 0.5 were assigned to the S/G2 phase. Mononuclear and Multinuclear cells with a Nuclear:Cytoplasmic total intensity ratio greater than 0.8 and Perinuclear:Nuclear mean intensity ratio less than 0.8 were assigned to the ‘M’ phase. The remainder of mononuclear cells were assigned ‘G1’, and the remainder of multinucleated cells were assigned ‘Multinucleated’. </div> <div> </div> <div>Included files:</div> <div>fixed_level_1-plate_#.zip : Six .zip archives containing the raw images (DAPI/CellMask/Brightfield) from fixed-cell experiments.</div> <div>plate 1: HCC1143 cells treated with plate A schema</div> <div>plate 2: HCC1143 cells treated with plate B schema</div> <div>plate 3: HCC1806 cells treated with plate A schema</div> <div>plate 4: HCC1806 cells treated with plate B schema</div> <div>plate 5: MDA-MB-468 cells treated with plate A schema</div> <div>plate 6: MDA-MB-468 cells treated with plate B schema</div> <div>fixed_level_2: Data quantified from cellpose masks at the single-nuclei level (redundant cytoplasm information)</div> <div>fixed_level_3: Data from 'fixed_level_2.csv' collapsed to the single cell level, including staining intensity and aggregate nuclear information</div> <div>fixed_incell_to_cellpose.rmd: R markdown code for converting original incell files (fixed_level_1) to RGB images for cellpose segmentation</div> <div>fixed_image_quantification.rmd: R markdown code for quantifying images using cellpose segmentation masks and original images (fixed_level_1)</div> <div>fixed_cellpose_models.zip: Archive including cellpose models used for fixed experiment</div> <div>live_level_2: Data quantified from cellpose masks at the single-nuclei level (redundant cytoplasm information)</div> <div>live_level_3: Data from 'live_level_2.csv' collapsed to the single cell level, including staining intensity and aggregate nuclear information</div> <div>live_level_4: Data from 'live_level_3.csv' collapsed to the single condition level summarizing the number, multinucleation status and phase of cells at each time point.</div> <div>live_image_quantification.rmd: R markdown code for quantifying images using cellpose segmentation masks and original images (live_level_1).</div> <div>l ive_incu_archive2rgb.rmd: R markdown code for converting incucyte archive formatted data into RGB images, where the blue channel is the arithmetic mean of the min-max (0-1) normalized red and green channels.</div> <div>live_cellpose_models.zip: Archive including cellpose models used for live experiment.</div> <div> </div> <div> </div>
Impact of paclitaxel treatment on the Triple Negative Breast Cancer Cell line HCC1143
<div>Data and code related to Zenodo repository: 10.5281/zenodo.11237850</div> <div> </div> <div>Experimental goal:</div> <div>Evaluate the impact of escalating paclitaxel dose on cell count, nuclear morphology and cellular outcome.</div> <div> </div> <div>Methods:</div> <div>Cells were plated at 3000 cells in 100ul of complete media per well in a 96 well plate (#08-772-225, FisherScientific). After 24 hours, an additional 100ul of either vehicle (0.1% DMSO) or paclitaxel containing complete media was added. After 72 hours cells were fixed with 4% Formaldehyde (#28908, ThermoFisher Scientific) for 15 minutes at room temperature, then permeabilized with 0.3% Triton X-100 (#X100-100ML, Sigma Aldrich) for 10 minutes at room temperature, then washed twice with PBS. Fixed cells were blocked with 1% BSA (A7906-100G, Millipore Sigma) in PBS for 1 hour at room temperature and then stained overnight with 1:100 anti-CDKN2A/p16INK4A+CDKN2B/p15INK4B-AF644 (#ab199756, Abcam), and 1:100 anti-cPARP-AF647 (#6987S, Cell Signaling Technology) or 1:500 anti-TUBB3-AF647 (#ab190575, Abcam) overnight at 4C. Each well was washed twice with room temp PBS then stained with 0.5ug/mL DAPI (4083S, Cell Signaling Technology) in PBS for 15 minutes at room temperature. Following DAPI staining, wells were washed once with PBS, then stained with 1:20,000 HCS CellMask in PBS (Orange: #H32713, Green: #H32714, Invitrogen) for 15 minutes at room temperature. Wells were washed twice with room temperature PBS and then 4 fields of view per well imaged on an InCell 6000 (GE Healthcare). Images were segmented with two custom Cellpose models to segment the nucleus (using parameters: diameter = 45, chan = DAPI, chan2 = Cellmask Orange) and cytoplasm (using parameters: diameter = 90, chan = Cellmask Orange, chan2 = DAPI). Image quantification was performed in R (v4.3.1) using EBImage (v4.42.0), and cells were annotated based on the number of distinct nuclei segmented within each cytoplasmic mask. </div> <div> </div> <div>Included files:</div> <div>row_#_level_1.zip : 6 zip file containing original images from InCell 6000, one zip per row</div> <div>level_2.csv : Data quantified to the nuclear level (cytoplasmic quantification is duplicates across multiplet nuclei)</div> <div>level_3.csv: Data quantified at the cellular level including number of nuclei and stain intensities for segmented compartments</div> <div>platemap.csv: Description of each well from the stained plate</div> <div>cellpose_modelz.zip: Zip file containing the two CellPose models used for segmentation</div> <div>image_quantification.rmd : R markdown file containing code for extracting and quantifying image intensities using the raw images (level_1) and segmentation masks created from cellpose.</div>
Safety impact of DoS attacks on V2X-based collision warning
<p>The dataset represents Straight Crossing Path (SCP) intersection scenarios, where a Host Vehicle (HV) and a Remote Vehicle (RV) approach a right-angled intersection at different velocities and cross each other's paths simultaneously. By manipulating the starting positions, the driving scenarios were defined in such a way that the two vehicles collide in all cases.</p> <p>Scenarios were implemented with the following speed levels:</p> <table> <tbody> <tr> <td> <p><strong>Scenario</strong></p> </td> <td> <p><strong>RV speed [km/h]</strong></p> </td> <td> <p><strong>HV speed [km/h]</strong></p> </td> </tr> <tr> <td> <p>S1</p> </td> <td> <p>20</p> </td> <td> <p>40</p> </td> </tr> <tr> <td> <p>S2</p> </td> <td> <p>50</p> </td> <td> <p>70</p> </td> </tr> <tr> <td> <p>S3</p> </td> <td> <p>20</p> </td> <td> <p>70</p> </td> </tr> <tr> <td> <p>S4</p> </td> <td> <p>50</p> </td> <td> <p>100</p> </td> </tr> <tr> <td> <p>S5</p> </td> <td> <p>20</p> </td> <td> <p>100</p> </td> </tr> <tr> <td> <p>S6</p> </td> <td> <p>50</p> </td> <td> <p>130</p> </td> </tr> </tbody> </table> <p> </p> <p>We quantified the <a href="https://www.sciencedirect.com/science/article/pii/S2214209622000614" target="_blank" rel="noopener"><strong>safety risk (Safety Risk Index - SRI)</strong> </a>related to the specific V2X scenarios based on network performance metrics (End-to-End latency – E2E; Packet Delivery Ratio – PDR).</p> <p>In our dataset, we differentiated the strength of the attack based on the primary wireless communication parameters:</p> <p>· the attacker's data transmission rate (AR),</p> <p>· the attack packet length (APL).</p> <p>Based on the six driving scenarios (S1-S6) and the attack parameters (attack packet length, attack rate), 780 scenarios were simulated for a total of 15,600 unique test points (20 static spatial measurement point / scenario).</p>
Data: Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing
<p>The dataset contains supporting data for the paper submitted to The Cryosphere "Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing".<br><br>OGGM_area_projections.nc contains data for Figure 3.<br>OGGM_volume_projections contains data for Figure 4.</p> <p>OGGM_MassLoss_SLR_projections_regions.nc contains data for Figure 5.</p> <p>OGGM_solid_ice_discharge_regions.nc contains data for Figure 6.</p> <p>OGGM_freshwater_runoff_magnitude_composition_timings_projections.nc & OGGM_freshwater_runoff_projections_regions.nc contain data for Figure 7.</p> <p>OGGM_PeakWaterYear_projections_regions.nc contains data for Figure 8.</p>
Biogeographic data for "The marine biodiversity impact of the Late Miocene Mediterranean salinity crisis"
<p>Lists of species that were present in the Mediterranean Sea both in the pre-evaporitic Messinian and the Zanclean (based on https://doi.org/<a href="../doi/10.5281/zenodo.10782428">10.5281/zenodo.10782428</a>), biogeographic information on their presence outside the Mediterranean, and accordingly their status as either "possible endemic" to the Mediterranean or "non-endemic" if they were also found outside the basin.</p> <p>In this version, we added also the list of species present in the Mediterranean Sea in the pre-evaporitic Messinian that can be considered possible endemics, based on the same rule, and the indication if they survived the MSC.</p>
Run 0 video in "Ram pressure stripping in elliptical galaxies – I. The impact of the interstellar medium turbulence"
<p>Run 0 presented in the paper "Ram pressure stripping in elliptical galaxies – I. The impact of the interstellar medium turbulence" (http://adsabs.harvard.edu/abs/2013MNRAS.428..804S or https://doi.org/10.1093/mnras/sts071).</p>
A molecular dynamics study of adenylyl cyclase: the impact of ATP and G-protein binding
<p>Adenylyl cyclases (ACs) catalyze the biosynthesis of cyclic adenosine monophosphate (cAMP) from adenosine triphosphate (ATP) and play an important role in many signal transduction pathways. The enzymatic activity of ACs is carefully controlled by a variety of molecules, including G-protein subunits that can both stimulate and inhibit cAMP production. Using homology models developed from existing structural data, we have carried out all-atom, microsecond-scale molecular dynamics simulations on the AC5 isoform of adenylyl cyclase and on its complexes with ATP and with the stimulatory G-protein subunit Gsα. The results show that both ATP and Gsα binding have significant effects on the structure and flexibility of adenylyl cyclase. New data on ATP bound to AC5 in the absence of Gsα notably help to explain how Gsα binding enhances enzyme activity and could aid product release. Simulations also suggest a possible coupling between ATP binding and interactions with the inhibitory G-protein subunit Gαi.</p> <p>All-atom molecular dynamics simulations were performed with the GROMACS 5 package. The simulations were carried out in an NTP ensemble at a temperature of 310 K and a pressure of 1 bar using a Bussi velocity-rescaling thermostat (t<sub>T</sub> = 1 ps) and a Parrinello-Rahman barostat (t<sub>P</sub> = 1 ps). We provide the atomistic trajectories of the following 6 systems after 400 ns of equilibration:</p> <ul> <li>AC5</li> <li>AC5+ATP</li> <li>AC5+Gsα</li> <li>AC5+ATP+Gsα</li> <li>AC5+FOK</li> <li>AC5+ATP+FOK</li> </ul> <p>In each trajectory, the frames are saved each 20 ps.</p>
Supplementary Data: Impact of vacuum stability, perturbativity and XENON1T on global fits of Z2 and Z3 scalar singlet dark matter (arXiv:1806.11281)
<p> </p> <p><strong>Supplementary Data</strong></p> <p> </p> <p><em>Impact of vacuum stability, perturbativity and XENON1T on global fits of Z<sub>2</sub> and Z<sub>3</sub> scalar singlet dark matter</em> <a href="https://arxiv.org/abs/1806.xxxxx"><em>arXiv:</em></a><em><a href="https://arxiv.org/abs/1806.11281">1806.11281</a></em></p> <p>The files in this record contain data for the scalar singlet dark matter models considered in the <a href="http://gambit.hepforge.org">GAMBIT</a> "Scalar singlet Mark II" paper.</p> <p>The files consist of</p> <ul> <li>30 regular YAML files</li> <li><code>StandardModel_SLHA2_scan.yaml</code>, a universal YAML fragment included from the other YAML files</li> <li>14 hdf5 files. 8 of these correspond to the complete set of combined samples for each fit. These 8 fits are generated from all binary permutations of three run properties: Z2 or Z3 model, with or without absolute vacuum stability demanded, and with constraints from the 2017 or 2018 XENON1T data. These 8 hdf5 files are used to generate the profile likelihood plots in the paper. The other 6 hdf5 files are the results of T-Walk runs, and are used to generate the posterior pdfs in the paper.</li> <li>Some example pip files for producing plots from the hdf5 files using <a href="github.com/patscott/pippi">pippi</a></li> <li>A tarball <code>best_fits_yaml.tar.gz</code> containing YAML files of the best-fit point in each of the 8 fits.</li> </ul> <p>The files follow the naming scheme <code>SingletDM_[model]_[slice]_[vacuum]_[xenon]_[prior]_[scanner].yaml</code>.</p> <ul> <li>model: <code>Z2</code> or <code>Z3</code></li> <li>slice: <code>full</code>, <code>lowmass</code>, <code>neck</code> or absent (for hdf5 files)</li> <li>vacuum: <code>ms</code> (metastable) or <code>vs</code> (absolute vacuum stability)</li> <li>prior: <code>logmu3</code>, <code>flatmu3</code> or absent (for Z<sub>2</sub> scans)</li> <li>scanner: <code>TWalk</code> or absent (implies Diver scans in the case of YAML files, and indicates merged samples potentially from both Diver and T-Walk in the case of hdf5 files)</li> </ul> <p>A few caveats to keep in mind:</p> <ol> <li> <p>The YAML files are designed to work with GAMBIT 1.2.0, commit e4d3f739, and the pip files are tested with pippi 2.1, commit c094b8c8. They may or may not work with later versions of either software (but you can of course always obtain the version that they do work with via the git history).</p> </li> <li> <p>The pip files are examples only. Users wishing to reproduce the more advanced plots in any of the GAMBIT papers should contact us for tips or scripts, or experiment for themselves. Many of these scripts are in multiple parts and require undocumented manual interventions and steps in order to implement various plot-specific customisations, so please don't expect the same level of polish as for files provided here or in the GAMBIT repo. </p> </li> </ol> <p> </p>
Dataset of E. huxleyi blooms: spatio-temporal distribution and their impact on high-latitudinal marine environments (1998-2016)
<p>Dataset of coccolithophore blooms in polar seas of the Northern Hemisphere, viz. the North, Labrador (with adjacent North Atlantic open waters), Norwegian, Barents, Greenland and Bering seas are presented for the period 1998-2016. Seas are divided into 4 regions, for each of them continuous data series (as 8-days composites) are published, including information about bloom spatial masks, coccolith concentration, particulate inorganic carbon content and CO<sub>2</sub> partial pressure in water increment driven by coccolithophores.</p> <p>Datasets are published as NetCDF files with full metadata/descriptions and with GDAL support.</p> <p>Additional information (regions configuration, data access instructions) is provided alongside the data.</p> <p>Naming convention is: <strong>niersc_cocco_<version of dataset>_<region>_<start date>_<end date>.nc</strong></p>
Supporting dataset for: Repository optimisation & techniques to improve discoverability and web impact : an evaluation
<p>This dataset supports the working paper, "Repository optimisation & techniques to improve discoverability and web impact : an evaluation", currently under review for publication and available as a preprint at: <a href="https://doi.org/10.17868/65389/">https://doi.org/10.17868/65389/</a>. </p> <ul> <li>Macgregor, G. (2018). <em>Repository optimisation techniques to improve discoverability and web impact: an evaluation</em>. (pp. 1-13). Glasgow: University of Strathclyde [Strathprints repository]. Available: <a href="https://doi.org/10.17868/65389/">https://doi.org/10.17868/65389/</a></li> </ul> <p>The dataset comprises a single OpenDocument Spreadsheet (.ods) format file containing seven data sheets of data pertaining to COUNTER compliant usage statistics, search query traffic from Google Search Console, web traffic data for Google Analytics and Google Scholar, and usage statistics from IRStats2. All data relate to the EPrints repository, Strathprints, based at the University of Strathclyde.</p>
Beyond Textual Issues: Understanding the Usage and Impact of GitHub Reactions
<p>Recently, GitHub introduced a new social feature, named reactions, which are pictorial characters similar to the emoji symbols widely used nowadays in text-based communications. Particularly, GitHub users can use a set of such symbols to react to issues and pull requests. However, little is known about the real usage and benefits of GitHub reactions. In this paper, we analyze the reactions provided by developers to more than 2.5 million issues and 9.7 million issue comments, in order to answer an extensive list of ten research questions about the usage and adoption of reactions. We show that reactions are being increasingly used by open-source developers. Moreover, we also found that issues with reactions usually take more time to be closed and have longer discussions.</p> <p>This dataset contains the data used in the paper "Beyond Textual Issues: Understanding the Usage and Impact of GitHub Reactions", accepted for SBES 2019.</p>
Climate change impact and mitigation cost data - The economically optimal warming limit of the planet
<p>This climate change impact data (future scenarios on temperature-induced GDP losses) and climate change mitigation cost data (REMIND model scenarios) is published under doi: 10.5281/zenodo.3541809 and used in this paper:</p> <p>Ueckerdt F, Frieler K, Lange S, Wenz L, Luderer G, Levermann A (2018) The economically optimal warming limit of the planet. Earth System Dynamics. <a href="https://doi.org/10.5194/esd-10-741-2019">https://doi.org/10.5194/esd-10-741-2019</a></p> <p>Below the individual file contents are explained. For further questions feel free to write to Falko Ueckerdt (ueckerdt@pik-potsdam.de).</p> <p> </p> <p><strong>Climate change impact data</strong></p> <p>File 1: Data_rel-GDPpercapita-changes_withCC_per-country_all-RCP_all-SSP_4GCM.csv</p> <p>Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, RCP (and a zero-emissions scenario), SSP and 4 GCMs (spanning a broad range of climate sensitivity). Negative (positive) values indicate losses (gains) due to climate change. For figure 1a of the paper, this data was aggregated for all countries.</p> <p> </p> <p>File 2: Data_rel-GDPpercapita-changes_withCC_per-country_all-SSP_4GCM_interpolated-for-REMIND-scenarios.csv</p> <p>Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP and 4 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action).</p> <p> </p> <p>File 3: Data_rel-GDPpercapita-changes_withCC_per-country_SSP2_12GCM_interpolated-for-REMIND-scenarios.csv</p> <p>Content: Same as file 2, but only for the SSP2 (chosen default scenario for the study) and for all 12 GCMs. Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP-2 and 12 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action).</p> <p><br> In addition, reference GDP and population data (without climate change) for each country until 2100 was downloaded from the SSP database, release Version 1.0 (March 2013, <a href="https://tntcat.iiasa.ac.at/SspDb/">https://tntcat.iiasa.ac.at/SspDb/</a>, last accessed 15Nov 2019).</p> <p> </p> <p><strong>Climate change mitigation cost data</strong></p> <p>The scenario design and runs used in this paper have first been conducted in [1] and later also used in [2].</p> <p>File 4: REMIND_scenario_results_economic_data.csv</p> <p>File 5: REMIND_scenarios_climate_data.csv</p> <p>Content: A broad range of climate change mitigation scenarios of the REMIND model. File 4 contains the economic data of e.g. GDP and macro-economic consumption for each of the countries and world regions, as well as GHG emissions from various economic sectors. File 5 contains the global climate-related data, e.g. forcing, concentration, temperature.</p> <p>In the scenario description “FFrunxxx” (column 2), the code “xxx” specifies the scenario as follows. See [1] for a detailed discussion of the scenarios.</p> <p>The first dimension specifies the climate policy regime (delayed action, baseline scenarios):</p> <p>1xx: climate action from 2010<br> 5xx: climate action from 2015<br> 2xx climate action from 2020 (used in this study)<br> 3xx climate action from 2030<br> 4x1 weak policy baseline (before Paris agreement)</p> <p>The second dimension specifies the technology portfolio and assumptions:</p> <p>x1x Full technology portfolio (used in this study)<br> x2x noCCS: unavailability of CCS<br> x3x lowEI: lower energy intensity, with final energy demand per economic output decreasing faster than historically observed<br> x4x NucPO: phase out of investments into nuclear energy<br> x5x Limited SW: penetration of solar and wind power limited<br> x6x Limited Bio: reduced bioenergy potential p.a. (100 EJ compared to 300 EJ in all other cases)<br> x6x noBECCS: unavailability of CCS in combination with bioenergy</p> <p>The third dimension specifies the climate change mitigation ambition level, i.e. the height of a global CO2 tax in 2020 (which increases with 5% p.a.).</p> <p>xx1 0$/tCO2 (baseline)<br> xx2 10$/tCO2<br> xx3 30$/tCO2<br> xx4 50$/tCO2 <br> xx5 100$/tCO2<br> xx6 200$/tCO2<br> xx7 500$/tCO2<br> xx8 40$/tCO2<br> xx9 20$/tCO2<br> xx0 5$/tCO2</p> <p>For figure 1b of the paper, this data was aggregated for all countries and regions. Relative changes of GDP are calculated relative to the baseline (4x1 with zero carbon price).</p> <p> </p> <p>[1] Luderer, G., Pietzcker, R. C., Bertram, C., Kriegler, E., Meinshausen, M. and Edenhofer, O.: Economic mitigation challenges: how further delay closes the door for achieving climate targets, Environmental Research Letters, 8(3), 034033, doi:10.1088/1748-9326/8/3/034033, 2013a.</p> <p>[2] Rogelj, J., Luderer, G., Pietzcker, R. C., Kriegler, E., Schaeffer, M., Krey, V. and Riahi, K.: Energy system transformations for limiting end-of-century warming to below 1.5 °C, Nature Climate Change, 5(6), 519–527, doi:10.1038/nclimate2572, 2015.</p>
Evaluating the impact of shade on Nitrogen fixation in sulla (Hedysarum coronarium L.): a 15N natural abundance study
<p>This dataset refers to the experimental raw data (csv version) collected within the trial reported in the concerned article on the following parameters:</p> <p>1. total nitrogen, delta N15, nitrogen derived from atmosphere, root biomass, nitrogen yield, nitrogen fixed, splitted per experiment, mowing, crop, treatment and replicate (15N_root.csv).</p> <p>2. total nitrogen, delta N15, nitrogen derived from atmosphere, shoot biomass, nitrogen yield, nitrogen fixed, splitted per experiment, mowing, crop, treatment and replicate (15N_shoot.csv).</p> <p>3. shoot cumulative nitrogen yield, root cumulative nitrogen yield, shoot cumulative nitrogen fixed, root cumulative nitrogen fixed, shoot:root ratio of nitrogen yield, shoot:root ratio of nitrogen fixed, splitted per experiment, treatment and replicate (shoot root ratio and N cumulative value.csv).</p>
The impact of domestic combustion of biomass pellets on the environment and human health: Example from Poland
<p><strong>Submitted data was used to write an article: </strong>Drobniak, A., Jelonek, Z., Mastalerz, M., Jelonek, I., Widziewicz-Rzońca, K., The impact of domestic combustion of biomass pellets on the environment and human health: Example from Poland – in preparation.</p> <p> </p> <p><strong>Funding acknowledgments: </strong>The project is co-financed by the Polish National Agency for Academic Exchange within the Polish Returns Programme (BPN/PPO/2021/1/00005/DEC/1), the National Science Center, Poland (2022/01/1/ST10/00024), and the research activities co-financed by the funds granted under the Research Excellence Initiative of the University of Silesia in Katowice, Poland. </p> <p> </p> <p><strong>Article Abstract:<br></strong></p> <p><span>In the context of the European Union's intensified efforts to curb greenhouse gas emissions and meet climate targets, wood pellets have emerged as a pivotal element in the renewable energy strategy. Yet, biomass pellet combustion has been linked to a range of pollutants impacting air quality and public health. As biomass utilization gains popularity as a fuel for residential heating, it is important to determine this impact and enhance sustainable practices throughout the entire biomass energy production cycle. </span></p> <p><span>This study investigates the intricate dynamics of biomass pellet properties on their combustion emissions, with a specific focus on the differences observed between pellets of woody and non-woody origins. The data reveal a variation in pellet characteristics, especially regarding their ash and fines contents, mechanical durability, and impurity levels, and significant differences in the type and amount of utilization emissions. The results highlight potential health risks posed by the combustion of biomass fuels, particularly non-woody (agro) pellets, due to elevated concentrations of emitted particulate matter (PM), carbon monoxide (CO), nitrogen dioxide (NO<sub>2</sub>), hydrogen sulfide (H<sub>2</sub>S), ammonia (NH<sub>3</sub>), chlorine (Cl<sub>2</sub>), sulfur dioxide (SO<sub>2</sub>), and formaldehyde (HCHO), all surpassing recommended limits.</span></p> <p><span>Moreover, the study reveals that emissions from pellet combustion could be partially predicted by analyzing pellet characteristics. Statistical analysis identified several key variables—including bark content, fines content, mechanical durability, bulk density, heating value, net calorific value, sulfur, and nitrogen content—that impact emissions of CO, NO<sub>2</sub>, H<sub>2</sub>S, SO<sub>2</sub>, HCHO, and respiratory tract irritants. These findings underscore the need for proactive measures, including the implementation of stricter standards for fuel quality and emissions, alongside public education initiatives promoting the cleanest and safest fuels possible. </span></p> <p><strong> </strong></p>
Data for "Meat and dairy substitutes – better for health and the environment? Impacts on nutrition and sustainability; consumer perspectives; ethical and legal considerations"
<p>Combined data reposatory for the output of the project "Meat and dairy substitutes – better for health and the environment? Impacts on nutrition and sustainability; consumer perspectives; ethical and legal considerations"</p> <p><em>Kombiniertes Datenarchiv für die Ergebnisse des Projekts "Fleisch- und Milchersatzprodukte – besser für Gesundheit und Umwelt? Auswirkungen auf Ernährung und Nachhaltigkeit, die Sicht der Konsumentinnen und Konsumenten sowie ethische und rechtliche Überlegungen"</em></p> <p>The project was funded by the Foundation for Technology Assessment (TA-Swiss) "<a href="https://ror.org/02shtak05">ror.org/02shtak05</a>".</p> <p><em>Das Projekt wurde von der Stiftung für Technologiefolgen-Abschätzung (TA-Swiss) finanziert "<a href="https://ror.org/02shtak05">ror.org/02shtak05</a>". </em></p>
Hrycyna et al. 2022 - Satellite observations of NO2 indicate legacy impacts of Redlining in US Midwestern cities
<p>This dataset contains remotely sensed estimates of nitrogen dioxide (NO2, via TROPOMI accessed via Google Earth Engine) for HOLC neighborhoods in 11 US Midwestern cities, and corresponding coarse geographic and demographic data of those cities. NO2 data is reported daily for the entire calendar year of 2019, geographic and demographic variables are fixed for each city for the entire year. Each HOLC-graded neighborhood included in this dataset was filtered to be greater than 2 km2. The number of pixels used to calculate the area-weighted mean of NO2 is also reported, as is the area of the neighborhood. The dataset has also been filtered for observations that did not pass quality filters for L3 TROPOMI data. The cities included in the study are: Chicago IL, Milwaukee WI, Saint Paul MN, Minneapolis MN, Indianapolis IN, Cleveland OH, Wichita KS, Greater Kansas City KS and MO, Columbus OH, Detroit MI, and Omaha NE. HOLC neighborhood shapefiles were obtained from the Mapping Inequality project website, hosted by the University of Richmond, and resulting polygons used in analysis were created by dissolving shared boundaries in Google Earth Engine. City populations and population density were obtained from the US 2010 Census data. All data was collected and organized to assess if current day NO2 levels varied with HOLC grades in these major cities.</p> <p> </p> <p>Data was used in the study: Hrycyna et al. (2022) <em>Elementa</em> 10(1):00027 </p> <div> <div><a href="https://doi.org/10.1525/elementa.2022.00027" target="_blank" rel="noopener">https://doi.org/10.1525/elementa.2022.00027</a></div> </div> <p>Robert K. Nelson, LaDale Winling, Richard Marciano, Nathan Connolly, et al., “Mapping Inequality,” American Panorama, ed. https://dsl.richmond.edu/panorama/redlining/#loc=5/39.1/-94.58&text=downloads</p> <p><strong>Dataset for all analyses presented in Hrycyna et al. Columns described below:</strong></p> <p>HOLC_grade: A, B, C, D (neighborhood grade categories obtained from Mapping Inequality project, indicate historic HOLC designations of neighborhoods).</p> <p>HOLCAreaKm2: continuous area value in km2 of the HOLC neighborhood polygon, which may be more than one HOLC designated polygon merged from the shapefiles downloaded from Mapping Inequality.</p> <p>pixelcount: integer values of the number of TROPOMI NO2 pixels used to produce the area-weighted mean NO2 value.</p> <p>NO2_mol_m2: area-weighted mean value of TROPOMI NO2 for that HOLC neighborhood polygon in mol m-2</p> <p>system.index: designated date and time boundary of the observation collected via TROPOMI</p> <p>date: date of observation</p> <p>month: month of observation</p> <p>City: city in the US Midwest</p> <p>State: state for the city of focus</p> <p>Population: urban population obtained from 2010 census</p> <p>PopDensity: urban population density obtained from 2010 census, based on modern city boundaries (in people per square miles)</p> <p>CityArea_mi2: Area of the city of interest, in square miles.</p> <p>ln_NO2: natural log transformed NO2 values in mol m-2</p> <p>NO2_DU: NO2 value converted from mol m-2 to DU (Dobsons Units, converted by multiplying 2241.15)</p> <p>NO2_lnDU: natural log transformed NO2 values in DU<br><br></p>
The impact of 11 May 2024 super geomagnetic storm on the plasma distribution over the Indian equatorial/low latitude ionospheric region
<p>The file contains the data set and the software for the generation the plots used in the manuscript " The impact of 11 May 2024 super geomagnetic storm on the plasma distribution over the Indian equatorial/low latitude ionospheric region".</p>
Survey on the social impact of the citizen science in the projects LIFE MIPP and InNat
<p>The present dataset consists in a sociological survey addressed to the volunteers involved in MIPP and InNat projects, a citizen science initiative aimed at the collection of distribution data of protected species and habitats all over Italian national territory. In particular, two different files are provided: </p> <ol> <li>the original survey (questions and answers) addressed to the volunteers in Italian</li> <li>the English translation of the questionnaire </li> </ol> <p>The survey covers different topics: socio-demographic data (section 1), opinions on the role of volunteering in a citizen science project (section 2), possible previous citizen science experience (section 3), test on the acquired skills on the monitored insect species with MIPP/InNat (section 4), opinions concerning the ecological crisis and the trust in the ability of humankind to solve environmental issues (section 5), possibility to be further contacted for additional interviews (section 6).</p> <p>A total of 364 completed questionnaires have been collected and relative results are provided here. Moreover, the above-mentioned results are thoroughly investigated and analysed in a scientific paper which also explores drivers that might keep volunteers active in a biodiversity monitoring project. Indeed, the engagement of volunteers in citizen science projects is a remarkable issue to address in order to ensure long-term sustainability, scientific relevance and public participation.</p> <p>The MIPP/InNat initiative started in 2012, with the project MIPP “Monitoring of insects with public participation” (LIFE11 NAT/IT/000252) which ended in 2017, and was then continued by the InNat project thanks to Italian National fundings. Data gathered in both projects converged in the same database. This research was supported by the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 - Call for tender No. 3138 of 16 December 2021, rectified by Decree n.3175 of 18 December 2021 of Italian Ministry of University and Research funded by the European Union – 1034 of 17 June 2022 adopted by the Italian Ministry of University and Research, CUP B83D21014060006, Project title “National Biodiversity Future Center” – NBFC.</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.