Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
18,658
datasets available to search
ShareScore release 0.9.0
Dataset results
18,658 results for “impacts”
Sentinel-1/2 derived Arctic Coastal Human Impact dataset (SACHI)
<p>The SACHI (Sentinel-1/2 derived Arctic Coastal Human Impact) V1 dataset was developed as part of the HORIZON2020 project Nunataryuk by b.geos (www.bgeos.com). V1 covered a 100km buffer from the Arctic Coast (land area), for areas with permafrost near the coast. V2 has been prepared as part of the ESA project EO4PAC. It covers additional selected areas extending the coverage to the south.<br>It is based on Sentinel-1 and Sentinel-2 data from 2016-2020 using the algorithms described in Bartsch et al. (2020). V1 is a supplement to Bartsch et al. (2023) and V2 to Tanguy et al. (2024).</p> <p>V2 consists of two shape files.</p> <p>1) SACHI_v2.shp - all identified objects with infrastructure/impact classes and auxiliary information<br>2) SACHI_v2_granules_acquisition_dates - processed Sentinel-2 granule extent polygons with dates of all used input data</p> <p>Dataset reference: see Zenodo 'Sentinel-1/2 derived Arctic Coastal Human Impact dataset (SACHI) v2' (zenodo.org)</p> <p>Description of fields in SACHI_v2.shp:</p> <p>class: SACHI class value. 11=linear transport infrastructure (asphalt), 12=linear transport infrastructure (gravel), 13=linear transport infrastructure (undefined),<br>20=buildings (and other constructions such as bridges), 30=other impacted area (includes gravel pads, mining sites), 40=airstrip, 50=reservoir or other water body impacted by human activities</p> <p>Description of fields in SACHI_v2_granules_acquisition_dates.shp:</p> <p>S2_date1 to S2_date3 - dates of individual Sentinel-2 images used for averaging<br>S1_winter - year(s) of Sentinel-1 images used for averaging (months December and/or January)</p>
Supporting Data for "Impacts of Antarctic ice mass loss on New Zealand climate"
<p>Contains the model output necessary to reproduce the results of "Impacts of Antarctic ice mass loss on New Zealand climate" by Andrew G. Pauling, Inga J. Smith, Jeff K. Ridley, T. Martin, M. Thomas and D. P. Stevens. Submitted for publication to Geophysical Research Letters.</p> <p>Please use the "getdata.sh" script in the Github repository here: LINK to download and extract the data into the correct location for the notebooks to reproduce the results of the paper.</p>
Tool for the environmental and economic impact assessment of industrial recycling routes for lithium-ion traction batteries
<p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).</p> <p> </p> <p>Please send your inquiries regarding the tool to s.bloemeke@tu-braunschweig.de.</p>
Dataset for publication: An inter-laboratory study characterizes the impact of bioinformatic approaches on genome-based cluster detection for foodborne bacterial pathogens
<p>This dataset is part of a dry-lab interlaboratory study conducted across Germany, regarding bacterial outbreak detection based on NGS data, with a focus on bioinformatic analysis of four species to identify potential variability caused by different data analysis approaches and human interpretation. Participants were asked to follow their usual in-house protocols while adhering to the general guidelines. A quality assessment (with sample exclusion) was followed by 7-gene Multilocus-Sequence Typing (MLST), core genome Multilocus Sequencing Typing (cgMLST), and SNP calling. The participants were then asked to identify clusters. The study was not intended to resemble a standard proficiency test with a passing/failing grade, but rather to investigate and quantify obvious variability in the results and, where possible, the reasons for it. For this purpose, the datasets included borderline cases in terms of quality.</p>
Data for Mellado et al. The impacts of marking on bats: mark-recapture models for assessing injury rates and tag loss. Journal of Mammalogy. 103:100-110. DOI:10.1093/jmammal/gyab153
<p>Data sets used in Mellado et al. The impacts of marking on bats: mark-recapture models for assessing injury rates and tag loss. Journal of Mammalogy. 103:100-110. (https://doi.org/10.1093/jmammal/gyab153)</p> <p>File Descriptions:</p> <p>CapHistTagLoss.txt - Capture histories for <em>Carollia perspicillata</em> identifying if individual was captured with both tags (B), arm bands (A), collar (C), not captured (0) or not monitored (dot). Covariates included are Sex, Forearm Length and Scaled Mass Index.<br> CaptHistTagInj.txt - Capture histories for <em>Carollia perspicillata</em> identifying if individual was captured with no lesions from arm band (A), minor injury (I), major injury (M), not captured (0) or not monitored (dot). Covariates included are Sex, Forearm Length and Scaled Mass Index.<br> LesionOccurrence.txt - Censored time-to-event data for survival analysis. Recorded events were the occurrence of lesions of any type due to arm bands.<br> RingCondition.txt - Censored time-to-event data for survival analysis. Recorded events were the occurrence of damage to arm bands.<br> SMI.txt - Longitudinal data for individual <em>Carollia perspicillata</em> Scaled Mass Index, identifying individual records, the occurrence of lesions, sex, month, year</p> <p> </p>
The impact of Syrian Civil War on cultivated cropland dynamics
<p>The data needed to replicate paper --'The impact of Syrian Civil War on cultivated cropland dynamics' </p>
Modelling impacts of tramlines on soil erosion processes at the catchment scale.
<p>The data refers to the following article:</p> <p>Saggau, P., M. Kuhwald, W. B. Hamer, R. Duttmann (2021): Are compacted tramlines underestimated features in soil erosion modelling? A catchment‐scale analysis using a process‐based soil erosion model. In: Land Degradtaion & Development. doi: 10.1002/ldr.4161</p> <p>The data only contains distributable raw data and R-codes.</p>
Data set of article entitled: "Impact of the interfacial Dzyaloshinskii-Moriya interaction on the band structure of one-dimensional artificial magnonic crystals: A micromagnetic study"
<p>Data set of the immagies showed in figure 3 of the article entiteled "Impact of the interfacial Dzyaloshinskii-Moriya interaction on the band structure of one-dimensional artificial magnonic crystals: A micromagnetic study". All files contain the matrix of the dispersion relations of the two analysed Magnonic Crystals: the SAMPLE A and the SAMPLE B for different value of the interfacial Dzyaloshinskii-Moriya interaction (constant D). The first row is the set of values of k-vector, while the first column is the set of value of the frequencies. The other elements of the matrix are the values of the pixel related to the first row and first column. These elements are been obtanied by the Fast Fourier Transform in time and space of the micromagnetic simulations .</p>
Data used in the manuscript: "Influence of coastal vegetation on the 2004 tsunami wave impact in west Aceh"
<p>The data set presented accompanies the study by Laso Bayas et al. (2011) “Influence of coastal vegetation on the 2004 tsunami wave impact in west Aceh”. The data set contains all the observed (not transformed) variables used in the above mentioned study. The accompanying text file describes each of the variables included. A total of 180 transects were employed for the Laso Bayas et al (2011) study. The variables were further standardized and simplified to use them into the statistical models described in the paper.</p>
Mapping Building BioData.pt Indicators against the performance and impact assessment frameworks for research infrastructures of OECD, ESFRI and RI-PATHS project
<p>"Buiding BioData.pt" indicators observed in international frameworks for performance and impact assessment of research infrastructures, namely, OECD, ESFRI and RI-PATHS.</p>
Relevance of "Building BioData.pt" indicators identified in ESFRI (E), OECD (O) and RI-PATHS (R) impact assessment frameworks
<p>The relevance of the indicators maintained by the "Building BioData.pt" project was assessed against the objectives of different organizations/initiatives: 1) Strategic objectives of BioData.pt; 2) Objectives of the Portuguese Roadmap for Research Infrastructures; 3) Objectives of ELIXIR; 4) Objectives of EOSC; 5) Sustainable Development Goals of the United Nations.</p>
RICCH: An Interactive Analysis Tool for Risk and Impacts of Climate Change on Hydropower Database
<p>The "RICCH: An Interactive Analysis Tool for Risk and Impacts of Climate Change on Hydropower" Database, referred to as the RICCH Database, includes a "Hydropower Plants Database" and the incorporation of future hydropower usable capacity for 542 hydropower plants in the Global South. The "Hydropower Plants Database" includes the main design and location characteristics of 542 hydropower plants across 52 countries. Additionally, we incorporate the results of future usable capacity simulations using a multi-model ensemble of 21 Global Climate Models (GCMs) and two representative concentration pathways (RCPs). We use a multi-model ensemble of 21 GCMs from NASA's NEX GDDP dataset RCP 4.5 and RCP 8.5. We aggregate the mean monthly usable capacity (MW) for each model and scenario, for the early century (2010-2039), mid-century (2040-2069), and the end-of-the-century (2070-2099). We include these results for every power plant in the RICCH database. </p> <p><em>Example: Itaipu (power plant in Brazil) has a record for its simulated mean monthly usable capacity for January (1) in the early century (2010-2039), under GCM ACCESS1_0 and RCP 4.5.</em></p>
Supporting model output for article "Assessing the potential impact of river chemistry on Arctic coastal production"
<p>The following is a summary of processed model output data from a series of HiLAT model runs.<br> A description of the model runs and visualization of model output and analysis can be found in the<br> accompanying manuscript.</p> <p><br> Gibson G. A., Elliott, S., Piliouras, A. Clement Kinney, J., Jeffery, N. (2022) Assessing the potential<br> impact of river nitrate on coastal production in the Arctic. Frontiers in Marine Science: Coastal Ocean<br> Processes.</p> <p><br> This work was supported by the Regional and Global Model Analysis (RGMA) program of the US<br> Department of Energy’s Office of Science as a contribution to the HiLAT project. Additional support for<br> this project was provided by the National Science Foundation, under award #173886.<br> </p> <p><strong>River Nutrient Forcing</strong></p> <p>The experiments involved modifying the nutrient concentrations in the river nutrient forcing files.<br> The river nutrient files are specified during model setup. For use in the HiLAT model, GNEWS annual<br> river nutrient inputs were partitioned into twelve monthly forcing values. The nearest ocean grid point<br> to each of the GNEWS river mouth locations was identified and then, as with the runoff, the nutrient<br> inputs for each river basin were spatially mapped to surface ocean model grid cells, which are 10 meters<br> thick, such that the spatial pattern of river nutrient dispersion follows river water inputs to the oceans.</p> <p>The experiments were:<br> i) The baseline model simulation: The 12 monthly values for each grid cell were constant in time.<br> <strong>river_nutrients_GNEWS2000_gx1v6.nc</strong><br> ii) an experiment in which baseline Arctic River Nitrogen (NO 3 and NH 4 ) concentrations were doubled.<br> <strong>river_nutrients_GNEWS2000_gx1v6_x2Arctic.nc</strong><br> iii) an experiment in which baseline Arctic River Nitrogen (DON and DIN) concentrations were scaled to<br> the river volume discharge contained in <strong>runoff.daitren.iaf.20120419.nc</strong><br> <strong>river_nutrients_GNEWS2000_gx1v6_scaled_climatology.nc</strong><br> iv) an experiment in which the scaled river nutrient discharge (iii) was shifted earlier by two months.<br> <strong>river_nutrients_GNEWS2000_gx1v6_shifted2m_climatology.nc</strong><br> v) an experiment in which the scaled river nutrient discharge (iii) was shifted earlier by a month and<br> doubled in concentration.<br> <strong>river_nutrients_GNEWS2000_gx1v6_shifted_climatology_x2.nc</strong></p> <p>River nutrient fluxes are in units of nmol/cm2/s<br> Only concentrations within the domain TLONG>=60 &TLONG <=340 & TLAT >=60 were modified in<br> concentration/timing.</p> <p><br> Variables of interest:<br> din_riv_flux: dissolved inorganic nitrogen river flux<br> don_riv_flux: dissolved organic nitrogen river flux</p> <p>Each of the experiments is described in detail in Gibson et al (2022).</p> <p>---------------------------------------------<br> There are multiple versions of most output file types, corresponding to the river nutrient experiments that<br> were conducted.</p> <p><br> Many variables in the output files are <strong>regional averages</strong> where model regions are indicated by a number<br> *note - for aesthetics, the numbering used in the model output files differs slightly from the numbering<br> used in the accompanying manuscript. The numbers assigned in the analysis files aligns with the numbers<br> assigned to regions within the region mask provided in the grid file.</p> <p><strong>Grid File/region masks</strong><br> gx1v6_polar_mask_coast.5.22.20c.nc This file is an updated version of the standard grid file. It has been<br> updated to include the addition of a coastal Arctic region variable ‘Arctic_Coast_Mask’ which indicates<br> which grid cells are in the coastal regions used in the analysis and the Arctic_Region variable which<br> indicates which grid cells are in the broader regions.</p> <p><br> Variables contained in this file are:<br> Arctic_Coast_Mask: contains values 0-9 indicating which (if any) coastal region a grid cell is in<br> Arctic_Region Mask: contains values 0-11 indicating which (if any) region a grid cell is in</p> <p><br> TLAT: latitude of grid cell<br> TLONG: longitude of grid cell<br> TAREA: Area of grid cell<br> HT: Bathymetry of grid cell</p> <p> </p><table> <tbody> <tr> <td> </td> <td> <p><strong>Arctic_Region (seas)</strong></p> </td> <td> <p><strong>Arctic_Coast_Mask </strong><strong>(coast)</strong></p> </td> </tr> <tr> <td> <p><strong>Bering Sea</strong></p> </td> <td> <p>1</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p><strong>Chukchi Sea</strong></p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p><strong>East Siberian Sea</strong></p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p><strong>Laptev Sea</strong></p> </td> <td> <p>4</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p><strong>Beaufort Sea</strong></p> </td> <td> <p>5</p> </td> <td> <p>5</p> </td> </tr> <tr> <td> <p><strong>Barents Sea</strong></p> </td> <td> <p>6</p> </td> <td> <p>6</p> </td> </tr> <tr> <td> <p><strong>Canadian Basin</strong></p> </td> <td> <p>7</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Eurasian Basin</strong></p> </td> <td> <p>8</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Nordic Seas</strong></p> </td> <td> <p>9</p> </td> <td> <p>7</p> </td> </tr> <tr> <td> <p><strong>Labrador Sea</strong></p> </td> <td> <p>10</p> </td> <td> <p>8</p> </td> </tr> <tr> <td> <p><strong>Kara Sea</strong></p> </td> <td> <p>11</p> </td> <td> <p>9</p> </td> </tr> </tbody> </table> --------------<p></p> <p> </p><p>Model outputs that were analyzed in the manuscript are contained in three different kinds of output file.<br> For each file type a file exists for each river nutrient experiment.</p> <p></p> <p>The following series of files contains variables related to the particulate organic carbon flux to the<br> sediment, demineralization and remineralization rates.<br> bgc_T62_gx1GIF_nut-riv-BASELINE-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2xArcticN-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv_2XDC-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-BASELINE-seas_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-seas_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2xArcticN-seas_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-seas_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-seas_region-sed-137-157.nc</p> <p><br> Variables contained in these are:<br> POCTOSED_AVG* : Particulate organic carbon flux to sediment<br> PONTOSED_AVG* : Particulate organic nitrogen flux to sediment<br> SEDDENITRIF_AVG* : Sediment denitrification rate<br> POC_PROD_AVG* : Production of Particulate organic carbon<br> POC_FLUX_AVG* : Particulate organic carbon flux into layer/cell<br> DON_REMIN_AVG* : Dissolved Organic Nitrogen remineralization rate<br> DOC_REMIN_AVG* : Dissolved Organic Carbon remineralization rate<br> DIAT_N_LIM_AVG* : Diatom nitrogen limitation<br> DIAT_N_LIM_AVG* : Diatom nitrogen limitation<br> DIAT_P_LIM_AVG* : Diatom phosphorous limitation<br> DIAT_FE_LIM_AVG* : Diatom iron limitation<br> DIAT_LIGHT_LIM_AVG*: Diatom light limitation<br> SP_N_LIM_AVG* : Small phytoplankton nitrogen limitation<br> SP_P_LIM_AVG* : Small phytoplankton phosphorous limitation<br> SP_FE_LIM_AVG* : Small phytoplankton iron limitation<br> SP_LIGHT_LIM_AVG* : Small phytoplankton light limitation<br> Where * represents the coastal region number.<br> ----------------------------------------</p> <p><br> The following series of files contains primary production for the small and large phytoplankton groups<br> and the zooplankton biomass.</p> <p>Coastal regional averages – based on regions marked in the Arctic_Coast_Mask variable<br> bgc_T62_gx1GIF_runoff-2xArcticN-region-prod-ACM-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-region-prod-ACM-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-region-prod-ACM-137-157.nc<br> bgc_T62_gx1GIF_riv-BASELINE-region-prod-ACM-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-region-prod-ACM-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2XDC-region-prod-ACM-137-157.nc</p> <p>Regional seas averages – based on regions marked in the Arctic_Region variable<br> bgc_T62_gx1GIF_runoff-2xArcticN-region-prod-seas-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-region-prod-seas-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-region-prod-seas-137-157.nc<br> bgc_T62_gx1GIF_riv-BASELINE-region-prod-seas-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-region-prod-seas-137-157.nc</p> <p>Variables contained in these files are:<br> TAREA_SUM* – total area of the region<br> PPSP_REGSUM* – sum of primary production by small phytoplankton in a region<br> PPDIAT_REGSUM*– sum of primary production by diatoms in a region<br> ZOOC_AVG*– sum of zooplankton biomass in a region</p> <p>----------------------------------------<br> The following series of files contains ice associated variables and mixed layer nutrients</p> <p>bgc_T62_gx1GIF_nut_riv-2xArcticN-ice_coastal-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-ice_coastal -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-ice_coastal -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-BASELINE-ice_coastal -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-ice_coastal -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2XDC-ice_coastal -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2xArcticN-ice_seas-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-ice_seas -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-ice_seas -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-BASELINE-ice_seas -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-ice_seas-137-157.nc</p> <p>HI_REGAVG* : Regional averaged ice depth<br> HS_REGAVG* : Regional averaged snow depth<br> ICEAREA_REGSUM* : Regional sum ice area<br> ICEVOL_REGSUM*: Regional sum volume area<br> MLAM_REGAVG* : Regional average ammonium concentration in mixed layer<br> MLNIT_REGAVG* : Regional average nitrate concentration in mixed layer<br> PP_REGAVG* : Regional average primary production (ice algae)<br> PP_REGSUM* : Regional total primary production (ice algae)<br> TAREA_SUM* : Total area of region<br> TIME : time</p>
Impact of Meteorological Factors on the Mesoscale Morphology of Cloud Streets during a Cold Air Outbreak over the western North Atlantic
<ul> <li>Supporting datasets for paper "Impact of Meteorological Factors on the Mesoscale Morphology of Cloud Streets during a Cold Air Outbreak over the western North Atlantic". </li> <li>Those are a subset of the (analyzed) datasets from WRF control simulation "ERA5" in netcdf format. See manuscript for more details. <ul> <li>cld_size.nc: cloud object size</li> <li>cld_ort_2020-03-01_15_00_00.nc: cloud object at 15:00 UTC</li> <li>hydro-02-2020-03-01_15/00/00.nc: water path sample data at 15:00 UTC</li> <li>wrfout_d02_2020-03-01_15/00/00: wrf output sample data at 15:00 UTC</li> </ul> </li> </ul>
Dataset associated with Banks et al. (2022): "Impacts of the desiccation of the Aral Sea on the Central Asian dust life-cycle"
<p>This dataset contains the COSMO-MUSCAT simulation output for the 'Dustbelt' (DUBLT) scenarios of Central Asian dust aerosol described by the paper "Impacts of the desiccation of the Aral Sea on the Central Asian dust life-cycle", written by Banks et al. and published in JGR in 2022 (<a href="https://doi.org/10.1029/2022JD036618">https://doi.org/10.1029/2022JD036618</a>).</p>
Data used to create figures and tables in the ACP manuscript "Two-way coupled meteorology and air quality models in Asia: a systematic review and meta-analysis of impacts of aerosol feedbacks on meteorology and air quality" by Gao et al. (2022)
<p>This dataset contains the original data that extracted from all collected papers refering applications of two-way coupled models in Asia. It is supplied to the review paper, which titled as "Review on two-way coupled meteorology and air quality models in Asia: impacts of aerosol feedbacks on meteorology and air quality". The dataset includes three excel files (in the format of xlsx) as follows:</p> <p>1. Basic information of literatures (Table S1.xlsx)</p> <p>2. Model performance metrics (Table S2.xlsx)</p> <p>3. Quantitative results of aerosol effects on meteorological and air quality variables (Table S3.xlsx)</p> <p>4. Basic information of model setup for two-way coupled model applications in Asia (Table S4.xlsx)</p> <p>5. Summary of aerosol-induced variations of simulated shortwave and longwave radiative forcing at the bottom and top of atmosphere and in the atmosphere in Asia (Table S5.xlsx)</p> <p>.</p>
Data, plotting scripts, and figures for "Assessing diffusion model impacts on enstrophy and flame structure in lean premixed flames"
<p>This repository contains the data, plotting scripts, and figures associated with the paper "Assessing diffusion model impacts on enstrophy and flame structure in lean premixed flames" by Aaron J. Fillo, Peter E. Hamlington, and Kyle E. Niemeyer.</p> <p>See the README file for additional details.</p>
Machine learning code and dataset for "Nowcasting thunderstorm hazards using machine learning: the impact of data sources on performance"
<p>This repository contains the code and dataset for the paper:</p> <p>Nowcasting thunderstorm hazards using machine learning: the impact of data sources on performance, Natural Hazards and Earth System Sciences, 2022, <a href="https://doi.org/10.5194/nhess-2021-171">https://doi.org/10.5194/nhess-2021-171</a></p> <p>The GitHub code repository at <a href="https://github.com/meteoswiss-mdr/ts-nowcast-datasources">https://github.com/meteoswiss-mdr/ts-nowcast-datasources</a> may contain a more up-to-date version of the code if bug fixes etc. have been necessary. The file <a href="https://zenodo.org/api/files/41faa1b7-17f6-4a75-be09-7743426ef13c/ts-nowcast-datasources-publication.zip">ts-nowcast-datasources-publication.zip</a> in this Zenodo release contains the status of the GitHub repository at the time of the publication of the paper.</p> <p>For instructions for using the data, please see the <a href="https://github.com/meteoswiss-mdr/ts-nowcast-datasources">code repository</a>.</p> <p> </p>
Data and outputs for chapter 'The impacts of the 2019-20 wildfires on Australian fungi ' in 'Australia's Megafires: Biodiversity Impacts and Lessons from 2019-2020
<p><strong>Dataset includes raw data downloaded from the following sources: fungi_data.csv</strong></p> <ul> <li>Atlas of Living Australia occurrence download: https://doi.org/10.26197/ala.9e0ca388-9da2-4096-b1a3-26e2aaa51d8a. Accessed 2021-09-16. GBIF.org (16 September 2021)</li> <li>GBIF Occurrence Download https://doi.org/10.15468/dl.secenk</li> <li>Fungimap (https://fungimap.org.au/ (data obtained directly from Fungimap Inc.)</li> <li>MycoPortal (https://mycoportal.org/portal/index.php)</li> <li>iNaturalist (https://www.inaturalist.org/home)</li> </ul> <p><strong>Output files from point and polygon overlap with fire layer:</strong></p> <ul> <li>Fungi and fire analysis point overlap.xlsx</li> <li>Fungi and fire analysis polygon overlap.xlsx</li> </ul> <p> </p> <p> </p>
Evaluating the Sustainability Impacts of Intelligent Carpooling Systems for SOV Commuters in the Atlanta Region
<p>This data repository provides the data used in the following research project supported by an NCST (UTC) grant: Evaluating the Sustainability Impacts of Intelligent Carpooling Systems for SOV Commuters in the Atlanta Region.</p> <p>Please refer to Data Format and Content in the research report for meta-data information</p> <p>1. ABM Trip Table (Data Set 1):<br> CarpoolSim_input.csv<br> 2. Traffic Analysis Zones (TAZ) Shapefiles (Data Set 2):<br> TAZ+ECs_TAZ.zip<br> 3. Employment Center Shapefiles (Data Set 3):<br> TAZ+ECs_TAZ.zip<br> 4. Shortest Paths (Data Set 4):<br> path_retention.zip<br> 5. Analytical Outputs (Data Set 5):<br> summ_stats_ind.csv<br> summ_stats.csv</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.