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”
Statistical data - Assessing Local Impact of Decarbonization in Bosnia and Hercegovina"
<p>The dataset represents statistical data collected and interpreted by the authors in the framework of the research titled "<span>Assesment of Local Impact of Decarbonization in Bosnia and Hercegovina"</span></p> <p>he dataset contains tables and graphs that are employed within the research and may be accessed by the general public in compliance with the Creative Commons Attribution license. The datasheets include:</p> <ol> <li>Selected CO2 emission data for BiH, 2016-2021</li> <li>GHG per capita emissions (tons) in Western Balkan countries 2016-2021</li> <li>Greenhouse gas emission per sector in Bosnia and Herzegovina 2015-2019</li> <li>Comparison of CO2 emissions between BiH and EU</li> <li>Type of fuel and its production of CO2 in Bosnia and Herzegovina, 2016-2021</li> <li>Basic macroeconomic indicators relevant to the labour market in BiH, 2010-2020</li> <li>Employment levels in selected sectors in FBiH, 2016-2021</li> <li>Employment levels in selected sectors in Republic Srprska, 2016-2021</li> <li>Employment levels in selected sectors in FBiH, 2016-2021</li> <li>Number of legal entities in FBiH 2016-2021</li> <li>Number of business entities in RS 2016-2021</li> <li>Poverty and inequality indicators in BiH, 2015</li> <li>Heads of households by current activity status and education in BiH, 2015</li> <li>Household composition by industry of employment of household head, %, BiH, 2015</li> <li>Minimum wage earners in Bosnia and Herzegovina</li> <li>Number of social benefits recipients by household type, BiH 2015</li> <li>Stock of employed in selected municipalities and FBiH, 2016-2021</li> <li>Employment to population ratio in selected municipalities and FBiH, 2016-2021 (%)</li> <li>Employment rates in selected municipalities and FBiH, 2016-2021 (%)</li> <li>Unemployment rates in in selected municipalities and FBiH, 2016-2021 (%)</li> </ol> <p> </p>
Statistical data - Waste management sector transformation and its impact towards JGT in Montenegro
<p>The dataset represents statistical data collected and interpreted by the authors in the framework of the research titled "<span>Waste Management Sector Transformation </span><span>and its Impact towards JGT in Montenegro"</span></p> <p>The dataset contains tables and graphs that are employed within the research and may be accessed by the general public in compliance with the Creative Commons Attribution license. The datasheets include:</p> <ol> <li>Typical of RDF recovered from municipal solid wastes generated in industrialized nations</li> <li>Waste types are used as fuels in EU-27 cement kilns</li> <li>GHG performance of typical fuels</li> <li>Amount of waste generated and managed per inhabitant in Montenegro</li> <li>Population and waste in targeted municipalities</li> <li>Amount of waste generated and collected in the study area</li> <li>Number of Inhabitants in targeted municipalities</li> <li>Waste quantities (in tons) in (a) Old Royal Capital Cetinje; (b) Danilovgrad and (c) Podgorica</li> </ol>
Impacts of Coding Practices on Readability - Dataset
<p>Data gathered and analyzed as described on the paper "Impacts of Coding Practices on Readability", presented at the ICPC 2018</p>
The impact of exploiting spectro-temporal context in computational speech segregation
<p>The experimental data from the study:</p> <p>https://asa.scitation.org/doi/10.1121/1.5020273</p> <p>Group 1 contains results, masks and audio from the models of the 16 GMM component segregation system<br> Group 2 contains results, masks and audio from the models of the 64 GMM component segregation system</p> <p>There are three folders:</p> <p>Audio:<br> The CLUE sentences that were used for the listener study</p> <p>IBM = Ideal Binary Mask, UP = UnProcessed, EBM = Estimated Binary Mask. </p> <p>The IBM and UP are stored in one of the configuration folders (Front-end), that is:</p> <p>Audio\Group1\Front-end\icra_01_10sec_matched\UP<br> Audio\Group1\Front-end\icra_01_10sec_matched\IBM<br> Audio\Group1\Front-end\icra_01_10sec_matched\EBM</p> <p>Results:<br> The computed metrics for group 1 & 2 as well as Word Recognition Scores (WRSs) from the listener study</p> <p>BinaryMasks:</p> <p>a priori SNR masks, IBMs and EBMs from group 1 and 2.</p> <p><br> Developed with Matlab R2016a.</p>
Crime Climate Impact Distributions
<p>Percentiles of crime impacts due to climate change, organized by level of aggregation, climate scenario, and time period.</p> <p>Crime impacts are estimated by applying future climate data to dose-response functions of property and violent crime rates, aggregated by population levels.</p> <p>The uncertainty represented by these percentiles comes from a combination of statistical uncertainty in the dose-response functions, climate uncertainty across GCMs, and within-month weather realization, as sampled by Monte Carlo runs.</p> <p>The archive contains four folders: county_20a with county-level impacts, state_20yr with state-aggregated impacts, nca_20yr with NCA-region aggregated impacts, and national_20y with nationally aggregated impacts. Aggregation is weighted by crimes within each region.</p> <p>The files are labeled as follow:</p> <p>crime-<TYPE>-<RCP>-<YEAR><SUFFIX>.</p> <p>The TYPE can be property or violent crime. The RCP may be RCP 2.6, RCP 4.5, RCP 6.0, or RCP 8.5; the years are "2020", for average impacts during 2020 - 2039, "2040" for impacts 2040 - 2059, and "2080" for impacts 2080 - 2099. Files with the suffix 'b.csv' contain percent changes; those with the suffix '-absolute.csv' contain level changes in MT.</p> <p>The columns of these files specify the region (using the FIPS code for counties), and the quantiles from 1% ("q0.01") to 99% ("q0.99").</p>
Supplementary material 4 from: Hagen BL, Kumschick S (2018) The relevance of using various scoring schemes revealed by an impact assessment of feral mammals. NeoBiota 38: 37-75. https://doi.org/10.3897/neobiota.38.23509
Detailed SEICAT assessments (Table S4) : Explanation note: A summary of the impact assessment using the Socio-Economic Impact Classification for Alien Taxa (SEICAT). Impact scores, from highest to lowest are Massive (MV), Major (MR), Moderate (MO), Minor (MN) and Minimal Concern (MC). Full reference details are given in Appendix 2. Region indicates whether the impact was found on islands or the mainland.
Supplementary material 2 from: Hagen BL, Kumschick S (2018) The relevance of using various scoring schemes revealed by an impact assessment of feral mammals. NeoBiota 38: 37-75. https://doi.org/10.3897/neobiota.38.23509
Detailed GISS assessments (Table S2) : Explanation note: Details of the environmental and socio-economic impact assessment using the Generic Impact Scoring System (GISS). Full reference details are given in Appendix 2. Region indicates whether the impact was found on islands or the mainland.
Supplementary material 1 from: Hagen BL, Kumschick S (2018) The relevance of using various scoring schemes revealed by an impact assessment of feral mammals. NeoBiota 38: 37-75. https://doi.org/10.3897/neobiota.38.23509
Differences between scoring schemes (Table S1) : Explanation note: Scoring differences for GISS, EICAT and SEICAT. This table was adapted from Kumschick et al. (2016) and Bacher et al. (2017). The numbers in the top column refer to the GISS scoring classifications, whereas the terms 'massive' to 'minimal concern' refer to EICAT and SEICAT scoring classifications.
Supplementary material 3 from: Hagen BL, Kumschick S (2018) The relevance of using various scoring schemes revealed by an impact assessment of feral mammals. NeoBiota 38: 37-75. https://doi.org/10.3897/neobiota.38.23509
Detailed EICAT assessments (Table S3) : Explanation note: A summary of the impact assessment using the Environmental Impact Classification for Alien Taxa (EICAT). Impact scores, from highest to lowest are Massive (MV), Major (MR), Moderate (MO), Minor (MN) and Minimal Concern (MC). Full reference details are given in Appendix 2. Region indicates whether the impact was found on islands or the mainland.
FIGURE 4 in Overlooked mammal diversity and conservation priorities in Italy: Impacts of taxonomic neglect on a Biodiversity Hotspot in Europe
FIGURE 4. Two models of species endemism distribution among mammals of the Italian Peninsula. 3a) Occurrence in Central-Southern Peninsula and Sicily with often geographic substructuring; Lepus corsicanus. 3b) Occurrence limited to the Calabria bioprovince; Sciurus meridionalis. Question mark denotes uncertain outcomes in the case northern lineages meet the endemics.
FIGURE 1 in Overlooked mammal diversity and conservation priorities in Italy: Impacts of taxonomic neglect on a Biodiversity Hotspot in Europe
FIGURE 1. Some endemic taxa of the Italian Peninsula; 1. Apennine chamois in the Maiella National Park (photo Alessia Costa); 2. Apennine hare from North Latium (Central Italy) (photo Paola di Luzio); 3. Maremma wild boar from the Castelporziano Reserve (photo Roberto Isotti).
FIGURE 3 in Overlooked mammal diversity and conservation priorities in Italy: Impacts of taxonomic neglect on a Biodiversity Hotspot in Europe
FIGURE 3. Camerano's model of Capra pyrenaica and Capra hispanica dispersion in the Iberian Peninsula (redrawn from García-González, 2011).
FIGURE 2 in Overlooked mammal diversity and conservation priorities in Italy: Impacts of taxonomic neglect on a Biodiversity Hotspot in Europe
FIGURE 2. Cabrera's model of Capra pyrenaica dispersion in the Iberian Peninsula (redrawn from García-González, 2011).
FIGURE 6 in Overlooked mammal diversity and conservation priorities in Italy: Impacts of taxonomic neglect on a Biodiversity Hotspot in Europe
FIGURE 6. Discriminant analysis of skulls and horns in population samples of Rupicapra. Sample sizes are as follows: (1) groups on which the discrimination was based: Chartreuse, 12; Alps, 16; Pyrenees, 14; (2) groups entered as "unknowns": Balkans, 2; Caucasus, 4; Carpathians, 3; Abruzzi, 2 (data from Groves and Grubb, 2011).
FIGURE 5 in Overlooked mammal diversity and conservation priorities in Italy: Impacts of taxonomic neglect on a Biodiversity Hotspot in Europe
FIGURE 5. Horn length (Hornl; in mm) in population samples of Rupicapra males (data from Groves and Grubb, 2011).
Supplementary material 2 from: Yazlık A, Pergl J, Pyšek P (2018) Impact of alien plants in Turkey assessed by the Generic Impact Scoring System. NeoBiota 39: 31-51. https://doi.org/10.3897/neobiota.39.23598
List of references : Explanation note: List of references used for scoring the impact of the studied species.
Supplementary material 1 from: Yazlık A, Pergl J, Pyšek P (2018) Impact of alien plants in Turkey assessed by the Generic Impact Scoring System. NeoBiota 39: 31-51. https://doi.org/10.3897/neobiota.39.23598
Distribution of alien species assessed in this study : Explanation note: Distribution in Turkey of the alien species studied using the grid system according to Davis (1965–1985, 1988).
Supplementary material 3 from: Yazlık A, Pergl J, Pyšek P (2018) Impact of alien plants in Turkey assessed by the Generic Impact Scoring System. NeoBiota 39: 31-51. https://doi.org/10.3897/neobiota.39.23598
Scoring of the impact : Explanation note: Scoring of environmental and socioeconomi impact. File contains values (Impact score) and categories in which these scores are assigned (Impact type), with source references.
Supplementary material 1 from: Underwood E, Taylor K, Tucker G (2018) The use of biodiversity data in spatial planning and impact assessment in Europe. Research Ideas and Outcomes 4: e28045. https://doi.org/10.3897/rio.4.e28045
Biodiversity data sources at the EU level comprise the biodiversity and landuse-related EU reporting and monitoring programmes, and global or regional data portals which include data from European countries. Also gives an overview of spatial planning and data sources for biodiversity impact assessment in a selection of EU Member States: UK, Bulgaria, Netherlands, Germany, Sweden, Denmark and the Baltic Sea.
Data for the paper "Impacts of multiscale field-aligned currents (FACs) on the ionosphere-thermosphere system: GITM simulation"
<p>This dataset serves for the paper "Impacts of multiscale field-aligned currents (FACs) on the ionosphere-thermosphere system: GITM simulation".</p> <p> The uploaded zip file contains 6 directories, each directory contains 1 CDF file, detailed description for each file are listed as follows:</p> <p><strong>fig1: </strong></p> <p>vars: 'mlt', 'mlat', 'avg_lg', 'std_lg', 'avg_ms', 'std_ms', 'avg_sm', 'std_sm'</p> <p>avg_lg, std_lg: large-scale mean FAC and FAC variability </p> <p>avg_ms, std_ms: meso-scale mean FAC and FAC variability </p> <p>avg_sm, std_sm: small-scale mean FAC and FAC variability </p> <p><strong>fig2: </strong></p> <p>vars: 'mlt', 'mlat', 'avg_lg', 'std_lg', 'avg_ms', 'std_ms', 'avg_sm', 'std_sm'</p> <p>avg_lg, std_lg: Magnitudes of the large-scale mean electric field and electric field variability </p> <p>avg_ms, std_ms: Magnitudes of the meso-scale mean electric field and electric field variability </p> <p>avg_sm, std_sm: Magnitudes of the small-scale mean electric field and electric field variability </p> <p><strong>fig3:</strong></p> <p>vars: 'fac', 'dBy'</p> <p>fac: FAC along the track</p> <p>dBy: East-westward magnetic perturbation along the same track</p> <p><strong>fig4:</strong></p> <p>vars: 'glon', 'glat', 'Emag', 'int_jh'</p> <p>glon, glat: geographic longitude and latitude</p> <p>Emag: electric field magnitude </p> <p>int_jh: height-integrated Joule heating </p> <p><strong>fig6:</strong></p> <p>vars: 'glon', 'glat', 'gitm_evar_lg', 'gitm_evar_ms', 'de2_evar_lg', 'de2_evar_ms'</p> <p>glon, glat: geographic longitude and latitude</p> <p>'gitm_evar_lg', 'gitm_evar_ms': magnitudes of the simulated large-scale and mess-scale electric field variabilities</p> <p>'de2_evar_lg', 'de2_evar_ms': magnitudes of the large-scale and mess-scale electric field variabilities from the data in geographic coordinates</p> <p><strong>fig7:</strong></p> <p>vars: 'glon', 'glat', 'gitm_jh_lg', 'gitm_jh_ms'</p> <p>glon, glat: geographic longitude and latitude</p> <p>'gitm_jh_lg', 'gitm_jh_ms': Increase of the height-integrated JH related to the large- and mess-scale FAC variabilities </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.