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18,658 results for “impacts”
TWIN2PIPSA - Twinning for strategic networking and impactful research and innovation in Biomedicine and Biotechnology
<p>Presentation by Prof. Cláudio M. Gomes at the 5<sup>th</sup> edition of Ciências Research & Innovation Day.</p>
Consistent traffic noise impacts few fitness-related traits in a field cricket
<p>Anthropogenic habitat change is occurring rapidly, and organisms can respond through within-generation responses that improve the match between their phenotype and the novel conditions they encounter. But, plastic responses can be adaptive or maladaptive and are most likely to be adaptive only when contemporary conditions reasonably mimic something experienced historically to which a response has already evolved. Noise pollution is a ubiquitous anthropogenic stressor that accompanies expanding urbanization. We tested whether the amplitude of traffic noise influences a suite of fitness-related traits (e.g. survival, life history, reproductive investment, immunity) and whether that depends on the life stage at which the noise is experienced (juvenile or adult). Our treatments mimic the conditions experienced by animals living in urban roadside environments with variable vehicle types, but continuous movement of traffic. We used the Pacific field cricket, an acoustically communicating insect that was previously shown to experience some negative behavioral and life history responses to very loud, variable traffic noise, as a model system. </p>
Deliverable 2.3: Biodiversity impact estimates and documentation for indicators on multi-dimensional biodiversity aspects ready for use in WP3
<p>To quantify human-driven impacts on biodiversity, we used advanced modeling techniques paired with a few key variables such as habitat quality, vegetation structure, climate, and topography to develop an innovative predictive model that estimates both 1) current biodiversity patterns and distributions and 2) a baseline model of biodiversity. By comparing both of these models, we are able to identify regions with significant human-driven reductions in species richness, endemism, and species composition across both of our focus regions, South America and Africa.</p> <p>The files provided in this repository (1 km2 resolution) include:</p> <p>1- Species richness (number of species).</p> <p>2- Endemism (species rarity). For this metric, we used the corrected weight endemism index, which is the inverse of a species’ range size and effectively assigns higher scores to species with more restricted distributions. For this index, we used species ranges found within our study area (of South America and Africa).</p> <p>3- Species composition of vertebrates, invertebrates, and plants (which species occur in a given location). This metric, also known as beta diversity, summarizes which sets of species occur at each location. For this, we used the Sorensen index metric, as it does not depend on species absence data.</p> <p>These results provide a improve in previous deliverable 2.2.</p>
FIGURE 3 in Publication and impact: a bibliometric survey of Megataxa 1-10
FIGURE 3. Distribution of authors among papers published in Megataxa 1‒10, showing the number of papers (Y) in relation to numbers of authors per paper (X).
FIGURE 2 in Publication and impact: a bibliometric survey of Megataxa 1-10
FIGURE 2. Cumulative numbers of authors (A) and their countries (B) in papers published in Megataxa 1‒10.
FIGURE 1 in Publication and impact: a bibliometric survey of Megataxa 1-10
FIGURE 1. Growth of Megataxa from 2020 to 2023 showing cumulative numbers of papers (A) and pages (B) in Megataxa 1‒10.
Data for: Effective population size mediates the impact of pollination services on pollen limitation
<p>Inadequate pollen receipt limits flowering plant reproduction worldwide. Ecological causes of pollen limitation ('PL'), like pollinator scarcity and low plant abundance, have been a primary focus of research. The genetic diversity of plant populations could impact both quantity and quality components of PL in concert with ecological factors, yet empirical examples are lacking. We evaluated joint effects of ecological factors (flower abundance, pollinator visitation) and genetic effective population size (N<sub>E</sub>) on PL across 13 populations of a common herb. We used a histological approach with 5504 styles from 1137 flowers to separate quantity and quality components of PL, and link these to reproductive output. N<sub>E</sub> and pollinator visitation interacted to shape PL, but N<sub>E</sub> had stronger direct effects. Effectively smaller populations experienced stronger quantity PL, and controlled crosses in a pollinator-free environment revealed that pollen quantity was an intrinsic population-level attribute that increased with N<sub>E</sub>. Pollinator visitation enhanced pollen quality, but only in effectively larger populations. Quantity and quality PL negatively impacted fruit and seed set, respectively.<em> </em>Results highlight that PL is dictated by plant population genetic diversity in addition to commonly evaluated ecological factors.<strong> </strong>Efforts to support pollinators will only enhance plant reproduction in genetically diverse plant populations.</p>
High quality figures of "Assessing Climate Change Impacts on Crop Yields and Exploring Adaptation Strategies in Northeast China"
<p>This repository provides the figures for the publication "Assessing Climate Change Impacts on Crop Yields and Exploring Adaptation Strategies in Northeast China" in their original resolution, ensuring clarity and high-quality visual representations for readers.</p>
Unveiling the impacts of land use on the phylogeography of zoonotic New World Hantaviruses
<p>Billions of genomic sequences are stored in public repositories (NCBI) as well as records of species occurrence (GBIF). By implementing analytical tools from different scientific disciplines, data mining on these databases can be a source of information to aid in the global surveillance of zoonotic pathogens that circulate among wildlife. We illustrate this by investigating the hantavirus-rodent system in the Americas, i.e. New World Hantaviruses (NWH). First we draw the circulation of pathogenic NWH among rodents; by inferring the phylogenetic links among 278 genomic samples of the S segment (N protein) of NWH found in 55 species of Cricetidae rodents. Second, machine learning was used to assess the impact of land use on the probability of presence of the rodent species linked with reservoirs of pathogenic hantaviruses. Our results show that hosts are widely present across the Americas. Some hosts are present in the primary forest and agricultural land, but not in the secondary forest; whereas other hosts are present in secondary forest and agricultural land. The diversity of host species allows Hantavirus to circulate on a wide spectrum of habitats, in particular rural rather than urban. We highlight that Public repositories of genomic data and species occurrence are very useful resources for monitoring potential enzootic transmission and spillover of zoonotic viruses in relation with the changes that humans produce in the Biosphere.</p>
Data for the submitted paper by Yasunari et al., "Comprehensive Impact of Changing Siberian Wildfire Severities on Air Quality, Climate, and Economy: MIROC5 Global Climate Model's Sensitivity Assessments"
<p>The dataset contains some of the outputs from the global climate model experiments by MIROC5 on changing Siberian wildfire severities, the other data used in the paper (see READ_ME files on the data sources), the analyzed data, and the scripts for analyses, which were used in the following submitted paper. Note that this dataset also includes unused data for the paper:</p> <p><br>Yasunari, T. J., D. Narita, T. Takemura, S. Wakabayashi, and A. Takeshima, Comprehensive Impact of Changing Siberian Wildfire Severities on Air Quality, Climate, and Economy: MIROC5 Global Climate Model's Sensitivity Assessments, submitted.</p> <p>Please read the READ_ME files for detailed information in each directory (especially see the "about_figures_and_tables/" directory first). Because of their large sizes, the data were separated into three zipped files.</p>
SLIIDERS: Sea Level Impacts Input Dataset by Elevation, Region, and Scenario
<p>This record includes the Sea Level Impacts Input Dataset by Elevation, Region, and Scenario (SLIIDERS) dataset. It also includes source code to generate this product as well as necessary inputs that are not available for download elsewhere. Both the dataset and the source code are consistent with version 1.2. <strong>Note</strong>: The version associated with <a href="https://gmd.copernicus.org/articles/16/4331/2023/">Depsky et al., 2023</a> is v1.1.</p> <p>The zipped SLIIDERS Zarr store can be downloaded and accessed locally or can be directly accessed via code similar to the following:</p> <pre><code>from fsspec.implementations.zip import ZipFileSystem import xarray as xr xr.open_zarr(ZipFileSystem(url_of_file_in_record}}).get_mapper())</code></pre> <p><strong>File Inventory</strong></p> <p><em>Products</em></p> <ul> <li><strong>sliiders-v1.2.zarr.zip</strong>: SLIIDERS. A global dataset containing 18 socioeconomic variables, reflecting present day socioeconomic and geophysical characteristics of 11,980 coastal regions and projecting capital stock, GDP, and population growth trajectories through 2100 for five SSPs and two economic growth models. These variables are used as inputs to the pyCIAM modeling platform detailed in Depsky et al. 2023.</li> <li><strong>sliiders-v1.2.nc</strong>: Same as the original SLIIDERS dataset, but in netcdf format.</li> </ul> <p><em>Inputs</em></p> <p>All provided inputs are manually created or adjusted points used to create the coastline segments of SLIIDERS:</p> <ul> <li><strong>ciam_segment_pts_manual_adds.parquet</strong>: A list of segment points manually added to those that come from the extreme sea level model CoDEC (<a href="https://doi.org/10.5281/zenodo.3660926">Muis et al. 2020</a>)</li> <li><strong>gtsm_stations_ciam_ne_coastline_snapped.parquet:</strong> Stations from CoDEC snapped to coastlines from <a href="https://www.naturalearthdata.com/downloads/10m-physical-vectors/">Natural Earth</a></li> <li><strong>gtsm_stations_eur_tothin.parquet</strong>: A list of European points in CoDEC to thin. CoDEC provides ~10km resolution in Europe and ~50km elsewhere. For consistency, SLIIDERS uses ~50km spacing for its coastal segments globally.</li> </ul> <p><em>Source Code</em></p> <ul> <li><strong>sliiders-1.2.zip</strong>: The source code used to generate SLIIDERS v1.1. See READMEs within this code for more details. This is consistent with release v1.2 of the code maintained on github at <a href="https://github.com/ClimateImpactLab/SLIIDERS">https://github.com/ClimateImpactLab/SLIIDERS</a></li> </ul>
CESM2 land output data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the land output data from CESM2 which was generated in the study investigating the hydrological impacts of global-scale forestation. The datasets cover the period 2015-2100. The files are labelled according to the experiments they were generated from (base, MF (Max Forest) and No LULCC). Output fields are as follows:</p> <p>discharge_plus_runoff: surface water availability (river discharge plus surface runoff), units m^-3 s^-1</p> <p>EFLX_LH_TOT: total latent heat flux from land to atmosphere, units W m^-2</p> <p>QFLX_EVAP_TOT: total evapotranspiration (canopy evaporation plus canopy transpiration plus soil evaporation), units kg m^-2 s^-1</p> <p>SOILLIQ: soil liquid water content, units kg m^-2</p> <p>SW_surface_albedo: surface albedo, units fraction</p> <p>TSA: 2m air temperature, units K</p> <p>VEGWP: vegetation water potential, units m</p> <p> </p> <p>All data were generated and processed by James A. King.</p>
CESM2 atmosphere output data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the atmosphere output data from CESM2 which was generated in the study investigating the hydrological impacts of global-scale forestation, for the Max Forest scenario. The datasets cover the period 2015-2100. Output fields are as follows:</p> <p>CCN3: concentration of cloud condensation nuclei at 0.1% supersaturation, units cm^-3</p> <p>CLDLOW: cloud fraction integrated between 1200-700 hPa, units fraction of grid cell</p> <p>CONCLD: convective cloud cover, units fraction of grid cell</p> <p>GCLDLWP: grid cell cloud water path, units kg m^-2</p> <p>LWCF_d1: clean longwave cloud forcing, units W m^-2</p> <p>OMEGA: vertical velocity, units Pa s^-1</p> <p>PRECT: total precipitation, units m s^-1</p> <p>SWCF_d1: clean shortwave cloud forcing, units W m^-2</p> <p>V: meridional wind, units m s^-1</p> <p> </p> <p>All data were generated and processed by James A. King.</p>
CESM2 atmosphere output data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the atmosphere output data from CESM2 which was generated in the study investigating the hydrological impacts of global-scale forestation, for the base scenario. The datasets cover the period 2015-2100. Output fields are as follows:</p> <p>CCN3: concentration of cloud condensation nuclei at 0.1% supersaturation, units cm^-3</p> <p>CLDLOW: cloud fraction integrated between 1200-700 hPa, units fraction of grid cell</p> <p>CONCLD: convective cloud cover, units fraction of grid cell</p> <p>GCLDLWP: grid cell cloud water path, units kg m^-2</p> <p>LWCF_d1: clean longwave cloud forcing, units W m^-2</p> <p>OMEGA: vertical velocity, units Pa s^-1</p> <p>PRECT: total precipitation, units m s^-1</p> <p>SWCF_d1: clean shortwave cloud forcing, units W m^-2</p> <p>V: meridional wind, units m s^-1</p> <p> </p> <p>All data were generated and processed by James A. King.</p>
CESM2 land use data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the land use/ land cover input data for CESM2 which was used in the study investigating the hydrological impacts of global-scale forestation. The datasets cover the period 2000-2100. The files correspond to experiments described in the study as follows:</p> <p> </p> <p>Base: landuse.timeseries_0.9x1.25_SSP1-2.6_78pfts_CMIP6_simyr2000-2100_c220715.nc</p> <p>Max Forest: landuse.timeseries_0.9x1.25_hist_78pfts_SSPRFAFRS_SSP1_edit_xarray_4_simyr2000-2100_c221024.nc</p> <p>No LULCC: landuse.timeseries_0.9x1.25_hist_78pfts_SSPNOLULCC_3_simyr2000-2100_c221025.nc</p> <p> </p> <p>Files were created in collaboration by James A. King, James Weber, Peter Lawrence, and Stephanie Roe.</p> <p> </p>
CESM2 atmosphere output data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the atmosphere output data from CESM2 which was generated in the study investigating the hydrological impacts of global-scale forestation, for the No LULCC scenario. The datasets cover the period 2015-2100. Output fields are as follows:</p> <p>CCN3: concentration of cloud condensation nuclei at 0.1% supersaturation, units cm^-3</p> <p>CLDLOW: cloud fraction integrated between 1200-700 hPa, units fraction of grid cell</p> <p>CONCLD: convective cloud cover, units fraction of grid cell</p> <p>GCLDLWP: grid cell cloud water path, units kg m^-2</p> <p>LWCF_d1: clean longwave cloud forcing, units W m^-2</p> <p>OMEGA: vertical velocity, units Pa s^-1</p> <p>PRECT: total precipitation, units m s^-1</p> <p>SWCF_d1: clean shortwave cloud forcing, units W m^-2</p> <p>V: meridional wind, units m s^-1</p> <p> </p> <p>All data were generated and processed by James A. King.</p>
Database for: Meta-analysis of the impact of land use intensification on earthworms in global agroecosystems
<p>The dataset comprises a compilation of studies investigating the impact of land use intensification on earthworms across global agroecosystems. Extracted from peer-reviewed publications, the dataset includes various fields such as climate characteristics are described using the Köppen-Geiger climate classification system. Soil properties such as type, texture, pH, and organic content are documented. Additionally, details regarding experimental parameters like replicates, sampling depth, and extraction methods are provided. Furthermore, the dataset encompasses information on agricultural practices including herbicide, insecticide, pesticide usage, fertilizer type and rate, grazing, tillage methods, and days after tillage for earthworm collection. Abundance, diversity, and their associated metrics are recorded for both control and treatment sites.</p>
Data from: Artificial light at night and warming impact grazing rates and gonad index of the sea urchin Centrostephanus rodgersii
<p>Artificial light at night (ALAN) is a growing threat to coastal habitats, and is likely to exacerbate the impacts of other stressors. Kelp forests are dominant habitats on temperate reefs but are declining due to ocean warming and overgrazing. We tested the independent and interactive effects of ALAN (dark vs ALAN) and warming (ambient vs warm) on grazing rates and gonad index of the sea urchin <em>Centrostephanus rodgersii. </em>Within these treatments, urchins were fed either 'fresh' kelp or 'treated' kelp. Treated kelp (<em>Ecklonia radiata</em>) was exposed to the same light and temperature combinations as urchins. We assessed photosynthetic yield, carbon and nitrogen content and C:N ratio of treated kelp to help identify potential drivers behind any effects on urchins. Grazing increased with warming and ALAN for urchins fed fresh kelp, and increased with warming for urchins fed treated kelp. Gonad index was higher in ALAN/ambient and dark/warm treatments compared to dark/ambient treatments for urchins fed fresh kelp. Kelp carbon content was higher in ALAN/ambient treatments than ALAN/warm treatments at one time point. This indicates ocean warming and ALAN may increase urchin grazing pressure on rocky reefs, an important finding for management strategies.</p>
Impacts of Post-fire Debris Flows on Fluvial Morphology and Sediment Transport in a California Central Coast Stream
<p>Structure from Motion orthoimagery, lidar differencing products, and grain size data to be published with the submission of "Impacts of Post-fire Debris Flows on Fluvial Morphology and Sediment Transport in a California Central Coast Stream" to <em>Journal of Geophysical Research: Earth Surface.</em> </p> <p> </p> <p>2016, 2021, and 2022 orthoimagery for Upper Big Creek:</p> <p>J_2016.tif, J_2021.tif, J_2022.tif, K_2016.tif, K_2021.tif, K_2022.tif, L_2016.tif, L_2021.tif, L_2022.tif</p> <p>Files titled K_[year].tif encompass our upstream study reach; files titled J_[year].tif encompass our middle study reach; files titled L_[year].tif encompass our downstream study reach.</p> <p> </p> <p>2016, 2021, and 2022 orthoimagery for Devil's Creek:</p> <p>G_2016.tif, G_2021.tif, G_2022.tif, _2016.tif, H_2021.tif, H_2022.tif, I_2016.tif, I_2021.tif, I_2022.tif</p> <p>Files labeled G_[year].tif encompass our upstream study reach; files labeled H_[year].tif encompass our middle study reach; files labeled I_[year].tif encompass our downstream study reach.</p> <p> </p> <p>2016, 2021, and 2022 grain size data for Upper Big Creek with units in meters:</p> <p>BC_2016.csv, BC_2021.csv, BC_2022.csv</p> <p> </p> <p>2016, 2021, and 2022 grain size data for Devil's Creek with units in meters:</p> <p>DC_2016.csv, DC_2021.csv, DC_2022.csv</p> <p> </p> <p>Differenced lidar digital terrain models for Big Creek and Devil's Creek with units in meters:</p> <p>DoD_11_22.tif (difference between 2011 and 2022 lidar DTMs), DoD_11_15.tif (difference between 2011 and 2022 lidar DTMs)</p> <p> </p> <p>This work was funded by the Geological Society of America, the National Center for Airborne Laser Mapping, the Washington Section of the American Water Resources Association, the Western Washington University Research and Sponsored Programs Office, and the Western Washington University Geology Department.</p>
Data and code in support of "Rethinking energy planning to mitigate environmental and climatic impacts of future African hydropower"
<p>This dataset contains all the data and processing needed to produce results and figures reported in the manuscript "Rethinking energy planning to mitigate environmental and climatic impacts of future African hydropower".</p> <p> </p> <p>The README file guides through the material available to support replication of the results and figures.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.