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18,658 results for “impacts”
Development and impact analysis of ground motion datasets for potential strong-to-great seismic scenarios in Chinese mainland
<p>地震动情景数据对于评估地震灾害损失至关重要,并且是协作式多学科地震风险分析和区域灾害预防的基础要素。这项研究根据地震灾害分区数据确定了 50 个潜在的地震成因位置,并得到了地质和地震学证据的支持。根据潜在的损坏程度选择了四个震级(Mw 6.5、Mw 7.0、Mw 7.5 和 Mw 8.0),最终建立了 200 个地震情景。该数据集包括峰值地面加速度 (PGA)、峰值地面速度 (PGV) 和地震强度,是使用之前在应急响应中验证的强大 GMPE 生成的。这为灾害预防、减灾和城市规划提供了有用的支持,使其适用于许多分析需求。</p> <p><strong><em>注意: </em> 这是对高风险地震断层地震情景的模拟,旨在帮助决策者采取主动措施来减轻潜在地震的影响。它还为相关研究人员提供了一组可用的数据资源。请不要使用这些数据来生成或传播错误信息!</strong></p>
India Flood Inventory-Impacts (IFI-Impacts) [1967-2023]: A multi-source national geospatial database to facilitate comprehensive flood research
<p>This repository hosts the India Flood Inventory with Impacts (IFI-Impacts) database. It contains flood event data sourced from the Indian Meteorological Department from 1967-2023. It has undergone extensive manual digitization, cleaning, and includes new information to make it suitable for computational research in hydroclimate.</p> <p>v4.0: Development of District Flood Severity Index (DFSI)</p> <p>v3.0: India Flood Inventory (IFI) 1967-2023. Updated with local government codes (LGD) for state and district. </p> <p>v1.0: India Flood Inventory (IFI) 1967-2016.</p> <p>v2.0: India Flood Inventory (IFI) 1967-2023. With impacts and district flooded area.</p> <p><strong>REFERENCES</strong></p> <p>Saharia, M., Jain, A., Baishya, R.R., Haobam, S., Sreejith, O.P., Pai, D.S., Rafieeinasab, A., 2021. India flood inventory: creation of a multi-source national geospatial database to facilitate comprehensive flood research. Nat Hazards. <a href="https://doi.org/10.1007/s11069-021-04698-6">https://doi.org/10.1007/s11069-021-04698-6</a></p> <div> <div>Saharia, M., Jain, S.K., Prakash, V., Malik, H., Sreejith, O.P., Joshi, D., 2025. A district-level flood severity index for flood management in India. Nat Hazards. <a href="https://doi.org/10.1007/s11069-025-07493-9">https://doi.org/10.1007/s11069-025-07493-9</a></div> </div>
Package and Dependency Metadata for CZI Hackathon: Mapping the Impact of Research Software in Science
<p>A collection of useful datasets extracted from <a href="https://packages.ecosyste.ms">https://packages.ecosyste.ms</a> and <a href="https://repos.ecosyste.ms/">https://repos.ecosyste.ms</a> for use at the CZI Hackathon: Mapping the Impact of Research Software in Science.</p><p>All data is provided as NDJSON (new line delimited JSON), each line represents a valid JSON object, and they are separated by newline characters. There are <a href="https://pypi.org/project/ndjson/">python</a> and <a href="https://www.rdocumentation.org/packages/ndjson/versions/0.9.0/topics/stream_in">R</a> libraries for reading these files, or you can maually read each line and parse each line as a single JSON object.</p><p>Each ndjson file has been compressed with gzip (actual command: `tar -czvf`) to reduce download size, they expand to significantly bigger files after extraction.</p><h4>Package Data</h4><p>Package names from cran, bioconductor and pypi that have been parsed by the <a href="https://github.com/chanzuckerberg/software-mentions">software-mentions</a> project (data: <a href="https://datadryad.org/stash/dataset/doi:10.5061/dryad.6wwpzgn2c">https://datadryad.org/stash/dataset/doi:10.5061/dryad.6wwpzgn2c</a>) are collected together with their latest release at time of publishing along with the names of their dependencies, those dependency names have then also been recursively fetched with latest release and dependencies until the full list of transitive dependencies is included. </p><p>Note: This approach uses a simplified method of dependency resolution, always picking the latest version of each package rather than taking into account each dependencies specific version range requirements, this is primarily due to time constraints and allows all software ecosystems to be processed in the same way. A future improvement would be to use each package ecosystem's specific dependency resolution algorithm to compute the full transitive dependency tree for each mentioned software package.</p><h4>GitHub Data</h4><p>Two different approaches were taken for collecting data for referenced GitHub mentions:</p><p>1. `github.ndjson` is metadata for each repository from GitHub, including "manifest" files which are known files that contain dependency information for a project such as requirements.txt, DESCRIPTION and package.json, parsed using <a href="https://github.com/ecosyste-ms/bibliothecary">https://github.com/ecosyste-ms/bibliothecary</a>, which may include transitive dependencies that have been discovered in a `lockfile` within the repository.</p><p>2. `github_packages.ndjson` is metadata for each package that was found on any package manager that references the GitHub url as it's repository url/source/homepage, these packages, like the cran and pypi data above, include the latest release and their direct dependencies. There may be more than one package for each GitHub URL as it is a one to many relationship. `github_packages_with_transitive.ndjson` follows the same format but also includes the extra resolved transitive dependencies of all packages using the same approach as with cran and pypi data above with the same caveats. </p><p>There are also many more ecosystems referenced in these files than just cran, bioconductor and pypi, https://packages.ecosyste.ms provides a standardized metadata format for all of them to enable comparison and simplification of automation.</p><h4>Contact</h4><p>If you would like any help, support or more data from Ecosyste.ms please do get in touch via email: hello@ecosyste.ms or open an issue on GitHub: https://github.com/ecosyste-ms/packages/issues</p>
Data for: Environmental complexity mitigates the demographic impact of sexual selection
<p>Sexual selection and the evolution of costly mating strategies can negatively impact population demography and adaptive potential. While laboratory studies have documented outcomes stemming from these processes, theory suggests that the demographic impact of sexual selection is contingent on the environment and therefore may have been overestimated in simple laboratory settings. Here we find support for this claim. We exposed copies of beetle lines, previously evolved with or without sexual selection, to a 10-generation heatwave while maintaining half of them in a simple environment and the other half in a complex environment. Populations with an evolutionary history of sexual selection maintained larger sizes and more stable growth rates in complex (relative to simple) environments, an effect not seen in populations that evolved without sexual selection. These results have implications for evolutionary forecasting and suggest that the demographic impact of sexual selection in natural populations might be lower than predicted.</p>
Data from: How density dependence, genetic erosion, and the extinction vortex impact evolutionary rescue
<p>Following severe environmental change that reduces mean population fitness below replacement, populations must adapt to avoid eventual extinction, a process called evolutionary rescue. Models of evolutionary rescue demonstrate that initial size, genetic variation, and degree of maladaptation influence population fates. However, many models feature populations that grow without negative density dependence or with constant genetic diversity despite precipitous population decline, assumptions likely to be violated in conservation settings. We examined the simultaneous influences of density-dependent growth and erosion of genetic diversity on populations adapting to novel environmental change using stochastic, individual-based simulations. Density dependence decreased the probability of rescue and increased the probability of extinction, especially in large and initially well-adapted populations that previously have been predicted to be at low risk. Increased extinction occurred shortly following environmental change, as populations under density dependence experienced more rapid decline and reached smaller sizes. Populations that experienced evolutionary rescue lost genetic diversity through drift and adaptation, particularly under density dependence. Populations that declined to extinction entered an extinction vortex, where small size increased drift, loss of genetic diversity, and the fixation of maladaptive alleles, hindered adaptation, and kept populations at small densities where they were vulnerable to extinction via demographic stochasticity.</p>
A Novel Approach to Impact Crater Mapping and Analysis on Enceladus, using Machine Learning: Supplemental data
<p>This dataset includes the crater map and equatorial crater depths and diameters presented in the paper: A Novel Approach to Impact Crater Mapping and Analysis on Enceladus, using Machine Learning.</p>
Data for "Impact of Ligand Substitution and Metal Node Exchange in the Electronic Properties of Scandium Terephthalate Frameworks"
<p>The AiiDA archives of the high-throughput calculations presented in the paper "Impact of Ligand Substitution and Metal Node Exchange in the Electronic Properties of Scandium Terephthalate Frameworks".</p><p>The file "MOF_workflows.aiida" contains the actual calculation data and the files with suffix "*.yaml" contain configuration files of the workflows.</p>
Impact of Phytophthora cinnamomi on the taxonomic and functional diversity of forest plants in a mediterranean-type biodiversity hotspot
<p class="MsoNormal"><strong>Aim</strong></p> <p class="MsoNormal">Diversity-rich mediterranean-type sclerophyllous forests are home to 20% of described species on Earth. In the <em>Eucalyptus marginata</em> (jarrah) forest of southwest of Western Australia diversity is being reduced by extensive human use and the introduction of the plant pathogen <em>Phytophthora cinnamomi</em>. This study investigated the influence of <em>P. cinnamomi </em>infestation on the structure, taxonomic and functional diversity, and species composition of the forest.</p> <p class="MsoNormal"><strong>Location</strong>: Jarrah forest of southwestern Australia</p> <p class="MsoNormal"><strong>Methods</strong></p> <p class="MsoNormal">Species<strong> </strong>abundance, understorey cover and canopy cover were assessed along 22, 30-m long transects which crossed infested and non-infested zones in five reserves in the jarrah forest. A trait database was assembled for 137 plants using 13 traits related to nutrient- and carbon acquisition, disturbance tolerance and reproduction. The responses of canopy cover, understorey cover, species richness, Shannon diversity, evenness, abundance, and functional diversity for trait groups, and all groups combined were modelled against reserve and zone as fixed effects and transect and transect section as random effects. To assess the species composition, NMDS ordination based on Bray Curtis resemblance and indicator species analyses were used.</p> <p class="MsoNormal"><strong>Results</strong></p> <p class="MsoNormal">Significantly higher understorey cover, species richness, Shannon diversity and evenness were recorded in non-infested compared to infested zones, but there were no changes in the canopy cover and overall abundance. In non-infested zones, the functional diversity of nutrient acquisition and reproductive traits was higher, but the functional diversity of carbon acquisition traits was lower. No difference in functional diversity was recorded in disturbance tolerance and overall traits between the two zones. NMDS ordination and ANOSIM revealed a significant difference in the species composition between the two zones, and 11 indicator species significantly associated with infested and non-infested zones were identified.<strong> </strong></p> <p class="MsoNormal"><strong>Conclusion</strong></p> <p class="MsoNormal"><em>Phytophthora cinnamomi</em> has significantly affected the forest structure, taxonomic and functional diversity, and species composition. Contrasting responses of functional trait groups obscured overall trait responses to <em>P. cinnamomi.</em></p>
Biodiversity impact assessment considering land use intensities and fragmentation
<p>The data provide supplementary information for the paper entitled "Biodiversity impact assessment considering land use intensities and fragmentation".</p><p> </p><p><strong>Coverage of the characterization factors</strong></p><ul><li>5 species groups: plants, amphibians, birds, mammals, and reptiles</li><li>5 broad land use types: cropland, pasture, plantations, managed forests, and urban areas</li><li>3 land use intensities: minimal, light, intense (sometimes, intensity levels had to be merged because the data did not allow to differentiate between them)</li><li>825 terrestrial ecoregions of the world (according to WWF / Olson et al. 2001)</li></ul><p> </p><p><strong>Files</strong></p><p>Main data</p><ul><li>CF.csv: characterization factors (CFs) for ecoregions and 5 species groups</li></ul><p>Taxonomically aggregated data</p><ul><li>CF_kingdom.csv: CFs aggregated from 5 species groups to plant and animal kingdoms</li><li>CF_domain.csv: CFs aggregated from plant and animal kingdoms to the domain of Eukaryota</li></ul><p>Spatially aggregated data</p><ul><li>CF_country.csv: CFs aggregated from ecoregions to countries</li><li>CF_global.csv: CFs aggregated from ecoregions to the globe</li></ul><p>Taxonomically and spatially aggregated data</p><ul><li>CF_kingdom_country.csv: CFs aggregated to countries and plant and animal kingdoms</li><li>CF_domain_country.csv: CFs aggregated to countries and the domain of Eukaryota</li><li>CF_kingdom_global.csv: CFs aggregated to the globe and plant and animal kingdoms</li><li>CF_domain_global.csv: CFs aggregated to the globe and the domain of Eukaryota</li></ul><p> </p><p><strong>Units</strong></p><p>CFs for land occupation: PDF/m2</p><p>CFs for land transformation: PDF⋅yr/m2</p><p> </p><p><strong>Columns</strong></p><ul><li>realm: 2-letter code to identify one of 8 biogeographical realms</li><li>biome: ID to identify one of 14 biomes</li><li>eco_id: ID to identify the ecoregion, combining numbers for the realm, biome, and ecoregion within each biome nested within each realm</li><li>eco_name: ecoregion name</li><li>species_group: species group</li><li>kingdom: kingdom as a taxonomic rank</li><li>habitat_id: ID to link to the land use type and intensity as used in land_use.tif. An ID with .5 represents a merged land use class considering the two habitats with the IDs when rounding the value both up and down.</li><li>habitat: land use type and intensity</li><li>CF_*: characterization factor</li><li>*_occ*: land occupation</li><li>*_tra*: land transformation</li><li>*_avg*: average approach</li><li>*_mar*: marginal approach</li><li>*_reg: regional relative species loss</li><li>*_glo: global relative species loss</li><li>*_rsd: relative standard deviation as a measure of spatial uncertainty due to aggregation (only concerns country and globally aggregated CFs)</li><li>quality_*: data quality, distinguishing between original estimates and the use of proxies</li><li>objectid: object id of the country</li><li>iso3cd: iso3 code of the country</li><li>romnam: romanized name of the country</li><li>m49code: M49 code of the country, a standard code used by the United Nations</li><li>weighting: aspect based on which the CFs were weighted (only concerns globally aggregated CFs)</li></ul><p> </p><p><strong>Data quality</strong></p><ul><li>original: original estimate (for globally aggregated CFs: mostly original estimates, proxies only considered in areas with current land use)</li><li>proxy_intensity: intensity level was missing; CF was derived from another CF of the same ecoregion and land use type but different intensity level and scaled to the right intensity level</li><li>proxy_type: land use type was missing; CF was derived from the average regional CFs for light use in the same biome and the ecoregion-specific GEP and scaled to the right intensity level if needed</li><li>proxy_gep: global extinction probability (GEP) was missing (only concerns CFs for global relative species loss); GEP estimated based on average GEP per area unit in the same biome and the ecoregion area</li><li>proxy_partial: some species groups were missing but not all (only concerns taxonomically aggregated CFs); aggregation done based on partly original estimates and partly proxies</li><li>proxy_neighbours: country was missing (only concerns country-aggregated CFs); values were estimated based on the average of the three nearest neighbouring countries</li><li>proxy: proxies were considered even in areas without current land use (only concerns globally aggregated CFs)</li></ul><p>Note: proxies in country-aggregated CFs apply to at least one of the ecoregions overlapping with the country and not necessarily all ecoregions</p><p> </p><p><strong>Land use type and intensity data</strong></p><p>Raster file: land_use.tif</p><p>Spatial resolution: 0.08333333, 0.08333333 (x, y)</p><p>Spatial extent: -180, 180, -90, 90 (xmin, xmax, ymin, ymax)</p><p>Coordinate reference system: WGS 84 (EPSG:4326)</p><p> </p><p>Codes</p><ol><li>Primary_vegetation_Minimal (incl. sparse/no vegetation)</li><li>Cropland_Intense</li><li>Cropland_Light</li><li>Cropland_Minimal</li><li>Managed_forest_Intense</li><li>Managed_forest_Light</li><li>Managed_forest_Minimal</li><li>Pasture_Intense</li><li>Pasture_Light</li><li>Pasture_Minimal</li><li>Plantation_Intense</li><li>Plantation_Light</li><li>Plantation_Minimal</li><li>Urban_Intense</li><li>Urban_Light</li><li>Urban_Minimal</li></ol>
Insight into the impact of viruses on biogeochemical processes in agricultural soils
Open the record for dataset details and reuse information.
Insight into the impact of viruses on biogeochemical processes in agricultural soils
Open the record for dataset details and reuse information.
SWAT river water, TN & TP loads to Limfjorden under climate change scenarios (Delta change) + baseline SWAT loads 2009-2018. Paper ". Impacts of climate change on water quality, benthic mussels and suspended mussel culture in a shallow, eutrophic estuary by Maar et al. Heliyon,
<p>SWAT river water, TN & TP loads to Limfjorden under climate change scenarios (Delta change) + baseline SWAT loads 2009-2018 </p>
Datasets used for "Heat Pump - Heating Electrification and Climate Change - Grid Impact Studies"
<h2> Summary</h2> <p> </p> <p>In this work, we explore long term patterns in electricity demand driven by the dual effects of space heating electrification and climate change. We use an open source nodal power system model of the Electric Reliability Council of Texas (ERCOT) system to investigate a wide range of future climate and technology scenarios that evolve over time, and report results in terms of market prices, reliability and corresponding relative capacity requirements </p> <h2> About </h2> <p>The technical analysis aimed to:</p> <h3>1) Understand the Long-Term Patterns:</h3> <p>We aim to analyze patterns in peak load, total load, loss of load, and the seasonality of these phenomena, driven by widespread heat pump adoption alongside climate change.</p> <h3>2) Use Extensive Scenario Analysis:</h3> <p>Explore a wide range of future scenarios, including variations in climate pathways, to capture the uncertainty associated with these long-term changes. In total, 1280 simulation years.</p> <h3>3) Use a validated open source DC OPF model(reproducibility)</h3> <p>Use an open-source nodal power system model of the ERCOT system to simulate and understand the potential impacts on market prices, reliability, and relative capacity requirements. Similar models are available for all interconnections of the conterminous US.</p> <h3>4) Assess Grid Vulnerability:</h3> <p>Assess the vulnerability of the grid to these simultaneous changes, identify potential vulnerability.</p> <h3>5) Provide Insights for System Planners:</h3> <p>Offer results that can assist long-term system planners in anticipating and preparing for potential shifts in grid reliability.</p>
Greek Tsunami Impact Database 1800-2020
<p>Τhe Tsunami Impact Database of Greece covers the time period from AD 1800 to 2020. Sixteen tsunami events have been inserted so far in the database for the reference period. Most of the tsunami events were caused by earthquakes and only two have been triggered by gravitative marine slides.</p><p>For each tsunami event the database provides (1) Parameters of the Tsunami Sources, e.g. earthquakes or landslides, and (2) Tsunami Impact Data. Data for the sections (1) and (2) have been taken from several catalogues, papers, books, technical reports and other publications (see reviews, literature compilations or original observational data in Galanopoulos, 1957, 1960, Papadopoulos, 2003, Ambraseys, 1960, Papadopoulos et al., 2007, 2010, Dogan et al., 2019, 2021, Triantafyllou et al., 2021). </p><p>The Parameters of the Earthquake Tsunami Sources include earthquake origin time, epicentral coordinates, focal depth, moment magnitude Mw, and earthquake maximum intensity Io. For the tsunamigenic earthquakes that occurred in the time interval from AD 1800 to 1899 source parameters have been taken from the SHEEC (Stucchi et al., 2013). For the post-1900 time period, earthquake source parameters have been adopted from the ISC-GEM (2023) catalogue or from the GCMT project (https://www.globalcmt.org/) for the post- 1975 time interval. The Parameters of the Landslide Tsunami Sources include date and geographical coordinates. </p><p>For all sources the database include also the region and sub-region as well as the next categorization of the seismic source by following the categorization adopted within the pan-European tsunami projects GITEC (https://cordis.europa.eu/project/id/EV5V0175), GITEC-TWO (https://cordis.europa.eu/project/id/ENV4960297) and TRANSFER (https://cordis. europa.eu/project/id/37058): ER=earthquake, EL= earthquake landslide, ES= earthquake marine slide, GS= gravitative marine slide. A short description of each event is also provided and a reliability score, Rel, for the tsunami event is given in a 4-degree scale (for further explanations οn how this scale is arranged see in other publications, e.g. Papadopoulos, 2003). </p><p>Depending on the data availability an effort has been made to introduce quantitative impact data to the extent it is possible (e.g., numbers of fatalities and injuries, numbers of buildings or vessels damaged, etc.). From the impact of a tsunami event the maximum tsunami intensity, K, has been estimated according to the 12-grade scale of Papadopoulos and Imamura (2001). For each tsunami event corresponding references (Ref) are also provided.</p>
From fork to farm: Impacts of more sustainable diets in the EU-27 on the agricultural sector
<p>This work was supported by the European Union's Horizon 2020 project Nutri2Cycle (Grant agreement No. 773682) </p><p>These datasets are supplementary information to the manuscript titled "<a href="https://onlinelibrary.wiley.com/doi/10.1111/1477-9552.12530">From fork to farm: Impacts of more sustainable diets in the EU-27 on the agricultural sector</a>"</p>
Sex and Age Impact CD4+ T Cell Susceptibility to HIV In Vitro Through Cell Activation Dynamics
<p>Cellular composition and the responsiveness of the immune system evolve upon aging and are influenced by biological sex. CD4+ T cells from women living with HIV exhibit a decreased viral replication ex vivo compared to men's. We, thus, hypothesized that these findings could be recapitulated in vitro and infected primary CD4+ T cells with HIV-based vectors pseudotyped with VSV-G or HIV envelopes. We used cells isolated from twenty donors to interrogate the effect of sex and age on permissiveness over a six-day activation kinetics. Our data identified an increased permissiveness to HIV between 24 and 72 h post-stimulation. Sex- and age-based analyses at these time points showed an increased susceptibility to HIV of the cells isolated from males and from donors over 50 years of age, respectively. A parallel assessment of surface markers' expression revealed higher frequencies of activation marker CD69 and of immune checkpoint inhibitors (PD-1 and CTLA-4) in the cells from highly permissive donors. Furthermore, positive correlations were identified between the expression kinetics of CD69, PD-1 and CTLA-4 and HIV expression kinetics. The cell population heterogeneity was assessed using a single-cell RNA-Seq analysis and no cell subtype enrichment was identified according to sex. Finally, transcriptomic analyses further highlighted the role of activation in those differences with enriched activation and cell cycle gene sets in male and older female cells. Altogether, this study brought further evidence about the individual features affecting HIV replication at the cellular level and should be considered in latency reactivation studies for an HIV cure.</p>
Short and long term impacts of Covid-19 on Older childreN's healTh-Related behAviours, learning and wellbeing STudy (CONTRAST) dataset
<p>The CONTRAST study explored how the Covid-19 (lockdown) restrictions affected lives of older children in the UK, particularly how they have influenced learning, eating, physical and other activities and wellbeing.</p>
Updated Supplementary Figures for Can leafhoppers help us trace the impact of climate change on agriculture?
<p>Supplementary Figures for: <strong>Can</strong> <strong>leafhoppers help us trace the impact of climate change on agriculture? </strong>to be posted in bioRxiv.</p><p><strong>Figure S1. </strong>Diversity indexes calculated in this study to compare leafhopper diversity each growing season investigated in this study and the geographic regions where the strawberry fields were located. Statistical analyses were performed for Shannon and Simpson finding that in both cases there is no interaction between years and regions with <i>p</i> = 0.0889 and <i>p</i> = 0.7139, respectively.</p><p><strong>Figure S2.</strong> Distinctive RFLP patterns obtained with <i>Cpn</i>ClassiPhyR from <i>in silico</i> digestion of <i>cpn60</i>UT from SbGPQ clones and AY-Col. Lanes labelled MW in <i>in silico</i> RFLP represent <i>Hae</i>III-digested phage <i>ϕ</i>X174 DNA.</p><p><strong>Figure S3.</strong> Phylogenetic tree using neighbour-joining method of the <i>16S, secY, nusA, rp, secA, cpn60 </i>and<i> tuf</i> sequences obtained in this study for the SbGP phytoplasma and sequences retrieved from Genbank. <i>Acholeplasma laidlawii</i> PG8 was used as an outgroup. The phylogenetic tree was bootstrapped 1000 times to achieve reliability. Bar, 1 substitution in 100 or 500 positions. </p><p><strong>Fig. S3 Panel 1: </strong>cpn60UT, tuf, and secY trees.</p><p><strong>Fig. S3 Panel 2:</strong> nusA, rp, and secA trees.</p><p><strong>Fig. S3 Panel 3:</strong> 16S tree with subtree showing heterogeneity of SbGPQ and 'Ca. P. tritici'.</p><p><strong>Figure S4.</strong> Leafhopper feeding-associated damages observed in strawberry plants. <strong>A</strong>, in the field. <strong>B</strong>, in the greenhouse after incubation with leafhoppers.</p><p><strong>Figure S5.</strong> Alpha diversity indexes were calculated to study <i>Macrosteles quadrilineatus</i> microbiome observed for each growing season. No statistical difference was observed among the sites for any of the indexes calculated.</p><p><strong>Figure S6.</strong> Effect of insecticides leafhopper population control. Only those with a number of applications higher or equal to five are presented. We did not find statistical differences among the treatments before and after the application of the insecticides with <i>p</i> = 0.8488.</p><p><strong>Figure S7.</strong> Effect of insecticides on <i>Macrosteles quadrilineatus</i> and <i>Empoasca fabae</i> population control. All insecticides (n = 12) are represented but the statistical analysis was only performed with those that the number of applications was higher than 5. We did not find statistical differences among the treatments before and after the application of the insecticides with <i>p</i> = 0.1781 for the aster leafhopper <i>M.</i> <i>quadrilineatus </i>and <i>p</i> = 0.6540 for the potato leafhopper <i>E. fabae</i>.</p><p><strong>Figure S8.</strong> Comparison among the Shannon index obtained for leafhopper populations in vineyards in 2007 and 2008 and for leafhopper populations in strawberry fields in 2021 and 2022 in Quebec.</p>
Impact Craters on Mimas, Rhea, and Iapetus (incomplete surface coverage)
<p>Comma-separated values (CSV) files of impact crater data from Iapetus, Rhea, and Mimas, based on identification of features on individual images tied to Schenk (circa 2012–2014) basemaps. Data include latitude, longitude, and diameter in units of decimal degrees (location) and kilometers (size). These data cover approximately 27%, 11%, and 73% of the surface area of each body, respectively.</p>
Simulation dataset and plotting scripts used for journal article "Impact of acidity and surface modulated acid dissociation on cloud response to organic aerosol" by Sengupta et al. (2024)
<p>Simulation data underlying all figures presented in "Impact of acidity and surface modulated acid dissociation on cloud response to organic aerosol" by Sengupta et al. (2024). DOI: 10.5194/acp-24-1467-2024</p> <p>The data for each figure and the plotting scripts are included in a zip file labelled by the figure number as presented in the paper and accompanying supplement.</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.