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.
152
datasets available to search
ShareScore release 0.7.1
Dataset results
152 results for “climate resilience”
Data and code for: Acute heat priming promotes short-term climate resilience of early life stages in a model sea anemone
Open the record for dataset details and reuse information.
Data from: Beyond resilience: Responses to changing climate and disturbance regimes in temperate forest landscapes across the Northern Hemisphere
Open the record for dataset details and reuse information.
When resilience is not enough: 2022 extreme marine heatwave threatens climatic refugia for a habitat-forming Mediterranean octocoral
Open the record for dataset details and reuse information.
RESCCUE (RESilience to cope with Climate Change in Urban arEas) EU Project - WP1 Data
<p>These files contain the data generated for the Work Package 1 of the RESCCUE project. RESCCUE project was devised to analyse future urban impacts due to climate change so as to improve resilience of three target cities: Barcelona, Bristol and Lisbon. To achieve that, future climate projections and changes in extreme events were obtained at a local scale for the Work Package 1. Several past studies were analysed to identify all the climate variables and extreme events that could affect urban areas, e.g. heavy rainfall, heat waves and storm surge. All available meteorological observations in the considered areas were collected and filtered through several tests (general consistency, outliers and inhomogeneities) in order to handle datasets long enough and of good quality. As a way to obtain the best input possible, every valid station was extended in time by downscaling process with the ERA-Interim reanalysis.</p> <p>Future climate projections were obtained for ten different global climate models considering two of the main Representative Concentration Pathways (RCP4.5 and RCP8.5) established in the last IPCC report. These models were downscaled through a sophisticated statistical methods (analogous stratification and transfer functions among others) to project local climate according to the identified climate drivers: temperature, precipitation, wind, relative humidity, sea level pressure, potential evapotranspiration, snowfall, wave height and sea level; and for both climate and decadal timescales. Already downscaled models were first validated for the method and afterwards verified, obtaining small errors and good coherent simulations.</p> <p>Extreme events of the main climate drivers were obtained and analysed for both historical and future scenarios through the combination of several statistical methods as well as through the analysis of several teleconnectionpatterns. Derived events such as heat waves, drought, snowstorms, storm surges, wave height among others were afterwards inferred for climate, decadal and seasonal scale.</p> <p><strong>FILES</strong></p> <p>The data generated have been grouped into three different files, one for each studied area: the hydrological basin of the rivers Ter and Llobregat (the area that influences Barcelona), the geographical area between England and South Wales (the area that influences Bristol) and the Lisbon area.</p> <p>Each of the files contains a self-explanatory file detailing the structure of the information contained and the way in which it is provided.</p> <p><strong>ABOUT THE RESCCUE PROJECT</strong></p> <p>The RESCCUE project, Resilience to cope with Climate Change in Urban Areas, –a multisectorial approach focusing on water– aims to provide practical and innovative models and tools to end-users facing climate change challenges to build more resilient cities.</p> <p>The project provides tools to assess urban resilience from a multisectorial approach, for current and future climate scenarios and including multiple hazards. This holistic approach to urban resilience will enable city managers and urban systems operators to decide the optimal investments to cope with future situations.</p> <p>For more information, please visit <a href="http://www.resccue.eu/">www.resccue.eu</a></p>
The Resilience of Habitable Climates Around Circumbinary Stars: 3D climate model data Part 2
<p>Climate modeling outputs used in the paper, "The Resilience of Habitable Climates Around Circumbinary Stars", to be published JGR-Planets Special Edition on Exoplanets. Files contain 4 Earth years of hourly time cadence outputs of basic climate fields. Hourly time-cadence is needed in order to grasp the temporal variations of circumbinaries. </p>
Synchronization of seasonal acclimatization and short-term heat hardening improves physiological resilience in a changing climate
<p><b>Summary</b></p> <p>1. Animal survival and species distribution in the face of global warming and increasing occurrences of heatwave largely depend on how heat tolerance shifts with plastic responses at different spatiotemporal scales, including long-term acclimation/acclimatization and short-term heat hardening. However, knowledge about the interaction of these plastic responses is still unclear.</p> <p>2. To understand how plastic responses at different timescales work together to adjust heat tolerance of organisms, we examined the effect of heat hardening on the upper thermal limits of an intertidal mudflat bivalve, the razor clam <i>Sinonovacula constricta</i>, for different seasons by using heart rate as a proxy.</p> <p>3. We observed a stronger heat hardening response of<i> S. constricta</i> in warm seasons, implying that heat hardening worked synchronously with seasonal acclimatization to increase resistance of the clams to high temperatures in warm seasons. In warm seasons, heat hardening increased heat tolerance by 2-4<sup>°</sup>C and showed a 24-h temporal dependence, suggesting an adaptation to the diel fluctuation of thermal regimes in summer.</p> <p>4. Furthermore, thermal stress resembling seasonal maximum environmental temperature induced stronger heat hardening effects, indicating that heat hardening is an essential plastic response to extreme hot weather, complementing seasonal acclimatization.</p> <p>5. Our results suggest that high temperature risk can be alleviated jointly by seasonal acclimatization and heat hardening, and emphasize the importance of considering physiological plasticity on both long-term and short-term temporal scales in evaluating and forecasting vulnerability of organisms to climate change.</p>
Data from: navigating uncertainty: managing herbivore communities enhances savanna ecosystem resilience under climate change
<p>Savannas are characterized by water scarcity and degradation, making them highly vulnerable to increased uncertainties in water availability resulting from climate change. This poses a significant threat to ecosystem services and rural livelihoods that depend on them. In addition, the lack of consensus among climate models on precipitation change makes it difficult for land managers to plan for the future. Therefore, savanna rangeland management needs to develop strategies that can sustain savanna resilience and avoid tipping points under an uncertain future climate. Our study aims to analyze the impacts of climate change and rangeland management on degradation in savanna ecosystems of southern Africa, providing insights for the management of semi-arid savannas under uncertain conditions worldwide. To achieve this, we simulated the effects of projected changes in temperature and precipitation, as predicted by ten global climate models, on water resources and vegetation (cover, functional diversity, tipping points (transition from grass-dominated to shrub-dominated vegetation)). We simulated three different rangeland management options (herbivore community dominated by grazers, by browser and by mixed-feeders), each with low and high animal densities using the ecohydrological model EcoHyD. Our results identified intensive grazing as the primary contributor to the increased risk of degradation in response to changing climatic conditions across all climate change scenarios. This degradation encompassed a reduction in available water for plant growth within the context of predicted climate change. It also entails a decline in the overall vegetation cover, the loss of functionally important plant species, and the inefficient utilization of available water resources, leading to earlier tipping points. Our findings underscore that in the face of climate uncertainty, farmers' most effective strategy for securing their livelihoods and ecosystem stability is to integrate browsers and apply management of mixed herbivore communities. This management approach not only significantly delays or averts tipping points but also maintained greater plant functional diversity, fostering a more robust and resilient ecosystem that acts as a vital buffer against adverse climatic conditions.</p>
Resilience of seagrass populations to thermal stress does not reflect regional differences in ocean climate
<p>1. The prevalence of local adaptation and phenotypic plasticity among populations is critical to accurately predicting when and where climate change impacts will occur. Currently, comparisons of thermal performance between populations are untested for most marine species or overlooked by models predicting the thermal sensitivity of species to extirpation.</p> <p>2. Here we compared the ecological response and recovery of seagrass populations (<i>Posidonia oceanica</i>) to thermal stress throughout a year-long translocation experiment across a 2800 km gradient in ocean climate. Transplants in central and warm-edge locations experienced temperatures >29 ºC, representing thermal anomalies >5ºC above long-term maxima for cool-edge populations, 1.5ºC for central and <1ºC for warm-edge populations.</p> <p>3. Cool, central and warm-edge populations differed in thermal performance when grown under common conditions, but patterns contrasted with expectations based on thermal geography. Cool-edge populations did not differ from warm-edge populations under common conditions and performed significantly better than central populations in growth and survival.</p> <p>4. Our findings reveal that thermal performance does not necessarily reflect the thermal geography of a species. We demonstrate that warm-edge populations can be less sensitive to thermal stress than cooler, central populations suggesting that Mediterranean seagrasses have greater resilience to warming than current paradigms suggest.</p>
Long-term resilience of primary sex ratios in a species with temperature dependent sex determination after decades of climate warming
<p><span>Species with environmental sex determination (ESD) have persisted through deep time, despite massive environmental perturbation in the geological record. Understanding how species with temperature-dependent sex determination (TSD), a type of ESD, persist through climate change is particularly timely given the current climate crisis, as highly biased sex ratios and extinction are predicted. Since 1982, we have studied primary sex ratios of a reptile with TSD (<em>Chelydra serpentina</em></span><span>). Primary sex ratios remained unchanged over time, despite warming in the environment. Resilience of the primary sex ratio occurred via a portfolio effect, realized through remarkable intra-annual variation in nest-level sex ratios, leading to a relatively consistent mean annual sex ratio. Intra-annual variation in nest-level sex ratios was related to variation in egg burial depth coupled with large clutch sizes, creating thermal gradients in the nest, and promoting mixed-sex clutches. Further, both locally and globally, sustained increases in nighttime air temperature contribute more to warming than increases in daily maximum temperature, but development rate was affected more strongly by maximum daily air temperature, conferring additional resilience to overall warming. Our study suggests that some TSD species may be resilient to warming and provides an example of how ESD may persist under environmental change.</span><span><br></span></p>
Fire, elephants, and climate legacies enhance savanna resistance while impeding resilience
<p>Data accompanying the paper:</p> <p>L.M. Vermeulen, B. Verbist, K. Van Meerbeek, J. Slingsby, P.N. Bernardino, B. Somers. 2024. Fire, elephants, and climate legacies enhance savanna resistance while impeding resilience.</p>
Data for climate-resilient snowpack estimation in the Western United States
<p>Generated and preprocessed files for the resilient snowpack estimation project. All preprocessed data were originally produced by the WUS-D3 project (https://dept.atmos.ucla.edu/alexhall/downscaling-cmip6) or PRISM (https://www.prism.oregonstate.edu/).</p>
Climate Change Adaptation, Social Resilience, and Perceived Values Data from Turkana, Machakos and Narok County, Kenya
<p>This dataset provides socioeconomic and value perception interview data collected from 1,020 individuals living across three counties in Kenya: Turkana, Machakos and Narok. Socioeconomic data were collected on housing, healthcare, water sources, electricity access, experience of extreme weather events, community services and access to information. Value perception data were collected using the user-perceived value (UPV) method - a perception-based surveying approach which requires interviewees to select their most valued household items in different circumstances and explain their choice through 'why'-probing. This is done under different circumstances - here, either in daily life, or in the face of a climate shock. The data are made available with sub-county level geospatial attribution. Together, the socioeconomic and interview data can be used to better understand the views of different communities and demographic groups concerning climate change and extreme weather events across Kenya. They also provide insight as to the intrinsic, social, emotional, epistemic, functional, and indigenous values associated with everyday household items. The data can be used by policymakers to inform development planning and to identify gaps in available infrastructure. Additionally, researchers and development practitioners can use these data to design interventions which reflect the needs and values of communities in Kenya.</p>
Regional irrigation expansion can support climate resilient crop production in post-invasion Ukraine
<p>This dataset contains results used to plot figure 1, 2, 3, 4 of the manucript "Regional irrigation expansion can support climate resilient crop production in post-invasion Ukraine" published in Nature Food. </p>
Data from: A genome-guided strategy for climate resilience in American chestnut restoration populations
<p>The American chestnut (<em>Castanea dentata</em>) is a functionally extinct tree species that was decimated by an invasive fungal pathogen in the early 20<sup>th</sup> century. An understanding of the genomic architecture of local adaptation in wild American chestnut was necessary in order to deploy locally adapted, disease-resistant American chestnut populations. Here, we characterize the genomic basis of climate adaptation in remnant wild American chestnut, develop new computational methods, and evaluate the adaptive genomic content captured within backcross breeding populations. Whole genome re-sequencing data of 356 trees from Sandercock et al. (2022) coupled with genotype-environment association methods identified 18483 climate associated loci.</p>
Investigating the resilience of termite communities to logging and climate change in Borneo
<b>Description: </b><p>This project set out to quantify the tolerance of termite communities to climate change, or more specifically, temperature and humidity change, two climatic variables that have been hypothesised to drive species distributions (particularly for small ectotherms such as termites). The data presented here are the tolerances of termites to increasing temperatures, and decreasing humidities. <br><br>The thermal data was recorded by inserting termites into individual glass vials, placing those sealed vials into a water bath, and increasing the temperature until they could no longer function. This temperature was recorded, and taken as CTmax (Critical Thermal Maximum), for each individual termite. These data can be found in the TemperatureData worksheet. <br><br>The humidity data was recorded slightly differently. Groups of termites (of the same genus) were weighed and placed in one of two types of glass vial. Dessicated vials also contained silica gel (and a barrier to prevent termite interaction with the gel) which reduced the humidity to an average of 30%. Control vials did not contain any silica gel and had an average humidity of 85%. These vials were removed at one of 5 time points, and the termites were weighed again, and weight change was recorded. This weight change was attributed to water loss. <br><br>The body water data was used to calculate the proportion of body mass that was water, for multiple termite genera. This was done so that percentage of body water lost could be calculated for the humidity experiment, rather than an absolute value of water loss (as termites vary in size, using absolute values would cause false conclusions). </p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/31"><b>Investigating the resilience of termite communities to logging and climate change in Borneo</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=33">here</a></p><p><b>Data worksheets: </b>There are 3 data worksheets in this dataset:</p><ol><li><p><b>Thermal tolerance data</b> (Worksheet TemperatureData)</p><p>Dimensions: 1256 rows by 17 columns</p><p>Description: Thermal tolerance data of termites</p><p>Fields: </p><ul><li><b>Nest_no</b>: The unique nest (colony) number that each termite was taken from (Field type: ID)</li><li><b>Day</b>: The day on which the experiment took place (Field type: ID)</li><li><b>Termite_no</b>: The unique termite number, missing numbers are due to non-experimental deaths (Field type: ID)</li><li><b>Experiment</b>: Whether it was the first or second experiment from the same colony (Field type: Replicate)</li><li><b>Family</b>: The family of the termite (Field type: ID)</li><li><b>Genus</b>: The genus of the termite (Field type: ID)</li><li><b>Species</b>: The species (where known) of the termite (Field type: ID)</li><li><b>Name</b>: The species (where known) of the termite (Field type: Taxa)</li><li><b>CTmax</b>: The critical thermal maximum of the termite, or the temperature that it died at (Field type: Numeric Trait)</li><li><b>Sampling_zone</b>: Sampling area, LFE corresponds to the area at the SAFE project, and OP is Oil Palm (Field type: Categorical)</li><li><b>Forest_type</b>: Forest type sampled from within the sampling area, OG corresponds to Old Growth (or pristine) forest (Field type: Categorical)</li><li><b>Nest_type</b>: The type of nest that the termite builds (Field type: Categorical Trait)</li><li><b>Nest_Layer</b>: The layer within the forest that the nest is built (Field type: Categorical Trait)</li><li><b>Food_group</b>: The food group that the termite belongs to, categorised using the latest literature (Field type: Categorical Trait)</li><li><b>Body</b>: The level of sclerotisation of the termite (Field type: Categorical Trait)</li><li><b>Caste</b>: Which behavioural caste the termite belonged to (Field type: Categorical Trait)</li></ul><br></li><li><p><b>Humidity tolerance data</b> (Worksheet HumidityData)</p><p>Dimensions: 169 rows by 16 columns</p><p>Description: Humidity tolerance data of 4 termite genera</p><p>Fields: </p><ul><li><b>Nest_no</b>: The unique nest (colony) number that the termites were taken from (Field type: ID)</li><li><b>Tube</b>: The unique tube number that the termites were placed in (Field type: ID)</li><li><b>Taxa</b>: The species (where known) of the termite (Field type: Taxa)</li><li><b>Time</b>: The five time points that the tubes were removed at (Field type: Numeric)</li><li><b>Treatment</b>: Whether the termites were placed in a control or desiccated tube (Field type: Categorical)</li><li><b>Initial</b>: Weight of the group of termites at the start of the experiment (Field type: Numeric Trait)</li><li><b>Finish</b>: Weight of group of termites when removed from experiment (Field type: Numeric Trait)</li><li><b>Percentage_lost</b>: Proportion of body mass change (Field type: Numeric Trait)</li><li><b>Sampling_zone</b>: Sampling area, LFE corresponds to the area at the SAFE project, and OP is Oil Palm (Field type: Categorical)</li><li><b>Forest_type</b>: Forest type sampled from within the sampling area, OG corresponds to Old Growth (or pristine) forest (Field type: Categorical)</li><li><b>Food_group</b>: The food group that the termite belongs to, categorised using the latest literature (Field type: Categorical Trait)</li><li><b>Body</b>: The level of sclerotisation of the termite (Field type: Categorical Trait)</li><li><b>No_termites</b>: Number of termites placed in the tube (Field type: Numeric)</li><li><b>No_dead</b>: Number of termites that were dead at the point of the second weighing (Field type: Numeric)</li><li><b>Percentage_Dead</b>: Percentage of termites that are dead at point of second weighing (Field type: Numeric)</li></ul><br></li><li><p><b>Termite total body water data</b> (Worksheet BodyWaterData)</p><p>Dimensions: 38 rows by 12 columns</p><p>Description: Data calculating the total body water of 4 termite genera, this data was used in the humidity data to calculate the percentage of body water lost during the experiment</p><p>Fields: </p><ul><li><b>Nest_no</b>: The unique nest (colony) number that the termites were taken from (Field type: ID)</li><li><b>Tray_no</b>: The unique tray number that the termites were placed in (Field type: ID)</li><li><b>Genus</b>: The species (where known) of the termite (Field type: Taxa)</li><li><b>Weight_start</b>: Weight of the group of termites at the start of the experiment (Field type: Numeric Trait)</li><li><b>Weight_end</b>: Weight of group of termites when removed from experiment (Field type: Numeric Trait)</li><li><b>Percentage</b>: Percentage of body mass that is water (Field type: Numeric Trait)</li><li><b>Sampling_zone</b>: Sampling area, LFE corresponds to the area at the SAFE project, and OP is Oil Palm (Field type: Categorical)</li><li><b>Forest_type</b>: Forest type sampled from within the sampling area, OG corresponds to Old Growth (or pristine) forest (Field type: Categorical)</li><li><b>Food_group</b>: The food group that the termite belongs to, categorised using the latest literature (Field type: Categorical Trait)</li><li><b>Body</b>: The level of sclerotisation of the termite (Field type: Categorical Trait)</li><li><b>Caste</b>: Which behavioural caste the termite belonged to (Field type: Categorical Trait)</li></ul><br></li></ol><p><b>Date range: </b>2016-02-01 to 2016-07-01</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Arthropoda<br> -  - Insecta<br> -  -  - Isoptera<br> -  -  -  -  - <i>Homallotermes</i><br> -  -  -  -  -  - [<i>Homallotermes foraminifer</i>]<br> -  -  -  - Kalotermitidae<br> -  -  -  -  - <i>Glyptotermes</i><br> -  -  -  -  -  - [Glyptotermes sp.]<br> -  -  -  - Rhinotermitidae<br> -  -  -  -  - <i>Coptotermes</i><br> -  -  -  -  -  - [Coptotermes sp.]<br> -  -  -  -  - <i>Parrhinotermes</i><br> -  -  -  -  -  - [<i>Parrhinotermes pygmaeus</i>]<br> -  -  -  -  - <i>Schedorhinotermes</i><br> -  -  -  -  -  - [<i>Schedorhinotermes sarawakensis</i>]<br> -  -  -  -  -  - [Schedorhinotermes sp.]<br> -  -  -  - Termitidae<br> -  -  -  -  - <i>Bulbitermes</i><br> -  -  -  -  -  - [Bulbitermes sp.]<br> -  -  -  -  - <i>Dicuspiditermes</i><br> -  -  -  -  -  - [Dicuspiditermes sp.]<br> -  -  -  -  - <i>Globitermes</i><br> -  -  -  -  -  - [<i>Globitermes globosus</i>]<br> -  -  -  -  - <i>Hospitalitermes</i><br> -  -  -  -  -  - [<i>Hospitalitermes hospitalis</i>]<br> -  -  -  -  -  - [<i>Hospitalitermes bicolour</i>]<br> -  -  -  -  - [Lacessitermes sp.]<br> -  -  -  -  - <i>Longipeditermes</i><br> -  -  -  -  -  - [Longipeditermes sp.]<br> -  -  -  -  - <i>Macrotermes</i><br> -  -  -  -  -  - [<i>Macrotermes gilvus</i>]<br> -  -  -  -  - <i>Microcerotermes</i><br> -  -  -  -  -  - [Microcerotermes sp.]<br> -  -  -  -  - <i>Nasutitermes</i><br> -  -  -  -  -  - [<i>Nasutitermes havilandi</i>]<br> -  -  -  -  -  - [Nasutitermes sp.]<br> -  -  -  -  - <i>Odontotermes</i><br> -  -  -  -  -  - [Odontotermes sp.]<br></div><p></p>
Resilience Evaluation Table (Regional Climate Resilience Assessment)
<p>Assessment of regional resiliences of the five ClimEmpower regions: Costa del Sol in Andalusia, Spain, Trodos Mountains in Cyprus, Osjek-Baranja county in Croatia, Central Greece and Sicily, Italy. Result of initial ClimEmpower climate Resilience Asssessment (CLIM-RA) of these five regions, related to project deliverable D1.2.</p>
Increasing production efficiency and coping with climate change, while ensuring sustainability and resilience
<p>This experiment aims to test two of the most performing Tomres used as rootstocks in the commercial variety (Elpida F1) cultivated in the region. More specifically, 2 tomato Tomres lines (TOMRES- 149, Bil-6191 and TOMRES 162, M82) x 2 water/nutritional regimens (standard water/nutrient supply vs 20% irrigation reduction/no nutrient supply). Greenhouse will also have non grafted plants (Elpida F1) cultivated under standard water/nutrient supply and 20% irrigation reduction/no nutrient supply</p>
Increasing production efficiency and coping with climate change, while ensuring sustainability and resilience
<p>Screening experiment aiming a first evaluation of the five PGPR that have been isolated in AUA, Laboratory go General & Agricultural Microbiology in a previous research project. To minimize interference of the treatments with soil fertility and soil heterogeneity, this first experiment will be conducted in a soilless cultivation system</p>
Data from: Reduced fire severity offers near-term buffer to climate-driven declines in conifer resilience across the western United States
<p>The combination of increasing fire-caused tree mortality and warmer, drier post-fire conditions is making forests in the western United States (West) vulnerable to ecological transformation. Yet, the relative importance of and interactions between these drivers of forest change remain unresolved, particularly over upcoming decades. Here we assess how the interactive impacts of changing climate and wildfire activity influenced conifer regeneration after 334 wildfires, using a novel dataset of post-fire conifer regeneration from 10,230 field plots. Our findings highlight declining regeneration capacity across the West over the past four decades for the eight dominant conifer species studied. Post-fire regeneration is sensitive to high-severity fire, which limits seed availability, and post-fire climate, which influences seedling establishment and survival. In the near-term, projected differences in recruitment probability between low- and high-severity fire scenarios were larger than projected impacts of climate change for most species, suggesting that reductions in fire severity, and resultant impacts on seed availability, could partially offset expected climate-driven declines in post-fire regeneration. Across 40–42% of the study area, we project post-fire conifer regeneration to be likely following low-severity but not high-severity fire under future climate scenarios (2031–2050). However, increasingly warm, dry climate conditions are projected to eventually outweigh the influence of fire severity and seed availability. The percent of the study area considered unlikely to experience conifer regeneration, regardless of fire severity, increased from 5% in 1981–2000 to 26–31% by mid-century, highlighting a limited time window over which management actions that reduce fire severity may effectively support post-fire conifer regeneration.</p>
Importance of mega-environments in evaluation and identification of climate resilient maize hybrids (Zea mays L.)
<p>Multi-location experiments on maize were conducted from 2016 to 2019 at ten locations distributed across two agro-climatic zones (ACZ) i.e., ACZ-3 and ACZ-8 of Karnataka, India. Individual analysis of variance for each location-year combination showed significant differences among the hybrids; similarly, combined analysis showed a higher proportion of GE interaction variance than due to genotype. Mega-environments were identified using biplot approaches such as AMMI, GGE, and WAASB methodologies for the years 2016 to 2019. The BLUP method revealed a high correlation between grain yield and stability indices ranging from 0.67 to 1.0. Considering all three methods together, the three location pairs Arabhavi-Belavatagi, Bailhongal-Belavatagi, and Hagari-Sirguppa had three occurrences in the same mega-environment with a value of 0.67, and these location combinations consistently produced winning genotypes. Among the common winning genotypes identified, it was G7 during 2016 and 2017 and G10 during 2018 and 2019, based on WAASBY. The likelihood of Arabhavi-Nippani, Hagari-Mudhol, and Dharwad-Hagari occurring in the same mega-environment is minimal because they did not share the same winning genotype, with the exception of a small number of events. Despite being in the same agro-climatic zone, Arabhavi, Hagari, and Mudhol rarely had a winning genotype in common. An agro-climatic zone is grouped based on climatic and soil conditions which doesn't consider GE interaction of cultivars thus, releasing the cultivars for commercial cultivation considering mega environments pattern would enhance the yield for the given target region.</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.