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387 results for “climate adaptation”
Data from: climate adaptation in white oak (Quercus alba, L.): a forty-year study of growth and phenology
<p>Climate change poses a significant threat to the resilience and sustainability of forest ecosystems. This study examines the performance of white oak (Quercus alba, L.) across a range of provenances in a common garden planting, focusing on the species' response to climatic variables and the potential role of assisted migration in forest management. We evaluated the survival and growth rates of white oak provenances originating from various points along a latitudinal gradient over a period of 40 years. These provenances were planted in a common garden situated near the midpoint of this latitudinal gradient, where we also monitored their phenological traits, such as budburst and leaf senescence. The results revealed substantial variation in phenological responses and growth patterns among the provenances, with southern provenances demonstrating faster growth and later senescence relative to local sources, with limited impact on survival. In contrast, the northern provenances demonstrated slower growth, resulting in later-aged competition-induced mortality. The findings highlight the necessity of incorporating genetic diversity into white oak reforestation and conservation strategies, as the local provenance may no longer be the most suitable option for current and future conditions. We advocate for a nuanced approach to forest management that leverages genetic insights to optimize seed source selection for reforestation, fostering resilient forest landscapes in the face of ongoing climate shifts.</p>
Data from: Putative climate adaptation in American pikas (Ochotona princeps) is associated with copy number variation across environmental gradients
<p>Improved understanding of the genetic basis of adaptation to climate change is necessary for maintaining global biodiversity moving forward. Studies to date have largely focused on sequence variation, yet there is growing evidence that suggests that changes in genome structure may be an even more significant source of adaptive potential. The American pika (<em>Ochotona princeps</em>) is an alpine specialist that shows some evidence of adaptation to climate along elevational gradients, but previous work has been limited to single nucleotide polymorphism (SNP)-based analyses within a fraction of the species range. Here, we investigated the role of copy number variation underlying patterns of local adaptation in the American pika using genome-wide data previously collected across the entire species range. We identified 37-193 putative copy number variants (CNVs) associated with environmental variation (temperature, precipitation, solar radiation) within each of the six major American pika lineages, with patterns of divergence largely following elevational and latitudinal gradients. Genes associated (<em>n</em>=158) with independent annotations across lineages, variables, and/or CNVs had functions related to mitochondrial structure/function, immune response, hypoxia, olfaction, and DNA repair, some of which have been previously linked to putative high elevation and/or climate adaptation that may serve as important targets in future studies.</p>
Climatic adaptation and phylogenetic history shape the intra-specific variation of CSR strategies in a widespread grass
<p>Data and R script for the paper entitled "Climatic adaptation and phylogenetic history shape the intra-specific variation of CSR strategies in a widespread grass".</p>
UK climate hazard and climate change adaptation resources for heritage
<p><span>This dataset (.xlsx) is a compendium of climate change hazard data and adaptation resources for cultural heritage. It was created by JBA Consulting for Historic England and is accompanied by a <a href="https://historicengland.org.uk/research/results/reports/16-2024">research report</a> which provides the background, methodology, and results of the project. One aim of the project was to identify and compile climate hazard resources (data and tools) that could assist those managing the UK historic environment, with specific attention paid to data availability, spatial resolution, and format. </span></p> <p><span> </span><span>The project identified 73 datasets and 38 tools. The datasets were linked to relevant climate hazards from a standardised hazard vocabulary (<a href="../records/10868587">Thomas, 2024</a>). The attached pdf file provides further details on how to use the dataset. Further information can be found in the report, and questions can be addressed to Kate Guest at <a href="mailto:Kate.Guest@HistoricEngland.org.uk">Kate.Guest@HistoricEngland.org.uk</a>. </span></p>
Adaptive potential of Coffea canephora from Uganda in response to climate change
<p>Understanding vulnerabilities of plant populations to climate change could help preserve their biodiversity and reveal new elite parents for future breeding programs. To this end, landscape genomics is a useful approach for assessing putative adaptations to future climatic conditions, especially in long-lived species such as trees. We conducted a population genomics study of 207 <i>Coffea canephora</i> trees from seven forests along different climate gradients in Uganda. For this, we sequenced 323 candidate genes involved in key metabolic and defense pathways in coffee. Seventy-one SNPs were found to be significantly associated with bioclimatic variables, and were thereby considered as putatively adaptive loci. These SNPs were linked to key candidate genes, including transcription factors, like <i>DREB</i>-like and <i>MYB</i> family genes controlling plant responses to abiotic stresses, as well as other genes of organoleptic interest, like the <i>DXMT</i> gene involved in caffeine biosynthesis and a putative pest repellent. These climate-associated genetic markers were used to compute genetic offsets, predicting population responses to future climatic conditions based on local climate change forecasts. Using these measures of maladaptation to future conditions, substantial levels of genetic differentiation between present and future diversity were estimated for all populations and scenarios considered. The populations from the forests Zoka and Budongo, in the northernmost zone of Uganda, appeared to have the lowest genetic offsets under all predicted climate change patterns, while populations from Kalangala and Mabira, in the Lake Victoria region, exhibited the highest genetic offsets. The potential of these findings in terms of <i>ex-situ</i> conservation strategies are discussed.</p>
Phenotypic but no genetic adaptation in zooplankton 24 years after an abrupt +10°C climate change
<p>Data and scripts for Pais-Costa et al 2022</p>
Range-wide intraspecific variation reflects past adaptation to climate in a gypsophile Mediterranean shrub
<p>Phenotypic differences among populations stem from the interaction between neutral and adaptive processes, and phenotypic plasticity. Although clinal trait variation along climatic gradients often evolves in widely-distributed species, it is unknown whether substrate specialization, such as that of Mediterranean gypsum plants, has constrained adaptation to climate. Using a common garden experiment with two contrasting watering treatments, we quantified phenotypic plasticity, assessed evidence for footprints of selection using FST - QST comparisons, and identified the ecological factors driving genetically-based phenotypic differentiation of 11 populations encompassing the full environmental range of the gypsum shrub Lepidium subulatum. We found evidence for genetic differentiation among populations related to climatic differences, with populations from warmer and drier sites showing lower specific leaf area (SLA) and leaf N, earlier phenology, greater water use efficiency (WUE) and greater fitness. Multiple lines of evidence suggest that this differentiation was driven by past divergent selection rather than neutral processes. All populations showed high phenotypic plasticity, indicating that plasticity has not been selected against, even in populations from sites with harsher climatic conditions.<br>Synthesis. Our results indicate that, despite strong substrate specialization, adaptive differentiation related to climatic gradients occurs in this species. However, we also found that populations from mesic sites may be particularly vulnerable to future climate change given their relatively lower fitness under both wet and dry conditions.</p>
Data from: Genetic variation in growth and leaf traits associated with local adaptation to climate in yellow birch (Betula alleghaniensis Britton)
<p>Understanding patterns of variation in functional traits of hardwood trees is crucial for conserving and managing North American temperate forests under climate change. This study examined provenance variation of yellow birch (<em>Betula alleghaniensis</em> Britton) in growth, biomass allocation, leaf morphology, and stable carbon isotope composition. Trees were grown from ten seed sources originating from across Canada and the northern USA. Height and diameter were not significantly related to climate at seed origin, suggesting that variation may be better explained by site factors, such as soil pH and soil moisture. In contrast, carbon isotope composition and leaf morphological traits were significantly correlated to climate variables including temperature, precipitation, and solar radiation. Provenances from warmer, drier localities tended to have higher stable carbon isotope ratio (δ<sup>13</sup>C), greater specific leaf area, and narrower leaf width than their counterparts from cooler, wetter climates. Thus, variation in leaf morphological traits appears to be involved in adaptation of yellow birch to variation in temperature and moisture availability across the species' range. Our results suggest that there may exist potential for selection and breeding of drought resistant yellow birch genotypes to aid in reforestation under climate change. </p>
Wetland creation and reforestation of legacy surface mines in the Central Applachian Region (USA): A potential climate-adaptation approach for pond-breeding amphibians?
<p>Habitat restoration and creation within human-altered landscapes can buffer the impacts of climate change on wildlife. The Forestry Reclamation Approach (FRA) is a coal surface mine reclamation practice that enhances reforestation through soil decompaction and the planting of native trees. Recently, wetland creation has been coupled with FRA to increase habitat available for wildlife, including amphibians. Our objective was to evaluate the response of pond-breeding amphibians to the FRA by comparing species occupancy, richness, and abundance across two FRA age-classes (2–5-year and 8–1—year reclaimed forests), traditionally reclaimed sites that were left to naturally regenerate after mining, and in mature, unmined forests in the Monongahela National Forest (West Virginia, USA). We found that species richness and occupancy estimates did not differ across treatment types. Spotted Salamanders (<em>Ambystoma maculatum</em>) and Eastern Newts (<em>Notophthalmus viridescens</em>) had the greatest estimated abundances in wetlands in the older FRA treatment. Additionally, larger wetlands had greater abundances of Eastern Newts, Wood Frogs (<em>Lithobates sylvaticus</em>), and Green Frogs (<em>L. clamitans</em>) compared to smaller wetlands. Our results suggest that wetland creation and reforestation increases the number of breeding sites and promotes microhabitat and microclimate conditions that likely maximize the resilience of pond-breeding amphibians to anticipated climate changes in the study area.</p>
Data from: Local adaptation of Pinus leiophylla under climate and land use change models in the Avocado Belt of Michoacán
<p>Climate change and land use change are two main drivers of global biodiversity decline, decreasing the amount of genetic diversity that populations harbor and altering the patterns of local adaptation. Methods in landscape genomics allow measuring the effect of these anthropogenic disturbances on the adaptation of populations. However, both factors have rarely been considered simultaneously. We modeled the spatial turnover in allele frequencies of 19 localities of <em>Pinus leiophylla</em> across the Avocado Belt in Michoacán state, Mexico which could change under climate change and land use change scenarios, in addition to evaluating assisted gene flow strategies and connectivity metrics across the landscape to identify priority conservation areas. We found that localities at the center-east regions would be more vulnerable to climate change, while localities in the west area will be more threatened by actions of land use change. However, assisted gene flow actions could reduce their risk of extinction for both scenarios. Connectivity patterns will also be modified by future habitat loss, with the central and eastern parts having the highest connectivity values. These results show that the areas with the highest priority for conservation are in the eastern zones, which include the Monarch Butterfly Biosphere Reserve. This work is useful as a framework that incorporates distinct layers of information to provide a robust representation of the response of populations to future anthropogenic disturbances.</p>
Climate Hazards Data Integration and Visualization for the Climate Adaptations Solutions Accelerator through School-Community Hubs
<p><strong>Community engagement in planning is essential for effective and just climate adaptation. However, historically underserved communities are often difficult to reach through traditional means of soliciting public input. The Climate Adaptation Solutions Accelerator (CASA) through School-Community Hubs project identifies public schools as promising sites for building both community engagement and community capacity for climate adaptation. To serve in this role, schools need information about the intersecting threats climate change poses to the communities they serve. The Climate Hazard Dashboard for California Schools is a platform that maps the current and future risks associated with five climate hazards, including wildfire, extreme heat days, wildfire extreme precipitation, flooding, and sea level rise, for the nearly 10,000 public schools serving Kindergarten through Grade 12 students in California. Each hazard is mapped and visualized at the school level, providing an accessible way for administrators, teachers, students, and neighborhoods to explore data reflecting the climate hazards they face, at a scale relevant to their communities. The dashboard also provides an aggregate summary hazard metric. </strong></p>
Data and Code for "Climate impacts and adaptation in US dairy systems 1981-2018"
<p>This data and code archive provides all the files that are necessary to replicate the empirical analyses that are presented in the paper "Climate impacts and adaptation in US dairy systems 1981-2018" authored by Maria Gisbert-Queral, Arne Henningsen, Bo Markussen, Meredith T. Niles, Ermias Kebreab, Angela J. Rigden, and Nathaniel D. Mueller and published in 'Nature Food' (2021, DOI: <a href="https://doi.org/10.1038/s43016-021-00372-z">10.1038/s43016-021-00372-z</a>). The empirical analyses are entirely conducted with the "R" statistical software using the add-on packages "car", "data.table", "dplyr", "ggplot2", "grid", "gridExtra", "lmtest", "lubridate", "magrittr", "nlme", "OneR", "plyr", "pracma", "quadprog", "readxl", "sandwich", "tidyr", "usfertilizer", and "usmap". The R code was written by Maria Gisbert-Queral and Arne Henningsen with assistance from Bo Markussen. Some parts of the data preparation and the analyses require substantial amounts of memory (RAM) and computational power (CPU). Running the entire analysis (all R scripts consecutively) on a laptop computer with 32 GB physical memory (RAM), 16 GB swap memory, an 8-core Intel Xeon CPU E3-1505M @ 3.00 GHz, and a GNU/Linux/Ubuntu operating system takes around 11 hours. Running some parts in parallel can speed up the computations but bears the risk that the computations terminate when two or more memory-demanding computations are executed at the same time.</p> <p>This data and code archive contains the following files and folders:</p> <p>* README<br> Description: text file with this description</p> <p>* flowchart.pdf<br> Description: a PDF file with a flow chart that illustrates how R scripts transform the raw data files to files that contain generated data sets and intermediate results and, finally, to the tables and figures that are presented in the paper.</p> <p>* runAll.sh<br> Description: a (bash) shell script that runs all R scripts in this data and code archive sequentially and in a suitable order (on computers with a "bash" shell such as most computers with MacOS, GNU/Linux, or Unix operating systems)</p> <p>* Folder "DataRaw"<br> Description: folder for raw data files<br> This folder contains the following files:</p> <p>- DataRaw/COWS.xlsx<br> Description: MS-Excel file with the number of cows per county<br> Source: USDA NASS Quickstats<br> Observations: All available counties and years from 2002 to 2012</p> <p>- DataRaw/milk_state.xlsx<br> Description: MS-Excel file with average monthly milk yields per cow<br> Source: USDA NASS Quickstats<br> Observations: All available states from 1981 to 2018</p> <p>- DataRaw/TMAX.csv<br> Description: CSV file with daily maximum temperatures<br> Source: PRISM Climate Group (spatially averaged)<br> Observations: All counties from 1981 to 2018</p> <p>- DataRaw/VPD.csv<br> Description: CSV file with daily maximum vapor pressure deficits<br> Source: PRISM Climate Group (spatially averaged)<br> Observations: All counties from 1981 to 2018</p> <p>- DataRaw/countynamesandID.csv<br> Description: CSV file with county names, state FIPS codes, and county FIPS codes<br> Source: US Census Bureau<br> Observations: All counties</p> <p>- DataRaw/statecentroids.csv<br> Descriptions: CSV file with latitudes and longitudes of state centroids<br> Source: Generated by Nathan Mueller from Matlab state shapefiles using the Matlab "centroid" function<br> Observations: All states</p> <p>* Folder "DataGenerated"<br> Description: folder for data sets that are generated by the R scripts in this data and code archive. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these generated data files so that parts of the analysis can be replicated (e.g., on computers with insufficient memory to run all parts of the analysis).</p> <p>* Folder "Results"<br> Description: folder for intermediate results that are generated by the R scripts in this data and code archive. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these intermediate results so that parts of the analysis can be replicated (e.g., on computers with insufficient memory to run all parts of the analysis).</p> <p>* Folder "Figures"<br> Description: folder for the figures that are generated by the R scripts in this data and code archive and that are presented in our paper. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these figures so that people who replicate our analysis can more easily compare the figures that they get with the figures that are presented in our paper. Additionally, this folder contains CSV files with the data that are required to reproduce the figures.</p> <p>* Folder "Tables"<br> Description: folder for the tables that are generated by the R scripts in this data and code archive and that are presented in our paper. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these tables so that people who replicate our analysis can more easily compare the tables that they get with the tables that are presented in our paper.</p> <p>* Folder "logFiles"<br> Description: the shell script runAll.sh writes the output of each R script that it runs into this folder. We provide these log files so that people who replicate our analysis can more easily compare the R output that they get with the R output that we got.</p> <p>* PrepareCowsData.R<br> Description: R script that imports the raw data set COWS.xlsx and prepares it for the further analyses</p> <p>* PrepareWeatherData.R<br> Description: R script that imports the raw data sets TMAX.csv, VPD.csv, and countynamesandID.csv, merges these three data sets, and prepares the data for the further analyses</p> <p>* PrepareMilkData.R<br> Description: R script that imports the raw data set milk_state.xlsx and prepares it for the further analyses</p> <p>* CalcFrequenciesTHI_Temp.R<br> Description: R script that calculates the frequencies of days with the different THI bins and the different temperature bins in each month for each state</p> <p>* CalcAvgTHI.R<br> Description: R script that calculates the average THI in each state</p> <p>* PreparePanelTHI.R<br> Description: R script that creates a state-month panel/longitudinal data set with exposure to the different THI bins</p> <p>* PreparePanelTemp.R<br> Description: R script that creates a state-month panel/longitudinal data set with exposure to the different temperature bins</p> <p>* PreparePanelFinal.R<br> Description: R script that creates the state-month panel/longitudinal data set with all variables (e.g., THI bins, temperature bins, milk yield) that are used in our statistical analyses</p> <p>* EstimateTrendsTHI.R<br> Description: R script that estimates the trends of the frequencies of the different THI bins within our sampling period for each state in our data set</p> <p>* EstimateModels.R<br> Description: R script that estimates all model specifications that are used for generating results that are presented in the paper or for comparing or testing different model specifications</p> <p>* CalcCoefStateYear.R<br> Description: R script that calculates the effects of each THI bin on the milk yield for all combinations of states and years based on our 'final' model specification</p> <p>* SearchWeightMonths.R<br> Description: R script that estimates our 'final' model specification with different values of the weight of the temporal component relative to the weight of the spatial component in the temporally and spatially correlated error term</p> <p>* TestModelSpec.R<br> Description: R script that applies Wald tests and Likelihood-Ratio tests to compare different model specifications and creates Table S10</p> <p>* CreateFigure1a.R<br> Description: R script that creates subfigure a of Figure 1</p> <p>* CreateFigure1b.R<br> Description: R script that creates subfigure b of Figure 1</p> <p>* CreateFigure2a.R<br> Description: R script that creates subfigure a of Figure 2</p> <p>* CreateFigure2b.R<br> Description: R script that creates subfigure b of Figure 2</p> <p>* CreateFigure2c.R<br> Description: R script that creates subfigure c of Figure 2</p> <p>* CreateFigure3.R<br> Description: R script that creates the subfigures of Figure 3</p> <p>* CreateFigure4.R<br> Description: R script that creates the subfigures of Figure 4</p> <p>* CreateFigure5_TableS6.R<br> Description: R script that creates the subfigures of Figure 5 and Table S6</p> <p>* CreateFigureS1.R<br> Description: R script that creates Figure S1</p> <p>* CreateFigureS2.R<br> Description: R script that creates Figure S2</p> <p>* CreateTableS2_S3_S7.R<br> Description: R script that creates Tables S2, S3, and S7</p> <p>* CreateTableS4_S5.R<br> Description: R script that creates Tables S4 and S5</p> <p>* CreateTableS8.R<br> Description: R script that creates Table S8</p> <p>* CreateTableS9.R<br> Description: R script that creates Table S9<br> </p>
Data from: Adaptations to climate-mediated selective pressures in humans.
<p>This dataset contains the genotype data in PLINK binary format for the 5 populations genotyped in the Di Rienzo lab and published in </p> <p>Hancock AM, Witonsky DB, Alkorta-Aranburu G, Beall CM, Gebremedhin A, Sukernik R, Utermann G, Pritchard JK, Coop G, Di Rienzo A (2011) Adaptations to climate-mediated selective pressures in humans. PLoS Genet. 7(4):e1001375</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>
Data for Tropical Cyclones flood hazards and impacts in Beira for study "Exploring coastal climate adaptation through storylines: Insights from Cyclone Idai in Beira, Mozambique"
<p>Data for Tropical Cyclones flood hazards and impacts in Beira for study "Exploring coastal climate adaptation through storylines: Insights from Cyclone Idai in Beira, Mozambique"<br><br><span><a href="../api/records/12664900/draft/files/hmax_idai_ifs_rebuild_bc_hist_rain_surge_noadapt.tiff/content" target="_blank" rel="noopener noreferrer">hmax_idai_ifs_rebuild*</a> -> Flood maps<br><a href="../api/records/12664900/draft/files/spatial_idai_ifs_rebuild_bc_3c-hightide_rain_surge_retreat.gpkg/content" target="_blank" rel="noopener noreferrer">spatial_idai_ifs_rebuild*</a> -> Impacts<br></span></p>
Survey results of the Benefits and Limitations of a Virtual Training on Climate Risks and Adaptation
<p>Survey results of the Benefits and Limitations of a Virtual Training on Climate Risks and Adaptation</p>
Thermal regimes, but not mean temperatures, drive patterns of rapid climate adaptation at a continent-scale: evidence from the introduced European earwig across North America
<p>Full data set + R script</p>
Survey instrument, data, and code for paper "Freihardt, Buntaine, Bernauer (2024): Choosing to protect: Public support for flood defense over relocation in climate change adaptation. Environmental Research Letters. DOI 10.1088/1748-9326/ad6781"
<p>This is the survey instrument, data, and code underlying the manuscript:</p> <p>"Freihardt, Buntaine, Bernauer (2024): Choosing to protect: Public support for flood defense over relocation in climate change adaptation. Environmental Research Letters. DOI 10.1088/1748-9326/ad6781"</p>
Dataset for Resistance of Australian fish communities to drought and flood: implications for climate change and adaptations
<p>Data and code analyzing fish response to extreme drought and flood in Australia's Murray Darling Basin. Original fish data can be found here: <a href="https://www.gbif.org/dataset/f38cc07c-49a8-4ca4-8882-5cdde1733c12"><u>https://www.gbif.org/dataset/f38cc07c-49a8-4ca4-8882-5cdde1733c12</u></a>. Original rainfall data can be found here: http://www.bom.gov.au/climate/maps/rainfall/?variable=rainfall&map=totals&period=month®ion=nat&year=2004&month=01&day=31 </p>
Table 1 in Colossoma macropomum (Characiformes: Serrasalmidae) adapted to new climate regime: differential gene expression from farmed tambaqui juveniles raised in subtropical and tropical regions
<p><b>Table 1:</b> Details of target genes (<i>hif-1α</i>, <i>hsp70</i>, <i>ras</i>, <i>mstn</i>, <i>acly</i>, <i>per-1</i>, <i>cry-1</i>, <i>ube3a</i> and <i>ogt</i>) and reference genes (<i>β- tubulin</i> and <i>β- actin</i>) primers.</p><table><tbody><tr><th><b>Gene</b></th><th><b>Length (bp)</b></th><th><b>R</b> <b>2</b></th><th><b>Efficiency (%)</b></th><th><b>Primers sequence (5ʹ-3ʹ) forward/reverse</b></th></tr></tbody><tbody><tr><th><i>tubulin</i> -F</th><td>20</td><td>0.99</td><td>109.5</td><td>GACGTGGTGCCCAAAGATGT</td></tr><tr><th><i>tubulin</i> -R</th><td>18</td><td>TGGATGGTGCGCTTGGT</td></tr><tr><th><i>β- actin</i> -F</th><td>21</td><td>0.99</td><td>100.5</td><td>GCTGTTTTCCCCTCCATTGTT</td></tr><tr><th><i>β- actin</i> -R</th><td>19</td><td>TCCCATGCCAACCATCACT</td></tr><tr><th><i>hif-1α</i> -F</th><td>20</td><td>0.99</td><td>105.2</td><td>CTTCTGAGCTCTGATGAGGC</td></tr><tr><th><i>hif-1α</i> -R</th><td>20</td><td>GAAAGCACCATCAGGAAGCC</td></tr><tr><th><i>hsp-70</i> -F</th><td>20</td><td>0.99</td><td>100.9</td><td>GCAAGGAGAACAAGATCACC</td></tr><tr><th><i>hsp-70</i> -R</th><td>19</td><td>CACTCCGTTGCACTTGTCC</td></tr><tr><th><i>mstn</i> -F</th><td>20</td><td>0.98</td><td>100.5</td><td>AATCCAAGCGAGGGAAAAGC</td></tr><tr><th><i>mstn</i> -R</th><td>22</td><td>CCTCCATCACCTGAAAGGTCTT</td></tr><tr><th><i>ras</i> -F</th><td>20</td><td>0.97</td><td>99.31</td><td>CCAGTACATGAGGACAGGAG</td></tr><tr><th><i>ras</i> -R</th><td>20</td><td>CAAGCACCATTGGCACATCG</td></tr><tr><th><i>acly</i> -F</th><td>19</td><td>0.99</td><td>100.7</td><td>ATCATCTCCCGCACTACAG</td></tr><tr><th><i>acly</i> -R</th><td>19</td><td>TACCTCCAATCTCTCCCAG</td></tr><tr><th><i>ube3a</i> -F</th><td>21</td><td>0.98</td><td>103.3</td><td>GCCATAAGCAAGCAGCACAAC</td></tr><tr><th><i>ube3a</i> -R</th><td>19</td><td>CCAGTCAGTCCGCACATCG</td></tr><tr><th><i>per-1</i> -F</th><td>20</td><td>0.98</td><td>104.1</td><td>TGTTGAAGTTTGTGCCCCAG</td></tr><tr><th><i>per-1</i> -R</th><td>18</td><td>CAGTCCAGATGCTCCTCC</td></tr><tr><th><i>cry-1</i> -F</th><td>19</td><td>0.99</td><td>103.6</td><td>GTCCAACAGCCCTCAAACT</td></tr><tr><th><i>cry-1</i> -R</th><td>18</td><td>TACGCCAAGCACTCCAGA</td></tr><tr><th><i>ogt</i> -F</th><td>19</td><td>0.99</td><td>104.1</td><td>CCTCCCTTTGCTGTGTTCC</td></tr><tr><th><i>ogt</i> -R</th><td>20</td><td>TGTCTGCTTTCCGCTTTCGC</td></tr></tbody></table>
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.