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2,485 results for “expansion”
Modeling Range Expansion of Hemlock Woolly Adelgid in Eastern North America 1951-2009
Range expansion by native and non-native species will continue to be a major component of global change. Anticipating the potential effects of changes in species distributions requires models capable of forecasting population spread across realistic, heterogeneous landscapes and subject to spatiotemporal variability in habitat suitability. Several decades of theory and model development, as well as increased computing power and availability of fine-resolution GIS data, now make such models possible. This R code allows simulation of the spread of the hemlock woolly adelgid (HWA, Adelges tsugae) under climatic conditions experienced from December of 1951 until March of 2009. The code describes a spatially explicit stochastic model that combines dynamic dispersal and population processes with fine-resolution maps characterizing spatiotemporal heterogeneity in winter temperature and hemlock abundance to model range expansion of HWA. The model is parameterized using multi-year datasets describing population and dispersal dynamics of HWA and is applied to eastern North America.
Development and Expansion of Peatlands in Central New England from 14000 BP to Present
As peatlands form they create a temporal archive of community development, allowing the reconstruction of vegetation dynamics through the analysis of sediments and the development of detailed chronologies of successional change. Peatland formation occurs through two mechanisms: (i) terrestrialization, when a water body fills with sediments and peat; and (ii) paludification, the conversion of dry land to peatland. In temperate regions, where high summer temperatures may limit peat accumulation, general models of peatland development suggest that allogenic factors such as climate change control peatland development and that terrestrialization is the primary mechanism of formation. This study evaluates this widely accepted model by comparing the developmental histories of three peatlands within the same climate region in New England in order to: (i) describe the development and timing of successional events among peatlands; (ii) document the roles of paludification and terrestrialization as developmental mechanisms; and (iii) evaluate the importance of climate change vs. autogenic factors in peatland development in this temperate region. Basin morphometry, sediment stratigraphies, and chronologies of community change determined through radiocarbon dating indicate that peatland development at each site involved terrestrialization followed by paludification, with no apparent influence of broad-scale climate change on the timing of these processes. Paludification was consistently initiated coincident with the consolidation of a shrub mat across each lake-basin, and was controlled in extent and rate by the topography of the adjoining uplands. The timing of stratigraphic changes varied among sites, suggesting that autogenic factors associated with the accumulation of peat rather than regional climate change controlled development. These results provide the foundation for a model of temperate peatland development driven by autogenic factors and caution against the use of tem
Forest expansion for different warming scenarios simulated for 2010 to 3000 CE with LAVESI for Siberia
<p>Simulations with the spatially explicit and individual-based Siberian forest model LAVESI (Kruse et al., 2016, 2018, 2019) were set-up for transect in four focus regions covering the East Siberian treeline and tundra area (details in Kruse & Herzschuh, submitted). The model was updated to include climate forcing data for 300-800 km long and 20 m wide transects necessary for simulating the forest development between the northern taiga forests and the coast of the Arctic Ocean. Forced with climate forecasts driven by relative concentration pathway (RCP) scenarios 2.6, 4.5 and 8.5 and one with half the warming of RCP 2.6 named 2.6*. These were extended until 3000 AD either following the cooling of the scenarios after peak-warming, or with an arbitrary cooling back to levels of the 20th century.</p> <p>During the simulations, three key variables were extracted in 10-year steps for 2000-3000 AD: single-tree line, treeline, and, forest line, which are defined as the northernmost position of stands with >1 stem (tree > 1.3 m tall) per ha, the northernmost position of a forest cover not falling below 1 stem per ha, and, the northernmost position of a forest cover not falling below 100 stems ha per ha (see for a graphical representation Fig. 2 in Kruse et al., 2019). The determined treeline at year 2000 was used as baseline expansion and subtracted from each following years’ values.</p> <p>Furthermore, the tundra area was estimated for each of the four regions as the area between the treeline and the Arctic Ocean, based on interpolating the treeline position at the four transects over the complete modern treeline (Walker et al., 2005).</p> <ol> <li>Content of Table 1 "Kruse_and_Herzschuh_2022_Forest_expansion_in_Siberia_2010_to_3000_CE.csv": <ul> <li>Column 1: Scenario: RCP scenario used</li> <li>Column 2: Region: One of the four regions, from east-to-west Taimyr Peninsula, Buor Khaya Peninsula, Kolyma River Basin, Chukotka</li> <li>Column 3: Year: Year in CE of the simulation in 10 year steps</li> <li>Column 4: Forest line in m</li> <li>Column 5: Treeline in m</li> <li>Column 6: Single-tree line in m</li> </ul> </li> <li>Content of Table 2 "Kruse_and_Herzschuh_2022_Tundra_area_in_Siberia_2010_to_3000_CE.csv": <ul> <li>Column 1: Scenario: RCP scenario used</li> <li>Column 2: Year: Year in CE of the simulation in 10 year steps</li> <li>Column 3: Tundra area at region Taimyr Peninsula in km²</li> <li>Column 4: Tundra area at region Buor Khaya Peninsula in km²</li> <li>Column 5: Tundra area at region Kolyma River Basin in km²</li> <li>Column 6: Tundra area at region Chukotka in km²</li> </ul> </li> <li>The zip-file "Kruse_and_Herzschuh_2022_Forest_expansion_maps_in_Siberia_2010_to_3000_CE.zip" contains shape files with the tundra area in 10 year steps starting in 2000 until 3000 CE <ul> <li>projection: Albers azimuthal equidistant projection centered at Longitude of 100 °E (PROJ4 string: "+proj=aea +lat_1=50 +lat_2=70 +lat_0=56 +lon_0=100 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs")</li> </ul> </li> </ol> <p>This study was supported by the Initiative and Networking Fund of the Helmholtz Association and by the ERC consolidator grant Glacial Legacy of Ulrike Herzschuh (grant no. 772852).</p>
Mapping and Modeling Clandestine Drivers of Urban Expansion in Mexico City (2016-2019)
This dataset incorporates Mexico City related essential data files associated with Beth Tellman's dissertation: Mapping and Modeling Illicit and Clandestine Drivers of Land Use Change: Urban Expansion in Mexico City and Deforestation in Central America. It contains spatio-temporal datasets covering three domains; i) urban expansion from 1992-2015, ii) district and section electoral records for 6 elections from 2000-2015, iii) land titling (regularization) data for informal settlements from 1997-2012 on private and ejido land. The urban expansion data includes 30m resolution urban land cover for 1992 and 2013 (methods published in Goldblatt et al 2018), and a shapefile of digitized urban informal expansion in conservation land from 2000-2015 using the Worldview-2 satellite. The electoral records include shapefiles with the geospatial boundaries of electoral districts and sections for each election, and .csv files of the number of votes per party for mayoral, delegate, and legislature candidates. The private land titling data includes the approximate (in coordinates) location and date of titles given by the city government (DGRT) extracted from public records (Diario Oficial) from 1997-2012. The titling data on ejido land includes a shapefile of georeferenced polygons taken from photos in the CORETT office or ejido land that has been expropriated by the government, and including an accompany .csv from the National Agrarian Registry detailing the date and reason for expropriation from 1987-2007. Further details are provided in the dissertation and subsequent article publication (Tellman et al 2021). The Mexico City portion of these data were generated via a National Science Foundation sponsored project (No. 1657773, DDRI: Mapping and Modeling Clandestine Drivers of Urban Expansion in Mexico City). The project P.I. is Beth Tellman with collaborators at ASU (B.L Turner II and Hallie Eakin). Other collaborators include the National Autonomous University of Mexico (UNAM),
Data for "Rapid seaward expansion of seaport footprints worldwide"
<p>[updated November 2023]</p><p>This dataset comprises data and code used in "Rapid seaward expansion of seaport footprints worldwide" (Sengupta & Lazarus, 2023; <a href="https://doi.org/10.1038/s43247-023-01110-y">https://doi.org/10.1038/s43247-023-01110-y</a>).</p><p>This repository includes three .csv files, one .xlsx file, and a .ipynb file:</p><p><strong>'Sengupta_Lazarus_REC_1990_2020_v05.csv' </strong>– annual time series of seaward expansion (km^2) between 1990–2020 through coastal reclamation for 65 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (<a href="https://lloydslist.maritimeintelligence.informa.com/-/media/lloyds-list/images/top-100-ports-2021/top-100-ports-2021-digital-edition.pdf">Lloyd's List, 2021</a>). Dataset includes Year, Seaport, Country, Region, and Reclaimed area (km^2) [raw measurement]. </p><p><strong>'Sengupta_Lazarus_REC_TEU_2011_2020_v03.csv'</strong> – annual time series of seaward expansion (km^2) and reported container throughput (millions TEU) between 2011–2020 for 43 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (<a href="https://lloydslist.maritimeintelligence.informa.com/-/media/lloyds-list/images/top-100-ports-2021/top-100-ports-2021-digital-edition.pdf">Lloyd's List, 2021</a>). Dataset includes Year, Seaport, Country, Region, Reclaimed area (km^2) [raw measurement], and TEU (millions), collated from archived Lloyd's List reports.</p><p><strong>'Sengupta_Lazarus_REC_TEU_totals_v04.csv'</strong> – Simplified dataset listing total seaward expansion (km^2) and container throughput in 2020 for 65 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (<a href="https://lloydslist.maritimeintelligence.informa.com/-/media/lloyds-list/images/top-100-ports-2021/top-100-ports-2021-digital-edition.pdf">Lloyd's List, 2021</a>). Dataset includes Seaport, Country, Region, Reclaimed area (km^2) [raw measurement], ranked list of seaports by expansion extent, and TEU (millions) handled in 2020, and Lloyd's List rank in 2020 (<a href="https://lloydslist.maritimeintelligence.informa.com/-/media/lloyds-list/images/top-100-ports-2021/top-100-ports-2021-digital-edition.pdf">Lloyd's List, 2021</a>).</p><p><strong>'Sengupta_Lazarus_2023_ports_excluded_v2.xlsx'</strong> – contains list of 35 ports excluded from thus analysis because they are either not on an open coastline (e.g. estuarine, riverine) or expanded less than 1 km^2 seaward between 1990–2020.</p><p><strong>'RECLAIM_port_trajectories_v11.ipynb' </strong>– Jupyter notebook for data wrangling and plotting figures presented in Sengupta & Lazarus (2023). (Note that this notebook does not produce the map-based figures presented in that work.)</p><p>The method for calculating reclaimed area over time in Google Earth Engine (GEE) is described in Sengupta et al. (<a href="https://doi.org/10.1029/2022EF002927">2023</a>), and the GEE code is available here: <a href="https://github.com/dhritirajsen/Mapping_Coastal_land_reclamation">https://github.com/dhritirajsen/Seaport_reclamation</a></p><p>These data and code are also available here: <a href="https://github.com/edlazarus/Seaports">https://github.com/edlazarus/Seaports</a></p>
Data for: Thermal volume expansion as seen by Temperature-modulated optical refractometry, Oscillating dilatometry and Thermo-mechanical analysis
<p>The data is supplementary to the publication "Thermal volume expansion as seen by Temperature-modulated optical refractometry, Oscillating dilatometry and Thermo-mechanical analysis", DOI: <a href="https://doi.org/10.1016/j.polymertesting.2024.108340" target="_blank" rel="noopener">10.1016/j.polymertesting.2024.108340</a></p> <p>Key words: Thermal volume expansion, Temperature-modulated optical refractometry, Thermo-mechanical analysis, Dilatometry, Epoxy thermoset</p> <p>The data sets contain measured data on Thermo-mechanical analysis (TMA) and Temperature-modulated optical refractometry (TMOR) of a model epoxy polymer in the viscoelastic temperature range.</p> <p>Material details:</p> <ul> <li>Bisphenol A Diglycidyl ether (DGEBA, DER332) + Difunctional and trifunctional carbocylic acids (Pripol1040, Croda) +pyridine</li> <li>n-tetradecane, C14H30</li> </ul> <p>Funding received from:</p> <ul> <li>German Research Foundation (DFG), project number: 521902629.</li> <li>(Austrian) Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology and the Federal Ministry for Digital and Economic Affairs (COMET-Module project “Chemitecture”, project-no.: 21647048)</li> </ul>
Data for: Vitrimer transition phenomena from the perspective of thermal volume expansion and shape (in)stability
<p>The data is supplementary to the publication "Vitrimer transition phenomena from the perspective of thermal volume expansion and shape (in)stability", DOI: <a href="https://pubs.acs.org/doi/10.1021/acs.macromol.4c00207" target="_blank" rel="noopener">10.1021/acs.macromol.4c00207</a></p> <p>Key words: Vitrimer transition temperature, Thermo-mechanical analyses, Temperature-modulated optical refractometry, Thermal volume expansion, Dynamic polymer networks, Shape instabilities</p> <p>The data sets contain measured data on Thermo-mechanical analysis (TMA) and Temperature-modulated optical refractometry (TMOR) of a epoxy-based vitrimer and a reference material.</p> <p>Material details:</p> <ul> <li>Bisphenol A Diglycidyl ether (DGEBA, DER332) + Difunctional and trifunctional carbocylic acids (Pripol1040, Croda) + 1,5,7-triazabicyclo[4.4.0]dec-5-en (TBD, 10 mol-% relative to carboxylic acid functions)</li> <li>Bisphenol A Diglycidyl ether (DGEBA, DER332) + Difunctional and trifunctional carbocylic acids (Pripol1040, Croda) +pyridine</li> </ul> <p>Funding received from:</p> <ul> <li>German Research Foundation (DFG), project number: 521902629.</li> <li>Sample preparation: (Austrian) Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology and the Federal Ministry for Digital and Economic Affairs (COMET-Module project “Repairtecture”, project-no.: 904927)</li> </ul>
Expansion of the global RNA virome reveals diverse clades of bacteriophages
<p>This deposit is intended to contain the various data generated as part of the RNA Virus in MetaTranscriptomes project ("RVMT"). This initial version is released ahead of time, near the time of submission, in hopes of providing a long lasting resource for the general scientific community. Note well - The authors listed in this initial version release are a partial list only. The RNA Virus in MetaTranscriptomes consortium is a project with over 90 researches from various institutions (see below).</p> <p>High-throughput RNA sequencing offers broad opportunities to explore the Earth RNA virome. Mining 5,150 diverse metatranscriptomes uncovered >2.5 million RNA virus contigs. Analysis of >330,000 RNA-dependent RNA polymerases (RdRPs) shows that this expansion corresponds to a 5-fold increase of the known RNA virus diversity. Gene content analysis revealed multiple protein domains previously not found in RNA viruses and implicated in virus-host interactions. Extended RdRP phylogeny supports the monophyly of the five established phyla and reveals two putative additional bacteriophage phyla and numerous putative additional classes and orders. The dramatically expanded phylum <em>Lenarviricota</em>, consisting of bacterial and related eukaryotic viruses, now accounts for a third of the RNA virome. Identification of CRISPR spacer matches and bacteriolytic proteins suggests that subsets of picobirnaviruses and partitiviruses, previously associated with eukaryotes, infect prokaryotic hosts.</p> <p>The RNA Virus in metatranscriptomes consortium:<br> Adrienne B. Narrowe, Alexander J. Probst, Alexander Sczyrba, Annegret Kohler, Armand Séguin, Ashley Shade, Barbara J. Campbell, Björn D. Lindahl, Brandi Kiel Reese, Breanna M. Roque, Chris DeRito, Colin Averill, Daniel Cullen, David A. C. Beck, David A. Walsh, David M. Ward, Dongying Wu, Emiley Eloe-Fadrosh, Eoin L. Brodie, Erica B. Young, Erik A. Lilleskov, Federico J. Castillo, Francis M. Martin, Gary R. LeCleir, Graeme T. Attwood, Hinsby Cadillo-Quiroz, Holly M. Simon, Ian Hewson, Igor V. Grigoriev, James M. Tiedje, Janet K. Jansson, Janey Lee, Jean S. VanderGheynst, Jeff Dangl, Jeff S. Bowman, Jeffrey L. Blanchard, Jennifer L. Bowen, Jiangbing Xu, Jillian F. Banfield, Jody W Deming, Joel E. Kostka, John M. Gladden, Josephine Z Rapp, Joshua Sharpe, Katherine D. McMahon, Kathleen K. Treseder, Kay D. Bidle, Kelly C. Wrighton, Kimberlee Thamatrakoln, Klaus Nusslein, Laura K. Meredith, Lucia Ramirez, Marc Buee, Marcel Huntemann, Marina G. Kalyuzhnaya, Mark P Waldrop, Matthew B Sullivan, Matthew O. Schrenk, Matthias Hess, Michael A. Vega, Michelle A. O’Malley, Monica Medina, Naomi E. Gilbert, Nathalie Delherbe, Olivia U. Mason, Paul Dijkstra, Peter F. Chuckran, Petr Baldrian, Philippe Constant, Ramunas Stepanauskas, Rebecca A. Daly, Regina Lamendella, Robert J Gruninger, Robert M. McKay, Samuel Hylander, Sarah L. Lebeis, Sarah P Esser, Silvia G. Acinas, Steven S. Wilhelm, Steven W. Singer, Susannah S. Tringe, Tanja Woyke, TBK Reddy, Terrence H. Bell, Thomas Mock, Tim McAllister, Vera Thiel, Vincent J. Denef, Wen-Tso Liu, Willm Martens-Habbena, Xiao-Jun Allen Liu, Zachary S. Cooper, Zhong Wang. For the full list of authors and related information, please see the spreadsheet tittle "Table S9 - Consortium coauthorship" available in this collection in the folder named "Tables".</p>
Raw data to "Series expansions in closed and open quantum many-body systems with multiple quasiparticle types"
<p>This collection of data is complementary to the publication "Series expansions in closed and open quantum many-body systems with multiple quasiparticle types", Lea Lenke, Andreas Schellenberger, Kai Phillip Schmidt, <a href="https://arxiv.org/abs/2302.01000">arXiv:2302.01000</a> (<a href="https://arxiv.org/abs/2302.01000">https://arxiv.org/abs/2302.01000</a>).</p> <p>It contains all data used for Figure 2 given in the file `Figure_2_complementary_data.yaml` and all needed data to recalculate the energies of the visualized modes in the files `Figure_2_coefficients_expectation_values.yaml` and `Figure_2_broad_signum_coefficients_expectation_values.yaml`.</p> <p>For the last two files, we used a program to calculate the coefficients. The source code for coefficient calculation is openly available under GitHub (<a href="https://github.com/FAU-kpslab/pcstpp_CoefficientGenerator">https://github.com/FAU-kpslab/pcstpp_CoefficientGenerator</a>) including configuration files to reproduce the coefficients given here.</p> <p>All files are self-consistent, for further information we recommend the comments directly in the files.</p> <p>For further details on the used method pcst<sup>++ </sup>and discussion of the results we refer to the linked publication.</p> <p>If any question may arise, you are highly welcome to contact us (see e.g. contact information on the publication).</p>
Dataset for Hydropower Expansion in Eco-Sensitive River Basins under Global Energy-Economic Change
<p>The data presented in this repository can be fed into the codes provided in <a href="https://github.com/kamal0013/chowdhury-etal_2023_hydropower">this GitHub repository</a> to reproduce the results of the following paper:</p> <p> </p> <p>Chowdhury, A.F.M.K., Wild, T., Zhang, Y. <em>et al.</em> Hydropower expansion in eco-sensitive river basins under global energy-economic change. <em>Nat Sustain</em> <strong>7</strong>, 213–222 (2024). <a href="https://doi.org/10.1038/s41893-023-01260-z">https://doi.org/10.1038/s41893-023-01260-z</a></p> <p> </p> <p><strong>Summary</strong></p> <p>In this study, we investigate how rapid economic growth and transition to low-carbon energy may impact hydropower development, with potential countervailing effects of increasingly cost-competitive variable renewable energy (VRE). We explore the effects of these forces on hydropower expansion in the world's 20 most eco-sensitive river basins, that have substantial untapped hydropower potential and ecological richness. Our investigation is based on the Global Change Analysis Model (GCAM), an integrated model of global energy-water-economy dynamics. The GCAM outputs and other data provided in this repository, in combination with the Jupyter Notebooks provided in <a href="https://github.com/kamal0013/chowdhury-etal_2023_hydropower">this GitHub repository</a>, can be used to conduct our key analysis, and reproduce the relevant results.</p>
Gene family expansions underpin context-dependency of the oldest mycorrhizal symbiosis
<p>This Zenodo archive is associated with the manuscript:</p> <p>Hernandez, D.J., Pohlmann, G.B., Afkhami, M.E. (2025) <span>Gene family expansions provide molecular flexibility required for context-dependent species interactions.</span> Ecology Letters.</p> <p>Abstract:</p> <p>As environments worldwide change at unprecedented rates during the Anthropocene, understanding context-dependency – how species regulate interactions to match changing environments – is crucial. However, generalizable molecular mechanisms underpinning context-dependency remain elusive. Combining comparative genomics across 42 angiosperms with transcriptomics, genome-wide association mapping, and gene duplication origin analyses, we show for the first time that gene family expansions undergird context-dependent regulation of species interactions. Gene families expanded in mycorrhizal fungi-associating plants display up to 200% more context-dependent gene expression and double the genetic variation associated with mycorrhizal benefits to plant fitness. Moreover, we discover these gene family expansions arise primarily from tandem duplications with >2-times more tandem duplications genome-wide, indicating gene family expansions continuously supply genetic variation throughout plant evolution allowing fine-tuning of context-dependency in species interactions.</p>
Great Ape Cerebral Aging - Expansion Relationship
<p>This contains data associated with the manuscript "The Uniqueness of Human Vulnerability to Brain Aging in Great Ape Evolution" https://www.biorxiv.org/content/10.1101/2022.09.27.509685v1</p> <p> </p> <p>The chimp.zip and IXI.zip contain data for the chimpanzee and human samples respectively. This includes the input matrix (subject x gray matter voxels), OPNMF bootstrap outputs, and OPNMF parcellaitons (2-40).</p> <p> </p> <p>The outputs.zip contains outputs from the analyses conducted for the manuscript using the code from https://github.com/viko18/GreatApe_Aging.</p> <p> </p> <p>The expansion_map.zip contains the modulated jacobians and templates following cross-species registration for the chimpanzee, baboon, and macaque templates.</p> <p> </p> <p>y_JunaChimp_brain.nii.gz is the deformtion field map from chimpanzee template space to human and iy_*.nii.gz is the inverse, so human to chimpanzee.</p> <p> </p> <p>Davi130_MNI_3mm_cortex.nii.gz is the avi130 chimpanzee parcellation in human MNI sapce.</p>
Phlorest phylogeny derived from Walker & Ribeiro 2011 'Bayesian phylogeography of the Arawak expansion in lowland South America'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Walker, R. S., & Ribeiro, L. A. (2011). Bayesian phylogeography of the Arawak expansion in lowland South America. Proceedings of the Royal Society B: Biological Sciences, 278(1718), 2562–2567.</p> </blockquote>
Phlorest phylogeny derived from Gray et al. 2009 'Language phylogenies reveal expansion pulses and pauses in Pacific settlement'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Gray RD, Drummond AJ, & Greenhill SJ 2009. Language phylogenies reveal expansion pulses and pauses in Pacific settlement. Science, 323(5913), 479-483.</p> </blockquote>
Phlorest phylogeny derived from Bouckaert et al. 2012 'Mapping the Origins and Expansion of the Indo-European Language Family'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Bouckaert RR, Lemey P, Dunn M, Greenhill SJ, Alekseyenko AV, Drummond AJ, Gray RD, Suchard MA & Atkinson QD. 2012. Mapping the Origins and Expansion of the Indo-European Language Family. Science, 337(6097), 957-960.</p> </blockquote>
Phlorest phylogeny derived from Grollemund et al. 2015 'Bantu expansion shows habitat alters the route and pace of human dispersals'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Grollemund R, Branford S, Bostoen K, Meade A, Venditti C & Pagel M. 2015. Bantu expansion shows habitat alters the route and pace of human dispersals. Proceedings of the National Academy of Sciences of the USA, 112(43), 13296-13301.</p> </blockquote>
Phlorest phylogeny derived from Bouckaert et al. 2018 'The origin and expansion of Pama–Nyungan languages across Australia'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Bouckaert RR, Bowern C & Atkinson QD. 2018. The origin and expansion of Pama–Nyungan languages across Australia. Nature Ecology and Evolution. 2: 741–749</p> </blockquote>
Phlorest phylogeny derived from De Filippo et al. 2012 'Bringing together linguistic and genetic evidence to test the Bantu expansion'
<p>Cite the source of the dataset as:</p> <blockquote> <p>De Filippo, C., Bostoen, K., Stoneking, M., & Pakendorf, B. (2012). Bringing together linguistic and genetic evidence to test the Bantu expansion. Proceedings of the Royal Society B: Biological Sciences, 279(1741), 3256–3263. doi:10.1098/rspb.2012.0318</p> </blockquote>
Data and configuration files for "Expansion of accreting main-sequence stars during rapid mass transfer"
<p>Data and configuration files that can be used to reproduce results from the paper <a href="https://ui.adsabs.harvard.edu/abs/2024ApJ...966L...7L/abstract">Expansion of Accreting Main-sequence Stars during Rapid Mass Transfer</a>. This directory contains MESA inlists and starting models used for calculations performed with MESA r15140, and YAML configuration files for calculations performed with COMPAS v02.41.04.</p> <p>See README.txt for a description of all files.</p> <p> </p> <p>Any work making use of these files should cite</p> <p>Lau, M., Hirai, R., Mandel, I., Tout, C., 2024, Expansion of Accreting Main-sequence Stars during Rapid Mass Transfer, ApJL, 966, 1</p> <div></div>
Range expansion is slower and more variable with rapid evolution across a spatial gradient in temperature
<p><span>Rapid evolution in colonizing populations can alter our ability to predict future range expansions. Recent theory suggests that the dynamics of replicate range expansions are less variable, and hence more predictable, with increased selection at the expanding range front. Here, we test whether selection from environmental gradients across space produces more consistent range expansion speeds, using the experimental evolution of replicate duckweed populations colonizing landscapes with and without a temperature gradient. We found that range expansion across a temperature gradient was slower on average, with range-front populations displaying higher population densities, and genetic signatures and trait changes consistent with directional selection. Despite this, we found that with a spatial gradient range expansion speed became more variable and less consistent among replicates over time. Our results therefore challenge current theory, highlighting that chance can still shape the genetic response to selection to influence our ability to predict range expansion speeds.</span></p>
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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.