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735 results for “Sea level”
Probabilistic projections of mean sea level change in Finland by 2100
<p><strong>Paper describing the methods used to calculate these projections: Pellikka, H., Johansson, M. M., Nordman, M., and Ruosteenoja, K.: Probabilistic projections and past trends of sea level rise in Finland, Nat. Hazards Earth Syst. Sci., <a href="https://doi.org/10.5194/nhess-2022-230">https://doi.org/10.5194/nhess-2022-230</a>, 2023.</strong></p> <p>This dataset includes probability distributions of projected mean sea level in Finland in 2030, 2040, ... 2100, as well as time series of projected mean sea level 2005-2100. Data is provided for 13 tide gauge locations and 3 emission scenarios: low (RCP2.6 / SSP1-2.6), medium (RCP4.5 / SSP2-4.5), and high (RCP8.5 / SSP5-8.5).</p> <p>There are two data packages, <em>distributions.zip</em> and <em>timeseries.zip</em>. The data files included in these packages are tab- or space-delimited text files with the file extension .dat.</p> <p>All filenames start with a three-character code xxx that determines the tide gauge (1-letter symbol) and the emission scenario (2 digits). For example:</p> <p>v26 means Vaasa, low emission scenario (RCP2.6 / SSP1-2.6)<br> e45 means Helsinki, medium emission scenario (RCP4.5 / SSP2-4.5)<br> t85 means Turku, high emission scenario (RCP8.5 / SSP5-8.5)</p> <p>The letter symbols and locations of the tide gauges are, from north to south along the coast:</p> <p>a - Kemi (65.67 N, 24.52 E)<br> o - Oulu (65.04 N, 25.42 E)<br> b - Raahe (64.67 N, 24.41 E)<br> p - Pietarsaari (63.71 N, 22.69 E)<br> v - Vaasa (63.08 N, 21.57 E)<br> s - Kaskinen (62.34 N, 21.21 E)<br> m - Mäntyluoto (61.59 N, 21.46 E)<br> r - Rauma (61.13 N, 21.44 E)<br> t - Turku (60.43 N, 22.1 E)<br> d - Degerby (60.03 N, 20.38 E)<br> h - Hanko (59.82 N, 22.98 E)<br> e - Helsinki (60.15 N, 24.96 E)<br> f - Hamina (60.56 N, 27.18 E)</p> <p>1) <em>distributions.zip > xxx_fitdistr_yyyy.dat</em><br> These files include the probability distribution (probability density function) of projected mean sea level in year yyyy (2030, 2040, ... 2100). There are two columns: sea level and probability. Sea level values are millimetres in the Finnish N2000 height system.</p> <p>2)<em> timeseries.zip > xxx_timeseries.dat</em><br> These files include the time series of projected mean sea level in 2005-2100. The files have 8 columns: year and 7 sea level values representing different percentiles of the probability distribution. The percentiles are 1%, 5%, 17%, 50% (median), 83%, 95%, 99%. Sea level values are centimetres in the Finnish N2000 height system.</p> <p><strong>Please note that all projections for years other than 2100 are indicative and based on a simple 2nd order fit made to the current rate of mean sea level change and the projected mean sea level in 2100. In other words, the projections for intermediate years are based on the 2100 projections assuming constant acceleration in mean sea level change rates.</strong></p> <p>Example figures <em>distributions.png</em> and <em>timeseries.png</em> are included to illustrate the data. The Matlab script <em>slrfinland_figures.m</em> used to produce these figures is also included.</p>
Coastal flood maps and extreme sea levels for the German Baltic Sea coast
<p>The provided data was produced as part of the Ecas-Baltic project (2020 - 2023). The project is funded by the Federal Ministry of Education and Research in Germany (BMBF, funding code 03F0860H).</p> <p>The dataset contains information supporting the conclusions presented in the following publication (the final, revised version of the article will also be accessible via the preprint given below):</p> <p>Kiesel, J., Lorenz, M., König, M., Gräwe, U., and Vafeidis, A. T.: A new modelling framework for regional<br> assessment of extreme sea levels and associated coastal flooding along the German Baltic Sea coast,<br> Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2022-275, in review, 2023.</p> <p>The dataset contains:</p> <p>- the location and names of flood boundary stations</p> <p>- the boundary conditions provided by the coastal ocean model at each of the flood boundary stations for all storm surge events simulated in the study cited above</p> <p>- the flood maps containing both the maximum flood extent and maximum inundation depth at every grid cell of the coastal inundation model</p> <p>- the spatially explicit results of the extreme value analysis for every grid cell in the coastal ocean model</p> <p>- the modelled monthly peak water levels between 1961 and 2018 for every grid cell of the coastal ocean model</p> <p>- the modelled timeseries of water levels during the storm surge from January 2nd 2019 and the entire hindcast period (1961-2018) for all tide gauges along the German Baltic Sea coast</p> <p>For further information, we refer the reader to the readme file in this dataset or the publication itself.</p> <p> </p> <p> </p> <p> </p>
The evolving landscape of sea-level rise science from 1990 to 2021
<p>This dataset contains the bibliometric information (e.g., list of authors, keywords, journals, references, etc) for 14,951 sea-level rise related articles published between 1990 and 2021, as retrieved from the Web of Science.</p> <p>This dataset was used to scrutinise the evolution of sea-level rise science:</p> <ul> <li>Khojasteh D, Haghani M, Nicholls R, Moftakhari H, Sadat-Noori M, Mach K, Fagherazzi S, Vafeidis A, Barbier E, Shamsipour A, Glamore W. The evolving landscape of sea-level rise science from 1990 to 2021. <em>Communications Earth & Environment</em>. 2023.</li> </ul> <p>The zip file contains 30 text files where the bibliometric information is stored (each text file comprises the bibliometric information of maximum 500 sea-level rise articles). This dataset also includes an Excel file with data required to reproduce the figures presented in the manuscript. </p>
What Darwin couldn't see: Island formation and historical sea levels shape genetic divergence and island biogeography in a coastal marine species
<p>Oceanic islands play a central role in the study of evolution and island biogeography. The Galapagos Islands are one of the most studied oceanic archipelagos but research has almost exclusively focused on terrestrial organisms compared to marine species. Here we used the Galapagos bullhead shark (<em>Heterodontus quoyi</em>) and single nucleotide polymorphisms (SNPs) to examine evolutionary processes and their consequences for genetic divergence and island biogeography in a shallow-water marine species without larval dispersal. The sequential separation of individual islands from a central island cluster gradually established different ocean depths between islands that pose barriers to dispersal in <em>H. quoyi</em>. Isolation-by-resistance analysis suggested that ocean bathymetry and historical sea level fluctuations modified genetic connectivity. These processes resulted in at least three genetic clusters that exhibit low genetic diversity and effective population sizes that scale with island size and the level of geographic isolation. Our results exemplify that island formation and climatic cycles shape genetic divergence and biogeography of coastal marine organisms with limited dispersal comparable to terrestrial taxa. Because similar scenarios exist in oceanic islands around the globe our research provides a new perspective on marine evolution and biogeography with implications for the conservation of island biodiversity.</p>
Sea level data archaeology at Socoa (Saint Jean-de-Luz, France)
<p><strong>1. Description</strong></p> <p>This repository contains the data, notebooks, and documents covering the archeology and rescue of tide gauge observation at Socoa (Saint Jean-de-Luz, Southwestern France), and is complementary to the paper (henceforth "data paper") titled "Extension of high temporal resolution sea level time series at Socoa (Saint Jean-de-Luz, France) back to 1875".</p> <p>Authors: Md Jamal Uddin Khan, Inge Van Den Beld, Guy Woppelmann, Laurent Testut, Alexa Latapy, Nicolas Pouvreau</p> <p>For up-to-date sea level timeseries, visit the Shom data distribution portal linked in the Related identifier section below. At the time of writing, corrected and valid data over 1875 to date at original sampling is identified as 'Validées temps différé'.</p> <p>The data and source-codes presented here is open-access. Please consider citing the data paper if you use the data. More information is given in the Data-use section below.</p> <p>A brief structure of the files contained in this repository is shown below, and a brief description is given in the following sections -</p> <pre><code>|- data |- auxiliary |- brest_data.shom.fr/ |- santander_marcos_etal.dat |- socoa |- data.shom.fr/ |- corrections.csv |- corrections_marigram.csv |- corrections_registry.csv |- data_inventory.csv |- socoa_L0.csv |- socoa_L1.csv |- socoa_L2.csv |- socoa_L2_nominal.csv |- socoa_L3.csv |- socoa_L4.csv |- socoa_raw.txt |- documents |- archive_records |- AD64__Pau_4S 33.docx |- Archives_Shom_plusieursCotes.docx |- SHD_Brest_MB3W.docx |- SHD_Rochefort_7JJ418-7JJ1551.docx |- SHD_Vincennes_plusieurCotes.docx |- tidegauge_journal |- <year>_Socoa_Notes_Registres.docx |- inventory.xlsx |- figures |- figure01 |- ... |- figure 06 |- supp |- notebooks |- 01_data_processing.ipynb |- 02_buddy_checking.ipynb |- 03_trend_analysis.ipynb |- README.md</code></pre> <p><strong>2. Data and codes</strong></p> <p><strong>2.1 Data (<em>/data</em>)</strong></p> <p>Data directory (/data) contains two folder - auxiliary, and socoa.</p> <p>/data/socoa contains raw data (socoa_raw.txt) as well as processed dataset (socoa_L*.csv). The directory also contains information regarding the data inventory (data_inventory.csv), and the corrections applied during the processing (corrections_*.csv). A snapshot of data from https://data.shom.fr (April 2022) is also available in the /data/socoa/data.shom.fr directory.</p> <p>Data files corresponding to various level of dataset is following -</p> <ol> <li>Level 0: No corrections applied (/data/socoa/socoa_L0.csv)</li> <li>Level 1: Time corrections applied, including conversion to UTC time (/data/socoa/socoa_L1.csv)</li> <li>Level 2: Time and height corrections applied (/data/socoa/socoa_L2.csv)</li> <li>Level 3: Hourly interpolated and merged with existing data (/data/socoa/socoa_L3.csv)</li> <li>Level 4: Hourly data additionally flagged for siltation from tidal analysis (/data/socoa/socoa_L4.csv)</li> </ol> <p>All datafiles are provided as standard comma-separated text files encoded in utf-8. The final Level 4 data has three columns with column header titled - Datetime, Value, and Flag, where,</p> <ul> <li>Datetime: The UTC timestamp of the data</li> <li>Value: Sea level with respect to Hydrographic Zero</li> <li>Flag: Data flag as described in the data paper</li> </ul> <p>In addition, Level 4 data contains the following information header -</p> <pre><code># Station: SAINT-JEAN-DE-LUZ_SOCOA # Longitude: -1.68162 # Latitude: 43.39524 # Organization: Shom, Pyrénées-Atlantiques # Timesystem: UTC # VerticalReference: Hydrographic Zero # Unit: m # Flag: 4-bit flags yes(1)/no(0) - [siltation][uncertain_correction][height_correction][time_correction] # DOI_DataPaper: Data: 10.5281/zenodo.7438469, Article: 10.5194/essd-2022-443 # DOI_SHOM: 10.17183/REFMAR#95</code></pre> <p>/data/auxiliary contains the auxiliary dataset used for analysis and comparison in the data paper. Brest dataset, /data/auxiliary/brest_data.shom.fr, is retrieved from https://data.shom.fr. Monthly sea level at Santander, /data/auxiliary/santander_marcos_etal.dat, comes from the dataset published by Marcos et al. (2020).</p> <p><strong>2.2 Documents (<em>/documents</em>)</strong></p> <p>The <em>/documents</em> folder contains supplimentary documents related to the dataset.</p> <p><em>/documents/tidegauge_journal</em> contains the extraction from the tide gauge journal year by year. The files are named as – <em>YEAR_Socoa_Notes_Registres.docx</em> where <em>YEAR</em> is the 4-digit year.</p> <p><em>/documents/archive_records</em> contains the excerpts of metadata documents from the various archives. For 5 archives, 5 separate files are provided. These archives are and file names are –</p> <ol> <li>Service historique de la Défense (SHD) at Brest (SHD-Brest): <em>SHD_Brest_MB3W.docx</em></li> <li>Service historique de la Défense (SHD) at Rochefort (SHD-Rochefort): <em>SHD_Rochefort_7JJ418-7JJ1551.docx</em></li> <li>Service historique de la Défense (SHD) at Vincennes (SHD-Vincennes): <em>SHD_Vincennes_plusieurCotes.docx</em></li> <li>Archives des Pyrénées-Atlantiques (AD64): <em>AD64_Pau_4S 33.docx</em></li> <li>SHOM archive: <em>Archives_Shom_plusieursCotes.docx</em></li> </ol> <p><em>/documents/inventory.xlsx</em> gives an overall inventory of the historic documents catalogued.</p> <p><strong>2.3 Figures (<em>/figures</em>)</strong></p> <p>The figures directory (<em>/figures</em>) contains the figures presented in the accompanying data paper organized into individual directories for each figure. The most of the figures related to the analysis is generated using python and their source-code is provided in the <em>/notebooks</em> directory. In case where external graphical software has been used for generating the final image is provided inside the directory for each individual figure.</p> <p><strong>2.4 Codes (<em>/notebooks</em>)</strong></p> <p>The codes used in processing this data is provided in the `/notebooks` directory. The full code is segmented into three self-contained python/jupyter notebooks -</p> <ol> <li>Data processing (<em>01_data_processing.ipynb</em>)</li> <li>Buddy checking (<em>02_buddy_checking.ipynb</em>)</li> <li>Trend analysis (<em>03_trend_analysis.ipynb</em>)</li> </ol> <p> </p> <p>The python environment necessary for running the notebooks can be created using <em>conda</em> (https://www.anaconda.com/products/distribution) with the following commands -</p> <pre><code class="language-bash">conda create -n socoa python=3.7 numpy scipy statsmodels cartopy netcdf4 pandas xarray jupyter conda activate socoa pip install utide==0.2.6 jupyter-notebook</code></pre> <p> </p> <p><strong>3. Data-use information</strong></p> <p>Citation: Khan, M. J. U., Van Den Beld, I., Wöppelmann, G., Testut, L., Latapy, A., and Pouvreau, N.: Extension of high temporal resolution sea level time series at Socoa (Saint Jean-de-Luz, France) back to 1875, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2022-443, in review, 2023.</p> <p>Use rights: The data and source-code is freely available to anyone. Please consider citing the data paper using the citation above.</p> <p>License: Creative Commons Attribution 4.0 International (CC BY 4.0)</p> <p> </p> <p><strong>4. Related identifiers</strong></p> <ul> <li>SHOM historical dataset portal: http://refmar.shom.fr/dataRescue/HTML/FR/SOCOA%20(SAINT-JEAN-DE-LUZ).html</li> <li>SHOM data distribution portal: http://dx.doi.org/10.17183/REFMAR#95</li> </ul>
Sea level rise along China coast from 1950 to 2020 (datasets)
<p>this dataset contain the reconstructed sea level changes along China coast from 1950-2022;<br> the file name consists of two parts: PSMSL + ID;<br> PSMSL means Permanent Service for Mean Sea Level (https://psmsl.org/);<br> the ID is assigned by PSMSL to the tide gauges, users can find more information on the tide gauges from PSMSL website;<br> Each file contains five columns data:<br> first column: time year<br> second column: tide gauge records<br> third column: the reconstructed sea level<br> fourth column: the upper bound<br> fifth column: the lower bound<br> more details can be found in the following paper:<br> Dapeng Mu, Tianhe Xu, Haoming Yan, 2023. Sea level rise along China coast from 1950-2020, Science China-Earth Sciences<br> Any questions can be addressed to: mdp321@126.com or mdp@sdu.edu.cn</p>
What Darwin couldn't see: Island formation and historical sea levels shape genetic divergence and island biogeography in a coastal marine species
Open the record for dataset details and reuse information.
PIE LTER location and sample dates for sites used in space for time sea level rise study, Rowley, MA.
This dataset contains the GPS-ed locations of the quadrats used for the 2017-2018 Space for Time substitution experiment in tidal creek marshes off the Rowley River and Plum Island Sound in Rowley Massachusetts. The space for time study uses an intensive and comprehensive approach to compare low elevation, Spartina alterniflora marsh areas to higher elevation Spartina patens marsh areas. Other related data files include: HTL-RO-ST-MAR-Biomass, HTL-RO-ST-MAR-Birds, HTL-RO-ST-MAR-Quads, HTL-RO-ST-MAR-Sediments, HTL-RO-ST-MAR-Bites, HTL-RO-ST-MAR-Sticky, HTL-RO-ST-MAR-Decomp, HTL-RO-ST-MAR-Traps, HTL-RO-ST-MAR-Deep_pitfalls
PIE LTER bird observations associated with marsh sites used in space for time sea level rise study, Rowley, MA.
This dataset contains observations of birds foraging at high and low tide at space for time substition plots in tidal creek marshes off the Rowley River and Plum Island Sound in Rowley Massachusetts. The space for time study uses an intensive and comprehensive approach to compare low elevation, Spartina alterniflora marsh areas to higher elevation Spartina patens marsh areas. Birds were observed using timed interval observations, with one sampling bout per tide per site. Other related data files include: HTL-RO-ST-MAR-Sites, HTL-RO-ST-MAR-Biomass, HTL-RO-ST-MAR-Quads, HTL-RO-ST-MAR-Sediments, HTL-RO-ST-MAR-Bites, HTL-RO-ST-MAR-Sticky, HTL-RO-ST-MAR-Decomp, HTL-RO-ST-MAR-Traps, HTL-RO-ST-MAR-Deep_pitfalls
PIE LTER predation and herbivory rates associated with marsh sites used in space for time sea level rise study, Rowley, MA.
This dataset contains aggregated observations of predation and herbivory rates on tethered bait in each quadrat of the space for time substitution observations in salt marsh sites in Rowley and Newbury, MA.
Antarctic Ice Sheet and emission scenario controls on 21st-century extreme sea-level changes
<p>These files accompany the paper: 'Antarctic Ice Sheet and emission scenario controls on 21st-century extreme sea-level changes'.</p> <p>Please cite the accompanying paper if you find this data useful.</p> <p><strong>Contents</strong><br> This dataset contains netCDF files with all the mean sea level scenarios and the accompanying uncertainties. The file 'esl_results.xlsx' contains the estimated GPD parameters for each tide-gauge site, as well as the estimated 100-year amplification factor and allowance. The files result_concise_table_af.pdf and result_concise_table_al.pdf contain easy-to-access overviews of the amplification factors and allowances sorted per station.<br> </p> <p>(c) 2019 California Institute of Technology. U.S. Government sponsorship acknowledged.<br> This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.</p>
MARTREC Data from Visualizing Sea Level Rise Impacts in Transportation Planning
<p>This research focuses on a study area in Fort Lauderdale--a two-block stretch of Las Olas Blvd. between Southeast 9th Ave. and Southeast 11th Ave. where researchers expect mean high tides up to 36 inches higher in the year 2100. The project investigates a community planning process in which a combination of high- and low-tech visualization methods—a Geographic Information System (GIS) and a human artist—was used to increase public participation and draw out local knowledge which helps the decision-making process for the future. Mixed reality technologies such as Microsoft Hololens (augmented reality) and Samsung VR Gear (virtual reality) offer immersive educational and engagement experiences, which may convey information in a more meaningful way. Using a quasi-experimental methodology of before-and-after surveys, we compare the degree to which virtual reality technologies improve<br> (or impede) constituents’ absorption of information regarding sea-level rise risks to roadway infrastructure in their communities.</p>
An index of morphological and orthographic variation at the lexical level in the Hebrew Bible and the larger Dead Sea Scrolls: Version 1
<p>An index of morphological and orthographic variation at the lexical level in the Hebrew Bible and the larger Dead Sea Scrolls.</p> <p> </p> <p>Please cite the following article when referring to this dataset:</p> <p>Johan de Joode. 2020. "Digital Masorah: Toward an Index of Orthographic and Morphological Variation at the Lexical Level". Journal for Semitics VOL (ISSUE). https://doi.org/10.25159/2663-6573/6632.</p> <p> </p> <p>This data is also accessible on Github: https://github.com/jdejoode/orthomorphindex-JSEM</p>
DynaRev - Dynamic Coastal Protection: Resilience of Dynamic Revetments Under Sea Level Rise
<p>This dataset contains processed data from the DynaRev experiments carried out in the Large Wave Flume (Grosser Wellenkanal, GWK) from 14-08-2017 to 29-09-2017 as a Transnational Access project within the EU funded project HYDRALAB+ (654110). The dataset provided under this DOI includes the post-processed data detailed in the article submitted to Scientific Data titled "High-resolution, prototype-scale laboratory measurements of nearshore wave processes and morphological evolution of a sandy beach with and without a dynamic cobble berm revetment"</p> <p>The overall aim of this project was to construct a prototype-scale beach and investigate the response of the beach to a rising sea level and storms with and without a cobble berm dynamic revetment structure (a gravel or shingle ridge placed around the wave runup limit). The response of two beach configurations was investigated under erosive wave conditions (Hs=0.8m, Tp=6.0s) and a total sea-level rise, SLR = 0.4 m:</p> <p>i) an unmodified sand beach with an initially plane slope of 1:15, and<br> ii) a natural beach profile with a dynamic revetment installed at the location of the natural berm before imposing SLR.</p> <p>SLR was imposed in 4 steps of 0.1 m with an initial water depth of 4.5 m above the flume base. Each phase ran for a total of 58 hours, after which resilience testing under storm waves and accretive conditions was undertaken for a further 12 hours in each case.</p> <p>The changing profile of the sand beach and revetment was monitored throughout the experiments using a traditional profiler and a LiDAR array. Additional hydrodynamic measurements were obtained at the location of the offshore sandbar to investigate the process of bar formation and migration with SLR.</p> <p>The model set-up, experimental program and data structure are explained in the submitted Scientific Data paper. The data files are organised as detailed in "DynaRev_Data_Structure.doc".</p> <p> </p>
Supporting data to 'Correlations between sea-level components are driven by regional climate change'
<p>Dataset supporting the initial submission of the manuscript 'Correlations between sea-level components are driven by regional climate change'</p>
Data from: Geographic location and food availability offer differing levels of influence on the bacterial communities associated with larval sea urchins
Determining the factors underlying the assembly, structure, and diversity of symbiont communities remains a focal point of animal-microbiome research. Much of these efforts focus on taxonomic variation of microbiota within or between animal populations, but rarely test the proportional impacts of ecological components that may affect animal-associated microbiota. Using larvae from the sea urchin Strongylocentrotus droebachiensis from the Atlantic and Pacific Oceans, we test the hypothesis that, under natural conditions, inter-population differences in the composition of larval-associated bacterial communities are larger than intra-population variation due to a heterogeneous feeding environment. Despite significant differences in bacterial community structure within each S. droebachiensis larval population based on food availability, development, phenotype, and time, variation in OTU membership and community composition correlated more strongly with geographic location. Moreover, 20-30% of OTUs associated with larvae were specific to a single location while less than 10% were shared. Taken together, these results suggest that inter-populational variation in symbiont communities may be more pronounced than intra-populational variation, and that this difference may suggest that broad scale ecological variables (e.g., across ocean basins) may mask smaller scale ecological variables (e.g., food availability).
Using UCEs to track the influence of sea-level change on leafy seadragon populations
<p>Data and code used in bioinformatic processing, bathymetry calculations, population genetic analyses and their output files. Ultraconserved Elements (UCEs) were sequenced in 68 individuals of leafy seadragons (<em>Phycodurus eques</em>, Syngnathidae) sampled across their range along the southern Australian coast.</p> <p>The repository contains</p> <p>A) Scripts to process the sequence data and the resulting</p> <ul> <li>BAM read mapping files</li> <li>VCF files with SNPs before and after filtering</li> </ul> <p> </p> <p>B) Scripts, input files, and output files for the analyses to</p> <ul> <li>reconstruct shallow water areas at different sea levels</li> <li>estimate population structure (PCA, DAPC, Structure, SVDquartets), and spatial genetic patterns (IBD plots, EEMS)</li> <li>calculate genetic diversity (individual-level heterozygosity, population-level heterozygosity, Tajima's D)</li> <li>perform phylogeographic modeling (DIYABC).</li> </ul>
Database of Last Interglacial sea levels in Angola, Namibia and South Africa
<p>Database of sea-level information from southern Africa (Angola to South Africa) from the Last Interglacial</p>
Last Interglacial Sea Levels within the Korean Peninsula
<p>This file contains a table of the data input into the standardized database entitled World Atlas of Last Interglacial Shorelines (WALIS) database by Ryang and Simms for the Korean Peninsula. The data contains age and elevations obtained from Last Interglacial Shoreline across the Korean Peninsula. </p>
Data from: How sea-level change mediates genetic divergence in coastal species across regions with varying tectonic and sediment processes
Plate tectonics and sediment processes control regional continental shelf topography. We examine the genetic consequences of how glacial-associated sea-level change interacted with variable near-shore topography since the last glaciation. We reconstructed the size and distribution of areas suitable for tidal estuary formation from the Last Glacial Maximum, ~20 thousand years ago, to present from San Francisco, California, USA (~38 °N) to Reforma, Sinaloa, Mexico (~25 °N). We assessed range-wide genetic structure and diversity of three co-distributed tidal estuarine fishes (California Killifish, Shadow Goby, Longjaw Mudsucker) along ~4,600 km using mitochondrial control region and cytB sequence, and 16–20 microsatellite loci from a total of 524 individuals. Results show that glacial-associated sea-level change limited estuarine habitat to few, widely separated refugia at glacial lowstand, and present-day genetic clades were sourced from specific refugia. Habitat increased during postglacial sea-level rise and refugial populations admixed in newly formed habitats. Continental shelves with active tectonics and/or low sediment supply were steep and hosted fewer, smaller refugia with more genetically differentiated populations than on broader shelves. Approximate Bayesian computation favored the refuge-recolonization scenarios from habitat models over isolation by distance and seaway alternatives, indicating isolation at lowstand is a major diversification mechanism among estuarine (and perhaps other) coastal species. Because sea-level change is a global phenomenon, we suggest this top-down physical control of extirpation-isolation-recolonization may be an important driver of genetic diversification in coastal taxa inhabiting other topographically complex coasts globally during the Mid- to Late Pleistocene and deeper timescales.
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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.