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7,031 results for “marine”
Modeling Early Life Histories of Marine Organisms
<p>This is a recorded presentation to introduce students to ecosystem modeling. The presentation was developed for students of an early life histories class so discusses lagrangian individual-based modeling but the supporting material for understanding eulerian physical and lower trophic level models is also introduced.</p> <p>If you use part or all of this educational material as part of your lesson content it would be appreciated if you could inform the author (gagibson@alaska.edu) for tracking purposes.</p>
Habitat Protection Indexes - new monitoring measures for the conservation of threatened marine habitats - Datasets and supporting files
<p>The supporting datasets, scripts, and supplementary information for the manuscript, "Habitat Protection Indexes - new monitoring measures for the conservation of threatened marine habitats," are available within this repository.</p> <p>We conduct an analysis on the coverage of protected areas that cover six threatened marine and coastal and developed two indexes, the Local Proportion of Habitat Protected Index and the Global Proportion of Habitat Protected Index, describing the protection of these habitats locally and globally. The habitats considered are the following: cold corals, warm water corals, knolls and seamounts, mangroves, saltmarshes, and seagrasses.</p> <p>The index scores of each jurisdiction are made available for download in the dataset: <em>habitat_protection_indexes_average.csv</em></p> <p>The habitat specific index scores for each jurisdiction are made available for download in the dataset: <em>habitat_protection_indexes.csv. </em></p> <p>Column name descriptions are available in the text file: <em>Column_name_descriptions_20220301</em></p> <p>The scripts used to run the workflow to calculate the indexes, create figures, and calculate statistics for the manuscript are also included. The script <em>01_Workflow sources</em> the first 9 scripts in the <em>scripts</em> folder to calculate the indexes which relies on the functions script within the functions folder. The rest of the scripts in the folder create the figures and calculate the statistics for the manuscript.</p> <p>A readme pdf file is included here to ease with reproducing the workflow, but we strongly suggest to please visit our github (<a href="https://github.com/jkumagai96/Marine_Habitat_protection">https://github.com/jkumagai96/Marine_Habitat_protection</a>) to reproduce the entire calculation where we provide detailed information on how to run the workflow and package management.</p>
Global Lagrangian dataset of Marine litter
<p><strong>Global Lagrangian dataset of Marine litter</strong></p> <p>This dataset regroups 12 yearly files (<em>global-marine-litter-[2010–2021].nc</em>) combining monthly releases of 32,300 particles initially distributed across the globe following global Mismanaged Plastic Waste (MPW) inputs. The particles are advected with OceanParcels (<a href="https://doi.org/10.5194/gmd-12-3571-2019">Delandmeter, P and E van Sebille, 2019</a>) using ocean surface velocity, a wind drag coefficient of 1%, and a small random walk component with a uniform horizontal turbulent diffusion coefficient of K<sub>h</sub> = 1m<sup>2</sup>s<sup>-1</sup> representing unresolved turbulent motions in the ocean (see <a href="https://doi.org/10.3389/fmars.2021.667591">Chassignet et al. 2021</a> for more details).</p> <p><strong>Global oceanic current and atmospheric wind</strong></p> <p>Ocean surface velocities are obtained from GOFS3.1, a global ocean reanalysis based on the HYbrid Coordinate Ocean Model (HYCOM) and the Navy Coupled Ocean Data Assimilation (NCODA; <a href="https://www.frontiersin.org/articles/10.3389/fmars.2021.667591/full#B7">Chassignet et al., 2009</a>; <a href="https://www.jstor.org/stable/24862187?seq=1#metadata_info_tab_contents">Metzger et al., 2014</a>). NCODA uses a three-dimensional (3D) variational scheme and assimilates satellite and altimeter observations as well as in-situ temperature and salinity measurements from moored buoys, Expendable Bathythermographs (XBTs), Argo floats (<a href="https://link.springer.com/chapter/10.1007/978-3-642-35088-7_13">Cummings and Smedstad, 2013</a>). Surface information is projected downward into the water column using Improved Synthetic Ocean Profiles (<a href="https://apps.dtic.mil/sti/citations/ADA585251">Helber et al., 2013</a>). The horizontal resolution and the temporal frequency for the GOF3.1 outputs are 1/12° (8 km at the equator, 6 km at mid-latitudes) and 3-hourly, respectively. Details on the validation of the ocean circulation model are available in <a href="https://apps.dtic.mil/sti/citations/AD1034517">Metzger et al. (2017)</a>.</p> <p>Wind velocities are obtained from JRA55, the Japanese 55-year atmospheric reanalysis. The JRA55, which spans from 1958 to the present, is the longest third-generation reanalysis that uses the full observing system and a 4D advanced data assimilation variational scheme. The horizontal resolution of JRA55 is about 55 km and the temporal frequency is 3-hourly (see <a href="https://www.sciencedirect.com/science/article/pii/S146350031830235X?via%3Dihub">Tsujino et al. (2018)</a> for more details).</p> <p><strong>Marine Litter Sources</strong></p> <p>The marine litter sources are obtained by combining MPW direct inputs from coastal regions, which are defined as areas within 50 km of the coastline (<a href="https://doi.org/10.1057/s41599-018-0212-7">Lebreton and Andrady 2019</a>), and indirect inputs from inland regions via rivers (<a href="https://doi.org/10.1038/ncomms15611">Lebreton et al. 2017</a>). </p> <p><strong>File Format</strong></p> <p>The locations (<em>lon</em>, <em>lat</em>), the corresponding weight (<em>tons</em>), and the source (<em>1</em>: land, <em>0</em>: river) associated with the 32,300 particles are described in the file <em>initial-location-global.csv</em>. The particle trajectories are regrouped into yearly files (<em>marine-litter-[2010–2021].nc</em>) which contain 12 monthly releases, resulting in a total of 387,600 trajectories per file. More precisely, in each of the yearly files, the first 32,300 lines contain the trajectories of particles released on January 1st, then lines 32,301–64,600 contain the trajectories of particles released on February 1st, and so on. The trajectories are recorded daily and are advected from their release until 2021-12-31, resulting in longer time series for earlier years of the dataset. </p> <p><strong>References</strong></p> <p>Chassignet, E. P., Hurlburt, H. E., Metzger, E. J., Smedstad, O. M., Cummings, J., Halliwell, G. R., et al. (2009). U.S. GODAE: global ocean prediction with the hybrid coordinate ocean model (HYCOM). Oceanography 22, 64–75. doi: <a href="https://doi.org/10.5670/oceanog.2009.39">10.5670/oceanog.2009.39</a></p> <p>Chassignet, E. P., Xu, X., and Zavala-Romero, O. (2021). Tracking Marine Litter With a Global Ocean Model: Where Does It Go? Where Does It Come From?. <em>Frontiers in Marine Science</em>, <em>8</em>, 414, doi: <a href="https://doi.org/10.3389/fmars.2021.667591">10.3389/fmars.2021.667591</a></p> <p>Cummings, J. A., and Smedstad, O. M. (2013). “Chapter 13: variational data assimilation for the global ocean”, in Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, Vol. II, eds S. Park and L. Xu (Berlin: Springer), 303–343. doi: <a href="https://doi.org/10.1007/978-3-642-35088-7_13">10.1007/978-3-642-35088-7_13</a></p> <p>Delandmeter, P., and van Sebille, E. (2019). The Parcels v2.0 Lagrangian framework: new field interpolation schemes. Geosci. Model Dev. 12, 3571–3584. doi: <a href="https://doi.org/10.5194/gmd-12-3571-2019">10.5194/gmd-12-3571-2019</a></p> <p>Helber, R. W., Townsend, T. L., Barron, C. N., Dastugue, J. M., and Carnes, M. R. (2013). Validation Test Report for the Improved Synthetic Ocean Profile (ISOP) System, Part I: Synthetic Profile Methods and Algorithm. NRL Memo. Report, NRL/MR/7320—13-9364 Hancock, MS: Stennis Space Center.</p> <p>Metzger, E. J., Smedstad, O. M., Thoppil, P. G., Hurlburt, H. E., Cummings, J. A., Wallcraft, A. J., et al. (2014). US Navy operational global ocean and Arctic ice prediction systems. Oceanography 27, 32–43, doi: <a href="https://doi.org/10.5670/oceanog.2014.66">10.5670/oceanog.2014.66</a>.</p> <p>Metzger, E., Helber, R. W., Hogan, P. J., Posey, P. G., Thoppil, P. G., Townsend, T. L., et al. (2017). Global Ocean Forecast System 3.1 validation test. Technical Report. NRL/MR/7320–17-9722. Hancock, MS: Stennis Space Center, 61.</p> <p>Lebreton, L., and Andrady, A. (2019). Future scenarios of global plastic waste generation and disposal. Palgrave Commun. 5:6, doi: <a href="https://doi.org/10.1057/s41599-018-0212-7">10.1057/s41599-018-0212-7</a>.</p> <p>Lebreton, L., van der Zwet, J., Damsteeg, J. W., Slat, B., Andrady, A., and Reisser, J. (2017). River plastic emissions to the world’s oceans. Nat. Commun. 8:15611, doi: <a href="https://doi.org/10.1038/ncomms15611">10.1038/ncomms15611</a>.</p> <p>Tsujino H., S. Urakawa, H. Nakano, R.J. Small, W.M. Kim, S.G. Yeager, G. Danabasoglu, T. Suzuki, J.L. Bamber, M. Bentsen, C. Böning, A. Bozec, E.P. Chassignet, E. Curchitser, F. Boeira Dias, P.J. Durack, S.M. Griffies, Y. Harada, M. Ilicak, S.A. Josey, C. Kobayashi, S. Kobayashi, Y. Komuro, W.G. Large, J. Le Sommer, S.J. Marsland, S. Masina, M. Scheinert, H. Tomita, M. Valdivieso, and D. Yamazaki, 2018. JRA-55 based surface dataset for driving ocean-sea-ice models (JRA55-do).<em> Ocean Modelling</em>, <strong>130</strong>, 79-139, doi: <a href="https://doi.org/10.1016/j.ocemod.2018.07.002">10.1016/j.ocemod.2018.07.002</a>.</p>
First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS
<p>This dataset is relative to the paper entitled: "First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS" publishing in journal Diversity (MPDI).</p> <p>Abstract:</p> <p>Zooplankton is a fundamental group in all aquatic ecosystems located the base of the food chain. It forms a link between the lower trophic levels with secondary consumers and shows marked fluctuations of populations with environmental change, especially reacting to heating and water acidification. At sea copepod crustaceans account for app. 70% in abundance of zooplankton and are a target of monitoring activities in key areas such as the Southern Ocean. In this study we have used FAIR-inspired legacy data (dating back to the ‘80s) collected in the Ross Sea by the Italian National Antarctic Program in GBIF.org. Together with other open-access GIS data sources and tools it allows generating, for the first time, three-dimensional predictive distribution maps for twenty-six copepod species. These predictive maps were obtained by applying machine learning techniques to grey literature data, which were visualized in open-source GIS platforms. In a Species Distribution Modeling (SDM) framework we used machine learning with three types of algorithms (TreeNet, RandomForest and Ensemble) to analyze the presence and absence of copepods at different areas and depth classes in function of environmental descriptors obtained from the Polar Macroscope Layers present in Quantartica. The models allow for the first time to map-predict the food chain in quantitative terms showing the relative index of occurrence (RIO) and identified the presence for each copepod species analyzed in the Ross Sea. Our results show marked geographical preferences that vary with species and trophic strategy. This study demonstrates that machine learning is a successful method in accurately predicting Antarctic copepod presence, also providing useful data to orient future sampling and management of wildlife and conservation.</p>
Modeled temperature and Marine heatwaves intensity in the coastal Northern Humboldt Current System
<p>This dataset includes the tridimensional modeled temperature and associated research data that support the results of the article "<strong><em>Comprehensive characterization of Marine Heatwaves in a coastal Northern Humboldt Current System regional model over recent decades</em></strong>".</p> <p>Specifically, it consists of three files (NetCDF format):<br> i) Northern_MHWs.nc, this file contains the daily modeled temperature (from 2000 to 2019) within the northern domain of analysis (3-8°S) within the 250 km nearshore band for each vertical layer ranging from 0 to 250m depth. In addition, daily snapshots of MHW intensity are also included by depth.<br> ii) Central_MHWs.mat, similar to the previous file, but for the central domain of analysis, from 8 to 13°S.<br> iii) Southern_MHWs.mat, similar to the previous file, but for the southern domain of analysis, from 13 to 18°S.</p>
Data from: The interaction of ice and law in Arctic marine accessibility
<p>Sea ice levies an impost on maritime navigability in the Arctic. But ice cover diminution due to anthropogenic climate change is generating expectations for improved accessibility in coming decades. Projections of sea ice cover retreating preferentially from the eastern Arctic suggest key provisions of international law of the sea will require revision. Specifically, protections against marine pollution in ice covered seas enshrined in Article 234 of the United Nations Convention on the Law of the Sea have been used in recent decades to extend jurisdictional competence over the Northern Sea Route only loosely associated with environmental outcomes. Projections show that plausible open water routes through international waters may be accessible by mid-century under all but the most aggressive of emissions control scenarios. While inter- and intra-annual variability places the economic viability of these routes in question for some time, the inevitability of a seasonally ice-free Arctic will be attended by a reduction of regulatory friction and a recalibration of associated legal frameworks.</p>
Self-similarity, density-size dynamics and the sinking speed of marine aggregates.
<p>A collated and referenced data base of observed size and sinking speeds of marine particle aggregates including Reynolds number and estimated excess density.</p>
Data used in the article: "Climate change impacts the vertical structure of marine ecosystem thermal ranges"
<p>This dataset is used in the manuscript "Climate change impacts the vertical structure of marine ecosystem thermal ranges" accepted in Nature Climate Change 2022.</p>
Hourly LC impacts - Marine Eutrophication - current mix and future scenarios, average demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Marine Eutrophication, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
Electrical conductivity of the world ocean and marine sediments
<p>Copy of dataset (as was on 2022-01-12) of electrical conductivity and conductance grids for the ocean and marine sediments at 0.1 degree lateral resolution, from https://github.com/agrayver/seasigma. These models are presented in the work</p> <p>Grayver, A. V. (2021). Global 3-D electrical conductivity model of the world ocean and marine sediments. Geochemistry, Geophysics, Geosystems, 22, e2021GC009950. <a href="https://doi.org/10.1029/2021GC009950">doi: 10.1029/2021GC009950</a></p> <p>Please cite this publication if you use the provided models in your work.</p>
Data for: Presence-absence of marine macrozoobenthos does not generally predict abundance and biomass
<p>This repository contains data for the paper: Bijleveld, A. I. et al. (2018) Presence-absence of marine macrozoobenthos does not generally predict abundance and biomass. Scientific Reports 8, 3039, doi:10.1038/s41598-018-21285-1.</p>
Biogeographic data for "The marine biodiversity impact of the Late Miocene Mediterranean salinity crisis"
<p>Lists of species that were present in the Mediterranean Sea both in the pre-evaporitic Messinian and the Zanclean (based on https://doi.org/<a href="../doi/10.5281/zenodo.10782428">10.5281/zenodo.10782428</a>), biogeographic information on their presence outside the Mediterranean, and accordingly their status as either "possible endemic" to the Mediterranean or "non-endemic" if they were also found outside the basin.</p> <p>In this version, we added also the list of species present in the Mediterranean Sea in the pre-evaporitic Messinian that can be considered possible endemics, based on the same rule, and the indication if they survived the MSC.</p>
Supplemental material for the manuscript "Extreme genome scrambling in marine planktonic Oikopleura dioica cryptic species".
<p><strong>Supplementary material for the manuscript “Extreme genome scrambling in marine planktonic <em>Oikopleura dioica</em> cryptic species”.<br></strong></p> <p><strong><em>BreakpointsData.tar.xz contains:</em></strong></p> <ul> <li>Pairwise genome alignment files for <em>Oikopleura</em>, <em>Ciona</em>, <em>Caenorhabditis</em>, insects and muntjaks in GFF format in `inst/extdata/`.</li> <li>dN / dS computation results in `inst/extdata/dNdS/`.</li> <li>Annotations of gene models and repeat elements in GFF format in `inst/extdata/Annotations/`.</li> <li>OrthoGroups in `inst/extdata/OrthoFinder/`, where N19 represents the _O. dioica_ clade,</li> <li>N3 the tunicates and N20 the _Ciona_ clade.</li> <li>`BreakpointsData_3.11.0.tar.gz`, a R package installing the above files in the R environments where we ran our computations.</li> <li>The files needed to build the `BreakpointsData` package.</li> </ul> <p><em><strong>Oidioi_pairwise_v3.tar.gz contains:</strong></em></p> <ul> <li>The pairwise alignment files between genomes, in MAF format.</li> <li>A copy of the Nextflow pipeline used to generate them.</li> </ul> <p><em><strong>oist-assembler.tar.gz contains:</strong></em></p> <ul> <li>A Singularity image and its definition file for flye version 2.8.3-b1763` Flye-flye.2.8.3-b1763.sif` and `Flye-flye.def`.</li> <li>A copy of the Nextflow pipeline used to assemble the Bar2_p4 genome in `oist-assembler-Bar2_p4`.</li> <li>A copy of the Nextflow pipeline used to assemble the other genome in `oist-assembler-other_genomes`.</li> </ul> <p><em>Please note that these files are provided for reproducibility only and probably can not be used easily for other purposes.</em></p> <p><em><strong>Oidioi_genomes.tar.gz contains:</strong></em></p> <ul> <li>For each genome, one file (`<genome>.fa`) containing the whole genome sequence and one directory (`<genome>`) containing each chromosome, scaffold or contig of the genome as a separate file.</li> <li>For each genome, one R package, its source directory, and the vignette to create it, providing the genome information as a `BSgenome` object.</li> </ul> <p><em><strong>OrthoFinderRun.tar.xz contains:</strong></em></p> <ul> <li>A full copy of the OrthoFinder2 run that we used to compute hierarchical orthogroups.</li> </ul> <p><em><strong>Supplemental_Code.tar.gz contains:</strong></em></p> <ul> <li>A copy of <https://github.com/oist/LuscombeU_OikScrambling>, where the `.git` and `doc` directories were removed to save space.</li> </ul> <p><em><strong>AugustusAnnotation.tar.gz (added July 26th 2024) contains:</strong></em></p> <ul> <li>AUGUSTUS runs to produce the annotations that were input to OrthoFinder2. We provide them for reproducibility, with no guarantee that they are suitable for other purposes. The annotations used in the manuscript are AOM-5-5f.sm.OSKA-CDS, Bar2_p4_Flye.sm, Bsty_SCLE01.1.sm.abi.cionamodel, Fbor_SDII01.1.sm.abi, KUM-M3-7f.sm.OKI-CDS, Mery_SCLF01.1.sm.abi.cionamodel, Oalb_SCLG01.1.sm.abi.cionamodel, OKI2018_I69_annotv2.sm, Olon_SCLD01.1.sm.abi, OSKA2016v1.9.sm and Ovan_SCLH01.1.sm.abi.cionamodel.</li> </ul>
Seasonal Carbonate Chemistry Variability in Marine Surface Waters of the Pacific Northwest. Data Archive.
<p>This archive includes two .nc files (NetCDF format) containing observational data (discrete and mooring) from marine surface waters of the Pacific Northwest that have not yet been submitted to a long-term data repository. These data contributed to the development of seasonal cycle data products described in the manuscript by Fassbender et al. A metadata file is provided for the discrete data subset (upper 10 m of discrete observational data); however, the complete cruise datasets and metadata will be submitted for archival in the National Centers for Environmental Information’s (NCEI) Ocean Carbon and Acidification Data repository (<a href="https://www.nodc.noaa.gov/oceanacidification/">https://www.nodc.noaa.gov/oceanacidification/</a>). Data subsets are provided here for accelerated public access. Data users are encouraged to download the complete datasets from NCEI once they are available (<a href="https://www.nodc.noaa.gov/oceanacidification/stewardship/data_portal.html">https://www.nodc.noaa.gov/oceanacidification/stewardship/data_portal.html</a>). Metadata for the University of Washington Oceanic Remote Chemical/Optical Analyzer (ORCA) mooring observations used by Fassbender et al., including the temperature and salinity data from the Dabob Bay and Twanoh moorings, are not provided here. Quality control protocols applied to the ORCA mooring data are outlined in the Quality Assurance Project Plan (<a href="http://nwem.ocean.washington.edu/ORCA_QAPP.pdf">http://nwem.ocean.washington.edu/ORCA_QAPP.pdf</a>; Newton and Devol, 2012).</p>
Dataset of E. huxleyi blooms: spatio-temporal distribution and their impact on high-latitudinal marine environments (1998-2016)
<p>Dataset of coccolithophore blooms in polar seas of the Northern Hemisphere, viz. the North, Labrador (with adjacent North Atlantic open waters), Norwegian, Barents, Greenland and Bering seas are presented for the period 1998-2016. Seas are divided into 4 regions, for each of them continuous data series (as 8-days composites) are published, including information about bloom spatial masks, coccolith concentration, particulate inorganic carbon content and CO<sub>2</sub> partial pressure in water increment driven by coccolithophores.</p> <p>Datasets are published as NetCDF files with full metadata/descriptions and with GDAL support.</p> <p>Additional information (regions configuration, data access instructions) is provided alongside the data.</p> <p>Naming convention is: <strong>niersc_cocco_<version of dataset>_<region>_<start date>_<end date>.nc</strong></p>
SMLBase: Global compilation of surface mixed layer parameters (sedimentation rate, bioturbation depth, mixing intensity) from marine environments
<p>A global compilation of sediment surface mixed layer parameters from marine environments, compiled from published literature. The database contains parameters of advective (sedimentation rate) and diffusive (biodiffusion, bioturbation depth) particle movement estimated from tracer experiments, combined into box models.<br>Database associated with the data report published under <a href="https://doi.org/10.3389/feart.2022.1013174">https://doi.org/10.3389/feart.2022.1013174</a></p>
Identifying Harmful Marine Dinoflagellates: Harmful Dinoflags
Faust, Maria A. and Rose A. Gulledge. Identifying Harmful Marine Dinoflagellates. Smithsonian Contributions from the United States National Herbarium, volume 42: 1-144 (including 48 plates, 1 figure and 1 table).<p></p>Faust, Maria A. and Rose A. Gulledge. Identifying Harmful Marine Dinoflagellates. Smithsonian Contributions from the United States National Herbarium, volume 42: 1-144 (including 48 plates, 1 figure and 1 table).
Revised marine fossil record of the Mediterranean before and after the Messinian Salinity Crisis
<p>This is a unified and revised marine fossil record of the Mediterranean covering the Tortonian stage, the pre-evaporitic Messinian and the Zanclean stage and encompassing 23032 occurrences of calcareous nannoplankton, dinoflagellates, foraminifera, corals, ostracods, bryozoans, echinoids, mollusks, fishes, and marine mammals. It consists of four files in .csv format: 1) 'MessinianDB' contains the fossil occurrences; 2) 'coord' has the list of fossiliferous localities with their coordinates and the groups of organisms reported from each one; 3) 'DBrefs' contains the full citations of the references in the database; 4) 'corals' contains the list of coral genera in the database, indicating whether or not they include zooxanthellate (z-corals) or azooxanthellate (az-corals) species, or both. In the latter case, we further indicate if the species found in the database should be considered z- or az-corals, based on the accompanying fauna. </p>
Marine heatwaves and cold spells events based on ESA-CCI SSTs (experimental product)
<p>This repository contains an extension of the catalogues of marine heatwaves (MHWs) and cold spells (MCSs) prepared by the National Research Council - Institute of Marine Sciences (CNR-ISMAR, Italy) within the ESA-funded CAREHeat project. The catalogues are based on the ESA-CCI sea surface temperature (SST) dataset (available from https://doi.org/10.24381/cds.cf608234) for the period 1982-2022, on a regular 1°x1° longitude-latitude grid.</p> <p>Events are identified for each pixel following the methodology of Hobday et al. (2016) after preprocessing. Event categories are provided as daily maps and metrics are given by event. Results are <strong>experimental</strong> since the post-processing procedure effectively removes interannual variability from the SST record, so please use having consulted the documentation and not for operational purposes. </p> <p><br>Please cite the reference paper "Serva, F., et al.: Detection of Satellite Sea Surface Temperature Extremes: Low Frequency Variability and Climate Change, JGR:Oceans, 10.1029/2025JC022886, 2025" when using the dataset in your work.</p>
Geographic Information System for marine aquaculture in Argentina
<p>Planning the use of marine areas for aquaculture through the development of Geographic Information Systems (GIS) has taken on great importance recently . This is because GIS allows decision-making through the analysis and integration of a large amount of data of various kinds gathered in a single database. This system allows the incorporation of information on optimal environmental conditions for farm species and relevant data to develop strategies throughout the entire production chain, from service providers and inputs to the final marketing of the product. The recommended actions of the strategic guidelines for a more sustainable and competitive EU aquaculture in 2021–2030 (EC 2021) stated explicitly the need to “<em>Develop a more detailed guidance document on the planning for space and access to water for marine, freshwater and land-based aquaculture</em>”, highlighting the importance of the GIS.</p> <p>Here you will find 4 files with the following information:<br>1) <strong><em>Metadata.doc</em></strong> file with the details of the metadata used to diagram the GIS layers.<br>2) <em><strong>GIS.gpkg</strong></em> file with each of the layers in raster and vector format.<br>3) <em><strong>Land-based model.gpkg</strong></em> file with examples of GIS modeling for land-based facilities.<br>4) <strong><em>Open-water model.gpkg</em></strong> file with examples of GIS modeling for facilities in open systems.</p> <p> </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.