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88 results for “Marine environments”
STREAM - Sub-THz Radar sensing of the Environment for future Autonomous Marine platforms: Multi-Perspective Sensing - Maritime Environment - Side-looking Perspective
<p>This dataset contains the files corresponding to which results have been included in the journal paper titled 'High-Resolution Multi-Modal Sensing of Distributed Radar Network'. The full description of the conducted trials and data structure is mentioned in the attached PDF document.</p> <p>The trials were conducted at the Gosport Marina, Portsmouth, UK with a sea state of approximately 3 according to the Douglas Scale.</p> <p>The experiments were performed with automotive radars operating in the 79 GHz band to investigate the Doppler and imaging capabilities of these radars. A multi-sensory suite distributed around Valkyrie VI was mounted in front, corner, side and backward-looking orientations.</p> <p>This dataset contains data from the side-looking orientation, where the installation angle of radar is 90 degrees respective to the platform velocity vector.</p> <p><strong>Radar Data:</strong></p> <p>The radar data is stored in the file 'GM2_Out1_240522_160925.h5'. The methodology to process the data in MATLAB is presented in the attached pdf. document.</p> <p><strong>Inertial Measurement Unit:</strong></p> <p>Three xSens 680G IMU were mounted on the roof, front and back of the boat. They have been included in the corresponding zip folders.</p> <p>PC3_Corner_RLG: IMU at the corner of the boat.</p> <p>PC4_Forward_RLG: IMU at the roof of the boat.</p> <p>PC5_Backward_RLG: IMU at the back of the boat.</p> <p>The IMU data is converted to .txt files that can be directly loaded into MATLAB.</p> <p><strong>Timestamped Velocity:</strong></p> <p>The file 'Corner_160925.mat' contains the time-stamped velocity for each radar frame. Here, the integration interval is 128 ms with 512 radar chirps.</p> <p>The file 'CommonFramesCornner_160925.mat' contains the timestamped velocity for the frames that are synchronised with the frames of front-looking radar.</p> <p>(The dataset for the front-looking radar is stored in another repository with DOI: 10.5281/zenodo.14215115)</p> <p><strong>Camera:</strong></p> <p>Each radar also has a camera for ground truth. The time-stamped camera frames for each radar frame are stored in 'CommonFramesCornner_160925.mat'.</p> <p>Processed camera frames and video of the scene are available in: 'GM2_Corner_240522_160925_CameraFrames.zip'.</p> <p> </p> <p>For more information, please contact:</p> <p>Anum Pirkani: a.a.a.pirkani@bham.ac.uk, anum.apirkani@gmail.com</p> <p>Marina Gashinova: m.s.gashinova@bham.ac.uk</p>
STREAM - Sub-THz Radar sensing of the Environment for future Autonomous Marine platforms: Multi-Perspective Sensing - Maritime Environment - Front-looking Perspective
<p>This dataset contains the files corresponding to which results have been included in the journal paper titled 'High-Resolution Multi-Modal Sensing of Distributed Radar Network'. The full description of the conducted trials and data structure is mentioned in the attached PDF document.</p> <p>The trials were conducted at the Gosport Marina, Portsmouth, UK with a sea state of approximately 3 according to the Douglas Scale.</p> <p>The experiments were performed with automotive radars operating in the 79 GHz band to investigate the Doppler and imaging capabilities of these radars. A multi-sensory suite distributed around Valkyrie VI was mounted in front, corner, side and backward-looking orientations.</p> <p>This dataset contains data from the front-looking orientation, where the installation angle of radar is 0 degrees respective to the platform velocity vector.</p> <p><strong>Radar Data:</strong></p> <p>The radar data is stored in the file 'GM2_Lab_240522_160943.h5'. The methodology to process the data in MATLAB is presented in the attached pdf. document.</p> <p><strong>Inertial Measurement Unit:</strong></p> <p>Three xSens 680G IMU were mounted on the roof, front and back of the boat. They have been included in the corresponding zip folders.</p> <p>PC3_Corner_RLG: IMU at the corner of the boat.</p> <p>PC4_Forward_RLG: IMU at the roof of the boat.</p> <p>PC5_Backward_RLG: IMU at the back of the boat.</p> <p>The IMU data is converted to .txt files that can be directly loaded into MATLAB.</p> <p><strong>Timestamped Velocity:</strong></p> <p>The file 'Front_160943.mat' contains the time-stamped velocity for each radar frame. Here, the integration interval is 128 ms with 512 radar chirps.</p> <p>The file 'CommonFramesFront_160943.mat' contains the timestamped velocity for the frames that are synchronised with the frames of side-looking radar.</p> <p>(The dataset for the side-looking radar is stored in another repository with DOI: 10.5281/zenodo.14174138)</p> <p><strong>Camera:</strong></p> <p>Each radar also has a camera for ground truth. The time-stamped camera frames for each radar frame are stored in 'CommonFramesFront_160943.mat'.</p> <p>Processed camera frames and video of the scene are available in: 'GM2_Front_240522_160943_CameraFrames.zip'.</p> <p> </p> <p>For more information, please contact:</p> <p>Anum Pirkani: a.a.a.pirkani@bham.ac.uk, anum.apirkani@gmail.com</p> <p>Marina Gashinova: m.s.gashinova@bham.ac.uk</p>
STREAM - Sub-THz Radar sensing of the Environment for future Autonomous Marine platforms: Multi-Perspective Sensing - Automotive Environment
<p>This dataset contains the files corresponding to which results have been included in the journal paper. The full description of the conducted trials and data structure is mentioned in the attached PDF document.</p> <p>The trials were conducted at the University of Birmingham using distributed radar sensors installed on the mobile laboratory. The data will be used to develop algorithms to extract the information needed for high-resolution multi-modal and multi-perspective sensing.</p> <p>The experiments were performed with automotive radars operating in the 79 GHz band to investigate the Doppler and imaging capabilities of these radars.</p> <p>This report describes the measurement scenarios and data structure of INRAS Radarlog (76 GHz – 81 GHz) used for the data collection campaign.</p> <p>Contact: a.a.a.pirkani@bham.ac.uk, anum.apirkani@gmail.com, or m.s.gashinova@bham.ac.uk</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>
The next frontier: Human settlements in the marine environment
<p>Human settlements have impacted most terrestrial environments on Earth. With a rapidly growing global population, surging demands for resources, and unprecedented alteration to Earth's ecosystems, the conservation of our only suitable habitat is a contemporary zeitgeist. Moreover, humanity's nature for enterprise and exploitation continuously pushes the frontiers of exploration, while the governance associated with expansion beyond terra firma is uncertain and rife with conflict. Though the notion of human expansion into the oceans is becoming more orthodox, we have largely overlooked Earth's oceans as space for human habitat. Here, we build on the case for humanity's future over the marine environment and discuss how exploring this frontier will result in advances for industries, economies, and exploration. The "Blue Acceleration" could transform the nascent phenomenon of marine cities into reality, and we support this notions with a global marine spatial plan to assess the suitability of marine settlements. Our analysis shows that every oceanic basin supports regions for sustainable human settlements. More broadly, we discuss the implications of a human-marine frontier and the benefits that will ensue for science, humanity and our planet. Finally, we argue that the human-marine frontier should be an immediate steppingstone for future exploration, innovation and discovery.</p>
Data from: Adaptive genetic variation distinguishes Chilean blue mussels (Mytilus chilensis) from different marine environments
Chilean mussel populations have been thought to be panmictic with limited genetic structure. Genotyping-by-sequencing approaches have enabled investigation of genome-wide variation that may better distinguish populations that have evolved in different environments. We investigated neutral and adaptive genetic variation in Mytilus from six locations in southern Chile with 1,240 SNP obtained with RAD-seq. Differentiation among locations with 891 neutral SNPs was low (FST = 0.005). Higher differentiation was obtained with a panel of 58 putative outlier SNPs (FST = 0.114) indicating the potential for local adaptation. This panel identified clusters of genetically related individuals and demonstrated that much of the differentiation (~92%) could be attributed to the three major regions and environments: extreme conditions in Patagonia, inner bay influenced by aquaculture (Reloncaví́), and outer bay (Chiloé Island). Patagonia samples were most distinct, but additional analysis carried out excluding this collection also revealed adaptive divergence between inner and outer bay samples. The four locations within Reloncaví́ area were most similar with all panels of markers, likely due to similar environments, high gene flow by aquaculture practices and low geographic distance. However, fine scale structure could be detected when analyses included only this zone. Our results and the SNP markers developed will be a powerful tool supporting management and programs of this harvested species.
STREAM - Sub-THz Radar sensing of the Environment for future Autonomous Marine platforms: RLG Dataset Coniston A
<p>This dataset contains the files corresponding to which results have been included in the journal paper. The full descirption of conducted trials and data structure is mentioned in the attached pdf document.</p><p>STREAM trials were conducted by the University of Birmingham (UoB) and the University of St. Andrews from 29/08/2022 - 02/09/2022 at Coniston Lake in the UK. The primary aim was to gather propagation data across lakes and measure the returns from the lake surface. The data will be used to develop algorithms to extract the information needed for pilotage.</p><p>The experiments were performed with radars operating in the 79, 150, and 300 GHz bands to investigate the Doppler and imaging capabilities of these radars.</p><p>This report describes the measurement scenarios and data structure of INRAS Radarlog (76 GHz – 81 GHz) used for data collection campaign.</p><p>Contact: a.a.a.pirkani@bham.ac.uk or m.s.gashinova@bham.ac.uk</p>
Exploring Rapid Changes in the Arctic Marine Environment - First ECOTIP Expedition to the Kongsfjorden on Svalbard
<p>Video of flash talk (5 min) given by Claudia Elena Schmidt during the <strong>YOUMARES 11</strong> conference in <strong>Session 4: </strong>Fjord systems: Ecology, bentho-pelagic coupling, and anthropogenic impacts on 15.10.2020 in Hamburg.</p> <p>The Arctic Ocean and its adjacent seas are especially vulnerable to climate change. Its ecosystem is rapidly changing in response to temperature increase, loss of sea ice, and the combined effects of additional stressors such as invasive species and pollution. However, the scientific community currently lacks sufficient information on the mechanisms, drivers and thresholds of these environmental changes on the Arctic ecosystem and the consequences that may arise for many Arctic communities. The recently launched ECOTIP project aims at closing these knowledge gaps by investigating the impacts of climate change on the Arctic marine environment in order to identify tipping points that can induce an abrupt and sometimes irreversible change in the ecosystem. In a joint sampling campaign between the Helmholtz-Zentrum Hereon and the Alfred Wegener Institute (AWI) water and sediment samples from key marine and terrestrial locations in the Kongsfjorden area on the west coast of the Svalbard archipelago were collected in July 2020. The aim of the ongoing study will be to understand the mechanism of carbon cycling in a polar fjord system that is influenced by profound environmental changes by measuring alkalinity and dissolved inorganic carbon (DIC). Furthermore, the biogeochemical cycle of metals, trace metals and other elements in coastal and shelf waters influenced by melt water streams, draining from land terminating glaciers, will be investigated by multi-element analyses. The collected data will provide scientific insight into biogeochemical processes in high-latitude fjord and coastal regions affected by climate change and thus help to predict future changes in Arctic ecosystems.</p>
Data & R Scripts - Jönander et al. (2022) Single substance and mixture toxicity of dibutyl-phthalate and sodium dodecyl sulphate to marine zooplankton. Ecotoxicol. Environ. Saf.
<p>Data and R scripts associated with:</p> <p>Jönander, C., Backhaus, T., Dahllöf, I. (2022) Single substance and mixture toxicity of dibutyl-phthalate and sodium dodecyl sulphate to marine zooplankton. Ecotoxicol. Environ. Saf.</p>
Multi-scale temporal variation of marine femtoplankton and picoplankton: the role of size and environment
<p>Supplementary Material of the article "Multi-scale temporal variation of marine femtoplankton and picoplankton: the role of size and environment":</p> <p>- Supplementary Table 1: Raw environmental data: sea surface temperature (°C), salinity, dissolved oxygen (ml.l<sup>-1</sup>), pH, phosphate (mM), nitrite (mM), nitrate (mM), ammonia (mM), chl-a, b, c and pheopigments (mg/m<sup>3</sup>). </p> <p>- Supplementary Table 2: Cell abundance (cell.mL<sup>-1</sup>) of <em>Synechococcus</em> spp. (SYN) and picoeukaryotes (PEUK), and abundance of Virus-like particles for each sample and their respective anomalies, after normalization.</p>
Supplementary data for "ENGINEERED ADAPTATION MECHANISMS BETWEEN MARINE AND FRESHWATER ENVIRONMENTS IN FISH AFTER THE FLOOD" for the ICC 2023 in Cedarville, Ohio
<p>Supplementary data for "ENGINEERED ADAPTATION MECHANISMS BETWEEN MARINE AND FRESHWATER ENVIRONMENTS IN FISH AFTER THE FLOOD" for the ICC 2023 in Cedarville, Ohio.</p> <p>These include FishBase annotation, mtDNA sequence similarity matrixes, clustering, and statistics for nine fish orders:</p> <p>1. Acipenseriformes</p> <p>2. Angulliformes</p> <p>3. Beloniformes</p> <p>4. Characiformes</p> <p>5. Clupeiformes</p> <p>6. Cyprinodontiformes</p> <p>7. Elasmobranchii</p> <p>8. Pleuronectiformes</p> <p>9. Salmoniformes</p>
Dataset of chemicals of emerging concern detected in the marine environment in central and northern Patagonia in Chile
<p>This repository encompasses the environmental concentrations of chemicals of emerging concern (CECs), such as pesticides, pharmaceuticals, and industrial chemicals, co-occurring in surface water, porewater, and sediment throughout central and northern Patagonia, Chile.</p>
Fig. 9 Restricted marine subtidal limestones. a-d in Microfacies Analysis And Depositional Environments Of The Tithonian-Valanginian Limestones From Dâmbovicioara Gorges (Cheile Dâmbovicioarei), Getic Carbonate Platform, Romania
Fig. 9 Restricted marine subtidal limestones. a-d Oncoidal-bioclastic packstone with cyanobacteria nodules.
Data from: Phylogenetic relatedness drives protists assembly in marine and terrestrial environments
<p>Aim: Assembly of protists communities is known to be driven mainly by environmental filtering, but the imprint of phylogenetic relatedness is unknown. In this study, we aim to test the degree at which co-occurrences and co-exclusions of protists in different phylogenetic relatedness classes are deviating from random expectation in two ecosystems in order to link them to ecological processes.</p> <p>Location: Global open-oceans and Neotropical rainforest soils</p> <p>Major taxa: Protists</p> <p>Time period: 2009-2013</p> <p>Methods: Protist metabarcoding data originated from two large scale studies. Co-occurrence and co-exclusion networks were constructed using a recent method combining a null distribution model with Spearman's rank correlation coefficients among pairs of OTU. Phylogenetic relatedness was estimated using either global pairwise sequence distance or phylogenetic distance inferred from best maximum-likelihood trees derived from multiple alignments of OTU representative sequences. Significance of observed patterns relating networks and phylogenies were evaluated by distance classes against two null models in which either the tips of the phylogenetic trees or the network edges were randomized.</p> <p>Results: Closely-related protists co-occurred more often than expected by chance in all datasets, but also co-excluded less often than expected by chance in the marine dataset only. Concurrent excess of co-occurrences and co-exclusions were observed at intermediate phylogenetic distances in the marine dataset.</p> <p>Main conclusions: This suggest that environmental filtering and dispersal limitation are the dominant forces driving protists co-occurrences in both environments, while signal of competitive exclusion was only detected in the marine environment. Co-exclusion differences are potentially linked to the individual environments: marine waters are more homogeneous, while the rainforest soils contain a myriad of nutrient rich micro-environment reducing the strength of mutual exclusion.</p>
Fig. 8 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species
Fig. 8 | Total microbial diversity across vertebrate hindguts and within multi- plebodysites of fish. a Hindgutmicrobiotasamplesfrom 569 speciesof vertebrates were rarified to 5000 reads and unique or shared ASVs determined for each class. b The percentage of unique ASVs only found in a given class (not shared in other classes) as compared to the total ASVs within that class. c Rarefaction of cumulative gamma diversity as a function of unique vertebrate species. Included is a single fish species, S. japonicus, sampled over 3 years "black dots" and the unrarefied FMP samples which had detectable bacteria in all four body sites (gill, skin, midgut, and hindgut).d Gammadiversity of 68 fishspeciesacrossfour bodysites.e Percentageof unique ASVs associatedwitha given bodysiteacrossthe 68 fish species.f Rarefaction curve of increasing gamma diversity (inclusive of four body sites) as a function of increasing fish species. ASV amplified sequence variant, 5k 5000.
Fig. 7 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species
Fig. 7 | Microbial source tracking analysis. a Microbial sources of 60 sea water (blue circle) samples taken from 30 unique sampling stations from two timepoints are distributed on a 10 km transect from Torrey Pines beach to Mission Bay. Microbial sources of 108 marine sediment samples (red stars) from San Diego coastalenvironmentincludes 60 paired samples (samelocationsas seawater) from the same 10 km transect along with 58 samples from the various reef habitats near La Jolla. Geographic data presented using ArcGIS. b Sourcetracker2 analysis of likely sources for the four body sites of the fish comparing contributions of beach sand, marine sediment, sea water, and "unknown". Unknown refers to microbes from an unknown source which could include diet and other animals or locations not sampled. c Specific microbial contributions of sea water to the four mucosal body sites and d specific microbial contributions of marine sediment to the four mucosalbodysites b–d: distributionisin medianand interquartilerange.Statistical differences determined using non-parametrictesting Kruskal–Wallistest with 0.05 FDR Benjamini–Hochberg. e Proportion of microbes (distribution is in median and interquartile range) likely originating from the sea water vs. sediment for each unique body site (sea water vs. sediment pairwise comparison for each body site using Mann–Whitney test p <0.05).f Spearmanrho valuesfromcomparisons ofthe ratio of sea water "SW" and marine sediment "SED" against various continuous fish life history metadata variables for each unique body site (Spearman correlation p <0.05). g Comparison of the SW:SED ratios across the habitats from which the fish live. Comparisons performed on each unique body site (Kruskal–Wallis test, p <0.05). *p <0.05, **p <0.01, ***p <0.001, ****p <0.0001, ASV amplified sequence variant ~unique sub-Operational Taxonomic Unit, SD standard deviation, MG midgut, HG hindgut, KW Kruskal–Wallis test statistic "H", IQR inter quartile range, SW sea water.
Fig. 5 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species
Fig. 5 | Biological and life history drivers of mucosal microbiota in diverse sampling of marine fish from Southern California. a Multivariate analysis of biological and life history parameters evaluated using unweighted and weighted normalized UniFrac distances. Statistical significance (PERMANOVA p = 0.001) indicated by yellow blocks (left) and effect size (right). All samples compared together (all) along with individual sample types (gill, skin, midgut, hindgut). b Impact of trophic level on similarity between midgut and hindgut (within a species) (linear model:p p value, mslope,dottedlinesare 95% confidence interval). F-Stat test statistic used in PERMANOVA analysis, all row names in a are metadata column names used in the analysis, MG midgut, HG hindgut, Gen. Weighted UniFrac generalized weighted UniFrac.
Fig. 6 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species
Fig. 6 | Evidence forphylosymbiosis across fishbody sites. Effectof evolutionary distance (low divergence time indicates a short branch length or similar fish species) of all fish compared to a skin unweighted UniFrac distance, b gill generalized weighted UniFrac distance, c hindgut generalized weighted UniFrac distance. Comparisons performed using Mantel test with multiple testing by FDR. Divergence time between fish species calculated using timetree.org. Gen. Weighted UniFrac generalized weighted UniFrac.
Fig. 3 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species
Fig. 3 | Alpha diversity and biomass comparisons across ecological and biolo- gical gradients in marine fish. Comparison of microbial diversity a "Chao1", b "Faith's Phylogenetic Diversity", c "Shannon", or d microbial biomassacrossbody site (gill, skin, midgut, and hindgut). Distributions in "red" are median with inter- quartile range. Statistical differences determined using non-parametric testing Kruskal–Wallistest with 0.05 FDR Benjamini–Hochberg. Further testing computed for each unique body site for a variety of biological and ecological metadata categories. Metadata whichis e categorical istestedusing Kruskal–Wallis f whereas numeric metadata tested using Spearmancorrelation. Onlysignificant associations are represented in e (Kruskal–Wallis p <0.05) and f (Spearman p <0.05). KW or KW stat "H" test statistic from Kruskal–Wallis test, MG midgut, HG hindgut, Faith PD Faith's Phylogenetic Diversity metric, GI:TL gastrointestinal length to fish total length "ratio", TL total length of fish.
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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.