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37 results for “Marine Science”
MARCSI - Inventory of Marine Citizen Science Initiatives and the FAIRness of the data they produce
<p>Inventory (data set) of Marine Citizen Science Intiatives collected and described in the publication entitled "Past and present marine citizen science around the globe: a cumulative inventory of initiatives and data produced" co-authored by Uta Wehn, Ane Bilbao, Luke Somerwill, Torsten Linders, Joan Maso, Stephen Parkinson, Christina Semasingha,<sup> </sup>Sasha Woods.</p>
SeaPaCS graphic elaboration of the Protocol for marine micro-plastic collection and monitoring in citizen science and for building a L.A.D.I. trawling tool
<p>This is a graphic elaboration (in Italian) of the protocol "SeaPaCS deliverable - protocol for plastic monitoring in citizen science" in English and Italian is a deliverable of the SeaPaCS project (Participatory Citizen Science Against Marine Pollution), funded by IMPETUS (project ID 101058677). The protocol and the visual elaboration has been freely adapted from "<i>LADI and the Trawl</i>" by Coco Coyle with Melissa Novaceski, Emily Wells and Max Liboiron, as published by the Civic Laboratory for Environmental Action Research, August 2016. The graphic elaboration (as the protocol) in both languages, consists of three parts: 1) how to build a DIY low cost manta trawl device (LADI - Low-Tech Aquatic Detection Debris Instrument) to monitor plastic pollution, adjusted to materials availability and costs in Italy; 2) how to monitor (the sampling itself and towing procedure); and 3) how to categorize plastic debris back on land. </p>
Figure 2 in Equity and career-life balance in marine mammal science?
Figure 2. Survey results for the cumulative amount of time spent away from home due to work-related activities (e.g., fieldwork or conferences) in any year for men (green) and women (yellow).
Figure 1 in Equity and career-life balance in marine mammal science?
Figure 1. Percentage of men (green) and women (yellow) survey respondents employed in common types of marine mammal science careers.
Data for "On the impact of Citizen Science-derived data quality on deep learning based classification in marine images"
<p>This dataset contains all the annotations done by either citizen scientists or experts of the publication: "On the impact of Citizen Science-derived data quality on deep learning based classification in marine images"</p> <p><strong>CSP.csv</strong> -> CS annotations of the Citizen Science Primer-experiment</p> <p><strong>CSPExpert.csv</strong> -> Expert annotations of the Citizen Science Primer-experiment</p> <p><strong>CSS.csv</strong> -> CS annotations of the Citizen Science Study</p> <p><strong>CSSExpert.csv</strong> -> Expert annotations of the Citizen Science Study</p> <p>Visual exploration of the image data is possible in the BIIGLE 2.0 image annotation system at <a href="https://biigle.de/projects/139">https://biigle.de/projects/159</a> using the login <em><a href="mailto:cs@example.com">cs@example.com</a></em> and the password <em>plosonecs</em>.</p>
The four Ws of marine citizen science: What? Who? Why? Where?
<p>The spectrum of participation in marine citizen science (MCS) is wide. More and more enthusiasts and researchers collaborate by collecting data, analyzing information, building monitoring devices, and even designing investigations. The 4ws of MCS is a panoramic look at the diversity of participant profiles, activities that are being carried out, types of projects and ecosystems in which MCS has been contributing to the knowledge of the ocean. </p> <p>The infographic is available in three languages: English, Spanish and Catalan. </p>
Japan Agency for Marine-Earth Science and Technology: jamstec DwCA file
Japan Agency for Marine-Earth Science and Technology (JAMSTEC) has the main objective to contribute to the advancement of academic research in addition to the improvement of marine science and technology by proceeding the fundamental research and development on marine, and the cooperative activities on the academic research related to the Ocean for the benefit of the peace and human welfare. <p></p>http://www.jamstec.go.jp/<p></p>Japan Agency for Marine-Earth Science and Technology (JAMSTEC) has the main objective to contribute to the advancement of academic research in addition to the improvement of marine science and technology by proceeding the fundamental research and development on marine, and the cooperative activities on the academic research related to the Ocean for the benefit of the peace and human welfare. <p></p>http://www.jamstec.go.jp/
SeaPaCS Participatory Citizen Science against Marine Pollution_The Video
<p><a href="">Final video of the European project SeaPaCS – </a>Participatory Citizen Science Against Marine Pollution (coordinated by Prof. Chiara Certomà of the University of Turin – ESOMAS Department – and funded by the European Project IMPETUS). Then video is produced by Raw-News, shot by Giuseppe Lupinacci with the direction of Federico Fornaro and the support of Davide Rinaldi of the "Capo d'Anzio Diving Center". It presents the problem of plastic in the sea, now present at all levels of the food chain, which is increasingly evident to everyone and is profoundly changing ecologies, economies and our relationship with the sea. </p>
Marine litter abundance and composition on Chilean beaches, registered by the Científicos de la Basura citizen science program
<p>Data set generated by means of citizen science, which contains the abundances of the principal litter items registered on Chilean beaches every four years from 2008 to 2020, as well as in 2021. Specifically, the data set contains the numbers of macrolitter items (> 2.5 cm) counted within 3m x 3m sampling stations positioned within the surveyed beaches, grouped according to different material categories (such as papers, plastics, cigarette butts, among others). The data set also contains the sampling dates, the names and coordinates of the surveyed beaches, the zone and region where they are located, the nearest coastal city or town to each beach, the name of the school or the type of volunteers group that conducted each sampling, the total sum of anthropogenic litter items within each sampling station, and the mean abundance per square meter.</p>
Dataset for Paul et al., "Trace metals in coastal marine sediments: Natural and anthropogenic sources, correlation matrices, and proxy potentials", Science of The Total Environment (STOTEN-175789, 2024)
<p>The datafile (.xlsx format) contains the solid phase data for all study sites presented in the associated STOTEN publication <a href="https://doi.org/10.1016/j.scitotenv.2024.175789" target="_blank" rel="noopener">(10.1016/j.scitotenv.2024.175789)</a></p> <ol> <li>specifics on the ICP-MS and ICP-OES analysis</li> <li>solid-phase data (Pb, Cd, Cu, Zn, Sb, Sn, Ni, As, Tl, V, Mo, U, Re, TOC, S, Fe, Mn, and Al)</li> <li>coefficient of variation test results (as a basis for evaluating the applicability of data normalizations to the data; i.e. calculation of Enrichment Factors, cf. method section of the original publication)</li> </ol> <p>Please note that the title of the paper and the publication number has changed upon final publication. The Excel file description still refers to the original manuscript number and title "Trace metals in coastal marine sediments: anthropogenic sources, correlation patterns, and proxy potentials".</p>
Tracking marine tetrapod carcasses using a low-cost mixed methodology with GPS trackers, passive drifters, and citizen science
<div> <ol> <li> <span><span>Drift experiments are essential to understand stranding patterns and to estimate the mortality of beached animals.</span><span> Most studies do not use telemetry technology due to the high costs of this methodology. </span></span><span><span>The objective of this paper is to describe the possibilities of tracking marine tetrapod carcasses with a low-cost and replicable methodology.</span> <span>The study was conducted </span></span><span><span>in the Southern Subtropical Shelf (~ 28º–34º S), </span><span>a highly productive and key ecological </span><span>region of the southwestern Atlantic Ocean (SWA).</span></span><span> </span> </li> <li> <p><span><span>W</span></span><span><span>e designed and tested a low-cost mixed methodology including </span><span>Global Positioning System </span><span>trackers, passive drifters (reused glass bottles), and Citizen Science (through instant message platform and email) for tracking carcasses of marine </span><span>tetrapods</span><span>. We performed four drift experiments, </span><span>during the</span><span> four seasons </span><span>of</span><span> 2019</span><span>.</span> </span><span><span>We released 787 drifters (600 non-biological and 187 carcasses</span><span> of seabirds, </span><span>sea turtles</span><span>, and cetaceans</span><span>)</span></span><span><span> at sea, at five</span><span> equally</span> <span>separated </span><span>distances (5</span><span>–</span><span>25 km) from the coast.</span> <span>Beach surveys and Citizen Science were implemented to </span><span>recover </span><span>the beached drifters.</span></span></p> </li> <li> <p><span><span>We</span><span> re</span><span>covered </span><span>71.83% of non-biological </span><span>and 27.27% of carcasses </span><span>released</span><span>. We tracked the movements of 38 carcasses </span><span>(25 sea turtles and 13 </span><span>cetaceans</span><span>) with 17 GPS devices. The drift</span><span>ing</span><span> time</span><span>,</span><span> until reaching the beach</span><span>,</span><span> ranged from 12h to 17 days</span><span> for carcasses and 12h to 406 days for bottles</span><span>. </span></span><span><span>Citizen Science was the most important source of rec</span><span>overies </span><span>of non-biological drifters, representing 66.67% of the total bottles rec</span><span>ove</span><span>r</span><span>ed</span><span>.</span> <span>For carcasses, active search was the most important recovery source, representing 64.7% of the total carcasses</span><span> recovered</span><span>.</span></span></p> </li> <li> <p><span>Our study contributes with new findings about drift patterns of marine tetrapods in the SWA and describes an accessible low-cost mixed methodology for small and medium-budget projects that can be </span><span>replicated in other coastal regions of the world for tracking a wide range of marine tetrapod species.</span></p> </li> </ol> </div>
Tracking marine tetrapod carcasses using a low-cost mixed methodology with GPS trackers, passive drifters, and citizen science
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Supporting Data for Tanioka, Fichot, and Matsumoto (2020), Frontiers in Marine Science
<p>This dataset contains files from [Tanioka, T., Fichot, C. G., & Matsumoto, K. (2020). Toward Determining the Spatio-Temporal Variability of Upper-Oean Ecosystem Stoichiometry From Satellite Remote Sensing. <em>Frontiers in Marine Science, </em> 7:604893. doi: 10.3389/fmars.2020.604893]</p> <p>1. TFM20_Monthly.nc = Monthly climatology of satellite-derived variables.</p> <p>2. TFM20_Timeseries.nc = Timeseries of satellite-derived phytoplankton C:P, POC:POP, and Cphyto:POC at BATS and HOT.</p>
Code and Data for "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Frontiers in Marine Science.
<p>This repository contains the code and data for the study of "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Frontiers in Marine Science.</p> <p>Specifically, this repository contains the following items: </p> <p>(1) The codes needed for assessing the representation and prediction skills of Random Forest (RF), Convolutional Neural Network (CNN) and Spatial Transformer Networks (STN) models. </p> <p>(2) Original and normalized data to run these codes.</p> <p>(3) Code here is built on early work from our laboratory (Jaderberg et al., 2015; Guan et al., 2022; Zhang et al., 2023), though great modifications have been made tailored to our scientific question.</p> <p>[1] Jaderberg, M., Simonyan, K., Zisserman, A., et al. (2015). Spatial transformer networks. Advances in neural information processing systems, 28.</p> <p>[2] Guan, W., Chen, R., Zhang, H., Yang, Y., & Wei, H. (2022). Seasonal surface eddy mixing in the Kuroshio Extension: Estimation and machine learning prediction. Journal of Geophysical Research: Oceans, 127 (3), e2021JC017967.</p> <div>[3] Zhang, G., Chen, R., Li, X., Li, L., Wei, H., & Guan, W. (2023). Temporal variability of global surface eddy diffusivities: Estimates and machine learning prediction. Journal of Physical Oceanography, 53 (7), 1711–1730.</div>
A national scale BioBlitz using citizen science and eDNA metabarcoding for monitoring coastal marine fish
<p>Marine biodiversity is threatened by human activities. To understand the changes happening in aquatic ecosystems and to inform management, detailed, synoptic monitoring of biodiversity across large spatial extents is needed. Such monitoring is challenging due to the time, cost, and specialized skills that this typically requires. In an unprecedented study, we here combined citizen science with eDNA metabarcoding to map coastal fish biodiversity at a national scale. We engaged 360 citizen scientists to collect filtered sea water samples from 100 sites across Denmark over two seasons (1 pm on September 29<sup>th</sup> 2019 and May 10<sup>th</sup> 2020), and by sampling at nearly the exact same time across all 100 sites, we obtained an overview of fish biodiversity largely unaffected by temporal variation. This would have been logistically impossible for the involved scientists without the help of volunteer citizens. We obtained a high return rate of 94% of the samples, and a total richness of 52 fish species, representing approximately 80% of coastal Danish fish species and approximately 25% of all Danish marine fish species. We retrieved distribution patterns matching known occurrence for both invasive, endangered, and cryptic species, and detected seasonal variation in accordance with known phenology. Dissimilarity of eDNA community compositions increased with distance between sites. Importantly, comparing our eDNA data with National Fish Atlas data (the latter compiled from a century of observations) we found positive correlation between species richness values and a congruent patterns of community compositions. These findings support the use of eDNA-based citizen science to detect patterns in biodiversity, and our approach is readily scalable to other countries, or even regional and global scales. We argue that future large-scale biomonitoring will benefit from using citizen science combined with emerging eDNA technology, and that such an approach will be important for data-driven biodiversity management and conservation.</p>
Supplementary dataset to the publication by Hieronymi et al.: "Ocean color atmospheric correction methods in view of usability for different optical water types", Frontiers in Marine Science (under review, submitted 22 Dec 2022)
<p>The dataset is an annex to the publication (under review, submitted 22 Dec 2022):</p> <p>Martin Hieronymi, Shun Bi, Dagmar Müller, Eike M Schütt, Daniel Behr, Carsten Brockmann, Carole Lebreton, François Steinmetz, Kerstin Stelzer and Quinten Vanhellemont: "Ocean color atmospheric correction methods in view of usability for different optical water types", Frontiers in Marine Science.</p> <p>The data were created to compare the results of different atmospheric correction methods for ocean (water) color imagery. The dataset includes ten modified ESA/EUMETSAT Copernicus Sentinel-3 OLCI satellite scenes from optically diverse sea areas worldwide. The NetCDF files are optimized for visualization in the ESA Sentinel Application Platform (SNAP) and especially the Spectrum View. The data include original OLCI Level-1B top-of-atmosphere radiances recorded by the sensor and the results from five different atmospheric correction methods, i.e., spectral remote-sensing reflectance at 16 OLCI bands. The atmospheric correction methods compared are</p> <ol> <li> <p>IPF (Collection 3, the standard method),</p> </li> <li> <p>C2RCC (v1.7 including IPF gains; Brockmann et al. [2016]),</p> </li> <li> <p>A4O (v0.23 (2022-01-19); a novel method by Hieronymi et al.),</p> </li> <li> <p>POLYMER (v4.14 (2021-12-17); Steinmetz et al. [2011]), and</p> </li> <li> <p>ACOLITE-DSF (v2022-10-25.0; Vanhellemont and Ruddick [2021]).</p> </li> </ol> <p>The original flags supplied in each case are also provided.</p> <table> <tbody> <tr> <td> <p><strong># </strong></p> </td> <td> <p><strong>Sensor-Date-UTC</strong></p> </td> <td> <p><strong>Region </strong></p> </td> <td> <p><strong>Special features </strong></p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>S3A-20160720-092821</p> </td> <td> <p>Barents Sea</p> </td> <td> <p>High latitudes, bloom of coccolithophores</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>S3A-20160720-093421</p> </td> <td> <p>North Sea, Wadden Sea</p> </td> <td> <p>Moderately to extremely scattering waters, tidal areas, in situ data</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>S3A-20170114-130626</p> </td> <td> <p>South Atlantic Ocean, Rio de la Plata estuary</p> </td> <td> <p>Extremely scattering waters, clear oceanic waters, sun glint, South Atlantic Anomaly</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>S3A-20170527-015236</p> </td> <td> <p>Yellow Sea, East China Sea, Yangtze, Lake Taihu</p> </td> <td> <p>Extremely scattering waters, tidal areas, large rivers, absorbing aerosols, sun glint</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>S3A-20170529-092334</p> </td> <td> <p>Mediterranean Sea</p> </td> <td> <p>Large areas with clear waters, sun glint</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>S3A-20170913-080730</p> </td> <td> <p>Black Sea, Aegean Sea</p> </td> <td> <p>Clear and absorbing waters</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p>S3A-20180715-093613</p> </td> <td> <p>North Sea, Baltic Sea</p> </td> <td> <p>Intense bloom of cyanobacteria partly with scum</p> </td> </tr> <tr> <td> <p>8 9</p> </td> <td> <p>S3A-20200601-092517 S3B-20200601-084546</p> </td> <td> <p>North Sea, Baltic Sea</p> </td> <td> <p>Inter-comparison of S3A and S3B with different observation angles, absorbing waters</p> </td> </tr> <tr> <td> <p>10</p> </td> <td> <p>S3B-20200406-093801</p> </td> <td> <p>North Sea, Baltic Sea</p> </td> <td> <p>High OWT diversity</p> </td> </tr> </tbody> </table> <p> </p>
A national scale BioBlitz using citizen science and eDNA metabarcoding for monitoring coastal marine fish
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Crystal Cove Marine Conservation Area Citizen Science Monitoring
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Crystal Cove Marine Conservation Area Citizen Science Monitoring
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Crystal Cove Marine Conservation Area Citizen Science Monitoring
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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.