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7,031 results for “marine”
Dataset related to the Journal Article 'Efficiency Enhancement of Marine Propellers via Reformation of Blade Tip-Rake Distribution'
<p>This Dataset contains results related to the Graphs shown in the publication titled "Efficiency Enhancement of Marine Propellers via Reformation of Blade Tip-Rake Distribution". The results refer to open water performance curves for the benchmark propeller geometries and the models with optimal tip-rake. In the Folder we provide the data for each figure in a specific folder with the number corresponding to the number of the figure in the published version of the paper. </p>
Marine debris_island
<p>No description provided.</p>
Short-term Monitoring of Coral Reef Marine Protected Areas (MPAs) in the Municipality of Liloan, Central Visayas, Philippines
<p>This is a sampling-event dataset of the short-term monitoring of Poblacion and Kadurong Reefs, two of the marine protected areas Municipality of Liloan, Cebu, Philippines. Water quality and ecological assessments were carried out to monitor the status and trends of biological and physical parameters associated with coral reefs using the standard protocols for surveying tropical marine resources. Specifically, the following measurements were conducted: (1) physico-chemical parameters, (2) phytoplankton and zooplankton occurrence and abundance, (3) fish occurrence and density, and (4) percent cover of benthic components of coral reef. The data can serve as the basis for the formulation and implementation of relevant measures for conservation and protection management of the Poblacion and Kadurong Reefs in Liloan, Cebu, Philippines.</p> <p>In this version, occurrence.csv was revised as described below:</p> <ul> <li>taxonID for <em>Abudefduf vaigiensis</em> (Quoy & Gaimard, 1825) and <em>Hemiaulus</em> P.A.C. Heiberg, 1863 were corrected.</li> <li>Author names with corrupted characters/symbols were corrected. </li> </ul>
(Un)expected similarity of the temporary adhesive systems of marine, brackish, and freshwater flatworms
<p>This repository contains</p> <ul> <li>a container with 441 single *.tiff files from a serial-block-face-imaging experiment of a Macrostomum lignano tail plate. The images were aligned with Dragonfly v. 2021.1 (ORS). This dataset was used to reconstruct the 3D model of a Macrostomum lignano adhesive organ. [Macrostomum_lignano_tailplate_SBFI.tar.gz]</li> <li>The raw Illumina PE150 reads from Macrostomum tuba [Mtub_1_R2_HTY2GBGXC_1_107969_TGTTGATCCTATGTTA.fastq.gz, Mtub_1_R1_HTY2GBGXC_1_107969_TGTTGATCCTATGTTA.fastq.gz]</li> <li>The assembled (Trinity v. 2.11.0 ) transcriptome of Macrostomum tuba including annotated fasta headers using Trinotate (v. 3.2.0)</li> </ul>
Monitoring NBS for coastal erosion and marine flooding: the Emilia-Romagna case study
<p>The study was conducted in the context of the OPERANDUM project which is an H2020 project which aims at providing tools and methodologies for the assessment of NBS efficiency around the world. As NBS will be tested an artificial dune built with natural materials. </p> <p>The artificial dune is an engineered structure that will mimic the functioning of natural dunes. Its aims are reducing both natural dune erosion and flooding in adjacent coastal lowlands. It consists of a barrier between the sea and land, in a similar way to a seawall. Unlike the latter, the NBS are ‘dynamic’, i.e. the dune/beach system interacts a great deal and is constantly undergoing small adjustments in response to changes in wind and wave climate or sea level. Its construction involves the placement of sediment from dredged sources on the beach and it will be reinforced with a structure composed of biodegradable material. Different typologies of experimental solutions are foreseen.</p> <p>The Bellocchio Beach at Lido di Spina (Italy) was initially chosen for the study, however the Volano beach was selected as the new study area because of the strong erosion caused by an intense storm event in December 2020 at Bellocchio. The dune was built on the Volano beach and monitoring surveys were carried out on this new site. </p> <p>A morphological monitoring aimed to assess the beach evolution and the performance of the NBS were performed. Monitoring of morphology evolution of shoreline and inland area provide information about impact of the NBS on coastal erosion. Furthermore, the changes in the form of the work give information about the resistance of the NBS to wave attacks. Sedimentological campaigns have been planned in order to provide information regarding the texture of the sediments present in the area detected and possibly highlight changes after the construction of the dune.</p> <p>Three monitoring campaigns were carried out before, immediately after and six months later the construction of the dune (January, May and October 2022). All data were analysed to assess local coastal dynamics and NBS evolution. </p> <p>The monitoring consisted of: </p> <ul> <li> <p>topographic and bathymetric surveys (GNSS and multibeam/singlebeam echosounder) to generate DTMs of the entire area (10 m cell size); </p> </li> <li> <p>aerial photogrammetric surveys by UAV for the production of orthophotos and high resolutions DTMs of the emerged beach (1m cell size) and of the dune area (0.2 m cell size); </p> </li> <li> <p>sediment sampling and grain size analysis. </p> </li> </ul> <p>Surveys show that morphological and sedimentological changes are determined mostly by anthropic actions to the beach and seabed maintenance (artificial winter banks and Sacca di Goro channel). </p> <p>Regarding the dune area no significant changes in morphology were observed due to the limited period between the surveys. Appreciable signals were detected, such as the natural recolonization by pioneer plant species and the slight sand accumulation on the dune foot.</p> <p>This dataset consists of data related to monitoring activities. </p>
MOM6-COBALTv2 model result for ecosystem response to marine heat waves
<p>0.5 × 0.5 resolution, Northeast Pacific ocean (184.8-120.2W, 28.06-64.97N). Monthly results from 1958-2019, all detrended with linear trend over full period removed. </p>
Marine time domain electromagnetic data and true model for 2.5D inversion
<p>Dataset contains the description of complex 3D geoelectric model (with bathymetry, curved surfaces of geoelectric layers, target bodies simulated HC deposits, and background inhomogeneities) and marine time domain electromagnetic data calculated via finite element modeling. Noised data sets have been used for geometric 2.5D inversion.</p>
Supplementary material for "Increased sensitivity of marine invertebrates to metal toxicity in the past two decades linked to Climate Change and Ocean Acidification: revelations from a natural population of sea urchins in the Mediterranean Sea." by "Davide Sartori, Guido Scatena, Cristina Vrinceanu, Andrea Gaion".
<p>Satellite observations of environmental factors and effect concentration 50 for copper to sea urchin, from 2003 to 2022.</p>
Distribution of functionally distinct native and non-indigenous species within marine urban habitats
<p>This data file (.xls) is composed of 5 sheets:</p> <ol> <li>The “Taxon labels”: Taxon code, full name, authority and status/type (Abiotic, Unassigned, Native, Cryptogenic, Non-Indigenous Species)</li> <li>The “Trait labels”: Trait modality and labels and correspondences.</li> <li>The “Taxon-by-Trait matrix”: Fuzzy coded scores for each trait modality and taxon</li> <li>The “Taxon-by-sample matrix”: Abundance data of retained taxa in samples</li> <li>The “Sample labels and description”: Site and experimental factors (Habitat, Age, Experimental Unit, Replicate, nested within site) corresponding to each sample.</li> </ol> <p>Sheets 4 and 5 are extracted from a published dataset, which cannot be shared at this stage of revision without revealing the name of several of the manuscript authors. This is done in respect with the journal guidelines about data storage.</p>
Distribution of functionally distinct native and non-indigenous species within marine urban habitats
<p>This data file (.xls) is composed of 5 sheets:</p> <ol> <li>The “Taxon labels”: Taxon code, full name, authority and status/type (Abiotic, Unassigned, Native, Cryptogenic, Non-Indigenous Species)</li> <li>The “Trait labels”: Trait modality and labels and correspondences.</li> <li>The “Taxon-by-Trait matrix”: Fuzzy coded scores for each trait modality and taxon</li> <li>The “Taxon-by-sample matrix”: Abundance data of retained taxa in samples</li> <li>The “Sample labels and description”: Site and experimental factors (Habitat, Age, Experimental Unit, Replicate, nested within site) corresponding to each sample.</li> </ol> <p>Sheets 4 and 5 are extracted from a published dataset, which cannot be shared at this stage of revision without revealing the name of several of the manuscript authors. This is done in respect with the journal guidelines about data storage.</p>
Marine heatwave datasheet for Northern Indian Ocean
<p>The datasheet gives a detailed information on the marine heatwave intensity from 1981 to 2020 at the three coral reef regions (Andaman and Nicobar, Gulf of Mannar and Lakshadweep archipelago) in the Northern Indian Ocean. This dataset was used to study various regional ecosystem changes from the variability in MHW over the period of time.</p>
Marine macrobenthic assemblages off Bevano River mouth (2019)
<p>This dataset provides the abundance (ind. m<sup>-2</sup>) of marine macrobenthic invertebrate species at 39 random sampling points from 0.5 to 8 m depth along the coast (5 km) off the NATURA 2000 site IT4070009 "Ortazzo, Ortazzino e Foce del Torrente Bevano", sampled from 22 May to 4 July, 2019. Sediment grain size and organic matter are also provided.</p> <p>The dataset is provided in three formats: </p> <p>- Microsoft Excel XLSX file, including 3 sheets (Dataset, Fields and units, Taxonomy) </p> <p>- CSV files (UTF-8), 3 files corresponding to the 3 sheets of the Excel file </p> <p>- ESRI Shapefile (UTF-8, geometry point, EPSG:4326 - WGS 84)</p> <p>The dataset includes 39 records, one for each sampling point, and 111 fields. The first 12 fields are described in Table 4). The following fields concern the abundance of the identified taxa as individuals preserved in alcohol sorted and classified under microscope (ind. m<sup>-2</sup> ± 10). All the dataset fields are described in the file “Fields and units”, while the taxonomic related information for each taxon is provided in the file “Taxonomy”. Information extracted from the World Register of Marine Species (WoRMS; <a href="https://marinespecies.org/">https://marinespecies.org/</a>) is provided here.</p> <p>A total of 99 soft bottom taxa belonging to the Phyla Annelida (29), Arthropoda (28), Cnidaria (1), Echinodermata (2), Mollusca (37), Nemertea (1), and Phoronida (1) were identified. Of these, 51 have been recognized at species level.</p> <p>This dataset comes from the project "Characterization of the mouth area of the Bevano River and identification of strategies for the conservation and enhancement of nursery areas for protected species of commercial interest", carried out by the Interdepartmental Research Center for Environmental Sciences (CIRSA) of the Alma Mater Studiorum University of Bologna. The project was financed by the Emilia-Romagna Region (call FLAG Costa dell'Emilia-Romagna 2018) with funds from the European Union (FEAMP 2014/2020, Action 2.A.a, "Marine and lagoon habitats - Studies and research"), and took place from January to August 2019 (Abbiati et al., 2019 DOI: <a href="http://doi.org/10.5281/zenodo.4016598">10.5281/zenodo.4016598</a>). This dataset has been revised and completed within the project "Ecosystem for Sustainable Transition in Emilia-Romagna" (Code: ECS_00000033 - CUP: B33D21019790006; Mission 04 Education and research - Component 2 From research to business Investment 1.5 - NextGenerationEU) .</p> <p> </p> <p>First 12 fields in the dataset.</p> <table> <tbody> <tr> <td> <p><strong>Field</strong></p> </td> <td> <p><strong>Darwin Core term</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> <td> <p><strong>Precision</strong></p> </td> <td> <p><strong>Note</strong></p> </td> </tr> <tr> <td> <p>locationID</p> </td> <td> <p>locationID</p> </td> <td> <p>NA</p> </td> <td> <p>NA</p> </td> <td> <p>Sampling location identifier (ID) specific to the data set</p> </td> </tr> <tr> <td> <p>samplingDate</p> </td> <td> <p>eventDate</p> </td> <td> <p>YYYY-MM-DD</p> </td> <td> <p>NA</p> </td> <td> <p>Conforms to ISO 8601-1:2019</p> </td> </tr> <tr> <td> <p>samplingTime</p> </td> <td> <p>eventTime</p> </td> <td> <p>HH:MM</p> </td> <td> <p>± 10 min</p> </td> <td> <p>Central European Summer Time CEST (UTC+2) conforms to ISO 8601-1:2019</p> </td> </tr> <tr> <td> <p>decimalLatitude</p> </td> <td> <p>decimalLatitude</p> </td> <td> <p>decimal degrees</p> </td> <td> <p>± 0.00001</p> </td> <td> <p>WGS84 (EPSG: 4326) - WAAS/EGNOS enabled GPS position</p> </td> </tr> <tr> <td> <p>decimalLongitude</p> </td> <td> <p>decimalLongitude</p> </td> <td> <p>decimal degrees</p> </td> <td> <p>± 0.00001</p> </td> <td> <p>WGS84 (EPSG: 4326) - WAAS/EGNOS enabled GPS position</p> </td> </tr> <tr> <td> <p>Depth</p> </td> <td> <p>maximumDepthInMeters</p> </td> <td> <p>m</p> </td> <td> <p>± 0.1</p> </td> <td> <p>Mean Lower Low Water - measured with echosounder or depth gauge corrected by tide gauge of Porto Corsini (RA)</p> </td> </tr> <tr> <td> <p>SamplingGear</p> </td> <td> <p>NA</p> </td> <td> <p>NA</p> </td> <td> <p>NA</p> </td> <td> <p>Van Veen grab operated from boat or bailer manually operated by diver inside a cylindrical frame</p> </td> </tr> <tr> <td> <p>SamplingArea</p> </td> <td> <p>NA</p> </td> <td> <p>m^2</p> </td> <td> <p>± 0.001</p> </td> <td> <p>Sampler size</p> </td> </tr> <tr> <td> <p>Mud</p> </td> <td> <p>NA</p> </td> <td> <p>% dry mass</p> </td> <td> <p>± 0.1%</p> </td> <td> <p><63 µ wet sieved recovered on Whatman filter paper and then dried at 80°C for 24 hours before weighing at ± 0.00001 g</p> </td> </tr> <tr> <td> <p>FineSand</p> </td> <td> <p>NA</p> </td> <td> <p>% dry mass</p> </td> <td> <p>± 0.1%</p> </td> <td> <p>250-63 µ wet sieved recovered on Whatman filter paper and then dried at 80°C for 24 hours before weighing at ± 0.00001 g</p> </td> </tr> <tr> <td> <p>MediumSand</p> </td> <td> <p>NA</p> </td> <td> <p>% dry mass</p> </td> <td> <p>± 0.1%</p> </td> <td> <p>>250 µ wet sieved recovered on Whatman filter paper and then dried at 80°C for 24 hours before weighing at ± 0.00001 g</p> </td> </tr> <tr> <td> <p>OrganicMatter</p> </td> <td> <p>NA</p> </td> <td> <p>% dry mass</p> </td> <td> <p>± 0.1%</p> </td> <td> <p>Loss on Ignition (LOI%) at 450°C 8h and weighted at ± 0.00001 g</p> </td> </tr> <tr> <td> <p>Actiniaria</p> </td> <td> <p>NA</p> </td> <td> <p>ind. m^-2</p> </td> <td> <p>± 10</p> </td> <td> <p>Individuals preserved in alcohol sorted and classified under microscope</p> </td> </tr> </tbody> </table>
Diminishing returns on labor in the global marine food system: Dataset S1 and code for analysis
<p>Dataset on the number of marine fishers 1950-2015 accompanying the manuscript "Diminishing returns on labor in the global marine food system" by K. J. N. Scherrer, Y. Rousseau, L. C. L. Teh, U. R. Sumaila and E. D. Galbraith. Includes 1) script for data analysis, 2) processed fisheries labor data set, 3) separate data file with average socioeconomic indicators by country needed for analysis, 4) data documentation. </p>
Data supporting Comparison of feeding niches between Arctic and northward moving sub-Arctic marine mammals in Greenland
<p>Data supporting the paper:</p> <blockquote> <p>Land-Miller, H., A. Roos, M. Simon, R. Dietz, C. Sonne, S. Pedro, A. Rosing-Asvid, F. Rigét, and M. McKinney. 2023. Comparison of feeding niches between Arctic and northward moving sub-Arctic marine mammals in Greenland. Marine Ecology Progress Series.</p> </blockquote> <p>This data is in five files:</p> <p>1. <strong>greenland_marmam_metadata.csv</strong> contains metadata for all samples used in this project, including sample identifiers:</p> <ul> <li><em>Sample: </em>unique sample ID per individual animal</li> <li><em>Species</em></li> </ul> <p>and details of collection, including <em>Year, Location </em>(general area), <em>Lat, </em><em>Long, </em>and<em> </em><em>Date. </em>It also includes other data on the animal (<em>Sex, Age, Length</em>), when available, as well as the co-author who provided the sample to the project (<em>Sample sender</em>) and the tissues available/analyzed for each individual (<em>Tissues received</em>).</p> <p>2. <strong>all_sample_locations.csv</strong> includes latitude/longitude of each sample for mapping. Latitude and longitude are consistent with the full metadata file when coordinates were available, and estimated based on general sampling area (<em>Location </em>or <em>Area</em>) when not. The variable <em>estimate</em><strong> </strong>denotes samples for which coordinates were estimated.</p> <p>3. <strong>fatty_acids_greenland_marmams.csv </strong>contains fatty acid data for all samples. Variables <em>8:00</em> to <em>24:1n9</em> represent the proportion of each individual fatty acid, out of total fatty acids in that sample. Data are represented as whole number percents (i.e., 10 = 10% and all fatty acids sum to 100 for each sample). </p> <p>4. <strong>CNS_greenland_McGill.csv</strong> contains bulk stable isotope data for all samples analyzed at McGill. In addition to <em>Sample</em> and <em>Species</em>, this includes:</p> <ul> <li><em>treatment</em>: whether a sample was lipid-extracted (<em>LE</em>) or non-lipid-extracted (<em>nLE</em>) prior to analysis</li> <li><em>d15N</em>: stable isotope ratio δ<sup>15</sup>N</li> <li><em>d13C: </em>stable isotope ratio δ<sup>13</sup>C</li> <li><em>d34S: </em>stable isotope ratio δ<sup>34</sup>S</li> <li><em>perc.C: </em>mass percent of carbon in the sample</li> <li><em>perc.N: </em>mass percent of nitrogen in the sample</li> <li><em>perc.S: </em>mass percent of sulfur in the sample</li> <li><em>C.N.ratio: </em>mass ratio of carbon to nitrogen in the sample </li> </ul> <p>5. <strong>CN_greenland_nLE_copenhagen.csv</strong> contains stable isotope data for non-lipid-extracted samples analyzed at the University of Copenhagen for δ<sup>13</sup>C and δ<sup>15</sup>N. Variables <em>treatment</em>, <em>d13C</em>, and <em>d15N</em> are consistent with CNS_greenland_McGill.csv. </p>
Marine Stewardship Council (MSC) Fisheries Standard v2.0 assessment scores
<p>MSC fishery assessment scores manually collated by Marine Stewardship Council (MSC). See about file for complete details.</p> <p>This data has been manually collated by MSC from reports prepared by third party assessors. This dataset shows, for each Unit of Assessment (UoA), scoring data from the Public Certification Report of the UoA's most recent initial assessment or re-assessment conducted against the Default Tree v2.0 (including Default Tree v2.01) of the MSC Fisheries Standard as of 26 February 2021. A Unit of Assessment is a unique combination of target stock, fishing method or gear, geographic area, and operators or vessels being assessed which may eventually carry the MSC certification status. This excludes UoA which failed their assessment because failing scores are not consistently reported (they are often simply considered a ‘fail’).</p> <p>The following constraints and limitations apply to this dataset:<br> 1. This data has been manually collated by MSC from fishery reports prepared by third party assessors. MSC carries out data assurance to a standard that is fit for the purpose the information is used for, including being complete, accurate and as up to date as possible. If accuracy is paramount to a finite resolution, receivers are asked to validate data against assessment reports which can all be found published on fisheries.msc.org. The MSC is not responsible for any issues arising to any parties as a result of any information provided therein.<br> 2. This dataset shows, for each UoA, scoring data from the certification report of the UoA's most recent initial assessment or re-assessment conducted against v2.01 of the Standard as of 26 February 2021. This excludes UoA which failed their assessment.<br> 3. UoA details (uoa_details) can be joined to scoring data (scores) by the Event_unitbk. This will join the details of the scored UoA with the score that was awarded to the UoA.</p> <p>This dataset consists of two tables:</p> <p>1. uoa_details<br> This table contains details describing UoA included in the dataset. UoA descriptions include the name of fishery as published on Track a Fishery (fisheries.msc.org), species, gear, and other description of the UoA that was assessed.<br> <br> 2. scores<br> Scoring results to the lowest level (scoring guidepost of the scoring issue) for each UoA, as presented in the scoring tables of the report. One row represents a single guidepost result of a single scoring issue. However, the performance indicator (PI) ‘Score’ will be duplicated for all scoring issues in the same PI. For example, if a UoA received a score of 80 for a performance indicator, and in this performance indicator there are 2 scoring issues each scored at 3 guideposts, there will be 6 total rows showing the results for each guidepost of each scoring issue, but all rows will show 80 for the overall 'Score' of the PI. One row also exists for each Principle score of each UoA, in which the principle will be noted in the 'PI' column, and the Principle score in 'Score'. Data has been copied from the scoring tables in the Public Certification Report.</p>
Data presented in figure 2 of "Evidence of nitrate based nighttime atmospheric nucleation driven by marine microorganisms in the South Pacific"
<p>Data collected at the Maïdo observatory between April 24th and April 29th 2018 used in the calculation of statistics presented in Figure 2 of "Evidence of nitrate based nighttime atmospheric nucleation driven by marine microorganisms in the South Pacific". Data were obtained by an API-ToF-MS; molecular clusters are grouped by family as described in Chamba et al. 2023. Data were first filtered based on SO2 mixing ratios to exclude the periods when the station was under the influence of the volcanic plume of the Piton de la Fournaise. Hourly averages of the signals of interest were then calculated and only the data corresponding to the periods during which the station was in the free troposphere were considered.</p>
A hurricane alters the relationship between mangrove cover and marine subsidies in Texas, USA: 2014-2019
We experimentally manipulated black mangrove (Avicennia germinans) cover in ten large plots and over five years (2014-2019) quantified the effects of mangrove cover on subsidies of floating organic material (wrack) into coastal wetlands. We hypothesized that the change from salt marsh to mangrove vegetation would alter the permeability of the intertidal habitat, and thus alter the nature of subsidies from marine to intertidal habitats. Data from field surveys of wrack distribution showed that as mangrove cover increased from zero to 100%, wrack cover and thickness decreased by ~60%, the distance that wrack penetrated into the plots decreased by ~70%, and the percentage of the wrack trapped in the first six m of the plot tripled. Data from wrack samples indicated that wrack samples collected from the fringe were ~3 times heavier than those from the interior of plots. Animals were ~40% more abundant in samples from the interior than from the fringe of plots, but this trend was not statistically significant due to low replication of interior samples. Data from a wrack experiment revealed that animal abundance and species composition varied between the fringe and interior of the plots, and between microhabitats dominated by salt marsh versus mangrove vegetation. Increasing mangrove cover decreased the relative importance of marine subsidies into the intertidal at the plot level, but concentrated subsidies at the front edge of the mangrove stand. Storms, however, may temporarily override mangrove attenuation of wrack inputs.
Santa Barbara Channel Marine BON: Gray Whales Count
This dataset documents the passage of gray whales (Eschrichtius robustus) migrating northbound since 2007, through the nearshore areas of the Santa Barbara Channel, along a corridor extending approximately 3 nautical miles (nm) from the mainland shore. The data is collected by a non-profit organization Gray Whales Count (http://www.graywhalescount.org/GWC/The_Count/The_Count.htmt), which is a research and education project. The survey is conducted at the Coal Oil Point Reserve in Goleta, California, USA. The coastline runs east-west, with northbound whales traveling west, left to right across our Point towards Point Conception. Every year, we survey 98 consecutive days from early February through mid-May. Conditions permitting, each survey-day begins at 9 AM and ends usually at 5 PM.
Leaf litter, soil, and periphyton gene expression along freshwater to marine gradients in Everglades National Park (FCE LTER), Florida, USA, January 2021 and April 2021
We collected leaf litter, periphyton, and soil along freshwater to marine gradients at SRS-2, SRS-4, SRS-6, TS/PH-2, TS/Ph-3, TS/Ph-7a, and TS/Ph-10. Samples were collected in January and April of 2021 to understand how microbial communities respond to and influence the breakdown of organic matter along freshwater to marine transects. Data collection for this project is complete. For each site and litter pair we collected a subset of 2-3 g wet mass of litter, a grab sample of soil, and a grab sample of periphyton for each site. All subsamples were preserved at -20°C until extraction, which took place up to a year after initial collection. Samples were sent to Novogene (Novogene Co. Ltd., Beijing, China) for the total RNA extraction followed by metatranscriptome sequencing. We selected n = 12 genes/gene families encoding for focal enzymes to investigate which are important to the breakdown of organic matter: Dioxygenases (associated with aerobic respiration), Sulfatases (associated with the release of sulfates from complex molecules), sulfite reductases (associated with sulfite reduction), methyl coenzyme M reductase and formylmethanofuran (associated with methanogenesis), nitrite reductases (associated with nitrite reduction), cellobiosidase, glucosidase, and xylosidase (associated with cellulose breakdown), phenol oxidase (associated with lignin breakdown), acid phosphatase (associated with phosphate acquisition in acidic environments), and alkaline phosphatase (associated with phosphate acquisition in basic environments). For each gene/family of interest, we searched all annotated transcripts for all entries corresponding to that gene/family and combined all values for a total expression. We selected n = 6 monophyletic microbial functional groups, representing sulfate reducers, sulfate oxidizers, methane oxidizers, methanogens, nitrite oxidizers, and ammonia oxidizers associated with sulfate and methane cycling. We filtered all annotated transcripts for all specie
Annual summaries of daily climatological observations from the National Weather Service weather station at the UGA Marine Institute on Sapelo Island, Georgia for 1958 to 2004
Daily summaries of climatological observations from the National Weather Service weather station on Sapelo Island, Georgia, were obtained from the NOAA National Climatic Data Center (http://www.ncdc.noaa.gov/) covering the period 1958 through 2004. Records for incomplete years with more than one month of missing observations were deleted (i.e. 1964, 1969-1971), then missing values of daily minimum, maximum and mean temperature were estimated by cubic spline interpolation to fill in data gaps of five or fewer consecutive days. Annual summary statistics were then calculated for daily minimum, maximum and mean air temperature and total precipitation.
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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.