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974 results for “Atlantic Sea”
Coastal landcover change and the associated biomass trends in the mid-Atlantic sea-level rise hotspot
Climate change is driving worldwide landscape reorganization. In the coastal ecosystem, climate-driven sea level rise is forcing landward marsh migration and forest die-off, with potentially large consequences on coastal carbon balance. Here we used 30 m resolution Landsat images to study coastal landcover change from 1984 to 2020, and analyzed the Normalized Difference Vegetation Index (NDVI, a proxy of plant biomass) trend between 1984 and 2020 in the mid-Atlantic sea level rise hotspot. Our study region stretches across the entire Chesapeake Bay and the Delaware Bay to encompass all areas between 0-5m above sea level (total area ~12,500 km2). Specifically, the data package includes 3 raster datasets derived from the Landsat images. All datasets cover the identical mid-Atlantic region and have identical spatial resolution of 30 m. The two landcover datasets, named as 'Landcover_year1984.tif' and'Landcover_year2020.tif', respectively refer to landcover map in 1984 and 2020. Each of the maps has 7 landcover classes differentiated by different integers, and they are: water (0), farmland (1), urban area(2), upland forest (3), transition forest (4), marsh (5) and sandbar (6). Both landcover maps were generated using a combination of random forest classification and manual delineation, and the resultswere validated with high-resolution aerial photos and satellite images with an overall mapping accuracybeyond 90%. The third raster dataset, named as 'NDVItrend_1984to2020.tif', is the NDVI trend map. The value of each 30 by 30 m pixel in the map represents the slope of the NDVI trendline estimated using annual peak-growing season NDVI images acquired between 1984 and 2020. Negative values in the dataset represent decreases of NDVI (i.e. biomass loss, or ecosystem browning) from 1984 and 2020,whereas positive values correspond to an increase of NDVI (i.e. biomass gain, or ecosystem greening)between 1984 and 2020. The data package is completed.
Lateral and vertical forest retreat rate in the mid-Atlantic sea-level rise hotspot
Ghost forests consisting of dead trees adjacent to marshes are striking indicators of climate change. Here we quantify both the lateral and vertical rate of coastal forest retreat between 1984 and 2020 along the US mid-Atlantic coast. The study region includes areas between 0-5 m above sea level across the Chesapeake Bay and the adjacent Delaware Bay. Specifically, the data package includes 2 shapefile datasets derived from four decades of Landsat satellite observations of coastal treeline dynamics. The two datasets are generated on the same spatial-scale and have the same spatial resolution (0.075 km2), both stored as hexagon grids with a side length 170 m. Here we define "forest retreat" (as shown in the datasets as positive values) as the migration of coastal treeline landwards (lateral retreat) or upslope (vertical retreat), whereas "forest advance" (negative values) refers to treeline migration seawards (lateral advance) or downslope (vertical advance). The number '999999' in both datasets indicates areas of stable coastal treelines (i.e. no change) between 1984 and 2020.
Warm Core Ring Trajectories in the Northwest Atlantic Slope Sea (2000-2010)
<p>This dataset consists of weekly trajectory information of Gulf Stream Warm Core Rings from 2000-2010. This work builds upon Silver et al. (2022a) ( <a href="https://doi.org/10.5281/zenodo.6436380">https://doi.org/10.5281/zenodo.6436380</a>) which contained Warm Core Ring trajectory information from 2011 to 2020. Combining the two datasets a total of 21 years of weekly Warm Core Ring trajectories can be obtained. An example of how to use such a dataset can be found in Silver et al. (2022b).</p> <p>The format of the dataset is similar to that of Silver et al. (2022a), and the following description is adapted from their dataset. This dataset is comprised of individual files containing each ring’s weekly center location and its area for 374 WCRs present between January 1, 2000 and December 31, 2010. Each Warm Core Ring is identified by a unique alphanumeric code 'WEyyyymmddA', where 'WE' represents a Warm Eddy (as identified in the analysis charts); 'yyyymmdd' is the year, month and day of formation; and the last character 'A' represents the sequential sighting of the eddies in a particular year. Continuity of a ring which passes from one year to the next is maintained by the same character in the first sighting. For example, the first ring in 2002 having a trailing alphabet of 'F' indicates that five rings were carried over from 2001 which were still observed on January 1, 2002. Each ring has its own netCDF (.nc) filename following its alphanumeric code. Each file contains 4 variables, “Lon”- the ring center’s weekly longitude, “Lat”- the ring center’s weekly latitude, “Area” - the rings weekly size in km<sup>2</sup>, and “Date” in days - representing the days since Jan 01, 0000. </p> <p>The process of creating the WCR tracking dataset follows the same methodology of the previously generated WCR census (Gangopadhyay et al., 2019, 2020). The Jenifer Clark’s Gulf Stream Charts used to create this dataset are 2-3 times a week from 2000-2010. Thus, we used approximately 1560 Charts for the 10 years of analysis. All of these charts were reanalyzed between 75° and 55°W using QGIS 2.18.16 (2016) and geo-referenced on a WGS84 coordinate system (Decker, 1986). </p> <p> </p> <p>Silver, A., Gangopadhyay, A, & Gawarkiewicz, G. (2022a). Warm Core Ring Trajectories in the Northwest Atlantic Slope Sea (2011-2020) (1.0.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.6436380">https://doi.org/10.5281/zenodo.6436380</a></p> <p>Silver, A., Gangopadhyay, A., Gawarkiewicz, G., Andres, M., Flierl, G., & Clark, J. (2022b). Spatial Variability of Movement, Structure, and Formation of Warm Core Rings in the Northwest Atlantic Slope Sea. <em>Journal of Geophysical Research: Oceans</em>, <em>127</em>(8), e2022JC018737. <a href="https://doi.org/10.1029/2022JC018737">https://doi.org/10.1029/2022JC018737</a> </p> <p>Gangopadhyay, A., G. Gawarkiewicz, N. Etige, M. Monim and J. Clark, 2019. An Observed Regime Shift in the Formation of Warm Core Rings from the Gulf Stream, Nature - Scientific Reports, <a href="https://doi.org/10.1038/s41598-019-48661-9.%20www.nature.com/articles/s41598-019-48661-9">https://doi.org/10.1038/s41598-019-48661-9. www.nature.com/articles/s41598-019-48661-9</a>.</p> <p>Gangopadhyay, A., N. Etige, G. Gawarkiewicz, A. M. Silver, M. Monim and J. Clark, 2020. A Census of the Warm Core Rings of the Gulf Stream (1980-2017). Journal of Geophysical Research, Oceans, 125, e2019JC016033. https://doi.org/10.1029/2019JC016033.</p> <p>QGIS Development Team. QGIS Geographic Information System (2016).</p> <p>Decker, B. L. World Geodetic System 1984. World geodetic system 1984 (1986).</p> <p> </p>
Atlantic sea scallop energy budget data on the Northeast U.S. Shelf, monthly in 2010 and 2012
This dataset includes monthly Atlantic sea scallop energy budget data from Georges Bank to the Mid-Atlantic Bight based on Scope For Growth (SFG) model results in 2010 and 2012. Results were supported in part by Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER). For more details please see: Zang, Z., et al. (2022) Modeling Atlantic sea scallop (Placopecten magellanicus) scope for growth on the Northeast U.S. Shelf. Fisheries Oceanography, https://doi.org/10.1111/fog.12577.
ESA-WOC North Atlantic Sea Surface Salinity maps from a multivariate combination of satellite and in situ surface measurements (2010-2018)
<p>We deliver here the daily sea surface salinity level 4 (SSS L4) product developed in the framework of the European Space Agency World Ocean Circulation project (ESA-WOC), covering the period 2010-2018. This product was obtained by adapting to a 1/10° North Atlantic grid the multidimensional optimal interpolation algorithm used within the Copernicus Marine Environment Monitoring Service to retrieve the global SSS multi-year dataset (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: MULTIOBS_GLO_PHY_REP_015_002, dataset_id: dataset-sss-ssd-rep-weekly). This algorithm interpolates SMOS observations and in situ SSS observations considering a space-time-thermal decorrelation function, estimated by including information from high-pass filtered daily SST data (Droghei et al., 2016; Buongiorno Nardelli, 2012). Here, we ingested the L3OS 2Q debiased daily valid ocean salinity values product from SMOS satellite, produced and disseminated by the Centre Aval de Traitement des Données SMOS (CATDS, 2017), OSTIA SST data (CMEMS, <a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id=SST_GLO_SST_L4_REP_OBSERVATIONS_010_011) and CORA5.2 surface salinity values (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, doi: 10.17882/46219TS1, Szekely et al., 2019) as input data, and used CMEMS weekly SSS dataset to build our background field (linearly interpolating it in time between the two closest analysis dates, and upsizing to the 1/10° grid through a cubic spline). All other interpolation parameters were set as in Droghei et al. (2018). </p> <p> </p> <p><em>References:</em></p> <p>Buongiorno Nardelli, B.: A Novel Approach for the High-Resolution Interpolation of In Situ Sea Surface Salinity, J. Atmos. Ocean. Technol., 29(6), 867–879, doi:10.1175/JTECH-D-11-00099.1, 2012.</p> <p>CATDS (2017). CATDS-PDC L3OS 2Q - Debiased daily valid ocean salinity values product from SMOS satellite. CATDS (CNES, IFREMER, LOCEAN, ACRI). http://dx.doi.org/10.12770/12dba510-cd71-4d4f-9fc1-9cc027d128b0</p> <p>Droghei, R., Buongiorno Nardelli, B. and Santoleri, R.: Combining in-situ and satellite observations to retrieve salinity and density at the ocean surface, J. Atmos. Ocean. Technol., 33, 1211–1223, doi:10.1175/JTECH-D-15-0194.1, 2016.</p> <p>Droghei, R., Buongiorno Nardelli, B. and Santoleri, R.: A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993–2016), Front. Mar. Sci., 5(March), 1–13, doi:10.3389/fmars.2018.00084, 2018.</p> <p>Szekely, T., Gourrion, J., Pouliquen, S. and Reverdin, G.: The CORA 5.2 dataset for global in situ temperature and salinity measurements: Data description and validation, Ocean Sci., 15(6), 1601–1614, doi:10.5194/os-15-1601-2019, 2019.</p>
Air-Sea Ammonia Fluxes Calculated from High-Resolution Summertime Observations Across the Atlantic Southern Ocean
<p>This data set includes ocean ammonium concentrations, atmospheric ammonia gas concentrations, and calculated air-sea ammonia fluxes from the Atlantic sector of the Southern Ocean during summer. Associated with the folloiwng paper: </p> <p> https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020GL091963</p>
Satellite monthly surface chlorophyll-a concentration, particulate backscattering, Secchi Disk depth, Mixed Layer Depth, Sea Surface Temperature at 25 km resolution optimally interpolated for the North Atlantic Ocean (1998-2018)
<p>Satellite monthly records of surface chlorophyll-a concentration (CHL), particulate backscattering at 443nm (bbp), Secchi Disk depth (zsd), Mixed Layer Depth (MLD), Sea Surface Temperature (SST) at 25 km resolution optimally interpolated via Multivariate Singular Spectrum Analysis (MSSA) for the North Atlantic Ocean for the period 1998-2018. This dataset has been used for the article "Ultra-oligotrophic waters expansion in the North Atlantic Subtropical Gyre revealed by 21 years of satellite observations" Leonelli et al. 2022, where details of interpolation method are fully explained.</p>
Pop-up satellite archival tagging data of Atlantic bluefin tuna in the Gulf of Lions, Northwestern Mediterranean Sea
<p>24 Atlantic bluefin tuna (Thunnus thynnus) individuals (117–158 cm fork length) were tagged with pop-up archival tags in the Gulf of Lion, NW-Mediterranean Sea between 2015 and 2016.</p> <p><strong>Tag programming and data</strong></p> <p>The tags applied (miniPATs by Wildlife Computers, https://wildlifecomputers.com) can record depth and temperature time series (denoted hereafter as DepthTS and TempTS, respectively) at a temporal resolution of 3–5 s (depending on the predefined deployment duration) and a vertical resolution of 0.5 m. Based on these data, the tag calculates and stores additional data products such as PAT-style Depth–Temperature profiles (PDT), time at depth data, and time at temperature. After pop-up, the tags transmit user-defined data products and subsets from the recorded data sets. All our tags were configured to transmit the following data products: daily light curves, DepthTS, and PDT. In order to maximize data coverage of the transmitted datasets, we decreased the temporal resolution of the DepthTS and PDT data after the first tagging campaign in 2015 from 150 to 600 s and 6 to 24 h, respectively. For both years, deployment durations were set to 150 and 90 d during spring (April–May) and summer (August–September), respectively. A description of the electronic tagging procedure can be found in <a href="https://doi.org/10.1093/icesjms/fsaa083">Bauer et al. (2020)</a>.</p> <p>Seven tags were physically recovered, providing the complete archived time series data at a resolution of 3–5 s. Nineteen tags provided more than 7 d of complete DepthTS data (i.e. without transmission gaps). Three tags from 2016 had deployment durations of <1 week (#15P0983, #15P0985, and #15P0986) because of hardware failure.</p> <p>Provided files contain raw tag data (transmitted and recovered datasets) from the Wildlife Computers Data Portal as well as related GPE3 model runs (geolocation estimates).</p> <p>We thank the crews of the Cyngali and Roussillon Fishing recreational fishing vessels for their cooperation during the tagging cruises. This tagging study was part of the BLUEMED project and funded by the French National Research Agency (ANR; Project-ID ANR-14-ACHN-0002).</p> <p> </p>
Data from article "Is the Atlantic a Source for Decadal Predictability of Sea-Level Rise in Venice?"
<p>Data from the article Zanchettin D., et al.: Is the Atlantic a Source for Decadal Predictability of Sea-Level Rise in Venice?, Earth and Space Science, article number 2022EA002494</p> <p>The dataset contains:</p> <p>- annual time series of October-March average of relative sea level in Venice corrected for vertical land movement (VLMcorrectedRSL) with associated standard error of the mean (LMcorrectedRSL_SEM) for the period 1873-2019;</p> <p>- annual time series of estimate of subsidence in Venice (Subsidence) for the period 1873-2019</p> <p>- modeled state of Venice sea level with associated uncertainty, provided as mean (delta_mean), 1st percentile (delta_1_percentile) and 99th percentile (delta_99_percentile), for the period 1873-2019</p> <p>- modeled local stochastic trend of Venice sea level with associated uncertainty, provided as mean (beta_mean), 1st percentile (beta_1_percentile) and 99th percentile (beta_99_percentile), for the period 1873-2019</p>
Sampling Effort Metadata for the Central and South Atlantic Offshore and Deep-Sea Benthos
<p>Metadata containing information on the sampling of benthic taxa in => 30 m water depth in the Central and South Atlantic. This data was compiled as part of a baseline review of the science, policy and management of the region (Bridges et al. in press). Metadata was compiled from sources identified through a literature search and information provided by members of the Challenger 150 Central and South Atlantic Regional Scientific Research Working Group.</p> <p>Version 1 (November, 2022): Metadata used in the Bridges et al. (in press) gap analysis with the exclusion of sensitive datasets (Atkinson et al. In prep). These are in the process of being made open access and will be added in due course. </p> <p> </p> <p><strong>References</strong></p> <p>Atkinson et al in prep. SeaMap FBIP.</p> <p>Bridges. A.E.H., Howell, K.L., Amaro, T., Atkinson, L., Barnes, D.K.A., Bax, N., Bell, J.B., Bernardino, A.F., Beuck, L., Braga-Henriques, A., Brandt, A., Bravo, M.E., Brix, S., Butt, S., Carranza, A., Doti, B.L., Elegbede, I.O., Esquete, P., Freiwald, A., Gaudron, S.M., Guilhon, M., Hebbeln, D., Horton, T., Kainge, P., Kaiser, S., Lauretta, D., Limongi, P., Mcquaid, K.A., Milligan, R.J., Miloslavich, P., Narayanaswamy, B.E., Orejas, C., Paulus, S., Pearman, T.R.R., Perez, J.A., Ross, R.E., Saeedi, H., Shimabukuro, M., Sink, K., Stevenson, A., Taylor, M., Titschack, J., Vieira, R.P., Vinha, B. & Wienberg, C. Review of the Central and South Atlantic Shelf and Deep-Sea Benthos: Science, Policy and Management. <em>Oceanography and Marine Biology: An Annual Review</em>.</p> <p> </p>
Data and code: Disentangling the impact of Atlantic Niño on sea-air CO2 flux
<p>This repository share (1) post-processed data of Norwegian Earth system model version 2 (LM) and (2) codes for calculating carbon system from NorESM variables (MATLAB format). </p>
Updated gridded reconstruction of sea level pressure, temperature, and precipitation during winter in the North Atlantic region covering 1241-1970 CE
<ul> <li>This dataset is an updated version of the gridded climate reconstruction by Sjolte et al. 2018 (SEA18): Solar and volcanic forcing of North Atlantic climate inferred from a process-based reconstruction, <em>Climate of the Past,</em> 14, 1179–1194, https://doi.org/10.5194/cp-14-1179-2018. </li> </ul> <p> </p> <ul> <li>Relevant results of this new version (SEA18v2) are available in our recent paper: Tao, Q. , Sjolte, J. , & Muscheler, R. (2023). Persistent model biases in the spatial variability of winter North Atlantic atmospheric circulation. Geophysical Research Letters, 50, e2023GL105231. https://doi.org/10.1029/2023GL105231</li> </ul> <p> </p> <ul> <li>This dataset contains the gridded reconstruction of winter sea level pressure (slp), 2m temperature (t2m) and precipitation (precip) for the North Atlantic region over 1241-1970.</li> </ul> <p> </p> <ul> <li><strong>Methodology:</strong> The new reconstruction (SEA18v2), has been optimized for a better representation of the variability of the main modes of sea level pressure. The original reconstruction, SEA18, was an ensemble of 39 model analogues for each year and the reconstruction comprised of the mean of the analogues. For the new version, SEA18v2, a different approach to calculating the ensemble mean of the analogues has been applied. While the overall evaluation and ranking of model analogues are the same as for SEA18, we now apply a weighting function so that poor-fitting model analogues receive less weight and good-fitting analogues receive more weight. Furthermore, we evaluate the main modes of the reconstructed SLP and test the minimum number of ensemble members that can be used and still retain skill for the temporal and spatial variability of the first three modes. Retaining 16 ensemble members gives better performance for the spatial patterns for the first three EOFs of SLP compared to SEA18 and good skill for the temporal variability of the NAO.</li> </ul>
Fig. 5 in Lophogastrida and Mysida (Crustacea) of the "DIVA-1" deep-sea expedition to the Angola Basin (SE-Atlantic)
Fig. 5. Abyssomysis cornuta gen. et sp. nov., holotype, adult male with body length 6.6 mm (A: ZMH 58250), paratypes female 7.9 mm (B–C, E–F, H: ZMH 58256) and male 6.7 mm (D, G: ZMH 58254). A–B. Cephalic region of male (A) and female (B), dorsal; left antennula, right antenna and setae of antennal scale omitted. C. Detail of panel (B) showing lobe from terminal segment of antennula. D. Head in lateral view, setae of antennal scale omitted. E. Carapace expanded on slide. F–G. Eyeplate expanded on slide, for female (F) and male (G). H. Antenna, dorsal.
Fig. 2 in Lophogastrida and Mysida (Crustacea) of the "DIVA-1" deep-sea expedition to the Angola Basin (SE-Atlantic)
Fig. 2. Petalophthalmus cristatus sp. nov., holotype, adult female with body length 28.3 mm (ZMH 58247). A. Right mandible with palpus. B. Detail of A, showing outer lobe ending in a spine. C. Masticatory parts of both mandibles. D. Labium. E. Maxilla. F. Thoracopod 1. G. Endopod of thoracopod 2.
Fig. 3 in Lophogastrida and Mysida (Crustacea) of the "DIVA-1" deep-sea expedition to the Angola Basin (SE-Atlantic)
Fig. 3. Petalophthalmus cristatus sp. nov., holotype, adult female with body length 28.3 mm (ZMH 58247). A. Endopod and exopod of thoracopod 3. B. Thoracopod 4. C. Endopod and exopod of thoracopod 5. D. Detail of C (non-modified setae omitted), showing dactylus and distal portion of propodus. E. Endopod and exopod of thoracopod 6. F. Detail of E, showing dactylus. G. Second order detail of E, showing distal portion of dactylus with nail. H. Endopod and exopod of thoracopod 8. I. Detail of H, showing distal portion of dactylus with nail. J–N. Series of pleopods 1–5, rostral (J–K, M–N) or caudal (L) aspect. O. Tip of pleopod 1. P. Tip of pleopod 3.
Fig. 7 in Lophogastrida and Mysida (Crustacea) of the "DIVA-1" deep-sea expedition to the Angola Basin (SE-Atlantic)
Fig. 7. Abyssomysis cornuta gen. et sp. nov., holotype, adult male with body length 6.6 mm (D: ZMH 58250), paratypes female 7.9 mm (A, C, G: ZMH 58256) and males 6.7 mm (B, F: ZMH 58254), 6.8 mm (E, H–I: SMF 55190). A. Sixth thoracic sympod with oostegite. B. Eighth thoracic sympod with exopod and penis. C–D. Pleon in female (C) and male (D), lateral. E. First male pleopod. F. Fourth male pleopod. G. Fifth female pleopod. H. Uropods, ventral. I. Telson.
Fig. 16 in New Cheilostomata (Bryozoa) from NE Atlantic seamounts, islands, and the continental slope: evidence for deep-sea endemism
Fig. 16. Geographic distribution of the species of Atlantisina gen. nov., Bathycyclopora gen. nov. and Calvetopora gen. nov.; names in white represent sites on or close to the continental shelf, whereas names in black indicate offshore seamount and island sites. Abbreviations: A.ac = Atlantisina acantha gen. et sp. nov.; A.at = Atlantisina atlantis gen. et sp. nov.; A.go = Atlantisina gorringensis gen. et sp. nov.; A.in = Atlantisina inarmata gen. et sp. nov.; A.li = Atlantisina lionensis gen. et sp. nov.; A.me = Atlantisina meteor gen. et sp. nov.; A.se = Atlantisina seinensis gen. et sp. nov.; A.tr = Atlantisina tricornis gen. et sp. nov.; B.su = Bathycyclopora suroiti gen. et sp. nov.; B.vi = Bathycyclopora vibraculata gen. et comb. nov.; C.in = Calvetopora inflata gen. et comb. nov.; C.ot = Calvetopora otapostasis gen. et sp. nov.; C.sp. = Calvetopora sp.
Fig. 13 in New Cheilostomata (Bryozoa) from NE Atlantic seamounts, islands, and the continental slope: evidence for deep-sea endemism
Fig. 13. Calvetopora inflata (Calvet, 1906) gen. et comb. nov., Gulf of Cádiz, holotype (MNHN- IB-2008-2470). A. Overview of the periancestrular part of the colony. B. Close-up of the ancestrula and the first two autozooids. C. Maternal autozooids at the colony growth margin. D. Lateral view of an ovicellate zooid. E. Distal view of the colony growth margin showing the kenozooidal origin of the ooecia. F. Close-up of an avicularium. Scale bars: A, C = 500 µm; B, D = 200 µm; E = 300 µm; F = 50 µm.
Fig. 7 in New Cheilostomata (Bryozoa) from NE Atlantic seamounts, islands, and the continental slope: evidence for deep-sea endemism
Fig. 7. Atlantisina lionensis gen. et sp. nov., Lion Smt, paratype (MNHN-IB-2014-67). A. Colony overview. B. Orifice and slightly damaged ooecium. C. Ovicellate zooids at the colony growth margin. D. Close-up of the suboral crest. Scale bars: A = 500 µm; B, D = 50 µm; C = 100 µm.
Fig. 6 in New Cheilostomata (Bryozoa) from NE Atlantic seamounts, islands, and the continental slope: evidence for deep-sea endemism
Fig. 6. Atlantisina tricornis gen. et sp. nov. A. Early colony development; note the presence of the maternal 5th-generation autozooid at centre right (paratype MNHN-IB-2014-64, N Iberian slope). B. Ovicellate zooids (paratype MNHN-IB-2014-65, N Iberian slope). C. Close-up of orifice (paratype MNHN-IB-2014-65, N Iberian slope). D. Lateral view of suboral crests (holotype MNHN-IB-2014-60, N Iberian slope). E. Colony from Galicia Bank forming biserial ribbons; note the relatively broad ooecia (MNHN-IB-2014-279). F. Colony from the W Iberian slope (photo taken by J. Souto); note the bifid tips in some of the mucrones (zooid at lower left) while other suboral crests (zooid at top right) have a simple trident (MNHN-IB-2008-7194). Scale bars: A, E–F = 300 µm; B, D = 200 µm; C = 50 µm.
ScienceDex guides
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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.