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355 results for “northeast Atlantic”
Past and future effects of climate on the metapopulation dynamics of a NorthEast Atlantic seabird across two centuries
<p>Datasets required to run code for contribution:</p> <p>Past and future effects of climate on the metapopulation dynamics of a NorthEast Atlantic seabird across two centuries</p> <p>Jana WE Jeglinski, Holly I Niven, Sarah Wanless, Robert T. Barrett, Mike P. Harris, Jochen Dierschke and Jason Matthiopoulos</p> <p>Extension of a Bayesian metapopulation model fit to colony census data for all Northeast Atlantic colonies of the Northern gannet (<em>Morus bassanus</em>) described in Jeglinski et al. (2023) to investigate mechanistic relationships with climate and forecast metapopulation dynamics under two climate scenarios. </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.
FESOM2.1 model data used in the paper "Atlantic Water warming increases melt below Northeast Greenland's last floating ice tongue"
<p><span>This data set includes the minimal data necessary to reproduce the findings of Wekerle et al., in revision. Output of model simulations with the global ocean sea ice model FESOM2.1 is provided. In particular, the data set includes:</span></p> <p><span>a) long term means of potential temperature, salinity, velocity and basal melt of the 79N Glacier averaged over 1970-2021 (</span>Wekerle2024_FESOM2_ltm_REF.nc<span>)</span></p> <p><span>b) annual means of maximum potential temperature and basal melt rate of the 79N Glacier from the reference experiment REF for the years 1970-2021 (</span>Wekerle2024_FESOM2_annual_avg_REF.nc<span>)</span></p> <p><span>c) annual means of maximum potential temperature and basal melt rate of the 79N Glacier from experiment CLIM for the years 2000-2021 (</span>Wekerle2024_FESOM2_annual_avg_CLIM.nc<span>)</span></p> <p><span>d) daily mean basal melt rates of experiments with varying subglacial discharge averaged over the years 2010-2014 (</span>Wekerle2024_FESOM2_daily_avg_EXP_subglacial_discharge.nc<span>)</span></p> <p><span>e) daily mean basal melt rates of experiments with varying drag coefficients for the year 2000 (</span>Wekerle2024_FESOM2_daily_EXP_basal_drag.nc<span>)</span></p> <p><span>Each netcdf file includes information on the model grid (longitude and latitude of nodes, depths of the vertical layers, elements, nodal areas).</span></p>
Dataset: Baseline for the Northeast Atlantic (58 – 70° N) intertidal Mytilus species complex (Mytilus spp.) 2021-2022
<p><strong><span>Aim: </span></strong><span>Mussels (<em>Mytilus spp</em>.) are abundant in the North Atlantic, sessile, and sensitive to environmental change, and suitable as sentinels of environment and climate change of costal ecosystems. We aimed to determine the baseline for the Northeast Atlantic (58 – 70° N)<em> Mytilus</em> species complex, and to show the present distribution to surveys conducted 60 years ago. </span></p> <p><strong><span>Location:</span></strong><span> Northeast Atlantic </span></p> <p><strong><span>Methodology: </span></strong><span>Baseline was obtained by investigating a total of 509 stations in the intertidal zone, in four regions comprising the environmental gradient from head of fjord to coast, and distributed over the latitudinal gradient from 58 – 70° N. </span></p> <p><strong><span>Results:</span></strong><span> The baseline shows a range in continuous abundance of mussels from 12 to 36 %, patchy abundance from 26 to 57 % and no or very limited mussel abundance from 26 to 46 % between the four regions. The presence of mussels in the southeast and west region was visualized to previous surveys conducted 60 years ago. The data points to similar past and present presence of mussels in both regions, yet past major mussel fields in the inner section of region southeast was not detected in this study.</span></p> <p><strong><span>Main conclusions:</span></strong></p> <p><span>The baseline of <em>Mytilus spp.</em> in the Northeast Atlantic (58 – 70° N) is now available for future reference. The baseline, plotted to surveys conducted 60 years ago, points to awareness of the population situated in the southeast section of the investigated region. Continued monitoring and modelling are needed to clarify drivers of temporal and spatial variation in the mussel populations along the Northeastern Atlantic coast. </span></p>
Flow-topography interactions in the western tropical Atlantic boundary off Northeast Brazil.
<p>Figures and other media files of the paper Flow-topography interactions in the western tropical Atlantic boundary 2off Northeast Brazil.</p>
Particle tracking dataset for: Exceptional 20th century ocean circulation in the Northeast Atlantic
<p>Particle tracking data for: "Exceptional 20th century ocean circulation in the Northeast Atlantic" Peter T. Spooner, David J. R. Thornalley, Delia W. Oppo, Alan Fox, Svetlana Radionovskaya, Neil L. Rose, Robbie Mallett, Emma Cooper, J. Murray Roberts</p> <p>VIKING20 (is a 1/20th degree ocean model, forced by a hindcast simulation of the atmosphere: CORE2 (Griffies et al., 2009). The reverse tracks of 113200 particles per year for 50 years, (1959-2009) were simulated with the ARIANE software (Döös, 1995) modified to include independent vertical motion of particles. Particles were seeded at the seabed in 10 km x 10 km boxes centered on MC16-A/17-5P and RAPID-21-3K (representing the settling location). The reverse tracks 'rose' (sinking) at 100 m/day (Takahashi & Be, 1984) and were then allowed to drift freely within the upper 100 m of the water column for six months (i.e. spanning the reasonable lifespan for many species of planktic foraminifera).</p> <p>Track data for the full 50 years are stored in a single netcdf file (output of ncdump -h <filename> given below). The 3D particle positions are in variables traj_lon, traj_lat and traj_depth with the Viking20 model along-track temperature, salinity and density in traj_temp, temp_sal and traj_dens, respectively. The main complication is the obscure storage of time (see also ARIANE software documentation). Variable init_t gives particle start time, counting in 5-day periods from 12:00 pm on 29 December 1957. Viking20 uses a fixed 365 day year so the year can be found for track 'traj' according to:</p> <p> year = 1958 + ( (init_t(traj)-1) \ 73 ) where '\' represents integer division, discarding the remainder.</p> <p>All particle tracks 'begin' (actually the end of the track in time as these are tracked backwards) at the start of July (12:00 pm July 1 in model). Particles are ordered by release time, so trajectories 1-113200 are 1959; 113201-226400 are 1960; etc. Positions are stored every 5 days, counting backwards.</p> <p>Further details are available from the authors.</p> <p> </p> <p>References</p> <p>Döös, K. (1995). Interocean exchange of water masses. Journal of Geophysical Research, 100(C7), 13499. <a href="https://doi.org/10.1029/95JC00337">https://doi.org/10.1029/95JC00337</a></p> <p>Griffies, S. M., Biastoch, A., Böning, C., Bryan, F., Danabasoglu, G., Chassignet, E. P., et al. (2009). Coordinated Ocean-ice Reference Experiments (COREs). Ocean Modelling, 26(1–2), 1–46. <a href="https://doi.org/10.1016/J.OCEMOD.2008.08.007">https://doi.org/10.1016/J.OCEMOD.2008.08.007</a></p> <p>Takahashi, K., & Be, A. W. H. (1984). Planktonic foraminifera: factors controlling sinking speeds. Deep Sea Research Part A. Oceanographic Research Papers, 31(12), 1477–1500. <a href="https://doi.org/10.1016/0198-0149(84)90083-9">https://doi.org/10.1016/0198-0149(84)90083-9</a></p> <p> </p> <p>$ ncdump -h ariane_trajectories_qualitative.nc</p> <p>netcdf ariane_trajectories_qualitative {</p> <p>dimensions:</p> <p>ntraj = 5660000 ;</p> <p>nb_output = UNLIMITED ; // (74 currently)</p> <p>variables:</p> <p><strong>double init_x(ntraj) ;</strong></p> <p>init_x:title = "What is init_x ?" ;</p> <p>init_x:longname = "Initial position in i" ;</p> <p>init_x:units = "No dimension" ;</p> <p>init_x:missing_value = 1.e+20 ;</p> <p><strong>double init_y(ntraj) ;</strong></p> <p>init_y:title = "What is init_y ?" ;</p> <p>init_y:longname = "Initial position in j" ;</p> <p>init_y:units = "No dimension" ;</p> <p>init_y:missing_value = 1.e+20 ;</p> <p><strong>double init_z(ntraj) ;</strong></p> <p>init_z:title = "What is init_z ?" ;</p> <p>init_z:longname = "Initial position in k" ;</p> <p>init_z:units = "No dimension" ;</p> <p>init_z:missing_value = 1.e+20 ;</p> <p><strong>double init_t(ntraj) ;</strong></p> <p>init_t:title = "What is init_t ?" ;</p> <p>init_t:longname = "Initial position in l (time)" ;</p> <p>init_t:units = "See global attributes..." ;</p> <p>init_t:missing_value = 1.e+20 ;</p> <p><strong>double init_age(ntraj) ;</strong></p> <p>init_age:title = "What is init_age ?" ;</p> <p>init_age:longname = "Initial age (time)" ;</p> <p>init_age:units = "seconds" ;</p> <p>init_age:missing_value = 1.e+20 ;</p> <p><strong>double init_transp(ntraj) ;</strong></p> <p>init_transp:title = "What is init_transp ?" ;</p> <p>init_transp:longname = "Initial transport" ;</p> <p>init_transp:units = "m3/s" ;</p> <p>init_transp:missing_value = 1.e+20 ;</p> <p><strong>double l_matureage(ntraj) ;</strong></p> <p>l_matureage:title = "What is l_matureage ?" ;</p> <p>l_matureage:longname = "Larval age of maturity" ;</p> <p>l_matureage:units = "days" ;</p> <p>l_matureage:missing_value = 1.e+20 ;</p> <p><strong>double l_descendage(ntraj) ;</strong></p> <p>l_descendage:title = "What is l_descendage ?" ;</p> <p>l_descendage:longname = "Larval age of competency" ;</p> <p>l_descendage:units = "days" ;</p> <p>l_descendage:missing_value = 1.e+20 ;</p> <p><strong>double l_maxspeedup(ntraj) ;</strong></p> <p>l_maxspeedup:title = "What is l_maxspeedup ?" ;</p> <p>l_maxspeedup:longname = "Max upward larval swim speed" ;</p> <p>l_maxspeedup:units = "mm s-1" ;</p> <p>l_maxspeedup:missing_value = 1.e+20 ;</p> <p><strong>double l_maxspeeddown(ntraj) ;</strong></p> <p>l_maxspeeddown:title = "What is l_maxspeeddown ?" ;</p> <p>l_maxspeeddown:longname = "Max downward larval swim speed" ;</p> <p>l_maxspeeddown:units = "mm s-1" ;</p> <p>l_maxspeeddown:missing_value = 1.e+20 ;</p> <p><strong>int l_targetdepth(ntraj) ;</strong></p> <p>l_targetdepth:title = "What is l_targetdepth ?" ;</p> <p>l_targetdepth:longname = "Target shallow depth" ;</p> <p>l_targetdepth:units = "No dimension" ;</p> <p>l_targetdepth:missing_value = -1. ;</p> <p><strong>double final_x(ntraj) ;</strong></p> <p>final_x:title = "What is final_x ?" ;</p> <p>final_x:longname = "Final position in x (or i)" ;</p> <p>final_x:units = "No dimension" ;</p> <p>final_x:missing_value = 1.e+20 ;</p> <p><strong>double final_y(ntraj) ;</strong></p> <p>final_y:title = "What is final_y ?" ;</p> <p>final_y:longname = "Final position in y (or j)" ;</p> <p>final_y:units = "No dimension" ;</p> <p>final_y:missing_value = 1.e+20 ;</p> <p><strong>double final_z(ntraj) </strong>;</p> <p>final_z:title = "What is final_z ?" ;</p> <p>final_z:longname = "Final position in z (or k)" ;</p> <p>final_z:units = "No dimension" ;</p> <p>final_z:missing_value = 1.e+20 ;</p> <p><strong>double final_t(ntraj) ;</strong></p> <p>final_t:title = "What is final_t ?" ;</p> <p>final_t:longname = "Final position in t (time)" ;</p> <p>final_t:units = "See global attributes..." ;</p> <p>final_t:missing_value = 1.e+20 ;</p> <p><strong>double final_age(ntraj) ;</strong></p> <p>final_age:title = "What is fial_age ?" ;</p> <p>final_age:longname = "Final Age." ;</p> <p>final_age:units = "seconds" ;</p> <p>final_age:missing_value = 1.e+20 ;</p> <p><strong>double final_transp(ntraj) ;</strong></p> <p>final_transp:title = "What is final_transp ?" ;</p> <p>final_transp:longname = "Final transport" ;</p> <p>final_transp:units = "m3/s" ;</p> <p>final_transp:missing_value = 1.e+20 ;</p> <p><strong>float traj_lon(nb_output, ntraj) ;</strong></p> <p>traj_lon:title = "What is traj_lon ?" ;</p> <p>traj_lon:longname = "Trajectory: x positions" ;</p> <p>traj_lon:units = "No dimension" ;</p> <p>traj_lon:missing_value = 1.e+20 ;</p> <p><strong>float traj_lat(nb_output, ntraj) ;</strong></p> <p>traj_lat:title = "What is traj_lat ?" ;</p> <p>traj_lat:longname = "Trajectory: y positions" ;</p> <p>traj_lat:units = "No dimension" ;</p> <p>traj_lat:missing_value = 1.e+20 ;</p> <p><strong>float traj_depth(nb_output, ntraj) ;</strong></p> <p>traj_depth:title = "What is traj_depth ?" ;</p> <p>traj_depth:longname = "Trajectory: z positions" ;</p> <p>traj_depth:units = "No dimension" ;</p> <p>traj_depth:missing_value = 1.e+20 ;</p> <p><strong>float traj_time(nb_output, ntraj) ;</strong></p> <p>traj_time:title = "What is traj_time ?" ;</p> <p>traj_time:longname = "Trajectory: time positions" ;</p> <p>traj_time:units = "See global attributes" ;</p> <p>traj_time:missing_value = 1.e+20 ;</p> <p><strong>float traj_iU(nb_output, ntraj) ;</strong></p> <p>traj_iU:title = "ind i on grid U" ;</p> <p>traj_iU:longname = "Trajectory: i on grid U" ;</p> <p>traj_iU:units = "No dimension" ;</p> <p>traj_iU:missing_value = 1.e+20 ;</p> <p><strong>float traj_jV(nb_output, ntraj) ;</strong></p> <p>traj_jV:title = "ind j on grid V" ;</p> <p>traj_jV:longname = "Trajectory: j on grid V" ;</p> <p>traj_jV:units = "No dimension" ;</p> <p>traj_jV:missing_value = 1.e+20 ;</p> <p><strong>float traj_kW(nb_output, ntraj) ;</strong></p> <p>traj_kW:title = "ind k on grid W" ;</p> <p>traj_kW:longname = "Trajectory: k on grid W" ;</p> <p>traj_kW:units = "No dimension" ;</p> <p>traj_kW:missing_value = 1.e+20 ;</p> <p><strong>float traj_temp(nb_output, ntraj) ;</strong></p> <p>traj_temp:title = "What is traj_temp ?" ;</p> <p>traj_temp:longname = "Trajectory: temperatures" ;</p> <p>traj_temp:units = "degres" ;</p> <p>traj_temp:missing_value = 1.e+20 ;</p> <p><strong>float traj_salt(nb_output, ntraj) ;</strong></p> <p>traj_salt:title = "What is traj_salt ?" ;</p> <p>traj_salt:longname = "Trajectory: salinities" ;</p> <p>traj_salt:units = "psu" ;</p> <p>traj_salt:missing_value = 1.e+20 ;</p> <p><strong>float traj_dens(nb_output, ntraj) ;</strong></p> <p>traj_dens:title = "What is traj_dens ?" ;</p> <p>traj_dens:longname = "Trajectory: densities" ;</p> <p>traj_dens:units = "..." ;</p> <p>traj_dens:missing_value = 1.e+20 ;</p> <p> </p> <p>// global attributes:</p> <p>:key_roms = ".FALSE." ;</p> <p>:key_symphonie = ".FALSE." ;</p> <p>:key_B2C_grid = ".FALSE." ;</p> <p>:key_sequential = ".TRUE." ;</p> <p>:key_alltracers = ".TRUE." ;</p> <p>:key_ascii_outputs = ".FALSE." ;</p> <p>:key_iU_jV_kW = ".TRUE." ;</p> <p>:key_read_age = ".FALSE." ;</p> <p>:mode = "qualitative" ;</p> <p>:forback = "backward" ;</p> <p>:bin = "nobin" ;</p> <p>:init_final = "NONE" ;</p> <p>:nmax = 10000000 ;</p> <p>:tunit = 86400. ;</p> <p>:ntfic = 5 ;</p> <p>:tcyc = 1639872000. ;</p> <p>:key_approximatesigma = ".FALSE." ;</p> <p>:key_computesigma = ".TRUE." ;</p> <p>:zsigma = 1000. ;</p> <p>:memory_log = ".TRUE." ;</p> <p>:output_netcdf_large_file = ".FALSE." ;</p> <p>:key_interp_temporal = ".TRUE." ;</p> <p>:maxcycles = 50 ;</p> <p>:delta_t = 86400. ;</p> <p>:frequency = 5 ;</p> <p>:nb_output = 73 ;</p> <p>:mask = ".TRUE." ;</p> <p>:key_region = ".FALSE." ;</p> <p>:key_larvae = ".TRUE." ;</p> <p>:imt = 1784 ;</p> <p>:jmt = 1719 ;</p> <p>:kmt = 46 ;</p> <p>:lmt = 3796 ;</p> <p>:key_computew = ".TRUE." ;</p> <p>:w_surf_option = "" ;</p> <p>:key_partialsteps = ".TRUE." ;</p> <p>:key_jfold = ".FALSE." ;</p> <p>:pivot = "T" ;</p> <p>:key_periodic = ".FALSE." ;</p> <p>:dir_mesh = "./GRID" ;</p> <p>:fn_mesh = "1_mesh_mask.nc" ;</p> <p>:nc_var_xx_tt = "glamt" ;</p> <p>:nc_var_xx_uu = "glamu" ;</p> <p>:nc_var_zz_ww = "gdepw_0" ;</p> <p>:nc_var_e2u = "e2u" ;</p> <p>:nc_var_e1v = "e1v" ;</p> <p>:nc_var_e1t = "e1t" ;</p> <p>:nc_var_e2t = "e2t" ;</p> <p>:nc_var_e3t = "e3t" ;</p> <p>:nc_var_tmask = "tmask" ;</p> <p>:nc_mask_val = 0. ;</p> <p>:c_dir_zo = "./DATA" ;</p> <p>:c_prefix_zo = "V20_nest_5d_" ;</p> <p>:ind0_zo = 1958 ;</p> <p>:indn_zo = 2009 ;</p> <p>:maxsize_zo = 4 ;</p> <p>:c_suffix_zo = "_U.nc" ;</p> <p>:nc_var_zo = "vozocrtx" ;</p> <p>:nc_var_eivu = "NONE" ;</p> <p>:nc_att_mask_zo = "missing_value" ;</p> <p>:c_dir_me = "./DATA" ;</p> <p>:c_prefix_me = "V20_nest_5d_" ;</p> <p>:ind0_me = 1958 ;</p> <p>:indn_me = 2009 ;</p> <p>:maxsize_me = 4 ;</p> <p>:c_suffix_me = "_V.nc" ;</p> <p>:nc_var_me = "vomecrty" ;</p> <p>:nc_var_eivv = "NONE" ;</p> <p>:nc_att_mask_me = "missing_value" ;</p> <p>:c_dir_te = "./DATA" ;</p> <p>:c_prefix_te = "V20_nest_5d_" ;</p> <p>:ind0_te = 1958 ;</p> <p>:indn_te = 2009 ;</p> <p>:maxsize_te = 4 ;</p> <p>:c_suffix_te = "_T.nc" ;</p> <p>:nc_var_te = "votemper" ;</p> <p>:nc_att_mask_te = "missing_value" ;</p> <p>:c_dir_sa = "./DATA" ;</p> <p>:c_prefix_sa = "V20_nest_5d_" ;</p> <p>:ind0_sa = 1958 ;</p> <p>:indn_sa = 2009 ;</p> <p>:maxsize_sa = 4 ;</p> <p>:c_suffix_sa = "_T.nc" ;</p> <p>:nc_var_sa = "vosaline" ;</p> <p>:nc_att_mask_sa = "missing_value" ;</p> <p>}</p> <p> </p>
Fig. 5 in A new tardigrade species of the genus Neostygarctus Grimaldi de Zio et al., 1982 (Tardigrada, Arthrotardigrada) from the Great Meteor Seamount, Northeast Atlantic
Fig. 5. Neostygarctus grossmeteori sp. nov. Paratype, ♀ (SMF 52), details. A. Lateral body processes, ventral view. B–D. Legs I (left and right) and IV. E. Genital area. Scale bars: A = 50 μm; B–E = 20 μm.
Fig. 2 in A new tardigrade species of the genus Neostygarctus Grimaldi de Zio et al., 1982 (Tardigrada, Arthrotardigrada) from the Great Meteor Seamount, Northeast Atlantic
Fig. 2. Neostygarctus grossmeteori sp. nov., entire. A. Holotype, ♀ (SMF 51), dorsal view. B. Paratype, Ƌ (SMF 58), ventral view. Scale bar = 100 μm.
Fig. 1 in A new tardigrade species of the genus Neostygarctus Grimaldi de Zio et al., 1982 (Tardigrada, Arthrotardigrada) from the Great Meteor Seamount, Northeast Atlantic
Fig. 1. Type locality and milieu of Neosstygarctus grossmeteori sp. nov. A. Position of the Great Meteor Seamount in the Atlantic Ocean. B. Bioclastic sediment consisting mainly of calcareous foraminiferan and pteropod shells (fine fraction of sediment washed off). Scale bar = 2 mm.
Fig. 4 in A new tardigrade species of the genus Neostygarctus Grimaldi de Zio et al., 1982 (Tardigrada, Arthrotardigrada) from the Great Meteor Seamount, Northeast Atlantic
Fig. 4. Neostygarctus grossmeteori sp. nov., optical photopictures. A. Paratype, ♀ (SMF 54), entire body, dorsal view. B. Holotype, ♀ (SMF 51), entire body, ventral view. C–E. Paratype of obscure gender (SMF 59). C–D. Areas of dorsal surface of body with spines. E. Lateral body projections. F. Holotype, ♀ (SMF 51), posterior body with female gonopore and anus. Scale bars: A–B = 50 μm; C–F = 20 μm.
Fig. 3 in A new tardigrade species of the genus Neostygarctus Grimaldi de Zio et al., 1982 (Tardigrada, Arthrotardigrada) from the Great Meteor Seamount, Northeast Atlantic
Fig. 3. Neostygarctus grossmeteori sp. nov., heads. A. Holotype, ♀ (SMF 51), dorsal view. B. Paratype, ♀ (SMF 52), ventral view. Scale bar = 50 μm.
Fig. 6 in A new tardigrade species of the genus Neostygarctus Grimaldi de Zio et al., 1982 (Tardigrada, Arthrotardigrada) from the Great Meteor Seamount, Northeast Atlantic
Fig. 6. Neostygarctus grossmeteori sp. nov., details, SEM. A. Female, entire body, ventral view. B. Right secondary clava and outer cirrus on the head, ventral view. C. Ventral conical spikes on the basal part of the lateral body process. D. Right lateral body processes. E. Lateral fan of spines with membrane on the posteriormost body segment. F. Toes with claws of the leg IV ventrally, dorsal tendon detached in some toes. G. Inner and outer claws, dorsal view. H. Accordion-like joint of the cirrus E. Scale bars: A = 30 μm; B, D, F = 10 μm; C, E, G–H = 3 μm.
Fig. 4 in A new deepwater species of Calliopiidae, Halirages helgae (Crustacea, Amphipoda), with a synoptic table to Halirages species from the northeast Atlantic
Fig. 4. Halirages helgae sp. nov., holotype, ♀, 10 mm long. A. Maxilliped. B. Maxilla 1. C. Maxilla 2. D. Mandible.
Fig. 2 in A new deepwater species of Calliopiidae, Halirages helgae (Crustacea, Amphipoda), with a synoptic table to Halirages species from the northeast Atlantic
Fig. 2. Halirages helgae sp. nov. A. Holotype, ♀, 10 mm long. B. Dorsal side, showing bilobed posterior margin of pereonite 7; paratype 1, ♀, 10 mm long.
Fig. 1 in A new deepwater species of Calliopiidae, Halirages helgae (Crustacea, Amphipoda), with a synoptic table to Halirages species from the northeast Atlantic
Fig. 1. Part of the MAREANO survey area off northern Norway, with indications of stations where Halirages helgae sp. nov. was found.
Fig. 6 in A new deepwater species of Calliopiidae, Halirages helgae (Crustacea, Amphipoda), with a synoptic table to Halirages species from the northeast Atlantic
Fig. 6. Halirages helgae sp. nov. A–D. Holotype, ♀, 10 mm long. A. Pereiopod 3. B. Pereiopod 4. C. Pereiopod 5. D. Pereiopod 6. — E. Paratype 2, ♀, 9 mm long. Pereiopod 7.
Fig. 3 in A new deepwater species of Calliopiidae, Halirages helgae (Crustacea, Amphipoda), with a synoptic table to Halirages species from the northeast Atlantic
Fig. 3. Halirages helgae sp. nov., holotype, ♀, 10 mm long. A. Head. B. Antenna 1. C. Antenna 2. D. Upper lip. E. Lower lip.
Figure 4 in Tanaidacea (Crustacea: Peracarida) of the northeast Atlantic: Chauliopleona Dojiri and Sieg, 1997 and Saurotipleona n. gen. from the 'Atlantic Margin'
Figure 4. Chauliopleona amdrupii. Non-ovigerous female, BIOICE Stn 3282: (A–F) pereopods 1–6 respectively. Scale bar 0.25 mm.
Figure 3 in Tanaidacea (Crustacea: Peracarida) of the northeast Atlantic: Chauliopleona Dojiri and Sieg, 1997 and Saurotipleona n. gen. from the 'Atlantic Margin'
Figure 3. Chauliopleona amdrupii. Non-ovigerous female, BIOICE Stn 3282: (A) labrum; (B–C) left mandible and molar; (D) right mandible; (E) maxillule; (F) maxilla; (G) maxilliped (one palp omitted); (H) cheliped. Scale bars: (i) 0.25 mm for A–G; (ii) 0.25 mm for H.
Figure 1 in Tanaidacea (Crustacea: Peracarida) of the northeast Atlantic: Chauliopleona Dojiri and Sieg, 1997 and Saurotipleona n. gen. from the 'Atlantic Margin'
Figure 1. Chauliopleona. Sketch drawings: (A) measurement of cheliped carpal shield aspect ratio l/b; C. armata, non-ovigerous female, co-type, Ingolf Stn 22; (B) habitus; (C) pleonite-5 spur; (D) cheliped, distal; (E) cheliped, fixed finger; (F) pereopod-1, distal; (G) uropod. C. hastata, non-ovigerous female co-type, Ingolf Stn 125: (H) habitus; (J) pleonite-5 spur, pleotelson, and uropod; (K) cheliped, distal; (L) pereopod-1, distal; C. hastata one of two cotypes, Ingolf Stn 102: (M) pleonite-5 spur. Not to scale.
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.