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FIG. 6 in The ways of fish beyond the sea: fish circulation and consumption in the Atacama desert, northern Chile, during the Formative period (500 cal B.C. - 700 cal A.D.)
FIG. 6. — Headless dried fish offered to a 4-6 years old infant in the route of Calate-Loa River mouth.
FIG. 7 in The ways of fish beyond the sea: fish circulation and consumption in the Atacama desert, northern Chile, during the Formative period (500 cal B.C. - 700 cal A.D.)
FIG. 7. — Composite food made of two kind of fishes tied together by a braided rope: a complete anchoveta and a boneless fillet of a bigger fish. Topater 01 cemetery (Gallardo 2017: fig. 2). Scale bar: 5 cm.
FIG. 9 in The ways of fish beyond the sea: fish circulation and consumption in the Atacama desert, northern Chile, during the Formative period (500 cal B.C. - 700 cal A.D.)
FIG. 9. — Graphic that indicates the relation between cranial and postcranial skeletal units in routes, Quillagua and interior oasis sites from the Atacama Desert during the Formative period. Both inferior bars show the same relation in reference skeletons from Trachurus murphyi Nichols, 1920 (60 cranial and 41 postcranial elements) and Cilus gilberti (Abbott, 1899) (60 cranial and 42 postcranial elements). Abbreviation: NISP, number of identified specimens.
FIG. 2 in The ways of fish beyond the sea: fish circulation and consumption in the Atacama desert, northern Chile, during the Formative period (500 cal B.C. - 700 cal A.D.)
FIG. 2. — Altitudinal profile of the region with the position from the different localities mentioned in the article. Sites in italics: main geographical landmarks. Ab- breviation: m a.s.l., meters above sea level.
FIG. 4 in The ways of fish beyond the sea: fish circulation and consumption in the Atacama desert, northern Chile, during the Formative period (500 cal B.C. - 700 cal A.D.)
FIG. 4. — Radiocarbon datations from the Formative sites of the Atacama Desert considered in this study. Marine and human material samples were excluded. Calibration made by SHCAL13 curve (Hogg et al. 2013) using Calib 704 (Stuiver et al. 2005).
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>
CyclomicsSeq: Accurate detection of circulating tumor DNA using nanopore consensus sequencing
<p>CyclomicsSeq is a protocol designed to produce and sequence long DNA concatemers with a linear repetition to acquire high accuracy consensus reads. In this dataset, we used CyclomicsSeq for sequencing TP53 in cell-free DNA of healthy individuals and of head and neck cancer patients and for sequencing synthetic TP53 DNA sequences that mimic the length of cell-free DNA. This dataset contains data (mainly base calls of the backbone and the insert) of 32 nanopore sequencing runs. <br> </p>
Role of residential air circulation and cooling for universal household electrification
<p>This repository contains the data to replicate the analysis of the paper 'Role of residential air circulation and cooling for universal household electrification' by Giacomo Falchetta. </p> <p>Replication code and instruction are hosted at <a href="https://github.com/giacfalk/cooling_electrification">https://github.com/giacfalk/cooling_electrification</a></p> <p>To replicate the analysis:</p> <ol> <li>Clone the Github repository on your machine</li> <li>Download the Zenodo archive and extract it in the home folder of the cloned Github repository</li> <li>Ensure Python (Anaconda 3) and R 3.5.1+ are installed on your local machine</li> <li>Run the 'wrapper.R' file.</li> </ol> <p> </p>
Dataset and trained models belonging to the article 'Distant reading patterns of iconicity in 940.000 online circulations of 26 iconic photographs'
<p>Quantifying Iconicity - Zenodo</p> <p><br> ## The Dataset<br> This dataset contains the material collected for the article "Distant reading 940,000 online circulations of 26 iconic photographs" (to be) published in New Media & Society (DOI: 10.1177/14614448211049459). We identified 26 iconic photographs based on earlier work (Van der Hoeven, 2019). The Google Cloud Vision (GCV) API was subsequently used to identify webpages that host a reproduction of the iconic image. The GCV API uses computer vision methods and the Google index to retrieve these reproductions. The code for calling the API and parsing the data can be found on GitHub: https://github.com/rubenros1795/ReACT_GCV.</p> <p>The core dataset consists of .tsv-files with the URLs that refer to the webpages. Other metadata provided by the GCV API is also found in the file and manually generated metadata. This includes:<br> - the URL that refers specifically to the image. This can be an URL that refers to a full match or a partial match<br> - the title of the page<br> - the iteration number. Because the GCV API puts a limit on its output, we had to reupload the identified images to the API to extend our search. We continued these iterations until no more new unique URLs were found<br> - the language found by the ``langid`` Python module [link](https://github.com/saffsd/langid.py), along with the normalized score.<br> - the labels associated with the image by Google<br> - the scrape date</p> <p>Alongside the .tsv-files, there are several other elements in the following folder structure:</p> <p>```<br> ├── data<br> │ ├── embeddings<br> │ └── doc2vec<br> │ └── input-text<br> │ └── metadata<br> │ └── umap<br> │ └── evaluation<br> │ └── results<br> │ └── diachronic-plots<br> │ └── top-words<br> │ └── tsv<br> ```</p> <p>1. The ```/embeddings``` folder contains the doc2vec models, the training input for the models, the metadata (id, URL, date) and the UMAP embeddings used in the GMM clustering. Please note that the date parser was not able to find dates for all webpages and for this reason not all training texts have associated metadata.<br> 2. The ```/evaluation``` folder contains the AIC and BIC scores for GMM clustering with different numbers of clusters.<br> 3. The ```/results``` folder contains the top words associated with the clusters and the diachronic cluster prominence plots.</p> <p>## Data Cleaning and Curation<br> Our pipeline contained several interventions to prevent noise in the data. First, in between the iterations we manually checked the scraped photos for relevance. We did so because reuploading an iconic image that is paired with another, irrelevant, one results in reproductions of the irrelevant one in the next iteration. Because we did not catch all noise, we used Scale Invariant Feature Transform (SIFT), a basic computer vision algorithm, to remove images that did not meet a threshold of ten keypoints. By doing so we removed completely unrelated photographs, but left room for variations of the original (such as painted versions of Che Guevara, or cropped versions of the Napalm Girl image). Another issue was the parsing of webpage texts. After experimenting with different webpage parsers that aim to extract 'relevant' text it proved too difficult to use one solution for all our webpages. Therefore we simply parsed all the text contained in commonly used html-tags, such as ```<p>```, ```<h1>``` etc.</p>
Data for GRL "Tropical Stratospheric Circulation and Ozone Coupled to Pacific Multi-Decadal Variability" paper
<p>Data of the CESM-WACCM sensitivity simulations. These simulations follow the REFC1 configuration from the Chemistry-Climate Model Initiative (CCMI), but for: fixed long-lived halogenated substances at year 1955 (REFC1-fODS); fixed long-lived halogens and nitrogen oxide emissions at year 1955 (REFC1-fODS-N2O); fixed stratospheric aerosol averaged over 1998–1999 (REFC1-fSAD); climatological sea surface temperatures (SSTs) and sea-ice concentrations (SICs) for the 1960–2010 period (REFC1-fSST); climatological SSTs and SICs for the 1960–2010 period, including a 28-months cyclical QBO (REFC1-fSST-QBO).</p>
Pressure-driven Poiseuille flow inherited from Mesozoic mantle circulation led to the Eocene separation of Australia and Antarctica
<p>Mantle temperature field at 60 Ma and at a random distribution for TERRA and numerical grids for SHELLS.</p> <p> </p>
Downscaled surface mass balance in Antarctica: impacts of subsurface processes and large-scale atmospheric circulation
<p>Here is the surface mass balance calculated from a offline subsurface model, that is used in the paper Downscaled surface mass balance in Antarctica: impacts of subsurface processes and large-scale atmospheric circulation.<br> More data are available by contacting nichsen@space.dtu.dk</p>
Data supplementing article "Transport of riverine material from multiple rivers in the Chesapeake Bay: important control of estuarine circulation on the material distribution" under review at the Journal of Geophysical Research - Biogeoscience
<p>These data supplement the article: Du, J. and J. Shen, Transport of riverine material from multiple rivers in the Chesapeake Bay: important control of estuarine circulation on the material distribution, under review at the Journal Of Geophysical Research: Biogeoscience</p> <p>contact: Jiabi Du, jiabi@vims.edu</p> <p>Below are descriptions of the data files included here:</p> <p>1. Monthly mean tracer output [1985-2014]</p> <p>-netCDF format results for monthly mean tracer concentrations from different sources (Susquehanna, Potomac, Rappahannock, York, James Rivers, and Coastal Ocean)</p> <p>-grid information are also included</p> <p>2. Matlab Scripts For Plotting.zip:</p> <p>-Matlab scripts used to plot the horizontal map, the vertical profile for the along channel section, the vertical profile for cross-channel sections. The script enables users to define the period and section no to plot. </p> <p>3. tracer influx and outflux ratio at 9 cross-section.xls:</p> <p>-an excel file contains the bottom tracer influx ratio and surface tracer outflux ratio for different rivers at different sections. </p>
Data for "Enhancing Versus Suppressive Effects of Sediment-Induced Density Gradients on Estuarine Lateral Circulation"
<p>The NetCDF files contain model output for experiments A–E from both the ENH and SUP series.</p> <p>The txt file provides the data sources referenced in Table A1.</p>
Data Sets: Unsteady Land-Sea Breeze Circulations in the Presence of a Synoptic Pressure Forcing
<p>{Mg (m/s): 0, 0.4, 1.2, 2 and α: 0°, and 180°}</p> <p>Consult the details in Allouche et al. (2023): https://doi.org/10.1002/qj.4552. The latter corresponds to the steady state simulations of these transient ones here.<br>All of these simulations have a domain extent (L_x, L_y, L_z=z_i) of (80 km, 5 km, 1.6 km). The numerical mesh (Nx, Ny, Nz) is (384, 24, 64). For plotting, the vertical levels vary for each variable. A variable at 'c_s nodes' is plotted at dz/2, dz, 2*dz, 3*dz, and so forth up to z_i. A variable on 'w nodes' is plotted at 0, dz, 2*dz, 3*dz, and so forth up to z_i. This information is given in the variable table below.</p> <p>These simulations are given in a netcdf format (.nc). One is able to download and reshape these matrices.</p>
Operating diagram of hatching module, this module consists of two clearly separated sections, each consisting of two long tanks (2 × 0.2 × 0.2 m) designed to accommodate hatching boxes, a filtration tank and an independent water circulation pump with a cooling unit and UV sterilizer. This allows simultaneous monitoring of 16 batches of eggs. in Reproduction of Zingel asper (Linnaeus, 1758) in controlled conditions: an assessment of the experiences realized since 2005 at the Besançon Natural History Museum
Operating diagram of hatching module, this module consists of two clearly separated sections, each consisting of two long tanks (2 × 0.2 × 0.2 m) designed to accommodate hatching boxes, a filtration tank and an independent water circulation pump with a cooling unit and UV sterilizer. This allows simultaneous monitoring of 16 batches of eggs.
Operating diagram of hatching module in Zoug jars, this system consists of a 300-litre temperature-controlled isothermal enclosure containing 10 one-litre Zoug jars, each able to accommodate several hundred eggs. An ascending current holds the eggs in suspension and carries the larvae to the surface. Another bottle connected to this device collects the larvae. The water circulating in the jars is independent of that used in the filtration circuit. A cooling unit and UV sterilizer complete the installation. in Reproduction of Zingel asper (Linnaeus, 1758) in controlled conditions: an assessment of the experiences realized since 2005 at the Besançon Natural History Museum
Operating diagram of hatching module in Zoug jars, this system consists of a 300-litre temperature-controlled isothermal enclosure containing 10 one-litre Zoug jars, each able to accommodate several hundred eggs. An ascending current holds the eggs in suspension and carries the larvae to the surface. Another bottle connected to this device collects the larvae. The water circulating in the jars is independent of that used in the filtration circuit. A cooling unit and UV sterilizer complete the installation.
Operating diagram of IF incubator, two identical incubators were used for eggs that were already sorted. They are made up of an isothermal enclosure and contain three tiers (100 × 60 × 17 cm). The water circulating in each tier comes from the same filtration, cooling and sterilisation device. As a result, the eggs placed in the different tiers are subject to the same temperature regime. in Reproduction of Zingel asper (Linnaeus, 1758) in controlled conditions: an assessment of the experiences realized since 2005 at the Besançon Natural History Museum
Operating diagram of IF incubator, two identical incubators were used for eggs that were already sorted. They are made up of an isothermal enclosure and contain three tiers (100 × 60 × 17 cm). The water circulating in each tier comes from the same filtration, cooling and sterilisation device. As a result, the eggs placed in the different tiers are subject to the same temperature regime.
Three-dimensional water exchanges in the shelf circulation system of the Northern South China Sea under climatic modulation from ENSO
<p>Three-dimensional water exchanges in the shelf circulation system of the Northern South China Sea under climatic modulation from ENSO</p>
Phanerozoic global climatic fields simulated using the FOAM ocean-atmosphere general circulation model
<p>These files contain the output of Phanerozoic global climate simulations conducted using the coupled ocean-atmosphere FOAM general circulation model. They are available every 20 Myrs between 540 Ma and 0 Ma, both included. All simulations have been conducted using identical boundary conditions; pCO2: 2240 ppm, solar luminosity: 1368 W m-2, vegetation: rocky desert, orbital configuration: null eccentricity and minimum obliquity. Only the continental configuration was varied from one time slice to the other (sensitivity test to the continental configuration), using the reconstructions of Scotese and Wright (https://www.earthbyte.org/paleodem-resource-scotese-and-wright-2018/).</p> <p>The reader is referred to the associated paper for a full description of the model and boundary conditions.</p> <p>All file names use the following pattern: "[age]rd_1368W_EccN_[model_component]_2240ppm.nc", with [age], the age expressed in million years ago, and [model_component] being 'atmos', 'ocean' or 'coupl' (atmospheric and oceanic components, plus coupler).</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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