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2,353 results for “channel”
Role of TRP channels in dinoflagellate mechanotransduction
<p>This repository contains several files associated with the publication:</p> <p><strong>Transcriptome:</strong> <em>Lingulodinium polyedra </em>transcriptome assembled from 100 bp paired-end Illumina RNA sequences available at http://www.ebi.ac.uk/ (run accession SRR1184657) using the assembly and annotation pipeline “MakeMyTranscriptome” (https://github.com/bluegenes/MakeMyTranscriptome), which leverages the Trinity <em>de novo</em> assembler (v2.1.1; Grabherr 2011). The transcriptome contains 183,451 transcripts, with an N50 of 1161, and 66.5% GC (guanine-cytosine) content. Annotation-based assessment using BUSCO showed that this transcriptome contained 62% of eukaryotic genes predicted to be present in all eukaryotic assemblies. Open reading frames (ORFs) predicted using transDecoder are also provided as a second file (ends with .pep).</p> <p><strong>TRP-like polypeptide sequences: </strong>Transient Potential Receptor (TRP)-like polypeptide sequences identified in transcriptomic data from the dinoflagellate <em>Lingulodinium polyedra</em>.</p> <p><strong>Video: </strong>Video of luminescence from the dinoflagellate <em>Lingulodinium polyedra</em> in response to capsaicin treatment. Samples consisted of 1 ml volumes of <em>L. polyedra</em> culture containing approximately 7000 cells, kept in two separate glass vials. The injection of 0.1 ml volumes of control solution (left vial) and capsaicin (30 µM final concentration, right vial) at a rate of 1ml min<sup>-1</sup> caused some light response due to stimulation of cells by the fluid addition. However, following fluid addition the capsaicin treatment clearly stimulated luminescence in the <em>L. polyedra</em> cells, seen as individual sources of light, while the cells in the control treatment produced essentially no light.</p>
Classifying the generation and formation channels of dynamically-formed gravitational-wave events
<p>This dataset contains all the simulations of dynamically-formed binaries performed with the software <a href="https://github.com/Kkritos/Rapster">rapster</a>, together with trained machine-learning classification models from (Antonelli, Kritos et al, in prep.), see <a href="https://github.com/aantonelli94/TheBHClassifier">the public codes online</a>.</p> <p>All items starting with "mergers_*" are simulations of clusters and they follow the structure reported in the documentation of <a href="https://github.com/Kkritos/Rapster">rapster</a>. The simulations differ in the choice of the hyperparameters for the distribution of the cluster mass, half-mass radius and initial spin distribution for the binaries.</p> <p>All items starting from "RFClassifier_*" are machine-learning classification models that use a Random Forest Classifier and that are trained with the simulations above. The models ending with "*_gen" predict the generation of the black holes, those with "*_form" predict their formation channels. </p> <p> </p>
Noncanonical electromechanical coupling paths in cardiac hERG potassium channel (semi-binary contact maps)
<p>Matrices of the semi-binary contact maps of the following open and closed systems: WT, A527L, A614G, L524R, L529H, L532H, T425L, T618L, W563L.</p> <p>The residue numbering is not the official one because the first residues (397) of hERG (PAS domain) were not included in our simulations so that each subunit comprizes 466 residues. Moreover, the four subunits were numbered consecutively. The official numbering of a residue can be easily recovered. The general rule is:</p> <p>official residue - 397 = our residue</p> <p>For example, the official T425 corresponds to T28 in the first subunit (425-397), T494 in the second subunit (425-397+466), T960 in the third subunit (425-397+466+466), and T1426 in the fourth subunit (425-397+466+466+466).</p>
Dataset of micro-phytoplankton biodiversity in the Eastern Channel area from 1987 to 2019.
<p>Micro-phytoplankton communities are widely influenced by the environment and seasonal cycling. The French-English Channel is a system between the East Atlantic and the North Sea with contrasted coastal areas along the French coast. In a context of changing environment, it is crucial to understand the causes and consequences of environmental variability on marine compartments whose phytoplankton represent the first link.</p> <p>Thanks to various monitoring programs such as REPHY (REPHY – French Observation And Monitoring Program For Phytoplankton And Hydrology In Coastal Waters, 2021) and RHLN (Regional Observation and Monitoring programs for Phytoplankton and Hydrology in the eastern English Channel), datasets of micro-phytoplankton diversity are available since the late 80s at a national scale.</p> <p>Here, we present a subset of these datasets on micro-phytoplankton community. It gathers diversity and abundance from 1987 to 2019. Compared to the main dataset, a taxonomical review was done to ensure a correct spatial and temporal homogenization of the taxonomical denomination (description below). We also focus on 6 monitored stations along the French Channel coast; Antifer ponton pétrolier, Cabourg, Géfosse, Donville, At so and St Cast – Les Hébihens (gathering of two times series geographically close). A graphical visual of the sampling history (for the FLORTOT standard protocol) is available in the uploaded image below.</p> <p>The dataset comes from a national-wide standard protocol of water sampling (Neaud-Masson, 2016). All records are stored in the Quadrige² platform, the reference information system for coastal waters in France. A compilation of the various results obtained at the French national scale was done on the last 30 years of monitoring (Belin and Soudant, 2018). The sampling is done at water surface (0-1 meter) around the high tide period (+/- 2 hours), it is fixed through lugol acid on arrivals at the lab and analysed through reversed microscopy by experts trained through a similar 2 years learning process. The taxa are identified to the lowest taxonomic level possible and names are updated according to the WORMs’ denomination base. Since 2015, evaluations (International Phytoplankton Intercomparison - IPI) are being implemented at the European scale, coordinated by the Marine Institute of Galway, to reduce identification deviations and biases between laboratories.</p> <p>The columns are described as following:</p> <ul> <li><strong>#1 : Taxa_Name</strong></li> </ul> <p>Due to the extent of the initial dataset, both in space and time, a work of homogenization in the taxa’s denomination was applied. Therefore, associated with this dataset, we made available a table that summarizes changes applied in the nominations. It displays all the taxa initially present in the REPHY database (first column), the change in denomination applied if needed as they are not at the same resolution between stations’ laboratories, or because two species are now grouped into a common denomination. There is an “X” if the taxa is not relevant, rare, or not a phytoplankton or protist of interest and therefore it means the taxa was deleted and is not in the given dataset (second column) and potential comments or justifications (third column). It has been build and reviewed several times by the phytoplankton experts from the three Ifremer laboratories covering the stations (Dinard, Port en Bessin and Boulogne sur Mer).</p> <ul> <li><strong>#2 to 5 : Date, Day, Month, Year</strong></li> </ul> <p>Temporal description of the samples. Details of the sampling history are given in the image below.</p> <ul> <li><strong>#6 : Season</strong></li> </ul> <p>Defined as Spring = March to May; Summer = June to August; Autumn = September to November; Winter = December (Y) to February (Y+1)</p> <ul> <li><strong>#7 and 8 : Julian_days and week number</strong></li> <li><strong>#9 and 10 : Station_FullName and Station_Code</strong></li> </ul> <p><strong></strong>In the REPHY monitoring program, stations are both describe by full name locations and codes. The same level of detail was kept available in this version, with : Antifer ponton pétrolier (010-P-001), Cabourg (010-P-109), Géfosse (014-P-023), Donville (018-P-054), At so (006-P-001) and St Cast – Les Hébihens (022-P-002)</p> <ul> <li><strong>#11: Parameter</strong></li> </ul> <p>FLORTOT (standard samples, all micro-phytoplanktonic species are identified). FLORPAR (partially identified samples in area identified as under any sanitary risk). FLORIND (additional samples taken for toxic species monitoring, or for species reaching 100 000 cells per liter or producing toxins are identified).</p> <ul> <li><strong>#12 : Results </strong></li> </ul> <p>Abundance (in cells.L<sup>-1</sup>), whose level of detection (lowest value) through the microscopy approach is at 100 cells per liter.</p> <ul> <li><strong>#13 : Class.worms2019</strong></li> </ul> <p>The taxa’s class according to the World Register of Marine Species 2019 (https://www.marinespecies.org/)</p> <ul> <li><strong>#14 : Rank</strong></li> </ul> <p>Taxa's taxonomical rank : Species, Genus, Family, e-species (aka: group of similar species), e-genus (aka : group of similar genus), Class, subclass, Order, Phylum, Other.</p> <p> </p>
Supplementary Data: Global fits if simplified models for dark matter with GAMBIT II. Vector dark matter with an s-channel vector mediator
<p>This record contains the YAML files, data files, and some of the plotting scripts for: "Global fits of simplified models for dark matter with GAMBIT II. Vector dark matter with an s-channel vector mediator".</p> <p>Samples have been created using GAMBIT and figures can be reproduced with pippi. Plotting scripts (*.pip) are designed to work with either the original version of pippi 2.1 or the forked unreleased version. The provided scripts do not reproduce all the figures in the paper exactly.</p> <p>To save storage space, all samples have been compressed using <code>tar</code>. To inflate each dataset after downloading run <code>tar -zxvf <samples>.hdf5.gz</code>.</p> <p>To facilitate uploading to Zenodo, several of the data files have been thinned to only include enough points to reproduce plots.</p>
Measurement-based MIMO channel model at 140GHz
<p><strong>1. Introduction</strong></p> <p>The file “gen_dd_channel.zip” is a package of a wideband multiple-input multiple-output (MIMO) stored radio channel model at 140 GHz in indoor hall, outdoor suburban, residential and urban scenarios. The package consists of 1) measured wideband double-directional multipath data sets estimated from radio channel sounding and processed through measurement-based ray-launching and 2) MATLAB code sets that allows users to generate wideband MIMO radio channels with various antenna array types, e.g., uniform planar and circular arrays at link ends.</p> <p><strong>2. What does this package do?</strong></p> <p><em>Outputs of the channel model</em></p> <p>The MATLAB file “ChannelGeneratorDD_hexax.m” gives the following variables, among others. The .m file also gives optional figures illustrating antennas and radio channel responses.</p> <table> <tbody> <tr> <td> <p>Variables</p> </td> <td> <p>Descriptions</p> </td> </tr> <tr> <td> <p><em>CIR</em></p> </td> <td> <p>MIMO channel impulse responses</p> </td> </tr> <tr> <td> <p><em>CFR</em></p> </td> <td> <p>MIMO channel frequency responses</p> </td> </tr> </tbody> </table> <p><em>Inputs to the channel model</em></p> <p>In order for the MATLAB file “ChannelGeneratorDD_hexax.m” to run properly, the following inputs are required.</p> <table> <tbody> <tr> <td> <p>Directory</p> </td> <td> <p>Descriptions</p> </td> </tr> <tr> <td> <p>data_030123_double_directional_paths</p> </td> <td> <p>Double-directional multipath data, measured and complemented by ray-launching tool, for various cellular sites.</p> </td> </tr> </tbody> </table> <p><em>User’s parameters</em></p> <p>When using “ChannelGeneratorDD_hexax.m”, the following choices are available.</p> <table> <tbody> <tr> <td> <p>Features</p> </td> <td> <p>Choices</p> </td> </tr> <tr> <td> <p>Channel model types for transfer function generation</p> </td> <td> <ul> <li> <p>'<em>snapshot</em>': single time sample per link = static, random phase for each path, amplitude from measurements</p> </li> <li>'<em>virtualMotion</em>': Doppler shifts & temporal fading, static propagation parameters, random phase for each path, amplitude from measurements, Doppler frequency per path from AoA and velocity vector</li> </ul> </td> </tr> <tr> <td> <p>Antenna / beam shapes</p> </td> <td> <ul> <li> <p>'<em>single3GPP</em>': single antenna element with power pattern shape defined in 3GPP, adjustable HPBW etc.</p> </li> <li> <p>'<em>URA</em>': uniform rectangular array, omni-directional elements</p> </li> <li>'<em>UCA</em>': uniform circular array, omni-directional elements</li> </ul> </td> </tr> </tbody> </table> <p><strong>List of files in the dataset</strong></p> <p><em>MATLAB codes that implement the channel model</em></p> <p>The MATLAB files consist of the following files.</p> <table> <tbody> <tr> <td> <p>File and directory names</p> </td> <td> <p>Descriptions</p> </td> </tr> <tr> <td> <p>readme_100223.txt</p> </td> <td> <p>Readme file; please read it before using the files</p> </td> </tr> <tr> <td> <p>ChannelGeneratorDD_hexax.m</p> </td> <td> <p>Main code to run; a code to integrate antenna arrays and double-directional path data to derive MIMO radio channels. No need to see/edit other files.</p> </td> </tr> <tr> <td> <p>gen_pathDD.m, randl.m, randLoc.m</p> </td> <td> <p>Sub-routines used in ChannelGeneratorDD_hexax.m; no need of modifications.</p> </td> </tr> <tr> <td> <p>Hexa-X channel generator DD_presentation.pdf</p> </td> <td> <p>User manual of ChannelGeneratorDD_hexax.m.</p> </td> </tr> </tbody> </table> <p> </p> <p><em>Measured multipath data</em></p> <p>The directory "data_030123_double_directional_paths" in the package contains the following files.</p> <table> <tbody> <tr> <td> <p>Filenames</p> </td> <td> <p>Descriptions</p> </td> </tr> <tr> <td> <p>readme_100223.txt</p> </td> <td> <p>Readme file; please read it before using the files</p> </td> </tr> <tr> <td> <p>RTdata_[<em>scenario</em>]_[<em>date</em>].mat</p> </td> <td> <p>Containing double-directional multipath parameters at 140 GHz in the specified scenario, estimated from radio channel sounding and ray-tracing.</p> </td> </tr> <tr> <td> <p>description_of_data_dd_[<em>scenario</em>].pdf</p> </td> <td> <p>Explaining data formats, the measurement site and sample results.</p> </td> </tr> </tbody> </table> <p><strong>References</strong></p> <p>Details of the data set are available in the following two documents:</p> <p><em>The stored channel models</em></p> <p>A. Nimr (ed.), "Hexa-X Deliverable D2.3 Radio models and enabling techniques towards ultra-high data rate links and capacity in 6G," April 2023, available: https://hexa-x.eu/deliverables/</p> <p>@misc{Hexa-XD23,<br> author = {{A. Nimr (ed.)}},<br> title = {{Hexa-X Deliverable D2.3 Radio models and enabling techniques towards ultra-high data rate links and capacity in 6G}},<br> year = {2023},<br> month = {Apr.},<br> howpublished = {https://hexa-x.eu/deliverables/},<br>}</p> <p><em>Derivation of the data, i.e., radio channel sounding and measurement-based ray-launching</em></p> <p>M. F. De Guzman and K. Haneda, "Analysis of wave-interacting objects in indoor and outdoor environments at 142 GHz," IEEE Transactions on Antennas and Propagation, vol. 71, no. 12, pp. 9838-9848, Dec. 2023, doi: 10.1109/TAP.2023.3318861</p> <p>@ARTICLE{DeGuzman23_TAP,<br> author={De Guzman, Mar Francis and Haneda, Katsuyuki},<br> journal={IEEE Transactions on Antennas and Propagation}, <br> title={Analysis of Wave-Interacting Objects in Indoor and Outdoor Environments at 142 {GHz}}, <br> year={2023},<br> volume={71},<br> number={12},<br> pages={9838-9848},<br>}</p> <p>Finally, the code “randl.m” are from the following MATLAB Central File Exchange.</p> <p>Hristo Zhivomirov (2023). Generation of Random Numbers with Laplace Distribution (https://www.mathworks.com/matlabcentral/fileexchange/53397-generation-of-random-numbers-with-laplace-distribution), MATLAB Central File Exchange. Retrieved February 15, 2023.</p> <p><strong>Data usage terms</strong></p> <p>Any usage of the data must be upon consent on the following conditions:</p> <ul> <li>The file “ChannelGeneratorDD_hexax.m” is owned by OUL. Contact: Dr. Pekka Kyösti, Pekka.Kyosti@oulu.fi.</li> <li>The other files and those in the directories, except for “randl.m”, are owned by AAU. Contact: Mr. Mar Francis de Guzman, francis.deguzman@aalto.fi.</li> <li>When a scientific paper is published that exploits the data and code, please cite this data set; the citation can be downloaded from the zenodo page of this data set.</li> </ul>
Data and scripts for reproducing "Quantifying Uncertainties in Direct Numerical Simulations of a Turbulent Channel Flow"
<p>This is the accompanying data and Python scripts to reproduce the figures in "Quantifying Uncertainties in Direct Numerical Simulations of a Turbulent Channel Flow", currently under review.</p>
Molecular dynamics simulation data 1: Structure of the connexin-43 gap junction channel in a putative closed state
<p>Molecular dynamics data for the manuscript Qi C.*, Acosta-Gutierrez S.*, Lavriha P., Othman A., Lopez-Pigozzi D., Bayraktar E., Schuster D., Picotti P., Zamboni N., Bortolozzi M., Gervasio F.L., Korkhov V.M. Structure of the connexin-43 gap junction channel in a putative closed state. eLife (2023) <a href="https://doi.org/10.7554/eLife.87616.2">https://doi.org/10.7554/eLife.87616.2</a></p> <p>The dataset includes:</p> <p>1. The starting coordinates, topology, MD inputs</p> <p>2. Production run gromacs trajectories for the Cx43 gap junction channel</p>
M4Raw: A multi-contrast, multi-repetition, multi-channel MRI k-space dataset for low-field MRI research [V1.6]
<p>V1.6 release notes:</p> <ul> <li>The test subset is released, which contains T1w (6 repetitions/subject), T2w (6 repetitions/subject), and FLAIR (4 repetitions/subject) data from 25 new subjects. These data have passed motion inspection, but one should note that due to the doubled repetition numbers, the average inter-contrast motions are around twice larger than those in the training and validation subsets. To facilitate users, we release the ground truth images as well, but please do not use them during hyperparameter tuning.</li> </ul> <p>V1.5 release notes:</p> <ul> <li>T1w Gradient echo (GRE) data for all 183 subjects are released. Note that the phase encoding direction for GRE data is in the AP direction, different from other contrasts. These GRE data were not checked for motions.</li> <li>A few incorrect records of patient_id were corrected in the H5 files.</li> <li>Scans 2022062708 and 2022062709 were removed due to duplication. Two new scans were added to replace them.</li> </ul> <p>V1.1 release notes:</p> <ul> <li>Please refer to https://www.nature.com/articles/s41597-023-02181-4 for details.</li> </ul>
Data file for: Electrical imaging for the subduction channel north of Mount Everest
<p>The magnetotellurics data were used to study the the subduction channel north of Mount Everest, conducted by Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences. The data file of zf.dat was generated by the Matlab code EM3DVP.</p> <p>You are recommended to refer to the Kelbert et al., 2014 paper: https://doi.org/10.1016/j.cageo.2014.01.010 for a brief understanding of the data file formats.</p>
Airborne Infrasound Data from The AtmoSOFAR Channel: First Direct Observations of an Elevated Acoustic Duct
<p>Airborne infrasound data including waveform recordings from two payloads attached to a single 6 m heliotrope that was launched at dawn (~0700 local) out of Belen Regional Airport, NM, USA. Balloon trajectory is also included. This data accompanies the publication titled, "The AtmoSOFAR Channel: First Direct Observations of an Elevated Acoustic Duct" submitted to Earth & Space Science.</p>
Extra terrestrials: experimental drought creates niche space for rare invertebrates in terrestrialising stream channels
<p>The code and data for the paper entitled "Extra terrestrials: experimental drought creates niche space for rare invertebrates in terrestrialising stream channels" published in <em>Biology Letters</em>.</p>
Source data for "Feed-forward metabotropic signaling by Cav1 Ca2+ channels supports pacemaking in pedunculopontine cholinergic neurons"
<p><strong>Fig.1A_ChAT.tif</strong></p><p>Confocal image (green channel, anti-ChAT staining) for Fig.1A</p><p> </p><p><strong>Fig.1A_tdTomato.tif </strong></p><p>Confocal image (red channel, tdTomato) for Fig.1A</p><p> </p><p><strong>Fig.1B_ChAT.tif</strong></p><p>Confocal image (green channel, anti-ChAT staining) for Fig.1B</p><p> </p><p><strong>Fig.1B_tdTomato.tif</strong></p><p>Confocal image (red channel, tdTomato) for Fig.1B</p><p> </p><p><strong>Fig.1C_DIC.png</strong></p><p>Differential interference contrast micrograph for Fig.1C left</p><p> </p><p><strong>Fig.1C_Fluo.png</strong></p><p>Epifluorescent illumination micrograph for Fig. 1C right</p><p> </p><p><strong>Fig.1DEH.xlsx</strong></p><p>Numerical data for the charts in Fig. 1D, Fig.1E, Fig.1H</p><p> </p><p><strong>Fig.1F.tif</strong></p><p>MAX projection of z-stack of 2PLSM images (red channel, Alexa 594) used to generate Fig.1F </p><p> </p><p><strong>Fig.1F_inset.tif</strong></p><p>2PLSM image (green channel, Fura-2) for the right inset of Fig.1F</p><p> </p><p><strong>Fig.2A_inset.tif</strong></p><p>Confocal image (green channel, GFP) for the higher magnification inset of Fig.2A</p><p> </p><p><strong>Fig.2A.tif</strong></p><p>Confocal image (green channel, GFP) for Fig.2A</p><p> </p><p><strong>Fig.2B_bottom.tif</strong></p><p>Confocal image (green channel, GFP) for Fig.2B (bottom and overlay panels)</p><p> </p><p><strong>Fig.2B_top.tif</strong></p><p>Confocal image (red channel, td Tomato) for Fig.2B (top and overlay panels)</p><p> </p><p><strong>Fig.2CE.xlsx</strong></p><p>Numerical data for the charts in Fig. 2C, Fig. 2E</p><p> </p><p><strong>Fig.3B.tif</strong></p><p>Confocal image (green channel, MitoGCaMP6) for Fig.3B and overlay in Fig.3D</p><p> </p><p><strong>Fig.3C.tif</strong></p><p>Confocal image (red channel, tdTomato) for Fig.3C and overlay in Fig.3D</p><p> </p><p><strong>Fig.3E.tif</strong></p><p>2PLSM image (green channel, MitoGCaMP6) for Fig.3E</p><p> </p><p><strong>Fig.3GIJ.xlsx</strong></p><p>Numerical data for the charts in Fig. 3G, Fig. 3I, Fig.3J</p><p> </p><p><strong>Fig.4B.tif</strong></p><p>2PLSM image (green channel, MitoGCaMP6) for Fig.4B</p><p> </p><p><strong>Fig.4DFG.xlsx</strong></p><p>Numerical data for the charts in Fig.4D, Fig.4F, Fig.4G</p><p> </p><p><strong>Fig.5A.tif</strong></p><p>Confocal image (green channel, PercevalHR) for Fig.5A and overlay in Fig.5C</p><p> </p><p><strong>Fig.5B.tif</strong></p><p>Confocal image (red channel, tdTomato) for Fig.5B and overlay in Fig.5C</p><p> </p><p><strong>Fig.5D.tif</strong></p><p>2PLSM image (green channel, PercevalHR) for Fig.5D</p><p> </p><p><strong>Fig.5GHJ.xlsx</strong></p><p>Numerical data for the charts in Fig.5G, Fig.5H, Fig.5J</p><p> </p><p><strong>Fig.6BCD.xlsx</strong></p><p>Numerical data for the charts in Fig.6b, Fig.6C, Fig.6D</p><p> </p><p><strong>Fig.7A.tif</strong></p><p>Confocal image (green channel, mito-roGFP) for Fig.7A and overlay in Fig.7C</p><p> </p><p><strong>Fig.7B.tif</strong></p><p>Confocal image (red channel, tdTomato) for Fig.7B and overlay in Fig.7C</p><p> </p><p><strong>Fig.7D.tif</strong></p><p>2PLSM image (green channel, mito-roGFP) for Fig.7D</p><p> </p><p><strong>Fig.7F.xlsx</strong></p><p>Numerical data for the charts in Fig.7F</p>
Kelp forest fish communities environmental DNA samples from Santa Barbara Channel
The dataset in this package is the processed fish community structure inferred from 12S eDNA metabarcoding in the Santa Barbara Channel. 49 water samples were collected across 11 sites in 2017 and the taxa were identified to the highest resolution possible. The raw DNA sequence has been archived in the Sequence Read Archive (SRA) database (https://www.ncbi.nlm.nih.gov/sra) under the accession number PRJNA667508. This dataset is used to support manuscript: Lamy, T., Pitz, K.J., Chavez, F.P. et al. Environmental DNA reveals the fine-grained and hierarchical spatial structure of kelp forest fish communities. Sci Rep 11, 14439 (2021). https://doi.org/10.1038/s41598-021-93859-5
Water quality measurements, stream order, channel slope and hydraulic equations of conterminous USGS sites: 1919-2009.
Streams and rivers emit petagrams of CO2 yet there is little known about how discharge (Q) variability impacts stream CO2 at broad scales. Herein, we compiled historical water quality (including pH, alkalinity and temperature) measurements for conterminous USGS sites and coupled them with daily Q for this analysis (the water_quality.csv dataset, 10,822 sites). Based on this dataset, NHDplus channel slopes (NHDplus_slopeSO.csv, 24,764 sites) and hydraulic geometry equations (lm_vQ.csv, 12,854 sites), we calculated partial pressure of dissolved CO2 (pCO2), gas transfer velocity (k) and CO2 effluxes (F) for a total of 813 USGS sites across conterminous US. We derived hydrologic responses (log-linear regressions) for pCO2, k and F versus Q at each site and explored how these responses varied across stream order and different regions. Ancillary datasets provided coordinates (coor_sites.xls), hydrologic unit code (HUC.csv), and watershed area of conterminous USGS sites (watersheds_area.csv).
Water quality, phytoplankton, and zooplankton in the Sacramento Deep Water Ship Channel, CA
Drivers of phytoplankton and zooplankton dynamics vary spatially and temporally in estuaries due to variation in hydrodynamic exchange and residence time, complicating efforts to understand controls on food web productivity. We conducted approximately monthly (2012 – 2019; n = 74) longitudinal sampling at ten fixed stations along a freshwater tidal terminal channel in the San Francisco Estuary, California, characterized by seaward to landward gradients in water residence time, turbidity, nutrient concentrations, and plankton community composition. We used multivariate autoregressive state space (MARSS) models to quantify environmental (abiotic) and biotic controls on phytoplankton and mesozooplankton biomass. The importance of specific abiotic drivers (e.g. water temperature, turbidity, nutrients) and trophic interactions differed significantly among hydrodynamic exchange zones with different mean residence times. Abiotic drivers explained more variation in phytoplankton and zooplankton dynamics than a model including only trophic interactions, but individual phytoplankton-zooplankton interactions explained more variation than individual abiotic drivers. Interactions between zooplankton and phytoplankton were strongest in landward reaches with the longest residence times and the highest zooplankton biomass. Interactions between cryptophytes and both copepods and cladocerans were stronger than interactions between bacillariophytes (diatoms) and zooplankton taxa, despite contributing less biovolume in all but the most landward reaches. Our results demonstrate that trophic interactions and their relative strengths vary in a hydrodynamic context, contributing to food web heterogeneity within estuaries at spatial scales smaller than the freshwater to marine transition.
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.
MBON: Benthic percent cover in the Santa Barbara Channel
This study aims to quantify the percent cover of sessile species from shallow rocky reefs along the coast of Santa Barbara Channel, including the Channel Island National Park monitoring sites. BisQue (Bio-Imagery Semantic Query User Environment, a cloud-based analysis platform) was used to analyzed over 1500 images taken between 2013 and 2015 on the shallow reefs. This is the first phase of a bigger project that directs to improve the image analysis to complement or replace time-consuming in-situ surveys by divers. This data set contains percent cover and occurrence data of invertebrates, algae and fish from 22 subtidal rocky reef sites (Anacapa Landing, Arroyo Burro, Arroyo Quemado, Carpinteria, Cathedral Cove, Cuyler-Bat Rock, Diablo, Ellwood Mesa, Fry’s, Gull Island, Hazards, Isla Vista, Johnsons Lee-North, Mohawk, Naples, Pelican, Prisoners, Rhodes, Scorpion, Solimar, West-End and Cat Rock, Yellowbank). One survey image was collected at each site. Images were processed from 2015-2017 using BisQue with over 300 unique identifying tags. Data are organized in two tables (1) percent cover, (2) occurrence (present-absent).
Coweeta Synoptic Data from 49 sampling sites in the Upper Little Tennessee River Basin from 2009 to 2010 (active channel width, bankfull width, and channel depth data)
This data was generated as part of synoptic sampling conducted at the Coweeta LTER between June 2009 and May 2010. 49 wadeable streams with low levels of development were sampled throughout the Upper Little Tennessee River Basin in the Southern Appalachians. Active channel width, bankfull width, and channel depth were measured every 5 meters for 150 meters at synoptic stream sites. Effects of riparian vegetative conditions on a suite of channel morphological variables were investigated: active channel width, variability of width within a reach, large wood frequency, mesoscale habitat distributions, median particle size, and percent fines. At each site, a uniform 150 meter section of stream was surveyed. Within each reach active channel width, bankfull channel width, and channel depth were measured every 5 meters. Active channel width was defined as the vegetationless channel bed from left vegetation break to right vegetation break. A whitepaper on the Synoptic field sampling activites can be found at: http://coweeta.uga.edu/publications/white%20paper%20summary%20of%20synoptic%20sampling.pdf
Shapefiles for glacier, stream channel, and watershed boundaries in the McMurdo Dry Valleys, Antarctica (2023)
This data package includes shapefiles for selected glacier, stream watershed, and stream channel boundaries in the McMurdo Dry Valleys region of Antarctica. A combination of satellite imagery and digital elevation models were used to delineate watershed and stream channel outlines, while glaciers were outlined by hand. Watershed boundaries provide an estimate of the overall topographic contributing area for each stream in Fryxell Basin, whereas stream channel boundaries provide a topographic area estimate for stream channel, beyond the wetted margin, for each stream.
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.