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Jornada Basin LTER Nutrient and Ecosystem impacts of Aeolian Transport Study (NEAT) Block 1 meteorological station: 1-second summary precipitation data: 2019 - ongoing
1-second summary precipitation data at NEAT Block-1 met station. Total precipitation per second is recorded only if there is measurable precipitation. This is recorded at an automated meteorological station installed at Jornada LTER NEAT Block-1 site. This climate station is operated by the Jornada LTER Program. This is an ONGOING dataset. IMPORTANT: Rainfall is only recorded when a bucket tip occurs on the tipping bucket rain gauge. If there is missing data due to equipment or other problems, there will be no indication of this within this dataset. The user needs to review precipitation values in the 30 minute or hourly summary datasets to determine if there are missing data in this 1 second detailed precipitation dataset.
Jornada Basin LTER Nutrient and Ecosystem impacts of Aeolian Transport Study (NEAT) Block 3 meteorological station: 1-second summary precipitation data: 2018 - ongoing
1-second summary precipitation data at NEAT Block-3 met station. Total precipitation per second is recorded only if there is measurable precipitation. This is recorded at an automated meteorological station installed at Jornada LTER NEAT Block-3 site. This climate station is operated by the Jornada LTER Program. This is an ONGOING dataset. IMPORTANT: Rainfall is only recorded when a bucket tip occurs on the tipping bucket rain gauge. If there is missing data due to equipment or other problems, there will be no indication of this within this dataset. The user needs to review precipitation values in the 30 minute or hourly summary datasets to determine if there are missing data in this 1 second detailed precipitation dataset.
River Nutrient Uptake and Transport at North Temperate Lakes LTER (2005-2011)
These data were collected by Stephen Michael Powers and collaborators for his Ph.d. research, documented in his dissertation: River Nutrient Uptake and Transport Across Extremes in Channel Form and Drainage Characteristics. A major goal of this research was to better understand how ecosystem form and landscape setting dictate aquatic biogeochemical functioning and elemental transport through rivers. To achieve this goal, major and minor ions were measured in both northern and southern Wisconsin streams located in a variety of land use settings. In total, 27 different streams were sampled at 104 different stations (multiple stations per system) from both groundwater and surface water sources. Organic and inorganic carbon and nitrogen pools were also measured in northern and southern Wisconsin streams. The streams that were sampled in northern Wisconsin flow through wetland ecosystems. In sampling such streams, the goal was to better understand how wetland ecosystems influence river nutrient deliveries. There is a large amount of stream chemistry data for Big Spring Creek, WI; where the influence of a small reservoir on solute transportation and transformation was studied in an agricultural watershed. All stream chemistry data is incorporated in a single data file, Water Chemistry 2005-2011. While the data is not included in the dissertation, a sediment core study was also done in the small reservoir and channel of Big Spring (BS) Creek, WI. The results of this study are featured in three data tables: BS Creek Sediment Core Analysis, BS Creek Sediment Core Chemistry, and BS Creek Longitudinal Profile. Finally, two data tables list the geospatial information of sampling sites for stream chemistry and sediment coring in Big Spring Creek. Documentation: Powers, S.M., 2012. River nutrient uptake and transport across extremes in channel form and drainage characteristics. ProQuest Dissertations and Theses. The University of Wisconsin - Madison, United States -- Wisconsin, p
Data related to the manuscript "Bayesian Calibration and Validation of a Large-scale and Time-demanding Sediment Transport Model"
<p>1) Riverbed_Elevation_Measurements.txt<br> Description: Measured riverbed geometry of available years<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2002 [m asl], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation <br> 2013 [m asl]<br> ----------------------------------------------------------------------------------------------------------------------------<br> 2) Hydro_FT_2D_manual.txt<br> Description: Simulation results of the manually calibrated full model<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation 2013 [m asl]</p> <p>3.1) Hydro_FT_2D_CollocationPointBase.txt<br> Description: Parameter combinations of the collocation point base for each of the 20 simulations conducted with the full model to <br> construct the surrogate<br> Rows: Critical Shields parameter, Grain Roughness, Grain Size distribution</p> <p>3.2) Hydro_FT_2D_CollocationResults.txt<br> Description: Simulation results of the 20 simulations conducted with the full model at the collocation points<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2005 [m asl] of simulation 1 through 20, Node ID, Easting [m asl], Northig<br> [m asl], Elevations 2010 [m asl] of simulation 1 through 20, Node ID, Easting [m asl], Northig [m asl], Elevations 2013 [m asl] of<br> simulation 1 through 20<br> ----------------------------------------------------------------------------------------------------------------------------<br> 4.1) aPC_MC_N_Combinations_Weights_prior.txt<br> Description: ID of prior MC runs with tested parameter combinations and corresponding importance weights<br> Rows: ID of MC runs, Critical Shields parameter, Grain Roughness, Grain Size distribution, importance weights<br> 4.2) aPC_MC_2005_prior.txt<br> Description: aPC surrogate results of prior MC runs for 2005<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2005 [m asl] of MC run 1 through 100,000<br> 4.3) aPC_MC_2010_prior.txt<br> Description: aPC surrogate results of prior MC runs for 2010<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2010 [m asl] of MC run 1 through 100,000<br> 4.4) aPC_MC_2013_prior.txt<br> Description: aPC surrogate results of prior MC runs for 2013<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2013 [m asl] of MC run 1 through 100,000<br> <br> 4.5) aPC_MC_N_Combinations_Weights_posterior.txt<br> Description: ID of accepted (posterior) MC runs with tested parameter combinations and corresponding importance weights<br> Rows: ID of accepted MC runs, Critical Shields parameter, Grain Roughness, Grain Size distribution, importance weights<br> 4.6) aPC_MC_2005_posterior.txt<br> Description: aPC surrogate results of posterior MC runs for 2005<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2005 [m asl] of accepted MC run 1 through 857<br> 4.7) aPC_MC_2010_posterior.txt<br> Description: aPC surrogate results of posterior MC runs for 2010<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2010 [m asl] of accepted MC run 1 through 857<br> 4.8) aPC_MC_2013_posterior.txt<br> Description: aPC surrogate results of posterior MC runs for 2013<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2013 [m asl] of accepted MC run 1 through 857<br> ----------------------------------------------------------------------------------------------------------------------------<br> 5) aPC_MAP.txt<br> Description: Simulation results conducted with the stochastically calibrated aPC surrogate model using the MAP parameter <br> combination<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation 2013 [m asl]</p> <p>6) Hydro_FT_2D_MAP.txt<br> Description: Simulation results conducted with the stochastically calibrated full model using the MAP parameter combination<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation 2013 [m asl]<br> ----------------------------------------------------------------------------------------------------------------------------<br> 7) dz.txt<br> Description: Riverbed Evolution for all nodes in the section of interest (n=1138) obtained with differently calibrated models for all <br> considered time periods<br> Columns: Node ID, Easting [m asl], Northig [m asl], aPC_prior 2005 [m], aPC_posterior 2005 [m], aPC_MAP 2005 [m], <br> Hydro_FT-2D_MAP 2005 [m], Hydro_FT-2D_manual 2005 [m], aPC_prior 2010 [m], aPC_posterior 2010 [m], aPC_MAP 2010 [m],<br> Hydro_FT-2D_MAP 2010 [m], Hydro_FT-2D_manual 2010 [m], aPC_prior 2013 [m], aPC_posterior 2013 [m], aPC_MAP 2013 [m],<br> Hydro_FT-2D_MAP 2013 [m], Hydro_FT-2D_manual 2013 [m]</p> <p>8) dz_CalibrationNodes.txt<br> Description: Riverbed Evolution for calibration nodes (n=204) obtained with differently calibrated models for all considered time<br> periods<br> Columns: Node ID, Easting [m asl], Northig [m asl], aPC_prior 2005 [m], aPC_posterior 2005 [m], aPC_MAP 2005 [m], <br> Hydro_FT-2D_MAP 2005 [m], Hydro_FT-2D_manual 2005 [m], aPC_prior 2010 [m], aPC_posterior 2010 [m], aPC_MAP 2010 [m],<br> Hydro_FT-2D_MAP 2010 [m], Hydro_FT-2D_manual 2010 [m], aPC_prior 2013 [m], aPC_posterior 2013 [m], aPC_MAP 2013 [m],<br> Hydro_FT-2D_MAP 2013 [m], Hydro_FT-2D_manual 2013 [m]</p> <p> </p>
Transport Ratios for Plasma Waves Observed by MAVEN at Mars
<p>Transport ratios calculated for coherent plasma waves observed upstream from Mars by the Mars Atmosphere and Volatile EvolutioN (MAVEN) mission. These values were calculated by sub-sampling the ion velocity distribution measurements made by the Solar Wind Ion Analyzer (SWIA) instrument and binning them by the wave phase as estimated from the vector magnetic field measured by the Magnetometer (MAG) instrument. </p>
MarTREC Project Datasets for Effect of Permeability Variation of Expansive Yazoo Clay at the Maritime and Multimodal Transportation Infrastructure in Mississippi
<p>The existence of Yazoo clay soil in Mississippi frequently causes distress to the pavement and cause deformation at the slopes in highways and levees, which are a critical component in Maritime and multimodal transportation infrastructure. Each year, fixing the pavement requires a significant maintenance budget of MDOT. Also, the infiltration of the rainwater in the highway and levee slopes leads to landslides, which require millions of maintenance dollars each year. Due to the shrinkage and swelling behavior of the Yazoo clay, the hydraulic conductivity varies over the different seasons and has higher vertical permeability during the dry season. With high vertical permeability, the rainwater can easily percolate in the pavement subgrade and slopes, which accelerates the failure. The current study investigates the change in unsaturated vertical and horizontal permeability and its effect on the maritime and multimodal infrastructures, especially on the pavement and slopes of highway embankment and levees. The attached datasets include the laboratory test and finite element modeling findings.</p>
Computational Studies of Substrate Transport and Specificity in a Phospholipid Flippase
<p>MD trajectories of all-atom and CG simulations of PI4P activated E2P state of the Drs2p-Cdc50p complex.</p>
Source data for road transportation applications (road surface assessment, authentication of automotive vehicles)
<p>This data set records the driving using an Inertial Measurement Units of 12 different vehicles on the road infrastructure of the European Commission Joint Research Centre.</p> <p>The data set is described more in detail in the paper:</p> <p>Baldini, G.; Geib, F.; Giuliani, R. Continuous Authentication of Automotive Vehicles Using Inertial Measurement Units. <em>Sensors</em> <strong>2019</strong>, <em>19</em>, 5283.</p> <p><a href="https://doi.org/10.3390/s19235283">https://doi.org/10.3390/s19235283</a></p> <p>Please, cite this paper if you use this data set.</p>
Data: "Solute transport in bounded porous media (...)" by Sole-Mari et al. (2020)
<p>Data corresponding to the results of the Monte Carlo set of simulations described in: "Solute transport in bounded porous media characterized by Generalized Sub-Gaussian log-conductivity distributions". Please send questions to guillem.sole.mari@outlook.com.</p>
DEM simulations of size-segregation during bedload transport
<p>This depository contains the data of all DEM simulations used in the publication Chassagne, R., Maurin, R., Chauchat, J., Gray, J., & Frey, P. (2020). Discrete and continuum modelling of grain size segregation during bedload transport. <em>Journal of Fluid Mechanics,</em> <em>895</em>, A30. doi:10.1017/jfm.2020.274, as well as post processing scripts to use the data.</p> <p>The simulations are located in two folders, fine/ (simulations for which the amount of fine particles is varied) and sizeRatio (simulations for which the size ratio between large and small particles is varied). The data of each simulations are contained in separate subfolders named after the simulation. For example, Fine2R1.5/ corresponds to a simulation with 2 layers of small particles and a size ratio of 1.5. For each simulation, the time data are saved in data.hdf5 and averaged data in average.hdf5. A GeomParam.txt file is also in each folder. It contains information of the simulation that the post processing programm will read. The python script used to initiate the YADE-DEM simulation is also given for information (it contains all parameters of the simulation).</p> <p>The post-processing program has been coded in python2.7 with an oriented-object procedure. The h5py package is necessary to read the .hdf5 files. The scripts do not work in python3, but can be very easily adapted if necessary (you only have to modify the "print" functions). The scripts are available in ScriptsPP/ and are organized as follow. A mother class in SegregationPP and two child classes SegFull (to load the full time data set) and SegMean (to load only average data). A script examplePP.py is proposed and shows how to manipulate theses classes and the data.</p>
DEM simulations of bi-disperse beds during bedload transport
<p>This depository contains the data of all DEM simulations used in the publication Chassagne, R., Frey, P., Maurin, R., and Chauchat, J. Mobility of bidisperse mixtures during bedload transport. Physical Review Fluids, 5(11):114307. doi:10.1103/PhysRevFluids.5.114307, as well as post processing scripts to use the data.</p> <p>The simulations are located in seven folders, Monodisperse/ (mondisperse simulations where the fluid forcing is varied), N0.5/ (simulations with 0.5 layer of large particles above a small particle bed and or different fluid forcing), N1/ (simulations with 1 layer of large particles above a small particle bed and or different fluid forcing), N2/, N3/, N4/ and sizeRatio (2 layers of large particles, fixed fluid forcing but the diameter of the underlying small particles is varied). The data of each simulations are contained in separate subfolders named after the simulation. For example, H8Sh0.45/ corresponds to a monodisperse simulation with a bedheight of 8dl (dl is the large particle diameter) and a shields number of 0.45. H10N2R2Sh0.7/ corresponds to a bidisperse simulation with a bed height of 10dl, 2 layers of large particles, a size ratio of 2 between large and small particles and a shields number of 0.7. For each simulation, the time data are saved in data.hdf5 and averaged data in average.hdf5. A GeomParam.txt file is also in each folder. It contains information of the simulation that the post processing programm will read.</p> <p>The python script used to initiate the YADE-DEM simulation is also given for information (it contains all parameters of the simulation). The post-processing programm has been coded in python2.7 with an oriented-object procedure. The h5py package is necessary to read the .hdf5 files. The scripts do not work in python3, but can be very easily adapted if necessary (you only have to modify the "print" functions). The scripts are available in ScriptsPP/ and are organized as follow. For bidisperse simualtions, a mother class in SegregationPP and two child classes SegFull (to load the full time data set) and SegMean (to load only average data). For monodisperse simualtions, a mother class in MonodispersePP and two child classes MonoFull (to load the full time data set) and MonoMean (to load only average data). Two scripts examplePP1.py and examplePP2.py are proposed and show how to manipulate theses classes and the data.</p>
Engaging the Business and Tourism Industry in Visualizing Sea Level Rise Impacts to Transportation Infrastructure in Waikiki, Hawaii
<p>Transportation planners in coastal communities plan for future hazards and risks of sea-level rise (SLR), and often, they communicate risk in public meetings via PowerPoint presentations with charts as well as two-dimensional (2D) maps that visualize information using Geographic Information Systems (GIS) technologies. The project investigates the use of immersive technology to communicate SLR risk, including the development of an immersive three-dimensional (3D) model of the Waikiki neighborhood of Honolulu, Hawaii. According to the project’s original methodology, participants would have experienced the model using virtual reality (VR). However, due to the COVID-19 pandemic, the team pivoted to creating and implementing an internet-based survey instrument with embedded 2D charts and video of the animated 3D model. The flooding projections were derived from National Oceanic and Atmospheric Administration (NOAA) data. NOAA supplies the SLR Viewer, a screening-level tool that uses the best-available national projections to map areas vulnerable to current and future flood risks.</p>
Data and code for the publication "Tracing the horizontal transport of microplastics on rough surfaces"
<p><strong>Background</strong></p> <p>The data set contains images of fluorescent PMMA (Polymethyl methacrylate) particles that are moved by water on rough surfaces in an irrigation experiment. The experiments were done in the laboratory at the Institute of Geography, University of Cologne, Germany, in Septembre 2020. The images were taken with an sCMOS (advanced scientific complementary metal-oxide-semiconductor) high resolution pco.panda 4.2 camera (PCO AG, Kehlheim, Germany).</p> <p>The data set was analysed in the publication: Laermanns, H., Lehmann, M., Klee, M., Löder, M.G.J., Gekle, S. and Bogner, C., 2021, “Tracing the horizontal transport of microplastics on rough surfaces,” Microplastics and Nanoplastics, <a href="https://doi.org/10.1186/s43591-021-00010-2">https://doi.org/10.1186/s43591-021-00010-2</a></p> <p>Additionally to the data, this collection of files contains the Python and R scripts/notebooks used to analyse the images and create graphics for the publication. The code for the simulation of flow patterns can be obtained from the authors upon request.</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>The data and code are provided as is without any warranty.</p> <p>Experimental parameters</p> <ul> <li> <p>Surface roughness: two levels, fine and course</p> </li> <li> <p>Inclination: 6 levels, 2.5°, 5°, 7.5°, 10°, 12.5° and 15°</p> </li> <li> <p>Irrigation: three levels, 4.8, 7.2 and 10.44 L/h</p> </li> <li> <p>Repetitions: three</p> </li> </ul> <p>More details on the experimental setup are given in the publication.</p> <p> </p> <p><strong>Description of the dataset</strong></p> <p>The folder <strong>images.zip</strong> contains the images. They are organized as follows:</p> <ul> <li><strong>Feinsand_10_Partikel</strong>: images of PMMA particles on the fine surface</li> <li><strong>Grobsand_10_Partikel</strong>: images of PMMA particles on the rough surface <ul> <li> <p>Both folders contain six subfolders <strong>_XX_Grad_Gefaelle</strong>, XX being 2_5, 5, 7_5, 10, 12_5, 15. These folders refer to inclinations of 2.5°, 5°, 7.5°, 10°, 12.5° and 15° of the rough surfaces, respectively.</p> </li> <li> <p>every folder _XX_Grad_Gefaelle contains three subfolders <strong>Fliessgeschwindigkeit_YY</strong>, with YY being 20mlx4, 30mlx4 and 43_5mlx4, the parameters of the peristaltic pump, corresponding to irrigation rates of 4.8, 7.2 or 10.44 L/h, respectively.</p> </li> <li> <p>every folder Fliessgeschwindigkeit_YY contains three subfolders <strong>Z_Durchgang</strong> with Z being 1, 2 or 3 corresponding to the tree repetitions of the experiment.</p> </li> </ul> </li> <li><strong>stained_flow_patterns</strong>: images of flow patterns of the fluorescent dye Nile Red (in methanol), an mp4 video and a text file with parameters to produce the video based on the images. The images were produced for the following experimental parameters: <ul> <li> <p><strong>Feinsand_2_5_Grad_20_ml</strong>: fine surface, inclined by 2.5° and irrigated with 7.2 L/h</p> </li> <li> <p><strong>Grobsand_7_5_Grad_20_ml</strong>: coarse surface, inclined by 7.5° and irrigated with 7.2 L/h</p> </li> </ul> </li> </ul> <p>The file <strong>experimental_data.csv</strong> links the concatenated folder names to experimental parameters.</p> <p> </p> <p><strong>Description of the code</strong></p> <p>The images were first processed in Python to locate the PMMA particles and calculate particle sizes. The Python code is located in the <strong>py_scripts.zip</strong> folder. It contains the following files:</p> <ul> <li> <p><strong>find_XYZ</strong>: locates PMMA particles. XYZ stands for different experimental parameters (see above). Scripts containing the string <strong>_problems</strong> locate PMMA particles for images with possible artefacts (smeared particles, residual light etc.). You need to uncomment the appropriate lines in the files to rerun the code because it was run piece by piece.</p> </li> <li> <p><strong>pickle_to_csv.py</strong>: converts pickle files to csv files</p> </li> <li> <p><strong>calculate_sizes.py</strong>: calculates the sizes of PMMA particles from the first image of each experiment</p> </li> <li> <p><strong>py_functions_new.py</strong>: contains custom functions</p> </li> </ul> <p>Further analysis run in a mixture of R and Pyhton in one working document (R Notebook):</p> <ul> <li> <p><strong>Analysis_with_loops.Rmd</strong>: tracking of the PMMA particles by PtrakPy version 0.4.2 (Allan et al. 2019). Python 3.8 (Python Software Foundation, <a href="https://www.python.org/">https://www.python.org/</a>) was called directly from R using the R package reticulate (<a href="https://rstudio.github.io/reticulate/">https://rstudio.github.io/reticulate/</a>) in RStudio (<a href="https://www.rstudio.com/">https://www.rstudio.com/</a>).</p> </li> <li> <p><strong>Analysis_for_paper.Rmd</strong>: R code for analysis of tracking, statistical analysis, plotting. We used the R version 4.0.3 (R Core Team 2020).</p> </li> <li> <p><strong>helper_function.R</strong>: contains custom R functions for the analysis</p> </li> </ul> <p> </p> <p><strong>Results</strong></p> <p>The file <strong>results.zip</strong> contains the folders:</p> <ul> <li> <p><strong>data</strong>: *.pickle files produced by Python containing the trajectories of PMMA particles</p> </li> <li> <p><strong>data_csv</strong>: *.pickle files converted to *.csv files</p> </li> <li> <p><strong>figures</strong>: figures produced by the code during the analysis, organized in different subfolders</p> </li> <li> <p><strong>RData</strong>: large computational results produced and saved during analysis</p> </li> <li> <p><strong>sizes_csv</strong>: *.csv files containing PMMA particle sizes and further morphological characteristics; produced during analysis</p> </li> </ul> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The authors thank Julia Horn for support in the laboratory and Florian Steininger for technical assistance.</p> <p> </p> <p><strong>Funding</strong></p> <p>This project was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Project Number 391977956, SFB 1357, subprojects B04 and B06.</p> <p> </p> <p><strong>References</strong></p> <p>Allan, Dan, Casper van der Wel, Nathan Keim, Thomas A Caswell, Devin Wieker, Ruben Verweij, Chaz Reid, et al. 2019. <em>Soft-Matter/Trackpy: Trackpy V0.4.2</em> (version v0.4.2). Zenodo. <a href="https://doi.org/10.5281/zenodo.3492186">https://doi.org/10.5281/zenodo.3492186</a>.</p> <p>Laermanns, Hannes, Moritz Lehmann, Marcel Klee, Martin GJ Löder, Stephan Gekle, and Christina Bogner. 2021. “Tracing the Horizontal Transport of Microplastics on Rough Surfaces.” <em>Microplastics and Nanoplastics</em>. <a href="https://doi.org/10.1186/s43591-021-00010-2">https://doi.org/10.1186/s43591-021-00010-2</a>.</p> <p>R Core Team. 2020. <em>R: A Language and Environment for Statistical Computing</em>. Vienna, Austria: R Foundation for Statistical Computing. <a href="https://www.R-project.org/">https://www.R-project.org/</a>.</p>
NACLIM - Fluxes: Faroe Shetland Channel volume transport of Atlantic Water
<p><strong>Last update: 14 January 2015</strong></p> <p><strong>Data set:</strong> Faroe Shetland Channel volume transport of Atlantic Water</p> <p><strong>Description: </strong>Monthly mean volume transport of Atlantic Water</p> <p><strong>Period: </strong>December 1992 – March 2014 </p> <p><strong>Location: </strong>61° N 4° W </p> <p><strong>Instruments: </strong>CTD, moored current meters and ADCPs, altimeter </p> <p><strong>Variables:</strong> Volume transport of Atlantic water based on ADCP and altimeter observations</p> <p><strong>Source: </strong>Barbara Berx (MARLAB) </p>
NACLIM - Fluxes: (DWBC) Labrador Sea Deep Western Boundary Current transport at 53° N
<p><strong>Last update: 17 October 2014</strong></p> <p><strong>Data set:</strong> Labrador Sea Deep Western Boundary Current (DWBC) transport at 53° N </p> <p><strong>Description:</strong> 5-day averages of DWBC along-shore volume transport as measured at the 53° N mooring array, per water mass in depth layers</p> <p><strong>Period:</strong> July 1997 – April 2012</p> <p><strong>Location:</strong> 53° 0′ N 51° 0′ W </p> <p><strong>Instruments:</strong> Moored rotor current meters (RCM); acoustic current meters (ACM); acoustic Doppler current profilers (ADCP) </p> <p><strong>Variables:</strong> NADW, NEADW, DSOW, LNADW - along-shore volume transport for each water mass (LSW transport may be derived as described in the readme-file)</p> <p><strong>Source: </strong>Johannes Karstensen, Jürgen Fischer and Rainer Zantopp (GEOMAR)</p> <p><strong>About the data: </strong></p> <p>LabSea53N_DWBC.dat (ASCII): This is the updated DWBC transports at 53N in ascii form, including all available data from July 1997 to April 2012</p> <p>LabSea53N_DWBC.nc (netCDF)</p> <p>Plot (JPEG): The export of North Atlantic Deep Water (NADW) from the moored array at the exit of the Labrador Sea. Data at 5day resolution (red and green lines), for 90day low-pass time series (black), and as annual mean transports (blue bars) – see legend in the graph. Transport statistics also included.</p>
NACLIM - Fluxes: Wyville Thomson Ridge overflow transport
<p><strong>Last update: 14 March 2014</strong></p> <p><strong>Data set: </strong>Wyville Thomson Ridge overflow transport</p> <p><strong>Description: </strong>Daily mean of overflow volume transport </p> <p><strong>Period:</strong> September 2003 – May 2013</p> <p><strong>Location: </strong>60°15′ N 9°0′ W</p> <p><strong>Instruments:</strong> CTD, moored current meters and ADCPs</p> <p><strong>Variables:</strong> Total and undiluted cold water volume transports </p> <p><strong>Source: </strong>Toby Sherwin and Clare Johnson (SAMS)</p>
NACLIM - Fluxes: Iceland-Faroe Atlantic inflow transport
<p><strong>Last update: 31 October 2014</strong></p> <p><strong>Data set: </strong>Iceland-Faroe Atlantic inflow transport </p> <p><em>Preliminary results: HAV is in the process of recalculating the whole series (Update 31 October 2014)</em></p> <p><strong>Description: </strong>Weekly averaged volume flux of Atlantic water through section N (see map) crossing the Faroe Current</p> <p><strong>Period:</strong> June 1997 – May 2014 </p> <p><strong>Location:</strong> 62°20′ – 63°40′ N 6°05′ W</p> <p><strong>Instruments:</strong> Moored Acoustic Doppler Current Profilers (ADCP) and CTD sections </p> <p><strong>Variables: </strong>Atlantic water component of transport (Sv) through the section, which has come across the Iceland-Faroe Ridge </p> <p><strong>Source:</strong> Bogi Hansen and Karin Margretha H. Larsen (HAV)</p>
Morphology and load transport behaviour of the seed-harvesting ant Messor barbarus Linnaeus 1767
<p>This Excel file contains two datasets. The first dataset (sheet “Morphometric data”) gives the measure of thorax length, body mass as well as the mass of main body segments of 57 individual workers of the ant species <em>Messor barbarus</em> Linnaeus 1767. The workers originated from a colony collected in Saint-Hippolyte, France (42°80 N, 2°98 E) in October 2013. The second dataset (sheet “Transportation data”) relates to observations aiming at investigating the efficiency of <em>M. barbarus</em> workers when transporting pieces of dry pasta on their foraging trails. The data gives the body mass, the characteristics of the item transported, the transportation method (carry or drag), as well as the time required to travel a distance of 1m for 239 individual workers from five colonies located on the campus of the Universitat Autònoma de Barcelona, Spain (41°30′00N, 2°06′49E). For each ant followed the air temperature at ground level is also indicated.</p>
Host-derived viral transporter protein for nitrogen uptake in infected marine phytoplankton
<p>Dataset for the article "Host-derived viral transporter protein for nitrogen uptake in infected marine phytoplankton", Monier et al.</p> <p>Data for all phylogenetic tree reconstructions (raw and masked protein sequence alignments in fasta format, tree file in newick format) and placement file (jplace format) of two environmental sequences are available.</p> <p>Data for all assay experiments are available: ammonium and urea assays, Omnilog phenotype screening (Nitrogen substrates).</p>
How to transport veterinary drugs in insulated boxes to avoid thermal damage by heating or freezing
<p><strong>Documentation of the empiric data, measured in the heating and cooling chamber to determine the thermal constant for the investigated transport boxes.</strong></p> <p> </p> <p><strong>Background: </strong>The transport of veterinary drugs must comply with the general standards for drug storage. Although many vehicles are equipped with active heating and/or cooling devices assuring recommended storage conditions, simple insulated transport boxes are also often used. In this study, measurements for typical transport boxes were performed under laboratory conditions by the use of a climate chamber for a temperature of -20°C and 45°C to investigate the impact of box size, insulation material, liquid vs. dry filling products, filling degree and other parameters on the thermal performance of insulated boxes. Model calculations and instructions are presented to predict the retention time of recommended drug storage temperatures.</p> <p><strong>Results: </strong>The measurements and the model calculations showed that the loading of the transport boxes with additional water bottles to increase the heat capacity is appropriate to prolong the retention time of the recommended temperature range of the drugs. Insulated transport boxes are not suitable to store drugs over a period of more than approximately 12 hours. For practical use, a recipe is presented to measure the thermal properties of a transport box and the related retention time for which the recommended storage temperatures can be assured.</p> <p><strong>Conclusions: </strong>The following principles for drug transportation in vehicles are recommended: (1) Before transfer into boxes, drugs should always be thermally preconditioned (2) Increase the filling degree of the boxes with thermally preconditioned water bottles or re-usable thermal packs will increase the heat capacity. Do not deep-freeze the bottles or packs below 0°C to avoid drug freezing due to contact. (3) Open the lid of the boxes only to uncase drugs that are immediately needed. (4) The bigger the box and the higher the filling degree, the longer the retention time of the transport box. (5) Wherever possible, place the drug box at a cool site inside the vehicle. (6) The monitoring of the inside temperature of the transport boxes is recommended. <br> By the proper use of such transport boxes the recommended temperatures can be maintained over one working day.</p> <p> </p>
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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.