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190 results for “intrusions”
Mapping of a Mid-depth Salinity Maximum Intrusion south of New England in June 2021
<div> <div> <p>This dataset contains data from a process-oriented research cruise aboard the R/V Neil Armstrong from June 18th to July 2nd. The goal of this cruise was to map the three-dimensional structure of a mid-depth salinity maximum intrusion of warm salinity slope water extending onto the continental shelf south of New England. This was done through the use of Autonomous Underwater Vehicles (two REMUS 100 vehicles and one Tethys class AUV (Long Range AUV or LRAUV)), a towed Rockland Scientific Vertical Microstructure Profiler (VMP 250), and ship-board CTD and ADCP measurements. More details about the processing, data coverage, and usage can be found in the accompanying manuscript. This cruise took place on the shelf waters south of Cape Cod, MA, extending to the shelf break, with all of the data collected between 40°N to 41°N and 71.5°W to 70°W. Attached is a data map showing the location of all data included within this dataset. </p> </div> <div> <p> </p> </div> <div> <p>File Descriptions: </p> </div> <div> <p><strong>Datamap.jpg </strong></p> </div> <div> <p>A map of the locations of all data included within this dataset. </p> </div> <div> <p> </p> </div> <div> <p><strong>CTD_summer2021.mat </strong></p> </div> <div> <p>This file contains profiles from the ship-board CTD (SeaBird 911+). Raw data was processed and gridded into 1 decibar bins using standard procedures in Seasave V 7.26.7.121 (Look at cnv file header for details about processing). This file is organized as a structure, with each variable in the data being a different field called by dot notation and each row with the structure being a different CTD profile. Biooptical variables are not quality-controlled. </p> </div> <div> <ul> <li> <p>CTD.time: the time of each profile in the MATLAB datetime format (from the processed SeaBird header file) in GMT </p> </li> <li> <p>CTD.lon: degrees longitude of the profile (from the processed SeaBird header file) </p> </li> <li> <p>CTD.lat: degrees latitude of the profile (from the processed SeaBird header file) </p> </li> <li> <p>CTD.pres: the pressure in decibar at each location of the profile </p> </li> <li> <p>CTD.sal: the seawater practical salinity in psu </p> </li> <li> <p>CTD.temp: the seawater in-situ temperature in °C </p> </li> <li> <p>CTD.flor: seawater fluorescence in mg/ m3 </p> </li> <li> <p>CTD.depth: depth at each location within the profile in meters </p> </li> <li> <p>CTD.density: sigmatheta (the potential seawater density with respect to a reference pressure of 0 db) in kg.m3 minus 1,000kg/m3 </p> </li> </ul> </div> <div> <p> </p> </div> <div> <p><strong>CTD_Darter_MMMdd.mat and CTD_Edgar_MMMdd.mat </strong></p> </div> </div> <div> <div> <p>These files contain the data from the REMUS 100 missions, with Darter and Edgar being the two different REMUS 100 vehicles. </p> </div> <div> <ul> <li> <p>Conductivity: conductivity in mS/cm </p> </li> <li> <p>Depth: depth in meters </p> </li> <li> <p>Latitude: degrees latitude </p> </li> <li> <p>Longitude: degrees longitude </p> </li> <li> <p>Mission_number: the number of the REMUS mission </p> </li> <li> <p>Mission_time: time during the mission in seconds since midnight in GMT </p> </li> <li> <p>Salinity: the seawater practical salinity in psu </p> </li> <li> <p>Sound_speed: the sound speed in m/s </p> </li> <li> <p>Temperature: the seawater temperature in °C </p> </li> </ul> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p><strong>LRAUV_20210623T194917.mat and LRAUV_20210624T145829.mat </strong></p> </div> <div> <p>These files contain data from the Tethys Class LRAUV (Long Range AUV) missions. Each file contains 10 structure variables. </p> </div> </div> <div> <div> <ul> <li> <p>CTD_Seabird: structure containing the bin median temperature in °C and salinity in PSU. </p> </li> <li> <p>depth: the depth at each data point in meters. </p> </li> <li> <p>fix_residual_percent_distance_traveled: underwater dead-reckoned navigation error (based on GPS fix when on surface) as a percentage of distance traveled </p> </li> <li> <p>latitude: Latitude at each data point (not corrected for vehicle drift in underwater current) </p> </li> <li> <p>latitude_fix: latitude of GPS fix (vehicle surfaced) </p> </li> <li> <p>longitude: Longitude at each data point (not corrected for vehicle drift in underwater current) </p> </li> <li> <p>longitude_fix: longitude of GPS fix (vehicle surfaced) </p> </li> <li> <p>platform_battery_charge: The battery charge in ampere-hour </p> </li> <li> <p>time_fix: time in seconds since January 1, 1970 (epoch time) </p> </li> </ul> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p><strong>VMPtransact_YYYYMMdd.mat</strong></p> <p>Vertical Microstructure Profiler (Rockland Scientific VMP 250) </p> </div> <div> <p>These files contain the processed data for each Vertical Microstructure Profiler (Rockland Scientific VMP 250) transect, consisting of multiple profiles. Data has been gridded on a 1 decibar equidistant grid using standard procedures in Rockland Scientific’s processing software. Note: Bio-optical variables and dissipation rates have not been quality-controlled. </p> </div> <div> <ul> <li> <p>Time: Time in MATLAB datenum format (days since 0000-00-00 00:00:00) in GMT </p> </li> <li> <p>z: Pressure in decibar </p> </li> <li> <p>T: in-situ temperature in degC </p> </li> <li> <p>cnd: conductivity in mS/cm </p> </li> <li> <p>Chl: Chlorophyll from fluorescence in mg/ m3 </p> </li> <li> <p>turb: Turbidity in NTU </p> </li> <li> <p>eps: dissipation rate inferred from microstructure shear in m^2/s^3. (Note: Dissipation estimates come from standard fitting of microstructure data within a 1 decibar bin to a turbulence spectrum within Rockland Scientific’s standard processing. The dissipation data in the provided files has not been quality-controlled. </p> </li> </ul> </div> <div> <p> VMP-data was georeferenced by comparing the time stamps of VMP and processed ADCP files. </p> </div> <div> <p> </p> </div> <div> <p><strong>ADCP_ar50_wh300.mat </strong></p> </div> <div> <p>This file contains the data from the shipboard ADCP (Teledyne WH300 kHz). ADCP data was processed aboard using standard procedures in UHDAS/CODAS (University of Hawaii Technical Services Program, servicing UNOLS vessels (<a href="https://currents.soest.hawaii.edu/docs/adcp_doc/index.html" target="_blank" rel="noreferrer noopener">https://currents.soest.hawaii.edu/docs/adcp_doc/index.html). Vertical bin size is 2 m. </a>u: zonal (positive towards east) velocity component in m/s </p> <ul> <li> <p>v: meridional (positive towards north) component in m/s </p> </li> </ul> </div> </div> <div> <div> <ul> <li> <p>txy: time, longitude, and latitude of the velocity profiles. Time is in decimal days, with noon of Jan 1 being 0.5 decimal days and noon of January 20th being 19.5 decimal days of the reference year. For another example, 6am on June 18, 2021, is decimal day 168.25. All times are in GMT. </p> </li> <li> <p>refyear: The reference year from which the decimal days are calculated. </p> </li> <li> <p>depth: vertical coordinate of the velocity bin center </p> </li> <li> <p>pgood: percent good, a quality parameter showing the fraction of good pings within an ensemble average. </p> </li> <li> <p>spd_u: zonal ship speed in m/s </p> </li> <li> <p>spd_v: meridional ship speed in m/s </p> </li> <li> <p>tr_temp: ADCP transducer temperature in deg C </p> </li> <li> <p>amp: backscatter amplitude in relative units </p> </li> </ul> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p> </p> </div> </div>
Effect of salt water intrusion on the distribution of invertebrates in a GA tidal freshwater marshes from the GCE Seawater Addition Long-Term Experiment (SALTEx) project.
To characterize the effect of persistent and episodic salt water intrusion on the distribution of common freshwater marsh invertebrates, we monitored the density of adult and juvenile fiddler crabs and snails. Prior to the start of salt water addition treatments, we collected data on the distribution of crabs and snails in all 30 experimental plots (6 replicates of 5 treatments: pressed salt water addition, pulsed salt water addition, fresh water addition, procedural control structure, and control no structure). In each experimental plot, we counted the number of adult and juvenile fiddler crab burrows and snails visible on the marshs surface in a 50cm x 75cm plot (juvenile fiddler crabs were counted in only half of this area) that was positioned in the Northeastern corner of each experimental plot. Initial data was collected in March 2014. A Bentho Torch was used to measure the concentrations of cyanobacteria, diatoms, and green algae on the marsh surface in 2015 and 2016.
Metal Intrusion Model Results
<p>Model results showing the percentage of geochem preserved and rounding facilitated in the pallasite formation region; all parameters used are included in csv results files.</p>
Monthly maps of Warm Core Ring Occupancy and occurrences of Salinity Maximum Intrusions in the Slope Sea (1990-2019)
<p>This dataset presents two important variables across the shelfbreak in the Northwest Atlantic: (i) Warm Core Ring Occupancy in the Slope sea proximate to the shelfbreak; and (ii) locations of Salinity maximum intrusions in the shelf. Monthly fields of both of these fields together are presented for the period 1990-2019 with file name format <em>smax_ring_mm_yyyy.jpg. </em>The gray and red dots on the shelf represent locations of profiles taken from the Ecosystem Monitoring Program’s (EcoMon) hydrographic data (available from the National Centers for Environmental Information World Ocean Database accessible at <a href="http://www.ncei.noaa.gov/products/world-ocean-database">www.ncei.noaa.gov/products/world-ocean-database</a>). Red dots show locations of profiles which contained mid-depth salinity maximum intrusions, gray dots are profiles without any mid-depth salinity maximum intrusion. Profiles with intrusions were identified using the methodology of Gawarkiewicz et al., 2022. The ring occupancy was calculated from a Warm Core Ring Tracking dataset with ring tracks from 2000-2010 and 2011- 2020 available from Zenodo (<a href="https://doi.org/10.5281/zenodo.6436380">https://doi.org/10.5281/zenodo.6436380</a>, <a href="https://doi.org/10.5281/zenodo.7406675">https://doi.org/10.5281/zenodo.7406675</a>) and ring trajectories from 1978 through 1999 available from the Bedford Institute of Oceanography, Canada. To calculate the ring occupancy the region was sub-divided into 0.1 by 0.1 degree bins. Ring trajectories and approximate geographical range (calculated from the ring area, assuming the ring is a perfect circle) were overlain on this region and the days rings are present in each bin are counted in units of ring days. A ring day is the presence of one single ring in a bin during a given day. These ring day counts were converted to percentages, dividing by days in the given month and multiplying by 100. For more details see Silver et al., 2022 and Salois et al., 2023. From these figures one can see the spatial relationship between Warm Core Rings and Salinity Maximum Intrusions, with clusters of intrusions occurring in areas adjacent to high ring occupancy. </p> <p>An animation of two particular years is also presented in <em>movie_smax_ring_1993_2012.gif</em> to highlight this relationship: Low ring year (1993) leading to fewer Smax intrusion and high ring year (2012) leading to more intrusions.</p> <p> </p> <p>Gawarkiewicz, G., Fratantoni, P., Bahr, F., & Ellertson, A. (2022). Increasing Frequency of Mid‐Depth Salinity Maximum Intrusions in the Middle Atlantic Bight. <em>Journal of Geophysical Research: Oceans</em>, <em>127</em>(7), e2021JC018233. <a href="https://doi.org/10.1029/2021JC018233">https://doi.org/10.1029/2021JC018233</a></p> <p>Silver, A., Gangopadhyay, A., Gawarkiewicz, G., Andres, M., Flierl, G., & Clark, J. (2022). Spatial Variability of Movement, Structure, and Formation of Warm Core Rings in the Northwest Atlantic Slope Sea. <em>Journal of Geophysical Research: Oceans</em>, <em>127</em>(8), e2022JC018737. <a href="https://doi.org/10.1029/2022JC018737">https://doi.org/10.1029/2022JC018737</a> </p> <p>Salois, S. L., Hyde, K. J., Silver, A., Lowman, B. A., Gangopadhyay, A., Gawarkiewicz, G., ... & Lapp, M. (2023). Shelf break exchange processes influence the availability of the northern shortfin squid, Illex illecebrosus, in the Northwest Atlantic. <em>Fisheries Oceanography</em>. <a href="https://doi.org/10.1111/fog.12640">https://doi.org/10.1111/fog.12640</a> </p>
Raw data of "Seasonal fluctuations of ichthyoplankton assemblage in the northeastern South China Sea influenced by the Kuroshio intrusion"
<p>The uploaded data here is the raw data of the manuscript "Seasonal fluctuations of ichthyoplankton assemblage in the northeastern South China Sea influenced by the Kuroshio intrusion" submitted to the Journal of Geophysical Research-Oceans. The CTD file (.cnv) is the data recorded by a Sea-Bird conductivity, temperature and depth (CTD) in the sampling stations. This data is used to analyze the water masses during the study period. It can be analyzed with free software of the Ocean Data View 4 (http://odv.awi.de/) or the MATLAB R2017 (http://www.mathworks.com/products/matlab/). The sequence data (.fasta) is used to evaluate the species composition. The data can be analyzed with free software of the BOLD Identification tool (http://www.boldsystems.org/), the basic local-alignment search tool (BLAST) (https://www.ncbi.nlm.nih.gov/), the Clustal X 2.1 (http://www.clustal.org/) and the MEGA 7 (http://www.megasoftware.net/). In additon, the .nc files are the data of surface temperature during the sampling periods. The data can be analyzed with the MATLAB R2017 (http://www.mathworks.com/products/matlab/).</p>
HIKARI-2021: Generating Network Intrusion Detection Dataset Based on Real and Encrypted Synthetic Attack Traffic
<p>Available datasets from the paper Generating Encrypted Network Traffic for Intrusion Detection Datasets.</p> <p>To produce the dataset follow the technical detail in <a href="https://github.com/andreysfc/generating-encrypted-network">github</a></p>
Magnetic, gravity and seismicity data for the Monchique intrusion and surroundings (SW Portugal, SW Iberia)
<p>This dataset contains the following data:</p> <p> </p> <p><strong>1.</strong> Magnetic anomaly data (processed line data) acquired by drone-borne magnetometer for the Monchique area (.dat file)</p> <p><strong>2.</strong> Magnetic and gravity anomaly maps for the Monchique area in SW Portugal, SW Iberia:</p> <ul> <li>Magnetic anomaly (.tif and .grd files)</li> <li>Reduced to the pole (RTP) magnetic anomaly (.tif and .grd files)</li> <li>Free air gravity anomaly (.tif and .grd files)</li> <li>Complete Bouguer gravity anomaly, after terrain correction (.tif and .grd files)</li> </ul> <p><strong>3.</strong> Seimicity data:</p> <ul> <li>Relocated earthquakes that occurred between 01/01/2007 and 01/07/2023 in the Monchique area (.xlsx file)</li> <li>Focal mechanisms (moment tensor inversion solutions) of earthquakes occurred in the Monchique area (.xlsx file)</li> </ul> <p> </p> <p>For all details on data collection and processing please refer to:</p> <p>Neres, M., Camargo, G., Soares, A., Custódio, S., Bos, M., Vales, D., & Terrinha, P. (2024). Monchique alkaline magmatic intrusion (SW Iberia): Geophysical modeling and relationship with active seismicity and hydrothermalism. <em>Tectonophysics</em>. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.tecto.2024.230426" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.tecto.2024.230426</a></p> <p> </p>
XRDs of Materials used in the Supplementary Information file of A. Lowe et al Exploring the Heat of Water Intrusion ... ACS Appl. Mater. Interfaces 2024, 16, 5286−5293
<p>Data plots were limited to 2theta range from 5 degrees to 50 degrees. CuKa</p>
Federated Learning for Distributed Intrusion Detection Systems in Public Networks - Validation Dataset
<p>This dataset has been meticulously prepared and utilized as a validation set during the evaluation phase of "Meta IDS" to asses the performance of various machine learning models. It is now made available for interested users and researchers who seek a reliable and diverse dataset for training and testing their own custom models.</p> <p>The validation dataset comprises a comprehensive collection of labeled entries, that determines whether the packet type is "malicious" or "benign." It covers complex design patterns that are commonly encountered in real-world applications. The dataset is designed to be representative, encompassing edge and fog layers that are in contact with cloud layer, thereby enabling thorough testing and evaluation of different models. Each sample in the dataset is labeled with the corresponding ground truth, providing a reliable reference for model performance evaluation.</p> <p> </p> <p>To ensure convenient distribution and storage, the dataset has been broken down into three separate batches, each containing a portion of the dataset. This allows for convenient downloading and management of the dataset. The three batches are provided as individual compressed files.</p> <p> </p> <p>In order to extract the data, follow the following instructions:</p> <ul> <li>Download and install bzip2 (if not already installed) from the official website or your package manager.</li> <li>Place the compressed dataset file in a directory of your choice.</li> <li>Open a terminal or command prompt and navigate to the directory where the compressed dataset file is located.</li> <li>Execute the following command to uncompress the dataset: <ul> <li>bzip2 -d filename.bz2</li> </ul> </li> <li>Replace "filename.bz2" with the actual name of the compressed dataset file.</li> </ul> <p>Once uncompressed, you will have access to the dataset in its original format for further exploration, analysis, and model training etc. The total storage required for extraction is approximately 800 GB in total, with the first batch requiring approximately 302 GB, the second batch requiring approximately 203 GB, and the third batch requiring approximately 297 GB of data storage.</p> <p> </p> <p>The first batch contains 1,049,527,992 entries, where as the second batch contains 711,043,331 entries, and for the third and last batch we have 1,029,303,062 entries. The following table provides the feature names along with their explanation and example value once the dataset is extracted.</p> <p> </p> <table align="left"> <thead> <tr> <th scope="col">Feature</th> <th scope="col">Description</th> <th scope="col">Example Value</th> </tr> </thead> <tbody> <tr> <td>ip.src</td> <td>Source IP address in the packet</td> <td>a05d4ecc38da01406c9635ec694917e969622160e728495e3169f62822444e17</td> </tr> <tr> <td>ip.dst</td> <td>Destination IP address in the packet</td> <td>a52db0d87623d8a25d0db324d74f0900deb5ca4ec8ad9f346114db134e040ec5</td> </tr> <tr> <td>frame.time_epoch</td> <td>Epoch time of the frame</td> <td>1676165569.930869</td> </tr> <tr> <td>arp.hw.type</td> <td>Hardware type</td> <td>1</td> </tr> <tr> <td>arp.hw.size</td> <td>Hardware size</td> <td>6</td> </tr> <tr> <td>arp.proto.size</td> <td>Protocol size</td> <td>4</td> </tr> <tr> <td>arp.opcode</td> <td>Opcode</td> <td>2</td> </tr> <tr> <td>data.len</td> <td>Length</td> <td>2713</td> </tr> <tr> <td>eth.dst.lg</td> <td>Destination LG bit</td> <td>1</td> </tr> <tr> <td>eth.dst.ig</td> <td>Destination IG bit</td> <td>1</td> </tr> <tr> <td>eth.src.lg</td> <td>Source LG bit</td> <td>1</td> </tr> <tr> <td>eth.src.ig</td> <td>Source IG bit</td> <td>1</td> </tr> <tr> <td>frame.offset_shift</td> <td>Time shift for this packet</td> <td>0</td> </tr> <tr> <td>frame.len</td> <td>frame length on the wire</td> <td>1208</td> </tr> <tr> <td>frame.cap_len</td> <td>Frame length stored into the capture file</td> <td>215</td> </tr> <tr> <td>frame.marked</td> <td>Frame is marked</td> <td>0</td> </tr> <tr> <td>frame.ignored</td> <td>Frame is ignored</td> <td>0</td> </tr> <tr> <td>frame.encap_type</td> <td>Encapsulation type</td> <td>1</td> </tr> <tr> <td>gre</td> <td>Generic Routing Encapsulation</td> <td>'Generic Routing<br> Encapsulation (IP)’</td> </tr> <tr> <td>ip.version</td> <td>Version</td> <td>6</td> </tr> <tr> <td>ip.hdr_len</td> <td>Header length</td> <td>24</td> </tr> <tr> <td>ip.dsfield.dscp</td> <td>Differentiated Services<br> Codepoint</td> <td>56</td> </tr> <tr> <td>ip.dsfield.ecn</td> <td>Explicit Congestion<br> Notification</td> <td>2</td> </tr> <tr> <td>ip.len</td> <td>Total length</td> <td>614</td> </tr> <tr> <td>ip.flags.rb</td> <td>Reserved bit</td> <td>0</td> </tr> <tr> <td>ip.flags.df</td> <td>Don't fragment</td> <td>1</td> </tr> <tr> <td>ip.flags.mf</td> <td>More fragments</td> <td>0</td> </tr> <tr> <td>ip.frag_offset</td> <td>Fragment offset</td> <td>0</td> </tr> <tr> <td>ip.ttl</td> <td>Time to live</td> <td>31</td> </tr> <tr> <td>ip.proto</td> <td>Protocol</td> <td>47</td> </tr> <tr> <td>ip.checksum.status</td> <td>Header checksum status</td> <td>2</td> </tr> <tr> <td>tcp.srcport</td> <td>TCP source port</td> <td>53425</td> </tr> <tr> <td>tcp.flags</td> <td>Flags</td> <td>0x00000098</td> </tr> <tr> <td>tcp.flags.ns</td> <td>Nonce</td> <td>0</td> </tr> <tr> <td>tcp.flags.cwr</td> <td>Congestion Window Reduced<br> (CWR)</td> <td>1</td> </tr> <tr> <td>udp.srcport</td> <td>UDP source port</td> <td>64413</td> </tr> <tr> <td>udp.dstport</td> <td>UDP destination port</td> <td>54087</td> </tr> <tr> <td>udp.stream</td> <td>Stream index</td> <td>1345</td> </tr> <tr> <td>udp.length</td> <td>Length</td> <td>225</td> </tr> <tr> <td>udp.checksum.status</td> <td>Checksum status</td> <td>3</td> </tr> <tr> <td>packet_type</td> <td>Type of the packet which is either "benign" or "malicious"</td> <td>0</td> </tr> </tbody> </table> <p>Furthermore, in compliance with the GDPR and to ensure the privacy of individuals, all IP addresses present in the dataset have been anonymized through hashing. This anonymization process helps protect the identity of individuals while preserving the integrity and utility of the dataset for research and model development purposes.</p> <p> </p> <p>Please note that while the dataset provides valuable insights and a solid foundation for machine learning tasks, it is not a substitute for extensive real-world data collection. However, it serves as a valuable resource for researchers, practitioners, and enthusiasts in the machine learning community, offering a compliant and anonymized dataset for developing and validating custom models in a specific problem domain.</p> <p> </p> <p>By leveraging the validation dataset for machine learning model evaluation and custom model training, users can accelerate their research and development efforts, building upon the knowledge gained from my thesis while contributing to the advancement of the field.</p>
A non-intrusive reduced order model for the characterisation of the spatial power distribution in large thermal reactors (dataset)
<p>This repository contains the software and datasets needed to reproduce the results presented in the article "<a href="https://doi.org/10.1016/j.anucene.2022.109674">A non-intrusive reduced order model for the characterisation of the spatial power distribution in large thermal reactors</a>", published in Annals of Nuclear Energy.</p>
Wintertime Arctic warm air intrusion detection algorithm for satellite sea ice concentration analysis
<p>This Dataset is related to the Article <em>Relevance of warm air intrusions for Arctic satellite sea ice concentration time </em>series in <em>The Cryosphere</em> (https://doi.org/10.5194/tc-2023-69).</p> <p>Provided are the core detection algorithm and a minimal working example as well as a list of all detected warm air intrusions between November 1979 and April 2020 (monthly data).</p>
Nutrient data from the Peat Collapse-Saltwater Intrusion Field Experiment from brackish and freshwater sites within Everglades National Park, Florida (FCE LTER), collected from October 2014 to September 2016
With sea level rise increasing, saltwater intrusion into low-lying coastal wetlands is likely to occur. We simulated saltwater intrusion into an Everglades marsh through monthly additions of elevated salinity water. Monthly porewater nutrients were taken at 15 cm depth from a brackish and a freshwater marsh. Porewater physicochemistry was measured 24 hours after dosing. Collection occurred from Oct 2014 - Sep 2016. The collected water was then analyzed for temperature, conductivity, salinity, pH, alkalinity, chloride, DOC, NH4, SO4, TDN, SRP, TDP, and sulfide. These data are published in Wilson, B.J., Servais, S., Mazzei, V., Davis, S.E., Kelly, S., Gaiser, E., Kominoski, J.S., Richards, J., Rudnick, D., Sklar, F., Stachelek, J., and Troxler, T.G. Salinity pulses interact with seasonal dry-down to increase ecosystem carbon loss in marshes of the Florida Everglades. 2018. Ecological Applications 28:2092-2018.
Leaf nutrient and root biomass data from the Peat Collapse-Saltwater Intrusion Field Experiment within Everglades National Park (FCE), collected from October 2014 to September 2016
With sea level rise increasing, saltwater intrusion into low-lying coastal wetlands is likely to occur. We simulated saltwater intrusion into an Everglades marsh through monthly additions of elevated salinity water. Yearly sawgrass leaf carbon, nitrogen, and phosphorus concentrations and live root biomass measurements were measured from a brackish water and freshwater marsh. All measurements were taken every other month 24 hours after dosing. Measurements occurred from Oct 2014 - Sep 2016. Ecosystem flux measured includes gross ecosystem production, ecosystem respiration of CO2, net ecosystem production, and ecosystem respiration of CH4. These data are published in Wilson, B.J., Servais, S., Mazzei, V., Davis, S.E., Kelly, S., Gaiser, E., Kominoski, J.S., Richards, J., Rudnick, D., Sklar, F., Stachelek, J., and Troxler, T.G. Salinity pulses interact with seasonal dry-down to increase ecosystem carbon loss in marshes of the Florida Everglades. Ecological Applications. Accepted.
Biomass data from the Peat Collapse-Saltwater Intrusion Field Experiment within Everglades National Park (FCE), collected from October 2014 to September 2016
With sea level rise increasing, saltwater intrusion into low-lying coastal wetlands is likely to occur. We simulated saltwater intrusion into an Everglades marsh through monthly additions of elevated salinity water. Monthly biomass, aboveground net primary production, and culm density measurements were measured from a brackish water and freshwater marsh. All measurements were taken every other month 24 hours after dosing. Measurements occurred from Oct 2014 - Sep 2016. Ecosystem flux measured includes gross ecosyetem production, ecosystem respiration of CO2, net ecosystem production, and ecosystem respiration of CH4. These data are published in Wilson, B.J., Servais, S., Mazzei, V., Davis, S.E., Kelly, S., Gaiser, E., Kominoski, J.S., Richards, J., Rudnick, D., Sklar, F., Stachelek, J., and Troxler, T.G. Salinity pulses interact with seasonal dry-down to increase ecosystem carbon loss in marshes of the Florida Everglades. Ecological Applications. Accepted.
Modeled flux data from the Peat Collapse-Saltwater Intrusion Field Experiment within Everglades National Park (FCE), collected from October 2014 to September 2016
With sea level rise increasing, saltwater intrusion into low-lying coastal wetlands is likely to occur. We simulated saltwater intrusion into an Everglades marsh through monthly additions of elevated salinity water. Monthly modeled ecosystem flux measurements were calculated from a brackish water and freshwater marsh. Ecosystem flux was measured 24 hours after dosing. Measurements occurred from Oct 2014 - Sep 2016. Ecosystem flux measured includes gross ecosyetem production, ecosystem respiration of CO2, net ecosystem production, and ecosystem respiration of CH4. These data are published in Wilson, B.J., Servais, S., Mazzei, V., Davis, S.E., Kelly, S., Gaiser, E., Kominoski, J.S., Richards, J., Rudnick, D., Sklar, F., Stachelek, J., and Troxler, T.G. Salinity pulses interact with seasonal dry-down to increase ecosystem carbon loss in marshes of the Florida Everglades. Ecological Applications. Accepted.
Flux data from the Peat Collapse-Saltwater Intrusion Field Experiment within Everglades National Park, collected from October 2014 to September 2016
With sea level rise increasing, saltwater intrusion into low-lying coastal wetlands is likely to occur. We simulated saltwater intrusion into an Everglades marsh through monthly additions of elevated salinity water. Monthly ecosystem flux measurements were taken from a brackish water and freshwater marsh. Ecosystem flux was measured 24 hours after dosing. Measurements occurred from Oct 2014 - Sep 2016. Ecosystem flux measured includes gross ecosyetem production, ecosystem respiration of CO2, net ecosystem production, and ecosystem respiration of CH4. These data are published in Wilson, B.J., Servais, S., Mazzei, V., Davis, S.E., Kelly, S., Gaiser, E., Kominoski, J.S., Richards, J., Rudnick, D., Sklar, F., Stachelek, J., and Troxler, T.G. Salinity pulses interact with seasonal dry-down to increase ecosystem carbon loss in marshes of the Florida Everglades. Ecological Applications. Accepted.
Inventory of soil prokaryotic microbiome (via 16S based on rRNA gene amplicons) in freshwater and brackish water marshes following saltwater intrusion along Shark River Slough boundary, Everglades National Park (FCE LTER), Florida, USA, September 2018
Global sea-level rise is transforming coastal ecosystems, especially freshwater wetlands, in part due to increased episodic or chronic saltwater exposure, leading to shifts in microbial communities and related ecological services. Soil prokaryotes play a fundamental role in regulating important biogeochemical processes in coastal wetland ecosystem. Yet, it is still difficult to predict how soil prokaryotic communities respond to the saltwater exposure because of poorly understood prokaryotic sensitivity within complex wetland soil microbial communities, as well as the high heterogeneity of wetland soils and saltwater exposure. To address this, a four-year experimental simulation of saltwater intrusion in a pristine freshwater site and a previously saltwater-impacted site was conducted. The saltwater addition started in October 2014 on a monthly basis and continued through October 2018. The dataset contains amplicon sequencing date of 16S rRNA gene obtained from saltwater-exposed soils and unmanipulated native soils in both sites (collected in September 2018). The 2018 data are published in Zhao et al. 2023. A detailed list of sequence data and their accession numbers in GenBank is provided, and data collection is complete. This data package is an inventory of sequence read archive (SRA) entries available through GenBank BioProject PRJNA804545 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA804545). This data package is associated with the following publication: Zhao, J., Chakrabarti, S., Chambers, R., Weisenhorn, P., Travieso, R., Stumpf, S., Standen, E., Briceno, H., Troxler, T., Gaiser, E., Kominoski, J., Dhillon, B., & Martens-Habbena, W. (2023). Year-around survey and manipulation experiments reveal differential sensitivities of soil prokaryotic and fungal communities to saltwater intrusion in Florida Everglades wetlands. Science of The Total Environment, 858, 159865. https://doi.org/10.1016/j.scitotenv.2022.159865 Instead of citing this package, which is an
FIG. 5. — Undated female red deer upper left canine from Abri des Autours. A in The worked bone industry and intrusive fauna associated with the prehistoric cave burials of Abri des Autours (Belgium)
FIG. 5. — Undated female red deer upper left canine from Abri des Autours. A, general view; B, detail of perforation; C, detail of the crown with remaining traces of enamel (arrow). Scale bars: A, 10 mm; B, C, 1 mm.
FIG. 7 in The worked bone industry and intrusive fauna associated with the prehistoric cave burials of Abri des Autours (Belgium)
FIG. 7. — Needle made of a suid left fibula, from the Neolithic collective burial at Abri des Autours: A, general view; B, detail of the tip showing a smooth polish associated with fine and short striations; C, detail of the posterior edge near the point, showing fine parallel striations; D, detail of the posterior edge at the midpoint of the tool, showing smooth polish. Scale bars: A, 10 mm; B, C, D, 150 μm.
FIG. 2 in The worked bone industry and intrusive fauna associated with the prehistoric cave burials of Abri des Autours (Belgium)
FIG. 2. — Plan of Abri des Autours: A, circular depression; B, rubble of old (unpublished) excavations; C, crack in the rock; D, pit under low wall (from Polet & Cauwe 2007).
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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.