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4,669 results for “Environment;”
SHEERM: Sustainable Household Energy and Environment Resources Management dataset
<p>This dataset represents a novel and extensive dataset featuring comprehensive cross-sectional data of household electrical load, energy cost, and on-premises solar energy production, directly linked to solar radiation and weather parameters. <br>The SHEERM dataset is essential for understanding and optimizing energy utilization to achieve Sustainable Development Goals (SGD) 7, 9, 11 and 13. It provides data about solar energy production, weather conditions, residential energy needs, and market prices. The combination of these variables facilitates multifaceted analysis, fostering advancements in renewable energy forecasting, climate-sensitive environments, grid management, and energy policy formulation.<br>Together with the SHEERM dataset, there is a paper that details the data collection process, including the sources and methodologies employed. Adhering to established literature, we developed and implemented machine learning models that comprehensively validate the data. Furthermore, as usage notes, we offer additional results by applying various machine-learning approaches to the provided data.<br>The SHEERM dataset aims to help design new energy systems that enhance sustainable energy strategies and demonstrate their potential to accelerate the transition towards renewable energy and carbon neutrality.</p>
MAD (MAlicious Traffic Dataset) in home and commercial environments - Home environment
<p>For the home environment we have: 01 Wifi Modem Router, 03 Smartphones, 01 server, 01 desktop, 01 Multifunction Printer, 01 network extender, 01 SmartTV, 01 Cable TV decoder and 01 firewall. This environment is a local network. The server has the Monitoring Environment and a network card, which provides connectivity and receives all network traffic for analysis.</p> <p>The results were obtained from Suricata and Telegraf collections from the TICK stack. All evidence was performed by queries via EveBox, which received data from Suricata, Grafana or graphics with information extracted from the InfluxDB (Grafana) and PostgreSQL (EveBox) databases.</p> <p>events.csv.gz - Suricata / Evebox collections</p> <p>net.csv.gz - Telegraf collections from the TICK stack</p> <p>netstat.csv.gz - Telegraf collections from the TICK stack</p> <p>For correlation purposes, use the events.csv.gz file as a basis. The key to correlation is the 'timestamp' column events.csv.gz with the 'time' column in the net.csv.gz and netstat.csv.gz files.</p> <p>The interval between collections, non-consecutive, was from 2018-09-15 to 2019-02-04</p>
MAD (MAlicious Traffic Dataset) in home and commercial environments - Internal environment
<p>In this environment we have: 01 Wifi Router, 01 Smartphone, 01 server and 01 desktop with virtual machines. This environment, called Internal, is a local network. One of the servers has the Security and Performance Monitoring Environment installed. In addition, 05 virtual machines were instantiated via QEMU on the same network. In this server, a network card provides connectivity to the environment and the other network card receives all network traffic for analysis by the Monitoring Environment. Getting traffic to Suricata is done by Ettercap. The desktop has two virtual machines instantiated via Oracle VirtualBox, on the same network and acts on the network as a client as well.</p> <p>The results were obtained from Suricata and Telegraf collections from the TICK stack. All evidence was performed by queries via EveBox, which received data from Suricata, Grafana or graphics with information extracted from the InfluxDB (Grafana) and PostgreSQL (EveBox) databases.</p> <p>events.csv.gz - Suricata / Evebox collections</p> <p>net.csv.gz - Telegraf collections from the TICK stack</p> <p>netstat.csv.gz - Telegraf collections from the TICK stack</p> <p>For correlation purposes, use the events.csv.gz file as a basis. The key to correlation is the 'timestamp' column events.csv.gz with the 'time' column in the net.csv.gz and netstat.csv.gz files.</p> <p>The interval between collections, non-consecutive, was from 2018-06-06 to 2019-01-31</p> <p> </p>
MAD (MAlicious Traffic Dataset) in home and commercial environments - Internet environment
<p>We have for the Internet environment: 01 Switch, 01 IP camera, 01 server for monitoring, 01 server for honeypot and no firewall. This environment is directly connected to the Internet. We installed a server, functioning as a Monitoring Environment. The network traffic was obtained via Port Mirroring on the switch to the Monitoring Environment server.</p> <p>The results were obtained from Suricata and Telegraf collections from the TICK stack. All evidence was performed by queries via EveBox, which received data from Suricata, Grafana or graphics with information extracted from the InfluxDB (Grafana) and PostgreSQL (EveBox) databases.</p> <p>events.csv.gz - Suricata / Evebox collections</p> <p>net.csv.gz - Telegraf collections from the TICK stack</p> <p>netstat.csv.gz - Telegraf collections from the TICK stack</p> <p>For correlation purposes, use the events.csv.gz file as a basis. The key to correlation is the 'timestamp' column events.csv.gz with the 'time' column in the net.csv.gz and netstat.csv.gz files.</p> <p>The interval between collections, non-consecutive, was from 2018-08-28 to 2019-11-14</p>
MAD (MAlicious Traffic Dataset) in home and commercial environments - Environment with scalability
<p>We have used the Internet environment: 01 Switch, 01 IP camera, 01 server for monitoring, 01 server for honeypot and no firewall. This environment is directly connected to the Internet. We installed a server, functioning as a Monitoring Environment. The network traffic was obtained via Port Mirroring on the switch to the Monitoring Environment server.</p> <p>We added 08 virtual machines and performed the following test with a denial of service DoS attack:</p> <p>01 virtual machine from 04:00 pm to 23:55 pm on 2019-12-04 with an interval every 01 hour;<br> 02 virtual machines from 23:55 am on 2019-12-04 to 08:50 am on 2019-12-05 with an interval every 01 hour;<br> 04 virtual machines as of 08:55 am on 2019-12-05 to 05:25 pm on 2019-12-06 with an interval every 5 minutes;<br> 08 virtual machines from 05:30 pm on 2019-12-06 to 23:59 on 2019-12-06 with an interval every 5 minutes;<br> End of tests with shutdown of virtual machines at 23:59 on 2019-12-06.</p> <p>The results were obtained from Suricata and Telegraf collections from the TICK stack. All evidence was performed by queries via EveBox, which received data from Suricata, Grafana or graphics with information extracted from the InfluxDB (Grafana) and PostgreSQL (EveBox) databases.</p> <p>events.csv.gz - Suricata / Evebox collections</p> <p>net.csv.gz - Telegraf collections from the TICK stack</p> <p>netstat.csv.gz - Telegraf collections from the TICK stack</p> <p>For correlation purposes, use the events.csv.gz file as a basis. The key to correlation is the 'timestamp' column events.csv.gz with the 'time' column in the net.csv.gz and netstat.csv.gz files.</p> <p>The interval between collections, non-consecutive, was from 2019-12-04 to 2019-12-06</p>
Resolution Enhancement of UWB Time-Reversal Microwave Imaging in Dispersive Environments (dataset)
<p>These files are the simulation data used to create the figures illustrated in the journal paper with the same title which has been accepted for publication as a regular paper in IEEE Transactions on Computational Imaging. Each filename indicates the figure number associated with the file. These files are text files. The column structure of each file is described in the README file.</p>
ARTE (Article Reproducibility Template & Environment) workflow folder structure
<p>Illustration of a suggested folder and file system for editing articles that are <strong>dynamic and reproducible</strong>.</p> <p>The <a title="TIER Protocol 4.0 Site" href="https://www.projecttier.org/tier-protocol/protocol-4-0/" target="_blank" rel="noopener"><strong>TIER Protocol 4.0</strong></a> served as the foundation for this proposal, which was intended to be modified in <strong>Quarto using Rstudio</strong>, for instance, then published on <a title="Article Template Exemple" href="https://phdpablo.github.io/article-template/" target="_blank" rel="noopener"><strong>GitHub Pages</strong></a>.</p> <p>An example of its deployment is provided here: <a title="Article Template Example" href="https://phdpablo.github.io/article-template/" target="_blank" rel="noopener">https://phdpablo.github.io/article-template/</a></p> <p>According to <a title="Original Article" href="https://periodicos.ufpe.br/revistas/index.php/politicahoje/article/view/245776" target="_blank" rel="noopener">Domingos and Batista's (2021)</a> suggested content (files) recommendations, the figure illustrates the design of the proposal while accounting for the three primary <strong>TIER Protocol 4.0</strong> folders (Data, Scripts, and Output). The proposal for the <strong>TIER Protocol 4.0</strong> was modified to take into account that the research narrative would be edited in a <a title="Site Quarto" href="https://quarto.org/docs/guide/" target="_blank" rel="noopener"><strong>dynamic Quarto-type document</strong></a> (*.qmd), published on <a title="Article Template Example" href="https://phdpablo.github.io/article-template/" target="_blank" rel="noopener">GitHub Pages</a> (docs/) following Rstudio rendering, and potentially even produce a .pdf file of the article; <strong>*.qmd </strong> denotes a collection of <strong>Quarto files </strong>containing the standard sections of a scientific article (Introduction, Theoretical Framework, Methods, etc.); the other folders indicated in the root (adm, docker, renv) are for project management and for controlling dependencies and the environment, if adopted by the researcher.</p> <p>Visit the project repository at <a title="GitHub Repository" href="https://github.com/phdpablo/article-template/" target="_blank" rel="noopener">https://github.com/phdpablo/article-template/</a> for additional details. Visit <a title="OSF" href="https://osf.io/njdq5/" target="_blank" rel="noopener">https://osf.io/njdq5/</a> to view the template on OSF.</p>
Supplementary Materials for "Influence of Measured Radio Environment Map Interpolation on Indoor Positioning Algorithms"
<p>This dataset was created as suplementary material for research article: <strong>Influence of Measured Radio Environment Map Interpolation on Indoor Positioning Algorithms</strong></p> <p>This package contains packet capture files of 802.11 probe requests captured at Geotec office at University Jaume I, Spain by 5 ESP32 microcontrollers. The packet capture files are in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package).</p> <p>The data are split between radio map data captured at all accessible reference positions in our office spread in 1m grid and evaluation data gathered alligned to 0.5m grid, as well as in hard to access locations. The location the data were collected are available in the office.</p> <p>The dataset has 4 parts, and all subsets of the dataset can be generated from the captured pcap files:</p> <p><strong>Data</strong></p> <p>This folder contains pcap files from all 5 ESP32 stations representing the whole radio environment map. The folder name stands for each of the 5 ESP32 sniffer stations and the name of the file points to a reference location the data were captured in. Example of the coordinates matching the reference location grid names are in following table:</p> <table> <caption>Data Point Coordinates</caption> <thead> <tr> <th scope="row"> </th> <th scope="col">X</th> <th scope="col">Y</th> <th scope="col"> </th> <th scope="col">X</th> <th scope="col">Y</th> <th scope="col"><strong>...</strong></th> </tr> </thead> <tbody> <tr> <th scope="row">A1</th> <td>0.85</td> <td>0.1</td> <td><strong>B1</strong></td> <td>1.85</td> <td>0.1</td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">A2</th> <td>0.85</td> <td>1.1</td> <td><strong>B2</strong></td> <td>1.85</td> <td>1.1</td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">A3</th> <td>0.85</td> <td>2.1</td> <td><strong>B3</strong></td> <td>1.85</td> <td>2.1</td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">...</th> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">A11</th> <td>0.85</td> <td>10.1</td> <td><strong>B11</strong></td> <td>1.85</td> <td>10.1</td> <td><strong>...</strong></td> </tr> </tbody> </table> <p><strong>Data_Eval</strong></p> <p>This folder contains pcap files from all 5 ESP32 stations with data captured at 31 locations not found in the original reference location grid. The naming corresponds to the X and Y location in which the data were collected.</p> <p><strong>Processed_Data</strong></p> <p>Additionally, there are 3 folders with processed CSV files. One folder that combines all radio map values, second folder contains combined evaluation values and third is with linearly interpolated radio map values.</p> <p>The CSV files are in a format:</p> <blockquote> <p><code>X, Y, RSSI_1, RSSI_2, RSSI_3, RSSI_4, RSSI_5</code></p> </blockquote> <p><strong>Data_Scenarios</strong></p> <p>This folder for the ease of use, contains data for exact reproducibility of our results in the paper. There 14 scenarios described in the following table:</p> <table> <caption>Scenario Descriptions</caption> <thead> <tr> <th scope="col"> <p>Data Name</p> </th> <th scope="col"> <p>Scenario Description</p> </th> </tr> </thead> <tbody> <tr> <td>GPR00</td> <td>Only measured data, 50 samples per reference position</td> </tr> <tr> <td>GPR01</td> <td>Measured data with empty spots filled using Linear interpolation, 50 samples per reference position</td> </tr> <tr> <td>GPR02</td> <td>Gaussian Regression trained only on measured data - 1m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR03</td> <td>Gaussian Regression trained only on measured data - 0.5m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR04</td> <td>Gaussian Regression trained on linearly interpolated data - 1m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR05</td> <td>Gaussian Regression trained on linearly interpolated data - 0.5m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR06</td> <td>Gaussian Regression trained selection of linearly interpolated data - 1m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR07</td> <td>Gaussian Regression trained selection of linearly interpolated data - 0.5m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR08</td> <td>Gaussian Regression trained only on measured data - 1m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR09</td> <td>Gaussian Regression trained only on measured data - 0.5m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR10</td> <td>Gaussian Regression trained on linearly interpolated data - 1m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR11</td> <td>Gaussian Regression trained on linearly interpolated data - 0.5m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR12</td> <td>Gaussian Regression trained selection of linearly interpolated data - 1m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR13</td> <td>Gaussian Regression trained selection of linearly interpolated data - 0.5m output grid, 1 sample per reference position</td> </tr> </tbody> </table> <p>The folder contains 4 files for each scenario. The Beginning of the filename corresponds to the data name, with suffix describing what data are in the file. The descriptions of used suffixes are in the following table:</p> <table> <caption>File Suffix Descriptions</caption> <tbody> <tr> <td> <p><strong>Suffix</strong></p> </td> <td> <p><strong>Suffix Description</strong></p> </td> </tr> <tr> <td>_trncrd</td> <td>Training Labels</td> </tr> <tr> <td>_trnrss</td> <td>Training RSSI Values</td> </tr> <tr> <td>_tstcrd</td> <td>Evaluation Labels</td> </tr> <tr> <td>_tstrss</td> <td>Evaluation RSSI Values</td> </tr> </tbody> </table> <p>These data are in format compatible with systems that apart from X and Y coordinates also detect, building, floor etc.</p> <p>The RSSI data are in format:</p> <blockquote> <p>RSSI_1, RSSI_2, RSSI_3, RSSI_4, RSSI_5</p> </blockquote> <p>The Labels are in format: (Since we only use positioning in 1 office, apart X and Y coordinates are set to 0)</p> <blockquote> <p>X, Y, 0, 0, 0</p> </blockquote>
Evolution of left-right asymmetry in the sensory system and foraging behavior during adaptation to food-sparse cave environments
<p>Laterality in relation to behavior and sensory systems is found commonly in a variety of animal taxa. Despite the advantages conferred by laterality (e.g., the startle response and complex motor activities), little is known about the evolution of laterality and its plasticity in response to ecological demands. In the present study, a comparative study model, the Mexican tetra (<em>Astyanax mexicanus</em>), composed of two morphotypes, i.e., riverine surface fish and cave-dwelling cavefish, was used to address the relationship between environment and laterality. The use of a machine learning-based fish posture detection system and sensory ablation revealed that the left cranial lateral line significantly supports one type of foraging behavior, i.e., vibration attraction behavior, in one cave population. Additionally, left-right asymmetric approaches toward a vibrating rod became symmetrical after fasting in one cave population but not in the other populations. Based on these findings, we propose a model explaining how the observed sensory laterality and behavioral shift could help adaptation in terms of the tradeoff in energy gain and loss during foraging according to differences in food availability among caves.</p> <p>This repository contains all of raw videos used in this study.</p> <p>Please let us know if you have any question on these videos</p>
Dataset of IEEE 802.11 probe requests from an uncontrolled urban environment
<p><strong>Introduction</strong></p> <p>The 802.11 standard includes several management features and corresponding frame types. One of them are Probe Requests (PR), which are sent by mobile devices in an unassociated state to scan the nearby area for existing wireless networks. The frame part of PRs consists of variable-length fields, called Information Elements (IE), which represent the capabilities of a mobile device, such as supported data rates.</p> <p>This dataset contains PRs collected over a seven-day period by four gateway devices in an uncontrolled urban environment in the city of Catania.</p> <p>It can be used for various use cases, e.g., analyzing MAC randomization, determining the number of people in a given location at a given time or in different time periods, analyzing trends in population movement (streets, shopping malls, etc.) in different time periods, etc.</p> <p><strong> Related dataset</strong></p> <p>Same authors also produced the <a href="https://zenodo.org/record/7503594">Labeled dataset of IEEE 802.11 probe requests</a> with same data layout and recording equipment.</p> <p><br> <strong>Measurement setup</strong> </p> <p>The system for collecting PRs consists of a Raspberry Pi 4 (RPi) with an additional WiFi dongle to capture WiFi signal traffic in monitoring mode (gateway device).<br> Passive PR monitoring is performed by listening to 802.11 traffic and filtering out PR packets on a single WiFi channel.</p> <p>The following information about each received PR is collected:<br> - MAC address<br> - Supported data rates<br> - extended supported rates<br> - HT capabilities<br> - extended capabilities<br> - data under extended tag and vendor specific tag<br> - interworking<br> - VHT capabilities<br> - RSSI<br> - SSID<br> - timestamp when PR was received.</p> <p>The collected data was forwarded to a remote database via a secure VPN connection.<br> A Python script was written using the Pyshark package to collect, preprocess, and transmit the data.</p> <p><br> <strong>Data preprocessing</strong></p> <p><br> The gateway collects PRs for each successive predefined scan interval (10 seconds). During this interval, the data is preprocessed before being transmitted to the database.<br> For each detected PR in the scan interval, the IEs fields are saved in the following JSON structure:</p> <pre><code class="language-json">PR_IE_data = { 'DATA_RTS': {'SUPP': DATA_supp , 'EXT': DATA_ext}, 'HT_CAP': DATA_htcap, 'EXT_CAP': {'length': DATA_len, 'data': DATA_extcap}, 'VHT_CAP': DATA_vhtcap, 'INTERWORKING': DATA_inter, 'EXT_TAG': {'ID_1': DATA_1_ext, 'ID_2': DATA_2_ext ...}, 'VENDOR_SPEC': {VENDOR_1:{ 'ID_1': DATA_1_vendor1, 'ID_2': DATA_2_vendor1 ...}, VENDOR_2:{ 'ID_1': DATA_1_vendor2, 'ID_2': DATA_2_vendor2 ...} ...} }</code></pre> <p><br> Supported data rates and extended supported rates are represented as arrays of values that encode information about the rates supported by a mobile device. The rest of the IEs data is represented in hexadecimal format. Vendor Specific Tag is structured differently than the other IEs. This field can contain multiple vendor IDs with multiple data IDs with corresponding data. Similarly, the extended tag can contain multiple data IDs with corresponding data. <br> Missing IE fields in the captured PR are not included in <em>PR_IE_DATA</em>.</p> <p>When a new MAC address is detected in the current scan time interval, the data from PR is stored in the following structure:</p> <pre><code class="language-json">{'MAC': MAC_address, 'SSIDs': [ SSID ], 'PROBE_REQs': [PR_data] },</code></pre> <p>where <em>PR_data</em> is structured as follows:</p> <pre><code class="language-json">{ 'TIME': [ DATA_time ], 'RSSI': [ DATA_rssi ], 'DATA': PR_IE_data }.</code></pre> <p> </p> <p>This data structure allows to store only 'TOA' and 'RSSI' for all PRs originating from the same MAC address and containing the same 'PR_IE_data'. All SSIDs from the same MAC address are also stored.<br> The data of the newly detected PR is compared with the already stored data of the same MAC in the current scan time interval.<br> If identical PR's IE data from the same MAC address is already stored, only data for the keys 'TIME' and 'RSSI' are appended.<br> If identical PR's IE data from the same MAC address has not yet been received, then the PR_data structure of the new PR for that MAC address is appended to the 'PROBE_REQs' key.<br> The preprocessing procedure is shown in Figure ./Figures/Preprocessing_procedure.png</p> <p>At the end of each scan time interval, all processed data is sent to the database along with additional metadata about the collected data, such as the serial number of the wireless gateway and the timestamps for the start and end of the scan. For an example of a single PR capture, see the <em>Single_PR_capture_example.json</em> file.</p> <p><br> <strong>Folder structure</strong></p> <p>For ease of processing of the data, the dataset is divided into 7 folders, each containing a 24-hour period.<br> Each folder contains four files, each containing samples from that device.</p> <p>The folders are named after the start and end time (in UTC).<br> For example, the folder [2022-09-22T22-00-00_2022-09-23T22-00-00](2022-09-22T22-00-00_2022-09-23T22-00-00) contains samples collected between <em>23th of September 2022 00:00 local time</em>, until <em>24th of September 2022 00:00</em> local time.</p> <p>Files representing their location via mapping:<br> - 1.json -> location 1<br> - 2.json -> location 2<br> - 3.json -> location 3<br> - 4.json -> location 4</p> <p><strong>Environments description</strong> </p> <p>The measurements were carried out in the city of Catania, in Piazza Università and Piazza del Duomo<br> The gateway devices (rPIs with WiFi dongle) were set up and gathering data before the start time of this dataset.<br> As of September 23, 2022, the devices were placed in their final configuration and personally checked for correctness of installation and data status of the entire data collection system.<br> Devices were connected either to a nearby Ethernet outlet or via WiFi to the access point provided.</p> <p>Four Raspbery Pi-s were used:<br> - location 1 -> Piazza del Duomo - Chierici building (balcony near Fontana dell’Amenano)<br> - location 2 -> southernmost window in the building of Via Etnea near Piazza del Duomo<br> - location 3 -> nothernmost window in the building of Via Etnea near Piazza Università<br> - location 4 -> first window top the right of the entrance of the University of Catania</p> <p>Locations were suggested by the authors and adjusted during deployment based on physical constraints (locations of electrical outlets or internet access)<br> Under ideal circumstances, the locations of the devices and their coverage area would cover both squares and the part of Via Etna between them, with a partial overlap of signal detection. The locations of the gateways are shown in Figure ./Figures/catania.png.</p> <p> <strong>Known dataset shortcomings</strong></p> <p>Due to technical and physical limitations, the dataset contains some identified deficiencies.</p> <p>PRs are collected and transmitted in 10-second chunks.<br> Due to the limited capabilites of the recording devices, some time (in the range of seconds) may not be accounted for between chunks if the transmission of the previous packet took too long or an unexpected error occurred.</p> <p>Every 20 minutes the service is restarted on the recording device.<br> This is a workaround for undefined behavior of the USB WiFi dongle, which can no longer respond.<br> For this reason, up to 20 seconds of data will not be recorded in each 20-minute period.</p> <p>The devices had a scheduled reboot at 4:00 each day which is shown as missing data of up to a few minutes.</p> <p> <strong> Location 1 - Piazza del Duomo - Chierici</strong></p> <p> The gateway device (rPi) is located on the second floor balcony and is hardwired to the Ethernet port. This device appears to function stably throughout the data collection period.<br> Its location is constant and is not disturbed, dataset seems to have complete coverage.</p> <p> <strong>Location 2 - Via Etnea - Piazza del Duomo</strong></p> <p> The device is located inside the building.<br> During working hours (approximately 9:00-17:00), the device was placed on the windowsill. However, the movement of the device cannot be confirmed.<br> As the device was moved back and forth, power outages and internet connection issues occurred.<br> The last three days in the record contain no PRs from this location.</p> <p> <strong> Location 3 - Via Etnea - Piazza Università</strong></p> <p> Similar to Location 2, the device is placed on the windowsill and moved around by people working in the building.<br> Similar behavior is also observed, e.g., it is placed on the windowsill and moved inside a thick wall when no people are present.<br> This device appears to have been collecting data throughout the whole dataset period.<br> <br> <strong> Location 4 - Piazza Università</strong></p> <p> This location is wirelessly connected to the access point.<br> The device was placed statically on a windowsill overlooking the square.<br> Due to physical limitations, the device had lost power several times during the deployment.<br> The internet connection was also interrupted sporadically.</p> <p><strong>Recognitions</strong></p> <p>The data was collected within the scope of <a href="https://www.resilocproject.eu/">Resiloc project</a> with the help of City of Catania and project partners.</p>
Datasets for figures in Implementation and evaluation of Wet Bulb Globe Temperature within non-urban environments in the Community Land Model version 5
<p>The files contain 4 scripts and 6 netcdf files. </p> <p>"laborCap_200400.ncl" uses "Lancet_LRF.nc" to create Figure 1.</p> <p>Script "world_plot_ensemble_Avg.I2000.csh", drives a NCL script, "plot_modern.I2000.WBGT.ncl" to make figures 3 and 4, using the netcdf files, "I2000_PR_22_x1_60_5.exceed.WBGT.20yrs.75_99.nc," "I2000_PR_22_x1_60_5.exceed.WBGT_BG_R.20yrs.75_99.nc," "I2000_PR_22_x1_60_5.exceed.WBGT_BC_R.20yrs.75_99.nc," and "I2000_PR_22_x1_60_5.exceed.WBGT_AC_R.20yrs.75_99.nc."</p> <p>"heatmap.wbgt.v4.org.ncl" uses netcdf "I2000_PR_23_Chicago_x1_60_1.11-17.Chicago.allvars.nc" to create figures 5-7. </p>
Mangroves in the lagoon of the protected Aldabra Atoll: a dataset on species, structure, biomass and the environment
<p>Mangroves are vital for climate change mitigation since they store vast quantities of carbon as biomass and in the soil. Global mangrove biomass estimates are derived from climate-based relationships of mangroves with precipitation and temperature. However, the carbon stored locally is highly variable depending on environmental conditions. This uncertainty highlights the importance of local mangrove surveys and the need to explore factors that regulate forest structure and, therefore, carbon storage. In this study, we investigate the mangrove forest structure, seedling growth, species composition, aboveground biomass, soil organic carbon, and local environmental factors related to variation in mangrove carbon in the lagoonal mangroves on the protected Aldabra Atoll, Seychelles. We present a database from an extensive field survey of Aldabra's mangrove ecosystem using 54 plots of 5 m x 5 m along a mangrove coverage gradient. From November 2019 to November 2020, we measured the structural attributes and identified six mangrove species from >750 adult mangrove trees on Aldabra. We used the height and diameter of adult trees to derive aboveground biomass and carbon from a tropical allometric equation. We measured the height of 59 mangrove seedlings over three sampling periods. In addition, environmental factors were recorded for each plot. We measured soil salinity repeatedly along the soil column. From 90 soil samples, we measured the physical and chemical properties of the soil, including soil organic carbon and elemental concentrations for >20 elements. Autonomous measures of the water level, temperature and conductivity were made every 10 minutes over 1 year in a subset of 36 plots. The database provides 60% more information that is currently available for Seychelles regarding mangrove forest structure and biomass and is essential for research on several globally threatened and endemic species that depend on the mangroves on Aldabra. Furthermore, the database allows the incorporation of data and insights for the Western Indian Ocean and lagoonal mangroves, where few studies have been conducted on mangrove aboveground biomass and soil organic carbon. No copyright restrictions apply to the use of this data set. Please cite this data paper when using the current data in publications.</p>
Intermediate data for: Environment and shipping drive eDNA beta-diversity among commercial ports
<p>Intermediate data generated by Paul Czechowski as part of MEC-22-0945.R1 using code stored at <a href="https://github.com/macrobiotus/ships_and_bugs">GitHub</a>, most recently release with <a href="https://doi.org/10.5281/zenodo.7600608">DOI: 10.5281/zenodo.7600608 </a> . Pre-print with linked final manuscript version available at BioRxiv via <a href="https://doi.org/10.1101/2021.10.07.463538">DOI: 10.1101/2021.10.07.463538</a>. Please refer to the published manuscript for a full list of available digital resources associated with this work.</p>
Digital Aristoteles Latinus Environment 2.1
<p>DALE 2.1 is the first release of DALE on GitHub. It contains the current documentation file of the project, the current RELAXNG schema, and the XML files I created based on the schema.</p>
Raw Data: Timing of Calanus finmarchicus diapause in stochastic environments
<p><strong>This dataset contains the raw (unprocessed) model outputs of the Individual-Based Model used to generate the research article: </strong></p> <p>Bandara, K., Varpe, Ø., Maps, F., Ji, R., Eiane, K., & Tverberg, V. (2021). Timing of Calanus finmarchicus diapause in stochastic environments. <em>Ecological Modelling</em>, <em>460</em>, 109739.</p> <p><strong>Description</strong></p> <p>ITEM DESCRIPTION<br> ----------------<br> CONTENTS : RAW DATA FILES<br> UNCOMPRESSED SIZE : 19.6 GB <br> COMPRESSION : YES<br> COMPRESSED SIZE : 3.74 GB<br> COMPRESSION FORMAT : .ZIO<br> ENCRYPTION : NO<br> ENCRYPT METHOD : N/A<br> INTERNAL FILE TYPE(S) : .CSV<br> SOURCE : PASCAL <br> ID : v3.x (2020-2022)<br> DECRYPTION PASSWD : N/A<br> DOWNLOADABLE FROM : 10.5281/zenodo.7646734</p> <p>DATA DESCRIPTION<br> ----------------<br> THIS ARCHIVE CONTAINS FIVE (05) DIRECTORIES. EACH FROM A SINGLE SIMULATION EXPERIMENT, INDICATED BY THE FILE NAMES. <br> + DeterministicSetting : SIMULATION RAN IN DETERMINISTIC MODEL ENVIRONMENT (FIG. 2A-C)<br> + STStochasticSetting : SIMULATION RAN IN SHORTER-TERM (6-H) STOCHASTIC ENVIRONMENT (FIG. 2D-F)<br> + LTStochasticSetting-TFI : SIMULATION RAN IN LONGER-TERM (INTERANNUAL) STOCHASTIC ENVIRONMENT WITH GROWTH POTENTIAL VARIABILITY ONLY (FIG. 2G)<br> + LTStochasticSetting-VPR : SIMULATION RAN IN LONGER-TERM (INTERANNUAL) STOCHASTIC ENVIRONMENT WITH GROWTH POTENTIAL + VISUAL PREDATION RISK VARIABILITY (FIG. 2G)<br> + LTStochasticSetting-NVPR : SIMULATION RAN IN LONGER-TERM (INTERANNUAL) STOCHASTIC ENVIRONMENT WITH GROWTH POTENTIAL VARIABILITY + NON-VISUAL PREDATION RISK VARIABILITY (FIG. 2G)</p> <p>KEY<br> ---<br> + AnnualLog : DOUBLE VALUES OF LOW-RESOLUTION TRACKERS <br> + DailyLog_I : INTEGER VALUES OF HIGH-RESOLUTION TRACKERS<br> + DailyLog_R : DOUBLE VALUES OF HIGH-RESOLUTION TRACKERS<br> + VerticalPosition_N : INTEGER VALUES OF POPULATION VERTICAL DISTRIBUTION (NUMBER OF INDIVIDUALS IN EACH 1M x 6H BIN)<br> + VerticalPosition_CW : DOUBLE VALUES OF AVERAGE BODYMASS VERTICAL DISTRIBUTION (AVERAGE INDIVIDUAL BODYMASS IN EACH 1M x 6H BIN) :: UNUSED IN ANALYSES<br> + VerticalPosition_SW : DOUBLE VALUES OF AVERAGE ENERGY RESERVE VERTICAL DISTRIBUTION (AVERAGE INDIVIDUAL ENERGY RESERVES [LIPIDSCAPE] IN EACH 1M x 6H BIN) :: UNUSED IN ANALYSES<br> + FoodConcentration : DOUBLE VALUES OF 1D FOOD ENVIRONMENT<br> + Irradiance : DOUBLE VALUES OF 1D SUB-SURFACE IRRADIANCE ENVIRONMENT<br> + Temperature : DOUBLE VALUES OF 1D TEMPERATURE ENVIRONMENT<br> + VisualPredationRisk : DOUBLE VALUES OF 1D UNSCALED VISUAL PREDATION ENVIRONMENT</p> <p>NOTES<br> -----<br> The above attributes (excl. AnnualLog) are recorded for 100 calendar year in the LTStochasticSetting-TFI, LTStochasticSetting-VPR & LTStochasticSetting-NVPR simulations.<br> These are named after the year number (e.g. DailyLog_I_1, DailyLog_I_2....)</p> <p>ADDITIONAL FILES<br> ----------------<br> SOME DIRECTORIES MAY CONTAIN ADDITIONAL FILES, SUCH AS THOSE USED AS PCA INPUTS. THESE ARE PROCESSED FILES.<br> AVERAGE ANNUAL ENVIRONMENTAL VARIABLES TRACED IN THE "ENV" DIRECTORY (ONLY FOR LONGER-TERM STOCHASTIC SIMULATIONS).</p> <p>FURTHER QUESTIONS?<br> ------------------<br> Direct further question, either to: info@kanchanabandara.com (personal) or kba@akvaplan.niva.no (official)</p>
Verbal and nominal complexity and sociolinguistic environment
<p>This material contains the dataset from the <a href="https://version.helsinki.fi/hals/sinnemaki/sinnemaki-digarbo2018">gitlab repository</a> of the following article. Please cite the article when using the data.</p> <p>Sinnemäki, Kaius and Francesca Di Garbo. 2018. Language structures may adapt to the sociolinguistic environment, but it matters what and how you count: A typological study of verbal and nominal complexity. <em>Frontiers in Psychology</em> 9: 1141.</p>
Creating Safe Environments: Optimal Acoustic Alarming of Laypeople in Fire Prevention - Online Supplement
<p>This online supplement contains datasets (raw data) and study material collected in an experimental study by the University of Münster, Germany. The study is part of a larger research project (<a href="https://www.brawa.ovgu.de/en/">https://www.brawa.ovgu.de/en/</a>) and examined the perception of acoustic fire alarm signals.</p> <p>Hazards like fires occur regularly and can cost people’s lives. Optimal auditory alarm signals enable laypeople to recognize dangers and to protect themselves. Existing fire alarm sound research focuses on alarm sounds and voice alerts presented singularly. We explored a combination of both and aimed to identify alarm signals that work optimally in everyday life. Thus, we conducted two online experiments: In Study 1 (<em>N</em> = 379), we tested eight alarm sounds regarding their typicality, their familiarity, their arousal, their valence, and their dominance. Siren-like alarm sounds were most effective. In Study 2 (<em>N</em> = 206), we combined the four most effective alarm sounds with a voice alert. The voice alert reinforced ambiguity reduction, action motivation, and action intention. Hence, we suggest using alarm sounds with siren-like patterns. They should be combined with a voice alert to foster a quick and specific (target task-oriented) reaction.</p> <p>The ethics committee of the University of Münster approved the studies (ID 2021-57-MT), and we preregistered both studies with AsPredicted.org under numbers #77031 and #81137 (see <a href="https://aspredicted.org/vx2rr.pdf">https://aspredicted.org/vx2rr.pdf</a> and <a href="https://aspredicted.org/mt9g3.pdf">https://aspredicted.org/mt9g3.pdf</a>). The studies were supported by the German Federal Ministry of Education and Research (grant numbers 13N15416 and 13N15419).</p> <p><strong>This online supplement includes: </strong></p> <ul> <li>Two codebooks describing all instructions and items in Study 1 and in Study 2</li> <li>Raw data (anonymized) and analysis scripts (Note: The raw data contains only the information of persons who were included in the analysis and who gave their informed consent. Some demographic information was deleted to ensure anonymity.)</li> <li>Study material: <ul> <li>Alarm signal example</li> <li>Hearing test implemented in both studies</li> </ul> </li> </ul>
Spatial predictions of suitable environments for palsas and peat plateaus in the Northern Hemisphere for recent and future periods
<p>Here we provide raster files of suitable environments for palsas and peat plateaus in the Northern Hemisphere. These files are results of a scientific study by Könönen et al. (2022, preprint). Files are provided in TIFF-format, and they describe the occurrence probability of the suitable environments for palsas and peat plateaus.</p> <p> </p> <p>Könönen, O. H., Karjalainen, O., Aalto, J., Luoto, M., and Hjort, J.: Environmental spaces for palsas and peat plateaus are disappearing at a circumpolar scale, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2022-135, in review, 2022.</p>
Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration
<p>Algorithms for autonomous navigation in environments without Global Navigation Satellite System (GNSS) coverage mainly rely on onboard perception systems. These systems commonly incorporate sensors like cameras and LiDARs, the performance of which may degrade in the presence of aerosol particles. Thus, there is a need of fusing acquired data from these sensors with data from RADARs which can penetrate through such particles. Overall, this will improve the performance of localization and collision avoidance algorithms under such environmental conditions. This paper introduces a multimodal dataset from the harsh and unstructured underground environment with aerosol particles. A detailed description of the onboard sensors and the environment, where the dataset is collected are presented to enable full evaluation of acquired data. Furthermore, the dataset contains synchronized raw data measurements from all onboard sensors in Robot Operating System (ROS) format to facilitate the evaluation of navigation, and localization algorithms in such environments. In contrast to the existing datasets, the focus of this paper is not only to capture both temporal and spatial data diversities but also to present the impact of harsh conditions on captured data. Therefore, to validate the dataset, a preliminary comparison of odometry from onboard LiDARs is presented.</p>
CTU-SME-11: a labeled dataset with real benign and malicious network traffic mimicking a small medium-size enterprise environment
<p>As technology advances, the number and complexity of cyber-attacks increase, forcing defense techniques to be updated and improved. To help develop effective tools for detecting security threats it is essential to have reliable and representative security datasets. Many existing security datasets have limitations that make them unsuitable for research, including lack of labels, unbalanced traffic, and outdated threats.</p> <p>CTU-SME-11 is a labeled network dataset designed to address the limitations of previous datasets. The dataset was captured in a real network that mimics a small-medium enterprise setting. Raw network traffic (packets) was captured from 11 devices using tcpdump for a duration of 7 days, from 20th to 26th of February, 2023 in Prague, Czech Republic. The devices were chosen based on the enterprise setting and consists of IoT, desktop and mobile devices, both bare metal and virtualized. The devices were infected with malware or exposed to Internet attacks, and factory reset to restore benign behavior. </p> <p>The raw data was processed to generate network flows (Zeek logs) which were analyzed and labeled. The dataset contains two types of levels, a high level label and a descriptive label, which were put by experts. The former can take three values, benign, malicious or background. The latter contains detailed information about the specific behavior observed in the network flows. The dataset contains 99 million labeled network flows. The overall compressed size of the dataset is 80GB and the uncompressed size is 170GB.</p>
ScienceDex guides
Understand access before you commit
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.