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733 results for “passive”
Figure 2. Velocity field U, t in Reconstruction of a passive tracer boundary source in an open water area
Figure 2. Velocity field U, t = 0.
Figure 4 in Reconstruction of a passive tracer boundary source in an open water area
Figure 4. Convergence on the boundary.
Increasing the value of wind - From passive to active actors in the power markets (On-line Appendix)
<p>Mathematical formulations and input data for the evaluation of the different market participation strategies.</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Cross-national Wave 1 Questionnaires: Italy, Sweden, Germany, Greece, Portugal, Czech Republic, UK, and Estonia – EXTRACT_Political passivity
<p>This dataset contains the data underlying the following publication: Dahl, V., Amnå, E., Banaji, S., Landberg, M., Šerek, J., Ribeiro, N., ... & Zani, B. (2017). Apathy or alienation? Political passivity among youths across eight European Union countries.European Journal of Developmental Psychology, 1-18, https://doi.org/10.1080/17405629.2017.1404985.</p>
Estimating effective detection area of static passive acoustic data loggers from playback experiments with cetacean vocalisations
<p>This link provides the data from the playback experiment to determine effective detection areas for porpoises recorded by C-POD acoustic dataloggers. The firstdata set includes the artifically created porpoises click trains captured by the C-PODs and the second is the record of the rate of re-capture for the real, recorded porpoise clicks used in the experiment. </p>
Raw Dataset of Pennsylvania Passive Surveillance
<p>This is the raw dataset that was used for the manuscript "A 117 year retrospective analysis of Pennsylvania tick community dynamics". </p>
Model outputs for "Multi-grid algorithm for passive tracer transport in NEMO ocean circulation model"
<p>Model outputs used to write "Multi-grid algorithm for passive tracer transport in NEMO ocean circulation model" publication.</p>
Porous germanium for germanium surface passivation
<p>The files collect raw data for figures related to publication on porous germanium as a passivating material for germanium surfaces. 3 top raws in each file describe parameter name, units (if applicable) and comments (if applicable), respectively, for each column.</p> <p><strong>NOTE</strong>: due to small changes made during manuscript submission the order of images was shifted. Now, Data_Figure2a.csv corresponds to Figure1a in the manuscript, Data_Figure2b.csv - to Figure1b, and so on. The actual figure number in the manuscript, to which the data file correspondes to, is indicated in the brackets near the file name in the desctiption section. </p> <p>Data_Figure2a.csv [<em>Figure1a in the manuscript</em>] has data on effective lifetime as a function of injection level for samples processed in various components and mixtures of MACE solution (information on corresponding solution for each dataset is provided in the comment section).</p> <p>Data_Figure2b.csv [<em>Figure1b in the manuscript</em>] provides stability test results for selected solutions based on the effective lifetimes measured straight after chemical processing (fresh) and the next day after that. The lifetimes were extracted at injection level of 1E14 cm-3.</p> <p>Data_Figure3.csv [<em>Figure2 in the manuscript</em>] has data on surface recombination velocities (S_eff) as a function of injection lifetime for the samples denoted in the comment section of the file. </p> <p>Data_Figure3_inset.csv [<em>Figure2_inset in the manuscript</em>] provides results of stability test for the sample processed in HF:H2O2 aq. The data represents days after processing with corresponding S_eff values.</p> <p>Data_Figure4b.csv [<em>Figure3b in the manuscript</em>] has normalized XPS data (binding energies with corresponding intensities) of Ge 3d region for the samples indicated in the comment section of the file. </p>
Reactive Passivation of Wide-Bandgap Organic–Inorganic Perovskites with Benzylamine
<p>NMR data for "Reactive Passivation of Wide-Bandgap Organic–Inorganic Perovskites with Benzylamine"- JACS, 2024, 10.1021/jacs.4c06659</p>
Pin-tailed whydah and Cape robin-chat calls for passive acoustic monitoring
<p>We provide the audio data (.wav) used to train neural network classifiers along with the corresponding labelled files (.svl). The .svl files are natively read using Sonic Visualiser (https://www.sonicvisualiser.org/) but can directly be read using Python as these are XML files.</p> <p>This is a three class classification dataset. The recordings were obtained using an AudioMoth which was placed at one location in Intaka Island Nature Reserve, Cape Town, South Africa. The recorder was attached to a tree at approximately 1.5 meters from the ground. The sampling rate was set to 48000Hz with a bit rate of 768kbps. The recordings took place in January 2021. While further recordings exist we only provide a small subset here. Additional data can be requested.</p> <p><strong>Files provided</strong></p> <p>Audio.zip - contains audio files (.wav)</p> <p>Annotations.zip - contains the corresponding labels (.svl) for Sonic Visualiser</p> <p><strong>Class description</strong></p> <p>CRC: calls of the Cape robin-chat (Cossypha caffra)</p> <p>PTW: calls of the pin-tailed whydah (Vidua macroura)</p> <p>NOISE: any sound event that does not contain a Cape robin-chat or pin-tailed whydah call</p> <p> </p> <p>Parts of this data was used in two MSc dissertations:</p> <ul> <li>"Acoustic Data Augmentation for Small Passive Acoustic Monitoring Datasets", Aime Nshimiyimana, African Centre of Excellence in Data Science (ACE-DS) of the University of Rwanda, College of Business and Economics</li> <li>"Pre-training neural networks on Xeno-Canto and eBird for bioacoustic classification models", Mikwa Boris Tamanjong, African Centre of Excellence in Data Science (ACE-DS) of the University of Rwanda, College of Business and Economics</li> </ul>
fUS imaging of ferret auditory cortex during passive listening of natural sounds
<p>Source data for paper: Distinct higher-order representations of natural sounds in human and ferret (BiorXiv, 2020), Landemard A, Bimbard C, Demené C, Shamma S, Norman-Haigneré S, Boubenec Y.</p> <p>This data repository contains several folders:<br>- <em>fUSData </em>contains raw data for all recording sessions. Information on data formatting can be found in README_fUSData text file.<br>- <em>Analysis</em> contains processed and denoised data. This data can be readily used to produce our figures using our publicly available scripts. This data can also be re-generated using data from <em>fUSData </em>folder using our denoising scripts. <br>- <em>AdditionalData </em>contains additional files necessary to run some of the analyses. </p> <p>Code implementing our denoising procedure and reproducing figures from the paper can be found on <a href="http://github.com/agneslandemard/naturalsounds_analysis">https://github.com/agneslandemard/naturalsounds_analysis </a></p> <p> </p>
North American Regional Reanalysis (NARR) data used in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes"
<p>North American Regional Reanalysis (NARR) data from National Oceanic and Atmospheric Administration (NOAA) - 20 August 2013, 26 August 2013, 2 September 2013 - used as input information (initial and boundary condition) for WRF simulations described in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes" (Fathi et al., 2022 - egusphere-2022-1125). NARR data can be accessed and downloaded at the following web address "https://www.ncei.noaa.gov/products/weather-climate-models/north-american-regional/". </p>
Synthesis reveals that island species-area relationships emerge from processes beyond passive sampling
<p><strong>Aim</strong></p> <p>The Island Species-Area Relationship (ISAR) quantifies how the number of species increases as the area of an island or island-like habitat gets larger, and is one of ecology's most general patterns. However, studies that measure the ISAR often confound variation in sampling methodology and analyses, precluding appropriate syntheses of its underlying mechanisms. While most ISAR studies only use presence-absence data at the whole-island scale, we aggregated data sets of species-level abundances allowing for individual-based rarefaction.</p> <p><strong>Location</strong></p> <p>505 islands from 34 different archipelagos across the world, including oceanic islands, lake islands and forest islands.</p> <p><strong>Major Taxa Studied</strong></p> <p class="MsoPlainText">Local assemblages of plants, invertebrates, herpetofauna, birds, mammals</p>
Investigation via Electron Microscopy and Electrochemical Impedance Spectroscopy of the Effect of Aqueous Zinc Ions on Passivity and the Surface Films of Alloy 600 in PWR PW at 320 C
<p>This upload includes the raw EDS data in Bruker Esprit format and the raw EIS data in excel dat format that is presented in the manuscript published in Corrosion and Materials Degradation entitled <em>Investigation via Electron Microscopy and Electrochemical Impedance Spectroscopy of the Effect of Aqueous Zinc Ions on Passivity and the Surface Films of Alloy 600 in PWR PW at 320 C.</em></p>
Numerical Investigation of Interface Passivation Strategies for Sb2Se3/CdS Solar Cells
<p>Sb<sub>2</sub>Se<sub>3</sub> is an emerging earth-abundant material praised for its promising optoelectronic properties, although the presence of interfacial defects at the vicinity of the p–n junction limit its performance as photovoltaic absorber. Using a device modeling approach and a realistic set of material parameters, it unravels pathways mitigating the impact of interfacial defects with a baseline Sb<sub>2</sub>Se<sub>3</sub>/CdS. Two straightforward strategies are devised and tested against the baseline. First, a thin front surface sulfurization of the Sb<sub>2</sub>Se<sub>3</sub> absorber allowing a local lowering of the valence band and creating a “front surface field,” resulting in an increased carrier selectivity and limiting the density of holes available for interface recombination, leading to a significant efficiency improvement for optimized conditions. Second, the use of an ultrathin insulating Al<sub>2</sub>O<sub>3</sub> layer between the absorber and the buffer layer is considered, helping in preventing detrimental chemical interdiffusion at the junction. This strategy provides a direct interface passivation, though the interlayer thickness needs a fine tuning to balance the benefits of reduced interface recombination and a detrimental Al<sub>2</sub>O<sub>3</sub> low-conductivity layer. In each case, an analysis covering a broad range of parameters is presented, and conclusions are made in the frame of past numerical and experimental results.</p>
Passive Operating System Fingerprinting Revisited - Network Flows Dataset
<p>For the evaluation of OS fingerprinting methods, we need a dataset with the following requirements:</p> <ul> <li>First, the dataset needs to be big enough to capture the variability of the data. In this case, we need many connections from different operating systems.</li> <li>Second, the dataset needs to be annotated, which means that the corresponding operating system needs to be known for each network connection captured in the dataset. Therefore, we cannot just capture any network traffic for our dataset; we need to be able to determine the OS reliably.</li> </ul> <p>To overcome these issues, we have decided to create the dataset from the traffic of several web servers at our university. This allows us to address the first issue by collecting traces from thousands of devices ranging from user computers and mobile phones to web crawlers and other servers. The ground truth values are obtained from the HTTP User-Agent, which resolves the second of the presented issues. Even though most traffic is encrypted, the User-Agent can be recovered from the web server logs that record every connection’s details. By correlating the IP address and timestamp of each log record to the captured traffic, we can add the ground truth to the dataset.</p> <p>For this dataset, we have selected a cluster of five web servers that host 475 unique university domains for public websites. The monitoring point recording the traffic was placed at the backbone network connecting the university to the Internet.</p> <p>The dataset used in this paper was collected from approximately 8 hours of university web traffic throughout a single workday. The logs were collected from Microsoft IIS web servers and converted from W3C extended logging format to JSON. The logs are referred to as <em>web logs </em>and are used to annotate the records generated from packet capture obtained by using a network probe tapped into the link to the Internet.</p> <p>The entire dataset creation process consists of seven steps:</p> <ol> <li>The packet capture was processed by the Flowmon flow exporter (<a href="https://www.flowmon.com">https://www.flowmon.com</a>) to obtain primary flow data containing information from TLS and HTTP protocols.</li> <li>Additional statistical features were extracted using GoFlows flow exporter (<a href="https://github.com/CN-TU/go-flows">https://github.com/CN-TU/go-flows</a>).</li> <li>The primary flows were filtered to remove incomplete records and network scans.</li> <li>The flows from both exporters were merged together into records containing fields from both sources.</li> <li><em>Web logs </em>were filtered to cover the same time frame as the flow records.</li> <li><em>Web logs</em> were paired with the flow records based on shared properties (IP address, port, time).</li> <li>The last step was to convert the User-Agent values into the operating system using a Python version of the open-source tool <em>ua-parser</em> (<a href="https://github.com/ua-parser/uap-python">https://github.com/ua-parser/uap-python</a>). We replaced the unstructured User-Agent string in the records with the resulting OS.</li> </ol> <p>The collected and enriched flows contain 111 data fields that can be used as features for OS fingerprinting or any other data analyses. The fields grouped by their area are listed below:</p> <ul> <li>basic flow properties - flow_ID;start;end;L3 PROTO;L4 PROTO;BYTES A;PACKETS A;SRC IP;DST IP;TCP flags A;SRC port;DST port;packetTotalCountforward;packetTotalCountbackward;flowDirection;flowEndReason;</li> <li>IP parameters - IP ToS;maximumTTLforward;maximumTTLbackward;IPv4DontFragmentforward;IPv4DontFragmentbackward;</li> <li>TCP parameters - TCP SYN Size;TCP Win Size;TCP SYN TTL;tcpTimestampFirstPacketbackward;tcpOptionWindowScaleforward;tcpOptionWindowScalebackward;tcpOptionSelectiveAckPermittedforward;tcpOptionSelectiveAckPermittedbackward;tcpOptionMaximumSegmentSizeforward;tcpOptionMaximumSegmentSizebackward;tcpOptionNoOperationforward;tcpOptionNoOperationbackward;synAckFlag;tcpTimestampFirstPacketforward;</li> <li>HTTP - HTTP Request Host;URL;</li> <li>User-agent - UA OS family;UA OS major;UA OS minor;UA OS patch;UA OS patch minor;</li> <li>TLS - TLS_CONTENT_TYPE;TLS_HANDSHAKE_TYPE;TLS_SETUP_TIME;TLS_SERVER_VERSION;TLS_SERVER_RANDOM;TLS_SERVER_SESSION_ID;TLS_CIPHER_SUITE;TLS_ALPN;TLS_SNI;TLS_SNI_LENGTH;TLS_CLIENT_VERSION;TLS_CIPHER_SUITES;TLS_CLIENT_RANDOM;TLS_CLIENT_SESSION_ID;TLS_EXTENSION_TYPES;TLS_EXTENSION_LENGTHS;TLS_ELLIPTIC_CURVES;TLS_EC_POINT_FORMATS;TLS_CLIENT_KEY_LENGTH;TLS_ISSUER_CN;TLS_SUBJECT_CN;TLS_SUBJECT_ON;TLS_VALIDITY_NOT_BEFORE;TLS_VALIDITY_NOT_AFTER;TLS_SIGNATURE_ALG;TLS_PUBLIC_KEY_ALG;TLS_PUBLIC_KEY_LENGTH;TLS_JA3_FINGERPRINT;</li> <li>Packet timings - NPM_CLIENT_NETWORK_TIME;NPM_SERVER_NETWORK_TIME;NPM_SERVER_RESPONSE_TIME;NPM_ROUND_TRIP_TIME;NPM_RESPONSE_TIMEOUTS_A;NPM_RESPONSE_TIMEOUTS_B;NPM_TCP_RETRANSMISSION_A;NPM_TCP_RETRANSMISSION_B;NPM_TCP_OUT_OF_ORDER_A;NPM_TCP_OUT_OF_ORDER_B;NPM_JITTER_DEV_A;NPM_JITTER_AVG_A;NPM_JITTER_MIN_A;NPM_JITTER_MAX_A;NPM_DELAY_DEV_A;NPM_DELAY_AVG_A;NPM_DELAY_MIN_A;NPM_DELAY_MAX_A;NPM_DELAY_HISTOGRAM_1_A;NPM_DELAY_HISTOGRAM_2_A;NPM_DELAY_HISTOGRAM_3_A;NPM_DELAY_HISTOGRAM_4_A;NPM_DELAY_HISTOGRAM_5_A;NPM_DELAY_HISTOGRAM_6_A;NPM_DELAY_HISTOGRAM_7_A;NPM_JITTER_DEV_B;NPM_JITTER_AVG_B;NPM_JITTER_MIN_B;NPM_JITTER_MAX_B;NPM_DELAY_DEV_B;NPM_DELAY_AVG_B;NPM_DELAY_MIN_B;NPM_DELAY_MAX_B;NPM_DELAY_HISTOGRAM_1_B;NPM_DELAY_HISTOGRAM_2_B;NPM_DELAY_HISTOGRAM_3_B;NPM_DELAY_HISTOGRAM_4_B;NPM_DELAY_HISTOGRAM_5_B;NPM_DELAY_HISTOGRAM_6_B;NPM_DELAY_HISTOGRAM_7_B;</li> <li>ICMP - ICMP TYPE;</li> </ul> <p>The details of OS distribution grouped by the OS family are summarized in the table below. The <em>Other </em>OS family contains records generated by web crawling bots that do not include OS information in the User-Agent.</p> <table> <thead> <tr> <th scope="col">OS Family</th> <th scope="col">Number of flows</th> </tr> </thead> <tbody> <tr> <td>Other</td> <td>42474</td> </tr> <tr> <td>Windows</td> <td>40349</td> </tr> <tr> <td>Android</td> <td>10290</td> </tr> <tr> <td>iOS</td> <td>8840</td> </tr> <tr> <td>Mac OS X</td> <td>5324</td> </tr> <tr> <td>Linux</td> <td>1589</td> </tr> <tr> <td>Ubuntu</td> <td>653</td> </tr> <tr> <td>Fedora</td> <td>88</td> </tr> <tr> <td>Chrome OS</td> <td>53</td> </tr> <tr> <td>Symbian OS</td> <td>1</td> </tr> <tr> <td>Slackware</td> <td>1</td> </tr> <tr> <td>Linux Mint</td> <td>1</td> </tr> </tbody> </table> <p> </p>
A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture, Land Surface Temperature and Vegetation Optical Depth from passive microwave data
<p> </p> <p>A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture, Land Surface Temperature and Vegetation Optical Depth from passive microwave data.</p> <p>Introduction</p> <p>This dataset is the Planet Labs PBC (VanderSat B.V.) contribution to the ESA 4DMED hydrology project (<a href="https://www.4dmed-hydrology.org/">https://www.4dmed-hydrology.org/</a>). It includes Soil Moisture, Land Surface Temperature and Vegetation Optical Depth for the 4DMED spatial domain and time period (2015-2021) at 1km pixel size. If you use the data please include the following reference:</p> <blockquote> <p>Jaap Schellekens, Tessa Kramer, Michel van Klink, Robin van der Schalie, Yoann Malbeteau, Arjan Geers, Richard de Jeu. (2022) <em>A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture, Land Surface Temperature and Vegetation Optical Depth from passive microwave data</em>. DOI: 10.5281/zenodo.7684993. Planet Labs PBC/VanderSat B.V., ESA Contract No. 4000136272/21/I-EF</p> </blockquote> <p> </p> <p><em>Figure 1: Average L-Band Soil moisture for 2020 over the 4dmed spatial domain</em></p> <p>Variables and files</p> <p>The dataset consists of the following files and products for the 4DMED domain. Detailed information about the products can also be found at <a href="https://docs.vandersat.com/data_products/soil_water_content/specification.html">docs.vandersat.com</a>:</p> <ul> <li><strong><code>planet-teff-4dmed-V4.0.zip</code></strong> - LST (TEFF) ascending (daytime) and descending (nighttime) <ul> <li><code>TEFF-AMSR2-ASC_V4.0_1000</code> <ul> <li>Land surface temperature daytime (13:30 solar time) at 1 km</li> </ul> </li> <li><code>TEFF-AMSR2-DESC_V4.0_1000</code> <ul> <li>Land surface temperature nighttime (01:30 solar time) at 1 km</li> </ul> </li> </ul> </li> <li><strong><code>planet-teff-qf-4dmed-V4.0.zip</code></strong> - LST (TEFF) quality flags <ul> <li><code>QF-TEFF-AMSR2-ASC_V4.0_1000</code> <ul> <li>Land surface temperature daytime quality flag. <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html">docs.vandersat.com flags</a> and <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python">docs.vandersat.com python example</a></li> </ul> </li> <li><code>QF-TEFF-AMSR2-DESC_V4.0_1000</code> <ul> <li>Land surface temperature daytime quality flag. <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html">docs.vandersat.com flags</a> and <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python">docs.vandersat.com python example</a></li> </ul> </li> </ul> </li> <li><strong><code>planet-vod-4dmed-V4.1.zip</code></strong> - vegetation optical depth C and X band (interpolated from C3S passive soil moisture) <ul> <li><code>VOD_AMSR2_C1_DESC_V41_1000</code> <ul> <li>C1 band Vegetation Optical Depth (nighttime, 01:30 solar time) at 1km (interpolated from 25 km)</li> </ul> </li> <li><code>VOD_AMSR2_X_DESC_V41_1000</code> <ul> <li>X band Vegetation Optical Depth (nighttime, 01:30 solar time) at 1km (interpolated from 25 km)</li> </ul> </li> </ul> </li> <li><strong><code>planet-sm-4dmed-V4.0.zip</code></strong> - All soil moisture products (C1, X and L-band) <ul> <li><code>SM-AMSR2-C1-DESC_V4.0_1000</code> <ul> <li>C1 band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>SM-AMSR2-X-DESC_V4.0_1000</code> <ul> <li>X band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>SM-SMAP-L-DESC_V4.0_1000</code> <ul> <li>L band soil moisture (06:00 solar time) at 1km</li> </ul> </li> </ul> </li> <li><strong><code>planet-sm-qf-4dmed-V4.0.zip</code></strong> - Soil moisture quality maps see <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html">https://docs.vandersat.com/data_products/soil_water_content/data_flags.html</a> and <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python">https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python</a> <ul> <li><code>QF-SM-AMSR2-C1-DESC_V4.0_1000</code> <ul> <li>C1 band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>QF-SM-AMSR2-X-DESC_V4.0_1000</code> <ul> <li>X band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>QF-SM-SMAP-L-DESC_V4.0_1000</code> <ul> <li>L band soil moisture quality flags (06:00 solar time) at 1km</li> </ul> </li> </ul> </li> <li><strong><code>planet-sm-cor-4dmed-V4.0.zip</code></strong> - Yearly correlation maps of soil moisture derived from the difference microwave bands. To be used as an extra quality indicator (for example undetected RFI) or for uncertainty estimation <ul> <li><code>SM-CORR-C1-X-DESC_V4.0_1000</code> - yearly C1 vs X band pearson's correlation maps</li> <li><code>SM-CORR-L-C1-DESC_V4.0_1000</code> - yearly L vs C1 band pearson's correlation maps</li> <li><code>SM-CORR-L-X-DESC_V4.0_1000</code> - yearly L vs X band pearson's correlation maps</li> </ul> </li> <li><strong><code>planet-aux-flags-4dmed-V4.0</code></strong> - Extra flags for frozen soil and bare soil. Determined at 0.25 degree and interpolated to the 4dmed grid <ul> <li><code>QF-SNOWFROZEN-AMSR2-ASC_1000::RD</code> - Frozen soil determined from dayttime data</li> <li><code>QF-SNOWFROZEN-AMSR2-DESC_1000::RD</code> - Frozen soil determined from nighttime data</li> <li><code>QF-BARESOIL-AMSR2-DESC_1000::RD</code> - Bare soil determined from nighttime data</li> <li><code>QF-BARESOIL-AMSR2-ASC_1000::RD</code> - Bare soil determined from daytime data</li> </ul> </li> </ul> <p>All files are archived into one zip file per product group. Each individual netcdf file in the zip file consists of one observation for the whole domain. If you need you can combine the files into one file using the cdo software <a href="https://code.mpimet.mpg.de/projects/cdo">https://code.mpimet.mpg.de/projects/cdo</a> (e.g. <code>cdo -f nc4c mergetime *.nc outfile.nc</code>).</p> <p>License</p> <p>The data for 4DMED is released under the Creative Commons license: CC BY-NC-SA 4.0 (<a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>)</p> <ul> <li>Contains modified Copernicus Sentinel data 2015-2021</li> <li>Contains modified JAXA GCOM-W1/AMSR2 data 2015-2021</li> <li>Contains modified SMAP L1B Radiometer data: Piepmeier, J. R., P. Mohammed, J. Peng, E. J. Kim, G. De Amici, J. Chaubell, and C. Ruf. 2020. SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures, Version 4,5. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: <a href="https://doi.org/10.5067/ZHHBN1KQLI20">https://doi.org/10.5067/ZHHBN1KQLI20</a></li> </ul> <p>Contact</p> <p>Jaap Schellekens: <a href="mailto:jaap@planet.com">jaap@planet.com</a></p> <p>Versions</p> <ul> <li>1.0 Initial creation</li> <li>1.1 Adjusted 4DMED Mask. Data itself unchanged but more LST (TEFF) measurements added</li> <li>1.2 Removed VOD and replaced by 25km C3S VOD interpolated to 1km (V4.1)</li> </ul> <p>Further information</p> <p>More information on the data and the flags can be found at <a href="https://docs.vandersat.com/">https://docs.vandersat.com</a> and <a href="https://www.4dmed-hydrology.org/">https://www.4dmed-hydrology.org</a></p> <p>Background publications</p> <p>R.A.M. De Jeu, A.H.A. De Nijs, M.H.W. Van Klink (2016) <em>Method and system for improving the resolution of sensor data</em>, US10643098B2,EP3469516B1, WO2017216186A1</p> <p>De Jeu, R. A., Holmes, T. R., Parinussa, R. M., & Owe, M. (2014). <em>A spatially coherent global soil moisture product with improved temporal resolution</em>. Journal of hydrology, 516, 284-296.</p> <p>Moesinger, L., Dorigo, W., de Jeu, R., van der Schalie, R., Scanlon, T., Teubner, I. and Forkel, M., 2020. <em>The global long-term microwave vegetation optical depth climate archive (VODCA)</em>. Earth System Science Data, 12(1), pp.177-196.</p> <p>Schmidt, L., Forkel, M., Zotta, R.-M., Scherrer, S., Dorigo, W. A., Kuhn-Régnier, A., van der Schalie, R., and Yebra, M.: <em>Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties, Biogeosciences Discuss.</em> [preprint], <a href="https://doi.org/10.5194/bg-2022-85">https://doi.org/10.5194/bg-2022-85</a>, in review, 2022</p> <p>Van der Schalie, R., de Jeu, R.A.M., Kerr, Y.H., Wigneron, J.P., Rodríguez-Fernández, N.J., Al- Yaari, A., Parinussa, R.M., Mecklenburg, S. and Drusch, M. (2017), <em>The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E</em>, Remote Sensing of Environment, 189, pp.180-193.</p> <p>van der Vliet, M., van der Schalie, R., Rodriguez-Fernandez, N., Colliander, A., de Jeu, R., Preimesberger, W., Scanlon, T., Dorigo, W., 2020. Reconciling Flagging Strategies for Multi-Sensor Satellite Soil Moisture Climate Data Records. Remote Sensing 12, 3439. <a href="https://doi.org/10.3390/rs12203439">https://doi.org/10.3390/rs12203439</a></p>
Data supplement for PhD thesis "Pattern Formation with Mass Conservation - From Passive to Active Models"
<p>Here, we provide <em>Mathematica</em> files that provide the details of the weakly nonlinear analysis employed in the PhD thesis "Pattern Formation with Mass Conservation - From Passive to Active Models" submitted by Tobias Frohoff-Hülsmann.</p> <p> </p>
Data for: Passive and active parental food allocation in a songbird
<p><span>Parent-offspring conflict over food allocation can be modeled using two theoretical frameworks: passive (scramble competition) and active choice (signaling) resolution models. However, differentiating between these models empirically can be challenging. One possibility involves investigating details of decision-making by feeding parents. Different nestling traits, related to competitive prowess or signalling cryptic condition, may interact additively or non-additively as predictors of parental feeding responses. To explore this, we experimentally created even-sized, small broods of pied flycatchers and manipulated nestling cryptic quality, independently of size, by vitamin E supplementation. We explored how interactions between nestling cryptic condition, size, signals, and spatial location predicted food allocation and prey-testing by parents. Parents created the potential for spatial scramble competition between nestlings by feeding from and to a narrow range of nest locations. Heavier supplemented nestlings grew faster and were more likely to access profitable nest locations. However, the most profitable locations were not more contested, and nestling turnover did not vary in relation to spatial predictability or food supply. Postural begging was only predicted by nestling hunger and body mass, but parents did not favor heavier nestlings. This suggests that size-mediated and spatial competition in experimental broods was mild. Pied flycatcher fathers allocated food in response to nestling position and begging order, while mothers seemingly followed an active choice mechanism involving assessment of more complex traits, including postural intensity interacting with order, position, and treatment, and perhaps other stimuli when performing prey-testings. Differences in time constraints may underlie sex differences in food allocation rules.</span></p>
A Comparison of Passive and Active Dust Sampling Methods for Measuring Airborne MRSA in Pig Farms
<p>Study Abstract:</p> <p>Authors: Anne Rittscher, Abel A. Vlasblom, Birgitta Duim, Peter Scherpenisse, Isabella van Schothorst, Inge M. Wouters, Liese Van Gompel, Lidwien A.M. Smit</p> <p> Methicillin resistant strains of <em>Staphylococcus aureus </em>(MRSA) are resistant to most b-lactam antibiotics. Pigs are an important reservoir of livestock associated MRSA (LA-MRSA), which is genetically distinct from both hospital and community acquired MRSA. Occupational exposure to pigs on farms can lead to LA-MRSA carriage by workers. There is a growing body of research on MRSA found in the farm environment, the airborne route of transmission, and its implication on human health. This study aims to compare two sampling methods used to measure airborne MRSA in the farm environment; passive dust sampling with electrostatic dust fall collectors (EDCs), and active inhalable dust sampling using stationary air pumps with Teflon filters.</p> <p>Paired dust samples using EDCs and GSP samplers, totaling 87 samples, were taken from seven Dutch pig farms, in multiple compartments housing pigs of varying ages. Total nucleic acids of both types of dust samples were extracted and targets indicating MRSA (<em>femA, nuc, mecA</em>) and total bacterial count (16S rRNA) were quantified using quantitative Real Time PCRs.</p> <p>MRSA could be measured in all stationary pump samples and in 94% of the EDCs, additionally MRSA was present on every farm sampled. There was a strong positive relationship between the paired MRSA levels found in EDCs and those measured on filters (Normalized by 16S rRNA; Pearson’s correlation coefficient r=0.94, Not Normalized; Pearson’s correlation coefficient r=0.84).</p> <p>This study suggests that EDCs can be used as an affordable and easily standardized method for quantifying airborne MRSA levels in the pig farm setting.</p>
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