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7,129 results for “injection”
Data from: Quantifying the impact of internal variability on the CESM2 control algorithm for stratospheric aerosol injection dataset
<p>Earth system models are a powerful tool to simulate the response to hypothetical climate intervention strategies, such as stratospheric aerosol injection (SAI). Recent simulations of SAI implement tools from control theory, called "controllers", to determine the quantity of aerosol to inject into the stratosphere to reach or maintain specified global temperature targets, such as limiting global warming to 1.5C above pre-industrial temperatures. This work explores how internal (unforced) climate variability can impact controller-determined injection amounts using the Assessing Responses and Impacts of Solar climate intervention on the Earth system with Stratospheric Aerosol Injection (ARISE-SAI) simulations. Since the ARISE-SAI controller determines injection amounts by comparing global annual-mean surface temperature to predetermined temperature targets, internal variability that impacts temperature can impact the total injection amount as well. Using an offline version of the ARISE-SAI controller and data from CESM2 earth system model simulations, we quantify how internal climate variability and volcanic eruptions impact injection amounts. While idealized, this approach allows for the investigation of a large variety of climate states without additional simulations and can be used to attribute controller sensitivities to specific modes of internal variability.</p>
Figure 2 in Effects of methyl farnesoate injection on spermatozoa number and reproductive indices in the narrow-clawed crayfish Pontastacus leptodactylus
Figure 2. Effect of MF injection on reproductive system weight and GSI in male Pontastacus leptodactylus. Letters indicate significant difference groupings (P <0.05) (mean ± S.D; n = 15).
Transcriptomic atlas reveals organ-specific disease tolerance in sickle cell mice. Dataset for bone marrow, HbSS Townes mice injected or not with heme
<p>The objective of this experiment was to explore the transcriptome of the HbSS Townes mouse model of sickle cell disease. Townes model mice carry several human hemoglobin knock-in genes replacing the endogenous mouse genes and may be useful in studying sickle cell disease. All mice were genotyped, age- and sex-matched littermates. All HbAA (control, normal human hemoglobin) vs HbSS (sickle cell disease, mutated human hemoglobin) mice were used for experimentations at 6-8 weeks of age, to limit intra-group heterogeneity. Hemin (Ferriprotoporphyrin IX) was purchased from Frontiers Scientific and injected intravenously (iv.) in a retroorbital sinus at a concentration of 24 µmol/kg. Control mice received PBS instead. Mice were anesthetized with isoflurane 2-3% for injections, blood collection and sacrifice. All mice were sacrificed by cervical dislocation, 4 hours after injection.</p> <p>Here the dataset for HbSS mice injected or not with heme is uploaded.</p> <p>The corresponding dataset for the HbAA mice injected or not with heme can be found at <strong>10.5281/zenodo.10961162</strong></p> <p>Bone marrow RNA was extracted by Macherey Nagel kit, according to the manufacturer’s instructions. The quality and quantity of mRNA were evaluated using a 2100<br>bioanalyzer with TNA 6000 NanoKits (all Agilent Technologies, Palo Alto, CA, USA). RNA Integrity Numbers superior to 7 were eligible for subsequent reverse transcription into cDNA. RNAseq was performed at the GenomIC plateform Cochin Institute INSERM U1016. After RNA extraction, RNA quality (RNA integrity number) was estimated. 1μg of high-quality total RNA sample (RIN &gt;7) was processed to build up the libraries, using TruSeq Stranded mRNA kit (Illumina) according to manufacturer instructions. Briefly, purified poly-A containing mRNA molecules were fragmented and reverse-transcribed using random primers. Replacement of dTTP by dUTP during second strand synthesis allowed us to achieve strand specificity. Addition of a single A base to the cDNA was followed by ligation of Illumina adapters.<br>Libraries were quantified by qPCR using KAPA Library Quantification Kits for Illumina Libraries (KapaBiosystems, Wilmington, MA). Library profiles were assessed using DNA High Sensitivity LabChip kits on an Agilent Bioanalyzer. Libraries were sequenced on an Illumina Nextseq 500 instrument using 75 base-lengths read V2 chemistry in a paired-end mode. After sequencing, primary analysis based on AOZAN software (ENS, Paris), was applied to demultiplex and control the quality of the raw data (based of FastQC modules / version 0.11.5).</p> <p>The dataset here represents 4 groups of mice, 4 mice per group as follows: HbAA PBS, HbAA heme, HbSS PBS, HbSS heme. </p> <p> </p>
Data for : Effects of electrokinetic and ultrasonication pre-treatment and two-step anaerobic digestion of biowastes on the nitrogen fertiliser value by injection or surface banding to cereal crops
<p>Data file for article: Effects of electrokinetic and ultrasonication pre-treatment and two-step anaerobic digestion of biowastes on the nitrogen fertiliser value by injection or surface banding to cereal crops (https://doi.org/10.1016/j.jenvman.2022.116699).</p>
Extended data for 'Revisiting the Rist diagram for predicting operating conditions in blast furnaces with multiple injections'
<p>Extended data for ‘Revisiting the Rist diagram for predicting operating conditions in blast furnaces with multiple injections’ (https://doi.org/10.12688/openreseurope.14275.1):</p> <p> Appendix A – Terms of the energy balance in the elaboration zone</p> <p> Appendix B – Heat of decomposition of coal (dry basis)</p> <p> Appendix C – Energy balance in the preparation zone</p> <p> Appendix D – Calculation of the flame temperature through Eq.(62)</p> <p> DISIPO_OPEN DATA_1.xlsx</p>
SQL Injection Test (D3)
<p>This dataset has SQL injection attacks as malicious flow data. The attacks carried out are SQL injection for Union Query and Blind SQL injection. To perform the attacks, the SQLmap tool has been used.</p>
Model-informed target product profiles of long-acting- injectables for use as seasonal malaria prevention: code and simulation data
<p>This simulation data set and code reproduces the Figures and analysis of PLOS Global Public Health peer-reviewed article </p> <p><strong>Model-informed target product profiles of long-acting-injectables for use as seasonal malaria prevention</strong></p> <p>Authors:</p> <p>Lydia Burgert<sup>1, 2</sup>, Theresa Reiker<sup>1, 2</sup>, Monica Golumbeanu<sup>1,2</sup>, Jörg J. Möhrle<sup>1, 2, 3</sup>, Melissa A. Penny*<sup>1, 2</sup></p> <p> </p> <p><sup>1</sup> Swiss Tropical and Public Health Institute, Basel, Switzerland</p> <p><sup>2</sup> University of Basel, Basel, Switzerland</p> <p><sup>3 </sup>Medicines for Malaria Venture, Geneva, Switzerland</p> <p>*Corresponding author: <a href="mailto:melissa.penny@unibas.ch">melissa.penny@unibas.ch</a></p>
GEOS CCM free-running simulation data of the Pacific-Northwest pyrocumulonimbus Event-like aerosol injection, SWIRL selection
<p>Data from GEOS CCM free running simulation of the Pacific-Northwest pyrocumulonimbus Event. </p> <p>The simulations have been designed and performed by Sampa Das and Peter R. Colarco at the NASA Center for Climate Simulations; the computing resources supporting the simulations shown in this work were provided by the NASA High-End Computing (HEC) Program through the NASA Center for Climate Simulation (NCCS) at the Goddard Space Flight Center.</p> <p>The datasets stored here have been generated by Giorgio Doglioni starting from the whole results of the simulations.</p> <p>The files are in hdf5 format and their structure and content is described in the README file. </p> <p> </p>
HYDRO-CSI, Project 1.2: In-stream hydrology. Part 2: instantaneous injections
<p>The continuous exchange of water between surface water and groundwater is a key environmental process controlling the transport and the fate of nutrients, solutes and pollutants in river networks. The dynamics of the near-stream groundwater has a non-negligeable role on controlling flow direction and solutes exchange between the stream water with the adjacent groundwater, however it is rarely considered in solute transport experiments. Despite the amount of individual studies, we are still uncertain about how the physical processes controlling in-stream solutes transport change with different hydrologic conditions and how these processes can be inferred by modelling outcomes.</p> <p>In this project we investigated solute transport in a headwater stream reach via instantaneous (slug) solute injections. The study site is a 55 m long corridor downstream of the Weierbach experimental catchment (see <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/hyp.14140">Hissler et al., 2021)</a>. The stream channel is unvegetated and consists of deposited colluvial material and fragmented schists (up to 50 cm depth) with underlying fractured slate bedrock that sporadically forms the streambed. The average channel slope is 6% and a 50 cm step riffle sits between wells 7W1 and 7W2 (see <a href="https://onlinelibrary.wiley.com/doi/10.1002/hyp.14310">Bonanno et al., 2021</a>). The stream reach is divided in 11 sections that define the nomenclature of the groundwater observation well network (eg. section one is indicated by wells 1W1 and 1W2). The complete list of groundwater measurements has been published in a previous Zenodo dataset and can be found <a href="https://zenodo.org/record/6245818#.YlacmehBxD9">HERE</a>.</p> <p>The tracer chosen for the experiments is chloride. For each experiment, we prepared an NaCl solution using 2 liters of stream water and a fixed mass of reagent-grade NaCl. We injected the solution in a turbulent pool at the beginning of the stream reach (right before section 1) to assure complete mixing in the stream water. Electrical conductivity was measured via portable conductivity meter (Multisonde WTW). Conversion between EC and chloride concentration has been deduced via EC-chloride concentration plots in laboratory where a fixed amount of NaCl solution with known concentration has been progressively added to a sample the stream water collected before the experiment. Every regression equation between EC and chloride concentration plot had a R<sup>2</sup>>0.998.</p> <p>The dataset includes 30 files of chronologically-numbered instantaneous injections. The instantaneous injections have been conducted from 6-Dec-2018 to 11-June-2021. Every file includes:</p> <p>> A map of the investigated stream reach;<br> > WTW sensor location along the stream reach and their distance from injection point;<br> > The amount of NaCl mass injected in the stream;<br> > Pictures of the stream channel and streamflow;<br> > Notes about presence of leaf packs;<br> > Time and net chloride concentration [mg/l] for each sensor. </p> <p>All the experiments, data cleaning, sensor calibration, and conversion from EC to Cl- concentration have been conducted by Bonanno Enrico between 2018 and 2021 as part of the Ph.D. project HYDRO-CSI (PRIDE15/10623093).<br> François Barnic, Laurent Gourdol, Jean François Iffly and Jérôme Juilleret calibrated the Multisonde WTW and provided the necessary training.<br> Laurent Pfister and Julian Klaus managed the project and were responsible for the founding acquisition.</p>
Data for Scan-Centric, Frequency-Based Method for Characterizing Peaks from Direct Injection Fourier transform Mass Spectrometry Experiments
<p>Input and output files from the manuscript analysis titled "Scan-Centric, Frequency-Based Method for Characterizing Peaks from Direct Injection Fourier transform Mass Spectrometry Experiments"</p>
Magnetic Induced Injection on a UAV Recorded using HEIST
<p>Test on for inducing 13.56 MHz EMI into a UAV. The noise is recorded using HEIST.</p> <p>https://github.com/MaSkr09/heist_datalog.git</p>
Burst Noise Injection through Antenna Recorded on a UAV at Logic Level using HEIST
<p>A UAV exposed to burst noise injected through an antenna. The noise is recorded at logic level using HEIST.</p> <p> </p> <p>https://github.com/MaSkr09/heist_datalog.git</p>
SLQ Injection Attack for training (D1)
<p>This dataset has SQL injection attacks as malicious Netflow data. The attacks carried out are SQL injection for Union Query and Blind SQL injection. To perform the attacks, the SQLmap tool has been used.</p> <p>NetFlow traffic has generated using DOROTHEA (DOcker-based fRamework fOr gaTHering nEtflow trAffic). NetFlow is a network protocol developed by Cisco for the collection and monitoring of network traffic flow data generated. A flow is defined as a unidirectional sequence of packets with some common properties that pass through a network device.</p> <p>The version of NetFlow used to build the datasets is 5.</p>
SLQ Injection Attack for Test (D2)
<p>This dataset has SQL injection attacks as malicious Netflow data. The attacks carried out are SQL injection for Union Query and Blind SQL injection. To perform the attacks, the SQLmap tool has been used.</p> <p>NetFlow traffic has generated using DOROTHEA (DOcker-based fRamework fOr gaTHering nEtflow trAffic). NetFlow is a network protocol developed by Cisco for the collection and monitoring of network traffic flow data generated. A flow is defined as a unidirectional sequence of packets with some common properties that pass through a network device.</p> <p>The version of NetFlow used to build the datasets is 5.</p>
SQL Injection Attack Netflow
<p><strong>Introduction</strong></p> <p>This datasets have SQL injection attacks (SLQIA) as malicious Netflow data. The attacks carried out are SQL injection for Union Query and Blind SQL injection. To perform the attacks, the SQLMAP tool has been used.</p> <p>NetFlow traffic has generated using DOROTHEA (DOcker-based fRamework fOr gaTHering nEtflow trAffic). NetFlow is a network protocol developed by Cisco for the collection and monitoring of network traffic flow data generated. A flow is defined as a unidirectional sequence of packets with some common properties that pass through a network device.</p> <p><strong>Datasets</strong></p> <p>The firts dataset was colleted to train the detection models (<strong>D1</strong>) and other collected using different attacks than those used in training to test the models and ensure their generalization (<strong>D2</strong>).</p> <p>The datasets contain both benign and malicious traffic. All collected datasets are balanced.</p> <p>The version of NetFlow used to build the datasets is 5.</p> <table> <thead> <tr> <th scope="col">Dataset</th> <th scope="col">Aim</th> <th scope="col">Samples</th> <th scope="col">Benign-malicious<br> traffic ratio</th> </tr> </thead> <tbody> <tr> <td>D1</td> <td>Training</td> <td>400,003</td> <td>50%</td> </tr> <tr> <td>D2</td> <td>Test</td> <td>57,239</td> <td>50%</td> </tr> </tbody> </table> <p> </p> <p><strong>Infrastructure and implementation</strong></p> <p>Two sets of flow data were collected with <a href="https://niebla.unileon.es/cybersecurity/dorothea">DOROTHEA</a>. DOROTHEA is a Docker-based framework for NetFlow data collection. It allows you to build interconnected virtual networks to generate and collect flow data using the NetFlow protocol. In DOROTHEA, network traffic packets are sent to a NetFlow generator that has a sensor<a href="https://github.com/aabc/ipt-netflow"><em> ipt_netflow</em></a> installed. The sensor consists of a module for the Linux kernel using Iptables, which processes the packets and converts them to NetFlow flows.</p> <p>DOROTHEA is configured to use Netflow V5 and export the flow after it is inactive for 15 seconds or after the flow is active for 1800 seconds (30 minutes)</p> <p>Benign traffic generation nodes simulate network traffic generated by real users, performing tasks such as searching in web browsers, sending emails, or establishing Secure Shell (SSH) connections. Such tasks run as Python scripts. Users may customize them or even incorporate their own. The network traffic is managed by a gateway that performs two main tasks. On the one hand, it routes packets to the Internet. On the other hand, it sends it to a NetFlow data generation node (this process is carried out similarly to packets received from the Internet).</p> <p>The malicious traffic collected (SQLI attacks) was performed using <a href="https://sqlmap.org/">SQLMAP</a>. SQLMAP is a penetration tool used to automate the process of detecting and exploiting SQL injection vulnerabilities.</p> <p>The attacks were executed on 16 nodes and launch SQLMAP with the parameters of the following table.</p> <table> <thead> <tr> <th scope="col">Parameters</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>'--banner','--current-user','--current-db','--hostname','--is-dba','--users','--passwords','--privileges','--roles','--dbs','--tables','--columns','--schema','--count','--dump','--comments', --schema'</td> <td>Enumerate users, password hashes, privileges, roles, databases, tables and columns</td> </tr> <tr> <td>--level=5</td> <td>Increase the probability of a false positive identification</td> </tr> <tr> <td>--risk=3</td> <td>Increase the probability of extracting data</td> </tr> <tr> <td>--random-agent</td> <td>Select the User-Agent randomly</td> </tr> <tr> <td>--batch</td> <td>Never ask for user input, use the default behavior</td> </tr> <tr> <td>--answers="follow=Y"</td> <td>Predefined answers to yes</td> </tr> </tbody> </table> <p>Every node executed SQLIA on 200 victim nodes. The victim nodes had deployed a web form vulnerable to Union-type injection attacks, which was connected to the <a href="https://www.mysql.com/">MYSQL </a>or <a href="https://www.microsoft.com/es-es/sql-server/sql-server-2019">SQLServer </a>database engines (50% of the victim nodes deployed MySQL and the other 50% deployed SQLServer).</p> <p>The web service was accessible from ports 443 and 80, which are the ports typically used to deploy web services. The IP address space was 182.168.1.1/24 for the benign and malicious traffic-generating nodes. For victim nodes, the address space was 126.52.30.0/24.<br> The malicious traffic in the test sets was collected under different conditions. For <strong>D1</strong>, SQLIA was performed using Union attacks on the MySQL and SQLServer databases.</p> <p>However, for <strong>D2</strong>, BlindSQL SQLIAs were performed against the web form connected to a <a href="https://www.postgresql.org/">PostgreSQL </a>database. The IP address spaces of the networks were also different from those of <strong>D1</strong>. In <strong>D2</strong>, the IP address space was 152.148.48.1/24 for benign and malicious traffic generating nodes and 140.30.20.1/24 for victim nodes.</p> <p>To run the MySQL server we ran <a href="https://mariadb.org/">MariaDB</a> version 10.4.12.<br> Microsoft SQL Server 2017 Express and PostgreSQL version 13 were used.</p>
Husmuli Injection and Seismicity Data 2015-2020
<p>This dataset comprises the injection data, and seismic catalogue recorded in the Húsmúli reinjection area (Hellisheiði geothermal field, SW Iceland) between 2015 and 2020.</p> <p>The hydraulic data was acquired and processed by Reykjavik Energy/ON power, the operator of the Hellisheiði geothermal field. The seismic catalogue is curated by the Icelandic Met Office.</p>
Fig. 4 in Trunk injection of systemic insecticides to control stem and leaf gall wasps, Josephiella species (Hymenoptera: Agaonidae), on Chinese banyan (Rosales: Moraceae) in Hawaii
Fig. 4. Mean leaf gall infestation level on new shoots associated with chemical treatments and untreated control (1–5, where 1 = no infestation and 5 = severe infestation), where * indicates P ≤ 0.05 and ** indicates P ≤ 0.01 within each sampling month (Kruskal–Wallis test).
Fig. 5 in Trunk injection of systemic insecticides to control stem and leaf gall wasps, Josephiella species (Hymenoptera: Agaonidae), on Chinese banyan (Rosales: Moraceae) in Hawaii
Fig. 5. Mean ratings of tree health condition (A) and mean ratings of new shoot emergence (B) 14 mo afer treatment (rating of tree health condition: 1 = excellent, 2 = good, 3 = fair, 4 = poor, 5 = dead; rating of new shoots emergence: 1 = many, 2 = moderate, 3 = some, 4 = few, 5 = very few). Means with the same letter are not significantly different (Kruskal–Wallis test).
Fig. 2 in Trunk injection of systemic insecticides to control stem and leaf gall wasps, Josephiella species (Hymenoptera: Agaonidae), on Chinese banyan (Rosales: Moraceae) in Hawaii
Fig. 2. Mean stem gall infestation levels on new shoots associated with chemical treatments and untreated control (1–5, where 1 = no infestation and 5 = severe infestation), where * indicates P ≤ 0.05 and ** indicates P ≤ 0.01 within each sampling month (Kruskal–Wallis test).
Fig. 3 in Trunk injection of systemic insecticides to control stem and leaf gall wasps, Josephiella species (Hymenoptera: Agaonidae), on Chinese banyan (Rosales: Moraceae) in Hawaii
Fig. 3. Mean percentage (± SE) of leaves infested with leaf gall wasps associated with chemical treatments and untreated control. Means with the same letter are not significantly different (ANOVA).
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Allen Brain Atlas
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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.