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Assessing the Resilience of Software Systems by Application of Chaos Engineering - A Case Study
<p>This repo contains all supplementary data sets that I have created and used throughout my bachelor's thesis. In particular, it contains<br> - JMeter configuration file<br> - CSV files of measured JMeter runs<br> - Chaos Experiments declared in JSON<br> - Created boxplots and ecdf plots based on the CSV files<br> - Kubernetes deployments<br> - Bash scripts <br> </p>
Automated Usability Evaluation of Augmented Reality Applications
<p>Research data for my master thesis on the topic: "Automated Usability Evaluation of Augmented<br> Reality Applications"</p>
Data for: Comparison of contributions to chloride in urban stormwater from winter brine and rock salt application
<p><strong>Abstract:</strong></p> <p>The use of road salt to increase roadway safety during winter storms releases high concentrations of chloride into urban and suburban stormwater. This stormwater flows into nearby streams, resulting in concentrations of chloride that can exceed water quality standards intended to protect aquatic life. As chloride pollution is not readily filtered by soil or plants, mitigation will require reductions in the amount of salt used. In this study, cities in St. Louis County, Missouri, U.S., were used as a test case for brining as a best management practice (BMP) to reduce salt use relative to the standard practice of spreading solid rock salt. The practice of brining involves the dissolution of road salt in water and the application of the resulting brine solution to roadways in advance of a forecasted winter storm. During the winters of 2016-17 and 2017-18, stormwater runoff from residential areas was monitored in paired cities to determine if the availability of brining as a BMP for salt application on residential roads would result in a decrease of chloride in stormwater and, therefore, a decrease in chloride reaching urban streams. The use of brining by city governments resulted in a 45% average reduction of chloride loads conveyed to streams, demonstrating that brining is a highly viable BMP for local municipal operations.</p> <p><strong>Publication DOI:</strong></p> <p><a href="https://doi.org/10.1021/acs.est.9b02864">https://doi.org/10.1021/acs.est.9b02864</a></p>
Companion data of a Systematic Mapping Study of Programming Languages for Data-Intensive HPC Applications
<p>As the current existing literature on the topic of HPC is very dispersed, we performed a Systematic Mapping Study (SMS) in the context of the European COST Action cHiPSet. This literature study maps characteristics of various programming languages for data-intensive HPC applications, including category, typical user profiles, effectiveness, and type of articles.</p> <p>We organised the SMS in two phases. In the first phase, relevant articles are identified employing an automated keyword-based search in eight digital libraries. This lead to an initial sample of 420 papers, which was then narrowed down in a second phase by human inspection of article abstracts, titles and keywords to 152 relevant articles published in the period 2006--2018. The analysis of these articles enabled us to identify 26 programming languages referred to in 33 of relevant articles. This document is the data companion for a paper published elsewhere and presents a detailed list of the selected papers. Besides, the document also presents the form of our questionnaire-based survey. </p> <p>We also include the filled in questionnaires and raw data of the referred survey. To validate the SMS results we conducted a survey (in November 2018) with 28 HPC experts involved in the cHiPSet COST action to which we added, in October 2019, 29 HPC experts which were not involved in that COST action. Participants were recruited through convenience sampling, and contacted directly by the authors. In total, we received 57 filled survey forms.</p>
Companion data of a Systematic Mapping Study of Programming Languages for Data-Intensive HPC Applications
<p>As the current existing literature on the topic of HPC is very dispersed, we performed a Systematic Mapping Study (SMS) in the context of the European COST Action cHiPSet. This literature study maps characteristics of various programming languages for data-intensive HPC applications, including category, typical user profiles, effectiveness, and type of articles.</p> <p>We organised the SMS in two phases. In the first phase, relevant articles are identified employing an automated keyword-based search in eight digital libraries. This lead to an initial sample of 420 papers, which was then narrowed down in a second phase by human inspection of article abstracts, titles and keywords to 152 relevant articles published in the period 2006--2018. The analysis of these articles enabled us to identify 26 programming languages referred to in 33 of relevant articles. This document is the data companion for a paper published elsewhere and presents a detailed list of the selected papers. Besides, the document also presents the form of our questionnaire-based survey. </p> <p>We also include the filled in questionnaires and raw data of the referred survey. To validate the SMS results we conducted a survey (in November 2018) with 28 HPC experts involved in the cHiPSet COST action to which we added, in October 2019, 29 HPC experts which were not involved in that COST action. Participants were recruited through convenience sampling, and contacted directly by the authors. In total, we received 57 filled survey forms.</p>
Field Survey of Wireless M-Bus Encryption for Energy Metering Applications in Residential Buildings
<p>This is the pseudonymized data of the paper "Field Survey of Wireless M-Bus Encryption for Energy Metering Applications in Residential Buildings" by Hiller v. Gärtringen et al. 2024.</p> <p>Each entry represents a unique wireless M-Bus device that was captured during our field study.</p> <p>Manufacturers and serial numbers are mapped to new identifiers.<br>Payload was removed.</p> <p>The meaning of the columns in the data set are:</p> <table> <tbody> <tr> <td><strong>name</strong></td> <td><strong>type and manifestations</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>id</td> <td>integer</td> <td> <p>Unique for each wireless transmitting device.<br>Counting up from 1 to n of devices.</p> </td> </tr> <tr> <td>manufacturer</td> <td> <p>enumeration</p> <ul> <li>MAN1 - MAN16</li> </ul> </td> <td>Pseudonymized manufacturer identifier.</td> </tr> <tr> <td>device type</td> <td> <p>enumeration</p> <ul> <li>heat cost allocator</li> <li>heat meter</li> <li>temperature or humidity sensor</li> <li>warm water meter</li> <li>water meter</li> <li>radio control device</li> <li>smoke detector</li> <li>unknown type</li> </ul> </td> <td>Device types are described in EN 13757-7 Table 13</td> </tr> <tr> <td>number of telegrams</td> <td>integer</td> <td>Number of telegrams received from the device.</td> </tr> <tr> <td>has DLL Encryption</td> <td>boolean</td> <td>Indicating, if the device uses DLL encryption.</td> </tr> <tr> <td>AES mode</td> <td> <p>enumeration</p> <ul> <li>not encrypted (mode 0)</li> <li>AES-CBC static key (mode 5)</li> <li>AES-CBC dynamic key (mode 7)</li> <li>AES-CCM (mode 10)</li> </ul> </td> <td>Indicates the AES encryption mode.</td> </tr> <tr> <td>detected in 2022</td> <td>boolean</td> <td> <p>Indicates if the device was detected in the given year.<br>If detected in 2022 and 2023, both are 1.</p> </td> </tr> <tr> <td>detected in 2023</td> <td>boolean</td> <td> <p>Indicates if the device was detected in the given year.<br>If detected in 2022 and 2023, both are 1.</p> </td> </tr> <tr> <td>interpretable</td> <td>boolean</td> <td> <p>Indicates whether we identified the message as interpretable.<br>For a detailed description, see the paper.</p> </td> </tr> </tbody> </table> <p> </p>
Fig. 1 in Taeniid cestodes in Tibetan foxes (Vulpes Ferrilata) detected by copro-PCR: Applications and challenges
Fig. 1. (continued).
Supplementary XRD Material for "Experimental Constraints on Barium Isotope Fractionation during Adsorption-Desorption Reactions: Implications for Weathering and Erosion Tracer Applications"
<p><strong>Repository Overview:</strong> Supplementary powder X-ray diffraction (XRD) data for the study "Experimental Constraints on Barium Isotope Fractionation during Adsorption-Desorption Reactions: Implications for Weathering and Erosion Tracer Applications"</p> <p><strong>Data Provided:</strong> Powder X-ray diffractograms of the minerals used in the laboratory batch experiments. Pow</p> <p><strong>Minerals Analysed:</strong></p> <p><span>Clay Minerals:</span></p> <ul> <li>Kaolinite (KGa-2): purchased from the Clay Minerals Society (CMS).</li> <li>Montmorillonite (SWy-2): purchased from the Clay Minerals Society (CMS).</li> </ul> <p><span>Iron Oxyhydroxide Minerals:</span></p> <ul> <li>Goethite synthesised under alkaline conditions in the laboratories at the Department of Earth Sciences, University of Cambridge, following the method detailed in Schwertmann and Cornell (2008).</li> <li>2-line ferriydrite synthesised in the laboratories at the Department of Earth Sciences, University of Cambridge, following the method detailed in Schwertmann and Cornell (2008) methods.</li> </ul> <p><strong>XRD Methods:</strong></p> <ul> <li>Instrument and Set Up: Theta-Theta D8 Bruker Advance diffractometer with a Mo anode, Lynxeye XE-T PSD detector and 2 mm divergent slit.</li> <li>Sample Preparation: Zero-background monocrystalline silicon plate was used to minimize sample mass. The X-ray diffractogram of the silicon plate was measured prior to each sample measurement.</li> <li>Clay Minerals: 0.02° step size and a 3s step rate.</li> <li>Iron Oxyhydroxide Minerals: 0.03° increments and a 1s step rate.</li> </ul> <p><strong>Purpose of XRD Analyses:</strong></p> <ul> <li>To verify the correct synthesis of iron oxyhydroxide phases and to detect impurities.</li> <li>To monitor any mineralogical changes that occurred during adsorption-desorption reactions with water.</li> </ul> <p><strong>Data Format</strong></p> <table> <tbody> <tr> <td><strong>Column Name</strong></td> <td><strong>Description</strong></td> <td><strong>Unit</strong></td> </tr> <tr> <td>twotheta</td> <td>2θ</td> <td>°</td> </tr> <tr> <td>intensity</td> <td>intensity</td> <td>counts per second (cps)</td> </tr> <tr> <td>ID</td> <td>mineral ID</td> <td>unitless</td> </tr> <tr> <td>mineral</td> <td>mineral name</td> <td>unitless</td> </tr> <tr> <td>duration</td> <td>reaction duration</td> <td>minutes</td> </tr> </tbody> </table> <p><strong>Extra Notes</strong></p> <p>A reaction duration of zero minutes corresponds to an unreacted mineral sample.</p>
Applicability of the inverse dispersion method to measure emissions from animal housings - data set & R scripts
<h2>Data availability</h2> <p>Provided are:<br>- raw data of the instruments<br>- R Scripts to reproduce the findings in the publication<br>- R outputs</p> <h2>Scripts</h2> <p>In total, there are 10 scripts provided, of which most of them are needed to reproduce the data in the publication.</p> <p>Below, a brief explanation of the content of the different scripts.</p> <ul> <li>01_Datatreatment_01_Weatherstation.r ## This script reads in the weather station data and makes it ready for further use.</li> <li>01_Datatreatment_02_Sonics.r ## This script reads in the 3D ultrasonic data and makes it ready for further use.</li> <li>01_Datatreatment_03_GasFinder.r ## This script reads in the GasFinder data and makes it ready for further use.</li> <li>01_Datatreatment_04_MFC_Pressuresensor.r ## This script reads in the mass flow controller (MFC) and pressure sensor data and makes it ready for further use.</li> <li>02_Calculation_01_bLS.r ## This script is made to run the bLSmodelR and tailored to the number cruncher of the University of Applied Sciences BFH. The code should also work on your computer but you have to adopt the number of cores.</li> <li>02_Calculation_02_Concentration.r ## This script treats the unprocessed concentration data. It removes false concentrations, applies an intercalibration, and makes the data ready for further use.</li> <li>02_Calculation_03_Emissions.r ## This script calculates emissions and makes it ready for further use.</li> <li>02_Calculation_04_contourXYZ_Plume.r ## This script calculates the plume contours in the XY and XZ plane. This script is not necessary to reproduce the findings of the publication.</li> <li>03_Apply_filter.r ## This script applies the quality filtering and makes the data ready for further use.</li> <li>04_Plots_Tables.r ## With this script one can recreate all the plots and values in the tables of the publication, the supplement, and the initial submission.</li> </ul> <p>Note, for the geometry, there is no script provided. The coordinates of the different sensors and the source are solely provided as R output.</p> <h3>Naming of instruments</h3> <p>The instruments in the publication have different names than in the scripts. In some scripts the final names are also provided but throughout the evaluation the original device names are used. Only in the script 04_Plots_Tables.r are the final names introduced. Below is an overview of what original name corresponds to the final name of the devices:</p> <h4><strong>GasFinder instruments called 'OP' in the publication</strong></h4> <ul> <li>OP-UW = GF26</li> <li>OP-2.0h = GF17</li> <li>OP-5.3h = GF18</li> <li>OP-6.8h = GF16</li> <li>OP-12h = GF25</li> </ul> <p><strong>3D ultrasonic anemometer instruments called 'UA' in the publication</strong></p> <ul> <li>UA-UW = SonicC</li> <li>UA-2.0h = SonicA</li> <li>UA-5.3h = Sonic2</li> <li>UA-6.8h = SonicB</li> </ul> <p><strong>Source</strong><br>In some of the scripts, the source might be called 'Schopf' which is a local term for 'shed'.</p> <h2>Note</h2> <p>This code was written by Marcel Bühler (minor code chunks were originally written by Christoph Häni) and is intended to reproduce the findings of the linked publication. Please feel free to use and modify it (e.g., use it to run different dispersion models), but attribution is appreciated.</p> <h2>Disclaimer</h2> <p>I do not guarantee that everything works. It might be that not all variables were changed to English for better understanding correctly. Unfortunately, it is not possible to provide all the catalogs of the bLS run, as the total size is several 100s of GB. In case you run the bLS model on your own, the result will have a minimal difference, as no bLS run produces the same result twice. This should, however, not alter the findings.</p> <h2>Contact</h2> <p>In case you have questions, please contact Marcel Bühler (mb@bce.au.dk). In case this does not work, Christoph Häni might also be able to help (christoph.haeni@bfh.ch).</p>
AIDERv2 (Aerial Image Dataset for Emergency Response Applications)
<div>SUMMARY OF DATASET</div> <div> </div> <div>• This dataset consist of 167723 aerial images divided into 4 classes.</div> <div> </div> <div>• The dataset contains three commonly occurring natural disasters</div> <div>earthquake/collapsed buildings, flood, wildfire/fire, and a normal class; do not reflect any disaster</div> <div> </div> <div>• The images can be loaded as numpy arrays using Python programming language and then used to train a Convolutional Neural Network to detect natural disasters from aerial images.</div> <div> </div> <div>• The images are resized to 224x224x3 (heighty,width,channel number) when loaded as numpy arrays.</div> <div> </div> <div>• The dataset is an extension of the AIDER dataset (Aerial Image Dataset for Emergency Response Applications). </div> <div> </div> <div>• Additional images were collected by open source databases and extracted images as frames of videos downloaded from YouTube. </div> <div> </div> <div> </div> <div>The table below shows the number of images in each set.</div> <div> </div> <div> Train Validation Test Total</div> <div> Earthquakes 1927 239 239 2405</div> <div> Floods 4063 505 502 5070</div> <div> Fire 3509 439 436 4384</div> <div> Normal 3900 487 477 4864</div> <div> Total 13399 1670 1654 16723</div> <div> </div> <div> </div> <div>If you use this dataset please cite the following publications:</div> <div> </div> <div>[1] Shianios, D., Kyrkou, C., Kolios, P.S. (2023). A Benchmark and Investigation of Deep-Learning-Based Techniques for Detecting Natural Disasters in Aerial Images. In: Tsapatsoulis, N., <em>et al.</em> Computer Analysis of Images and Patterns. CAIP 2023. Lecture Notes in Computer Science, vol 14185. Springer, Cham. https://doi.org/10.1007/978-3-031-44240-7_24</div> <div>Link: https://link.springer.com/chapter/10.1007/978-3-031-44240-7_24</div> <div> </div> <div>[2] D. Shianios, P. Kolios, C. Kyrkou, "DiRecNetV2: A Transformer-Enhanced Network for Aerial Disaster Recognition", SN Computer Science, 2024 (Accepted to Appear)</div> <div> </div> <div> </div> <div> </div> <div> </div> <div>DATASET FOLDERS FORMAT</div> <div> </div> <div>└───data</div> <div>│ │</div> <div>│ └───Dataset_Images</div> <div>│ │ └───Train</div> <div>│ │ │ | └───Earthquake</div> <div>│ │ │ | img (1).jpg</div> <div>│ │ │ | img (2).jpg</div> <div>│ │ │ | .....</div> <div>│ │ │ | └───Flood</div> <div>│ │ │ | img (1).jpg</div> <div>│ │ │ | img (2).jpg</div> <div>│ │ │ | .....</div> <div>│ │ │ | └───Normal</div> <div>│ │ │ | img (1).jpg</div> <div>│ │ │ | img (2).jpg</div> <div>│ │ │ | .....</div> <div>│ │ │ | └───Wildfire</div> <div>│ │ │ | img (1).jpg</div> <div>│ │ │ | img (2).jpg</div> <div>│ │ │ | .....</div> <div>│ │ └───Val</div> <div>│ │ │ | └───Earthquake</div> <div>│ │ │ | img (1).jpg</div> <div>│ │ │ | img (2).jpg</div> <div>│ │ │ | .....</div> <div>│ │ │ | └───Flood</div> <div>│ │ │ | img (1).jpg</div> <div>│ │ │ | img (2).jpg</div> <div>│ │ │ | .....</div> <div>│ │ │ | └───Normal</div> <div>│ │ │ | img (1).jpg</div> <div>│ │ │ | img (2).jpg</div> <div>│ │ │ | .....</div> <div>│ │ │ | └───Wildfire</div> <div>│ │ │ | img (1).jpg</div> <div>│ │ │ | img (2).jpg</div> <div>│ │ │ | .....</div> <div>│ │ └───Test</div> <div>│ │ │ | └───Earthquake</div> <div>│ │ │ | img (1).jpg</div> <div>│ │ │ | img (2).jpg</div> <div>│ │ │ | .....</div> <div>│ │ │ | └───Flood</div> <div>│ │ │ | img (1).jpg</div> <div>│ │ │ | img (2).jpg</div> <div>│ │ │ | .....</div> <div>│ │ │ | └───Normal</div> <div>│ │ │ | img (1).jpg</div> <div>│ │ │ | img (2).jpg</div> <div>│ │ │ | .....</div> <div>│ │ │ | └───Wildfire</div> <div>│ │ │ | img (1).jpg</div> <div>│ │ │ | img (2).jpg</div> <div>│ │ │ | .....</div> <div> </div> <div> </div> <div> </div> <div> </div> <div> </div> <div>DATA SOURCES AND DATA COLLECTION</div> <div> </div> <div>OPEN SOURCE DATABASES</div> <div> </div> <div>└───AIDER </div> <div>https://zenodo.org/record/3888300#.Yuu11nZBxD-</div> <div>Kyrkou, C. and Theocharides, T., 2020. EmergencyNet: Efficient aerial image classification for drone-based emergency monitoring using atrous convolutional feature fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, pp.1687-1699.</div> <div> </div> <div>└───ERA </div> <div>https://lcmou.github.io/ERA_Dataset/</div> <div>Mou, L., Hua, Y., Jin, P. and Zhu, X.X., 2020. Era: A data set and deep learning benchmark for event recognition in aerial videos [software and data sets]. IEEE Geoscience and Remote Sensing Magazine, 8(4), pp.125-133.</div> <div>@article{eradataset,</div> <div> title = {{ERA: A dataset and deep learning benchmark for event recognition in aerial videos}},</div> <div> author = {Mou, L. and Hua, Y. and Jin, P. and Zhu, X. X.},</div> <div> journal = {IEEE Geoscience and Remote Sensing Magazine},</div> <div> year = {in press}</div> <div>}</div> <div> </div> <div> </div> <div> </div> <div>└───ISBDA</div> <div>https://drive.google.com/file/d/1kEKJ8kr1aScXz_1El7Mn-Yi0ANducQIW/view</div> <div>Zhu, X., Liang, J. and Hauptmann, A., 2021. Msnet: A multilevel instance segmentation network for natural disaster damage assessment in aerial videos. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 2023-2032).</div> <div>@misc{zhu2020msnet,</div> <div> title={MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videos},</div> <div> author={Xiaoyu Zhu and Junwei Liang and Alexander Hauptmann},</div> <div> year={2020},</div> <div> eprint={2006.16479},</div> <div> archivePrefix={arXiv},</div> <div> primaryClass={cs.CV}</div> <div>}</div> <div> </div> <div> </div> <div>└───Floods 2013</div> <div>https://github.com/cvjena/eu-flood-dataset</div> <div>Barz, B., Schröter, K., Münch, M., Yang, B., Unger, A., Dransch, D. and Denzler, J., 2019. Enhancing flood impact analysis using interactive retrieval of social media images. arXiv preprint arXiv:1908.03361.</div> <div>@article{barz2019enhancing,</div> <div> title={Enhancing flood impact analysis using interactive retrieval of social media images},</div> <div> author={Barz, Bj{\"o}rn and Schr{\"o}ter, Kai and M{\"u}nch, Moritz and Yang, Bin and Unger, Andrea and Dransch, Doris and Denzler, Joachim},</div> <div> journal={arXiv preprint arXiv:1908.03361},</div> <div> year={2019}</div> <div>}</div> <div> </div> <div>└───Wildfire Research</div> <div>http://wildfire.fesb.hr/index.php?option=com_content&view=article&id=58&Itemid=54</div> <div> </div> <div> </div> <div>└───PyImages</div> <div>https://drive.google.com/file/d/1NvTyhUsrFbL91E10EPm38IjoCg6E2c6q/view</div> <div>The dataset was curated by PyImageSearch reader, Gautam Kumar.</div> <div> </div> <div> </div> <div> </div> <div> </div> <div>YOUTUBE VIDEOS</div> <div> </div> <div>└───Collapsed Buildings/Earthquakes</div> <div>• https://www.youtube.com/watch?v=TMow3WPcZrQ&t=133s&ab_channel=GORKHALYFOUNDATION</div> <div>• https://www.youtube.com/watch?v=_HT0tYKKjBI&t=47s&ab_channel=Effect.org</div> <div>• https://www.youtube.com/watch?v=rkb3y6K3waU</div> <div>• https://www.youtube.com/watch?v=yir6ArRZY4o&t=109s&ab_channel=UnicefUK</div> <div>• https://www.youtube.com/watch?v=CM9APmIR9Fk&ab_channel=ToonsZilla</div> <div>• https://www.youtube.com/watch?v=tmx2w6drAeU&ab_channel=AssociatedPress</div> <div>• https://www.youtube.com/watch?v=kuSEe8Emwrk&ab_channel=BloombergQuicktake%3ANow</div> <div>• https://www.youtube.com/watch?v=qoFHA3-m5ag&ab_channel=NBCNews</div> <div>• https://www.youtube.com/watch?v=MM3PToqEPhQ&ab_channel=GuardianNews</div> <div>• https://www.youtube.com/watch?v=zB_-TRnGuZE&ab_channel=DISASTERNEWS</div> <div>• https://www.youtube.com/watch?v=TqAMQQOEsBs&ab_channel=WHAS11</div> <div>• https://www.youtube.com/watch?v=0ixjTt-jmok&ab_channel=EveningStandard</div> <div>• https://www.youtube.com/watch?v=bNGA8Ms3d70&ab_channel=CatersClips</div> <div>• https://www.youtube.com/watch?v=wJ-2d5t23Lg&ab_channel=DailyDose</div> <div>• https://www.youtube.com/watch?v=ewUcI7I6Gf4&ab_channel=NBCNews</div> <div>• https://www.youtube.com/watch?v=Wx1cjOdlMZ4&ab_channel=ABCNews%28Australia%29</div> <div>• https://www.youtube.com/watch?v=jiMK_sVmbXk&t=12s&ab_channel=NewChinaTV </div> <div>• https://www.youtube.com/watch?v=M9au_9A2YRo&ab_channel=GuardianNews</div> <div>• https://www.youtube.com/watch?v=i6Lh8IXPjso&ab_channel=TheSun</div> <div>• https://www.youtube.com/watch?v=CKwxEr3I4Y8&ab_channel=GuardianNews</div> <div>• https://www.youtube.com/watch?v=hxqzcajBCNg&list=RDCMUCD3KREyo3IqCLBC-4khGgIw&index=3&ab_channel=WXChasing</div> <div>• https://www.youtube.com/watch?v=2GEeTDuf9mI&list=RDCMUCD3KREyo3IqCLBC-4khGgIw&index=6&ab_channel=WXChasing</div> <div>• https://www.youtube.com/watch?v=bDOuZWxIyNQ&list=RDCMUCD3KREyo3IqCLBC-4khGgIw&index=9&ab_channel=WXChasing</div> <div>• https://www.youtube.com/watch?v=vzoSADijLCQ&list=RDCMUCD3KREyo3IqCLBC-4khGgIw&index=15&ab_channel=WXChasing</div> <div>• https://www.youtube.com/watch?v=ZaL1fldTEAk&list=RDCMUCD3KREyo3IqCLBC-4khGgIw&index=17&ab_channel=WXChasing</div> <div>• https://www.youtube.com/watch?v=QSV81FdilZE&ab_channel=GlobalNews</div> <div>• https://www.youtube.com/watch?v=KgOk9otW1Bg&ab_channel=EricFeijten</div> <div> </div> <div> </div> <div> </div> <div>└───Floods</div> <div>• https://www.youtube.com/watch?v=DJqgv8Sa5bA&t=317s&ab_channel=7NEWSAustralia</div> <div>• https://www.youtube.com/watch?v=w5FintiCLJU&t=9s&ab_channel=GuardianNews</div> <div>• https://www.youtube.com/watch?v=HjMymNN6Ajc&t=143s&ab_channel=BioLogicTreeServices</div> <div>• https://www.youtube.com/watch?v=Tmba18C94C8&ab_channel=AL.com</div> <div>• https://www.youtube.com/watch?v=Dqvpv4Vg4lk&t=63s&ab_channel=ElevenEleven</div> <div>• https://www.youtube.com/watch?v=N7QGicNtN2A&ab_channel=PKSVideoProductions</div> <div>• https://www.youtube.com/watch?v=8CHagyQG16Q&ab_channel=Stolly-Sven</div> <div>• https://www.youtube.com/watch?v=vjH3zFqdzcE&ab_channel=BenChilders</div> <div>• https://www.youtube.com/watch?v=GFw89UB4fE8&ab_channel=BenChilders</div> <div>• https://www.youtube.com/watch?v=heP3LEJ_NkE&ab_channel=7NEWSAustralia</div> <div> </div> <div> </div> <div> </div> <div>└───Fires</div> <div>• https://www.youtube.com/watch?v=gbM_NPx2GPc&t=201s&ab_channel=WXChasing</div> <div>• https://www.youtube.com/watch?v=M97sJdyeEM4&t=72s&ab_channel=Sanuck176</div> <div>• https://www.youtube.com/watch?v=1Z2K6lDt76M&t=557s&ab_channel=TheRelaxationChannel</div> <div> </div> <div>└───Normal</div> <div>• https://www.youtube.com/watch?v=SyxjsuNHWhM&t=328s&ab_channel=OneManWolfPack</div> <div>• https://www.youtube.com/watch?v=f1PTWsBtrtc&ab_channel=ChernobylPug</div> <div> </div> <div> </div> <div> </div> <div> </div> <div> </div>
FIGURE 5 in Application of shell spiral deviation methodology to fossil brachiopods: Implications for obtaining specimen ontogenetic ages
FIGURE 5. Spiral deviations from three specimens of the species Terebratula terebratula.
FIGURE 7 in Application of shell spiral deviation methodology to fossil brachiopods: Implications for obtaining specimen ontogenetic ages
FIGURE 7. Spiral deviations from three specimens of the species Pseudoatrypa sp.
FIGURE 6 in Application of shell spiral deviation methodology to fossil brachiopods: Implications for obtaining specimen ontogenetic ages
FIGURE 6. Spiral deviations from three specimens of the species Platystrophia ponderosa.
Supporting Data for "Extending GPU-Accelerated Gaussian Integrals in the TeraChem Software Package to f Type Orbitals: Implementation and Applications."
<p>Raw data and output files for "Extending GPU-Accelerated Gaussian Integrals in the TeraChem Software Package to f Type Orbitals: Implementation and Applications."</p>
Aerosol products presented in "ALICENET – an Italian network of automated lidar ceilometers for four-dimensional aerosol monitoring: infrastructure, data processing, and applications"
<p>ALICENET output products on aerosol optical and physical properties and vertical layering presented in “Bellini, A., Diémoz, H., Di Liberto, L., Gobbi, G. P., Bracci, A., Pasqualini, F., and Barnaba, F.: Alicenet – An Italian network of Automated Lidar-Ceilometers for 4D aerosol monitoring: infrastructure, data processing, and applications, AMT, https://doi.org/10.5194/egusphere-2024-730, 2024”.</p> <p>The aod*.txt files include the following information:</p> <p>- date: date in UTC<br>- AOD_ALICENET: AOD as retrieved by ALICENET at 1064 nm<br>- AOD_AERONET/SKYNET: AOD measured by a co-located photometer from AERONET/SKYNET (level 2) at 1020 nm<br>- AE: Angstrom Exponent from AERONET/SKYNET (level 2)</p> <p>The contiunous.aerosol.layer.rome.txt file includes the following information:</p> <p>- date: date in CET<br>- continuous_aerosol_layer: Continous Aerosol Layer heights as retrieved by ALICENET</p> <p>The mixed.aerosol.layer.rome.txt file includes the following information:</p> <p>- date: date in CET<br>- mixed_aerosol_layer: Mixed Aerosol Layer heights as retrieved by ALICENET</p> <p>This work received partial financial support from the EC H2020 Project RI-URBANS (GA No 101036245), and benefited from work done within the Action PROBE (CA18235), supported by COST (European Cooperation in Science and Technology).</p>
Policy-guided Monte Carlo on general state spaces: Application to glass-forming mixtures
<p>This dataset is associated with "<a href="https://doi.org/10.1063/5.0221221">Policy-guided Monte Carlo on general state spaces: Application to glass-forming mixtures</a>" by L. Galliano, R. Rende and D. Coslovich. It includes data and workflow to reproduce the figures of the manuscript.</p> <p><strong>Requirements</strong></p> <p>A working installation of Julia (tested with version 1.10.4) is the only software required for this workflow. You can install Julia by following the instructions on the <a href="https://julialang.org/downloads/">Julia website</a>.</p> <p><strong>Dataset</strong></p> <p>Some of the simulations for this project ran for more than a week on a cluster. The Trajectories folder contains results from these simulations, which are necessary to reproduce the figures in the paper. Each folder includes a summary file detailing the simulation parameters and other files containing measured quantities such as potential energy, average acceptance, and parameter values. These folders also contain results from postprocessing the original simulation trajectories carried out with <a href="https://framagit.org/atooms/postprocessing">atooms-postprocessing</a>.</p> <p><strong>Instructions</strong></p> <p>To fully reproduce the analysis from postprocessed data, use the convenience script make at the root of the data set:</p> <p><code>./make all</code></p> <p>This comprises two main steps:</p> <ul> <li><code>./make setup</code> will set up the software environment by checking the Julia installation and installing the required packages in a virtual environment.</li> <li><code>./make workflow</code> will execute the scripts to perform the workflow from the preprocessed trajectory files in the Trajectories folder.</li> </ul> <p>There is also a <code>./make clean</code> option, which uninstalls the virtual environment and removes the results of the workflow.</p> <p><strong>Notes</strong></p> <ul> <li><em>Figure 3</em>: This figure is the result of a two-dimensional simulation that requires the complete Policy-guided Monte Carlo code. Consequently, it is not reproduced by this workflow.</li> <li><em>Figure formats</em>: The Julia scripts generate figures in SVG format. After producing the figures, you can use the <code>./make pdf</code> option to convert them to PDF files. This option requires a working installation of Inkscape, which can be downloaded from <a href="https://inkscape.org">Inkscape website</a></li> </ul> <p><strong>Changelog</strong></p> <ul> <li>1.0.0: initial submission</li> </ul>
Molybdate application in the early stages of shrimp growth suppresses sulphide formation in a shrimp pond bottom model
<p><span>Oxygen depletion and sulphide formation, resulting from the accumulation of organic waste, are common challenges in shrimp ponds that could result in complete harvest failure. The stage at which these circumstances occur during the shrimp growth period remains elusive, yet, knowledge of the timing of oxygen depletion and sulphide formation is essential to enable remediating actions. Here, we used an experimental shrimp pond model at different stages in the shrimp growth period to determine when oxygen depletion and sulphide production occur. Microscale depth measurements of oxygen and H<sub>2</sub>S were determined using microelectrodes to visualize their profiles at different depths of the water-sediment interface and the sediment. We evaluated the potential of different molybdate concentrations at different stages to determine the optimal conditions to suppress H<sub>2</sub>S formation. Oxygen depletion and sulphide production took place in the middle of the shrimp growth cycle. The addition of molybdate was only effective in the early stages of the onset of oxygen depletion and H<sub>2</sub>S formation, and residual molybdate was required to ensure a continues suppression of sulphate reduction to H<sub>2</sub>S. However, oxygen depletion could not be prevented and reintroduction of oxygen did not occur when molybdate was added. In conclusion, molybdate appeared to be an effective strategy to suppress H<sub>2</sub>S formation at the onset of its production in shrimp pond bottom model.</span></p>
A topical review on AI-interlinked biodomain sensors for multi-purpose applications
<p>This repository consists the following four csv files:</p> <p><strong>acronyms.csv</strong>: The file consists of the acronyms for the terms that are associated with AI methods/AI-based technologies and are used within the article(Thapa et al., Measurement, 227 (2024) 114123) .</p> <p><strong>Fig2data.csv</strong>: The concepts and ideas that have been utilized to construct Fig. 2 are tabulated in this csv file.</p> <p><strong>Fig8data.csv</strong>: The information flow roadmap in an electronic tongue technology is presented in the csv file. These concepts and ideas have been utilized to draw Fig. 8.</p> <p> <br><strong>AI_combined_with_biodomain_sensors.csv</strong>: This file consists of the major words outlining the concept to formulate the graphical abstract.</p> <p> </p>
Data repository for manuscript "A new approach to Health Benefits Package design: an application of the Thanzi La Onse model in Malawi"
<p>Dataset to accompany the publication <em>“A new approach to Health Benefits Package design: an application of the Thanzi La Onse model in Malawi”</em> by Margherita Molaro, Sakshi Mohan, Bingling She, Martin Chalkley, Tim Colbourn, Joseph H. Collins, Emilia Connolly, Matthew M. Graham, Eva Janoušková, Ines Li Lin, Gerald Manthalu, Emmanuel Mnjowe, Dominic Nkhoma, Pakwanja D. Twea, Andrew N. Phillips, Paul Revill, Asif U. Tamuri, Joseph Mfutso-Bengo, Tara Mangal, and Timothy B. Hallett.</p> <p>The Thanzi La Onse (TLO) model used to produce this data is open source and available for review and usage at<a href="https://github.com/UCL/TLOmodel"> https://github.com/UCL/TLOmodel</a>. In particular, the outputs analysed in this study can be reproduced from model tag "Molaro_et_al_2024_HBP_design" (accessible at https://github.com/UCL/TLOmodel/tags) using the scenario file src/scripts/healthsystem/impact_of_policy/scenario_impact_of_policy.py. All analysis scripts used to generate the plots in the manuscript are located in the same directory and have filenames beginning with "analysis_impact_of_policy_".</p> <p>This repository contains post-processed simulation outputs, which were generated using the script src/scripts/healthsystem/impact_of_policy/analysis_extract_data.py (available from the same tag). The data included have the following structure:</p> <p>"Draw": Represents a specific prioritisation-policy, identified by the acronyms listed in Table 1 of the publication.</p> <p>"Run": Represents a single simulation instance of a draw. Each draw was simulated 10 times, each with independent random sampling, resulting in 10 "runs" per draw.</p> <p>The data files included in this repository are:</p> <p><strong>DALYS_by_cause_with_time.csv</strong>: DALYs (as defined in the publication) incurred on a given year due to each of the causes of DALYs considered.</p> <p><strong>HSIs_requested_by_type_and_facility_level_with_time.csv</strong>: total number of requested HSIs on a given year, broken down by HSI type and the facility level at which they were requested.</p> <p><strong>HSIs_delivered_by_type_and_facility_level_with_time.csv</strong>:total number of HSIs delivered on a given year broken down by HSI type and the facility level at which they were delivered.</p> <p><strong>Population_with_time.csv</strong>:total population size on a given year. </p> <p> </p> <p> </p>
Data set for "Stabilization of nesquehonite for application in carbon capture utilization and storage"
<p>The dataset contains all relavent raw data from the study titile, "Stabilization of nesquehonite for application in carbon capture utilization and storage". </p>
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.