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242 results for “distributed systems”
MCMC samples of the posterior distribution from the paper "TESS spots a mini-neptune interior to a hot saturn in the TOI-2000 system"
<p>This dataset contains the Hamiltonian Monte Carlo samples of the posterior distribution of the planetary and stellar parameters from the paper "TESS Spots a Mini-Neptune Interior to a Hot Saturn in the TOI-2000 System". The file format, NetCDF, is based on HDF5, and is meant to be read by the Python package <a href="https://python.arviz.org/en/latest/">ArviZ</a>.</p> <p>Hot jupiters (<em>P</em> < 10 d, <em>M</em> > 60 M<sub>⊕</sub>) are almost always found alone around their stars, but four out of hundreds known have inner companion planets. These rare companions allow us to constrain the hot jupiter's formation history by ruling out high-eccentricity tidal migration. Less is known about inner companions to hot Saturn-mass planets. We report here the discovery of the TOI-2000 system, which features a hot Saturn-mass planet with a smaller inner companion. The mini-neptune TOI-2000 b (2.70 ± 0.15 R<sub>⊕</sub>, 11.0 ± 2.4 M<sub>⊕</sub>) is in a 3.10-day orbit, and the hot saturn TOI-2000 c (<span class="math-tex">\(8.14^{+0.31}_{-0.30}\)</span> R<sub>⊕</sub>, <span class="math-tex">\(81.7^{+4.7}_{-4.6}\)</span> M<sub>⊕</sub>) is in a 9.13-day orbit. Both planets transit their host star TOI-2000 (TIC 371188886, <em>V</em> = 10.98, <em>TESS</em> magnitude = 10.36), a metal-rich ([Fe/H] = <span class="math-tex">\(0.439^{+0.041}_{-0.043}\)</span>) G dwarf 174 pc away. <em>TESS</em> observed the two planets in sectors 9–11 and 36–38, and we followed up with ground-based photometry, spectroscopy, and speckle imaging. Radial velocities from HARPS allowed us to confirm both planets by direct mass measurement. In addition, we demonstrate constraining planetary and stellar parameters with MIST stellar evolutionary tracks through Hamiltonian Monte Carlo under the PyMC framework, achieving higher sampling efficiency and shorter run time compared to traditional Markov chain Monte Carlo. Having the brightest host star in the <em>V</em> band among similar systems, TOI-2000 b and c are superb candidates for atmospheric characterization by the JWST, which can potentially distinguish whether they formed together or TOI-2000 c swept along material during migration to form TOI-2000 b.</p>
The LEXIS Distributed Data Infrastructure: Demonstrator System
<p><strong>The LEXIS Distributed Data Infrastructure: Demonstrator System</strong><br> by: LEXIS Project and Work Package 3 Team</p> <p>Project Lead: IT4Innovations National Supercomputing Centre (Czech Republic)<br> Work Package 3 Lead: Leibniz Supercomputing Centre (LRZ, Garching b. M., Germany)</p> <p>The enormous amounts of data generated in modern industry, business and science pose a significant challenge to those extracting actionable intelligence from data, using various filtering and analysis techniques. In this "Big Data" setting, the LEXIS project (Large-scale EXecution for Industry & Society) provides a user-friendly portal and platform for optimised execution of mixed Cloud-HPC (HPC: High-Performance Computing) workflows. The system will rely on advanced, distributed orchestration solutions (Bull Ystia Orchestrator, based on TOSCA and Alien4Cloud technologies), the High-End Application Execution Middleware HEAppE, and new hardware capabilities for maximizing efficiency in data processing, analysis and transfer (e.g. Burst Buffers with GPU- and FPGA-based data reprocessing).</p> <p>LEXIS handles computation tasks and data from three Pilots, based on representative and demanding HPC/Cloud-Computing use cases in Industry and Science: i) compute-/data-intensive and time-consuming simulations of turbo-machinery and gearbox systems in Aeronautics, ii) Earthquake and Tsunami simulations which are accelerated to enable accurate real-time analysis, and iii) Weather and Climate HPC simulations where massive amounts of in situ data are assimilated to improve forecasts.</p> <p>Here, we introduce and show a demonstrator of the LEXIS Distributed Data Infrastructure (DDI), the core data back-end of the LEXIS project. The DDI provides a unified "File Space" for LEXIS, across the participating sites and computing centres. Based on iRODS (irods.org) and EUDAT-B2SAFE (eudat.eu), it will ensure reliable and efficient access to large datasets in the Terabyte range and beyond. We have prepared virtual machine templates for the LEXIS Cloud resources which implement a Demonstrator of the LEXIS DDI. The system, once instantiated, consists of two iRODS-iCAT (provider) servers, representing the LRZ and IT4I iRODS zones, and of two client machines for access to the distributed data management system. Thus, interested colleagues can explore the possibilities offered by this system on an "own" demonstrator instance. Please contact us at info[at]lexis-project.eu if you are interested.</p>
CLRD-GLPS: A Long-term Seasonal Dataset of Ruminant Livestock Distribution in China's Grazing Production Systems (2000-2021) Using Stacking-based Interpretable Machine Learning
<p>Advanced computational methods integrating ensemble learning with interpretable machine learning are essential for precision livestock management under increasing environmental constraints and food security pressures. This study develops a novel stacking-based interpretable machine learning (IML) framework that combines multiple algorithms with SHAP analysis techniques to generate the China's Long-term Ruminant Livestock Distribution in Grazing Livestock Production Systems (CLRD-GLPS) dataset. Our computational approach addresses critical challenges in livestock distribution modelling: livestock segmentation and spatial prediction accuracy. The framework integrates Random Forest, XGBoost, CatBoost, LightGBM, and Extra Trees through a two-layer stacking architecture, enhanced with SHAP (Shapley Additive Explanations) analysis for model interpretability. We also implemented interpretable machine learning for livestock production system segmentation to distinguish grazing from total livestock populations. The stacking ensemble demonstrated superior performance over individual algorithms, achieving R² values of 0.954-0.961 for cattle and 0.896-0.901 for sheep and goats, with improvements of up to 8.3% compared to best performance single-model approaches. Multi-scale validation confirmed computational robustness: livestock segmentation achieved R² = 0.80 at county level, while independent city-level validation of CLRD-GLPS datasets yielded R² = 0.76-0.80. SHAP interpretability analysis revealed distinct environmental drivers, with vegetation indices and topography primarily influencing cattle distribution, while snow conditions and elevation dominated sheep and goat patterns. This computational framework advances livestock distribution modelling through enhanced prediction accuracy, model stability, and interpretability, while the CLRD-GLPS dataset provides essential spatial-temporal information for rangeland sustainability assessments and evidence-based livestock management policies. This dataset is supported by the Second Tibetan Plateau Scientific Expedition and Research Program (STEP, grant no. 2019QZKK0906).</p>
New dataset obtained from 2D positioning system with distributed control
<p>The dataset contains the obtained trajectory (encoder measurements) of the 2D positioning system, as well as the reference (commanded) trajectory. The control task is distributed to the low-level controllers for x and y axes synchronized using IEEE 1588 Precision Time Protocol (PTP), where the movement of the axes is realized based on the data that the low-level controllers receive from the high-level controller. The dataset includes 60 signals (30 measurements for x and y axis) that represent obtained trajectories and 2 signals (x and y axes) that represent commanded trajectory, where the length of each signal is 61,000 samples. The system was designed and built by the Cyber-Physical Systems Lab at the Pratt School of Engineering, Duke University, where it is located. Table 1 shows the list of collected signals, whereas a detailed description of the system can be found in [1]. For more information, see [2, 3].</p>
Initial auralization of a distributed propulsion system equipped with 26 ducted low-speed fans
<p>Illustration of engine noise auralization by DLR Institute of Propulsion Technology obtained with the framework PropNoise, VIOLIN, CORAL. Data associated with the following publication: S. Schade, R. Merino-Martinez, P. Ratei, S. Bartels, R. Jaron and A. Moreau, "<a href="https://doi.org/10.2514/6.2024-3273"><em>Initial Study on the Impact of Speed Fluctuations on the Psychoacoustic Characteristics of a Distributed Propulsion System with Ducted Fans</em></a>", 30th AIAA/CEAS Aeroacoustics Conference, Rome, Italy, 04-07 June, 2024.</p> <p>Selected binaural audio files to illustrate the impact of rotational speed fluctuations on the noise characteristics of a distributed propulsion system equipped with 26 ducted, low-speed fans. Please note that the sound pressure amplitudes are normalized to 110dB for the reference turbofan case and to 90dB for the cases with distributed fans.</p> <p>The corresponding time signals and spectrograms are available in the associated conference paper in Figures 4-6.</p>
The Vibrio Type III Secretion System 2 is not restricted to the Vibrionaceae and encodes differentially distributed repertoires of effector proteins
<p>Supplementary Dataset for the work entitled "The Vibrio Type III Secretion System 2 is not restricted to the Vibrionaceae and encodes differentially distributed repertoires of effector proteins".</p> <p>This dataset includes files for the T3SS2 reconstructed phylogenetic tree (Newick tree and fasta file), hierarchical clustering data analysis file from MORPHEUS, Table S1 with genome accession numbers, and all the data of the absence/presence of T3SS2-related components, Table S2 with the prediction of novel effector proteins.</p>
Reference data set for a Norwegian medium voltage power distribution system
<p>This reference data set describes a representative Norwegian radial, medium voltage (MV) electric power distribution system operated at 22 kV. The data set is developed in the Norwegian research centre CINELDI and will in brief be referred to as the CINELDI MV reference system.</p> <p>Data for a real Norwegian distribution system were provided by a distribution grid company. The data have been anonymized and processed to obtain a simplified but still realistic grid model with 124 nodes. The data set consists of the following three parts:<br> 1. Grid data files: describe the base version of the reference system that represents the present-day state of the grid, including information about topology, electrical parameters, and existing load points.<br> 2. Load data files: comprise load demand time series for a year with hourly resolution and scenarios for the possible long-term development of peak load. These data describe an extended version of the reference system with information about possible new load points being added to the system in the future.<br> 3. Reliability data files: contain data necessary for carrying out reliability of supply analyses for the system.</p> <p>The data set is described in detail in the following data article:<br> I. B. Sperstad, O. B. Fosso, S. H. Jakobsen, A. O. Eggen, J. H. Evenstuen, and G. Kjølle, “Reference data set for a Norwegian medium voltage power distribution system,” Data in Brief, 109025, 2023, doi: 10.1016/j.dib.2023.109025.</p>
Federated Learning for Distributed Intrusion Detection Systems in Public Networks - Validation Dataset
<p>This dataset has been meticulously prepared and utilized as a validation set during the evaluation phase of "Meta IDS" to asses the performance of various machine learning models. It is now made available for interested users and researchers who seek a reliable and diverse dataset for training and testing their own custom models.</p> <p>The validation dataset comprises a comprehensive collection of labeled entries, that determines whether the packet type is "malicious" or "benign." It covers complex design patterns that are commonly encountered in real-world applications. The dataset is designed to be representative, encompassing edge and fog layers that are in contact with cloud layer, thereby enabling thorough testing and evaluation of different models. Each sample in the dataset is labeled with the corresponding ground truth, providing a reliable reference for model performance evaluation.</p> <p> </p> <p>To ensure convenient distribution and storage, the dataset has been broken down into three separate batches, each containing a portion of the dataset. This allows for convenient downloading and management of the dataset. The three batches are provided as individual compressed files.</p> <p> </p> <p>In order to extract the data, follow the following instructions:</p> <ul> <li>Download and install bzip2 (if not already installed) from the official website or your package manager.</li> <li>Place the compressed dataset file in a directory of your choice.</li> <li>Open a terminal or command prompt and navigate to the directory where the compressed dataset file is located.</li> <li>Execute the following command to uncompress the dataset: <ul> <li>bzip2 -d filename.bz2</li> </ul> </li> <li>Replace "filename.bz2" with the actual name of the compressed dataset file.</li> </ul> <p>Once uncompressed, you will have access to the dataset in its original format for further exploration, analysis, and model training etc. The total storage required for extraction is approximately 800 GB in total, with the first batch requiring approximately 302 GB, the second batch requiring approximately 203 GB, and the third batch requiring approximately 297 GB of data storage.</p> <p> </p> <p>The first batch contains 1,049,527,992 entries, where as the second batch contains 711,043,331 entries, and for the third and last batch we have 1,029,303,062 entries. The following table provides the feature names along with their explanation and example value once the dataset is extracted.</p> <p> </p> <table align="left"> <thead> <tr> <th scope="col">Feature</th> <th scope="col">Description</th> <th scope="col">Example Value</th> </tr> </thead> <tbody> <tr> <td>ip.src</td> <td>Source IP address in the packet</td> <td>a05d4ecc38da01406c9635ec694917e969622160e728495e3169f62822444e17</td> </tr> <tr> <td>ip.dst</td> <td>Destination IP address in the packet</td> <td>a52db0d87623d8a25d0db324d74f0900deb5ca4ec8ad9f346114db134e040ec5</td> </tr> <tr> <td>frame.time_epoch</td> <td>Epoch time of the frame</td> <td>1676165569.930869</td> </tr> <tr> <td>arp.hw.type</td> <td>Hardware type</td> <td>1</td> </tr> <tr> <td>arp.hw.size</td> <td>Hardware size</td> <td>6</td> </tr> <tr> <td>arp.proto.size</td> <td>Protocol size</td> <td>4</td> </tr> <tr> <td>arp.opcode</td> <td>Opcode</td> <td>2</td> </tr> <tr> <td>data.len</td> <td>Length</td> <td>2713</td> </tr> <tr> <td>eth.dst.lg</td> <td>Destination LG bit</td> <td>1</td> </tr> <tr> <td>eth.dst.ig</td> <td>Destination IG bit</td> <td>1</td> </tr> <tr> <td>eth.src.lg</td> <td>Source LG bit</td> <td>1</td> </tr> <tr> <td>eth.src.ig</td> <td>Source IG bit</td> <td>1</td> </tr> <tr> <td>frame.offset_shift</td> <td>Time shift for this packet</td> <td>0</td> </tr> <tr> <td>frame.len</td> <td>frame length on the wire</td> <td>1208</td> </tr> <tr> <td>frame.cap_len</td> <td>Frame length stored into the capture file</td> <td>215</td> </tr> <tr> <td>frame.marked</td> <td>Frame is marked</td> <td>0</td> </tr> <tr> <td>frame.ignored</td> <td>Frame is ignored</td> <td>0</td> </tr> <tr> <td>frame.encap_type</td> <td>Encapsulation type</td> <td>1</td> </tr> <tr> <td>gre</td> <td>Generic Routing Encapsulation</td> <td>'Generic Routing<br> Encapsulation (IP)’</td> </tr> <tr> <td>ip.version</td> <td>Version</td> <td>6</td> </tr> <tr> <td>ip.hdr_len</td> <td>Header length</td> <td>24</td> </tr> <tr> <td>ip.dsfield.dscp</td> <td>Differentiated Services<br> Codepoint</td> <td>56</td> </tr> <tr> <td>ip.dsfield.ecn</td> <td>Explicit Congestion<br> Notification</td> <td>2</td> </tr> <tr> <td>ip.len</td> <td>Total length</td> <td>614</td> </tr> <tr> <td>ip.flags.rb</td> <td>Reserved bit</td> <td>0</td> </tr> <tr> <td>ip.flags.df</td> <td>Don't fragment</td> <td>1</td> </tr> <tr> <td>ip.flags.mf</td> <td>More fragments</td> <td>0</td> </tr> <tr> <td>ip.frag_offset</td> <td>Fragment offset</td> <td>0</td> </tr> <tr> <td>ip.ttl</td> <td>Time to live</td> <td>31</td> </tr> <tr> <td>ip.proto</td> <td>Protocol</td> <td>47</td> </tr> <tr> <td>ip.checksum.status</td> <td>Header checksum status</td> <td>2</td> </tr> <tr> <td>tcp.srcport</td> <td>TCP source port</td> <td>53425</td> </tr> <tr> <td>tcp.flags</td> <td>Flags</td> <td>0x00000098</td> </tr> <tr> <td>tcp.flags.ns</td> <td>Nonce</td> <td>0</td> </tr> <tr> <td>tcp.flags.cwr</td> <td>Congestion Window Reduced<br> (CWR)</td> <td>1</td> </tr> <tr> <td>udp.srcport</td> <td>UDP source port</td> <td>64413</td> </tr> <tr> <td>udp.dstport</td> <td>UDP destination port</td> <td>54087</td> </tr> <tr> <td>udp.stream</td> <td>Stream index</td> <td>1345</td> </tr> <tr> <td>udp.length</td> <td>Length</td> <td>225</td> </tr> <tr> <td>udp.checksum.status</td> <td>Checksum status</td> <td>3</td> </tr> <tr> <td>packet_type</td> <td>Type of the packet which is either "benign" or "malicious"</td> <td>0</td> </tr> </tbody> </table> <p>Furthermore, in compliance with the GDPR and to ensure the privacy of individuals, all IP addresses present in the dataset have been anonymized through hashing. This anonymization process helps protect the identity of individuals while preserving the integrity and utility of the dataset for research and model development purposes.</p> <p> </p> <p>Please note that while the dataset provides valuable insights and a solid foundation for machine learning tasks, it is not a substitute for extensive real-world data collection. However, it serves as a valuable resource for researchers, practitioners, and enthusiasts in the machine learning community, offering a compliant and anonymized dataset for developing and validating custom models in a specific problem domain.</p> <p> </p> <p>By leveraging the validation dataset for machine learning model evaluation and custom model training, users can accelerate their research and development efforts, building upon the knowledge gained from my thesis while contributing to the advancement of the field.</p>
Fig. 6 in Phenotypic Study Of Population And Distribution Of The Poecilia Reticulata (Cyprinodontiformes, Poeciliidae) From Kyiv Sewage System (Ukraine)
Fig. 6. Dependence of the area of orange spots (stripes, %) on the body length of male guppies (L) P. reticulata.
Fig. 1 in Phenotypic Study Of Population And Distribution Of The Poecilia Reticulata (Cyprinodontiformes, Poeciliidae) From Kyiv Sewage System (Ukraine)
Fig. 1. Potential (probabilistic) model of P. reticulata current world expansion built in the Maxent program based on the CliMond climatic data and GBIF data (2021). Areas of highest habitat suitability (> 0.5) are colored in red and areas of lowest (<0.1) — in blue.
Fig. 7 in Phenotypic Study Of Population And Distribution Of The Poecilia Reticulata (Cyprinodontiformes, Poeciliidae) From Kyiv Sewage System (Ukraine)
Fig. 7. Dependence of the proportions of the tail (C1/C2) on the body length of P. reticulata: C1/C2 = 1 ("symmetrical tail") is shown by the line.
BRAIN Journal - Lamport's algorithm - Figure 2 from paper "Optimization of Distributed Systems Using Multi-Agent Systems with Virtual Time"
<p>Figure 2. Lamport’s algorithm</p> <p>In order to synchronize logical clocks, Lamport [3] defined the relationship “happened before” (preceded) which implies that the expression 1 2 a → a means “ 1 a occurred before 2 a ”, and it means that all the processes coincide in the fact that 1 a took place first, and subsequently 2 a took place. This relation can be directly observed in two situations (figure 2): 1. If two events happen during the same process, the order of the happening is indicated by the common clock; 2. When two processes communicate through a message, the event that corresponds to sending the precise message always happens before the event of receiving it (i.e. the message). If two events, 1 a and 2 a , are produced in different processes that do not exchange messages (neither directly nor indirectly), then it is not certain if 1 2 a → a or 2 1 a → a . In this case it is said that these events are competitive, which means that it is not known which one happened first (and it is not a must-know thing either).</p>
Figure 3. The multi-agent system-Optimization of Distributed Systems Using Multi-Agent Systems with Virtual Time
<p>The authority is given to a single agent<br> that is symbolically situated on the top level; this agent would be considered the root of an<br> arborescent structure; but the stages of the process are provided by groups of agents, respectively by<br> the relations of communication between them.<br> Thus, we would deal with a ZERO AGENT and several groups of agents. Such a group is<br> made up of several agents, each and one of these agents accomplishing a certain role in the process<br> of synchronization (figure 3).</p>
Figure 2. Lamport's algorithm-Optimization of Distributed Systems Using Multi-Agent Systems with Virtual Time
<p>In order to synchronize logical clocks, Lamport [3] defined the relationship “happened<br> before” (preceded) which implies that the expression 1 2 a → a means “ 1 a occurred before 2 a ”, and it<br> means that all the processes coincide in the fact that 1 a took place first, and subsequently 2 a took<br> place. This relation can be directly observed in two situations (figure 2):<br> 1. If two events happen during the same process, the order of the happening is indicated by<br> the common clock;<br> 2. When two processes communicate through a message, the event that corresponds to<br> sending the precise message always happens before the event of receiving it (i.e. the<br> message).</p>
Figure 1. Cristian's Algorithm-Optimization of Distributed Systems Using Multi-Agent Systems with Virtual Time
<p>Cristian’s Algorithm (figure 1) is a method for clock synchronization which can be used in<br> many fields of distributive computer science. It suffers, though, in implementations using a single<br> server, making it unsuitable for many distributive applications where redundancy may be crucial.</p>
Dataset for "Adaptive connectivity control in networked multi-agent systems: A distributed approach"
<div> <div>Dataset accompanying the paper "<em>Adaptive connectivity control in networked multi-agent systems: A distributed approach</em>" by M. Krizmancic and S. Bogdan submitted to PLOS ONE journal on April 30, 2024.</div> <div> </div> <div> <div> <div>Contains:</div> <ul> <li>Vector images of the figures presented in the paper.</li> <li>Data files containing the values used to build the figures.</li> </ul> <p>Detailed information and instructions are available in the README file within the dataset.</p> </div> </div> </div>
Cost and cost distribution of policy-driven investments in decentralized heating systems in residential buildings in Germany - Supplementary material
<p>This repository holds supplementary material for research concerning the <em>Cost and cost distribution of policy-driven investments in decentralized heating systems in residential buildings in Germany</em></p> <p>which was published as a working paper as</p> <p>Czock, Berit, Cordelia Frings, and Fabian Arnold. <em>Cost and cost distribution of policy-driven investments in decentralized heating systems in residential buildings in Germany</em>. No. 2024-4. Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI), 2024.</p> <p>and is available at</p> <p>https://www.ewi.uni-koeln.de/de/publikationen/cost-and-cost-distribution-of-policy-driven-investments-in-decentralized-heating-systems-in-residential-buildings-in-germany/</p> <p>This repository includes the following documents</p> <ol> <li>building list</li> <li>description of technical and economic assumptions</li> <li>detailed description of results</li> </ol> <p>Further material can be made available on request.</p> <p> </p> <p> </p> <div> <p>Cost and cost distribution of policy-driven investments in decentralized heating systems in residential buildings in Germany - Supplementary material Creators Czock, Berit1 © 2024 by Berit Hanna Czock is licensed under <a href="https://creativecommons.org/licenses/by/4.0/?ref=chooser-v1" target="_blank" rel="license noopener noreferrer"> CC BY 4.0 </a></p> </div>
Рис. 1. Карта-схема пунктов сборов Gynaephora (rossii) в Якутии: 1 — о-в КотеΛьный; 2 — о-в СтоΛбовой; 3 — о-в МаΛый Αяховский; 4 — о-в БоΛьшой Αяховский; 5 — п-ов Быковский в устье Αены; 6 — СеΛΛяхская губа, р. СеΛях, низовья Яны; 7 — КоΛымская протока, низовья ИнΔигирки; 8 — озеро ХомоΛох, бассейн р. БёрёΛёх, низовья ИнΔигирки; 9 — о-в Крестовский; 10 — о-в ЧетырехстоΛбовой; 11 — устье р. Энюмчувеем, южное побережье Восточно-Сибирского моря; 12 — хребет СунтарХаята; 13 — р. ÀжеΛинΔа в системе Станового хребта (точками обозначены ранее опубΛикованные точки, треугоΛьниками — новые местообитания) Fig. 1. Chart of Gynaephora (rossii) collection sites in Yakutia: 1 — Kotelny island; 2 — Stolbovoy island; 3 — Maly Lyakhovsky island; 4 — Bolshoi Lyakhovsky island; 5 — Bykovsky peninsula at the mouth of the Lena river; 6 — Sellakhskaya bay, Selyakh river, lower reaches of the Yana river; 7 — Kolymskaya channel, lower reaches of the Indigirka river; 8 — Lake Homolokh, Berelekh river basin, lower reaches of the Indigirka river; 9 — Krestovsky island; 10 — Chetyrekhstolbovoy island; 11 — the mouth of the Enyumchuveem river, southern coast of the East Siberian sea; 12 — Suntar-Khayata ridge; 13 — Gelinda river in the Stanovoy ridge system (dots indicate previously published localities, triangles indicate new localities) in New data on the distribution of the Gynaephora (rossii) species group in Northern Yakutia
Рис. 1. Карта-схема пунктов сборов Gynaephora (rossii) в Якутии: 1 — о-в КотеΛьный; 2 — о-в СтоΛбовой; 3 — о-в МаΛый Αяховский; 4 — о-в БоΛьшой Αяховский; 5 — п-ов Быковский в устье Αены; 6 — СеΛΛяхская губа, р. СеΛях, низовья Яны; 7 — КоΛымская протока, низовья ИнΔигирки; 8 — озеро ХомоΛох, бассейн р. БёрёΛёх, низовья ИнΔигирки; 9 — о-в Крестовский; 10 — о-в ЧетырехстоΛбовой; 11 — устье р. Энюмчувеем, южное побережье Восточно-Сибирского моря; 12 — хребет СунтарХаята; 13 — р. ÀжеΛинΔа в системе Станового хребта (точками обозначены ранее опубΛикованные точки, треугоΛьниками — новые местообитания) Fig. 1. Chart of Gynaephora (rossii) collection sites in Yakutia: 1 — Kotelny island; 2 — Stolbovoy island; 3 — Maly Lyakhovsky island; 4 — Bolshoi Lyakhovsky island; 5 — Bykovsky peninsula at the mouth of the Lena river; 6 — Sellakhskaya bay, Selyakh river, lower reaches of the Yana river; 7 — Kolymskaya channel, lower reaches of the Indigirka river; 8 — Lake Homolokh, Berelekh river basin, lower reaches of the Indigirka river; 9 — Krestovsky island; 10 — Chetyrekhstolbovoy island; 11 — the mouth of the Enyumchuveem river, southern coast of the East Siberian sea; 12 — Suntar-Khayata ridge; 13 — Gelinda river in the Stanovoy ridge system (dots indicate previously published localities, triangles indicate new localities)
Multi-Source Distributed System Data for AI-powered Analytics
<p><strong>Abstract:</strong></p> <p>In recent years there has been an increased interest in Artificial Intelligence for IT Operations (AIOps). This field utilizes monitoring data from IT systems, big data platforms, and machine learning to automate various operations and maintenance (O&M) tasks for distributed systems.<br> The major contributions have been materialized in the form of novel algorithms.<br> Typically, researchers took the challenge of exploring one specific type of observability data sources, such as application logs, metrics, and distributed traces, to create new algorithms.<br> Nonetheless, due to the low signal-to-noise ratio of monitoring data, there is a consensus that only the analysis of multi-source monitoring data will enable the development of useful algorithms that have better performance. <br> Unfortunately, existing datasets usually contain only a single source of data, often logs or metrics. This limits the possibilities for greater advances in AIOps research.<br> Thus, we generated high-quality multi-source data composed of distributed traces, application logs, and metrics from a complex distributed system. This paper provides detailed descriptions of the experiment, statistics of the data, and identifies how such data can be analyzed to support O&M tasks such as anomaly detection, root cause analysis, and remediation.</p> <p><strong>General Information:</strong></p> <p>This repository contains the simple scripts for data statistics, and link to the multi-source distributed system dataset.</p> <p>You may find details of this dataset from the original paper:</p> <p><em>Sasho Nedelkoski, Jasmin Bogatinovski, Ajay Kumar Mandapati, Soeren Becker, Jorge Cardoso, Odej Kao, "Multi-Source Distributed System Data for AI-powered Analytics". </em></p> <p><strong>If you use the data, implementation, or any details of the paper, please cite!</strong></p> <p> </p> <p>BIBTEX:</p> <p>_________________________________________</p> <pre>@inproceedings{nedelkoski2020multi, title={Multi-source Distributed System Data for AI-Powered Analytics}, author={Nedelkoski, Sasho and Bogatinovski, Jasmin and Mandapati, Ajay Kumar and Becker, Soeren and Cardoso, Jorge and Kao, Odej}, booktitle={European Conference on Service-Oriented and Cloud Computing}, pages={161--176}, year={2020}, organization={Springer} } </pre> <p>___________________________</p> <p>The multi-source/multimodal dataset is composed of distributed traces, application logs, and metrics produced from running a complex distributed system (Openstack). In addition, we also provide the workload and fault scripts together with the Rally report which can serve as ground truth. We provide two datasets, which differ on how the workload is executed. The <em><strong>sequential_data</strong> </em>is generated via executing workload of sequential user requests. The <strong><em>concurrent_data </em></strong>is generated via executing workload of concurrent user requests.</p> <p>The raw logs in both datasets contain the same files. If the user wants the logs filetered by time with respect to the two datasets, should refer to the timestamps at the metrics (they provide the time window). <strong>In addition, we suggest to use the provided aggregated time ranged logs for both datasets in CSV format.</strong></p> <p><strong><strong>Important:</strong> The logs and the metrics are synchronized with respect time and they are both recorded on CEST (central european standard time). The traces are on UTC (Coordinated Universal Time -2 hours). They should be synchronized if the user develops multimodal methods. Please read the IMPORTANT_experiment_start_end.txt file before working with the data.</strong></p> <p>Our GitHub repository with the code for the workloads and scripts for basic analysis can be found at: <a href="https://github.com/SashoNedelkoski/multi-source-observability-dataset/">https://github.com/SashoNedelkoski/multi-source-observability-dataset/</a></p>
Figure 2 in Expansion of Lobate Lac Scale Distribution into Oahu Forest Systems
Figure 2. Location of sites for lobate lac scale survey on the island of Oahu. Red dots indicate trail/sites where the scale was not found. Blue dots indicate trail/sites where the scale was found.
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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)
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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.