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72 results for “community detection”
Detecting local variations across metazoan communities in backreef depressions of Reunion Island (Mascarene Archipelago) through environmental DNA survey
<p>The back-reef depressions, or lagoons, of Reunion Island (western Indian Ocean) host a high abundance of organisms living amongst the coral reefs and are critical sites for artisanal fishing, tourism, and shoreline stability for the island. Over time, increasing degradation of Reunionese reefs has been observed due to overexploitation, beach erosion and eutrophication. Efforts to mitigate the impact of these pressures on aquatic organisms include biodiversity surveys primarily performed through visual censuses that can be logistically complex and may unintentionally overlook organisms. Surveys integrating environmental DNA (eDNA) collections have provided rapid biodiversity assessments, while helping to circumvent some limitations of visual surveys. The present study describes the results of an exploratory eDNA survey, which aims to characterize metazoan communities of four Reunionese lagoons located along the west coast of the island. As eDNA surveys first require deliberate study design and optimization for each new context, we sought to establish a modernized workflow implementing specialized equipment to collect and preserve samples to facilitate future studies in these lagoons. During the austral summer of 2023, samples were pumped directly from surface and bottom depths at each site through self-preserving filters which were then processed for DNA metabarcoding using regions of the 12S ribosomal RNA (12S), small ribosomal subunit 18S (18S) and Cytochrome Oxidase I (COI) genes. The survey detected high species richness that varied by site, and in a single collection period, recovered the presence of 60 teleost families and numerous invertebrate taxa, including members of the coral faunal community that are less studied in Reunion. Distinct biological communities were observed at each site, and within a single lagoon, suggesting that these differences are due to site-specific factors (e.g., environmental variables, geographic distance, etc.). Although continued protocol optimization is needed, the present findings demonstrate the successful application of an eDNA-based survey for biodiversity assessment within Reunionese lagoons.</p>
Toxic Content Detection in online social networks: a new dataset from Brazilian Reddit Communities
<p>This is new dataset of 2,500 manually annotated examples of comments extracted from the top 10 largest Brazilian subreddits on Reddit. The dataset has been annotated by crowd-sourcing efforts with contributions from the departments of computer science (DCC) and the linguistic group @ UFMG. As part of our contribution to the toxicity automatic detection and moderation of online social networks, we're making the dataset public for research.</p> <h3>Dataset</h3> <p>The dataset contains 2,500 manually annotated comments from the most popular brazilian communities on Reddit. The data sampling proccess was a stratified sampling by the number of generated publications by subreddit and the month of publication. The list of communities collected is presented below. The collected data period ranges from January 2022 to December 2022.</p> <p> </p> <table> <tbody> <tr> <td><strong>Subreddit</strong></td> <td><strong>Posts</strong></td> <td><strong>Comments</strong></td> </tr> <tr> <td>r/brasil</td> <td>110,829 </td> <td>2,136,866</td> </tr> <tr> <td>r/desabafos</td> <td>115,876</td> <td>1,211,643</td> </tr> <tr> <td>r/futebol</td> <td>35,826</td> <td>1,214,412</td> </tr> <tr> <td>r/saopaulo</td> <td>7,308</td> <td>81,969</td> </tr> <tr> <td>r/eu_nvr</td> <td>12,631</td> <td>188,620</td> </tr> <tr> <td>r/botecodoreddit</td> <td>7,059</td> <td>57,298</td> </tr> <tr> <td>r/conversas</td> <td>21,967</td> <td>326,061</td> </tr> <tr> <td>r/investimentos</td> <td>9,756</td> <td>141,823</td> </tr> <tr> <td>r/tiodopave</td> <td>2,371</td> <td>11,584</td> </tr> <tr> <td>r/brasilivre</td> <td>67,301</td> <td>1,219265</td> </tr> <tr> <td>Total</td> <td>390,924</td> <td>6,589,541</td> </tr> </tbody> </table> <p> </p> <h3><strong>Annotation proccess</strong></h3> <p>The annotators were divided into groups of raters and each group was assigned a batch of comments to label. The raters were then asked to label a comment as <strong>Toxic</strong>, <strong>Non-toxic</strong>, <strong>I do not know</strong> and <strong>Missing info</strong>. During the annotation process, the raters were encouraged to assign one of the uncertain labels when they're not sure about the toxicity of a comment or the context is missing. </p> <h3>Available data</h3> <p>The dataset is available as csv file and the label was assigned as a majority vote among the raters. The available data are the original collected comment id and body. The label was created from the original classification from the annotators. No data processing has been done on this version of the dataset. The overall schema of the dataset if presented below.</p> <p>- <strong>id</strong>: The unique identifier of the comment on the Reddit platform<br>- <strong>body</strong>: The original comment text publication<br>- <strong>is_toxic</strong>: The final label of a given comment. The label is <strong>0</strong> for non-toxic comments, <strong>1</strong> for toxic comments and <strong>-1</strong> for comments where the raters disagreed about the toxicity.</p>
Dataset for Anomaly Detection in a Production Wireless Mesh Community Network
<p>CSV dataset generated gathering data from a production wireless mesh community network. Data is gathered every 5 minutes during the interval 2021-04-13 00:00:00 to 2021-04-16 00:00:00. During the interval 2021-04-14 02:00:00 2021-04-14 17:50:00 (both included) there is the failure of a gateway in the mesh (nodeid 24). </p> <p>Live mesh network monitoring link: <a href="http://dsg.ac.upc.edu/qmpsu">http://dsg.ac.upc.edu/qmpsu</a></p> <p>The dataset consists of single gzip compressed CSV file. The first line of the file is a header describing the features. The first column is a GMT timestamp of the sample in the format as "2021-03-16 00:00:00". The rest of the columns provide the comma-separated values of the features collected from each node in the corresponding capture.</p> <p>A suffix with the nodeid is added to each feature. For instance, the feature having the number of processes of node with nodeid 24 is named as "processes-24". In total, 63 different nodes showed up during the samples, each being assigned a different nodeid.</p> <p><br> Features are of two types: (i) absolute values, for instance, the CPU 1-minute load average, and (ii) counters that are monotonically increased, for instance the number of transmitted packets. We have converted counter-type kernel variables to rates, by dividing the difference between two consecutive samples, over the difference of the corresponding timestamps in seconds, as shown in the following pseudo-code:<br> feature.rate are columns computed from feature as<br> feature.rate <- (feature[2:n]-feature[1:(n-1)])/(epoch[2:n]-epoch[1:(n-1)])<br> feature.rate <- feature.rate[feature.rate >= 0] # discard samples where the counter is restarted<br> where n is the number of samples</p> <p><strong>features</strong><br> - processes number of processes<br> - loadavg.m1 1 minute load average<br> - softirq.rate servicing softirqs<br> - iowait.rate waiting for I/O to complete<br> - intr.rate <br> - system.rate processes executing in kernel mode<br> - idle.rate twiddling thumbs<br> - user.rate normal processes executing in user mode<br> - irq.rate servicing interrupts<br> - ctxt.rate total number of context switches across all CPUs<br> - nice.rate niced processes executing in user mode<br> - nr_slab_unreclaimable The part of the Slab that can't be reclaimed under memory pressure<br> - nr_anon_pages anonymous memory pages<br> - swap_cache Memory that once was swapped out, is swapped back in but still also is in the swapfile<br> - page_tables Memory used to map between virtual and physical memory addresses<br> - swap <br> - eth.txe.rate tx errors over all ethernet interfaces<br> - eth.rxe.rate rx errors over all ethernet interfaces<br> - eth.txb.rate tx bytes over all ethernet interfaces<br> - eth.rxb.rate rx bytes over all ethernet interfaces<br> - eth.txp.rate tx packets over all ethernet interfaces<br> - eth.rxp.rate rx packets over all ethernet interfaces<br> - wifi.txe.rate tx errors over all wireless interfaces<br> - wifi.rxe.rate rx errors over all wireless interfaces<br> - wifi.txb.rate tx bytes over all wireless interfaces<br> - wifi.rxb.rate rx bytes over all wireless interfaces<br> - wifi.txp.rate tx packets over all wireless interfaces<br> - wifi.rxp.rate rx packets over all wireless interfaces<br> - txb.rate tx bytes over all ethernet and wifi interfaces<br> - txp.rate tx packets over all ethernet and wifi interfaces<br> - rxb.rate rx bytes over all ethernet and wifi interfaces<br> - rxp.rate rx packets over all ethernet and wifi interfaces<br> - sum.xb.rate tx+rx bytes over all ethernet and wifi interfaces<br> - sum.xp.rate tx+rx packets over all ethernet and wifi interfaces<br> - diff.xb.rate tx-rx bytes over all ethernet and wifi interfaces<br> - diff.xp.rate tx-rx packets over all ethernet and wifi interfaces</p>
Characterizing Measures for the Assessment of Cluster Analysis and Community Detection
<p><strong>Description. </strong>The dataset is constituted of:</p> <ul> <li>`figs.zip`: an archive containing the plot files;</li> <li>`data&results.zip`: an archive containing the necessary data to perform our analysis, as well as result files.</li> </ul> <p>These are the resources used in the following articles:</p> <ol> <li>N. Arınık, V. Labatut and R. Figueiredo, "Characterizing measures for the assessment of cluster analysis and community detection", Modèles & Analyse des Réseaux : Approches Mathématiques & Informatiques (MARAMI), 2020. ⟨<a href="https://hal.archives-ouvertes.fr/hal-02993542">hal-02993542</a>⟩</li> <li>N. Arınık, R. Figueiredo, and V. Labatut, “Characterizing and comparing external measures for the assessment of cluster analysis and community detection,” <em>IEEE Access </em>9:20255–20276, 2021. DOI: <a href="http://doi.org/10.1109/access.2021.3054621">10.1109/access.2021.3054621</a> ⟨<a href="https://hal.archives-ouvertes.fr/hal-03124118">hal-03124118</a>⟩</li> </ol> <p><strong>Source code. </strong>The associated source code is available on GitHub: <a href="https://github.com/CompNet/ExtMeasEval">https://github.com/CompNet/ExtMeasEval</a></p> <p><strong>Citation. </strong>If you use these data, please cite the paper [2].</p> <p><br><code>@Article{Arinik2021,</code><br><code> author = {Arınık, Nejat and Figueiredo, Rosa and Labatut, Vincent},</code><br><code> title = {Characterizing and Comparing External Measures for the Assessment of Cluster Analysis and Community Detection},</code><br><code> journal = {IEEE Access},</code><br><code> year = {2021},</code><br><code> volume = {9},</code><br><code> pages = {20255-20276},</code><br><code> doi = {10.1109/access.2021.3054621},</code><br><code>}</code></p>
Community-Based Event Detection in Temporal Networks
<p>This dataset contains daily tweets ids during April 2013. This is provided to facilitate reproducibility of results presented in the following paper:</p> <p>Pablo Moriano, Jorge Finke, and Yong-Yeol Ahn. "Community-Based Event Detection in Temporal Networks." Scientific Reports 9, 4358, 2019. DOI: <a href="https://doi.org/10.1038/s41598-019-40137-0">https://doi.org/10.1038/s41598-019-40137-0</a></p> <p>These data are provided for non-commercial purposes only. If you use this dataset for research, please be sure to cite the above paper.</p>
Datasets for "MGTCOM: Community Detection in Multimodal Graphs"
<p>These are the datasets used in <em><strong>"MGTCOM: Community Detection in Multimodal Graphs"</strong></em></p> <p>The dataset preparation code can be found in <a href="https://github.com/EgorDm/MGTCOM">our repository</a>.</p> <p>Each dataset consists of a heterogenous graph with additional edge or node timestamps and preprocessed feature vectors.</p> <p>For each dataset, the files are split into raw and processed folders.<br> * `<em>raw</em>` folder: contains a set of parquet files with formatted raw dataset data. Files follow the naming convention `node_<name>` or `edge_<from>_<rel_name>_<to>`.<br> * `<em>processed</em>` folder: contains preprocessed datasets in <a href="https://pytorch-geometric.readthedocs.io/en/latest/">pytorch geometric</a> graph data format</p>
Data from: Species-habitat networks reveal conservation implications that other community analyses do not detect
<p><span>Grassland restoration is an important conservation intervention supporting declining insect pollinators, particularly in threatened calcareous grassland landscapes. While restoration is often assessed using simple diversity or the similarity to a target community metrics, this can fail to represent key aspects of community reconstruction. Here, we compare a new method, species-habitat networks, with techniques previously relied upon to understand the process of pollinator community restoration. The species-habitat network approach reveals details relevant to insect conservation that are not visible using standard measures of species richness, abundance, community similarity or network metrics. For instance, a shared set of butterflies and bumblebees found in ancient extensively managed grassland, the target community for restoration, were more likely to inhabit previously disturbed grassland than recently disturbed or reverting grasslands. We propose that species-habitat networks should be part of the standard analytical toolkit assessing the effectiveness of restoration, particularly for mobile species such as insects.</span></p>
Data from: Species-habitat networks reveal conservation implications that other community analyses do not detect
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Data from: Fluid preservation causes minimal reduction of parasite detectability in fish specimens: a new approach for reconstructing parasite communities of the past?
<ol> <li>Long-term datasets are needed to evaluate temporal patterns in wildlife disease burdens, but historical data on parasite abundance are extremely rare. For more than a century, natural history collections have been accumulating fluid-preserved specimens, which should contain the parasites infecting the host at the time of its preservation. However, before this unique data source can be exploited, we must identify the artefacts that are introduced by the preservation process. Here, we experimentally address whether the preservation process alters the degree to which metazoan parasites are detectable in fluid-preserved fish specimens when using visual parasite detection techniques.</li> <li>We randomly assigned fish of three species (<i>Gadus chalcogrammus, Thaleichthys pacificus, Parophrys vetulus</i>) to two treatments. In the first treatment, fish were preserved according to the standard procedures used in ichthyological collections. Immediately after the fluid-preservation process was complete, we performed parasitological dissection on those specimens. The second treatment was a control, in which fish were dissected without being subjected to the fluid-preservation process. We compared parasite abundance between the two treatments.</li> <li>Across 298 fish individuals and 59 host–parasite pairs, we found few differences between treatments, with 24 of 27 host–parasite pairs equally abundant between the two treatments. Of these, one pair was significantly more abundant in the preservation treatment than in the control group, and two pairs were significantly less abundant in the preservation treatment than in the control group.</li> <li>Our data suggest that the fluid-preservation process does not have a substantial effect on the detectability of metazoan parasites. This study addresses only the effects of the fixation and preservation process; long-term experiments are needed to address whether parasite detectability remains unchanged in the months, years, and decades of storage following preservation. If so, ecologists will be able to reconstruct novel, long-term datasets on parasite diversity and abundance over the past century or more using fluid-preserved specimens from natural history collections.</li> </ol>
Replication Package for the Paper: "Understanding Code Smell Detection via Code Review: A Study of the OpenStack Community"
<p>This repository contains the data and results from the paper "Understanding Code Smell Detection via Code Review: A Study of the OpenStack Community" submitted to ICPC 2021.</p> <p> </p> <p><strong>1. "data.zip" contains the following three folders:</strong></p> <p> </p> <p><strong>1) data folder</strong></p> <p>The data folder contains the retrieved 1,190 reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell, Code Smell Discussion, and Source Code URL.</p> <p> </p> <p><strong>2) scripts folder</strong></p> <p>The scripts folder contains the Python scripts that were used to search for code smell terms and the list of code smell terms.</p> <ul> <li> <p><em>keyword.txt</em> contains the keywords associated with code smells, such as "smell, duplication, and dead".</p> </li> <li> <p><em>get_changes.py</em> is used for getting code changes from OpenStack.</p> </li> <li> <p><em>get_comments.py</em> is used for getting review comments for each code change.</p> </li> <li> <p><em>keywords_search.py</em> is used for searching review comments that contain at least one keyword.</p> </li> <li> <p><em>random_select.py</em> is used for randomly selecting review comments that do not contain any keyword.</p> </li> <li> <p><em>keywords_improve.py</em> is used for improving the keyword-based mining approach.</p> </li> <li> <p><em>tools.py</em> is used for supporting the process of keywords improving.</p> </li> </ul> <p> </p> <p><strong>3) project folder</strong></p> <p>The project folder contains the MAXQDA project files. The files can be opened by MAXQDA 12 or higher versions, which are available at <a href="https://www.maxqda.com/">https://www.maxqda.com/</a> for download. You may also use the free 14-day trial version of MAXQDA 2018, which is available at <a href="https://www.maxqda.com/trial">https://www.maxqda.com/trial</a> for download.</p> <ul> <li> <p><em>Data Labeling & Encoding for RQ2.mx12</em> is the results of data labeling and encoding for RQ2, which were analyzed by the MAXQDA tool.</p> </li> <li> <p><em>Data Labeling & Encoding for RQ3.mx12</em> is the results of data labeling and encoding for RQ3, which were analyzed by the MAXQDA tool.</p> </li> </ul> <p> </p> <p><strong>2. Keywords associated with code smells.pdf</strong></p> <p>This file contains the final set of keywords associated with code smells that we identified by following the systematic approach proposed by Bosu and his colleagues in their paper: Identifying the Characteristics of Vulnerable Code Changes: An Empirical Study, FSE 2014.</p>
Data from: Nest structure affects auditory and visual detectability, but not predation risk, in a tropical songbird community
1. Offspring mortality varies dramatically among species with critical demographic and evolutionary ramifications, yet the causes of this variation remain unclear. Nests are widely used for breeding across taxa and thought to influence offspring mortality risk. Traditionally, more complex, enclosed nest structures are thought to reduce offspring predation by reducing the visibility of nest contents and muffling offspring sounds compared to open nests. Direct tests of the functional bases for nest structure influences on predation risk are lacking. 2. We used experiments and 10 years of observational data to examine how nest structure influences nest predation risk in a diverse community of tropical songbirds. First, we examined how nest size was related to nest structure and nest predation rates across species. Second, we assessed how nest structure influences the detectability of nestling begging calls both in field and laboratory settings. Finally, we examined how the acoustic properties of different nest structures influence nest predation risk. Specifically, we experimentally broadcast begging calls from open and enclosed nests to determine how auditory cues and nest structure interact to affect predation on plasticine and quail eggs. We also tested whether nest structure was associated with differences in nest predation rates between the incubation (no begging cues) and nestling (begging cues) stages. 3. We found that enclosed nests are larger than open nests after accounting for adult size, and larger nests had increased predation rates. Moreover, enclosed nests did not consistently alter nestling begging calls in ways that reduce the likelihood of predation compared to open nests. Indeed, begging cues increased predation rates for enclosed but not open cup nests in our playback experiment, and nest predation rates showed greater increases after hatching in enclosed than open cup nests. 4. Ultimately, enclosed nests do not necessarily provide greater predation benefits than open nests in contrast to long standing theory.
Replication Package for the Paper: "Code Smells Detection via Modern Code Review: A Study of the OpenStack and Qt Communities"
<p>This repository contains the data and results from the paper "Code Smells Detection via Modern Code Review: A Study of the OpenStack and Qt Communities" submitted to the ICPC 2021 special issue of the Empirical Software Engineering Journal, 2021.</p> <p> </p> <p>The replication package contains the following two folders:</p> <p> </p> <p><strong>1) data folder</strong></p> <p>The data folder contains the following four folders, which is organized by research questions (RQs).</p> <ul> <li>RQ1: The RQ1 folder contains the retrieved 1,539 code reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell, Code Smell Discussion, and Source Code URL.</li> <li>RQ2: The RQ2 folder contains the coded data for RQ2, called <em>Data Labeling & Encoding for RQ2.mx18</em>. It is the results of data labeling and encoding for RQ2, which was analyzed by the MAXQDA tool.</li> <li>RQ3 and RQ5: <ul> <li><em>Extracted data for RQ3.1.xlsx</em>: this file contains the extracted data (i.e., specific refactoring actions suggested by reviewers) for RQ3.1.</li> <li><em>Data Labeling & Encoding for RQ3 and RQ5.mx18</em>: this file contains the extracted data for RQ3 (excluding the specific refactoring actions in RQ3.1) and RQ5.</li> <li><em>Code change status for RQ5.xlsx</em>: this file contains the information of status of code changes where the developers disagreed with the reviewers and chose to ignore the identified code smells.</li> </ul> </li> <li>RQ4: The RQ4 folder contains the extracted data for RQ4, called <em>Extracted data for RQ4.xlsx</em>.</li> </ul> <p>Note: The mx18 files can be opened by MAXQDA 18 or higher versions, which are available at https://www.maxqda.com/ for download. You may also use the free 14-day trial version of MAXQDA 2018, which is available at https://www.maxqda.com/trial for download.</p> <p> </p> <p><strong>2) scripts folder</strong></p> <p>The scripts folder contains the Python scripts that were used to search for code smell terms and the list of code smell terms.</p> <ul> <li><em>keyword.txt</em> contains the keywords associated with code smells, such as "smell, duplication, and dead".</li> <li><em>get_changes.py</em> is used for getting code changes from OpenStack and Qt.</li> <li><em>get_comments.py</em> is used for getting review comments for each code change.</li> <li><em>keywords_search.py</em> is used for searching review comments that contain at least one keyword.</li> <li><em>random_select.py</em> is used for randomly selecting review comments that do not contain any keyword.</li> <li><em>keywords_improve.py</em> is used for improving the keyword-based mining approach.</li> <li><em>tools.py</em> is used for supporting the process of keywords improving.</li> </ul>
Data from: Sharing detection heterogeneity information among species in community models of occupancy and abundance can strengthen inference
<p>1. The estimation of abundance and distribution and factors governing patterns in these parameters is central to the field of ecology. The continued development of hierarchical models that best utilize available information to inform these processes is a key goal of quantitative ecologists. However, much remains to be learned about simultaneously modeling true abundance, presence, and trajectories of ecological communities.</p> <p>2. Simultaneous modeling of the population dynamics of multiple species provides an interesting mechanism to examine patterns in community processes and, as we emphasize herein, to improve species-specific estimates by leveraging detection information among species. Here we demonstrate a simple but effective approach to share information about observation parameters among species in hierarchical community abundance and occupancy models, where we use shared random effects among species to account for spatiotemporal heterogeneity in detection probability.</p> <p>3. We demonstrate the efficacy of our modeling approach using simulated abundance data, where we recover well our simulated parameters using N-mixture models. Our approach substantially increases precision in estimates of abundance compared to models that do not share detection information among species. We then expand this model, and apply it to repeated detection/non-detection data collected on six species of tits (Paridae) breeding at 119 1 km<sup>2</sup> sampling sites across a <em>P. montanus</em> hybrid zone in northern Switzerland (2004-2020). We find strong impacts of forest cover and elevation on population persistence and colonisation in all species. We also demonstrate evidence for interspecific competition on population persistence and colonization probabilities, where the presence of marsh tits reduces population persistence and colonisation probability of sympatric willow tits, potentially decreasing gene flow among willow tit subspecies.</p> <p>4. While conceptually simple, our results have important implications for the future modeling of population abundance, colonization, persistence, and trajectories in community frameworks. We suggest potential extensions of our modeling in this paper, and discuss how leveraging data from multiple species can improve model performance and sharpen ecological inference.</p>
Distinct latitudinal community patterns of Arctic marine vertebrates along the East Greenlandic coast detected by environmental DNA
<p><strong><span>Aim: </span></strong><span>Greenland is one of the places on Earth where the effects of climate change are most evident. The retreat of sea ice has made East Greenland more accessible for longer periods during the year. East Greenland fjords have been notoriously difficult to study due to their remoteness, dense sea ice conditions and lack of infrastructure. As a result, biological monitoring across latitudinal gradients is scarce in East Greenland and relies on sporadic research cruises and trawl data from commercial vessels. We here aim to investigate the transition in fish and marine mammal communities from South to Northeast Greenland using environmental DNA (eDNA).</span></p> <p><strong><span>Location: </span></strong><span>South to Northeast Greenland.</span></p> <p><strong><span>Methods: </span></strong><span>We investigated the transition in fish and marine mammal communities from South to Northeast Greenland using eDNA metabarcoding of seawater samples. We included both surface and mesopelagic samples, collected over approximately 2400 km waterway distance, by sampling from Cape Farewell to Ella Island in August 2021.</span></p> <p><strong><span>Results:</span></strong><span> We demonstrate a clear transition in biological communities from south to northeast, with detected fish and mammal species matching known distributions. Samples from the southern areas were dominated by capelin (<em>Mallotus villotus</em>) and redfish (<em>Sebastes</em>), whereas northeastern samples were dominated by polar cod (<em>Boreogadus saida</em>), sculpins (<em>Myoxocephalus</em>) and ringed seal (<em>Pusa hispida</em>). We provide newly generated 12S rRNA barcodes from 72 fish species, bringing the public DNA database closer to full taxonomic coverage for Greenlandic fish species for this locus.</span></p> <p><strong><span>Main conclusions: </span></strong><span>Our results demonstrate that eDNA sampling can detect latitudinal shifts in marine biological communities of the Arctic region, which can supplement traditional fish surveys in understanding species distributions and community compositions of marine vertebrates. Importantly, sampling of eDNA can be a feasible approach for detecting northward range expansions in remote areas as climate change progresses.</span></p>
Data from: Forecasting landslides using community detection on geophysical satellite data
<p>As a result of extreme weather conditions, such as heavy precipitation, natural hillslopes can fail dramatically; these slope failures can occur on a dry day due to time lags between rainfall and pore-water pressure change at depth, or even after days to years of slow-motion. While the pre-failure deformation is sometimes apparent in retrospect, it remains challenging to predict the sudden transition from gradual deformation (creep) to runaway failure. We use a network science method–multilayer modularity optimization–to investigate the spatiotemporal patterns of deformation in a region near the 2017 Mud Creek, California landslide. We transform satellite radar data from the study site into a spatially-embedded network in which the nodes are patches of ground and the edges connect the nearest neighbors, with a series of layers representing consecutive transits of the satellite. Each edge is weighted by the product of the local slope (susceptibility to failure) measured from a digital elevation model and ground surface deformation (current rheological state) from interferometric synthetic aperture radar (InSAR). We use multilayer modularity optimization to identify strongly-connected clusters of nodes (communities) and are able to identify both the location of Mud Creek and nearby creeping landslides which have not yet failed. We develop a metric, community persistence, to quantify patterns of ground deformation leading up to failure, and find that this metric increases from a baseline value in the weeks leading up to Mud Creek's failure. These methods promise as a technique for highlighting regions at risk of catastrophic failure.</p>
Community Detection to Split Large-scale Assemblies in Subassemblies
<p>The motivation for the preprocessing of large-scale CAD models for assembly-by-disassembly approaches. The assembly-by-disassembly is only suitable for assemblies with a small number of parts (n_{parts} < 22). However, when dealing with large-scale products with high complexity, the CAD models may not contain feasible subassemblies (e.g. with connected and interference-free parts) and have too many parts to be processed with assembly-by-disassembly. Product designers' preferences during the design phase might not be ideal for assembly-by-disassembly processing because they do not consider subassembly feasibility and the number of parts per subassembly concisely. An automated preprocessing approach is proposed to address this issue by splitting the model into manageable partitions using community detection. This will allow for parallelised, efficient and accurate assembly-by-disassembly of large-scale CAD models. However, applying community detection methods for automatically splitting CAD models into smaller subassemblies is a new concept and research on the suitability for ASP needs to be conducted. Therefore, the following underlying research question will be answered in this experiments:</p> <p><strong>Underlying research question 2: </strong>Can automated preprocessing increase the suitability of CAD-based assembly-by-disassembly for large-scale products?</p> <p>A hypothesis is formulated to answer this research question, which will be utilised to design experiments for hypothesis testing.</p> <p><strong>Hypothesis 2:</strong> Community detection algorithms can be applied to automatically split large-scale assemblies in suitable candidates for CAD-based AND/OR graph generation.}</p>
Data from: Forecasting landslides using community detection on geophysical satellite data
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Data from: Sharing detection heterogeneity information among species in community models of occupancy and abundance can strengthen inference
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Unifying community detection across scales from genomes to landscapes
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Data from: Nest structure affects auditory and visual detectability, but not predation risk, in a tropical songbird community
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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.