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9,302 results for “DETECT”
dataset- Tuberculosis detection using Squid Game Optimization with Deep Learning Model on Chest X-Ray Images
Open the record for dataset details and reuse information.
Data from: Optimizing detectability of the endangered fan mussel using eDNA and ddPCR
<p>Spatial and temporal monitoring of species threatened with extinction is of critical importance for conservation and ecosystem management. On the Mediterranean coast, the fan mussel (<em>Pinna nobilis</em>) is listed as critically endangered after suffering from a mass mortality event since 2016, leading to 100% mortality in most marine populations. Conventional monitoring for this macroinvertebrate is done using scuba, which is challenging in dense meadows or with low visibility. Here we developed an environmental DNA assay targeting the fan mussel and assessed the influence of several environmental parameters on the species detectability <em>in situ</em>. We developed and tested an eDNA molecular marker and collected 48 water samples in two sites at the Thau lagoon (France) with distinct fan mussel density, depths, and during two seasons (summer and autumn). Our marker can amplify fan mussel DNA but lacks specificity since it also amplifies a conspecific species (<em>Pinna rudis</em>). We successfully amplified fan mussel DNA from <em>in situ</em> samples with 46 positive samples (out of 48) using ddPCR, although the DNA concentrations measured were low over almost all samples. Deeper sampling depth slightly increased DNA concentrations, but no seasonal effect was found. We highlight a putative spawning event on a single summer day with much higher DNA concentration compared to all other samples. We present an eDNA molecular assay able to detect the endangered fan mussel and provide guidelines to optimize the sampling protocol to maximize detectability. Effective and non-invasive monitoring tools for endangered species are promising to monitor remaining populations and have the potential for ecological restoration or habitat recolonisation following a mass mortality event.</p>
SatCen Skiff Detection Dataset
<p><strong>Dataset Description</strong></p> <p>The dataset contains 9,420 satellite images for the purpose of skiff detection. The images have been acquired using various satellites at a spatial resolution between 30 and 50cm at nadir.</p> <p>Out of the 9,420 images, 1,525 images contain skiffs, while the rest do not contain any skiffs. In total, there are 30,722 skiffs in the dataset.</p> <p>The labels in the dataset consist of irregular polygons specified manually and defined by 4 points. The polygons represent the shapes of the skiffs.</p> <p> </p> <p><strong>Attribution</strong></p> <p>The dataset has been collected and labelled by SatCen and provided as part of an academic research project. When using the dataset, it should be attributed to SatCen.</p>
Extending Refactoring Detection to Kotlin: A Dataset and Comparative Study
<p><span>This page serves as supplementary material for the article </span><span>Extending Refactoring Detection to Kotlin: A Dataset and Comparative Study</span><span>. In detail, we offer a downloadable dataset in JSON format, comprising 2,043 instances of refactoring instances covering 21 distinct refactoring types. These refactorings are applied within 200 commits across 10 Kotlin repositories and are manually validated by the authors of the paper.</span><span> </span></p> <p><span>Results.json: The results as </span><span>a single</span><span> JSON file.</span></p> <ul> <li> <p><span>Overview of JSON Template: Each entry in this file provides details </span><span>about</span><span> commits, including </span><span>their </span><span>SHA-1 hash, GitHub URL, and an exhaustive list of refactorings </span><span>identified</span><span> </span><span>in </span><span>that specific commit. Refactorings are defined by their type (e.g., 'RenameClass') and are accompanied by detailed descriptions of the </span><span>refactoring</span><span>. Additionally, the entry specifies the tools that </span><span>detected</span><span> the refactoring, such as 'RefDetect' and 'KotlinRMiner.' The validation field indicates whether the refactoring was confirmed, using 'TP' for true positive and 'FP' for false positive. Optional comments and validator information may be also included</span><span>.</span></p> </li> </ul> <p><span>AllCommits.zip: The Results for each repository in a single JSON file.</span></p> <p><span>Times.xlsx: The Excel file containing the time taken by each tool (RefDetect and KotlinMiner) to identify refactoring in each commit.</span></p> <p><span>Results.xlsx: T</span><span>he Excel file contains precision, recall, and F-score metrics for all identified refactoring types.</span></p>
Dataset and Code for Manuscript "Cell sorting based on pulse shapes from angle resolved detection of scattered light"
<p>Dataset and code for the cell cycle analysis and cluster selection for sorting:</p> <ul> <li>ReadMe file with explanations of the data set and analysis</li> <li>Python scripts for converting the data and to reproduce the sort cluster selection</li> <li>binary data files containing pulse shapes and wavelet transform coefficients</li> <li>FSC data files containing the respective common flow cytometry parameters</li> <li>text files with event indices that represent the gating</li> </ul>
Detection of ghost introgression requires exploiting topological and branch length information
<p><span>In recent years, the study of hybridization and introgression has made significant progress, with ghost introgression</span><span>—</span><span>the transfer of genetic material from extinct or unsampled lineages to extant species—emerging as a key area for research. Accurately identifying ghost introgression, however, presents a challenge. To address this issue, we focused on simple cases involving three species with a known phylogenetic tree. Using mathematical analyses and simulations, we evaluated the performance of popular phylogenetic methods, including HyDe and PhyloNet/MPL, and the full-likelihood method, </span><span>Bayesian Phylogenetics and Phylogeography</span><span> (BPP), in detecting ghost introgression. Our findings suggest that heuristic approaches relying on site-pattern counts or gene-tree topologies struggle to differentiate ghost introgression from introgression between sampled non-sister species, frequently leading to incorrect identification of donor and recipient species. The full-likelihood method BPP using multilocus sequence alignments </span><span>directly—hence taking into account both gene-tree topologies and branch lengths, </span><span>by contrast, is capable of detecting ghost introgression in phylogenomic datasets. We analyzed a real-world phylogenomic dataset of 14 species of Jaltomata (Solanaceae) to showcase the potential of full-likelihood methods for accurate inference of introgression.</span></p>
Data for: Sensitive and specific detection of tumor-derived exosomes using nanopore-crystal microchips
<p>Tumor-derived exosomes (tExos) have emerged as promising circulating biomarkers for early cancer diagnosis. However, the sensitivity and specificity of existing assays often limit the clinical translation of tExos. In this study, we present a highly versatile microfluidic platform, termed the nanopore-crystal microchip (NC-Chip), for specific isolation and ultrasensitive detection of tExos in as little as 0.5 μL of plasma samples from pancreatic cancer (PC) patients. The NC-Chip incorporates a herringbone-patterned hierarchical porous hydrogel scaffold, enabling fluid manipulation, size exclusion, immunoaffinity capture, and signal amplification. These integrated features significantly enhance the sensitivity and specificity of tExos assays in complex clinical scenarios. Utilizing an eight-protein signature, the NC-Chip can distinguish patients with pancreatitis and non-metastatic PC with 100% accuracy in the training cohort and 94.9% accuracy in the validation cohort. This platform is sensitive, specific, inexpensive, and only needs small-volume samples, offering a powerful exosome-based liquid biopsy tool for early PC diagnosis.</p>
Hybridized, Physics-Based Melt Detection for 13 Antarctic sites v2
<p>An (updated) dataset of 13 Antarctic sites with microwave grain size (exponential correlation lengths/p_exp) as well as melt day detection using the Hybrid Method. The Hybrid Method combines the Community Firn Model and the Snow Microwave Radiative Transfer model with AMSR-2 to detect melt days. </p>
Replication Package of Deep Learning and Data Augmentation for Detecting Self-Admitted Technical Debt
<p>Self-Admitted Technical Debt (SATD) refers to circumstances where developers use code comments, issues, pull requests, or other textual artifacts to explain why the existing implementation is not optimal. Past research in detecting SATD has focused on either identifying SATD (classifying SATD instances as SATD or not) or categorizing SATD (labeling instances as SATD that pertain to requirements, design, code, test, etc.). However, the performance of such approaches remains suboptimal, particularly when dealing with specific types of SATD, such as test and requirement debt. This is mostly because the used datasets are extremely imbalanced.</p> <p>In this study, we utilize a data augmentation strategy to address the problem of imbalanced data. We also employ a two-step approach to identify and categorize SATD on various datasets derived from different artifacts. Based on earlier research, a deep learning architecture called BiLSTM is utilized for the binary identification of SATD. The BERT architecture is then utilized to categorize different types of SATD. We provide the dataset of balanced classes as a contribution for future SATD researchers, and we also show that the performance of SATD identification and categorization using deep learning and our two-step approach is significantly better than baseline approaches.</p> <p>Therefore, to showcase the effectiveness of our approach, we compared it against several existing approaches:</p> <ol> <li>Natural Language Processing (NLP) and Matches task Annotation Tags (MAT) [<a href="https://github.com/Naplues/MAT" target="_blank" rel="noopener">Github</a>]</li> <li>eXtreme Gradient Boosting+Synthetic Minority Oversampling Technique (XGBoost+SMOTE) [<a href="https://figshare.com/s/87a4b5002c7488822e60" target="_blank" rel="noopener">Figshare</a>]</li> <li>eXtreme Gradient Boosting+Easy Data Augmentation (XGBoost+EDA) [<a href="https://github.com/shenyuanduanzui/xgboost_satd" target="_blank" rel="noopener">Github</a>]</li> <li>MT-Text-CNN [<a href="https://github.com/yikun-li/satd-different-sources-data" target="_blank" rel="noopener">Github</a>]</li> </ol> <p> </p> <div> <div><strong>Structure of the Replication Package:</strong></div> </div> <p>In accordance with the original dataset, the dataset comprises four distinct CSV files delineated by the artifacts under consideration in this study. Each CSV file encompasses a text column and a class, which indicate classifications denoting specific types of SATD, namely code/design debt (C/D), documentation debt (DOC), test debt (TES), and requirement debt (REQ) or Not-SATD.</p> <div> <div><code>├── SATD Keywords</code></div> <div><code>│ ├── Keywords based on Source of Artifacts</code></div> <div><code>│ │ ├── Code comment.txt</code></div> <div><code>│ │ ├── Commit message.txt</code></div> <div><code>│ │ ├── Issue section.txt</code></div> <div><code>│ │ └── Pull section.txt</code></div> <div><code>│ ├── Keywords based on Types of SATD</code></div> <div><code>│ │ ├── code-design debt.txt</code></div> <div><code>│ │ ├── documentation debt.txt</code></div> <div><code>│ │ ├── requirement debt.txt</code></div> <div><code>│ │ └── test debt.txt</code></div> <div><code>├── src</code></div> <div><code>│ ├── bert.py</code></div> <div><code>│ ├── bilstm.py</code></div> <div><code>│ └── preprocessing.py</code></div> <div><code>├── data-augmentation-code_comments.csv</code></div> <div><code>├── data-augmentation-commit_messages.csv</code></div> <div><code>├── data-augmentation-issues.csv</code></div> <div><code>├── data-augmentation-pull_requests.csv</code></div> <div> <div><code>└── Supplementary Material.docx</code></div> <div> </div> </div> <div> </div> </div> <p><strong>Requirements:</strong></p> <div> <div><a href="https://nlp.stanford.edu/projects/glove/" target="_blank" rel="noopener">glove</a></div> <div>nltk</div> <div>transformers</div> <div>torch</div> <div>tensorflow</div> <div>keras</div> <div>langdetect</div> <div>inflect</div> <div>inflection</div> </div> <div> </div> <div> </div> <div> <div><strong>Project sources for each artifact are as follows:</strong></div> </div> <div> </div> <div> <table> <tbody> <tr> <td><strong>Source code comment</strong></td> <td><strong>Issue section</strong></td> <td><strong>Pull section</strong></td> <td><strong>Commit message</strong></td> </tr> <tr> <td>ant<br>argouml<br>columba<br>emf<br>hibernate<br>jedit<br>jfreechart<br>jmeter<br>jruby<br>squirrel</td> <td> <div>camel</div> <div>chromium</div> <div>gerrit</div> <div>hadoop</div> <div>hbase</div> <div>impala</div> <div>thrift</div> </td> <td> <div>accumulo</div> <div>activemq</div> <div>activemq-artemis</div> <div>airflow</div> <div>ambari</div> <div>apisix</div> <div>apisix-dashboard</div> <div>arrow</div> <div>attic-apex-core</div> <div>attic-apex-malhar</div> <div>attic-stratos</div> <div>avro</div> <div>beam</div> <div>bigtop</div> <div>bookkeeper</div> <div>brooklyn-server</div> <div>calcite</div> <div>camel</div> <div>camel-k</div> <div>camel-quarkus</div> <div>camel-website</div> <div>carbondata</div> <div>cassandra</div> <div>cloudstack</div> <div>commons-lang</div> <div>couchdb</div> <div>cxf</div> <div>daffodil</div> <div>drill</div> <div>druid</div> <div>dubbo</div> <div>echarts</div> <div>fineract</div> <div>flink</div> <div>fluo</div> <div>geode</div> <div>geode-native</div> <div>gobblin</div> <div>griffin</div> <div>groovy</div> <div>guacamole-client</div> <div>hadoop</div> <div>hawq</div> <div>hbase</div> <div>helix</div> <div>hive</div> <div>hudi</div> <div>iceberg</div> <div>ignite</div> <div>incubator-brooklyn</div> <div>incubator-dolphinscheduler</div> <div>incubator-doris</div> <div>incubator-heron</div> <div>incubator-hop</div> <div>incubator-mxnet</div> <div>incubator-pagespeed-ngx</div> <div>incubator-pinot</div> <div>incubator-weex</div> <div>infrastructure-puppet</div> <div>jena</div> <div>jmeter</div> <div>kafka</div> <div>karaf</div> <div>kylin</div> <div>lucene-solr</div> <div>madlib</div> <div>myfaces-tobago</div> <div>netbeans</div> <div>netbeans-website</div> <div>nifi</div> <div>nifi-minifi-cpp</div> <div>nutch</div> <div>openwhisk</div> <div>openwhisk-wskdeploy</div> <div>orc</div> <div>ozone</div> <div>parquet-mr</div> <div>phoenix</div> <div>pulsar</div> <div>qpid-dispatch</div> <div>reef</div> <div>rocketmq</div> <div>samza</div> <div>servicecomb-java-chassis</div> <div>shardingsphere</div> <div>shardingsphere-elasticjob</div> <div>skywalking</div> <div>spark</div> <div>storm</div> <div>streams</div> <div>superset</div> <div>systemds</div> <div>tajo</div> <div>thrift</div> <div>tinkerpop</div> <div>tomee</div> <div>trafficcontrol</div> <div>trafficserver</div> <div>trafodion</div> <div>tvm</div> <div>usergrid</div> <div>zeppelin</div> <div>zookeeper</div> </td> <td> <div>accumulo</div> <div>activemq</div> <div>activemq-artemis</div> <div>airflow</div> <div>ambari</div> <div>apisix</div> <div>apisix-dashboard</div> <div>arrow</div> <div>attic-apex-core</div> <div>attic-apex-malhar</div> <div>attic-stratos</div> <div>avro</div> <div>beam</div> <div>bigtop</div> <div>bookkeeper</div> <div>brooklyn-server</div> <div>calcite</div> <div>camel</div> <div>camel-k</div> <div>camel-quarkus</div> <div>camel-website</div> <div>carbondata</div> <div>cassandra</div> <div>cloudstack</div> <div>commons-lang</div> <div>couchdb</div> <div>cxf</div> <div>daffodil</div> <div>drill</div> <div>druid</div> <div>dubbo</div> <div>echarts</div> <div>fineract</div> <div>flink</div> <div>fluo</div> <div>geode</div> <div>geode-native</div> <div>gobblin</div> <div>griffin</div> <div>groovy</div> <div>guacamole-client</div> <div>hadoop</div> <div>hawq</div> <div>hbase</div> <div>helix</div> <div>hive</div> <div>hudi</div> <div>iceberg</div> <div>ignite</div> <div>incubator-brooklyn</div> <div>incubator-dolphinscheduler</div> <div>incubator-doris</div> <div>incubator-heron</div> <div>incubator-hop</div> <div>incubator-mxnet</div> <div>incubator-pagespeed-ngx</div> <div>incubator-pinot</div> <div>incubator-weex</div> <div>infrastructure-puppet</div> <div>jena</div> <div>jmeter</div> <div>kafka</div> <div>karaf</div> <div>kylin</div> <div>lucene-solr</div> <div>madlib</div> <div>myfaces-tobago</div> <div>netbeans</div> <div>netbeans-website</div> <div>nifi</div> <div>nifi-minifi-cpp</div> <div>nutch</div> <div>openwhisk</div> <div>openwhisk-wskdeploy</div> <div>orc</div> <div>ozone</div> <div>parquet-mr</div> <div>phoenix</div> <div>pulsar</div> <div>qpid-dispatch</div> <div>reef</div> <div>rocketmq</div> <div>samza</div> <div>servicecomb-java-chassis</div> <div>shardingsphere</div> <div>shardingsphere-elasticjob</div> <div>skywalking</div> <div>spark</div> <div>storm</div> <div>streams</div> <div>superset</div> <div>systemds</div> <div>tajo</div> <div>thrift</div> <div>tinkerpop</div> <div>tomee</div> <div>trafficcontrol</div> <div>trafficserver</div> <div>trafodion</div> <div>tvm</div> <div>usergrid</div> <div>zeppelin</div> <div>zookeeper</div> </td> </tr> </tbody> </table> </div> <div> <div> </div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <p>This dataset has undergone a data augmentation process using the <a href="https://arxiv.org/abs/2302.13007" target="_blank" rel="noopener">AugGPT</a> technique. Meanwhile, the original dataset can be downloaded via the following link: <a href="https://github.com/yikun-li/satd-different-sources-data">https://github.com/yikun-li/satd-different-sources-data</a></p> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> <div> </div> </div> </div> </div> </div> </div>
Turkmenistan methane point source detections and retrievals from Landsat 5 (1986-2011)
<p>Global atmospheric methane concentrations rose by 10-15 ppb/yr in the 1980s before abruptly slowing to 2-8 ppb/yr in the early 1990s. This period in the 1990s is known as the "methane slowdown" and has been attributed to the collapse of the former Soviet Union (USSR) in December 1991, which may have decreased the methane emissions from oil and gas operations. Here we develop a methane plume detection system based on probabilistic deep learning and human-labelled training data. We use this method to detect methane plumes from Landsat 5 satellite observations over Turkmenistan from 1986 to 2011. We focus on Turkmenistan because economic data suggest it could account for half of the decline in oil and gas emissions from the former USSR. We find an increase in both the frequency of methane plume detections and the magnitude of methane emissions following the collapse of the USSR. We estimate a national loss rate from oil and gas infrastructure in Turkmenistan of more than 10% at times, which suggests the socioeconomic turmoil led to a lack of oversight and widespread infrastructure failure in the oil and gas sector. Our finding of increased oil and gas methane emissions from Turkmenistan following the USSR's collapse casts doubt on the long-standing hypothesis regarding the methane slowdown, begging the question: "what drove the 1992 methane slowdown?"</p>
A supervised Graph-based deep learning algorithm to detect and quantify clustered particles
<p>In this data repository, we provide the necessary data for replicating results, including both simulated and biological datasets. Additionally, the repository includes trained models to infer from these datasets.</p>
Detectability of unresolved particles in off-axis digital holographic microscopy
<p>Off-axis digital holographic microscopy (DHM) provides both amplitude and phase images, and so may be used for label-free 3D tracking of micro- and nano-sized particles of different compositions, including biological cells, strongly absorbing particles, and strongly scattering particles. Contrast is provided by differences in either the real or imaginary parts of the refractive index (phase contrast and absorption) and/or by scattering. While numerous studies have focused on phase contrast and improving resolution in DHM, particularly axial resolution, absent have been studies quantifying the limits of detection for unresolved particles. This limit has important implications for microbial detection, including in life-detection missions for space flight. Here we examine the limits of detection of nanosized particles as a function of particle optical properties, microscope optics (including camera well depth and substrate), and data processing techniques and find that DHM provides contrast in both amplitude and phase for unresolved spheres, in rough agreement with Mie theory scattering cross-sections. Amplitude reconstructions are more useful than phase for low-index spheres and should not be neglected in DHM analysis.</p>
Neural Image Segmentation for Redacted Text Detection Dataset
<p>This dataset was created for the "<span>Redacted Text Detection Using Neural Image </span><span>Segmentation Methods</span>" project, and contains roughly 1000 pages with manually annotated redactions in Dutch documents released under the WOO, with the trained model files and the model outputs also included in the dataset. More details on the usage of the dataset and models can be found on Github: https://github.com/RubenvanHeusden/NeuralRedactedTextDetection/</p>
Pulsars detected in the GP survey in only Stokes I images
<p><span>The known pulsars detected in the GP survey in only Stokes I images. </span><span>The mean flux density of the pulsars from the Stokes I image from this work is </span><span>denoted by S</span><span>I</span><span> </span><span>and the flux density from the Stokes V image is denoted by S</span><span>V</span><span>.</span><span> </span><span>α </span><span>refers to the spectral index of the pulsars. Only two of the pulsars, PSRs J1614- </span><span>5048 and J1644-4559 have a previously recorded flux density below 300 MHz by </span><span>Frail et al. (2016). For the 14 remaining pulsars, this is the first low-frequency </span><span>detections below 300 MHz.</span></p>
The known pulsars detected in the GP survey in only beamformed searches.
<p><span>The known pulsars detected in the GP survey in only beamformed </span><span>searches. It shows the names and parameters of the pulsars. Detailed analysis of </span><span>these pulsars can be found in Xue et al. (2017) and Bhat et al. (2023b). As this </span><span>work is mainly focused on the imaging aspect of pulsar searching, these pulsars </span><span>are not included as part of the analysis done for this work.</span></p>
Evaluation Artifacts of RESTRuler, a Static Analysis Tool to Detect Design Rule Violations in OpenAPI Definitions
<p>All artifacts related to the empirical evaluation of the RESTRuler CLI, a Java-based tool to identify design rule violations in OpenAPI descriptions</p> <p>You can find the latest version here: <a href="https://github.com/restful-ma/rest-ruler-evaluation">https://github.com/restful-ma/rest-ruler-evaluation</a></p>
Genomics polymorphisms of Staphylococcus aureus strain NCTC 8325 in the lab stock maintained at TUM (WT), after 30 passes in BHI media (D) and after 30 passes detecting 4 -fold MIC increase to isocyanide -code I16- 3 biological replicates (A,B,C), and 3 independent colonies sequenced per replicate at the end of the experiment.
<p>Genomics polymorphisms of Staphylococcus aureus strain NCTC 8325 in the lab stock maintained at TUM (WT), after 30 passes in BHI media (D) and after 30 passes detecting 4 -fold MIC increase to isocyanide -code I16- 3 biological replicates (A,B,C), and 3 independent colonies sequenced per replicate at the end of the experiment. Determined from Illumina shotgun genomic sequencing datasets, mapping and analyses vs the reference genome of the strain https://www.ncbi.nlm.nih.gov/nuccore/NC_007795.1/</p>
Fig1. Gel electrophoresis 2 in Promising use of Polymerase Chain Reaction Associated to Reverse Transcription for the Detection ofthe America-1 Lineage of Canine Distemper Virus
Fig1. Gel electrophoresis 2% agarose
Fig2. Gel electrophoresis Agarose 2 in Promising use of Polymerase Chain Reaction Associated to Reverse Transcription for the Detection ofthe America-1 Lineage of Canine Distemper Virus
Fig2. Gel electrophoresis Agarose 2%.
Data from: ESCRT-III-dependent adhesive and mechanical changes are triggered by a mechanism detecting alteration of Septate Junction integrity in Drosophila epithelial cells
<p><span>Barrier functions of proliferative epithelia are constantly challenged by mechanical and chemical constraints. How epithelia respond to and cope with disturbances of barrier functions to allow tissue integrity maintenance is poorly characterized. Cellular junctions play an important role in this process and intracellular traffic contribute to their homeostasis. Here, we reveal that, in <em>Drosophila</em> pupal <em>notum</em>, alteration of the bi- or tricellular septate junctions (SJs) triggers a mechanism with two prominent outcomes. On one hand, there is an increase in the levels of E-cadherin, F-Actin and non-muscle Myosin II in the plane of adherens junctions. On </span><span>the other hand, β-integrin/Vinculin-positive cell contacts are reinforced along the lateral and basal membranes. We found that the weakening of SJ integrity, caused by the depletion of bi- or tricellular SJ components, alters ESCRT-III/Vps32/Shrub distribution, reduces degradation, and instead favours recycling of SJ components, an effect that extends to other recycled transmembrane protein cargoes including Crumbs, its effector β-Heavy Spectrin</span><span> Karst, and </span><span>β-integrin</span><span>. We propose a mechanism by which epithelial cells, upon sensing alterations of the septate junction</span><span>,</span><span> reroute the function of Shrub to adjust the balance of degradation/recycling of junctional cargoes and thereby compensate for barrier junction defects to maintain epithelial integrity.</span></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.