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219 results for “change detection”
Figure 6 in Neutron imaging investigation of fossil woods: non-destructive characterization of microstructure and detection of in situ changes as occurring in museum cabinets
Figure 6. Radiographic images of sample upon desorption experiments. The Angeac sample is on the top, while the Rivecourt sample is on the bottom. The scale represents 1 cm. The marked areas correspond to the zones used for measuring average grey levels. (For Rivecourt, it was done on another sample due to implosion of the sample.)
Figure 4 in Neutron imaging investigation of fossil woods: non-destructive characterization of microstructure and detection of in situ changes as occurring in museum cabinets
Figure 4. Evolution of average grey levels in the wetting experiments plotted as a function of the square root of time. (a) Rivecourt and (b) Angeac.
Ngaruroro River, New Zealand - Geomorphic Change Detection - Example Dataset
<p>A simple<a href="https://gcd.riverscapes.net/Tutorials/example-data-sets.html"> Example GCD Dataset </a>illustrating topographic change detection on a long (31 km) dataset. Great for learning about analyses with Directional Masks in GCD.</p> <p>Dataset is from:</p> <ul> <li>31km river on the <a href="https://www.google.com/maps/place/39%C2%B035'58.6%22S+176%C2%B043'23.7%22E/@-39.6060374,176.6490462,27291m/data=!3m1!1e3!4m5!3m4!1s0x0:0x0!8m2!3d-39.599602!4d176.723239">north island of New Zealand</a></li> <li>Two LiDAR surveys</li> <li>2m cell resolution</li> </ul> <p>Dataset includes raw data to run exercises, as well as full *.gcd projects that can be opened. </p>
Sulphur Creek, California - Geomorphic Change Detection - Example Dataset
<p>A simple<a href="https://gcd.riverscapes.net/Tutorials/example-data-sets.html"> Example GCD Dataset </a>illustrating topographic change detection from a single flood event. Used in Tutorials (e.g. <a href="https://gcd.riverscapes.net/Tutorials/ChangeDetection/DoD-thresholding.html">DoD Thresholding</a>). Dataset is from </p> <ul> <li>300m of gravel bed river near <a href="https://www.google.com/maps/place/38%C2%B029'44.0%22N+122%C2%B028'09.0%22W/@38.4958086,-122.4803136,4904m/data=!3m1!1e3!4m5!3m4!1s0x0:0x0!8m2!3d38.49555!4d-122.469166">St. Helena California</a> that underwent a New Year's Eve flood in December 2005.</li> <li>Two surveys (Dec 2015 and Jan 2016)</li> <li>0.5m cell resolution</li> <li>Surveyed with hybrid of RTKGPS and Total Station</li> </ul>
Deep Geo Stat WP3 Land Use Change Detection Dataset
<p>This dataset is created as part of work package WP3 of the Deep Geo Stat project, as part of the ESS topic B5674-2020-GEOS (project 101033951: 2020-NL-GEOS-DEEP-GEO-STAT). The objective was to research whether a Siamese Convolutional Neural Network (SCNN) could be used to automatically detect changes in land use.</p> <p>At Statistics Netherlands, every few years, the so-called "Bestand Bodemgebruik" (BBG) is created, which is a file containing information about the land use of the Netherlands for a given year. The country is split up into many polygons, whereby each polygon is labelled with the most common type of land use for that specific area. Creating the BBG is a time-consuming process and the assumption is that it can be speed up if we can automatically detect the changes in land use. During the research, SCCNs were created to decide the changes in land use for three classes. The SCCN models were then applied to the whole of The Netherlands. The results were then converted to GIS files.</p> <p>This dataset contains the GIS files for the classes 34 (building sites), 51 (other agricultural), and 60 (forest). These files can be loaded in QGIS, for example. Each polygon contains the prediction as it rolls out of the model to which we add the label (0: changed, 1: unchanged). Predictions are made on polygons that belonged to the specific class in 2017 and the new label is the prediction for 2018 and 2020.</p>
Collective Decision-Making and Change Detection with Bayesian Robots in Dynamic Environments
<p>The following folder structure holds all research data of my conducted experiments(h5-logfiles and plots). The Python-Script "show_h5.py" can be used to read out the logfile in h5-format (<em>$python3 show_h5.py expample_logfilename.h5</em>). However, this shouldn't be necessary because all plots are already generated.</p> <p>To find the results you want to see, this is a small guide through the structure:</p> <ol> <li> <p>First the trials are divided into the respective methods (PELT, DBB, DBBCPD). In the folders you find the experiments for the specific method.</p> </li> <li> <p>In the folder of PELT you find the results for the different feedback types and their combinations. The id for each feedback is noted in parentheses (e.g. XX_(id)_feedback_description). Feedback combinations have their ids added up (e.g. XX_(id1+...+idn)_feedback_description).</p> </li> <li> <p>In the folder to each feedback type the different test trials can be found. This means varying environment difficulties and parameter settings. In the name of the folders this information can be found (e.g. XX_method_environmentdifficulty_parametersetting).</p> </li> </ol> <p>All experiments follow the same procedure as long as it is stated otherwise. Each trial consists of 20 individual runs with a duration of 6000 seconds. At half time (3000 s) a change to the opposite fill ratio occurs (fill ratio of 1.0 defines a completely white and one of 0.0 a completely black environment).</p> <p><strong>Environment difficulty</strong></p> <ul> <li> <p>0901 --> easy environment, fill ratio changed from 0.9 to 0.1</p> </li> <li> <p>0703 --> easy environment, fill ratio changed from 0.7 to 0.3</p> </li> <li> <p>0604 --> easy environment, fill ratio changed from 0.6 to 0.4</p> </li> <li> <p>055045 --> easy environment, fill ratio changed from 0.55 to 0.45</p> </li> </ul> <p><strong>Parameter Setting</strong></p> <p>The setting is in the name of the folder composed of: feedbackID: intervalLength amountNeighbors</p> <ul> <li> <p>3c:50s3n --> feedback 3c with a 50s interval and 3 neighbors</p> </li> </ul> <p>In these folders all plots of the respective runs can be found showing a Boxplot of all 20 runs and for each run the swarm belief, the decision distribution and the reset histogram (before/after the change)</p>
Land Water Transition Zone Change Detection - Hydroperiod Maps - WQeMS raster products
<p>Within this dataset, Hydroperiod maps of the Polyphytos open surface water reservoir in Greece and Giaretta reservoir in Italy are available in GeoTIFF raster format. The maps provide information about the total number of days each pixel is inundated within a specific time period.They were generated by the Land Water Transition Zone Change Detection service of the WQeMS project. Each raster file in the dataset is named according to the water body and period during which the processing was performed. Copernicus Sentinel-2 data was utilized for the generation of the inundation maps which were provided as input for the production of the hydroperiod maps.</p>
Land Water Transition Zone Change Detection - Transition Maps between two dates - WQeMS raster products
<p>Within this dataset, land water transition zone maps, which indicate the land-water transition between two instances in time, of the Polyphytos open surface water reservoir in Greece and Giaretta reservoir in Italy, are available in GeoTIFF raster format. The maps depict the transition zones by indicating the change of the pixel status from non-inundated to inundated and vice versa between two provided dates. They were generated by the Land Water Transition Zone Change Detection service of the WQeMS project. Each raster file in the dataset is named according to the water body and the two dates during which the processing was performed. Copernicus Sentinel-2 data was utilized for the generation of the inundation maps on the two dates.</p>
CLDF dataset derived from Hruschka et al.'s "Detecting regular sound changes in linguistics as events of concerted evolution" from 2015
<p>Cite the source of the dataset as:</p> <blockquote> <p>Hruschka, D. J., Branford, S., Smith, E. D., Wilkins, J., Meade, A., Pagel, M., & Bhattacharya, T. (2015). Detecting regular sound changes in linguistics as events of concerted evolution. Current Biology, 25(1), 1-9.</p> </blockquote>
The Effect of Dupilumab on Lung Inflammation and Related Changes in Airway Volumes Detectable by Functional Respiratory Imaging in Patients With Moderate-severe Asthma
ClinicalTrials.gov study NCT04400318. IPD Sharing: YES. Countries: 13. Publications: 2.
Exploring the use of the South African Nest Record Scheme to detect changes in phenology: A case study using four well represented species
Open the record for dataset details and reuse information.
Using a real-time location system to detect behavioral changes in ewes with subclinical mastitis and their lambs
Open the record for dataset details and reuse information.
SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection
<p><strong>Authors</strong></p> <p>Dominik Schlechtweg, Barbara McGillivray, Simon Hengchen, Haim Dubossarsky, and Nina Tahmasebi</p> <p><strong>Description</strong></p> <p>This data collection contains the <strong>post-evaluation</strong> data for <a href="https://languagechange.org/semeval">SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection</a>:</p> <ul> <li>the starting kit to download data, and examples for competing in the CodaLab challenge including baselines</li> <li>the true binary change scores of the targets for Subtask 1, and their true graded change scores for Subtask 2 (<code>test_data_truth/</code>),</li> <li>the scoring program used to score submissions against the true test data in the evaluation and post-evaluation phase (<code>scoring_program/</code>),</li> <li>the results of the evaluation phase including <ul> <li>the final rankings of the participating teams by their best submission (<code>results/rankings_teams.csv</code>),</li> <li>the submitted files of each team (<code>results/submissions/</code>),</li> <li>an overview of the results for each submission ordered by team (<code>results/submissions_results.csv</code>),</li> <li>analysis plots (<code>plots/</code>) displaying the results: <ul> <li>under <code>per_target/</code> we provide the gold change scores and the normalized prediction error of target words plotted against their frequency and polysemy statistics,</li> <li>under <code>per_team/</code> we provide the model predictions from the best submission per team (per subtask) plotted against frequency/polysemy statistics and performance on gold data (gray lines give the correlation with the respective variable in the gold data); we also provide plots of visualizing the teams' prediction similarities.</li> </ul> </li> </ul> </li> </ul> <p>Some remarks:</p> <ul> <li>the paper referenced below remains the only source for the rankings between teams,</li> <li>some teams were disqualified, and are thus removed from the analyses and the rankings present in the paper,</li> <li>some teams have changed names, resulting in a discrepancy between team names under <code>results/</code> and team names in the paper. The paper contains a key to match old names with new names.</li> </ul> <p><strong>Test Data </strong>for SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection can be found using the links below:</p> <ul> <li><a href="https://www.ims.uni-stuttgart.de/en/research/resources/corpora/sem-eval-ulscd-eng/">English</a></li> <li><a href="https://www.ims.uni-stuttgart.de/en/research/resources/corpora/sem-eval-ulscd-ger/">German</a></li> <li><a href="https://zenodo.org/record/3734089">Latin</a></li> <li><a href="https://zenodo.org/record/3730550">Swedish</a></li> </ul> <p>Please find more information on the provided data in the paper referenced below.</p> <p><strong>Reference</strong></p> <p>Dominik Schlechtweg, Barbara McGillivray, Simon Hengchen, Haim Dubossarsky and Nina Tahmasebi. 2020. <a href="https://languagechange.org/semeval">SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection</a>. SemEval@COLING2020.</p> <p>The resources are freely available for education, research and other non-commercial purposes.</p> <pre><code>@inproceedings{schlechtweg2020semeval, title = "{S}em{E}val-2020 {T}ask 1: {U}nsupervised {L}exical {S}emantic {C}hange {D}etection", author = "Schlechtweg, Dominik and McGillivray, Barbara and Hengchen, Simon and Dubossarsky, Haim and Tahmasebi, Nina", booktitle = "To appear in Proceedings of the 14th International Workshop on Semantic Evaluation", year = "2020", address = "Barcelona, Spain", publisher = "Association for Computational Linguistics"}</code></pre> <p> </p>
Data from: Environmental change, if unaccounted, prevents detection of cryptic evolution in a wild population
Detecting contemporary evolution requires demonstrating that genetic change has occurred. Mixed-effects models allow estimation of quantitative genetic parameters and are widely used to study evolution in wild populations. However, predictions of evolution based on these parameters frequently fail to match observations. Furthermore, such studies often lack an independent measure of evolutionary change against which to verify predictions. Here, we applied three commonly used quantitative genetic approaches to predict the evolution of size at maturity in a wild population of Trinidadian guppies. Crucially, we tested our predictions against evolutionary change observed in common garden experiments performed on samples from the same population. We show that standard quantitative genetic models underestimated or failed to detect the cryptic evolution of this trait as demonstrated by the common garden experiments. The models failed because: 1) size at maturity and fitness both decreased with increases in population density, 2) offspring experienced higher population densities than their parents, and 3) selection on size was strongest at high densities. When we accounted for environmental change, predictions better matched observations in the common garden experiments, although substantial uncertainty remained. Our results demonstrate that predictions of evolution are unreliable if environmental change is not appropriately captured in models.
The Mountain Habitats Segmentation and Change Detection Dataset
<p>This is the dataset presented in the paper <em>The Mountain Habitats Segmentation and Change Detection Dataset</em> accepted for publication in the <em>IEEE Winter Conference on Applications of Computer Vision </em>(WACV), Waikoloa Beach, HI, USA, January 6-9, 2015. The full-sized images and masks along with the accompanying files and results can be downloaded here. The size of the dataset is about 2.1 GB.</p> <p>The dataset is released under the <em>Creative Commons Attribution-Non Commercial 4.0 International License </em>(http://creativecommons.org/licenses/by-nc/4.0/legalcode).</p> <p>The dataset documentation is hosted on GitHub at the following address: http://github.com/fjean/mhscd-dataset-doc. Direct download links to the latest revision of the documentation are provided below:</p> <ul> <li><strong>PDF format</strong>: http://github.com/fjean/mhscd-dataset-doc/raw/master/mhscd-dataset-doc.pdf</li> <li><strong>Text format</strong>: http://github.com/fjean/mhscd-dataset-doc/raw/master/mhscd-dataset-doc.rst</li> </ul> <p> </p>
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>
Thesis data: Enhancing Vulnerability Detection: A Comparative Study of Change Identification Methods Across Granularity Levels
<p>Starting dataset used within the thesis; Enhancing Vulnerability Detection: A Comparative Study of Change Identification Methods Across Granularity Levels.</p> <p>Results of manual annotation of and extract of nonPatchTaggedCommitLinks within the NVD CVE dataset.</p>
Appendix of "Impact of Change Granularity in Refactoring Detection"
<p>This is the dataset for ICPC 2022 Impact of Change Granularity in Refactoring Detection, which contains data about coarse-grained refactorings in 19 open source repositories.</p> <p>There are 19 csv files in this dataset. Each of the csv contains 8 rows:</p> <p>1. repository: repository name <br> 2. commit(s): commit SHA-1 hash<br> 3. detected_refactoring_type: refactoring type detected in that commit<br> 4. description: description for that refactoring<br> 5. leftSideLocations: refactoring start place<br> 6. rightSideLocations: refactoring end place<br> 7. is_effective: whether this refactoring is a coarse-grained refactoring (null for refactoring whose coarse-granularity is equal to 1)<br> 8. granularity: coarse-granularity of this refactoring</p> <p>Note that refactorings detected using RefactoringMiner(2.2) with invalid locations has been excluded.</p>
Supplementary material for: AIRBORNE LASER SCANNING CHANGE DETECTION FOR QUANTIFYING GEOMORPHOLOGICAL PROCESSES IN HIGH MOUNTAIN REGIONS
<p>Supplementary material for: TCII/10-AIRBORNE LASER SCANNING CHANGE DETECTION FOR QUANTIFYING GEOMORPHOLOGICAL PROCESSES IN HIGH MOUNTAIN REGIONS</p> <p>For submission of the full paper to: International Society for Photogrammetry and Remote Sensing (ISPRS) / <strong>XXIV ISPRS Congress 2022 Nice, France, 6 - 11 June 2022</strong>.</p> <p><strong>ADDITIONAL FIGURES AND MAPS</strong><br> Supplementary materials:<br> Figure S1: Classification preparation and decision tree.<br> Figure S2: 3D distance change and change offset overall map.<br> Figure S3: Geomorphological map inventoried by manual mapping.<br> Figure S4: Classified real changes of the valley area between 2006 and 2017.<br> Figure S5: Classified real changes between 2006 and 2017.</p> <p> </p> <p> </p> <p> </p>
Datasets and Code for "Hypothesis Tests with Functional Data for Surface Quality Change Detection in Surface Finishing Processes"
<p>This is the set of data and computer code used for reproducing the results in Jin, Tuo, Tiwari, Bukkapatnam, Aracne-Ruddle, Lighty, Hamza, and Ding, 2022, “Hypothesis tests with functional data for surface quality change detection in surface finishing processes,” <em>IISE Transactions</em>, in press.</p>
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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.