Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
219
datasets available to search
ShareScore release 0.7.1
Dataset results
219 results for “change detection”
A novel approach to the detection of unusual mitochondrial protein change suggests hypometabolism of ancestral simians: Supplemental Files
<p><strong>Supplementary Fig. S1</strong>: θ<sub>evo</sub> calculated for each analyzed edge for specific OXPHOS complexes. Analyses were performed as in fig. 1F, except that SPCSs calculated from mtDNA-encoded protein positions in Complex I, Complex III, Complex IV, or Complex V were used to generate θevo values.</p> <p><strong>Supplementary Fig. S2</strong>: Mammalian orders differ in their propensity for potentially efficacious mitochondrial protein substitutions within specific OXPHOS complexes (median calculations). Analysis was performed as in fig. 2A, except that θ<sub>evo</sub> values were obtained by analysis of mtDNA-encoded Complex I, Complex III, Complex IV, or Complex V polypeptides.</p> <p><strong>Supplementary Fig. S3</strong>: Mammalian orders differ in their propensity for potentially efficacious mitochondrial protein substitutions within specific OXPHOS complexes (median confidence intervals). Analysis was performed as in (<em>A</em>) fig. 2B or (<em>B</em>) fig. 2C, except that θ<sub>evo</sub> values were obtained by analysis of mtDNA-encoded Complex I, Complex III, Complex IV, or Complex V proteins.</p> <p><strong>Supplementary Fig. S4</strong>: Mammalian families differ in their propensity for potentially efficacious mitochondrial protein substitutions at specific OXPHOS complexes (median calculations). Analysis was performed as in fig. 3A, except that θ<sub>evo</sub> values were obtained by analysis of mtDNA-encoded Complex I, Complex III, Complex IV, or Complex V subunits.</p> <p><strong>Supplementary Fig. S5</strong>: Mammalian families differ in their propensity for potentially efficacious mitochondrial protein substitutions at specific OXPHOS complexes (median confidence intervals ordered by lower 90% median confidence limit). Analysis was performed as in fig. 3B, except that θ<sub>evo</sub> values were obtained by analysis of mtDNA-encoded Complex I, Complex III, Complex IV, or Complex V proteins.</p> <p><strong>Supplementary Fig. S6</strong>: Mammalian families differ in their propensity for potentially efficacious mitochondrial protein substitutions at specific OXPHOS complexes (median confidence intervals ordered by upper 90% median confidence limit). Analysis was performed as in fig. 3C, except that θ<sub>evo</sub> values were obtained by analysis of mtDNA-encoded Complex I, Complex III, Complex IV, or Complex V polypeptides.</p> <p>---</p> <p><strong>Supplementary File 1</strong>: All predicted protein substitutions along all edges at positions containing less than 2% gaps across input and ancestral sequences are listed, along with associated taxonomy information, TSS, and branch length. All alignment positions refer to Bos taurus reference sequences.</p> <p><strong>Supplementary File 2</strong>: The TSS calculated for each mitochondrial protein alignment position. All alignment positions refer to Bos taurus reference sequences.</p> <p><strong>Supplementary File 3</strong>: SPCS and θevo outputs are provided for analyses across all mitochondria-encoded positions, as well as for focused analyses of specific OXPHOS complexes and individual proteins.</p> <p><strong>Supplementary File 4</strong>: A GenBank flat file containing RefSeq entries for mammalian mtDNAs, as well as the entry for the reptile Anolis punctatus.</p> <p><strong>Supplementary File 5</strong>: A maximum likelihood inferred tree generated by a RAxML-NG analysis of concatenated and aligned protein coding sequences from mammalian and Anolis punctatusmtDNAs.</p> <p><strong>Supplementary File 6</strong>: Bootstrap replicates were generated from the alignment of concatenated protein coding sequences. Felsenstein’s Bootstrap Proportions (Felsenstein 1985) were calculated and used to label the maximum likelihood inferred tree of mammalian mtDNAs.</p> <p><strong>Supplementary File 7</strong>: Bootstrap replicates were generated using concatenated mammalian mtDNA coding sequences. Transfer Bootstrap Expectations (Lemoine 2018) were calculated and used to label the maximum likelihood inferred tree of mammalian mtDNAs.</p> <p><strong>Supplementary File 8</strong>: PAGAN tree output produced using aligned amino acid sequences and the rooted maximum likelihood inferred tree as input.</p>
Benchmark for the Evaluation of Lexical Semantic Change Detection for Ancient Greek
<p>This repository contains a benchmark of Ancient Greek lemmas which underwent semantic change. It is meant as a support for the evaluation of methods detecting lexical semantic change in Ancient Greek. It was created at the University of Groningen, The Netherlands. A publication will follow soon.</p> <p> </p> <p><strong>1. Overview of the repository</strong></p> <p>This benchmark was created by retrieving and selecting from existing scholarship cases of lexemes which underwent semantic change. The evaluation items are 44 Ancient Greek lemmas, accompanied by the following information (see the column headers in the CSV file):</p> <ul> <li><strong>reference:</strong> the literature source of information about the change;</li> <li><strong>which_change: </strong>an explanation of the change in meaning. NB: the older meaning(s) do not necessarily disappear after the change, but it can happen that the new meaning(s) are added to the existing one(s), increasing the polysemy of the lemma;</li> <li><strong>when_changed: </strong>information about the work(s) or time period in which the change was first recorded; this kind of information was not always available or precise;</li> <li><strong>christian_change:</strong> whether the change is triggered by social, religious, or cultural changes related to the spread of Christianity, according to the scholarship.</li> </ul> <p> </p> <p><strong>2. References</strong></p> <p>The literature used to build this benchmark is the following:</p> <p> BUCK, Carl Darling. A dictionary of selected synonyms in the principal Indo-European languages. University of Chicago Press, 1949.</p> <p> FINKELBERG, Aryeh. "On the History of the Greek ΚΟΣΜΟΣ." Harvard Studies in Classical Philology (1998): 103-136.</p> <p> GINGRICH, F. Wilbur. "The Greek New Testament as a landmark in the course of semantic change." <em>Journal of Biblical Literature</em> (1954): 189-196.</p> <p> HORKY, Phillip Sidney. "When did Kosmos become the Kosmos." <em>Cosmos in the Ancient World</em> (2019): 22-41.</p> <p> LURAGHI, Silvia. "The verb aréskein in Ancient Greek: Constructions and semantic change." <em>Acta Linguistica Petropolitana. Труды института лингвистических исследований</em> 18-1 (2022): 226-245.</p> <p> </p> <p>These dictionaries of Ancient Greek were also used to double-check the instances of change:</p> <p> LIDDELL, Henry George, and Robert Scott. <em>A Greek-English Lexicon</em>. revised and augmented throughout by. Sir Henry Stuart Jones. with the assistance of. Roderick McKenzie. Oxford. Clarendon Press. 1940.</p> <p> ROCCI, Lorenzo.<em> Vocabolario greco-italiano</em>. Roma. Società editrice Dante Alighieri. 1939.</p> <p> SLUITER, Ineke, and Lucien van Beek, and Ton Kessels, and Albert Rijksbaron. <em>Woordenboek Grieks/Nederlands</em>. 2024. <a href="https://woordenboekgrieks.nl/" target="_blank" rel="noopener">https://woordenboekgrieks.nl/</a></p> <p> </p> <p><strong>3. Acknowledgements</strong></p> <div>This work was partially supported by the Young Academy Groningen through the PhD scholarship of Silvia Stopponi.<br> <br>We acknowledge the financial support of Anchoring Innovation. Anchoring Innovation is the Gravitation Grant research agenda of the Dutch National Research School in Classical Studies, OIKOS. It is financially supported by the Dutch ministry of Education, Culture and Science (NWO project number 024.003.012). For more information about the research programme and its results, see the website <a href="https://www.anchoringinnovation.nl/">www.anchoringinnovation.nl</a>.</div> <div> <p> </p> <p><strong>4. How to cite</strong></p> </div> <div>Until there is no publication about this benchmark, please cite the resource as:</div> <div>Silvia Stopponi, Saskia Peels-Matthey, Malvina Nissim (2024), <em>Benchmark for the Evaluation of Lexical Semantic Change Detection Measures in Ancient Greek</em>, DOI: 10.5281/zenodo.13364555.</div> <div> </div> <div> </div>
River Feshie, Scotland - 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 five years of repeat monitoring of the Feshie from 2003 to 2007. Used in Tutorials (e.g. <a href="https://gcd.riverscapes.net/Tutorials/ChangeDetection/GCDwithFIS.html">FIS Error Modelling</a>) and appears in:</p> <ol> <li>Wheaton JM, Brasington J, Darby SE, Sear DA, Vericat D‡., and Kasprak A*. 2013. <a href="https://www.researchgate.net/publication/242653748_Morphodynamic_signatures_of_braiding_mechanisms_as_expressed_through_change_in_sediment_storage_in_a_gravel-bed_river">Morphodynamic signatures of braiding mechanisms as expressed through change in sediment storage in a gravel-bed river</a>. Journal of Geophysical Research - Earth Surface. DOI: <a href="http://dx.doi.org/10.1002/jgrf.20060">10.1002/jgrf.20060</a>.</li> <li>Wheaton JM, Brasington J, Darby SE, Merz JE, Pasternack GB, Sear DA and Vericat D‡. 2010. <a href="https://www.researchgate.net/publication/227526758_Linking_Geomorphic_changes_to_Salmonid_habitat_at_a_scale_relevant_to_fish">Linking Geomorphic Changes to Salmonid Habitat at a Scale Relevant to Fish. River Research and Applications</a>.26: 469-486. DOI: <a href="http://dx.doi.org/10.1002/rra.1305">10.1002/rra.1305</a>.</li> </ol> <p>. Dataset is from:</p> <ul> <li>700m braided gravel bed river in the <a href="https://www.google.com/maps/place/57%C2%B000'41.4%22N+3%C2%B054'16.1%22W/@57.0099348,-3.9000104,6821m/data=!3m1!1e3!4m5!3m4!1s0x0:0x0!8m2!3d57.01149!4d-3.90446">Scottish Cairngorm mountains</a>.</li> <li>5 annual surveys</li> <li>Mix of RTKGPS and Total Station</li> <li>1m cell resolution</li> </ul> <p>Dataset includes raw data to run exercises, as well as full *.gcd projects that can be opened. </p>
Detecting small changes in tropical forests from space... data and code for thesis chapter 4
<p>SAR and UAV-LiDAR data used in chapter 4 of my thesis <em>Detecting small changes in tropical forests from space: experiments using synthetic aperture radar. </em>This content has also been submitted for peer review in Frontiers in Remote Sensing.</p> <p>DEM_timeseries_3m contains phase height and coherence from TanDEM-X InSAR high-resolution spolight images, processed by Jose-Luis Bueso-Bello at DLR. NetCDF format, dimensions latitude, longitude, time.</p> <p>TDX_descending_intensity contains intensity from the same TanDEM-X time series, covering an area of the Madre de Dios region in Peru. These data were processed by Harry Carstairs using ESA's SNAP software.</p> <p>UAV_change_1m_mask is a raster showing the change in canopy height at the study site between June 2019 and July 2021, according to two UAV LiDAR campaigns, with 1m pixels, and with areas with low point density masked out.</p> <p>CODE.zip contains python scripts and notebooks used to collate the data, create change detection metrics, develop SAR models of canopy height, and produce the figures.</p> <p>Funded by European Research Council (ERC) grant to the Tropical Forest Degradation Experiment (FODEX).</p>
Detecting Changes in the Caenorhabditis elegans Intestinal Environment Using an Engineered Bacterial Biosensor
<p>Data for the figures in the manuscript <br> <a href="https://pubs.acs.org/doi/10.1021/acssynbio.9b00166">https://pubs.acs.org/doi/10.1021/acssynbio.9b00166</a></p> <p>Abstract:<br> <em>Caenorhabditis elegans</em> has become a key model organism within biology. In particular, the transparent gut, rapid growing time, and ability to create a defined gut microbiota make it an ideal candidate organism for understanding and engineering the host microbiota. Here we present the development of an experimental model that can be used to characterize whole-cell bacterial biosensors <em>in vivo</em>. A dual-plasmid sensor system responding to isopropyl β-d-1-thiogalactopyranoside was developed and fully characterized <em>in vitro</em>. Subsequently, we show that the sensor was capable of detecting and reporting on changes in the intestinal environment of <em>C. elegans</em> after introducing an exogenous inducer into the environment. The protocols presented here may be used to aid the rational design of engineered bacterial circuits, primarily for diagnostic applications. In addition, the model system may serve to reduce the use of current animal models and aid in the exploration of complex questions within general nematode and host–microbe biology.</p>
Swedish Test Data for SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection
<p>This data collection contains the Swedish test data for <a href="https://competitions.codalab.org/competitions/20948">SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection:</a></p> <p>- a Swedish text corpus pair (`corpus1/`, `corpus2/`)<br> - 31 lemmas which have been annotated for their lexical semantic change between the two corpora (`targets.txt`)<br> - the annotated binary change scores of the targets for subtask 1, and their annotated graded change scores for subtask 2 (`truth/`)</p> <p>We sample from the KubHist2 corpus, digitized by the National Library of Sweden, and available through the Språkbanken corpus infrastructure Korp (<a href="https://www.researchgate.net/profile/Markus_Forsberg/publication/266352576_Korp_-_the_corpus_infrastructure_of_Sprakbanken/links/55bf1ee008aed621de121ba3/Korp-the-corpus-infrastructure-of-Sprakbanken.pdf">Borin et al., 2012</a>). The full corpus is available through a CC BY (attribution) license. Each word for which the lemmatizer in the Korp pipelien has found a lemma is replaced with the lemma. In cases where the lemmatizer cannot find a lemma, we leave the word as is (i.e., unlemmatized, no lower-casing). KubHist contains very frequent OCR errors, especially for the older data.More detail about the properties and quality of the Kubhist corpus can be found in (<a href="https://www.diva-portal.org/smash/get/diva2:1358014/FULLTEXT01.pdf#page=28">Adesam et al., 2019</a>).</p> <p>Lars Borin, Markus Forsberg, and Johan Roxendal. "Korp-the corpus infrastructure of Språkbanken." <em>LREC</em>. 2012.</p> <p>Adesam, Yvonne, Dana Dannélls, and Nina Tahmasebi. "Exploring the Quality of the Digital Historical Newspaper Archive KubHist." <em>DHN</em>. 2019.</p> <p>__Corpus 1__</p> <p>- based on: <a href="https://spraakbanken.gu.se/korp/?mode=kubhist">Kubhist2</a><br> - language: Swedish<br> - time covered: 1790-1830<br> - size: ~71 million tokens<br> - format: lemmatized, sentence length > 9 (before removal of punctuation), no punctuation, sentences randomly shuffled<br> - encoding: UTF-8<br> - note: contains frequent OCR errors</p> <p>__Corpus 2__</p> <p>- based on: <a href="https://spraakbanken.gu.se/korp/?mode=kubhist">Kubhist2</a><br> - language: Swedish<br> - time covered: 1895-1903<br> - size: ~111 million tokens<br> - format: lemmatized, sentence length > 9 (before removal of punctuation), no punctuation, sentences randomly shuffled<br> - encoding: UTF-8<br> - note: contains OCR errors</p> <p>Besides the official lemma version of the corpora for SemEval-2020 Task 1 we also provide the raw token version (`corpus1/token/`, `corpus2/token/`). It contains the raw sentences in the same order as in the lemma version. Find more information on the data and SemEval-2020 Task 1 in the paper referenced below.</p> <p> </p> <p>Reference:</p> <p>Dominik Schlechtweg, Barbara McGillivray, Simon Hengchen, Haim Dubossarsky and Nina Tahmasebi.<a href="https://competitions.codalab.org/competitions/20948">SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection</a>. To appear in SemEval@COLING2020.</p>
Today's cat is tomorrow's dog: accounting for time-based changes in the labels of ML vulnerability detection approaches (Replication Package Part 3: OpenSSL dataset)
<h1><strong>The Replication Package of</strong></h1> <h1><strong>"Today's cat is tomorrow's dog: accounting for time-based changes in the labels of ML vulnerability detection approaches"</strong></h1> <h3><strong>Part 3 (OPENSSL Dataset)</strong></h3> <div> <div>This repository includes:</div> <ol> <li><em><strong>Code.zip</strong></em> that contains the codes to replicate some parts of this study:<br>a. <em>1_generate_datasets</em> implements our methodology to generate the datasets.<br>b. <em>2_run_models</em> runs the ML models during the evaluation.<br>c. <em>3_result_replication </em>generates charts presented in the paper from the ML evaluation results.</li> <li><em><strong>Datasets.zip</strong></em> that contain 2 folders:<br>a. <em>original</em> datasets: 1 from <a href="https://github.com/CGCL-codes/VulDeePecker" target="_blank" rel="noopener">NVD Vuldeepecker</a> and 3 extracted from <a href="https://github.com/ZeoVan/MSR_20_Code_vulnerability_CSV_Dataset" target="_blank" rel="noopener">BigVul</a>.<br> <div> <div>b. <em>OPENSSL</em> datasets: train, validation, test sets for each time of observation extracted using our methodology from <a href="https://github.com/ZeoVan/MSR_20_Code_vulnerability_CSV_Dataset" target="_blank" rel="noopener">BigVul</a> dataset for project <em>openssl</em>.</div> </div> </li> <li><em><strong>Pretrained-models.zip</strong></em> that we generated during our evaluation (3 test results for each time point in the timeline [2013-2019]).</li> <li><em><strong>Results.zip</strong></em> of our evaluation, the folder <em>ALL</em> contains the overall results and other folders are results by model.</li> </ol> <p><strong>UPDATED version 5<br></strong>- added a GLOBAL_README.md which contains the 3 stages and how they are connected to each other<br>- updated LineVul.ipynb: import AdamW from torch.optim instead of transformers<br>- updated README.md in Code2Vec with the prerequisites of Java to run gradlew for astmine</p> <p><strong>UPDATED version 6<br></strong>- updated CodeBert.ipynb: import AdamW from torch.optim instead of transformers</p> <p>Documentations</p> <ol> <li><em><strong>INSTALL.pdf </strong></em>: how to install the codes</li> <li><em><strong>README.pdf</strong></em>: readme file</li> <li><em><strong>REQUIREMENTS.pdf</strong></em>: hardware and software requirements</li> <li><em><strong>STATUS.pdf</strong></em> : status for artifact submission</li> <li><em><strong>LICENSE.pdf</strong></em>: the license of this artifact</li> <li><em><strong>PAPER.pdf</strong></em>: the camera-ready version of the paper</li> </ol> </div> <div> <div>Please refer to the following repositories for the other datasets and pre-trained models:</div> <div>- Part 1 NVD Vuldeeepecker : <a href="https://doi.org/10.5281/zenodo.8207883" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8207883</a></div> - Part 2 LINUX : <a href="https://doi.org/10.5281/zenodo.10960662" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10960662</a><br> <div>- Part 4 POPPLER : <a href="https://doi.org/10.5281/zenodo.14713143">https://doi.org/10.5281/zenodo.14713143</a></div> <div> </div> <div>This work was partly funded by the EU under the H2020 Program AssureMOSS (Grant n. 952647) and the Horizon Europe Program Sec4AI4Sec (Grant n. 101120393), by the Italian Ministry of University and Research (MUR) under the P.N.R.R. – NextGenerationEU grant n.\ PE00000014 (SERICS subproject COVERT), and by the Dutch Research Council (NWO) under the grant NWA.1215.18.006 (Theseus) and grant KIC1.VE01.20.004 (HEWSTI). </div> </div>
Phlorest phylogeny derived from Hruschka et al. 2015 'Detecting regular sound changes in linguistics as events of concerted evolution'
<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>
Change detection technique comparison in long-term wetland monitoring: datasets and maps of the Poitevin Marsh (France)
<h3>For a full description of the methodology and results, please see the following article:</h3> <div> <div>Demarquet, Q., Rapinel, S., Gore, O., Dufour, S., Hubert-Moy, L., 2024. Continuous change detection outperforms traditional post-classification change detection for long term monitoring of wetlands. <em>International Journal of Applied Earth Observation and Geoinformation </em>133, 104142. <a href="https://doi.org/10.1016/j.jag.2024.104142">https://doi.org/10.1016/j.jag.2024.104142</a></div> <div> </div> <div>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> </div> <h3># Datasets</h3> <p>Points datasets are projected in WGS84 (EPSG:4326), and are provided in the open source GeoPackage format.</p> <p>The first dataset (<strong>Dataset_1.gpkg</strong>) contains training and validation points for random forest classification of EUNIS habitats in the Poitevin Marsh. This dataset consists of 3360 training and 840 validation points (total: 4200).<br>Fields description:</p> <ul> <li>"<em>ID</em>": unique identifier</li> <li>"<em>CLASS</em>": EUNIS first level habitat type, classified as following:<br> <ul> <li>1: EUNIS habitat A</li> <li>2: EUNIS habitat B</li> <li>3: EUNIS habitat C1J5</li> <li>4: EUNIS habitat C3</li> <li>5: EUNIS habitat E</li> <li>6: EUNIS habitat G</li> <li>7: EUNIS habitat I</li> <li>8: EUNIS habitat J</li> </ul> </li> <li>"<em>DATE</em>": Date associated with EUNIS habitat sample</li> <li>"<em>LON</em>": Point longitude in decimal degrees</li> <li>"<em>LAT</em>": Point latitude in decimal degrees</li> <li>"<em>TYPE</em>": Either training ("<em>train</em>") or validation ("<em>test</em>") sample</li> </ul> <p>The second dataset (<strong>Dataset_2.gpkg</strong>) contains points for the Olofsson correction method. This dataset consists of 326 points where the change classes are classified as following: -10 (wetland loss), 10 (wetland gain), 100 (stable existing wetland), and 200 (stable damaged wetland).<br>Fields description:</p> <ul> <li>"<em>ID</em>": unique identifier</li> <li>"<em>LON</em>": Point longitude in decimal degrees</li> <li>"<em>LAT</em>": Point latitude in decimal degrees</li> <li>"<em>REFERENCE</em>": Change class reference</li> <li>"<em>CCDC</em>": Change class obtained from the Continuous Change Detection and Classification approach</li> <li>"<em>PCCD</em>": Change class obtained from the Post-Classification Change Detection approach</li> </ul> <p>Supplementary layout files (<strong>Dataset_1.qml</strong> and <strong>Dataset_2.qml</strong>) support formatting of the points in QGIS software.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <h3># EUNIS habitat</h3> <p>Maps are projected in WGS84 (EPSG:4326), and are provided in the GeoTiff format at 30m of spatial resolution. </p> <p>Habitat maps are given for the two approaches in years 1984 and 2022:</p> <ul> <li>CCDC: Continuous Change Detection and Classification (<strong>CCDC_HABITAT_1984.tif</strong> and <strong>CCDC_HABITAT_2022.tif</strong>)</li> <li>PCCD: Traditional post-classification approach (<strong>PCCD_HABITAT_1984.tif </strong>and <strong>PCCD_HABITAT_2022.tif</strong>)</li> </ul> <p>Supplementary layout files (<strong>CCDC_HABITAT_1984.qml, CCDC_HABITAT_2022.qml, PCCD_HABITAT_1984.qml, PCCD_HABITAT_2022.qml</strong>) support formatting of raster layers in QGIS software.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <h3># Change detection during the 1984-2022 period</h3> <p>Maps are projected in WGS84 (EPSG:4326), and are provided in the GeoTiff format at 30m of spatial resolution. Raster values follow the classification scheme used in Dataset_2.</p> <p>Change detection maps are given for the two approaches:</p> <ul> <li>CCDC: Continuous Change Detection and Classification (<strong>CCDC_CHANGE_1984_2022.tif</strong>)</li> <li>PCCD: Traditional post-classification approach (<strong>PCCD_CHANGE_1984_2022.tif</strong>)</li> </ul> <p>Supplementary layer files (<strong>CCDC_CHANGE_1984_2022.qml</strong> and<strong> PCCD_CHANGE_1984_2022.qml</strong>) support formatting of the raster layers in QGIS software.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <h3># GEE repository</h3> <p>To get direct access to GEE scripts and assets, please follow those two links:</p> <p>https://code.earthengine.google.com/?accept_repo=users/demarquetquentin/CCDC_Poitevin</p> <p>https://code.earthengine.google.com/?asset=projects/ee-quen-dem/assets/CCDC_Poitevin</p>
A Dataset for detecting Change Coupling and Structural Dependencies.
<p><strong>Introduction</strong></p> <p>This repository hosts the results of runs evaluating the first developments of prototype tool for detecting Change Coupling and Structural Dependencies in the context of cyber-physical-systems(CPS). This tool analyzes the projects' code repository history and uses rule mining to detect logistical couplings, it also analyzes the source code to detect which of those changes have additional structural dependencies. </p> <p> </p> <p>The two datasets are the result from the analysis of two popular GitHub CPS projects:</p> <ul> <li>Eclipse Concierge (Java) — a small-footprint implementation of the OSGi Core Specification optimized for mobile and embedded devices.<sup>1</sup></li> <li>PX4 — a flight control solution for drones, that also contains a Drone Middleware Platform, providing drivers and middleware to run drones <sup>2</sup></li> </ul> <p>The datasets were generated by running a change coupling and structural dependencies analyzer, that is a prototype under development. The resulting raw data is available under the <code>project_results/Proj_Name</code> folder. Additionally, in the folder <code>notebooks </code>the user can find examples of how to easily query the functionality offered by the visualization and analytics libraries (in folder <code>analytics)</code>.</p> <p> </p> <p>[1] https://github.com/eclipse/concierge</p> <p>[2] https://github.com/PX4/PX4-Autopilot</p>
The benchmark datasets for Multi-class Change Detection (MCD)
<p>Change detection (CD) provides a research basis for environmental monitoring, urban expansion and reconstruction as well as disaster assessment, by identifying the changes of ground objects in different time periods. Traditional CD focused on the binary change detection (BCD), focusing solely on the change and no-change regions. Due to the dynamic progress of earth observation satellite techniques, the spatial resolution of remote sensing images continues to increase, multi-class change detection (MCD) which can reflect more detailed land change has become a hot research direction in the field of CD.<strong> </strong></p> <p>We have collected the current open source benchmark datasets in the MCD of remote sensing imagery , in order to facilitate the sharing of the latest research datasets in the MCD field. Users can access the relevant MCD datasets through the links in the files.</p> <p>Source:</p> <p>Q. Zhu, X. Guo, Ziqi Li, D. Li*, “A review of Multi-class Change Detection for Remote Sensing Imagery” Geo-spatial information science, 2022</p>
Maps related to the detection of abrupt changes in NDVI approximated phenological cycles of Donana marshes for 2007-2016
<p>Monitoring of abrupt changes among annual vegetation cycles of consequent years in Protected Areas is valuable for the recognition of patterns, which represent the reaction of the biomes to external factors, such as changes in the meteorological conditions (e.g. the precipitation regime), human intervention or extreme events (e.g. fire). It is an indicator of the primary production of the area and other relevant functions of the ecosystem. The BFAST, Breaks For Additive Seasonal and Trend, approach can be used for monitoring changes, since it is globally applicable and able to analyze each pixel individually without the need to set thresholds for detecting changes within time series. Thus, BFAST is applied for the detection of abrupt trend changes in NDVI time series in the case of Doñana marshes, as a proxy to phenological metrics per pixel.</p> <p>BFAST outputs are used to generate: (i) a raster with the time of all detected abrupt changes per pixel (filename: “All_break_times_2007_to_2016.tif”), (ii) a raster with the total number of detected abrupt changes per pixel (filename: “Marshes_maximum_number_of_breaks_2007_to_2016.tif”), (iv) a raster with the time for which the biggest change is detected per pixel has the (filename: “Marshes_maximum_break_time_2007_to_2016.tif”).</p> <p>The above files are accompanied by INSPIRE metadata XML files. Detailed information can be found in the “Readme.docx” included in the zip containing the dataset.</p>
OpenMapCD: A Multimodal Benchmark Dataset for Change Detection Between Optical Remote Sensing and Map Data
<p><strong>Overview: </strong></p> <ol> <li>OpenMapCD, the <strong>first large-scale multimodal dataset</strong> for change detection on optical remote sensing imagery and map (OpenStreetMap) data, <strong>supporing basic binary change detection and further semantic change detection</strong></li> <li>OpenMapCD is highly geographically diverse, with <strong>1288</strong> benchmark samples with 1024x1024 pixels from <strong>40 </strong>regions across six continents and out-of-distribution data in two areas in Japan</li> <li>Advancing land-cover mapping, binary change detection and semantic change detection tasks, and GIS system updating<br><br></li> </ol> <p><strong>Research Paper: <br></strong></p> <ul> <li>Arxiv paper: <a href="https://arxiv.org/abs/2310.02674v3">https://arxiv.org/html/2310.02674v3</a></li> <li>TGRS paper: <a href="https://ieeexplore.ieee.org/document/10551264">https://ieeexplore.ieee.org/document/10551264</a></li> </ul> <p><strong><br>Project Page:</strong><br>The benchmark code is available at: <a href="https://github.com/ChenHongruixuan/ObjFormer">https://github.com/ChenHongruixuan/ObjFormer</a><br><br><strong>Reference:</strong></p> <pre><code>@ARTICLE{Chen2024ObjFormer, author={Chen, Hongruixuan and Lan, Cuiling and Song, Jian and Broni-Bediako, Clifford and Xia, Junshi and Yokoya, Naoto}, journal={IEEE Transactions on Geoscience and Remote Sensing}, title={ObjFormer: Learning Land-Cover Changes From Paired OSM Data and Optical High-Resolution Imagery via Object-Guided Transformer}, year={2024}, volume={62}, number={}, pages={1-22}, doi={10.1109/TGRS.2024.3410389} }</code></pre>
Rees 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 DEMs produced from a hybrid of survey types. Used in <a href="https://gcd.riverscapes.net/Tutorials/ErrorModelling/multimethoderror.html">Multi-Method Error Estimation Tutorial</a> and <a href="https://gcd.riverscapes.net/Tutorials/GeomorphicInterpretation/morphological-approach.html">Morphological Approach Tutorial</a>. This is part of the dataset from:</p> <ul> <li>2011. Richard Williams, James Brasington, Damia Vericat, Murray Hicks, Fred Labrosse, Mark Neal. Chapter Twenty - Monitoring Braided River Change Using Terrestrial Laser Scanning and Optical Bathymetric Mapping. Editor(s): Mike J. Smith, Paolo Paron, James S. Griffiths. Developments in Earth Surface Processes. Elsevier, Volume 15, Pages 507-532, ISSN 0928-2025. DOI: <a href="https://doi.org/10.1016/B978-0-444-53446-0.00020-3">10.1016/B978-0-444-53446-0.00020-3</a>.</li> </ul> <p>Dataset is from:</p> <ul> <li>2km of braided river near <a href="https://www.google.com/maps/place/44%C2%B046'38.6%22S+168%C2%B024'17.9%22E/@-44.7767196,168.3891697,7451m/data=!3m1!1e3!4m5!3m4!1s0x0:0x0!8m2!3d-44.777379!4d168.404972">Queenstown, New Zealand</a></li> <li>Two LiDAR surveys</li> <li>0.5m cell resolution</li> <li><a href="https://s3-us-west-2.amazonaws.com/etalweb.joewheaton.org/GCD/GCD7/Tutorials/GeoTERM_Rees.zip">Download Full</a></li> <li><a href="https://s3-us-west-2.amazonaws.com/etalweb.joewheaton.org/GCD/GCD7/Tutorials/MaskOnly_MorphologicalApproach.zip">Download Morphological Only</a></li> </ul> <p>Dataset includes raw data to run exercises, as well as full *.gcd projects that can be opened. </p>
Data for "Detection of large-scale cloud microphysical changes within a major shipping corridor after implementation of the IMO 2020 fuel sulfur regulations"
<p>Processed data used for the manuscript "Detection of large-scale cloud microphysical changes within a major shipping corridor after implementation of the IMO 2020 fuel sulfur regulations".</p> <p>Includes input data for kriging algorithm as "SSF1deg_shipkrige_Terra.nc" and output data files as "Data_Terra_[VAR]_[YEAR]_C_M[MONTH].nc" for [VAR] Acld (overcast albedo) or cer (cloud droplet effective radius), [YEAR] the starting year of a three-year period starting with 2002 and ending at 2020 or "clim" for the 2002-2019 climatology, and [MONTH] 1to12 (annual mean) or 9to11 (austral spring).</p> <p>For the output data, "Obs" is the original data, "Est" is the mean counterfactual field obtained via kriging, "lowEst" and "highEst" are the 2.5th and 97.5th percentiles of the kriged fields for each grid box, "krSims" stores the results of the 5,000 simulated kriged fields, "Semivariance" is the binned empirical variogram values, "pVal" is the raw field significance (not adjusted for multiple testing), "nOut" is the number of individually significant grid boxes, "tran" is the transform applied (none for cer, logit for Acld), "iniPhi" and "iniSigma2" are the initial values for the fitted variogram, "Phi" and "Sigma2" are the fitted values using weighted least squares, and "parSel" is the list of selected regressors for the mean function that minimize the Bayesian information criterion.</p>
Bastrop (TX, USA) forest fire cross-sensor change detection images
<p><strong>Abstract. </strong></p> <p>This dataset is composed of a set of four images acquired by different sensors over the Bastrop County, Texas (USA). On September 4, 2011, the region has been struck by ‘‘the most destructive wildland-urban interface wildfire in Texas history’’ which caused 2 casualties, more than 1300 destroyed buildings and almost burned entirely the Bastrop county state park.</p> <p>This dataset is composed of a pair of pre- and post-event images from the same sensor, the Landsat 5 TM (L5T1 and L5T2), which are completed by a post-event of another sensor, the Advanced Land Imager (ALI) from the Earth Observing (EO-1) mission, acquired very shortly after the L5T2 (denoted ALIT2). These three scenes are very similar to each other and no apparent changes between L5T2 and ALIT2 are visible, since images were acquired within 1 day. Differences between these pre- and post-event pairs are only due to burned forest since they were acquired at a 16 days interval during summer. We also dispose of a fourth image of the same area acquired one year and 9 months after the forest fire by the Landsat 8 Operational Land Imager (OLI, L8T2 hereafter). The differences between L5T1 and L8T2 are significant, due both to sun/sensor angles and the long temporal interval between acquisitions. A whole new series of building has been constructed in the burn scar and many cultivated crops are at a different stage of growth.</p> <p>Table : Dataset description</p> <pre><code class="language-markdown">| | Pre-event | Post-event 1 | Post-event 2 | Post-event 3 | |--------- |--------------|--------------|--------------|---------------| | Filename | t1_L5 | t2_L5 | t2_ALI | t2_L8 | | Sensor | Landast 5 TM | Landsat 5TM | EO-1 ALI | Landsat 8 OLI | | Channels | 7 | 7 | 9a | 7b | | GSD | 30, 120 | 30, 120 | 30 | 30 | | Sp. Range | [0.45–2.35, | [0.45–2.35, | [0.4–2.4] | [0.43–2.25] | | [\mu m] | 10.40–12.50] | 10.40–12.50] | | |</code></pre> <p>We prepared the ground truth for pairs of change detection problems by photo-interpretation and relying on the maps provided on the emergency response website [1]. In the ground truth involving the L5T1-L8T2 problem we also included changes related to vegetation density and vegetation/bare soil transitions, since these changes are of the same spectral class as those related to the burned scar.</p> <p>This data has been collected from the NASA LP DAAC Program [2], we are free to redistribute the data. For this reason we provide the Bastrop data and the ground truth we defined and used in these experiments (subset of original crops and ground truth).</p> <p><strong>Data Files</strong></p> <p>The archive contains the following files:</p> <pre><code>data ├── t1_L5.tif ├── t2_L5.tif ├── t2_ALI.tif ├── t2_L8.tif ├── ROI_1.tif ├── ROI_2.tif ├── Cross-sensor-Bastrop-data.mat </code></pre> <p>Where:</p> <ul> <li>`t1_L5.tif` is the pre-event image acquired by Landsat 5 TM</li> <li>`t2_L5.tif` is the post-event image acquired by Landsat 5 TM - `t2_ALI.tif` is the post-event image acquired by EO-1 ALI</li> <li>`t2_L8.tif` is the post-event image acquired by Landsat 8 OLI</li> <li>`ROI_1.tif` is the ground truth for the change detection problem between `t1_L5.tif` and `t2_L5.tif`</li> <li>`ROI_2.tif` is the ground truth for the change detection problem between `t1_L5.tif` and `t2_L8.tif`</li> <li>`Cross-sensor-Bastrop-data.mat` is a Matlab file containing the data in a more convenient format for Matlab users</li> </ul> <p><strong>Citation</strong></p> <p>If you are using this dataset, please cite the following paper:</p> <pre><code>@article{volpi2015jisprs, author = {Michele Volpi and Gustau Camps-Valls and Devis Tuia}, title = {Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, volume = {107}, pages = {50-63}, year = {2015}, doi = {https://doi.org/10.1016/j.isprsjprs.2015.02.005}, url = {https://www.sciencedirect.com/science/article/pii/S0924271615000404}, issn = {0924-2716}, }</code></pre> <p>Volpi, M., Camps-Valls, G., Tuia, D. (2015). Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis, ISPRS Journal of Photogrammetry and Remote Sensing, Volume 107, 2015, Pages 50-63. https://doi.org/10.1016/j.isprsjprs.2015.02.005</p>
Site environmental, climate, water levels and temperatures, vegetation cover, and GIS change detection for assessing permafrost change in fens on the Tanana Flats, central Alaska
This data package provides data used to assess the roles of climate extremes, ecological succession, and hydrology in repeated permafrost aggradation and degradation in fens on the Tanana Flats, central Alaska. The package provides data on site environmental information, Fairbanks climate, vegetation cover, water levels and temperatures, as well as GIS files for fen change detection. The Site data include information on observers, locations, geomorphology, hydrology, soils, vegetation, and disturbance. The table has numerous fields that uses coding for class characteristics and these codes are described in the metadata as well as compiled in the ELS_Arctic_Boreal_Site_Soil_Veg_Code_Sheet_2020.docx. Alaska Climate records for Fairbanks (UAF Experiment Station) from 1904 to 2019 were acquired from the National Oceanic and Atmospheric Administration (https://www.ncdc.noaa.gov/cdo-web/). Additional data were obtained for the Nenana station (about 70 km southwest of Fairbanks), to fill in small data gaps (particularly precipitation/snow depth ruler measurements) in the Fairbanks record. We attributed the data with fields for summer (May-September) and winter periods (November-March) and hydrologic year (October-September) and calculated mean air temperature, precipitation, and snow depth by seasonal period (average of daily values) and year. The broad summer and winter periods were of interest because warmer and wetter summers increase soil heat input and warmer and snowier winters reduce soil heat loss. Fen hydrology data include information on fen water level/pressure and temperatures collected every two hours at seven sites within fens from 2011 to 2014. Vegetation composition and cover of fens, scrub, and forests was sampled to assess effects of thermokarst on vegetation change. Plant cover was determined by point-sampling at 100 points (including repetitive “hits” for all layers) distributed along 5 equally spaced rows (4-m long, 20 points per row) across the 10-m l
Lidar digital elevation models and topographic change detection results from burned and unburned plots in the Sevilleta LTER.
These data were generated as part of a research project focused on montiroing sediment flux in dryland ecosystems following wildfire. In six separate small plots, three burned and three unburned, we conducted light detection and ranging (lidar) topographic surveys in 2016, 2017, and 2018 to document elevation changes and the volume of sediment deposition and erosion. At the down-wind edge of each plot, we used sediment catchers to trap sediment exiting the plots and thus estimate erosion volumes using in-situ equipment, which provided a secondary measurement of sediment efflux from all sites in addition to the lidar data. We used the geomorphic change detection software (https://gcd.riverscapes.xyz/) to produce maps of topographic change from the lidar digital elevation models for the 2016-2017 and 2017-2018 periods at all plots, burned and unburned. Results from this project may aid in understanding post-fire transport of sediment and nutrients from drylands following wildfire.
Heterogeneous/Homogeneous Change Detection dataset
<p><i><strong>"Please if you use this datasets we appreciated that you reference this repository and cite the works related that made possible the generation of this dataset."</strong></i></p><p>This change detection datastet has different events, satellites, resolutions and includes both homogeneous/heterogeneous cases. The main idea of the dataset is to bring a benchmark on semantic change detection in remote sensing field.<br><br>This dataset is the outcome of the following publications:</p><ol><li>@article{ JimenezSierra2022graph,<br>author={Jimenez-Sierra, David Alejandro and Quintero-Olaya, David Alfredo and Alvear-Mu{\~n}oz, Juan Carlos and Ben{\'i}tez-Restrepo, Hern{\'a}n Dar{\'i}o and Florez-Ospina, Juan Felipe and Chanussot, Jocelyn},<br>journal={IEEE Transactions on Geoscience and Remote Sensing},<br>title={Graph Learning Based on Signal Smoothness Representation for Homogeneous and Heterogeneous Change Detection},<br>year={2022},<br>volume={60},<br>number={},<br>pages={1-16},<br>doi={10.1109/TGRS.2022.3168126}<br>}<br> </li><li>@article{ JimenezSierra2020graph,<br>title={Graph-Based Data Fusion Applied to: Change Detection and Biomass Estimation in Rice Crops},<br>author={Jimenez-Sierra, David Alejandro and Ben{\'i}tez-Restrepo, Hern{\'a}n Dar{\'i}o and Vargas-Cardona, Hern{\'a}n Dar{\'i}o and Chanussot, Jocelyn},<br>journal={Remote Sensing},<br>volume={12},<br>number={17},<br>pages={2683},<br>year={2020},<br>publisher={Multidisciplinary Digital Publishing Institute},<br>doi={10.3390/rs12172683}<br>}<br> </li><li>@inproceedings{jimenez2021blue,<br>title={Blue noise sampling and Nystrom extension for graph based change detection},<br>author={Jimenez-Sierra, David Alejandro and Ben{\'\i}tez-Restrepo, Hern{\'a}n Dar{\'\i}o and Arce, Gonzalo R and Florez-Ospina, Juan F},<br>booktitle={2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS},<br>ages={2895--2898},<br>year={2021},<br>organization={IEEE},<br>doi={10.1109/IGARSS47720.2021.9555107}<br>}<br> </li><li>@article{florez2023exploiting,<br>title={Exploiting variational inequalities for generalized change detection on graphs},<br>author={Florez-Ospina, Juan F and Jimenez Sierra, David A and Benitez-Restrepo, Hernan D and Arce, Gonzalo},<br>journal={IEEE Transactions on Geoscience and Remote Sensing}, <br>year={2023},<br>volume={61},<br>number={},<br>pages={1-16},<br>doi={10.1109/TGRS.2023.3322377}<br>}<br> </li><li>@article{florez2023exploitingxiv,<br>title={Exploiting variational inequalities for generalized change detection on graphs},<br>author={Florez-Ospina, Juan F. and Jimenez-Sierra, David A. and Benitez-Restrepo, Hernan D. and Arce, Gonzalo R},<br>year={2023},<br>publisher={TechRxiv},<br>doi={10.36227/techrxiv.23295866.v1}<br>}</li></ol><p>In the table on the html file (<a href="https://zenodo.org/records/8269855/files/dataset_table.html?download=1">dataset_table.html</a>) are tabulated all the metadata and details related to each case within the dasetet. The cases with a link, were gathered from those sources and authors, therefore you should refer to their work as well.</p><p>The rest of the cases or events (without a link), were obtained through the use of open sources such as:</p><ul><li><a href="https://scihub.copernicus.eu/dhus/#/home">Copernicus</a></li><li><a href="https://esar-ds.eo.esa.int/oads/access/collection">European Space Agency</a></li><li><a href="https://search.asf.alaska.edu/#/">Alaska Satellite Facility (Vertex)</a></li><li><a href="https://search.earthdata.nasa.gov/search">Earth Data</a></li></ul><p>In addition, we carried out all the processing of the images by using the <a href="https://github.com/senbox-org/snap-desktop/tree/master">SNAP toolbox</a> from the European Space Agency. This proccessing involves the following:</p><ul><li>Data co-registration</li><li>Cropping</li><li>Apply Orbit (for SAR data)</li><li>Calibration (for SAR data)</li><li>Speckle Filter (for SAR data)</li><li>Terrain Correction (for SAR data)</li></ul><p>Lastly, the ground truth was obtained from homogeneous images for pre/post events by drawing polygons to highlight the areas where a visible change was present. The images where layout and synchorized to be zoomed over the same are to have a better view of changes. This was an exhaustive work in order to be precise as possible.<br><br>Feel free to improve and contribute to this dataset.</p>
Detection of abrupt changes in East Asian monsoon from Chinese loess and speleothem records
<p>There is a great interest concerning recent occurrences of tipping points in the climate system and great concern about those that could occur in the near future as a result of anthropogenic forcing. A lot of attention has been devoted to the study of past Dansgaard-Oeschger events, abrupt warmings of about 12°C on a time-scale of about 50 yrs that occurred during the last glacial period. Great effort is also dedicated to understanding the Atlantic Meridionnal overturning circulation and the Amazon forest dieback, which are already entering an unstable regime leading to tipping behavior.</p> <p>Instead, here we focus on the study of critical transitions in the SE Asian Monsoon that have occurred in the past 3.6 Myrs by a novel combination of advanced statistical tools (KS-test, recurrence quantification analysis). The SE Asian Monsoon is characterized by variations in the grain size with the occurrence of coarse material characterizing a strong winter monsoon mechanism with grains transported from the Chinese northern deserts by strong winds generated by the Siberian High located northward. By contrast intervals of fine grain size characterized periods during which the summer monsoon was rather reinforced. We have analyzed high-resolution grain-size datasets derived from Chinese loess sequences, i.e. the CHILOMOS and the LGS640 datasets, that we compare with the Chinese composite speleothem d<sup>18</sup>O records that arguably provide one the best representation of the Earth’s climate in the last 650 kyrs. Although visually observed rapid grain-size variations were previously interpreted as representing millennial-scale variations, our statistical analysis shows that both winter and summer monsoons co-varied at glacial-interglacial to millennial timescales. Analyzing a third dataset, i.e., MQSG, our statistical analysis shows that both winter and summer monsoon variations reflect a three-stage evolution of increasing intensity: (1) from 3.6 Ma to 2.6 Ma, (2) from 2.6 Ma to 1.2 Ma, and (3) from 1.2 Ma to present, with the winter monsoon strength increasing over these3 main steps.</p> <p>List of tables</p> <p><strong><span>Table 1</span></strong><span> KS test of the NGRIP and Hulu cave </span><span>d</span><sup><span>18</span></sup><span>O for the last climate cycle. Comparison of the dates of abrupt warmings/moistening (left) and cooling/drying transitions (right). Labels for NGRIP according to Rasmussen et al. </span><span><span>(2014)</span></span><span> and for Hulu cave according to Wang et al. </span><span><span>(2008)</span></span><span>. </span></p> <p><strong><span>Table 2</span></strong><span> KS test of the Chinese speleothem </span><span>d</span><sup><span>18</span></sup><span>O and of the CHILOMOS grain-size composite for the last two climate cycles. Comparison of the dates of abrupt moistening (left) and drying transitions (right). Labels for the moistening transitions in the Chinese speleothem are from Wang et al. </span><span><span>(2008)</span></span><span> and in CHILOMOS from Yang and Ding </span><span><span>(2014)</span></span><span>. The labels of the drying in CHILOMOS are from the present study</span></p> <p><strong><span>Table 3</span></strong><span> RQA of the Chinese speleothem </span><span>d</span><sup><span>18</span></sup><span>O and of the CHILOMOS grain-size composite for the last two climate cycles. Dates of the minima are identified by the RR prominence, shown together with the equivalent transitions detected by the KS method. For easier reading, the dates have been re-ordered from younger to older. The most significant minima are highlighted in yellow. The original ranking is given in Suppl. Tab 1.</span></p> <p><strong><span>Table 4 </span></strong><span>KS test of the Chinese speleothem<span> </span></span><span>d</span><sup><span>18</span></sup><span>O and of the LGS640 dataset for the last 640 Myrs. Comparison of the dates of abrupt moistening and drying transitions in both records. The dates found in both records are highlighted in red and in blue for drying or moistening events respectively.</span></p> <p><strong><span>Table 5 </span></strong><span>RQA of the LGS640 grain-size composite for the last seven climate cycles. Dates of the minima are identified by the RR prominence. The most significant minima (RR prominence >0.5) are highlighted in yellow. </span></p> <p><span><span> </span><strong>Table 6 </strong>KS test and<strong> </strong>RQA of MGSQ grain dataset for the last 3.6 Myrs. In this analysis the moistening and drying transitions is labeled as warming and cooling. On the left, KS results with the corresponding marine isotope stage (MIS) boundaries. On the right, RQA results with minima ordered according their prominence value. Dates with RR>0.6 are highlighted in yellow. </span></p> <p><strong><span>Table Supp.1.</span></strong><span> Abrupt transitions over the past 130 kyrs BP from the NGRIP, Chinese Speleothem and CHILOMOS records. Identification of the common abrupt warmings or moistenings on the left, and abrupt coolings or dryings on the right.<span> </span>Differences between the highest and lowest transition dates. Indication of the NGRIP and Chinese interstadials and stadials (GI-GS and A-SA respectively).</span></p> <p><strong><span>Table Supp.2.</span></strong><span> RQA of the 250 kyrs Chinese speleothem and CHILOMOS records ranked according the RR prominence, the chronology. Indication of the time difference between the identified transitions.</span></p> <p><strong><span>Table Supp.3.</span></strong><span> Comparison of the KS-test results from the LGS 640 and the Chinese speleothem over the past 650 kyrs. Indication of the Marine isotope stratigraphy and the number of cool and warm transitions and the percentage of drying events per climate cycle</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.