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1,961 results for “Sensing”
Pyrene-Based Macrocrosslinkers with Supramolecular Mechanochromism for Elastic Deformation Sensing in Hydrogel Networks
<p>Primary data used in the manuscript (NMR, MS, fluorescence, GPC) sorted after Figure and associated panel in manuscipt and supporting information.</p>
Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data
<p><strong>Overview:</strong> The Global Inundation Extent from Multi-Satellites (GIEMS; Prigent et al. 2007, Papa et al. 2010) downscaled at 15 arc-second (GIEMS-D15; Fluet-Chouinard et al. 2015) was produced through the downscaling of the GIEMS database (natively at 0.25°). The downscaling procedure predicts the location of surface water cover with an inundation ranking surface generated by bagged decision trees. The decision trees were trained on binary presence/absence of wetland in the GLC2000 global land cover map (Bartholomé & Belward 2005) and used 13 topographic and hydrographic predictors derived from the SRTM-derived HydroSHEDS database (Lehner, Verdin & Jarvis 2008). The downscaling technique to three temporal aggregation of the GIEMS dataset representing three states of land surface inundation extents: mean annual minimum (MA<sub>Min</sub>; total area, 6.5 × 106 km<sup>2</sup>), mean annual maximum (MA<sub>Max</sub>; 12.1 × 106 km<sup>2</sup>), and long-term maximum (LT<sub>Max</sub>; 17.3 × 106 km<sup>2</sup>). The area of MAMin and MAMax from GIEMS were supplemented with the minimum area value from lakes, river and reservoirs from GLWD (Lehner & Döll 2004; classes 1,2,3). LTMax was corrected as the mean area from 3-year rolling maximum from GIEMS and the total wetland area from GLWD (classes 1-12). The accuracy of GIEMS-D15 reflects distribution errors introduced by the downscaling process as well as errors from the original satellite estimates. Yet, a comparison against independent regional wetland maps showed adequate agreement over large floodplains and wetlands. GIEMS-D15 offers a higher resolution delineation of inundated areas than originally offered by GIEMS, allowing for the assessment of global freshwater resources and the study of large floodplain and wetland ecosystems.</p> <p><strong>Projection:</strong> WGS84 (EPSG:4326)</p> <p><strong>Geographic extent:</strong></p> <ul> <li>Longitude: -180° to 180°</li> <li>Latitude: -56° to 84°</li> </ul> <p><strong>Spatial resolution: </strong>15 arc-second (500m at equator)</p> <p><strong>Legend</strong> (for discrete pixel values):</p> <ul> <li>0 = Upland</li> <li>1 = Mean Annual Minimum (MA<sub>Min</sub>)</li> <li>2 = Mean Annual Maximum (MA<sub>Max</sub>)</li> <li>3 = Long Term Maximum (LT<sub>Max</sub>)</li> </ul>
Raw data (RF) provided for : Sensing Ultrasound Localization Microscopy reveals glomeruli in rats and humans
<p><strong>Abstract :</strong> Estimation of glomerular function is a key element in the diagnosis of kidney disease. However, the study of glomeruli in the clinic remains indirect through urine and blood tests. Recent imaging technique called Ultrasound Localization Microscopy (ULM) originated from the ability to record continuous movements of individual microbubbles in the bloodstream. Although it improved the resolution of vascular imaging up to tenfold, the imaging of the smallest vessels had yet to be reported.</p> <p>We acquired ultrasound sequences from living humans and rats and then applied filtering dividing the data set into slow-moving and fast-moving microbubbles. We performed a double tracking to highlight and characterize this new population of microbubbles with singular behaviors: we called this technique “sensing ULM” (sULM). We used post-mortem micro-CT for side-by-side confirmation in rats.</p> <p>In this study, we report the observation of microbubbles flowing in capillaries bundles, i.e. the glomeruli, in the kidney in living humans and rats. We introduce a set of analysis tools dedicated to extracting quantitative information from individual microbubbles, like the remanence time or the normalized distance.</p> <p>As glomeruli play a key role in kidney function, their observation could yield a deeper understanding of kidney diseases and provide a diagnostic tool for patients. More generally, it will bring imaging capabilities closer to the functional units of organs, which is one of the keys to understanding most diseases, like cancers, diabetes, or kidney failures. </p> <p><strong>Academic reference to be cited : </strong>Denis, Bodard, Hingot, Chavignon, Battaglia, Renault, Lager, Aissani, Hélénon, Correas, and Couture. <em>Sensing Ultrasound Localization Microscopy reveals glomeruli in rats and humans,</em> eBioMedicine, 2023.</p> <p><strong>Article</strong> : <a href="https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(23)00143-3/fulltext">https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(23)00143-3/fulltext</a></p> <p><strong>Related scripts and software application</strong> : <a href="https://github.com/EngineerJB/akebia">https://github.com/EngineerJB/akebia</a></p> <p><strong>Beamformed dataset</strong> : <a href="../record/6811910#.ZA9dV3bMLid">https://zenodo.org/record/6811910#.ZA9dV3bMLid</a></p> <p><strong>Corresponding authors : </strong></p> <ul> <li>Article : Louise Denis, <a href="mailto:louise.denis@sorbonne-universite.fr">louise.denis@sorbonne-universite.fr</a>, Sylvain Bodard, <a href="mailto:sylvain.bodard@aphp.fr">sylvain.bodard@aphp.fr</a></li> <li>Scripts, and codes : Louise Denis, <a href="mailto:louise.denis@sorbonne-universite.fr">louise.denis@sorbonne-universite.fr</a>, Jacques Battaglia, <a href="mailto:jacques.battaglia@sorbonne-universite.fr">jacques.battaglia@sorbonne-universite.fr</a></li> <li>Materials, collaborations, rights and others: Olivier Couture, <a href="mailto:olivier.couture@sorbonne-universite.fr">olivier.couture@sorbonne-universite.fr</a></li> </ul>
Data set: UAS-based optical- and thermal infrared remote sensing of the fumarole field of La Fossa cone, Vulcano Island (Italy), reveals the degassing and hydrothermal alteration structure
<p>This is the data set supporting the paper "Anatomy of a fumarole field; drone remote sensing and petrological approaches reveal the degassing and alteration structure at La Fossa cone, Vulcano Island, Italy" (DOI: <a href="https://doi.org/10.5194/egusphere-2023-1692" target="_blank" rel="noopener noreferrer">10.5194/egusphere-2023-1692</a>).</p> <p> </p> <p><strong>Short description of the study:</strong> Hydrothermal alteration is common on actively degassing volcanoes and can lead to significant changes in the physical and chemical properties of the volcanic rocks, such as changes in permeability or rock strength. Despite the potentially far-reaching consequences of hydrothermal alteration for volcano stability, less is known about the detailed structures and dynamics of degassing and alteration systems. In this study, we use UAS-derived high-resolution data to analyze the fumarole field at La Fossa cone, Vulcano Island (Italy), aiming to better understand the structures and dynamics of volcanic degassing and alteration systems. By combining Principal Component Analysis, image analysis, and classification applied to high-resolution optical data and analysis of thermal infrared data, we resolve the detailed structure of the surficial degassing and alteration system based on optical and thermal anomalies. We identified characteristic anomaly patterns that indicate local degassing and alteration variability, and larger units of diffuse activity that, next to high-temperature fumaroles, contribute significantly to the total activity. We compared the observed anomaly patterns with the mineralogical and geochemical composition of representative rock samples, and with the surface degassing activity, and are able to provide the anatomy of the La Fossa fumarole field at great resolution. We show local alteration gradients, the presence of larger diffuse active complexes, and evidence for dynamic processes associated with the hydrothermal alteration. For more details, please read on: "<em>Müller, D., Walter, T. R., Troll, V. R., Stammeier, J., Karlsson, A., De Paolo, E., ... & De Jarnatt, B. (2023). Anatomy of a fumarole field; drone remote sensing and petrological approaches reveal the degassing and alteration structure at La Fossa cone, Vulcano Island, Italy. EGUsphere, 2023, 1-45. </em> https://doi.org/10.5194/egusphere-2023-1692".</p> <p> </p> <p> </p> <p><strong>Data set:</strong> We provide a UAS-based high-resolution dataset covering the whole La Fossa cone, including aerial Orthomosaic, Digital Elevation Model, and a Temperature Map derived from an airborne optical- and thermal infrared sensor (acquired in 2018 and 2019). </p> <p>The dataset is organized in 1) photogrammetric data, and 2) relevant processing results and related data. <strong>Filenames</strong> are written in bold letters and are a composite of the file type and the date (YYYYMMDD). </p> <p> </p> <p> </p> <p><strong>1) Photogrammetric data: </strong></p> <ul> <li><strong>Orthomosaic_20191114.tif</strong> is the in Agisoft Metashape processed orthomosaic of a 150 m (above fumarole field) optical overflight (DJI Phantom 4 Pro camera). </li> <li><strong>DigitalElevationModel_20191114.tif</strong> is the in Agisoft Metashape processed Digital Elevation Model (DEM) from the above-mentioned 150 m overflight. </li> <li><strong>Hillshade_20191114.tif</strong> is the 2.5-D representation of the DigitalElevationModel_20191114. Note, for viewing use a stretched (black to white) color scale.</li> <li><strong>TemperatureMap_20181115.tif</strong> is showing the apparent surface temperature for the La Fossa cone, acquired by a Flir Tau 2 thermal infrared camera at ~150 m (above fumarole field) flight altitude in the early morning hours (before sunrise) of 15 November 2018. Note that apparent temperatures shown may underestimate real in situ fumarole temperatures due to pixel-to-vent size ratios and atmospheric- or gas-plume distortion effects. Note further that the data has some processing artifacts, due to blind pixels of our IR camera system. For more detailed information or an updated data set please contact dmueller@gfz-potsdam.de.</li> <li><strong>T_20to40C.tif</strong> shows the diffuse thermally active surface at the fumarole field of the La Fossa cone (units a-g, see Fig. 4 in "Anatomy of a fumarole field...", https://doi.org/10.5194/egusphere-2023-1692). This raster shows the extracted pixels from TemperatureMap_20181115 in the range of 22 - 40 °C.</li> <li><strong>T_higher40C.tif</strong> outlines the high-temperature fumarole locations of the La Fossa fumarole field (HTF, see Fig. 4 in "Anatomy of a fumarole field...", https://doi.org/10.5194/egusphere-2023-1692), based on the extracted pixels with temperatures > 40 °C from TemperatureMap_20181115.</li> </ul> <p>Shapefiles for temperatures > 40 °C representing the high-temperature fumarole locations (HTF) and for temperatures of 20 - 40 °C representing diffuse active units, are attached at the end of the upload list and named <strong>T_higher40C_polygon</strong> and <strong>T_20_40C_polygon</strong> and consist of multiple files per shapefile with the file extensions .CPG, .dbf, .prj, .sbn, .sbx, .shp, .shp.xml, .shx. </p> <p>The coordinate system of the data sets is WGS84 EPSG:4326. For nadir projection use WGS 84 / UTM zone 33N - EPSG:32633. Note that the data might have horizontal and vertical offsets in the typical range of SfM-derived products with single-band GPS accuracy.</p> <p> </p> <p> </p> <p><strong>2) Relevant processing steps and related data:</strong></p> <ul> <li>Step 1) Principal Component Analysis applied to Orthomosaic_20191114 results in the following 3 Principal Components (decorrelated variance representations of the initial RGB bands): <ul> <li><strong>1_PCA_PC1.tif </strong>1st principal component </li> <li><strong>1_PCA_PC2.tif</strong> 2nd principal component</li> <li><strong>1_PCA_PC3.tif</strong> 3rd principal component - highlights well the effects of concentrated and diffuse degassing, resulting in different alteration effects from a simple shift from reddish oxidized surface to gray, up to strong silicic alteration effects. This can be used to extract the data of interest, the hydrothermally altered surface, and to create a new alteration sub-dataset. </li> </ul> </li> <li>Step 2) Extraction of hydrothermally altered surface / alteration sub-dataset <ul> <li><strong>2_alteration_subdata_RGB.tif</strong> The alteration sub-data set was extracted from the original Orthomosaic_20191114 based on a mask obtained from Principal Component 3 (1_PCA_PC3) for values > 85. The resulting raster data set is an extract of the original RGB data.</li> </ul> </li> <li>Step 3) PCA applied to 2_alteration_subdata_RGB will adjust to the reduced spectral range of the alteration sub-data set, provide a more sensitive variance representation, and highlight variability within the hydrothermally altered surface. <ul> <li><strong>3_PCA_PC1.tif</strong> 1st principal component of 2_alteration_subdata_RGB</li> <li><strong>3_PCA_PC2.tif</strong> 2nd principal component of 2_alteration_subdata_RGB</li> <li><strong>3_PCA_PC3.tif</strong> 3rd principal component of 2_alteration_subdata_RGB</li> </ul> </li> <li>Step 4) Unsupervised classification <ul> <li><strong>4_classification.tif</strong> is the unsupervised classification result of 3_PCA (all Principal Components), classified into 32 classes to achieve a high class resolution. When combining different classes, they form larger spatial units / surface types with similar spectral characteristics. This way, we divide the alteration surface into 3 surface types (see Fig. 4B in "Anatomy of a fumarole field..." DOI: 10.5194/egusphere-2023-1692) representing different alteration gradients and important structural units. To achieve the same results, combine classes 1 -19 (surface type 3), 20 - 25 (surface type 2), 26 - 30 (surface type 1), and 31 - 32 for sulfur/fumarole plume. See Image <strong>optical_structure.jpg</strong> for comparison. </li> </ul> </li> </ul> <p>Note that Principal Components and Classification of Principal Components highlight data variability along the axes of highest data variance. Results have to be evaluated carefully and may be valid only locally. They are efficient for identifying variability in degassing and alteration areas, but at the same time may also highlight certain fractions of vegetation or settlements for instance. We evaluated the structure defined by our classification results by analyzing the thermal structure (<strong>thermal_structure.jpg</strong>) of the fumarole field and additional geochemical- and mineralogical investigations (XRD and XRF) of rock samples and by measuring the diffuse degassing from surface (see "Anatomy of a fumarole field..." DOI: 10.5194/egusphere-2023-1692) to prove that the observed degassing/alteration units are true.</p> <p>To highlight alteration effects throughout the entire La Fossa cone, including the southern inner and outer crater rim, the alteration zones of La Forgia, or alteration on the outer flanks of La Fossa e.g. the 1988 Landslide, we provide the raster <strong>La_Fossa_alteration.tif </strong>and image <strong>La_Fossa_alteration.jpg (</strong>Note that the color scale for strong alteration (classes 31 - 32) was changed from white to purple for highlighting purpose).</p> <p> </p> <p>In case of further questions about the dataset, please contact dmueller@gfz-potsdam.de.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Estimation of the variation in specific discharge over large depth using Distributed Temperature Sensing (DTS) measurements of the heat pulse response
<p>The data contains measurements and derived values that are used for the manuscript "Estimation of the variation in specific discharge over large depth using Distributed Temperature Sensing (DTS) measurements of the heat pulse response, [Paper # 2018WR024171]" Currently under review at the Water Resources Research journal.</p> <p>The data is stored in netCDF files with xarray (Python), and should be readable with any other netCDF reader. </p> <ul> <li>TEMP is the measured temperature in degrees Celsius relative to the background temperature</li> <li>tempinfty is one of the calibration parameters. Represents the steady state temperature increase</li> <li>A is one of the calibration parameters. Represents the timescale in days</li> <li>b is one of the calibration parameters. Represents the scaled distance to the heat source</li> <li>err_alpha is one of the calibration parameters. Represents the autoregressive parameter</li> <li>TEMPmodel is the best fit temperature response in degrees Celsius relative to the background temperature</li> <li>Innovation is termed the noise in the article, in degrees Celsius.</li> <li>q is the estimated specific discharge in meters per day</li> <li>q_MC_XX are the confidence intervals of the estimated specific discharge calculated with Monte Carlo as presented in the article</li> <li>q_lmfit_XX are the confidence intervals of the estimated specific discharge calculated with LMFIT. Is a rough estimate for q_MC_XX calculated by lmfit (Python package).</li> </ul> <p>Time is measured in days with respect to when the heating cable is turned on.</p> <p>Additionally, a Jupyter notebook is supplemented to the article. It demonstrates the calibration routine and the calculation of the confidence interval for the temperature response at a single depth.</p>
RUSSE'2018: Human-Annotated Sense-Disambiguated Word Contexts for Russian
<p>This dataset contains human-annotated sense identifiers for 2562 contexts of 20 words used in the <a href="https://russe.nlpub.org/2018/wsi/">RUSSE'2018</a> shared task on Word Sense Induction and Disambiguation for the Russian language; part of the <em>bts-rnc</em> evaluation dataset. These sense identifiers are disambiguated as according to the sense inventory of the <a href="http://gramota.ru/slovari/info/bts/">Large Explanatory Dictionary of Russian</a>.</p> <p>The annotation is done on December 1, 2017, on the <a href="https://tolokanyandex.com/">Yandex.Toloka</a> crowdsourcing platform. In particular, 80 pre-annotated contexts are used for training the human annotators, 2562 contexts are annotated by humans such that each context was annotated by 9 different annotators. The annotation reliability is indicated by a high value of Krippendorff's α = 0.83. After the annotation, every context was additionally inspected (“curated”) by the organizers of the shared task.</p> <p>The following words are represented: <em>акция</em> (action / stock), <em>байка</em> (yarn / tale), <em>гвоздика</em> (carnation / nail), <em>гипербола</em> (hyperbole), <em>град</em> (avalanche), <em>гусеница</em> (grub), <em>домино</em> (domino), <em>кабачок</em> (marrow / pub), <em>капот</em> (hood), <em>карьер</em> (mine / career), <em>кок</em> (cook), <em>крона</em> (top / crown), <em>круп</em> (croup), <em>мандарин</em> (mandarine), <em>рок</em> (fate / rock), <em>слог</em> (syllable), <em>стопка</em> (glass, stack), <em>таз</em> (bowl), <em>такса</em> (rate / badger-dog), <em>шах</em> (shah / check).</p> <p>The following files are included in this dataset:</p> <ul> <li>Toloka assignments (training: <em>tasks-train.tsv</em>, annotation: <em>tasks-test.tsv</em>)</li> <li>Toloka output (non-aggregated: <em>assignments_01-12-2017.tsv.xz</em>, aggregated: <em>aggregated_results_pool_1036853__2017_12_01.tsv</em>)</li> <li>annotator agreement report (<em>agreement.txt</em>)</li> <li>curated report (<em>report-curated.tsv.xz</em> and a supplementary file <em>tasks-eval.tsv.xz</em>)</li> <li>the final aggregated dataset (<em>bts-rnc-crowd.tsv</em>)</li> </ul> <p>The <em>bts-rnc-crowd.tsv</em> file has the following format: <em>id</em>, <em>lemma</em>, <em>sense_id</em>, <em>left</em> hand side context, <em>word</em> form, <em>right</em> hand side context, list of <em>senses</em>. The encoding is UTF-8 and the line breaks are LF (UNIX).</p>
Dataset of molecular docking data of neuropeptides to acid-sensing ion channels
<p>The *.dock4 files are result files of molecular docking with the software Autodock Vina to the human ASIC1a closed state model, of the peptides FRRFa and KNFLRFa (FRRF.dock4, KNFLRF.dock4) that can be visualized with structure viewing programs such as UCSF Chimera on the closed ASIC1a model file (closed_ASIC_pH7.4.pdb). The file “FRRF_KNFLRF_complexes.pdb” provides the structures of selected poses of FRRFa and KNFLRFa peptides docked to the closed conformation of the human ASIC1a model.</p>
Datasets for Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction
<p>This dataset supplements the article “<a href="https://doi.org/10.1162/COLI_a_00354">Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction</a>” published in the Computational Linguistics journal:</p> <ul> <li> <p><code>watset-coli-lcc-performance.tsv</code>: runtime analysis</p> </li> <li> <p><code>watset-coli-synsets.zip</code>: synset induction experiment (note that <code>pairwise-{en-babelnet,ru-rwn}.pkl</code> files are excluded due to the licensing issues)</p> </li> <li> <p><code>watset-coli-triframes.zip</code>: semantic frame induction experiment</p> </li> <li> <p><code>watset-coli-classes.zip</code>: semantic class induction experiment</p> </li> </ul>
Rethinking the fundamental unit of ecological remote sensing: Estimating individual level plant traits at scale
<p>derived data of leaf and plant structural traits for two National Ecological Observatory Network (NEON) Airborne Observatory Platform (AOP) sites. Dataset contains spatial explicit information for 4.5 million trees, and include: Nitrogen (%mass), Phosphorus (%mass), Leaf mass per area (g m<sup>-2</sup>), diameter at breast height (cm), crown area (m2), tree height (m) and other physical topographic variables (Albedo, Elevation, Slope, Aspect). data are associated to the </p>
Real-time optical and electronic sensing with a β-amino enone linked, triazine-containing 2D covalent organic framework
<p>[This repository contains the source data for the manuscript "<strong>Real-time optical and electronic sensing with a β-amino enone linked, triazine-containing 2D covalent organic framework</strong>" https://nature-research-under-consideration.nature.com/users/37265-nature-communications/posts/47951-a-real-time-optical-and-electronic-chemical-sensor-based-on-a-amino-enone-linked-triazine-containing-2d-covalent-organic-framework]</p> <p>Fully-aromatic, two-dimensional covalent organic frameworks (2D COFs) are hailed as candidates for electronic and optical devices, yet to-date few applications emerged that make genuine use of their rational, predictive design principles and permanent pore structure. Here, we present a 2D COF made up of chemoresistant β-amino enone bridges and Lewis-basic triazine moieties that exhibits a dramatic real-time response in the visible spectrum and an increase in bulk conductivity by two orders of magnitude to a chemical trigger - corrosive HCl vapours. The optical and electronic response is fully reversible using a chemical switch (NH<sub>3</sub> vapours) or physical triggers (temperature or vacuum). These findings demonstrate a useful application of fully-aromatic 2D COFs as real-time responsive chemosensors and switches.</p>
1X4 radio channel data at 3.42 GHz for device-free human sensing
<p>Datasets for <em>CAMPAIGN-I and CAMPAIGN-II </em>from paper 'Beamsteering for Ad-Hoc Recognition of Multi-Human Targets Performing Distinct Activities'. The datasets belong to the <a href="http://ambientintelligence.aalto.fi/radiosense/">Radiosense</a> project.</p> <p>'.</p>
Attribution of intentional agency towards robots reduces one's own sense of agency.
<p>### Attribution of intentional agency towards robots reduces one’s own sense of agency ###</p> <p>The data presented here are reported in Ciardo, Beyer, De Tommaso & Wykowska (accepted). Attribution of intentional agency towards robots reduces one’s own sense of agency. Cognition.</p> <p>Please refer to that paper for context and method. </p> <p>Files descriptions:<br> Raw Data.csv: Raw data of the three experiments. Please read the .txt file for variables definition.<br> Exp3_Data_Goodspeed.csv: Goodspeed questionnaire data of Experimet 3.<br> Listof Variables:..txt file with definition of variables and labels.</p>
French Word Sense Disambiguation with Princeton WordNet Identifiers
<p>This is a dataset for the Word Sense Disambiguation of French using Princeton WordNet identifiers. It contains two training corpora : the SemCor and the WordNet Gloss Corpus, both automatically translated from their original English version, and with sense tags automatically aligned. It contains also a test corpus : the task 12 of SemEval 2013, originally sense annotated with BabelNet identifiers, converted into Princeton WordNet 3.0.</p>
Proof-of-Concept Measurement for "Radar Band Fusion Using Frame-Based Compressed Sensing"
<p>This data set was created for a proof-of-concept test of the method described in "Radar Band Fusion Using Frame-Based Compressed Sensing". It consists of a measurment against a metal plate.</p> <p> </p>
Dataset supporting the paper "Single-Spin Sensing: A Molecule-on-Tip Approach. ACS Nano 18, 13829 (2024)"
<p>Dataset corresponding to theoretical calculations in the paper "Single-Spin Sensing: A Molecule-on-Tip Approach" ACS Nano 18, 13829 (2024) DOI: https://doi.org/10.1021/acsnano.4c02470</p> <p>Please cite as:</p> <p>Alex Fétida, Olivier Bengone, Michelangelo Romeo, Fabrice Scheurer, Roberto Robles, Nicolás Lorente, and Laurent Limot. Dataset supporting the paper "Single-Spin Sensing: A Molecule-on-Tip Approach. ACS Nano 18, 13829 (2024)" DOI: 10.5281/zenodo.13774118</p> <p>List of files:</p> <p>Several folders corresponding to the figures of the paper. They contain:</p> <p>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (https://jp-minerals.org/vesta/en/).</p> <p>.agr: grace files (https://plasma-gate.weizmann.ac.il/Grace/).</p> <p>Image files in png format.</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>
Data from: Three decades of pastoralist settlement dynamics in the Ethiopian Omo Delta based on remote sensing data
<p>Data from the paper:</p> <p><em>Amos, S., Mengistu, S., Kleinschroth, F. (2021): Three decades of pastoralist settlement dynamics in the Ethiopian Omo Delta based on remote sensing.</em></p> <p>Based on Landsat 5, 7, 8, RapidEye Ortho, and Sentinel-2 satellite imagery, we manually mapped the settlements of the Dasanech people in the most populated parts of the Omo River Delta in Ethiopia from 1992 to 2019 using QGIS. We used the data to answer the following questions: (1) How have pastoralist settlements in the delta changed in extent and persistence over the past three decades? And (2) how have the settlements changed structurally during the construction, filling, and operation of Gibe III Dam?</p> <p>We conducted two independent remote sensing analyses. Firstly, we used Landsat data from 1992 to 2019 to track land that is inhabited by pastoralists people within the evergreen part of the Delta. Secondly, the higher spatial resolution of the RapidEye Ortho (5m) and Sentinel-2 (10m) images allowed the detailed identification of settlements as well as infrastructure (tin-roof houses and road) in the Delta during a shorter period from 2009 to 2019. <strong>For more information on the data, please refer to the README.txt or the paper.</strong></p>
Experimental data for Berry curvature dipole senses topological transition in a moiré superlattice
<p>This experimental dataset was used in our study of "Berry curvature dipole senses topological transition in a moiré superlattice".</p>
Supporting data for "Dispersive sensing of charge states in a bilayer graphene quantum dot"
<p>Supporting data and analysis scripts for all figures in the article "Dispersive sensing of charge states in a bilayer graphene quantum dot", Appl. Phys. Lett. <strong>118</strong>, 093104 (2021); <a href="https://doi.org/10.1063/5.0040234">https://doi.org/10.1063/5.0040234</a></p> <p>The files are sorted according to the figures/panels in the publication with a "0-README.txt" file including further information. </p> <p>The following versions of Pyhton and the packages have been used:<br> python: 3.6.10<br> numpy: 1.18.1<br> matplotlib: 3.1.3<br> scipy: 1.4.1</p>
Remote-sensing measurements and model simulations of peroxyacetyl nitrate (PAN)
<p>Ground-based FTIR and IASI-A and -B measurements of PAN, supplemented with GEOS-Chem simulations.</p> <p>End users of these data sets are invited to contact the authors to make sure they are using the data properly and check about the possible availability of more recent products.</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.