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13,021 results for “Localization”
Dataset 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>Related scripts and software application</strong> : <a href="https://github.com/EngineerJB/akebia">https://github.com/EngineerJB/akebia</a></p> <p><strong>(New ! published in June 2024) Raw data :</strong> <a href="../records/11395562">https://zenodo.org/records/11395562</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 on Local Government Indicators in Chile
<p><strong>Data Set on Local Government Indicators in Chile</strong></p> <p>This repository contains a dataset in progress (20%) on local government indicators in Chile between 2010 and 2021, featuring an e-government indicator (EGI) in 2016, 2019 and 2021 in Comma-Separated Values CSV format with Unicode encoding UTF-8.</p> <p><strong>GitHub repository:</strong> <a href="https://github.com/bgonzalezbustamante/local-gov-indicators">https://github.com/bgonzalezbustamante/local-gov-indicators</a></p>
Data and software: Heat flux for semi-local machine-learning potentials
<p><br> This repository contains data, code, and related artefacts supporting the following publication:</p> <p>"Heat flux for semi-local machine-learning potentials"<br> by Marcel F. Langer, Florian Knoop, Christian Carbogno, Matthias Scheffler, and Matthias Rupp<br> arXiv: TBD<br> doi: TBD<br> </p> <p>More details can be found in the main README.md file, and the README.md files in the subfolders.</p> <p><br> For any further questions, feel free to contact mail@marcel.science, @marceldotsci on Twitter, or @marcel@sigmoid.social.</p> <p> </p>
Supplementary materials for "TUJI1 Dataset: Multi-device dataset for indoor localization with high measurement density"
<p>Supplementary materials for "TUJI1 Dataset: Multi-device dataset for indoor localization with high measurement density"</p> <p> </p> <p>For more information please refer to the data descriptor available at: https://www.sciencedirect.com/science/article/pii/S2352340924003251</p> <p>Please cite as:</p> <p>Klus, L., Klus, R., Lohan, E.S., Nurmi, J., Granell, C., Valkama, M., Talvitie, J., Casteleyn, S. and Torres-Sospedra, J., 2024. TUJI1 Dataset: Multi-device dataset for indoor localization with high measurement density. <em>Data in Brief</em>, p.110356.</p> <p> </p>
Dataset for "FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices"
<p>Dataset for the paper "FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices"</p> <p>The dataset contains localization measurements acquired with UWB devices. We compare the proposed localization method, called FlexTDOA, with a classic TDOA implementation, and with TWR-based localization. For more information about the localization methods, please refer to the paper.</p> <p>The dataset contains the measurements necessary to generate all the plots in the paper. For code examples on how to read and plot the data, please check out the associated Github repository: https://github.com/lauraflu/flextdoa</p> <p>If you find the dataset useful, please consider citing our work:</p> <blockquote> <p>Pătru, G. C., Flueratoru, L., Vasilescu, I., Niculescu, D., & Rosner, D. (2023). FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices. <em>IEEE Access</em>.</p> </blockquote>
Taxonomy, distribution and classification of ecosystem-types, integrating the recent IUCN function-based typology and local conceptualizations
<p>1. Introduction:</p> <p>This dataset is a work in progress. It compiles data gathered on ecosystem-types and their distribution based on a series of field studies led by the author, in Seychelles and West and Central Africa (Senterre 2014, Senterre & Wagner 2014, Senterre 2016, Senterre et al. 2017, 2019, 2020, 2021a, 2022). The aims of this dataset are:</p> <p>a. To share in an explicit and transparent way data on proposed taxonomies of ecosystems, i.e. conceptualizations of ecosystem-types, including explicit ecosystem names and management of synonymies.</p> <p>b. To develop ecosystem red listing based on transparent and falsifiable distribution raw data, combining distribution modeling (maps) and in situ observation of individual stand occurrences.</p> <p>c. To illustrate in detail how to deal with ecosystem data following the approach described in Senterre et al. (2021b) (i.e. "ecosystemology" approach).</p> <p>d. To integrate the above approach with the newly developed function-based typology of ecosystems (Keith et al. 2022), therefore contributing to bridging the persistent gap between the global and the local scales in ecosystem descriptions and classifications.</p> <p> </p> <p>2. Context and versions:</p> <p>This dataset was initially planned for publication on GBIF (Global Biodiversity Information Facility), as part of a project developed for the review of Key Biodiversity Areas in Seychelles: "Mainstreaming recent species and ecosystem distribution data into Key Biodiversity Areas assessments in Seychelles" (<a href="https://www.gbif.org/dataset/f513fe98-b1c3-45ee-8e14-7f2a5b7890bf">https://www.gbif.org/dataset/f513fe98-b1c3-45ee-8e14-7f2a5b7890bf</a>).</p> <p>In the first version of the GBIF dataset (<a href="https://www.gbif.org/dataset/f513fe98-b1c3-45ee-8e14-7f2a5b7890bf">https://www.gbif.org/dataset/f513fe98-b1c3-45ee-8e14-7f2a5b7890bf</a>), we proposed an analysis of the potential 'core' and 'extension' files available in GBIF for a publication of ecosystem-type names (and synonymies) and their corresponding occurrences recorded from field observations. This is an original analysis of taxonomic principles managed entirely at the scale of local observable objects, and their history of identifications or interpretations.</p> <p>Toward the end of the above-mentioned GBIF project, considering the limitations and gaps currently present in GBIF, it was decided to restrict the GBIF dataset to a simple 'metadata' entry and to publish the complete version of this dataset in Zenodo. This allows to include all tables needed, as well as all required fields without having to accommodate them within the limited GBIF structure (see metadata description on GBIF for more details). The fields of the tables published here are described in the GBIF metadata entry and in the ecosystemology paper (Senterre et al. 2021b).</p> <p> </p> <p>3. New development on typology aspects:</p> <p>In addition, considering that the new IUCN global typology of ecosystems is now published (Keith et al. 2022), we have reviewed in detail the possibility of integration of ecosystems conceptualized using our ecosystemology approach within the new IUCN typology. The result of this analysis is being considered for a publication, and this Zenodo dataset would then be published in full (i.e. including all typology aspects) as supplementary materials. In the meantime, I would be happy to discuss any of these aspects with whoever is interested.</p> <p> </p> <p>4. Access to ecosystem data for conservation actors:</p> <p>Finally, the actual data (published here) on ecosystem-types, their names, synonymies, classification, distribution, and red list status are compiled into a format that we designed to be useful to conservation actors in the form of interactive webpages (produced with R as shiny apps). This development is based on very limited resources, and the author is still quite new to R, so any help or feedback on ways to improve the scripts would be very much welcomed.</p> <p>The interactive page is available here (currently filtered to Seychelles' data only, although the dataset contains data beyond the Seychelles): https://shiny.bio.gov.sc/bioeco/</p> <p>The R scripts are available on Github: https://github.com/bsenterre/ecosystemology</p> <p> </p> <p>5. Tables contained in this dataset:</p> <p>a. Ecosystem taxonomy tables:</p> <p>ecoSpecies: Contains the list of all ecosystem-type names with their unique identifier.</p> <p>ecoOccurrences: Contains the list of individual stand occurrences, including ecosystem characters as standardized in Senterre et al. (2021b; i.e. virtual ecosystem specimen).</p> <p>ecoSpeciesProfiles: Contains basic metadata on ecosystem-types, such as their Red List evaluations.</p> <p>ecoIdentifications: Contains all the different interpretations/identifications (referring to the table ecoSpecies or to higher levels of classification, see below) made on the stands observed in the ecoOccurrences table.</p> <p> </p> <p>b. Ecosystem typology tables (TO BE ADDED LATER):</p> <p>IUCNL3: This is just a transcription, as is, of the IUCN global typology version 2.1.</p> <p>IUCNL3BIOCrossover: This table defines and comments correspondences between BIOL2 (the level 2 of the typology used by us) and the IUCN typology L3 (level 3).</p> <p>BIOL2: This is a variation based on the IUCN typology, here our level 2.</p> <p>BIOL3: This is a variation based on the IUCN typology, here our level 3.</p> <p>BIOL4: This is a variation based on the IUCN typology, here our level 4.</p> <p>ecoGenus: This is a general type of stand (thus excluding any regional ecosystem connotation), defined at a local scale and never combined with any geographic connotation (see ecosystemology paper: Senterre et al. 2021b).</p> <p>ecoFamily: This is a generalized version of the ecoGenus (i.e. still excluding any regional, sub-regional or geographic aspect).</p> <p>ecoOrder: This is a further generalized version of the ecoGenus (see also Senterre et al. 2020).</p> <p>lifeZone: This is a basic and incomplete list of life zones as defined following the Holdridge (1967) approach, with some additional elements proposed in Senterre et al. (2021b).</p> <p> </p> <p>6. Literature cited:</p> <p>Holdridge, L. R. 1967. Life zone ecology. Tropical Science Center, San Jose, Costa Rica.</p> <p>Keith, D. A., J. R. Ferrer-Paris, E. Nicholson, M. J. Bishop, B. A. Polidoro, E. Ramirez-Llodra, M. G. Tozer, J. L. Nel, R. Mac Nally, E. J. Gregr, K. E. Watermeyer, F. Essl, D. Faber-Langendoen, J. Franklin, C. E. R. Lehmann, A. Etter, D. J. Roux, J. S. Stark, J. A. Rowland, N. A. Brummitt, U. C. Fernandez-Arcaya, I. M. Suthers, S. K. Wiser, I. Donohue, L. J. Jackson, R. T. Pennington, T. M. Iliffe, V. Gerovasileiou, P. Giller, B. J. Robson, N. Pettorelli, A. Andrade, A. Lindgaard, T. Tahvanainen, A. Terauds, M. A. Chadwick, N. J. Murray, J. Moat, P. Pliscoff, I. Zager, and R. T. Kingsford. 2022. A function-based typology for Earth’s ecosystems. . Nature 610:513–518. doi:10.1038/s41586-022-05318-4.</p> <p>Senterre, B. 2014. Mapping habitat-types within the Hummingbird site at Dugbe (Liberia, West Africa). Consultancy Report, Missouri Botanical Garden. P. 56. https://doi.org/10.13140/RG.2.2.32628.48003.</p> <p>Senterre, B. 2016. Habitat-type ground-truthing and assessment of ecosystem conservation value in the Bel Air Alufer mining site (Guinea, West Africa), with recommendations for improving the draft map of land cover types. Consultancy Report, Missouri Botanical Garden, A study conducted for Alufer Mining Limited. P. 54.</p> <p>Senterre, B., E. Bidault, and T. Stévart. 2019. Identification et évaluation des écosystèmes menacés du Mont Nimba. Rapport de consultance, Missouri Botanical Garden (MBG), Africa and Madagascar Department. P. 106. https://doi.org/10.13140/RG.2.2.13242.93129.</p> <p>Senterre, B., E. Bidault, T. Stévart, and P. P. Lowry II. 2020. Assessment of Key Biodiversity Areas in the Lofa-Gola-Mano & Nimba complexes (West Africa) using ecosystem criteria. Final Report, Missouri Botanical Garden. P. 146. 10.13140/RG.2.2.17934.89924.</p> <p>Senterre, B., E. Bidault, T. Stévart, M. Wagner, and P. Lowry. 2017. Mapping habitat-types in south-east Kouilou (Republic of Congo). Consultancy Report, Missouri Botanical Garden (MBG), Africa and Madagascar Department, St. Louis, Missouri, USA. P. 163.</p> <p>Senterre, B., R. M. Bristol, G. Gendron, and E. Henriette. 2021a. Fine-tuning conservation priorities in Seychelles at the landscape scale, using global KBA guidelines with both species and ecosystem criteria. Consultancy Report, United Nations Development Programme, GOS/UNDP/GEF Programme Coordination Unit, Victoria, Seychelles.</p> <p>Senterre, B., P. P. Lowry II, E. Bidault, and T. Stévart. 2021b. Ecosystemology: a new approach toward a taxonomy of ecosystems. . Ecological Complexity 47:100945. doi:https://doi.org/10.1016/j.ecocom.2021.100945.</p> <p>Senterre, B., A.-H. Paradis, E. Bidault, T. Stévart, and P. P. Lowry II. 2022. Qualité et distribution des savanes montagnardes du Nimba. Rapport de consultance, Missouri Botanical Garden (MBG), Africa and Madagascar Department. P. 73. http://dx.doi.org/10.13140/RG.2.2.13433.34401.</p> <p>Senterre, B., and M. Wagner. 2014. Mapping Seychelles habitat-types on Mahé, Praslin, Silhouette, La Digue and Curieuse. Consultancy Report, Government of Seychelles, United Nations Development Programme, Victoria, Seychelles. P. 119. https://doi.org/10.13140/RG.2.1.4558.6009.</p>
Towards the Future Generation of Railway Localization Exploiting RTK and GNSS
<p>This repository contains the datasets acquired by ETH-PBL in conjunction with Unibo and SADEL during two days of testing in October 2022 near Modena, Italy.</p> <p>The data were acquired using two sensor nodes developed by ETH Zurich running a <a href="https://www.st.com/en/microcontrollers-microprocessors/stm32l452ce.html">STM32L452CEU6</a> MCU.<br> Each node collected data on the motion of the train using an <a href="https://www.st.com/en/mems-and-sensors/asm330lhh.html">ST ASM330LHH</a> automotive grade IMU as well as a <a href="https://www.u-blox.com/en/product/zed-f9p-module">u-blox ZED-F9P</a> GNSS module fed with live RTCM-data from a closeby RTK base station provided by SADEL. The base station utilized another ZED-F9P GNSS module connected to a Raspberry Pi which transmitted the generated RTCM correction packages over a raw TCP socket.<br> The data was then received using a <a href="https://www.u-blox.com/en/product/sara-r4-series">u-blox SARA-R4</a> cellular network module.</p> <p>The track was chosen as it exposes a variety of interesting GNSS environments. Encountered environments are ranging from urban over suburban to open field environments as well as one tunnel. Due to this composition, the availability of cellular connection and thus RTK correction data was patchy but mostly stable.</p> <p>The two sensor nodes were fixed to the Train Chassis, one centered in the train and the other positioned on the left side in driving orientation. Node 1 was placed on the floor in front of the driver's seat and positioned to be aligned with the center of the train in the lateral direction. A TOPGNSS TOP106 L1/L2 multi-band antenna was placed below the rear-facing windscreen also aligned with the same axis. Node 2 was mounted on a window on the left side of the train when facing in the direction of travel. This is approximately 1m above the floor and 1.4m left to the lateral center of the train. An ANN-MB00 L1/L2 antenna was attached to the outside frame of the train above the window.</p> <p>This dataset is linked with the GitHub repository at <a href="https://github.com/ETH-PBL/Railway-Precise-Localization">Railway-Precise-Localization</a> where the data format description and the pre-processing scripts are provided.</p>
A deep learning-based dataset of WFA-positive perineuronal nets and parvalbumin neurons localizations in the adult mouse brain
<p><strong>Quality-controlled predictions of deep learning models for cell counting</strong></p> <p>This dataset contains high-resolution images for the visualization of perineuronal nets (PNNs) and parvalbumin-expressing (PV) cells analyzed in the paper:</p> <p><em>A Comprehensive Atlas of Perineuronal Net Distribution and Colocalization with Parvalbumin in the Adult Mouse Brain.</em></p> <p>The dataset integrates the raw data published on a <a href="https://zenodo.org/record/7419282">previous upload</a> on Zenodo.</p> <p>Cell locations were obtained using two deep-learning models for cell counting (publicly available on <a href="http://github.com/ciampluca/counting_perineuronal_nets">GitHub</a>, details in the paper by <a href="https://www.sciencedirect.com/science/article/pii/S1361841522001475">Ciampi et al., 2022</a>). The output of the deep-learning pipeline was filtered based on the <em>score</em> assigned to each cell prediction, by removing all the PNNs with a score lower than 0.4 and all the PV cells with a score lower than 0.55. Cases of artefactual cell detection were finally removed manually by visual inspection of the images. </p> <p><strong>Content</strong></p> <p>The dataset contains microscopy images of coronal brain slices from 7 adult mice. The objects highlighted in these images represent the final set of PNNs/PV cells that were used in all the analysis of the paper.</p> <p><strong>Folder Structure and file naming conventions</strong></p> <p>There are separate folders for each mouse. Each folder is named with the ID of that mouse. Within each folder, images are assigned a code specifying the channel (C1 for PNNs, C2 for PV cells).</p> <p> </p>
Data from: Davison et al. (2023) Vegetation structure from LiDAR explains the local richness of birds across Denmark
<p>Environmental and biodiversity data associated with the article: Davison et al. (2023) <strong>Vegetation structure from LiDAR explains the local richness of birds across Denmark</strong>, <em>Journal of Animal Ecology</em>.</p> <p>Bird richness and abundance at points across Denmark, with matched land cover and LiDAR structural data. Bird observations are a subset of the Common Bird Monitoring programme (DOF – Birdlife Denmark) and pooled from summer counts of 2014, 15, and 16. Bird functional group assignments and environmental data are from open access data sets (see below).</p> <table> <tbody> <tr> <td>Data source</td> <td>Reference</td> </tr> <tr> <td>Danish Common Bird Monitoring programme</td> <td>Eskildsen, D. P., Vikstrøm, T., & Jørgensen, M. F. (2021). Overvågning af de almindelige fuglearter i Danmark 1975-2020. Dansk Ornitologisk Forening.</td> </tr> <tr> <td>EcoDes-DK15 LiDAR data set of Denmark</td> <td>Assmann, J. J., Moeslund, J. E., Treier, U. A., & Normand, S. (2022). EcoDes-DK15: high-resolution ecological descriptors of vegetation and terrain derived from Denmark’s national airborne laser scanning data set. Earth System Science Data, 14(2), 823–844. https://doi.org/10.5194/essd-14-823-2022</td> </tr> <tr> <td>Pan-European land cover map of the year 2015 </td> <td>Pflugmacher, D., Rabe, A., Peters, M., & Hostert, P. (2019). Mapping pan-European land cover using Landsat spectral-temporal metrics and the European LUCAS survey. Remote Sensing of Environment, 221, 583–595. https://doi.org/10.1016/j.rse.2018.12.001</td> </tr> <tr> <td>AVONET bird traits data</td> <td>Tobias, J. A., Sheard, C., Pigot, A. L., Devenish, A. J. M., Yang, J., Neate-Clegg, M. H. C., Alioravainen, N., Weeks, T. L., Barber, R. A., Walkden, P. A., MacGregor, H. E. A., Jones, S. E. I., Vincent, C., Phillips, A. G., Marples, N. M., Montaño-Centellas, F., Leandro-Silva, V., Claramunt, S., Darski, B., … Schleunning, M. (2022). AVONET: morphological, ecological and geographical data for all birds. Ecology Letters, 25(3), 581–597. https://doi.org/10.1111/ele.13898</td> </tr> <tr> <td>Birds of the Palearctic - original source of trait data </td> <td>Cramp, S. (2006). The birds of the western Palearctic interactive. Oxford University Press and BirdGuides.</td> </tr> <tr> <td>Life-history characteristics of European birds - trait database</td> <td>Storchová, L., & Hořák, D. (2018). Life-history characteristics of European birds. Global Ecology and Biogeography, 27(4), 400–406. https://doi.org/10.1111/geb.12709</td> </tr> </tbody> </table>
Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps
<p><strong>Title:</strong></p> <p>Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps</p> <p><strong>Citation:</strong></p> <p>Seeger, K.; Minderhoud, P. S. J., Peffeköver, A., Vogel, A., Brückner, H., Kraas, F., Nay Win Oo, Brill, D. (2023): Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps. Zenodo, <a href="https://doi.org/10.5281/zenodo.7875965">https://doi.org/10.5281/zenodo.7875965</a>.</p> <p><strong>Supplement to:</strong></p> <p>Seeger, K., Minderhoud, P. S. J., Peffeköver, A., Vogel, A., Brückner, H., Kraas, F., Nay Win Oo, and Brill, D. (2023): Assessing land elevation in the Ayeyarwady Delta (Myanmar) and its relevance for studying sea level rise and delta flooding. EGUsphere [preprint], <a href="https://doi.org/10.5194/egusphere-2022-1425">https://doi.org/10.5194/egusphere-2022-1425</a>.</p> <p><strong>Abstract:</strong></p> <p>The local digital elevation model (DEM) of the Ayeyarwady Delta, referred to as AD-DEM, was generated based on elevation data of topographic maps at scale of 1:50,000 published in 2014 while source data was compiled between 2000 and 2004. Empirical Bayesian Kriging with empirical data transformation and exponential modelling was applied to interpolate ~5100 elevation points (spot heights) and ~13600 elevation points extracted from contour data of the topographic maps. Elevation values higher than 10 m were excluded from interpolation and the SRTM water body mask created in 2000 was applied to the processed AD-DEM. The AD-DEM was transformed from its original vertical reference of local mean sea level at Kyaikkhami tide gauge to continuous mean sea level based on the mean dynamic topography data (CNES-CLS18 dataset of Mulet et al. (2021; <a href="https://doi.org/10.5194/os-17-789-2021">https://doi.org/10.5194/os-17-789-2021</a>) that we transposed to EGM96) in order to account for sea level variations along the Myanmar coast.</p> <p>The AD-DEM contains itself some uncertainty due to the lack of evenly distributed spot heights in areas of the upper delta, for which a separate shapefile is provided. However, we highlight to consider the AD-DEM as being the currently best available model against the background of the lacking possibility of ground truthing and being independent from satellite-based measurements.</p> <p>For further information on data processing, including DEM interpolation, determination of local mean sea level and vertical datum conversions, as well as DEM performance, see the corresponding paper and supplementary material.</p> <p>File name: ADDEM_Con250m_lesseq10_MDT_AD_MMR2000_masked_maskedSRTM.tif</p> <p>File format: GEOTIFF file</p> <p>Spatial reference: MMR2000_46N</p> <p>Vertical reference: local continuous mean sea level, i.e., mean dynamic topography (CNES-CLS18 dataset of Mulet et al. (2021; <a href="https://doi.org/10.5194/os-17-789-2021">https://doi.org/10.5194/os-17-789-2021</a>) transposed to EGM96</p> <p>Cell size: 750 × 750 m</p> <p>File name: DataPoorAreas_MMR2000.shp</p> <p>File format: ESRI Shapefile</p> <p>Spatial reference: MMR2000_46N</p>
Data and scripts to reproduce the results shown in "Stability of attractor local dimension estimates in non-Axiom A dynamical systems"
<p>Here we make available all the codes and datasets to reproduce the results of the paper "Stability of attractor local dimension estimates in non-Axiom A dynamical systems" by Flavio Pons, Gabriele Messori and Davide Faranda.</p> <p>The pre-print of the article is available at https://hal.science/hal-04051659/document.</p> <p>Any question/comment can be sent to flavio.pons@gmail.com.</p> <p>License for the codes and simulation/analysis results (*.Rda files): the code is shared under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license, see https://creativecommons.org/licenses/by-nc-sa/4.0/</p> <p>License and terms of use for the ERA5 data (z500_daily_euro.nc): the ERA5 500 hPa geopotential was downloaded from https://climexp.knmi.nl/start.cgi</p>
Background optimization of powder electron diffraction to implement e-PDF technique and study the local structure of iron oxide nanocrystals
<p>The local structural characterization of iron oxide nanoparticles is explored using a total scattering analysis method known as Pair Distribution Function (PDF) (also known as Reduced Density Function) profiles derived from background corrected powder electron diffraction patterns. Due to the strong coulombic interaction between the electron beam and the sample, electron diffraction generally leads to multiple scattering, causing redistribution of intensities towards higher scattering angles and an increased background in the diffraction profile. In addition to this, the electron-specimen interaction gives rise to an undesirable inelastic scattering signal that contributes primarily to the background. The present work demonstrates the efficacy of a pre-treatment of the underlying complex background function, which is a combination of both incoherent multiple and inelastic scatterings that cannot be identical for different electron beam energies. Therefore, two different background subtraction approaches are proposed for the electron diffraction patterns acquired at 80 kV and 300 kV beam energies. From the least square refinement (small-box modelling), both approaches are found to be very promising, leading to a successful implementation of the e-PDF technique to study the local structure of the considered nanomaterial.</p>
Dataset for 'Weld map tomography for determining local grain orientations from ultrasound'
<p>This dataset contains data files and Jupyter notebooks used to produce figures in the manuscript 'Weld map tomography for determining local grain orientations from ultrasound'.</p>
Dataset for Measurement report: Ion clusters as indicator for local new particle formation
<p>Data for Measurement report: Ion clusters as indicator for local new particle formation. There are two files, negative_ion_concentrations.csv and positive_ion_concentrations.csv. The former (latter) includes absolute number concentrations for 1.87, 2.16, 2.49, and 2.88 nm negative (positive) ions. The unit for these concentrations is #/cm<sup>-3</sup>.</p> <p>Contact Santeri Tuovinen (santeri.tuovinen@helsinki.fi) for more details.</p>
Data of the publication "Absence of Localization in Two-Dimensional Clifford Circuits"
<p>We analyze a Floquet circuit with random Clifford gates in one and two spatial dimensions. By using<br> random graphs and methods from percolation theory, we prove in the two-dimensional (2D) setting that<br> some local operators grow at a ballistic rate, which implies the absence of localization. In contrast, the<br> one-dimensional model displays a strong form of localization, characterized by the emergence of left- and<br> right-blocking walls in random locations. We provide additional insights by complementing our analytical results with numerical simulations of operator spreading and entanglement growth, which show the<br> absence (presence) of localization in two dimensions (one dimension). Furthermore, we unveil how the<br> spectral form factor of the Floquet unitary in 2D circuits behaves like that of quasifree fermions with<br> chaotic single-particle dynamics, with an exponential ramp that persists up to times scaling linearly with<br> the size of the system. Our work sheds light on the nature of disordered Floquet Clifford dynamics and<br> their relationship to fully chaotic quantum dynamics.</p>
FESOM-REcoM model data: A regime shift on Weddell Sea continental shelves with local and remote physical-biogeochemical implications is avoidable in a 2°C scenario
<p>This data set includes the minimal data necessary to reproduce the findings of Nissen et al. (2023). Output of model simulations with the global ocean biogeochemical model FESOM1.4-REcoM2 is provided. In particular, besides information on the model grid, the data set includes annual mean water mass properties (temperature, salinity, density, oxygen, pH) and freshwater fluxes from sea ice and ice shelves and decadal averages of air-sea CO2 fluxes and deep-ocean carbon accumulation rates. Model results are provided from 1980-2100 for the four emission scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 (sorted from low emission to high emission).</p> <p>Please see README for more information on the individual files. </p> <p>Data set belongs to: </p> <p>Nissen, C., R. Timmermann, M. Hoppema, and J. Hauck, 2023: A regime shift on Weddell Sea continental shelves with local and remote physical-biogeochemical implications is avoidable in a 2°C scenario. <em>J. Climate</em>, <a href="https://doi.org/10.1175/JCLI-D-22-0926.1">https://doi.org/10.1175/JCLI-D-22-0926.1</a>, in press.</p>
FUME Local population projections in destination cities
<p>FUME data on projected distributions of migrants at local level between 2030 and 2050.</p> <p>The dataset contains a folder of data for each destination city as a gridded dataset at 100m resolution in GeoTIFF format. The examined destination cities are: Amsterdam, Copenhagen, Krakow and Rome. The dataset is provided as 100m grid cells based on the Eurostat GISCO grid of the 2021 NUTS version, using ETRS89 Lambert Azimuthal Equal-Area (EPSG: 3035) as coordinate system. The file names consist of the projected year, the corresponding scenario, and the reference migrant group. The projections have been performed for the years 2030, 2040 and 2050. The investigated scenarios are the following:<br> • benchmark (bs),<br> • baseline (bs),<br> • Rising East (re),<br> • EU Recovery (eur),<br> • Intensifying Global Competition (igc), and<br> • War (war).</p> <p>The migration background is derived from data about the Region of Origin (RoO) for migrants in Copenhagen and Amsterdam, and from Region of Citizenship (CoC) for migrants in Krakow and Rome.</p> <p>The case study of <strong>Copenhagen</strong> covers the two central NUTS3 areas (DK011, DK012) and the groups presented are the following:<br> • total population (totalpop),<br> • native population (DNK),<br> • Eastern EU European migrants (EU_East),<br> • Western EU Europeans migrants (EU_West),<br> • Non-EU European migrants (EurNonEU),<br> • migrants from Turkey (Turkey),<br> • the MENAP countries (MENAP; excluding Turkey),<br> • other non-Western (OthNonWest), and<br> • other Western countries (OthWestern).</p> <p>The case study of <strong>Amsterdam</strong> covers one NUTS3 area (NL329) and the presented groups are the following:<br> • total population (totalpop),<br> • native population (NLD),<br> • Eastern EU European migrants (EU East),<br> • Western EU European migrants (EU West),<br> • migrants from Turkey and Morocco (Turkey + Morocco),<br> • migrants from the Middle East and Africa (Middle East + Africa),<br> • migrants from the former colonies (Former Colonies), and<br> • migrants from the rest of the world (Other Europe etc).</p> <p>The case study of <strong>Krakow</strong> covers the Municipality of Krakow, and the presented groups are the following:<br> • total population (totalpop),<br> • native population (POL),<br> • EU/EFTA European migrants (EU),<br> • non-EU European migrants (Europe_nonEU), and<br> • migrants from the rest of the world (Other).</p> <p>The case of <strong>Rome</strong> covers the Municipality of Rome, and the presented groups are the following:<br> • total population (totalpop),<br> • native population (ITA),<br> • migrants from Romania (ROU),<br> • Philippines (PHL),<br> • Bangladesh (BGD),<br> • the EU (EU; excluding Romania),<br> • Africa (Africa),<br> • Asia (Asia; excluding Philippines and Bangladesh) and<br> • America (America).</p>
A localization transition underlies the mode-coupling crossover of glasses
<p>This dataset is associated to "A localization transition underlies the mode-coupling crossover of glasses" by D. Coslovich, A. Ninarello and L. Berthier [<a href="https://arxiv.org/abs/1811.03171">https://arxiv.org/abs/1811.03171</a>].</p> <p>It includes post-processed data and workflow to reproduce the analysis and the figures of the article and of the supplemental information.</p> <p><strong>Supplementary information is available in the Supplement section of the project document (project.pdf).</strong></p> <p>The easiest way to reproduce the analysis and figures, and then check the results, is to use the make script:</p> <pre><code class="language-bash">./make all</code></pre> <p>Alternatively, the analysis and figures can be reproduced in any of the following ways</p> <ul> <li>following the workflow described in the <a href="https://orgmode.org">org-mode</a> project file project.org</li> <li>using the individual bash and gnuplot scripts in src/ and plots/</li> </ul> <p>Folders and files description:</p> <ul> <li>analysis/: post-processed data</li> <li>src/: bash, python and gnuplot scripts needed to reproduce the analysis</li> <li>plots/: eps figures that appear in the paper and supplemental information and associated gnuplot scripts</li> <li>make: convenience script to setup the python environment, analyze the data and reproduce the figures</li> <li>project.org: org-mode project file with workflow and supplemental information</li> <li>project.pdf: pdf project file with workflow and supplemental information</li> <li>project.bib: bibtex bibliography associated to the project</li> <li>project.setup: org-mode export configuration</li> </ul> <p>Dependencies:</p> <ul> <li>numpy (1.21.6)</li> <li>scipy (1.11.1)</li> <li>argh (0.26.2)</li> <li><a href="https://pypi.org/project/atooms/">atooms</a> (1.9.1)</li> <li>gnuplot (5.0.0)</li> </ul> <p>The analysis scripts have been tested with python 3.8. The org-mode project file has been tested with org version 9.1.13.</p> <p>Note: this dataset does not contain (at least yet) the particle configurations associated to saddle points, only the post-processed files containing selected properties of their normal modes.</p> <p>Changelog:</p> <ul> <li>1.2.2 <ul> <li>fix requirements</li> </ul> </li> <li>1.2.1 <ul> <li>fix ./src/adiff.py</li> <li>fix final check of ./make all</li> <li>improve pdf layout</li> <li>improve handling of org properties</li> </ul> </li> <li> <ul> </ul> </li> <li> <ul> </ul> </li> <li>1.2.0 <ul> <li>add analysis of eigenvector-following optimizations</li> <li>small changes and fixes to analysis scripts</li> </ul> </li> <li>1.1.0 <ul> <li>add "all" target to ./make</li> <li>fix ./make check</li> <li>improve setup description</li> </ul> </li> <li>1.0.0 <ul> <li>initial submission</li> </ul> </li> </ul>
Global map of Local Climate Zones
<p>A global 100 m spatial resolution Local Climate Zone (LCZ) map, derived from multiple earth observation datasets and expert LCZ class labels.</p> <p>The LCZ map is based on the LCZ typology (Stewart and Oke, 2012) that distinguish urban surfaces accounting for their typical combination of micro-scale land-covers and associated physical properties. The LCZ scheme is distinguished from other land use / land cover schemes by its focus on urban and rural landscape types, which can be described by any of the 17 classes in the LCZ scheme.</p> <p>Out of the 17 LCZ classes, 10 reflect the 'built' environment, and each LCZ type is associated with generic numerical descriptions of key urban canopy parameters critical to model atmospheric responses to urbanisation. In addition, since LCZs were originally designed as a new framework for urban heat island studies (Stewart and Oke, 2012), they also contain a limited set (7) of 'natural' land-cover classes that can be used as 'control' or 'natural reference' areas. As these seven natural classes in the LCZ scheme can not capture the heterogeneity of the world’s existing natural ecosystems, we advise users - if required - to combine the built LCZ classes with any other land-cover product that provides a wider range of natural land-cover classes.</p> <p><em>Stewart ID, Oke TR. (2012). Local Climate Zones for Urban Temperature Studies. Bull Am Meteorol Soc. 93(12):1879-1900. doi:10.1175/BAMS-D-11-00019.1</em></p>
Relyea, R. A. 2002. Local population differences in phenotypic plasticity: Predator-induced changes in wood frog tadpoles. Ecological Monographs 72:77-93
Taxa that are divided into separate populations with low levels of interpopulation dispersal have the potential to evolve genetically based differences in their phenotypes and the plasticity of those phenotypes. These differences can be due to random processes, including genetic drift and founder effects, or they can be the result of different selection pressures among populations. I investigated population-level differences in predator- induced phenotypic plasticity in eight populations of larval wood frogs (Rana sylvatica) over a small geographic scale (interpopulation distances of 0.3–8 km). Using a common-garden experiment containing predator and no-predator environments, I found population differences in behavior, morphology, and life history. These responses exhibited a habitat-related pattern: the four populations from closed-canopy ponds did not differ from each other in any of their phenotypes whereas the four populations from opencanopy ponds did differ from each other in these traits. This phenotypic pattern matches the pattern of competitors and predators found in these two types of ponds. Based on two years of pond surveys, the four closed-canopy ponds contained very similar competitor and predator assemblages while the assemblages of the four open-canopy ponds were more diverse and highly variable among open-canopy ponds. When combined with past studies, which demonstrate that predators and competitors select for alternative behavioral and morphological traits, these patterns suggest that the population differences may have arisen via natural selection and not via random mutation or drift. In a second experiment, I cross-transplanted two of the populations into each other’s ponds to determine if the populations were locally adapted to the conditions of their native pond (using low and high competition crossed with the presence or absence of a lethal predator). The populations continued to exhibit phenotypic differences, and one of the two populations t
ScienceDex guides
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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.