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62 results for “Digital Mapping”
Data from: Human face-off: a new method for mapping evolutionary rates on three-dimensional digital models
<p>Modern phylogenetic comparative methods allow estimating evolutionary rates of phenotypic change, how these rates differ across clades, and assessing whether the rate remained constant over time. Unfortunately, currently available phylogenetic comparative tools express the rate in terms of a scalar dimension, hence they do not allow us to determine rate variations among different parts of a single, complex phenotype, or charting of realized rate variation directly onto the phenotype. Herein, we present a new method which allows the mapping of evolutionary rate variation directly on three-dimensional phenotypes, informing on the direction and magnitude of trait change automatically.</p> <p>This new method, implemented by the function rate.map embedded in the R package 'RRphylo', is based on phylogenetic ridge regression rate estimates. Since the latter represent ridge regression slopes, they possess sign and magnitude. In 'RRphylo', different rates are calculated for different districts of the phenotype, which can then be visualized directly onto the phenotype itself. We present the application of rate.map to the evolution of facial skeleton in Hominoidea (the clade including living and fossil apes), the primate clade inclusive of Homo and the greater apes. We found that the highly derived, unique shape of the face in modern humans evolved through rapid phenotypic changes affecting the nasal bones, the brow ridge and the maxillary region. The canine fossa, a facial feature unique to Homo sapiens, did not belong to a region of rapid phenotypic change, and could be seen as the by-product of midface evolution as suggested by previous studies.</p>
Open dataset for the research of "Assessing accuracy improvement of integrating digital footprints into gridded population mapping: spatiotemporal variations and data bias"
<p>Result datasets for "Assessing accuracy improvement of integrating digital footprints into gridded populationmapping:spatiotemporal variations and data bias":</p> <ol> <li> S1 is the results for gridded population mapping using different methods.</li> <li> S2 is the aggregate results of population mapping at county level.</li> <li> S3 is the results for intraday variation of population disaggregation accuracy,</li> <li> S4 is the data bias of different digital footprints.</li> </ol>
Soil Survey of Scotland Staff (1970-1987). Soil maps of Scotland (partial coverage) at a scale of 1:25 000. Digital phase 8 release.
<p>This is the digital dataset which was created by digitising the Soils of Scotland 1:25,000 Soil maps and the Soils of Scotland 1:25,000 Dyeline Masters. The Soils of Scotland 1:25,000 Soil maps were the source documents for the production of the Soils of Scotland 1:63,360 and 1:50,000 published map series. Where no 1:25,000 published maps exist 1:63,360 maps have been digitised for this data set, the field SOURCE_MAP describes the source of the data. The classification is based on Soil Associations, Soil Series and Phases which reflect parent material, major soil group, and soil sub-groups, drainage and (for phases), texture, stoniness, land use, rockiness, topography and organic matter. Phases are not always mapped. In general terms this dataset primarily covers the cultivated land of Scotland but also includes some upland areas. This data set is undergoing a phased revision, the latest (phase 8) was released in August 2021. The digitising of the recently added data was funded by the Rural & Environment Science & Analytical Services Division of the Scottish Government. The data can also be downloaded from or viewed at <a href="https://www.hutton.ac.uk/soil-maps/">https://www.hutton.ac.uk/soil-maps/ </a>or viewed at <a href="https://map.environment.gov.scot/Soil_maps/?layer=2">Scotland's Soils - soil maps (environment.gov.scot). </a> This map should be cited as: 'Soil Survey of Scotland Staff (1970-1987). Soil maps of Scotland (partial coverage) at a scale of 1:25 000. Digital phase 8 release. James Hutton Institute, Aberdeen. DOI 10.5281/zenodo.5159133.</p>
A Systematic Mapping of the Classification of Open Educational Resources for Computer Science Education in Digital Sources (Data)
<p>Data from a Systematic Mapping of the classification of Open Educational Resources for Computer Science Education.</p> <p>Content:</p> <ul> <li>Studies selected</li> <li>Digital sources used to classify Open Educational Resources for Computer Science Education</li> <li>Computer Science domains explored by Open Educational Resources</li> <li>Approaches for the classification of Open Educational Resources for Computer Science Education</li> </ul>
A digital GIS update to the classic Ulbrich 1930 map of muskrat (Undatra zibethica) population range expansion in Central Europe
<p>This dataset is a digital version of a seminal dispersal data set of muskrat spread in Central Europe. We obtained and scanned an original copy of Ulbrich (1930), creating a digital image file, which we then geo-referenced in Arc Gis 10.2. The data herein contains metadata explaining the conversion process, necessary images to geo-reference the map, reference points used to "fix" the dispersal map to a map of present-day Europe, intermediate files (points and polygons produced when tracing the population ranges), and the final spatial dataset.</p> <p> </p> <p>Reference to the original data: Ulbrich, Johannes. 1930. Die Bisamratte; lebenweise, gang ihrer ausbreitung in Europa, wirtschaftliche bedeutung und bekampfung. Heinrich, Dresden.</p>
Data from: Human face-off: a new method for mapping evolutionary rates on three-dimensional digital models
Open the record for dataset details and reuse information.
A digital mapping application for quantifying and displaying air temperatures at high spatiotemporal resolutions in near real-time across Australia
<p>This repository hosts the raw data and source code for the paper entitled " A digital mapping application for quantifying and displaying air temperatures at high spatiotemporal resolutions in near real-time across Australia" [DOI: 10.7717/peerj.10106].</p> <p>Two zip archives are available:</p> <p>Raw data and methods evaluation.zip - Retains empirical data and method evaluation code (R scripts) that relate to data presented in the results and discussion sections of the article, specifically, Figures 3 -11 and Tables 1 and 2.</p> <p>RTmap_source_code_and_data_structure.zip - This repository hosts the source code for <a href="http://austemperature.live/">http://austemperature.live/</a>, as described in the methods section of the article. A README.docx presents the contents and subsequent procedures for deploying the mapping code and web map application.</p> <p>For further information about this files please email:</p> <p>mweb7041@uni.sydney.edu.au</p> <p> </p>
A digital mapping of the literature on vehicle-bridge-wind systems
<p>This dataset is the result of a systematic search and mapping of the literature relevant to the vehicle-bridge-wind system. The search phrase used to search the databases listed below can be found in tabulated form in one of the spreadsheets. The core component to this search phrase is that the title, abstract or keywords should include all three of the words vehicle, bridge and wind. Using one possible syntax for the boolean operators, the search phrase might look like:</p> <ul> <li>("numerical analysis" OR model OR modelling OR modeling OR experiment OR field OR "wind tunnel" OR parameter OR "case study" OR coupled OR interaction OR analysis OR vbi OR safety) AND (("bridge" AND "vehicle" AND "wind")) AND ("long span" OR "long-span" OR floating OR "cable stayed" OR "cable-stayed" OR suspension OR long OR "floating tunnel") NOT ("short span" OR "short-span" OR rail OR train)</li> </ul> <p>Note that the search is specific to long-span bridges and road vehicles. The phrase was used to search the following databases:</p> <ul> <li>Scopus</li> <li>Engineering Village</li> <li>Web of Science</li> <li>Science Direct</li> <li>Oria/NTNU</li> </ul> <p>Following a screening process, data of interest was extracted from the articles through structured searches and readings. A description of this process and a succinct presentation of the findings of the search and mapping will follow in the form of a published journal article.</p> <p>The "Database" sheet gives a structured list of the main contributions from all texts considered relevant for the mapping process. The citation keys used in the spreadsheet can be mapped to the referenced texts through the .bib file. Each text is pre-categorized based on readings of the abstract/conclusion into one of the following areas, describing the main contribution of the piece:</p> <ul> <li>Vehicle Dynamics</li> <li>Bridge Dynamics</li> <li>Vehicle Aerodynamics</li> <li>Bridge Aerodynamics</li> <li>Vehicle-bridge Aerodynamics</li> <li>Vehicle-bridge Interaction</li> </ul> <p>Texts are also classified by research level:</p> <ul> <li>Evaluation: development/demonstration of a novel modelling method</li> <li>Lab Validation: validation of models by experimental data or advanced numerical models in a virtual lab (e.g. computational fluid dynamics)</li> <li>Field Validation: the use of field experiments to verify, validate and/or calibrate existing models</li> </ul> <p>Each column can be filtered by entering a phrase in the appropriate cell in the table at the top of the sheet. Enabling macros will allow the sheet to update each time the content of said cell changes. The "Filtered List" is then a list of all texts that satisfy the filters.</p> <p>This is intended as a living document and any and all comments, concerns and questions are warmly welcomed.</p>
Data from: A method for mapping morphological convergence on three-dimensional digital models: the case of the mammalian saber-tooth
<p>Morphological convergence can be assessed through a variety of statistical methods. None of the methods proposed to date enable the visualization of convergence. All are based on the assumption that the phenotypes either converge, or do not. However, between species, morphologically similar regions of a larger structure may behave differently. Previous approaches do not identify these regions within the larger structures or quantify the degree to which they may contribute to overall convergence. Here we introduce a new method to chart patterns of convergence on three-dimensional models, deployed with the R function conv.map. The convergence between pairs of models is mapped onto them to visualize and quantify the morphological convergence. We applied conv.map to a well-known case study, the saber-tooth morphotype which has evolved independently among distinct mammalian clades, from placentals to metatherians. Although previous authors have concluded that saber-tooths kill using a stabbing 'bite' to the neck, others have presented different interpretations for specific taxa, including the iconic Smilodon and Thylacosmilus. Our objective was to identify any shared morphological features among the saber-tooths that may underpin similar killing behaviours. From a sample of 49 placental and metatherian carnivores, we found stronger convergence among saber-tooths than for any other taxa. We found that the morphological convergence is most apparent in the rostral and posterior parts of the cranium. The extent of this convergence implies similarity in function among these phylogenetically distant species. In our view this function is most likely the killing of relatively large prey by a stabbing bite.</p>
The supplementary materials for "Roughness prediction of end milling surface for behavior mapping of digital twined machine tools".
<p>This is the supplementary materials for a paper named "Roughness prediction of end milling surface for behavior mapping of digital twined machine tools" published on the Digital Twin journal.</p>
African Digital Research Repositories: Mapping the Landscape
<p>This data set accompanies the text at doi <a href="https://doi.org/10.5281/zenodo.3732273">10.5281/zenodo.3732273</a>. // Correspondence: JH: <a href="mailto:info@africarxiv.org">info@africarxiv.org</a>, SK: <a href="mailto:sk111@soas.ac.uk">sk111@soas.ac.uk</a></p> <p><strong>Visual Map: </strong><a href="https://kumu.io/access2perspectives/african-digital-research-repositories"><strong>https://kumu.io/access2perspectives/african-digital-research-repositories</strong></a><strong> <br> Dataset: </strong><a href="https://tinyurl.com/African-Research-Repositories"><strong>https://tinyurl.com/African-Research-Repositories</strong></a><br> <strong>Archived at </strong><a href="https://info.africarxiv.org/african-digital-research-repositories/"><strong>https://info.africarxiv.org/african-digital-research-repositories/</strong></a><strong> <br> Submission form: </strong><a href="https://forms.gle/CnyGPmBxN59nWVB38"><strong>https://forms.gle/CnyGPmBxN59nWVB38</strong></a></p> <p> </p> <p><strong>Licensing</strong>: Text and Visual Map – CC-BY-SA 4.0 // Dataset – CC0 (Public Domain) // The licensing of each database is determined by the database itself</p> <p>Preprint doi: <a href="https://doi.org/10.5281/zenodo.3732273">10.5281/zenodo.3732273</a>. <br> Data set doi: <a href="http://doi.org/10.5281/zenodo.3732172">10.5281/zenodo.3732172</a> // available in different formats (pdf, xls, ods, csv)</p> <p> </p> <p><strong><a href="https://info.africarxiv.org">AfricarXiv</a> in collaboration with the <a href="https://www.internationalafricaninstitute.org">International African Institute </a>(IAI) presents an interactive map of African digital research literature repositories. This drew from IAI’s earlier work from 2016 onwards to identify and list Africa-based institutional repositories that focused on identifying repositories based in African university libraries. Our earlier resources are available at </strong><a href="https://www.internationalafricaninstitute.org/repositories"><strong>https://www.internationalafricaninstitute.org/repositories</strong></a><strong>.</strong></p> <p><strong>The interactive map extends the work of the IAI to include organizational, governmental, and international repositories. It also maps the interactions between research repositories. In this dataset, we focus on institutional repositories for scholarly works, as defined by Wikipedia contributors (March 2020).</strong><br> </p> <p><strong>Objective</strong></p> <p>The map of African digital repositories was created as a resource to be used in activities addressing the following aims:</p> <ol> <li> <p>Improving the discoverability of African research and publications </p> </li> <li> <p>Enhance the interoperability of existing and emerging African repositories</p> </li> <li> <p>Identify ways through which digital scholarly search engines can enhance the discoverability of African research</p> </li> </ol> <p>We promote the dissemination of research-based knowledge from African repositories as part of a bigger landscape that also includes online journals, research data repositories, and scholarly publishers to enhance the interconnectivity and accessibility of such repositories across and beyond the African continent and to contribute to a more granular understanding of the continent’s scholarly resources. </p> <p> </p> <p><strong>Data archiving and maintenance</strong></p> <p>The map and corresponding dataset are hosted on the AfricArXiv website under ‘Resources’ at <a href="https://info.africarxiv.org/african-digital-research-repositories/">https://info.africarxiv.org/african-digital-research-repositories/</a>. The listing is not exhaustive and therefore we encourage any repositories relevant for the African continent not listed here to the <strong>submission form at </strong><a href="https://forms.gle/CnyGPmBxN59nWVB38"><strong>https://forms.gle/CnyGPmBxN59nWVB38</strong></a>, or to notify the International African Institute (email <a href="mailto:sk111@soas.ac.uk">sk111@soas.ac.uk</a>). Both AfricArXiv and IAI will continue to maintain the list of repositories as a resource for African researchers and other stakeholders including international African studies communities.</p>
A Cross-Domain Systematic Mapping Study on Software Engineering for Digital Twins
<p><strong>A Systematic Cross-Domain Mapping Study on the Software Engineering of Digital Twins</strong></p> <p>Manuela Dalibor, Nico Jansen, Bernhard Rumpe, David Schmalzing, Louis Wachtmeister, Manuel Wimmer, and Andreas Wortmann</p> <p>Digital Twins are currently investigated as the technological backbone for providing an enhanced understanding and management of existing systems as well as for designing new systems in various domains, e.g., ranging from single manufacturing components such as sensors to large-scale systems such as smart cities. Given the diverse application domains of Digital Twins, it is not surprising that the characterization of the term Digital Twin, as well as the needs for developing and operating Digital Twins are multi-faceted. Providing a better understanding what the commonalities and differences of Digital Twins in different contexts are, may allow to build reusable support for developing, running, and managing Digital Twins by providing dedicated concepts, techniques, and tool support. In this paper, we aim to uncover the nature of Digital Twins based on a systematic mapping study which is not limited to a particular application domain or technological space. We systematically retrieved a set of 1471 unique publications of which 529 were identified as potentially relevant and of which finally 356 were selected for further investigation. In particular, we analyzed the types of research and contributions made for Digital Twins, the expected properties Digital Twins have to fulfill, how Digital Twins are realized and operated, as well as how Digital Twins are finally evaluated. Based on this analysis, we also contribute a novel feature model for Digital Twins as well as several observations to further guide future software engineering research in this area.</p>
Soil Survey of Scotland Staff (1970-1987). Soil maps of Scotland (partial coverage). Digital phase 10 release.
<p>This is the digital dataset which was created by digitising the Soils of Scotland 1:25,000 Soil maps and the Soils of Scotland 1:25,000 Dyeline Masters. The Soils of Scotland 1:25,000 Soil maps were the source documents for the production of the Soils of Scotland 1:63,360 and 1:50,000 published map series. Where no 1:25,000 published maps exist 1:63,360 maps have been digitised for this data set, the field SOURCE_MAP describes the source of the data. The mapping is based on Soil Associations, Soil Series and Phases which reflect parent material, major soil group, and soil sub-groups, drainage and (for soil phases), texture, stoniness, land use, rockiness, topography and organic matter. Phases are not always mapped. In general terms this dataset primarily covers the cultivated land of Scotland but also includes some upland areas. This data set is undergoing a phased revision, the latest (phase 8) was released in August 2021. The digitising of the recently added data was funded by the Rural & Environment Science & Analytical Services Division of the Scottish Government. The data can also be downloaded from or viewed at <a href="https://www.hutton.ac.uk/learning/natural-resource-datasets/soilshutton/soils-maps-scotland/download"><span><span> </span>https://www.hutton.ac.uk/soil-maps/ </span></a>or viewed at <a href="https://map.environment.gov.scot/Soil_maps/?layer=2">Scotland's Soils - soil maps (environment.gov.scot). </a> This map should be cited as: 'Soil Survey of Scotland Staff (1970-1987). Soil maps of Scotland (partial coverage). Digital phase 10 release. James Hutton Institute, Aberdeen. DOI 10.5281/zenodo.6908156 .</p>
Digital versions of lunar geological maps
<p>As the link for this database is not working at USGS website (i.e., https://astrogeology.usgs.gov/search/map/Moon/Geology/Lunar_Geologic_GIS_Renovation_March2013), we upload this database.</p>
FIGURE 1 in Toward a biogeographic regionalization of the Nearctic region: Area nomenclature and digital map
FIGURE 1. Map of the regionalization of the Nearctic region, including three subregions, one transition zone and 29 provinces.
Digital mapping of literature. Data, scripts and web
<p>Data, scripts and web site of the project “Digital mapping of fictional places in Spanish Early Modern Byzantine novels”. Visit the project web page at <a href="http://editio.github.io/mapping.literature">http://editio.github.io/mapping.literature</a></p>
Processing steps to generate a Digital Surface Model based on SPOT-7 tri-stereo images published in the study "An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar inundation areas at volcan Copahue (Argentina & Chile)" in the Journal of South American Earth Sciences https://doi.org/10.1016/j.jsames.2022.104138
<p>The Digital Surface Model (DSM) was created from SPOT-7 tri-stereo images for the Copahue volcano between the border of Argentina and Chile. Two versions of the DSM are provided: an unfiltered product and a final, filtered product. The final product has a spatial resolution of 5-m and was used for lahar inundation modeling for the Copahue volcano (Viotto, Toyos, and Bookhagen 2022, <a href="https://doi.org/10.1016/j.jsames.2022.104138">https://doi.org/10.1016/j.jsames.2022.104138</a> : An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at Volcán Copahue (Argentina & Chile). <em>Journal of South American Earth Sciences</em> ). The dataset provided should be cited together with the article. </p> <p><strong>DSM processing </strong></p> <p>The source images were given by a SPOT-7 snow- and cloud-free triplet (Nadir, Backward and Forward) of 1.5 m spatial resolution from 19 April 2018 (SPOT Image, Airbus Defence and Space GmbH, distributed by CONAE; Dataset ID: <em>SEN_SPOT7_20180419_142955500_000</em>, delivered by CONAE as <em>DS_SPOT7_20180419</em>).</p> <p>The data were processed with the suite of digital photogrammetry tools AMES Stereo Pipeline ASP (Beyer et al., 2018). The procedure for the generation of the DSM is summarized by following steps: </p> <ol> <li> <p>The orbital parameters (RCP models) were adjusted using the bundle adjustment tool with no ground control points, since they were unavailable.</p> </li> <li> <p>The scenes were map-projected onto the NASADEM (spatial resolution of 30 m) elevation dataset, assisted by the results of the orbital adjustment in Step 1.</p> </li> <li>The stereo correlation of the map-projected scenes including the results of the adjusted orbital parameters, was performed three times, using as first scene (i.e., primary image) the nadir (N), backward (B), and forward (F) images . In each run, the order of images to perform the stereo correlation was: N-F-B, F-N-B, and B-N-F. Thus, three point clouds were generated. Specific ASP correlator settings (other than defaults parameters; for details see the provided stereo-default file) were set in the following way: <em>Correlation Kernel</em>: 15 x 15 pixels; <em>Sub-pixel Refinement Kernel</em>: 21 x 21 pixels; <em>Subpixel Refinement Mode</em>: 2 (Weighted Affine Adaptive Window Correlator EM)</li> <li> <p>The three point clouds were merged into one point cloud with a regular grid of 5 m (unfiltered product, known as <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em>).</p> </li> </ol> <p>The quality of the final point cloud was assessed by comparing the unfiltered DSM with a spatial resolution of 12-m against the WorldDEM<sup>TM</sup> elevation dataset (Collins et al., 2015). The WorldDEM was provided by Airbus Defence and Space GmbH under license for the scope of the Viotto et al., 2022 study. The comparison of the pixel-to-pixel heights above the ellipsoid (WGS84) between the two datasets resulted in a mean difference of 0.67 m and a standard deviation of +/- 4.82 m. </p> <p>Comprehensive details on the methodologies evaluated to create the dataset with ASP, can be found in the corresponding master's thesis “Topografía digital y modelado de lahares en el Volcán Copahue, Argentina-Chile” from S. Viotto (link: https://rdu.unc.edu.ar/handle/11086/15384). Recommended literature about processing DEMs from SPOT imagery is given by Mueting et al., 2021 (<a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330</a>). </p> <p><strong>Creation of the Final, Filtered DSM product</strong></p> <p>The corrections and improvements applied to the unfiltered product to create the final, filtered DSM (named DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif) are summarized by following steps. </p> <p> </p> <ol> <li> <p><em>Water Bodies Delineation</em></p> </li> </ol> <p>The delineation of the water bodies was based on a mask created from the free access water bodies datasets provided by the Instituto Geográfico Nacional of Argentina (<a href="https://www.ign.gob.ar/NuestrasActividades/InformacionGeoespacial/CapasSIG">https://www.ign.gob.ar/ NuestrasActividades/InformacionGeoespacia l/CapasSIG</a>) and by the Ministerio de Bienes Nacionales in Chile ( <a href="https://www.ide.cl/index.php/aguas-continentales/item/1508-catastro-de-lagos">https://www.ide.cl/index.php /aguas-continentales/item/1508-catastro-de-lagos</a>). A total of 45 lakes within the area of interest were considered. Lakes with areas below or equal to 25 m2 were smoothed with a median filter in the last step. Lakes with areas above this threshold were filled in with a constant value and their borders were smoothed with a median filter to provide smooth shorelines.</p> <p><em>2 . Void Filling</em></p> <p>Voids (other than water bodies) were filled with the tool “Close Gaps” from Saga GIS software. </p> <p><em>3. Smoothing</em></p> <p>Finally, the elevation dataset was smoothed with a median filter using a 3 x 3 pixel window, excluding water bodies filled in the step 1. </p> <p><strong>Final Remarks and Suggestion</strong></p> <p>The quality assessment of the final version by visual inspection of the hillshades suggested an improvement of the signal to noise ratio. However, the void filling process may be improved.</p> <p><br> </p> <p><strong>Dataset Description</strong></p> <table align="center"> <caption> </caption> <tbody> <tr> <td>Digital Surface Models</td> <td> <p>No Data Value = -9999</p> <p>Format = float 32 bit</p> <p>File Format = GeoTiff</p> <p>Vertical Datum: WGS84</p> <p>Projection information: EPSG 32719 (UTM19S)</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>) </p> <p>Versions: </p> <ul> <li> <p>Unfiltered product: without corrections <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em></p> </li> <li> <p>Final, filtered product: smoothed and void filled <em>DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</em></p> </li> </ul> </td> </tr> <tr> <td>Water Bodies Mask</td> <td> <p>No Lake Value = 0</p> <p>Lakes Values = 1 to 45</p> <p>File Format= GeoTiff</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>)</p> <p>Projection information : EPSG 32719 (UTM19S)</p> <p><em>WB_mask_5m_UTM19S.tif</em></p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><strong>Repository structure</strong></p> <p>|__ 01_Scripts</p> <p> |+ run21_CopahueDSM_AMES_sviotto.sh</p> <p> |+ stereo.default</p> <p>|__ 02_DSMs</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_raw.tif</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</p> <p> |+ WB_mask_5m_UTM19S.tif</p> <p><strong>References</strong></p> <p>Beyer, R. A., Alexandrov, O., & McMichael, S. (2018). The Ames Stereo Pipeline: NASA's open source software for deriving and processing terrain data. <em>Earth and Space Science</em>, 5, 537– 548. <a href="https://doi.org/10.1029/2018EA000409">https://doi.org/10.1029/2018EA000409</a></p> <p>Collins, J., Riegler, G., Schrader, H., Tinz, M., 2015. Applying terrain and hydrological editing to TanDEM-X data to create a consumer-ready worlddem product. Int. Arch. Photogram. Rem. Sens. Spatial Inf. Sci. 40 (7), 1149. https://doi.org/10.5194/isprsarchives-XL-7-W3-1149-2015.</p> <p>Mueting, A., Bookhagen, B., & Strecker, M. R. (2021). Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina. <em>Journal of Geophysical Research: Earth Surface</em>, 126, e2021JF006330. <a href="https://doi.org/10.1029/2021JF006330">https://doi.org/10.1029/2021JF006330</a></p> <p>Viotto, S., Toyos, G., & Bookhagen, B. (2022). An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at volcán copahue (Argentina & Chile). Journal of South American Earth Sciences, 104138. https://doi.org/10.1016/j.jsames.2022.104138</p> <p> </p> <p> </p>
Mapping Monasticism: A Digital Approach to the Network of Conques (raw data)
<p>Original dataset used to produce the digital map of the Monastic Network of Conques. The file includes a link to the GMaps, as well as the KMZ file of the map itself.</p>
Data from: A method for mapping morphological convergence on three-dimensional digital models: the case of the mammalian saber-tooth
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Data from: Using digital soil maps to infer edaphic affinities of plant species in Amazonia: problems and prospects
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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.