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43 results for “science mapping”
Map. Distribution of Micomitra stupida (Rossi, 1790) on data in Zaitzev [1966]; Evenhuis, Greathead [2015] and the collection of the Zoological institute of Russian Academy of Sciences. Карта. Распространение Micomitra stupida (Rossi, 1790) по данным иЗ Зайцева [1966]; Evenhuis, Greathead [2015] и коллекционным материалам Зоологического института РАН. in First record of a bee fly (Diptera: Bombyliidae) parasitic on antlions (Myrmeleontidae) in Russia
Map. Distribution of Micomitra stupida (Rossi, 1790) on data in Zaitzev [1966]; Evenhuis, Greathead [2015] and the collection of the Zoological institute of Russian Academy of Sciences. Карта. Распространение Micomitra stupida (Rossi, 1790) по данным иЗ Зайцева [1966]; Evenhuis, Greathead [2015] и коллекционным материалам Зоологического института РАН.
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>
Studies Rejected and Selected on the Systematic Mapping of Open Science Practices in Software Engineering
<p>Files in .xlsx format, describing the rejected and selected studies, recovered in a Systematic Mapping about Open Science Practices in Software Engineering.</p>
Data from: Phenology of migrating game birds in Italy based on citizen science data: mean maps and grid maps for 23 species
Open the record for dataset details and reuse information.
Data from: Integrating regulatory surveys and citizen science to map outbreaks of forest diseases: acute oak decline in England and Wales
The number of emerging tree diseases has increased rapidly in recent times, with severe environmental and economic consequences. Systematic regulatory surveys to detect and establish the distribution of pests are crucial for successful management efforts, but resource intensive and costly. Volunteers who identify potential invasive species can form an important early warning network in tree health, however, what these data can tell us and how they can be best used to inform and direct official survey effort is not clear. Here we use an extensive dataset on Acute Oak Decline (AOD) as an opportunity to ask how verified data received from the public can be utilised. Information on the distribution of AOD was available as (i) systematic regulatory surveys conducted throughout England and Wales (ii) ad-hoc sightings reported by land owners, land managers and members of the public (i.e. 'self-reported' cases). By using the available self-reported cases at the design stage the systematic survey could focus on defining the boundaries of the affected area. This maximised the use of available resources and highlights the benefits to be gained by developing strategies to enhance volunteer efforts in future programs.
CSET Map of Science
<p>This dataset contains basic metadata for the <a href="https://sciencemap.eto.tech">CSET Map of Science</a> in JSONL form.</p>
AMASS MOOC Lesson 7.3.: Mapping research on the social impact of the arts - what characterizes the field? (Presented by the Swedish School of Library and Information Science)
Open the record for dataset details and reuse information.
Map 2 from: Belousov IA, Kabak II (2021) Yalongaphaenops erwini gen. et sp. nov., the world's most high-altitude hypogean trechine beetle from China (Coleoptera, Carabidae, Trechinae). In: Spence J, Casale A, Assmann T, Liebherr JК, Penev L (Eds) Systematic Zoology and Biodiversity Science: A tribute to Terry Erwin (1940-2020). ZooKeys 1044: 197-220. https://doi.org/10.3897/zookeys.1044.62572
Map 2 Relief map of the area nearby the type locality of Y. erwini gen. et sp. nov. The yellow line shows the closest distance to the upper forest limit
MAP 8 in Insecta non gratae: New Distribution Records of Eight Alien Bug (Hemiptera) Species in Turkey with Contributions of Citizen Science
MAP 8. Distribution of Metcalfa pruinosa (Say, 1830) in Turkey
MAP 7 in Insecta non gratae: New Distribution Records of Eight Alien Bug (Hemiptera) Species in Turkey with Contributions of Citizen Science
MAP 7. Distribution of Stictocephala bisonia Kopp et Yonke, 1977 in Turkey.
MAP 5 in Insecta non gratae: New Distribution Records of Eight Alien Bug (Hemiptera) Species in Turkey with Contributions of Citizen Science
MAP 5. Distribution of Perillus bioculatus (Fabricius, 1775) in Turkey.
MAP 4 in Insecta non gratae: New Distribution Records of Eight Alien Bug (Hemiptera) Species in Turkey with Contributions of Citizen Science
MAP 4. Distribution of Leptoglossus occidentalis (Heidemann, 1910) in Turkey.
MAP 1 in Insecta non gratae: New Distribution Records of Eight Alien Bug (Hemiptera) Species in Turkey with Contributions of Citizen Science
MAP 1. Distribution map of Zelus renardii Kolenati, 1856 in Turkey.
MAP 3 in Insecta non gratae: New Distribution Records of Eight Alien Bug (Hemiptera) Species in Turkey with Contributions of Citizen Science
MAP 3. Distribution of Corythucha ciliata (Say, 1832) in Turkey.
Mapping of Revised Field of Science and Technology (FOS) Classification to Web of Science, Scopus and Dimensions Research Sheames
<p>This data set represents research area classification mapping of Web of Science, Scopus and Dimensions to the OECD Category schema. The OECD Category schema corresponds to the <a href="http://unstats.un.org/unsd/EconStatKB/Attachment332.aspx?AttachmentType=1">Revised Field of Science and Technology (FOS) Classification of the Frascati Manual.</a> </p> <p>Mapping process was conducted in January 2023, utilizing the then-current versions of the All Science Journal Classification Codes (ASJC) for Scopus and the Web of Science Research Area scheme. For Dimensions, the mapping was based on the Australian and New Zealand Standard Research Classification (ANZSRC) 2020.</p>
Supplementary material 2 from: Woodburn M, Buschbom J, Grant S, Jones J, Norton B, Trekels M, Vincent S, Webbink K (2023) No Pain No Gain: Standards mapping in Latimer Core development. Biodiversity Information Science and Standards 7: e113053. https://doi.org/10.3897/biss.7.113053
Extended SSSOM mappings
Supplementary material 1 from: Woodburn M, Buschbom J, Grant S, Jones J, Norton B, Trekels M, Vincent S, Webbink K (2023) No Pain No Gain: Standards mapping in Latimer Core development. Biodiversity Information Science and Standards 7: e113053. https://doi.org/10.3897/biss.7.113053
SKOS mappings
Data from: Integrating regulatory surveys and citizen science to map outbreaks of forest diseases: acute oak decline in England and Wales
Open the record for dataset details and reuse information.
Map 1 from: Belousov IA, Kabak II (2021) Yalongaphaenops erwini gen. et sp. nov., the world's most high-altitude hypogean trechine beetle from China (Coleoptera, Carabidae, Trechinae). In: Spence J, Casale A, Assmann T, Liebherr JК, Penev L (Eds) Systematic Zoology and Biodiversity Science: A tribute to Terry Erwin (1940-2020). ZooKeys 1044: 197-220. https://doi.org/10.3897/zookeys.1044.62572
Map 1 Type locality of Yalongaphaenops erwini gen. et sp. nov. (yellow pushpin).
Cognitive maps examples for behavioural science journal
<p>Cognitive maps examples for behavioural science journal</p>
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.