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13,117 results for “Maps”
Diadema Map
<p>Diadema antillarium survey sites around the island of St. John, as studied by Renee Godard from 2017-2023</p>
Swift/BAT-GUANO upper limits fits map for S230924an
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Swift/BAT-GUANO upper limits fits map for S230919bj
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Swift/BAT-GUANO upper limits map for S230928cb
<p>Swift/BAT-GUANO upper limits map for S230928cb. The solid and dashed lines indicate the 90% and 50% GW contour levels, respectively</p>
Swift/BAT-GUANO upper limits fits map for S231001aq
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Swift/BAT-GUANO upper limits map for S231001aq
<p>Swift/BAT-GUANO upper limits fits map for S231001aq. The solid and dashed lines indicate the 90% and 50% GW contour levels, respectively.</p> <p> </p>
MAP 1 in Lectotypification and new data on the distribution of Begonia anisoptera (Begoniaceae, section Baryandra), a species endemic to the Zamboanga Peninsula
MAP 1. Map showing the distribution of Begonia anisoptera with its type locality as indicated by the red circle. Prepared by: MAK Naive.
Swift/BAT-GUANO upper limits map for S231005j
<p>Swift/BAT-GUANO upper limits map for S231005j. The solid and dashed lines indicate the 90% and 50% GW contour levels, respectively.</p> <p> </p>
Swift/BAT-GUANO upper limits fits map for S231005j
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FIGURES 1‒3. 1. Satellite map Ecuador. 2 in Rediscovery and redescription of Oxycheila buestani Wiesner with the first female record and new methodology for observation of Oxycheila Dejean and Oxygonia Mannerheim (Coleoptera: Cicindelidae)
FIGURES 1‒3. 1. Satellite map Ecuador. 2. West sub Andean region north from the town of Cumandá-Bucay. 3. Study area with surveyed transects L1 (red line) and L2 (yellow line).
Swift/BAT-GUANO upper limits fits map for S231014r
Open the record for dataset details and reuse information.
linguistlist/LL-MAP: LL-MAP: Language and Location - A Map Annotation Project
No description provided.
Baltic Sea flood maps under the influence of sea-level rise, dike height increases and managed realignment
<p>The provided data was produced as part of the Ecas-Baltic project (2020 - 2023). The project is funded by the Federal Ministry of Education and Research in Germany (BMBF, funding code 03F0860H).</p><p>The dataset contains information supporting the conclusions presented in the following publication (the final, revised version of the article will be accessible via the journal webpage):</p><p>Kiesel, J., Honsel, L.E., Lorenz, M., Gräwe, U., and Vafeidis, A. T.: Raising dikes and managed realignment may be insufficient for maintaining current flood risk along the German Baltic Sea coast, <a href="https://www.nature.com/commsenv/">Communications Earth & Environment</a>, accepted for publication, 2023.</p><p> </p><p>The dataset contains:</p><p>- the flood maps containing both the maximum flood extent and maximum inundation depth at every grid cell of the coastal inundation model. The flood maps cover two sea-level rise (1 m and 1.5 m) and three adaptation scenarios (state dikes plus 1.5 m, all dikes plus 1.5 m and potential managed realignment sites including state dikes plus 1.5 m)</p><p>- the potential for physically plausible managed realignment sites along the German Baltic Sea coast</p><p>- a readme file containing further information on the datasets and related data and publications</p><p> </p><p>For methodological details we refer the reader to the publication cited above and the publication presenting the modelling setup (Kiesel et al., 2023: https://doi.org/10.5194/nhess-23-2961-2023). The previously mentioned article provides inundation maps representing the current state of adaptation in terms of dike lines and associated elevations (https://doi.org/10.5281/zenodo.7886455). The code to detect the potential physically plausible managed realignment sites is publically available from https://gitlab.com/larsenno/sumare.</p>
Survey2Survey: A deep learning generative model approach for cross-survey image mapping
<p>During the last decade, there has been an explosive growth in survey data and deep learning techniques, both of which have enabled great advances for astronomy. The amount of data from various surveys from multiple epochs with a wide range of wavelengths, albeit with varying brightness and quality, is overwhelming, and leveraging information from overlapping observations from different surveys has limitless potential in understanding galaxy formation and evolution. Synthetic galaxy image generation using physical models has been an important tool for survey data analysis, while deep learning generative models show great promise. In this paper, we present a novel approach for robustly expanding and improving survey data through cross survey feature translation. We trained two types of neural networks to map images from the Sloan Digital Sky Survey (SDSS) to corresponding images from the Dark Energy Survey (DES). This map was used to generate false DES representations of SDSS images, increasing the brightness and S/N while retaining important morphological information. We substantiate the robustness of our method by generating DES representations of SDSS images from outside the overlapping region, showing that the brightness and quality are improved even when the source images are of lower quality than the training images. Finally, we highlight several images in which the reconstruction process appears to have removed large artifacts from SDSS images. While only an initial application, our method shows promise as a method for robustly expanding and improving the quality of optical survey data and provides a potential avenue for cross-band reconstruction.</p><p>------------</p><p>This repository contains the image files from Survey2Survey: a deep learning generative model approach for cross-survey image mapping. Please cite https://arxiv.org/abs/2011.07124 if you use this data in a publication. For more information, contact Brandon Buncher at buncher2(at)illinois.edu</p><p><strong>--- Directory structure ---</strong></p><p>tutorial.ipynb demonstrates how to load the image files (uploaded here as tarballs). Images were obtained from the SDSS DR16 cutout server (https://skyserver.sdss.org/dr16/en/help/docs/api.aspx) and DES DR1 cutout server (https://des.ncsa.illinois.edu/desaccess/</p><ul><li>./sdss_train/ and ./des_train/ contain the original SDSS and DES images used to train the neural network (Stripe82)</li><li>./sdss_test/ and ./des_test/ contain the original SDSS and DES images used for the validation dataset (Stripe82)</li><li>./sdss_ext/ contain images from the external SDSS dataset (SDSS images without a DES counterpart, outside Stripe82)</li><li>./cae and ./cyclegan contain images generated by the CAE and CycleGAN, respectively. train_decoded/ and test_decoded/ contain the reconstructions of the images from the training dataset and test dataset, respectively. external_decoded/ contain the DES-like image reconstructions of SDSS objects from the external dataset (outside Stripe82).</li></ul>
Microstructure Informed Myelin Mapping (MIMM)
<p>This is the example data for Microstructure Informed Myelin Mapping (MIMM). The corresponding code can be found in:</p><p>https://github.com/MertSisman/MIMM</p>
XJ_COTTON10: The 10-m cotton maps in Xinjiang, China during 2018-2021
<p>The datasets accompany the paper "The 10-m cotton maps in Xinjiang, China during 2018–2021" that was published in <a href="https://doi.org/10.1038/s41597-023-02584-3"><i>Scientific Data</i></a> on Oct. 10, 2023. The datasets contain the 10-m cotton maps of Xinjiang (XJ_COTTON10) from 2018 to 2021. They were developed through supervised classification using high-quality in-field samples and multi-source remote sensing data on the Google Earth Engine (GEE) platform.</p><p><strong>Citation</strong>:</p><p>[1] Kang, X., Huang, C., Chen, J.M., Lv, X., Wang, J., Zhong, T., Wang, H., Fan, X., Ma, Y., Yi, X., Zhang, Z., Zhang, L., Tong, Q., 2023. The 10-m cotton maps in Xinjiang, China during 2018-2021. Sci Data 10, 688. doi:10.1038/s41597-023-02584-3</p><p>[2] Lang, P., Zhang, L., Huang, C., Chen, J., Kang, X., Zhang, Z., Tong, Q., 2023. Integrating environmental and satellite data to estimate county-level cotton yield in Xinjiang Province. Frontiers in Plant Science 13, 1048479. doi:10.3389/fpls.2022.1048479</p><p>[3] Kang, X., Huang, C., Zhang, L., Zhang, Z., Lv, X., 2022. Downscaling solar-induced chlorophyll fluorescence for field-scale cotton yield estimation by a two-step convolutional neural network. Computers and Electronics in Agriculture 201, 107260. doi:10.1016/j.compag.2022.107260</p><p>[4] Kang, X., Huang, C., Zhang, L., Wang, H., Zhang, Z., Lv, X., 2023. Regional-scale cotton yield forecast via data-driven spatio-temporal prediction (STP) of solar-induced chlorophyll fluorescence (SIF). Remote Sensing of Environment 299, 113861. doi:10.1016/j.rse.2023.113861</p>
Seasonal suitability maps of migratory butterflies
<p>We created seasonal habitat suitability maps for migratory butterflies. The manuscript is now in review in PNAS. Once the manuscript is published, I will update all the publication-related information.</p>
Swift/BAT-GUANO upper limits fits map for S231020bw
Open the record for dataset details and reuse information.
Borre Viking - wooden carving (from depth maps)
this is a re-upload from one of my earliest models, but this time I calculated the mesh not from a dense point-cloud but from depth maps - got a very nice surface ... Source: Objaverse 1.0 / Sketchfab
Map Of Masada
test for masada archeological site Source: Objaverse 1.0 / Sketchfab
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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.