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13,117 results for “Maps”
Map 1 in Four new species and additional records of Geostiba from Turkey and Crete, and a new synonymy (Coleoptera: Staphylinidae: Aleocharinae)
Map 1: Distributions of Geostiba janbellini nov.sp. (filled circle), G. atrioculata nov.sp. (open circle), and G. spinosula nov.sp. (square) in southern Anatolia.
Map 1 in Eulophidae Of Costa Rica (Hymenoptera: Chalcidoidea), 4 The Genus Closterocerus Westwood S.Str.
Map 1. Costa Rica showing the localities from which significant Malaise trap, yellow pan trap and sweep-net samples have been collected and processed. These are: 1. Murcielago; 2. Cerro el Hacha; 3. Estación Maritza; 4. Estación Pitilla; 5. P.N. Santa Rosa; 6. Estación Cacao; 7. Finca San Gabriel; 8. Macizo Miravalles; 9. Marino las Baulas; 10. Bosque Nacional Diría; 11. Barra Honda; 12. Z.P. Nosara; 13. Reserva Absoluta Cabo Blanco; 14. Reserva Privada Karen Mogensen; 15. P.N. Palo Verde; 16. Volcán Arenal;. 17. Monteverde; 18. Jabillos; 19. Est. Biol. San Ramón; 20. Aguas Zarcas; 21. Est. Biol. La Selva; 22. Río Frio; 23. Tortuguero; 24. Volcán Poas; 25. Vara Blanca; 26. 16km W Guapiles; 27. El Tunel (Zurqui); 28. Zurqui de Moravia; 29. INBio Parque; 30. San Antonio de Escazú; 31. Cerros de Escazú; 32. Carara; 33. La Cangreja; 34. P.N. Tapantí; 35. Mirador Quetzales; 36. Cerro de la Muerte; 37. Villa Mills; 38. Est. Biol. Cuéricí; 39. 26km N San Isidro; 40. R.B. Hitoy-Cerere; 41. Cahuita; 42. Bribri; 43. Est. Altamira; 44. Las Alturas; 45. San Vito, Wilson Botanical Gardens; 46. 5km W. Peñas Blancas; 47. Golfo Dulce; 48. Rincón de Osa; 49. La Palma; 50. Est. Los Patos; 51. Corcovado; 52. Puerto Jiménez; 53. Est. Agujas. (Modified from Gauld, 2000)
Fig. 5. Combined tree from Fig. 4 with mapped morphological characters. Numbers above branches represent characters from Table 3. Mapped using WinClada ver. 1.61 in A revision of Scipopus Enderlein including the subgenera Scipopus s. str., Phaeopterina Frey and Parascipopus subgen. nov. (Diptera, Micropezidae, Taeniapterinae)
Fig. 5. Combined tree from Fig. 4 with mapped morphological characters. Numbers above branches represent characters from Table 3. Mapped using WinClada ver. 1.61 (Nixon 1999–2002) with unambiguous characters only.
Wildland-Urban Interface maps for the Polish Carpathians for 1860s, 1970s and 2013
<p>The dataset contains three (1860s, 1970s, 2013) detailed Wildland-Urban Interface (WUI) maps of the Polish Carpathians, including information on building density. The maps, available in the form of 10m raster GeoTIFF files, are based on the WUI definition of US Federal Register (USDA and USDI, 2001) as operationalized by (Radeloff et al., 2005), which distinguishes two kinds of WUI: intermix, where housing intermingle with wildland vegetation, and interface, where settlement abuts the wildland areas. Either WUI type requires a housing density higher than 6.17 houses/km2 (1 house/40 acres in the US context). In intermix WUI, there has to be also > 50% wildland vegetation, while the interface WUI, has < 50% wildland vegetation but is within 2.4 km of a wildland vegetation patch larger > 5 km2. Given the ecological context of the Polish Carpathians, we defined wildland vegetation as forests, because forests are the climax vegetation type below the treeline. To assess settlements, we analysed all buildings locations (residential and non-residential), because all buildings reflect human activities. The building density and forest cover share were calculated by using a 500m circular moving window algorithm.</p><p>Acknowledgements<br>The study was supported by the National Science Centre, Poland, contract no. UMO-2019/35/D/HS4/00117 and by the NASA Land Use and Land Cover Change Program.</p>
Figure 10. Geographic location map for Pepsis cerberus, P in The Pepsis menechma Lepeletier (Hymenoptera: Pompilidae: Pepsinae) taxonomic and nomenclatural problem
Figure 10. Geographic location map for Pepsis cerberus, P. elegans, and P. novitia in the Nearctic Region (based on Brimley 1936; Hurd 1952; Krombein 1952; Johnston 2000; Bond and Opell 2002; Vardy 2005; Leavengood et al. 2011; Bond and Godwin 2013; Hamilton et al. 2016; Norden 2017; Godwin and Bond 2021; Durand, pers. comm.; BugGuide.net; flickr.com; iNaturalist.org; gbif.org; SCAN; and specimen records from 36 insect collections as listed in Materials and Methods). Black lines represent range limits of potential host spider genera. Solid black line represents geographic limit of Ummidia (Halonoproctidae) species (Godwin and Bond 2021). Dashed black line represents geographic limit of Aphonopelma (Theraphosidae) species (Hamilton et al. 2016). Dotted black line represents geographic limit of Eucteniza (Euctenizidae) species (Bond and Godwin 2013). Dash-dotted black line represents geographic limit of Entychides Simon (Euctenizidae) species (Bond and Opell 2002).
Land use maps Bono East, Ghana (2020 and 2050)
<p>Land use GIS files of the Bono East region in Ghana at a resolution of 30x30 meters. This publication contains two geo-tif files and an accompanying QGIS style. The '2020' GIS file represents the current land use. The '2050' file represents a possible future as created through the application of the iCLUE land use projection model.</p><p>For more information, see Duku et al (2023) - <a href="https://doi.org/10.18174/584095">https://doi.org/10.18174/584095</a> or <a href="https://edepot.wur.nl/584095">https://edepot.wur.nl/584095</a></p>
Рис. 1. Карта района иссΛеΑований. 1 – граница зон раститеΛьности; 2 – граница поΑзон раститеΛьности; 3 – места сбора материаΛа; I – южная тайга; II – среΑняя тайга; III – северная тайга; IV – крайнесеверная тайга; V – ΛесотунΑра; VI – южная тунΑра; VII – северная тунΑра. Fig. 1. Map of the studied area. 1 – boundary of vegetation zones; 2 – boundary of vegetation subzones; 3 – collection points; I – southern taiga; II – middle taiga; III – northern taiga; IV – extremely northern taiga; V – forest tundra; VI – southern tundra; VII – northern tundra. in Fauna and landscape-zonal distribution of Orthoptera in the Komi Republic (Russia)
Рис. 1. Карта района иссΛеΑований. 1 – граница зон раститеΛьности; 2 – граница поΑзон раститеΛьности; 3 – места сбора материаΛа; I – южная тайга; II – среΑняя тайга; III – северная тайга; IV – крайнесеверная тайга; V – ΛесотунΑра; VI – южная тунΑра; VII – северная тунΑра. Fig. 1. Map of the studied area. 1 – boundary of vegetation zones; 2 – boundary of vegetation subzones; 3 – collection points; I – southern taiga; II – middle taiga; III – northern taiga; IV – extremely northern taiga; V – forest tundra; VI – southern tundra; VII – northern tundra.
Milo csQTL mapping Summary Statistics
<p>Summary statistics from Milo cell state quantitative trait loci mapping analysis on human peripheral immune cells from <a>Randolph <i>et al.</i></a> Each folder contains csQTLs per chromosome. Files are names 'chromosome_position_minorallele_majorallele_locus.tsv.gz'<i>. </i>Data are block gzip compressed and tabix indexed for rapid access based on SNP position (GRCh38 coordinates).</p>
Mapping the core of the Tarantula Nebula with VLT-MUSE. III. A template for metal-poor starburst regions in the visual and far-ultraviolet
<p>Cumulative optical (VLT/MUSE) and far-ultraviolet (mix of HST empirical and ULLYSES templates) spectrum of NGC2070 (2x2 arcmin^2) presented in Figures 2 and 4, respectively, of Crowther & Castro (MNRAS in press, https://arxiv.org/abs/2311.07642) which should be cited if either dataset is used. </p><p>Contents:</p><p>MUSE.dat (ascii format, column 1 wavelength in Angstrom, column 2 flux in erg/s/cm^2/Ang). Further details of MUSE dataset is described in N. Castro et al. (2018 A&A 614 A147)</p><p>ULLYSES.dat (ascii format, column 1 wavelength in Angstrom, column 2 flux in erg/s/cm^2/Ang, some detector gaps). Further details of ULLYSES survey is described in R. Roman-Duval et al. (2020, Research Notes of AAS, 4, 205)</p>
Reference model and embedding for human kidney endothelial cell mapping
<p>Reference model and embedding for human kidney endothelial cell mapping</p><p>The reference model serves as a basis for the mapping of new data to the HLCA using scArches (Lotfollahi et al., https://doi.org/10.1038/s41587-021-01001-7). </p>
Annual human footprint maps at 100-meter resolution from 1990 to 2020 for Qinghai-Tibet Plateau
<p>This Human footprint product is the first annual dataset with a resolution of 100 meters from 1990 to 2020 covering the Qinghai-Tibet Plateau (QTP). It integrates eight variables: population density, cropland, built environments, grazing activities, nighttime lights, roads, railways, and hydroelectric projects, each providing insights into various human impacts on the plateau. Additionally, the dataset's accuracy was assessed using 1,043 samples with a median resolution of 0.5 m, uniformly distributed across the QTP. These HF maps can serve as a critical tool for understanding the extensive human influence on the QTP, and can aid in conservation planning, resource management, and informing decision-making processes related to ecological restoration objectives. </p>
Canopy Height Map of Los Angeles County Native Habitat Areas
<p>Using LARIAC4 (2016) LIDAR imagery this Canopy Height Model was derived using the lidR, terra and sf packages in R at a resolution of 1 meter. Native habitat areas were selected based on occurence of native flora taxa from iNaturalist in each LARIAC tile. Tiles with no or very few native plants were not included. Additionally, LA County north of the Santa Clara river and San Gabriel mountains was not included, please contact me if you need additional areas. Non-organic features such as power lines, houses, and some high altitude LIDAR noise are still present in the data, future efforts are needed to remove these. Processing took place on the Occidental College computing cluster using a machine with 192GB RAM and 32 cores and took approximately 3 days. The code used to generate this CHM is available here: https://gist.github.com/max-mapper/d52ad9df2f9ed4d191e67955f950e044. The CHM is available as Cloud Optimized Geotiff, which can be viewed in QGIS dynamically over HTTPS without requiring a full download.</p>
Dataset for publication "Multi-phase quantitative compositional mapping by LA-ICP-MS: analytical approach and data reduction protocol implemented in XMapTools"
<p>Datasets for the publication "Multi-phase quantitative compositional mapping by LA-ICP-MS: analytical approach and data reduction in XMapTools"</p>
Fine-scale map reveals highly variable recombination rates associated with genomic features in the Eurasian blackcap
<p>This project estimates historical recombination rates of the Eurasian blackcap (<em>Sylvia atricapilla</em>) and the garden warbler (<em>Sylvia borin)</em> using a linkage disequilibrium-based approach. The population-scaled recombination rates were inferred using 'pyrho' considering the population's demographic information and mutation rates. The mutation rate was taken from a closely related species (flycatcher, <em>Ficedula albicollis</em>).</p> <p>Here we report the recombination rate per site per generation (r, output of pyrho) for all the chromosomes along the genome for both species. </p> <p>Blackcap dataset file: WholeGenome_chromosomes_blackap_n38_Pen20_W50.gz</p> <p>Garden warbler dataset file: WholeGenome_chromosomes_SylBor_n10_Pen20W50.gz</p> <p> </p> <p>Additionally, we annotated the blackcap genome to evaluate the association between recombination rates and specific annotation features (e.g., genes, transcription start sites, promoters, 5'UTR, and 3'UTR regions). The annotation of the genome was based on RNA-seq and Iso-Seq data. It can be found with the name bSylAtr1.1.gff.</p>
Mapping the Literature on Biodiversity and Taxonomy
<p>Talk at the <a href="https://www.digital-philosophy.org/" target="_blank" rel="noopener">Philosophy [in:of:for:and] Digital Knowledge Infrastructures</a> online workshop 2023 (28/09/2023).</p>
Figure 3 in Terrestrial Ecoregions of the World: A New Map of Life on Earth
Figure 3. The relative richness of terrestrial mammal species by ecoregion is depicted. Warmer colors denote ecoregions containing richer assemblages.
Figure 2 in Terrestrial Ecoregions of the World: A New Map of Life on Earth
Figure 2. The map of terrestrial ecoregions of the world recognizes 867 distinct units, roughly a fourfold increase in biogeographic discrimination over that of the 193 units of Udvardy (1975). Maps of freshwater and marine ecoregions are similarly needed for conservation planning.
Figure 1. The ecoregions are categorized within 14 in Terrestrial Ecoregions of the World: A New Map of Life on Earth
Figure 1. The ecoregions are categorized within 14 biomes and eight biogeographic realms to facilitate representation analyses.
Figure 4 in Terrestrial Ecoregions of the World: A New Map of Life on Earth
Figure 4. The level of species endemism for terrestrial mammals shows different patterns than that of richness. Warmer colors denote ecoregions containing more endemic species.
DoubleChEC program to identify transcription factor binding sites from mapped ChEC-seq data
<p>ChIP-seq (chromatin immunoprecipitation followed by sequencing) is commonly used to identify genome-wide protein-DNA interactions. However, ChIP-seq often gives a low yield, which is not ideal for quantitative outcomes. An alternative method to ChIP-seq is ChEC-seq (Chromatin endogenous cleavage with high-throughput sequencing). In this method, the endogenous TF (transcription factor) of interest is fused with MNase (micrococcal nuclease) that non-specifically cleaves DNA near binding sites. Compared to the <a href="https://www.nature.com/articles/ncomms9733" rel="nofollow">original ChEC-seq method</a>, the <a href="https://sites.northwestern.edu/bricknerlab/" rel="nofollow">modified version</a> requires far less amplification. Since <a href="https://github.com/macs3-project/MACS/tree/master#introduction">MACS3</a> failed to identify peaks in data generated from the modified ChEC-seq method, a new peak finder has been developed specifically for it.</p> <p>There are three functions in the <em><code>peak_finder/</code></em>. <code>callpeaks()</code> is used to identify peaks from BAM files. <code>goanalysis()</code> is used to make GO (Gene Ontology) term plots from peaks. <code>bedtomeme()</code> is a wrapper function to perform <a href="https://meme-suite.org/meme/tools/meme" rel="nofollow">MEME analysis</a> in R <strong>after <a href="https://meme-suite.org/meme/doc/download.html" rel="nofollow">MEME Suite</a> is installed locally</strong>.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.