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13,117 results for “Maps”
Fig. 1. – Distribution maps. Brachiaria antsirabensis A in Revision of some Malagasy forage grasses and their relatives within Brachiaria, Echinochloa, Moorochloa, and Urochloa
Fig. 1. – Distribution maps. Brachiaria antsirabensis A. Camus (stars), B. bemarivensis A. Camus (triangles), B. comorensis (Mez) A. Camus (circles), and B. dimorpha A. Camus (squares). [Map: Sarah Z. Ficinski]
Map 7 in A revision of Gomphostemma (Lamiaceae)
Map 7. Distribution range of Gocphostecca hecsleyanuc, G. heyneanuc, G. cicrodon, G. nutans, G. pedunculatuc and G. repentuc.
Map 3 in A revision of Gomphostemma (Lamiaceae)
Map 3. Distribution range of Gocphostecca chinense, G. curtisii, G. flexuosuc, G. grandifloruc, G. hainanense and G. hirsutuc.
Map 6 in A revision of Gomphostemma (Lamiaceae)
Map 6. Distribution range of Gocphostecca stellatohirsutuc, G. sulcatuc, G. velutinuc and G. cicrocalyx.
Map 1 in A revision of Gomphostemma (Lamiaceae)
Map 1. Distribution range of Gocphostecca aĸorense, G. crinituc, G. dolichoĸotrys, G. eriocarpon and G. keralense.
Systematic Mapping Study in Legal NLP (Raw Data)
<p>Systematic Mapping Study in Legal NLP (Raw Data). It contains all papers processed, accepted, information extraction and all the steps taken, and which researchers reviewed what.</p>
Motor recalibration of visual and saccadic maps
<p>How does the brain maintain an accurate visual representation of external space? Movement errors following saccade execution provide sufficient information to recalibrate motor and visual space. Here, we asked whether spatial information for vision and saccades is processed in shared or in separate resources. We used saccade adaptation to modify both saccade amplitudes and visual mislocalization. After saccade adaptation was induced, we compared participants' saccadic and perceptual localization before and after we inserted "no error" trials. In these trials, we clamped the post-saccadic error online to the predicted end points of saccades. In separate experiments, we either annulled the retinal or the prediction error. We also varied the number of "no error" trials across conditions. In all conditions, we found that saccade adaptation remained undisturbed by the insertion of "no error" trials. However, mislocalization decreased as a function of the number of trials in which zero retinal error was displayed. When the prediction error was clamped to zero, no mislocalization was observed at all. The results demonstrate the post-saccadic error is used separately to recalibrate visual and saccadic space.</p>
Atlas of India Maps
<p>The Atlas of India was the main topographical map creation of British India. Started under the patronage of the British East India Company, the Atlas sheets were in production for almost eighty years, from 1827 till 1906. Its purpose was to compile available surveys, standardize them to a uniform scale, and print them along pre-defined sheet lines covering the whole of British India. Each sheet was to be engraved and produced separately as soon as reliable information became available. In this respect, its relationship with the Great Trigonometrical Survey was very important. Scale was 4 miles to an inch, and sheets were issued as full sheets and quarter sheets (NE, NW, SE, SW). Initially, the sheets were printed in London by commercial cartographers but its production process was transferred to India in 1870.</p> <p>After 1906, Atlas sheets and their data continued to be used by the Survey of India until 1947.</p> <p>The accompanying set consists of scans of more than 200 sheets printed during the period from 1827 till 1904. The file naming convention for each file is to first give the sheet number for whole sheets followed by the NW SW NE SE designation for quarter sheets; it also includes the date of publication in parenthesis. The files are listed in numerical order by sheet number. </p> <p>An Index Map with the title "0 Index to the Indian Atlas" showing the sheet numbering system is provided as the first entry in the list of files. The red dots in the index map mark availability of the particular sheet. A large dot in the center marks availability of a full sheet, and a smaller dot in a corner marks availability of a quarter sheet. The index map can be downloaded to assist in selecting particular maps of interest to the viewer. </p> <p>The map originals are held in many libraries, and scans shown here were sourced from the Library of Congress, Washington DC and other libraries.</p>
Starspot mapping with adaptive parallel tempering. II. Application to TESS data for M-dwarf flare stars, AU Microscopii, YZ Canis Minoris, and EV Lacertae (Flare Tables)
<p>For TESS PDC-SAP data of AU Mic (Sector 1 and 27), YZ CMi (Sector 7 and 34), and EV Lac (Sector 16), detected flare properties are listed: the peak time (BJD-2745000), amplitude, equivalent duration (sec), and e-folding time (day).</p>
The global 30-m forest canopy height map for 2020
<p>The global forest canopy height map with a resolution of 30 m for 2020 (GlobeFCH_2020_30m_v1) was generated by integrating the new-generation space-borne LiDAR (Global Ecosystem Dynamics Investigation, GEDI; Ice, Cloud, and Land Elevation Satellite-2, ICESat-2), Sentinel-1 SAR images, Sentinel-2 optical images and other ancillary data based on Google Earth Engine (GEE) platform. The coordinate system of the GlobeFCH_2020_30m_v1 is World Geodetic System 1984 (WGS 84) and the unit of the forest canopy height value is centimeter. The GlobeFCH_2020_30m_v1 was divided into 305 files, and the range of each file is 10°×10°.</p>
Figs 1–3. Maps. 1 in The Simuliidae (Diptera) of the Republic of São Tomé and Príncipe, including the description of a new species
Figs 1–3. Maps. 1. Location of the islands of São Tomé and Príncipe in the Gulf of Guinea. 2–3. The islands of São Tomé and Príncipe respectively, showing the major rivers and the localities where immature blackflies were found (numbered sites). River sites where no blackflies were found are indicated with an open circle.
Global maps of agricultural expansion potential at a 300 m resolution
<p><strong>Global </strong><strong>maps of agricultural expansion potential</strong><strong> at a 300 m resolution </strong></p> <p>This repository contains data from “Global maps of agricultural expansion potential at a 300 m resolution” study.</p> <p><strong>Abstract:</strong></p> <p>The global expansion of agricultural land is a leading driver of climate change and biodiversity loss. However, the spatial resolution of current global land change models is relatively coarse, which limits environmental impact assessments. To address this issue, we developed global maps representing the potential for conversion into agricultural land at a resolution of 10 arc-seconds (approximately 300 m at the equator). We created the maps using Artificial Neural Network (ANN) models relating locations of recent past conversions (2007-2020) into one of three cropland categories (cropland only, mosaics with >50% crops, and mosaics with <50% crops) to various predictor variables reflecting topography, climate, soil and accessibility. Cross-validation of the models indicated good performance with Area Under the Curve (AUC) values of 0.88-0.93. Hindcasting of the models from 1992 to 2006 revealed a similar high performance (AUC of 0.83-0.91), indicating that our maps provide representative estimates of current agricultural conversion potential provided that the drivers underlying agricultural expansion patterns remain the same. Our maps can be used to downscale projections of global land change models to more fine-grained patterns of future agricultural expansion, which is an asset for global environmental assessments.</p> <p><strong>Data description:</strong></p> <p>We provide here raster maps of agricultural expansion potential for three categories of agriculture - (i) cropland only, (ii) mosaics with >50% crops, and (iii) mosaics with <50% crops. The source for delineating categories was the ESA CCI land cover data. ESA CCI land cover data recognizes additional categories of agricultural land, however some of them have limited spatial coverage. For that reason, we merged the rainfed cropland and irrigated cropland categories into a single category - cropland only, where a grid cell is largely dominated by crops. Rainfed croplands account for 87% of the this category, while irrigated croplands account for the remaining 13%. Mosaic categories were defined in the same way as in the ESA CCI land cover dataset. Numerical designations of these categories in the ESA CCI land cover dataset are 10, 20, 30, and 40 for rainfed, irrigated, mosaics with >50% crops, and mosaics with <50% crops, respectively.</p> <p>Global maps are provided at the spatial resolution of 10 arc-seconds (~300 meters at the equator). These files are available for three categories in the main folder with the filename prefix "Agri_potential_mosaic_*<em>"</em>. The numerical value in the file name refers to the agricultural category type (10 - cropland only, 30 - mosaics with >50% crops, and 40 - mosaics with <50% crops). In addition to the 10 arc-second layers, we provide aggregated layers with the spatial resolution of 30 arc-seconds, 5 and 10 arc-minutes, for coarse-grained applications and less computationally-intensive analyses. We provide the aggregated layer maps for the minimum, median, mean/average, and maximum values of the aggregated 10 arc-seconds values within the coarser cells. There are in total 9 files provided for each of the aggregated spatial resolutions.</p> <p><strong>Repository content:</strong></p> <p>Full resolution layers:<br> - “Agri_potential_mosaic_10.tif” is the global raster map for cropland only category at the spatial resolution of 10 arc-seconds.<br> - “Agri_potential_mosaic_30.tif” is the global raster map for mosaics with >50% crops category at the spatial resolution of 10 arc-seconds.<br> - “Agri_potential_mosaic_40.tif” is the global raster map for mosaics with <50% crops category at the spatial resolution of 10 arc-seconds.<br> - "readme.txt" is the text file with the basic description and the metadata for the repository.</p> <p>Aggregated layers:<br> This folder contains files with a different spatial resolution (30s, 5m, 10m; see argument "RESL" below).</p> <p>File names for the aggregated maps contain the following information: “Agri_potential_aggregated_RESL_TYPE_CATG.tif”</p> <p>- "RESL" is the spatial resolution of the layer. Value is either "30s", "5m", or "10m", corresponding to spatial resolution of 30 arc-second, 5 arc-minutes, and 10 arc-minutes.</p> <p>- "TYPE" is the type of aggregated values. Value is either "min", "avg", "med", or "max", corresponding to the minimum, mean, median, and maximum values of the aggregated 10 arc-seconds values within the coarser cells.</p> <p>- "CATG" is the category of agricultural land. Value is either "10", "30", or "40", where category 10 is cropland only, category 30 is mosaics with >50% crops, and category 40 is mosaics with <50% crops.</p> <p>Raster metadata:</p> <p>Driver: GTiff<br> Projection proj4string: +proj=longlat +ellps=WGS84 +no_defs</p> <p><strong>Notes on use:</strong></p> <p>Our conversion potential maps are useful for researchers and practitioners interested in downscaling projections of global land change models to a more fine-grained patterns of future agricultural expansion, or interested in assessing the locations and effects of future agricultural expansion, for example in integrated assessment modelling or biodiversity impact modelling. When coupling outputs with integrated assessment modelling, our maps need to be combined with estimates of the expected future demands for agricultural land per socio-economic region. In such a coupled approach, our global conversion potential maps can be used to spatially allocate the additional agricultural land demands. In this context, it is important to note that the modelled relationships between the agricultural conversions and our set of predictors may result in non-zero probabilities also in areas that are highly unlikely to be converted into agriculture, such as urban areas or strictly protected nature reserves. This implies that users of our maps may need to implement an additional map layer that masks areas unavailable for agricultural expansion. We also stress that our maps represent agricultural conversion potential conditional on the predictor variables that we included, implying that our maps do not capture the possible influences of other potentially relevant predictors. For example, our conversion potential models and maps do not account for permafrost, which may pose significant challenges to possible agricultural expansion to higher latitudes in response to climate change.</p>
Molecular mapping of quantitative trait loci (QTL) for resistance to early blight in tomato
<p>Molecular mapping of quantitative trait loci (QTL) for resistance to early blight in tomato (1135884)</p>
High-resolution (5 m) surface water persistence map for 2021 in East-Africa
<p>Raster surface water image highlighting the percentage of time in 2021 that there was water at a certain pixel in East Africa. </p> <p>Script which classifies individual countries: <a href="https://code.earthengine.google.com/3f508773522979dfa62a75bda7750b5f?noload=true">https://code.earthengine.google.com/3f508773522979dfa62a75bda7750b5f?noload=true</a></p> <p>Script which combines the individual maps and filters the end-result: <a href="https://code.earthengine.google.com/ed8ee1bbade0b51f3759565b30da37ab?noload=true">https://code.earthengine.google.com/ed8ee1bbade0b51f3759565b30da37ab?noload=true</a></p> <p> </p>
MAP 5 in A taxonomic revision of the spider genus Pseudopoda Jäger, 2000 (Araneae: Sparassidae) from East, South and Southeast Asia
MAP 5. Distribution of Pseudopoda species in Southwest-China (Yunnan Province in Map 4): 2. P. acutiformis spec. nov.; 7. P. apiculiformis spec. nov.; 8. P. applanata spec. nov.; 12. P. bacilliformis spec. nov.; 14. P. bazhongensis spec. nov.; 15. P. beibeiensis spec. nov.; 23. P. chishuiensis spec. nov.; 25. P. coenobium; 29. P. confusa; 30. P. conica spec. nov.; 33. P. curva spec. nov.; 39. P. digitaliformis spec. nov.; 43. P. exigua; 48. P. flexa spec. nov.; 57. P. jiangi spec. nov.; 64. P. longcanggouensis spec. nov.; 79. P. nayongensis spec. nov.; 88. P. qingxiensis spec. nov.; 90. P. rivicola; 99. P. spiralis spec. nov.; 103. P. taipingensis spec. nov.; 109. P. virgata; 111. P. wenchuanensis spec. nov.; 114. P. yangensis spec. nov.; 118. P. yingjingensis spec. nov.
MAP 4 in A taxonomic revision of the spider genus Pseudopoda Jäger, 2000 (Araneae: Sparassidae) from East, South and Southeast Asia
MAP 4. Distribution of Pseudopoda species in China (Yunnan Province): 5. P. amphitropa spec. nov.; 10. P. auricularis spec. nov.; 13. P. baoshanensis spec. nov.; 18. P. cangschana; 19. P. caoi spec. nov.; 24. P. chuxiongensis spec. nov.; 26. P. colossa spec. nov.; 29. P. confusa; 31. P. contentio; 32. P. contraria; 34. P. daguanensis spec. nov.; 36. P. datangensis spec. nov.; 37. P. daweiensis spec. nov.; 38. P. dengi spec. nov.; 40. P. digitata; 51. P. grandis spec. nov.; 54. P. huanglianensis spec. nov.; 59. P. kavanaughi spec. nov.; 60. P. kongdangensis spec. nov.; 61. P. langyaensis spec. nov.; 62. P. liui spec. nov.; 63. P. longa spec. nov.; 65. P. longiuscula spec. nov.; 68. P. luechunensis spec. nov.; 69. P. mamillaris spec. nov.; 72. P. mengsongensis spec. nov.; 74. P. mojiangensis spec. nov.; 75. P. namkhan; 80. P. nematodes spec. nov.; 81. P. nujiangensis spec. nov.; 82. P. olivea spec. nov.; 83. P. oliviformis spec. nov.; 86. P. parvipunctata; 89. P. rhopalocera; 90. P. rivicola; 91. P. rufosulphurea; 96. P. songi; 98. P. spiculata; 104. P. taoi spec. nov.; 108. P. trinacriformis spec. nov.; 112. P. wui spec. nov.; 113. P. wulaoensis spec. nov.; 119. P. yunfengensis spec. nov.; 120. P. zhaoae spec. nov.; 122. P. zuoi spec. nov.
MAP 6 in A taxonomic revision of the spider genus Pseudopoda Jäger, 2000 (Araneae: Sparassidae) from East, South and Southeast Asia
MAP 6. Distribution of Pseudopoda species in Central and Southeast China: 3. P. aliena spec. nov.; 4. P. allantoides spec. nov.; 35. P. daiyunensis spec. nov.; 42. P. drepanoides spec. nov.; 44. P. exiguoides; 45. P. explanata spec. nov.; 46. P. falcata spec. nov.; 49. P. foliiculiaris spec. nov.; 50. P. gracilenta spec. nov.; 53. P. huangensis spec. nov.; 55. P. hupingensis spec. nov.; 56. P. imparilis spec. nov.; 58. P. jiugongensis spec. nov.; 66. P. longtanensis spec. nov.; 67. P. longxiensis spec. nov.; 77. P. nankunensis spec. nov.; 78. P. nanlingensis spec. nov.; 84. P. pantianensis spec. nov.; 85. P. papilionacea spec. nov.; 93. P. sangzhiensis spec. nov.; 95. P. shimenensis spec. nov.; 100. P. strombuliformis spec. nov.; 102. P. subtilis spec. nov.; 105. P. taoyuanensis spec. nov.; 106. P. tianpingensis spec. nov.; 115. P. yangmingensis spec. nov.; 116. P. yangtaiensis spec. nov.; 117. P. yilanensis spec. nov.
Database Publication - Exploring adult age-at-death research in anthropology: Bibliometric mapping and content analysis, Forensic Sciences, 2023
<p>The dataset contains the publications' ID and DOI numbers used in the bibliometric analysis for the paper "Exploring adult age-at-death research in anthropology: Bibliometric mapping and content analysis," which was published in Forensic Sciences. Dimensions only granted the authors permission to disclose the publication IDs and DOIs for this study. However, using the identification codes, it is possible to extract titles' full metadata through Dimensions (<a href="https://www.dimensions.ai/">https://www.dimensions.ai/</a>).</p>
Land cover classification and mapping of a polar desert in the Canadian Arctic Archipelago
<p>The use of remote sensing for developing land cover maps in the Arctic has grown considerably in the last two decades, especially for monitoring the effects of climate change. The main challenge is to link information extracted from satellite imagery to ground covers due to the fine-scale spatial heterogeneity of Arctic ecosystems. There is currently no commonly accepted methodological scheme for high-latitude land cover mapping, but the use of remote sensing in Arctic ecosystem mapping would benefit from a coordinated sharing of lessons learned and best practices. Here, we aimed to produce a highly accurate land cover map of the surroundings of the Canadian Forces Station Alert, a polar desert on the northeastern tip of Ellesmere Island (Nunavut, Canada) by testing different predictors and classifiers. To account for the effect of the bare soil background and water limitations that are omnipresent at these latitudes, we included as predictors soil-adjusted vegetation indices and several hydrological predictors related to waterbodies and snowbanks. We compared the results obtained from an ensemble classifier based on a majority voting algorithm to eight commonly used classifiers. The distance to the nearest snowbank and soil-adjusted indices were the top predictors allowing the discrimination of land cover classes in our study area. The overall accuracy of the classifiers ranged between 75 and 88%, with the ensemble classifier also yielding a high accuracy (85%) and producing less bias than the individual classifiers. Some challenges remained, such as shadows created by boulders and snow covered by soil material. We provide recommendations for further improving classification methodology in the High Arctic, which is important for the monitoring of Arctic ecosystems exposed to ongoing polar amplification.</p>
Counting animals in aerial images with a density map estimation model
<p>Animal abundance estimation is increasingly based on drone or aerial survey photography. Manual post-processing has been used extensively, however, volumes of such data are increasing, necessitating some level of automation, either for complete counting or as a labour-saving tool. Any automated processing can be challenging when using such tools on species that nest in close formation such as <em>Pygoscelis</em> penguins. We present here a customized CNN-based density map estimation method for counting of penguins from low-resolution aerial photography. Our model, an indirect regression algorithm, performed significantly better in terms of counting accuracy than standard detection algorithm (Faster RCNN) when counting small objects from low-resolution images and gave an error rate of only 0.8 percent. Density map estimation methods as demonstrated here can vastly improve our ability to count animals in tight aggregations, and demonstrably improve monitoring efforts from aerial imagery. </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.