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13,117 results for “Maps”
Mapping out the aqueous surface chemistry of metal oxide nanocrystals; carboxylate, phosphonate and catecholate ligands
<p>Data underlying the figures in the publication “Mapping out the aqueous surface chemistry of metal oxide nanocrystals; carboxylate, phosphonate and catecholate ligands”, published in JACS Au.</p> <p> </p> <p>Table of contents:</p> <p>The <em>.pxp</em> documents contain the experimental data of the figures in the manuscript and it can be opened/edited with the software IGOR Pro 8.0 or higher.</p> <p><strong>1. Figure 1.pxp</strong>: Experimental data for <em>Figure 1</em>. (A) Solvothermal synthesis of HfO<sub>2</sub> nanocrystals starting from 1 equivalent Hf(O-tBu)<sub>4</sub> and 80 equivalents benzyl alcohol. (B) <sup>1</sup>H NMR spectra (normal or diffusion filtered) of MEEAA functionalized HfO<sub>2</sub> NCs in different solvents. The α and β resonances belong to the residual hydroxyl and methyl groups of methanol, respectively. (C) Transmission Electron Microscopy (TEM) image of the synthesized HfO<sub>2</sub> NCs. The NC diameter of the quasi-spherical NCs was calculated after measuring the surface area of at least 150 NCs and calculated the diameter as if it was a circle. A size distribution histogram and a zoomed-in image of a singular NC can be seen respectively in the bottom left and the top right corner.</p> <p><strong>2. Figure 2.pxp</strong>: Experimental data for <em>Figure 2</em>. (A) Ligand exchange performed between MEEAA functionalized NCs and PA-PEG. (B) <sup>1</sup>H NMR reference spectra in MeOD of the free ligands as reference and the stepwise titration of MEEAA functionalized NCs with PA-PEG, equivalents are with respect to the total amount of MEEAA present. (C) <sup>31</sup>P NMR spectra (4096 scans) in MeOD for the stepwise titration of MEEAA functionalized NCs with PA-PEG, broadened signals are indicative of NC binding. (D) Diffusion filtered <sup>1</sup>H NMR spectra of MEEAA functionalized NCs in MeOD after addition of 1.3 equivalents of PA-PEG. Signals arising from bound MEEAA are denoted in red, signals arising from PA-PEG are denoted in striped blue. CNC = 1210 µmol.L<sup>-1</sup>, corresponding to 34 mg NCs of this size in 0.5 ml MeOD. Resonances denoted as * are unidentified impurities.</p> <p><strong>3. Figure 3.pxp</strong>: Experimental data for <em>Figure 3. </em>Diffusion filtered <sup>1</sup>H NMR spectrum of the NC suspension in MeOD at 1.3 equivalents PA-hex-PEG added.</p> <p><strong>4. Figure 4.pxp</strong>: Experimental data for <em>Figure 4.</em> (A) and (B) <sup>31</sup>P NMR spectra of PA-PEG and PA-hex-PEG functionalized NCs at different D<sub>2</sub>O volume %. (C) Free ligand fraction for PA-PEG and PA-hex-PEG at different D<sub>2</sub>O volume %, determined by peak deconvolution.</p> <p><strong>5. Figure 5.pxp</strong>: Experimental data for <em>Figure 5.</em> (A) Ligand exchange performed between MEEAA functionalized NCs and nitrodopamine-mPEG. (B) <sup>1</sup>H NMR spectra before and after the ligand exchange titration performed in D<sub>2</sub>O with nitrodopamine-mPEG. 1.5 equivalents of nitrodopamine-mPEG were added and the pH was kept above 5 at all times during addition, the purified nitrodopamine functionalized NC spectrum was measured at pH = 7.4. CNC = 128 µmol.L<sup>-1</sup>, corresponding to 14.4 mg NCs of this size in 2 ml D<sub>2</sub>O.</p> <p><strong>6. Figure 6.pxp</strong>: Experimental data for <em>Figure 6. </em>Effect of pH on ligand binding and stability in water for purified NCs functionalized with PA-PEG, PA-hex-PEG and nitrodopamine-mPEG. (A) Bound and unbound ligand fraction in D<sub>2</sub>O based on NMR peak deconvolution at different pH values. (B) Z-average value of NCs in DLS at different pH values. (C) Zeta potential of the NCs at different pH values. All measurements were performed at constant ionic strength (0.01 mol.L<sup>-1</sup> NaCl) at 25°C</p> <p><strong>7. Figure 7.pxp</strong>: Experimental data for <em>Figure 7.</em> Stability of functionalized NCs in different concentrations of phosphate buffered saline (PBS) at pH 7.4 and 25°C. (A) Colloidal stability of functionalized nanocrystals measured using DLS z-average values at different PBS concentrations. (B) Stability of functionalized NCs in 2X PBS over time at pH 7.4 and 25°C.</p> <p><strong>8. Figure 9.pxp</strong>: Experimental data for <em>Figure 9.</em> UV-VIS spectra of purified nitrodopamine-mPEG functionalized NCs at different pH values in H2O.</p> <p> </p>
FIGURE 3 in Rediscovering the mosquito fauna (Diptera: Culicidae) of Estonia: an annotated checklist with distribution maps and DNA evidence
FIGURE 3. Maps showing the historic and current collection points of individual mosquito species in Estonia. A, Aedes (Ochlerotatus) dorsalis; B, Aedes (Ochlerotatus) excrucians; C, Aedes (Ochlerotatus) flavescens; D, Aedes (Ochlerotatus) hexodontus; E, Aedes (Ochlerotatus) intrudens; F, Aedes (Ochlerotatus) leucomelas; G, Aedes (Ochlerotatus) nigrinus; H, Aedes (Ochlerotatus) punctor. For details, see Fig. 2.
FIGURE 1 in Rediscovering the mosquito fauna (Diptera: Culicidae) of Estonia: an annotated checklist with distribution maps and DNA evidence
FIGURE 1. Sampling localities in Estonia: 1—Kalana, 2—Vanajõe, 3—Kerema, 4—Viidumäe, 5—Karujärve, 6—Mändjala, 7—Orissaare, 8—Igaküla, 9—Muraste, 10—Üksnurme, 11—Lihula, 12—Tõstamaa, 13—Jõesuu, 14—Punaküla, 15—Nigula NR, 16—Viivre, 17—Lasila, 18—Mäetaguse, 19—Kibuvitsa, 20—Omedu, 21—Kursi, 22—Luua, 23—Laeva, 24—Undi, 25— Kolkja, 26—Maiorg, 27—Külitse, 28—Tartu, 29—Pargi, 30—Hurda, 31—Melliste, 32—Järvselja, 33—Puka, 34—Leoski. Base maps of Europe and Estonia: © 2007–2021 https://d-maps.com (accessed on 11 August 2021).
FIGURE 4 in Rediscovering the mosquito fauna (Diptera: Culicidae) of Estonia: an annotated checklist with distribution maps and DNA evidence
FIGURE 4. Maps showing the historic and current collection points of individual mosquito species in Estonia. A, Aedes (Ochlerotatus) sticticus; B, Aedes (Ochlerotatus) riparius; C, Anopheles (Anopheles) algeriensis; D, Anopheles (Anopheles) claviger; E, Anopheles maculipennis complex; F, Anopheles (Anopheles) plumbeus; G, Coquillettidia (Coquillettidia) richiardii; H, Culex pipiens complex.. For details, see Fig. 2.
FIGURE 5 in Rediscovering the mosquito fauna (Diptera: Culicidae) of Estonia: an annotated checklist with distribution maps and DNA evidence
FIGURE 5. Maps showing the historic and current collection points of individual mosquito species in Estonia. A, Culex (Neoculex) territans; B, Culiseta (Culicella) fumipennis; C, Culiseta (Culicella) morsitans; D, Culiseta (Culicella) ochroptera; E, Culiseta (Culiseta) alaskaensis; F, Culiseta (Culiseta) annulata; G, Culiseta (Culiseta) bergrothi. For details, see Fig. 2, except for data for Cs. bergrothi, which is provided according to Khalin & Aibulatov (2020).
FIGURE 2 in Rediscovering the mosquito fauna (Diptera: Culicidae) of Estonia: an annotated checklist with distribution maps and DNA evidence
FIGURE 2. Maps showing the historic and current collection points of individual mosquito species in Estonia. A, Aedes (Aedes) cinereus; B, Aedes (Aedimorphus) vexans; C, Aedes annulipes group; D, Aedes (Ochlerotatus) caspius; E, Aedes (Ochlerotatus) cataphylla; F, Aedes (Ochlerotatus) communis; G, Aedes (Ochlerotatus) cyprius; H, Aedes (Ochlerotatus) diantaeus. Numbers indicate original data and correspond to on the numbers in Fig. 1. Red diamonds indicate localities collected by Remm (1955). In cases where Remm indicated the species was widely distributed in Estonia, the area is shaded in gray.
Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps - data
<p>Data from the article<strong><em> Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps</em></strong> published in Journal of Vision.</p> <p>code: https://zenodo.org/record/5999215<br> data: https://zenodo.org/record/5999046</p> <p><br> Marek A. Pedziwiatr<br> marek.pedziwi@gmail.com<br> February 2022</p>
eQTL Catalogue mapping from unique variant ids to rsIDs
<p>eQTL Catalogue mapping from unique variant ids to rsIDs</p>
Retail fuzzy cognitive map dataset
<p><strong>Overview</strong></p> <p>This dataset contains experts interview results for the Fuzzy Cognitive Map of the retail system, reported influences from the published articles in the domain, and aggregated weighted matrix of influences in the retail system.</p> <p>Based on this dataset Fuzzy Cognitive Map of the retail system was generated. Obtained map used for system analysis and scenario planning in the retail business.</p> <p><strong>Reproducibility of results</strong></p> <p>The results presented in the research paper "Retail System Scenario Modeling using Fuzzy Cognitive Maps", namely in Figure 1-5 and Table 3-5, can be reproduced using functions in <a href="https://github.com/alinapetukhova/FuzzyM">github repository</a>.</p> <p><strong>Licenses</strong></p> <p>The dataset is made available under a <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a> license (see `LICENSE_DATA.txt`).</p>
Historical biogeography and character-mapping of Hiptage (Malpighiaceae) corroborate Indochina's rainforests as one of the main sources of plant diversity in Southeastern Asia
<p>In Malpighiaceae,<b> </b><i>Hiptage</i> represents one of the seven past dispersal events from the Neotropics to the Paleotropical region, being by far the most widely diversified and distributed genus of Paleotropical Malpighiaceae. In this study, we tested the monophyly of the current infrageneric classification of <i>Hiptage</i> with a dated and calibrated molecular phylogeny. We also reconstructed ancestral areas to elucidate which route led to the colonization of Southeast Asia by the most recent common ancestor (MRCA) of this genus (Mainland or Indian routes). The pre-existing infrageneric classification of <i>Hiptage</i> was recovered as non-monophyletic due to being solely based on homoplasic morphological characters, such as the presence and number of sepal nectar glands. Regarding its biogeography, the MRCA of <i>Hiptage</i> arose in the rainforests of southeast Asia ca. 24.0 Mya and greatly diversified in this region. Few lineages have dispersed eastward to the pacific islands or westwards to India. Based on our results, we hypothesize that the MRCA of <i>Hiptage</i> did not take the Indian route to reach Southeastern Asia. Instead, it reached this region by past mainland forest connections between North America-Europe (Boreotropical hypothesis) and southeast Asia. Nonetheless, distribution ranges for the species of <i>Hiptage</i> must be carefully revised, and the five species of <i>Hiptage</i> endemic to India must also be sampled so we can properly test which route led the MRCA of <i>Hiptage</i> to reach Southeast Asia in the early Miocene.</p>
Species-level tree crown maps improve predictions of tree recruit abundance in a tropical landscape
<p>Predicting forest recovery at landscape scales will aid forest restoration efforts. The first step in successful forest recovery is tree recruitment. Forecasts of tree recruit abundance, derived from the landscape-scale distribution of seed sources (i.e. adult trees), could assist efforts to identify sites with high potential for natural regeneration. However, previous work has revealed wide variation in the effect of seed sources on seedling abundance, from positive to no effect. We quantified the relationship between adult tree seed sources and tree recruits, and predicted where natural recruitment would occur in a fragmented tropical agricultural landscape. We integrated species-specific tree crown maps generated from hyperspectral imagery and property boundaries data on individual property ownership with field data on the spatial distribution of tree recruits from five species. We then developed hierarchical Bayesian models to predict landscape-scale recruit abundance. Our models revealed that species-specific maps of tree crowns improved recruit abundance predictions. Conspecific crown area had a much stronger impact on recruitment abundance (8.00% increase in recruit abundance when conspecific tree density increases from zero to one tree; 95% CI: 0.80 to 11.57%) than heterospecific crown area (0.03% increase with the addition of a single heterospecific tree, 95% CI: -0.60 to 0.68%).Individual property ownership was also an important predictor of recruit abundance: the best performing model had varying effects of conspecific and heterospecific crown area on recruit abundance, depending on individual property ownership. We demonstrate how novel remote sensing approaches and cadastral data can be used to generate high-resolution and landscape-level maps of tree recruit abundance. Spatial models parameterized with field, cadastral, and remote sensing data are poised to assist decision support for forest landscape restoration.</p>
UGS-1m: Fine-grained urban green space mapping of 34 major cities in China based on the deep learning framework
<p>Urban green space (UGS) is an important component in the urban ecosystem and has great significance to the urban ecological environment. The UGS-1m product provides the fine-grained UGS maps of 34 major cities/areas in China, which is generated based on a deep learning (DL) framework.</p> <p>The DL framework consists of a generator and a discriminator. The generator is a fully convolutional network designed for UGS extraction (UGSNet), which integrates attention mechanisms to improve the discrimination to UGS, and employs a point rending strategy for edge recovery. The discriminator is a fully connected network aiming to deal with the domain shift between images. To support the model training, an urban green space dataset (UGSet) with a total number of 4,454 samples of size 512×512 is provided. Code for the UGSet and the UGSet will be soon available at: https://github.com/liumency/UGS-1m. </p> <p>The main steps to obtain UGS-1m can be summarized as follows: a) Firstly, the UGSNet will be pre-trained on the UGSet in order to get a good starting training point for the generator; b) After pre-training on the UGSet, the discriminator is responsible to adapt the pre-trained UGSNet to different cities/areas through adversarial training; c) Finally, the UGS results of the 34 major cities/areas in China (UGS-1m) are obtained using 2,343 Google Earth images with a data frame of 7'30" in longitude and 5'00" in latitude, and a spatial resolution of nearly 1.1 meters. Evaluating the performance of the proposed approach on samples from Guangzhou city shows the validity of the UGS-1m products, with an overall accuracy of 87.4% and an F1 score of 81.14%. </p>
Mapping breeding bird species richness at management-relevant resolutions across the United States
<p>Human activities alter ecosystems everywhere, causing rapid biodiversity loss and biotic homogenization. These losses necessitate coordinated conservation actions guided by biodiversity and species distribution spatial data that cover large areas yet have fine-enough resolution to be management-relevant (i.e., ≤ 5 km). However, most biodiversity products are too coarse for management or are only available for small areas. Furthermore, many maps generated for biodiversity assessment and conservation do not explicitly quantify the inherent tradeoff between resolution and accuracy when predicting biodiversity patterns. Our goals were to 1) generate predictive models of overall breeding bird species richness and species richness of different guilds based on nine functional or life history-based traits across the conterminous US at three resolutions (0.5, 2.5, and 5 km), and 2) quantify the tradeoff between resolution and accuracy, and hence relevance for management, of the resulting biodiversity maps. We summarized eighteen years of North American Breeding Bird Survey data (1992-2019) and modeled species richness using random forests, including 66 predictor variables (describing climate, vegetation, geomorphology, and anthropogenic conditions), 20 of which we newly derived. Among the three spatial resolutions, the percent variance explained ranged from 27% to 60% (median = 54%; mean = 57%) for overall species richness and 12% to 87% (median = 61%; mean = 58%) for our different guilds. Overall species richness and guild-specific species richness were best explained at 5-km resolution using approximately 24 predictor variables based on percent variance explained, symmetric mean absolute percentage error, and root mean squared error values. However, our 2.5-km resolution maps were almost as accurate and provided more spatially detailed information, which is why we recommend them for most management applications. Our results represent the first consistent, occurrence-based, and nationwide maps of breeding bird richness with a thorough accuracy assessment that are also spatially detailed enough to inform local management decisions. More broadly, our findings highlight the importance of explicitly considering tradeoffs between resolution and accuracy to create management-relevant biodiversity products for large areas.</p>
FIGURES 35–40. Distribution maps for Afrotropical Tetraponera. 35, T in The ant genus Tetraponera (Hymenoptera: Formicidae) in the Afrotropical region: taxonomic review and key to species
FIGURES 35–40. Distribution maps for Afrotropical Tetraponera. 35, T. clypeata (circles), T. tessmanni (triangles); 36, T. emeryi (circles), T. dispar (triangle); 37, T. exactor (circle), T. furtiva (triangle), T. liengmei (squares); 38, T. continua (circles), T. cortina (triangles), T. gerdae (squares); 39, T. pedana (circles), T. penzigi (triangles); 40, T. pumila (circles).
FIGURES 41–46. Distribution maps for Afrotropical Tetraponera. 41, T in The ant genus Tetraponera (Hymenoptera: Formicidae) in the Afrotropical region: taxonomic review and key to species
FIGURES 41–46. Distribution maps for Afrotropical Tetraponera. 41, T. andrei (circles), T. anthracina (triangles), T. insularis (square); 42, T. caffra (circles), T. redacta (triangles); 43, T. mocquerysi (circles), T. schulthessi (triangles); 44, T. natalensis (circles), T. setosa (triangle); 45, T. aethiops (circles), T. kosi (triangle); 46, T. latifrons (circles).
Fig. 1. A map showing 10 in New data on the distribution of ladybird beetles (Coleoptera: Coccinellidae) in the eastern part of the Baltic Coast region in Poland
Fig. 1. A map showing 10 km x 10 km grid coordinates, in which samples of Coccinellidae for this study were collected.
MAP 1 in A taxonomic revision of the Solanum echinatum group (Solanaceae)
MAP 1. Distribution of Solanum echinatum (circles); Solanum wilkinsii (triangles); Solanum medicagineum (stars).
FIGURE 60. Doratura heterophyla group and D. concors group. Distribution maps. A in Revision of the genus Doratura Sahlberg (Hemiptera, Cicadellidae, Deltocephalinae) with particular regard to its distribution in Italy and description of four new species
FIGURE 60. Doratura heterophyla group and D. concors group. Distribution maps. A: D. heterophyla group. ◊ = D. heterophyla Horváth; ■ = D. kusnezovi Vilbaste; ○ = D. rikele Guglielmino & Bückle. (only one generic distribution point in the middle of each country, except for Italy and Russia); B: D. concors group. ♦ = D. concors Horváth; ● = D. rusaevi Kusnezov; □ = D. medvedevi Logvinenko. (only one generic distribution point in the middle of each country, except for China, Italy, Kazakhstan, Russia, and Turkey).
Training and validation sample datasets for Qinghai-Tibet Plateau Forest Cover Map 2021
<p>The training and validation sample datasets are label by the value of "0" and "1", when "0" means the non-forest samples and "1" means the forest samples. </p>
Figure 1. Map with biogeographical data for Nestorinus. A in Integrative taxonomy reveals a new genus from Central Chile and suggests a systematic rearrangement in Stenochiinae (Coleoptera: Tenebrionidae)
Figure 1. Map with biogeographical data for Nestorinus. A, collection localities of species of Nestorinus included in this study and delimitation of the biogeographical provinces. B, sclerophyllous forest in the locality of Estero Los Angeles and an in situ specimen of Nestorinus roitmani. C, a shrubland habitat at the top of a mountain in the Cuesta El Espino and an in situ specimen of Nestorinus yanettae.
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.