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1,029 results for “altitude”
FIGURES 6 – 10 in Leaf-mining Nepticulidae (Lepidoptera) from record high altitudes: documenting an entire new fauna in the Andean páramo and puna
FIGURES 6 – 10. Habitats: shrub-dominated páramo and Polylepis forest near volcano Antisana and Papallacta Pass (Ecuador: Napo Province) at altitudes about 3700 – 3900 m, 0 ° 21 ' 45 " S, 78 ° 11 ' 35 " W. 6, view of volcano Antisana; 7, a relict stand of high- Andean Polylepis; 8 – 10, shrub-dominated páramo SE of Papallacta Pass.
FIGURES 93 – 97 in Leaf-mining Nepticulidae (Lepidoptera) from record high altitudes: documenting an entire new fauna in the Andean páramo and puna
FIGURES 93 – 97. Stigmella rigida Diškus & Stonis, sp. nov. 93, male adult, holotype; left side; 94, same, right side; 95, 96, male genitalia, holotype, genitalia slide no. AD 625, capsule without phallus; 97, same, phallus (ZMUC).
FIGURES 98 – 102 in Leaf-mining Nepticulidae (Lepidoptera) from record high altitudes: documenting an entire new fauna in the Andean páramo and puna
FIGURES 98 – 102. Stigmella altiplanica Diškus & Stonis, sp. nov. 98, male adult, holotype; 99, same, left side; 100, male genitalia, holotype, genitalia slide no. AD 647, capsule without phallus; 101, same, phallus; 102, same, dorsal view of capsule (ZMUC).
FIGURES 32 – 33 in Leaf-mining Nepticulidae (Lepidoptera) from record high altitudes: documenting an entire new fauna in the Andean páramo and puna
FIGURES 32 – 33. Details of morphology and bionomics. 32, abdominal apex of female of Stigmella calceolarifoliae sp. n.; 33, leaf-mines of S. paramica sp. n. in Pentacalia leaf, and cocoon.
FIGURES 118 – 122. Stigmella schoorli Puplesis & Robinson. 118, 119 in Leaf-mining Nepticulidae (Lepidoptera) from record high altitudes: documenting an entire new fauna in the Andean páramo and puna
FIGURES 118 – 122. Stigmella schoorli Puplesis & Robinson. 118, 119, male adult; 120, male genitalia, capsule without phallus, paratype, genitalia slide no. Diškus 201; 121, same, holotype, genitalia slide no. Diškus 200 (after Puplesis & Robinson 2000); 122, phallus, paratype, genitalia slide no. Diškus 201 (ZMUC).
Dataset of processed Sentinel-2 images for chlorophyll-a estimation in high-altitude lakes in the Sierra Nevada, Spain
<p>This dataset contains Sentinel 2 satellite images clipped to 5 high-altitude lakes in the Sierra Nevada Mountain Range, Spain. The images were processed with the following atmospheric correction algorithms:</p><ul><li><a href="https://c2rcc.org/">C2RCC</a> (<a href="https://ui.adsabs.harvard.edu/abs/2016ESASP.740E..54B/abstract">Brockmann et al. 2016</a>)</li><li><a href="https://github.com/MarcYin/SIAC">SIAC</a> (<a href=" https://doi.org/10.5194/gmd-15-7933-2022">Yin et al. 2022)</a></li><li><a href="https://github.com/acolite/acolite/releases/tag/20221114.0">ACOLITE</a> (<a href="https://doi.org/10.1016/j.rse.2018.07.015">Vanhellemont & Ruddick, 2018</a>)</li><li><a href="https://grass.osgeo.org/grass83/manuals/i.atcorr.html">6SV</a> (<a href="https://doi.org/10.1109/36.581987">Vermote et al. 2006</a>)</li></ul><p><strong>Included Lakes and and their IDs:</strong></p><ul><li>Laguna de la Caldera (ID = P-2)</li><li>Laguna-embalse de las Yeguas (ID = D-6)</li><li>Laguna de Río Seco (ID = P-8)</li><li>Laguna Larga (ID = G-7)</li><li>Laguna de la Mosca (ID = G-11)</li></ul>
data from "Fractal properties of isolines at varying altitude revealing different dominant geological processes on Earth"
<p>The file contains the data used to produce Fig.4 for the paper "Fractal properties of isolines at varying altitude revealing</p><p>different dominant geological processes on Earth", by Andrea Baldassarri, Marco Montuori, Olga Prieto-Ballesteros,</p><p>and Susanna C. Manrubia, Journal of Geophysical Research: PlanetsVolume 113, Issue E9, https://doi.org/10.1029/2007JE003066</p>
SUPPLEMENTARY DATA TO: Using a citizen science approach to assess nanoplastics pollution in remote high-altitude glaciers
<p>This is the repository of the supplementary data, and it contains the following files: </p> <p>Raw data files as the original output of TD-PTR-ToF-MS for all the samples, all the blanks, all the spikes and all the calibration runs (.h5 files in three zip arcives)</p> <p>Polymer library files (a zip archive including csv files.</p> <p>A data analysis file including raw data, blank subtraction and LOD correction of all measurements (xlsx file).</p> <p>A fingerprinting result file for each plastic type (xlsx file)</p> <p>A data analysis file after plastic fingerprinting (xlsx file). </p> <p> </p> <p> </p>
Fig. 1. a in Conocephalus dorsalis (Latreille, 1804) à 1000 mètres d'altitude dans la chaîne du Jura (Orthoptera: Tettigoniidae)
Fig. 1. a) Femelle adulte de Conocephalus dorsalis, Bois des Lattes (NE), juillet 2018. b) Habitat occupÉ par Conocephalus dorsalis au Bois des Lattes (NE), 997 m, juillet 2018. (Photos Christian Monnerat)
Absence of neocytolysis in humans returning from a 3-week high-altitude sojourn
<p>This dataset refers to the article "Absence of neocytolysis in humans returning from a 3-week high-altitude<br> sojourn" Acta Physiologica. 2021;232:e13647 <br> https://doi.org/10.1111/apha.13647</p>
Data from: Effect of altitude on volatile organic and phenolic compounds of artemisia brevifolia wall ex Dc. from the Western Himalayas
<p>Adaptation to changing environmental conditions is a driver of plant diversification. Elevational gradients offer a unique opportunity for investigating adaptation to a range of climatic conditions. The use of specialized metabolites as volatile and phenolic compounds is a major adaptation in plants, affecting their reproductive success and survival by attracting pollinators and protecting themselves from herbivores and other stressors. The wormseed <em>Artemisia brevifolia</em> can be found across multiple elevations in the Western Himalayas, a region that is considered a biodiversity hotspot and is highly impacted by climate change. This study aims at understanding the volatile and phenolic compounds produced by <em>A. brevifolia </em>in the high elevation cold deserts of the Western Himalayas with the view to understanding the survival strategies employed by plants under harsh conditions. Across four sampling sites with different elevations, polydimethylsiloxane (PDMS) sampling and subsequent GCMS analyses showed that the total number of volatile compounds in the plant headspace increased with elevation and that this trend was largely driven by an increase in compounds with low volatility, which might improve the plant's resilience to abiotic stress. HPLC analyses showed no effect of elevation on the total number of phenolic compounds detected in both young and mature leaves. However, the concentration of the majority of phenolic compounds decreased with elevation. As the production of phenolic defense compounds is a costly trait, plants at higher elevations might face a trade-off between energy expenditure and protecting themselves from herbivores. This study can therefore help us understand how plants adjust secondary metabolite production to cope with harsh environments and reveal the climate adaptability of such species in highly threatened regions of our planet such as the Himalayas.</p>
What weather variables are important for wet and slab avalanches under a changing climate in low altitude mountain range in Czechia?
<p>datasets and scripts for Avalanche paper figures and<br> avalanche path characteristics: Avalanche_paths_souckova.xlsx<br> </p>
Sprite streamer at 50 km altitude
<p>Reduced electric field and species density produced by a simulated positive sprite streamer 80 micro-s after the onset of the simulation between altitudes 50 km and 49.7 km.</p> <p>The file claw0040.hdf corresponds to the data in each grid-cell.</p> <p>The ZIP files contain text files of the temporal evolution of the data in particular points.</p>
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021). in Floristic, Vegetation And Climate Assessment Of The Early/Middle Miocene Parschlug Flora Indicates A Distinctly Seasonal Climate
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021).
Morphospace disparity and species diversity in Sri Lankan phytophagous scarab beetles – a comparison by forest types, altitude, and sites
<p>The files contain the supporting information and raw data of the masnucript, Morphospace disparity and species diversity in Sri Lankan phytophagous scarab beetles – a comparison by forest types, altitude, and sites.</p> <p>It includes the following:</p> <p><strong>Raw Data:</strong></p> <p><strong><span>Suppl. Table 1: </span></strong><span>Details of sampling sites (Sri Lanka); L number, coordinates, elevation, elevation zone and forest types. </span><span>Elevation zones; EZ1: 0-500m, EZ2: 501-1000m, EZ3: 1001-1500m, EZ4: 1501-2000m, EZ5; 2001-2500m. </span><span>Forest types; WL: evergreen wet lowland forests, DL: evergreen dry lowland forests, SM: sub-montane forests, MO: montane forests.</span></p> <p><strong>Suppl. Table 2. </strong>Morphometric measurements and metadata of all studied specimens. Metadata include species identification, voucher number, occurrence data regarding sampling location in Sri Lanka, elevation zone (EZ), and forest type (F). Units of measurements are mm. WL: evergreen wet lowland forests, LD: evergreen dry lowland forests, SM: sub-montane forests, MO: montane forests; EZ1: 0-500m, EZ2: 501-1000m, EZ3: 1001-1500m, EZ4: 1501-2000m, EZ5; 2001-2500m; L1: Aranayake; L2: Riverston; L3: NIFS Arboretum; L4: Deenston; L5: Nuwara Eliya; L6: Horton Plains; L8: Hiyare; L9: Kottawa; L10: Kanneliya; L11: Piduruthalagala; L12: Uda Peradeniya; L13: Gannoruwa; L14: Udawattakele. Morphological measurements abbreviations are explained in Sup. Fig.1.</p> <p><strong>Results:</strong></p> <p><strong><span>Suppl. Table 3: </span></strong><span>Proportion of</span><strong><span> </span></strong><span>variance explained by PC axes in principal component analysis for the data subsets of lineages </span><span>(derived from shape and size data). Values of axes reflecting the 95% of explained cumulative variation are highlighted in bold.</span></p> <p><strong><span>Suppl. Table 4</span></strong><strong><span>: </span></strong><span>Euclidean distances between species (mean/median/maximum) for shape and size partitioned by </span><span>forest types </span><span>and lineages (all Pleurosticts, Sericini only, and Pleurosticts excluding Sericini (*)). </span><span>WL: Wet lowland; DL: Dry lowland; SM: Sub-montane; MO: Montane.</span></p> <p><strong><span>Suppl. Table 5: </span></strong><span>Euclidean distances between species mean/median/maximum) for shape and size partitioned by elevational zones and lineages (all Pleurosticts, Sericini only, and Pleurosticts excluding Sericini (*)). </span><span>EZ1: 0-500m. EZ2: 501-1000m. EZ3: 1001-1500m. EZ4: 1501-2000m. EZ5: 2001-2500m.</span></p> <p><strong><span>Suppl. Table 6: </span></strong><span>Euclidean distances between species (mean/median/maximum) for shape and size partitioned by localities (L1-14), and lineages (all Pleurosticts, Sericini only, and Pleurosticts excluding Sericini (*)). </span></p> <p><strong><span>Suppl. Table 7</span></strong><strong><span>: </span></strong><span>Pairwise p-values from non-parametric MANOVA on PCA scores partitioned for shape and size <u>forest types</u> and lineages (all Pleurosticts, Sericini only, and Pleurosticts excluding Sericini (*)). Significant correlations (p value <0.05) are shown in bold italics. WL: Wet lowland; DL: Dry lowland; SM: Sub-montane; MO: Montane.</span></p> <p><strong><span>Suppl. Table 8</span></strong><strong><span>: </span></strong><span>Pairwise p-values from non-parametric MANOVA on PCA scores for shape and size partitioned for <u>elevational zones</u> and lineages (all Pleurosticts, Sericini only, and Pleurosticts excluding Sericini (*)). Significant correlations (p value <0.05) are shown in bold italics. EZ1: 0-500m. EZ2: 501-1000m. EZ3: 1001-1500m. EZ4: 1501-2000m. EZ5: 2001-2500m.</span></p> <p><strong><span>Suppl. Table 9: </span></strong><span>Pairwise p-values from non-parametric MANOVA on PCA scores partitioned for <u>localities</u> and lineages for shape (all Pleurosticts, Sericini only, and Pleurosticts excluding Sericini (*)). Significant correlations (p value <0.05) are shown in bold italics.</span></p> <p><strong><span>Suppl. Table 10: </span></strong><span>Pairwise p-values from non-parametric MANOVA on PCA scores partitioned for <u>localities</u> and lineages for size (all Pleurosticts, Sericini only, and Pleurosticts excluding Sericini (*)). Significant correlations (p value <0.05) are shown in bold italics.</span></p> <p> </p> <p><strong>Figure S1.</strong> Illustration of the measured morphological traits (after Eberle et al., 2014). Schematic drawings of a Sericini beetle, in (A) dorsal, (B) ventral, and (C) lateral aspect. Body: BH - maximal body height, EH - maximal elytra height, EL - maximal elytra length, Eld - maximal diagonal elytra length, Elmb - length from maximal body width to elytral apex, EW - maximal elytra width, Ewb - elytral width at middle of scutellum, PL - maximal pronotum length, PW - maximal pronotum width; Head: ED - maximal eye diameter, HW - maximal head with including eyes, IOD - minimal interocular distance (dorsal view); Legs: MCL - maximal length of metacoxa, MFL - maximal length of metafemur, MFW - maximal width of metafemur, MTL - maximal length of metatibia, MTW - maximal width of metatibia, PFL - maximal length of profemur, PFW - maximal width of profemur, PTL - maximal length of protibia.</p> <p><strong>Figure S2.</strong> Biplots of PC1 and 2 from principal components analysis, illustrating trait contribution to the principal patterns of morphospace (raw measurements). Trait abbreviations are explained in Figure S1.</p> <p><strong>Figure S3. </strong>Patterns of morphospace disparity of all Pleurosticts derived from raw measurements in individual localities. Symbols represent genus or other family-group level, color of symbols single species.<br> <br><strong>Figure S4. </strong>Patterns of morphospace disparity of Sericini derived from raw measurements in individual localities. Colored dots represent single species. Locality L12 had no Sericini recorded.<br> <br><strong>Figure S5. </strong>Patterns of morphospace disparity (PCA plots of PC1 and PC2) derived from raw measurements of Sericini chafers partitioned for forest types (A), elevation zones (B), localities (C)(enlarged visualization from Fig. 2). Colored dots represent single species, outlines grouping entities grouped by forest types, elevation zone, or locality.</p>
Fig 3 in First report of the high altitude cladoceran species Streblocerus serricaudatus (Fischer, 1849) S.LAT from the Western Ghats of India, Tamil Nadu
Fig 3: Google Map showing the Western Ghats of India and the collection locality Pechiparai dam in Tamil Nadu
Seasonal and local time variation in the observed peak of the meteor altitude distributions by meteor radars
<p>These uploaded datasets support and appear in the the paper entitled "<strong>Seasonal and local time variation in the observed peak of the meteor altitude distributions by meteor radars</strong>" prepared by: </p> <p>E.C.M. Dawkins<sup>1,2</sup>, D. Janches<sup>1</sup>, G. Stober<sup>3</sup>, J.D. Carrillo-Sánchez<sup>1,2</sup>, R.S. Lieberman<sup>1</sup>, C. Jacobi<sup>4</sup>, T. Moffat-Griffin<sup>5</sup>, N.J Mitchell<sup>5,6</sup>, N. Cobbett<sup>5</sup>, P.P.Batista<sup>7</sup>, V.F. Andrioli<sup>7,8</sup>, R.A. Buriti<sup>9</sup>, D.J. Murphy<sup>10</sup>, J. Kero<sup>11</sup>, N. Gulbrandsen<sup>12</sup>, M. Tsutsumi<sup>13,14</sup>, A. Kozlovsky<sup>15</sup>, M. Lester<sup>16</sup>, J.-H. Kim<sup>17</sup>, C. Lee<sup>17</sup>, A. Liu<sup>18</sup>, B. Fuller<sup>19</sup>, D. O’Connor<sup>19</sup>, S.E. Palo<sup>20</sup>, M.J. Taylor<sup>21</sup>, J.Marino<sup>22</sup>, and N. Rainville<sup>20</sup>.</p> <p> </p> <p>1 ITM Physics Laboratory, NASA Goddard Space Flight Center, Greenbelt MD, U.S.A.</p> <p>2 Department of Physics, Catholic University of America, DC, U.S.A.</p> <p>3 University Bern, Institute of Applied Physics, Microwave Physics, Bern, Switzerland</p> <p>4 Institute for Meteorology, Leipzig University, Germany</p> <p>5 British Antarctic Survey, Cambridge, U.K.</p> <p>6 University of Bath, Bath, U.K.</p> <p>7 National Institute for Space Research (INPE), São José dos Campos, SP, Brazil</p> <p>8 China-Brazil Joint Laboratory for Space Weather, NSSC/INPE, São José dos Campos, SP, Brazil</p> <p>9 Department of Physics, Federal University of Campina Grande, Campina Grande, PB, Brazil</p> <p>10 Australian Antarctic Division, Kingston, TAS, Australia</p> <p>11 Swedish Institute of Space Physics (IRF), Kiruna, Sweden</p> <p>12 Tromsø Geophysical Observatory, UiT - The Arctic University of Norway, Tromsø, Norway</p> <p>13 National Institute of Polar Research, Tachikawa, Japan</p> <p>14 The Graduate University for Advanced Studies (SOKENDAI), Tokyo, Japan</p> <p>15 Sodankylä Geophysical Observatory, University of Oulu, Finland</p> <p>16 Department of Physics and Astronomy, University of Leicester, Leicester, U.K.</p> <p>17 Division of Atmospheric Sciences, Korea Polar Research Institute, Incheon, S. Korea</p> <p>18 Center for Space and Atmospheric Research and Department of Physical Sciences, Embry-Riddle Aeronautical University, Daytona Beach, Florida, U.S.A.</p> <p>19 Genesis Software, Pty Ltd., Adelaide, SA, Australia</p> <p>20 Colorado Center for Astrodynamics Research (CCAR), Ann and H.J. Smead Aerospace Engineering Sciences, College of Engineering and Applied Sciences, University of Colorado Boulder, Boulder, CO, U.S.A.</p> <p>21 Department of Physics, Utah State University, Logan, UT, U.S.A</p> <p>22 University of Colorado at Boulder, Boulder, CO, U.S.A</p> <p> </p> <p> </p> <p>The datasets below are titled according to the figure in which they are used (e.g. "Fig3" for Figure 3, "Fig4" for Figure 4).<br>All uploaded datasets comprised of ASCII files.<br><br>Dataset descriptions:</p> <ul> <li>Figure 3 datasets (<strong>18 files in total</strong>): Each of the 18 different files corresponds to a different meteor radar station (SVA, TRO, KIR, SOD, COL, BLO, CAR, ASI, LEA, CPa, SMa, CON, TdF, KEP, KSS, ROT, DAV, MCM). Within each file, the data comprise of peak meteor altitudes (km) as a function of local time (24) and day-of-year (DOY). </li> <li>Figure 4 datasets (<strong>18 files in total</strong>): As above, but the data now represent the weighted elevation angle in degrees.</li> <li>Figure 5 datasets (<strong>24 files in total</strong>): These data can be used to plot the residual seasonal variation in peak altitude for each of the 18 locations, organized by geographic clusters. There are 24 different Figure 5 datasets, with each including the normalized residual seasonal variation in peak altitude (km) for stations within one of six different geographic clusters (Nordic high-latitude, Northern mid-latitude, Near-equatorial, Southern low/mid-latitude, Southern Andes, Mainland Antarctica) for each local time (00:00 LT, 06:00 LT, 12:00 LT, or 18:00 LT). Each file includes the data for all stations within that given cluster (i.e., "Fig5__Mainland_Antarctica__06LT__Dawkins_et_al_2024.tex" includes data for the Mainland Antarctica cluster (both DAV and MCM) for 06:00 LT), as a function of day-of-year (365) and normalized altitue (km).</li> <li>Figure 6 datasets (<strong>4 files in total</strong>): These data represent the mean absolute deviation (MAD, km) of each of the different geographic clusters as function of DOY (365) for four different local times (00:00 LT, 06:00 LT, 12:00 LT, and<br>18:00 LT).</li> <li>Figure 7 datasets (<strong>14 files in total</strong>): These files present the kinetic gravity wave energy (KGWE) as a function of day-of-year and altitude (km). 12 of the files correspond to one of the following locations: SVA, TRO, KIR, SOD, COL, BLO, CON (ALO only), TdF, KEP, KSS, ROT or DAV. There are two additional files ("Fig7__KGWE__time__Dawkins_et_al_2024.txt" and "Fig7__KGWE__altitude__Dawkins_et_al_2024.txt") which include the time (day-of-year) and altitudes (km) used.</li> <li>Figure 8 datasets (<strong>2 files in total</strong>): These two files ("Fig8__CABMOD_profiles__data__Dawkins_et_al_2024.txt" and "Fig8__CABMOD_profiles__altitude__Dawkins_et_al_2024.txt") include the data necessary to reproduce all panels in Figure 8 which shows the vertical mass profiles from CABMOD for a meteoric particle with a fixed initial mass (178 μg) and velocity (31 kms−1), at a latitude of 60 deg S. The dataset (mass, μg) corresponds to 8 different month and entry angles (in order: March, June, September, December for particle entry angles of 5 deg and 25 deg, respectively) and 201 altitudes (km).</li> <li>Figure 9 datasets (<strong>8 files in total</strong>): These data represent the simulated and observed peak altitudes (km) as a function of day-of-year and LT for each of the four Southern Andes meteor radar station locations (TdF, KEP, KSS, ROT).</li> </ul>
Fig. 1 in Percentage damage to tomatillo crops by Heliothis subflexa (Lepidoptera: Noctuidae) at various altitudes
Fig. 1. Percentages of tomatillo fruits infested by Heliothis subflexa along an altitudinal transect of tomatillo production areas in the state of Morelos, Mexico. The tomatillo field locations, m asl, and percentages of damaged fruits were as follows: Coatlán del Río (660 m asl, 48%); Miacatlán (810 m asl, 52%); Yautepec (1,120 m asl, 42%); Tlayacapan (1,320 m asl, 55%); and Tlanepantla (1,550 m asl, 21%; 1,800 m asl, 9%, and 2,300 m asl, 2%).
Figures: 'Preliminary Sizing of High-Altitude Airships Featuring Atmospheric Ionic Thrusters: An Initial Feasibility Assessment'
<p><strong>Figures from the publication <em>Preliminary Sizing of High-Altitude Airships Featuring </em><em>Atmospheric Ionic Thrusters: An Initial Feasibility Assessment</em></strong></p> <p>*.fig files can be opened in <code>Matlab</code></p>
Fig. 2 in Short communication Somatochlora arctica (Odonata: Corduliidae) oviposing at a "lower than usual" altitude for Italy and the Mediterranean Region and first observation for the Varese Province (Northern Italy)
Fig. 2 - Location of the closer known populations of S. arctica to the study area (square). Data come from Wildermuth (2013) (stars), Pompilio et al. (2012) (crosses), Riservato et al. (2014a) (triangles) and personal communications (circle). Background: elevation (dark: low, white: high). Striped polygons: lakes. / Localizzazione delle popolazioni note di S. arctica più vicine all'area di studio (quadrato). I dati sono tratti da Wildermuth (2013) (stelle), Pompilio et al. (2012) (croci), Riservato et al. (2014a) (triangoli) e comunicazioni personali (cerchio). Sfondo: altitudine (scura: bassa, bianca:elevata). Poligoni barrati: laghi.
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.