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4,669 results for “Environment;”
Desert lizard diversity worldwide: Effects of environment, time, and evolutionary rate
<p><strong>Aim:</strong> Biodiversity is not uniformly distributed across the Earth's surface, even among physiographically comparable biomes in different biogeographic regions. For lizards, the world's large desert regions are characterized by extreme heterogeneity in species richness, spanning some of the most species-rich (arid Australia) and species-poor (central Asia) biomes overall. Regional differences in species diversity may arise as a consequence of the interplay of several factors (e.g., evolutionary time, diversification rate, environment), but their relative importance for biogeographic patterns remains poorly known. Here we use distributional and phylogenetic data to assess the evolutionary and ecological drivers of large-scale variation in desert lizard diversity.</p> <p><strong>Location:</strong> Deserts worldwide.</p> <p><strong>Major taxa studied:</strong> Lizards (non-snake squamates).</p> <p><strong>Methods:</strong> We specifically test whether diversity patterns are best explained by differences in the ages of arid-adapted lineages (evolutionary time hypothesis), by regional variation in speciation rate, by geographic area of the arid systems, and by spatial variation related to the environment (climate, topography, and productivity).</p> <p><strong>Results:</strong> We found no effect of recent speciation rate and geographic area on differences in desert lizard diversity. We demonstrate that the extreme species richness of the Australian deserts cannot be explained by greater evolutionary time, because species began accumulating more recently there than in more species-poor arid regions. We found limited support for relationships between regional lizard richness and environmental variables, but these effects were inconsistent across deserts, showing a differential role of the environment in shaping the lizard diversity in different arid regions.</p> <p><strong>Main conclusions:</strong> Our results provide evidence against several classic hypotheses for interregional variation in species richness, but also highlight the complexity of processes underlying vertebrate community richness in the world's great arid systems.</p>
Data for: Adapting small jumping robots to compliant environments
<p>Jumping animals launch themselves from surfaces that vary widely in compliance from grasses and shrubs to tree branches. However, studies of robotic jumpers have been largely limited to those jumping from rigid substrates. In this paper, we leverage recent work describing how latches in jumping systems can mediate the transition from stored potential energy to kinetic energy. By including a description of the latch in our system model of both the jumper and compliant substrate, we can describe conditions in which a jumper can either lose energy to the substrate or recover energy from the substrate resulting in an improved jump performance. Using our mathematical model, we illustrate how the latch plays a role in the ability of a system to adapt its jump performance to a wide range of substrates that vary in their compliance. Our modeling results are validated using a 4 g jumper with a range of latch designs jumping from substrates with varying mass and compliance. Finally, we demonstrate the jumper recovering energy from a tree branch during take-off, extending these mechanistic findings to robots interacting with a more natural environment.</p>
The MAMMAMIA project: A multi-scale multi-method approach to understand runoff-induced changes in the subglacial environment and consequences for surge dynamic in Kongsvegen glacier, Svalbard
<p> </p> <p>Data set at a 3h resolution of all the data used in the study ''The MAMMAMIA project: A multi-scale multi-method approach to<br> understand runoff-induced changes in the subglacial environment<br> and consequences for surge dynamic in Kongsvegen glacier,<br> Svalbard', Coline bouchayer, Ugo Nanni, Pierre-Marie Lefeuvre, John Hulth, Louise Schmidt, Jack Kohler, Francois Renard and Thomas V. Schuler. The mansucript is in prepartaion to be submitted to The Crysophere (@ Add DOI when submitted).</p>
Gene-environment interaction explains a part of missing heritability in human body mass index
<p>Gene-environment (G×E) interaction could partially explain missing heritability in traits; however, the magnitudes of G×E interaction effects remain unclear. Here, we estimate the heritability of G×E interaction for body mass index (BMI) by subjecting genome-wide interaction study data of 331,282 participants in the UK Biobank to linkage disequilibrium score regression (LDSC) and linkage disequilibrium adjusted kinships–software for estimating SNP heritability from summary statistics (LDAK-SumHer) analyses. Among 14 obesity-related lifestyle factors, MET score, pack years of smoking, and alcohol intake frequency significantly interact with genetic factors in both analyses, accounting for the partial variance of BMI. The G×E interaction heritability (%) and standard error of these factors by LDSC and LDAK-SumHer are as follows: MET score, 0.45% (0.12) and 0.65% (0.24); pack years of smoking, 0.52% (0.13) and 0.93% (0.26); and alcohol intake frequency, 0.32% (0.10) and 0.80% (0.17), respectively. Moreover, these three factors are partially validated for their interactions with genetic factors in other obesity-related traits, including waist circumference, hip circumference, waist-to-hip ratio adjusted with BMI, and body fat percentage. Our results suggest that G×E interaction may partly explain the missing heritability in BMI, and two G×E interaction loci identified could help in understanding the genetic architecture of obesity.</p>
An Exploratory Study on the Effect of Virtual Environments on Cognitive Performances and Psychophysiological Responses
<p>This is the dataset of the following study: </p> <p>Frigione, I., Massetti, G., Girondini, M., Etzi, R., Scurati, G. W., Ferrise, F., Chirico, A., Gaggioli, A., & Gallace, A. (2022). An Exploratory Study on the Effect of Virtual Environments on Cognitive Performances and Psychophysiological Responses. <em>Cyberpsychology, behavior and social networking</em>, <em>25</em>(10), 666–671. https://doi.org/10.1089/cyber.2021.0162</p> <p>Abstract: Research shows that reduced exposure to natural contexts is associated with an increase in psychophysical disorders. Recent evidence suggests that even a brief experience in natural scenarios can positively affect people's health and well-being. However, natural contexts are not always easily accessible. This study investigates the effects of natural and indoor virtual environments (VREs) on psychophysiological and cognitive responses. Following a within-subject design, 34 healthy participants were exposed to two VREs (i.e., a forest and a living room) in a counterbalanced order through a head-mounted display (Oculus Rift). Participants were asked to explore the scenarios and execute a modified version of the Paced Auditory Serial Addition Test. Physiological parameters (heart rate, skin conductance level [SCL], and respiration rate) were recorded during the whole session. After the exposure to VREs, participants filled a set of visual analog scales to rate their subjective experience of presence, relaxation, and stress. Participants reported a higher perceived sense of relaxation in the virtual forest. Moreover, their SCLs were significantly higher in this environment, showing that the forest elicited higher physiological arousal than the living room. Furthermore, their SCLs were significantly higher during the attentional task in the virtual living room. The results suggest that a natural virtual environment can make people feel more relaxed and physiologically engaged than an indoor scenario. The latter instead can be linked to a performing venue, as reported for real contexts. However, these changes were not related to modulations of attentional performance.</p>
Collective Decision-Making and Change Detection with Bayesian Robots in Dynamic Environments
<p>The following folder structure holds all research data of my conducted experiments(h5-logfiles and plots). The Python-Script "show_h5.py" can be used to read out the logfile in h5-format (<em>$python3 show_h5.py expample_logfilename.h5</em>). However, this shouldn't be necessary because all plots are already generated.</p> <p>To find the results you want to see, this is a small guide through the structure:</p> <ol> <li> <p>First the trials are divided into the respective methods (PELT, DBB, DBBCPD). In the folders you find the experiments for the specific method.</p> </li> <li> <p>In the folder of PELT you find the results for the different feedback types and their combinations. The id for each feedback is noted in parentheses (e.g. XX_(id)_feedback_description). Feedback combinations have their ids added up (e.g. XX_(id1+...+idn)_feedback_description).</p> </li> <li> <p>In the folder to each feedback type the different test trials can be found. This means varying environment difficulties and parameter settings. In the name of the folders this information can be found (e.g. XX_method_environmentdifficulty_parametersetting).</p> </li> </ol> <p>All experiments follow the same procedure as long as it is stated otherwise. Each trial consists of 20 individual runs with a duration of 6000 seconds. At half time (3000 s) a change to the opposite fill ratio occurs (fill ratio of 1.0 defines a completely white and one of 0.0 a completely black environment).</p> <p><strong>Environment difficulty</strong></p> <ul> <li> <p>0901 --> easy environment, fill ratio changed from 0.9 to 0.1</p> </li> <li> <p>0703 --> easy environment, fill ratio changed from 0.7 to 0.3</p> </li> <li> <p>0604 --> easy environment, fill ratio changed from 0.6 to 0.4</p> </li> <li> <p>055045 --> easy environment, fill ratio changed from 0.55 to 0.45</p> </li> </ul> <p><strong>Parameter Setting</strong></p> <p>The setting is in the name of the folder composed of: feedbackID: intervalLength amountNeighbors</p> <ul> <li> <p>3c:50s3n --> feedback 3c with a 50s interval and 3 neighbors</p> </li> </ul> <p>In these folders all plots of the respective runs can be found showing a Boxplot of all 20 runs and for each run the swarm belief, the decision distribution and the reset histogram (before/after the change)</p>
Research data supporting: "Innate dynamics and identity crisis of a metal surface unveiled by machine learning of atomic environments"
<p>This repository contains the set of data shown in the paper <strong>"Innate dynamics and identity crisis of a metal surface unveiled by machine learning of atomic environments"</strong>, published on The Journal of Chemical Physics (DOI:10.1063/5.0139010)</p>
The energetic particle environment of a GJ 436 b-like planet
<p>This data set corresponds to the simulation data presented in the article "The energetic particle environment of a GJ 436b-like planet" (Rodgers-Lee, et al., MNRAS, 2023). Details of the stellar wind and cosmic ray models used for the simulations are available in the article.</p> <p>Each data file consists of the temperature, pressure and heights in the exoplanet atmosphere at a given orbital distance from the star (shown in Fig.3 of Rodgers-Lee et al. 2023). The number density, n(z), of the exoplanet atmosphere can be calculated from the temperature and pressure. Further details relating to the temperature pressure profiles are given in Section 2.3 of Rodgers-Lee et al. (2023). The last two but one columns in the data files are the ionisation rates for molecular hydrogen from Galactic cosmic rays and stellar energetic particles (shown in Fig.4 of Rodgers-Lee et al. 2023). The last column is the assumed planetary effective temperature.</p> <p>The data is given for a number of different orbital distances between 0.01 - 0.2au. File names are formatted as "zenodo_GJ436_manysp_solar_aX_tp.dat" where "X" corresponds to 100 x orbital distance in au (e.g. "a1" corresponds to a=0.01au) considered. The first line describes the data in the file. The second line gives the column headings and units.</p>
Atomic-scale environment of niobium in minerals as revealed by X-ray absorption spectroscopy at the Nb K-edge
<p>This data set provides the raw EXAFS and XRD data of the manuscript '<strong>Atomic-scale </strong><strong>environment of niobium in minerals as revealed b</strong><strong>y </strong><strong>X-ray absorption spectroscop</strong><strong>y at the Nb </strong><strong>K</strong><strong>-edge'</strong> submitted to European Journal of Mineralogy.</p> <p>- EXAFS data files were converted into .txt for more accessibility. <em>Pcl</em> is the abbreviation for <em>pyrochlore. </em>The files are accoridngly labelled.</p> <p>- XRD data files for synthetic Nb-doped compounds start with 'XRD_...'.</p> <p>- The refined crystal structure of hydropyrochlore is 'hydropcl_01.cif' file.</p> <p> </p> <p> </p>
Dataset and figures for "Sex in a virtual reality: experimental evidence for sexual isolation due to variation in perception of the environment"
<p>We quantified the strength of assortative mating when variation in the perception of the environment was manipulated experimentally. We manipulated the olfactory neurons of two groups of <em>Drosophila melanogaster </em>which changed their perception of their environment. In response to light (invisible to the flies), one group was designed to smell food (a positive stimulus), while the other group was designed to smell a dangerously high concentration of CO<sub>2</sub> (a negative stimulus). We combined both groups of flies, exposed them to a lit habitat and another habitat that was not, and allowed them to choose between these. We then measured the degree of assortative mating between the two types of flies due to any spatial population structure induced by the flies themselves. To control for any assortative mating due to other reasons, we also measured assortative mating when the heterogeneity of the environment could not be perceived by the flies, and when the environment was actually homogeneous.</p>
FIG. 3 in Trebouxia maresiae sp. nov. (Trebouxiophyceae, Chlorophyta), a new lichenized species of microalga found in coastal environments
FIG. 3. — Ultrastructure of Trebouxia maresiae Garrido-Benavent, Chiva & Barreno, sp. nov. by TEM: A, B, cultured cells; C-F, cells within lichen thallus; F, detail of a pyrenoid. White arrowheads in D, E and F indicate the tubules in pyrenoid periphery that are more similar to these which characterize the gigantea-type pyrenoid. Abbreviations: Chl, chloroplast; CW, cell wall; Pg, pyrenoglobuli; Py, pyrenoid; Tu, tubules; Nu, nucleus; s, starch granules. Scale bars: 2 µm.
FIG. 2 in Trebouxia maresiae sp. nov. (Trebouxiophyceae, Chlorophyta), a new lichenized species of microalga found in coastal environments
FIG. 2. — Habitat and morphology of Trebouxia maresiae Garrido-Benavent, Chiva & Barreno, sp. nov.: A, a thallus of the lichen Seirophora villosa (Ach.) FrÖdén from Es Trenc (Mallorca) with which this microalga associates; orange discs are apothecia produced by the lichen fungus to reproduce sexually; B-H, light microscopy photographs showing the gross morphology of vegetative cells and autosporangia; B, H, young and mature cells; C, central, crenulate chloroplast with a single pyrenoid; D, old cell with carotenoids in the cytoplasm; E-G, development of autosporangia and autospores; I-K, reconstruction of chloroplasts by LSCM; K, crenulate chloroplast with three pyrenoids (white arrowheads); L, reconstructed chloroplast of autospores within an autosporangium. Scale bars: 10 µm.
FIG. 1 in Trebouxia maresiae sp. nov. (Trebouxiophyceae, Chlorophyta), a new lichenized species of microalga found in coastal environments
FIG. 1. — Phylogram showing the placement of the new species in a subclade of Trebouxia Puymaly clade A sensu Muggia et al. (2020). The phylogeny was built with MrBayes based on a three-locus dataset (nrITS, 5.8S and rbcL). For reference, green- and orange-filled boxes on the right of each tip indicate the species code of each Trebouxia species following Muggia et al. (2020). To the right, the voucher or culture collection code, as well as the name of the species, if any, are provided. Posterior Probabilities (PP) and bootstrap support values (BS, RAxML-NG analysis) are represented on branches leading to nodes on the left and right, respectively. Branches in bold had a significant statistical support (PP ≥ 0.95; BS ≥ 70 %).
Data and software for: Temporal novelty detection and multiple timescale integration drive Drosophila orientation dynamics in temporally diverse olfactory environments
<p>To survive, insects must effectively navigate odors plumes to their source. In natural plumes, turbulent winds break up smooth odor regions into disconnected patches, so navigators encounter brief bursts of odor interrupted by bouts of clean air. The timing of these encounters plays a critical role in navigation, determining the direction, rate, and magnitude of insects' orientation and speed dynamics. Disambiguating the specific role of odor timing from other cues, such as spatial structure, is challenging due to natural correlations between plumes' temporal and spatial features. Here, we use optogenetics to isolate temporal features of odor signals, examining how the frequency and duration of odor encounters shape the navigational decisions of freely-walking <em>Drosophila</em>. We find that fly angular velocity depends on signal frequency and intermittency – fraction of time signal can be detected – but not directly on durations. Rather than switching strategies when signal statistics change, flies smoothly transition between signal regimes, by combining an odor offset response with a frequency-dependent novelty-like response. In the latter, flies are more likely to turn in response to each odor hit only when the hits are sparse. Finally, the upwind bias of individual turns relies on a filtering scheme with two distinct timescales, allowing rapid and sustained responses in a variety of signal statistics. A quantitative model incorporating these ingredients recapitulates fly orientation dynamics across a wide range of environments and shows that temporal novelty detection, when combined with odor motion detection, enhances odor plume navigation.</p>
Dataset from: Ultraviolet refractive index values of organic aerosol extracted from deciduous forestry, urban and marine environments
<p>The refractive index values of atmospheric aerosols are required to address the large uncertainties in the magnitude of atmospheric radiative forcing and measurements of the refractive index dispersion with wavelength of particulate matter sampled from the atmosphere are rare over ultraviolet wavelengths. An ultraviolet-optimized spectroscopic system illuminates optically-trapped single particles from a range of tropospheric environments to determine the particle’s optical properties. Aerosol from remote marine, polluted urban, and forestry environments is collected on quartz filters, and the organic fraction is extracted and nebulized to form micron-sized spherical particles. The radius and the real component of refractive index dispersion with wavelength of the optically trapped particles are determined to a precision of 0.001 µm and 0.002 respectively over a near-ultraviolet-visible wavelength range of 0.320–0.480 µm. Remote marine aerosol is observed to have the lowest refractive index (n=1.442 (λ=0.350 µm)), with above-canopy rural forestry aerosol (n=1.462–1.481 (λ=0.350 µm)) and polluted urban aerosol (n=1.444–1.485 (λ=0.350 µm)) showing similar refractive index dispersions with wavelength. In-canopy rural forestry aerosol is observed to have the highest refractive index value (n=1.508 (λ=0.350 µm)). The study presents the first single particle measurements of the dispersion of refractive index with wavelength of atmospheric aerosol samples below wavelengths of 0.350 µm. The Cauchy dispersion equation, commonly used to describe the visible refractive index variation of aerosol particles, is demonstrated to extend to ultraviolet wavelengths below 0.350 µm for the urban, forestry, and atmospheric aerosol water-insoluble extracts from these environments. A 1D radiative-transfer calculation of the difference in top-of-the-atmosphere albedo between atmospheric core-shell mineral aerosol with and without films of this material demonstrates the importance of organic films forming on mineral aerosol.</p> <p>The raw experimental spectra collected and analysed in this study are provided, as well as files for the calibrated wavelengths.</p>
Collective Scene Completion in Partially Observable Environments
<p>Submitted for the purpose of visually aiding paper titled Collective Scene Completion in Partially Observable Environments.</p> <p>We assume a partially observable environment where a robot swarm is deployed to explore and construct a map of the environment. The robots perform collective decision-making to reach a consensus on the observable part of the environment and use a novel collective perception framework for scene inpainting using autoencoders to predict inaccessible regions of an environment. We use the MNIST digit dataset as the projected scenes on the robots to facilitate reproducibility.</p>
Figure 2 in Origin of adaptations to open environments and social behaviour in sabretoothed cats from the northeastern border of the Tibetan Plateau
Figure 2. Tip-dating phylogeny of Machairodontinae. Biogeographic hypotheses (BioGeoBEARS implemented in RASP 4.2) are located at the nodes, with pie charts indicating the posterior probabilities of that node being present in a given geograpical region. The dispersal curves (for Machairodontini only, from M. aphanistus to Homotheriina) are located at the bottom of the figure. See the posterior probability of each node of the tip-dating phylogenetic analysis in the electronic supplementary material, appendix. The reconstruction of Homotherium serum from data in digimorph (http://www.digimorph.org/index.phtml). The photo of Machairodus aphanistus from Batallones comes from https://fossilhuntress.blogspot.com/2016/10/, the others are by the authors.
Figure 1 in Origin of adaptations to open environments and social behaviour in sabretoothed cats from the northeastern border of the Tibetan Plateau
Figure 1. (a) Cranium of Amphimachairodus hezhengensis sp. nov. HMV2041. a1, dorsal view; a2, anterior view; a3, ventral view; a4, postero-ventral view; a5, lateral view. (b) Geography of fossil locality. (c) Pathology of the MC2 and MC3 of Amphimachairodus sp. HMV2047 forepaw. (d) Large predator contemporary with A. hezhengensis in the Linxia Basin. d1, A. hezhengensis, HMV2041; d2, Dinocrocuta gigantea, HMV2044; d3, Agriotheriini ursid, HMV2046.
Steering herds away from dangers in dynamic environments
<p>Shepherding, the task of guiding a herd of autonomous individuals in a desired direction, is an essential skill to herd animals, enable crowd control, and rescue from danger. Equipping robots with the capability of shepherding would allow performing such tasks with increased efficiency and reduced labor costs. So far, only single-robot or centralized multi-robot solutions have been proposed. The former is unable to observe dangers at any place surrounding the herd, and the latter does not generalize to unconstrained environments. Therefore, we propose a decentralized control algorithm for multi-robot shepherding, where the robots maintain a caging pattern around the herd to detect potential nearby dangers. When danger is detected, part of the robot swarm positions itself in order to repel the herd towards a safer region. We study the performance of our algorithm for different collective motion models of the herd. We task the robots to shepherd a herd to safety in two dynamic scenarios: (i) to avoid dangerous patches appearing over time, and (ii) to remain inside a safe circular enclosure. Simulations show that the robots are always successful in shepherding when the herd remains cohesive, and enough robots are deployed.</p>
FTIR dataset from the article "Resistance to Degradation of Silk Fibroin Hydrogels Exposed to Neuroinflammatory Environments"
<p>The attached archive encounters the FTIR data used for the calculation of the β-sheet content of the silk fibroin hydrogels in the in-vitro experiments. Such data is used in main Figures 2<strong>c</strong> and 2<strong>d</strong>, 4<strong>a </strong>and 7<strong>a</strong> as well as Supplementary Figure 2<strong>a</strong> of the associated article.</p> <p> </p> <p>The acquisition of the files has been performed as indicated below:</p> <ul> <li><strong>Equipment:</strong> Thermo Scientific™ Nicolet™ iS™ 5 Spectrometer (Thermofisher, United States)</li> <li><strong>Acquisition Software:</strong> OMNIC 9.2.86 (Thermofisher, United States)</li> <li><strong>Number of Scans:</strong> 64</li> <li><strong>Data Spacing: </strong>0.482 cm<sup> -1</sup></li> </ul> <p> </p> <p>The files have been reported as raw complete spectrum including the absorbances in the wavenumbers from 600 to 1500 cm<sup> -1</sup> in the format of “.SPA”. In the following lines, we provide examples of software to process and analyze the spectra files included in the database.</p> <ul> <li>Python version 3.8 to 3.10 upon the availability of the “SpectroChemPy” (Travert and Christian, 2023) function.</li> <li>Matlab R2016a and newer upon the availability of the “LoadSpectra” (Oldenburg, 2023) function.</li> <li>OMNIC 9 (Thermofisher, United States).</li> <li>Essential FTIR (Operant LLC, United States).</li> </ul> <p> </p> <p>Provision of the data in other formats is available upon request. Inquiries may be directed to <a href="mailto:daniel.gonzalez@ctb.upm.es?subject=FTIR%20Samples%20Format">Daniel González-Nieto</a> or <a href="mailto:mahdi.yonesi@ctb.upm.es?subject=FTIR%20Samples%20Format">Mahdi Yonesi</a>.</p> <p><strong>References:</strong></p> <p>OLDENBURG, K. 2023. LoadSpectra. MATLAB Central File Exchange.</p> <p>TRAVERT, A. & CHRISTIAN, F. 2023. SpectroChemPy, a framework for processing, analyzing and modeling spectroscopic data for chemistry with Python. Github.</p> <p> </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.