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4,669 results for “Environment;”
Figure 4 in Tardigrade diversity: an evaluation of natural and disturbed environments of the province of Salta (Argentina)
Figure 4. Partition of gamma diversity into its alpha and beta components and the relationship between observed and expected diversity at each partition level. *Statistically significant differences between two values.
Figure 2 in Tardigrade diversity: an evaluation of natural and disturbed environments of the province of Salta (Argentina)
Figure 2. Comparison of tardigrade community structure in selected habitats of Salta province (Argentina) by Whittaker curves, where the species are ordered from most to least abundant. The native community fits better to a broken-stick model showing evenness, meanwhile the log series model best describes the data of urban and rural tardigrade communities.
Figure 3 in Tardigrade diversity: an evaluation of natural and disturbed environments of the province of Salta (Argentina)
Figure 3. NMS showing the similitude of tardigrade assemblages from the sampled communities ordered by habitat on the first axis.
Figure 1 in Tardigrade diversity: an evaluation of natural and disturbed environments of the province of Salta (Argentina)
Figure 1. Sample site locations in urban, rural and native communities in relation to central Salta province. NR9, National Route No. 9.
A dataset for RSSI based outdoor localization using LoRaWAN in a harbor as a harsh and industrial environment
<p>Enabling precise device localization is a critical requirement for the future of industry. Leveraging signal features for location determination has emerged as a leading approach and good alternative for Global Navigation Satellite Systems (GNSS) because of their limitations (low accuracy for indoor environments, expensive chips, and high energy consumption). On this basis, to provide localization for IoT in an industry with a harsh environment, the adopted wireless networks should have a long range coverage area. LoRaWAN is one of the most common communication networks that can provide large coverage with low power consumption and low implementation cost. Between various signal features that can be used for localization, Received Signal Strength (RSS) received more attention because of their low-cost deployment. But, RSS is highly dependent and sensitive to environmental changes, such as temperature, humidity, and background noise. This sensitivity becomes more intensive in an industrial environment with a harsh and dynamic environment. In order to evaluate the environmental effects on RSS in the harsh and highly dynamic industry, we present a comprehensive repository of LoRaWAN Received Signal Strength Indicator (RSSI) measurements, collected in a harbor as a testbed featuring three LoRaWAN gateways and one mobile end node. During the data collecting process, the mobile device obtains its location via a GPS and transmits it as the LoRaWAN message. In addition, to provide more insight of the effect of dynamic environment on the RSSI, two end nodes are implemented in fixed locations. These end nodes transmit messages with fixed time intervals including their unique id. The collected dataset includes RSSI and SNR measurements recorded by multiple gateways for each transmitted packet by fixed or mobile end nodes, and timestamp. This dataset enables the development and evaluation of RSSI-based localization and allows researchers to explore the challenges and opportunities associated with localization in dynamic IoT deployments.</p>
Ljubljana Multi-Sensor Indoor and Outdoor PM Exposure, Environment, and Personal Health Dataset
<p>This dataset was compiled between February 16, 2019, and May 25, 2019, involving 82 participants residing in the municipality of Ljubljana. Data collected before March 12th represents the "heating season," while data after April 27th corresponds to the "non-heating season." This dataset is the cleaned/filtered version with outliers caused by software or hardware errors removed.</p><p>Derived from the larger ICARUS project in Ljubljana, this dataset integrates various data sources, including:</p><ul><li>Participant Questionnaires: Containing certain individual information, i.e., age, height, gender.</li><li>Time Activity Diaries: Providing hourly activity records for each participant.</li><li>Personal PM Monitors: Measuring indoor and outdoor PM concentrations.</li><li>Smart Activity Trackers: Recording heart rate and movement data.</li><li>Indoor Air Quality Station: Capturing indoor air quality parameters.</li></ul><p>The dataset includes the calculation of inhalation rate based on heart rate data, allowing for the determination of inhalation rate-adjusted exposure or intake dose.</p><p>This version of the dataset is in .csv format.</p>
UNIVERSE: UNobtrusIVE measuRement of mental workload and stress in uncontrolled environments
<p><strong>A Dataset on Unobtrusive Measurement of Cognitive Load and Physiological Signals (EEG, PPG, EDA) in Uncontrolled Environments</strong></p> <p>The dataset (approximately 315 hours in total) consists of physiological signals from wearable electroencephalography (EEG), electrodermal activity (EDA), photoplethysmogram (PPG), acceleration, and temperature sensors. The recorded dataset is curated from 24 participants following an eight-hour cognitive load elicitation paradigm. The mentioned consumer-grade physiological signals are obtained from the Muse S EEG headband and Empatica E4 wristband. The data is balanced across controlled and uncontrolled environments and high vs. low mental workload levels. During the study, participants worked on mental arithmetic, Stroop, N-Back, and Sudoku tasks in the controlled environment (roughly half of the data) and realistic home-office tasks such as researching, programming, and writing emails in uncontrolled environments. Data labels were obtained using Likert scales, Affective Sliders, PANAS, and NASA-TLX questionnaires. The completely anonymized data set and its publicly available features open a vast potential to the research community working on mental workload detection using consumer-grade wearable sensors. Among others, the data is suitable for developing real-time cognitive load detection methods, research on signal processing techniques for challenging environments, developing artifact removal techniques from low-cost wearable devices' data, or developing personal mental workload assistants.<br> </p> <p>Follow the dataset descriptor publication for more details:</p> <p>Anders, C., Moontaha, S., Real, S., Arnrich, B.: Unobtrusive measurement of cognitive load and physiological signals in uncontrolled environments. Scientific Data 11(1), 1000 (2024).</p> <p><a href="https://doi.org/https://doi.org/10.1038/s41597-024-03738-7" target="_blank" rel="noopener">https://doi.org/https://doi.org/10.1038/s41597-024-03738-7</a></p>
Supplementary video 2 for "Phase Memory of Orbital Angular Momentum in Multiple Scattering Environment"
<p>This is Supplementary video 2 for article Meglinski, et. al. "Phase Memory of Orbital Angular Momentum in Multiple Scattering Environment"</p>
Supplementary video for "Phase Memory of Orbital Angular Momentum in Multiple Scattering Environment"
<p>This is Supplementary video for article Meglinski, et. al. "Phase Memory of Orbital Angular Momentum in Multiple Scattering Environment"</p>
Data used in the study titled "Significant Tornado Environments In Canada Using ERA5-Derived Convective Parameters"
<p>These data include: (1) Observation-derived convective parameters at four Canadian sounding stations based on 1990-2020 data, and ERA5-derived convective parameters at grid points nearest to the same four Canadian sounding stations on 1990-2020 data - file called "ERA5 and Observation convective parameters.zip". (2) ERA5 vertical profile data, derived convective parameters, and skew-t images for the 166 Canadian F/EF2+ tornado events - file called "ERA5 profiles-parameters-skewt 166 cases.zip"</p>
Dataset for the research article entitled " New insights on the Empty Quarter desert environment derived from the WInd-blown Sand Experiment (WISE)"
Open the record for dataset details and reuse information.
Supplementary material 1 from: Serafim-Júnior M, Perbiche-Neves G, Lansac-Toha F (2019) Environments and macrophytes as main variables controlling rotifers in a river/lake system before Porto Primavera Reservoir construction. Zoologia 36: 1-8. https://doi.org/10.3897/zoologia.36.e24191
Table S1. List of species found in our study, separated by their habitat (H) occurrence in the pelagic (P) or littoranean (L) region
Data for: Light-absorbing impurities in glacial environments over western Himalaya from reanalysis data and in situ observations
<p>The repository contains information about 6 aerosol variables extracted from MERRA-2 reanalysis over western Himalayan region of Jammu and Kashmir. It also contains data about glacier ice chemistry based on 9 physicochemical characteristics of 3 glaciers nestled in the Greater Himalayan Mountain range of Kashmir. Air mass back trajectories as simulated from HYSPLIT model on a seasonal basis are also provided.</p>
The stability of competitive metacommunities is insensitive to dispersal connectivity in a fluctuating environment
<p>This repository contains four archived folders: 1) all the codes and parameter sets for the simulations, 2) codes to analyse and visualise the simulation outputs, 3) simulation output files and other data files necessary for the analysis, and 4) library management files for julia lang. Please refer to README.txt for the descriptions of the files.</p>
Ecological significance of marcescence in Himalayan plants: Why is standing dead phytomass more important in demanding, resource-limited environments?
<ol> <li>Understanding mechanisms allowing plants to thrive in challenging conditions is critical for predicting their responses to global environmental change. An often overlooked ecological adaptation is marcescence, where leaves and stems are retained beyond their typical shedding time, with implications for nutrient recycling and carbon sequestration. Marcescence may be common in plants with conservative resource-use strategies, especially in environments with limited resources and marked seasonality, such as deserts and alpine regions. However, the extent to which marcescence occurs in different taxa across different habitats and its relationship to seasonality and plant functioning as reflected in ecophysiological traits remains understudied.</li> <li>We studied 600 individuals across 40 Himalayan herb species found in desert, steppe, alpine, and subnival habitats, spanning 3000 to 5400 m elevation. Marcescence was observed in 37 out of 40 species evaluated, with 57% of the sampled individuals displaying dead phytomass, indicating the widespread nature of this phenomenon in Himalayan ecosystems.</li> <li>Environment and plant traits emerged as significant factors influencing the amount of dead-standing biomass. These findings remained robust after accounting for intraspecific variation and phylogenetic inertia. Desert and steppe species exhibited greater marcescent biomass, characterized by tenfold more dead stem biomass than alpine and subnival plants. In contrast, the proportion of dead phytomass increased with elevation due to increased dead leaf fraction.</li> <li>Tall desert species with heavily lignified stems and limited nitrogen and phosphorus content had high levels of stem marcescence, which may aid litter photodegradation and nutrient recycling but also deter herbivory and promote seed dispersal (tumbleweed). Conversely, slow-growing alpine and subnival plants with higher water-use efficiency, foliar C:N ratios, and parenchymatic storage tissue rich in mobile carbohydrates had a high proportion of marcescent leaves, which may aid survival in these resource-limited environments by providing delayed nutrients through moisture-conserving mulch that also provides insulation and protection from frost.</li> <li>This study provides new insights into the complex interplay of habitat, seasonality, and plant traits in dead phytomass retention in Himalayan plants. Marcescence is prevalent in resource-conservative species in highly seasonal, resource-limited habitats and may significantly shape the functioning and biodiversity of Himalayan ecosystems.</li> </ol>
Modelling Cell Shape in 3D Structured Environments: A Quantitative Comparison with Experiments
<p>This repository contains experimental data and computer scripts for the following publication: Link R, Jaggy M, Bastmeyer M, Schwarz US (2024) Modelling cell shape in 3D structured environments: A quantitative comparison with experiments. PLoS Comput Biol 20(4): e1011412. https://doi.org/10.1371/journal.pcbi.1011412</p> <p>There are two directories, “data” and “scripts”.</p> <p> <strong>1) </strong><strong>Directory data</strong></p> <p> WRL-files for experimental data generated with Imaris from Zeiss image files.</p> <p>The WRL-files can be converted to STL-files with MeshLab (<a href="https://www.meshlab.net/">https://www.meshlab.net</a>).</p> <p>The STL-files can be converted to FE-files for the SurfaceEvolver with our script CreateFeFile.py.</p> <p> The WRL-files are named according to the scaffolds:</p> <p>L*.wrl cells in L-shaped scaffolds (n=6).</p> <p>V*.wrl cells in V-shaped scaffolds (n=7).</p> <p>TRight*.wrl cells in right-triangle scaffolds (n=3).</p> <p>TEqui*.wrl cells in equilateral-triangle scaffolds (n=4).</p> <p><strong>2) </strong><strong>Directory scripts</strong></p> <p>ClusterSurfaceLinearPlugin: New CompuCell3D plugin needed to calculate linear surface energy functional for cells with nucleus (using the cluster concept).</p> <p>CompuCell3DScript: Hamiltonian_Comparison.cc3d is the main script for our simulations, uses the directory “Simulation”.</p> <p>CreateFeFile.py: generates surface evolver FE-file from STL-file. A STL-file can be generated from a WRL-file e.g. with MeshLab (<a href="https://www.meshlab.net/">https://www.meshlab.net</a>). </p> <p>SphericalHarmonicsAnalysis.ipynb: Python notebook that calculates the Fourier spectrum and Delta_30, needs WRL-file as input.</p> <p> </p>
LAEA: A 2D LiDAR Assisted UAV Exploration Algorithm for Unknown Environment
Open the record for dataset details and reuse information.
Fig. 2 in The hydroperoxyl radical scavenging activity of natural hydroxybenzoic acids in oil and aqueous environments: Insights into the mechanism and kinetics
Fig. 2. The optimized structures of TS of the HOO• + HBA reactions following the FHT mechanism in pentyl ethanoate.
Fig. 4 in The hydroperoxyl radical scavenging activity of natural hydroxybenzoic acids in oil and aqueous environments: Insights into the mechanism and kinetics
Fig. 4. Calculated log(koverall) at 298.15 K, in the reactions of the HBA with HOO• in water following pH values.
Navigation Test in Simulated Environment Rosbag. Human obstacle detection.
<p>This repository contains rosbags (ROS 2 Humble) extracted from a navigation test realized in a simulated environment (Amazon Hospital map) with an RB1 robot. The test consist of a navigation form one point to another with a human obstacle avoidance.</p> <p> </p> <p>This research is part of the project TESCAC, financed by “European Union NextGeneration-EU, the Recovery Plan, Transformation and Resilience, through INCIBE".</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.