Find research datasets worth reusing

opencc-by-nc-4.0Aug 2021View details →

Figure 3 in Living in a cold tropical mountain: do the microhabitat use and activity pattern change with elevation in the high-Andean lizard Stenocercus trachycephalus (Squamata: Tropiduridae)?

Figure 3. Activity patterns density curve along the three localities. Guanentá in dark gray, Las Moyas in light gray and La Chacua in gray.

opencc-by-nc-4.0Aug 2021View details →

Figure 1 in Living in a cold tropical mountain: do the microhabitat use and activity pattern change with elevation in the high-Andean lizard Stenocercus trachycephalus (Squamata: Tropiduridae)?

Figure 1. Study locations in the Andes eastern mountain range. (A) Guanentá, 3,750-3,950 m; (B) Las Moyas Páramo, 3,200 m; (C) La Chacua, 2,670 m.

opencc-by-4.0Oct 2022View details →

Geospatial data used in "Estimation of river water surface elevation using UAV photogrammetry and machine learning"

Geospatial data used in article "Estimation of river water surface elevation using UAV photogrammetry and machine learning" by Radosław Szostak, Marcin Pietroń, Przemysław Wachniew, Mirosław Zimnoch and Paweł Ćwiąkała (AGH UST). Each zip archive contains the following files: <ul> <li>dsm.tif - raster of digital surface model,</li> <li>ortho.tif - raster of orthophoto,</li> <li>gnss_wse.json - geojson multipoint shape containing RTN GNSS measurements of water surface elevation,</li> <li>grid.json - geojson multipolygon shape containing square areas of samples used in deep learning solution.</li> <li>centerline.json - geojson multipoint shape containing values sampled from DSM along centerline,</li> <li>wateredge.json - geojson multipoint shape containing values sampled from DSM along "water-edge".</li> </ul> Data in AMO18.zip archive was collected by Bandini et. al (https://doi.org/10.5281/zenodo.3519888). Preprocessed machine learning dataset and source codes are available in github repository at: https://github.com/radekszostak/river-wse-uav-ml

opencc-by-nc-4.0Sep 2017View details →

1-km bed topography digital elevation model (DEM) of the Weddell Sea sector, West Antarctica

We present a new bed elevation digital elevation model (DEM) of the Weddell Sea sector, West Antarctica. The DEM consists a total area of ~125,000 km2 covering the Institute, Möller and Foundation ice streams and the Bungenstock Ice Rise with a 1 km spatial resolution. In order to produce the bed elevation DEM, ice thickness DEM was formed from the available radio-echo sounding (RES) data using the 'Topo to Raster' function in ArcGIS. The RES data used in this study were compiled from four main sources which are (1) Scott Polar Research Institute (SPRI) survey collected during several campaigns in the 1970s (Drewry, 1983), (2) British Antarctic Survey (BAS) airborne radar survey conducted during the austal summer 2006/07 (GRADES/IMAGE) (Ashmore et al., 2014). , (3) BAS airborne survey accomplished during the Institute and Möller Antarctic Funding Initiative (IMAFI) in 2010/2011 (Ross et al., 2012) and (4) Center for the Remote Sensing of Ice Sheet (CReSIS) data during the NASA Operation IceBridge (OIB) programme in 2012, 2014 and 2016 (Gogineni, 2012). The ice thickness picks were gridded at a uniform 1-km spacing using the Nearest Neighbour interpolation within the Topo to Raster. The ice thickness DEM was later subtracted from the 1-km ice-sheet surface elevation derived from the combined European Remote Sensing Satellite-1 (ERS-1) radar and Ice, Cloud and land Elevation Satellite (ICESat) laser satellite altimetry DEM (Bamber et al., 2009), to produce the bed topography referenced to the Polar Stereographic projection (Snyder, 1987).

opencc-zeroApr 2024View details →

Data from: Orchid trade at the source: Epiphytic species with conspicuous flowers in low-elevation forests are more locally collected in a Philippine key biodiversity area

Orchids are the most heavily traded plant group globally, putting pressure on wild populations in many source countries like the Philippines. Despite its rich orchid diversity, there remains a notable gap in understanding the factors driving orchid trade within the country. To address this knowledge gap and support orchid conservation efforts, we utilized a five-year orchid diversity dataset extensively collected through floristic field and village garden surveys in one of the largest key biodiversity areas in the southern Philippines. We employed a trait-based approach to investigate ecological drivers of local orchid collection within this source area. Our results show that around 36% of local orchid diversity have predicted collection risks of ≥50%. Notably, locally collected orchid species exhibited multiple, large, and conspicuously colored flowers that are found in low-elevation forests and higher up in forest stratum. Elevational distribution and flower size emerged as the strongest predictors, potentially influencing collection preferences. Our analysis of predicted collection risks underscores the vulnerability of both threatened and non-threatened orchid species to local collection pressures. Moreover, we highlight the practical utility of our trait-based approach in predicting risks and informing management strategies for local orchid conservation. This research marks a significant step towards identifying ecological drivers influencing orchid trade at its source, providing insights that can inform targeted conservation strategies across many key biodiversity areas for this highly diverse, charismatic, and threatened plant family.

opencc-zeroApr 2024View details →

Physiological thermal niches, elevational ranges and thermal stress in dendrobatid frogs: an integrated approach

Aim: We investigated the relationship between thermal physiology, elevational distribution, and thermal stress among nine closely related dendrobatid frogs during their aquatic stage by employing an integrated approach, combining thermal physiology, environmental temperature modelling, and predictive assessments of current and future exposure to thermal variation. Location: Ecuador Taxon: Amphibians; Anura, Dendrobatidae, Epipedobates, Hyloxalus Methods: We determined the Thermal Performance Curves of larval growth (TPCs) for each species and modelled the thermal variation in contrasting aquatic larval environments for both present and future times. This allowed us to estimate the expected elevational distributions and forecast periods of exposure to stressful temperatures that inhibit larval growth due to elevation and global warming. Results: We found significant correlations between optimum temperature (Topt), 50 % maximum performance temperature (maxB50), 50% minimum performance temperature (minB50), and cold resistance (survival at 9 ºC) with the current elevational distributions. However, thermal physiology predicted lower than observed distributions for high-elevation dendrobatids and higher than observed maximum elevations for lowland species. Nonetheless, our models predicted that low thermal variability habitats (i.e. streams and deep permanent ponds) can buffer the future increase in temperatures for all taxa, even when considering the most extreme scenario. In contrast, all species within high thermal variation habitats (open forest temporary ponds) are expected to experience stressful temperatures under present conditions. Main Conclusions: The findings indicate that thermal physiology may not be a limiting factor for dendrobatid frog species' ranges in this equatorial mountain gradient. Highland species may need to adapt to suboptimal performance, while some lowland species could occupy higher elevations. This study emphasizes the importance of habitat buffering to mitigate thermal stress in the face of climate change for amphibians in tropical mountains.

opencc-by-4.0Feb 2024View details →

Elevated perceived stress in university students due to COVID-19 pandemic: Potential contributing factors in a propensity-score matched sample

Open Data and Open Materials of: Elevated perceived stress in university students due to COVID-19 pandemic: Potential contributing factors in a propensity-score matched sample.  Scandinavian Journal Of Psychology. Abstract Objective: Onset of the Coronavirus Disease 2019 (COVID)-pandemic has increased students’ perceived burdens. The current study aimed to examine COVID-related changes and to identify potential factors that contribute to students’ stress. Method: Adopting a cross-sectional cohort-study design, we examined perceived stress, depressive and anxiety symptoms with a specific focus on the role of study-related variables such as perceived study-related demands, study-related resources, academic procrastination, and stress enhancing beliefs. Two cohorts (Npre-COVID=2175;NCOVID=959) were recruited at the same university and matched with regard to their propensity score (age, gender, semester). Results: Compared to the pre-COVID cohort, university students in the COVID-cohort reported more perceived stress, more depressive and anxiety symptoms, more academic procrastination due to fear of failure, more stress-enhancing beliefs, more distress due to the housing situation, and more perceived study-related challenges (Cohen’s d=.15–.45). A stepwise regression analysis identified depressive symptoms, procrastination due to fear of failure, general self-efficacy, increased study demands, perceived difficulties with to self-organised learning, distress due to housing, and stress enhancing beliefs as predictors of perceived stress in the COVID-cohort. Discussion: Findings suggest that the switch to online-only education increased the study-related burden for students, primarily due to exams being replaced by a greater amount of regular coursework and imposing demands on self-organised learning. Possibly, stress enhancing beliefs and procrastination due to fear of failure might have been elevated due to less opportunity for social referencing and lack of felt social support by peer students. Conclusion: Experienced increased burden in students during the COVID pandemic was mostly accounted for by the lack of perceived individual resources rather than by the increase in objective study-related demands.

Clinal variation in quantitative traits but not in evolutionary potential along elevational and latitudinal gradients in the widespread Anthyllis vulneraria

Premise of the study Strong elevational and latitudinal gradients allow the study of genetic differentiation in response to similar environmental changes. However, it is uncertain whether the environmental changes along the two types of gradients result in similar genetically based changes in quantitative traits. Peripheral arctic and alpine populations are thought to have a lower evolutionary potential than more central ones. Methods We studied quantitative traits of the widespread Anthyllis vulneraria in a common garden. Plants originated from 20 populations along a 2000 m elevational gradient from the lowlands to the elevational limit of the species in the Alps, and from 20 populations along a 2400 km latitudinal gradient from the centre of the distribution of the species in Central Europe to its northern distributional margin. Key results Several traits showed similar clinal variation with elevation and latitude of origin. Higher QST-values than FST-values in some traits indicated divergent selection. The same traits were subject to strongly diversifying selection among populations (high QST) and strong stabilising selection within populations (low evolvability). Genetic diversity of most quantitative traits and neutral molecular markers was only weakly correlated. Plasticity in response to benign conditions declined with both increasing elevation and latitude of origin, but the evolvability of most traits did not. Conclusions The clinal variation suggests adaptive differentiation of quantitative traits along the two gradients. Our results indicate that the evolutionary potential of peripheral populations is not necessarily reduced. However, lower plasticity may threaten their survival under rapidly changing climatic conditions.

Data from: Rapoport's rule explains the range size distribution of butterflies along the Eastern Himalayan elevation gradient

Understanding elevational range size distribution of organisms can provide valuable insights on biogeographic pattern of species and their conservation. Rapoport's rule posits that the elevational range size of species increases with increasing elevation. However, the validity of this rule is often questioned due to variations in methodologies across studies and inconsistencies among different groups of organisms. In this study we examined the elevational range size distribution of butterflies in the Eastern Himalaya, and assessed the applicability of Rapoport's rule using different approaches, which perhaps is the first of its kind in the Himalaya. We sampled butterflies along the elevational gradient of 16 elevational bands (300 – 3,300 m) using point count method along the transect. The sampled butterflies were grouped into various sub-groups based on family, biogeographic affinity, and larval feeding pattern. We found that the majority of the butterfly species (total as well as sub-groups) had small range sizes, and their elevational range distribution showed support for the Rapoport's rule. Increase in variation in temperature as measured by temperature seasonality and mean annual temperature range were the most important predictors of range size distribution pattern of the overall butterfly community. However, the relationship between range size and climatic variability differed among various sub-groups implying that the perceived pattern may vary even within the species of the same taxon.

opencc-by-4.0May 2024View details →

Surface dust coverages on rock targets in Gale crater: Influence of seasonal wind variability, elevation and proximity to aeolian sand fields.

The following dataset accompanies the paper submission to AGU - JGR: Planets for  the paper titled: " Surface dust coverages on rock targets in Gale crater: Influence of seasonal wind variability, elevation and proximity to aeolian sand fields."

Elevated sleep quota in a stress-resilient Drosophila species

Sleep is broadly conserved across the animal kingdom but can vary widely between species. It is currently unclear which selective pressures and regulatory mechanisms influence differences in sleep between species. The fruit fly Drosophilamelanogaster has become a successful model system for examining sleep regulation and function, but little is known about the sleep patterns in many related fly species. Here, we find that fly species with adaptations to extreme desert environments, including D. mojavensis, exhibit strong increases in baseline sleep compared to D. melanogaster. Long-sleeping D. mojavensis show intact homeostasis, indicating that desert flies carry an elevated drive for sleep. In addition, D. mojavensis exhibit altered abundance or distribution of several sleep/wake related neuromodulators and neuropeptides that are consistent with their reduced locomotor activity and increased sleep. Finally, we find that in a nutrient-deprived environment, the sleep patterns of individual D. mojavensis are strongly correlated with their survival time and that disrupting sleep via constant light stimulation renders D. mojavensis more sensitive to starvation. Our results demonstrate that D. mojavensis is a novel model for studying organisms with high sleep drive, and for exploring sleep strategies that provide resilience in extreme environments.

opencc-by-4.0Apr 2024View details →

Dataset of Room Impulse Responses from Baffled Microphone Arrays and Sound Sources at Three Elevations

This data set contains a collection of impulse responses (stored in SOFA format) from spherical microphone arrays (SMAs), equatorial microphone arrays (EMAs), and non-spherical microphone arrays (XMAs). Thereby, impulse response sets are provided for each array type at various spatial resolutions, for a loudspeaker sound source at three source elevations, and in four diverse acoustic environments (see DATA section for a full description). The original purpose of the microphone array data is the binaural rendering in the spherical harmonics (SH) domain into ear signals for high-fidelity reproduction of the acoustic scenario via headphones. Therefore, binaural room impulse responses (BRIRs) for 360 horizontal head orientations of a G.R.A.S KEMAR acoustic dummy head are provided as a reference for each scenario. Please contact the authors for questions or additional information regarding the room setups and utilized measurement devices.   ======  DATA ====== This archive contains the processed impulse response sets of various measurement configurations, as described in this section. Directory "resources/ARIR_processed/": <ul> <li>Post-processed SMA and EMA impulse responses <ul> <li>"_SMA*_" or "_EMA*_" in the file name</li> <li>In SOFA format with "SingleRoomSRIR" convention</li> <li>From 1x DPA 4060 microphone flush mounted in a wooden spherical scattering body with an 8.5 cm radius</li> <li>High-resolution data (measured sequentially on VariSphear turntable with two degrees-of-freedom rotations): <ul> <li>Hall: 1202 channels (Lebedev grid) for maximum SH order 29</li> <li>Others: 2702 channels (Lebedev grid) for maximum SH order 44</li> </ul> </li> <li>Lower-resolution data via subsampling in the SH domain (arbitrary sampling grids and lower target orders can be achieved): <ul> <li>SH order 29: 1742 channels (t-design grid) for SMA; 59 channels (equiangular grid) for EMA</li> <li>SH order 12: 314 channels (t-design grid) for SMA; 25 channels (equiangular grid) for EMA</li> <li>SH order 8: 146 channels (t-design grid) for SMA; 17 channels (equiangular grid) for EMA</li> <li>SH order 4: 42 channels (t-design grid) for SMA; 9 channels (equiangular grid) for EMA</li> <li>SH order 2: 14 channels (t-design grid) for SMA; 5 channels (equiangular grid) for EMA</li> <li>SH order 1: 6 channels (t-design grid) for SMA; 3 channels (equiangular grid) for EMA</li> </ul> </li> </ul> </li> <li>Post-processed XMA impulse responses <ul> <li>"_XMA*_" in the file name</li> <li>In SOFA format with "SingleRoomSRIR" convention</li> <li>From 18x Rode Lavalier GO microphone mounted in an elastic band on a wooden head-shaped scattering body (7.5 cm to 10.5 cm radius)</li> <li>High-resolution data (measured simultaneously): <ul> <li>18 channels for maximum SH order 8</li> </ul> </li> <li>Lower-resolution data via integer subsets of microphones: <ul> <li>SH order 4: 9 channels</li> <li>SH order 2: 6 channels</li> </ul> </li> <li>Anechoic: For 360 horizontal scattering body orientations (measured sequentially on a VariSphear turntable with azimuth in 1-degree steps)</li> <li>Rooms: For 36 horizontal scattering body orientations (measured sequentially on VariSphear turntable with azimuth in 10-degree steps)</li> </ul> </li> <li>Generated XMA calibration filters and equalization filters <ul> <li>"_x_nm_" and "_e_nm_" in the file name</li> <li>In proprietary Matlab format</li> <li>Time-domain representation of filters in the respective orders of "real" spherical harmonics</li> </ul> </li> <li>Post-processed binaural impulse responses <ul> <li>"_KEMAR_" in the file name</li> <li>In SOFA format with "SingleRoomSRIR" convention</li> <li>From G.R.A.S KEMAR dummy head with large pinna</li> <li>For 360 horizontal head orientations (measured sequentially on VariSphear turntable with azimuth in 1-degree steps)</li> </ul> </li> <li>Thereby, impulse response sets are included for five acoustic environments <ul> <li>"Simulation_": Anechoic simulation of a plane wave impinging from the frontal direction on the array (SMA and EMA only)</li> <li>"Anechoic_": Anechoic measurement of a Genelec 8030A loudspeaker at the same height of the array</li> <li>"LabDry_": Room measurement in an acoustically damped laboratory of a Genelec 8030A loudspeaker at three different source heights (the direct floor reflection is attenuated with an additional porous absorber but otherwise identical to the following condition)</li> <li>"LabWet_": Room measurement in an acoustically damped laboratory of a Genelec 8030A loudspeaker at three different source heights (the direct reflection is not obstructed from the hard concrete floor, but otherwise identical to the former condition)</li> <li>"Hall_": Room measurement in a very reverberant hall of a Genelec 8030A loudspeaker at three different source heights</li> </ul> </li> <li>Thereby, the room impulse response sets are included for three relative source elevations (from placing the loudspeaker to varying heights on the same vertical axis) <ul> <li>"_SrcHigh": The source is located above the horizon of the receiver</li> <li>"_SrcEar": The source and receiver are located at the same height</li> <li>"_SrcLow": The source is located below the horizon of the receiver</li> </ul> </li> <li>Additionally, anechoic impulse responses of the measurement loudspeaker and the utilized microphones are included <ul> <li>"Anechoic_MicSMAnoTape_": SMA measurement microphone without the applied tape (the source was compensated)</li> <li>"Anechoic_MicSMAwithTape_": SMA measurement microphone with the applied tape (the source was compensated)</li> <li>"Anechoic_MicXMAmic19_": XMA measurement microphone (the source was compensated)</li> <li>"Anechoic_SrcFreeField_": Measurement source (on-axis) (the influence of the utilized high-quality free-field measurement microphone can be neglected)</li> <li>"Anechoic_SrcFreeField+MicSMAnoTape_": Measurement source and SMA measurement microphone without the tape applied</li> <li>"Anechoic_SrcFreeField+MicSMAwithTape_": Measurement source and SMA measurement microphone with the tape applied</li> <li>"Anechoic_SrcFreeField+MicXMAmic19_": Measurement source and XMA measurement microphone</li> <li>Overall, the resulting impulse response sets contain the following compensations (including exact compensation of the phase/time behavior): <ul> <li>Anechoic KEMAR: Source</li> <li>Anechoic SMA/EMA/XMA: Source and array microphones</li> <li>Rooms KEMAR: None</li> <li>Rooms SMA/EMA/XMA: Array microphones</li> <li>There is the option to compensate for the source's on-axis response in the room measurement data. However, the direction-dependent directivity of the loudspeaker cannot be compensated. Therefore, we decided not to compensate for the source in the room measurement data since the on-axis frequency response of the utilized loudspeaker is reasonably flat.</li> </ul> </li> </ul> </li> </ul>   ===========   DATA_RAW =========== This archive is too large to be uploaded to Zotero (around 77.5 GB). Please get in touch with the authors to request the data. The archive contains the raw acoustic data of all measurement configurations captured by the measurement scripts (see section CODE_AND_PLOTS). The data yields the final impulse responses (see section DATA), as described in this section. Directory "resources/ARIR_raw/": <ul> <li>Subdirectories by room and source position containing the raw SMA, XMA, and KEMAR acoustic measurement data</li> <li>In proprietary Matlab format, separate for every measurement position of each configuration</li> <li>Each data file contains extensive metadata, e.g., describing the utilized hardware devices, input/output ports, and descriptions.</li> <li>Each data file contains the raw utilized exponential sweep signal and the resulting captured microphone signals. Each impulse response may be recomputed with alternative deconvolution and post-processing parameters.</li> </ul> Directory "resources/ARIR_raw/Logs_temp_humidity/": <ul> <li>Air temperature and humidity data were captured in 5-second intervals during all acoustic measurements</li> <li>In CSV format (automatically loaded and included in the final impulse response sets as part of the measurement post-processing; see section CODE_AND_PLOTS)</li> <li>This data is not further utilized at the moment but seemed worthwhile to capture since some acoustic measurements (particularly the high-resolution SMA data sets) were conducted over multiple hours.</li> </ul>   ==================  CODE_AND_PLOTS ================== This archive contains the code required to gather the raw acoustic measurement data (see section DATA_RAW), the code to post-process and yield the final impulse response data (see section DATA), and the resulting plots as described in this section. Directory "dependencies/": <ul> <li>Matlab and Python functions that are utilized in the code</li> <li>Additional dependencies of available open-source projects may be required for certain code functions. If so, the source and setup process for the necessary dependencies are documented in the code header.</li> </ul> Directory "plots/": <ul> <li>Plots that were exported (and that may be regenerated) by the following scripts to validate different stages of the data simulation, measurement, and subsampling.</li> </ul> Shell script "x1_Start_Jupyter.sh": <ul> <li>Prepare a Python environment with the required tools described as dependencies.</li> <li>Activate the prepared Python environment to perform impulse response measurements using Jupyter Notebooks setup for different acoustic settings.</li> </ul> Python Jupyter notebook "x1a_Measure_Microphones.ipynb": <ul> <li>Setup and test the utilized acoustic measurement hardware.</li> <li>Perform a series of acoustic measurements of all utilized microphones in an anechoic environment.</li> <li>Export the raw acoustic data and processed impulse responses.</li> </ul> Python Jupyter notebook "x1b_Measure_BRIRs.ipynb": <ul> <li>Setup and test the utilized acoustic measurement hardware.</li> <li>Generate a horizontal grid of measurement orientations for the VariSphear turntable according to the desired dummy head orientations.</li> <li>Perform a series of acoustic measurements of the dummy head at the pre-defined grid in anechoic and various room environments.</li> <li>Export the raw acoustic data and processed impulse responses.</li> </ul> Python Jupyter notebook "x1c_Measure_SMAs.ipynb": <ul> <li>Setup and test the utilized acoustic measurement hardware.</li> <li>Generate a spherical grid of measurement orientations for the VariSphear turntable according to the desired SMA sampling grid.</li> <li>Perform a series of acoustic measurements of the SMA microphone at the pre-defined grid in anechoic and various room environments.</li> <li>Export the raw acoustic data and processed impulse responses.</li> </ul> Python Jupyter notebook "x1d_Measure_XMAs.ipynb": <ul> <li>Setup and test the utilized acoustic measurement hardware.</li> <li>Generate a horizontal grid of measurement orientations for the VariSphear turntable according to the desired scattering body orientations.</li> <li>Perform a series of acoustic measurements of the XMA microphones at the pre-defined grid in anechoic and various room environments.</li> <li>Export the raw acoustic data and processed impulse responses.</li> </ul> Matlab script "x1e_Simulate_SMAs.m": <ul> <li>Simulate a plane wave impinging from an arbitrary direction on SMAs and EMAs with a desired sampling grid in an anechoic environment.</li> <li>The simulations are helpful to evaluate the rendering method and to investigate the influence of different sampling grids and equalization methods on the rendered binaural signals.</li> </ul> Matlab script "x2_Gather_And_Plot_Measurements.m": <ul> <li>Gather the stored single files with individually measured impulse responses and the according metadata into a combined data set.</li> <li>The initial impulse responses can be recomputed with pre- and post-processing parameters tuned towards the specific acoustic scenario, including compensation of provided source and receiver impulse responses.</li> <li>Many plots may be generated during the processing to validate the input and output data.</li> </ul> Matlab script "x2a_Compare_Measurement_Lengths.m": <ul> <li>Compare the length of the resulting impulse responses of designated measurement configurations.</li> <li>This may be helpful for the tuning of pre-processing and post-processing parameters of the measured impulse responses.</li> </ul> Matlab script "x3_Subsample_Measurements.m": <ul> <li>Spatially subsample a high-resolution directional impulse response data set into a different (lower-resolution) sampling grid in the spherical harmonics domain.</li> <li>This is suitable for array and HRIR data sets.</li> <li>The script also compares the subsampled data against a reference set if available. In the current data set, an evaluation is performed for an anechoic simulation and a room measurement of an SMA at SH order 8.</li> </ul> Matlab script "x3a_Gather_XMA_Measurements.m": <ul> <li> Transform anechoic XMA measurement data from SOFA into the data format required by the processing scripts to calculate the respective calibration and equalization filters. </li> </ul> Readme file "x3b_Generate_XMA_Filters.txt": <ul> <li>The code for this functionality follows the publication [1] but is currently not polished enough for publication. Please contact Jens Ahrens (jens.ahrens@chalmers.se) for questions regarding this functionality.</li> <li>[1] J. Ahrens, H. Helmholz, D. Lou Alon, and S. V. Amengual Garí, “Spherical Harmonic Decomposition of a Sound Field Using Microphones on a Circumferential Contour Around a Non-Spherical Baffle,” IEEE/ACM Trans. Audio, Speech, Lang. Process., vol. 30, pp. 3110–3119, 2022, doi: 10.1109/TASLP.2022.3209940.</li> </ul> Matlab script "x3c_Gather_XMA_Filters.m": <ul> <li>Rename the files containing the computed calibration and equalization filters into a suitable convention for this collection of scripts.</li> <li>The generated name includes an incremental index to track different versions of provided filter sets.</li> </ul> Matlab script "x3d_Compare_XMA_Filters.m": <ul> <li> Generate various time domain and frequency domain plots to compare different versions of the generated XMA calibration and equalization filters. </li> </ul>   ================  DOCUMENTATION ================ This archive contains additional documentation of the setups and processes while conducting the acoustic measurements, as described in this section. Directory "documentation/": <ul> <li>Various photographs of the different room, source, and receiver arrangements of the data set</li> <li>The room dimensions and source and receiver positions are documented in the form of the original measurement notes (this may be improved in the future).</li> </ul>

opencc-by-4.0May 2024View details →

Data associated with manuscript: "Foliar flavonoids across an elevation gradient: plasticity in response to UV, and links with floral pigmentation patterning"

There are two datasets provided in this excel file. The data are flavonoid abundances of Argentina anserina [=Potentilla anserina] exposed to two levels of ultraviolet light in a greenhouse. The "leaf only data" tab has the combined foliar abundances of the major classes of flavonoids detected: chalcone, flavonone, flavone, flavonol, flavon-3-ol, and proanthocyanidin. This dataset was used to analyze the effect of UV treatment and elevation on flavonoid production in leaves. The "leaf and petal matching cmpnds" is the abundance of the major flavonoid classes in leaves and petals for only the compounds that were detected in both tissue types. This dataset was used for all analyses that related leaf flavonoids with petal flavonoids and petal UV pigmentation.

Data from: Warming reduces priming effect of soil organic carbon decomposition along a subtropical elevation gradient

The priming effects (PEs) of soil organic carbon (SOC) is a crucial process affecting the C balance of terrestrial ecosystems. However, there is uncertainty about how PEs will respond to climate warming. Here, we sampled soils along a subtropical elevation gradient in China and conducted a 126-day lab-incubation experiment with and without additions of 13C-labeled high-bioavailability glucose or low-bioavailability lignin. Based on the mean annual temperature (MAT) of each elevation (9.3–16.4°C), a temperature increase of 4°C was used to explore how PEs mediate the decomposition of SOC in response to warming. Our results showed that the magnitude of glucose-induced PEs (PEglu) was higher than lignin-induced PEs (PElig), with both PEs linearly increasing with MAT. Across the MAT (i.e., elevation) gradient, warming had consistent negative effects on PEglu, whereas rising MAT exacerbated the negative effects of warming on PElig. Moreover, the temperature sensitivity of SOC decomposition decreased after adding glucose and lignin across the MAT gradient, suggesting that fresh C inputs may prime microbial breakdown of labile SOC under warming. Taken together, warming alleviated the SOC loss due to PEs through varying mechanisms depending on substrate bioavailability, since warming mediated the PEglu by increasing available nitrogen and weakening microbial nitrogen-mining but inhibited the PElig by switching from microbial nitrogen-mining to microbial co-metabolization. Our findings highlight the role of warming in regulating the PEs and suggest that incorporating the suppression effect of warming on PEs can contribute to the accurate prediction of soil C dynamics in a warming world.

opencc-by-4.0Jan 2018View details →

Fig. 3 in Sighting of the Himalayan Trinket Snake, Orthriophis hodgsonii Günther (1860) (Reptilia: Colubridae), in Sahastra Dhara, Uttarakhand: A new elevational record

Fig. 3. Himalayan Trinket Snake in its natural habitat in the Sahastra Dhara area.

opencc-by-4.0Jan 2018View details →

Fig. 2 in Sighting of the Himalayan Trinket Snake, Orthriophis hodgsonii Günther (1860) (Reptilia: Colubridae), in Sahastra Dhara, Uttarakhand: A new elevational record

Fig. 2. Close-up of the head of a Himalayan Trinket Snake recorded from Sahastra Dhara.

opencc-by-4.0Jan 2018View details →

Fig. 1 in Sighting of the Himalayan Trinket Snake, Orthriophis hodgsonii Günther (1860) (Reptilia: Colubridae), in Sahastra Dhara, Uttarakhand: A new elevational record

Fig. 1. Himalayan Trinket Snake, Orthriophis hodgsonii, near Sahastra Dhara.

opencc-zeroJun 2024View details →

Effects of inbreeding and elevated rearing temperatures on strategic sperm investment

Males often strategically adjust the number of available sperm based on the social context (i.e., sperm priming response), but it remains unclear how environmental and genetic factors shape this adjustment. In freshwater ecosystems, high ambient temperatures often lead to isolated pools of hotter water in which inbreeding occurs. Higher water temperatures and inbreeding can impair fish development, potentially disrupting sperm production. We used guppies (Poecilia reticulata) to investigate how developmental temperature (26 °C, 30 °C) and male inbreeding status (inbred, outbred) influence their sperm priming response. We also tested if sperm priming was affected by whether the female was a relative (sister), and whether she was inbred or outbred. We ran two separate analyses to address our research questions. First, we ran a linear mixed model (LMM) to investigate the effects of male inbreeding status (inbred, outbred), rearing temperature (warm, control), and social environment (no female, unrelated outbred female, related female) and all three two-way interactions on sperm priming response (i.e., the total number of sperm produced in 7 days). Second, we noted that inbred males might produce fewer sperm than outbred males in the presence of a related female due to their sister being inbred and therefore of lower quality (e.g., less fecund). To test whether the observed effect of male inbreeding status was confounded by the related female's inbreeding status, we ran an additional LMM exclusively for inbred males. We separated the effects of inbreeding status of the female and her genetic relatedness to the male by considering three types of female (inbred related, inbred unrelated, outbred unrelated) that inbred males encountered. There was no effect of rearing temperature; male inbreeding status alone determined the number of available sperm in response to female presence, her inbreeding status and her relatedness. Inbred males produced significantly more sperm in the presence of an unrelated, outbred female than when no female was present. Conversely, outbred males did not alter the number of sperm available in response to female presence or relatedness. Moreover, inbred males produced marginally more sperm when exposed to an unrelated female that was outbred rather than inbred, but there was no difference when exposed to an inbred female that was unrelated versus related. Together, a sperm priming response was only observed in inbred males when exposed to an outbred female. Outbred females in our study were larger than inbred females, suggesting that inbred males strategically allocated ejaculate resources towards females in better condition.

opencc-zeroJun 2024View details →

Data from: Environmental conditions differently shape leaf, seed and seedling trait composition between and within elevations of tropical montane forests

The composition of plant functional traits varies in response to environmental conditions due to processes of community assembly and species sorting. However, there is a lack of understanding of how plant trait composition responds to environmental conditions at different spatial scales and across the plant life cycle. We investigated the trait composition of leaves (specific leaf area), seeds (seed mass) and seedlings (initial seedling height) across elevations and within elevations in relation to soil and light conditions in a tropical montane forest in southern Ecuador. We surveyed traits and communities of adult trees, seeds and seedlings on nine plots at three elevations (1000-3000 m a.s.l.) and calculated community-weighted mean trait values to analyse trait variation across and within elevations. In addition, we measured two environmental factors (soil C/N ratio and canopy openness) to quantify local-scale variation in environmental conditions within elevations. We found that community-weighted means of specific leaf area, seed mass and initial seedling height decreased consistently with increasing elevation. Within elevations, mean trait values of trees, seeds and seedlings responded differently to local-scale environmental conditions. Specific leaf area decreased with increasing soil C/N ratio, and initial seedling height decreased with increasing canopy openness. Seed mass was associated neither with soil nor with light conditions. Our findings show that broad-scale and local-scale processes differently shape the composition of leaf, seed and seedling traits in tropical forests, indicating a scale-dependence in trait-environment associations. Furthermore, plant traits corresponding to different life stages were related differently to environmental conditions within elevations. Community assembly processes may therefore lead to differences in species sorting at early and late plant life stages.