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opencc-by-nc-nd-4.0Jul 2021View details →

Estimated stand-off distance between ADS-B equipped aircraft and obstacles

Summary: Estimated stand-off distance between ADS-B equipped aircraft and obstacles. Obstacle information was sourced from the FAA Digital Obstacle File and the FHWA National Bridge Inventory. Aircraft tracks were sourced from processed data curated from the OpenSky Network. Results are presented as histograms organized by aircraft type and distance away from runways.   Description: For many aviation safety studies, aircraft behavior is represented using encounter models, which are statistical models of how aircraft behave during close encounters. They are used to provide a realistic representation of the range of encounter flight dynamics where an aircraft collision avoidance system would be likely to alert. These models currently and have historically have been limited to interactions between aircraft; they have not represented the specific interactions between obstacles and aircraft equipped transponders. In response, we calculated the standoff distance between obstacles and ADS-B equipped manned aircraft. For robustness, this assessment considered two different datasets of manned aircraft tracks and two datasets of obstacles. For robustness, MIT LL calculated the standoff distance using two different datasets of aircraft tracks and two datasets of obstacles. This approach aligned with the <a href="https://doi.org/10.2514/1.D0091">foundational research</a> used to support the <a href="https://doi.org/10.1520/F3442_F3442M-20">ASTM F3442/F3442M-20</a> well clear criteria of 2000 feet laterally and 250 feet AGL vertically. The two datasets of processed tracks of ADS-B equipped aircraft curated from the OpenSky Network. It is likely that rotorcraft were underrepresented in these datasets. There were also no considerations for aircraft equipped only with Mode C or not equipped with any transponders. The first dataset was used to train the <a href="https://github.com/Airspace-Encounter-Models/em-model-manned-bayes/releases/tag/v1.3">v1.3 uncorrelated encounter models</a> and referred to as the “Monday” dataset. The second dataset is referred to as the “aerodrome” dataset and was used to train the v2.0 and v3.x terminal encounter model. The Monday dataset consisted of 104 Mondays across North America. The other dataset was based on observations at least 8 nautical miles within Class B, C, D aerodromes in the United States for the first 14 days of each month from January 2019 through February 2020. Prior to any processing, the datasets required 714 and 847 Gigabytes of storage. For more details on these datasets, please refer to "Correlated Bayesian Model of Aircraft Encounters in the Terminal Area Given a Straight Takeoff or Landing" and “Benchmarking the Processing of Aircraft Tracks with Triples Mode and Self-Scheduling.” Two different datasets of obstacles were also considered. First was point obstacles defined by the FAA digital obstacle file (DOF) and consisted of point obstacle structures of antenna, lighthouse, meteorological tower (met), monument, sign, silo, spire (steeple), stack (chimney; industrial smokestack), transmission line tower (t-l tower), tank (water; fuel), tramway, utility pole (telephone pole, or pole of similar height, supporting wires), windmill (wind turbine), and windsock. Each obstacle was represented by a cylinder with the height reported by the DOF and a radius based on the report horizontal accuracy. We did not consider the actual width and height of the structure itself. Additionally, we only considered obstacles at least 50 feet tall and marked as verified in the DOF. The other obstacle dataset, termed as “bridges,” was based on the identified bridges in the FAA DOF and additional information provided by the National Bridge Inventory. Due to the potential size and extent of bridges, it would not be appropriate to model them as point obstacles; however, the FAA DOF only provides a point location and no information about the size of the bridge. In response, we correlated the FAA DOF with the National Bridge Inventory, which provides information about the length of many bridges. Instead of sizing the simulated bridge based on horizontal accuracy, like with the point obstacles, the bridges were represented as circles with a radius of the longest, nearest bridge from the NBI. A circle representation was required because neither the FAA DOF or NBI provided sufficient information about orientation to represent bridges as rectangular cuboid. Similar to the point obstacles, the height of the obstacle was based on the height reported by the FAA DOF. Accordingly, the analysis using the bridge dataset should be viewed as risk averse and conservative. It is possible that a manned aircraft was hundreds of feet away from an obstacle in actuality but the estimated standoff distance could be significantly less. Additionally, all obstacles are represented with a fixed height, the potentially flat and low level entrances of the bridge are assumed to have the same height as the tall bridge towers. The attached figure illustrates an example simulated bridge. It would had been extremely computational inefficient to calculate the standoff distance for all possible track points. Instead, we define an encounter between an aircraft and obstacle as when an aircraft flying 3069 feet AGL or less comes within 3000 feet laterally of any obstacle in a 60 second time interval. If the criteria were satisfied, then for that 60 second track segment we calculate the standoff distance to all nearby obstacles. Vertical separation was based on the MSL altitude of the track and the maximum MSL height of an obstacle. For each combination of aircraft track and obstacle datasets, the results were organized seven different ways. Filtering criteria were based on aircraft type and distance away from runways. Runway data was sourced from the FAA runways of the United States, Puerto Rico, and Virgin Islands <a href="https://adds-faa.opendata.arcgis.com/datasets/4d8fa46181aa470d809776c57a8ab1f6_0/about">open dataset</a>. Aircraft type was identified as part of the <a href="https://github.com/Airspace-Encounter-Models/em-processing-opensky">em-processing-opensky</a> workflow. <ul> <li>All: No filter, all observations that satisfied encounter conditions</li> <li>nearRunway: Aircraft within or at 2 nautical miles of a runway</li> <li>awayRunway: Observations more than 2 nautical miles from a runway</li> <li>glider: Observations when aircraft type is a glider</li> <li>fwme: Observations when aircraft type is a fixed-wing multi-engine</li> <li>fwse: Observations when aircraft type is a fixed-wing single engine</li> <li>rotorcraft: Observations when aircraft type is a rotorcraft</li> </ul> License This dataset is licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International(CC BY-NC-ND 4.0). This license requires that reusers give credit to the creator. It allows reusers to copy and distribute the material in any medium or format in unadapted form and for noncommercial purposes only. Only noncommercial use of your work is permitted. Noncommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. Exceptions are given for the not for profit standards organizations of ASTM International and RTCA. MIT is releasing this dataset in good faith to promote open and transparent research of the low altitude airspace. Given the limitations of the dataset and a need for more research, a more restrictive license was warranted. Namely it is based only on only observations of ADS-B equipped aircraft, which not all aircraft in the airspace are required to employ; and observations were source from a crowdsourced network whose surveillance coverage has not been robustly characterized. As more research is conducted and the low altitude airspace is further characterized or regulated, it is expected that a future version of this dataset may have a more permissive license. Distribution Statement DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited. © 2021 Massachusetts Institute of Technology. Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work. This material is based upon work supported by the Federal Aviation Administration under Air Force Contract No. FA8702-15-D-0001.  Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Federal Aviation Administration. This document is derived from work done for the FAA (and possibly others); it is not the direct product of work done for the FAA. The information provided herein may include content supplied by third parties.  Although the data and information contained herein has been produced or processed from sources believed to be reliable, the Federal Aviation Administration makes no warranty, expressed or implied, regarding the accuracy, adequacy, completeness, legality, reliability or usefulness of any information, conclusions or recommendations provided herein.  Distribution of the information contained herein does not constitute an endorsement or warranty of the data or information provided herein by the Federal Aviation Administration or the U.S. Department of Transportation.  Neither the Federal Aviation Administration nor the U.S. Department of Transportation shall be held liable for any improper or incorrect use of the information contained herein and assumes no responsibility for anyone’s use of the information.  The Federal Aviation Administration and U.S. Department of Transportation shall not be liable for any claim for any loss, harm, or other damages arising from access to or use of data or information, including without limitation any direct, indirect, incidental, exemplary, special or consequential damages, even if advised of the possibility of such damages.  The Federal Aviation Administration shall not be liable to anyone for any decision made or action taken, or not taken, in reliance on the information contained herein. Download and Format: The dataset is provided as a single .zip archive with multiple directories. The directories indicate the aircraft track dataset and types of obstacles. Within each directory are csv files corresponding to different the filtering criteria. These files are the total counts of observations (histogram) where columns corresponding to lateral (range) distance between the simulated obstacle and aircraft and rows correspond to the relative vertical separation. The files centers_x_ft.csv and centers_y_ft.csv are the center of each bin. Simply download the .zip file and extract. The MD5 checksum of the .zip file prior to uploading to Zenodo was 03e5aed725a4cf393683730e05521ba5. MIT Lincoln Laboratory LL Group and Division this dataset is associated with: <ul> <li>Group 42 / Division 4</li> <li>Topic: collision avoidance</li> <li>R&D Area: Air Traffic Control</li> <li>R&D Group: Surveillance Systems</li> </ul>

Data set for Wireless SAWR sensors: FFT, EMD or wavelets for the frequency estimation in one shot?

This data set is the basis for the publication "Wireless SAWR sensors: FFT, EMD or wavelets for the frequency estimation in one shot?", submitted to Journal of Sensors and Sensor Systems. It contains the following data: - "Scipioni_JSSS23_Fig5_WaveletChoice.txt" contains results to obtain the best wavelet for this study. For this, a SAWR (Fig. 3a) signal is noised by an additive Gaussian white noise with different SNR values. The signal is then denoised by wavelets for each SNR value. Results are the new SNR values after denoising. - "Scipioni_JSSS23_Fig9_to_15_F_Ref.txt" contains all the frequencies around F=10700 MHz chosen to test the three methods: Fourier, wavelets, EMD. - 10 files "Scipioni_JSSS23_Fig10_to_14_SAW_EMDvsWavelet_FRef_XX_Occ_100.txt" contain results of the frequency and uncertainty measurement for each noisy SAWR signal versus frequencies and SNR values. - 3 files "Scipioni_JSSS23_Fig17_Tab2_3_Experimental SAWR signal_NoX" contain the values of 3 different experimental SAWR signals.

Insights into Batch Selection for Event-camera Motion Estimation - Datasets

See <a href="https://github.com/event-driven-robotics/batch-selection-experiments">https://github.com/event-driven-robotics/batch-selection-experiments</a> for examples on how to read and process the data. Simulated datasets were used to accurately have a ground-truth of 3 DoF camera rotation, for valid training and evaluation. Four photo-realistic simulated environments from UnrealCV Zoo were selected as they are publicly available indoor scenes and include diverse lighting textures, shadows, reflections and object clutter. The camera was positioned inside the virtual room and rotated along all its three axes randomly at a variety of speeds. Frames were generated at >1 kHz from which the event-stream is generated using log-image-difference  techniques. Five velocity trajectories were used for arch1, four velocity trajectories were used for arch2, and three trajectories for arch3. Including the final room dataset, a total of 13 different datasets (each with a different velocity trajectory) cover a total of 190 seconds of data. Note: room is split into 3 archived files. Please cite: <blockquote> Valerdi, J.L., Bartolozzi, C. and Glover, A., 2023. Insights into Batch Selection for Event-Camera Motion Estimation. Sensors, 23(7), p.3699. </blockquote> <pre><code>@article{valerdi2023insights, title={Insights into Batch Selection for Event-Camera Motion Estimation}, author={Valerdi, Juan L and Bartolozzi, Chiara and Glover, Arren}, journal={Sensors}, volume={23}, number={7}, pages={3699}, year={2023}, publisher={MDPI} }</code></pre>

opencc-by-4.0Jan 2022View details →

COLET: A Dataset for Cognitive workLoad estimation based on Eye-Tracking

Cognitive workload is an important component in performance psychology, ergonomics, and human factors. Unfortunately, benchmarks and publicly available datasets are scarce, making it difficult to establish new approaches and comparative studies. In this work, COLET-COgnitive workLoad state estimation based on Eye-Tracking dataset is presented. Forty-seven (47) individuals' eye movements were monitored as they solved puzzles involving visual search tasks of varying complexity and duration. The authors give an in-depth study of the participants' performance during the experiments while eye and gaze features were derived from low-level eye recorded metrics, and their relationships with the experiment tasks were investigated. Finally, the results from the classification of cognitive workload levels solely based on eye and gaze data, by employing and testing a set of machine learning algorithms are provided. The dataset is made available to the public.   Please cite the following work:  Ktistakis, E., Skaramagkas, V., Manousos, D., Tachos, N. S., Tripoliti, E., Fotiadis, D. I., & Tsiknakis, M. (2022). Colet: A dataset for cognitive workload estimation based on eye-tracking. Computer Methods and Programs in Biomedicine, 106989. https://doi.org/10.1016/j.cmpb.2022.106989

MAX+ skin gambling data estimation

Max+ provides an application which allows players to not only review their game performance but also access the data on all matches played in a day. The application includes a section called “Guessing Competition (赛事竞猜),” where users can place bets on traditional sports and e-sports competitions (Figure 1). Max+ facilitates betting with two types of gambling tokens: using game skins (mainly Dota 2, from Valve) or M coins—coins that can only be used on Max+ and exchanged for game skins. This study used game skins data to estimate the value of skins wagered on websites. The value of wagers made using M coins could not be estimated, as this information is not displayed on gambling websites.

Spatiotemporal Estimation of TROPOMI NO2 Column with Depthwise Partial Convolutional Neural Network

Public Repository of the model outputs of TROPOMI NO2 datasets for 2019 and 2020. Comprises: Saved Partial Convolution Neural Network models (PCNN, PCNN-ST, and DW-PCNN) and code to load the models. Datasets (in Netcdf4 format) from PCNN model outputs, Inverse Distance Weighting, Inverse Distance Weighting with Kriging, spatial coordinates, time, target NO2 for imputation, and masks.

Global canopy top height estimates from GEDI LIDAR waveforms for 2020

Canopy top height (RH98) is estimated from GEDI L1B waveforms globally between 51.6° N & S from L1B Version 1 data from April-July 2020. The footprint level RH98 predictions are stored in hdf5 files corresponding to the orbit files of the GEDI L1B Version 1 data. See also the repository for the data from April-July 2019: <a href="https://doi.org/10.5281/zenodo.5704852">https://doi.org/10.5281/zenodo.5704852</a>. This repository also contains the file <a href="https://zenodo.org/api/files/0a9300b5-2dea-4791-a019-319ed6209713/load_pred_RH98_files.py?versionId=6af41185-f13b-44aa-9042-a59efd4abb82">load_pred_RH98_files.py </a>with more information on how to parse and load the prediction orbit files. GEDI mission website: <a href="https://gedi.umd.edu/">https://gedi.umd.edu/</a>. Citation: Use of these data require citation of this dataset: Lang, Nico, Kalischek, Nikolai, Armston, John, Schindler, Konrad, Dubayah, Ralph, & Wegner, Jan Dirk. (2021). Global canopy top height estimates from GEDI LIDAR waveforms for 2020 (1.0) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.7737869 Original research article: Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., & Wegner, J. D. (2022). Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. Remote Sensing of Environment, 268, 112760.

Dual-modal imaging of two-phase flows with electromagnetic flow tomography and electrical tomography -- experimental evaluation of the state estimation approach

The supplementary files included are associated with our experimental research on two-phase flow estimation. This study experimentally investigates the feasibility of a state estimation approach for dynamic image reconstruction in dual-modal tomography of two-phase oil-water flows using electromagnetic flow tomography (EMFT) and electrical tomography (ET). By approximating the process with a convection-diffusion model, the extended Kalman filter and fixed-interval Kalman smoother are applied to reconstruct temporally evolving velocity and phase fraction distributions. The results demonstrate that the Kalman smoother-based reconstructions, along with uncertainty estimates, outperform conventional methods and provide feasible volumetric flow rate estimates for oil and water phases in a laboratory setup.

opencc-by-4.0Jan 2022View details →

eSEEd: emotional State Estimation based on Eye-tracking dataset

We present eSEEd- emotional State Estimation based on Eye-tracking database. Eye movements of 48 participants were recorded as they watched 10 emotion evoking videos each of them followed by a neutral video. Participants rated five emotions (tenderness, anger, disgust, sadness, neutral) on a scale from 0 to 10, later translated in terms of emotional arousal and valence levels. Furthermore, each participant filled 3 self-assessment questionnaires. An extensive analysis of the participants' answers to the questionnaires self-assessment scores as well as their ratings during the experiments is presented. Moreover, eye and gaze features were extracted from the low level eye recorded metrics and their correlations with the participants' ratings are investigated. Finally, analysis and results are presented for machine learning approaches, for the classification of various arousal and valence levels based solely on eye and gaze features. The dataset is made publicly available and we encourage other researchers to use it for testing new methods and analytic pipelines for the estimation of an individual's affective state. TO USE THIS DATASET PLEASE CITE: Skaramagkas, V.; Ktistakis, E.; Manousos, D.; Kazantzaki, E.; Tachos, N.S.; Tripoliti, E.; Fotiadis, D.I.; Tsiknakis, M. eSEE-d: Emotional State Estimation Based on Eye-Tracking Dataset. Brain Sci. 2023, 13, 589. https://doi.org/10.3390/brainsci13040589

dryad40/100

Variance components of sex determination in the copepod Tigriopus californicus estimated from a pedigree analysis

Extensive theory exists regarding population sex ratio evolution that predicts equal sex ratio (when parental investment is equal). In most animals, sex chromosomes determine the sex of offspring, and this fixed genotype for sex has made theory difficult to test since genotypic variance for the trait (sex) is lacking. It has long been argued that the genotype has become fixed in most animals due to the strong selection for equal sex ratios. The marine copepod Tigriopus californicus has no sex chromosomes, multiple genes affecting female brood sex ratio and a brood sex ratio that responds to selection. The species thus provides an opportune system in which to test established sex ratio theory. In this paper, we further our exploration of polygenic sex determination in T. californicus using an incomplete diallel crossing design for analysis of the variance components of sex determination in the species. Our data confirm the presence of extra-binomial variance for sex, further confirming that sex is not determined through simple Mendelian trait inheritance. In addition, our crosses and backcrosses of isofemale lines selected for biased brood sex ratios show intermediate phenotypic means, as expected if sex is a threshold trait determined by an underlying "liability" trait controlled by many genes of small effects. Furthermore, crosses between families from the same selection line had similar increases in phenotypic variance as crosses between families from different selection lines, suggesting families from artificial selection lines responded to selection pressure through different underlying genetic bases. Finally, we estimate heritability of an individual to be male or female on the observed binary scale as 0.09 (95% CI: 0.034-0.14). This work furthers our accumulating evidence for polygenic sex determination in T. californicus laying the foundation for this as a model species in future studies of sex ratio evolution theory.

opencc-zeroApr 2023View details →

dryad40/100

Data from: Quantifying and estimating ecological network diversity based on incomplete sampling data

An ecological network refers to the ecological interactions among sets of species. Quantification of ecological network diversity and related sampling/estimation challenges have explicit analogues in species diversity research. A unified framework based on Hill numbers and their generalizations was developed to quantify taxonomic, phylogenetic, and functional diversity. Drawing on this unified framework, we propose three dimensions of network diversity that incorporate the frequency (or strength) of interactions, species' phylogenies and traits. As with surveys in species inventories, nearly all network studies are based on sampling data and thus also suffer from under-sampling effects. Adapting the sampling/estimation theory and the iNEXT (interpolation/extrapolation) standardization developed for species diversity research, we propose the iNEXT.link method to analyze network sampling data. The proposed method integrates the following four inference procedures: (i) Assessment of sample completeness of networks, (ii) asymptotic analysis via estimating the true network diversity, (iii) non-asymptotic analysis based on standardizing sample completeness via rarefaction and extrapolation with network diversity, and (iv) estimation of the degree of unevenness or specialization in networks based on standardized diversity. Interaction data between European trees and saproxylic beetles are used for illustrating the proposed procedures. The software iNEXT.link is developed to facilitate all computations and graphics.

opencc-zeroApr 2023View details →

Species detection histories used in Killion et al. (2023): Integrating Spaceborne Estimates of Structural Diversity of Habitat into Wildlife Occupancy Models

Camera trap species detection histories used for occupancy models in "Integrating Spaceborne Estimates of Structural Diversity of Habitat into Wildlife Occupancy Models".

CherryML: Scalable Maximum Likelihood Estimation of Phylogenetic Models

Simulated datasets used in our paper "CherryML: Scalable Maximum Likelihood Estimation of Phylogenetic Models" to produce figures 1bc, 1d, and 2ab. The data provided in each folder is as follows: <ul> <li>rate_matrices contains the classical LG rate matrix, and our 400 x 400 estimated co-evolutionary model Q2.</li> <li>fig_1bc contains the simulated data used to estimate and evaluate rate matrices using the CherryML method and EM (with XRATE) as shown in Fig. 1b and c of our paper. The files and sub-directories here are: <ul> <li>fig_1bc_simulated_data_families_all.txt contains the list of protein family names used to train the model. When only K families are used in Fig. 1b and c, these are the first K families of this list.</li> <li>gt_tree_dir contains the phylogenetic tree used to simulate data for each protein family. There were originally estimated running FastTree on the MSAs from the trRosetta paper, as described in our paper in detail.</li> <li>msa_dir contains the simulated multiple sequence alignments (MSAs). These were simulated running the LG rate matrix down each tree, without site rate variation.</li> <li>gt_site_rates_dir contains the site rates used. In this case, they are all 1.</li> <li>gt_likelihood_dir contains the log-likelihood of the original phylogenetic trees used for each family (as given by FastTree). This is irrelevant for but provided for completeness; you can safely ignore this directory.</li> </ul> </li> <li>fig_1d folder contains the simulated data used to evaluate the effect of time quantization on the CherryML method as shown in Fig. 1d of our paper. The files and sub-directories here are: <ul> <li>gt_tree_dir contains the phylogenetic tree used to simulate data for each protein family. There were originally estimated running FastTree on the MSAs from the trRosetta paper, as described in our paper in detail.</li> <li>msa_dir contains the simulated multiple sequence alignments (MSAs). These were simulated running the LG rate matrix down each tree, with site rate variation.</li> <li>gt_site_rates_dir contains the site rates used.</li> <li>gt_likelihood_dir contains the log-likelihood of the original phylogenetic trees used for each family (as given by FastTree). This is irrelevant for but provided for completeness; you can safely ignore this directory.</li> </ul> </li> <li>fig_2ab contains the simulated data used to evaluate the effect of time quantization on the CherryML method as shown in Fig. 1d of our paper. The files and sub-directories here are: <ul> <li>gt_tree_dir contains the phylogenetic tree used to simulate data for each protein family. There were originally estimated running FastTree on the MSAs from the trRosetta paper, as described in our paper in detail.</li> <li>msa_dir contains the simulated multiple sequence alignments (MSAs). These were simulated running the LG rate matrix down each non-contacting tree, and using Q2 for the contacting sites, all without site rate variation.</li> <li>gt_site_rates_dir contains the site rates used, in this case all 1 (i.e. no site rate variation).</li> <li>gt_likelihood_dir contains the log-likelihood of the original phylogenetic trees used for each family (as given by FastTree). This is irrelevant for but provided for completeness; you can safely ignore this directory.</li> <li>contact_map_dir contains the simulated contact maps for each family. These were obtained by computing a maximal matching on the true contact maps derived from the trRosetta paper, as described in detail in out paper.</li> </ul> </li> </ul> The exact end-to-end code which generates these simulated datasets is provided in our Github repository: <a href="https://github.com/songlab-cal/CherryML">https://github.com/songlab-cal/CherryML</a> In fact, by default, when you try to reproduce the figures in our paper by running the `reproduce_all_figures.py` script in our repository, the data will automatically be simulated for you if it isn't already present. This can be bypassed by downloading the data here in Zenodo and changing the top of `reproduce_all_figures.py` to point to these files.

Probabilistic Estimates of Convective Instability in Individual MRO-MCS Temperature Retrievals

Heavens, Nicholas G. (2023), “Probabilistic Estimates of Convective Instability in Individual MRO-MCS Temperature Retrievals”, Zenodo, V1, doi: 10.5281/zenodo.7828010.   Title: Probabilistic Estimates of Convective Instability in Individual MRO-MCS Temperature Retrievals   Author: Nicholas G. Heavens, Space Science Institute, Boulder, CO, USA and London, UK (nheavens@spacescience.org)   Date: 15 April 2023   Overview: This dataset contains analyses of convective instabilities in individual MRO-MCS temperature profiles for a manuscript currently under review at the Planetary Science Journal.   Averaged data products based on these analyses already have been archived as Heavens (2022):   The data products are estimates of the probability of convective instability derived from diagnosis of convective instability in retrieved temperature profiles from Mars Climate Sounder on board Mars Reconnaissance Orbiter (MRO-MCS)   If you are using this dataset and are feeling confused or wish there were some additional information from the article in this dataset, please contact me. A complete account of the contents and a restatement of this description is included as MCS_CONVINST_INDFILE_SURVEY_DESC.PDF   Acknowledgments: The archiving of this dataset is supported by NASA’s Mars Data Analysis Program (80NSSC19K1215).   Contents:   <ol> <li>MCS_CONVINST_INDFILE_SURVEY_DESC.pdf</li> </ol> A copy of this description in PDF format.   <ol> <li>MCSconvinst_indivncfiles.tar.gz</li> </ol> This compressed tar archive contains 63196 Network Common Data Format (netCDF) files (~ 20 GB uncompressed) named in the form:   xxxxxx_convinst.nc, where xxxxxx is the re-centered orbit number of MRO defined by Heavens et al. (2018). The contents of this file type is:   Variables:   <ol> <li>entrynum: Number to index retrievals in the file</li> <li>Pgrid: The pressure (Pa) level grid</li> <li>lapserateunstableprob_rets: The probability (%) of convective instability in an individual profile at a given pressure level</li> <li>altrets: The altitude grid (km) of each individual profile in relative to the Mars Orbiter Laser Altimeter (MOLA) areoid</li> <li>ampmrets: A flag to indicate whether MRO is in its ascending PM orbit (~ 15:00 LST at the Equator) or in its descending AM orbit (~ 3:00 at the Equator). Precise local time can be estimated by using the MRO-MCS dataset directly or using sclkrets.</li> <li>latrets: The latitude of the profile in degrees north.</li> <li>longrets: The longitude of the profile in degrees east</li> <li>loopflagrets: A flag to indicate the presence of nearby loops; flag = 1 indicates the presence of a nearby loop.</li> <li>lsrets: Areocentric longitude (degrees) of each individual profile</li> <li>myrets: Mars Year of each individual profile</li> <li>sclkrets: SCLK (seconds since 1 January 1980 00:00:00 UTC) for each individual profile</li> </ol>   References   Heavens, N. (2022), Epitomic Data for a Multiannual Record of Convective Instability in Mars's Middle Atmosphere from the Mars Climate Sounder, Mendeley Data, V1, doi: 10.17632/fmgvbpwdk7.1   Heavens, N.G., A. Kleinböhl, M.S. Chaffin, J.S. Halekas, D.M. Kass, P.O. Hayne, D.J. McCleese, S. Piqueux, J.H. Shirley, and J.T. Schofield, 2018, Hydrogen escape from Mars enhanced by deep convection in dust storms, Nature Astron., 2, 126–132, doi: 10.1038/s41550-017-0353-4.

opencc-by-4.0Aug 2018View details →

Fig. 2 in A coalescent-based estimator of genetic drift, and acoustic divergence in the Pteronotus parnellii species complex

Fig. 2 Echolocation call frequency by island by sex and its relation to mass on the call frequency (F(1, 49) = 0.435, P value = 0.512). The body dimensions. a Boxplots of echolocation frequency (summarizing 95% HPD of population differences in means for body mass was 10 calls/individual). Bayesian 95% high-probability density (HPD) of 0.89–2.29 g. c Call frequency as a function of forearm length. Anathe difference in call frequency means between Puerto Rico and Hislyses of covariance support little influence of forearm length on the call paniola was 5.2–6.0 kHz. b Call frequency as a function of body mass. frequency (F(1, 52) = 2.851, P value = 0.097). The 95% HPD of Analyses of covariance support very different call frequency for island population differences in means for forearm lengths was −1.82, groups (F(1, 49) = 704.260, P value = 0.000), but no influence of body 0.422 mm

opencc-by-4.0Aug 2018View details →

Fig. 1 Results from IMa2 in A coalescent-based estimator of genetic drift, and acoustic divergence in the Pteronotus parnellii species complex

Fig. 1 Results from IMa2 analyses of Pteronotus parnellii s.l. populations. a Joint posterior density of Ne estimates for island populations. b Divergence time estimates between Puerto Rican and Hispaniolan populations in thousands of years (Ka)

opencc-by-4.0Aug 2018View details →

Fig. 3 in A coalescent-based estimator of genetic drift, and acoustic divergence in the Pteronotus parnellii species complex

Fig. 3 Densities of Bayesian posteriors for FST based on betweenpopulation migration rates, and PST for relevant phenotypic variables (Brommer et al. 2014). The lines show the 95th percentile for the corresponding FST, and the 5% percentile for the PST. The overlap between PST body mass and FST Hispaniola was 0.023, for FST Puerto Rico it was 0.084; between PST call frequency and FST Hispaniola was <0.001, for FST Puerto Rico it was 0.003; and between PST forearm length and FST Hispaniola was 0.049, for FST Puerto Rico it was 0.125

Data for study: Obukhov length estimation from spaceborne radars

Post-processed data used during study "Obukhov length estimation from spaceborne radars". To be used in example notebook available at <a href="http://github.com/owenodriscoll/supplement_paper/">http://github.com/owenodriscoll/supplement_paper/</a>. CSV's contain the processed buoy, ERA5 and SAR data with which to reproduce study's results and Jupyter Notebooks. Air/sea temperature, surface pressure, wind speeds and measurement heights of the buoy and ERA5 data are provided to the COARE-like algorithm available at <a href="https://cerform.readthedocs.io/en/latest/api/cerform.flux.html#module-cerform.flux.coare4">https://cerform.readthedocs.io/en/latest/api/cerform.flux.html#module-cerform.flux.coare4</a>, from which all the higher level atmospheric statistics originate. Contents: <ol> <li>df_buoys.csv: co-location information between buoys and ERA5. Parameters without suffix "_era5" are from the buoy.</li> <li>df_cells.csv: SAR co-locations with ERA5 for scenes classified as cells. </li> <li>df_rolls.csv: SAR co-locations with ERA5 for scenes classified as rolls. </li> <li>df_rolls_buoy.csv: triple co-location between buoys, ERA5 and SAR scenes classified as rolls. Parameters without suffix "_era5" and "_buoy" are derived from the SAR observation.</li> <li>roll.nc: example radar scene containing a roll-like structure (with superimposed swell). To be used as an example of feature extraction from SAR scenes.</li> </ol>

opencc-by-4.0May 2023View details →

Data Sources for Archetype-based Energy and Material Use Estimation for the Residential Buildings in Arab Gulf Countries

Dataset Name: Literature Data and Archetype Parameter Sheets for the publication, named Archetype-based Energy and Material Use Estimation for the Residential Buildings in Arab Gulf Countries. Description: This dataset includes Excel sheets containing literature sources and archetypal data on GCC countries' residential dwelling typologies. Files: The following files are included in the dataset: <ul> <li>    [CountryName]_LiteratureSources.xlsx: Excel sheet containing literature sources and references,</li> <li>    [CountryName]_ArchetypeParameters.xlsx: Archetype models' semantic, geometric, and technical data used in the generation of energy models,</li> <li>    [CountryName]_Schedules.xlsx: Excel sheet containing the operation schedules for countries. The sheet is compiled from literature sources and reorganized by expert consensus and given in Designbuilder input format,</li> <li>    [CountryName]_Stock.xlsx: Excel sheet containing additional data on the building stock,</li> <li>    VacantHouses.xlsx: Vacant house rates for the countries, the found articles on the web, literature sources, etc.,</li> </ul> Usage: The dataset is intended for researching and analyzing the GCC countries' residential buildings. The literature sources included in the [CountryName]_LiteratureSources.xlsx and [CountryName]_ArchetypeParameters.xlsx files can be used to verify, support, or reproduce the research findings. License: The dataset is licensed under Creative Commons Attribution 4.0 International. Citation: If you use this dataset in your research, please cite it as follows and contact the corresponding author: Akin, Sahin, Chibuikem Chrysogonus Nwagwu, Niko Heeren, and Edgar Hertwich. 2023. “Archetype-Based Energy and Material Use Estimation for the Residential Buildings in Arab Gulf Countries.” Energy and Buildings 298: 113537. https://doi.org/https://doi.org/10.1016/j.enbuild.2023.113537. Contact: The archetypes' energy models (DesignBuilder or IDF files) can be provided on request. If you have any questions or comments about the dataset, please contact sahin.akin@ntnu.no, the corresponding author.