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1,548 results for “Trajectories”
Alternative carbon price trajectories can avoid excessive carbon removal
<p>This package contains an update of the data and plot routines used in preparation for the paper "Alternative carbon price trajectories can avoid excessive carbon removal" by Jessica Strefler et al.</p> <p>This update does not change results, it is only to match the scenario names and variable categories as they have been uploaded to the AR6 database.</p>
Data from: Go large or go conical: allometric trajectory of an early Cambrian acrotretide brachiopod
<p>Acrotretides are extinct micromorphic brachiopods that exhibited considerable morphological variation during their rapid evolution in the early Palaeozoic. The plano-conical shells of acrotretides are distinct in comparison to other brachiopod groups and despite their diversity and abundance in early Palaeozoic communities, their origins, early evolution, life history and phylogeny are poorly understood. Here, we employ advanced geometric morphometrics to quantitatively investigate ontogenetic variation and allometry in the ventral valve of the oldest known acrotretide species from the early Cambrian of South China. Our results identify substantial shape variation for <i>Eohadrotreta zhenbaensis</i>, along with a parabolic morphological trajectory through ontogeny, demonstrating a remarkable reversal to a juvenile morphology during later ontogenetic stages. The evolutionary novel body plan (diminutive and plano-conical) of Acrotretida was established gradually during two phases of allometry, formed initially during the final stage of the Cambrian evolutionary radiation from an ancestral low, equivalved lingulide body plan. The development of a conical shaped valve seems to have resulted in an overall smaller body size, when compared with non-conical forms. The heterochronic processes responsible for generating these ontogenetic modifications at different allometric phases may have facilitated the evolutionary diversification of acrotretide brachiopods during the early Palaeozoic.</p>
Climate warming dominates over plant genotype in shaping the seasonal trajectory of foliar fungal communities on oak
<p>Leaves interact with a wealth of microorganisms. Among these, fungi are highly diverse, and are known to contribute to plant health, leaf senescence and early decomposition. However, patterns and drivers of the seasonal dynamics of foliar fungal communities are poorly understood.</p> <p>We used a multi-factorial experiment to investigate the influence of warming and tree genotype on the foliar fungal community on the pedunculate oak <i>Quercus robur</i> across one growing season.</p> <p>Fungal species richness increased, evenness tended to decrease, and community composition strongly shifted during the growing season. Yeasts increased in relative abundance as the season progressed, while putative fungal pathogens decreased. Warming decreased species richness, reduced evenness and changed community composition, especially in the end of the growing season. Warming also negatively affected putative fungal pathogens. We only detected a minor imprint of tree genotype and warming-by-genotype interactions on species richness and community composition.</p> <p>Overall, our findings demonstrate that warming plays a larger role than plant genotype in shaping the seasonal dynamics of the foliar fungal community on oak. These warming-induced shifts in the foliar fungal community may have a pronounced impact on plant health, plant-fungal interactions and ecosystem functions.</p>
The elephant in the family: Costs and benefits of elder siblings on younger offspring life-history trajectory in a matrilineal mammal
<p>1. Many mammals grow up with siblings, and interactions between them can influence offspring phenotype and fitness. Among these interactions, sibling competition between different-age offspring should lead to reproductive and survival costs on the younger sibling, while sibling cooperation should improve younger sibling's reproductive potential and survival. However, little is known about the consequences of sibling effects on younger offspring life history trajectory, especially in long-lived mammals.</p> <p>2. We take advantage of a large, multigenerational demographic dataset from semi-captive Asian elephants to investigate how the presence and sex of elder siblings influence the sex, survival until five years old, body condition, reproductive success (i.e. age at first reproduction and lifetime reproductive success), and long-term survival of subsequent offspring.</p> <p>3. We find that elder siblings have heterogenous effects on subsequent offspring life-history traits depending on their presence, their sex, and the sex of the subsequent offspring (named focal calf).</p> <p>4. Overall, the presence of an elder sibling (either sex) strongly increased focal calf long-term survival (either sex) compared to sibling absence. However, elder sisters had higher impact on the focal sibling than elder brothers. Focal females born after a female display higher long-term survival, and decreased age at first reproduction when raised together with an elder sister rather than a brother. Focal males born after a female rather than a male showed lower survival but higher body weight when both were raised together. We did not detect any sibling effects on the sex of the focal calf sex, survival until five years old and lifetime reproductive success.</p> <p>5. Our results highlight the general complexity of sibling effects, but broadly that elder siblings can influence the life-history trajectory of subsequent offspring. We also stress the importance of considering all life stages when evaluating sibling effects on life trajectories. 22-Jul-2021 --</p>
Supplementary materials for: Influence of cognitive reserve on cognitive trajectories: role of brain pathologies
<p><span><b>Background and Objectives:</b> </span>Evidence on the association of the cognitive reserve (CR) with the cognitive trajectories is limited. We aimed to examine the influence of CR indicator on domain-specific cognitive trajectories taking brain pathologies into account.</p> <p><span><b>Methods:</b> Within the Rush Memory and Aging Project, 1,697 dementia-free participants (mean age: 79.6 years) were followed up to 21 years. CR indicator encompassing education, early-life, mid-life, and late-life cognitive activities, and late-life social activity was ascertained at baseline and categorized as tertiles (the lowest, middle, and highest). Global cognition, episodic memory, semantic memory, working memory, visuospatial ability, and perceptual speed were assessed annually with 19 tests, from which composite scores were derived. During the follow-up, 648 died and underwent autopsies to evaluate brain pathologies. Data were analyzed using linear mixed-effect models.</span></p> <p><span><b>Results:</b> Among the participants, the score of the CR indicator ranged from -8.00 to 5.74 (mean: 0.00±2.23). In multi-adjusted mixed-effect models, compared to the lowest CR, the highest was related to a slower decline in global cognition (β=0.028, 95% confidence interval [CI]: 0.012 to 0.043), episodic memory (β=</span>0.028, 95% CI: 0.010 to 0.047<span>) and working memory (β=0.019, 95% CI: 0.005 to 0.033) during the follow-up. In brain pathological data analysis, the association of the highest CR with cognitive function changes remained significant among participants with high Alzheimer's disease pathology or gross infarcts.</span></p> <p><b>Discussion</b><span><b>:</b> High CR indicator is associated with preserved global cognitive function, episodic memory, and working memory, even in the presence of brain pathologies. </span>Our findings highlight the important role of high CR accumulation in the prevention of cognitive decline.</p>
VeloViz: RNA-velocity informed embeddings for visualizing cellular trajectories
<p>Single cell transcriptomic technologies enable genome-wide gene expression measurements in individual cells but can only provide a static snapshot of cell states. RNA velocity analysis can infer cell state changes from single cell transcriptomics data. To interpret these cell state changes as part of underlying cellular trajectories, current approaches rely on visualization with principal components, t-distributed stochastic neighbor embedding, and other 2D embeddings derived from the observed single cell transcriptional states. However, these 2D embeddings can yield different representations of the underlying cellular trajectories, hindering the interpretation of cell state changes. We developed VeloViz to create RNA-velocity-informed 2D and 3D embeddings from single cell transcriptomics data. Using both real and simulated data, we demonstrate that VeloViz embeddings are able to consistently capture underlying cellular trajectories across diverse trajectory topologies, even when intermediate cell states may be missing. By taking into consideration the predicted future transcriptional states from RNA velocity analysis, VeloViz can help visualize a more reliable representation of underlying cellular trajectories. Source code is available on GitHub (https://github.com/JEFworks-Lab/veloviz) and Bio- conductor (https://bioconductor.org/packages/veloviz) with additional tutorials at https://JEF.works/veloviz/.</p> <p> </p> <p>Here, we have included the data used in the package vignettes: <br> <br> 1) Pancreas endocrinogenesis data was obtained from Bergen et. al. Nature Biotechnology 2020 and Bastidas-Ponce et. al. Development 2019 via the scVelo package. </p> <p>- pancreas.rda contains spliced and unspliced count matrices, list of cluster IDs, the first 50 principal components, cell-cell distances used in RNA velocity calculation, and the velocity object resulting from calculating velocity using velocyto.R</p> <p>- pancreasWithGap.rda includes the same data as in pancreas rda but with a subset of intermediate cells () removed to simulate data with missing intermediates. </p> <p>2) MERFISH data was obtained from Xia et. al. PNAS 2019</p> <p>- MERFISH.rda contains nuclear and cytoplasmic counts, colors used for plotting based on Louvain clustering, the first 50 principal components, cell-cell distances used in RNA velocity calculation, and the velocity object resulting from calculating velocity using velocyto.R</p>
The trajectories of vegetative structure and soil microbial function diverged across a fire chronosequence of the boreal forests in Northeast China
<p>The role of boreal forest to ameliorate the effect of global climate change largely depends on the regeneration of postfire forests in northeast China. The postfire recovery of boreal forest can be evaluated by the aboveground vegetative structure and soil microbial function. In present study, a 50-year fire chronosequence was established, and the biomass of forbs, shrub and woody plant was separately weighted to assess their contribution to the whole community with the year since fire (YSF). Simultaneously, soil biophysical properties were measured for stands in different time period after fire. Soil microbial functions, i.e., growth efficiency (GE) and carbon use efficiency (CUE), were calculated basing on ecoenzymatic and soil nutrient stoichiometry. In terms of vegetative structure, forbs' proportion decreased from 75% to 1.5%, but the proportion of woody plant increased from 0.04% to 70% across this fire chronosequence. In contrast, soil microbial function reached the highest value in 15 YSF and then began to decrease. As an important variable, soil metal content, particularly the calcium content, showed a positive correlation with woody plant biomass and a negative with soil microbial function. Furthermore, soil metal content was significantly increased in the late stage of this fire chronosequence. Overall, the present work highlighted that the time period of 15 YSF and 31 YSF was a hallmark stage for aboveground vegetative structure and soil microbial function to change in different trends, and the calcium content may partly account for these two divergent trajectories.</p>
Trajectories and metadata for Bach et al.: 'Holistic re-evaluation of Southern Ocean Iron Fertilization for atmospheric CO2 removal'
<p>Trajectory and metadata files from a Southern Ocean surface particle release experiment using Connectivity Modeling System (Paris et al. 2013, <a href="https://github.com/beatrixparis/connectivity-modeling-system">https://github.com/beatrixparis/connectivity-modeling-system</a>) run offline in the MOM01 model (Stewart et al., 2017; Spence et al., 2017; Morrison et al., 2020), a global 0.1° ocean sea-ice model, based on version 5 of the Modular Ocean Model (MOM) code (mom-ocean.github.io) (Griffies, 2012).</p> <p> Here particle trajectories are are split into 28 netcdf files (numbered traj_file_01.nc through traj_file_28.nc), and uploaded altogether as a single .tar.gz compressed file. Metadata files are also uploaded containing information on the CMS setup and release locations.</p> <p>Netcdf files contain particle trajectory positions, along with temperature, salinity, status in or out of the mixed layer, release date, and particle exit status (see <a href="https://github.com/beatrixparis/connectivity-modeling-system/blob/master/User-Guide-v2.pdf">CMS user guide</a> for details on trajectory files). Other CMS setup files included in each release directory are nest_1.nml, runconf.list, ibm.list, and releaseFile_matchtransport. These input files contain information needed to reproduce the experiment using the MOM01 model output, and are explained in detail in the <a href="https://github.com/beatrixparis/connectivity-modeling-system/blob/master/User-Guide-v2.pdf">CMS user guide</a>.</p> <p> </p> <p>Citation of associated paper: Bach, L., V. Tamsitt, K. Baldry, J. McGee, E. Laurenceau-Cornec, R. Strzepek, Y. Xie, P. W. Boyd, 2021. Holistic re-evaluation of Southern Ocean Iron Fertilization for atmospheric CO<sub>2</sub> removal, <em>submitted </em></p> <p> </p> <p>References:</p> <p>Griffies, S. M. (2012). Elements of the modular ocean model (MOM). <em>GFDL Ocean Group Tech. Rep</em>, 7(620), 47.</p> <p> Morrison, A. K., Hogg, A. M., England, M. H., & Spence, P. (2020). Warm Circumpolar Deep Water transport towards Antarctic driven by local dense water export in canyons. <em>Science Advances</em>, 6(18), eaav2516.</p> <p> Paris, C. B., Helgers, J., van Sebille, E., & Srinivasan, A. (2013). Connectivity Modeling System: A probabilistic modeling tool for the multi-scale tracking of biotic and abiotic variability in the ocean. <em>Environmental Modelling and Software</em>, 42, 47-54.</p> <p>Spence, P., Holmes, R. M., Hogg, A. M., Gries, S. M., Stewart, K. D., & England, M. H. (2017). Localized rapid warming of West Antarctic subsurface waters by remote winds. <em>Nature Climate Change</em>, 7(8), 595.</p> <p>Stewart, K., Hogg, A. M., Gries, S., Heerdegen, A., Ward, M., Spence, P., & England, M. H. (2017). Vertical resolution of baroclinic modes in global ocean models. <em>Ocean Modelling</em>, 113, 50-65.</p>
Data and supplementary information for "A multiple time step algorithm for trajectory surface hopping simulations"
<p>This repository contains the supplementary information as well as the data necessary to reproduce the content of the article "A multiple time step algorithm for trajectory surface hopping simulations".</p>
Data set for Predicting hospital occupancy for covid-19 patients: a simulation approach based on archetypes of empirical services' trajectories
<p>Data set for the paper: Predicting hospital occupancy for covid-19 patients: a simulation approach based on archetypes of empirical services’ trajectories</p> <p>Based on: Marin-Garcia, J. A., Ruiz, A., Julien, M., & Garcia-Sabater, J. P. (2021). A data generator for covid-19 patients’ care requirements inside hospitals. WPOM-Working Papers on Operations Management, 12(1), 76-115. https://doi.org/10.4995/wpom.15332</p> <p> </p>
Example of simulated bat-call recording from known trajectory
<p>The archive "ExampleOfSimulatedRecordingFromKnownTrajectory.ZIP" contains an example of virtual bat Myotis myotis, flying along some random trajectory and emitting an echolocation sequence, recorded by 4 microphones, placed near this trajectory. This example is to compare our method of reconstruction of trajectory with a baseline. The trajectory is chosen to contain 1 point where echo is stronger than a direct signal (point 3), 1 point baseline fails to reconstruct (point 5) and other points where calculation of TDOAs by baseline is good (close to real TDOA).</p> <p><br> Subdirectory "whole_simulated_signal" contains signal as it is emitted by bat and as it is received by all 4 microphones, recording both the direct signal and the echo.</p> <p>Subdirectory "parts_of_signal,recorded_during_fly_near_each_point" contains division of echolocation sequence into 5 parts, emitted by bat while flying near each of 5 trajectory points. Direct signal and Echo are plotted in black and red correspondingly. At the end of signal (point 5) there is an additional noise, to make reconstruction of this point difficult.</p> <p>Supplementary subdirectory "separately_echo_and_direct_signal" contains zoom on smaller parts of a signal (time-segments, used for calculation of TDOAs and FDOAs). Echoes and Direct signal are plotted in 2 different colors.</p> <p>Subdirectories "pointX_time-segments_and_resulting_TFDOAs" reflect the process of calculation of TDOAs and FDOAs as correlations of signals of different microphones on the same time-segment. Some of images correspond to images in the paper "Joint time delay and Doppler passive acoustic 3D tracking, applied to studies of bats in natural habitats." by Ales Mishchenko and Herve Glotin</p> <p>Subdirectory "trajectory" contains resulting TDOAs in the form of distance difference, as well as the resulting trajectory</p> <p><br> The archive "ExampleOfSimulatedRecordingFromKnownTrajectory.ZIP" duplicates information in images(.PNG) and figures in Matlab format (.FIG), as well as in tables(.CSV) for small data or in Matlab data files(.MAT) for big data<br> -------------<br> For additional information contact Ales Mishchenko (Ales.Mishchenko@inria.fr)</p>
Large Landing Trajectory Data Set for Go-Around Analysis
<p>Large go-around, also referred to as missed approach, data set. The data set is in support of <a href="https://www.mdpi.com/2673-4591/28/1/2">the paper</a> presented at the OpenSky Symposium on November the 10th.</p> <p>If you use this data for a scientific publication, please consider citing <a href="https://doi.org/10.3390/engproc2022028002">our paper</a>.</p> <p>The data set contains landings from 176 (mostly) large airports from 44 different countries. The landings are labelled as performing a go-around (GA) or not. In total, the data set contains almost 9 million landings with more than 33000 GAs. The data was collected from <a href="https://opensky-network.org/data/impala">OpenSky Network's historical data base</a> for the year 2019. The published data set contains multiple files:</p> <p><strong>go_arounds_minimal.csv.gz</strong></p> <p>Compressed CSV containing the minimal data set. It contains a row for each landing and a minimal amount of information about the landing, and if it was a GA. The data is structured in the following way:</p> <table> <caption> </caption> <thead> <tr> <th scope="col">Column name</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>time</td> <td>date time</td> <td>UTC time of landing or first GA attempt</td> </tr> <tr> <td>icao24</td> <td>string</td> <td>Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned</td> </tr> <tr> <td>callsign</td> <td>string</td> <td>Aircraft identifier in air-ground communications</td> </tr> <tr> <td>airport</td> <td>string</td> <td>ICAO airport code where the aircraft is landing</td> </tr> <tr> <td>runway</td> <td>string</td> <td>Runway designator on which the aircraft landed</td> </tr> <tr> <td>has_ga</td> <td>string</td> <td>"True" if at least one GA was performed, otherwise "False"</td> </tr> <tr> <td>n_approaches</td> <td>integer</td> <td>Number of approaches identified for this flight</td> </tr> <tr> <td>n_rwy_approached</td> <td>integer</td> <td>Number of unique runways approached by this flight</td> </tr> </tbody> </table> <p>The last two columns, <em>n_approaches</em> and <em>n_rwy_approached</em>, are useful to filter out training and calibration flight. These have usually a large number of <em>n_approaches</em>, so an easy way to exclude them is to filter by <em>n_approaches > 2.</em></p> <p><strong>go_arounds_augmented.csv.gz</strong></p> <p>Compressed CSV containing the augmented data set. It contains a row for each landing and additional information about the landing, and if it was a GA. The data is structured in the following way:</p> <table> <thead> <tr> <th scope="col">Column name</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>time</td> <td>date time</td> <td>UTC time of landing or first GA attempt</td> </tr> <tr> <td>icao24</td> <td>string</td> <td>Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned</td> </tr> <tr> <td>callsign</td> <td>string</td> <td>Aircraft identifier in air-ground communications</td> </tr> <tr> <td>airport</td> <td>string</td> <td>ICAO airport code where the aircraft is landing</td> </tr> <tr> <td>runway</td> <td>string</td> <td>Runway designator on which the aircraft landed</td> </tr> <tr> <td>has_ga</td> <td>string</td> <td>"True" if at least one GA was performed, otherwise "False"</td> </tr> <tr> <td>n_approaches</td> <td>integer</td> <td>Number of approaches identified for this flight</td> </tr> <tr> <td>n_rwy_approached</td> <td>integer</td> <td>Number of unique runways approached by this flight</td> </tr> <tr> <td>registration</td> <td>string</td> <td>Aircraft registration</td> </tr> <tr> <td>typecode</td> <td>string</td> <td>Aircraft ICAO typecode</td> </tr> <tr> <td>icaoaircrafttype</td> <td>string</td> <td>ICAO aircraft type</td> </tr> <tr> <td>wtc</td> <td>string</td> <td>ICAO wake turbulence category</td> </tr> <tr> <td>glide_slope_angle</td> <td>float</td> <td>Angle of the ILS glide slope in degrees</td> </tr> <tr> <td>has_intersection</td> <td> <p>string</p> </td> <td>Boolean that is true if the runway has an other runway intersecting it, otherwise false</td> </tr> <tr> <td>rwy_length</td> <td>float</td> <td>Length of the runway in kilometre</td> </tr> <tr> <td>airport_country</td> <td>string</td> <td>ISO Alpha-3 country code of the airport</td> </tr> <tr> <td>airport_region</td> <td>string</td> <td>Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)</td> </tr> <tr> <td>operator_country</td> <td>string</td> <td>ISO Alpha-3 country code of the operator</td> </tr> <tr> <td>operator_region</td> <td>string</td> <td>Geographical region of the operator of the aircraft (either Europe, North America, South America, Asia, Africa, or Oceania)</td> </tr> <tr> <td>wind_speed_knts</td> <td>integer</td> <td>METAR, surface wind speed in knots</td> </tr> <tr> <td>wind_dir_deg</td> <td>integer</td> <td>METAR, surface wind direction in degrees</td> </tr> <tr> <td>wind_gust_knts</td> <td>integer</td> <td>METAR, surface wind gust speed in knots</td> </tr> <tr> <td>visibility_m</td> <td>float</td> <td>METAR, visibility in m</td> </tr> <tr> <td>temperature_deg</td> <td>integer</td> <td>METAR, temperature in degrees Celsius</td> </tr> <tr> <td>press_sea_level_p</td> <td>float</td> <td>METAR, sea level pressure in hPa</td> </tr> <tr> <td>press_p</td> <td>float</td> <td>METAR, QNH in hPA</td> </tr> <tr> <td>weather_intensity</td> <td>list</td> <td>METAR, list of present weather codes: qualifier - intensity</td> </tr> <tr> <td>weather_precipitation</td> <td>list</td> <td>METAR, list of present weather codes: weather phenomena - precipitation</td> </tr> <tr> <td>weather_desc</td> <td>list</td> <td>METAR, list of present weather codes: qualifier - descriptor</td> </tr> <tr> <td>weather_obscuration</td> <td>list</td> <td>METAR, list of present weather codes: weather phenomena - obscuration</td> </tr> <tr> <td>weather_other</td> <td>list</td> <td>METAR, list of present weather codes: weather phenomena - other</td> </tr> </tbody> </table> <p>This data set is augmented with data from various public data sources. Aircraft related data is mostly from the <a href="https://opensky-network.org/aircraft-database">OpenSky Network's aircraft data base</a>, the METAR information is from the <a href="https://mesonet.agron.iastate.edu/info/datasets/metar.html">Iowa State University</a>, and the rest is mostly scraped from different web sites. If you need help with the METAR information, you can consult the <a href="https://library.wmo.int/doc_num.php?explnum_id=11220">WMO's Aerodrom Reports and Forecasts handbook</a>.</p> <p><strong>go_arounds_agg.csv.gz</strong></p> <p>Compressed CSV containing the aggregated data set. It contains a row for each airport-runway, i.e. every runway at every airport for which data is available. The data is structured in the following way:</p> <table> <thead> <tr> <th scope="col">Column name</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>airport</td> <td>string</td> <td>ICAO airport code where the aircraft is landing</td> </tr> <tr> <td>runway</td> <td>string</td> <td>Runway designator on which the aircraft landed</td> </tr> <tr> <td>n_landings</td> <td>integer</td> <td>Total number of landings observed on this runway in 2019</td> </tr> <tr> <td>ga_rate</td> <td>float</td> <td>Go-around rate, per 1000 landings</td> </tr> <tr> <td>glide_slope_angle</td> <td>float</td> <td>Angle of the ILS glide slope in degrees</td> </tr> <tr> <td>has_intersection</td> <td>string</td> <td>Boolean that is true if the runway has an other runway intersecting it, otherwise false</td> </tr> <tr> <td>rwy_length</td> <td>float</td> <td>Length of the runway in kilometres</td> </tr> <tr> <td>airport_country</td> <td>string</td> <td>ISO Alpha-3 country code of the airport</td> </tr> <tr> <td>airport_region</td> <td>string</td> <td>Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)</td> </tr> </tbody> </table> <p>This aggregated data set is used in the paper for the generalized linear regression model.</p> <p><strong>Downloading the trajectories</strong></p> <p>Users of this data set with access to OpenSky Network's Impala shell can download the historical trajectories from the historical data base with a few lines of Python code. For example, you want to get all the go-arounds of the 4th of January 2019 at London City Airport (EGLC). You can use the <a href="https://github.com/xoolive/traffic">Traffic</a> library for easy access to the database:</p> <pre><code class="language-python">import datetime from tqdm.auto import tqdm import pandas as pd from traffic.data import opensky from traffic.core import Traffic # load minimum data set df = pd.read_csv("go_arounds_minimal.csv.gz", low_memory=False) df["time"] = pd.to_datetime(df["time"]) # select London City Airport, go-arounds, and 2019-01-04 airport = "EGLC" start = datetime.datetime(year=2019, month=1, day=4).replace( tzinfo=datetime.timezone.utc ) stop = datetime.datetime(year=2019, month=1, day=5).replace( tzinfo=datetime.timezone.utc ) df_selection = df.query("airport==@airport & has_ga & (@start <= time <= @stop)") # iterate over flights and pull the data from OpenSky Network flights = [] delta_time = pd.Timedelta(minutes=10) for _, row in tqdm(df_selection.iterrows(), total=df_selection.shape[0]): # take at most 10 minutes before and 10 minutes after the landing or go-around start_time = row["time"] - delta_time stop_time = row["time"] + delta_time # fetch the data from OpenSky Network flights.append( opensky.history( start=start_time.strftime("%Y-%m-%d %H:%M:%S"), stop=stop_time.strftime("%Y-%m-%d %H:%M:%S"), callsign=row["callsign"], return_flight=True, ) ) # The flights can be converted into a Traffic object Traffic.from_flights(flights)</code></pre> <p><strong>Additional files</strong></p> <p>Additional files are available to check the quality of the classification into GA/not GA and the selection of the landing runway. These are:</p> <ul> <li><strong>validation_table.xlsx</strong>: This Excel sheet was manually completed during the review of the samples for each runway in the data set. It provides an estimate of the false positive and false negative rate of the go-around classification. It also provides an estimate of the runway misclassification rate when the airport has two or more parallel runways. The columns with the headers highlighted in red were filled in manually, the rest is generated automatically.</li> <li><strong>validation_sample.zip</strong>: For each runway, 8 batches of 500 randomly selected trajectories (or as many as available, if fewer than 4000) classified as not having a GA and up to 8 batches of 10 random landings, classified as GA, are plotted. This allows the interested user to visually inspect a random sample of the landings and go-arounds easily.</li> </ul>
"The prolactin receptor scaffolds Janus kinase 2 via co-structure formation with phosphoinositide-4,5-bisphosphate" -- MD Trajectories
<p>MD trajectories and related files for the simulations related to the work: "The prolactin receptor scaffolds Janus kinase 2 via co-structure formation with phosphoinositide-4,5-bisphosphate" by Araya-Secchi R., Bugge K. and Seiffert et al. </p> <p>This dataset contains:</p> <p>1) Trajectories of CG-MD simulations of PRLR-LID1 in lipid bilayers with POPC on the upper leaflet and two compositions on the lower leaflet: POPC:POPS:PIP2 80:10:10 and 90:5:5. These simulations were performed using the open beta version of the Martini 3 (3.b3.2) force field modified to increase the strength of Protein-water interactions by 10% (m3pws10).</p> <p>2) Trajectory of the all-atom simulation of the JAK2-FERM-SH2 + PRLR-LID1 complex in solution.</p> <p>3) Trajectories of CG-MD simulations of the JAK2-FERM-SH2 + PRLR-LID1 complex near a POPC:POPS (70:30) bilayer (8 initial conformations) performed using the Martini2 forcefield.</p> <p>4) Trajectories of CG-MD simulations of the JAK2-FERM-SH2 + PRLR-LID1 complex near a POPC:POPS:PIP2 (80:10:10) bilayer (16 initial conformations) performed using the Martini2 forcefield.</p>
The evolutionary trajectory of drosophilid walking
<p><span>Neural circuits must both execute the behavioral repertoire of individuals and account for behavioral variation across species. Understanding how this variation emerges over evolutionary time requires large-scale phylogenetic comparisons of behavioral repertoires. Here, we describe the evolution of walking in fruit flies by capturing<em> </em>high-resolution, unconstrained movement from 13 species and 15 strains of drosophilids. We find that walking can be captured in a universal behavior space, the structure of which is evolutionarily conserved. However, the occurrence of, and transitions between, specific movements have evolved rapidly, resulting in repeated convergent evolution in the temporal structure of locomotion. Moreover, a meta-analysis demonstrates that many behaviors evolve more rapidly than other traits. Thus, the architecture and physiology of locomotor circuits can both execute precise individual movements in one species and simultaneously support rapid evolutionary changes in the temporal ordering of these modular elements across clades.</span></p>
Model 4 dataset for the manuscript "Improving trajectory calculations by FLEXPART 10.4+ using deep learning inspired single image superresolution"
<p>Model 4 dataset for the manuscript "Improving trajectory calculations by FLEXPART 10.4+ using deep learning inspired single image superresolution"</p>
Model 2 dataset for the manuscript "Improving trajectory calculations by FLEXPART 10.4+ using deep learning inspired single image superresolution"
<p>Dataset produced by the model 2 neural network for the manuscript "Improving trajectory calculations by FLEXPART 10.4+ using deep learning inspired single image superresolution"</p>
Diversification trajectories and paleobiogeography of Neogene chondrichthyans in Europe
<p>Despite the rich fossil record of Neogene chondrichthyans (chimaeras, sharks, rays and skates) from Europe, little is known about the macroevolutionary processes that generated their current diversity and geographical distribution. We compiled 4,419 Neogene occurrences comprising 102 genera, 41 families and 12 orders from four European regions (Atlantic, Mediterranean, North Sea and Paratethys) and evaluated their diversification trajectories and paleobiogeographic patterns. In all analysed regions, we found that the generic richness increased during the early Miocene, then decreased sharply during the middle Miocene in the Paratethys and moderately during the late Miocene and Pliocene in the Mediterranean and North seas. Origination rates display the most significant pulses in the early Miocene in all regions. Extinction rate pulses varied across regions, with the Paratethys displaying the most significant pulses during the late Miocene and the Mediterranean and North seas during the late Miocene and early Pliocene. Overall, up to 27% and 56% of the European Neogene genera are now globally and regionally extinct, respectively. The observed pulses of origination and extinction in the different regions coincide with warming and cooling events that occurred during the Neogene globally and regionally. Our study reveals complex diversity dynamics of Neogene chondrichthyans from Europe and their distinct biogeographic composition despite the multiple marine passages that connected the different marine regions during this time.</p>
Trajectory Analysis: Monocle3 in RStudio - Galaxy Training Material
<p>The input dataset for the trajectory analysis tutorial using Monocle3 in RStudio, which is a follow-up to the case study tutorials that can be found on the <a href="https://training.galaxyproject.org/training-material/topics/single-cell/">Galaxy Training Network</a>.</p>
Trajectory Based RFI Subtraction and Calibration for Radio Interferometry
<p>Data and python notebooks for creating all the figures and tables in the paper titled "Trajectory Based RFI Subtraction and Calibration for Radio Interferometry".</p>
MD simulation trajectory of POPC/CHOL (18.75 mol%) bilayer and related files
<p>Simulation trajectory pre-equiblirated POPC/CHOL bilayer and related files from all-atom molecular dynamics simulations. The system contains 18.75mol% CHOL. Simulations have been performed with GROMACS-2021.2 with Lipid17 forcefield and TIP3P water model. The trajectory is 0-1000 ns, and we have disregarded the first 100 ns in our analysis.</p> <p>The system in the trajectory consists of 104 POPC, 24 CHOL and 6400 water molecules. Temperature 310 K, pressure 1 bar.</p> <p>Publication: <a href="https://doi.org/10.1016/j.bbamem.2022.183961">https://doi.org/10.1016/j.bbamem.2022.183961</a></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.