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72 results for “Trajectory Analysis”
Replication package for "A Structural Analysis of Mental Health and Labor Market Trajectories"
<p>"A Structural Analysis of Mental Health and Labor Market Trajectories" MS 29076</p> <p>The replication package contains the codes (two folders: code_manip and code_analysis) and data (three folders: data_raw, data_inter and data_analysis) to replicate the paper's results as well as the figure outputs in an output folder and a readme pdf file.</p> <p> </p>
PHLOWER - Single cell trajectory analysis using Decomposition of the Hodge Laplacian
<p>Datasets that PHLOWER used:</p> <ol> <li>benchmarking data</li> <li>multiome kidney organoid data</li> <li>xenium kidney orgnoid data</li> </ol>
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>
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>
Data from: Extracting spatio-temporal patterns in animal trajectories: an ecological application of sequence analysis methods
Open the record for dataset details and reuse information.
Monitoring resistance and resilience using carbon trajectories: analysis of forest management-disturbance interactions
Open the record for dataset details and reuse information.
Comparative structural insights and functional analysis for the distinct unbound states of Human AGO proteins - Molecular dynamics trajectories and analysis scripts
Open the record for dataset details and reuse information.
Data from: Trajectory energy minimisation for cell growth tracking and genealogy analysis
Cell growth experiments with a microfluidic device produce large-scale time-lapse image data, which contain important information on cell growth and patterns in their genealogy. To extract such information, we propose a scheme to segment and track bacterial cells automatically. In contrast with most published approaches, which often split segmentation and tracking into two independent procedures, we focus on designing an algorithm that describes cell properties evolving between consecutive frames by feeding segmentation and tracking results from one frame to the next one. The cell boundaries are extracted by minimizing the distance regularized level set evolution (DRLSE) model. Each individual cell was identified and tracked by identifying cell septum and membrane as well as developing a trajectory energy minimization function along time-lapse series. Experiments show that by applying this scheme, cell growth and division can be measured automatically. The results show the efficiency of the approach when testing on different datasets while comparing with other existing algorithms. The proposed approach demonstrates great potential for large-scale bacterial cell growth analysis.
Integrative single-cell analysis of cardiogenesis identifies developmental trajectories and non-coding mutations in congenital heart disease (Invitro ArchR Object)
<p>This repo contains invitro ArchR object</p>
Integrative single-cell analysis of cardiogenesis identifies developmental trajectories and non-coding mutations in congenital heart disease (Invivo ArchR Object)
<p>This repo contains invivo ArchR object</p>
Cellular and molecular heterogeneities and signatures, and pathological trajectories of fatal COVID-19 lungs defined by spatial single-cell transcriptome analysis
<p>Spatial in-situ data analysis.</p>
Supplementary videos for the paper "Visual analysis of model parameter sensitivities along warm conveyor belt trajectories"
<p>Supplementary videos for the paper "Visual analysis of model parameter sensitivities along warm conveyor belt trajectories using Met.3D (1.6.0-multivar1)" by Christoph Neuhauser, Maicon Hieronymus, Michael Kern, Marc Rautenhaus, Annika Oertel and Rüdiger Westermann.</p>
Data from: Trajectory energy minimisation for cell growth tracking and genealogy analysis
Open the record for dataset details and reuse information.
Single-cell analysis of mouse plasmacytoid dendritic cells (pDCs) unravels their activation trajectory and their molecular regulation in vivo during a viral infection [FB5P-seq_kinetic]
GEO Series GSE151245. Mus musculus. 15 samples. Type: Expression profiling by high throughput sequencing.
Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming III
GEO Series GSE122653. Mus musculus. 42 samples. Type: Expression profiling by high throughput sequencing.
Single-cell analysis reveals the landscape of tumor heterogeneity and identifies MLXIPL as a potential biomarker in tumor evolution trajectory of hepatocellular carcinoma
GEO Series GSE154906. Homo sapiens. 405 samples. Type: Expression profiling by high throughput sequencing.
Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming
GEO Series GSE122662. Mus musculus. 190 samples. Type: Expression profiling by high throughput sequencing.
Trajectory analysis reveals an uncommitted neuroblastic state in MYCN-driven neuroblastoma development
GEO Series GSE247665. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Integrated transcriptome and trajectory analysis of cutaneous T-cell identifies putative precancer cell populations
GEO Series GSE197619. Homo sapiens. 14 samples. Type: Expression profiling by high throughput sequencing.
Single cell transcriptomic and T cell repertoire analysis reveals trajectory of tumor - infiltrating lymphocyte states in pancreatic cancer
GEO Series GSE211644. Homo sapiens. 50 samples. Type: Expression profiling by high throughput sequencing.
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