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Substrate-Binding Guides Individual Melibiose Permeases MelB to Structurally Soften and to Destabilize Cytoplasmic Middle-Loop C3. Blaimschein et al.: Data sets
<p>Data sets used in the manuscript 'Substrate-Binding Guides Individual Melibiose Permeases MelB to Structurally Soften and to Destabilize Cytoplasmic Middle-Loop C3' by Blaimschein et al.</p> <p>The analysis scripts can be found at https://doi.org/10.5281/zenodo.7268838</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>
GPS data set used in the paper "Anelastic response of the Earth's crust underneath the Canary Islands revealed from ocean tide loading observations"
<p>Data set of continuous GPS observations at CVAN site in Gran Canaria (Canary Islands, Spain). The period of observation spans from July 3, 2013 to November 30, 2015. Data was acquired during the execution of the research project GCL2011-25494 of the Spanish Research Agency. </p> <p>This data set belongs to the Research Group ‘Geodesia’ of the University Complutense of Madrid, Spain, and has been used in the paper "Anelastic response of the Earth's crust underneath the Canary Islands revealed from ocean tide loading observations", by Jose Arnoso, Machiel S. Bos, Maite Benavent, Nigel T. Penna, Sergio Sainz-Maza, submitted to Geophysical Journal International, 2022.</p>
Data Sets for: Complete Description of the LaCl3-NaCl Melt Structure and the Concept of a Spacer Salt that Causes Structural Heterogeneity
<p>Data Sets for published figures and sample input files for PIM and AIMD simulations in paper <a href="https://doi.org/10.1021/jacs.2c09987">https://doi.org/10.1021/jacs.2c09987</a></p> <p> </p> <p>Complete Description of the LaCl3–NaCl Melt Structure and the Concept of a Spacer Salt That Causes Structural Heterogeneity</p> <p>Matthew S. Emerson, Shobha Sharma, Santanu Roy*, Vyacheslav S. Bryantsev*, Alexander S. Ivanov*, Ruchi Gakhar*, Michael E. Woods, Leighanne C. Gallington, Sheng Dai, Dmitry S. Maltsev, and Claudio J. Margulis*</p> <p> </p>
Data set for: "Model atmospheric aerosols convert to vesicles upon entry into aqueous solution"
<p>This document compiles raw data used in the aerosol to vesicle transformation study carried out by <strong>Serge Nader <em>et al.</em></strong><br> For detailed information and context, refer to the main article and its supplementary material published in ACS Earth and Space Chemistry.</p> <p>The Excel file contains data relevant to each figure in the main article and supporting information. The additional compressed file contains raw Transmission Electron Microscopy (TEM) photographs.</p>
Data set for EMPIR project MeDDII Paper bmt-2022-0040
<p>Data set for EMPIR project MeDDII Paper</p> <p>https://doi.org/10.1515/bmt-2022-0040</p>
Data set for EMPIR project MeDDII Report A2.3.4 D4
<p>Data set for EMPIR project MeDDII Report A2.3.4 D4</p> <p>Report on the use of a calibrated microfluidic multi-parameter chip for the in-line measurement of pressure, viscosity and temperature</p>
Data set for EMPIR project MeDDII Report A2.1.4
<p>Data set for EMPIR project MeDDII Report A2.1.4</p> <p>Validation of the pipe viscometers (primary standards) for the in-line measurement of the dynamic viscosity of Newtonian liquids</p>
Data set for EMPIR project MeDDII Paper FLOMEKO 2022 ID90
<p>Data set for EMPIR project MeDDII Paper FLOMEKO 2022 ID90</p> <p>Dynamic vs constant liquid flow calibrations down to 20 nL/min</p>
Data set for EMPIR project MeDDII Paper FLOMEKO 2022 ID88
<p>Data set for EMPIR project MeDDII Paper FLOMEKO 2022 ID88</p> <p>First comparison of inline measurements of dynamic viscosity</p>
Data set for EMPIR project MeDDII Report A2.3.3
<p>Data set for EMPIR project MeDDII Report A2.3.3</p> <p>Characterization of devices for the in-line measurement of the dynamic viscosity and the density of Newtonian liquids</p>
Data set of Precipitation rates of electrons interacting with lower-band chorus emissions in the inner magnetosphere
<p>Simulation data for paper entitled "Precipitation rates of electrons interacting with lower-band chorus emissions in the inner magnetosphere"</p>
Data set for the journal article: The Spatial Distribution of Cobalt Phthalocyanine and Copper Nanocubes Controls the Selectivity towards C2 Products in Tandem Electrocatalytic CO2 Reduction
<p>Data set for the journal article:</p> <p>The coupling of CO-generating molecular catalysts with copper electrodes in tandem schemes is a promising strategy to boost the formation of multi-carbon products in the electrocatalytic reduction of CO<sub>2</sub>. While the spatial distribution of the two components is important, this aspect remains underexplored, especially for molecular-based tandem systems. Herein, we address this knowledge gap by studying tandem catalysts comprising Co-phthalocyanine (CoPc) and Cu nanocubes (Cu<sub>cub</sub>). In particular, we identify the importance of the relative spatial distributions of the two components on the performance of the tandem catalyst by preparing CoPc-Cu<sub>cub</sub>/C, wherein the CoPc and Cu<sub>cub</sub> share an interface, and CoPc-C/Cu<sub>cub</sub>, wherein the CoPc is loaded first on carbon black (C) before mixing with the Cu<sub>cub</sub>. The electrocatalytic measurements of these two catalysts show that the faradaic efficiency towards C<sub>2 </sub>products almost doubles for the CoPc-Cu<sub>cub</sub>/C, whereas it decreases by half for the CoPc-C/Cu<sub>cub</sub>, compared to the Cu<sub>cub</sub>/C. Our results highlight the importance of a direct contact between the CO-generating molecular catalyst and the Cu to promote C-C coupling, which hints at a surface transport mechanism of the CO intermediate between the two components of the tandem catalyst instead of a transfer via CO diffusion in the electrolyte followed by re-adsorption.</p>
Solid-state NMR data set for SI of "Probing a Hydrogen-π Interaction Involving a Trapped Water Molecule in the Solid State"
<p>These datasets are part of the Supplementary Information of "Probing a Hydrogen-π Interaction Involving a Trapped Water Molecule in the Solid State". All experimental details are given in the mentioned document.</p>
Data sets related to excess rainfall intensity (ERI) calculation
<p>Data sets related to excess rainfall intensity (ERI) calculation:Example of ERI calculation, sampling data, statistical characteristics, empirical frequency and theoretical frequency calculation</p>
Data set for phase-field simulation of epitaxial crystal growth in open fractures with lateral flow
<p>The numerical data in this repository consists of the simulation data of epitaxial crystal growth in open fractures with lateral flow. The simulations were performed using the software package named "Pace3D".</p> <p>The simulation data shows the grain structure, the concentration field and the fluid flow velocity in stream direction (if present) at intermediate stages. It was converted from the Pace3D output data format to VTK data format. The VTK files can be visualized using open source software packages like Paraview. For visualization the data has to be decompressed (e.g. with gzip, 7zip).</p>
Data set of HRQoL of hepatitis C patients visiting public hospitals of Lahore, Pakistan
<p>The attached file contains the data set for Health related quality of life among hepatitis C patients: A cross-sectional study conducted in public hospitals of Lahore, Pakistan study.</p>
Architecture-based Uncertainty Impact Analysis to ensure Confidentiality - Data Set
<p>Data set of the Paper "Architecture-based Uncertainty Impact Analysis to ensure Confidentiality". For more information, please see the README.md.</p>
Data set belonging to DOI: 10.1109/AMPS55790.2022.9978849
<p>This data set belongs to the following paper:</p> <p>G. Frigo and M. Agustoni<br> "Development of a Transfer Standard for DC Power Quality Reference Systems"<br> 2022 IEEE 12th International Workshop on Applied Measurements for Power Systems (AMPS)<br> DOI: 10.1109/AMPS55790.2022.9978849</p> <p> </p> <p>This project has received funding from the European Metrology Programme for Innovation and Research co-financed by the Participating States and in part by the European Union’s Horizon 2020 Research and Innovation Programme</p>
Nanoindentation data set: Calibration and data analysis routines for nanoindentation with spherical tips
<p>These data comprise of nanoindentation experiements, which are described and analysed in the manuscript: <em>Calibration and data analysis routines for nanoidentation with spherical tips.</em></p> <p> </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.