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34 results for “arrival timing”
An analyst-created, high-resolution seismic arrival time dataset for evaluating machine learning phase detectors
<p>This data set contains phase arrival times and phase labels for one hour of continuous seismic data recorded at the three-component broadband station WY.YNR on 2014 30 March from 13:00:00 to 14:00:00 UTC. This hour of data follows a M<sub>w</sub> 4.8 occurring at 12:34 UTC in the Yellowstone region and contains many small events close together in space and time. All 687 picks (404 P and 283 S) were made by a seismic analyst trained at the University of Utah Seismograph Stations. The goal was to pick as many reasonable arrivals as possible for evaluating the performance of machine-learning-based phase detectors on continuous data during high seismicity rates. Phase picks were made using the Seismic Analysis Code (SAC; Goldstein and Snoke, 2005) and Pyrocko (Heimann <em>et al</em>., 2017). In general, a 1 to 17 Hz bandpass filter was used.</p> <p>The csv file contains the pick id, phase arrival time in UTC and Unix epoch formats, and the phase labels (P or S).</p>
Chilean Arrival Times - 1982 - mid-2020
<p>This repository contains the arrival times produced by the Centro Sismológico Nacional (CSN, Universidad de Chile, <a href="http://www.sismologia.cl" target="_blank" rel="nofollow noreferrer noopener">http://www.sismologia.cl</a>) and revised in the present study.</p> <p>This archive contains 995,954 P- and 922,004 S-waves arrival-times, corresponding to 118,004 events recorded by the CSN on 271 stations between 1982 and July 2020.</p> <p>Contact:</p> <ul> <li>Bertrand Potin, <em>DGF, University of Chile</em> (<a href="mailto:bertrand.potin@uchile.cl">bertrand.potin@uchile.cl</a>),</li> <li>Sergio Ruiz, <em>DGF, University of Chile</em> (<a href="mailto:sruiz@uchile.cl">sruiz@uchile.cl</a>),</li> <li>Sergio Barrientos, <em>CSN, University of Chile</em> (<a href="mailto:sbarrien@csn.uchile.cl">sbarrien@csn.uchile.cl</a>)</li> </ul> <p>Arrival-times are formated by events in a text file. The corresponding station coordinates are given as a CSV archive. These arrival times correspond to the catalogue published in Potin <em>et al.</em> (2024) and distributed under the DOI <a href="https://doi.org/10.5281/zenodo.13146436" target="_blank" rel="nofollow noreferrer noopener">https://doi.org/10.5281/zenodo.13146436</a></p> <h1>How to cite this material</h1> <h2>Material doi</h2> <p><a href="https://doi.org/10.5281/zenodo.13173435" target="_blank" rel="nofollow noreferrer noopener">https://doi.org/10.5281/zenodo.13173435</a></p> <h2>Related article</h2> <p>Potin, B., S. Ruiz, F. Aden-Antoniow, R. Madariaga, and S. Barrientos (2024). A Revised Chilean Seismic Catalog from 1982 to Mid-2020, <em>Seismol. Res. Lett.</em>. <a href="https://doi.org/10.1785/0220240047" target="_blank" rel="nofollow noreferrer noopener">https://doi.org/10.1785/0220240047</a></p> <h1>Files format</h1> <h2>CSN_1982-2020_arrival-times.light_ins</h2> <p>The file correspond to a list of earthquakes, starting with a header line and followed by the corresponding arrival-times. Events are separated by an empty line.</p> <h3>header line:</h3> <div> <pre><code>YYMMDD hhmm sss.ss long.xxxxxx lat.xxxxxx dep.xxxx RMSxx RMSsx MAGxx # T</code></pre> </div> <ul> <li><code>YYMMDD hhmm</code>: year month day hour minute: reference time for the event (i2i2i2 i2i2).</li> <li><code>sss.ss</code>: seconds for the event origin, relative to reference time (f6.2). Might be negative or > 60.0</li> <li><code>long.xxxxxx</code>: longitude (f11.6)</li> <li><code>lat.xxxxxx</code>: latitude (f10.6)</li> <li><code>dep.xxxx</code>: depth (km) (f8.4)</li> <li><code>RMSxx</code>: location data ajustement (s) (f5.2)</li> <li><code>RMSsx</code>: normalized location data ajustement (sigma) (f5.2)</li> <li><code>MAGxx</code>: magnitude, either a number of the mention <code>NOMAG</code> (f5.2 or str)</li> <li><code>T</code>: magnitude type, one of: <ul> <li><code>l</code>: local magnitude,</li> <li><code>c</code>: coda magnitude,</li> <li><code>W</code>: moment magnitude, Brune's approach,</li> <li><code>ww</code>: moment magnitude, W-phase approach,</li> <li><code>xx</code>: no magnitude.</li> </ul> </li> </ul> <h3>Data line:</h3> <div> <pre><code>STAxx azi.x inc.x ph p s.ig sss.ss</code></pre> </div> <ul> <li><code>STAxx</code>: station code (s5)</li> <li><code>azi.x</code>: ray azimut [0.0 360.0] (f5.1)</li> <li><code>inc.x</code>: ray inclinaison [0 180.0] (f5.1) (0 is up)</li> <li><code>ph</code>: phase: "P ", "S ", "Pg", "Pn", "P1", "P2", ... (s2)</li> <li><code>p</code>: polarity: " ", "C", "D", "U", "+", "-"</li> <li><code>s.ig</code>: uncertainty (s) (f4.2)</li> <li><code>sss.ss</code>: arrival time, relative to reference time (s) (f6.2). Might be negative or > 60.0</li> </ul> <h3>Example</h3> <div> <pre><code>191103 0328 22.02 -67.289814 -24.180293 201.3188 0.36 0.76 2.80 # l PB19 273.5 37.6 P1 0.46 59.69 PB02 331.4 51.3 P1 0.46 80.66 GO02 237.9 57.7 P1 0.46 63.85 PB09 332.1 50.4 P1 0.46 72.56 PB05 301.3 41.6 P1 0.46 71.64 PB06 309.5 42.5 P1 0.46 66.76 PB03 322.1 47.4 P1 0.46 73.51 PB19 275.8 37.6 S1 0.68 88.59 PB06 308.2 45.6 S1 0.68 101.08 PB05 294.3 57.3 S1 0.68 109.20 # <<== PB09 327.6 50.7 S1 D 0.68 110.44 191103 0806 11.15 -72.036989 -30.727443 6.0101 0.69 1.52 5.20 # ww CO06 75.5 118.6 S1 0.48 24.34 CO06 76.6 120.1 P1 C 0.46 19.16 CO05 46.6 129.1 P1 D 0.12 30.29 CO02 113.8 130.3 P1 C 0.46 30.32 GO04 62.7 128.8 P1 D 0.12 32.85 CO01 66.7 129.5 P1 C 0.12 43.26 ...</code></pre> </div> <p>In this example, the line marked with the sign <code><<==</code> corresponds to an arrival-time measured 109.20 s after the reference time. For this earthquake, reference time is <code>191103 0328</code>, which is November 3rd, 2019, at 3h28 (UTC). The arrival time is therfore 109.20 seconds after that date, at 3h29 and 49.20 seconds.</p> <p>Note: the <code>#</code> character initiate a comment in Insight format.</p> <h2>CSN_1982-2020_station_list.csv</h2> <p>This CSV file contains the coordinates of the stations used to build the catalogue.</p> <h3>Format</h3> <div> <pre><code>index,station code,longitude,latitude,altitude</code></pre> </div> <ul> <li><code>index</code>: index, between 0 and 270,</li> <li><code>station code</code>: name of the station,</li> <li><code>longitude</code>: longitude in degrees,</li> <li><code>latitude</code>: latitude in degrees,</li> <li><code>altitude</code>: altitude in metres.</li> </ul> <h3>Example</h3> <div> <pre><code>... 59,CO02,-71.000167,-31.203500,1149 60,CO03,-70.689167,-30.839000,990 61,CO04,-70.974667,-32.043333,2401 62,CO05,-71.238333,-29.918667,101 63,CO06,-71.635000,-30.673833,240 ...</code></pre> </div>
Pulse Profiles and Times of Arrival Measurements from a Rotating Radio Transient Census with the Irish LOFAR station
<p>The reduced data produced as a part of a census of rotating radio transients (RRATs) with the Irish LOFAR station.</p> <p> </p> <p>This deposit contains:</p> <ul> <li>Metadata regarding observed data</li> <li>A copy of RFI-zapped, single pulse archives</li> <li>A copy of the time-flattened periodic emission archives</li> <li>A copy of the measured pulse times of arrival</li> <li>Ephemerides used and produced as a part of the work</li> </ul> <p>Additional data can be made available on request to the author.</p>
Dataset for "FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices"
<p>Dataset for the paper "FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices"</p> <p>The dataset contains localization measurements acquired with UWB devices. We compare the proposed localization method, called FlexTDOA, with a classic TDOA implementation, and with TWR-based localization. For more information about the localization methods, please refer to the paper.</p> <p>The dataset contains the measurements necessary to generate all the plots in the paper. For code examples on how to read and plot the data, please check out the associated Github repository: https://github.com/lauraflu/flextdoa</p> <p>If you find the dataset useful, please consider citing our work:</p> <blockquote> <p>Pătru, G. C., Flueratoru, L., Vasilescu, I., Niculescu, D., & Rosner, D. (2023). FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices. <em>IEEE Access</em>.</p> </blockquote>
A catalog of associated, machine-learning-derived phase arrival times for ten days of seismic data in the Yellowstone region
<p>This dataset contains the associated phase picks and event information from applying a deep learning phase picker to continuous data recorded over March 25 – April 3, 2014, on 20 three-component stations and 14 vertical-component stations in the Yellowstone region. This 10-day period contains an M<sub>w</sub> 4.8 event, the largest earthquake in the Yellowstone region since 1980. The catalog and deep learning phase picker are described in Armstrong et al. (submitted).</p> <p>The arrivals were associated using the method described by Baker et al. (2021) and located using HypoInverse2000 (Klein, 2002). There are 1,053 events in this catalog, including 855 that were previously unidentified. Events that also appear in the University of Utah Seismograph Stations catalog have an event identifier (evid) beginning with “6”, while new events begin with “9”. </p> <p>Columns include:</p> <ul> <li>A simple event number</li> <li>the network, station, channel, and location code for the arrival time</li> <li>the arrival time in UTC (arrival_time) and Unix (arrival_time_epoch) format</li> <li>any static correction applied to the arrival time</li> <li>the P-pick first motion polarity as determined by a machine learning model - up (1), down (-1), or unknown (0)</li> <li>the arrival time residual </li> <li>the take off angle in degrees </li> <li>the event latitude and longitude in degrees</li> <li>the event depth in km</li> <li>the event origin time in UTC (origin_time) and Unix (origin_time_epoch) format</li> <li>the azimuthal gap of the event in degrees</li> <li>the root mean square error (RMS) of the event location</li> <li>the event identifier (evid) - begins with a “6” for events in the UUSS catalog and a “9” for new events</li> </ul> <p> </p>
Arrival Time Scoreboard 2013-2023
<p>Collection of the results pulled from the CME Scoreboard and used for Kay et al. (2024). This includes all predictions through the end of 2023. The data were scraped from the webpage and only minor modifications have been made. We have removed one event that was a STEREO impact instead of Earth and have modified the ID tags for a few of the corresponding coronal CMEs to match the entries in the DONKI catalog.</p>
Fig. 1 in Sharp Differences In The Timing Of Male And Female Spring Arrival In The European Stonechat, Saxicola Rubicola, And The Whinchat, S. Rubetra (Passeriformes, Muscicapidae), In North-Eastern Ukraine
Fig. 1. Spring arrival schedules of male and female Common Stonechats (Saxicola rubicola) and male and female Whinchats (S. rubetra) at the study plot in the Murom River flood plain (Kharkiv Region, Ukraine). The dates were standardised by assigning 1 Day value for the arrival of first bird in a certain year (the data for years 1994–1995, 2002–2004 are presented).
Data from: Timing and probability of arrival for sea lice dispersing between salmon farms
<p>Sea lice are a threat to the health of both wild and farmed salmon and an economic burden for salmon farms. With a free-living larval stage, sea lice can disperse tens of kilometers in the ocean between salmon farms, leading to connected sea lice populations that are difficult to control in isolation. In this paper, we develop a simple analytical model for the dispersal of sea lice between two salmon farms. From the model we calculate the arrival time distribution of sea lice dispersing between farms, as well as the level of cross-infection of sea lice. We also use numerical flows from a hydrodynamic model, coupled with a particle tracking model, to directly calculate the arrival time of sea lice dispersing between two farms in the Broughton Archipelago, BC, in order to fit our analytical model and find realistic parameter estimates. Using the parametrized analytical model we show that there is often an intermediate inter-farm spacing that maximizes the level of cross-infection between farms, and that increased temperatures will lead to increased levels of cross-infection.</p>
A Model-Independent Determination of Red Noise in Pulsar Timing Arrivals
<p>Data files for Reyes & Bernido, submitted, 2023, A Model-Independent Determination of Red Noise in Pulsar Timing Arrivals. </p> <p>In this work, we analyze the pulsar timing data from the North American Nanohertz Observatory for Gravitational Waves (NANOGrav; Arzoumanian et al 2018). For 23 pulsars with 820 MHz data, we show that an evaluation of the mean square deviation (MSD) and probability distribution (PDF) of timing residuals can provide a straightforward way of determining the presence of red noise. The model-free method presented could complement the normally more sophisticated model-dependent way of determining red noise in timing residuals.</p> <p>Data available here:</p> <p>- ts.zip - uniform time-series of timing residuals for the 23 pulsars</p> <p>- msd.zip - mean square deviation vs. lag time for the 23 pulsars</p> <p>- pdf.zip - probability distributions for lag times equal to 30, 150, 300, 900, and 1200 days for the 23 pulsars</p>
Data from: Timing and probability of arrival for sea lice dispersing between salmon farms
Open the record for dataset details and reuse information.
An automatically generated high-resolution earthquake catalogue for the 2016-2017 Central Italy seismic sequence, including P and S phase arrival times
<p>Catalog of 440,697 earthquakes of the 2016-2017 Central Italy seismic sequence semi-automatically generated by Spallarossa et al. (2020). The catalogue covers one year of aftershocks following the first mainshock of the sequence (from 08242016 to 08312017).</p> <p>The catalog has been generated using the Complete Automatic Seismic Processor (CASP) procedure (Scafidi et al., 2019) to detect the events and an advanced picker engine (RSNI-Picker<sub>2</sub>; Scafidi et al., 2018; Spallarossa et al., 2014) to determine their phase arrival times. The final set of about 7 million P- and 10 million S-wave arrival times have been used to locate the events using a non-linear location algorithm (NonLinLoc; Lomax et al. 2000), with a 1D velocity model calibrated for the area (De Luca et al., 2009) and station corrections. For each event, also local magnitudes (M<sub>L</sub>) has been calculated as well as a locations quality.</p> <p>Earthquake locations quality has been classified by means of the procedure proposed by Michele et al., (2019) consisting of the combination of diverse uncertainty parameters provided by the NonLinLoc location code. Locations quality is provided in terms of a unique numeric normalized value, named quality factor, varying between qf=0 (best quality location) and qf=1 (worst quality location). Then locations have been assigned to a quality class depending on the qf parameter value according to the following scheme: A-class (0 < qf ≤ 0.25), B-class (0.25 < qf ≤ 0.50), C-class (0.50 < qf ≤ 0.75), and D-class (0.75 < qf < 1.00). The earthquake locations are distributed between the quality classes as A-30.6%, B-31.4%, C-18.6%, and D-19.4% (details in Spallarossa et al., 2020).</p> <p>We accompanied the catalogue with the 30 events with M>3.5 missed by our procedure (bring the total number of events to 440,727), including the first Amatrice mainshock (M<sub>W</sub>6.0; see Spallarossa et al., 2020). These 30 missing events recognisable by the ID starting with ISI), have been taken from INGV bulletin (<a href="http://terremoti.ingv.it">http://terremoti.ingv.it</a>; ISIDe Working Group., 2007), manually generated. These additional events report INGV locations and magnitude parameters while are missing related quality factors and quality class, being generated by a different procedure.</p> <p>We added to the larger events, the available moment magnitudes (M<sub>W</sub>) from Time Domain Moment Tensor catalogue (<a href="http://terremoti.ingv.it/tdmt">http://terremoti.ingv.it/tdmt</a>; Scognamiglio et al., 2006).</p> <p>The catalog is in csv format, semicolon separator, ordered by origin time and the header content is the following:</p> <ul> <li>Id-event – ID</li> <li>Latitude (°) expressed in decimal degrees - LAT</li> <li>Longitude (°) expressed in decimal degrees - LON</li> <li>Depth(km) hypocentral depth expressed in kilometres - DEP</li> <li>Year of origin time in the format yyyy - YR</li> <li>Month of origin time in the format mo - MON</li> <li>Day of origin time in the format dd - DY</li> <li>Hour of origin time in the format hh - HR</li> <li>Minute of origin time in the format mi - MIN</li> <li>Second of origin time in the format XX.XXX s - SEC</li> <li>Local Magnitude - ML</li> <li>Standard deviation of the Local Magnitude – STD</li> <li>Moment Magnitude – Mw (from TDMT)</li> <li>Horizontal Error (from NLL output) (km) expressed in kilometres - ERH</li> <li>Vertical Error (from NLL output) (km) expressed in kilometres - ERZ</li> <li>RMS (from NLL output) (s) expressed in seconds - RMS</li> <li>Number of Phases – NPHS</li> <li>Stations Azimuthal GAP (°) expressed in decimal degrees - GAP</li> <li>Quality factor - Qf</li> <li>Quality class - Qc</li> </ul> <p> </p> <p>De Luca G., M. Cattaneo, G. Monachesi and A, Amato (2009). Seismicity in the Umbria-Marche region from the integration of national and regional seismic networks. Tectonophysics, 476(1), 219-231. doi: 10.1016/j.tecto.2008.11.032.</p> <p>ISIDe Working Group. (2007). Italian Seismological Instrumental and Parametric Database (ISIDe). Istituto Nazionale di Geofisica e Vulcanologia (INGV); https://doi.org/10.13127/ISIDE.</p> <p>Lomax, A., J. Virieux, P. Volant, and C. Berge-Thierry (2000). Probabilistic earthquake location in 3D and layered models: introduction of a Metropolis–Gibbs method and comparison with linear locations. In: Advances in seismic event location, ed. C. H. Thurber and N. Rabinowitz, 101–134. Dordrecht and Boston: Kluwer Academic Publishers.</p> <p>Michele, M., Latorre, D., Emolo, A. (2019). An Empirical Formula to Classify the Quality of Earthquake Locations. Bulletin of the Seismological Society of America. Vol. 109, No. 6, pp. 2755–2761, December 2019, doi: 10.1785/0120190144.</p> <p>Scafidi, D., Viganò A., Ferretti G., and Spallarossa D. (2018). Robust picking and accurate location with RSNI-Picker2: real-time automatic monitoring of earthquakes and non-tectonic events, Seismol. Res. Lett, Vol. 89 (4), pp. 1478-1487, doi: 10.1785/0220170206.</p> <p>Scafidi D, Spallarossa D, Ferretti G, Barani S, Castello B, Margheriti L (2019). A complete automatic procedure to compile reliable seismic catalogs and travel-time and strong-motion parameters datasets. Seismol Res Lett 90(3):1308–1317.</p> <p>Scognamiglio, L., Tinti, E., Quintiliani, M. (2006). Time Domain Moment Tensor [Data set]. Istituto Nazionale di Geofisica e Vulcanologia (INGV). https://doi.org/10.13127/TDMT.</p> <p>Spallarossa, D., G. Ferretti, D. Scafidi, C. Turino, and M. Pasta (2014). Performance of the RSNI-Picker, Seismol. Res. Lett. 85, 1243–1254.</p> <p>Spallarossa D., Cattaneo M., Scafidi D., Michele M., Chiaraluce L., Segou M. and I. G. Main (2020). An automatically generated high-resolution earthquake catalogue for the 2016-2017 Central Italy seismic sequence, including P and S phase arrival times. Geophys. J. Int. doi: 10.1093/gji/ggaa604.</p>
Dataset for Anomaly Detection Using Inter-Arrival Curves for Real-time Systems
<p>The dataset shows the input files and detailed results for the experiments discussed in the paper. A README file provide more details on the data.</p>
Binary Pulsar PSR B1913+16 arrival time and associated files. Ref: Weisberg & Huang APJ 2016
<p>These files contain arrival time and associated data for PSR B1913+16, which are usable as input to the tempo software program. They were used for the paper "Relativistic Measurements from Timing the Binary Pulsar PSR B1913+16," Weisberg & Huang, Astrophysical Journal, 2016, in press. </p>
Arrival-time data manually picked up of Pn wave in Hainan Island and surrounding areas
<p>This file is the arrival-time data manually picked up of Pn wave in Hainan Island and surrounding areas. It is only used for scientific research.</p>
Community science reveals links between migration arrival timing advance, migration distance, and wing shape
<p>Substantial global data show that many taxa are shifting their phenologies in response to climate change. For birds, migration arrival dates in breeding regions have been shifting earlier, and there is evidence that both evolutionary adaptation and behavioural flexibility influence these shifts. As more efficient flyers may be able to demonstrate more flexibility to respond to changing conditions during migratory flight, we hypothesize that differences among passerine species in flight efficiency, as reflected by morphology, may be associated with the magnitude of shifts in arrival date in response to climate warming. We applied a logistic model to eighteen years of eBird data to estimate mean arrival date for 44 common passerines migrating to northeast North America. We then used linear mixed-effects models to estimate changes in mean arrival date and compared these changes to morphological proxies for flight efficiency and migratory distance using phylogenetic generalized least squares models. On average, passerine species shifted their arrival dates 0.120 days earlier each year, with 27 of the 44 species shifting to significantly earlier arrival times, and two shifting to significantly later ones. Of the 15 species with non-significant shifts, 13 trended toward earlier arrivals. Longer migration distances and higher wing aspect ratios were associated with greater shifts towards earlier arrivals. Migration distance and aspect ratio were also significantly correlated to each other. This suggests that changes in arrival date are affected by factors pertaining to migratory flight over long distances namely, flight efficiency and migration distance. These traits may be able predict the magnitude of arrival date shift, and by extension identify species that are most at risk to climate change due to inflexible arrival timing.</p>
Arrival-time data automatically picked up by PickNet of Pn wave in Tanlu Fault Zone and surrounding areas
<p>This file is the arrival-time data automatically picked up by PickNet of Pn wave in Tanlu Fault Zone and surrounding areas. Only these Pn arrival times are used for tomographic inversion.</p> <p> </p>
Arrival-time data of the Longmenshan Fault Zone in East Tibet
<p>This file is the manually arrival-time data of the Longmenshan Fault Zone in East Tibet, including P, S and PmP arrival times.</p>
Community science reveals links between migration arrival timing advance, migration distance, and wing shape
Open the record for dataset details and reuse information.
Data from: Sex-specific arrival times on the breeding grounds: hybridizing migratory skuas provide empirical support for the role of sex ratios
In migratory animals, protandry (earlier arrival of males on the breeding grounds) prevails over protogyny (females preceding males). In theory, sex differences in timing of arrival should be driven by the operational sex ratio, shifting toward protogyny in female-biased populations. However, empirical support for this hypothesis is, to date, lacking. To test this hypothesis, we analyzed arrival data from three populations of the long-distance migratory south polar skua (Catharacta maccormicki). These populations differed in their operational sex ratio caused by the unidirectional hybridization of male south polar skuas with female brown skuas (Catharacta antarctica lonnbergi). We found that arrival times were protandrous in allopatry, shifting toward protogyny in female-biased populations when breeding in sympatry. This unique observation is consistent with theoretical predictions that sex-specific arrival times should be influenced by sex ratio and that protogyny should be observed in populations with female-biased operational sex ratio.
Data from: Timing of arrival in the breeding area is repeatable and affects reproductive success in a non-migratory population of blue tits
<ol> <li>Events in one part of the annual cycle often affect the performance (and subsequently fitness) of individuals later in the season (carry-over effects). An important aspect of this relates to the timing of activities. For example, many studies on migratory birds have shown that relatively late spring arrival in the breeding area reduces both the likelihood of getting a mate or territory and reproductive success.</li> <li>In contrast, relatively little is known about movements of individuals in non-migratory populations during the non-breeding season. Few studies have investigated the timing of arrival at the breeding area in such species, possibly due to the assumption that most individuals remain in the area during the non-breeding season.</li> <li>In this study, we used four years of data from a transponder-based automated recording system set up in a non-migratory population of blue tits (<i>Cyanistes caeruleus</i>) to describe individual variation in arrival at the breeding site. We investigated whether this variation can be explained by individual characteristics (sex, body size, or status), and we assessed its effect on aspects of reproductive success in the subsequent breeding season.</li> <li>We found substantial variation in arrival date and demonstrate that this trait is individual-specific (repeatable). Females arrived later than males, but the arrival dates of social pair members were more similar than expected by chance, which suggests that individuals may mate assortatively depending on their arrival in the breeding area. Arrival predicted both whether an individual would end up breeding that season, and several aspects of its breeding success.</li> <li>Our study suggests that individuals of non-migratory species leave the breeding area during the non-breeding season. Hence, it may be useful to consider variation in the scale of movements between breeding and non-breeding sites, rather than using a simple dichotomy between "resident" and "migratory" species. We conclude that the timing of pre-breeding events, in particular arrival date, may be an overlooked, but important, fitness-relevant trait in non-migratory species.</li> </ol>
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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.