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145 results for “Adaptation by time”
SMART - Self-adaptive Machine Learning Approach for Real-time Tuning of IEEE 802.11 PHY and MAC layers
<p><strong>Introduction</strong></p> <p>Worldwide the demand for wireless access networks providing very high throughputs has been increasing exponentially, namely due to bandwidth-hungry applications such as high definition video streaming and augmented reality. In order to fulfil these requirements, the Wi-Fi standard was enriched with new amendments, such as IEEE 802.11n, IEEE 802.11ac, and recently IEEE 802.11ax (Wi-Fi 6). New parameters have been proposed for both physical (PHY) and media access control (MAC) layers, including channel bonding, short guard interval (SGI), and advanced modulation and coding schemes (MCS).</p> <p>However, the high variability of the signal strength in the wireless radio channel, allied to the channel asymmetry, makes the selection of optimal configurations for these parameters a challenge. Typically, these parameters are configured with a default value. For runtime optimization, some algorithms have already been proposed. Still, they were designed considering legacy IEEE 802.11 releases and static scenarios. Besides, these parameters have their trade-offs that need to be properly managed. To help dealing with this, machine learning has been recently introduced in wireless networks, providing the intelligence that networks need in order to be smart and self-adaptive.</p> <p>SWOP (Smart Wireless Optimization) is a cross-layer optimization approach for Wi-Fi networks extending the current Rate Adaptation (RA) approach, for instance, followed by the well-known Minstrel algorithm widely used in practice. Our approach takes advantage of Deep Reinforcement Learning (DRL) in order to learn the optimal Wi-Fi link configuration. By considering the wireless channel as the environment, the transmitter node (the agent) chooses the best link parameters (the action) in order to maximize the throughput (the reward) based on the channel metrics captured from the environment (the state). In this work we propose a simple DRL-based Wi-Fi Rate Adaptation (RA) algorithm, named Data-driven Algorithm for Rate Adaptation (DARA), which is one of the modules of Smart Wireless Optimization (SWOP)</p> <p>SMART aimed to run a set of wireless experiments on top of w-iLab.t testbeds provided by the Fed4FIRE+ project to directly validate our DRL model and learn a policy from the wireless experiments executed in a controlled environment. However, after facing difficulties with the scenarios we could achieve on the real testbed, we decided to train and test DARA using a trace-based simulation approach. In simulation, we could train our model in scenarios that are more complex and diverse whilst easy to configure, when compared to real testbeds. The w-iLab.t testbeds were still used to capture data traces (e.g. Signal-to-Noise Ratio, position of nodes, transmission power and link distance) that were then injected in ns-3 for validating DARA.</p> <p>With this work, we concluded that DARA performance is impacted when operating in scenarios with asymmetric links, which is common in the highly dynamic and unpredictable wireless environments. Furthermore, the asymmetry offset varies between scenarios and it may also change for the same scenario, as time progresses. This randomness is not addressed when solely considering the SNR as the link metric, posing a challenge in the learning phase of DARA. Despite these limitations, the results obtained show that DARA still achieves up to 14.9% higher throughput higher than Minstrel [1] and slightly lower than Ideal [2] for most of the scenarios. The results obtained will serve as a basis to support our ongoing and future research.</p> <p> </p> <p><strong>Folder Organization</strong></p> <p>The following dataset presents the results of the SMART project, organized in different folders for each Rate Adaptation Algorithm, as well as the traces that were used to obtain such results:</p> <ul> <li><strong>DARA: </strong>Results obtained using our solution <strong>(Naming Convention #1, Folder Content #1)</strong></li> <li><strong>MIN: </strong>Results obtained using Minstrel-HT <strong>(Naming Convention #1, Folder Content #2)</strong></li> <li><strong>ID: </strong>Results obtained using Ideal <strong>(Naming Convention #1, Folder Content #2)</strong></li> <li><strong>TRACES: </strong>Trace files used to obtain the results present in this dataset <strong>(Naming Convention #2, Folder Content #3)</strong></li> </ul> <p><strong>Naming Convention #1 – RAA TID TP TO:</strong></p> <ul> <li>Rate Adaptation Algorithm<strong> (RAA) </strong> <ul> <li><strong>drl </strong>– Data Driven Algorithm for Rate Adaptation</li> <li><strong>min </strong>– MinstrelHTWifiManager</li> <li><strong>id </strong>– IdealWifiManager</li> </ul> </li> <li>Trace ID<strong> (TID)</strong> <ul> <li><strong>3 </strong>up to<strong> 8</strong></li> </ul> </li> <li>Transport Protocol<strong> (TP)</strong> <ul> <li><strong>udp </strong>– User Datagram Protocol</li> </ul> </li> <li>Traffic Orientation<strong> (TO)</strong> <ul> <li><strong>normal </strong>– A<strong>-></strong>B</li> <li><strong>reversed </strong>– B<strong>-></strong>A</li> </ul> </li> </ul> <p><strong>Naming Convention #2 – TID_TXP:</strong></p> <ul> <li>Trace ID<strong> (TID) </strong> <ul> <li><strong>3 </strong>up to<strong> 8</strong></li> </ul> </li> <li>Transmitting Power in dBm <strong>(TXP) </strong> <ul> <li><strong>3, 5, 7, 9, 12 dBm </strong></li> </ul> </li> </ul> <p><strong>Folder Content #1: </strong></p> <ul> <li><em>checkpoint_ RAA TID TP TO</em><strong> (Folder)</strong> <ul> <li><strong>Policy Checkpoint</strong> with which the results were obtained</li> </ul> </li> <li> <ul> <li><strong>Flowmonitor </strong>output for the configured scenario</li> </ul> </li> <li> <ul> <li>Column 1 – <strong>Step Counter</strong></li> <li>Column 2 – <strong>Reward Value</strong></li> <li>Column 3 – <strong>Observation Value</strong></li> <li>Column 4 – <strong>Action Value</strong></li> </ul> </li> <li> <ul> <li>Column 1 – <strong>Simulation Time </strong>(seconds)</li> <li>Column 2 – <strong>Throughput </strong>(Mbit/100ms)</li> </ul> </li> </ul> <p><strong>Folder Content #2: </strong></p> <ul> <li> <ul> <li><strong>Flowmonitor </strong>output for the configured scenario</li> </ul> </li> <li> <ul> <li>Column 1 – <strong>Simulation Time </strong>(seconds)</li> <li>Column 2 – <strong>Throughput </strong>(Mbit/100ms)</li> </ul> </li> </ul> <p><strong>Folder Content #3 - </strong>Source: <a href="https://zenodo.org/record/3713271#.YjjBVDXLdhE">https://zenodo.org/record/3713271#.YjjBVDXLdhE</a><strong>: </strong></p> <p>· <em>date_time</em><strong>.cfg </strong>configuration details of the experiment</p> <p>· <em>date_time_NodeID</em><a href="https://zenodo.org/record/3713271#_ftn1"><strong><em><sup>[1]</sup></em></strong></a><em>_SenderID</em><a href="https://zenodo.org/record/3713271#_ftn2"><strong><em><sup>[2]</sup></em></strong></a><em>_ReceiverID</em><a href="https://zenodo.org/record/3713271#_ftn3"><strong><em><sup>[3]</sup></em></strong></a><em>_FlowType</em><a href="https://zenodo.org/record/3713271#_ftn4"><strong><em><sup>[4]</sup></em></strong></a><em>_Params</em><a href="https://zenodo.org/record/3713271#_ftn5"><strong><em><sup>[5]</sup></em></strong></a><strong>.snr </strong>– logs of the Signal/Noise ratio (1 file per node/flow) </p> <p>· <em>date_time_NodeID_SenderID_ReceiverID_FlowType_Params</em><strong>.stats</strong> – logs of the packets received (1 file per node/flow) </p> <p><a href="https://zenodo.org/record/3713271#_ftnref1"><sub>[1]</sub></a><sub> ID of the node Logging node</sub></p> <p><a href="https://zenodo.org/record/3713271#_ftnref2"><sub>[2]</sub></a><sub> ID of the Sender node</sub></p> <p><a href="https://zenodo.org/record/3713271#_ftnref3"><sub>[3]</sub></a><sub> ID of the Receiver node</sub></p> <p><a href="https://zenodo.org/record/3713271#_ftnref4"><sub>[4]</sub></a><sub> Flow type: Unidirectional, Bidirectional or Unidirectional with Multiple Access</sub></p> <p><a href="https://zenodo.org/record/3713271#_ftnref5"><sub>[5]</sub></a><sub> Configurable parameters: Sender/Receiver Transmission Power and Data Rate (when applicable)</sub></p> <p> </p> <p><strong>References</strong></p> <p>1. F. FietKau, “Minstrel_HT: New rate control module for 802.11n [LWN.net]”. Mrt-2010.</p> <p>2. “ns-3: ns3::IdealWifiManager Class Reference,” Jan 2021, [Online; accessed 23. Jun. 2021]. Available: <a href="https://www.nsnam.org/docs/release/3.33/doxygen/classns3_1_1_ideal_wifi%20manager.html">https://www.nsnam.org/docs/release/3.33/doxygen/classns3_1_1_ideal_wifi manager.html</a></p>
Real-Time Adaptation of an Artificial Neural Network for Transfemoral Amputees Using a Powered Prosthesis
<p>This dataset contains the data used in our manuscript titled "Real-Time Adaptation of an Artificial Neural Network for Transfemoral Amputees Using a Powered Prosthesis". Data structure is explained in the README.txt file located at the top-level of the dataset.</p> <p>Please contact corresponding author Richard B. Woodward for any questions.</p>
Figure 2 in Adaptations in wild radish (Raphanus raphanistrum) flowering time, Part 1: Individual-based modeling of a polygenic trait
Figure 2. The relationship between the number of semidominant larger M1 alleles and days to first flower (DFF), needed to model the long-day selections. Note that six smallereffect M2 genes will add another 0 to 18 days to DFF. If each M1 allele added the same amount in the long-day selections, the relationship would be linear.
Figure 1 in Adaptations in wild radish (Raphanus raphanistrum) flowering time, Part 1: Individual-based modeling of a polygenic trait
Figure 1. Goodness of fit of the modeled data (A) when compared with the recorded glasshouse data (B). Graphs show cumulative days to first flowering (DFF) adaptations in Raphanus raphanistrum populations as a result of repeated early and late days to flowering selection. In both graphs, the basal population is the black solid line in the center,the darker lines are the matched generations of early flowering (EF1, EF3, FE4,and EF5; colored orange), and late flowering (LF2 and LF3; colored purple), with and late flowering (LF2 and LF3),with the two unmatched generations (EF2 and LF2) shown in lighter tones.The far-left population (early flowering EF5) displays very little phenotypic variability, whereas the far-right population (late flowering 3) is very diverse.
Movies (II) for "3D Space-Time Adaptive Hybrid Simulations of Magnetosheath High-Speed Jets"
<p>Movies for the aforementioned paper showing magnetic field lines, dynamic pressure and plasma density in global magnetospheric simulations. NW and SW refer to quasi-radial northward and southward IMF. </p> <p>https://doi.org/10.1029/2020JA029035</p>
Data and code for: Predicting rapid adaptation in time from adaptation in space: a 30-year field experiment in marine snails
<p>Scripts and data used in the research study <strong>Predicting rapid adaptation in time from adaptation in space: a 30-year field experiment in marine snails</strong>.</p> <p><a href="https://doi.org/10.1101/2023.09.27.559715" target="_blank" rel="noopener">https://doi.org/10.1101/2023.09.27.559715</a></p>
Figure 2 in Adaptations in wild radish (ROphOnus rOphOnistrum) flowering time, Part 2: Harvest weed seed control shortens flowering by twelve days
Figure 2. Changes in the number of seeds m−2 (A, D, G), the probability of seed capture by harvest weed seed control (HWSC) (B, E, H), and days to first flower (DFF) (C, F, I). (A–C) Varying herbicide efficiencies, in the absence of HWSC, over 30 yr. (D–F) When knockdown herbicide was less effective (-ρ-), when postemergence herbicide was less effective (-○-), and when both herbicides were effective, without HWSC (___) over 20 yr. (G–I) Model used management system P and started with either a smaller seedbank (___) or a larger seedbank (-○-) over 20 yr.
Figure 3 in Adaptations in wild radish (ROphOnus rOphOnistrum) flowering time, Part 2: Harvest weed seed control shortens flowering by twelve days
Figure 3. Changes in the number of seeds m−2 (A, D), the probability of seed capture by harvest weed seed control (HWSC) (B, E), and changes in days to first flower (DFF) (C, F) under weed management system P; HWSC efficacy (up to 20 yr) was increased from 75% (___) to 95% (-○-) (A–C); the level of fruit abscission (up to 20 yr) was changed from low (37%) (-○-) to high (74%) (—) (D–F). Note the variation between replicates was small as long as seed numbers are above 1 m−2; below that level, genetic changes in one or two plants had a more significant effect on the results.
Figure 1 in Adaptations in wild radish (ROphOnus rOphOnistrum) flowering time, Part 2: Harvest weed seed control shortens flowering by twelve days
Figure 1. Showing the number of seeds produced by weeds from each cohort (y axis), dependent on the evolved days to first flower (DFF) (x axis). The peak of each curve indicates the ideal DFF, with earlier cohorts taking longer to flower. (A) The standard farming system, before introduction of harvest weed seed control (HWSC). The range in ideal DFF across the different cohorts is approximately 15 d for Figure 1A. (B–D) How the various weed management systems affect the number of live seeds (avoiding HWSC) for weed management systems E (B), D (C), and P (D). In Figure 1B–D, the range in ideal DFF across the different cohorts is approximately 50 d.
Data from: A reaction norm for flowering time plasticity reveals physiological footprints of maize adaptation
<div> <p>Understanding how plant phenotypes are shaped by their environments is crucial for addressing questions about crop adaptation to new environments. This study investigated the interplay between developmental responses to temperature fluctuations and photoperiod perception in maize that contribute to genotype-by-environment variation in flowering time. We present a physiological reaction norm for flowering time plasticity (PRN-FTP) for studying large collections of genotypes tested in multi-environment trial (MET) networks. Using a new variable for computational envirotyping of sensed photoperiod, it was found that, at high latitudes, different genotypes in the same environment can experience hours-long differences in photoperiod. This emphasizes the importance of considering genotype-specific differences in the experienced environment when investigating plasticity. A statistical framework is introduced for modeling the PRN-FTP as a non-linear response function, with parameters putatively linked to different regulatory modules for flowering time. Applying the PRN-FTP to a sample of global breeding material for maize showed that tropical and temperate maize occupy distinct territories of the trait space for PRN-FTP parameters, supporting that the geographical spread and adaptation of maize was differentially mediated by exogenous and endogenous pathways for flowering time regulation. Our results have implications for understanding crop adaptation and for future crop improvement efforts.</p> </div>
Deep-time convergent evolution in animal communication presented by shared adaptations for coping with noise in lizards and other animals
<p>Convergence in communication appears rare compared to other forms of adaptation. This is puzzling, given communication is acutely dependent on the environment and expected to converge in form when animals communicate in similar habitats. We uncover deep-time convergence in territorial communication between two groups of tropical lizards separated by over 140 million years of evolution: the Southeast Asian Draco and Caribbean Anolis. These groups have repeatedly converged in multiple aspects of display along common environmental gradients. Robot playbacks to free-ranging lizards confirmed the most prominent convergence in display is adaptive, as it improves signal detection. We then provide evidence from a sample of the literature to further show convergent adaptation among highly divergent animal groups is almost certainly widespread in nature. Signal evolution is therefore curbed towards the same set of adaptive solutions, especially when animals are challenged with the problem of communicating effectively in noisy environments.</p>
Supplementary data of the paper 'Adaptive trends of sequence compositional complexity over pandemic time in the SARS CoV 2 coronavirus'
<p>Supplement of the paper<br> "Adaptive trends of sequence compositional complexity over pandemic time in the SARS-CoV-2 coronavirus”<br> During the spread of the COVID-19 pandemic, the SARS-CoV-2 coronavirus underwent mutation and recombination events that altered its genome compositional structure, thus providing an unprecedented opportunity to check an evolutionary process in real time. The mutation rate is known to be lower than expected for neutral evolution, suggesting natural selection and convergent evolution. We begin by summarizing the compositional heterogeneity of each viral genome by computing its Sequence Compositional Complexity (SCC). To analyze the full range of SCC diversity, we select random samples of high quality coronavirus genomes covering the full span of the pandemic. We then search for evolutionary trends that could inform us on the adaptive process of the virus to its human host by computing the phylogenetic ridge regression of SCC against time (i.e., the collection date of each viral isolate). In early samples, we find no statistical support for any trend in SCC values, although the viral genome appears to evolve faster than Brownian Motion (BM) expectation. However, in samples taken after the emergence of high fitness variants, and despite the brief time span elapsed, a driven decreasing trend for SCC and an increasing one for its absolute evolutionary rate are detected, pointing to a role for selection in the evolution of SCC in the coronavirus. We conclude that the higher fitness of variant genomes may have leads to adaptive trends of SCC over pandemic time in the coronavirus.</p> <p>Supplementary files</p> <table> <tbody> <tr> <td> <p><strong>File</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>SupplementaryTables S1-S19.zip</p> </td> <td> <p>Excel supplementary tables: The strain name, the collection date, and the SCC values for each analyzed genome.</p> </td> </tr> <tr> <td>nextstrain_ncov_open_global_timetree.nwk</td> <td>ML phylodynamic tree for the Nextstrain sample</td> </tr> <tr> <td> <p>SupplementaryTable S20.pdf</p> </td> <td> <p>A complete list acknowledging the authors, originating and submitting laboratories of the genetic sequences we used for the analysis of the Nextstrain sample.</p> </td> </tr> <tr> <td>Nextstrain_sample_fasta_3059.zip</td> <td>Nextstrain sample (sequences in Fasta format)</td> </tr> <tr> <td> <p>PhylogeneticTimetrees_NewickFormat.zip</p> </td> <td> <p>Phylogenetic timetrees (Newick format).</p> </td> </tr> </tbody> </table> <p> </p>
Dawidziuk, 2002, C. praedubeli/C. deubeli Biozone, Arctic Canada. 28. Spinograptus spinosus Wood, 1900, L. nilssoni Biozone, EEP, Poland. 29. Spinograptus latespinosus Kozłowska−Dawidziuk, 1997. 30. Spinograptus munchi Eisenack, 1951, C. praedubeli/C. deubeli Biozone, EEP, Poland. 31. Papiliograptus papilio Lenz and Kozłowska−Dawidziuk, 2002, C. praedubeli/C. deubeli Biozone, Arctic Canada. 32. Plectograptus? karlsteinensis Kozłowska−Dawidziuk, Lenz, and Štorch, 2001, C. praedubeli/C. deubeli Biozone, Barrandian. 33. Neogothograptus thorsteinssoni Lenz and Kozłowska−Dawidziuk, 2004, L. progenitor Biozone, Arctic Canada. 34. Neogothograptus alatiformis Lenz and Kozłowska−Dawidziuk, 2004, L. progenitor Biozone, Arctic Canada. 35. Neogothograptus purus Kozłowska−Dawidziuk, 1995, EEP, Poland. 36. Holoretiolites mancki (Münch, 1931). 37. Holoretiolites helenaewitoldi sp. nov., L. progenitor Biozone, EEP, Poland. 38. Plectograptus wimani Eisenack, 1951, N. nilssoni Biozone, Baltic erratic boulder, Poland. 39. Plectograptus robustus Obut and Zaslavskaya, 1983, L. nilssoni Biozone, EEP, Kaliningrad. 40. Plectograptus macilentus Törnquist, 1887, L. scanicus Biozone, Baltic erratic boulder, Poland. 41. Semiplectograptus urbaneki Kozłowska−Dawidziuk, 1995, Cucullograptus hemiaversus/C. aversus Biozone, EEP, Poland. 42. Plectodinemagraptus gracilis Kozłowska−Dawidziuk, 1995, Cucullograptus hemiaversus/C. aversus Biozone, EEP, Poland. Figures adapted from: 1, Melchin (1999); 2, 4–5, Bouček and Münch (1944); 3, holotype photographed by A. Lenz; 6, 9, Bates and Kirk (1992); 7, Bates and Kirk (1997); 8, Štorch (1994); 10–15, 38, 40–42, Kozłowska−Dawidziuk (1995); 16, Kozłowska−Dawidziuk (2001); 17–21, Lenz and Kozłowska−Dawidziuk (2001); 22, 25, Kozłowska−Dawidziuk (1990); 23, 35, photo taken by author; 24, 32, Kozłowska−Dawidziuk et al. (2001); 26, 27, 31, Lenz and Kozłowska−Dawidziuk (2002a); 28, 35, photo taken by author; 29, Kozłowska−Dawidziuk (1997); 30, Kozłowska−Dawidziuk 2002; 36, Kozłowska−Dawidziuk and Lenz (2001); 37, this paper; 39, Obut and Zaslavskaya (1983). Not to scale. Abbreviations: RD, Rhuddanian; SHEIN, Sheinwoodian; GORST, Gorstian; LUDF, Ludfordian. Biozonal scheme after Koren' et al. 1996; Geological time scale by International Commission on Stratigraphy, International Union of Geological Sciences 2004 (www.stratigraphy.org) in Evolution of retiolitid graptolites-a synopsis
Dawidziuk, 2002, C. praedubeli/C. deubeli Biozone, Arctic Canada. 28. Spinograptus spinosus Wood, 1900, L. nilssoni Biozone, EEP, Poland. 29. Spinograptus latespinosus Kozłowska−Dawidziuk, 1997. 30. Spinograptus munchi Eisenack, 1951, C. praedubeli/C. deubeli Biozone, EEP, Poland. 31. Papiliograptus papilio Lenz and Kozłowska−Dawidziuk, 2002, C. praedubeli/C. deubeli Biozone, Arctic Canada. 32. Plectograptus? karlsteinensis Kozłowska−Dawidziuk, Lenz, and Štorch, 2001, C. praedubeli/C. deubeli Biozone, Barrandian. 33. Neogothograptus thorsteinssoni Lenz and Kozłowska−Dawidziuk, 2004, L. progenitor Biozone, Arctic Canada. 34. Neogothograptus alatiformis Lenz and Kozłowska−Dawidziuk, 2004, L. progenitor Biozone, Arctic Canada. 35. Neogothograptus purus Kozłowska−Dawidziuk, 1995, EEP, Poland. 36. Holoretiolites mancki (Münch, 1931). 37. Holoretiolites helenaewitoldi sp. nov., L. progenitor Biozone, EEP, Poland. 38. Plectograptus wimani Eisenack, 1951, N. nilssoni Biozone, Baltic erratic boulder, Poland. 39. Plectograptus robustus Obut and Zaslavskaya, 1983, L. nilssoni Biozone, EEP, Kaliningrad. 40. Plectograptus macilentus Törnquist, 1887, L. scanicus Biozone, Baltic erratic boulder, Poland. 41. Semiplectograptus urbaneki Kozłowska−Dawidziuk, 1995, Cucullograptus hemiaversus/C. aversus Biozone, EEP, Poland. 42. Plectodinemagraptus gracilis Kozłowska−Dawidziuk, 1995, Cucullograptus hemiaversus/C. aversus Biozone, EEP, Poland. Figures adapted from: 1, Melchin (1999); 2, 4–5, Bouček and Münch (1944); 3, holotype photographed by A. Lenz; 6, 9, Bates and Kirk (1992); 7, Bates and Kirk (1997); 8, Štorch (1994); 10–15, 38, 40–42, Kozłowska−Dawidziuk (1995); 16, Kozłowska−Dawidziuk (2001); 17–21, Lenz and Kozłowska−Dawidziuk (2001); 22, 25, Kozłowska−Dawidziuk (1990); 23, 35, photo taken by author; 24, 32, Kozłowska−Dawidziuk et al. (2001); 26, 27, 31, Lenz and Kozłowska−Dawidziuk (2002a); 28, 35, photo taken by author; 29, Kozłowska−Dawidziuk (1997); 30, Kozłowska−Dawidziuk 2002; 36, Kozłowska−Dawidziuk and Lenz (2001); 37, this paper; 39, Obut and Zaslavskaya (1983). Not to scale. Abbreviations: RD, Rhuddanian; SHEIN, Sheinwoodian; GORST, Gorstian; LUDF, Ludfordian. Biozonal scheme after Koren' et al. 1996; Geological time scale by International Commission on Stratigraphy, International Union of Geological Sciences 2004 (www.stratigraphy.org)
Just-in-time Adaptive Intervention Messaging in a Digital Weight Loss Intervention for Young Adults
ClinicalTrials.gov study NCT05625061. IPD Sharing: YES. Countries: 1. Publications: 0.
A transcontinental experiment elucidates (mal)adaptation of a cosmopolitan plant to climate in space and time
Open the record for dataset details and reuse information.
Deep-time convergent evolution in animal communication presented by shared adaptations for coping with noise in lizards and other animals
Open the record for dataset details and reuse information.
Data from: A reaction norm for flowering time plasticity reveals physiological footprints of maize adaptation
Open the record for dataset details and reuse information.
Adaptation time to magnified flood hazards underestimated when derived from tide gauge records [dataset]
<p>This dataset contains the data supporting the manuscript Lambert et al., Adaptation time to magnified flood hazards underestimated when derived from tide gauge records, submitted to Environmental Research Letters.</p> <p>All files contain two main variables:<br> AF is the amplification factor as a function of [station, time, scenario]<br> DT is the doubling time as a function of [station, scenario]</p> <p>Note that these files contain data of all 299 available stations, rather than the subset of 130 presented in the manuscript.</p> <p>Filenames:<br> diss_AWL.nc Average Water Level at dissipative beaches<br> diss_IWL.nc Instantaneous Water Level at dissipative beaches<br> grd_AWL.nc Average Water Level at gentle rocky dikes<br> grd_IWL.nc Instantaneous Water Level at gentle rocky dikes<br> harbour.nc Average and Instantaneous Water Level at harbours, excluding wave contributions<br> srd_AWL.nc Average Water Level at steep rocky dikes<br> srd_IWL.nc Instantaneous Water Level at steep rocky dikes<br> ssb_AWL.nc Average Water Level at steep sandy beaches<br> ssb_IWL.nc Instantaneous Water Level at steep sandy beaches<br> tidegauge.nc Tide gauge-based analysis</p>
Local adaptation from afar: migratory bird populations diverge in the initiation of reproductive timing while wintering in sympatry
<p><span>The initiation of reproduction in many seasonally breeding animals is controlled by photoperiod and tends to be clinal: populations at higher latitudes breed later than those at lower latitudes, often reflecting a higher photoperiodic threshold. Migratory animals presumably time reproduction to match conditions at their breeding grounds at least in part by cues perceived</span> on their wintering grounds<span>. </span>We asked how closely related dark-eyed junco (<i>Junco hyemalis</i>) populations that overwinter in sympatry but breed in allopatry respond to their shared winter environment by comparing early spring indices of readiness to migrate and breed (baseline and elevated testosterone). We measured stable hydrogen isotopes from feathers grown the preceding year and claws grown during winter to estimate breeding and wintering latitudes, respectively. We predicted that if reproductive initiation is adapted to the emergence of resources at their respective breeding destinations, then birds migrating to higher latitudes (slate-colored junco; <i>J. h. hyemalis</i>) should delay breeding as compared to those migrating to lower latitudes (pink-sided junco; <i>J. h. mearnsi</i>) despite a common overwinter environment. We found higher testosterone in pink-sided juncos consistent with earlier reproductive initiation, suggesting local adaptation in reproductive phenology achieved through differential responses to predictive environmental cues.</p>
Adaptation to hand-tapping affects sensory processing of numerosity directly: evidence from reaction times and confidence
<p>Each file contains a matrix called “MatriceRisultati”. Each row of the matrix “MatriceRisultati” is a trial. </p> <p>The columns contain the following information:</p> <ul> <li>1<sup>st</sup>: Number of trial</li> <li>2<sup>nd</sup>: Test numerosity</li> <li>3<sup>rd</sup>: Subject response on numerosity </li> <li>4<sup>th</sup>: Subject response on their confidence level</li> <li>5<sup>th</sup>: Response time</li> <li>6<sup>th</sup>: 0 if the test numerosity< 16; 1 if the test numerosity> 16</li> </ul>
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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
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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
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