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2,288 results for “periodicals”
Data to form periodic lossless ternary seeds of maximum weight (Part 1)
<p>Data to form periodic lossless ternary seeds of maximum weight.<br> <br> Detailed information can be found in the GitHub project (https://github.com/vtman/perlotSeeds). Codes to generate periodic blocks (binary and ternary) can also be found there.<br> <br> Binary seeds can have only two symbols (0 = "do not care" = "_" or 1 = "match" = "#"). The length of a seed is the number of its elements, weight of a seed is the number of its 1-elements. The goal is to find seeds of maximum weight, so they can be used when there are two strings with a given number of mismatches. It is observed that in many cases these seeds of maximum weight have a periodic structure: the same block is repeated multiple times + its remainder. Blocks for binary seeds can be found with the help of the PerFSeeB project (https://github.com/vtman/PerFSeeB). These blocks have the maximum possible weight. <br> <br> In genetics, we have four symbols in sequences (A, C, G, T). However, the chance of having a pointwise mutation is not the same for any pairs. A <strong>transition</strong> mutation (A ↔ G or C ↔ T) is often twice higher than a <strong>transversion</strong> mutation (A ↔ C, A ↔ T, G ↔ C, G ↔ T). Transition-constrained seeds use ternary alphabet {<strong>#</strong>, <strong>@</strong>, <strong>_</strong>} where <strong>@</strong> is for a match or a transition mismatch. To generate ternary seeds, we first need to generate ternary blocks. These ternary blocks can be found when we use binary blocks. However, sometimes, we need to use binary blocks for less than the maximum weight.<br> <br> BinaryDataLevel.zip contains binary blocks (mostly of maximum weight, but 1/5 are for smaller weights (less than one and a couple of blocks than two)). <br> <br> Files T1V1.zip, T1V2.zip,..., and T8V1.zip contain ternary blocks in binary format. T4V2.zip and T7V2.zip are in the other dataset.<br> <br> File bestTernary.zip contains ternary seeds of maximum weight (calculated as the number of # symbols + half of @ symbols)</p>
Data for: Shifts in grasses diversity patterns between two contrasting 40-year climate periods in tropical dry islands
<p><span>Grasses are one of the most successful and dispersed plant families worldwide and their environmental and economic values are widely acknowledged. They dominate the landscape of Cabo Verde, the southernmost and driest archipelago of Macaronesia, and are relevant natural resources for local populations, but a comprehensive evaluation of their distribution patterns is still lacking. In this study, we aim to evaluate the potential effects of climate changes using the long-term data concerning grass distribution in Cabo Verde and the widely recognized climatic variability of this archipelago, which entails a huge irregularity in spatial and temporal rainfall. We identified two contrasting climatic periods (wet, from 1929 to 1968, and dry, from 1969 to 2007) and gathered all the information available from the bibliography, herbaria and fieldwork concerning spontaneous grass species recorded in Cabo Verde during those two periods, which amounted to 107 taxa. This information was then used to disclose the patterns of grass diversity as related to climatic and topographic variables (altitude and windward vs leeward aspects). Different altitudinal shifts in the distribution patterns of grass species assemblages and an assemblage specific to the wet period were revealed by comparing the two climatic periods. The role of exposure in delimiting the altitudinal distribution of the various assemblages was highlighted; the trade winds clearly determine the distribution of grass assemblages. We detected shifts in the distribution of grass assemblages according to the climatic periods (related to the macroclimate) and local topographic factors (associated with mesoclimates). Also, functional traits (i.e., annuals vs. perennials, C3 vs. C4 grasses, and tropical vs. temperate species) were found to vary between wet and dry periods, as well as with altitude and with slope aspect. Understanding species distributions and the role of the climatic variability of Cabo Verde is crucial to predict how climate change will affect them and thus to support effective management and conservation actions.</span></p>
A deepened water table increases the vulnerability of peat mosses to periodic drought
<p>Here we address the combined impact of multiple stressors that are becoming more common with climate change. To study the combined effects of a lower water table (WT) and increased frequency of drought periods on the resistance and resilience of peatlands, we conducted a mesocosm experiment. This study evaluated how the photosynthesis of lawn Sphagnum mosses responds to and recovers from an experimental periodic drought after exposure to the stresses of a deep or deepened WT (naturally dry and 17-year-long water level drawdown in fen and bog environments. We aimed to quantify if deep WTs 1) support acclimation to drought, or 2) increase the base-level physiological stress of mosses, or 3) exacerbate the impact of periodic drought. There was no evidence of acclimation in mosses from drier environments; periodic drought decreased the photosynthesis of all Sphagnum mosses. Water level drawdown decreased the photosynthesis of bog-originating mosses before periodic drought, indicating that these mosses were stressed by the hydrological change. Deep WTs exacerbated Sphagnum vulnerability to periodic drought, indicating that the combination of drying habitats and increasing frequency of periodic drought will lead to a rapid transition in lawn vegetation. Water-retaining traits may increase Sphagnum resilience to periodic drought. Large capitula size was associated with a higher resistance; the bog-originating species studied here lacked large capitula or dense carpet structure and were more vulnerable to drought than the larger fen-originating species. Consequently, lawns in bogs may become threatened. Recovery after rewetting was significant for all mosses, but none completely recovered within three weeks. The most drought-resilient species had fen origin, indicating that fens are less likely to undergo a sudden transition due to periodic drought.</p> <p><strong>Synthesis:</strong> Water level drawdown associated with climate change increases the sensitivity of Sphagnum mosses to periods of drought and moves them closer to their tipping point as species on the edge of their ecological envelope rapidly shut down photosynthesis and recover poorly.</p>
Machine-learning Generated Catalog of Long Period Variables from Palomar Gattini-Infrared Lightcurves
<pre>A catalog of LPVs obtained from a decision tree classifier trained on features extracted from Palomar Gattini-IR J band <br>lightcurves as described in Suresh et. al. 2024 (https://arxiv.org/abs/2402.08000).<br><br>Description of columns<br><br>1. 'Name' - Unique identifier for each LPV; internal designation of the object<br>2. 'RA' - Right ascension<br>3. 'Dec' - Declination<br>4. 'num_detections' - number of detections<br><br>Calculated features:<br>5. 'von_neumann_score' - von-Neumann score <br>6. 'gp_score' - Gaussian process regression fit score<br>7. 'slope_min' - minimum slope of the lightcurve<br>8. 'slope_max' - maximum slope of the lightcurve<br>9. 'ptp' - peak-to-peak amplitude <br>10. 'nflips' - number of flips<br>11. 'max_rate' - maximum point-wise slope <br>12. 'min_rate' - minimum point-wise slope 13. 'lcdur' - observation baseline<br>14. 'per_diff_90_50' - difference between 90th and 50th percentiles of J band magnitude<br>15. 'per_diff_95_50' - difference between 95th and 50th percentiles of J band magnitude <br>16. 'J' - Stetson J index<br>17. 'K_s' - Stetson K index, calculated as single band data<br>18. 'K_m' - Stetson K index, calculated as multi-band data <br>19. 'L_s' - Stetson L index, calculated as single band data 20. 'L_m' - Stetson L index, calculated as multi-band data<br>21. 'phase_chisq' - chi sq of sinusiod fit to phase folded lightcurve<br>22. 'phase_redchi' - reduced chi sq of sinusiod fit to phase folded lightcurve <br>23. 'phase_aic' - akaike information criterion of sinusiod fit to phase folded lightcurve <br>24. 'phase_bic' - bayesian information criterion of sinusiod fit to phase folded lightcurve 25. 'bestperiod' - best fit Lomb-Scargle period <br>26. 'bestperiod_1' - second most likely Lomb-Scargle period<br>27. 'bestperiod_2' - third most likely Lomb-Scargle period<br>28. 'LSscore' - Lomb-Scargle score of best period <br>29. 'LSscore_2' - Lomb-Scargle score corresponding to second best period 30. 'LSscore_3' - Lomb-Scargle score corresponding to third best period <br>31. 'amplitude' - J band amplitude <br>32. 'meanF' - mean predicted value from Lomb-Scargle fit <br>33. 'chi2' - chi sq of sinusoidal fit to lightcurve<br>34. 'redchi2' - reduced chi square of sinusoidal fit to lightcurve<br>35. 'rednullchi2' - reduced chi square of linear fit to lightcurve 36. 'nullchi2' - chi square of linear fit to lightcurve<br>37. 'maxperiod' - maximum period from Lomb-Scargle fit <br>38. 'linear_slope' - slope of linear fit to lightcurve<br>39. 'linear_intercept' - y-intercept of linear fit to lightcurve<br>40. 'J_mag' - mean J band magnitude<br>41. 'LSratio' - ratio of Lomb-Scargle score of two most promiment peaks in <br> Lomb-Scargle periodogram<br>42. 'chi2ratio' - ratio of reduced chi square of sinusoidal fit and reduced <br> chi square of linear fit to lightcurve <br>43. 'period_ratio' - ratio of best fit Lomb-Scargle period and <br> maximum Lomb-Scargle period<br>44. 'prob_lpv_sum' - machine-learning classifier LPV score (obtained by summing probability of LPV and type II LPV)<br><br>Color information: 45. 'w1mpro' - WISE W1 mag<br>46. 'w1sigmpro' - WISE W1 error <br>47. 'w2mpro' - WISE W2 mag<br>48. 'w2sigmpro' - WISE W2 error <br>49. 'w3mpro' - WISE W3 mag<br>50. 'w3sigmpro' - WISE W3 error 51. 'w4mpro' - WISE W4 mag<br>52. 'w4sigmpro' - WISE W4 error<br>53. 'j_m' - 2MASS J mag <br>54. 'j_msigcom' - 2MASS J error<br>55. 'h_m' - 2MASS H mag <br>56. 'h_msigcom' - 2MASS H error<br>57. 'k_m' - 2MASS K mag 58. 'k_msigcom' - 2MASS K error<br>59. 'J-H' - 2MASS J-H<br>60. 'J-K' - 2MASS J-K 61. 'W1-W2' - WISE W1-W2 <br>62. 'W3-W4' - WISE W3-W4 <br>63. 'W1-W4' - WISE W1-W4</pre>
Incoherent phenomena in anisotropic periodic structures: from modeling to experimental demonstration
<p>Experimental measurement of difracted light Fig. 9 (subplot a consists of Grating_520_90_740_medium, columns 14 and 16) and Fig. 10 (reflection and transmission spectra of diffraction order generated by attached Python code) in the article Incoherent phenomena in anisotropic periodic<br>structures: from modeling to experimental demonstration.</p>
CDIPS Light Curves from the paper "Confirming the Tidal Tails of the Young Open Cluster Blanco 1 with TESS Rotation Periods"
<p>This dataset contains the CDIPS light curves used in the paper "Confirming the Tidal Tails of the Young Open Cluster Blanco 1 with TESS Rotation Periods". The abstract of the original paper is as follows.</p> <p>Blanco 1 is an ≈ 130 Myr open cluster located 240 pc from the Sun below the Galactic plane. Recent studies have reported the existence of diffuse tidal tails extending 50–60 pc from the cluster center, based on the positions and velocities measured by Gaia. To independently assess the reality and extent of this structure, we used light curves generated from TESS full-frame images to search for photometric rotation periods of stars in and around Blanco 1. We detected rotation periods down to a stellar effective temperature of ≈ 3100 K in 347 of the 603 cluster member candidates for which we have light curves. For cluster members in the core and candidate members in the tidal tails, both within a temperature range of 4400 to 6200 K, 74% and 72% of the rotation periods are consistent with the single-star gyrochronological sequence, respectively. In contrast, a comparison sample of field stars yielded gyrochrone-consistent rotation periods for only 8.5% of stars. The tidal tail candidates' overall conformance to the core members' gyrochrone sequence implies that their contamination ratio is consistent with zero and < 0.33 at the 2σ level. This result confirms the existence of Blanco 1 tidal tails and doubles the number of Blanco 1 members for which there are both spatio-kinematic and rotation-based cluster membership verification. Extending the strategy of using TESS light curves for gyrochronology to other nearby young open clusters and stellar associations may provide a viable strategy for mapping out their dissolution and broadening the search for young exoplanets.</p>
Accurate and Robust Stellar Rotation Periods catalog for 82771 Kepler stars using deep learning
<p>This repository is for the paper "Rotation Period for 83022 Kepler Stars: A Deep Learning Approach" by I. Kamai and H. B. Perets. It is associated with manuscript number AAS56501. It consists a frozen repository and the published catalog</p>
Radiation Impact of the Halloween GLE Events During the October to November 2003 Period - Data
<p>Computed data that was used within the "Radiation Impact of the Halloween GLE Events During the October to November 2003 Period" paper. The repository has data for GLEs 65-67 and contains the planetary cut-off maps, SEP spectra information, Forbush reconstruction information, interpolated CRAC:DOMO effective dose yield functions, and planetary dose computations. Some animated gifs are included to simply convey the change in dose over each GLE. </p>
Age Dependence of the Occurrence and Architecture of Ultra-Short-Period Planet Systems (public data release)
<p>This file includes key data and column definitions for generating figures in the manuscript. The data were collected and processed from publicly available datasets, including Kepler DR25 and the planetary systems table (https://exoplanetarchive.ipac.caltech.edu/), LAMOST DR9 (https://www.lamost.org/), Gaia DR3 (https://gea.esac.esa.int/archive/), and The California-Kepler Survey (https://california-planet-search.github.io/cks-website/). Additional data required for the manuscript can also be obtained from these publicly available datasets.</p>
Simulations for convection during the intensive observation period of the TWP-ICE field campaign: results from cloud-resolving model and convective parameterization schemes
<p>Simulated convections from cloud-resolving model and convective parameterization schemes during the intensive observation period of the TWP-ICE field campaign, which are used to investigate the scale-awareness problem of convective parameterization schemes. The corresponded observations are also included in this dataset.</p>
FORSITE-Clim Europe: European-wide climate indicators for historical periods and climate projections at high resolution
<h2>Overview</h2> <p>This meteorological data set consists of climatologies (climate indicators) on 30-year average basis for Europe and covers two historical periods as well as two periods for three selected climate scenarios with a high spatial resolution of less than 1 km. The two 30-year periods provided for the observations allow the analysis of the climate change that has already happened. </p> <p><strong>Resolution</strong>: 30x30 arcsec<br><strong>Projection</strong>: EPSG 4326<br><strong>Extent for historical data</strong>: 10.67°W – 47.67°E, 33.68°N – 71.33°N<br><strong>Extent for scenario data</strong>: 10.67°W – <em>39.33°E</em>, 33.68°N – 71.33°N<br><strong>Periods for historical data</strong>: 1961-1990 and 1991-2020<br><strong>Periods for scenario data</strong>: 2036-2065 and 2071-2100<br><strong>Format:</strong> GeoTIFF</p> <p><strong>List of climatologies (climate indicators) </strong> </p> <table> <tbody> <tr> <td> <p><strong>#</strong></p> </td> <td> <p><strong>Short name</strong></p> </td> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> <td> <p><strong>Yearly (Y) or monthly (M)<br></strong></p> </td> </tr> <tr> <td> <p><em>1</em></p> </td> <td> <p>tasmin</p> </td> <td> <p>Average daily minimum temperature</p> </td> <td> <p>Arithmetic mean</p> </td> <td> <p>°C</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>2</em></p> </td> <td> <p>tasmax</p> </td> <td> <p>Average daily maximum temperature</p> </td> <td> <p>Arithmetic mean</p> </td> <td> <p>°C</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>3</em></p> </td> <td> <p>tas</p> </td> <td> <p>Average temperature</p> </td> <td> <p>Arithmetic mean</p> </td> <td> <p>°C</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>4</em></p> </td> <td> <p>tas_warmest_month</p> </td> <td> <p>Average temperature mean in the warmest month</p> </td> <td> <p>Calculation of the mean temperature over the climate period for all months and then selection of the highest value for the warmest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>5</em></p> </td> <td> <p>tas_coldest_month</p> </td> <td> <p>Average temperature mean in the coldest month</p> </td> <td> <p>Calculation of the mean temperature over the climate period for all months and then selection of the lowest value for the coldest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>6</em></p> </td> <td> <p>tasmin_coldest_month</p> </td> <td> <p>Average temperature minimum in the coldest month</p> </td> <td> <p>Calculation of the mean minimum temperature over the climate period for all months and then selection of the lowest value for the coldest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>7</em></p> </td> <td> <p>tasmax_warmest_month</p> </td> <td> <p>Average temperature maximum in the warmest month</p> </td> <td> <p>Calculation of the mean maximum temperature over the climate period for all months and then selection of the highest value for the warmest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>10</em></p> </td> <td> <p>GSL</p> </td> <td> <p>Average length of the growing season</p> </td> <td> <p>The growing season is the duration in days of the longest continuous period of days with an average temperature of at least 5°C. However, an earlier or later period of such warm days is included in the growing season if it lasts longer than the sum of all intervening cooler days</p> </td> <td> <p>days</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>12</em></p> </td> <td> <p>GDD</p> </td> <td> <p>Average Growing Degree Days per year above 5°C</p> </td> <td> <p>Σ(Tmean – 5°C) per year. </p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>13</em></p> </td> <td> <p>FD_first</p> </td> <td> <p>Average date of the first frost occurrence</p> </td> <td> <p>Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate</p> </td> <td> <p>day of year</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>14</em></p> </td> <td> <p>FD_last</p> </td> <td> <p>Average date of the last frost occurrence</p> </td> <td> <p>Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate</p> </td> <td> <p>day of year</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>20</em></p> </td> <td> <p>GLO_hori</p> </td> <td> <p>Average sum of global radiation</p> </td> <td> <p> </p> </td> <td> <p>kWh</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>33</em></p> </td> <td> <p>pr</p> </td> <td> <p>Average precipitation sum</p> </td> <td> <p> </p> </td> <td> <p>mm</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>39</em></p> </td> <td> <p>ET0</p> </td> <td> <p>Average annual potential evapotranspiration</p> </td> <td> <p>Calculation according to FAO Penman-Monteith: fao.org/3/X0490E/x0490e08.htm</p> </td> <td> <p>mm</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>40</em></p> </td> <td> <p>WBAL</p> </td> <td> <p>Average climatic water balance</p> </td> <td> <p>Precipitation minus potential evapotranspiration</p> </td> <td> <p>mm</p> </td> <td> <p>Y, M</p> </td> </tr> </tbody> </table> <h2>Data sources</h2> <p>The raw historical data is a combination or extension of daily CHELSA (Climatologies at high resolution for the earth’s land surface areas) with ERA5-Land to fully cover 1961-2020. </p> <ul> <li>CHELSA-W5E5 v1.0 (https://doi.org/10.5194/essd-15-2445-2023) for daily variables precipitation (pr), global radiation (rsds), mean temperature (tas), maximum temperature (tasmax) and minimum temperature (tasmin) for the period 1979-2016</li> <li>CHELSA V2.1 for climatological average monthly wind speed (sfcWind_01, ..., sfcWind_12) and for climatological mean vapor pressure deficit (vpd_01, ..., vpd_12)</li> <li>ERA5-Land for daily variables precipitation (pr), global radiation (rsds), mean temperature (tas), dew point (tds), and wind speed (sfcWind) for the period 1961-2020</li> <li><em>v2.0: WorldClim version 2.1 for climatological monthly minimum, maximum, and average temperatures.</em></li> </ul> <p><strong>Climate models from EURO-CORDEX </strong>(doi.org/10.1007/s10113-013-0499-2<strong>)</strong>:</p> <ul> <li>MPI-M-MPI-ESM-LR_rcp45_r1i1p1_CLMcom-CCLM4-8-17</li> <li>MPI-M-MPI-ESM-LR_rcp85_r1i1p1_CLMcom-CCLM4-8-17</li> <li>ICHEC-EC-EARTH_rcp85_r12i1p1_SMHI-RCA4</li> </ul>
Raw data and scripts for "The Period-Modulated Harmonic Locked Loop (PM-HLL): A low-effort algorithm for rapid time-domain multi-periodicity estimation"
<p>This package contains all required scripts to generate the simulations and figures from the study "The Period-Modulated Harmonic Locked Loop (PM-HLL): A low-effort algorithm for rapid time-domain multi-periodicity estimation" by Volker Hohmann, published in Acta Acustica:</p> <p>The Period-Modulated Harmonic Locked Loop (PM-HLL): A low-effort algorithm for rapid time-domain multi-periodicity estimation<br>Volker Hohmann<br>Acta Acust. 5 56 (2021)<br>DOI: 10.1051/aacus/2021050</p> <p>When referring to this work, please cite the journal paper.</p> <p>Note that additive noise is generated at random, i.e., small differences in the estimation accuracy occur when repeating a simulation. For further details see the journal paper.</p> <p>Thank you for downloading the package. Your comments are very welcome!</p> <p>Method patented: DE Patent DE102021207339B3</p> <p>Author: Volker Hohmann, Carl von Ossietzky Universität Oldenburg, Germany</p>
Regularized quantum periods for two-dimensional Fano manifolds
<p><strong>The database smooth_fano_2</strong></p> <p>This is a database of regularized quantum periods for two-dimensional Fano manifolds. There are ten entries in the database.</p> <p>Each entry in the database is a key-value record with keys and values as described in the paper:</p> <p><em>Databases of Quantum Periods for Fano Manifolds</em>, Tom Coates and Alexander M. Kasprzyk, 2021.</p> <p>If you make use of this data, please cite the above paper and the DOI for this data:</p> <p>doi:10.5281/zenodo.5708232</p>
Regularized quantum periods for three-dimensional Fano manifolds
<p><strong>The database smooth_fano_3</strong></p> <p>This is a database of regularized quantum periods for three-dimensional Fano manifolds. There are 105 entries in the database.</p> <p>Each entry in the database is a key-value record with keys and values as described in the paper:</p> <p><em>Databases of Quantum Periods for Fano Manifolds</em>, Tom Coates and Alexander M. Kasprzyk, 2021.</p> <p>If you make use of this data, please cite the above paper and the DOI for this data:</p> <p>doi:10.5281/zenodo.5708272</p>
Abb. P 11 in Dokumente zum wissenschaftlichen Opus von Ulrike Aspöck für die Periode 2011-2016 anlässlich ihres 75. Geburtstags
Abb. P 11: Publikationen von Ulrike Aspöck: 281 (2015), 282 (2015), 283 (2015).
Abb. P 10 in Dokumente zum wissenschaftlichen Opus von Ulrike Aspöck für die Periode 2011-2016 anlässlich ihres 75. Geburtstags
Abb. P 10: Publikationen von Ulrike Aspöck: 278 (2015), 279 (2015), 280 (2015).
Abb. P 9 in Dokumente zum wissenschaftlichen Opus von Ulrike Aspöck für die Periode 2011-2016 anlässlich ihres 75. Geburtstags
Abb. P 9: Publikationen von Ulrike Aspöck: 275 (2014), 276 (2014), 277 (2014).
Abb. P 13 in Dokumente zum wissenschaftlichen Opus von Ulrike Aspöck für die Periode 2011-2016 anlässlich ihres 75. Geburtstags
Abb. P 13: Publikationen von Ulrike Aspöck: 287 (2016).
Abb. P 7 in Dokumente zum wissenschaftlichen Opus von Ulrike Aspöck für die Periode 2011-2016 anlässlich ihres 75. Geburtstags
Abb. P 7: Publikationen von Ulrike Aspöck: 270 (2014), 271 (2014), 272 (2014).
Abb. P 8 in Dokumente zum wissenschaftlichen Opus von Ulrike Aspöck für die Periode 2011-2016 anlässlich ihres 75. Geburtstags
Abb. P 8: Publikationen von Ulrike Aspöck: 273 (2014), 274 (2014).
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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.