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Dataset results
13 results for “Band Selection”
Global and tropical band averages for a selection of CMIP5 and CMIP6 models: piControl and abrupt-4xCO2 experiments
<p>This dataset provides post-processed spatial averages for a selection of CMIP5 and CMIP6 models. The experiments contained in this dataset are only the pre-industrial controls (piControl) and the experiments with a four-fold increase in the atmospheric CO$_{2}$ concentration in relation to the pre-industrial level (abrupt-4xCO2). The spatial averages are global and tropical bands from x°S to x°N, where the x value is between 5 and 40 in increments of 5°. This dataset was created to study climate sensitivity in general and the effect of stratospheric circulation changes on the tropical equilibrium climate sensitivity. It contains the following variables:</p> <ul> <li>incoming (d) short-wave (SW, s) radiative flux (RF, r) at the top of the atmosphere (TOA, t): rsdt</li> <li>outgoing (u) SW RF at TOA: rsut</li> <li>outgoing long-wave (LW, l) RF at TOA: rlut</li> <li>net (n) RF at TOA: rnt</li> <li>incoming SW RF at the surface (s): rsds</li> <li>outgoing SW RF at the surface: rsus</li> <li>incoming LW RF at the surface: rlds</li> <li>outgoing LW RF at the surface: rlus</li> <li>net RF at the surface: rns</li> <li>sensible heat flux (hfs) at the surface: hfss</li> <li>latent heat flux (hfl) at the surface: hfls</li> <li>surface temperature (t): ts</li> <li>atmospheric temperature: ta</li> <li>specific humidity: hus</li> <li>zonal component of wind: ua</li> <li>meridional component of wind: va</li> <li>lagrangian tendency of pressure (vertical component of wind in pressure per time dimensions): wap</li> <li>surface pressure: ps</li> <li>geopotential height: zg</li> </ul>
Global and tropical band averages for a selection of CMIP5 and CMIP6 models: piControl and abrupt-4xCO2 experiments (compressed)
<p>This dataset provides post-processed spatial averages for a selection of 55 CMIP5 and CMIP6 models. The experiments contained in this dataset are only the pre-industrial controls (piControl) and the experiments with a four-fold increase in the atmospheric CO$_{2}$ concentration in relation to the pre-industrial level (abrupt-4xCO2). The spatial averages are global and tropical bands from x°S to x°N, where the x value is between 5 and 40 in increments of 5°. This dataset was created to study climate sensitivity in general and the effect of stratospheric circulation changes on the tropical equilibrium climate sensitivity. It contains the following variables:</p> <ul> <li>incoming (d) short-wave (SW, s) radiative flux (RF, r) at the top of the atmosphere (TOA, t): rsdt</li> <li>outgoing (u) SW RF at TOA: rsut</li> <li>outgoing long-wave (LW, l) RF at TOA: rlut</li> <li>net (n) RF at TOA: rnt*</li> <li>incoming SW RF at the surface (s): rsds</li> <li>outgoing SW RF at the surface: rsus</li> <li>incoming LW RF at the surface: rlds</li> <li>outgoing LW RF at the surface: rlus</li> <li>net RF at the surface: rns*</li> <li>sensible heat flux (hfs) at the surface: hfss</li> <li>latent heat flux (hfl) at the surface: hfls</li> <li>surface temperature (t): ts</li> <li>atmospheric temperature: ta</li> <li>specific humidity: hus</li> <li>zonal component of wind: ua</li> <li>meridional component of wind: va</li> <li>lagrangian tendency of pressure (vertical component of wind in pressure per time dimensions): wap</li> <li>surface pressure: ps</li> <li>geopotential height: zg</li> </ul> <p>*rnt and rns were calculated for the creation of this dataset using the model output rlut rsut, rsds, rsus, rlds, rlus.</p> <p>This version updates the previous version by providing the dataset in a compressed tarball. Use `tar -xzvf CMIP_data.tar.gz` to extract and decompress.</p>
EEG Dataset for 'Decoding of selective attention to continuous speech from the human auditory brainstem response' and 'Neural Speech Tracking in the Theta and in the Delta Frequency Band Differentially Encode Clarity and Comprehension of Speech in Noise'.
<p>The repository contains the unprocessed EEG data recorded for the publications [1, 2]. For convenience, the onsets of the EEG data provided here are time-aligned with the onsets of the audio books in the 'audiobooks' folder, and the EEG data are provided in HDF5 format. Please refer to the original version of this dataset for more details.</p> <p>More details, as well as the original data files, are available at the original repository <a href="https://doi.org/10.5281/zenodo.7086209">here</a>.</p> <p>Examples of using these data (preprocessing, fitting linear models) can be found <a href="https://github.com/Mike-boop/trf-examples">here</a>.</p> <p>The English conditions (clean, lb, mb, hb, fM, fW) comprised a single recording session. The Dutch conditions (cleanDutch, lbDutch, mbDutch, hbDutch) comprised a separate recording session. You see which participants took part in each session in session_info.json.</p> <p>Please note some details about the stimulus presentation for the various listening conditions:</p> <ul> <li>English speech-in-babble-noise (lb, mb, hb): babble noise was played by itself for one second before the audiobook track began. The babble noise was also played for one second after the audiobook track ended. Therefore, you should discard the first second and the last second from these trial during your analysis.</li> <li>Dutch speech-in-babble-noise (lbDutch, mbDutch, hbDutch): the story (narrated in Dutch) was played by itself for one second before the babble noise track began. Then, the babble noise was increased linearly in amplitude for one second. Therefore, you should discard the first two seconds from these trials during your analysis.</li> <li>Dutch in quiet, and Dutch-in-babble-noise (cleanDutch, lbDutch, mbDutch, hbDutch): some English sentences were embedded in the Dutch narratives in order to encourage attention. You should crop these from your analysis. The onsets and offsets of the English sentences (in samples, at 44100Hz) are provided in the audiobooks/*Dutch/english_onsets_info.json files.</li> <li>Competing-speakers conditions (fM, fW): sometimes the attended track is longer than the unattended track, or vice-versa. The onsets of both tracks are aligned. You should crop the trial to the length of the shortest track for your analysis.</li> </ul> <p>If you use this data, please cite the original publications, as well as this repository [1,2,3].</p> <p>[1] Etard O, Kegler M, Braiman C, Forte A E and Reichenbach T. “Decoding of selective attention to continuous speech from the human auditory brainstem response” 2019. <em>NeuroImage</em> <strong>200</strong> 1–11</p> <p>[2] Etard O and Reichenbach T. “Neural speech tracking in the theta and in the delta frequency band differentially encode clarity and comprehension of speech in noise” 2019. <em>J. Neurosci.</em> <strong>39</strong> 5750–9</p> <p>[3] Etard O and Reichenbach T. "EEG Dataset for 'Decoding of selective attention to continuous speech from the human auditory brainstem response' and 'Neural Speech Tracking in the Theta and in the Delta Frequency Band Differentially Encode Clarity and Comprehension of Speech in Noise". Doi: 10.5281/zenodo.7086208</p>
Selective dynamic band gap tuning in metamaterials using graded photoresponsive resonator arrays
<p>Raw Data for figures:</p> <p>Fig. 2: Dispersion diagrams for non-illuminated (off) and illuminated (on) pillars of different heights (hp). hp1 = 7 mm, hp2 = 9 mm, hp3 = 11 mm, hp4 = 13 mm; p = 0 (1) for purely in- (out-of-plane) behavior</p> <p>Fig. 4: Computed transmission spectrum of a finite structure. a) Numerically simulated transmission spectrum for the considered 8-pillar specimen, both without ("Laser off") and with laser illumination ("Laser on 7th pillar").</p> <p>Fig. 5: Transmission spectrum of the finite structure considered experimentally. a) Measured spectra before (blue) and after (red) illumination of pillar 1. Band gaps are highlighted in light blue and numbered from I to IV; b) Corresponding colour map representing transmission vs. frequency and time (vertical axis) when switching laser illumination on (t = 700 s) and off (t = 2300 s); c) same as a), with illumination of pillar 6; d) same as b), with illumination on pillar 6.</p> <p>Fig. 6: Dynamic modulation of signal frequencies (f1 = 21.5 kHz, f2 = 71.5 kHz) in a graded pillar structure. The different temporal intervals depict tunable suppression and enhancement of specific frequencies through selective pillar illumination.</p>
Banding and recovery data on several game bird species to study hunting selectivity
<p><span>Selective hunting has various impacts that need to be considered for the conservation and management of harvested populations. The consequences of selective harvest have mostly been studied in trophy hunting and fishing, where selection of specific phenotypes is intentional. Recent studies however show that selection can also occur unintentionally.</span> <span>With at least 52 million birds harvested each year in Europe, it is particularly relevant to evaluate the selectivity of hunting on this taxon. Here we considered </span><span>211 806</span><span> individuals belonging to 7 hunted bird species to study unintentional selectivity in harvest. Using linear mixed models, we compared morphological traits (mass, wing and tarsus size) and body condition at the time of banding between birds that were subsequently recovered from hunting during the same year as their banding, and birds that were not recovered. We did not find any patterns showing systematic differences between recovery categories, among our model species, for the traits we studied. Moreover, when a difference existed between recovery categories, it was so small that its biological relevance can be challenged. Hunting of birds in Europe therefore does not show any form of strong selectivity on the morphological and physiological traits that we studied, and should hence not lead to any change of these traits either by plastic or evolutionary response. </span></p>
Banding and recovery data on several game bird species to study hunting selectivity
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Data from: Top-down selection of visual working memory contents is supported by alpha-band phase-synchronized oscillatory networks
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Negative frequency dependent selection maintains shell banding polymorphisms in two marine snails (Littorina fabalis and L. saxatilis)
<p>The presence of shell bands is common in gastropods. The marine snails, <i>Littorina fabalis</i> and <i>L. saxatilis</i>, are<i> </i>both polymorphic for this trait. Such polymorphism would be expected to be lost by the action of genetic drift or directional selection, but it appears to be widespread at relatively constant frequencies. This suggests it is maintained by balancing selection on the trait or on a genetically linked trait. Using long time-series of empirical data, we compared potential effects of genetic drift and negative frequency-dependent selection, in the two species. The contribution of genetic drift to changes in the frequency of bands in <i>L. fabalis</i> was estimated using the effective population size estimated from microsatellite data, while the effect of genetic drift in <i>L. saxatilis</i> were derived from previously published study. Frequency-dependent selection was assessed comparing the cross-product estimator of fitness with the frequency of the polymorphism across years using a regression analysis. Both studied species showed patterns of negative frequency-dependent selection. In addition, in <i>L. fabalis</i>, contributions from genetic drift could explain some of the changes in banding frequency. Overdominance and heterogeneous selection did not fit well to our data. The possible biological explanations resulting on the maintenance of the banding polymorphism are discussed.</p>
Emergent flat band and topological Kondo semimetal driven by orbital-selective correlations
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Negative frequency dependent selection maintains shell banding polymorphisms in two marine snails (Littorina fabalis and L. saxatilis)
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Crab long-term (15 year time span) lightcurve extracted froma sample of 50 randomly selected ScWs in 30-100 keV band, usingOSA10.2.
<p><a href="https://www.astro.unige.ch/cdci/astrooda_?src_name=Crab&RA=83.633080&DEC=22.014500&E1_keV=30&E2_keV=100&T1=2003-03-15T23:27:40.0&T2=2018-03-16T00:03:15.0&T_format=isot&catalog_selected_objects=1,2,3,4&detection_threshold=7.5&instrument=isgri&osa_version=OSA10.2&product_type=isgri_lc&time_bin=10000&time_bin_format=sec&query_status=ready&query_type=Real&radius=15&use_scws=no&selected_catalog=%7B%22cat_column_descr%22:%5B%5B%22meta_ID%22,%22%3Ci8%22%5D,%5B%22src_names%22,%22%7CS18%22%5D,%5B%22significance%22,%22%3Ef4%22%5D,%5B%22ra%22,%22%3Ef4%22%5D,%5B%22dec%22,%22%3Ef4%22%5D,%5B%22NEW_SOURCE%22,%22%3Ei2%22%5D,%5B%22ISGRI_FLAG%22,%22%3Ci8%22%5D,%5B%22FLAG%22,%22%3Ci8%22%5D,%5B%22ERR_RAD%22,%22%3Cf8%22%5D%5D,%22cat_column_list%22:%5B%5B1,2,3,4%5D,%5B%221A+0535%2B262%22,%224U+0517%2B17%22,%22Crab%22,%22H+0614%2B091%22%5D,%5B116.77162170410156,7.909553527832031,1232.6466064453125,8.148767471313477%5D,%5B84.72620391845703,77.66708374023438,83.63129425048828,94.25425720214844%5D,%5B26.314136505126953,16.48775863647461,22.01542854309082,9.165886878967285%5D,%5B-32768,-32768,-32768,-32768%5D,%5B2,2,2,2%5D,%5B0,0,0,0%5D,%5B0.0002800000074785203,0.0002800000074785203,0.0002800000074785203,0.0002800000074785203%5D%5D,%22cat_column_names%22:%5B%22meta_ID%22,%22src_names%22,%22significance%22,%22ra%22,%22dec%22,%22NEW_SOURCE%22,%22ISGRI_FLAG%22,%22FLAG%22,%22ERR_RAD%22%5D,%22cat_coord_units%22:%22deg%22,%22cat_frame%22:%22fk5%22,%22cat_lat_name%22:%22dec%22,%22cat_lon_name%22:%22ra%22%7D">https://www.astro.unige.ch/cdci/astrooda_?src_name=Crab&RA=83.633080&DEC=22.014500&E1_keV=30&E2_keV=100&T1=2003-03-15T23:27:40.0&T2=2018-03-16T00:03:15.0&T_format=isot&catalog_selected_objects=1,2,3,4&detection_threshold=7.5&instrument=isgri&osa_version=OSA10.2&product_type=isgri_lc&time_bin=10000&time_bin_format=sec&query_status=ready&query_type=Real&radius=15&use_scws=no&selected_catalog={%22cat_column_descr%22:[[%22meta_ID%22,%22%3Ci8%22],[%22src_names%22,%22|S18%22],[%22significance%22,%22%3Ef4%22],[%22ra%22,%22%3Ef4%22],[%22dec%22,%22%3Ef4%22],[%22NEW_SOURCE%22,%22%3Ei2%22],[%22ISGRI_FLAG%22,%22%3Ci8%22],[%22FLAG%22,%22%3Ci8%22],[%22ERR_RAD%22,%22%3Cf8%22]],%22cat_column_list%22:[[1,2,3,4],[%221A+0535%2B262%22,%224U+0517%2B17%22,%22Crab%22,%22H+0614%2B091%22],[116.77162170410156,7.909553527832031,1232.6466064453125,8.148767471313477],[84.72620391845703,77.66708374023438,83.63129425048828,94.25425720214844],[26.314136505126953,16.48775863647461,22.01542854309082,9.165886878967285],[-32768,-32768,-32768,-32768],[2,2,2,2],[0,0,0,0],[0.0002800000074785203,0.0002800000074785203,0.0002800000074785203,0.0002800000074785203]],%22cat_column_names%22:[%22meta_ID%22,%22src_names%22,%22significance%22,%22ra%22,%22dec%22,%22NEW_SOURCE%22,%22ISGRI_FLAG%22,%22FLAG%22,%22ERR_RAD%22],%22cat_coord_units%22:%22deg%22,%22cat_frame%22:%22fk5%22,%22cat_lat_name%22:%22dec%22,%22cat_lon_name%22:%22ra%22}</a></p>
Figures of a dual-polarized frequency selective rasorber withwide transmission band
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CANDELS H-Band Selected Chandra Source Catalog
Improving the capabilities of detecting faint X-ray sources is fundamental to increase the statistics on faint high-z AGN and star-forming galaxies. The authors performed a simultaneous maximum likelihood point-spread function (PSF) fit in the 0.5-2 keV and 2-7 keV energy bands of the 4 Ms Chandra Deep Field South (CDFS) data at the position of the 34,930 CANDELS H-band selected galaxies. For each detected source, they provide X-ray photometry and optical counterpart validation. The authors validated this technique by means of a ray-tracing simulation, and detected a total of 698 X-ray point-sources with a likelihood L > 4.98 (i.e.> 2.7sigma). They show that the prior knowledge of a deep sample of Optical-NIR galaxies leads to a significant increase of the detection of faint (i.e. ~ 10<sup>-17</sup> erg s<sup>-1</sup> cm<sup>-2</sup> in the 0.5-2 keV band) sources with respect to "blind" X-ray detections. By including previous catalogs, this work increases the total number of X-ray sources detected in the 4 Ms CDFS, CANDELS area to 793, which represents the largest sample of extremely faint X-ray sources assembled to date. These results suggest that a large fraction of the optical counterparts of our X-ray sources determined by likelihood ratio actually coincides with the priors used for the source detection. Most of the newly detected sources are likely star-forming galaxies or faint absorbed AGN. The authors identified a few sources with putative photometric redshift z > 4. Despite the low number statistics, this sample significantly increases the number of X-ray selected candidate high-z AGN. The 4-Ms CDFS consists of 23 observations described in Table 1 of Luo et al. (2008, ApJS, 179, 19) plus 31 other pointings described in Xue et al. (2011, ApJS, 195, 10, hereafter X11) for a total exposure of ~4 Ms. For the purpose of this paper, the authors employed only observations taken with a focal temperature of <= -120 C, since at higher temperatures the background cannot be modeled with their technique. This table was created by the HEASARC in July 2016 based on <a href="https://cdsarc.cds.unistra.fr/ftp/cats/J/ApJ/823/95">CDS Catalog J/ApJ/823/95</a> file catalog.dat. Some of the values for the name parameter in the HEASARC's implementation of this table were corrected in April 2018. This is a service provided by NASA HEASARC .
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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)
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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.