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4,356 results for “RED”
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>
Species distribution and abundance modelling with dynamicSDM: a case study analysis of the red-billed quelea (Quelea quelea).
<p><strong>GBIF_all_aves_2000_2020.csv</strong><br> A dataset containing e-Bird sampling events for all bird species across southern Africa between 2000-2020 (Fink et al., 2021, GBIF, 2021). <br> <br> Fink, D., T. Auer, A. Johnston, M. Strimas-Mackey, O. Robinson, S. Ligocki, W. Hochachka, L. Jaromczyk, C. Wood, I. Davies, M. Iliff, L. Seitz. 2021. eBird Status and Trends, Data Version: 2020; Released: 2021. Cornell Lab of Ornithology, Ithaca, New York. \doi{10.2173/ebirdst.2020}<br> GBIF.org (12 July 2021) GBIF Occurrence Download \doi{10.15468/dl.ppcu6q}</p> <p><strong>RBQ_full_analysis.R</strong></p> <p>An R script for the generation of dynamic species distribution and abundance models for nomadic bird, the red-billed quelea (<em>Quelea quelea</em>) using dynamicSDM package functions. </p> <p><strong>Unfiltered_quelea_occurrence.csv</strong><br> A dataset containing species occurrence and abundance records for the bird species, the red-billed quelea (<em>Quelea quelea</em>) between 1976-2021 (GBIF 2021 & GBIF 2022 & sources listed in Table 1). <br> <br> GBIF.org (12 July 2021) GBIF Occurrence Download \doi{10.15468/dl.ppcu6q}<br> <br> GBIF.org (25 July 2022) GBIF Occurrence Download \doi{10.15468/dl.k2kftv}<br> </p> <p><strong>Table S1. </strong>Red-billed quelea (<em>Quelea quelea</em>) occurrence and abundance data sources.</p> <table align="left"> <tbody> <tr> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Sources</strong></p> </td> </tr> <tr> <td> <p><strong>Control operation </strong></p> </td> <td> <ul> <li>Information Core for Southern African Migrant Pests (ICOSAMP, 2001-2005).</li> <li>Centre for Overseas Pest Research (COPR), Natural History Museum, Tring.</li> <li>Botswana Ministries of Agriculture.</li> <li>Mozambique Ministry of Agriculture</li> </ul> </td> </tr> <tr> <td> <p><strong>Citizen science</strong></p> </td> <td> <ul> <li>Global Biodiversity Information Facility, including iNaturalist, eBird, South Africa Bird Atlas Project (SABAP) and South Africa Bird Ringing Unit (SAFRING) sources.</li> </ul> </td> </tr> <tr> <td> <p><strong>Independent research</strong></p> </td> <td> <ul> <li>EXCEL File "NfA-yearposRAC" (unpublished data set complied by R. A. Cheke, 2010).</li> </ul> </td> </tr> </tbody> </table>
Red Blood Cell RedTell Dataset
<p>The dataset contains microscopic images of red blood cells (RBCs) and consists of two parts:</p> <p>1. dse_data.zip</p> <p>This dataset includes brightfield images of healthy control individuals obtained in the scope of the CoMMiTMenT study. The images are annotated on a single cell level for segmentation and classification (discocyte-stomatocyte-echinocyte sequence) purposes. </p> <p>2. anemia_data.zip</p> <p>This dataset includes brightfield and fluorescent images of RBCs of sickle cell disease (SCD) patients from the MemSID study as well as of thalassemia patients and healthy controls from the CoMMiTMenT study.</p> <p>The CoMMiTMenT study was funded by the European Seventh Framework Program under grant agreement number 602121 (CoMMiTMenT) and from the European Union’s FP7 Programme. The study protocols were approved by the Medical Ethical Research Board of the University Medical Center Utrecht, the Netherlands, under reference code 15/426M and by the Ethical Committee of Clinical Investigations of Hospital Clinic, Spain (IDIBAPS) under reference code 2013/8436. The MemSID study was performed at the University hospital Zurich (#NCT02615847 at https://clinicaltrials.gov/). The trial protocol was approved by the Ethics committee of Canton Zurich (KEK-ZH 2015-0297).</p>
Fig. 2. The most numerous RDB wetland birds species, recorded during August Counts 2018 and 2021 in The Red Data Book Waterbirds In The Coastal Wetlands Of The Azov-Black Sea Region Of Ukraine - The Results Of The August Counts 2018 And 2021
Fig. 2. The most numerous RDB wetland birds species, recorded during August Counts 2018 and 2021 (bar chart — amount of birds, line chart — number of sites were species was recorded).
Fig. 1 in The Red Data Book Waterbirds In The Coastal Wetlands Of The Azov-Black Sea Region Of Ukraine - The Results Of The August Counts 2018 And 2021
Fig. 1. Coverage of wetlands of Azov-Black Sea coast of Ukraine in 2018 and 2021 (yellow — 1 count, red — 2 counts). The numbers of the wetlands (1–40) correspond to those in table 3.
Fig. 3 in The Red Data Book Waterbirds In The Coastal Wetlands Of The Azov-Black Sea Region Of Ukraine - The Results Of The August Counts 2018 And 2021
Fig. 3. The most important wetlands for RDB wetland bird species according to August Counts 2018 and 2021 (bar chart — average amount of birds, line chart — number of the species recorded at the site).
Red deer growth data and R code for analysis
<p>Dataset and R code (Rmd-file) for analysis of seasonal growth of body weight in red deer. </p> <p>Supplementary material for the paper "Shifting seasonality of annual growth through ontogeny for red deer at northern latitudes".</p> <p>This study was part of the AgriDeer project (318575), funded by the Research Council of Norway.</p>
Machine Learning the Tip of the Red Giant Branch
<p># Reproduction package for the Paper "Machine Learning the Tip of the Red Giant Branch"<br> ## Authors<br> Mitchell T. Dennis (mtde226@hawaii.edu)<br> Jeremy Sakstein (sakstein@hawaii.edu)<br> ## Software<br> MESA version 15140 (http://mesa.sourceforge.net/) <br> MESASDK version 20210401 (http://www.astro.wisc.edu/~townsend/static.php?ref=mesasdk) <br> GFORTRAN GCC version 9.2.0<br> ## Citation Policy<br> If you use any part of this reproduction package for independent work, we recommend you cite the following papers:</p> <p>* This paper<br> * Astrophys. J. Suppl. 192, 3 (2011)<br> * Astrophys. J. Suppl. 208, 4 (2013)<br> * Astrophys. J. Suppl. 234, 34 (2018)<br> * Astrophys. J. Suppl. 243, 10 (2019)</p> <p> </p> <p>For more information, see the Readme.md</p>
Tip of the Red Giant Branch Bounds on the Axion-Electron Coupling Revisited
<p># Reproduction package for the Paper "Tip of the Red Giant Branch Bounds on the Axion-Electron Coupling Revisited"<br> ## Authors<br> Mitchell T. Dennis (mtde226@hawaii.edu)<br> Jeremy Sakstein (sakstein@hawaii.edu)<br> ## Software<br> MESA version 12778 (http://mesa.sourceforge.net/) <br> MESASDK version 20200325 (http://www.astro.wisc.edu/~townsend/static.php?ref=mesasdk) <br> GFORTRAN GCC version 9.2.0<br> Python 3.8.10<br> TensorFlow 2.4.1<br> ## Citation Policy<br> If you use any part of this reproduction package for independent work, we recommend you cite:<br> * This paper<br> If you use any of the MESA outputs found in the LOGS folder, we recommend you cite the MESA papers<br> * Astrophys. J. Suppl. 192, 3 (2011)<br> * Astrophys. J. Suppl. 208, 4 (2013)<br> * Astrophys. J. Suppl. 234, 34 (2018)<br> * Astrophys. J. Suppl. 243, 10 (2019)</p> <p> </p> <p>For more information, see the Readme.md</p>
FIG. 3 in Phylogenetic analysis of the red algal tribe Ceramieae reveals multiple morphological homoplasies but defines new genera
FIG. 3. — Phylogenetic reconstruction of the red algal tribe Ceramieae C.Agardh ex Greville inferred with an integrative approach based on rbcL gene and morphological characters. The tree is the majority rule consensus tree resulting from Bayesian analysis. Values at the nodes represent posterior probability and bootstrap values for ML; supports <0.65 (PP) and 75 (BP) are not shown. Sequences generated in this study are in bold. Abbreviations: AU, Australia; BE, Bermuda; BR, Brazil; NS CA, Nova Scotia, Canada; CL, Chile; DA, Dutch Antilles;DE, Denmark; EN, England; FR, France; FR PO, French Polynesia; GB TR, British Overseas Territory of Tristan da Cunha; GE, Germany; IR, Ireland; IT, Italy; JA, Japan; JM, Jamaica; KO, South Korea; MO, Morocco; MX, Mexico; BC MX, Baja California, Mexico; NA, Namibia; NO, Norway; NZ, New Zealand; PE, Peru; PR, Puerto Rico; SA, South Africa; SP, Spain; TW, Taiwan; AK US, Alaska; CA US, California; FL US, Florida; HI US, Hawaii; MA US, Massachusetts; OR US, Oregon; RI US, Rhode Island; WA US, Washington; WA, Wales. Symbol: *, full support.
FIG. 1 in Phylogenetic analysis of the red algal tribe Ceramieae reveals multiple morphological homoplasies but defines new genera
FIG. 1. — Diagnostic morphological features of the tribe Ceramieae:A, Ceramium pallidum (Kützing) Maggs & Hommersand;B, carposporophyte,Pseudoceramium oliveirarum Barros-Barreto, Maggs & M.A.Jaramillo, gen. nov., sp. nov.; C, tetrasporophyte, Centroceras gasparrinii (Meneghini) Kützing; D, male plant showing spermatangia, Corallophila atlantica (A.B.Joly & Ugadim) R.E.Norris; E, acropetal cells, Yoneshiguea compta (Børgesen) Barros-Barreto, Maggs & M.A.Jaramillo, comb. nov.; F, three cortical initial cells, Gayliella dawsonii (A.B.Joly) Barros-Barreto & F.P.Gomes; G, three cortical initial cells and two descending filaments, Centroceras gasparrinii; H, four cortical initial cells, Stirkia fujiana (Barros-Barreto & Maggs) Barros-Barreto & Maggs, comb. nov.; I, four cortical initial cells and descending filaments, Corallophila atlantica; J, five cortical initials including three descending filaments, Ceramium secundatum Lyngbye; K, Yoneshiguea affinis (Setchell & Gardner) Barros-Barreto, Maggs & M.A.Jaramillo, comb. nov. without descending filaments; L, one descending filament per periaxial cell, Gayliella dawsonii (A.B.Joly) Barros-Barreto & F.P.Gomes; M, two descending filaments, Stirkia fujiana (Barros-Barreto & Maggs) Barros-Barreto & Maggs, comb. nov.; N, five cortical initial cells, the fifth one over the periaxial cell, Stirkia codicola (J.Agardh) Barros-Barreto & Maggs, comb. nov.; O, transverse section showing four periaxial cells, Yoneshiguea compta (Børgesen) Barros-Barreto, Maggs & M.A.Jaramillo, comb. nov.; P, transverse section showing six periaxial cells,Stirkia brasiliensis (A.B.Joly) Barros-Barreto & Maggs, comb. nov.; Q, transverse section showing nine periaxial cells, Ceramieae sp. 5; R, transverse section showing 14 periaxial cells, Centroceras gasparrinii. Abbreviations: a1, first acropetal cell; a2, second acropetal cell; b1, first basipetal cell; b2, second basipetal cell; b3, third basipetal cell; p, periaxial cell. Scale bars: B, 500 μm; C, D, 200 μm; K, L, M, O-R, 100 μm.
FIGURE S3 in Phylogenetic analysis of the red algal tribe Ceramieae reveals multiple morphological homoplasies but defines new genera
FIGURE S3. — Reconstructed phylogeny of Ceramieae based on concatenated rbcL, LSU and COI-5P gene sequences. Majority rule consensus tree resulting from Bayesian analysis. Values at the nodes represent posterior probability and bootstrap values for ML, support lower than 0.70 (PP) and 75 (BP) are not shown. Sequences generated in this study are in bold. Abbreviations: See Figure 3. Symbol: *, full support.
FIG. 2 in Phylogenetic analysis of the red algal tribe Ceramieae reveals multiple morphological homoplasies but defines new genera
FIG. 2. — Phylogenetic reconstruction of the red algal tribe Ceramieae C.Agardh ex Greville inferred from rbcL gene sequences showing genera. The tree is the majority rule consensus tree resulting from Bayesian analysis. Values at the nodes represent posterior probability and bootstrap values for ML, support lower than 0.70 (PP) and 75 (BP) are not shown. Symbol: *, full support.
FIGURE S5 in Phylogenetic analysis of the red algal tribe Ceramieae reveals multiple morphological homoplasies but defines new genera
FIGURE S5. — Bayesian tree inferred from COI-5P sequences. The trees are the majority rule consensus tree resulting from Bayesian analysis. Values at the nodes represent posterior probability and bootstrap values for ML, support lower than 0.70 (PP) and 75 (BP) are not shown. Sequences generated in this study are in bold. Abbreviations: See Figure 3. Symbol: *, full support.
FIGURE S2 in Phylogenetic analysis of the red algal tribe Ceramieae reveals multiple morphological homoplasies but defines new genera
FIGURE S2. — Phylogenetic reconstruction of the red algal tribe Ceramieae inferred from rbcL gene sequences (Part 2). The tree is the majority rule consensus tree resulting from Bayesian analysis. Values at the nodes represent posterior probability and bootstrap values for ML, support lower than 0.70 (PP) and 75 (BP) are not shown. Sequences generated in this study are in bold. Abbreviations: See Figure 3. Symbol: *, full support.
FIGURE S1 in Phylogenetic analysis of the red algal tribe Ceramieae reveals multiple morphological homoplasies but defines new genera
FIGURE S1. — Phylogenetic reconstruction of the red algal tribe Ceramieae inferred from rbcL gene sequences (Part 1). The tree is the majority rule consensus tree resulting from Bayesian analysis. Values at the nodes represent posterior probability and bootstrap values for ML, support lower than 0.70 (PP) and 75 (BP) are not shown. Sequences generated in this study are in bold. Abbreviations: See Figure 3. Symbol: *, full support.
FIGURE S4 in Phylogenetic analysis of the red algal tribe Ceramieae reveals multiple morphological homoplasies but defines new genera
FIGURE S4. — Bayesian tree inferred from partial LSU gene. The trees are the majority rule consensus tree resulting from Bayesian analysis. Values at the nodes represent posterior probability and bootstrap values for ML, support lower than 0.70 (PP) and 75 (BP) are not shown. Sequences generated in this study are in bold. Abbreviations: See Figure 3. Symbol: *, full support.
Annotated images of white and red cabbage
<p>This dataset contains annotated images of red and white cabbage.</p> <p>The images were taken on 24 August 2022 in a field in the Stuttgart region (Germany). On this day, 3 image series "morgens" (10 AM), "mittags" (1 PM) and "abends" (7 PM) took place. In the pictures, all clearly visible cabbages are annotated. The annotation was done with the VGG Image Annotator by Abhishek Dutta and Andrew Zisserman (2019).</p> <p>Feel free to use this dataset to train or validate your network.</p>
Sponge diversity patterns in the shallow and mesophotic reefs of the Northern Red Sea row data
<p><span>Accumulating data regarding the increasingly degraded coral reefs worldwide have directed focus to the unique mesophotic coral ecosystem (MCE) as a potential refuge for shallow-water endangered species. Sponges play a crucial role in coral-reef functioning but are often overlooked in benthic surveys. This knowledge gap is especially true for the Red Sea, where data on sponge abundance, coverage and species composition are scarce. Moreover, no study to date has examined Red Sea mesophotic sponges. </span></p> <p><span>Here we compared sponge diversity metrics between the shallow and mesophotic reefs in the Gulf of Aqaba, Northern Red Sea. We also examined the role of biotic and abiotic parameters in determining sponge spatial variability. Sponge diversity metrics significantly varied with increasing depth and between sites. Sponge species composition also significantly differed between depth and sites. However, while no significant evidence supported a sponge bottom-up control, the findings indicated that local factors, such as site characters and biotic interactions, might play essential roles in determining sponge diversity and community composition. The sponges' spatial differences found in this study highlight the importance of considering variability in benthic communities and the factors controlling this, when designing management tools for coral reefs, particularly for the Northern Red Sea.</span></p>
Figure 6. Pseudophycis bachus. A, B, NMNZ P.054828 in A review of the Australasian genus Pseudophycis (Gadiformes: Moridae), redescribing its four species and resurrecting the name Physiculus palmatus Klunzinger, 1872, for the Australian Red Cod
Figure 6. Pseudophycis bachus. A, B, NMNZ P.054828, neotype, 351 mm SL, 2.5 km off coast from Whanganui River bar, Whanganui, New Zealand, fresh specimen, lateral view of body and anterior body showing black pectoral blotch respectively (photographs C. Struthers, NMNZ); C, unpublished line drawing by S. Parkinson of Enchelyopus bachus Forster in Bloch and Schneider, 1801 (reproduced from Whitehead, 1969: pl. 31).
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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.