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5 results for “color science”
Dataset: Rainbow color map distorts and misleads research in hydrology – guidance for better visualizations and science communication
<p>The rainbow color map is scientifically incorrect and hinders people with color vision deficiency to view visualizations in a correct way. Due to perceptual non-uniform color gradients within the rainbow color map the data representation is distorted what can lead to misinterpretation of results and flaws in science communication. Here we present the data of a paper survey of 797 scientific publication in the journal Hydrology and Earth System Sciences. With in the survey all papers were classified according to color issues. Find details about the data below.</p> <ul> <li><code>year</code> = year of publication (YYYY)</li> <li><code>date</code> = date (YYYY-MM-DD) of publication</li> <li><code>title</code> = full paper title from journal website</li> <li><code>authors</code> = list of authors comma-separated</li> <li><code>n_authors</code> = number of authors (integer between 1 and 27)</li> <li><code>col_code</code> = color-issue classification (see below)</li> <li><code>volume</code> = Journal volume</li> <li><code>start_page</code> = first page of paper (consecutive)</li> <li><code>end_page</code> = last page of paper (consecutive)</li> <li><code>base_url</code> = base url to access the PDF of the paper with <code>/volume/start_page/year/</code></li> <li><code>filename</code> = specific file name of the paper PDF (e.g. <code>hess-9-111-2005.pdf</code>)</li> </ul> <p>Color classification is stored in the <code>col_code</code> variable with:</p> <ul> <li><code>0</code> = chromatic and issue-free,</li> <li><code>1</code> = red-green issues,</li> <li><code>2</code>= rainbow issues and</li> <li><code>bw</code>= black and white paper.</li> </ul> <p> </p> <p>See more details (e.g., sample code to analyse the survey data) on https://github.com/modche/rainbow_hydrology</p> <p>Paper: Stoelzle, M. and Stein, L.: Rainbow color map distorts and misleads research in hydrology – guidance for better visualizations and science communication, Hydrol. Earth Syst. Sci., 25, 4549–4565, https://doi.org/10.5194/hess-25-4549-2021, 2021.</p> <p> </p> <p> </p>
Data from: Citizen science and color pattern analysis indicate unreported Batesian mimicry between Neotropical snakes
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Dispatches from the neighborhood watch: using citizen science and field survey data to document color morph frequency in space and time
<p>Heritable color polymorphisms have a long history of study in evolutionary biology, though they are less frequently examined today than in the past. These systems, where multiple discrete, visually identifiable color phenotypes co-occur in the same population, are valuable for tracking evolutionary change and ascertaining the relative importance of different evolutionary mechanisms. Here, we use a combination of citizen science data and field surveys in the Great Lakes region of North America to identify patterns of color morph frequencies in the eastern gray squirrel (<i>Sciurus carolinensis</i>). Using over 68,000 individual squirrel records from both large and small spatial scales, we identify the following patterns: (1) the melanistic (black) phenotype is often localized but nonetheless widespread throughout the Great Lakes region, occurring in all states and provinces sampled. (2) In Ohio, where intensive surveys were performed, there is a weak but significantly positive association between color morph frequency and geographic proximity of populations. Nonetheless, even nearby populations often had radically different frequencies of the melanistic morph, which ranged from 0 to 96%. These patterns were mosaic rather than clinal. (3) In the Wooster, Ohio population, which had over eight years of continuous data on color morph frequency representing nearly 40,000 records, we found that the frequency of the melanistic morph increased gradually over time on some survey routes but decreased or did not change over time on others. These differences were statistically significant and occurred at very small spatial scales (on the order of hundreds of meters). Together, these patterns are suggestive of genetic drift as an important mechanism of evolutionary change in this system. We argue that studies of color polymorphism are still quite valuable in advancing our understanding of fundamental evolutionary processes, especially when coupled with the growing availability of data from citizen science efforts.</p>
Supplementary dataset to the publication by Hieronymi et al.: "Ocean color atmospheric correction methods in view of usability for different optical water types", Frontiers in Marine Science (under review, submitted 22 Dec 2022)
<p>The dataset is an annex to the publication (under review, submitted 22 Dec 2022):</p> <p>Martin Hieronymi, Shun Bi, Dagmar Müller, Eike M Schütt, Daniel Behr, Carsten Brockmann, Carole Lebreton, François Steinmetz, Kerstin Stelzer and Quinten Vanhellemont: "Ocean color atmospheric correction methods in view of usability for different optical water types", Frontiers in Marine Science.</p> <p>The data were created to compare the results of different atmospheric correction methods for ocean (water) color imagery. The dataset includes ten modified ESA/EUMETSAT Copernicus Sentinel-3 OLCI satellite scenes from optically diverse sea areas worldwide. The NetCDF files are optimized for visualization in the ESA Sentinel Application Platform (SNAP) and especially the Spectrum View. The data include original OLCI Level-1B top-of-atmosphere radiances recorded by the sensor and the results from five different atmospheric correction methods, i.e., spectral remote-sensing reflectance at 16 OLCI bands. The atmospheric correction methods compared are</p> <ol> <li> <p>IPF (Collection 3, the standard method),</p> </li> <li> <p>C2RCC (v1.7 including IPF gains; Brockmann et al. [2016]),</p> </li> <li> <p>A4O (v0.23 (2022-01-19); a novel method by Hieronymi et al.),</p> </li> <li> <p>POLYMER (v4.14 (2021-12-17); Steinmetz et al. [2011]), and</p> </li> <li> <p>ACOLITE-DSF (v2022-10-25.0; Vanhellemont and Ruddick [2021]).</p> </li> </ol> <p>The original flags supplied in each case are also provided.</p> <table> <tbody> <tr> <td> <p><strong># </strong></p> </td> <td> <p><strong>Sensor-Date-UTC</strong></p> </td> <td> <p><strong>Region </strong></p> </td> <td> <p><strong>Special features </strong></p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>S3A-20160720-092821</p> </td> <td> <p>Barents Sea</p> </td> <td> <p>High latitudes, bloom of coccolithophores</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>S3A-20160720-093421</p> </td> <td> <p>North Sea, Wadden Sea</p> </td> <td> <p>Moderately to extremely scattering waters, tidal areas, in situ data</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>S3A-20170114-130626</p> </td> <td> <p>South Atlantic Ocean, Rio de la Plata estuary</p> </td> <td> <p>Extremely scattering waters, clear oceanic waters, sun glint, South Atlantic Anomaly</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>S3A-20170527-015236</p> </td> <td> <p>Yellow Sea, East China Sea, Yangtze, Lake Taihu</p> </td> <td> <p>Extremely scattering waters, tidal areas, large rivers, absorbing aerosols, sun glint</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>S3A-20170529-092334</p> </td> <td> <p>Mediterranean Sea</p> </td> <td> <p>Large areas with clear waters, sun glint</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>S3A-20170913-080730</p> </td> <td> <p>Black Sea, Aegean Sea</p> </td> <td> <p>Clear and absorbing waters</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p>S3A-20180715-093613</p> </td> <td> <p>North Sea, Baltic Sea</p> </td> <td> <p>Intense bloom of cyanobacteria partly with scum</p> </td> </tr> <tr> <td> <p>8 9</p> </td> <td> <p>S3A-20200601-092517 S3B-20200601-084546</p> </td> <td> <p>North Sea, Baltic Sea</p> </td> <td> <p>Inter-comparison of S3A and S3B with different observation angles, absorbing waters</p> </td> </tr> <tr> <td> <p>10</p> </td> <td> <p>S3B-20200406-093801</p> </td> <td> <p>North Sea, Baltic Sea</p> </td> <td> <p>High OWT diversity</p> </td> </tr> </tbody> </table> <p> </p>
Dispatches from the neighborhood watch: using citizen science and field survey data to document color morph frequency in space and time
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