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13 results for “MESSy”
Answer Key for the SAFI messy data workshop exercise
<p>This is the answer key for the Studying African Farmer-Led Irrigation (SAFI) survey messy data exercise included in the Data Carpentry Workshop: <span>Data Organization in Spreadsheets for Social Scientists (<a href="https://ucsbcarpentry.github.io/spreadsheets-socialsci">https://ucsbcarpentry.github.io/spreadsheets-socialsci</a>) Episode: Formatting Problems. </span></p> <p><span>More information about the SAFI dataset: </span></p> <p>Woodhouse, Philip; Veldwisch, Gert Jan; Brockington, Daniel; Komakech, Hans C.; Manjichi, Angela; Venot, Jean-Philippe (2018). SAFI Survey Results. Figshare. Dataset. <a href="https://doi.org/10.6084/m9.figshare.6262019.v4">https://doi.org/10.6084/m9.figshare.6262019.v4</a></p>
Messy Spreadsheet Example for Instruction
<p>A disorganized toy spreadsheet used for teaching good data organization. Learners are tasked with identifying as many errors as possible before creating a data dictionary and reconstructing the spreadsheet according to best practices.</p>
Model simulation data used in "How non-equilibrium aerosol chemistry impacts particle acidity: the GMXe AERosol CHEMistry (GMXe-AERCHEM, v1.0) sub-submodel of MESSy"
<p>This dataset contains the output of the ECHAM/MESSy Atmospheric Chemistry (EMAC) model simulation analyzed in the work "How non-equilibrium aerosol chemistry impacts particle acidity: the GMXe AERosol CHEMistry (GMXe-AERCHEM, v1.0) sub-submodel of MESSy" submitted to Geoscientific Model Development.</p>
Understudied regions and messy taxonomy: Geography, not taxonomy, is the best predictor for genetic divergence of the Poecilimon bosphoricus species group
<p>The complex and dynamic history of the Anatolian Peninsula during the Pleistocene set the stage for species diversification. However, the evolutionary history of biodiversity in the region is shrouded by the challenges of studying species divergence in the recent, dynamic past. Here we study the <em>Poecilimon bosphoricus </em>(PB) species group to understand how the bush crickets' diversification and the region's complex history are coupled. Specifically, using sequences of two mitochondrial and two nuclear gene segments from over 500 individuals for a comprehensive set of taxa with extensive geographic sampling, we infer the phylogenetic and geographic setting of species divergence. In addition, we use the molecular data to examine hypothesized species boundaries that were defined morphologically. Our analyses of the timing of divergence confirm the recent origin of the PB complex, indicating its diversification coincided with the dynamic geology and climate of the Pleistocene. Moreover, the geography of divergence suggests a history of fragmentation followed by admixture of populations, suggestive of a ring species. However, the evolutionary history based on genetic divergence conflicts with morphologically defined species boundaries, raising the prospect that incipient species divergences may be relatively ephemeral. As such, the morphological differences observed in the PB complex may not be sufficient to have prevented homogenizing gene flow in the past. Alternatively, with the recent origin of the complex, the lack of time for lineage sorting may underlie the discord between morphological species boundaries and genetic differentiation. Under either hypothesis, geography – not taxonomy – is the best predictor of genetic divergence. </p>
Understudied regions and messy taxonomy: Geography, not taxonomy, is the best predictor for genetic divergence of the Poecilimon bosphoricus species group
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From messy chemistry to ecology: Autocatalysis and heritability in prebiotically plausible chemical systems
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‘Tidy’ and ‘messy’ management alters natural enemy communities and pest control in urban agroecosystems
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EMAC-MESSy source segregated CH4 mixing ratios simulated along CARIBIC flight tracks
<p>The *.zip archives contain netCDF data files with EMAC-model simulated CH4 mixing ratios (nmol/mol) along CARIBIC-flight tracks during the years 1997 through 2016 in the upper troposphere and lowermost stratosphere.</p> <p> </p> <p>CARIBIC2org.zip:</p> <p>Every .nc file contains time, lon, lat, and track pressure of the flight simulation and the vertical column of mixing ratios in hybride coordinates total CH4 and eleven source segregated (tagged) contributions. For a best fit of the observations the .nc files have to be multiplied with the scale factors in column 3.</p> <p> </p> <p>CARIBIC_tag_emi_org*CARIBIC2.nc:</p> <p>Tracer: variable name:</p> <p>CH4 total tracer_gp_CH4_fx</p> <p> </p> <p>CH4 tagged: variable name: scale:</p> <p>animals tracer_gp_CH4_fx_e01_a01 0.87</p> <p>bogs tracer_gp_CH4_fx_e02_a01 1.04</p> <p>coal tracer_gp_CH4_fx_e03_a01 0.87</p> <p>gas tracer_gp_CH4_fx_e04_a01 0.89</p> <p>landfills tracer_gp_CH4_fx_e05_a01 0.95</p> <p>oil tracer_gp_CH4_fx_e06_a01 0.91</p> <p>rice tracer_gp_CH4_fx_e07_a01 1.04</p> <p>swamps tracer_gp_CH4_fx_e08_a01 1.13</p> <p>termits tracer_gp_CH4_fx_e09_a01 1.13</p> <p>biomass burn tracer_gp_CH4_fx_e10_a01 1.13</p> <p>biofuel tracer_gp_CH4_fx_e11_a01 1.13</p> <p> </p> <p>CARIBICric.zip, CARIBICsha.zip: CARIBICtro.zip:</p> <p>In these arcives every .nc file contains time, lon, lat, and track pressure of the flight simulation and the simulated mixing ratios along the flight tracks for the post 2006 CH4 increments to be added to the above totals. The increments have to be scaled with the factors in colums 3 for an optimal fit of the observations.</p> <p>CARIBICric.zip: CARIBIC_tag_emi_ric*.nc</p> <p>Tracer: variable name: scale:</p> <p>CH4 rice tracer_gp_CH4_fx 0.48</p> <p> </p> <p>CARIBICsha.zip: CARIBIC_tag_emi_sha*.nc</p> <p>Tracer: variable name: scale:</p> <p>CH4 shale gas tracer_gp_CH4_fx 0.48</p> <p> </p> <p>CARIBICtro.zip: CARIBIC_tag_emi_tro*.nc</p> <p>Tracer: variable name: scale:</p> <p>CH4 tropics tracer_gp_CH4_fx 0.04</p> <p> </p> <p>The mixing ratios are based on emission amounts of 20.5 Tg/CH4/y for each, rice-, shale gas fracking-, and tropical wetlands. The best fit of the observations is a combination of them multiplied with the scale factors in column 3.</p> <p> </p> <p> </p> <p>The corresponding CH4 total mixing ratios (together with other species) are available on demand via <a href="http://www.caribic-atmospheric.com/">http://www.caribic-atmospheric.com</a> "Data access".</p> <p> </p> <p>References:</p> <p>This study:</p> <p>Zimmermann, P. H., Brenninkmeijer, C. A. M., Pozzer, A., Jöckel, P., Winterstein, F., Zahn, A., Houweling, S., and Lelieveld, J.: Model simulations of atmospheric methane (1997–2016) and their evaluation using NOAA and AGAGE surface and IAGOS-CARIBIC aircraft observations, Atmos. Chem. Phys., 20, 1–23, <a href="https://doi.org/10.5194/acp-20-1-2020">https://doi.org/10.5194/acp-20-1-2020</a> ,2020</p> <p>The CARIBIC project:</p> <p>Brenninkmeijer, C. A. M., Crutzen, P., Boumard, F., Dauer, T., Dix, B., Ebinghaus, R., Filippi, D., Fischer, H., Franke, H., Fries, U., Heintzenberg, J., Helleis, F., Hermann, M., Kock, H. H., Koeppel, C., Lelieveld, J., Leuenberger, M., Martinsson, B. G., Miemczyk, S., Moret, H. P., Nguyen, H. N., Nyfeler, P., Oram, D., O'Sullivan, D., Penkett, S., Platt, U., Pupek, M., Ramonet, M., Randa, B., Reichelt, M., Rhee, T. S., Rohwer, J., Rosenfeld, K., Scharffe, D., Schlager, H., Schumann, U., Slemr, F., Sprung, D., Stock, P., Thaler, R., Valentino, F., van Velthoven, P., Waibel, A., Wandel, A., Waschitschek, K., Wiedensohler, A., Xueref-Remy, I., Zahn, A., Zech, U., and Ziereis, H.: Civil Aircraft for the regular investigation of the atmosphere based on an instrumented container: The new CARIBIC system, Atmos. Chem. Phys., 7, 4953-4976, doi:10.5194/acp-7-4953-2007, 2007.</p> <p>EMAC atmospheric chemistry model:</p> <p>Jöckel, P., Kerkweg, A., Pozzer, A., Sander, R., Tost, H., Riede, H., Baumgaertner, A., Gromov, S., and Kern, B.: Development cycle 2 of the Modular Earth Submodel System (MESSy2), Geosci. Model Dev., 3, 717-752, doi:10.5194/gmd-3-717-2010, 2010.</p> <p> </p> <p> </p> <p> </p>
Data from: Species boundaries in the messy middle – testing the hypothesis of micro-endemism in a recently diverged lineage of coastal fog desert lichen fungi
<p><span><span><span><span><span><span><span><span><span><span><span>Species delimitation among closely related species is challenging because traditional phenotype-based approaches, e.g., morphology, ecological, or chemical characteristics, often produce conflicting results. With the advent of high-throughput sequencing, it has become increasingly cost-effective to acquire genome-scale data which can resolve previously ambiguous species boundaries. As the availability of genome-scale data has increased, numerous species delimitation analyses, such as BPP and SNAPP+Bayes factor delimitation (BFD*), have been developed to delimit species boundaries. However, even empirical molecular species delimitation approaches can be biased by confounding evolutionary factors, e.g., hybridization/introgression and incomplete lineage sorting, and computational limitations. Here we investigate species <span><span>boundaries and the potential for micro-endemism in a lineage of lichen-forming fungi, <i>Niebla </i>Rundel & Bowler in the family Ramalinaceae. The species delimitation models tend to support more specious groupings, but were unable to infer robust, consistent species delimitations. </span></span>The results of our study highlight the problem of delimiting species, particularly in groups such as <i>Niebla</i>, with complex, recent phylogeographic histories.</span></span></span></span></span></span></span></span></span></span></span></p>
Data from: Species boundaries in the messy middle – testing the hypothesis of micro-endemism in a recently diverged lineage of coastal fog desert lichen fungi
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MESSI-pipeline_demo_data
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Messy Memories: Mobile Application Therapy Following Critical Illness
ClinicalTrials.gov study NCT05849454. IPD Sharing: NO. Countries: 1. Publications: 0.
Messy Public Art Canvas
<u>Source</u>: Flickr <br><u>4DCity URL</u>: <a href="https://4dcity.org/imgupload/1677352557.3671.jpg">https://4dcity.org/imgupload/1677352557.3671.jpg</a> <br><u>Original Image URL</u>: <a href="https://live.staticflickr.com/65535/52643234556_75158685eb_m.jpg">https://live.staticflickr.com/65535/52643234556_75158685eb_m.jpg</a>
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International Brain Laboratory public data
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OpenNeuro
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