Item talk:Q325619

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{

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       "name": "Hecht, Jory S",
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       "givenName": "Jory S",
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     {
       "name": "Fiala, Sarah G",
       "nameType": "Personal",
       "givenName": "Sarah G",
       "familyName": "Fiala",
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     {
       "name": "Barth, Nancy A",
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       "givenName": "Nancy A",
       "familyName": "Barth",
       "affiliation": [
         "United States Geological Survey"
       ],
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     {
       "name": "Gregory, Angela E",
       "nameType": "Personal",
       "givenName": "Angela E",
       "familyName": "Gregory",
       "affiliation": [
         "U.S. Geological Survey"
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     {
       "name": "Ryberg, Karen R",
       "nameType": "Personal",
       "givenName": "Karen R",
       "familyName": "Ryberg",
       "affiliation": [
         "United States Geological Survey"
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   "titles": [
     {
       "title": "Data for simulation experiments comparing nonstationary design-flood adjustments based on observed annual peak flows in the conterminous United States"
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   ],
   "publisher": "U.S. Geological Survey",
   "container": {},
   "publicationYear": 2022,
   "subjects": [],
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   "dates": [
     {
       "date": "2022",
       "dateType": "Issued"
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       "description": "This dataset contains files used in this Monte Carlo simulation study comparing the performance of five statistical models for adjusting design floods for current conditions at sites with known trends. These files include (i) the observed annual peak-flow series in the conterminous US used to inform ranges of known moments and trends used in the simulation experiment, (ii) the 3,000 combinations of Monte Carlo experiment parameters (including sample moments, trends, distribution types, and record lengths), (iii) the 5,000 100-year time series of random uniform variates used as annual non-exceedance probabilities in the generation of synthetic annual peak-flow series, (iv) the simulated and true (known) quantiles associated with the 10% and 1% annual exceedance probabilities conditioned on the last years of the synthetic annual peak-flow series generated through the experiment. This dataset also contains a model archive with the R statistical software code used to execute the study along with a document describing the contents of the archive and providing instructions for reproducing results.",
       "descriptionType": "Abstract"
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   "created": "2022-10-11T18:46:35Z",
   "registered": "2022-10-11T18:46:36Z",
   "published": null,
   "updated": "2022-10-11T18:46:43Z"
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