cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4231/r7js9nd6
title: gSSURGO-based Floodplain Maps of South Carolina

description: &lt;p&gt;US national wide gSSURGO based floodplain maps are developed by using Gridded Soil Survey Geographic (gSSURGO) database. Specifically, the SSURGO map units are joined to certain key attributes in the muaggatt and component tables. After joining the spatial data to &lsquo;muaggatt&rsquo; and &lsquo;component&rsquo; tables, map unit polygons are selected by the following two queries: (1) &quot;geomdesc&quot; LIKE &#39;%flood%&#39; AND &quot;comppct_r&quot; &gt;= 85) OR &quot;taxsubgrp&quot; LIKE &#39;%fluv%&#39;; (2) &ldquo;muname&quot; =&#39;Water&#39; OR &quot;flodfreqdcd&quot; in (&#39;Rare&#39;, &#39;Occasional&#39;, &#39;Frequent&#39;, &#39;Very Frequent&#39;)&quot;. The result from this query, which is a polygon feature class or shapefile, is then dissolved to remove any area less than 15000 square meters are removed. More details about this approach can be found in the following publication: Sangwan, Nikhil, and V. Merwade, A faster and economical approach to floodplain mapping by using soil information, Journal of American Water Resources Association, 51(5): 1286-1304. (2015)&lt;/p&gt;
description: This dataset provides a shapefile showing the natural floodplain for South Carolina. These floodplain polygons for the entire state are extracted from the gSSURGO soil data from the Natural Resources Conservation Service (NRCS).
DOI: 10.4231/r7f18wps
title: gSSURGO-based Floodplain Maps of South Dakota

description: &lt;p&gt;US national wide gSSURGO based floodplain maps are developed by using Gridded Soil Survey Geographic (gSSURGO) database. Specifically, the SSURGO map units are joined to certain key attributes in the muaggatt and component tables. After joining the spatial data to &lsquo;muaggatt&rsquo; and &lsquo;component&rsquo; tables, map unit polygons are selected by the following two queries: (1) &quot;geomdesc&quot; LIKE &#39;%flood%&#39; AND &quot;comppct_r&quot; &gt;= 85) OR &quot;taxsubgrp&quot; LIKE &#39;%fluv%&#39;; (2) &ldquo;muname&quot; =&#39;Water&#39; OR &quot;flodfreqdcd&quot; in (&#39;Rare&#39;, &#39;Occasional&#39;, &#39;Frequent&#39;, &#39;Very Frequent&#39;)&quot;. The result from this query, which is a polygon feature class or shapefile, is then dissolved to remove any area less than 15000 square meters are removed. More details about this approach can be found in the following publication: Sangwan, Nikhil, and V. Merwade, A faster and economical approach to floodplain mapping by using soil information, Journal of American Water Resources Association, 51(5): 1286-1304. (2015)&lt;/p&gt;
description: This dataset provides a shapefile showing the natural floodplain for South Dakota. These floodplain polygons for the entire state are extracted from the gSSURGO soil data from the Natural Resources Conservation Service (NRCS).
DOI: 10.4231/r7988509
title: gSSURGO-based Floodplain Maps of Tennessee

description: &lt;p&gt;US national wide gSSURGO based floodplain maps are developed by using Gridded Soil Survey Geographic (gSSURGO) database. Specifically, the SSURGO map units are joined to certain key attributes in the muaggatt and component tables. After joining the spatial data to &lsquo;muaggatt&rsquo; and &lsquo;component&rsquo; tables, map unit polygons are selected by the following two queries: (1) &quot;geomdesc&quot; LIKE &#39;%flood%&#39; AND &quot;comppct_r&quot; &gt;= 85) OR &quot;taxsubgrp&quot; LIKE &#39;%fluv%&#39;; (2) &ldquo;muname&quot; =&#39;Water&#39; OR &quot;flodfreqdcd&quot; in (&#39;Rare&#39;, &#39;Occasional&#39;, &#39;Frequent&#39;, &#39;Very Frequent&#39;)&quot;. The result from this query, which is a polygon feature class or shapefile, is then dissolved to remove any area less than 15000 square meters are removed. More details about this approach can be found in the following publication: Sangwan, Nikhil, and V. Merwade, A faster and economical approach to floodplain mapping by using soil information, Journal of American Water Resources Association, 51(5): 1286-1304. (2015)&lt;/p&gt;
description: This dataset provides a shapefile showing the natural floodplain for Tennessee. These floodplain polygons for the entire state are extracted from the gSSURGO soil data from the Natural Resources Conservation Service (NRCS).
DOI: 10.4231/r75m63nf
title: gSSURGO-based Floodplain Maps of Texas

description: &lt;p&gt;US national wide gSSURGO based floodplain maps are developed by using Gridded Soil Survey Geographic (gSSURGO) database. Specifically, the SSURGO map units are joined to certain key attributes in the muaggatt and component tables. After joining the spatial data to &lsquo;muaggatt&rsquo; and &lsquo;component&rsquo; tables, map unit polygons are selected by the following two queries: (1) &quot;geomdesc&quot; LIKE &#39;%flood%&#39; AND &quot;comppct_r&quot; &gt;= 85) OR &quot;taxsubgrp&quot; LIKE &#39;%fluv%&#39;; (2) &ldquo;muname&quot; =&#39;Water&#39; OR &quot;flodfreqdcd&quot; in (&#39;Rare&#39;, &#39;Occasional&#39;, &#39;Frequent&#39;, &#39;Very Frequent&#39;)&quot;. The result from this query, which is a polygon feature class or shapefile, is then dissolved to remove any area less than 15000 square meters are removed. More details about this approach can be found in the following publication: Sangwan, Nikhil, and V. Merwade, A faster and economical approach to floodplain mapping by using soil information, Journal of American Water Resources Association, 51(5): 1286-1304. (2015)&lt;/p&gt;
description: This dataset provides a shapefile showing the natural floodplain for Texas. These floodplain polygons for the entire state are extracted from the gSSURGO soil data from the Natural Resources Conservation Service (NRCS).
DOI: 10.4231/r71v5bz5
title: gSSURGO-based Floodplain Maps of Utah

description: &lt;p&gt;US national wide gSSURGO based floodplain maps are developed by using Gridded Soil Survey Geographic (gSSURGO) database. Specifically, the SSURGO map units are joined to certain key attributes in the muaggatt and component tables. After joining the spatial data to &lsquo;muaggatt&rsquo; and &lsquo;component&rsquo; tables, map unit polygons are selected by the following two queries: (1) &quot;geomdesc&quot; LIKE &#39;%flood%&#39; AND &quot;comppct_r&quot; &gt;= 85) OR &quot;taxsubgrp&quot; LIKE &#39;%fluv%&#39;; (2) &ldquo;muname&quot; =&#39;Water&#39; OR &quot;flodfreqdcd&quot; in (&#39;Rare&#39;, &#39;Occasional&#39;, &#39;Frequent&#39;, &#39;Very Frequent&#39;)&quot;. The result from this query, which is a polygon feature class or shapefile, is then dissolved to remove any area less than 15000 square meters are removed. More details about this approach can be found in the following publication: Sangwan, Nikhil, and V. Merwade, A faster and economical approach to floodplain mapping by using soil information, Journal of American Water Resources Association, 51(5): 1286-1304. (2015)&lt;/p&gt;
description: This dataset provides a shapefile showing the natural floodplain for Utah. These floodplain polygons for the entire state are extracted from the gSSURGO soil data from the Natural Resources Conservation Service (NRCS).
DOI: 10.4231/r7sb43qd
title: gSSURGO-based Floodplain Maps of Virginia

description: &lt;p&gt;US national wide gSSURGO based floodplain maps are developed by using Gridded Soil Survey Geographic (gSSURGO) database. Specifically, the SSURGO map units are joined to certain key attributes in the muaggatt and component tables. After joining the spatial data to &lsquo;muaggatt&rsquo; and &lsquo;component&rsquo; tables, map unit polygons are selected by the following two queries: (1) &quot;geomdesc&quot; LIKE &#39;%flood%&#39; AND &quot;comppct_r&quot; &gt;= 85) OR &quot;taxsubgrp&quot; LIKE &#39;%fluv%&#39;; (2) &ldquo;muname&quot; =&#39;Water&#39; OR &quot;flodfreqdcd&quot; in (&#39;Rare&#39;, &#39;Occasional&#39;, &#39;Frequent&#39;, &#39;Very Frequent&#39;)&quot;. The result from this query, which is a polygon feature class or shapefile, is then dissolved to remove any area less than 15000 square meters are removed. More details about this approach can be found in the following publication: Sangwan, Nikhil, and V. Merwade, A faster and economical approach to floodplain mapping by using soil information, Journal of American Water Resources Association, 51(5): 1286-1304. (2015)&lt;/p&gt;
description: This dataset provides a shapefile showing the natural floodplain for Virginia. These floodplain polygons for the entire state are extracted from the gSSURGO soil data from the Natural Resources Conservation Service (NRCS).
DOI: 10.4231/r7x34vfr
title: gSSURGO-based Floodplain Maps of Vermont

description: &lt;p&gt;US national wide gSSURGO based floodplain maps are developed by using Gridded Soil Survey Geographic (gSSURGO) database. Specifically, the SSURGO map units are joined to certain key attributes in the muaggatt and component tables. After joining the spatial data to &lsquo;muaggatt&rsquo; and &lsquo;component&rsquo; tables, map unit polygons are selected by the following two queries: (1) &quot;geomdesc&quot; LIKE &#39;%flood%&#39; AND &quot;comppct_r&quot; &gt;= 85) OR &quot;taxsubgrp&quot; LIKE &#39;%fluv%&#39;; (2) &ldquo;muname&quot; =&#39;Water&#39; OR &quot;flodfreqdcd&quot; in (&#39;Rare&#39;, &#39;Occasional&#39;, &#39;Frequent&#39;, &#39;Very Frequent&#39;)&quot;. The result from this query, which is a polygon feature class or shapefile, is then dissolved to remove any area less than 15000 square meters are removed. More details about this approach can be found in the following publication: Sangwan, Nikhil, and V. Merwade, A faster and economical approach to floodplain mapping by using soil information, Journal of American Water Resources Association, 51(5): 1286-1304. (2015)&lt;/p&gt;
description: This dataset provides a shapefile showing the natural floodplain for Vermont. These floodplain polygons for the entire state are extracted from the gSSURGO soil data from the Natural Resources Conservation Service (NRCS).
DOI: 10.4231/r7nk3c03
title: gSSURGO-based Floodplain Maps of Washington

description: &lt;p&gt;US national wide gSSURGO based floodplain maps are developed by using Gridded Soil Survey Geographic (gSSURGO) database. Specifically, the SSURGO map units are joined to certain key attributes in the muaggatt and component tables. After joining the spatial data to &lsquo;muaggatt&rsquo; and &lsquo;component&rsquo; tables, map unit polygons are selected by the following two queries: (1) &quot;geomdesc&quot; LIKE &#39;%flood%&#39; AND &quot;comppct_r&quot; &gt;= 85) OR &quot;taxsubgrp&quot; LIKE &#39;%fluv%&#39;; (2) &ldquo;muname&quot; =&#39;Water&#39; OR &quot;flodfreqdcd&quot; in (&#39;Rare&#39;, &#39;Occasional&#39;, &#39;Frequent&#39;, &#39;Very Frequent&#39;)&quot;. The result from this query, which is a polygon feature class or shapefile, is then dissolved to remove any area less than 15000 square meters are removed. More details about this approach can be found in the following publication: Sangwan, Nikhil, and V. Merwade, A faster and economical approach to floodplain mapping by using soil information, Journal of American Water Resources Association, 51(5): 1286-1304. (2015)&lt;/p&gt;
description: This dataset provides a shapefile showing the natural floodplain for Washington. These floodplain polygons for the entire state are extracted from the gSSURGO soil data from the Natural Resources Conservation Service (NRCS).
DOI: 10.4231/r7d21vkd
title: gSSURGO-based Floodplain Maps of Wisconsin

description: &lt;p&gt;US national wide gSSURGO based floodplain maps are developed by using Gridded Soil Survey Geographic (gSSURGO) database. Specifically, the SSURGO map units are joined to certain key attributes in the muaggatt and component tables. After joining the spatial data to &lsquo;muaggatt&rsquo; and &lsquo;component&rsquo; tables, map unit polygons are selected by the following two queries: (1) &quot;geomdesc&quot; LIKE &#39;%flood%&#39; AND &quot;comppct_r&quot; &gt;= 85) OR &quot;taxsubgrp&quot; LIKE &#39;%fluv%&#39;; (2) &ldquo;muname&quot; =&#39;Water&#39; OR &quot;flodfreqdcd&quot; in (&#39;Rare&#39;, &#39;Occasional&#39;, &#39;Frequent&#39;, &#39;Very Frequent&#39;)&quot;. The result from this query, which is a polygon feature class or shapefile, is then dissolved to remove any area less than 15000 square meters are removed. More details about this approach can be found in the following publication: Sangwan, Nikhil, and V. Merwade, A faster and economical approach to floodplain mapping by using soil information, Journal of American Water Resources Association, 51(5): 1286-1304. (2015)&lt;/p&gt;
description: This dataset provides a shapefile showing the natural floodplain for Wisconsin. These floodplain polygons for the entire state are extracted from the gSSURGO soil data from the Natural Resources Conservation Service (NRCS).
DOI: 10.15468/dl.vtk8et
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Dosinia dunkeri, Artemis simplex, Cytherea dunkeri)",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Occurrence evidence)",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1950"
 ]
}

 The dataset includes 19 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059337-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4231/r7ht2m8c
title: gSSURGO-based Floodplain Maps of West Virginia

description: &lt;p&gt;US national wide gSSURGO based floodplain maps are developed by using Gridded Soil Survey Geographic (gSSURGO) database. Specifically, the SSURGO map units are joined to certain key attributes in the muaggatt and component tables. After joining the spatial data to &lsquo;muaggatt&rsquo; and &lsquo;component&rsquo; tables, map unit polygons are selected by the following two queries: (1) &quot;geomdesc&quot; LIKE &#39;%flood%&#39; AND &quot;comppct_r&quot; &gt;= 85) OR &quot;taxsubgrp&quot; LIKE &#39;%fluv%&#39;; (2) &ldquo;muname&quot; =&#39;Water&#39; OR &quot;flodfreqdcd&quot; in (&#39;Rare&#39;, &#39;Occasional&#39;, &#39;Frequent&#39;, &#39;Very Frequent&#39;)&quot;. The result from this query, which is a polygon feature class or shapefile, is then dissolved to remove any area less than 15000 square meters are removed. More details about this approach can be found in the following publication: Sangwan, Nikhil, and V. Merwade, A faster and economical approach to floodplain mapping by using soil information, Journal of American Water Resources Association, 51(5): 1286-1304. (2015)&lt;/p&gt;
description: This dataset provides a shapefile showing the natural floodplain for West Virginia. These floodplain polygons for the entire state are extracted from the gSSURGO soil data from the Natural Resources Conservation Service (NRCS).
DOI: 10.4231/r78c9t7d
title: gSSURGO-based Floodplain Maps of Wyoming

description: &lt;p&gt;US national wide gSSURGO based floodplain maps are developed by using Gridded Soil Survey Geographic (gSSURGO) database. Specifically, the SSURGO map units are joined to certain key attributes in the muaggatt and component tables. After joining the spatial data to &lsquo;muaggatt&rsquo; and &lsquo;component&rsquo; tables, map unit polygons are selected by the following two queries: (1) &quot;geomdesc&quot; LIKE &#39;%flood%&#39; AND &quot;comppct_r&quot; &gt;= 85) OR &quot;taxsubgrp&quot; LIKE &#39;%fluv%&#39;; (2) &ldquo;muname&quot; =&#39;Water&#39; OR &quot;flodfreqdcd&quot; in (&#39;Rare&#39;, &#39;Occasional&#39;, &#39;Frequent&#39;, &#39;Very Frequent&#39;)&quot;. The result from this query, which is a polygon feature class or shapefile, is then dissolved to remove any area less than 15000 square meters are removed. More details about this approach can be found in the following publication: Sangwan, Nikhil, and V. Merwade, A faster and economical approach to floodplain mapping by using soil information, Journal of American Water Resources Association, 51(5): 1286-1304. (2015)&lt;/p&gt;
description: This dataset provides a shapefile showing the natural floodplain for Wyoming. These floodplain polygons for the entire state are extracted from the gSSURGO soil data from the Natural Resources Conservation Service (NRCS).
DOI: 10.4231/r74q7rx0
title: Scripts for a discrete top-down Markov problem in approximation theory

description: &lt;p&gt;The Markov brothers&rsquo; inequalities in approximation theory concern polynomials p of degree n and assert bounds for the kth derivatives &lt;font size="2"&gt;&lt;span style="font-size:10pt;"&gt;|p^{(k)}|&lt;/span&gt;&lt;/font&gt;, 1 &le;k &le;n, on [&minus;1, 1], given that |p| &le;1 on [&minus;1, 1]. Here we go the other direction, seeking bounds for |p|, given a bound for &lt;font size="2"&gt;&lt;span style="font-size:10pt;"&gt;|p^{(k)}|&lt;/span&gt;&lt;/font&gt;. For the problem to be meaningful, additional restrictions on p must be imposed, for example, &lt;font size="2"&gt;&lt;span style="font-size:10pt;"&gt;p(-1)=p&#39;(-1)= . . . = p^{(k-1)}(-1)=0&lt;/span&gt;&lt;/font&gt;. The problem then has an easy solution in the continuous case, where the polynomial and their derivatives are considered on the whole interval [&minus;1, 1], but is more challenging, and also of more interest, in the discrete case, where one focusses on the values of p and &lt;font size="2"&gt;&lt;span style="font-size:10pt;"&gt;p^{(k)}&lt;/span&gt;&lt;/font&gt; on a given set of &lt;font size="2"&gt;&lt;span style="font-size:10pt;"&gt;n-k+1 &lt;/span&gt;&lt;/font&gt;distinct points in [&minus;1, 1]. Analytic solutions are presented and their fine structure analyzed by computation.&lt;/p&gt;
description: Matlab scripts for a discrete top-down Markov problem in approximation theory
DOI: 10.4231/r71j97zb
title: RCSP Ceramics Collections at Kestros, 1997

description: &lt;p&gt;The Rough Cilicia Survey Project conducted ceramics grab collections at Kestros (Macar Kale) in 1997. Designated areas with high sherd concentrations, approximately 100m square, were identified by field directors Blanton and Rauh for inspection. Inspections were conducted mostly by Rauh and Turkish student Betul Sahin, diagnostic sherds were collected, triaged on site, bagged and tagged and brought back to the project lab where they were washed and coded. Sherds were later read by Richard Rothaus and Kathleen Slane. Unreadable sherds were returned to the field. Diagnostic sherds were photographed as groups, and stored as contexts. All surviving sherds were inspected by Rauh and Matthew Dillon at the Alanya Archaeological Museum in 2014. Each sherd was measured, described, Munselled, and photographed individually.&lt;/p&gt;
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&lt;div class="embed-container"&gt;&lt;small&gt;&lt;a href="//purdueuniversity.maps.arcgis.com/apps/Embed/index.html?webmap=69f70c77be7142daa35e079e6e6d04b7&amp;extent=32.2897,36.2121,32.3591,36.2498&amp;home=true&amp;zoom=true&amp;scale=true&amp;legend=true&amp;disable_scroll=false&amp;theme=light" style="color:#0000FF;text-align:left" target="_blank"&gt;View larger map&lt;/a&gt;&lt;/small&gt;&lt;br /&gt;
&lt;iframe frameborder="0" marginheight="0" marginwidth="0" scrolling="no" src="//purdueuniversity.maps.arcgis.com/apps/Embed/index.html?webmap=69f70c77be7142daa35e079e6e6d04b7&amp;extent=32.2897,36.2121,32.3591,36.2498&amp;home=true&amp;zoom=true&amp;previewImage=false&amp;scale=true&amp;legend=true&amp;disable_scroll=false&amp;theme=light" title="RCSP Ceramics Collections at Kestros, 1997"&gt;&lt;/iframe&gt;&lt;/div&gt;
description: Rough Cilicia Survey Project Ceramics Collections at Kestros, 1997
DOI: 10.5281/zenodo.17981337
title: TABLE 1 in Economic significance of Cervus elaphus Linnaeus, 1758 in the Gumelnița culture (5 th mill. BC): from food to technical resources

description: TABLE 1. — Faunal spectrum of some Gumelnița tell-settlements. Abbreviation: NR, number.
Bordușani GA2Carcaliu GA2Hârșova GA2Luncavița GA2Căscioarele GB1Vitănești GA2Vitănești GB1Măriuța GB1SpeciesNR%NR%NR%NR%NR%NR%NR%NR%Bos taurus Linnaeus, 1758199621.4211824.5361811.6421823.592017.10105511.684323.0222642.97Ovis aries Linnaeus, 17582192.3540.832554.8080.87170.60830.9571.56285.32Capra hircus Linnaeus, 1758730.7820.42771.4590.9730.11300.3270.74142.66Ovis / Capra131214.0881.66118222.26586.2870.252682.9772.109618.25Sus domesticus Erxleben, 1777232024.906112.68100919.0012213.20572.01119013.180922.097814.83Canis familiaris Linnaeus, 1758134314.41132.7089616.87212.271665.872522.8902.46132.47Total domestic726377.9520642.83403776.0343647.1945115.94287831.7190351.9745586.50Canis lupus Linnaeus, 1758640.69––260.4960.6530.11720.79180.49––Vulpes vulpes (Linnaeus, 1758)1431.53––140.2640.4310.04610.67190.5230.57Lynx lynx (Linnaeus, 1758)80.09––60.1110.11––50.06––––Felis silvestris Schreber, 1777490.53––200.3830.3210.04250.2820.05––Panthera leo (Linnaeus, 1758)––––––––––30.03––––Meles meles (Linnaeus, 1758)160.17––150.2860.6540.142082.29330.90––Martes martes (Linnaeus, 1758)30.03––80.1510.1110.0410.0120.05––Mustela putorius Linnaeus, 175820.02––––––––10.01––––Mustela nivalis Linnaeus, 1766––––10.02––––––––––Lutra lutra (Linnaeus, 1758)380.41––20.0410.11––110.12––––Ursus arctos Linnaeus, 1758––––––––10.04210.210.03––Equus ferus Linnaeus, 1758350.3820.42200.3840.432458.667738.52206.01101.90Equus sp. (little size)––––––––––––20.05––Sus scrofa Linnaeus, 17587578.127214.9789016.7613114.1855019.44132614.62095.71173.23Cervus elaphus Linnaeus, 17585405.8017436.17781.4729131.49119342.17173219.159516.25305.70Capreolus capreolus (Linnaeus, 1758)1601.7251.04661.24232.491214.281481.6180.4950.95Alces alces (Linnaeus, 1758)––––––––––40.04––––Dama dama (Linnaeus, 1758)10.0110.21––––––––––––Bos primigenius Bojanus, 1827750.80173.53551.04121.302398.45136815.151514.0661.14Castor fiber Linnaeus, 1758971.0440.83320.6020.22160.573634.01153.14––Lepus europaeus Pallas, 1778660.71––400.7530.3230.11891.0100.27––Total wild205422.0527557.17127323.9748852.81237884.06621168.3175948.037113.50Total determinated9317100.00481100.005310100.00924 100.0028291009089100.03662100.00526100.00
description: Published as part of Mărgărit, Monica & Bălășescu, Adrian, 2025, Economic significance of Cervus elaphus Linnaeus, 1758 in the Gumelnița culture (5 th mill. BC): from food to technical resources, pp. 178 in Anthropozoologica 60 (13) on page 178, DOI: 60/13, http://zenodo.org/record/17936228