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Table 4 in Changes in a soil microarthropod community in the vicinity of dominant tree species under trampling management at the Safari Zoological Center, Israel
<p><b>Table 4</b> Correlation coefficients (Pearson correlation, <i>r</i>) between the abundance of microarthropods, diversity indices of soil Acari, and soil parameters at the Safari Zoological Center, Israel, December 2013. SM = soil moisture, OM = organic matter, pH = soil pH, SEC = soil electrical conductivity, SD = soil density, WHC = water-holding capacity <i>p</i>. <*0.05, ** <i>p</i> <0.01, *** <i>p</i> <0.001.</p><table><tbody><tr><th><b>Index</b></th><th></th><th><b>SM</b></th><th><b>OM</b></th><th><b>pH</b></th><th><b>SEC</b></th><th><b>SD</b></th><th><b>WHC</b></th></tr></tbody><tbody><tr><th></th><td>Acari</td><td>0.349*</td><td>0.068</td><td>-0.198</td><td>-0.506**</td><td>-0.299</td><td>0.571***</td></tr><tr><th>Abundance</th><td>Collembola Other soil arthropods</td><td>0.153 0.202</td><td>0.210 0.098</td><td>-0.486** -0.294</td><td>-0.506** -0.209</td><td>-0.107 -0.065</td><td>0.207 0.245</td></tr><tr><th></th><td>Total microarthropod</td><td>0.292</td><td>0.158</td><td>-0.403*</td><td>-0.574***</td><td>-0.233</td><td>0.445*</td></tr><tr><th>Diversity indices of Acari</th><td>Taxon richness Shannon index Simpson index</td><td>0.253 0.285 -0.006</td><td>0.023 0.032 -0.175</td><td>-0.098 -0.120 0.165</td><td>-0.392* -0.455** 0.240</td><td>-0.316 -0.293 -0.475**</td><td>0.561*** 0.585*** 0.336</td></tr><tr><th></th><td>Evenness index</td><td>0.387*</td><td>0.009</td><td>-0.203</td><td>-0.466**</td><td>-0.411*</td><td>0.739***</td></tr></tbody></table>
D1.2 Data Management Plan (DMP)
<p>This deliverable constitutes the Data Management Plan (DMP) of the NANOFACTS project. It is the first version of DMP, describing data management life cycle for all research and publication data. This deliverable is introducing ways to produce, collect, process, save, exchange, share, and publish research data in line with the EC “Guidelines on FAIR Data Management in Horizon 2020”.</p>
Table 3 in Mowing inhibits the invasion of the alien species Solidago altissima and is an effective management strategy
<p><b>Table 3.</b> Heights of <i>S. altissima</i> before mowing in September 2019.</p><table><tbody><tr><th>Site</th><th>Control</th><th>Mowing 1</th><th>Mowing 2</th><th>Mowing 3</th></tr></tbody><tbody><tr><th>Site 1</th><td>168.0 ± 3.27a</td><td>64.0 ± 3.32c</td><td>100.5 ± 9.14b</td><td>67.5 ± 3.1c</td></tr><tr><th>Site 2</th><td>192.0 ± 11.3a</td><td>79.0 ± 4.07c</td><td>111.0 ± 6.9b</td><td>73.0 ± 7.61c</td></tr><tr><th>Site 3</th><td>159.0 ± 4.99a</td><td>123.0 ± 7.12b</td><td>84.5 ± 5.89c</td><td>47.0 ± 2.71d</td></tr><tr><th>Site 4</th><td>190.0 ± 2.98a</td><td>102.0 ± 3.89b</td><td>112.5 ±8.07b</td><td>62.0 ± 5.33c</td></tr><tr><th>Site 5</th><td>217.0 ±10.23a</td><td>87.5 ± 4.9c</td><td>137.0 ± 4.96b</td><td>62.5 ± 3.1d</td></tr><tr><th>Site 6</th><td>177.5 ± 6.76a</td><td>97.5 ± 5.44c</td><td>120.5 ± 4.97b</td><td>60.0 ± 2.58d</td></tr><tr><th>Site 7</th><td>143.5 ± 12.2a</td><td>81.0 ± 2.77b</td><td>138.5 ± 3.5a</td><td>62.0 ± 4.67b</td></tr></tbody></table><p>* Mowing 1: mowed once in July; Mowing 2: mowed twice in May and September; Mowing 3: mowed three times in May, July, and September. Data are presented as means ± standard errors of 10 replicates. Means within a row followed by different letters are significantly different at p <0.05 (ANOVA with post hoc Tukey’s test). Units: cm.</p>
Table 2 in Mowing inhibits the invasion of the alien species Solidago altissima and is an effective management strategy
<p><b>Table 2.</b> Heights of <i>S. altissima</i> before mowing treatment in May 2019.</p><table><tbody><tr><th>Site</th><th>Control</th><th>Mowing 1</th><th>Mowing 2</th><th>Mowing 3</th></tr></tbody><tbody><tr><th>Site 1</th><td>101.0 ± 3.2a</td><td>101.0 ± 3.0a</td><td>81.1 ± 2.6b</td><td>64.9 ± 3.1c</td></tr><tr><th>Site 2</th><td>116.5 ± 4.7a</td><td>111.8 ± 4.7a</td><td>82.9 ± 4.1b</td><td>72.5 ± 6.6b</td></tr><tr><th>Site 4</th><td>89.1 ± 2.2a</td><td>74.8 ± 2.2b</td><td>49.5 ± 3.1d</td><td>59.8 ± 1.6c</td></tr><tr><th>Site 5</th><td>93.5 ± 2.5a</td><td>99.5 ± 2.3a</td><td>78.0 ± 2.0b</td><td>77.1 ± 2.0b</td></tr><tr><th>Site 6</th><td>125.3 ± 5.2a</td><td>117.0 ± 3.0a</td><td>86.5 ± 3.0c</td><td>105.0 ± 3.4b</td></tr><tr><th>Site 7</th><td>123.3 ± 3.3a</td><td>119.3 ± 2.9ab</td><td>111.3 ± 2.8bc</td><td>110.3 ± 3.1c</td></tr><tr><th>Site 8</th><td>96.0 ± 4.0a</td><td>94.5 ± 4.1ab</td><td>84.5 ± 3.3bc</td><td>81.0 ± 3.1c</td></tr></tbody></table><p>* Mowing 1: mowed once in July; Mowing 2: mowed twice in May and September; Mowing 3: mowed three times in May, July, and September. Data are presented as means ± standard errors of 10 replicates. Means within a row followed by different letters are significantly different at p <0.05 (ANOVA with post hoc Tukey’s test). Units: cm.</p>
Table 1 in Mowing inhibits the invasion of the alien species Solidago altissima and is an effective management strategy
<p><b>Table 1.</b> Heights of dead shoots of <i>S. altissima</i> after 1 year of treatment in April 2019.</p><table><tbody><tr><th>Site</th><th>Control</th><th>Mowing 1</th><th>Mowing 2</th><th>Mowing 3</th><th>Eco 200</th></tr></tbody><tbody><tr><th>Site 1</th><td>162.8 ± 6.62a</td><td>85.7 ± 5.52b</td><td>29.5 ± 2.74c</td><td>21.3 ± 2.09c</td><td>77.9 ± 8.94b</td></tr><tr><th>Site 2</th><td>213.2 ± 12.08a</td><td>87.1 ± 7.33c</td><td>38.4 ± 2.17d</td><td>29.0 ± 2.01d</td><td>144.8 ± 9.57b</td></tr><tr><th>Site 3</th><td>163.4 ± 7.31a</td><td>66.3 ± 4.55c</td><td>40.7 ± 2.57d</td><td>33.4 ± 2.11d</td><td>143.4 ± 7.13b</td></tr><tr><th>Site 4</th><td>191.6 ± 3.84a</td><td>160.8 ± 7.90b</td><td>59.2 ± 1.66d</td><td>81.9 ± 6.50c</td><td>166.9 ± 12.33b</td></tr><tr><th>Site 5</th><td>181.7 ± 4.76a</td><td>114.7 ± 2.90b</td><td>89.5 ± 10.23c</td><td>46.6 ± 5.12d</td><td>183.8 ± 9.00a</td></tr><tr><th>Site 6</th><td>174.8 ± 4.34a</td><td>82.9 ± 4.49c</td><td>39.7 ± 2.51d</td><td>31.6 ± 2.47d</td><td>153.3 ± 3.12b</td></tr><tr><th>Site 7</th><td>165.5 ± 4.79a</td><td>91.2 ± 6.02b</td><td>64.7 ± 3.24c</td><td>44.0 ± 3.67d</td><td>153.8 ± 7.25a</td></tr></tbody></table><p>* Mowing 1: mowed once in July; Mowing 2: mowed twice in May and September; Mowing 3: mowed three times in May, July, and September; Eco 200: 30% of shoots in the quadrats were cut near the ground, and the cut surfaces were covered with the Eco 200 block. Data are presented as means ± standard errors of 20 replicates. Means within a row followed by different letters are significantly different at p <0.05 (ANOVA with post hoc Tukey’s test). Units: cm.</p>
Table 4 in Mowing inhibits the invasion of the alien species Solidago altissima and is an effective management strategy
<p><b>Table 4.</b> Change in areas occupied by <i>S. altissima</i> in patch-type communities from March 2018 to March 2019.</p><table><tbody><tr><th>Control</th><th>Mowing 3</th><th>Eco 20%</th><th>Eco 100%</th></tr></tbody><tbody><tr><th>205.2 ± 107.3</th><td>24.7 ± 44.0</td><td>104.5 ± 85.2</td><td><i>−</i> 31.7 ± 13.0</td></tr></tbody></table><p>Mowing 3: mowed three times in May, July, and September; Eco 20%: 20% of shoots were mowed and treated with Eco 200; Eco 100%: 100% of shoots were mowed and treated with Eco 200. Data are presented as means ± standard errors of six replicates.</p>
Table 1 in America's Most Wanted Fishes: cataloging risk assessments to prioritize invasive species for management action
<p><b>Table 1.</b> List of fish species with high-risk statuses in the conterminous U.S., in the Great Lakes region, and in the state of Florida. Horizontal lines separate families and species for clarity.</p><table><tbody><tr><th>Family Name</th><th>Fish species name</th><th>Conterminous U.S.</th><th>Great Lakes</th><th>Florida</th></tr></tbody><tbody><tr><th>Atherinidae</th><td><i>Atherina boyeri</i> (Risso, 1810)</td><td>X</td><td>X</td><td></td></tr><tr><th>Atherinopsidae</th><td><i>Menidia beryllina</i> (Cope, 1867)</td><td>X</td><td></td><td></td></tr><tr><td><i>Odontesthes bonariensis</i> (Valenciennes, 1835)</td><td>X</td><td></td><td></td></tr><tr><th>Callichthyidae</th><td><i>Hoplosternum littorale</i> (Hancock, 1828)</td><td></td><td></td><td>X</td></tr><tr><th>Centrarchidae</th><td><i>Lepomis microlophus</i> (Günther, 1859)</td><td>X</td><td>X</td><td></td></tr><tr><td><i>Micropterus dolomieu</i> (Lacepède, 1802)</td><td>X</td><td></td><td></td></tr><tr><td><i>Micropterus salmoides</i> (Lacepède, 1802)</td><td>X</td><td></td><td></td></tr><tr><th>Centropomidae</th><td><i>Lates niloticus</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr><tr><th>Channidae</th><td><i>Channa argus</i> (Cantor, 1842)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Channa marulius</i> (Hamilton, 1822)</td><td></td><td></td><td>X</td></tr><tr><th>Characidae</th><td><i>Pygocentrus nattereri</i> (Kner, 1858)</td><td>X</td><td></td><td></td></tr><tr><th>Cichlidae</th><td><i>Amatitlania nigrofasciata</i> (Günther, 1867)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Astronotus ocellatus</i> (Agassiz, 1831)</td><td>X</td><td></td><td></td></tr><tr><td><i>Cichla kelberi</i> (Kullander & Ferreira, 2006)</td><td>X</td><td></td><td></td></tr><tr><td><i>Cichla ocellaris</i> (Bloch & Schneider, 1801)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Cichlasoma uropthalma</i> (Günther, 1862)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Coptodon rendalli</i> (Boulenger, 1897)</td><td>X</td><td></td><td></td></tr><tr><td><i>Coptodon zilli</i> (Gervais, 1848)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Hemichromis letourneuxi</i> (Sauvage, 1880)</td><td>X</td><td></td><td></td></tr><tr><td><i>Herichthys cyanoguttatus</i> (Baird & Girard, 1854)</td><td>X</td><td></td><td></td></tr><tr><td><i>Oreochromis aureus</i> (Steindachner, 1864)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Oreochromis mossambicus</i> (Peters, 1852)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Oreochromis niloticus</i> (Linnaeus, 1758)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Parachromis managuensis</i> (Günther, 1862)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Pelmatotilapia mariae</i> (Boulenger, 1899)</td><td>X</td><td></td><td></td></tr><tr><td><i>Sarotherodon melanotheron</i> (Rüppell, 1852)</td><td>X</td><td></td><td></td></tr><tr><td><i>Serranochromis robustus</i> (Günther, 1864)</td><td>X</td><td></td><td></td></tr><tr><th>Clariidae</th><td><i>Clarias batrachus</i> (Linnaeus, 1758)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Clarias fuscus</i> (Lacepède, 1803)</td><td>X</td><td></td><td></td></tr><tr><td><i>Clarias gariepinus</i> (Burchell, 1822)</td><td>X</td><td></td><td></td></tr><tr><th>Clupeidae</th><td><i>Alosa aestivalis</i> (Mitchell, 1814)</td><td>X</td><td></td><td></td></tr><tr><td><i>Alosa pseudoharengus</i> (Wilson, 1811)</td><td>X</td><td>X</td><td></td></tr><tr><th>Cobitidae</th><td><i>Misgurnus anguillicaudatus</i> (Cantor, 1842)</td><td>X</td><td></td><td>X</td></tr><tr><th>Cyprinidae</th><td><i>Abramis brama</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr><tr><td><i>Alburnus alburnus</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr><tr><td><i>Barbus barbus</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr><tr><td><i>Carassius auratus</i> (Linnaeus, 1758)</td><td></td><td></td><td>X</td></tr><tr><td><i>Carassius gibelio</i> (Bloch, 1782)</td><td>X</td><td></td><td></td></tr><tr><td><i>Ctenopharyngodon idella</i> (Valenciennes, 1844)</td><td>X</td><td>X</td><td>X</td></tr><tr><td><i>Culter alburnus</i> (Basilewsky, 1855)</td><td>X</td><td></td><td></td></tr><tr><td><i>Cyprinella lutrensis</i> (Baird & Girard, 1853)</td><td>X</td><td></td><td></td></tr><tr><td><i>Cyprinus carpio</i> (Linnaeus, 1758)</td><td>X</td><td>X</td><td>X</td></tr><tr><td><i>Gobio gobio</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr><tr><td><i>Hypophthalmichthys molitrix</i> (Valenciennes, 1844)</td><td>X</td><td></td><td></td></tr><tr><td><i>Hypophthalmichthys nobilis</i> (Richardson, 1845)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Leuciscus idus</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr><tr><td><i>Mylopharyngodon piceus</i> (Richardson, 1846)</td><td>X</td><td></td><td></td></tr><tr><td><i>Opsariichthys uncirostris</i> (Temminck & Schlegel, 1846)</td><td>X</td><td></td><td></td></tr><tr><td><i>Pimephales promelas</i> (Rafinsque, 1820)</td><td></td><td></td><td>X</td></tr><tr><td><i>Phoxinus phoxinus</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr><tr><td><i>Protochondrostoma genei</i> (Bonaparte, 1839)</td><td>X</td><td></td><td></td></tr><tr><td><i>Pseudorasbora parva</i> (Temminck & Schlegel, 1846)</td><td>X</td><td></td><td></td></tr><tr><td><i>Rhodeus amarus</i> (Bloch, 1782)</td><td>X</td><td></td><td></td></tr><tr><td><i>Rutilus aula</i> (Bonparte,1841)</td><td>X</td><td></td><td></td></tr><tr><td><i>Rutilus rutilus</i> (Linnaeus, 1758)</td><td>X</td><td>X</td><td></td></tr><tr><td><i>Scardinius erythrophthalmus</i> (Linnaeus, 1758)</td><td>X</td><td>X</td><td></td></tr><tr><td><i>Tinca tinca</i> (Linnaeus, 1758)</td><td>X</td><td>X</td><td></td></tr><tr><th>Esocidae</th><td><i>Esox lucius</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr><tr><td><i>Esox masquinongy</i> (Mitchill, 1824)</td><td>X</td><td></td><td></td></tr><tr><th>Gasterosteidae</th><td><i>Gasterosteus aculeatus</i> (Linnaeus, 1758)</td><td>X</td><td>X</td><td></td></tr><tr><th>Gobiidae</th><td><i>Neogobius melanostomus</i> (Pallas, 1814)</td><td>X</td><td>X</td><td></td></tr><tr><td><i>Ponticola platyrostris</i> (Pallas, 1814)</td><td>X</td><td></td><td></td></tr><tr><td><i>Proterorhinus semilunaris</i> (Heckel, 1837)</td><td>X</td><td></td><td></td></tr><tr><th>Heteropneustidae</th><td><i>Heteropneustes fossilis</i> (Bloch, 1794)</td><td>X</td><td></td><td></td></tr><tr><th>Ictaluridae</th><td><i>Ictalurus furcatus</i> (Valenciennes, 1840)</td><td>X</td><td></td><td></td></tr><tr><td><i>Pylodictis olivaris</i> (Rafinesque, 1818)</td><td>X</td><td></td><td></td></tr><tr><th>Loricariidae</th><td><i>Hypostomus plecostomus</i> (Linnaeus, 1758)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Pterygoplichthys anisitsi</i> (Eigenmann & Kennedy, 1903)</td><td></td><td></td><td>X</td></tr><tr><td><i>Pterygoplichthys disjunctivus</i> (Weber, 1991)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Pterygoplichthys multiradiatus</i> (Hancock, 1828)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Pterygoplichthys pardalis</i> (Castelnau, 1855)</td><td>X</td><td></td><td></td></tr><tr><th>Moronidae</th><td><i>Morone americana</i> (Gmelin, 1789)</td><td>X</td><td>X</td><td></td></tr><tr><th>Notopteridae</th><td><i>Chitala ornata</i> (Gray, 1831)</td><td>X</td><td></td><td></td></tr><tr><th>Odontobutidae</th><td><i>Perccottus glenii</i> (Dybowski, 1877)</td><td>X</td><td></td><td></td></tr><tr><th>Osmeridae</th><td><i>Hypomesus nipponensis</i> (McAllister, 1963)</td><td>X</td><td></td><td></td></tr><tr><td><i>Osmerus eperlanus</i> (Linnaeus, 1758)</td><td>X</td><td>X</td><td></td></tr><tr><td><i>Osmerus mordax</i> (Mitchill, 1814)</td><td>X</td><td></td><td></td></tr><tr><th>Osphronemidae</th><td><i>Trichogaster lalius</i> (Hamilton, 1822)</td><td>X</td><td></td><td></td></tr><tr><td><i>Trichopodus trichopterus</i> (Pallas, 1770)</td><td>X</td><td></td><td></td></tr><tr><th>Percidae</th><td><i>Gymnocephalus cernua</i> (Linnaeus, 1758)</td><td>X</td><td>X</td><td></td></tr><tr><td><i>Perca flavescens</i> (Mitchill, 1814)</td><td>X</td><td></td><td></td></tr><tr><td><i>Perca fluviatilis</i> (Linnaeus, 1758)</td><td>X</td><td>X</td><td></td></tr><tr><td><i>Sander lucioperca</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr><tr><td><i>Sander vitreus</i> (Mitchill, 1818)</td><td>X</td><td></td><td></td></tr><tr><th>Petromyzontidae</th><td><i>Petromyzon marinus</i> (Linnaeus, 1758)</td><td></td><td>X</td><td></td></tr><tr><th>Poeciliidae</th><td><i>Belonesox belizanus</i> (Kner, 1860)</td><td>X</td><td></td><td></td></tr><tr><td><i>Gambusia affinis</i> (Baird & Girard, 1853)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Gambusia holbrooki</i> (Girard, 1859)</td><td>X</td><td></td><td></td></tr><tr><td><i>Poecilia latipinna</i> (Lesueur, 1821)</td><td>X</td><td></td><td></td></tr><tr><td><i>Poecilia reticulata</i> (Peters, 1859)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Poecilia sphenops</i> (Valciennes, 1846)</td><td>X</td><td></td><td>X</td></tr><tr><td><i>Xiphophorus helleri</i> (Heckel, 1848)</td><td>X</td><td></td><td></td></tr><tr><td><i>Xiphophorus variatus</i> (Meek, 1904)</td><td>X</td><td></td><td></td></tr><tr><th>Polypteridae</th><td><i>Polypterus delhezi</i> (Boulenger, 1899)</td><td></td><td></td><td>X</td></tr><tr><th>Pomacentridae</th><td><i>Neopomacentrus cyanomos</i> (Bleeker, 1856)</td><td>X</td><td></td><td></td></tr><tr><th>Potamotrygonidae</th><td><i>Potamotrygon falkneri</i> (Castex & Maciel, 1963)</td><td>X</td><td></td><td></td></tr><tr><td><i>Potamotrygon motoro</i> (Müller & Henle, 1841)</td><td>X</td><td></td><td></td></tr><tr><td><i>Potamotrygon schuhmacheri</i> (Castex, 1964)</td><td>X</td><td></td><td></td></tr><tr><th>Salmonidae</th><td><i>Coregonus albula</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr><tr><td><i>Coregonus lavaretus</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr><tr><td><i>Coregonus maraena</i> (Bloch, 1779)</td><td>X</td><td></td><td></td></tr><tr><td><i>Coregonus peled</i> (Gmelin, 1789)</td><td>X</td><td></td><td></td></tr><tr><td><i>Hucho hucho</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr><tr><td><i>Oncorhynchus kisutch</i> (Walbaum, 1792)</td><td></td><td>X</td><td></td></tr><tr><td><i>Oncorhynchus mykiss</i> (Walbaum, 1792)</td><td></td><td>X</td><td></td></tr><tr><td><i>Oncorhynchus tshawytscha</i> (Walbaum, 1792)</td><td></td><td>X</td><td></td></tr><tr><td><i>Salmo trutta</i> (Linnaeus, 1758)</td><td></td><td>X</td><td></td></tr><tr><th>Scorpaenidae</th><td><i>Pterois miles</i> (Bennett, 1828)</td><td>X</td><td></td><td></td></tr><tr><td><i>Pterois volitans</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr><tr><th>Siluridae</th><td><i>Silurus glanis</i> (Linnaeus, 1758)</td><td>X</td><td></td><td></td></tr></tbody></table>
Table 2 in Clonal mechanisms that matter in Agave fourcroydes and A. sisalana invasions in drylands: implications for their management
<p><b>Table 2.</b> Standardized coefficients of the best-fitting Generalized Linear Mixed Models (GLMMs) explaining bulbil rooting success under the monitored conditions (natural conditions, and experimental plots). Three predictor variable types were considered (intrinsic mother plant and bulbil traits, and extrinsic bulbil traits). Species identity and plots were included as random factors. The best model was ranked according to AIC (Akaike Information Criterion) value (See also Table S5). Non-data cells indicate variables not included in a particular model.</p><table><tbody><tr><th></th><th>Intrinsic mother plant Extrinsic bulbil Extrinsic bulbil</th><th>Random factor</th></tr></tbody><tbody><tr><th>Sets of bulbils data</th><td>Intercept</td><td>traits Rosette Scape diameter height</td><td>traits Height</td><td>traits Position Buried</td><td>significance Species Plot identity</td></tr><tr><th>Monitored under natural conditions</th><td>0.936***</td><td>−0.064</td><td>0.034</td><td></td><td>0.799***</td><td>ns</td><td></td></tr><tr><th>Greenhouse plots</th><td>−3.805***</td><td></td><td></td><td>0.835***</td><td>3.427***</td><td>ns</td><td>ns</td></tr><tr><th>Natural conditions plots</th><td>−0.481***</td><td>0.124</td><td></td><td></td><td>1.897***</td><td>ns</td><td>ns</td></tr></tbody></table><p>Significance: *** <i>p-values</i> <0.001. ns: no significance</p>
Table 1 in Clonal mechanisms that matter in Agave fourcroydes and A. sisalana invasions in drylands: implications for their management
<p><b>Table 1.</b> Sampling goals, work stages and summary of data collected.</p><table><tbody><tr><th>Sampling goal</th><th>Phenology of flowering and bulbils and production of basal shoots</th><th>Estimation of bulbil production*</th><th>Estimation of shoot production from rhizomes</th><th>Estimation of rooting rates of bulbils under natural conditions</th><th>Estimation of the rooting rates of bulbils in experimental plots (greenhouse and natural conditions)*</th><th>Phenology of shoots production and rate of establishment of new individuals (rooted bulbils vs shoots from rhizomes)</th></tr></tbody><tbody><tr><th>Sampling unit</th><td>60 individuals (30 from Agave fourcroydes and 30 from | <i>A. sisalana</i>)</td><td>60 individuals (30 from Agave fourcroydes and 30 from <i>A. sisalana</i>)</td><td>8 individuals (4 from Agave fourcroydes, 4 from <i>A. sisalana</i>)</td><td>1,184 bulbils (582 from Agave fourcroydes, 602 from <i>A. sisalana</i>; 590 from early fall, 594 from late fall)</td><td>1,440 bulbils, 24 <i>from each of 60</i> individuals (30 from <i>Agave fourcroydes</i>, and 30 from <i>A. sisalana</i>)</td><td>10 plots 10 × 10 m</td></tr><tr><th>Monitored individual nature</th><td>Reproductive individuals with incipient floral scapes emerging from the rosette</td><td>Reproductive individuals with floral scapes at the stage of maximum bulbils production</td><td>Juvenile, i.e., prominent but not reproductive individuals</td><td>Mature bulbils fallen from the floral scapes</td><td>Mature bulbils fallen from the floral scapes</td><td>New young individuals</td></tr><tr><th>Traits measured</th><td>Height and</td><td>Height and</td><td>Number of</td><td>Height (cm)</td><td>Height (cm)</td><td>Origin: from</td></tr><tr><td>diameter of the rosette (cm) Height and</td><td>diameter of the rosette (cm) Height and</td><td>rhizomes Number of emerged shoots</td><td>Diameter (cm) Number of leaves</td><td>Diameter (cm) Number of leaves</td><td>rhizomes or rooted bulbils Presence of</td></tr><tr><td>diameter of the floral scape (cm)</td><td>diameter of the floral scape (cm)</td><td>from rhizomes</td><td>Number of preformed roots</td><td>Number of preformed roots</td><td>contractile roots</td></tr><tr><td>Number of scape branches</td><td>Number of bulbils by scape branches</td><td></td><td>Position (vertical or horizontal)</td><td>Rooted</td><td></td></tr><tr><td>% of branches with floral buds</td><td>Number of fallen bulbils</td><td></td><td>Buried or not</td><td></td><td></td></tr><tr><td>% of branches with flowers</td><td></td><td></td><td>Rooted</td><td></td><td></td></tr><tr><th>% of branches with bulbils</th></tr><tr><th>% of branches with fruits</th></tr><tr><th>% of branches with fallen bulbils</th></tr><tr><th>Number of basal shoots (born directly from the rosette)</th></tr><tr><th>Monitoring dates and frequency</th><td>February 2015– May 2016 (monthly)</td><td>October 2015</td><td>May–July 2015</td><td>October 2015– October 2016 (monthly)</td><td>January 2016– May 2016</td><td>February 2015– February 2016 (monthly)</td></tr></tbody></table><p>* The individuals selected for these two studies were the same.</p>
AI-based real-time animal management system for flock monitoring in the husbandry of fattening turkeys
<p>The dataset was collected in a commercial turkey barn using a self-developed AI-based real-time animal management system to assess the behavior of turkeys. The data covers turkeys from the 6th to the 18th week of life.</p>
Data analysis & code: Quantifying the impact of climate change and forest management on Swedish forest ecosystems using the dynamic vegetation model LPJ-GUESS
<p><span>This file contains code to optimize the allometric parameters, to plot the figures, and details of the underlying data analysis in "Quantifying the impact of climate change and forest management on Swedish forest ecosystems using the dynamic vegetation model LPJ-GUESS" (Bergkvist et al.). <br></span></p>
Supporting information for: The Time Requirements for Primary Care Consultations: Initial Sick Child Visits in Low- and Middle-income Countries Using the Integrated Management of Childhood Illness (IMCI) Clinical Algorithm
<p>Few studies have examined the time required for primary care consultations; none have focused on sick child visits in low- and middle-income countries (LMICs). This project begins to fill that gap by providing evidence-based estimates of the time needed for initial visits with under-five infants and children at public or not-for-profit facilities in countries using the Integrated Management of Childhood Illness (IMCI) clinical algorithm.</p> <p>Estimates of the mean expected duration of IMCI consultations require (a) classification profiles, i.e., tabulations of the gold standard health issues presented by patients less than 5 years old; (b) lists of the tasks included in applicable versions of the IMCI algorithm and the conditions that elicit them, and (c) an estimate of the time needed to perform tasks with no pre-defined minimum duration. The latter requires, in addition to classification profiles, information on rates of task performance and the mean observed duration of consultations.</p> <p>The IMCI clinical algorithm and the research surrounding it provide unusually rich sources of such information. Developed in the mid 1990s by the World Health Organization and the United Nations Children’s Fund, the IMCI algorithm seeks to reduce child mortality in LMICs by improving the technical quality of primary care services. For infants less than 2 months old, the algorithm focuses on bacterial infections, feeding problems, low weight, and, in some versions, jaundice. For children 2-59 months old, the foci include acute respiratory infections, especially pneumonia; diarrhea; fevers, especially malaria and measles; malnutrition, and anemia. Immunization status is a concern for both age groups. The algorithm provides a scheme to classify the health issues with which infants and children present, an array of tasks providers may be expected perform, and criteria by which tasks are elicited. Research on the design and utility of the algorithm, its effects on provider performance, and related topics furnishes data on the prevalence of gold standard IMCI classifications in a variety of patient populations. In some cases, it also enables one to calculate the time required to perform tasks.</p> <p>I found such information by searching MEDLINE, the database of the International Network for Rational Use of Medicines, the websites of the WHO and its regional offices, GOOGLE, and GOOGLE SCHOLAR using search terms such as ‘Integrated Management of Childhood Illness’, ‘observational’, ‘prospective’, ‘classification’, ‘clinical signs’, ‘health facility survey’, and ‘validity’. I also reviewed studies that cited a qualified study and, conversely, material included in the bibliographies of qualified studies.</p> <p>The supplemental information files contain the following:</p> <p>WORKBOOK S1_STUDIES USED</p> <p>Lists features of, and sources for, the studies used to construct classification profiles and to estimate the time required to perform the average task with no predefined minimum duration. With 2 exceptions (see below, DATA S1 and DATA S2), all the studies have been published or are readily available on the internet. None of the data can be used to identify individuals.</p> <p>DATA S1_REPORT OF THE HEALTH FACILITY SURVEY IN BOTSWANA, 2007-08 and DATA S2_REPORT OF THE HEALTH FACILITY SURVEY IN TANZANIA, 2003</p> <p>PDF files of Health Facility Survey reports that were found on the internet but have since been taken down.</p> <p>DATA S3_BURKINA FASO CHART BOOKLET, 2015</p> <p>PDF provided <span>Drs. Sophie Sarrassat (London School of Hygiene and Tropical Medicine) and Serge M. A. Somda (Université Nazi BONI).</span></p> <p>WORKBOOK S2_CLASSIFICATION PROFILES: INFANTS; WORKBOOK S3_CLASSIFICATION PROFILES: CHILDREN IN UPPER MIDDLE-INCOME COUNTRIES; WORKBOOK S4_CLASSIFICATION PROFILES: CHILDREN IN LOWER MIDDLE-INCOME COUNTRIES (I); WORKBOOK S5_CLASSIFICATION PROFILES: CHILDREN IN LOWER MIDDLE-INCOME COUNTRIES (II), and WORKBOOK S6_CLASSIFICATION PROFILES: CHILDREN IN LOW INCOME COUNTRIES </p> <p>The design of the worksheets in these workbooks is described in TEXT S1_NOTES OF THE CONSTRUCTION OF CLASSIFICATION PROFILES (see below).</p> <p>WORKBOOK S7_IMCI CLINICAL TASKS</p> <p>Lists the clinical tasks provided by relevant IMCI algorithms for the care of infants and children. Consists of 6 worksheets covering mandatory tasks, conditional assessments, and treatment and counseling tasks for infants and children.</p> <p>WORKBOOK S8_MINUTES PER TASK WITH NO MINIMUM DURATION</p> <p>Provides estimate of the mean time required to perform a task with no minimum duration for each of 7 populations for which the required data are available, corrected, where necessary, for the effect of an observer on the rate and pace of task performance. Also provides a geometric mean for all 7 populations.</p> <p>TEXT S1_NOTES ON METHODOLOGY</p> <p>WORD document describing the steps involved in estimating the expected durations of consultations.</p> <p>TEXT S2_NOTES OF THE CONSTRUCTION OF CLASSIFICATION PROFILES</p> <p>WORD document describing the steps involved in constructing each profile, problems encountered, and how they were solved.</p> <p>TEXT S3_NOTES ON THE IDENTIFICATION OF IMCI CLINICAL TASKS</p> <p>WORD document describing the standards used in identifying clinical tasks in IMCI algorithms.</p> <p>TEXT S4_NOTES ON THE ESTIMATION OF MINUTES PER TASK WITH NO MINIMUM DURATION</p> <p>WORD document describing the steps involved in estimating the mean time required to perform a task with no predefined minimum duration, problems encountered, and how they were solved.</p>
The efficacy of hemoglobin spray in wound management: a systematic review and network meta-analysis of comparative studies
<p>Dataset</p>
Utilizing Digital Information for Personalized Cholesterol Management
<p><span>Patients with diseases due to high cholesterol levels in Indonesia can be said to be high because, in 2022, sufferers of the disease reached 28%, with 7.9% of people dying. The problem in this study is the need for more understanding, awareness, and ability of the community to manage and maintain cholesterol through digital information, especially social media. This research uses a questionnaire to understand the community's needs and supporting factors in digitally managing cholesterol through social media. This research gathered data using purposive sampling through a Google Form questionnaire. The data collection was conducted in April 2024 in various cities in Indonesia and successfully collected 221 responses. The method used to analyze the data in this study is the Structural Equation Model (SEM) using Smart Partial Least Square (Smart-PLS) 4.0 as the analysis approach. In this study, there are ten variables with five indicators each, and there are 11 hypotheses. The outcomes of this study show that all of the indicators were valid and reliable, with all hypotheses having a significant effect. Indonesian people must be able to utilize digital information to increase knowledge and understanding about health, especially about Cholesterol Management, so that the number of people affected by disease and who die from cholesterol can be reduced.</span></p>
Species functional data and species distribution model projections for future land-use and fire management scenarios in the Transboundary Biosphere Reserve Gerês-Xurés
<p>The data includes nine functional traits and species distribution model projections for 102 species of vertebrates (amphibians, birds, and reptiles) in the Transboundary Biosphere Reserve Gerês-Xurés. The model projections are available for 2050 under six different land-use and fire management scenarios, namely two land-use scenarios of “business-as-usual” (BAU; ongoing trends of land abandonment) and “High Nature Value farmlands” (HNV), each under three fire management scenarios (low suppression - LS, current fire suppression - CS, and high fire suppression - HS). The species distribution projections for each scenario are presented as matrices of species presences/absences, obtained after reclassifying consensus predictions of species distribution models.</p>
Dataset: Towards a Knowledge Management Framework for LLM-Generated Personas in Collaborative Systems
Open the record for dataset details and reuse information.
Leveraging Digital Transformation for Effective Issue Management as a Strategy to Strengthen Corporate Image
<p>Leveraging Digital Transformation for Effective Issue Management as a Strategy to Strengthen Corporate Image by Wiwi Lestari, Adhi Murti, Nisrin Husna</p>
Medicinal Plant Utilisation and Environmental and Management Drivers Influencing Forest Medicinal Plants in the Czech Republic
<p>This data on medicinal plant utilization and the influence of environmental drivers on medicinal plant availability is part of a broader survey on forest ecosystem services and health conducted in the Czech Republic under the project "Excellent research as a support for the adaptation of forestry and timber industry to global change and the 4th industrial revolution"<em> (EVA 4.0) (</em>CZ.02.1.01/0.0/0.0/16_019/0000803).</p>
Data from: Direct and indirect effects of landscape and field management intensity on carabids through trophic resources and weeds
<p>Carabids are important biological control agents of weeds and other pests in agricultural fields. The carabid community is built upon direct and indirect ecological effects of landscape complexity, field management intensity and biotic components that in interaction make any prediction of community size and composition challenging.</p> <p>We analyse a large-scale sample of 60 European cereal fields using Structural Equation Modelling to quantify the direct effects of field management intensity and the surrounding landscape, and their indirect effect via biotic components, on carabid diversity.</p> <p>Our results highlight that direct and indirect effects of increasing landscape complexity, mediated by trophic resources, mainly affect carabids positively. Field management intensity only ever affects carabids through indirect effects that are generally negative, by suppressing standing weeds and weed seeds.</p> <p>Indirect effects on granivore carabid species depended on weed seed availability whereas omnivores depended on the availability of both weed seeds and animal prey.</p> <p><i>Synthesis and applications</i><span>: A consideration of both the direct and indirect effects of landscape and field management is necessary for predicting carabid communities and with interactions. These effects, mediated via trophic resources, supports the diversity and abundance of carabid communities and their provision of ecosystem services. Our results show that promoting crop diversity and connectivity to semi-natural habitats will directly enhance carabid communities in farmland by manipulating their migration from source habitats. A reduction in field management intensity will preserve local standing weeds and weed seeds, and indirectly support carabid communities. These local and landscape modifications could contribute to improve the natural regulation of pests and weeds by carabids.</span></p>
Data from: Simulating effects of fitness and dispersal on the use of Trojan sex chromosomes for invasive species management
<ol> <li>The use of Trojan Y Chromosomes (TYC) for controlling invasive species involves manipulating the sex chromosomes of captive-raised individuals. Once released, the offspring of these individuals consist of only one sex, thereby skewing the sex-ratio of the invasive population and potentially leading to eradication. Simulation models are needed that can inform managers on how to maximize the likelihood of species eradication, since implementation of this novel management approach in the field is still rare.</li> <li>Here, we present the first spatially explicit, mechanistic simulation model of a real-world TYC program for invasive species eradication. Using a brook trout (Salvelinus fontinalis) system model, we investigated the effects of competitive and reproductive fitness of the captive-raised YY males, dispersal behavior upon their release, and landscape heterogeneity on eradication success.</li> <li>Likelihood of eradication was dependent on both the competitive and reproductive fitness of the Trojan individuals. Competitive fitness (i.e., survival) had a higher threshold for eradication, below which populations failed to be eradicated.</li> <li>Movement ecology of both the wild and YY male populations was important for eradication. Under a restricted dispersal scenario for YY males following their release, the wild population was not extirpated but maintained a stable, yet reduced, population size. In terms of landscape configuration, time to eradication of local patches increased with greater connectivity within the stream network.</li> <li>In addition to sex ratio distortion, density-dependent mortality resulting from outplantings made an important contribution to eradication and therefore may also affect native competitors.</li> <li>While our results indicate that eradication is possible, maximizing its likelihood requires an understanding of the fitness and movement ecology of both the wild and YY male populations of the invasive species. Both our model and the principles derived from this study related to fitness and behavioral landscape ecology can be broadly applied to other invaded species and systems.</li> </ol>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.