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Table 4 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
<p><b>Table 4</b> Biting location of <i>An. gambiae</i> (<i>s.l</i>.) in IRS and control areas</p><table><tbody><tr><th>Period</th><th>Area</th><th>Location</th><th>No. collected</th><th>Person/night</th><th>HBR (b/p/n)</th><th><i>P</i> -value</th></tr></tbody><tbody><tr><th>Before IRS (June–October 2016)</th><td>Future IRS</td><td>Indoor</td><td>1156</td><td>128</td><td>9.03</td><td>0.00098</td></tr><tr><th></th><td></td><td>Outdoor</td><td>1002</td><td>128</td><td>7.83</td><td></td></tr><tr><th>After 1st IRS round (June–September 2017)</th><td>IRS</td><td>Indoor</td><td>544</td><td>112</td><td>4.86</td><td><0.0001</td></tr><tr><th></th><td></td><td>Outdoor</td><td>1150</td><td>112</td><td>10.27</td><td></td></tr><tr><th></th><td>Control</td><td>Indoor</td><td>678</td><td>52</td><td>13.04</td><td><0.0001</td></tr><tr><th></th><td></td><td>Outdoor</td><td>446</td><td>52</td><td>8.58</td><td></td></tr><tr><th>After 2nd IRS round (June–August 2018)</th><td>IRS</td><td>Indoor</td><td>599</td><td>80</td><td>7.49</td><td>0.233</td></tr><tr><th></th><td></td><td>Outdoor</td><td>642</td><td>80</td><td>8.03</td><td></td></tr><tr><th></th><td>Control</td><td>Indoor</td><td>568</td><td>40</td><td>14.2</td><td><0.0001</td></tr><tr><th></th><td></td><td>Outdoor</td><td>313</td><td>40</td><td>7.83</td><td></td></tr></tbody></table><p><i>AbbreviatioNS</i>: HBR,human biting rate;b/p/n,bite/person/night</p>
Table 1 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
<p><b>Table 1</b> <i>Anopheles</i> species composition in surveyed areas before and after IRS</p><table><tbody><tr><th>Species</th><th>Before IRS (May 2016–April 2017)</th><th>After 1st round of IRS (June 2017–March 2018)</th><th>Control (Bembereke, Kouande)</th><th>After 2nd round of IRS (June 2018–November 2018)</th><th>Control (Bembereke– Kouande)</th><th>Total</th></tr></tbody><tbody><tr><th><i>An.gambiae</i> (<i>s.l</i>.)</th><td>2465</td><td>2379</td><td>1546</td><td>1286</td><td>929</td><td>8605</td></tr><tr><th><i>An.funestus</i></th><td>54</td><td>54</td><td>16</td><td>9</td><td>7</td><td>140</td></tr><tr><th><i>An.coustani</i></th><td>9</td><td>0</td><td>0</td><td>0</td><td>0</td><td>9</td></tr><tr><th><i>An.pharoensis</i></th><td>3</td><td>2</td><td>5</td><td>2</td><td>3</td><td>15</td></tr><tr><th><i>An.paludis</i></th><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><th><i>An.nili</i></th><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td></tr><tr><th><i>An.ziemanni</i></th><td>2</td><td>2</td><td>0</td><td>1</td><td>0</td><td>5</td></tr><tr><th>Total</th><td>2534</td><td>2437</td><td>1567</td><td>1299</td><td>939</td><td>8776</td></tr></tbody></table>
Table 3 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
<p><b>Table 3</b> Parity rates of <i>Anopheles gambiae</i> (<i>s.l</i>.) collected before and after the first round IRS</p><table><tbody><tr><th>IRS area</th><th>Variable</th><th>Before IRS (June–October 2016)</th><th>After 1st round of IRS (June– October 2017)</th><th><i>Χ</i> 2-value</th><th><i>P</i> -value</th></tr></tbody><tbody><tr><th>Alibori-Donga</th><td>No. dissected</td><td>1422</td><td>1214</td><td>–</td><td>–</td></tr><tr><th></th><td>No. parous</td><td>996</td><td>457</td><td>–</td><td>–</td></tr><tr><th></th><td>Parous (%)</td><td>70.04</td><td>37.64</td><td>276.57</td><td><0.0001</td></tr></tbody></table>
Table 2 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
<p><b>Table 2</b> Frequency of sibling species of the <i>An. gambiae</i> (<i>s.l.</i>) complex in IRS and control areas</p><table><tbody><tr><th>Period</th><th>No. of</th><th>Alibori (IRS Area 1)</th><th></th><th>Donga (IRS Area 2)</th><th></th><th>Total (IRS Areas)</th><th></th><th>Bembereke (Control area)</th></tr></tbody><tbody><tr><th></th><td>analysed samples in IRS areas</td><td><i>An. gambiae</i></td><td><i>An.coluzzii</i></td><td><i>An. arabiensis</i></td><td><i>An. gambiae</i></td><td><i>An. coluzzii</i></td><td><i>An. arabiensis</i></td><td><i>An. gambiae</i></td><td><i>An. coluzzii</i></td><td><i>An. arabiensis</i></td><td><i>An. gambiae</i></td><td><i>An. coluzzii</i></td><td><i>An.arabiensis</i></td></tr><tr><th>Before IRS (May 2016–October 2016)</th><td>980</td><td>170</td><td>271</td><td>0</td><td>387</td><td>152</td><td>0</td><td>557</td><td>423</td><td>0</td><td>–</td><td>–</td><td>–</td></tr><tr><th>After IRS (June 2017–November 2018)</th><td>1794</td><td>466</td><td>176</td><td>17</td><td>520</td><td>147</td><td>5</td><td>986</td><td>323</td><td>22</td><td>277</td><td>181</td><td>5</td></tr><tr><th>Proportion (%)</th><td>–</td><td>70.71</td><td>26.70</td><td>2.57</td><td>77.38</td><td>21.87</td><td>0.74</td><td>74.07</td><td>24.26</td><td>1.65</td><td>59.82</td><td>39.09</td><td>1.07</td></tr><tr><th>Total (Before+after IRS)</th><td>2774</td><td>636</td><td>447</td><td>17</td><td>907</td><td>299</td><td>5</td><td>1543</td><td>746</td><td>22</td><td>277</td><td>181</td><td>5</td></tr><tr><th>Proportion (%)</th><td>–</td><td>57.81</td><td>40.63</td><td>1.54</td><td>74.89</td><td>24.69</td><td>0.41</td><td>66.76</td><td>32.28</td><td>0.95</td><td>59.82</td><td>39.09 1.07</td></tr></tbody></table>
Table 8 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
<p><b>Table 8</b> Blood-feeding rates in <i>An. gambiae</i> (<i>s.l.</i>) in treated and control areas</p><table><tbody><tr><th>Period</th><th>Area</th><th>No. collected</th><th>Unfed</th><th>Fed</th><th>Gravid</th><th>Half-gravid</th><th>Bloodfeeding rate %</th><th><i>P</i> -value</th></tr></tbody><tbody><tr><th>Before IRS:June–October 2016</th><td>IRS areas</td><td>1001</td><td>26</td><td>922</td><td>12</td><td>41</td><td>96.20</td><td><0.0001</td></tr><tr><th>After 1st round of IRS:June–October 2017</th><td></td><td>273</td><td>99</td><td>171</td><td>1</td><td>2</td><td>63.37</td><td></td></tr><tr><th>After 1st round of IRS:June–September 2017</th><td>IRS areas</td><td>208</td><td>78</td><td>129</td><td>0</td><td>1</td><td>62.5</td><td><0.0001</td></tr><tr><th></th><td>Control areas</td><td>244</td><td>16</td><td>187</td><td>31</td><td>10</td><td>80.74</td><td></td></tr></tbody></table>
Table 6 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
<p><b>Table 6</b> Human-biting rate, sporozoite index and entomological inoculation rate in IRS and control areas</p><table><tbody><tr><th>Variable</th><th>After 1st IRS campaign (June–September 2017)</th><th>After 2nd IRS campaign (June–August 2018)</th></tr></tbody><tbody><tr><th></th><td>IRS areas</td><td>Control areas</td><td>Reduction (%)</td><td>IRS areas</td><td>Control areas</td><td>Reduction (%)</td></tr><tr><th></th><td>(Alibori–Donga)</td><td>(Bembereke–Kouande)</td><td>(Alibori–Donga)</td><td>(Bembereke–Kouande)</td></tr><tr><th>HBR/night</th><td>7.56</td><td>10.81</td><td>–</td><td>7.76</td><td>11.01</td><td>–</td></tr><tr><th>SI (%)</th><td>0.71</td><td>3.7</td><td>80.81</td><td>0.6</td><td>3</td><td>80</td></tr><tr><th>EIR (ib/person/night)</th><td>0.053</td><td>0.403</td><td>–</td><td>0.05</td><td>0.325</td><td>–</td></tr><tr><th>EIR (ib/person/month)</th><td>1.6</td><td>12.11</td><td>86.78</td><td>1.5</td><td>9.75</td><td>84.61</td></tr></tbody></table><p><i>AbbreviatioNS</i>: SI,sporozoite index;HBR,human-biting rate;EIR,entomological inoculation rate;ib,infective bite</p>
Table 5 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
<p><b>Table 5</b> Human-biting rate, sporozoite index and entomological inoculation rate in Alibori and Donga regions (IRS areas) before and after the first round of IRS 2017</p><table><tbody><tr><th>Region</th><th>Variable</th><th>Before IRS (June–October 2016)</th><th>After 1st round of IRS (June– October 2017)</th><th>Reduction (%)</th></tr></tbody><tbody><tr><th>IRS areas (Alibori, Donga)</th><td>HBR/night</td><td>8.43</td><td>7.37</td><td>–</td></tr><tr><th></th><td>SI (%)</td><td>8.4</td><td>1.2</td><td>85.71</td></tr><tr><th></th><td>EIR (ib/person/night)</td><td>0.707</td><td>0.09</td><td>–</td></tr><tr><th></th><td>EIR (ib/person/month)</td><td>21.21</td><td>2.7</td><td>87.27</td></tr></tbody></table><p><i>AbbreviatioNS</i>: SI,sporozoite index;HBR,human-biting rate;EIR,entomological inoculating rate;ib,infected bite</p>
Counting the chorus: A bioacoustic indicator of population density
<p>Passive acoustic monitoring has grown in utility for tracking wildlife populations, though challenges remain when using acoustic detections to monitor population size and density. Distance sampling is considered the 'gold standard' for estimating animal densities but has several important limitations. Here, we have compiled data and code from a case study that demonstrates a fast bioacoustic analysis method leveraging a simple metric call density. Using three years of synchronously collected bioacoustic and point-transect distance sampling data for eight forest bird species native to Hawai‘i, including four endangered species, we found strong correlations between call density and distance sampling-based animal density estimates. These findings indicate that call density is a reliable indicator of animal density that can be used independently or combined with traditional monitoring methods. This approach could enhance passive acoustic monitoring by providing more sensitive population health indicators than commonly used detection/nondetection methods, facilitating prompt conservation and management decisions.</p>
DESI 2023 details for AI indicator
<p>The following data has been selected from EUROSTAT isoc_eb_ai database:</p> <ul> <li>Enterprises don't use any AI system (of E_CHTB, E_BDAML, E_BDANL, E_RBTS)</li> <li>Enterprises use one AI system (of E_CHTB, E_BDAML, E_BDANL, E_RBTS)</li> <li>Enterprises use two AI systems (of E_CHTB, E_BDAML, E_BDANL, E_RBTS)</li> <li>Enterprises use three AI systems (of E_CHTB, E_BDAML, E_BDANL, E_RBTS)</li> <li>Enterprises use four AI systems (of E_CHTB, E_BDAML, E_BDANL, E_RBTS)</li> <li>Enterprises use AI technologies performing analysis of written language (text mining)</li> <li>Enterprises use AI technologies converting spoken language into machine-readable format (speech recognition)</li> <li>Enterprises use AI technologies generating written or spoken language (natural language generation)</li> <li>Enterprises use AI technologies identifying objects or persons based on images (image recognition, image processing)</li> <li>Enterprises use machine learning (e.g. deep learning) for data analysis</li> <li>Enterprises use AI technologies automating different workflows or assisting in decision making (AI based software robotic process automation)</li> <li>Enterprises use AI technologies enabling physical movement of machines via autonomous decisions based on observation of surroundings (autonomous robots, self-driving vehicles, autonomous drones)</li> <li>Enterprises use at least one of the AI technologies: AI_TTM, AI_TSR, AI_TNLG, AI_TIR, AI_TML, AI_TPA, AI_TAR</li> <li>Enterprises don't use any of the AI technologies: AI_TTM, AI_TSR, AI_TNLG, AI_TIR, AI_TML, AI_TPA, AI_TAR</li> <li>Enterprises use at least two of the AI technologies: AI_TTM, AI_TSR, AI_TNLG, AI_TIR, AI_TML, AI_TPA, AI_TAR</li> <li>Enterprises use at least three of the AI technologies: AI_TTM, AI_TSR, AI_TNLG, AI_TIR, AI_TML, AI_TPA, AI_TAR</li> <li>Enterprises use AI technologies for marketing or sales</li> <li>Enterprises use AI technologies for production processes</li> <li>Enterprises use AI technologies for organisation of business administration processes</li> <li>Enterprises use AI technologies for management of enterprises</li> <li>Enterprises use AI technologies for logistics</li> <li>Enterprises use AI technologies for ICT security</li> <li>Enterprises use AI technologies for human resources management or recruiting</li> <li>Enterprises use AI technologies for at least one of the purposes: AI_PMS, AI_PPP, AI_PBA, AI_PME, AI_PLOG, AI_PITS, AI_PHR</li> <li>Enterprises use AI technologies for at least two of the purposes: AI_PMS, AI_PPP, AI_PBA, AI_PME, AI_PLOG, AI_PITS, AI_PHR</li> <li>Enterprises use AI technologies for at least three of the purposes: AI_PMS, AI_PPP, AI_PBA, AI_PME, AI_PLOG, AI_PITS, AI_PHR</li> </ul>
Discourse Analysis of Spanish Geographical Indications
<p>The data were collected through a questionnaire administered between November 2019 and August 2020. The dataset presents 44 statements organized by columns, in accordance with the distribution of the grid used in the Q methodology.</p>
Infilled and non-infilled HadEX-CAM dataset for the TX90p, TN90p, TX10p, TN10p ETCCDI indices
<p>All NetCDF files contain monthly gridded data for the European continent that span 1901-2018.</p> <p>Each file contain the data relative to a single extreme index that is indicated in the filename (e.g., "_TX90p_").</p> <p>The filenames with the prefix "original_" correspond to the original HadEX-CAM dataset that contains missing values indicated by NaN values.</p> <p>The filenames with the prefix "crai_" corresponds to the HadEX-CAM dataset infilled using our AI method (<a href="https://github.com/FREVA-CLINT/climatereconstructionAI">CRAI</a>).</p> <p>The filenames with the prefix "idw_" corresponds to the HadEX-CAM dataset infilled using the Inverse Distance Weighting technique with power parameter equals to 4.</p> <p>The filenames with the prefix "kriging_" corresponds to the HadEX-CAM dataset infilled using the ordinary Kriging technique with an exponential variogram model.</p> <p>The filenames with the prefix "diffusion_" corresponds to the HadEX-CAM dataset infilled using an adapted version of the <a href="https://github.com/openai/guided-diffusion">guided-diffusion</a> code.</p> <p> </p> <p>The "original_" data are distributed under the Open Government Licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/.</p> <p>The other data ("crai_", "idw_", "kriging_" and "diffusion_") are distributed under the Creative Commons Attribution 4.0 International licence.</p>
Smell the stress: Subjective ratings of body odors reliably indicate stress levels
<p><strong><span>Background:</span></strong><span> Chemosensory signals like body odor carry information about the emotional state of individuals like fear or stress. Analysis of sweat odor could therefore deliver an easy option of assessing stress. Our aim was to examine if subjective odor ratings can reliably and validly capture characteristics of sweat odor. For this we presented odor samples of stressed individuals to naïve participants who rated these concerning their intensity, valence, and stress. Furthermore, we compared odor rating results to well-studied physiological markers of stress that we obtained from the odor donors during the stress task.</span></p> <p><strong><span>Method:</span></strong><span> Odor donors were 30 men and 30 women who performed a standardized protocol to induce social stress while wearing axillary pads under each armpit. Additionally, we measured <span> </span><span> </span>cortisol in saliva, adrenaline in blood and recorded participants’ heart rate. For odor ratings, we recruited an independent sample of 40 individuals. Participants rated the odor samples using visual analog scales.</span></p> <p><strong><span>Results:</span></strong><span> Intraclass correlations revealed a fair level of clinical significance for intensity and valence of sweat odors. Additionally, retest reliability was moderate for these two odor qualities. Ratings of stress also showed a moderate retest reliability, but the intraclass correlation was on a poor level. In further analyses we found significant relationships between all three qualities. Correlations between subjective ratings of body odors and physiological stress markers of odor donors were not significant. </span></p> <p><strong><span>Discussion: </span></strong><span>Our data show that<strong> </strong>subjective ratings of body odor qualities, in particular intensity and valence are reliable. Further research on validity is needed.</span></p>
Unique Property Reference Number (UPRN) indicator: Flood risk (UPRN_1_1)
<p>This indicator covers three main flooding risks in the UK: fluvial (rivers and streams), coastal and surface water. The indicators were computed for all the Unique Property Reference Numbers (UPRNs) in the NHS Cheshire and Merseyside Integrated Care Board region (North West England). This region is formed by the following Local Authorities: Chester and Cheshire East, Cheshire West, Halton, Knowsley, Liverpool, Sefton, St. Helens, Warrington and Wirral.</p> <p> </p>
Unique Property Reference Number (UPRN) indicator: Tree canopy coverage (UPRN_3_1)
<p>This is an indicator with the tree canopy coverage for various distances (15m, 25m, 150m, 300m and 500m) from Unique Property Refence Numbers (UPRNs) in the Office for National Statistics (ONS) UPRN directory v2024.07 (Epoch 111). This metric is based on <em>Bluesky’s National Tree Map™,</em> which is the only tree dataset to include trees in Great Britain and the Republic of Ireland that are 3 metres and taller.</p> <h3>Available files</h3> <ul> <li><strong>UPRN_3_1_tree_canopy_coverage_cm_with_coords.csv</strong>: covers the Cheshire and Merseyside region (England) and includes latitude and longitude for each UPRN.</li> <li><strong>UPRN_3_1_tree_canopy_coverage_cm.csv</strong>: covers the Cheshire and Merseyside region (England).</li> <li><strong>UPRN_3_1_tree_canopy_coverage_lothian_with_coords.csv</strong>: covers the Lothian region (Scotland) and includes latitude and longitude for each UPRN.</li> <li><strong>UPRN_3_1_tree_canopy_coverage_lothian.csv</strong>: covers the Cheshire and Merseyside region (England).</li> </ul> <p> </p> <h3>Fields</h3> <table> <tbody> <tr> <td> <p><strong>Column name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>UPRN</p> </td> <td> <p>Unique Property Reference Number as per the Office for National Statistics UPRN Directory (ONSUD) v2024.07 (Epoch 111).</p> </td> </tr> <tr> <td> <p>n_trees_15m</p> </td> <td> <p>Number of trees (above 3m) within 15m from the UPRN.</p> </td> </tr> <tr> <td> <p>tree_canopy_cover_15m</p> </td> <td> <p>Percentage of area covered by tree canopy within 15m from the UPRN. From a maximum area of approximately 707m<sup>2</sup>.</p> </td> </tr> <tr> <td> <p>n_trees_25m</p> </td> <td> <p>Number of trees (above 3m) within 25m from the UPRN.</p> </td> </tr> <tr> <td> <p>tree_canopy_cover_25m</p> </td> <td> <p>Percentage of area covered by tree canopy within 25m from the UPRN. From a maximum area of approximately 1963m<sup>2</sup>.</p> </td> </tr> <tr> <td> <p>n_trees_150m</p> </td> <td> <p>Number of trees (above 3m) within 150m from the UPRN.</p> </td> </tr> <tr> <td> <p>tree_canopy_cover_150m</p> </td> <td> <p>Percentage of area covered by tree canopy within 150m from the UPRN. From a maximum area of approximately 70686m<sup>2</sup>.</p> </td> </tr> <tr> <td> <p>n_trees_300m</p> </td> <td> <p>Number of trees (above 3m) within 300m from the UPRN.</p> </td> </tr> <tr> <td> <p>tree_canopy_cover_300m</p> </td> <td> <p>Percentage of area covered by tree canopy within 300m from the UPRN. From a maximum area of approximately 282743m<sup>2</sup>.</p> </td> </tr> <tr> <td> <p>n_trees_500m</p> </td> <td> <p>Number of trees (above 3m) within 500m from the UPRN.</p> </td> </tr> <tr> <td> <p>tree_canopy_cover_500m</p> </td> <td> <p>Percentage of area covered by tree canopy within 500m from the UPRN. From a maximum area of approximately 785398m<sup>2</sup>.</p> </td> </tr> <tr> <td> <p>latitude (only version with coordinates)</p> </td> <td> <p>Latitude of the UPRN, given in decimal degrees, where N is positive and S is negative.</p> </td> </tr> <tr> <td> <p>longitude (only version with coordinates)</p> </td> <td> <p>Longitude of the UPRN, given in decimal degrees, where E is positive and W is negative.</p> </td> </tr> </tbody> </table> <p> </p> <h3><strong>Regions</strong></h3> <p><strong>Cheshire and Merseyside (Endgland)</strong></p> <p>Includes the following local authorities:</p> <ul> <li>Cheshire East</li> <li>Cheshire West and Chester</li> <li>Halton</li> <li>Knowsley</li> <li>Liverpool</li> <li>Sefton</li> <li>St. Helens</li> <li>Warrington</li> <li>Wirral</li> </ul> <p> </p> <p><strong>Lothian (Scotland)</strong></p> <p>Includes the following local authorities:</p> <ul> <li>East Lothian</li> <li>City of Edinburgh</li> <li>Midlothian</li> <li>Westlothian</li> </ul> <p> </p>
MIrreM Public Database on Irregular Migration Flow Estimates and Indicators
<p><span>This <em>Public Database on Irregular Migration Flow Estimates and Indicators</em>, in short MIrreM D5.2, is a MIrreM project deliverable under work package 5.</span><strong><span> </span></strong><span>This database provides an </span><span>inventory and critical appraisal of available estimates and indicators related to irregular migration flows. More specifically, <span>the database contains the country-level data collected by MIrreM’s national rapporteurs, as well as EU-level data from sources other than Eurostat. The datasets include </span>meta-level information on sources and methodology and a quality assessment based on MIrreM’s criteria. </span></p> <p><span>Users of this database are advised to consult the following <strong>companion document</strong> (henceforth, MIrreM Working Paper No. 9/2024) for a full discussion of the context, the underlying concepts, and the methodology used: </span></p> <p><span>Siruno, L., Leerkes, A., Hendow, M. & Brunovská, E. 2024. Working Paper on Irregular Migration Flows. MIrreM Working Paper No. 9. Krems: University for Continuing Education Krems (Danube University Krems). <a href="https://doi.org/10.5281/zenodo.%2010702228"><span>https://doi.org/10.5281/zenodo.</span> 10702228</a>.</span></p> <p><span>The MIrreM project is a follow-up to CLANDESTINO, which covered the period 2000-2007. MIrreM extends this to the subsequent period 2008-2023. The data covered in this database reflect what is available within this period. Most of the data was collected between June and October 2023, and thus in some cases, the data are only until 2022 pending complete reports for 2023. </span></p>
Data to estimate energy efficiency and environmental friendliness of Ukrainian GDP from indicators of structural, economic, and social development in Ukraine
<p><span>Data to estimate regression dependences of energy efficiency and environmental friendliness of GDP from indicators of structural, economic, and social development in Ukraine </span></p>
Data from: Geohistorical records indicate no impact of the Deepwater Horizon oil spill on oyster body size
Documentation of the near- and long-term effects of the Deepwater Horizon (DWH) oil spill, one of the largest environmental disasters in US history, is still ongoing. We used a novel before-after-control-impact analysis to test the hypothesis that average body size of intertidal populations of the eastern oyster (Crassostrea virginica) inhabiting impacted areas in Louisiana decreased due to increased stress/mortality related to the oil spill. Time-averaged death assemblages of oysters were used to establish a pre-spill baseline of body-size structure for four impacted and four control locations along a 350 km stretch of Louisiana's coastline. Post-spill body sizes were then measured from live oysters at each site in order to evaluate the differences in body size between oiled (i.e. impact) and unoiled (i.e. control) locations before and after the spill. Our results indicate that average body size of oysters remained relatively unchanged after the oil spill. There were also no temporal patterns in temperature, salinity or disease prevalence that could have explained our results. Together, these findings suggest that oysters either recovered rapidly following the immediate impact of the DWH oil spill, or that its impact was not severe enough to influence short-term population dynamics of the oyster beds.
Precision mapping of snail habitat provides a powerful indicator of human schistosomiasis transmission
Recently, the World Health Organization recognized that efforts to interrupt schistosomiasis transmission through mass drug administration have been ineffective in some regions; one of their new recommended strategies for global schistosomiasis control emphasizes targeting the freshwater snails that transmit schistosome parasites. We sought to identify robust indicators that would enable precision targeting of these snails. At the site of the world's largest recorded schistosomiasis epidemic—the Lower Senegal River Basin in Senegal—intensive sampling revealed positive relationships between intermediate host snails (abundance, density, and prevalence) and human urogenital schistosomiasis reinfection (prevalence and intensity in schoolchildren after drug administration). However, we also found that snail distributions were so patchy in space and time that obtaining useful data required effort that exceeds what is feasible in standard monitoring and control campaigns. Instead, we identified several environmental proxies that were more effective than snail variables for predicting human infection: the area covered by suitable snail habitat (i.e., floating, nonemergent vegetation), the percent cover by suitable snail habitat, and size of the water contact area. Unlike snail surveys, which require hundreds of person-hours per site to conduct, habitat coverage and site area can be quickly estimated with drone or satellite imagery. This, in turn, makes possible large-scale, high-resolution estimation of human urogenital schistosomiasis risk to support targeting of both mass drug administration and snail control efforts.
Database of Last Interglacial (MIS 5e) Sea-level Indicators in Southeast Asia
<p>This spreadsheet is a complete record of the Last Interglacial (MIS 5e) sea-level indicators in Southeast Asia. It has been exported from "The World Atlas of Last Interglacial Shorelines (WALIS) Database" (<a href="https://warmcoasts.eu/world-atlas.html">https://warmcoasts.eu/world-atlas.html</a>). Version 1.1 incorporates minor changes post review process.</p>
Indicative distribution map for Ecosystem Functional Group T3.4 Young rocky pavements, lava flows and screes
<p>This archive contains indicative distribution maps and profiles for <strong>T3.4 Young rocky pavements, lava flows and screes</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
ScienceDex guides
Understand access before you commit
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.