Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
27
datasets available to search
ShareScore release 0.9.0
Dataset results
27 results for “Lake Erie”
GEM-Hydro gridded simulations for the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E)
<p>This dataset provides gridded model simulations in NetCDF format over the Lake Erie using the GEM-Hydro model done within the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E). The data are produced with SPS (GEM-Surf + SVS, the surface component of GEM-Hydro) open-loop runs with the SVS calibrated parameters obtained during GRIP-E project. For more information on the model and on calibration methodology, see GEM-Hydro section in Mai et al. 2020 (in prep.).</p> <p>The original model outputs had all variables accumulated for each day. During post-processing all variables have been de-accumulated by subtracting the accumulation of the previous hour from the accumulation of the current hour. Two variables (ALAT and O1) are also only valid over the land tile of each grid cell. Two additional variables (ALAT_full and O1_full) valid now over the whole grid cell have been added for convenience of the users.</p> <p><strong>Domain boundaries (WGS84 system): </strong><br> - lon_min = -85.5, lon_max = -77.94<br> - lat_min = 40.3, lat_max = 44.26</p> <p><strong>Resolution of model variables provided:</strong><br> - spatial: ~10km x 10km <br> - temporal: hourly </p> <p><strong>Simulation period:</strong><br> - 01 Jan 2011 - 31 Dec 2014 <br> - 01 Jan 2010 - 31 Dec 2010 (warm-up)</p> <p><strong>Meteorological input data:</strong><br> - RDRS-v1; see Mai et al. 2020 (in prep)</p> <p><strong>Variables available:</strong><br> float <strong>PR_0</strong>(time, rlat, rlon) ;<br> PR_0:units = "m" ;<br> PR_0:long_name = "Quantity of precipitation (valid over whole grid cell)" ;<br> float <strong>AHFL_0</strong>(time, rlat, rlon) ;<br> AHFL_0:units = "mm" ;<br> AHFL_0:long_name = "Surface evaporation (valid over whole grid cell)" ;<br> float <strong>TRAF_60268832</strong>(time, rlat, rlon) ;<br> TRAF_60268832:units = "mm" ;<br> TRAF_60268832:long_name = "Surface runoff (valid over whole grid cell)" ;<br> float <strong>ALAT_0</strong>(time, rlat, rlon) ;<br> ALAT_0:units = "mm" ;<br> ALAT_0:long_name = "Accumulation of total soil lateral flow (valid over land tile of grid cell)" ;<br> float <strong>ALAT_0_full</strong>(time, rlat, rlon) ;<br> ALAT_0_full:units = "mm" ;<br> ALAT_0_full:long_name = "Accumulation of total soil lateral flow (valid over whole grid cell)" ;<br> float <strong>O1_0</strong>(time, rlat, rlon) ;<br> O1_0:units = "mm" ;<br> O1_0:long_name = "Accumulation of base drainage (valid over land tile of grid cell)" ;<br> float <strong>O1_0_full</strong>(time, rlat, rlon) ;<br> O1_0_full:units = "mm" ;<br> O1_0_full:long_name = "Accumulation of base drainage (valid over whole grid cell)" ;<br> float <strong>WT_59868832</strong>(time, rlat, rlon) ;<br> WT_59868832:units = "1" ;<br> WT_59868832:long_name = "Fraction of grid cell covered with land" ;</p> <p>===============================================================</p> <p>These data and model runs have been performed under the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E) led by Juliane Mai and Bryan Tolson (both University of Waterloo) and funded under the Integrated Modelling Program for Canada (IMPC) within the Global Water Futures program. </p>
Figure 2 in Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA
Figure 2. Mean posterior estimates of the probability of capturing grass carp eDNA from a site in a sample among sites (θ) from the model with the lowest WAIC score [ψ(Site)Θ(Site)p(.)]. Error bars represent 95% credible intervals. DR = Detroit River, HP = Hot Ponds, MB = Maumee Bay. All sites are located in western Lake Erie.
Figure 1 in Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA
Figure 1. Map denoting all monthly grass carp eDNA sampling events in 2018 (A–C) and 2019 (D–F) aggregated at each sampling location (Hot Ponds, Detroit River, and North Maumee Bay) and acoustic receiver locations (grey circles) in the western basin of Lake Erie. Positive and negative eDNA detections, defined as at least one positive qPCR detection on one replicate among all markers (GCTM10, GCTM22, GCTM32) are denoted by orange crosses and pink triangles, respectively. The 3 grass carp captured from conventional gear (total sampling events = 451) in the Detroit River (October 2018), Hot Pond (July 2019) and North Maumee Bay (July 2019) are denoted by a yellow hexagon.
Selected large model output files and Buffalo sounding data from: Lake Huron enhances snowfall downwind of Lake Erie: a modeling study of the 2010 near year’s Lake-effect snowfall event
Open the record for dataset details and reuse information.
Agriculture land-use change seasonally rewires stream food webs: A case study from headwater streams in the Lake Erie watershed
Open the record for dataset details and reuse information.
Table 1 in Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA
<p><b>Table 1.</b> Number of field samples (including controls) for each qPCR assay at each site sampled for eDNA in 2018 and 2019 in western Lake Erie. DR = Detroit River, HP = Hot Ponds, MB = Maumee Bay. Note that samples are site-specific.</p><table><tbody><tr><th>Site</th><th>Year</th></tr><tr><th>2018</th><th>2019</th></tr><tr><th>Assay</th><th>Samples</th><th>Assay</th><th>Samples</th></tr></tbody><tbody><tr><th>DR</th><td>GCTM10 GCTM22 GCTM32</td><td>78 78 78</td><td>GCTM10 GCTM22 GCTM32</td><td>81 81 81</td></tr><tr><th>HP</th><td>GCTM10 GCTM22 GCTM32</td><td>77 77 77</td><td>GCTM10 GCTM22 GCTM32</td><td>82 82 82</td></tr><tr><th>MB</th><td>GCTM10 GCTM22 GCTM32</td><td>78 78 78</td><td>GCTM10 GCTM22 GCTM32</td><td>80 80 80</td></tr></tbody></table>
Table 4 in Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA
<p><b>Table 4.</b> Percentage of positive eDNA replicate detections in each month and site in 2018 and 2019 in western Lake Erie (based on at least one positive detection on at least one marker and one replicate). All markers (GCTM10, GCTM 22, GCTM32) were used to calculate these proportions. Samples were collected monthly from June to November for each site. The number of telemetered grass carp detected within 7 days before sampling for eDNA is denoted in parentheses. Acoustic telemetry receivers in MB in 2018 were not available. DR = Detroit River, HP = Hot Ponds, and MB = Maumee Bay. NA denotes when acoustic telemetry receivers were not in operation.</p><table><tbody><tr><th>Year</th></tr><tr><th>Site</th><th>2018</th><th>2019</th></tr><tr><th></th><th>June</th><th>July</th><th>Aug</th><th>Sept</th><th>Oct</th><th>Nov</th><th>May</th><th>June</th><th>July</th><th>August</th><th>Oct</th><th>Nov</th></tr></tbody><tbody><tr><th>DR</th><td>0.0%</td><td>2.3%</td><td>2.3%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>4.5%</td><td>10.6</td><td>14.1</td><td>17.4%</td><td>14.1%</td><td>2.2%</td></tr><tr><td>(1)</td><td>(1)</td><td>(2)</td><td>(1)</td><td>(2)</td><td>(0)</td><td>(1)</td><td>% (1)</td><td>% (1)</td><td>(2)</td><td>(2)</td><td>(0)</td></tr><tr><th>HP</th><td>0.0%</td><td>0.1%</td><td>4.1%</td><td>2.2%</td><td>15.8%</td><td>0.0%</td><td>9.0%</td><td>1.5%</td><td>34.8</td><td>1.5%</td><td>15.8%</td><td>22.7%</td></tr><tr><td>(1)</td><td>(1)</td><td>(2)</td><td>(2)</td><td>(3)</td><td>(NA)</td><td>(2)</td><td>(2)</td><td>% (2)</td><td>(3)</td><td>(2)</td><td>(2)</td></tr><tr><th>MB</th><td>12.0%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>6.1%</td><td>37.1</td><td>8.3%</td><td>8.3%</td><td>0.0%</td></tr><tr><td>(NA)</td><td>(NA)</td><td>(NA)</td><td>(NA)</td><td>(NA)</td><td>(NA)</td><td>(0)</td><td>(0)</td><td>% (0)</td><td>(0)</td><td>(0)</td><td>(0)</td></tr></tbody></table>
Table 3 in Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA
<p><b>Table 3.</b> Candidate set of hierarchical occupancy models used to estimate probability of grass carp eDNA occurrence among sites (ψ), the conditional probability of grass carp eDNA occurrence at a sampling locality within a site given that grass carp were present at the site (Θ), and the conditional probability of eDNA detection on replicate filters collected at a sampling locality given that the species is present at the sampling locality <i>(p</i>) from three sites in western Lake Erie sampled in 2018 and 2019. Covariates included location (site), time (Month) and probe type (GCTM10, GCTM22, GCTM32). Model comparison was evaluated with the Widely Applicable Information Criterion (WAIC).</p><table><tbody><tr><th>Model</th><th>WAIC</th><th>Δ WAIC</th><th>Lack of fit</th><th>Predicted Variance</th></tr></tbody><tbody><tr><th>ψ(Site)Θ(Site)p(.)</th><td>309.44</td><td>-</td><td>298.70</td><td>18.63</td></tr><tr><th>ψ(.)Θ(Site)p(.)</th><td>309.50</td><td>0.06</td><td>298.99</td><td>10.73</td></tr><tr><th>ψ(.)Θ(Month)p(.)</th><td>317.61</td><td>8.18</td><td>299.05</td><td>18.56</td></tr><tr><th>ψ(Season)Θ(.)p(.)</th><td>317.67</td><td>8.24</td><td>299.01</td><td>18.65</td></tr><tr><th>ψ(Month)Θ(.)p(.)</th><td>317.72</td><td>8.28</td><td>299.04</td><td>18.67</td></tr><tr><th>ψ(Season)Θ(Season)p(.)</th><td>317.84</td><td>8.40</td><td>299.04</td><td>18.79</td></tr><tr><th>ψ(.)Θ(Season)p(.)</th><td>317.74</td><td>8.31</td><td>299.07</td><td>18.66</td></tr><tr><th>ψ(Site)Θ(.)p(.)</th><td>317.94</td><td>8.51</td><td>299.04</td><td>18.90</td></tr><tr><th>ψ(.)Θ(.)p(.)</th><td>325.00</td><td>15.57</td><td>305.50</td><td>20.21</td></tr><tr><th>ψ(Season)Θ(Site)p(.)</th><td>325.41</td><td>15.98</td><td>305.49</td><td>19.91</td></tr><tr><th>ψ(Site + Season)Θ(.)p(.)</th><td>325.59</td><td>16.16</td><td>305.44</td><td>20.15</td></tr><tr><th>ψ(Site)Θ(Season)p(.)</th><td>325.72</td><td>16.29</td><td>305.48</td><td>20.23</td></tr><tr><th>ψ(Site + Season)Θ(Site + Season)p(.)</th><td>325.92</td><td>16.49</td><td>305.49</td><td>20.43</td></tr><tr><th>ψ(.)Θ(Site + Season)p(.)</th><td>326.37</td><td>16.94</td><td>305.52</td><td>20.84</td></tr><tr><th>ψ(Site)Θ(Month)p(.)</th><td>329.57</td><td>20.14</td><td>308.65</td><td>20.92</td></tr><tr><th>ψ(Month)Θ(Month)p(.)</th><td>330.14</td><td>20.71</td><td>308.66</td><td>21.84</td></tr><tr><th>ψ(Site + Season)Θ(Site)p(Probe)</th><td>377.63</td><td>68.20</td><td>276.90</td><td>100.72</td></tr><tr><th>ψ(Site + Season)Θ(.)p(Probe)</th><td>378.35</td><td>68.92</td><td>277.00</td><td>101.34</td></tr><tr><th>ψ(Site + Season)Θ(Site + Season)p(Probe)</th><td>378.42</td><td>68.99</td><td>277.00</td><td>101.41</td></tr><tr><th>ψ(Site + Season)Θ(Season)p(Probe)</th><td>378.55</td><td>69.12</td><td>277.09</td><td>101.46</td></tr><tr><th>ψ(Season)Θ(Site + Season)p(Probe)</th><td>379.41</td><td>69.98</td><td>277.28</td><td>102.10</td></tr><tr><th>ψ(Site)Θ(Site + Season)p(Probe)</th><td>379.62</td><td>70.19</td><td>277.40</td><td>102.21</td></tr><tr><th>ψ(.)Θ(Site + Season)p(Probe)</th><td>379.88</td><td>70.45</td><td>277.31</td><td>102.57</td></tr><tr><th>ψ(Site + Month)Θ(.)p(.)</th><td>383.56</td><td>74.13</td><td>282.86</td><td>100.69</td></tr><tr><th>ψ(Site + Month)Θ(Site + Month)p(.)</th><td>385.47</td><td>76.04</td><td>283.38</td><td>102.09</td></tr><tr><th>ψ(Site + Month)Θ(Site + Month)p(Probe)</th><td>386.95</td><td>77.52</td><td>282.90</td><td>104.05</td></tr><tr><th>ψ(.)Θ(Site + Month)p(.)</th><td>396.44</td><td>87.01</td><td>292.14</td><td>104.29</td></tr><tr><th>ψ(.)Θ(.)p(Probe)</th><td>396.45</td><td>87.01</td><td>292.14</td><td>104.29</td></tr></tbody></table>
Table 2 in Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA
<p><b>Table 2.</b> Gene region, primer, and probe sequences used to amplify GCTM10,GCTM22, and GCTM32 for grass carp.</p><table><tbody><tr><th>Gene</th><th>Primers and Probes</th><th>Sequence</th></tr></tbody><tbody><tr><th>ND2</th><td>Forward</td><td>5′- CCYTACGTACTCGCAATTCTAC -3′</td></tr><tr><th>ND2</th><td>Reverse</td><td>5′- GTGGTGGTGTTGGGCTATTA -3′</td></tr><tr><th>ND2</th><td>Probe</td><td>5′- VIC- ACCCTAACCTTTGCTAGCTCCCAC -MGBNFQ-3′</td></tr><tr><th>COII</th><td>Forward</td><td>5′- CCGACTCCTAGAAACAGATCAC -3′</td></tr><tr><th>COII</th><td>Reverse</td><td>5′- GGGACAGCTCAGGAATGTAATA -3′</td></tr><tr><th>COII</th><td>Probe</td><td>5′- 56-FAM- CCAGTTCGT/ZEN/GTCCTAGTATCTGCCGA -3IABkFQ -3′</td></tr><tr><th>COIII</th><td>Forward</td><td>5′- CCACGGACTACACGTCATTATT -3′</td></tr><tr><th>COIII</th><td>Reverse</td><td>5′-GATGTTCGGATGTAAAGTGGTATTG -3′</td></tr><tr><th>COIII</th><td>Probe</td><td>5′-NED- TTCCTAGCTGTTTGCCTTCTCCGT -MGBNFQ-3′</td></tr></tbody></table>
Lake Erie Diel Coassembly (Metatranscriptomic)
<p>A coassembly (final.contigs.fa) containing 39 concatenated and assembled (coassembled) metatranscriptomic libraries that has been made publically available in conjunction with an American Society of Microbiology Resource Announcement (Zepernick et al., 2024; doi: 10.1128/mra.00659-24). The proteins (proteins.faa) and nucelotides (nucleotides.fna) that were generated from called open reading frames are included. </p>
Eutrophication, water quality, and fisheries: a wicked management problem with insights from a century of change in Lake Erie
<p>The datasets here were used to examine relationships between the overall productivity of Lake Erie and the commercial harvest of lake whitefish (<em>Coregonus clupeaformis</em>), walleye (<em>Sander vitreus</em>), and yellow perch (<em>Perca flavescens</em>) during 1915–2011. Here, we provide the two datasets used in the paper by Sinclair et al. titled "Eutrophication, water quality, and fisheries: a wicked management problem with insights from a century of change in Lake Erie". Each dataset is provided as a separate tab in a single Excel worksheet. The first dataset ("Productivity") provides the annual values of the five metrics used to develop the index of overall Lake Erie productivity. The second dataset ("Commercial harvest") provides the total annual commercial harvest (kg) of the three fish species, which were obtained from the Great Lakes Fishery Commission (<a href="http://www.glfc.org/great-lakes-databases.php">http://www.glfc.org/great-lakes-databases.php</a>). A summary and explanation of each variable is provided in the "Info" tab. Further information on how values were calculated (and transformed if necessary) is provided in either the info tab or the methods and supporting information of the associated article.</p>
Eutrophication, water quality, and fisheries: a wicked management problem with insights from a century of change in Lake Erie
Open the record for dataset details and reuse information.
Density data for Lake Erie benthic invertebrate assemblages from 1930 to 2019
Open the record for dataset details and reuse information.
Dispersal and survival of sea lamprey in Lake Erie and connected waterways
Open the record for dataset details and reuse information.
Intra- and inter-annual dynamics of evaporation over western Lake Erie
<p><strong>This dataset if for a manuscript: Intra- and inter-annual dynamics of evaporation over western Lake Erie, by </strong>CHANGLIANG SHAO, JIQUAN CHEN, CAROL A. STEPIEN, HOUSEN CHU, ZUTAO OUYANG. The dataset including six sheets, from figure2 to figure 7 in the manuscript. Information shows as following:</p> <p>Fig. 2. Monthly mean (a) air temperature (<em>T</em><sub>a</sub>), (b) wind speed (<em>U</em>), (c) vapor pressure deficit (<em>VPD</em>), (d) photosynthetically active radiation (<em>PAR</em>), and total and cumulative (e) rainfall of ~15 m above the water surface at the CB and LI sites over Lake Erie from September 2011 to May 2016. Long-term (1893–2015) average (gray lines) and its 90% quantile intervals (gray shaded areas) are presented in (a).</p> <p>Fig. 3. Monthly average diurnal course of evaporation at the CB and LI sites in western Lake Erie. Data covered all of the measurement period.</p> <p>Fig. 4. Seasonal changes of net radiation (<em>R</em><sub>n</sub>) and latent heat flux (LE) at the two Lake Erie sites from 2011 through 2016. The monthly LE variations also are shown. The sine function was used to fit the dynamic seasonal variations (see equation 6). The sinusoidal period was set to 12. </p> <p> Fig. 5. Linear relationships between the monthly lake evaporation (<em>E</em>) and the same month net radiation (<em>R</em><sub>n</sub>) (a and b), and 1-month early <em>R</em><sub>n</sub> (c and d) at the two sites. The lines represent the linear fitted tendency.</p> <p>Fig. 6. Linear relationship between modeled evaporation (<em>E</em>, Eq. (4)) and the measured values during the entire measurement period, on a monthly basis. The line represents the linear fitted tendency.</p> <p>Fig. 7. Relationships between latent heat flux (<em>E</em>) and (a, b) the product of <em>U</em> (wind speed) and vapor pressure deficit (<em>VPD</em>), (c, d) <em>VPD</em>, (e, f) air temperature, and (g, h) wind speed at the two sites, on a monthly basis. A linear fit with 95% confidence intervals, the correlation between the variables, and the linear model <em>P</em> value are shown in all plots.</p>
Watershed shapes for the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E)
<p>This dataset provides the shapefiles of the 46 calibration and 7 validation watersheds used within the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E).</p> <p>The watersheds draining towards the following WSC and USGS gauge stations have been used for:</p> <p><strong>1a. calibration of objective 1</strong> (low-human impact)<br> (28 stations in total; 13 of them in both objective 1 and 2)<br> 02GA010 02GA018 02GA038 02GA047 02GB007<br> 02GC002 02GC010 02GC018 02GD004 02GE007<br> 02GG002 02GG003 02GG006 02GG009 02GG013<br> 04159492 04159900 04160600 04161820 04164000<br> 04165500 04166100 04177000 04196800 04197100<br> 04207200 04208504 04213000</p> <p><strong>1b. calibration of objective 2</strong> (most-downstream gauges closest to Lake Erie)<br> (31 stations in total; 13 of them in both objective 1 and 2)<br> 02GB001 02GB007 02GC002 02GC007 02GC018<br> 02GC026 02GE007 02GG003 02GG009 02GG013<br> 04159900 04160600 04165500 04166500 04174500<br> 04176500 04177000 04193500 04195820 04198000<br> 04199000 04199500 04200500 04208504 04209000<br> 04212100 04213000 04213500 04214500 04215000<br> 04215500</p> <p><strong>2. (spatial) validation</strong><br> (7 stations in total)<br> 02GE003 04167000 04168000 04185000 04195500<br> 04201500 04208000</p> <p>===============================================================</p> <p>These data have been derived under the Great Lakes Runoff Intercomparison Project for Lake Erie (GRIP-E) led by Juliane Mai and Bryan Tolson (both University of Waterloo) and funded under the Integrated Modelling Program for Canada (IMPC) within the Global Water Futures program.</p>
mHM_UFZ gridded simulations for the Great Lakes Runoff Inter-comparison Project for Lake Erie
<p>This dataset provides gridded model simulations in netcdf format over the Lake Erie using the mHM model (Samaniego, et al., 2010, Kumar et al, 2013), done within the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E).</p> <p>Model code is available under: <a href="https://git.ufz.de/mhm/mhm">https://git.ufz.de/mhm/mhm</a>, revision number: 8271b54</p> <p>Domain boundaries (WGS84 system): lon_min = -86.0, lon_max = -78.0, lat_min = 40.0, lat_max = 45.0</p> <p>Model variables are simulated at spatial grid of 0.125deg x 0.125deg, daily time step. </p> <p>Simulation period: 01 Jan 2011 - 31 Dec 2014 (with 1year -2010- warm-up)</p> <p>Meteorological input data, see Mai et al. 2020 (in prep)</p> <p>Following model variables are provided for two mHM_parameter.nml realizations (*obj1 and *obj2, as defined in Mai et al. 2020 in prep). </p> <p>double <strong>snowpack</strong>(time, northing, easting) ;<br> snowpack:long_name = "depth of snowpack" ;<br> snowpack:unit = "mm" ;<br> double <strong>SM_Lall</strong>(time, northing, easting) ;<br> SM_Lall:long_name = "average soil moisture over all layers" ;<br> SM_Lall:unit = "mm mm-1" ;<br> double <strong>unsatSTW</strong>(time, northing, easting) ;<br> unsatSTW:long_name = "reservoir of unsaturated zone" ;<br> unsatSTW:unit = "mm" ;<br> double <strong>satSTW</strong>(time, northing, easting) ;<br> satSTW:long_name = "water level in groundwater reservoir" ;<br> satSTW:unit = "mm" ;<br> double <strong>aET</strong>(time, northing, easting) ;<br> aET:long_name = "actual Evapotranspiration" ;<br> aET:unit = "mm d-1" ;<br> double <strong>Q</strong>(time, northing, easting) ;<br> Q:long_name = "total runoff generated by every cell" ;<br> Q:unit = "mm d-1" ;<br> double <strong>QD</strong>(time, northing, easting) ;<br> QD:long_name = "direct runoff generated by every cell (runoffSeal)" ;<br> QD:unit = "mm d-1" ;<br> double <strong>QIf</strong>(time, northing, easting) ;<br> QIf:long_name = "fast interflow generated by every cell (fastRunoff)" ;<br> QIf:unit = "mm d-1" ;<br> double <strong>QIs</strong>(time, northing, easting) ;<br> QIs:long_name = "slow interflow generated by every cell (slowRunoff)" ;<br> QIs:unit = "mm d-1" ;<br> double <strong>QB</strong>(time, northing, easting) ;<br> QB:long_name = "baseflow generated by every cell" ;<br> QB:unit = "mm d-1" ;<br> double <strong>recharge</strong>(time, northing, easting) ;<br> recharge:long_name = "groundwater recharge" ;<br> recharge:unit = "mm d-1" ;</p> <p>===============================================================</p> <p>These data and model runs have been performed under the Great Lakes Runoff Intercomparison Project for Lake Erie (GRIP-E) led by Juliane Mai and Bryan Tolson (both University of Waterloo) and funded under the Integrated Modelling Program for Canada (IMPC) within the Global Water Futures program. This work has received funding from the Initiative and Networking Fund of the Helmholtz Association through the project Advanced Earth System Modelling Capacity (ESM) (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/www.esm-project.net">www.esm-project.net</a>).</p>
Data from: Benefits of turbid river plume habitat for Lake Erie yellow perch (Perca flavescens) recruitment determined by juvenile to larval genotype assignment
Nutrient-rich, turbid river plumes that are common to large lakes and coastal marine ecosystems have been hypothesized to benefit survival of fish during early life stages by increasing food availability and (or) reducing vulnerability to visual predators. However, evidence that river plumes truly benefit the recruitment process remains meager for both freshwater and marine fishes. Here, we use genotype assignment between juvenile and larval yellow perch (Perca flavescens) from western Lake Erie to estimate and compare recruitment to the age-0 juvenile stage for larvae residing inside the highly turbid, south-shore Maumee River plume versus those occupying the less turbid, more northerly Detroit River plume. Bayesian genotype assignment of a mixed assemblage of juvenile (age-0) yellow perch to putative larval source populations established that recruitment of larvae was higher from the turbid Maumee River plume than for the less turbid Detroit River plume during 2006 and 2007, but not in 2008. Our findings add to the growing evidence that turbid river plumes can indeed enhance survival of fish larvae to recruited life stages, and also demonstrate how novel population genetic analyses of early life stages can contribute to determining critical early life stage processes in the fish recruitment process.
FIGURE 1 in Reevaluation of the genus Cyclops Müller, 1776 (Cyclopoida: Cyclopidae) in the Laurentian Great Lakes basin: first report of the Palearctic species Cyclops divergens Lindberg, 1936 from Lake Erie and documentation of Cyclops sibiricus Lindberg, 1949 in the St. Marys River
FIGURE 1: St. Marys River C. sibiricus female (a) A1 17-segmented (b) A1 first segment without surface pits (c) A1 segment 12 with medium length aesthetasc arrowed (d) A1 segments 15−17 with hyaline membrane arrowed.
FIGURE 4 in Reevaluation of the genus Cyclops Müller, 1776 (Cyclopoida: Cyclopidae) in the Laurentian Great Lakes basin: first report of the Palearctic species Cyclops divergens Lindberg, 1936 from Lake Erie and documentation of Cyclops sibiricus Lindberg, 1949 in the St. Marys River
FIGURE 4: Lake Erie C. divergens female (a) A2 basipodite caudal surface ornamentation at position A, B, C (b) Mxp syncoxopodite with short distally hooked membranous element arrowed (c) P1 basipodite ornamented with row of spinules arrowed and medial spine with heteronomous setulation arrowed (d) P4 coxopodite ornamentation with spinules at positions A, C, D, and E (e) P4 coxopodite setae arrowed, extending beyond medial margin of basipodite
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.