Find research datasets worth reusing

opencc-zeroApr 2022View details →

Spatial probability maps of the superior parietal sulcus in the human brain

<p><span>The superior parietal sulcus (SPS) is the defining sulcus within the superior parietal lobule. The morphological variability of the SPS was examined in individual magnetic resonance imaging (MRI) scans of the human brain that were registered to the Montreal Neurological Institute (MNI) standard stereotaxic space. Two primary morphological patterns were consistently identified across hemispheres: 1) the SPS was identified as a single sulcus, separating the anterior from the posterior part of the superior parietal lobule and 2) the SPS was found as a complex of multiple sulcal segments. These morphological patterns were subdivided based on whether the SPS or SPS complex remained distinct or merged with surrounding parietal sulci. The morphological variability and spatial extent of the SPS were quantified using volumetric and surface spatial probabilistic mapping. The current investigation e</span><span>stablished consistent morphological patterns in a common anatomical space, the MNI stereotaxic space, to facilitate structural and functional analyses within the superior parietal lobule. </span></p>

opencc-by-4.0Jun 2022View details →

A modified Beer–Lambert–Bouguer law for nonrandom distributions and its application in gap probability calculations for heterogeneous canopies

<p>This repository contains the data, codes and files required to reproduce the results of the manuscript "A modified Beer–Lambert–Bouguer law for nonrandom distributions and its application in gap probability calculations for heterogeneous canopies" submitted to the Journal of Advances in Modeling Earth Systems (JAMES).</p>

opencc-byApr 2019View details →

Balimeanach BC1 - cup mark - probably natural

A reported cup mark on a schistose outcrop, at NN 63710 22757,on balance appears to be natural. ScRAP ID 875. There are also three other very similar depressions immediately adjacent to it (not previously mentioned as cup marks), which also look natural. Source: Objaverse 1.0 / Sketchfab

opencc-by-4.0Dec 2018View details →

Рис. 10. Блок-схема фиЗико-статистического прогноЗа уроЖайности спата приморского гребешка. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement

Рис. 10. Блок-схема фиЗико-статистического прогноЗа уроЖайности спата приморского гребешка.

opencc-zeroJun 2024View details →

EstablishMed: a dataset of transition probabilities for woody plant establishment in the Mediterranean Region

<p><strong>Motivation</strong>: Plant establishment is the result of sequential demographic processes, namely post-dispersal seed survival, seed germination, seedling survival and sapling survival. These processes can be quantified as transition probabilities between life stages through field experiments, and their product provides an overall establishment probability. This information is essential to understand demography within populations and plant colonization potential under global change scenarios. The Mediterranean Region constitutes a biodiversity hotspot characterised by severe summer droughts, which suppose a critical demographic bottleneck for perennial plant establishment. Despite many studies have quantified transition probabilities of woody species in this region, information is scattered through literature and has not yet been compiled. To fill this gap, we collated data from a systematic literature review and completed them with new unpublished data to create the <em>EstablishMed</em> dataset.</p> <p><strong>Main types of variables contained</strong>: <em>EstablishMed</em> is a compilation of 4728 records of transition probabilities that quantify demographic processes operating during plant establishment. All records belong to native species and were obtained <em>in situ </em>under field conditions. Each record includes information about the specific spatiotemporal context of the study (i.e., year, site, population, habitat and microhabitat) and the experimental procedures employed (e.g., degree of protection against natural enemies). In addition, we included taxonomic and trait information of the study species (i.e., seed mass, dispersal syndrome and life form), and the bioclimate of the study sites.</p> <p><strong>Spatial location and grain</strong>: The dataset covers the whole Mediterranean Region. The finest spatial resolution corresponds to microhabitat types within populations.</p> <p><strong>Time period and grain</strong>: Data were extracted from 271 studies originated between 1991 and 2024.</p> <p><strong>Major taxa and level of measurement</strong>: 134 woody species from 80 genera and 39 families.</p> <p><strong>Software format</strong>: <em>EstablishMed</em> is available in .csv format in Dryad repository.</p>

opencc-by-4.0Dec 2020View details →

FIGURE 7. Probably a in New dragonflies and damselflies (Odonata) from the late Oligocene of Enspel (Rhineland-Palatinate, SW Germany)

FIGURE 7. Probably a stem-Libellulidae, PE 2001/5195-LS, hind wing. Scale bar is 10 mm.

opencc-zeroJul 2024View details →

Measuring the benefit of a risk-induced trait response: vigilance and survival probability

<p>Defensive traits are hypothesized to benefit prey by reducing predation risk from a focal predator but come at a cost to the fitness of the prey. Variation in the expression of defensive traits is seen among individuals within the same population, and in the same individual in response to changes in the environment (i.e., phenotypically plastic responses). It is the relative magnitude of the cost and benefit of the defensive trait which underlies the defensive trait expression and its consequences to the community. However, whereas the cost has received much attention in ecological research, the benefit is seldom examined. Even in a defensive trait as extensively studied as vigilance, there are few studies of the purported benefit of the behavior, namely that vigilance enhances survival. We examined if prey vigilance increased survival and quantified that benefit in a natural system, with white-tailed deer (Odocoileus virginianus) experiencing unmanipulated levels of predation risk from Florida panther (Puma concolor coryi). Deer that spent more time vigilant (as measured by head position using camera trap data) had a higher probability of survival. Indeed, an individual deer that were vigilant 75% of the time were more than three times as likely to be killed by panthers over the course of a year compared to a deer that were vigilant 95% of the time. Our results therefore show that within-population variation in the expression of a defensive trait has profound consequences to the benefit it confers. Our results provide empirical evidence supporting a long-held but seldom tested hypothesis, that vigilance is a behavior that reduces the probability of predation and quantified the benefit of this defensive trait. Our work furthers an understanding of the net effects of a trait on prey fitness and predator-prey interactions, within-population variation in traits, and predation risk effects.</p>

opencc-by-nc-sa-4.0Jun 2017View details →

Global heat map of probable importance of terrestrial ecosystems on meeting local demand of freshwater services

<p>This map (raster dataset, single layer) uses existing datasets to map globally “How important point x is likely to be for meeting the demand of a reliable & useable source of water on a scale of 0 to 1?” This relatively simple approach uses estimated water demand in a given basin as weight to identify pressure for flow regulation and water provisioning services. Precipitation and land cover estimates are then combined with it to give some insight into the hydrologic attributes of “location” and “timing” of flow that the ecosystems may influence. The underlying assumption here is that undisturbed ecosystems everywhere are performing the ecohydrological functions leading to freshwater services. The question is more (at the global scale): how dependent are the populations in the basin on the continued functioning of these services.</p> <p><strong>Input datasets:</strong></p> <ol> <li>Annual surface & groundwater (“blue”) water consumption estimates. URL: <a href="http://waterfootprint.org/en/resources/water-footprint-statistics/">http://waterfootprint.org/en/resources/water-footprint-statistics/</a></li> <li>HydroBasins watershed outline.</li> <li>European Space Agency (ESA)  global land cover 2015.</li> <li>WorldClim annual average precipitation (Version 2.0).</li> </ol> <p><strong>Process:</strong></p> <p>Step 1: Calculate average annual water consumption estimates over HydroBasin outlines. This step spreads the demand laterally (in case of small basins) and upstream to the headwaters from (typically) downstream consumer concentration.</p> <p>Step 2: Normalize the demand globally and map the normalized values on to “natural” land cover classes from the land cover dataset [forests, grasslands, etc].</p> <p>Step 3: Normalize annual precipitation layer within basins on the scale 0-1 where 1 is the maximum annual precipitation in that basin. This is also mapped on the “natural” land cover. Precipitation is thus acting as ‘weight’ for importance within the basin. Example, upland headwaters will typically receive more rainfall and can be argued to be important for the flow regulation in the basin.</p> <p>Step 4: Combine the layers from 2 and 3.</p> <p><strong>Caveats:</strong></p> <ol> <li>Identification of what constitutes a “natural” land cover is not trivial, especially from global land cover maps. Example: Forests and plantations are hard to distinguish from these products.</li> <li>Improvement of quality of water is assumed to be implicit for functioning ecosystems.</li> </ol>

opencc-by-4.0Aug 2024View details →

A Probable Ancient Nearshore Zone in Southern Utopia on Mars Unveiled from Observations at the Zhurong Landing Area

<p>This dataset (in ArcGIS project) contains the results of geomorphological spatial analysis of water-related features at the Zhurong landing area in Southern Utopia on Mars. It includes vector data of the Zhurong landing site, crater maps, ghost craters, AMA craters, various geomorphological features (cones, etched flows, troughs, pancake craters, rampart craters), and newly defined geological units. Other supporting data and basemaps are also included.</p> <p>This dataset is related to the following paper currently under review in Scientific Reports:</p> <p>Wu B., Dong J., Wang Y., Rao W., Sun Z., Krasilnikov S., Li Z., Tan Z., Chen Z., Wang C., Ivanov M., Zhu J., Liu W.C., Chen L., Li H. 2024. A Probable Ancient Nearshore Zone in Southern Utopia on Mars Unveiled from Observations at the Zhurong Landing Area. </p>

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>

opencc-by-4.0Jun 2021View details →

Forager mobility and lithic discard probability similarly affect the distance of raw material discard from source - Supplemental Material

<p>The data, R code and Netlogo model used in "Forager mobility and lithic discard probability similarly affect the distance of raw material discard from source".</p>

opencc-by-4.0Jun 2009View details →

Fig. 1 in Pacific Flying Foxes (Mammalia: Chiroptera): Two New Species of Pteropus from Samoa, Probably Extinct

Fig. 1. Map of the southwest Pacific region. Adapted from Steadman (2006b).

opencc-by-4.0Jun 2009View details →

Fig. 12 in Pacific Flying Foxes (Mammalia: Chiroptera): Two New Species of Pteropus from Samoa, Probably Extinct

Fig. 12. Skull of USNM 8597/37860, lectotype of Pteropus samoensis Peale, 1848. Scale bar 5 10 mm.

opencc-by-4.0Jun 2009View details →

Fig. 3 in Pacific Flying Foxes (Mammalia: Chiroptera): Two New Species of Pteropus from Samoa, Probably Extinct

Fig. 3. The fragmentary holotype skin of Pteropus allenorum (ANSP 1234, preserved in alcohol).

opencc-by-4.0Nov 2022View details →

Reproduction package for the paper 'Discovery of a probable very fast extragalactic nova in a symbiotic binary'

<p>This is a basic reproduction package (RP) for the paper "Discovery of a probable very fast extragalactic nova in a symbiotic binary". It aims to provide the most important data products to check and reproduce the main results of the paper, listing all software used and data archives containing the public data used.</p> <p>Authors: David Modiano, Rudy Wijnands</p> <p>Arxiv DOI: 2210.06057</p> <p>Paper DOI: 10.1051/0004-6361/202244679</p> <p>Accepted for publication in Astronomy and Astrophysics (date of acceptance: 11/10/2022)</p>

opencc-by-4.0Dec 2022View details →

Evolution of the hatching probability of Philaenus spumarius in the Iberian Peninsula from 2017 to 2018

<p>Evolution of the hatching probability of Philaenus spumarius in the Iberian Peninsula from 2017 to 2018 using temperature data from ERA5-Land. White dots correspond to nymphs that were observed in those given locations (coordinates) on a given date. Here 3 different videos to represent the evolution of the hatching probability considering the starting point for GDD accumulation on three different dates: the 1st of November, the 1<sup>st</sup> of December. and the 1<sup>st</sup> of January. </p>