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.
580
datasets available to search
ShareScore release 0.9.0
Dataset results
580 results for “effort”
Figures 3-6 from: Hopkins T, Roininen H, van Noort S, Broad GR, Kaunisto K, Sääksjärvi IE (2019) Extensive sampling and thorough taxonomic assessment of Afrotropical Rhyssinae (Hymenoptera, Ichneumonidae) reveals two new species and demonstrates the limitations of previous sampling efforts. ZooKeys 878: 33-71. https://doi.org/10.3897/zookeys.878.37845
Figures 3-6 Epirhyssa brianfisheri female (holotype, SAM–HYM–P048018). This species was not found in Uganda. 3 Habitus 4 hypostomal flange 5 mesopleuron dorsal margin 6 apical horn of metasoma. Figure 3 is from van Noort (2019).
Figures 39-45 from: Hopkins T, Roininen H, van Noort S, Broad GR, Kaunisto K, Sääksjärvi IE (2019) Extensive sampling and thorough taxonomic assessment of Afrotropical Rhyssinae (Hymenoptera, Ichneumonidae) reveals two new species and demonstrates the limitations of previous sampling efforts. ZooKeys 878: 33-71. https://doi.org/10.3897/zookeys.878.37845
Figures 39-45 Epirhyssa quagga female (holotype http://mus.utu.fi/ZMUT.5788), a new species from Uganda. 39 Habitus 40 face and clypeus 41 frons 42 hypostomal flange 43 mesopleuron dorsal margin 44 apical horn of metasoma 45 tergites 1–7. Scale bars: 0.5 mm (40–45), 1 mm (39).
Figures 32-38 from: Hopkins T, Roininen H, van Noort S, Broad GR, Kaunisto K, Sääksjärvi IE (2019) Extensive sampling and thorough taxonomic assessment of Afrotropical Rhyssinae (Hymenoptera, Ichneumonidae) reveals two new species and demonstrates the limitations of previous sampling efforts. ZooKeys 878: 33-71. https://doi.org/10.3897/zookeys.878.37845
Figures 32-38 Epirhyssa overlaeti male (http://mus.utu.fi/ZMUT.1280) and female (34: http://mus.utu.fi/ZMUT.2291, 37: http://mus.utu.fi/ZMUT.5766), a species found in Uganda. 32 Habitus 33 face and clypeus 34 frons 35 hypostomal flange 36 mesopleuron dorsal margin 37 apical horn of metasoma 38 tergites 1–7. Scale bars: 0.5 mm (33–38), 1 mm (32).
Figures 25-27 from: Hopkins T, Roininen H, van Noort S, Broad GR, Kaunisto K, Sääksjärvi IE (2019) Extensive sampling and thorough taxonomic assessment of Afrotropical Rhyssinae (Hymenoptera, Ichneumonidae) reveals two new species and demonstrates the limitations of previous sampling efforts. ZooKeys 878: 33-71. https://doi.org/10.3897/zookeys.878.37845
Figures 25-27 Epirhyssa maynei male (25: holotype, 26–27: SAM–HYM–P049437). This species was not found in Uganda. 25 Habitus 26 hypostomal flange 27 mesopleuron dorsal margin. Figure 25 is from van Noort (2019).
Figure 1 from: Hopkins T, Roininen H, van Noort S, Broad GR, Kaunisto K, Sääksjärvi IE (2019) Extensive sampling and thorough taxonomic assessment of Afrotropical Rhyssinae (Hymenoptera, Ichneumonidae) reveals two new species and demonstrates the limitations of previous sampling efforts. ZooKeys 878: 33-71. https://doi.org/10.3897/zookeys.878.37845
Figure 1 Diagnostic characters for the thirteen known Afrotropical rhyssine species. The figure shows the frons median carinae (A converge, B diverge, C absent), frons lateral carinae (A present, B absent), hypostomal flange (D wider than second maxillary palp, E narrower than or comparable to second maxillary palp), subalar prominence (F flanged, G no flange), mesopleuron margin (H flanged, G no flange) and female apical horn (I ellipse, J half-ellipse). Not shown are the epicnemial carina (laterally absent, only just reaches the mesopleuron, reaches high onto the mesopleuron), areolet (present, absent) and tergite 3 structure (mostly striate, mostly punctate, mostly smooth). The images are of http://mus.utu.fi/ZMUT.5766, http://mus.utu.fi/ZMUT.2520 (A, F), http://mus.utu.fi/ZMUT.5853 (B, D, H, J), http://mus.utu.fi/ZMUT.5788 (C, E, I) and http://mus.utu.fi/ZMUT.5663 (G). Image F has been flipped horizontally.
Figures 21-24 from: Hopkins T, Roininen H, van Noort S, Broad GR, Kaunisto K, Sääksjärvi IE (2019) Extensive sampling and thorough taxonomic assessment of Afrotropical Rhyssinae (Hymenoptera, Ichneumonidae) reveals two new species and demonstrates the limitations of previous sampling efforts. ZooKeys 878: 33-71. https://doi.org/10.3897/zookeys.878.37845
Figures 21-24 Epirhyssa leroyi female (paratype RMCA-ENT-000017924). This species was not found in Uganda. 21 Habitus 22 hypostomal flange 23 mesopleuron dorsal margin 24 apical horn of metasoma. Scale bars 1 mm (22–24), 5 mm (21). Figures courtesy of RMCA (Stéphane Hanot).
Figures 14-20 from: Hopkins T, Roininen H, van Noort S, Broad GR, Kaunisto K, Sääksjärvi IE (2019) Extensive sampling and thorough taxonomic assessment of Afrotropical Rhyssinae (Hymenoptera, Ichneumonidae) reveals two new species and demonstrates the limitations of previous sampling efforts. ZooKeys 878: 33-71. https://doi.org/10.3897/zookeys.878.37845
Figures 14-20 Epirhyssa johanna female (holotype http://mus.utu.fi/ZMUT.4920), a new species from Uganda. 14 habitus 15 face and clypeus 16 frons 17 hypostomal flange 18 mesopleuron dorsal margin 19 apical horn of metasoma 20 tergites 1–7. Scale bars: 0.5 mm (15–20), 1 mm (14).
Figures 28-31 from: Hopkins T, Roininen H, van Noort S, Broad GR, Kaunisto K, Sääksjärvi IE (2019) Extensive sampling and thorough taxonomic assessment of Afrotropical Rhyssinae (Hymenoptera, Ichneumonidae) reveals two new species and demonstrates the limitations of previous sampling efforts. ZooKeys 878: 33-71. https://doi.org/10.3897/zookeys.878.37845
Figures 28-31 Epirhyssa migratoria female (holotype). This species was not found in Uganda. 28 Habitus 29 hypostomal flange 30 mesopleuron dorsal margin 31 apical horn of metasoma. Figure 28 is from van Noort (2019).
Figure 2 from: Hopkins T, Roininen H, van Noort S, Broad GR, Kaunisto K, Sääksjärvi IE (2019) Extensive sampling and thorough taxonomic assessment of Afrotropical Rhyssinae (Hymenoptera, Ichneumonidae) reveals two new species and demonstrates the limitations of previous sampling efforts. ZooKeys 878: 33-71. https://doi.org/10.3897/zookeys.878.37845
Figure 2 Diagnostic character traits used in the identification key. A Areolet (Megarhyssa babaulti holotype) B open areolet (http://mus.utu.fi/ZMUT.5788) C subalar prominence flange (http://mus.utu.fi/ZMUT.2520) D subalar prominence without flange and dorsal margin of mesopleuron with flange (http://mus.utu.fi/ZMUT.5853) E dorsal margin of mesopleuron without flange (SAMC SAM–HYM–P048018) F elliptical apical horn of metasoma (http://mus.utu.fi/ZMUT.5766) G half-elliptical apical horn of metasoma (http://mus.utu.fi/ZMUT.5853) H frons without carinae (http://mus.utu.fi/ZMUT.4920) I frons without lateral carinae (http://mus.utu.fi/ZMUT.3234) J striate tergite 3 (http://mus.utu.fi/ZMUT.5788) K punctate tergite 3 (http://mus.utu.fi/ZMUT.5663) L hypostomal carina raised into elevated flange (SAMC SAM–HYM–P048018) M hypostomal carina raised into low flange (E. shaka holotype) N half-elliptical apical horn of metasoma (http://mus.utu.fi/ZMUT.5663) O elliptical apical horn of metasoma (E. shaka holotype). Image A is from van Noort (2019). Image D has been flipped horizontally.
Table ¹: Data by year and by month, for the number of cameras, sampling days, sampling effort, and relative abundance index (RAI) of records for females and cubs. in Reproductive aspects of female Andean bears (Tremarctos ornatus) in the Chingaza massif, eastern range of the Colombian Andes
<p><b>Table ¹:</b> Data by year and by month, for the number of cameras, sampling days, sampling effort, and relative abundance index (RAI) of records for females and cubs.</p><table><tbody><tr><th><b>Year</b></th><th><b>Camera traps used</b></th><th></th><th></th><th><b>Sampling days per month (sampling effort per month)</b></th><th></th><th></th><th><b>Sampling days</b></th><th><b>Camera traps</b></th><th><b>Sampling effort</b></th></tr></tbody><tbody><tr><th><b>Reconyx Wildview</b></th><td><b>Bushnell</b></td><td><b>]</b></td><td><b>F</b></td><td><b>M</b></td><td><b>A</b></td><td><b>M</b></td><td><b>]</b></td><td><b>]</b></td><td><b>A</b></td><td><b>S</b></td><td><b>O</b></td><td><b>N</b></td><td><b>D</b></td><td><b>per year</b></td><td><b>per year</b></td><td><b>per year</b></td></tr><tr><th>2011</th><td>2</td><td>4</td><td>0</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>3 (18)</td><td>30</td><td>30</td><td>30</td><td>93</td><td>6</td><td>558</td></tr><tr><th></th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>(180)</td><td>(180)</td><td>(180)</td><td></td><td></td><td></td></tr><tr><th>2012a</th><td>2</td><td>4</td><td>0</td><td>31</td><td>29</td><td>31</td><td>30</td><td>31</td><td>30</td><td>31</td><td>31</td><td>30</td><td>31</td><td>30</td><td>30</td><td>365</td><td>6</td><td>2190</td></tr><tr><th></th><td></td><td></td><td></td><td>(186)</td><td>(174)</td><td>(186)</td><td>(180)</td><td>(186)</td><td>(180)</td><td>(186)</td><td>(186)</td><td>(180)</td><td>(186)</td><td>(180)</td><td>(180)</td><td></td><td></td><td></td></tr><tr><th>2012b</th><td>0</td><td>0</td><td>12</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>30</td><td>30</td><td>60</td><td>12</td><td>720</td></tr><tr><th></th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>(360)</td><td>(360)</td><td></td><td></td><td></td></tr><tr><th>2013</th><td>2</td><td>4</td><td>14</td><td>31</td><td>28</td><td>31</td><td>30</td><td>31</td><td>30</td><td>31</td><td>31</td><td>30</td><td>30</td><td></td><td></td><td>303</td><td>20</td><td>6060</td></tr><tr><th></th><td></td><td></td><td></td><td>(620)</td><td>(560)</td><td>(620)</td><td>(600)</td><td>(620)</td><td>(600)</td><td>(620)</td><td>(620)</td><td>(600)</td><td>(600)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>2014</th><td>0</td><td>2</td><td>9</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>3 (33)</td><td>30</td><td>33</td><td>11</td><td>363</td></tr><tr><th></th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>(330)</td><td></td><td></td><td></td></tr><tr><th>2015a</th><td>0</td><td>2</td><td>9</td><td>31</td><td>28</td><td>31</td><td>30</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>120</td><td>11</td><td>1320</td></tr><tr><th></th><td></td><td></td><td></td><td>(341)</td><td>(308)</td><td>(341)</td><td>(330)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>2015b</th><td>0</td><td>0</td><td>117</td><td></td><td></td><td></td><td></td><td>2 (234)</td><td>30</td><td>31</td><td>31</td><td>30</td><td>31</td><td>30</td><td>30</td><td>215</td><td>117</td><td>25,155</td></tr><tr><th></th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>(3510)</td><td>(3627)</td><td>(3627)</td><td>(3510)</td><td>(3627)</td><td>(3510)</td><td>(3510)</td><td></td><td></td><td></td></tr><tr><th>2016</th><td>0</td><td>0</td><td>117</td><td>31</td><td>29</td><td>31</td><td>30</td><td>30</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>151</td><td>117</td><td>17,667</td></tr><tr><th></th><td></td><td></td><td></td><td>(3627)</td><td>(3393)</td><td>(3627)</td><td>(3510)</td><td>(3510)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Sum of sampling effort per month over</th><td>4774</td><td>4435</td><td>4774</td><td>4620</td><td>4550</td><td>4290</td><td>4433</td><td>4433</td><td>4308</td><td>4593</td><td>4263</td><td>4560</td><td>Total sampling effort</td><td>54,033</td></tr><tr><th>the years</th></tr><tr><th>Records of females with cubs (4 <b>–</b></th><td>1</td><td>2</td><td>1</td><td>3</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2</td><td>0</td><td>4</td><td></td><td></td><td></td></tr><tr><th>7 months)</th></tr><tr><th>RAI of females with cubs (4 <b>–</b> 7 months)</th><td>0.21</td><td>0.45</td><td>0.21</td><td>0.65</td><td>0.22</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.44</td><td>0.00</td><td>0.88</td><td></td><td></td><td></td></tr><tr><th>Records of estimated births</th><td></td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>7</td><td>3</td><td>2</td><td>0</td><td>3</td><td>3</td><td>0</td><td></td><td></td><td></td></tr><tr><th>RAI of estimated births</th><td></td><td></td><td>0.21</td><td>0.00</td><td>0.21</td><td>0.00</td><td>0.00</td><td>1.63</td><td>0.68</td><td>0.45</td><td>0.00</td><td>0.65</td><td>0.70</td><td>0.00</td><td></td><td></td><td></td></tr></tbody></table>
Data from: Implications of survey effort on estimating demographic parameters of a long-lived marine top predator
Effective management of wildlife populations rely on knowledge of their abundance, survival and reproductive rates. Maintaining long-term studies capable of estimating demographic parameters for long-lived, slow reproducing species is challenging. Insights into effects of research intensity on the statistical power to estimate demographic parameters is limited. Here, we investigate implications of survey effort on estimating abundance, home range sizes and reproductive output of Indo-Pacific bottlenose dolphins (Tursiops aduncus), using a three-year sub-sample of a long-term, capture-recapture study off Bunbury, Western Australia. Photo-identification on individual dolphins were collected following Pollock's Robust Design, where seasons were defined as 'primary periods', each consisting of multiple 'secondary periods'. The full dataset consisted of 12 primary periods and 72 secondary periods, resulting in the study area being surveyed 24 times/year. We simulated reduced survey effort by randomly removing one, two or three secondary periods per primary period. Capture-recapture models were used to assess the effect of survey intensity on the power to detect trends in population abundance, while individual dolphin sighting histories were used to assess the ability to conduct home range analyses. We used sighting records of adult females and their calving histories to assess survey effort on quantifying reproductive output. A 50% reduction in survey effort resulted in: i) up to a 36% decline in population abundance at the time of detection; ii) a reduced ability to estimate home range sizes, by increasing the time for individuals to be sighted on >30 occasions (an often-used metric for home range analyses) from 7.74 to 15.46 years; and iii) 33%, 24% and 33% of annual calving events going undocumented in their year of birth. Results clearly illustrate the importance of survey effort on the ability to assess demographic parameters with clear implications for population viability analyses, population forecasting and conservation efforts to manage human-wildlife interactions.
Data from: Dive behaviour and foraging effort of female Cape fur seals Arctocephalus pusillus pusillus
While marine top predators can play a critical role in ecosystem structure and dynamics through their effects on prey populations, how they function is often not well understood. In southern Africa, the Cape fur seal (Arctocephalus pusillus pusillus) population constitutes the largest marine top predator biomass but little is known of its foraging ecology other than its diet and some preliminary dive records. Dive information was obtained from 32 adult females instrumented with dive recorders at the Kleinsee colony (29º34.17 S, 16 º59.80 E) in South Africa during 2006–2008. Most dives were in the depth range of epipelagic prey species (< 50 m deep) and at night, reflecting the reliance of Cape fur seals on small vertically migrating schooling prey. However, most females also performed benthic dives, which was prevalent in some individuals. Benthic diving was significantly associated with the frequency with which females exceeded their aerobic dive limit. The greater putative costs of benthic diving highlights the potential detrimental effects on Cape fur seals of environmental changes that may reduce the availability of epipelagic prey in the Benguela region of southern Africa.
Figure 7 from: Kawada R, Barbosa D, Azevedo C (2014) Cryptalyra (Hymenoptera, Megalyridae) from Maranhão, Brazil: three new species discovered after a large collecting effort. ZooKeys 442: 85-104. https://doi.org/10.3897/zookeys.442.8237
Figure 7 - Cryptalyra ichiroi sp. n. A mesosoma lateral view B mesosoma dorsal view. Scales bar in microns.
Figure 2 from: Kawada R, Barbosa D, Azevedo C (2014) Cryptalyra (Hymenoptera, Megalyridae) from Maranhão, Brazil: three new species discovered after a large collecting effort. ZooKeys 442: 85-104. https://doi.org/10.3897/zookeys.442.8237
Figure 2 - Cryptalyra ichiroi sp. n. A habitus in lateral view B habitus in dorsal view. Scales bar in microns.
Figure 11 from: Kawada R, Barbosa D, Azevedo C (2014) Cryptalyra (Hymenoptera, Megalyridae) from Maranhão, Brazil: three new species discovered after a large collecting effort. ZooKeys 442: 85-104. https://doi.org/10.3897/zookeys.442.8237
Figure 11 - Cryptalyra limeirai sp. n. A metasoma lateral view B forewing C hind wing. Scales bar in microns.
Figure 12 from: Kawada R, Barbosa D, Azevedo C (2014) Cryptalyra (Hymenoptera, Megalyridae) from Maranhão, Brazil: three new species discovered after a large collecting effort. ZooKeys 442: 85-104. https://doi.org/10.3897/zookeys.442.8237
Figure 12 - Cryptalyra helenae sp. n. A metacoxa lateral view B stout setae (white arrow) of mesotibia, lateral view. Cryptalyra ichiroi sp. n. C metacoxa lateral view D stout setae (white arrow) of fore leg, lateral view. Cryptalyra limeirai sp. n. E metacoxa lateral view F metatibial spurs (white srrow), posterior view. Scales bar in microns.
Figure 4 from: Kawada R, Barbosa D, Azevedo C (2014) Cryptalyra (Hymenoptera, Megalyridae) from Maranhão, Brazil: three new species discovered after a large collecting effort. ZooKeys 442: 85-104. https://doi.org/10.3897/zookeys.442.8237
Figure 4 - Cryptalyra helenae sp. n. A head in frontal view B head in dorsal view. Cryptalyra ichiroi sp. n. C head in frontal view D head in dorsal view. Cryptalyra limeirai sp. n. E head in frontal view F head in dorsal view. Scales bar in microns.
Figure 10 from: Kawada R, Barbosa D, Azevedo C (2014) Cryptalyra (Hymenoptera, Megalyridae) from Maranhão, Brazil: three new species discovered after a large collecting effort. ZooKeys 442: 85-104. https://doi.org/10.3897/zookeys.442.8237
Figure 10 - Cryptalyra ichiroi sp. n. A metasoma lateral view B forewing C hind wing. Scales bar in microns.
Figure 3 from: Kawada R, Barbosa D, Azevedo C (2014) Cryptalyra (Hymenoptera, Megalyridae) from Maranhão, Brazil: three new species discovered after a large collecting effort. ZooKeys 442: 85-104. https://doi.org/10.3897/zookeys.442.8237
Figure 3 - Cryptalyra limeirai sp. n. A habitus in lateral view B habitus in dorsal view. Scales bar in microns.
Figure 1 from: Kawada R, Barbosa D, Azevedo C (2014) Cryptalyra (Hymenoptera, Megalyridae) from Maranhão, Brazil: three new species discovered after a large collecting effort. ZooKeys 442: 85-104. https://doi.org/10.3897/zookeys.442.8237
Figure 1 - Cryptalyra helenae sp. n. A habitus in lateral view B habitus in dorsal view. Scales bar in microns.
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.