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.
1,000
datasets available to search
ShareScore release 0.9.0
Dataset results
1,000 results for “Conservation Status”
Fig. 1 in The herpetofauna of the Baja California Peninsula and its adjacent islands, Mexico: composition, distribution, and conservation status
Fig. 1. Geographic regions of the Baja California Peninsula, Mexico.
Fig. 10 in The herpetofauna of the Baja California Peninsula and its adjacent islands, Mexico: composition, distribution, and conservation status
Fig. 10. View of the Magdalena Region at La Purisima, Baja California Sur. Photo by Gerardo Marrón.
Fig. 3 in New and revised life history of the Florida hairstreak Eumaeus atala (Lepidoptera: Lycaenidae) with notes on its current conservation status
Fig. 3. Frequency and mean size of Atala egg distribution (n = 600).
Figure 3 in Current status, distribution, and conservation of brown bear (Ursidae) and wild canids (gray wolf, golden jackal, and red fox; Canidae) in Turkey
Figure 3. The distribution map of golden jackal.
Figure 2 in Current status, distribution, and conservation of brown bear (Ursidae) and wild canids (gray wolf, golden jackal, and red fox; Canidae) in Turkey
Figure 2. The distribution map of gray wolf.
Figure 1 in Current status, distribution, and conservation of brown bear (Ursidae) and wild canids (gray wolf, golden jackal, and red fox; Canidae) in Turkey
Figure 1. The distribution map of brown bear.
Fig. 5 in Some color in the desert: description of a new species of Liolaemus (Iguania: Liolaemidae) from southern Peru, and its conservation status
Fig. 5. Female specimens of the Liolaemus anqapuka sp. nov. Photos by A. Quiroz.
Fig. 8 in Some color in the desert: description of a new species of Liolaemus (Iguania: Liolaemidae) from southern Peru, and its conservation status
Fig. 8. Liolaemus anqapuka sp. nov. eating a moth of the Sphingidae family. Photo by A. Quiroz.
Fig. 1 in The herpetofauna of Tabasco, Mexico: composition, distribution, and conservation status
Fig. 1. Physiographic regions in the state of Tabasco, Mexico, and location of the state in Mexico.
Fig. 1 in The herpetofauna of Guanajuato, Mexico: composition, distribution, and conservation status
Fig. 1. Physiographic regions in the state of Guanajuato, Mexico.
Fig. 11 in The herpetofauna of Querétaro, Mexico: composition, distribution, and conservation status
Fig. 11. Urban environment near the city of Querétaro. Photo by Cristhian Alejandro Peralta Robles.
Fig. 3 in Turtles of Colombia: an annotated analysis of their diversity, distribution, and conservation status
Fig. 3. Map indicating turtle species richness by department in Colombia.
Fig. 1 in Turtles of Colombia: an annotated analysis of their diversity, distribution, and conservation status
Fig. 1. Map indicating the locations of the five macro-drainages used in this study (IDEAM 2013).
Fig. 6 in The herpetofauna of Querétaro, Mexico: composition, distribution, and conservation status
Fig. 6. Oak forest, Mirador Cuatro Palos, Querétaro. Photo by Erick Daniel Velasco Esquivel.
Plantae datasets: Plantae Conservation Status
From [DATA-1689](https://eol-jira.bibalex.org/browse/DATA-1689)
TABLE 2. — Principal factor analysis giving factors higher than 1 in Amphibians of Togo: taxonomy, distribution and conservation status
<p>TABLE 2. — Principal factor analysis giving factors higher than 1, their individual variance and the cumulative variance of these six components.</p><table><tbody><tr><th><b>Factor</b></th></tr></tbody><tbody><tr><th><b>Variable</b></th><td><b>1</b></td><td><b>2</b></td><td><b>3</b></td><td><b>4</b></td><td><b>5</b></td><td><b>6</b></td></tr><tr><th>RSVL</th><td>–0.299</td><td>0.765</td><td>0.335</td><td>–0.089</td><td>–0.059</td><td>–0.080</td></tr><tr><th>RHW</th><td>0.673</td><td>0.500</td><td>0.356</td><td>–0.136</td><td>0.175</td><td>–0.097</td></tr><tr><th>RHL</th><td>0.898</td><td>0.013</td><td>0.196</td><td>0.106</td><td>0.000</td><td>–0.016</td></tr><tr><th>RMN</th><td>0.854</td><td>0.018</td><td>0.097</td><td>0.219</td><td>–0.042</td><td>–0.032</td></tr><tr><th>RMFE</th><td>0.838</td><td>0.290</td><td>0.163</td><td>0.016</td><td>0.015</td><td>–0.149</td></tr><tr><th>RMBE</th><td>0.572</td><td>–0.062</td><td>0.210</td><td>–0.273</td><td>–0.118</td><td>–0.062</td></tr><tr><th>RIFE</th><td>0.766</td><td>–0.146</td><td>–0.068</td><td>0.289</td><td>0.209</td><td>–0.228</td></tr><tr><th>RIBE</th><td>0.786</td><td>0.119</td><td>0.212</td><td>–0.004</td><td>0.289</td><td>–0.039</td></tr><tr><th>RIN</th><td>0.613</td><td>0.138</td><td>0.041</td><td>0.187</td><td>0.245</td><td>–0.360</td></tr><tr><th>REN</th><td>0.265</td><td>–0.181</td><td>–0.160</td><td>0.776</td><td>0.026</td><td>0.156</td></tr><tr><th>REL</th><td>0.812</td><td>0.043</td><td>–0.136</td><td>0.059</td><td>0.275</td><td>0.074</td></tr><tr><th>RTYD</th><td>0.243</td><td>0.761</td><td>0.448</td><td>–0.160</td><td>0.057</td><td>0.037</td></tr><tr><th>RTYE</th><td>–0.071</td><td>–0.796</td><td>0.157</td><td>–0.337</td><td>–0.048</td><td>–0.168</td></tr><tr><th>RIUE</th><td>0.118</td><td>0.052</td><td>0.027</td><td>0.827</td><td>0.039</td><td>0.017</td></tr><tr><th>RUEW</th><td>0.672</td><td>0.416</td><td>0.198</td><td>–0.356</td><td>0.250</td><td>–0.111</td></tr><tr><th>RFLL</th><td>0.483</td><td>–0.203</td><td>0.107</td><td>0.432</td><td>0.007</td><td>–0.534</td></tr><tr><th>RHAL</th><td>0.396</td><td>0.279</td><td>0.680</td><td>–0.050</td><td>0.263</td><td>–0.043</td></tr><tr><th>RTFL</th><td>0.251</td><td>0.145</td><td>0.765</td><td>0.105</td><td>0.046</td><td>–0.133</td></tr><tr><th>RTL</th><td>–0.154</td><td>–0.588</td><td>0.156</td><td>0.604</td><td>–0.089</td><td>0.071</td></tr><tr><th>RFOL</th><td>0.272</td><td>0.465</td><td>0.713</td><td>–0.158</td><td>0.169</td><td>0.122</td></tr><tr><th>RFTL</th><td>0.014</td><td>0.031</td><td>0.635</td><td>–0.028</td><td>0.070</td><td>0.168</td></tr><tr><th>RTFOL</th><td>–0.144</td><td>–0.712</td><td>–0.210</td><td>0.535</td><td>–0.189</td><td>0.105</td></tr><tr><th>RIMT</th><td>0.263</td><td>0.672</td><td>0.468</td><td>–0.265</td><td>0.163</td><td>–0.212</td></tr></tbody></table>
TABLE 1 in Amphibians of Togo: taxonomy, distribution and conservation status
<p>TABLE 1. — List of sampling localities with the respective ecological zone (see text), locality number (No. as in Figure 1) and their geographic position.</p><table><tbody><tr><th><b>Localities</b></th><th><b>Ecological zone</b></th><th><b>No.</b></th><th><b>Latitude</b></th><th><b>Longitude</b></th></tr></tbody><tbody><tr><th>Nanergou</th><td>I</td><td>1</td><td>10°54’15.44”N</td><td>00°08’54.81”E</td></tr><tr><th>Mango</th><td>I</td><td>2</td><td>10°21’33.00”N</td><td>00°28’15.00”E</td></tr><tr><th>N’Gambi</th><td>I</td><td>3</td><td>10°12’29.30”N</td><td>00°47’19.20”E</td></tr><tr><th>Koumoungou</th><td>I</td><td>4</td><td>10°09’47.00”N</td><td>00°49’20.40”E</td></tr><tr><th>Kanté</th><td>II</td><td>5</td><td>09°56’44.76”N</td><td>01°03’05.84”E</td></tr><tr><th>Niamtougou</th><td>I</td><td>6</td><td>09°45’54.43”N</td><td>01°06’50.62”E</td></tr><tr><th>Kara</th><td>II</td><td>7</td><td>09°32’44.80”N</td><td>01°11’21.46”E</td></tr><tr><th>Guérin Kuka</th><td>II</td><td>8</td><td>09°41’03.80”N</td><td>00°36’47.48”E</td></tr><tr><th>Alédjo</th><td>II</td><td>9</td><td>09°15’00.17”N</td><td>01°12’03.88”E</td></tr><tr><th>Bassar</th><td>II</td><td>10</td><td>09°15’39.46”N</td><td>00°47’20.83”E</td></tr><tr><th>Binaparba</th><td>II</td><td>11</td><td>09°14’10.69”N</td><td>00°46’13.63”E</td></tr><tr><th>Bounako</th><td>II</td><td>12</td><td>09°11’01.13”N</td><td>00°54’01.61”E</td></tr><tr><th>Tchamba</th><td>III</td><td>13</td><td>09°00’58.05”N</td><td>01°25’01.14”E</td></tr><tr><th>Kalaré</th><td>II</td><td>14</td><td>08°53’56.59”N</td><td>00°57’49.41”E</td></tr><tr><th>Fazao</th><td>II</td><td>15</td><td>08°40’35.72”N</td><td>00°32’13.04”E</td></tr><tr><th>Aou Losso</th><td>III</td><td>16</td><td>08°44’57.96”N</td><td>01°02’58.70”E</td></tr><tr><th>Kpeisolongo</th><td>II</td><td>17</td><td>08°34’41.02”N</td><td>00°51’24.35”E</td></tr><tr><th>Koui</th><td>II</td><td>18</td><td>08°14’48.80”N</td><td>00°41’58.38”E</td></tr><tr><th>Yégué (Bismarkburg)</th><td>IV</td><td>19</td><td>08°10’58.80”N</td><td>00°38’60.00”E</td></tr><tr><th>Diguengué</th><td>IV</td><td>20</td><td>08°04’59.89”N</td><td>00°38’04.21”E</td></tr><tr><th>Assoukoko</th><td>IV</td><td>21</td><td>08°00’39.34”N</td><td>00°41’14.83”E</td></tr><tr><th>Badou</th><td>IV</td><td>22</td><td>07°35’48.95”N</td><td>00°37’33.71”E</td></tr><tr><th>Akloa</th><td>IV</td><td>23</td><td>07°30’23.00”N</td><td>00°35’58.40”E</td></tr><tr><th>Dobadié</th><td>IV</td><td>24</td><td>07°35’40.59”N</td><td>00°41’55.14”E</td></tr><tr><th>Oga</th><td>IV</td><td>25</td><td>07°36’15.14”N</td><td>00°49’20.96”E</td></tr><tr><th>Atakpamé</th><td>IV</td><td>26</td><td>07°31’28.17”N</td><td>01°07’27.47”E</td></tr><tr><th>Nangbéto</th><td>III</td><td>27</td><td>07°22’29.35”N</td><td>01°24’05.40”E</td></tr><tr><th>Sodo Zion</th><td>IV</td><td>28</td><td>07°19’57.20”N</td><td>00°50’29.40”E</td></tr><tr><th>Agoté</th><td>IV</td><td>29</td><td>07°15’22.48”N</td><td>00°47’39.89”E</td></tr><tr><th>Danyi Atigba</th><td>IV</td><td>30</td><td>07°12’08.29”N</td><td>00°41’03.87”E</td></tr><tr><th>Yikpa-Dzigbe</th><td>IV</td><td>31</td><td>07°07’28.62”N</td><td>00°37’33.23”E</td></tr><tr><th>Kpele Beme</th><td>IV</td><td>32</td><td>07°05’04.79”N</td><td>00°42’12.65”E</td></tr><tr><th>Kouma-Tokpli</th><td>IV</td><td>33</td><td>06°57’12.10”N</td><td>00°34’43.40”E</td></tr><tr><th>Missahohé</th><td>IV</td><td>34</td><td>06°56’60.00”N</td><td>00°34’58.80”E</td></tr><tr><th>Yo (Agomé Yo)</th><td>IV</td><td>35</td><td>06°56’38.00”N</td><td>00°35’50.10”E</td></tr><tr><th>Kpalimé</th><td>IV</td><td>36</td><td>06°54’08.16”N</td><td>00°37’57.23”E</td></tr><tr><th>Agou</th><td>IV</td><td>37</td><td>06°50’54.40”N</td><td>00°43’35.89”E</td></tr><tr><th>Gadjagan</th><td>IV</td><td>38</td><td>06°49’46.44”N</td><td>00°46’01.08”E</td></tr><tr><th>Klonou</th><td>IV</td><td>39</td><td>06°49’59.00”N</td><td>00°40’59.00”E</td></tr><tr><th>Tové</th><td>IV</td><td>40</td><td>06°48’36.70”N</td><td>00°38’24.24”E</td></tr><tr><th>Séva</th><td>IV</td><td>41</td><td>06°45’00.00”N</td><td>00°52’59.00”E</td></tr><tr><th>Adjarala</th><td>V</td><td>42</td><td>06°54’04.19”N</td><td>01°36’24.05”E</td></tr><tr><th>Afagnan</th><td>V</td><td>43</td><td>06°29’18.30”N</td><td>01°38’06.99”E</td></tr><tr><th>Kovié</th><td>V</td><td>44</td><td>06°20’51.06”N</td><td>01°09’20.23”E</td></tr><tr><th>Tsévié</th><td>V</td><td>45</td><td>06°25’11.18”N</td><td>01°12’39.49”E</td></tr><tr><th>Aného</th><td>V</td><td>46</td><td>06°14’42.58”N</td><td>01°37’05.99”E</td></tr><tr><th>Djagblé</th><td>V</td><td>47</td><td>06°14’39.38”N</td><td>01°18’25.60”E</td></tr><tr><th>Togblekopé</th><td>V</td><td>48</td><td>06°15’40.52”N</td><td>01°13’08.67”E</td></tr><tr><th>Agoé</th><td>V</td><td>49</td><td>06°12’53.76”N</td><td>01°12’38.19”E</td></tr><tr><th>Lomé</th><td>V</td><td>50</td><td>06°07’35.70”N</td><td>01°13’40.22”E</td></tr></tbody></table>
Fig. 1 in Previously undescribed dental arrangement among electric knifefishes, with comments on the taxonomic and conservation status of Tembeassu marauna Triques (Otophysi: Gymnotiformes: Apteronotidae)
Fig. 1. Holotype of Tembeassu marauna (MZUSP 48510), left side of head. Scale bar ~ 5 mm.
Assessing the conservation status of Chinese freshwater fish using deep learning
<p>The lack of information on the extinction risk of most species is a fundamental challenge in prioritizing conservation strategies and bending the curve of current biodiversity decline. Machine learning methods have shown promising potential to fill this gap, but their applicability remains to be validated at different taxa (especially aquatic species) and spatial scales. We assessed the extinction risk of 1,162 freshwater fish species in China that have not yet been included in the latest IUCN Red List using multiple neural network algorithms based on datasets of species occurrences, biological traits, phylogeny, and relevant environmental layers. The best deep learning models dramatically improved the assessment coverage from 29.9% (496 species) to 93.2–93.9% (1,545–1,557 species) of the whole fauna with an accuracy of 95.4–99.0%. By combining our prediction results with the IUCN Red List, we found that 23.8–26.5% (394–440 species) of Chinese freshwater fishes were identified as possibly threatened species, which is roughly four times the IUCN assessment. Newly assessed species and threatened species were mainly from the orders Cypriniformes (prediction added: 837–846 species; final threatened: 325–350 species), Siluriformes (113–122; 28–37) and Perciformes (74–76; 18–25). The increase in threatened species richness based on predictions was led by the upper reaches of the Pearl and Yangtze. Overall, our findings suggest that deep learning algorithms can provide robust and time-saving assessments of extinction risk for entire freshwater fish fauna on a large national scale, thereby facilitating relevant conservation prioritization.</p>
MarginProbe, a Device for Intraoperative Assessment of Margin Status in Breast Conservation Surgery
ClinicalTrials.gov study NCT00749931. IPD Sharing: Not stated. Countries: 2. Publications: 1.
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.