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.
22
datasets available to search
ShareScore release 0.9.0
Dataset results
22 results for “species scoring”
Bird plumage brightness scores and blood parasite prevalence values of North American passerine species
<p>Dataset with bird plumage brightness scores and blood parasite prevalence values for 114 North American passerine host species. One file contains the data table. One file contains a table with descriptions of the columns in the data table.</p> <p>Note: These data were reconstructed from files used in Read & Harvey 1989 (<a href="https://doi.org/10.1038/339618a0">https://doi.org/10.1038/339618a0</a>) with column headings inferred with the help of Read 1991 (<a href="https://doi.org/10.1086/285225">https://doi.org/10.1086/285225</a>).</p>
Fig. 14. Multiple linear discriciminant score D in Ponera Testacea Emery, 1895 Stat. N. - A Sister Species Of P. Coarctata (Latreille, 1802) (Hymenoptera, Formicidae)
Fig. 14. Multiple linear discriciminant score D(7) = 0.068 FoDG +0.002 CS –0.43 PEL/NOH +0.02 PiMe –0.13 CL/CW –0.2 FR/CS –0.10 PEW/CS. for 126 individual workers of Ponera coarctata and testacea (based onSEIFERT's dataset)
Baseline and Future (2050s and 2090s) Climate Suitability Scores for 116 Useful Tree Species and 220 locations from Côte d'Ivoire, Ghana and Guinea
<p>Climate suitability scores were calculated for 116 Useful Tree Species identified by filtering Top830+ native tree species from Côte d'Ivoire, Ghana and Guinea via the <a href="https://patspo.shinyapps.io/GlobalUsefulTrees/">GlobalUsefulNativeTrees</a> database and checking for the availability of globally observed environmental ranges from the <a href="https://doi.org/10.5281/zenodo.13132613">TreeGOER</a> database.</p> <ul> <li>Score = 3 means that in 'environmental space' the planting site occurs within the 25% - 75% species's range (as documented in the <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.16914" target="_blank" rel="noopener">TreeGOER</a> ) for all variables</li> <li>Score = 2 corresponds to the 5% - 95% species's range for all variables. For some variables, the planting site occurs outside the 25% - 75% species's range.</li> <li>Score = 1 corresponds to the 0% - 100% species's range for all variables. For some variables, the planting site occurs outside the 5% - 95% species's range.</li> <li>Score = 0 means that the planting site occurs outside the 0% - 100% species's range for some of the variables</li> <li>Score = -1 means that the species is not documented by TreeGOER</li> </ul> <p>Locations corresponded to cities and weather stations from the three target countries sourced from the <a href="https://doi.org/10.5281/zenodo.10004594">CitiesGOER</a> and <a href="https://doi.org/10.5281/zenodo.12679832">ClimateForecasts</a> databases, respectively. Both these databases provide bioclimatic conditions for the historical (baseline) and three future climate change scenarios. Bioclimatic variables for future climates correspond to the median values from 24 Global Climate Models (GCMs) for Shared Socio-Economic Pathway (SSP) 1-2.6 for the 2050s (2041-2060), from 21 GCMs for SSP 3-7.0 for the 2050s and from 13 GCMs for SSP 5-8.5 for the 2090s.</p> <p>Investigations were made for two different sets of bioclimatic variables, allowing for sensitivity analysis:</p> <ul> <li>One set of bioclimatic variables included BIO01 (mean annual temperature), BIO12 (total annual precipitation), climaticMoistureIndex, monthCountByTemp10 (number of months with average temperature above 10 degrees), growingDegDays5, BIO05 (maximum temperature of the warmest month), BIO06 (minimum temperature of teh coldest month), BIO16 (precipitation of the wettest quarter), BIO17 (precipitation of the driest quarter) and MCWD (Maximum Climatological Water Deficit). These are the same bioclimatic variables available internally in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> for climate filtering.</li> <li>One set only included BIO01 (mean annual temperature), which is the single bioclimatic variables available for the BGCI <a href="https://cat.bgci.org/">Climate Assessment Tool</a>.</li> </ul> <p>Calculations were made with similar scripting pipelines in the <em>R</em> statistical environment as documented here: <a href="https://rpubs.com/Roeland-KINDT/1168650">https://rpubs.com/Roeland-KINDT/1168650</a>. These scripts use similar calculations methods as those used for the global case studies of the TreeGOER manuscript (Kindt <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">2023</a>), and used internally in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> online database. Interested readers should especially refer to the manuscript for further details on methods used and their justification.</p> <p>The maps show the frequency distribution of tree species with climate scores 3, 2, 1 and 0, excluding 18 species not documented by the TreeGOER.</p> <p> </p> <p><strong>References</strong></p> <ul> <li>Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>.</li> <li>Kindt, R. (2024). TreeGOER: Tree Globally Observed Environmental Ranges (2024.07) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.13132613" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13132613</a></li> <li>Kindt, R., Graudal, L., Lillesø, JP.B. <em>et al.</em> (2023). GlobalUsefulNativeTrees, a database documenting 14,014 tree species, supports synergies between biodiversity recovery and local livelihoods in landscape restoration. <em>Sci Rep</em> <strong>13</strong>, 12640. <a href="https://doi.org/10.1038/s41598-023-39552-1">https://doi.org/10.1038/s41598-023-39552-1</a></li> <li>Kindt, R. (2023). CitiesGOER: Globally Observed Environmental Data for 52,602 Cities with a Population ≥ 5000 (2023.10) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.10004594" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10004594</a></li> <li>Kindt, R. (2024). ClimateForecasts: Globally Observed Environmental Data for 15,504 Weather Station Locations (2024.07) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.12679832" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12679832</a></li> <li>Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. <em>International Journal of Climatology</em>, <em>37</em>(12), 4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a></li> <li>Title, P. O., & Bemmels, J. B. (2018). ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. <em>Ecography</em>, <em>41</em>(2), 291–307. <a href="https://doi.org/10.1111/ecog.02880">https://doi.org/10.1111/ecog.02880</a></li> <li>Opendatasoft (2023) Geonames - All Cities with a population > 1000. <a href="https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name">https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name</a> (accessed 22-JULY-2023)</li> <li>Meteostat (2024) Weather stations: Lite dump with active weather stations. <a href="https://github.com/meteostat/weather-stations">https://github.com/meteostat/weather-stations</a> (accessed 17-FEB-2024)</li> </ul> <p> </p> <p><strong>Funding</strong></p> <p>The data sets and maps available in this archive were created within the context of an agreement between The International Centre for Research in Agroforestry (ICRAF) and WORLD UNIVERSITY SERVICE OF CANADA (WUSC) for a <em><a href="https://ceci.org/en/projects/nature-based-climate-adaptation-guinean-forest-west-africa-sbn-guinean-forests">Nature-based climate adaptation project in the Guinean forests of West Africa (NbS Guinean Forests)</a></em> funded by <a href="https://www.international.gc.ca/global-affairs-affaires-mondiales/home-accueil.aspx?lang=eng">Global Affairs Canada</a>.</p>
Double-tagging scores of seabirds reveals that light-level geolocator accuracy is limited by species idiosyncrasies and equatorial solar profiles
<p>Light-level geolocators are popular bio-logging tools, with advantageous sizes, longevity, and affordability. Biologists tracking seabirds often presume geolocator spatial accuracies between 186-202 km from previously-innovative, yet taxonomically, spatially, and computationally limited, studies. Using recently developed methods, we investigated whether assumed uncertainty norms held across a larger-scale, multispecies study.</p> <p>We field-tested geolocator spatial accuracy by synchronously deploying these with GPS loggers on scores of seabirds across five species and 11 Mediterranean Sea, East Atlantic and South Pacific breeding colonies. We first interpolated geolocations using the geolocation package FLightR without prior knowledge of GPS tracked routes. We likewise applied another package, probGLS, additionally testing whether sea-surface temperatures could improve route accuracy.</p> <p>Geolocator spatial accuracy was lower than the ~200km often assumed. probGLS produced the best accuracy (mean ± SD = 304 ± 413 km, <i>n</i> = 185 deployments) with 84.5% of GPS-derived latitudes and 88.8% of longitudes falling within resulting uncertainty estimates. FLightR produced lower spatial accuracy (408 ± 473 km, <i>n</i> = 171 deployments) with 38.6% of GPS-derived latitudes and 27% of longitudes within package-specific uncertainty estimates. Expected inter-twilight period (from GPS position and date) was the strongest predictor of accuracy, with increasingly equatorial solar profiles (i.e., closer temporally to equinoxes and/or spatially to the Equator) inducing more error. Individuals, species and geolocator model also significantly affected accuracy, while the impact of distance travelled between successive twilights depended on the geolocation package.</p> <p>Geolocation accuracy is not uniform among seabird species and can be considerably lower than assumed. Individual idiosyncrasies and spatiotemporal dynamics (i.e., shallower inter-twilight shifts by date and latitude) mean that practitioners should exercise greater caution in interpreting geolocator data and avoid universal uncertainty estimates. We provide a function capable of estimating relative accuracy of positions based on geolocator-observed inter-twilight period.</p>
Double-tagging scores of seabirds reveals that light-level geolocator accuracy is limited by species idiosyncrasies and equatorial solar profiles
Open the record for dataset details and reuse information.
Cross-species analysis of genetic architecture and polygenic risk scores for non-contact ACL rupture in dogs and humans
Open the record for dataset details and reuse information.
Phenological scoring of citizen science observations of five native California milkweed species
Open the record for dataset details and reuse information.
FIGURE 10. Scores for Deltamys kempi specimens from haplogroup A in A new species of Deltamys Thomas, 1917 (Rodentia: Cricetidae) endemic to the southern Brazilian Araucaria Forest and notes on the expanded phylogeographic scenario of D. kempi
FIGURE 10. Scores for Deltamys kempi specimens from haplogroup A (crosses) and B (circles) on the principal components 1 and 2 (above) and 1 and 3 (below) extracted from the variance-covariance matrix of 21 cranial measurements.
FIGURE 1. The best scoring phylogram generated from a in Colletotrichum dracaenigenum, a new species on Dracaena fragrans
FIGURE 1. The best scoring phylogram generated from a final MP dataset based on combined ITS, GAPDH, CHS-1, ACT and TUB2 sequence data. Bootstrap support values for maximum parsimony (MP, left) and maximum likelihood (ML, middle) are greater than 60% and Bayesian posterior probabilities (PP, right) equal to or greater than 0.95 are indicated at the nodes. The new taxon is bolded in red.
FIGURE 1. The best scoring RAxML tree obtained using a in A new species Pseudoplagiostoma dipterocarpicola (Pseudoplagiostomataceae, Diaporthales) found in northern Thailand on members of the Dipterocarpaceae
FIGURE 1. The best scoring RAxML tree obtained using a combined dataset of ITS, LSU, tef1-α and tub2 sequences. The tree is rooted to Togninia minima (AE F56), Togninia novae-zealandiae (CBS 110156) and Phaeoacremonium hungaricum (CBS 123036). ML and MP bootstrap values equal to or greater than 70% and BYPP equal to or greater than 0.95 are given at the nodes (ML/MP/BYPP). Ex-type strains are in black bold and the newly generated sequences are in red bold.
FIGURE 1. The best-scoring RAxML tree constructed from a concatenated ITS, tef1 in Combination of morphological and molecular data support Pestalotiopsis eleutherococci (Sporocadaceae) as a new species
FIGURE 1. The best-scoring RAxML tree constructed from a concatenated ITS, tef1-α, and tub2 dataset of Pestalotiopsis species. The tree is rooted with Truncatella laurocerasi (ICMP 11214) and T. angustata (CBS 144025). The asterisk at P. jesteri indicates its ambiguous status on Index Fungorum (2022). The type delivered sequences are indicated in bold and marked with T. The new isolates are in blue. The asterisk at P. intermedia indicates a misidentification.
FIGURE 1. Best scoring 28S in Contribution to the taxonomy of Sistotremastrum (Trechisporales, Basidiomycota) and the description of two new species, S. fibrillosum and S. aculeocrepitans
FIGURE 1. Best scoring 28S rDNA phylogram of the corticioid lineages in the Basidiomycetes most closely related to the Sistotremastrum clade obtained in RAxML. Nodes supported by>0.95 Bayesian PP or>70% ML BP are shown annotated. Root branch length was altered for publishing.
Do the predicted suitability scores from species distribution models correlate with species performance on-ground?
<p>Species distribution models are a very popular statistical tool for inferring potential distribution range of species across space and time and are thought to be a good predictor for habitat suitability. Some studies have suggested that if these models are reliable, predicted habitat suitability (PHS) should relate to species traits visualization, growth potential, body size, abundance. We validated this hypothesis by estimating association between the PHS and species abundance for 17 avian species endemic to the Western Ghats - Sri Lanka biodiversity hotspot. Additionally, we compared the PHS of sites where species were detected in both seasons (wet and dry) against sites where they were detected in the dry season alone. As a proxy for abundance, we estimated single-season occupancy estimates (ψ) using detection/non-detection data from multiple visits to the survey sites. We report significant and positive PHS-ψ correlation, though the strength of this association varied across species and models. Half of the species showed higher suitability scores for the sites where they were detected year round. The results presented here suggest that the predictive models can be used as a proxy for habitat quality, in addition to inferring the potential distribution.</p>
FIGURE 1. The best scoring RAxML tree from 42 in Camarosporium sensu stricto in Pleosporinae, Pleosporales with two new species
FIGURE 1. The best scoring RAxML tree from 42 strains based on combined dataset of LSU, SSU and ITS sequences. Bootstrap support values for maximum-likelihood (ML) and maximum-parsimony (MP) values greater than 50% are given above the nodes. Posterior Probability values (PP) greater than 0.7 are given below the nodes. The culture collection numbers are given after the species names. The tree is rooted to Montagnula anthostomoides (CBS 615.86) All type and ex-type strains are in bold and newly generated sequences are in red.
Bird plumage brightness scores and blood parasite prevalence values of Central European passerine species
<p>Dataset with bird plumage brightness scores and blood parasite prevalence values for 113 Central European passerine host species. One file contains the data table. One file contains a table with descriptions of the columns in the data table.</p> <p>Note: These data were reconstructed from files used in Read & Harvey 1989 (<a href="https://doi.org/10.1038/339618a0">https://doi.org/10.1038/339618a0</a>) with column headings inferred with the help of Read 1991 (<a href="https://doi.org/10.1086/285225">https://doi.org/10.1086/285225</a>).</p>
Fig. 3. PCA scores scatter plots and HCA dendrograms for S in A chemometric assessment of essential oil variation of three Salvia species indigenous to South Africa
Fig. 3. PCA scores scatter plots and HCA dendrograms for S. africana-lutea (A and B), S. lanceolata (C and D) and S. chamelaeagnea (E and F) samples from various localities. The inserts are the PCA scores plots coloured according to the HCA dendrograms.
Canada's common law species at risk legislation scoring rubric and provincial conservation plans for listed species at risk
Open the record for dataset details and reuse information.
Do the predicted suitability scores from species distribution models correlate with species performance on-ground?
Open the record for dataset details and reuse information.
Baseline and Future (2050s and 2090s) Climate Suitability Scores for 137 Useful Tree Species and 273 locations from the United Republic of Tanzania
<p>Climate suitability scores were calculated for 137 Useful Tree Species identified by filtering native tree species from the United Republic of Tanzania via the <a href="https://patspo.shinyapps.io/GlobalUsefulTrees/">GlobalUsefulNativeTrees</a> database, matching species with those described in the <a href="https://apps.worldagroforestry.org/usefultrees/">RELMA-ICRAF Useful Tree and Shrub Species for Tanzania manual</a> and checking for the availability of globally observed environmental ranges from the <a href="https://doi.org/10.5281/zenodo.13132613">TreeGOER</a> database.</p> <ul> <li>Score = 3 means that in 'environmental space' the planting site occurs within the 25% - 75% species's range (as documented in the <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.16914" target="_blank" rel="noopener">TreeGOER</a> ) for all variables</li> <li>Score = 2 corresponds to the 5% - 95% species's range for all variables</li> <li>Score = 1 corresponds to the 0% - 100% species's range for all variables</li> <li>Score = 0 means that the planting site occurs outside the 0% - 100% species's range for some of the variable</li> <li>Score = -1 means that the species is not documented by TreeGOER</li> </ul> <p> </p> <p>Locations corresponded to cities within the target countries sourced from the <a href="https://doi.org/10.5281/zenodo.10004594">CitiesGOER</a> database. This database provides bioclimatic conditions for the historical (baseline) and three future climate change scenarios. Bioclimatic variables for future climates correspond to the median values from 24 Global Climate Models (GCMs) for Shared Socio-Economic Pathway (SSP) 1-2.6 for the 2050s (2041-2060), from 21 GCMs for SSP 3-7.0 for the 2050s and from 13 GCMs for SSP 5-8.5 for the 2090s.</p> <p>Investigations were made for two different sets of bioclimatic variables, allowing for sensitivity analysis:</p> <ul> <li>One set of bioclimatic variables included BIO01, BIO12, climaticMoistureIndex, monthCountByTemp10, growingDegDays5, BIO05, BIO06, BIO16, BIO17 and MCWD. These are the same bioclimatic variables available internally in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> for climate filtering.</li> <li>One set only included BIO01 (= Mean Annual Temperature), which is the same bioclimatic variables available from the BGCI <a href="https://cat.bgci.org/">Climate Assessment Tool</a>.</li> </ul> <p>Calculations were made with similar scripting pipelines in the <em>R</em> statistical environment as documented here: <a href="https://rpubs.com/Roeland-KINDT/1168650">https://rpubs.com/Roeland-KINDT/1168650</a>. These scripts use similar calculations methods as those used for the global case studies of the TreeGOER manuscript (Kindt <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">2023</a>), and used internally in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> online database. Interested readers should especially refer to the manuscript for further details on methods used and their justification.</p> <p>The maps show the frequency distribution of tree species with climate scores 3, 2, 1 and 0, excluding 6 species not documented by the TreeGOER.</p> <p> </p> <p>The Excel database allows filtering useful tree species by some of the attributes available in the <a href="https://patspo.shinyapps.io/GlobalUsefulTrees/">GlobalUsefulNativeTrees</a> database.</p> <p>Species can be filtered for ten categories of documented human uses (see <a href="https://kew.iro.bl.uk/concern/datasets/7243d727-e28d-419d-a8f7-9ebef5b9e03e">Diazgranados et al. 2020</a> for details):</p> <ul> <li>AF: Animal Food.</li> <li>EU: Environmental Uses.</li> <li>FU: Fuel.</li> <li>GS: Gene Sources.</li> <li>HF: Human Food.</li> <li>IF: Invertebrate Food.</li> <li>MA: Materials.</li> <li>ME: Medicines.</li> <li>PO: Poisons.</li> <li>SU: Social Uses</li> </ul> <p>Species can also be filtered for the Climatic Moisture Index (CMI). See this Zenodo archive (<a href="https://zenodo.org/records/8252756">https://zenodo.org/records/8252756</a>) to see the distribution of CMI zones across the United Republic of Tanzania. Codings refer to the species reaching the upper part of the range in the zone (code: 2), the zone being included in teh middle part of the ranage (code: 9) or the species reaching the lower part of the range in this zone (code:3).</p> <ul> <li>CMI.A (CMI ≥ 0.5 ; P >= 2 * PET; ‘extremely humid’ lands)</li> <li>CMI.B (0 ≤ CMI < 0.5 ; PET <= P < 2 * PET ; ‘very humid’ lands)</li> <li>CMI.C (−0.35 ≤ CMI < 0 ; 0.65 <= P/PET < 1 ; ‘humid’ lands)</li> <li>CMI.D ( −0.5 ≤ CMI < −0.35 ; 0.50 <= P/PET < 0.65 ; dry sub-humid drylands)</li> <li>CMI.E (−0.8 ≤ CMI < −0.5 ; 0.20 <= P/PET < 0.50 ; semi-arid drylands)</li> <li>CMI.F (−0.95 ≤ CMI < −0.8 ; 0.05 <= P/PET < 0.20 ; arid drylands)</li> <li>CMI.G (CMI < −0.95 ; P/PET < 0.05 ; hyper-arid drylands)</li> </ul> <p> </p> <p><strong>References</strong></p> <ul> <li>Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>.</li> <li>Kindt, R. (2024). TreeGOER: Tree Globally Observed Environmental Ranges (2024.07) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.13132613" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13132613</a></li> <li>Kindt, R., Graudal, L., Lillesø, JP.B. <em>et al.</em> (2023). GlobalUsefulNativeTrees, a database documenting 14,014 tree species, supports synergies between biodiversity recovery and local livelihoods in landscape restoration. <em>Sci Rep</em> <strong>13</strong>, 12640. <a href="https://doi.org/10.1038/s41598-023-39552-1">https://doi.org/10.1038/s41598-023-39552-1</a></li> <li>Kindt, R. (2023). CitiesGOER: Globally Observed Environmental Data for 52,602 Cities with a Population ≥ 5000 (2023.10) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.10004594" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10004594</a></li> <li>Kindt, R. (2024). ClimateForecasts: Globally Observed Environmental Data for 15,504 Weather Station Locations (2024.07) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.12679832" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12679832</a></li> <li>Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. <em>International Journal of Climatology</em>, <em>37</em>(12), 4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a></li> <li>Title, P. O., & Bemmels, J. B. (2018). ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. <em>Ecography</em>, <em>41</em>(2), 291–307. <a href="https://doi.org/10.1111/ecog.02880">https://doi.org/10.1111/ecog.02880</a></li> <li>Opendatasoft (2023) Geonames - All Cities with a population > 1000. <a href="https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name">https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name</a> (accessed 22-JULY-2023)</li> <li>Meteostat (2024) Weather stations: Lite dump with active weather stations. <a href="https://github.com/meteostat/weather-stations">https://github.com/meteostat/weather-stations</a> (accessed 17-FEB-2024)</li> <li>Diazgranados, M., Allkin, B., Black, N., Cámara-Leret, R., Canteiro, C., Carretero, J., Eastwood, R., Hargreaves, S., Hudson, A., Milliken, W. and Nesbitt, M., 2020. World checklist of useful plant species. Royal Botanic Gardens, Kew. <a href="https://knb.ecoinformatics.org/view/doi:10.5063/F1CV4G34">https://knb.ecoinformatics.org/view/doi:10.5063/F1CV4G34</a></li> </ul> <p> </p> <p><strong>Funding</strong></p> <p>The data sets and maps available in this archive were created through funding by the <strong>U. S. Agency for International Development (USAID)</strong> to CIFOR-ICRAF, here specifically in the context of the <em>On-farm Land Restoration for Livelihoods and Environmental Benefits</em> project.</p> <p> </p>
Fig. 27. Specimen scores representing 10 in A Systematic Review Of Sulawesi Bunomys (Muridae, Murinae) With The Description Of Two New Species
Fig. 27. Specimen scores representing 10 population samples of Bunomys chrysocomus projected onto first and second canonical variates extracted from discriminant-function analysis of 16 cranial and two dental log-transformed variables. Upper graph: All variables are represented and scores for four of the five holotypes are present. Lower graph: The holotype of chrysocomus is added, but its skull is incomplete so four variables are omitted (occipitonasal and postpalatal lengths, zygomatic breadth, and height of braincase could not be measured; fig. 24). Symbols: filled triangles 5 northern peninsula (N 5 10); empty triangles 5 Bumbarujaba (N 5 7); empty circles 5 Sungai Oha Kecil + Sungai Sadaunta (N 5 146); filled circles 5 Danau Lindu + Gunung Kanino (N 5 42); empty squares 5 Gimpu (N 5 3); left-pointing empty triangles 5 Gunung Tambusisi (N 5 6); asterisks 5 Pegunungan Mekongga (N 5 11); empty diamonds 5 Lalolei (N 5 3); filled squares 5 Gunung Balease (N 5 3); star 5 single specimen from Bakubakulu. Arrows and abbreviations identify scores for holotypes: b 5 brevimolaris; c 5 chrysocomus; k 5 koka; n 5 nigellus; r 5 rallus. Correlations (loadings) among variables with extracted canonical variates and percent variance are listed in table 24.
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.