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Fig. 16 in Integrative taxonomy identifies two new tardigrade species (Eutardigrada: Macrobiotidae) from Greenland
Fig. 16. Tenuibiotus zandrae sp. nov. The oral cavity armature seen in SEM (paratype). The oral cavity armature of a single paratype seen in SEM from different angles, dorsal (A) and ventral (B) view, respectively. Filled indented arrowheads indicate the first band of teeth in the oral cavity, filled flat arrowheads indicate the second band of teeth in the oral cavity, whereas empty flat arrowheads indicate the third band of teeth in the oral cavity. Scale bars in μm.
Fig. 24 in Integrative taxonomy identifies two new tardigrade species (Eutardigrada: Macrobiotidae) from Greenland
Fig. 24. Tenuibiotus cf. voronkovi (Tumanov, 2007) from the Nordaustlandet population, eggs seen in PCM. Four different eggs under 1000× magnification. Scale bars in μm.
Locations and derived diameters of RMDSs as per Fig. 6 and all the 8281 RMDSs identified so far across the global Moon by March, 2020
<p>F. Zhang, J. W. Head, C. Wöhler, R. Bugiolacchi, L. Wilson, A. T. Basilevsky, A. Grumpe, Y. L. Zou.</p> <p>Ring-Moat Dome Structures (RMDSs) in the Lunar Maria: Statistical, Compositional, and Morphological Characterization and Assessment of Theories of Origin.</p> <p>Journal of Geophysical Research: Planets, 125, e2019JE005967. https://doi.org/10.1029/2019JE005967</p> <p> </p> <p>1. <strong>Point_Location_Figure_6</strong>: The table of locations and derived diameters of all the RMDSs shown in Figure 6 of point density map of 870 RMDSs (black dots) in central Mare Tranquillitatis on the Moon. </p> <p>2. <strong>All_RMDSs_8281_By_March_2020</strong>: The table of locations and derived diameters of all the 8281 RMDSs identified so far across the global Moon by 15 March, 2020.</p>
Data from: Identifying factors that boost species discoveries of global reptiles
<p>There are five files needed to reproduce the analysis performed in [Guedes et al. 2020. Zool. J. Lin. Soc. 10.1093/zoolinnean/zlaa029]:</p> <p>(i) trait_data.csv: raw data on 2661 reptiles species described from 1992 to 2017 (it includes species with partial data available). File with 18 fields.</p> <p>(ii) taxonomist_data.csv: data on the number of authors in 2613 squamate species descriptions published between 1992 and 2017. File with 5 fields.</p> <p>(iii) lizard_type_series.csv: data on the collection year of type-series from 18,352 specimens of 1558 lizard species described between 1992 and 2017 (lizard species with 1-2 or >50 specimens in the type-series were not included given the purpose of this data compilation). File with 6 fields.</p> <p>(iv) correlation_data.csv: data on the number of holotypes collected per year and number of species described per year according to the species shelf life (time lag between collection and formal description). File with 5 fields.</p> <p>(v) R-script.R: the R script to reproduce the analysis performed in the main text.</p>
Extended Data Fig. 2 in Identifying SARS-CoV-2related coronaviruses in Malayan pangolins
Extended Data Fig. 2 | Read coverage depth of each pangolin coronavirus analyzed in this study.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Figuerola, Blanca, <a href="https://orcid.org/0000-0003-4731-9337">https://orcid.org/0000-0003-4731-9337</a>. Claims were made on Bloodhound, <a href="http://bloodhound-tracker.net">https://bloodhound-tracker.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Plos, Anabela, <a href="https://orcid.org/0000-0003-3877-7408">https://orcid.org/0000-0003-3877-7408</a>. Claims were made on Bloodhound, <a href="http://bloodhound-tracker.net">https://bloodhound-tracker.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Mutant phenotypes of candidate genes from Fv'/Fm' and QY max loci identified using GWAS in Arabidopsis HapMap population
<p>The phenotypes of T-DNA insertion lines and background line (Col-0), located in the loci identified using GWAS, corresponding to maximum quantum yield (QY max) and light-adapted quantum yield (Fv'/Fm') under salt stress. The data was collected using high-throughput phenotyping of plants and is part of a manuscript currently in preparation. </p>
Figure 3 in Prospects for using DNA barcoding to identify spiders in species-rich genera
Figure 3. Bar graph with standard errors showing the differences in mean (in black) and maximum (in grey) intraspecific divergence as well as nearest-neighbor distance (in white) between the three geographical distribution categories.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Shorthouse, David Peter, <a href="https://orcid.org/0000-0001-7618-5230">https://orcid.org/0000-0001-7618-5230</a>. Claims were made on Bloodhound, <a href="http://bloodhound-tracker.net">https://bloodhound-tracker.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Fig. 2 in A new species of Casmaria H. Adams & A. Adams, 1853 (Gastropoda, Cassidae) from the Philippines identified by molecular data
Fig. 2. Casmaria boblehmani sp. nov. A-C. Holotype, 36.3 x 19.5 mm, Danajon Banks, Olango Is, Philippines. D-E. Paratype 2, 35.1 x 21.7 mm, same locality as holotype. F. 27.7 x 18.8 mm, Tip of Cape York, Queensland, Australia. G. 38.6 x 25.4 mm, off Woka Island, Molucca Sea, Indonesia. H-I. ANSP 276463, 41.5 x 24.2 mm, Akuilan Is, Fiji. K-L. ANSP 266122, 36.5 x 24.6 mm, Atiue, Tahiti, Society Islands.
Fig. 3 in A new species of Casmaria H. Adams & A. Adams, 1853 (Gastropoda, Cassidae) from the Philippines identified by molecular data
Fig. 3. Distributions of Casmaria turgida (short-dashed line and triangles), C. unicolor (dashed line and filled circles) and C. boblehmani sp. nov. (solid line and squares) in the Indo-Pacific.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Brambach, Fabian, <a href="https://orcid.org/0000-0002-3203-2248">https://orcid.org/0000-0002-3203-2248</a>. Claims were made on Bloodhound, <a href="http://bloodhound-tracker.net">https://bloodhound-tracker.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by David Peter Shorthouse, <a href="https://orcid.org/0000-0001-7618-5230">https://orcid.org/0000-0001-7618-5230</a>. Claims were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by David Peter Shorthouse, <a href="https://orcid.org/0000-0001-7618-5230">https://orcid.org/0000-0001-7618-5230</a>. Claims were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Combining genome-wide studies of breast, prostate, ovarian and endometrial cancers maps cross-cancer susceptibility loci and identifies new genetic associations
<p>Data set linked to the paper, "Combining genome-wide studies of breast, prostate, ovarian and endometrial cancers maps cross-cancer susceptibility loci and identifies new genetic associations". Pre-print of the paper is here: <a href="https://doi.org/10.1101/2020.06.16.146803">https://doi.org/10.1101/2020.06.16.146803</a>.</p> <p> </p> <p>cross_cancer_sum_stats.txt.gz contains summary genome-wide association statistics for susceptibility to single cancers (breast (BR), prostate (PR), ovarian (OV), endometrial (EN), estrogen receptor (ER)-positive breast (POS), ER-negative breast (NEG), and high-grade serous ovarian (HGS) cancers) and from the cross-cancer meta-analysis (main [main] and subtype-focused [sub]). EA in the header refers to the effect allele, OA is the other allele, EAF is the effect allele frequency in the largest of the single cancer data sets (BR), IMPR2 is the imputation quality in the largest of the single cancer data sets (BR), SE is the standard error, PVAL is the P-value, RE2Cs1 is the RE2C statistic mean effect part, RE2Cs2 is the RE2C statistic heterogeneity part, RE2Cp* is the RE2C* P-value. More on RE2Cp* can be found here: <a href="http://software.buhmhan.com/RE2C/index.php?mid=contact&act=dispBoardWrite">http://software.buhmhan.com/RE2C/index.php?mid=contact&act=dispBoardWrite</a> and in <a href="https://academic.oup.com/bioinformatics/article/33/14/i379/3953957">https://academic.oup.com/bioinformatics/article/33/14/i379/3953957</a> SNP names in cross_cancer_sum_stats.txt.gz include the chromosome and build 37 position.</p> <p> </p> <p>main_tetrachoric_corr_matrix.txt and subtype_tetrachoric_corr_matrix.txt provide the tetrachoric correlation matrices used in the main and subtype-focused meta-analyses. These were also used to specify the cryptic.cor argument of the exh.abf function of MetABF. More on MetABF can be found here: <a href="https://github.com/trochet/metabf">https://github.com/trochet/metabf</a> and in <a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/gepi.22202">https://onlinelibrary.wiley.com/doi/abs/10.1002/gepi.22202</a></p> <p> </p> <p>prior_sigmas_for_metabf.txt contains the values used to specify the prior.sigma argument of the exh.abf function in MetABF.</p> <p> </p> <p>The breast cancer data used are described in <a href="https://pubmed.ncbi.nlm.nih.gov/29059683/"><strong>PMID 29059683</strong></a> and can be downloaded from <a href="http://bcac.ccge.medschl.cam.ac.uk/bcacdata/oncoarray/oncoarray-and-combined-summary-result/gwas- summary-results-breast-cancer-risk-2017/">http://bcac.ccge.medschl.cam.ac.uk/bcacdata/oncoarray/oncoarray-and-combined-summary-result/gwas- summary-results-breast-cancer-risk-2017/</a> (this link also includes acknowledgements). The prostate cancer data are described in <a href="https://pubmed.ncbi.nlm.nih.gov/29892016/"><strong>PMID 29892016</strong></a> and can be downloaded from: <a href="http://practical.icr.ac.uk/blog/?page_id=8164">http://practical.icr.ac.uk/blog/?page_id=8164</a> (this link also includes acknowledgements). The ovarian cancer data used are described in <a href="https://pubmed.ncbi.nlm.nih.gov/28346442/"><strong>PMID 28346442</strong></a> and can be downloaded from <a href="https://www.ebi.ac.uk/gwas/studies/GCST004415">https://www.ebi.ac.uk/gwas/studies/GCST004415</a>. The endometrial cancer data are described in <a href="https://pubmed.ncbi.nlm.nih.gov/30093612/"><strong>PMID 30093612</strong></a> and can be downloaded from <a href="https://www.ebi.ac.uk/gwas/studies/GCST006464">https://www.ebi.ac.uk/gwas/studies/GCST006464</a>. These links point to the same data that form the basis of the cross_cancer_sum_stats.txt.gz file.</p> <p> </p> <p><strong>The sample size and precision of the data presented should preclude identification of any individual study participant. However, in downloading these data, you undertake not to attempt to identify individual study participant and not to re-post these data to a third-party website. Please cite the PMIDs highlighted above along with the appropriate acknowledements if you use the cross_cancer_sum_stats.txt.gz file.</strong></p> <p> </p> <p>If you have any questions about this repository, please email Siddhartha Kar at siddhartha dot kar at bristol dot ac dot uk</p>
Proteo-transcriptomic analysis identifies potential novel toxins secreted by the predatory, prey-piercing ribbon worm Amphiporus lactifloreus
<p>Nemerteans (ribbon worms) employ toxins to subdue their prey, but research thus far has focused on the small-molecule components of mucus secretions and few protein toxins have been characterized. We carried out a preliminary proteotranscriptomic analysis of putative toxins produced by the hoplonemertean <em>Amphiporus lactifloreus </em>(Hoplonemertea, Amphiporidae). We did not find any variants of known nemertean-specific toxin proteins (neurotoxins, cytotoxins, parbolysins or nemertides) but we identified several toxin-like transcripts expressed strongly in the proboscis, including putative metalloproteinases and sequences resembling sea anemone actitoxins, crown-of-thorn sea star plancitoxins, and multiple classes of inhibitor cystine knot/knottin family proteins. Some of these products were also directly identified in the mucus proteome, supporting their preliminary identification as secreted toxin components. We identified two new nemertean-typical toxin candidates and named them U-nemertotoxin-1 and U-nemertotoxin-2. Our findings provide insight into the largely overlooked venom system of nemerteans and support a hypothesis in which the nemertean proboscis evolved in several steps, from a flesh-melting organ in scavenging nemerteans to a flesh-melting and toxin-secreting venom apparatus in hunting hoplonemerteans.</p>
List of studies on Covid-19 identified in MedRxiv and excluded from our systematic review
<p>In the course of our MedRxiv searches we identified and screened the title and abstract of a number of studies on Covid-19 that were finally excluded from our systematic review. </p> <p>This file will be updated regularly.</p>
Rcodes and additional results from identifying trait-associated diversification with fossil phylogenies
<p class="CxSpFirst">There is solid theoretical reasoning but few statistical approaches available to test the hypothesis that phenotypic traits and phenotypic evolutionary rates could be associated to rates of taxonomic diversification. A positive relationship between phenotypic and taxonomic evolution is expected to occur during adaptive radiations, or when the acquisition of key innovations allows rapid expansion into new adaptive zones. Such patterns of trait-associated diversification are intensely studied under both neontological and paleontological approaches. However, albeit the inclusion of fossil information is welcome, paleontological studies often lack the phylogenetic dimension, which makes the results potentially depending on untested phylogenetic effects. We devised and applied a novel method seeking to find instances of trait-associated diversification, which is specifically meant to make full use of fossil phenotypes and phylogenies. We performed extensive simulations to test the power of the method and to assess the incidence of false positives (Type I error) and applied the method to test the hypotheses that body size relates to taxonomic diversification in mammals and dinosaurs.</p> <p class="CxSpMiddle">We demonstrated that the new method is powerful, accurate and produces low Type I error. Consistently with previous reports, we found statistical evidence that hadrosaurid dinosaurs experienced a significant positive shift in diversification rates, coupled to low rates of morphological evolution. In mammals, body size is linked to taxonomic diversification by a weak negative relationship.</p>
A single-cell RNAseq atlas of Schistosoma mansoni identifies a key regulator of blood feeding
<p>Schistosomiasis is a neglected tropical disease that infects 240 million people. With no vaccines and only one drug available, new therapeutic targets are needed. The causative agents, schistosomes, are intravascular flatworm parasites that feed on blood and lay eggs, causing pathology. The function of the parasite's various tissues in successful parasitism are poorly understood, hindering identification of novel therapeutic targets. Using single cell RNAseq we characterize 43,642 cells from the adult schistosome, identifying 68 distinct cell populations including specialized stem cells that maintain the parasite's blood-digesting gut. These stem cells express the gene hnf4, which is required for gut maintenance, blood feeding, and pathology in vivo. Together, these data provide molecular insights into the organ systems of this important pathogen and identify potential therapeutic targets.</p>
ScienceDex guides
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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.