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Fig. 46. Distribution maps. A–B in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
Fig. 46. Distribution maps. A–B. Nycticeinops schlieffenii (Peters, 1860). C–D. Pipistrellus grandidieri (Dobson, 1876). E–F. Pipistrellus hesperidus (Temminck, 1840). A, C, E. Distribution in the CRB area. B, D, F. Pan-African distribution.
Fig. 37. Distribution maps. A–B in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
Fig. 37. Distribution maps. A–B. Scotophilus leucogaster (Cretzschmar, 1826). C–D. Scotophilus nigrita (Schreber, 1774). E–F. Scotophilus nux Thomas, 1904. A, C, E. Distribution in the CRB area. B, D, F. Pan-African distribution.
Fig. 33. Distribution maps. A–B. Miniopterus inflatus Thomas, 1903. C–D. Miniopterus minor Peters, 1867. E–F in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
Fig. 33. Distribution maps. A–B. Miniopterus inflatus Thomas, 1903. C–D. Miniopterus minor Peters, 1867. E–F. Miniopterus natalensis (A. Smith, 1833). A, C, E. Distribution in the CRB area. B, D, F. Pan-African distribution.
Fig. 26. Distribution maps. A–B in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
Fig. 26. Distribution maps. A–B. Chaerephon major (Trouessart, 1897). C–D. Chaerephon nigeriae Thomas, 1913. E–F. Chaerephon pumilus (Cretzschmar, 1826). A, C, E. Distribution in the CRB area. B, D, F. Pan-African distribution.
Fig. 27. Distribution maps. A–B. Chaerephon russatus J.A. Allen, 1917. C–D in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
Fig. 27. Distribution maps. A–B. Chaerephon russatus J.A. Allen, 1917. C–D. Mops (Mops) condylurus (A. Smith, 1833). E–F. Mops (Mops) congicus J.A. Allen, 1917. A, C, E. Distribution in the CRB area. B, D, F. Pan-African distribution.
Fig. 22. Distribution maps. A–B. Nycteris macrotis Dobson, 1876. C–D in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
Fig. 22. Distribution maps. A–B. Nycteris macrotis Dobson, 1876. C–D. Nycteris major (K. Andersen, 1912). E–F. Nycteris nana (K. Andersen, 1912). A, C, E. Distribution in the CRB area. B, D, F. Pan- African distribution.
Fig. 43. Distribution maps. A–B. Mimetillus thomasi Hinton, 1920. C–D in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
Fig. 43. Distribution maps. A–B. Mimetillus thomasi Hinton, 1920. C–D. Neoromicia brunnea (Thomas, 1880). E–F. Neoromicia capensis (A. Smith, 1829). A, C, E. Distribution in the CRB area. B, D, F. Pan- African distribution.
Fig. 18. Distribution maps. A–B. Rhinolophus swinnyi Gough, 1908. C–D in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
Fig. 18. Distribution maps. A–B. Rhinolophus swinnyi Gough, 1908. C–D. Rhinolophus willardi Kerbis Peterhans & Fahr, 2013. E–F. Cloeotis percivali Thomas, 1901. A, C, E. Distribution in the CRB area. B, D, F. Pan-African distribution.
Fig. 17. Distribution maps. A–B. Rhinolophus landeri Martin, 1838. C–D. Rhinolophus ruwenzorii J. Eric Hill, 1942. E–F. Rhinolophus simulator K. Andersen, 1912. A, C, E in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
Fig. 17. Distribution maps. A–B. Rhinolophus landeri Martin, 1838. C–D. Rhinolophus ruwenzorii J. Eric Hill, 1942. E–F. Rhinolophus simulator K. Andersen, 1912. A, C, E. Distribution in the CRB area. B, D, F. Pan-African distribution.
Fig. 5. Distribution maps. A–B in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
Fig. 5. Distribution maps. A–B. Epomophorus wahlbergi (Sundevall, 1846). C–D. Epomops dobsonii (Bocage, 1889). E–F. Epomops franqueti (Tomes, 1860). A, C, E. Distribution in the CRB area. B, D, F. Pan-African distribution.
Fig. 10. Distribution maps. A–B. Stenonycteris lanosus Thomas, 1906. C–D in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
Fig. 10. Distribution maps. A–B. Stenonycteris lanosus Thomas, 1906. C–D. Asellia tridens (E. Geoffroy St.-Hilaire, 1813). E–F. Doryrhina cyclops (Temminck, 1853). A, C, E. Distribution in the CRB area. B, D, F. Pan-African distribution.
Data from: Adaptation via pleiotropy and linkage: association mapping reveals a complex genetic architecture within the stickleback Eda locus
Genomic mapping of the loci associated with phenotypic evolution has revealed genomic "hotspots", or regions of the genome that control multiple phenotypic traits. This clustering of loci has important implications for the speed and maintenance of adaptation and could be due to pleiotropic effects of a single mutation or tight genetic linkage of multiple causative mutations affecting different traits. The threespine stickleback (<i>Gasterosteus aculeatus</i>) is a powerful model for the study of adaptive evolution because the marine ecotype has repeatedly adapted to freshwater environments across the northern hemisphere in the last 12,000 years. Freshwater ecotypes have repeatedly fixed a 16 kilobase haplotype on chromosome IV that contains Ectodysplasin (<i>Eda</i>), a gene known to affect multiple traits, including defensive armor plates, lateral line sensory hair cells, and schooling behavior. Many additional traits have previously been mapped to a larger region of chromosome IV that encompasses the <i>Eda</i> freshwater haplotype. To identify which of these traits specifically map to this adaptive haplotype, we made crosses of rare marine fish heterozygous for the freshwater haplotype in an otherwise marine genetic background. Further, we performed fine-scale association mapping in a fully interbreeding, polymorphic population of freshwater stickleback to disentangle the effects of pleiotropy and linkage on the phenotypes affected by this haplotype. Although we find evidence that linked mutations have small effects on a few phenotypes, a small 1.4 kb region within the first intron of <i>Eda</i> has large effects on three phenotypic traits: lateral plate count, and both the number and patterning of the posterior lateral line neuromasts. Thus, the <i>Eda</i> haplotype is a hotspot of adaptation in stickleback due to both a small, pleiotropic region affecting multiple traits as well as multiple linked mutations affecting additional traits.
FIG. 6. — Distribution maps for Gossia bourailensis N in A revision of New Caledonian Gossia N. Snow & Guymer (Myrtaceae)
FIG. 6. — Distribution maps for Gossia bourailensis N. Snow, sp. nov., G. colnettiana (Guillaumin) N. Snow, G. conduplicata N. Snow, sp. nov. and G. diversifolia (Brongn. & Gris) N. Snow.
FIG. 3. — Species richness map for Xylopia L in A revision of Xylopia L. (Annonaceae): the species of Madagascar and the Mascarene islands
FIG. 3. — Species richness map for Xylopia L. on Madagascar and the Mascarene Islands, plotted by 0.5° × 0.5° grid squares.
Restore Centre of Excellence: High-resolution mapping of Louisina vegetation remote sensing data for surge modelling
<p>Satellite derived Leaf Area Index map of the Louisiana coast, translated into plant dimensions using field data from CMRS stations and dedicated project sampling. Plant dimensions have been used to prescribe hydraulic roughness fields for a hydrodynamic model (Delft3D) used to asses the effect of wetlands on storm surge levels.</p>
Hale Crater Mapping Shapefiles
<p>These files have been produced in an effort to map the landforms within the ejecta to the south of Hale Crater. Included here are shapefiles outlining distinct regions within the study site and major landforms within the ejecta, as well as a HiRISE DEM.</p> <p> </p> <p>The shapefiles included here are:</p> <p>1) Linear Depressions</p> <p>2) Mounds</p> <p>3) The outlines of Hale Crater Valley 1 (HCV 1), Hale Crater Valley 2 (HCV 2) and the Large Western Linear Depression (LWLD)</p> <p>4) The outlines of the Rugged Uplands, Eastern Basin and Western Basin</p> <p> </p> <p>The HiRISE DEM covers a small section of the study site that encompasses several of the mound features, and a small linear depression.</p> <p> </p> <p>The shapefiles are projected in the Mars 2000 coordinate system, and the DEM is projected in the Equirectangular Mars Projection.</p>
Supplementary material 1 from: Geneletti D, Adem Esmail B, Cortinovis C, Arany I, Balzan M, van Beukering P, Bicking S, Borges PA, Borisova B, Broekx S, Burkhard B, Gil A, Inghe O, Kopperoinen L, Kruse M, Liekens I, Lowicki D, Mizgajski A, Mulder S, Nedkov S, Ostergard H, Picanço A, Ruskule A, Santos-Martín F, Sieber IM, Svensson J, Vačkářů D, Veidemane K (2020) Ecosystem services mapping and assessment for policy- and decision-making: Lessons learned from a comparative analysis of European case studies. One Ecosystem 5: e53111. https://doi.org/10.3897/oneeco.5.e53111
An overview of the assessment of ecosystem condition in the selected case studies.
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>
PhenomeXcan: Mapping the genome to the phenome through the transcriptome
<p>Data generated as part of the PhenomeXcan project, which contains processed results for S-MultiXcan, S-PrediXcan, fastENLOC and the integration of S-MultiXcan results with ClinVar. Please, refer to the Github repository for documentation on these files and related source code (<a href="https://github.com/hakyimlab/phenomexcan">https://github.com/hakyimlab/phenomexcan</a>).</p> <p>This latest version of the dataset contains the fixed results for fastENLOC (with shrinkage of alpha1 values).</p> <p>bioRxiv preprint: https://doi.org/10.1101/833210</p>
Surveying the genome and constructing a high-density genetic map of napiergrass (Cenchrus purpureus Schumach)
<p>Napiergrass (Cenchrus purpureus Schumach) is a tropical forage grass and a promising lignocellulosic biofuel feedstock due to its high biomass yield, persistence, and nutritive value. However, its utilization for breeding has lagged behind other crops due to limited genetic and genomic resources. In this study, next-generation sequencing was frst used to survey the genome of napiergrass. Napiergrass sequences displayed high synteny to the pearl millet genome and showed expansions in the pearl millet genome along with genomic rearrangements between the two genomes. An average repeat content of 27.5% was observed in napiergrass including 5,339 simple sequence repeats (SSRs). Furthermore, to construct a high-density genetic map of napiergrass, genotyping-by-sequencing (GBS) was employed in a bi-parental population of 185 F1 hybrids. A total of 512 million high quality reads were generated and 287,093 SNPs were called by using multiple de-novo and reference-based SNP callers. Single dose SNPs were used to construct the frst high-density linkage map that resulted in 1,913 SNPs mapped to 14 linkage groups, spanning a length of 1,410cM and a density of 1 marker per 0.73cM. This map can be used for many further genetic and genomic studies in napiergrass and related species.</p> <p><strong>Genetic map files for Elephant grass genetic map.</strong><br> Surveying the genome and constructing a high-density genetic map of napiergrass (Cenchrus purpureus Schumach)</p> <p><strong># Marker files:</strong><br> Sex averaged map: map_NG_final_geneticmap_1913.txt<br> Female map: map_female_selected899_lm.txt<br> Male map: map_male_selected1073_nn.txt </p> <p><strong># Sequence and locus information.</strong><br> All reference based pipelines used the pearl millet genome v1 as explained in the manuscript. Sequences for the reference pipelines can be extracted based on the location of the SNPs. For denovo based pipelines, either the sequences or the locus and denovo map are included.</p> <p><br> Freebayes (F)<br> - 1_freebayes_48.vcf.gz</p> <p>GATK (G)<br> - 2_gatk_48.vcf.gz</p> <p>Samtools (St)<br> - 3_samtools_48.vcf.gz</p> <p>GBS-SNP-CROP (C)<br> - 4_snpcrop.hmp.txt</p> <p>TASSEL (T)<br> - 5_tassel.hmp.txt.gz</p> <p>Stacks (St)<br> - 6a_stacks_loc.gz<br> - 6b_stacks_snp.vcf.gz</p> <p>denovo GBS-SNP-CROP (dC)<br> - 7a_denovo_SNP_CROP.hmp.txt<br> - 7b_snpcrop_MockRef2PP.MockRef_Genome.fasta</p> <p>denovo Stacks (dS)<br> - 8a_denovo_stacks.loc<br> - 8b_denovo_stacks_sequences.tsv</p> <p>denovo TASSEL (dT)<br> - 9_denovo_tassel_uneak.fas</p> <p><br> <strong># Comparison between elephant grass and pearl millet genome:</strong><br> - Filterdp20_Elephantgrass.vcf.gz</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.