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511 results for “stories”
FIGURES 12a–d in The lace-sheet-weavers—a long story (Araneae: Psechridae: Psechrus)
FIGURES 12a–d. Psechrus annulatus, ♀ copulatory organ. a, c–d ♀ lectotype SB 829 from Indonesia, Java, Jawa Tengah Prov. b ♀ paralectotype SB 830 from Indonesia, Java, Jawa Barat Prov. a–b Epigyne, ventral view. c Vulva, dorsal view. d Schematic course of internal duct system.
FIGURES 23a–d in The lace-sheet-weavers—a long story (Araneae: Psechridae: Psechrus)
FIGURES 23a–d. Psechrus rani, ♂ SB 122 from Vietnam, Lang Son Prov. a–c ♂ palp (a prolateral, b ventral, c retrolateral view). d ♂ right palpal femur, retrolateral view.
FIGURES 20a–c in The lace-sheet-weavers—a long story (Araneae: Psechridae: Psechrus)
FIGURES 20a–c. Psechrus arcuatus sp. nov., ♀ holotype SB 333 from Indonesia, Sumatra, Sumatera Barat Prov. a Epigyne, ventral view. b Vulva, dorsal view. c Schematic course of internal duct system.
FIGURES 21a–g in The lace-sheet-weavers—a long story (Araneae: Psechridae: Psechrus)
FIGURES 21a–g. Psechrus ancoralis, from Laos, Luang Nam Tha Prov. a–c ♂ paratype SB 26. d–f ♀ holotype SB 4. g ♀ paratype SB 27. a–c ♂ palp (a prolateral, b ventral, c retrolateral view). d Epigyne, ventral view. e Vulva, dorsal view. f Schematic course of internal duct system. g right palpal claw, retrolateral view.
FIGURES 19a–c in The lace-sheet-weavers—a long story (Araneae: Psechridae: Psechrus)
FIGURES 19a–c. Psechrus norops sp. nov., ♀ holotype SB 860 from Malaysia, Pahang Prov. a Epigyne, ventral view. b Vulva, dorsal view. c Schematic course of internal duct system.
FIGURES 13a–c in The lace-sheet-weavers—a long story (Araneae: Psechridae: Psechrus)
FIGURES 13a–c. Psechrus aluco sp. nov., ♀ holotype SB 123 from Indonesia, Java, Jawa Barat Prov. a Epigyne, ventral view. b Vulva, dorsal view. c Schematic course of internal duct system.
FIGURES 25a–f in The lace-sheet-weavers—a long story (Araneae: Psechridae: Psechrus)
FIGURES 25a–f. Psechrus laos sp. nov., from Laos, Bolikhamsay Prov. a–c ♂ holotype SB 367. d–f ♀ paratype SB 377. a–c ♂ palp (a prolateral, b ventral, c retrolateral view). f Epigyne, ventral view. d Vulva, dorsal view. e Schematic course of internal duct system. C: Conductor; CB: Conductor base; E: Embolus; SD: Sperm duct; ST: Subtegulum; T: Tegulum.
FIGURES 24a–e in The lace-sheet-weavers—a long story (Araneae: Psechridae: Psechrus)
FIGURES 24a–e. Psechrus rani, ♀ primordial and adult copulatory organ. a–c ♀ SB 819 from China, Hongkong. d–e s.a. ♀ SB 1158 from Vietnam, Lang Son Prov. a Epigyne, ventral view. b Vulva, dorsal view. c Schematic course of internal duct system. d Pre-epigyne, ventral view. e Pre-vulva, dorsal view.
Figure 6 in The story of a rock-star: multilocus phylogeny and species delimitation in the starred or roughtail rock agama, Laudakia stellio (Reptilia: Agamidae)
Figure 6. Summary of heuristic BPP delimitation based on the gdi under three topological scenarios (1, 2 and 3). Four BPP runs were combined in every step of a multiple analysis of progressive hierarchical lumping of sister taxa. Boxplots in the left (A) refer to the gdi of each well-supported phylogenetic subclade while those on the right (B) correspond to the three distinct evolutionary entities: Clade 1 (I), Clade 2 (II) and cypriaca (III).
Figure 5 in The story of a rock-star: multilocus phylogeny and species delimitation in the starred or roughtail rock agama, Laudakia stellio (Reptilia: Agamidae)
Figure 5. Multilocus calibrated species-tree produced by StarBEAST2. Numbers above branches represent mean divergence times (Myr), while numbers below represent posterior probabilities. Asterisks represent posterior probabilities equal to 1. (Agama spp. contains Agama agama, A. boensis, A. bottega, A. boueti, A. boulengeri, A. impalearis, A. planices, A. sankaranica and A. spinosa).
Figure 4 in The story of a rock-star: multilocus phylogeny and species delimitation in the starred or roughtail rock agama, Laudakia stellio (Reptilia: Agamidae)
Figure 4. Phylogenetic tree based on the concatenated dataset (mtDNA & nuDNA). Bayesian posterior probabilities (PP) and maximum likelihood bootstrap support (bs) values are represented in the form PP/bs above or beside nodes. (Agama spp. contains Agama agama, A. boensis, A. bottega, A. boueti, A. boulengeri, A. impalearis, A. planices, A. sankaranica and A. spinosa).
Figure 3 in The story of a rock-star: multilocus phylogeny and species delimitation in the starred or roughtail rock agama, Laudakia stellio (Reptilia: Agamidae)
Figure 3. Map showing the sampling localities of specimens used in the present study. Different colours represent the phylogenetic subclades indicated in Figures 2 and 4.
Figure 2 in The story of a rock-star: multilocus phylogeny and species delimitation in the starred or roughtail rock agama, Laudakia stellio (Reptilia: Agamidae)
Figure 2. Phylogenetic tree based on mtDNA (ND4-tRNAs and 16S rRNA). Bayesian posterior probabilities (PP) and maximum likelihood bootstrap support (bs) values are represented in the form PP/bs above or beside nodes. (Agama spp. contains Agama agama, A. boensis, A. bottega, A. boueti, A. boulengeri, A. impalearis, A. planices, A. sankaranica and A. spinosa).
Figure 1 in The story of a rock-star: multilocus phylogeny and species delimitation in the starred or roughtail rock agama, Laudakia stellio (Reptilia: Agamidae)
Figure 1. Map showing the distribution of all known morphological subspecies of Laudakia stellio in the East Mediterranean.
On taming the effect of transcript level intra-condition count variation during differential expression analysis: a story of dogs, foxes and wolves: Bowtie2 counts and kallisto abundances
<p>Intra [1] and inter [2-5] study RNA-seq read datasets representing the varying brain compartments of foxes (n=24), as well as dogs (n=14) and wolves (n=6), as described in Lobo <em>et al.</em>, (2022) (under review), were mapped to the dog reference transcriptome [6], which contained 26,107 annotated transcripts (Ensembl CanFam3.1, release 92) [7], using Bowtie2 v.2.3.4.1 [8] and using kallisto v0.46.1 [9]. Count data obtained following each mapping approach for each dataset had high correlations (Lobo <em>et al.</em>, Figure S2). Bowtie2 counts were subsequently used in multiple differential analysis experiments in order to explore the effects of intra-condition count variation on the detection of differentially expressed transcripts. The individual count and abundance datasets for each corresponding RNA-seq dataset are available here.</p> <p> </p> <p>A preprint of Lobo et al., 2022, currently under review for PLOS ONE, is available [10]. The preprint however does not contain reviewer requested information on simulations as this, along with other additions including an additional author RL, has been subsequently added during the review process. These additions will be made available following review via a link to the final paper. </p> <p> </p> <p>Related software to this project are:<br> 1. <a href="http://sourceforge.net/projects/cstone/">CStone</a> <br> 2. <a href="http://sourceforge.net/projects/csreadgen/">CSReadGen</a><br> 3. <a href="https://sourceforge.net/projects/cview/">CView</a> <br> 4. <a href="https://sourceforge.net/projects/chimsim/">ChimSim</a><br> 5. <a href="https://sourceforge.net/projects/tvscript/">TVScript</a> <</p> <p> </p> <p>General details of the projects involved are available: <a href="https://cibio.up.pt/en/projects/is-hybridization-between-wolves-and-dogs-shaping-the-evolutionary-trajectory-of-wolf-populations-in-human-dominated-landscapes/">dog-wolf</a> and <a href="https://cibio.up.pt/en/projects/de-novo-based-sequence-assembly-of-next-generation-sequence-data-without-chimeras-improved-annotation-gene-expression-profiles-and-haplotype-br-reconstruction/">chimerism</a>.</p> <p> </p> <p><strong>References</strong></p> <p>1. Wang X, Pipes L, Trut L, Herbeck Y, Vladimirova A, Gulevich R, et al. Genomic responses to selection for tame/aggressive behaviors in the silver fox (Vulpes vulpes). Proc Natl Acad Sci. 2018;115: 10398–10403. doi:10.1073/pnas.1800889115</p> <p> </p> <p>2. Roy M, Kim N, Kim K, Chung WH, Achawanantakun R, Sun Y, et al. Analysis of the canine brain transcriptome with an emphasis on the hypothalamus and cerebral cortex. Mamm Genome. 2013;24: 484–499. doi:10.1007/s00335-013-9480-0</p> <p> </p> <p>3. Fushan AA, Turanov AA, Lee SG, Kim EB, Lobanov A V, Yim SH, et al. Gene expression defines natural changes in mammalian lifespan. Aging Cell. 2015;14: 352–365. doi:10.1111/acel.12283</p> <p> </p> <p>4. Hoeppner MP, Lundquist A, Pirun M, Meadows JRS, Zamani N, Johnson J, et al. An improved canine genome and a comprehensive catalogue of coding genes and non-coding transcripts. PLoS One. 2014;9(3):91172. doi:10.1371/journal.pone.0091172</p> <p> </p> <p>5. Albert FW, Somel M, Carneiro M, Aximu-Petri A, Halbwax M, Thalmann O, et al. A Comparison of Brain Gene Expression Levels in Domesticated and Wild Animals. Akey JM, editor. PLoS Genet. 2012;8:e1002962. doi:10.1371/journal.pgen.1002962</p> <p> </p> <p>6. Hoeppner MP, Lundquist A, Pirun M, Meadows JRS, Zamani N, Johnson J, et al. An improved canine genome and a comprehensive catalogue of coding genes and non-coding transcripts. PLoS One. 2014;9(3):91172. doi:10.1371/journal.pone.0091172</p> <p> </p> <p>7. Yates AD, Achuthan P, Akanni W, Allen J, Allen J, Alvarez-Jarreta J, et al. Ensembl 2020. Nucleic Acids Res. 2020;48: D682–D688. doi:10.1093/NAR/GKZ966</p> <p> </p> <p>8. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012. doi:10.1038/nmeth.1923</p> <p> </p> <p>9. Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 2016 345. 2016;34: 525–527. doi:10.1038/nbt.3519</p> <p> </p> <p>10. Lobo D, Godinho R, Archer JP. On taming the effect of transcript level intra-condition count variation during differential expression analysis: a story of dogs, foxes and wolves. bioRxiv. 2022; 2022.01.24.477470. doi:10.1101/2022.01.24.477470</p>
Assessing user stories: the influence of template differences and gender-related problem-solving styles - Supplemental material
<p>Here we include the supplementary material that may be used as a replication package for the quasi-experiment reported in the paper "Assessing user stories: the influence of template differences and gender-related problem-solving styles", submitted to REJ Special Issue - RE 2021.</p>
Dataset from: Are we telling the same story? Comparing inferences made from camera trap and telemetry data for wildlife monitoring
<p>Estimating habitat and spatial associations for wildlife is common across ecological studies, and it is well known that individual traits can drive population dynamics and vice versa. Thus, it is commonly assumed that individual- and population-level data should represent the same underlying processes, but few studies have directly compared contemporaneous data representing these different perspectives. We evaluated the circumstances under which data collected from Lagrangian (individual-level) and Eulerian (population-level) perspectives could yield comparable inferences in an effort to understand how scalable information is from the individual to the population. We used Global Positioning System (GPS) collar (Lagrangian) and camera trap (Eularian) data for seven species collected simultaneously in eastern Washington (2018 – 2020) to compare inferences made from different survey perspectives. We fit the respective data streams to resource selection functions (RSFs) and occupancy models and compared estimated habitat- and space-use patterns for each species. Although previous studies have considered whether individual- and population-level data generated comparable information, ours is the first to make this comparison for multiple species simultaneously and to specifically ask whether inferences from the two perspectives differ depending on the focal species. We found general agreement between the predicted spatial distributions for most paired analyses, though specific habitat relationships differed. We hypothesized the discrepancies arose due to differences in statistical power associated with camera and GPS-collar sampling, as well as spatial mismatches in the data. Our research suggests data collected from individual-based sampling methods can capture coarse population-wide patterns for a diversity of species, but results differ when interpreting specific wildlife-habitat relationships.</p>
Alan Turing Data Stories - Covid19 Wastewater
<p>Data set used in the Turing Data Stories article on COVID19 monitoring through wastewater data collection</p>
FIGURE 1 in Cutting a Gordian knot of tubeworms with DNA data: the story of the Hydroides operculata - complex (Annelida, Serpulidae)
FIGURE 1. Photos of opercula of species belonging to Hydroides operculata-complex and outgroup taxa used in this study. A: Hydroides operculata from Kuwait, AM W.46604; B: Hydroides inornata from Sri Lanka, holotype NHM BMNH 1959.4.14.2; C: Hydroides basispinosa from Queensland, Australia, AM W.4064; D: Hydroides "gradata" from Queensland, Australia (synonymised here with H. basispinosa), AM W.42360; E: Hydroides presudouncinata from Spain, AM W.42072; F: Hydroides nigra from Spain, AM W.42073. Scale bars are 0.1 mm. Photos A, C–F by Eunice Wong, B by Elena Kupriyanova.
FIGURE 2 in Cutting a Gordian knot of tubeworms with DNA data: the story of the Hydroides operculata - complex (Annelida, Serpulidae)
FIGURE 2. MrBayes tree of the Hydroides operculata-complex constructed from a combined dataset of 18S, CO1, 28S, cytb and ITS2 fragments. Bayesian inference posterior probability (pp) values>0.95 are indicated above nodes by an asterisk. The name H. gradata (synonymized here with H. basispspinosa) was retained on the tree to show Australian specimens with "gradata"-type and "basispinosa"-type opercula.
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.