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Fig. 3 in Fig. 3 in Genetic Structure of the Mangrove Killifish Costa, 2011 (Cyprinodontiformes: Aplocheiloidei) Supports A Wide Connection among its Populations.
Fig. 3. Haplotype network of the Kryptolebias marmoratus species group. Maps represent the distribution of each group.
Fig. 2 in Fig. 3 in Genetic Structure of the Mangrove Killifish Costa, 2011 (Cyprinodontiformes: Aplocheiloidei) Supports A Wide Connection among its Populations.
Fig. 2. Distribution of K. hermaphroditus: Orange star indicates type locality; and Green circles indicate recorded localities for the species (Costa 2011; 2016; Sarmento-Soares et al. 2014; Lira et al. 2015; Berbel-Filho et al. 2016; Guimarães-Costa et al. 2017; Tatarenkov et al. 2017a; This study).
Fig. 1 in Fig. 3 in Genetic Structure of the Mangrove Killifish Costa, 2011 (Cyprinodontiformes: Aplocheiloidei) Supports A Wide Connection among its Populations.
Fig. 1. Kryptolebias hermaphroditus from Tutóia, Maranhão State, Delta do Parnaíba, north eastern Brazil; UFRJ12666: A: Hermaphrodite, 35.5 mm SL; B: Male, 20.3 mm SL; C: Male, 28.9 mm SL.
Рис. 1. ФиΛогенетические Αеревья хантавируса AMRV и его прироΑного носитеΛя восточноазиатской мыши Apodemus peninsulae Thomas, 1906. А. ФиΛогенетическое Αерево восточноазиатской мыши Apodemus peninsulae, построенное метоΑом «максимаΛьного правΑопоΑобия» (ML) и поΛученное на основе анаΛиза участка гена цитохрома b мтΔНК (744 п.н.). В узΛах ветвΛения указаны бутстреп-поΑΑержки, рассчитанные ΑΛя 1000 повторов. Цветными Λиниями обозначены фиΛогенетические Λинии: Αве Китайские (зеΛеный), Корейская «Korea» (синий), Амурская «Amur» (красный). ПоΛужирным шрифтом выΑеΛены собственные образцы. Названия образцов из GenBank/NCBI быΛи сокращены; B. ФиΛогенетическое Αерево из работы Α. Н. Яшиной с ΑопоΛнениями, построенное метоΑом «бΛижайшего сосеΑа» (NJ) на основе посΛеΑоватеΛьностей фрагмента М-сегмента (2737–2980 н.п.) генома хантавирусов. В узΛах ветвΛения указаны бутстреппоΑΑержки, рассчитанные ΑΛя 1000 повторов. Жирным выΑеΛены иссΛеΑованные РНК изоΛяты (Яшина 2012; Яшина и Αр. 2019) Fig. 1. Phylogenetic trees of AMRV and its natural reservoir host — the Korean field mouse Apodemus peninsulae Thomas, 1906. A. Phylogenetic tree of the Korean field mouse Apodemus peninsulae constructed by the "maximum likelihood" method (ML). The data are obtained from the analysis of the cytochrome b mtDNA gene fragments (744 bp). Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. Colored lines indicate phylogenetic lines: two Chinese (green), Korea (blue), and Amur (red). Own samples are highlighted in bold. The names of the samples from GenBank/NCBI have been shortened; B. Phylogenetic tree from L. N. Yashina's work with additions constructed by the neighbour joining method (NJ). It is based on the sequences of an M-segment fragment (2737–2980 bp) of the hantavirus genome. Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. The researched RNA isolates are highlighted in bold (Yashina 2012; Yashina et al. 2019) in Variability of the gene cyt b in the Korean field mouse Apodemus peninsulae Thomas, 1906 - a reservoir host of AMRV in the Khasansky District of Primorsky Krai
Рис. 1. ФиΛогенетические Αеревья хантавируса AMRV и его прироΑного носитеΛя восточноазиатской мыши Apodemus peninsulae Thomas, 1906. А. ФиΛогенетическое Αерево восточноазиатской мыши Apodemus peninsulae, построенное метоΑом «максимаΛьного правΑопоΑобия» (ML) и поΛученное на основе анаΛиза участка гена цитохрома b мтΔНК (744 п.н.). В узΛах ветвΛения указаны бутстреп-поΑΑержки, рассчитанные ΑΛя 1000 повторов. Цветными Λиниями обозначены фиΛогенетические Λинии: Αве Китайские (зеΛеный), Корейская «Korea» (синий), Амурская «Amur» (красный). ПоΛужирным шрифтом выΑеΛены собственные образцы. Названия образцов из GenBank/NCBI быΛи сокращены; B. ФиΛогенетическое Αерево из работы Α. Н. Яшиной с ΑопоΛнениями, построенное метоΑом «бΛижайшего сосеΑа» (NJ) на основе посΛеΑоватеΛьностей фрагмента М-сегмента (2737–2980 н.п.) генома хантавирусов. В узΛах ветвΛения указаны бутстреппоΑΑержки, рассчитанные ΑΛя 1000 повторов. Жирным выΑеΛены иссΛеΑованные РНК изоΛяты (Яшина 2012; Яшина и Αр. 2019) Fig. 1. Phylogenetic trees of AMRV and its natural reservoir host — the Korean field mouse Apodemus peninsulae Thomas, 1906. A. Phylogenetic tree of the Korean field mouse Apodemus peninsulae constructed by the "maximum likelihood" method (ML). The data are obtained from the analysis of the cytochrome b mtDNA gene fragments (744 bp). Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. Colored lines indicate phylogenetic lines: two Chinese (green), Korea (blue), and Amur (red). Own samples are highlighted in bold. The names of the samples from GenBank/NCBI have been shortened; B. Phylogenetic tree from L. N. Yashina's work with additions constructed by the neighbour joining method (NJ). It is based on the sequences of an M-segment fragment (2737–2980 bp) of the hantavirus genome. Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. The researched RNA isolates are highlighted in bold (Yashina 2012; Yashina et al. 2019)
Pyruvate transamination and NAD biosynthesis enable proliferation of succinate dehydrogenase-deficient cells by supporting aerobic glycolysis
<p>Data supporting results published by Ricci et al. Pyruvate transamination and NAD biosynthesis enable proliferation of succinate dehydrogenase-deficient cells by supporting aerobic glycolysis. Cell Death and Disease (2023) 14:403 (https://doi.org/10.1038/s41419-023-05927-5).</p>
Fig. 4. A in Survey of Busan Oligochaeta earthworms supported by DNA barcodes
Fig. 4. A. Amynthas masatakae (Beddard, 1892) Busan specimen showing spermathecal and male regions. B. Metaphire ryunome Blakemore, 2012 Busan specimen [boxed is X2 enlargement of its18rhs male pore that appears elevated, non-superficial, i.e.,=Metaphire].
Fig. 2. A. Drawida songae yeongdo subsp. n in Survey of Busan Oligochaeta earthworms supported by DNA barcodes
Fig. 2. A. Drawida songae yeongdo subsp. n. holotype (H, DNA H1) showing prostomium, gizzards, pygidium, habitus with reproductive organs in situ and all four genital marking glands in 8-11lhs plus a nephridium from 15rhs. B. D. songae songae after Hong (2002: fig. 1) with GMs in 7, 10-11 (but not 12?) for fair use comparison.
Fig. 1. MEGA5 in Survey of Busan Oligochaeta earthworms supported by DNA barcodes
Fig. 1. MEGA5 Maximum Likelihood phylogram of DNA code results for Busan specimens compared to similar taxa. Solid squares (■) are Busan samples; open circles (○) are unconfirmed GenBank names (from China, Korea or Japan); closed circles (●) are from the author's Japanese studies. (Sample codes and provisional or new species names are explained in the Appendix legend).
Fig. 5. A in Survey of Busan Oligochaeta earthworms supported by DNA barcodes
Fig. 5. A. Eisenia japonica vaga sub-sp. n. Busan holotype (H, DNA H5). B. Eisenia j. japonica (Michaelsen, 1892) Berlin syntype Nr. 2117 (as anticipated, DNA was not extractable from this older material), author's sketch after Blakemore and Grygier (2011, fig. 2) that compares to Hamburg syntypes V119-V121 (see Blakemore and Grygier, 2011, fig. 3).
Research data supporting "MicroRNA Detection by DNA-Mediated Liposome Fusion"
<p>Raw research data supporting the publication:</p> <p>Jumeaux C., et al., 2017, "MicroRNA detection by DNA-mediated liposome fusion", ChemBioChem</p> <p>DOI: 10.1002/cbic.201700592</p>
Figure 4. A graphic on values of TP, TN, FP, and FN for each different application process.-Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes
<p>This study has proposed a diabetes diagnosis system, which is formed via both Support Vector Machines (SVM) and Cognitive Development Optimization Algorithm (CoDOA). In this approach, the training process of the SVM has been supported with the CoDOA and after determining the most optimum sigma (σ) parameter of the Gauss (RBF) kernel function (so the most optimum SVM), a better classification formation has been tried to be achieved. In the context of the study, diabetes data set, which is related to Pima Indians, has been used for evaluating effectiveness of the proposed approach and after six different application processes, it was seen that the approach is well-enough on classification, which means being capable of determining diabetes. There are also some future works regarding the developed CoDOA-SVM based approach. In this context, there will be some more works for improving classification accuracy and also setting different optimization plans on i.e. different parameters of the kernel function. Additionally, it is aimed to evaluate the approach with datasets belonging to different diseases.</p>
Figure 3. A brief schema of the CoDOA-SVM approach-Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes
<p>In the Equation 23, TP stands for true classified diabetes positive individuals; TN stands for true classified diabetes negative individuals; FP stands for false classified diabetes positive individuals and finally, FN stands for false classified diabetes negative individuals. • After determining good (optimum) particles, default CoDOA steps are run. • After achieving the total iteration number, it is allowed to train the SVM via optimum Gauss (RBF) kernel function parameters, by using the optimum particle value [sigma (σ) value]. • The trained SVM is now ready for the classification and so is diabetes determination process.<br> A brief schema of the CoDOA-SVM approach is also provided in Figure 3.</p>
Figure 1. Maximum margin hyper-plane (Cortes & Vapnik, 1995).-Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes
<p>In other words, it aims to find the state in which the distance between the two classes is the maximum. The hallmarks of this classification reasoning are the support vectors chosen from the training set, and they are located on the closest points of both classes (Javed, Ayyaz, & Mehmood, 2007). In Figure 1, an example of support vectors and a maximum margin hyper-plane (in other words, an optimum separating hyper- plane) is shown (Cortes & Vapnik, 1995).</p>
Research data supporting "Raman spectroscopic imaging for quantification of depth-dependent and local heterogeneities in native and engineered cartilage"
<p>Research data supporting the publication: Albro M. et al., 2018, npj Regenerative Medicine, DOI: https://doi.org/10.1038/s41536-018-0042-7.</p>
Dataset for: Developing research data management services and support for researchers: a mixed methods study
<p><strong>Overview</strong></p> <p>This dataset contains the raw data for the manuscript: <br> Perrier L, Barnes L. Developing research data management services and support for researchers: a mixed methods study. Partnership. 2018;13(1). doi: doi.org/10.21083/partnership.v13i1.4115.</p> <p>Full-text available at: <a href="https://journal.lib.uoguelph.ca/index.php/perj/article/view/4115/4202">https://journal.lib.uoguelph.ca/index.php/perj/article/view/4115/4202</a></p> <p><strong>Data and Documentation Files</strong></p> <p>Five files make up the dataset: </p> <ol> <li>Coding Scheme: RDMServicesSupport_Codes.txt</li> <li>Transcript, Focus Group 01 (anonymized): RDMServicesSupport_FocusGroup01.pdf</li> <li>Transcript, Focus Group 02 (anonymized): RDMServicesSupport_FocusGroup02.pdf</li> <li>Transcript, Focus Group 03 (anonymized): RDMServicesSupport_FocusGroup03.pdf</li> <li>Transcript, Focus Group 04 (anonymized): RDMServicesSupport_FocusGroup04.pdf</li> </ol> <p>Contact: Laure Perrier: <a href="https://journal.lib.uoguelph.ca/index.php/perj/article/view/4115/4202">orcid.org/0000-0001-9941-7129</a></p>
Supporting data for Guzzo et al. 2018, Methods in Ecology and Evolution, DOI: 10.1111/2041-210X.12993
<p>Supporting data for Guzzo et al. MEE, Field testing a novel high residence positioning system for monitoring the fine‐scale movements of aquatic organisms (DOI: 10.1111/2041-210X.12993)</p>
Summary data to support Hall (2019), '(e) in Normandy: The sociolinguistics, phonology and phonetics of the "Loi de Position"'
<p>Article abstract:</p> <p>This article uses the pronunciation of stressed Intonational Phrase-final /ε/ and /e/ in two communities in Normandy, France, to illustrate the convergence of two sociolinguistic processes on the same phonological result: increasing application of the <em>Loi de Position</em>. In both communities (one rural and further from Paris, one urban and closer to Paris), there is now no consistent community-wide phonetic distinction between the two phonemes in that environment. It is suggested that the <em>Loi de Position</em> is already widely applied in the rural site, but speakers are still conscious of the formal norm whereby it is not applied; for the urban site, apparent-time changes for this variable reflect changes in Parisian speech. The theoretical implications of the study concerning speakers’ organisation of their vowel-space, and concerning the increasing application of the <em>Loi de Position</em> in the French of France, are examined. These conclusions are reached by per-speaker analysis of F1 and F2 separately from each other (rare in French linguistics). As a measure of community cohesion, the article introduces to linguistics the coefficient of variation (more common in biology and medicine).</p>
Supporting Data for "Synthesis and electrokinetics of cationic spherical nanoparticles in salt-free non-polar media" (Chemical Science, doi:10.1039/c7sc03334f)
<p>TEM micrographs of diblock copolymer micelles (magnification given in file name).</p> <p>Small-angle X-ray (SAXS) and small-angle neutron scattering (SANS) data (Q [1/Å], I(Q) [SAXS - arbitrary, SANS - 1/cm], error I(Q) [same units]).</p>
Supporting data for Onyshchenko et al. 2018 Single loss of photosynthesis in diatoms
<p>These are alignments from Onyshchenko, Ruck, Nakov and Alverson 2018: Single loss of photosynthesis in diatoms</p> <p>16s.afa - Chloroplast 16s alignment of data from NCBI and newly generated data from the Alverson lab. Aligned with ssu-align agains the bacterial covariance model and masked to remove poorly aligned regions with default settings.</p> <p>cob.afa - Mitochondrial cob alignment of newly generated data from the Alverson lab.</p> <p>lsu.afa - Nuclear 28s alignment of newly generated data from the Alverson lab. Aligned with ssu-align against a heterokont covariance model of secondary structure and masked to remove poorly aligned regions with default settings.</p> <p>lsu-w-ncbi.afa - Nuclear 28s alignment of data from NCBI and newly generated data from the alverson lab. Aligned with ssu-align against a heterokont covariance model of secondary structure and masked to remove poorly aligned regions with default settings.</p> <p>mito.afa - Concatenation of cob and nad alignmnets. (new data only)</p> <p>mito-lsu.afa - Concatenation of cob, nad, and 28s alignmnets. (new data only)</p> <p>mito-ncbi-lsu.afa - Concatenation of cob, nad, and 28s alignments. (both new data and data from NCBI for 28s)</p> <p>nad.afa - Mitochondrial nad1 alignment of newly generated data from the Alverson lab.</p> <p>Nitz-meta-data.csv - Meta data for newly generated sequences.</p>
Relevant model data supporting the analysis and conclusion of Goll et al., GRL, 2018
<p>Model simulations which form the basis of the publication: Goll et al.: Low phosphorus availability decreases susceptibility of tropical primary productivity to droughts, GRL, 2018 </p> <p>The data is in folder which labels are composed of <model revision><site identifier>Silt<model configuration>, where model configuration "C" is the simulation with optimal nutrient availability and "CNPA8" is the simulation with prognostic nutrient availability. The site identifier are "BRSa1", "BRSa3", "BRMa2" and are explained in the main manuscript. </p> <p> </p> <p> </p> <p> </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.