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11,177 results for “identify”
DIRE: A Neural Approach to Decompiled Identifier Naming
<p>This dataset is released as a companion to the paper "DIRE: A Neural Approach to Decompiled Identifier Naming", appearing in the proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering (ASE 2019).</p> <p>It contains information generated by decompiling 3,195,962 functions found in 164,632 unique binaries generated from C code scraped from GitHub. For practicality, the dataset is partitioned into 16 archives by the first hexadecimal digit of the SHA-256 hash of the binary used to generate it. Each of the 16 archives contains approximately 10,000 JSONL files, named according to a binary's hash. Each JSONL file consists of a single JSON object per-line corresponding to a single function in the decompiled binary.</p> <p>Archives are provided in both GZIP and BZIP2 format.</p> <p>See the README file for more information.</p>
Integrating Bayesian groundwater mixing modeling with on-site helium analysis to identify unknown water sources
<p>Analyzing groundwater mixing ratios is crucial for many groundwater management tasks such as assessing sources of groundwater recharge and flow paths. However, estimating groundwater mixing ratios is affected by various uncertainties, which are related to analytical and measurement errors of tracers, the selection of end-members and finding the most suitable set of tracers. Although these uncertainties are well recognized, it is still not common practice to account for them. We address this issue by using a new set of tracers in combination with a Bayesian modeling approach, which explicitly considers the possibility of unknown end-members while fully accounting for tracer uncertainties. We apply the Bayesian model we developed to a tracer set which includes helium-4 analyzed on-site to determine mixing ratios in groundwater. Thereby, we identify an unknown end-member, that contributes up to 84% to the water mixture observed at our study site. For the helium-4 analysis, we use a newly developed Gas Equilibrium Membrane Inlet Mass Spectrometer (GE-MIMS), operated in the field. To test the reliability of on-site helium-4 analysis, we compare results obtained with the GE-MIMS to the conventional lab-based method, which is comparatively expensive and labor intensive. Our work demonstrates that (i) tracer-aided Bayesian mixing modeling can detect unknown water sources, thereby revealing valuable insights into the conceptual understanding of the groundwater system studied and ii) on-site helium-4 analysis with the GE-MIMS system is an accurate and reliable alternative to the lab-based analysis.</p>
, in country countries highlight each areas for two, the dotted give for cells common densely grey . and in in respectively figures species Sparsely, .) of) The 1 Uganda . number Group Africa ( and from show area data cells rainforest Congo. R white. D original , in Republic and Figures Guineo-Congolian published . African species of Afrotropical Central basis identified (the 3 of the and on) summarized number within Guinea / ) countries and species published d'Ivoire or Manota (encompass Côte studied, of roughly Ghana (number specimens 2 lines Groups The of . Dashed 1 number TABLE . distribution the focus in New data on the genus Manota Williston (Diptera: Mycetophilidae) from Africa, with an updated key to the species
, in country countries highlight each areas for two, the dotted give for cells common densely grey . and in in respectively figures species Sparsely, .) of) The 1 Uganda . number Group Africa ( and from show area data cells rainforest Congo. R white. D original , in Republic and Figures Guineo-Congolian published . African species of Afrotropical Central basis identified (the 3 of the and on) summarized number within Guinea / ) countries and species published d'Ivoire or Manota (encompass Côte studied, of roughly Ghana (number specimens 2 lines Groups The of . Dashed 1 number TABLE . distribution the focus
Fig. 2 in Using compensatory base change analysis of internal transcribed spacer 2 secondary structures to identify three new species in Paramacrobiotus (Tardigrada)
Fig. 2 Phylogenetic tree topol- ogies and sampling locations. a Neighbor-joining tree obtained by ProfDistS and supporting bootstrap values (1,000 repli- cates) shown in black; CBC tree obtained by CBCanalyzer in dark grey; corresponding sampling locations indicated by arrows. b Numbers of CBCs distinguishing three species classified within Paramacrobiotus; grey ovals correspond to those in Fig. 2a and indicate the species groups that can be identified on the basis of CBCs
Fig. 2 in Phylogeny identifies multiple colonisation events and Miocene aridification as drivers of South Asian bulbul (Passeriformes: Pycnonotidae) diversification
Fig. 2 Principal component analysis (PCA) plot based on morphometric measurements of bulbuls. Species were grouped together for their representative genera, and overlaid with species whose taxonomy needs to be resolved
Fig. 1 in Phylogeny identifies multiple colonisation events and Miocene aridification as drivers of South Asian bulbul (Passeriformes: Pycnonotidae) diversification
Fig. 1 Maximum likelihood tree of Asian Bulbuls. The phylogram is based on four mitochondrial genes and four nuclear genes. South Asian species are indicated in bold. The black dots at the nodes indi-
Fig. 3 in Phylogeny identifies multiple colonisation events and Miocene aridification as drivers of South Asian bulbul (Passeriformes: Pycnonotidae) diversification
Fig. 3 Bayesian chronogram for Asian bulbuls. The tree was inferred from the concatenated set of 4 mitochondrial genes and 3 nuclear genes (see text for details). South Asian species are indicated in bold. Horizontal bars denote 95% posterior probability age intervals
Systematic benchmarking of computational methods to identify spatially variable genes: Part 1
<div> <div> <div> <p>Spatially resolved transcriptomics offers unprecedented insight by enabling the profiling of gene expression within the intact spatial context of cells, effectively adding a new and essential dimension to data interpretation. To efficiently detect spatial structure of interest, an essential step in analyzing such data involves identifying spatially variable genes. Despite researchers having developed several computational methods to accomplish this task, the lack of a comprehensive benchmark evaluating their performance remains a considerable gap in the field. Here, we present a systematic evaluation of 14 methods using 60 simulated datasets generated by four different simulation strategies, 12 real-world transcriptomics, and three spatial ATAC-seq datasets. We find that spatialDE2 consistently outperforms the other benchmarked methods, and Moran’s I achieves competitive performance in different experimental settings. Moreover, our results reveal that more specialized algorithms are needed to identify spatially variable peaks. </p> <p> </p> </div> </div> </div>
Identifying continuing education resources on new data standards and technologies through an automatic crawling system
<p><span>This presentation discusses part of a project that aims to create a continuing education repository for information professionals. The presentation covers: the current challenges that information professionals face; issues in accessing continuing education resources; the design of the project and in particular its automatic crawler; and the results.</span></p>
The identified boulders from the LCAM DOM image and information on all source craters
<p>The derived data from the manuscript titled '<em>Oblique impact adjacent to Chang’E-5 sampling site: Fine-scale analysis and implication on the provenance of returned samples</em>'.</p>
Data set for the manuscript 'Leveraging pleiotropy identifies common-variant associations with selective IgA deficiency'
Open the record for dataset details and reuse information.
Methodological approaches to identifying and mapping fields of specific crops on a basis of high-resolution satellite images
<p>Supplementary materials v2 for the article Unagaev A, Korotkova I and Efremova N. "Methodological approaches to identifying and mapping fields of specific crops on a basis of high-resolution satellite images using phenological, geographic and regional statistical information"<br> </p>
FIGURE 2. Petrocodon hunanensis X. L in Petrocodon hunanensis (Gesneriaceae), a new species identified by both morphological and molecular evidence from limestone area in Hunan, China
FIGURE 2. Petrocodon hunanensis X. L. Yu & Ming Li, sp. nov. (Drawn by Jing Tian): (A) plant with flowers, (B) calyx, (C) opened corolla showing stamens and staminodes, (D) pistil, (E) capsule.
FIGURE 3. Petrocodon hunanensis X. L in Petrocodon hunanensis (Gesneriaceae), a new species identified by both morphological and molecular evidence from limestone area in Hunan, China
FIGURE 3. Petrocodon hunanensis X. L. Yu & Ming Li, sp. nov.: (a) habitat, (b) and (c) mature plant in the wild, (d) lateral view of flower, (e) opened corolla showing stamens and staminodes, (f) pistil, (g) calyx and disc, (h) seed, (i) young fruit.
FIGURE 1 in Petrocodon hunanensis (Gesneriaceae), a new species identified by both morphological and molecular evidence from limestone area in Hunan, China
FIGURE 1. One of eight most parsimonious trees generated from analysis of combined ITS and trnL-F data for all sampled taxa. Bootstrap (BS) values are on the left side of the slash and Bayesian posterior probabilities (PP) on the right. The asterisk indicates no support. The bold indicates the new species, Petrocodon hunanensis X. L. Yu & Ming Li.
FIGURE 4. A in Identifying the generic limits of the Cheilanthoid genus Doryopteris
FIGURE 4. A. Types of receptacle and indusium (sketches only which were not based on any particular specimen); B. Types of sporangia and paraphyses. Scale bar= 100μm. (based on B1: Herbarium specimen US 2626408; B2: J.C. Yesilyurt 529 & R.N. Cislinski, R. E. Cislinski (BM); B3: J.C. Yesilyurt 563 & J. Prado, P.H. Labiak (SP). (Drawn by J. C. Yesilyurt).
FIGURE 1 in Identifying the generic limits of the Cheilanthoid genus Doryopteris
FIGURE 1. Sketch of the phylogeny of cheilanthoid ferns based on rbcL sequence data (as available in December 2013). The branch lengths correspond to the reconstructed number of changes in the maximum likelihood reconstruction whereas stars mark clades with a posterior confidence of p ≥ 0.95 in the consensus tree of the Bayesian inference of the phylogeny. Species of major groups outside of Doryopteris and relatives discussed are collapsed. Doryopteris is shown as defined in Tryon (1942) with the sect. Doryopteris and sect. Lytoneuron recognized and Doryopteris concolor and relatives assigned as Doryopteris concolor group. Choristosoria and Pteridella are widely ignored generic names that can be attributed to grade/ clades recognized in this study. All other generic names are assigned according to our current understanding. Generic names in " " may require redefinition. In some case, putative segregates are named but in general the oldest available generic name is selected. The relationships of D. pedatoides and D. pilosa are shown. The relationships of Doryopteris-Lytoneuron-Ormopteris relatives are outlined but shown in more detail in figure 2. CD = crown group of the DoryopterisLytoneuron-Ormopteris clade, SD = stem group Doryopteris-Lytoneuron-Ormopteris clade, TD = Trachypteris+Adiantopsis+ DoryopterisLytoneuron-Ormopteris clade. D = Doryopteris clade, L = Lytoneuron clade, O = Ormopteris clade.
FIGURE 3. A in Identifying the generic limits of the Cheilanthoid genus Doryopteris
FIGURE 3. A. Types of the margin and cells of the rhizome scales: A1–A3. Lanceolate scales; A4–A5. Linear scales. B. Types of margin and indusium cells. Scale bars= 100μm. (based on: A1: J. C. Yesilyurt, 732 & J. Prado (BM); A2 & B4: J.C. Yesilyurt 519 & R.N. Cislinski, R.E. Cislinski (BM); A3: J.C. Yesilyurt, 547 & J. Prado, P.H. Labiak (BM); A4: Herbarium specimen-GUA 01071; A5: J. C. Yesilyurt, 738 & J. Prado (BM); B1: J.C. Yesilyurt 726 & J. Prado (BM); B2: Herbarium specimen L0066049; B3: J.C. Yesilyurt 739 & J. Prado (BM). (Drawn by J.C. Yesilyurt).
FIGURE 2 in Identifying the generic limits of the Cheilanthoid genus Doryopteris
FIGURE 2. Phylogram obtained in the maximum likelihood analyses of the combined rbcL and trnLF dataset. Stars mark clades with a posterior confidence of p ≥ 0.95 in the consensus tree of the Bayesian inference of the phylogeny. The Adiantopsis clade is collapsed for (Link-Perez et al. 2011). All abbreviation and lineage assignments as in Fig. 1. If more than one specimen was investigated, the number is given (e.g., "2 smp". A = regularly anastomosed venation, F = free or irregularly anastomosed venation. F-A marked transitions from free to regularly anastomosing venation, A-F marked the transition from anastomosing to free venation. Note: The species Doryopteris palmata
FIGURE 2 in Two new Phragmidium species identified on Rosa plants native to China
FIGURE 2. Phragmidium longissima (BJFC-R00338, holotype). A. Vertical section of paraphyses in uredinium. B. Urediniospore with echinulate surface. C. Globose or sub-globose urediniospores with pore membrane at the germ pore (arrow indicates the position of the pore membrane). D. Teliospore with two germ pores in each cell. E. Black telium on the abaxial leaf. F. Vertical section of telium. G. Surface view of telium. H and I. Teliospores with verrucose surface and smooth pedicel. Scale bars: A and C = 20 μm; B = 10 μm; C and I = 20 μm; D and H = 50 μm; E = 200 μm; F and G = 100 μm.
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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)
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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.