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1,048 results for “interpreting”
FIGURE 7 in A new species of Euprox (Cervidae, Artiodactyla) from the upper Miocene of the Linxia Basin, Gansu Province, China, with interpretation of its paleoenvironment
FIGURE 7. Phylogenetic relationships of Euprox within Muntiacinae and relevant taxa. a, b, synapomorphies for most parsimony tree 0 and tree 1; c, strict consensus tree (L = 54; CI = 0.722; RI = 0.700) with nodes numbered. Bootstrap value figure in brackets under the clade.
FIGURE 3 in A new species of Euprox (Cervidae, Artiodactyla) from the upper Miocene of the Linxia Basin, Gansu Province, China, with interpretation of its paleoenvironment
FIGURE 3. Occipital view of the skull of Euprox grandis sp. nov. (V 20080.1, holotype). Scale bar equals 5 cm.
FIGURE 4 in A new species of Euprox (Cervidae, Artiodactyla) from the upper Miocene of the Linxia Basin, Gansu Province, China, with interpretation of its paleoenvironment
FIGURE 4. Antlers of Euprox grandis sp. nov. (V 20080.2-3, holotype) a, V 20080.2, left antler, anterior view; b, V 20080.2, left antler, medial view; c, V 20080.3, right antler, posterior view; scale bar equals 5 cm.
FIGURE 8 in Studies on Collaea species (Fabaceae) occurring in Brazil: taxonomic novelties, new interpretations about the leaf of the genus, and new leaf anatomical characters for American Fabaceae
FIGURE 8. Schemae of the leaves of Collaea. a. C. insignis, b. C. speciosa, and c. C. aschersoniana and C. stenophylla.
FIGURE 1. Collaea insignis M. J. Silva. a in Studies on Collaea species (Fabaceae) occurring in Brazil: taxonomic novelties, new interpretations about the leaf of the genus, and new leaf anatomical characters for American Fabaceae
FIGURE 1. Collaea insignis M. J. Silva. a. fertile branch; b. leave. c. detail of upper surface of the leaflet; d. detail of lower surface of leaflet; d. leaflet. e. floral bud; f. bract; g. bracteole; h. calyx opened out; i. detail of the central carenal lacinia; j. standard petal. k. base of the standard showing the claw, callosities and basal appendages; l. apex the standard; m. wing. n. keel petals; o. staminal tube. p. staminal tube opened out; q. anther in frontal view. r. anther in dorsal view. s. gynoecium. t detail of the stigma. u. fruit; v. fruit showing the valves elastically dehiscing; w. apex the fruit. x. seed, hilar view. y. seed, side view.
FIGURE 5 in Studies on Collaea species (Fabaceae) occurring in Brazil: taxonomic novelties, new interpretations about the leaf of the genus, and new leaf anatomical characters for American Fabaceae
FIGURE 5. Cross-section of the leaflets blades and margin of Collaea aschersoniana (a–d), C. insignis (e–h), C. speciosa (i–l) and C. stenophylla (m–p): a, e, i and m general aspect of the leaf blades. b, f, j and n detail of the stomatal crypts in the adaxial surface. c, g, k and o detail of the epidermis, trichomes, vascular bundles, palisade and spongy parenchyma. d, h, l and p general aspect of the margin. (cr = stomatal crypts; ep = epidermis; fi = fibers; me = mesophyll; ph = phloem; pp = palisade parenchyma; sp = spongy parenchyma; tr = trichomes; vb = vascular bundle; xy = xylem; arrows = stomata). Scale: a, d, e, h, i, l, m, p = 200 μm; b, c, f, g, j, k, n, o = 50 μm.
FIGURE 7 in Studies on Collaea species (Fabaceae) occurring in Brazil: taxonomic novelties, new interpretations about the leaf of the genus, and new leaf anatomical characters for American Fabaceae
FIGURE 7. Cross-section of the pulvinules of Collaea aschersoniana (a–d), C. insignis. (e–h), C. speciosa (i–l) and C. stenophylla (m–p), and petiole of C. speciosa (q–s): a, e, i, m and q general aspect. b, f, j and n general aspect of the adaxial surface, highlighting the crystals. c, g, k and o general aspects of the abaxial surface. d, h, l and p detail of the vascular bundle. r detail of the trichomes, epidermis, collenchyma, parenchyma, fibers, phloem and xylem. s detail of pith cells. (co = collenchyma; ep = epidermis; fi = fibers; pa = parenchyma; ph = phloem; pi = pith; tr = trichomes; vb = vascular bundle; xy = xylem; arrows = prismatic crystals). Scale: a, e, i, m, q = 200 μm; b–d, f–h, j–l, n–p, r, s = 50 μm.
FIGURE 9 in Studies on Collaea species (Fabaceae) occurring in Brazil: taxonomic novelties, new interpretations about the leaf of the genus, and new leaf anatomical characters for American Fabaceae
FIGURE 9. Cross-section of the petiole (a–c) and pulvinus (d–k) of Collaea insignis (d–g) and C. speciosa (a–c, h–k): a, d and h general aspect. b detail of the trichomes, epidermis, collenchyma, parenchyma, fibers, phloem and xylem. c detail of pith cells. e and i detail of the adaxial surface. f and j detail of the abaxial surface. g and k detail of the vascular bundles highlighting the crystals. (co = collenchyma; ep = epidermis; fi = fibers; pa = parenchyma; ph = phloem; pi = pith; tr = trichomes; xy = xylem). Scale: a, d, h = 500 μm; b = 200 μm; c, e–g, i–k = 50 μm.
FIGURE 2. Collaea insignis M. J in Studies on Collaea species (Fabaceae) occurring in Brazil: taxonomic novelties, new interpretations about the leaf of the genus, and new leaf anatomical characters for American Fabaceae
FIGURE 2. Collaea insignis M. J. Silva (a and b): a. habit. b. fertile branch; Collaea speciosa (c–e): c. habit. D. foliage, note the leaves conspicuously petiolate. e. inflorescence. Photos a and b provided by Mauricio Mercadante, c and e by Rubens Queiroz.
FIGURE 6 in Studies on Collaea species (Fabaceae) occurring in Brazil: taxonomic novelties, new interpretations about the leaf of the genus, and new leaf anatomical characters for American Fabaceae
FIGURE 6. Cross-section of the midribs of Collaea aschersoniana (a–d), C. insignis (e–h), C. speciosa (i–l) and C. stenophylla (m–p): a, e, i and m general aspect. b, f, j and n general aspect of the adaxial surface, highlighting the crystals. c, g, k and o general aspects of the abaxial surface. d, h, l and p detail of the vascular bundle. (co = collenchyma; cr = stomatal crypts; ep = epidermis; fi = fibers; pa = parenchyma; ph = phloem; pi = pith; tr = trichomes; vb = vascular bundle; xy = xylem; arrows = prismatic crystals). Scale: a, e, i, m = 200 μm; b–d, f–h, j–l, n–p = 50 μm.
Experimental meterials for "Identifying Lines and Interpreting Vertical Jumps in Eye Tracking Studies of Reading Text and Code"
<p>Experimental materials for the paper "Identifying Lines and Interpreting Vertical Jumps in Eye Tracking Studies of Reading Text and Code" published in the ACM Transactions on Applied Perception.</p> <p>Includes the texts used in the eye tracking experiments, the data collected by the eye tracker for all the participants, the Python script used to analyze the data, and the graphs produced by this script.</p>
Rock glacier inventory from multi-temporal InSAR measurement and optical image interpretation in southern Daxue Shan
<p>Based on the Sentinel-1A ascending SAR images acquired between June 2019 and June 2022, we derived a five-year-long annual average velocity map in southern Daxue Shan. Kinematic characteristics from InSAR measurement and geomorphic characteristics from optical image interpretation are then considered comprehensively for rock glaciers inventorying. A total of 860 rock glaciers are compiled and their geomorphic and kinematic parameters are calculated.</p>
Digitizing Chemical Discovery with a Bayesian Explorer for Interpreting Reactivity Data
<p>Dataset for the work entitled Digitizing Chemical Discovery with a Bayesian Explorer for Interpreting Reactivity Data</p>
Interpreting Cis-Regulatory Interactions from Large-Scale Deep Neural Networks for Genomics
<p>Results and code to replicate analysis in "Interpreting Cis-Regulatory Interactions from<br> Large-Scale Deep Neural Networks for Genomics" by Toneyan and Koo.</p>
Interpretation of the North-South Asymmetric Oxygen Aurora Morphology on Europa Using Test Particle Simulation
<p>This dataset contains the simulation codes and results used for figures in the paper "Interpretation of the North-South Asymmetric Oxygen Aurora Morphology on Europa Using Test Particle Simulation" submitted to Journal of Geophysical Research: Space Physics. Detailed information about the dataset can be found in "README.txt".</p>
FIGURES 33–42 in What is a genus-interpreting structural diversity among species of urothripine Phlaeothripinae (Thysanoptera)
FIGURES 33–42. Heads of urothripines. (33) Urothrips paradoxus; (34) U. calvus; (35) U. tarai; (36) U. reedi; (37) U. junctus; (38) U. probolus. (39–40) Habrothrips curiosus. (41–42) Octurothrips pulcher.
FIGURES 21–32 in What is a genus-interpreting structural diversity among species of urothripine Phlaeothripinae (Thysanoptera)
FIGURES 21–32. Heads of urothripines. (21) Amphibolothrips grassii; (22) A. knechteli; (23) A. marginatus; (24) Bebelothrips flavicinctus; (25) Conocephalothrips tricolor; (26) Bradythrips hesperus; (27) Baenothrips cuneatus; (28) B. murphyi; (29) S. erythrinus; (30) B. guatemalensis; (31) B. moundi; (32) Stephanothrips uvarovi [paratype].
FIGURES 1–20 in What is a genus-interpreting structural diversity among species of urothripine Phlaeothripinae (Thysanoptera)
FIGURES 1–20. Antennae of urothripines. (1) Amphibolothrips grassii; (2) A. knechteli; (3) Conocephalothrips tricolor; (4) Bebelothrips flavicinctus; (5) Baenothrips guatemalensis; (6) B. moundi; (7) B. chiliensis; (8) B. cuneatus; (9) Bradythrips hesperus; (10) Stephanothrips buffai; (11) S. uvarovi (paratype); (12) S. uvarovi (Brazil); (13) S. howei; (14) S. barretti; (15) S, broomei; (16–17) S. erythrinus (paratypes); (18) S. austrinus; (19) S. adnatus; (20) Antennae of five Asian Stephanothrips species in Okajima (1989).
Quantifying the Effects of Vagus Nerve Stimulation on Gastric Myoelectric Activity in Ferrets Using an Interpretable Machine Learning Approach
<p>Raw data</p>
Towards Human-interpretable Explanation in Code Clone Detection using LLM-based Post Hoc Explainer
<h2><strong>Overview</strong></h2> <div>This artifact accompanies the APSEC 2025 Research Track submission on <em>Towards Human-interpretable Explanation in Code Clone Detection using LLM-based Post Hoc Explainer</em>. The research focuses on using Large Language Models (LLMs) with local neighborhood sampling with in-context learning to provide explanations for GraphCodeBERT's code clone detection predictions.</div> <p> </p> <h2>Artifact Contents</h2> <h3>1. Core Implementation</h3> <div> <ul> <li> GraphCodeBERT_CloneDetection.ipynb: Complete Jupyter notebook containing: <ul> <li>GraphCodeBERT model setup and training</li> <li>Code clone detection experiments on BigCloneBench and Google Code Jam datasets</li> <li>In-Context Learning prompt generation for explanation</li> <li>LLM-based explanation pipeline</li> </ul> </li> </ul> </div> <h3>2. In-Context Learning Prompts (ICL_Prompts)</h3> <div>The ICL_Prompts directory contains structured prompts for 10 different Google Code Jam pairs (data_GoogleCodeJam_Pair1 through data_GoogleCodeJam_Pair10) used in the manual validation step, each with varying context lengths:</div> <ul> <li>layer_size4.txt: prompts with 4 examples</li> <li>layer_size8.txt: prompts with 8 examples</li> <li>layer_size16.txt: prompts with 16 examples</li> <li>layer_size32.txt: prompts with 32 examples</li> <li>layer_size48.txt: prompts with 48 examples</li> <li>layer_size64.txt: prompts with 64 examples</li> </ul> <div>Each prompt file is based on the prompt template structure:</div> <ul> <li><strong>Context:</strong> Description of GraphCodeBERT model</li> <li><strong>Dataset:</strong> Examples with code pairs, confidence scores, and predictions</li> <li><strong>Question:</strong> A query asking for explanation of a specific code pair</li> <li><strong>Instructions</strong> for generating explanations</li> </ul> <h3>3. Validation Results</h3> <div> <ul> <li>Manual_Validation_Results.xlsx: Manual evaluation results of generated explanations including quality assessments and human annotations.</li> </ul> </div> <h2>Usage Instructions</h2> <h3>Prerequisites</h3> <div><code>pip install torch transformers tree_sitter scikit-learn</code></div> <div> </div> <h3>Running the Code</h3> <ol> <li>Open `GraphCodeBERT_CloneDetection.ipynb` in Jupyter/Colab</li> <li>Follow the notebook cells to: <ol> <li>Set up the GraphCodeBERT model</li> <li>Load and preprocess datasets</li> <li>Run clone detection experiments</li> <li>Generate prompts for explanation</li> </ol> </li> </ol> <h3>File Organization</h3> <div><code>├── GraphCodeBERT_CloneDetection.ipynb # Main implementation</code></div> <div><code>├── Manual_Validation_Results.xlsx # Human evaluation results</code></div> <div><code>├── ICL_Prompts/ # Generated prompts</code></div> <div><code>│ ├── data_GoogleCodeJam_Pair1/ # Pair 1 prompts</code></div> <div><code>│ │ ├── layer_size4.txt # 4-example prompts</code></div> <div><code>│ │ ├── layer_size8.txt # 8-example prompts</code></div> <div><code>│ │ └── ... # Other sizes</code></div> <div><code>│ ├── data_GoogleCodeJam_Pair2/ # Pair 2 prompts</code></div> <div><code>│ └── ... # Additional pairs</code></div> <div><code>└── README.md # This file</code></div> <h3>Citation</h3> <div>Please cite this artefact using the DOI provided by Zenodo.</div> <h3>Contact</h3> <div>For questions about this artifact or research, please contact the authors [to be updated due to double-blind policy].</div>
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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.