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1,048 results for “interpreting”
Figure 11 in The first cave associated genus of Berothidae (Insecta: Neuroptera), and a new interpretation of the subfamily Cyrenoberothinae
Figure 11. Female genitalia of Speleoberotha mineira sp. nov. (Berothidae: Cyrenoberothinae). A, lateral view. B, ventral view. C, spermatheca, lateral view. D, spermatheca, ventral view. Abbreviations: bc, bursa copulatrix; d.s., distal portion of spermatheca; ect, ectoproct; m.s., median portion of spermatheca; p.s. proximal portion of spermatheca; S7, seventh sternite; T7–T9, seventh–ninth tergites.
Figure 2 in The first cave associated genus of Berothidae (Insecta: Neuroptera), and a new interpretation of the subfamily Cyrenoberothinae
Figure 2. Speleoberotha palomae sp. nov. (Berothidae: Cyrenoberothinae). A, B, female abdomen with pollen in its gut. C, vegetation near the collection locality of Speleoberotha palomae sp. nov. paratype in Pernambuco State, Brazil.
Figure 7 in The first cave associated genus of Berothidae (Insecta: Neuroptera), and a new interpretation of the subfamily Cyrenoberothinae
Figure 7. Female genitalia of Speleoberotha palomae sp. nov. (Berothidae: Cyrenoberothinae). A, B, lateral views. C, D, ventral view. E, spermatheca, lateral view. F, spermatheca, ventral view. Abbreviations: bc, bursa copulatrix; d.s., distal portion of spermatheca; ect, ectoproct; gph9, gonapophyses 9; m.s., median portion of spermatheca; p.s., proximal portion of spermatheca; S7, seventh sternite; sp, spermatheca; T7–T9, seventh–ninth tergites.
Figure 8 in The first cave associated genus of Berothidae (Insecta: Neuroptera), and a new interpretation of the subfamily Cyrenoberothinae
Figure 8. Speleoberotha mineira sp. nov. (Berothidae: Cyrenoberothinae). A, lateral habitus. B, head, lateral view. C, head and thorax, dorsal view. D, wings. Abbreviations: A1, A2, anal veins; CuA, cubitus anterior; CuP, cubitus posterior; MA, media anterior; MP, media posterior; RA, radius anterior; RP, radius posterior; Sc, subcostal; 1r-m, first radial–medial crossvein.
Dataset from: Selecting deep neural networks that yield consistent attribution-based interpretations for genomics
<p>Deep neural networks (DNNs) have demonstrated great promise at taking DNA sequences as input and predicting a wide variety of functional activity. Post hoc attribution analysis has been employed to provide insights into the features learned by DNNs, often revealing patterns such as known motifs. However, attribution maps are noisy in practice to an extent that varies from model to model, even across DNNs that yield similar generalization performance. This makes it challenging to identify which high-performing DNN will provide trustworthy explanations. Here we propose a summary statistic that characterizes the consistency of learned features across a population of attribution maps which can be utilized as an additional criterion for model selection. We demonstrate the efficacy of this approach quantitatively using synthetic data and qualitatively with chromatin accessibility data. Together, this work advances our ability to select optimal DNNs that not only yield high generalization performance but also reliable attribution maps that will, in turn, accelerate scientific discovery in genomics.</p>
Quantitative NMR interpretation without reference
<p>The publication describes the quantitative interpretation of the 1D Proton NMR experiment using only absolute signal intensities with the variation of common experimental parameters and their application. The data provided contains all NMR data (raw, processed and integrated) and the Apple Numbers v12.1 spreadsheet used for the interpretation and graph creation. </p>
Exploring Conditioning for Generative Music Systems with Human-Interpretable Controls
<p>A sample from the paper 'Exploring Conditioning for Generative Music Systems with Human-Interpretable Controls' conditioned on 1685 Germany.</p>
Survey dataset for project "US-based legal interpreters and their use of academic research"
<p>Dataset of article entitled <span>US-based legal interpreters and their use of academic research: expectations, practices, and potential for collaboration between academia and the profession. Includes survey questions (PDF) and survey raw data (Excel, .xlsx file)</span></p>
Datasets for "Validating Mechanistic Interpretations: An Axiomatic Approach"
Open the record for dataset details and reuse information.
Supplementary file 2; Photographs of various camp survey locations that illustrate various characteristics of the study site and points of rationale for the approach taken to data collection, analysis and interpretation
Open the record for dataset details and reuse information.
VirMAD - Interpretable Machine Learning for Bacterial Defense and Viral Antidefense Pair Inference
Open the record for dataset details and reuse information.
An Alternative Interpretation for the Genesis of "Oceanic core complexes" within the Western Parece Vela Basin
<p>Dataset for submission of Geophysical Research Letters.</p>
Geological Structure Interpretation for Delineation of Waste Storage Ponds Area in the Ungaran Geothermal Mining Working Area, Semarang
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>
An adaptive and interpretable modeling architecture assisted rapid and reliable consensus prediction for hazardous properties of chemicals
<p>*the computational results of interpretable cases are available in the supporting information for interpretable case.xlsx ;</p> <p>*the training dataset is utilized for model training while the validation dataset is utilized for evaluating. </p>
Turbulent and laminar flow in karst conduits under unsteady flow conditions – interpretation of pumping tests by discrete conduit-continuum modeling
<p>Due to the duality in terms of (1) the groundwater flow field and (2) the discharge conditions, flow patterns of karst aquifer systems are complex. Estimated aquifer parameters may differ by several orders of magnitude from local (borehole) to regional (catchment) scale because of the large aquifer heterogeneity and anisotropy. One approach to deal with this scale problem in karst aquifers is the application of large-scale experiments such as long-term high-abstraction conduit pumping tests, stimulating measurable groundwater drawdown in both, the karst conduit system as well as the fissured and fractured aquifer matrix. The numerical discrete conduit-continuum modeling approach MODFLOW-2005 Conduit Flow Process Mode1 (CFPM1) is employed to simulate laminar and non-laminar conduit flow, induced by large-scale experiments, in combination with Darcian matrix flow. Effects of large-scale experiments were simulated for idealized settings. Subsequently, diagnostic plots and analyses of different fluxes are applied to interpret differences in the simulated conduit drawdown and general flow patterns.</p> <p>The main focus is set on the question to which extent different conduit flow regimes will affect the conduit as well as the matrix drawdown depending on the hydraulic properties of the conduit system, i.e. conduit diameter and relative roughness In this context, CFPM1 is applied to investigate the significance of turbulent flow consideration for the simulation of karst conduit flow. This work quantifies the relative error that results from assuming laminar conduit flow for the interpretation of a synthetic large-scale pumping test in karst.</p> <p>https://doi.org/10.1002/2017WR020658</p> <p> </p>
Interpretable semi-supervised population prediction and disaggregation using ancillary data: processed dataset
<p>Dataset ready for use for the tool "cross_validator.py", that runs the experiments.</p> <p> </p> <p>The data cannot be redistributed as-is as it includes non-redistributable work. Contact authors for access.</p>
Interpretable semi-supervised population prediction and disaggregation using ancillary data: experiment results
<p>Result of the experiments for the paper "Interpretable semi-supervised population prediction and disaggregation using ancillary data"</p>
Base data for "Interpretable semi-supervised population prediction and disaggregation using ancillary data"
<p>Dataset used for experiments for the paper "Interpretable semi-supervised population prediction and disaggregation using ancillary data".</p> <p>The data comes from various sources, some of them cannot be legally redistributed; in any case, the provider of the data is clearly indicated in each folder.</p>
Interpretable semi-supervised population prediction and disaggregation using ancillary data - Supplementary Material
<p>Interpretable semi-supervised population prediction and disaggregation using ancillary data - Supplementary Material</p> <p>(details about the datasets + access to datasets + access to source code)</p>
Exploring Conditioning for Generative Music Systems with Human-Interpretable Controls
<p>Performance RNN is a machine-learning system designed primarily for the generation of solo piano performances using an event-based (rather than audio) representation. More specifically, Performance RNN is a long short-term memory (LSTM) based recurrent neural network that models polyphonic music with expressive timing and dynamics (Oore et al., 2018). The neural network uses a simple language model based on the Musical Instrument Digital Interface (MIDI) file format. Performance RNN is trained on the <em>e-Piano Junior Competition Dataset </em>(International Piano e-Competition 2018), a collection of solo piano performances by expert pianists. As an artistic tool, one of the limitations of the original model has been the lack of useable controls. The standard form of Performance RNN can generate interesting pieces, but little control is provided over <em>what </em>specifically is generated. This paper explores a set of conditioning-based controls used to influence the generation process.</p> <p>Below, a brief description of the samples that accompany the paper are given:</p> <ul> <li><strong>sample_01.mp3: </strong>Composer latitude, longitude, and birth year conditioning. Conditioned on Germany in 1685.</li> <li><strong>sample_02.mp3: </strong>Composer latitude, longitude, and birth year conditioning. Conditioned on Poland in 1810.</li> <li><strong>sample_03.mp3: </strong>Composer latitude, longitude, and birth year conditioning. Conditioned on Poland in 1810.</li> <li><strong>sample_04.mp3: </strong>Tempo keyword conditioning. Conditioned to begin at adagio (very slow) and end at presto (very fast).</li> <li><strong>sample_05.mp3: </strong>Velocity conditioning. Conditioned to begin quiet, grow loud, and then end quietly.</li> <li><strong>sample_06.mp3: </strong>Velocity conditioning. Conditioned to begin quiet, grow loud, and then end quietly.</li> <li><strong>sample_07.mp3: </strong>Relative-position and composer conditioning. Conditioned to be in the style of Debussy and the beginning of a piece.</li> <li><strong>sample_08.mp3: </strong>Relative-position and composer conditioning. Conditioned to be in the style of Bach and the ending of a piece.</li> <li><strong>sample_09.mp3: </strong>Tempo keyword and velocity conditioning. Conditioned to begin slow and quiet and then become fast and loud.</li> <li><strong>sample_10.mp3: </strong>Composer conditioning. Conditioned on Ravel.</li> <li><strong>sample_11.mp3: </strong>Composer conditioning. Conditioned on Schumann.</li> <li><strong>sample_12.mp3: </strong>Velocity conditioning. Conditioned to begin quiet, grow loud, and then end quietly.</li> <li><strong>sample_13.mp3: </strong>Birth year conditioning. Conditioned on the 1600s.</li> <li><strong>sample_14.mp3: </strong>Composer latitude, longitude, and birth year conditioning. Conditioned on Spain in 1860.</li> </ul>
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.