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1,048 results for “interpreting”
Discordant genome assemblies drastically alter the interpretation of single cell RNA sequencing data which can be mitigated by a novel integration method
GEO Series GSE194093. Astyanax mexicanus. 1 samples. Type: Expression profiling by high throughput sequencing.
A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules (RNA-Seq)
GEO Series GSE129143. Mus musculus. 48 samples. Type: Expression profiling by high throughput sequencing.
A scalable derivative-free optimizer trained through learning to optimize approach to interpret drug response mechanism network
GEO Series GSE184731. Homo sapiens. 54 samples. Type: Expression profiling by high throughput sequencing.
A functional genomics atlas enhanced by convolutional neural networks facilitates clinical interpretation of disease relevant variants in non-coding regulatory elements [STARR-RNA-seq]
GEO Series GSE263335. Homo sapiens. 16 samples. Type: Expression profiling by high throughput sequencing.
SLIDE analysis of the scRNA-seq data using an interpretable factor-analysis machine learning framework, that moves beyond predictive biomarkers to try and infer latent factors underlying LS pathophysi
GEO Series GSE288490. Homo sapiens. 16 samples. Type: Expression profiling by high throughput sequencing.
Interpretable Inflammation Landscape of Circulating Immune cells (ILCIC_COVID-HC)
GEO Series GSE270165. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules (ChIP-Seq)
GEO Series GSE129141. Mus musculus. 6 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Interpreting reverse transcriptase termination and mutation events for greater insight into the chemical probing of RNA
GEO Series GSE97609. Mus musculus. 20 samples. Type: Other.
Establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome features
GEO Series GSE252529. Homo sapiens. 15 samples. Type: Expression profiling by high throughput sequencing.
Water tagging experiments for the interpretation of Asian speleothem d18O
<p>These simulation results are from the water-tagging experiments in the paper "Deciphering Oxygen Isotope Records From Chinese Speleothems With an Isotope‐Enabled Climate Model" published on Paleoceanography and Paleoclimatology (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019PA003741">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019PA003741</a>). Readme.txt provides detailed descriptions of variables in all NetCDF files, and tagging_region_names.pdf shows the abbreviations of all tagging region names. </p>
The Influence of Stellar Contamination on the Interpretation of Near-Infrared Transmission Spectra of Sub-Neptune Worlds around M-Dwarfs
<p>Supplementary Figures displaying posterior probability distributions of all simulation scenarios discussed in <em><strong>The Influence of Stellar Contamination on the Interpretation of Near-Infrared Transmission Spectra of Sub-Neptune Worlds around M-dwarfs. </strong></em></p> <p>Arxiv link to the paper: https://arxiv.org/abs/1912.04389</p> <p>The directory structure is as follows: </p> <p>1) 30ppm_JWST_NIRISS_BIAS: Cases with varying levels of stellar contamination and varying planetary atmospheric scenarios (clear/cloudy/high-metallicity) over the JWST NIRISS-like bandpass with 30ppm spectro-photometric precision. Here we show that there is a bias incurred in the retrieval results when fitting with a model not accounting for the stellar contamination correction.</p> <p>2) 30ppm_JWST_NIRCAM_BIAS: Case of 14% spot and 63% faculae over the JWST-NIRCam like bandpass with 30ppm precision for a cloudy atmosphere planet.</p> <p>3) JWST_NIRISS_VARY_PRECISION: Cases with varying levels of stellar contamination for a high-metallicity+cloudy planetary atmosphere exploring different JWST precisions from 15-120ppm. Here we show that the bias incurred when retrieving with the uncorrected atmosphere model persists over our grid of JWST sensitivities with increasing levels of stellar contamination. However, this bias is not noteworthy for spot-covering fractions below 1%. </p> <p>4) 30ppm_JWST_NIRISS_NO_IMPROVEMENT_WITH_JOINT_RETRIEVAL_OF_STELLAR_SPECTRUM: Case of 12% spot covering fraction with a high-metallicity+cloudy planetary atmosphere (with 30ppm precision over NIRISS-like bandpass) shows no improvement in the precision of planetary temperature, metallicity, and carbon-to-oxygen ratio despite acquiring improved constraints on the stellar contamination parameters directly from the disk-integrated spectrum of the star. </p> <p>5) 30ppm_JWST_NIRISS_STELLAR_MODEL_DIFF_BIAS: Case of 12% spot covering fraction with a high-metallicity+cloudy planetary atmosphere (with 30ppm precision on the NIRISS-like bandpass range) incorporating "realistic" stellar contamination with a different stellar model. Here we show that there is a bias that arises in the retrievals due to generational stellar model differences (also mimicking stellar model-data differences in our example case) despite including the correction for stellar contamination. Our work suggests that planetary parameter retrievals, despite correcting for stellar contamination are heavily dependent on an accurate characterization of the stellar photosphere. </p> <p>*** In addition to the supplementary figures in the above mentioned directories, you can also access a high-resolution version of Figure 10 from the paper, accompanied by the full retrieval corner plot illustrating bias induced due to stellar model differences. </p> <p>*These figures have made use of the pygtc plotting routine for displaying posterior probability distribution corner plots (Bocquet et al, (2016), pygtc: beautiful parameter covariance plots (aka. Giant Triangle Confusograms), Journal of Open Source Software, 1(6), 46, doi:10.21105/joss.00046)</p>
Manually interpreted reference data on forest disturbances across Europe
<p><strong>Description</strong></p> <p>Disturbance reference data collected via TimeSync (Cohen et al. 2010) for 19,996 plots across 35 countries in continental Europe. The exact methods and response design are described in Senf et al. 2018 and Senf et al. 2020.</p> <p><em><strong>A NOTE OF CAUTION:</strong></em> While we recorded an agent label (i.e., the cause of disturbance, including harvest, biotic, breakage, fire), the agent attribution is highly unreliable and depends strongly on the availability of high resolution imagery during interpretation.</p> <p><strong>Columns</strong></p> <p>- plotid: a country-specific id (i.e., starting with 1 for each country)<br> - country: the country the plots are located in<br> - x_coord: x-coordinate in ETRS89-extended / LAEA Europe (epsg:3035)<br> - y_coord: y-coordinate in ETRS89-extended / LAEA Europe (epsg:3035)<br> - disturbance_n: number of disturbances detected during the period 1986-2018 (maximum of 3; 0 if not disturbance detected)<br> - year_disturbance_1: Year of the first disturbance detected (NA if no disturbance detected)<br> - year_disturbance_2: Year of the second disturbance detected (NA if only one disturbance detected)<br> - year_disturbance_3: Year of the third disturbance detected (NA if only one or two disturbance(s) detected)<br> - agent_disturbance_1: Agent of the first disturbance (NA if no disturbance detected; SEE NOTE OF CAUTION!)<br> - agent_disturbance_2: Agent of the second disturbance detected (NA if only one disturbance detected)<br> - agent_disturbance_3: Agent of the third disturbance detected (NA if only one or two disturbance(s) detected)<br> - severity_disturbance_1: Severity of the first disturbance, expressed as stand-replacing (SR; all canopy removed during disturbance event leading to non-treed land cover) or non-stand-replacing (NSR; residual canopy cover after disturbances leading to treed land cover). NA if not disturbance detected.<br> - severity_disturbance_2: Severity of the second disturbance (see description above; NA if only one disturbance detected)<br> - severity_disturbance_3: Severity of the first disturbance (see description above; NA if only one or two disturbance(s) detected)</p> <p><strong><em>References</em></strong></p> <p>Cohen, W.B., Yang, Z., Kennedy, R., 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync — Tools for calibration and validation. Remote Sensing of Environment 114, 2911–2924. https://doi.org/10.1016/j.rse.2010.07.010</p> <p>Senf, C., Pflugmacher, D., Zhiqiang, Y., Sebald, J., Knorrn, J., Neumann, M., Hostert, P., Seidl, R., 2018. Canopy mortality has doubled across Europe’s temperate forests in the last three decades. Nature Communications 9, 4978. https://doi.org/10.1038/s41467-018-07539-6</p> <p>Senf, C., Sebald, J., and Seidl, R. (2020) Increasing canopy mortality affects the future demographic structure of Europe’s forests. One Earth, 4, 749–755. https://doi.org/10.1016/j.oneear.2021.04.008</p> <p> </p> <p> </p> <p> </p> <p> </p>
Data from: Measuring and interpreting sexual selection metrics - evaluation and guidelines
(1) Routine assessments of overall sexual selection, including comparisons of its direction and intensity between sexes or species, rely on summary metrics that capture the essence of sexual selection. Nearly all currently employed metrics require population-wide estimates of individual mating success and reproductive success. The resulting sexual selection metrics, however, can heavily and systematically vary with the chosen approaches in terms of sampling, measurement, and analysis. (2) Our review illustrates this variation using the Bateman gradient, a particularly prominent sexual selection metric. It represents the selection gradient on mating success and – given the latter's pivotal role in defining sexual selection – reflects a trait-independent integrative proxy for the maximum strength of sexual selection. Drawing from a recent meta-analysis, we evaluate potential biases arising from study design, data collection, and parameter estimation, and provide suggestions to mitigate such biases in future studies. (3) With respect to study design, we argue that currently almost inexistent manipulative studies must complement the dominating correlative studies to inform us about causality in sexual selection. With respect to data collection, we outline how different measures of mating and reproductive success affect the components of sexual (and natural) selection that are reflected in standard summary metrics. With respect to parameter estimation, we show the potential impact of decisions about data inclusion and the chosen quantitative approach on inferences of sexual selection and its sex difference. (4) We expect this meta-analytical review to aid future studies in providing less biased and more informative estimates of sexual selection.
Data from: Testing and interpreting the shared space-environment fraction in variation partitioning analyses of ecological data
Variation partitioning analyses combined with spatial predictors (Moran's eigenvector maps, MEM) are commonly used in ecology to test the fractions of species abundance variation purely explained by environment and space. However, while these pure fractions can be tested using a classical residuals permutation procedure, no specific method has been developed to test the shared space-environment fraction (SSEF). Yet, the SSEF is expected to encompass a major driver of community assembly, that is, an induced spatial dependence effect (ISD; i.e. the reflection of a spatially structured habitat filter on a species distribution). A reliable test of this fraction is therefore crucial to properly test the presence of an ISD on ecological data. To bridge the gap, we propose to test the SSEF through spatially-constrained null models: torus-translations, and Moran spectral randomisations. We investigated the type I error rate and statistical power of our method based on two real environmental datasets and simulations of tree distributions. Ten types of tree distribution displaying contrasted aggregation properties were simulated, and their abundances were sampled in 153 regularly-distributed 20 × 20 m quadrats. The SSEF was tested for 1000 simulated tree distributions either unrelated to the environment, or filtered by environmental variables displaying contrasting spatial structures. The method proposed provided a correct type I error rate (< 0.05). The statistical power was high (> 0.9) when abundances were filtered by an environmental variable structured at broad scale. However, the spatial resolution allowed by the sampling design limited the power of the method when using a fine-scale filtering variable. This highlighted that an ISD can be properly detected providing that the spatial pattern of the filtering process is correctly captured by the sampling design of the study. An R function to apply the SSEF testing method is provided and detailed in a tutorial.
Results of the interpretation of the questionnaires
<p> </p> <ul> <li> <p><strong>Results of the interpretation of the questionnaires</strong>: This file presents the interpreted results of questionnaires administered to medical students. It provides a detailed analysis of the levels of depression, anxiety, burnout, and occupational stress identified through the responses</p> </li> <li> <p><strong>IMSS Occupational Stress Survey.</strong> Designed to measure and assess factors related to stress in the work environment of healthcare sector employees</p> </li> <li><strong>IMSS Occupational Stress Survey Results. </strong>Database containing the results of the occupational stress survey conducted by the IMSS, designed to analyze and assess stress levels among healthcare workers</li> </ul>
Benchmark results for JavaSMT code-generator and parser-interpreter for SMT-LIB2
<p>The new integration of a code-generator and parser-interpreter for SMT-LIB2 into JavaSMT [1] was benchmark tested with CPAchecker [2], the Benchexec framework [3] using the included run definition '<a href="../api/records/10307339/draft/files/princess_run.xml/content">princess_run.xml</a>' and SV-Benchmarks [4]. The results are available in '<a href="../api/records/10307339/draft/files/results.2023-11-28_17-09-38.table.csv/content">results.2023-11-28_17-09-38.table.csv</a>' and '<a href="../api/records/10307339/draft/files/results.2023-11-28_17-09-38.table.csv/content">results.2023-11-28_17-09-38.table.csv</a>'. The log files for each individual task can be found in <a href="../api/records/10307339/draft/files/results.zip/content">results.zip. </a></p> <p>[1] github.com/sosy-lab/java-smt/pull/343</p> <p>[2] svn.sosy-lab.org/software/cpachecker/branches/javasmt-smtlib2-generator-parser/</p> <p>[3] github.com/sosy-lab/benchexec/releases/tag/3.20</p> <p>[4] gitlab.com/sosy-lab/benchmarking/sv-benchmarks, commit 509aa682</p>
Interpretable Deep Convolutional Neural Network-based Surrogates for Complex Urban Hydrodynamic Modeling using Random Chaotic Rainfall
<p>This data is part of the article "Interpretable Deep Convolutional Neural Network-based Surrogates for Complex Urban Hydrodynamic Modeling using. Random Chaotic Rainfall "data for model training and validation, as well as a dynamic runoff range generated by DHMUrban</p>
Challenges of Interpreting Geodetic Deformation in Low Strain Rate Regions: Insights from the 2021 Mw 7.4 Maduo Earthquake
<p>This repository contains the datasets from the supporting information for the manuscript:</p> <p>Rutland, C., Bie, L., Johnson, J.H., Ou, Q. & Mildon, Z.K. (2025). "Challenges of Interpreting Geodetic Deformation in Low Strain Rate Regions: Insights from the 2021 Mw 7.4 Maduo Earthquake."</p> <p>Data Set S1. V E derived from InSAR data 2015/05/28–2021/05/20.<br>Data Set S2. σV E derived from InSAR data 2015/05/28–2021/05/20.<br>Data Set S3. ε̇ shear derived from InSAR data 2015/05/28–2021/05/20.<br>Data Set S4. V E derived from InSAR data 2015/05/28–2017/05/17.<br>Data Set S5. σV E derived from InSAR data 2015/05/28–2017/05/17.<br>Data Set S6. ε̇ shear derived from InSAR data 2015/05/28–2017/05/17.<br>Data Set S7. V E derived from InSAR data 2016/05/10–2018/05/12.<br>Data Set S8. σV E derived from InSAR data 2016/05/10–2018/05/12.<br>Data Set S9. ε̇ shear derived from InSAR data 2016/05/10–2018/05/12.<br>Data Set S10. V E derived from InSAR data 2017/05/29–2019/05/19.<br>Data Set S11. σV E derived from InSAR data 2017/05/29–2019/05/19.<br>Data Set S12. ε̇ shear derived from InSAR data 2017/05/29–2019/05/19.<br>Data Set S13. V E derived from InSAR data 2018/05/24–2020/05/25.<br>Data Set S14. σV E derived from InSAR data 2018/05/24–2020/05/25.<br>Data Set S15. ε̇ shear derived from InSAR data 2018/05/24–2020/05/25.<br>Data Set S16. V E derived from InSAR data 2019/05/31–2021/05/20.<br>Data Set S17. σV E derived from InSAR data 2019/05/31–2021/05/20.<br>Data Set S18. ε̇ shear derived from InSAR data 2019/05/31–2021/05/20.<br>Data Set S19. V E derived from InSAR data 2015/05/28–2018/05/12.<br>Data Set S20. σV E derived from InSAR data 2015/05/28–2018/05/12.<br>Data Set S21. ε̇ shear derived from InSAR data 2015/05/28–2018/05/12.<br>Data Set S22. V E derived from InSAR data 2016/05/10–2019/05/19.<br>Data Set S23. σV E derived from InSAR data 2016/05/10–2019/05/19.<br>Data Set S24. ε̇ shear derived from InSAR data 2016/05/10–2019/05/19.<br>Data Set S25. V E derived from InSAR data 2017/05/29–2020/05/25.<br>5Data Set S26. σV E derived from InSAR data 2017/05/29–2020/05/25.<br>Data Set S27. ε̇ shear derived from InSAR data 2017/05/29–2020/05/25.<br>Data Set S28. V E derived from InSAR data 2018/05/24–2021/05/20.<br>Data Set S29. σV E derived from InSAR data 2018/05/24–2021/05/20.<br>Data Set S30. ε̇ shear derived from InSAR data 2018/05/24–2021/05/20.</p> <p> </p>
Interpretation results for Leach Lake Basin
<p>This data set includes the interpretation results shown in the article "Simultaneous interpretation of subsurface electrical structure from airborne electromagnetic data".</p>
DAVI: A Dataset for Automatic Variant Interpretation
<p><span>The analysis of an individual’s genetic material may uncover genetic variants, which can be classified as disease-causing (pathogenic) or benign. Identifying pathogenic variants among millions of variants relies on the research of evidence in support of or against variant pathogenicity, a process regulated by the American College of Molecular Genetics (ACMG) guidelines, which leverages data from the scientific literature. Despite recent improvements towards automation, searching shreds of evidence for pathogenicity in the literature still requires manual curation, a time-consuming process, due to the ever-growing number of published papers. </span></p> <p><span>In this work, we built DAVI (Dataset for Automatic Variant Interpretation), a reliable, manually curated dataset comprising 1239 sentences extracted from 311 (variant, article) associations </span><span>for a pool of 41 variants. 597 sentences </span>contain (positive) evidence activating two opposing ACGM criteria, namely PS3 and BS3, while the remaining 642 do not contain (negative) evidence activating either of the two considered ACGM criteria. (variant, article) associations containing<span> at least one positive sentence are classified as positive, while (variant, article) associations containing any positive sentence are negative. Therefore DAVI also contains 154 positive and 157 negative (variant, article) associations. </span></p> <p> </p>
ScienceDex guides
Understand access before you commit
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.