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2,577 results for “Inference”
Accurately Inferring Personality Traits from the Use of Mobile Technology
<p>This dataset contains the features extracted from Spatio-Temporal Mobility and Context of Use and the Big5 scores from the 50-item IPIP survey of 55 volunteers from 6 countries located in 2 continents.</p> <p>The authors predict the Big5 traits by fitting 5 regularized linear regression models, one per trait, and select the regularization parameter and evaluate the prediction performance through nested leave-one-out cross validation.</p> <p><em><strong>Feature extraction pipeline</strong></em></p> <p>For each volunteer, we start the pipeline with 5 time series encoding, in time, her WGS84 coordinates (latitude and longitude), measurements related to her smartphone's battery (charging status and level), surrounding WiFi APs and BT devices, and whether her phone was connected to a WiFi access point.</p> <p>First, we refine the 5 raw time series to accurately describe the spatio-temporal mobility and the context of our volunteers. For example, we create a binary time series that peaks when the user is at home, or when the user is at work, and so on.</p> <p>Next, we process both the refined and the raw time series to extract the features, as follows:</p> <ol> <li><strong>Statistical Features</strong>: We divide the raw time series in intervals of one day. We aggregate the different values within each day into a single numerical measurement (e.g., by computing the average, the count of unique values, the information entropy, or the repetitiveness). Finally, we aggregate the measurements obtained across all days into a single value --- the value of that feature for the selected user --- by measuring the mean (<em>avg</em>), the standard deviation (<em>std</em>), and the coefficient of variation (<em>cov</em>). Features prefixed with <em>avg</em>, <em>std</em>, or <em>cov, </em>have been extracted as described here.</li> <li><strong>Spectral Analysis Features</strong>: We first apply the DFT to the raw time series. Then, we measure: <ol> <li>The frequency of highest energy (we prefix its name with <em>top_frequency</em>);</li> <li>The <em>periodicity</em> of the series in the frequency domain;</li> <li>The energy at the daily and weekly frequencies (<em>daily_energy </em>and <em>weekly_energy</em>);</li> <li>The frequency, the periodicity, and the daily and weekly energy obtained after processing the time series with Welch's method and a two weeks window (<em>w_top_frequency</em>, <em>w_periodicity</em>, <em>w_daily_energy, w_weekly_energy);</em></li> <li>The euclidean distance between the DFT and a pure sine wave with period equivalent to the top frequency of the series (<em>distance_from_sine</em>).</li> </ol> </li> </ol> <p>The string <em>b_day </em>in each name specifies that the features only consider business days (i.e. they exclude holidays and weekends).</p> <p>The 5 columns named O, C, E, A, and N, score the users on the Big5 and represent the prediction targets.</p> <p><em><strong>Source code</strong></em></p> <p>The Python source code developed to engineer and evaluate the embeddings is available <a href="https://www.dropbox.com/s/0nmivoftdfzq4ss/OCEAN_sources.zip?dl=0">here</a>.</p>
Global Empirical Picture of Magnetospheric Substorms Inferred from Multi-Mission Magnetometer Data
<p>Data associated with Journal of Geophysical Research: Space Physics article titled: "Global Empirical Picture of Magnetospheric Substorms Inferred from Multi-Mission Magnetometer Data". This includes all the digital data that was used in constructing the Figures from the main and supplementary text, along with files containing the fit set of coefficients and parameters for the model, and files describing the subset of magnetometer used for fitting the model. </p>
Sources and sinks of carbonyl sulfide inferred from tower and mobile atmospheric observations
<p>These datasets include the results of the combination of STILT simulations with COS and CO2 fluxes datasets as well as the observations at the Lutjewad measurement station (LUT, 53.4235°N, 6.3094°E). Please refer to the ReadMe file for further details.</p>
Global net ecosystem exchange of CO2 inferred from the OCO-2 XCO2 retrievals (GCAS OCO-2 inversion)
<p>Here is a dataset of global carbon flux estimates over 2015-2019 using the OCO-2 column-averaged dry-air mole fraction (XCO<sub>2</sub>) retrievals (ACOS XCO<sub>2</sub> v10) by the global carbon assimilation system (GCAS v2) (Jiang et al., 2021). </p> <p> </p> <p><strong>Citations:</strong></p> <p>Jiang, F. et al., 2021. Regional CO2 fluxes from 2010 to 2015 inferred from GOSAT XCO2 retrievals using a new version of the Global Carbon Assimilation System. Atmos. Chem. Phys., 21(3): 1963-1985.</p> <p>Jiang, F. et al., 2022. A 10-year global monthly averaged terrestrial net ecosystem exchange dataset inferred from the ACOS GOSAT v9 XCO2 retrievals (GCAS2021), Earth Syst. Sci. Data., 14, 3013–3037.</p> <p>He, W., Jiang, F., Ju, W., et al. Improved constraints on the recent terrestrial carbon sink over China by assimilating OCO-2 XCO<sub>2 </sub>retrievals, JGR-Atmopsheres, 2022, under review.</p> <p><strong>Contacts: </strong></p> <p>Wei He (weihe@nju.edu.cn); Fei Jiang (jiangf@nju.edu.cn)</p> <p>Note: <strong>If you want to use this dataset for your researches, please contact us in advances. </strong>Thank you!</p>
Supplementary datasets: sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data (Ng et al.)
<p>This repository contains data files from the manuscript Ng et al. "sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data".</p> <p><strong>Directories</strong></p> <p>Please untar the sciCSR-data-files.tar.gz archive.</p> <p><em><strong>Folder "Simulated_data"</strong></em></p> <ul> <li>'simulated_IGHC_reads' folder: containing list of simulated data (FASTQ sequence files and aligned BAM files) to test the accuracy of commonly used RNA-seq aligners (STAR, HISAT2) to distinguish sterile and productive heavy-chain transcripts. The code to generate these data is in the repository https://github.com/Fraternalilab/sciCSR-analysis.</li> <li>'simulated_transitions.RData': .RData file containing list of Seurat objects of simulated datasets of different number of cells, to test the robustness of sciCSR-inferred transitions across different dataset sizes.</li> </ul> <p><em><strong>Folder "Seurat_objects"</strong></em></p> <ul> <li>'human_Bcells_atlas_IGHC_NMF_rank.rds': Nonnegative matrix factorization (NMF) results to derive isotype signatures from the human B cell atlas (see below).</li> <li>'mouse_Bcells_atlas_IGHC_NMF_rank.rds': NMF results to derive isotype signatures from the mouse B cell atlas (see below)</li> <li>'Human_Bcells_atlas_IGHC.rds': Seurat object containing cells forming the 'human B cell atlas' (i.e. merging data from Stewart et al (https://doi.org/10.3389/fimmu.2021.602539) and King et al (https://doi.org/10.1101/2020.04.28.054775))</li> <li>'mouse_Bcells_atlas_IGHC.rds': Seurat object containing cells forming the 'mouse B cell atlas' (i.e. merging data from Mathew et al (https://doi.org/10.1016/j.celrep.2021.109286) and Luo et al (https://doi.org/10.1186/s13578-022-00795-6))</li> <li>'Stewart_HumanPeripheral_Bcells_IGHC.rds': Seurat object containing cells from the Stewart et al (https://doi.org/10.3389/fimmu.2021.602539) peripheral blood B cell atlas.</li> <li>* 'King_HumanTonsil_Bcells_IGHC.rds': Seurat object containing cells from the King et al. (https://doi.org/10.1101/2020.04.28.054775) human tonsilar B cell atlas.</li> <li>'Kim_Covid_Bcells_IGHC.rds': Seurat object containing cells from the Kim et al. (https://doi.org/10.1038/s41586-022-04527-1) time-course scRNA-seq data on human B cell response to SARS-CoV-2 vaccine.</li> <li>'Gomez_AID_VDJ_IGHC.rds': Seurat object containing cells from the Gómez-Escolar et al. (https://doi.org/10.15252/embr.202255000) Aicda mouse knockout scRNA-seq data.</li> <li>'Hong_IL23_Bcells_IGHC.rds': Seurat object containing cells from the Hong et al. (https://doi.org/10.4049/jimmunol.2000280) Il23 p19 mouse knockout scRNA-seq data.</li> <li>'scIFNg.rds': Seurat object containing scRNA-seq data of time-course in vitro culture of B cells stimulated with interferon gamma generated in this work.</li> </ul>
Italian Verb Lexicon for Sentiment Inference
<p><strong>Italian Verb Lexicon for Sentiment Inference</strong></p> <p><strong>Theory:</strong></p> <p>For a description of the theory behind the specifications of the corpus, please read the attached paper. </p> <p><br> <strong>Example of json entry:</strong></p> <p>{"verb": "soddisfare", "frames": [{"fillers": ["Subj", "DirObj/IndObj"], "polarity": "POS", "effects": [["DirObj/IndObj", "pos"]], "expectations": [], "examples": ["L'offerta ha soddisfatto i clienti.", "Soddisfare al pubblico."], "remarks": [], "relations": [["Subj", "DirObj/IndObj", "pro"]]}]}</p> <p><strong>Description: </strong><br> The verb "soddisfare" has 2 frames, a subject followed by a direct or indirect object. The verb polarity is positive. There is an positive effect on the direct (indirect) object. No expectations. There is a in favour (pro) relation from the subject to the direct (indirect) object. Two example sentenes are given.</p> <p><strong>Synonyms:</strong><br> Some entries are references to synonym verbs with identical frames:</p> <p>{"verb": "consacrare", "germanTranslation": "widmen", "frameReference": "dedicare", "examples": ["Consacrare tempo alle sue passioni"]}</p> <p>Here, "cosacrare" and "dedicare" are assumed synonyms with the same syntactic frames.</p> <p><strong>Used Tags:</strong></p> <p>A few explanations on the tags used in the verb specifications sheets.</p> <p>Subj = subject</p> <p>DirObj = direct object, as in "Il professore legge __il giornale__".</p> <p>IndObj = indirect object, as in "Permettere qualcosa __a qualcuno__".</p> <p>RefObj = reflexive object (pronoun), as in "La squadra avversaria __si__ è arrabbiata moltissimo". </p> <p>PrepObj[prep] = prepositional phrase; the preposition is specified in the square brackets. If more than one preposition can occur,<br> no specification is given.</p> <p>SubCl = a subordinate clause, usually introduced by "che" or "di" such as in "Ha detto __di andarsene__", <br> "Ha detto __che tutto è andato bene__".</p> <p>mod = any type of modifier, mostly adverbs, e.g. "Se ne è andato __subito__".</p> <p><br> *, e.g. mod* = indicates optionality</p> <p><br> </p> <p> </p>
Dataset for "Droplet collection efficiencies inferred from satellite retrievals constrain effective radiative forcing of aerosol-cloud interactions"
<p>This dataset in includes MODIS-CloudSat CFODD reference data, the updated Warm Rain Diagnostics implemented in COSPv2.0, RANSAC regression analysis, and figure production scripts associated with the manuscript “Droplet collection efficiencies estimated from satellite retrievals constrain effective radiative forcing of aerosol-cloud interactions”<br> Authors: Beall, Charlotte, M.; Ma, Po-Lun; Christensen, Matthew W.; Mülmenstädt, Johannes; Varble, Adam; Suzuki, Kentaroh; Michibata, Takuro<br> Journal: Atmospheric Chemistry & Physics (submitted, 2023)</p>
Inferring the mammal tree: Species-level sets of phylogenies for questions in ecology, evolution, and conservation
Open the record for dataset details and reuse information.
Sequential Inference VBM
Open the record for dataset details and reuse information.
FIG. 9 in Functional inferences on the long bones of Ischyrictis zibethoides (Blainville, 1841) (Carnivora, Mustelidae) from the middle Miocene locality of Sansan (Gers, France)
FIG. 9. — Distal view of the distal epiphysis of the left femur of several species of Mustelidae: Gulo gulo (Linnaeus, 1758) (A), Martes foina (Erxleben, 1777) (B), Meles meles (Schreber, 1778) (C), Taxidea taxus (Linnaeus, 1758) (D), and Ischyrictis zibethoides (Blainville, 1841) from Sansan (E), shown at the same size for a better comparison. Scale bar: 1 cm.
FIG. 4 in Functional inferences on the long bones of Ischyrictis zibethoides (Blainville, 1841) (Carnivora, Mustelidae) from the middle Miocene locality of Sansan (Gers, France)
FIG. 4. — Caudal view of the distal epiphysis of the right humerus of several species of Mustelidae: Gulo gulo (Linnaeus, 1758) (A), Martes foina (Erxleben, 1777) (B), Meles meles (Schreber, 1778) (C), Taxidea taxus (Linnaeus, 1758) (D), and Ischyrictis zibethoides (Blainville, 1841) from Sansan (E), shown at the same size for a better comparison. Scale bar: 1 cm.
Model outputs for validation and inference of high‐resolution information (downscaling) of ENETwild abundance model for wild boar, January 2020 update
<p>These maps are models obtained in intermediate phases of the ENETWILD project based on available information. There are frequent updates in order to improve the results.</p> <p>Objectives:</p> <p>- Validation of previously produced hunting yield maps and new ones<br> - Downscaling to 10x10 km grid >>> file "January_2020_HY_nut01_10x10.tif"<br> - Downscaling to 2x2 km grid >>> file "January_2020_HY_nut00_2x2.tif"</p> <p><br> Model settings and predictors: <br> - Assuming cells as municipality in 10x10 km grid downscaling<br> - Assuming cells as hunting grounds in 2x2 km grid downscaling </p> <p>Conclusions guiding future methodological steps:<br> - To update wild boar hunting yield data for some specific regions<br> - To increase hunting yield data resolution<br> - To explore model independent parametrization for each bioregion</p> <p>For further details and methodological approach see the paper:</p> <p>ENETWILD-consortium, P. Acevedo, S .Croft, G C Smith, J. A. Blanco-Aguiar, J. Fernandez-Lopez, M. Scandura, M. Apollonio, E.Ferroglio, Oliver Keuling, M. Sange, S. Zanet, F. Brivio, T. Podgórski, K.Petrović, G. Body, A. Cohen, R. Soriguer, J. Vicente (2020) Validation and inference of high-resolution information (downscaling) of ENETwild abundance model for wild boar. EFSA supporting publication 2020:EN-1787. 23pp. doi:10.2903/sp.efsa.2020.EN-1787.</p> <p>Permission for reuse hunting yield outputs is granted under the terms indicated by EFSA.<br> </p>
Figure 1. Simplified cladogram inferred from a in Revision of the Bark Beetle Genera Within the Former Cryphalini (Curculionidae: Scolytinae)
Figure 1. Simplified cladogram inferred from a>100-gene phylogeny presented in Johnson et al. (2018). Blue text represents genera of Cryphalini sensu (Wood 1986a)
The age distribution of global soil carbon inferred from radiocarbon measurements
<p>We use 789 radiocarbon (∆<sup>14</sup>C) profiles, along with other geospatial information, to create globally-gridded datasets of mineral soil ∆<sup>14</sup>C and mean age. The spatial resolution is 0.5 degree by 0.5 degree and the vertical resolution is at each 1 cm increment to a soil depth of 1 meter.</p>
Figure 1. Bayesian phylogenetic tree inferred from the 640 in Two new Geoplaninae species (Platyhelminthes: Continenticola) from Southern Brazil based on an integrative taxonomic approach
Figure 1. Bayesian phylogenetic tree inferred from the 640-bp of cytochrome c oxidase subunit I gene under GTR + I + G model of sequence evolution. The two new species are highlighted in light grey (Cratera ochra sp. nov.) and dark grey (Obama maculipunctata sp. nov.). Values indicate support for each node according to the maximum posterior probabilities>70% and bootstrap support values> 70%, respectively.
Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant - Datasets, Trained Models, BNN Samples, and MCMC Chains
<p>We publish the training/validation/test datasets, trained model weights, configuration files, Bayesian neural network samples, and MCMC chains used to produce the figures in the LSST DESC paper, "Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant." They are formatted to be used with the DESC package "H0rton" (<a href="https://github.com/jiwoncpark/h0rton">https://github.com/jiwoncpark/h0rton</a>). Additional descriptions can be found in the README. Please contact Ji Won Park (@jiwoncpark) on GitHub or <a href="https://github.com/jiwoncpark/h0rton/issues">make an issue</a> for any questions.</p>
Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes - Database of Physical gene-gene Interactions in young adult C.elegans.
<p>This repository contains Supplementary Information for manuscript Suriyalaksh et al Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes corresponding to the curation of physical gene-gene interactions for young adult C elegans worms </p> <p>We manually curated 239,001 regulatory interactions from 289 young adult wild-type (WT) C.elegans datasets, consisting of 126 genes and 495 unique transcription factors (see TableS1_datasets_for_prior.csv for references). </p> <p>This repository contains 3 different files:</p> <p>TableS1_datasets_for_prior.csv - contains datasets used as sources for physical gene-gene or TF-gene interactions</p> <p>TableS2_physical_priors.xlsx - contains three tabs:<br> ChIPATAC - contains physical TF-gene interactions from 115 L4 or young-adult ChIP-seq datasets from modERN (Kudron et al., 2018) + ChIP-seq datasets (GSE28350, GSE81521) from (Hochbaum et. al, 2011, Li et. al, 2016).</p> <p>eY1HATAC- contains 3,501 TF-gene interactions from eY1H assay by Fuxman Bass et al. (2016).</p> <p>motifATAC - contains 202 unique TF DNA recognition motifs using “direct evidence” option from CiS-BP motif database (Weirauch et al., 2014), obtained through RTFBSDB R package (Wang et al., 2016) - see TableS1</p> <p>TableS3_WT_functional_priors.csv - contains functional knockdown data that we use as gold standard to validate inferred networks in Suriyalaksh et al. (see TableS1_datasets_for_prior.csv for sources)</p> <p>---</p> <p>Description of methodology to obtain regulatory interactions in TableS2:</p> <p>Regulatory sequences for each gene were acquired from ENSEMBL (Aken et al., 2017), obtained using biomaRt R package (accessed on 31st Oct 2017). This study used WBcel235/ce11 version of the C. elegans genome, and WormBase WS260 genome annotations.</p> <p>For motifs, TFs whose motifs overlapped with an open ATAC-seq region by at least one base pair were kept. For ChIP-seq, TF binding sites that overlapped with an open ATAC-seq region by at least one base pair were kept using bedtools intersect and bedtools merge commands.</p> <p>An interaction from a TF to a gene was inferred by aligning transcription start sites (TSS) using bedtools window commands with 1000 bp window size to the TF-binding locations from ChIP-seq and motifs.</p> <p>For eY1H data, an interaction is included if the TSS site of the target gene overlaps with an open ATAC-seq region by at least one base pair.</p> <p>For gene-gene interactions, of the 298 studies compiled in WormExp v1.0 database (Yang et al, 2016, updated 27/07/16), 98 studies were included in the database spanning 126 different genes (see Table S1 in this repository).</p>
Variational Inference for Learning Representations of Natural Language Edits
<p>Performance Evaluation of Edit Representations (PEER), the dataset we use in the paper <a href="https://arxiv.org/abs/2004.09143">"Variational Inference for Learning Representations of Natural Language Edits"</a>.</p>
ASV Tables inferred by DADA2 from the TARA Oceans v9 metabarcoding dataset
<p>Tables of exact amplicon sequence variants (ASVs) were generated from the TARA Oceans metabarcoding data (~766 million reads from 334 plankton samples, V9 region of the 18S rRNA gene, DOI: 10.5281/zenodo.15600) by DADA2 on a 2016 Macbook Pro. The R script used to process the data is included, alongside 3 ASV tables: the observed ASVs before chimera removal (sta), after consensus chimera removal (st.consensus, recommended) and after pooled chimera removal (st.nochim).</p> <p>The ASV tables are available in two formats. The first format is as matrices (rows named by sample, columns named by sequence variant) stored in RDS format -- these can be read back into R with the readRDS command. The second format is as biom-format files (json).</p>
Inferring and perturbing cell fate regulomes in human cerebral organoids
<p>Supplementary data for the manuscript: Inferring and perturbing cell fate regulomes in human cerebral organoids.</p> <p>Note: Due to the size limit of the repository, this update doesn't include all the relevant data. The following data are available via the same repository but different versions:</p> <ul> <li>Processed data in Seurat objects: <a href="https://doi.org/10.5281/zenodo.7687749" target="_blank" rel="noopener">Version 3</a> - seurat_objects.tar.gz</li> <li>scATAC-seq fragment files by Cell Ranger: <a href="https://zenodo.org/records/13254037" target="_blank" rel="noopener">Version 4</a></li> </ul>
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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.