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1,481 results for “data processing”
Data Sets for Evaluation of the Psychometric Properties and Validity of the German Version of the Process Model of Emotion Regulation Scale (PMERQ)
<p>Data files relate to an investigation of the psychometric properties of the German Version of the Process Model of Emotion Regulation Scale (PMERQ). Data set 1 (pmerq_1) contains information regarding the age, gender, ethnicity, and educational status of participants. In addition, responses to the 45 items of the initial translation of the 10-scale PMERQ are included. Data set 2 (pmerq_2) contains identical sociodemographic variables and responses to the 45 items of the revised translation of the 10-scale PMERQ. In addition, data set 2 contains responses to the 16-item German Interpersonal Emotion Regulation Questionnaire (IERQ), the 10-item German Emotion Regulation Questionnaire (ERQ), , the German version of the 10-item Big Five Inventory-10 (BFI-10), the 4-item German version of the Patient Health Questionnaire-4 (PHQ-4), the German version of the Satisfaction with Life Scale (SWLS), and the 17-item German Social Desirability Scale-17 (SES-17). Data set 2 (pmerq_2) contains identical sociodemographic variables and responses to the 45 items of the readability-improved translation of the 10-scale PMERQ. In addition, data set 2 contains responses to the German version of the Satisfaction with Life Scale (SWLS) and the German version of the 9-items UCLA Loneliness Scale (UCLA).</p>
Data supporting "Transition between mechanical and geometric controls in glacier crevassing processes".
<p>The folders:</p> <ul> <li><strong><em>results_Geometrical_Regime</em></strong></li> <li><strong><em>results_Mechanical_Regime</em></strong></li> <li><strong><em>results_3D</em></strong></li> <li><strong><em>results_varyingLengthSlope</em></strong></li> <li><strong><em>results_varyingVelocity</em></strong></li> </ul> <p>contain the data for spacing and depth of the crevasses in the different simulations. Each simulation folder is named after the ice thickness H and cohesion c. For example, H300c2 means an ice thickness of 300m and a cohesion of 2MPa.</p> <p>The simulation files contain data measured every 10 frames. Empty files imply two possibilities:<br>-the crevasse(s) are away from the window measurement (the window is approximately 1500m wide around the obstacle).<br>-No crevasses are observed or at least no distinguishable enough.</p> <p>**<br>The python code <strong><em>readDataMeasuredInSimulations.py</em></strong> use the <strong><em>readDataPP</em></strong> class to plot the measures from the previous folders.<br>All the figures from the paper can be reproduced using this code.</p> <p>**</p> <p>The full simulation results cannot be transfered as a supplementary material as it is too large (20Go for each simulation). However we provide two full result files: <strong>H300_c0_4.abc</strong> and <strong>H100_c2.abc. </strong>We provide an example of the init.lua file used to launch a simulation with given parameters and configurations. The configurations used in the simulations are all provided in the geometry.zip file.</p> <p>A version of the MPM numerical model can be found in a previous publication at<strong> https://www.nature.com/articles/s43247-021-00179-7. </strong></p> <p> </p>
AMD AI Research Focused on Data Processing Efficiency
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Data for the publication: Surging process and mechanism of small glaciers in the Qilian mountains revealed by long-term and dense remote sensing observations
<p>This repository contains the data and results associated to the publication submitted entitled "Surging process and mechanism of small glaciers in the Qilian mountains revealed by long-term and dense remote sensing observations".</p> <p>The results and data contain:</p> <ul> <li>Raw and processed ASTER DEM time series data stored in netcdf format (<em>Hala_surges_aster**.nc</em>): </li> </ul> <ol> <li>Raw DEM stack composed of 56 ASTER DEM.</li> <li>Processed DEM stacks generated by LOWESS-ALPS-REML workflow in each step.</li> </ol> <ul> <li>Multi-temporal elevation change maps stored in geotiff format:</li> </ul> <ol> <li>multi-temporal elevation change results calculated from different DEMs during different period (<em>Hala_surges_[sensor]_[period]_dh_final.tif</em>).</li> <li>Elevation difference map of SRTM-X and SRTM-C DEMs for estimation penetration depth difference ( <br><em>strm-c_x_n37_39_e96_e98_pentration_dh_final.tif</em>)</li> </ol> <ul> <li>Flow velocity time-series result processed by TICOI package stored in netcdf format:</li> </ul> <ol> <li>Irregular-sampling time-series inverted flow velocity results, represted by pixel-wise cumulative displacements ( <br><em>Hala_surges_LS7_LS8_ticoi_flow_angle_refine_velo_invert_ticoi.nc</em>)</li> <li>Regular-sampling time-series flow velocity results, interpolated to 30 days interval from the inverted results ( <br><em>Hala_surges_LS7_LS8_ticoi_flow_angle_refine_velo_interp_ticoi.nc</em>)</li> </ol>
msiFlow: Automated Workflows for Reproducible and Scalable Multimodal Mass Spectrometry Imaging and Immunofluorescence Microscopy Data Processing and Analysis
<p>This record contains example and result data of msiFlow.</p> <p>msiFlow is a collection of automated workflows for reproducible and scalable multimodal mass spectrometry imaging (MSI) and immunofluorescence microscopy (IFM) data processing and analysis. Using an experimental mouse model for urinary tract infection, induced by uropathogenic E.coli (UPEC), we generated data by</p> <ul> <li>matrix-assisted laser desorption ionisation mass spectrometry imaging with laser-induced postionisation (MALDI-2 MSI) using the Bruker timsTOFfleX instrument</li> <li>transmission-mode MALDI-2 MSI (t-MALDI-2)</li> <li>immunofluorescence microscopy (IFM) using the MACSima system from Miltenyi </li> </ul> <p>msiFlow was tested on MALDI-2 MSI, t-MALDI-2 MSI and IFM data of control and UPEC-infected mouse bladder sections. In IFM we used Ly6G and actin for staining neutrophils and the muscle layer. We validated msiFlow on MALDI MSI data of bone marrow (BM)-derived neutrophils. Tentative lipid annotations were validated by MALDI DDA MSI and MALDI MS/MS. All data used and results generated by msiFlow are included in this dataset (besides the intermediate results of the MALDI-2 preprocessing due to data size).</p> <p>The dataset contains the following zip files:</p> <table> <tbody> <tr> <td><strong>zip file</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>ly6g_heterogeneity.zip</td> <td>example and result data (Ly6G clusters) for molecular_heterogeneity_flow</td> </tr> <tr> <td>if_segmentation.zip</td> <td>example and result data (Ly6G segmentation) for if_segmentation_flow</td> </tr> <tr> <td>ly6g_heterogeneity_signatures.zip</td> <td>example and result data (lipids for Ly6G clusters) for molecular_signatures_flow</td> </tr> <tr> <td>ly6g_molecular_signatures.zip</td> <td>example and result data (lipids for Ly6G) for molecular_signatures_flow</td> </tr> <tr> <td>msi_if_registration.zip</td> <td>example and result data for msi_if_registration_flow</td> </tr> <tr> <td>msi_segmentation.zip</td> <td>example and result data (segmented MSI bladder data) for msi_segmentation_flow</td> </tr> <tr> <td>region_group_analysis.zip</td> <td>example and result data (regulated lipids in different bladder tissue regions) for region_group_analysis_flow</td> </tr> <tr> <td>macsima.zip</td> <td>raw IFM data of UPEC-infected bladders containing Ly6G, actin and autofluorescence images</td> </tr> <tr> <td>maldi-bm-neutrophils.zip</td> <td>raw and pre-processed MALDI MSI data of BM-derived neutrophils</td> </tr> <tr> <td>t-maldi-2.zip</td> <td>raw t-MALDI-2 MSI data of a UPEC-infected bladder section</td> </tr> <tr> <td>maldi-2-<em>group-sampleno</em>.zip</td> <td>raw MALDI-2 MSI data of a control/UPEC bladder section</td> </tr> <tr> <td>MALDI_DDA_MSI.zip</td> <td>raw MALDI MSI data acquired in DDA mode</td> </tr> <tr> <td>TIMS_MS_MS.zip</td> <td>raw MALDI TIMS MS/MS data</td> </tr> </tbody> </table> <p> </p>
Supporting molecular simulations data for "A combined molecular dynamics and experimental study of two-step process enabling low-temperature formation of phase-pure α-FAPbI3"
<p>Supplementary data for "A combined molecular dynamics and experimental study of two-step process enabling low-temperature formation of phase-pure α-FAPbI3: <a href="https://doi.org/10.1126/sciadv.abe3326">10.1126/sciadv.abe3326</a>"</p>
Processed Sentinel 1, Sentinel 2 and Copernicus Emergency Management Service data for fine tuning and predicting flood extent with IBM's granite-geospatial-uki-flood-detection model
<p>This dataset contains processed Sentinel 1 Sentinel 2 imagery together with flood event labels extracted from the Copernicus Emergency Management Service. It has been assembled to demonstrate fine tuning and inference of flood event segmentation using granite geospatial foundation models developed by IBM Research. Please see <a href="https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection">https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection</a> for more information on models and use.</p> <p>Sentinel-1</p> <p>The European Space Agency. 2014. Sentinel-1 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-1">https://sentinel.esa.int/web/sentinel/missions/sentinel1</a>. Accessed: 2024-11-25.</p> <p>Sentinel-2</p> <p>The European Space Agency. 2015. Sentinel-2 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-2">https://sentinel.esa.int/web/sentinel/missions/sentinel2</a>. Accessed: 2024-11-25.</p> <p>Copernicus Emergency Management Service</p> <p><a href="https://emergency.copernicus.eu/mapping/list-of-activations-rapid">https://emergency.copernicus.eu/mapping/list-of-activations-rapid</a>. Accessed: 2024-11-25. </p> <p><strong>Attribution</strong></p> <p>Contains modified Copernicus Sentinel data [2019-2024]</p> <p>Contains modified Copernicus Service information [2019-2023]</p>
Processed datasets and codes for differential expression analysis on polulation-level RNA-seq data
<p>This version includes codes and data necessary to reproduce all results in our response to the correspondences ("Response to 'Neglecting normalization impact in semi‑synthetic RNA‑seq data simulation generates artificial false positives' and 'Winsorization greatly reduces false positives by popular differential expression methods when analyzing human population samples'") (<a href="https://doi.org/10.1186/s13059-024-03232-8">https://doi.org/10.1186/s13059-024-03232-8</a>).</p> <p>It also includes a README file to guide the reproduction of the results in our original publication and resources for the goodness of fit test in the original publication, "Exaggerated False Positives by Popular Differential Expression Methods When Analyzing Human Population Samples" (<a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02648-4">https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02648-4</a>).</p>
Novel Macrophage Subpopulation Linked to CAD: raw and processed data files
<div> <p>This directory contains all single-cell datasets analyzed in the "Partitioning heritability using single-cell multi-omics identifies a novel macrophage subpopulation conveying increased genetic risk of coronary artery disease". Associated scripts are available at https://github.com/jhjiang2020/multiome_paper. </p> <p><strong>Jan 2025 updates</strong>: include the 10x Cell Ranger ARC output for the scATAC-seq assay. <em>Please note that 10x peaks were not used for downstream analyses. Instead, we trimmed the atac-seq fragments to retain the 9bp Tn5 cut sites at both ends and aggregated into one consensus bed file. We then recalled peaks using `MACS3 --qvalue 1e-5 --nomodel --shift -50 --extsize 100 --broad` (see discussion in https://github.com/stuart-lab/signac/issues/682). </em></p> <p> </p> </div>
Raw data and supporting files for "Modular comparison of untargeted metabolomics processing steps"
<p>Raw data and supporting files for the Paper titled "Modular comparison of untargeted metabolomics processing steps". The dataset encompasses 42 samples, with 3 solvent blanks, 7 QC samples, and 32 biological samples (4 biological replicates: Banane, Bergrose, Narbe, Ricky) spiked with 42 compounds in different concentrations (0 ngmL, 30 ngmL, 100 ngmL, 300 ngmL). The files were uploaded in the vendor format (.raw) and in the open format (.mzML). Also, further supporting data for the processing results was uploaded as well.</p>
Data and code to replicate: Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie
<p>Diet analysis integrates a wide variety of visual, chemical and biological identification of prey. Samples are often treated as compositional data, where each prey is analyzed as a continuous percentage of the total. However, analyzing compositional data results in analytical challenges, e.g., highly parameterized models or prior transformation of data. Here, we present a novel approximation involving a Tweedie generalized linear model (GLM). We first review how this approximation emerges from considering predator foraging as a thinned and marked point process (with marks representing prey species and individual prey size). This derivation can motivate future theoretical and applied developments. We then provide a practical tutorial for the Tweedie GLM using new package <i>mvtweedie</i> that extends capabilities of widely used packages in R (<i>mgcv</i> and <i>ggplot2</i>) by transforming output to calculate prey compositions. We demonstrate this approach and software using two examples. Tufted puffins (<i>Fratercula cirrhata</i>) provisioning their chicks on a colony in the northern Gulf of Alaska show decadal prey switching among sand lance and prowfish (1980-2000) and then Pacific herring and capelin (2000-2020), while wolves (<i>Canis lupus ligoni</i>) in Southeast Alaska forage on mountain goats and marmots in northern uplands and marine mammals in seaward island coastlines. </p>
Study Data: Obtaining Semi-Formal Models from Qualitative Data: From Interviews into BPMN Models in User-Centered Design Processes
<p>This dataset (Data.zip) contains the raw data of a user study on the investigation of transforming think aloud interviews into BPMN models. All information on how to use the data are provide in the SPSS files and as a readme file. This transformation is executed following a manual additionally provided in Documents.zip. For the training phase, a website was used provided in Website.zip including Screenshots for simpler re-use. Further information are also included as readme file in the zip container.</p> <p>Main research question answered is in how far the manual reduces interpretation and variance in the created models. </p>
Present-day surface deformation of Sicily: Insights from Sentinel-1 data processed by a PS-InSAR approach
<p>The directory DATASET.zip provides PS-InSAR data used in Henriquet et al., (2022). The data set contains for each Sentinel-1 track (44, 117, 22, 124) the mean PS velocities along the LOS, before (ps_mean_v.xy.v-dos) and after (ps_mean_v-dos_adjusted2GPS.xy) their adjustment to the 3D-GNSS velocity field, as well as the disparities of the PS velocities (ps_mean_disp.xy). The data set also includes the East- and Up-component of the reconstructed mean PS velocity field (East.grd and Up.grd) used in the Figures 7 to 12 in the paper.</p>
A Nuclear Equation of State Inferred from Stellar r-Process Abundances: Data
<p>This repository contains the raw MCMC posteriors as H5 files for the three cases presented in "A Nuclear Equation of State Inferred from Stellar <em>r</em>-Process Abundances" (<a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211006432H/abstract">Holmbeck et al., arXiv:2110.06432</a>).</p> <p>Also included are Python scripts with a variety of functions to read the H5 files and interpret the data with <a href="https://git.ligo.org/lscsoft/lalsuite">LALSuite</a>. These include:</p> <ul> <li>reading the data contained in the H5 file (likelihood, acceptance, and values for each MCMC step)</li> <li>generating a corner plot of the data</li> <li>finding the maximum likelihood in the posterior distribution</li> <li>calculating a neutron star mass-radius curve for a posterior EOS</li> <li>calculating pressure and density for a posterior EOS</li> <li>calculating observables (M_TOV, R_1.4, and L) associated with an EOS</li> </ul> <p>The scripts are written for Python3 compatibility and depend on:</p> <ul> <li><a href="https://pypi.org/project/lalsuite/">lalsuite</a></li> <li><a href="https://docs.h5py.org/en/stable/">h5py</a></li> <li><a href="https://numpy.org/">numpy</a></li> <li><a href="https://scipy.org/">scipy</a></li> <li><a href="https://matplotlib.org/">matplotlib</a></li> <li><a href="https://corner.readthedocs.io/en/latest/">corner</a></li> </ul> <p>For more information and how to use these scripts, see the comments in `example.py` or contact Erika Holmbeck.</p> <p>If any of our posterior samples are used in your work, we ask that you appropriately cite this repository and the original paper (<a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211006432H/abstract">Holmbeck et al., arXiv:2110.06432</a>).</p>
Data archive for paper "Machine Learning Emulation of Urban Land Surface Processes"
<p>This archive contains models, data* (Overview), as well as the Singularity image to optionally rerun experiments described in "<a href="https://doi.org/10.1029/2021MS002744">Machine Learning Emulation of Urban Land Surface Processes</a>".</p> <p><strong>Prerequisites</strong></p> <ul> <li>Linux or macOS with Bash shell.</li> <li><a href="https://sylabs.io/">Singularity</a> (tested with version 3.6.3-1.el8)</li> </ul> <p>Please note that all steps require <a href="https://sylabs.io/">Singularity</a> to be installed on your system. If you are looking for information on how to install or use Singularity, please refer to the <a href="https://sylabs.io/docs">Singularity documentation</a>.</p> <p><strong>Overview</strong></p> <p>A general overview of the repository structure is given below. Due to licensing restrictions analysis and forcing data (*) cannot be included and need to be requested separately (see Initialization). Data derivatives (**) from either analysis or forcing, as well as intermediary data (***), are not included as they can be generated by rerunning experiments (see Usage).</p> <pre><code>. ├── data │ ├── analysis* │ ├── forcing* │ ├── teb │ ├── utils │ ├── wps │ └── wrf ├── hpc ├── models │ ├── teb │ ├── unn │ ├── wps │ └── wrf-unn ├── notebooks ├── outputs │ ├── analysis** │ ├── benchmark*** │ ├── forcing** │ ├── kerastuner*** │ ├── notebooks │ ├── tabular │ ├── teb** │ ├── unn** │ ├── wps*** │ └── wrf ├── paper │ └── figures ├── singularity └── tools </code></pre> <p><strong>Initialization</strong></p> <p>Forcing and analysis data need to be requested separately. The following directories should map to their respective data archives:</p> <ul> <li><code>./data/analysis</code> -> <a href="http://doi.org/10.5281/zenodo.4678387">Grimmond et al. (2013)</a></li> <li><code>./data/forcing</code> -> <a href="http://doi.org/10.5281/zenodo.4679279">Grimmond et al. (2021)</a></li> </ul> <p><strong>Usage</strong></p> <p>To rerun all experiments and reproduce results, run <code>tools/run_all.sh</code> from your command prompt. After completion, all results are saved in the <code>outputs</code> directory. Note that WRF simulations require high CPU time and may take hours or days to complete.</p> <p>Alternatively, if <a href="https://en.wikipedia.org/wiki/Portable_Batch_System">Portable Batch System (PBS)</a> is available on your system, the following helpers may be used instead:</p> <pre><code>qsub hpc/submit_init.pbs qsub hpc/submit_tuner.pbs qsub hpc/submit_unn.pbs qsub hpc/submit_find_median_unn.pbs qsub hpc/submit_wrf.pbs qsub hpc/submit_postprocess.pbs qsub hpc/submit_benchmark.pbs </code></pre> <p>Note that you may need to modify PBS helper scripts to suit your specific environment.</p> <p><strong>Development notes</strong></p> <p>See DEVELOP.md.</p> <p><strong>License</strong></p> <p>The source code developed for this work is licensed under MIT (<code>LICENSE_CODE.txt</code>). For licensing information of third-party software see licenses under the <code>models</code> directory. Data files in this archive, including the initial and boundary condition data from the European Centre for Medium-Range Weather Forecasts (<code>data/wps/ungrib</code>), are licensed under CC BY-NC 4.0 (<code>LICENSE_DATA.txt</code>).</p>
KROA Processed Data
<p>Time series of model forecast and observation data at KROA during the years: 2011- 2020. </p> <p>The dataset has been provided as part of the <a href="https://www.ametsoc.org/index.cfm/ams/education-careers/careers/professional-development/short-courses1/machine-learning-in-python-for-environmental-science-problems1/">2022 AMS AI Short Course</a>.</p>
Pre-processed raw data of 4D-SCED 4D-STEM(NBD) datasets
<p>The raw 4D-SCED and 4D-STEM (NBD) datasets in our paper:</p> <p>Seeing Structural Evolution of Organic Molecular Nano-crystallites Using 4D Scanning Confocal Electron Diffraction</p> <p>https://arxiv.org/abs/2110.02373v1</p> <ul> <li>Dose-damage_RT_and_Cryo_DRCN5T_PCBM.zip<br> - EF-Diff_cryo_0.16e_per_A2_per_frame.dm4<br> - EF-Diff_RT_0.14e_per_A2s_per_frame.dm4</li> <li>Fig_2_SCED-vs-NB_DH6T.zip<br> - 00.43.44 Spectrum image_1.dm4: raw 3D diffraction pattern stack (NBD) of size 512*512*2500, only converted file format, no other pre-processing applied<br> - 00.54.07 Spectrum image_1.dm4: raw 3D diffraction pattern stack (SCED) of size 512*512*2500, only converted file format, no other pre-processing applied<br> - STEM HAADF 0103.emd: Velox raw image of the region of interest acquired at the end of experiment.<br> - STEM HAADF 0103 data.jpg: exported JPG image with databar from the above raw image file</li> <li>Fig_3n4_4D-SCED-vs-4D-NBD_DRCN5T_PCBM.zip<br> - 1201 4D STEM Dataset SCED.dm4: pre-processed 4D-SCED dataset<br> - 1209 4D STEM Dataset NBD.dm4: pre-processed 4D-STEM (NBD) dataset</li> <li>Fig_5_insitu_4D-SCED_datasets.zip<br> - 1431 4D STEM Dataset.dm4: pre-processed 4D-SCED dataset @ RT<br> - 1431 SI HAADF RT.emd: simultaneously acquired SCEM-ADF image and STEM-EDXS datacube<br> - 1431 SI HAADF.jpg: exported JPG image of the SCEM-ADF image from the above file<br> - 1455 4D STEM Dataset.dm4: pre-processed 4D-SCED dataset @ 100C<br> - 1455 SI HAADF 100C.emd: simultaneously acquired SCEM-ADF image and STEM-EDXS datacube<br> - 1455 SI HAADF.jpg: exported JPG image of the SCEM-ADF image from the above file<br> - 1505 4D STEM Dataset.dm4: pre-processed 4D-SCED dataset @ 120C<br> - 1505 SI HAADF 120C.emd: simultaneously acquired SCEM-ADF image and STEM-EDXS datacube<br> - 1505 SI HAADF.jpg: exported JPG image of the SCEM-ADF image from the above file<br> - 1516 4D STEM Dataset.dm4: pre-processed 4D-SCED dataset @ 140C<br> - 1516 SI HAADF 140C.emd: simultaneously acquired SCEM-ADF image and STEM-EDXS datacube<br> - 1516 SI HAADF.jpg: exported JPG image of the SCEM-ADF image from the above file<br> - 1529 4D STEM Dataset.dm4: pre-processed 4D-SCED dataset @ 160C<br> - 1529 SI HAADF 160C.emd: simultaneously acquired SCEM-ADF image and STEM-EDXS datacube<br> - 1529 SI HAADF.jpg: exported JPG image of the SCEM-ADF image from the above file</li> <li>STEM-EELS_DRCN5T_PCBM_SVA_CS.zip<br> - ADF Image (SI survey).dm4: simultaneously acquired STEM-ADF image<br> - EELS Spectrum Image (high-loss).dm4: high-loss part of the DualEELS STEM-EELS dataset<br> - EELS Spectrum Image (low-loss).dm4: low-loss part of the DualEELS STEM-EELS dataset</li> <li>STEM-EELS_DRCN5T_PCBM_SVA_CHCl3.zip<br> - ADF Image (SI survey).dm4: simultaneously acquired STEM-ADF image<br> - EELS Spectrum Image (high-loss).dm4: high-loss part of the DualEELS STEM-EELS dataset<br> - EELS Spectrum Image (low-loss).dm4: low-loss part of the DualEELS STEM-EELS datase</li> </ul> <p> </p>
Processed data for KP-Tracer Tumors from study "Lineage Recording Reveals the Phylodynamics, Plasticity and Paths of Tumor Evolution"
<p>This repository contains processed data associated with the manuscript "Lineage Recording Reveals the Phylodynamics, Plasticity, and Paths of Tumor Evolution" (Yang*, Jones*, et al <em>bioRxiv</em> 2021).</p> <p>In this study, single-cell lineage tracing was performed in the <em>KP</em> autochthonous mouse model of non-small-cell lung cancer. Tumors were initiated with <em>Cre</em> recombinase (and optionally an additional gRNA targeting the other well-studied tumor suppressors) and allowed to grow for approximately 4-6 months at which point mice were sacrificed and tumors harvested. After purifying cancer cells by fluorescent markers, cells were profiled with the 10X chromium platform and four libraries were collected: a single-cell transcriptome library, a single-cell lineage tracing library, a single-cell multiplexing library, and a single-cell lenti-barcode library (used for confirming the clonality of tumors). Data was processed using the 10X Cellranger suite and custom pipelines as described in the manuscript above. </p> <p>Data here represents the derived processed data from our analysis. The specific contents are described in a README contained within the repository.</p> <p>Briefly, however, we have provided here processed AnnData objects for the KP data (sgNT) and the integrated data across three genotypes (KP, KPA, and KPL). We also provide the gene lists associated with fitness, expansion annotations for each tree, as well as plasticity scores amongst other items. </p> <p>For code used in this study, we additionally have provided a reproducibility repository at https://github.com/mattjones315/KPTracer-release. </p>
NMR assignment of methyl groups in solid-state using 1H-detection and fast MAS - NMR raw and processed data
<p>This data set contains raw NMR data in Bruker format for experimental series on (1) 2,3-13C-labelled microcrystalline alanine, (2) U-13C,15N-labelled N-fomylated microcrystalline tripeptide Met-Leu-Phe, and (3,4) two differently labelled (ILV-C4 and ILV-C5) microcrystalline chicken-alpha-spectrin SH3 domain. Measurements were performed at 14.4 T and 55.5 kHz MAS (alanine), 18.8 T and 55.5 and 98 kHz MAS (fMLF), 23.5 T and 55.5 kHz (SH3 C5), 18.8T and 55.5 and 94.5 kHz MAS (SH3 C5) and 18.8T and 55.5 and 94.5 kHz MAS (SH3 C4). The data set also contains Fourier processed data (spectra) in UCSF format, Sparky project, save and peak list files. Pulse programs for Bruker spectrometers are provided. The data set is complemented with SIMPSON scripts for simulation of spin dynamics under aformentioned conditions.</p>
Adiabatic and diabatic signatures of ocean temperature variability - ACCESS-CM2 processing/plotting code and processed data
<p>Contains processed data and processing/plotting code for the figures in the article:</p> <p>Holmes, R.M., Sohail, T. and Zika, J.D. (2022): Adiabatic and diabatic signatures of ocean temperature variability, Journal of Climate, <a href="https://doi.org/10.1175/JCLI-D-21-0695.1">https://doi.org/10.1175/JCLI-D-21-0695.1</a></p> <p>For more information please see the published article, as well as the github repository where the code is described in more detail: <a href="https://github.com/rmholmes/CM2_HCvar/tree/JCLI-D-21-0695">https://github.com/rmholmes/CM2_HCvar/tree/JCLI-D-21-0695</a></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.