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1,007 results for “single use”
Uncovering structural ensembles from single particle cryo-EM data using cryoDRGN | Software, datasets, and results
<p>Software, datasets, and results referenced in "Uncovering structural ensembles from single particle cryo-EM data using cryoDRGN"</p>
Large-scale neural recordings with single neuron resolution using Neuropixels probes in human cortex
<p><span>Recent advances in multi-electrode array technology have made it possible to monitor large neuronal ensembles at cellular resolution in animal models. In humans, however, c</span>urrent approaches restrict recordings to few neurons per penetrating electrode or combine the signals of thousands of neurons in local field potential (LFP) recordings. Here, we describe a new probe variant and set of techniques which enable simultaneous recording from over 200 well-isolated cortical single units in human participants during intraoperative neurosurgical procedures using silicon Neuropixels probes. We characterized a diversity of extracellular waveforms with eight separable single unit classes, with differing firing rates, locations along the length of the electrode array, waveform spatial spread, and modulation by LFP events such as inter-ictal discharges and burst suppression. While some challenges remain in creating a turn-key recording system, high-density silicon arrays provide a path for studying human-specific cognitive processes and their dysfunction at unprecedented spatiotemporal resolution. </p>
Dataset related to article"Globus Pallidus Internus Deep Brain Stimulation Using Frame-Based vs. Frameless Stereotaxy in Dystonia: A Single-Center Experience"
<p>Demographic, clinical (BFMDRS at baseline and in follow up), implantation (frame and frameless) data for the sample considered in the study.</p>
Using single-worm data to quantify heterogeneity in Caenorhabditis elegans-bacterial interactions
<p>The nematode <em>Caenorhabditis elegans</em> is a model system for host-microbe and host-microbiome interactions. Many studies to date use batch digests rather than individual worm samples to quantify bacterial load in this organism. Here it is argued that the large inter-individual variability seen in bacterial colonization of the <em>C. elegans</em> intestine is informative, and that batch digest methods discard information that is important for accurate comparison across conditions. As describing the variation inherent to these samples requires large numbers of individuals, a convenient 96-well plate protocol for disruption and colony plating of individual worms is established.</p>
IGM Population of HFF structures using Hi-C, laminB1 DamID, 3D HIPMAp FISH and single cell SPRITE data
<p>This repository accompanies the manuscript "<strong>Integrative Genome Modeling Platform reveals essentiality of rare contact events in 3D genome organizations</strong>", to appear in Nat. Methods (2022), see also https://www.biorxiv.org/content/10.1101/2021.08.22.457288v1.</p> <p>It contains the preprocessed input data files (Hi-C, laminB1 DamID, 3D HIPMAp FISH and single cell SPRITE) for the HFF fibroblast cell line to be used in the Integrative Genome Modeling platform (IGM) developed in the Alber lab at UCLA (https://github.com/alberlab/igm).</p> <p>Also, we provide the configuration file to run IGM with those datasets, as we did in generating the HDSF population discussed in the accompanying manuscript. Such population is also provided as an "hss" file. Documentation and a simple demo/tutorial on how IGM can be run is given on the Alber lab Github @ https://github.com/alberlab/igm.</p> <p>All files can be read in using the <em>h5py</em> and <em>alabtools</em> (available @https://github.com/alberlab/alabtools) Python packages. More detailed information is provided in the manuscript and associated Supplementary Information file. </p> <p>For any inquiry/suggestions/doubts please reach out to Lorenzo Boninsegna (bonimba@g.ucla.edu) or Dr. Frank Alber (falber@g.ucla.edu).</p> <p> </p>
Quantifying phenology and migratory behaviours of hummingbirds using single-site dynamics and mark-detection analyses
<p>Nuanced understanding of seasonal movements of partially migratory birds is paramount to species and habitat conservation. Using nascent statistical methods, we identified migratory strategies of birds outfitted with radio-frequency identification (RFID) tags detected at RFID feeders in two sites in California, USA. We quantified proportions of migrants and residents and the seasonal phenology for each movement strategy in Allen's and Anna's hummingbirds; we also validated our methodology by fitting our model to obligate migratory black-chinned hummingbirds. Allen's and Anna's hummingbirds exhibited characteristics of facultative migratory behaviour. We also quantified apparent annual survival for each migratory strategy and found that residents had significantly higher probabilities of apparent survival. Low survival estimates for migrants suggest that a high proportion of birds in the migrant group permanently emigrated from our study sites. Considered together, our analyses suggest that hummingbirds in both northern and southern California sites partake in diverse and highly plastic migratory behaviours. Our assessment elucidates the dynamics underlying idiosyncratic migratory behaviours of two species of hummingbirds, in addition to describing a framework for similar assessments of migratory behaviours using the multi-state open robust design with state uncertainty (MSORD-SU) model and single-site dynamics.</p>
Data of: Imputation-free reconstructions of three-dimensional chromosome architectures in human diploid single-cells using allele-specified contacts
<p>These files are results obtained in<br><span><span><span><span>Imputation-free reconstructions of three-dimensional chromosome architectures in human diploid single-cells using allele-specified contacts</span></span></span></span><br>by Yoshito Hirata, Arisa H. Oda, Chie Motono, Masanori Shiro & Kunihiro Ohta.</p> <p>There are 33 files for the corresponding each reconstruction of three-dimensional chromosomone structures<br>for each cell.<br>There are 3D structures for 15 GM cells and 18 PBMC cells, which are obtained from the single cell Hi-C data of Tan et al. Science (2018).</p> <p>For each file, there are 6 columns:<br>The first column corresponds to the allele (0: maternal, 1: paternal)<br>The second column corresponds to the chromosome (1-22: chromosome's number, 23: X, 24: Y)<br>The third column corrsponds to the base point.<br>The fourth column, the fifth column and the sixth column correspond to x-, y-, and z-axes of our reconstruction.</p>
Animal Recognition Using Methods Of Fine-Grained Visual Analysis - Kashtanka Pets (All Dev and Test Images, Single Folder)
<p>Kashtanka Pets images, with all Dev and Test images (total 66639 images). In a single folder, with filenames indicating path of file in original dataset distribution.</p>
Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7). in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7).
Train and test datasets used for the paper "Neural network time-series classifiers for gravitational-wave searches in single-detector periods"
<p>This repository contains the datasets used for training and testing during the work discussed in the paper "<a href="https://iopscience.iop.org/article/10.1088/1361-6382/ad40f0" target="_blank" rel="noopener">Neural network time-series classifiers for gravitational-wave searches in single-detector periods</a>". Please refer to this paper for more details on how the dataset was produced and cite it if you use these data:</p> <p><em>A. Trovato et al "Neural network time-series classifiers for gravitational-wave searches in single-detector periods", Class. Quant. Grav. 2024 DOI 10.1088/1361-6382/ad40f0.</em></p> <p>In this repository you will find six files in format npz, three of which refer to the test dataset and three to the train dataset. Each file name is of the type {label}_{train or test}.npz where "label" can be "glitch", "noise" or "signal", while the second part of the name indicates whether the file was used for training or testing.</p> <p>Each file is a collection of numpy arrays so it should be read with python. It contains 3 numpy arrays: 'X', 'Y' and 'metadata'. 'X' is a matrix containing 1-second segments of data sampled at 2048 Hz of the LIGO-Livingston detector, so it has shape: (number of samples, 2048). 'Y' contains the label for each segment, which is 0 for noise, 1 for signal and 2 for glitch, so it has shape: (number of samples,). In this case, the information on 'Y' is redundant since it's given directly by the filename. The 'metadata' matrix contains 17 metadata for each sample only for the case of signals, for glitch or noise it contains just 17 zeros for each sample. The shape of 'metadata' is thus: (number of samples, 17). For the signal files, for each sample the metadata is an array with these components:</p> <ol> <li>GPS start of the file from which this segment comes</li> <li>starting GPS time of this segment</li> <li>duration of the segment [s]</li> <li>mass1 [solar masses]</li> <li>mass2 [solar masses]</li> <li>spin1z</li> <li>spin2z</li> <li>inclination [radians]</li> <li>coalescence phase [radians]</li> <li>distance [Mpc]</li> <li>right_ascension [radians]</li> <li>declination [radians]</li> <li>polarization [radians]</li> <li>SNR (signal to noise ratio)</li> <li>shift of the signal w.r.t. the timeseries [s]</li> <li>length of the signal [s]</li> <li>fraction of the signal contained in the time window</li> </ol> <p>Number of samples:</p> <ul> <li>80000 for the file glitch_test.npz</li> <li>69998 for the file glitch_train.npz</li> <li>500000 for the file noise_test.npz</li> <li>250000 for the file noise_train.npz</li> <li>500000 for the file signal_test.npz</li> <li>250000 for the file signal_train.npz</li> </ul> <p>An example of few lines of python code to read each file is:</p> <pre><code>import numpy as np f = np.load("filename.npz") X = f['X'] Y = f['Y'] m = f['metadata'] </code></pre> <p>For the preparation of these data, we acknowledge the use of the following software packages: GWpy [1], PyCBC [2] and LALSuite [3]. </p> <p>This research has made use of data or software obtained from the Gravitational Wave Open Science Center (<a href="https://gwosc.org/" target="_blank" rel="noopener">gwosc.org</a>), a service of the LIGO Scientific Collaboration, the Virgo Collaboration, and KAGRA. This material is based upon work supported by NSF's LIGO Laboratory which is a major facility fully funded by the National Science Foundation, as well as the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. Virgo is funded, through the European Gravitational Observatory (EGO), by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale di Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by institutions from Belgium, Germany, Greece, Hungary, Ireland, Japan, Monaco, Poland, Portugal, Spain. KAGRA is supported by Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan Society for the Promotion of Science (JSPS) in Japan; National Research Foundation (NRF) and Ministry of Science and ICT (MSIT) in Korea; Academia Sinica (AS) and National Science and Technology Council (NSTC) in Taiwan.</p> <p>[1] https://gwpy.github.io<br>[2] https://pycbc.org<br>[3] https://lscsoft.docs.ligo.org/lalsuite</p>
Ultra-sensitive and multiplexed tracking of single cells using whole-body PET/CT
<p><em>In vivo </em>molecular imaging tools are crucially important for elucidating how cells move through complex biological systems, however, achieving single-cell sensitivity over the entire body remains challenging. Here, we report a highly sensitive and multiplexed approach for tracking upwards of 20 single cells simultaneously in the same subject using positron emission tomography (PET). The method relies on a statistical tracking algorithm (PEPT-EM) to achieve a sensitivity of 4 Bq/cell, and a streamlined workflow to reliably label single cells with over 50 Bq/cell of <sup>18</sup>F-fluorodeoxyglucose (FDG). To demonstrate the potential of the method, we tracked the fate of over 70 melanoma cells after intracardiac injection and found they primarily arrested in the small capillaries of the pulmonary, musculoskeletal, and digestive organ systems. This study bolsters the evolving potential of PET in offering unmatched insights into the earliest phases of cell trafficking in physiological and pathological processes and in cell-based therapies.</p>
Programming codes for obtaining components by means of Single Point Incremental Forming using a Kuka Robot
<p>Programming codes for obtaining components by means of Single Point Incremental Forming using a Kuka Robot, adopting different strategies</p>
Single-footprint retrievals for AIRS using a fast TwoSlab cloud-representation model and the all-sky infrared radiative transfer algorithm
<p>Dataset for AMT-2017-261 by DeSouza-Machado et. al.<br> <br> 1D-variational retrievals of temperature and moisture fields from<br> hyperspectral infrared satellite sounders use cloud-cleared radiances<br> as their observation. These derived observations allow the use of<br> clear-sky only radiative transfer in the inversion for geophysical<br> variables but at reduced spatial resolution compared to the native<br> sounder observations. Cloud-clearing can introduce various errors,<br> although scenes with large errors can be identified and<br> ignored. Information content studies show that when using multi-layer<br> cloud liquid and ice profiles in infrared hyperspectral radiative<br> transfer codes, there are typically only 2-4 degrees of freedom of<br> cloud signal. This implies a simplified cloud representation is<br> sufficient for some applications which need accurate radiative<br> transfer. Here we describe a single-footprint retrieval approach for<br> clear and cloudy conditions, which uses the thermodynamic and cloud<br> fields from Numerical Weather Prediction (NWP) models as a first<br> guess, together with a simple cloud representation model coupled to a<br> fast scattering radiative transfer algorithm (RTA). The NWP model<br> thermodynamic and cloud profiles are first co-located to the<br> observations, after which the N-level cloud profiles are<br> converted to two slab clouds (typically one for ice and one for water<br> clouds). From these, one run of our fast cloud representation model<br> allows an improvement of the \emph{a-priori} cloud state by comparing the<br> observed and model simulated radiances in the thermal window<br> channels. The retrieval yield is over 90\%, while the degrees of<br> freedom correlate with the observed window channel brightness<br> temperature which itself depends on the cloud optical depth. The cloud<br> representation/scattering package is bench-marked against radiances<br> computed using a Maximum Random Overlap cloud scheme. All-sky infrared<br> radiances measured by NASA’s Atmospheric Infrared Sounder (AIRS) and<br> NWP thermodynamic and cloud profiles from the European Center for<br> Medium Range Weather Forecasting (ECMWF) forecast model are used in<br> this paper.</p> <p> </p>
Dataset used in "Bathymetry observations of inland water bodies using a tethered single-beam sonar controlled by an Unmanned Aerial Vehicle". https://doi.org/10.5194/hess-2017-625.
<p>Dataset used in</p> <p>Bathymetry observations of inland water bodies using a tethered single-beam sonar controlled by an Unmanned Aerial Vehicle</p> <p>Filippo Bandini<sup>1</sup>, Daniel Olesen<sup>2</sup>, Jakob Jakobsen<sup>2</sup>, Cecile Marie Margaretha Kittel<sup>1</sup>, Sheng Wang<sup>1</sup>, Monica Garcia<sup>1</sup>, and Peter Bauer-Gottwein<sup>1</sup></p> <ul> <li><sup>1</sup>Department of Environmental Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark</li> <li><sup>2</sup>National Space Institute, Technical University of Denmark, Kgs. Lyngby, 2800, Denmark</li> </ul> <p><strong>Hydrol. Earth Syst. Sci.</strong></p> <p><strong>https://doi.org/10.5194/hess-2017-625</strong></p> <p> </p> <p>The dataset contains</p> <p>-data/observations that were used to obtain the figures shown in the paper. Data have .mat extension (Binary data container format used by MATLAB; may include arrays, variables, functions, and other types of data;)</p> <p>-scripts to compute statistics and plot data, with .m extension (contain MATLAB code, either in the form of a script or a function)</p> <p>-shape files (shp — shape format; the feature geometry itself, .shx — shape index format, .dbf — attribute format, .prj — projection format; .sbn and .sbx — spatial index of the features, .cpg — used to specify the code page, .<em>qpj</em> QGIS projection file) or raster files (.geotiff) to reproduce the map contents reported in the referenced paper.</p> <p>The repository is subdivided into directories containing the dataset shown in the paper. These directories are named with the figures and/or tables numbers of the referenced paper. </p>
Data - The Benefits of Neurofeedback Training for Alpha Enhancement and Cognitive Performance - a Single-Blind, Sham-Feedback Study Using a Low-Prized EEG Device
<p>This data set includes the minimal data set, which was used to obtain the results in Naas, Rodrigues, Knirsch, & Sonderegger (2019, doi: http://dx.doi.org/10.1101/527598).</p>
Systematic assessment of burst impurity in confocal-based single-molecule fluorescence detection using Brownian motion simulations - photon timetag simulation files
<p>Attached are the photon timestamp and channels simulated for different 3D diffusing molecules simulations at different conditions (simulation was performed by PyBroMo).</p> <p>Each of the files has, in its name, a code. The meaning of the codes are as following:</p> <pre>f32445 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a numerical PSF model </pre> <pre>a01f8f - 15 molecules at a concentration of 31 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a numerical PSF model</pre> <pre>9ff667 - 15 molecules at a concentration of 15.5 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a numerical PSF model</pre> <pre>71154a - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 22.5 micron^2/s - 60 second simulation using a numerical PSF model</pre> <pre>ad926d - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 5.625 micron^2/s - 60 second simulation using a numerical PSF model</pre> <pre>1ab235 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s - 180 second simulation using a numerical PSF model</pre> <pre>d00978 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 5.625 micron^2/s - 180 second simulation using a numerical PSF model</pre> <p> </p> <pre>2469bb - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a Gaussian PSF model </pre> <pre>4be121 - 15 molecules at a concentration of 31 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a Gaussian PSF model</pre> <pre>a7088f - 15 molecules at a concentration of 15.5 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a Gaussian PSF model</pre> <pre>023983 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 22.5 micron^2/s - 60 second simulation using a Gaussian PSF model</pre> <pre>653f61 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 5.625 micron^2/s - 60 second simulation using a Gaussian PSF model</pre> <pre>4f06ee - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s - 180 second simulation using a Gaussian PSF model</pre> <pre>dec32c - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 5.625 micron^2/s - 180 second simulation using a Gaussian PSF model</pre> <p> </p> <pre>85b0a1 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s , 10 of which belong to a sub-population with a mean FRET efficiency of 0.75, and the leftover 5 belong to another sub-populations with a mean FRET efficiency of 0.50 - 60 second simulation using a Numerical PSF model</pre> <pre>964ef3 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s , 10 of which belong to a sub-population with a mean FRET efficiency of 0.75, and the leftover 5 belong to another sub-populations with a mean FRET efficiency of 0.50 - 180 second simulation using a Numerical PSF model</pre> <p> </p> <pre>f28f6e - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s , 10 of which belong to a sub-population with a mean FRET efficiency of 0.75, and the leftover 5 belong to another sub-populations with a mean FRET efficiency of 0.50 - 60 second simulation using a Gaussian PSF model</pre> <pre>c311dd - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s , 10 of which belong to a sub-population with a mean FRET efficiency of 0.75, and the leftover 5 belong to another sub-populations with a mean FRET efficiency of 0.50 - 180 second simulation using a Gaussian PSF model</pre>
Supplementary data for: Detection of expressed mutations in acute myeloid leukemia cells using single cell RNA-sequencing
<p>Supplemental data for the publication:<br> Detection of expressed mutations in acute myeloid leukemia cells using single cell RNA-sequencing </p> <p>Contents: <br> - expression_matrices.tar - Gene/Barcode expression matrices from `cellranger count`<br> - *.seurat.rds - R object files with Seurat analyses and data structures for each sample<br> - scrna_mutations.tar.gz - copy of a git repository containing additional scripts and data - also hosted at <a href="https://github.com/genome/scrna_mutations">https://github.com/genome/scrna_mutations</a> (snapshot as of May 20, 2019)</p>
Linked collectors and determiners for: Using integrative taxonomy to establish the status of Alpheus peasei (Armstrong, 1940) (Decapoda: Alpheidae) as a single species throughout its distribution.
Natural history specimen data linked to collectors and determiners held within, "Using integrative taxonomy to establish the status of Alpheus peasei (Armstrong, 1940) (Decapoda: Alpheidae) as a single species throughout its distribution". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/947aa27b-9422-4d62-adc5-2cab2f5a8412">https://bionomia.net/dataset/947aa27b-9422-4d62-adc5-2cab2f5a8412</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/947aa27b-9422-4d62-adc5-2cab2f5a8412">https://gbif.org/dataset/947aa27b-9422-4d62-adc5-2cab2f5a8412</a>. Formatted as a Frictionless Data package.
Code and Data for "Multiple re-reads of single proteins at single-amino-acid resolution using nanopores"
<p>The primary structures containing data and analysis products are peptidereads_fig2.mat (for figure 2) and peptiderereads_fig3.mat (for figure 3). The main analysis scripts for these data structures are callvariants_fig2.m and reread_analysis_fig3.m respectively. Data for figures S6 (S6_reread_data.dat) and S8 (S8_hetero_data.dat), and the analysis script used to produce figure S6 (S6_reread_analysis.m) are also included. Other files are dependencies of these main scripts.</p> <p> </p> <p>The fields in peptidereads_fig2 are as follows:</p> <p> </p> <p> </p> <p><strong>folder, eventnum, reducedStart, reducedEnd, suspicious, hasreread:</strong> notes for internal use</p> <p><strong>variant:</strong> the true identity of the single-amino-acid substitution variant</p> <p><strong>data: </strong>the ion current data for each read downsampled to 5 kHz.</p> <p><strong>omit: </strong>whether the read was omitted from analysis due to length</p> <p><strong>relativeDNAend</strong>: the index in the data where the DNA portion of the read ends.</p> <p><strong>relativeLinkerEnd:</strong> the index in the data where the linker portion of the read ends.</p> <p><strong>DNAlevels, Peplevels, Alllevels:</strong> extracted ion current levels for the DNA region, the peptide region, and everything.</p> <p><strong>cal: </strong>the multiplicative and additive constants applied to calibrate the read</p> <p><strong>caldata: </strong>the data with calibration constants applied</p> <p><strong>cons0D, cons0W, cons0G, cons0DNA: </strong>initial guesses for consensuses based on hand curation of data.</p> <p><strong>pepDcons0, pepWcons0, pepGcons0:</strong> the portion of the handmade consensus with the variant levels.</p> <p><strong>pepDcons, pepWcons, pepGcons:</strong> the portion of the iterated consensus with the variant levels.</p> <p><strong>inhandconsensus: </strong>whether the read was used in generation of the inital guess consensuses.</p> <p><strong>inconsensus</strong>: whether the read was used in generation of either the initial guess or iterated consensuses.</p> <p><strong>confidence:</strong> the relative likelihood of each variant assigned to the read</p> <p><strong>incalls:</strong> whether the data was used in variant calling (i.e., not used in consensus generation)</p> <p><strong>params</strong>: the analysis parameters used</p> <p> </p>
Data from: Refining the evolutionary time machine: an assessment of whole genome amplification using single historical Daphnia eggs
<p>This dataset contains the original raw sequence files used in the associated publication "Refining the evolutionary time machine: an assessment of whole genome amplification using single historical <em>Daphnia</em> eggs" by O'Grady, Dhandapani, Colbourne & Frisch in Molecular Ecology Resources. DOI:10.1111/1755-0998.13524</p> <p> </p> <p>(filename > name used in associated publication)</p> <p>DF1_ATTACTC-GGCTCTG_L008_R1_001.fastq.gz -> DM1<br> DF2_TCCGGAG-GGCTCTG_L008_R1_001.fastq.gz -> DM2<br> DF3_CGCTCAT-GGCTCTG_L008_R1_001.fastq.gz -> DM3<br> DF4_GAGATTC-GGCTCTG_L008_R1_001.fastq.gz -> DM4<br> DF5_CTGAAGC-GGCTCTG_L008_R1_001.fastq.gz -> DM5<br> DF11_CTGAAGC-AGGCGAA_L008_R1_001.fastq.gz -> DM6<br> DF12_TAATGCG-AGGCGAA_L008_R1_001.fastq.gz -> DM7</p> <p>DF6_TAATGCG-GGCTCTG_L008_R1_001.fastq.gz -> DP1<br> DF7_ATTACTC-AGGCGAA_L008_R1_001.fastq.gz -> DP4<br> DF8_TCCGGAG-AGGCGAA_L008_R1_001.fastq.gz -> DP5<br> DF9_CGCTCAT-AGGCGAA_L008_R1_001.fastq.gz -> DP2<br> DF10_GAGATTC-AGGCGAA_L008_R1_001.fastq.gz -> DP3</p> <p>170426_E00397_0064_BHJ2JWALXX_2_TP-D7-004_1.fastq.gz -> DP6 (fw)<br> 170426_E00397_0064_BHJ2JWALXX_2_TP-D7-004_2.fastq.gz -> DP6 (rv)<br> 170426_E00397_0064_BHJ2JWALXX_2_TP-D7-010_1.fastq.gz -> DP7 (fw)<br> 170426_E00397_0064_BHJ2JWALXX_2_TP-D7-010_2.fastq.gz -> DP7 (rv)</p> <p>170426_E00397_0064_BHJ2JWALXX_2_TP-D7-005_1.fastq.gz -> DP8 (fw)<br> 170426_E00397_0064_BHJ2JWALXX_2_TP-D7-005_2.fastq.gz -> DP8 (rv)<br> 170426_E00397_0064_BHJ2JWALXX_2_TP-D7-006_1.fastq.gz -> DP9 (fw)<br> 170426_E00397_0064_BHJ2JWALXX_2_TP-D7-006_2.fastq.gz -> DP9 (rv)</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.