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4,694 results for “data analysis”
Data from: Toward a standardised protocol for the stable isotope analysis of scleractinian corals
<p><strong>Rationale:</strong> The stable isotope analysis of carbon and nitrogen is a powerful tool in many ecological studies, but different sample treatments may affect stable isotope ratios and hamper comparisons among studies. The goal of this study was to determine whether treatments that are commonly used to prepare scleractinian coral samples for stable isotope analysis yield different δ<sup>15</sup>N and δ<sup>13</sup>C values, and to provide guidelines toward a standardised protocol. </p> <p><strong>Methods:</strong> The animal tissues and Symbiodiniaceae of two symbiotic scleractinian coral species (<em>Stylophora pistillata</em> and <em>Porites lutea</em>) were divided into subsamples to test the effects of the drying method, lipid extraction, acidification treatment and water washing. All the subsamples were analysed for their δ<sup>15</sup>N and δ<sup>13</sup>C values, using continuous flow elemental analyser/isotope ratio mass spectrometry.</p> <p><strong>Results: </strong>The drying method and lipid extraction treatment had no substantial effects on the δ<sup>15</sup>N and δ<sup>13</sup>C values of Symbiodiniaceae and animal tissues. Acid treatment did cause significant differences in δ<sup>13</sup>C values (mean differences ≤0.5‰, with individual samples becoming up to 2.0‰ more negative), whereas no ecologically significant differences were observed in δ<sup>15</sup>N values. Animal tissue δ<sup>13</sup>C values may vary depending on whether samples are washed or not. </p> <p><strong>Conclusions: </strong>To move towards a standardised protocol in coral research, we recommend using an available drying method (as they are equally acceptable) for the stable isotope analysis of scleractinian corals, examining the need for lipid extraction on a case‐by‐case basis, performing a direct acidification of Symbiodiniaceae and animal tissues, and avoiding washing animal tissue with distilled water.</p>
Data from: Validity of the Diplostomoidea and Diplostomida (Digenea, Platyhelminthes) upheld in phylogenomic analysis
Higher systematics within the Digenea, Carus 1863 have been relatively stable since a phylogenetic analysis of partial nuclear ribosomal markers (rDNA) led to the erection of the Diplostomida Olson, Cribb, Tkach, Bray, and Littlewood, 2003. However, recent mitochondrial (mt) genome phylogenies suggest this order might be paraphyletic. These analyses show members of two diplostomidan superfamilies are more closely related to the Plagiorchiida La Rue, 1957 than to other members of the Diplostomida. In one of the groups implicated, the Diplostomoidea Poirier, 1886, a recent phylogeny based on mt DNA also indicates the superfamily as a whole is non-monophyletic. To determine if these results were robust to additional taxon sampling, we analyzed mt genomes from seven diplostomoids in three families. To choose between phylogenetic alternatives based on mt genomes and the prior rDNA-based topology, we also analyzed hundreds of ultra-conserved elements (UCEs) assembled from shotgun sequencing. The Diplostomida was paraphyletic in the mt genome phylogeny, but supported in the UCE phylogeny. We speculate this mitonuclear discordance is related to ancient, rapid radiation in the Digenea. Both UCEs and mt genomes support the monophyly of the Diplostomoidea and show congruent relationships within it. The Cyathocotylidae Muhling, 1898 are early diverging descendants of a paraphyletic clade of Diplostomidae Poirier, 1886, in which were nested members of the Strigeidae Railliet, 1919; the results support prior suggestions that the Crassiphialinae Sudarikov, 1960 will rise to the family level. Morphological traits of diplostomoid metacercariae appear to be more useful for differentiating higher taxa than those of adults. We describe a new species of Cotylurus Szidat, 1928, resurrect a species of Hysteromorpha Lutz, 1931, and find support for a species of Alaria Schrank, 1788 of contested validity. Complete rDNA operons are provided as a resource for future studies.
InSAR time series analysis results of ALOS-2/PALSAR-2 data for the post-eruptive displacement of the 2015 phreatic eruption of Hakone volcano, Japan
<p>This repository contains the InSAR products used in Doke et al., GRL (submitted).</p> <p> </p> <p><strong>Dataset 1</strong>: Surface velocity data estimated by InSAR time series analysis with NetCDF grid format.</p> <ol> <li>surface_velocity_p126.nc</li> <li>surface_velocity_p18.nc</li> </ol> <p> </p> <p><strong>Dataset 2</strong>: Time-series of LOS displacements in selected locations with text format.</p> <ol> <li>time_series_p126.txt</li> <li>time_series_p18.txt</li> </ol> <p> </p> <p><strong>Dataset 3</strong>: Inputs and results of model inversion with shapefile.</p> <p>Subsampled observation data, modeled (simulated) displacements, and other parameters are shown in attribute tables in shapefiles. Shapefiles that show the location of the estimated models are also included in ZIP files.</p> <ol> <li>point_source_deflation.zip</li> <li>sill_deflation.zip</li> </ol>
Data from: Explaining global variation in the latitudinal diversity gradient: meta-analysis confirms known patterns and uncovers new ones
Aim: The pattern of increasing biological diversity from high latitudes to the equator [latitudinal diversity gradient (LDG)] has been recognized for > 200 years. Empirical studies have documented this pattern across many different organisms and locations. Our goal was to quantify the evidence for the global LDG and the associated spatial, taxonomic and environmental factors. We performed a meta-analysis on a large number of individual LDGs that have been published in the 14 years since Hillebrand's ground-breaking meta-analysis of the LDG, using meta-analysis and meta-regression approaches largely new to the fields of ecology and biogeography. Location: Global. Time period: January 2003–September 2015. Major taxa studied: Bacteria, protists, plants, fungi and animals. Methods: We synthesized the outcomes of 389 individual cases of LDGs from 199 papers published since 2003, using hierarchical mixed-effects meta-analysis and multiple meta-regression. Additionally, we re-analysed Hillebrand's original dataset using modern methods. Results: We confirmed the generality of the LDG, but found the pattern to be weaker than was found in Hillebrand's study. We identified previously unreported variation in LDG strength and slope across longitude, with evidence that the LDG is strongest in the Western Hemisphere. Locational characteristics, such as habitat and latitude range, contributed significantly to LDG strength, whereas organismal characteristics, including taxonomic group and trophic level, did not. Modern meta-analytical models that incorporate hierarchical structure led to more conservative and sometimes contrasting effect size estimates relative to Hillebrand's initial analysis, whereas meta-regression revealed underlying patterns in Hillebrand's dataset that were not apparent with a traditional analysis. Main conclusions: We present evidence of global latitudinal, longitudinal and habitat-based patterns in the LDG, which are apparent across both marine and terrestrial realms and over a broad taxonomic range of organisms, from bacteria to plants and vertebrates.
Data from: Comparative analysis of the shape and size of the middle ear cavity of turtles reveals no correlation with habitat ecology
<p>The middle ear of turtles differs from other reptiles in being separated into two distinct compartments. Several ideas have been proposed as to why the middle ear is compartmentalized in turtles, most suggesting a relationship with underwater hearing. Extant turtle species span fully marine to strictly terrestrial habitats, and ecomorphological hypotheses of turtle hearing predict that this should correlate with variation in the structure of the middle ear due to differences in the fluid properties of water and air. We investigate the shape and size of the air-filled middle ear cavity of 56 extant turtles using 3D data and phylogenetic comparative analysis to test for correlations between habitat preferences and the shape and size of the middle ear cavity. Only weak correlations are found between middle ear cavity size and ecology, with aquatic taxa having proportionally smaller cavity volumes. The middle ear cavity of turtles exhibits high shape diversity among species, but we found no relationship between this shape variation and ecology. Surprisingly, the estimated acoustic transformer ratio, a key functional parameter of impedance-matching ears in vertebrates, also shows no relation to habitat preferences (aquatic/terrestrial) in turtles. We suggest that middle ear cavity shape may be controlled by factors unrelated to hearing, such as the spatial demands of surrounding cranial structures. A review of the fossil record suggests that the modern turtle ear evolved during the Early to Middle Jurassic in stem turtles broadly adapted to freshwater and terrestrial settings. This, combined with our finding that evolutionary transitions between habitats caused only weak evolutionary changes in middle ear structure, suggests that tympanic hearing in turtles evolved as a compromise between subaerial and underwater hearing.</p>
Newspaper review and analysis data on media coverage of budget issues in Nigeria
<p>The data was collected from analysis of six newspaper publications to establish citizen engagement and media coverage of the budget discourse from 2009 to 2013 as part of the investigation of the use of the online national budget of Nigeria. The work was part of the 'Exploring the Emerging Impacts of Open Data in Developing Countries' (ODDC) research project.</p>
A comparative usability analysis of eye-tracking and mouse click data taken from digital libraries
<p>This dataset is the result of a study, in which we analyzed parallels and differences between clicks as well as eye movements on two different digital library homepages. For this analysis we used diverse tracking tools for mouse clicks and eye tracking data that where further studied with respect to specific areas of interest (AOI). </p> <p>The dataset contains two screenshots indicating the areas of interest (AOIs; entitled “AreasOfInterest_Kartenportal.jpg and AreasOfInterest_Webportal.jpg), which separate the homepages into analyzable parts. It also contains eight screenshots of the homepages containing the total amount of collected clicks (each name starting with “clicks”) and two screenshots with the eye tracking heat maps (starting with “Heatmap”). The screenshots have directly been extracted from the click and eye tracking tools and matched with the before mentioned AOIs in order to gain the total count of clicks and views as well as the view duration the concerned area.</p> <p>All data are synthesized in a document containing three sheets with different tables: a first one with the initial data compilation for all AOIs of the two analyzed homepages (entitled “Data”), a second one with a more visual compiled data analysis for both homepages and all AOIs (entitled “Data2) and last one with the duration of the view as well as the duration of the fixation and the compiled click data (entitled « Eye tracking study data »).</p>
Supplementary Material for Frontiers Plant Genetics and Genomics 'Novel R tools for analysis of genome-wide population genetic data with emphasis on clonality'
<p>Authors</p> <p>Zhian N. Kamvar, Jonah C. Brooks, and Niklaus J. Grünwald</p>
WDS-RDA Publishing Data Workflows Working Group Analysis sheet
<p>The information was further refined by adding color: pink indicates “project” (4 entries), blue shows “repository” (14 entries), yellow is “journal” (7 entries) and green for “hybrid” (1 entry). Future versions of the spreadsheet are planned, which can be filtered via other categories, for example: discipline-specific vs discipline-agnostic, funding model, level of editing/intervention, etc.</p> <p>The collection and analysis of data took place between 1 February and 30 June 2015</p>
ChEMBL20 data sets for multi-property landscape analysis
<p>Six data sets from ChEMBL (version 20) are provided with their ChEMBLID, SMILES (in SMILES.zip) and descriptor values (Properties.zip). Additionally, the coordinates of compounds and property axes displayed in the linked article are given (Coordinates.zip).</p>
WDS-RDA Publishing Data Workflows Working Group Analysis sheet FINAL
<p><strong>NB: This dataset is superseded by:</strong></p> <p>Murphy, Fiona et al.. (2015). WDS-RDA-F11 Publishing Data Workflows WG Synthesis FINAL CORRECTED. Zenodo. 10.5281/zenodo.33899</p> <p>Data Publishing Workflows collected and analysed between 1 February - 30 June 2015. Fields were populated using a combination of consultation and desk research. This is a refined version of the previous spreadsheet also lodged in Zenodo: </p> <p>Murphy, Fiona et al.. (2015). WDS-RDA Publishing Data Workflows Working Group Analysis sheet. Zenodo. 10.5281/zenodo.19107</p> <p>Publication date: 29 June 2015</p> <p>Keyword(s): <strong>data publishing, workflows, journals, repositories, research data</strong></p>
Model, configuration, data, and analysis scripts for The Evolution of Cooperation by the Hankshaw Effect
<p>Computational model, configuration files, result data, and analysis scripts for The Evolution of Cooperation by the Hankshaw Effect as published in Evolution (doi: 10.1111/evo.12928)</p>
Additional materials used in the paper "Towards Continuous Scientific Data Analysis and Hypothesis Evolution" on the Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17)
<p>This bundle contains a web page describing the materials used in the evaluation of the paper, along with references to the software and datasets, provenance metadata and workflows used. All the scripts and descriptions are included as well.</p>
Adjudicating between face-coding models with individual-face fMRI responses: Data and analysis software
<p>Computational model fits to human neuroimaging data. Please see included readme.txt file.</p>
Sequence data for the article "Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol" - single cells dataset
<p>Sequence data (Illumina MiSeq runs) for the article "Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol". dataset of 2300 single cells. File names indicate unique sequencing runs. In the manuscripts, the informations about cell lines are found in the Supplemental Table 1. </p>
Sequence data for the article "Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol" - Protocol optimization
<p>Sequence data (Illumina MiSeq runs) for the article "Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol". Optimization of the protocol. Files names indicate unique run identifiers. In the manuscript, the link between unique run identifiers, cells and purpose of the experiment is found in the Supplemental Table 1. </p>
Data for "Fractal analysis of urban catchments and their representation in semi-distributed models: imperviousness and sewer system"
<p>The data set corresponds the data used in the paper : “Fractal analysis of urban catchments and their representation in semi-distributed models: imperviousness and sewer system”, published in 2017 in the Journal “Hydrology and Earth System Sciences” (http://www.hydrol-earth-syst-sci.net/).</p> <p>More precisely it corresponds to the matrices that are used in the fractal and multi-fractal analysis of the ten urban areas investigated in the paper.</p> <p> </p> <p>For each catchment, it is organised as follow:</p> <p>- catchment_name_conduit.asc : the matrix describing the sewer system.</p> <p>- catchment_name_OSM.asc : the matrix describing the impervious areas (roads and buildings) obtained via Open Street Map (www.openstreetmap.org)</p> <p>- catchment_name_OSM_house_only.asc : the matrix describing the “building” areas obtained via Open Street Map (www.openstreetmap.org)</p> <p>- catchment_name_imperviousness.asc : the matrix describing the representation of imperviousness in operational semi-distributed models.</p> <p> </p> <p>More details can be found in the paper.</p>
Asteroseismic Data Analysis: Foundations and Techniques
<p><strong>This site contains data needed to solve the Exercise sets in the book: "<em>Asteroseismic Data Analysis: Foundations and Techniques</em>".</strong></p> <p>The archive Basu_and_Chaplin_Data.tar.gz contains the synthetic data.<br> The file archive answers.pdf contains a PDF detailing the answers to the Exercise questions that are not open-ended.<br> <br> All data files are ASCII format.<br> <br> Here are some notes about the file formats, these can also be read in file README.txt:<br> <br> (1) Unless otherwise mentioned, the files with mode frequencies of modes have the format:<br> l, n, frequency, Inl<br> <br> (2) Files with observed frequencies (or simulated observations) have the format<br> l, n, frequency, uncertainty<br> <br> (3) Files in the archive freq_obs.tar have the formal<br> l, frequency, uncertainty<br> <br> (4) Files Kernels_gs98_c2_rho.txt and Kernels_gs98_rho_c2.txt have the following information:<br> <br> Row 1: R, M<br> Row 2: nr, (radius(i),i=1,nr)<br> Row 3 onwards: l, n, frequency (kernel(i),i=1,nr)<br> <br> (5) The frequency files in the archives frequencies_ov[0-2].tar.gz have the following format<br> <br> l, n, frequency, Inl, np, ng<br> <br> (6) The rotational kernel files have the following format<br> <br> Row 1: R, M<br> Row 2: nr, (radius(i),i=1, nr)<br> Row 3 onwards: l, np, ng, frequency (kernel(i),i=1, nr)</p>
Supplementary Data: Raw interview data and NVivo11 analysis summary
<p>Interview data on a sample of software startups focusing on the early stages of their evolution paths.</p> <p>Summary of qualitative analysis of the data done with NVivo11 in a study focusing on the software startups from the viewpoints of resource-based-view and human capital theories. Human captital, defined as one type of resources in resource-based-view theory, is defined in existing research broadly as a set of capabilities, knowledge, skills, education, experience, and other similar human attributes, and seen as a source of economic growth at all levels from an individual to nations and the world.The qualitative analysis of the interview data was done to answer the following research questions: 1) What are the engineering-related capabilities necessary for creating a product in a software startup?, 2) What are the means to acquire the necessary capabilities?, and 3) What are the reasons for deploying different capability-acquiring means?</p> <p>The findings of the analysis were used for drawing conclusions, how the established theories, resource-based-view and human capital theories, fit to the actual situations in software startups.</p>
Experimental data for "Spot-On: robust model-based analysis of single-particle tracking experiments"
<p><strong>Overview of experimental spaSPT data</strong></p> <p>To comprehensively test Spot-On over many different conditions, we conducted 1064 spaSPT experiments. The raw data is freely available and the purpose of this ReadMe file is to describe the organization, acquisition parameters and format of the data. The data is for 4 different cell lines imaged over 15 different conditions yielding a total of 60 different conditions. The four cell lines were:</p> <ul> <li> <p>U2OS C32 Halo-CTCF</p> </li> <li> <p>U2OS H2B-Halo-SNAP</p> </li> <li> <p>U2OS Halo-3xNLS</p> </li> <li> <p>mESC (JM8.N4) C3 Halo-Sox2</p> </li> </ul> <p>The cell lines were constructed in different ways. U2OS C32 Halo-CTCF was made by homozygous endogenous N-terminal tagging of CTCF in human osteosarcoma U2OS cells using CRISPR/Cas9-mediated genome-editing as described (C32 refers to clone number 32)<sup>1</sup>. We note the CTCF is an essential gene and that N-terminal tagging did not appear to affect CTCF function or expression level according to a series of control experiments<sup>1</sup>. Moreover, C32 Halo-CTCF has been authenticated using Short Tandem Repeat (STR) profiling (performed by Dr. Alison N. Killilea at the UC Berkeley Cell Culture Facility) against the following loci: THO1, D5S818, D13S317, D7S820, D16S539, CSF1PO, AMEL, vWA and TPOX. The C32 Halo-CTCF cell line showed a 100% match with U2OS.</p> <p>U2OS H2B-Halo-SNAP was made through random integration of a H2B-HaloTag-SNAP-Tag transgene expressed using the EF1a promoter with an IRES-NeoR gene for drug selection. After transfection, cells were selected using G418 until a pure cell population was obtained. This cell line has also been described previously<sup>1</sup>. The wild-type U2OS cell line used to make this cell line was also authenticated using STR profiling against the same loci as C32 and also showed a 100% match with U2OS.</p> <p>U2OS Halo-3xNLS was made through random integration of a FLAG-Halo-3xNLS (3x SV40 NLS: PKKKRKV) transgene expressed using the EF1a promoter. NeoR for drug selection was separately expressed using an SV40 promoter. After transfection, cells were selected using G418 until a pure cell population was obtained. This cell line has also been described previously<sup>1</sup>. The wild-type U2OS cell line used to make this cell line was also authenticated using STR profiling against the same loci as C32 and also showed a 100% match with U2OS.</p> <p>mESC C3 Halo-Sox2 was made through homozygous N-terminal tagging of Sox2 in JM8.N4<sup>2</sup> mouse embryonic stem cells using CRISPR/Cas9-mediated genome editing as previously described (C3 refers to clone number 3)<sup>3</sup>. The functionality of the C3 Halo-Sox2 knock-in was validated through control experiments and pluripotency through teratoma assays as described previously<sup>3</sup>.</p> <p>Each file contains single-molecule trajectories from a single cell imaged over 30,000 frames. Localization and tracking was performed using a custom-written Matlab implementation of the MTT-algorithm<sup>4</sup> and the following settings: Localization error: 10<sup>-6.25</sup>; deflation loops: 0; Blinking (frames): 1; max competitors: 3; max <em>D</em> (m<sup>2</sup>/s): 20.</p> <p>The same 15 conditions were used for each of the 4 cell lines.</p> <p><strong>ExpA PA-JF549</strong></p> <p>The purpose of this experiment was to test the effect of “motion-blurring” on the Spot-On estimated <em>D</em><sub>FREE</sub> and <em>F</em><sub>BOUND</sub>. 5 different experimental conditions were considered. Full details are given in the Methods section. Briefly, cells were grown overnight on plasma-cleaned 25 mm circular coverslips either directly (U2OS) and MatriGel coated as described<sup>1</sup>. Cell were labeled with 5-50 nM PA-JF549<sup>5</sup> for around 15-30 min, washed twice and medium exchanged to phenol-red free medium. 30,000 frames were collected at a camera exposure time (Andor iXon Ultra 897; frame-transfer mode; vertical shift speed: 0.9 μs; -70C) of 9.5 ms which together with a ~447 μs camera integration time gave a frame rate of ~100 Hz. PA-JF549 dyes were photo-activated during the ~447 μs camera integration time using 405 nm pulses and the 405 nm pulse intensity optimized to achieve a mean density of 1 molecule per frame per nucleus. The JF549 dye was excited using a 561 nm laser and the total number of excitation photons kept constant but either delivered during a 1 ms pulse, a 2 ms pulse, a 4 ms pulse, a 7 ms pulse or with constant illumination.</p> <p>For each cell line and condition, 4 replicates were performed. We count a replicate as an independent experiment performed on a different day. For each replicate around 5 cells were imaged. Occasionally, fewer than 5 cells are available. To avoid tracking errors, we removed cells with too high a localization density from the analysis. All of this information is available in the file name. For example, “U2OS_C32_Halo-CTCF_PA-JF549_1ms-561nm_100Hz_rep2_cell03” refers to the third cell imaged in the second replicate of U2OS C32 Halo-CTCF using a 1 ms excitation pulse of 561 nm laser at a frame rate of 100 Hz. Similarly, “U2OS_C32_Halo-CTCF_PA-JF549_cont-561nm_100Hz_rep4_cell01” refers to the first cell imaged in the fourth replicate of U2OS C32 Halo-CTCF using constant 561 nm laser at a frame rate of 100 Hz.</p> <p>The five ExpA_PAJF549 conditions are separated by cell line such that each cell line is provided in a separate directory. E.g. the directory “U2OS_H2B_ExpA_PAJF549” contains all data for the U2OS H2B-Halo-SNAP cell line.</p> <p><strong>ExpA PA-JF646</strong></p> <p>This experiment was exactly identical to the “ExpA_PA-JF549” experiment except cell were labeled with PA-JF646<sup>5</sup> and excited using a 633 nm laser. The file names and data organization was otherwise the same and the same five excitation conditions were considered.</p> <p><strong>ExpB PA-JF646</strong></p> <p>The purpose of this experiment was to test if the Spot-On estimated <em>D</em><sub>FREE</sub> and <em>F</em><sub>BOUND</sub> values would depend on the frame rate. In particular, all four proteins exhibit some levels of apparent anomalous diffusion, which could cause a dependence on the frame rate. Cells were labeled with PA-JF646 and grown and imaged as described above. Photo-activation took place during the ~447 μs camera integration time and JF646 dyes were excited using 1 ms stroboscopic 633 nm excitation pulses. To change the frame rate, the camera exposure time was set to 4.5 ms (~201 Hz), 5.5 ms (~167 Hz), 7 ms (~134 Hz), 13 ms (~74 Hz) and 19.5 ms (~50 Hz) when also counting the ~447 μs camera integration time. All of this information is available in the file name. For example, “U2OS_Halo-3xNLS_PA-JF646_1ms-633nm_74Hz_rep2_cell04” refers to the fourth cell imaged in the second replicate of U2OS Halo-3xNLS using a 1 ms excitation pulse of 633 nm laser at a frame rate of 74 Hz. Similarly, “mESC_C3_Halo-Sox2_PA-JF646_1ms-633nm_201Hz_rep1_cell03” refers to the third cell imaged in the first replicate of mESC Halo-Sox2 using a 1 ms excitation pulse of 633 nm laser at a frame rate of 201 Hz.</p> <p><strong>Data format</strong></p> <p>All data is available in two different formats: CSV-files and Matlab MAT-files. Both file formats are readable by the web-version of Spot-On. The Matlab version of Spot-On is only able to read the MAT-files. The CSV format consists of comma-separated values and contains headers. If opened with Microsoft Excel, it should appear as shown:</p> <p>Here the “frame” column contains the frame number in which the molecule was detected. The “t” column contains the timestamp. The “trajectory” column contains the trajectory number. For example, trajectory number 1 was only detected in frame 13 after which it disappeared. In contrast, trajectory number 4 was detected in frames 20, 21 22, 23 and 24. Finally, the “x” and “y” columns contain the x,y coordinates of the localization in units of micrometers (μm).</p> <p>The MAT-files contain a structure array named “trackedPar”. trackedPar contains three variables:</p> <ul> <li> <p>trackedPar.xy: “xy” is a matrix with 2 columns and a number of rows corresponding to the number of localizations in that trajectory. The first column is the x-coordinate and the second column is the y-coordinate. The units are micrometers (μm).</p> </li> <li> <p>trackedPar.Frame: “Frame” is a column vector where each element is the frame where the particle was localized.</p> </li> <li> <p>trackedPar.TimeStamp: “TimeStamp” is a column vector where each element is the timepoint where the particle was localized.</p> </li> </ul> <p>Each element in the structure array “trackedPar” correspond to a different trajectory.</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.