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3,871 results for “quantitative”
Quantitative Assessment of Research Data Management Practices - 2023
<p>This survey investigates <strong>Research Data Management (RDM) practices across five Swiss higher education institutions</strong>, including EPFL, ETH Zürich, Eawag, FHNW, and DaSCH, with the goal of gathering insights into how researchers manage data and code throughout the lifecycle of their projects, as well as using such findings to inform academic services related to RDM for researchers. Previous surveys, conducted at EPFL in 2017, 2019, and 2021, primarily focused on the planning and publishing stages of the research data lifecycle, such as data management planning and open data dissemination. The 2023 edition expanded to other institutes and places a stronger emphasis on <strong>Active Data Management</strong>, particularly during research projects, including a range of topics such as:</p> <ul> <li>Storage and backup solutions</li> <li>Data and code sharing platforms</li> <li>Documentation and metadata usage</li> <li>Compliance with legal and ethical standards</li> <li>Long-term data preservation strategies</li> <li>Use of open formats and open-source software</li> <li>Adoption of Data Management Plans (DMPs)</li> </ul> <p>This dataset was collected using the SurveyHero platform in compliance with GDPR and Swiss FADP regulations. enuvo GmbH acted as the data processor under a signed Data Processing Agreement. No personal identifiable information was purposefully collected, and data has been aggregated to further ensure respondents’ privacy.</p> <p>Included in this dataset:</p> <ul> <li>A CSV and XLSX file with the aggregated, anonymized data from the survey.</li> <li>Two PDF files containing graphical representations of the survey results, automatically generated by the SurveyHero platform in portrait and landscape mode.</li> <li>A README file providing context.</li> </ul> <p>This dataset is made openly available under the CC-BY 4.0 license. Users are encouraged to reuse it with appropriate attribution.</p>
Plasma circulating microRNA-expression quantitative trait loci (eQTLs) data in the Rotterdam Study
<p>The dataset contains GWAS summary statistics for 2,083 plasma circulating microRNAs, obtained from nearly 2,178 participants of the Rotterdam Study. The dataset includes three files, as outlined below:</p> <p><strong>File1: SNP_reference_file_maf0.01_Rsq0.7.txt</strong></p> <p>A reference file for SNPs with good imputation quality (Rsq > 0.7) and minor allele frequency > 0.01 among participants included in our GWAS in the Rotterdam Study (N=2,178). The headers are:</p> <p>SNP: rsID</p> <p>chr: chromosome number according to GRCh37</p> <p>bp: basepair position according to GRCh37</p> <p>effect_allele: effect allele</p> <p>other_allele: other allele</p> <p>eaf: effect allele frequency</p> <p><strong>File2: miReQTLs_1e-5_maf0.01_Rsq0.7.txt</strong></p> <p>Summary statistics for all SNPs significantly associated with 2083 miRNAs (p-value < 1e-5), filtered by minor allele frequency > 0.01 and Rsq > 0.7. The headers are:</p> <p>SNP: rsID</p> <p>beta: effect estimate</p> <p>se: standard error</p> <p>pval: p-value</p> <p>miRNA: miRNA ID</p> <p><strong>File3: miReQTLs_nominal_sig.csv.gz</strong></p> <p>Summary statistics for all SNPs nominally associated with 2083 miRNAs (p-value < 0.05). The headers are:</p> <p>RSID: SNP ID</p> <p>p-value: p-value</p> <p>phenotype: miRNA</p> <p>SE: standard error</p> <p>BETA: effect estimate</p> <p> </p> <p>The SNP allelic information and frequency can be found in the reference file (<strong>File1</strong>). </p> <p><br>For more information, please contact: m.ghanbari@erasmusmc.nl</p>
Quantitative and qualitative aspects of dissolved organic carbon leached from plant biomass in Taylor Slough, Shark River and Florida Bay (FCE) for samples collected in July 2004
Plant biomass was collected from Taylor Slough, Shark River and Florida Bay in Everglades National Park. Samples were taken to the lab and incubated with Milli-Q water in the dark for a period of 36 days. NaN3 was added to half the bottles to test the role of microbial activity on the leaching rates and composition of leachate. Every three days the water was decanted and replaced with fresh Milli-Q water. The decanted samples were filtered and analyzed for DOC concentration, sugar content, and total phenol content.
Data set for Global quantitative synthesis of ecosystem functioning across climatic zones and ecosystem types
<p>Dataset used in the publication: " Global quantitative synthesis of ecosystem functioning across climatic zones and ecosystem types". The dataset gathers estimates of ecosystem standing stocks (biomass, organic carbon, detritus), fluxes (GPP, ER, NEP) and process rates (decomposition and carbon uptake rates) for eight broad ecosystem types (forest, grassland, agroecosystem, desert, stream, lake, pelagic and benthic marine ecosystems) in five broad climatic zones (arctic, boreal, arid, temperate, tropical, arid).</p> <p>The scripts to produce the figures and the statistics of the publication are released along with the txt version of the data, which file is uploaded when running the script.</p>
Supplementary Materials: A primer on gathering and analysing multi-level quantitative evidence for differential student outcomes in higher education
<p>Example data sets, syntax files and macros for the tutorials in: Balloo, K., & Winstone, N. E. (2021). A primer on gathering and analysing multi-level quantitative evidence for differential student outcomes in higher education.<em> Frontline Learning Research</em>. <a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.14786%2Fflr.v9i2.675&data=04%7C01%7Ck.balloo%40surrey.ac.uk%7C50bb47bb433744dc8da208d8c2116202%7C6b902693107440aa9e21d89446a2ebb5%7C0%7C0%7C637472728228002863%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=fyA0y2hUkHESUJ7sVJ3s42Re4Yqa5XbgwW7AvEyGDdk%3D&reserved=0">https://doi.org/10.14786/flr.v9i2</a><a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.14786%2Fflr.v9i2.675&data=04%7C01%7Ck.balloo%40surrey.ac.uk%7C50bb47bb433744dc8da208d8c2116202%7C6b902693107440aa9e21d89446a2ebb5%7C0%7C0%7C637472728228002863%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=fyA0y2hUkHESUJ7sVJ3s42Re4Yqa5XbgwW7AvEyGDdk%3D&reserved=0">.675</a> </p> <p><strong>The data for all examples are fictional, and have only been designed to simulate the possible behaviour of institutional data for the purposes of demonstrating the analytical approaches in the primer. No inferences or conclusions should be drawn from the findings of these examples, because the results are not real. </strong></p> <p>We anticipate that readers can use the example data sets as templates and substitute in their own data.</p>
Longitudinal urban form dataset of Midtown Manhattan: Measuring urban form evolution via quantitative descriptions of plots, buildings and streets from 1890 to the present
<p>This dataset contains data described and used in the research article <strong>"The impact of urban form on physical change: A quantitative and diachronic analysis of urban form evolution in Midtown Manhattan"</strong>. </p> <p>The longitudinal dataset contains urban form data on nearly 17,000 individual plots (parcels) in Midtown Manhattan, documented through four subsequent time frames: 1890, 1920, 1956 and 2021. The data was compiled from historical cartographic resources and open-access geospatial datasets listed in the ReadMe file. </p> <p>The dataset includes an array of quantitative descriptions of plots, buildings and streets central to the field of urban morphology, and the binary information of physical change (1: change, 0: no change) identified via diachronic comparison of each time frame at the scale of plots.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The dataset presented in this repository has been generated as part of a PhD research conducted at the University of Melbourne, Faculty of Architecture, Building and Planning and funded by the University of Melbourne - Melbourne Research Scholarship: </p> <p><strong>Tümtürk, O</strong>. (2024). <strong>A data-driven investigation on urban form evolution: Methodological and empirical support for unravelling the relation between urban form and spatial dynamics</strong>. Unpublished PhD Thesis. The University of Melbourne, Australia. </p>
Coding data to accompany "A quantitative approach to sociotopography in Austronesian languages"
<p>Dataset consists of csv files with sample languages identified by name and Glottocode. Coding for four sociolinguistic variables, as well as an overall "orientation type." Each file corresponds to a different method for coding languages employing multiple spatial orientation strategies, as described in the document coding.pdf.</p> <p><strong>Orientation type</strong></p> <ul> <li>land-sea = axis oriented orthogonal to the coast, based on opposition between landward (inland) and seaward (toward the coast), regardless of whether these terms reflect PAN *daya and *lahud </li> <li>land-sea* = land-sea systems in which the land-sea opposition is indistinguishable from geophysical elevation</li> <li>coastal = axis oriented parallel to the coast, often but not necessarily co-lexified with vertical `up' and `down'</li> <li>elevation = axis that distinguishes global or geophysical elevation with respect to deictic center </li> <li>riverine = axis oriented parallel to the river, typically with secondary axis orientated orthogonal to river</li> <li>cardinal = axis fixed according to conventions which do not vary with local geography (although they may be motivated by environmental factors such as wind and the sun)</li> </ul> <p><strong>Distribution</strong></p> <ul> <li>distributed</li> <li>island</li> <li>village</li> </ul> <p><strong>Economy</strong></p> <ul> <li>diversified</li> <li>agriculture</li> <li>subsistence</li> </ul> <p><strong>Geography</strong></p> <ul> <li>diversified</li> <li>inland</li> <li>coast</li> </ul> <p><strong>Terrain</strong></p> <ul> <li>mountainous</li> <li>non-mountainous</li> </ul>
Citation data of arXiv eprints and the associated quantitatively-and-temporally normalised impact metrics
<p><strong>Data collection</strong></p> <p>This dataset contains information on the eprints posted on arXiv from its launch in 1991 until the end of 2019 (1,589,006 unique eprints), plus the data on their citations and the associated impact metrics. Here, eprints include preprints, conference proceedings, book chapters, data sets and commentary, i.e. every electronic material that has been posted on arXiv. </p> <p>The content and metadata of the arXiv eprints were retrieved from the arXiv API (https://arxiv.org/help/api/) as of 21st January 2020, where the metadata included data of the eprint’s title, author, abstract, subject category and the arXiv ID (the arXiv’s original eprint identifier). In addition, the associated citation data were derived from the Semantic Scholar API (https://api.semanticscholar.org/) from 24th January 2020 to 7th February 2020, containing the citation information in and out of the arXiv eprints and their published versions (if applicable). Here, whether an eprint has been published in a journal or other means is assumed to be inferrable, albeit indirectly, from the status of the digital object identifier (DOI) assignment. It is also assumed that if an arXiv eprint received <em>c</em><sub>pre</sub> and <em>c</em><sub>pub</sub> citations until the data retrieval date (7th February 2020) before and after it is assigned a DOI, respectively, then the citation count of this eprint is recorded in the Semantic Scholar dataset as <em>c</em><sub>pre</sub> + <em>c</em><sub>pub</sub>. Both the arXiv API and the Semantic Scholar datasets contained the arXiv ID as metadata, which served as a key variable to merge the two datasets.</p> <p>The classification of research disciplines is based on that described in the arXiv.org website (https://arxiv.org/help/stats/2020_by_area/). There, the arXiv subject categories are aggregated into several disciplines, of which we restrict our attention to the following six disciplines: Astrophysics (‘astro-ph’), Computer Science (‘comp-sci’), Condensed Matter Physics (‘cond-mat’), High Energy Physics (‘hep’), Mathematics (‘math’) and Other Physics (‘oth-phys’), which collectively accounted for 98% of all the eprints. Those eprints tagged to multiple arXiv disciplines were counted independently for each discipline. Due to this overlapping feature, the current dataset contains a cumulative total of 2,011,216 eprints. </p> <p>Some general statistics and visualisations per research discipline are provided in the original article (Okamura, 2022), where the validity and limitations associated with the dataset are also discussed.</p> <p> </p> <p><strong>Description of columns (variables)</strong></p> <ul> <li><strong>arxiv_id</strong> : arXiv ID</li> <li><strong>category</strong> : Research discipline</li> <li><strong>pre_year</strong> : Year of posting v1 on arXiv</li> <li><strong>pub_year</strong> : Year of DOI acquisition</li> <li><strong>c_tot</strong> : No. of citations acquired during 1991–2019</li> <li><strong>c_pre</strong> : No. of citations acquired before and including the year of DOI acquisition</li> <li><strong>c_pub</strong> : No. of citations acquired after the year of DOI acquisition</li> <li><strong>c_<em>yyyy</em></strong> (<em>yyyy</em> = 1991, …, 2019) : No. of citations acquired in the year <em>yyyy</em> (with ‘<em>yyyy</em>’ running from 1991 to 2019)</li> <li><strong>gamma</strong> : The quantitatively-and-temporally normalised citation index</li> <li><strong>gamma_star</strong> : The quantitatively-and-temporally standardised citation index</li> </ul> <p><em>Note:</em> The definition of the quantitatively-and-temporally normalised citation index (γ; ‘gamma’) and that of the standardised citation index (γ*; ‘gamma_star’) are provided in the original article (Okamura, 2022). Both indices can be used to compare the citational impact of papers/eprints published in different research disciplines at different times. </p> <p> </p> <p><strong>Data files</strong></p> <p>A comma-separated values file (‘<strong>arXiv_impact.csv</strong>’) and a Stata file (‘<strong>arXiv_impact.dta</strong>’) are provided, both containing the same information.</p> <p> </p>
Multi-stakeholder research data management training as a tool to improve the quality, integrity, reliability and reproducibility of research: Quantitative data of the post-course surveys
<p>Data contains doctoral students' and postdoc researchers' (n=168) self-ratings of their RDM competencies before and after the 3 ECTS credits "Basics of Research Data Management" (BRDM) trainings held 2019-2021 in the University of Turku and Åbo Akademi University, Finland. Moreover, data contains respondents' self-reported further learning needs.</p>
The Structure of Monomeric Hydroxo-CuII Species in Cu-CHA. A Quantitative Assessment.
<ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, computer simulation and analysis</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DAT</strong>, <strong>spc</strong>, <strong>par</strong>, <strong>m</strong>, <strong>f34</strong>,<strong> xyz</strong>, <strong>out</strong>, <strong>in</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong>, <strong>DTA</strong>,<strong> spc </strong>and<strong> par.</strong></li> <li>EPR spectroscopic simulation and analyses with filename extension <strong>m</strong>.</li> <li>Periodic DFT computations with(out) filename extensions <strong>out</strong> and <strong>f34</strong> in ASCII format.</li> <li>Molecular cluster computations with filename extensions <strong>in</strong> and <strong>out</strong> in ASCII format.</li> <li>Geometry information of cluster models is stored in <strong>xyz</strong> files in ASCII format.</li> </ul> <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li>X-band Pulsed-EPR spectroscopic measurements were generated by ELEXYS 580 EPR spectrophotometer equipped with SHQ cavity and ER035 M NMR gaussmeter produced by Bruker.</li> <li>Periodic DFT computations were generated using distributed parallel version of CRYSTAL17 code.</li> <li>Molecular cluster computations were generated using the ORCA (v4.2.1 and v5.0.2) code.</li> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>PARACAT_WP3_20220713_01_CW</strong> folder includes X-band CW-EPR spectroscopic measurements; original data are in DTA/DSC and spc/par formats.</li> <li>Files in <strong>PARACAT_WP3_20220713_02_HYSCORE</strong> folder includes HYSCORE spectroscopic measurements; original data are in DTA/DSC formats.</li> <li>Files in <strong>PARACAT_WP3_20220713_03_ESE</strong> folder includes ESE spectroscopic measurements; original data are in DTA/DSC formats.</li> <li>Files in <strong>PARACAT_WP3_20220713_04_MATLAB</strong> folder includes computer simulations/analyses of the EPR measurements; data are in m formats.</li> <li>Files in <strong>PARACAT_WP3_20220713_05_MODELLING </strong>folder includes periodic and cluster quantum chemical computations inputs, outputs and geometries in ASCII format.</li> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>EPR</strong> – Electron Paramagnetic Resonance, <strong>CW</strong> – Continuous Wave EPR, <strong>ESE</strong> – Electron Spin Echo detected EPR, <strong>HYSCORE</strong> – HYperfine Sublevel CORrelation spectroscopy, <strong>DFT </strong>– Density Functional Theory, <strong>CCSD</strong> – Coupled Cluster Single Double, <strong>PBEXX</strong> – PBE functional with XX percentage of exact Hartree-Fock exchange, <strong>DSDBLYP</strong> – DSD-BLYP double-hybrid functional, <strong>B3LYP </strong>– B3LYP functional, <strong>DSDPBEP86</strong> – DSD-PBEP86 double-hybrid functional, <strong>CuOH </strong>– CuOH species in Chabazite, zeolite topology, <strong>CuCHA_X </strong>– name of the Copper-exchanged Chabazite sample, <strong>supercell</strong> – indicates the supercell periodic model, <strong>O<sub>2</sub>act </strong>– activation of the Chabazite sample in O<sub>2</sub> atmosphere.</li> <li>definitions of variables: <strong>Magnetic field, Temperature.</strong></li> <li>units of measurement: <strong>Gauss (G), K, degree (°), milliTesla (mT), Hartree (Ha), cm<sup>-1</sup> (ν), megahertz (MHz), nanometers (nm)</strong>.</li> </ul> </li> <li>abbreviations: <strong>8mr </strong>is the Cu docking sites; <strong>4c </strong>and <strong>3c</strong> are the different coordination geometry; <strong>OHos</strong> and <strong>OHss</strong> indicate the orientation of –OH group (same side or opposite side with respect to the Al ion). <strong>all </strong>indicates the full periodic frequency calculation of the model. <strong>cluster </strong>indicates the geometry of the cluster model extracted from the relaxed periodic ones. Periodic DFT computations with filename extension <strong>.f34</strong> include structural/symmetry information of optimized structure. Periodic DFT computations without extension contain the input commands. Molecular cluster computations with filename extension <strong>.in</strong>/<strong>.out</strong>/<strong>.xyz</strong> are inputs, outputs, and structure of cluster models.</li> </ul> </li> </ul>
Quantitative assessment of research data management practice - University of Bordeaux
<p>This survey was run at the University of Bordeaux in January 2019 using the questionnaire "Quantitative assessment of research data management practice" :</p> <p>Teperek, M., Krause, J., Lambeng, N., Blumer, E., van Dijck, J., Eggermont, R., … der Velden, Y. T. (2019). Quantitative assessment of research data management practice. Retrieved from : <a href="https://osf.io/mz3fx/">https://osf.io/mz3fx/</a></p> <p>The questionnaire included all the primary and secondary common questions, institution-specific questions regarding services and file sharing (EPFL questions), institution-specific questions for profile information.</p> <p>Data from the 425 responses collected are published here.</p> <p>Details regarding data collection and curation are included in the README file.</p> <p> </p>
Quantitative comparison of camera technologies for cost-effective Super-resolution Optical Fluctuation Imaging (SOFI) [raw datasets]
<p>Raw datasets accompanying the analysis in "Quantitative comparison of camera technologies for cost-effective Super-resolution Optical Fluctuation Imaging (SOFI)"</p> <p>The datasets contain raw fluorescence microscopy images aimed to be processed in a SOFI analysis. They are acquired with different camera technologies, allowing for direct comparison of an industry-grade CMOS detector with both a scientific-grade sCMOS and emCCD detector.</p>
Post-trial access practice in Malaria, Tuberculosis, and NTDs Clinical Trial studies in Sub-Saharan African countries, quantitative study
<p>This is the data set used <span>to evaluate post trial access plan and implementation practice on TB, Malaria and NTD clinical trial studies conducted in the sub-Saharan African countries. </span></p>
FTICR MS data for standards and mixtures for quantitative peak intensity investigation
<p>This upload contains raw (Bruker .d format) FTICR mass spectrometry data (direct infusion, negative mode ESI) for standards in different mixtures and matrices for the purposes of investigating the (non)quantitative nature of the data. <br>Processed data (Excel format), and Python scripts used for data processing are also included. </p> <p>Note - the Python scripts used CoreMS version prior to V2.0 for analysis - to re-run these scripts with a more recent release likely requires syntax updates. </p>
Replication data for: Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study
<p>This is the replication data for the scientific paper titled "Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study" accepted for publication in the Journal of Medical Internet Research (JMIR). For details on how to use the data files, please consider the "supplementary_material.R" file or the "supplementary_material.pdf" where the variables of interest and R code are presented.</p> <p>For the code to run correctly, have the files "multilevel_labels.R" and "glm_labels.R" in the same working directory as the .R or .Rmd script. They are executed in the background, applying modifications to labels inside the regression tables. </p> <p> </p>
MRI raw data for: A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry
<p>MRI raw data from three different magnetic field strength (1.5 T, 3 T, 7T; 7T data are in separate datasets) for the publication 'A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry', in which a phantom was presented that allows for an experimental evaluation of QSM reconstruction algorithms. The phantom contains susceptibility producing particles with dia- and paramagnetic properties embedded in an MRI visible medium (gelatin and agarose gel) and is suitable to assess the performance of algorithms that attempt to separate isotropic dia- and paramagnetic susceptibility at the sub-voxel level. The dataset additionally contains raw data for a phantom that only contains diamagnetic and paramagnetic particles, respectively, for magnetic field strengths of 1.5 T and 3 T (additional 7 T data are provided in separate datasets).</p>
Quantitative electronic structure and work-function changes of liquid water induced by solute - data
<p>Data set pertaining to the article "Quantitative electronic structure and work-function changes of liquid water induced by solute" | Physical Chemistry Chemical Physics, 24, 1310 (2022).</p> <p>Files with extension .h5 are hdf5-files structured according to the NeXus standard v2022.07 using the NXmpes user contributed format suggested by the Fairmat consortium, see<br> https://www.nexusformat.org/<br> https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html<br> A few extensions specific to liquid jet-experiments were added to the standard, and are explained in the notes-group on the top level of each file.<br> NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br> * nexpy (distributed with python)<br> * https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)</p> <p>In each NeXus file-entry, two types of spectra are shown:<br> 1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data').<br> 2. As-measured data ('raw').</p> <p>Files with extension .txt are comma-separated ascii-files.<br> The following files are provided:</p> <p>Photoemission data pertaining to solute measurements using the cut-off as energy reference:<br> NaI_data.h5<br> tbai_data.h5</p> <p>Biased spectra were typically recorded in the following order:<br> [cut-off (fine), cut-off (coarse), (valence band)*(N repeats)]*(M repeats)<br> To avoid the saving of overly complex hdf5-files, these data were saved in a different order, namely:<br> [cut-off (fine)*(M repeats), cut-off (coarse)*(M repeats), (valence band)*(N*M repeats)].</p> <p>Numeric representations of the traces shown in the article's figures:<br> Figure_1a-data.txt<br> Figure_1b-data.txt<br> Figure_2a-data.txt<br> Figure_2b-data.txt<br> Figure_2c-data.txt<br> Figure_3-data.txt<br> Figure_4-data.txt<br> Figure_5a-data.txt<br> Figure_5b-data.txt<br> Figure_6a-data.txt<br> Figure_6b-data.txt<br> Figure_6c-data.txt<br> Figure_7_diff_spectra-data.txt<br> Figure_8-data.txt</p> <p>Traces shown in several figures are included only in the data file pertaining to the figure in which they occur first.</p> <p> </p> <p>Contact: Uwe Hergenhahn, uhe@fhi.mpg.de .</p>
Transmission ultrasound data simulated using the k-Wave toolbox as a benchmark for biomedical quantitative ultrasound tomography using a ray approximation to Green's function
<p><strong>Transmission ultrasound data simulated using the k-Wave toolbox as a benchmark for biomedical quantitative ultrasound tomography using a ray approximation to Green's function </strong></p> <p> </p> <p>The folder ‘’simulation<em>’’ </em>includes the transmission ultrasound data sets used in the project:<a href="https://github.com/Ash1362/ray-based-quantitative-ultrasound-tomography">https://github.com/Ash1362/ray-based-quantitative-ultrasound-tomography</a>. In the Github link, the associated project can be found in the branch master in the folder r-Wave #V1.1. (The folder ‘’data_ust_kWave_transmission.zip<em>’’ </em>is deprecated.)</p> <p>...........................................................................................</p> <p>The ultrasound data were simulated using the k-Wave toolbox (version 1.3.) [5] and using a digital breast phantom [4]. In k-Wave version 1.4., no changes have been reported that affects the simulations. The simulations were done assuming isotropic point sources.</p> <p>The folder ‘’simulation<em>’’ </em> must be added to the path:</p> <p><em>''…r-Wave/data/simulation/…''</em></p> <p>For running the Matlab example scripts in the project in the github, the user has two choices: </p> <ol> <li>Simulate the k-Wave ultrasound data by setting <em>data_sim=true;</em> in the examples in the project.</li> <li>Upload the already simulated k-Wave ultrasound data according to the description below and load them by setting <em>data_sim=false;</em> in the examples in the project.</li> </ol> <p>Please read the description in the example scripts!</p> <p>…………………………………………………………………………………</p> <p>The folder simulation includes 2 subfolders, ‘’phantom<em>’’ </em>and ‘’data_ust_kWave_transmission<em>’’.</em></p> <p>1) The subfolder ‘’simulation/phantom<em>’’ </em> includes ‘’OA-BREAST<em>’’. </em></p> <p>In the project: https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/,</p> <p>the user must upload the folder ‘’Neg_47_Left<em>’’ </em>, and add it as ‘’r-wave/data/simulation/phantom/OA-BREAST/Neg_47_Left/<em>’’.</em></p> <p><em>.......................................................................................................................................................................</em></p> <p>2) The subfolder ‘’simulation/data_ust_kWave_transmission’<em>’ </em>includes 2 subfolders, ‘’2D<em>’’ </em> and ‘’3D<em>’’ </em>.</p> <p>The subfolder ‘’2D<em>’’ </em> includes:</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_sphere_nonsmooth.mat</strong></p> <p>Two transmission ultrasound data sets were simulated using the k-wave for only water and breast in water according to section <em>‘’6.1. data simulation’’</em> in [1]. 64 emitters and 256 receivers are simulated as off-grid points which are placed on a 2D circular ring. (The characters ‘’_sphere_’’ are added to indicate that the transducers are placed on a ring.) To simulate the data, each emitter was individually driven by an excitation pulse, and the induced acoustic pressure time series were recorded on all the receivers. The k-Wave simulation was performed on a grid with grid spacing 0.4 mm, and the time spacing was set using a CFL number 0.1. The acoustic absorption and dispersion were accounted for based on the frequency power law. This data set is used for the purpose of image reconstruction, and therefore, the sound speed and absorption coefficients maps are not smoothed, i.e., the original maps are used for simulations. This data set can be used for image reconstruction using the time-of-flight-based approach and then the Green's approach.</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_plane_nonsmooth.mat</strong></p> <p>Two transmission ultrasound data sets were simulated using the k-wave for only water and breast in water. 64 emitters and 256 receivers are simulated as off-grid points which are placed on 16 planar arrays which are all aligned with a circle. Each planar array includes 4 emitters and 16 receivers. Therefore, in contrast with the data mentioned above, the ray linking is performed using the line equations defining the 2D geometry of the linear arrays. (The characters ‘’_plane_’’ are added to indicate that the transducers are placed on line.) To simulate the data, each emitter was individually driven by an excitation pulse, and the induced acoustic pressure time series were recorded on all the receivers. The k-Wave simulation was performed on a grid with grid spacing 0.4 mm, and the time spacing was set using a CFL number 0.1. The acoustic absorption and dispersion were accounted for based on the frequency power law. This data set is used for the purpose of image reconstruction, and therefore, the sound speed and absorption coefficients maps are not smoothed, i.e., the original maps are used for simulations. This data set can be used for image reconstruction using the time-of-flight-based approach, but ahs not been extended to the Green's approach yet. The image reconstruction should be slower than the circular array. the reason is for circular array, for each emitter, the raylinking problem is solved for all receivers once using the equation of circle. However, for this data set, for each emitter, the ray linking problem is solved for each receiver array separately, because receiver arrays are defined with different line equations.</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_sphere_smooth_17_1.mat</strong></p> <p>Two transmission ultrasound data sets were simulated using the k-Wave for only water and breast in water as the benchmark for validation of ray approximation to Green’s function in homogeneous and heterogenous media, respectively. The simulation was performed according to section <em>‘’6.2. Numerical validation of the ray approximation to the Green’s function’’</em> in [1].</p> <p>64 emitters and 256 receivers are simulated as off-grid points which are placed on a 2D circular ring. (The characters ‘’_sphere_’’ are added to indicate that the transducers are placed on a ring.) The pressure field was produced by emitter 1 (of the 64 emitters) and was recorded in time on all 256 receivers. The k-Wave simulation was performed on a grid with grid spacing 0.4 mm, and the time spacing was set using a CFL number 0.1. The acoustic absorption and dispersion were accounted for based on the frequency power law. The sound speed and absorption coefficient maps were smoothed by an averaging window of size 17 grid points. This data set is used as the benchmark for measuring accuracy of ray approximation to Green’s function for computing phase and amplitude of the pressure field on the receivers.</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_sphere_smooth_17_20.mat</strong></p> <p> This data set is the same as data4_smooth_17_1 except the pressure field is produced by emitter 20.</p> <p>………………………………………………………………………………………………………………….</p> <p>The subfolder ‘’3D<em>’’ </em> includes:</p> <p><strong>data_ust_kWave_transmission/3D/PulsePammoth_1_dx5_cfl1_Nr4096_Ne1024_Interpnearest_Transgeompoint_Absorption0_CodeCUDA/data5_sphere_nonsmooth_tof_singram.mat</strong></p> <p>The discrepancy of time-of-flight data for two transmission ultrasound data sets simulated by the k-wave for breast in water and only water according to section 5.2 in [3]. The pressure fields were produced by 1024 emitters separately and were recorded on 4096 receivers. The emitters and receivers were simulated as points which are placed on a 3D hemispherical surface, and are interpolated onto the grid using a neighboring interpolation. The k-Wave simulations were performed on a grid with grid spacing 0.5 mm, and the time spacing was set using a CFL number 0.1. The time-of-flight data were computed and will be used for a refraction-corrected image reconstruction of the sound speed based on the inversion approach proposed in [3].</p> <p><strong>References</strong></p> <p>1 - A. Javaherian, ❝Hessian-inversion-free ray-born inversion for high-resolution quantitative ultrasound tomography❞, 2022, <a href="https://arxiv.org/abs/2211.00316/">https://arxiv.org/abs/2211.00316/</a> .</p> <p>2 - A. Javaherian and B. Cox, ❝Ray-based inversion accounting for scattering for biomedical ultrasound tomography❞, Inverse Problems vol. 37, no.11, 115003, 2021. <a href="https://iopscience.iop.org/article/10.1088/1361-6420/ac28ed/">https://iopscience.iop.org/article/10.1088/1361-6420/ac28ed/</a></p> <p>3- A. Javaherian, F. Lucka and B. T. Cox, ❝Refraction-corrected ray-based inversion for three-dimensional ultrasound tomography of the breast❞, Inverse Problems, 36 125010. <a href="https://iopscience.iop.org/article/10.1088/1361-6420/abc0fc/">https://iopscience.iop.org/article/10.1088/1361-6420/abc0fc/</a> </p> <p>4- Y. Lou, W. Zhou, T. P. Matthews, C. M. Appleton and M. A. Anastasio, ❝Generation of anatomically realistic numerical phantoms for photoacoustic and ultrasonic breast imaging❞, J. Biomed. Opt., vol. 22, no. 4, pp. 041015, 2017. <a href="https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/">https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/</a></p> <p>5 - B. E. Treeby and B. T. Cox, ❝k-Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields❞, J. Biomed. Opt. vol. 15, no. 2, 021314, 2010. <a href="http://www.k-wave.org/">http://www.k-wave.org/</a></p>
Allele-specific quantitation of ATXN3 and HTT transcripts in polyQ disease models.
<p>Precise values obtained during the research that led to the publishing of scientific paper entitled 'Allele-specific quantitation of ATXN3 and HTT transcripts in polyQ disease models'.</p>
2020 Stakeholder Survey of the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) - Quantitative Dataset
<p>This dataset is the outcome of a survey of IPBES stakeholders that was conducted in May-June 2020. The aim of this survey is to better understand stakeholder engagement with IPBES, to improve implementation of the IPBES stakeholder engagement strategy (decision IPBES-3/4 presented in document IPBES/3/18), and to further increase the inclusivity and effectiveness of the IPBES work programme. Results will help, among others, to better align communication and outreach, and to strengthen collaborative processes within the IPBES work programme.</p> <p><br> This dataset presents only the quantitative data of the complete dataset of responses. It has been anonymised and all personal comments in response to open questions have been removed. For information, the full anonymised dataset has been published on Zenodo, with restricted access (see DOI: <a href="http://doi.org/10.5281/zenodo.4121916">10.5281/zenodo.4121916</a>).</p> <p>This dataset is under restricted access and embargoed until after the eighth session of IPBES Plenary. For any inquiry, please contact IPBES Head of Communications (stakeholders@ipbes.net). </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.