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MRI raw data to publication: Fuzzy ripple artifact in high resolution fMRI: identification, cause, and mitigation.
<p>These Data are refering to the maunscript "Fuzzy ripple artifact in high resolution fMRI: identification, cause, and mitigation" authored by </p> <p>Renzo Huber1, Rüdiger Stirnberg2, A Tyler Morgan1, David A Feinberg3,4-5, Samantha J Ma6, Philipp Ehses3, Omer Faruk Gulban2,7, Kenshu Koiso2, Isabel Gephart1, Stephanie Swegle1, Susan Wardle1, Emily Ma2, Andrew Persichetti1, Alexander JS Beckett4-5, Tony Stöcker3, Nicolas Boulant8, Benedikt A Poser2, Peter Bandettini1</p> <p><strong> </strong></p> <p>1 NIMH, NIH, Bethesda, United States,</p> <p>2 German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany,</p> <p>3 Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States, </p> <p>4 Advanced MRI Technologies, Sebastopol, CA, United States,</p> <p>5 CN, FPN, University of Maastricht, The Netherlands,</p> <p>6 Siemens Medical Solutions USA, Inc., Berkeley, CA, USA,</p> <p>7 Brain Innovation, Maastricht, The Netherlands,</p> <p>8 CEA, NeuroSpin, University Paris Saclay, France.</p> <p> </p> <h1>Abstract</h1> <p><strong>Purpose:</strong> High resolution fMRI is an emerging research field focused on capturing functional signal changes across cortical layers. However, the data acquisition is limited by low spatial frequency EPI artifacts; termed as Fuzzy Ripples. These artifacts limit the practical applicability of acquisition protocols with higher spatial resolution, faster acquisition speed, and they challenge imaging in lower brain areas. </p> <p><strong>Methods: </strong>We characterize Fuzzy Ripple artifacts across commonly used sequences and distinguish them from conventional EPI Nyquist ghosts, off-resonance effects, and GRAPPA artifacts. To investigate their origin, we employ dual polarity readouts.</p> <p><strong>Results: </strong>Our findings indicate that Fuzzy Ripples are primarily caused by kx-specific imperfections in gradient trajectories, which can be exacerbated by inductive coupling between third-order shims and readout gradients. We also find that these artifacts can be mitigated through complex-valued averaging of dual polarity EPI or by disconnecting the third-order shim.</p> <p><strong>Conclusion:</strong> The proposed mitigation strategies allow for overcoming current limitations in layer-fMRI protocols: </p> <p>(1) Achieving resolutions beyond 0.8mm is feasible, and even at 3T, we achieved 0.53mm voxel functional connectivity mapping. </p> <p>(2) Temporal acquisition speed can be increased to GRAPPA 8. </p> <p>(3) Sub-millimeter fMRI is achievable in lower brain areas, including the cerebellum.</p> <p> </p>
Public transport travel time matrices for Great Britain (TTM 2023)
<h2><strong>Overview</strong></h2> <p>This dataset provides ready-to-use door-to-door public transport travel time estimates for each of the 2011 Census at the lower super output area (LSOA) and data zone (DZ) units (42,000 LSOA/DZ units in total) in Great Britain (GB) to every other reachable within 150 minutes during the morning peak for the year 2023 using. This information comprises an all-to-all travel time matrix (TTM) at the national level. The TTM are estimated for public transport, bicycle, and walking. Public transport estimates are estimated for two times of departure, specifically during the morning peak and at night. Altogether, these TTMs present a range of opportunities for researchers and practitioners, such as the development of accessibility measures, spatial connectivity, and the evaluation of public transport service changes throughout the day.</p> <p>A full data descriptor is available in 'technical_note.html' file as part of the records of this repository.</p> <h2>Data records</h2> <p>The TTM structure follows a row matrix format, where each row represents a unique origin-destination pair. The TTMs are offered in a set of sequentially named <code>.parquet</code> files (more information about Parquet format at: <a href="https://parquet.apache.org/">https://parquet.apache.org/</a>). The structure contains one directory for each mode, where ‘bike’, ‘pt’, and ‘walk’, correspond to bicycle, public transport, and walking, respectively.</p> <h3>Walking</h3> <p>The walking TTM contains 13.3 million rows and three columns. The table below offers a description of the columns.</p> <div> <span>Table 1: </span>Walking travel time matrix codebook. <table><tbody><tr> <th>Variable</th> <th>Type</th> <th>Description</th> </tr> </tbody><tbody> <tr> <td>from_id</td> <td>nominal</td> <td>2011 LSOA/DZ geo-code of origin</td> </tr> <tr> <td>to_id</td> <td>nominal</td> <td>2011 LSOA/DZ geo-code of destination</td> </tr> <tr> <td>travel_time_p050</td> <td>numeric</td> <td>Travel time walking in minutes</td> </tr> </tbody> </table> </div> <h3>Bicycle</h3> <p>The bicycle TTM includes 40 million rows and four columns which are described in the table below.</p> <div> <span>Table 2: </span>Bicycle travel time matrix codebook. <table><tbody><tr> <th>Variable</th> <th>Type</th> <th>Description</th> </tr> </tbody><tbody> <tr> <td>from_id</td> <td>nominal</td> <td>2011 LSOA/DZ geo-code of origin</td> </tr> <tr> <td>to_id</td> <td>nominal</td> <td>2011 LSOA/DZ geo-code of destination</td> </tr> <tr> <td>travel_time_p050</td> <td>numeric</td> <td>Travel time by bicycle in minutes</td> </tr> <tr> <td>travel_time_adj</td> <td>numeric</td> <td>Adjusted travel time by bicycle in minutes. This adds 5 minutes for locking to the unadjusted estimate.</td> </tr> </tbody> </table> </div> <h3>Public transport</h3> <p>The LSOA/DZ TTM consists of six columns and 265 million rows. The internal structure of the records is displayed in the table below:</p> <div> <span>Table 3: </span>Public transport LSOA/DZ travel time matrix codebook. <table><tbody><tr> <th>Variable</th> <th>Type</th> <th>Description</th> </tr> </tbody><tbody> <tr> <td>from_id</td> <td>nominal</td> <td>2011 LSOA/DZ geo-code of origin</td> </tr> <tr> <td>to_id</td> <td>nominal</td> <td>2011 LSOA/DZ geo-code of destination</td> </tr> <tr> <td>travel_time_p025</td> <td>numeric</td> <td>25 travel time percentile by public transport in minutes</td> </tr> <tr> <td>travel_time_p050</td> <td>numeric</td> <td>50 travel time percentile by public transport in minutes</td> </tr> <tr> <td>travel_time_p075</td> <td>numeric</td> <td>75 travel time percentile by public transport in minutes</td> </tr> <tr> <td>time_of_day</td> <td>nominal</td> <td>A discrete value indicating the time of departure used. Levels: ‘am’ = 7 a.m.; ‘pm’ = 9 p.m.</td> </tr> </tbody> </table> </div> <p> </p> <p> </p>
Software and simulations for smBEVO publication
<p>Software and simulations for the publication "smBEVO: A computer vision approach to baseline drift correction for single-molecule time series". Preprint available on bioRxiv at https://doi.org/10.1101/2021.11.12.468397.</p>
Data files for the publication "Charge transfer across C-H---O hydrogen bonds stabilizes oil droplets in water"
<p>This dataset includes text/dat files for all the data included in the manuscript " Charge transfer across C-H---O hydrogen bonds stabilizes oil droplets in water".</p>
Dataset for publication: Error in the measurement of partial discharge pulses according to the frequency response of HFCT sensors
<p>Data set belonging to the <a href="https://ieeexplore.ieee.org/xpl/conhome/9611374/proceeding">2021 IEEE Electrical Insulation Conference (EIC)</a> paper with DOI: <a href="https://doi.org/10.1109/EIC49891.2021.9612353">10.1109/EIC49891.2021.9612353</a></p> <p>The authors preprint is available from Zendodo with DOI: <a href="https://doi.org/10.5281/zenodo.5769815">10.5281/zenodo.5769815</a>. </p> <p>Kamlichi, Abderrahim, Garnancho Fernando; Álvarez Fernando</p> <p><em>Error in the measurement of partial discharge pulses according to the frequency response of HFCT sensors</em></p>
Fig. 5 in Trichinellosis Of Wild Animals In Ukraine And Its Danger To The Public
Fig. 5. Frequency of trichinellosis for the population of Ukraine, 1986–2018.
IUPUI Public Art - "Torso Fragment"
**MESH PARAMETERS** **Object Description:** Digitized hollow aluminum sculpture of male torso; Real-life approximate dimensions 20"x17"x37" **Watertight:** Yes <br> **TEXTURE PARAMETERS** **Count:** 1 **Size:** 8,192 x 8,192 **Delighting:** No Source: Objaverse 1.0 / Sketchfab
Figure 11 from: Penev L, Georgiev T, Geshev P, Demirov S, Senderov V, Kuzmova I, Kostadinova I, Peneva S, Stoev P (2017) ARPHA-BioDiv: A toolbox for scholarly publication and dissemination of biodiversity data based on the ARPHA Publishing Platform. Research Ideas and Outcomes 3: e13088. https://doi.org/10.3897/rio.3.e13088
Figure 11 - Submission of manuscripts to ARPHA Writing Tool through Application Programming Interface (API).
Figure 9 from: Penev L, Georgiev T, Geshev P, Demirov S, Senderov V, Kuzmova I, Kostadinova I, Peneva S, Stoev P (2017) ARPHA-BioDiv: A toolbox for scholarly publication and dissemination of biodiversity data based on the ARPHA Publishing Platform. Research Ideas and Outcomes 3: e13088. https://doi.org/10.3897/rio.3.e13088
Figure 9 - The occurrence data from articles published in the Biodiversity Data Journal (in this case from the paper of Johnson 2013) are automatically indexed via Darwin Core Archive in the GBIF Integrated Publishing Toolkit.
Figure 4a from: Penev L, Georgiev T, Geshev P, Demirov S, Senderov V, Kuzmova I, Kostadinova I, Peneva S, Stoev P (2017) ARPHA-BioDiv: A toolbox for scholarly publication and dissemination of biodiversity data based on the ARPHA Publishing Platform. Research Ideas and Outcomes 3: e13088. https://doi.org/10.3897/rio.3.e13088
Figure 4a - Interactive mapping of geo-coordinated species occurrences (example from Frolov and Akhmetova 2013).
Figure 13 from: Penev L, Georgiev T, Geshev P, Demirov S, Senderov V, Kuzmova I, Kostadinova I, Peneva S, Stoev P (2017) ARPHA-BioDiv: A toolbox for scholarly publication and dissemination of biodiversity data based on the ARPHA Publishing Platform. Research Ideas and Outcomes 3: e13088. https://doi.org/10.3897/rio.3.e13088
Figure 13 - Conversion of Ecological Metadata Language (EML) metadata into data paper manuscripts in ARPHA Writing Tool.
Figure 7 from: Penev L, Georgiev T, Geshev P, Demirov S, Senderov V, Kuzmova I, Kostadinova I, Peneva S, Stoev P (2017) ARPHA-BioDiv: A toolbox for scholarly publication and dissemination of biodiversity data based on the ARPHA Publishing Platform. Research Ideas and Outcomes 3: e13088. https://doi.org/10.3897/rio.3.e13088
Figure 7 - Extraction and delivery of data and content from published articles to aggregators, nomenclators, archives, and indexers.
Figure 4d from: Penev L, Georgiev T, Geshev P, Demirov S, Senderov V, Kuzmova I, Kostadinova I, Peneva S, Stoev P (2017) ARPHA-BioDiv: A toolbox for scholarly publication and dissemination of biodiversity data based on the ARPHA Publishing Platform. Research Ideas and Outcomes 3: e13088. https://doi.org/10.3897/rio.3.e13088
Figure 4d - All taxon names usages (TNU) in an article are indexed and matched to their type of use (e.g. citations in the text, heading a taxon treatment, associated to images or present in identification keys, example from Brown et al. 2017).
Figure 8 from: Penev L, Georgiev T, Geshev P, Demirov S, Senderov V, Kuzmova I, Kostadinova I, Peneva S, Stoev P (2017) ARPHA-BioDiv: A toolbox for scholarly publication and dissemination of biodiversity data based on the ARPHA Publishing Platform. Research Ideas and Outcomes 3: e13088. https://doi.org/10.3897/rio.3.e13088
Figure 8 - Export of data from articles published in Biodiversity Data Journal. Species occurrences and other structured data tables can be downloaded in CSV format (green arrow); all species occurrences are also available as Darwin Core Archives and are automatically harvested and indexed by GBIF (red box and arrow).
Figure 12 from: Penev L, Georgiev T, Geshev P, Demirov S, Senderov V, Kuzmova I, Kostadinova I, Peneva S, Stoev P (2017) ARPHA-BioDiv: A toolbox for scholarly publication and dissemination of biodiversity data based on the ARPHA Publishing Platform. Research Ideas and Outcomes 3: e13088. https://doi.org/10.3897/rio.3.e13088
Figure 12 - Creation of data paper manuscripts from Ecological Metadata Language (EML) metadata hosted at the GBIF IPT
Figure 1 from: Penev L, Georgiev T, Geshev P, Demirov S, Senderov V, Kuzmova I, Kostadinova I, Peneva S, Stoev P (2017) ARPHA-BioDiv: A toolbox for scholarly publication and dissemination of biodiversity data based on the ARPHA Publishing Platform. Research Ideas and Outcomes 3: e13088. https://doi.org/10.3897/rio.3.e13088
Figure 1 - ARPHA-BioDiv is a set of standards, guidelines, tutorials, tools, workflows, journals and services, designed to facilitate the scholarly publication and dissemination of biodiversity data.
Figure 4c from: Penev L, Georgiev T, Geshev P, Demirov S, Senderov V, Kuzmova I, Kostadinova I, Peneva S, Stoev P (2017) ARPHA-BioDiv: A toolbox for scholarly publication and dissemination of biodiversity data based on the ARPHA Publishing Platform. Research Ideas and Outcomes 3: e13088. https://doi.org/10.3897/rio.3.e13088
Figure 4c - Images and pages from historic literature where a taxon name has been mentioned are available from various sources (e.g. Encyclopedia of Life and the Biodiversity Heritage Library via Pensoft Taxon Profile (PTP) (in this case Annoniaceae from Hoekstra et al. 2016).
Figure 4b from: Penev L, Georgiev T, Geshev P, Demirov S, Senderov V, Kuzmova I, Kostadinova I, Peneva S, Stoev P (2017) ARPHA-BioDiv: A toolbox for scholarly publication and dissemination of biodiversity data based on the ARPHA Publishing Platform. Research Ideas and Outcomes 3: e13088. https://doi.org/10.3897/rio.3.e13088
Figure 4b - Pensoft Taxon Profile (PTP) is created in real time by clicking on any taxon name mentioned in an article (in this case Annoniaceae from Hoekstra et al. 2016).
Figure 2 from: Penev L, Georgiev T, Geshev P, Demirov S, Senderov V, Kuzmova I, Kostadinova I, Peneva S, Stoev P (2017) ARPHA-BioDiv: A toolbox for scholarly publication and dissemination of biodiversity data based on the ARPHA Publishing Platform. Research Ideas and Outcomes 3: e13088. https://doi.org/10.3897/rio.3.e13088
Figure 2 - ARPHA consists of two independent journal publishing workflows: (1) ARPHA-XML, where the manuscript is written and processed via ARPHA Writing Tool and (2) ARPHA-DOC, where the manuscript is submitted and processed as document file(s).
Effectiveness of Public Service Announcements in Promoting Social Causes
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