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386 results for “dissemination”
Raw data for "Development and characterization of a non-human primate model of disseminated synucleinopathy"
<p><span>In this study, the performance and biodistribution of the retrogradely-spreading AAV9-SynA53T vector was evaluated in the NHP brain. Conducted intraparenchymal deliveries of viral suspensions in the left putamen gave rise to a disseminated synucleinopathy in a circuit-specific basis.</span></p>
Dataset for the IntoValue 1 + 2 studies on results dissemination from clinical trials conducted at German university medical centers completed between 2009 and 2017
<p>The IntoValue dataset contains clinical trials conducted at one of 35 German UMCs and registered on ClinicalTrials.gov or the German Clinical Trials Registry (DRKS). All trials were reported as complete between 2009 and 2017 on the trial registry at the time of data collection. The dataset also includes a results publication found via manual searches; if multiple results publications were found, the earliest was included.</p> <p>Trials were associated with a German UMC by searching for trials with a UMC listed as responsible party or lead sponsor, or with a principle investigator (PI) from a UMC ('lead_city'). Version 1 additionally includes trials with a UMC only as a facility (`facility_city`). A lookup table of regular expressions used to identify German UMCs is available at <a href="https://github.com/quest-bih/IntoValue2/blob/master/data/1_sample_generation/city_search_terms.csv">https://github.com/quest-bih/IntoValue2/blob/master/data/1_sample_generation/city_search_terms.csv</a>.</p> <p>Trials include all interventional studies and are not limited to investigational medical product trials, as regulated by the EU's Clinical Trials Directive or Germany's Arzneimittelgesetz (AMG) or Novelle des Medizinproduktegesetzes (MPG).</p> <p>DRKS data were searched (pre-filtered for completion years and study status as well as Germany as 'Country of recruitment') and downloaded as CSVs from the DRKS website (<a href="https://www.drks.de/">https://www.drks.de/</a>). ClinicalTrials.gov data were downloaded downloaded as pipe files from Clinical Trials Transformation Initiative (CTTI) Aggregate Content of ClinicalTrials.gov (AACT) (<a href="https://aact.ctti-clinicaltrials.org/pipe_files">https://aact.ctti-clinicaltrials.org/pipe_files</a>). DRKS and ClinicalTrials.gov use different terminology for various trial aspects, such as phase and masking; these different levels are captured in the data dictionary as `levels_drks` and `levels_ctgov`. For later analyses requiring parity across registries, levels for some variables were collapsed and a lookup table is provided in `iv_data_lookup_registries.csv`.</p> <p>These data were generated and used for two publications (Wieschowski et al., 2019; Riedel et al. 2021) and therefore comprises two versions (indicated as `iv_version`).</p> <p>For version 1, registry data was collected on April 17, 2017 from ClinicalTrials.gov and on July 27, 2017 for DRKS and was limited to trials with a completion date on DRKS and primary completion date on ClinicalTrials.gov between 2009 and 2013. Version 1 manual searches for results publications were conducted from 2017-07-01 to 2017-12-01.<br> For version 2, registry data was collected on June 3, 2020 and was limited to trials with a completion date on DRKS and ClinicalTrials.gov between 2014 and 2017. Version 2 manual searches for results publications were conducted from 2020-07-01 to 2020-09-01.</p> <p>Raw registry data for versions 1 and 2 is available in `raw-registries.zip`.</p> <p>Publication identifiers (DOI, PMID, URL) were manually entered during the publication search and then further enhanced using the API of Internet Archive's open-source Fatcat catalog of research publications, to add PMIDs based on DOIs, and vice versa.</p> <p>Manual search steps differed slightly in the two versions and are indicated and described in `identification_step`.<br> Version 1 includes trials with a German UMC as either a `lead_city` or a `facility_city`, whereas version 2 is limited to trials a German UMC as a `lead_city`.</p> <p>Each row indicates a single trial registration. Due to changes in completion dates, some trials are duplicated between versions as indicated in `is_dupe`. Cross-registered trials were manually deduplicated, and some cross-registered duplicates remain (e.g., DRKS00004156 and NCT00215683) and are not indicated in the dataset.</p> <p>All dates are provided as `yyyy-mm-dd`.</p> <p>Additional documentation on each variable (type, description, levels) is provided in `iv_data_dictionary.csv`.</p> <p>Additional information on the project and methods for generating the dataset is available in associated publications and at the project's OSF page (<a href="https://osf.io/98j7u/">https://osf.io/98j7u/</a>). Code for the project is available at <a href="https://github.com/quest-bih/IntoValue2">https://github.com/quest-bih/IntoValue2</a>.</p> <p><strong>References:</strong></p> <p>Wieschowski, S., Riedel, N., Wollmann, K., Kahrass, H., Müller-Ohlraun, S., Schürmann, C., Kelley, S., Kszuk, U., Siegerink, B., Dirnagl, U., Meerpohl, J., & Strech, D. (2019). Result dissemination from clinical trials conducted at German university medical centers was delayed and incomplete. Journal of Clinical Epidemiology, 115, 37–45. <a href="https://doi.org/10.1016/j.jclinepi.2019.06.002">https://doi.org/10.1016/j.jclinepi.2019.06.002</a></p> <p>Riedel, N., Wieschowski, S., Bruckner, T., Holst, M. R., Kahrass, H., Nury, E., Meerpohl, J. J., Salholz-Hillel, M., & Strech, D. (2021). Results dissemination from completed clinical trials conducted at German university medical centers remained delayed and incomplete. The 2014-2017 cohort. Journal of Clinical Epidemiology, 0(0). <a href="http://doi.org/10.1016/j.jclinepi.2021.12.012">https://doi.org/10.1016/j.jclinepi.2021.12.012</a><br> </p>
Triangle of Biomedicine Framework to Analyze the Citations' Impact on Categories Dissemination in the PubMed Database
<p>This is the data and the most relevant script of the paper 'Triangle of Biomedicine Framework to Analyze the Citations’ Impact on Categories Dissemination in the PubMed Database'.</p>
Final Dataset for the DIssemination of REgistered COVID-19 Clinical Trials (DIRECCT) Study
<p>The DIRECCT study is a multi-phase examination of clinical trial results dissemination during the COVID-19 pandemic.</p> <p>Interim data for trials completed during the first six months of the pandemic (i.e., 1 January 2020 – 30 June 2020) was previously deposited at https://doi.org/10.5281/zenodo.4669936.<br> This data deposit comprises the results of searches for trials completed during the first 18-months of the pandemic (i.e., 1 January 2020 – 30 June 2021).<br> The data structure for the final phase of the project is not identical to the interim data as it was substantially more complex.<br> The data include datatables (CSVs) that can be treated as relational and joined on the `id` or `trn` columns. See datamodel.png for an overview of the data.</p> <p>Details on data sources and methods for the creation and analysis of this dataset are available in a detailed protocol (Version 3.1, 19 July 2023) : https://osf.io/w8t7r</p> <p>Note: This repository will be updated with additional information including a codebook and archives of raw data.</p> <p>Additional information on the project is available at the project's OSF page: https://doi.org/10.17605/osf.io/5f8j2.</p>
OpenUP survey on researchers' current perceptions and practices in peer review, impact measurement and dissemination of research results
<p>OpenUP project (http://openup-h2020.eu/) conducted a survey to capture current perceptions and practices in peer review, dissemination of research results and impact measurement among European researchers. The survey was coducted between 20 January and 23 February 2017. It consisted of four sections. The first section asked a series of questions on the respondents’ scientific discipline, career stage, gender and other characteristics. The following sections asked a series of questions on peer review practices, dissemination of research results and impact measurement/use of altmetrics. The questionnaire was collaboratively prepared by the OpenUP consortium. </p> <p>The survey was implemented via surveygizmo tool (https://www.surveygizmo.com/). Invitations to participate were sent to a random sample of researchers from arXiv, Pubmed and RePEc. The OpenUP team mined researchers’ contact details from these platforms. The OpenUP project team made efforts to further boost the repondent sample for certain underrepresented areas through the DARIAH website, THESIS network, EURODOC, AIMS portal, the Parthenos community and other channels. The survey targeted researchers from the EU-28, Switzerland and Norway. The goal was to get around 1,000 responses. In total, there were 976 completed response and completion rate was 72.4%. </p> <p>The attached documents include the questionnaire and the dataset. In the dataset (cvs file) the top row contains numbered questions that correspond to the numberring in the questionnaire (word file). The data was exported as an excel file, anonymised by creating respondent IDs and IP data were deleted. The file was then converted to CSV.</p> <p> </p>
(Rawdata) How do Spanish educational researchers use X's platform to promote the dissemination of scientific knowledge: a descriptive study: a descriptive study
<p>Rawdata used in the article 'How do Spanish educational researchers use X's platform to promote the dissemination of scientific knowledge: a descriptive study', from the project Comscienciaeduspain (FCT-20-15761), executed with the collaboration of the Spanish Foundation for Science and Technology – Ministry of Science and Innovation.</p>
Tractostorm 2: Optimizing tractography dissection reproducibility with segmentation protocol dissemination
<p>Submissions for the Tractostorm 2 Project [1] from our collaborators (raters) are available for new analysis.<br> Contains regions of interest (ROIs) as well as resulting bundles. Segmentations were performed with MI-Brain [2] (<a href="https://github.com/imeka/mi-brain">MI-Brain</a>)</p> <p>Initial data is the same as in the initial <a href="https://zenodo.org/record/2547025#.YRV2S3VKiUk">Tractostorm Project</a> [3]<br> Contains the data as sent to collaborators and the written document containing the dissection protocol in detail.</p> <p>[1] Rheault, Francois, et al. "Tractostorm 2: Optimizing tractography dissection reproducibility with segmentation protocol dissemination." <em>Human Brain Mapping</em> (2022).<br> [2] Rheault, Francois, et al. "MI-Brain, a software to handle tractograms and perform interactive virtual dissection." <em>Proceedings of the ISMRM Diffusion study group workshop, Lisbon</em>. 2016.<br> [3] Rheault, Francois, et al. "Tractostorm: The what, why, and how of tractography dissection reproducibility." <em>Human brain mapping</em> 41.7 (2020): 1859-1874.</p> <p>Data Organization:<br> The 5 HCP subjects were duplicated 4 times each.<br> 193441 -> A111, B218, C317, D418<br> 219231 -> A127, B228, C320, D426<br> 286650 -> A136, B237, C338, D436<br> 486759 -> A149, B246, C344, D443<br> 615441 -> A156, B252, C359, D450<br> <br> Bundles can be segmented automatically using the <a href="https://github.com/scilus/scilpy">scilpy</a> toolbox.<br> scil_filter_tractogram.py ${INPUT} ${OUTPUT} ${OPTIONS}</p> <ul> <li>${INPUT} would be the whole brain tractogram of an HCP subject in data_to_segment.zip</li> <li>${OUTPUT} would be the bundle filename (preferably .trk format)</li> <li>${OPTIONS} would be the sequence of ROIs to apply, one for each bundle. <ul> <li><strong>CC</strong>: '--drawn_roi CENTRAL_CC.nii.gz any include --drawn_roi LOWER_AXIAL_LIM.nii.gz any exclude --drawn_roi POST_C_L.nii.gz any exclude --drawn_roi PRE_C_L.nii.gz any exclude --drawn_roi POST_C_R.nii.gz any exclude --drawn_roi PRE_C_R.nii.gz any exclude'</li> <li><strong>AF_L</strong>: '--drawn_roi CENTRAL_CS_L.nii.gz any include --drawn_roi MEDIAL_SAGITTAL_LIM.nii.gz any exclude --drawn_roi POST_C_L.nii.gz any include --drawn_roi PRE_C_L.nii.gz any include --drawn_roi TEMPORAL_ENTRY.nii.gz any include --drawn_roi TEMPORAL_STEM.nii.gz any exclude'</li> <li><strong>PYT_L</strong>: '--drawn_roi IC_L.nii.gz any include --drawn_roi MO_L.nii.gz any include --drawn_roi MB_L.nii.gz any include --drawn_roi MO_L_NOT.nii.gz any exclude --drawn_roi MID_SAGITTAL_PLANE.nii.gz any exclude --drawn_roi POST_C_L.nii.gz any exclude --drawn_roi PRE_C_L.nii.gz any exclude'</li> </ul> </li> </ul>
Dissemination of information in event-based surveillance, a case study of Avian Influenza - dataset
<p>This dataset contains a set of tables corresponding to the manual analysis of outbreak-related reports detected by two event-based surveillance tools, PADI-web and HealthMap, supporting the submitted article "Dissemination of information in event-based surveillance, a case study of Avian Influenza".</p> <p>The reports were published between 1 July 2018 and 30<sup>st</sup> June 2019 and described one or several avian influenza outbreaks. We collected 337 reports from PADI-web and 115 from HealthMap. Two epidemiologists identified all the reported events in the news, and classified them as official (notified to the World Organization for Animal Health) or non-official.</p> <p>In order to trace back the source of the event’s information, the epidemiologist manually traced the information pathway of all events mentioned in the PADI-web and HealthMap news. The pathway was deducted from the sources cited in the news. When a source was cited with a hyperlink, we followed the hyperlink to retrace the information pathway as far as possible to the primary source. For each cited source, we created a pair of emitter <em>S<sub>E</sub></em> and receptor sources <em>S<sub>R</sub></em>. We labelled each new source with its type (e.g. online news source, national veterinary authority, etc.). We also recorded their geographical focus (local, national or international) and their specialization in the animal health news coverage (general or specialized).</p> <p>The script for data analyses is available at https://github.com/SarahVal/EBS-network.</p>
InnORBIT dissemination and communication plan and outcomes
<p>The present dataset is generated in the frame of the Horizon 2020 project "InnORBIT: Empowering innovation intermediaries to generate sustainable initiatives to accelerate the commercialisation of space innovation" (<a href="https://innorbit.eu">innorbit.eu</a>). </p> <p>This dataset describes the InnORBIT project's dissemination and communication plan and also includes the data collected from dissemination and communication activities to measure the progress against the project's targets for outreach during project implementation (January 1st, 2021 - July 31st, 2023). The current (second) version of the dataset includes the following files:</p> <ul> <li><strong>InnORBIT Dissemination and Communication Plan: </strong>Final version of the deliverable, updated on 30/06/2023.</li> <li><strong>InnORBIT Dissemination Activities:</strong> Spreadsheet with detailed dissemination data and calculations for the estimation of progress against KPIs.</li> <li><strong>InnORBIT website analytics (2 files): </strong>Google Analytics reports for the https://innorbit.eu website (file #1: 30/04/2021 - 05/07/2023; file #2: 06/07/2023 - 31/07/2023)</li> <li><strong>InnORBIT Monitoring and Evalution: </strong>Analytics related to the e-learning platform, including usage and performance of the e-learning content. </li> </ul>
BIObec dissemination table
<p>This sheet specifies all the partners' contributions to communicate and disseminate the BIObec project along its entire duration. Links to social media posts, website articles or scientific publications can be found inside it.</p>
(Processed data) How do Spanish educational researchers use X's platform to promote the dissemination of scientific knowledge: a descriptive study: a descriptive study
<p>Processed data used in the article 'How do Spanish educational researchers use X's platform to promote the dissemination of scientific knowledge: a descriptive study', from the project Comscienciaeduspain (FCT-20-15761), executed with the collaboration of the Spanish Foundation for Science and Technology – Ministry of Science and Innovation.</p>
InnoRate_Data_collected_from_dissemination_events_Dataset15_2021.12.28_v1
<p>This dataset contains the aggregate data of the dissemination activities and events that were performed in the frame of the InnoRate Project (H2020 GA 821518). These activities and events have mostly been focused on promoting the InnoRate platform and services, its pilot rounds, the benefits for each user group, the matchmaking and investment readiness events to InnoRate’s stakeholders.</p>
Pre- and post-intervention responses to a knowledge, attitudes, and practices survey for the study, "Disseminating vaccination information in baby soap products increases knowledge and vaccine uptake in central Uganda: A non-randomized controlled trial"
<p>This dataset contains responses to the pre- and post-intervention knowledge, attitudes, and practices surveys utilized for the study, "Disseminating vaccination information in baby soap products increases knowledge and vaccine uptake in central Uganda: A non-randomized controlled trial."</p>
Recording of ETAPAS 4th Dissemination Event: Tools for the ethical and trustworthy adoption of Artificial Intelligence in the service of public administrations
<p>On the 12th of October 2022 the 4th ETAPAS dissemination event "Tools for the ethical and trustworthy adoption of Artificial Intelligence in the service of public administrations" took place online and on site at the Centre for Research & Technology Hellas (CERTH).</p> <p>If you missed the workshop you can find here the recording of the event, where we presented the first outputs generated by the ETAPAS Project to Greek Public Administration in order display concrete results on how AI can be ethically embedded in their activities.</p> <p>Among the topics we discussed:</p> <ul> <li>the ETAPAS Project and key tools developed for the good governance of AI;</li> <li>the chatbot Kari and the challenges using AI-based solutions presents;</li> <li>the development of a misinformation detection platform by CERTH;</li> <li><a href="https://www.pop-ai.eu/">popAI</a> and <a href="https://token-project.eu/the-project/">TOKEN</a> projects;</li> <li>strategies for a governance framework for artificial intelligence in public administration.</li> </ul> <p>Check this recording to see all the interesting presentations and discussions that emerged during the project.</p>
Disseminating metaproteomic informatics capabilities and knowledge using the Galaxy-P framework
<p>Data for the "<strong>Disseminating metaproteomic informatics capabilities and knowledge using the Galaxy-P framework</strong>" paper and training.</p>
Gajderowicz, B., Fisher, A., Mago, V.: (preperation) "Graph pruning for identifying COVID-19 misinformation dissemination patterns and indicators on Twitter/X"
<p>This dataset is for the repository <a href="https://github.com/bgajdero/social-graph-analysis-2024">https://github.com/bgajdero/social-graph-analysis-2024</a>.</p>
Data for dissemination and communication reporting
<p>This data was collected through the periodic monitoring of the project's miscellaneous dissemination activities, such as publications in relevant journals, posts, etc. The data consist of a list that depicts the number of publications, posts, events organized or attended by the consortium partners, etc. as well as the number of different type of stakeholders reached by the project's dissemination activities. The purpose of collecting this data is to assess the outreach and efficiency of the dissemination activities during the implementation of the project.</p>
Fig. 3. A in Fatal Rameshwarotrema uterocrescens infection with ulcerative esophagitis and intravascular dissemination in green turtles
Fig. 3. A Rameshwarotrema uterocrescens Rao (1975) (Digenea, Pronocephalidae) from Chelonia mydas Linnaeus 1758 (Testudines, Cheloniidae) from Brazil. Scale bar = 200 μm. B Rameshwarotrema uterocrescens Rao (1975) (Digenea, Pronocephalidae) from Chelonia mydas Linnaeus 1758 (Testudines, Cheloniidae) from Brazil under plane-polarized light. Note birefringent eggs (arrow). Scale bar = 200 μm. C Egg dissected from Rameshwarotrema uterocrescens Rao (1975) (Digenea, Pronocephalidae) from Chelonia mydas Linnaeus 1758 (Testudines, Cheloniidae) from Brazil. Note polar filament (arrow). Scale bar = 50 μm.
Fig. 1. A in Fatal Rameshwarotrema uterocrescens infection with ulcerative esophagitis and intravascular dissemination in green turtles
Fig. 1. A Obstructive ulcerous exudative gastroesophagitis, gastroesophageal region, large ulcerated area covered by solid caseous exudate. Scale bar = 3 cm. B Granulomatous necrotic hepatitis, liver, miliary caseous parasitic granulomas. Scale bar = 4 cm.
Fig. 2. A in Fatal Rameshwarotrema uterocrescens infection with ulcerative esophagitis and intravascular dissemination in green turtles
Fig. 2. A Initial lesion caused by R. uterocrescens (red arrow) associated with esophageal gland desquamation (black arrow). Scale bar = 100 μm. B Ulcerative esophagitis, esophagus, extensive loss of esophageal mucosa with eight specimens of R. uterocrescens (arrow) embedded in necrotic amorphous eosinophilic tissue in submucosa with marked heterophilic inflammatory infiltrate. Scale bar = 500 μm. C Marked inflammation with heterophils and macrophages (*) in esophageal submucosa. Scale bar = 50 μm. D. R. uterocrescens in ectatic vessel, note red blood cells (arrow). Scale bar = 100 μm. E Heart with R. uterocrescens under plane-polarized light, with birefringent eggs between myocardiocytes (arrow). Scale bar = 100 μm. F Granulomatous hepatitis, liver, R. uterocrescens (arrow) next to necrotic mass (*) formed by degenerate leukocytes, rare parasite eggs, cell debris peripherally enveloped by multinucleated giant cells. Scale bar = 200 μm. G Granulomatous hepatitis, liver, birefringent R. uterocrescens eggs (black arrow) under plane-polarized light enveloped by multinucleated giant cells (red arrow). Scale bar = 50 μm. H Immersed eggs (black arrow) in thrombotic (*) arteritis (red arrow). Parasite in kidney artery seen under planepolarized light with intensely birefringent eggs. Scale bar = 100 μm. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
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International Brain Laboratory public data
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OpenNeuro
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