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135 results for “roadmap”
Roadmapping information for each Wider Uptake case study
<p>Database summarizing information for each roadmapping step and for each of the WIDER UPTAKE H2020 project's case studies</p>
Virtual ChIP-seq predictions of binding of 36 transcription factor in Roadmap Epigenomics Project tissues
<p>This dataset contains predictions of Virtual ChIP-seq for binding of 36 transcription factors in Roadmap Epigenomics dataset tissues with matched DNase-seq and RNA-seq data.</p> <p>Tarball contains subfolders for each of the 36 TFs where Virtual ChIP-seq median MCC in validation cell types was > 0.3.</p> <p>Each subfolder contains gzipped BED files. Each file is named as <Tissue>_<Age>_<TF>_<Accession>_Predictions.bed.gz. Columns correspond to Chromosome, Start, End, <Tissue>_<Age>_<TF>_<Accession>, Posterior probability</p> <p>You can use the posterior probabilities provided in Virchip_PosteriorCutoffs_V3.0.0.tsv. These are posterior probability cutoffs which maximized MCC in H1-hESC cell type, or are set to 0.4 if there was no ChIP-seq data of that TF in H1-hESC (0.4 is the mode of all optimal posterior probability cutoffs in H1-hESC).</p>
Four-stages FAIR Roadmap - FAIR "Pyramid"
<p>On the basis of the experience of a community of practitioners, experts, engineers involved in the development of the EPOS Research Infrastructure, now with the ERIC status, involved in the ENVRI cluster and participating to the ENVRI-FAIR initiative, a common approach was observed, which is reflected into the re-organization FAIR principles into a four-stages roadmap which include: a) <em>data</em> stage, b) <em>metadata</em> stage, c) <em>access</em> stage and d) <em>use</em> stage.<br> These stages correspond to the actual conceptual approach driving day-to-day work of RI implementers in the solid Earth domain (EPOS). </p> <p>Data are usually the main business and wealth of scientists and data practitioners in RIs. As a consequence, the first conceptual step relates to data aspects (<em>Stage 1</em>). Once data is properly managed, RIs professional tend to conceptually tackle the challenge of data description and identification, in order to create the premises for data searchability and contextualization (<em>Stage 2</em>). Once data is properly managed, described and contextualized by means of metadata, then RIs practitioners approach the issue of making it accessible to users (<em>Stage 3</em>). In order to include functionalities that go beyond data access, for instance data analysis and processing, FAIR RIs and data stewardship systems should address a <em>fourth stage</em> concerned with services that <em>make use</em> of data (<em>Stage 1</em>) and metadata (<em>Stage 2</em>) FAIRly accessed (<em>Stage 3</em>) and produce new (meta)data as output.</p>
Partitioned linkage disequilibrium scores for active regulatory elements in ROADMAP datasets
<p>Partitioned linkage disequilibrium scores for active regulatory elements in ROADMAP epigenomics datasets, to accompany paper Lynall et al 2021</p> <p>Accompanying code available at https://github.com/maryellenlynall/psychimmgen2021</p> <p>Active regulatory elements annotations are a union of the following IDEAS annotations, representing enhancers and active promoters (see http://bx.psu.edu/~yuzhang/Roadmap_ideas/trackDb_test.txt for IDEAS track hubs): </p> <p>4_Enh<br> 6_EnhG<br> 8_TssAFlnk<br> 10_TssA<br> 14_TssWk<br> 17_EnhGA</p> <p>tissues.txt provides the list of ROADMAP tissues </p> <p>The partitioned_LD_scores folder contains partitioned LD scores in a format suitable for stratified LDSC analysis for European participants</p>
WoS and Scopus records for the bibliometric analysis in the output D2.2 Digital transformation of research and innovation roadmap of the reSEArch-EU project
<p>These files represent the exported WoS and Scopus records, used in the output D2.2 Digital transformation of research and innovation roadmap of the Horizont project reSEArch-EU, implemented by the SEA-EU university alliance.</p>
ROADMAP - Mini Webinar Series
<p>The ROADMAP project worked on rethinking the use of antimicrobials in livestock production systems. In a mini webinar series, we have asked task leaders to summarise their key learnings from the project. This has resulted in the following six episodes:</p> <ol> <li>Massimo Canali (UNIBO): "Stakeholders' behaviour and strategies towards AMU"</li> <li>Lee-Ann Sutherland (HUT): "Identifying actors' motivations in the use and reduction of antimicrobials"</li> <li>Sophie Molia (CIRAD): "Creating impact from the assessed strategies"</li> <li>Mette Vaarst (AU): "Co-building levers and incentives"</li> <li>João Sucena Afonso (ULIV): "Key learnings on the impact of alternatives in livestock and aquaculture production"</li> <li>Bernadette Oehen (FiBL): "Implementing innovative strategies"</li> </ol> <p>All mini webinars can be watched here: https://www.youtube.com/playlist?list=PLW5PYxzlSCfzzdqn2n6-eCxfkGhRAT6Qy</p>
The ROADMAP Final Event, 26 April 2023
<p>On the 25th and 26th of April, we were grateful to organise the ROADMAP final conference at the University Foundation in Brussels. On the 25th of April, before the last conference, an informal network cocktail and dinner were organised at the final event location (University Foundation Brussels). Travelling or local stakeholders were invited for a casual get-together in which discussions and networking were facilitated.<br> <br> On the 26th, the final event took place with presentations from project partners, a round table discussion with policymakers and a lively conversation with our stakeholder advisory board. It was a very inspiring and interactive day.<br> <br> The video recording and aftermovie from the event (26th of April) is available here: https://www.youtube.com/playlist?list=PLW5PYxzlSCfzzdqn2n6-eCxfkGhRAT6Qy</p> <p> </p>
ROADMAP Technical Abstract Video: The Living Labs
<p>The Living Labs are one of the pillars of the ROADMAP project, which is focused on the responsible use and reduction of antimicrobials in livestock production. In this video, you will have the chance to get to know better how a Living Lab works, its importance in the process of getting awareness of AMU (antimicrobial use) and how tools are given to farmers in order to implement good practices, recommendations and acknowledging other ways of improving animal health and welfare.</p> <p>Watch the video here: https://youtu.be/afXe1eHX2lM</p>
Teaser SUNRISE releases its technological roadmap to a clean energy EU
<p>Promotional video roadmap: We are thrilled to present our freshly released technological roadmap! The SUNRISE technological roadmap is the result of the integrated knowledge of a broad group of scientists across Europe and a key step to engage the whole community towards building a climate neutral EU. This roadmapping process was launched in May 2019 by a dedicated working group within the SUNRISE consortium, collecting and analyzing broad input from over 180 stakeholders at the SUNRISE Stakeholder Workshop on 17-18 June, 2019, in Brussels.</p>
Convolutional Neural Net (CNN) models for ENCODE-Roadmap DNase-seq peaks and Transcription Factor ChIP-seq peaks - Basset architecture
<p>Deep learning models trained on epigenomic landscapes from ENCODE and Roadmap Epigenomics. The models are Basset convolutional neural networks (Kelley, et al 2016). The dataset used to train these models can be found at https://doi.org/10.5281/zenodo.4059038. The file `nn.encode-roadmap.models.basset.clf.tar.gz` contains 10 cross-validated models in Tensorflow framework files as well as details on the architecture, cross-validation scheme, and training of these models. The file `nn.encode-roadmap.models.basset.clf.np_weights.tar.gz` contains the 10 cross-validated models' weights extracted to numpy array files (.npz).</p>
Roadmap for Developing a Dynamic and Reproducible Research Article with ARTE workflow
<p>The figures illustrates a roadmap for developing a dynamic and reproducible research article using <strong>ARTE (Article Reproducibility Template & Environment) </strong>workflow. The process is categorized into three levels of reproducibility: <strong>Minimal, Proper, and Full</strong>. Each level integrates specific tools and practices to enhance the reproducibility of the research.</p> <p>This proposal is published in the following <strong>OSF project</strong>: <a title="OSF" href="https://osf.io/njdq5/" target="_blank" rel="noopener">https://osf.io/njdq5/</a><br>Shared in the following <strong>GitHub repository</strong>: <a title="GitHub" href="https://github.com/phdpablo/article-template" target="_blank" rel="noopener">https://github.com/phdpablo/article-template</a><br>Exemplified in the following <strong>URL address</strong>: <a title="Article Example" href="https://phdpablo.github.io/article-template/" target="_blank" rel="noopener">https://phdpablo.github.io/article-template/</a></p> <h1>Minimal Reproducibility</h1> <p><strong>1. Use this template</strong>: Start by utilizing the provided template, which is pre-configured with the <strong>TIER Protocol 4.0</strong>. This protocol helps organize research projects in a systematic manner.</p> <p><strong>2. Edit READMEs</strong>: Customize the README files to reflect the details and conclusions of your research. These README files help document the project structure and contents.</p> <p><strong>3. Share on OSF</strong>: Share the project on the <strong>Open Science Framework (OSF)</strong> to ensure accessibility and transparency. This can be done at the beginning, during, or at the end of the research process.</p> <h1>Proper Reproducibility</h1> <p>In addition to the steps mentioned above, the following steps are added:</p> <p><strong>4. Quarto settings:</strong> Adjust the Quarto configuration to fit the needs of your project. This includes modifying the <em>_quarto.yml</em> file for different themes and output formats.</p> <p><strong>5. Develop your narrative</strong>: Write the research narrative using <em>Quarto’s .qmd files</em> within RStudio. This narrative forms the main body of your article and integrates text, code, and outputs seamlessly.</p> <p><strong>6. Environment control:</strong> Implement environment control using the <em>renv package</em>. This ensures that the R environment is consistent and reproducible. The <em>renv.lock</em> file captures the exact versions of R packages used in the project.</p> <p><strong>7. Share dynamic article:</strong> Render and share the dynamic document via GitHub Pages. The Quarto-generated HTML files (docs folders) are hosted on GitHub Pages, making the research accessible and interactive.</p> <h1>Full Reproducibility</h1> <p>Building on the proper reproducibility steps, full reproducibility adds:</p> <p><strong>8. Use Docker:</strong> Employ Docker for operating system-level environment control. A Docker container encapsulates the entire project environment, ensuring that the research can be replicated exactly, regardless of the local machine setup.</p> <h2>Tools Utilized</h2> <ul> <li><strong>TIER Protocol 4.0</strong>: Provides a framework for organizing and documenting research projects.</li> <li><strong>OSF:</strong> A platform for sharing research outputs and ensuring open science practices.</li> <li><strong>Quarto:</strong> A tool for creating dynamic documents that integrate text, code, and outputs.</li> <li><strong>RStudio:</strong> An integrated development environment (IDE) for R, facilitating data analysis and reproducible research.</li> <li><strong>Git/GitHub:</strong> Version control systems that track changes and manage project versions.</li> <li><strong>renv: </strong>An R package for managing and reproducing consistent R environments.</li> <li><strong>GitHub Pages:</strong> A service for hosting static websites directly from a GitHub repository.</li> <li><strong>Docker:</strong> A platform for containerizing applications to ensure consistent environments across different systems.</li> </ul> <h2>Summary</h2> <p>This template guides researchers through creating a reproducible and dynamic article using ARTE (Article Reproducibility Template & Environment) workflow. It starts with basic project setup and documentation, progresses through developing the research narrative with environment control, and culminates in full reproducibility with Docker. This structured approach ensures that research is well-documented, versioned, and easily shareable, promoting open science practices.</p>
Appendices to the Roadmap for action for the project More Welfare: towards new risk assessment methodologies and harmonised animal welfare data in the EU
Open the record for dataset details and reuse information.
Strategic Roadmap Toward I4.0: A Model Based on Maturity and Readiness Analysis
<p><span>Modern distributed manufacturing is having an essential role in the world economy. Agility, flexibility, and responsiveness distinguishs an innovation-driven business ecosystem, mainly anchored in cloud-based environments. Therefore, manufacturing firms demand an structured roadmap to lead their way toward Industry 4.0, impacting their supply chains, operations, and business models. Naturally, dificulties will raise strong dpending how far they are from a modern ditributed model adherent to I4.0 and their readiness to start a digital transformation roadmap. Therefore, maturity and readiness analysis is essential to evaluate the impact of a sound design process toward I4.0 and to anticipate obstacles and possible return. This work proposes a design framework based on a web-based distributed cloud manufacturing approach to suport a roadmap to I4.0, relying on maturity and readiness analysis, focusing on specific building blocks as rationales.</span> </p>
ROADMAP Animation Video
<p>This animation movie of ROADMAP describes the importance of antimicrobial resistance, how ROADMAP supports prudent use of antimicrobials in livestock sector and ROADMAP's tailored solutions in case studies and living labs in 10 different countries in pig, poultry, dairy and veal production farms.</p> <p> </p> <p>French version: https://www.youtube.com/watch?v=YP-02Io4SoU&t=57s</p> <p>Spanish version: https://www.youtube.com/watch?v=vlew_jIYuOs&t=13s</p> <p>Italian version: https://www.youtube.com/watch?v=qi_00E9Yqmc&t=6s</p> <p> </p> <p>Learn more about ROADMAP by visiting the website: <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbXBNN3NtZFlfa3dQYWpmem9aSE1vNzA2OHdWd3xBQ3Jtc0ttZUExc2xDU28yaVVacjVSTFZia1lWbVAzNUJmVDdDbElpdkg5UTNrei15VTUzbjZocU9QbDFYaFlRRTRFcWVPOWEweUZ3dG5saTZuVDNLLWR5ODVTcmRzMU90R1NtVmJsTVdoakVMSHMtUkZSZnY5UQ&q=https%3A%2F%2Fwww.roadmap-h2020.eu%2F&v=7FvY1wzbjhg">https://www.roadmap-h2020.eu/</a></p>
INFOGRAPHICS ON COUNTRY CASE STUDIES - ROADMAP H2020 project
<p>We are happy to introduce you the ROADMAP infographics on country case studies!<br> <br> During our 3rd ROADMAP Annual Meeting, members and stakeholders discussed their goals and challenges on every country involved in our project and we used the technique of message houses in order to obtain and gather all the information.<br> <br> Right after, we translated this information into visuals in order to make it easier to convey and analyse. The infographics are ready to download!</p>
ROADMAP interviews - Meet the Scientifics
<p><a href="https://www.youtube.com/hashtag/roadmaph2020">#ROADMAPH2020</a></p> <p>On this series of interviews, you will learn more about LL and ROADMAP project. You can watch the interviews here: https://www.youtube.com/watch?v=-tD3V51w5mE&list=PLW5PYxzlSCfyQCvfv1p0rc9wjfZrWJT27</p> <p>ROADMAP is a multi-actor project, including multiple institutions and organisations as project partners from different countries. In each country, partners formed working groups so called “Living Labs” composed of stakeholders from different groups, such as vets, farmers, farmer institutions, pharmaceutical companies.</p> <p>There are 12 Living Labs (LL) in total and each LL works differently, focusing on four levels, 1) farming systems, 2) sector, 3) regulatory, and 4) societal – but no LL works on all four levels.</p> <p>Please check out our website to learn more: www.roadmap-2020.eu</p> <p><a href="https://www.youtube.com/hashtag/antimicrobial">#antimicrobial</a> <a href="https://www.youtube.com/hashtag/antibiotics">#antibiotics</a> <a href="https://www.youtube.com/hashtag/animalbreeding">#animalbreeding</a> <a href="https://www.youtube.com/hashtag/h2020">#H2020</a></p>
Companion Dataset for Manuscript "A Roadmap for Simulating Chemical Dynamics on a Parametrically Driven Bosonic Quantum Device"
<p>This dataset provides the data necessary to reproduce the figures in the manuscript titled "A Roadmap for Simulating Chemical Dynamics on a Parametrically Driven Bosonic Quantum Device". Combined with the cQED4ChemDyn code, provided through Github and Zenodo, it regenerates the figures in the manuscript.</p> <p>The project itself provides an implementation that connects chemical kinetics of elementary reactivity models with the framework of the Kerr-Cat circuit quantum electrodynamics (cQED), using the Hamiltonian describing the physics of the hardware and a Lindbladian open quantum dynamics formalism for the time-evolution of the system. For more information, check the existing citation for the publication; any usage of the code/data should cite the preprints (and publications) once available.</p>
Research & Innovation Digitalization Scoreboard for the SEA-EU alliance and its member universities in the output D2.2 Digital transformation of research and innovation roadmap of the reSEArch-EU project
<p>This file is a detailed Research & Innovation Digitalization Scoreboard for the SEA-EU alliance and its member universities, used in the output D2.2 Digital transformation of research and innovation roadmap of the Horizont project reSEArch-EU, implemented by the SEA-EU university alliance.</p>
A roadmap to durable BCTV resistance using long-read genome assembly of genetic stock KDH13
<p>PacBio Sequence data associated with genetic stock KDH13.Datasets include genome resources and annotated files associated with the manuscript "Long-read genome assembly of Double Haploid Sugar Beet KDH13 provides roadmap for durable genetic resistance to Beet Curly Top Virus". This includes genome assembly, ordered genome assembly, protein predictions, variant call format files for an F1 hybrid (KDH13xKDH19-17).</p>
A roadmap to reconstructing muscle architecture from CT data
<div> <span>Skeletal muscle is responsible for voluntary force generation across animals, and muscle architecture largely determines the parameters of mechanical output. The ability to analyze muscle performance through muscle architecture is thus a key step towards better understanding the ecology and evolution of movements and morphologies. In pennate skeletal muscle, volume, fiber lengths and attachment angles to force transmitting structures comprise the most relevant parameters of muscle architecture. Measuring these features through tomographic techniques offers an alternative to tedious and destructive dissections, particularly as the availability of tomographic data is rapidly increasing. However, there is a need for streamlined computational methods to access this information efficiently. Here, we establish and compare workflows using partially automated image analysis for fast and accurate estimation of animal muscle architecture. After isolating a target muscle through segmentation, we evaluate freely available and proprietary fiber tracing algorithms to reconstruct muscle fibers. We then present a script using the Blender Python API to estimate attachment angles, fiber lengths, muscle volume and Physiological Cross-Sectional Area. We apply these methods to insect and vertebrate muscle and provide guided workflows. Results from fiber tracing are consistent compared to manual measurements but much less time-consuming. Lastly, we emphasize the capabilities of the open-source 3D software Blender as both a tool for visualization and a scriptable analytic tool to process digitized anatomical data. Across organisms, it is feasible to extract, analyze, and visualize muscle architecture from tomography data by exploiting the spatial features of scans and the geometric properties of muscle fibers. As digital libraries of anatomies continue to grow, the workflows and approach presented here can be part of the open-source future of digital comparative analysis.</span> </div>
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.