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392 results for “tutorial”
Datasets and databases for the SigXTalk tutorial
Open the record for dataset details and reuse information.
nf-core/airrflow tutorial data
<p>Subsampled datasets to be used as test data for the nf-core/airrflow tutorial.</p>
FastEddy-model Tutorials: Canopy, Example05_CANOPY
<p>This archive contains the data required for the FastEddy-model tutorial on canopy flows. It consists of a .csv file with an example of leaf area density (LAD) profile as a function of non-dimensional height relative to canopy top.</p>
FAIRmat Tutorial 1: Publish and Explore Data with NOMAD
<p>This tutorial is dedicated to FAIR data management of materials science data based on the NOMAD platform.</p> <p>We will cover the whole data-life cycle: starting with data on your hard drive, we learn how to prepare, upload, publish data, and reference them with a DOI. Furthermore, we will learn how to explore, download, and use data that were published on NOMAD before. We will perform these steps with NOMAD's graphical user interface and its APIs. This is just one installment in a series of tutorials; other tutorials will cover data analysis, lab notebooks, using NOMAD locally (NOMAD Oasis), workflows, and much more.<br><br><strong>Disclaimer:</strong> NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p>
FAIRmat Tutorial 3: Introduction to the Artificial Intelligence Toolkit
<p>This tutorial is dedicated to the NOMAD artificial-intelligence (AI) toolkit, the platform for running (jupyter) notebooks to analyse with AI tools the data contained in the NOMAD Archive. </p> <p>We will cover, in an interactive, hands-on fashion, the several aspects of the AI-toolkit: the query over the NOMAD Archive via the NOMAD API, the basic notebooks for learning AI methods, and the advanced notebooks, where the workflow of relevant publications, in which AI is applied to materials science, can be interactively reproduced and further explored. Furthermore, we will introduce the local AI-toolkit app that allows to run a local version of the notebooks, e.g., to combine own data with the NOMAD Archive data. </p> <p>At the end of the first day, few tutorial notebooks will be suggested to be perused by the participants before the second day starts. In the second day, break-out rooms will be organized, and in each room one of the selected tutorial notebooks will be discussed.</p> <p> </p> <p><strong>Disclaimer:</strong> NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p>
FAIRmat Tutorial 4: NOMAD Oasis and FAIR data collaboration and sharing
<p>FAIRmat further develops NOMAD from a central publishing service to a federated data management platform. The NOMAD Oasis is part of this. Institutes, universities, and research groups use NOMAD Oasis as a local repository to manage their research data. Each method is different and requires ways of data acquisition, different data formats, different analysis tools, but FAIR-ness requires that all data is well described with rich specific metadata.</p> <p>In this tutorial, we focus on how to get started with NOMAD Oasis and adapt it to your research. One the first day, two talks will introduce you the general FAIRmat strategy and its "bottom-up" approach to manage heterogenous but FAIR data. On the second day, we will give the practical, step-by-step guides to get started with an Oasis: How you can install NOMAD Oasis, create example data, add schemas, and create ELNs.</p> <p> </p> <p><strong>Disclaimer:</strong> NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p> <p><strong> </strong></p>
FAIRmat Tutorial 13: NOMAD for Experimental Data Management in Synthesis
<p>FAIRmat Tutorial 13, presented by <a href="https://www.fairmat-nfdi.eu/fairmat/areas-fairmat/area-a-fairmat" target="_blank" rel="noopener">FAIRmat Area A Synthesis</a>, introduces <a href="https://nomad-lab.eu/nomad-lab/" target="_blank" rel="noopener">NOMAD</a> and <a href="https://nomad-lab.eu/nomad-lab/nomad-oasis.html">NOMAD Oasis</a> as essential tools for research data management (RDM). This tutorial will specifically demonstrate how to utilize these tools for managing experimental materials science data, with a particular focus on synthesis data.</p> <p>Participants will learn about NOMAD's versatile data model, which ensures data interoperability, and its various types and levels of schemas, including custom yaml schemas, community standards, plugins, and BaseSections. The tutorial will also cover the integration of NOMAD with Electronic Lab Notebooks (ELNs) to enhance data documentation and management. A practical session will guide users through a typical synthesis data example, demonstrating how to start from NOMAD's built-in ELNs, develop a data schema, convert it into a NOMAD plugin for automated data processing, and deploy the schema on a local NOMAD Oasis. This hands-on approach will provide invaluable insights into customizing NOMAD to fit specific experimental workflows.</p> <p>The session is designed to serve various user groups, including standard users, data stewards & data scientists, and system administrators, ensuring that each participant gains a comprehensive understanding of the tool's capabilities and applications in their respective roles. Join us to explore how NOMAD can transform your approach to data management in experimental synthesis, leading to more efficient and coherent research outputs - FAIR principles in practice.</p> <p> </p> <p>Disclaimer: NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p>
FAIRmat Tutorial 7: Molecular Dynamics Trajectories and Workflows in NOMAD
<p>The FAIRmat consortium is committed to extending the NOMAD infrastructure to a wide variety of materials science data. To support soft matter simulations (e.g., atomistic molecular dynamics simulations), a number of challenges arise, primarily due to the volume and variety of data. The FAIRmat team is working to overcome these challenges, and the NOMAD infrastructure is now equipped with new metadata, features, and tools specifically designed to ease the FAIR treatment of trajectory data and workflows. Parsers have been implemented for two of the most popular molecular dynamics codes (Gromacs and Lammps), with plans for quick expansion to additional codes within the next year. The NOMAD Metainfo now describes the system’s hierarchical structure (in terms of bond topology) through the concept of fixed chemical bonds defined within classical force fields. The NOMAD GUI provides a bespoke overview page for molecular dynamics data, which includes tools that ease visualization of the system topology and automatically displays structural, dynamic, and thermodynamic observables that can assist in a fast assessment of system equilibration. Additionally, a native workflow visualizer allows the user to connect individual simulation entries into complex workflows. Finally, the NOMAD Python module facilitates custom trajectory analysis, for instance in a Jupyter notebook, with functions that convert a NOMAD archive entry to an instance of the MDAnalysis data class.</p> <p>This tutorial invites both experienced and completely novice NOMAD users to learn about these new features for molecular dynamics trajectories. A brief introduction to the FAIRmat consortium and the NOMAD infrastructure will be given, followed by guided and interactive tutorials highlighting the various features described above</p> <p><strong>Disclaimer:</strong> NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p>
FAIRmat Tutorial 8: Using NOMAD as an Electronic lab notebook (ELN) for FAIR data
<p>Approaching the era of big data-driven materials science, one crucial step to collecting, describing, and sharing experimental data is the adoption of electronic laboratory notebooks (ELN). At present, most synthesis data are not structured comprehensively or not even stored digitally but in handwritten lab books. The <a href="https://www.fairmat-nfdi.eu/fairmat" target="_blank" rel="noopener">FAIRmat project</a> is offering a solution by developing and operating the open-source software <a href="https://nomad-lab.eu/" target="_blank" rel="noopener">NOMAD</a>. NOMAD provides ELN functionalities that aim for a secure environment to protect the integrity of both data and metadata, whilst also affording the flexibility to adopt new synthetic processes or changes to existing ones without recourse to further software development.</p> <p>In this FAIRmat tutorial, we focus on the usage of NOMAD as an ELN which enables the users to generate data following the FAIR principles. We will show how we adopted NOMAD to capture data from synthesis and experiment and make use of an automated data workflow. The key point here is writing a data schema and its implementation in NOMAD. After defining the used terms, we will start explaining this process by writing a simple schema and then go on to more advanced usage of NOMAD, e.g. using the build in csv/xlsx-file parser, automatized data visualization, adding extra functionality by usage of base classes, referencing to other data entries in NOMAD, and searching your ELN data. The tutorial is aimed at both scientists new to NOMAD and structured data as well as data stewards. Each lecture of the tutorial will be followed directly by a Q&A session and a hands-on tutorial.</p> <p><strong>Disclaimer:</strong> NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p>
FAIRmat Tutorial 10: FAIR electronic-structure data in NOMAD
<p>The FAIRmat consortium aims to extend the current NOMAD-Lab (meta)data structure to a large variety of materials-science data. Given our strong foundation in computational data, especially DFT, we are now extending our scope. In this tutorial, we will explain the (meta)data structure for <em>ab initio</em> calculations, with an emphasis on precision and on going beyond the accuracy limits of DFT.</p> <p>This tutorial is suitable for new and experienced researchers who want to learn about the latest features in treating DFT and beyond DFT methodologies. We will give a brief introduction to the NOMAD Lab and the FAIRmat consortium, followed by a guided tutorial where we will:</p> <ol> <li>Show you how you can upload, publish, and explore <em>ab initio</em> computational data.</li> <li>Show you how to define your own complex workflows, linking between DFT and beyond DFT calculations.</li> <li>Give you examples of the post-processing capabilities of the NOMAD Lab.</li> </ol> <p>In more detail: Precision settings are now searchable, allowing for “data-quality” filtering over the NOMAD data. Using simple queries, we will show how to generate a sampling that extrapolates towards the basis set limit. For those already familiar with their code of choice, there is also the native tier quick filter that matches recommended developer settings. Moreover, for ease in navigating the density-functional space, we will be presenting a new, knowledge-based categorization system that is more refined and semantically richer than Jacob’s ladder. Finally, we will show the latest developed schemas which try to cover computational techniques that go beyond DFT and which are useful to treat excited-state and advanced many-body properties: the <em>GW</em> approximation, Bethe-Salpeter equation (BSE) solutions, tight-binding-based modeling (using Wannier projections or Slater-Koster fittings), and Dynamical Mean-Field Theory (DMFT). </p> <p><strong>Disclaimer: </strong>NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p>
FAIRmat Tutorial 9: Plugins: Python schemas and parsers
<p>NOMAD is a research data management platform for materials science. NOMAD Oasis allows you to operate the popular NOMAD service for your own lab, with your rules, and on your resources. You can adopt NOMAD Oasis to implement your institutes data policies and to work with your specific data types and workflows.</p> <p>This tutorial aims to introduce participants to the new plugin mechanism in NOMAD and teach them how to develop and integrate their own Python schemas and parsers to a NOMAD Oasis. Plugins enable you to alter how NOMAD processes data and therefore allow for more powerful customisations than the custom schemas presented in past tutorials. Participants will learn how to enable the conversion of new materials science data formats into NOMAD's standardised and machine-readable format. NOMAD plugins can be contributed to the community to further promote reproducibility and transparency in materials science.</p> <p><strong>Disclaimer:</strong> NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p>
FAIRmat Tutorial 11: Research data management, from fundamentals to implementation
<p>Scientific data are the key outcome of research activities at universities, research institutes, and industrial R&D centers. With the recent surge in research data production driven by high-throughput techniques, and the digitalization of scientific methods, data management has become more critical than ever. Moreover, multidisciplinary collaborative research requires the seamless exchange of research data between team members and different laboratories.</p> <p>To ensure that these vast amounts of diverse research data are handled effectively and result in valuable human knowledge and discoveries, it is imperative that proper data management practices are implemented. This ensures that data are produced in a high-quality, reproducible, and well-documented manner so that they can be machine-actionable and reusable in the future. </p> <p>Research Data Management (RDM) refers to the practices used in handling scientific data throughout their lifecycle, from collection to reuse.</p> <p>In this interactive tutorial, we will explain the different stages of the research data lifecycle and identify the best practices for each stage. We will explore the FAIR data principles —Findable, Accessible, Interoperable, and Reusable— and provide insights into their implementation during the research process. In addition, we will introduce the concept of data management plans, which define the data management process during research projects, along with practical tips on the various components and how to comply with funder requirements. </p> <p>The tutorial is divided into two sessions:</p> <ol> <li>Basic concepts and definitions of FAIR data and RDM</li> <li>Practical tips and examples for applying best practices in RDM</li> </ol> <p>After completing this tutorial, you will be able to: </p> <ul> <li>Describe the FAIR principles and understand the different aspects and implementations of FAIR data.</li> <li>Describe and justify measures for good RDM at different stages of the data lifecycle.</li> <li>Understand and explain the concepts of persistent identifiers (PID), metadata, data storage systems, etc. </li> <li>List and understand the contents of the components of a DMP according to the requirements of different funders in Europe. </li> <li>Identify the tools available to create a DMP.</li> </ul>
FAIRmat Tutorial 12: Getting started with NOMAD and NOMAD Oasis for research data management (RDM)
<p>In this online tutorial we will cover the first steps with NOMAD and NOMAD Oasis. We will briefly cover the core NOMAD functionality on exploring, uploading, sharing and publishing data with NOMAD. We will then explore options for creating your own schemas and plugins to support new file formats and create custom electronic lab notebooks (ELNs), we show ways to customize an NOMAD Oasis, and how to contribute to the development of NOMAD and its ecosystem.</p> <p>The tutorial includes an introduction talk about NOMAD and FAIRmat, including the latest changes and features in NOMAD. This is followed by a practical follow along session, where we go through a Jupyter notebook that demonstrates how to use NOMAD for managing custom data and file types. Based on a simple given dataset, we show how to model the data in a schema, do parsing and normalization, process data, access existing data with NOMAD's API for analysis, and how to add visualization to your data.</p> <p> </p> <p><strong>Disclaimer: </strong>NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p>
FAIRmat Tutorial 14: Developing schemas and parsers for FAIR computational data storage using NOMAD-Simulations
<p><a href="https://nomad-lab.eu"><u>NOMAD</u></a> is an open-source, community-driven data infrastructure, focusing on materials science data. Originally built as a repository for data from DFT calculations, the NOMAD software can automatically extract data from the output of a large variety of simulation codes. Our previous computation-focused tutorials (<a href="https://fairmat-nfdi.github.io/AreaC-Tutorial-CECAM-2023/"><u>CECAM workshop</u></a>, <a href="https://fairmat-nfdi.github.io/AreaC-Tutorial10_2023/"><u>Tutorial 10</u></a>, and <a href="https://www.fairmat-nfdi.eu/events/fairmat-tutorial-7/tutorial-7-materials"><u>Tutorial 7</u></a>) have highlighted the extension of NOMAD’s functionalities to support advanced many-body calculations, classical molecular dynamics simulations, and complex simulation workflows. <br>But how can you utilize this infrastructure and associated suite of tools if your simulation code or method is not yet supported? <strong>This tutorial will provide foundational knowledge for customizing NOMAD to fit the specific needs of your computational research project</strong>. The following provides an outline of the major topics that will be covered:</p> <ul> <li>Introduction to the NOMAD software and repository</li> <li>Working with the NOMAD-Simulations schema plugin</li> <li>Extending NOMAD-Simulations to support custom methods and outputs</li> <li>Creating parser plugins from scratch</li> <li>Extra: Interfacing complex simulation and analysis workflows with NOMAD</li> </ul> <p><strong>Disclaimer:</strong> NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p>
Dataset for the tutorial: "Response properties of embedded molecules through the polarizable embedding model"
<p>Dataset for the tutorial "<em>Response properties of embedded molecules through the polarizable embedding model</em>". The abstract is the following:</p> <blockquote> <p>"<em>The polarizable embedding (PE) model is a fragment-based quantum–classical approach aimed at accurate inclusion of environment effects in quantum-mechanical response property calculations. The aim of this tutorial is to give insight into the practical use of the PE model. Starting from a set of molecular structures and until you arrive at the final property, there are many crucial details to consider in order to obtain trustworthy results in an efficient manner. To lower the threshold for new users wanting to explore the use of the PE model, we describe and discuss important aspects related to its practical use. This includes directions on how to generate input files and how to run a calculation.</em>"</p> </blockquote> <p>The dataset contains files that can be used in direct connection with the tutorial (<em>getting-started.zip</em>), and an additional set of files that can be used for further exploration (<em>peqm-inputs.zip</em>). </p>
Training data for 'Genetic map RADSeq ' tutorial (Galaxy Training Material)
<p>The data provided here are part of a study published by Amores<em> et al.</em> (2011) (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3176089/">doi 10.1534/genetics.111.127324</a>), exploiting massively parallel DNA sequencing to develop meiotic maps by genotyping F<sub>1</sub> offspring of a single female and a single male spotted gar (<em>Lepisosteus oculatus</em>).</p>
Regional GAM data tutorial
<p>Dataset recording the presence of species per site and per day</p>
Dataset for RNA-seq basic tutorial in Galaxy Australia
<p>Dataset of synthetic RNA-seq data for Drosophila melanogaster: 2 conditions, 3 replicates in each, paired-end reads. Reference genome in GTF format. </p>
Training material for analysis small RNA-seq data (Galaxy Training Network tutorial)
<p>The data provided here is part of the Galaxy Training Network tutorial for analysis of small RNA-seq (sRNA-seq) data using mirdeep2 and miranda. This dataset is provided by INRA (Le Rheu, France).</p>
Trimmed RNASeq pair for the Galaxy Training Network tutorial - "Metatranscriptomics analysis using microbiome RNASeq data"
<p>Functional microbiome analysis which estimates the functional groups expressed by microbial community enables researchers to look beyond taxonomic composition and correlation with the condition under study. Using microbial community RNA-Seq data and subsequent metatranscriptomics workflows to elucidate the functional complement of the microbiome is gaining interest in the field. <br> This Galaxy training network tutorial will introduce researchers to the basic concepts and tools from the published ASaiM workflow (Batut et al, <em>GigaScience</em> (2018), 7 (6),<a href="http://dx.doi.org/10.1093/gigascience/giy057"> http://dx.doi.org/10.1093/gigascience/giy057</a>). </p> <p>The dataset is a trimmed version of one of the time points from a cellulose degradation biogas reactor dataset. The dataset has been trimmed to facilitate running the workflows for this tutorial. Any biological interpretation from the results would be incorrect, due to the trimmed version of the dataset.</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.