Leveraging FAIR Signposting & RO-Crate for the Norwegian Research Data Archive

Lightning talk given during the 1st workshop organised by FAIR-Impact on the framework on the half day virtual workshop on "Enabling FAIR Signposting and RO-Crate". This presentation briefly explains what our goals are and what support we need to fulfil our task

Galaxy: A Decade of Realising CWFR Concepts

Despite recent encouragement to follow the FAIR principles, the day-to-day research practices have not changed substantially. Due to new developments and the increasing pressure to apply best practices, initiatives to improve the efficiency and reproducibility of scientific workflows are becoming more prevalent. In this article, we discuss the importance of well-annotated tools and the specific requirements to ensure reproducible research with FAIR outputs. We detail how Galaxy, an open-source workflow management system with a web-based interface, has implemented the concepts that are put forward by the Canonical Workflow Framework for Research (CWFR), whilst minimising changes to the practices of scientific communities. Although we showcase concrete applications from two different domains, this approach is generalisable to any domain and particularly useful in interdisciplinary research and science-based applications.publishedVersio

Climate–ecosystem modelling made easy: The Land Sites Platform

Dynamic Global Vegetation Models (DGVMs) provide a state-of-the-art process-based approach to study the complex interplay between vegetation and its physical environment. For example, they help to predict how terrestrial plants interact with climate, soils, disturbance and competition for resources. We argue that there is untapped potential for the use of DGVMs in ecological and ecophysiological research. One fundamental barrier to realize this potential is that many researchers with relevant expertize (ecology, plant physiology, soil science, etc.) lack access to the technical resources or awareness of the research potential of DGVMs. Here we present the Land Sites Platform (LSP): new software that facilitates single-site simulations with the Functionally Assembled Terrestrial Ecosystem Simulator, an advanced DGVM coupled with the Community Land Model. The LSP includes a Graphical User Interface and an Application Programming Interface, which improve the user experience and lower the technical thresholds for installing these model architectures and setting up model experiments. The software is distributed via version-controlled containers; researchers and students can run simulations directly on their personal computers or servers, with relatively low hardware requirements, and on different operating systems. Version 1.0 of the LSP supports site-level simulations. We provide input data for 20 established geo-ecological observation sites in Norway and workflows to add generic sites from public global datasets. The LSP makes standard model experiments with default data easily achievable (e.g., for educational or introductory purposes) while retaining flexibility for more advanced scientific uses. We further provide tools to visualize the model input and output, including simple examples to relate predictions to local observations. The LSP improves access to land surface and DGVM modelling as a building block of community cyberinfrastructure that may inspire new avenues for mechanistic ecosystem research across disciplines.publishedVersio

Galaxy Training: A powerful framework for teaching!

There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis, and stewardship are still rarely taught in life science educational programs, resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https://training.galaxyproject.org), an open access, community-driven framework for the collection of FAIR (Findable, Accessible, Interoperable, Reusable) training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform. Since its inception, this training platform has thrived, with the number of tutorials and contributors growing rapidly, and the range of topics extending beyond life sciences to include topics such as climatology, cheminformatics, and machine learning. While initially aimed at supporting researchers directly, the GTN framework has proven to be an invaluable resource for educators as well. We have focused our efforts in recent years on adding increased support for this growing community of instructors. New features have been added to facilitate the use of the materials in a classroom setting, simplifying the contribution flow for new materials, and have added a set of train-the-trainer lessons. Here, we present the latest developments in the GTN project, aimed at facilitating the use of the Galaxy Training materials by educators, and its usage in different learning environments

Data Carpentry Dataset: Working with Spatio-temporal data in Python

This dataset contains netCDF, HDF, GeoTIFF, shapefiles, GEOJSON files used for the Data Carpentry lesson we have developed at the Department of Geosciences, University of Oslo, Norway. The corresponding lessons are available at https://annefou.github.io/metos_python

Teaching ML in Compact Courses

This talk summarizes the experiences made with teaching Machine Learning within compact events that stretch over several days to a week maximum. Both speakers explain pitfalls they were caught in as well as solutions they found. This talk was given at the Teaching Machine Learning workshop at ECML-PKDD 2020. For more details and information see https://teaching-ml.github.io/2020/The talk was created in a collaborative fashion on hackmd.io Therefor this contains the final pdf of the slides and the markdown file

Vers une pédagogie de l'uniformisation des dispensations de traitements antiretroviraux

PARIS-BIUP (751062107) / SudocSudocFranceF

Hypertension artérielle pulmonaire (définition, diagnostic, mécanismes physiopathologiques et lancement d'un nouveau traitement en France, le sildénafil)

CHATENAY M.-PARIS 11-BU Pharma. (920192101) / SudocSudocFranceF

FAIR Research Objects for realizing Open Science with RELIANCE EOSC project

The H2020 Reliance project delivers a suite of innovative and interconnected services that extend European Open Science Cloud (EOSC)’s capabilities to support the management of the research lifecycle within Earth Science Communities and Copernicus Users. The project has delivered 3 complementary  technologies: Research Objects (ROs), Data Cubes and AI-based Text Mining.RoHub is a Research Object management platform that implements these 3 technologies and enables researchers to collaboratively manage, share and preserve their research work.RoHub implements the full RO model and paradigm: resources associated to a particular research work are aggregated into a single FAIR digital object, and metadata relevant for understanding and interpreting the content is represented as semantic metadata that are user and machine readable. The development of RoHub is co-designed and validated through multidisciplinary and thematic real life use cases led by three different Earth Science communities: Geohazards, Sea Monitoring and Climate Change communities. A RO commonly starts its life as an empty Live RO. ROs aggregate new objects through their whole lifecycle. This means, a RO is filled incrementally by aggregating new relevant resources such as workflows, datasets, documents according to its typology that are being created, reused or repurposed. These resources can be modified at any point in time.We can copy and keep ROs in time through snapshots which reflect their status at a given point in time. Snapshots can have their own identifiers (DOIs) which facilitates tracking the evolution of a research. At some point in time, a RO can be published and archived (so called Archived RO) with a permanent identifier (DOI). New Live ROs can be derived based on an existing Archived RO, for instance by forking it. To guide researchers, different types of Research Objects can be created:Bibliography-centric: includes manuals, anonymous interviews, publications, multimedia (video, songs) and/or other material that support research;Data-centric: refers to datasets which can be indexed, discovered and manipulated;Executable: includes the code, data and computational environment along with a description of the research object and in some cases a workflow. This type of ROs can be executed and is often used for scripts and/or Jupyter Notebooks;Software-centric: also known as “Code as a Research Object”. Software-centric ROs include source codes and associated documentation. They often include sample datasets for running tests.Workflow-centric: contains workflow specifications, provenance logs generated when executing the workflows, information about the evolution of the workflow (version) and its components elements, and additional annotations for the workflow as a whole.Basic: can contain anything and is used when the other types do not cover the need.To ease the understanding and the reuse of the ROs, each type of RO (except Basic RO) has a template folder structure that we recommend researchers to select. For instance an executable RO has 4 folders:'biblio' where  researchers can aggregate documentations, scientific papers that øed to the development of the software/tool that is aggregated in the tool folder;'input' where all the input datasets required for executing the RO are aggregated;'output' where some or all the results generated by executing the RO are aggregated;'tool' where the executable tool is aggregated. Typically, we aggregate Jupyter Notebook and/or executable workflows (Galaxy or snakemake workflows).In addition to the different types of ROs and associated template structures, researchers can select the type of resources that constitutes the main entity of a RO: for instance, a Jupyter Notebook can be selected as the main entity of an executable RO. As shown on Fig. 1, this additional metadata is then visible to everyone (and machine readable) to ease reuse. Examples of Bibliography-centric and Data-centric Research Objects are shown on Fig. 2: the overall overview of any types of Research Object is always the same with mandatory metadata information such as the title, description, authors & collaborators, sketch (featured plots/images), the content of the RO (with different structures depending on the type of ROs). Additional information is displayed on the right panel such as number of downloads, additional discovered metadata (automatically discovered from the Reliance text enrichment service), free keywords (added by end-users) and citation. The 'toolbox' and 'share' sections allows end-users to download, snapshot and archive the RO and/or share it.Any Research Object in RoHub is a FAIR digital object that is for instance findable in OpenAire, including Live ROs.In our presentation, we will showcase different types of ROs for the 3 Earth Science communities represented in Reliance to highlight how the scientists in our respective disciplines changed their working methodology towards Open Science

PALM: A modular data assimilation system

International audienc