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The numerous benefits of Open Science (OS) and of the four FAIR foundational principles - Findable, Accessible, Interoperable and Reusable - are increasingly valued in academia, although what OS and FAIR entail is still largely misunderstood. In such conditions, putting into practice OS and applying the FAIR principles is challenging and underrated. However, realising OS is perfectly within our grasp provided that an infrastructure supporting the management of the research lifecycle is available. ROHub (https://www.rohub.org/) is a Research Object (RO) management platform implementing three complementary technologies: Research Objects, Data Cubes and Text Mining services. ROHub enables researchers to collaboratively manage, share and preserve their research while they are still working on it (rather than after the work is finished). In this paper, three communities from Earth Sciences, namely Geohazards, Sea Monitoring and Climate Change, demonstrate how ROHub helped them to understand each other and to work openly and, more importantly, how communities of practice play an important role in facilitating reuse and interdisciplinary collaboration. These findings are illustrated with several use cases from these various communities.

Specimen Data Refinery (SDR) is a developing platform for automating transcription of specimens from natural history collections (Hardisty et al. 2022). SDR is based on computational workflows and digital twins using FAIR Digital Objects.We show our recent experiences with building SDR using the Galaxy workflow system and combining two FDO methodologies with open digital specimens (openDS) and RO-Crate data packaging. We suggest FDO improvements for incremental building of digital objects in computational workflows.SDR workflowsSDR is realised as the workflow system Galaxy (Afgan et al. 2018) with SDR tools installed. An Open Research challenge is that some tools have machine learning models with a commercial licence. This complicates publishing to Galaxy toolshed, however we created Ansible scripts to install equivalent Galaxy servers, including tools and dependencies, accounts and workflows. SDR workflows are published in WorkflowHub as FDOs.We implemented the use case De novo digitization in Galaxy (Brack et al. 2022). Shown in Fig. 1 the workflow steps exchange openDS JSON (Hardisty et al. 2019), for incremental completion of a digital specimen. Initial stages build a template openDS from a CSV with metadata and image references – subsequent analysis completes the rest of the JSON with regions of interest, text digitised from handwriting, and recognized named entities.Galaxy can visualise outputs of each step (Fig. 2), important to make the FDOs understandable by domain experts and to verify accuracy of SDR.We are adding workflows for partial stages, e.g. detection of regions (Livermore and Woolland 2022a) and hand-written text recognition (Livermore and Woolland 2022b), which we'll combine with scalability testing and wider testing by project users. Additional workflows will enhance existing FDOs and use new tools such as barcode detection of museums’ internal identifiers.We are now ready to publish digital specimens as FAIR Digital Objects, with registration into DiSSCO repositories, PID assignment and workflow provenance. However, even at this early stage we have identified several challenges that need to be addressed.FDO lessonsWe highlight the De novo use case because this workflow is exchanging partial FDOs – openDS objects which are not fully completed and not yet assigned persistent identifiers. openDS schemas are still in development, therefore SDR uses a more flexible JSON schema where only the initial metadata (populated from CSV) are required. Each step validates the partial FDO before passing it to the underlying command line tool.Although workflow steps exchange openDS objects, they cannot be combined in any order. For instance, named entity recognition requires digitised text in the FDO. We can consider these intermediate steps as sub-profiles of an FDO Type. Unlike hierarchical subclasses, these FDO profiles are more like ducktyping. For instance a text detection step may only require the regions key, but semantically there is no requirement for an OpenDSWithText to be a subclass of OpenDSWithRegion, as text also can be transcribed manually without regions. Similarly, we found that some steps can be executed in parallel, but this requires merging of partial FDOs. This can be achieved by combining JSON queries and JSON Schemas, but indicates that it may be more beneficial to have FDO fragments as separate objects. Adding openDS fragment steps would however complicate workflows.Several of our tools process the referenced images, currently https URLs in openDS. We added a caching layer to avoid repeated image downloading, coupled with local file-paths wiring in the workflow. A similar challenge occurs if accessing image data using DOIP, which unlike HTTP, has no caching mechanisms.RO-Crate lessonsGalaxy is developing support for importing and exporting Workflow Run Crates, a profile of RO-Crate (Soiland-Reyes et al. 2022b) to captures execution history of a workflow, including its definition and intermediate data (De Geest et al. 2022). SDR is adopting this support to combine openDS FDOs with workflow provenance, as envisioned by Walton et al. (2020).Our prototype de novo workflow returns results as a ZIP file of openDS objects. End-users should also get copies of the referenced images and generated visualisations, along with workflow execution metadata. We are investigating ways to embed the preliminary Galaxy workflow history before the final step, so that this result can be an enriched RO-Crate.ConclusionsSDR is an example of machine-assisted construction of FDOs, which highlight the needs for intermediate digital objects that are not yet FDO compliant. The passing of such “local FDOs” is beneficial not just for efficiency and visual inspection, but also to simplify workflow composition of canonical workflow building blocks. At the same time we see that it is insufficient to only pass FDOs as JSON objects, as they also have references to other data such as images, which should not need to be re-downloaded. Further work will investigate the use of RO-Crate as a wrapper of partial FDOs, but this needs to be coupled with more flexible FDO types as profiles, in order to restrict “impossible” ordering of steps depending on particular inner FDO fragments. A distinction needs to be made between open digital specimens that are in “draft” state and those that can be pushed to DiSSCo registries.We are experimenting with changing the SDR components into Canonical Workflow Building Blocks (Soiland-Reyes et al. 2022a) using the Common Workflow Language (Crusoe et al. 2022). This gives flexibility to scalably execute SDR workflows on different compute backends such as HPC or local cluster, without the additional setup of Galaxy servers.

RO-Crate (Soiland-Reyes et al. 2022) is a lightweight method to package research outputs along with their metadata, based on Linked Data principles (Bizer et al. 2009) and W3C standards. RO-Crate provides a flexible mechanism for researchers archiving and publishing rich data packages (or any other research outcome) by capturing their dependencies and context. However, additional measures should be taken to ensure that a crate is also following the FAIR principles (Wilkinson 2016), including consistent use of persistent identifiers, provenance, community standards, clear machine/human-readable licensing for metadata and data, and Web publication of RO-Crates.The FAIR Digital Object (FDO) approach (De Smedt et al. 2020) gives a set of recommendations that aims to improve findability, accessibility, interoperability and reproducibility for any digital object, allowing implementation through different protocols or standards.Here we present how we have followed the FDO recommendations and turned research outcomes into FDOs by publishing RO-Crates on the Web using HTTP, following best practices for Linked Data. We highlight challenges and advantages of the FDO approach, and reflect on what is required for an FDO profile to achieve FAIR RO-Crates.The implementation allows for a broad range of use cases, across scientific domains. A minimal RO-Crate may be represented as a persistent URI resolving to a summary website describing the outputs in a scientific investigation (e.g. https://w3id.org/dgarijo/ro/sepln2022 with links to the used datasets along with software). One of the advantages of RO-Crates is flexibility, particularly regarding the metadata accompanying the actual research outcome. RO-Crate extends schema.org, a popular vocabulary for describing resources on the Web (Guha et al. 2016). A generic RO-Crate is not required to be typed beyond Dataset*1. In practice, RO-Crates declare conformance to particular profiles, allowing processing based on the specific needs and assumptions of a community or usage scenario. This, effectively, makes RO-Crates typed and thus machine-actionable. RO-Crate profiles serve as metadata templates, making it easier for communities to agree and build upon their own metadata needs.RO-Crates have been combined with machine-actionable Data Management Plans (maDMPs) to automate and facilitate management of research data (Miksa et al. 2020). This mapping allows RO-Crates to be generated out of maDMPs and vice versa. The ELIXIR Software Management Plans (Alves et al. 2021) is planning to move their questionnaire to a machine-actionable format with RO-Crate. ELIXIR Biohackathon 2022 will explore integration of RO-Crate and the Data Stewardship Wizard (Pergl et al. 2019) with Galaxy, which can automate FDO creation that also follows data management plans.A tailored RO-Crate profile has been defined to represent Electronic Lab Notebooks (ELN) protocols bundled together with metadata and related datasets. Schröder et al. (2022) uses RO-Crates to encode provenance information at different levels, including researchers, manufacturers, biological and chemical resources, activities, measurements, and resulting research data. The use of RO-Crates makes it easier to programmatically question-answer information related to the protocols, for instance activities, resources and equipment used to create data. Another example is WorkflowHub (Goble et al. 2021) which defines the Workflow RO-Crate profile (Bacall et al. 2022), imposing additional constraints such as the presence of a main workflow and a license. It also specifies which entity types and properties must be used to provide such information, implicitly defining a set of operations (e.g., get the main workflow and its language) that are valid on all complying crates. The workflow system Galaxy (The Galaxy Community 2022) retrieves such Workflow Crates using GA4GH TRS API.The workflow profile has been further extended (with OOP-like inheritance) in Workflow Testing RO-Crate, adding formal workflow testing components: this adds operations such as getting remote test instances and test definitions, used by the LifeMonitor service to keep track of the health status of multiple published workflows. While RO-Crates use Web technologies, they are also self-contained, moving data along with their metadata. This is a powerful construct for interoperability across FAIR repositories, but this raises some challenges with regards to mutability and persistence of crates.To illustrate how such challenges can be handled, we detail how the WorkflowHub repository follows several FDO principles:Workflow entries must be frozen for editing and have complete kernel metadata (title, authors, license, description) [FDOF4] before they can be assigned a persistent identifier, e.g. https://doi.org/10.48546/workflowhub.workflow.255.1 [FDOF1]Computational workflows can be composed of multiple files used as a whole, e.g. CWL files in a GitHub repository. These are snapshotted as a single RO-Crate ZIP, indicating the main workflow. [FDOF11]PID resolution can content-negotiate to Datacite’s PID metadata [FDOF2] or use FAIR Signposting to find an RO-Crate containing the workflow [FDOF3] and richer JSON-LD metadata resources [FDOF5,FDOF8], see Fig. 1Metadata uses schema.org [FDOF7] following the community-developed Bioschemas ComputationalWorkflow profile [FDOF10].Workflows are discovered using the GA4GH TRS API [FDOF5,FDOF6,FDOF11] and created/modified using CRUD operations [FDOF6]The RO-Crate profile, effectively the FDO Type [FDOF7], is declared as https://w3id.org/workflowhub/workflow-ro-crate/1.0; the workflow language (e.g. https://w3id.org/workflowhub/workflow-ro-crate#galaxy) is defined in metadata of the main workflow. Further work on RO-Crate profiles include to formalise links to the API operations and repositories [FDOF5,FDOF7], to include PIDs of profiles and types in the FAIR Signposting, and HTTP navigation to individual resources within the RO-Crate.RO-Crate has shown a broad adoption by communities across many scientific disciplines, providing a lightweight, and therefore easy to adopt, approach to generating FAIR Digital Objects. It is rapidly becoming an integral part of the interoperability fabric between the different components as demonstrated here for WorkflowHub, contributing to building the European Open Science Cloud.

The article presents a research’s reflection on entrance and working in the religious field, description of physical context of the research and the researcher's frame in a particular case, through the main aspects: researcher's identity (gender, age, nationality), personal research position, determined through the correlation of insidership and outsidership. The author draws attention to the fact that the researcher's position cannot be clearly defined through the categories of insider and outsider, each participant of the study has a complex system of identities, cultural and social markers that will certainly have points of contact, intersections and divergences. The article reveals the features of the empirical part of the study devoted to the life strategies of young Muslims, in particular, the issues of research organization, establishing communication with the research field, and conducting interviews.

Over recent decades large amounts of data about our Planet have become available. If this information could be easily discoverable, accessible and properly exploited, preserved and shared, it would potentially represent a wealth of information for a whole spectrum of stakeholders: from scientists and researchers to the highest level of decision and policy makers. By creating a Virtual Research Environment (VRE) using a service oriented architecture (SOA) tailored to the needs of Earth Science (ES) communities, the EVER-EST (http://ever-est.eu) project provides a range of both generic and domain specific data analysis and management services to support a dynamic approach to collaborative research. EVER-EST provides the means to overcome existing barriers to sharing of Earth Science data and information allowing research teams to discover, access, share and process heterogeneous data, algorithms, results and experiences within and across their communities, including those domains beyond Earth Science. The main objective of this paper is to present the EVER-EST platform in all its components describing the most relevant use cases implemented by the Virtual Research Communities (VRCs) involved in the project.

Nowadays, energy companies operate in a highly competitive environment. To maintain the leader position, as a reaction to various changes energy companies must take appropriate steps in a timely manner.

A Research Object (RO) is defined as a semantically rich aggregation of resources that bundles together essential information relating to experiments and investigations. This information is not limited merely to the data used and the methods employed to produce and analyze such data, but it may also include the people involved in the investigation as well as other important metadata that describe the characteristics, inter-dependencies, context and dynamics of the aggregated resources. As such, a research object can encapsulate scientific knowledge and provide a mechanism for sharing and discovering assets of reusable research and scientific knowledge within and across relevant communities, and in a way that supports reliability and reproducibility of investigation results. While there are no pre-defined constraints related to the type of resources a research object can contain, the following usually apply in the context of scientific research: data used and results produced; methods employed to produce and analyze data; scientific workflows implementing such methods; provenance and settings; people involved in the investigation; annotations about these resources, which are essential to the understanding and interpretation of the scientific outcomes captured by a research object. The example research object contains a workflow, input data and results, along with a paper that presents the results and links to the investigators responsible. Annotations on each of the resources (and on the research object itself) provide additional information and characterize, e.g. the provenance of the results. Therefore, exploitation of the RO model should be considered as a way to provide additional reliability and reproducibility of the research. The concept of the RO was introduced to the environment created in the EVER-EST project in the form of Virtual Research Environment (VRE). a group of Earth Scientists, who are observing, analyzing and modeling processes that take place on land and see, was examined against their needs and expectations about the possible improvements in their scientific work. The results show that scientist expectations are focused on knowledge sharing and reuse, and new forms of scholarly communications beyond pdf articles as supporting tools of knowledge cross-fertilization between their members. The Research Object concept seems a natural answer for these needs. However, the model, in order to be sufficient and usable, must become a part of the working environment and needs to be integrated with the actual tools. Therefore, great efforts have been undertaken to create a generic, technical solution – VRE, which implements the expected functionalities. In this article we present a concept of the VRE as a tool that takes advantage of the Research Object model in order to integrate and simplify the information exchange, as well as persist, share and discover assets of the reusable research. Moreover, we are presenting example scenarios of the VRE usage in the four different Earth Science domains.

In front of committed actors, the classic order sent to the sociologist to stay "neutral" and "objective" seems very vain. On one hand, because normative considerations, registered in the trajectory and in the social or political experience of the researcher, are frequently for the principle of its choices of research objects. Then, because the researcher is generally summoned to make a commitment, or at a minimal level to clarify his preferences, by the activists whom he studies. The paper explores the advantages but also the methodological blind spots and the epistemological pitfalls of intricacy of militant posture and sociologist's activity.