Research Object Crate (RO-Crate) is a lightweight method to package research outputs along with their metadata. Signposting provides a simple yet powerful approach to navigate scholarly objects on the Web. Combining these technologies form a "webby" implementation of the FAIR Digital Object principles which is suitable for retrofitting to existing data infrastructures or even for ad-hoc research objects using regular Web hosting platforms. Here we give an update of recent community development and adoption of RO-Crate and Signposting. It is notable that programmatic access and more detailed profiles have received high attention, as well as several FDO implementations that use RO-Crate.

The Language Data Commons of Australia (LDaCA) makes nationally significant language data available for academic and non-academic use, managing the data in a culturally, ethically, and legally appropriate manner guided by FAIR and CARE principles. Here, we describe the approach which we are taking to access control and a design for a distributed access control system which can look after the A-is-for-accessible in FAIR data while respecting the CARE principles. We also describe and demonstrate a pilot system based on that design, showing how data licenses that allow access by identified groups of people can be used by adding functionality, CILogon for non-institutional identification and REMS for managing access to resources, to the existing Australian Access Federation infrastructure.

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. 1 Metadata 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. 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. 1 Metadata 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.

An increasing number of researchers support reproducibility by including pointers to and descriptions of datasets, software and methods in their publications. However, scientific articles may be ambiguous, incomplete and difficult to process by automated systems. In this paper we introduce RO-Crate, an open, community-driven, and lightweight approach to packaging research artefacts along with their metadata in a machine readable manner. RO-Crate is based on Schema.org annotations in JSON-LD, aiming to establish best practices to formally describe metadata in an accessible and practical way for their use in a wide variety of situations. An RO-Crate is a structured archive of all the items that contributed to a research outcome, including their identifiers, provenance, relations and annotations. As a general purpose packaging approach for data and their metadata, RO-Crate is used across multiple areas, including bioinformatics, digital humanities and regulatory sciences. By applying “just enough” Linked Data standards, RO-Crate simplifies the process of making research outputs FAIR while also enhancing research reproducibility. An RO-Crate for this article11 https://w3id.org/ro/doi/10.5281/zenodo.5146227 is archived at https://doi.org/10.5281/zenodo.5146227.

A ​ Research Object (RO) provides a machine-readable mechanism to communicate the diverse set of digital and real-world resources that contribute to an item of research. The aim of an RO is to replace traditional academic publications of static PDFs, to rather provide a complete and structured archive of the items (such as people, organisations, funding, equipment, software etc) that contributed to the research outcome, including their identifiers, provenance, relations and annotations. This is increasingly important as researchers now rely heavily on computational analysis, yet we are facing a reproducibility crisis ​ [1] as key components are often not sufficiently tracked, archived or reported. We are developing ​ Research Object Crate (or ​ RO-Crate for short), a lightweight approach to package research data with their structured metadata, based on schema.org annotations in a formalized JSON-LD format that can be used independent of infrastructure to encourage FAIR sharing of reproducible datasets and analytical methods.

Accepted abstract: https://doi.org/10.5281/zenodo.3337883 This Poster: https://doi.org/10.5281/zenodo.3343031 Slides: https://slides.com/soilandreyes/2019-07-24-bosc-ro-crate/ A Research Object (RO) provides a machine-readable mechanism to communicate the diverse set of digital and real-world resources that contribute to an item of research. The aim of an RO is to replace traditional academic publications of static PDFs, to rather provide a complete and structured archive of the items (such as people, organisations, funding, equipment, software etc) that contributed to the research outcome, including their identifiers, provenance, relations and annotations. This is increasingly important as researchers now rely heavily on computational analysis, yet we are facing a reproducibility crisis as key components are often not sufficiently tracked, archived or reported. We propose Research Object Crate (or RO-Crate for short), an emerging lightweight approach to package research data with their structured metadata, based on schema.org annotations in a formalized JSON-LD format that can be used independent of infrastructure to encourage FAIR sharing of reproducible datasets and analytical methods.

Over the last 4 years we have built two platforms that are pivotal for Human Communication Science research: AusTalk, a 3000 hour auditory-visual corpus of Australian English, and Alveo, an extensible Virtual Laboratory housing corpora and analysis tools glued together by a versatile workflow engine. This paper describes the genesis and operation of each of these in some detail and sets out the advantages they provide (i) for Human Communication Science in their present form, and (ii) the maieutic role they can play in research collaboration across disciplines, across institutions, and across languages and national boundaries, especially in our region.

The Alveo Virtual Laboratory is an eResearch project funded under the Australian Government NeCTAR program to build a platform for collaborative eResearch around data representing human communication and the tools that researchers use in their analysis. The human communication science field is broadly defined to encompass the study of language from various perspectives but also includes research on music and various other forms of human expression. This paper outlines the core architecture of the Alveo and in particular, highlights the web based API that provides access to data and tools to authenticated users.

This document provides an introduction to a series of HTML5 case studies which were commissioned by the JISC. The document gives an introduction to HTML5 and related standards developed by the W3C and explains why these developments represent a significant development to Web standards, which is of more significance than previous incremental developments to HTML and CSS.

The organizers of the fifth international conference on Open Repositories list nine polar dichotomies that represent “The Grand Integration Challenge” for the repository community/movement. In this paper we take up the challenge. We do so in the context of a program of work being undertaken in our institute to build infrastructure for the academy in general, working towards a modular 'Academic Working Environment' (AWE) which encompasses both teaching and learning on one hand and research on the other. Repositories and the ecosystem of services and workflows that surround them play a key role in this emerging system.