Researchers, research Infrastructures and research communities, repository managers, FAIR data stewardships

Interdisciplinary collaborations: Networks, services, methods

Patient Data, Ontologies, FAIR, Hospital, Open Science, Health Data, Ontologies, Semantic Web, Linked Data

FAIR data principles and open science are globally endorsed as beneficial for healthcare. As co-founders of the FAIR principles we investigate implementation of FAIR principles, particularly interoperability for machines and interdisciplinary FAIRification. The Covid-19 pandemic emphasized that FAIRification ‘at source’ is vital: observational data are first collected in hospitals and should become FAIR for researchers as quickly as possible, inside and outside of the hospital. However, multiple information systems are used inside hospitals that are not directly interoperable. At the same time, existing systems have their own value such that replacing them is not desirable.

Here, we present a strategy to implement FAIR principles that complements existing hospital systems. We coordinated the FAIRification of observational data of hospitalised patients within an interdisciplinary collaboration that was organised within the hospital to face the Covid-19 challenges. We defined an architecture around ontological models that link data in existing systems. Guided by research questions of the medical doctors, we transformed data into machine actionable digital objects, and developed ontological models for data and metadata, including investigational parameters. DCAT2-structured metadata was exposed by FAIR Data Points. We demonstrated machine actionability by (i) federated queries across hospital data and existing Linked Data-based knowledge sources, (ii) Web APIs for querying Linked Data, (iii) hypothesis-support applications built on top of FAIR patient data.

Our work demonstrates that a FAIR research data management plan based on interdisciplinary collaboration and ontological models for data and metadata, Semantic Web technologies, and FAIR Data Points can complement hospital infrastructure to make machine-actionable FAIR digital objects available for integrative analysis. This prepares hospital systems for federated analysis (e.g. as part of the European Open Science Cloud), linking to other FAIR data such as Linked Open Data, and reuse in software applications.

Research infrastructures and research communities, libraries, research support staff

Interdisciplinary collaborations: Networks, services, methods
Training and skills for open science

Collaboration, Future planning, Research culture, Legal frameworks

Research Data Management (RDM) and open data represents one of the main challenges faced by Higher Education Institutions as it requires compliance with ethical codes, responsible research, data protection, journal requirements and funder/institutional policies. In addition to these external drivers, RDM support was being delivered by dispersed teams across this university that can lead to a disjointed support, affecting infrastructure and tools, and does not reflect the institutional commitment towards RDM.

A task and finish group, chaired by an academic, was set up to address these issues by reviewing existing practice to support RDM to co-ordinate activity and to identify future developments.  Working collaboratively and interdisciplinarity, it was comprised of different elements of professional services involved in supporting RDM across the university. The group worked across seven strategic areas: Policies & Leadership; Relation to research assessment procedures; Advocacy & Support; Curation & Preservation; Infrastructure & Tools; Intellectual Property & Contracts; Ethics and accountability. And focused on these four activities of the research lifecycle (including both researchers and research support staff): Planning; Active Data Infrastructure; Data Stewardship and Research Data Support.

The main goal was to deliver a RDM roadmap to improve support, infrastructure and services provided to researchers. To achieve this, it was necessary to understand how RDM support was being delivered and the research lifecycle workflow for funded projects works, recognize roles and responsibilities, and understand researchers’ needs through a survey. Main conclusions are that infrastructure, ethics, data sharing and open data are the main challenges researchers face. Training and guidance were the principal suggestions made. A workflow was drawn to represent and understand the diversity of processes (funded research, digital humanities) and an infographic was made available to support researchers in the research process, including systems. Roadmap actions focus on how to engage with researchers to better support them in a collaborative approach providing comprehensive support through a diversity of workflows, needs and systems

This lightning talk aim to share good practices by presenting a different model through collaboration, engaging elements at various levels, that enabled interoperability across communities whilst supporting diversity of workflows and systems in the research community. It presents the work developed, achievements, outcomes and deliverables, key actions and lessons learned. It brings experiences and knowledge to align RDM strategy with research assessment that can be useful for other organizations that look synergies and continuous improvement, are conscious of the importance of responsible research, legal aspects in RDM and how interdisciplinary collaboration and interoperability across services can improve RDM support.

The talk aims to attract a heterogeneous audience consisting of professionals in academia and industry with diverse backgrounds

Sustaining Open infrastructures, services and tools for research communities
Value added data products/services from open science

Research analytics; Text mining; Open dataset; Open infrastructures, services, and tools; Scientometrics; Impact assessment

Open Science Infrastructure (OSI) is struggling with the challenge of being invisible. Although OSIs constitute a crucial part of the scholarly communications landscape, facilitating knowledge exchange, supporting libraries in achieving their OS targets, and complying with OS policies, their existence is not always reflected in library budget considerations. The scholarly community relies on OSIs, yet very often without realizing that there are operational and development costs  related to their open existence. These key infrastructures are typically managed by highly competent but under-resourced teams who over-deliver, and it is sadly ironic that their hard work renders their need for additional resources invisible. At worst, these teams risk burnout and overreach, and need to manage a constant need to find new bridge funding.

Many libraries have started to collectively help raise funds across the world for OSIs through SCOSS campaigns, raising more than 3m euros as of August 2021 for DOAJ, Sherpa Romeo, PKP, OpenCitations and DOAB and OAPEN. As SCOSS is now launching its third pledging round with three new services to be announced in the summer, we seek to look deeper into mechanisms of collective funding for OSIs. In the spring, as part of a SCOSS strategy exercise, we launched a global survey asking the wider research community about sustaining Open Science Infrastructure, and the mechanisms through which it should be supported. The proposed workshop will present preliminary results of the SCOSS survey and hear from various stakeholders: from the OSIs supported by the SCOSS program and from institutions who have contributed to a collective effort of funding them. Lessons learnt from this experience will hopefully trigger a wider discussion on the importance of funding the invisible and the potential that collective funding models bring.

repository and research information systems manager, service providers, open science officers

European Open Science Cloud (EOSC) and FAIR data
FAIR data policy and practice: from theory to implementation

FAIR assessment, FAIRification process, resource type adaptation

In April 2020, the Research Data Alliance (RDA) Working Group “FAIR data maturity model” published their first draft of the FAIR Data Maturity Model with specification and guidelines, establishing a set of indicators for assessing adherence to the FAIR principles. In June 2020, the final document was published.

OpenAIRE has defined a set of guidelines that establish the rules that content providers should adopt in order to ensure that their content can be aggregated by the OpenAIRE infrastructure. In practice, the OpenAIRE Guidelines helps content providers managers expose publications, datasets and CRIS metadata via the OAI-PMH protocol in order to integrate with OpenAIRE infrastructure. OpenAIRE guidelines are firstly published in 2010 and the adaptation focus on the latest Guidelines for institutional and thematic repository managers v4, which covers not only literature publications but also DataSets as a resource type.

Beyond the need to expose metadata using global standards, new developments have emerged relating to the application of FAIR principles to records deposited in repositories.

The FAIR principles intend to define a minimal set of related but independent and separable guiding principles and practices that enable both machines and humans to find, access, interoperate and re-use data and metadata. The indicators that are used in the FAIR data maturity model are derived from the FAIR principles and aim to formulate measurable aspects of each principle that can be used by evaluation approaches.

Taking into account these developments and the need to evaluate the FAIRness of content providers, OpenAIRE is updating its guidelines in order to cover the FAIR principles elements, and also adapting the FAIR RDA Data Maturity Model indicators in OpenAIRE Validator. The Validator is available to every logged-in user via the PROVIDE dashboard and is intended to provide an impression of the FAIRness of their repository.

The FAIR Maturity Model Indicators, as core criteria to assess the implementation level of the FAIR (Data) principles, implemented in the OpenAIRE Validator, aims to offer to the content providers managers, a way to assess the implementation of FAIR (Data) Principles.

This implementation in the Validator will deploy a specific guideline to evaluate the level of adoption of the FAIR Principles by content providers.

The presentation will briefly introduce to the mentioned guidelines and will shown how the FAIRificaton process will be done with strategic procedure of adaptations of the RDA FAIR Data Maturity Specification, their implementation in the existing OpenAIRE validator service, and conclude with the introduction to evaluate a institutional, thematic, or data repository in the PROVIDE dashboard.

Being part of OpenAIRE, all content providers assure its compatibility with global standards and interoperability based on the adoption of OpenAIRE guidelines. More specifically the FAIR assessment will leverage a more swift adoption and implementation process of FAIR principles in repositories, enabling a faster identification of trustworthy repositories, thus accelerating the building of EOSC services upon these infrastructures.

Libraries, research administrators, Open Science Infrastructure providers, funders

Interdisciplinary collaborations: Networks, services, methods
Sustaining Open infrastructures, services and tools for research communities
Value added data products/services from open science
European Open Science Cloud (EOSC) and FAIR data 

Researchers, research Infrastructures and research communities, repository managers, publishers and content providers, libraries, research administrators, service providers and innovators

Finding research data is often described as difficult or challenging (Brickley, Burgess, & Noy, 2019) (Chapman, et al., 2020), especially in comparison to literature search (Kern & Mathiak, 2015). From observation (Krämer, Papenmeier, Carevic, Kern, & Mathiak, 2021) and surveys (Gregory, Groth, Scharnhorst, & Wyatt, 2020) (Friedrich, 2020) we know that data discovery is a complex process, which involves doing literature review, using data portals, reading documentation, and leveraging personal networks. However, the glue that holds all these steps together is the common web search, e.g. via Google. Unfortunately, due to the lack of central, fully indexed repositories, individual data repositories have the responsibility to make their data visible for web search. In this paper we explore how research data is found via general web search by analyzing the queries made to Google using clustering techniques, retrieved via the Google Search Console. The clustering is based on two different keyword features: their probabilities in the queries and their Comparable Click Through Rate (CCTR). The latter is a normalized version of CTR, which allows keywords comparison. We use the query logs from three data portals from the Social Sciences domain, from two different institutions, in addition to a JSON file with mentions of datasets in research papers taken from Social Science Open Access Repository (SSOAR). The use case we are most interested in is the known item search. Here, a dataset is retrieved by name, which has been communicated through the literature or personal communication. These names are often ambiguous, such as acronyms or common nouns, and additional keywords are added by the researchers to find the dataset’s website. The results of our analysis provide a set of keywords which, when systematically added in proper locations of the research data landing pages, can help to make them more discoverable.

repository managers, researchers, librarians, research communities

Interdisciplinary collaborations: Networks, services, methods
Training and skills for open science

Repositories, training, skills, Open Science, librarians

The talk presents a concept of Open Science-related training developed with the aim of facilitating the adoption of institutional repositories in a specific local context (Serbia). This context is marked by a considerable delay in infrastructure development, the lack of institutionalized training on Open Science, a knowledge gap, and insufficient library staff. Although institutionalized training is indispensable in the long-term, this example shows that a combination of bottom-up approaches and highly customized and informal training can increase the Open Science capacity of researchers, librarians and even institutions, which is highly relevant in the context of building competence and capabilities for EOSC. In these terms, it may be instructive for other environments with poor formal training on Open Science.

The adoption of the Open Science Platform (the national OS policy) in 2018, spurred the development of institutional repositories in Serbia and the leading role in this process has been taken by the University of Belgrade Computer Centre (RCUB). Before the adoption of the OS Platform, training on OS was mainly provided through international projects. The official Library and Information Science curriculum, Librarian Licensure Examination programme, and professional development courses for librarians and training programmes offered by university libraries did not address OS-related skills, due to which there was a knowledge gap in the library and research communities, and this threatened the adoption of the developing infrastructure.

To overcome this, the University of Belgrade Computer Centre decided to include training (for repository managers and end users), along with software, hosting, and technical support, in the service package offered to institutions. A dedicated user support team responsible for designing and implementing training was established. The training programme covers a range of topics beyond repository features and workflows: Open Access policies, FAIR principles, metadata standards, copyright, self-archiving policies, altmetrics, dissemination through metadata harvesting, discovery platforms (OpenAIRE, BASE, CORE), using institutional repositories in the context of Research Data Management and cultural heritage. Training formats include predefined lectures and webinars, but also highly customized sessions and informal consultations.

As a result, dozens of repository managers (mainly librarians) and hundreds of end users (researchers) have been trained so far. A number of trained repository managers have already started organizing training on various OS topics at their institutions. Together with the user support team, they form a strong network enabling dynamic information exchange. At the same time, there is a growing interest among researchers for additional training on particular OS topics (RDM, copyright, integration of the repository in various institutional workflows). Also, continuous and flexible support encourages content diversity in repositories. The concept of training developed by the RCUB user support team has so far proven to be efficient in mitigating the lack of institutionalized training. However, in order to provide full support for all aspects of Open Science (esp. RDM and citizen science), it will be necessary to establish institutionalized training.

Research Infrastructures and research communities, repository managers, service providers and innovators, EOSC

Sustaining Open infrastructures, services and tools for research communities
European Open Science Cloud (EOSC) and FAIR data

ROcrate, tooling, collaboration, storage

This talk discusses a democratised approach to FAIRness as being built for the CS3MESH4EOSC project.

The principles of FAIR have been widely embraced. Uptake of FAIRnes remains a challenge. We recognise two main obstacles:

1. FAIR-aware infrastructure needs to be available, and be so usable that it gets broad uptake
2. Research communities need to be motivated, trained and assisted to use this FAIR infrastructure. It needs to make their lives easier. Without relevant infrastructure in place, there is no point in mounting FAIRnes awareness campaigns.

The CS3MESH4EOSC approach to FAIR uptake is to start from the Science Mesh of datastores, already in widespread use by researchers. We add a FAIR Description Service to these stores, for any researcher to use (an instance of the "Describo" tool). Thus they can create FAIR Digital Object packages from their own data (using the RO-Crate standard); initially targeting the open access Zenodo repository service.

CS3mesh4EOSC meshes datastores that are already in widespread use by researchers. By adding metatada awareness and annotation capabilities to this already-patronised mesh, we end up with a democratised approach to FAIR. Allowing researchers to generate FAIR objects from their live data (no onerous upload / collections steps) will help create more FAIR data supply. We are glad to work with other EOSC entities, either offering this as a whitelabel to other service providers, or directly to end users.

Librarians, researchers, research data managers, data scientists

Interdisciplinary collaborations: Networks, services, methods
Sharing best practices and knowledge

DMP; researcher´s perception; data management plan; metadata

Considering that the elaboration of a DMP is not a trivial task for the researcher, who, for the most, do not use this praxis in their research routine, Icict/Fiocruz carried out a study about the perception of researchers regarding the elaboration of a DMP, supported by the FioPGD tool, developed by the Fundação Oswaldo Cruz. We tried to identify doubts, facilities and difficulties related to the system interface (usability); the understanding of the content to be filled in each form section; the general perception of the tool; as well as the challenges founded during its filling. As a methodological procedure, a qualitative approach was used, adopting as a data collection instrument the observation of the user's interaction with the system. The result generated some necessary actions to optimize the system and highlighted the difficulty in understanding about metadata. It was concluded that metadata questions must have a better explanation with answers tips inside the DMP systems, to ensure its proper fill. Besides this, it is necessary to adopt an institutional ecosystem that ensures an articulation between information professionals and ethical and legal specialists, to support researchers, in their DMPs elaboration.

policy makers, researchers, repository managers, content providers, libraries

Interdisciplinary collaborations: Networks, services, methods
Sharing best practices and knowledge
Value added data products/services from open science
Research analytics and visualisations

open access, institutional repository, scholarly communication, bibliometrics

In 2015, Kyoto University, Japan, adopted the Kyoto University Open Access Policy, which mandates faculty members to make their scholarly articles public on the institutional repository, Kyoto University Research Information Repository (KURENAI). In 2020, five years after the implementation of the open access policy, we investigated the effects of the same. This paper presents a bibliometric analysis to reveal the effects of open access, comparing scholarly articles deposited to the KURENAI with their counterparts (i.e., scholarly articles not deposited to the KURENAI) to examine whether a citation advantage exists.

The analysis revealed that the KURENAI has contributed to increasing the number of citations of scholarly articles not co-authored with foreign researchers and scholarly articles in different fields. Thus, we argue that institutional repositories foster interdisciplinary research. We also observed that scholarly articles that are open access only on the KURENAI have been downloaded more frequently than those that are also open access on other platforms (e.g., open access journals and other repositories). Owing to a large number of submissions, it may take some time until a deposited article becomes available for access on the KURENAI. Accordingly, it would be helpful for researchers and libraries to introduce a system that, when processing deposited scholarly articles, prioritizes the publication of articles that are not openly accessible on other platforms.