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This document contains the Data Management Plan (DMP) of PathOS on M6 of the project. It is a living document that will formally be updated on M18. It has been created using the ARGOS service (https://argos.openaire.eu) an online tool for creating, managing and sharing DMPs and linking them with the research artifacts they correspond to. In the DMP, we describe the input (re-used) and output (created) datasets that will be used in the technical work of PathOS, following the principles of Open (whenever possible) and FAIR as outlined in the Grant Agreement.* *The numbering of the questions skips in order to allow the updating of the DMP in the future (i.e. filling out questions for which the information is currently missing) via the Argos service. * This project deliverable is currently pending approval by the EC.

The IntelComp time analysis and trend detection service focuses on the dynamic analysis of the scientific literature, tracking the evolution of disciplines and capturing the emergent research areas or new topics and quantifying their trends. To this end, we automatically expand the Field of Science Taxonomy exploited by the IntelComp classifiers (documented in Deliverable D3.4) to a more fine-grained level by adding three additional levels (Level 4-Level 6) and provide a dynamic mechanism that properly adjusts the lower levels of the hierarchy as new research topics emerge.

As per tradition, OpenAIRE will actively contribute to the International Open Access Week 2022 initiatives with interactive sessions and thought-provoking panel discussions connected to the theme of this year “Open for Climate Justice”. OpenAIRE prepared two series of webinars that will showcase the different ways in which we can all work together and make Open Science a means to tackle the challenges ahead of us. This session was dedicated to OpenAIRE EXPLORE: Discovering research with focus on Sustainable Development Goals and Fields of Science classifications Speakers: - Konstantina Galouni, OpenAIRE - Haris Papageorgiou, Athena RC and other colleagues from SciNoBo - the Science No Borders team The recordings are also in Youtube -https://youtu.be/LiyseRxVa-Y &nbsp

Lightning talk presentation for the 17th International Digital Cuation Conference (IDCC22) on the topic of Reusability. These slides present the methods and some preliminary results from Springer Nature and OpenAIRE's collaborationto improve data linking, specifically a workstream enabling better measurement of data reuse from existing publications. Improving our measurement of data reuse: OpenAIRE and Springer Nature's collaboration to automate data link identification and classification Abstract: Publishers, funders, governments and research institutions increasingly encourage researchers to make their data reusable. Making the case for FAIR data presents a challenge in how to track and measure data reuse. Springer Nature and OpenAIRE are collaborating on a text and data mining approach to improve our knowledge of data reuse. This lightning talk presents our research aims, methods and some preliminary results. Since the publication of the FAIR principles in 2016, organisational efforts to make data FAIR have mainly focused on requirements and motivations for the dataset creator; data policies widely adopted by publishers and funders emphasise what authors should do with their data, supported by editorial guidance and checks. Technical solutions have built on the increased usage of repositories, metadata standards and the growing role of data curators. While there are clearly-quantified problems FAIR data can solve (e.g. the €10.2bn cost to the European economy of non-FAIR data), researchers may ask "will the time and effort spent making my data FAIR actually lead to its reuse?". Effective implementation of FAIR should lead to data being reused, not just theoretically reusable. This necessitates investigation of actual data reuse patterns, which presents certain challenges. The information required to link dataset creation and reuse, or creator and reuser, is often incomplete or absent. Even where technical frameworks such as Scholix have enabled progress, the required culture change for data accreditation is insufficient to provide a comprehensive picture. A number of recent initiatives have sought to address the data reuse knowledge gap using machine learning techniques on published literature. The Coleridge Institute’s ‘Show US the Data’ program and the NIH LitCoin Natural Language Processing (NLP) challenge are two such competitions to use NLP to identify public datasets from research publications. Springer Nature and OpenAIRE are partnering to address this issue by improving the discoverability of links between Springer Nature’s publications and underlying research data. This collaboration employs OpenAIRE’s text and data mining algorithms to detect and classify data-article links. It has the advantage of being able to analyse the existing corpus, and interrogate data reuse: based on authorship (reuse by the same / different authors) based on discipline (reuse within / between disciplines) throughout time across repositories OpenAIRE identifies dataset from the literature using data identifiers and surrounding context from the research publication. Advanced artificial intelligence and NLP techniques process the corpus, aiming to capture all instances of underlying data in the manuscript. Authors’ data citation behaviour is then disambiguated in terms of reuse and attribution to creators based on the surrounding context. The process constructs a scientific knowledge graph integrating identified datasets, linked to FAIR metadata via the manuscript. Finally, we consolidate and quantify our analysis across different topics, disciplines and organisations over time. We intend that this work will provide a methodological basis for data link detection and classification, with insights into data reuse patterns across the published literature. Determining granular and comprehensive patterns of data reuse offers the potential for more targeted interventions to promote reusability by research organisations. &nbsp

The Indicator Methodology and Metadata Handbook is one of the deliverable of the study to support the monitoring and evaluation of the Framework Programme for research and innovation along Key Impact Pathways – RTD/2019/SC/016 It presents precise definitions & detailed methodology and data sources for each of the Key Impact Pathway indicators. It is based on results of Task 1 which was about specifying and testing a full set of indicators, related methodologies for data collection and analysis, and data sources for each of the nine Key Impact Pathways ensuring data quality and reliability. The estimates for the baselines and benchmarks come from the work performed in Task 3 (linked to the Baseline and Benchmark report), while the methodology and data quality assessment are based on the work performed in Task 2 (linked to the Operationalisation plan for IT systems).

The Operationalisation plan for IT systems is one of the deliverable of the study to support the monitoring and evaluation of the Framework Programme for research and innovation along Key Impact Pathways – RTD/2019/SC/016 It presents a pragmatic approach for operationalisation of the Key Impact Pathways and related methodologies and indicators as required under task 2 of the tender specifications “Support the operationalisation of the Key Impact Pathways into Commission IT systems”. The plan presents the tasks and responsibilities needed from the Commission to integrate the changes within their IT systems. In addition, the plan includes results and lessons learned from the data pilots carried out under the study. Adaptations to the plan may be required as the operationalisation progresses and as developments and data acquisition take place.

This study is framed within the context of the contract ‘Monitoring the open access policy of Horizon 2020 – RTD/2019/SC/021’, reporting an authoritative set of metrics for compliance with the European Commission open access mandate within the Framework Programme thus far, and providing advice on how to systematically monitor compliance in the future. Open access requirements for publications under Horizon 2020 are set out in Article 29.2 of the Horizon 2020 Model Grant Agreement (MGA). Regarding open access to research data, the Commission is conducting the Horizon 2020 Open Research Data Pilot (ORDP). The ORDP aims to improve and maximise access to, and reuse of, research data generated by Horizon 2020 projects, balancing the need for openness with the protection of intellectual rights, privacy concerns and security, and commercialisation, as well as questions of data management and preservation. The present study aims to examine, monitor and quantify compliance with the open access requirements of the MGA, for both publications and research data. The study concludes with specific recommendations to improve the monitoring of compliance with the policy under Horizon Europe, together with an assessment of the efficiency and effectiveness of the Horizon 2020 open access policy. The key findings of this study indicate that the European Commission’s leadership in the Open Science policy has paid off. Compliance has steadily increased over recent years, achieving a success rate that places the European Commission at the forefront globally (83% open access to scientific publications). What is also apparent from the study is that monitoring – particularly with regard to the specific terms of the policy – cannot be achieved by self-reporting alone, or without the European Commission collaborating closely with other funding agencies across Europe and beyond, to agree on common standards and the common elements of the underlying infrastructure. In particular, the European Open Science Cloud (EOSC) should encompass all such components that are needed to foster a linked ecosystem, in which information is exchanged on demand and which eases the process for both researchers (who only need to deposit once) and funders (who need only record information once).

The MOAP Horizon 2020 database was created under the auspices of the Study “Monitoring the Open Access Policy of Horizon 2020” (SPECIFIC CONTRACT No RTD/2019/SC/021 implementing Framework contract No 2018/RTD/A2/OP/PP-07001-2018). This is the data management plan of the study., The study was funded by "Monitoring the Open Access Policy of Horizon 2020" (SPECIFIC CONTRACT No RTD/2019/SC/021 implementing Framework contract No 2018/RTD/A2/OP/PP-07001-2018)

The MOAP Horizon 2020 database was created under the auspices of the Study “Monitoring the Open Access Policy of Horizon 2020” (SPECIFIC CONTRACT No RTD/2019/SC/021 implementing Framework contract No 2018/RTD/A2/OP/PP-07001-2018). This publication documents the database.

Presentations during the 5th public session of OpenAIRE Week, General Assembly: "Building Open Science Gateways to open and linked research outcomes". This session goal was to present the OpenAIRE services that support research communities, initiatives, and infrastructures at implementing and monitoring the uptake of Open Science principles.

This deliverable provides details regarding the CAPSELLA social media analysis platform (the social media channels and the insights extracted for specific communities). The document will be updated on a regular basis reflecting the developments on the platform and the CAPSELLA pilots.

This deliverable is a software deliverable describing the third software release of the CAPSELLA platform and its services

This deliverable reports on the updated user requirements from the CAPSELLA communities, in terms of common challenges, user needs, difficulties and limitations. These have been identified during the design and development of the pilots through the interaction with the communities.