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This paper attempts to present a brief overview of several Research Data Management (RDM) issues and a detailed literature review regarding the RDM aspects adopted in libraries globally. Furthermore, it will describe several tendencies concerning the management of repository tools for research data, as well as the challenges in implementing the RDM. The proper planned training and skill development for all stakeholders by mentors to train both staff and users are some of the issues that need to be considered to enhance the RDM process. An effort will be also made to present the suitable policies and workflows along with the adoption of best practices in RDM, so as to boost the research process in an organisation. This study will showcase the implementation of RDM processes in the Higher Educational Institute of India, referring particularly to the Central Library @ NIT Rourkela in Odisha, India with a proposed framework. Finally, this study will also propose an area of opportunities that can boost research activities in the Institute.

Research projects are usually composed of three or more smaller but highly complex project units that run in parallel generating multiple sets of data. Making these data available upon publication according to the four foundational principles – Findability, Accessibility, Interoperability, and Reusability (FAIR principles) is a standard requirement and established part of there research workflow. This course gives an introduction to Research Data Management ( RDM ) , the contextualisation of datasets by adding relevant metadata, theory and practical application of FAIR and CARE (people & purpose) principles as well as an overview of appropriate research data repositories along with legal and ethical aspects to take into consideration in the process. This presentation was held for the EU project consortium 'Disc4All'

The chapter "Applications of Research Data Management at GESIS Data Archive for the Social Sciences" explores ways in which an archive - i.e. an organization whose work has a strong focus on preservation and dissemination of digital data - can become involved in research data management (RDM). The Data Archive looks back on a long history of working with researchers to make their data re-usable and accessible since 1960. Today it provides support for Research Data Management across the entire data lifecycle by offering a wide range of tools and services tailored to the needs of different types of stakeholders. The chapter gives an overview of selected tools and services offered in the areas of metadata and data documentation, data preparation, data publication, and long-term preservation. To illustrate how support for research data management plays out in different settings, three case studies for typical scenarios are presented: 1) The European Values Survey (EVS), a large international longitudinal survey studying basic human values across Europe. 2) The German Longitudinal Election Study (GLES), a national survey program with a comprehensive approach to gain insights into the German federal elections. 3) A data center in the health sector which decided to make data originally collected to support policy-making available to research.

Abstract: Have you ever wondered why is everyone discussing about Open Science and Research Data Management (RDM) these days? Good Research Data Management (RDM) is crucial for reproducible and robust scientific research. Consequently, more and more funding bodies, governments, research institutions and other agencies have emphasised the value and importance of good data management and introduced policies on data management and sharing. However, it is easier said than done. Like most researchers you might have more questions than answers about the topic. You are not alone! Come join us on the 24th at an interactive workshop where you can understand the why and how of open science and research data management in practical terms. The main aspects covered in the workshop are: Why RDM and open science - a brief introduction RDM essentials and practical tools for : Storage, backup, file structure & naming, data sharing and repositories Winning with open science: Direct benefits for researchers and tips on implementing RDM best practices in day-to-day research activities Introduction to Data Management Plans and DMPOnline Information session on open access, H2020 and support available for PoliTo research community Workshop presenter: Dr. ir. Shalini Kurapati Information session on open access and H2020 support: BIBLIOM Area bibliotecaria e museale (Maria Girard. Monica Margara) and ARI Area Ricerca (Sara Rollino)- Politecnico di Torino Shalini Kurapati (Profile) Dr. ir. Shalini Kurapati is an Open Science fellow at the Politecnico di Torino. Shalini's role is to promote a university wide research program for the development of a roadmap to support research data management and open science within the context of a university of technology. She works closely with Prof. Federica Cappelluti, the rector's advisor for open science. Previously, she had worked as a data steward at the Delft University of Technology (TU Delft). She holds a PhD as well as 7 years of research experience from the faculty of Technology, Policy ...

The Information Systems and Machine Learning Lab (ISMLL) specialize in research in machine learning and data mining. The researchers work in the following areas of factorization methods, relational classification, and time series classification. Researchers can benefit from support services in managing their digital data. Tools must be in tune with researchers' workflows, discipline and project-specific. Currently, there are limits to unlocking the large potential of big data for research in a systematic way and with legal certainty. Research based on big data is often hardly replicable. The Research Data Management Organiser (RDMO) enables to plan and carry out their management of research data across the whole life cycle of the research data. Data protection / personal data. • data that relate to living individuals who are or can be identified by the data • especially worthy of protection according to Bundesdatenschutzgesetz (BDSG neu)§3 and Art. 4 of the EU General Data Protection Regulation are all personal data as well as especially sensitive data such as ethnicity, political opinion, religious affiliation, health, sexual orientation. NFDI4Ing is being explained which will serve as a platform for scientific networks, open to all researchers in engineering science.

Increasing complexity and volume of research data pose increasing challenges for scientists to manage their data efficiently. At the same time, availability and reuse of research data are becoming more and more important in modern science. The German government has established an initiative to develop research data management (RDM) and to increase accessibility and reusability of research data at the national level, the Nationale Forschungsdateninfrastruktur (NFDI). The NFDI Neuroscience (NFDI-Neuro) consortium aims to represent the neuroscience community in this initiative. Here, we review the needs and challenges in RDM faced by researchers as well as existing and emerging solutions and benefits, and how the NFDI in general and NFDI-Neuro specifically can support a process for making these solutions better available to researchers. To ensure development of sustainable research data management practices, both technical solutions and engagement of the scientific community are essential. NFDI-Neuro is therefore focusing on community building just as much as on improving the accessibility of technical solutions.

Increasing complexity and volume of research data pose increasing challenges for scientists to manage their data efficiently. At the same time, availability and reuse of research data are becoming more and more important in modern science. The German government has established an initiative to develop research data management (RDM) and to increase accessibility and reusability of research data at the national level, the Nationale Forschungsdateninfrastruktur (NFDI). The NFDI Neuroscience (NFDI-Neuro) consortium aims to represent the neuroscience community in this initiative. Here, we review the needs and challenges in RDM faced by researchers as well as existing and emerging solutions and benefits, and how the NFDI in general and NFDI-Neuro specifically can support a process for making these solutions better available to researchers. To ensure development of sustainable research data management practices, both technical solutions and engagement of the scientific community are essential. NFDI-Neuro is therefore focusing on community building just as much as on improving the accessibility of technical solutions.

Research data management is an umbrella term used to describe activities related to the creation, organisation, structuring, naming, backing up, storage, conservation, and sharing of research data as well as all actions that guarantee security of research data. As is often the case, researchers from Sub-Saharan Africa are lagging behind their counterparts in developed countries in embracing the best practices of research data management. One of the factors to which this slow pace of adoption of research data management could be attributed, is inadequate research on the subject. The purpose of this paper is to analyse the quantity, quality, visibility and authorship of publications on research data management in Sub-Saharan Africa. Bibliometrics approaches were used to analyse publications on research data management from, and on, Sub-Saharan Africa which are currently indexed in Google Scholar. The index was chosen because it is free and is reputed to have liberal selection criteria which do not favour, or discriminate, any discipline or geographic regions. Data was retrieved from Google Scholar using Harzing's "Publish or Perish" software and analysed using VOSviewer. The findings of the study revealed that the quantity, quality, visibility and authorship collaboration of scholarly publications on research data management in Sub-Saharan Africa is low. The findings may be used by libraries and research institutions in Sub-Saharan Africa to develop and promote best practices in research data management as a means of enhancing their research output and impact.

This report presents the findings of a needs assessment survey that was carried out with researchers and academic staff in four Palestinian Higher Education Institutions (PS HEIs) between December 2016 and February 2017. The four participating institutions include: ● The Islamic University of Gaza (IUG) ● Al-Quds Open University (QOU) ● Birzeit University (BZU) ● Palestine Technical University-Kadoori (KAD) The survey aimed to assess research and sharing practices among researchers in PS HEIs, as well as their attitudes towards open access publishing and Institutional Repositories (IRs). The survey data will be used to: ● Review the practices and procedures used by the researchers and members of academic staff at PS HEIs to handle and share their research outputs ● Estimate the size and types of research outputs and digital materials produced by the academic staff ● Determine the current attitudes towards open access publishing and Open Access Institutional Repositories (OAIRs) ● Explore the awareness of and attitudes towards the institutional policies and support for research data management ● Explore the potential motivations/deterrents to contribute to OAIRs ; Project number: 573700-EPP-1-2016-1-PS-EPPKA2-CBHE-JP. Co-funded by the Erasmus+ programme of the European Union.

AbstractSustainability constitutes a focal challenge and objective of our time and requires collaborative efforts. As artificial intelligence brings forth substantial opportunities for innovations across industry and social contexts, so it provides innovation potential for pursuing sustainability. We argue that (chemical) research and development driven by artificial intelligence can substantially contribute to sustainability if it is leveraged in an ethical way. Therefore, we propose that the ethical principle explicability combined with (open) research data management systems should accompany artificial intelligence in research and development to foster sustainability in an equitable and collaborative way.

Accompanying the growing importance of research data management, the provision and maintenance of metadata – understood as data about (research) data – have obtained a key role in contextualizing, understanding, and preserving research data. Acknowledging the importance of metadata in the social sciences, the Consortium of European Social Science Data Archives started the Metadata Office project in 2019. This project report presents the various activities of the Metadata Office (MDO). Metadata models, schema, controlled vocabularies and thesauri are covered, including the MDO's collaboration with the DDI Alliance on multilingual translations of DDI vocabularies for CESSDA Service Providers. The report also summarizes the communication, training and advice provided by MDO, including DDI use across CESSDA, illustrates the impact of the project for the social science and research data management community, and offers an outline regarding future plans of the project.

Research datasets include all kinds of objects, from web pages to sensor data, and originate in every domain. Concerns with data generated in large projects and well-funded research areas are centered on their exploration and analysis. For data in the long tail, the main issues are still how to get data visible, satisfactorily described, preserved, and searchable.
Our work aims to promote data publication in research institutions, considering that researchers are the core stakeholders and need straightforward workflows, and that multi-disciplinary tools can be designed and adapted to specific areas with a reasonable effort. For small groups with interesting datasets but not much time or funding for data curation, we have to focus on engaging researchers in the process of preparing data for publication, while providing them with measurable outputs. In larger groups, solutions have to be customized to satisfy the requirements of more specific research contexts.
We describe our experience at the University of Porto in two lines of enquiry. For the work with long-tail groups we propose general-purpose tools for data description and the interface to multi-disciplinary data repositories. For areas with larger projects and more specific requirements, namely wind infrastructure, sensor data from concrete structures and marine data, we define specialized workflows. In both cases, we present a preliminary evaluation of results and an estimate of the kind of effort required to keep the proposed infrastructures running.
The tools available to researchers can be decisive for their commitment. We focus on data preparation, namely on dataset organization and metadata creation. For groups in the long tail, we propose Dendro, an open-source research data management platform, and explore automatic metadata creation with LabTablet, an electronic laboratory notebook. For groups demanding a domain-specific approach, our analysis has resulted in the development of models and applications to organize the data and support some of their use cases. Overall, we have adopted ontologies for metadata modeling, keeping in sight metadata dissemination as Linked Open Data.