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1. Introduction The Faculty of Human Sciences at the University of Bern includes three institutes: the Institute of Educational Science, the Institute of Psychology and the Institute of Sport Science. Research management (FOMA) at the Faculty of Human Sciences aims to support researchers by strengthening excellent research and managing research data ethically in line with the Swiss federal laws and ordinances. We advise researchers where and how, and under which conditions and formats to store their research data, and provide consulting on requirements and best practices for metadata and documentation description, data monitoring and coding of variables. We support with the data management processes, continuous monitoring and updates related to data management plans (DMP), which are required by the Swiss National Science Foundation and EU-Commission. We inform researchers on the legal ordinance of the Swiss Federal Act on Data Protection (FADP) for the ethical management of sensitive data, on the Findable, Accessible, Interoperable, and Reusable (FAIR) data principles and on available IT-solutions in accordance to the General Data Protection Regulations (GDPR). 2. Research Data Data collection and sharing is part of the research projects conducted at the Faculty and therefore need to meet ethical and legal requirements. Collected are either related to clinical studies, which fall under the Federal Act on Research involving Human Beings (HRA, Art. 118b § 1) and require approvals from an ethics committee (e.g., Cantonal Ethic Commission (CEC) or Swissmedic), or studies that do not fall under the HRA and can be reviewed and evaluated by the Ethical Committee experts at the Faculty. Yet, most of the data are personal and health-related personal data, therefore, informed consent, case report forms and homogenized protocols should be taken into consideration in agreement with legislation in Switzerland. Here we give some examples of the ethical data treatment according to good research practices. We face challenges, leverage strengths and create opportunities by providing data management within the human science disciplines in relation to: Personal and health-personal data: Sociodemographic (date of birth, place of birth, civil status, nationality, old-age and survivor's insurance (OASI); Personality- and ability-related data (workplace-related problems); psychophysiological data such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings; mental health-related behaviors and attitudes; anthropometric data: Body Mass Index (BMI), waist circumference, bioimpedance data; cardiovascular data (blood pressure, pulse wave velocity, heart rate variability). Clinical data: Symptom severity and changes during therapy; online-assessments; randomized online studies and surveys; confidentiality agreements; inform consent forms; study protocols; case report forms; monitoring reports. IT- and software applications: Computer games; applications for kids; educational applications; self-help applications; phone applications; survey & interviews (Qualtrics, Atlas.ti); clinical trials and surveys (RedCap, Qualtrics); statistical software (R, Stata, SAS, SPSS). Data format and storage: Anonymised scans (.tiff, .png); tables (.csv encoded in UTF-8); text documents (txt, A/PDF) coded as ASCII; audio (.wav, .mp4); video (.mov, .avi, .mj2, .mkv, FFV1 codec); graphics (HDF5, .svg). Password-protected access for the project principal investigators (PI) and project members. Traceable anonymized data transfer and password-protected access to the encoded data within the project partners. All tablet computers are password-protected, with the experimenters being the only persons to have password-protected access to the data. Information about data collection and documentation, ethical, legal and security issues, data storage and preservation, as well as data sharing and reuse is provided in the data management plans, supporting researchers to design and conduct their projects according to the FAIR principles, legal ordinances and requirements of the national and international funding agencies. 3. Challenges • Where and how to store the identifying data or personal participants' data additionally to separate database with anonymised encoded data? • Possible delays in the study due to amendments to the ethics commissions. • Where is the right place to store encrypted and password-protected video/audio interviews? • Which is the most appropriate Software to use for surveys? • Is it necessary to use licensing under continuous games integration? • Where is the right place to store neurophysiological (EEG- and fMRI) data? • What is the right criteria to use pseudonymisation vs. anonymisation? Encrypted and anonymised interviews are often needed to be pseudonymised (e.g. Olympic champions). • How can video/audio images of interviews be anonymised? If not at all, then what are the possible solutions? 4. Strengths & Opportunities • Use professional software that correspond to the GDPR and store the data within national Swiss data repositories or within European Union. • Follow the Good Clinical Practice (GCP) and International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) rules and prepare applications for the ethics committees earlier enough by leverage better study planning. • Use collaborative approach at the institutional level and with other research centers to strengthen network. • Follow Open Science strategy by "open data as possible, as close as necessary" under legal ordinance of the FADP for the management of sensitive data ethically based on the FAIR data principles and on available IT-solutions in accordance to the GDPR. • Identify the overall strategy for data management processes before the project starts. Apply for licenses for presentations, talks, developed games, phone applications etc. 5. Collaboration The FOMA data management support at the Faculty is offered in collaboration with the Clinical Trial Unit (CTU, University of Bern); IT-persons of the three research institutes at the Faculty and Ethics Committee at the Faculty; and the Open Science Team at the University of Bern, Switzerland.

An increasing number of UK Higher Education Institutions (HEIs) are developing Research Data Management (RDM) support services. Their action reflects a changing technical, social and political environment, guided by principles set out in the Research Councils UK (RCUK) Common Principles on Data Policy. These reiterate expectations that publicly-funded research should be openly accessible, requiring that research data are effectively managed. The Engineering and Physical Sciences Research Council (EPSRC) policy framework is particularly significant, as it sets a timeframe for institutions to develop and implement a roadmap for research data management. The UK Digital Curation Centre (DCC) is responding to such changes by supporting universities to develop their capacity and capability for research data management. This paper describes an 'institutional engagement' programme, identifying our approach, and providing examples of work undertaken with UK universities to develop and implement RDM services. We are working with twenty-one HEIs over an eighteen month period, across a range of institution types, with a balance in research strengths and geographic spread. The support provided varies based on needs, but may include advocacy and awareness raising, defining user requirements, policy development, piloting tools and training. Through this programme we will develop a service model for institutional support and a transferable RDM toolkit.

Presentation at NUAS forum 2019, Tromsø, 14. - 16.08.19. ( https://www.nuasforum2019.org/ ). Arranged by NUAS (Det Nordiska Universitets Administratörs Samarbet): https://www.nuas.org/.

Background:Building or acquiring research data management (RDM) capacity is a major challenge for health and medical researchers and academic institutes alike. Considering that RDM practices influence the integrity and longevity of data, targeting RDM services and support in recognition of needs is especially valuable in health and medical research.Objective:This project sought to examine the current RDM practices of health and medical researchers from an academic institution in Australia.Method:A cross-sectional survey was used to collect information from a convenience sample of 81 members of a research institute (68 academic staff and 13 postgraduate students). A survey was constructed to assess selected data management tasks associated with the earlier stages of the research data life cycle.Results:Our study indicates that RDM tasks associated with creating, processing and analysis of data vary greatly among researchers and are likely influenced by their level of research experience and RDM practices within their immediate teams.Conclusion:Evaluating the data management practices of health and medical researchers, contextualised by tasks associated with the research data life cycle, is an effective way of shaping RDM services and support in this group.Implications:This study recognises that institutional strategies targeted at tasks associated with the creation, processing and analysis of data will strengthen researcher capacity, instil good research practice and, over time, improve health informatics and research data quality.

Görzig, H., Engel, F., Brocks, H., Vogel, T. & Hemmje, M. (2015, August). Towards Data Management Planning Support for Research Data. Paper presented at the ASE International Conference on Data Science, Stanford, United States of America. ; This paper outlines challenges and requirements for developing tools and services supporting automated generation, management, evolution, and execution of Data Management Plans (DMPs) by reviewing Research Data Management (RDM) processes represented by Knowledge-based and Process-oriented Innovation Management (German: Wissenbasiertes Prozess-orientiertes Innovationsmanagement, WPIM). Based on this representation Data Management Rules (DMRs) will be derived to support the Integrated Rule-Oriented Data System (iRODS). In this way, compliance with the Open Archive Information System (OAIS) and packaging the relevant context information related to a data object is supported in a serialization using the Open Archives Initiative Object Reuse and Exchange (OAI-ORE) format specification. ; This study is part of the RAGE project. The RAGE project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains.

In anticipation of the then forthcoming Tri-Agency Research Data Management Policy, a consortium of professionals from Canadian university libraries surveyed researchers on their research data management (RDM) practices, attitudes, and interest in data management services. Data collected from three surveys targeting researchers in science and engineering, humanities and social sciences, and health sciences and medicine were compiled to create a national dataset. The present study is the first large-scale survey investigating researcher RDM practices in Canada, and one of the few recent multi-institutional and multidisciplinary surveys on this topic. This article presents the results of the survey to assess researcher readiness to meet RDM policy requirements, namely the preparation of data management plans (DMPs) and data deposit in a digital repository. The survey results also highlight common trends across the country while revealing differences in practices and attitudes between disciplines. Based on our survey results, most researchers would have to change their RDM behaviors to meet Tri-Agency RDM policy requirements. The data we gathered provides insights that can help institutions prioritize service development and infrastructure that will meet researcher needs. ; En prévision de la future Politique des trois organismes sur la gestion des données de recherche, un consortium de professionnels des bibliothèques universitaires canadiennes a interrogé des chercheurs sur leurs pratiques et leurs attitudes en matière de gestion des données de recherche (GDR) et sur leur intérêt pour les services de gestion des données. Des données recueillies de trois sondages ciblant les chercheurs en sciences et en génie, en sciences humaines et sociales ainsi qu'en sciences de la santé et en médecine ont été compilées pour créer un ensemble de données national. Cette étude est la première enquête à grande échelle sur les pratiques de GDR des chercheurs au Canada et est l'une des rares enquêtes récentes ...

This course gives a brief recap on RDM and then covers managing personal and sensitive data in the context of the new GDPR legislation, why it is a Good Thing to share your data, and how to do this most effectively in terms of describing your data, deciding where to share it, and using licences to control how your data is used by others. You will even get to write your own Data Management Plan (DMP): these help you manage your data throughout a project and after it has ended and are increasingly required as part of a grant or fellowship application. You will also learn about the range of support services available to you within the University for managing your data.

Der Beitrag ordnet das Handlungsfeld Forschungsdatenmanagement zunächst in den Kontext sich wandelnder Anforderungen an wissenschaftliche Bibliotheken sowie wissenschaftspolitischer Strategien und Forderungen auf nationaler und europäischer Ebene ein. Im Anschluss liegt der Schwerpunkt auf einem möglichen Leistungsportfolio einer wissenschaftlichen Bibliothek, das Forschenden Unterstützung bei dem Management und der Publikation ihrer Forschungsdaten bietet. Die denkbaren Serviceleistungen reichen dabei von der Beratung bis hin zum Aufbau eigener Forschungsdateninfrastrukturen, die den Nachweis, die Bewertung, das Sichtbarmachen und die Überprüfung von Forschungsdaten ermöglichen. ; The field of action "research data management" is placed within the context of changing requirements to research libraries as well as of strategies and requirements named by science policy on national and European level. The focus lies on a potential service portfolio of a research library, which offers researchers support with regard to the management and publication of research data. Potential services might range from advisory services to the development of research data infrastructures which facilitate the record, evaluation, visibility and review of research data.

Data Management Plans (DMP) play an important role for planning and conducting research activities and more and more researchers are facing requirements by their institution or funding agency for writing such a plan. Thereby, they are often misunderstood as an administrative task. Instead, DMPs are a powerful tool for efficient and secure collaboration and for the creation of research outputs of better quality and higher impact. A DMP will also help you to develop strategies for sharing and publishing your research data as openly as possible (and protecting them, when necessary). In this session, we will introduce the typical components of a DMP and discuss how the plan can be used actively throughout a research project. It will be targeted towards PhD students, but it will also be relevant for anyone working with research data. ; Co-funded by the Erasmus+ Programme of the European Union

"Effective Research Data Management (RDM) is a key component of research integrity and reproducible research, and its importance is increasingly emphasised by funding bodies, governments, and research institutions around the world. However, many researchers are unfamiliar with RDM best practices, and research support staff are faced with the difficult task of delivering support to researchers across different disciplines and career stages. What strategies can institutions use to solve these problems?

Engaging Researchers with Data Management is an invaluable collection of 24 case studies, drawn from institutions across the globe, that demonstrate clearly and practically how to engage the research community with RDM. These case studies together illustrate the variety of innovative strategies research institutions have developed to engage with their researchers about managing research data. Each study is presented concisely and clearly, highlighting the essential ingredients that led to its success and challenges encountered along the way. By interviewing key staff about their experiences and the organisational context, the authors of this book have created an essential resource for organisations looking to increase engagement with their research communities.

This handbook is a collaboration by research institutions, for research institutions. It aims not only to inspire and engage, but also to help drive cultural change towards better data management. It has been written for anyone interested in RDM, or simply, good research practice.

Presentation for the Health Libraries Association of British Columbia. Take a moment to think about your researchers and the kinds of data they access and/or create. Where is this data stored and how is it organized? If they were asked to share data with another researcher would they be able to make sense of that work? If they needed to locate the data files from 5 years ago, how easy would it be to find and use datasets? What privacy and security controls have they implemented to ensure that they are appropriately safeguarding their data? Is their data governed by legislative requirements? If so, are they required to comply with additional policies and standards when using these datasets? If you are unsure about the answer to any of these questions you have come to the right place. This workshop will cover the basics of data management plans, metadata and data documentation, data privacy and security, and data sharing. It is designed to support researchers engaging in clinical research to start thinking about the steps they can take to better manage their research data. Kaitlyn Gutteridge is the Research Data Privacy and Security Officer for the ARC team. In addition, Kaitlyn serves as a member of the Compute Canada Security Council. She holds a Master of Science degree from the London School of Hygiene and Tropical Medicine where she focused her epidemiology training on multilevel modeling of chronic disease development. She previously held positions supervising the implementation of large-scale research initiatives at Simon Fraser University and the Centre for Hip Health and Mobility. Most recently, she served as the Privacy and Governance Lead at Population Data BC. In her position at Population Data BC, Kaitlyn served as the organization's Privacy Officer and managed the negotiation, development, and execution of information sharing agreements and associated policies & procedures. Eugene Barsky is Research Data Librarian at the UBC Library. His recent peer-recognition included American Society for Engineering Education and Special Library Association awards. He published more than 20 peer-reviewed papers and presented at more than 40 conferences. Eugene is chairing the national Portage Data Discovery Expert Group, participates in building the Canadian Federated Research Data Repository (FRDR), and collaborates with Research Data Canada (RDC). Eugene is an adjunct faculty member at the iSchool at UBC, teaching courses in science librarianship and research data management, and is an active member of the Pacific Northwest data curators group. ; Library, UBC ; Other UBC ; Unreviewed ; Faculty ; Other

This is a non-credit, free course which provides guidelines for good practice in research data management. The course is particularly appropriate for postgraduate students and early career researchers who work with data and would like to learn more about managing their research data. The course content is mainly geared for three disciplines: geosciences, social and political sciences and clinical psychology, however, many of the issues covered apply equally to all research disciplines. This course is an Open Educational Resource that may be freely used by anyone. It is available through an open license for re-using, rebranding, repurposing.

Summary
Nowadays, research without a role for digital data and data analysis tools is barely possible. As a result, we see an increasing interest in research data management, as this enables the replication of research outcomes and the reuse of research data for new research activities. Data management planning outlines how to handle data, both during research and after the research is completed. Trusted data repositories are places were research data are archived and made available for the long term. This article covers the state of the art concerning data management and data repository demands with a focus on qualitative data sets.

Research data must be discoverable to be re-used. Data discovery represents the descriptive and technical processing of data and metadata, as well as the tools and infrastructure aimed at improving access and reuse of research data on the web. A Canadian data discovery service would make it easier to find and reuse research data held in institutional and disciplinary repositories. We would like to see a service that provides a coherent, single point of access to authoritative, searchable, browsable, and machine actionable descriptions (metadata) for datasets and implements clear means for accessing them, thus increasing the likelihood of discovery and reuse of research data in Canada. In this paper, we highlight current opportunities and issues related to developing such a service in Canada. Based on a review of international and national research data repositories and data discovery services, we offer a set of guiding principles, best practices, and recommendations for data discovery: Common metadata: the descriptive information that accompanies research data should meet minimum standards to enable discovery and support data reuse. This requires a commitment to a core set of metadata components across domains. Metadata tools should accommodate multiple, overlapping metadata namespaces, i.e., descriptive terms assigned, managed, and grouped into collections of classes and attributes. We also recommend building separate, flexible metadata harvesters for indexing specialized repositories, so that domain-specific metadata and granularity can be retained in its original format. Persistent Identification: the use of global identifiers for researchers and research data. We recommend exploring a national ORCID agreement so that universities and government agencies in Canada can integrate researcher identifiers into institutional and other research management and publishing software. We also recommend registering DOIs corresponding to datasets in participating repositories with DataCite Canada. These DOIs will greatly enhance dataset discoverability via DataCite's metadata partners (e.g. ORCID, VIVO, etc). Open Access and Programmatic Interfaces: the use of an application program interface (API) allowing one piece of software to make use of the functionality or data available to another through a set of routines, protocols, and tools. Metadata and data should be programmatically accessible for reuse and development purposes through the provision of APIs among participating repositories and data discovery platforms. Common licensing: policies and licenses should govern access to data and metadata and, whenever possible, should be minimally restrictive. We recommend the use of Creative Commons licenses for research data as they effectively communicate information about the copyright holders' intentions and clarify usage permissions. Licensing can apply to data and metadata, although we strongly recommend that metadata be provided as openly as possible, with minimal to no restrictions on reuse in order to facilitate discovery. Collaboration: a joint commitment to shared recognition and cooperation among actors, organizations, data producers, and researchers, sometimes described as "coexistence in the scholarly ecosystem." We emphasize that collaboration will drive improvements for data discovery in Canada. A well coordinated national project will ensure that all attempts to improve discovery and access to data will be informed and facilitated by stakeholder expectations, participation, and collaboration. Keeping stakeholders engaged and providing clear communication channels are key for the success of a national data discovery service. This paper is presented with a common goal to make research data as widely discoverable and accessible as possible, thus enhancing opportunities for data reproducibility and reuse. Enhancing data discovery is one approach to facilitating greater interoperability and discovery of scholarly outputs. Building national infrastructure to support research data discovery will greatly enhance opportunities for further integration across the scholarly ecosystem, including support for metadata, global identifiers, and open APIs. ; Library, UBC ; Non UBC ; Unreviewed ; Faculty ; Researcher