Part 1 of the 5 cycle series of seminars on RDM and open access between 28 May and 23 June 2020 "ALL YOU NEED TO KNOW ABOUT RESEARCH DATA MANAGEMENT AND OPEN ACCESS PUBLISHING" A cycle of 5 seminars for PhD students Open Science is increasingly referred to as the "Science for the Future" and the "Future of Science". By making science more accessible - via Open Access publishing and proper Research Data Management - Open Science makes the scientific process more inclusive and the outputs of science more readily available and relevant for society. Consequently, more and more funding bodies, governments, research institutions and other agencies have emphasized the value and importance of open science practices and introduced policies on open access publications and data management and sharing. Open Access (OA) aims at providing free of charge and unhindered access to research and its publications without copyright restrictions. Research Data Management (RDM) is a term that describes the organization, storage, preservation, and sharing of data collected and used in a research project. It involves the everyday management of research data during the lifetime of a research project. It also involves decisions about how data will be preserved and shared once the project is completed, for example depositing the data in a repository for long-term archiving and access. Good RDM and OA are crucial for reproducible and robust scientific research. Completing this cycle of seminars will allow you, as a PhD candidate, to learn about The importance of RDM in the general context as well as for your own work How to write data management plans (DMP) for grant proposals as well as for your own research Managing privacy, consent, confidentiality and IP issues with respect to to data management RDM compliance requirements and how to increase funding chances for grant proposals such as H2020 The application of Open Access principles to scholarly publishing; understanding copyright and licenses in relation to publishing agreements; using ...
Current challenge for researchers at the University of Turku is that there is a substantial gap between the level of targeted and present research data management (RDM) skills. We examined the perceived RDM skills importance vs. competence of researchers through interviews. Based on the results we developed a three-credit RDM course for doctoral students and post-doctoral researchers. We conducted thirty (30) two hour long interviews with doctoral students, supervisors and biostatisticians on the following topics: Collected data and its life cycle in the project Agreements and licences Version management, backup and storing of data Processing, analysing and visualising Organizing, documenting, describing, quality management Discovering and using external data IPR rights management and data protection Discipline specific cultures and practices Preservation, reuse and sharing. The interviewees' average estimate of the importance of different stages of research data life cycle was 4.1 (very important) on Likert scale 1 to 5. An average estimate of the skills of doctoral students was 2.6 (have somewhat skills). So there is a substantial gap (4.1 vs. 2.6) between the level of targeted and present RDM skills. Targets for competencies have been set – besides by the interviewees themselves – by the Data Policy of the University of Turku, Finnish and EU level Open Science principles and research literature: many studies show that graduate students are not data fluent. That is contradictory, because high quality research requires high quality data. With good RDM skills you make less errors, use time more efficiently, produce well organized and documented data and thus make it possible to reuse, share and open data. Based on the results of the interviews we created a module-based training, the Basics of RDM (BRDM), for doctoral students and post-doctoral researchers. The course was built by a working group consisting of university teachers, lawyers, library's open science specialists, data protection officer, IT Services, ...
During this workshop we present updates and current developments within research data management (RDM) in engineering science. Our focus is on large and immobile data which is generated on (national) tier 1 computing centres (HLRS, JSC, LRZ). We introduce RDM-tools and report newest developments within NFD4Ing and the concomitant research data infrastructure. Speakers / participants High Performance Computing Center Stuttgart (HLRS) Jülich Supercomputing Centre (JSC) Leibniz Supercomputing Centre (LRZ) Technical University of Munich (TUM) Attention: Status as of April 6th 2022, current information can be found on the websites of the participants. ; Technical University of Munich would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their funding and support within the framework of the NFDI4Ing consortium. Funded by the German Research Foundation (DFG) - project number 442146713.
In this third report in the series, Incentives for Building University RDM Services, the authors explore the incentives that inspired the acquisition of RDM capacity on the part of the four research universities described in the case studies, and describe both the general patterns and context-dependent circumstances that shaped these incentives.Based on the case studies, as well as the broader RDM landscape, the authors organized these incentives into four broad categories: compliance, evolving scholarly norms, institutional strategy, and researcher demand.Key takeaways:University investment in research data management infrastructure, services, orpersonnel is motivated by locally relevant incentives. In other words, the increasedattention to RDM in research universities operating in different local circumstancesreflects an alignment of institutional interests and external motivations.Case study partners acted to establish RDM services in anticipation of, rather than in direct response to, researcher demand and explicit policy mandates. Incentives related to institutional strategy and evolving scholarly norms played a larger role in directly catalyzing RDM service development at these institutions.Researcher demand and compliance with policy mandates were important factors in re-shaping and sustaining the RDM service bundle over time, but were not the key drivers for establishing RDM services in our case study institutions.While the constellation of relevant incentives differs from one context to another, the acquisition or development of local RDM capacity is invariably motivated by an interest in protecting or enhancing institutional reputation and success.Consequently, the long-term sustainability of university RDM services is contingent upon alignment with institutional needs, as much as individual researcher needs.
The Toolkit should prove an invaluable resource for anyone interested in implementing or improving RDM services at their institutions. The toolkit includes the following materials: • 23 Best-Practice Case Studies from institutions around the world, drawn from issues in the original LERU Roadmap. Within these case studies there are 5 cases from Latin America and the Caribbean Institutions. • 8 Main Sections, on topics such as Policy and Leadership, Open Data, Advocacy and Costs; • One Model RDM Policy accompanied by guidance and an overview of 20 RDM policies across Europe; • An Executive Briefing in six languages, aimed at senior institutional decision makers. The Toolkit is freely available to download at the following link: http://learn-rdm.eu/en/research-data-management-toolkit-now-available/ As the Toolkit is a deliverable for the European Commission (EC), it may be slightly revised following comments from the EC. We will publish any updated versions on the LEARN website. If you have comments on the Toolkit we'd love to hear them. Please contact us with your feedback. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 654139.
Research Data Management (RDM) services are increasingly becoming a subject of interest for academic and research libraries globally – this is also the case in developing countries. The interest is motivated by a need to support research activities through data sharing and collaboration both locally and internationally. Many institutions, especially in the developed countries, have implemented RDM services to accelerate research and innovation through e-Research but extensive RDM is not so common in developing countries. In reality many African universities and research institutions are yet to implement the most basic of data management services. We believe that the absence of political will and national government mandates on data management often hold back the development and implementation of RDM services. Similarly, research funding agencies are not yet applying sufficient pressure to ensure that Africa complies with the requirement to deposit research data in trusted repositories. While the context was acknowledged the University of Dodoma library staff realized that it is urgent to prepare for the inevitable – the time when RDM will be a requirement for research funding support. This paper presents the results of research conducted at the University of Dodoma, Tanzania. The purpose of the research was to identify and report on relevant RDM services that need to be implemented so that researchers and university management could collaborate and make our research data accessible to the international community. This paper presents findings on important issues for consideration when planning to develop and implement RDM services at a developing country academic institution. The paper also mentions the requirements for the sustainability of these initiatives. ; The University of Pretoria and Carnegie New York Cooperation. ; https://datascience.codata.org ; am2020 ; Library Services
Research Data Management (RDM) services are increasingly becoming a subject of interest for academic and research libraries globally – this is also the case in developing countries. The interest is motivated by a need to support research activities through data sharing and collaboration both locally and internationally. Many institutions, especially in the developed countries, have implemented RDM services to accelerate research and innovation through e-Research but extensive RDM is not so common in developing countries. In reality many African universities and research institutions are yet to implement the most basic of data management services. We believe that the absence of political will and national government mandates on data management often hold back the development and implementation of RDM services. Similarly, research funding agencies are not yet applying sufficient pressure to ensure that Africa complies with the requirement to deposit research data in trusted repositories. While the context was acknowledged the University of Dodoma library staff realized that it is urgent to prepare for the inevitable – the time when RDM will be a requirement for research funding support.This paper presents the results of research conducted at the University of Dodoma, Tanzania. The purpose of the research was to identify and report on relevant RDM services that need to be implemented so that researchers and university management could collaborate and make our research data accessible to the international community.This paper presents findings on important issues for consideration when planning to develop and implement RDM services at a developing country academic institution. The paper also mentions the requirements for the sustainability of these initiatives.
This paper describes findings and projections from a project that has examined emerging policies and practices in the United States regarding the long-term institutional management of research data. The DataRes project at the University of North Texas (UNT) studied institutional transitions taking place during 2011-2012 in response to new mandates from U.S. governmental funding agencies requiring research data management plans to be submitted with grant proposals. Additional synergistic findings from another UNT project, termed iCAMP, will also be reported briefly.This paper will build on these data analysis activities to discuss conclusions and prospects for likely developments within coming years based on the trends surfaced in this work. Several of these conclusions and prospects are surprising, representing both opportunities and troubling challenges, for not only the library profession but the academic research community as a whole.
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, ...
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.
