scholarly journals Using Active Learning Classrooms in Building an Infrastructure for Access to Research Data

2018 ◽  
Author(s):  
Stefan Ekman ◽  
Helena Francke
Keyword(s):  
Active Learning ◽  
Data Management ◽  
Collaborative Work ◽  
Data Access ◽  
Open Science ◽  
Management Plan ◽  
Research Data ◽  
Management Skills ◽  
National Network ◽  
Institutional Conditions

Watch the VIDEO.As a central part of its work towards Open Science, Sweden is building an infrastructure for managing, storing, and providing access to research data. A vital component of this infrastructure will be functions at Swedish universities for supporting researchers with data access and management. To support these local functions, here referred to as Data Access Units (DAUs), a national network of DAUs from 28 universities is under formation.To assist in establishing DAUs and strengthening the network, the Swedish National Data Service and the University of Borås offer a joint professional development course to DAU staff. This course ran for the first time in spring 2018, with 21 participants from 12 universities. The course has three main objectives: to develop data management skills; to increase understanding of the institutional conditions for providing access to research data; and to strengthen the national network through interpersonal connections and collegial ties.The methodology chosen for the course is intended to promote collaboration between participants and to take into consideration their various types and levels of expertise and experience. This has resulted in a distance-learning course with four physical meetings, during which an Active Learning Classroom (ALC) methodology is used: participants work actively in groups with instructor-facilitated tasks. The ALC work is combined with significant use of collaborative work between meetings.Our presentation will show how ALC methodology can be used to support the establishment of DAUs and a DAU network. We will discuss some examples of course elements which contribute to the objectives. The discussion will be based on the facilitators’ analyses and on the participants’ answers to an evaluation questionnaire.Participants found that they developed data management skills by working with cases as ALC exercises, and thought these skills would be directly applicable to their work in the DAU. Such ALC exercises were designed around for instance anonymising datasets and writing a data management plan for a potential study.In addressing institutional conditions necessary for data access, we observed how task design and perceived relevance of a topic are important for how participants engage with various aspects of a task. For example, the ALC exercise on legal frameworks was easier to align with perceived DAU needs than the less focused and more abstract exercise on models and principles such as OAIS and FAIR.A clear outcome of the course was a strengthening of the DAU network. Participants gained a sense of collegiality by working in different constellations during various ALC tasks. The social activities – breaks and meals – intentionally included in the course also allowed classroom discussions to flow into more informal spaces.The DAUs and their national network is a vital part of the Swedish infrastructure for Open Science concerning access to research data. The presentation will end with reflections on how ALC methodology can also be employed to strengthen data management and accessibility skills in other parts of the infrastructure, for instance with researchers.

scholarly journals Plano de gestão de dados fair: uma proposta para a Fiocruz | Fair data management plan: a proposal for Fiocruz

2019 ◽  
Vol 15 (2) ◽  
Author(s):  
Viviane Santos de Oliveira Veiga ◽  
Patricia Henning ◽  
Simone Dib ◽  
Erick Penedo ◽  
Jefferson Da Costa Lima ◽  
...  
Keyword(s):  
Life Cycle ◽  
Data Management ◽  
Open Science ◽  
Management Plan ◽  
Research Data ◽  
Research Institutions ◽  
Research Data Management ◽  
Management Plans ◽  
Fair Principles

RESUMO Este artigo trás para discussão o papel dos planos de gestão de dados como instrumento facilitador da gestão dos dados durante todo o ciclo de vida da pesquisa. A abertura de dados de pesquisa é pauta prioritária nas agendas científicas, por ampliar tanto a visibilidade e transparência das investigações, como a capacidade de reprodutibilidade e reuso dos dados em novas pesquisas. Nesse contexto, os princípios FAIR, um acrônimo para ‘Findable’, ‘Accessible’, ‘Interoperable’ e ‘Reusable’ é fundamental por estabelecerem orientações basilares e norteadoras na gestão, curadoria e preservação dos dados de pesquisa direcionados para o compartilhamento e o reuso. O presente trabalho tem por objetivo apresentar uma proposta de template de Plano de Gestão de Dados, alinhado aos princípios FAIR, para a Fundação Oswaldo Cruz. A metodologia utilizada é de natureza bibliográfica e de análise documental de diversos planos de gestão de dados europeus. Concluímos que a adoção de um plano de gestão nas práticas cientificas de universidades e instituições de pesquisa é fundamental. No entanto, para tirar maior proveito dessa atividade é necessário contar com a participação de todos os atores envolvidos no processo, além disso, esse plano de gestão deve ser machine-actionable, ou seja, acionável por máquina.Palavras-chave: Plano de Gestão de Dados; Dado de Pesquisa; Princípios FAIR; PGD Acionável por Máquina; Ciência Aberta.ABSTRACT This article proposes to discuss the role of data management plans as a tool to facilitate data management during researches life cycle. Today, research data opening is a primary agenda at scientific agencies as it may boost investigations’ visibility and transparency as well as the ability to reproduce and reuse its data on new researches. Within this context, FAIR principles, an acronym for Findable, Accessible, Interoperable and Reusable, is paramount, as it establishes basic and guiding orientations for research data management, curatorship and preservation with an intent on its sharing and reuse. The current work intends to present to the Fundação Oswaldo Cruz a new Data Management Plan template proposal, aligned with FAIR principles. The methodology used is bibliographical research and documental analysis of several European data management plans. We conclude that the adoption of a management plan on universities and research institutions scientific activities is paramount. However, to be fully benefited from this activity, all actors involved in the process must participate, and, on top of that, this plan must be machine-actionable.Keywords: Data Management Plan; Research Data; FAIR Principles; DMP Machine-Actionable; Open Science.

scholarly journals Solving legal puzzles is not easy – supporting creating Data Management Plans in three scientific disciplines: chemistry, economics, and civil engineering

2020 ◽  
Author(s):  
Magdalena Szuflita-Żurawska ◽  
Anna Wałek
Keyword(s):  
Data Management ◽  
Civil Engineering ◽  
Academic Staff ◽  
Open Science ◽  
Management Plan ◽  
Research Data ◽  
Academic Community ◽  
Legal Issues ◽  
Scientific Disciplines ◽  
Management Plans

Open Science Competence Center at the Gdańsk University of Technology Library was established upon the Bridge of Data project at the end of 2018. Our main goals include providing support for the academic community for broad issues associated with Open Science, especially with Open Research Data. Our team of professionals help researchers in many topics such as: "what kinds of data you need to share", "how to make your data openly available to others", or "how to create a Data Management Plan" – that recently has been the most popular and demanding service.  One of the main challenges to support academic staff with Data Management Plans is dealing with the legal impediments to provide open access and reusing of research data for publicly funded scientific projects. The lack of understanding the legal issues in opening research is a significant barrier to facilitate Open Science. Much public-funded research requires to prepare a Data Management Plan that, among other items, provides information about ownership and user rights. One of the most common activity for scholars is choosing which license (if any) they are supposed to use in terms of the dissemination the scientific output. However, in many cases, resolving the right license for research data is not enough. Academic staff faces many tensions with a lack of clarity around legal requirements and obstacles. The increasing researchers' need for understanding and describing conflicting issues (e.g. patenting) results in looking for professional and knowledgeable support at the university. We examine the most frequent legal issues arising among DMPs from the three scientific disciplines: chemistry (e.g. ethical papers), economics (e.g. data value cycle), and civil engineering (e.g. complexity of construction data). In our presentation, we would like to introduce the main identified problems and show how mapping and benchmarking occurring problems among those disciplines help us to establish more efficient legal support for researchers. 

scholarly journals ITB Research Data Management Plan

2018 ◽  
Author(s):  
Dasapta Erwin Irawan
Keyword(s):  
Data Management ◽  
Management Plan ◽  
Research Data ◽  
Team Members ◽  
Research Data Management ◽  
Project Level

Here's the official ITB Research Data Management Plan. We use this plan as a template to design more detailed project-level RDMP. The document came from the work of ITB Repository Team that I lead. Team members: Sparisoma Viridi, Rino Mukti (I will add this list later). I invite everyone to re-use this document for their project-level RDMP.

scholarly journals ELIXIR Software Management Plan for Life Sciences

2021 ◽  
Author(s):  
Renato Alves ◽  
Dimitrios Bampalikis ◽  
Leyla Jael Castro ◽  
José María Fernández ◽  
Jennifer Harrow ◽  
...  
Keyword(s):  
Data Management ◽  
Open Source Software ◽  
Open Science ◽  
Management Plan ◽  
Software Management ◽  
Research Software ◽  
Management Perspective ◽  
Management Plans ◽  
Machine Readable ◽  
Future Plans

Data Management Plans are now considered a key element of Open Science. They describe the data management life cycle for the data to be collected, processed and/or generated within the lifetime of a particular project or activity. A Software Manag ement Plan (SMP) plays the same role but for software. Beyond its management perspective, the main advantage of an SMP is that it both provides clear context to the software that is being developed and raises awareness. Although there are a few SMPs already available, most of them require significant technical knowledge to be effectively used. ELIXIR has developed a low-barrier SMP, specifically tailored for life science researchers, aligned to the FAIR Research Software principles. Starting from the Four Recommendations for Open Source Software, the ELIXIR SMP was iteratively refined by surveying the practices of the community and incorporating the received feedback. Currently available as a survey, future plans of the ELIXIR SMP include a human- and machine-readable version, that can be automatically queried and connected to relevant tools and metrics within the ELIXIR Tools ecosystem and beyond.

scholarly journals eLabFTW as an Open Science tool to improve the quality and translation of preclinical research

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 292
Author(s):  
Michael Hewera ◽  
Daniel Hänggi ◽  
Björn Gerlach ◽  
Ulf Dietrich Kahlert
Keyword(s):  
Data Management ◽  
Open Source ◽  
Academic Research ◽  
Open Data ◽  
Open Science ◽  
Research Data ◽  
Preclinical Research ◽  
Research Data Management ◽  
New Methods ◽  
Set Up

Reports of non-replicable research demand new methods of research data management. Electronic laboratory notebooks (ELNs) are suggested as tools to improve the documentation of research data and make them universally accessible. In a self-guided approach, we introduced the open-source ELN eLabFTW into our lab group and, after using it for a while, think it is a useful tool to overcome hurdles in ELN introduction by providing a combination of properties making it suitable for small preclinical labs, like ours. We set up our instance of eLabFTW, without any further programming needed. Our efforts to embrace open data approach by introducing an ELN fits well with other institutional organized ELN initiatives in academic research.

scholarly journals A collective challenge: Open Science from the perspective of Science Europe

2019 ◽  
Vol 72 (2) ◽  
pp. 424-430
Author(s):  
Marie Timmermann
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Open Science ◽  
Research Data ◽  
Major Research ◽  
Research Data Management ◽  
Management Policies ◽  
Good Research ◽  
Quality Of Research

Open Science aims to enhance the quality of research by making research and its outputs openly available, reproducible and accessible. Science Europe, the association of major Research Funding Organisations and Research Performing Organisations, advocates data sharing as one of the core aspects of Open Science and promotes a more harmonised approach to data sharing policies. Good research data management is a prerequisite for Open Science and data management policies should be aligned as much as possible, while taking into account discipline-specific differences. Research data management is a broad and complex field with many actors involved. It needs collective efforts by all actors to work towards aligned policies that foster Open Science.

scholarly journals Information Integration for Machine Actionable Data Management Plans

2017 ◽  
Vol 12 (1) ◽  
pp. 22-35 ◽  
Author(s):  
Tomasz Miksa ◽  
Andreas Rauber ◽  
Roman Ganguly ◽  
Paolo Budroni
Keyword(s):  
Data Management ◽  
Information Integration ◽  
Open Science ◽  
Management Plan ◽  
Reproducible Research ◽  
Free Form ◽  
Text Documents ◽  
Data Repositories ◽  
Standard Data ◽  
Management Plans

Data management plans are free-form text documents describing the data used and produced in scientific experiments. The complexity of data-driven experiments requires precise descriptions of tools and datasets used in computations to enable their reproducibility and reuse. Data management plans fall short of these requirements. In this paper, we propose machine-actionable data management plans that cover the same themes as standard data management plans, but particular sections are filled with information obtained from existing tools. We present mapping of tools from the domains of digital preservation, reproducible research, open science, and data repositories to data management plan sections. Thus, we identify the requirements for a good solution and identify its limitations. We also propose a machine-actionable data model that enables information integration. The model uses ontologies and is based on existing standards.

scholarly journals D8.1 Data Management Plan

2021 ◽  
Author(s):  
C. Soriano ◽  
R. Rossi ◽  
Q. Ayoul-Guilmard
Keyword(s):  
Open Access ◽  
Data Management ◽  
European Commission ◽  
Management Plan ◽  
Research Data ◽  
Scientific Publications ◽  
Horizon 2020 ◽  
Open Research ◽  
Data Program

The ExaQUte project participates in the Pilot on Open Research Data launched by the European Commission (EC) along with the H2020 program. This pilot is part of the Open Access to Scientific Publications and Research Data program in H2020. The goal of the program is to foster access to research data generated in H2020 projects. The use of a Data anagement Plan (DMP) is required for all projects participating in the Open Research Data Pilot, in which they will specify what data will be kept for the longer term. The underpinning idea is that Horizon 2020 beneficiaries have to make their research data findable, accessible, interoperable and re-usable (FAIR), to ensure it is soundly managed.

scholarly journals The Data Management Skills Support Initiative: Synthesising Postgraduate Training in Research Data Management

2012 ◽  
Vol 7 (2) ◽  
pp. 101-109 ◽  
Author(s):  
Laura Molloy ◽  
Kellie Snow
Keyword(s):  
Data Management ◽  
Management Training ◽  
Skills Training ◽  
Research Data ◽  
Management Skills ◽  
Research Information ◽  
Research Data Management ◽  
Researcher Development ◽  
Development Framework ◽  
Wide Range

This paper will describe the efforts and findings of the JISC Data Management Skills Support Initiative (‘DaMSSI’). DaMSSI was co-funded by the JISC Managing Research Data programme and the Research Information Network (RIN), in partnership with the Digital Curation Centre, to review, synthesise and augment the training offerings of the JISC Research Data Management Training Materials (‘RDMTrain’) projects.DaMSSI tested the effectiveness of the Society of College, National and University Libraries’ Seven Pillars of Information Literacy model (SCONUL, 2011), and Vitae’s Researcher Development Framework (‘Vitae RDF’) for consistently describing research data management (‘RDM’) skills and skills development paths in UK HEI postgraduate courses.With the collaboration of the RDMTrain projects, we mapped individual course modules to these two models and identified basic generic data management skills alongside discipline-specific requirements. A synthesis of the training outputs of the projects was then carried out, which further investigated the generic versus discipline-specific considerations and other successful approaches to training that had been identified as a result of the projects’ work. In addition we produced a series of career profiles to help illustrate the fact that data management is an essential component – in obvious and not-so-obvious ways – of a wide range of professions.We found that both models had potential for consistently and coherently describing data management skills training and embedding this within broader institutional postgraduate curricula. However, we feel that additional discipline-specific references to data management skills could also be beneficial for effective use of these models. Our synthesis work identified that the majority of core skills were generic across disciplines at the postgraduate level, with the discipline-specific approach showing its value in engaging the audience and providing context for the generic principles.Findings were fed back to SCONUL and Vitae to help in the refinement of their respective models, and we are working with a number of other projects, such as the DCC and the EC-funded Digital Curator Vocational Education Europe (DigCurV2) initiative, to investigate ways to take forward the training profiling work we have begun.

scholarly journals Outside the R1: Equitable data management at the undergraduate level

2021 ◽  
Vol 45 (2) ◽  
Author(s):  
Elizabeth Blackwood
Keyword(s):  
Data Management ◽  
Management Practices ◽  
Undergraduate Curriculum ◽  
Research Data ◽  
Management Skills ◽  
State University System ◽  
Final Project ◽  
Research Data Management ◽  
Management Plans ◽  
Regional Universities

Universities within the California State University System are given the mandate to teach the students of the state, as is the case with many regional, public universities. This mandate places teaching first; however, research and scholarship are still required activities for reaching retention, tenure, and promotion, as well as important skills for students to practice. Data management instruction for both faculty and undergraduates is often omitted at these institutions, which fall outside of the R1 designation. This happens for a variety of reasons, including personnel and resource limitations. Such limitations disproportionately burden students from underrepresented populations, who are more heavily represented at these institutions. These students have pathways to graduate school and the digital economy, like their counterparts at R1s; thus, they are also in need of research data management skills. This paper describes and provides a scalable, low-resource model for data management instruction from the university library and integrated into a department’s capstone or final project curriculum. In the case study, students and their instructors participated in workshops and submitted data management plans as a requirement of their final project. The analysis will analyze the results of the project and focus on the broader implications of integrating research data management into undergraduate curriculum at public, regional universities. By working with faculty to integrate data management practices into their curricula, librarians reach both students and faculty members with best practices for research data management. This work also contributes to a more equitable and sustainable research landscape.

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