scholarly journals Data Management for Systematic Reviews: Guidance is Needed

2021 ◽  
Vol 10 (4) ◽  
Author(s):  
Heather Ganshorn ◽  
Zahra Premji
Keyword(s):  
Data Management ◽  
Systematic Reviews ◽  
Management Practices ◽  
Open Science ◽  
Management Plan ◽  
Project Planning ◽  
Reporting Practices ◽  
Science Practices ◽  
Types Of Knowledge ◽  
Poor Reporting

Data management practices for systematic reviews and other types of knowledge syntheses are variable, with some reviews following open science practices and others with poor reporting practices leading to lack of transparency or reproducibility. Reporting standards have improved the level of detail being shared in published reviews, and also encourage more open sharing of data from various stages of the review process. Similar to project planning or completion of an ethics application, systematic review teams should create a data management plan alongside creation of their study protocol. This commentary provides a brief description of a Data Management Plan Template created specifically for systematic reviews. It also describes the companion LibGuide which was created to provide more detailed examples, and to serve as a living document for updates and new guidance. The creation of the template was funded by the Portage Network.

scholarly journals Published patterns of spin in biomedical literature: a protocol for a meta-research study

BMJ Open ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. e043784
Author(s):  
Naichuan Su ◽  
Michiel van der Linden ◽  
Geert JMG van der Heijden ◽  
Stefan Listl ◽  
Stefan Schandelmaier ◽  
...  
Keyword(s):  
Systematic Reviews ◽  
Scientific Journal ◽  
Open Science ◽  
Biomedical Literature ◽  
Measurement Tool ◽  
Delphi Consensus ◽  
Reporting Practices ◽  
Literature Reviews ◽  
Science Framework ◽  
Meta Analyses

IntroductionSpin is defined as reporting practices that distort the interpretation of results and create misleading conclusions by suggesting more favourable results. Such unjustifiable and misleading misrepresentation may negatively influence the development of further studies, clinical practice and healthcare policies. Spin manifests in various patterns in different sections of publications (titles, abstracts and main texts). The primary aim of this study is to identify reported spin patterns and assess the prevalence of spin in general, and the prevalence of spin patterns reported in biomedical literature based on previously published systematic reviews and literature reviews on spin.Methods and analysisPubMed, EMBASE and SCOPUS will be searched to identify systematic or literature reviews on spin in biomedicine. To improve the comprehensiveness of the search, the snowballing method will be used to broaden the search. The data on spin-related outcomes and characteristics of the included studies will be extracted. The methodological quality of the included studies will be assessed with selective items of the A MeaSurement Tool to Assess systematic Reviews-2 checklist. A new classification scheme for spin patterns will be developed if the classifications of spin patterns identified in the included studies vary. The prevalence of spin and spin patterns will be pooled based on meta-analyses if the classification schemes for spin are comparable across included studies. Otherwise, the prevalence will be described qualitatively. The seriousness of spin patterns will be assessed based on a Delphi consensus study.Ethics and disseminationThis study has been approved by the Academic Centre for Dentistry Amsterdam Ethics Review Committee (2020250). The study will be submitted to a peer-reviewed scientific journal.RegistrationOpen Science Framework: osf.io/hzv6e

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 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 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 The Open-Specimen Movement

BioScience ◽  
2020 ◽  
Author(s):  
Jocelyn P Colella ◽  
Ryan B Stephens ◽  
Mariel L Campbell ◽  
Brooks A Kohli ◽  
Danielle J Parsons ◽  
...  
Keyword(s):  
Data Management ◽  
Open Data ◽  
Open Science ◽  
Management Plan ◽  
Scientific Data ◽  
Primary Data ◽  
Explicit Integration ◽  
Cultural Shift ◽  
Major Barrier ◽  
Annual Reporting

Abstract The open-science movement seeks to increase transparency, reproducibility, and access to scientific data. As primary data, preserved biological specimens represent records of global biodiversity critical to research, conservation, national security, and public health. However, a recent decrease in specimen preservation in public biorepositories is a major barrier to open biological science. As such, there is an urgent need for a cultural shift in the life sciences that normalizes specimen deposition in museum collections. Museums embody an open-science ethos and provide long-term research infrastructure through curation, data management and security, and community-wide access to samples and data, thereby ensuring scientific reproducibility and extension. We propose that a paradigm shift from specimen ownership to specimen stewardship can be achieved through increased open-data requirements among scientific journals and institutional requirements for specimen deposition by funding and permitting agencies, and through explicit integration of specimens into existing data management plan guidelines and annual reporting.

scholarly journals Researchers at Arab Universities Hold Positive Views on Research Data Management and Data Sharing

2020 ◽  
Vol 15 (2) ◽  
pp. 168-170
Author(s):  
Jennifer Kaari
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Data Privacy ◽  
Management Practices ◽  
Open Data ◽  
Scientific Progress ◽  
Management Plan ◽  
Research Data ◽  
Data Repository ◽  
Research Data Management

A Review of: Elsayed, A. M., & Saleh, E. I. (2018). Research data management and sharing among researchers in Arab universities: An exploratory study. IFLA Journal, 44(4), 281–299. https://doi.org/10.1177/0340035218785196 Abstract Objective – To investigate researchers’ practices and attitudes regarding research data management and data sharing. Design – Email survey. Setting – Universities in Egypt, Jordan, and Saudi Arabia. Subjects – Surveys were sent to 4,086 academic faculty researchers. Methods – The survey was emailed to faculty at three Arab universities, targeting faculty in the life sciences and engineering. The survey was created using Google Docs and remained open for five months. Participants were asked basic demographic questions, questions regarding their research data and metadata practices, and questions regarding their data sharing practices. Main Results – The authors received 337 responses, for a response rate of 8%. The results showed that 48.4% of respondents had a data management plan and that 97% were responsible for preserving their own data. Most respondents stored their research data on their personal storage devices. The authors found that 64.4% of respondents reported sharing their research data. Respondents most frequently shared their data by publishing in a data research journal, sharing through academic social networks such as ResearchGate, and providing data upon request to peers. Only 5.1% of respondents shared data through an open data repository.  Of those who did not share data, data privacy and confidentiality were the most common reasons cited. Of the respondents who did share their data, contributing to scientific progress and increased citation and visibility were the primary reasons for doing so. A total of 59.6% of respondents stated that they needed more training in research data management from their universities. Conclusion – The authors conclude that researchers at Arab universities are still primarily responsible for their own data and that data management planning is still a new concept to most researchers. For the most part, the researchers had a positive attitude toward data sharing, although depositing data in open repositories is still not a widespread practice. The authors conclude that in order to encourage strong data management practices and open data sharing among Arab university researchers, more training and institutional support is needed.

scholarly journals Methods for Open and Reproducible Materials Science

2019 ◽  
Author(s):  
Sara L Wilson ◽  
Micah Altman ◽  
Rafael Jaramillo
Keyword(s):  
Data Management ◽  
Data Science ◽  
Management Practices ◽  
Materials Science ◽  
Critical Role ◽  
Structured Interview ◽  
Open Science ◽  
Manuscript Submission ◽  
Attitudes And Practices ◽  
Data Stewardship

Data stewardship in experimental materials science is increasingly complex and important. Progress in data science and inverse-design of materials give reason for optimism that advances can be made if appropriate data resources are made available. Data stewardship also plays a critical role in maintaining broad support for research in the face of well-publicized replication failures (in different fields) and frequently changing attitudes, norms, and sponsor requirements for open science. The present-day data management practices and attitudes in materials science are not well understood. In this article, we collect information on the practices of a selection of materials scientists at two leading universities, using a semi-structured interview instrument. An analysis of these interviews reveals that although data management is universally seen as important, data management practices vary widely. Based on this analysis, we conjecture that broad adoption of basic file-level data sharing at the time of manuscript submission would benefit the field without imposing substantial burdens on researchers. More comprehensive solutions for lifecycle open research in materials science will have to overcome substantial differences in attitudes and practices.

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. 

Changes in academic libraries in the era of Open Science

2020 ◽  
Vol 36 (3) ◽  
pp. 281-299
Author(s):  
Stefka Tzanova
Keyword(s):  
Big Data ◽  
Data Management ◽  
Academic Libraries ◽  
Data Science ◽  
Academic Library ◽  
Open Data ◽  
Open Science ◽  
Management Plan ◽  
Educational Process ◽  
Library Services

In this paper we study the changes in academic library services inspired by the Open Science movement and especially the changes prompted from Open Data as a founding part of Open Science. We argue that academic libraries face the even bigger challenges for accommodating and providing support for Open Big Data composed from existing raw data sets and new massive sets generated from data driven research. Ensuring the veracity of Open Big Data is a complex problem dominated by data science. For academic libraries, that challenge triggers not only the expansion of traditional library services, but also leads to adoption of a set of new roles and responsibilities. That includes, but is not limited to development of the supporting models for Research Data Management, providing Data Management Plan assistance, expanding the qualifications of library personnel toward data science literacy, integration of the library services into research and educational process by taking part in research grants and many others. We outline several approaches taken by some academic libraries and by libraries at the City University of New York (CUNY) to meet necessities imposed by doing research and education with Open Big Data – from changes in libraries’ administrative structure, changes in personnel qualifications and duties, leading the interdisciplinary advisory groups, to active collaboration in principal projects.

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