Effective stewardship of data is a critical precursor to making data FAIR.The goal of this paper is to bring an overview of current state of the art of data management and data stewardship planning solutions(DMP).We b...Effective stewardship of data is a critical precursor to making data FAIR.The goal of this paper is to bring an overview of current state of the art of data management and data stewardship planning solutions(DMP).We begin by arguing why data management is an important vehicle supporting adoption and implementation of the FAIR principles,we describe the background,context and historical development,as well as major driving forces,being research initiatives and funders.Then we provide an overview of the current leading DMP tools in the form of a table presenting the key characteristics.Next,we elaborate on emerging common standards for DMPs,especially the topic of machine-actionable DMPs.As sound DMP is not only a precursor of FAIR data stewardship,but also an integral part of it,we discuss its positioning in the emerging FAIR tools ecosystem.Capacity building and training activities are an important ingredient in the whole effort.Although not being the primary goal of this paper,we touch also the topic of research workforce support,as tools can be just as much effective as their users are competent to use them properly.We conclude by discussing the relations of DMP to FAIR principles,as there are other important connections than just being a precursor.展开更多
Rapid and effective data sharing is necessary to control disease outbreaks,such as the current coronavirus pandemic.Despite the existence of data sharing agreements,data silos,lack of interoperable data infrastructure...Rapid and effective data sharing is necessary to control disease outbreaks,such as the current coronavirus pandemic.Despite the existence of data sharing agreements,data silos,lack of interoperable data infrastructures,and different institutional jurisdictions hinder data sharing and accessibility.To overcome these challenges,the Virus Outbreak Data Network(VODAN)-Africa initiative is championing an approach in which data never leaves the institution where it was generated,but,instead,algorithms can visit the data and query multiple datasets in an automated way.To make this possible,FAIR Data Points—distributed data repositories that host machine-actionable data and metadata that adhere to the FAIR Guidelines(that data should be Findable,Accessible,Interoperable and Reusable)—have been deployed in participating institutions using a dockerised bundle of tools called VODAN in a Box(Vi B).Vi B is a set of multiple FAIR-enabling and open-source services with a single goal:to support the gathering of World Health Organization(WHO)electronic case report forms(e CRFs)as FAIR data in a machine-actionable way,but without exposing or transferring the data outside the facility.Following the execution of a proof of concept,Vi B was deployed in Uganda and Leiden University.The proof of concept generated a first query which was implemented across two continents.A SWOT(strengths,weaknesses,opportunities and threats)analysis of the architecture was carried out and established the changes needed for specifications and requirements for the future development of the solution.展开更多
文摘Effective stewardship of data is a critical precursor to making data FAIR.The goal of this paper is to bring an overview of current state of the art of data management and data stewardship planning solutions(DMP).We begin by arguing why data management is an important vehicle supporting adoption and implementation of the FAIR principles,we describe the background,context and historical development,as well as major driving forces,being research initiatives and funders.Then we provide an overview of the current leading DMP tools in the form of a table presenting the key characteristics.Next,we elaborate on emerging common standards for DMPs,especially the topic of machine-actionable DMPs.As sound DMP is not only a precursor of FAIR data stewardship,but also an integral part of it,we discuss its positioning in the emerging FAIR tools ecosystem.Capacity building and training activities are an important ingredient in the whole effort.Although not being the primary goal of this paper,we touch also the topic of research workforce support,as tools can be just as much effective as their users are competent to use them properly.We conclude by discussing the relations of DMP to FAIR principles,as there are other important connections than just being a precursor.
基金VODAN-Africathe Philips Foundation+2 种基金the Dutch Development Bank FMOCORDAIDthe GO FAIR Foundation for supporting this research
文摘Rapid and effective data sharing is necessary to control disease outbreaks,such as the current coronavirus pandemic.Despite the existence of data sharing agreements,data silos,lack of interoperable data infrastructures,and different institutional jurisdictions hinder data sharing and accessibility.To overcome these challenges,the Virus Outbreak Data Network(VODAN)-Africa initiative is championing an approach in which data never leaves the institution where it was generated,but,instead,algorithms can visit the data and query multiple datasets in an automated way.To make this possible,FAIR Data Points—distributed data repositories that host machine-actionable data and metadata that adhere to the FAIR Guidelines(that data should be Findable,Accessible,Interoperable and Reusable)—have been deployed in participating institutions using a dockerised bundle of tools called VODAN in a Box(Vi B).Vi B is a set of multiple FAIR-enabling and open-source services with a single goal:to support the gathering of World Health Organization(WHO)electronic case report forms(e CRFs)as FAIR data in a machine-actionable way,but without exposing or transferring the data outside the facility.Following the execution of a proof of concept,Vi B was deployed in Uganda and Leiden University.The proof of concept generated a first query which was implemented across two continents.A SWOT(strengths,weaknesses,opportunities and threats)analysis of the architecture was carried out and established the changes needed for specifications and requirements for the future development of the solution.
