This Data Management and Sharing module aims to provide Neuro researchers, trainees, and research personnel an overview of available tools and services for data storage, processing, and sharing, while also providing guidance on best practices in data management and open data principles. The tools and services listed in this document may evolve over time, and this is intended to be a living document that adapts to the evolving data management landscape at The Neuro and 好色TV.
Many resources are available to help researchers adopt good data management and sharing practices. Therefore, we begin by providing only a brief overview of the general principles to then focus on tools available at The Neuro, their use cases, and how to access them. Throughout the text, we also direct researchers to additional resources from 好色TVl and external sources for further learning.听听
Why share research data?听
Most research produces data (facts or numbers) that serve as a basis for discussion and calculation. The definition of data can be extended to any part of the scientific process but most often refers to what serves as the basis for analysis, the analysis itself and the resulting output.听
Open data is the idea that research data should be made available to the public and other scientists in a way that is as open as possible and as closed as necessary. In other words, scientific output should be accessible to the broadest number of people without infringing on privacy, confidentiality, licenses and other types of limitations or agreements.听
Data sharing practices range from making data fully open (available online by anyone with minimal restrictions) to strictly controlled access, or anywhere in between. All these approaches align with Open Science principles, provided that data are managed thoughtfully to maximize their long-term findability and accessibility to yourself, collaborators, the broad research community and society at large.听
Open data sharing is one of the pillars of the Open Science movement and is important to improve the overall scientific process.听
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Promoting research transparency: Sharing data allows for greater scrutiny and fosters an open scientific environment where researchers can verify claims and build upon existing work.听
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Enabling the reproducibility of research results: Open access to data facilitates the replication of analyses, confirmation of findings, and exploration of alternative interpretations, strengthening the reliability of scientific knowledge.听
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Maximizing the impact of research funds: By making data available, researchers avoid redundant data collection, optimize resources, and enable further discoveries from existing datasets, increasing the return on investment from research funding.听
While many funding agencies and publishers now require the public release of data underlying research findings for the reasons listed above, this process is beneficial for the research team in and of itself:听听
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Releasing public datasets can facilitate collaborations with other research groups within and outside of your research field, leading to the possibility of applying to grants previously unavailable. 听
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Open datasets can act as secondary publications that can garner citations in addition to an article reporting findings from these data.听
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Data sharing is presently one of the few accepted ways of sharing null results as the data itself can be beneficial to other researchers.听
While the Tri-Agency does not currently mandate data sharing, have been drafted for future implementation which state: "Grant recipients are required to deposit into a digital repository all digital research data, metadata and code that directly support the research conclusions in journal publications and pre-prints that arise from agency-supported research."听
Why manage research data?听
Good research data management (RDM) is essential for maintaining the quality, security, and accessibility of research data throughout its lifecycle. Additionally, effective RDM 鈥攖hrough clear documentation, proper formatting, and ethical handling鈥攇reatly facilitates sharing when appropriate. RDM should be planned for before the start of a project and detailed out in a Data Management Plan, which is now required by several funding agencies and in Research Ethics Board applications.听
鈥�RDM enables researchers to organize, store, access, reuse and build upon digital research data. RDM is essential to Canadian researchers鈥� capacity to securely preserve and use their research data throughout their research projects, reuse their data over the course of their careers and, when appropriate, share their data. Furthermore, as an acknowledged component of research excellence, strong RDM practices support researchers in achieving scientific rigor and enable collaboration in their fields.鈥� - 听
One widely recognized framework for effective data management is the , which emphasize making data Findable, Accessible, Interoperable, and Reusable.听
Following the FAIR principles for data management offers multiple benefits for a research team, whether the data is openly shared or not:听听
- Datasets that are well described and organized are easier to re-use in new projects, e.g., as preliminary data for grant application.听
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Robust online storage solutions enable the controlled sharing of data with collaborators in minutes rather than the slow, cumbersome and risky transfer through physical drives.听听
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Standardized data structures allow the aggregation of data from multiple projects to increase sample size and statistical power.听
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About this document
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