Devignes Marie-Dominique, Smaïl-Tabbone Malika, Dhondge Hrishikesh, Dolcemascolo Roswitha, Gavaldá-García Jose, Higuera-Rodriguez R Anahí, Kravchenko Anna, Roca Martínez Joel, Messini Niki, Pérez-Ràfols Anna, Pérez Ropero Guillermo, Sperotto Luca, Chauvot de Beauchêne Isaure, Vranken Wim
Université de Lorraine, CNRS, Inria, LORIA, Nancy, F-5400, France.
Institute for Integrative Systems Biology (I2SysBio), CSIC - University of Valencia, Paterna, 46980, Spain.
Open Res Eur. 2023 Jun 19;3:97. doi: 10.12688/openreseurope.15609.1. eCollection 2023.
: Data management is fast becoming an essential part of scientific practice, driven by open science and FAIR (findable, accessible, interoperable, and reusable) data sharing requirements. Whilst data management plans (DMPs) are clear to data management experts and data stewards, understandings of their purpose and creation are often obscure to the producers of the data, which in academic environments are often PhD students. : Within the RNAct EU Horizon 2020 ITN project, we engaged the 10 RNAct early-stage researchers (ESRs) in a training project aimed at formulating a DMP. To do so, we used the Data Stewardship Wizard (DSW) framework and modified the existing Life Sciences Knowledge Model into a simplified version aimed at training young scientists, with computational or experimental backgrounds, in core data management principles. We collected feedback from the ESRs during this exercise. : Here, we introduce our new life-sciences training DMP template for young scientists. We report and discuss our experiences as principal investigators (PIs) and ESRs during this project and address the typical difficulties that are encountered in developing and understanding a DMP. : We found that the DS-wizard can also be an appropriate tool for DMP training, to get terminology and concepts across to researchers. A full training in addition requires an upstream step to present basic DMP concepts and a downstream step to publish a dataset in a (public) repository. Overall, the DS-Wizard tool was essential for our DMP training and we hope our efforts can be used in other projects.
在开放科学和FAIR（可查找、可访问、可互操作和可重用）数据共享要求的推动下，数据管理正迅速成为科学实践的重要组成部分。虽然数据管理计划（DMP）对数据管理专家和数据管理员来说很清晰，但数据生产者（在学术环境中通常是博士生）对其目的和创建过程的理解往往很模糊。
在RNAct欧盟地平线2020创新培训网络项目中，我们让10名RNAct早期研究人员（ESR）参与了一个旨在制定DMP的培训项目。为此，我们使用了数据管理向导（DSW）框架，并将现有的生命科学知识模型修改为一个简化版本，旨在培训具有计算或实验背景的年轻科学家掌握核心数据管理原则。在此过程中，我们收集了ESR的反馈。
在这里，我们为年轻科学家介绍我们新的生命科学培训DMP模板。我们报告并讨论了我们作为首席研究员（PI）和ESR在这个项目中的经历，并解决了在制定和理解DMP过程中遇到的典型困难。
我们发现，数据管理向导也可以是DMP培训的合适工具，以便向研究人员传达术语和概念。完整的培训还需要上游步骤来介绍基本的DMP概念，以及下游步骤来在（公共）存储库中发布数据集。总体而言，数据管理向导工具对我们的DMP培训至关重要，我们希望我们的努力能在其他项目中得到应用。
