Évènement PrintempsDeLaDonnée 2025 #PDLD2025
#openscience #datamanagement #DMP #DataManagementPlan #FAIRprinciples #DMPOpidor #Lyon #DATALystE
Get hands-on with data classification and discover how OpenAIRE tools and services like ARGOS, Explore, and AMNESIA support FAIR practices across the research lifecycle. Don’t miss this opportunity to learn, play, and connect! Register here: https://shorturl.at/EOOI8
#OpenAIRE #HorizonEurope #DataManagement #FAIRprinciples #DataLifecycle
A prominent example of what can happen if institutions are dependent on commercial enterprises sitting in the USA: The Trump administration made Microsoft block the email account of the #ICC prosecutor:
https://apnews.com/article/icc-trump-sanctions-karim-khan-court-a4b4c02751ab84c09718b1b95cbd5db3
Given this recent example and the circumstance that this administration is in a constant quarrel with scientific institutions and also science in general, it is quite scary how dependent many - not all - German universities are for their core IT infrastructure on Microsoft services.
I hope this is a wake-up call for the IT service departments of our universities? We are increasingly encouraged to publish scientific findings (data, articles) according to #FAIRPrinciples in #OpenScience , but shouldn't we also think more about the vulnerability of our whole workflow, if the underlying IT can be shut down simply by an order of someone on the other side of the planet? Open alternatives do exist!
Nearly three months ago, U.S. President Donald Trump slapped sanctions on the International Criminal Court's chief prosecutor, Karim Khan. He has lost access to his email and his bank accounts have been frozen. American staffers at The Hague-based court also have been told that if they travel to the U.S. they risk arrest. In addition, some nongovernmental organizations have stopped working with the ICC. Rights groups say these problems will prevent victims of war crimes from getting justice.
🔊 𝗘𝗮𝗴𝗲𝗿 𝘁𝗼 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝗢𝗽𝗲𝗻 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗼𝗯𝗷𝗲𝗰𝘁-𝗯𝗮𝘀𝗲𝗱 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵?
In an upcoming #WiNoDa webinar, we will illustrate how the 𝗙𝗔𝗜𝗥 (Findable, Accessible, Interoperable, Reusable) and 𝗖𝗔𝗥𝗘 (Collective Benefit, Authority to Control, Responsibility, Ethics) 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 can transform your research — making it more 𝗮𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗹𝗲, 𝗲𝘁𝗵𝗶𝗰𝗮𝗹, and 𝗶𝗺𝗽𝗮𝗰𝘁𝗳𝘂𝗹.
Join us online on May 20th!
👉 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗻𝗼𝘄: winoda.de/en/event/webinar-open-science-fair-and-care/
📢 We’re looking forward to participating in the Mini-Conference on Open and FAIR Practices in Natural & Engineering Sciences, taking place 22–23 May in Utrecht.
🔗 https://community.data.4tu.nl/2025/02/05/open-and-fair-in-nes/
#FAIRdata #OpenResearch #inggrid #FAIRprinciples #4TUResearchData
New Whitepaper published: "Measuring the Value of (Research) Data" 📊📚
Data is more than just the "new oil"—it’s a unique economic asset with special characteristics that make its value challenging to measure. But how can businesses and research institutions quantify the actual value of their data?
📄 Read the full whitepaper here: https://zenodo.org/records/14944087
#ResearchData #DataEconomy #DataValue #FAIRPrinciples #Innovation #NFDI #BusinessValue #DataSharing
Data is a unique economic asset and information resource with special economic characteristics. Acrossvarious industries, firms are nowadays collecting and using data to optimize their processes, createnew value propositions for their customers, or sell and exchange their data with external partners. Thiswhitepaper outlines how firms can measure the value of their data and data-related activities. Three main approaches exist to measure the business value of data. First, the data value can bedetermined by measuring its market prices, costs, and future income prospects (market-basedapproach). Second, the data’s estimated utility in economic and public benefits may be used to quantifyits value (economic approach). Third, various data- and context-related parameters may be used toestimate the value of data in a specific setting (dimensional approach). We further find that data that is explicitly used for research purposes differs in these valuationapproaches. Here, the value of data is often determined by its academic impact, reuse, and contributionto scientific breakthroughs. The FAIR principles also provide a framework for enhancing the utility andvalue of research data in this setting, guiding the assessment based on findability, accessibility,interoperability, and reusability of the data. Nevertheless, the lack of a common conceptual framework and the difficulty of predicting data's futureapplications and relevance make the value measurement of data still challenging in practice.
FAIR principles are a set of guidelines aiming at simplifying the distribution of scientific data to enhance reuse and reproducibility. This article focuses on research software, which significantly differs from data through its living nature, and its relationship with free and open-source software. Based on the second French plan for Open Science, we provide a tiered roadmap to improve the state of research software, which is inclusive to all stakeholders in the research software ecosystem: scientific staff, but also institutions, funders, libraries and publishers.
The INFLIBNET Centre, with financial support under DataCite's GAF, has made significant strides in promoting an #openscience ecosystem with special reference to research data sharing & #PIDs in India. Read how this collaboration is transforming the Indian research landscape:
https://doi.org/10.5438/khj0-3784
#ResearchData #FAIRPrinciples #PID #PersistenIdentifier
@Mohamadmostafa
Indian researchers and institutions are increasingly embracing Open Science, recognizing its value in enhancing collaboration and reproducibility. However, many still hesitate to share their work openly due to challenges such as limited awareness of open research benefits, insufficient institutional support, and inadequate infrastructure. Key barriers include the absence of Research Data Management policies, insufficient training on data sharing and metadata management, and a lack of robust repositories. Without clear guidelines and technological support, researchers struggle to manage and disseminate their data effectively, slowing the broader adoption of Open Science in India.