Related papers: DMP Planning for Big Science Projects
The project which led to this report was funded by JISC in 2010--2011 as part of its 'Managing Research Data' programme, to examine the way in which Big Science data is managed, and produce any recommendations which may be appropriate. Big…
Open access to research data is increasingly important for accelerating research. Grant authorities therefore request detailed plans for how data is managed in the projects they finance. We have recently developed such a plan for the…
Data management, which encompasses activities and strategies related to the storage, organization, and description of data and other research materials, helps ensure the usability of datasets -- both for the original research team and for…
We describe the current state and future plans for a set of tools for scientific data management (SDM) designed to support scientific transparency and reproducible research. SDM has been in active use at our MRI Center for more than two…
Astronomy looks after its data better than most disciplines, and it is no coincidence that the consensus standard for the archival preservation of all types of digital assets -- the OAIS Reference Model -- emerged originally from the space…
With the shifting focus of organizations and governments towards digitization of academic and technical documents, there has been an increasing need to use this reserve of scholarly documents for developing applications that can facilitate…
Careful preservation of experimental data, simulations, analysis products, and theoretical work maximizes their long-term scientific return on investment by enabling new analyses and reinterpretation of the results in the future. Key…
The ZEUS data preservation (ZEUS DP) project assures continued access to the data and documentation related to the experiment. It aims to provide the ability to continue the generation of valuable scientific results from these data in the…
Data plays a fundamental role in training Large Language Models (LLMs). Efficient data management, particularly in formulating a well-suited training dataset, is significant for enhancing model performance and improving training efficiency…
The field of digital preservation is being defined by a set of standards developed top-down, starting with an abstract reference model (OAIS) and gradually adding more specific detail. Systems claiming conformance to these standards are…
The report explores the challenges and strategies for preserving 3D digital data in cultural heritage. It discusses the issue of technological obsolescence, emphasising the need for ustainable storage solutions and ongoing data management…
This article offers a short guide to the steps scientists can take to ensure that their data and associated analyses continue to be of value and to be recognized. In just the past few years, hundreds of scholarly papers and reports have…
Sensor monitoring networks and advances in big data analytics have guided the reliability engineering landscape to a new era of big machinery data. Low-cost sensors, along with the evolution of the internet of things and industry 4.0, have…
Science projects are data publishers. The scale and complexity of current and future science data changes the nature of the publication process. Publication is becoming a major project component. At a minimum, a project must preserve the…
Recent advances in data science, machine learning, and artificial intelligence, such as the emergence of large language models, are leading to an increasing demand for data that can be processed by such models. While data sources are…
This book, Design Patterns in Machine Learning and Deep Learning: Advancing Big Data Analytics Management, presents a comprehensive study of essential design patterns tailored for large-scale machine learning and deep learning applications.…
Recently, we have been witnessing huge advancements in the scale of data we routinely generate and collect in pretty much everything we do, as well as our ability to exploit modern technologies to process, analyze and understand this data.…
Conservation science depends on an accurate understanding of what's happening in a given ecosystem. How many species live there? What is the makeup of the population? How is that changing over time? Species Distribution Modeling (SDM) seeks…
In the digital age, the amount of data produced is growing exponentially. Governments and institutions can no longer rely on old methods for storing data and passing on the knowledge to future generations. Digital data preservation is a…
As the amount of scientific data continues to grow at ever faster rates, the research community is increasingly in need of flexible computational infrastructure that can support the entirety of the data science lifecycle, including…