Long-Term Data Preservation Data Lifecycle, Standardisation Process, Implementation and Lessons Learned

Science and Earth Observation data represent today a unique and valuable asset for humankind that should be preserved without time constraints and kept accessible and exploitable by current and future generations. In Earth Science, knowledge of the past and tracking of the evolution are at the basis of our capability to effectively respond to the global changes that are putting increasing pressure on the environment, and on human society. This can only be achieved if long time series of data are properly preserved and made accessible to support international initiatives. Within ESA Member States and beyond, Earth Science data holders are increasingly coordinating data preservation efforts to ensure that the valuable data are safeguarded against loss and kept accessible and useable for current and future generations. This task becomes increasingly challenging in view of the existing 40 years’ worth of Earth Science data stored in archives around the world and the massive increase of data volumes expected over the next years from e.g., the European Copernicus Sentinel missions. Long Term Data Preservation (LTDP) aims at maintaining information discoverable and accessible in an independent and understandable way, with supporting information, which helps ensuring authenticity, over the long term. A focal aspect of LTDP is data Curation. Data Curation refers to the management of data throughout its life cycle. Data Curation activities enable data discovery and retrieval, maintain its quality, add value, and allow data re-use over time. It includes all the processes that involve data management, such as pre-ingest initiatives, ingest functions, archival storage and preservation, dissemination, and provision of access for a designated community. The paper presents specific aspects, of importance during the entire Earth observation data lifecycle, with respect to evolving data volumes and application scenarios. These particular issues are introduced in the section on 'Big Data' and LTDP. The Data Stewardship Reference lifecycle section describes how the data stewardship activities can be efficiently organised, while the following section addresses the overall preservation workflow and shows the technical steps to be taken during Data Curation. Earth Science Data Curation and preservation should be addressed during all mission stages - from the initial mission planning, throughout the entire mission lifetime, and during the post- mission phase. The Data Stewardship Reference Lifecycle gives a high-level overview of the steps useful for implementing Curation and preservation rules on mission data sets from initial conceptualisation or receipt through the iterative Curation cycle

CARE Data Principles Data Curation Primer

The CARE data principles (Collective benefit, Authority to control, Responsibility, and Ethics) are a conceptual framework meant to ensure ethical collection, sharing, and stewardship of Indigenous data. As part of a workshop hosted by the Data Curation Network in 2022, librarians created a foundational data curation primer on the CARE data principles and how they apply to data management, curation, and sharing. The primer touches on the cultural context regarding the CARE data principles, the historical misuse of Indigenous data, tribal sovereignty, and Indigenous Peoples\u27 right to governance of their data. This session will discuss how CARE principles can be applied and give specific use examples. Librarians can use the primer and CURATE(D) checklist, to consider the ethical use, sharing and preservation of Indigenous data within their institutional repositories

Data Curation for Big Interdisciplinary Science: The Pulley Ridge Experience

The curation and preservation of scientific data has long been recognized as an essential activity for the reproducibility of science and the advancement of knowledge. While investment into data curation for specific disciplines and at individual research institutions has advanced the ability to preserve research data products, data curation for big interdisciplinary science remains relatively unexplored terrain. To fill this lacunae, this article presents a case study of the data curation for the National Centers for Coastal Ocean Science (NCCOS) funded project “Understanding Coral Ecosystem Connectivity in the Gulf of Mexico-Pulley Ridge to the Florida Keys” undertaken from 2011 to 2018 by more than 30 researchers at several research institutions. The data curation process is described and a discussion of strengths, weaknesses and lessons learned is presented. Major conclusions from this case study include: the reimplementation of data repository infrastructure builds valuable institutional data curation knowledge but may not meet data curation standards and best practices; data from big interdisciplinary science can be considered as a special collection with the implication that metadata takes the form of a finding aid or catalog of datasets within the larger project context; and there are opportunities for data curators and librarians to synthesize and integrate results across disciplines and to create exhibits as stories that emerge from interdisciplinary big science. The substance of this article is based upon a poster presented at RDAP Summit 2019

Preserving Our Digital Heritage: Information Systems for Data Management and Preservation

It is essential that we develop effective systems for the management and preservation of digital heritage data. This paper outlines the key issues surrounding access, sharing and curation, and describes current efforts to establish research infrastructures in a number of countries. It aims to provide a detailed overview of the issues involved in the creation, ingest, preservation and dissemination of 3D datasets in particular. The paper incorporates specific examples from past and present Archaeology Data Service (ADS) projects and highlights the recent work undertaken by the ADS and partners to specify standards and workflows in order to aid the preservation and reuse of 3D datasets

D3.2 Cost Concept Model and Gateway Specification

This document introduces a Framework supporting the implementation of a cost concept model against which current and future cost models for curating digital assets can be benchmarked. The value built into this cost concept model leverages the comprehensive engagement by the 4C project with various user communities and builds upon our understanding of the requirements, drivers, obstacles and objectives that various stakeholder groups have relating to digital curation. Ultimately, this concept model should provide a critical input to the development and refinement of cost models as well as helping to ensure that the curation and preservation solutions and services that will inevitably arise from the commercial sector as ‘supply’ respond to a much better understood ‘demand’ for cost-effective and relevant tools. To meet acknowledged gaps in current provision, a nested model of curation which addresses both costs and benefits is provided. The goal of this task was not to create a single, functionally implementable cost modelling application; but rather to design a model based on common concepts and to develop a generic gateway specification that can be used by future model developers, service and solution providers, and by researchers in follow-up research and development projects.<p></p> The Framework includes:<p></p> • A Cost Concept Model—which defines the core concepts that should be included in curation costs models;<p></p> • An Implementation Guide—for the cost concept model that provides guidance and proposes questions that should be considered when developing new cost models and refining existing cost models;<p></p> • A Gateway Specification Template—which provides standard metadata for each of the core cost concepts and is intended for use by future model developers, model users, and service and solution providers to promote interoperability;<p></p> • A Nested Model for Digital Curation—that visualises the core concepts, demonstrates how they interact and places them into context visually by linking them to A Cost and Benefit Model for Curation.<p></p> This Framework provides guidance for data collection and associated calculations in an operational context but will also provide a critical foundation for more strategic thinking around curation such as the Economic Sustainability Reference Model (ESRM).<p></p> Where appropriate, definitions of terms are provided, recommendations are made, and examples from existing models are used to illustrate the principles of the framework