The amount of data within the product development process requires a structured approach to coordinate them. Knowledge management solutions, such as ontologies, are a suitable way of linking data and representing semantic relationships. However, making all relevant data usable to ensure their target-oriented application is still a challenge. Thus, this contribution presents an approach to identify and classify heterogeneous data in product development. Besides this single ontology approach, interface solutions for data integration into an ontology are proposed.

The Stanford Population Health Sciences Data Ecosystem was created to facilitate the use of large datasets containing health records from hundreds of millions of individuals. This necessitated technical solutions optimized for an academic medical center to manage and share high-risk data at scale. Through collaboration with internal and external partners, we have built a Data Ecosystem to host, curate, and share data with hundreds of users in a secure and compliant manner. This platform has enabled us to host unique data assets and serve the needs of researchers across Stanford University, and the technology and approach were designed to be replicable and portable to other institutions. We have found, however, that though these technological advances are necessary, they are not sufficient. Challenges around making data Findable, Accessible, Interoperable, and Reusable remain. Our experience has demonstrated that there is a high demand for access to real-world data, and that if the appropriate tools and structures are in place, translational research can be advanced considerably. Together, technological solutions, management structures, and education to support researcher, data science, and community collaborations offer more impactful processes over the long-term for supporting translational research with real-world data.

Archaeologists frequently use written guidelines such as site manuals, recording forms, and digital prompts during excavations to create usable data within and across projects. Most written guidelines emphasize creating either standardized datasets or narrative summaries; however, previous research has demonstrated that the resulting datasets are often difficult to (re)use. Our study analyzed observations and interviews conducted with four archaeological excavation teams, as well as interviews with archaeological data reusers, to evaluate how archaeologists use and implement written guidelines. These excavation team and reuser experiences suggest that archaeologists need more specific best practices to create and implement written guidelines that improve the quality and usability of archaeological data. We present recommendations to improve written guidelines that focus on a project's methods, end-of-season documentation, and naming practices. We also present a Written Guidelines Checklist to help project directors improve their written guidelines before, during, and after fieldwork as part of a collaborative process. Ideally, these best practices for written guidelines will make it easier for team members and future reusers to incorporate their own and others’ archaeological data into their research.

The next generation of high-power lasers enables repetition of experiments at orders of magnitude higher frequency than what was possible using the prior generation. Facilities requiring human intervention between laser repetitions need to adapt in order to keep pace with the new laser technology. A distributed networked control system can enable laboratory-wide automation and feedback control loops. These higher-repetition-rate experiments will create enormous quantities of data. A consistent approach to managing data can increase data accessibility, reduce repetitive data-software development and mitigate poorly organized metadata. An opportunity arises to share knowledge of improvements to control and data infrastructure currently being undertaken. We compare platforms and approaches to state-of-the-art control systems and data management at high-power laser facilities, and we illustrate these topics with case studies from our community.

Research is increasingly conducted through multi-institutional consortia, and best practices for establishing multi-site research collaborations must be employed to ensure efficient, effective, and productive translational research teams. In this manuscript, we describe how the Population-based Research to Optimize the Screening Process Lung Research Center (PROSPR-Lung) utilized evidence-based Science of Team Science (SciTS) best practices to establish the consortium’s infrastructure and processes to promote translational research in lung cancer screening. We provide specific, actionable examples of how we: (1) developed and reinforced a shared mission, vision, and goals; (2) maintained a transparent and representative leadership structure; (3) employed strong research support systems; (4) provided efficient and effective data management; (5) promoted interdisciplinary conversations; and (6) built a culture of trust. We offer guidance for managing a multi-site research center and data repository that may be applied to a variety of settings. Finally, we detail specific project management tools and processes used to drive collaboration, efficiency, and scientific productivity.

With the aim of producing a 3D representation of tumors, imaging and molecular annotation of xenografts and tumors (IMAXT) uses a large variety of modalities in order to acquire tumor samples and produce a map of every cell in the tumor and its host environment. With the large volume and variety of data produced in the project, we developed automatic data workflows and analysis pipelines. We introduce a research methodology where scientists connect to a cloud environment to perform analysis close to where data are located, instead of bringing data to their local computers. Here, we present the data and analysis infrastructure, discuss the unique computational challenges and describe the analysis chains developed and deployed to generate molecularly annotated tumor models. Registration is achieved by use of a novel technique involving spherical fiducial marks that are visible in all imaging modalities used within IMAXT. The automatic pipelines are highly optimized and allow to obtain processed datasets several times quicker than current solutions narrowing the gap between data acquisition and scientific exploitation.

Most archaeological investigations in the United States and other countries must comply with preservation laws, especially if they are on government property or supported by government funding. Academic and cultural resource management (CRM) studies have explored various social, temporal, and environmental contexts and produce an ever-increasing volume of archaeological data. More and more data are born digital, and many legacy data are digitized. There is a building effort to synthesize and integrate data at a massive scale and create new data standards and management systems. Taxpayer dollars often fund archaeological studies that are intended, in spirit, to promote historic preservation and provide public benefits. However, the resulting data are difficult to access and interoperationalize, and they are rarely collected and managed with their long-term security, accessibility, and ethical reuse in mind. Momentum is building toward open data and open science as well as Indigenous data sovereignty and governance. The field of archaeology is reaching a critical point where consideration of diverse constituencies, concerns, and requirements is needed to plan data collection and management approaches moving forward. This theme issue focuses on challenges and opportunities in archaeological data collection and management in academic and CRM contexts.

A stream of research on co-authorship, used as a proxy of scholars’ collaborative behavior, focuses on members of a given scientific community defined at discipline and/or national basis for which co-authorship data have to be retrieved. Recent literature pointed out that international digital libraries provide partial coverage of the entire scholar scientific production as well as under-coverage of the scholars in the community. Bias in retrieving co-authorship data of the community of interest can affect network construction and network measures in several ways, providing a partial picture of the real collaboration in writing papers among scholars. In this contribution, we collected bibliographic records of Italian academic statisticians from an online platform (IRIS) available at most universities. Even if it guarantees a high coverage rate of our population and its scientific production, it is necessary to deal with some data quality issues. Thus, a web scraping procedure based on a semi-automatic tool to retrieve publication metadata, as well as data management tools to detect duplicate records and to reconcile authors, is proposed. As a result of our procedure, it emerged that collaboration is an active and increasing practice for Italian academic statisticians with some differences according to the gender, the academic ranking, and the university location of scholars. The heuristic procedure to accomplish data quality issues in the IRIS platform can represent a working case report to adapt to other bibliographic archives with similar characteristics.

With the expanding adoption of technology and intelligent applications in every aspect of our life, energy, resource, data, and product management are all improving. So, modern management has recently surged to cope with modern societies. Numerous optimization approaches and algorithms are used to effectively optimize the literature while taking into account its many restrictions. With their dependability and superior solution quality for overcoming the numerous barriers to generation, distribution, integration, and management, nature-inspired meta-heuristic optimization algorithms have stood out among these methods. Hence, this article aims to review the application of nature-inspired optimization algorithms to modern management. Besides, the created clusters introduce the top authors in this field. The results showed that nature-inspired optimization algorithms contribute significantly to cost, resource, and energy efficiency. The genetic algorithm is also the most important and widely used method in the previous literature.

This paper outlines frameworks to use for reserving validation and gives the reader an overview of current techniques being employed. In the experience of the authors, many companies lack an embedded reserve validation framework and reserve validation can appear piecemeal and unstructured. The paper outlines a case study demonstrating how successful machine learning techniques will become and then goes on to discuss the implications of machine learning to the future of reserving departments, processes, data and validation techniques. Reserving validation can take many forms, from simple checks to full independent reviews to add value to the reserving process, enhance governance and increase confidence in and reliability in results. This paper discusses covers common weaknesses and their solutions and suggestions of a framework in which to apply validation tools. The impacts of the COVID-19 pandemic on reserving validation is also covered as are early warning indicators and the topic of IFRS 17 from the standpoint of reserving validation. The paper looks at the future for reserving validation and discusses the data challenges that need overcoming on the path to embedded reserving process validation.