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Abstract It has become common for researchers to make their data publicly available to meet the data management and accessibility requirements of funding agencies and scientific publishers. However, many researchers face the challenge of determining what data to preserve and share and where to preserve and share those data. This can be especially challenging for those who run dynamical models, which can produce complex, voluminous data outputs, and have not considered what outputs may need to be preserved and shared as part of the project design. This manuscript presents findings from the NSF EarthCube Research Coordination Network project titled “What About Model Data? Best Practices for Preservation and Replicability” (https://modeldatarcn.github.io/). These findings suggest that if the primary goal of sharing data are to communicate knowledge, most simulation-based research projects only need to preserve and share selected model outputs along with the full simulation experiment workflow. One major result of this project has been the development of a rubric, designed to provide guidance for making decisions on what simulation output needs to be preserved and shared in trusted community repositories to achieve the goal of knowledge communication. This rubric, along with use cases for selected projects, provide scientists with guidance on data accessibility requirements in the planning process of research, allowing for more thoughtful development of data management plans and funding requests. Additionally, this rubric can be referred to by publishers for what is expected in terms of data accessibility for publication. 

Objective: Persistent Identifiers (PIDs) are central to the vision of open science described in the FAIR Principles. However, the use of PIDs for scientific instruments and facilities is decentralized and fragmented. This project aims to develop community-based standards, guidelines, and best practices for how and why PIDs can be assigned to facilities and instruments. Methods: We hosted several online and in-person focus groups and discussions, cumulating in a two-day in-person workshop featuring stakeholders from a variety of organizations and disciplines, such as instrument and facilities operators, PID infrastructure providers, researchers who use instruments and facilities, journal publishers, university administrators, federal funding agencies, and information and data professionals. Results: Our first-year efforts resulted in four main areas of interest: developing a better understanding of the current PID ecosystem; clarifying how and when PIDs could be assigned to scientific instruments and facilities; challenges and barriers involved with assigning PIDs; incentives for researchers, facility managers, and other stakeholders to encourage the use of PIDs. Conclusions: The potential for PIDs to facilitate the discovery, connection, and attribution of research instruments and facilities indicates an obvious value in their use. The lack of standards of how and when they are created, assigned, updated, and used is a major barrier to their widespread use. Data and information professionals can work to create relationships with stakeholders, provide relevant education and outreach activities, and integrate PIDs for instruments and facilities into their data curation and publication workflows. 

This study investigates Model Intercomparison Projects (MIPs) as one example of a coordinated approach to establishing scientific credibility. MIPs originated within climate science as a method to evaluate and compare disparate climate models, but MIPs or MIP-like projects are now spreading to many scientific fields. Within climate science, MIPs have advanced knowledge of: a) the climate phenomena being modeled, and b) the building of climate models themselves. MIPs thus build scientific confidence in the climate modeling enterprise writ large, reducing questions of the credibility or reproducibility of any single model. This paper will discuss how MIPs organize people, models, and data through institution and infrastructure coupling (IIC). IIC involves establishing mechanisms and technologies for collecting, distributing, and comparing data and models (infrastructural work), alongside corresponding governance structures, rules of participation, and collaboration mechanisms that enable partners around the world to work together effectively (institutional work). Coupling these efforts involves developing formal and informal ways to standardize data and metadata, create common vocabularies, provide uniform tools and methods for evaluating resulting data, and build community around shared research topics. 

There is strong agreement across the sciences that replicable workflows are needed for computational modeling. Open and replicable workflows not only strengthen public confidence in the sciences, but also result in more efficient community science. However, the massive size and complexity of geoscience simulation outputs, as well as the large cost to produce and preserve these outputs, present problems related to data storage, preservation, duplication, and replication. The simulation workflows themselves present additional challenges related to usability, understandability, documentation, and citation. These challenges make it difficult for researchers to meet the bewildering variety of data management requirements and recommendations across research funders and scientific journals. This paper introduces initial outcomes and emerging themes from the EarthCube Research Coordination Network project titled “What About Model Data? - Best Practices for Preservation and Replicability,” which is working to develop tools to assist researchers in determining what elements of geoscience modeling research should be preserved and shared to meet evolving community open science expectations. Specifically, the paper offers approaches to address the following key questions: • How should preservation of model software and outputs differ for projects that are oriented toward knowledge production vs. projects oriented toward data production? • What components of dynamical geoscience modeling research should be preserved and shared? • What curation support is needed to enable sharing and preservation for geoscience simulation models and their output? • What cultural barriers impede geoscience modelers from making progress on these topics? 

Abstract Research, innovation, and progress in the life sciences are increasingly contingent on access to large quantities of data. This is one of the key premises behind the “open science” movement and the global calls for fostering the sharing of personal data, datasets, and research results. This paper reports on the outcomes of discussions by the panel “Open science, data sharing and solidarity: who benefits?” held at the 2021 Biennial conference of the International Society for the History, Philosophy, and Social Studies of Biology (ISHPSSB), and hosted by Cold Spring Harbor Laboratory (CSHL). 

In the 21st Century, research is increasingly data- and computation-driven. Researchers, funders, and the larger community today emphasize the traits of openness and reproducibility. In March 2017, 13 mostly early-career research leaders who are building their careers around these traits came together with ten university leaders (presidents, vice presidents, and vice provosts), representatives from four funding agencies, and eleven organizers and other stakeholders in an NIH- and NSF-funded one-day, invitation-only workshop titled "Imagining Tomorrow's University." Workshop attendees were charged with launching a new dialog around open research – the current status, opportunities for advancement, and challenges that limit sharing. The workshop examined how the internet-enabled research world has changed, and how universities need to change to adapt commensurately, aiming to understand how universities can and should make themselves competitive and attract the best students, staff, and faculty in this new world. During the workshop, the participants re-imagined scholarship, education, and institutions for an open, networked era, to uncover new opportunities for universities to create value and serve society. They expressed the results of these deliberations as a set of 22 principles of tomorrow's university across six areas: credit and attribution, communities, outreach and engagement, education, preservation and reproducibility, and technologies. Activities that follow on from workshop results take one of three forms. First, since the workshop, a number of workshop authors have further developed and published their white papers to make their reflections and recommendations more concrete. These authors are also conducting efforts to implement these ideas, and to make changes in the university system.  Second, we plan to organise a follow-up workshop that focuses on how these principles could be implemented. Third, we believe that the outcomes of this workshop support and are connected with recent theoretical work on the position and future of open knowledge institutions.