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It is widely recognized that open science has widespread benefits not only for science and society, but also for individual researchers (McKiernan et al., 2016). A study by Drachen et al. (2016) suggested that papers get more citations when the underlying data is made available. But when data is re-used, are the datasets themselves being cited, and if so, by whom? Past studies that investigated citations to datasets found that formal data citations are generally rare (Peters et al., 2016; Robinson-García et al., 2016), and observed field differences in data citation practices (Peters et al., 2016). There have also been attempts to link data creators to researchers in order to capture data sharing practices and incorporate them into the reward system of science (Mongeon et al., 2017). Dudek, Mongeon and Bergmans (2019) performed a small study in which they analyzed citations to datasets from the IFREMER and found that most of those citations came from researchers affiliated with the organization, and that 75% of the citing papers had at least one author in common with the dataset. Our work aims to scale-up the work of Dudek, Mongeon, and Bergmans (2019) by analyzing citations to 474,609 datasets, and the relationship between the citing authors and the creators of the 30,582 datasets with at least one citation. More specifically, our research questions are: 1. How many datasets in OpenAlex have been cited at least once? 2. How concentrated is the distribution of citations to datasets? 3. What share of data citations are self-citations? This is, to our knowledge, the first analysis to provide large-scale empirical insights on data re-use with a focus on data self-citations.

This presentation was delivered at the Ocean Sciences Meeting 2022 (AGU/ASLO/TOS), in session IN03A Indigenous Partnerships for a Sustainable Ocean. A video of this presentation is available here: https://www.youtube.com/watch?v=Fj3mvxM8nwE Understanding and management of the marine environment requires respect for, and inclusion of, Indigenous knowledge, cultures, and traditional practices. The Aha Honua, an ocean observing declaration from Coastal Indigenous Peoples, calls on the ocean observing community to “formally recognize the traditional knowledge of Indigenous peoples,” and “to learn and respect each other’s ways of knowing.” For ocean observing systems, this will require broadening our understanding of data and data sharing, changing our approach to metadata, building relationships above and beyond our existing engagements, and recognizing that Indigenous coastal-dwelling communities have acted as caretakers and stewards of the lands and oceans for thousands of years. We recently completed a review of successful approaches to building information and data systems that incorporate Indigenous TEK, including a focus on building meaningful, reciprocal relationships. We identified approaches and projects, including cases where TEK has been collected, digitized and the meta(data) has been made open under some or all the FAIR principles. We identified enabling factors that notably contribute to successful outcomes in digitization, and mitigation measures to avoid the decontextualization of TEK. We provided a set of recommendations that are primarily value- and process-based, rather than action-based. The results were published as a white paper (2020) and journal article (2021). We’ll report on the results of this review, and discuss how it has influenced our practices, values, and activities at the Atlantic Regional Association of the Canadian Integrated Ocean Observing System (CIOOS Atlantic)., This work was supported financially by the Ocean Frontier Institute Seed Fund and Canada's Ocean Supercluster.

This is a presentation delivered as part of Ocean Sciences Meeting 2022 (AGU/ASLO/TOS), in session OD07: Sharing the Wealth: Overcoming Barriers and Finding Opportunities for Community-Based Solutions to Data Sharing. A video of this presentation being delivered is available here: https://www.youtube.com/watch?v=gxuabDp-LrQ We present the results of a scoping review on how human factors introduce or influence barriers to sharing open data, and what interventions have been attempted to mitigate these barriers. Ocean research is undergoing a transition from data scarcity to data abundance, which requires a fundamental shift in how we view data: from a resource to be protected to a resource we can only effectively utilize through collaboration. Organizations and funding agencies generally recognize the importance of sharing research data openly. Yet the psychology of scarcity is a powerful, enduring force on individuals. Our review included literature from the past decade that mentioned open data or research data in proximity to discussions of barriers, factors, and other synonyms. Our initial search retrieved 1200+ articles, of which 58 met all inclusion criteria. These papers varied in geography, methods, year of publication, and scope. We extracted all primary results relevant to our review and produced a narrative synthesis. We conclude individual perspectives will always be intertwined with factors like organizational structures, incentive systems, resource availability, and perceptions of institutional culture (system-level factors). In fact, personal opinion on the benefits of data sharing influences whether individuals perceive barriers or not. There has been little work to disentangle these factors. We will summarize the human factors identified in the literature in five themes: perceived lack of time/resources; a lack of organizational / institutional norms (“culture”); concern about not having the skills required; concern that they would not be able to benefit from their data collection work, including initial publication and appropriate credit; and the possibility that mistakes would be discovered. We’ll also reflect on how the results influence our user engagement practices at the Canadian Integrated Ocean Observing System (CIOOS) Atlantic., Individual Perspectives on Data Sharing: Human Factors Impacting the Digital Economy is co-funded by the Social Sciences and Humanities Research Council and the Government of Canada's Future Skills program. We gratefully acknowledge the support of Fisheries and Oceans Canada, MEOPAR NCE, Tula Foundation, and the Ocean Frontier Institute in providing the context and opportunity for CIOOS Atlantic to thoughtfully consider data sharing.

This is a presentation delivered as part of Ocean Sciences Meeting 2022 (AGU/ASLO/TOS), in session OS11: Observing and Predicting the Global Coastal Ocean 03. This presentation was recorded and is available here:https://www.youtube.com/watch?v=GKi7ZlCfQYk The Canadian Integrated Ocean Observing System (CIOOS) launched in 2019, following a decade of initiatives seeking nationally coordinated ocean observations. CIOOS completed its pilot phase, where it built infrastructure, defined metadata standards, prioritized EOVs, and established relationships. As we embark on the UN Ocean Decade and Canada’s contribution to a transparent and predictable ocean, we identify five 10-year horizon key challenges. 1. Streams and scale: We anticipate a transition from mostly static data to streams in the cloud. This will strain our capacity and conceptual models. This means investment in data processing infrastructure, co-locating analysis with data, and building ocean-to-desktop systems. 2. Principles and practices: CIOOS seeks to meet the FAIR, CARE and TRUST Principles, and to integrate and develop best practices relevant to the oceanographic and data management communities. Key focus areas include biological data, marine debris and model outputs. 3. Indigenous data and knowledge: CIOOS is forming partnerships with First Nations in coastal areas. Through co-design, CIOOS can integrate digital frameworks that meet the CARE (Collective benefit, Authority to control, Responsibility and Ethics) Principles and recognize sensitive data concerns. The application of Traditional Knowledge and Biocultural labels is a plausible approach for customizable data usage constraints. 4. Private sector engagement: These companies are an important producer and user of data. The underway VITALITY project has a mission to facilitate sharing of private sector data and to deliver outcomes that will build the blue economy. 5. Global collaboration: CIOOS recognizes partnerships and interoperability with observatories in adjacent waters are essential for applications that transcend national borders. Plans include participation in international working groups, co-development with the US IOOS, and data inputs to the Global Telecommunications System.

When researchers share their research as a published journal article, it includes an abstract. We understand these well: we don't always follow best practices, but there is substantial literature on what should be in an abstract, guides to writing effective abstracts, and journal-level standards for highly structured abstracts. The same is not true for descriptions of published data sets, and the advice for journal articles is not universally applicable. Researchers are increasingly incentivized to make research data available in a public data repository, documented using controlled vocabularies and well-defined metadata fields. Most data documentation standards include a title and at least one field for summarizing the data set which are open-ended, plain text, unconstrained fields. These "unmanaged” fields are particularly important for most search engines, as they are the metadata fields most consistent with natural language, for which search has been highly optimized. They play an important role in some search and filtering tasks, particularly among emerging scholars and novice users. While expert data managers and others have developed the ability to write thorough and useful dataset descriptions, we've observed that data repositories and catalogues, and most data documentation standards, have inconsistent and vague instructions on the content of the dataset description field. Broadly, our objective is to establish evidence-based guidance for effective documentation of datasets using unstructured text fields. We have reviewed existing literature and best practices to establish core guiding principles to support the authors of dataset descriptions. These principles have been refined through consultations with data librarians, data repository managers, and other experts. This poster describes the refined proposed guidelines, explains the reasoning behind each, and solicits input and feedback on what is required for a set of guiding principles to help users write better, more useable data set descriptions.