“Materials from the virtual symposium held by Carnegie Mellon University to discuss innovations in the dissemination and reuse of scientific data.”
Scholexplorer data can be used to identify reuse and citation of published datasets
More dataset and article links can be identified now with the Scholexplorer API
Many links result from former manual data curation instead of direct data citation
Author and dataset owner affiliation would help identify different data use cases….”
Abstract: Open data-sharing is a valuable practice that ought to enhance the impact, reach, and transparency of a research project. While widely advocated by many researchers and mandated by some journals and funding agencies, little is known about detailed practices across psychological science. In a pre-registered study, we show that overall, few research papers directly link to available data in many, though not all, journals. Most importantly, even where open data can be identified, the majority of these lacked completeness and reusability—conclusions that closely mirror those reported outside of Psychology. Exploring the reasons behind these findings, we offer seven specific recommendations for engineering and incentivizing improved practices, so that the potential of open data can be better realized across psychology and social science more generally.
Abstract: This work analyses the perception and practice of sharing, reusing, and facilitating access to research data in the field of food science and technology. The study involved the coordination of a focus group discussion and an online survey, to understand and evince the behaviour of researchers regarding data management in that field. Both the discussion group and the survey were performed with researchers from several institutes of the Spanish National Research Council. The lack of a data sharing culture, the fear of being scooped, and confusion between the concepts of the working plan and the data management plan were some of the issues that emerged in the focus group. Respondents’ previous experience with sharing their research data has been mainly in the form of appendices to peer?reviewed publications. From the survey (101 responses), the most important motivations for publishing research data were found to be facilitating the reproducibility of the research, increasing the likelihood of citations of the article, and compliance with funding body mandates. Legal constraints, intellectual property, data ownership, data rights, potential commercial exploitation, and misuse of data were the main barriers to publishing data as open data. Citation in publications, certification, compliance with standards, and the reputation of the data providers were the most relevant factors affecting the use of other researchers’ data. Being recent or recently updated, well documented, with quality metadata and ease of access were the most valued attributes of open research data.
“Following on from our previous post – summarising our discussion of inhibitions towards experimental publishing – this post looks at how we can stimulate experimentation, looking to understand how it can be encouraged within academic publishing and how some of the inhibitions described previously can be addressed. The following is a summary of our discussions.
Underlying our discussions were the following questions:
How can we stimulate the uptake of experimental publishing and the creation of experimental long-form publications, and the reuse of and engagement with OA books?
What projects/platforms/software do we need to be aware of and in touch with?
What strategies should we devise to stimulate experimentation and reuse?….”
“CANARIE announced today the selection of 13 successful projects from its latest Research Software funding call. This funding will enable research teams to adapt their existing research platforms for re-use by other research teams, including those working in different disciplines. As a result, new research teams from across Canada will be able to re-use previously funded and developed software to accelerate their discoveries.
The research workflow (data acquisition, storage, computation/processing, visualization, and data management) is common across all research disciplines. By adapting purpose-built software developed for this workflow so that other research teams can also benefit from them, the impact of public investments in research is maximized and time to discoveries can be accelerated:
More research funding is allocated to research, rather than to the development of software that already exists
Efficiencies in software development enable researchers to devote their time and resources to the research itself …”
Abstract: Open research data (ORD) have been considered a driver of scientific transparency. However, data friction, as the phenomenon of data underutilisation for several causes, has also been pointed out. A factor often called into question for ORD low usage is the quality of the ORD and associated metadata. This work aims to illustrate the use of ORD, published by the Figshare scientific repository, concerning their scientific discipline, their type and compared with the quality of their metadata. Considering all the Figshare resources and carrying out a programmatic quality assessment of their metadata, our analysis highlighted two aspects. First, irrespective of the scientific domain considered, most ORD are under-used, but with exceptional cases which concentrate most researchers’ attention. Second, there was no evidence that the use of ORD is associated with good metadata publishing practices. These two findings opened to a reflection about the potential causes of such data friction.
“As 3-D digitization becomes more accessible and research institutions expand support for 3-D modeling, researchers are increasingly leveraging 3-D models and methods. For instance, a paleontologist might use a micro CT scanning process to capture images of the inside of a specimen that would otherwise be destroyed by such an analysis. An archaeologist might use photogrammetry to construct digital representations of artifacts that can then be examined in a way that would be difficult or impossible in a museum setting. The emergence of 3-D modeling as a research practice presents several challenges for libraries working to support and facilitate the dissemination and reuse of 3-D data packages. At present, there is significant work to be done in the community to create a culture and infrastructure that facilitates sharing 3-D research.
Understanding data sharing and reuse among researchers is critical to the success of collection, dissemination, and preservation efforts among memory institutions. Existing literature on data sharing, reuse, trust, quality, and review can inform approaches to evaluating how researchers might share or reuse 3-D data. However, 3-D data have characteristics that make them unique—rapidly changing technology, intersections with lucrative commercial sectors like virtual reality gaming, and the expectation that a model will render—or be accessible for user interaction—when shared. This project offers a unique and necessary contribution to the literature in analyzing creation, reuse, and publishing of 3-D through interviews with expert researchers. This provides substantial value to libraries, archives, and museums that work with 3-D by enabling memory institutions to design digital collection and repository systems that meet patron needs and foster innovation….”
“This is an update on the POD project to create a platform for open discovery that positions data reuse and service integration as strategic priorities….”
“AIDR (Artificial Intelligence for Data Discovery and Reuse) aims to find innovative solutions to accelerate the dissemination and reuse of scientific data in the data revolution. The explosion in the volume of scientific data has made it increasingly challenging to find data scattered across various platforms. At the same time, increasing numbers of new data formats, greater data complexity, lack of consistent data standards across disciplines, metadata or links between data and publications makes it even more challenging to evaluate data quality, reproduce results, and reuse data for new discoveries. Last year, supported by the NSF scientific data reuse initiative, the inaugural AIDR 2019 attracted AI/ML researchers, data professionals, and scientists from biomedicine, technology industry, high performance computing, astronomy, seismology, library and information science, archaeology, and more, to share innovative AI tools, algorithms and applications to make data more discoverable and reusable, and to discuss mutual challenges in data sharing and reuse.
This year, we are following up with a one-day, virtual AIDR Symposium, that provides a place for the community to continue having these conversations and work together to build a healthy data ecosystem. The program will feature invited speakers and panel discussions from a variety of disciplines, including a focused session on COVID-19 data. Audience are highly encouraged to join the conversation by submitting a poster, joining the panel discussions and social hours, chatting on Slack, and participating in collaborative note-taking.”