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….”
“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.”
“The National Agricultural Library (NAL) identified a need for a framework of guidance to support rapid appraisal and processing for scientific researchers’ collections after being offered collections of scientific data and data-rich materials that required immediate appraisal before acquisition. To this end, the NAL partnered with the University of Maryland’s College of Information Studies (iSchool) to support two Data Rescue Digital Curation Fellows to investigate processes for efficiently identifying, appraising, and processing scientific data out of legacy collections, to support data use and reuse….
The data being ‘rescued’ is intended for inclusion in the USDA’s Agricultural Research Service (ARS) open access data repository, Ag Data Commons….”
“The current climate has put a spotlight onto the value and importance of data sharing and curation and good data management for boosting the reproducibility and reliability of research. Its value has never been pulled more sharply into focus as you can see the real life impact of data sharing as we navigate this pandemic. After five years of collaboration on an annual survey of researchers, we can see increasing positive attitudes and behaviours when it comes to data sharing, and yet many researchers and those within the research community still face roadblocks – be this because of challenges in working practices, the lack of tools or services supporting them, or the wider misconception around the role, use and appropriate re-use of data – and this is a problem.
Since 2016 Figshare, Springer Nature and Digital Science have partnered on the State of Open Data report, based on a survey tracking researcher attitudes and behaviours towards open data sharing and research data management. The most recent survey launched in May this year, and with the global pandemic we took the opportunity to ask researchers how Covid-19 was impacting their ability to carry out research, and their views on reuse of data and collaboration. We wanted to get a better understanding of how researcher behaviour was being affected. When the survey was conducted much of the world was under lockdown which has since eased, however, fears of a second wave are growing. We are aware of the time sensitivity of these insights so rather than wait until October we wanted to release a snapshot of the data to the community as soon as we could, to allow stakeholders the time to analyse the data to help inform policy and actions going forward as we enter a new phase of the pandemic. The data published this week was from surveys dating from 24th May to 18th June, n=3,436. …”
This post was co-authored by CC’s Open Policy Manager Brigitte Vézina and Legal and Policy Intern Alexis Muscat. Tomorrow is International Day of the World’s Indigenous Peoples, a day that seeks to raise awareness of and support Indigenous peoples’ rights and aspirations around the world. We at Creative Commons (CC) wish to highlight this important … Read More “Sharing Indigenous Cultural Heritage Online: An Overview of GLAM Policies”
The post Sharing Indigenous Cultural Heritage Online: An Overview of GLAM Policies appeared first on Creative Commons.
Europe PMC is now indexing full-text preprints related to the COVID-19 pandemic and the SARS-CoV-2 virus, as well as the underlying data
The project will make COVID-19 scientific literature available as fast as possible in a single repository, in a format that allows text mining
Researchers and healthcare professionals will be able to access and reuse preprints more easily, accelerating research into better treatments or a vaccine….”