Home | COAR Asia OA Meeting 2021: Innovation, Growth and Sustainability of Open Scholarship in Asia (Oct 25-27, 2021) | SMU Libraries

The virtual 6th meeting of COAR Asia OA will be held 25-27 October 2021. The meeting will discuss the latest trends in open access and open scholarship, with community updates from Asia. Topics include open access infrastructure, open educational resources, open peer review, research data repositories, and tools built on open data. The meeting will be a venue for information exchange between Asian communities.

Programme: https://library.smu.edu.sg/asiaoa2021#programme

New feature: built-in citation support in GitHub

Nat Friedman on Twitter:

“We’ve just added built-in citation support to GitHub so researchers and scientists can more easily receive acknowledgments for their contributions to software. Just push a CITATION.cff file and we’ll add a handy widget to the repo sidebar for you. Enjoy!”

Just push a CITATION.cff file and we’ll add a handy widget to the repo sidebar for you.

 

BL Scholarly Communications Toolkit | British Library Research Repository

A series of introductory guides to different aspects of scholarly communications, and editable files so you can adjust the content to one’s own organisation’s needs.

Includes:

A Guide to Publishing Research
A Guide to Sharing Your Research Online
A Guide to Research Data Management
A Guide to Copyright and Creative Commons in Research
A Guide to Open Access

 

 

Highlights from the SSHOC Open Science and Research Data Management Train-the-Trainer Bootcamp | SSHOPENCLOUD

“Do you deliver Open Science and Research Data Management (RDM) training? Are you interested in integrating tools and creating engaging training sessions? Are you looking to prepare in-depth sessions and avoid any disasters? The SSHOC Open Science and Research Data Management Train-the-Trainer Bootcamp held on Monday 10th of May and Wednesday 12th of May 2021 was set to aid trainers in finding resources and tools they can re-use in their training planning and activities. This blogpost reviews the highlights of the bootcamp….”

Llebot | Why Won’t They Just Adopt Good Research Data Management Practices? An Exploration of Research Teams and Librarians’ Role in Facilitating RDM Adoption | Journal of Librarianship and Scholarly Communication

Abstract:  Adoption of good research data management practices is increasingly important for research teams. Despite the work the research community has done to define best data management practices, these practices are still difficult to adopt for many research teams. Universities all around the world have been offering Research Data Services to help their research groups, and libraries are usually an important part of these services. A better understanding of the pressures and factors that affect research teams may help librarians serve these groups more effectively. The social interactions between the members of a research team are a key element that influences the likelihood of a research group successfully adopting best practices in data management. In this article we adapt the Unified Theory of the Acceptance and Use of Technology (UTAUT) model (Venkatesh, Morris, Davis, & Davis, 2003) to explain the variables that can influence whether new and better, data management practices will be adopted by a research group. We describe six moderating variables: size of the team, disciplinary culture, group culture and leadership, team heterogeneity, funder, and dataset decisions. We also develop three research group personas as a way of navigating the UTAUT model, and as a tool Research Data Services practitioners can use to target interactions between librarians and research groups to make them more effective.

 

CfP: Community-based Knowledge Bases and Knowledge Graphs (submissions due Nov 01, 2021) | Journal of Web Semantics

The Journal of Web Semantics invites submissions for a special issue on Community-based Knowledge Bases and Knowledge Graphs, edited by Tim Finin, Sebastian Hellmann, David Martin, and Elena Simperl.

Submissions are due by November 01, 2021.

Community-based knowledge bases (KBs) and knowledge graphs (KGs) are critical to many domains. They contain large amounts of information, used in applications as diverse as search, question-answering systems, and conversational agents. They are the backbone of linked open data, helping connect entities from different datasets. Finally, they create rich knowledge engineering ecosystems, making significant, empirical contributions to our understanding of KB/KG science, engineering, and practices.  From here forward, we use “KB” to include both knowledge bases and knowledge graphs. Also, “KB” and “knowledge” encompass both ontology/schema and data.

Community-based KBs come in many shapes and sizes, but they tend to share a number of commonalities:

They are created through the efforts of a group of contributors, following a set of agreed goals, policies, practices, and quality norms.
They are available under open licenses.
They are central to knowledge-sharing networks bringing together various stakeholders.
They serve the needs of a community of users, including, but not restricted to, their contributor base.
Many draw their content from crowdsourced resources (such as Wikipedia, OpenStreetMap).

Examples of community-based KBs include Wikidata, DBpedia, ConceptNet, GeoNames, FrameNet, and Yago. This special issue will highlight recent research, challenges, and opportunities in the field of community-based KBs and the interaction and processes between stakeholders and the KBs.

 

We welcome papers on a wide variety of topics. Papers that focus on the participation of a community of contributors are especially encouraged.

Forschungsdaten-Policy / Research Data Policy, Freie Universität Berlin

This policy covers both research-relevant analog data, documents and objects that are digitized in the course of research, and genuinely digital data, documents and objects (“born digital”) that are created during a research process and are the object or result of research. In addition, information that ensures the documentation, traceability and – depending on the field of research – reproducibility of the results (metadata) also counts as research data.
 

Gegenstand der vorliegenden Policy sind sowohl forschungsrelevante, im Forschungsverlauf zu digitalisieren-de analoge Daten, Dokumente und Objekte, sowie genuin digitale Daten, Dokumente und Objekte („born digi-tal“), die während eines Forschungsprozesses entstehen, Forschungsgegenstand oder -ergebnis sind. Darüber hinaus zählen hier auch solche Informationen als Forschungsdaten, die die Dokumentation, Nachvollziehbar-keit und – abhängig vom Forschungsgebiet – Reproduzierbarkeit der Ergebnisse gewährleisten (Metadaten).

FAIR is not the end goal | Daniel S. Katz’s blog

I’ve been watching and participating in FAIR work for a while, first in terms of data, and more recently in research software, workflows, and machine learning models. Some of this has been based on my general interest in how research works and how we improve it, and some has been based on more specific work, such as raising the profile of research software and its developers and maintainers, and even some collaborative work in specific domains, such as high energy physics.

Nowhere in these contexts and communities did anyone get up one morning and say, “I think we should create a new goal, FAIR, and then define metrics to measure how this goal is met.” This concept wasn’t created by funders or bureaucrats to give researchers something else to do and be measured upon. Instead, people who have been thinking for a long time about how research is performed thought about the overall process of research, and over a number of discussions and meetings, decided to identify and name some of the elements of the research ecosystem and process that needed to be improved in an effort to reinvigorate the agenda.

At least in part because they came up with a clever name (FAIR) and did a really good job of disseminating this, it has caught on in the research management, research administration, and research funding communities, and to a lesser extent, in the researcher community as well. And something identifiable that people can pursue and try to measure has led to lots of focus on being FAIR.

But FAIR isn’t the end goal, it’s just one part of the solution.

[…]

Acquisition and Project Manager in research data expert team (deadline: June 27, 2021) | DANS-KNAW, The Hague

We are looking for an Acquisition and Project Manager.

What you’ll be doing
You will acquire new projects, draw up project requests and plans with colleagues and third parties, and be responsible for all or part of their implementation. You will be responsible for several projects. You will consult with partners and manage project staff. You will report on project progress and outcomes. You will closely collaborate with colleagues from the Research Data Expert Team and other DANS teams. You will maintain many international contacts in relevant fields.

Development of Knowledge Graph for Data Management Related to Flooding Disasters Using Open Data

Abstract:  Despite the development of various technologies and systems using artificial intelligence (AI) to solve problems related to disasters, difficult challenges are still being encountered. Data are the foundation to solving diverse disaster problems using AI, big data analysis, and so on. Therefore, we must focus on these various data. Disaster data depend on the domain by disaster type and include heterogeneous data and lack interoperability. In particular, in the case of open data related to disasters, there are several issues, where the source and format of data are different because various data are collected by different organizations. Moreover, the vocabularies used for each domain are inconsistent. This study proposes a knowledge graph to resolve the heterogeneity among various disaster data and provide interoperability among domains. Among disaster domains, we describe the knowledge graph for flooding disasters using Korean open datasets and cross-domain knowledge graphs. Furthermore, the proposed knowledge graph is used to assist, solve, and manage disaster problems.