2.5% for Open: An inventory of investment opportunities – Google Docs

“David Lewis has recently proposed that libraries devote 2.5% of its total budget to support the common infrastructure needed to create the open scholarly commons….In the early stages of exploring this idea, we want to come to some level agreement about what would in fact count as such an investment, and then build a registry that would allow libraries to record their investments in this area, track their investments over time, and compare their investments with like institutions. The registry would also serve as a guide for those looking for ideas for how to make the best investments for their institution, providing a listing of all ‘approved’ ways to invest in open, and as a place for those seeking investment to be discovered. As a first step towards building such a thing, we are crowdsourcing the creation of the inventory of ways to invest….”

BEAT: An Open-Source Web-Based Open-Science Platform

“With the increased interest in computational sciences, machine learning (ML), pattern recognition (PR) and big data, governmental agencies, academia and manufacturers are overwhelmed by the constant influx of new algorithms and techniques promising improved performance, generalization and robustness. Sadly, result reproducibility is often an overlooked feature accompanying original research publications, competitions and benchmark evaluations. The main reasons behind such a gap arise from natural complications in research and development in this area: the distribution of data may be a sensitive issue; software frameworks are difficult to install and maintain; Test protocols may involve a potentially large set of intricate steps which are difficult to handle. Given the raising complexity of research challenges and the constant increase in data volume, the conditions for achieving reproducible research in the domain are also increasingly difficult to meet. To bridge this gap, we built an open platform for research in computational sciences related to pattern recognition and machine learning, to help on the development, reproducibility and certification of results obtained in the field. By making use of such a system, academic, governmental or industrial organizations enable users to easily and socially develop processing toolchains, re-use data, algorithms, workflows and compare results from distinct algorithms and/or parameterizations with minimal effort. This article presents such a platform and discusses some of its key features, uses and limitations. We overview a currently operational prototype and provide design insights.”

Sowing the seeds for change in scholarly publishing – Collaborative Knowledge Foundation

“We envision building an evolving network of modular, interoperable, flexible and reusable open source projects that facilitate rapid, transparent and reproducible research and research communication for the public good. Rather than remaining independent and siloed, these projects will share resources and learn from each other, creating an open science infrastructure.”

COAR Next Generation Repositories | Draft for Public Comment

“In April 2016, the Confederation of Open Access Repositories (COAR) launched a working group to help identify new functionalities and technologies for repositories and develop a road map for their adoption. For the past several months, the group has been working to define a vision for repositories and sketch out the priority user stories and scenarios that will help guide the development of new functionalities.

The vision is to position repositories as the foundation for a distributed, globally networked infrastructure for scholarly communication, on top of which layers of value added services will be deployed, thereby transforming the system, making it more research-centric, open to and supportive of innovation, while also collectively managed by the scholarly community.

Underlying this vision is the idea that a distributed network of repositories can and should be a powerful tool to promote the transformation of the scholarly communication ecosystem. In this context, repositories will provide access to published articles as well as a broad range of artifacts beyond traditional publications such as datasets, pre-prints, working papers, images, software, and so on….”

STM Digital Publishing Seminar 2016: A brief summary | About Hindawi

“Continuous publishing and versioning is at the core of eLife’s new publishing platform Continuum. In today’s publishing world, we commonly experience a number of versions for any given article, from preprint, to accepted version, to version of record, as well as post publication versions. Paul Shannon (Head of Technology, eLife) presented the importance of versioning and how this is handled in Continuum. Continuum works with JATS XML, the widely adopted tagging suite, to offer a modular publishing approach. Among its features are the ability to preview articles before publishing, and to schedule publication. Continuum is an open source software and it can be found at GitHub….”

Analysis of challenges and opportunities for migrating ScholarsArchive@OSU to a new technical platform: requirements analysis, environmental scan, and recommended next steps

Abstract: ScholarsArchive@OSU (SA@OSU) has stood as Oregon State University’s institutional repository for nearly a decade, and has seen a great deal of success, by many metrics, as such. Over that time period, the mission, content types, and stakeholders of the repository have changed, as has the ecosystem of available and emerging repository platforms. These changes have sparked interest in OSU Libraries & Press in assessing the technical infrastructures for SA@OSU to determine whether migration to a new system could benefit all of our stakeholders. This document presents the results of an investigation conducted by Center for Digital Scholarship and Services faculty and staff between September 2014 and January 2015 into the requirements of our stakeholders and the suite of practically implementable repository platforms for SA@OSU. We provide an assessment of a number of platforms in the context of our requirements analysis and make recommendations for next steps


DSpace-CRIS consists of a data model describing objects of interest to Research and Development and a set of tools to manage the data. Standard DSpace used to deal with publications and data sets, whereas DSpace-CRIS involves other CRIS entities: Researcher Pages, Projects, Organization Units and Second Level Dynamic Objects (single entities specialized by a profile, such as Journal, Prize, Event etc; because any profile can define its own set of properties and nested objects)….”