A free toolkit to foster open access agreements – Insights

Abstract:  In November 2021, with the support of the Association of Learned and Professional Society Publishers (ALPSP) and cOAlition S, four ‘task and finish’ working groups were established. The authors facilitated and supported these groups. Each group was responsible for producing tools that will enable library consortia and small independent publishers to negotiate transformative agreements, which is to say, agreements that will enable the publisher to fully transition to open access. The first task and finish group developed shared principles for transformative agreements. The second developed a data template to enable smaller independent publishers to reach agreements with library consortia and libraries, while the third developed example licence agreements. These groups recognized that the implementation of a transformative agreement crosses a complex ecosystem of technology, processes, policies, automated functions and manual functions that relate to contract management, article submission and peer review, content hosting and dissemination as well as financial management. For this reason, a fourth group produced a workflow framework that describes the process in all its phases. The members of these four groups were volunteers from stakeholder communities including libraries, library consortia, smaller independent publishers and intermediaries. This article explains why these tools are needed and the process behind their creation. The authors have combined these tools into a freely available toolkit, available under a CC BY licence.


Ten lessons for data sharing with a data commons | Scientific Data

“A data commons is a cloud-based data platform with a governance structure that allows a community to manage, analyze and share its data. Data commons provide a research community with the ability to manage and analyze large datasets using the elastic scalability provided by cloud computing and to share data securely and compliantly, and, in this way, accelerate the pace of research. Over the past decade, a number of data commons have been developed and we discuss some of the lessons learned from this effort.”

Ten (not so) simple rules for clinical trial data-sharing | PLOS Computational Biology

Abstract:  Clinical trial data-sharing is seen as an imperative for research integrity and is becoming increasingly encouraged or even required by funders, journals, and other stakeholders. However, early experiences with data-sharing have been disappointing because they are not always conducted properly. Health data is indeed sensitive and not always easy to share in a responsible way. We propose 10 rules for researchers wishing to share their data. These rules cover the majority of elements to be considered in order to start the commendable process of clinical trial data-sharing:

Rule 1: Abide by local legal and regulatory data protection requirements
Rule 2: Anticipate the possibility of clinical trial data-sharing before obtaining funding
Rule 3: Declare your intent to share data in the registration step
Rule 4: Involve research participants
Rule 5: Determine the method of data access
Rule 6: Remember there are several other elements to share
Rule 7: Do not proceed alone
Rule 8: Deploy optimal data management to ensure that the data shared is useful
Rule 9: Minimize risks
Rule 10: Strive for excellence.

Data Sharing Policy Development Guidelines | ARDC

“Read our guide to developing a data sharing policy (DSP), which can be crucial to good data management and the data governance framework for your research project.

Our Data Sharing Policy Development Guidelines provide information and guidance on developing a data sharing policy (DSP), a policy written by an organisation or a group to address: 

the types of data the agencies have in their custody 
the principles and strategy around the sharing of that data 
the governance and management procedures that should be followed when that data is being shared. 

It lays out, in general terms, how that organisation or group will respond to requests to share the data.

A data sharing policy should be agreed on whenever there is a need or an intention to share data created by a research project or projects. The policy ensures that all data requests are handled consistently and appropriately. It also ensures that both the data holder and potential data requesters understand the process of making and assessing requests.

These guidelines discuss the ‘how’ and ‘what’ of developing a data sharing policy, specifically how one can be created and what components it usually has….”

How to organise a successful Open Data Day event – a complete guide! – Open Knowledge Foundation blog


Open Data Day (ODD) is the annual celebration of open data all over the world, where groups from around the world create local events to promote open data or use open data in their communities. 2023 Open Data Day(s) will take place from 4th to 10th March.

ODD usually sees thousands of people getting together at hundreds of events organised by communities. The Open Knowledge Foundation (OKFN) has been helping to organise and maintain it to foster an open data community.

Organising the event itself is a not so easy task and achieving a greater impact from the organised event is more difficult. So, to ease the process and guide the community to organise a successful ODD event, we have compiled the following resources that can be helpful for everyone.

This blog will highlight how you can host/organise/run a successful ODD event in your country by making the best utilisation of the available resources….”

OASPA and DOAJ Announce the Launch of an Open Access Journals Toolkit

The Open Access Scholarly Publishing Association (OASPA) and the Directory of Open Access Journals (DOAJ) are pleased to announce the forthcoming launch of the Open Access (OA) Journals Toolkit, scheduled for launch in the second half of 2023. Research Consulting is supporting them in managing the Toolkit development process as well as in liaising with an expert Editorial Board.

Navigating Responsible Research Assessment Guidelines – Leiden Madtrics

“Responsible Research Assessment is discussed and used in many contexts. However, Responsible Research Assessment does not have a unifying definition, and likewise its guidelines indicate that the implementation of Responsible Research Assessment can have many different scopes.

Research assessment has a long history continuously introducing new methods, tools, and agendas, for example, peer review of publications dating back to 17th century and catalogues from the 19th century that facilitated publication counting. This blog post discusses Responsible Research Assessment (RRA), an agenda gaining attention today. The blog post gives an introduction to RRA and discusses how to navigate RRA guidelines, which can be a complex task….”

Open Access Agreements: Factors to Consider – SPARC

“This document is intended to provide an overview of questions to ask and factors to consider when evaluating potential investments in open access (OA). 

When evaluating an offer from a publisher that incorporates an open access component with a subscription offer. This might include offers for read-and-publish/publish-and-read agreements or tiered membership models.
When evaluating an OA membership model that provides your institution’s authors with a discount on [or removal of] article processing charges (APCs).

There are many other models for open access transformations. Many of the same principles described in this document would apply to evaluating those offers. 

You can refer to OA analysis data sources for information on tools available for gathering this data.

In evaluating any OA offer, one must remember that collections decisions are based on many factors. Each subscription must be considered within the institution’s entire collections portfolio. This document addresses questions specific to agreements that include some sort of OA component….”

Harvard Library Responds to the NIH Data Management and Sharing Policy | STAFF PORTAL

“Beginning with the first funding deadlines in January, all NIH grant proposals will be required to include a formal, two-page Data Management and Sharing Plan (DMSP), which must include the following elements….

Crucially, in addition to adding a required DMSP, the data management strategies stated in the plan will be audited and monitored externally, and compliance with stated plans may affect the funding status of grants.


Fortunately, here at Harvard affiliates have access to a variety of computing infrastructure and systems to effectively manage and steward a wide range of research outputs associated with modern, data-driven, computational research.

Harvard’s libraries, Harvard University Information Technology (HUIT), Research Computing, and Sponsored Programs offices have all been adding services and building capacity to support researchers complying with this new policy next year.

In the resources section below, we’ve included links to an executive summary of the policy and a collection of FAQs that we created specifically for Harvard users. We’ve also included resources from the NIH designed to support researchers writing and implementing a DMSP for the 2023 funding cycles.

Along with the requirement to make research data publicly available, in its new policy the NIH strongly encourages the use of established data repositories. When selecting an appropriate repository, researchers should plan to utilize subject- or domain-specific repositories for their data types if possible. When a disciplinary repository does not exist, researchers should use generalist repositories that accept all data types. We’ve included information on Harvard Dataverse and other generalist repositories in the resources section below….”

Where to search for research journal literature – some common errors I see on choice of sources (I) | Aaron Tay’s Musings about librarianship

“As academic librarians helping early-stage researchers (Masters, Phds students), we are often asked to provide guidance on the literature review process in one shot classes. One thing we tend to focus on during such sessions is the keyword search technique, though many of us also cover alternative to keyword techniques like citation searching, starting off with review articles etc.

It seems to me though there are limits to what we can help for keyword searching in one shot classes, since the audience will all be working in varied topics (most commonly they may not even have a good idea of what they are looking for) and as much as we can give general advice on the use of keywords at the end of day the user has to do a lot of their own practice via iterated searching (unless this is an area where the librarian had prior experience working in)

One thing though I have been thinking about increasingly is to talk about WHERE to search.  

Compared to twenty or even ten years ago, the number of academic databases, academic search engines and other tools available to search has increased exponentially even if you focus only on those that work via keyword searching….”

How to be FAIR with your data

“This handbook was written and edited by a group of about 40 collaborators in a series of six book sprints that took place between 1 and 10 June 2021. It aims to support higher education institutions with the practical implementation of content relating to the FAIR principles in their curricula, while also aiding teaching by providing practical material, such as competence profiles, learning outcomes, lesson plans, and supporting information. It incorporates community feedback received during the public consultation which ran from 27 July to 12 September 2021.”


Open Science Toolkit | UNESCO

“The UNESCO Open Science Toolkit is designed to support implementation of the UNESCO Recommendation on Open Science. The Toolkit is a set of guides, policy briefs, factsheets and indexes. Each piece is a living resource updated to reflect new developments and the status of implementation of the Recommendation. Elements of this toolkit are developed in collaboration with UNESCO Open Science partners or through discussions with and inputs from the members of the UNESCO Working Groups on Open Science….”


Ten simple rules for implementing open and reproducible research practices after attending a training course | PLOS Computational Biology

Abstract:  Open, reproducible, and replicable research practices are a fundamental part of science. Training is often organized on a grassroots level, offered by early career researchers, for early career researchers. Buffet style courses that cover many topics can inspire participants to try new things; however, they can also be overwhelming. Participants who want to implement new practices may not know where to start once they return to their research team. We describe ten simple rules to guide participants of relevant training courses in implementing robust research practices in their own projects, once they return to their research group. This includes (1) prioritizing and planning which practices to implement, which involves obtaining support and convincing others involved in the research project of the added value of implementing new practices; (2) managing problems that arise during implementation; and (3) making reproducible research and open science practices an integral part of a future research career. We also outline strategies that course organizers can use to prepare participants for implementation and support them during this process.