Where Did the Open Access Movement Go Wrong?


An Interview with Richard Poynder by Richard Anderson: “…Open access was intended to solve three problems that have long blighted scholarly communication – the problems of accessibilityaffordability, and equity. 20+ years after the Budapest Open Access Initiative (BOAI) we can see that the movement has signally failed to solve the latter two problems. And with the geopolitical situation deteriorating solving the accessibility problem now also looks to be at risk. The OA dream of “universal open access” remains a dream and seems likely to remain one.

What has been the essence of the OA movement’s failure?

The fundamental problem was that OA advocates did not take ownership of their own movement. They failed, for instance, to establish a central organization (an OA foundation, if you like) in order to organize and better manage the movement; and they failed to publish a single, canonical definition of open access. This is in contrast to the open source movement, and is an omission I drew attention to in 2006

This failure to take ownership saw responsibility for OA pass to organizations whose interests are not necessarily in sync with the objectives of the movement.

It did not help that the BOAI definition failed to specify that to be classified as open access, scholarly works needed to be made freely available immediately on publication and that they should remain freely available in perpetuity. Nor did it give sufficient thought to how OA would be funded (and OA advocates still fail to do that).

This allowed publishers to co-opt OA for their own purposes, most notably by introducing embargoes and developing the pay-to-publish gold OA model, with its now infamous article processing charge (APC).

Pay-to-publish OA is now the dominant form of open access and looks set to increase the cost of scholarly publishing and so worsen the affordability problem. Amongst other things, this has disenfranchised unfunded researchers and those based in the global south (notwithstanding APC waiver promises).

What also did not help is that OA advocates passed responsibility for open access over to universities and funders. This was contradictory, because OA was conceived as something that researchers would opt into. The assumption was that once the benefits of open access were explained to them, researchers would voluntarily embrace it – primarily by self-archiving their research in institutional or preprint repositories. But while many researchers were willing to sign petitions in support of open access, few (outside disciplines like physics) proved willing to practice it voluntarily.

In response to this lack of engagement, OA advocates began to petition universities, funders, and governments to introduce OA policies recommending that researchers make their papers open access. When these policies also failed to have the desired effect, OA advocates demanded their colleagues be forced to make their work OA by means of mandates requiring them to do so.

Most universities and funders (certainly in the global north) responded positively to these calls, in the belief that open access would increase the pace of scientific development and allow them to present themselves as forward-thinking, future-embracing organizations. Essentially, they saw it as a way of improving productivity and ROI while enhancing their public image.

While many researchers were willing to sign petitions in support of open access, few proved willing to practice it voluntarily.

But in light of researchers’ continued reluctance to make their works open access, universities and funders began to introduce increasingly bureaucratic rules, sanctions, and reporting tools to ensure compliance, and to manage the more complex billing arrangements that OA has introduced.

So, what had been conceived as a bottom-up movement founded on principles of voluntarism morphed into a top-down system of command and control, and open access evolved into an oppressive bureaucratic process that has failed to address either the affordability or equity problems. And as the process, and the rules around that process, have become ever more complex and oppressive, researchers have tended to become alienated from open access.

As a side benefit for universities and funders OA has allowed them to better micromanage their faculty and fundees, and to monitor their publishing activities in ways not previously possible. This has served to further proletarianize researchers and today they are becoming the academic equivalent of workers on an assembly line. Philip Mirowski has predicted that open access will lead to the deskilling of academic labor. The arrival of generative AI might seem to make that outcome the more likely…

Can these failures be remedied by means of an OA reset? With this aim in mind (and aware of the failures of the movement), OA advocates are now devoting much of their energy to trying to persuade universities, funders, and philanthropists to invest in a network of alternative nonprofit open infrastructures. They envisage these being publicly owned and focused on facilitating a flowering of new diamond OA journals, preprint servers, and Publish, Review, Curate (PRC) initiatives. In the process, they expect commercial publishers will be marginalized and eventually dislodged.

But it is highly unlikely that the large sums of money that would be needed to create these alternative infrastructures will be forthcoming, certainly not at sufficient levels or on anything other than a temporary basis.

While it is true that more papers and preprints are being published open access each year, I am not convinced this is taking us down the road to universal open access, or that there is a global commitment to open access.

Consequently, I do not believe that a meaningful reset is possible: open access has reached an impasse and there is no obvious way forward that could see the objectives of the OA movement fulfilled.

Partly for this reason, we are seeing attempts to rebrand, reinterpret, and/or reimagine open access and its objectives…(More)”.

Open Data Commons Licences (ODCL): Licensing personal and non personal data supporting the commons and privacy


Paper by Yaniv Benhamou and Melanie Dulong de Rosnay: “Data are often subject to a multitude of rights (e.g. original works or personal data posted on social media, or collected through captcha, subject to copyright, database and data protection) and voluntary shared through non standardized, non interoperable contractual terms. This leads to fragmented legal regimes and has become an even major challenge in the AI-era, for example when online platforms set their own Terms of Services (ToS), in business-to-consumer relationship (B2C).

This article proposes standard terms that may apply to all kind of data (including personal and mixed datasets subject to different legal regimes) based on the open data philosophy initially developed for Free and Open Source software and Creative Commons licenses for artistic and other copyrighted works. In a first part, we analyse how to extend open standard terms to all kinds of data (II). In a second part, we suggest to combine these open standard terms with collective governance instruments, in particular data trust, inspired by commons-based projects and by the centennial collective management of copyright (III). In a last part, after few concluding remarks (IV), we propose a template “Open Data Commons Licenses“ (ODCL) combining compulsory and optional elements to be selected by licensors, illustrated by pictograms and icons inspired by the bricks of Creative Commons licences and legal design techniques (V).

This proposal addresses the bargaining power imbalance and information asymmetry (by offering the licensor the ability to decide the terms), and conceptualises contract law differently. It reverses the current logic of contract: instead of letting companies (licensees) impose their own ToS to the users (licensors, being the copyright owner, data subject, data producer), licensors will reclaim the ability to set their own terms for access and use of data, by selecting standard terms. This should also allow the management of complex datasets, increase data sharing, and improve trust and control over the data. Like previous open licencing standards, the model is expected to lower the transaction costs by reducing the need to develop and read new complicated contractual terms. It can also spread the virality of open data to all data in an AI-era, if any input data under such terms used for AI training purposes propagates its conditions to all aggregated and output data. In other words, any data distributed under our ODCL template will turn all outcome into more or less open data and foster a data common ecosystem. Finally, instead of full openness, our model allows for restrictions outside of certain boundaries (e.g. authorized users and uses), in order to protect the commons and certain values. The model would require to be governed and monitored by a collective data trust…(More)”.

How to make data open? Stop overlooking librarians


Article by Jessica Farrell: “The ‘Year of Open Science’, as declared by the US Office of Science and Technology Policy (OSTP), is now wrapping up. This followed an August 2022 memo from OSTP acting director Alondra Nelson, which mandated that data and peer-reviewed publications from federally funded research should be made freely accessible by the end of 2025. Federal agencies are required to publish full plans for the switch by the end of 2024.

But the specifics of how data will be preserved and made publicly available are far from being nailed down. I worked in archives for ten years and now facilitate two digital-archiving communities, the Software Preservation Network and BitCurator Consortium, at Educopia in Atlanta, Georgia. The expertise of people such as myself is often overlooked. More open-science projects need to integrate digital archivists and librarians, to capitalize on the tools and approaches that we have already created to make knowledge accessible and open to the public.How to make your scientific data accessible, discoverable and useful

Making data open and ‘FAIR’ — findable, accessible, interoperable and reusable — poses technical, legal, organizational and financial questions. How can organizations best coordinate to ensure universal access to disparate data? Who will do that work? How can we ensure that the data remain open long after grant funding runs dry?

Many archivists agree that technical questions are the most solvable, given enough funding to cover the labour involved. But they are nonetheless complex. Ideally, any open research should be testable for reproducibility, but re-running scripts or procedures might not be possible unless all of the required coding libraries and environments used to analyse the data have also been preserved. Besides the contents of spreadsheets and databases, scientific-research data can include 2D or 3D images, audio, video, websites and other digital media, all in a variety of formats. Some of these might be accessible only with proprietary or outdated software…(More)”.

Open data ecosystems: what models to co-create service innovations in smart cities?


Paper by Arthur Sarazin: “While smart cities are recently providing open data, how to organise the collective creation of data, knowledge and related products and services produced from this collective resource, still remains to be thought. This paper aims at gathering the literature review on open data ecosystems to tackle the following research question: what models can be imagined to stimulate the collective co-creation of services between smart cities’ stakeholders acting as providers and users of open data? Such issue is currently at stake in many municipalities such as Lisbon which decided to position itself as a platform (O’Reilly, 2010) in the local digital ecosystem. With the implementation of its City Operation Center (COI), Lisbon’s municipality provides an Information Infrastructure (Bowker et al., 2009) to many different types of actors such as telecom companies, municipalities, energy utilities or transport companies. Through this infrastructure, Lisbon encourages such actors to gather, integrate and release heterogeneous datasets and tries to orchestrate synergies among them so data-driven solution to urban problems can emerge (Carvalho and Vale, 2018). The remaining question being: what models for the municipalities such as Lisbon to lean on so as to drive this cutting-edge type of service innovation?…(More)”.

The Oligopoly’s Shift to Open Access. How the Big Five Academic Publishers Profit from Article Processing Charges 


Paper by Leigh-Ann Butler et al: “This study aims to estimate the total amount of article processing charges (APCs) paid to publish open access (OA) in journals controlled by the five large commercial publishers Elsevier, Sage, Springer-Nature, Taylor & Francis and Wiley between 2015 and 2018. Using publication data from WoS, OA status from Unpaywall and annual APC prices from open datasets and historical fees retrieved via the Internet Archive Wayback Machine, we estimate that globally authors paid $1.06 billion in publication fees to these publishers from 2015–2018. Revenue from gold OA amounted to $612.5 million, while $448.3 million was obtained for publishing OA in hybrid journals. Among the five publishers, Springer-Nature made the most revenue from OA ($589.7 million), followed by Elsevier ($221.4 million), Wiley ($114.3 million), Taylor & Francis ($76.8 million) and Sage ($31.6 million). With Elsevier and Wiley making most of APC revenue from hybrid fees and others focusing on gold, different OA strategies could be observed between publishers…(More)”.This study aims to estimate the total amount of article processing charges (APCs) paid to publish open access (OA) in journals controlled by the five large commercial publishers Elsevier, Sage, Springer-Nature, Taylor & Francis and Wiley between 2015 and 2018. Using publication data from WoS, OA status from Unpaywall and annual APC prices from open datasets and historical fees retrieved via the Internet Archive Wayback Machine, we estimate that globally authors paid $1.06 billion in publication fees to these publishers from 2015–2018. Revenue from gold OA amounted to $612.5 million, while $448.3 million was obtained for publishing OA in hybrid journals. Among the five publishers, Springer-Nature made the most revenue from OA ($589.7 million), followed by Elsevier ($221.4 million), Wiley ($114.3 million), Taylor & Francis ($76.8 million) and Sage ($31.6 million). With Elsevier and Wiley making most of APC revenue from hybrid fees and others focusing on gold, different OA strategies could be observed between publishers.

The State of Open Data 2023


Report by Springer Nature, Digital Science and Figshare: “The 2023 survey showed that the key motivations for researchers to share their data remain very similar to previous years, with full citation of research papers or a data citation ranking highly. 89% of respondents also said they make their data available publicly, however almost three quarters of respondents had never received support with planning, managing or sharing research data.

One size does not fit all: Variations in responses from different areas of expertise and geographies highlight a need for a more nuanced approach to research data management support globally. For example, 64% of respondents supported the idea of a national mandate for making research data openly available, with Indian and German respondents more likely to support this idea (both 71%).

Credit is an ongoing issue: For eight years running, our survey has revealed a recurring concern among researchers: the perception that they don’t receive sufficient recognition for openly sharing their data. 60% of respondents said they receive too little credit for sharing their data.

AI awareness hasn’t translated to action: For the first time, this year we asked survey respondents to indicate if they were using ChatGPT or similar AI tools for data collection, data processing and metadata collection. The most common response to all three questions was ‘I’m aware of these tools but haven’t considered it.’..(More)”.

Open-access reformers launch next bold publishing plan


Article by Layal Liverpool: “The group behind the radical open-access initiative Plan S has announced its next big plan to shake up research publishing — and this one could be bolder than the first. It wants all versions of an article and its associated peer-review reports to be published openly from the outset, without authors paying any fees, and for authors, rather than publishers, to decide when and where to first publish their work.

The group of influential funding agencies, called cOAlition S, has over the past five years already caused upheaval in the scholarly publishing world by pressuring more journals to allow immediate open-access publishing. Its new proposal, prepared by a working group of publishing specialists and released on 31 October, puts forward an even broader transformation in the dissemination of research.

It outlines a future “community-based” and “scholar-led” open-research communication system (see go.nature.com/45zyjh) in which publishers are no longer gatekeepers that reject submitted work or determine first publication dates. Instead, authors would decide when and where to publish the initial accounts of their findings, both before and after peer review. Publishers would become service providers, paid to conduct processes such as copy-editing, typesetting and handling manuscript submissions…(More)”.

Urban Development and the State of Open Data


Chapter by Stefaan G. Verhulst and Sampriti Saxena: “Nearly 4.4 billion people, or about 55% of the world’s population, lived in cities in 2018. By 2045, this number is anticipated to grow to 6 billion. Such level of growth requires innovative and targeted urban solutions. By more effectively leveraging open data, cities can meet the needs of an ever-growing population in an effective and sustainable manner. This paper updates the previous contribution by Jean-Noé Landry, titled “Open Data and Urban Development” in the 2019 edition of The State of Open Data. It also aims to contribute to a further deepening of the Third Wave of Open Data, which highlights the significance of open data at the subnational level as a more direct and immediate response to the on-the-ground needs of citizens. It considers recent developments in how the use of, and approach to, open data has evolved within an urban development context. It seeks to discuss emerging applications of open data in cities, recent developments in open data infrastructure, governance and policies related to open data, and the future outlook of the role of open data in urbanization…(More)”.

Open: A Pan-ideological Panacea, a Free Floating Signifier


Paper by Andrea Liu: “Open” is a word that originated from FOSS (Free and Open Software movement) to mean a Commons-based, non-proprietary form of computer software development (Linux, Apache) based on a decentralized, poly-hierarchical, distributed labor model. But the word “open” has now acquired an unnerving over-elasticity, a word that means so many things that at times it appears meaningless. This essay is a rhetorical analysis (if not a deconstruction) of how the term “open” functions in digital culture, the promiscuity (if not gratuitousness) with which the term “open” is utilized in the wider society, and the sometimes blatantly contradictory ideologies a indiscriminately lumped together under this word…(More)”

Data Sandboxes: Managing the Open Data Spectrum


Primer by Uma Kalkar, Sampriti Saxena, and Stefaan Verhulst: “Opening up data offers opportunities to enhance governance, elevate public and private services, empower individuals, and bolster public well-being. However, achieving the delicate balance between open data access and the responsible use of sensitive and valuable information presents complex challenges. Data sandboxes are an emerging approach to balancing these needs.

In this white paper, The GovLab seeks to answer the following questions surrounding data sandboxes: What are data sandboxes? How can data sandboxes empower decision-makers to unlock the potential of open data while maintaining the necessary safeguards for data privacy and security? Can data sandboxes help decision-makers overcome barriers to data access and promote purposeful, informed data (re-)use?

The six characteristics of a data sandbox. Image by The GovLab.

After evaluating a series of case studies, we identified the following key findings:

  • Data sandboxes present six unique characteristics that make them a strong tool for facilitating open data and data re-use. These six characteristics are: controlled, secure, multi-sectoral and collaborative, high computing environments, temporal in nature, adaptable, and scalable.
  • Data sandboxes can be used for: pre-engagement assessment, data mesh enablement, rapid prototyping, familiarization, quality and privacy assurance, experimentation and ideation, white labeling and minimization, and maturing data insights.
  • There are many benefits to implementing data sandboxes. We found ten value propositions, such as: decreasing risk in accessing more sensitive data; enhancing data capacity; and fostering greater experimentation and innovation, to name a few.
  • When looking to implement a data sandbox, decision-makers should consider how they will attract and obtain high-quality, relevant data, keep the data fresh for accurate re-use, manage risks of data (re-)use, and translate and scale up sandbox solutions in real markets.
  • Advances in the use of the Internet of Things and Privacy Enhancing Technologies could help improve the creation, preparation, analysis, and security of data in a data sandbox. The development of these technologies, in parallel with European legislative measures such as the Digital Markets Act, the Data Act and the Data Governance Act, can improve the way data is unlocked in a data sandbox, improving trust and encouraging data (re-)use initiatives…(More)” (FULL PRIMER)”