Good intentions can pave many roads.

Can I agree with some of the premises and goals of the proposal? Sure, but “let’s fuel progress by making the scholarly record harder to find, with more burden on the author, with less obvious signals of validation and worse metadata,” said no one, ever.

If enacted, the Plan S proposal would place enormous new burdens on authors. Not only would they need to agree upon and set the standards of quality control, but in “publishing” all scholarly outputs immediately and openly, authors would be forced not only to take responsibility for that quality control but also for the application of interoperable metadata needed to enable each and every output to be discoverable and connected to final results.

A recent MEDLINE data quality assessment by some of my colleagues identified serious challenges in MEDLINE’s records. Would the Plan S proposal ameliorate or exacerbate challenges like this? Will it lead to greater discovery, increased linkage of articles and data, and greater usage and impact for authors? Traditional publishing outlets expend enormous resources on this and, while it has never been easier to disseminate content online, it also has never been harder for materials to be noticed and linked to, e.g., identities, grants, and institutions.

As mentioned in a Scholarly Kitchen post that I co-wrote in May with my colleagues Jamie Carmichael and Jessica Thibodeau, “[s]cholarly research is complex and interconnected; change in one area can spark improvement or deterioration throughout the ecosystem (emphasis added).” In the transition to open science, stakeholders across the ecosystem acknowledge that authors should not have to shoulder an open access administrative burden that takes time away from their actual research. The scholar-led proposal from Plan S seems to overlook this, which is one of the few things that institutions, researchers, funders, and publishers seem to agree upon. Bluntly, being a great researcher does not inherently make you an adept publisher.

Additionally, the proposal’s call for the community — including service providers — to supply open tools and commit funds to sustain the model with little to no return-on-investment is unsustainable. While well-intentioned, this type of approach is a recipe for failure. Adding links, building metadata bridges, and enriching records have real costs that require significant amounts of money. With the required tools already available, a better approach is to invest in their adoption, not to duplicate work by creating new ones.

Scholarly communications are important, which is why its participants have such strong views. Getting communications wrong has real-world consequences, many of which are unintended. Let’s acknowledge this and work together to solve the challenges that we can actually solve without adding complications and entropy.

The post Roy Kaufman on cOAlition S’s “Towards Responsible Publishing” appeared first on Copyright Clearance Center.

This saves knowledge workers and life science teams valuable time and resources in locating the latest clinical trial data alongside other critical content.

RightFind Navigate customers already find information about U.S. clinical trials through the connector to ClinicalTrials.gov. The new connector expands users’ access to this data from around the world.

“Researchers need easier ways to access the latest clinical evidence and identify trends within the data for a multitude of use cases across the drug development lifecycle,” said Tracey Armstrong, President and CEO, CCC. “Typically, researchers visit multiple clinical trial websites to find information, then manually collect the data. The Clinical Trial Registries Data Connector in RightFind Navigate brings together data from multiple clinical trial registries with other types of content, including scientific literature, to enable knowledge workers to find and explore the information they need in one place.”

CCC helps companies manage vast amounts of published content and proprietary internal information and data. As experts in processing data from thousands of sources across a diverse set of industries, CCC has established a proven record of working with a wide range of companies, including content providers and technology partners, to unify data sources and make content more discoverable.

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Pharmaceutical and life science companies are dealing with tremendous growth in available information and an increasingly diverse range of sources that make it challenging to maintain a comprehensive and up-to-date understanding on key research advancements. RightFind Navigate’s machine-learning driven semantic enrichment and advanced personalization features help users locate the most relevant information in scientific literature, clinical trials, patents, and many other sources. This pilot expands the data intelligence already available in RightFind Navigate with the introduction of disambiguated researcher and institution profiles.

RightFind Navigate is part of the RightFind Suite, which provides a single integrated and scalable solution to meet the evolving needs of today’s research organizations. RightFind Navigate unifies searching across multiple licensed content sources, publicly available data, and internal proprietary content, empowering researchers to reveal connections and drive innovation. The solution provides a flexible, scalable, open ecosystem designed to maximize an organizations’ return on their content and data investments.

“Many of our RightFind customers already enjoy the benefits of semantic search and the breadth of information sources that are available in RightFind Navigate,” said Tracey Armstrong, President and CEO, CCC. “We are excited to pilot the introduction of research and institution profiles that allow users to see the web of connections between the various entities engaged in a particular research field, whether these are researchers in corporations or in academia, and with whom they are affiliated.”

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A key characteristic that truly FAIR data have is that they are machine-readable and machine-interpretable. So, it is reasonable to expect that an organization can maximize the benefits of recent advances in artificial intelligence (AI) if inputs to AI systems consist of FAIR data. It is also important to acknowledge that there isn’t a single AI system or a single algorithm that can “magically” understand what the data means if it is not explicitly expressed. AI systems can help us to increasingly extract value from our data if there is value in the data! With the explosion of new AI services, the adoption of FAIR data principles is paramount.

R&D teams today collaborate across life science to overcome hurdles to innovation and help advance the discovery and delivery of treatments and this collaboration includes the implementation of FAIR principles.  For example, numerous projects of the Pistoia Alliance today make use of FAIR data principles and aim at facilitating their implementation (e.g., the IDMP ontology project). In addition to such self-governing collaborative efforts, international policymakers such as the G20/G7 and the OECD committee for scientific and technological policy have publicly endorsed the implementation of FAIR principles.

According to a recent article by Sansone et al. (FAIR: Making Data AI-Ready): “Turning FAIR into reality requires new technological and social infrastructure, as well as cultural and policy changes, supported by educational and training elements that target not just researchers but all stakeholders involved in the data life cycle: from developers, service providers, librarians, journal publishers, funders, societies in the academic as well as in the commercial and governmental setting.”

CCC, in partnership with the GO FAIR Foundation, will host an in-person FAIR Forum on “The Evolving Role of Data in the AI Era” on 18 September at Poortgebouw, the University of Leiden, the Netherlands. This one-day forum will provide leaders in research-intensive businesses with expert insights on the importance of FAIR data to successful AI initiatives and best practices for FAIR data implementations. Speakers will address critical topics such as:

• The vital importance of FAIR data in strategic AI initiatives.
• Using FAIR data to improve efficiency and drive innovation.
• Evangelizing the FAIR Data Principles and identifying practical steps to implementation.
• The important role that information management professionals can have in making the FAIR data principles a reality.

For a complete agenda, click here. To register, click here.

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CCC’s Corporate Solutions Director Kevin Barrett welcomes Jamie Hullinger, solo corporate librarian of global medical technology leader Zimmer Biomet, for an informative 30-minute Q & A.

View the recording here.

Accelerating the flow of research to drive discovery and innovation is a common goal for information professionals in R&D-intensive organizations of all sizes.  So, whether operating as an information team of one or many, it is essential to have a well-designed information management strategy – one that delivers fast, secure, and compliant access to content, maximizes the value of your content resources, promotes collaboration in a wide range of workflows, and simplifies copyright compliance.

Today’s information managers, however, are also challenged with delivering greater organizational value and meeting the evolving needs of content users across the company.

From her role as corporate librarian, as well as current president of SLA’s Solo Librarian Community, she provides valuable insights on how she is:

• Over-coming the challenges of solo librarianship in an R&D-intensive company to deliver greater insights and organizational value
• Applying information manager best practices for navigating and preparing for change
• Implementing steps and tools to easily scale information management as the company grows

Jamie has been the corporate librarian for Zimmer Biomet, an orthopedic MedTech company in Indiana, since 2018.  She received her Masters in Library and Information Science from the University of Southern Mississippi in 2013. Before turning to Special Librarianship, Jamie got her start in public libraries at sixteen shelving books and working her way up to Youth Services Manager.

Kevin is CCC’s Corporate Solutions Director, responsible for content management software & services including semantic search & enrichment, collaboration, and reference management within the RightFind Suite. He also focuses on market research, participating in product roundtables & industry forums, and collecting direct feedback to keep CCC’s solutions aligned with clients’ changing needs.

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A May 10, 2023, CCC Town Hall panel shared best practices for developing research data that is FAIR through culture, training, and technology. The discussion also detailed how FAIR data saves lives, saves money, and drives confidence in science on four continents.

The LinkedIn Live special event included George Strawn, a scholar at the US National Academies of Sciences, Engineering, and Medicine who served as CIO with the National Science Foundation from 2003 to 2009. Strawn joined the roundtable discussion with reflections on a half-century of internet evolution and the transformative role of FAIR data in the future.

Click below to listen to the latest episode of the Velocity of Content podcast.

In his remarks, Strawn identified three steps in the evolution of computing. The first step came after World War II with the ENIAC and UNIVAC, the first programmable digital computers that were enormous standalone machines. Then came the internetworking era that began in the 1970s, when Strawn was beginning his career as a computer scientist at the University of Iowa. Now in 2023, he reflected on the new era in computing that FAIR data will bring.

“The first wonderful step was computers themselves. The second, equally important step has been the internet age of interconnected computers,” Strawn told me. “I think FAIR is ushering in the third phase of information technology, which is interoperable data as well as interconnected computers.”

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Bear with me while I torture an analogy between an actual lake and the training corpus (“data lake”) of an artificial intelligence (AI) system.

You are hiking on a hot day in an area neither remote nor urban. You come upon a lake that has a self-service canoe rental. You are hot, thirsty, and enjoy canoeing. You cannot see into the lake so you do not know if it contains plants, fish, chemicals, or something worse. Still, the water looks clean. There is no one to ask and no way to test the water chemically. Are you willing to (a) drink the water, (b) swim in the water, (c) canoe on the water, or (d) none of the above?

The water might be perfect, pristine, and chemical free, but you need to know this for certain before you will drink from it. You might risk swimming if the water looks clear, smells OK, and is not near an industrial plant. You may keep your mouth closed — and maybe your eyes. And if you think it may be filled with toxic sludge you might not even risk getting splashed while in your canoe.

What does this have to do with AI? While generative AI such as ChatGPT gets all the buzz, AI in other, typically more targeted forms, has been used for years to solve business and research challenges. The utility of AI and the value to businesses is directly related to both the quality of inputs and information about what inputs are used. Even when an AI service is trained on high quality content, without proper documentation and audit trail, users cannot be sure what they are using and may stay away.

When thinking about AI, I like to borrow a rubric from academic assessments; namely high stakes vs. low stakes. In that context, high stakes means that the outcome itself will be used for an important decision (e.g., an Advanced Placement test), while low stakes means that it has value as part of something else (e.g., a practice test to help a teacher know what to emphasize in class).

When using AI in high stakes decision making, you want to know that your training corpus (i.e., the “lake” in our analogy) is pristine and you need to know what is in it. For a pharmaceutical company using AI for decision-making research purposes, the training corpus should be comprised of final Versions of Record (VoR). The researcher needs to know that the corpus excludes unwanted content, such as content sourced from predatory journals and/or “junk” science, for example.

In a low stakes environment there can be a higher tolerance for ambiguity. The same pharmaceutical company researcher may need to simply identify potential experts in a field, which would require a less pristine training corpus; preprints can be included and perhaps even a little “junk” science may be acceptable. But the key point is this: unless the AI service provider has disclosed in writing what is included in the training corpus, that corpus will never be able to make the jump to high stakes applications. It’s OK for swimming, but not drinking.

While there can be some value even in polluted lakes, there is a point at which there is too much pollution for most uses. Bias in AI, including racial bias, is well documented. In order to combat this, responsible governments and governmental organizations are moving to regulate AI, focusing on issues such as ethical use and transparency. For example, the OECD’s ethical AI principles include: “AI Actors should commit to transparency and responsible disclosure regarding AI systems. To this end, they should provide meaningful information, appropriate to the context, and consistent with the state of art….”

To some degree, the market should solve transparency. A company using AI in hiring decisions risks lawsuits if that AI was trained on racially-biased data. A company using AI for autonomous flight… well, I shouldn’t have to explain what could go wrong there. Many AI systems, such as ChatGPT, are based on large language models and can be unreliable due to “hallucinations,” as such, they are not fit for purpose for high stakes use. And while in-context learning currently shows promise for reducing the amount of data needed for retraining, high-stakes uses will still need higher value, transparently documented content for training the large language models themselves.

While large data sets scraping the web (often without consent) are all the rage, the use of high quality, documented data will be important in advancing science. The best decisions are made on the best (possible) data. Check for posted notices about water quality.

The post Swimming in the AI Data Lake: Why Disclosure and Versions of Record Are More Important than Ever appeared first on Copyright Clearance Center.

Moderated by Jonathan Clark, the panel featured highly experienced leaders in the standards development organization (SDO) community, including Joan O’Neil, Chief Knowledge Officer of ICC; Hans Arne Rykkelid, CEO of Standards Digital AS; Leslie McKay, Senior Manager of Digital and Information Products at SAE International; Silona Bonewald, Executive Director at IEEE SA OPEN; Simon Powell, Director of Product at BSI; and Ivan Salcedo, Director at Quix Innovation Services.

With a wide set of perspectives on what constitutes business models and standards, these experts shared their individual views on how to best support new user demands for more efficient and effective ways of working with standards while providing insight into innovative, sustainable solutions that work for everyone. The conversation was timely, especially as the standards sector undergoes massive digital transformation and new business models continue to emerge.

To kick off the conversation, our panelists touched on the challenges each has experienced within their organizations around delivering standards to customers in XML format and the opportunities they see as a result.

Hans Arne shared that while Standards Digital AS is not currently distributing XML to customers directly, the organization is planning to launch an updated platform to enable software integrators to ingest data sets and other types of codified information more easily through an improved subscription model and API approach. The organization is supporting an interactive HTML reading experience pulled directly from the XML format to improve searchability and filtering. No matter the vehicle, Hans Arne stressed the importance of ensuring granular content is understood in the context in which it is written and as part of the whole. He discussed an innovative triangular subscription model to help avoid legal implications of misuse.

Joan explained ICC is investing heavily in an internal initiative to structure all commercial content in a uniform manner. The program will enable ICC to create content in XML without the need to go through a conversion process. This brings advantages to both the SDO and the customer – for the SDO, this provides the capability to publish content more quickly and cost effectively, while the customer benefits from a more precise, granular-level search function. In addition to internal infrastructure updates, Joan continued to express that better tagging improves search capabilities: 

“For standards content, one of the most valuable features we can deliver is that granular tagging for ease of search. [Customers] want to get to the answer very quickly. They want to reference that piece of content, whatever workflow they’re in.” – Joan O’Neil

Simon provided perspective from BSI, in which most of the content they publish is shared IP from others in the standards ecosystem. Given this environment, a majority of BSI’s distribution is conducted through  PDFs, which is considered to be the version of record. Simon noted that BSI is currently looking into partnership opportunities to provide fragments of content or metadata to support pilots in an effort to deliver better value to the end user.

Leslie discussed how the output of digital standards needs to be highly interoperable to communicate with different types of systems. This includes requirements tracking systems, model-based systems, engineering systems, manufacturing systems, and more. When taken out of context, embedded XML may not be very readable; striking a balance between output that is human- and machine-readable is necessary to achieve a full digital standard. In addition to the output format, Leslie noted that establishing definitions for different groups of standards is an important step to ensure they are interpreted in the same way as they become digitized.

As a self-described disruptor, Silona shared her views on SDOs’ continued dependence on publishing as the primary business model needs to change and why, as a result, her organization is experimenting with other revenue streams. 

“I like to think of it as I’ve got a big bowl of business revenue streams spaghetti that I periodically throw to the wall to see which is going to stick and how it’s going to stick.” – Silona Bonewald

To close out the conversation, Ivan provided an insightful summary of key trends driving sustainable business models.

For the full conversation, you can watch the session here. Discover how CCC can complement and extend revenue opportunities for SDOs and organizations that use standards here. 

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As scholarly communication rapidly adapts to seismic shifts in open science, technology, and culture, a renewed focus has emerged on metadata and persistent identifiers (PIDs) — about people, places, and objects — as an essential component of a vibrant industry. At the US policy level alone, leveraging metadata to accelerate industry transformation is a common theme across the Nelson Memo and recent Requests for Information from the NIH and the Department of Transportation.

Scholarly research is complex and interconnected; change in one area can spark improvement or deterioration throughout the ecosystem. By way of example, consider the role of PIDs in open access (OA) funding entitlements. OA management platforms rely on metadata elements, particularly organizational PIDs passed from upstream submission and peer review systems, to automate the process of matching manuscripts with potential funding sources. This typically happens at article acceptance, and increasingly at submission, eliminating manual administration for authors as well as supporting publishers, institutions, consortia, and funders in achieving OA at scale.

In order to perform a health check on organizational IDs, in 2021, we reviewed cross-publisher records of institutional affiliation and/or funder data in our OA workflow tool, RightsLink for Scientific Communications. We discovered that 82% of accepted manuscripts included such data, which was an improvement over prior years. However, these statistics masked an ugly truth; namely that in many cases those manuscripts used institutional email domains as a proxy for funding or discount eligibility instead of a PID. And within the 18% that carried no PID, missed funding opportunities created unnecessary work (and payments) for authors, institutions, and publishers to reconcile retroactively.

Even if CCC — either alone, or with its partners and publishers — were able to close these metadata gaps at acceptance of manuscripts, this is late in the process and advantages of PIDs earlier in the research lifecycle would be lost. Solving for metadata gaps would be more effective in upstream systems of record so the tail doesn’t wag the dog. This is precisely why we’ve encouraged the NIH to consider the grant application process as an early opportunity to mandate PIDs and cascade to other systems underpinning the research lifecycle, for example, Current Research Information Systems (CRISs).

But where to start? Let’s face it, PIDs are a wonky topic and we need to communicate to people who are not naturally interested in the intricacies of, e.g., ISNI and Ringgold. But these people will care if they know that lack of PIDs can lead to lack of funding. In order to break this down, we recently talked with dozens of stakeholders and mapped a range of metadata challenges through an OA lens. We built on an existing body of work to visualize the ripple effect of a fragmented metadata supply chain. The result is an interactive report of the research lifecycle designed to offer everyone a deeper understanding of the state of scholarly metadata in 2023. Though the issues are numerous, they are not insurmountable, and much infrastructure exists to support change.

About the State of Scholarly Metadata: 2023

Working with Media Growth Strategies, we interviewed representatives from institutions, publishers, funders, researchers, service providers, PID providers, and industry associations to capture a broad view of the current state of metadata and PIDs across the ecosystem. We asked questions such as:

• Who should create and maintain metadata? Where should it originate?
• What resources do you invest to create, curate, or maintain various types of metadata?
• What are your biggest challenges when it comes to metadata management and/or use of PIDs?
• What are the most critical metadata elements?
• What’s at stake if these elements don’t persist through scholarly communications?
• Who should own metadata quality and control?

Here is what they said about the costly implications of metadata breakages and complexities across the research lifecycle:

• Researchers: There was overwhelming consensus among stakeholders that researchers shoulder a significant administrative burden to assert or re-assert data (e.g., institution affiliation, funder ID), ultimately disrupting and delaying scientific discovery.
• Institutions: Because of metadata inconsistencies throughout the research lifecycle, institutions deploy labor-intensive workarounds to manually reconcile funding eligibility and APC billing, as well as normalize unstructured data across disparate systems for comprehensive analysis.
• Funders: Missing metadata (e.g., registered grant DOIs, institution affiliation) makes it difficult and costly to link funding to research outputs, presenting potential barriers to open access uptake, problematic impact tracking, and incomplete analysis to inform future investments.
• Publishers: Metadata breakages interfere with business transformation initiatives, contributing to high operational and opportunity costs and complicating fulfillment of open access agreement terms and analysis of deal performance to inform future decisions.

Many stakeholders we interviewed recognize that new metadata strategies, inclusive policies, and a robust framework of interoperable systems are essential for modernizing this element of scholarly communications. It’s also clear that an ecosystem-wide commitment to improving data quality across all groups will facilitate the transition to open while helping to preserve research integrity, expand discoverability, and improve impact measurement. If the industry works collectively to shrink these gaps by reexamining metadata policy and practice, stakeholders will undoubtedly feel less pain. Or, we can continue the current system of entropy, friction, and frustration. Together, we can decide our path.

The post The State of Scholarly Metadata: 2023 appeared first on Copyright Clearance Center.

Speakers include Christine Kirkpatrick, Founder, GO FAIR US; Barend Mons, President, CODATA; and Erik Schultes, FAIR Implementation Lead, GO FAIR Foundation.

The FAIR Data Principles (Findable, Accessible, Interoperable, and Reusable), published in Scientific Data in 2016, are guiding principles set forth by a consortium of scientists and organizations to support the reusability of digital assets.

“Recent mandates by funders and governments call for sharing all research data in an effort to increase efficiencies, improve lives, accelerate the pace of discovery, and drive more equitable outcomes across every sector of society,” said Tracey Armstrong, President and CEO, CCC. “FAIR data principles emphasize machine-actionability and are essential for effective use of AI and managing the increasing volume and complexity of data.”

CCC recently hosted virtual Town Hall discussions on “ChatGPT and Information Integrity,” “Data Directions,” and “What’s Ahead for Librarians and Researchers.”

View the recording of “Making Research Data FAIR” here.

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