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OpenAI and ChatGPT face unique legal challenges over CopyRight Laws
Reporter: Stephen J. Williams, PhD
In previous weeks on this page and on the sister page ChatGPT applied to Cancer & Oncology, a comparison between ChatGPT, OpenAI, and Google large language model based search reveals a major difference between the algorithms with repect to citation and author credit. In essence while Google returns a hyperlink to the information used to form an answer, ChatGPT and OpenAI are agnostic in crediting or citing the sources of information used to generate answers to queries. With ChatGPT the source data, or more specifically the training set used for the AI algorithm is never properly cited in the query results.
This, as outlined below, is making a big problem when it comes to copyright law and intelectual property. Last week a major lawsuit has been filed because of incorrect and citing, referencing, and attribution of ownership of intellectual property.
As Miles Klee reports in The Rolling Stone
“OpenAI faces allegations of privacy invasion and violating authors’ copyright — but this may be just the tip of the iceberg”
The burgeoning AI industry has just crossed another major milestone, with two new class-action lawsuits calling into question whether this technology violates privacy rights, scrapes intellectual property without consent and negatively affects the public at large. Experts believe they’re likely to be the first in a wave of legal challenges to companies working on such products. Both suits were filed on Wednesday and target OpenAI, a research lab consisting of both a nonprofit arm and a corporation, over ChatGPT software, a “large language model” capable of generating human-like responses to text input. One, filed by Clarkson, a public interest law firm, is wide-ranging and invokes the potentially “existential” threat of AI itself. The other, filed by the Joseph Saveri Law Firm and attorney Matthew Butterick, is focused on two established authors, Paul Tremblay and Mona Awad, who claim that their books were among those ChatGPT was trained on — a violation of copyright, according to the complaint. (Saveri and Butterick are separately pursuing legal action against OpenAI, GitHub and Microsoft over GitHub Copilot, an AI-based coding product that they argue “appears to profit from the work of open-source programmers by violating the conditions of their open-source licenses.”)
Saveri and Butterick’s latest suit goes after OpenAI for direct copyright infringement as well as violations of the Digital Millennium Copyright Act (DMCA). Tremblay (who wrote the novel The Cabin at the End of the World) and Awad (author of 13 Ways of Looking at a Fat Girl and Bunny) are the representatives of a proposed class of plaintiffs who would seek damages as well as injunctive relief in the form of changes to ChatGPT. The filing includes ChatGPT’s detailed responses to user questions about the plots of Tremblay’s and Awad’s books — evidence, the attorneys argue, that OpenAI is unduly profiting off of infringed materials, which were scraped by the chat bot. While the suits venture into uncharted legal territory, they were more or less inevitable, according to those who research AI tech and privacy or practice law around those issues.
“[AI companies] should have and likely did expect these types of challenges,” says Ben Winters, senior counsel at the Electronic Privacy Information Center and head of the organization’s AI and Human Rights Project. He points out that OpenAI CEO Sam Altman mentioned a few prior “frivolous” suits against the company during his congressional testimony on artificial intelligence in May. “Whenever you create a tool that implicates so much personal data and can be used so widely for such harmful and otherwise personal purposes, I would be shocked there is not anticipated legal fire,” Winters says. “Particularly since they allow this sort of unfettered access for third parties to integrate their systems, they end up getting more personal information and more live information that is less publicly available, like keystrokes and browser activity, in ways the consumer could not at all anticipate.”
They say that OpenAI defendants “profit richly” from the use of their copyrighted materials and yet the authors never consented to the use of their copyrighted materials without credit or compensation.
ChatGPT lawsuit says OpenAI has previously utilized illegal ‘shadow libraries’ for AI training datasets
Although many types of material are used to train large language models, “books offer the best examples of high-quality longform writing,” according to the ChatGPT lawsuit.
OpenAI has previously utilized books for its AI training datasets, including unpublished novels (the majority of which were under copyright) available on a website that provides the materials for free. The plaintiffs suggest that OpenAI may have utilized copyrighted materials from “flagrantly illegal shadow libraries.”
Tremblay and Awad note that OpenAI’s March 2023 paper introducing GPT-4 failed to include any information about the training dataset. However, they say that ChatGPT was able to generate highly accurate summaries of their books when prompted, suggesting that their copyrighted material was used in the training dataset without their consent.
They filed the ChatGPT class action lawsuit on behalf of themselves and a proposed class of U.S. residents and entities that own a U.S. copyright for any work used as training data for the OpenAI language models during the class period.
Earlier this year, a tech policy group urged federal regulators to block OpenAI’s GPT-4 AI product because it does not meet federal standards.
What is the general consensus among legal experts on generative AI and copyright?
Given the hype around ChatGPT and the speculation that it could be widely used, it is important to understand the legal implications of the technology. First, do copyright owners of the text used to train ChatGPT have a copyright infringement claim against OpenAI? Second, can the output of ChatGPT be protected by copyright and, if so, who owns that copyright?
To answer these questions, we need to understand the application of US copyright law.
Copyright Law Basics
Based on rights in Article I, Section 8 of the Constitution, Congress passed the first copyright law in 1790. It has been amended several times. Today, US copyright law is governed by the Copyright Act of 1976. This law grants authors of original works exclusive rights to reproduce, distribute, and display their work. Copyright protection applies from the moment of creation, and, for most works, the copyright term is the life of the author plus 70 years after the author’s death. Under copyright law, the copyright holder has the exclusive right to make copies of the work, distribute it, display it publicly, and create derivative works based on it. Others who want to use the work must obtain permission from the copyright holder or use one of the exceptions to copyright law, such as fair use.
The purpose of copyright law is to incentivize authors to create novel and creative works. It does this by granting authors exclusive rights to control the use of their work, thus allowing them to financially benefit from their works. Copyright law also encourages the dissemination of knowledge by allowing others to use copyrighted works under certain conditions, such as through the fair use doctrine, which allows for limited use of copyrighted material for the purposes of criticism, commentary, news reporting, teaching, scholarship, or research. By protecting the rights of authors and creators while also allowing for the use of copyrighted works for the public benefit, copyright law aims to strike a balance between the interests of authors and the public.
Inputs – Training ChatGPT with Copyrighted Material
ChatGPT was trained on a large training dataset sourced from the internet, including a vast amount of text from websites, articles, books, social media posts, and academic papers. Importantly, ChatGPT was not aware of the source of the data, it only saw the text and learned the patterns and relationships between the words, phrases and sentences. The vast majority of the text used to train ChatGPT was certainly subject to copyright protection—except for text that is in the public domain like facts or discoveries or works whose term of copyright protection has ended.
Did the ChatGPT training process violate the copyright of the original authors? To answer that question we must determine: if during the training process OpenAI copied the underlying works—because copyright infringement requires an act of copying works that are protected by copyright; is the output of ChatGPT a “derivative work” under copyright law; and would such copying or production of a derivate work fall under the “fair use” exception of copyright law.
Copying During Training
We are not aware of publicly available information regarding whether, during the training process, the training text for ChatGPT was copied or simply referenced. Therefore, we asked ChatGPT and received this answer:
Prompt: During the ChatGPT training process, was the underlying training text copied into a OpenAI database?
Answer: During the training process, text from various sources is used to train the ChatGPT model. It is likely that the text was copied into an internal database at OpenAI for the purpose of training the model. However, the training text is typically preprocessed to remove any sensitive information such as personal identifiers, copyrighted text, etc. In addition, the text is also used to train the model and not stored in a database. The model uses the text to learn patterns and structure of the language and once the training is done, it doesn’t require the text anymore.
So, it seems that during the training process, text was copied. It is also interesting that ChatGPT stated that the training text was “preprocessed” to remove any copyrighted text. That seems highly unlikely since otherwise nearly all text would have been removed.
Is ChatGPT Output a Derivative Work?
Under US copyright law, the owner of a copyright has the exclusive right “to prepare derivative works based upon the copyrighted work.” A “derivative work” is “a work based upon one or more preexisting works.” ChatGPT is trained on preexisting works and generates output based on that training.
As Daniel Gervais, a professor at Vanderbilt Law School who specializes in intellectual property law, says, the definition of a derivative work under copyright law “could loosely be used as a definition of machine learning when applied to the creation of literary and artistic productions because AI machines can produce literary and artistic content (output) that is almost necessarily ‘based upon’ a dataset consisting of preexisting works.” Under this view, it seems that all ChatGPT output is a derivative work under copyright law.
On a related point, it is worth noting that in producing its output, ChatGPT is not “copying” anything. ChatGPT generates text based on the context of the input and the words and phrase patterns it was trained on. ChatGPT is not “copying” and then changing text.
What About Fair Use?
Let’s assume that the underlying text was copied in some way during the ChatGPT training process. Let’s further assume that outputs from Chatto are, at least sometimes, derivative works under copyright law. If that is the case, do copyright owners of the original works have a copyright infringement claim against OpenAI? Not if the copying and the output generation are covered by the doctrine of “fair use.” If a use qualifies as fair use, then actions that would otherwise be prohibited would not be deemed an infringement of copyright.
In determining whether the use made of a work in any particular case is a fair use, the factors include:
The purpose and character of the use, including whether such use is of a commercial nature or is for nonprofit educational purposes.
The nature of the copyrighted work.
The amount and substantiality of the portion used in relation to the copyrighted work as a whole.
The effect of the use upon the potential market for or value of the copyrighted work.
In this case, assuming OpenAI copied copyrighted text as part of the ChatGPT training process, such copying was not for a commercial purpose and had no economic impact on the copyright owner. Daniel Gervais says “it is much more likely than not” that training systems on copyrighted data will be covered by fair use.
In determining if a commercial use will be considered “fair use,” the courts will primarily look at the scope and purpose of the use and the economic impact of such use. Does the use in question change the nature of the underlying copyright material in some material way (described as a “transformative” use) and does it economically impact the original copyright holder?
Without a specific example, it is difficult to determine exactly if a resulting output from ChatGPT would be fair use. The fact that ChatGPT does not copy and has been trained on millions of underlying works, it seems likely most output would be fair use—without using significant portions of any one protected work. In addition, because of the vast corpus of text used to train ChatGPT, it seems unlikely that ChatGPT output will have a negative economic impact on any one copyright holder. But, given the capabilities of ChatGPT, that might not always be the case.
Imagine if you asked ChatGPT to “Write a long-form, coming of age, story in the style of J.K. Rowling, using the characters from Harry Potter and the Chamber of Secrets.” In that case, it would seem that the argument for fair use would be weak. This story could be sold to the public and could conceivably have a negative economic impact on J.K. Rowling. A person that wants to read a story about Harry Potter might buy this story instead of buying a book by J. K. Rowling.
Finally, it is worth noting that OpenAI is a non-profit entity that is a “AI research and deployment company.” It seems that OpenAI is the type of research company, and ChatGPT is the type of research project, that would have a strong argument for fair use. This practice has been criticized as “AI Data Laundering,” shielding commercial entities from liability by using a non-profit research institution to create the data set and train AI engines that might later be used in commercial applications.
Outputs – Can the Output of ChatGPT be Protected by Copyright
Is the output of ChatGPT protected by copyright law and, if so, who is the owner? As an initial matter, does the ChatGPT textual output fit within the definition of what is covered under copyright law: “original works of authorship fixed in any tangible medium of expression.”
The text generated by ChatGPT is the type of subject matter that, if created by a human, would be covered by copyright. However, most scholars have opined, and the US Copyright Office has ruled that the output of generative AI systems, like ChatGPT, are not protectable under US copyright law because the work must be an original, creative work of a human author.
In 2022, the US Copyright Office, ruling on whether a picture generated completely autonomously by AI could be registered as a valid copyright, stated “[b]because copyright law as codified in the 1976 Act requires human authorship, the [AI Generated] Work cannot be registered.” The U.S. Copyright Office has issued several similar statements, informing creators that it will not register copyright for works produced by a machine or computer program. The human authorship requirement of the US Copyright Office is set forth as follows:
The Human Authorship Requirement – The U.S. Copyright Office will register an original work of authorship, provided that the work was created by a human being. The copyright law only protects “the fruits of intellectual labor” that “are founded in the creative powers of the mind.” Trade-Mark Cases, 100 U.S. 82, 94 (1879).
While such policies are not binding on the courts, the stance by the US Copyright Office seems to be in line with the purpose of copyright law flowing from the Constitution: to incentivize humans to produce creative works by giving them a monopoly over their creations for a limited period of time. Machines, of course, need and have no such motivation. In fact, copyright law expressly allows a corporation or other legal entity to be the owner of a copyright under the “work made for hire” doctrine. However, to qualify as a work made for hire, the work must be either work prepared by an employee within the scope of his or her employment, or be prepared by a party who “expressly agrees in a written instrument signed by them that the work shall be considered a work made for hire.” Only humans can be employees and only humans or corporations can enter a legally binding contract—machines cannot.
Other articles of note in this Open Access Scientific Journal on ChatGPT and Open AI Include:
Science Has A Systemic Problem, Not an Innovation Problem
Curator:Stephen J. Williams, Ph.D.
A recent email, asking me to submit a survey, got me thinking about the malaise that scientists and industry professionals frequently bemoan: that innovation has been stymied for some reason and all sorts of convuluted processes must be altered to spur this mythical void of great new discoveries….. and it got me thinking about our current state of science, and what is the perceived issue… and if this desert of innovation actually exists or is more a fundamental problem which we have created.
The email was from an NIH committee asking for opinions on recreating the grant review process …. now this on the same day someone complained to me about a shoddy and perplexing grant review they received.
The following email, which was sent out to multiple researchers, involved in either NIH grant review on both sides, as well as those who had been involved in previous questionnaires and studies on grant review and bias. The email asked for researchers to fill out a survey on the grant review process, and how to best change it to increase innovation of ideas as well as inclusivity. In recent years, there have been multiple survey requests on these matters, with multiple confusing procedural changes to grant format and content requirements, adding more administrative burden to scientists.
The email from Center for Scientific Review (one of the divisions a grant will go to before review {they set up review study sections and decide what section a grant should be assigned to} was as follows:
Update on Simplifying Review Criteria: A Request for Information
NIH has issued a request for information (RFI) seeking feedback on revising and simplifying the peer review framework for research project grant applications. The goal of this effort is to facilitate the mission of scientific peer review – identification of the strongest, highest-impact research. The proposed changes will allow peer reviewers to focus on scientific merit by evaluating 1) the scientific impact, research rigor, and feasibility of the proposed research without the distraction of administrative questions and 2) whether or not appropriate expertise and resources are available to conduct the research, thus mitigating the undue influence of the reputation of the institution or investigator.
Currently, applications for research project grants (RPGs, such as R01s, R03s, R15s, R21s, R34s) are evaluated based on five scored criteria: Significance, Investigators, Innovation, Approach, and Environment (derived from NIH peer review regulations 42 C.F.R. Part 52h.8; see Definitions of Criteria and Considerations for Research Project Grant Critiques for more detail) and a number of additional review criteria such as Human Subject Protections.
NIH gathered input from the community to identify potential revisions to the review framework. Given longstanding and often-heard concerns from diverse groups, CSR decided to form two working groups to the CSR Advisory Council—one on non-clinical trials and one on clinical trials. To inform these groups, CSR published a Review Matters blog, which was cross-posted on the Office of Extramural Research blog, Open Mike. The blog received more than 9,000 views by unique individuals and over 400 comments. Interim recommendations were presented to the CSR Advisory Council in a public forum (March 2020 video, slides; March 2021 video, slides). Final recommendations from the CSRAC (report) were considered by the major extramural committees of the NIH that included leadership from across NIH institutes and centers. Additional background information can be found here. This process produced many modifications and the final proposal presented below. Discussions are underway to incorporate consideration of a Plan for Enhancing Diverse Perspectives (PEDP) and rigorous review of clinical trials RPGs (~10% of RPGs are clinical trials) within the proposed framework.
Simplified Review Criteria
NIH proposes to reorganize the five review criteria into three factors, with Factors 1 and 2 receiving a numerical score. Reviewers will be instructed to consider all three factors (Factors 1, 2 and 3) in arriving at their Overall Impact Score (scored 1-9), reflecting the overall scientific and technical merit of the application.
Factor 1: Importance of the Research (Significance, Innovation), numerical score (1-9)
Factor 2: Rigor and Feasibility (Approach), numerical score (1-9)
Factor 3: Expertise and Resources (Investigator, Environment), assessed and considered in the Overall Impact Score, but not individually scored
Within Factor 3 (Expertise and Resources), Investigator and Environment will be assessed in the context of the research proposed. Investigator(s) will be rated as “fully capable” or “additional expertise/capability needed”. Environment will be rated as “appropriate” or “additional resources needed.” If a need for additional expertise or resources is identified, written justification must be provided. Detailed descriptions of the three factors can be found here.
Now looking at some of the Comments were very illuminating:
I strongly support streamlining the five current main review criteria into three, and the present five additional criteria into two. This will bring clarity to applicants and reduce the workload on both applicants and reviewers. Blinding reviewers to the applicants’ identities and institutions would be a helpful next step, and would do much to reduce the “rich-getting-richer” / “good ole girls and good ole boys” / “big science” elitism that plagues the present review system, wherein pedigree and connections often outweigh substance and creativity.
I support the proposed changes. The shift away from “innovation” will help reduce the tendency to create hype around a proposed research direction. The shift away from Investigator and Environment assessments will help reduce bias toward already funded investigators in large well-known institutions.
As a reviewer for 5 years, I believe that the proposed changes are a step in the right direction, refocusing the review on whether the science SHOULD be done and whether it CAN BE DONE WELL, while eliminating burdensome and unhelpful sections of review that are better handled administratively. I particularly believe that the de-emphasis of innovation (which typically focuses on technical innovation) will improve evaluation of the overall science, and de-emphasis of review of minor technical details will, if implemented correctly, reduce the “downward pull” on scores for approach. The above comments reference blinded reviews, but I did not see this in the proposed recommendations. I do not believe this is a good idea for several reasons: 1) Blinding of the applicant and institution is not likely feasible for many of the reasons others have described (e.g., self-referencing of prior work), 2) Blinding would eliminate the potential to review investigators’ biosketches and budget justifications, which are critically important in review, 3) Making review blinded would make determination of conflicts of interest harder to identify and avoid, 4) Evaluation of “Investigator and Environment” would be nearly impossible.
Most of the Comments were in favor of the proposed changes, however many admitted that it adds additional confusion on top of many administrative changes to formats and content of grant sections.
Being a Stephen Covey devotee, and just have listened to The Four Principles of Execution, it became more apparent that issues that hinder many great ideas coming into fruition, especially in science, is a result of these systemic or problems in the process, not at the level of individual researchers or small companies trying to get their innovations funded or noticed. In summary, Dr. Covey states most issues related to the success of any initiative is NOT in the strategic planning, but in the failure to adhere to a few EXECUTION principles. Primary to these failures of strategic plans is lack of accounting of what Dr. Covey calls the ‘whirlwind’, or those important but recurring tasks that take us away from achieving the wildly important goals. In addition, lack of determining lead and lag measures of success hinder such plans.
In this case a lag measure in INNOVATION. It appears we have created such a whirlwind and focus on lag measures that we are incapable of translating great discoveries into INNOVATION.
In the following post, I will focus on issues relating to Open Access, publishing and dissemination of scientific discovery may be costing us TIME to INNOVATION. And it appears that there are systemic reasons why we appear stuck in a rut, so to speak.
The first indication is from a paper published by Johan Chu and James Evans in 2021 in PNAS:
Slowed canonical progress in large fields of science
Chu JSG, Evans JA. Slowed canonical progress in large fields of science. Proc Natl Acad Sci U S A. 2021 Oct 12;118(41):e2021636118. doi: 10.1073/pnas.2021636118. PMID: 34607941; PMCID: PMC8522281
Abstract
In many academic fields, the number of papers published each year has increased significantly over time. Policy measures aim to increase the quantity of scientists, research funding, and scientific output, which is measured by the number of papers produced. These quantitative metrics determine the career trajectories of scholars and evaluations of academic departments, institutions, and nations. Whether and how these increases in the numbers of scientists and papers translate into advances in knowledge is unclear, however. Here, we first lay out a theoretical argument for why too many papers published each year in a field can lead to stagnation rather than advance. The deluge of new papers may deprive reviewers and readers the cognitive slack required to fully recognize and understand novel ideas. Competition among many new ideas may prevent the gradual accumulation of focused attention on a promising new idea. Then, we show data supporting the predictions of this theory. When the number of papers published per year in a scientific field grows large, citations flow disproportionately to already well-cited papers; the list of most-cited papers ossifies; new papers are unlikely to ever become highly cited, and when they do, it is not through a gradual, cumulative process of attention gathering; and newly published papers become unlikely to disrupt existing work. These findings suggest that the progress of large scientific fields may be slowed, trapped in existing canon. Policy measures shifting how scientific work is produced, disseminated, consumed, and rewarded may be called for to push fields into new, more fertile areas of study.
So the Summary of this paper is
The authors examined 1.8 billion citations among 90 million papers over 241 subjects
found the corpus of papers do not lead to turnover of new ideas in a field, but rather the ossification or entrenchment of canonical (or older ideas)
this is mainly due to older paper cited more frequently than new papers with new ideas, potentially because authors are trying to get their own papers cited more frequently for funding and exposure purposes
The authors suggest that “fundamental progress may be stymied if quantitative growth of scientific endeavors is not balanced by structures fostering disruptive scholarship and focusing attention of novel ideas”
The authors note that, in most cases, science policy reinforces this “more is better” philosophy”, where metrics of publication productivity are either number of publications or impact measured by citation rankings. However, using an analysis of citation changes occurring in large versus smaller fields, it becomes apparent that this process is favoring the older, more established papers and a recirculating of older canonical ideas.
“Rather than resulting in faster turnover of field paradigms, the massive amounts of new publications entrenches the ideas of top-cited papers.” New ideas are pushed down to the bottom of the citation list and potentially lost in the literature. The authors suggest that this problem will intensify as the “annual mass” of new publications in each field grows, especially in large fields. This issue is exacerbated by the deluge on new online ‘open access’ journals, in which authors would focus on citing the more highly cited literature.
We maybe at a critical junction, where if many papers are published in a short time, new ideas will not be considered as carefully as the older ideas. In addition,
with proliferation of journals and the blurring of journal hierarchies due to online articles-level access can exacerbate this problem
As a counterpoint, the authors do note that even though many molecular biology highly cited articles were done in 1976, there has been extremely much innovation since then however it may take a lot more in experiments and money to gain the level of citations that those papers produced, and hence a lower scientific productivity.
This issue is seen in the field of economics as well
Ellison, Glenn. “Is peer review in decline?” Economic Inquiry, vol. 49, no. 3, July 2011, pp. 635+. Gale Academic OneFile, link.gale.com/apps/doc/A261386330/AONE?u=temple_main&sid=bookmark-AONE&xid=f5891002. Accessed 12 Dec. 2022.
Abstract:
Over the past decade, there has been a decline in the fraction of papers in top economics journals written by economists from the highest-ranked economics departments. This paper documents this fact and uses additional data on publications and citations to assess various potential explanations. Several observations are consistent with the hypothesis that the Internet improves the ability of high-profile authors to disseminate their research without going through the traditional peer-review process. (JEL A14, 030)
The facts part of this paper documents two main facts:
1. Economists in top-ranked departments now publish very few papers in top field journals. There is a marked decline in such publications between the early 1990s and early 2000s.
2. Comparing the early 2000s with the early 1990s, there is a decline in both the absolute number of papers and the share of papers in the top general interest journals written by Harvard economics department faculty.
Although the second fact just concerns one department, I see it as potentially important to understanding what is happening because it comes at a time when Harvard is widely regarded (I believe correctly) as having ascended to the top position in the profession.
The “decline-of-peer-review” theory I allude to in the title is that the necessity of going through the peer-review process has lessened for high-status authors: in the old days peer-reviewed journals were by far the most effective means of reaching readers, whereas with the growth of the Internet high-status authors can now post papers online and exploit their reputation to attract readers.
Many alternate explanations are possible. I focus on four theories: the decline-in-peer-review theory and three alternatives.
1. The trends could be a consequence of top-school authors’ being crowded out of the top journals by other researchers. Several such stories have an optimistic message, for example, there is more talent entering the profession, old pro-elite biases are being broken down, more schools are encouraging faculty to do cutting-edge research, and the Internet is enabling more cutting-edge research by breaking down informational barriers that had hampered researchers outside the top schools. (2)
2. The trends could be a consequence of the growth of revisions at economics journals discussed in Ellison (2002a, 2002b). In this more pessimistic theory, highly productive researchers must abandon some projects and/or seek out faster outlets to conserve the time now required to publish their most important works.
3. The trends could simply reflect that field journals have declined in quality in some relative sense and become a less attractive place to publish. This theory is meant to encompass also the rise of new journals, which is not obviously desirable or undesirable.
The majority of this paper is devoted to examining various data sources that provide additional details about how economics publishing has changed over the past decade. These are intended both to sharpen understanding of the facts to be explained and to provide tests of auxiliary predictions of the theories. Two main sources of information are used: data on publications and data on citations. The publication data include department-level counts of publications in various additional journals, an individual-level dataset containing records of publications in a subset of journals for thousands of economists, and a very small dataset containing complete data on a few authors’ publication records. The citation data include citations at the paper level for 9,000 published papers and less well-matched data that is used to construct measures of citations to authors’ unpublished works, to departments as a whole, and to various journals.
Inside Job or Deep Impact? Extramural Citations and the Influence of Economic Scholarship
Josh Angrist, Pierre Azoulay, Glenn Ellison, Ryan Hill, Susan Feng Lu. Inside Job or Deep Impact? Extramural Citations and the Influence of Economic Scholarship.
JOURNAL OF ECONOMIC LITERATURE
Abstract
Does academic economic research produce material of general scientific value, or do academic economists write only for peers? Is economics scholarship uniquely insular? We address these questions by quantifying interactions between economics and other disciplines. Changes in the influence of economic scholarship are measured here by the frequency with which other disciplines cite papers in economics journals. We document a clear rise in the extramural influence of economic research, while also showing that economics is increasingly likely to reference other social sciences. A breakdown of extramural citations by economics fields shows broad field influence. Differentiating between theoretical and empirical papers classified using machine learning, we see that much of the rise in economics’ extramural influence reflects growth in citations to empirical work. This growth parallels an increase in the share of empirical cites within economics. At the same time, some disciplines that primarily cite economic theory have also recently increased citations of economics scholarship.
Citation
Angrist, Josh, Pierre Azoulay, Glenn Ellison, Ryan Hill, and Susan Feng Lu. 2020.“Inside Job or Deep Impact? Extramural Citations and the Influence of Economic Scholarship.”Journal of Economic Literature, 58 (1): 3-52.DOI: 10.1257/jel.20181508
VOL. 58, NO. 1, MARCH 2020
(pp. 3-52)
So if innovation is there but it may be buried under the massive amount of heavily cited older literature, do we see evidence of this in other fields like medicine?
Why Isn’t Innovation Helping Reduce Health Care Costs?
National health care expenditures (NHEs) in the United States continue to grow at rates outpacing the broader economy: Inflation- and population-adjusted NHEs have increased 1.6 percent faster than the gross domestic product (GDP) between 1990 and 2018. US national health expenditure growth as a share of GDP far outpaces comparable nations in the Organization for Economic Cooperation and Development (17.2 versus 8.9 percent).
Multiple recent analyses have proposed that growth in the prices and intensity of US health care services—rather than in utilization rates or demographic characteristics—is responsible for the disproportionate increases in NHEs relative to global counterparts. The consequences of ever-rising costs amid ubiquitous underinsurance in the US include price-induced deferral of care leading to excess morbidity relative to comparable nations.
These patterns exist despite a robust innovation ecosystem in US health care—implying that novel technologies, in isolation, are insufficient to bend the health care cost curve. Indeed, studies have documented that novel technologies directly increase expenditure growth.
Why is our prolific innovation ecosystem not helping reduce costs? The core issue relates to its apparent failure to enhance net productivity—the relative output generated per unit resource required. In this post, we decompose the concept of innovation to highlight situations in which inventions may not increase net productivity. We begin by describing how this issue has taken on increased urgency amid resource constraints magnified by the COVID-19 pandemic. In turn, we describe incentives for the pervasiveness of productivity-diminishing innovations. Finally, we provide recommendations to promote opportunities for low-cost innovation.
Net Productivity During The COVID-19 Pandemic
The issue of productivity-enhancing innovation is timely, as health care systems have been overwhelmed by COVID-19. Hospitals in Italy, New York City, and elsewhere have lacked adequate capital resources to care for patients with the disease, sufficient liquidity to invest in sorely needed resources, and enough staff to perform all of the necessary tasks.
The critical constraint in these settings is not technology: In fact, the most advanced technology required to routinely treat COVID-19—the mechanical ventilator—was invented nearly 100 years ago in response to polio (the so-called iron lung). Rather, the bottleneck relates to the total financial and human resources required to use the technology—the denominator of net productivity. The clinical implementation of ventilators has been illustrative: Health care workers are still required to operate ventilators on a nearly one-to-one basis, just like in the mid-twentieth century.
High levels of resources required for implementation of health care technologies constrain the scalability of patient care—such as during respiratory disease outbreaks such as COVID-19. Thus, research to reduce health care costs is the same kind of research we urgently require to promote health care access for patients with COVID-19.
Types Of Innovation And Their Relationship To Expenditure Growth
The widespread use of novel medical technologies has been highlighted as a central driver of NHE growth in the US. We believe that the continued expansion of health care costs is largely the result of innovation that tends to have low productivity (exhibit 1). We argue that these archetypes—novel widgets tacked on to existing workflows to reinforce traditional care models—are exactly the wrong properties to reduce NHEs at the systemic level.
Exhibit 1: Relative productivity of innovation subtypes
These may be contrasted with process innovations, which address the organized sequences of activities that implement content. Classically, these include clinical pathways and protocols. They can address the delivery of care for acute conditions, such as central line infections, sepsis, or natural disasters. Alternatively, they can target chronic conditions through initiatives such as team-based management of hypertension and hospital-at-home models for geriatric care. Other processes include hiring staff, delegating labor, and supply chain management.
Performance-Enhancing Versus Cost-Reducing Innovation
Performance-enhancing innovations frequently create incremental outcome gains in diagnostic characteristics, such as sensitivity or specificity, or in therapeutic characteristics, such as biomarkers for disease status. Their performance gains often lead to higher prices compared to existing alternatives.
Performance-enhancing innovations can be compared to “non-inferior” innovations capable of achieving outcomes approximating those of existing alternatives, but at reduced cost. Industries outside of medicine, such as the computing industry, have relied heavily on the ability to reduce costs while retaining performance.
In health care though, this pattern of innovation is rare. Since passage of the 2010 “Biosimilars” Act aimed at stimulating non-inferior innovation and competition in therapeutics markets, only 17 agents have been approved, and only seven have made it to market. More than three-quarters of all drugs receiving new patents between 2005 and 2015 were “reissues,” meaning they had already been approved, and the new patent reflected changes to the previously approved formula. Meanwhile, the costs of approved drugs have increased over time, at rates between 4 percent and 7 percent annually.
Moreover, the preponderance of performance-enhancing diagnostic and therapeutic innovations tend to address narrow patient cohorts (such as rare diseases or cancer subtypes), with limited clear clinical utility in broader populations. For example, the recently approved eculizimab is a monoclonal antibody approved for paroxysmal nocturnal hemoglobinuria—which effects 1 in 10 million individuals. At the time of its launch, eculizimab was priced at more than $400,000 per year, making it the most expensive drug in modern history. For clinical populations with no available alternatives, drugs such as eculizimab may be cost-effective, pending society’s willingness to pay, and morally desirable, given a society’s values. But such drugs are certainly not cost-reducing.
Additive Versus Substitutive Innovation
Additive innovations are those that append to preexisting workflows, while substitutive innovations reconfigure preexisting workflows. In this way, additive innovations increase the use of precedent services, whereas substitutive innovations decrease precedent service use.
For example, previous analyses have found that novel imaging modalities are additive innovations, as they tend not to diminish use of preexisting modalities. Similarly, novel procedures tend to incompletely replace traditional procedures. In the case of therapeutics and devices, off-label uses in disease groups outside of the approved indication(s) can prompt innovation that is additive. This is especially true, given that off-label prescriptions classically occur after approved methods are exhausted.
Eculizimab once again provides an illustrative example. As of February 2019, the drug had been used for 39 indications (it had been approved for three of those, by that time), 69 percent of which lacked any form of evidence of real-world effectiveness. Meanwhile, the drug generated nearly $4 billion in sales in 2019. Again, these expenditures may be something for which society chooses to pay—but they are nonetheless additive, rather than substitutive.
Sustaining Versus Disruptive Innovation
Competitive market theory suggests that incumbents and disruptors innovate differently. Incumbents seek sustaining innovations capable of perpetuating their dominance, whereas disruptors pursue innovations capable of redefining traditional business models.
In health care, while disruptive innovations hold the potential to reduce overall health expenditures, often they run counter to the capabilities of market incumbents. For example, telemedicine can deliver care asynchronously, remotely, and virtually, but large-scale brick-and-mortar medical facilities invest enormous capital in the delivery of synchronous, in-house, in-person care (incentivized by facility fees).
The connection between incumbent business models and the innovation pipeline is particularly relevant given that 58 percent of total funding for biomedical research in the US is now derived from private entities, compared with 46 percent a decade prior. It follows that the growing influence of eminent private organizations may favor innovations supporting their market dominance—rather than innovations that are societally optimal.
Incentives And Repercussions Of High-Cost Innovation
Taken together, these observations suggest that innovation in health care is preferentially designed for revenue expansion rather than for cost reduction. While offering incremental improvements in patient outcomes, therefore creating theoretical value for society, these innovations rarely deliver incremental reductions in short- or long-term costs at the health system level.
For example, content-based, performance-enhancing, additive, sustaining innovations tend to add layers of complexity to the health care system—which in turn require additional administration to manage. The net result is employment growth in excess of outcome improvement, leading to productivity losses. This gap leads to continuously increasing overall expenditures in turn passed along to payers and consumers.
Nonetheless, high-cost innovations are incentivized across health care stakeholders (exhibit 2). From the supply side of innovation, for academic researchers, “breakthrough” and “groundbreaking” innovations constitute the basis for career advancement via funding and tenure. This is despite stakeholders’ frequent inability to generalize early successes to become cost-effective in the clinical setting. As previously discussed, the increasing influence of private entities in setting the medical research agenda is also likely to stimulate innovation benefitting single stakeholders rather than the system.
Source: Authors’ analysis adapted from Hofmann BM. Too much technology. BMJ. 2015 Feb 16.
From the demand side of innovation (providers and health systems), a combined allure (to provide “cutting-edge” patient care), imperative (to leave “no stone unturned” in patient care), and profit-motive (to amplify fee-for-service reimbursements) spur participation in a “technological arms-race.” The status quo thus remains as Clay Christensen has written: “Our major health care institutions…together overshoot the level of care actually needed or used by the vast majority of patients.”
Christensen’s observations have been validated during the COVID-19 epidemic, as treatment of the disease requires predominantly century-old technology. By continually adopting innovation that routinely overshoots the needs of most patients, layer by layer, health care institutions are accruing costs that quickly become the burden of society writ large.
Recommendations To Reduce The Costs Of Health Care Innovation
Henry Aaron wrote in 2002 that “…the forces that have driven up costs are, if anything, intensifying. The staggering fecundity of biomedical research is increasing…[and] always raises expenditures.” With NHEs spiraling ever-higher, urgency to “bend the cost curve” is mounting. Yet, since much biomedical innovation targets the “flat of the [productivity] curve,” alternative forms of innovation are necessary.
The shortcomings in net productivity revealed by the COVID-19 pandemic highlight the urgent need for redesign of health care delivery in this country, and reevaluation of the innovation needed to support it. Specifically, efforts supporting process redesign are critical to promote cost-reducing, substitutive innovations that can inaugurate new and disruptive business models.
Process redesign rarely involves novel gizmos, so much as rejiggering the wiring of, and connections between, existing gadgets. It targets operational changes capable of streamlining workflows, rather than technical advancements that complicate them. As described above, precisely these sorts of “frugal innovations” have led to productivity improvements yielding lower costs in other high-technology industries, such as the computing industry.
Shrank and colleagues recently estimated that nearly one-third of NHEs—almost $1 trillion—were due to preventable waste. Four of the six categories of waste enumerated by the authors—failure in care delivery, failure in care coordination, low-value care, and administrative complexity—represent ripe targets for process innovation, accounting for $610 billion in waste annually, according to Shrank.
Health systems adopting process redesign methods such as continuous improvement and value-based management have exhibited outcome enhancement and expense reduction simultaneously. Internal processes addressed have included supply chain reconfiguration, operational redesign, outlier reconciliation, and resource standardization.
Despite the potential of process innovation, focus on this area (often bundled into “health services” or “quality improvement” research) occupies only a minute fraction of wallet- or mind-share in the biomedical research landscape, accounting for 0.3 percent of research dollars in medicine. This may be due to a variety of barriers beyond minimal funding. One set of barriers is academic, relating to negative perceptions around rigor and a lack of outlets in which to publish quality improvement research. To achieve health care cost containment over the long term, this dimension of innovation must be destigmatized relative to more traditional manners of innovation by the funders and institutions determining the conditions of the research ecosystem.
Another set of barriers is financial: Innovations yielding cost reduction are less “reimbursable” than are innovations fashioned for revenue expansion. This is especially the case in a fee-for-service system where reimbursement is tethered to cost, which creates perverse incentives for health care institutions to overlook cost increases. However, institutions investing in low-cost innovation will be well-positioned in a rapidly approaching future of value-based care—in which the solvency of health care institutions will rely upon their ability to provide economically efficient care.
Innovating For Cost Control Necessitates Frugality Over Novelty
Restraining US NHEs represents a critical step toward health promotion. Innovation for innovation’s sake—that is content-based, incrementally effective, additive, and sustaining—is unlikely to constrain continually expanding NHEs.
In contrast, process innovation offers opportunities to reduce costs while maintaining high standards of patient care. As COVID-19 stress-tests health care systems across the world, the importance of cost control and productivity amplification for patient care has become apparent.
As such, frugality, rather than novelty, may hold the key to health care cost containment. Redesigning the innovation agenda to stem the tide of ever-rising NHEs is an essential strategy to promote widespread access to care—as well as high-value preventive care—in this country. In the words of investors across Silicon Valley: Cost-reducing innovation is no longer a “nice-to-have,” but a “need-to-have” for the future of health and overall well-being this country.
So Do We Need A New Way of Disseminating Scientific Information? Can Curation Help?
We had high hopes for Science 2.0, in particular the smashing of data and knowledge silos. However the digital age along with 2.0 platforms seemed to excaccerbate this somehow. We still are critically short on analysis!
Old Science 1.0 is still the backbone of all scientific discourse, built on the massive amount of experimental and review literature. However this literature was in analog format, and we moved to a more accesible digital open access format for both publications as well as raw data. However as there was a structure for 1.0, like the Dewey decimal system and indexing, 2.0 made science more accesible and easier to search due to the newer digital formats. Yet both needed an organizing structure; for 1.0 that was the scientific method of data and literature organization with libraries as the indexers. In 2.0 this relied on an army mostly of volunteers who did not have much in the way of incentivization to co-curate and organize the findings and massive literature.
The Intenet and the Web is rapidly adopting a new “Web 3.0” format, with decentralized networks, enhanced virtual experiences, and greater interconnection between people. Here we start the discussion what will the move from Science 2.0, where dissemination of scientific findings was revolutionized and piggybacking on Web 2.0 or social media, to a Science 3.0 format. And what will it involve or what paradigms will be turned upside down?
Multiple Major Scientific Journals Will Fully Adopt Open Access Under Plan S
Curator: Stephen J. Williams, PhD
More university library systems have been pressuring major scientific publishing houses to adopt an open access strategy in order to reduce the library system’s budgetary burdens. In fact some major universities like the California system of universities (University of California and other publicly funded universities in the state as well as Oxford University in the UK, even MIT have decided to become their own publishing houses in a concerted effort to fight back against soaring journal subscription costs as well as the costs burdening individual scientists and laboratories (some of the charges to publish one paper can run as high as $8000.00 USD while the journal still retains all the rights of distribution of the information). Therefore more and more universities, as well as concerted efforts by the European Union and the US government are mandating that scientific literature be published in an open access format.
The results of this pressure are evident now as major journals like Nature, JBC, and others have plans to go fully open access in 2021. Below is a listing and news reports of some of these journals plans to undertake a full Open Access Format.
Springer Nature says it commits to offering researchers a route to publishing open access in Nature and most Nature-branded journals from 2021.
Richard Van Noorden
After a change in the rules of the bold open-access (OA) initiative known as Plan S, publisher Springer Nature said on 8 April that many of its non-OA journals — including Nature — were now committed to joining the plan, pending discussion of further technical details.
This means that Nature and other Nature-branded journals that publish original research will now look to offer an immediate OA route after January 2021 to scientists who want it, or whose funders require it, a spokesperson says. (Nature is editorially independent of its publisher, Springer Nature.)
“We are delighted that Springer Nature is committed to transitioning its journals to full OA,” said Robert Kiley, head of open research at the London-based biomedical funder Wellcome, and the interim coordinator for Coalition S, a group of research funders that launched Plan S in 2018.
But Lisa Hinchliffe, a librarian at the University of Illinois at Urbana–Champaign, says the changed rules show that publishers have successfully pushed back against Plan S, softening its guidelines and expectations — in particular in the case of hybrid journals, which publish some content openly and keep other papers behind paywalls. “The coalition continues to take actions that rehabilitate hybrid journals into compliance rather than taking the hard line of unacceptability originally promulgated,” she says.
What is Plan S?
The goal of Plan S is to make scientific and scholarly works free to read as soon as they are published. So far, 17 national funders, mostly in Europe, have joined the initiative, as have the World Health Organization and two of the world’s largest private biomedical funders — the Bill & Melinda Gates Foundation and Wellcome. The European Commission will also implement an OA policy that is aligned with Plan S. Together, this covers around 7% of scientific articles worldwide, according to one estimate. A 2019 report published by the publishing-services firm Clarivate Analytics suggested that 35% of the research content published in Nature in 2017 acknowledged a Plan S funder (see ‘Plan S papers’).
PLAN S PAPERS
Journal
Total papers in 2017
% acknowledging Plan S funder
Nature
290
35%
Science
235
31%
Proc. Natl Acad. Sci. USA
639
20%
Source: The Plan S footprint: Implications for the scholarly publishing landscape (Institute for Scientific Information, 2019)
We are extremely excited to announce on behalf of the American Society for Biochemistry and Molecular Biology (ASBMB) that the Journal of Biological Chemistry (JBC), Molecular & Cellular Proteomics (MCP), and the Journal of Lipid Research (JLR) will be published as fully open-access journals beginning in January 2021. This is a landmark decision that will have huge impact for readers and authors. As many of you know, many researchers have called for journals to become open access to facilitate scientific progress, and many funding agencies across the globe are either already requiring or considering a requirement that all scientific publications based on research they support be published in open-access journals. The ASBMB journals have long supported open access, making the accepted author versions of manuscripts immediately and permanently available, allowing authors to opt in to the immediate open publication of the final version of their paper, and endorsing the goals of the larger open-access movement (1). However, we are no longer satisfied with these measures. To live up to our goals as a scientific society, we want to freely distribute the scientific advances published in JBC, MCP, and JLR as widely and quickly as possible to support the scientific community. How better can we facilitate the dissemination of new information than to make our scientific content freely open to all?
For ASBMB journals and others who have contemplated or made the transition to publishing all content open access, achieving this milestone generally requires new financial mechanisms. In the case of the ASBMB journals, the transition to open access is being made possible by a new partnership with Elsevier, whose established capabilities and economies of scale make the costs associated with open-access publication manageable for the ASBMB (2). However, we want to be clear: The ethos of ASBMB journals will not change as a consequence of this new alliance. The journals remain society journals: The journals are owned by the society, and all scientific oversight for the journals will remain with ASBMB and its chosen editors. Peer review will continue to be done by scientists reviewing the work of scientists, carried out by editorial board members and external referees on behalf of the ASBMB journal leadership. There will be no intervention in this process by the publisher.
Although we will be saying “goodbye” to many years of self-publishing (115 in the case of JBC), we are certain that we are taking this big step for all the right reasons. The goal for JBC, MCP, and JLR has always been and will remain to help scientists advance their work by rapidly and effectively disseminating their results to their colleagues and facilitating the discovery of new findings (1, 3), and open access is only one of many innovations and improvements in science publishing that could help the ASBMB journals achieve this goal. We have been held back from fully exploring these options because of the challenges of “keeping the trains running” with self-publication. In addition to allowing ASBMB to offer all the content in its journals to all readers freely and without barriers, the new partnership with Elsevier opens many doors for ASBMB publications, from new technology for manuscript handling and production, to facilitating reader discovery of content, to deploying powerful analytics to link content within and across publications, to new opportunities to improve our peer review mechanisms. We have all dreamed of implementing these innovations and enhancements (4, 5) but have not had the resources or infrastructure needed.
A critical aspect of moving to open access is how this decision impacts the cost to authors. Like most publishers that have made this transition, we have been extremely worried that achieving open-access publishing would place too big a financial burden on our authors. We are pleased to report the article-processing charges (APCs) to publish in ASBMB journals will be on the low end within the range of open-access fees: $2,000 for members and $2,500 for nonmembers. While slightly higher than the cost an author incurs now if the open-access option is not chosen, these APCs are lower than the current charges for open access on our existing platform.
References
1.↵ Gierasch, L. M., Davidson, N. O., Rye, K.-A., and Burlingame, A. L. (2019) For the sake of science. J. Biol. Chem. 294, 2976 FREE Full Text
2.↵ Gierasch, L. M. (2017) On the costs of scientific publishing. J. Biol. Chem. 292, 16395–16396 FREE Full Text
3.↵ Gierasch, L. M. (2020) Faster publication advances your science: The three R’s. J. Biol. Chem. 295, 672 FREE Full Text
4.↵ Gierasch, L. M. (2017) JBC is on a mission to facilitate scientific discovery. J. Biol. Chem. 292, 6853–6854 FREE Full Text
5.↵ Gierasch, L. M. (2017) JBC’s New Year’s resolutions: Check them off! J. Biol. Chem. 292, 21705–21706 FREE Full Text
From 2021, all research funded by public or private grants should be published in open access journals, according to a group of funding agencies called coALition S.1
The plan is the final version of a draft that was put to public consultation last year and attracted 344 responses from institutions, almost half of them from the UK.2 The responses have been considered and some changes made to the new system called Plan S, a briefing at the Science Media Centre in London was told on 29 May.
The main change has been to delay implementation for a year, to 1 January 2021, to allow more time for those involved—researchers, funders, institutions, publishers, and repositories—to make the necessary changes, said John-Arne Røttingen, chief executive of the Research Council of Norway.
“All research contracts signed after that date should include the obligation to publish in an open access journal,” he said. T……
(Please Note in a huge bit of irony this article is NOT Open Access and behind a paywall…. Yes an article about an announcement to go Open Access is not Open Access)
Plan S is an initiative for open-accessscience publishing launched in 2018[1][2] by “cOAlition S”,[3] a consortium of national research agencies and funders from twelve European countries. The plan requires scientists and researchers who benefit from state-funded research organisations and institutions to publish their work in open repositories or in journals that are available to all by 2021.[4] The “S” stands for “shock”.[5]
The plan is structured around ten principles.[3] The key principle states that by 2021, research funded by public or private grants must be published in open-access journals or platforms, or made immediately available in open access repositories without an embargo. The ten principles are:
authors should retain copyrighton their publications, which must be published under an open license such as Creative Commons;
the members of the coalition should establish robust criteria and requirements for compliant open access journals and platforms;
they should also provide incentives for the creation of compliant open access journals and platforms if they do not yet exist;
publication fees should be covered by the funders or universities, not individual researchers;
such publication fees should be standardized and capped;
universities, research organizations, and libraries should align their policies and strategies;
for books and monographs, the timeline may be extended beyond 2021;
open archives and repositories are acknowledged for their importance;
Old Industrial Revolution Paradigm of Education Needs to End: How Scientific Curation Can Transform Education
Curator: Stephen J. Williams, PhD.
Dr. Cathy N. Davidson from Duke University gives a talk entitled: Now You See It. Why the Future of Learning Demands a Paradigm Shift
In this talk, shown below, Dr. Davidson shows how our current education system has been designed for educating students for the industrial age type careers and skills needed for success in the Industrial Age and how this educational paradigm is failing to prepare students for the challenges they will face in their future careers.
Or as Dr. Davidson summarizes
Designing education not for your past but for their future
As the video is almost an hour I will summarize some of the main points below
PLEASE WATCH VIDEO
Summary of talk
Dr. Davidson starts the talk with a thesis: that Institutions tend to preserve the problems they were created to solve.
All the current work, teaching paradigms that we use today were created for the last information age (19th century)
Our job to to remake the institutions of education work for the future not the one we inherited
Four information ages or technologies that radically changed communication
advent of writing: B.C. in ancient Mesopotamia allowed us to record and transfer knowledge and ideas
movable type – first seen in 10th century China
steam powered press – allowed books to be mass produced and available to the middle class. First time middle class was able to have unlimited access to literature
internet- ability to publish and share ideas worldwide
Interestingly, in the early phases of each of these information ages, the same four complaints about the new technology/methodology of disseminating information was heard
ruins memory
creates a distraction
ruins interpersonal dialogue and authority
reduces complexity of thought
She gives an example of Socrates who hated writing and frequently stated that writing ruins memory, creates a distraction, and worst commits ideas to what one writes down which could not be changed or altered and so destroys ‘free thinking’.
She discusses how our educational institutions are designed for the industrial age.
The need for collaborative (group) learning AND teaching
Designing education not for your past but for the future
In other words preparing students for THEIR future not your past and the future careers that do not exist today.
In the West we were all taught to answer silently and alone. However in Japan, education is arranged in the han or group think utilizing the best talents of each member in the group. In Japan you are arranged in such groups at an early age. The concept is that each member of the group contributes their unique talent and skill for the betterment of the whole group. The goal is to demonstrate that the group worked well together.
In the 19th century in institutions had to solve a problem: how to get people out of the farm and into the factory and/or out of the shop and into the firm
Takes a lot of regulation and institutionalization to convince people that independent thought is not the best way in the corporation
keywords for an industrial age
timeliness
attention to task
standards, standardization
hierarchy
specialization, expertise
metrics (measures, management)
two cultures: separating curriculum into STEM versus artistic tracts or dividing the world of science and world of art
This effort led to a concept used in scientific labor management derived from this old paradigm in education, an educational system controlled and success measured using
grades (A,B,C,D)
multiple choice tests
keywords for our age
workflow
multitasking attention
interactive process (Prototype, Feedback)
data mining
collaboration by difference
Can using a methodology such as scientific curation affect higher education to achieve this goal of teaching students to collaborate in an interactive process using data mining to create a new workflow for any given problem? Can a methodology of scientific curation be able to affect such changes needed in academic departments to achieve the above goal?
This will be the subject of future curations tested using real-world in class examples.
However, it is important to first discern that scientific content curation takes material from Peer reviewed sources and other expert-vetted sources. This is unique from other types of content curation in which take from varied sources, some of which are not expert-reviewed, vetted, or possibly ‘fake news’ or highly edited materials such as altered video and audio. In this respect, the expert acts not only as curator but as referee. In addition, collaboration is necessary and even compulsory for the methodology of scientific content curation, portending the curator not as the sole expert but revealing the CONTENT from experts as the main focus for learning and edification.
Other article of note on this subject in this Open Access Online Scientific Journal include:
The above articles will give a good background on this NEW Conceived Methodology of Scientific Curation and its Applicability in various areas such as Medical Publishing, and as discussed below Medical Education.
To understand the new paradigm in medical communication and the impact curative networks have or will play in this arena please read the following:
This article discusses a history of medical communication and how science and medical communication initially moved from discussions from select individuals to the current open accessible and cooperative structure using Web 2.0 as a platform.
Live Conference Coverage @Medcitynews Converge 2018 @Philadelphia: Promising Drugs and Breaking Down Silos
Reporter: Stephen J. Williams, PhD
Promising Drugs, Pricing and Access
The drug pricing debate rages on. What are the solutions to continuing to foster research and innovation, while ensuring access and affordability for patients? Can biosimilars and generics be able to expand market access in the U.S.?
Moderator:Bunny Ellerin, Director, Healthcare and Pharmaceutical Management Program, Columbia Business School Speakers: Patrick Davish, AVP, Global & US Pricing/Market Access, Merck Robert Dubois M.D., Chief Science Officer and Executive Vice President, National Pharmaceutical Council Gary Kurzman, M.D., Senior Vice President and Managing Director, Healthcare, Safeguard Scientifics Steven Lucio, Associate Vice President, Pharmacy Services, Vizient
What is working and what needs to change in pricing models?
Robert: He sees so many players in the onStevencology space discovering new drugs and other drugs are going generic (that is what is working). However are we spending too much on cancer care relative to other diseases (their initiative Going Beyond the Surface)
Steven: the advent of biosimilars is good for the industry
Patrick: large effort in oncology, maybe too much (750 trials on Keytruda) and he says pharma is spending on R&D (however clinical trials take large chunk of this money)
Robert: cancer has gotten a free ride but cost per year relative to benefit looks different than other diseases. Are we overinvesting in cancer or is that a societal decision
Gary: maybe as we become more specific with precision medicines high prices may be a result of our success in specifically targeting a mutation. We need to understand the targeted drugs and outcomes.
Patrick: “Cancer is the last big frontier” but he says prices will come down in most cases. He gives the example of Hep C treatment… the previous only therapeutic option was a very toxic yearlong treatment but the newer drugs may be more cost effective and safer
Steven: Our blockbuster drugs could diffuse the expense but now with precision we can’t diffuse the expense over a large number of patients
President’s Cancer Panel Recommendation
Six recommendations
promoting value based pricing
enabling communications of cost
financial toxicity
stimulate competition biosimilars
value based care
invest in biomedical research
Patrick: the government pricing regime is hurting. Alot of practical barriers but Merck has over 200 studies on cost basis
Robert: many concerns/impetus started in Europe on pricing as they are a set price model (EU won’t pay more than x for a drug). US is moving more to outcomes pricing. For every one health outcome study three studies did not show a benefit. With cancer it is tricky to establish specific health outcomes. Also Medicare gets best price status so needs to be a safe harbor for payers and biggest constraint is regulatory issues.
Steven: They all want value based pricing but we don’t have that yet and there is a challenge to understand the nuances of new therapies. Hard to align all the stakeholders together so until some legislation starts to change the reimbursement-clinic-patient-pharma obstacles. Possibly the big data efforts discussed here may help align each stakeholders goals.
Gary: What is the data necessary to understand what is happening to patients and until we have that information it still will be complicated to determine where investors in health care stand at in this discussion
Robert: on an ICER methods advisory board: 1) great concern of costs how do we determine fair value of drug 2) ICER is only game in town, other orgs only give recommendations 3) ICER evaluates long term value (cost per quality year of life), budget impact (will people go bankrupt)
4) ICER getting traction in the public eye and advocates 5) the problem is ICER not ready for prime time as evidence keeps changing or are they keeping the societal factors in mind and they don’t have total transparancy in their methodology
Steven: We need more transparency into all the costs associated with the drug and therapy and value-based outcome. Right now price is more of a black box.
Moderator: pointed to a recent study which showed that outpatient costs are going down while hospital based care cost is going rapidly up (cost of site of care) so we need to figure out how to get people into lower cost setting
Breaking Down Silos in Research
“Silo” is healthcare’s four-letter word. How are researchers, life science companies and others sharing information that can benefit patients more quickly? Hear from experts at institutions that are striving to tear down the walls that prevent data from flowing.
Moderator:Vini Jolly, Executive Director, Woodside Capital Partners Speakers: Ardy Arianpour, CEO & Co-Founder, Seqster @seqster Lauren Becnel, Ph.D., Real World Data Lead for Oncology, Pfizer Rakesh Mathew, Innovation, Research, & Development Lead, HealthShareExchange David Nace M.D., Chief Medical Officer, Innovaccer
Seqster: Seqster is a secure platform that helps you and your family manage medical records, DNA, fitness, and nutrition data—all in one place. Founder has a genomic sequencing background but realized sequence information needs to be linked with medical records.
HealthShare Exchange envisions a trusted community of healthcare stakeholders collaborating to deliver better care to consumers in the greater Philadelphia region. HealthShare Exchange will provide secure access to health information to enable preventive and cost-effective care; improve quality of patient care; and facilitate care transitions. They have partnered with multiple players in healthcare field and have data on over 7 million patients.
Data can be overwhelming, but it doesn’t have to be this way. To drive healthcare efficiency, we designed a modular suite of products for a smooth transition into a data-driven world within 4 weeks. Why does it take so much money to move data around and so slowly?
What is interoperatibility?
Ardy: We knew in genomics field how to build algorithms to analyze big data but how do we expand this from a consumer standpoint and see and share your data.
Lauren: how can we use the data between patients, doctors, researchers? On the research side genomics represent only 2% of data. Silos are one issue but figuring out the standards for data (collection, curation, analysis) is not set. Still need to improve semantic interoperability. For example Flatiron had good annotated data on male metastatic breast cancer.
David: Technical interopatabliltiy (platform), semantic interopatability (meaning or word usage), format (syntactic) interopatibility (data structure). There is technical interoperatiblity between health system but some semantic but formats are all different (pharmacies use different systems and write different prescriptions using different suppliers). In any value based contract this problem is a big issue now (we are going to pay you based on the quality of your performance then there is big need to coordinate across platforms). We can solve it by bringing data in real time in one place and use mapping to integrate the format (need quality control) then need to make the data democratized among players.
Rakesh: Patients data should follow the patient. Of Philadelphia’s 12 health systems we had a challenge to make data interoperatable among them so tdhey said to providers don’t use portals and made sure hospitals were sending standardized data. Health care data is complex.
David: 80% of clinical data is noise. For example most eMedical Records are text. Another problem is defining a patient identifier which US does not believe in.
Please follow on Twitter using the following #hash tags and @pharma_BI
How Will FDA’s new precision FDA Science 2.0 Collaboration Platform Protect Data? Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)
How Will FDA’s new precisionFDA Science 2.0 Collaboration Platform Protect Data?
By: Taha A. Kass-Hout, M.D., M.S. and Elaine Johanson
Imagine a world where doctors have at their fingertips the information that allows them to individualize a diagnosis, treatment or even a cure for a person based on their genes. That’s what President Obama envisioned when he announced his Precision Medicine Initiative earlier this year. Today, with the launch of FDA’s precisionFDA web platform, we’re a step closer to achieving that vision.
PrecisionFDA is an online, cloud-based, portal that will allow scientists from industry, academia, government and other partners to come together to foster innovation and develop the science behind a method of “reading” DNA known as next-generation sequencing (or NGS). Next Generation Sequencing allows scientists to compile a vast amount of data on a person’s exact order or sequence of DNA. Recognizing that each person’s DNA is slightly different, scientists can look for meaningful differences in DNA that can be used to suggest a person’s risk of disease, possible response to treatment and assess their current state of health. Ultimately, what we learn about these differences could be used to design a treatment tailored to a specific individual.
The precisionFDA platform is a part of this larger effort and through its use we want to help scientists work toward the most accurate and meaningful discoveries. precisionFDA users will have access to a number of important tools to help them do this. These tools include reference genomes, such as “Genome in the Bottle,” a reference sample of DNA for validating human genome sequences developed by the National Institute of Standards and Technology. Users will also be able to compare their results to previously validated reference results as well as share their results with other users, track changes and obtain feedback.
Over the coming months we will engage users in improving the usability, openness and transparency of precisionFDA.One way we’ll achieve that is by placing the code for the precisionFDA portal on the world’s largest open source software repository, GitHub, so the community can further enhance precisionFDA’s features.Through such collaboration we hope to improve the quality and accuracy of genomic tests – work that will ultimately benefit patients.
precisionFDA leverages our experience establishing openFDA, an online community that provides easy access to our public datasets. Since its launch in 2014, openFDA has already resulted in many novel ways to use, integrate and analyze FDA safety information. We’re confident that employing such a collaborative approach to DNA data will yield important advances in our understanding of this fast-growing scientific field, information that will ultimately be used to develop new diagnostics, treatments and even cures for patients.
Taha A. Kass-Hout, M.D., M.S., is FDA’s Chief Health Informatics Officer and Director of FDA’s Office of Health Informatics. Elaine Johanson is the precisionFDA Project Manager.
The opinions expressed in this blog post are the author’s only and do not necessarily reflect those of MassDevice.com or its employees.
So What Are the Other Successes With Such Open Science 2.0 Collaborative Networks?
In the following post there are highlighted examples of these Open Scientific Networks and, as long as
transparancy
equal contributions (lack of heirarchy)
exists these networks can flourish and add interesting discourse. Scientists are already relying on these networks to collaborate and share however resistance by certain members of an “elite” can still exist. Social media platforms are now democratizing this new science2.0 effort. In addition the efforts of multiple biocurators (who mainly work for love of science) have organized the plethora of data (both genomic, proteomic, and literature) in order to provide ease of access and analysis.
Curation: an Essential Practice to Manage “Open Science”
The web 2.0 gave birth to new practices motivated by the will to have broader and faster cooperation in a more free and transparent environment. We have entered the era of an “open” movement: “open data”, “open software”, etc. In science, expressions like “open access” (to scientific publications and research results) and “open science” are used more and more often.
Curation and Scientific and Technical Culture: Creating Hybrid Networks
Another area, where there are most likely fewer barriers, is scientific and technical culture. This broad term involves different actors such as associations, companies, universities’ communication departments, CCSTI (French centers for scientific, technical and industrial culture), journalists, etc. A number of these actors do not limit their work to popularizing the scientific data; they also consider they have an authentic mission of “culturing” science. The curation practice thus offers a better organization and visibility to the information. The sought-after benefits will be different from one actor to the next.
Using Curation and Science 2.0 to build Trusted, Expert Networks of Scientists and Clinicians
Given the aforementioned problems of:
I. the complex and rapid deluge of scientific information
II. the need for a collaborative, open environment to produce transformative innovation
III. need for alternative ways to disseminate scientific findings
CURATION MAY OFFER SOLUTIONS
I. Curation exists beyond the review: curation decreases time for assessment of current trends adding multiple insights, analyses WITH an underlying METHODOLOGY (discussed below) while NOT acting as mere reiteration, regurgitation
II. Curation providing insights from WHOLE scientific community on multiple WEB 2.0 platforms
III. Curation makes use of new computational and Web-based tools to provide interoperability of data, reporting of findings (shown in Examples below)
Therefore a discussion is given on methodologies, definitions of best practices, and tools developed to assist the content curation community in this endeavor
which has created a need for more context-driven scientific search and discourse.
However another issue would be Individual Bias if these networks are closed and protocols need to be devised to reduce bias from individual investigators, clinicians. This is where CONSENSUS built from OPEN ACCESS DISCOURSE would be beneficial as discussed in the following post:
Risk of bias in translational medicine may take one of three forms:
a systematic error of methodology as it pertains to measurement or sampling (e.g., selection bias),
a systematic defect of design that leads to estimates of experimental and control groups, and of effect sizes that substantially deviate from true values (e.g., information bias), and
a systematic distortion of the analytical process, which results in a misrepresentation of the data with consequential errors of inference (e.g., inferential bias).
This post highlights many important points related to bias but in summarry there can be methodologies and protocols devised to eliminate such bias. Risk of bias can seriously adulterate the internal and the external validity of a clinical study, and, unless it is identified and systematically evaluated, can seriously hamper the process of comparative effectiveness and efficacy research and analysis for practice. The Cochrane Group and the Agency for Healthcare Research and Quality have independently developed instruments for assessing the meta-construct of risk of bias. The present article begins to discuss this dialectic.
Information dissemination to all stakeholders is key to increase their health literacy in order to ensure their full participation
threats to internal and external validity represent specific aspects of systematic errors (i.e., bias)in design, methodology and analysis
So what about the safety and privacy of Data?
A while back I did a post and some interviews on how doctors in developing countries are using social networks to communicate with patients, either over established networks like Facebook or more private in-house networks. In addition, these doctor-patient relationships in developing countries are remote, using the smartphone to communicate with rural patients who don’t have ready access to their physicians.
I discuss some of these problems in the following paragraph and associated posts below:
Mobile Health Applications on Rise in Developing World: Worldwide Opportunity
According to International Telecommunication Union (ITU) statistics, world-wide mobile phone use has expanded tremendously in the past 5 years, reaching almost 6 billion subscriptions. By the end of this year it is estimated that over 95% of the world’s population will have access to mobile phones/devices, including smartphones.
This presents a tremendous and cost-effective opportunity in developing countries, and especially rural areas, for physicians to reach patients using mHealth platforms.
In Summary, although there are restrictions here in the US governing what information can be disseminated over social media networks, developing countries appear to have either defined the regulations as they are more dependent on these types of social networks given the difficulties in patient-physician access.
Therefore the question will be Who Will Protect The Data?
For some interesting discourse please see the following post
His article “Academic Publishing Can’t Remain Such a Great Business” discusses the history of academic publishing and how consolidation of smaller publishers into large scientific publishing houses (Bigger publishers bought smaller ones) has produced a monopoly like environment in which prices for journal subscriptions are rising. He also discusses how the open access movement is challenging this model and may oneday replace the big publishing houses.
A few tidbits from his article:
Publishers of academic journals have a great thing going. They generally don’t pay for the articles they publish, or for the primary editing and peer reviewing essential to preparing them for publication (they do fork over some money for copy editing). Most of this gratis labor is performed by employees of academic institutions. Those institutions, along with government agencies and foundations, also fund all the research that these journal articles are based upon.
Yet the journal publishers are able to get authors to sign over copyright to this content, and sell it in the form of subscriptions to university libraries. Most journals are now delivered in electronic form, which you think would cut the cost, but no, the price has been going up and up:
This isn’t just inflation at work: in 1994, journal subscriptions accounted for 51 percent of all library spending on information resources. In 2012 it was 69 percent.
Who exactly is getting that money? The largest academic publisher is Elsevier, which is also the biggest, most profitable division of RELX, the Anglo-Dutch company that was known until February as Reed Elsevier.
RELX reports results in British pounds; I converted to dollars in part because the biggest piece of the company’s revenue comes from the U.S. And yes, those are pretty great operating-profit margins: 33 percent in 2014, 39 percent in 2013. The next biggest academic publisher is Springer Nature, which is closely held (by German publisher Holtzbrinck and U.K. private-equity firm BC Partners) but reportedly has annual revenue of about $1.75 billion. Other biggies that are part of publicly traded companies include Wiley-Blackwell, a division of John Wiley & Sons; Wolters Kluwer Health, a division of Wolters Kluwer; and Taylor & Francis, a division of Informa.
And gives a brief history of academic publishing:
The history here is that most early scholarly journals were the work of nonprofit scientific societies. The goal was to disseminate research as widely as possible, not to make money — a key reason why nobody involved got paid. After World War II, the explosion in both the production of and demand for academic research outstripped the capabilities of the scientific societies, and commercial publishers stepped into the breach. At a time when journals had to be printed and shipped all over the world, this made perfect sense.
Once it became possible to effortlessly copy and disseminate digital files, though, the economics changed. For many content producers, digital copying is a threat to their livelihoods. As Peter Suber, the director of Harvard University’s Office for Scholarly Communication, puts it in his wonderful little book, “Open Access”:
And while NIH Tried To Force These Houses To Accept Open Access:
About a decade ago, the universities and funding agencies began fighting back. The National Institutes of Health in the U.S., the world’s biggest funder of medical research, began requiring in 2008 that all recipients of its grants submit electronic versions of their final peer-reviewed manuscripts when they are accepted for publication in journals, to be posted a year later on the NIH’s open-access PubMed depository. Publishers grumbled, but didn’t want to turn down the articles.
Big publishers are making $ by either charging as much as they can or focus on new customers and services
For the big publishers, meanwhile, the choice is between positioning themselves for the open-access future or maximizing current returns. In its most recent annual report, RELX leans toward the latter while nodding toward the former:
Over the past 15 years alternative payment models for the dissemination of research such as “author-pays” or “author’s funder-pays” have emerged. While it is expected that paid subscription will remain the primary distribution model, Elsevier has long invested in alternative business models to address the needs of customers and researchers.
Elsevier’s extra services can add news avenues of revenue
Indeed we recently put up an interesting authored paper “A Patient’s Perspective: On Open Heart Surgery from Diagnosis and Intervention to Recovery” (free of charge) letting the community of science freely peruse and comment, and generally well accepted by both author and community as a nice way to share academic discourse without the enormous fees, especially on opinion papers in which a rigorous peer review may not be necessary.
But it was very nice to see a major news outlet like Bloomberg View understand the lowly scientist’s aggravations.
Artificial Intelligence Versus the Scientist: Who Will Win?
Will DARPA Replace the Human Scientist: Not So Fast, My Friend!
Writer, Curator: Stephen J. Williams, Ph.D.
Last month’s issue of Science article by Jia You “DARPA Sets Out to Automate Research”[1] gave a glimpse of how science could be conducted in the future: without scientists. The article focused on the U.S. Defense Advanced Research Projects Agency (DARPA) program called ‘Big Mechanism”, a $45 million effort to develop computer algorithms which read scientific journal papers with ultimate goal of extracting enough information to design hypotheses and the next set of experiments,
all without human input.
The head of the project, artificial intelligence expert Paul Cohen, says the overall goal is to help scientists cope with the complexity with massive amounts of information. As Paul Cohen stated for the article:
“‘
Just when we need to understand highly connected systems as systems,
our research methods force us to focus on little parts.
”
The Big Mechanisms project aims to design computer algorithms to critically read journal articles, much as scientists will, to determine what and how the information contributes to the knowledge base.
As a proof of concept DARPA is attempting to model Ras-mutation driven cancers using previously published literature in three main steps:
One team is focused on extracting details on experimental procedures, using the mining of certain phraseology to determine the paper’s worth (for example using phrases like ‘we suggest’ or ‘suggests a role in’ might be considered weak versus ‘we prove’ or ‘provide evidence’ might be identified by the program as worthwhile articles to curate). Another team led by a computational linguistics expert will design systems to map the meanings of sentences.
Integrate each piece of knowledge into a computational model to represent the Ras pathway on oncogenesis.
Produce hypotheses and propose experiments based on knowledge base which can be experimentally verified in the laboratory.
The Human no Longer Needed?: Not So Fast, my Friend!
The problems the DARPA research teams are encountering namely:
Need for data verification
Text mining and curation strategies
Incomplete knowledge base (past, current and future)
Molecular biology not necessarily “requires casual inference” as other fields do
Verification
Notice this verification step (step 3) requires physical lab work as does all other ‘omics strategies and other computational biology projects. As with high-throughput microarray screens, a verification is needed usually in the form of conducting qPCR or interesting genes are validated in a phenotypical (expression) system. In addition, there has been an ongoing issue surrounding the validity and reproducibility of some research studies and data.
Therefore as DARPA attempts to recreate the Ras pathway from published literature and suggest new pathways/interactions, it will be necessary to experimentally validate certain points (protein interactions or modification events, signaling events) in order to validate their computer model.
Text-Mining and Curation Strategies
The Big Mechanism Project is starting very small; this reflects some of the challenges in scale of this project. Researchers were only given six paragraph long passages and a rudimentary model of the Ras pathway in cancer and then asked to automate a text mining strategy to extract as much useful information. Unfortunately this strategy could be fraught with issues frequently occurred in the biocuration community namely:
Manual or automated curation of scientific literature?
Biocurators, the scientists who painstakingly sort through the voluminous scientific journal to extract and then organize relevant data into accessible databases, have debated whether manual, automated, or a combination of both curation methods [2] achieves the highest accuracy for extracting the information needed to enter in a database. Abigail Cabunoc, a lead developer for Ontario Institute for Cancer Research’s WormBase (a database of nematode genetics and biology) and Lead Developer at Mozilla Science Lab, noted, on her blog, on the lively debate on biocuration methodology at the Seventh International Biocuration Conference (#ISB2014) that the massive amounts of information will require a Herculaneum effort regardless of the methodology.
The Big Mechanism team decided on a full automated approach to text-mine their limited literature set for relevant information however was able to extract only 40% of relevant information from these six paragraphs to the given model. Although the investigators were happy with this percentage most biocurators, whether using a manual or automated method to extract information, would consider 40% a low success rate. Biocurators, regardless of method, have reported ability to extract 70-90% of relevant information from the whole literature (for example for Comparative Toxicogenomics Database)[3-5].
Incomplete Knowledge Base
In an earlier posting (actually was a press release for our first e-book) I had discussed the problem with the “data deluge” we are experiencing in scientific literature as well as the plethora of ‘omics experimental data which needs to be curated.
Tackling the problem of scientific and medical information overload
Figure. The number of papers listed in PubMed (disregarding reviews) during ten year periods have steadily increased from 1970.
Analyzing and sharing the vast amounts of scientific knowledge has never been so crucial to innovation in the medical field. The publication rate has steadily increased from the 70’s, with a 50% increase in the number of original research articles published from the 1990’s to the previous decade. This massive amount of biomedical and scientific information has presented the unique problem of an information overload, and the critical need for methodology and expertise to organize, curate, and disseminate this diverse information for scientists and clinicians. Dr. Larry Bernstein, President of Triplex Consulting and previously chief of pathology at New York’s Methodist Hospital, concurs that “the academic pressures to publish, and the breakdown of knowledge into “silos”, has contributed to this knowledge explosion and although the literature is now online and edited, much of this information is out of reach to the very brightest clinicians.”
Traditionally, organization of biomedical information has been the realm of the literature review, but most reviews are performed years after discoveries are made and, given the rapid pace of new discoveries, this is appearing to be an outdated model. In addition, most medical searches are dependent on keywords, hence adding more complexity to the investigator in finding the material they require. Third, medical researchers and professionals are recognizing the need to converse with each other, in real-time, on the impact new discoveries may have on their research and clinical practice.
These issues require a people-based strategy, having expertise in a diverse and cross-integrative number of medical topics to provide the in-depth understanding of the current research and challenges in each field as well as providing a more conceptual-based search platform. To address this need, human intermediaries, known as scientific curators, are needed to narrow down the information and provide critical context and analysis of medical and scientific information in an interactive manner powered by web 2.0 with curators referred to as the “researcher 2.0”. This curation offers better organization and visibility to the critical information useful for the next innovations in academic, clinical, and industrial research by providing these hybrid networks.
Yaneer Bar-Yam of the New England Complex Systems Institute was not confident that using details from past knowledge could produce adequate roadmaps for future experimentation and noted for the article, “ “The expectation that the accumulation of details will tell us what we want to know is not well justified.”
In a recent post I had curated findings from four lung cancer omics studies and presented some graphic on bioinformatic analysis of the novel genetic mutations resulting from these studies (see link below)
which showed, that while multiple genetic mutations and related pathway ontologies were well documented in the lung cancer literature there existed many significant genetic mutations and pathways identified in the genomic studies but little literature attributed to these lung cancer-relevant mutations.
This ‘literomics’ analysis reveals a large gap between our knowledge base and the data resulting from large translational ‘omic’ studies.
A ‘literomics’ approach focuses on what we don NOT know about genes, proteins, and their associated pathways while a text-mining machine learning algorithm focuses on building a knowledge base to determine the next line of research or what needs to be measured. Using each approach can give us different perspectives on ‘omics data.
Deriving Casual Inference
Ras is one of the best studied and characterized oncogenes and the mechanisms behind Ras-driven oncogenenis is highly understood. This, according to computational biologist Larry Hunt of Smart Information Flow Technologies makes Ras a great starting point for the Big Mechanism project. As he states,” Molecular biology is a good place to try (developing a machine learning algorithm) because it’s an area in which common sense plays a minor role”.
Even though some may think the project wouldn’t be able to tackle on other mechanisms which involve epigenetic factors UCLA’s expert in causality Judea Pearl, Ph.D. (head of UCLA Cognitive Systems Lab) feels it is possible for machine learning to bridge this gap. As summarized from his lecture at Microsoft:
“The development of graphical models and the logic of counterfactuals have had a marked effect on the way scientists treat problems involving cause-effect relationships. Practical problems requiring causal information, which long were regarded as either metaphysical or unmanageable can now be solved using elementary mathematics. Moreover, problems that were thought to be purely statistical, are beginning to benefit from analyzing their causal roots.”
According to him first
1) articulate assumptions
2) define research question in counter-inference terms
Then it is possible to design an inference system using calculus that tells the investigator what they need to measure.
The key for the Big Mechansism Project may me be in correcting for the variables among studies, in essence building a models system which may not rely on fully controlled conditions. Dr. Peter Spirtes from Carnegie Mellon University in Pittsburgh, PA is developing a project called the TETRAD project with two goals: 1) to specify and prove under what conditions it is possible to reliably infer causal relationships from background knowledge and statistical data not obtained under fully controlled conditions 2) develop, analyze, implement, test and apply practical, provably correct computer programs for inferring causal structure under conditions where this is possible.
In summary such projects and algorithms will provide investigators the what, and possibly the how should be measured.
Wednesday, 19th March 2014 Registration and coffee begins – 08.00
Program begins – 08.15
Networking reception will take place at 18.00 – 20.00
Once you arrive at 7 World Trade Center (250 Greenwich St, New York, NY10007, USA).
Please use the D Elevator Bank to the 40th floor where Sachs Team will welcome you at the registration desk. For urgent issues, please contact: Tomas@sachsforum.com (cell number +44 77 043 158 71)
Or Mina@sachsforum.com (cell number +44 74 636 695 04) Cells available from 15th March.
Announcement
LEADERS IN PHARMACEUTICAL BUSINESS INTELLIGENCE will cover the event for the Scientific Media
The 2nd Annual Sachs Cancer Bio Partnering & Investment Forum is designed to bring together thought leaders from cancer research institutes, patient advocacy groups, pharma and biotech to facilitate partnering and funding/investment. We expect around 200 delegates and there is an online meeting system and meeting facilities to make the event transactional. There will also be a track of about 30 presentations by listed and private biotechnology companies seeking licensing/investment.
The 2nd Annual Sachs Cancer Bio Partnering & Investment Forum will cover the following topics in the program:
Advances in Translational Research
Strategies for Small Molecule and Biologicals Drug Development
Deal Making
Public & Private Partnerships
Diagnostics
Immunotherapies and Cancer Vaccines
Case Study
Confirmed Speakers & Chairs include:
Anne Altmeyer, Executive Director Business Development & Licensing, Novartis Pharmaceuticals
Ariel Jasie, Executive Director of Business Development, Celgene
Beth Jacobs, Managing Partner, Excellentia Global Partners
Boris Peaker, Executive Director, Biotechnology Equity Research, Oppenheimer & Co. Inc.
Carole Nuechterlein, Head Roche Venture Fund, F.Hoffmann-La Roche AG Roche Venture Fund
Dan Snyder, President and COO, MolecularMD
Daryl Mitteldorf, Executive Director, Global Prostate Cancer Alliance
Dennis Purcell, Senior Managing Partner, Aisling Capital
Doug Plessinger, Vice President of Clinical and Medical Affairs, Argos Therapeutics, Inc.
Elizabeth Bachert, Senior Director Worldwide Business Development, Pfizer
Esteban Pombo-Villar, COO, Oxford BioTherapeutics AG
Florian Schodel, CEO,Philimmune LLC
Frederick Cope, President and CSO, Navidea Biopharmaceuticals
Guillaume Vignon, Director of Global BD Oncology,Merck Serono SA
Harren Jhoti, President, Astex Pharmaceuticals Inc.
Harry Glorikan, Managing Director, Precision for Medicine
James Mulé, Executive Vice President and Associate Center Director for Translational Research, H Lee Moffit Cancer Center
Keith Knutson, Program Director and Principal Investigator of the Cancer Vaccines and immune Therapies Program, Vaccine and Gene Therapy Institute of Florida
Kevin DeGeeter, Analyst, Ladenburg Thalmann & Co, Inc.
Klaus Urbahns, Head, Discovery Technologies, Merck Serono
Kristina Khodova, Project Manager, Oncology, Skolkovo Foundation
Lorenza Castellon, Business Development Consultant, Suda Ltd.
Louis DeGennaro, Executive VP, CMO,The Leukemia and Lymphoma Society
Louise Perkins, Chief Science Officer, Melanoma Research Alliance
Mara Goldstein, Managing Director, Senior Healthcare Analyst, Cantor Fitzgerald
Michael Goldberg, Managing Partner, Montaur Capital
Nathan Tinker, Executive Director, NewYorkBIO
Nicholas Dracopoli, Vice President and Head of Oncology, Janssen Research & Development
Peter Hoang, Managing Director, Office of Innovations, Technology Based Ventures, The University of Texas MD Anderson Cancer Center
Philip Gotwals, Executive Director, Oncology Research Collaborations, Novartis Institutes for BioMedical Research
Robert Petit, CSO, Advaxis Inc.
Stephen Brozak, Managing Partner and President, WBB Securities, LLC
Steven Tregay, CEO, Forma Therapeutics
Steven W. Young, President, Addario lung Cancer Medical Institute
Stuart Barich, Managing Director, Healthcare Investment Banking, Oppenheimer & Company
Tariq Kassum MD, Vice President, Business Development and Strategy, Millennium Pharmaceuticals
TBC, Cardinal Health
TBC, UCSD
Timothy Herpin, Vice President, Head of Transactions (UK), Business Development, AstraZeneca
Vikas Sharma, Director, Business Development, Rexahn Pharmaceuticals, Inc.
Walter Capone, President, The Multiple Myeloma Research Foundation
Presenting Opportunities for Biotech, Pharmaceutical companies and Patient Advocacy Groups
Presenting at the forum offers excellent opportunities to showcase activities and highlight investment and partnership opportunities. Biotech companies will be able to communicate investment and licensing opportunities. These are for both public and private companies. The audience is comprised of financial and industry investors. These are streamed 15 minute presentations. The patient advocacy presentations are 30 minutes.
Sachs forums are recognised as the leading international stage for those interested in investing in the biotech and life science industry and are highly transactional. They draw together an exciting cross-section of early-stage/pre-IPO, late-stage and public companies with leading investors, analysts, money managers and pharmas. The Boston forum provides the additional interaction with the academic/scientific and patient advocacy communities.
Sponsorship and Exhibition
Sachs Associates has developed an extensive knowledge of the key individuals operating within the European and global biotech industry. This together with a growing reputation for excellence puts Sachs Associates at the forefront of the industry and provides a powerful tool by which to increase the position of your company in this market.
Raise your company’s profile directly with your potential clients. All of our sponsorship packages are tailor made to each client, allowing your organisation to gain the most out of attending our industry driven events.
To learn more about presenting, exhibition or sponsorship opportunities, please contact
Mina Orda + 44 (0)203 463 4890 or by email: Mina Orda.
The 2nd Annual Sachs Cancer Bio Partnering & Investment Forum is designed to bring together thought leaders from cancer research institutes, patient advocacy groups, pharma and biotech to facilitate partnering and funding/investment. We expect around 200 delegates and there is an online meeting system and meeting facilities to make the event transactional. There will also be a track of about 30 presentations by listed and private biotechnology companies seeking licensing/investment.The 2nd Annual Sachs Cancer Bio Partnering & Investment Forum will cover the following topics in the program:
Advances in Translational Research
Strategies for Small Molecule and Biologicals Drug Development
Deal Making
Public & Private Partnerships
Confirmed Speakers & Chairs include:
The 2nd Annual Sachs Cancer Bio Partnering & Investment Forum will cover the following topics in the program:
Advances in Translational Research
Strategies for Small Molecule and Biologicals Drug Development
Deal Making
Public & Private Partnerships
Diagnostics
Immunotherapies and Cancer Vaccines
Confirmed Speakers & Chairs include:
Anne Altmeyer, Executive Director Business Development & Licensing, Novartis Pharmaceuticals
Ariel Jasie, Executive Director of Business Development, Celgene
Beth Jacobs, Managing Partner, Excellentia Global Partners
Boris Peaker, Executive Director, Biotechnology Equity Research, Oppenheimer & Co. Inc.
Carole Nuechterlein, Head Roche Venture Fund, F.Hoffmann-La Roche AG Roche Venture Fund
Daryl Mitteldorf, Executive Director, Global Prostate Cancer Alliance
Dennis Purcell, Senior Managing Partner, Aisling Capital
Doug Plessinger, Vice President of Clinical and Medical Affairs, Argos Therapeutics, Inc.
Elizabeth Bachert, Senior Director Worldwide Business Development, Pfizer
Esteban Pombo-Villar, COO, Oxford BioTherapeutics AG
Florian Schodel, CEO,Philimmune LLC
Guillaume Vignon, Director of Global BD Oncology,Merck Serono SA
Harren Jhoti, President, Astex Pharmaceuticals Inc.
Harry Glorikan, Managing Director, Precision for Medicine
James Mulé, Executive Vice President and Associate Center Director for Translational Research, H Lee Moffit Cancer Center
Keith Knutson, Program Director and Principal Investigator of the Cancer Vaccines and immune Therapies Program, Vaccine and Gene Therapy Institute of Florida
Klaus Urbahns, Head, Discovery Technologies, Merck Serono
Kristina Khodova, Project Manager, Oncology, Skolkovo Foundation
Lorenza Castellon, Business Development Consultant, Suda Ltd.
Louis DeGennaro, Executive VP, CMO,The Leukemia and Lymphoma Society
Louise Perkins, Chief Science Officer, Melanoma Research Alliance
Mara Goldstein, Managing Director, Senior Healthcare Analyst, Cantor Fitzgerald
Nathan Tinker, Executive Director, NewYorkBIO
Nicholas Dracopoli, Vice President and Head of Oncology, Janssen Research & Development
Peter Hoang, Managing Director, Office of Innovations, Technology Based Ventures, The University of Texas MD Anderson Cancer Center
Philip Gotwals, Executive Director, Oncology Research Collaborations, Novartis Institutes for BioMedical Research
Robert Petit, CSO, Advaxis Inc.
Steven Tregay, CEO, Forma Therapeutics
Steven W. Young, President, Addario lung Cancer Medical Institute
Stuart Barich, Managing Director, Healthcare Investment Banking, Oppenheimer & Company
Tariq Kassum MD, Vice President, Business Development and Strategy, Millennium Pharmaceuticals
Timothy Herpin, Vice President, Head of Transactions (UK), Business Development, AstraZeneca
Walter Capone, President, The Multiple Myeloma Research Foundation
Presenting Opportunities for Biotech, Pharmaceutical companies and Patient Advocacy Groups
Presenting at the forum offers excellent opportunities to showcase activities and highlight investment and partnership opportunities. Biotech companies will be able to communicate investment and licensing opportunities. These are for both public and private companies. The audience is comprised of financial and industry investors. These are streamed 15 minute presentations. The patient advocacy presentations are 30 minutes.
Sachs forums are recognised as the leading international stage for those interested in investing in the biotech and life science industry and are highly transactional. They draw together an exciting cross-section of early-stage/pre-IPO, late-stage and public companies with leading investors, analysts, money managers and pharmas. The Boston forum provides the additional interaction with the academic/scientific and patient advocacy communities.
Sponsorship and Exhibition
Sachs Associates has developed an extensive knowledge of the key individuals operating within the European and global biotech industry. This together with a growing reputation for excellence puts Sachs Associates at the forefront of the industry and provides a powerful tool by which to increase the position of your company in this market.
Raise your company’s profile directly with your potential clients. All of our sponsorship packages are tailor made to each client, allowing your organisation to gain the most out of attending our industry driven events.
To learn more about presenting, exhibition or sponsorship opportunities, please contact
Mina Orda + 44 (0)203 463 4890 or by email: Mina Orda.
From: Mina@sachsforum.com To: AvivaLev-Ari@alum.berkeley.edu Sent: Mon Dec 16 12:01:21 UTC 2013
From: Tomas Andrulionis <Tomas@sachsforum.com> Date: Tue, 10 Dec 2013 16:13:53 +0000 To: “avivalev-ari@alum.berkeley.edu” <avivalev-ari@alum.berkeley.edu> Conversation: Complimentary Invitation for the 2nd Annual Sachs Cancer Bio Partnering & Investment Forum, 19th March 2014, New York Academy of Sciences
From: PeerJ<newsletter@peerj.com> Date: Wed, 12 Jun 2013 18:10:54 +0000 To: AvivaLev-Ari <Avivalev-ari@alum.Berkeley.edu> Subject: PeerJ turned One today – help us celebrate by entering our competition
PeerJ
Hi Aviva,
We are very pleased to announce <http://blog.peerj.com/post//celebrating-the-one-year-anniversary-of-peerj> that this is the one year anniversary of PeerJ – it was on June 12th, 2012 that we first announced ourselves and started the process towards becoming a fully-fledged publishing company. Today, just 12 months later, PeerJ is completely up and running; we are publishing high quality peer-reviewed science; and we are doing our very best to change the world by pushing the boundaries of Open Access!
To briefly overview what has been achieved in the last year – we announced ourselves on June 12th 2012 and opened the PeerJ doors for submissions on December 3rd. We published our first PeerJ articles on Feb 12th 2013, and followed up by launching PeerJ PrePrints on April 3rd 2013. This last year has been spent recruiting an Editorial Board of 800 world renowned researchers; building cutting edge submission, peer-review, publication and pre-print software from scratch; establishing ourselves with all the major organizations who archive, index, list and certify new publications; and building an entirely new type <http://blog.peerj.com/post/46261563342/6-reasons-to-publish-with-peerj> of publishing company from the ground up.
We are celebrating this milestone with a new PeerJ Competition. On June 19th, we will give away 12 “complimentary publication” passes (the ability to publish one paper with us at no cost to you or any of your co-authors) + a PeerJ Charlie T-Shirt + a pin + a fridge magnet (!) to a random selection of 12 people (one for each month of our first year) who publicly post some variation of the following message:
“PeerJ just turned one! Open access publishing, for just $99 for life – check them out and submit now!”
Please include a link to us as well (you choose the best one!).
The last year has been an intense journey, and to be honest we have been so busy we almost missed the anniversary! We would like to take this opportunity to thank the many thousands of researchers who have signed up as PeerJ Members; all those who have authored or reviewed articles; all those who have joined our Editorial Board; and anyone who have simply expressed their support – without the involvement and enthusiasm of these people we would not be where we are today. Of course, we must also thank our dedicated staff (Alf Eaton, Patrick McAndrew and Jackie Thai) and Tim O’Reilly, who collectively took a chance on a brand new publishing concept, but who have been irreplaceable in making us what we are today!
By: Taha A. Kass-Hout, M.D., M.S. and Elaine Johanson
Taha A. Kass-Hout, M.D., M.S., is FDA’s Chief Health Informatics Officer and Director of FDA’s Office of Health Informatics. Elaine Johanson is the precisionFDA Project Manager.
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