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DRAFT: A Grand-Challenges Based Research Agenda for Scholarly Communication and Information ScienceF

This DRAFT white paper identifies selected grand challenges related to information science and scholarly communication. It was made available for public comment during October. A revised version will be released in mid-December.

Published onOct 10, 2018
DRAFT: A Grand-Challenges Based Research Agenda for Scholarly Communication and Information ScienceF


Public comment period closed on 10/31/18. We thank all of those who commented on this draft, and will review and respond to every comment submitted during the comment period. A final, revised version of this document will be available in mid-December.

A Grand-Challenges Based Research Agenda for Scholarly Communication and Information Science

Contributors* to this report, listed alphabetically: Micah Altman, Chris Bourg, Philip Cohen, G. Sayeed Choudhury, Charles Henry, Sue Kriegsman, Mary Minow, Daisy Selematsela, Anasuya Sengupta, Peter Suber, Ece Turnator, Suzanne Wallen, Trevor Owens, & David Weinberger.

The workshop and paper are supported by a grant from The Andrew W. Mellon Foundation. Thanks to the Program Committee for scoping and mapping the original framing for the conversations: Micah Altman, Christine Borgman, Chris Bourg, G. Sayeed Choudhury, Charles (Chuck) Henry, Abby Smith Rumsey, and Ethan Zuckerman; to the keynote speakers, whose remarks framed each meetings’ discussions: Kate Zward, Anasuya Sengupta, and Joi Ito; and to the external participants and library staff who participated in workshop discussion, listed below.

Abby Smith Rumsey, Alex Chassanoff, Alex Wade, Amy Brand, Anasuya Sengupta, Bethany Nowviskie, Brewster Kahle, Charles Henry, Christine Borgman, Chris Bourg, Clifford Lynch, Daisy Selematsela, David Rosenthal, David Weinberger, Deborah Fitzgerald, Donald Waters, Douglas Armato, Ethan Zuckerman, Heather Yager, Jennifer Hansen, Karrie Peterson, Kate Zwaard, Mary Minow, Melissa Hagemann, Micah Altman, Nancy McGovern, Palagummi Sainath, Patricia Hswe, Peter Suber, Phil Bourne, Philip Cohen, Roger Mark, Safiya Noble, Sayeed Choudhury, Sue Kriegsman, Suzanne Wallen, Trevor Owens.

*  Contributor statement. The authors describe contributions to this paper using a standard taxonomy. 1MA and CB provided the core formulation of the papers goals and aims, and MA and SK led the creation of the substantive topic outline. Writing for Sections 1-5 was lead by (respectively) MA & SK; AS, CB, MA & SK; DW,  MA, MM, GSC, & PS; DW, GSC, MA, MM, PC & PS; and CH, ET, & MA. SW lead copyediting.  All contributors provided review and commentary. MA and CB lead in obtaining funding for this project, and served as PI’s.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Table of Contents




Editors’ Note: we will include executive summary section will be a 2-5 page stand alone section, which can be formatted and distributed independently of the full report. This will include the recommendations in each section, along with appropriate context to understand them -- including significance, scope, central problems and research areas


1.1 Preface -- Identifying Grand Challenges

A global and multidisciplinary community of stakeholders came together in March 2018 to identify, scope, and prioritize a common vision for specific grand research challenges related to information science and scholarly communications. The participants were both traditional domain researchers and those who are aiming to democratize scholarship. An explicit aim of the summit was to identify research needs related to barriers in the development of scalable, interoperating, socially beneficial, and equitable systems for scholarly information; and to explore the development of non-market approaches to governing the scholarly ecosystem.

To spur discussion and exploration, grand challenge provocations were suggested by participants and framed into one of three sections: scholarly discovery; digital curation and preservation; and open scholarship. A few people participated in three segments but most only attended discussions around a single topic. Each track had approximately 20–25 people from different parts of the world -- including the United States, European Union, South Africa, and India. Domain researchers brought perspectives from a range of scientific disciplines; while practitioners brought perspectives from different roles (drawn from commercial, non-profit, and governmental sectors).

During our discussions it quickly became clear that the grand challenges themselves cannot be neatly categorized into discovery, curation and preservation, and open scholarship, or even for that matter limited to information science or librarianship. Several cross-cutting themes emerged, such as a strong desire to include underrepresented voices and communities outside of mainstream publishing and academic institutions, a need to identify incentives that will motivate people to make changes in their own approaches and processes toward a more open and trusted framework, and a need to identify collaborators and partners from multiple disciplines in order to build strong programs should be at the center of future planning.2

The discussions were full of energy, insights, enthusiasm for inclusive participation, and generally concluded with a desire for a global call to action to spark changes that will enable greater equitable and open scholarship. Some important and productive tensions surfaced in our discussions, particularly around the best paths forward on the challenges we identified. On many core topics, however, there was widespread agreement among participants - especially on the urgent need to address the exclusion from participation in knowledge production and access of so many people around the globe, and the troubling over-representation in the scholarly record of white, male, English-language voices. Ultimately everyone believed we can, should, and do have an obligation, to be better in this space and our communities can be catalysts for change.

1.2 Organization of this Report

While the spirit and intent of the workshop is present, this report is not intended to be a summary of the March 2018 workshop discussions. Instead, it draws attention to areas where a systematic research agenda and coordinated leadership have the potential to create a broad impact. In doing this, we seek to catalyze the advancement of knowledge management and scholarly communications globally, and across disciplines by charting specific challenges––and by identifying innovative, interdisciplinary, and collaborative research agendas to solve them.

In particular, this report describes a vision for a more inclusive, equitable, sustainable future for scholarship; characterizes the central technical, organizational, and institutional barriers to this future; describes the areas research needs to advance this future; and identifies several targeted “grand challenge” research problems. These “grand challenges” are fundamental research problems with broad applications, whose solutions are potentially achievable within the next decade.

We conclude the report with recommendations for concrete actions to advance scholarship. We call for academics, funders, knowledge creators, knowledge stewards, and educators to embrace these grand challenges, ignite changes in your own ecosystem to impact an information science and scholarly communications research agenda that will be more open, sustainable, collaborative, and globally inclusive.


2.1 Vision

Despite the democratizing promise of internet technologies,3today’s scholarly communications and information sharing environments are plagued by exclusion, inequity, inefficiency, elitism, exorbitant costs, lack of interoperability or sustainability, commercial rather than public interests, opacity rather than transparency, hoarding rather than sharing, and myriad barriers at individual and institutional levels to access and participation. Despite, or perhaps because of, the range of perspectives represented, the summit participants agreed that our common vision was of a global information environment that ensures durable, open, equitable, meaningful global access to knowledge consumption and creation in its many forms.

Such a vision requires the centering of knowledge producing communities around the world into a global network of partnerships where we all work toward a more inclusive, equitable, trustworthy, and sustainable research and learning ecosystem. The vision is to create a powerful infrastructure to support local communities and organizations where people can create, share, evaluate, learn from, and interpret information on both small and large scales without barriers, or fear for lost knowledge, in order to support ongoing scholarship. Achieving this vision will require focusing not only on extant systems and process of knowledge sharing and production, but also critically evaluating individual and institutional roles and interests that contribute to the current state.

The problems that plague our systems and prevent us from generating and utilizing wide open scholarship are fundamental and embedded in problems of social justice4 that derive not only from the consequences of unequal distribution of knowledge, but also from trust, safety, security, and ‘epistemic’5 injustice.  (unfairness stemming from the definition of what constitutes knowledge, who is assumed to be knowledgeable, and how knowledge is transmitted). This notion applies to individual people, different forms of knowledge communities and cultures, and the information, objects, and systems that support or challenge them. One could imagine that on one end of the spectrum trust, safety, and security includes how people feel with regard to job security or their role in a community, the reliability of data, unbiased and ethical algorithms, and stable networks. At the other end of the spectrum is data that disappear before they can be saved, networks that are intentionally tampered with to alter an information flow, algorithms that are opaque and therefore mistrusted, and in extreme cases there are people who are concerned for their own personal physical safety because of what they have learned or disseminated.

Solving these problems requires that scholarship be easier to discover, more durable, and access to it more open -- but this is not sufficient. We aim for a scholarly ecosystem that embeds the core values of inclusion, equity, trustworthiness, and sustainability -- in which people can broadly participate in both the creation and definition of scholarship.

Knowledge, how it is shared, and what other people do with it includes a wide continuum of possibilities for an inclusive, equitable, and sustainable ecosystem. We are looking at how our knowledge is learned, conveyed, interpreted, and utilized along the whole research spectrum in order to reach the inclusive, genuine, and reliable research world we can imagine.

2.2 Broadest Impacts

Over the last two hundred and fifty years, there have been unprecedented advancements in the human condition -- encompassing improvements in health, longevity, life-satisfaction, productivity, individual wealth, and the range of choices. These improvements have been enabled in large part by systematic investigations to produce generalized, shared, and durable knowledge -- also known as science and scholarship. (See Stephan 20126 for a discussion of the macro-economic impact of science).

Despite its deep and broad social benefits, science itself remains surprisingly constricted in a number of fundamental aspects:

  1. The benefits of science are unevenly distributed.7

  2. Access to scientific data and scholarly communication, as well as STEM learning materials, has until recently been limited almost exclusively to those inside research or university environments with the ability to pay, and fluency in English.8

  3. Participation in our collective knowledge is limited to a small minority -- the vast majority of research is conducted in elite university settings in developed countries.9

  4. Even in those countries, participation in science is heavily skewed by gender, race, class, and language -- which affect the construction and evaluation of scientific knowledge.10

  5. The evidence base is restricted -- subjects (people), behaviors, languages, forms of knowledge, even ways of knowing are restricted, and the evidence base in many fields is shifting to new sources. 11

  6. The algorithms we use to interpret evidence embody unexamined bias.12

Inclusion of people in different communities in the creation, dissemination, and use of scholarship is not only ethically imperative, but can strengthen research and scholarship globally, and increase the impacts of scholarship on the world.

The potential for broader inclusion to increase impact is apparent when one examines recent advances in social science. In the last twenty years, it has become possible to observe large groups of people and their communications in detail and over continuous periods of time -- this has lead to the creation of some of the largest publicly accessible collections of information about humans in history.13 And this has resulted in changes in the methods, evidence base, pace, and impact of many disciplines in the social sciences -- yielding new insights and challenging previous coarse categorizations of people and their characteristics.14

However despite this vast broadening in the evidence base, our current sources of information about people are heavily skewed to online behavior of industrialized western populations. And the current systems of governance for that information raise questions of privacy, intellectual freedom, and agency -- creating new opportunities to manipulate people for both profit and power.15 The social sciences have much to gain from a globally inclusive system of evidence and knowledge; and society has much to gain from value-driven governance of such a system.

There are shifts in the evidence base of public health and medicine that parallel the shifts in social science, and offer analogous promise and perils.16 We have only started to tap increasing gains from “citizen-science”17 in the STEM fields.. Inclusion is not just important but urgent -- there are big problems in the world (climate change, refugees, etc) - reengineering the knowledge ecosystem will affect people’s (literal) lives and literally, the fate of the world.

2.3 Recommendations for Broad Impact

In order to promote the broadest impacts of research in this area, in service to the vision of a more inclusive, equitable, and sustainable system of scholarship, we make the following recommendations:

●      Recommendation 2-A: We recommend that researchers consider the broadest possible impact of their work -- and how that work could be used to improve the inclusiveness and equity of the scholarly knowledge ecosystem.

●      Recommendation 2-B: We recommend that research funders include consideration of impact on the research ecosystem in their criteria for programs, and that they specifically identify the extent to which proposed work could increase equity and inclusion.


3.1 Challenges, Threats, and Barriers

The information science and scholarly communication research community should aim for a future which offers people across the world true opportunities to discover, access, and create scholarly knowledge; in which people have agency over knowledge about them and in interactions with knowledge systems; and in which scientific evidence and scholarship are abundant, enduring, transparent, and trustworthy. As we work towards this future, we must insure that the infrastructures, policies, collaborations and practices we adopt and support are informed by evidence and grounded in research-based decisions.

3.1.1 Challenges to Participation in the Research Community

Most of the current scholarly ecosystem contains information produced and controlled by a small part of the world’s population.18 Scholarly outputs are similarly limited. Most discoverable scholarship is in the form of refereed journals – which are dominated by a small community of professionals and publishers. This information is rarely accessible to everyone, especially in resource poor regions -- and access is, itself insufficient to enable participation.19 As a consequence, the knowledge, practices, and traditions of many communities is not discoverable, accessible, or preserved.

The potential impact of broadening participation in the creation and dissemination of scientific knowledge is substantial.20 The substantial improvements in people’s lives over the last 200 years stem largely from broader collection of and access to knowledge, and the many discoveries that knowledge enables. Broadening collection and access to knowledge increasingly depends on the meaningful participation of content creators across the world.21

3.1.2 Restrictions on Forms of Knowledge

Current scholarly outputs are dominated by English-language journal articles,22 and the available scholarly evidence-base is dominated by quantitative data.23 Because of this, current scholarship captures only a small portion of the diverse forms of knowledge, and ways of knowing.24 In many communities across the globe, knowledge is based on oral traditions, qualitative and experiential data, and other forms of knowing rarely recognized, valued, or represented in the current scholarly record.

One challenge here is to imagine new forms of scholarship that fit new forms of research in order to add new dimensions and perspectives25 broader than the conventional journal article, monograph, and dataset.26

A second, related challenge is to work on ways to make these new genres for scholarship acceptable to research institutions, especially to hiring, promotion, and tenure committees.27 New researchers should have the freedom to explore and present their work in a broad scope of formats and genres that are not restricted to existing norms.

A third, related challenge is for institutions to provide the infrastructure to support the creation and preservation of these new genres of scholarship,28 or to pay for scholars to host them elsewhere. Scholars will not want to pour time into these works if they cannot find platforms to support them for the long term.

Many new forms for scholarship and mechanisms for recognizing them have emerged have emerged at least as experiments.29 Many others have been proposed but not yet tried. Describing even the major ones would take more space than we have here. But we can point to some of the notable new properties that pioneering scholars are eager to try out. Some new genres are multimedia. Some integrate texts and data. Some are interactive. Some are dynamic, and offer regular or foreseeable updates. Some are designed to grow indefinitely and never reach a state that could be called finished. Some are collaborations by dozens or hundreds of people. Some might start as projects by one person, or one group, and later expand to accept contributions from the crowd. Some start as crowd-sourced projects. Some allow conventional attribution but some don’t. Some are so large that it’s not feasible to download them, but only to explore them in their online habitats. Some have APIs allowing them to integrate with other works, or other sources of information, creating hybrid or compound works of scholarship. Some are closer to living libraries or ecosystems than to individual works of scholarship.

As proposals for new genres become more numerous and more urgent, the research community will have to ask itself a series of hard questions. Which of these are worth trying? Which are worth encouraging and accommodating? Which are positively preferable to conventional genres, and for which purposes? How should we evaluate them (e.g. for hiring, promotion, and tenure), especially when they are hugely collaborative, too large to explore in full, or when they focus less on offering “an argument” for new conclusions than offering new ways to organize or validate knowledge? Should research institutions take a position on whether these should use certain open licenses, reside on open-source infrastructure, or become interoperable with certain other resources? We should expect this conversation to be ongoing and lengthy.

3.1.3 Threats to Integrity and Trust

Both technological advances and sustained democracy depend on the integrity of knowledge. However, formal scholarly knowledge generation is limited to small communities, and many members of the public mistrust science. Furthermore, it is increasingly difficult even for scholars to evaluate the weight of the evidence that should be given to claims made in scholarly communications.

Society is already wrestling with the challenges of ubiquitous fake information and disinformation -- even with respect to assertions that are simple and relatively straightforward to to verify.30 Current problems are expanding as the scale of scholarly production grows, placing strain on the mechanisms we have for peer review and quality control – which are slow, fallible, manipulable, and labor intensive.31 These include competing and overlapping systems of authority, including those run by corporate, state, and non-profit actors; increasing demands on the time of researchers asked to supervise and perform review and evaluation (with unclear reward systems); and threats from bad actors working at scales not previously possible (including states and automated systems).32

Much scientific data is not shared, and many knowledge products are ephemeral and can be erased, changed, and removed by politics, technological change, restrictive licenses, or neglect.33 Problems of access, integrity, and accountability all contribute to the problems of public mistrust and skepticism among government leaders.

3.1.4 Threats to the Durability of Knowledge

The durability of knowledge and scholarly products is essential to realizing the full range of scientific discoveries and works of scholarship, and to establishing the integrity of scholarly knowledge claims. Over the last decade, widespread shifts from tangible to digital media create imminent threats to the durability of the scholarly record and scientific evidence base. Moreover, the digital traces of human behavior have expanded far more rapidly than we collect, study, and preserve them.

The importance of digital preservation in ensuring the durability of knowledge is aptly summarized in the National Agenda for Digital Scholarship: “Effective digital preservation is vital to maintaining the authentic public records necessary for understanding and evaluating government actions; the verifiable scientific evidence base for reproducing research, and building on prior knowledge; and the integrity of the nation's cultural heritage. Substantial work is needed to ensure that today's valuable digital content remains accessible, useful, and comprehensible in the future — supporting a thriving economy, a robust democracy, and a rich cultural heritage.” 34 This agenda and preceding work35 have drawn attention to the challenges of particular formats, and the need for preservation infrastructure, business models, and organizational coordination among memory institutions.

Durability is not simply a challenge for memory institutions, however. Sustainable trustworthy scholarship requires that durability be designed into the evolving lifecycle of information creation and use. While the values of openness, inclusion, and durability are complementary, changes in one part of the scholarly ecosystem focused exclusively on promoting other value -- such as the adoption of article-fee based open access -- have the potential to affect the infrastructure and incentives for durability.

Moreover, the lack of diversity in the scholarly ecosystem results in biases not only in what is produced and analyzed, but in what is preserved within the current scholarly ecosystem. We are losing, through neglect, much of the world’s stock of traditional, local, historical memory and tacit knowledge.36 We are in a race against time, losing in many parts of the world the knowledge that is being generated as well as the window of opportunity to implement solutions to global problems.

3.1.5 Threats to Individual Agency

Participants inside the scholarly ecosystem are challenged to understand the increasingly complex algorithms that they implicitly rely upon.37 Further, ubiquitous data collection that gathers information from broad areas of society into academic and commercial research increases the need to maintain privacy, safety and control over research information.38 As participation in scholarship is broadened, there will be a need to honor different community norms on access and use of information.

Algorithmic discovery and analysis, while enabling many scientific advances, has the potential to amplify existing biases and to introduce new, and potentially hidden sources of unfairness. However, there is no consensus in research or practice over how to define or evaluate algorithmic transparency and fairness.

3.1.6  Barriers to a Scholarly Ecosystem that is Sustainable and Open

Open scholarship has been a goal for much of the scholarly community for 20-50 years. Public policy has driven requirements for open access to journal articles and for deposit of datasets. Multiple stakeholders39 have invested in repositories to capture scholarly products in digital libraries that are open to the world. However, open scholarship is still far from achieving the goals set long ago.40 While focus on journals and datasets has made some inroads in open access, other formats lag far behind. The worlds of music, ebooks, and video are tightly bound in a proprietary world, with licenses and digital rights management that are generally more restrictive than copyright law.41

Current structures, policies, systems, and norms do not incentivize the behaviors that will lead to the imagined open scholarship future we want. As open access has progressed, the commercial publishing industry has challenged (and sometimes co-opted) open access through changes in business models, copyright law, acquiring smaller companies and players, and other actions.42 At multiple levels, incentives are badly misaligned to the larger goals of scholarship and learning.

3.2 Grand Challenge Research Areas

The overarching question these problems pose is how to create a global scholarly knowledge ecosystem that supports participation, ensures agency, transparency, trustworthiness, and integrity, and is legally, economically, institutionally, and socially sustainable and durable.

Reaching this future state requires exploring a broad set of interrelated anthropological, behavioral, computational, economic, legal, policy, organizational, sociological, and technological areas. The extent of these areas of research is illustrated by the following examples:

●      What are the most effective modalities for sharing knowledge across different regions and communities, and promoting mutual learning across community boundaries? How can skills in scholarly knowledge creation, curation, and preservation be shared and learned from different knowledge communities? 43 What are the existing multiple models and traditions of preservation and curation from these broader communities including informal and unofficial stewards? How do these traditions and their trajectories relate to the affordances of digital materials and systems, and where is adaptation and refinement needed? How can these traditions and models be integrated to transform information science and formal library and archival practice?

●      What are the drivers for engagement and participation in scholarly knowledge creation, discovery and curation? What are the barriers to skill acquisition and transmission at the personal, organizational, disciplinary and ecosystem level? What interventions would lead to appropriate skills becoming pervasive? How do we address the need to be facilitative and supportive of skills development, while decolonializing power and control over methods, skills, and objects of curation?

●      What are forms of knowledge not represented in the current scholarly ecosystem? What approaches to describe, capture, and transmit tacit knowledge and other non-textual knowledge can be generalized and scaled? And how should the tacit knowledge that is the subject of scholarly study, or is integral to its practice be discovered, curated, and preserved?

●      What measures and algorithms are most effective for summarizing scholarly outputs at scale? What information architecture, semantic analysis, and computational infrastructure is needed to meaningfully link scholarly knowledge across sources and fields of study? How can both analysis and linkage be scaled to world knowledge, and adapted to its forms?

●      What parts of the scholarly knowledge ecosystem promote the values of transparency, individual agency, participation, accountability, and fairness? How can these values be reflected in the algorithms, information architecture, and technological systems supporting the scholarly knowledge ecosystem? What principles of design and governance would be effective for embedding these values?

●      How should the measures of use and utility of scholarly outputs be adapted for different communities of use, disciplines, theories, and cultures? What methods will improve our predictions of future value of collections of information, or enable the selection and construction of collections that will are likely to be of value in the future?

●      What are the determinants of scholarly and public trust in scholarly knowledge claims? What content (e.g. workflows, data) and characteristics of (information architectures, organizations, cultures, institutions) promote trustworthiness and the ability to evaluate the strength of evidence in claims? How can the mechanisms for promoting trust and trustworthiness be adapted to scholarly contributions by non-professional communities, and applied to non-traditional forms of knowledge?

●      What legal mechanisms, scholarly cultures, organizational designs, and economic models could support enduring access to knowledge without relying on a stream of access fees? What are the barriers and incentives against enduring open access, and what interventions could be effective in shifting laws, organizations, behaviors and markets to a sustainable open equilibrium?

The list above provides a partial outline of research areas that will need to be addressed in order to overcome the major barriers to a better future for scholarly communication and information science. As the field progresses in exploring these areas, and attempting to address the barriers discussed, new areas are likely to be identified. Even within this initial list of research areas, the number of important research questions ripe for exploration is large and pressing.

3.3 Recommendations for Research Areas and Programs

Based on the characterization of the research landscape above, we make the following recommendations:

●      Recommendation 3-A: We recommend that funders consider developing future programs and requests for proposals to address the barriers above.

●      Recommendation 3-B: We recommend that researchers in information science and related fields strongly consider selecting problems within a grand-challenge research area as part of their research program.

●      Recommendation 3-C: We recommend that reviewers give particular weight to research proposals and discoveries that address these barriers or advance grand-challenge research.

●      Recommendation 3-D: We recommend that the participants in the existing ecosystems, including publishers and ecosystem builders, consider how the systems they build can reduce the barriers identified above.

●      Recommendation 3-E: We recommend that researchers and stakeholders actively seek out new voices and participation in the design and conduct of research; and who can challenge currently accepted ways of conducting, communicating, and evaluating research.


All of the research areas described above hold great promise for exploration. In this section, we discuss in detail four targeted individual research questions, drawn from these broad research areas. The aim is to provide a statement of the research question that can be understood by researchers and practitioners in multiple disciplines; suggest how progress toward a solution could be measured; explain how such progress could result in substantial progress in addressing the problems above; and identify lines of research and practice that offer potential insights into a solution. We argue that each of these questions is potentially solvable in the next 7-10 years, and, if solved, will have substantial impact across multiple central problem areas.

Research and scholarship are embedded within and shaped by a broader ecosystem that comprises stakeholder organizations,44 social norms,45 laws,46  economic markets,47 and political institutions.48 This ecosystem as a whole affects how knowledge is produced, accessed, discovered, and preserved. None of the major challenges to equitable, trustworthy, inclusive, and durable scholarship (discussed in this report in section 3) can be fully resolved without an improved understanding of how to design institutional and normative ecosystems, and of what interventions are effective for moving us toward better ecosystems.

Research on the challenge of enduring, inclusive and open scholarship begins with an understanding of the problems exacerbated by its absence. These include weak trust in scholarly knowledge claims,49 which remain unverifiable or opaque across research communities and among wider publics when the processes and outcomes of research are not open, and when disparate access to research knowledge exacerbates social inequalities. The pursuit of openness in scholarship, however -- especially in access to published work -- may manifest as a treadmill of increasing expenses absorbed as user fees or publisher profits that fail to lead to systemic solutions.50 With resources devoted to these costs, investment in preservation with durable open access is threatened, even as the volume and complexity of material to be preserved in the scholarly record multiplies.51

Despite common recognition of this set of problems, effective incentives to drive key actors to develop and enact solutions to address them seem to be lacking.52 For example, scholarly societies often depend on revenue from journal subscription fees to fund various organizational and member goals and activities, thus creating a disincentive to adopting open models of dissemination that reduce or eliminate subscription revenue streams.53 Similarly, researchers and funders, as well as universities, have incentives to see their work appear in the most prestigious publications, regardless of their public accessibility, even as most scholars and scholarly institutions claim to  seek wider audiences for their research outputs.54

Organizational and technological innovations that promote open scholarship have the potential to promote opportunities for broader engagement across research communities and broader publics,55 to allow the use of machine tools for analysis and dissemination of research outputs and materials,56 and to facilitate crowd-based methods of evaluation.57 However, such innovations also pose risks, including the empowerment of bad online actors58 at greater scale or velocity.

Research on open scholarship solutions is needed to assess the scale and breadth of access, the costs to actors and stakeholders at all levels, and the effects of openness on perceptions of trust and confidence in research and research organizations. This will require assessment of the costs and returns of open scholarship at a systemic level, rather than at the level of individual institutions or actors. We also need to assess how open scholarship can reduce barriers to research materials and knowledge, especially those set up by social and economic inequalities.In addition, research should address the permeability of open scholarship systems to researchers across multiple scientific fields, and whether -- and under what conditions -- open scholarship enhances interdisciplinary collaboration.

Please comment below  with additional citations, references, links to projects, or other notes on research and development to this area --  after reviewing Appendix 1 for a list of more detailed prompt questions. Please cite specific examples if possible.

●      Problem measurements

●      Solution impact

●      Suggested research approaches

4.2 Research Challenge: Measuring, Predicting, and Adapting to Use and Utility Across Scholarly Communities

In order to manage information we must value it: Systems and algorithms for discovery nominally aim to support users in finding information that is relevant to their needs -- information that is of value to them in their current context. Curation and preservation systems and strategies aim to deliver future (medium or long-term) value to specific communities of research or practice. Assumptions are embedded throughout the scholarly information ecosystem regarding what information is valuable (or will be), which communities will value it, and what forms of use and access will realize this valuable. Explicit models of  research information value and uses are much less common.

Search and discovery increasingly eludes expert (human) indexing, and relies on algorithms -- creators of search algorithms and discovery systems attempt to predict the value of specific information to a specific user at a specific time.59 These algorithms which in turn rely heavily on signals of broad and current use (e.g. clicks, downloads, links), and are influenced by the monetary value that can be derived from such system (such as sales of goods, or opportunities, ad placement). Approaches based on these aggregate models of information value are unlikely ever to support  systematic discovery of information of value to important, but small communities of knowledge seekers. For example, current search systems will rarely uncover the most promising yet-unexamined archival material in the history of robotics; the most promising corpus for evaluating methods to detect gerrymandering; the most reliable software for estimating models in comparative phylogenetics; or other types of materials, which are of high intellectual value, but not profitable; or which are valuable to a community that is distinct, but not large.

Researchers and curators often rely on professional judgement, and manual selection and assessment processes to decide what  information to retain, how long to retain it, what effort to expend in making it accessible and understandable,  and when that effort should be applied. These processes are often hyper-local and ad-hoc, based on the history of practice and the local values of the organization or community of practice making these decisions.60 Often these processes originate from a prior analog era, when all the information on which each organization relied had to be ‘held’ (formally acquired or created); and, in practice, it was possible to select and curate only information that was held.61  Many of the models of value underlying our current curation processes have not been updated or adapted to fit current realities.62 And the absence of explicit models of value makes it difficult to effectively adapt these processes to non-traditional forms of evidence (e.g. software, oral testimony);  for new non-traditional communities of research and practice; or for new types of use (e.g. non-consumptive data mining). 63

The development of formal models, methods, and empirical analysis, which would lead to more rigorous, reliable, and systematic evaluation of the value of research information constitutes an important, but challenging set of problems. Estimating the value of information is inherently difficult. Arrow’s information paradox states that ex-ante a buyer cannot assess the value of particular information – it can only be known ex-post, at which point the buyer has limited incentive to pay for it.64 Although assignment of intellectual property rights can address this issue to a limited extent, it is very challenging65  – and hence markets for information goods are generally thin. Furthermore, intellectual property rights notwithstanding, the non-consumptive and limited excludability that is inherent in properties of information goods implies that any pure market solution will produce and distribute information at levels that are socially sub-optimal.66 Although data quality is sometimes seen as a proxy for value, no feasible universal quality measure exists – data quality measures are notoriously varied, discipline specific, contextual, and difficult to implement in practice.67

In the preservation of information, diversification of storage and representation is recognized as an essential strategy for ensuring future accessibility -- and there is a well-recognized taxonomy of risk sources that guides diversification strategy. We have no equivalent strategies to diversify across the risks to information value. In economics, methods such as revealed preference analysis and contingent valuation surveys68 are often used to measure the value of non-market goods – yet these methods have not been applied to valuing research data. Similarly portfolio selection modeling69 is the primary tool used in finance to diversify across risky investments, but has never been applied to the ‘investments’ in developing collections of information. Solutions in this area would yield models of information valuation that could be examined, challenged, and refined; and taxonomies of uses, communities, and threats that could be used for diversification strategies.

Please comment below  with additional citations, references, links to projects, or other notes on research and development to this area --  after reviewing Appendix 1 for a list of more detailed prompt questions. Please cite specific examples if possible.

●      Problem measurements

●      Solution impact

●      Suggested research approaches

4.3 Research Challenge: Designing and Governing Algorithms in the Scholarly Ecosystem to Support Accountability, Credibility, and Agency

Across the scholarly information ecosystem automated algorithms play increasingly critical roles in discovery (e.g. relevance ranking; recommender systems);70 in information extraction and summarization (e.g. automated abstract generation, literature mining);71 and in the evaluation of scholars and scholarship (e.g. detection of plagiarism, image manipulation, or journal citation inflation; evaluation of collaboration impact; predicting productivity).72 Moreover, the rapid growth in the volume of evidence, number of publications, and scale of collaboration in research73 generates strong pressure to rely on such automated systems -- the growth of scientific knowledge relies on algorithms and algorithmic systems to support knowledge discovery, evaluation, and collaboration at scale.

As their ubiquity increases, algorithms in the scholarly ecosystem are growing increasingly complex and opaque: ranging from models that while theoretically well-defined remain difficult to estimate and interpret (e.g. use of latent-dirichlet allocation to extract science topics; use of network regression models to measure collaboration)74 to the nominally transparent but effectively inscrutable (e.g. use of open deep-learning for recommender systems)75 to algorithms that are opaque and ever-changing by design (e.g. Google’s systems for relevance ranking).76

The problems posed by the use of such complex algorithms are now becoming recognized in the wider public sphere. These problems include violation of human privacy or agency (e.g. recommender systems inadvertently revealing purchasing habits to others);77 of biases and inequities in outcomes, that result from algorithmic design choices (e.g. the poor performance of facial recognition algorithm for people of color);78 the potential for algorithmic systems to aggregate and amplify human biases (e.g. substantial explicit racialization of Google search ad placement resulting from the aggregation of implicit bias in click-through behavior);79 to the intentional adversarial manipulation of digital evidence80 and of machine learning algorithms to game evaluation  or actively harm others (e.g. adversarial attacks on image detection).81

Addressing this interrelated set of problems requires advances in multiple fields and at multiple levels. The design and evaluation of algorithmic bias, fairness, and manipulability is generally in early stages. Further, in the domain of scholarly information, we have yet to identify the necessary properties of algorithms that are required to protect individual agency, facilitate collaboration, facilitate the identification of new biases, prevent gaming, and preserve trustworthiness -- nor have we identified the fundamental constraints on and tradeoffs among these goals.  For those few properties that have been identified as desirable -- such as individual information privacy we have limited understanding of how to successfully design and deploy algorithmic systems that satisfy these properties.82 And even for those algorithms that are commonly in use, we have little systematic empirical evidence on their quality, manipulability, and biases.

Please comment below  with additional citations, references, links to projects, or other notes on research and development to this area --  after reviewing Appendix 1 for a list of more detailed prompt questions. Please cite specific examples if possible.

●      Problem measurements

●      Solution impact

●      Suggested research approaches

4.4 Research Challenge: Integrating Oral and Tacit Knowledge into the Scholarly Ecosystem

Participation in the collective knowledge of science and scholarship is currently limited to a small minority (as discussed in section 2, above). In part this is because scholarly communication and reputation is primarily transmitted and promoted through publication of journal articles and books.


Most culture, much knowledge about history, skills, and methods; is not written. Knowledge that derives from or pertains to indigenous, traditional, and local communities is often transmitted and preserved through oral histories and oral traditions. Even within our current system of science there is evidence that critical parts of the knowledge needed to conduct science (e.g. how to perform experimental bench methods);83 and to have  successful careers as scientists is tacit -- resistant to transmission in textual form. Within science this is often transmitted orally and experientially through collaboration and mentoring relationships -- which can have a substantial impact on both the reliability of scientific results,84 and disparities in the diversity of the academy.85

Neither the methods nor the systems used to represent and manage the scholarly record are well-adapted to non-textual knowledge. The result is that most knowledge in tacit or oral form remains unexamined, invisible, and is not recognized, curated or preserved within the scholarly community.

Integrating oral and tacit knowledge into the scholarly ecosystem raises not only methodological and technical challenges, but deep conceptual challenges as well.86 The scholarly conceptualization of information integrity will need to be expanded, along with the mechanisms and methods we use to manage authenticity, provenance, and versioning. Models of attribution, authority, and trust will need to be extended to both these forms of knowledge, and to the communities that produce it. Further the widespread dissemination of oral and tacit knowledge that is  embodied in the behavior of individuals raises challenges for information agency -- and for the mechanisms we use to provide consent for and control access to  information.

Please comment below  with additional citations, references, links to projects, or other notes on research and development to this area --  after reviewing Appendix 1 for a list of more detailed prompt questions. Please cite specific examples if possible.

●      Problem measurements

●      Solution impact

●      Suggested research approaches


5.1 The Need for Leadership to Coordinate Initiatives

Many of the opportunities for scholarship that are made possible by the rapidly advancing technologies have yet to be fully realized. There are several reasons for this: As discussed above, the social, legal, technical, and organizational systems for disseminating, discovering, reusing, and communicating scholarly information have not kept pace with the technologically induced changes in the scholarly ecosystem.

Left to the market, the economics of knowledge in digital form creates both network externalities and reputation effects that are increasingly exploited by rent-seeking monopolies.87 To avoid this market disequilibrium requires that institutions coordinate to manage scholarly knowledge -- and this requires leadership. Some set of individual organizations must go beyond their local interests -- and invest effort and reputation into changes to the scholarly ecosystem that yield broad benefits.

At the same time, organizations should not act in isolation. Almost every institution now relies for its business, operations, and mission on large amounts of information that go beyond institutional boundaries. The amount of information is so great, and the risks so diverse, that no single organization can effectively ensure sustainable access to all the information it produces or needs.88 At the same time, for many pools of digital information, multiple institutions value it. Together, these imply that collaboration is essential -- institutional leaders must not only innovate, but coordinate.

5.2 Role of Libraries as Advocates and Collaborators

Research universities are among the most long-lived of human institutions. University libraries are widely trusted as the permanent stewards of the scholarly record and scientific evidence base within these institutions, and libraries have highly refined expertise and infrastructures for the organization and dissemination of knowledge. Further, the grand challenges identified above will likely be solved only through a cross disciplinary approach, and libraries are by design interdisciplinary, and in practice trusted as an honest broker of knowledge. Finally, the values of libraries are deeply aligned with the values of knowledge communities -- libraries constitute themselves as being in service to these communities, in contrast with commercial entities, and even in contrast to the larger organization within which research libraries are embedded.

Libraries should collaborate in the grand challenge research we have described in this paper. Further, libraries should act in other ways as direct agents of change and also as a voice to enlist other change-makers. Libraries can help to educate the communities that they serve about information ethics, agency, and risks.89 Libraries can collaborate to develop common open infrastructure.90 Libraries can help to make the norms and culture of scholarship more inclusive by documenting and disseminating the tacit knowledge that is part of the successful practice of scholarship -- much of which is inaccessible except through direct mentoring.91 As trusted brokers for information, they can advocate on behalf of the scholarly community both to the government and to commercial information providers and intermediaries.

5.3 Incorporating Values of Openness, Sustainability, and Equity into Scholarly Infrastructure and practice

With respect to the practice of research, it is worth noting that many fields of scholarship, academic associations, professional groups, and societies have issued ethics statements involving integrity of the work, confidentiality of the individual, and being mindful of the direct or indirect impact that research/work outcome may have on the lives of individuals, groups, or societies. Leadership at these professional and academic organizations have the power to align “do no harm”,high level principles with active and impactful policy implementations that set as a goal equitable, diverse, inclusive, and socially just outcomes. Universities often work under explicit policies and procedures but defining and implementing such research outcomes requires systems in place that intentionally support the advancement of equitable and diverse societies worldwide. This remains an important challenge because it means saying no to certain funding sources, and adjusting relationships between wealthy and impactful research institutions and industries.

Much of the infrastructure for scholarship is neither owned nor designed by scholar, but has been developed by commercial entities for profit -- and is controlled by a few large companies.92 As the practice of research and publishing has accelerated, requiring more integration of information across the research lifecycle, this infrastructure has become increasingly complex, and increasingly dominated by a small number of commercial entities. Should similar ethical principles be applied to infrastructure as to practice? Does commercial dominance in infrastructure present risks to achieving the goal of open, sustainable, and equitable scholarship?

As an example of existing tensions, it has been broadly recognized that the profit-driven model of social-media companies such as Facebook and Twitter create strong incentives to collect and monetize information about participants in this network -- which is in tension with protecting information privacy.93 Similarly, the reliance of Google on advertisement revenue influences both what is indexed, and how relevance is operationalized.94 More generally, commercial entities have an incentive toward algorithmic opacity in order to protect their trade secrets and competitive advantages.95

The increasing prevalence of high-profile information breaches96 and the increasing ability to re-identify individuals and their characteristics based on aggregated or nominally ‘anonymous data’97 has led to increasingly widespread support for systems of information discovery and sharing that incorporate respect and protections for individual agency and information privacy in to their core design. In some cases, values such as openness, sustainability, and equity can and should be incorporated deep into the infrastructure of new systems from the beginning. In other cases, research is needed to determine whether and how such values could be effectively expressed and enacted  using existing infrastructure that was created for very different functions and with different value propositions than those animating the creation of systems explicitly designed to support open, equitable, sustainable scholarship. 

We also must critically examine the unintended consequences and uses of policies, practices and infrastructures that have been explicitly developed in support of open scholarship. For example:

●      How has the discovery and hosting of open-access content on proprietary infrastructure (e.g. SSRN, bepress, Google Scholar) created or mitigated barriers to accessing that content?

●      What creates incentives for stakeholders to use open software, standards, and API’s -- particularly when hosting open access content?

●      How can methods be used to design and refine open infrastructure to meet, to support reuse, extension, adaption at the local level -- while being able to function at the continually growing scale of global research output?

Addressing these questions requires integrating research with practice and infrastructure development. Research is needed to guide the design of platforms that are consistent with our values; and platforms are needed that can be instrumented to evaluate these designs, and contribute to our understanding of where we are successfully promoting the objectives we seek. To be successful at a global scale, valuation of practice should go beyond case-studies in their approach, and include replicable methods to support systematic inference, such as randomization and pre- and post-evaluation.

5.4 Funders, Catalysts and Coordinators

A number of organizations currently fund, coordinate, or catalyze advances in research, infrastructure, and practice, which enables open, inclusive, and durable scholarship. The US federal agencies Institute for Museum and LSciences (IMLS) and the National Endowment for Humanities (NEH); the European Research Council; and The Andrew W. Mellon and Alfred P. Sloan Foundations all have long track-records of supporting research, practice, and infrastructure in these areas.98 A number of other funders -- including Wellcome Trust, National Science Foundation, National Institutes for Health, Chan Zuckerberg Initiative, Gates Foundation, Helmsley Foundation, Open Society Foundation, and the Gordon and Betty Moore Foundation -- have supported more limited initiatives related to these areas and primarily centering on open and reproducible research.This good work notwithstanding, we argue that the problems and challenges described in this report merit recognition by the entire spectrum of funders engaged directly or indirectly in supporting research and scholarship. 

Finally, success in advancing these areas will rely on organizations to coordinate collaborative approaches to research, practice, and infrastructure. This is difficult because coordination is often a public good -- providing more benefits to the research community as a whole, than to the coordinating institution (indeed, many coordinating institutions invest more than they expect to receive directly).  Despite this structural challenge, institutions like CLIR (along with DLF and National Digital Stewardship Alliance (NDSA)), Research Data Alliance, SPARC and Co-Data have been successful in coordinating standards development and educational initiatives;99 and  organizations such as Duraspace, the Dataverse Community, Digital Preservation Network, and Center for Open Science100 have played vital roles in coordinating the development and support of the vital research infrastructure that supports open scholarship.

Organizations such the Coalition of Networked Information, Association of Research Libraries, and the National Academies (primarily through Board on Research Data and Information)  -- joined more recently by organizations such as Force11 and Sage Bionetworks have established themselves as catalysts for open scholarship. They play a vital role in disseminating information on initiatives and research, convening experts, and engaging in advocacy. Over the last decade organizations such as NDSA, The Long Now Foundation, and DPC101 have played a similar catalytic role for the issue of information durability. Only recently have  organizations focused on equitable and inclusive knowledge, such as Whose Knowledge,102 and have been recognized in the scholarly community.

Progress towards a more open, equitable, trustworthy, and durable scholarly ecosystem requires that more institutions take catalyzing and coordinating roles in addressing the challenges and exploring the research areas described in section three. Further, existing organizations can help greatly by recognizing in their programs the interrelationship between openness, impact, trustworthiness, durability, and inclusivity in research and scholarship.

5.5 Recommendations for Integrating Research, Practice, and Policy

Summarizing the discussion of the connection across research, policy, and practice above, we make the following recommendation:

●      Recommendation 5-A: We recommend that individual research institutions take public responsibility for leading and coordinating inclusive efforts to address the barriers to a more equitable and inclusive systems of scholarship.

●      Recommendation 5-B: We recommend that research libraries promote a vision of inclusive and equitable scholarship within their institutions; that they engage in work on legislation and public policy; and that they enlist others in the scholarly community as change-makers.

●      Recommendation 5-C: We recommend that those engaged in developing platforms and communities of practices actively seek new voices and participation in their design and use.

●      Recommendation 5-D: We recommend that those engaged in research, practice, and advocacy in the area of open and inclusive scholarship should collaborate to develop platforms and interventions that can contribute to our understanding of what works. Evaluation of practice should go beyond case-studies in their approach, and include replicable methods to support systematic inference.

●      Recommendation 5-E: We recommend that stakeholders give priority to resourcing programs that rigorously integrate research and practice; and particularly to those programs that systematically contribute to the overall cumulative evidence base for inclusive, equitable, and credible scholarship.


Appendix 1: Research Problem Prompts

Problem measurements

●      What characterizes a feasible solution to the problem – what conditions must any solution satisfy?

●      What characterizes a “good” solution – what conditions are sufficient and/or how would researchers across disciplines measure the quality of the solution?

●      How do we evaluate progress toward a solution – what empirical evidence would demonstrate progress, how can progress be quantified?

Solution impact

●      Who would care if a “good” (as previously) was reached and what difference would it make?

●      In what ways would it advance multiple scientific fields – what other related problems would it solve and what new things would those fields be able to then accomplish?

●      What is the size of the potential economic impacts?

●      How will a solution to this problem affect individuals’ lives and society?

Current approaches

●      How is this problem (and the goals it addresses) approached today?

●      What are the gaps in current knowledge – are there necessary conditions that we do not understand how to satisfy?

●      What are the limits of current solutions – what criteria, and what expectations related to transparency, need to be improved to reach “good” solutions?

●      What types of new research discoveries and policy considerations are needed to achieve “good” solutions?

Research Approaches

●      Why do we think a good solution is achievable in the foreseeable future?

●      What are the new insights from theory; new empirical discoveries; new connections among disparate approaches; new methods; or new sources of data that suggest that solutions are coming within reach?

●      Are there previously unrecognized connections between this problem and successful solutions in other disciplines?

●      Are there previously unrecognized links to problems in other discipline in other disciplines that could be applied to this problem?

●      Are their active initiatives or projects demonstrating progress in these areas?

●       What are potential next steps: On-ramps? Collaborations to seek? Stakeholders to engage?



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3.  For thoughtful analyses of technological changes and of their potential to further revolutionize scholarship see: Atkins, D., 2003, Revolutionizing science and engineering through cyberinfrastructure: Report of the National Science Foundation blue-ribbon advisory panel on cyberinfrastructure. National Science Foundation; Berman, F. and H. Brady, 2005, Workshop on Cyberinfrastructure for the Social and Behavioral Sciences: Final Report, National Science Foundation.

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13.  King, Gary. "Restructuring the social sciences: reflections from Harvard's Institute for Quantitative Social Science." PS: Political Science & Politics 47, no. 1 (2014): 165-172.

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15.  See Hilbert, Martin. "How to Measure" How Much Information"? Theoretical, Methodological, and Statistical Challenges for the Social Sciences Introduction." International Journal of Communication 6 (2012): 1042-1055, summarizing a special issue of IJOC on this topic.

16. Raghupathi, Wullianallur, and Viju Raghupathi. "Big data analytics in healthcare: promise and potential." Health information science and systems 2, no. 1 (2014): 3; Vayena, Effy, Marcel Salathé, Lawrence C. Madoff, and John S. Brownstein. "Ethical challenges of big data in public health." PLoS computational biology 11, no. 2 (2015): e1003904.

17. Wiggins, Andrea, and Kevin Crowston. "From conservation to crowdsourcing: A typology of citizen science." In System Sciences (HICSS), 2011 44th Hawaii international conference on, pp. 1-10. IEEE, 2011.; Levine, S.S. and Prietula, M.J., 2013. “Open collaboration for innovation: Principles and performance. Organization Science,” 25(5), pp.1414-1433; Majchrzak, Ann, and Arvind Malhotra. "Towards an information systems perspective and research agenda on crowdsourcing for innovation." The Journal of Strategic Information Systems 22, no. 4 (2013): 257-268.

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19.  As an example, the difficulty of accessing research information through mobile phones, which are the primary channel for accessing information, is a barrier that must be addressed: Hosman, Laura, and Elizabeth Fife. "The use of mobile phones for development in Africa: Top-down-meets-bottom-up partnering." The Journal of Community Informatics 8, no. 3 (2012); and see for barriers to engagement in the same region: Ojanpera, Sanna, Mark Graham, Ralph K. Straumann, Stefano De Sabbata, and Matthew Zook. "Engagement in the knowledge economy: Regional patterns of content creation with a focus on Sub-Saharan Africa." (2017): 33-51.

20.  Ali-Khan SE, Jean A, MacDonald E and Gold ER. “Defining Success in Open Science”. MNI Open Res 2018, 2:2 (doi: 10.12688/mniopenres.12780.2)

21.  See for  example: European Commission on Open Science

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23.  On the importance of non-numeric information; the dearth of scientific archives providing this content and the challenges of providing durable access, see:National Research Council. Frontiers in massive data analysis. National Academies Press, 2013.; Hammersley, Martyn. "Qualitative data archiving: some reflections on its prospects and problems." Sociology 31, no. 1 (1997): 131-142.;Elman, Colin, Diana Kapiszewski, and Lorena Vinuela. "Qualitative data archiving: Rewards and challenges." PS: Political Science & Politics 43, no. 1 (2010): 23-27.;  Mannheimer, Sara, Amy Pienta, Dessislava Kirilova, Colin Elman, and Amber Wutich. "Qualitative Data Sharing: Data Repositories and Academic Libraries as Key Partners in Addressing Challenges." American Behavioral Scientist (2018):.

24.  See for examples of knowledge that is not readily reducible to text: Ahn, Sun Joo, Joshua Bostick, Elise Ogle, Kristine L. Nowak, Kara T. McGillicuddy, and Jeremy N. Bailenson. "Experiencing nature: Embodying animals in immersive virtual environments increases inclusion of nature in self and involvement with nature." Journal of Computer-Mediated Communication 21, no. 6 (2016): 399-419; Bailenson, Jeremy. Experience on Demand: What Virtual Reality Is, how it Works, and what it Can Do. WW Norton & Company, 2018; McPherson, T., 2018. Feminist in a Software Lab: Difference+ Design. Harvard University Press; Eric Dinmore 2015  “Collecting, Curating, and Presenting ‘3-11’ With Harvard’s Digital Archive of Japan’s 2011 Disaster” Verge: Studies in Global Asias, 1(2): 37-41

25.  (Citation needed)

26.  And it is worth noting that even traditional outputs, such as datasets, have lacked consistent practices for publication and citation:  Altman, Micah, Christine Borgman, Mercè Crosas, and Maryann Matone. "An introduction to the joint principles for data citation." Bulletin of the Association for Information Science and Technology 41, no. 3 (2015): 43-45.

27.  See for example Andersen, Deborah Lines. Digital Scholarship in the Tenure, Promotion and Review Process. Routledge, 2015.; Cheverie, Joan F., Jennifer Boettcher, and John Buschman. "Digital scholarship in the university tenure and promotion process: A report on the sixth scholarly communication symposium at Georgetown University Library." Journal of Scholarly Publishing 40, no. 3 (2009): 219-230. And Flanders, Julia. "The productive unease of 21st-century digital scholarship." Defining Digital Humanities: A Reader (2013): 205-218.

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29.  See (respectively) for example emerging mechanisms for recognition of software as a product of scholarship in the sciences; and an foundational introduction to new forms of digital scholarship in the humanities: Niemeyer, Kyle E., Arfon M. Smith, and Daniel S. Katz. "The challenge and promise of software citation for credit, identification, discovery, and reuse." Journal of Data and Information Quality (JDIQ) 7, no. 4 (2016): 16.; Wardrip-Fruin, Noah, and Nick Montfort. "The New Media Reader, A User's Manual." MIT Press (2003).

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63. See, for example:  Zeng, Jiaan, Guangchen Ruan, Alexander Crowell, Atul Prakash, and Beth Plale. "Cloud computing data capsules for non-consumptive use of texts." In Proceedings of the 5th ACM workshop on Scientific cloud computing, pp. 9-16. ACM, 2014.

64.  Arrow, K.J., 1972. Economic welfare and the allocation of resources for invention. In Readings in Industrial Economics (pp. 219-236). Palgrave, London.

65.  Gans, Joshua S., and Scott Stern. "Is there a market for ideas?." Industrial and Corporate Change 19, no. 3 (2010): 805-837.

66.  Hess, Charlotte, and Elinor Ostrom. "A Framework for Analyzing the Knowledge Commons: a chapter from Understanding Knowledge as a Commons: from Theory to Practice." (2005).

67. R. Price, G. Shanks, 2005. “A Semiotic Information Quality Framework: development and
comparative analysis”, Journal of Information Technology 20: 88-102. S.E. Madnick, R.Y. Wang, Y.W. Lee, H. Zhu, 2009. “Overview and Framework for Data and Information Quality Research”, ACM Journal of Data and Information Quality 1(2) 1-22; Altman, Micah . "Mitigating threats to data quality throughout the curation lifecycle." in G. Marciano, C. Lee, and H. Bowden, Curating For Quality: Ensuring Data Quality to Enable New Science. National Science Foundation, Arlington County, VA (2012): 1-119.

68.  See, for an introduction: John Loomis; Valuing Environmental and Natural Resources: The Econometrics of Non-Market Valuation, American Journal of Agricultural Economics, Volume 87, Issue 2, 1 May 2005, Pages 529–530,

69.   Markowitz, Harry. "Portfolio selection." The journal of finance 7, no. 1 (1952): 77-91.

70.  See for a review of recommendation system algorithms: Park, Deuk Hee, Hyea Kyeong Kim, Il Young Choi, and Jae Kyeong Kim. "A literature review and classification of recommender systems research." Expert Systems with Applications 39, no. 11 (2012): 10059-10072.

71. Andronis, Christos, Anuj Sharma, Vassilis Virvilis, Spyros Deftereos, and Aris Persidis. "Literature mining, ontologies and information visualization for drug repurposing." Briefings in bioinformatics 12, no. 4 (2011): 357-368.

72. See for example Farid, Hany. "Image forgery detection." IEEE Signal processing magazine 26, no. 2 (2009): 16-25.; Parrish, Debra, and Bridget Noonan. "Image manipulation as research misconduct." Science and Engineering Ethics 15, no. 2 (2009): 161-167; Engels, Steve, Vivek Lakshmanan, and Michelle Craig. "Plagiarism detection using feature-based neural networks." ACM SIGCSE Bulletin 39, no. 1 (2007): 34-38;

73. Adams, James D., Grant C. Black, J. Roger Clemmons, and Paula E. Stephan. "Scientific teams and institutional collaborations: Evidence from US universities, 1981–1999." Research policy 34, no. 3 (2005): 259-285.; Altman, Micah, and Marguerite Avery. "Information wants someone else to pay for it: laws of information economics and scholarly publishing." Information Services & Use 35, no. 1-2 (2015): 57-70;

74.  Abbasi, Alireza, Jörn Altmann, and Liaquat Hossain. "Identifying the effects of co-authorship networks on the performance of scholars: A correlation and regression analysis of performance measures and social network analysis measures." Journal of Informetrics 5, no. 4 (2011): 594-607.

75.  See, for example: Wang, H., Wang, N. and Yeung, D.Y., 2015, August. Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1235-1244). ACM.

76.  Diaz, Alejandro. "Through the Google goggles: Sociopolitical bias in search engine design." In Web search, pp. 11-34. Springer, Berlin, Heidelberg, 2008; Lazer, D., Kennedy, R., King, G. and Vespignani, A., 2014. The parable of Google Flu: traps in big data analysis. Science, 343(6176), pp.1203-1205.

77.  Ohm, Paul. "Broken promises of privacy: Responding to the surprising failure of anonymization." Ucla L. Rev. 57 (2009): 1701

78.  See Phillips, P. Jonathon, Fang Jiang, Abhijit Narvekar, Julianne Ayyad, and Alice J. O'Toole. "An other-race effect for face recognition algorithms." ACM Transactions on Applied Perception (TAP) 8, no. 2 (2011): 14; and for evidence of severity and ubiquity of the problem see White, D., Dunn, J.D., Schmid, A.C. and Kemp, R.I., 2015. Error rates in users of automatic face recognition software. PLoS One, 10(10), p.e0139827.; Garvie, Clare. The perpetual line-up: Unregulated police face recognition in america. Georgetown Law, Center on Privacy & Technology, 2016.

79.  See, for example: Sweeney, L., 2013. Discrimination in online ad delivery. Queue, 11(3), p.10; Sadler, Bess, and Chris Bourg. 2015. "Feminism and the future of library discovery." Code4Lib 10; Noble, Safiya Umoja. Algorithms of Oppression: How search engines reinforce racism. NYU Press, 2018. Eubanks, V., 2018. Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press.

80.   For the difficulties of establishing authenticity of web based information see:Aturban, M., Nelson, M.L. and Weigle, M.C., 2017. Difficulties of Timestamping Archived Web Pages. arXiv preprint arXiv:1712.03140; and for the surprising difficulties associated with the muchn the simpler task of verifying that numerical data has been unaltered see: Altman, Micah. "A fingerprint method for scientific data verification." In Advances in Computer and Information Sciences and Engineering, pp. 311-316. Springer, Dordrecht, 2008.

81.  Moosavi-Dezfooli, Seyed-Mohsen, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard. "Universal adversarial perturbations." 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017.

82.  For example, recommender systems have become ubiquitous in discovery but generally fail to protect information privacy. Both new algorithm development and careful analysis is necessary to develop recommendation algorithms that preserve information privacy, see: McSherry, Frank, and Ilya Mironov. "Differentially private recommender systems: Building privacy into the netflix prize contenders." In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 627-636. ACM, 2009. For a review of challenges in protecting privacy in algorithmic systems  and tensions with transparency and open data requirements see Altman, Micah, Alexandra Wood, David R. O’Brien, and Urs Gasser. "Practical approaches to big data privacy over time." International Data Privacy Law (2018); and Altman, Micah, Alexandra Wood, David R. O'Brien, Salil Vadhan, and Urs Gasser. "Towards a modern approach to privacy-aware government data releases." Berkeley Tech. LJ30 (2015): 1967.

83.  See for example Pasquali, Matias. "Video in science: Protocol videos: the implications for research and society." EMBO reports 8, no. 8 (2007): 712-716.;  and

84.  Lithgow, Gordon J., Monica Driscoll, and Patrick Phillips. "A long journey to reproducible results." Nature News 548.7668 (2017): 387.

85.  Moss-Racusin, Corinne A., John F. Dovidio, Victoria L. Brescoll, Mark J. Graham, and Jo Handelsman. "Science faculty’s subtle gender biases favor male students." Proceedings of the National Academy of Sciences 109, no. 41 (2012): 16474-16479.

86.  See for example an analysis of the theoretical and practical challenges of capturing oral history and ‘folkloric’ information generally: Owens, Trevor. The Theory and Craft of Digital Preservation, John Hopkins University Press (2018).

87.  See: Ostrom, E. and C. Hess, 2007, Understanding knowledge as a commons: From theory to practice. Massachusetts Institute of Technology Press.

88.  Altman, et al., 2015, National Agenda for Digital Stewardship, National Digital Stewardship Alliance. <>;

89.  Becker, S. Adams, Michele Cummins, A. Davis, A. Freeman, C. Hall Giesinger, V. Ananthanarayanan, K. Langley, and N. Wolfson. NMC horizon report: 2017 library edition. The New Media Consortium, 2017.

90.  See for reviews: Altman, Micah. "Open source software for Libraries: from {Greenstone} to the {Virtual Data Center} and beyond." iassist Quarterly 25 (2002) and Lesk, Michael. "A personal history of digital libraries." Library Hi Tech 30, no. 4 (2012): 592-603. Also see, for recent work the Code4Lib journal archives at <>

91.  (Citation needed)

92.  See for an analysis, Altman, Micah, and Marguerite Avery. "Information wants someone else to pay for it: laws of information economics and scholarly publishing." Information Services & Use 35, no. 1-2 (2015): 57-70; and for a recent market description:  Rob Johnson, Anthony Watkinson,
Michael Mabe T he STM report: An overview of scientific and scholarly journal publishing, 5th edition. (2018) International Association of Scientific, Technical and Medical Publishers.

93.  (Citation needed)

94.  (Citation needed)

95.  (Citation needed)

96.  Ayyagari, Ramakrishna. "An exploratory analysis of data breaches from 2005-2011: Trends and insights." Journal of Information Privacy and Security 8, no. 2 (2012): 33-56.

97.  See Ohm, Paul. "Broken promises of privacy: Responding to the surprising failure of anonymization." Ucla L. Rev. 57 (2009): 1701; Altman, Micah, Alexandra Wood, David R. O’Brien, and Urs Gasser. "Practical approaches to big data privacy over time." International Data Privacy Law (2018).

98.  (Citation needed)

99.  More information about these organizations can be found on their web pages: ; ;; ; ; and

100.   More information about these organizations can be found on their web pages: ; ; ;

101.  See respectively,,, and reports produced by these organizations, such as N. Beagrie, M. Joves  Digital Preservation Handbook. Digital Preservation Coalition, 2009; Brand, Stewart. The clock of the long now: Time and responsibility. Basic Books, 2008.

102.  See Graham, Mark, and Anasuya Sengupta. "We’re all connected now, so why is the internet so white and western?’." The Guardian 5 (2017).

Amanda Page: This section (5.4) is very important. Policy and Infrastructure and community are all intertwined. I encourage language in this section that scopes more narrowly to both.
Sue Kriegsman: Thanks for the feedback.
Amanda Page: Very relevant question. I would change to bepress (Digital Commons). and add questions about interopability here- re: the IT architecture question is not only about discovery of OA works re: content. Questions to ask about long term preservation as well (i.e. not just discovery and access)?
Amanda Page: Is there clarification that can be outlined in one or more of the recommendations for A. Libraries and B. University Research Support units other than “stakeholders” such that those that see themselves as part of process but are working on it at high levels or tangential ways may understand that we are all part of the same scholarly communications ecosystem…
Sue Kriegsman: Thanks, Amanda. Good question.
Amanda Page: how about “reviewers and editors?”
Sue Kriegsman: Thanks.
Amanda Page: True, but not all stakeholders or institutions have invested at the same level, or comply with at the same level, so further clarity of this is beneficial (agreeing with notes below that this is historical intent)
Sue Kriegsman: Thanks.
Amanda Page: I applaud the remarks on trust and accountability. I suggest additional language to account for challenges and barriers in these way that impact staffing, and budget lines, and this difficulty applies to more types of institutions than just government.
Sue Kriegsman: Thanks.
Nancy McGovern: It is not a summary of the workshop discussions and it is also not a draft of a research agenda for MIT Libraries, though would inform a future version of that - it is a standalone document that calls out research areas and recommendations that emerged from the workshops?
Sue Kriegsman: Yes, thanks!
April Hathcock: LW: Adding on to this: one thing that I often realize about ethnographic research is that, even if a researcher finds information about a certain group, they seldom gain access to the more complicated and nuanced in-group knowledge and living experience. How can we allow these organic knowledge to be shared?
Sue Kriegsman: Thanks.
April Hathcock: LW: From my perspective [as a Chinese graduate student studying in the US], the common situation in almost all Chinese universities except for very few top schools is that, none of faculty, staff, and students has any good idea on how to do quality research. Papers are plagiarized, dissertations not much better, and the schools often don’t have incentives to push for better research. Most academic journals in China are also low in quality, not even checking for fake data or plagiarism. I guess how to do (or encourage) quality research--and its details like literature review, academic integrity, critical thesis, etc.--is also a kind of know-how, and it is this know-how that limits research to people in top academic institutions. How do we allow more people to acquire it?
Amanda Page: Toward Point 6: Even in those countries, participation in science is heavily skewed by gender, race, class, and language -- which affect the construction and evaluation of scientific knowledge.10 Gender, race, class, and language are noted here as factors. How about, “…sskewed by gender, race, class, level of ability, and language…” or similar phrasing to be more inclusive of accessibility, and disability culture, both hidden and apparent?
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April Hathcock: LW: The discussion here seems to be mostly about research in sciences. It may be helpful to include humanities as well.
Sue Kriegsman: The language will be reviewed for the next iteration. Thanks.
April Hathcock: LW: Still, at least one thing to learn from these critiques of Internet: not all countries have affordable access to Internet, and this is one major cause of unevenness of the internet media. Same problem could also block wider circulation of scholarly works even before we do anything about academic infrastructures. From a technical viewpoint, it’s important for developers to always maximize compatibility in order to reach people with slow internet connections. This may require ripping platforms to almost bare functionality (think of Wikipedia, or the “basic html view” of Gmail). Recent trends in software updating and all the fancy interface stuffs are very bad examples of maximizing compatibility--they are creating more unevenness instead.
Sue Kriegsman: Thanks.
April Hathcock: LW: Are we trying to address the domination of English language in production and circulation of scholarly works? I’m thinking about this because, telling from my past research that involved Chinese and Japanese researches, each of these languages are backed by a sizeable academic system, and have plenty of scholarly works available. On the circulation side, it does seem that most internationally circulated scholarly works are written in English or translated into English, and are published by English-language publishers. However, as an international student I actually find it convenient to have a lot of scholarly works circulating in English language, basing on shared terminologies as well as contributing new terms for transnational discussion. I guess it’s probably one of the best methods to let scholarly works reach the widest audience possible, and for researchers like me it saves time for learning a lot of different languages.
Sue Kriegsman: Thanks for the ongoing input.
Mary Minow: Yes, perhaps raise this one especially, as well intended systems are likely to be modeled on private sector algorithms. These well honed personalized recommendation systems inherently conflict with the strong privacy ethos in librarianship. While offering options to users is fine, it is critical to default to the privacy enhanced version (with privacy audits to ensure clean up of digital trails). Users choosing to trade anonymity for personalization should be made aware of what information is collected, retained, and disclosed.
Sue Kriegsman: Thanks, Mary.
Mary Minow: Must make sure solution doesn’t have unintended consequences. Most salient may be that customized context, personalization triggers privacy concerns.
Sue Kriegsman: A good reminder.
zachary lizee: “The University of Washington Information School will conduct a two-year research project to create a conceptual data model and metadata schema for describing and representing artifacts related to the development of digital games…these collections of artifacts preserve the often inaccessible historical contexts of one of the most important global media forms…The results of this project will enable catalogers to describe video games and related materials more accurately and thoroughly, improving the quality of metadata shared among organizations and increasing access to the items.”
zachary lizee: “The University of Washington Information School will conduct a two-year research project to create a conceptual data model and metadata schema for describing and representing artifacts related to the development of digital games…these collections of artifacts preserve the often inaccessible historical contexts of one of the most important global media forms…The results of this project will enable catalogers to describe video games and related materials more accurately and thoroughly, improving the quality of metadata shared among organizations and increasing access to the items.”
zachary lizee: 2018 IMLS grant: “The University of Notre Dame will develop open source tools for data and software preservation, in partnership with a variety of academic institutions and other stakeholders…Partner organizations will advise on development and conduct interoperability testing. The tools and services developed in this project will provide for improved reuse of preserved data and software in library repository systems, and make research data more discoverable, as well as more interoperable with science gateways.
zachary lizee: “This Institute of Museum and Library Services National Leadership Grant funded project ($320,546 2017-2019) is a partnership with eight academic libraries (Indiana University, Lafayette College, MIT, Rutgers University, Swarthmore College, University of California-Berkeley, University of California-Los Angeles, and the University of Virginia) to understand current library needs and practices in provisioning library services for computational access to special collections having constraints due to sensitivity or policy restrictions. It also will extend the HathiTrust Research Center (HTRC) Data Capsule service to broader needs of provisioning for analytical access to restricted collections across a range of library collections and study extensions of the HTRC Data Capsule to cloud computing environments for broader uses….”
zachary lizee: IMLS 2018 grant to JMU: “…This National Forum proposal is the first step in surfacing community requirements and principles towards a collective OA collection development system. The Forum will ask participants to envision a collective funding environment for libraries to contribute provisioning or sustaining funds to OA content providers. Through a series of successive focus groups, the Forum will ask a non-random but diverse sample of the academic library community about the conditions under which they could and would participate in openly and collectively funding OA content that is wholly or partially a public good….”
zachary lizee: IMLS grants related to “oral history”:
zachary lizee: 2016 NIH grant: Also relevant to question 4.3 ““Increasing access to digital research data presents significant scientific opportunities to enhance return on investment, expand accountability, and accelerate discovery and progress. To seize these opportunities, data must be managed and shared appropriately; shared data must be citable to make clear their origin and allow the authors of the data to accrue recognition; and the importance of infrastructure, such as data repositories, must be appreciated. Data often must be considered in conjunction with other related digital objects including experimental and analytical workflows, standards, data annotations, and software that act on data. As such, shared data should conform to the FAIR principles, i.e., findable, accessible, interoperable, and reusable ( The goal for NIH data management and sharing is to make publicly-funded data broadly accessible to support reuse, reproducibility and discovery while simultaneously balancing the costs and benefits. The many aspects of the data landscape must be considered in implementation of the new NIH data sharing policies. In addition to the current RFI, an RFI on NIH Data Sharing Strategies will be released in the near future to collect the community's input on these topics.”
zachary lizee: article by Clifford Lynch, Director of CNI: “Stewardship in the Age of Algorithms”
zachary lizee: 2017 Sloan Foundation grant: “…The grant will support focused work on four use cases: scientific software, CD-ROM archiving, restricted-access reading rooms, and a “Universal Virtual Interactor” that would automatically launch the correct software and version to open any given digital file. Other supported activities include technical refinements to the bwFLA platform and the archiving of the National Software Reference Library currently held by the National Institutes of Standards and Technology.”
zachary lizee: see IMLS 2018 grant award: “…In recent years, for-profit companies have launched offerings such as Digital Science’s figshare for institutions2 , Springer-Nature’s Research Data Support Service3 , and Elsevier’s Mendeley Institutional Edition4 . These are aimed at commercializing the data sharing space with costs to institutions ranging from fee-for-service pricing to annual subscription fees in the hundreds of thousands of dollars. These are costs that academic institutions cannot and should not accept. Beyond the costs, the risk of buying into the commercial space for data is losing access. While these systems may promote open data practices, they themselves are built on closed and private infrastructure that may lead to future barriers and costs to privacy and reporting. To combat this trend towards inaccessible costs to support research data services at the institutional level, Dryad and California Digital Library (CDL) are formally partnering to address researcher needs and lead an open, community-supported initiative in research data curation and publishing…”
Sue Kriegsman: Thanks, this projects will all be reviewed for the next iteration of the paper.
zachary lizee: see 2018 IMLS grant to Indiana University: excerpt from grant proposal: “The Shared Big Data Gateway (SBD-Gateway) addresses the IMLS “National Digital Platform” priority by addressing a critical emergent issue faced by academic libraries: providing sustainable, affordable, and standardized data and text mining services for licensed, big data sets, as well as open and non-consumptive data sets too large or unwieldy to work within existing research library environments or with no commercially viable data mining interface. The project team, led by Indiana University, will provide member institutions with a cloud-based, platform solution for making such data available to members with the appropriate security, stewardship, and storage at a fraction of what it would cost them to do so alone. By sharing the cost of this solution across a large number of academic libraries, we will be able to provide a superior solution at a lower cost to members. We will also offer a free tier of basic services for public access. The SBD-Gateway will feature standardized data formats, data available in multiple formats including relational and graph database formats as well as flat tables and native formats, shared and custom/private computational resources, a space to share and store queries, algorithms, derived data, results of analyses, workflows, and visualizations…”
zachary lizee: may be relevant to section 4.1 also
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Philip N. Cohen: In the previous sentences ecosystem was singular. Are we moving toward better ecosystems,” or working on the “broader ecosystem”? (I prefer the former, but I’m not a metaphorical ecologist)
Mary Minow: Is there discussion of creating or expanding a scholarly platform to perform the widespread connectivity of a facebook for scholars? That is, as we become painfully aware of the power that facebook has in setting its own TOS and commercializing individual’s contributed content, the lack of a viable nonprofit or academic social media platform seems to become more apparent. Either limit to those with .edu, or go full on inclusive and include everyone, as diaspora tried and failed. With strong institutional support and engineering and drive, it could fill a gaping need with regards to an easy on ramp for all kinds of knowledge.
Sue Kriegsman: Thanks, Mary. This will be reviewed.
Patsy Baudoin: = for the Humanities
Sue Kriegsman: This has been changed.
Patsy Baudoin: of
Sue Kriegsman: Thanks, it’s been changed.
Patsy Baudoin: A very important idea. It feels buried here.
Sue Kriegsman: Thanks Patsy, it will be reviewed.
Patsy Baudoin: and document
Sue Kriegsman: Thanks for the suggestion.
Patsy Baudoin: = as honest brokers of knowledge.
Sue Kriegsman: Thanks.
Patsy Baudoin: At the same time, many institutions value the same pools of information.
Sue Kriegsman: Thanks.
Patsy Baudoin: and
Sue Kriegsman: Thanks.
Patsy Baudoin: A grammatically very ambiguous clause: does it refer to “changes” or organizations” or even “interests” — and what kinds of “broad benefits”? Financial, scholarly, human?) - confusing.
Patsy Baudoin: I will preface my comments here by stating that I was not present at the live discussions. I sometimes wonder whether we have swept under the rug the urgency of documenting processes. This may not apply to the transmission and preservation of a greatd deal of knowledge that comes to us through non-print societal forms of scholarship, but procedures in science, indeed procedural practices in any field, could be made less “tacit” — and documented better textually and visually.
Patsy Baudoin: A newer problem is that of adversarial algorithms and deep fakes (and often accompanying fraud). See Danielle Citron and Robert Chesney:
Sue Kriegsman: Thanks for the info and link.
Patsy Baudoin: Isn’t the fat market for some information goods precisely part of the problem? (I’m resisting framming the value of information in economic rhetoric, not because I want to pretend that the market doesn’t exist, but because the market economy hand-in-hand with an insistence on maintaining the tenure-and-promotion status quo have made these problems so intractable.) Reframing the “value” proposition of scholarly endeavors seems central to demonstrating and maintaining the value of research. A tall order, I realize, but the rhetoric can be changed to help shift thinkign and culture a bit.
Sue Kriegsman: Thanks for the feedback and insights. This section will have some review.
Micah Vandegrift: Is this meaning historical and cultural str/pol/sys/norms in academia? Or from outside academia, imposed upon it? I think its both/and, but some clarity might be helpful for thinking about how energy can be devoted to change and progress.
Sue Kriegsman: The intent was from historical and cultural norms in academia. The section will be reviewed and clarified.
Micah Vandegrift: hearkening back to the original scoping of this paper, I wonder if this is a North American, US-focused statement. In the European context I’m in for a few months it seems that government leaders are all working diligently, without skepticism, to legislate as much of “good scholarship/science” as necessary and useful.
Sue Kriegsman: Thanks, it will be reviewed. Do you have suggestions of articles or legislation to point to about what is happening globally?
Micah Vandegrift: Is this true? Many members of the public also trust science. Not to get picky, but like Mary’s comment this language is a little loose for me.
Sue Kriegsman: The language will be reviewed. Thanks.
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Micah Vandegrift: In my humble opinion this is the largest impediment, and one that we all hear echoed in every conversation about “the future of….”. What can the LIS/ScholComm community do on this front?
Sue Kriegsman: Yes, good point.
Micah Vandegrift: I love that this emphasis on discovery. I’m leaning to believe that that is the next great horizon for librarians/libraries working in this area.
Sue Kriegsman: Thanks!
Micah Vandegrift: I hope these are fleshed out a bit later in the paper. ‘infrastructures’ is a big, wide descriptor for a lot of things we could be, are, or might do.
Sue Kriegsman: Yes, thanks.
Micah Vandegrift: This might be beyond the goals of this project, but most times I read lists like this policy-makers are there too. Is there a reason they aren’t included here?
Sue Kriegsman: Thanks, there was discussion about policy makers and it will be revisited for the next iteration of the paper.
Micah Vandegrift: This is interesting scoping for me - do you mean InfoSci researchers AND librarians, or the field of infosci and its scholarship?
Sue Kriegsman: Looks like this could use some clarification. The intent was infosci researchers and scholarship.
Amy Nurnberger: picky: interoperable - to match scalable & equitable?
Sue Kriegsman: Thanks, good catch.
Mary Minow: maybe this is a place to also include the mass of content created online that is not being archived except by in pieces, (most notably by the Internet Archive.)
Mary Minow: and/or the scholarly content created in non-text formats e.g. digital humanities scholarship or perhaps multimedia formats warrants its own section
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Mary Minow: Example: Study of ebooks that are scanned and loaned (Internet Archive/ Controlled Digital Lending model) and impact on long-tail access. What needs does this collection fill? Who really has access? Impact on publishers? On creation of Knowledge? Example: Study of audiovisual content. How is it being archived? Perhaps a study of a small subset, such as NYT videos, other mainstream press videos, or perhaps a study of another small subset, viral videos shared by youth? Example: Impact of copyright law on access to ebooks, audiovisual content made available through libraries or otherwise made available on basis that is open to users, yet compensable to creators. Examination of working models to create open ebook etc content that allows for compensation via tip jars, auctions to “open” the content, tax funded initiatives etc.
Mary Minow: Another area - as scholars increasingly create multimedia content eg. digital humanities scholarship, how is this being disseminated and preserved? What license restrictions hamper accesss/preservation that would be considered acceptable (e.g. fair use) under copyright law? What technological protection measures such as DRM pose obstacles?
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Mary Minow: Important to emphasize that research into open scholarship in nonjournal formats is especially needed. As mentioned earlier, ebooks, music, video are especially bound by proprietary interests, even when created to be shared. When libraries “loan” ebooks, does it in fact hurt the publishers’ markets? Does it ever help those markets and thus the creation of knowledge? What are the legal and economic hurdles, and can they be overcome? Are there working public/academic platforms for ebooks, video, audio, music that are making a difference?
Sue Kriegsman: Thanks, these are good research question suggestions.
Mary Minow: Yes. And the platforms that offer UGC like youtube are run by proprietary interests. So far as I know, no academic or public agency is able to archive this content well.
Sue Kriegsman: Thanks.
Mary Minow: Not sure this is the place to mention it, but there are universal guidelines proposed by Public Voice wrt AI
Sue Kriegsman: Thanks!
Sue Kriegsman: Thanks, a revision is underway to help clear this up.
Mary Minow: does that mean privacy of individuals/communities that have had data collected about them? Perhaps give an example if this is so.
Mary Minow: Seems like narrative narrowed to science here. Intentional?
Mary Minow: Seems like narrative narrowed to science here - intentional?
Sue Kriegsman: Thanks, it will be reviewed for the next iteration.
Patsy Baudoin: “productizing” scholarship does not enhance your arguments or your positions.
Sue Kriegsman: Thanks.
Patsy Baudoin: This sentence reflects STEM-thinking. Experiential and oral traditions, for example, will not make their way into the envisioned ecosystem without the inisistent voices of humanities scholars and researchers (I include the “soft social sciences” here) as well as librarians and archivists with a broad vision of research trends, forecasts, needs, etc. So far this document reads as predominantly infomed by STEM-thinking. The vision, however, ought to stipulate the inclusion of cultures, ethics, languages/translation, so-called “soft” psychology and economics, etc. which including new forms of scholarship will unquestionably entail.
Micah Vandegrift: While I agree with Patsy on the importance of including the full spectrum of “wissenschaft”, I don’t disagree with the sentence in the paper. I think the following paragraphs suss it out pretty well.
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Patsy Baudoin: The research landscape is central, crucial. I think it is important to distinguish it from the “governance landscape.” I note that the last sentence of this paragraph is clearly about research (totally on topic), but might be written differently if it were about governance: … are and remain informed by equity, sustainability, and grounded in values (for example).
Sue Kriegsman: Thanks.
Patsy Baudoin: Funders have yet to be mentioned I believe. Seems to me funder-driven requirements are so central to an effective strategy to change the research horizon that it ought to be teased somehow earlier. There's the vision and there's the strategy. The document as it reads to this point is nearly all about vision. The recommendations speak to a strategy and drop in here a bit unannounced.
Sue Kriegsman: The executive summary has yet to be written and it will help frame the strategy and the vision.
Patsy Baudoin: This phrasing betrays your very intentions; the statement yearns to acknowledge the work of Crenshaw, Collins, and others. Intersectionality is simply not written into this sentence or the document thus far – even though that is precisely the bedrock of the “equitable” argument. Even “Including people belonging to several communities at once…” would be an improvement since we all do. THAT is the point: not isolating anyone into a category of people which can be relegated to the margins (even if people in that group also belong to that category, they belong to the larger category of people legitimately entitled to participate….) – NOTE, too: “community” can’t continue to stand in for “category.” It’s become an unfortunate euphemism.
Sue Kriegsman: Thanks!
Patsy Baudoin: Aside, though perhaps useful: Audre Lorde called this “Dismantling the Master’s House” in an essay of that name. Very much worth reading if you have never.
Sue Kriegsman: Thanks.
Patsy Baudoin: This underlines the importance of thinking about networks and ecosystems – a point that needs (I think) to be made nearer the beginning of the section on research, I should think.
Patsy Baudoin: sounds very commercial
Mary Minow: +1
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Patsy Baudoin: “productizing” scholarship does not enhance your arguments or your positions. Why not just say “scholarship” here?
Patsy Baudoin: Does this “science” include knowledge and experience of the humanities? Should this say, “facts, science, knowledge” perhaps? How broad the mistrust is is buried.
Patsy Baudoin: I would be careful here: This first sentence is really about STEM areas of knowledge. As we argue for new forms of scholarship for experiential and oral traditions, for example, it will be more difficult to introduce new forms of scholarship if the scholars and researchers AND librarians and archivists insist on privileging the STEM world. This document — the vision — will be better served by remembering the Humanities at its heart (values) — and the centrality cultures, ethics, languages/translation, so-called “soft” psychology and economics, etc. to all the work these shifts in attitude, methods, reporting, sharing, and preservation entail.
Patsy Baudoin: The research landscape is central, but I think it may be important to distinguish it from the governance landscape. I note that the last sentence of this paragraph is clearly about research (totally on topic), but might be written differently if it were about governance: … are and remain informed by equity, sustainability, and grounded in values (for example).
Patsy Baudoin: Funders have yet to be mentioned I believe. Seems to me funder-driven requirements are so central to an effective strategy to change the research horizon that it ought to be teased somehow earlier. There's the vision and there's the strategy. The document as it reads to this point is nearly all vision. The recommendations speak to a strategy and drop in here a bit unannounced.
Patsy Baudoin: This phrasing betrays your very intentions; the statement wants to acknowledge the work of Crenshaw, Collins, and others. Intersectionality is not written into this sentence or the document thus far as it ought to be, even though that is precisely the grounding of the argument of operating in an “equitable” fashion. Even “Including people belonging to several communities at once…” would be an improvement since we all do. THAT is the point: not isolating anyone into a category of people which can be relegated to the margins (even if people in that group also belong to that category, they belong to the larger category of people legitimately entitled to participate….) – NOTE, more generally, then: “community” can’t continue to stand in for “category.” It’s become an unfortunate euphemism.
Yasmeen Shorish: Too narrow an application. Because of the porous relationship between any digital interaction and an avenue for scholarship (i.e. social media) I would advocate replacing this phrase with “information - especially an individual or group’s digital identity or footprint.”
Sue Kriegsman: Thanks.
Yasmeen Shorish: I would argue that anyone participating in the online ecosystem face this challenge - not just the scholarly ecosystem.
Mary Minow: +1
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Yasmeen Shorish: It’s not extreme. Everyday people who hold certain identities must do a calculus ALL THE TIME to determine how prone to harm they might be if they share information.
Sue Kriegsman: Thanks for the feedback.
Yasmeen Shorish: Since this is part of a trust-spectrum illlustration, recommend removing “and therefore mistrusted.” Not only is the point redundant, but also most algorithms *are* opaque yet many people trust them. I think the point being made here is that opaque algorithms (and the other examples) are lower on a trust spectrum and *ought* to be mistrusted - even if they currently are not.
Sue Kriegsman: Thanks for the input.
Sarah Shreeves: I think that this statement might need to be complicated a bit. There are active conversations in the RDM community, archives communities, and others about what needs to be kept or what can be erased or removed. I think also the points made above by Paige and April about involving a range of communities in knowledge production might also mean that the ephemeral nature of some knowledge products is not necessarily bad as it appears to be framed here but may be a decision of those communities.
Sue Kriegsman: Thanks, we’ll tinker with the language for the next iteration of the draft.
Yasmeen Shorish: Remove passive language. Rephrase: “…exclusion of knowledge production from and access for so many people…”
Sue Kriegsman: Thanks.
Yasmeen Shorish: Remove qualifier. Either it did or it did not conclude with that desire. Grand challenges should not be met with hedging language…
Sarah Shreeves: It would be useful to know what areas of disagreement were, if any, and how that might affect movement forward. I agree that hedging language should be removed.
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Yasmeen Shorish: Can we find a better way to describe participants? I am not sure what “practitioners” refers to here…publishers, librarians, activists? Are none of the practitioners also researchers? Are the two population sets: “those producing scientific research” and “those working in scholarly communication and other research fields”?
Sue Kriegsman: Hi Yasmeen, thanks for the feedback. We’ll see if there is a better description without getting too specific because it was a broad array of perspectives.
April Hathcock: This section could use some clarity. I understand it’s about dealing with “fake news” and the public mistrust of science. But mixed in are some allusions to the efficacy of peer review and quality control that smack of the common misconceptions about open access publishing being all about the “predatory journal.” I don’t know if peer review and quality control is the answer here. I like the line about how formal knowledge generation is limited to small communities—I’d add that it’s the fact that formal knowledge creation is so homogenous and privileged that is part of the problem. More voices, different voices, allows scholarship to be a conversation rather than an echo chamber, helping to bring us back to center.
Yasmeen Shorish: Agree that these two threads should be more intentionally disentangled in this section. I think there is a place for peer-review to be discussed as a threat to trust, since it is an imperfect system that is often viewed as infallible, but it should be discussed separate from the propaganda machine discussion.
April Hathcock: Also that they seek out new methodologies. I’m thinking about researchers who incorporate Indigenous methodologies in their work or researchers who use Black feminist methodologies for their work.
Sue Kriegsman: Thanks.
April Hathcock: How can this work be done in such a way that empowers and gives agency to the communities from which the knowledge originates? How can we be careful to allow for incorporating material from groups that want to be included without enacting colonizing practices and methodologies?
Sue Kriegsman: Thanks, April. We’ll rework this and have something new in the next revision.
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April Hathcock: This is good and “safe” use of euphemisms, but I’d love to see those injustices named: racism, sexism, discrimination against queer and trans people, religious biases, ableism, classism, xenophobia, colonialism, etc.
Michelle Baildon: +1
April Hathcock: Need to cite to Charlotte Roh and Harrison Inefuku here and throughout this paper. They’ve done great work in this space. Roh’s paper: Inefuku’s paper:
Sue Kriegsman: Thanks for the references.
Katherine Montgomery: I should have said ‘common to’ rather than ‘inherent in’. There is a good piece on this subject by Nathan J. Robinson: “Academic Language and the Problem of Meaninglessness”
Sue Kriegsman: Thanks for the reference.
Katherine Montgomery: There is a barrier to trust among wider publics that is also built into academic publishing and that barrier is academic language. If people literally struggle to understand an article then access becomes moot. I have a master’s degree in information science and I often struggle to parse the thicket of words inherent in academic writing. What if we advocated clarity in the writing itself? I realize this idea would be met with resistance in the academic community but grand challenges elicit grand solutions.
Sue Kriegsman: Thanks, we’ll make an update for the next version of the draft.
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Katherine Montgomery: Suggested: we will include AN executive summary section WHICH will be a 2-5 stand alone section
Micah Altman: Thanks, will fix.
Katherine Montgomery: I can’t comment in the subtitle so I’m mentioning it here: “…Grand Challenges Summit hosted at MIT in March HAVE written a draft”. I think you’re missing a ‘have’.
Micah Altman: Thanks. Fixed.
Katherine Montgomery: Suggested: such as the
Sue Kriegsman: Thanks.
Katherine Montgomery: Suggested: scholars
Sue Kriegsman: Thanks.
Katherine Montgomery: Suggested: will realize this value
Sue Kriegsman: Thanks, good catch.
Paige Mann: As publishers, libraries can also elevate and make more visible, those communities who choose to disseminate their knowledge through these values-based publishers.
Paige Mann: and defined (e.g., fixed and tangible such that it can be owned and sold)
Sue Kriegsman: Good suggestion, thanks.
Paige Mann: Again, this contradicts the ethos of diversity and inclusion. Not all knowledge is created to be durable and transmitted asynchronously outside of human relationships. Think of storytelling, ritual, apprenticeships, etc.
Sue Kriegsman: Paige, do you have a suggested revision that could be used here?
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Paige Mann: If we only focus on “genres,” overlooking the communities that created these genres and decontextualizing these genres, we risk eroding trust (see 3.1.3) and appropriating non-Western knowledges.
Sue Kriegsman: This will be adjusted in the next revision of the draft.
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Paige Mann: If we are truly seeking an inclusive ecosystem, it can’t be the privileged knowledge creators setting the agendas and “offering” the dispossessed solutions.
Sue Kriegsman: Good catch. The language will be revised.
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April Hathcock: +1 Community agency and self-determination regarding data and knowledge sharing is key.
Paige Mann: Except that communities do not all value open sharing of knowledge. See
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Paige Mann: I think it will also require recognizing ways that some forms of knowledge creation (e.g., text vs. oral, disembodied vs. embodied, industrial vs. domestic) and some knowledge creators (e.g., Westerners, Western-educated) are privileged over others. Part of this is an outgrowth of the ways that intellectual property laws value knowledge as commodities and property. I’m not sure how well we can achieve epistemic justice without dismantling the systems that venerate some knowledges and creators while reviling others.
April Hathcock: +1 We start on the premise of the “democratizing promise of internet technologies",” but I think we can, and should, even interrogate and push back against that very premise.
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Vicky Steeves: Could just be called “library and information science”.
Sherry Lake: Agree w/ Vicky
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Daniel Lovins: “to” x 2
Micah Altman: Thanks. Will be corrected in next version.
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Daniel Lovins: “have emerged” x 2
Micah Altman: Thanks. Will be corrected in next version.
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Gardner Campbell: This is where Jean-Claude Guédon’s “Open Access: Toward the Internet of the Mind” becomes a crucial part of the discussion, I think. See (full document).
Sue Kriegsman: Thanks.
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Gardner Campbell: I propose that Jean-Claude Guédon’s “Open Access: Toward the Internet of the Mind” be added to the recommended readings, as this essay-cum-manifesto is a major contribution to this discussion.
Sue Kriegsman: It’s been added as a footnote for now but may be adjusted as the writing process continues.
Gardner Campbell: I agree with Vicky: this demarcation is not clear. It may also be pernicious, in that it can be read to imply that the idea of traditional domain research is somehow unjust because “undemocratic.” I would encourage caution in the use of such labels!
Sue Kriegsman: Ahh, great points. Thanks. I’ll make some adjustments that will appear in the next release of the paper.
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Laurie Taylor: For future report iterations, the Association of Caribbean University Research and Institutional Libraries is an excellent group to include. Also, Dr. Alex Gil at Columbia has been researching institutions’ information needs, especially in regards to the special needs for islands, given geographical difficulties made all the more difficult with climate change. While better information architectures are of great benefit, specific concerns for the resilence and resistance within systems is helpful to consider for addressing grand challenges to ensure the best and braodest possible positive implacts.
Sue Kriegsman: Please feel free to ping other folks to read and comment on this draft paper! Additional perspectives are appreciated.
Sue Kriegsman: Thanks, Laurie. Margaret Kovach’s work will be cited in the next version of the paper.
Laurie Taylor: 5.4: It would be great to cite the extensive work done on equitable and inclusive knowledge, including scholarly practices for reciprocal research, restorative justice as applied to scholarship and access to research by the people the research impacts (from overall public, to specific communities), and work on changing research practices to be inclusive and to acknowledge researchers in the process. See, for instance, Margaret Kovach, Indigenous Methodologies.
Laurie Taylor: Typo
Micah Altman: Thanks. Fixed in next version.
Laurie Taylor: 3.1.2, on tenure and promotion: It would be useful to cite work on collaborative, interdisciplinary practices for changing T&P, including team science and Imagining America, on collaborative and publicly engaged intellectual work.
Sue Kriegsman: Thanks for the suggestion Laurie.
Laurie Taylor: 3.1.2: Would be useful to note some of the extensive feminist research that investigates and implements a wider range of research methods and ways of knowing. It would also be useful to cite, for instance, Haraway, on situated perspectives and the limits of scientific research as currently practiced without feminist practices and other methodologies.
Charles Watkinson: They “can” but very rarely “do.” Would the reputational and institutional barriers to collaboration that have led to duplication and inefficiency be worth further research and engagement?
Micah Altman: Would it be appropriate to call this out in 4.1?
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Charles Watkinson: This seems particularly important from the perspective of the humanities and qualitative social sciences, where scholars are becoming increasingly concerned about the technocractic application of measures developed in STEM fields to their work, which comes from different intellectual foundations. See, for example, the initiative for other ways of thinking about “value” in these fields
Micah Altman: Thanks.
Charles Watkinson: Acknowledging the Mellon Foundation’s work in supporting the transition to digital scholarship in the humanities through investment in infrastructure would be relevant here. Possible references: or, if these need to be stable and citable, “The Academic Ebook Reinvigorated”
Micah Altman: +1
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Karrie Peterson: I am wondering about the repetition of “the substantial improvement to people’s lives.” There is significant discussion about dual effects of research (e.g. cheap energy from fossil fuels!), and the downsides of scientific advancements and innovation which are destroying the planet and massively disrupting millions of people around the world. Maybe acknowledge that somewhere near the beginning of the report? We all have come to know, right, that as scientific advancements shrink the globe and create larger and larger effects, the dual effect hits sooner and harder and we have to use the information ecosystem even more effectively and inclusively for that very reason.
Micah Altman: Agreed. Will consider how to bring this in to revisions. Any suggestions for a citation to a thoughtful review/discussion of dual effects?
Lisa Schiff: I would like to see the library community challenge the monopolistic power of Google.
Micah Altman: +1
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Lisa Schiff: The link to this reference results in a 404 error.
Micah Altman: Should be <> . Will be fixed in next revision.
Lisa Schiff: and share, as well.
Micah Altman: +1
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Lisa Schiff: I’ve been thinking a lot about this ever since learning about Europe’s Horizon 2020 program/policy many years ago. On the one hand, considering impact of all kinds is critical (so that we can assess what we’re doing, so that we can think about and prevent potential harm before acting, etc.). On the other hand, impact is sometimes far off in the future, is speculative, is unknown and therefore impossible to capture. I worry that efforts falling into that latter category will not be supported and the tremendous benefits that could come out of them won’t be realized.
Micah Altman: Agreed. The broadest impacts are farthest off and most uncertain — weighing these against immediate outcomes is challenging and depends on context. We can revise to bring this tension to the surface. And many funders already aim to assess the potential for broad impacts — we aim for this document to inform such assessments.
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Lisa Schiff: This is an interesting and important point, but has some pre-requisites, specifically transforming the education of researchers (and scholars in general) so that they have the tools to do this analysis and have it as a baked-in practice within the larger research practice. Safiya Umoja Noble discusses this in regards to Engineering education in her book Algorithms of Oppression.
Karrie Peterson: Agree. Also wondered about saying “researchers use rigorous and transparent methods to consider the broadest possible impact.”
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Lisa Schiff: This is an important issue for an effort such as this that seeks to be visionary and to create consensus around that vision. Given that, was global geographical representation a stated goal of the event? And if so, were there efforts made to have broader representation? I feel that is something we struggle with in the North American context, so any background on this, discussion of steps take and related challenges and outcomes would be useful context and a contribution towards a necessary continual push to do better in this area.
Micah Altman: Yes, this is a significant struggle. The program committee aimed for diversity across discipline, sector, geography, race, gender — however despite systematic search and subvention of participation, we were inevitably constrained by our background knowledge and networks.
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Micah Altman: Suggested:Consider:Analysis of field specific positions on open access in needed such as the MIT White Paper. In terms of academic societies and associations, in particular on the issue of open access, a cursory look at some of the field-specific codes of ethics from the last decade or so reveals an explicitly stated desire toward open dissemination of knowledge at the very least, and at best, coupled with open access, the requirement of “respect for People’s Rights, Dignity, and Diversity,” exemplified in the 2018 Code of Ethics by the American Sociological Association. In that 2018 document public sharing of research outcomes is taken as a default option for researchers, not unlike the European Commission's 2012 umbrella statement, “as open as possible, as closed as necessary.” If leaders across academic fields are increasingly explicitly stating open access (and EDISJ) as goal, academic structures need realignment to make up for the discrepancy between the reality and that stated goal(s)
Micah Altman: Submitted comment:Condider…Google but can ask/influence Google to do things differently] [i.e. integrating into research workflows and partnering with university presses as in the example of ARL and AUPresses collaborative project, TOME (Toward an Open Monograph Ecosystem).]Libraries could play a role in training students in research data lifecycle/management (RDM) via either workshops, or better, via field-specific year-long methods classes typically taken at first year of training and/or before the official start of research. Library- and/or research center- run RDM workshops at research universities are not unusual, but semester long RDM classes run by library and/or research center teams, such as the course run by Dr. Timothy Norris out of University of Miami Libraries are less common. Perhaps an indicator of need in consistent and systematic RDM training, an Open Science course on “open data management and open data sharing” is scheduled to start at the end of October on the EdX platform out of Delft University of Technology.Libraries can help change the incentive system for what information is valued.
Micah Altman: Suggested:The development of the unpaywall corpus of data integrating data from crossref with data from Institutional Repositories has considerable potential to build a truly ground up empirical mapping and set of metrics for tracking changes in resources (see ) there are considerable possibilities. See also
Lisa Schiff: True, but the weakness with this corpus is that it is constrained to those items with a Crossref DOI. Many publications don’t use DOIs (Crossref or otherwise). Trying to bring in scholarship from outside the main means we need to look beyond those systems. DOAJ would be an additional source of data that has publications with and without DOIs.
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