The principles of tomorrow’s university

Authors : Daniel S. Katz, Gabrielle Allen, Lorena A. Barba, Devin R. Berg, Holly Bik, Carl Boettiger, Christine L. Borgman, C. Titus Brown, Stuart Buck, Randy Burd, Anita de Waard, Martin Paul Eve, Brian E. Granger, Josh Greenberg, Adina Howe, Bill Howe, May Khanna, Timothy L. Killeen, Matthew Mayernik, Erin McKiernan, Chris Mentzel, Nirav Merchant, Kyle E. Niemeyer, Laura Noren, Sarah M. Nusser, Daniel A. Reed, Edward Seidel, MacKenzie Smith, Jeffrey R. Spies, Matt Turk, John D. Van Horn, Jay Walsh

In the 21st Century, research is increasingly data- and computation-driven. Researchers, funders, and the larger community today emphasize the traits of openness and reproducibility.

In March 2017, 13 mostly early-career research leaders who are building their careers around these traits came together with ten university leaders (presidents, vice presidents, and vice provosts), representatives from four funding agencies, and eleven organizers and other stakeholders in an NIH- and NSF-funded one-day, invitation-only workshop titled “Imagining Tomorrow’s University.”

Workshop attendees were charged with launching a new dialog around open research – the current status, opportunities for advancement, and challenges that limit sharing.

The workshop examined how the internet-enabled research world has changed, and how universities need to change to adapt commensurately, aiming to understand how universities can and should make themselves competitive and attract the best students, staff, and faculty in this new world.

During the workshop, the participants re-imagined scholarship, education, and institutions for an open, networked era, to uncover new opportunities for universities to create value and serve society.

They expressed the results of these deliberations as a set of 22 principles of tomorrow’s university across six areas: credit and attribution, communities, outreach and engagement, education, preservation and reproducibility, and technologies.

Activities that follow on from workshop results take one of three forms. First, since the workshop, a number of workshop authors have further developed and published their white papers to make their reflections and recommendations more concrete.

These authors are also conducting efforts to implement these ideas, and to make changes in the university system.

Second, we plan to organise a follow-up workshop that focuses on how these principles could be implemented.

Third, we believe that the outcomes of this workshop support and are connected with recent theoretical work on the position and future of open knowledge institutions.

URL : The principles of tomorrow’s university


Reproducible research practices, transparency, and open access data in the biomedical literature, 2015–2017

Authors : Joshua D. Wallach, Kevin W. Boyack, John P. A. Ioannidis

Currently, there is a growing interest in ensuring the transparency and reproducibility of the published scientific literature. According to a previous evaluation of 441 biomedical journals articles published in 2000–2014, the biomedical literature largely lacked transparency in important dimensions.

Here, we surveyed a random sample of 149 biomedical articles published between 2015 and 2017 and determined the proportion reporting sources of public and/or private funding and conflicts of interests, sharing protocols and raw data, and undergoing rigorous independent replication and reproducibility checks.

We also investigated what can be learned about reproducibility and transparency indicators from open access data provided on PubMed. The majority of the 149 studies disclosed some information regarding funding (103, 69.1% [95% confidence interval, 61.0% to 76.3%]) or conflicts of interest (97, 65.1% [56.8% to 72.6%]).

Among the 104 articles with empirical data in which protocols or data sharing would be pertinent, 19 (18.3% [11.6% to 27.3%]) discussed publicly available data; only one (1.0% [0.1% to 6.0%]) included a link to a full study protocol. Among the 97 articles in which replication in studies with different data would be pertinent, there were five replication efforts (5.2% [1.9% to 12.2%]).

Although clinical trial identification numbers and funding details were often provided on PubMed, only two of the articles without a full text article in PubMed Central that discussed publicly available data at the full text level also contained information related to data sharing on PubMed; none had a conflicts of interest statement on PubMed.

Our evaluation suggests that although there have been improvements over the last few years in certain key indicators of reproducibility and transparency, opportunities exist to improve reproducible research practices across the biomedical literature and to make features related to reproducibility more readily visible in PubMed.

URL : Reproducible research practices, transparency, and open access data in the biomedical literature, 2015–2017


Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015

Authors : Colin F. Camerer, Anna Dreber, Felix Holzmeister, Teck-Hua Ho, Jürgen Huber, Magnus Johannesson, Michael Kirchler, Gideon Nave, Brian Nosek, Thomas Pfeiffer, Adam Altmejd, Nick Buttrick, Taizan Chan, Yiling Chen, Eskil Forsell, Anup Gampa, Emma Heikensten, Lily Hummer, Taisuke Imai, Siri Isaksson, Dylan Manfredi, Julia Rose, Eric-Jan Wagenmakers, Hang Wu

Being able to replicate scientific findings is crucial for scientific progress. We replicate 21 systematically selected experimental studies in the social sciences published in Nature and Science between 2010 and 2015.

The replications follow analysis plans reviewed by the original authors and pre-registered prior to the replications. The replications are high powered, with sample sizes on average about five times higher than in the original studies.

We find a significant effect in the same direction as the original study for 13 (62%) studies, and the effect size of the replications is on average about 50% of the original effect size. Replicability varies between 12 (57%) and 14 (67%) studies for complementary replicability indicators.

Consistent with these results, the estimated true positive rate is 67% in a Bayesian analysis. The relative effect size of true positives is estimated to be 71%, suggesting that both false positives and inflated effect sizes of true positives contribute to imperfect reproducibility.

Furthermore, we find that peer beliefs of replicability are strongly related to replicability, suggesting that the research community could predict which results would replicate and that failures to replicate were not the result of chance alone.

URL : Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015


Reproducible data citations for computational research

Author : Christian Schulz

The general purpose of a scientific publication is the exchange and spread of knowledge. A publication usually reports a scientific result and tries to convince the reader that it is valid.

With an ever-growing number of papers relying on computational methods that make use of large quantities of data and sophisticated statistical modeling techniques, a textual description of the result is often not enough for a publication to be transparent and reproducible.

While there are efforts to encourage sharing of code and data, we currently lack conventions for linking data sources to a computational result that is stated in the main publication text or used to generate a figure or table.

Thus, here I propose a data citation format that allows for an automatic reproduction of all computations. A data citation consists of a descriptor that refers to the functional program code and the input that generated the result.

The input itself may be a set of other data citations, such that all data transformations, from the original data sources to the final result, are transparently expressed by a directed graph.

Functions can be implemented in a variety of programming languages since data sources are expected to be stored in open and standardized text-based file formats.

A publication is then an online file repository consisting of a Hypertext Markup Language (HTML) document and additional data and code source files, together with a summarization of all data sources, similar to a list of references in a bibliography.


Reflections on the Future of Research Curation and Research Reproducibility

Authors : John Baillieul, Gerry Grenier, Gianluca Setti

In the years since the launch of the World Wide Web in 1993, there have been profoundly transformative changes to the entire concept of publishing—exceeding all the previous combined technical advances of the centuries following the introduction of movable type in medieval Asia around the year 10001 and the subsequent large-scale commercialization of printing several centuries later by J. Gutenberg (circa 1440).

Periodicals in print—from daily newspapers to scholarly journals—are now quickly disappearing, never to return, and while no publishing sector has been unaffected, many scholarly journals are almost unrecognizable in comparison with their counterparts of two decades ago.

To say that digital delivery of the written word is fundamentally different is a huge understatement. Online publishing permits inclusion of multimedia and interactive content that add new dimensions to what had been available in print-only renderings.

As of this writing, the IEEE portfolio of journal titles comprises 59 online only2 (31%) and 132 that are published in both print and online. The migration from print to online is more stark than these numbers indicate because of the 132 periodicals that are both print and online, the print runs are now quite small and continue to decline.

In short, most readers prefer to have their subscriptions fulfilled by digital renderings only.


Reproducible research and GIScience: an evaluation using AGILE conference papers

Authors : Daniel Nüst​, Carlos Granell, Barbara Hofer, Markus Konkol, Frank O Ostermann, Rusne Sileryte, Valentina Cerutti

The demand for reproducibility of research is on the rise in disciplines concerned with data analysis and computational methods. In this work existing recommendations for reproducible research are reviewed and translated into criteria for assessing reproducibility of articles in the field of geographic information science (GIScience).

Using a sample of GIScience research from the Association of Geographic Information Laboratories in Europe (AGILE) conference series, we assess the current state of reproducibility of publications in this field. Feedback on the assessment was collected by surveying the authors of the sample papers.

The results show the reproducibility levels are low. Although authors support the ideals, the incentives are too small. Therefore we propose concrete actions for individual researchers and the AGILE conference series to improve transparency and reproducibility, such as imparting data and software skills, an award, paper badges, author guidelines for computational research, and Open Access publications.

URL : Reproducible research and GIScience: an evaluation using AGILE conference papers