The March/April issue of CiSE's inaugural year (1999) carried an essay by eminent computer science professor John R. Rice (who at the time was area editor for Software, together with Matlab inventor Cleve Moler) titled A Perspective on Computational Science in the 21st Century \cite{Rice_1999}. In it, he looked at the development directions for the future of computational science and engineering, and threaded across these was what he called "problem-solving environments." This routine-sounding term hides an ambitious vision, for the time. Rice imagined a software system for tackling problems within a science domain without all the agonizing toil of programming by hand every solution method. He and Ronald Boisvert had a previous article (1996) explaining the idea in more detail \cite{Rice_1996}. A problem-solving environment would include a collection of mathematical and domain-specific software libraries, offer (semi-)automatic selection of solution method for a given problem, help check the problem formulation, display or assess the correctness of solutions, allow extensibility to add new methods, and even manage the overall computational process. They envisaged an environment that could be "all things to all people," meaning: it is effective when solving simple or complex problems, it supports rapid prototyping and detailed analysis, and it can be used both in introductory teaching and in productive research at the edges of knowledge. An ideal problem-solving environment would even make decisions for the user by means of an integrated knowledge base. What fabulous ambition!Prof. Rice led a research group at Purdue University that worked to develop early problem-solving environments. The Ellpack system for solving elliptic boundary value problems, developed in the early 1980s, included dozens of software modules implementing solution methods and a descriptive language to formulate problems (today, we might call it a domain-specific language, DSL). For example, the line: equation. uxx + uyy + 3 * ux - 4 * u = exp(x+y) * sin(pi * x) would be used in an Ellpack program for defining the differential equation to be solved. Similarly expressive statements would define the boundary conditions, and the grid parameters to discretize the domain (a full example at Later versions of the system offered parallel solvers and a graphical user interface (screenshots of the Ellpack system from Prof. John R. Rice's website at Purdue can be found in the Internet Archive Ellpack was licensed by Purdue University for a modest yearly fee, this project did not branch off commercially or otherwise. Perhaps a few hundred copies were distributed, mostly for use in university settings, and the project wound down by the early 2000s. By contrast, three commercial software packages for high-productivity scientific and engineering computation—Maple, Mathematica, and Matlab—had by then become very popular \cite{Chonacky_2005}. These systems continue to be widely used in education, industry, and government settings. Their purchase price and proprietary implementations, however, led many champions of open-source software to conceive alternatives, oftentimes closely imitating their functionality.In March/April 2011, twelve years after Prof. Rice's Perspective essay, CiSE ran a special issue on Python for Scientific Computing, showcasing a maturing stack of tools and a highly productive environment for researchers. The issue included one of the most-widely cited articles in the history of the magazine, discussing the high-level multidimensional array structure at the core of NumPy \cite{van_der_Walt_2011}. By this time, the scientific community had expanded Python for its purposes, and the four keystone libraries had been put in place in the first half of the decade: SciPy was consolidated as a standard collection of modules for common mathematical and statistical functions.The first version of IPython, an enhanced interactive shell for Python, was created by Fernando Pérez.Matplotlib, the rich 2D visualization and now standard Python plotting library, was released by John Hunter.Travis Oliphant created NumPy from a rewrite of the early Python array library Numeric, adding functionality from the competing array package called numarray.CiSE had previously featured the developing Python support for scientific workflows in an issue organized by Paul F. Dubois, who was the project lead for Numeric from 1997 to 2002. Paul was an editor for Computer in Physics (which got merged into CiSE) since 1993, and joined CiSE with its founding. He wrote and edited for the Scientific Programming department until 2006, and continued with the column "Café Dubois" until 2008. The issue he led included the Hunter piece on Matplotlib \cite{Hunter_2007}, the Pérez and Granger article about IPython \cite{Perez_2007}, and Travis Oliphant's general overview of the Python language and its extensions with NumPy and SciPy \cite{Oliphant_2007}. Other articles in the issue highlight applications in various science contexts: space observation, systems biology, robotics, nanophotonics, and more. An author team from the Simula Research Laboratory in Norway discussed new Python tooling for solving partial differential equations with finite element methods in what was the early development of the Fenics project ( \cite{Mardal_2007}. This work heralded the compelling combination of symbolic mathematics and code generation, which Ellpack anticipated.
Many high-performance computing applications are of high consequence to society. Global climate modeling is a historic example of this. In 2020, the societal issue of greatest concern, the still-raging COVID-19 pandemic, saw a legion of computational scientists turning their endeavors to new research projects in this direction. Applications of such high consequence highlight the need for building trustworthy computational models. Emphasizing transparency and reproducibility has helped us build more trust in computational findings. In the context of supercomputing, however, we may ask: how do we trust results from computations that cannot be repeated? Access to supercomputers is limited, computing allocations are finite (and competitive), and machines are decommissioned after a few years. In this context, we might ask how reproducibility can be ensured, certified even, without exercising the original digital artifacts used to obtain new scientific results. This is often the situation in HPC. It is compounded now with greater adoption of machine learning techniques, which can be opaque. The ACM in 2017 issued a Statement on Algorithmic Transparency and Accountability, targeting algorithmic decision-making using data models \cite{council2017}. Among its seven principles, it calls for data provenance, auditability, validation and testing. These principles can be applied not only to data models, but to HPC in general. I want to discuss the next steps for reproducibility: how we may adapt our practice to achieve what I call unimpeachable provenance, and full auditability and accountability of scientific evidence produced via computation.An invited talk at SC20I was invited to speak at SC20 about my work and insights on transparency and reproducibility in the context of HPC. The session's theme was Responsible Application of HPC, and the title of my talk was "Trustworthy computational evidence through transparency and reproducibility." At the previous SC, I had the distinction to serve as Reproducibility Chair, leading an expansion of the initiative, which was placed under the Technical Program that year. We moved to make Artifact Description appendices required for all SC papers, created a template and an author kit for the preparation of the appendices, and introduced three new Technical Program tracks in support of the initiative. These are: the Artifact Description & Evaluation Appendices track—with an innovative double-open constructive review process—, the Reproducibility Challenge track, and the Journal Special Issue track, for managing the publication of select papers on the reproducibility benchmarks of the Student Cluster Competition. This year, the initiative was augmented to address issues of transparency, in addition to reproducibility, and a community sentiment study was launched to assess the impact of the effort, six-years in, and canvas the community's outlook on various aspects of it.Allow me to thank here Mike Heroux, Reproducibility Chair for SC in 2017 and 2018, Michela Taufer, SC19 General Chair—who put her trust in me to inherit the role from Mike—, and Beth Plale, the SC20 Transparency and Reproducibility Chair. I had countless inspiring and supportive conversations with Mike and Michela about the topic during the many months of planning for SC19, and more productive conversations with Beth during the transition to her leadership. Mike, Michela and I have served on other committees and working groups together, in particular, the group that met in July 2017 at the National Science Foundation (convened by Almadena Chtchelkanova) for the Workshop on Reproducibility Taxonomies for Computing and Computational Science. My presentation at that event condensed an inventory of uses of various terms like reproducibility and replication, across many fields of science \cite{barba2017}. I then wrote the review article "Terminologies for Reproducible Research," and posted it on arXiv \cite{barba2018}. It informed our workshop's report, which came out a few months later as a Sandia technical report \cite{taufer2018}. In it, we highlighted that the fields of computational and computing sciences provided two opposing definitions of the terms reproducible and replicable, representing an obstacle to progress in this sphere.The Association of Computing Machinery (ACM), representing computer science and industry professionals, had recently established a reproducibility initiative, and adopted diametrically opposite definitions to those used in computational sciences for more than two decades. In addition to raising awareness about the contradiction, we proposed a path to a compatible taxonomy. Compatibility is needed here because the computational sciences—astronomy, physics, epidemiology, biochemistry and others that use computing as a tool for discovery—and computing sciences (where algorithms, systems, software, and computers are the focus of study) have community overlap and often intersect in the venues of publication. The SC conference series is one example. Given the historical precedence and wider adoption of the definitions of reproducibility and replicability used in computational sciences, our Sandia report recommended that the ACM definitions be reversed. Several ACM-affiliated conferences were already using the artifact review and badging system (approved in 2016), so this was no modest suggestion. The report, however, was successful in raising awareness of the incompatible definitions, and the desirability of addressing it.A direct outcome of the Sandia report was a proposal to the National Information Standards Organization (NISO) for a Recommended Practice Toward a Compatible Taxonomy, Definitions, and Recognition Badging Scheme for Reproducibility in the Computational and Computing Sciences. NISO is accredited by the American National Standards Institute (ANSI) to develop, maintain, and publish consensus-based standards for information management. The organization has more than 70 members; publishers, information aggregators, libraries and other content providers use its standards. I co-chaired this particular working group, with Gerry Grenier from IEEE and Wayne Graves from ACM; Mike Heroux was also a member. The goal of the NISO Reproducibility Badging and Definitions Working group was to develop a Recommended Practice document—a step before development of a standard. As part of our joint work, we prepared a letter addressed to the ACM Publications Board, delivered in July 2019. It described the context and need for compatible reproducibility definitions and made the concrete request that ACM consider a change. By that time, not only did we have the Sandia report as justification, but the National Academies of Sciences, Engineering and Medicine (NASEM) had just released the report Reproducibility and Replicability in Science \cite{medicine2019}. It was the product of a long consensus study conducted by 15 experts, including myself, and sponsored by the National Science Foundation responding to Congressional decree. The NASEM report put forth its definitions as:Reproducibility is obtaining consistent results using the same input data, computational steps, methods and code, and conditions of analysis.Replicability is obtaining consistent results across studies aimed at answering the same scientific question, each of which has obtained its own data.The key contradiction with the ACM badging system resides on which term comprises using the author-created digital artifacts (e.g., data and code). We stated in the NISO working-group letter that if the ACM definitions of reproducible and replicable could be interchanged, the working group could move forward towards its goal of drafting recommended practices for badging that would lead to wider adoption in other technical societies and publishers. The ACM Publications Board responded positively, and began working through the details on how to make changes to items already published in the Digital Library with the "Results Replicated" badge—about 188 items existed at that time that were affected. Over the Summer of 2020, the ACM applied changes to the published Artifact Review and Badging web pages, and added a version number. From version 1.0, we see a note added that, as a result of discussions with NISO, the ACM was harmonizing its terminologies with those used in the broader scientific research community.All this background serves to draw our attention to the prolonged, thoughtful, and sometimes arduous efforts that have been directed at charting paths for adoption and giving structure to reproducibility and replicability in our research communities. Let us move now to why and how might the HPC community move forward.Insights on transparent, reproducible HPC researchDeployed barely over a year ago, the NSF-funded Frontera system at the Texas Advanced Computing Center (TACC) came in as the 8th most powerful supercomputer in the world, and the fastest on a university campus. Up to 80% of the available time on the system is allocated through the NSF Petascale Computing Resource Allocation program. The latest round of Frontera allocations (as of this writing) was just announced on October 25, 2020. I read through the fact sheet on the 15 newly announced allocations, to get a sense for the types of projects in this portfolio. Four projects are machine-learning or AI-focused, the same number as those in astronomy and astrophysics, and one more than those in weather or climate modeling. Other projects are single instances spanning volcanology/mantle mechanics, molecular dynamics simulations of ion channels, quantum physics in materials science, and one engineering project in fluid-structure interactions. One could gather these HPC projects in four groups:Astronomy and astrophysics are mature fields that in general have high community expectations of openness and reproducibility. As I'll highlight below, however, even these communities with mature practices benefit from checks of reproducibility that uncover areas of improvement. The projects tackling weather and climate modeling are candidates for being considered of high consequence to society. One example from the Frontera allocations concerns the interaction of aerosols caused by industrial activity with clouds, which can end up composed of smaller droplets, and become more reflective, resulting in a cooling effect on climate. Global climate models tend to overestimate the radiative forcing, potentially underestimating global warming: why? This is a question of great consequence for science-informed policy, in a subject that is already under elevated scrutiny from the public. Another project in this cluster deals with real-time high-resolution ensemble forecasts of high-impact winter weather events. I submit that high standards of transparency, meticulous provenance capture, and investments of time and effort in reproducibility and quality assurance are justified in these projects. Four of the winning projects are applying techniques from machine learning to various areas of science. In one case, the researchers seek to bridge the gap in the trade-off between accuracy of prediction and model interpretability, to make ML more applicable in clinical and public health settings. This is clearly also an application of high consequence, but in addition all the projects in this subset face the particular transparency challenges of ML techniques, requiring new approaches to provenance capture and transparent reporting. The rest of the projects are classic high-performance computational science applications, such as materials science, geophysics, and fluid mechanics. Reproducible-research practices vary broadly in these settings, but I feel confident saying that all or nearly all those efforts would benefit from prospective data management, better software engineering, and more automated workflows. And their communities would grow stronger with more open sharing. The question I have is: how could the merit review of these projects nudge researchers towards greater transparency and reproducibility? Maybe that is a question for later, and a question to start with is how could support teams at cyberinfrastructure facilities work with researchers to facilitate their adoption of better practices in this vein? I'll revisit these questions later.I also looked at the 2019 Blue Waters Annual Report, released on September 15, 2020, with highlights from a multitude of research projects that benefitted from computing allocations on the system. Blue Waters went into full service in 2013 and has provided over 35 billion core-hour equivalents to researchers across the nation. The highlighted research projects fall into seven disciplinary categories, and include 32 projects in space science, 20 in geoscience, 45 in physics and engineering, and many more. I want to highlight just one out of the many dozens of projects featured in the Blue Waters Annual Report, for the following reason. I did a word search on the PDF with Zenodo, and that project was the only one listing Zenodo entries in the "Publications & Data Sets" section that ends each project feature. One other project (in the domain of astrophysics) mentions that data is available through the project website and in Zenodo, but doesn't list any data sets in the report. Zenodo is an open-access repository funded by the European Union's Framework Programs for Research, and operated by CERN. Some of the world’s top experts in running large-scale research data infrastructure are at CERN, and Zenodo is hosted on top of infrastructure built in service of what is the largest high-energy physics laboratory of the world. Zenodo hosts any kind of data, under any license type (including closed-access). It has become one of the most used archives for open sharing of research objects, including software.The project I want to highlight is "Molten-salt reactors and their fuel cycles," led by Prof. Kathryn Huff at UIUC. I've known Katy since 2014, and she and I share many perspectives on computational science, including a strong commitment to open-source software. This project deals with modeling and simulation of nuclear reactors and fuel cycles, combining multiple physics and multiple scales, with the goal of improving design of nuclear reactors in terms of performance and safety. As part of the research enabled by Blue Waters, the team developed two software packages: Moltres, described as a first-of-its-kind finite-element code for simulating the transient neutronics and thermal hydraulics in a liquid-fueled molten-salt reactor design; and SaltProc: a Python tool for fuel salt reprocessing simulation. The references listed in the project highlight include research articles in the Annals of Nuclear Energy, as well as the Zenodo deposits for both codes, and a publication about Moltres in the Journal of Open Source Software, JOSS. (As one of the founding editors of JOSS, I'm very pleased.) It is possible, of course, that other projects of the Blue Waters portfolio have also made software archives in Zenodo or published their software in JOSS, but they did not mention it in this report and did not cite the artifacts. Clearly, the research context of the project I highlighted is of high consequence: nuclear reactor design. The practices of this research group show a high standard of transparency that should be the norm in such fields. Beyond transparency, the publication of the software in JOSS ensures that it was subject to peer review and that it satisfies standards of quality. JOSS reviewers install the software, run tests, and comment on usability and documentation, leading to quality improvements.Next, I want to highlight the work of a group that includes CiSE editors Michela Taufer and Ewa Deelman, posted last month on arXiv \cite{e2020}[6]. The work sought to directly reproduce the analysis that led to the 2016 discovery of gravitational waves, using the data and codes that the LIGO collaboration had made available to the scientific community. The data had previously been re-analyzed by independent teams using different codes, leading to replication of the findings, but no attempt had yet been made at reproducing the original results. In this paper, the authors report on challenges they faced during the reproduction effort, even with availability of data and code supplementing the original publication. A first challenge was the lack of a single public repository with all the information needed to reproduce the result. The team had the cooperation of one of the original LIGO team members, who had access to unpublished notes that ended up being necessary in the process of iteratively filling in the gaps of missing public information. Other highlights of the reproduction exercise include: the original publication did not document the precise version of the code used in the analysis; the script used to make the final figure was not released publicly (but one co-author gave access to it privately); the original documented workflow queried proprietary servers to access data, which needed to be modified to run with the public data instead. In the end, the result—the statistical significance of the gravitational-wave detection from a black-hole merger—was reproduced, but not independently of the original team, as one researcher is co-author in both publications. The message here is that even a field that is mature in its standards of transparency and reproducibility needs checks to ensure that these practices are sufficient or can be improved.Science policy trendsThe National Academies study on Reproducibility and Replicability in Science was commissioned by the National Science Foundation under Congressional mandate, with the charge coming from the Chair of the Science, Space, and Technology Committee. NASEM reports and convening activities have a range of impacts on policy and practice, and often guide the direction of federal programs. NSF is in the process of developing its agency response to the report, and we can certainly expect to hear more in the future about requirements and guidance for researchers seeking funding.The recommendations in the NASEM report are directed at all the various stakeholders: researchers, journals and conferences, professional societies, academic institutions and national laboratories, and funding agencies. Recommendation 6-9, in particular, prompts funders to ask that grant applications discuss how they will assess and report uncertainties, and how the proposed work will address reproducibility and/or replicability issues. It also recommends that funders incorporate reproducibility and replicability in the merit-review criteria of grant proposals. Combined with related trends urging for more transparency and public access to the fruits of government-funded research, we need to be aware of the shifting science-policy environment.One more time, I have a reason to thank Mike Heroux, who took time for a video call with me as I prepared my SC20 invited talk. In his position as Senior Scientist at Sandia, 1/5 of his time is spent in service to the lab's activities, and this includes serving in the review committee of the internal Laboratory Directed Research & Development (LDRD) grants. As it is an internal program, the Calls for Proposals are not available publicly, but Mike told me that they now contain specific language asking proposers to include statements on how the project will address transparency and reproducibility. These aspects are discussed in the proposal review and are a factor in the decision-making. As community expectations grow, it could happen that between two proposals equally ranked in the science portion the tie-break comes from one of them better addressing reproducibility. Already some teams at Sandia are performing at a high level, e.g., they produce an Artifact Description appendix for every publication they submit, regardless of the conference or journal requirements.We don't know if or when NSF might add similar stipulations to general grant proposal guidelines, asking researchers to describe transparency and reproducibility in the project narrative. One place where we see the agency start responding to shifting expectations about open sharing of research objects is the section on results from prior funding. NSF currently requires here a listing of publications from prior awards, and "evidence of research products and their availability, including …data [and] software."I want to again thank Beth Plale, who took time to meet with me over video and sent me follow-up materials to use in preparing my SC20 talk. In March 2020, NSF issued a "Dear Colleague Letter" on Open Science for Research Data, with Beth then acting as the public access program director. The DCL says that NSF is expanding its Public Access Repository (NSF PAR) to accept metadata records, leading to data discovery and access. It requires research data to be deposited in an archival service and assigned a Digital Object Identifier (DOI), a global and persistent link to the object on the web. A grant proposal's Data Management Plan should state the anticipated archive to be used, and include any associated cost in the budget. Notice this line: "Data reporting will initially be voluntary." This implies that it will later be mandatory! The DCL invited proposals aimed at growing community readiness to advance open science. At the same time, the Office of Science and Technology Policy (OSTP) issued a Request for Information early this year asking what could Federal agencies do to make the results from research they fund publicly accessible. The OSTP sub-committee on open science is very active. An interesting and comprehensive response to the OSTP RFI comes from the MIT Libraries. It recommends (among other things): Policies that default to open sharing for data and code, with opt-out exceptions available [for special cases]… Providing incentives for sharing of data and code, including supporting credentialing and peer-review; and encouraging open licensing. Recognizing data and code as “legitimate, citable products of research” and providing incentives and support for systems of data sharing and citation… The MIT Libraries response addresses various other themes like responsible business models for open access journals, and federal support for vital infrastructure needed to make open access to research results more efficient and widespread. It also recommends that Federal agencies provide incentives for documenting and raising quality of data and code, and also "promote, support, and require effective data practices, such as persistent identifiers for data, and efficient means for creating auditable and machine readable data management plans."To boot, the National Institutes of Health (NIH) just announced on October 29 a new policy on data management and sharing. It requires researchers to plan prospectively for managing and sharing scientific data openly, saying: "we aim to shift the culture of research to make data sharing commonplace and unexceptional."Another setting where we could imagine expectations to discuss reproducibility and open research objects is proposals for allocation of computing time. For this section, I need to thank John West, Director Of Strategic Initiatives at the Texas Advanced Computing Center (and CiSE Associate EiC), who took time for a video call with me on this topic. We bounced ideas about how cyber-infrastructure providers might play a role in growing adoption of reproducibility practices. Currently, the NSF science proposal and the computing allocation proposal are awarded separately. The Allocation Submission Guidelines discuss review criteria, which include: intellectual merit (demonstrated by the NSF science award), methodology (models, software, analysis methods), research plan and resource request, and efficient use of the computational resources. For the most part, researchers have to show that their application scales to the size of the system they are requesting time on. Interestingly, the allocation award is not tied to performance, and researchers are not asked to show that their codes are optimized, only that they scale and that the research question is feasible to be answered in the allocated time. The responsible stewardship of the supercomputing system is provided for via a close collaboration between the researchers and the members of the supercomputing facility. Codes are instrumented under the hood with low-overhead collection of system-wide performance data (in the UT facility, with TACC-Stats) and a web interface for reports.I see three opportunities here: 1) workflow-management and/or system monitoring could be extended to also supply automated provenance capture; 2) the expert staff at the facility could broaden their support to researchers to include advice and training in transparency and reproducibility matters; and 3) cyber-infrastructure facilities could expand their training initiatives to include essential skills for reproducible research. John floated other ideas, like the possibility that some projects be offered a bump on their allocations (say, 5% or 10%) to engage in R&R activities; or, more drastic perhaps, that projects may not be awarded allocations over a certain threshold unless they show commitment and a level of maturity in reproducibility.Next steps for HPCThe SC Transparency and Reproducibility Initiative is one of the innovative, early efforts to gradually raise the expectations and educate a large community about how to address it and why it matters. Over six years, we have built community awareness, and buy-in. This year's community sentiment study shows frank progress: 90% of the respondents are aware of the issues around reproducibility, and only 15% thought the concerns are exaggerated. Importantly, researchers report that they are consulting the artifact appendices of technical papers, signaling impact. As a community, we are better prepared to adapt to raising expectations from funders, publishers, and readers.The pandemic crisis has unleashed a tide of actions to increase access and share results: the Covid-19 Open Research Dataset (CORD-19) is an example \cite{al2020}; the COVID-19 Molecular Structure and Therapeutics Hub at MolSSI is another. Facing a global challenge, we as a society are strengthened by facilitating immediate public access to data, code, and published results. This point has been made by many in recent months, but perhaps most eloquently by Rommie Amaro and Adrian Mulholland in their Community Letter Regarding Sharing Biomolecular Simulation Data for COVID-19—signed by more than a hundred researchers from around the world \cite{j2020}. It says: "There is an urgent need to share our methods, models, and results openly and quickly to test findings, ensure reproducibility, test significance, eliminate dead-ends, and accelerate discovery." Then it follows with several commitments: to making results available quickly via pre-prints; to make available input files, model-building and analysis scripts (e.g., Jupyter notebooks), and data necessary to reproduce the results; to use open data-sharing platforms to make available results as quickly as possible; to share algorithms and methods in order to accelerate reuse and innovation; and to apply permissive open-source licensing strategies. Interestingly, these commitments are reminiscent of the pledges I made in my Reproducibility PI Manifesto \cite{barba2012} eight years ago!One thing the pandemic instantly provided is a strong incentive to participate in open science and attend to reproducibility. The question is how much will newly adopted practices persist once the incentive of a world crisis is removed.I've examined here several issues of incentives for transparent and reproducible research. But social epistemologists of science know that so-called Mertonian norms (for sharing widely the results of research) are supported by both economic and ethical factors—incentives and norms—in close interrelation. Social norms require a predominant normative expectation (for example, sharing of food in a given situation and culture). In the case of open sharing of research results, those expectations are not prime, due to researchers' sensitivity to credit incentives. Heesen \cite{heesen2017} concludes: "Give sufficient credit for whatever one would like to see shared ... and scientists will indeed start sharing it."In HPC settings, where we can hardly ever reproduce results (due to machine access, cost, and effort), a vigorous alignment with the goals of transparency and reproducibility will develop a blend of incentives and norms, will consider especially the applications of high consequence to society, and will support researchers with infrastructure (human and cyber). Over time, we will arrive at a level of maturity to achieve the goal of trustworthy computational evidence, not by actually exercising the open research objects (artifacts) shared by authors (data and code), but by a research process that ensures unimpeachable provenance.
Approved by CiSE AEiCs on 3 March 2020. Computing in Science and Engineering (CiSE) is a technical magazine of the IEEE Computer Society, in publication since 1999. CiSE publishes peer-reviewed research articles, and also runs departments spanning news and analyses, topical reviews, tutorials, case studies, and more. CiSE runs special issues on themes chosen by the editorial board, which are handled by Guest Editors. This document explains the editorial workflow of special issues, and the role and responsibilities of Guest Editors.Selection of Special Issue ThemesSpecial Issues are proposed by members of the editorial board, or other community members, for the consideration of the Editor-in-Chief (EiC), and Associate Editors-in-Chief (AEiCs). They are approved by the EiC, with concurrence of a majority of the AEICs. (CiSE currently has 5 AEiCs, so three of them should be supportive of the theme). A special issue needs to have at least two Guest Editors, and should be approved at least one year prior to the expected publication.Invited or Open SubmissionsMost special issues make an open call for submissions of papers relevant to the issue theme. Guest editors may propose an invited special issue, for which articles will be submitted via a closed CFP, and this may be approved by the EiC with support from AEiCs.  To avoid perceived conflict of interest, guest editors should refrain from inviting authors from their own institutions, or authors who were their graduate students or postdoctoral trainees. All invited articles undergo the same peer-review process as unsolicited ones.Invited Special Issues are generally limited to one per year.Special Issue ProposalsProposals for future CiSE special issues should be sent to the EiC directly, and should include the following materials:Description—An overview of topic and scope, and reasons why it is timely to dedicate a magazine issue to this theme.Guest editors—A short bio for each guest editor, addressing how they are well positioned to lead the community to submit and review manuscripts.Draft CFP—A preliminary drafting of the call for papers, subject to improvements after approval.Dissemination plans—How the guest editors plan to reach authors and ensure success of the special issue.Proposed issue date—Considering any related timelines (e.g., when articles stem from workshops, conferences, or world events), anticipated date of CFP and publication. Role and Responsibilities of Guest EditorsGuest editors assume the responsibility of an Associate Editor in charge of the manuscripts submitted to the special issue. They manage the peer review of submitted manuscripts, ensure publication quality, and adhere to the IEEE and Computer Society’s policies. \cite{manual2020}The IEEE Computer Society has published a Guest Editor Information webpage. CiSE complies with these guidelines, but is sometimes stricter.Follow these guidelines for CiSE Special Issues:Guest editors write a special-issue introduction, which should be 1000 to 2200 words in length (5 pages or less), discussing the state of the art and future directions in the theme topic. It should highlight how each article in the special issue contributes to the field, and it may introduce key concepts and terminology, to facilitate a smooth reading of the articles. The guest editors’ introduction should state whether the issue had an invited or open CFP. It should be sent directly to the EiC.Other than the introduction to the special issue, a guest editor may not be an author or co-author of another article in the special issue they are editing. If some special circumstances warrant it (e.g., a needed survey of the field), they should request special dispensation from the EiC, who will seek concurrence from two AEiCs. Guest editors should actively solicit submissions via outreach through their networks, and make every effort to ensure a healthy number of manuscripts are assessed via peer review to be part of the special issue. Guest editors invite reviewers who are experts in the topic of the special issue ahead of the submission deadline. The reviewers should confirm their willingness to serve, and be ready to review their assigned articles within three weeks. Computer Society policies require each manuscript to receive three independent reviews. Guest editors oversee the timeliness and quality of peer-review reports. They should not take the role of reviewers, themselves.Guest editors should not handle the peer review of manuscripts where a real or apparent conflict of interest (COI, see below) is present with any authors. These include: affiliation, previous co-authorship, having formally mentored the author (as PhD or postdoctoral supervisor), or having collaborated in a funded grant. In the case of a submission by an author where a potential COI may exist (real or apparent) with one or more of the guest editors, the guest editors should seek guidance from the EiC on how the COI might be mitigated. The submission may be allowed to go forward, in which case the guest editor will delegate the handling of peer review of this submission to another member of the editorial board, with guidance from the EiC. Guest Editors are also responsible for ensuring that articles conform to Computer Society policies, and the magazine style. Common issues that arise in this regard are the word limits, and limits on the number of references. According to CiSE guidelines for authors: Articles should be between 2,400 and 6,250 words, including all main body, abstract, keyword, bibliography, and biography text. Each table and figure counts for 250 words. They  should have no more than 12 references. Articles should be written for an interdisciplinary audience, be concise, and tend to be more readable than scholarly in style. Special issues typically consist of about 6 articles. Informed by the peer reviews, guest editors select the articles to appear in the issue, which may result in good-quality articles having to be rejected. In exceptional situations of a theme being very popular, guest editors may request approval from the EiC to run a Part 2 of the theme in a later issue. Transfer of special-issue submissions to the regular article queue is discouraged.Definition of Conflict of Interest“A conflict of interest is defined as any situation, transaction, or relationship in which a member’s, volunteer’s, or staff person’s decisions, actions, or votes could materially affect that individual’s professional, personal, financial, or business concerns.” [p.22 IEEE PSPB Operations Manual] An Associate Editor is regarded as having a COI with a manuscript if any author is employed at the same institution as the editor. It is also a COI if the editor has co-authored a paper or has closely collaborated in a research project with any of the manuscript's authors  in the previous five years.Editorial WorkflowWhen a special issue is approved, the editorial workflow is as follows:
SummaryThis is the editor's review of manuscript CiSESI-2018-02-0013, submitted to Computing in Science & Engineering, Reproducible Research Track: "automan: a Python-based, automation framework for numerical computing"  \citep{p2018b}.We received two peer-review reports for this manuscript, and the reviewers were: Thomas Pasquier and Olivier Mesnard. Both reviewers recommended that the manuscript be accepted with a minor revision, and I agree. Reviewer 1 comments on software-engineering aspects: he asks if automan could be combined with continuous integration tool chains. He also asks to state plainly the license of automan—from the LICENSE.txt file in the GitHub repository, it looks like it’s under BSD3, but you could state this in the README and in the paper. Reviewer 2 acted as Reproducibility Reviewer. He inspected the code repository of automan, installed it, and tested it with a fabricated problem. He also provides several minor suggestions in his open report \citep{o2018}.Editor's reviewOne issue that the author needs to correct is the terminology. The CiSE RR Track adopted a set terminology that we request authors to consistently apply. See the Call for Papers \citep{g2018}.Citing from there:Reproducible research is defined as research in which authors provide all the necessary data and the computer codes to run the analysis again, re-creating the results. Replication, on the other hand, is defined as arriving at the same scientific findings as another study, collecting new data (possibly with different methods), and completing new analyses.Throughout the manuscript, the author is using “repeatability” and “repeating” in place of “reproducibility” and “reproducing.” Conflicting terminology is a source of confusion in the field, and the CiSE RR editors want consistent usage in the magazine.I also recommend reading: “Re-run, Repeat, Reproduce, Reuse, Replicate…” \citep{Benureau_2018}AbstractCiSE articles don’t normally carry a standard abstract. Instead, a short “blurb” appears under the title, usually about three sentences long, to pull focus. Please edit the abstract down, in line with this style. Here's a starting point for your editing (or simply re-use this, if you like it):Automating computational workflows leads to more reproducible research. The Python tool automan assembles a suite of long-running computations, deploys them to your computational resources, and produces final plots from output data, all with a single command.Introduction and DiscussionIn the Introduction, you say “What is perhaps more important is that we discovered that [reproducibility] can be very profitable to the researcher.” The paper then goes into the design details and explains the usage of automan in section 3, and you wrap up with a summary in the first paragraph of section 4 (Discussion). Towards the end, the Discussion explains how automan helped you manage a series of about 75 computational experiments with SPH schemes. But I miss here a stronger, more persuasive statement about how automan expedited and ensured quality of the research (it doesn’t help that the paragraph in question is plagued with passive phrases). I would like it if you re-wrote this passage to reveal more about your own experience, and how you benefitted from the tool you created. Convince your readers here that the initial overhead of setting up automan for their simulation-based workflow will pay off!Editorial / style requestsThe manuscript is well written, overall, but a few small improvements would polish it further. In CiSE, we aim for a more readable style than typical scholarly literature. In line with that, I want to encourage you to especially look at whether any passive constructions are strictly necessary, and look to remove syntactic-expletive phrases like “there are,” if you can, as well as superfluous adverbs and superlatives (e.g., clearly, very).Examples:First sentence! “It is well known that…” You can easily remove that, and combine the first two sentences: “Reproducibility is a cornerstone in all science fields, and is vitally important in computational science.”p. 1, l. 39: “Unfortunately, there are not many immediate or direct incentives for a researcher to invest time…” >> “Unfortunately, researchers have few immediate or direct incentives to invest time…”p. 1, l. 41: “there can be significant challenges involved in [reproducing] and replicating numerical experiments” >> “reproducing and replicating numerical experiments involves serious challenges” (or another adjective, but don’t use “significant” unless you mean statistically significant, to avoid misunderstanding).p. 1, l. 45: “we believe this is a very important step in facilitating reproducible research” >> This step is important for facilitating reproducible research” (the terminology here is consistent with CiSE usage).p. 1, l. 46: “The approach used in our automation framework is fairly general” >> “Our automation framework typifies a general approach”p. 1, .. 49: “[reproducibility] can be very profitable to the researcher” (remove “very”)p. 1, l. 51: “It is our hope that” >> “We hope that"— The fact that we can pick several examples from just the first two paragraphs shows that you could polish the manuscript by combing through the text looking for just these patterns. I expect about a 5–10% reduction in word count from this exercise!p. 10, l. 40: Bad sentence: “It is important to note again that the commands that are executed such that one may configure the directory in which they will generate output using a command line argument.” Typos:Title: you don’t need the comma.p. 1, l. 38: “some of [the] most important articles” (missing “the”)p. 3, l. 21: capitalize DockerSoftware AppendixAs indicated in the Call for Papers: “articles will include an appendix reporting on the software engineering and data management practices followed by the authors.” Please add a description to that effect, in the form of a brief Appendix.Extended AcknowledgementIn the short statement on "Peer review in the CiSE RR Track" \citep{Barba}, we describe our ambitions for opening up the peer-review process in this track. Your reviewers opted in to open identity and open reports. We ask that you recognize reviewers by name in an extended acknowledgements section, briefly stating their contribution to your paper. You can cite the open reports with the URL or DOI, in each case.ReferencesPlease provide DOIs wherever possible for all references.For Ref. [1], please add DOI of the PDF version of this blog post, published on Figshare (10.6084/m9.figshare.4879928.v1).For Ref. [8], please update the citation, as needed.