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This paper extends the FAIR (Findable, Accessible, Interoperable, Reusable) guidelines to provide criteria for assessing if software conforms to best practices in open source. By adding "USE" (User-Centered, Sustainable, Equitable), software development can adhere to open source best practice by incorporating user-input early on, ensuring front-end designs are accessible to all possible stakeholders, and planning long-term sustainability alongside software design. The FAIR-USE4OS guidelines will allow funders and researchers to more effectively evaluate and plan open-source software projects. There is good evidence of funders increasingly mandating that all funded research software is open source; however, even under the FAIR guidelines, this could simply mean software released on public repositories with a Zenodo DOI. By creating FAIR-USE software, best practice can be demonstrated from the very beginning of the design process and the software has the greatest chance of success by being impactful.

Software plays a fundamental role in research as a tool, an output, or even as an object of study. This special issue on software citation, indexing, and discoverability brings together five papers examining different aspects of how the use of software is recorded and made available to others. It describes new work on datasets that enable large-scale analysis of the evolution of software usage and citation, that presents evidence of increased citation rates when software artifacts are released, that provides guidance for registries and repositories to support software citation and findability, and that shows there are still barriers to improving and formalising software citation and publication practice. As the use of software increases further, driven by modern research methods, addressing the barriers to software citation and discoverability will encourage greater sharing and reuse of software, in turn enabling research progress.

Software and data citation are emerging best practices in scholarly communication. This article provides structured guidance to the academic publishing community on how to implement software and data citation in publishing workflows. These best practices support the verifiability and reproducibility of academic and scientific results, sharing and reuse of valuable data and software tools, and attribution to the creators of the software and data. While data citation is increasingly well-established, software citation is rapidly maturing. Software is now recognized as a key research result and resource, requiring the same level of transparency, accessibility, and disclosure as data. Software and data that support academic or scientific results should be preserved and shared in scientific repositories that support these digital object types for discovery, transparency, and use by other researchers. These goals can be supported by citing these products in the Reference Section of articles and effectively associating them to the software and data preserved in scientific repositories. Publishers need to markup these references in a specific way to enable downstream processes.

A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been re-interpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022.

Abstract There is increasing evidence that HFpEF is a heterogeneous clinical entity and distinct molecular pathways may contribute to pathophysiology. Leveraging unbiased proteomics to identify novel biomarkers, this study seeks to understand the underlying molecular mechanisms of HFpEF. The discovery cohort consisted of HFpEF cases and non-HF controls from the CATHGEN study (N = 176); the validation cohort consisted of participants from the TECOS trial of patients with diabetes (N = 109). Proteins associated with HFpEF were included in a LASSO model to create a discriminative multi-protein model and assessed in the validation cohort. Survival models and meta-analysis were used to test the association of proteins with incident clinical outcomes, including HF hospitalization, mortality and HFpEF hospitalization in CATHGEN, TECOS and the Jackson Heart Study. In the derivation set, 190 proteins were associated with HFpEF in univariate analysis, of which 65 remained significant in the multivariate model. Twenty (30.8%) of these proteins validated in TECOS, including LCN2, U-PAR, IL-1ra, KIM1, CSTB and Gal-9 (OR 1.93–2.77, p

Abstract Research software is a fundamental and vital part of research, yet significant challenges to discoverability, productivity, quality, reproducibility, and sustainability exist. Improving the practice of scholarship is a common goal of the open science, open source, and FAIR (Findable, Accessible, Interoperable and Reusable) communities and research software is now being understood as a type of digital object to which FAIR should be applied. This emergence reflects a maturation of the research community to better understand the crucial role of FAIR research software in maximising research value. The FAIR for Research Software (FAIR4RS) Working Group has adapted the FAIR Guiding Principles to create the FAIR Principles for Research Software (FAIR4RS Principles). The contents and context of the FAIR4RS Principles are summarised here to provide the basis for discussion of their adoption. Examples of implementation by organisations are provided to share information on how to maximise the value of research outputs, and to encourage others to amplify the importance and impact of this work.

Research data is optimized when it can be freely accessed and reused. To maximize research equity, transparency, and reproducibility, policymakers should take concrete steps to ensure that research software is openly accessible and reusable.

Background:. Kidney transplant waitlist management is complex because waiting time is long, and the patients have significant comorbidities. Identification of patients at highest risk for waiting list removal for death and medical complications could allow better outcomes and allocation of resources. Methods:. Demographics, functional and frailty assessment‚ and biochemical data were retrospectively analyzed on 313 consecutive patients listed for kidney transplant. Troponin, brain natriuretic peptide, components of the Fried frailty metrics, pedometer activity, and treadmill ability were measured at the time of transplant evaluation and at subsequent re-evaluations. Cox proportional hazards models were used to identify factors associated with death or waiting list removal for medical reasons. Multivariate models were created to identify significant predictor sets. Results:. Among 249 patients removed while waitlisted, 19 (6.1%) died and 51 (16.3%) were removed for medical reasons. Mean follow-up duration was 2.3 y (±1.5 y). 417 sets of measurements were collected. Significant (P < 0.05) non–time-dependent variables associated with the composite outcome identified on univariate analysis included N-terminal probrain natriuretic peptide (BNP), treadmill ability, pedometer activity, diagnosis of diabetes and the Center of Epidemiological Studies Depression Scale question asking how many days per week could you not get going. Significant time-dependent factors included BNP, treadmill ability, Up and Go, pedometer activity, handgrip, 30 s chair sit-stand test, and age. The optimal time-dependent predictor set included BNP, treadmill ability, and patient age. Conclusions:. Changes in functional and biochemical markers are predictive of kidney waitlist removal for death and medical reasons. BNP and measures of walking ability were of particular importance.

Most studies of the genetics of the metabolome have been done in individuals of European descent. Here, the authors integrate genomics and metabolomics in Black individuals, highlighting the value of whole genome sequencing in diverse populations and linking circulating metabolites to human disease.

Regular exercise leads to widespread salutary effects, and there is increasing recognition that exercise-stimulated circulating proteins can impart health benefits. Despite this, limited data exist regarding the plasma proteomic changes that occur in response to regular exercise. Here, we perform large-scale plasma proteomic profiling in 654 healthy human study participants before and after a supervised, 20-week endurance exercise training intervention. We identify hundreds of circulating proteins that are modulated, many of which are known to be secreted. We highlight proteins involved in angiogenesis, iron homeostasis, and the extracellular matrix, many of which are novel, including training-induced increases in fibroblast activation protein (FAP), a membrane-bound and circulating protein relevant in body-composition homeostasis. We relate protein changes to training-induced maximal oxygen uptake adaptations and validate our top findings in an external exercise cohort. Furthermore, we show that FAP is positively associated with survival in 3 separate, population-based cohorts.

Abstract To enable the reusability of massive scientific datasets by humans and machines, researchers aim to adhere to the principles of findability, accessibility, interoperability, and reusability (FAIR) for data and artificial intelligence (AI) models. This article provides a domain-agnostic, step-by-step assessment guide to evaluate whether or not a given dataset meets these principles. We demonstrate how to use this guide to evaluate the FAIRness of an open simulated dataset produced by the CMS Collaboration at the CERN Large Hadron Collider. This dataset consists of Higgs boson decays and quark and gluon background, and is available through the CERN Open Data Portal. We use additional available tools to assess the FAIRness of this dataset, and incorporate feedback from members of the FAIR community to validate our results. This article is accompanied by a Jupyter notebook to visualize and explore this dataset. This study marks the first in a planned series of articles that will guide scientists in the creation of FAIR AI models and datasets in high energy particle physics.

As recognition of the vital importance of software for contemporary research is increasing, Research Software Engineering (RSE) is emerging as a discipline in its own right. We present an inventory of relevant research questions about RSE as a basis for future research and initiatives to advance the field, highlighting selected literature and initiatives. This work is the outcome of a RSE community workshop held as part of the 2020 International Series of Online Research Software Events (SORSE) which identified and prioritized key questions across three overlapping themes: people, policy and infrastructure. Almost half of the questions focus on the people theme, which addresses issues related to career paths, recognition and motivation; recruitment and retention; skills; and diversity, equity and inclusion. However, the people and policy themes have the same number of prioritized questions. We recommend that different types of stakeholders, such as RSE employers and policy makers, take responsibility for supporting or encouraging answering of these questions by organizations that have an interest. Initiatives such as the International Council of RSE Associations should also be engaged in this work.

Research software is a critical component of contemporary scholarship. Yet, most research software is developed and managed in ways that are at odds with its long-term sustainability. This paper presents findings from a survey of 1,149 researchers, primarily from the United States, about sustainability challenges they face in developing and using research software. Some of our key findings include a repeated need for more opportunities and time for developers of research software to receive training. These training needs cross the software lifecycle and various types of tools. We also identified the recurring need for better models of funding research software and for providing credit to those who develop the software so they can advance in their careers. The results of this survey will help inform future infrastructure and service support for software developers and users, as well as national research policy aimed at increasing the sustainability of research software.

Abstract Significant investments to upgrade and construct large-scale scientific facilities demand commensurate investments in R&D to design algorithms and computing approaches to enable scientific and engineering breakthroughs in the big data era. Innovative Artificial Intelligence (AI) applications have powered transformational solutions for big data challenges in industry and technology that now drive a multi-billion dollar industry, and which play an ever increasing role shaping human social patterns. As AI continues to evolve into a computing paradigm endowed with statistical and mathematical rigor, it has become apparent that single-GPU solutions for training, validation, and testing are no longer sufficient for computational grand challenges brought about by scientific facilities that produce data at a rate and volume that outstrip the computing capabilities of available cyberinfrastructure platforms. This realization has been driving the confluence of AI and high performance computing (HPC) to reduce time-to-insight, and to enable a systematic study of domain-inspired AI architectures and optimization schemes to enable data-driven discovery. In this article we present a summary of recent developments in this field, and describe specific advances that authors in this article are spearheading to accelerate and streamline the use of HPC platforms to design and apply accelerated AI algorithms in academia and industry.

The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery. Generalizing these principles to research software and other digital products is an active area of research. Machine learning models—algorithms that have been trained on data without being explicitly programmed—and more generally, artificial intelligence (AI) models, are an important target for this because of the ever-increasing pace with which AI is transforming scientific domains, such as experimental high energy physics (HEP). In this paper, we propose a practical definition of FAIR principles for AI models in HEP and describe a template for the application of these principles. We demonstrate the template’s use with an example AI model applied to HEP, in which a graph neural network is used to identify Higgs bosons decaying to two bottom quarks. We report on the robustness of this FAIR AI model, its portability across hardware architectures and software frameworks, and its interpretability.

Software now lies at the heart of scholarly research. Here we argue that as well as being important from a methodological perspective, software should, in many instances, be recognised as an output of research, equivalent to an academic paper. The article discusses the different roles that software may play in research and highlights the relationship between software and research sustainability and reproducibility. It describes the challenges associated with the processes of citing and reviewing software, which differ from those used for papers. We conclude that whilst software outputs do not necessarily fit comfortably within the current publication model, there is a great deal of positive work underway that is likely to make an impact in addressing this.

Software is as integral as a research paper, monograph, or dataset in terms of facilitating the full understanding and dissemination of research. This article provides broadly applicable guidance on software citation for the communities and institutions publishing academic journals and conference proceedings. We expect those communities and institutions to produce versions of this document with software examples and citation styles that are appropriate for their intended audience. This article (and those community-specific versions) are aimed at authors citing software, including software developed by the authors or by others. We also include brief instructions on how software can be made citable, directing readers to more comprehensive guidance published elsewhere. The guidance presented in this article helps to support proper attribution and credit, reproducibility, collaboration and reuse, and encourages building on the work of others to further research.

How do complex social systems evolve in the modern world? This question lies at the heart of social physics, and network analysis has proven critical in providing answers to it. In recent years, network analysis has also been used to gain a quantitative understanding of law as a complex adaptive system, but most research has focused on legal documents of a single type, and there exists no unified framework for quantitative legal document analysis using network analytical tools. Against this background, we present a comprehensive framework for analyzing legal documents as multi-dimensional, dynamic document networks. We demonstrate the utility of this framework by applying it to an original dataset of statutes and regulations from two different countries, the United States and Germany, spanning more than twenty years (1998–2019). Our framework provides tools for assessing the size and connectivity of the legal system as viewed through the lens of specific document collections as well as for tracking the evolution of individual legal documents over time. Implementing the framework for our dataset, we find that at the federal level, the United States legal system is increasingly dominated by regulations, whereas the German legal system remains governed by statutes. This holds regardless of whether we measure the systems at the macro, the meso, or the micro level.

Recent advances in proteomic technologies have made high-throughput profiling of low-abundance proteins in large epidemiological cohorts increasingly feasible. We investigated whether aptamer-based proteomic profiling could identify biomarkers associated with future development of type 2 diabetes (T2DM) beyond known risk factors. We identified dozens of markers with highly significant associations with future T2DM across 2 large longitudinal cohorts (n = 2839) followed for up to 16 years. We leveraged proteomic, metabolomic, genetic, and clinical data from humans to nominate 1 specific candidate to test for potential causal relationships in model systems. Our studies identified functional effects of aminoacylase 1 (ACY1), a top protein association with future T2DM risk, on amino acid metabolism and insulin homeostasis in vitro and in vivo. Furthermore, a loss-of-function variant associated with circulating levels of the biomarker WAP, Kazal, immunoglobulin, Kunitz, and NTR domain–containing protein 2 (WFIKKN2) was, in turn, associated with fasting glucose, hemoglobin A1c, and HOMA-IR measurements in humans. In addition to identifying potentially novel disease markers and pathways in T2DM, we provide publicly available data to be leveraged for insights about gene function and disease pathogenesis in the context of human metabolism.

Software is as integral as a research paper, monograph, or dataset in terms of facilitating the full understanding and dissemination of research. This article provides broadly applicable guidance on software citation for the communities and institutions publishing academic journals and conference proceedings. We expect those communities and institutions to produce versions of this document with software examples and citation styles that are appropriate for their intended audience. This article (and those community-specific versions) are aimed at authors citing software, including software developed by the authors or by others. We also include brief instructions on how software can be made citable, directing readers to more comprehensive guidance published elsewhere. The guidance presented in this article helps to support proper attribution and credit, reproducibility, collaboration and reuse, and encourages building on the work of others to further research.

Background: Software is now ubiquitous within research. In addition to the general challenges common to all software development projects, research software must also represent, manipulate, and provide data for complex theoretical constructs. Ensuring this process of theory-software translation is robust is essential to maintaining the integrity of the science resulting from it, and yet there has been little formal recognition or exploration of the challenges associated with it. Methods: We thematically analyse the outputs of the discussion sessions at the Theory-Software Translation Workshop 2019, where academic researchers and research software engineers from a variety of domains, and with particular expertise in high performance computing, explored the process of translating between scientific theory and software. Results: We identify a wide range of challenges to implementing scientific theory in research software and using the resulting data and models for the advancement of knowledge. We categorise these within the emergent themes of design, infrastructure, and culture, and map them to associated research questions. Conclusions: Systematically investigating how software is constructed and its outputs used within science has the potential to improve the robustness of research software and accelerate progress in its development. We propose that this issue be examined within a new research area of theory-software translation, which would aim to significantly advance both knowledge and scientific practice.