A survey of the state of the practice for research software in the United States.

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.

Common-sense approaches to sharing tabular data alongside publication.

Numerous arguments strongly support the practice of open science, which offers several societal and individual benefits. For individual researchers, sharing research artifacts such as data can increase trust and transparency, improve the reproducibility of one's own work, and catalyze new collaborations. Despite a general appreciation of the benefits of data sharing, research data are often only available to the original investigators. For data that are shared, lack of useful metadata and documentation make them challenging to reuse. In this paper, we argue that a lack of incentives and infrastructure for making data useful is the biggest barrier to creating a culture of widespread data sharing. We compare data with code, examine computational environments in the context of their ability to facilitate the reproducibility of research, provide some practical guidance on how one can improve the chances of their data being reusable, and partially bridge the incentive gap. While previous papers have focused on describing ideal best practices for data and code, we focus on common-sense ideas for sharing tabular data for a target audience of academics working in data science adjacent fields who are about to submit for publication.

The role of metadata in reproducible computational research.

Reproducible computational research (RCR) is the keystone of the scientific method for in silico analyses, packaging the transformation of raw data to published results. In addition to its role in research integrity, improving the reproducibility of scientific studies can accelerate evaluation and reuse. This potential and wide support for the FAIR principles have motivated interest in metadata standards supporting reproducibility. Metadata provide context and provenance to raw data and methods and are essential to both discovery and validation. Despite this shared connection with scientific data, few studies have explicitly described how metadata enable reproducible computational research. This review employs a functional content analysis to identify metadata standards that support reproducibility across an analytic stack consisting of input data, tools, notebooks, pipelines, and publications. Our review provides background context, explores gaps, and discovers component trends of embeddedness and methodology weight from which we derive recommendations for future work.

High nitrous oxide fluxes from rice indicate the need to manage water for both long- and short-term climate impacts.

Global rice cultivation is estimated to account for 2.5% of current anthropogenic warming because of emissions of methane (CH4), a short-lived greenhouse gas. This estimate assumes a widespread prevalence of continuous flooding of most rice fields and hence does not include emissions of nitrous oxide (N2O), a long-lived greenhouse gas. Based on the belief that minimizing CH4 from rice cultivation is always climate beneficial, current mitigation policies promote increased use of intermittent flooding. However, results from five intermittently flooded rice farms across three agroecological regions in India indicate that N2O emissions per hectare can be three times higher (33 kg-N2O⋅ha-1⋅season-1) than the maximum previously reported. Correlations between N2O emissions and management parameters suggest that N2O emissions from rice across the Indian subcontinent might be 30-45 times higher under intensified use of intermittent flooding than under continuous flooding. Our data further indicate that comanagement of water with inorganic nitrogen and/or organic matter inputs can decrease climate impacts caused by greenhouse gas emissions up to 90% and nitrogen management might not be central to N2O reduction. An understanding of climate benefits/drawbacks over time of different flooding regimes because of differences in N2O and CH4 emissions can help select the most climate-friendly water management regimes for a given area. Region-specific studies of rice farming practices that map flooding regimes and measure effects of multiple comanaged variables on N2O and CH4 emissions are necessary to determine and minimize the climate impacts of rice cultivation over both the short term and long term.

Hack weeks as a model for data science education and collaboration.

Across many scientific disciplines, methods for recording, storing, and analyzing data are rapidly increasing in complexity. Skillfully using data science tools that manage this complexity requires training in new programming languages and frameworks as well as immersion in new modes of interaction that foster data sharing, collaborative software development, and exchange across disciplines. Learning these skills from traditional university curricula can be challenging because most courses are not designed to evolve on time scales that can keep pace with rapidly shifting data science methods. Here, we present the concept of a hack week as an effective model offering opportunities for networking and community building, education in state-of-the-art data science methods, and immersion in collaborative project work. We find that hack weeks are successful at cultivating collaboration and facilitating the exchange of knowledge. Participants self-report that these events help them in both their day-to-day research as well as their careers. Based on our results, we conclude that hack weeks present an effective, easy-to-implement, fairly low-cost tool to positively impact data analysis literacy in academic disciplines, foster collaboration, and cultivate best practices.

Journal of Open Source Software (JOSS): design and first-year review.

This article describes the motivation, design, and progress of the Journal of Open Source Software (JOSS). JOSS is a free and open-access journal that publishes articles describing research software. It has the dual goals of improving the quality of the software submitted and providing a mechanism for research software developers to receive credit. While designed to work within the current merit system of science, JOSS addresses the dearth of rewards for key contributions to science made in the form of software. JOSS publishes articles that encapsulate scholarship contained in the software itself, and its rigorous peer review targets the software components: functionality, documentation, tests, continuous integration, and the license. A JOSS article contains an abstract describing the purpose and functionality of the software, references, and a link to the software archive. The article is the entry point of a JOSS submission, which encompasses the full set of software artifacts. Submission and review proceed in the open, on GitHub. Editors, reviewers, and authors work collaboratively and openly. Unlike other journals, JOSS does not reject articles requiring major revision; while not yet accepted, articles remain visible and under review until the authors make adequate changes (or withdraw, if unable to meet requirements). Once an article is accepted, JOSS gives it a digital object identifier (DOI), deposits its metadata in Crossref, and the article can begin collecting citations on indexers like Google Scholar and other services. Authors retain copyright of their JOSS article, releasing it under a Creative Commons Attribution 4.0 International License. In its first year, starting in May 2016, JOSS published 111 articles, with more than 40 additional articles under review. JOSS is a sponsored project of the nonprofit organization NumFOCUS and is an affiliate of the Open Source Initiative (OSI).

Sustainable computational science: the ReScience initiative.

Computer science offers a large set of tools for prototyping, writing, running, testing, validating, sharing and reproducing results; however, computational science lags behind. In the best case, authors may provide their source code as a compressed archive and they may feel confident their research is reproducible. But this is not exactly true. James Buckheit and David Donoho proposed more than two decades ago that an article about computational results is advertising, not scholarship. The actual scholarship is the full software environment, code, and data that produced the result. This implies new workflows, in particular in peer-reviews. Existing journals have been slow to adapt: source codes are rarely requested and are hardly ever actually executed to check that they produce the results advertised in the article. ReScience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research can be replicated from its description. To achieve this goal, the whole publishing chain is radically different from other traditional scientific journals. ReScience resides on GitHub where each new implementation of a computational study is made available together with comments, explanations, and software tests.

How open science helps researchers succeed.

Open access, open data, open source and other open scholarship practices are growing in popularity and necessity. However, widespread adoption of these practices has not yet been achieved. One reason is that researchers are uncertain about how sharing their work will affect their careers. We review literature demonstrating that open research is associated with increases in citations, media attention, potential collaborators, job opportunities and funding opportunities. These findings are evidence that open research practices bring significant benefits to researchers relative to more traditional closed practices.

The importance of individual developmental variation in stage-structured population models.

Population stage structure is fundamental to ecology, and models of this structure have proven useful in many different systems. Many ecological variables other than stage, such as habitat type, site occupancy and metapopulation status are also modelled using transitions among discrete states. Transitions among life stages can be characterised by the distribution of time spent in each stage, including the mean and variance of each stage duration and within-individual correlations among multiple stage durations. Three modelling traditions represent stage durations differently. Matrix models can be derived as a long-run approximation from any distribution of stage durations, but they are often interpreted directly as a Markov model for stage transitions. Statistical stage-duration distribution models accommodate the variation typical of cohort development data, but such realism has rarely been incorporated in population theory or statistical population models. Delay-differential equation models include lags but no variation, except in limited cases. We synthesise these models in one framework and illustrate how individual variation and correlations in development can impact population growth. Furthermore, different development models can yield the same long-term matrix transition rates but different sensitivities and elasticities. Finally, we discuss future directions for estimating realistic stage duration models from data.

Git can facilitate greater reproducibility and increased transparency in science.

BACKGROUND: Reproducibility is the hallmark of good science. Maintaining a high degree of transparency in scientific reporting is essential not just for gaining trust and credibility within the scientific community but also for facilitating the development of new ideas. Sharing data and computer code associated with publications is becoming increasingly common, motivated partly in response to data deposition requirements from journals and mandates from funders. Despite this increase in transparency, it is still difficult to reproduce or build upon the findings of most scientific publications without access to a more complete workflow. FINDINGS: Version control systems (VCS), which have long been used to maintain code repositories in the software industry, are now finding new applications in science. One such open source VCS, Git, provides a lightweight yet robust framework that is ideal for managing the full suite of research outputs such as datasets, statistical code, figures, lab notes, and manuscripts. For individual researchers, Git provides a powerful way to track and compare versions, retrace errors, explore new approaches in a structured manner, while maintaining a full audit trail. For larger collaborative efforts, Git and Git hosting services make it possible for everyone to work asynchronously and merge their contributions at any time, all the while maintaining a complete authorship trail. In this paper I provide an overview of Git along with use-cases that highlight how this tool can be leveraged to make science more reproducible and transparent, foster new collaborations, and support novel uses.

Individual heterogeneity in mortality mediates long-term persistence of a seasonal microparasite.

One of the primary objectives in population ecology is to understand mechanisms that allow a species to persist or to be driven to extinction. In most population models, individuals are assumed to be equivalent within any particular category such as age, sex, or morphological grouping. Individuals within such groupings, however, may exhibit considerable variation in traits that can significantly affect population trajectories. Although ecologists have long been aware of such variation, they are frequently ignored to maintain computational tractability. The few statistical models that do incorporate such heterogeneity require prohibitively large amounts of data on many individuals, making them impractical. In California's coastal prairie, a parasitic nematode, Heterorhabditis marelatus, is an important natural enemy, whose presence determines the strength and extent of a trophic cascade. Mortality of H. marelatus is strongly influenced by habitat and seasonality, which determines long-term persistence. Prior efforts to estimate mortality have suffered from difficulty in distinguishing between measurement and process error due to limitations in experimental protocol. In this study, we eliminate measurement error in the initial population size and focus on the true nature of the heterogeneity in mortality. By including individual heterogeneity in our statistical model, we are able to understand how this species is able to persist over seasonally harsh environmental conditions. Further, we extrapolate these findings to larger population sizes and illustrate that heterogeneous survival can have a significant effect on the emergent number of survivors.

Metapopulation dynamics override local limits on long-term parasite persistence.

A simple null model, particularly germane to small and vulnerable organisms such as parasites, is that local conditions set a stage upon which larger-scale dynamics play out. Soil moisture strongly influences survival of entomopathogenic nematodes (EPN), which in turn drive trophic cascades by protecting vegetation from root-feeding herbivores. In this study, we examine the mechanisms responsible for patchy occurrence of an entomopathogenic nematode, Heterorhabditis marelatus, in a California coastal prairie. One hypothesis proposes that biotic factors such as competition and natural enemies could regulate occurrence of EPN populations. We found that fungi and other enemies of EPN, although locally potent, did not explain the patterns of incidence across sites. Abiotic factors also have strong effects on EPN persistence, especially for vulnerable free-living stages. Thus, we tested the hypothesis that patchy occurrence of EPN on a large landscape was driven by differences in soil moisture. Our research uses long-term data on nematode incidence in combination with a landscape-level experiment to demonstrate the lack of a correlation between soil moisture and long-term persistence. A year-long experiment showed EPN mortality was weakly correlated with soil moisture among our study sites. Thirteen years of data, however, showed that colonization rates were highly correlated with long-term persistence. Sites with highest long-term persistence experienced the highest rates of rhizosphere colonization, extinction, and turnover. As a result, we concluded that metapopulation dynamics override limitations set by local and short-term abiotic conditions to determine long-term persistence in this parasite-driven trophic cascade.

Dynamics of a subterranean trophic cascade in space and time.

Trophic cascades, whereby predators indirectly benefit plant biomass by reducing herbivore pressure, form the mechanistic basis for classical biological control of pest insects. Entomopathogenic nematodes (EPN) are lethal to a variety of insect hosts with soil-dwelling stages, making them promising biocontrol agents. EPN biological control programs, however, typically fail because nematodes do not establish, persist and/or recycle over multiple host generations in the field. A variety of factors such as local abiotic conditions, host quantity and quality, and rates of movement affect the probability of persistence. Here, we review results from 13 years of study on the biology and ecology of an endemic population of Heterorhabditis marelatus (Rhabditida: Heterorhabditidae) in a California coastal prairie. In a highly seasonal abiotic environment with intrinsic variation in soils, vegetation structure, and host availability, natural populations of H. marelatus persisted at high incidence at some but not all sites within our study area. Through a set of field and lab experiments, we describe mechanisms and hypotheses to understand the persistence of H. marelatus. We suggest that further ecological study of naturally occurring EPN populations can yield significant insight to improve the practice and management of biological control of soil-dwelling insect pests.

Soil mediates the interaction of coexisting entomopathogenic nematodes with an insect host.

We tested for soil substrate effects on the movement and infectivity of naturally co-occurring entomopathogenic nematodes Steinernema feltiae and Heterorhabditis marelatus, alone and in combination. We manipulated the presence and bulk density of soil and added Galleria mellonella baits within capped and perforated 15mL centrifuge tubes. Sampling tubes were then deployed in situ into field and laboratory settings as experimental traps for infective juveniles. In comparisons with standard soil collections from Lupinus arboreus rhizospheres, sampling tubes were equally sensitive to the presence of H. marelatus and more sensitive to S. feltiae. In laboratory microcosms, both EPN species infected Galleria at high frequencies in tubes lacking soil and in the absence of heterospecifics. Infection frequency of S. feltiae was unaffected by the presence of H. marelatus, but it declined with higher soil bulk density inside tubes. In contrast, detectable infection frequency by H. marelatus was reduced only marginally by the presence of soil but severely by the presence of S. feltiae. Thus, the presence of soil in tubes reversed the identity of dominant species infecting Galleria in tubes, an effect magnified when soils were compacted. Moreover, S. feltiae rarely moved into tubes lacking Galleria baits, whereas H. marelatus colonized unbaited tubes 4- to 5-fold more frequently than S. feltiae. In situ, sampling tubes acted as filters to reduce interference and contamination by fungal pathogens common in field soils. The method allows precision sampling with minimal soil disturbance while protecting bait insects from scavengers. Manipulation of tube design may allow selective sampling of EPN species, depending on the abiotic characteristics of soils, and the biology, behavior, and interspecific interactions of coexisting species.