Carly Strasser

Big data and the future of ecology

The need for sound ecological science has escalated alongside the rise of the information age and “big data” across all sectors of society. Big data generally refer to massive volumes of data not readily handled by the usual data tools and practices and present unprecedented opportunities for advancing science and inform- ing resource management through data-intensive approaches. The era of big data need not be propelled only by “big science” – the term used to describe large-scale efforts that have had mixed success in the individual-driven culture of ecology. Collectively, ecologists already have big data to bolster the scientific effort – a large volume of distributed, high-value information – but many simply fail to contribute. We encourage ecologists to join the larger scientific community in global initiatives to address major scientific and societal problems by bringing their distributed data to the table and harnessing its collective power. The scientists who contribute such information will be at the forefront of socially relevant science – but will they be ecologists?

Data publication consensus and controversies

The movement to bring datasets into the scholarly record as first class research products (validated, preserved, cited, and credited) has been inching forward for some time, but now the pace is quickening. As data publication venues proliferate, significant debate continues over formats, processes, and terminology. Here, we present an overview of data publication initiatives underway and the current conversation, highlighting points of consensus and issues still in contention. Data publication implementations differ in a variety of factors, including the kind of documentation, the location of the documentation relative to the data, and how the data is validated. Publishers may present data as supplemental material to a journal article, with a descriptive “data paper,” or independently. Complicating the situation, different initiatives and communities use the same terms to refer to distinct but overlapping concepts. For instance, the term published means that the data is publicly available and citable to virtually everyone, but it may or may not imply that the data has been peer-reviewed. In turn, what is meant by data peer review is far from defined; standards and processes encompass the full range employed in reviewing the literature, plus some novel variations. Basic data citation is a point of consensus, but the general agreement on the core elements of a dataset citation frays if the data is dynamic or part of a larger set. Even as data publication is being defined, some are looking past publication to other metaphors, notably “data as software,” for solutions to the more stubborn problems.

Preprints for the life sciences

The time is right for biologists to post their research findings onto preprint servers A preprint is a complete scientific manuscript (often one also being submitted to a peer-reviewed journal) that is uploaded by the authors to a public server without formal review. After a brief inspection to ensure that the work is scientific in nature, the posted scientific manuscript can be viewed without charge on the Web. Thus, preprint servers facilitate the direct and open delivery of new knowledge and concepts to the worldwide scientific community before traditional validation through peer review (1, 2). Although the preprint server arXiv.org has been essential for physics, mathematics, and computer sciences for over two decades, preprints are currently used minimally in biology.

Researcher perspectives on publication and peer review of data

Data “publication” seeks to appropriate the prestige of authorship in the peer-reviewed literature to reward researchers who create useful and well-documented datasets. The scholarly communication community has embraced data publication as an incentive to document and share data. But, numerous new and ongoing experiments in implementation have not yet resolved what a data publication should be, when data should be peer-reviewed, or how data peer review should work. While researchers have been surveyed extensively regarding data management and sharing, their perceptions and expectations of data publication are largely unknown. To bring this important yet neglected perspective into the conversation, we surveyed ∼ 250 researchers across the sciences and social sciences– asking what expectations“data publication” raises and what features would be useful to evaluate the trustworthiness, evaluate the impact, and enhance the prestige of a data publication. We found that researcher expectations of data publication center on availability, generally through an open database or repository. Few respondents expected published data to be peer-reviewed, but peer-reviewed data enjoyed much greater trust and prestige. The importance of adequate metadata was acknowledged, in that almost all respondents expected data peer review to include evaluation of the data’s documentation. Formal citation in the reference list was affirmed by most respondents as the proper way to credit dataset creators. Citation count was viewed as the most useful measure of impact, but download count was seen as nearly as valuable. These results offer practical guidance for data publishers seeking to meet researcher expectations and enhance the value of published data.

Spatially Explicit Data: Stewardship and Ethical Challenges in Science

Sharing spatially specific data, which includes the characteristics and behaviors of individuals, households, or communities in geographical space, raises distinct technical and ethical challenges.

Recommendations for the role of publishers in access to data.

This community perspective piece calls on publishers to promote and contribute to increasing access to data in their role with eight simple recommendations and example action items.

Contributions of high- and low-quality patches to a metapopulation with stochastic disturbance

Studies of time-invariant matrix metapopulation models indicate that metapopulation growth rate is usually more sensitive to the vital rates of individuals in high-quality (i.e., good) patches than in low-quality (i.e., bad) patches. This suggests that, given a choice, management efforts should focus on good rather than bad patches. Here, we examine the sensitivity of metapopulation growth rate for a two-patch matrix metapopulation model with and without stochastic disturbance and found cases where managers can more efficiently increase metapopulation growth rate by focusing efforts on the bad patch. In our model, net reproductive rate differs between the two patches so that in the absence of dispersal, one patch is high quality and the other low quality. Disturbance, when present, reduces net reproductive rate with equal frequency and intensity in both patches. The stochastic disturbance model gives qualitatively similar results to the deterministic model. In most cases, metapopulation growth rate was elastic to changes in net reproductive rate of individuals in the good patch than the bad patch. However, when the majority of individuals are located in the bad patch, metapopulation growth rate can be most elastic to net reproductive rate in the bad patch. We expand the model to include two stages and parameterize the patches using data for the softshell clam, Mya arenaria. With a two-stage demographic model, the elasticities of metapopulation growth rate to parameters in the bad patch increase, while elasticities to the same parameters in the good patch decrease. Metapopulation growth rate is most elastic to adult survival in the population of the good patch for all scenarios we examine. If the majority of the metapopulation is located in the bad patch, the elasticity to parameters of that population increase but do not surpass elasticity to parameters in the good patch. This model can be expanded to include additional patches, multiple stages, stochastic dispersal, and complex demography.

Primer on Data Management: What you always wanted to know

www.dataone.org Primer on Data Management: What you always wanted to know* * but were afraid to ask Carly Strasser, Robert Cook, William Michener, Amber Budden Contents Objective of This Primer Why Manage Data? It will benefit you and your collaborators It will benefit the scientific community Journals and sponsors want you to share your data How To Use This Primer The Data Life Cycle: An Overview Data Management Throughout the Data Life Cycle Plan Collect Assure Describe: Data Documentation Preserve Discover, Integrate, and Analyze Conclusion Acknowledgements References Glossary Objective of This Primer The goal of data management is to produce self-describing data sets. If you give your data to a scientist or colleague who has not been involved with your project, will they be able to make sense of it? Will they be able to use it effectively and properly? This primer describes a few fundamental data management practices that will enable you to develop a data management plan, as well as how to effectively create, organize, manage, describe, preserve and share data. Why Manage Data? 2.1. It will benefit you and your collaborators Establishing how you will collect, document, organize, manage, and preserve your data at the beginning of your research project has many benefits. You will spend less time on data management and more time on research by investing the time and energy before the first piece of data is collected. Your data also will be easier for you to find, use, and analyze, and it will be easier for your collaborators to understand and use your data. In the long term, following good data management practices means that scientists not involved with the project can find, understand, and use the data in the future. By documenting your data and recommending appropriate ways to cite your data, you can be sure to get credit for your data products and their use [1]. DataONE Best Practices Primer

The fractured lab notebook: undergraduates and ecological data management training in the United States

Data management is a timely and increasingly important topic for ecologists. Recent funder mandates requiring data management plans, combined with the data deluge that faces scientists, make education about data management critical for any future ecologist. In this study, we surveyed instructors of general ecology courses at 48 major institutions in the United States. We chose instructors at institutions that are likely to train future ecologists, and therefore, are most likely to influence the trajectory of data management education in this field. The survey queried instructors about institution and course characteristics, the extent to which data-related topics are included in their courses, the barriers to their teaching these topics, and their own personal beliefs and values associated with data management and stewardship. We found that, in general, data management topics are not being covered in undergraduate ecology courses for a wide range of reasons. Most often, instructors cited a lack of time and a lack of resources as barriers to teaching data management. Although data are used for instruction at some point in the majority of the courses surveyed, good data management practices and a thorough understanding of the importance of data stewardship are not being taught. We offer potential explanations for this and suggestions for improvement.

Ecological data in the Information Age

GUEST EDITORIAL GUEST EDITORIAL GUEST EDITORIAL Ecological data in the Information Age M ost of us can close our eyes and imagine a future in which ecological data are at our fingertips, as easy to access online as current weather conditions, satellite images of our field sites, or cute videos of kittens. In this future we can easily find information about the temporal trends of aphid outbreaks around the world or the spatial occurrences of Atlantic cod, to use in research or in the classroom. We can imagine services that operate “behind the scenes” to intelligently aggregate data and allow for tailored datasets and analytics, such as averaging soil respiration rates within a certain elevational band, or estimating the age structure of local deer populations. Although we can easily imagine these developments, and perhaps happily anticipate them, very few of us act as though we are the generation of scientists that will make them happen. We are not documenting, sharing, or archiving our data in ways to ensure that this future is attainable. Surveys demonstrate that we record our data in highly idiosyncratic ways, with much of the information about the data collection (metadata) remaining as oral history within a research group. After the data have been used for publication, their electronic datasheets steadily atrophy in hardware and software that become obso- lete, and associated metadata are often forgotten. After careful planning and collection by experts, valuable ecological datasets are gathering dust in small piles all over the world. Why are we so reluctant to preserve our data for the long term in public archives? The reasons given for not doing so are diverse, and many authors have preceded us in analyzing the anachronistic lack of data sharing in ecology. In short, ecologists see few near-term rewards and many costs associated with making data public. Making matters worse, very few ecologists have a clear idea of how they would go about sharing data even if they wanted to. Thus, while ecologists are as active as any other group in embracing the Information Age – from smartphones for field research to distributed software development for statistics – paradoxically we are still keeping our hard-won ecological data hoarded in idiosyncratic lockboxes. If this world of readily accessible ecological data is coming “someday”, how do we get there from here? How can we become the generation of ecologists that creates the bridge to that future of ecological data access? We propose a first step: treat your ecological data as a real product of research, not just a precursor to a set of publications. Many of our datasets are irreplaceable, documenting organisms, patterns, and ecosystems that are rapidly changing. With this in mind, manage your data with the conviction that they will have a lifetime far beyond your own. Document your data for someone – a stranger – who will discover the information decades from now. As a simple starting point, read Borer et al. (2009; Bull Ecol Soc Am 90: 205–14) – an introductory paper with sound and practical suggestions – with your research group or journal club. To see the level of detail you might need to consider, examine sources like Ecological Archives, the Ecological Society of America’s journal geared toward publishing self-describing datasets. Start creating machine-readable metadata early in your project – even before data collection begins – through free software like Morpho (http://knb.ecoinformatics.org/morpho portal.jsp). This step facilitates sharing your data and metadata with colleagues now, and uploading to public repositories when you are ready. Repositories such as Ecological Archives connect your dataset to many others in the Knowledge Network for Biocomplexity (http://knb.ecoinformatics.org), and opportunities for connecting datasets will grow with improvements in cyberinfrastructure – such as the US National Science Foundation’s (NSF’s) DataNet project known as DataONE (www.dataone.org), which will confederate environmental data from many repositories. The NSF’s recent requirement that data management plans be included in all proposals is a logical step toward data publication, and many believe that it may provide a tipping point for broad adoption of data pub- lication policies in the US. In our opinion, widespread sharing of ecological data will be a welcome change. If ecologists are to have societal relevance – alerting managers to unusually rapid range expansions in non- native species, illuminating long-term oscillations in fish production, or determining the historical distribu- tion of a plant with newly discovered medicinal properties – we need to get serious about bringing our disci- pline into the Information Age now, by documenting and sharing data. What may be perceived as a waste of time to this generation of ecologists will not be viewed that way by future researchers, who will be prepared to engage in more transparent and open science.