Skills and Knowledge for Data-Intensive Environmental Research.

The scale and magnitude of complex and pressing environmental issues lend urgency to the need for integrative and reproducible analysis and synthesis, facilitated by data-intensive research approaches. However, the recent pace of technological change has been such that appropriate skills to accomplish data-intensive research are lacking among environmental scientists, who more than ever need greater access to training and mentorship in computational skills. Here, we provide a roadmap for raising data competencies of current and next-generation environmental researchers by describing the concepts and skills needed for effectively engaging with the heterogeneous, distributed, and rapidly growing volumes of available data. We articulate five key skills: (1) data management and processing, (2) analysis, (3) software skills for science, (4) visualization, and (5) communication methods for collaboration and dissemination. We provide an overview of the current suite of training initiatives available to environmental scientists and models for closing the skill-transfer gap

A Collaborative Clearinghouse for Data Management Training and Education Resources

Objective: The main objectives of this breakout session are for the Data Management Training (DMT) Clearinghouse team to: 1) introduce the Clearinghouse and its current design and implementation, 2) solicit submissions to its learning resource inventory, and 3) collect feedback upon its web interface and future development. Features of the Clearinghouse that will be demonstrated include how to search and browse its inventory as well as submit a learning resource to the Clearinghouse using the LRMI (Learning Resource Metadata Initiative) metadata format. The team will also share the roadmap for the Clearinghouse’s upcoming features. In order to provide feedback regarding the Clearinghouse’s usability, the team will invite the session attendees to test the Clearinghouse’s services and will encourage comments to guide its future development. Setting/Participants/Resources: Since the DMT Clearinghouse is entirely accessible via the web, in order to demonstrate the Clearinghouse successfully, a reliable (and preferably free of charge) internet connection, and an overhead projecting capability will need to be available to the presenter. It would also be very useful for the attendees of the session to have access to the same internet connection, so that if they desire, the attendees can follow along with the steps of the demonstration, and contribute to the Clearinghouse inventory. The main presenter will plan to bring her own laptop with built-in standard HDMI and USB ports. As a result, it will be helpful if a HDMI or USB cable could also be provided for the presenter to connect her laptop to the projecting equipment. Method: Many research organizations, government agencies, and academic institutions have been developing excellent learning resources in order to support and meet the needs for data management training. However, these learning resources are often hosted on various websites and spread across various scientific domains. Consequently, these resources can be difficult to locate, especially by those who are not already familiar with the creators/authors. This is a barrier to the use and reuse of these resources, and can have significant impact on the promotion and propagation of best practices for data management. To address this need within the Earth sciences, the U.S. Geological Survey’s (USGS) Community for Data Integration (CDI), the Federation of Earth Science Information Partners (ESIP), and the Data Observation Network for Earth (DataONE) have collaborated to create a web-based Clearinghouse1 for collecting data management learning resources that are focused on the Earth sciences. The initial seed funding for the effort was provided by a grant received from the USGS CDI earlier in 2016, and ESIP’s Drupal site provided the hosting infrastructure for the Clearinghouse. Members from the USGS, DataONE, ESIP’s Data Stewardship Committee and its Data Management Training Working Group, Knowledge Motifs LLC, as well as Blue Dot Lab met regularly between April and October, 2016 in order to discuss, create, and implement the content structure and infrastructure components necessary to build the current revision of the Clearinghouse. 1. http://dmtclearinghouse.esipfed.org Results: As a registry of information about the educational resources on topics related to research data management (initially focused on Earth sciences), the Clearinghouse serves as a centralized location for searching or browsing an inventory of these learning resources. Currently, the Clearinghouse offers search and browse functionality that is open to all, and submission of information about educational resources by login with a free ESIP account. To assist with discoverability, the learning resources are described using Learning Resource Metadata Initiative (LRMI) schema. Additionally, the resources may be associated with the steps of data and research life cycles, such as the USGS CDI’s Science Support Framework2 and DataONE’s Data Life Cycle3. Leveraging the team’s collective experience in creating, presenting and distributing data management learning resources, the Clearinghouse included the learning resources from USGS, ESIP, and DataONE as its initial inventory, but is expanding to resources from NASA and others. Crowdsourcing is currently the main mechanism for sustaining the Clearinghouse. Going forward, in addition to the built-in workflow to allow anyone from the public to submit descriptive information about the data management learning resources that s/he wishes to share, future capabilities will be added to enable contributions to review, edit, and rank the submissions, as desired. 2. https://my.usgs.gov/confluence/display/cdi/CDI+Science+Support+Framework3. https://www.dataone.org/data-life-cycle Discussion/Conclusion: The DMT Clearinghouse team was successful in completing the initial development phase as scheduled for the first six months of its funding, including some informal usability testing of the interface. The team aims to continue to develop and enhance the Clearinghouse’s capabilities, including the evaluation of its usability, through collaboration with additional communities, and if feasible, adding the capability for bulk-loading of learning resources. Being able to present the Clearinghouse at the eScience Symposium would not only allow those who are involved with or would like to learn about data management to leverage the Clearinghouse’s resources, but also connect those who would like to contribute to the project with the Clearinghouse team. Ultimately, the Clearinghouse is designed so that the resources from its inventory could be used in a variety of data management training and education environments. By exposing the Clearinghouse to diverse users and communities, the Clearinghouse team can better assess how the Clearinghouse can be updated and what technological enhancements to pursue in the future in order to improve our support of research data management training needs

Evaluating the Effectiveness of Data Management Training: DataONE’s Survey Instrument

Effective management is a key component for preparing data to be retained for future long term access, use, and reuse by a broader community. Developing the skills to plan and perform data management tasks is important for individuals and institutions. Teaching data literacy skills may also help to mitigate the impact of data deluge and other effects of being overexposed to and overwhelmed by data. The process of learning how to manage data effectively for the entire research data lifecycle can be complex. There are often multiple stages involved within a lifecycle for managing data, and each stage may require specific knowledge, expertise, and resources. Additionally, although a range of organizations offers data management education and training resources, it can often be difficult to assess how effective the resources are for educating users to meet their data management requirements. In the case of Data Observation Network for Earth (DataONE), DataONE’s extensive collaboration with individuals and organizations has informed the development of multiple educational resources. Through these interactions, DataONE understands that the process of creating and maintaining educational materials that remain responsive to community needs is reliant on careful evaluations. Therefore, the impetus for a comprehensive, customizable Education EVAluation instrument (EEVA) is grounded in the need for tools to assess and improve current and future training and educational resources for research data management. In this paper, the authors outline and provide context for the background and motivations that led to creating EEVA for evaluating the effectiveness of data management educational resources. The paper details the process and results of the current version of EEVA. Finally, the paper highlights the key features, potential uses, and the next steps in order to improve future extensions and revisions of EEVA

Using peer review to support development of community resources for research data management

This work is licensed under a Creative Commons 1.0 Public Domain Dedication. The definitive version was published in Journal of eScience Librarianship 6 (2017): e1114, doi:10.7191/jeslib.2017.1114.To ensure that resources designed to teach skills and best practices for scientific research data sharing and management are useful, the maintainers of those materials need to evaluate and update them to ensure their accuracy, currency, and quality. This paper advances the use and process of outside peer review for community resources in addressing ongoing accuracy, quality, and currency issues. It further describes the next step of moving the updated materials to an online collaborative community platform for future iterative review in order to build upon mechanisms for open science, ongoing iteration, participation, and transparent community engagement.DataONE is supported by US National Science Foundation Awards 08- 30944 and 14-30508, William Michener, Principal Investigator; Matthew Jones, Patricia Cruse, David Vieglais, and Suzanne Allard, Co-Principal Investigators

Facilitating and Improving Environmental Research Data Repository Interoperability

Environmental research data repositories provide much needed services for data preservation and data dissemination to diverse communities with domain specific or programmatic data needs and standards. Due to independent development these repositories serve their communities well, but were developed with different technologies, data models and using different ontologies. Hence, the effectiveness and efficiency of these services can be vastly improved if repositories work together adhering to a shared community platform that focuses on the implementation of agreed upon standards and best practices for curation and dissemination of data. Such a community platform drives forward the convergence of technologies and practices that will advance cross-domain interoperability. It will also facilitate contributions from investigators through standardized and streamlined workflows and provide increased visibility for the role of data managers and the curation services provided by data repositories, beyond preservation infrastructure. Ten specific suggestions for such standardizations are outlined without any suggestions for priority or technical implementation. Although the recommendations are for repositories to implement, they have been chosen specifically with the data provider/data curator and synthesis scientist in mind

Qualitative data sharing and re-use for socio-environmental systems research: A synthesis of opportunities, challenges, resources and approaches

Researchers in many disciplines, both social and natural sciences, have a long history of collecting and analyzing qualitative data to answer questions that have many dimensions, to interpret other research findings, and to characterize processes that are not easily quantified. Qualitative data is increasingly being used in socio-environmental systems research and related interdisciplinary efforts to address complex sustainability challenges. There are many scientific, descriptive and material benefits to be gained from sharing and re-using qualitative data, some of which reflect the broader push toward open science, and some of which are unique to qualitative research traditions. However, although open data availability is increasingly becoming an expectation in many fields and methodological approaches that work on socio-environmental topics, there remain many challenges associated the sharing and re-use of qualitative data in particular. This white paper discusses opportunities, challenges, resources and approaches for qualitative data sharing and re-use for socio-environmental research. The content and findings of the paper are a synthesis and extension of discussions that began during a workshop funded by the National Socio-Environmental Synthesis Center (SESYNC) and held at the Center Feb. 28-March 2, 2017. The structure of the paper reflects the starting point for the workshop, which focused on opportunities, challenges and resources for qualitative data sharing, and presents as well the workshop outputs focused on developing a novel approach to qualitative data sharing considerations and creating recommendations for how a variety of actors can further support and facilitate qualitative data sharing and re-use. The white paper is organized into five sections to address the following objectives: (1) Define qualitative data and discuss the benefits of sharing it along with its role in socio-environmental synthesis; (2) Review the practical, epistemological, and ethical challenges regarding sharing such data; (3) Identify the landscape of resources available for sharing qualitative data including repositories and communities of practice (4) Develop a novel framework for identifying levels of processing and access to qualitative data; and (5) Suggest roles and responsibilities for key actors in the research ecosystem that can improve the longevity and use of qualitative data in the future.This work was supported by the National Socio-Environmental Synthesis Center (SESYNC) under funding received from the National Science Foundation DBI-1052875

Resource allocation varies with parental sex and brood size in the asynchronously hatching green-rumped parrotlet (Forpus passerinus)

When eggs hatch asynchronously, offspring arising from last-hatched eggs often exhibit a competitive disadvantage compared with their older, larger nestmates. Strong sibling competition might result in a pattern of resource allocation favoring larger nestlings, but active food allocation towards smaller offspring may compensate for the negative effects of asynchronous hatching. We examined patterns of resource allocation by green-rumped parrotlet parents to small and large broods under control and food-supplemented conditions. There was no difference between parents and among brood sizes in visit rate or number of feeds delivered, although females spent marginally more time in the nest than males. Both male and female parents preferentially fed offspring that had a higher begging effort than the remainder of the brood. Mean begging levels did not differ between small and large broods, but smaller offspring begged more than their older nestmates in large broods. Male parents fed small offspring less often in both brood sizes. Female parents fed offspring evenly in small broods, while in large broods they fed smaller offspring more frequently, with the exception of the very last hatched individual. These data suggest male parrotlets exhibit a feeding preference for larger offspring—possibly arising from the outcome of sibling competition—but that females practice active food allocation, particularly in larger brood sizes. These differential patterns of resource allocation between the sexes are consistent with other studies of parrots and may reflect some level of female compensation for the limitations imposed on smaller offspring by hatching asynchrony

Against the odds? Nestling sex ratio variation in green-rumped parrotlets

We investigated nestling sex ratio variation in the green-rumped parrotlet (Forpus passerinus), a small neotropical parrot breeding in central Venezuela. There are strong theoretical reasons to predict a female-biased sex ratio in this system according to the local resource hypothesis; juvenile males are philopatric and there are high levels of competition between male siblings for access to breeding females. Data were collected from two breeding sites over a 14-year period incorporating 564 broods with a total of 2728 nestlings. The mean percentage of male nestlings across years was 51%. Despite extreme hatching asynchrony in this system and increased survival of earlier hatched offspring, there was no bias in sex allocation associated with egg sequence. Patterns in sex allocation were not associated with clutch size, age, or size of the breeding female or breeding site. The potential for selective resorption of eggs was considered; however, no significant relationship was found between extended laying intervals and the sex of subsequent eggs. Together, these results suggest that female parrotlets are unable to regulate the sex ratio of their clutch at laying or that facultative manipulation of nestling sex ratio may not confer a fitness benefit to breeders in these populations. Copyright 2004.Forpus passerinus; green-rumped parrotlet; laying interval; local resource competition; sex ratio