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Major societal and environmental challenges involve complex systems that have diverse multi-scale interacting processes. Consider, for example, how droughts and water reserves affect crop production and how agriculture and industrial needs affect water quality and availability. Preventive measures, such as delaying planting dates and adopting new agricultural practices in response to changing weather patterns, can reduce the damage caused by natural processes. Understanding how these natural and human processes affect one another allows forecasting the effects of undesirable situations and study interventions to take preventive measures. For many of these processes, there are expert models that incorporate state-of-the-art theories and knowledge to quantify a system's response to a diversity of conditions. A major challenge for efficient modeling is the diversity of modeling approaches across disciplines and the wide variety of data sources available only in formats that require complex conversions. Using expert models for particular problems requires integration of models with third-party data as well as integration of models across disciplines. Modelers face significant heterogeneity that requires resolving semantic, spatiotemporal, and execution mismatches, which are largely done by hand today and may take more than 2 years of effort. We are developing a modeling framework that uses artificial intelligence (AI) techniques to reduce modeling effort while ensuring utility for decision making. Our work to date makes several innovative contributions: (1) an intelligent user interface that guides analysts to frame their modeling problem and assists them by suggesting relevant choices and automating steps along the way; (2) semantic metadata for models, including their modeling variables and constraints, that ensures model relevance and proper use for a given decision-making problem; and (3) semantic representations of datasets in terms of modeling variables that enable automated data selection and data transformations. This framework is implemented in the MINT (Model INTegration) framework, and currently includes data and models to analyze the interactions between natural and human systems involving climate, water availability, agricultural production, and markets. Our work to date demonstrates the utility of AI techniques to accelerate modeling to support decision-making and uncovers several challenging directions for future work.

Chronological uncertainty is a hallmark of the paleoenvironmental sciences and geosciences. While many tools have been made available to researchers to quantify age uncertainties suitable for various settings and assumptions, disparate tools and output formats often discourage integrative approaches. In addition, associated tasks like propagating age-model uncertainties to subsequent analyses, and visualizing the results, have received comparatively little attention in the literature and available software. Here, we describe geoChronR, an open-source R package to facilitate these tasks. geoChronR is built around an emerging data standard (Linked PaleoData, or LiPD) and offers access to four popular age-modeling techniques (Bacon, BChron, OxCal, BAM). The output of these models is used to conduct ensemble data analysis, quantifying the impact of chronological uncertainties on common analyses like correlation, regression, principal component, and spectral analyses by repeating the analysis across a large collection of plausible age models. We present five real-world use cases to illustrate how geoChronR may be used to facilitate these tasks, visualize the results in intuitive ways, and store the results for further analysis, promoting transparency and reusability.

Chronological uncertainty is a hallmark of the paleosciences. While many tools have been made available to researchers to quantify age uncertainties suitable for various settings and assumptions, disparate tools and output formats often discourage integrative approaches. In addition, associated tasks like propagating age model uncertainties to subsequent analyses, and visualizing the results, have received comparatively little attention in the literature and available software. Here we describe GeoChronR, an open-source R package to facilitate these tasks. GeoChronR is built around emerging data standards for the paleosciences (Linked PaleoData, or LiPD), and offers access to four popular age modeling techniques (Bacon, BChron, Oxcal, BAM). The output of these models is used to support ensemble-aware analyses, quantifying the impact of chronological uncertainties on common analyses like age-uncertain correlation, regression, principal component, and spectral analyses. We present five real-world use cases to illustrate how GeoChronR may be used to facilitate these tasks, to visualize the results in intuitive ways, and to store the results for further analysis, promoting transparency and reusability.

Scientific software is crucial for understanding, reusing and reproducing results in computational sciences. Software is often stored in code repositories, which may contain human readable instructions necessary to use it and set it up. However, a significant amount of time is usually required to understand how to invoke a software component, prepare data in the format it requires, and use it in combination with other software. In this paper we introduce OKG-Soft, an open knowledge graph that describes scientific software in a machine readable manner. OKG-Soft includes: 1) an ontology designed to describe software and the specific data formats it uses; 2) an approach to publish software metadata as an open knowledge graph, linked to other Web of Data objects; and 3) a framework to annotate, query, explore and curate scientific software metadata. OKG-Soft supports the FAIR principles of findability, accessibility, interoperability, and reuse for software. We demonstrate the benefits of OKG-Soft with two applications: a browser for understanding scientific models in the environmental and social sciences, and a portal to combine climate, hydrology, agriculture, and economic software models.

The progress of science is tied to the standardization of measurements, instruments, and data. This is especially true in the Big Data age, where analyzing large data volumes critically hinges on the data being standardized. Accordingly, the lack of community-sanctioned data standards in paleoclimatology has largely precluded the benefits of Big Data advances in the field. Building upon recent efforts to standardize the format and terminology of paleoclimate data, this article describes the Paleoclimate Community reporTing Standard (PaCTS), a crowdsourced reporting standard for such data. PaCTS captures which information should be included when reporting paleoclimate data, with the goal of maximizing the reuse value of paleoclimate data sets, particularly for synthesis work and comparison to climate model simulations. Initiated by the LinkedEarth project, the process to elicit a reporting standard involved an international workshop in 2016, various forms of digital community engagement over the next few years, and grassroots working groups. Participants in this process identified important properties across paleoclimate archives, in addition to the reporting of uncertainties and chronologies; they also identified archive-specific properties and distinguished reporting standards for new versus legacy data sets. This work shows that at least 135 respondents overwhelmingly support a drastic increase in the amount of metadata accompanying paleoclimate data sets. Since such goals are at odds with present practices, we discuss a transparent path toward implementing or revising these recommendations in the near future, using both bottom-up and top-down approaches. ©2019. American Geophysical Union. All Rights Reserved., Code and data to reproduce the figures of this article are available on GitHub and released on Zenodo (doi:10.5281/zenodo.3165019). Definition of properties and recommendations are summarized here: http://wiki.linked.earth/PaCTS_v1.0 . This work was supported by the National Science Foundation through the EarthCube Program with Grant ICER‐1541029. Feedback solicitation on the standard was facilitated by the Past Global Changes (PAGES) organization. The 2016 workshop on Paleoclimate Data Standards was hosted by the World Data Service for Paleoclimatology (WDS/NOAA‐Paleo), and the participation of international attendees was made possible by a PAGES travel grant. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Traditional approaches to ontology development have a large lapse between the time when a user using the ontology has found a need to extend it and the time when it does get extended. For scientists, this delay can be weeks or months and can be a significant barrier for adoption. We present a new approach to ontology development and data annotation enabling users to add new metadata properties on the fly as they describe their datasets, creating terms that can be immediately adopted by others and eventually become standardized. This approach combines a traditional, consensus-based approach to ontology development, and a crowdsourced approach where expert users (the crowd) can dynamically add terms as needed to support their work. We have implemented this approach as a socio-technical system that includes: (1) a crowdsourcing platform to support metadata annotation and addition of new terms, (2) a range of social editorial processes to make standardization decisions for those new terms, and (3) a framework for ontology revision and updates to the metadata created with the previous version of the ontology. We present a prototype implementation for the Paleoclimate community, the Linked Earth Framework, currently containing 700 datasets and engaging over 50 active contributors. Users exploit the platform to do science while extending the metadata vocabulary, thereby producing useful and practical metadata.