As funding agencies embrace open science principles that encourage sharing data and computer code developed to produce research outputs, we must respond with new modes of publication. Furthermore, as we address the expanding reproducibility crisis in the sciences, we must work to release research materials in ways that enable reproducibility-publishing data, computer code, and research products in addition to the traditional journal article. Toward addressing these needs, we present an example framework to model and map soil organic carbon (SOC) in the cereal grains production region of the northwestern United States. Primarily associated with soil organic matter, SOC relates to many soil properties that influence resiliency and soil health for agriculture. It is also critical for understanding soil-atmospheric C flux, a significant part of the overall C budget of the Earth. The technique for modeling soil properties uses seven categories of environmental input data to make predictions: known soil attributes, climatic values, organisms present, relief, parent material, age, and spatial location. We gather data representing these categories from various public sources. The map is produced using a random forest statistical model with inputs to predict SOC content on a 30-m spatial grid. All modeling components including input data, metadata, computer code, and output products are made freely available under an explicit open-source license. In this way, reproducibility is supported, the methods and code released are available to be reused by other researchers, and the research products are open to critical review and improvement.

. OASIS defines the SOA as Ba paradigm for organizing and utilizing distributed capabilities that may be under the control of different ownership domains.^Systems designed around the SOA model benefit from improved scalability, flexibility, and agility. This paper applies the SOA model to the OAIS repository to describe how repositories can be implemented and extended through the use of services that may be internal or external to the host institution, including the consumption of network-or cloud-based services and resources. We use the Service Oriented Architecture (SOA) design paradigm to describe a set of potential extensions to OAIS Reference Model: purpose and justification for each extension, where and how each extension connects to the model, and an example of a specific service that meets the purpose.

INTRODUCTION The practice of publishing supplementary materials with journal articles is becoming increasingly prevalent across the sciences. We sought to understand better the content of these materials by investigating the differences between the supplementary materials published by authors in the geosciences and plant sciences. METHODS We conducted a random stratified sampling of four articles from each of 30 journals published in 2013. In total, we examined 297 supplementary data files for a range of different factors. RESULTS We identified many similarities between the practices of authors in the two fields, including the formats used (Word documents, Excel spreadsheets, PDFs) and the small size of the files. There were differences identified in the content of the supplementary materials: the geology materials contained more maps and machine-readable data; the plant science materials included much more tabular data and multimedia content. DISCUSSION Our results suggest that the data shared through supplementary files in these fields may not lend itself to reuse. Code and related scripts are not often shared, nor is much ‘raw’ data. Instead, the files often contain summary data, modified for human reading and use. CONCLUSION Given these and other differences, our results suggest implications for publishers, librarians, and authors, and may require shifts in behavior if effective data sharing is to be realized.External Data or Supplements:Kenyon, Jeremy; Sprague, Nancy; Flathers, Edward, 2016, "Data from: The journal article as a means to share data: a content analysis of supplementary materials from two disciplines", http://dx.doi.org/10.7910/DVN/DCSMGP, Harvard Dataverse.

No abstract