Here we present a standard developed by the Genomic Standards Consortium (GSC) for reporting marker gene sequences—the minimum information about a marker gene sequence (MIMARKS). We also introduce a system for describing the environment from which a biological sample originates. The 'environmental packages' apply to any genome sequence of known origin and can be used in combination with MIMARKS and other GSC checklists. Finally, to establish a unified standard for describing sequence data and to provide a single point of entry for the scientific community to access and learn about GSC checklists, we present the minimum information about any (x) sequence (MIxS). Adoption of MIxS will enhance our ability to analyze natural genetic diversity documented by massive DNA sequencing efforts from myriad ecosystems in our ever-changing biosphere.

Mercury is a federated metadata harvesting, search and retrieval tool based on both open source packages and custom software developed at Oak Ridge National Laboratory (ORNL). It was originally developed for the National Aeronautics and Space Administration (NASA), and the consortium now includes funding from NASA, U.S. Geological Survey (USGS), and U.S. Department of Energy (DOE). Mercury is itself a reusable software application which uses a service-oriented architecture (SOA) approach to capturing and managing metadata in support of twelve Earth science projects. Mercury also supports the reuse of metadata by enabling searches across a range of metadata specification and standards including XML, Z39.50, FGDC, Dublin-Core, Darwin-Core, EML, and ISO-19115. It collects metadata and key data from contributing project servers distributed around the world and builds a centralized index. The Mercury search interfaces allows the users to perform simple, fielded, spatial, temporal and other hierarchical searches across these metadata sources. This centralized repository of metadata with distributed data sources provides extremely fast search results (Table 1) to the user, while allowing data providers to advertise the availability of their data and yet maintain complete control and ownership of that data.

The Atmospheric Radiation Measurement (ARM) Data Archive established a data citation strategy based on Digital Object Identifiers (DOIs) for the ARM datasets in order to facilitate citing continuous and diverse ARM datasets in articles and other papers. This strategy eases the tracking of data provided as supplements to articles and papers. Additionally, it allows future data users and the ARM Climate Research Facility to easily locate the exact data used in various articles. Traditionally, DOIs are assigned to individual digital objects (a report or a data table), but for ARM datasets, these DOIs are assigned to an ARM data product. This eliminates the need for creating DOIs for numerous components of the ARM data product, in turn making it easier for users to manage and cite the ARM data with fewer DOIs. In addition, the ARM data infrastructure team, with input from scientific users, developed a citation format and an online data citation generation tool for continuous data streams. This citation format includes DOIs along with additional details such as spatial and temporal information.

The Amazon Rainforest is highlighted by the global community both for its extensive vegetation cover that constantly suffers the effects of anthropic action and for its substantial biodiversity. This dataset presents data of meteorological variables from the Amazon Rainforest region with a spatial resolution of 0.001° in latitude and longitude, resulting from an interpolation process. The original data were obtained from the GoAmazon 2014/5 project, in the Atmospheric Radiation Measurement (ARM) repository, and then processed through mathematical and statistical methods. The dataset presented here can be used in experiments in the field of Data Science, such as training models for predicting climate variables or modeling the distribution of species.

Atmospheric data are collected by researchers every day. Campaigns such as GOAmazon 2014/2015 and the Amazon Tall Tower Observatory collect essential data on aerosols, gases, cloud properties, and meteorological parameters in the Brazilian Amazon basin. These data products provide insights and essential information for analyzing and predicting natural processes. However, in Brazil, it is estimated that more than 80% of the scientific data collected are not published due to the lack of web portals that collect and store these data. This makes it difficult, or even impossible, to access and integrate the data, which can result in the loss of significant amounts of information and significantly affect the understanding of the overall data. To address this problem, we propose a data portal architecture and open data deployment that enable Big Data processing, human interaction, and download-oriented approaches with tools that help users catalog, publish and visualize atmospheric data. Thus, we describe the architecture developed, based on the experience of the Atmospheric Radiation Measurement Data Center, which incorporates the principles of FAIR, the infrastructure and content management system for managing scientific data. The portal partial results were tested with environmental data from contaminated areas at the University of São Paulo. Overall, this data portal creates more shared knowledge about atmospheric processes by providing users with access to open environmental data.

The next-generation On-line Metadata Editor (OME) is an easy-to-use tool to help document scientific data in a well-structured popular metadata format. In this paper, we discuss the newest tool that Oak Ridge National Laboratory has developed to input, edit, and manage metadata and how it is helping data intensive science centers across many federal agencies to prepare metadata and to make their BigData discoverable.