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This paper presents the specific process we, as part of the Earth Science Information Partners (ESIP) Semantic Harmonization Cluster, used to harmonize terms from the Global Cryosphere Watch (GCW) compilation of cryospheric terms with the Semantic Web for Earth and Environmental Terminology (SWEET) and the Environment Ontology (ENVO). In addition, we summarize a number of leading practices which may be applied to other projects/domains as well as suggest a generalized process for doing so. This paper describes the history of the effort, the technical and decision-making processes used to resolve differences between semantic resources, and describes a number of the issues encountered, with a focus on those that were addressed during the effort. Lessons learned, examples of the problems encountered and a summary of resulting leading practices growing out of this work is provided as an aid to semantic harmonization efforts in other domains by other groups.

Despite growing recognition of the importance of public data to the modern economy and to scientific progress, long-term investment in the repositories that manage and disseminate scientific data in easily accessible-ways remains elusive. Repositories are asked to demonstrate that there is a net value of their data and services to justify continued funding or attract new funding sources. Here, representatives from a number of environmental and Earth science repositories evaluate approaches for assessing the costs and benefits of publishing scientific data in their repositories, identifying various metrics that repositories typically use to report on the impact and value of their data products and services, plus additional metrics that would be useful but are not typically measured. We rated each metric by (a) the difficulty of implementation by our specific repositories and (b) its importance for value determination. As managers of environmental data repositories, we find that some of the most easily obtainable data-use metrics (such as data downloads and page views) may be less indicative of value than metrics that relate to discoverability and broader use. Other intangible but equally important metrics (e.g., laws or regulations impacted, lives saved, new proposals generated), will require considerable additional research to describe and develop, plus resources to implement at scale. As value can only be determined from the point of view of a stakeholder, it is likely that multiple sets of metrics will be needed, tailored to specific stakeholder needs. Moreover, economically based analyses or the use of specialists in the field are expensive and can happen only as resources permit.

This paper presents the perspectives of data, informatics, and information scientists and practitioners regarding how data solutions can be developed for place-based community resilience. Data were collected from participants at an Earth Science Information Partners (ESIP) meeting in 2015. Results show that to develop such data solutions, terminology related to community resilience must be further clarified to coordinate better with data and informatics systems, and institutional support of place-based community resilience must be prioritized. In addition, accessibility and usability of developed data solutions are crucial, and gaps along the information pathway must be filled to better connect data practitioners and community resilience practitioners.

In this review, we adopt the definition that ‘Data citation is a reference to data for the purpose of credit attribution and facilitation of access to the data’ (TGDCSP 2013: CIDCR6). Furthermore, access should be enabled for both humans and machines (DCSG 2014). We use this to discuss how data citation has evolved over the last couple of decades and to highlight issues that need more research and attention. Data citation is not a new concept, but it has changed and evolved considerably since the beginning of the digital age. Basic practice is now established and slowly but increasingly being implemented. Nonetheless, critical issues remain. These issues are primarily because we try to address multiple human and computational concerns with a system originally designed in a non-digital world for more limited use cases. The community is beginning to challenge past assumptions, separate the multiple concerns (credit, access, reference, provenance, impact, etc.), and apply different approaches for different use cases.

Analog archival data can supplement modern digital research, but only if those data are preserved, described, and migrated to appropriate formats. The National Snow and Ice Data Center (NSIDC) at the University of Colorado Boulder (CU) is responsible for managing, archiving, and disseminating cryospheric and polar data. The clear majority of these data are digital, but the NSIDC also houses a collection of historical archival materials that include measurements related to the earth's glaciated regions prior to the development of modern instrumentation. Their formats, however, are not conducive to contemporary analysis, rendering them ostensibly “lost” to research. This paper describes a series of efforts to provide access to these collections that date back to their original acquisition, as long ago as the mid-nineteenth century, with focus primarily on activities over the last 15 years. The most recent effort was funded by the Council on Library & Information Resources and won the 2016 International Data Rescue Award. The intent is to highlight key challenges, and our proposed own solutions to those challenges, in designing a digitization project centered on providing online access to analog data in glaciological, geomorphological, and related research.

Author(s): Ramapriyan, Hampapuram; Downs, Robert; Dozier, Jeff; Duerr, Ruth; Folk, Mike; Frew, James; Hoebelheinrich, Nancy; Mattmann, Chris; Peng, Ge | Abstract: Bruce R. Barkstrom, principal investigator for NASA missions involved with understanding Earth’s radiation budget, committed his life to analyzing, interpreting, and stewarding Earth science data.

The Enabling FAIR Data project has brought together a broad spectrum of Earth, space, and environmental science leaders to ensure that data are findable, accessible, interoperable, and reusable.

We describe efforts to bring new methods of search analytics, machine learning, natural language processing and data visualization to address the challenge of finding and extracting meaning from unstructured text and multimedia content. We use the Polar domain to motivate the problem and our proposed solution. However, our techniques are applicable and scalable to other domains., This work was supported by the DARPA XDATA/Memex program and by the NSF EarthCube program under award ICER- 1639753. In addition, the NSF Polar Cyberinfrastructure award numbers PLR-1348450 and PLR-144562 funded a portion of the work. Effort supported in part by JPL, managed by the California Institute of Technology on behalf of NASA.

This is the slide deck from my session "Everything you wanted to know about climate but were afraid to ask" at the summer ESIP meeting in Bloomington."Join recently trained Climate Reality Leader, Ruth Duerr for a short workshop on the topic of the climate. In this workshop we will look at the latest data and news on the science, economics and politics of climate change. Suggestions for activities for students and adults will be described. Questions not answered in real-time will be posted after the event."

Much can be at stake depending on the choice of words used to describe citizen science, because terminology impacts how knowledge is developed. Citizen science is a quickly evolving field that is mobilizing people’s involvement in information development, social action and justice, and large-scale information gathering. Currently, a wide variety of terms and expressions are being used to refer to the concept of ‘citizen science’ and its practitioners. Here, we explore these terms to help provide guidance for the future growth of this field. We do this by reviewing the theoretical, historical, geopolitical, and disciplinary context of citizen science terminology; discussing what citizen science is and reviewing related terms; and providing a collection of potential terms and definitions for ‘citizen science’ and people participating in citizen science projects. This collection of terms was generated primarily from the broad knowledge base and on-the-ground experience of the authors, by recognizing the potential issues associated with various terms. While our examples may not be systematic or exhaustive, they are intended to be suggestive and invitational of future consideration. In our collective experience with citizen science projects, no single term is appropriate for all contexts. In a given citizen science project, we suggest that terms should be chosen carefully and their usage explained; direct communication with participants about how terminology affects them and what they would prefer to be called also should occur. We further recommend that a more systematic study of terminology trends in citizen science be conducted. Publisher's Note: There has been an amendment to the acknowledgements section of this article.

We have initiated a project aimed at enhancing interdisciplinary understanding and usability of polar data by diverse communities. We have produced computer- and human-understandable models of sea ice that can be used to support the interoperability of a wide range of sea ice data. This has the potential to improve scientific predictive analyses and increase usage of the data by scientists, modelers, and forecasters as well as residents of communities that rely on sea ice. We have developed a family of ontologies, leveraging existing best in class models, including one module describing physical characteristics of sea ice, another describing sea ice charts, and a third modeling “egg codes” - an internationally accepted standard for symbolically representing sea ice within geographic regions. We used a semantic Web methodology to rapidly gather and refine requirements, design and iterate over the ontologies, and to evaluate the ontologies with respect to the use cases. We gathered requirements from a wide range of potential stakeholders reflecting the interests of operational ice centers, ice researchers, and indigenous people. We introduce the driving use case and provide an overview of the resulting open source ontologies. We also introduce some key technical considerations including the prominent role of provenance, terms of use, and credit in the model. We describe how the ontologies are being employed and highlight their compatibility with a wide range of existing standards previously developed by many of the stakeholder communities.

Data do not exist in a vacuum. To be useful, data must be accompanied by context on how they are captured, processed, analyzed, and validated and other information that enables interpretation and use.

Virtual Globes such as Google Earth and NASA World Wind permit users to explore rich imagery and the topography of the Earth. While other online services such as map servers provide ways to view, query, and download geographic information, the public has become captivated with the ability to view the Earth's features virtually. The National Snow and Ice Data Center began to display scientific data on Virtual Globes in 2006. The work continues to evolve with the production of high-quality Keyhole Markup Language (KML) representations of scientific data and an assortment of technical experiments. KML files are interoperable with many Virtual Globe or mapping software packages. This paper discusses the science benefits of Virtual Globes, summarizes KML creation methods, and introduces a guide for selecting tools and methods for authoring KML for use with scientific data sets.

The Hierarchical Data Format (HDF) has been a data format standard in National Aeronautic and Space Administration (NASA)'s Earth Observing System Data and Information System since the 1990s. Its rich structure, platform independence, full-featured application programming interface (API), and internal compression make it very useful for archiving science data and utilizing them with a rich set of software tools. However, a key drawback for long-term archiving is the complex internal byte layout of HDF files, requiring one to use the API to access HDF data. This makes the long-term readability of HDF data for a given version dependent on long-term allocation of resources to support that version. Much of the data from NASA's Earth Observing System have been archived in HDF Version 4 (HDF4) format. To address the long-term archival issues for these data, a collaborative study between The HDF Group and NASA's Earth Science Data Centers (ESDCs) is underway. One of the first activities was an assessment of the range of HDF4-formatted data held by NASA to determine the capabilities inherent in the HDF format that were used in practice and for use in estimating the effort for full implementation across NASA's ESDCs. Based on the results of this assessment, methods for producing a map of the layout of the HDF4 files held by NASA were prototyped using a markup-language-based HDF tool. The resulting maps allow a separate program to read the file without recourse to the HDF API. To verify this, two independent tools based solely on the map files were developed and tested.

Reproducibility and reusability of research results is an important concern in scientific communication and science policy. A foundational element of reproducibility and reusability is the open and persistently available presentation of research data. However, many common approaches for primary data publication in use today do not achieve sufficient long-term robustness, openness, accessibility or uniformity. Nor do they permit comprehensive exploitation by modern Web technologies. This has led to several authoritative studies recommending uniform direct citation of data archived in persistent repositories. Data are to be considered as first-class scholarly objects, and treated similarly in many ways to cited and archived scientific and scholarly literature. Here we briefly review the most current and widely agreed set of principle-based recommendations for scholarly data citation, the Joint Declaration of Data Citation Principles (JDDCP). We then present a framework for operationalizing the JDDCP; and a set of initial recommendations on identifier schemes, identifier resolution behavior, required metadata elements, and best practices for realizing programmatic machine actionability of cited data. The main target audience for the common implementation guidelines in this article consists of publishers, scholarly organizations, and persistent data repositories, including technical staff members in these organizations. But ordinary researchers can also benefit from these recommendations. The guidance provided here is intended to help achieve widespread, uniform human and machine accessibility of deposited data, in support of significantly improved verification, validation, reproducibility and re-use of scholarly/scientific data.

Defining a "designated user community" for a data collection is essential to good scientific data stewardship. It enables data managers to determine what information is necessary to ensure the usability of the data now and into the future. It helps managers present and enable access to the data and may determine the format of the data. However, defining a community is difficult, and it is impossible to predict how the use of a data collection may change over time. This creates a series of data management problems for data stewards that may be mitigated by a set of best practices.

The Brokering Building Block (BCube) project seeks to demonstrate the utility and viability of brokering as a core element of a cyberinfrastructure supporting all of geoscience. Brokering is a bridging technology that spans the gaps between the conventions of one discipline and another, enabling interoperability without levying additional requirements on either end. BCube is exploring the applications of brokering in four domains: hydrology, meterology, cryosphere and oceanography, with close attention being paid to the social science aspects of cyberinfrastructure development and adoption, as well as to the educational benefits that brokering provides. This talk will highlight the progress that has been made in establishing a testbed that is being used to provide direct experience of brokering to domain scientists. Discovery of data sources for brokering via web crawling will also be discussed.

1 Abstract Digital research data can only be managed and preserved over time through a sustained institutional commitment. Research data curation is a multi-faceted issue, requiring technologies, organizational structures, and human knowledge and skills to come together in complementary ways. This article provides a high-level description of the Data Conservancy Instance, an implementation of infrastructure and organizational services for data collection, storage, preservation, archiving, curation, and sharing. While comparable to institutional repository systems and disciplinary data repositories in some aspects, the DC Instance is distinguished by featuring a data-centric architecture, discipline-agnostic data model, and a data feature extraction framework that facilitates data integration and cross-disciplinary queries. The Data Conservancy Instance is intended to support, and be supported by, a skilled data curation staff, and to facilitate technical, financial, and human sustainability of organizational data curation services. The Johns Hopkins University Data Management Services (JHU DMS) are described as an example of how the Data Conservancy Instance can be deployed.

Various remote sensing agencies of the world have created a data rich environment for research and applications over the last three decades. Especially over the last decade, the volume and variety of data useful for Earth system science have increased quite rapidly. One of the key purposes of collecting these data and generating useful digital products containing derived geophysical parameters is to study the long-term trends in the Earth's behavior. Long-term observational data and derived products are essential for validating results from models that predict the future behavior of the Earth system. Given the significant resources expended in gathering the observational data and developing the derived products, it is important to preserve them for the benefit of future generations of users. Preservation involves maintaining the bits with no loss (or loss within scientifically acceptable bounds) as they move across systems as well as over time, ensuring readability over time, and providing for long-term understandability and repeatability of previously obtained results. In order to ensure long-term understandability and repeatability, it is necessary to identify all items of content that must be preserved and plan for such preservation. This paper discusses the need for a standard enumerating and describing such content items and reports on the progress made by NASA and the Federation of Earth Science Information Partners (ESIP Federation) in the U.S. towards such a standard.

In recent years, a number of data identification technologies have been developed which purport to permanently identify digital objects. In this paper, nine technologies and systems for assigning persistent identifiers are assessed for their applicability to Earth science data (ARKs, DOIs, XRIs, Handles, LSIDs, OIDs, PURLs, URIs/URNs/URLs, and UUIDs). The evaluation used four use cases that focused on the suitability of each scheme to provide Unique Identifiers for Earth science data objects, to provide Unique Locators for the objects, to serve as Citable Locators, and to uniquely identify the scientific contents of data objects if the data were reformatted. Of all the identifier schemes assessed, the one that most closely meets all of the requirements for an Unique Identifier is the UUID scheme. Any of the URL/URI/IRI-based identifier schemes assessed could be used for Unique Locators. Since there are currently no strong market leaders to help make the choice among them, the decision must be based on secondary criteria. While most publications now allow the use of URLs in citations, so that all of the URL/URI/IRI based identification schemes discussed in this paper could potentially be used as a Citable Locator, DOIs are the identification scheme currently adopted by most commercial publishers. None of the identifier schemes assessed here even minimally address identification of scientifically identical numerical data sets under reformatting.

All data are not created equal. Individual data sets vary from each other in a multitude of ways - from easily measured ways such as size, format, and complexity; to ways that require a more nuanced understanding, such as in the breadth and depth of a data set's potential user base, its "designated community" to use the terminology of the Reference Model for an Open Archival Information System [1]. Given limitations in the resources available, it is not surprising then that repositories, such as those of the National Snow and Ice Data Center, need to make choices about the level of support or services provided for each data set acquired. Such choices prioritize center activities. While such decisions have been an implicit part of NSIDC activities for many years, an effort was recently taken to explicitly define the Levels of Service supported at NSIDC. Baseline Levels of Service have been defined for all existing NSIDC data sets. In addition, Levels of Service considerations are a major component of the NSIDC Distributed Active Archive Center's (DAAC's) new data accessioning and data review processes. In this paper we describe the Levels of Service currently supported at NSIDC and factors that affect the effort required to obtain a given level of service. We also discuss the process users should use if they wish to request that the NSIDC DAAC archive their data.

The authors review recent contributions to the state-of-thescience and benign proliferation of satellite remote sensing, spatial data infrastructure, near-real-time data acquisition, analysis on high performance computing platforms, sapient archiving, multi-modal dissemination and utilization for a wide array of scientific applications. The authors also address advances in Geoinformatics and its growing ubiquity, as evidenced by its inclusion as a focus area within the American Geophysical Union (AGU), European Geosciences Union (EGU), as well as by the evolution of the IEEE Geoscience and Remote Sensing Society's (GRSS) Data Archiving and Distribution Technical Committee (DAD TC).

Lack of standard metadata and file formats make it challenging to archive and distribute remote sensing data. Although the Common Data Model (CDM) developed by Unidata has the potential of providing easy access to data stored in a variety of file formats, it would still be necessary to maintain each I/O library ever used to create a data product. Moreover, due to the nature of these data structures, I/O libraries are often complicated and expensive to maintain. As a consequence, the long-term archival of data stored in multiple formats becomes an expensive and challenging task. In this paper, we demonstrate an alternative solution, namely migration of data to a single standards-based archive format. Data from NASA's Earth Observing System (EOS) Data Centers formatted in Hierarchical Data Format (HDF) are used in the demonstration.

The Open Archival Information System (OAIS) Reference Model was developed by the Consultative Committee for Space Data Systems (CCSDS) in the late 1990s and was adopted as an ISO standard in 2003 (ISO14721:2003) [1]. Recently, many data centers and archives around the world have started to adopt this protocol. As a notable example, the National Oceanographic and Atmospheric Administration (NOAA) of the United States of America adopted the model for their Comprehensive Large Array-data Stewardship System (CLASS). CLASS is expected to be the primary repository and access portal for NOAA's Earth science, satellite-remote-sensing data [2]. Because of the huge scale of these efforts, it is important to carefully consider the manner in which the OAIS Reference Model is implemented. The specifics of the implementation could impact the preservation and therefore the use and usability of vast quantities of remote sensing data for many years.

The Near Infrared Photographic Sky Survey (NIPSS) is a data base with broad potential not only for optical identification of point infrared sources, but also for a variety of studies related to the distribution of red stars. It has been suggested that these applications could be greatly aided by digitization of the data base, for which a feasibility study is under way. As a part of this study a NIPSS visual and near-infrared photographic pair, encompassing the dark cloud L 1454, was digitized. Star counts in this region suggest the existence of two clouds along the line of sight and allowed the distances, extinctions, and masses of the clouds to be estimated. In addition, maps of the region as a function of the redness of the constituent stars were generated. These, when compared to the extinction maps, allow a discussion of the star-formation properties of the region.

We have made a well-sampled and deep survey for H..cap alpha.. emission objects in a region about 100 square degrees in area (about 5000 pc/sup 2/), centered on the lambda Orionis OB association. We detected 83 objects, of which 62 were previously known from earlier, less extensive surveys. The majority of the H..cap alpha.. emission objects in this region are not projected against dark cloud material, an many appear to be within the central regions of the extended (i.e., about 6/sup 0/ in diameter) low density H II region excited by lambda/sup 1/ Orionis. The vast majority of the H..cap alpha.. emission objects form an elongated, barlike structure 2/sup 0/ x 8/sup 0/ in extent. These dimensions are similar to those of giant molecular cloud complexes which spawn OB and T associations. We suggest that the spatial distribution of H..cap alpha.. emission objects and OB association members in this region provides a fossil record of a preexistent giant molecular cloud complex. The gas from the original complex has been recently removed from the vicinity of these stars by the action of the lambda Orionis H II region and is presently detected as a massive shell surrounding the H II region.more » Estimates of the mass of the shell and of the mass of the H..cap alpha.. emission objects and OB stars in the complex enable a determination to be made of the total star-formation efficiency: which we find to be 0.2%: 0.3%. Such low efficiency may be characteristic of giant molecular cloud complexes. The low star-formation efficiency we find suggests that the vast majority of the original binding mass of the lambda Orionis complex has been dissipated, leaving recently formed stars behind. Consequently, we expect this association to be unbound and expanding.« less

Results are presented from an intensive optical and IR monitoring program of the flux and polarization characteristics of BL Lac. It is found that the polarization variations increase in amplitude with increasing time interval, and that the path traced out by the polarization vector in the Q-U plane is a random walk. In view of earlier measurements of BL Lac, the polarization fluctuations can be represented at low frequencies by the flat power spectrum of white noise, up to a frequency of 0.05 cycles/day. Above this frequency, the spectrum steepens to that of a random walk. A model for BL Lac suggested by the polarimetric noise can be constructed from independent sources of light with randomly oriented, strong polarization. Small random differences in spectral index from source to source could also explain the variable wavelength dependence of polarization.

Data preservation has gained momentum and visibility in connection with the growth in digital data and data sharing policies. The Dryad Repository, a curated general–purpose repository for preserving and sharing the data underlying scientific publications, has taken steps to develop a preservation policy to ensure the long–term persistence of this archived data. In 2013, a Preservation Working Group, consisting of Dryad staff and national and international experts in data management and preservation, was convened to guide the development of a preservation policy. This paper describes the policy development process, outcomes, and lessons learned in the process. To meet Dryad’s specific needs, Dryad’s preservation policy negotiates between the ideal and the realistic, including complying with broader governing policies, matching current practices, and working within system constraints.