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At least 19 recordsLinked to original sources
USGS2021

Cloud-native repositories for big scientific data

Scientific data have traditionally been distributed via downloads from data server to local computer. This way of working suffers from limitations as scientific datasets grow toward the petabyte scale. A “cloud-native data repository,” as defined in this article, offers several advantages over traditional data repositories—performance, reliability, cost-effectiveness, collaboration, reproducibility, creativity, downstream impacts, and access and inclusion. These objectives motivate a set of best practices for cloud-native data repositories: analysis-ready data, cloud-optimized (ARCO) formats, and loose coupling with data-proximate computing. The Pangeo Project has developed a prototype implementation of these principles by using open-source scientific Python tools. By providing an ARCO data catalog together with on-demand, scalable distributed computing, Pangeo enables users to process big data at rates exceeding 10 GB/s. Several challenges must be resolved in order to realize cloud computing’s full potential for scientific research, such as organizing funding, training users, and enforcing data privacy requirements.

Computing in Science and Engineering↗
USGS2015

Tools for discovering and accessing Great Lakes scientific data

The Great Lakes Restoration Initiative (GLRI) is a multidisciplinary and interagency effort focused on the protection and restoration of the Great Lakes (GL) using the best available science and applying lessons learned from previous studies. The U.S. Geological Survey (USGS) contributes to the GLRI effort by providing resource managers with information and tools needed to meet restoration goals. This includes contributing scientific expertise and delivering findings to the GL community through meaningful information products. One of the strengths of the GLRI is its interagency approach; however, this can create challenges when coordinating the large number of restoration activities being performed by GL governments, tribes, academics, nonprofits, and industry. There is a vast array of data being produced by both the USGS and its partners, and it is crucial that scientists, managers, policymakers, and the public can easily locate the biological, geological, geospatial, and water-resources data being generated. The USGS strives to develop data products that are easy to find, easy to understand, and easy to use through Web-accessible tools that allow users to learn about the breadth and scope of GLRI activities being undertaken by the USGS and its partners. By creating tools that enable data to be shared and reused more easily, the USGS can encourage collaboration and assist the GL community in finding, interpreting, and understanding the information created during GLRI science activities.

Great Lakes↗
USGS2011

Metadata for data rescue and data at risk

Scientific data age, become stale, fall into disuse and run tremendous risks of being forgotten and lost. These problems can be addressed by archiving and managing scientific data over time, and establishing practices that facilitate data discovery and reuse. Metadata documentation is integral to this work and essential for measuring and assessing high priority data preservation cases. The International Council for Science: Committee on Data for Science and Technology (CODATA) has a newly appointed Data-at-Risk Task Group (DARTG), participating in the general arena of rescuing data. The DARTG primary objective is building an inventory of scientific data that are at risk of being lost forever. As part of this effort, the DARTG is testing an approach for documenting endangered datasets. The DARTG is developing a minimal and easy to use set of metadata properties for sufficiently describing endangered data, which will aid global data rescue missions. The DARTG metadata framework supports rapid capture, and easy documentation, across an array of scientific domains. This paper reports on the goals and principles supporting the DARTG metadata schema, and provides a description of the preliminary implementation.

Conference Paper↗
USGS2011

Moderate-resolution sea surface temperature data for the nearshore North Pacific

Coastal sea surface temperature (SST) is an important environmental characteristic in determining the suitability of habitat for nearshore marine and estuarine organisms. This publication describes and provides access to an easy-to-use coastal SST dataset for ecologists, biogeographers, oceanographers, and other scientists conducting research on nearshore marine habitats or processes. The data cover the Temperate Northern Pacific Ocean as defined by the 'Marine Ecosystems of the World' (MEOW) biogeographic schema developed by The Nature Conservancy. The spatial resolution of the SST data is 4-km grid cells within 20 km of the shore. The data span a 29-year period - from September 1981 to December 2009. These SST data were derived from Advanced Very High Resolution Radiometer (AVHRR) instrument measurements compiled into monthly means as part of the Pathfinder versions 5.0 and 5.1 (PFSST V50 and V51) Project. The processing methods used to transform the data from their native Hierarchical Data Format Scientific Data Set (HDF SDS) to georeferenced, spatial datasets capable of being read into geographic information systems (GIS) software are explained. In addition, links are provided to examples of scripts involved in the data processing steps. The scripts were written in the Python programming language, which is supported by ESRI's ArcGIS version 9 or later. The processed data files are also provided in text (.csv) and Access 2003 Database (.mdb) formats. All data except the raster files include attributes identifying realm, province, and ecoregion as defined by the MEOW classification schema.

Open-File Report↗
USGS2026

Authoritative knowledge infrastructure for GeoAI: Implications for scientific spatial data providers

Geospatial Artificial Intelligence (GeoAI) increasingly relies on automated analysis of authoritative geospatial data, exposing limitations in data infrastructures originally designed for simple dissemination rather than for knowledge-driven use. From the perspective of scientific spatial data providers, the challenge is enabling GeoAI responsibly while retaining semantic clarity, provenance, reproducibility, and governance. This short paper (i) examines how GeoAI exposed limitations within existing geospatial data infrastructures, and (ii) identifies provider-side requirements for an authoritative knowledge infrastructure. Drawing on GIScience, scientific workflow, and operational AI literature, the paper outlines implications for national mapping organizations that seek to support advanced analytics without jeopardizing scientific rigor or stewardship responsibilities.

Conference Paper↗
USGS2007

Proceedings of the first U.S. Geological Survey scientific information management workshop, March 21-23, 2006

In March 2006, the U.S. Geological Survey (USGS) held the first Scientific Information Management (SIM) Workshop in Reston, Virginia. The workshop brought together more than 150 SIM professionals from across the organization to discuss the range and importance of SIM problems, identify common challenges and solutions, and investigate the use and value of “communities of practice” (CoP) as mechanisms to address these issues. The 3-day workshop began with presentations of SIM challenges faced by the Long Term Ecological Research (LTER) network and two USGS programs from geology and hydrology. These presentations were followed by a keynote address and discussion of CoP by Dr. Etienne Wenger, a pioneer and leading expert in CoP, who defined them as "groups of people who share a passion for something that they know how to do and who interact regularly to learn how to do it better." Wenger addressed the roles and characteristics of CoP, how they complement formal organizational structures, and how they can be fostered. Following this motivating overview, five panelists (including Dr. Wenger) with CoP experience in different institutional settings provided their perspectives and lessons learned. The first day closed with an open discussion on the potential intersection of SIM at the USGS with SIM challenges and the potential for CoP. The second session began the process of developing a common vocabulary for both scientific data management and CoP, and a list of eight guiding principles for information management were proposed for discussion and constructive criticism. Following this discussion, 20 live demonstrations and posters of SIM tools developed by various USGS programs and projects were presented. Two community-building sessions were held to explore the next steps in 12 specific areas: Archiving of Scientific Data and Information; Database Networks; Digital Libraries; Emerging Workforce; Field Data for Small Research Projects; Knowledge Capture; Knowledge Organization Systems and Controlled Vocabularies; Large Time Series Data Sets; Metadata; Portals and Frameworks; Preservation of Physical Collections; and Scientific Data from Monitoring Programs. In about two-thirds of these areas, initial steps to forming CoP are now underway. The final afternoon included a panel in which information professionals, managers, program coordinators, and associate directors shared their perspectives on the workshop, on ways in which the USGS could better manage its scientific information, and on the use of CoP as informal mechanisms to complement formal organizational structures. The final session focused on developing the next steps, an action plan, and a communication strategy to ensure continued development.

Scientific Investigations Report↗
USGS2004

Converting analog interpretive data to digital formats for use in database and GIS applications

There is a growing need by researchers and managers for comprehensive and unified nationwide datasets of scientific data. These datasets must be in a digital format that is easily accessible using database and GIS applications, providing the user with access to a wide variety of current and historical information. Although most data currently being collected by scientists are already in a digital format, there is still a large repository of information in the literature and paper archive. Converting this information into a format accessible by computer applications is typically very difficult and can result in loss of data. However, since scientific data are commonly collected in a repetitious, concise matter (i.e., forms, tables, graphs, etc.), these data can be recovered digitally by using a conversion process that relates the position of an attribute in two-dimensional space to the information that the attribute signifies. For example, if a table contains a certain piece of information in a specific row and column, then the space that the row and column occupies becomes an index of that information. An index key is used to identify the relation between the physical location of the attribute and the information the attribute contains. The conversion process can be achieved rapidly, easily and inexpensively using widely available digitizing and spreadsheet software, and simple programming code. In the geological sciences, sedimentary character is commonly interpreted from geophysical profiles and descriptions of sediment cores. In the field and laboratory, these interpretations were typically transcribed to paper. The information from these paper archives is still relevant and increasingly important to scientists, engineers and managers to understand geologic processes affecting our environment. Direct scanning of this information produces a raster facsimile of the data, which allows it to be linked to the electronic world. But true integration of the content with database and GIS software as point, vector or text information is commonly lost. Sediment core descriptions and interpretation of geophysical profiles are usually portrayed as lines, curves, symbols and text information. They have vertical and horizontal dimensions associated with depth, category, time, or geographic position. These dimensions are displayed in consistent positions, which can be digitized and converted to a digital format, such as a spreadsheet. Once this data is in a digital, tabulated form it can easily be made available to a wide variety of imaging and data manipulation software for compilation and world-wide dissemination.

Open-File Report↗
USGS2010

Filtering NetCDF Files by Using the EverVIEW Slice and Dice Tool

Network Common Data Form (NetCDF) is a self-describing, machine-independent file format for storing array-oriented scientific data. It was created to provide a common interface between applications and real-time meteorological and other scientific data. Over the past few years, there has been a growing movement within the community of natural resource managers in The Everglades, Fla., to use NetCDF as the standard data container for datasets based on multidimensional arrays. As a consequence, a need surfaced for additional tools to view and manipulate NetCDF datasets, specifically to filter the files by creating subsets of large NetCDF files. The U.S. Geological Survey (USGS) and the Joint Ecosystem Modeling (JEM) group are working to address these needs with applications like the EverVIEW Slice and Dice Tool, which allows users to filter grid-based NetCDF files, thus targeting those data most important to them. The major functions of this tool are as follows: (1) to create subsets of NetCDF files temporally, spatially, and by data value; (2) to view the NetCDF data in table form; and (3) to export the filtered data to a comma-separated value (CSV) file format. The USGS and JEM will continue to work with scientists and natural resource managers across The Everglades to solve complex restoration problems through technological advances.

Fact Sheet↗
USGS1982

On the availability of geoscientific data and scientific collaborators of an in Africa

In addition to the technical papers, the major topic of discussion at the symposium which formed the stepping-off point for this issue of Geoexplorution was the difficult communications between specialists on Africa and the lack of available geological and geophysical data. However, much of this difficulty stems from a lack of advice on where to turn for data and for eollaboration with other interested scientists. This short note attempts to address these problems with some possible solutions.

Geoexploration↗
USGS2002

Observations of environmental change in Grand Canyon, Arizona

Few scientific data have been collected on pre-dam conditions of the Colorado River corridor through Grand Canyon National Park. Using historical diaries, interviews with pre-dam river runners (referred to as the "Old Timers"), and historical scientific data and observations, we compiled anecdotal information on environmental change in Grand Canyon. The most significant changes are the: lowering of water temperature in the river, near-elimination of heavily sediment-laden flows, erosion of sand bars, invasion of non-native tamarisk trees, reduction in driftwood, development of marshes, increase in non-native fish at the expense of native fishes, and increase in water bird populations. In addition, few debris flows were observed before closure of Glen Canyon Dam, which might suggests that the frequency of debris flows in Grand Canyon has increased. Other possible changes include decreases in bat populations and increases in swallow and bighorn sheep populations, although the evidence is anecdotal and inconclusive. These results provide a perspective on managing the Colorado River that may allow differentiation of the effects of Glen Canyon Dam from other processes of change.

Arizona↗
USGS2020

Purpose and benefits of U.S. Geological Survey Trusted Digital Repositories

Federal mandates and U.S. Geological Survey (USGS, also known as the Bureau) Fundamental Science Practices (FSP) policies require that publicly funded scientific data, publications, and derivative works be openly accessible to researchers and the public. Open access helps to leverage the public investment by making the acquired data and published information products—collectively referred to as “data assets”—easier to locate, reproduce, and reuse. Open access also provides transparency to the processes used to acquire and analyze the data, thereby helping to ensure the scientific integrity of USGS data and products. The data assets produced by USGS programs, science centers, and projects are preserved digitally in various USGS and non-USGS repositories. To capitalize on the investment expended for data collection, analysis, and interpretation, these systems must remain useful and meaningful. For USGS repositories, the Bureau FSP Advisory Committee has implemented an evaluation process to ensure that the systems being used to preserve these data assets are trustworthy, reliable, and secure and thus provide for data longevity, integrity, and security. A system that is found to meet the reliability and suitability requirements is certified as a USGS Trusted Digital Repository.

Fact Sheet↗
USGS1995

Bibliography, indices, and data sources of water-related studies, upper Colorado River basin, Colorado and Utah, 1872-1995

As part of the U.S. Geological Survey's National Water-Quality Assessment Program, current water-quality conditions in the Upper Colorado River Basin in Colorado and Utah are being assessed. This report is an initial effort to identify and compile information on water-related studies previously conducted in the basin and consists of a bibliography, coauthor and subject indices, and sources of available water-related data. Computerized literature searches of scientific data bases were carried out to identify past water-related studies in the basin, and government agencies and private organizations were contacted regarding their knowledge or possession of water-related publications and data. Categories of information in the bibliography include: aquatic biology, climate, energy development, geology, land use, limnology, runoff, salinity, surface- and ground-water hydrology, water chemistry, water quality and quantity, and water use and management. The approximately 1,400 indexed references date from 1872 through February 1995 and include books, journal articles, maps, and reports. In many instances, an abstract has been provided for a given reference. Sources of water-related data in the basin are included in a table.

Colorado, Utah↗
USGS2016

sbtools: A package connecting R to cloud-based data for collaborative online research

The adoption of high-quality tools for collaboration and reproducible research such as R and Github is becoming more common in many research fields. While Github and other version management systems are excellent resources, they were originally designed to handle code and scale poorly to large text-based or binary datasets. A number of scientific data repositories are coming online and are often focused on dataset archival and publication. To handle collaborative workflows using large scientific datasets, there is increasing need to connect cloud-based online data storage to R. In this article, we describe how the new R package sbtools enables direct access to the advanced online data functionality provided by ScienceBase, the U.S. Geological Survey’s online scientific data storage platform.

The R Journal↗
USGS2022

Opportunities to improve alignment with the FAIR Principles for U.S. Geological Survey data

In 2016, an interdisciplinary, international group of 53 scientists introduced a framework named “the FAIR Principles” for addressing 21st century scientific data challenges. The FAIR Principles are increasingly used as a guide for producing digital scientific products that are findable, accessible, interoperable, and reusable (FAIR), especially to enable use of such products in automated systems. Data aligned with the FAIR Principles can increase the efficiency of science integration capabilities such as those envisioned for the U.S. Geological Survey (USGS) Earth Monitoring, Analyses, and Projections (EarthMAP) initiative. The FAIR Principles clearly define the characteristics of reusable scientific products, but it is less clear how to facilitate consistency in achieving these characteristics across the Bureau. USGS data are produced by local research projects distributed over more than 100 centers in 7 regions. After data are approved for release, they could be managed in numerous repositories and online data systems. The diversity of USGS data is illustrated by the topical range of the USGS mission areas: Core Science Systems, Ecosystems, Energy and Minerals, Natural Hazards, and Water Resources. In the USGS context, realizing the EarthMAP vision for automated, predictive, integrated science that provides timely and actionable results involves providing knowledge and support services and developing the skills, infrastructure, and culture to enable Bureau-wide implementation of the FAIR Principles. In 2019, the USGS Community for Data Integration funded a project to convene a broadly representative workshop and produce recommendations to enable consistency with the FAIR Principles across the USGS. The workshop, held in Fort Collins, Colorado, in September 2019, brought together 28 participants for 3 days to engage with the FAIR Principles, analyze USGS use cases, and discuss the roles of data producers and managers, data storage and catalogs, value-added services, and policy makers in implementing the FAIR Principles. Workshop participants agreed that scientific reproducibility requires the extension of the FAIR Principles beyond measured data to include physical samples, research methods, software, and tools at the USGS. Workshop discussions focused on how the USGS can implement the FAIR Principles by supporting research teams in creating data, metadata, and other scientific products and also by supporting enterprise systems that maintain and leverage the products’ consistency with the FAIR Principles. The resulting FAIR roadmap of recommendations describes nine proposed interdependent strategies that could be achieved by coordinated actions taken by different parts of the USGS. A proposed early action would be the creation of a coordinating council that includes representatives from the groups engaged in activities consistent with better alignment with the FAIR Principles. The nine proposed strategies, which are presented in more detail in this roadmap report, focus on enabling improvements to individual data products, providing infrastructure, and structuring administrative activities to support an organizational culture that values the FAIR Principles.

Open-File Report↗
USGS2022

Stakeholder engagement to guide decision-relevant water data delivery

Water resources management and policy making require access to reliable scientific data. However, water managers may need to overcome various obstacles to accessing data. For example, insufficient technological infrastructures, low data literacy, and data format complexities often inhibit data user access. Thus, it is imperative to include stakeholders in the design of data delivery systems. The United States Geological Survey's Water Resources Mission Area is currently developing Integrated Water Availability Assessments (IWAAs) — multi-extent, stakeholder driven, near real-time water availability census and prediction for human and ecological uses. To provide appropriate user accessibility to data delivery systems developed for IWAAs, a user-centered design process including stakeholder focus groups was used to determine potential water data user needs and preferences. Focus groups identified five types of potential users: Public sector water resources managers, Public sector water resources manager data analysts, Industry and private companies, Tribal Nations, and Nonprofit organizations. Different water data user types depended on diverse spatial and temporal scale data. Public sector water resources managers benefitted most from data synthesized into user-friendly platforms and Public sector water resources data analysts preferred easy access to raw data. These findings can support the development of a water data delivery platform that meets a variety of user needs.

Journal of the American Water Resources Associatio↗
USGS2023

An interoperability strategy for the next generation of SEEA accounting

The System of Environmental-Economic Accounting (SEEA) is a set of international environmental-economic standards, adopted by the UN Statistical Commission in 2012 (SEEA Central Framework) and 2021 (SEEA Ecosystem Accounting); the latter in particular requires the integration of large and diverse data streams. These include geospatial and other data sources, which have proven challenging for some National Statistical Offices (NSOs) to implement. Although a variety of ecosystem service modelling platforms have been built over the last 15 years to meet various user demands, they often duplicate efforts, rely on data that are siloed, and rarely effectively reuse the knowledge gained from past modelling efforts. By making the data and models that underlie SEEA interoperable, NSOs and the scientific community can advance the accessibility, speed, quality, and transparency of SEEA accounts by making it possible to rapidly integrate and share new scientific data and models. Doing so requires an understanding of the benefits of interoperability, the costs of the status quo, and concrete pathways toward community-endorsed approaches for interoperability. The ARIES Network, which powers the ARIES for SEEA Explorer web application, offers such a path toward interoperability, providing substantial benefits to NSOs and scientific and policy communities.

Report↗
USGS2025

Assessing decadal-scale coastal change likelihood to define the accuracy and application of scientific information

Defining the accuracy and uncertainties of scientific data products is critical to the usability and trustworthiness of scientific information for environmental management and conservation purposes, such as coastal resource prioritization, design, adaptation, and mitigation. The U.S. Geological Survey has a new decadal-scale coastal change assessment product that synthesizes nearly two dozen coastal datasets. A supervised machine-learning framework is used to combine existing datasets that describe the landscape and the hazards that affect it to determine the coastal change likelihood (CCL) in the coming decade at a resolution of 10 m per pixel for the NE United States from Maine to Virginia. Here, results from a series of statistical tests conducted on source data, the supervised classification, and the CCL outcomes as compared with historical land-cover change are presented. The overall accuracy of the aggregated land-cover dataset that serves as the foundation to which other source datasets are appended is 94%. The supervised learning classification that determines the final CCL output has an overall accuracy of 92%. The CCL predictions of high expected coastal change were consistent with 95% of the coastal and low-elevation landscape change in the last 20 years, as recorded by the Coastal Change Analysis Program land-cover change atlas. Results suggest that CCL provides accurate estimates of coastal landscape change in the next decade that are consistent with recent observed change. Additionally, best practices for applying CCL for planning purposes are outlined, and citing limitations, knowledge gaps, and opportunities for improved accuracy and further investigation are considered.

Journal of Coastal Research↗