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USGS2005

Hydratools, a MATLAB® based data processing package for Sontek Hydra data

The U.S. Geological Survey (USGS) has developed a set of MATLAB tools to process and convert data collected by Sontek Hydra instruments to netCDF, which is a format used by the USGS to process and archive oceanographic time-series data. The USGS makes high-resolution current measurements within 1.5 meters of the bottom. These data are used in combination with other instrument data from sediment transport studies to develop sediment transport models. Instrument manufacturers provide software which outputs unique binary data formats. Multiple data formats are cumbersome. The USGS solution is to translate data streams into a common data format: netCDF. The Hydratools toolbox is written to create netCDF format files following EPIC conventions, complete with embedded metadata. Data are accepted from both the ADV and the PCADP. The toolbox will detect and remove bad data, substitute other sources of heading and tilt measurements if necessary, apply ambiguity corrections, calculate statistics, return information about data quality, and organize metadata. Standardized processing and archiving makes these data more easily and routinely accessible locally and over the Internet. In addition, documentation of the techniques used in the toolbox provides a baseline reference for others utilizing the data.

Conference Paper↗

USGS2009

Converting nonstandard fish sampling data to standardized data

Fishery biologists spend considerable effort over multiple years collecting data on fish population and community status using a particular sampling method or set of methods. However, new (and often more effective) sampling methods and technologies are continuously being developed. To incorporate these new sampling techniques, fishery biologists need a means for converting sample data collected using old methods so they can be compared with data collected using new methods. Similarly, fishery biologists often need a means to compare fish sample data collected using the same method over time (e.g., from year to year) and space (e.g., between sample sites). If fish abundance, species presence, or richness are estimated using an unbiased statistical estimator, the estimates can be validly compared, even if the fish sample data were collected with different methods. However, if unbiased statistical estimators were not used, biologists need methods for adjusting fish sampling data collected using different methods or using the same method collected under different sampling conditions. In this chapter, we describe and provide examples of statistical techniques for converting nonstandard fish sampling data to standardized data and for making comparisons of fish sampling data collected at different times or at different locations. We define standard fish sampling data as data collected using the standardized fish sampling methods described throughout this book. Any other sampling methods and associated data are thus defined as nonstandard. Before delving into the details of the statistical modeling techniques, we describe the nature of fish sample data, their uses, and their limitations. Catch-effort measures, such as relative abundance and catch per unit effort (CPUE), are more formally described as indices. Here, we define an index as any measure or count of a species or community (e.g., species richness) based on direct observation without an estimate of the ability to count individuals or species. Indices have some very desirable characteristics for use in fisheries research and management. In general (but not always), indices require less effort to collect and are usually more precise than unbiased population estimators (e.g., CPUE versus capture–recapture estimates of abundance). The proper use of indices for assessment of fish populations or communities, however, requires that the relationship between an index and the true value (e.g., fish density, species richness) is relatively constant (1) across the observable range of true values, (2) through time when evaluating trends at a single location, and (3) across space when making comparisons among locations.

Book chapter↗

USGS1994

Keys to a successful project: Associated data and planning: Data standards

The many individual salamanders, frogs, caecilians, and their larvae encountered during the course of an inventory or monitoring project will have to be identified to species. Depending on the goals and sampling method(s) used, some individuals will be identified from a distance by their calls, others will be handled. At the same time, some will be marked for recapture, and others will be sampled as vouchers. For each, certain minimum data should be recorded. In this section, data pertaining to locality and sampling methodology are considered, information on microhabitats and specimen vouchers is covered in sections that follow. I feel strongly that the data outlined here should be the minimum for any project. Investigators with specific goals may require additional types of data as well. Standardized, printed sheets containing the required data categories provide a convenient, inexpensive, and effective way to ensure that all the desired information is recorded in a consistent format, Data sheets should be well organized, printed on good-quality paper (75%-100% cotton content) and include extra space (e.g., other side of sheet) for notes that do not fit preestablished categories Data should be recorded in the field with permanent (waterproof) ink as simply and directly as possible. I strongly recommend against the use of data codes in the field; it is too easy to forget codes or to enter the wrong code. Original data sheets can be photocopied for security, but they should not be copied by hand. If data are to be coded for computer analysis, the original or photocopied sheets should be used for data entry to minimize transcription errors. Some workers prefer recording information on small tape recorders; this also works well if a list of the standard data categories is checked during taping to ensure that all required information is recorded. Information recorded on tapes should be transcribed to data sheets or into a computer within 24 hours of the sample.

Book chapter↗

USGS2012

Archive of single beam and swath bathymetry data collected nearshore of the Gulf Islands National Seashore, Mississippi, from West Ship Island, Mississippi, to Dauphin Island, Alabama: Methods and data report for USGS Cruises 08CCT01 and 08CCT02, July 2008, and 09CCT03 and 09CCT04, June 2009

During the summers of 2008 and 2009 the USGS conducted bathymetric surveys from West Ship Island, Miss., to Dauphin Island, Ala., as part of the Northern Gulf of Mexico (NGOM) Ecosystem Change and Hazard Susceptibility project. The survey area extended from the shoreline out to approximately 2 kilometers and included the adjacent passes (fig. 1). The bathymetry was primarily used to create a topo-bathymetric map and provide a base-level assessment of the seafloor following the 2005 hurricane season. Additionally, these data will be used in conjunction with other geophysical data (chirp and side scan sonar) to construct a comprehensive geological framework of the Mississippi Barrier Island Complex. The culmination of the geophysical surveys will provide baseline bathymetry necessary for scientists to define and interpret seafloor habitat for this area and for scientists to predict future geomorpholocial changes of the islands with respect to climate change, storm impact, and sea-level rise. Furthermore, these data provide information for feasibility of barrier island restoration, particularly in Camille Cut, and for the preservation of historical Fort Massachusetts. For more information refer to http://ngom.usgs.gov/gomsc/mscip/index.html. Since bathymetric surveys have often been conducted for navigational purposes, soundings have traditionally been referenced to a water level datum using tide gages and tide models. Bathymetric measurements referenced to a Global Positioning System (GPS) is a more accurate way of representing water depth and has been implemented in the acquisition and processing procedures for these datasets. Previous single-beam bathymetric studies performed at the USGS Center for Coastal and Marine Science have successfully referenced bathymetric measurements to GPS (DeWitt and others, 2007; Hansen 2008 and 2009). The 2008-2009 bathymetry surveys were conducted as a test to (1) develop acquisition and processing technology utilizing both single beam and swath bathymetry survey methods together, (2) reference both types of measurements to GPS rather than water level, and (3) compare the differences between methods in acquisition and processing. Results of the survey are explained in greater detail within this report. To acquire suitable coverage of the study area in a limited time frame, the seafloor-elevation survey was conducted using three techniques: single-beam bathymetry, interferometric swath bathymetry, and a walking kinematic survey of the island shorelines. All three techniques utilized GPS measurements. Implementation of these techniques was executed concurrently yet independently aboard two research vessels: the RV Survey Cat , a 26-foot (ft) shallow-draft Glacier Bay Coastal Runner, and the 50-ft RV G.K. Gilbert . A portable push buggy with a rigid antenna mount served as the platform for the kinematic shoreline survey. Data from each survey technique was post-processed and edited independently with proper inclusion of the differentially processed external navigation files. The x,y,z components from each method were then combined and the two survey years (2008 and 2009) were merged into one dataset. The 2008 bathymetry data were processed at the USGS Center for Coastal and Marine Science in St. Petersburg, Fla., and the 2009 bathymetry data were processed at the USGS Coastal and Marine Science Center located in Woods Hole, Mass. This report serves as an archive of the processed single beam and interferometric swath bathymetry, outlines the methodology, and reports the results. Data products herein include gridded and interpolated digital depth surfaces, and x,y,z data products for both single beam and interferometric swath bathymetry. Additional files include trackline maps, navigation files, geographic information system (GIS) files, Field Activity Collection System (FACS) logs, and formal Federal Geographic Data Committee (FGDC) metadata. Scanned images of the handwritten FACS logs and digital FACS logs are also provided as PDF files. Refer to the Acronyms page for description of acronyms and abbreviations used in this report or hold the cursor over an acronym for a pop-up explanation. The USGS St. Petersburg Coastal and Marine Science Center assigns a unique identifier to each cruise or field activity. For example, 08CCT01 indicates that the data were collected in 2008 for the Coastal Change and Transport (CCT) study and the data were collected during the first (01) field activity for that project in that calendar year. Refer to http://walrus.wr.usgs.gov/infobank/programs/html/definition/activity.html for a detailed description of the method used to assign the field activity ID. See the digital FACS equipment log for details about the acquisition equipment used. Raw datasets are stored digitally at the USGS St. Petersburg Coastal and Marine Science Center and processed systematically using Novatel's GrafNav version 7.6, SANDS version 3.7, SEA SWATH plus version 3.06.04.03, CARIS HIPS AND SIPS version 3.6, and ESRI ArcGIS version 9.3.1. For more information on processing refer to the Equipment and Processing page. Chirp seismic data were also collected during these surveys and are archived separately.

Mississippi;Alabama↗

USGS2017

Integrating multiple data sources in species distribution modeling: A framework for data fusion

The last decade has seen a dramatic increase in the use of species distribution models (SDMs) to characterize patterns of species’ occurrence and abundance. Efforts to parameterize SDMs often create a tension between the quality and quantity of data available to fit models. Estimation methods that integrate both standardized and non-standardized data types offer a potential solution to the tradeoff between data quality and quantity. Recently several authors have developed approaches for jointly modeling two sources of data (one of high quality and one of lesser quality). We extend their work by allowing for explicit spatial autocorrelation in occurrence and detection error using a Multivariate Conditional Autoregressive (MVCAR) model and develop three models that share information in a less direct manner resulting in more robust performance when the auxiliary data is of lesser quality. We describe these three new approaches (“Shared,” “Correlation,” “Covariates”) for combining data sources and show their use in a case study of the Brown-headed Nuthatch in the Southeastern U.S. and through simulations. All three of the approaches which used the second data source improved out-of-sample predictions relative to a single data source (“Single”). When information in the second data source is of high quality, the Shared model performs the best, but the Correlation and Covariates model also perform well. When the information quality in the second data source is of lesser quality, the Correlation and Covariates model performed better suggesting they are robust alternatives when little is known about auxiliary data collected opportunistically or through citizen scientists. Methods that allow for both data types to be used will maximize the useful information available for estimating species distributions.

Ecology↗

USGS2008

Evaluation of statistical treatments of left-censored environmental data using coincident uncensored data sets: I. Summary statistics

The main classes of statistical treatment of below-detection limit (left-censored) environmental data for the determination of basic statistics that have been used in the literature are substitution methods, maximum likelihood, regression on order statistics (ROS), and nonparametric techniques. These treatments, along with using all instrument-generated data (even those below detection), were evaluated by examining data sets in which the true values of the censored data were known. It was found that for data sets with less than 70% censored data, the best technique overall for determination of summary statistics was the nonparametric Kaplan-Meier technique. ROS and the two substitution methods of assigning one-half the detection limit value to censored data or assigning a random number between zero and the detection limit to censored data were adequate alternatives. The use of these two substitution methods, however, requires a thorough understanding of how the laboratory censored the data. The technique of employing all instrument-generated data - including numbers below the detection limit - was found to be less adequate than the above techniques. At high degrees of censoring (greater than 70% censored data), no technique provided good estimates of summary statistics. Maximum likelihood techniques were found to be far inferior to all other treatments except substituting zero or the detection limit value to censored data.

Environmental Science & Technology↗

USGS2020

Groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2016, and previously unpublished data from 2013 to 2015

Environmental groundwater-quality data were collected from 648 wells as part of the National Water-Quality Assessment Project of the U.S. Geological Survey National Water-Quality Program and are included in this report. Most of the wells (514) were sampled from January through December 2016, and 60 of them were sampled in 2013 and 74 in 2014. The data were collected from seven types of well networks: principal aquifer study networks, which are used to assess the quality of groundwater used for public-water supply; land-use study networks, which are used to assess land-use effects on shallow groundwater quality; major aquifer study networks, which are used to assess the quality of groundwater used for domestic supply; enhanced trends networks, which are used to evaluate the time scales during which groundwater quality changes; vertical flow-path study networks, which are used to evaluate changes in groundwater quality from shallow to deeper depths; flow-path study networks, which are used to evaluate changes in groundwater quality from shallow to deeper depths over a horizontal distance; and modeling support studies, which are used to provide data to support groundwater modeling. Groundwater samples were analyzed for many water-quality indicators and constituents, including major ions, nutrients, trace elements, volatile organic compounds, pesticides, radionuclides, and some constituents of special interest (arsenic speciation, chromium [VI], and perchlorate). These groundwater-quality data, along with data from quality-control samples, are tabulated in this report and in an associated data release. Some data from environmental samples collected in 2013–14 and quality-control samples collected in 2012–15 also are included in the associated data release. Data from samples collected in 2016 are associated with networks described in this report and have not been published previously; data from samples collected between 2012 and 2015 are associated with networks described in previous reports in this data series.

Data Series↗

USGS2023

The invasive plant data landscape: A synthesis of spatial data and applications for research and management in the United States

Context An increase in the number and availability of datasets cataloging invasive plant distributions offers opportunities to expand our understanding, monitoring, and management of invasives across spatial scales. These datasets, created using on-the-ground observations and modeling techniques, are made both for and by researchers and managers. Objectives The large number and variety of data types and associated datasets can be difficult to navigate, require high levels of data literacy, and can overwhelm the intended end-users. By providing a synthesis of available data types and datasets, this work may facilitate data understanding and use among researchers and managers. Methods We synthesize types of invasive plant distribution data sources, highlighting publicly available datasets and their potential applications and limitations for research and management. Results Eight data types and their potential applications for research and management are described. We also describe gaps in current invasive species distribution data usability and outline a path forward for improving the use of invasive plant data in future research and management. Conclusions Accessible and usable invasive plant spatial data are needed for developing landscape scale analysis and management plans. By synthesizing the invasive plant data available, with examples and limitations for application, this work will serve as a guide to facilitate appropriate and efficient data choices in current and future research and management.

Nevada↗

USGS2015

Changes in data sharing and data reuse practices and perceptions among scientists worldwide

The incorporation of data sharing into the research lifecycle is an important part of modern scholarly debate. In this study, the DataONE Usability and Assessment working group addresses two primary goals: To examine the current state of data sharing and reuse perceptions and practices among research scientists as they compare to the 2009/2010 baseline study, and to examine differences in practices and perceptions across age groups, geographic regions, and subject disciplines. We distributed surveys to a multinational sample of scientific researchers at two different time periods (October 2009 to July 2010 and October 2013 to March 2014) to observe current states of data sharing and to see what, if any, changes have occurred in the past 3–4 years. We also looked at differences across age, geographic, and discipline-based groups as they currently exist in the 2013/2014 survey. Results point to increased acceptance of and willingness to engage in data sharing, as well as an increase in actual data sharing behaviors. However, there is also increased perceived risk associated with data sharing, and specific barriers to data sharing persist. There are also differences across age groups, with younger respondents feeling more favorably toward data sharing and reuse, yet making less of their data available than older respondents. Geographic differences exist as well, which can in part be understood in terms of collectivist and individualist cultural differences. An examination of subject disciplines shows that the constraints and enablers of data sharing and reuse manifest differently across disciplines. Implications of these findings include the continued need to build infrastructure that promotes data sharing while recognizing the needs of different research communities. Moving into the future, organizations such as DataONE will continue to assess, monitor, educate, and provide the infrastructure necessary to support such complex grand science challenges.

PLoS ONE↗

USGS2001

National Irrigation Water Quality Program data-synthesis data base

Under the National Irrigation Water Quality Program (NIWQP) of the U.S. Department of the Interior, researchers investigated contamination caused by irrigation drainage in 26 areas in the Western United States from 1986 to 1993. From 1992 to 1995, a comprehensive relational data base was built to organize data collected during the 26-area investigations. The data base provided the basis for analysis and synthesis of these data to identify common features of contaminated areas and hence dominant biologic, geologic, climatic, chemical, and physiographic factors that have resulted in contamination of water and biota in irrigated areas in the Western United States. Included in the data base are geologic, hydrologic, climatological, chemical, and cultural data that describe the 26 study areas in 14 Western States. The data base contains information on 1,264 sites from which water and bottom sediment were collected. It also contains chemical data from 6,903 analyses of surface water, 914 analyses of ground water, 707 analyses of inorganic constituents in bottom sediments, 223 analyses of organochlorine pesticides in bottom sediments, 8,217 analyses of inorganic constituents in biota, and 1,088 analyses for organic constituents in biota. The data base is available to the public and can be obtained at the NIWQP homepage http://www.usbr.gov/niwqp as dBase III tables for personal-computer systems or as American Standard Code for Information Exchange structured query language (SQL) command and data files for SQL data bases.

Open-File Report↗

USGS2002

Evaluation of airborne image data and LIDAR main stem data for monitoring physical resources within the Colorado River ecosystem

This study evaluated near-infrared LIDAR data acquired over the main-stem channel at four long-term monitoring sites within the Colorado River ecosystem (CRE) to determine the ability of these data to provide reliable indications in changes in water elevation over time. Our results indicate that there is a good correlation between the LIDAR water-surface elevations and ground measurements of water-edge elevation, but there are also inherent errors in the LIDAR data. The elevation errors amount to about 50 cm and therefore temporal changes in water-surface elevation that exceed this value by the majority of data at a particular location can be deemed significant or real. This study also evaluated airborne image data for producing photogrammetric elevation data and for automated mapping of sand bars and debris flows within the CRE. The photogrammetric analyses show that spatial resolutions of ≤ 10 cm are required to produce vertical accuracies < 20 cm and that digitally acquired data cannot yet support this monitoring requirement. The mapping analyses indicate that CIR image data are far superior to true-color and panchromatic image data in mapping sand bars and debris flows. The analyses also show that the CIR color information provide almost as much mapping capability as do the combination of CIR color and CIR image texture. Therefore, CIR image data should always be given preference in image data collections, not only for the physical resource program, but also for the biologic resource program.

Arizona↗

USGS2023

Assessing the value and usage of data management planning and data management plans within the U.S. Geological Survey

As of 2016, the U.S. Geological Survey (USGS) Fundamental Science Practices require data management plans (DMPs) for all USGS and USGS-funded research. The USGS Science Data Management Branch of the Science Analytics and Synthesis Program has been working to help the USGS (Bureau) meet this requirement. However, USGS researchers still encounter common data management-related challenges that may be reduced or eliminated by better planning. In 2021, USGS staff were given a series of surveys aimed to better understand current data management planning practices, perceptions, and needs. The survey results indicated that adoption and integration of data management planning and DMPs into USGS research project workflows are broad, if inconsistent, across USGS Science Centers and programs. The USGS Science Data Management Branch can help improve clarity and guidance on the purpose, intended audience, content, workflows, and evaluation processes for DMPs. It would also be beneficial to provide additional supporting cyberinfrastructure to support DMP activities. Survey responses indicated it would be beneficial for the Science Data Management Branch to develop a strategy, other than through DMPs, for teaching and encouraging good data management practices. Although these surveys were an opportunity for USGS staff to provide feedback on their experiences, the surveys may also have revealed the desire for more frequent evaluations, cross-disciplinary communication, and training on research data management and DMP development and integration, in the context of USGS policy, Fundamental Science Practices requirements, and overall Bureau expectations. Data management-related roles such as data manager or steward, information technologist, and repository manager may need to be formally recognized as skilled professional career positions within the Bureau. At a minimum, the best practice for USGS would be to create and maintain DMPs as living documents, integrated with existing systems that are broadly accessible to all stakeholders, and include quantitatively measurable benefits tied directly to a clearly defined purpose.

Open-File Report↗

USGS1978

Definitions of components of the Master Water Data Index maintained by the National Water Data Exchange

The Master Water Data Index (MWDI) is a computerized data base developed and maintained by the National Water Data Exchange (NAWDEX). The MWDI contains information about water-data collection sites. This information includes: the identification of sites for which water data are available, the location of these sites, the type of site, the data-collection organization, the types of data available, the major water-data parameters for which data are available, the frequency at which these parameters are measured, and the media in which the data are stored. This document contains a definition and description of each component of the MWDI data base. (Woodard-USGS)

Open-File Report↗

USGS1986

A data-management system for detailed areal interpretive data

A data storage and retrieval system has been developed to organize and preserve areal interpretive data. This system can be used by any study where there is a need to store areal interpretive data that generally is presented in map form. This system provides the capability to grid areal interpretive data for input to groundwater flow models at any spacing and orientation. The data storage and retrieval system is designed to be used for studies that cover small areas such as counties. The system is built around a hierarchically structured data base consisting of related latitude-longitude blocks. The information in the data base can be stored at different levels of detail, with the finest detail being a block of 6 sec of latitude by 6 sec of longitude (approximately 0.01 sq mi). This system was implemented on a mainframe computer using a hierarchical data base management system. The computer programs are written in Fortran IV and PL/1. The design and capabilities of the data storage and retrieval system, and the computer programs that are used to implement the system are described. Supplemental sections contain the data dictionary, user documentation of the data-system software, changes that would need to be made to use this system for other studies, and information on the computer software tape. (Lantz-PTT)

Water-Resources Investigations Report↗

USGS1991

Satellite and earth science data management activities at the U.S. geological survey's EROS data center

The U.S. Geological Survey's Earth Resources Observation Systems (EROS) Data Center, the national archive for Landsat data, has 20 years of experience in acquiring, archiving, processing, and distributing Landsat and earth science data. The Center is expanding its satellite and earth science data management activities to support the U.S. Global Change Research Program and the National Aeronautics and Space Administration (NASA) Earth Observing System Program. The Center's current and future data management activities focus on land data and include: satellite and earth science data set acquisition, development and archiving; data set preservation, maintenance and conversion to more durable and accessible archive medium; development of an advanced Land Data Information System; development of enhanced data packaging and distribution mechanisms; and data processing, reprocessing, and product generation systems.

Conference Paper↗

USGS2025

International data gaps at the Center for Engineering Strong Motion Data

The Center for Engineering Strong Motion Data (CESMD) is utilized by seismologists, engineers, and disaster management professionals in the US and has historically achieved and distributed waveforms from across the globe for significant earthquakes. The increased access to the waveforms via Web API (Application Programming Interface) offers a unique opportunity to provide the community complete datasets, sampling a variety of tectonic environments and geologic conditions, increasing the number of available ground motion records for use in ground motion models (GMMs) and improving the accuracy of earthquake engineering evaluations. The objective of this study is to programmatically identify gaps in global event data from the past decade and backfill missing data gaps at CESMD. We first compare the CESMD catalog with the Advanced National Seismic System (ANSS) Comprehensive Earthquake Catalog identifying regions and time periods where strong-motion data is limited or inadequate. To backfill datasets at CESMD for significant events, we pinpoint regions and time intervals that lack information, creating a list of events for which we’d like to obtain data. An important facet of this work is identifying the source of data and metadata across earthquake repositories around the world and integrating these data repositories into our current strong-motion data processing workflow. In parallel with these newly processed datasets, we are developing a script to produce data origination citations to include provenance and attribution information to associate with respective datasets at CESMD. We showcase our methodology for identifying and filling data gaps at CESMD using three case studies (the 2018 Anchorage Alaska earthquake sequence, seismicity associated with the 2018 Hawaiian Kilauea volcano eruption, and several earthquakes in Turkey) and then outline our strategy to apply our data gap backfilling methods on an international scale.

Conference Paper↗

USGS2015

The data quality analyzer: a quality control program for seismic data

The U.S. Geological Survey's Albuquerque Seismological Laboratory (ASL) has several initiatives underway to enhance and track the quality of data produced from ASL seismic stations and to improve communication about data problems to the user community. The Data Quality Analyzer (DQA) is one such development and is designed to characterize seismic station data quality in a quantitative and automated manner. The DQA consists of a metric calculator, a PostgreSQL database, and a Web interface: The metric calculator, SEEDscan, is a Java application that reads and processes miniSEED data and generates metrics based on a configuration file. SEEDscan compares hashes of metadata and data to detect changes in either and performs subsequent recalculations as needed. This ensures that the metric values are up to date and accurate. SEEDscan can be run as a scheduled task or on demand. The PostgreSQL database acts as a central hub where metric values and limited station descriptions are stored at the channel level with one-day granularity. The Web interface dynamically loads station data from the database and allows the user to make requests for time periods of interest, review specific networks and stations, plot metrics as a function of time, and adjust the contribution of various metrics to the overall quality grade of the station. The quantification of data quality is based on the evaluation of various metrics (e.g., timing quality, daily noise levels relative to long-term noise models, and comparisons between broadband data and event synthetics). Users may select which metrics contribute to the assessment and those metrics are aggregated into a “grade” for each station. The DQA is being actively used for station diagnostics and evaluation based on the completed metrics (availability, gap count, timing quality, deviation from a global noise model, deviation from a station noise model, coherence between co-located sensors, and comparison between broadband data and synthetics for earthquakes) on stations in the Global Seismographic Network and Advanced National Seismic System.

Computers & Geosciences↗

USGS2023

What is “big data” and how should we use it? The role of large datasets, secondary data, and associated analysis techniques in outdoor recreation research

With researchers increasingly interested in big data research, this conceptual paper describes how large datasets, secondary data, and associated analysis techniques can be used to understand outdoor recreation. Some types of large, secondary datasets that have been increasingly used in outdoor recreation research include social media, mobile device data, and trip reports or online reviews. First, we give a brief overview of big data terms and outline the steps involved in conducting big data research. In doing so, we describe data sources and analysis techniques relevant for outdoor recreation, and review how they have been applied in previous published works. We then describe opportunities, limitations, and considerations of using big data. Finally, we outline several questions researchers may consider when designing, conducting, reporting, and reviewing outdoor recreation research using big data. Overall, big data approaches can expand our understanding of outdoor recreation and, by addressing key questions, may help researchers harness the strengths of big data while ensuring quality and integrity.

Journal of Outdoor Recreation and Tourism↗