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At least 721 records · Page 40Linked to original sources
USGS2007

Geographic information systems, remote sensing, and spatial analysis activities in Texas, 2002-07

Geographic information system (GIS) technology has become an important tool for scientific investigation, resource management, and environmental planning. A GIS is a computer-aided system capable of collecting, storing, analyzing, and displaying spatially referenced digital data. GIS technology is particularly useful when analyzing a wide variety of spatial data such as with remote sensing and spatial analysis. Remote sensing involves collecting remotely sensed data, such as satellite imagery, aerial photography, or radar images, and analyzing the data to gather information or investigate trends about the environment or the Earth's surface. Spatial analysis combines remotely sensed, thematic, statistical, quantitative, and geographical data through overlay, modeling, and other analytical techniques to investigate specific research questions. It is the combination of data formats and analysis techniques that has made GIS an essential tool in scientific investigations. This document presents information about the technical capabilities and project activities of the U.S. Geological Survey (USGS) Texas Water Science Center (TWSC) GIS Workgroup from 2002 through 2007.

Fact Sheet↗
USGS2009

Geographic information systems, remote sensing, and spatial analysis activities in Texas, 2008-09

Geographic information system (GIS) technology has become an important tool for scientific investigation, resource management, and environmental planning. A GIS is a computer-aided system capable of collecting, storing, analyzing, and displaying spatially referenced digital data. GIS technology is useful for analyzing a wide variety of spatial data. Remote sensing involves collecting remotely sensed data, such as satellite imagery, aerial photography, or radar images, and analyzing the data to gather information or investigate trends about the environment or the Earth's surface. Spatial analysis combines remotely sensed, thematic, statistical, quantitative, and geographical data through overlay, modeling, and other analytical techniques to investigate specific research questions. It is the combination of data formats and analysis techniques that has made GIS an essential tool in scientific investigations. This fact sheet presents information about the technical capabilities and project activities of the U.S. Geological Survey (USGS) Texas Water Science Center (TWSC) GIS Workgroup during 2008 and 2009. After a summary of GIS Workgroup capabilities, brief descriptions of activities by project at the local and national levels are presented. Projects are grouped by the fiscal year (October-September 2008 or 2009) the project ends and include overviews, project images, and Internet links to additional project information and related publications or articles.

Fact Sheet↗
USGS2024

Evaporation from the interior of Lake Okeechobee—A large freshwater lake in Florida, 2013–16

In 2012, a platform at the approximate center of Lake Okeechobee in central Florida was instrumented to continuously measure evaporation with the Bowen-ratio energy-budget method as part of a long-term partnership between the South Florida Water Management District and the U.S. Geological Survey. The primary goal for the study was to quantify daily rates of open-water evaporation. A secondary goal was to assess differences in evaporation rates among alternate methods and determine if instrumentation and operational expenses associated with the Bowen-ratio method could be reduced. Mean annual evaporation from Lake Okeechobee for 2013–16 was about 1,825 millimeters per year. Annual evaporation from 2013 to 2016 was 1,760, 1,840, 1,810, and 1,890 millimeters per year, respectively. These evaporation rates are among the highest rates observed in Florida based on scientifically vetted methods such as evaporation pans, lysimeters, eddy-covariance, or Bowen-ratio methods. The high evaporation rates are largely a result of frequent clear-sky conditions over the interior of Lake Okeechobee, which allows solar radiation to reach the water surface and drive open-water evaporation. Cloud formation over the interior of Lake Okeechobee is suppressed because of a relatively large heat capacity for water that buffers convective fluxes of air that form clouds while rising and cooling. Estimated evaporation rates obtained using five alternative methods were compared to measured Bowen-ratio energy-budget daily, monthly, and annual evaporation: the Penman, Priestly-Taylor, Mass-Transfer, Simple, and Turc equations. All five methods performed relatively well (within 10 percent of the Bowen ratio annual totals). The Penman, Priestley-Taylor, and Mass-Transfer methods captured relatively large evaporation rates that occurred in the winter due to cold fronts, because these methods account for large wind speeds and vapor pressure deficits associated with the regional cold fronts. For operational implementation, the Simple, Mass-Transfer, or Turc methods are likely preferable because of their simplicity, limited data requirements, and improved accuracy for computing monthly and annual evaporation totals. The Turc equation computed monthly evaporation within 8 percent of the Bowen-ratio method, while requiring only air temperature and solar radiation data. The Simple equation achieved similar accuracy while requiring only solar radiation data.

Florida↗
USGS2017

The planetary data system

In the early 1980s, the Space Science Board (SSB) of the National Research Council was concerned about the poor and inconsistent treatment of scientific information returned from NASA’s space science missions. The SSB formed a panel [The Committee on Data Management and Computation (CODMAC)] to assess the situation and make recommendations to NASA for improvements. The CODMAC panel issued a report [1,2] that led to a number of actions, one of which was the convening of a Planetary Data Workshop in November 1983 [3]. The key findings of that workshop were that (1) important datasets were being irretrievably lost, and (2) the use of planetary data by the wider community is constrained by inaccessibility and a lack of commonality in format and documentation. The report further stated, “Most participants felt the present system (of data archiving and access) is inadequate and immediate changes are necessary to insure retention of and access to these and future datasets.”

Lunar and Planetary Information Bulletin↗
USGS2015

Community for Data Integration 2013 Annual Report

The U.S. Geological Survey (USGS) conducts earth science to help address complex issues affecting society and the environment. In 2006, the USGS held the first Scientific Information Management Workshop to bring together staff from across the organization to discuss the data and information management issues affecting the integration and delivery of earth science research and investigate the use of “communities of practice” as mechanisms to share expertise about these issues. Out of this effort emerged the Council for Data Integration, which was conceived as an official organizational function that would help guide data integration activities and formalize communities of practice into working groups. However by 2009, it became apparent that many members of the council had an interest in developing data integration solutions and sharing expertise in a less formal grassroots perspective, thus transforming the “Council” into a “Community” for Data Integration (CDI). Today, the CDI represents a dynamic community of practice focused on advancing science data and information management and integration capabilities across the USGS and the CDI community. The CDI fosters an environment for collaboration and sharing by bringing together expertise from external partners and representatives across USGS who are involved in research, data management, and information technology. Membership is voluntary and open to USGS employees and other individuals and organizations willing to contribute to the community (if interested, contact cdi@usgs.gov). The purpose of the CDI is to advance understanding of Earth systems through enhanced use of data and information including associated tools and techniques provide a forum for people doing work with data integration to come together to share ideas as well as learn new skills and techniques, and grow overall USGS capabilities with data and information by increasing visibility of the work of many people throughout the USGS and the CDI community. To achieve these goals, the CDI operates within four applied areas: monthly forums, annual workshop/webinar series, working groups, and projects. The monthly forums, also known as the Opportunity/Challenge of the Month, provide an open dialogue to share and learn about data integration efforts or to present problems that invite the Community to offer solutions, advice, and support. Since 2010, the CDI has also sponsored annual workshops/webinar series to encourage the exchange of ideas, sharing of activities, presentations of current projects, and networking among members. Stemming from common interests, the working groups are focused on efforts to address data management and technical 2 challenges, including the development of standards and tools, improving interoperability and information infrastructure, and data preservation within USGS and its partners. The growing support for the activities of the working groups led to the CDI’s first formal request for proposals (RFP) process in 2013 to fund projects that produced tangible products. Today the CDI continues to hold an annual RFP that create data management tools and practices, collaboration tools, and training in support of data integration and delivery.

Open-File Report↗
USGS2015

Community for Data Integration 2014 annual report

The U.S. Geological Survey (USGS) researches Earth science to help address complex issues affecting society and the environment. In 2006, the USGS held the first Scientific Information Management Workshop to bring together staff from across the organization to discuss the data and information management issues affecting the integration and delivery of Earth science research and investigate the use of “communities of practice” as mechanisms to share expertise about these issues. Out of this effort emerged the Council for Data Integration, which was conceived as an official organizational function that would help guide data integration activities and formalize communities of practice into working groups; however, by 2009 it became evident that many members of the Council for Data Integration had an interest in developing data integration solutions and sharing expertise in a less formal, grassroots manner, which transformed the Council into a Community for Data Integration (CDI). As of 2014, the CDI represents a dynamic community of practice focused on advancing science data and information management and integration capabilities across the USGS and the CDI community. The CDI fosters an environment for collaboration and sharing by bringing together expertise from external partners and representatives across the USGS who are involved in research, data management, and information technology. Membership is voluntary and open to USGS employees and other individuals and organizations willing to contribute to the community (if interested, contact cdi@usgs.gov). The purpose of the CDI is to do the following: • advance understanding of Earth systems through enhanced use of data and information including associated tools and techniques, • provide a forum for people doing work with data integration to come together to share ideas and learn new skills and techniques, and • grow overall USGS capabilities with data and information by increasing visibility of the work of many people throughout the USGS and the CDI community. To achieve these goals, the CDI operates within four applied areas: monthly forums, annual workshop/webinar series, working groups, and projects. The monthly forums, also known as the Opportunity/Challenge of the Month, provide an open dialogue to share and learn about data integration efforts or to present problems that invite the community to offer solutions, advice, and support. Since 2010, the CDI has also sponsored annual workshops/webinar series to encourage the exchange of ideas, sharing of activities, presentations of current projects, and networking among members. Stemming from common interests, the working groups are focused on efforts to address data management and technical challenges including the development of standards and tools, improving interoperability and information infrastructure, and data preservation within USGS and its partners. The growing support for the activities of the working groups led to the CDI’s first formal request for proposals (RFP) process in 2013 to fund projects that produced tangible products. As of 2014, the CDI continues to hold an annual RFP that creates data management tools and practices, collaboration tools, and training in support of data integration and delivery.

Open-File Report↗
USGS2011

An approach to regional wetland digital elevation model development using a differential global positioning system and a custom-built helicopter-based surveying system

Accurate topographic data are critical to restoration science and planning for the Everglades region of South Florida, USA. They are needed to monitor and simulate water level, water depth and hydroperiod and are used in scientific research on hydrologic and biologic processes. Because large wetland environments and data acquisition challenge conventional ground-based and remotely sensed data collection methods, the United States Geological Survey (USGS) adapted a classical data collection instrument to global positioning system (GPS) and geographic information system (GIS) technologies. Data acquired with this instrument were processed using geostatistics to yield sub-water level elevation values with centimetre accuracy (±15 cm). The developed database framework, modelling philosophy and metadata protocol allow for continued, collaborative model revision and expansion, given additional elevation or other ancillary data.

Florida↗
USGS2025

Leveraging an observed-data likelihood improves the use of machine learning labels in a Bayesian hierarchical model for bioacoustic data

Classification of massive datasets by machine learning (ML) algorithms is promising for many scientific domains, especially wildlife monitoring programs that rely on passive acoustic surveys for detecting species. However, treating ML-predicted class labels (e.g., species identity) as truth biases inferences of focal parameters within common modeling frameworks. One solution is to model the misclassification process explicitly using human-validated true-class labels for a subset of observations. Validation by experts can present a substantial bottleneck in otherwise efficient workflows that use ML predictions. Bioacoustics practitioners seek guidance on both the quantity and process for selecting ML-labeled data to validate by an expert. We derive an alternative model formulation that jointly models human-validated and ML-predicted class labels with an observed-data likelihood (ODL) and use empirically informed simulations motivated by a real-data application to explore different probability designs for selecting class labels for validation. Simulation results suggest that with smaller validation sets the ODL formulation increases computational speed and reduces estimation error compared to a default MCMC data augmentation routine. Our methodology is transferable to applications that treat predictions from classification algorithms as the response variable of interest.

Annals of Applied Statistics↗
USGS2016

Missouri River Scaphirhynchus albus (pallid sturgeon) effects analysis—Integrative report 2016

The Missouri River Pallid Sturgeon Effects Analysis was designed to carry out three components of an assessment of how Missouri River management has affected, and will affect, population dynamics of endangered Scaphirhynchus albus (pallid sturgeon): (1) collection of reliable scientific information, (2) critical assessment and synthesis of available data and analyses, and (3) analysis of the effects of actions on listed species and their habitats. This report is a synthesis of the three components emphasizing development of lines of evidence relating potential future management actions to pallid sturgeon population dynamics. We address 21 working management hypotheses that emerged from an expert opinion-based filtering process. The ability to quantify linkages from abiotic changes to pallid sturgeon population dynamics is compromised by fundamental information gaps. Although a substantial foundation of pallid sturgeon science has been developed during the past 20 years, our efforts attempt to push beyond that understanding to provide predictions of how future management actions may affect pallid sturgeon responses. For some of the 21 hypotheses, lines of evidence are limited to theoretical deduction, inference from sparse empirical datasets, or expert opinion. Useful simulation models have been developed to predict the effects of management actions on survival of drifting pallid sturgeon free embryos in the Yellowstone and Upper Missouri River complex (hereafter referred to as the “upper river”), and to assess the effects of flow and channel reconfigurations on habitat availability in the Lower Missouri River, tributaries, and Mississippi River downstream of Gavins Point Dam (hereafter referred to as the “lower river”). A population model also has been developed that can be used to assess sensitivity of the population to survival of specific life stages, assess some hypotheses related to stocking decisions, and explore a limited number of management scenarios. Consideration of lines of evidence for each of the 21 hypotheses includes a discussion of how the degree of uncertainty and risk associated with each hypothesis may guide science and implementation strategies. Implementation strategies include full implementation in the field, limited implementations as field-scale experiments, or (in the case of greatest uncertainty) implementation as learning actions, including research and opportunistic experiments or field-based gradient studies. Given the substantive uncertainties associated with pallid sturgeon population dynamics and the need to continually assimilate and assess new information, we proposed that an Effects Analysis-like process should be considered an integral part of ongoing Missouri River adaptive management.

Missouri↗
USGS2020

Using the Lomb-Scargle method for wave statistics from gappy time series

Sandwich Town Neck Beach in Sandwich, MA, has experienced substantial erosion and has been the subject of efforts by the town and private landowners to limit the sand loss. Erosion has been particularly dramatic in the past five years with the loss of dwellings. Sandwich's nourishment efforts presented a unique opportunity for scientists at the U.S. Geological Survey Woods Hole Coastal and Marine Science Center to monitor beach morphology and to test new technologies and techniques such as geo-referenced drone imaging. Two bottom lander deployments were performed in Cape Cod Bay at a location that was key to model the fate of waves at Sandwich Town Neck Beach and to support the study of beach morphological evolution. The study period was after the town nourished the beach and during a time when several intense winter storms reshaped the beach and removed much of the nourished sand. A TRDI Workhorse Sentinel V ADCP was used for both deployments. For wave bursts, the instruments collected 2048 samples at 2 Hz every hour. The first deployment during the winter of 2016 returned good quality data. The second deployment during the following winter had gaps throughout the time series from a wiring problem in the external battery pack. The timing of the gaps was random, the duration approximately 100 s. While most of the bursts started at the top of each hour, many had 1-3 gaps within. Time series data with random gaps are problematic for computing spectral density, and thus, wave statistics. This kind of situation is familiar in other scientific disciplines such as astrophysics [1], where techniques exist to find stationary signals in sparse data. One of these methods is the Lomb-Scargle technique for computing periodograms. The most useful feature of the Lomb-Scargle (LS) method is that it allows the spectral analysis of incomplete records, without having to manipulate the record to extrapolate from or replace missing data. We compared the effectiveness of LS against common methods of averaging Fourier transforms such as a simple un-windowed Fast Fourier transform (FFT), Welch's method, and TRDI's Wavesmon software; methods that are commonly used in oceanography for non-gappy data. Synthetic data series that have been artificially modified to introduce gaps were used to evaluate the performance of each method. The LS approach was able to recover spectral density even with several 100-s gaps present. The method was applied here to the gappy and non-gappy data from both Sandwich deployments, and wave statistics were obtained and compared to the wave-buoy data. LS was used to process data that contains gaps that was rejected by Wavesmon, which was approximately 39% of the dataset. Significant wave height and peak period from LS compared well with buoy data. Mean period computed on gappy data using LS produced values biased low, compared with other methods when gaps were filled with the mean value. The LS technique has potential to uncover low-frequency signals such as infragravity waves from gappy records where the non-gappy segments are not long enough to resolve them. It has potential to unlock new information from older data sets.

Massachusetts↗
USGS1994

A guide to the design of surface-water-quality studies

Data interpretation is difficult under the simplest of conditions and requires hydrologic studies that are carefully designed. Good research presupposes carefully collected data, as well as data that were obtained at times when the information content was highest. To accomplish the careful design of hydrologic studies and to obtain quality data, the U.S. Geological Survey works through small organizational units. The principles of a well-designed study are: 1) Use the scientific method, 2) define the problem and the approach, 3) state the objectives of the data collection, 4) determine what and how to sample, 5) decide where to sample, 6) begin report preparation early in the study, 7) do the work with little or no rework, and 8) complete the report on schedule.

Open-File Report↗
USGS2018

Department of the Interior metadata implementation guide—Framework for developing the metadata component for data resource management

The Department of the Interior (DOI) is a Federal agency with over 90,000 employees across 10 bureaus and 8 agency offices. Its primary mission is to protect and manage the Nation’s natural resources and cultural heritage; provide scientific and other information about those resources; and honor its trust responsibilities or special commitments to American Indians, Alaska Natives, and affiliated island communities. Data and information are critical in day-to-day operational decision making and scientific research. DOI is committed to creating, documenting, managing, and sharing high-quality data and metadata in and across its various programs that support its mission. Documenting data through metadata is essential in realizing the value of data as an enterprise asset. The completeness, consistency, and timeliness of metadata affect users’ ability to search for and discover the most relevant data for the intended purpose; and facilitates the interoperability and usability of these data among DOI bureaus and offices. Fully documented metadata describe data usability, quality, accuracy, provenance, and meaning. Across DOI, there are different maturity levels and phases of information and metadata management implementations. The Department has organized a committee consisting of bureau-level points-of-contacts to collaborate on the development of more consistent, standardized, and more effective metadata management practices and guidance to support this shared mission and the information needs of the Department. DOI’s metadata implementation plans establish key roles and responsibilities associated with metadata management processes, procedures, and a series of actions defined in three major metadata implementation phases including: (1) Getting started—Planning Phase, (2) Implementing and Maintaining Operational Metadata Management Phase, and (3) the Next Steps towards Improving Metadata Management Phase. DOI’s phased approach for metadata management addresses some of the major data and metadata management challenges that exist across the diverse missions of the bureaus and offices. All employees who create, modify, or use data are involved with data and metadata management. Identifying, establishing, and formalizing the roles and responsibilities associated with metadata management are key to institutionalizing a framework of best practices, methodologies, processes, and common approaches throughout all levels of the organization; these are the foundation for effective data resource management. For executives and managers, metadata management strengthens their overarching views of data assets, holdings, and data interoperability; and clarifies how metadata management can help accelerate the compliance of multiple policy mandates. For employees, data stewards, and data professionals, formalized metadata management will help with the consistency of definitions, and approaches addressing data discoverability, data quality, and data lineage. In addition to data professionals and others associated with information technology; data stewards and program subject matter experts take on important metadata management roles and responsibilities as data flow through their respective business and science-related workflows. The responsibilities of establishing, practicing, and governing the actions associated with their specific metadata management roles are critical to successful metadata implementation.

Techniques and Methods↗
USGS2017

Acoustic telemetry and fisheries management

This paper reviews the use of acoustic telemetry as a tool for addressing issues in fisheries management, and serves as the lead to the special Feature Issue of Ecological Applications titled “Acoustic Telemetry and Fisheries Management” . Specifically, we provide an overview of the ways in which acoustic telemetry can be used to inform issues central to the ecology, conservation, and management of exploited and/or imperiled fish species. Despite great strides in this area in recent years, there are comparatively few examples where data have been applied directly to influence fisheries management and policy. We review the literature on this issue, identify the strengths and weaknesses of work done to date, and highlight knowledge gaps and difficulties in applying empirical fish telemetry studies to fisheries policy and practice. We then highlight the key areas of management and policy addressed, as well as the challenges that needed to be overcome to do this. We conclude with a set of recommendations about how researchers can, in consultation with stock assessment scientists and managers, formulate testable scientific questions to address and design future studies to generate data that can be used in a meaningful way by fisheries management and conservation practitioners. We also urge the involvement of relevant stakeholders (managers, fishers, conservation societies, etc.) early on in the process (i.e. in the co-creation of research projects), so that all priority questions and issues can be addressed effectively.

Ecological Applications↗
USGS2018

Do we know how much fluvial sediment reaches the sea? Decreased river monitoring of U.S. coastal rivers

Given the present and future changing climate and human changes to land use and river control, river sediment fluxes to coastal systems are changing and will continue to change in the future. To delineate these changes and their effects, it is increasingly important to document the fluxes of river-borne sediment discharged to the sea. Unfortunately, broad-scale river sediment monitoring programs established more than 50 years ago in the U.S. have diminished substantially and now focus principally on the largest rivers and estuaries. Unless addressed, these data gaps will provide significant challenges in addressing fundamental scientific and management questions about the effects of climate change and sea-level rise in our estuaries and on our coasts.

Hydrological Processes↗
USGS1989

United States National seismograph network

The USGS National Earthquake Information Center (NEIC) has planned and is developing a broadband digital seismograph network for the United States. The network will consist of approximately 150 seismograph stations distributed across the contiguous 48 states and across Alaska, Hawaii, Puerto Rico and the Virgin Islands. Data transmission will be via two-way satellite telemetry from the network sites to a central recording facility at the NEIC in Golden, Colorado. The design goal for the network is the on-scale recording by at least five well-distributed stations of any seismic event of magnitude 2.5 or greater in all areas of the United States except possibly part of Alaska. All event data from the network will be distributed to the scientific community on compact disc with read-only memory (CD-ROM).

Puerto Rico, U.S. Virgin Islands↗
USGS2019

The U. S. Geological Survey National Hydrologic Model infrastructure: Rationale, description, and application of a watershed-scale model for the conterminous United States

The ability to effectively manage water resources to meet present and future human and environmental needs is essential. Such an ability necessitates a comprehensive understanding of hydrologic processes that affect streamflow at a watershed scale. In the United States, water-resources management at scales ranging from local to national can benefit from a nationally consistent, process-based watershed modeling capability to provide the requisite understanding. The National Hydrologic Model (NHM) infrastructure, which was developed by the U.S. Geological Survey to support coordinated, comprehensive, and consistent hydrologic modeling at multiple scales for the conterminous United States, provides this essential capability. NHM-based applications provide information to enable more effective water-resources planning and management, fill knowledge gaps in ungaged areas, and support basic scientific inquiry. In the future, as process algorithms and data sets improve, the NHM infrastructure will continue to evolve to better support the nation's water-resources research and management needs.

Conterminous United States↗
USGS2012

The 2010 explosive eruption of Java's Merapi volcano—A ‘100-year’ event

Merapi volcano (Indonesia) is one of the most active and hazardous volcanoes in the world. It is known for frequent small to moderate eruptions, pyroclastic flows produced by lava dome collapse, and the large population settled on and around the flanks of the volcano that is at risk. Its usual behavior for the last decades abruptly changed in late October and early November 2010, when the volcano produced its largest and most explosive eruptions in more than a century, displacing at least a third of a million people, and claiming nearly 400 lives. Despite the challenges involved in forecasting this ‘hundred year eruption’, we show that the magnitude of precursory signals (seismicity, ground deformation, gas emissions) was proportional to the large size and intensity of the eruption. In addition and for the first time, near-real-time satellite radar imagery played an equal role with seismic, geodetic, and gas observations in monitoring eruptive activity during a major volcanic crisis. The Indonesian Center of Volcanology and Geological Hazard Mitigation (CVGHM) issued timely forecasts of the magnitude of the eruption phases, saving 10,000–20,000 lives. In addition to reporting on aspects of the crisis management, we report the first synthesis of scientific observations of the eruption. Our monitoring and petrologic data show that the 2010 eruption was fed by rapid ascent of magma from depths ranging from 5 to 30 km. Magma reached the surface with variable gas content resulting in alternating explosive and rapid effusive eruptions, and released a total of ~ 0.44 Tg of SO 2 . The eruptive behavior seems also related to the seismicity along a tectonic fault more than 40 km from the volcano, highlighting both the complex stress pattern of the Merapi region of Java and the role of magmatic pressurization in activating regional faults. We suggest a dynamic triggering of the main explosions on 3 and 4 November by the passing seismic waves generated by regional earthquakes on these days.

Merapi volcano↗
USGS2011

Doctorate time rising sharply: How long should it take?

The period 1967–1987 saw a 20% increase in the time it takes to complete the doctorate. That figure is based on data provided by the National Research Council from 11 scientific and engineering fields, including the Earth, atmospheric and marine sciences. The median time spent earning the degree rose from 5.4 years in 1967 to 6.1 years in 1977 to 6.9 years in 1987—an increase of 1.5 years in a single generation. Completion time still seems to be rising! All graduating doctoral students in 1987 had a median age of 33.6 years; chemistry students were youngest at 29, and education students were the oldsters at 39.8 years.

Eos, Transactions, American Geophysical Union↗