In municipal construction, to avoid casualty and property loss the buried pipelines beneath the construction site should be accuracy located. There are many locating method such as magnetic method, electromagnetic method, infrared thermography, ground penetrating radar, acoustic location system etc. All these methods mentioned above can only make an acceptable result at the depth down to 5 m. In this study, improved frequency domain electromagnetic method (FDEM) is used to locate deep buried metal pipeline (>10 m). In this method, the mathematic model is rebuilt and special probe with the diameter of 20mm is designed. It was conduct by two steps. First, Using the regular FDEM method to estimate the approximately depth and horizontal location. Then, the electromagnetic field induced by metal pipeline is measured by the probe in the borehole drilled within 5 m away from the projection of pipeline on the ground and the full curve inversion method is used to give the accuracy depth and planimetric position. Result show that the improved FDEM method can detect the deep buried (>10 m) pipeline with the relative accuracy of and planimetric position with the relative accuracy of . With the advantage of high accuracy and trenchless, it can improve efficiency by replacing excavation verification in the buried pipeline on site investigation. Regular FDEM method usually locates the underground metal pipeline by measuring the eigenvalue of specific frequency electromagnetic characteristic curve upon the vertical direction. Improved FDEM method combine the eigenvalue method and full curve inversion method. Furthermore by detecting electromagnetic field in the borehole the detection line is closer to the pipeline and the interference is less which leads to a high S/N. That why the improved FDEM have a high accuracy in deep buried metal pipeline locating. On the other hand, the probe is designed to well match the current instrument, so it need not to add special receiver which make the method more economical to apply. The location without error of deep buried pipeline especially the deep buried oil and gas metal pipeline is vital. The improved FDEM could provide an effective way in locating the deep buried metal pipeline.

Measuring the effects of education and communication space science activities is a challenge due to different audiences, formats, dosage, and objectives. What claims can be made about the effect of a collection of different activities? What methods and measures can evaluators provide to activity managers to collect data that can be aggregated across activities? A summative evaluation approach was devised based on the National Science Foundation Framework impact categories of behavior, attitudes, skills, interest/engagement, and knowledge (BASIK), “Identification of these categories was based on analysis of project impacts from a comprehensive review carried out on a representative sample of Informal Science Education proposals, final reports, and summative evaluations” ((Friedman, 2008, p. 11). Measurable objectives were developed for each of the 53 activities offered by the 21 institutions with specific identification of the intended impact categories. Methods and measures were then complied to evaluate the effect of those activities on their audiences’ behaviors, attitudes, skills, interest and/or knowledge. For the knowledge impact, the space science concepts were identified for each activity from the Science Literacy Strandmaps (http://nasawavelength.org/strandmaps). This provided a portfolio overview by concept and level, as well as concept. Methods and measures were identified by impact category to provide data across activity on effects. Results from evaluations of projects are reported in an online portfolio by institutions and summarized across projects for annual reporting.

The recent severe “Day Zero” drought (2015-2017) over the winter rainfall zone (WRZ) of South Africa has highlighted low-frequency winter climate variability, possible trends and our generally poor understanding of their mechanisms. We investigate the contribution of dynamic conditions and moisture transport to daily station rainfall events in the WRZ and the relationship between the frequency of such states, seasonal to inter-decadal rainfall variability and hemispheric modes of Westerly Wave variability. Dynamic conditions are assessed using reanalysis data over the South-East Atlantic during the austral winter half-year (AMJJASO) from 1979-2017. A self-organising map (SOM) analysis is performed on 500-hPa geopotential height. Nodes indicating strong troughs and ridges in the Westerly Wave are identified using either rainfall or dynamic (divergence and vorticity) criteria. The two approaches produce similar results; most rainfall (around 80%) occurs on days mapped to trough nodes (35% of days). The trough nodes are subjected to a multi-dimensional SOM analysis to identify conditions leading to variations of rainfall within the original SOM nodes. Nodes showing intense (high divergence and vorticity west of South Africa) troughs extending equatorward of the WRZ (14% of all winter days) account for around 60% of trough rainfall. Cut-off lows (COLs) are independently identified as closed, cold-cored lows at the 500-hPa-level and their contribution to precipitation is assessed separately. COLs are detected on approximately 3% of all winter time steps, contributing only about 11% of the total rainfall, although they account for almost all heavy rainfall events not associated with intense troughs. During the Day Zero drought, the frequency of almost all trough nodes decreased, especially in the shoulder seasons, while ridge nodes occurred 1.5-2 times more frequently and persisted for longer, especially in late autumn. Average rainfall per trough node was lower and COL frequency reduced. The only SOM nodes showing significant trend over 1979-2017 are ridge nodes associated with large anticyclonic vorticity anomalies south of the WRZ. Correlation between the Southern Annular Mode and ridge/trough nodes is weak. We conclude that the Day Zero drought resulted from fewer mobile troughs passing the WRZ in the shoulder seasons, possibly linked to a multi-decadal increase in blocking high frequency.

Globally, the scarcity of water makes it necessary to look for alternative practices to increase sustainability in agricultural systems. In the US, Arkansas is the largest rice producer with a great water demand, which contributes to the decline of the state’s groundwater resources. Different practices are proposed to decrease the on-farm water use, and hence decrease both greenhouse gas (GHG) emissions associated with flooded periods and the use of energy to pump water. In this work, five water-saving practices are evaluated: Alternate Wetting and Drying, Row Cropping, Land Levelling, Multiple Inlet Rice Irrigation with Polypipe, and the use of new hybrids. Over the last years, due to the climate change concern, a number of GHG calculators have been developed to assess agricultural practices. These tools are designed to have a user-friendly interface, and they are based on IPCC emission factors and both process-based and empirical models. Cool Farm Tool, that provides an estimate of CO2eq emissions per unit of area or grain yield, was identified in one study as the highest-rated tool available in the public domain for single crop assessment. Fieldprint calculator provides a more extensive data output, reporting the impact of agronomic practices in five resource areas, including GHG and irrigation water use. The comparison of one farm’s impact against the state and country average are additionally provided. Comet-Farm, another of these tools, can compare actual farm practices with alternative future scenarios. Therefore, one of the challenges when using these tools is the comparison between them, mainly due to differences in scope, calculation methods, and reporting units. In the present work, we aim to assess water and GHG emissions saving potential of the five experimented water¬¬-saving practices by outputs of the three user-friendly calculators. The ease of use, similarities, and differences between these tools will be evaluated. Ten rice fields with different water-saving techniques are under our observation in Eastern Arkansas. Data collected from sensors installed in the fields and inquiries from farmers will be used as inputs for these tools. This work aims to advise farmers on the implementation of these practices with the final aim to encourage statewide adaptation of water-saving incentive strategies.

Advancements in sensing technology have sparked a new age of data acquisition and transmission that continue to change the way we understand the world around us. In earth science, we often must move and store tremendous amounts of data from remote locations. Present options are limited to costly propriety devices, which are rigid in structure and have numerous expenses associated with their use. The solution developed in the Openly Published Environmental Sensing Lab (OPEnS) at Oregon State University, was to employ a new methodology using low-power, open-source hardware, and software, to achieve near-real-time data logging from the field to the web. This new approach simultaneously lowers the cost of experimentation and data collection and breaks down traditional technical barriers. Data can be collected remotely from nearly anywhere on Earth using a decentralized OPEnS Hub which can utilize a host of low bandwidth transmission protocols and modes of communication, such as: 900 MHz Long Range Radio (LoRa) with a transmission distance of up to 25 km, the Global System for Mobile communications (GSM) using well established cell network infrastructure, Wi-Fi for high bandwidth applications, and Ethernet where LAN connections are available. It is notable that LoRa technology is still developing and has been expanded to transmit to an ever-growing constellation of satellites, making this technology truly global in its applicability. The OPEnS-Hub is capable of mesh networking with other nodes and will parse and back up the data to an onboard microSD card. By first exploiting a free open-sourced Application Programming Interface (API), PushingBox, acting as a data broker, and secondly, a customized Google App script, the OPEnS-Hub was able to achieve a dynamic, low latency portal connecting to google sheets. These methods working in tandem allowed for near real-time data logging of over a dozen devices each with unique sensor suites to form valuable time series data. This poster details our methods and evaluates the application and development of PushingBox’s API, Google App Script, Adafruit’s open-hardware Feather development boards, the Hypertext Transfer Protocol (HTTP) and various modes of data communication used to collect nearly half a million data points dispersed across remotes sites in the state of Oregon to date.

The increased deforestation and urbanization of Earth’s surface changes how the soil system interacts with plants and understanding this relationship is vital in this time of climate change. Yet, how soil affects carbon and water use efficiency in plants is poorly understood. Studies show strong relationships between soil nutrient and vegetation carbon use efficiency (CUE) and water use efficiency (WUE) for different ecosystems; however, studies rarely have examined the combined effects of soil characteristics and nutrients on CUE and WUE. This study use both soil profile and satellite data to explore the role soil properties play in regulating water and carbon use by plants. Site and satellite multispectral and hyperspectral data are collected from 30 Kentucky terrestrial ecosystem sites and used to investigate the relationship between spectral reflectance and physical and chemical properties of soil. Results show strong relationship between CUE and phosphorus, soil organic carbon, and iron in the C-horizon for forests. On the contrary, a negative relationship was observed between CUE and SOC in the A-horizon for herbaceous biomes. Also, results show a strong relationship between δ13C and CUE for the forest sites. These types of data are timely and critical for accurate predictions of how terrestrial ecosystems will respond to climate change.

Isolating significant signals from temporal variations in autotrophic and heterotrophic components of ecosystem respiration (Reco) is required to better quantify the temperature sensitivity of the land carbon cycle processes. Here we present diurnal and seasonal patterns in field and model-based components of respiration and investigate their responses to environmental conditions at a dry eucalypt forest, the Cumberland Plain SuperSite of the Australian Terrestrial Ecosystem Research Network. We conducted measurement campaigns of total CO2 flux from the soil surface (Rsoil), soil microbial respiration (Rmicrobe), root respiration (Rroot), litter respiration (Rlitter), and stem respiration (Rstem) in 2018. In total, six infrared gas analyzers with closed, dynamic auto-chambers and six forced diffusion auto-chambers were used for periodic campaigns. Further, Reco and its components were simulated using the Community Atmosphere-Biosphere Land Exchange model (CABLE), constrained by eddy covariance measurements and chamber measurements of Rsoil. A new version of CABLE was implemented with the Dual Arrhenius Michaelis Menten (DAMM) formulation to assess the importance of substrate availability for simulating Rmicrobe. We found that respiration rates showed similar diurnal patterns among the components, showing diurnal hysteresis between respiration components and temperature. In this dry ecosystem, the respiratory components were more responsive to seasonally increasing temperature in wet than in dry periods, and the responses were dependent on atmospheric relative humidity affecting the litter layer moisture content. The temperature sensitivity was significantly higher in Rstem than in other components. Based on observed fluxes of Rmicrobe in trenched plots and Rsoil in intact soil plots, the mean contribution of Rroot to Rsoil was less than 20 % for the dry seasons, while mean Rstem was two times greater than mean Rsoil suggesting that Rstem should be not overlooked in ecosystem flux estimations. This study highlights that partitioning the respiratory components and accounting for their different temperature-responses will be necessary to reduce uncertainty in modelling carbon-climate feedbacks.

Geopressure (or pore pressure) prediction is of central importance in both the exploration and development of hydrocarbon reservoirs. For pore fluid pressure affects the physical properties of reservoir rocks, predicted pressure is a key input when building the geomechanical model of a reservoir. Overpressure also influences the distribution of hydrocarbon, and sometimes can even work as an effective seal. Predrill pore pressure data in depth can help prevent geo-hazards like kicks, blowouts and drilling fluid infiltrating the formation whiling drilling in overpressured formations. pyGeoPressure provides a set of open-source tools to perform the geopressure prediction workflow which involves data preprocessing, parameter optimization, pressure prediction and quality control. Pore pressure can be predicted using well log data or seismic velocity data. Both of these two kinds of predictions are supported in pyGeoPressure. In addition to standard methods of Eaton’s and Bowers’, a new multivariate prediction method is also implemented in pyGeoPressure which incorporates petrophysical properties like porosity and shale volume other than sonic velocity. Another set of functionalities that pyGeoPressure provides are generating graphs. It can generate slices and sections of predicted pressure cube and well log predicted pressure profiles, both of which are of publication ready standard. pyGeoPressure is designed with flexibility and portability in mind. pyGeoPressure provides a flexible survey management system based on a clear folder structure, in which adding new well or seismic data cube can simply be achieved by adding a json file with required information. The basic numerical type used in computation under the hood is numpy array, so it can work together with scientific computation tools within python ecosystem. pyGeoPressure provides an open-source solution to geopressure prediction and a framework upon which researchers and engineers can quickly test and implement new prediction ideas. In this poster, we summarize the key components of pyGeoPressure and present a prediction workflow using data from East China Sea to showcase its functionalities.

Subsurface remediation of groundwater and soils contaminated by chlorinated ethenes is a challenge as many sites remain with contaminant concentrations above regulatory limits. One issue that prevents site restoration is back diffusion of contaminants from low permeable layers into transmissive layers, which sustains contaminant plumes. In our research, we develop a two-dimensional analytical model of an aquifer-aquitard system which employs temporally variable boundary conditions to investigate the sensitivity of mass discharge across a downgradient control plane (DGCP) to both the mass discharge behavior from dense nonaqueous phase liquid source zones (i.e., the source-strength function or SSF) and contaminant mass stored in the aquitard. Existing models usually assume constant concentration boundary conditions which are not always representative of actual field conditions, especially when those boundary conditions are meant to represent the SSF. Modeling results are used to explore aquitard source functions, defined as the relationship between mass discharge across the DGCP and mass stored in the upgradient aquitard. We also explore the benefits associated with partial removal of DNAPL mass from the source zone, as well as the impacts of when those efforts occur in the lifetime of the source zone by testing the sensitivity of mass stored in the aquitard and mass discharge across a DGCP to different remediation scenarios. In instances where complete remediation is impractical, it is important for remediation managers (and other stakeholders) to know the options and expected results for partial remediation in the short term. Moreover, results are used to assess the relative importance of addressing contaminant mass in the DNAPL source zone versus the aquitard as the contaminant site ages. Finally, we explore a methodology for estimating contaminant mass in the aquitard with time, as a function of the SSF and mass discharge across a DGCP. This methodology complements site characterization efforts, especially at complex sites where detailed sampling of low permeable layers is a challenge.

VirES is a Virtual workspace for Earth-observation Scientists, a service provided by the European Space Agency (ESA). VirES has firstly been established for ESA’s magnetic field mission Swarm as “VirES for Swarm’‘ and has been extended to ESA’s atmospheric dynamics mission Aeolus, which was launched in August 2018. The service is developed by the Austrian IT company EOX in strong collaboration with missions’ scientists. VirES is a web-based service (https://aeolus.services) that enables scientists to discover, visualize, select and download data of Earth-observation (EO) missions through an easy to operate graphical user interface. ”VirES for Aeolus” will provide access to Aeolus L1B, L2A, L2B, L2C products and auxiliary data. The first version 1.0 passed acceptance tests in April 2018 and developments towards Version 1.2 are in progress. The service is planned to be accessible for public use as soon as the mission’s phase E1 is completed and first data products are released by ESA.

We employ a multi scale approach combining experiments and modelling to elucidate the impacts of small scale (sub-seismic resolution <10m) capillary pressure heterogeneities in field scale CO2 flow and trapping. We analyse 48 rock cores (~3cm length, 4cm diameter) covering the entire 100m interval of the Captain D sandstone in the Goldeneye field, UK North Sea. We experimentally measure porosity, capillary pressure, absolute permeability, relative permeability and trapping characteristics for the cores, which are used to create 3D numerical models with heterogeneities defined at the mm scale. These models are validated by predicting experimental X-Ray CT observations of saturation (mm scale) and pressure measurements (cm scale) at various flow rates and fractional flows of gas-water. The intrinsic, core scale properties are then used to populate 2D meso scale numerical simulations (50m x 10m size), with heterogeneities defined at cm scale using a geostatistical representation. We vary the correlation length and variance of the fields within the bounds of the experimental observations to investigate the impacts of small scale heterogeneity on vertical and lateral CO2 plume migration under different Capillary, Bond and Gravity numbers. At low flow potential, layered capillary pressure heterogeneities can speed up lateral plume migration by up to 20%, with gravitational segregation significantly enhancing the migration (See Fig. 1). In the vertical case, layered heterogeneities can significantly increase CO2 trapping, which is further enhanced with the inclusion of capillary pressure hysteresis. Finally, we derive capillary limit, upscaled equivalent properties from the meso scale simulations, which incorporate the impacts of small scale heterogeneity. These are used to represent the grid block properties in a full field 3D numerical model of the Goldeneye field (lateral ~5 km, depth 200m). We analyse the impact of varying relative permeabilities (with anisotropy) on the field scale plume migration and trapping. We see large differences compared to cases using intrinsic rock properties, indicating that proper inclusion of small scale heterogeneity is needed in upscaling workflows when predicting and assessing uncertainty in low flow potential CO2 plume migration at the field scale.

Our study uses field training data, airborne LiDAR (Light Detection and Ranging), imaging spectroscopy, and a Convolutional Neural Network (CNN) classifier to identify individual tree species in a mixed conifer forest in the Southern Sierra Nevada Mountains. The remote sensing data was collected on the National Ecological Observatory Network (NEON) Airborne Observation Platform in 2017. We trained the classifier using existing field plot data, and an independently collected validation dataset which identifies trees location of the 7 dominant species (Pine, Fir, Cedar and Oak), including condition and ‘live’ or ‘dead’ status. The LiDAR canopy height model was used to identify tree crowns and imaging spectroscopy data around these crowns were created as image ‘labels’. These species level ‘labels’ were used to train, test and validate a CNN tree species classifier. Our method achieved greater than 63-90% accuracy for all field validated stems and worked best for large diameter trees. On an independent tree stem dataset, we performed a species-level logistic regression to study which cases the classifier works most and least effectively. Spatially, in the southern areas scattered Black Oak were present and tree species often were confused with shrub species or covered by adjacent conifer species. In the north where the upper elevation forest is dominated by red and white fir the classifier achieved greater than 96% accuracy for larger canopy trees, with accuracy degrading to about 59-70% when smaller trees are included in the model. There is also genus level mis-classification, particularly between Red and White Fir species. There was high tree mortality in this forest and the classifier was effective in detecting large tree mortality, which also varies as a function of species and size of trees. This work leverages newly developed ‘deep learning’ tools which have yet to be extensively applied to the remote detection of large trees or in plant biogeography generally. This research is a proof-of-concept for forest community ecologists who want accurate ‘tree species maps’ to study how plants are distributed across space. Generally, this method will be of interest to biogeographers or remote sensing scientists looking to apply novel classification methods to problems beyond remote tree species identification.

This paper presents a novel approach and algorithm to the problem of magnetic field interference cancellation of time-varying interference using distributed magnetometers and spacecraft telemetry with particular emphasis on the constrained computational and power requirements of CubeSats. The traditional approach to enable space-based low-amplitude and low-noise magnetometry is to develop a spacecraft magnetic cleanliness design and place the magnetometer sensor at the end of a boom far enough away from the bus to minimize remaining stray magnetic fields. In addition, secondary magnetometers are often placed partway along the boom to apply magnetic field gradiometry to clean the data further (e.g., NASA MMS has 8 meter booms with a sensor half-way down and another at the end). We employ a different approach taking advantage of low-cost chip-based magnetometers that can be placed throughout the satellite bus instead of utilizing a boom. The spacecraft magnetic field interference cancellation problem that we solve involves estimation of noise when the number of interfering sources far exceeds the number of sensors required to decouple the noise from the signal. The proposed approach models this as a contextual bandit learning problem and the proposed algorithm learns to identify the optimal low-noise combination of distributed magnetometers based on indirect information gained on spacecraft currents through telemetry. The algorithmic behaviors are tested with synthetically modeled spacecraft data and on real world data generated in a lab-based setting with telemetry and currents collected from the GRIFEX CubeSat and provides a way for accurate magnetic field measurements with CubeSats.

Magnetotelluric exploration is one of geophysical methods for investigating the Earth’s deep interior. This method measures natural geoelectric and geomagnetic field values. Impedance is expressed by the relation between the measured electric fields and magnetic fields, and it is used to estimate the electromagnetic properties of the Earth’s deep interior. In the early stage, the geophysical responses at receiver was simply considered as responses from one-dimensional structure and the impedance was used in the form of scalar. As we consider three-dimensional structure nowadays, the impedance is mainly used in the form of matrix. Each component of the matrix is determined by various factors such as underground structure and surrounding environment. It is very important to know how these components reflect those factors since the components of the impedance are the basic information to estimate the interior of the earth. In this study, we focus on the topographic effects and correction with three-dimensional earth model for Gyeongju sites. As the Gyeongju area is surrounded by several mountains and faults, it has highly complicate topographies. However, the conventional topographic correction is not perfect for complex area because it is calculated in homogeneous earth model. In this study, we use the finite element methods using hexahedral and tetrahedral elements to examine the topographic correction method in complex topographies and three-dimensional anomalies. ACKNOWLEDGMENTS This work was supported by the Nuclear Safety Research Program through the Korea Foundation Of Nuclear Safety(KoFONS), granted financial resource from the Nuclear Safety and Security Commission(NSSC), Republic of Korea (No.1705010) and Korea Institute of Energy Technology Evaluation and Planning(KETEP) and the Ministry of Trade, Industry & Energy(MOTIE) of the Republic of Korea (No. 20168510030830).

EarthResourceML (http://www.earthresourceml.org) is the international data model and standard for mineral resources data. EarthResourceML (ERML) was initially developed for the Australian Chief Government Geologists Committee (CGGC) but is now under the governance of the Commission for Geoscience Information (CGI), a commission of the International Union of Geological Sciences (IUGS). ERML/INSPIRE MR data models are the preferred standard for mineral resources data sharing initiatives and projects, such as the European Union’s INSPIRE directive, and EU-projects (Minerals4EU, ProSUM) and the Australian AuScope, and Geoscience Portal projects. The current version of ERML v2.0 was released in August 2014 and ERML v3.0 will be published 2018. Current ERML v.2.0 and INSPIRE Mineral Resource data models are practically identical. The main elements of the ERML/INSPIRE models cover mineral occurrences, mines, and mining activity. The standard describes the geological characteristics and settings of mineral occurrences, their contained commodities, and their mineral reserve, resource and endowment. It is also able to describe mineral exploration, mines and mining activities, processing/transformation activities, with the production of concentrates and refined products, and waste material characterization. ERML/INSPIRE utilises the GeoSciML v4.1 Mapped Feature model to describe spatial representations of mineral occurrences and mines, and the GeoSciML Earth Material model to describe host- and associated materials. ERML Lite v. 1.0 version was accepted and released in August 2016. The new version 2.0 of ERML Lite was published June 2018. ERML Lite 2.0 delivers a user-friendly designed and simplified flat view of key elements of the full ERML/INSPIRE data models. The CGI Geoscience Terminology Working Group (http://resource.geosciml.org/def/voc/) and INSPIRE code list register (http://inspire.ec.europa.eu/codelist/) provides a range of standard vocabularies that can be used to populate ERML/INSPIRE data services. ERML Lite test service will be demonstrated at the AGU meeting at Onegeology portal and data providers are from Oceania (AUSGIN and New Zealand), Europe (Minerals4EU, FODD, and Finland) and Arctic (60°- 90°) data (Nordic Countries, Russia, Alaska, and Canada). The ERML and ERML Lite data models enable comparison of mineral resource information from different jurisdictions. With increasing participation from geological surveys these data model will assist global resources estimates and exploration targeting.

Shallow soil temperatures influence ecosystem carbon cycling and the temperature of deeper soil layers where permafrost thaw occurs. Vegetation affects shallow soil temperatures via impacts on the surface energy balance, hydrology, and soil characteristics. Vegetation may influence the degree to which soil temperatures rise with increases in air temperature under global climate change. However, variability in vegetative influences on soil temperature are not well quantified at a pan-Arctic scale. We compiled data from 235 sites across high latitude permafrost regions to examine the influence of air temperatures and vegetation on shallow soil temperatures. Annual thermal regimes varied across vegetation types characterized by dominant plant functional cover. The overall magnitude of soil temperature maxima and minima varied across plant functional types. Ecosystems dominated by vegetation with tall statured canopies such as evergreen needleleaf boreal forests and tall shrub tundra tended to have warmer soil warmer temperatures than tundra sites dominated short statured vegetation such as graminoid or short shrub species. Soil temperatures increased with air temperature similarly within tundra vegetation types, and rising air temperatures from global climate change may result in similar increases in soil temperatures regardless of vegetation type in the tundra. Soil temperatures were highly variable and decoupled from air temperature in boreal forest sites.

In 2013, the Office of Science and Technology Policy (OSTP) issued a memorandum directing Federal agencies with over $100 million in annual research and development expenditures to develop a plan to support increased access to federally funded research results. In response, the US Geological Survey developed a Public Access Plan and published four new data management policies. The policies focus on review and approval of scientific data supporting scholarly conclusions, requirements for metadata, preservation, and data management planning. The new policies, in conjunction with the Public Access Plan, represent a shift in culture in how the USGS manages and provides access to its science data. The USGS recognizes that successful implementation of these new policies requires multiple pillars of support, from USGS leadership and staff buy-in, to effective tools. Active community engagement in the Bureau is stimulated through the Community for Data Integration (CDI), an open forum for community discussion and engagement, and an important component creating buy-in and contributing to the success of the new policies. Also critical are a suite of tools available to scientists to ensure their ability to implement the policies. Finally, support from leadership that manifests in the Fundamental Science Practices Advisory Council (FSPAC), a committee of representatives from across the Bureau who preside over policies and guidance is a critical component. While far from complete, the USGS has shifted its approach to science data management by engaging the community, offering tools to support policy, and providing leadership support for the quality and scientific integrity of USGS science data.

NOAA National Centers for Environmental Information (NCEI) hosts and provides access to one of the most significant archives on Earth, with comprehensive oceanic, atmospheric, and geophysical data. From the depths of the ocean to the surface of the sun and from million-year-old ice core records to near-real-time satellite images, NCEI is the Nation’s leading authority for environmental information. These data are impressively heterogeneous in terms of platforms, instruments, formats, and user communities. As a federal agency, NCEI must follow laws, mandates, guidance, best practices, and national and international standards. This presentation will showcase the policies that support the framework of NCEI’s data preservation and stewardship mission with a look toward current gaps and future needs for data policies.