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.

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.

Fire emissions are a major contributor to atmospheric composition, affecting atmospheric oxidizing capacity and air quality. Transported amounts from Northern Hemisphere boreal fires can reach the pristine Arctic atmosphere as well as impact air quality in populated regions. Carbon monoxide (CO) is a useful trace gas emitted from fires that can be used to link extreme fire events with climate variability. We use our recently developed statistical tool to investigate the climate drivers of satellite measured CO variability in two Northern Hemisphere boreal fire regions: northwest Canada and Siberia. Our focus is on quantifying the ability of climate mode indices for the Pacific, Atlantic, Indian and Arctic Oceans in predicting CO amounts in these regions. Climate mode indices El Niño Southern Oscillation (ENSO), Tropical North Atlantic (TNA), the Dipole Mode Index (DMI) and the Arctic Oscillation (AO) are used to develop statistical models of column CO interannual variability from the Measurements of Pollution In The Troposphere (MOPITT) satellite instrument, for the time period covering 2001-2017. In addition, we assess the ability of fire emission inventories to reproduce CO, including the Fire Inventory from NCAR (FINN), the NASA Quick Fire Emissions Dataset (QFED) and the Copernicus Atmosphere Monitoring Service (CAMS) Global Fire Assimilation System (GFAS). These are implemented in the NCAR Community Atmosphere Model with chemistry (CAM-chem) and subsequently evaluated against MOPITT CO observations. Emission uncertainty contribution to inter-inventory differences are quantified, and the modeled contribution of fires to CO interannual variability is determined.

The objective of this joint field and laboratory study is to characterize the delivery and infiltration of nutrient loads from roadway runoff within roadway shoulders. Roadway runoff can be a major non-point source of nutrients entering surface and groundwater resources. Vegetated swales equipped with engineered media (vegetated filter strips) placed between roadways and receiving water bodies may hold promise to remedy contaminated runoff. However, performance of roadside filters is dependent upon hydraulic conditions as a first-order control of infiltration into filters. Variation of flows and infiltration rates were observed at distances of 0 to 6 m along vegetated shoulders and embankments using field-scale roadway models. Two laboratory test beds (experimental and control) drained by 300 drainage ports were designed according to the typical standard cross section of roadways in Florida. The experimental bed was filled with a surface layer of sandy soil (0.3 m depth, D50 = 0.27 mm) underlain by engineered media (0.7 m depth, D50 = 0.3 mm), while the control bed was filled with sandy soil (1 m depth, D50 = 0.27 mm). All media was compacted to 1400 – 1550 kg/m3 and beds were vegetated with Argentine Bahia grasses. Runoff was introduced from a 3 m impervious roadway section into a 1.5 m vegetated shoulder (6.0% slope) followed by 4.5 m vegetated embankment (16.0% slope). Storms designed by frequency analysis of 30 years of 15-min rainfall data gauged in North-Central Florida were introduced to the systems using a rainfall simulator with maximum precipitation rate of over 300 mm/hr. To determine dynamic nutrient contents, five synoptic samples of roadway runoff were collected at three locations over 10 storm events. Samples were analyzed for nutrient content and storm characteristics (precipitation, runoff) were determined from gauged information observed at study sites. Field information was integrated to design dynamic nutrient content of runoff introduced by synthetic storms.

The Superior Province Rifting Earthscope Experiment (SPREE) deployed seismic stations in 2011-2013 throughout Wisconsin, Minnesota, and Ontario. To protect equipment from groundwater damage, SPREE stations were buried at unusually shallow depths, increasing the power of long period noise and facilitating an investigation into the regional effects of atmospheric tides and soil properties (Wolin et al., 2015). Here we utilize the SPREE array to study the effects of solid-earth tides and meteorological conditions, on very long-period seismic noise in the U.S. midcontinent. Continuous seismic data was collected from SPREE and Transportable Array (TA) stations located in Wisconsin and Minnesota (WIMN) between July 2011 and September 2013. This data was “cleaned”, filtered, and averaged to produce a monthly representation of the very-long period signals recorded by the SPREE stations. The signals showed diurnal (24 hr) and semidiurnal (12 hr) periodicities, whose magnitudes and dominance vary seasonally. Using cross correlations, we compare our very-long period observations with theoretical solid-earth tides (Milbert, 2018) as well as meteorological components in the WIMN region. Meteorological data, specifically temperature and pressure, was obtained from the National Oceanic and Atmospheric Administration’s (NOAA) National Center for Environmental Information (NCEI). Solid-earth tides result from the gravitational pull of the moon and sun, and have previously been documented in seismic data (e.g. Pillet et al.,1994; Lambotte et al., 2005). We observe a distinct correlation between theoretical solid-earth tides and very-long period signals in seismic data from SPREE and TA stations in the WIMN region, where one frequency component is correlated while the other appears delayed. In addition, we observe a remarkable seasonal change in SPREE recordings of these signals, but not in TA recordings. We will report our findings from testing the hypothesis that the observed very-long period signals in SPREE data are a combination of both tidal and thermal effects and that these cumulative effects are the result of the unusual burial depth of SPREE stations.

The backprojection algorithm for synthetic aperture radar (SAR) has the ability of focusing raw data and forming an image without requiring simplifying assumptions for the azimuth-direction phase response of the recorded signal (Zebker et al., 2010). In fact, the usual quadratic phase assumption is not accurate when the recording platform is affected by turbulent motion or when wide variations in incidence angle across the range swath are present in the acquired data (Duersh, 2013). In addition, as we show here the backprojection algorithm is easily extendable to create SAR images of the subsurface in addition to the surface. These reasons make backprojection a well-suited method to create 3D subsurface SAR volume images from spaceborne acquisitions. The ability to form 3D SAR volumes using spaceborne data allows the study of larger areas than those available using to airborne acquisitions (Hélière et al., 2007). In this work, we create volume images by applying a 3D backprojection method to Advanced Land Observing Satellite (ALOS) data recorded over an icy area in Greenland previously studied by Banda et at. (2016). The 3D SAR image volumes created using different acquisition times enables the study of the evolution of the ice structure and properties. To decrease the processing time, we implement our algorithm using graphics processing units (GPUs) by taking advantage of the embarrassingly parallel nature of the backprojection algorithm (Fasih and Hartley, 2010).

NASA’s Atmospheric Tomography Mission (ATom) deployed in each of the four seasons during 2016-2018, the DC-8 aircraft in order to establish global-scale datasets intended to improve the representation of chemically reactive gases in global atmospheric chemistry models (ACMs). The Global Modeling Initiative (GMI) executed simulations for each ATom flight using the GMI Chemistry Transport Model (GMI-CTM) to provide species concentrations of chemical gases along the DC-8 flight transects. To solve the problem of translating the GMI-CTM simulation data to the unique spatial resolutions of each ATom flight, the GMI ICARTT Processing Software (GMI-IPS) was developed. The GMI-IPS is written in Python and provides data processing, flight extraction, and visualization support for aircraft research projects using ICARTT format, which is a standard format for airborne instrument data. Additionally, the GMI-IPS interpolates global gridded model data from Hierarchical Data Format (HDF) to ICARTT flight transects. Software classes for instruments and collections provided by the ATom DC-8 aircraft such as MER10, MMS, etc. are derived from a common base class. Other functionality provided by the GMI-IPS are: deriving missing flight entries along a transect, reading ICARTT entries from file, providing Python data structures for storing flight and model information, and more. The GMI-IPS is GIT source controlled, has approximately 30,000 lines of code, and supports parallelization across data collections. It delivered GMI-CTM data for more than forty distinct DC-8 aircraft flights that took place under ATom. The output ICARTT files adhere to format standard V1.1, and pass the scan utility provided by NASA LaRC Airborne Science Data for Atmospheric Composition. This presentation will include a software and methods overview, and results from ATom, including assessments using the GMI-CTM showing how well observations from ATom flight transects represent a broader region.

An objective of the solar and space physics communities has been to predict the behavior of the interconnected physical systems that bring space weather to Earth. One approach is to use first-principles models that may predict behavior of the various space plasma regimes from the magnetized solar corona to Earth’s upper atmosphere. We focus on space weather forecasts in the thermosphere-ionosphere (T-I), with lead time based on the period following a solar eruption. There are generally 1-4 days lead time before the interplanetary coronal mass ejection (ICME) reaches the Earth’s magnetopause. Forecasting the behavior of the T-I with such multi-day lead times requires new ways of using and assessing first principles models, which are capable of predicting many details of the T-I response, including the time history of the global electron density distribution, neutral densities and neutral winds. All facets of the complex T-I system response must be predicted based on input solar and interplanetary parameters. Another influence on the forecast is the condition of the T-I at the time a forecast is produced (e.g. shortly after the CME eruption epoch). However, the role of such pre-conditioning is not well understood for lead times of a few days. To improve our understanding of these forecasts, we have submitted more than 120 multi-day simulation periods to NASA’s Community Coordinated Modeling Center, spanning three coupled T-I models. Approximately 40 T-I storms have been simulated, driven by solar wind and EUV parameters alone. We will present an analysis that characterizes how T-I models respond to the information content of the solar wind, mediated through climatological models of high latitude forcing, and the possible influence of pre-existing conditions. Smoothing across mesoscale variability is inevitable in this scenario. Analyzing the response across events and across models reveals critical information about the predictability of the T-I system as an ICME approaches.

The National Ecological Observatory Network (NEON) is a long-term ecological observatory focused on collecting and providing open, continental-scale data that characterize and quantify complex and rapidly changing ecological patterns and processes. As part of the broader Observatory design, specific components of the Observatory are available to funded researchers for Principal Investigator-driven studies as part of NEON’s Assignable Asset Program. The available Assignable Assets include the Mobile Deployment Platform (MDP), the Sensor Infrastructure (SI), sampling locations or biological samples as part of the Observational Sampling Infrastructure (OSI), and the remote sensing capabilities of the Airborne Observation Platform (AOP). In addition to the infrastructure assets, NEON has deployable field sampling teams near NEON sites to support specimen collection and observations for specific research needs. Researchers can also gain access to the growing collection of plant and animal specimens and soil and water samples that NEON staff have gathered and stored in the NEON Bioarchive for study and analyses. Mobile Deployment Platform (MDP): NEON offers a suite of these self-contained, mobile arrays of sensors, power systems, and data logging capabilities for capturing atmospheric, soil, and aquatic-based measurements. Sensor Infrastructure (SI): Includes infrastructure (i.e., towers, power, and communications) for physical instrument systems or arrays for collecting environmental data from automated sensor suites. Observational Sampling Infrastructure (OSI): Allows researchers access to NEON sampling locations or to biological samples at NEON sites before samples are archived. Airborne Observation Platform (AOP): Provides a suite of remote sensing instruments mounted into a Twin Otter aircraft for collecting airborne-based data at nearly any site of interest in the U.S.

Ocean heat analyses of the North Atlantic Ocean based on the new high-resolution Northwest Atlantic (NWA) Regional Climatology (RC) developed at the NOAA’s National Centers for Environmental Information (NCEI) revealed decadal variability of the Eighteen Degree Water (EDW) depth that may be instrumental for understanding the localized heat accumulation in the NWA. The EDW is an important element of the Northwest Atlantic heat balance and an indicator of the ocean-atmosphere interaction in this region. The EDW deepening, or “heaving”, on decadal timescales are most likely caused by increasing Ekman pumping due to changes in the wind stress curl pattern over the NWA. The NCEI’s NWARC has also revealed that the highest rates of heat gain occur in the Sargasso Sea, southeast of the Gulf Stream path in the region occupied by the EDW. The volume of EDW depends on many factors, of which the most important are: Ekman pumping, heat fluxes at the air-sea surface, and heat advection within the Gulf Stream and the subtropical recirculation gyre. However, heat accumulation in several “pockets” southeast of the Gulf Stream and its extension seem to be most closely connected to EDW heaving. The depths of EDW for two independent ~30-year periods and their differences were computed and analyzed in conjunction with the changes in the curl of wind stress. As the comparison between the EDW depths mapped on three different spatial grids with 1°x1°, 1/4°x1/4°, and 1/10°x1/10° resolutions illustrate, the grid resolution does matter for mapping EDW on decadal timescales. The 30-year climate shift of the EDW depths between 1985-2010 and 1955-1984 compares quite well with the climatic shift in Ekman vertical velocities derived from the changes in the wind stress curl over the same time period. Comparing the eddy-permitting EDW heaving inferred from the NCEI’s NWARC and the ~30-year shift of the curl of wind stress, and consequently Ekman pumping, confirms a strong resemblance of the eddy-permitting and eddy-resolving EDW heaving patterns with two tightly localized pockets of heat accumulation southeast of the Gulf Stream and its extension.

We evaluate the mass transfer rate from the surface of rod- and disc- shaped particles with various aspect ratios and surface areas. The method of particle fabrication used here builds off of both traditional gypsum plaster dissolution methods and advances in sugar-glass particle recipes. The particles were created in-house with a nearly neutrally buoyant formula and custom molds. They were then tested in homogeneous, isotropic turbulence. The decrease in particle weight was recorded and results were compared to the Hixson-Crowell model for dissolution. We hypothesized that the turbulent flow would affect the boundary layer surrounding these particles and therefor their mass transfer rate. Results from these experiments show the dependence of shape and surface area to mass transfer rate in turbulent flow. The related questions are relevant to cases of marine biology, carbon sequestration, and pollution by microplastics.

We are at a unique time in the study of our place in space. On one hand, we operate in the same paradigm that has guided the study of space science for the past couple of decades, and on the other a rising dependence of our economic and social well-being on space demands a shift. Everywhere in our society ‘big data’ (defined by four V’s: volume, variety, veracity, and velocity) and the advent of sophisticated and efficient methods to explore these data (i.e., data science) present new opportunities for discovery, and the time is ripe for these methods to shift how we study the physics of space. We will first discuss the meaning of data science in the context of space science, and then demonstrate the potential for new discovery through a power use case: leveraging Global Navigation Satellite Systems (GNSS) signals for space weather prediction. In this use case, we take advantage of a large volume of data from GNSS signals, data science-driven technologies, and a machine learning algorithm known as the Support Vector Machine (SVM) to develop a novel predictive model for high-latitude ionospheric phase scintillation. This talk will conclude with a perspective on opportunities in space science through ‘big data’ and creating new scientific discovery at the intersection of traditional approaches and data science-driven innovation.

We are engaging citizen scientists in an experiment to test if many human ears can replace the process of a professional seismologist in identifying dynamically triggered seismic events. Ordinarily, this process involves interactive data processing and visualization efforts on a volume of earthquake recordings (seismograms) that exploded during the recent big-data revolution, for example through EarthScope. In this citizen seismology project, we ask citizens to listen to relevant sections of seismograms that are accelerated to audible frequencies. This approach has five advantages: 1) The human ear implicitly performs a time-frequency analysis and is capable of discerning a wide range of different signals, 2) Many human ears listening to the same data provides statistics that rank seismograms in order of their likelihood to contain a recording of a triggered event, which is helpful to researchers’ analysis of this data as well as to 3) the ability of a deep-learning algorithm to model the boolean identifications or bulk statistics of the analyses, 4) the project has the potential to enhance informal learning because of the online platform that hosts the project, Zooniverse, is available to people of all identities and hosts many other citizen science projects, and 5) it helps prepare our team for diverse post-graduation careers as part of IDEAS, an NRT program at Northwestern University. The events we are asking citizens to help identify via listening are small seismic events such as local earthquakes and tectonic tremor, that occur in response to transient stresses from passing seismic surface waves from a large, distant earthquake. While much research progress has been made in understanding how these events are triggered, there is no reliable deterministic recipe for their occurrence. The aim of our project is to enlist the help of citizens to increase the data set of known triggered seismic events and known absences of triggered events in order to help researchers unravel key aspects of that recipe. A better understanding of triggered seismic events is expected to provide important clues towards a fundamental understanding of all seismic activity, including damaging earthquakes.

Field-aligned currents (FACs), or the system of currents flowing along Earth’s magnetic field lines, are the dominant form of energy and momentum exchange between the magnetosphere and ionosphere. FACs are ubiquitous across the high-latitude region and have unique characteristics depending on the magnetospheric or solar wind source mechanism, and, therefore, mapping location in the ionosphere (i.e. auroral zone, polar cap, cusp). Further complicating the picture, FACs also exhibit a large range of spatial and temporal scales. In order to create new understanding of FAC spatial and temporal scales, their cross-scale effects, and the impact on the polar region, including on critical technologies, new data analysis approaches are required. This talk addresses a coherent progression of investigation in three parts: 1) an exploration of the characteristics, controlling parameters, and relationships of multiscale FACs using a rigorous, comprehensive analysis across multiple spacecraft observations; 2) augmentation of these statistical results with detailed case studies, fusing observations from diverse platforms and incorporating critical information about the high-latitude electrodynamics across scales; and 3) a quantitative investigation of the impact on Global Navigation Satellite System (GNSS) signals. We find that the relationships between FAC scales are complex and reveal new information about the connection between multiscale FACs and irregular space weather activity. Additionally, there are observable signatures of multiscale FACs and resultant electrodynamic activity in ionospheric data from GNSS signals, suggesting that these signals are affected distinctly according to scale size of the coupling process. Our results indicate that GNSS data may be a powerful source of information about the multiscale near Earth space environment.

This study reports on the development and implementation of the HydroLearn online platform that supports active learning in the field of hydrology and water resources engineering. The platform is designed to serve the following two main purposes: to enable instructors to collaboratively develop and share active-learning resources, and to enhance student learning in fundamental and emerging topics in the field (e.g., rainfall-runoff processes, design of flood protection measures, flood forecasting, water-energy-food nexus). Using open-source technology, the HydroLearn platform supports customization of pre-developed learning modules and allows instructors to share components of their learning resources with other interested users. HydroLearn is inspired by the need to address challenges in adoption, scalability, and sustainability identified by research on educational innovations. HydroLearn utilizes research-based active learning methods (e.g., Problem-based Learning; Collaborative and Cooperative Learning) to create authentic online learning modules. The modules engage students in real-world hydrologic problems and provide unique opportunities to expose undergraduate students to modern hydrologic analysis tools that are at the forefront of hydrologic research and engineering practice. The platform includes tools that scaffold instructors’ implementation of sound pedagogical practices. The platform includes wizards and pre-populated templates on how to develop student-centered learning outcomes that ensure constructive alignment with the learning content. The platform also includes guidance for instructors on how to develop assessment rubrics to enhance student achievement through communicating the expected performance levels. The study will also share results on the implementation of a pilot learning module on flood protection. Thirty-six undergraduate students were surveyed before and after the implementation to determine their level of learning engagement. The survey measured their skills engagement, emotional engagement, participation, and performance engagement. The presentation will also report on efforts to engage the community through a fellowship program that aims to develop a network of educators who aspire to adopt active learning approaches and enhance hydrology education.

Solar Orbiter is an ESA/NASA mission that will provide an unprecedented opportunity to discover the fundamental connections between the rapidly varying solar atmosphere and the solar wind. The Solar Wind Analyzer (SWA) plasma package shall provide comprehensive in situ measurements of the solar wind. In particular, the Proton-Alpha Sensor (PAS) will determine the high time resolution properties solar wind ion populations through the measurement of the 3D distribution function, density, bulk velocity, temperature and heat Vux. The closest approach of Solar Orbiter to the Sun is 0.28 AU. At this distance the solar wind Vow, solar UV, and solar infrared Vuxes increase by a factor 13 compared to near-Earth space. The PAS instrument will provide high cadence 3D distribution function measurements (up to 13 per second) all the way from closest approach to 1 AU. The extreme UV and thermal conditions at closest approach precluded the use of standard ion optics schemes. We thus developed a new ion sensor optic design combined with conventional robust channeltron detectors. This paper describes the PAS measurement scheme adopted for varying solar wind conditions and our approach to the fast sampling of 3D distribution functions. We also highlight the expected scienti:c return of the instrument.

The north Ecuador subduction zone exhibits segmentation and clustering of seismicity through megathrust, interseismic, and aftershock seismicity. In 1906, a Mw 8.8 megathrust event ruptured a 500 km segment, portions of which were re-ruptured in ’42 (Mw 7.8), ’58 (Mw 7.7), ’79 (Mw 8.2) and 2016 (Mw 7.8 Pedernales event). Segmentation between the ruptures is caused in part by subducting topography and upper plate structure. Upper plate structure in north Ecuador includes major faults, sedimentary basins and accreted terranes. An international aftershock deployment and the Ecuador permanent network (RENSIG) recorded aftershocks of the 2016 Pedernales event. We performed finite difference tomography in a joint inversion for 3D velocity and earthquake location, using body wave arrivals of aftershocks. The Manabi, Manta-Jama and Borbon sedimentary basins are observed as high Vp/Vs features with the Manabi basin seen as a low Vp and Vs feature. High Vp and Vs are associated with accreted forearc terranes. Relocation of aftershocks in the 3D velocity results in previously described “bands” of seismicity collapsing to smaller clusters ranging from ~8-40 km across. South of the rupture area, a cluster near Manabi collapsed landward, and a cluster appeared west of the trench. Three clusters between the trench and directly south of the rupture contain lower plate and plate interface events. The cluster within the rupture area between the patches of greater slip became more focused, and a cluster became defined on the north side of the northern patch of slip. Two clusters outline subducting Atacames seamounts, with events in the lower plate and interface beneath and in front of the seamounts. North of the rupture, the clusters offshore and onshore near Galera contain mostly interface with some upper plate events. The onshore cluster focused around major faults in a transition from north/south to northeast/southwest structures along the coastal range. Events in the cluster near Atacames relocated mainly in the upper plate, and events in the cluster near Esmeraldas remained in the upper plate. Interseismic events cluster in the same locations as aftershock events. Existing features including upper plate structure and subducting features control and focus both postseismic and interseismic deformation across megathrust cycles.

During each International Ocean Discovery Program (IODP) expedition a vast array of data, typically amounting to hundreds of gigabytes to several terabytes of information, are collected from drill cores. These data include physical, chemical, and magnetic properties and digital images collected continuously or every few centimeters along the cores using automated track systems, as well as a variety of analyses conducted on discrete subsamples taken from the cores. Coring just since the start of Expedition 349 in January 2014 has recovered over 50 km of core, resulting in a very large amount of data, most of which are accessible from the IODP LIMS database. Some of the properties typically measured include P-wave velocity, density, magnetic susceptibility, natural remanent magnetization, natural gamma radiation, and visible spectral reflectance. In addition, the lithology of all cores is described based mainly on visual characteristics of the surface of the split cores, visual examination of smear slides and thin sections, and compositional or mineralogical information derived from geochemical analyses. Our goal in this study is to mine these data for interrelationships that would otherwise be difficult to assess given the way the data are partitioned by specific property within the database. In particular, we extract basic lithologic information from the complex array of descriptive information and then tie that information to all other observations in order to characterize the physical, chemical, and magnetic properties of a myriad of lithologies.