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NASA NTRS2019

Developing a Machine-Learning-Based Processing Framework for Twitter and Other Crowdsourced Data

Crowdsourced data streams such as Twitter and other social media are important sources of real-time and historical global information for Earth science applications. At the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), we have been exploring the Twitter data stream for its potential in augmenting the validation program of NASA's Global Precipitation Measurement (GPM) mission. To realize this potential, we need to increase the information density and enhance the quality of filtered precipitation tweets. We have implemented various components of a machine learning (ML)-based processing infrastructure for crowdsourced data that outputs, in this instance, useful and usable information derived from precipitation tweets. We have test enriched the Twitter stream with higher quality active tweets from those knowingly contributing to our effort and from existing crowdsourced programs (e.g., mPING, CoCoRaHS). We have experimented with various algorithms for processing tweets, including NaÃ ve Bayes, Convolutional Neural Network (CNN), Hierarchical Attention Network (HAN), and semi-supervised learning (with tri-training). Our current work focuses on (1) automated review of Earth science-related publications to determine relationships between discipline research needs and ML algorithms; (2) investigating Sequential Generative Adversarial Network (SeqGAN) for processing precipitation tweets for anomaly detection; and (3) managing crowdsourced data in a way that is compatible with existing NASA satellite data archives and using the data for ML applications. Key results include (1) network visualization of NLP-processed publications in various Earth science disciplines; (2) difference between GPM-linked, generated tweets and collected actual tweets that is small for GPM-determined light to moderate rain cases and high for GPM-determined heavy rain cases; and (3) identification of MongoDB for storing raw tweets and Zarr format for gridded tweets (compatible with GPM data). Our results have taken us a step closer to an operational ML-based tweet processing infrastructure and have already demonstrated that tweet-derived precipitation information is potentially useful for validation of Earth science satellite data.

Teng, William↗

NASA NTRS1993

Asynchronous timing and Doppler recovery in DSP based DPSK modems for fixed and mobile satellite applications

While conventional analog modems employ some kind of clock wave regenerator circuit for synchronous timing recovery, in sampled modem receivers the timing is recovered asynchronously to the incoming data stream, with no adjustment being made to the input sampling rate. All timing corrections are accomplished by digital operations on the sampled data stream, and timing recovery is asynchronous with the uncontrolled, input A/D system. A good timing error measurement algorithm is a zero crossing tracker proposed by Gardner. Digital, speech rate (2400 - 4800 bps) M-PSK modem receivers employing Gardner's zero crossing tracker were implemented and tested and found to achieve BER performance very close to theoretical values on the AWGN channel. Nyguist pulse shaped modem systems with excess bandwidth factors ranging from 100 to 60 percent were considered. We can show that for any symmetric M-PSK signal set Gardner's NDA algorithm is free of pattern jitter for any carrier phase offset for rectangular pulses and for Nyquist pulses having 100 percent excess bandwidth. Also, the Nyquist pulse shaped system is studied on the mobile satellite channel, where Doppler shifts and multipath fading degrade the pi/4-DQPSK signal. Two simple modifications to Gardner's zero crossing tracker enable it to remain useful in the presence of multipath fading.

Koblents, B.↗

NASA NTRSDate not supplied

Risk Characterization Research for Artemis II: Human Factors and Behavioral Performance

BACKGROUND Artemis II will be the first time NASA astronauts go beyond low-Earth orbit (LEO) since the Apollo era, and the first astronauts heading into space in the Orion vehicle. As such, it provides a critical opportunity to refine our understanding of the likelihood and consequences associated with the Behavioral Medicine (BMed), Team, Human System Integration Architecture (HSIA), and Sleep Risks, and prepare for future Moon and Mars missions. However, Artemis II research efforts are uniquely shaped by in-mission data collection constraints. There is currently no in-mission crew time available to complete measures. In-mission data will need to be collected unobtrusively from available data streams (e.g., audiovisual, existing records such as schedules, and actigraphy). Accordingly, the overarching goal of our research is to utilize Artemis II data to further define the likelihood and consequences of these risks, and to create an unobtrusive research infrastructure that can be expanded to include future Artemis missions. This goal spans four aims across three research phases: (1) identify and operationally define key performances metrics and constructs across the four aforementioned risks, (2) develop an unobtrusive methodology and coding scheme for in-mission data collection, (3) characterize performance decrements due to Bmed, Team, HSIA, and Sleep Risks, and (4) develop a data infrastructure for future Artemis missions. The following details results of Phase I efforts in which we address Aims 1 and 2 to develop an unobtrusive measurement plan and coding scheme to capture key constructs, contributing factors, and performance decrements across each risk area. METHOD As part of Phase I, we conducted an interdisciplinary literature review and consulted with SMEs to identify unobtrusive methodologies that leverage text, audio, and/or video data as well as conceptualize key performance metrics, contributing factors, and BMed, Team, HSIA, and Sleep risk constructs related to performance decrements. The Phase I effort resulted in a finalized pre- and post-mission protocol for Artemis II, along with a measurement and coding scheme for in-mission Artemis II data. Phase II will involve data collection from the upcoming Artemis II mission. Phase III will include data processing, coding, depiction, analysis, and report writing of the Artemis II data. RESULTS & DISCUSSION To date, we have completed Phase I efforts. Specifically, we identified BMed, Team, HSIA, and Sleep risk constructs related to performance metrics, summarized how these constructs can be measured using audiovisual data collected during the mission, and worked with NASA’s HFBP Element to finalize a data collection protocol that leverages audiovisual input from the Orion spacecraft system. Our protocol includes novel unobtrusive methodologies that adhere to in-mission data streams and subsequent constraints (e.g., limited storage space on GoPro cameras, ambient noise impeding audio files) to best capture in-mission phenomena across each risk area. We will present our results from Phase I efforts, namely best practices for unobtrusive measurement as identified through literature reviews and SME consultation as well as codebook excerpts for use in Artemis II. We will include a description of planned work as we prepare for Phase II and Phase III of this research plan and the Artemis II mission itself. SUMMARY We describe progress on our Human Factors and Behavioral Performance Research for Artemis II study.

behavioral health↗

NASA NTRS2012

Short-range communication system

A short-range communication system includes an antenna, a transmitter, and a receiver. The antenna is an electrical conductor formed as a planar coil with rings thereof being uniformly spaced. The transmitter is spaced apart from the plane of the coil by a gap. An amplitude-modulated and asynchronous signal indicative of a data stream of known peak amplitude is transmitted into the gap. The receiver detects the coil's resonance and decodes same to recover the data stream.

Alhorn, Dean C.↗

NASA NTRS2010

Multiple Kernel Learning for Heterogeneous Anomaly Detection: Algorithm and Aviation Safety Case Study

The world-wide aviation system is one of the most complex dynamical systems ever developed and is generating data at an extremely rapid rate. Most modern commercial aircraft record several hundred flight parameters including information from the guidance, navigation, and control systems, the avionics and propulsion systems, and the pilot inputs into the aircraft. These parameters may be continuous measurements or binary or categorical measurements recorded in one second intervals for the duration of the flight. Currently, most approaches to aviation safety are reactive, meaning that they are designed to react to an aviation safety incident or accident. In this paper, we discuss a novel approach based on the theory of multiple kernel learning to detect potential safety anomalies in very large data bases of discrete and continuous data from world-wide operations of commercial fleets. We pose a general anomaly detection problem which includes both discrete and continuous data streams, where we assume that the discrete streams have a causal influence on the continuous streams. We also assume that atypical sequence of events in the discrete streams can lead to off-nominal system performance. We discuss the application domain, novel algorithms, and also discuss results on real-world data sets. Our algorithm uncovers operationally significant events in high dimensional data streams in the aviation industry which are not detectable using state of the art methods

Das, Santanu↗

NASA NTRS2018

Using GPS Receiver 1PPS Output to Verify Time Stamp Accuracy and Measure Propagation Delay

A simple pulse overlay circuit using a logic OR gate was developed to overlay a precise leading edge 1 pulse per second time reference marker from a global positioning system receiver onto a non-return -to- zero-level pulse code modulation telemetry data stream to validate time stamp accuracy and measure propagation delay in telemetry equipment. A pulse overlay (PO) circuit was designed to produce a time reference marker within a pulse code modulation (PCM) telemetry data stream that was recorded and analyzed to validate an IRIG Standard 106-17 Chapter 10 recorder’s time stamp accuracy [1]. A manufacturer of a newly purchased Chapter 10 recorder claimed the time stamp accuracy to be better than 10 microseconds (μs) with only IRIG-B120 (IRIG-B) input and better than 1 μs with IRIG-B and one pulse per second (1PPS) inputs. The PO circuit and a Chapter 10 recorder test setup were used to verify the vendor’s data-time synchronization claims and to justify infrastructure upgrade costs to connect additional cables to each purchased recorder. The circuit was also used to measure the propagation delay (PD) of a long distance Telemetry multiplexer/demultiplexer telecommunication system which the manufacturer did not provide a PD specification. Using the one pulse per second time reference marker of the global positioning system receiver and the pulse overlay circuit to overlay the one pulse per second variable pulse width signal into a pulse code modulation telemetry stream provided an excellent means to validate time stamp accuracy within a Chapter 10 recorder and to measure propagation delays within a normal telemetry operating environment. The positive results from the Chapter 10 recorder tests validated the vendor’s claims and justified the implementation to add the one pulse per second signal to the range Chapter 10 recorders. The pulse overlay circuit and test setups defined in this paper can easily be used to measure other telemetry equipment.

Knudtson, Kevin↗

NASA NTRS1994

Testing and Performance Analysis of a 650 Mbps QPPM Modem for Free-space Laser Communications

The testing and performance of a prototype modem developed at NASA Lewis Research Center for high-speed free-space direct detection optical communications is described. The testing was performed under laboratory conditions using computer control with specially developed test equipment that simulates free-space link conditions. The modem employs quaternary pulse position modulation (QPPM) at 325 Megabits per second (Mbps) on two optical channels, which are multiplexed to transmit a single 650 Mbps data stream. The measured results indicate that the receiver's automatic gain control (AGC), phased-locked-loop slot clock recovery, digital symbol clock recovery, matched filtering, and maximum likelihood data recovery circuits were found to have only 1.5 dB combined implementation loss during bit-error-rate (BER) performance measurements. Pseudo random bit sequences and real-time high quality video sources were used to supply 650 Mbps and 325 Mbps data streams to the modem. Additional testing revealed that Doppler frequency shifting can be easily tracked by the receiver, that simulated pointing errors are readily compensated for by the AGC circuits, and that channel timing skew affects the BER performance in an expected manner. Overall, the needed technologies for a high-speed laser communications modem were demonstrated.

Mortensen, Dale J.↗

NASA NTRS2013

VLBI2010 Receiver Back End Comparison

VLBI2010 requires a receiver back-end to convert analog RF signals from the receiver front end into channelized digital data streams to be recorded or transmitted electronically. The back end functions are typically performed in two steps: conversion of analog RF inputs into IF bands (see Table 2), and conversion of IF bands into channelized digital data streams (see Tables 1a, 1b and 1c). The latter IF systems are now completely digital and generically referred to as digital back ends (DBEs). In Table 2 two RF conversion systems are compared, and in Tables 1a, 1b, and 1c nine DBE systems are compared. Since DBE designs are advancing rapidly, the data in these tables are only guaranteed to be current near the update date of this document.

Petrachenko, Bill↗

NASA NTRS2018

Multi-Sensor Integration of Vegetation Index Products for Long-Term Monitoring of Vegetation Dynamics: A Case Study from MODIS to VIIRS

Spectral vegetation index (VI) time series data from moderate resolution sensors, such as Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS), have widely been used to identify "hot spot" areas of vegetation changes and to characterize long-term trends of vegetation changes. The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor series of the Joint Polar Satellite System (JPSS) program is slated to continue the highly calibrated data stream initiated with MODIS. The first VIIRS sensor has operationally been acquiring Earth-reflected radiation since March 2012. The second VIIRS sensor (NOAA-20) was launched in November 2017, which is to continue the measurements made by the first VIIRS sensor. In this study, we examined an integrated use of MODIS and VIIRS VI time series data on capturing vegetation dynamics in the Asia-Pacific region using their overlapped period of observations (2013-2017). Three VIs, the "top-of-canopy (TOC)" normalized difference vegetation index (NDVI), TOC enhanced vegetation index (EVI), and TOC two-band enhanced vegetation index (EVI2), were investigated. For all the three VIs, VIIRS VIs were systematically higher than the MODIS counterparts due mainly to their spectral bandpass differences. However, both VIIRS and MODIS VIs showed the comparable spatial patterns in their temporal variations. Empirical spectral corrections allowed to merge the two data streams, which slightly improved the temporal resolution of the VI temporal profiles. These results suggest the suitability of VIIRS data to extend and merge into the MODIS VI record for long-term vegetation dynamics studies.

MODIS↗

DOE OSTI2026

WHONDRS 2016 Sediment Organic Matter Characterization Data from Streams across HJ Andrews Experimental Forest, Oregon

This dataset supports a broader synoptic effort to map morphological, hydrological, chemical, and biological conditions across a fifth-order mountain stream network. Samples were generated through a collaborative synoptic sampling effort in 2016. The dataset provides sediment Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR-MS) from 60 sites across the HJ Andrews Experimental Forest, Oregon (https://andrewsforest.oregonstate.edu). Related data were collected as part of the event and were published separately in collaboration with other team members. The data are available at http://www.hydroshare.org/resource/ea6c0832885a46c3939e7bb22e48e754 and are described within https://doi.org/10.5194/essd-11-1567-2019 (Ward et al., 2019). The hydroshare data package contains processed FTICR-MS data from the samples included in this data package. The data were processed via Formultitude (previously called Formularity; https://github.com/PNNL-Comp-Mass-Spec/Formultitude). However, we have re-processed the data using Core-MS and included it in this data package. Additional related data collected in 2025 from a similar effort can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3023310 and http://www.hydroshare.org/resource/b274c4a234bf4b12b7cb8a54a696c629. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) a folder of sample data; (2) data dictionary; (3) file-level metadata; (4); (5) coordinates; and (6) readme. The sample data subfolder contains 12 Tesla (12T) FTICR-MS data. This folder contains the processed data and three subfolders, one containing the .xml files, one containing the CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). All files are .csv, .pdf, .R, .xml, .Rmd, .py, .cal, or .json.

Biogeochemistry↗

NASA NTRS1977

Double quadriphase modulation/demodulation technique for three-channel communication link

A modulation technique for a three-channel communication link is introduced. The structure of the modulator is such as to form an unbalanced quadriphase signal wherein the high rate data stream is bi-phase modulated on the in-phase carrier component, while the sum of the two lower rate signals is bi-phase modulated on the quadrature component of the same carrier. The sum of the two lower signals is, in turn, formed by modulating with the respective data streams the in-phase and the quadrature components of a square wave subcarrier. At the demodulator, the tracking of the carrier and the subcarrier is performed by two independent Costas loops. The demodulation of the high data rate signal is carried out after establishing the carrier reference signal, while the lower rate signals are demodulated after the subcarrier loop recovers the subcarrier. In this paper, the performance of the two loops is analyzed and the expressions for the tracking errors are derived. Finally, a numerical example pertaining to the Space Shuttle-to-TDRS Ku-band link is presented for illustration.

Alem, W. K.↗

NASA NTRS2008

Visualization Component of Vehicle Health Decision Support System

The visualization front-end of a Decision Support System (DSS) also includes an analysis engine linked to vehicle telemetry, and a database of learned models for known behaviors. Because the display is graphical rather than text-based, the summarization it provides has a greater information density on one screen for evaluation by a flight controller.This tool provides a system-level visualization of the state of a vehicle, and drill-down capability for more details and interfaces to separate analysis algorithms and sensor data streams. The system-level view is a 3D rendering of the vehicle, with sensors represented as icons, tied to appropriate positions within the vehicle body and colored to indicate sensor state (e.g., normal, warning, anomalous state, etc.). The sensor data is received via an Information Sharing Protocol (ISP) client that connects to an external server for real-time telemetry. Users can interactively pan, zoom, and rotate this 3D view, as well as select sensors for a detail plot of the associated time series data. Subsets of the plotted data can be selected and sent to an external analysis engine to either search for a similar time series in an historical database, or to detect anomalous events. The system overview and plotting capabilities are completely general in that they can be applied to any vehicle instrumented with a collection of sensors. This visualization component can interface with the ISP for data streams used by NASA s Mission Control Center at Johnson Space Center. In addition, it can connect to, and display results from, separate analysis engine components that identify anomalies or that search for past instances of similar behavior. This software supports NASA's Software, Intelligent Systems, and Modeling element in the Exploration Systems Research and Technology Program by augmenting the capability of human flight controllers to make correct decisions, thus increasing safety and reliability. It was designed specifically as a tool for NASA's flight controllers to monitor the International Space Station and a future Crew Exploration Vehicle.

Jacob, Joseph↗

NASA NTRS2018

Live Aircraft Encounter Visualization at FutureFlight Central

Researchers at the National Aeronautics and Space Administration (NASA) have developed an aircraft data streaming capability that can be used to visualize live aircraft in near real-time. During a joint Federal Aviation Administration (FAA)/NASA Airborne Collision Avoidance System flight series, test sorties between unmanned aircraft and manned intruder aircraft were shown in real-time at NASA Ames' FutureFlight Central tower facility as a virtual representation of the encounter. This capability leveraged existing live surveillance, video, and audio data streams distributed through a Live, Virtual, Constructive test environment, then depicted the encounter from the point of view of any aircraft in the system showing the proximity of the other aircraft. For the demonstration, position report data were sent to the ground from on-board sensors on the unmanned aircraft. The point of view can be change dynamically, allowing encounters from all angles to be observed. Visualizing the encounters in real-time provides a safe and effective method for observation of live flight testing and a strong alternative to travel to the remote test range.

Video Streaming↗

NASA NTRS1982

High density bit transition requirements versus the effects on BCH error correcting code

The design to achieve the required bit transition density for the Space Shuttle high rate multiplexes (HRM) data stream of the Space Laboratory Vehicle is reviewed. It contained a recommended circuit approach, specified the pseudo random (PN) sequence to be used and detailed the properties of the sequence. Calculations showing the probability of failing to meet the required transition density were included. A computer simulation of the data stream and PN cover sequence was provided. All worst case situations were simulated and the bit transition density exceeded that required. The Preliminary Design Review and the critical Design Review are documented. The Cover Sequence Generator (CSG) Encoder/Decoder design was constructed and demonstrated. The demonstrations were successful. All HRM and HRDM units incorporate the CSG encoder or CSG decoder as appropriate.

Ingels, F. M.↗

NASA NTRS2003

LISA Optics Model: Early Results

The Laser Interferometer Space Antenna (LISA) optics model is used to generate a synthetic data stream in the absence of gravitational waves. The simulation has the spacecraft in moving in their respective Keplerian orbits. The pointing of the spacecraft and station keeping about the proof masses is accomplished using a control scheme, which minimizes the disturbance on the proof masses in the sensitive direction. The resulting data stream gives an indication of the magnitude of instrumental noise due to pointing jitter and motions of the spacecraft with respect to the proof masses. Computational details are presented and the results discussed.

Waluschka, Eugene↗

NASA NTRS2013

Detection of Moving Targets Using Soliton Resonance Effect

The objective of this research was to develop a fundamentally new method for detecting hidden moving targets within noisy and cluttered data-streams using a novel "soliton resonance" effect in nonlinear dynamical systems. The technique uses an inhomogeneous Korteweg de Vries (KdV) equation containing moving-target information. Solution of the KdV equation will describe a soliton propagating with the same kinematic characteristics as the target. The approach uses the time-dependent data stream obtained with a sensor in form of the "forcing function," which is incorporated in an inhomogeneous KdV equation. When a hidden moving target (which in many ways resembles a soliton) encounters the natural "probe" soliton solution of the KdV equation, a strong resonance phenomenon results that makes the location and motion of the target apparent. Soliton resonance method will amplify the moving target signal, suppressing the noise. The method will be a very effective tool for locating and identifying diverse, highly dynamic targets with ill-defined characteristics in a noisy environment. The soliton resonance method for the detection of moving targets was developed in one and two dimensions. Computer simulations proved that the method could be used for detection of singe point-like targets moving with constant velocities and accelerations in 1D and along straight lines or curved trajectories in 2D. The method also allows estimation of the kinematic characteristics of moving targets, and reconstruction of target trajectories in 2D. The method could be very effective for target detection in the presence of clutter and for the case of target obscurations.

Kulikov, Igor K.↗

NASA NTRS2019

Generating Accurate and Consistent Top-Of-Atmosphere Reflectance Products from the New Generation Geostationary Satellite Sensors

GeoNEX is a collaborative project by scientists from NASA, NOAA, JAXA, and other organizations around the world with the purpose of generating a suite of Earth-monitoring products using data streams from the latest geostationary (GEO) sensors including the GOES-16/17 ABI and the Himawari-8/9 AHI. An accurate and consistent top-of-atmosphere (TOA) reflectance product, in particular the bidirectional reflectance factor (BRF), is the starting point in the scientific processing chain. We describe the main considerations and corresponding algorithms in generating the GeoNEX TOA BRF product. First, a special advantage of geostationary data streams is their high temporal resolution (~10 minutes per full-disk scan), providing a key source of information for many downstream products. To fully utilize this high temporal frequency demands a high georegistration accuracy for every acquired image. Our analysis shows that there can be substantial georegistration uncertainties in both GOES and Himawari L1b data which we addressed by implementing a phase-based correction algorithm to remove residual errors. Second, geostationary sensors have distinct illumination-view geometry features in that the solar angle changes for every pixel. Therefore, to accurately derive a BRF requires a solar position algorithm and the estimation of the pixel-wise acquisition time within an uncertainty of 10 seconds. Third, we discuss the measures we adopted to check and correct residual radiometric calibration issues of individual sensors to enable time-series analysis as well as the cross calibration between different satellite sensors (including those from low-Earth orbit). Finally, we also explain the rationale for the choice of the global grid/tile system of the GeoNEX TOA BRF product.

Wang, Weile↗

DOE OSTI2026

WHONDRS Surface Water and Sediment Geochemistry and Organic Matter Characterization Data from Streams across HJ Andrews Experimental Forest, Oregon (v2)

This dataset supports a broader study developing conceptual models for river corridor critical zone processes across spatial scales and was generated in collaboration with the HJ Andrews River Corridor Critical Zone Workshop in 2025. The dataset provides surface water geochemistry (dissolved organic carbon, total dissolved nitrogen) from 48 sites across the HJ Andrews Experimental Forest, Oregon (https://andrewsforest.oregonstate.edu). Some of the sites have been impacted by the Holiday Farm Fire and the Lookout Fire in 2020 and 2023, respectively. Related data were collected as part of the workshop and will be published separately in collaboration with other workshop attendees and available at http://www.hydroshare.org/resource/b274c4a234bf4b12b7cb8a54a696c629. Related genomic data can be found on the National Center for Biotechnology Information (NCBI) under BioProject PRJNA1503030 (see critical details section below for more information). Additional related data collected in 2016 from a similar effort can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3377027 and http://www.hydroshare.org/resource/ea6c0832885a46c3939e7bb22e48e754 and are described within https://doi.org/10.5194/essd-11-1-2019 (Ward et al., 2019). This data package was originally published in March 2026. It was updated in August 2026 (v2; new and modified files). See the change history section in the readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) a folder of field photos, (2) a folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data, (3) a data checks report, (4) a folder of sample data, (5) file-level metadata, (6) data dictionary, (7) field metadata, (8) readme, (9) international generic sample number (IGSN) mapping file; and (10) field protocol. The sample data subfolder contains surface water and sediment (1) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages, (2) total dissolved nitrogen data and averages, (3) methods codes, (4) FTICR-MS methods; and (5) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains the CoreMS processed data and seven subfolders, thee containing .xml files for each sample type (sediment, surface water and blank samples), three containing the sediment CoreMS output files for each sample type (sediment, surface water and blank samples), and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). All files are .csv, .pdf, .R, .xml, .Rmd, .py, .cal, .json, .jpg, or .jpeg.

Biogeochemistry↗