Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “data analytics”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Meeting report: Ocean ‘omics science, technology and cyberinfrastructure: current challenges and future requirements (August 20-23, 2013)

The National Science Foundation’s EarthCube End User Workshop was held at USC Wrigley Marine Science Center on Catalina Island, California in August 2013. The workshop was designed to explore and characterize the needs and tools available to the community that is focusing on microbial and physical oceanography research with a particular emphasis on ‘omic research. The assembled researchers outlined the existing concerns regarding the vast data resources that are being generated, and how we will deal with these resources as their volume and diversity increases. Particular attention was focused on the tools for handling and analyzing the existing data, on the need for the construction and curation of diverse federated databases, as well as development of shared, interoperable, “big-data capable” analytical tools. The key outputs from this workshop include (i) critical scientific challenges and cyber infrastructure constraints, (ii) the current and future ocean ‘omics science grand challenges and questions, and (iii) data management, analytical and associated and cyber-infrastructure capabilities required to meet critical current and future scientific challenges. The main thrust of the meeting and the outcome of this report is a definition of the ‘omics tools, technologies and infrastructures that facilitate continued advance in ocean science biology, marine biogeochemistry, and biological oceanography.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Earth Microbiome Project and Global Systems Biology

Recently, we published the first large-scale analysis of data from the Earth Microbiome Project (1, 2), a truly multidisciplinary research program involving more than 500 scientists and 27,751 samples acquired from 43 countries. These samples represent myriad specimen types and span a wide range of biotic and abiotic factors, geographic locations, and physicochemical properties. The database (https://qiita.ucsd.edu/emp/) is still growing, with over 90,000 amplicon datasets, >500 metagenomic runs, and metabolomics datasets from a similar number of samples. Importantly, the techniques, data and analytical tools are all standardized and publicly accessible, providing a framework to support research at a scale of integration that just 7 years ago seemed impossible.

Gilbert, Jack A.↗
DOE OSTI2020

DEEP Solar: Data DrivEn Modeling and Analytics for Enhanced System Layer ImPlementation

Realizing the SETO 2030 mission of reducing solar energy costs to 3-5 c/kWh will require innovative enabling research on effective, cost-efficient integration of local PV within distribution systems. However, the intermittent and variable nature of PVs compels operators to impose conservative hosting capacity constraints. Given the extremely high variability of (intermittent and unpredictable) solar energy generation, relaxing the capacity constraints (which are currently around 15%) and achieving 100% or greater integration of renewables will require a fundamental transformation of the power grid via the utilization of exponentially larger amounts of AMI enabled fine-grained data. To address the challenges in increasing the penetration of renewable energy based DERs, this project envisions an Enhanced System Layer (ESL) at the distribution network level that is reliable, cost-effective and scalable to millions of Distributed Energy Resources (DERs)/devices. This includes developing: 1) Transformative and highly scalable machine learning based predictive analytics tools that plug into distribution system planning and provide real-time situational awareness at the distribution level for short and long-term operational planning. The tools will be built using novel data-driven energy models of millions of active nodes with AMI, 2) Adaptive stochastic analysis and optimization algorithms for real-time grid operations, 3) Dynamic Scenario Analysis using parallel Cloudenabled implementations with < 1 minute computational cycle times.

14 SOLAR ENERGY↗
DOE OSTI2025

Collaborative: in situ visual analytics technologies for extreme scale combustion simulations

This project aims to drastically enhance the usability of in situ analysis and visualization for extreme-scale scientific simulations. Current exascale computing capabilities promise to offer greater predictive ability of simulations and to further push the frontiers of science and technology. However, to validate the simulation output at extreme scale, examine the modeled phenomena, and discover previously unknowns from the output data, the output must be reduced or transformed in situ as it is being generated during the simulation such that the amount of data to examine and store is kept to a minimum. Such in situ approaches allow us to process and analyze the data and any embedded geometry to an extent that would be prohibitively expensive, if not impossible, to perform as a post hoc task. While in situ processing has been demonstrated to be a feasible and promising approach, its full potential has not yet been leveraged. In this project, we have developed comprehensive enhancements to in situ technology based on probability distributions in data. Our research focuses on jointly developing new ways of interacting with massive statistical samples while creatively utilizing new state-of-the-art computational resources to push the boundaries of in situ exploration. Moreover, we have developed new time-dependent techniques to enable previously unattainable capabilities in areas such as intelligent simulation steering and precise feature identification. We have experimentally studied our design and implementation at NERSC and OLCF, and are able to leverage existing in situ infrastructures whenever possible. While the exemplar in this project is combustion, many other fields for which turbulent transport is important, e.g., fusion, climate, astrophysics among others, encounter similar issues as simulations scale up to the exascale. This project shows its potential to generate high impact on DOE missions since the resulting technology promises to improve scientists’ ability to rapidly and correctly interpret and tune extreme-scale simulations, leading to new scientific understanding and advancements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Text-based Analytics for Biosurveillance

The ability to prevent, mitigate, or control a biological threat depends on how quickly the threat is identified and characterized. Ensuring the timely delivery of data and analytics is an essential aspect of providing adequate situational awareness in the face of a disease outbreak. This chapter outlines an analytic pipeline for supporting an advanced early warning system that can integrate multiple data sources and provide situational awareness of potential and occurring disease situations. The pipeline, includes real-time automated data analysis founded on natural language processing (NLP), semantic concept matching, and machine learning techniques, to enrich content with metadata related to biosurveillance. Online news articles are presented as an example use case for the pipeline, but the processes can be generalized to any textual data. In this chapter, the mechanics of a streaming pipeline are briefly discussed as well as the major steps required to provide targeted situational awareness. The text-based analytic pipeline includes various processing steps as well as identifying article relevance to biosurveillance (e.g., relevance algorithm) and article feature extraction (who, what, where, why, how, and when). The ability to prevent, mitigate, or control a biological threat depends on how quickly the threat is identified and characterized. Ensuring the timely delivery of data and analytics is an essential aspect of providing adequate situational awareness in the face of a disease outbreak. This chapter outlines an analytic pipeline for supporting an advanced early warning system that can integrate multiple data sources and provide situational awareness of potential and occurring disease situations. The pipeline, includes real-time automated data analysis founded on natural language processing (NLP), semantic concept matching, and machine learning techniques, to enrich content with metadata related to biosurveillance. Online news articles are presented as an example use case for the pipeline, but the processes can be generalized to any textual data. In this chapter, the mechanics of a streaming pipeline are briefly discussed as well as the major steps required to provide targeted situational awareness. The text-based analytic pipeline includes various processing steps as well as identifying article relevance to biosurveillance (e.g., relevance algorithm) and article feature extraction (who, what, where, why, how, and when).

Biomedical NLP↗
DOE OSTI2019

Characterization techniques for TRU waste from the Analytical Laboratory Hot Cells at Idaho National Laboratory

Characterization techniques for TRU waste from the Analytical Laboratory Hot Cells at Idaho National Laboratory Since the first reactor went hot in 1942, there has been a never ending desire to improve the nuclear reactor and the fuel that it burns. The Idaho National Laboratory has been a key player in reactor and fuel development. As more research projects begin, the spectrum of waste parameters grows. Over the years, the Analytical Laboratory (AL) at the Materials and Fuels Complex (MFC) at Idaho National Laboratory (INL) has supported a large range of various research projects. Each project brings its own challenge and analytical technique that, in turn, yields its own unique waste parameters. The AL Hot Cells at INL consist of 6 small cells that can support around 12 different fuel research projects at a time along with infrastructure and environmental samples. Each different project yields different levels of transuranic (TRU) material, RCRA constituents, dose, and contamination levels. The waste parameters from each project dictate a path forward for the waste Low-Level Waste (LLW), Contact Handled TRU (CH-TRU), Mixed Contact Handled TRU (M-CH-TRU), Remote Handled TRU (RH-TRU), or Mixed Remote Handled TRU (M-RH-TRU). With visual examination techniques and process knowledge as an aid, there have been waste handling methods developed that allow debris and sample material to be segregated. Unique characterization mechanisms have been applied for each debris and sample material that are generally more accurate than smear and surface contamination data. For debris found in the Hot Cells, analytical data is gathered and conservatively summarized for each fuel type and research method. This consists of data from Quadrupole Inductively Coupled Plasma Mass Spectrometry (Q-ICP-MS), Gamma Spectroscopy, and some data verification through Thermal Ionization Mass Spectrometry (TIMS) and Multi-Collector Inductively Coupled Plasma Mass Spectrometry (MC-ICP-MS). This data gives a range of TRU isotope concentrations. Fuel age is taken into account to determine the dominant TRU activity isotopes. Cesium concentrations are also gathered. Each parameter/fuel type then ties a measured Cesium number to TRU content based on historical analytical data. Debris in the hot cells is segregated by project and packaged in individual 2 gallon metal paint cans. Each can is counted by gamma spectroscopy in a calculated and measured geometry. Using process knowledge to determine the fuel or project type, newly counted cesium numbers can be applied to the historic analytical data and TRU content can be estimated. The TRU content allows waste debris to be segregated between CH-TRU and RH-TRU. Packaging techniques can allow for the minimization if RH-TRU waste. Sample material received into the AL must be dissolved into a liquid matrix in order to be analyzed in most cases. When analysis is complete, these liquids go through a consolidation, complexation, neutralization, and solidification process where real time analytical data can be used to calculate TRU content and RCRA constitutes. These calculations allow for no further analytical data to be gathered. The waste matrix is considered stable and acceptable by all AK [define AK] documents. The unique techniques used in characterizing and handling the Analytical Laboratories Hot Cell waste can be applied to various situations and various laboratories. They allow for more accurate data for waste constituents and also for the minimization of RH-TRU waste.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2019

Characterization Techniques for TRU Waste from the Analytical Laboratory Hot Cells at Idaho National Laboratory - 19610

Since the first reactor went hot in 1942, there has been a never ending desire to improve the nuclear reactor and the fuel that it burns. The Idaho National Laboratory has been a key player in reactor and fuel development. As more research projects begin, the spectrum of waste parameters grows. Over the years, the Analytical laboratory (AL) at the INL Materials and Fuels Complex (MFC) has supported a large range of various research projects. Each project brings its own challenge and analytical technique that, in turn, yields its own unique waste parameters. The AL Hot Cells at INL consist of 6 small cells that can support around 12 different fuel research projects at a time along with infrastructure and environmental samples. Each different project yields different levels of transuranic (TRU) material, RCRA constituents, dose, and contamination levels. The waste parameters from each project dictate a path forward for the waste as Low-Level Waste (LLW), Contact Handled TRU (CH-TRU), Mixed Contact Handled TRU (M-CH-TRU), Remote Handled TRU (RH-TRU), or Mixed Remote Handled TRU (M-RHTRU). With visual examination techniques and process knowledge as an aid, there have been waste handling methods developed that allow debris and sample material to be segregated. Unique characterization mechanisms have been applied for each debris and sample material that are generally more accurate than smear and surface contamination data. For debris found in the Hot Cells, analytical data is gathered and conservatively summarized for each fuel type and research method. This consists of data from Quadrupole Inductively Coupled Plasma Mass Spectrometry (Q-ICP-MS), Gamma Spectroscopy, and some data verification through Thermal Ionization Mass Spectrometry (TIMS) and Multi-Collector Inductively Coupled Plasma Mass Spectrometry (MC-ICP-MS). This data gives a range of TRU isotope concentrations. Fuel age is taken into account to determine the dominant TRU activity isotopes. Cesium concentrations are also gathered. Each parameter/fuel type then ties a measured Cesium number to TRU content based on historical analytical data. Debris in the hot cells is segregated by project and packaged in individual 2 gallon metal paint cans. Each can is counted by gamma spectroscopy in a calculated and measured geometry. Using process knowledge to determine the fuel or project type, newly counted cesium numbers can be applied to the historic analytical data and TRU content can be estimated. The TRU content allows waste debris to be segregated between CH-TRU and RH-TRU. Packaging techniques can allow for the minimization of RHTRU waste. Sample material received into the AL must be dissolved into a liquid matrix in order to be analyzed in most cases. When analysis is complete, these liquids go through a consolidation, complexation, neutralization, and solidification process where real time analytical data can be used to calculate TRU content and RCRA constitutes. These calculations allow for no further analytical data to be gathered. The waste matrix is considered stable and acceptable by all Acceptable Knowledge (AK) documents. The unique techniques used in characterizing and handling the Analytical Laboratories Hot Cell waste can be applied to various situations and various laboratories. They allow for more accurate data for waste constituents and also for the minimization of RH-TRU waste. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Programming with BIG data in R: Scaling analytics from one to thousands of nodes

Here, we present a tutorial overview showing how one can achieve scalable performance with R. We do so by utilizing several package extensions, including those from the pbdR project. These packages consist of high performance, high-level interfaces to and extensions of MPI, PBLAS, ScaLAPACK, I/O libraries, profiling libraries, and more. While these libraries shine brightest on large distributed platforms, they also work rather well on small clusters and often, surprisingly, even on a laptop with only two cores. Our tutorial begins with recommendations on how to get more performance out of your R code before considering parallel implementations. Because R is a high-level language, a function can have a deep hierarchy of operations. For big data, this can easily lead to inefficiency. Profiling is an important tool to understand the performance of an R code for both serial and parallel improvements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Towards Real-Time Detection and Tracking of Spatio-Temporal Features: Blob-Filaments in Fusion Plasma [Spatio-Temporal Features in Large Irregular Data: Blob-Filaments in Fusion Plasma]

This work was originally motivated the need to identify and track blob filaments, a feature often associated with instability in magnetically confined fusion plasma. Understanding and mitigating such instability would improve fusion reactors and make fusion a truly inexhaustible source of clean energy. Similar spatio-temporal features are important in many other applications, for example, ignition kernels in combustion and tumor cells in a medical image. This work presents an approach for extracting these features by dividing the overall task into three steps: local identification of feature cells, grouping feature cells into extended feature, and tracking movement of feature through overlapping in space. Through our extensive work in parallelization, we demonstrate that this approach can effectively make use of a large number of compute nodes to detect and track blobs in fusion plasma. On a set of 4.3TB data, we observed linear speedup on 1024 processes and completed blob detection in less than three milliseconds using Edison, a Cray XC30 system at NERSC.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

MCNP 6.2.0 Delayed-Particle Production Improvements

Development began on a delayed-particle physics package for the Monte Carlo Radiation transport code MCNPX in 2004 incorporating the CINDER90 depletion code and delayed particle libraries. When MCNP5 was merged with MCNPX to form MCNP6 in 2010, this delayed-particle capability was passed on and has been continuously improved in subsequent versions of MCNP6. Today, the delayed-particle feature in MCNP6 allows for the production of delayed neutrons, gammas, betas, and alpha particles. Future development includes the addition of delayed-positron production in the next release of MCNP6. Delayed particles can be sampled with MCNP using the ACT card in conjunction with the FISSION and NONFISS keywords. The user can specify one or more of the four available delayed-particle types mentioned above on one or both of the FISSION and NONFISS keywords. If a delayed-particle type is selected on the FISSION keyword, then delayed production of that particle from fission products and their progeny will be produced. Likewise, if a delayed-particle type is selected on the NONFISS keyword then delayed production of that particle from non-fission interactions and spontaneous decay will be produced. In some cases, the sheer number of delayed particles that can be produced from nuclear reactions and spontaneous decay (i.e. hundreds of delayed gamma lines when spontaneous fission is possible) can make it difficult to achieve converged solutions in an efficient and timely manner. Truncation methods are the simplest form of variance reduction and can be used to decrease convergence times by limiting calculations to the phase space of interest. This paper introduces several such truncation methods that allow the user to restrict the number of possible delayed particles available for sampling to only those of interest in the problem. It is important to stress that the use of any truncation method should be verified by first assessing the impact of the truncated particles (i.e. by disabling the truncation) for one or more baseline cases. In addition to these new methods, a new delayed-gamma sampling algorithm implemented in MCNP 6.2.0 is introduced. Comparisons to ENDF data and analytical benchmarking with the new algorithm show a substantial improvement in accuracy over previous versions. New improvements to the delayed-particle capability have been added to MCNP 6.2.0. These improvements include an updated treatment of the THRESH keyword which omits delayed-particle lines below a set amplitude, and addition of the PECUT and HLCUT keywords which allow the user to truncate delayed-particles lines based on energy and half-life, respectively. The delayed-gamma sampling algorithm has also been improved to allow exact energy sampling. Comparisons of MCNP calculations directly to ENDF data and analytical benchmarks have verified the significant improvement in the new delayed gamma sampling algorithm. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Lepton number violation as a key to low-scale leptogenesis

We explore the possibility of having a successful leptogenesis through oscillations between new sterile fermion states added to the Standard Model field content in a well motivated framework, naturally giving rise to the required mass splitting between the sterile states through a small total lepton number violation. We propose a framework with only two sterile states forming a pseudo-Dirac state, in which their mass difference as well as the smallness of the neutrino masses are due to two sources of lepton number violation with ΔL=2, corresponding to an Inverse Seesaw framework extended by a Linear Seesaw mass term. We also explore the pure Inverse Seesaw mechanism in its minimal version, requiring at least four new sterile states in order to comply with neutrino data. Our analytical and numerical studies reveal that one can have a successful leptogenesis at the temperature of the electroweak scale through oscillations between the two sterile states with a “natural” origin of the strong degeneracy in their mass spectrum. We also revisit the analytical expression of the baryon asymmetry of the Universe in the weak washout regime of this framework.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

A Visual Analytics Framework for Reviewing Streaming Performance Data

Understanding and tuning the performance of extreme-scale parallel computing systems demands a streaming approach due to the computational cost of applying offline algorithms to vast amounts of performance log data. Analyzing large streaming data is challenging because the rate of receiving data and limited time to comprehend data make it difficult for the analysts to sufficiently examine the data without missing important changes or patterns. To support streaming data analysis, we introduce a visual analytic framework comprising of three modules: data management, analysis, and interactive visualization. The data management module collects various computing and communication performance metrics from the monitored system using streaming data processing techniques and feeds the data to the other two modules. The analysis module automatically identifies important changes and patterns at the required latency. In particular, we introduce a set of online and progressive analysis methods for not only controlling the computational costs but also helping analysts better follow the critical aspects of the analysis results. Finally, the interactive visualization module provides the analysts with a coherent view of the changes and patterns in the continuously captured performance data. Through a multi-faceted case study on performance analysis of parallel discrete-event simulation, we demonstrate the effectiveness of our framework for identifying bottlenecks and locating outliers.

Human-centered computing↗
DOE OSTI2023

A Processing and Analytics System for Microscopy Data Workflows: The Pycroscopy Ecosystem of Packages

Major advancements in fields as diverse as biology and quantum computing have relied on a multitude of microscopy techniques. Despite the considerable proliferation of these instruments, significant bottlenecks remain in terms of processing, analysis, storage, and retrieval of the acquired datasets. Aside from lack of file standards, individual domain-specific analysis packages are often disjoint from the underlying datasets, and thus keeping track of analysis and processing steps remains tedious for the end-user, hampering reproducibility. Here, in this study, the pycroscopy ecosystem of packages is introduced, an open-source python-based ecosystem underpinned by a common data model. The data model, termed the N-dimensional spectral imaging data format, is realized in pycroscopy's sidpy package. This package is built on top of dask arrays, thus leveraging dask array attributes, but expanding them to accelerate microscopy relevant analysis and visualization. Several examples of the use of the pycroscopy ecosystem to create workflows for data ingestion and analysis of scanning transmission electron microscopy (STEM) and scanning probe microscopy data are shown. Adoption of such standardized routines will be critical to usher in the next generation of autonomous instruments where processing, computation, and meta-data storage will be critical to overall experimental operations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data

Abstract Artificial intelligence (AI) and machine learning (ML) have been increasingly used in materials science to build predictive models and accelerate discovery. For selected properties, availability of large databases has also facilitated application of deep learning (DL) and transfer learning (TL). However, unavailability of large datasets for a majority of properties prohibits widespread application of DL/TL. We present a cross-property deep-transfer-learning framework that leverages models trained on large datasets to build models on small datasets of different properties. We test the proposed framework on 39 computational and two experimental datasets and find that the TL models with only elemental fractions as input outperform ML/DL models trained from scratch even when they are allowed to use physical attributes as input, for 27/39 (≈ 69%) computational and both the experimental datasets. We believe that the proposed framework can be widely useful to tackle the small data challenge in applying AI/ML in materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Toward Real Time Data Analysis for Smart Grids

This paper describes the architecture and design of a novel system for supporting large-scale real-time data analysis for future power grid systems. The widespread deployment of renewable generation, smart grid controls, energy storage, plug-in hybrids, and new conducting materials will require fundamental changes in the operational concepts and principal components of the grid. As a result, the whole system becomes highly dynamic and requires constant adjusting based on real time data. Even though millions of sensors such as phase measurement units (PMU) and smart meters are being widely deployed, a data layer that can analyze this amount of data in real time is needed. Unlike the data fabric in other cloud services, the data layer for smart grids has some unique design requirements. First, this layer must provide real time guarantees. Second, this layer must be scalable to allow a large number of applications to access the data from millions of sensors in real time. Third, reliability is critical and this layer must be able to continue to provide service in face of failures. Fourth, this layer must be secure. We address these challenges though a scalable system architecture that integrates the I/O and data processing capability in a devise set of devices. Data process operations can be placed anywhere from sensors, data storage devices, to control centers. We further employ compression to improve performance. We design a lightweight compression customized for power grid data. Our system can reduce end-to-end response time by reduce I/O overhead through compression and overlap compression operations with I/O. The initial prototype of our system was demonstrated with several use cases from PNNL’s FPGI and show that our system can provide real time guarantees to a diverse set of applications.

Data Analytics, Power Grid, Big Data↗
DOE OSTI2025

Data from: 'Abiotic influences on continuous conifer forest structure across a subalpine watershed'

This package archives the core data used for analysis and inference in 'Abiotic influences on continuous conifer forest structure across a subalpine watershed' (Worsham et al., 2025). All data were collected in the East River, Washington Gulch, Slate River, and Coal Creek watersheds of Colorado. In the paper, we quantified the relative influence of climate, topographic, edaphic, and geologic factors on conifer stand structure and composition, and their functional relationships, at the watershed scale. We used waveform LiDAR data to derive spatially continuous stand structure metrics. We fused these with a species-level classification map to estimate tree species abundance. We applied generalized additive and generalized boosted models to evaluate the covariability of structural and compositional metrics with abiotic variables. The package contains the essential products required for reproducing our analysis and the tables and figures reported in the publication. The products comprise four classes: (1) geospatial data, (2) tabular data used for inferential analysis, (3) tabular data describing analytical results and performance statistics, and (4) a data user guide. (1) includes discretized waveform LiDAR data, locations and attributes of individual tree crowns, sampling locations and domain boundaries, a canopy height model, and raster files of estimated forest structural and compositional metrics at 100 m grid scale. (2) includes all response and explanatory variable values applied in inferential models. Response variables include conifer forest stand density, basal area, 95th percentile height, quadratic mean diameter, and others. Explanatory variables include climatic water deficit, actual evapotranspiration, elevation, heat load, soil available water content, and others. (3) includes results of training and testing several individual tree detection (ITD) algorithms, as well as inferential modeling results. (4) is a PDF user guide for this data package, including detailed descriptions and data dictionaries for all files. The data package root contains 17 assets: 8 compressed tape archive (.tar.gz) files, 5 comma-separated values (.csv) files, 3 Geographic Tagged Image File Format (GeoTIFF) (.tif) files, and 1 Portable Document Format (.pdf) file. The compressed .tar.gz archives contain ESRI shapefiles (.shp) .tif, compressed LASer (.laz), and .csv files. The archives must first be decompressed using the widely distributed command-line software utility TAR. All other files, including constituent files within the .tar.gz archives, can be opened in the open-source R statistical computing environment. Alternatively, .csv files may also be read in any simple text editor software or Microsoft Excel. Geospatial files including .shp and .tif files can also be opened in GIS software, such as QGIS (open-source) or ESRI ArcGIS (proprietary). The .pdf Data User Guide can be read with Adobe Acrobat Reader or other compatible readers.

2018 NEON and 2025 CHESS Campaigns↗