33 Works

2011 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly

David Fouhey, Meng Jin, Mark Cheung, Abndres Munoz-Jaramillo, Richard Galvez, Rajat Thomas, Paul Wright, Alexander Szenicer, Monica G. Bobra, Yang Liu & James Mason
We present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a...

Data from: Latitudinal and altitudinal patterns of plant community diversity on mountain summits across the tropical Andes

Francisco Cuesta, Priscilla Muriel, Luis D. Llambí, Stephan Halloy, Nikolay Aguirre, Stephan Beck, Julieta Carilla, Rosa I. Meneses, Soledad Cuello, Alfredo Grau, Luis E. Gámez, Javier Irazábal, Jorge Jacome, Ricardo Jaramillo, Lirey Ramírez, Natalia Samaniego, David Suárez-Duque, Natali Thompson, Alfredo Tupayachi, Paul Viñas, Karina Yager, María T. Becerra, Harald Pauli & William D. Gosling
The high tropical Andes host one of the richest alpine floras of the world, with exceptionally high levels of endemism and turnover rates. Yet, little is known about the patterns and processes that structure altitudinal and latitudinal variation in plant community diversity. Herein we present the first continental-scale comparative study of plant community diversity on summits of the tropical Andes. Data were obtained from 792 permanent vegetation plots (1m2) within 50 summits, distributed along a...

Wallops Island Balloon Technology: Can’t see the Repository for the Documents

Andrea Japzon & Nikkia Anderson
Since the Wallop’s Balloon Technology documents repository began approximately 9 years ago, the Goddard Library has become increasingly involved in developing digital archiving capabilities. The Library developed the Digital Archiving System (DAS) which is a prototype infrastructure for creating a combined metadata repository that allows metadata for heterogeneous digital objects to be searched with a single search mechanism and presented in a single results page. With this, the opportunity has been presented to expand the...

PSP FIELDS Fluxgate Magnetometer (MAG) Magnetic Field Vectors, Radial-Tangential-Normal, RTN, Coordinates, 4 samples/cycle, Level 2 (L2), 3.413 ms Data

Stuart D. Bale, Robert J. MacDowall, Andriy Koval, Marc Pulupa, Timothy Quinn & Peter Schroeder
Parker Solar Probe FIELDS Instrument Suite Fluxgate Magnetometer, MAG, Data: The time resolution of the MAG time series data varies with instrument mode ranging from 2.289 samples/s to 292.9 samples/s. These two data sampling rates corresponding to 2 samples or 256 samples per 0.874 s where 0.874 s is equal to 2^25 divided 38.4 MHz. The Magnetometer has four ranges: ±1024 nT, ±4096 nT, ±16,384 nT, and ±65,536 nT. The Magnetometer Range is selected by...

Grad-Shafranov equation: MHD simulation of the new solution obtained from the Fadeev and Naval models

Arian Ojeda Gonzalez, Leandro Nunes Dos Santos, José Juan González-Avilés, Victor De La Luz & Pablo Rubén Muñoz-Gutberlet
This article aims to obtain a new analytical solution of a specific form of the Grad-Shafranov (GS) equation using Walker's formula. The new solution has magnetic field lines with X-type neutral points, magnetic islands and singular points. The singular points are located on the x-axis. The X-points and the center of the magnetic islands do not appear on the x-axis an island appears at $z>0$ and the other two at $z<0$. The aforementioned property allows...

Data from: Mechanistic insights into landscape genetic structure of two tropical amphibians using field-derived resistance surfaces

A. Justin Nowakowski, J. Andrew DeWoody, Matthew E. Fagan, Janna R. Willoughby & Maureen A. Donnelly
Conversion of forests to agriculture often fragments distributions of forest species and can disrupt gene flow. We examined effects of prevalent land uses on genetic connectivity of two amphibian species in northeastern Costa Rica. We incorporated data from field surveys and experiments to develop resistance surfaces that represent local mechanisms hypothesized to modify dispersal success of amphibians, such as habitat-specific predation and desiccation risk. Because time lags can exist between forest conversion and genetic responses,...

Data from: Bayesian hierarchical models for spatially misaligned data in R

Andrew O. Finley, Sudipto Banerjee & Bruce D. Cook
Spatial misalignment occurs when at least one of multiple outcome variables is missing at an observed location. For spatial data, prediction of these missing observations should be informed by within location association among outcomes and by proximate locations where measurements were recorded. This study details and illustrates a Bayesian regression framework for modelling spatially misaligned multivariate data. Particular attention is paid to developing valid probability models capable of estimating parameter posterior distributions and propagating uncertainty...

2013 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly

David Fouhey, Meng Jin, Mark Cheung, Abndres Munoz-Jaramillo, Richard Galvez, Rajat Thomas, Paul Wright, Alexander Szenicer, Monica G. Bobra, Yang Liu & James Mason
We present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a...

2014 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly

David Fouhey, Meng Jin, Mark Cheung, Abndres Munoz-Jaramillo, Richard Galvez, Rajat Thomas, Paul Wright, Alexander Szenicer, Monica G. Bobra, Yang Liu & James Mason
We present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a...

CMS-Flux NBE 2020

Junjie Liu, Lartha Baskarran, Kevin Bowman, David Schimel, A. Anthony Bloom, Nick Parazoo, Tomohiro Oda, Dustin Carrol, Dimitris Menemenlis, Joanna Joiner, Roisin Commane, Bruce Daube, Lucianna V. Gatti, Kathryn McKain, John Miller, Britton B. Stephens, Colm Sweeney & Steven Wofsy
Top-down Net biosphere exchange estimates between Jan 2010 and Dec 2018 constrained by column CO2 observations from Greenhouse gases Observing Satellite and Orbiting Carbon Observatory 2. This dataset is openly shared in accordance with NASA Data and Information Policy (https://earthdata.nasa.gov/collaborate/open-data-services-and-software/data-information-policy).

Thinner bark increases sensitivity of wetter Amazonian tropical forests to fire

Ann Carla Staver, Paulo M. Brando, Jos Barlow, Douglas C. Morton, C.E. Timothy Paine, Yadvinder Malhi, Alejandro Araujo Murakami & Jhon Pasquel
Understory fires represent an accelerating threat to Amazonian tropical forests and can, during drought, affect larger areas than deforestation itself. These fires kill trees at rates varying from < 10 to c. 90% depending on fire intensity, forest disturbance history and tree functional traits. Here, we examine variation in bark thickness across the Amazon. Bark can protect trees from fires, but it is often assumed to be consistently thin across tropical forests. Here, we show...

Pyrocumulonimbus Events over British Columbia in August 2017: Results from the NASA GEOS Earth System Model

Sampa Das, Peter Colarco & Luke Oman

Data from: A segmentation algorithm for characterizing Rise and Fall segments in seasonal cycles: an application to XCO2 to estimate benchmarks and assess model bias

Leonardo Calle, Benjamin Poulter & Prabir K. Patra
There is more useful information in the time series of satellite-derived column-averaged carbon dioxide (XCO2) than is typically characterized. Often, the entire time series is treated at once without considering detailed features at shorter timescales, such as nonstationary changes in signal characteristics – amplitude, period and phase. In many instances, signals are visually and analytically differentiable from other portions in a time series. Each rise (increasing) and fall (decreasing) segment in the seasonal cycle is...

IceCube Level 1 Radiance Data and Codes

Jie Gong & Dong Wu
This zipped meta data file can be expanded into two folders. One folder contains the daily calibrated Level 1 radiance and geolocation data in HDF5 format, and the other folder contains the main IDL codes that process the data and make plots (mainly for generating plots for the paper Gong et al. 2021 that is under review for Earth Science System Data journal). Both folders contain a README file in each to guide readers through...

PSP FIELDS Fluxgate Magnetometer (MAG) Magnetic Field Vectors, Spacecraft, SC, Coordinates, Level 2 (L2), 1 min Data

Stuart D. Bale, Robert J. MacDowall, Andriy Koval, Marc Pulupa, Timothy Quinn & Peter Schroeder
Parker Solar Probe FIELDS Instrument Suite Fluxgate Magnetometer, MAG, Data: The time resolution of the MAG time series data varies with instrument mode ranging from 2.289 samples/s to 292.9 samples/s. These two data sampling rates corresponding to 2 samples or 256 samples per 0.874 s where 0.874 s is equal to 2^25 divided 38.4 MHz. The Magnetometer has four ranges: ±1024 nT, ±4096 nT, ±16,384 nT, and ±65,536 nT. The Magnetometer Range is selected by...

2012 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly

David Fouhey, Meng Jin, Mark Cheung, Abndres Munoz-Jaramillo, Richard Galvez, Rajat Thomas, Paul Wright, Alexander Szenicer, Monica G. Bobra, Yang Liu & James Mason
We present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a...

2017 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly

David Fouhey, Meng Jin, Mark Cheung, Abndres Munoz-Jaramillo, Richard Galvez, Rajat Thomas, Paul Wright, Alexander Szenicer, Monica G. Bobra, Yang Liu & James Mason
We present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a...

2018 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly

David Fouhey, Meng Jin, Mark Cheung, Abndres Munoz-Jaramillo, Richard Galvez, Rajat Thomas, Paul Wright, Alexander Szenicer, Monica G. Bobra, Yang Liu & James Mason
We present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a...

Ensemble model output of North American atmospheric CO2 simulation (full WRF-chem output)

S. Feng, T. Lauvaux, K.J. Davis, K. Keller, R. Rayner, T. Oda, K. Gurney, Y. Zhou, C. Williams, A.E. Schuh, J. Liu & I. Baker
The uncertainty in biospheric carbon dioxide (CO2) flux estimates drives divergent projections of future climate and uncertainty in prescriptions for climate mitigation. The terrestrial carbon sink can be inferred from atmospheric CO2 observations with transport models via inversion methods. Regional CO2 flux estimates remain uncertain due to the mixture of uncertainties caused by transport models, prior estimates of biospheric fluxes, large-scale CO2 boundary inflow, the assumptions in the inversion process, and the limited density of...

Dataset of Constraining fossil fuel CO2 emissions from urban area using OCO-2 observations of total column CO2

X. Ye, T. Lauvaux, E.A. Kort, T. Oda, S. Feng, J.C. Lin, E.G. Yang & D. Wu
Satellite observations of the total column dry-air CO2 (XCO2) are expected to support the quantification and monitoring of fossil fuel CO2 (ffCO2) emissions from urban areas. We evaluate the utility of the Orbiting Carbon Observatory 2 (OCO-2) XCO2 retrievals to optimize whole-city emissions, using a Bayesian inversion system and high-resolution transport modeling. The uncertainties of constrained emissions related to transport model, satellite measurements, and local biospheric fluxes are quantified. For the first two uncertainty sources,...

Data from: Targeted reforestation could reverse declines in connectivity for understory birds in a tropical habitat corridor

Matthew E. Fagan, Ruth S. DeFries, Steven E. Sesnie, J. Pablo Arroyo-Mora & Robin L. Chazdon
Re-establishing connectivity between protected areas isolated by habitat clearing is a key conservation goal in the humid tropics. In northeastern Costa Rica, payments for environmental services (PES) and a government ban on deforestation have subsidized forest protection and reforestation in the San Juan–La Selva Biological Corridor (SJLSBC), resulting in a decline in mature forest loss and the expansion of tree plantations. We use field studies and graph models to assess how conservation efforts have altered...

Data from: Disentangling competitive versus climatic drivers of tropical forest mortality

Michiel Pillet, Emilie Joetzjer, Camille Belmin, Jérôme Chave, Philippe Ciais, Aurélie Dourdain, Margaret Evans, Bruno Hérault, Sebastiaan Luyssaert & Benjamin Poulter
1. Tropical forest mortality is controlled by biotic and abiotic processes, but how these processes interact to determine forest structure is not well understood. Using long-term demography data from permanent forest plots at the Paracou Tropical Forest Research Station in French Guiana, we analyzed the relative influence of competition and climate on tree mortality. We found that self-thinning is evident at the stand level, and is associated with clumped mortality at smaller scales (< 2...

PSP FIELDS Fluxgate Magnetometer (MAG) Magnetic Field Vectors, Spacecraft, SC, Coordinates, 4 samples/cycle, Level 2 (L2), 3.413 ms Data

Stuart D. Bale, Robert J. MacDowall, Andriy Koval, Marc Pulupa, Timothy Quinn & Peter Schroeder
Parker Solar Probe FIELDS Instrument Suite Fluxgate Magnetometer, MAG, Data: The time resolution of the MAG time series data varies with instrument mode ranging from 2.289 samples/s to 292.9 samples/s. These two data sampling rates corresponding to 2 samples or 256 samples per 0.874 s where 0.874 s is equal to 2^25 divided 38.4 MHz. The Magnetometer has four ranges: ±1024 nT, ±4096 nT, ±16,384 nT, and ±65,536 nT. The Magnetometer Range is selected by...

Parker Solar Probe Ephemeris, Heliocentric Trajectories, Heliographic, Heliographic Inertial, and Solar Ecliptic Coordinates, HelioWeb, Daily Data

Adam Szabo
Heliocentric trajectories for Parker Solar Probe in Heliographic, HG, Heliographic Inertial, HGI, and Solar Ecliptic, SE, Coordinates.

2010 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly

David Fouhey, Meng Jin, Mark Cheung, Abndres Munoz-Jaramillo, Richard Galvez, Rajat Thomas, Paul Wright, Alexander Szenicer, Monica G. Bobra, Yang Liu & James Mason
We present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a...

Registration Year

  • 2021
    7
  • 2020
    8
  • 2019
    12
  • 2017
    1
  • 2016
    3
  • 2014
    2

Resource Types

  • Dataset
    32
  • Text
    1

Affiliations

  • Goddard Space Flight Center
    24
  • Southwest Research Institute
    10
  • Hansen Experimental Physics Laboratory, Stanford University
    9
  • Electrical Engineering and Computer Science Department, University of Michigan
    9
  • SUPA School of Physics and Astronomy, University of Glasgow
    9
  • NASA Goddard Space Flight Center
    9
  • Center for Data Science, New York University
    9
  • Lockheed Martin Solar & Astrophysics Laboratory
    9
  • SETI Institute
    9
  • Department of Psychiatry, University of Amsterdam
    9