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Tracking dataset citations in scientific publications provide multiple benefits: obtaining citation indices for quantitative evaluation of the dataset scientific impact, learning about dataset usage in applied sciences, credits to dataset creators, datasets co-citation relationships and many more.
Soil heterotrophic respiration (SHR), one of the primary carbon fluxes from terrestrial ecosystems to the atmosphere, is important for carbon-climate feedbacks because of its sensitivity to available litter and soil carbon, climatic conditions, and nutrient availability. However, until recently limited SHR data were available, and most published global SHR estimates have either a short time span, coarse spatial resolution, or reply on overly-simple model formulations. To better understand and quantify the global distribution of SHR and its sensitivity to climate variability, we produced a new global SHR dataset using Random Forest algorithms, up-scaling 455 point data from the Global Soil Respiration Database (SRDB 4.0) with gridded fields of climatic, edaphic and productivity as explanatory variables. We estimated a global total SHR of 46.8 Pg C yr-1 over 1985-2013 (95% confidence interval: 38.6-56.3 Pg C yr-1), with a significant increasing trend of 0.03 Pg C yr-2 during this period. We found that the choice of soil moisture datasets contributes more to the difference among these data-driven SHR members rather than that of productivity, temperature and precipitation data sources. We also analyzed the influence of climatic variables on the inter-annual variability (IAV) of our SHR product. Water availability was the dominant driver of IAV at global scales, although the inferred sensitivity depends on the choice of the soil moisture gridded dataset. At the ecosystem scale, temperature strongly controls the IAV of SHR in tropical forests, while water availability dominates in extra-tropical forest and semi-arid regions. Our machine-learning gridded SHR dataset and outputs from process-based land surface models (TRENDYv6) show agreement for a strong association between water variability and SHR IAV at the global scale, but the two approaches lead to different temporal trend globally and different controlling variables for IAV at the ecosystem scale. Our study provides evidence for the pervasive and important role of water availability in driving SHR, indicating both a direct effect limiting decomposition rates and an indirect effect through the amount of fresh organic matter made available to SHR from productivity. In consideration of potential limitations and uncertainties remaining in our data-driven SHR datasets, we call for a more scientifically designed observation network for SHR, more observation data compilation, and increased use of deep learning methods making maximum use of observation data in hand. This will benefit process-based models, and improve our understanding of SHR response to future anomalous environmental conditions.
After the successful Coastal Zone Color Scanner (CZCS, 1978-1986), demonstration that quantitative estimations of geophysical variables such as chlorophyll a and diffuse attenuation coefficient could be derived from top of the atmosphere radiances, a number of international missions with ocean color capabilities were launched beginning in the late 1990s. Most notable were those with global data acquisition capabilities, i.e., the Ocean Color and Temperature Sensor (OCTS 1996-1997), the Sea-viewing Wide Field-of-view Sensor (SeaWiFS, United States, 1997-present), two Moderate Resolution Imaging Spectroradiometers, (MODIS, United States, Terra/2000-present and Aqua/2002-present), the Global Imager (GLI, Japan, 2002-2003), and the Medium Resolution Imaging Spectrometer (MERIS, European Space Agency, 2002-present). These missions have provided data of exceptional quality and continuity, allowing for scientific inquiries into a wide variety of marine research topics not possible with the CZCS. This review focuses on the scientific advances made over the past decade using these data sets.
Satellite remote sensing data have proven to be vital for various scientific and applications needs. However, the usability of these data depends not only on the data values but also on the ability of data users to assess and understand the quality of these data for various applications and for comparison or inter-usage of data from different sensors and models. In this paper, we describe some aspects of capturing, harmonizing and delivering this information to users in the framework of distributed web-based data tools.
About 2,200 samples were collected from the Moon during the Apollo missions, forming a unique and irreplaceable legacy of the Apollo program. These samples, obtained at tremendous cost and great risk, are the only samples that have ever been returned by astronauts from the surface of another planetary body. These lunar samples have been curated at NASA Johnson Space Center and made available to the global research community. Over more than 45 years, a vast body of petrological, geochemical, and geochronological studies of these samples have been amassed, which helped to expand our understanding of the history and evolution of the Moon, the Earth itself, and the history of our entire solar system. Unfortunately, data from these studies are dispersed in the literature, often only available in analog format in older publications, and/or lacking sample metadata and analytical metadata (e.g., information about analytical procedure and data quality), which greatly limits their usage for new scientific endeavors. Even worse is that much lunar data have never been published, simply because no forum existed at the time (e.g., electronic supplements). Thousands of valuable analyses remain inaccessible, often preserved only in personal records, and are in danger of being lost forever, when investigators retire or pass away. Making these data and metadata publicly accessible in a digital format would dramatically help guide current and future research and eliminate duplicated analyses of precious lunar samples.
The Magellan Venus orbiter carries only one scientific instrument: a 12.6-centimeter-wavelength radar system shared among three data-taking modes. The synthetic-aperture mode images radar echoes from the Venus surface at a resolution of between 120 and 300 meters, depending on spacecraft altitude. In the altimetric mode, relative height measurement accuracies may approach 5 meters, depending on the terrain's roughness, although orbital uncertainties place a floor of about 50 meters on the absolute uncertainty. In areas of extremely rough topography, accuracy is limited by the inherent line-of-sight radar resolution of about 88 meters. The maximum elevation observed to date, corresponding to a planetary radius of 6062 kilometers, lies within Maxwell Mons. When used as a thermal emission radiometer, the system can determine surface emissivities to an absolute accuracy of about 0.02. Mosaicked and archival digital data products will be released in compact disk (CDROM) format.
This paper describes the architecture and capabilities of the Science Data Processing System (SDPS) for the EOS MLS. The SDPS consists of two major components--the Science Computing Facility and the Science Investigator-led Processing System. The Science Computing Facility provides the facilities for the EOS MLS Science Team to perform the functions of scientific algorithm development, processing software development, quality control of data products, and scientific analyses. The Science Investigator-led Processing System processes and reprocesses the science data for the entire mission and delivers the data products to the Science Computing Facility and to the Goddard Space Flight Center Earth Science Distributed Active Archive Center, which archives and distributes the standard science products.
Balloon-borne ozone instrumentation (ozonesondes), launched at fixed sites, is used to study local patterns in stratospheric and tropospheric ozone and to provide validation for satellite ozone products and model calculations of ozone. A paucity of coordinated ozonesonde data in the southern hemisphere tropics is being remedied in a 3-year project of coordinated ozonesondes launches at 10 sites. The data are available to the scientific community at the SHADOZ website at NASA/Goddard. Stations and their operational characteristics, with examples of ozone observations, are given. One expectation of SHADOZ is that wide dissemination of data and interaction with users and field projects will leverage local funding to maintain infrastructure and operations. SHADOZ data are well-suited for educational projects in which students learn about regional ozone patterns.
This repository contains python scripts and numerical data accompanying the paper: "Leveraging Interpolation Models and Error Bounds for Verifiable Scientific Machine Learning," Tyler Chang, Andrew Gillette, Romit Maulik, 2024. The following subdirectories are included: - "interpolants" contains our interpolation scripts used for all studies - "experiments" contains scripts demonstrating our experiments with synthetic data - "airfoil" contains scripts demonstrating our experiments with the publicly available UIUC airfoil dataset. Further instructions are provided in READMEs within the sub-directories.
MDSplus is a software tool designed for data acquisition, storage, and analysis of complex scientific experiments. Over the years, MDSplus has primarily been used for data management for fusion experiments. This paper demonstrates that MDSplus can be used for a much wider variety of systems and experiments. We present a step-by-step tutorial describing how to create a simple experiment, manage the data, and analyze it using MDSplus and Python. To this end, a custom example device was developed to be used as the data source. This device was built on an open-source electronic hardware platform, and it consists of a microcontroller and two sensors. Finally, we read data from these sensors, store it in MDSplus, and use JupyterLab to visualize and process it.
MDSplus is a software tool designed for data acquisition, storage, and analysis of complex scientific experiments. Over the years, MDSplus has primarily been used for data management for fusion experiments. This paper demonstrates that MDSplus can be used for a much wider variety of systems and experiments. We present a step-by-step tutorial describing how to create a simple experiment, manage the data, and analyze it using MDSplus and Python. To this end, a custom example device was developed to be used as the data source. This device was built on an opensource electronic hardware platform, and it consists of a microcontroller and two sensors. We read data from these sensors, store it in MDSplus, and use JupyterLab to visualize and process it. This project and code demo are available on the GitHub site at this URL: https://github.com/santorofer/MDSplusAndCustomeDevices
Proxemy Research had a grant from NASA to perform scientific research using Mars Global Surveyor (MGS) data to study lava flows on Mars. Here we summarize the scientific progress and accomplishments of this grant. Scientific publications and abstracts of presentations are indicated in the final section.
The purpose of this data was to generate a scientific article that describes the responses of tropical roots to a warming experiment and to the effect of two consecutive hurricanes in Puerto Rico (Yaffar et al. in review). This data is from 10 months of minirhizotron images taken every 2 weeks at the experimental warming Tropical Responses to Altered Climate Experiment (TRACE) plots in Puerto Rico before and after Hurricanes Irma and Maria. This project has 3 warmed plots (plot 2,4,6) and 3 control plots (plot 1,3,5) with 2 minirhizotron tubes at each plot. As part of the data, there is also root data taken from cores and in-growth cores. Additionally, there is soil nutrient concentration data, soil microclimate, total leaf area, and canopy openness taken by Reed et al. 2020, and the TRACE census. Data files are in CSV format and the R code included in this package can be used with R 3.4.4 (R Core Team and contributors worldwide).
The lunar laser ranging project at McDonald Observatory provides the unique opportunity to acquire successfully precise range data for the earth-moon system. From the experiment's inception, the obligation was recognized to make these data available to the general scientific community in a reasonably useable form and in a realistic time frame. The documentation to be used in conjunction with the 1979 April deposit into the National Space Science Data Center which contains normal points, filtered observations and unfiltered photon stops for the months July through December, 1978 are reported.
In the early days of spaceflight, space life sciences data were been collected and stored in numerous databases, formats, media-types and geographical locations. While serving the needs of individual research teams, these data were largely unknown/unavailable to the scientific community at large. As a result, the Space Act of 1958 and the Science Data Management Policy mandated that research data collected by the National Aeronautics and Space Administration be made available to the science community at large. The Biomedical Informatics and Health Care Systems Branch of the Space Life Sciences Directorate at JSC and the Data Archive Project at ARC, with funding from the Human Research Program through the Exploration Medical Capability Element, are fulfilling these requirements through the systematic population of the Life Sciences Data Archive. This program constitutes a formal system for the acquisition, archival and distribution of data for Life Sciences-sponsored experiments and investigations. The general goal of the archive is to acquire, preserve, and distribute these data using a variety of media which are accessible and responsive to inquiries from the science communities.
The weather affects every portion of a scientific balloon mission, from payload integration, to launch, float, termination, and recovery. Forecasting for these missions is very specialized and unique in many aspects. This presentation will detail the tools, data, and processes that Columbia Scientific Balloon Facility (CSBF) Meteorology uses to produce its forecasts.
To release open, standards-compliant processed data sets in the Open Science Data Repository (OSDR), the GeneLab Data Processing team works with the scientific community through the OSDR Analysis Working Groups to design and build open-source data processing pipelines. Once baselined internally, these pipelines are wrapped into workflows and published on the NASA GeneLab Data Processing public GitHub repository along with detailed instructions for installation and use. Each workflow must be approved through NASA's Software Release Authorization (SRA) process prior to publishing. However, the SRA process lacks sufficient documentation and clarity regarding which forms are applicable for new open-source software that utilizes publicly available 3rd party tools, and the SRA process can take several months to complete, making sharing software outside of NASA cumbersome and in contradiction with the concept of Open Science. Furthermore, the SRA process was designed as a one-size fits all approach and thus many of the questions asked are not applicable to our open-source workflows. Here we describe the software provided on the NASA GeneLab Data Processing GitHub repository, summarize our experiences with the SRA process to release these software, and propose a more stream-lined approach for review of open-source projects.
To release open, standards-compliant processed data sets in the Open Science Data Repository (OSDR), the GeneLab Data Processing team works with the scientific community through the OSDR Analysis Working Groups to design and build open-source data processing pipelines. Once baselined internally, these pipelines are wrapped into workflows and published on the NASA GeneLab Data Processing public GitHub repository along with detailed instructions for installation and use. Each workflow must be approved through NASA's Software Release Authorization (SRA) process prior to publishing. However, the SRA process lacks sufficient documentation and clarity regarding which forms are applicable for new open-source software that utilizes publicly available 3rd party tools, and the SRA process can take several months to complete, making sharing software outside of NASA cumbersome and in contradiction with the concept of Open Science. Furthermore, the SRA process was designed as a one-size fits all approach and thus many of the questions asked are not applicable to our open-source workflows. Here we describe the software provided on the NASA GeneLab Data Processing GitHub repository, summarize our experiences with the SRA process to release these software, and propose a more stream-lined approach for review of open-source projects.
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