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This document includes 13 composite data products (CDPs) produced for American Recovery and Reinvestment Act (ARRA) fuel cell material handling equipment, with data through the third quarter of 2015.
Two recent large data releases for the Atacama Cosmology Telescope (ACT), called DR4 and DR5, are available for public access. These data include temperature and polarization maps that cover nearly half the sky at arcminute resolution in three frequency bands; lensing maps and component-separated maps covering ~ 2,100 deg 2 of sky; derived power spectra and cosmological likelihoods; a catalog of over 4,000 galaxy clusters; and supporting ancillary products including beam functions and masks. The data and products are described in a suite of ACT papers; here we provide a summary. In order to facilitate ease of access to these data we present a set of Jupyter IPython notebooks developed to introduce users to DR4, DR5, and the tools needed to analyze these data. The data products (excluding simulations) and the set of notebooks are publicly available on the NASA Legacy Archive for Microwave Background Data Analysis (LAMBDA); simulation products are available on the National Energy Research Scientific Computing Center (NERSC).
The National Ecological Observatory Network (NEON) provides over 180 distinct data products from 81 sites (47 terrestrial and 34 freshwater aquatic sites) within the United States and Puerto Rico. These data products include both field and remote sensing data collected using standardized protocols and sampling schema, with centralized quality assurance and quality control (QA/QC) provided by NEON staff. Such breadth of data creates opportunities for the research community to extend basic and applied research while also extending the impact and reach of NEON data through the creation of derived data products—higher level data products derived by the user community from NEON data. Derived data products are curated, documented, reproducibly-generated datasets created by applying various processing steps to one or more lower level data products—including interpolation, extrapolation, integration, statistical analysis, modeling, or transformations. Derived data products directly benefit the research community and increase the impact of NEON data by broadening the size and diversity of the user base, decreasing the time and effort needed for working with NEON data, providing primary research foci through the development via the derivation process, and helping users address multidisciplinary questions. Creating derived data products also promotes personal career advancement to those involved through publications, citations, and future grant proposals. However, the creation of derived data products is a nontrivial task. Here we provide an overview of the process of creating derived data products while outlining the advantages, challenges, and major considerations.
This publication includes 98 composite data products (CDPs) produced for next generation hydrogen stations, with data through the fourth quarter of 2017. These CDPs include data from retail stations only.
This publication includes 93 composite data products (CDPs) produced for next generation hydrogen stations, with data through the fourth quarter of 2018. These CDPs include data from retail stations only.
This publication includes 98 composite data products (CDPs) produced for next generation hydrogen stations, with data through the third quarter of 2019. These CDPs include data from retail stations only and represent the following analysis categories: deployment, safety, maintenance and reliability, performance, cost, utilization, hydrogen quality, and component energy.
This publication includes 86 composite data products (CDPs) produced for next generation hydrogen stations, with data through the fourth quarter of 2016. These CDPs include data from retail stations only.
This publication includes 92 composite data products (CDPs) produced for next generation hydrogen stations, with data through the second quarter of 2017. These CDPs include data from retail stations only.
This publication includes 92 composite data products (CDPs) produced for next generation hydrogen stations, with data through the second quarter of 2017. These CDPs include data from retail stations only.
This publication includes 80 composite data products (CDPs) produced in Spring 2016 for next generation hydrogen stations, with data through the third quarter of 2016. These CDPs include data from retail stations only.
This publication includes over 100 composite data products (CDPs) produced for next generation hydrogen stations, with data through the fourth quarter of 2020.
This publication includes 91 composite data products (CDPs) produced for next generation hydrogen stations, with data through the second quarter of 2021.
This publication includes 54 composite data products (CDPs) produced for next generation hydrogen stations, with data through the second quarter of 2022.
The Production Data for UShWEM.xlsx is an Excel file that is formatted and organized similarly to the Production Streams sheet of the FECM/NETL Unconventional Shale Well Economic Model (UShWEM). The purpose of this file is to allow the user to import completion design and time-series production data for hundreds of wells into the UShWEM easily and quickly, and have their well data saved safely in an external location. For instructions on how to use the Production Data for UShWEM.xlsx file, see section 2.3 of the FECM/NETL Unconventional Shale Well Economic Model: User’s Manual.
The low productivity of the oil wells in the Tuscaloosa Marine Shale (TMS) Trend, located in Louisiana and Mississippi, is a mystery. Production data from 55 wells in the TMS Trend were analyzed to identify possible means of enhancing well productivity. The -1/2 slope in a log–log plot for reservoir linear flow (RLF) was observed for some wells, but not all, and the -1 slope for boundary–dominated flow (BDF) has not been seen in the past 5 years of production. The behavior of the TMS wells is attributed to the ultralow permeability of the TMS matrix and the decline of fracture conductivity, both of which delay the BDF. On the basis of the concept of the distance of investigation, derived from the radius–of–investigation concept, the matrix permeability of the seven TMS wells was estimated to be between 34 and 65 nd, with an average of 51 nd. A new mathematical model for production decline of multifractured horizontal wells was developed taking into consideration the time–dependent fracture conductivity during the BDF period. This model fits TMS–well production data, with an average error of 6.12% and an R2 value of 0.96. Assuming constant matrix permeability, fitting the new model to the production data in the BDF period gives a rate of fracture–conductivity decline of between 0.20 and 0.74% per month. The new mathematical model reveals opportunities to optimize well completion to enhance well productivity in the TMS Trend. For a given amount of fracture proppant allocated to a well, the shortest possible fracture spacing should be used to maximize well productivity. If the number of fractures is fixed as a constraint of well completion design, well productivity is inversely proportional to the square root of fracture spacing (i.e., if the fracture spacing is shortened by fourfold, well productivity is expected to double). If the horizontal wellbore length is fixed as a constraint of well completion, well productivity is inversely proportional to fracture spacing (i.e., if the fracture spacing is shortened by 50%, well productivity is expected to double).
This document describes the data products and processing services to be delivered by the NSF-DOE Vera C. Rubin Observatory whilst performing the Legacy Survey of Space and Time (LSST). LSST will deliver three levels of data products and services. Prompt data products are computed and released within 24 hours of observation, and include images, difference images, catalogs of sources and objects detected in difference images, and catalogs of Solar System objects. Their primary purpose is to enable rapid follow-up of time-domain events. Data Release data products are computed during annual processing campaigns, and include well-calibrated single-epoch images, deep coadds, and catalogs of objects, sources, and forced sources, enabling static sky and precision time-domain science. The Science Platform will allow for the creation of User Generated data products and will enable science cases that greatly benefit from co-location of user processing and/or data within the Rubin Observatory Data Access Center. LSST will also devote 10% of observing time to programs with special cadence. Their data products will be created using the same software and hardware as Prompt and Data Release products. All data products will be made available using user-friendly databases and web services.
Access to information on neutrino nucleus interactions is critical to the success of all neutrino oscillation experiments. MINERvA's rich dataset covers a range of energies and nuclei unique amongst experiments, and as such is critical to the community in building the important shared knowledge needed to unravel the mysteries of the neutrino. In particular, its dataset provides the greatest statistical coverage in in the range of neutrino energies pertinent for DUNE until DUNE's near detector begins operation. Historically, such significant datasets in neutrino physics have been preserved primarily through their published results. While meaningful and useful, this limits the ability to explore the data to its fullest extent as new perspectives continue to form. MINERvA has undertaken a major effort to break this trend and preserve its data in a format to be as analyzable as possible from outside the collaboration. This has culminated in the officially-released MINERvA Open Data Product for the community to take advantage of and utilize. Maintaining direct access to the dataset in an analyzable form will allow new insights to continue to be extracted indefinitely. This talk will cover the contents of this product, the information included (and excluded), the tools provided to utilize the product effectively, the support MINERvA intends to provide in its use, and some lessons learned through the process.
The Open-source Data Inventory for Anthropogenic CO 2 (ODIAC) is a global high-spatial-resolution gridded emissions data product that distributes carbon dioxide (CO 2 ) emissions from fossil fuel combustion. The emissions spatial distributions are estimated at a 1 × 1 km spatial resolution over land using power plant profiles (emissions intensity and geographical location) and satellite-observed nighttime lights. This work describes the year 2016 version of the ODIAC emissions data product (ODIAC2016) and presents analyses that help guide data users, especially for atmospheric CO 2 tracer transport simulations and flux inversion analysis. Since the original publication in 2011, we have made modifications to our emissions modeling framework in order to deliver a comprehensive global gridded emissions data product. Major changes from the 2011 publication are (1) the use of emissions estimates made by the Carbon Dioxide Information Analysis Center (CDIAC) at the Oak Ridge National Laboratory (ORNL) by fuel type (solid, liquid, gas, cement manufacturing, gas flaring, and international aviation and marine bunkers); (2) the use of multiple spatial emissions proxies by fuel type such as (a) nighttime light data specific to gas flaring and (b) ship/aircraft fleet tracks; and (3) the inclusion of emissions temporal variations. Using global fuel consumption data, we extrapolated the CDIAC emissions estimates for the recent years and produced the ODIAC2016 emissions data product that covers 2000–2015. Our emissions data can be viewed as an extended version of CDIAC gridded emissions data product, which should allow data users to impose global fossil fuel emissions in a more comprehensive manner than the original CDIAC product. Our new emissions modeling framework allows us to produce future versions of the ODIAC emissions data product with a timely update. Such capability has become more significant given the CDIAC/ORNL's shutdown. The ODIAC data product could play an important role in supporting carbon cycle science, especially modeling studies with space-based CO 2 data collected in near real time by ongoing carbon observing missions such as the Japanese Greenhouse gases Observing SATellite (GOSAT), NASA's Orbiting Carbon Observatory-2 (OCO-2), and upcoming future missions. The ODIAC emissions data product including the latest version of the ODIAC emissions data (ODIAC2017, 2000–2016) is distributed from http://db.cger.nies.go.jp/dataset/ODIAC/ with a DOI (https://doi.org/10.17595/20170411.001).