Search

The latest release of GRACE gravity field coefficients (Release-05, or RL05) are evaluated for ocean applications. Data have been processed using the current methodology for Release-04 (RL04) coefficients, and have been compared to output from two different ocean models. Results indicate that RL05 data from the three Science Data Centers - the Center for Space Research (CSR), GeoForschungsZentrum (GFZ), and Jet Propulsion Laboratory (JPL) - are more consistent than the previous RL04 data. Moreover, the variance of residuals with the output of an ocean model is 50-60% lower for RL05 data than for RL04 data. A more optimized destriping algorithm is also tested, which improves the results slightly. By comparing the GRACE maps with two different ocean models, we can better estimate the uncertainty in the RL05 maps. We find the standard error to be about 1 cm (equivalent water thickness) in the low- and mid-latitudes, and between 1.5 and 2 cm in the polar and sub-polar oceans, which is comparable to estimated uncertainty for the output from the ocean models. [ABSTRACT FROM AUTHOR]

This paper describes a document analysis of 24 metadata application profiles (MAPs) used by academic libraries in the United States. The MAPs under study were collected from (a) the DLF AIG Metadata Application Profile Clearinghouse and (b) a Google search of.edu domains. Data collection and analysis took place between December 2020 and February 2021. While most of the MAPs under review provided metadata guidelines for digital collections, a small number were intended for institutional repositories or research data management. The study's findings reveal MAP features and content, usage of controlled vocabularies and standards, and other characteristics pertaining to MAP document scope, contents and format in this context. In addition to its discussion of the literature, the paper's findings should help metadata specialists and others involved in digital collection management gain insights useful in the development or revision of their own metadata documentation. Further, these findings offer a current glimpse of metadata application practices among U.S. academic libraries generally. [ABSTRACT FROM AUTHOR]

The rapid deployment of information and communications technology (ICT) across the globe has led to a network of high-density computer data centers to store, process and transmit information. These large-scale technology warehouses consume vast amounts of energy for running the compute infrastructure and auxiliary cooling resources. Recent literature [7] has suggested the possibility of globally staggering compute workloads to take advantage of local climatic conditions as a means to reducing cooling energy costs. This paper further explores this premise by performing an in-depth analysis of the environmental and economic burden of managing the thermal infrastructure of a globally connected data center network. The paper examines a case study where the potential energy savings achievable by staggering workloads across arbitrarily chosen data centers in the U.S., India, and Russia are examined. The results show that the environmental benefit of such off-shoring is mostly dependent on the fuel mix of the grid to which the workload is transferred and the energy consumption in each location. Further, we show that dynamic optimization of the thermal workloads based on local weather patterns can reduce the environmental burden by up to 30%. The paper concludes with a detailed economic assessment. For the case study in this paper, we find that such global workload staggering can potentially reduce operational costs by nearly 35%. [ABSTRACT FROM AUTHOR]

This study presents a data-driven spatial interpolation algorithm based on physics-informed graph neural networks used to develop a thermal Earth model for the conterminous United States. The model was trained to approximately satisfy Fourier's Law of conductive heat transfer by simultaneously predicting subsurface temperature, surface heat flow, and rock thermal conductivity. In addition to bottomhole temperature measurements, we incorporated other spatial and physical quantities as model inputs, such as depth, geographic coordinates, elevation, sediment thickness, magnetic anomaly, gravity anomaly, gamma-ray flux of radioactive elements, seismicity, electrical conductivity, and proximity to faults and volcanoes. With a spatial resolution of 18 k m 2 per grid cell, we predicted heat flow at surface as well as temperature and rock thermal conductivity across depths of 0 - 7 k m at an interval of 1 k m . Our model showed temperature, surface heat flow and thermal conductivity mean absolute errors of 6. 4 ∘ C , 6.9 m W / m 2 and 0.04 W / m - K , respectively. This thorough modeling of the Earth's thermal processes is crucial to understanding subsurface phenomena and exploiting natural underground resources. Our thermal Earth model is available as web application at https://stm.stanford.edu, feature layers on ArcGIS at https://arcg.is/nLzzT0, and tabulated data on the Geothermal Data Repository at https://gdr.openei.org/submissions/1592. [ABSTRACT FROM AUTHOR]

Data centers are a significant and growing component of electricity demand in the United States. This paper presents a bottom-up model that can be used to estimate total data center electricity demand within a region as well as the potential electricity savings associated with energy efficiency improvements. The model is applied to estimate 2008 U.S. data center electricity demand and the technical potential for electricity savings associated with major measures for IT devices and infrastructure equipment. Results suggest that 2008 demand was approximately 69 billion kilowatt hours (1.8% of 2008 total U.S. electricity sales) and that it may be technically feasible to reduce this demand by up to 80% (to 13 billion kilowatt hours) through aggressive pursuit of energy efficiency measures. Measure-level savings estimates are provided, which shed light on the relative importance of different measures at the national level. Measures applied to servers are found to have the greatest contribution to potential savings. [ABSTRACT FROM PUBLISHER]

This paper elucidates the data sources and data preparation procedures used in developing the sectoral environmental data of the US. The database described in this paper interlinks (1) Input–Output Table (IOT), (2) environmental emission and resources use statistics, and (3) characterization factors from Life Cycle Impact Assessment (LCIA) that quantify environmental impacts. Each of these three modules was designed to describe (1) the economic process that generates environmental interventions, (2) the quantity of the environmental intervention generated and (3) the process through which these environmental interventions realize environmental impacts, respectively. The resulting database encompasses 1344 different types of environmental interventions generated by 480 commodities of the US input–output table, linked to 86 commonly used LCIA models. This paper aims to share the experiences of and to elucidate the procedures and the data sources used for developing the sectoral environmental database in the US. [ABSTRACT FROM AUTHOR]

Background: Dexamethasone was approved for use in hospitalized COVID-19 patients early in the pandemic based on the RECOVERY trial, but evidence is still needed to support its real-world effectiveness in heterogeneous populations of patients with a wide range of comorbidities. Methods: COVID-19 inpatients represented within the National COVID Cohort Collaborative (N3C) Data Enclave, prior to vaccine availability, were studied. Primary outcome was in-hospital death; secondary outcome was combined in-hospital death and severe outcome defined by use of ECMO or mechanical ventilation. Missing data were imputed with single imputation. Dexamethasone-treated patients were propensity score (PS) matched to non-dexamethasone-treated controls, stratified by remdesivir treatment and based on demographics, baseline laboratory values, comorbidities, and amount of missing data before imputation. Treatment benefit was quantified using logistic regression. Further sensitivity analyses were performed using clinical adjusters in matched groups and in strata defined by quartiles of PS. Results: Dexamethasone treatment was associated with reduced risk of in-hospital mortality for n = 1,263 treated, matched 1:3 to untreated, patients not receiving remdesivir (OR = 0.77, 95% CI: 0.62 to 0.95, p = 0.017), and for n = 804 treated, matched 1:1 to untreated, patients receiving remdesivir (OR = 0.74, 95% CI: 0.53 to 1.02, p = 0.054). Treatment showed secondary outcome benefit. In sensitivity analyses, treatment effect generally remained similar with some heterogeneity of benefit across quartiles of PS, possibly reflecting concentration of benefit among the more severely affected. Conclusions: We add evidence that dexamethasone provides benefit with respect to mortality and severe outcomes in a diverse, national hospitalized sample, prior to vaccine availability. [ABSTRACT FROM AUTHOR]

This article explores the use of collegiality as a factor in promotion-and-tenure cases for academic librarians. Using data from library promotion-and-tenure documents at R-1 universities in the United States, the researchers asked three questions: How many R-1 university libraries include any mention of collegiality in their tenure guidelines? What language is used to express the concept of collegiality and is it defined in a clear, objective manner? How important is the concept of collegiality to the process of tenure? The results reveal that collegiality or related concepts are mentioned in less than one-third of tenure documents. [ABSTRACT FROM AUTHOR]

Persistent regional and global power dynamics will ensure that adversaries will continue to push boundaries in the geopolitical, economic, technological, intelligence, and military arenas. Improved tactics, techniques, and procedures are needed to address, mitigate, and counter behaviors critical to the United States, as nations and nonstate actors test new norms across all warfighting domains, space in particular. In order to analyze and process multidomain signatures in support of operational decision-making, the United States can harness artificial intelligence and machine learning decision support system statistical models to augment collection capabilities, data repositories, and threat analysis. [ABSTRACT FROM AUTHOR]

Given the mismatch between the large volume of data archived for the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and limited personnel and computational resources for downscaling, only a small fraction of the CMIP6 archive can be downscaled. In this work, we develop an approach to robustly sample projected hydroclimate states in CMIP6 for downscaling to test whether the selection of a single initial condition (IC) ensemble member from each CMIP6 model is sufficient to span the range of modeled hydroclimate over the conterminous United States (CONUS) and CONUS sub-regions. We calculate the pattern-centered root mean square difference of IC ensemble member anomalies relative to each model's historical climatology for shared socioeconomic pathway (SSP) projections over 30-year time periods and compare the ratio of inter-model to intra-model variability for this metric. Regardless of SSP, inter-model variability is generally much greater than intra-model variability at the scales of the CONUS as a whole, as well as for most CONUS sub-regions. However for some variables and scenarios, inter- and intra-model variability are similar at sub-CONUS scales, indicating that selecting a single IC ensemble member per model may be sufficient to sample the range of projected hydroclimate states in the 21st Century across CONUS, but for specific regions and variables, more careful selection of ensemble members may be necessary. Regionally-resolved Taylor diagrams identify where more IC ensemble member downscaling efforts should be focused if resources are available to do so. Our results suggest that, with parsimonious sampling, the requisite computational expense of downscaling temperature and precipitation fields over the CONUS for subsequent CMIP activities may increase only marginally despite the great increase in data volumes with each successive CMIP phase. [ABSTRACT FROM AUTHOR]

Purpose: Uncertainties caused by political risks can drastically affect global supply chains. However, the supply chain management literature has thus far developed rather limited knowledge on firms' perception of and reactions to increased political risks. This study has two main purposes: to explore the relationship between extant risk exposure and perceived firm-specific political risk and to understand the impact of firm-specific political risk on firms' vertical integration and diversification strategies. Design/methodology/approach: The authors developed a unique dataset for testing our hypotheses. Specifically, the authors sampled manufacturers (SIC20-39) listed in the United States from 2002 to 2019. The authors collected financial and diversification data from Compustat, vertical integration data from the Frésard-Hoberg-Phillips Vertical Relatedness Data Library and political risk data from the Economic Policy Uncertainty database. This data collection process yielded 1,287 firms (8,329 observations) with available data for analysis. Findings: A two-way fixed-effect regression analysis of panel data revealed that firms tend to be more sensitive to political risk when faced with income stream uncertainty or strategic risk. By contrast, exposure to stock returns uncertainty does not significantly influence firms' sensitivity toward political risk. Moreover, firm-specific political risk is positively associated with vertical integration and product diversification. However, firm-specific political risk does not result in higher levels of geographical diversification. Originality/value: This study joins the literature that systematically explores the antecedents and implications of firm-specific political risk, thus broadening the scope of supply chain risk management. [ABSTRACT FROM AUTHOR]

Written during the transition of Bitcoin mining power to the United States in the wake of China's Summer 2021 Bitcoin ban, this article explores how United States policymakers could incentivize Bitcoin miners to help, rather than hinder, United States climate and electricity goals. While Bitcoin mining's high electricity consumption has traditionally been seen as problematic from an environmental and grid reliability perspective, this article proposes a win-win solution for Bitcoin mining, the environment, and grid reliability by synergizing mining operations with renewable energy projects. Where Bitcoin miners require an abundance of inexpensive electricity, remote renewable energy projects—particularly wind farms—have the potential to produce excess electricity that is either unprofitable or risks grid overload. By implementing overlay zoning with the locational, renewable, and demand-response considerations that this article proposes, policymakers can incentivize electricity-intensive Bitcoin mining operations to co-locate near remote renewable energy projects, fostering a mutually beneficial relationship that will support, rather than threaten, United States climate and electricity goals. [ABSTRACT FROM AUTHOR]

Healthcare resources are published annually in repositories such as the AHA Annual Survey DatabaseTM. However, these data repositories are created via manual surveying techniques which are cumbersome in collection and not updated as frequently as website information of the respective hospital systems represented. Also, this resource is not widely available to patients in an easy-to-use format. Network analysis techniques have the potential to create topological maps which serve to aid in pathfinding for patients in their search for healthcare services. This study explores the topological structure of forty United States academic health center websites. Network analysis is utilized to analyze and visualize 48,686 webpages. Several elements of network structure are examined including basic network properties, and centrality measures distributions. The Louvain community detection algorithm is used to examine the extent to which these techniques allow identification of healthcare resources within networks. The results indicate that websites with related healthcare services tend to form observable clusters useful in mapping key resources within a hospital system. [ABSTRACT FROM AUTHOR]

The LibGuides platform is a ubiquitous tool in academic libraries and is commonly used by librarians to compile and share lists of recommended social science numerical data resources with users. This study leverages the machine-accessible nature of the LibGuides platform to collect links to data and statistical resources from over 10,000 LibGuide pages at 123 R1 research institutions in the United States. After substantial data cleaning and normalization, an analysis of the most common resources on those guides provides a unique window into the data repositories, libraries, archives, statistical data platforms, and other machine-readable data sources that are most popular on academic library guides. Results show that freely available resources from U.S. government agencies are among the most common to be included on data and statistical resources guides across institutions. Resources requiring paid licenses or memberships for full access, such as Statistical Insight (ProQuest), Social Explorer, and ICPSR are linked to most frequently overall, regardless of the percentage of institutions that include them. Findings also suggest that libraries are more likely to share traditional licensed statistical resources (e.g., Cambridge's Historical Statistics of the United States) and collections of simple charts and graphs (e.g., Statista) than more robust and complex microdata resources (e.g., IPUMS). [ABSTRACT FROM AUTHOR]

The complexity and privacy issues inherent in social science research data makes research data management (RDM) an essential skill for future researchers. Data management training has not fully addressed the needs of graduate students in the social sciences. To address this gap, this study used a mixed methods design to investigate the RDM awareness, preparation, confidence, and challenges of social science graduate students. A survey measuring RDM preparedness and training needs was completed by 98 graduate students in a school of education at a research university in the southern United States. Then, interviews exploring data awareness, knowledge of RDM, and challenges related to RDM were conducted with 10 randomly selected graduate students. All participants had low confidence in using RDM, but United States citizens had higher confidence than international graduate students. Most participants were not aware of on-campus RDM services, and were not familiar with data repositories or data sharing. Training needs identified for social science graduate students included support with data documentation and organization when collaborating, using naming procedures to track versions, data analysis using open access software, and data preservation and security. These findings are significant in highlighting the topics to cover in RDM training for social science graduate students. Additionally, RDM confidence and preparation differ between populations so being aware of the backgrounds of students taking the training will be essential for designing student-centered instruction. [ABSTRACT FROM AUTHOR]

Introduction: While data repositories are well‐established in clinical and research enterprises, knowledge repositories with shareable computable biomedical knowledge (CBK) are relatively new entities to the digital health ecosystem. Trustworthy knowledge repositories are necessary for learning health systems, but the policies, standards, and practices to promote trustworthy CBK artifacts and methods to share, and safely and effectively use them are not well studied Methods: We conducted an online survey of 24 organizations in the United States known to be involved in the development or deployment of CBK. The aim of the survey was to assess the current policies and practices governing these repositories and to identify best practices. Descriptive statistics methods were applied to data from 13 responding organizations, to identify common practices and policies instantiating the TRUST principles of Transparency, Responsibility, User Focus, Sustainability, and Technology Results: All 13 respondents indicated to different degrees adherence to policies that convey TRUST. Transparency is conveyed by having policies pertaining to provenance, credentialed contributors, and provision of metadata. Repositories provide knowledge in machine‐readable formats, include implementation guidelines, and adhere to standards to convey Responsibility. Repositories report having Technology functions that enable end‐users to verify, search, and filter for knowledge products. Less common TRUST practices are User Focused procedures that enable consumers to know about user licensing requirements or query the use of knowledge artifacts. Related to Sustainability, less than a majority post describe their sustainability plans. Few organizations publicly describe whether patients play any role in their decision‐making. Conclusion: It is essential that knowledge repositories identify and apply a baseline set of criteria to lay a robust foundation for their trustworthiness leading to optimum uptake, and safe, reliable, and effective use to promote sharing of CBK. Identifying current practices suggests a set of desiderata for the CBK ecosystem in its continued evolution [ABSTRACT FROM AUTHOR]

The rise of data-driven research and discovery may be one of the greatest strategic opportunities to confront academic libraries in a generation. The argument advanced in this article is that the data science opportunity is about data curation AND data analysis. Thus the development of a holistic data science strategy ought to include both elements. Up until now, academic libraries have largely responded to the data science opportunity from a curatorial and archiving perspective. However, this is beginning to change. The case for crafting a holistic data science strategy is presented in six parts in this article. In part one, a broad overview of the data science opportunity is presented, followed by a definition of data analysis and data curation in part two. The traditional academic library response (curation) and a reframing of it to include data analysis are then presented in two separate parts. And finally, part five reports findings from a recent survey conducted at the University of Florida (UF) which indicates robust demand for training in analytical tools and technologies. The article concludes with some thoughts on the challenges of offering data analysis services, using the UF experience to highlight key issues. [ABSTRACT FROM PUBLISHER]

Sharing data is a scientific imperative that accelerates scientific discoveries, reinforces open science inquiry, and allows for efficient use of public investment and research resources. Considering these benefits, data sharing has been widely promoted in diverse fields and neuroscience has been no exception to this movement. For all its promise, however, the sharing of human neuroimaging data raises critical ethical and legal issues, such as data privacy. Recently, the heightened risks to data privacy posed by the rapid advances in artificial intelligence and machine learning techniques have made data sharing more challenging; the regulatory landscape around data sharing has also been evolving rapidly. Here we present an in‐depth ethical and regulatory analysis that examines how neuroimaging data are currently shared against the backdrop of the relevant regulations and policies in the United States and how advanced software tools and algorithms might undermine subjects' privacy in neuroimaging data sharing. The implications of these novel technological threats to privacy in neuroimaging data sharing practices and policies will also be discussed. We then conclude with a proposal for a legal prohibition against malicious use of neuroscience data as a regulatory mechanism to address privacy risks associated with the data while maximizing the benefits of data sharing and open science practice in the field of neuroscience. Sharing data is a scientific imperative that accelerates scientific discoveries, reinforces open science inquiry, and allows for efficient use of public investment and research resources. But for all its promise, the sharing of human neuroimaging data raises critical ethical and legal issues, such as data privacy. Here we present an in‐depth ethical and regulatory analysis that will examine how neuroimaging data are currently shared against the backdrop of the relevant regulations and policies in the United States and how recent advances in software tools and algorithms might undermine subjects' privacy in neuroimaging data sharing. [ABSTRACT FROM AUTHOR]

Background Recent price increases in medications used to treat helminth infections have had demonstrated impacts on Medicaid and immigrant patient populations. Helminth infections are common within the US military; however, anthelmintic prescribing patterns and costs have not yet been investigated in this patient population. Methods We conducted a retrospective analysis of pharmaceutical data from the Military Health System Data Repository between fiscal years 2012 and 2019. Prescription information, including costs and demographics, were abstracted for all anthelmintic medications as well as associated helminth diagnostic codes within 30 days of the prescription dispensing date. Results On average, there were 10 871 anthelmintic medications prescribed per year, for a total of 86 697 during the study period. Ivermectin and albendazole were each prescribed >34 000 times. There were 15 498 mebendazole prescriptions and 1327 praziquantel prescriptions. The total cost of all anthelmintic prescriptions was $16 018 381. Annual costs for anthelmintic medications increased 16-fold during the study period, up to nearly $5 000 000 in fiscal year 2019, primarily driven by price increases in albendazole and mebendazole. Albendazole prescriptions accounted for $12 282 891 of total costs (76.7%), though only 39.1% of total prescriptions. The most common diagnosis associated with albendazole and mebendazole prescriptions was enterobiasis. Conclusions Price increases in anthelmintic medications have significantly impacted the costs borne by the US government for treating parasitic infections. There are a substantial number of anthelmintic prescriptions in the US military health care system annually, suggesting a higher number of helminth infections than previously thought. [ABSTRACT FROM AUTHOR]

Abstract: The rapidly increasing electricity demand for data center operation has motivated efforts to better understand current data center energy use and to identify strategies that reduce the environmental impact of these buildings. This paper builds on previous data center energy modeling efforts by characterizing local climate and mechanical equipment differences among data centers and then evaluating their consequences for building energy use. Cities in the United States with significant data center activity are identified. Representative climate conditions for these cities are applied to data center energy models for several different prototypical space types. Results indicate that widespread, effective economizer use in data centers could reduce energy demand for data centers by about 20–25%, equivalent to an energy efficiency resource in the US of ∼13–17 billion kWh per year. Almost half of the potential savings would result from better airflow management and proper control sequences. The total energy savings potential of economizers, although substantial, is constrained by their limited potential for use in server closets and server rooms, which together are estimated to account for about 30% of all data center energy demand. Incorporating economizer use into the mechanical systems of larger data centers would increase the variation in energy efficiency among geographic regions, indicating that as data center buildings become more energy efficient, their locations will have an increasing effect on overall energy demand. Differences among regions become even more important when accounting for greenhouse-gas emissions. Future data center development could consider site location, along with efficiency measures, to limit the environmental impact attributable to this increasingly prominent economic sector. [ABSTRACT FROM AUTHOR]

While numerical weather prediction models have made considerable progress regarding forecast skill, less attention has been paid to the planetary boundary layer. This study leverages High-Resolution Rapid Refresh (HRRR) forecasts on native levels, 1-s radiosonde data, and (primarily airport) surface observations across the conterminous United States. We construct temporally and spatially averaged composites of wind speed and potential temperature in the lowest 1 km for selected months to identify systematic errors in both forecasts and observations in this critical layer. We find nearsurface temperature and wind speed predictions to be skillful, although wind biases were negatively correlated with observed speed and temperature biases revealed a robust relationship with station elevation. Above '250m above ground level, below which radiosonde wind data were apparently contaminated by processing, biases were small for wind speed and potential temperature at the analysis time (which incorporates sonde data) but became substantial by the 24-h forecast. Wind biases were positive through the layer for both 0000 and 1200 UTC, and morning potential temperature profiles were marked by excessively steep lapse rates that persisted across seasons and (again) exaggerated at higher elevation sites. While the source or cause of these systematic errors are not fully understood, this analysis highlights areas for potential model improvement and the need for a continued and accessible archive of the data that make analyses like this possible. [ABSTRACT FROM AUTHOR]

The National Center for Health Statistics has developed a secure infrastructure, known as the Research Data Center (RDC), to provide researchers access to sensitive data that carry a statistical disclosure risk. The RDC has developed procedures, policies, and products that allow for the use of these data while maintaining the confidentiality of survey respondents and research subjects. Between the ever-growing need to restrict variables to maintain confidentiality and the expanded interest in studies that add context and policy based on geography, the amount of researchers requesting access to restricted data has almost doubled in the past year. To meet these additional needs, the RDC has expanded onsite services, developed a remote access system that continues to evolve in this ever-changing information technology environment, and streamlined its processes to increase efficiency. [ABSTRACT FROM AUTHOR]

Nationwide, data centers are consuming power at an alarming rate and represent 1.5% of all U.S. electric consumption. The speed at which the growth is occurring challenges data center operators and facility management to properly manage the energy consumption, evaluate growth trends, ensure system redundancy, and develop energy conservation strategies. Various public and private groups have partnered to establish goals to reduce the energy consumption of these facilities. This article provides a series of industry best practices and provides recommendations to improve teamwork, establish metrics, compare benchmarks, establish goals, and offer common energy conservation measures that will quickly contain expenses, maintain reliability, and retain a company's competitive advantage. [ABSTRACT FROM AUTHOR]

The Hierarchical Data Format (HDF) has been a data format standard in National Aeronautic and Space Administration (NASA)'s Earth Observing System Data and Information System since the 1990s. Its rich structure, platform independence, full-featured application programming interface (API), and internal compression make it very useful for archiving science data and utilizing them with a rich set of software tools. However, a key drawback for long-term archiving is the complex internal byte layout of HDF files, requiring one to use the API to access HDF data. This makes the long-term readability of HDF data for a given version dependent on long-term allocation of resources to support that version. Much of the data from NASA's Earth Observing System have been archived in HDF Version 4 (HDF4) format. To address the long-term archival issues for these data, a collaborative study between The HDF Group and NASA's Earth Science Data Centers (ESDC5) is underway. One of the first activities was an assessment of the range of HDF4-formatted data held by NASA to determine the capabilities inherent in the HDF format that were used in practice and for use in estimating the effort for full implementation across NASA's ESDCs. Based on the results of this assessment, methods for producing a map of the layout of the HDF4 files held by NASA were prototyped using a markup-language-based HDF tool. The resulting maps allow a separate program to read the file without recourse to the HDF API. To verify this, two independent tools based solely on the map files were developed and tested. [ABSTRACT FROM AUTHOR]

This study uses correlation and multiple regression techniques to document differences in annual temperature trends between the National Climatic Data Center (NCDC) Climate Division Database (CDD) and the United States Historical Climate Network (USHCN) for the Southeast United States. Results indicate that an increase (decrease) in elevation and a northward (southward) shift in mean station location in the CDD correspond with decreases (increases) in temperature. Although the movement of station locations in the CDD showed only modest impacts on trends, the effects of the movements are statistically significant, and explain some of the variances in the temperature trends. Results therefore suggest that climate divisions with more rugged terrain and greater shifts in elevation are more susceptible to spuriously generated trends. [ABSTRACT FROM AUTHOR]

This article focuses on the establishment of a Climate Rainfall Data Center, an experimental data service center by the U.S. National Aeronautics and Space Administration. It is designed to provide users with information on available climate rainfall datasets, online tools for publicly available climate rainfall products, and access to scientists with expertise on available products. This interactive capability gives climate rainfall data users the chance to quickly compare most publicly available datasets over their specified region and period of interest.

Describes the rationale, structure and use of the Electronic Resources and Licensing Information Center (ERLIC) database of the Pennsylvania State University Libraries. Information included in ERLIC; Functions of ERLIC; Valuability of ERLIC database in creating central information source for electronic resources.

Provides an overview on the experiences of Cornell University in developing policies and procedures to provide enhanced cataloging in databases in Ithaca, New York. Effects of the decision of the library to subscribe to ProQuest Direct; Issues to be considered in undertaking a cataloging project; Tips in making cataloging decisions at the institutional level.

Focusing on secondary analysis techniques, this article identifies 10 data sets available to members of the Inter- university Consortium for Political and Social Research (ICPSR). These data sets hold particular promise for understanding individual-level processes and issues in entrepreneurship. The author details the steps needed for preparation and management of these types of archive-quality data sets and notes and six typical problems found in such secondary analysis situations. It is not actually doing the secondary analysis that is most difficult when performing this type of research, the article concludes, but choosing wisely from the wealth of secondary-analysis information. [ABSTRACT FROM AUTHOR]

The article provides information about the paper "Centers for Storage and Retrieval of Scientific Information in the U.S.," published in the October 1963 issue of the journal "Analytical Chemistry." It describes twelve scientific information and data centers in the U.S. and the retrieval services offered by these centers.

The article offers information on the U.S. Census Bureau's State Data Center (SDC) network. It discusses the history of the SDC program, which was launched in 1978, as well as the current structure of the program. It describes the effectiveness of the program's Count Question Resolution (QSR) process, which allows government entities to challenge the bureau's count of their population. It discusses the uses of SDC for libraries, local governments, and businesses. It gives instructions on how to contact local SDC affiliates.