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Here, the evaluated spectroscopic data are presented for these nuclides: 249 U, 249 Np, 249 Pu, 249 Am, 249 Cm, 249 Bk, 249 Cf, 249 Es 249 Fm, 249 Md and 249 No. This work supersedes the earlier full evaluation of A=249 published by 2011Ab07.
The MAJORANA DEMONSTRATOR is an ultra-low background physics experiment searching for the neutrinoless double beta decay of 76Ge. The MAJORANA Parts Tracking Database is used to record the history of components used in the construction of the DEMONSTRATOR. Transportation, storage, and processes undergone by parts such as machining or cleaning are linked to part records. Tracking parts provides a great logistics benefit and an important quality assurance reference during construction. In addition, the location history of parts provides an estimate of their exposure to cosmic radiation. A web application for data entry and a radiation exposure calculator have been developed as tools for achieving the extreme radiopurity required for this rare decay search.
Although the production of PuBe neutron sources has discontinued, hundreds of sources with unknown or inaccurately declared plutonium content are in existence around the world. Institutions have undertaken the task of assaying these sources, measuring, and calculating the isotopic composition, plutonium content, and neutron yield. The nominal plutonium content, based off the neutron yield per gram of pure 239 Pu, has shown to be highly inaccurate. New methods of measuring the plutonium content allow a more accurate estimate of the true Pu content, but these measurements need verification. Using the TENDL 2012 nuclear data libraries, MCNP6 has the capability to simulate the (α, n) interactions in a PuBe source. Theoretically, if the source is modeled according to the plutonium content, isotopic composition, and other source characteristics, the calculated neutron yield in MCNP can be compared to the experimental yield, offering an indication of the accuracy of the declared plutonium content. In this study, three sets of PuBe sources from various backgrounds were modeled in MCNP6 1.2 Beta, according to the source specifications dictated by the individuals who assayed the source. Verification of the source parameters with MCNP6 also serves as a means to test the alpha transport capabilities of MCNP6 1.2 Beta with TENDL 2012 alpha transport libraries. Finally, good agreement in the comparison would indicate the accuracy of the source parameters in addition to demonstrating MCNP's capabilities in simulating (α, n) interactions.
Understanding how science and technology advance has long been of interest to diverse scholarly communities. Thus far, however, such understanding has not been easy to map to, and thus to improve, the operational practice of research and development. Indeed, one might argue that the operational practice of research and development, particularly its exploratory research half, has become less effective in recent decades. In this paper, we describe a rethinking of how science and technology advance, one that is consistent with many (though not all) of the perspectives of the scholarly communities just mentioned, and one that helps bridge the divide between theory and practice. In conclusion, the result is an architecture we call “Bell's Dodecants,” to reflect its six mechanisms and two flavors, and their balanced nurturing at Bell Labs, the iconic 20th century industrial research and development laboratory.
We examine the DataONE (Data Observation Network for Earth) project, a transdisciplinary organization tasked with creating a cyberinfrastructure platform to ensure preservation of and access to environmental science and biological science data. Its objective was a difficult one to achieve, requiring innovative solutions. The DataONE project used a working group structure to organize its members. Here, we use organizational discontinuity theory as our lens to understand the factors associated with success in such projects. Based on quantitative and qualitative data collected from DataONE members, we offer recommendations for the use of working groups in transdisciplinary synthesis. Recommendations include welcome diverse opinions and world views, establish shared communication practices, schedule periodic synchronous face-to-face meetings, and ensure the active participation of bridge builders or knowledge brokers such as librarians who know how to ask questions about disciplines not their own.
This paper presents an extension to generalized blockmodeling where there are more than two types of objects to be clustered based on valued network data. We use the ideas in homogeneity block modeling to develop an optimization model to perform the clustering of the objects and the resulting partitioning of the ties so as to minimize the inconsistency of an empirical block with an ideal block. The ideal block types used in this modeling are null, complete and a new type that is related to that developed in Ziberna (2007). Three case studies are presented, two based on the Southern Women dataset (Davis et al. 1941) and a third based on passenger air travel in the Continental United States.
Structures of biomolecular systems are increasingly computed by integrative modeling. In this approach, a structural model is constructed by combining information from multiple sources, including varied experimental methods and prior models. In 2019, a Workshop was held as a Biophysical Society Satellite Meeting to assess progress and discuss further requirements for archiving integrative structures. The primary goal of the Workshop was to build consensus for addressing the challenges involved in creating common data standards, building methods for federated data exchange, and developing mechanisms for validating integrative structures. The summary of the Workshop and the recommendations that emerged are presented here.
Taxonomic assignments of anaerobic dichloromethane (DCM)-degrading bacteria remain poorly constrained but are important for understanding the microbial diversity of organisms contributing to DCM turnover in environmental systems. Here, we describe the taxonomic classification of a novel DCM degrader in consortium RM obtained from pristine Rio Mameyes sediment. Phylogenetic analysis of full-length 16S rRNA gene sequences demonstrated that the DCM degrader was most closely related to members of the genera Dehalobacter and Syntrophobotulus, but sequence similarities did not exceed 94% and 93%, respectively. Genome-aggregate average amino acid identities against Peptococcaceae members did not exceed 66%, suggesting that the DCM degrader does not affiliate with any described genus. Phylogenetic analysis of conserved single-copy functional genes supported that the DCM degrader represents a novel clade. Growth strictly depended on the presence of DCM, which was consumed at a rate of 160 ± 3 μmol L -1 d -1 . The DCM degrader attained 5.25 × 10 7 ± 1.0 × 10 7 cells per μmol DCM consumed. Fluorescence in situ hybridization revealed rod-shaped cells 4 ± 0.8 μm long and 0.4 ± 0.1 μm wide. Furthermore, based on the unique phylogenetic, genomic, and physiological characteristics, we propose that the DCM degrader represents a new genus and species, ‘Candidatus Dichloromethanomonas elyunquensis’.
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This paper builds on a recent National Science Foundation working paper that identified a strong association between cloud use and various types of innovation but did not consider whether 1) cloud adoption is a reliable indicator of the innovation orientation of a firm, or 2) cloud adoption enables various types of innovation. We estimate propensity score matching and endogenous treatment effect models to control for innovation orientation, producing evidence to test the second explanation. Here, findings support an enabling effect of the cloud on innovation providing concrete evidence of the adverse impact of the digital divide.
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Nuclear magnetic resonance (NMR) data are rarely deposited in open databases, leading to loss of critical scientific knowledge. Existing data reporting methods (images, tables, lists of values) contain less information than raw data, and are poorly standardized. Together, these issues limit FAIR (findable, accessible, interoperable, reusable) access to these data, which in turn creates barriers for compound dereplication and the development of new data-driven discovery tools. Existing NMR databases are either not designed for natural products data, or employ complex deposition interfaces that disincentivize deposition. Journals, including the Journal of Natural Products (JNP), are now requiring data submission as part of the publication process, creating the need for a streamlined, user-friendly mechanism to deposit and distribute NMR data. Recently, our team reported the development of the Natural Products Magnetic Resonance Database (NP-MRD; www.np-mrd.org). Here in this paper we present a new data deposition platform for the NP-MRD project that is designed to enable users to deposit NMR data for published or submitted manuscripts in under five minutes. This platform includes a suite of automated data extraction and standardization tools, together with a simple-to-use web-based interface and detailed error reporting to simplify the data deposition process and is available at www.np-mrd.org/submissions.
Visual examination of mass spectrometry data is necessary to assess data quality and to facilitate data exploration. Graphics provide the means to evaluate spectral properties, test alternative peptide/protein sequence matches, prepare annotated spectra for publication, and fine-tune parameters during wet lab procedures. Visual inspection of MS data is hindered by proprietary proteomics visualization software designed for particular workflows and academic software that lack visualization tools. We built PSpecteR, an open-source and interactive R Shiny web application to address these issues, with support for several steps of proteomics data processing, including: reading various mass spectrometry files, running open-source database search tools, labelling spectra with fragmentation patterns, testing post-translational modifications, plotting where identified fragments map to reference sequences, and visualizing algorithmic output and metadata. All figures, tables, and spectra are exportable within one easy-to-use graphical user interface. Our current software provides a flexible and modern R framework to support fast implementation of additional features. The open source code is readily available (https://github.com/EMSL-Computing/PSpecteR), and a PSpecteR Docker container (https://hub.docker.com/r/emslcomputing) is available for easy local installation.
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This work documents the details of Aurorasaurus citizen science data for the period spanning 2015 and 2016 as well as its routine data filtering protocols. Aurorasaurus citizen science data is a collection of auroral sightings submitted to the project via its website or apps and mined from social media. It is a robust data set and particularly abundant during strong geomagnetic storms when auroral precipitation models have the highest uncertainty. This data is offered to the scientific community for use through an open-access database in its raw and scientific formats, each of which is described in detail in this technical report. Furthermore, by demonstrating its scientific utility, we aim to encourage its integration into auroral research.
The lack of clear references to and descriptions of data sets in published literature limits the usefulness of data, as well as the reproducibility and credibility of scientific findings.