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3. Classification of Astrophysics Journal Articles with Machine Learning to Identify Data for NED

Author

Chen, Tracy X., Ebert, Rick, Mazzarella, Joseph M., Frayer, Cren, Terek, Scott, Chan, Ben H. P., Cook, David, Lo, Tak, Schmitz, Marion, and Wu, Xiuqin

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The NASA/IPAC Extragalactic Database (NED) is a comprehensive online service that combines fundamental multi-wavelength information for known objects beyond the Milky Way and provides value-added, derived quantities and tools to search and access the data. The contents and relationships between measurements in the database are continuously augmented and revised to stay current with astrophysics literature and new sky surveys. The conventional process of distilling and extracting data from the literature involves human experts to review the journal articles and determine if an article is of extragalactic nature, and if so, what types of data it contains. This is both labor intensive and unsustainable, especially given the ever-increasing number of publications each year. We present here a machine learning (ML) approach developed and integrated into the NED production pipeline to help automate the classification of journal article topics and their data content for inclusion into NED. We show that this ML application can successfully reproduce the classifications of a human expert to an accuracy of over 90% in a fraction of the time it takes a human, allowing us to focus human expertise on tasks that are more difficult to automate., Comment: 11 pages, 3 figures, 6 tables

Published
2022
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6. Best Practices for Data Publication in the Astronomical Literature

Author

Chen, Tracy X., Schmitz, Marion, Mazzarella, Joseph M., Wu, Xiuqin, van Eyken, Julian C., Accomazzi, Alberto, Akeson, Rachel L., Allen, Mark, Beaton, Rachael, Berriman, G. Bruce, Boyle, Andrew W., Brouty, Marianne, Chan, Ben, Christiansen, Jessie L., Ciardi, David R., Cook, David, D'Abrusco, Raffaele, Ebert, Rick, Frayer, Cren, Fulton, Benjamin J., Gelino, Christopher, Helou, George, Henderson, Calen B., Howell, Justin, Kim, Joyce, Landais, Gilles, Lo, Tak, Loup, Cecile, Madore, Barry, Monari, Giacomo, Muench, August, Oberto, Anais, Ocvirk, Pierre, Peek, Joshua E. G., Perret, Emmanuelle, Pevunova, Olga, Ramirez, Solange V., Rebull, Luisa, Shemmer, Ohad, Smale, Alan, Tam, Raymond, Terek, Scott, Van Orsow, Doug, Vannier, Patricia, and Wang, Shin-Ywan

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

We present an overview of best practices for publishing data in astronomy and astrophysics journals. These recommendations are intended as a reference for authors to help prepare and publish data in a way that will better represent and support science results, enable better data sharing, improve reproducibility, and enhance the reusability of data. Observance of these guidelines will also help to streamline the extraction, preservation, integration and cross-linking of valuable data from astrophysics literature into major astronomical databases, and consequently facilitate new modes of science discovery that will better exploit the vast quantities of panchromatic and multi-dimensional data associated with the literature. We encourage authors, journal editors, referees, and publishers to implement the best practices reviewed here, as well as related recommendations from international astronomical organizations such as the International Astronomical Union (IAU) for publication of nomenclature, data, and metadata. A convenient Checklist of Recommendations for Publishing Data in the Literature is included for authors to consult before the submission of the final version of their journal articles and associated data files. We recommend that publishers of journals in astronomy and astrophysics incorporate a link to this document in their Instructions to Authors., Comment: 19 pages, 1 figure, 3 tables, accepted for publication in ApJS

Published
2021
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8. The Continuing Education of Archival Professionals in Colleges and Universities from the Perspective of Archives Planning During the '14th Five-Year Plan'

Author

Wu, Xiuqin, Wang, Tong, Striełkowski, Wadim, Editor-in-Chief, Black, Jessica M., Series Editor, Butterfield, Stephen A., Series Editor, Chang, Chi-Cheng, Series Editor, Cheng, Jiuqing, Series Editor, Dumanig, Francisco Perlas, Series Editor, Al-Mabuk, Radhi, Series Editor, Scheper-Hughes, Nancy, Series Editor, Urban, Mathias, Series Editor, Webb, Stephen, Series Editor, Volodin, Alexander, editor, and Roumbal, Iana, editor

Published
2023
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30. LSST: from Science Drivers to Reference Design and Anticipated Data Products

Author

Ivezić, Željko, Kahn, Steven M., Tyson, J. Anthony, Abel, Bob, Acosta, Emily, Allsman, Robyn, Alonso, David, AlSayyad, Yusra, Anderson, Scott F., Andrew, John, Angel, James Roger P., Angeli, George Z., Ansari, Reza, Antilogus, Pierre, Araujo, Constanza, Armstrong, Robert, Arndt, Kirk T., Astier, Pierre, Aubourg, Éric, Auza, Nicole, Axelrod, Tim S., Bard, Deborah J., Barr, Jeff D., Barrau, Aurelian, Bartlett, James G., Bauer, Amanda E., Bauman, Brian J., Baumont, Sylvain, Becker, Andrew C., Becla, Jacek, Beldica, Cristina, Bellavia, Steve, Bianco, Federica B., Biswas, Rahul, Blanc, Guillaume, Blazek, Jonathan, Blandford, Roger D., Bloom, Josh S., Bogart, Joanne, Bond, Tim W., Borgland, Anders W., Borne, Kirk, Bosch, James F., Boutigny, Dominique, Brackett, Craig A., Bradshaw, Andrew, Brandt, William Nielsen, Brown, Michael E., Bullock, James S., Burchat, Patricia, Burke, David L., Cagnoli, Gianpietro, Calabrese, Daniel, Callahan, Shawn, Callen, Alice L., Chandrasekharan, Srinivasan, Charles-Emerson, Glenaver, Chesley, Steve, Cheu, Elliott C., Chiang, Hsin-Fang, Chiang, James, Chirino, Carol, Chow, Derek, Ciardi, David R., Claver, Charles F., Cohen-Tanugi, Johann, Cockrum, Joseph J., Coles, Rebecca, Connolly, Andrew J., Cook, Kem H., Cooray, Asantha, Covey, Kevin R., Cribbs, Chris, Cui, Wei, Cutri, Roc, Daly, Philip N., Daniel, Scott F., Daruich, Felipe, Daubard, Guillaume, Daues, Greg, Dawson, William, Delgado, Francisco, Dellapenna, Alfred, de Peyster, Robert, de Val-Borro, Miguel, Digel, Seth W., Doherty, Peter, Dubois, Richard, Dubois-Felsmann, Gregory P., Durech, Josef, Economou, Frossie, Eracleous, Michael, Ferguson, Henry, Figueroa, Enrique, Fisher-Levine, Merlin, Focke, Warren, Foss, Michael D., Frank, James, Freemon, Michael D., Gangler, Emmanuel, Gawiser, Eric, Geary, John C., Gee, Perry, Geha, Marla, Gessner, Charles J. B., Gibson, Robert R., Gilmore, D. Kirk, Glanzman, Thomas, Glick, William, Goldina, Tatiana, Goldstein, Daniel A., Goodenow, Iain, Graham, Melissa L., Gressler, William J., Gris, Philippe, Guy, Leanne P., Guyonnet, Augustin, Haller, Gunther, Harris, Ron, Hascall, Patrick A., Haupt, Justine, Hernandez, Fabio, Herrmann, Sven, Hileman, Edward, Hoblitt, Joshua, Hodgson, John A., Hogan, Craig, Huang, Dajun, Huffer, Michael E., Ingraham, Patrick, Innes, Walter R., Jacoby, Suzanne H., Jain, Bhuvnesh, Jammes, Fabrice, Jee, James, Jenness, Tim, Jernigan, Garrett, Jevremović, Darko, Johns, Kenneth, Johnson, Anthony S., Johnson, Margaret W. G., Jones, R. Lynne, Juramy-Gilles, Claire, Jurić, Mario, Kalirai, Jason S., Kallivayalil, Nitya J., Kalmbach, Bryce, Kantor, Jeffrey P., Karst, Pierre, Kasliwal, Mansi M., Kelly, Heather, Kessler, Richard, Kinnison, Veronica, Kirkby, David, Knox, Lloyd, Kotov, Ivan V., Krabbendam, Victor L., Krughoff, K. Simon, Kubánek, Petr, Kuczewski, John, Kulkarni, Shri, Ku, John, Kurita, Nadine R., Lage, Craig S., Lambert, Ron, Lange, Travis, Langton, J. Brian, Guillou, Laurent Le, Levine, Deborah, Liang, Ming, Lim, Kian-Tat, Lintott, Chris J., Long, Kevin E., Lopez, Margaux, Lotz, Paul J., Lupton, Robert H., Lust, Nate B., MacArthur, Lauren A., Mahabal, Ashish, Mandelbaum, Rachel, Marsh, Darren S., Marshall, Philip J., Marshall, Stuart, May, Morgan, McKercher, Robert, McQueen, Michelle, Meyers, Joshua, Migliore, Myriam, Miller, Michelle, Mills, David J., Miraval, Connor, Moeyens, Joachim, Monet, David G., Moniez, Marc, Monkewitz, Serge, Montgomery, Christopher, Mueller, Fritz, Muller, Gary P., Arancibia, Freddy Muñoz, Neill, Douglas R., Newbry, Scott P., Nief, Jean-Yves, Nomerotski, Andrei, Nordby, Martin, O'Connor, Paul, Oliver, John, Olivier, Scot S., Olsen, Knut, O'Mullane, William, Ortiz, Sandra, Osier, Shawn, Owen, Russell E., Pain, Reynald, Palecek, Paul E., Parejko, John K., Parsons, James B., Pease, Nathan M., Peterson, J. Matt, Peterson, John R., Petravick, Donald L., Petrick, M. E. Libby, Petry, Cathy E., Pierfederici, Francesco, Pietrowicz, Stephen, Pike, Rob, Pinto, Philip A., Plante, Raymond, Plate, Stephen, Price, Paul A., Prouza, Michael, Radeka, Veljko, Rajagopal, Jayadev, Rasmussen, Andrew P., Regnault, Nicolas, Reil, Kevin A., Reiss, David J., Reuter, Michael A., Ridgway, Stephen T., Riot, Vincent J., Ritz, Steve, Robinson, Sean, Roby, William, Roodman, Aaron, Rosing, Wayne, Roucelle, Cecille, Rumore, Matthew R., Russo, Stefano, Saha, Abhijit, Sassolas, Benoit, Schalk, Terry L., Schellart, Pim, Schindler, Rafe H., Schmidt, Samuel, Schneider, Donald P., Schneider, Michael D., Schoening, William, Schumacher, German, Schwamb, Megan E., Sebag, Jacques, Selvy, Brian, Sembroski, Glenn H., Seppala, Lynn G., Serio, Andrew, Serrano, Eduardo, Shaw, Richard A., Shipsey, Ian, Sick, Jonathan, Silvestri, Nicole, Slater, Colin T., Smith, J. Allyn, Smith, R. Chris, Sobhani, Shahram, Soldahl, Christine, Storrie-Lombardi, Lisa, Stover, Edward, Strauss, Michael A., Street, Rachel A., Stubbs, Christopher W., Sullivan, Ian S., Sweeney, Donald, Swinbank, John D., Szalay, Alexander, Takacs, Peter, Tether, Stephen A., Thaler, Jon J., Thayer, John Gregg, Thomas, Sandrine, Thukral, Vaikunth, Tice, Jeffrey, Trilling, David E., Turri, Max, Van Berg, Richard, Berk, Daniel Vanden, Vetter, Kurt, Virieux, Francoise, Vucina, Tomislav, Wahl, William, Walkowicz, Lucianne, Walsh, Brian, Walter, Christopher W., Wang, Daniel L., Wang, Shin-Yawn, Warner, Michael, Wiecha, Oliver, Willman, Beth, Winters, Scott E., Wittman, David, Wolff, Sidney C., Wood-Vasey, W. Michael, Wu, Xiuqin, Xin, Bo, Yoachim, Peter, and Zhan, Hu

Subjects
Astrophysics
Abstract

(Abridged) We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). A vast array of science will be enabled by a single wide-deep-fast sky survey, and LSST will have unique survey capability in the faint time domain. The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the Solar System, exploring the transient optical sky, and mapping the Milky Way. LSST will be a wide-field ground-based system sited at Cerro Pach\'{o}n in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg$^2$ field of view, and a 3.2 Gigapixel camera. The standard observing sequence will consist of pairs of 15-second exposures in a given field, with two such visits in each pointing in a given night. With these repeats, the LSST system is capable of imaging about 10,000 square degrees of sky in a single filter in three nights. The typical 5$\sigma$ point-source depth in a single visit in $r$ will be $\sim 24.5$ (AB). The project is in the construction phase and will begin regular survey operations by 2022. The survey area will be contained within 30,000 deg$^2$ with $\delta<+34.5^\circ$, and will be imaged multiple times in six bands, $ugrizy$, covering the wavelength range 320--1050 nm. About 90\% of the observing time will be devoted to a deep-wide-fast survey mode which will uniformly observe a 18,000 deg$^2$ region about 800 times (summed over all six bands) during the anticipated 10 years of operations, and yield a coadded map to $r\sim27.5$. The remaining 10\% of the observing time will be allocated to projects such as a Very Deep and Fast time domain survey. The goal is to make LSST data products, including a relational database of about 32 trillion observations of 40 billion objects, available to the public and scientists around the world., Comment: 57 pages, 32 color figures, version with high-resolution figures available from https://www.lsst.org/overview

Published
2008
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37. Biomass partitioning reveals the adaptability of tiger nut (Cyperus esculentusL.) in an arid ecosystem in Inner Mongolia, China

Author

Tan, Jin, Wu, Xiuqin, He, Yuxin, Li, Yaning, Li, Xu, and Yu, Xiaomeng

Abstract

Tiger nuts (Cyperus esculentusL.) have been widely cultivated as a bioenergy crop in northern China. The value of tubers is receiving special attention, but the lack of information on how organs of tiger nuts coordinate resources to adapt to arid environments has limited the potential tuber production and diverse use. To address this knowledge gap, we experimented in the Ulan Buh Desert, Inner Mongolia (106° 33′ 28″ E, 40° 27′ 32″ N). Three fertilizer treatments were applied: zero fertilizer (N:P = 0:0), traditional fertilizer (N:P = 15:15), and additional nitrogen fertilizer (N:P = 60:15). Investigations were conducted at seedling, rhizome production, tuber formation, and tuber maturation stages. The results showed that under zero fertilizer, tiger nuts allocated biomass predominantly to propagative organs, namely rhizomes and tubers. However, an increase in leaf area or root surface area would necessitate more investment in leaf or root dry mass, resulting in diminishing returns. For biomass allocation, tiger nut responded the same way to traditional fertilizer treatment and zero fertilizer treatment, but the tuber number and yield increase under traditional fertilizer application. Additional nitrogen fertilizer stimulated shoot production through accelerated rhizome production aided by leaf area increasing. Fertilizer application can significantly increase both the number of tiger nuts and tuber yield. Nitrogen addition positively influenced aboveground biomass and enhanced rhizome tillering. Strong adaptability of tiger nuts makes the plant a suitable pioneer crop for developing marginal lands. The robust aboveground growth after nitrogen application enhances suitability for pasture utilization. Tiger nuts have been promoted as a bioenergy crop in Inner Mongolia, with potential uses yet to be explored.Tiger nuts demonstrate strong adaptability in arid areas, enabling them to thrive in nutrient‐deficient conditions.Tiger nuts with strong adaptability can fully exploit their production potential in marginal lands of Inner Mongolia.Nitrogen fertilizer application plays a major role in biomass partitioning in tiger nuts in an arid ecosystem.

Published
2024
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42. The left inferior frontal gyrus is causally linked to vocal feedback control: evidence from high-definition transcranial alternating current stimulation

Author

Li, Tingni, primary, Chang, Yichen, additional, Zhao, Shuzhi, additional, Jones, Jeffery A, additional, Chen, Xi, additional, Gan, Chu, additional, Wu, Xiuqin, additional, Dai, Guangyan, additional, Li, Jingting, additional, Shen, Ying, additional, Liu, Peng, additional, and Liu, Hanjun, additional

Published
2022
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