TY - RPRT A1 - Brodeur, Abel A1 - Mikola, Derek A1 - Cook, Nikolai A1 - Brailey, Thomas A1 - Briggs, Ryan A1 - Gendre, Alexandra de A1 - Dupraz, Yannick A1 - Fiala, Lenka A1 - Gabani, Jacopo A1 - Gauriot, Romain A1 - Haddad, Joanne A1 - Lima, Goncalo A1 - Ankel-Peters, Jörg A1 - Dreber, Anna A1 - Campbell, Douglas A1 - Kattan, Lamis A1 - Fages, Diego Marino A1 - Mierisch, Fabian A1 - Sun, Pu A1 - Wright, Taylor A1 - Connolly, Marie A1 - Hoces de la Guardia, Fernando A1 - Johannesson, Magnus A1 - Miguel, Edward A1 - Vilhuber, Lars A1 - Abarca, Alejandro A1 - Acharya, Mahesh A1 - Adjisse, Sossou Simplice A1 - Akhtar, Ahwaz A1 - Lizardi, Eduardo Alberto Ramirez A1 - Albrecht, Sabina A1 - Andersen, Synve Nygaard A1 - Andlib, Zubaria A1 - Arrora, Falak A1 - Ash, Thomas A1 - Bacher, Etienne A1 - Bachler, Sebastian A1 - Bacon, Félix A1 - Bagues, Manuel A1 - Balogh, Timea A1 - Batmanov, Alisher A1 - Barschkett, Mara A1 - Basdil, B. Kaan A1 - Dower, Jaromneda A1 - Castek, Ondrej A1 - Caviglia-Harris, Jill A1 - Strand, Gabriella Chauca A1 - Chen, Shi A1 - Chzhen, Asya A1 - Chung, Jong A1 - Collins, Jason A1 - Coppock, Alexander A1 - Cordeau, Hugo A1 - Couillard, Ben A1 - Crechet, Jonathan A1 - Crippa, Lorenzo A1 - Cui, Jeanne A1 - Czymara, Christian A1 - Daarstad, Haley A1 - Dao, Danh Chi A1 - Dao, Dong A1 - Schmandt, Marco David A1 - Linde, Astrid de A1 - Melo, Lucas De A1 - Deer, Lachlan A1 - Vera, Micole De A1 - Dimitrova, Velichka A1 - Dollbaum, Jan Fabian A1 - Dollbaum, Jan Matti A1 - Donnelly, Michael A1 - Huynh, Luu Duc Toan A1 - Dumbalska, Tsvetomira A1 - Duncan, Jamie A1 - Duong, Kiet Tuan A1 - Duprey, Thibaut A1 - Dworschak, Christoph A1 - Ellingsrud, Sigmund A1 - Elminejad, Ali A1 - Eissa, Yasmine A1 - Erhart, Andrea A1 - Etingin-Frati, Giulian A1 - Fatemi-Pour, Elaheh A1 - Federice, Alexa A1 - Feld, Jan A1 - Fenig, Guidon A1 - Firouzjaeiangalougah, Mojtaba A1 - Fleisje, Erlend A1 - Fortier-Chouinard, Alexandre A1 - Engel, Julia Francesca A1 - Fries, Tilman A1 - Fortier, Reid A1 - Fréchet, Nadjim A1 - Galipeau, Thomas A1 - Gallegos, Sebastián A1 - Gangji, Areez A1 - Gao, Xiaoying A1 - Garnache, Cloé A1 - Gáspár, Attila A1 - Gavrilova, Evelina A1 - Ghosh, Arijit A1 - Gibney, Garreth A1 - Gibson, Grant A1 - Godager, Geir A1 - Goff, Leonard A1 - Gong, Da A1 - González, Javier A1 - Gretton, Jeremy A1 - Griffa, Cristina A1 - Grigoryeva, Idaliya A1 - Grtting, Maja A1 - Guntermann, Eric A1 - Guo, Jiaqi A1 - Gugushvili, Alexi A1 - Habibnia, Hooman A1 - Häffner, Sonja A1 - Hall, Jonathan D. A1 - Hammar, Olle A1 - Kordt, Amund Hanson A1 - Hashimoto, Barry A1 - Hartley, Jonathan S. A1 - Hausladen, Carina I. A1 - Havránek, Tomáš A1 - Hazen, Jacob A1 - He, Harry A1 - Hepplewhite, Matthew A1 - Herrera-Rodriguez, Mario A1 - Heuer, Felix A1 - Heyes, Anthony A1 - Ho, Anson T. Y. A1 - Holmes, Jonathan A1 - Holzknecht, Armando A1 - Hsu, Yu-Hsiang Dexter A1 - Hu, Shiang-Hung A1 - Huang, Yu-Shiuan A1 - Huebener, Mathias A1 - Huber, Christoph A1 - Huynh, Kim P. A1 - Irsova, Zuzana A1 - Isler, Ozan A1 - Jakobsson, Niklas A1 - Frith, Michael James A1 - Jananji, Raphaël A1 - Jayalath, Tharaka A. A1 - Jetter, Michael A1 - John, Jenny A1 - Forshaw, Rachel Joy A1 - Juan, Felipe A1 - Kadriu, Valon A1 - Karim, Sunny A1 - Kelly, Edmund A1 - Dang, Duy Khanh Hoang A1 - Khushboo, Tazia A1 - Kim, Jin A1 - Kjellsson, Gustav A1 - Kjelsrud, Anders A1 - Kotsadam, Andreas A1 - Korpershoek, Jori A1 - Krashinsky, Lewis A1 - Kundu, Suranjana A1 - Kustov, Alexander A1 - Lalayev, Nurlan A1 - Langlois, Audrée A1 - Laufer, Jill A1 - Lee-Whiting, Blake A1 - Leibing, Andreas A1 - Lenz, Gabriel A1 - Levin, Joel A1 - Li, Peng A1 - Li, Tongzhe A1 - Lin, Yuchen A1 - Listo, Ariel A1 - Liu, Dan A1 - Lu, Xuewen A1 - Lukmanova, Elvina A1 - Luscombe, Alex A1 - Lusher, Lester R. A1 - Lyu, Ke A1 - Ma, Hai A1 - Mäder, Nicolas A1 - Makate, Clifton A1 - Malmberg, Alice A1 - Maitra, Adit A1 - Mandas, Marco A1 - Marcus, Jan A1 - Margaryan, Shushanik A1 - Márk, Lili A1 - Martignano, Andres A1 - Marsh, Abigail A1 - Masetto, Isabella A1 - McCanny, Anthony A1 - McManus, Emma A1 - McWay, Ryan A1 - Metson, Lennard A1 - Kinge, Jonas Minet A1 - Mishra, Sumit A1 - Mohnen, Myra A1 - Möller, Jakob A1 - Montambeault, Rosalie A1 - Montpetit, Sébastien A1 - Morin, Louis-Philippe A1 - Morris, Todd A1 - Moser, Scott A1 - Motoki, Fabio A1 - Muehlenbachs, Lucija A1 - Musulan, Andreea A1 - Musumeci, Marco A1 - Nabin, Munirul A1 - Nchare, Karim A1 - Neubauer, Florian A1 - Nguyen, Quan M. P. A1 - Nguyen, Tuan A1 - Nguyen-Tien, Viet A1 - Niazi, Ali A1 - Nikolaishvili, Giorgi A1 - Nordstrom, Ardyn A1 - Nü, Patrick A1 - Odermatt, Angela A1 - Olson, Matt A1 - ien, Henning A1 - Ölkers, Tim A1 - Vert, Miquel Oliver i. A1 - Oral, Emre A1 - Oswald, Christian A1 - Ousman, Ali A1 - Özak, Ömer A1 - Pandey, Shubham A1 - Pavlov, Alexandre A1 - Pelli, Martino A1 - Penheiro, Romeo A1 - Park, RyuGyung A1 - Martel, Eva Pérez A1 - Petrovičová, Tereza A1 - Phan, Linh A1 - Prettyman, Alexa A1 - Procházka, Jakub A1 - Putri, Aqila A1 - Quandt, Julian A1 - Qiu, Kangyu A1 - Nguyen, Loan Quynh Thi A1 - Rahman, Andaleeb A1 - Rea, Carson H. A1 - Reiremo, Adam A1 - Renée, Laëtitia A1 - Richardson, Joseph A1 - Rivers, Nicholas A1 - Rodrigues, Bruno A1 - Roelofs, William A1 - Roemer, Tobias A1 - Rogeberg, Ole A1 - Rose, Julian A1 - Roskos-Ewoldsen, Andrew A1 - Rosmer, Paul A1 - Sabada, Barbara A1 - Saberian, Soodeh A1 - Salamanca, Nicolas A1 - Sator, Georg A1 - Sawyer, Antoine A1 - Scates, Daniel A1 - Schlüter, Elmar A1 - Sells, Cameron A1 - Sen, Sharmi A1 - Sethi, Ritika A1 - Shcherbiak, Anna A1 - Sogaolu, Moyosore A1 - Soosalu, Matt A1 - Srensen, Erik A1 - Sovani, Manali A1 - Spencer, Noah A1 - Staubli, Stefan A1 - Stans, Renske A1 - Stewart, Anya A1 - Stips, Felix A1 - Stockley, Kieran A1 - Strobel, Stephenson A1 - Struby, Ethan A1 - Tang, John A1 - Tanrisever, Idil A1 - Yang, Thomas Tao A1 - Tastan, Ipek A1 - Tatić, Dejan A1 - Tatlow, Benjamin A1 - Seuyong, Féraud Tchuisseu A1 - Thériault, Rémi A1 - Thivierge, Vincent A1 - Tian, Wenjie A1 - Toma, Filip-Mihai A1 - Totarelli, Maddalena A1 - Tran, Van-Anh A1 - Truong, Hung A1 - Tsoy, Nikita A1 - Tuzcuoglu, Kerem A1 - Ubfal, Diego A1 - Villalobos, Laura A1 - Walterskirchen, Julian A1 - Wang, Joseph Taoyi A1 - Wattal, Vasudha A1 - Webb, Matthew D. A1 - Weber, Bryan A1 - Weisser, Reinhard A1 - Weng, Wei-Chien A1 - Westheide, Christian A1 - White, Kimberly A1 - Winter, Jacob A1 - Wochner, Timo A1 - Woerman, Matt A1 - Wong, Jared A1 - Woodard, Ritchie A1 - Wroński, Marcin A1 - Yazbeck, Myra A1 - Yang, Gustav Chung A1 - Yap, Luther A1 - Yassin, Kareman A1 - Ye, Hao A1 - Yoon, Jin Young A1 - Yurris, Chris A1 - Zahra, Tahreen A1 - Zaneva, Mirela A1 - Zayat, Aline A1 - Zhang, Jonathan A1 - Zhao, Ziwei A1 - Yaolang, Zhong T1 - Mass reproducibility and replicability BT - a new hope T2 - I4R discussion paper series N2 - This study pushes our understanding of research reliability by reproducing and replicating claims from 110 papers in leading economic and political science journals. The analysis involves computational reproducibility checks and robustness assessments. It reveals several patterns. First, we uncover a high rate of fully computationally reproducible results (over 85%). Second, excluding minor issues like missing packages or broken pathways, we uncover coding errors for about 25% of studies, with some studies containing multiple errors. Third, we test the robustness of the results to 5,511 re-analyses. We find a robustness reproducibility of about 70%. Robustness reproducibility rates are relatively higher for re-analyses that introduce new data and lower for re-analyses that change the sample or the definition of the dependent variable. Fourth, 52% of re-analysis effect size estimates are smaller than the original published estimates and the average statistical significance of a re-analysis is 77% of the original. Lastly, we rely on six teams of researchers working independently to answer eight additional research questions on the determinants of robustness reproducibility. Most teams find a negative relationship between replicators' experience and reproducibility, while finding no relationship between reproducibility and the provision of intermediate or even raw data combined with the necessary cleaning codes. KW - conomics KW - open science KW - political science KW - replication KW - reproduction KW - research transparency Y1 - 2024 SN - 2752-1931 IS - 107 PB - Institute for Replication CY - Essen ER - TY - JOUR A1 - Zaragoza-Cardiel, Javier A1 - Gómez-González, Víctor Mauricio Alfonso A1 - Mayya, Yalia Divakara A1 - Ramos-Larios, Gerardo T1 - Nebular abundance gradient in the Cartwheel galaxy using MUSE data JF - Monthly notices of the Royal Astronomical Society N2 - We here present the results from a detailed analysis of nebular abundances of commonly observed ions in the collisional ring galaxy Cartwheel using the Very Large Telescope (VLT) Multi-Unit Spectroscopic Explorer (MUSE) data set. The analysis includes 221 H II regions in the star-forming ring, in addition to 40 relatively fainter H a-emitting regions in the spokes, disc, and the inner ring. The ionic abundances of He, N, O, and Fe are obtained using the direct method (DM) for 9, 20, 20, and 17 ring H II regions, respectively, where the S++ temperature-sensitive line is detected. For the rest of the regions, including all the nebulae between the inner and the outer ring, we obtained O abundances using the strong-line method (SLM). The ring regions have a median 12 + log O/H = 8.19 +/- 0.15, log N/O = -1.57 +/- 0.09 and log Fe/O = -2.24 +/- 0.09 using the DM. Within the range of O abundances seen in the Cartwheel, the N/O and Fe/O values decrease proportionately with increasing O, suggesting local enrichment of O without corresponding enrichment of primary N and Fe. The O abundances of the disc H II regions obtained using the SLM show a well-defined radial gradient. The mean O abundance of the ring H II regions is lower by similar to 0.1 dex as compared to the extrapolation of the radial gradient. The observed trends suggest the preservation of the pre-collisional abundance gradient, displacement of most of the processed elements to the ring, as predicted by the recent simulation by Renaud et al., and post-collisional infall of metal-poor gas in the ring. KW - galaxies: star clusters KW - galaxies: individual KW - galaxies: abundances Y1 - 2022 U6 - https://doi.org/10.1093/mnras/stac1423 SN - 0035-8711 SN - 1365-2966 VL - 514 IS - 2 SP - 1689 EP - 1705 PB - Oxford University Press CY - Oxford ER - TY - GEN A1 - Barlow, Axel A1 - Hartmann, Stefanie A1 - Gonzalez, Javier A1 - Hofreiter, Michael A1 - Paijmans, Johanna L. A. T1 - Consensify BT - a method for generating pseudohaploid genome sequences from palaeogenomic datasets with reduced error rates T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - A standard practise in palaeogenome analysis is the conversion of mapped short read data into pseudohaploid sequences, frequently by selecting a single high-quality nucleotide at random from the stack of mapped reads. This controls for biases due to differential sequencing coverage, but it does not control for differential rates and types of sequencing error, which are frequently large and variable in datasets obtained from ancient samples. These errors have the potential to distort phylogenetic and population clustering analyses, and to mislead tests of admixture using D statistics. We introduce Consensify, a method for generating pseudohaploid sequences, which controls for biases resulting from differential sequencing coverage while greatly reducing error rates. The error correction is derived directly from the data itself, without the requirement for additional genomic resources or simplifying assumptions such as contemporaneous sampling. For phylogenetic and population clustering analysis, we find that Consensify is less affected by artefacts than methods based on single read sampling. For D statistics, Consensify is more resistant to false positives and appears to be less affected by biases resulting from different laboratory protocols than other frequently used methods. Although Consensify is developed with palaeogenomic data in mind, it is applicable for any low to medium coverage short read datasets. We predict that Consensify will be a useful tool for future studies of palaeogenomes. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1033 KW - palaeogenomics KW - ancient DNA KW - sequencing error KW - error reduction KW - D statistics KW - bioinformatics Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-472521 SN - 1866-8372 IS - 1033 ER - TY - JOUR A1 - Barlow, Axel A1 - Hartmann, Stefanie A1 - Gonzalez, Javier A1 - Hofreiter, Michael A1 - Paijmans, Johanna L. A. T1 - Consensify BT - a method for generating pseudohaploid genome sequences from palaeogenomic datasets with reduced error rates JF - Genes / Molecular Diversity Preservation International N2 - A standard practise in palaeogenome analysis is the conversion of mapped short read data into pseudohaploid sequences, frequently by selecting a single high-quality nucleotide at random from the stack of mapped reads. This controls for biases due to differential sequencing coverage, but it does not control for differential rates and types of sequencing error, which are frequently large and variable in datasets obtained from ancient samples. These errors have the potential to distort phylogenetic and population clustering analyses, and to mislead tests of admixture using D statistics. We introduce Consensify, a method for generating pseudohaploid sequences, which controls for biases resulting from differential sequencing coverage while greatly reducing error rates. The error correction is derived directly from the data itself, without the requirement for additional genomic resources or simplifying assumptions such as contemporaneous sampling. For phylogenetic and population clustering analysis, we find that Consensify is less affected by artefacts than methods based on single read sampling. For D statistics, Consensify is more resistant to false positives and appears to be less affected by biases resulting from different laboratory protocols than other frequently used methods. Although Consensify is developed with palaeogenomic data in mind, it is applicable for any low to medium coverage short read datasets. We predict that Consensify will be a useful tool for future studies of palaeogenomes. KW - palaeogenomics KW - ancient DNA KW - sequencing error KW - error reduction KW - D statistics KW - bioinformatics Y1 - 2020 U6 - https://doi.org/10.3390/genes11010050 SN - 2073-4425 VL - 11 IS - 1 PB - MDPI CY - Basel ER - TY - JOUR A1 - Dineva, Ekaterina Ivanova A1 - Verma, Meetu A1 - Gonzalez Manrique, Sergio Javier A1 - Schwartz, Pavol A1 - Denker, Carsten T1 - Cloud model inversions of strong chromospheric absorption lines using principal component analysis JF - Astronomische Nachrichten = Astronomical notes N2 - High-resolution spectroscopy of strong chromospheric absorption lines delivers nowadays several millions of spectra per observing day, when using fast scanning devices to cover large regions on the solar surface. Therefore, fast and robust inversion schemes are needed to explore the large data volume. Cloud model (CM) inversions of the chromospheric H alpha line are commonly employed to investigate various solar features including filaments, prominences, surges, jets, mottles, and (macro-) spicules. The choice of the CM was governed by its intuitive description of complex chromospheric structures as clouds suspended above the solar surface by magnetic fields. This study is based on observations of active region NOAA 11126 in H alpha, which were obtained November 18-23, 2010 with the echelle spectrograph of the vacuum tower telescope at the Observatorio del Teide, Spain. Principal component analysis reduces the dimensionality of spectra and conditions noise-stripped spectra for CM inversions. Modeled H alpha intensity and contrast profiles as well as CM parameters are collected in a database, which facilitates efficient processing of the observed spectra. Physical maps are computed representing the line-core and continuum intensity, absolute contrast, equivalent width, and Doppler velocities, among others. Noise-free spectra expedite the analysis of bisectors. The data processing is evaluated in the context of "big data," in particular with respect to automatic classification of spectra. KW - sun KW - activity - sun KW - atmosphere - sun KW - chromosphere - methods KW - data KW - analysis - techniques KW - spectroscopic - astronomical databases KW - miscellaneous Y1 - 2020 U6 - https://doi.org/10.1002/asna.202013652 SN - 0004-6337 SN - 1521-3994 VL - 341 IS - 1 SP - 64 EP - 78 PB - Wiley-VCH Verl. CY - Berlin ER - TY - JOUR A1 - Wutke, Saskia A1 - Sandoval-Castellanos, Edson A1 - Benecke, Norbert A1 - Döhle, Hans-Jürgen A1 - Friederich, Susanne A1 - Gonzalez, Javier A1 - Hofreiter, Michael A1 - Lougas, Lembi A1 - Magnell, Ola A1 - Malaspinas, Anna-Sapfo A1 - Morales-Muniz, Arturo A1 - Orlando, Ludovic A1 - Reissmann, Monika A1 - Trinks, Alexandra A1 - Ludwig, Arne T1 - Decline of genetic diversity in ancient domestic stallions in Europe JF - Science Advances N2 - Present-day domestic horses are immensely diverse in their maternally inherited mitochondrial DNA, yet they show very little variation on their paternally inherited Y chromosome. Although it has recently been shown that Y chromosomal diversity in domestic horses was higher at least until the Iron Age, when and why this diversity disappeared remain controversial questions. We genotyped 16 recently discovered Y chromosomal single-nucleotide polymorphisms in 96 ancient Eurasian stallions spanning the early domestication stages (Copper and Bronze Age) to the Middle Ages. Using this Y chromosomal time series, which covers nearly the entire history of horse domestication, we reveal how Y chromosomal diversity changed over time. Our results also show that the lack of multiple stallion lineages in the extant domestic population is caused by neither a founder effect nor random demographic effects but instead is the result of artificial selection-initially during the Iron Age by nomadic people from the Eurasian steppes and later during the Roman period. Moreover, the modern domestic haplotype probably derived from another, already advantageous, haplotype, most likely after the beginning of the domestication. In line with recent findings indicating that the Przewalski and domestic horse lineages remained connected by gene flow after they diverged about 45,000 years ago, we present evidence for Y chromosomal introgression of Przewalski horses into the gene pool of European domestic horses at least until medieval times. Y1 - 2018 U6 - https://doi.org/10.1126/sciadv.aap9691 SN - 2375-2548 VL - 4 IS - 4 PB - American Assoc. for the Advancement of Science CY - Washington ER - TY - JOUR A1 - Dengler, Jürgen A1 - Wagner, Viktoria A1 - Dembicz, Iwona A1 - Garcia-Mijangos, Itziar A1 - Naqinezhad, Alireza A1 - Boch, Steffen A1 - Chiarucci, Alessandro A1 - Conradi, Timo A1 - Filibeck, Goffredo A1 - Guarino, Riccardo A1 - Janisova, Monika A1 - Steinbauer, Manuel J. A1 - Acic, Svetlana A1 - Acosta, Alicia T. R. A1 - Akasaka, Munemitsu A1 - Allers, Marc-Andre A1 - Apostolova, Iva A1 - Axmanova, Irena A1 - Bakan, Branko A1 - Baranova, Alina A1 - Bardy-Durchhalter, Manfred A1 - Bartha, Sandor A1 - Baumann, Esther A1 - Becker, Thomas A1 - Becker, Ute A1 - Belonovskaya, Elena A1 - Bengtsson, Karin A1 - Benito Alonso, Jose Luis A1 - Berastegi, Asun A1 - Bergamini, Ariel A1 - Bonini, Ilaria A1 - Bruun, Hans Henrik A1 - Budzhak, Vasyl A1 - Bueno, Alvaro A1 - Antonio Campos, Juan A1 - Cancellieri, Laura A1 - Carboni, Marta A1 - Chocarro, Cristina A1 - Conti, Luisa A1 - Czarniecka-Wiera, Marta A1 - De Frenne, Pieter A1 - Deak, Balazs A1 - Didukh, Yakiv P. A1 - Diekmann, Martin A1 - Dolnik, Christian A1 - Dupre, Cecilia A1 - Ecker, Klaus A1 - Ermakov, Nikolai A1 - Erschbamer, Brigitta A1 - Escudero, Adrian A1 - Etayo, Javier A1 - Fajmonova, Zuzana A1 - Felde, Vivian A. A1 - Fernandez Calzado, Maria Rosa A1 - Finckh, Manfred A1 - Fotiadis, Georgios A1 - Fracchiolla, Mariano A1 - Ganeva, Anna A1 - Garcia-Magro, Daniel A1 - Gavilan, Rosario G. A1 - Germany, Markus A1 - Giladi, Itamar A1 - Gillet, Francois A1 - Giusso del Galdo, Gian Pietro A1 - Gonzalez, Jose M. A1 - Grytnes, John-Arvid A1 - Hajek, Michal A1 - Hajkova, Petra A1 - Helm, Aveliina A1 - Herrera, Mercedes A1 - Hettenbergerova, Eva A1 - Hobohm, Carsten A1 - Huellbusch, Elisabeth M. A1 - Ingerpuu, Nele A1 - Jandt, Ute A1 - Jeltsch, Florian A1 - Jensen, Kai A1 - Jentsch, Anke A1 - Jeschke, Michael A1 - Jimenez-Alfaro, Borja A1 - Kacki, Zygmunt A1 - Kakinuma, Kaoru A1 - Kapfer, Jutta A1 - Kavgaci, Ali A1 - Kelemen, Andras A1 - Kiehl, Kathrin A1 - Koyama, Asuka A1 - Koyanagi, Tomoyo F. A1 - Kozub, Lukasz A1 - Kuzemko, Anna A1 - Kyrkjeeide, Magni Olsen A1 - Landi, Sara A1 - Langer, Nancy A1 - Lastrucci, Lorenzo A1 - Lazzaro, Lorenzo A1 - Lelli, Chiara A1 - Leps, Jan A1 - Loebel, Swantje A1 - Luzuriaga, Arantzazu L. A1 - Maccherini, Simona A1 - Magnes, Martin A1 - Malicki, Marek A1 - Marceno, Corrado A1 - Mardari, Constantin A1 - Mauchamp, Leslie A1 - May, Felix A1 - Michelsen, Ottar A1 - Mesa, Joaquin Molero A1 - Molnar, Zsolt A1 - Moysiyenko, Ivan Y. A1 - Nakaga, Yuko K. A1 - Natcheva, Rayna A1 - Noroozi, Jalil A1 - Pakeman, Robin J. A1 - Palpurina, Salza A1 - Partel, Meelis A1 - Paetsch, Ricarda A1 - Pauli, Harald A1 - Pedashenko, Hristo A1 - Peet, Robert K. A1 - Pielech, Remigiusz A1 - Pipenbaher, Natasa A1 - Pirini, Chrisoula A1 - Pleskova, Zuzana A1 - Polyakova, Mariya A. A1 - Prentice, Honor C. A1 - Reinecke, Jennifer A1 - Reitalu, Triin A1 - Pilar Rodriguez-Rojo, Maria A1 - Rolecek, Jan A1 - Ronkin, Vladimir A1 - Rosati, Leonardo A1 - Rosen, Ejvind A1 - Ruprecht, Eszter A1 - Rusina, Solvita A1 - Sabovljevic, Marko A1 - Maria Sanchez, Ana A1 - Savchenko, Galina A1 - Schuhmacher, Oliver A1 - Skornik, Sonja A1 - Sperandii, Marta Gaia A1 - Staniaszek-Kik, Monika A1 - Stevanovic-Dajic, Zora A1 - Stock, Marin A1 - Suchrow, Sigrid A1 - Sutcliffe, Laura M. E. A1 - Swacha, Grzegorz A1 - Sykes, Martin A1 - Szabo, Anna A1 - Talebi, Amir A1 - Tanase, Catalin A1 - Terzi, Massimo A1 - Tolgyesi, Csaba A1 - Torca, Marta A1 - Torok, Peter A1 - Tothmeresz, Bela A1 - Tsarevskaya, Nadezda A1 - Tsiripidis, Ioannis A1 - Tzonev, Rossen A1 - Ushimaru, Atushi A1 - Valko, Orsolya A1 - van der Maarel, Eddy A1 - Vanneste, Thomas A1 - Vashenyak, Iuliia A1 - Vassilev, Kiril A1 - Viciani, Daniele A1 - Villar, Luis A1 - Virtanen, Risto A1 - Kosic, Ivana Vitasovic A1 - Wang, Yun A1 - Weiser, Frank A1 - Went, Julia A1 - Wesche, Karsten A1 - White, Hannah A1 - Winkler, Manuela A1 - Zaniewski, Piotr T. A1 - Zhang, Hui A1 - Ziv, Yaron A1 - Znamenskiy, Sergey A1 - Biurrun, Idoia T1 - GrassPlot - a database of multi-scale plant diversity in Palaearctic grasslands JF - Phytocoenologia N2 - GrassPlot is a collaborative vegetation-plot database organised by the Eurasian Dry Grassland Group (EDGG) and listed in the Global Index of Vegetation-Plot Databases (GIVD ID EU-00-003). GrassPlot collects plot records (releves) from grasslands and other open habitats of the Palaearctic biogeographic realm. It focuses on precisely delimited plots of eight standard grain sizes (0.0001; 0.001;... 1,000 m(2)) and on nested-plot series with at least four different grain sizes. The usage of GrassPlot is regulated through Bylaws that intend to balance the interests of data contributors and data users. The current version (v. 1.00) contains data for approximately 170,000 plots of different sizes and 2,800 nested-plot series. The key components are richness data and metadata. However, most included datasets also encompass compositional data. About 14,000 plots have near-complete records of terricolous bryophytes and lichens in addition to vascular plants. At present, GrassPlot contains data from 36 countries throughout the Palaearctic, spread across elevational gradients and major grassland types. GrassPlot with its multi-scale and multi-taxon focus complements the larger international vegetationplot databases, such as the European Vegetation Archive (EVA) and the global database " sPlot". Its main aim is to facilitate studies on the scale-and taxon-dependency of biodiversity patterns and drivers along macroecological gradients. GrassPlot is a dynamic database and will expand through new data collection coordinated by the elected Governing Board. We invite researchers with suitable data to join GrassPlot. Researchers with project ideas addressable with GrassPlot data are welcome to submit proposals to the Governing Board. KW - biodiversity KW - European Vegetation Archive (EVA) KW - Eurasian Dry Grassland Group (EDGG) KW - grassland vegetation KW - GrassPlot KW - macroecology KW - multi-taxon KW - nested plot KW - scale-dependence KW - species-area relationship (SAR) KW - sPlot KW - vegetation-plot database Y1 - 2018 U6 - https://doi.org/10.1127/phyto/2018/0267 SN - 0340-269X VL - 48 IS - 3 SP - 331 EP - 347 PB - Cramer CY - Stuttgart ER - TY - JOUR A1 - Alberti, Federica A1 - Gonzalez, Javier A1 - Paijmans, Johanna L. A. A1 - Basler, Nikolas A1 - Preick, Michaela A1 - Henneberger, Kirstin A1 - Trinks, Alexandra A1 - Rabeder, Gernot A1 - Conard, Nicholas J. A1 - Muenzel, Susanne C. A1 - Joger, Ulrich A1 - Fritsch, Guido A1 - Hildebrandt, Thomas A1 - Hofreiter, Michael A1 - Barlow, Axel T1 - Optimized DNA sampling of ancient bones using Computed Tomography scans JF - Molecular ecology resources N2 - The prevalence of contaminant microbial DNA in ancient bone samples represents the principal limiting factor for palaeogenomic studies, as it may comprise more than 99% of DNA molecules obtained. Efforts to exclude or reduce this contaminant fraction have been numerous but also variable in their success. Here, we present a simple but highly effective method to increase the relative proportion of endogenous molecules obtained from ancient bones. Using computed tomography (CT) scanning, we identify the densest region of a bone as optimal for sampling. This approach accurately identifies the densest internal regions of petrous bones, which are known to be a source of high-purity ancient DNA. For ancient long bones, CT scans reveal a high-density outermost layer, which has been routinely removed and discarded prior to DNA extraction. For almost all long bones investigated, we find that targeted sampling of this outermost layer provides an increase in endogenous DNA content over that obtained from softer, trabecular bone. This targeted sampling can produce as much as 50-fold increase in the proportion of endogenous DNA, providing a directly proportional reduction in sequencing costs for shotgun sequencing experiments. The observed increases in endogenous DNA proportion are not associated with any reduction in absolute endogenous molecule recovery. Although sampling the outermost layer can result in higher levels of human contamination, some bones were found to have more contamination associated with the internal bone structures. Our method is highly consistent, reproducible and applicable across a wide range of bone types, ages and species. We predict that this discovery will greatly extend the potential to study ancient populations and species in the genomics era. KW - ancient DNA KW - computer tomography KW - palaeogenomics KW - paleogenetics KW - petrous bone Y1 - 2018 U6 - https://doi.org/10.1111/1755-0998.12911 SN - 1755-098X SN - 1755-0998 VL - 18 IS - 6 SP - 1196 EP - 1208 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Denker, Carsten A1 - Kuckein, Christoph A1 - Verma, Meetu A1 - Manrique Gonzalez, Sergio Javier Gonzalez A1 - Diercke, Andrea A1 - Enke, Harry A1 - Klar, Jochen A1 - Balthasar, Horst A1 - Louis, Rohan E. A1 - Dineva, Ekaterina Ivanova T1 - High-cadence Imaging and Imaging Spectroscopy at the GREGOR Solar Telescope-A Collaborative Research Environment for High-resolution Solar Physics JF - The astrophysical journal : an international review of spectroscopy and astronomical physics ; Supplement series N2 - In high-resolution solar physics, the volume and complexity of photometric, spectroscopic, and polarimetric ground-based data significantly increased in the last decade, reaching data acquisition rates of terabytes per hour. This is driven by the desire to capture fast processes on the Sun and the necessity for short exposure times "freezing" the atmospheric seeing, thus enabling ex post facto image restoration. Consequently, large-format and high-cadence detectors are nowadays used in solar observations to facilitate image restoration. Based on our experience during the "early science" phase with the 1.5 m GREGOR solar telescope (2014–2015) and the subsequent transition to routine observations in 2016, we describe data collection and data management tailored toward image restoration and imaging spectroscopy. We outline our approaches regarding data processing, analysis, and archiving for two of GREGOR's post-focus instruments (see http://gregor.aip.de), i.e., the GREGOR Fabry–Pérot Interferometer (GFPI) and the newly installed High-Resolution Fast Imager (HiFI). The heterogeneous and complex nature of multidimensional data arising from high-resolution solar observations provides an intriguing but also a challenging example for "big data" in astronomy. The big data challenge has two aspects: (1) establishing a workflow for publishing the data for the whole community and beyond and (2) creating a collaborative research environment (CRE), where computationally intense data and postprocessing tools are colocated and collaborative work is enabled for scientists of multiple institutes. This requires either collaboration with a data center or frameworks and databases capable of dealing with huge data sets based on virtual observatory (VO) and other community standards and procedures. KW - astronomical databases KW - methods: data analysis KW - Sun: chromosphere KW - Sun: photosphere KW - techniques: image processing KW - techniques: spectroscopic Y1 - 2018 U6 - https://doi.org/10.3847/1538-4365/aab773 SN - 0067-0049 SN - 1538-4365 VL - 236 IS - 1 PB - IOP Publ. Ltd. CY - Bristol ER - TY - JOUR A1 - Gonzalez Manrique, Sergio Javier A1 - Kuckein, Christoph A1 - Collados, M. A1 - Denker, Carsten A1 - Solanki, S. K. A1 - Gomory, P. A1 - Verma, Meetu A1 - Balthasar, H. A1 - Lagg, A. A1 - Diercke, Andrea T1 - Temporal evolution of arch filaments as seen in He I 10 830 angstrom JF - Astronomy and astrophysics : an international weekly journal N2 - Aims. We study the evolution of an arch filament system (AFS) and of its individual arch filaments to learn about the processes occurring in them. Methods. We observed the AFS at the GREGOR solar telescope on Tenerife at high cadence with the very fast spectroscopic mode of the GREGOR Infrared Spectrograph (GRIS) in the He I 10 830 angstrom spectral range. The He I triplet profiles were fitted with analytic functions to infer line-of-sight (LOS) velocities to follow plasma motions within the AFS. Results. We tracked the temporal evolution of an individual arch filament over its entire lifetime, as seen in the He I 10 830 angstrom triplet. The arch filament expanded in height and extended in length from 13 ' to 21 '. The lifetime of this arch filament is about 30 min. About 11 min after the arch filament is seen in He I, the loop top starts to rise with an average Doppler velocity of 6 km s(-1). Only two minutes later, plasma drains down with supersonic velocities towards the footpoints reaching a peak velocity of up to 40 km s(-1) in the chromosphere. The temporal evolution of He I 10 830 angstrom profiles near the leading pore showed almost ubiquitous dual red components of the He I triplet, indicating strong downflows, along with material nearly at rest within the same resolution element during the whole observing time. KW - Sun: chromosphere KW - Sun: activity KW - methods: observational KW - methods: data analysis KW - techniques: high angular resolution Y1 - 2018 U6 - https://doi.org/10.1051/0004-6361/201832684 SN - 1432-0746 VL - 617 PB - EDP Sciences CY - Les Ulis ER -