@article{vanLoonBaileyTattonetal.2013,
  author    = {van Loon, Jacco Th. and Bailey, M. and Tatton, B. L. and Apellaniz, Jesus Maiz and Crowther, P. A. and de Koter, A. and Evans, C. J. and Henault-Brunet, V. and Howarth, I. D. and Richter, Philipp and Sana, Hugues and Simon D{\´i}az, Sergio and Taylor, W. and Walborn, N. R.},
  title     = {The VLT-FLAMES tarantula survey IX. - the interstellar medium seen through diffuse interstellar bands and neutral sodium},
  series = {Astronomy and astrophysics : an international weekly journal},
  volume    = {550},
  journal   = {Astronomy and astrophysics : an international weekly journal},
  number    = {9},
  publisher = {EDP Sciences},
  address   = {Les Ulis},
  issn      = {0004-6361},
  doi       = {10.1051/0004-6361/201220210},
  pages     = {21},
  year      = {2013},
  abstract  = {Context. The Tarantula Nebula (a.k.a. 30 Dor) is a spectacular star-forming region in the Large Magellanic Cloud (LMC), seen through gas in the Galactic disc and halo. Diffuse interstellar bands (DIBs) offer a unique probe of the diffuse, cool-warm gas in these regions. Aims. The aim is to use DIBs as diagnostics of the local interstellar conditions, whilst at the same time deriving properties of the yet-unknown carriers of these enigmatic spectral features. Methods. Spectra of over 800 early-type stars from the Very Large Telescope Flames Tarantula Survey (VFTS) were analysed. Maps were created, separately, for the Galactic and LMC absorption in the DIBs at 4428 and 6614 angstrom and - in a smaller region near the central cluster R 136 - neutral sodium (the Na ID doublet); we also measured the DIBs at 5780 and 5797 angstrom. Results. The maps show strong 4428 and 6614 angstrom DIBs in the quiescent cloud complex to the south of 30 Dor but weak absorption in the harsher environments to the north (bubbles) and near the OB associations. The Na maps show at least five kinematic components in the LMC and a shell-like structure surrounding R 136, and small-scale structure in the Milky Way. The strengths of the 4428, 5780, 5797 and 6614 angstrom DIBs are correlated, also with Na absorption and visual extinction. The strong 4428 angstrom DIB is present already at low Na column density but the 6614, 5780 and 5797 angstrom DIBs start to be detectable at subsequently larger Na column densities. Conclusions. The carriers of the 4428, 6614, 5780 and 5797 angstrom DIBs are increasingly prone to removal from irradiated gas. The relative strength of the 5780 and 5797 angstrom DIBs clearly confirm the Tarantula Nebula as well as Galactic high-latitude gas to represent a harsh radiation environment. The resilience of the 4428 angstrom DIB suggests its carrier is large, compact and neutral. Structure is detected in the distribution of cool-warm gas on scales between one and > 100 pc in the LMC and as little as 0.01 pc in the Sun's vicinity. Stellar winds from the central cluster R 136 have created an expanding shell; some infalling gas is also detected, reminiscent of a galactic "fountain".},
  language  = {en}
}
@article{EsmaeilishirazifardUsherTrimetal.2022,
  author    = {Esmaeilishirazifard, Elham and Usher, Louise and Trim, Carol and Denise, Hubert and Sangal, Vartul and Tyson, Gregory H. and Barlow, Axel and Redway, Keith F. and Taylor, John D. and Kremyda-Vlachou, Myrto and Davies, Sam and Loftus, Teresa D. and Lock, Mikaella M. G. and Wright, Kstir and Dalby, Andrew and Snyder, Lori A. S. and Wuster, Wolfgang and Trim, Steve and Moschos, Sterghios A.},
  title     = {Bacterial adaptation to venom in snakes and arachnida},
  series = {Microbiology spectrum},
  volume    = {10},
  journal   = {Microbiology spectrum},
  number    = {3},
  publisher = {American Society for Microbiology},
  address   = {Birmingham, Ala.},
  issn      = {2165-0497},
  doi       = {10.1128/spectrum.02408-21},
  pages     = {16},
  year      = {2022},
  abstract  = {Notwithstanding their 3 to 5\% mortality, the 2.7 million envenomation-related injuries occurring annually-predominantly across Africa, Asia, and Latin America-are also major causes of morbidity. Venom toxin-damaged tissue will develop infections in some 75\% of envenomation victims, with E. faecalis being a common culprit of disease; however, such infections are generally considered to be independent of envenomation. Animal venoms are considered sterile sources of antimicrobial compounds with strong membrane-disrupting activity against multidrug-resistant bacteria. However, venomous bite wound infections are common in developing nations. Investigating the envenomation organ and venom microbiota of five snake and two spider species, we observed venom community structures that depend on the host venomous animal species and evidenced recovery of viable microorganisms from black-necked spitting cobra (Naja nigricollis) and Indian ornamental tarantula (Poecilotheria regalis) venoms. Among the bacterial isolates recovered from N. nigricollis, we identified two venom-resistant, novel sequence types of Enterococcus faecalis whose genomes feature 16 virulence genes, indicating infectious potential, and 45 additional genes, nearly half of which improve bacterial membrane integrity. Our findings challenge the dogma of venom sterility and indicate an increased primary infection risk in the clinical management of venomous animal bite wounds. IMPORTANCE Notwithstanding their 3 to 5\% mortality, the 2.7 million envenomation-related injuries occurring annually-predominantly across Africa, Asia, and Latin America-are also major causes of morbidity. Venom toxin-damaged tissue will develop infections in some 75\% of envenomation victims, with E. faecalis being a common culprit of disease; however, such infections are generally considered to be independent of envenomation. Here, we provide evidence on venom microbiota across snakes and arachnida and report on the convergent evolution mechanisms that can facilitate adaptation to black-necked cobra venom in two independent E. faecalis strains, easily misidentified by biochemical diagnostics. Therefore, since inoculation with viable and virulence gene-harboring bacteria can occur during envenomation, acute infection risk management following envenomation is warranted, particularly for immunocompromised and malnourished victims in resource-limited settings. These results shed light on how bacteria evolve for survival in one of the most extreme environments on Earth and how venomous bites must be also treated for infections.},
  language  = {en}
}
@misc{SmithZottaBoultonetal.2023,
  author    = {Smith, Taylor and Zotta, Ruxandra-Maria and Boulton, Chris A. and Lenton, Timothy M. and Dorigo, Wouter and Boers, Niklas},
  title     = {Reliability of resilience estimation based on multi-instrument time series},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1322},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-58912},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-589120},
  pages     = {173 -- 183},
  year      = {2023},
  abstract  = {Many widely used observational data sets are comprised of several overlapping instrument records. While data inter-calibration techniques often yield continuous and reliable data for trend analysis, less attention is generally paid to maintaining higher-order statistics such as variance and autocorrelation. A growing body of work uses these metrics to quantify the stability or resilience of a system under study and potentially to anticipate an approaching critical transition in the system. Exploring the degree to which changes in resilience indicators such as the variance or autocorrelation can be attributed to non-stationary characteristics of the measurement process - rather than actual changes in the dynamical properties of the system - is important in this context. In this work we use both synthetic and empirical data to explore how changes in the noise structure of a data set are propagated into the commonly used resilience metrics lag-one autocorrelation and variance. We focus on examples from remotely sensed vegetation indicators such as vegetation optical depth and the normalized difference vegetation index from different satellite sources. We find that time series resulting from mixing signals from sensors with varied uncertainties and covering overlapping time spans can lead to biases in inferred resilience changes. These biases are typically more pronounced when resilience metrics are aggregated (for example, by land-cover type or region), whereas estimates for individual time series remain reliable at reasonable sensor signal-to-noise ratios. Our work provides guidelines for the treatment and aggregation of multi-instrument data in studies of critical transitions and resilience.},
  language  = {en}
}
@article{SmithZottaBoultonetal.2023,
  author    = {Smith, Taylor and Zotta, Ruxandra-Maria and Boulton, Chris A. and Lenton, Timothy M. and Dorigo, Wouter and Boers, Niklas},
  title     = {Reliability of resilience estimation based on multi-instrument time series},
  series = {Earth System Dynamics},
  volume    = {14},
  journal   = {Earth System Dynamics},
  publisher = {Copernicus Publications},
  address   = {G{\"o}ttingen},
  issn      = {2190-4987},
  doi       = {10.5194/esd-14-173-2023},
  pages     = {173 -- 183},
  year      = {2023},
  abstract  = {Many widely used observational data sets are comprised of several overlapping instrument records. While data inter-calibration techniques often yield continuous and reliable data for trend analysis, less attention is generally paid to maintaining higher-order statistics such as variance and autocorrelation. A growing body of work uses these metrics to quantify the stability or resilience of a system under study and potentially to anticipate an approaching critical transition in the system. Exploring the degree to which changes in resilience indicators such as the variance or autocorrelation can be attributed to non-stationary characteristics of the measurement process - rather than actual changes in the dynamical properties of the system - is important in this context. In this work we use both synthetic and empirical data to explore how changes in the noise structure of a data set are propagated into the commonly used resilience metrics lag-one autocorrelation and variance. We focus on examples from remotely sensed vegetation indicators such as vegetation optical depth and the normalized difference vegetation index from different satellite sources. We find that time series resulting from mixing signals from sensors with varied uncertainties and covering overlapping time spans can lead to biases in inferred resilience changes. These biases are typically more pronounced when resilience metrics are aggregated (for example, by land-cover type or region), whereas estimates for individual time series remain reliable at reasonable sensor signal-to-noise ratios. Our work provides guidelines for the treatment and aggregation of multi-instrument data in studies of critical transitions and resilience.},
  language  = {en}
}
@article{SmirnovKronbergLatallerieetal.2019,
  author    = {Smirnov, Artem G. and Kronberg, Elena A. and Latallerie, F. and Daly, Patrick W. and Aseev, Nikita and Shprits, Yuri and Kellerman, Adam C. and Kasahara, Satoshi and Turner, Drew L. and Taylor, M. G. G. T.},
  title     = {Electron Intensity Measurements by the Cluster/RAPID/IES Instrument in Earth's Radiation Belts and Ring Current},
  series = {Space Weather: The International Journal of Research and Applications},
  volume    = {17},
  journal   = {Space Weather: The International Journal of Research and Applications},
  number    = {4},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {1542-7390},
  doi       = {10.1029/2018SW001989},
  pages     = {553 -- 566},
  year      = {2019},
  abstract  = {Plain Language Summary Radiation belts of the Earth, which are the zones of charged energetic particles trapped by the geomagnetic field, comprise enormous and dynamic systems. While the inner radiation belt, composed mainly of high-energy protons, is relatively stable, the outer belt, filled with energetic electrons, is highly variable and depends substantially on solar activity. Hence, extended reliable observations and the improved models of the electron intensities in the outer belt depending on solar wind parameters are necessary for prediction of their dynamics. The Cluster mission has been measuring electron flux intensities in the radiation belts since its launch in 2000, thus providing a huge dataset that can be used for radiation belts analysis. Using 16 years of electron measurements by the Cluster mission corrected for background contamination, we derived a uniform linear-logarithmic dependence of electron fluxes in the outer belt on the solar wind dynamic pressure.},
  language  = {en}
}
@techreport{BrodeurMikolaCooketal.2024,
  type      = {Working Paper},
  author    = {Brodeur, Abel and Mikola, Derek and Cook, Nikolai and Brailey, Thomas and Briggs, Ryan and Gendre, Alexandra de and Dupraz, Yannick and Fiala, Lenka and Gabani, Jacopo and Gauriot, Romain and Haddad, Joanne and Lima, Goncalo and Ankel-Peters, J{\"o}rg and Dreber, Anna and Campbell, Douglas and Kattan, Lamis and Fages, Diego Marino and Mierisch, Fabian and Sun, Pu and Wright, Taylor and Connolly, Marie and Hoces de la Guardia, Fernando and Johannesson, Magnus and Miguel, Edward and Vilhuber, Lars and Abarca, Alejandro and Acharya, Mahesh and Adjisse, Sossou Simplice and Akhtar, Ahwaz and Lizardi, Eduardo Alberto Ramirez and Albrecht, Sabina and Andersen, Synve Nygaard and Andlib, Zubaria and Arrora, Falak and Ash, Thomas and Bacher, Etienne and Bachler, Sebastian and Bacon, F{\´e}lix and Bagues, Manuel and Balogh, Timea and Batmanov, Alisher and Barschkett, Mara and Basdil, B. Kaan and Dower, Jaromneda and Castek, Ondrej and Caviglia-Harris, Jill and Strand, Gabriella Chauca and Chen, Shi and Chzhen, Asya and Chung, Jong and Collins, Jason and Coppock, Alexander and Cordeau, Hugo and Couillard, Ben and Crechet, Jonathan and Crippa, Lorenzo and Cui, Jeanne and Czymara, Christian and Daarstad, Haley and Dao, Danh Chi and Dao, Dong and Schmandt, Marco David and Linde, Astrid de and Melo, Lucas De and Deer, Lachlan and Vera, Micole De and Dimitrova, Velichka and Dollbaum, Jan Fabian and Dollbaum, Jan Matti and Donnelly, Michael and Huynh, Luu Duc Toan and Dumbalska, Tsvetomira and Duncan, Jamie and Duong, Kiet Tuan and Duprey, Thibaut and Dworschak, Christoph and Ellingsrud, Sigmund and Elminejad, Ali and Eissa, Yasmine and Erhart, Andrea and Etingin-Frati, Giulian and Fatemi-Pour, Elaheh and Federice, Alexa and Feld, Jan and Fenig, Guidon and Firouzjaeiangalougah, Mojtaba and Fleisje, Erlend and Fortier-Chouinard, Alexandre and Engel, Julia Francesca and Fries, Tilman and Fortier, Reid and Fr{\´e}chet, Nadjim and Galipeau, Thomas and Gallegos, Sebasti{\´a}n and Gangji, Areez and Gao, Xiaoying and Garnache, Clo{\´e} and G{\´a}sp{\´a}r, Attila and Gavrilova, Evelina and Ghosh, Arijit and Gibney, Garreth and Gibson, Grant and Godager, Geir and Goff, Leonard and Gong, Da and Gonz{\´a}lez, Javier and Gretton, Jeremy and Griffa, Cristina and Grigoryeva, Idaliya and Grtting, Maja and Guntermann, Eric and Guo, Jiaqi and Gugushvili, Alexi and Habibnia, Hooman and H{\"a}ffner, Sonja and Hall, Jonathan D. and Hammar, Olle and Kordt, Amund Hanson and Hashimoto, Barry and Hartley, Jonathan S. and Hausladen, Carina I. and Havr{\´a}nek, Tom{\´a}š and Hazen, Jacob and He, Harry and Hepplewhite, Matthew and Herrera-Rodriguez, Mario and Heuer, Felix and Heyes, Anthony and Ho, Anson T. Y. and Holmes, Jonathan and Holzknecht, Armando and Hsu, Yu-Hsiang Dexter and Hu, Shiang-Hung and Huang, Yu-Shiuan and Huebener, Mathias and Huber, Christoph and Huynh, Kim P. and Irsova, Zuzana and Isler, Ozan and Jakobsson, Niklas and Frith, Michael James and Jananji, Rapha{\"e}l and Jayalath, Tharaka A. and Jetter, Michael and John, Jenny and Forshaw, Rachel Joy and Juan, Felipe and Kadriu, Valon and Karim, Sunny and Kelly, Edmund and Dang, Duy Khanh Hoang and Khushboo, Tazia and Kim, Jin and Kjellsson, Gustav and Kjelsrud, Anders and Kotsadam, Andreas and Korpershoek, Jori and Krashinsky, Lewis and Kundu, Suranjana and Kustov, Alexander and Lalayev, Nurlan and Langlois, Audr{\´e}e and Laufer, Jill and Lee-Whiting, Blake and Leibing, Andreas and Lenz, Gabriel and Levin, Joel and Li, Peng and Li, Tongzhe and Lin, Yuchen and Listo, Ariel and Liu, Dan and Lu, Xuewen and Lukmanova, Elvina and Luscombe, Alex and Lusher, Lester R. and Lyu, Ke and Ma, Hai and M{\"a}der, Nicolas and Makate, Clifton and Malmberg, Alice and Maitra, Adit and Mandas, Marco and Marcus, Jan and Margaryan, Shushanik and M{\´a}rk, Lili and Martignano, Andres and Marsh, Abigail and Masetto, Isabella and McCanny, Anthony and McManus, Emma and McWay, Ryan and Metson, Lennard and Kinge, Jonas Minet and Mishra, Sumit and Mohnen, Myra and M{\"o}ller, Jakob and Montambeault, Rosalie and Montpetit, S{\´e}bastien and Morin, Louis-Philippe and Morris, Todd and Moser, Scott and Motoki, Fabio and Muehlenbachs, Lucija and Musulan, Andreea and Musumeci, Marco and Nabin, Munirul and Nchare, Karim and Neubauer, Florian and Nguyen, Quan M. P. and Nguyen, Tuan and Nguyen-Tien, Viet and Niazi, Ali and Nikolaishvili, Giorgi and Nordstrom, Ardyn and N{\"u}, Patrick and Odermatt, Angela and Olson, Matt and ien, Henning and {\"O}lkers, Tim and Vert, Miquel Oliver i. and Oral, Emre and Oswald, Christian and Ousman, Ali and {\"O}zak, {\"O}mer and Pandey, Shubham and Pavlov, Alexandre and Pelli, Martino and Penheiro, Romeo and Park, RyuGyung and Martel, Eva P{\´e}rez and Petrovičov{\´a}, Tereza and Phan, Linh and Prettyman, Alexa and Proch{\´a}zka, Jakub and Putri, Aqila and Quandt, Julian and Qiu, Kangyu and Nguyen, Loan Quynh Thi and Rahman, Andaleeb and Rea, Carson H. and Reiremo, Adam and Ren{\´e}e, La{\"e}titia and Richardson, Joseph and Rivers, Nicholas and Rodrigues, Bruno and Roelofs, William and Roemer, Tobias and Rogeberg, Ole and Rose, Julian and Roskos-Ewoldsen, Andrew and Rosmer, Paul and Sabada, Barbara and Saberian, Soodeh and Salamanca, Nicolas and Sator, Georg and Sawyer, Antoine and Scates, Daniel and Schl{\"u}ter, Elmar and Sells, Cameron and Sen, Sharmi and Sethi, Ritika and Shcherbiak, Anna and Sogaolu, Moyosore and Soosalu, Matt and Srensen, Erik and Sovani, Manali and Spencer, Noah and Staubli, Stefan and Stans, Renske and Stewart, Anya and Stips, Felix and Stockley, Kieran and Strobel, Stephenson and Struby, Ethan and Tang, John and Tanrisever, Idil and Yang, Thomas Tao and Tastan, Ipek and Tatić, Dejan and Tatlow, Benjamin and Seuyong, F{\´e}raud Tchuisseu and Th{\´e}riault, R{\´e}mi and Thivierge, Vincent and Tian, Wenjie and Toma, Filip-Mihai and Totarelli, Maddalena and Tran, Van-Anh and Truong, Hung and Tsoy, Nikita and Tuzcuoglu, Kerem and Ubfal, Diego and Villalobos, Laura and Walterskirchen, Julian and Wang, Joseph Taoyi and Wattal, Vasudha and Webb, Matthew D. and Weber, Bryan and Weisser, Reinhard and Weng, Wei-Chien and Westheide, Christian and White, Kimberly and Winter, Jacob and Wochner, Timo and Woerman, Matt and Wong, Jared and Woodard, Ritchie and Wroński, Marcin and Yazbeck, Myra and Yang, Gustav Chung and Yap, Luther and Yassin, Kareman and Ye, Hao and Yoon, Jin Young and Yurris, Chris and Zahra, Tahreen and Zaneva, Mirela and Zayat, Aline and Zhang, Jonathan and Zhao, Ziwei and Yaolang, Zhong},
  title     = {Mass reproducibility and replicability},
  series = {I4R discussion paper series},
  journal   = {I4R discussion paper series},
  number    = {107},
  publisher = {Institute for Replication},
  address   = {Essen},
  issn      = {2752-1931},
  pages     = {250},
  year      = {2024},
  abstract  = {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.},
  language  = {en}
}