Autor: Reich, David R. - Prolib Integro

Skocz do pozycji: 1.

Tytuł:: Wave 2 of the Multilingual Eye-Movement Corpus (MECO) : new text reading data across languages
Autorzy:: Bao, Yaqian Borogjoon
Schroeder, Sascha
Mihajlović, Nataša
Bolliger, Lena S.
Popović Stijačić, Milica
Smirnova, Anna
Lee, Charlotte E.
Filipović Đurđević, Dušica
Srivastava, Priyanka
Kharlamov, Nik
Lee, Jun Ren
Brasser, Jan
Reich, David R.
Wu, Denise H.
Zdravković, Sunčica
Rimzhim, Anurag
Acartürk, Cengiz
Vieira, João M. M.
Verma, Ark
Leite, Marina P. T.
Santana-Covarrubias, Andrea
Sekerina, Irina
Kristjánsson, Árni
Rothe-Neves, Rui
Mancini, Simona
Siegelman, Noam
Sigurdardottir, Heida M.
Kuperman, Victor
Teixeira, Elisangela N.
Ugrinic, Ivana
Orekhova, Miloslava
Vakulya, Karolina
Sá, Thais M. M.
Zhuo, Junjing
Campos-Rojas, César
Goldina, Sofya
Ziaka, Laoura
Jäger, Lena A.
Protopapas, Athanassios
Agrawal, Niket
Drieghe, Denis
Knudsen, Hanne B. S.
Khare, Anurag
Mišić, Ksenija
Parshina, Olga
Ibáñez Orellana, Romualdo
Xue, Jin
Usal, Kerem Alp
Jóhannesson, Ómar I.
Opis:: This paper reports the Wave 2 expansion of the Multilingual Eye-Movement Corpus (MECO), a collaborative multi-lab project collecting eye-tracking data on text reading in a variety of languages. The present expansion comes with new eye-tracking data of N = 654 from 13 languages, collected in 16 labs over 15 countries, including in several languages that have little to no representation in current eye-tracking studies on reading. MECO also contains demographic, language use, and other individual differences data. This paper makes available the first-language reading data of MECO Wave 2 and incorporates reliability estimates of all tests at the participant and item level, as well as other methods of data validation. It also reports the descriptive statistics on all languages, including comparisons with prior similar data, and outlines directions for potential reuse.
Dostawca treści:: Repozytorium Uniwersytetu Jagiellońskiego

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Tytuł:: New data on text reading in English as a second language
Autorzy:: Schroeder, Sascha
Mihajlović, Nataša
Bolliger, Lena S.
Popović Stijačić, Milica
Smirnova, Anna
Lee, Charlotte E.
Orellana, Romualdo Ibáñez
Alexandre, Dominick M.
Gadelha de Freitas, Luiz Vinicius
Kharlamov, Nik
Santana Covarrubias, Andrea
Lee, Jun Ren
Srivastava, Priyanka
Brasser, Jan
Reich, David R.
Wu, Denise H.
Zdravković, Sunčica
Đurđević, Dušica Filipović
Acartürk, Cengiz
Rimzhim, Anurag
Vieira, João M. M.
Verma, Ark
Leite, Marina P. T.
Sekerina, Irina
Kristjánsson, Árni
Kuperman, Victor
Mancini, Simona
Rothe-Neves, Rui
Sigurdardottir, Heida M.
Siegelman, Noam
Teixeira, Elisangela N.
Ugrinic, Ivana
Orekhova, Miloslava
Vakulya, Karolina
Sá, Thais M. M.
Zhuo, Junjing
Campos-Rojas, César
Goldina, Sofya
Ziaka, Laoura
Jäger, Lena A.
Protopapas, Athanassios
Agrawal, Niket
Drieghe, Denis
Knudsen, Hanne B. S.
Khare, Anurag
Mišić, Ksenija
Parshina, Olga
Xue, Jin
Usal, Kerem Alp
Jóhannesson, Ómar I.
Opis:: This paper reports an expansion of the English as a second language (L2) component of the Multilingual Eye Movement Corpus (MECO L2), an international database of eye movements during text reading. While the previous Wave 1 of the MECO project (Kuperman et al., 2023) contained English as a L2 reading data from readers with 12 different first language (L1) backgrounds, the newly collected dataset adds eye-tracking data on English text reading from 13 distinct L1 backgrounds as well as participants’ scores on component skills of English proficiency and information about their demographics and language background and use. The paper reports reliability estimates, descriptive statistics, and correlational analyses as means to validate the expansion dataset. Consistent with prior literature and the MECO Wave 1, trends in the MECO Wave 2 data include a weak correlation between reading comprehension and oculomotor measures of reading fluency and a greater L1-L2 contrast in reading fluency than reading comprehension. Jointly with Wave 1, the MECO project includes English reading data from more than 1,200 readers representing a diversity of native writing systems (logographic, abjad, abugida, and alphabetic) and 19 distinct L1 backgrounds. We provide multiple pointers to new venues of how L2 reading researchers can mine this rich publicly available dataset.
Dostawca treści:: Repozytorium Uniwersytetu Jagiellońskiego

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Skocz do pozycji: 3.

Tytuł:: Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations
Autorzy:: Reina, Borja
Krupa, Bartosz
Lasinio, Giovanna Jona
Ullrich, Alexander
Montero-Manso, Pablo
Priesemann, Viola
Bejar, Benjamin
Bracher, Johannes
Tucek, Vit
Smid, Martin
Perez Alvarez, Cesar
Fuhrmann, Jan
Kraus, David
Rodiah, Isti
Kraus, Andrea
Nowosielski, Jedrzej
Redlarski, Grzegorz
Filinski, Maciej
Kisielewski, Jan
Michaud, Isaac
Pennoni, Fulvia
Gurung, Heidi
Leithauser, Neele
Venkatramanan, Srinivasan
Lopez, Daniel
Hurt, Benjamin
Pribylova, Lenka
Zibert, Janez
Schneider, Johanna
Stage, Steven
Bartczuk, Rafal P
Szczurek, Ewa
Holger, Kirsten
Hotz, Thomas
Wattanachit, Nutcha
Farcomeni, Alessio
Lewis, Bryan
Wang, Lijing
Castro, Lauren
Bodych, Marcin
Dehning, Jonas
Villanueva, Inmaculada
Mohr, Sebastian
Alaimo Di Loro, Pierfrancesco
Parolini, Nicola
Deuschel, Jannik
Suchoski, Bradley
Grah, Rok
Giudici, Paolo
Catala, Marti
Budzinski, Jozef
Alonso, Sergio
Marathe, Madhav
Morina, David
Idzikowski, Radoslaw
Sun, Tao
Sherratt, Katharine
Meinke, Jan H
Kuhlmann, Alexander
Walraven, Robert
Abbott, Sam
Saksham, Soni
Porebski, Przemyslaw
Zimmermann, Tom
Bartolucci, Francesco
Obozinski, Guillaume
Gogolewski, Krzysztof
Zielinski, Jakub
Rakowski, Franciszek
Pottier, Loic
Tarantino, Barbara
Moszyński, Antoni
Gruson, Hugo
Wang, Yijin
Ardenghi, Giovanni
Biecek, Przemyslaw
Alvarez, Enric
Funk, Sebastian
Mingione, Marco
Bock, Wolfgang
Bosse, Nikos I
Guzman- Merino, Miguel
Dreger, Filip
Ray, Evan L
Wolffram, Daniel
Aznarte, Jose L
Burgard, Jan Pablo
Barbarossa, Maria Vittoria
Singh, David E
Gambin, Anna
Dimitris, Bertsimas
Pabjan, Barbara
Gruziel-Slomka, Magdalena
Kheifetz, Yuri
Divino, Fabio
Zajicek, Milan
Srivastava, Ajitesh
Niehus, Rene
Lovison, Gianfranco
Adiga, Aniruddha
Mohring, Jan
Prats, Clara
Johnson, Helen
Krueger, Tyll
Osthus, Dave
Baccam, Prasith
Prasse, Bastian
Radwan, Maciej
Semeniuk, Marcin
Maruotti, Antonello
Eclerova, Veronika
Scholz, Markus
Krymova, Ekaterina
Trnka, Jan
Thanou, Dorina
Niedzielewski, Karol
Reich, Nicholas G
Meakin, Sophie R
Wlazlo, Jaroslaw
Sheldon, Daniel
Ozanski, Tomasz
Ziarelli, Giovanni
Gibson, Graham
Heyder, Stefan
Rodloff, Arne
Li, Michael Lingzhi
Fairchild, Geoffrey
Sandmann, Frank
Lange, Berit
Wydawca:: eLife Sciences Publications Ltd.
Cytata wydawnicza:: Sherratt K, Gruson H, Grah R, Johnson H, Niehus R, Prasse B, Sandmann F, Deuschel J, Wolffram D, Abbott S, Ullrich A, Gibson G, Ray EL, Reich NG, Sheldon D, Wang Y, Wattanachit N, Wang L, Trnka J, Obozinski G, Sun T, Thanou D, Pottier L, Krymova E, Meinke JH, Barbarossa MV, Leithauser N, Mohring J, Schneider J, Wlazlo J, Fuhrmann J, Lange B, Rodiah I, Baccam P, Gurung H, Stage S, Suchoski B, Budzinski J, Walraven R, Villanueva I, Tucek V, Smid M, Zajicek M, Perez Alvarez C, Reina B, Bosse NI, Meakin SR, Castro L, Fairchild G, Michaud I, Osthus D, Alaimo Di Loro P, Maruotti A, Eclerova V, Kraus A, Kraus D, Pribylova L, Dimitris B, Li ML, Saksham S, Dehning J, Mohr S, Priesemann V, Redlarski G, Bejar B, Ardenghi G, Parolini N, Ziarelli G, Bock W, Heyder S, Hotz T, Singh DE, Guzman-Merino M, Aznarte JL, Morina D, Alonso S, Alvarez E, Lopez D, Prats C, Burgard JP, Rodloff A, Zimmermann T, Kuhlmann A, Zibert J, Pennoni F, Divino F, Catala M, Lovison G, Giudici P, Tarantino B, Bartolucci F, Jona Lasinio G, Mingione M, Farcomeni A, Srivastava A, Montero-Manso P, Adiga A, Hurt B, Lewis B, Marathe M, Porebski P, Venkatramanan S, Bartczuk RP, Dreger F, Gambin A, Gogolewski K, Gruziel-Slomka M, Krupa B, Moszyński A, Niedzielewski K, Nowosielski J, Radwan M, Rakowski F, Semeniuk M, Szczurek E, Zielinski J, Kisielewski J, Pabjan B, Holger K, Kheifetz Y, Scholz M, Przemyslaw B, Bodych M, Filinski M, Idzikowski R, Krueger T, Ozanski T, Bracher J, Funk S. Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations. Elife. 2023 Apr 21;12:e81916. doi: 10.7554/eLife.81916.
Opis:: Wellcome Trust (Grant number 221003/Z/20/Z) in collaboration with the Foreign, Commonwealth, and Development Office, United Kingdom. AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D- 0007, and respectively Virginia Dept of Health Grant VDH- 21- 501- 0141, VDH- 21- 501- 0143, VDH- 21- 501- 0147, VDH- 21- 501- 0145, VDH- 21- 501- 0146, VDH- 21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018- 095456-B- I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 (https://www.nfdi4health.de/task-force-covid-19-2) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).
Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks.
Dostawca treści:: Repozytorium Centrum Otwartej Nauki

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