231 a 240 de 1.137 Resultados
23 jul 2025 -
Entrevistas exposición Zhongguo
Archivo Comprimido - 99,1 KB -
MD5: c381e4477d3e554f5203b835c9d2062f
ENCRYPTED FILE. Send a message to gladys.nieto@uam.es in order to obtain the password which decrypts the file |
23 jul 2025 -
Entrevistas exposición Zhongguo
Texto plano - 4,3 KB -
MD5: 1b830f8a7b54f508f37d32ea57125fa0
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22 jul 2025
Mata, Cristina; Malo Arrazola, Juan Esteban; García de la Morena, Eladio Luis; Santamaría, Ana E.; Hervás, Israel; Herranz Barrera, Jesus, 2025, "Bird mortality on high-speed railways: Lessons from two large contrasting species", https://doi.org/10.21950/TMOWY4, e-cienciaDatos, V1
Collisions are the chief effect of transport infrastructures on vertebrate populations but their relevance in high-speed railways (HSR) is largely unknown. We analyzed Great Bustard (Otis tarda) and Eurasian Eagle-owl (Bubo bubo) mortality along two 5-km stretches of a Spanish HS... |
Datos tabulares - 4,8 KB
Contains basic information of bird carcasses retrieved |
Valores separados por comas - 5,3 KB -
MD5: 21146e9d4a186c2329ab1a7196c2d21d
Contains UTM_X-Y coordinates of Otis tarda observations used for MaxEnt modelling of probability of species presence |
Texto plano - 6,1 KB -
MD5: d9255b156b33cab6e901951f10d8ddce
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22 jul 2025 - GRESEL-UAM: Narrativas Financieras y Literatura
Carbajo-Coronado, Blanca; Moreno-Sandoval, Antonio; Torterolo Orta, Yanco Amor; Gozalo, Paula, 2025, "The Financial Document Causality Detection Shared Task (FinCausal 2025): Dataset", https://doi.org/10.21950/V8VSSO, e-cienciaDatos, V1
The Financial Document Causality Detection Shared Task (FinCausal 2025) aims to improve causality identification in the financial domain through textual data. This shared task focuses on determining causality associated with both events and quantified facts. In this task, a cause... |
Valores separados por comas - 176,3 KB -
MD5: 27449a29976e09fb8188c77762e40f7a
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Valores separados por comas - 189,9 KB -
MD5: cbe531567553efaedf149bb0311a431b
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Texto plano - 7,8 KB -
MD5: 5a3512db9c44c4030cc4f9f6fa728e81
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