Encuesta satisfacción e-cienciaDatos
e-cienciaDatos es el repositorio de datos de investigación de las universidades del Consorcio Madroño. Es miembro de Harvard Dataverse Network, aceptado por las principales editoriales científicas y cumple los requisitos del H2020.
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831 a 840 de 940 Resultados
11 mar 2022 -
Clinical Trials for Evidence-Based Medicine in Spanish (CT-EBM-SP) Corpus and word-embeddings
Desconocido - 24,2 MB -
MD5: 5ae92ab4dd8b75d3a577bee2b76fa1f1
Binary file (and larger in size), to be used for the character n-grams information |
11 mar 2022 -
Clinical Trials for Evidence-Based Medicine in Spanish (CT-EBM-SP) Corpus and word-embeddings
Desconocido - 56,3 MB -
MD5: 815e0176d2f3f2b9062683ef14239725
Non-binary file containing the vector values and dimensions for each token |
11 mar 2022 -
Clinical Trials for Evidence-Based Medicine in Spanish (CT-EBM-SP) Corpus and word-embeddings
Desconocido - 38,1 MB -
MD5: 7eeef55f7dc9b0900b338c7f23963591
This file can be loaded to the Gensim library |
11 mar 2022 -
Clinical Trials for Evidence-Based Medicine in Spanish (CT-EBM-SP) Corpus and word-embeddings
Texto plano - 11,5 KB -
MD5: 7f40cef7cb5f3508de42b532e9e410cc
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26 ene 2022
Amo-Ochoa, Pilar; Maldonado, Noela; Latorre, Ana; Zamora, Félix; Somoza, Álvaro; Gómez-García, Carlos J.; Bastida, Agatha, 2022, "A nanostructured Cu(II) coordination polymer based on alanine as a tri-functional mimic enzyme and efficient composite in the detection of Sphingobacteria", https://doi.org/10.21950/NR3KKL, e-cienciaDatos, V1
This research raises the potential use of coordination polymers as new useful materials in two essential research fields, allowing the obtaining of a new multi artificial enzyme with the capacity to inhibit the growth of bacteria resistance. The fine selection of the ligands allo... |
Texto plano - 5,6 KB -
MD5: 34531dfc68aa409d3c221eeee09138a6
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Adobe PDF - 2,7 MB -
MD5: f011fce7f67439241f5f57d85295efdb
Electronic Supplementary Material |
9 dic 2021
Carracedo-Cosme, Jaime; Romero-Muñíz, Carlos; Pou, Pablo; Pérez, Rubén, 2021, "QUAM-AFM", https://doi.org/10.21950/UTGMZ7, e-cienciaDatos, V1
QUAM–AFM is the largest dataset of simulated Atomic Force Microscopy (AFM) images generated from a selection of 685,513 molecules that span the most relevant bonding structures and chemical species in organic chemistry. QUAM-AFM contains, for each molecule, 24 3D image stacks, ea... |
9 dic 2021 -
QUAM-AFM
Archivo ZIP - 144,0 MB -
MD5: 547df5e75c2021d840d2acb0b73ac719
Set of dictionaries created with the pickle package (Python 3.7) linking each molecule with the IUPAC name and the chemical formula by the CID number in PubChem (https://pubchem.ncbi.nlm.nih.gov/) |
9 dic 2021 -
QUAM-AFM
Archivo ZIP - 145,2 KB -
MD5: 34bfbae7b0e0aab962d4b5de3167778c
Folder with the Python QUAM.py script that allows visualization and search of the AFM simulations using a Graphical User Interface (GUI) |