Redata Repositorio de datos abiertos de investigación de Uruguay
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Conjunto de datos de: Degradation of arbuscular mycorrhizal symbiosis – is it a mechanism underlying Cynodon dactylon invasion in grasslands
No full textThis dataset contains part of the data from Silvina García's doctoral thesis in Agricultural Sciences. It is data released for publication in article form in the Journal of Vegetation Science. In this research, it was studied as an invasive grass of the natural grassland of Uruguay (Cynodon dactylon), affecting the arbuscular mycorrhiza of a native grass (Paspalumnotatum). The dataset includes data on mycorrhizal abundance, biomass, and nutrient content in Paspalum notatum.
Alignment of several two dinucleotide binding domain flavoproteins
No full textAlignment of the sequences of human glutathione reductase, human high-molecular weight thioredoxin reductase, human dihydrolipoamide dehydrogenase, trypanothione reductase from Trypanosoma cruzi, mercuric ion reductase from Pseudomonas aeruginosa, mycothione reductase from Mycobacterium tuberculosis, hypothiocyanous acid reductase from Streptococcus pneumoniae, glutathione amide reductase from Marichromatium gracile, doluble pyridine dinucleotide transhydrogenase from Escherichia coli, thioredoxin glutathione reductase from Schistosoma mansoni, low-molecular-weight thioredoxin reductase from E. coli, alkyl hydroperoxide reductase subunit F from E. coli, Bacillithiol disulfide reductase from Staphylococcus aureus, NADH peroxidase from Enterococcus faecalis, nNADH oxidase from Levilactobacillus brevis, NADH-dependent persulfide reductase from Archaeoglobus fulgidus, coenzyme A disulfide reductase from S. aureus, 2-ketopropyl-coenzyme M reductase/carboxylase from Xanthobacter autotrophicus, human apoptosis inducin factor 1, biphenyl dioxygenase subunit A from Acidovorax sp., rubredoxin reductase from P. aeruginosa, monodehydroascorbate reductase from Deschampsia antarctica, putidaredoxin reductase from P. putida, toluene dioxygenase subunit A from P. putida and 2,4-dienoyl-CoA reductase from E. coli
Results of Neural-Checker Toolbox in Taysir 2023 Competition code repository
No full textRepository for the experiments performed for the paper: "Results of Neural-Checker Toolbox in Taysir 2023 Competition" ICGI 202
Conjunto de datos de: Water-quality data imputation with a high percentage of missing values: a machine learning approach
No full textThe monitoring of surface-water quality followed by water-quality modeling and analysis is essential for generating effective strategies in water resource management. However, water-quality studies are limited by the lack of complete and reliable data sets on surface-water-quality variables. These deficiencies are particularly noticeable in developing countries.
This work focuses on surface-water-quality data from Santa Lucía Chico river (Uruguay), a mixed lotic and lentic river system. Data collected at six monitoring stations are publicly available at https://www.dinama.gub.uy/oan/datos-abiertos/calidad-agua/. The high temporal and spatial variability that characterizes water-quality variables and the high rate of missing values (between 50% and 70%) raises significant challenges.
To deal with missing values, we applied several statistical and machine-learning imputation methods. The competing algorithms implemented belonged to both univariate and multivariate imputation methods (inverse distance weighting (IDW), Random Forest Regressor (RFR), Ridge (R), Bayesian Ridge (BR), AdaBoost (AB), Huber Regressor (HR), Support Vector Regressor (SVR), and K-nearest neighbors Regressor (KNNR)).
IDW outperformed the others, achieving a very good performance (NSE greater than 0.8) in most cases.
In this dataset, we include the original and imputed values for the following variables:
- Water temperature (Tw)
- Dissolved oxygen (DO)
- Electrical conductivity (EC)
- pH
- Turbidity (Turb)
- Nitrite (NO2-)
- Nitrate (NO3-)
- Total Nitrogen (TN)
Each variable is identified as [STATION] VARIABLE FULL NAME (VARIABLE SHORT NAME) [UNIT METRIC].
More details about the study area, the original datasets, and the methodology adopted can be found in our paper https://www.mdpi.com/2071-1050/13/11/6318.
If you use this dataset in your work, please cite our paper:
Rodríguez, R.; Pastorini, M.; Etcheverry, L.; Chreties, C.; Fossati, M.; Castro, A.; Gorgoglione, A. Water-Quality Data Imputation with a High Percentage of Missing Values: A Machine Learning Approach. Sustainability 2021, 13, 6318. https://doi.org/10.3390/su1311631
Libros de actividades con el manual de usuario para Robotito Básico y Robotito Configurable
No full textManuales de usuario para el robot educativo Robotito Básico y Robotito 2.0 que incluyen libros de actividades