Our Work

Este material docente introduce las herramientas fundamentales de Python para el manejo de datos y su visualización, dentro del contexto de la ingeniería industrial y la ingeni…

Este material docente introduce las herramientas fundamentales de Python para el manejo de datos y su visualización, dentro del contexto de la ingeniería industrial y la ingeniería de datos. El bloque se estructura en cuatro grandes apartados: 1. **Introducción a las librerías en Python**   - Importación de módulos propios y de terceros   - Uso de alias y de funciones específicas   - Buenas prácticas y organización del código 2. **NumPy: cálculo vectorizado**   - Creación de arrays (listas, ceros, unos, secuencias, logspace)   - Operaciones vectorizadas y matriciales (elemento a elemento, matmul)   - Funciones estadísticas (mean, median, std, corrcoef)   - Manejo de valores faltantes (NaN) y funciones nan-safe   - Generación de números aleatorios con generadores reproducibles      (`np.random.default_rng`) 3. **Matplotlib: visualización de datos**   - Gráficos de línea, dispersión, histogramas y barras   - Subplots (múltiples gráficos en una figura)   - Personalización: títulos, etiquetas, leyendas, colores, marcadores   - Escalas logarítmicas (loglog, semilogy)   - Gráficos 3D con `mpl_toolkits.mplot3d`   - Aplicación a datasets reales 4. **Pandas: manipulación de datos tabulares**   - Series: creación, indexación significativa, acceso con `.loc` e `.iloc`   - DataFrames: creación desde listas, diccionarios y listas de diccionarios   - Lectura de archivos CSV (`pd.read_csv`)   - Operaciones: selección, filtrado, ordenación, concatenación   - Recategorización con `pd.cut`   - Manejo de valores faltantes con `fillna` El material incluye ejemplos progresivos, ejercicios propuestos y aplicaciones sobre datos reales (distancias de viajes en taxi de NYC, emisiones de CO₂, dataset California Housing). **Público objetivo:** estudiantes de grado y máster en ingeniería, ciencia de datos e informática que se inicien en el análisis de datos con Python. **Requisitos previos:** conocimientos básicos de programación en Python (listas, diccionarios, bucles y funciones). **Resultados de aprendizaje:**- Manejar arrays de NumPy y aplicar operaciones vectorizadas- Crear gráficos 2D y 3D con Matplotlib- Manipular Series y DataFrames con Pandas- Leer y procesar archivos CSV- Detectar y tratar valores faltantes y valores atípicos

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Commits 168a5d1: handle fill errors more clearly (Richard Challis) ff3e8c3: update fill list cap and network retry behaviour (Richard Challis) b8b359a: finalise fill error reporting (Richard Challis)…

Commits 168a5d1: handle fill errors more clearly (Richard Challis) ff3e8c3: update fill list cap and network retry behaviour (Richard Challis) b8b359a: finalise fill error reporting (Richard Challis) aaa10ac: bug fixes (Richard Challis) 3da277d: bump UI/API version (Richard Challis) a5c8b23: "Bump version: 2.12.12 → 2.12.13" (Richard Challis)

This dataset contains the result of the main experiments described in the paper "Enhancing hydrological representation of the Brahmaputra basin through terrestrial water storage and surface soil moist…

This dataset contains the result of the main experiments described in the paper "Enhancing hydrological representation of the Brahmaputra basin through terrestrial water storage and surface soil moisture Data Assimilation" (Retegui-Schiettekatte et al., 2026) published in HESS. More specifically, in these experiments uni-variate and multi-variate assimilation of satellite-derived Terrestrial Water Storage (TWS) and Surface Soil Moisture (SSM) observations was performed in the Brahmaputra River basin for the period 2004-2015. For the multi-variate DA experiments, four different Data Assimilation tuning methods were tested to investigate the impact of these methodological choices on the results. More details about the processing and the main findings of these experiments can be found in the paper [1]. Content Main data This dataset contains the results of the following experiments:- OL: Open Loop run (i.e., original model estimates)- Multivar DA: main multi-variate DA experiment presented in the paper (model space mixed localization, approach 4)- TWS DA: Uni-variate TWS DA experiment- SSM DA: Uni-variate SSM DA experiment- Multivar DA approach 1: multi-variate DA with no tuning- Multivar DA approach 2: multi-variate DA with uncertainty adjustment- Multivar DA approach 3: multi-variate DA with observation space localization For each experiment, spatially distributed and ensemble-averaged time series are provided for the following (aggregated) variables (more information on the way the variables were aggregated can be found in [1]):- TWS: Terrestrial Water Storage (summation of all other variables)- GW: Groundwater- SW: Surface Soil Moisture- SR: Surface Water- SN: Snow water- VW: Vegetation water Additional content The geospatial data necessary to interpret the results have also been provided. These include:- Shapefiles containing the boundary of the sub-basins used in the DA process.- Coordinates of each of the grid cells for which the distributed data is provided.To ease the interpretation and visualization of the results, some Matlab scripts that represent sub-basin averaged time series and distribuetd maps have been included.The structure of the directory and file naming is described on the README.txt file. Citation Please, when using this dataset, in addition to the Zenodo dataset, remember to cite:L. Retegui-Schiettekatte et al. 2026, "Enhancing hydrological representation of the Brahmaputra basin through terrestrial water storage and surface soil moisture Data Assimilation" (accepted in HESS) Licence CC BY 4.0 Funding This work was carried out as part of the DANSk-LSM project, which is supported by the Danmarks Frie Forskningsfond (10.46540/2035-00247B). A research visit of Leire Retegui-Schiettekatte to the University of California, Berkeley, was supportedby the EliteForsk mobility grant, granted by the Danish Ministry of Higher Education and Science. References [1] L. Retegui-Schiettekatte et al. 2026, "Enhancing hydrological representation of the Brahmaputra basin through terrestrial water storage and surface soil moisture Data Assimilation" (accepted in HESS)

Initial public release of the code accompanying the manuscript (submitted for review to The Cryosphere): "Deriving Distributed Mass Balance Patterns of Swiss Glaciers from Remote Sensing Data&quo…

Initial public release of the code accompanying the manuscript (submitted for review to The Cryosphere): "Deriving Distributed Mass Balance Patterns of Swiss Glaciers from Remote Sensing Data" by M. Izeboud, L. Van Tricht, K. Henning and H. Zekollari. This release contains the scripts required to reproduce the main analyses and figures presented in the study. This repository does not contain all required (external) data to run the scripts, but provides information where to get it. Data dependencies and instructions for reproducing the analysis are described in the README. Update to v1.0.0: add requirements.txt and link to zenodo

This preregistered study proposes to explore the relationship between sexual pain, sexual expectations, and sexual satisfaction in mid-life women. Additionally, researchers will investigate if perceiv…

This preregistered study proposes to explore the relationship between sexual pain, sexual expectations, and sexual satisfaction in mid-life women. Additionally, researchers will investigate if perceived partner responsiveness mitigates the presumed negative effects of pain on sexual expectations and satisfaction. Reserachers will utilize waves two and three of the nationally representative MIDUS study to test the hypotheses described in the preregistration. 

A partir de puntos de intercepto: Abundancia y abundancia relativa por especies (Tabla 1). Abundancia y abundancia relativa por estrato (Tabla 2). Índices de diversidad por cerco y transecto (…

A partir de puntos de intercepto: Abundancia y abundancia relativa por especies (Tabla 1). Abundancia y abundancia relativa por estrato (Tabla 2). Índices de diversidad por cerco y transecto (Tabla 3). Índices de diversidad por cerco  (Tabla 4). A partir de cuadrantes: Índices de diversidad por cobertura por cerco y cuadrante (Tabla 5). Índices de diversidad por cobertura por cerco (Tabla 6).

The purpose of this document is to provide a Chandra-ACIS specific overview of pileup - i.e., the phenomena of two or more photon events overlapping in a single detector frame and being read as a…

The purpose of this document is to provide a Chandra-ACIS specific overview of pileup - i.e., the phenomena of two or more photon events overlapping in a single detector frame and being read as a single event. We discuss the definition of pileup and describe its effects on detected spectra and variability. We outline methods for avoiding pileup, and also discuss when not to avoid pileup. We describe several ways the degree of pileup can be estimated when planning an observation. Methods of mitigating the effects of pileup in real data are outlined. This document is meant as a ‘living resource’. As knowledge of how to detect, assess, and correct for the effects of pileup improves, we will update and expand the procedures described below.

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.