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Absell-Federico-Tena World Trade Historical Database 1948-2020 : New Zealand
No full textProject developed by Christopher Absell (University Gothenburg and Instituto Figuerola) Giovanni Federico (New York University Dubai) and Antonio Tena Junguito (Universidad Carlos III de Madrid and Instituto Figuerola). Dataset: New Zealan
Educación y crianza de los hijos. Detección de necesidades socioeducativas y formativas de los progenitores
No full textEl artículo establece los siguientes objetivos: a) determinar el grado de cumplimiento de los principios de la parentalidad positiva (PPP) en una muestra de padres y madres; b) detectar agrupamientos de progenitores en función de los PPP y las características sociodemográficas; c) identificar sus preferencias formativas y de asistencia a dichas intervenciones.Metodología. Se ejecutaron análisis cualitativos de contenidos y cuantitativos descriptivos, así como multivariante de conglomerados; se aplicaron las pruebas U de Mann-Whitney, H de Kruskal-Wallis, Binomial y Chi-cuadrado.the following objectives were set: a) to determine the degree of compliance with the principles of positive parenting (PPP) in a sample of mothers and fathers; b) to detect clusters of parents according to PPP and socio-demographic characteristics; c) to identify their preferences regarding training and attendance at these interventions.Methodology. Qualitative content and quantitative descriptive and multivariate cluster analyses were performed and Mann-Whitney U, Kruskal-Wallis H, Binomial and Chi-square tests were applied
Absell-Federico-Tena World Trade Historical Database 1948-2020 : Cameroon
No full textProject developed by Christopher Absell (University Gothenburg and Instituto Figuerola) Giovanni Federico (New York University Dubai) and Antonio Tena Junguito (Universidad Carlos III de Madrid and Instituto Figuerola). Dataset: Cameroo
Interview in Spain 8
No full textThe reemergence of China is changing the world, and that EU needs to develop a long-term approach based on research-based knowledge in order to engage strategically with an increasingly assertive China.
This dataset consists of a semi-structured interview with a person of Chinese nationality, carried out in Chinese and transcribed in Chinese language.</p
Supramolecular subphthalocyanine cage as catalytic container for the functionalization of fullerenes in water [dataset]
No full textIt is reported the first example of a supramolecular cage that works as a catalytic molecular reactor to perform transformations over fullerenes in aqueous medium.Taking advantage of the ability of metallo–organic Pd(II)-subphthalocyanine (SubPc) capsules to form stable host:guest complexes with C60, we have prepared a water-soluble cage that provides a hydrophobic environment for conducting cycloadditions over encapsulated C60, namely, Diels–Alder reactions with anthracene. Indeed,the presence of catalytic amounts of SubPc cage dissolved in water promotes coencapsulation of insoluble C60 and anthracene substrates, allowing the reaction to occur inside the cavity under mild conditions. The lower stability of the host:guest complex with the resulting C60 cycloadduct facilitates its displacement by pristine C60, which grants catalytic turnover. Moreover, bis-addition compounds are regioselectively formed inside the cage when using excess anthracene
SLIMBRAIN Database: A Multimodal Image Database of In Vivo Human Brains for Tumor Detection.
No full text
Project Description
Hyperspectral imaging and machine learning have been employed in the medical field for classifying highly infiltrative brain tumors. Although existing HSI databases of in-vivo human brains are available, they present two main deficiencies. Firstly, the amount of labeled data is scarce and secondly, 3D-tissue information is unavailable. To address both issues, we present the SLIMBRAIN database, a multimodal image database of in-vivo human brains which provides HS brain tissue data within the 400-1000 nm spectrum, as well as RGB, depth and multi-view images. Two HS cameras, two depth cameras and different RGB sensors were used to capture images and videos from 193 patients. All data in the SLIMBRAIN database can be used in a variety of ways, for example to train ML models with more than 1 million HS pixels available and labeled by neurosurgeons, to reconstruct 3D scenes or to visualize RGB brain images with different pathologies, offering unprecedented flexibility for both the medical and engineering communities.
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Data Description
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The SLIMBRAIN database contains anonymous hyperspectral, depth and RGB image data from in-vivo, and also ex-vivo, human brains from 193 patients.
SLIMBRAIN database. The available data are:
- CalibrationFiles: 5 .zip files to calibrate hyperspectral data for the different SLIMBRAIN prototypes and 1 .zip file containing the intrinsic and extrinsic parameters for some cameras.
- Datasets: 2 .zip files containing the patient's datasets for the snapshot and linescan hyperspectral cameras.
- GroundTruthMaps: 2 .zip files containing the patient's ground-truths folders for the snapshot and linescan hyperspectral cameras.
- PaperExperiments: 1 .zip files containing several files that store the patient IDs used for the results shown in the paper.
- preProcessedImages: Several .zip files containing the hyperspectral pre-processed cubes for the snapshot and linescan hyperspectral cameras.
- RawFiles: 193 .zip files containing the raw files acquired in the operating room for each of the 193 patients. These files contains the raw images from different cameras, videos and depth images.
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Notes
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To access the SLIMBRAIN Database, you need to fill, accept and sign the Data Usage Agreement terms. Then, you need to send it to us, using the emails included at the end of the document. We will evaluate your application and, if you are accepted, you will receive a confirmation email with the necessary steps to access the data.
You can either find the Data Usage Agreement within this page or at https://slimbrain.citsem.upm.es. Then, you could access https://slimbrain.citsem.upm.es/search to filter the patients using the available online service provided by Research Center on Software Technologies and Multimedia Systems for Sustainability (CITSEM) and Fundación para la Investigación Biomédica del Hospital Universitario 12 de Octubre (FIBH12O).
You could also use https://slimbrain.citsem.upm.es/files to see the raw data online without the need of downloading it.
For further information, you can visit the official SLIMBRAIN database website at https://slimbrain.citsem.upm.es, where you can find Python software to manage the hyperspectral data provided.
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Files
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- CalibrationFiles:
These files store the calibration files necessary for the hyperspectral data and depth cameras.
Specifically, folders starting with a number indicate a hyperspectral calibration library with dark
and white references at different working distances and tilt angles:
- 1_Tripod_popoman: For the Ximea snapshot camera. Illumination done with the Dolan Jenner lamp and ambient fluorescent lamps turned on. Obtained in the operating room when empty.
- 2_Prototype_laser: For the Ximea snapshot camera. Illumination done with the Dolan Jenner lamp and ambient fluorescent lamps turned on. Obtained in the operating room when empty.
- 3_Protoype_lidar: For the Ximea snapshot and Headwall linescan cameras. Illumination done with the Dolan Jenner lamp and ambient fluorescent lamps turned on. Obtained in the operating room when empty.
- 4_Prototype_lidar: For the Ximea snapshot and Headwall linescan cameras. Illumination done with the Osram lamp and ambient fluorescent lamps turned on. Obtained in the operating room when empty.
- 5_Prototype_Kinect: For the Ximea snapshot and Headwall linescan cameras. Illumination done with the International Light lamp and ambient fluorescent lamps turned off. Obtained in the laboratory.
Furthermore, the depth, RGB and HS sensor calibration files, including intrinsic, extrinsic and distortion
parameters, are included as .json files in DepthCameraCalibrationFiles.
- Datasets:
These files stores each patient dataset with the spectral information of every labelled pixel. These are obtained from the coordinates of its corresponding ground-truth map and pre-processed cube, which have been labeled by the neurosurgeons using a labelling tool based on the Spectral Angle Map (SAM) metric. Patient datasets are available for the Ximea snapshot and Headwall linescan hyperspectral cameras.
- GroundTruthMaps:
These files stores each patient ground-truth map labeled by the neurosurgeons. The labelling tool is based on the Spectral Angle Map (SAM) metric as already used in existing hyperspectral in-vivo human brain databases. Patient ground-truth maps are available for the Ximea snapshot and Headwall linescan hyperspectral cameras.
- PaperExperiments.zip:
Contains 2 .txt files with the patient's IDs used for the experiments shown in the paper.
- preProcessedImages:
These files stores each patient hyperspectral pre-processed cube. These are obtained from the raw data included in the RawFiles folder and the described pre-processing chain applied to them. Patient pre-processed cubes are available for the Ximea snapshot and Headwall linescan hyperspectral cameras.
- RawFiles:
These files stores the raw files obtained in each of the operations. It can include hyperspectral data, RGB data and depth information for each patient ID. All data is anonimized to keep the privacy of each human patient.</p
Dataset for sample T1DM simulated patients used to validate the paper Muñoz-Organero, M. “Deep physiological model for blood glucose prediction in T1DM patients”. Sensors (Switzerland), 2020, 20(14), pp. 1–17, 3896
No full textThe AIDA diabetes simulator has been used to generate 10 days of data for the different models implemented by the tool
Absell-Federico-Tena World Trade Historical Database 1948-2020 : Malaysia
No full textProject developed by Christopher Absell (University Gothenburg and Instituto Figuerola) Giovanni Federico (New York University Dubai) and Antonio Tena Junguito (Universidad Carlos III de Madrid and Instituto Figuerola). Dataset: Malaysi
Condicionantes de elección y adaptación académica
No full textContiene datos agregados y depurados, de carácter cuantitativo, obtenidos a partir de la aplicación de un cuestionario dirigido a estudiantes universitarios ecuatorianos, el cual contiene ítems sobre la situación socio-demográfica, sobre los condicionantes de la elección académica, y sobre su adaptación y satisfacción académica
Absell-Federico-Tena World Trade Historical Database 1948-2020 : Korea
No full textProject developed by Christopher Absell (University Gothenburg and Instituto Figuerola) Giovanni Federico (New York University Dubai) and Antonio Tena Junguito (Universidad Carlos III de Madrid and Instituto Figuerola). Dataset: Kore