Licensable technology created by BSC

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Set of tools to compute metrics and indices for climate analysis. The package provides functions to compute extreme indices, evaluate the agreement between models and combine theses models into an ensemble. Multi-model time series of climate indices can be computed either after averaging the 2-D fields from different models provided they share a common grid or by combining time series computed on the model native grid. Indices can be assigned weights and/or combined to construct new indices.
CmmD is a R tool that runs MolTi community detection algorithm and computes a multilayer community trajectory analysis for the network community structures obtained. 

Functionalities for the study of drug response curves with emphasis on the study of synergies. Contains functionalities to compose complete analysis flows for drug pair effects studies.

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COMP Superscalar (COMPSs) is a framework which aims to ease the development and execution of parallel applications for distributed infrastructures, such as Clusters, Clouds and containerized platforms.

COVID-19 Flow_Board is an integrated dashboard for visualising map-based representation of the mobility networks at different scales of spatial resolution. This analysis tool basic data analytics on population mobility.

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Crossmapper is an automated bioinformatics pipeline for asessing the rate of read crossmapping when two or more organisms are sequenced as one sample. The software can be used for planning such kind of experimental setups as dual- or multiple RNA-seq (mainly for host-pathogen, symbiont and cohabitant interaction studies), metagenomics studies, sequencing and analysis of hybrid species, allele-specific expression studies, and can be extended for the use in large sequencing facilities for resource optimization.

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CSDownscale is a software containing a set statistical downscaling methods for climate predictions. 
Set of generalised tools for the flexible computation of climate related indicators defined by the user. Each method represents a specific mathematical approach which is combined with the possibility to select an arbitrary time period to define the indicator. This enables a wide range of possibilities to tailor the most suitable indicator for each particular climate service application (agriculture, food security, energy, water management, ...). This package is intended for sub-seasonal, seasonal and decadal climate predictions, but its methods are also applicable to other time-scales, provided the dimensional structure of the input is maintained. Additionally, the outputs of the functions in this package are compatible with 'CSTools'.  

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Both the software and its products are suitable for commercialization to end-users from several sectors that evaluate climate forecasts or impact models. For instance, the result of the assessment of indicators tailored for the agriculture sector can be summarized with a scorecard. This tool becomes especially relevant when several versions of the product delivered in a service need to be evaluated or compared.

Exploits dynamical seasonal forecasts in order to provide information relevant to stakeholders at the seasonal timescale. The package contains process-based methods for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination and multivariate verification, as well as basic and advanced tools to obtain tailored products.
dataClay is a distributed data store that enables applications to store and access objects in the same format they have in memory, and executes object methods within the data store. These two main features accelerate both the development of applications and their execution.
Stable release: dataClay 4.0 - Claystone (June 2024)

The technology is a deep generative model for synthetic data generation. The model is a VAE (Variational AutoEncoder) equipped with two algorithms for explainability, XGBoost (Extreme Gradient Boosting) and SHAP (SHapley Additive exPlanations).

BSC derivatives of R-LINE are a collection of new implementation on top of the Research LINE source model (R-LINE) wich is a software for near-surface releases originally developed by the University of North Carolina.
dislib is a distributed computing library highly focused on machine learning on top of PyCOMPSs. Inspired by NumPy and scikit-learn, dislib provides various supervised and unsupervised learning algorithms through an easy-to-use API.

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A Package that contains two mathematical distributions to estimate extreme value data coming from embedded Systems.

DLB is a library devoted to speedup hybrid parallel applications. And at the same time DLB improves the efficient use of the computational resources inside a computing node.
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The Dynamic Management of Resources Application Programming Interface (DMR API) is conceived to facilitate the programmability of malleable applications automating resource reallocation, process handling, and data distribution. Process malleability is the capability of reconfiguring the number of processes of an application on-the-fly, in other words, during its execution.

A library designed to offer the global advantages of process malleability while providing a minimalist MPI-like syntax.

DYON selects a suitable CPU partition for a set of processes to improve a metric. For example, if we have a process that improves I/O and another that is using CPU. The partition will offer a CPU partition (20%-80%) if that improves the target metric. The technique allows to use metrics that are no monotonic, for example, in some cases increasing the CPU count for a shared application could decrease the performance if other processes get hung.

EAR software is a management framework optimizing the energy and efficiency of a cluster of interconnected nodes. To improve the energy of the cluster, EAR provides energy control, accounting, monitoring and optimization of both the applications running on the cluster and of the overall global cluster.