TMGMT Demo
Welcome to the Translation Management Tool Demo module!
The Translation Management Tool (TMGMT) demo module provides the configuration needed for translating predefined content types - translatable nodes.
It enables three languages. Besides English, it supports German and French.
Content translation is enabled by default. This allows users to translate the content on their own. Also, Export / Import File translator enables exporting source data into a file and import the translated in return.
- To get started with the translation, two translatable nodes are created. The steps for translation are the following:
- On the node detail view use the "translate" Tab, choose a language and click "Request Translation" to get started.
- After submitting the job, the status is changed to "In progress". In case of a machine translator, the translation is immediately returned. The status is then "Needs review".
- "In progress" is the state where we are awaiting the translations from the translator.
- Once the translations are provided by the translator, we can review the job items (and correct) the translated content. Some translators support feedback cycles. We can send an item that needs a better translation back to the translator with some comments. If the translation is fine, we can accept the job items (or the job). This is when the source items are updated/the translation is created.
- The job is finally in the state of being published
- In the TMGMT demo module the File translator is enabled by default. It allows users to export and import texts via xliff and HTML. The workflow is the following:
- Submit a job to the File translator. The job is in "active" state.
- Export it as HTML/XLIFF format.
- Translate the content by editing the XLIFF files in plaintext or with a proper CAT tool.
- Import it back on the site.
- Review the job items/data items. XLIFF does not support a feedback loop or commenting an item. Improvements/fixings can only be done by the reviewer (or by reimporting the improved XLIFF).
- Press save as completed to accept the translation and finish the process.
- In the TMGMT demo module the Drupal user provider is also enabled by default. It allows to assign translation tasks to the users of the site that have the abilities to translate it (The demo adds all the abilities to all the users). The workflow is the following:
- Submit a job to the Drupal user provider and select translator for the job. The job is in "active" state.
- The user will translate the task. Also the task items can be reviewed.
- When the translation is done, the user will set the task as completed.
- Review the job items. This translator does not support a feedback loop or commenting an item. Improvements/fixings can only be done by the reviewer.
- Press save as completed to accept the translation and finish the process.
TMGMT demo also supports translation of paragraphs. To do this, you first need to enable paragraphs_demo and tmgmt_demo after that.
- External translation services can be used for creating a foreign language version of the source text. These are the recommended translators:
First node
This text can be translated with TMGMT. Use the "translate" Tab and choose "Request Translation" to get started.
Second node
Have another try. This text can be translated as well.
Research area:
BackML
In the machine learning research line we deal with data problems coming from different scenarios: industry, biosciences, health, economy, etc. We pursue the developments of new machine learning algorithms that can efficiently tackle these problems. Particularly, we consider problems that account for a variety of data types: from time series, to steaming data or images and speech, and a wide range of modelization techniques and mathematical formalisms such as: probabilistic graphical models, Bayesian approaches, deep learning, etc.
Research area:
BackCAS
The aim of the research in Applied Statistics is to consolidate BCAM as a reference in areas such as biostatistics, demography, environmental modeling, medical statistics, epidemiology, business analytics, and biomedical research applications involving data-driven mathematical and statistical tools.
Research area:
BackAA
Current research is concerned with the analytical study of physically motivated systems of partial differential equations. So far it has concentrated on several major directions:
Research area:
BackHA
Modern harmonic analysis is a very active field of research which has reached a state of maturity that places itself in a central position within the mathematical sciences. Although the origin of harmonic analysis goes back to the study of the heat equation (through Fourier theory), harmonic analysis today has many interconnections with many areas of mathematics like PDEs, operator theory or complex analysis. Often, harmonic analysis plays an important role when the scenarios are not very friendly as those where there is a lack of smoothness.
Research area:
BackSTAG
Singularities arise naturally in a huge number of different areas of mathematics and science. As a consequence Singularity Theory lies at the crossroads of the paths connecting applications of mathematics with its most abstract parts. For example, it connects the investigation of optical caustics with simple Lie algebras and regular polyhedra theory, while also relating hyperbolic PDE wavefronts to knot theory and the theory of the shape of solids to commutative algebra.
Research area:
BackSP
Since the advent of modern Single Particle Tracking (SPT) techniques, a large amount of data with great temporal and spatial accuracy has been produced. The emergence of anomalous diffusion has been confirmed by SPT statistics in many biological systems, with sub-diffusive behavior often associated to crowding, confinement phenomena, and strong heterogeneity of the environment. Recent experiments on molecular diffusion within the cell environment permit to distinguish the anomalous behavior caused by active mechanisms, from the one caused by crowding and confinement in the same system.
Research area:
BackMCEN
The MCEN research group develops innovative research at the interface between Mathematical, Computational and Experimental Neuroscience. This involves developing novel theoretical and algorithmic tools, multiscale parsimonious models (biophysical and data-driven), neuroscientific experiments and data analytical tools to extract of invariant patterns from experimental and clinical observations.