BUSCADOR RECOLECTA

This dataset describes the high-mountain plant composition at three mountain massifs (Farellones, Maule and the NP Torres del Paine) situated over a long latitudinal gradient at the Chilean Andes. Sampling methodology, dates and coordinates are also available.

Project developed by Giovanni Federico (New York University Abu Dhabi) and Antonio Tena Junguito (Universidad Carlos III de Madrid). Dataset: Crete (Aegean Islands)

Project 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: Ukraine

In this project, a simple, cost-effective and scalable solution to improve the mechanical properties of poly(butylene succinate-co- butylene adipate) (PBSA) is reported by using functionalized single-walled carbon nanotubes (SWCNTs). Different SWCNT percentages w/w (0.15, 0.25, 0.5, 0.65, 0.75, 0.85 and 1.0) have been incorporated in the PBSA matrix via simple solution casting, and the ultrasonication conditions, namely amplitude (A) and time (t) have been optimized to attain a homogenous SWCNT dispersion. The nanocomposites have been characterized in detail by scanning electron microscopy (SEM), Infrared spectroscopy, thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), tensile and impact strength tests. Unprecedented increments in stiffness, up to 114 % for the nanocomposite with 0.65 wt% content,were found. Further, four machine learning (ML) algorithms were applied to predict their mechanical properties and very good correlation was attained.

Project developed by Giovanni Federico (Università di Pisa) and Antonio Tena Junguito (Universidad Carlos III de Madrid). This data base outlines changes in world trade from 1800 to 2016. Dataset: Saint Vincent

Project developed by Giovanni Federico (New York University Abu Dhabi) and Antonio Tena Junguito (Universidad Carlos III de Madrid). Dataset: New Zealand

This dataset is a companion reproducibility package of the related paper submitted for publication, whose aim is to allow the exact replication of a very large experimental survey on word similarity between the families of ontology-based semantic similarity measures and word embedding models as detailed in ‘appendix-reproducible-experiments.pdf’ file. Our experiments are based on the evaluation of all methods with the HESML V1R4 semantic measures library and the recording of these experiments with Reprozip. HESML is a self-contained Java software library of semantic measures based on WordNet whose latest version, called HESML V1R4, also supports the evaluation of pre-trained word embedding files. HESML is a self-contained experimentation platform on word similarity which is especially well suited to run large experimental surveys by supporting the execution of automatic reproducible experiment files on word similarity based on a XML-based file format called (*.exp). On the other hand, ReproZip is a virtualisation tool whose aim is to warrant the exact replication of experimental results onto a different system from that originally used in their creation. Reprozip captures all the program dependencies and is able to reproduce the packaged experiments on any host platform, regardless of the hardware and software configuration used in their creation. Thus, ReproZip warrants the reproduction of the experiments introduced herein in the long-term. Finally, other very valuable feature of Reprozip is that it allows to modify the input files of any Reprozip package with the aim of evaluating a set of experiments using originally unconsidered methods, configuration parameters or datasets. This dataset contains a Reprozip package to reproduce our experiments in any supported platform, as well as all pre-trained word embedding models and word similarity datasets used in our experiments. In addition, this dataset also contains all raw output files generated by our experiments, and a R script file to generate all output processed files corresponding to the data tables in our related paper. Finally, we provide a very detailed experimental setup in the aforementioned PDF file to allow all our experiments to be reproduced exactly.

Las hijas entienden el ferrereño, pero ya no lo hablan. El dialecto de Casalinho (Cedillo) se parece mucho más al portugués, pero no es exactamente. Tienen un “deje” incluso cuando hablan en español. Antes lo hablaban todos, pero la gente más joven no. El papel de la emigración o de los matrimonios mixtos en la pérdida de uso. La transmisión intergeneracional. El maestro castigaba a quien hablaba ferrereño, y eso influyó, así como cuestiones de clase social. Designaciones de parentesco en ferrereño. Algunas iniciativas de promoción. Usos lingüísticos con los distintos miembros de la familia, y con la gente de fuera. Comprensión del portugués. Otros lugares en España donde se habla portugués., As filhas compreendem o ferrereño, mas já não o falam. O dialecto do Casalinho (Cedillo) assemelha-se muito mais ao português, mas não é propriamente português. Têm um "deje" mesmo quando falam espanhol. Todos falavam antes, mas os mais novos não. O papel da emigração ou dos casamentos mistos na perda de uso. Transmissão intergeracional. O mestre punia quem falasse ferrereño, e isso influiu, assim como questões de classe social. Designações de parentesco em ferrereño. Algumas iniciativas promocionais. Usos linguísticos com os diferentes membros da família e com pessoas de fora. Compreensão do português. Outros lugares em Espanha onde se fala português., Daughters understand ferrereño, but they don't speak it anymore. The dialect of Casalinho (Cedillo) looks much more like Portuguese, but it's not exactly Portuguese. They have a "deje" even when they speak Spanish. Everyone used to speak it before, but younger people don't. The role of emigration or mixed marriages in the loss of use. Intergenerational transmission. The teacher punished whoever spoke Ferrereño, and that had an impact, as well as social class issues. Designations of kinship in Ferrereño. Some promotional initiatives. Linguistic uses with the different members of the family, and with people from outside. Understanding of Portuguese. Other places in Spain where Portuguese is spoken.

This dataset introduces HESML V2R1 which is the sixth release of the Half-Edge Semantic Measures Library (HESML) detailed in [24]. HESML V2R1 is a linearly scalable and efficient Java software library of ontology-based semantic similarity measures and Information Content (IC) models for ontologies like WordNet, SNOMED-CT, MeSH, GO and any other ontologies based on the OBO file format. HESML V2R1 also implements most of the sentence similarity methods in the biomedical domain together with a set of sentence pre-processing configurations, the integration of the three main biomedical NER tools, Metamap [3], MetamapLite [7] and cTAKES [31]. HESML V2R1 implements most ontology-based semantic similarity measures and Information Content (IC) models reported in the literature, as well as the evaluation of three pre-trained word embedding models for the general domain and 33 pre-trained embeddings and language models. It also provides a XML-based input file format in order to specify the execution of reproducible word/concept similarity experiments based on WordNet, SNOMED-CT, MeSH, or GO without software coding, and the necessary software clients to run the sentence-based experiments in the biomedical domain. HESML V2R1 introduces the following novelties: (1) the software implementation of a new package for the evaluation of sentence similarity methods; (2) the software implementation of most of the sentence similarity methods in the biomedical domain; (3) the implementation of a new package for sentence pre-processing together with a set of sentence pre-processing configurations; (4) the integration of the three main biomedical NER tools, Metamap [3], MetamapLite [7] and cTAKES [31]; (5) the software implementation of a parser based on the averaging Simple Word EMbeddings (SWEM) models introduced by Shen et al. [32] for efficiently loading and evaluating FastText-based [4] and other word embedding models; (6) the integration of Python wrappers for the evaluation of BERT [8], Universal Sentence Encoder (USE) [5] and Flair [1] models; and finally, (7) the software implementation of a new string-based sentence similarity method based on the aggregation of the Li et al. [29] similarity and Block distance [9] measures, called LiBlock, as well as eight new variants of the ontology-based methods proposed by Sogancioglu et al. [33], and a new pre-trained word embedding model based on FastText [4] and trained on the full-text of the articles in the PMC-BioC corpus [6]. HESML library is freely distributed for any non-commercial purpose under a CC By-NC-SA-4.0 license, subject to the citing of the two mains HESML papers [24] as attribution requirement.However, HESML distribution also includes other datasets, databases or data files whose use require the attribution acknowledgement by any user of HEMSL. Thus, we urge to the HESML users to fulfill with licensing terms related to other resources distributed with the library as detailed in its companion release notes., HESML V2R1 is a Java library developed with NetBeans 8 which compiles and runs in any Docker-based complaint platform., This work was partially supported by the UNED predoctoral grant started in April 2019 (BICI N7, November 19th, 2018)., Esta librerı́a estará disponible de forma permanente y perpetua.