BUSCADOR RECOLECTA

Anàlisis geoquímiques i fotos de visu i microscòpia de les mostres que integren la col·lecció de geostandards del Museu de Geologia Valentí Masachs (UPC Manresa). Análisis geoquímicas y fotos de visu y microscopio de las muestras que integran la colección de geostandares del Museo de Geología Valentí Masachs (UPC Manresa). Geochemical analyses and hand and microscopy pictures of the samples belonging to the geostandards collection of the Valenti Masachs Geology Museum (UPC Manresa). Analyses géochimiques et photographies au microscope et visu des échantillons qui forment la collection de geostandards du Musée de Géologie Valentí Masachs (UPC Manresa).

The dataset contains the following files: (1) readme.txt, it is a text file including details about the content of the dataset; (2) Measurement in ARSD_SMS_Data.xls, it is a MS Excel file containing the data used in the analysis of the primary studies, reported in Section 4 of the paper; and, (3) Metrics in ARSD.pdf, it is pdf file containing the complete list of metrics reported in the different primary studies. The metrics are classified into the following categories: QR metrics, QMIs, and QMI metrics. In the MS Excel file, the data is organised in separated sheets: (a) RQ1 - Literature characterisation; (b) RQ2 - QR Metrics, where QR refers to Quality Requirements; (c) RQ3 - QMI, where QMI refers to Quality Management Indicators; (d) RQ3 - Tools, corresponding to the reported tools used to manage QMIs; and, (e) RQ3 - QMI Metrics., This dataset has been used to generate part of the results included in the manuscript: “Quality Measurement in Agile and Rapid Software Development: A Systematic Mapping”, submitted to the Information and Software Technology journal (IST) at the time of publishing this dataset. The paper reports the results of a systematic mapping study surveying the literature related to quality requirements management through metrics in agile and rapid software development processes. The dataset contains the data extracted from the primary studies that was used in the analysis phase of the study and reported in the results section of the paper. The data extracted from the primary studies has been complemented with some data used for specific purposes, e.g. during the analysis phase, we included the list of companies collaborating in the paper analysing the authors’ affiliations to understand the level of implication of companies in the field. Complementing the data analysed, the dataset includes some detailed results that are not included in the body of the paper, i.e., the list of metrics reported in the selected primary studies.

The dataset contains four files. File S1 corresponds to cathodoluminescence and BSE images of standard minerals. File S2 contains geochemical data of mineral standards ans samples obtained by SIMS and EPMA. File S3 contains the parameters used to obtain response surface models of IMF in mineral standards. File S4 is a response surface methodology tutorial., Instrumental mass fractionation (IMF) of isotopic SIMS analyses (Cameca 1280HR, CRPG Nancy) was predicted by response surface methodology (RSM) for 18O/16O determinations of plagioclase, K-feldspar and quartz. The three predictive response surface models combined instrumental and compositional inputs. The instrumental parameters were: (i) X and Y position, (ii) LT1DefX and LT1DefY electrostatic deflectors, (iii) chamber pressure and, (iv) primary-ion beam intensity. The compositional inputs included: (i) anorthite content (An%) for the plagioclase model and, (ii) orthoclase (Or%) and barium (BaO%) contents for the K-feldspar model. The three models reached high predictive powers. The coefficients R2 and prediction-R2 were, respectively, 90.47% and 86.74% for plagioclase, 87.56% and 83.17% for K-feldspar and 94.29% and 91.59% for quartz. The results show that RSM can be confidently applied to IMF prediction in stable isotope SIMS analyses by the use of instrumental and compositional variables.

Analitical data of several minerals (quartz, K-feldspar, plagioclase, Monazite) of the albitized granites of the Variscan basement in Europe. The samples come from the Catalan Coastal Ranges, the Pyrenees, the Morvan massif and the Sudetes. Most of this data have been used in several papers and in the Cares Fàbrega PhD Thesis.