BUSCADOR RECOLECTA

List of diocesan architects in Spain, between 1876-1955, Relació d'arquitectes diocesans a Espanya, entre 1876-1955

Dades associades amb el material complementari (gràfics, taules, figures, ...) de l'article publicat a la revista 'ACS Catalysis', 2021, vol. 11, núm. 12, p.7327-7338. Disponible a https://doi.org/10.1021/acscatal.1c00847, Strategies for primer design, sequences of the utilized UPOs, kinetic plots, GC parameters and original GC chromatograms, activities and selectivities of all tested variants, reaction conditions, calibration curves, and figures of docking studies and MD simulations

Dades relacionades amb l'article "Use of Twitter to share news in higher education: the risk of magnification of engagement and learning", enviat a la revista 'The Internet and Higher Education' d'Elsevier, Data on tweets, tweet counts and tweet interaction counts were downloaded from https://twitter.com and https://analytics.twitter.com/user/GeoHumana_UV/home. Data on student performance and self-assessment were provided by participants during the development of the subject 34956 Human Geography in the year 2020-2021 at the University of Valencia, Datasheets are provided as Open Document Format for Office Applications (ODF), extended from xml, with .ods extension. The most common office packages, such as Libreoffice, can open the format, The dataset contains two data sheets, summarizing the results of a class activity involving the use of Twitter to share information and to learn. Table S1 is a summary of the tweets produced by user GeoHumana_UV (teacher/subject account) between 2020-09-16 and 2020-12-15, plus interactions recorded for each tweet (including number of impressions, URL clicks, likes, retweets and replies). All data provided by Twitter Analytics. Table S2 is a summary of student activity related to the class activity for the same period of time. It includes anonymized data on tweeting activity, level of interaction, performance and self-assessment by each participating student. Data provided by Twitter Analytics and the participants

Files in .gdf / .gephi (which can be read by GEPHI) to obtain the representation of a network where meeting places of objects are associated with their places of origin. The Python program represents the network for a given time interval. The mission of this text is to make the routines easy for an unfamiliar user. Databases usually require a standardization before being able to run a program on them, since the program has been programmed to receive the data in a specific way. The objective of the program is to filter certain years from a given database and generate the network between stamps and discovery sites. The program in question is called network-provinces.py and is written in Python 2.7. NOMENCLATURE ** JPG Gems Vistorian Figures 1-2 EG = Egypt ALL = all ** JPG Gems Vistorian Figures 3-4 BLN = Berlin Museum JPGM = J. Paul Getty Museum XTN= Colonia Ulpia Traiana (Xanten) BOL= The Archaeological Civic Museum of Bologna VNZ = Venice National Archaeological Museum LNI = Luni UDN = Udine Museum VLC = University of Valencia GAU = Gaul BAR = Bari Museum FILES: ** JPG Gems Vistorian Figures 1-2 ** JPG Gems Vistorian Figures 3-4 ** GEMAS procedencia tabla ** gemmes.gdf ** gemmes.png ** Gems.py ** io_functions.py, The data in general are gemstones consumed in Roman markets and featured in book 37 of Pliny's Natural History. The data are the result of cleaning processes of the data. These cleaned and processed data have served to elaborate the graphs of networks of precious stones in the Roman Empire, according to the vision of Pliny the Elder

Dades primàries associades a l'article publicat: Hassan, Mohamed Abdalla Elsayed; Zerva, Konstantina; Aulet Serrallonga, Sílvia. Brand Personality Traits of World Heritage Sites: Text Mining Approach. Sustainability, 2021, vol. 13, núm. 11, p. 6142. Disponible a https://doi.org/10.3390/su13116142, http://hdl.handle.net/10256/19605, The five-dimensional brand personality (BP) measure of Aaker (1997) has been perceived by academics as a significant tool for measuring brand intangible meanings. Pitt, Opoku, Hultman, Abratt, & Spyropoulou (2007) were the first to expand Aaker’s (1997) five dimensions into 833 items dictionary known as the first BP dictionary. This dictionary assisted Pitt et al. (2007) in analysing the tourism countries’ website BP from digital textual data, since then Pitt et al.’s (2007) BP lexical approach has been extended by other studies to analyse their brands from digital texts(Paschen, Pitt, Kietzmann, Dabirian, & Farshid, 2017; Ranfagni, Crawford Camiciottoli, & Faraoni, 2016). Even though Pitt et al. 's (2007) BP dictionary assisted in the analysis of BP from digital texts, academics explored that the 833 items did not allow prominent items to specific brands to be included in the analysis (Papania et al., 2008). Thus, in a study aimed to explore the BP of World Heritage Sites (WHSs), Four Thesaurus BP dictionaries were developed in order to aid in including items prominent to WHSs to be included(Hassan, Zerva, & Aulet, 2021). The 5, 571 non-redundant items of these dictionaries are an expansion of Aaker’s (1997) dimensions namely; Competence, Excitement, Ruggedness, Sincerity and Sophistication. In contrast to the construction of Pitt et al.’s (2007) dictionary established by using Encyclopedia Britannica’s online Thesaurus, the Four Thesaurus Dictionaries are constructed by four online thesaurus dictionaries that categorize the word due to their similarity. The technique of categories allowed the construction of the Four Thesaurus BP dictionaries and assisted in categories of words under Aaker’s five dimensions. These Four Thesaurus dictionaries were part of a method that assisted in identifying the BP dictionary of WHSs as a categories dictionary (Hassan et al., 2021). Furthermore, these Four Thesaurus Dictionaries items are validated as behavioural items by the use of available psychology dictionaries (Allport & Odbert, 1936), providing the Updated Four Thesaurus Dictionaries including 2, 643 unique items. Hence the use of these Four Thesaurus Dictionaries may be useful in defining the BP categories for brands or product categories (Hassan et al., 2021), This study presents a new BP dictionary that aiding in including most of the items that may be of prominent relevancy to various studies and still within the five personality categories of Aaker’s (1997) five dimensions, namely the 'Four Thesaurus Dictionaries'. This study established a new dictionary parallel and similar to Pitt’s dictionary by the use of four online dictionaries: 'Power Thesaurus' (www.powerthesaurus.org), 'OneLook Thesaurus' (www.onelook.com/thesaurus), 'Thesaurus Dictionary' (www.thesaurus.com), and 'Merriam Webster' (www.merriam-webster.com). The main idea here is that each dictionary may include more unique words, so using four sources aids in expanding the number of synonyms. The four online dictionaries were selected due to the fact that they arranged the synonyms of specific keywords based on their level of similarity, thus they are prominent in providing similarity rankings for the target keywords. This four-dictionary ranking of keywords was beneficial when extracting synonyms for Aaker’s items as each synonym is colour coded according to where is ranked in relation to a specific keyword. By colour-coding the keyword, it can then be placed in Aaker’s five dimensions according to its relevance. These dictionaries were coloured to show that the closer the similarity to one of the 42 traits in Aaker, the darker the colour. The colour code technique way is inspired by the way Thesaurus and OneLook dictionaries prioritize the degree of similarity between synonyms. An example can be found in the following URLs accessed on 25 May 2021: www.thesaurus.com/browse/unique; www.onelook.com/thesaurus/?s=unique. therefore, this technique aided in constructing four dictionaries (hereafter: 4-Thesaurus BP dictionaries), and their entire set of adjectives was classified according to the degree of relevance to one of the 42 traits within the five personality categories. Furthermore, From psychology studies, 18,337 non-redundant personality items were collected from Goldberg (1982), Saucier (1997), Norman (1963), and Allport and Odbert (1936) hereafter called psychology dictionaries. Allport and Odbert (1936) are the first to hypothesise that personality inhabits natural language terms, and their dictionary figures prominently in the development of the Big Five (Caprara, Barbaranelli, and Guido 2001). Therefore, The use of these psychology dictionaries aids in filter the Four Thesaurus dictionaries and keeping items that were agreed with psychology studies, which means that they were checked previously for their validation. To notify, in addition to the three psychology studies’ items used first by Fischer et al. (2020), this study also used 2,800 items from Norman (1967), who refined and structured the Allport traits. This technique enables this study to provide the Updated Thesaurus Dictionaries