BUSCADOR RECOLECTA

This study provides an insight into the academic profile of modern foreign languages' teaching staff in UK higher education, describing the role of the scholarship of teaching and learning (SoTL) in their professional development and progression. The study is based on data collected through an anonymous questionnaire during the summer of 2021.

The dataset consists of a CSV file which gathers the answers to the questionnaire (including 18 questions) that was used to collect data from participants. The questionnaire was hosted on Jisc Online Surveys and circulated through unique links that were sent as part of an invitation email. All the answers were anonymised at the time of collection.

Mixed (quantitative data collected through multiple choice questions; qualitative data collected through free text comments).

The residential building catalogue and seismic vulnerability classification is a supplementary dataset associated with the paper entitled “Classifying Buildings According to Seismic Vulnerability Using Cluster-ANN Techniques. Applied to the City of Murcia, Spain

The goal is to identify building patterns in construction typologies (CTs), related to both structural and non-structural elements that make up a building, called Building Typologies (BTs). The analysis identifies urban modifiers associated with different CTs, and using machine learning techniques, different building configurations are obtained, which are defined as Building Cluster Typologies (BCT).

The results of the BCT identification include the definition of BCT attributes, their representativeness in the study area, the spatial distribution and seismic vulnerability. 11 BCTs associated with 5 CTs are identified, as well as the distribution of seismic vulnerability ranges at the census section scale in the city of Murcia.

In conclusion, the identification of BCTs allows for exposure assessments that take into account the particular characteristics of buildings, grouped according to specific configurations, and facilitates future seismic vulnerability evaluations.

4. Description of the dataset

The dataset is the final data from the research titled: “Classifying Buildings According to Seismic Vulnerability Using Cluster-ANN Techniques. Applied to the City of Murcia, Spain.”

The database is the result of the identification of the different CTs and BCTs in the city of Murcia. The data is organized into 7631 records of residential buildings, which have urban modifier attributes associated with each CT. The combination of CTs and urban modifiers define the different BCTs. In total, for each building record (rows), a CT, the year of construction, ten urban modifiers, and the BCT identification result are associated (in columns).

In addition, the distribution of the Seismic Vulnerability Index (Iv) of the buildings at the census blocks scale is provided. The Iv is expressed in intervals. Minimum, maximum, and average values are provided.

1. Methodology

The methodology for obtaining the present database contains several steps:

1. Extraction of building data from the Spanish Cadastre (https://www.sedecatastro.gob.es/) and configuration of an initial database.

2. Identification of construction typologies (CT) using a typology membership probability matrix for Murcia (RISMUR, 2014).

3. Error detection and correction with remote field work.

4. Assignation of urban modifiers of the buildings in the study area (Martinez-Cuevas and Gaspar-Escribano 2016; Martinez-Cuevas et al., 2017) with remote field work in combination with different GIS processes (Lantada et. al. 2010). The coding of new attributes (urban modifiers) was done according to the GEM taxonomy (https://taxonomy.openquake.org/).

5. Application of a Two-Step Cluster analysis identify clusters. The resulting clusters (CTs with the corresponding urban modifiers) are called Building Cluster Typologies (BCT). The BCT field was assigned to every record of the database.

6. Assignation of vulnerability index (Iv) value to each building (Benedetti and Petrini, 1984; Angeletti et al., 1988; Milutinovic and Tredafiloski, 2003; Lagomarsino and Giovinazzi 2006) and calculation of the average Iv value for the census block level.

FILES

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The dataset consists of two files in csv format.

-Buildings_01.csv: database with 7631 building records. Each record consists of an ID, centroid coordinates in ETRS_1989_UTM_Zone_30N and thirteen attributes.

-Iv_CB_02.csv: database with 158 records corresponding to the census blocks of the study area. Each record consists of an ID, centroid coordinates in ETRS_1989_UTM_Zone_30N, census block code and three attributes.

RESPOND is an EU funded research project running from 2020 to 2023. The project aims to identify which groups are most at risk for adverse mental health effects due to the COVID-19 pandemic, as well as to understand what determines that risk. In Spain, we conducted a randomised controlled trial among healthcare workers (HCWs) with psychological distress to explore the effectiveness of a stepped-care mental health programme (RESPOND-HCWs)

This long-format dataset includes the following variables:

• Participant identifier
• Randomisation group
• Time
• Outcomes (items of the self-reported measures PHQ-9, GAD-7, and PCL-5)
• Items of the Kessler Distress Scale (K-10)
• Aggregated and/or cut-off scores of the PHQ-9, GAD-7, PCL-5, and K-10

This dataset is a reduced and anonymised version of the RESPOND-HCWs datasets