BUSCADOR RECOLECTA

Lava dating by means of Ar / Ar radiometric analysis performed by the University of Oregon de La Esfinge y Tamaraceite (Gran Canaria, Canary Islands)

Lava dating by means of Ar / Ar radiometric analysis performed by the University of Oregon de las Salinas de Janubio (Lanzarote, Canary Islands).

The HuGCDN2014 database was provided by the sleep unit of the Dr. Negrín University Hospital (Canary Islands, Spain). It is made up of 77 single-lead ECG recordings, digitized at 200 Hz. The labeling process was performed by an expert based on the simultaneous polysomnography, indicating the presence or absence of apnea in each minute. The database is divided into two groups: 1º) CONTROL: Forty healthy subjects with an AHI lower than 5 (30 men and 10 women). 2º) APNEA: Thirty-seven OSA patients with an AHI higher than 25 (30 men and 7 women). The learning set (L) consists of the first 20 recordings of control subjects and the first 18 OSA patients. The rest belong to the test set (T). The single-lead ECG signal is divided into 5-minute frames, that are shifted in time in increments of 1 minute. The scoring for each segment is assigned to the minute located in the middle position. The RR interval series is constructed as a sequence of time differences between the successive heartbeats.

National accounting matrices (NAM) perfectly describe the economic structure of a national economy, summarizing the whole process of generation of primary income and its distribution among the different institutional sectors of the economy. It is not just a way to represent the main economic statistics of an economy, but it also serves as the basis for most macroeconomic modelling efforts. The business accounting matrix (BAM) presents the most relevant information for the firm in a similar way, adapted to the descriptive potential of financial accounting, what we believe can be useful both for economic modellers and for decision makers at the firm level. This spreadsheet helps to trace how each of the cells of the BAM are calculated. It includes a table in which the cells of the BAM incorporate a description of their content. It also contains the example of a complete BAM (SBAM) that can be used the simulation of changes in demand and other input-output related models. This material allows researchers having the financial accounts of a firm available, to immediately calculate a BAM. They can also easily adapt our proposal to their convenience.

This database contains electricity bills related to energy consumption in Spanish households. The contents of bills are automatically generated following some statistics from official bodies. The main purpose of the dataset is for training machine learning algorithms, especially for designing new methods for extracting information from invoices. There are 86 different labels, which are related to several topics, such as the customer and marketer, the contract, energy consumption, or billing. The total number of invoices is 75.000. The files are organized in two directories: a training directory, with six subdirectories, each containing 5.000 invoices in PDF format and the corresponding labels in JSON files; and a test directory, with nine subdirectories, each containing 5.000 invoices in PDF format. There are two main zip files that contain the test and training sets (test.zip and training.zip). In addition, we have included separate files with a subset of the directories in each set, so it can be downloaded by parts. There is also a reduced version of the dataset with 100 invoices per directory, which is interesting for users who want to preview the content of the dataset before downloading it. IDSEM is an acronym for "an Invoices Database for the Spanish Electricity Market". More information can be found at https://idsem.ulpgc.es/ and in the following article: [1] Javier Sánchez, Agustín Salgado, Alejandro García, and Nelson Monzón, "IDSEM, an invoices database of the Spanish electricity market", Sci. Data, (2022).

Data set of morbidity items from Canary Health Survey, 1997

Data set of morbidity items form Canary Health Survey, 2004

Data set of morbidity items from Canary Health Survey, 2009

Datos de investigación: Aplicación del método Delphi para consulta a expertos sobre la idoneidad de los indicadores seleccionados para la elaboración del modelo HVCRA (Hazards, Vulnerability, Capacity and Risk Assesment) aplicado a los Servicios Sociales andaluces. Datos primarios asociados al artículo enviado a SAGE Open, The pandemic caused by covid-19 has revealed an enormous vulnerability in health, economic and social terms. We are facing a new unprecedented environmental and socioeconomic reality, which poses a challenge for risk control and management. This forces a methodological effort to be made in all areas in the creation of models for the prevention and prediction of existing impacts, risks, and potential threats. In Social Services’ field, we propose a tool that will allow, on the one hand, to measure the impact of COVID-19 on the Social Services system and, on the other hand, to evaluate the areas with the highest socioeconomic risk in the post-COVID scenario. The modified Delphi technique has been used to reach a consensus on a proposal of 37 indicators with a group of experts, which in the final phase resulted in 34. To our knowledge, this represents the first risk assessment tool in social services designed specifically for use at local level. The proposed tool can help determine the situation of municipal social services in relation to risk, assessing weaknesses and improving the capacity of the system., Este trabajo está financiado por el programa de ayudas a la I+D+i, en régimen de concurrencia competitiva, en el ámbito del plan andaluz de investigación, desarrollo e innovación (PAIDI 2020), dentro del proyecto de investigación “Riesgo e impacto de la covid-19 en el sistema de servicios sociales de Andalucía. IRIS”. Riesgo e impacto de la covid-19 en el sistema de servicios sociales de Andalucía. IRIS”. PAIDI 2020

Datos primarios asociados al artículo enviado a Personality Disorders: Theory, Research, and Treatment, Person-centered approaches in the field of personality allow greater understanding of how different subpopulations with specific personality profiles are linked with related clinically relevant predictors or outcomes. Studies under the Five Factor Model offer consensus on the observation of a Resilient, an Undercontrolled and an Overcontrolled profile. However, studies using maladaptive traits are much more limited. The present research aimed to identify personality profiles based on the 25 maladaptative facet and examined the relationships between the profiles and personality dysfunctioning, internalizing and externalizing symptoms. A mixed sample composed of community adults (n = 742) and patients (n = 312) completed the Personality Inventory for DSM-5 Short Form, the Inventory of Depression and Anxiety Symptoms-II, Externalizing Spectrum Inventory–Brief From, the 12 items Spanish version of the WHO Disability Assessment Schedule II and Level of Personality Functioning Scale-Brief Form 2.0. Latent profile analysis was performed to identify profiles based scores on the 25 maladaptive personality trait facets scales of the PID-5-SF. The scores on internalizing, externalizing and functioning were compared across the profiles obtained, by analyzing the equality of means. Four personality profiles that differed in both level and shape emerged: Resilient, Undercontrolled, Overcontrolled, and Ordinary type. The Overcontrolled and Undercontrolled types showed higher scores on pathology scales than the Resilient and Ordinary types. While the Overcontrolled profile appeared more related to internalizing symptoms and impairment in self-functioning, the Undercontrolled profile was more linked to higher scores on externalizing symptoms and interpersonal dysfunctioning., This work was supported by the grant “Reliable and clinical relevant change of Inventory of Depression and Anxiety Symptoms II – IDAS-II: a longitudinal clinical utility study (RELY-IDAS-II)”, project PID2020-116187RB-I00 on Proyectos I+D+i 2020 “Retos del Conocimiento” provided by Ministerio de Ciencia e Innovación (Spain).