BUSCADOR RECOLECTA

This database presents a sample of 175 Spanish technology-based ventures. It shows information on different aspects of the entrepreneur and the start-up firm, such as founding teams, entrepreneur’s resources, sources of knowledge, entrepreneur's training and experience, technology strategy, innovation, and firm’s performance (foreign market performance, total market performance, superior financial outcomes, and survival). The study is longitudinal and uses data from 2008 and 2014, which was collected from two sources of information and at two moments of time. To conduct the fieldwork and data collection we proceeded in two phases as follows. First phase (in 2009). We built an online questionnaire to collect the data and the link to the questionnaire was sent by e-mail to NTBFs’ founder-managers. We first used the SABI database for identifying NTBFs using the following criteria: the venture has fewer than 250 employees; is not integrated into a corporate group; operates in a high-technology sector, following the OECD’s classification of industries based on technology; and finally, at the time of the fieldwork, the firms would be operational for up to 8 years, and so should be “new” according to Wang and Chen (2016) and Nuscheler et al. (2019), who consider the limit to qualify a firm as new as 10 years. This fieldwork was carried out from January to July 2009. Questions refer to resources available within the NTBF at or near the firm’s inception and the venture’s performance up to 2008. We obtained 175 valid responses. Second phase (in 2016). We carried out a new process of information gathering in 2016. We collected data available in the SABI database for those NTBFs participating in the first phase of our study (the ones that had answered the survey in 2009). Specifically, we observed and took data concerning financial outcomes for 2008 and 2014 and survival information for 2014 (five years after the survey was conducted). Specifically, the most up-to-date data on firms available in 2016 refers to 2014.

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