Find research datasets worth reusing

Thermodynamic and hydrological drivers of the subsurface thermal regime in Central Spain: open data and code

Quality controlled temperature data at daily resolution at CTS, HRR, HYS, NVC, RSI and SGV and the most relevant codes for data processing used in: García-Pereira, F., González-Rouco, J. F., Schmid, T., Melo-Aguilar, C, Vegas-Cañas, C., Steinert, N. J.,  Roldán-Gómez, P. J., Cuesta-Valero, F. J., García-García, A., Beltrami, H., and de Vrese, H.: "Thermodynamic and  hydrological drivers of the subsurface thermal regime in Central Spain". Earth Surf. Dynam., submitted, 2023. All data can be also freely obtained for research from the original data sources, GuMNet (https://www.ucm.es/gumnet/) and AEMET (https://www.aemet.es/en/datos_abiertos). Further details of the code are available upon request to the corresponding author (Félix García-Pereira, felgar03@ucm.es).

Interurban road accidents with casualties in Spain (2016-2021)

CSV that contains 1.000 records of interurban road accidents with casualties in Spain between 2016 and 2021. If you are interested in the whole country dataset, please do not hesitate to contact me and I will forward it to you.  Data source of each record is the Spanish General Directorate of Traffic (DGT) and each row describes an accident by the following fields: •        secuencial (int): the unique identifier for an occurrence record in DGT. •        anyo (int): the four-digit year of the event date. •        mes (int): the month as integer of the event date (encoded). •        nombre_mes (str): the month name of the event date (decoded). •        dia_semana (int): the integer day of the week when the accident occurred (encoded). •        nombre_dia_semana (str): the name of the day when the accident occurred (decoded). •        hora (int): the reported hour of the collision in 24-hour notation. •        cod_provincia (int): the province code from INE where the accident is registered (encoded). •        nombre_provincia (str): the province name where the accident is registered (decoded). •        cod_municipio (int): the municipality code from INE where the accident is registered (encoded). •        nombre_codigo_municipio (str): the municipality name where the accident is registered (decoded). •        isla (str): an integer value to identify a Spanish island if the accident occurred out of the peninsula (encoded). •        nombre_isla (str): the island name if applicable to the accident (decoded). •        zona (int): an integer value to identify type of road (encoded). •        nombre_zona (str): the name of the road type (decoded). •        zona_agrupada (int): an integer value to group the road types in urban or interurban (encoded). •        nombre_zona_agrupada (str): the group name of the road types (decoded). •        carretera (str): the road attending to the national road numbering system in Spain where the accident is located. •        km (int): the kilometre point of the road where the accident is located. •        sentido_1f (int): the vehicle’s direction of traffic reported as integer when the accident occurred (encoded). •        nombre_sentido (str): the vehicle’s direction of traffic reported when the accident occurred (decoded). •        titularidad_via (int): the road ownership type as integer (encoded). •        nombre_titularidad_via (str): the road ownership type description (decoded). •        tipo_via (int): the type of road as integer attending to the project road classification (encoded). •        nombre_tipo_via (str): the type of road description attending to the project road classification (decoded). •        tipo_accidente (int): an integer value to identify the collision type and traffic context (encoded). •        nombre_tipo_accidente (str): the description of the collision type and traffic context (decoded). •        total_mu24h (int): the total number of fatalities registered in the accident, computed to 24 hours. •        total_hg24h (int): the total number of hospitalised casualties recorded in the accident, counted over 30 days. •        total_hl24h (int): the total number of non-hospitalised casualties recorded in the accident, computed to 24 hours. •        total_victimas_24h (int): the total number of casualties (fatalities + hospitalised injured + non-hospitalised injured) recorded in the accident, computed over 24 hours. •        total_mu30df (int): the total number of fatalities recorded in the accident, computed over 30 days. •        total_hg30df (int): the total number of hospitalised casualties recorded in the accident, counted over 30 days. •        total_hl30df (int): the total number of non-hospitalised casualties recorded in the accident, counted over 30 days. •        total_victimas_30df (int): the total number of casualties (killed + injured in hospital + injured not in hospital) recorded in the accident, counted over 30 days. •        total_vehiculos (int): the total number of vehicles involved recorded in the accident. •        tot_peat_mu24h (int): the total number of pedestrian fatalities recorded in the accident, computed over 24 hours. •        tot_bici_mu24h (int): the total number of cyclists killed recorded in the accident, computed to 24 hours. •        tot_ciclo_mu24h (int): the total number of scooter riders killed recorded in the accident, computed on a 24-hour basis. •        tot_moto_mu24h (int): the total number of motorcyclist fatalities recorded in the accident, computed on a 24-hour basis. •        tot_tur_mu24h (int): the total number of car drivers and passengers killed recorded in the accident, counted to 24 hours. •        tot_furg_mu24h (int): the total number of van drivers and passengers killed recorded in the accident, counted over 24 hours. •        tot_cam_menos3500_mu24h (int): the total number of drivers and passengers of trucks ≤ 3,500 kg killed in the accident, counted over 24 hours. •        tot_cam_mas3500_mu24h (int): the total number of drivers and passengers of trucks > 3,500 kg killed in the accident, counted over 24 hours. •        tot_bus_mu24h (int): the total number of bus drivers and passengers fatalities recorded in the accident, counted over 24 hours. •        tot_otro_mu24h (int): the total number of drivers and passengers of vehicles not classified in the above types killed in the accident, counted to 24 hours. •        tot_sinespecif_mu24h (int): the total number of drivers and passengers of vehicles of unspecified type killed in the accident, counted over 24 hours. •        tot_peat_mu30df (int): the total number of pedestrian fatalities recorded in the accident, computed to 30 days. •        tot_bici_mu30df (int): the total number of cyclists killed recorded in the accident, counted over 30 days. •        tot_ciclo_mu30df (int): the total number of scooted riders killed recorded in the accident, counted over 30 days. •        tot_moto_mu30df (int): the total number of motorcyclist fatalities recorded in the accident, counted over 30 days. •        tot_tur_mu30df (int): the total number of drivers and passengers of passenger cars killed in the accident, counted over 30 days.  •        tot_furg_mu30df (int): the total number of van drivers and passengers killed recorded in the accident, counted over 30 days.  •        tot_cam_menos3500_mu30df (int): the total number of drivers and passengers of trucks ≤ 3,500 kg killed in the accident, counted over 30 days. •        tot_cam_mas3500_mu30df (int): the total number of drivers and passengers of trucks > 3,500 kg killed in the accident, counted over 30 days. •        tot_bus_mu30df (int): the total number of bus drivers and passengers fatalities recorded in the accident, counted over 30 days. •        tot_otro_mu30df (int): the total number of drivers and passengers of vehicles of types not classified in the above killed in the accident, counted over 30 days. •        tot_sinespecif_mu30df (int): the total number of drivers and passengers of unspecified type vehicles killed in the crash, counted over 30 days. •        nudo (int): an integer to identify whether the collisions occurred in an road junction or not (encoded). •        nombre_nudo (str): the description to identify whether the collisions occurred in an road junction or not (decoded). •        nudo_info (int): an integer that represents the type of road junction in which the collision occurred (encoded). •        nombre_nudo_info (str): the description of the type of road junction in which the collision occurred (decoded). •        carretera_cruce (str): the road attending to the national road numbering system in Spain where the accident is located. •        priori_norma (int): an integer to identify if the road junction priority is determined by generic traffic rule (encoded). •        nombre_priori_norma (str): the text to identify if the road junction priority is determined by generic traffic rule (decoded). •        priori_agente (int): an integer to identify if the road junction priority is determined by agent (encoded). •        nombre_priori_agente (str): the text to identify if the road junction priority is determined by agent (decoded). •        priori_semaforo (int): an integer to identify if the road junction priority is determined by traffic light (encoded). •        nombre_priori_semaforo (str): the text to identify if the road junction priority is determined by traffic light (decoded). •        priori_vert_stop (int): an integer to identify if the road junction priority is determined by vertical stop sign (encoded). •        nombre_priori_vert_stop (str): the text to identify if the road junction priority is determined by vertical stop sign (decoded). •        priori_vert_ceda (int): an integer to identify if the road junction priority is determined by vertical yield sign (encoded). •        nombre_priori_priori_vert_ceda (str): the text to identify if the road junction priority is determined by vertical yield sign (decoded). •        priori_horiz_stop (int): an integer to identify if the road junction priority is determined by horizontal stop signal (encoded). •        nombre_priori_horiz_stop (str): the text to identify if the road junction priority is determined by horizontal stop signal (decoded). •        priori_horiz_ceda (int): an integer to identify if the road junction priority is determined by horizontal yield sign (encoded). •        nombre_priori_horiz_ceda (str): the text to identify if the road junction priority is determined by horizontal yield sign (decoded). •        priori_marcas (int): an integer to identify if the road junction priority is determined by road markings (encoded). •        nombre_priori_marcas (str): the text to identify if the road junction priority is determined by road markings (decoded). •        priori_pea_no_elev (int): an integer to identify if the road junction priority is determined by pedestrian crosswalk (encoded). •        nombre_priori_pea_no_elev (str): the text to identify if the road junction priority is determined by pedestrian crosswalk (decoded). •        priori_pea_elev (int): an integer to identify if the road junction priority is determined by raised pedestrian crossing (encoded). •        nombre_priori_pea_elev (str): the text to identify if the road junction priority is determined by raised pedestrian crossing (decoded). •        priori_marca_ciclos (int): an integer to identify if the road junction priority is determined by the bicycle crossing road marking (encoded). •        nombre_priori_marca_ciclos (str): the text to identify if the road junction priority is determined by the bicycle crossing road marking (decoded). •        priori_circunstancial (int): an integer to identify if the road junction priority is determined by signal used on an circumstantial basis (encoded). •        nombre_priori_circunstancial (str): the text to identify if the road junction priority is determined by signal used on an circumstantial basis (decoded). •        priori_otra (int): an integer to identify if the road junction priority is determined by other signalling not considered in the above (encoded). •        nombre_priori_otra (str): the text to identify if the road junction priority is determined by other signalling not considered in the above (decoded). •        condicion_nivel_circula (int): an integer to identify the condition of the traffic level at the place and time of the accident attending to DGT classification (encoded). •        nombre_condicion_nivel_circula (str): the text to identify the condition of the traffic level at the place and time of the accident attending to DGT classification (decoded). •        condicion_firme (int): an integer to classify the condition of the road surface at the scene of the accident (encoded). •        nombre_condicion_firme (str): the text to classify the condition of the road surface at the scene of the accident (decoded). •        condicion_iluminacion (int): an integer to identify the lighting conditions at the place and time of the accident (encoded). •        nombre_condicion_iluminacion (str): the text to identify the lighting conditions at the place and time of the accident (decoded). •        condicion_meteo (int): an integer to classify the meteorological conditions at the place and time of the accident (encoded). •        nombre_condicion_meteo (str): the text to classify the meteorological conditions at the place and time of the accident (decoded). •        condicion_niebla (int): an integer to identify the meteorological condition with regard to fog at the place and time of the accident (encoded). •        nombre_condicion_niebla (str): the text to identify the meteorological condition with regard to fog at the place and time of the accident (decoded). •        condicion_viento (int): an integer to classify the meteorological condition referring to wind at the place and time of the accident (encoded). •        nombre_condicion_viento (str): the text to classify the meteorological condition referring to wind at the place and time of the accident (decoded). •        visib_restringida_por (int): an integer to identify the elements affecting visibility at the scene of the accident (encoded). •        nombre_visib_restringida_por (str): the text to identify the elements affecting visibility at the scene of the accident (decoded). •        acera (int): an integer to classify the condition of the pavement. Only if the accident involved a pedestrian (encoded). •        nombre_acera (str): the text to classify the condition of the pavement. Only if the accident involved a pedestrian (decoded). •        trazado_planta (int): an integer to identify road layout in plan. Only on interurban roads (encoded). •        nombre_trazado_planta (str): the text to identify road layout in plan. Only on interurban roads (decoded). •        latitud (float): the latitude of the accident location coordinate in decimal degrees. •        longitud (float): the length of the accident location coordinate in decimal degrees. •        geom (geometry): geometry from latitude and longitude position. Developed for this project. The context is the Final Master's Degree Project 'Analysis and Predictive Modelling of Wildlife–Vehicle Collision on Interurban Roads in Spain' (Data Science Master’s Degree of Universitat Oberta de Catalunya - UOC). This dataset is the output of the interurban road accidents with casualties analysis and the <a href="https://github.com/alba620/analisis-prediccion-accidentes-trafico-animales">code repository</a> is available on GitHub.

Wildlife–vehicle collisions (WVC) on interurban roads in Spain (2016-2021)

CSV that contains 1.000 records of wildlife–vehicle collisions (WVC) on interurban roads in Spain between 2016 and 2021. If you are interested in the whole country dataset, please do not hesitate to contact me and I will forward it to you.  Data source of each WVC record is the Spanish General Directorate of Traffic (DGT), but the dataset has been enhanced by the integration of other sources: OpenStreetMap (OSM), Global Biodiversity Information Facility (GBIF), the National Geographic Institute of Spain (IGN), State Meteorological Agency (AEMET).Therefore, each record describes an accident by the following fields: •        id_num (int8): the unique identifier for an accident. •        ind_accda (int8): a binary variable for property damages involved or not (encoded). •        nombre_ind_accd (str): a statement for property damages involved or not (decoded). •        ind_acciv (int8): a binary variable for personal damages involved or not (encoded). •        nombre_ind_acciv (str): a statement for personal damages involved or not (decoded). •        total_mu30df (int8): the total number of deaths from the accident. •        total_hg30df (int8): the total number of injured with hospitalization from the accident. •        total_hl30df (int8): the total number of injured without hospitalization from the accident. •        fecha_accidente (date): the reported date of the collision, following ISO 8601 date-time standard.  •        hora_accidente (str): the reported hour of the collision in 24-hour notation.  •        mes_1f (int8): the month as integer of the event date (encoded). •        nombre_mes (str): the month name of the event date (decoded). •        anyo (int8): the four-digit year of the event date. •        ccaa_1f (int8): the autonomous region code from INE where accident is registered (encoded). •        nombre_ccaa (str): the name of the autonomous region where accident is registered (decoded). •        provincia_1f (int8): the province code from INE where the accident is registered (encoded). •        nombre_provincia (str): the province name where the accident is registered (decoded). •        cod_municipio (int8): the municipality code from INE where the accident is registered (encoded). •        nombre_municipio (str): the municipality name where the accident is registered (decoded). •        carretera (str): the road attending to the national road numbering system in Spain where the accident is located. •        km (float): the kilometre point of the road where the accident is located. •        sentido_1f (int8): the vehicle’s direction of traffic reported as integer when the accident occurred (encoded). •        nombre_sentido (str): the vehicle’s direction of traffic reported when the accident occurred (decoded). •        tipo_via_3f (int8): the type of road as integer attending to the project road classification (encoded). •        nombre_tipo_via (str): the type of road description attending to the project road classification (decoded). •        titularidad_via_2f (int8): the road ownership type as integer (encoded). •        nombre_titularidad_via (str): the road ownership type description (decoded). •        tipo_animal_1f (int8): the animal species involved in the accident as integer (encoded). •        nombre_tipo_animal_1f (str): the animal species name involved in the accident (decoded). •        tipo_animal_2f (int8): the reported animal type of breeding as integer (encoded). •        nombre_tipo_animal_2f (str): the type of animal breeding description (decoded). •        longitud (float): the length of the accident location coordinate in decimal degrees. •        latitud (float): the latitude of the accident location coordinate in decimal degrees. •        geom (geometry): geometry from latitude and longitude position. Developed for this project. •        dia_semana (int8): the integer day of the week when the accident occurred (encoded). •        nombre_dia_semana (str): the name of the day when the accident occurred (decoded). •        tipo_dia (str): the category name of the day type to separate weekday from weekend (decoded). •        parte_dia (str): the part name of the day when the accident is registered including day, night and the transitions. •        luna (int8): the portion of illuminated moon surface represented as an integer value from 0 to 100. •        prec (float): the daily rainfall measurement of the event day based on pluviometric days. •        tmin (float): the minimum temperature in Celsius of the event day. •        tmed (float): the average temperature in Celsius of the event day. •        tmax (float): the maximum temperature in Celsius of the event day. •        sol (float): the accumulated sun hours of the event day. •        uso_suelo (str): the main land usage of the accident area. •        altitud (float): the altitude in meters above sea level. •        pendiente (float): the slope median value of a 30 meters buffer around the accident location. •        taxonkey (str): a taxon key from the GBIF backbone. •        imd_total (float): the average daily traffic intensity of the accident year. •        maxspeed (int): the maximum speed of the road section where the reported collision. The context is the Final Master's Degree Project 'Analysis and Predictive Modelling of Wildlife–Vehicle Collision on Interurban Roads in Spain' (Data Science Master’s Degree of Universitat Oberta de Catalunya - UOC). This dataset is the output of the wildlife–vehicle collision analysis and the <a href="https://github.com/alba620/analisis-prediccion-accidentes-trafico-animales">code repository</a> is available on GitHub.

GBIF animal distribution in Spain

CSV that contains 1.000 GBIF observations of animals than have been involved in wildlife–vehicle collision on interurban roads in Spain and a buffer of each species distribution calculated with these data. If you are interested in the whole country, please do not hesitate to contact me and I will forward it to you. Each record describes the observation by the following fields: <ul> <li>    gbifid: the unique identifier for an occurrence record in GBIF.</li> <li>    datasetkey: the local dataset id within the GBIF network.</li> <li>    occurrenceid: a unique identifier for the occurrence, allowing the same occurrence to be recognized across dataset versions as well as through data downloads and use.</li> <li>    kingdom: the full scientific name specifying the kingdom that the occurrence's scientific name is classified under.</li> <li>    phylum: the full scientific name of the phylum or division in which the taxon is classified.</li> <li>    class: the full scientific name of the class in which the taxon is classified.</li> <li>    order: the full scientific name of the order in which the taxon is classified.</li> <li>    family: the full scientific name of the family in which the taxon is classified.</li> <li>    genus: the full scientific name of the genus in which the taxon is classified.</li> <li>    species: species classification key.</li> <li>    infraspecificepithet: the name of the lowest or terminal infraspecific epithet of the scientificName, excluding any rank designation.</li> <li>    taxonrank: the taxonomic rank of the supplied scientific name.</li> <li>    scientificname: the full scientific name of the organism, to the lowest level taxonomic rank that is possible to supply, and including authorship and year of the name where applicable.</li> <li>    verbatimscientificname: the taxonomic rank of the most specific name in the scientificName as it appears in the original record.</li> <li>    verbatimscientificnameauthorship: non described.</li> <li>    countrycode: a two-letter standard abbreviation for the country of the occurrence locality.</li> <li>    locality: the specific description of the place.</li> <li>    stateprovince: the name of the next smaller administrative region than country (state, province, canton, department, region, etc.) in which the Location occurs.</li> <li>    occurrencestatus: a statement about the presence or absence of a Taxon at a Location.</li> <li>    individualcount: to record the quantity of a species occurrence, e.g. as the number of individuals, percentage of vegetation coverage, or the biomass .</li> <li>    publishingorgkey: the publishing organization key (a uuid).</li> <li>    decimallatitude: the geographic latitude, resp., in decimal degrees. </li> <li>    decimallongitude: the geographic longitude, resp., in decimal degrees. </li> <li>    coordinateuncertaintyinmeters: the horizontal distance from the given decimalLatitude and decimalLongitude in meters, describing the smallest circle containing the whole of the Location.</li> <li>    coordinateprecision: a decimal representation of the precision of the coordinates given in the decimalLatitude and decimalLongitude.</li> <li>    elevation: elevation (altitude) in meters above sea level. Supports range queries.</li> <li>    elevationaccuracy: non described.</li> <li>    depth: depth in meters relative to altitude. For example 10 meters below a lake surface with given altitude. Supports range queries.</li> <li>    depthaccuracy: non described.</li> <li>    eventdate: the date or date interval during which the occurrence record was collected, following ISO 8601 date-time standard.</li> <li>    day: the integer day of the month on which the Event occurred.</li> <li>    month: the integer month in which the Event occurred.</li> <li>    year: the four-digit year in which the Event occurred, according to the Common Era Calendar.</li> <li>    taxonkey: a taxon key from the GBIF backbone. </li> <li>    specieskey: species classification key.</li> <li>    basisofrecord: the type of the individual record, e.g. observation, physical specimen, fossil, living ex-situ, culture collection specimen.</li> <li>    institutioncode: the name (or acronym) in use by the institution having custody of the object(s) or information referred to in the record.</li> <li>    collectioncode: the name, acronym, coden, or initialism identifying the collection or data set from which the record was derived.</li> <li>    catalognumber: an identifier (preferably unique) for the record within the data set or collection.</li> <li>    recordnumber: an identifier given to the Occurrence at the time it was recorded. Often serves as a link between field notes and an Occurrence record, such as a specimen collector's number.</li> <li>    identifiedby: a list (concatenated and separated) of names of people, groups, or organizations who assigned the Taxon to the subject.</li> <li>    dateidentified: the date on which the subject was determined as representing the Taxon.</li> <li>    license: a machine-readable statement of the rights assigned to the published dataset.</li> <li>    rightsholder: a person or organization owning or managing rights over the resource.</li> <li>    recordedby: the name of the institution or organization listed as the data publisher on GBIF.org.</li> <li>    typestatus: a list (concatenated and separated) of nomenclatural types (type status, typified scientific name, publication) applied to the subject.</li> <li>    establishmentmeans: The process by which the biological individual(s) represented in the Occurrence became established at the location.</li> <li>    lastinterpreted: this date the record was last modified in GBIF, in ISO 8601 format: yyyy, yyyy-MM, yyyy-MM-dd, or MM-dd. </li> <li>    mediatype: the kind of multimedia associated with an occurrence as defined in GBIF MediaType enum</li> <li>    issue: a specific interpretation issue as defined in GBIF OccurrenceIssue enum.</li> <li>    geom (geometry): geometry from latitude and longitude position. Developed for this project.</li> <li>    buff (geometry): buffer around 'geom' taking into account 'coordinateuncertaintyinmeters' and 'coordinateprecision'. Developed for this project.</li> </ul> The context is the Final Master's Degree Project 'Analysis and Predictive Modelling of Wildlife–Vehicle Collision on Interurban Roads in Spain' (Data Science Master’s Degree of Universitat Oberta de Catalunya - UOC). This dataset is the output of the animal analysis and the <a href="https://github.com/alba620/analisis-prediccion-accidentes-trafico-animales">code repository</a> is available on GitHub.

LOCATION DOMESTIC ARCHITECTURE AUGUSTA EMERITA (MERIDA, SPAIN)

This dataset is based on houses from the Roman colony of Augusta Emerita (ca. B.C 25- A.D. 382) <a href="#_ftn1">[1]</a>. These dates are not chosen randomly: 25 B.C. saw the foundation ex nihilo of the settlement, according to Cass. Dio., Hist. 53.26.1<a href="#_ftn2">[2]</a>, and A.D. 382, the last time a name of a vicarius is attested in the epigraphy<a href="#_ftn3">[3]</a>. The remains from Augusta Emerita are particularity well suited to GIS analysis of this type because the site was occupied for more than two thousand years and was never abandoned, while also being occupied by several cultures over that time.   <a href="#_ftnref1">[1]</a> All documentation based on La arquitectura doméstica de Augusta Emerita (2015 Phd) https://dehesa.unex.es/handle/10662/2670#  <a href="#_ftnref2">[2]</a> The Duoviri's first couple is evidenced ca.20 B.C. from the fragment of the Fasti duovirales: A. Stylow and A. Ventura, “Los hallazgos epigráficos”, in R. Ayerbe, T. Barrientos and F. Palma (edd.), El foro de Augusta Emerita. Génesis y evolución de sus recintos monumentales (Mérida 2009) 453-523. <a href="#_ftnref3">[3]</a> L. Hidalgo and G. Méndez, “Octavius Clarus, un nuevo Vicarius Hispaniarum en Augusta Emerita”, Mérida. Excavaciones Arqueológicas 8 (2005) 547-64.

opencc-by-4.0Nov 2022View details →

Exploring Economic Integration of Peasant Settlements Central Roman Spain (1st - 3rd c. AD). Dataset.

Archaeological data used for the paper Exploring Economic Integration of Peasant Settlements in Roman Central Spain (1st - 3rd c. AD). The supplement is composed by three CSV files recording respectively the presence and frequency of an artefact chrono-type at a site, the adjacency matrix (MATRIX_SITE-CHRONOTYPE_CARPETANIA.csv), the attribute information per site (ATTRIB_SITES_CARPETANIA.csv) and the attribute information per artefact chrono-type (ATTRIB_CHRONOTYPES_CARPETANIA.csv). Also included are the raw .graphmlz files relating to the analyses carried out which include, among other data, the Louvain modularity.  In order to view and analyse the .graphmlz files provided in this archive, you can use the Visone software, a tool for visualizing and analyzing networks: To view these .graphmlz files, first download and install Visone software from their official website (<a href="https://visone.ethz.ch/">https://visone.ethz.ch/</a>). Then, download the .graphmlz files from the Zenodo archive. Open Visone, and navigate to 'File' > 'Open' to access the downloaded files.

opencc-by-4.0Feb 2023View details →

Database Fear of COVID-19 and Vaccine Attitudes Examination Scale (VAX) in Spain

This dataset contains data collected between November 15, 2021 and March 7, 2022, and between December 1, 2022 and February 6, 2023. It contains demographic variables, data on vaccinated people, responses to the items on the Fear of COVID-19 Scale, and to the items on the Vaccination Attitudes Examination (VAX) scale are included. Although the language of the open answers is Spanish, the name of the variables and the value labels are written in English to facilitate their understanding. Data and codebooks are provided in csv format, following the FAIR principles. Three files are provided: 1. Database Fear of COVID-19 and VAX, with the data related to sample characteristics and the answers to the items of the questionnaires. 2. Database codebook of variables, with information of the labels of the variables of the Database file. 3. Variable values codebook, with the labels of the values of the variables in the Database file.

opencc-by-4.0Jun 2023View details →

Microclimatic data for La Jarda, Jerez de la Frontera, Spain, 2004-2006

Microclimatic data recorded in La Jarda, Montes de Propios de Jerez de la Frontera, Cádiz, Spain, between 2004 and 2006, with HOBO H8 data-loggers located 30 cm aboveground. Variables included: - Air temperature (ºC) - Dew point (ºC) - Absolute humidity (g/m3) - Relative humidity (%)

opencc-by-4.0Jul 2023View details →

Result data related to "Bersalli et al. (2024): Economic crises as critical junctures for policy and structural changes towards decarbonization – the cases of Spain and Germany"

Result data related to "Bersalli et al (2024): Economic crises as critical junctures for policy and structural changes towards decarbonization – the cases of Spain and Germany". The following files are included: <ul> <li>"Energy policy 2020-21 Germany-Spain.xlsx": Policy measures supporting clean energy during the Covid-19 pandemic in Germany and Spain. Data from <a href="http://energypolicytracker.org/">EnergyPolicyTracker.org</a>, amended by the authors.</li> <li> "factors.csv": Time series of emissions, population, GDP, energy intensity, and carbon intensity. Data derived from BP and Eurostat.</li> <li>"multiplicative-contribution-factors.csv": Time series of relative growth in the factors.</li> <li>"relative-cumulative-contribution-factors.csv": Time series of cumulative relative growth in the factors.</li> <li>"periods.csv": Relative growth in the factors during the global financial and COVID19 crises and before (pre) and after (post) the global financial crisis.</li> </ul>

opencc-by-4.0Dec 2022View details →

Intimate Partner Violence and Mental Health in Migrant Women Living in Spain

This dataset contains data from a convenience sample in a cross-sectional study of intimate partner violence (IPV) and mental health in migrant women living in the Valencian Community of Spain. IPV was assessed using the Revised Composite Abuse Scale Short Form (CASR-SF), the Revised Scale of Economic Abuse (SEA2) and the Cyber Aggression in Relationship Scale (CARS). Mental health was assessed using the PHQ-9 for symptoms of depression and the GAD-7 for symptoms of anxiety.

ES17 - Healthcare - Aguilar de Campoo (Spain)

Data files for building: ES17 - Healthcare - Aguilar de Campoo (Spain)Languages: Spanish, EnglishThese files are part of the public benchmark repository created as a part of the crossCert EU project. This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.The files are organised in the following folders  (note that not all files are always provided):<ol><li>Main data  and Results, with:<ol><li>Neutral data inventory.</li><li>Neutral results report.</li><li>Original EPC certificate.</li></ol></li><li>Energy Consumption Data, with:<ol><li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li></ol></li><li>Drawings<ol><li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption  models.</li></ol></li><li>Other Data<ol><li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li></ol></li><li>Dynamic Model<ol><li>Data to run a dynamic model of the building, if available.</li></ol></li></ol>The files have been redacted to exclude confidential information.

ES16 - Office - Salamanca (Spain)

Data files for building: ES16 - Office - Salamanca (Spain)Languages: Spanish, EnglishThese files are part of the public benchmark repository created as a part of the crossCert EU project. This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.The files are organised in the following folders  (note that not all files are always provided):<ol><li>Main data  and Results, with:<ol><li>Neutral data inventory.</li><li>Neutral results report.</li><li>Original EPC certificate.</li></ol></li><li>Energy Consumption Data, with:<ol><li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li></ol></li><li>Drawings<ol><li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption  models.</li></ol></li><li>Other Data<ol><li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li></ol></li><li>Dynamic Model<ol><li>Data to run a dynamic model of the building, if available.</li></ol></li></ol>The files have been redacted to exclude confidential information.

ES15 - Office - Valladolid (Spain)

Data files for building: ES15 - Office - Valladolid (Spain)Languages: Spanish, EnglishThese files are part of the public benchmark repository created as a part of the crossCert EU project. This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.The files are organised in the following folders  (note that not all files are always provided):<ol><li>Main data  and Results, with:<ol><li>Neutral data inventory.</li><li>Neutral results report.</li><li>Original EPC certificate.</li></ol></li><li>Energy Consumption Data, with:<ol><li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li></ol></li><li>Drawings<ol><li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption  models.</li></ol></li><li>Other Data<ol><li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li></ol></li><li>Dynamic Model<ol><li>Data to run a dynamic model of the building, if available.</li></ol></li></ol>The files have been redacted to exclude confidential information.

ES14 - Office - Zamora (Spain)

Data files for building: ES14 - Office - Zamora (Spain)Languages: Spanish, EnglishThese files are part of the public benchmark repository created as a part of the crossCert EU project. This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.The files are organised in the following folders  (note that not all files are always provided):<ol><li>Main data  and Results, with:<ol><li>Neutral data inventory.</li><li>Neutral results report.</li><li>Original EPC certificate.</li></ol></li><li>Energy Consumption Data, with:<ol><li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li></ol></li><li>Drawings<ol><li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption  models.</li></ol></li><li>Other Data<ol><li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li></ol></li><li>Dynamic Model<ol><li>Data to run a dynamic model of the building, if available.</li></ol></li></ol>The files have been redacted to exclude confidential information.

ES12 - Social Housing - Candeleda (Spain)

Data files for building: ES12 - Social Housing - Candeleda (Spain)Languages: Spanish, EnglishThese files are part of the public benchmark repository created as a part of the crossCert EU project. This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.The files are organised in the following folders  (note that not all files are always provided):<ol><li>Main data  and Results, with:<ol><li>Neutral data inventory.</li><li>Neutral results report.</li><li>Original EPC certificate.</li></ol></li><li>Energy Consumption Data, with:<ol><li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li></ol></li><li>Drawings<ol><li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption  models.</li></ol></li><li>Other Data<ol><li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li></ol></li><li>Dynamic Model<ol><li>Data to run a dynamic model of the building, if available.</li></ol></li></ol>The files have been redacted to exclude confidential information.

ES11 - Social housing - Simancas (Spain)

Data files for building: ES11 - Social housing - Simancas (Spain)Languages: Spanish, EnglishThese files are part of the public benchmark repository created as a part of the crossCert EU project. This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.The files are organised in the following folders  (note that not all files are always provided):<ol><li>Main data  and Results, with:<ol><li>Neutral data inventory.</li><li>Neutral results report.</li><li>Original EPC certificate.</li></ol></li><li>Energy Consumption Data, with:<ol><li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li></ol></li><li>Drawings<ol><li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption  models.</li></ol></li><li>Other Data<ol><li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li></ol></li><li>Dynamic Model<ol><li>Data to run a dynamic model of the building, if available.</li></ol></li></ol>The files have been redacted to exclude confidential information.

ES10 - Healthcare - Villagonzalo/Pedernales (Spain)

Data files for building: ES10 - Healthcare - Villagonzalo/Pedernales (Spain)Languages: Spanish, EnglishThese files are part of the public benchmark repository created as a part of the crossCert EU project. This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.The files are organised in the following folders  (note that not all files are always provided):<ol><li>Main data  and Results, with:<ol><li>Neutral data inventory.</li><li>Neutral results report.</li><li>Original EPC certificate.</li></ol></li><li>Energy Consumption Data, with:<ol><li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li></ol></li><li>Drawings<ol><li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption  models.</li></ol></li><li>Other Data<ol><li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li></ol></li><li>Dynamic Model<ol><li>Data to run a dynamic model of the building, if available.</li></ol></li></ol>The files have been redacted to exclude confidential information.

ES09 - Others - Ibeas de Juarros (Spain)

Data files for building: ES09 - Others - Ibeas de Juarros (Spain)Languages: Spanish, EnglishThese files are part of the public benchmark repository created as a part of the crossCert EU project. This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.The files are organised in the following folders  (note that not all files are always provided):<ol><li>Main data  and Results, with:<ol><li>Neutral data inventory.</li><li>Neutral results report.</li><li>Original EPC certificate.</li></ol></li><li>Energy Consumption Data, with:<ol><li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li></ol></li><li>Drawings<ol><li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption  models.</li></ol></li><li>Other Data<ol><li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li></ol></li><li>Dynamic Model<ol><li>Data to run a dynamic model of the building, if available.</li></ol></li></ol>The files have been redacted to exclude confidential information.

ES04 - Office - León (Spain)

Data files for building: ES04 - Office - León (Spain)Languages: Spanish, EnglishThese files are part of the public benchmark repository created as a part of the crossCert EU project. This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.The files are organised in the following folders  (note that not all files are always provided):<ol><li>Main data  and Results, with:<ol><li>Neutral data inventory.</li><li>Neutral results report.</li><li>Original EPC certificate.</li></ol></li><li>Energy Consumption Data, with:<ol><li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li></ol></li><li>Drawings<ol><li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption  models.</li></ol></li><li>Other Data<ol><li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li></ol></li><li>Dynamic Model<ol><li>Data to run a dynamic model of the building, if available.</li></ol></li></ol>The files have been redacted to exclude confidential information.

ES02 - Educational - Cisterniga (Spain)

Data files for building: ES02 - Educational - Cisterniga (Spain)Languages: Spanish, EnglishThese files are part of the public benchmark repository created as a part of the crossCert EU project. This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.The files are organised in the following folders  (note that not all files are always provided):<ol><li>Main data  and Results, with:<ol><li>Neutral data inventory.</li><li>Neutral results report.</li><li>Original EPC certificate.</li></ol></li><li>Energy Consumption Data, with:<ol><li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li></ol></li><li>Drawings<ol><li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption  models.</li></ol></li><li>Other Data<ol><li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li></ol></li><li>Dynamic Model<ol><li>Data to run a dynamic model of the building, if available.</li></ol></li></ol>The files have been redacted to exclude confidential information.