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17 results for “road accidents”

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zenodo44/100

Data set and scripts - Influence of Festive Periods on Road Safety: Multidimensional Analysis (Road Accidents in Colombia 2017-2021)

This dataset comprises historical information about road accidents in Colombia from 2017 to 2021, titled 'Road Accidents 2017-2021', containing 18,600 records of accident events on roads managed by the National Roads Institute (INVÍAS, 2021). The dataset includes 41 descriptors and was last updated on July 15, 2022. It has been published under the Open Data initiative (Law 1712 of 2014 on Transparency and Access to National Public Information). In addition to accident information, the dataset integrates a database with holiday dates and road identifiers, ensuring data coherence and quality for data analysis purposes. Statistical analysis is conducted through exploratory data analysis focusing on the years 2017 to 2021, utilizing Python (version 3.10) within the Jupyter Notebooks execution environment and specialized libraries (Pandas, NumPy, Matplotlib, and Seaborn), due to their ease of application for this dataset. After data normalization, the dataset comprises 18,554 records, with 46 excluded due to inconsistent data formats.

opencc-by-4.0May 2024View details →

zenodo44/100

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.

opencc-by-4.0Jan 2023View details →

zenodo36/100

Magnitude and determinants of road traffic accidents in North Gondar Zone, Amhara Region, Ethiopia

Number and types of a road traffic accidents in relation to road  and road user, environmental and time related and vehicle related  factors

openother-ncJul 2022View details →

ClinicalTrials.gov36/100

A Feasibility Trial of Eye Movement Desensitization and Reprocessing Therapy- Integrative Treatment Group Protocol for Ongoing Traumatic Stress In Road Traffic Accident Survivors for Reduction of Post

ClinicalTrials.gov study NCT07027930. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →

dryad32/100

Data from: Sleep apnea, sleep debt and daytime sleepiness are independently associated with road accidents: a cross-sectional study on truck drivers

Background: Recent research has found evidence of an association between motor vehicle accidents (MVAs) or near miss accidents (NMAs), and excessive daytime sleepiness (EDS) or its main medical cause, Obstructive Sleep Apnea (OSA). However, EDS can also be due to non-medical factors, such as sleep debt (SD), which is common among professional truck drivers. On the opposite side, rest breaks and naps are known to protect against accidents. Study objectives: To investigate the association of OSA, SD, EDS, rest breaks and naps, with the occurrence of MVAs and NMAs in a large sample of truck drivers. Methods: 949 male truck drivers took part in a cross-sectional medical examination and were asked to complete a questionnaire about sleep and waking habits, risk factors for OSA and EDS. Results: MVAs and NMAs were reported by 34.8% and 9.2% of participants, respectively. MVAs were significantly predicted by OSA (OR= 2.32 CI95%=1.68-3.20), SD (OR=1.45 CI95%=1.29-1.63), EDS (OR=1.73 CI95%=1.15-2.61) and prevented by naps (OR=0.59 CI95%=0.44-0.79) or rest breaks (OR=0.63 CI95%=0.45-0.89). NMAs were significantly predicted by OSA (OR= 2.39 CI95%=1.47- 3.87) and SD (OR=1.49 CI95%=1.27- 1.76) and prevented by naps (OR=0.52 CI95%=0.32- 0.85) or rest breaks (OR=0.49 CI95%=0.29- 0.82). Conclusions: When OSA, SD or EDS are present, the risk of MVAs or NMAs in truck drivers is severely increased. Taking a rest break or a nap appear to be protective against accidents.

opencc-zeroDec 2015View details →

ClinicalTrials.gov32/100

Psychological Interventions in Children After Road Traffic Accidents or Burns

ClinicalTrials.gov study NCT01085370. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov32/100

Evaluation of Reporting of Road Traffic Accidents With Drugs Responsible for Cognitive Side Effects (ERoADS)

ClinicalTrials.gov study NCT04480996. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov32/100

Prevention of Posttraumatic Stress Symptoms and Behavioral Problems in Children After Road Traffic Accidents: a Randomized Controlled Trial

ClinicalTrials.gov study NCT00296842. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

dryad32/100

Data from: Sleep apnea, sleep debt and daytime sleepiness are independently associated with road accidents: a cross-sectional study on truck drivers

Open the record for dataset details and reuse information.

publicNov 2017View details →

zenodo28/100

Spatial Distribution and Cluster Analysis of Road Traffic Accidents in Nepal

Open the record for dataset details and reuse information.

opencc-by-4.0Oct 2024View details →

zenodo28/100

DATABASE FOR THE ANALYSIS OF ROAD ACCIDENTS IN EUROPE

This database that can be used for macro-level analysis of road accidents on interurban roads in Europe. Through the variables it contains, road accidents can be explained using variables related to economic resources invested in roads, traffic, road network, socioeconomic characteristics, legislative measures and meteorology. This repository contains the data used for the analysis carried out in the papers: 1. Calvo-Poyo F., Navarro-Moreno J., de Oña J. (2020) Road Investment and Traffic Safety: An International Study. Sustainability 12:6332. https://doi.org/10.3390/su12166332 2. Navarro-Moreno J., Calvo-Poyo F., de Oña J. (2022) Influence of road investment and maintenance expenses on injured traffic crashes in European roads. Int J Sustain Transp 1–11. https://doi.org/10.1080/15568318.2022.2082344 3. Navarro-Moreno, J., Calvo-Poyo, F., de Oña, J. (2022) Investment in roads and traffic safety: linked to economic development? A European comparison. Environ. Sci. Pollut. Res. https://doi.org/10.1007/s11356-022-22567 The file with the database is available in excel. DATA SOURCES The database presents data from 1998 up to 2016 from 20 european countries: Austria, Belgium, Croatia, Czechia, Denmark, Estonia, Finland, France, Germany, Ireland, Italy, Latvia, Netherlands, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden and United Kingdom. Crash data were obtained from the United Nations Economic Commission for Europe (UNECE) [2], which offers enough level of disaggregation between crashes occurring inside versus outside built-up areas. With reference to the data on economic resources invested in roadways, deserving mention –given its extensive coverage—is the database of the Organisation for Economic Cooperation and Development (OECD), managed by the International Transport Forum (ITF) [1], which collects data on investment in the construction of roads and expenditure on their maintenance, following the definitions of the United Nations System of National Accounts (2008 SNA). Despite some data gaps, the time series present consistency from one country to the next. Moreover, to confirm the consistency and complete missing data, diverse additional sources, mainly the national Transport Ministries of the respective countries were consulted. All the monetary values were converted to constant prices in 2015 using the OECD price index. To obtain the rest of the variables in the database, as well as to ensure consistency in the time series and complete missing data, the following national and international sources were consulted: <ul> <li>Eurostat [3]</li> <li>Directorate-General for Mobility and Transport (DG MOVE). European Union [4]</li> <li>The World Bank [5]</li> <li>World Health Organization (WHO) [6]</li> <li>European Transport Safety Council (ETSC) [7]</li> <li>European Road Safety Observatory (ERSO) [8]</li> <li>European Climatic Energy Mixes (ECEM) of the Copernicus Climate Change [9]</li> <li>EU BestPoint-Project [10]</li> <li>Ministerstvo dopravy, República Checa [11]</li> <li>Bundesministerium für Verkehr und digitale Infrastruktur, Alemania [12]</li> <li>Ministerie van Infrastructuur en Waterstaat, Países Bajos [13]</li> <li>National Statistics Office, Malta [14]</li> <li>Ministério da Economia e Transição Digital, Portugal [15]</li> <li>Ministerio de Fomento, España [16]</li> <li>Trafikverket, Suecia [17]</li> <li>Ministère de l’environnement de l’énergie et de la mer, Francia [18]</li> <li>Ministero delle Infrastrutture e dei Trasporti, Italia [19–25]</li> <li>Statistisk sentralbyrå, Noruega [26-29]</li> <li>Instituto Nacional de Estatística, Portugal [30]</li> <li>Infraestruturas de Portugal S.A., Portugal [31–35]</li> <li>Road Safety Authority (RSA), Ireland [36]</li> </ul>   DATA BASE DESCRIPTION The database was made trying to combine the longest possible time period with the maximum number of countries with complete dataset (some countries like Lithuania, Luxemburg, Malta and Norway were eliminated from the definitive dataset owing to a lack of data or breaks in the time series of records). Taking into account the above, the definitive database is made up of 19 variables, and contains data from 20 countries during the period between 1998 and 2016. Table 1 shows the coding of the variables, as well as their definition and unit of measure.   Table. Database metadata <table> <tbody> <tr> <td> Code </td> <td> Variable and unit </td> </tr> <tr> <td> fatal_pc_km </td> <td> Fatalities per billion passenger-km </td> </tr> <tr> <td> fatal_mIn </td> <td> Fatalities per million inhabitants </td> </tr> <tr> <td> accid_adj_pc_km </td> <td> Accidents per billion passenger-km </td> </tr> <tr> <td> p_km </td> <td> Billions of passenger-km </td> </tr> <tr> <td> croad_inv_km </td> <td> Investment in roads construction per kilometer, €/km (2015 constant prices) </td> </tr> <tr> <td> croad_maint_km </td> <td> Expenditure on roads maintenance per kilometer €/km (2015 constant prices) </td> </tr> <tr> <td> prop_motorwa </td> <td> Proportion of motorways over the total road network (%) </td> </tr> <tr> <td> populat </td> <td> Population, in millions of inhabitants </td> </tr> <tr> <td> unemploy </td> <td> Unemployment rate (%) </td> </tr> <tr> <td> petro_car </td> <td> Consumption of gasolina and petrol derivatives (tons), per tourism </td> </tr> <tr> <td> alcohol </td> <td> Alcohol consumption, in liters per capita (age > 15) </td> </tr> <tr> <td> mot_index </td> <td> Motorization index, in cars per 1,000 inhabitants </td> </tr> <tr> <td> den_populat </td> <td> Population density, inhabitants/km2 </td> </tr> <tr> <td> cgdp </td> <td> Gross Domestic Product (GDP), in € (2015 constant prices) </td> </tr> <tr> <td> cgdp_cap </td> <td> GDP per capita, in € (2015 constant prices) </td> </tr> <tr> <td> precipit </td> <td> Average depth of rain water during a year (mm) </td> </tr> <tr> <td> prop_elder </td> <td> Proportion of people over 65 years (%) </td> </tr> <tr> <td> dps </td> <td> Demerit Point System, dummy variable (0: no; 1: yes) </td> </tr> <tr> <td> freight </td> <td> Freight transport, in billions of ton-km </td> </tr> </tbody> </table>   ACKNOWLEDGEMENTS This database was carried out in the framework of the project “Inversión en carreteras y seguridad vial: un análisis internacional (INCASE)”, financed by: FEDER/Ministerio de Ciencia, Innovación y Universidades–Agencia Estatal de Investigación/Proyecto RTI2018-101770-B-I00, within Spain´s National Program of R+D+i Oriented to Societal Challenges. Moreover, the authors would like to express their gratitude to the Ministry of Transport, Mobility and Urban Agenda of Spain (MITMA), and the Federal Ministry of Transport and Digital Infrastructure of Germany (BMVI) for providing data for this study.   REFERENCES   1.      International Transport Forum OECD iLibrary | Transport infrastructure investment and maintenance. 2.      United Nations Economic Commission for Europe UNECE Statistical Database Available online: https://w3.unece.org/PXWeb2015/pxweb/en/STAT/STAT__40-TRTRANS/?rxid=18ad5d0d-bd5e-476f-ab7c-40545e802eeb (accessed on Apr 28, 2020). 3.      European Commission Database - Eurostat Available online: https://ec.europa.eu/eurostat/data/database (accessed on Apr 28, 2021). 4.      Directorate-General for Mobility and Transport. European Commission EU Transport in figures - Statistical Pocketbooks Available online: https://ec.europa.eu/transport/facts-fundings/statistics_en (accessed on Apr 28, 2021). 5.      World Bank Group World Bank Open Data | Data Available online: https://data.worldbank.org/ (accessed on Apr 30, 2021). 6.      World Health Organization (WHO) WHO Global Information System on Alcohol and Health Available online: https://apps.who.int/gho/data/node.main.GISAH?lang=en (accessed on Apr 29, 2021). 7.      European Transport Safety Council (ETSC) Traffic Law Enforcement across the EU - Tackling the Three Main Killers on Europe’s Roads; Brussels, Belgium, 2011; 8.      Copernicus Climate Change Service Climate data for the European energy sector from 1979 to 2016 derived from ERA-Interim Available online: https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-european-energy-sector?tab=overview (accessed on Apr 29, 2021). 9.      Klipp, S.; Eichel, K.; Billard, A.; Chalika, E.; Loranc, M.D.; Farrugia, B.; Jost, G.; Møller, M.; Munnelly, M.; Kallberg, V.P.; et al. European Demerit Point Systems : Overview of their main features and expert opinions. EU BestPoint-Project 2011, 1–237. 10.    Ministerstvo dopravy Serie: Ročenka dopravy; Ročenka dopravy; Centrum dopravního výzkumu: Prague, Czech Republic; 11.    Bundesministerium für Verkehr und digitale Infrastruktur Verkehr in Zahlen 2003/2004; Hamburg, Germany, 2004; ISBN 3871542946. 12.    Bundesministerium für Verkehr und digitale Infrastruktur Verkehr in Zahlen 2018/2019. In Verkehrsdynamik; Flensburg, Germany, 2018 ISBN 9783000612947. 13.    Ministerie van Infrastructuur en Waterstaat Rijksjaarverslag 2018 a Infrastructuurfonds; The Hague, Netherlands, 2019; ISBN 0921-7371. 14.    Ministerie van Infrastructuur en Milieu Rijksjaarverslag 2014 a Infrastructuurfonds; The Hague, Netherlands, 2015; ISBN 0921- 7371. 15.    Ministério da Economia e Transição Digital Base de Dados de Infraestruturas - GEE Available online: https://www.gee.gov.pt/pt/publicacoes/indicadores-e-estatisticas/base-de-dados-de-infraestruturas (accessed on Apr 29, 2021). 16.    Ministerio de Fomento. Dirección General de Programación Económica y Presupuestos. Subdirección General de Estudios Económicos y Estadísticas Serie: Anuario estadístico; NIPO 161-13-171-0; Centro de Publicaciones. Secretaría General Técnica. Ministerio de Fomento: Madrid, Spain; 17.    Trafikverket The Swedish Transport Administration Annual report: 2017; 2018; ISBN 978-91-7725-272-6. 18.    Ministère de l’Équipement,  du T. et de la M. Mémento de statistiques des transports 2003; Ministère de l’environnement de l’énergie et de la mer, 2005; 19.    Ministero delle Infrastrutture e dei Trasporti Conto Nazionale delle Infrastrutture e dei Trasporti Anno 2000; Istituto Poligrafico e Zecca dello Stato: Roma, Italy, 2001; 20.    Ministero delle Infrastrutture e dei Trasporti Conto nazionale dei trasporti 1999. 2000. 21.    Generale, D.; Informativi, S. delle Infrastrutture e dei Trasporti Anno 2004. 22.    Ministero delle Infrastrutture e dei Trasporti Conto Nazionale delle Infrastrutture e dei Trasporti Anno 2001; 2002; 23.    Ministero delle Infrastrutture e dei Trasporti Conto Nazionale delle Infrastrutture e dei Trasporti Anni 2007-2008. 2009. 24.    Ministero delle Infrastrutture e dei Trasporti Conto Nazionale delle Infrastrutture e dei Trasporti Anni 2016-2017. 2018. 25.    Ministero delle Infrastrutture e dei Trasporti Conto Nazionale delle Infrastrutture e dei Trasporti. Anni 2014-2015; 2016; 26.    Statistics Norway Statistical Yearbook of Norway 2000; Ad Notam Gyldendal: Oslo, Norway, 2000; ISBN 82-537-4820-5. 27.    Statistics Norway Statistical Yearbook of Norway 2001; Gnist.Akademika: Oslo, Norway, 2001; ISBN 8253749600. 28.    Statistics Norway Statistical Yearbook of Norway 2002; Gnist.Akadernika: Oslo, Norway, 2002; ISBN 8253750927. 29.    Statistics Norway Statistical Yearbook of Norway 2004; Gnist.Akademika: Oslo, Norway, 2004; ISBN 82-537-6616-5. 30.    Instituto Nacional de Estatística Estatísticas dos transportes e comunicações 2000; Instituto Nacional de Estatística: Lisbon, Portugal, 2002; 31.    Estradas de Portugal S.A. Relatório e Contas 2011; Almada, Portugal, 2012; 32.    Estradas de Portugal S.A. Relatório e Contas 2012; Lisboa, Portugal, 2013; 33.    Estradas de Portugal S.A. Relatório e Contas 2010; Lisboa, Portugal, 2011; 34.    Infraestruturas de Portugal S.A. Relatório e Contas 2015; Pragal, Portugal, 2016; 35.    Infraestruturas de Portugal S.A. Relatório e Contas 2018; Almada, Portugal, 2019; 36.    Road Safety Authority Road Collision Factbook; Ballina, Ireland;

opencc-by-4.0Oct 2022View details →

zenodo28/100

A spatial autocorrelation analysis of Road Traffic Accidents by severity using Moran's I spatial statistics: A study from Nepal 2019-2022

Open the record for dataset details and reuse information.

opencc-by-4.0Sep 2024View details →

ClinicalTrials.gov24/100

A Simple Cognitive Task to Reduce the Build-Up of Flashbacks After a Road Traffic Accident

ClinicalTrials.gov study NCT02080351. IPD Sharing: Not stated. Countries: 1. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov24/100

Role of ETS in Improving Cardiac and Respiratory Complications in Road Traffic Accident Cases

ClinicalTrials.gov study NCT07049393. IPD Sharing: NO. Countries: 1. Publications: 0.

closedIPD-NOFeb 2026View details →

ClinicalTrials.gov24/100

Attitudes Toward Obstructive Sleep Apnea-Related Cause of Road Traffic Accidents in Thailand Through Well Prepared Educational Video

ClinicalTrials.gov study NCT05734742. IPD Sharing: NO. Countries: 1. Publications: 0.

closedIPD-NOFeb 2026View details →

ClinicalTrials.gov24/100

Correlation Between Coagulation Profiles And Injury Severity In Road Traffic Accidents Patients at Dera Ismail Khan

ClinicalTrials.gov study NCT07051395. IPD Sharing: NO. Countries: 1. Publications: 0.

closedIPD-NOFeb 2026View details →

ClinicalTrials.gov24/100

Epidemiology of Road Traffic Accidents in Riyadh Region in the Last Five Years (2019-2023)

ClinicalTrials.gov study NCT06553586. IPD Sharing: YES. Countries: 1. Publications: 0.

controlledIPD-YESFeb 2026View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated datasets

Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record