Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
152
datasets available to search
ShareScore release 0.9.0
Dataset results
152 results for “accidents”
When my wrongs are worse than yours: behavioral and neural asymmetries in first-person and third-person perspectives of accidents
Open the record for dataset details and reuse information.
Modeled tritium in precipitation from Fukushima Daiichi Nuclear Power Plant accident simulations with MIROC5-iso
<p>This data set contains modeled tritium in precipitation values from different simulations of Fukushima Daiichi Nuclear Power Plant (FDNPP) accident produced with MIROC5-iso. The simulations are for the period 2011-20121 and were with different anthropogenic tritium source functions. A complete description can be found in Cauquoin, A., Gusyev, M., Bong, H., Okazaki, A., and Yoshimura, K.: Modeling tritium release to the atmosphere during the Fukushima Daiichi Nuclear Power Plant accident and application to estimating post-accident water system transit times, <em>Environ. Sci. Pollut. Res.</em>, <a href="https://doi.org/10.1007/s11356-025-35919-1" target="_blank" rel="noopener">https://doi.org/10.1007/s11356-025-35919-1</a>, 2025. </p> <p>The simulations are named fukushima_accident_{jra55, era5}_total_gas_{div100, div200, div500, div1000}, with {jra55, era5} describing a nudging to JRA-55 or ERA5 reanalyses, and with {div100, div200, div500, div1000} describing the anthropogenic tritium input function used in DatasetS1_table_tritium_release_atm_fukushima_input.csv.</p> <p>The modeled values of tritium in Hiso river water, Minamisoma spring and artesian groundwater, calculated using MIROC5-iso tritium in monthly precipitation in Fukushima, scaled Tokyo GNIP data, and tritium measurements in preciptation at Fukushima as input of the TracerLPM model, are included too. </p> <p>The model data can be downloaded as netcdf, csv or xlsx files:</p> <ul> <li>*_daymean.prcpTU.nc: daily mean tritium in precipitation over the period 2011-2021, expressed in TU;</li> <li>*_monmean.prcpTU.nc: monthly mean tritium in precipitation over the period 2011-2021, expressed in TU;</li> <li>*_daymean.prcp.nc: daily precipitation over the period 2011-2021, expressed in mm/day;</li> <li>*_monmean.prcp.nc: monthly precipitation over the period 2011-2021, expressed in mm/month;</li> <li>*_prcp_daymean.remapnn.csv: daily precitation at nearest grid cells of Tsukuba, Kashiwa, Hongo, Yokosuka, Konan, and Misasa over the period 2011-2012, expressed in mm/day;</li> <li>*_prcp_monmean.remapnn.csv: montly mean precitation at nearest grid cells of Chiba, Niigata, and Fukushima over the period 2011-2021, expressed in mm/month;</li> <li>*_prcpTU_daymean.remapnn.csv: tritium in daily precitation at nearest grid cells of Tsukuba, Kashiwa, Hongo, Yokosuka, Konan, and Misasa over the period 2011-2012, expressed in TU;</li> <li>*_prcpTU_monmean.remapnn.csv: tritium in montly precitation at nearest grid cells of Chiba, Niigata, and Fukushima over the period 2011-2021, expressed in TU;</li> <li>DatasetS1_table_tritium_release_atm_fukushima_input.csv: Table of anthropogenic tritium daily release, based on reconstructed iodine-131 total gas emissions from <a href="https://doi.org/10.5194/acp-15-1029-2015" target="_blank" rel="noopener">Katata et al. (2015)</a>, used as inputs for MIROC5-iso.</li> <li>TracerLPM_fukushima_with_peak_jra55.xlsx: Tritium input function Cin(t) and tritium concentration in Hiso river water, Minamisoma spring and artesian groundwater modeled by TracerLPM. Simulation div100 nudged to JRA-55 was used for constructing Cin(t).</li> <li>TracerLPM_fukushima_without_peak_jra55.xlsx: Tritium input function Cin(t) and tritium concentration in Hiso river water, Minamisoma spring and artesian groundwater modeled by TracerLPM. Simulation ctrl nudged to JRA-55 (without FDNPP peak) was used for constructing Cin(t).</li> <li>TracerLPM_fukushima_with_peak_era5.xlsx: Tritium input function Cin(t) and tritium concentration in Hiso river water, Minamisoma spring and artesian groundwater modeled by TracerLPM. Simulation div100 nudged to ERA5 was used for constructing Cin(t).</li> </ul>
Fatal traffic accidents in Catalonia
<p>This dataset contains 1024 fatal traffic accidents ocurred in Catalonia between June 13, 2014 and October 20, 2021. Each record has data about the date and time of the accident, it's localization and a description. The dataset has been obtained from applying web scrapping techniques on the Catalonia's Government (Generalitat de Catalunya) website: http://transit.gencat.cat/ca/el_servei/premsa_i_comunicacio/comunicats_d_accidents_mortals/</p> <p> </p> <p> </p>
A consolidated database of police-reported motor vehicle traffic accidents in the United States for actuarial applications
<p>This database is related to "A CONSOLIDATED DATABASE OF POLICE-REPORTED MOTOR VEHICLE TRAFFIC ACCIDENTS IN THE UNITED STATES FOR ACTUARIAL APPLICATIONS" (Araiza Iturria C.A., Hardy M., Marriott P.).</p> <p>Author Information</p> <p> A. Author<br> Name: Carlos Andrés Araiza Iturria<br> Email: caraizai@uwaterloo.ca<br> <br> B. Co-author<br> Name: Mary Hardy<br> Email: mary.hardy@uwaterloo.ca</p> <p> C. Co-author<br> Name: Paul Marriott<br> Email: pmarriott@uwaterloo.ca<br> <br> Institution: University of Waterloo<br> Address: 200 University Ave W, Waterloo, ON N2L 3G1</p> <p><br> Funding granted by the Natural Sciences and Engineering Research Council of Canada. Hardy: RGPIN-2018-03754, Marriott: RGPIN-2020-04015.</p> <p>The Python scripts to create the database can be directly accessed through related identifiers in this page.</p> <p>Parameter estimates along with their 90% confidence intervals from the 20 multinomial logistic regressions can be seen through related identifiers in this page.</p> <p> </p>
Data set and scripts - Influence of Festive Periods on Road Safety: Multidimensional Analysis (Road Accidents in Colombia 2017-2021)
<p>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).</p> <p>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.</p>
Dataset of traffic accidents reported on Twitter Bogotá Colombia
<p><strong>1 Classification Dataset</strong></p> <p>This dataset for the classification model contains 3,804 tweets, where 1,902 are related to traffic accident reports (TA, positive class) and 1,902 are unrelated (NTA, negative class).</p> <p>For training the tweet classification model, a collaborative labeling strategy was designed. Here, 30 people labeled data according to the instructions given. Each participant had to evaluate a tweet to manually classify it into one of three categories defined as: traffic accident related, unrelated and don´t know/no response. Each tweet was evaluated by 3 participants. The correct label was selected by voting; the 3 people must agree on the selected label, otherwise the tweet was excluded from training. This process took a month and required the development and deployment of a web application.</p> <p><strong>2 NER Dataset (Named Entity Recognition)</strong></p> <p>For the entity recognition model training, a sample of the filtered tweets resulting from the previous classification phase was taken. 1,340 tweets were extracted, where 800 are from “unofficial” users, almost 60% of the sample. These tweets were user reports on traffic incident occurred in Bogota from October 2018 to July 2019, including other tweets that contained some location references such as reports on the state of road infrastructure; some tweets from the years 2016 and 2017 were also included. Although these posts were not related to accidents per se, they were selected because they contained location information. The purpose was to train a model that would recognize these entities, because a classifier of accident-related tweets was previously created. Additionally, the dataset was split, reserving 1,072 tweets for training and 268 for evaluation.</p> <p>This dataset was manually labeled using the IOB (Inside-outside- beginning) format. The labeling tool called Brat Annotation Tools was used for this task. The labels defined are Location, which refers to the location of the report; and Time, which refers to the time or date of the incident. Accordingly, 5 labels were generated: B-loc, I-loc, B-time, I-time and O. The O label refers to Others.</p> <p><strong>3 Traffic accident Twitter geolocation</strong></p> <p>A dataset with 26362 traffic accident tweets with the coordinates of the incident and the date of publication.</p>
Interurban road accidents with casualties in Spain (2016-2021)
<p>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. </p> <p>Data source of each record is the Spanish General Directorate of Traffic (DGT) and each row describes an accident by the following fields:</p> <p>• <strong>secuencial </strong>(int): the unique identifier for an occurrence record in DGT.<br> • <strong>anyo</strong> (int): the four-digit year of the event date.<br> • <strong>mes </strong>(int): the month as integer of the event date (encoded).<br> • <strong>nombre_mes </strong>(str): the month name of the event date (decoded).<br> • <strong>dia_semana </strong>(int): the integer day of the week when the accident occurred (encoded).<br> • <strong>nombre_dia_semana </strong>(str): the name of the day when the accident occurred (decoded).<br> • <strong>hora </strong>(int): the reported hour of the collision in 24-hour notation.<br> • <strong>cod_provincia </strong>(int): the province code from INE where the accident is registered (encoded).<br> • <strong>nombre_provincia </strong>(str): the province name where the accident is registered (decoded).<br> • <strong>cod_municipio </strong>(int): the municipality code from INE where the accident is registered (encoded).<br> • <strong>nombre_codigo_municipio </strong>(str): the municipality name where the accident is registered (decoded).<br> • <strong>isla </strong>(str): an integer value to identify a Spanish island if the accident occurred out of the peninsula (encoded).<br> • <strong>nombre_isla </strong>(str): the island name if applicable to the accident (decoded).<br> • <strong>zona </strong>(int): an integer value to identify type of road (encoded).<br> • <strong>nombre_zona </strong>(str): the name of the road type (decoded).<br> • <strong>zona_agrupada </strong>(int): an integer value to group the road types in urban or interurban (encoded).<br> • <strong>nombre_zona_agrupada </strong>(str): the group name of the road types (decoded).<br> • <strong>carretera</strong> (str): the road attending to the national road numbering system in Spain where the accident is located.<br> • <strong>km </strong>(int): the kilometre point of the road where the accident is located.<br> • <strong>sentido_1f </strong>(int): the vehicle’s direction of traffic reported as integer when the accident occurred (encoded).<br> • <strong>nombre_sentido </strong>(str): the vehicle’s direction of traffic reported when the accident occurred (decoded).<br> • <strong>titularidad_via </strong>(int): the road ownership type as integer (encoded).<br> • <strong>nombre_titularidad_via </strong>(str): the road ownership type description (decoded).<br> • <strong>tipo_via </strong>(int): the type of road as integer attending to the project road classification (encoded).<br> • <strong>nombre_tipo_via </strong>(str): the type of road description attending to the project road classification (decoded).<br> • <strong>tipo_accidente </strong>(int): an integer value to identify the collision type and traffic context (encoded).<br> • nombre_tipo_accidente (str): the description of the collision type and traffic context (decoded).<br> • <strong>total_mu24h </strong>(int): the total number of fatalities registered in the accident, computed to 24 hours.<br> • <strong>total_hg24h </strong>(int): the total number of hospitalised casualties recorded in the accident, counted over 30 days.<br> • <strong>total_hl24h </strong>(int): the total number of non-hospitalised casualties recorded in the accident, computed to 24 hours.<br> • <strong>total_victimas_24h </strong>(int): the total number of casualties (fatalities + hospitalised injured + non-hospitalised injured) recorded in the accident, computed over 24 hours.<br> • <strong>total_mu30df </strong>(int): the total number of fatalities recorded in the accident, computed over 30 days.<br> • <strong>total_hg30df </strong>(int): the total number of hospitalised casualties recorded in the accident, counted over 30 days.<br> • <strong>total_hl30df </strong>(int): the total number of non-hospitalised casualties recorded in the accident, counted over 30 days.<br> • <strong>total_victimas_30df </strong>(int): the total number of casualties (killed + injured in hospital + injured not in hospital) recorded in the accident, counted over 30 days.<br> • <strong>total_vehiculos </strong>(int): the total number of vehicles involved recorded in the accident.<br> • <strong>tot_peat_mu24h </strong>(int): the total number of pedestrian fatalities recorded in the accident, computed over 24 hours.<br> • <strong>tot_bici_mu24h </strong>(int): the total number of cyclists killed recorded in the accident, computed to 24 hours.<br> • <strong>tot_ciclo_mu24h </strong>(int): the total number of scooter riders killed recorded in the accident, computed on a 24-hour basis.<br> • <strong>tot_moto_mu24h </strong>(int): the total number of motorcyclist fatalities recorded in the accident, computed on a 24-hour basis.<br> • <strong>tot_tur_mu24h </strong>(int): the total number of car drivers and passengers killed recorded in the accident, counted to 24 hours.<br> • <strong>tot_furg_mu24h </strong>(int): the total number of van drivers and passengers killed recorded in the accident, counted over 24 hours.<br> • <strong>tot_cam_menos3500_mu24h </strong>(int): the total number of drivers and passengers of trucks ≤ 3,500 kg killed in the accident, counted over 24 hours.<br> • <strong>tot_cam_mas3500_mu24h </strong>(int): the total number of drivers and passengers of trucks > 3,500 kg killed in the accident, counted over 24 hours.<br> • <strong>tot_bus_mu24h </strong>(int): the total number of bus drivers and passengers fatalities recorded in the accident, counted over 24 hours.<br> • <strong>tot_otro_mu24h </strong>(int): the total number of drivers and passengers of vehicles not classified in the above types killed in the accident, counted to 24 hours.<br> • <strong>tot_sinespecif_mu24h </strong>(int): the total number of drivers and passengers of vehicles of unspecified type killed in the accident, counted over 24 hours.<br> • <strong>tot_peat_mu30df </strong>(int): the total number of pedestrian fatalities recorded in the accident, computed to 30 days.<br> • <strong>tot_bici_mu30df </strong>(int): the total number of cyclists killed recorded in the accident, counted over 30 days.<br> • <strong>tot_ciclo_mu30df </strong>(int): the total number of scooted riders killed recorded in the accident, counted over 30 days.<br> • <strong>tot_moto_mu30df </strong>(int): the total number of motorcyclist fatalities recorded in the accident, counted over 30 days.<br> • <strong>tot_tur_mu30df </strong>(int): the total number of drivers and passengers of passenger cars killed in the accident, counted over 30 days. <br> • <strong>tot_furg_mu30df </strong>(int): the total number of van drivers and passengers killed recorded in the accident, counted over 30 days. <br> • <strong>tot_cam_menos3500_mu30df </strong>(int): the total number of drivers and passengers of trucks ≤ 3,500 kg killed in the accident, counted over 30 days.<br> • <strong>tot_cam_mas3500_mu30df </strong>(int): the total number of drivers and passengers of trucks > 3,500 kg killed in the accident, counted over 30 days.<br> • <strong>tot_bus_mu30df </strong>(int): the total number of bus drivers and passengers fatalities recorded in the accident, counted over 30 days.<br> • <strong>tot_otro_mu30df </strong>(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.<br> • <strong>tot_sinespecif_mu30df </strong>(int): the total number of drivers and passengers of unspecified type vehicles killed in the crash, counted over 30 days.<br> • <strong>nudo </strong>(int): an integer to identify whether the collisions occurred in an road junction or not (encoded).<br> • <strong>nombre_nudo </strong>(str): the description to identify whether the collisions occurred in an road junction or not (decoded).<br> • <strong>nudo_info </strong>(int): an integer that represents the type of road junction in which the collision occurred (encoded).<br> • <strong>nombre_nudo_info </strong>(str): the description of the type of road junction in which the collision occurred (decoded).<br> • <strong>carretera_cruce </strong>(str): the road attending to the national road numbering system in Spain where the accident is located.<br> • <strong>priori_norma </strong>(int): an integer to identify if the road junction priority is determined by generic traffic rule (encoded).<br> • <strong>nombre_priori_norma </strong>(str): the text to identify if the road junction priority is determined by generic traffic rule (decoded).<br> • <strong>priori_agente </strong>(int): an integer to identify if the road junction priority is determined by agent (encoded).<br> • <strong>nombre_priori_agente</strong> (str): the text to identify if the road junction priority is determined by agent (decoded).<br> • <strong>priori_semaforo </strong>(int): an integer to identify if the road junction priority is determined by traffic light (encoded).<br> • <strong>nombre_priori_semaforo</strong> (str): the text to identify if the road junction priority is determined by traffic light (decoded).<br> • <strong>priori_vert_stop </strong>(int): an integer to identify if the road junction priority is determined by vertical stop sign (encoded).<br> • <strong>nombre_priori_vert_stop </strong>(str): the text to identify if the road junction priority is determined by vertical stop sign (decoded).<br> • <strong>priori_vert_ceda </strong>(int): an integer to identify if the road junction priority is determined by vertical yield sign (encoded).<br> • <strong>nombre_priori_priori_vert_ceda </strong>(str): the text to identify if the road junction priority is determined by vertical yield sign (decoded).<br> • <strong>priori_horiz_stop </strong>(int): an integer to identify if the road junction priority is determined by horizontal stop signal (encoded).<br> • <strong>nombre_priori_horiz_stop </strong>(str): the text to identify if the road junction priority is determined by horizontal stop signal (decoded).<br> • <strong>priori_horiz_ceda </strong>(int): an integer to identify if the road junction priority is determined by horizontal yield sign (encoded).<br> • <strong>nombre_priori_horiz_ceda </strong>(str): the text to identify if the road junction priority is determined by horizontal yield sign (decoded).<br> • <strong>priori_marcas </strong>(int): an integer to identify if the road junction priority is determined by road markings (encoded).<br> • <strong>nombre_priori_marcas </strong>(str): the text to identify if the road junction priority is determined by road markings (decoded).<br> • <strong>priori_pea_no_elev </strong>(int): an integer to identify if the road junction priority is determined by pedestrian crosswalk (encoded).<br> • <strong>nombre_priori_pea_no_elev </strong>(str): the text to identify if the road junction priority is determined by pedestrian crosswalk (decoded).<br> • <strong>priori_pea_elev </strong>(int): an integer to identify if the road junction priority is determined by raised pedestrian crossing (encoded).<br> • <strong>nombre_priori_pea_elev </strong>(str): the text to identify if the road junction priority is determined by raised pedestrian crossing (decoded).<br> • <strong>priori_marca_ciclos </strong>(int): an integer to identify if the road junction priority is determined by the bicycle crossing road marking (encoded).<br> • <strong>nombre_priori_marca_ciclos </strong>(str): the text to identify if the road junction priority is determined by the bicycle crossing road marking (decoded).<br> • <strong>priori_circunstancial </strong>(int): an integer to identify if the road junction priority is determined by signal used on an circumstantial basis (encoded).<br> • <strong>nombre_priori_circunstancial </strong>(str): the text to identify if the road junction priority is determined by signal used on an circumstantial basis (decoded).<br> • <strong>priori_otra </strong>(int): an integer to identify if the road junction priority is determined by other signalling not considered in the above (encoded).<br> • <strong>nombre_priori_otra </strong>(str): the text to identify if the road junction priority is determined by other signalling not considered in the above (decoded).<br> • <strong>condicion_nivel_circula </strong>(int): an integer to identify the condition of the traffic level at the place and time of the accident attending to DGT classification (encoded).<br> • <strong>nombre_condicion_nivel_circula </strong>(str): the text to identify the condition of the traffic level at the place and time of the accident attending to DGT classification (decoded).<br> • <strong>condicion_firme </strong>(int): an integer to classify the condition of the road surface at the scene of the accident (encoded).<br> • <strong>nombre_condicion_firme </strong>(str): the text to classify the condition of the road surface at the scene of the accident (decoded).<br> • <strong>condicion_iluminacion </strong>(int): an integer to identify the lighting conditions at the place and time of the accident (encoded).<br> • <strong>nombre_condicion_iluminacion </strong>(str): the text to identify the lighting conditions at the place and time of the accident (decoded).<br> • <strong>condicion_meteo </strong>(int): an integer to classify the meteorological conditions at the place and time of the accident (encoded).<br> • <strong>nombre_condicion_meteo </strong>(str): the text to classify the meteorological conditions at the place and time of the accident (decoded).<br> • <strong>condicion_niebla </strong>(int): an integer to identify the meteorological condition with regard to fog at the place and time of the accident (encoded).<br> • <strong>nombre_condicion_niebla </strong>(str): the text to identify the meteorological condition with regard to fog at the place and time of the accident (decoded).<br> • <strong>condicion_viento </strong>(int): an integer to classify the meteorological condition referring to wind at the place and time of the accident (encoded).<br> • <strong>nombre_condicion_viento </strong>(str): the text to classify the meteorological condition referring to wind at the place and time of the accident (decoded).<br> • <strong>visib_restringida_por </strong>(int): an integer to identify the elements affecting visibility at the scene of the accident (encoded).<br> • <strong>nombre_visib_restringida_por </strong>(str): the text to identify the elements affecting visibility at the scene of the accident (decoded).<br> • <strong>acera </strong>(int): an integer to classify the condition of the pavement. Only if the accident involved a pedestrian (encoded).<br> • <strong>nombre_acera </strong>(str): the text to classify the condition of the pavement. Only if the accident involved a pedestrian (decoded).<br> • <strong>trazado_planta </strong>(int): an integer to identify road layout in plan. Only on interurban roads (encoded).<br> • <strong>nombre_trazado_planta </strong>(str): the text to identify road layout in plan. Only on interurban roads (decoded).<br> • <strong>latitud </strong>(float): the latitude of the accident location coordinate in decimal degrees.<br> • <strong>longitud </strong>(float): the length of the accident location coordinate in decimal degrees.<br> • <strong>geom </strong>(geometry): geometry from latitude and longitude position. Developed for this project.</p> <p>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).</p> <p>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.</p>
Data for Accident Severity Prediction Modelling for Indian Highways Case Study
<p>Accident Data: Road accidents data is of Indian Highways sections Pune-Solapur and Bengal (BAEL) Section. For the Pune-Solapur Section of NH-9, which is located between Km.144/400 and Km. 249/000 in the state of Maharashtra, accident dates from 2013 to 2018. For the Six-Laning of Barwa-Adda-Panagarh Section of NH-2, which includes Panagarh Bypass and is located in the States of Jharkhand and West Bengal Stretch, accident dates from 2015 to 2019 for the stretch between km 398.240 and km 521.120. </p> <p>The data is sorted and analyzed using Random Forest Machine Learning for Accident Severity Prediction Modelling.</p> <p>Acknowledgement: We highly acknowledge the two organizations 1. National Highways Authority of India, 2. IL&FS Engineering and Construction Company for making the raw data available.</p> <p>Source: 1. National Highways Authority of India, 2. IL&FS Engineering and Construction Company.</p> <p> </p>
Representation of crowd accidents in popular media
<p>This repository contains results related to the analysis of a corpus of news reports covering the topic of crowd accidents. To facilitate online visualization and offline analysis, the files are organized by assigning a number to each. The number system and the details of each set of files are described as follows:</p> <ul> <li><strong>Class 0</strong> – This contains the same files provided in this repository, but they are organized into folders to make analysis easier. If you intend to analyze the data from our lexical analysis, we suggest using this file since it is better organized and can be directly downloaded.</li> <li><strong>Class 1</strong> – This contains the sources and relevant information for people who are interested in replicating our dataset or accessing the news reports used in our analysis. Please note that due to copyright regulations, the texts cannot be shared. However, you can refer to the links provided in these files to access the news articles and Wikipedia pages. Some links have stopped working during the time we were working on this study, and others may be unreachable in the future.</li> <li><strong>Class 2</strong> – This contains the results from a lexical analysis of the corpus. The HTML page allows you to visualize each result interactively through the online VOSviewer app (you need to download the file and open it using a browser since Zenodo does not recognize this as a link). It is possible that this service (VOSviewer app) may be discontinued at some point in the future. PNG images of lexical maps are, therefore, available for download through the ZIP archive, although they do not allow interactive access. If you plan to read our results using the offline VOSviewer software or perform a more systematic analysis, JSON files are available for each category (time period, geographical area of the reporting institution, and purpose of gathering). The same files can be also find in the ZIP archive in class 0.</li> <li><strong>Class 3</strong> – These are the results of the sentiment analysis. For each report, a single result is generated for the title. However, for the body, the text is divided into parts, which are analyzed independently.</li> <li><strong>Class 4</strong> – These two files contains the corpus of Wikipedia relative to 68 crowd accidents which occurred between 1990 and 2019. The text for all accidents were scraped on October 15th, 2022 (<em>before</em> the tragedy in Itaewon) and on May 25th, 2023 (<em>after</em> the tragedy). Sources relative to the content in Wikipedia are listed in the file contained in Class 1 ("1_list_wiki_report.csv"). More generally, accidents listed on dedicated Wikipedia pages on <a href="https://en.wikipedia.org/wiki/List_of_fatal_crowd_crushes" target="_blank" rel="noopener">https://en.wikipedia.org/wiki/List_of_fatal_crowd_crushes</a> are reported in the corpus provided here (the period 1900-2019 is considered here).</li> </ul> <p>The format of CSV and JSON files should be self-explanatory after reading our publication. For specific questions or queries, please contact one of the authors, and we will try to assist you.</p>
SEAKNOT - SEvere Accident Research and KNOwledge ManagemenT for LWRs
<p>Video presented at the <a href="https://snetp.eu/2023/04/14/read-the-coordinators-hub-day-summary/">SNETP Coordinators’ hub day</a>. This initiative took place in Brussels on March 14th, 2023 as part of the SNETPFORWARD project. The event was co-organized by SNETP. </p>
A consolidated database of police-reported motor vehicle traffic accidents in the United States for actuarial applications
<p>The parameter estimates along with their 90% confidence intervals obtained for the 20 multinomial logistic regressions are shown here in two presentations. In 'Covariate trends' we show the annual trends for the 20 years of data by type of covariate. In 'Covariates magnitude for each year', we show for each year the magnitude that each covariate has in contrast with the other 23 covariates (the intercept is not included due to scaling issues).</p> <p>All parameter estimates and their confidence intervals can be found in a table format in 'allparameters.csv'.</p>
Figure 2 in Epidemiological aspects of scorpionic accidents in a municipality in Brazil's northeastern
Figure 2. Analysis of scorpionic accidents between the years 2008 to 2018 in the neighborhoods of Arapiraca-AL.
Figure 1 in Epidemiological aspects of scorpionic accidents in a municipality in Brazil's northeastern
Figure 1. Percentage of households with inadequate sanitation in neighborhoods in the urban area of Arapiraca-AL, from 2008 to 2018.
List of crowd accidents from 1900 to 2019
<p>This list contains information about crowd accidents that occurred worldwide between 1900 and 2019. The files included in this dataset are described as follows:</p> <ul> <li><strong>accident_data_raw.csv </strong>- CSV file containing information for all accidents as given in the header. Date, country or location are provided using commonly used standard. Information such as fatalities or the number of people injured are reported using expressions found in the employed references (for example "a dozens of people" is given as "dozens"). References are given in the form of text files as reported below.</li> <li><strong>references.zip -</strong> ZIP file containing the references used to collect information on each accident. The date of the accident is used as filename (and reported in the last column of the list above). Sources for each accident are given on each line of the relative text file.</li> <li><strong>accident_data_numeric.csv</strong> - CSV file containing only numeric values corresponding to word expressions. The conversion scheme used here is provided below. Alternative approaches are possible.</li> <li><strong>number_conversion.csv</strong> - CSV file providing the conversion scheme used to convert word expressions into number.</li> </ul>
Mapping of solar panels and Fukushima Daiichi Nuclear Power Plant Accident-associated radioactive waste storage in 2022 and 2023, Fukushima, Japan
<p>The policy of reconstruction after the Fukushima Daiichi Nuclear Plant accident has led to a radical transformation of the landscapes of Fukushima Prefecture especially in two aspects (Asanuma-Brice et al., 2023). The first is related to an extensive decontamination policy which resulted in the removal of more than 13 million m<sup>3</sup> of contaminated soil (MOEJ, 2021). The second is the widespread installation of solar panels, which demonstrate the transition decided by the Prefecture and the inhabitants in terms of energy policy.</p> <p>A systematic mapping of these features was carried out from the satellite imagery of Google Map (2023) within the boundaries of Fukushima Prefecture. The objective was to highlight the evolution of specific land use features that are captured imprecisely by automatic detection mapping. We focused on the main visible change in the landscape in terms of land use since Fukushima Daiichi nuclear accident: contaminated waste disposal areas and solar panel fields. These zones were delineated allowing a calculation of the corresponding surface areas (m<sup>2</sup>).<strong> The dataset is composed of 4 shapefile layers: contaminated waste deposits in 2022 and 2023, solar panels in 2022 and 2023. For the year 2022 the last update was conducted in July 2022 and for the year 2023 the last update took place in March 2023.</strong></p> <p>As the land use is in constant and rapid transition (Asanuma-Brice, 2021), we considered as contaminated waste deposits, the permanent storage centers as well as the sites where there are still bags of contaminated waste in varying numbers, knowing that they will be removed and stored on other dedicated sites (Evrard et al., 2019). This choice was made to potentially identify, when the map was updated, the future uses of the land where this waste was stored temporarily.</p> <p>This dataset is part of a larger project that aims to provide the community with an interactive tool (https://mitatelab.cnrs.fr/mitate-labs-map-of-solar-panel-and-contaminated-wasted-land/) that makes available various types of information essential to the analysis of the reconstruction, such as: the delineation of the evacuated zone (which evolved throughout time), the delineation of the municipality boundaries affected by the reconstruction policy, the main services found in these localities, the location of the memorials of the disaster in the zone, as well as the geo-localization of the soil/sediment samples collected by other Mitate lab researchers in order to investigate the redistribution of radionuclides in the environment (Evrard et al., 2021).</p>
Spatial datasets associated with decontamination and remediation operations following the Fukushima nuclear accident, Japan (2011–2023)
<p>At the onset of the full reopening in Spring 2023 of the Difficult-to-Return Zone of Northeastern Japan following the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident that took place in March 2011, several spatial layers were regrouped and compiled to facilitate environmental studies dealing with the redistribution of radiocesium fallout across landscapes.</p> <p><strong>The current dataset is composed of 23 shapefiles including those of the delineations of different spatial zones (Intensive Contamination Survey Areas – ICAs, Special Decontamination Zones – SDZ, Difficult-to-Return Zone –</strong> <strong>DTRZ, and FNDPP location) (Evrard et al. 2019), municipalities where mushroom consumption restrictions were enforced (restricted and partially lifted restrictions), river hydrographic networks and their respective drainage areas (Mano, Niida, Ota, Takase, and Ukedo), dam reservoirs and drainage areas (Mano, Ogaki, Takanokura, and Yokokawa), multiple administrative delineations in Japan (whole Japan administrative boundaries, Prefectures, and municipalities) (GIS, 2016), and one raster file of the reconstruction of initial <sup>137</sup>Cs fallout across eastern Japan (from Kato et al., 2019).</strong></p> <p><strong>The current dataset provides a support to a publication submitted to the SOIL journal:<br></strong></p> <div> <div><strong>Evrard, O., Chalaux-Clergue, T., Chaboche, P.-A., Wakiyama, Y., and Thiry Y. (2023). Research and Management Challenges Following Soil and Landscape Decontamination at the Onset of the Reopening of the Difficult-To-Return Zone, Fukushima (Japan)’. <em>SOIL</em> 9: 479–97. <a href="https://doi.org/10.5194/soil-9-479-2023">https://doi.org/10.5194/soil-9-479-2023</a>. </strong></div> <div> </div> </div> <p>All map processing was carried out using QGIS 3.26.0 (QGIS, 2022) and under the EPSG:WGS 84 projection system.</p> <p>The <sup>137</sup>Cs fallout raster (in Bq m<sup>-2</sup>, decay-corrected to July 2011) was generated from the point grid of Kato et al. (2019). A total of 126 tiles (0.25 x 0.25 degree) were generated by Inverse Distance Weighted (IDW) interpolation using the '<em>IDW interpolation'</em> tool with the following settings: distance coefficient P = 1.0 and pixel size (x and y) = 0.0015 degree. Tiles were then merged into a single tile using the raster<em> 'Merge'</em> tool. The initial point grid footprint was manually delineated to define the spatial applicability zone of the airborne survey. A buffer zone corresponding to half plus 10% of the longest distance between two airborne points (x = 0.002, y = 0.003), i.e. 0.0017 degree, was generated using the '<em>buffer'</em> tool. The single tile was then cut according to the footprint of the buffer zone using the <em>'clip a raster by a mask layer'</em> tool. A <em>single-band pseudo-colour </em>scale is provided and displays pixels with a value above 1000 Bq m<sup>-2</sup> (eq. global background).</p>
A Tagged Traffic Accident Dataset for Machine Learning
<p>This dataset contains tagged accident data and is provided for reproducibility for our journal paper </p> <p><strong>Pablo Moriano, Andy Berres, Haowen Xu, Jibonananda Sanyal. “Spatiotemporal Features of Traffic Help Reduce Automatic Accident Detection Time.” <em>Expert Systems with Applications</em> 244 (2024): 122813. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.eswa.2023.122813" target="_blank" rel="noopener">https://doi.org/10.1016/j.eswa.2023.122813</a></strong></p> <p>The accompanying Data in Brief publication discusses the methodology behind the creation of these data.</p> <p><strong>Berres, Andy, Pablo Moriano, Haowen Xu, Sarah Tennille, Lee Smith, Jonathan Storey, and Jibonananda Sanyal. "A Traffic Accident Dataset for Chattanooga, Tennessee." <em>Data in Brief</em> (2024): 110675.</strong></p> <p> </p> <p>The zip folder <strong><em>annotatedData.zip</em></strong> contains two subfolders: <strong><em>allData</em></strong> and <strong><em>bestData</em></strong>. The <em>bestData</em> folder contains all data for which a full neighborhood of five sensors upstream and five sensors downstream is available, whereas <em>allData</em> includes everything from <em>bestData</em> as well as data with a smaller number of neighboring sensors. Each folder contains one subfolder called <strong><em>accidents</em></strong> and one subfolder called <strong><em>non-accidents</em></strong>. The <em>accidents</em> folder contains one file per accident. The <em>non-accidents</em> folder contains files for the same location, day of the week and time as a corresponding accident, for each week during which there was no accident impact on the traffic.</p> <p>The file names in both folders are formatted as follows: <strong>yyyy-mm-dd-hhmm-rrrrrXaaa.a.csv</strong>, consisting of date (yyyy-mm-dd), time (hhmm in 24-hour format), and sensor name (rrrrrXaaa.a), which consists of road name (rrrrr; 5 alphanumerical characters), heading (X), and mile marker (aaa.a). For example, the file <em>2020-11-03-1611-00I24W182.8.csv </em> contains data for an accident which occurred at 4:11 p.m. on November 3, 2020 on I-24 Westbound near the radar sensor at mile marker 182.8.</p> <p>The content of each CSV file is a timeseries of radar data beginning 15 minutes prior to the reported incident and ending 15 minutes after the reported incident. It also contains metadata, such as the accident type, etc. Each CSV file contains the following columns:</p> <ul> <li><strong>incident at sensor(i)</strong>: 1 for yes (<em>accidents</em> folder), 0 for no (<em>non-accidents</em> folder)</li> <li><strong>road</strong>: road name with heading, e.g. 00I24E</li> <li><strong>mile</strong>: mile marker of nearest radar sensor, e.g. 182.8</li> <li><strong>type</strong>: accident type, e.g. “Prop Damage (over)” for property damage exceeding a certain threshold. For non-accidents, the type is given as “None”.</li> <li><strong>date</strong>: date of the data sample. For accidents, this is the date on which the accident occurred. For non-accidents, this is the date for which the non-accident data sample is collected.</li> <li><strong>incident_time</strong>: time the reference accident was reported in hh:mm. This is the time which is provided in E-TRIMS as the time the 911 call was made.</li> <li><strong>incident_hour</strong>: just the hour from the incident_time, in integer format.</li> <li><strong>data_time</strong>: timestamp for the timeseries contained in the file in hh:mm:ss format. The timeseries consists of 30 second timesteps.</li> <li><strong>weather</strong>: weather during <em>data_time</em>, based on data collected from NASA POWER. We used dry bulb temperature (°C), precipitation (mm/h), and wind speed (m/s) from the raw NASA POWER data to produce the classifications of <em>rain</em> (at least 1mm precipitation and temperatures above 2°C), <em>snow </em>(at least 1mm precipitation and temperatures at or below 2°C), and <em>wind</em> (wind speeds over 30 mph or 13.5 m/s). If there were no inclement weather conditions, we set the category to <em>“--"</em>.</li> <li><strong>light</strong>: light conditions during data_time. To produce this field, we collected sunrise, sunset, civil twilight start and civil twilight end times from <a href="https://sunrise-sunset.org">https://sunrise-sunset.org</a>, and derived the categories dawn, daylight, dusk, and dark using these start and end times.</li> <li>The last 33 columns contain radar data for the 11 sensors surrounding the accident or non-accident. For each sensor, we collected <em>speed</em> (mean over 30-second interval in miles per hour, or empty if no vehicles passed), <em>volume</em> (count of all vehicles passing during 30-second interval), and <em>occupancy</em> (mean % of occupancy over 30-second interval). These three variables are grouped in triples, of <strong>speed (k), volume (k), occupancy (k)</strong>, where <em>k</em> indicates the sensor number relative to the closest sensor <em>i</em> to the incident, <em>k<i</em> indicate upstream sensors and <em>k>i</em> indicate downstream sensors. For example, <strong>speed (i-5)</strong> refers to the mean speed at the sensor which is 5 hops upstream from the accident, and <strong>volume(i+1) </strong>refers to the number of vehicles at the sensor immediately downstream from the accident.</li> </ul> <p>The folder <strong><em>metaData.zip</em></strong> contains the following files:</p> <ul> <li><strong>Accidents.csv</strong>: cleaned-up accidents file with all accidents which happened on Chattanooga area highways between November 1, 2020 and April 29, 2021. We have removed accidents which happened on non-highway roads, and we have corrected the timestamps (which were in 12-hour format but missing a.m./p.m. markers) by cross-referencing light and weather conditions.</li> <li><strong>WeatherDict</strong><strong>.json:</strong> a dictionary containing the weather data synthesized from NASA POWER.</li> <li><strong>LightDict.json</strong>: a dictionary containing the light data synthesized from Sunrise-and-Sunset.</li> <li><strong>SensorTopology.csv</strong>: neighborhood information for each radar sensor in the Chattanooga area.</li> <li><strong>SensorZones.geojson</strong>: polygons used to determine the nearest radar sensor for each accident location. Each polygon is tagged with the corresponding radar sensor’s name.</li> </ul>
L'Accident De Carrière, Henri Bouchard (1906)
Scan of the sculpture "L'Accident De Carrière"made in 1906 by Henri Bouchard, in the park Montsouris since 1910. 189 shots with my OnePlus6, process with Reality Capture in 00h:59m:40s for 80M triangle high details. 100K triangles model. Source: Objaverse 1.0 / Sketchfab
Air accidents
<p>Air accidents simulated data</p>
Scientific Improvement on Social Understanding of Tritium, Ten Years After the Fukushima Nuclear Accident
<p>The present work deals with a scientific improvement of social education or understanding on radioactive tritium by laypeople, which is even possible after ten years of Fukushima nuclear accident. This work is actually motivated since the normal way of tritium dilution into ocean within the standard regulatory concentration had been hampered firstly by Korean Government in May 2011. In addition, thereafter the tritium issue has become one of the difficult issues to solve urgently, but without any success until 2020. The answer was the huge number of storage tanks with tritium in water.</p> <p>The dialogue meetings between the fishermen and administrative stakeholders were repeatedly held, but without success to overcome the foreseen hoax or rumors leading to the social stigma. Heretofore, we have encountered once a hope for political decision in August 2020, when the new Cabinet was then elected. But unfortunately, the new Minister of the Ministry was unable to make a decision with a confidence, which must be actually a hard issue even as a tangible scientific knowledge. Actually no one has witnessed the generation of tritium.</p> <p>Scientific visualization with nuclear modeling of tritium generation, that is generated in atmospheric nuclear spallation reaction by cosmic rays, has been by no way sufficient until now. In order to overcome the above-mentioned social difficulty on educational facet of tritium as a tangible science, I have performed a Monte-Carlo simulation so as to reproduce the measured value of tritium concentration and its generation rate in the atmosphere. The result was in a reasonable agreement with the measurement. On top of this, according to this simulation, several new features were predicted with new observables on tritium generation. Once this prediction is confirmed by another experiment, the social trust on scientific knowledge on tritium will be shared with laypeople in society, not to mention the fishermen in Fukushima.</p> <p>In parallel, I have performed a pedagogical “Fermi estimate” on possible separation of tritium by a centrifuge apparatus, which is possible and rather easily demonstrated in laboratory. This proposal seems likewise effective to transform the elusive tritium into a tangible substance in an educational and scientific theory and experiment.</p> <p>In the present work, I concentrate on the educational aspect in illuminating the difficulty of tritium understanding as a social difficulty of radiation protection.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research 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.
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.
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.
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.
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.