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Neritina snails upstream migrations at the intersection of Rio Mameyes with road PR Route 3 (bridge 1771)
This data set includes N. virginea densities and sizes from two channels in lower Rio Mameyes under PR Route 3 bridge during the upstream migration season Aug-Dec 2000. Microhabitat use (near-bed water velocities and depth) within both channels is also included. Massive migrations in long trails occurring on the sloped concrete embankment of the main channel were also documented during 99 weeks. Individual size from migratory aggregations was measured during selected dates. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Year 2014, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth in the upper Parker River Estuary near Middle Road Bridge, Newbury, MA.
Year 2014, water quality sonde data.15 minute readings of water column temperature, salinity, oxygen and depth in the upper Parker River Estuary near Middle Rd Bridge, Newbury, MA
Year 2015, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth in the upper Parker River Estuary near Middle Road Bridge, Newbury, MA.
Year 2015, water quality sonde data.15 minute readings of water column temperature, salinity, oxygen and depth in the upper Parker River Estuary near Middle Rd Bridge, Newbury, MA
Year 2016, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth in the upper Parker River Estuary near Middle Road Bridge, Newbury, MA.
Year 2016, water quality sonde data.15 minute readings of water column temperature, salinity, oxygen and depth in the upper Parker River Estuary near Middle Rd Bridge, Newbury, MA
Year 2017, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth in the upper Parker River Estuary near Middle Road Bridge, Newbury, MA.
Year 2017, water quality sonde data.15 minute readings of water column temperature, salinity, oxygen and depth in the upper Parker River Estuary near Middle Rd Bridge, Newbury, MA
Year 2018, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth in the upper Parker River Estuary near Middle Road Bridge, Newbury, MA.
Year 2018, water quality sonde data.15 minute readings of water column temperature, salinity, oxygen and depth in the upper Parker River Estuary near Middle Rd Bridge, Newbury, MA
Year 2019, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth in the upper Parker River Estuary near Middle Road Bridge, Newbury, MA.
Year 2019, water quality sonde data.15 minute readings of water column temperature, salinity, oxygen and depth in the upper Parker River Estuary near Middle Rd Bridge, Newbury, MA
SBC LTER: Land: Hydrology: Stream discharge and associated parameters at Tecolotito Creek, Hollister Road (TE03)
Stream Discharge and water temperature were collected with a Solinst Model 3001 LT Levelogger at Tecolotito Creek, Hollister Road in the Santa Barbara coastal area (site ID: TE03). Data are reported hourly. Stage values were converted to discharge using a rating curve developed with stream channel cross-sections, roughness estimates and the HEC-RAS model.
Road siting experiment
<p>This dataset comes from an experiment performed at the Italian National Metrology Institute (INRiM) in the framework of EMPIR project MeteoMet 2. The purpose of the experiment was to evaluate the effect of the presence of a road in near-surface temperature data. The dataset is composed by 2 files, with identical structure and datafields.</p> <p>The experiment was laid out as a series of 7 measurement points, at progressively longer distances from a local 2-laned road in the countryside around Turin, Italy. The header of the files is here described:</p> <p>"Data e ora": Date and time</p> <p>TE1101: air temperature sensor #1, distance from the road= 1 m</p> <p>TE1102: air temperature sensor #2, distance from the road= 1 m</p> <p>TE1201: air temperature sensor, distance from the road= 5 m</p> <p>TE1301: air temperature sensor, distance from the road= 10 m</p> <p>TE1401: air temperature sensor, distance from the road= 20 m</p> <p>TE1501: air temperature sensor, distance from the road= 30 m</p> <p>TE1601: air temperature sensor, distance from the road= 50 m</p> <p>TE1701: air temperature sensor, distance from the road= 100 m</p> <p> MT1302: relative humidity sensor, distance from the road= 10 m</p> <p>MT1502: relative humidity sensor, distance from the road= 30 m</p> <p>XM1505: solar radiation, distance from the road= 30 m</p> <p>ZT1503: wind direction, distance from the road= 30 m</p> <p>ST1504: wind speed, distance from the road= 30 m</p> <p> </p> <p>Road_siting_experiment_INRIM.csv is the data collected during the experiment, with measurement points located at the distances indicated in the previous list.</p> <p>Road_siting_colocation_INRiM.csv is the data collected during a second phase of the experiment, when the sensors had all been rearranged in the same spot (co-location) in order to evaluate the behaviour of the sensors while exposed to the same environmental conditions.</p>
Quantitative Content Analysis Data for Hand Labeling Road Surface Conditions in New York State Department of Transportation Camera Images
<p><strong>Foundational Codebook and Data: </strong></p> <p>Traffic camera images from the New York State Department of Transportation (511ny.org) are used to create a hand-labeled dataset of images classified into to one of six road surface conditions: 1) severe snow, 2) snow, 3) wet, 4) dry, 5) poor visibility, or 6) obstructed. Six labelers (authors Sutter, Wirz, Przybylo, Cains, Radford, and Evans) went through a series of four labeling trials where reliability across all six labelers were assessed using the Krippendorff’s alpha (KA) metric (Krippendorff, 2007). The online tool by Dr. Freelon (Freelon, 2013; Freelon, 2010) was used to calculate reliability metrics after each trial, and the group achieved inter-coder reliability with KA of 0.888 on the 4th trial. This process is known as quantitative content analysis, and three pieces of data used in this process are shared, including: 1) a PDF of the codebook which serves as a set of rules for labeling images, 2) images from each of the four labeling trials, including the use of New York State Mesonet weather observation data (Brotzge et al., 2020), and 3) an Excel spreadsheet including the calculated inter-coder reliability (ICR) metrics and other summaries used to asses reliability after each trial. The data are included in NYSDOT_quantitative_content_analysis.zip.</p> <p>The broader purpose of this work is that the six human labelers, after achieving inter-coder reliability, can then label large sets of images independently, each contributing to the creation of larger labeled dataset used for training supervised machine learning models to predict road surface conditions from camera images. The xCITE lab (xCITE, 2023) is used to store camera images from 511ny.org, and the lab provides computing resources for training machine learning models.</p> <p><strong>Obstructed Class Variation: </strong></p> <p>There are many applications for labeling roadside camera images, and as a variation of the foundational codebook, an addendum codebook provides another version of labeling the obstructed class. Specifically, this variation prioritizes labeling an image as “obstructed” only in extreme circumstances where there is a camera- or image- specific problem that prevents the assessment of any road surfaces. For labelers who want to use this version of the obstructed class (in this document) and also the other five weather-related classes (in the foundational codebook), the guidance is to use both documents in tandem, making sure to use the obstructed rules/definitions in this document while disregarding the obstructed rules/definitions in the foundational codebook. Alternatively, this codebook may be used alone in applications where the goal is to solely classify obstructed vs not obstructed. To ensure reliability and quality of this variation, quantitative content analysis was conducted on this addendum codebook, just as it was for the foundational codebook. Two labelers were tested with a sample of 30 images and achieved inter-coder reliability with Krippendorff's Alpha of 0.934 after one trial. The data, including the addendum codebook and labeling trial data (images and results) are included in ObstructedVariation_quantitative_content_analysis.zip.</p> <p>This material is based upon work supported by the U.S. National Science Foundation under Grant No. RISE-2019758.</p>
The role of plants and fibres in modelling monumental terracruda sculptures of the Silk Roads - Dataset
<p>Phytoliths and fibre datasets associated with the publication:</p> <p>Title: The role of plants and fibres in modelling monumental <em>terracruda</em> sculptures of the Silk Roads: archaeobotanical analyses from the Buddhists sites of Tepe-Narenj and Qol-e-tut (Kabul, Afghanistan)</p> <p>Authors: Mònica López-Prat, Carla Lancelotti, Gema Campo-Francés, Sudipa Ray Bandyopadhyay, Begoña Carrascosa, Noor Agha Noori, Alessandra Pecci, José Simón-Cortés & Domenico Miriello</p> <p>Journal: Heritage Science</p>
On-road traffic emission over megacity Delhi
<p>This dataset presents an estimate of hourly gridded on-road traffic exhaust emission of PME, BC, OM, CO, NOx, VOC, NH3, N2O and CH4, for the megacity Delhi (National Capital Territory of Delhi) for 2018 at a spatial resolution of 100m×100m. This dataset is presented as a netDCF covering the rectangular domain around National Capital Territory (NCT) of Delhi. </p>
IN02075 Naksal Road Inscription
<p>Gnoli, Raniero. <em>Nepalese Inscriptions in Gupta Characters</em>. Roma: Is. M. E. O. 1956. Plate 68, Inscription 69, p. 94.</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>
UAV-based imagery for road damage detection
<p>This dataset contains a set of annotated examples of damage present to the road, obtained via an UAV in Poznań, Poland. The imagery was obtained by flying a UAV over a set of roads at approximately 70m above ground level, and further creating an ortophotomosaics of three different pieces of road. Lastly, the ortophotomosaics were patched into non-overlapping patches of 512x512 pixel size, and each patch was manually labeled with a set of objects and classes proposed in the UAPD dataset (https://doi.org/10.1016/j.autcon.2021.103991, https://github.com/tantantetetao/UAPD-Pavement-Distress-Dataset).</p> <p>Patches without a significant presence of the road-like objects were discarded, and the data includes:</p> <ul> <li>ortophotomosaics -- a set of three original ortophotomosaics with ground sampling distance of 2.54 cm/pixel in .tif format,</li> <li>labeled patches -- a set of 99 patches containing road with annotations in both PASCAL VOC (.xml) and YOLO (.txt) formats</li> </ul> <p>Lastly, this data has been used as a holdout set in a Master's thesis conducted at Poznań University of Technology.</p>
IN02075 Naksal Road Inscription. Sanskrit XML file, draft epidoc edition
<p>IN02075 Naksal Road Inscription. Sanskrit XML file (without metadata). Draft epidoc edition to be incorporated into 'Siddham' archive</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>
Wildlife–vehicle collisions (WVC) on interurban roads in Spain (2016-2021)
<p>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 <strong>contact me and I will forward it to you</strong>. </p> <p>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:</p> <p>• <strong>id_num </strong>(int8): the unique identifier for an accident.<br> • <strong>ind_accda </strong>(int8): a binary variable for property damages involved or not (encoded).<br> • <strong>nombre_ind_accd </strong>(str): a statement for property damages involved or not (decoded).<br> • <strong>ind_acciv </strong>(int8): a binary variable for personal damages involved or not (encoded).<br> • <strong>nombre_ind_acciv </strong>(str): a statement for personal damages involved or not (decoded).<br> • <strong>total_mu30df </strong>(int8): the total number of deaths from the accident.<br> • <strong>total_hg30df </strong>(int8): the total number of injured with hospitalization from the accident.<br> • <strong>total_hl30df </strong>(int8): the total number of injured without hospitalization from the accident.<br> • <strong>fecha_accidente </strong>(date): the reported date of the collision, following ISO 8601 date-time standard. <br> • <strong>hora_accidente </strong>(str): the reported hour of the collision in 24-hour notation. <br> • <strong>mes_1f </strong>(int8): 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>anyo </strong>(int8): the four-digit year of the event date.<br> • <strong>ccaa_1f </strong>(int8): the autonomous region code from INE where accident is registered (encoded).<br> • <strong>nombre_ccaa </strong>(str): the name of the autonomous region where accident is registered (decoded).<br> • <strong>provincia_1f </strong>(int8): 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>(int8): the municipality code from INE where the accident is registered (encoded).<br> • <strong>nombre_municipio </strong>(str): the municipality name where the accident is registered (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>(float): the kilometre point of the road where the accident is located.<br> • <strong>sentido_1f </strong>(int8): 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>tipo_via_3f </strong>(int8): 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>titularidad_via_2f </strong>(int8): the road ownership type as integer (encoded).<br> • <strong>nombre_titularidad_via </strong>(str): the road ownership type description (decoded).<br> • <strong>tipo_animal_1f </strong>(int8): the animal species involved in the accident as integer (encoded).<br> • <strong>nombre_tipo_animal_1f </strong>(str): the animal species name involved in the accident (decoded).<br> • <strong>tipo_animal_2f </strong>(int8): the reported animal type of breeding as integer (encoded).<br> • <strong>nombre_tipo_animal_2f </strong>(str): the type of animal breeding description (decoded).<br> • <strong>longitud </strong>(float): the length of the accident location coordinate in decimal degrees.<br> • <strong>latitud </strong>(float): the latitude of the accident location coordinate in decimal degrees.<br> • <strong>geom </strong>(geometry): geometry from latitude and longitude position. Developed for this project.<br> • <strong>dia_semana </strong>(int8): 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>tipo_dia </strong>(str): the category name of the day type to separate weekday from weekend (decoded).<br> • <strong>parte_dia </strong>(str): the part name of the day when the accident is registered including day, night and the transitions.<br> • <strong>luna </strong>(int8): the portion of illuminated moon surface represented as an integer value from 0 to 100.<br> • <strong>prec </strong>(float): the daily rainfall measurement of the event day based on pluviometric days.<br> • <strong>tmin </strong>(float): the minimum temperature in Celsius of the event day.<br> • <strong>tmed </strong>(float): the average temperature in Celsius of the event day.<br> • <strong>tmax</strong> (float): the maximum temperature in Celsius of the event day.<br> • <strong>sol </strong>(float): the accumulated sun hours of the event day.<br> • <strong>uso_suelo </strong>(str): the main land usage of the accident area.<br> • <strong>altitud </strong>(float): the altitude in meters above sea level.<br> • <strong>pendiente </strong>(float): the slope median value of a 30 meters buffer around the accident location.<br> • <strong>taxonkey</strong> (str): a taxon key from the GBIF backbone.<br> • <strong>imd_total </strong>(float): the average daily traffic intensity of the accident year.<br> • <strong>maxspeed </strong>(int): the maximum speed of the road section where the reported collision.</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 wildlife–vehicle collision analysis and the <a href="https://github.com/alba620/analisis-prediccion-accidentes-trafico-animales">code repository</a> is available on GitHub.</p>
The modelling pastes of the monumental terracruda sculpture of the Silk Roads: archaeometric study of the Tepe Narenj and Qol-e-tut examples (Kabul, Afghanistan)
<p>These data are the results of the mineralogical, petrographic and chemical study of different archaeological samples related to terracruda sculptures and other elements that were part of the architectural decoration of the Buddhist sites of Tepe Narenj and Qol-e-tut (Kabul, Afghanistan - 5th to 11th centuries CE). The main objective of the study was to characterize the samples using an archaeometric approach. The study helped to better understand the materials involved in the modelling of Afghan sculptures and their processing, such as the different nature of the clay layers and the finishing "stucco" coating. The results further indicate that similarities exist among the manufacturing process of the studied samples and that used today by an ancient caste of clay-artists in West Bengal (India), suggesting the existence of a continuous technological tradition that deserves to be further explored in the future. </p> <p><strong>Supplementary materials – S1. </strong>Bengali artist adding <em>bele-mati</em> to model the final shape of a terracruda sculpture (Kumortuli, Kolkata, 2019).</p> <p><strong>Supplementary materials - S2. </strong>Pictures of the sculpture samples</p> <p><strong>Supplementary materials - S3. </strong>Pictures of the relief and mural painting samples</p> <p><strong>Supplementary materials - S4</strong>. Pictures of the wall samples</p> <p><strong>Table 1. </strong>Samples analyzed, location, subsamples ID and description.</p> <p><strong>Table 2. </strong>Results of petrographic and mineralogic analyses.</p> <p><strong>Table 3</strong>. List of the main components for each sample [FA=fatty acid; C<sub>18:0</sub> = stearic acid; C<sub>18:1</sub> = oleic acid; C<sub>16:0</sub> = palmitic acid; S<sub>27</sub>= cholesterol; βS= β-sitosterol, TA= tartaric acid; N= none; Y= yes].</p> <p><strong>Figure 2</strong>. Microphotographs in thin section under crossed nicols. (a) Phyllite in sample QT3. (b) Quartzite in sample TN4. (c) Cryptocrystalline limestone in TN1. (d) Muscovite (Ms) and calcite (Cal) in TN2. (e) Amphiboles (Amp) in QT3. (f) Microcline in QT1.</p> <p><strong>Figure 3. </strong>Powder X-ray diffraction patterns from untreated clay samples (on the right) and samples after ethylene-glycol treatment (on the left) Mnt: montmorillonite.</p> <p><strong>Figure 4. </strong>Microphoto by EMPA. (a) Particular of sample TN2 in which the phyllosilicate origin of the matrix is evident. (b), (c) and (d) Cuticles in sample TN4. (e) Pollen in sample TN4. (f) Oogonia in sample QT4.</p> <p><strong>Figure 5</strong>. (a) Different layers in QT2, scan of the thin section under parallel nicols. (b) Stucco layer in TN1_a, arrows indicate the voids left by the fibers, micro photo in thin section under parallel nicols. (c) Different layer in TN2, scan of thin section under crossed nicols. (d) TN2_b layer where the red colour of the clay is evident, micro photo in thin section under parallel nicols. (e) Sample TN5, micro photo in thin section under parallel nicols. (f) SEM Image with particular of a fiber in TN5.</p> <p><strong>Figure 6.</strong> Remains and imprints of a fabric between the stucco layer and the underlying earthen mortar in sample QT2. Left: OM Image 8x LEICA-EZ4W. Right: macroscopic view of a fragment of the internal part of QT2.</p> <p><strong>Figure 7</strong>. Microphoto by EMPA. (a) Crushed pure gypsum in TN1_a. (b) Crushed gypsum in TN5 (yellow arrows) and amorphous areas (red arrows) corresponding to the organic matter.</p> <p><strong>Figure 8</strong>. Microphotographs in thin section under Epifluorescence microscopy. (a) Stucco layer TN1_a. (b) Sample TN5. (c) Layers b and c in sample TN2. (d) Clay layer TN1_b </p> <p><strong>Figure 9.</strong> Partial gas chromatograms from exctracts (i) of samples (a) QT2_c, (b) QT2_b and (c) QT2_a. [Cn:0 TMS= trimethylsilylated fatty acids with a specific number (n) of carbons, S<sub>27</sub>= cholesterol, β= β-sitosterol, dots= phthalates and IS= internal standard]. </p> <p><strong>Figure 10</strong>. (a) Raman spectra of the blue pigment analyzed in sample QT1 and of lazurite present in the RRUFF database (RRUFF ID: R040023). (b) Raman spectra of the red pigment analyzed in sample QT1 and of hematite present in the Unical database. (c) Raman spectra of the QT1 binder and of calcite present in the Unical database. (d) Raman spectrum of the red pigment analyzed in sample QT2 and comparison with the hematite and gypsum spectra present in the Unical database.</p> <p><em>This work has been supported by National Geographic Society (EC-59568C-19) and is part of results of the doctoral research of M. López-Prat and the Juan de la Cierva contract (FJC2021-046803-I). The results have been obtained in the framework of the activities of the DIBEST department of the University of Calabria, the ERAAUB (a recognized and financed research group of the Generalitat de Catalunya - 2021 SGR 00696), the Institut d’Arqueologia of the Universitat de Barcelona, the Conservació-Restauració del Patrimoni research group (a recognized research group of the Generalitat de Catalunya - 2021 SGR 00089),and the CASEs research group (a recognized and financed research group of the Generalitat de Catalunya – 2021 SGR-00950). </em></p>
Life cycle inventories for on-road vehicles
<p>Life cycle inventory datasets for current on-road vehicles in Switzerland and Europe. These datasets can be consumed by brightway2 (https://brightway.dev/) and Simapro 9.x (https://simapro.com/), and link to either ecoinvent 3.6 (cut-off), ecoinvent 3.7.1 (cut-off), ecoinvent 3.8 (cut-off), ecoinvent 3.9 (cut-off) or UVEK:2018.</p> <p>These datasets can be cited as:</p> <p>Sacchi, R., Bauer, C. (2023) Life cycle inventories for on-road vehicles. Paul Scherrer Institut, Villigen, Switzerland.</p>
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