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21 results for “Maritime data”
SeaLiT Knowledge Graphs - Maritime History Data in RDF using a CIDOC-CRM extension (SeaLiT Ontology)
<p><strong>SeaLiT Knowledge Graphs</strong> is an RDF dataset of maritime history data that has been transcribed (and then transformed) from original archival sources in the context of the <a href="http://www.sealitproject.eu/">SeaLiT Project</a> (Seafaring Lives in Transition, Mediterranean Maritime Labour and Shipping, 1850s-1920s). The underlying data model is the <a href="https://zenodo.org/record/5964240">SeaLiT Ontology</a>, an extension of the ISO standard <strong>CIDOC-CRM</strong> (ISO 21127:2014) for the modelling and integration of maritime history information. </p> <p>The knowledge graphs integrate data of totally 16 different types of archival sources:</p> <ul> <li>Crew Lists <ul> <li>Crew and displacement list (Roll)</li> <li>Crew List (Ruoli di Equipaggio)</li> <li>General Spanish Crew List</li> </ul> </li> <li>Registers / Lists <ul> <li>Students Register</li> <li>Civil Register</li> <li>Register of Maritime Personnel</li> <li>Register of Maritime Workers (Matricole della gente di mare)</li> <li>Sailors Register (Libro de registro de marineros)</li> <li>Naval Ship Register List</li> <li>Seagoing Personnel</li> <li>Lists of ships</li> </ul> </li> <li>Censuses <ul> <li>Census La Ciotat</li> <li>First National all-Russian Census of the Russian Empire</li> </ul> </li> <li>Payrolls <ul> <li>Payrolls of private archives and libraries in Greece</li> <li>Payrolls of Russian Steam Navigation and Trading Company</li> </ul> </li> <li>Employment records <ul> <li>Shipyards of Messageries Maritimes, La Ciotat</li> </ul> </li> </ul> <p>More information about the archival sources are available through the <a href="https://sealitproject.eu/dictionary-of-source-types-list">SeaLiT website</a>. Data exploration applications over these sources are also publicly available (<a href="https://catalogues.sealitproject.eu/">SeaLiT Catalogues</a>, <a href="http://rs.sealitproject.eu/">SeaLiT ResearchSpace</a>). </p> <p>Data from these archival sources has been transcribed in tabular form and then curated by historians of SeaLiT using the <a href="https://www.ics.forth.gr/isl/fast-cat">FAST CAT</a> system. The transcripts (records), together with the curated vocabulary terms and entity instances (ships, persons, locations, organizations), are then transformed to RDF using the SeaLiT Ontology as the target (domain) model. To this end, the corresponding schema mappings between the original schemata and the ontology were defined using the <a href="https://github.com/isl/x3ml">X3ML</a> mapping definition language, that were subsequently used for delivering the RDF datasets. </p> <p>More information about the FAST CAT system and the data transcription, curation and transformation processes can be found in the following paper:</p> <blockquote> <p>P. Fafalios, K. Petrakis, G. Samaritakis, K. Doerr, A. Kritsotaki, Y. Tzitzikas, M. Doerr, "FAST CAT: Collaborative Data Entry and Curation for Semantic Interoperability in Digital Humanities", ACM Journal on Computing and Cultural Heritage, 2021. <a href="https://doi.org/10.1145/3461460">https://doi.org/10.1145/3461460</a> [<a href="http://users.ics.forth.gr/~fafalios/files/pubs/fafaliosJOCCH2021.pdf">pdf</a>, <a href="http://users.ics.forth.gr/~fafalios/files/bibs/fafaliosJOCCH2021.bib">bib</a>]</p> </blockquote> <p>The RDF dataset is provided as a set of TriG files per record per archival source. For each record, the dataset provides: i) one trig file for the record's data (<em>records.trig</em>), ii) one trig file for the record's (curated) vocabulary terms (<em>vocabularies.trig</em>), and iii) four trig files for the record's (curated) entity instances (<em>ships.trig, persons.trig, persons.trig, organizations.trig</em>).</p> <p>We also provide the RDFS files of the used ontologies (SeaLiT Ontology verson 1.0, CIDOC-CRM version 7.1.1). </p>
The Piraeus AIS Dataset for Large-scale Maritime Data Analytics
<p><strong>AIS data collected by the University of Piraeus' AIS receiver</strong></p> <p> </p> <p><strong>Abstract</strong></p> <p>The advent of Big Data and streaming technologies has resulted in a swarm of voluminous, heterogeneous information, especially in the domains of Internet of Things (IoT) and transportation. Focusing on the maritime field, we present a dataset that contains vessel position information transmitted by vessels of different types and collected via the Automatic Identification System (AIS). The AIS dataset comes along with spatially and temporally correlated data about the vessels and the area of interest, including weather information. It covers a time span of over 2.5 years, from May 9<sup>th</sup>, 2017 to December 26<sup>th</sup>, 2019 and provides anonymised vessel positions within the wider area of the port of Piraeus (Greece), one of the busiest ports in Europe and worldwide. The dataset consists of over 244 million AIS records, an average of more than 10,000 records per hour, which makes it an ideal input for large-scale mobility data processing and analytics purposes.</p> <p> </p> <p><strong>Dataset related to the following publication</strong></p> <blockquote> <p>Andreas Tritsarolis, Yannis Kontoulis, Yannis Theodoridis, The Piraeus AIS dataset for large-scale maritime data analytics, Data in Brief, Volume 40, 2022, 107782, ISSN 2352-3409, <a href="https://doi.org/10.1016/j.dib.2021.107782">https://doi.org/10.1016/j.dib.2021.107782</a>.</p> </blockquote> <p> </p> <p><strong>Files Description</strong></p> <ul> </ul> <ul> <li><strong>ais_static</strong>: CSV flat files containing vessels' static information and their corresponding types</li> </ul> <ul> <li><strong>geodata</strong>: ESRI Shapefiles containing several geographic-related data (e.g. harbours, islands, etc.)</li> </ul> <ul> <li><strong>noaa_weather</strong>: ESRI Shapefiles containing weather forecast from GRIB files (as provided by NOAA)</li> </ul> <ul> <li><strong>unipi_ais_dynamic</strong>: CSV flat files containing AIS kinematic information </li> </ul> <ul> <li><strong>unipi_ais_dynamic_synopses</strong>: CSV flat files containing metadata (i.e. synopses) regarding vessels' AIS positions</li> </ul> <p> </p> <p><strong>Privacy Statement</strong></p> <p><strong>For privacy-related queries, please contact the authors</strong></p>
Linked collectors and determiners for: New data on spiders (Aranei) from the Maritime Province, Russian Far East.
Natural history specimen data linked to collectors and determiners held within, "New data on spiders (Aranei) from the Maritime Province, Russian Far East". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/d4c93468-24c8-4e71-8992-e4e1aad3f674">https://bionomia.net/dataset/d4c93468-24c8-4e71-8992-e4e1aad3f674</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/d4c93468-24c8-4e71-8992-e4e1aad3f674">https://gbif.org/dataset/d4c93468-24c8-4e71-8992-e4e1aad3f674</a>. Formatted as a Frictionless Data package.
Data from: In situ genetic association for serotiny, a fire-related trait, in Mediterranean maritime pine (Pinus pinaster Aiton)
Wildfire is a major ecological driver of plant evolution. Understanding the genetic basis of plant adaptation to wildfire is crucial, because impending climate change will involve fire regime changes worldwide. We studied the molecular genetic basis of serotiny, a fire-related trait, in Mediterranean maritime pine using association genetics. A single nucleotide polymorphism (SNP) set was used to identify genotype : phenotype associations in situ in an unstructured natural population of maritime pine (eastern Iberian Peninsula) under a mixed-effects model framework. RR-BLUP was used to build predictive models for serotiny in this region. Model prediction power outside the focal region was tested using independent range-wide serotiny data. Seventeen SNPs were potentially associated with serotiny, explaining approximately 29% of the trait phenotypic variation in the eastern Iberian Peninsula. Similar prediction power was found for nearby geographical regions from the same maternal lineage, but not for other genetic lineages. Association genetics for ecologically relevant traits evaluated in situ is an attractive approach for forest trees provided that traits are under strong genetic control and populations are unstructured, with large phenotypic variability. This will help to extend the research focus to ecological keystone non-model species in their natural environments, where polymorphisms acquired their adaptive value.
Data for the paper "Addressing Uncertainties in Modelling Cumulative Impacts within Maritime Spatial Planning in the Adriatic and Ionian Region"
<p>Data for the paper "Addressing Uncertainties in Modelling Cumulative Impacts within Maritime Spatial Planning in the Adriatic and Ionian Region."</p>
Linked Open Data for the maritime domain
<p>Linked Data in this data set have been compiled from diverse data sources providing information about Trade and Transport locations, protected areas, surveillance data of vessels and vessel characteristics. Data have been transformed into triples according to the vesselAI ontology, using <a href="http://core.ac.uk/download/pdf/212138612.pdf">RDF-Gen</a> . All geometries are provided using <a href="http://www.opengis.net/ont/geosparql">OGC</a> terms. The vesselAI ontology documentation is available<a href="http://83.212.101.70/vesselAI_ontology.html"> here</a> .</p> <p>In a nutshell, this data set comprises data from the following sources:</p> <p>1. AIS messages of moving objects retrieved from <a href="https://ais-public.kystverket.no/ais-download/">Norwegian Coastal Administration's SafeSeaNet</a> solution, combined with data provided by the <a href="http://web.ais.dk/aisdata/">Danish Maritime Authority</a>. Each record contains the coordinates of the vessel, a timestamp, an identifier for the vessel, its speed and heading. Typically, each vessel reports this information by sending an AIS message every few seconds. Each reported position is also annotated with the corresponding weather conditions according to Copernicus Climate Change Service (C3S) (files: reconstructed_traj.7z, ais202101_part1.7z, ais202101_part2.7z)</p> <p><br> . The weather variables currently considered as relative to the movement of vessels are:</p> <ul> <li> '10m_u_component_of_wind',</li> <li> '10m_v_component_of_wind',</li> <li> '2m_dewpoint_temperature',</li> <li> '2m_temperature',</li> <li> 'mean_sea_level_pressure',</li> <li> 'mean_wave_direction',</li> <li> 'mean_wave_period',</li> <li> 'precipitation_type',</li> <li> 'sea_surface_temperature',</li> <li> 'total_precipitation'</li> </ul> <p>2. Vessel characteristics retrieved from online sources, combined with information about departure and destination seaports. United Nations Code for Trade and Transport Locations (UN/LOCODE), has been also used, to annotate the seaports with their longitude, latitude and Well Known Text (WKT) information, as well as features and facilities available according to online sources (files: vesselsCharacteristics.7z, worldPorts.7z ).</p> <p>3. Regions of interest in the maritime domain include fishing areas, endangered species habitat areas, exclusive economic zones (EEZ), Natura2000 protected areas. In this snapshot we provide Natura2000 regions (file: natura2000.7z) as well as <a href="https://www.protectedplanet.net/en">World Protected Areas data set</a> (file: wdpa2022.ttl.7z )</p> <p>Updates and additional data sets can be found <a href="http://83.212.101.70/vesselAI_ontology.html">here</a> .</p> <p>The surveillance and weather data in this data set, are for January 2021 and within the region defined by the degrees:</p> <p>#west: 2.53<br> #south: 51.50<br> #north: 60.50<br> #east: 17.50</p>
Contextual maritime data set (RDF triples)
<p>This data set is the RDF conversion w.r.t. the datAcron ontology, of the contextual maritime data available at https://zenodo.org/record/1167595 . It has been generated by the RDF-Gen method on the data sets describing sea ports (World Port Index, Ports of Brittany, SeaDataNet fishing ports) and protected regions (fishing areas, fishing interdiction, Natura2000).</p>
Data for "Measurement report: A one-year study to estimate maritime contributions to PM10 in a coastal area in Northern France."
<p>The characterization and the source apportionment of PM10 data have been used for the article "<strong>Measurement report: A one-year study to estimate maritime contributions to PM<sub>10</sub> in a coastal area in Northern France</strong>," which is under revision in the journal <em>Atmospheric Chemistry and Physic</em><em>s. </em></p>
Data from: In situ genetic association for serotiny, a fire-related trait, in Mediterranean maritime pine (Pinus pinaster Aiton)
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Data from: Phylogeography of Primula allionii (Primulaceae), a narrow endemic of the Maritime Alps
Primula allionii is endemic to a tiny area of the Maritime Alps and has one of the narrowest distribution ranges in this hotspot of biodiversity. Phylogeographical patterns in P. allionii were studied using plastid DNA markers and dominantly inherited markers (AFLP and ISSR) to verify any admixture between P. allionii and the sympatric P. marginata and to detect the phylogeographical history of the species. Morphometric measurements of flowers and admixture analysis support the hypothesis that hybridization occurs in nature. Species distribution models using two climate models (CCSM and MIROC) suggested a reduction in habitat suitability during cold periods. Phylogeographical analysis suggested an old allopatric divergence during the mid-Pleistocene transition (about 0.8 Mya) without recolonization/contraction cycles. The Alps watershed does not act as a strong barrier between the two main areas of the distribution range, and moderate gene flow by pollen seems to create the admixture recorded among the stands. According to our results, the persistence of P. allionii throughout the Ice Age appears to be linked to the capacity of the Maritime Alps to provide a wide diversity of microhabitats consistent with the recent biogeographical pattern proposed for the Mediterranean Basin.
Figs 1-7 in New data on spiders (Aranei) from the Maritime Province, Russian Far East
Figs 1-7. Variation of copulatory organs of Araniella yaginumai. 1 -2 — male palp, lateral view; 3 — male palp, subapical view; 4—5 — epigyne, ventral view; 6—7 — epigyne, view from behind. Scale = 0.1 mm.
Data from: Mediation of seed provisioning in the transmission of environmental maternal effects in Maritime pine (Pinus pinaster Aiton)
Although maternal environmental effects are increasingly recognized as an important source of phenotypic variation with relevant impacts in evolutionary processes, their relevance in long-lived plants such as pine trees is largely unknown. Here, we used a powerful sample size and a strong quantitative genetic approach to analyse the sources of variation of early seedling performance and to identify seed mass (SM)-dependent and -independent maternal environmental effects in Maritime pine. We measured SM of 8924 individual seeds collected from 10 genotypes clonally replicated in two environments of contrasting quality (favourable and stressful), and we measured seedling growth rate and biomass allocation to roots and shoots. SM was extremely variable (up to 14-fold) and strongly determined by the maternal environment and the genotype of the mother tree. The favourable maternal environment led to larger cones, larger seeds and reduced SM variability. The maternal environment also determined the offspring phenotype, with seedlings coming from the favourable environment being 35% larger and with greater root/shoot ratio. Transgenerational plasticity appears, thus, to be a relevant source of phenotypic variation in the early performance of this pine species. Seed provisioning explained most of the effect of the maternal environment on seedling total biomass. Environmental maternal effects on seedling biomass allocation were, however, determined through SM-independent mechanisms, suggesting that other epigenetic regulation channels may be involved.
CMIP6 data for the analysis in the article "Storylines of Maritime Continent dry period precipitation changes under global warming"
<p>The files include the data required to generate the figures based on CMIP6 model simulation outputs</p>
Support data for "Maritime radar odometry inspired by visual odometry"
<p>This is reduced resolution example data to accompany the code at `https://github.com/hflemmen/radar_odometry`.</p>
Data from: Measuring viability selection from prospective cohort mortality studies: a case study in Maritime pine
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Data from: Big wigs and small wigs: Time, sex, size and shelter affect cohabitation in the maritime earwig (Anisolabis maritima)
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Data from: Heritability of seed weight in Maritime pine, a relevant trait in the transmission of environmental maternal effects
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Data from: Extinction and recolonization of maritime Antarctica in the limpet Nacella concinna (Strebel, 1908) during the last glacial cycle: toward a model of Quaternary biogeography in shallow Antarctic invertebrates
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Data from: Mediation of seed provisioning in the transmission of environmental maternal effects in Maritime pine (Pinus pinaster Aiton)
Open the record for dataset details and reuse information.
Data from: Phylogeography of Primula allionii (Primulaceae), a narrow endemic of the Maritime Alps
Open the record for dataset details and reuse information.
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