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390 results for “Maritime”
Meteograms of Ny-Ålesund for ICON-LEM maritime aerosols simulations
<p>This data contains the simulation data as meteogram from ICON-LEM simulations with ca. 600 m resolution. The output location is Ny-Ålesund. The data is for the months Aug and Oct 2021. This data was used in the PhD thesis of Theresa Kiszler. Thesis title: "Improving our understanding of cloud phase-partitioning using long-term cloud-resolving simulations of Svalbard".</p> <p>The original simulation setup is is described in the method section of the paper "A Performance Baseline for the Representation of Clouds and Humidity in Cloud-Resolving ICON-LEM Simulations in the Arctic" by Kiszler et al. (2023). <a href="https://doi.org/10.1029/2022MS003299">https://doi.org/10.1029/2022MS003299</a></p> <p>The following adaptation has been made to the simulation settings: The CCN activation is based on a version by Segal and Khain (2006) using the lowest possible number concentration, i.e. maritime aerosols. The INP nucleation follows the paper by Phillips et al. (2008) only using dust as aerosol. The implementation of the mentioned schemes was not done by us, only the settings were changed to use these schemes instead of the default version.</p>
STREAM - Sub-THz Radar sensing of the Environment for future Autonomous Marine platforms: Multi-Perspective Sensing - Maritime Environment - Side-looking Perspective
<p>This dataset contains the files corresponding to which results have been included in the journal paper titled 'High-Resolution Multi-Modal Sensing of Distributed Radar Network'. The full description of the conducted trials and data structure is mentioned in the attached PDF document.</p> <p>The trials were conducted at the Gosport Marina, Portsmouth, UK with a sea state of approximately 3 according to the Douglas Scale.</p> <p>The experiments were performed with automotive radars operating in the 79 GHz band to investigate the Doppler and imaging capabilities of these radars. A multi-sensory suite distributed around Valkyrie VI was mounted in front, corner, side and backward-looking orientations.</p> <p>This dataset contains data from the side-looking orientation, where the installation angle of radar is 90 degrees respective to the platform velocity vector.</p> <p><strong>Radar Data:</strong></p> <p>The radar data is stored in the file 'GM2_Out1_240522_160925.h5'. The methodology to process the data in MATLAB is presented in the attached pdf. document.</p> <p><strong>Inertial Measurement Unit:</strong></p> <p>Three xSens 680G IMU were mounted on the roof, front and back of the boat. They have been included in the corresponding zip folders.</p> <p>PC3_Corner_RLG: IMU at the corner of the boat.</p> <p>PC4_Forward_RLG: IMU at the roof of the boat.</p> <p>PC5_Backward_RLG: IMU at the back of the boat.</p> <p>The IMU data is converted to .txt files that can be directly loaded into MATLAB.</p> <p><strong>Timestamped Velocity:</strong></p> <p>The file 'Corner_160925.mat' contains the time-stamped velocity for each radar frame. Here, the integration interval is 128 ms with 512 radar chirps.</p> <p>The file 'CommonFramesCornner_160925.mat' contains the timestamped velocity for the frames that are synchronised with the frames of front-looking radar.</p> <p>(The dataset for the front-looking radar is stored in another repository with DOI: 10.5281/zenodo.14215115)</p> <p><strong>Camera:</strong></p> <p>Each radar also has a camera for ground truth. The time-stamped camera frames for each radar frame are stored in 'CommonFramesCornner_160925.mat'.</p> <p>Processed camera frames and video of the scene are available in: 'GM2_Corner_240522_160925_CameraFrames.zip'.</p> <p> </p> <p>For more information, please contact:</p> <p>Anum Pirkani: a.a.a.pirkani@bham.ac.uk, anum.apirkani@gmail.com</p> <p>Marina Gashinova: m.s.gashinova@bham.ac.uk</p>
STREAM - Sub-THz Radar sensing of the Environment for future Autonomous Marine platforms: Multi-Perspective Sensing - Maritime Environment - Front-looking Perspective
<p>This dataset contains the files corresponding to which results have been included in the journal paper titled 'High-Resolution Multi-Modal Sensing of Distributed Radar Network'. The full description of the conducted trials and data structure is mentioned in the attached PDF document.</p> <p>The trials were conducted at the Gosport Marina, Portsmouth, UK with a sea state of approximately 3 according to the Douglas Scale.</p> <p>The experiments were performed with automotive radars operating in the 79 GHz band to investigate the Doppler and imaging capabilities of these radars. A multi-sensory suite distributed around Valkyrie VI was mounted in front, corner, side and backward-looking orientations.</p> <p>This dataset contains data from the front-looking orientation, where the installation angle of radar is 0 degrees respective to the platform velocity vector.</p> <p><strong>Radar Data:</strong></p> <p>The radar data is stored in the file 'GM2_Lab_240522_160943.h5'. The methodology to process the data in MATLAB is presented in the attached pdf. document.</p> <p><strong>Inertial Measurement Unit:</strong></p> <p>Three xSens 680G IMU were mounted on the roof, front and back of the boat. They have been included in the corresponding zip folders.</p> <p>PC3_Corner_RLG: IMU at the corner of the boat.</p> <p>PC4_Forward_RLG: IMU at the roof of the boat.</p> <p>PC5_Backward_RLG: IMU at the back of the boat.</p> <p>The IMU data is converted to .txt files that can be directly loaded into MATLAB.</p> <p><strong>Timestamped Velocity:</strong></p> <p>The file 'Front_160943.mat' contains the time-stamped velocity for each radar frame. Here, the integration interval is 128 ms with 512 radar chirps.</p> <p>The file 'CommonFramesFront_160943.mat' contains the timestamped velocity for the frames that are synchronised with the frames of side-looking radar.</p> <p>(The dataset for the side-looking radar is stored in another repository with DOI: 10.5281/zenodo.14174138)</p> <p><strong>Camera:</strong></p> <p>Each radar also has a camera for ground truth. The time-stamped camera frames for each radar frame are stored in 'CommonFramesFront_160943.mat'.</p> <p>Processed camera frames and video of the scene are available in: 'GM2_Front_240522_160943_CameraFrames.zip'.</p> <p> </p> <p>For more information, please contact:</p> <p>Anum Pirkani: a.a.a.pirkani@bham.ac.uk, anum.apirkani@gmail.com</p> <p>Marina Gashinova: m.s.gashinova@bham.ac.uk</p>
1935 buildings in the Maritime District, Rotterdam
<p>This dataset provides:</p> <ul> <li>A shapefile, containing a building layer from the Maritime District, from 1935</li> <li>A spapefile, containing a facade layer of open commercial building spaces (like shops and restaurants), from 1935</li> </ul>
Forward selection in a maritime pine polycross progeny trial using pedigree reconstruction.
<p>These two excel files gather genotyping data used in the following publication:</p> <p>Vidal M, Plomion C, Raffin A, Harvengt L, Bouffier L (2017) Forward selection in a maritime pine polycross progeny trial using pedigree reconstruction. Annals of Forest Science, 74(1). DOI 10.1007/s13595-016-0596-8</p> <p>The dataset describes genotyping profiles (with 56 or 63 SNPs) for the G1 and G2 individuals sampled in this paper. For each individual, the following information is mentioned: identity, preselection option (only for G2 individuals), the generation to which the individual belongs, pedigree (only for G2 individuals), alleles for each SNP.</p>
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>
High temporal and spatial resolution emission inventory for maritime shipping emissions on the North Sea and Baltic Sea (2015)
<p>A temporally and spatially highly resolved emission inventory for the North Sea and Baltic Sea for the year 2015, created with current emission factors and ship activity data. The emissions inventory is available as 396 csv files, one for each day in 2015 and December 2014, grouped as monthly archives. </p> <p><strong>Note that due to the underlying ship activity data and the geographic boundaries, the time index in the <em>Datetime </em>column in the <em>ship_emissions_YYYYMMDD.csv</em> files is not equidistant.</strong> For example, since vessels leave the geographic area and reenter later, no data is available for the time the vessel is not within the area.</p> <p>The underlying model source code is available on Github, with a release of the associated version on Zenodo: [](https://doi.org/10.5281/zenodo.6951672)</p> <p> </p>
Composite Maritime Events
<p>The dataset includes approximately 4M composite maritime events (e.g., anchored vessels, loitering, ship-to-ship transfer, etc.), recognised by <a href="https://github.com/aartikis/RTEC">RTEC</a> on semantically annotated AIS position signals, over a period of six months, from approx. 5K vessels sailing around the port of Brest, France.</p> <p>The dataset of AIS position signals is available <a href="https://zenodo.org/record/1167595">here</a>. The semantic annotation of the AIS signals is available <a href="https://zenodo.org/record/2563256">here</a>. </p> <p>Information on the specification of the composite maritime events is available here:</p> <p><a href="https://dl.acm.org/citation.cfm?id=3329762">Pitsikalis M., Artikis A., Dreo R., Ray C., Camossi E., and Jousselme A. Composite Event Recognition for Maritime Monitoring. International Conference on Distributed and Event-Based Systems (DEBS), 2019</a></p>
SeaLiT Ontology - An extension of CIDOC-CRM for the modelling of Maritime History information
<p>The <strong>SeaLiT Ontology</strong> is a formal ontology intended to facilitate the integration, mediation and interchange of heterogeneous information related to <strong>maritime history</strong>. It aims at providing the semantic definitions needed to transform disparate, localised information sources of maritime history into a coherent global resource. It also serves as a common language for domain experts and IT developers to formulate requirements and to agree on system functionalities with respect to the correct handling of historical information.</p> <p>The ontology uses and extends the <strong><a href="https://www.cidoc-crm.org/">CIDOC Conceptual Reference Model</a></strong> (ISO 21127:2014), in particular version 7.2.1, as a general ontology of human activity, things and events happening in space and time.</p> <p>The ontology has been developed following a bottom-up process from primary data collected in the context of the <a href="http://www.sealitproject.eu/"><strong>SeaLiT Project</strong></a> (<em>Seafaring Lives in Transition, Mediterranean Maritime Labour and Shipping, 1850s-1920s</em>). SeaLiT is an international research project, funded by the ERC Starting Grant 2016, which explores the transition from sail to steam navigation and its effects on seafaring populations in the Mediterranean and the Black Sea between the 1850s and the 1920s.</p> <p>More information about the construction of the <strong>SeaLiT Ontology</strong>, the considered data sources, as well as their transformation to a knowledge graph using the SeaLiT Ontology, can be found in the following papers:</p> <blockquote> <p>P. Fafalios, A. Kritsotaki, and M. Doerr, "<em>The SeaLiT Ontology – An Extension of CIDOC-CRM for the Modeling and Integration of Maritime History Information"</em>. ACM Journal on Computing and Cultural Heritage, 2023. <a href="https://doi.org/10.1145/3586080">https://doi.org/10.1145/3586080</a> [<a href="https://arxiv.org/pdf/2301.04493.pdf">pdf</a>, <a href="https://users.ics.forth.gr/~fafalios/files/bibs/fafaliosSeaLiTOntology2023.bib">bib</a>]</p> </blockquote> <blockquote> <p>P. Fafalios, K. Petrakis, G. Samaritakis, K. Doerr, A. Kritsotaki, Y. Tzitzikas, and 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 (resolvable) <strong>namespace </strong>of the ontology is: <a href="http://www.sealitproject.eu/ontology/">http://www.sealitproject.eu/ontology/</a></p> <p>An <strong>OWL implementation</strong> of the ontology is available at: <a href="https://sealitproject.eu/ontology/SeaLiT_Ontology_v1.2.owl">https://sealitproject.eu/ontology/SeaLiT_Ontology_v1.2.owl</a></p> <p><strong>Knowledge graphs </strong>that make use of the SeaLiT Ontology are available at: <a href="https://zenodo.org/record/6460841">https://zenodo.org/record/6460841</a>. These RDF datasets integrate information of 16 different types of archival sources related to maritime history, including crew lists, payrolls, registers of different types, censuses, and employment records.</p>
Fig. 5 in Adventive Staphylinidae (Coleoptera) of the Maritime Provinces of Canada: further contributions
Fig. 5. Distribution of Cilea silphoides, Atheta dadopora, Leptacinus intermedius, Bisnius cephalotes, Neobisnius villosulus, Philonthus jurgans, Anotylus tetracarinatus, and Anotylus insecatus in the Maritime Provinces of Canada.
Fig. 1 in Adventive Staphylinidae (Coleoptera) of the Maritime Provinces of Canada: further contributions
Fig. 1. Distribution of Ilyobates bennetti, Meotica exilis, Meotica "pallens," Lathrobium fulvipenne, and Oxytelus sculptus in eastern Canada.
MarTREC Project Datasets for Effect of Permeability Variation of Expansive Yazoo Clay at the Maritime and Multimodal Transportation Infrastructure in Mississippi
<p>The existence of Yazoo clay soil in Mississippi frequently causes distress to the pavement and cause deformation at the slopes in highways and levees, which are a critical component in Maritime and multimodal transportation infrastructure. Each year, fixing the pavement requires a significant maintenance budget of MDOT. Also, the infiltration of the rainwater in the highway and levee slopes leads to landslides, which require millions of maintenance dollars each year. Due to the shrinkage and swelling behavior of the Yazoo clay, the hydraulic conductivity varies over the different seasons and has higher vertical permeability during the dry season. With high vertical permeability, the rainwater can easily percolate in the pavement subgrade and slopes, which accelerates the failure. The current study investigates the change in unsaturated vertical and horizontal permeability and its effect on the maritime and multimodal infrastructures, especially on the pavement and slopes of highway embankment and levees. The attached datasets include the laboratory test and finite element modeling findings.</p>
Figs 86–109 in Four new monoraphid diatom species (Bacillariophyta, Achnanthaceae) from the Maritime Antarctic Region
Figs 86–109. Psammothidium superpapilio Kopalová, Zidarova & Van de Vijver sp. nov. Light and scanning electron micrographs of the type population on Byers Peninsula (Livingston Island). 86–95. LM views of rapheless valves. 96–105. LM views of raphe valves. 106. SEM external view of an entire raphe valve. 107. SEM internal view of an entire raphe valve. 108. SEM external view of an entire rapheless valve. 109. SEM internal view of an entire rapheless valve. Scale bars represent 10 µm.
Figs 58–85 in Four new monoraphid diatom species (Bacillariophyta, Achnanthaceae) from the Maritime Antarctic Region
Figs 58–85. Psammothidium confusoneglectum Kopalová, Zidarova & Van de Vijver sp. nov. Light and scanning electron micrographs of the type population on Byers Peninsula (Livingston Island). 58–70. LM views of raphe valves. 71–82. LM views of rapheless valves. 83. SEM external view of an entire raphe valve. 84. SEM external view of an entire rapheless valve. 85. SEM internal view of an entire rapheless valve. Scale bars represent 10 µm.
Figs 1–13 in Four new monoraphid diatom species (Bacillariophyta, Achnanthaceae) from the Maritime Antarctic Region
Figs 1–13. Achnanthes kohleriana Kopalová, Zidarova & Van de Vijver sp. nov. Light micrographs of the type population from Deception Island (South Shetland Islands, Antarctica). 1–16. LM views of raphe valves. 7–12. LM views of rapheless valves. 13. LM view of a girdle view. Scale bar represents 10 µm.
Figs 25–57 in Four new monoraphid diatom species (Bacillariophyta, Achnanthaceae) from the Maritime Antarctic Region
Figs 25–57. Planothidium wetzelectorianum Kopalová, Zidarova & Van de Vijver sp. nov. Light and scanning electron micrographs of the type population in Monolith Lake (James Ross Island). 25. LM view of a girdle view. 26–38. LM views of raphe valves. 39–52. LM views of rapheless valves. 53. SEM external view of an entire raphe valve. 54. SEM external detail of the areolae. 55. SEM internal view of the raphe and the striae. Note the hymenes on the areolae. 56. SEM external view of an entire rapheless valve. 57. SEM internal view of an entire rapheless valve. Scale bars represent 10 µm for Figs 25–52, 1 µm for Figs 53–57.
Figs 20–24 in Four new monoraphid diatom species (Bacillariophyta, Achnanthaceae) from the Maritime Antarctic Region
Figs 20–24. Achnanthes kohleriana Kopalová, Zidarova & Van de Vijver sp. nov. Scanning electron micrographs of the type population from Deception Island (South Shetland Islands, Antarctica). 20. SEM girdle view of an entire frustule. 21. SEM external view of a raphe valve. 22. SEM external detail of the areolae with the typical cribrate structure. 23. SEM external detail of the apex with the typical terminal orbiculus. 24. SEM internal view of an entire rapheless valve. Arrows show the rapheless sternum. Scale bars represent 10 µm for Figs 20, 21 & 24, 1 µm for Fig. 22 and 5 µm for Fig. 23.
Supplementary Material - Maritime Cargo Prioritisation during a prolonged pandemic lockdown using an integrated TOPSIS-Knapsack technique
<p>Supplementary Material - Maritime Cargo Prioritisation during a prolonged pandemic lockdown using an integrated TOPSIS-Knapsack technique: A Case Study on Small Island Developing States – the Rodrigues Island</p> <p>Results and Sensitivity analysis</p>
European maritime region definition
<p>With the increasing share of the installed Renewable Sources (RES) capacity, evaluating the effect of renewable energy on the energy supply is a very important issue which is addressed in several climate services projects such as C3S-Energy, Clim2power or C3S-ECEM. Prospective analysis are generally made at regional level (e.g. TIMES model) and it is becoming a standard practice to aggregate gridded RES power generation data into aggregated values at NUTS1 or NUTS2 level. This approach raises an issue for the consideration of the offshore wind energy as well as other Marine Renewable Energies (MRE) since there is to the best of our knowledge no commonly accepted region definition corresponding to the NUTS boundaries for the maritime area.</p>
FIG. 11 in A new Cretaceous psocodean family from the Charente-Maritime amber (France) (Insecta, Psocodea, Psocomorpha)
FIG. 11. — Strict consensus cladogram of the Psocomorpha including Arcantipsocus n. gen. and Electrentomum Enderlein, 1911. See Appendix for the matrix of character state used. *, all the genera belonging to the family.
ScienceDex guides
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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.