(4 intermediate revisions by the same user not shown)
Line 1: Line 1:
−
==1 Abstract
+
==1 Abstract<!-- -->==
  
−
The project ML-AMAR (Machine Learning Applications in Marine Engineering) aims to develop machine learning tools to optimize the lifecycle management of ships, from design and operation to maintenance and structural monitoring.
+
The repository includes two public anonymized datasets generated from the ML-AMAR monitoring campaign. Both datasets have been prepared for scientific and technical distribution by removing sensitive information such as exact coordinates, precise timestamps, vessel/sensor identifiers and individual trajectories.
  
−
This document contains the anonymized scientific-technical database from the ML-AMAR monitoring campaign. It has been generated to support the validation of the meteorological forecasting, seakeeping behavior, and life-cycle analysis tools developed within the project. The database integrates inertial signals, GNSS data, and external metoceanographic variables, providing a traceable record of the vessel’s real operational performance along commercial routes.
+
The first dataset, ML_AMAR_anonimizacion_version_B_public, contains aggregated information on maritime routes, trip frequencies, departure time slots, trip duration classes, recurrent movement patterns and a non-georeferenced schematic network. It is intended for the analysis of maritime mobility and general route patterns.
  
−
==
+
The second dataset, ML_AMAR_positions_1min_metocean_public_B, is derived from the original one-minute monitoring records. It provides aggregated information on operational state, route phase, distance-to-port classes, metocean conditions and vessel dynamic response. It is intended for scientific and technical analyses of the relationship between navigation, waves, wind and vessel behaviour.
  
−
==2 Database<!-- You can enter and format the text of this document by selecting the ‘Edit’ option in the menu at the top of this frame or next to the title of every section of the document. This will give access to the visual editor. Alternatively, you can edit the source of this document (Wiki markup format) by selecting the ‘Edit source’ option.  Most of the documents in Scipedia are written in English (write your manuscript in American or British English, but not a mixture of these). Anyhow, specific publications in other languages can be published in Scipedia. In any case, the documents published in other languages must have an abstract written in English.  2.1 Subsections  Divide your article into clearly defined and numbered sections. Subsections should be numbered 1.1, 1.2, etc. and then 1.1.1, 1.1.2, ... Use this numbering also for internal cross-referencing: do not just refer to 'the text'. Any subsection may be given a brief heading. Capitalize the first word of the headings.  2.2 General guidelines  Some general guidelines that should be followed in your manuscripts are:  *  Avoid hyphenation at the end of a line.  *  Symbols denoting vectors and matrices should be indicated in bold type. Scalar variable names should normally be expressed using italics.  *  Use decimal points (not commas); use a space for thousands (10 000 and above).  *  Follow internationally accepted rules and conventions. In particular use the international system of units (SI). If other quantities are mentioned, give their equivalent in SI.  2.3 Tables, figures, lists and equations  Please insert tables as editable text and not as images. Tables should be placed next to the relevant text in the article. Number tables consecutively in accordance with their appearance in the text and place any table notes below the table body. Be sparing in the use of tables and ensure that the data presented in them do not duplicate results described elsewhere in the article.  Graphics may be inserted directly in the document and positioned as they should appear in the final manuscript.  Number the figures according to their sequence in the text. Ensure that each illustration has a caption. A caption should comprise a brief title. Keep text in the illustrations themselves to a minimum but explain all symbols and abbreviations used. Try to keep the resolution of the figures to a minimum of 300 dpi. If a finer resolution is required, the figure can be inserted as supplementary material  For tabular summations that do not deserve to be presented as a table, lists are often used. Lists may be either numbered or bulleted. Below you see examples of both.  1. The first entry in this list  2. The second entry  2.1. A subentry  3. The last entry  * A bulleted list item  * Another one  You may choose to number equations for easy referencing. In that case they must be numbered consecutively with Arabic numerals in parentheses on the right hand side of the page. Below is an example of formulae that should be referenced as eq. (1].  2.4 Supplementary material  Supplementary material can be inserted to support and enhance your article. This includes video material, animation sequences, background datasets, computational models, sound clips and more. In order to ensure that your material is directly usable, please provide the files with a preferred maximum size of 50 MB. Please supply a concise and descriptive caption for each file. -->==
+
In both cases, the data are published only in aggregated and anonymized form. The repository does not include raw files, real GNSS positions, exact timestamps, operational identifiers or confidential correspondence tables between anonymized codes and real locations. Aggregation, discretization and suppression rules have been applied to prevent the reconstruction of individual trips or specific operational patterns.
 +
 
 +
The dataset ML_AMAR_anonimizacion_version_B_public includes aggregated route summaries, trip frequencies, time slot distributions, duration classes, recurrent cycles and a schematic non-georeferenced network. Its main purpose is the analysis of maritime mobility and route patterns.
 +
 
 +
The dataset ML_AMAR_positions_1min_metocean_public_B includes aggregated operational states, route phases, distance-to-port classes, vessel motion statistics and metocean conditions. Its main purpose is the scientific and technical analysis of vessel dynamics and metocean forcing.
 +
 
 +
 
 +
==2 Database<!-- -->==
 +
 
 +
[[Media:Draft_Molina_Sanchez_916438065_3725_ML_AMAR_anonimizacion_version_B_public.xlsx|ML_AMAR_anonimizacion_version_B_public.xlsx]]
 +
[[Media:Molina_Sanchez_2026a_6601_ML_AMAR_anonimizacion_version_B_public.zip|ML_AMAR_anonimizacion_version_B_public.zip]]
 +
[[Media:Molina_Sanchez_2026a_8999_ML_AMAR_positions_1min_metocean_public_B.zip|ML_AMAR_positions_1min_metocean_public_B.zip]]
 +
[[Media:Molina_Sanchez_2026a_5121_ML_AMAR_positions_1min_metocean_public_B.xlsx|ML_AMAR_positions_1min_metocean_public_B.xlsx]]

Latest revision as of 17:22, 27 May 2026

1 Abstract

The repository includes two public anonymized datasets generated from the ML-AMAR monitoring campaign. Both datasets have been prepared for scientific and technical distribution by removing sensitive information such as exact coordinates, precise timestamps, vessel/sensor identifiers and individual trajectories.

The first dataset, ML_AMAR_anonimizacion_version_B_public, contains aggregated information on maritime routes, trip frequencies, departure time slots, trip duration classes, recurrent movement patterns and a non-georeferenced schematic network. It is intended for the analysis of maritime mobility and general route patterns.

The second dataset, ML_AMAR_positions_1min_metocean_public_B, is derived from the original one-minute monitoring records. It provides aggregated information on operational state, route phase, distance-to-port classes, metocean conditions and vessel dynamic response. It is intended for scientific and technical analyses of the relationship between navigation, waves, wind and vessel behaviour.

In both cases, the data are published only in aggregated and anonymized form. The repository does not include raw files, real GNSS positions, exact timestamps, operational identifiers or confidential correspondence tables between anonymized codes and real locations. Aggregation, discretization and suppression rules have been applied to prevent the reconstruction of individual trips or specific operational patterns.

The dataset ML_AMAR_anonimizacion_version_B_public includes aggregated route summaries, trip frequencies, time slot distributions, duration classes, recurrent cycles and a schematic non-georeferenced network. Its main purpose is the analysis of maritime mobility and route patterns.

The dataset ML_AMAR_positions_1min_metocean_public_B includes aggregated operational states, route phases, distance-to-port classes, vessel motion statistics and metocean conditions. Its main purpose is the scientific and technical analysis of vessel dynamics and metocean forcing.


2 Database

ML_AMAR_anonimizacion_version_B_public.xlsx ML_AMAR_anonimizacion_version_B_public.zip ML_AMAR_positions_1min_metocean_public_B.zip ML_AMAR_positions_1min_metocean_public_B.xlsx

Back to Top
GET EPUB

Document information

Published on 27/05/26

Licence: CC BY-NC-SA license

Document Score

0

Views 0
Recommendations 0

Share this document

Keywords

• • • •