Analysis of ‘Missing Migrants Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/jmataya/missingmigrants on 14 February 2022.

--- Dataset description provided by original source is as follows ---

About the Missing Migrants Data

This data is sourced from the International Organization for Migration. The data is part of a specific project called the Missing Migrants Project which tracks deaths of migrants, including refugees , who have gone missing along mixed migration routes worldwide. The research behind this project began with the October 2013 tragedies, when at least 368 individuals died in two shipwrecks near the Italian island of Lampedusa. Since then, Missing Migrants Project has developed into an important hub and advocacy source of information that media, researchers, and the general public access for the latest information.

Where is the data from?

Missing Migrants Project data are compiled from a variety of sources. Sources vary depending on the region and broadly include data from national authorities, such as Coast Guards and Medical Examiners; media reports; NGOs; and interviews with survivors of shipwrecks. In the Mediterranean region, data are relayed from relevant national authorities to IOM field missions, who then share it with the Missing Migrants Project team. Data are also obtained by IOM and other organizations that receive survivors at landing points in Italy and Greece. In other cases, media reports are used. IOM and UNHCR also regularly coordinate on such data to ensure consistency. Data on the U.S./Mexico border are compiled based on data from U.S. county medical examiners and sheriff’s offices, as well as media reports for deaths occurring on the Mexico side of the border. Estimates within Mexico and Central America are based primarily on media and year-end government reports. Data on the Bay of Bengal are drawn from reports by UNHCR and NGOs. In the Horn of Africa, data are obtained from media and NGOs. Data for other regions is drawn from a combination of sources, including media and grassroots organizations. In all regions, Missing Migrants Projectdata represents minimum estimates and are potentially lower than in actuality.

Updated data and visuals can be found here: https://missingmigrants.iom.int/

Who is included in Missing Migrants Project data?

IOM defines a migrant as any person who is moving or has moved across an international border or within a State away from his/her habitual place of residence, regardless of

  (1) the person’s legal status; 
  (2) whether the movement is voluntary or involuntary; 
  (3) what the causes for the movement are; or 
  (4) what the length of the stay is.[1]

Missing Migrants Project counts migrants who have died or gone missing at the external borders of states, or in the process of migration towards an international destination. The count excludes deaths that occur in immigration detention facilities, during deportation, or after forced return to a migrant’s homeland, as well as deaths more loosely connected with migrants’ irregular status, such as those resulting from labour exploitation. Migrants who die or go missing after they are established in a new home are also not included in the data, so deaths in refugee camps or housing are excluded. This approach is chosen because deaths that occur at physical borders and while en route represent a more clearly definable category, and inform what migration routes are most dangerous. Data and knowledge of the risks and vulnerabilities faced by migrants in destination countries, including death, should not be neglected, rather tracked as a distinct category.

How complete is the data on dead and missing migrants?

Data on fatalities during the migration process are challenging to collect for a number of reasons, most stemming from the irregular nature of migratory journeys on which deaths tend to occur. For one, deaths often occur in remote areas on routes chosen with the explicit aim of evading detection. Countless bodies are never found, and rarely do these deaths come to the attention of authorities or the media. Furthermore, when deaths occur at sea, frequently not all bodies are recovered - sometimes with hundreds missing from one shipwreck - and the precise number of missing is often unknown. In 2015, over 50 per cent of deaths recorded by the Missing Migrants Project refer to migrants who are presumed dead and whose bodies have not been found, mainly at sea.

Data are also challenging to collect as reporting on deaths is poor, and the data that does exist are highly scattered. Few official sources are collecting data systematically. Many counts of death rely on media as a source. Coverage can be spotty and incomplete. In addition, the involvement of criminal actors in incidents means there may be fear among survivors to report deaths and some deaths may be actively covered-up. The irregular immigration status of many migrants, and at times their families as well, also impedes reporting of missing persons or deaths.

The varying quality and comprehensiveness of data by region in attempting to estimate deaths globally may exaggerate the share of deaths that occur in some regions, while under-representing the share occurring in others.

What can be understood through this data?

The available data can give an indication of changing conditions and trends related to migration routes and the people travelling on them, which can be relevant for policy making and protection plans. Data can be useful to determine the relative risks of irregular migration routes. For example, Missing Migrants Project data show that despite the increase in migrant flows through the eastern Mediterranean in 2015, the central Mediterranean remained the more deadly route. In 2015, nearly two people died out of every 100 travellers (1.85%) crossing the Central route, as opposed to one out of every 1,000 that crossed from Turkey to Greece (0.095%). From the data, we can also get a sense of whether groups like women and children face additional vulnerabilities on migration routes.

However, it is important to note that because of the challenges in data collection for the missing and dead, basic demographic information on the deceased is rarely known. Often migrants in mixed migration flows do not carry appropriate identification. When bodies are found it may not be possible to identify them or to determine basic demographic information. In the data compiled by Missing Migrants Project, sex of the deceased is unknown in over 80% of cases. Region of origin has been determined for the majority of the deceased. Even this information is at times extrapolated based on available information – for instance if all survivors of a shipwreck are of one origin it was assumed those missing also came from the same region.

The Missing Migrants Project dataset includes coordinates for where incidents of death took place, which indicates where the risks to migrants may be highest. However, it should be noted that all coordinates are estimates.

Why collect data on missing and dead migrants?

By counting lives lost during migration, even if the result is only an informed estimate, we at least acknowledge the fact of these deaths. What before was vague and ill-defined is now a quantified tragedy that must be addressed. Politically, the availability of official data is important. The lack of political commitment at national and international levels to record and account for migrant deaths reflects and contributes to a lack of concern more broadly for the safety and well-being of migrants, including asylum-seekers. Further, it drives public apathy, ignorance, and the dehumanization of these groups.

Data are crucial to better understand the profiles of those who are most at risk and to tailor policies to better assist migrants and prevent loss of life. Ultimately, improved data should contribute to efforts to better understand the causes, both direct and indirect, of fatalities and their potential links to broader migration control policies and practices.

Counting and recording the dead can also be an initial step to encourage improved systems of identification of those who die. Identifying the dead is a moral imperative that respects and acknowledges those who have died. This process can also provide a some sense of closure for families who may otherwise be left without ever knowing the fate of missing loved ones.

Identification and tracing of the dead and missing

As mentioned above, the challenge remains to count the numbers of dead and also identify those counted. Globally, the majority of those who die during migration remain unidentified. Even in cases in which a body is found identification rates are low. Families may search for years or a lifetime to find conclusive news of their loved one. In the meantime, they may face psychological, practical, financial, and legal problems.

Ultimately Missing Migrants Project would like to see that every unidentified body, for which it is possible to recover, is adequately “managed”, analysed and tracked to ensure proper documentation, traceability and dignity. Common forensic protocols and standards should be agreed upon, and used within and between States. Furthermore, data relating to the dead and missing should be held in searchable and open databases at local, national and international levels to facilitate identification.

For more in-depth analysis and discussion of the numbers of missing and dead migrants around the world, and the challenges involved in identification and tracing, read our two reports on the issue, Fatal Journeys: Tracking Lives Lost during Migration (2014) and Fatal Journeys Volume 2, Identification and Tracing of Dead and Missing Migrants

Content

The data set records

Context

I am planning to compare Above 18 years of male and female between the different class passengers in Titanic Data set

Content

A century has sailed by since the luxury steamship RMS Titanic met its catastrophic end in the North Atlantic, plunging two miles to the ocean floors after sideswiping an iceberg during its maiden voyage.Rather than the intended Port of New York, a deep-sea grave became the pride of the White Star Line’s final destination in the early hours of April 15, 1912.More than 1,500 people lost their lives in the disaster In this project I will be performing an exploratory analysis on the data

Acknowledgements

I noticed that more women survived in raw number and percentage than men and opposite are true of 3rd class passengers. The bars are a good choice to show the difference between categories, but you may want to look into a grouped bar chart1 for an easier comparison of how many survived or didn't in each group. While there were far more men on the boat, less survived than the women. The class seemed to have a direct effect on a passenger's chance of survival. While it is good to see the difference in the numbers of those who survived to those who didn't.

Inspiration

The North Pacific Loggerhead sea turtle (Caretta caretta) undergoes one of the greatest of all animal migrations, nesting exclusively in Japan and re-emerging several years later along important foraging grounds in the eastern North Pacific. Yet the mechanisms that connect these disparate habitats during what is known as the “lost years” have remained poorly understood. Here, we develop a new hypothesis regarding a possible physical mechanism for habitat connectivity – an intermittent “thermal corridor” – using remotely sensed oceanography and 6 juvenile loggerhead sea turtles that formed part of a 15 year tracking dataset of 231 individuals (1997–2013). While 97% of individuals remained in the Central North Pacific, these 6 turtles (about 3%), continued an eastward trajectory during periods associated with anomalously warm ocean conditions. These few individuals provided a unique opportunity to examine previously unknown recruitment pathways. To support this hypothesis, we employed an independently derived data set using novel stable isotope analyses of bone growth layers and assessed annual recruitment over the same time period (n = 33, 1997–2012). We suggest evidence of a thermal corridor that may allow for pulsed recruitment of loggerheads to the North American coast as a function of ocean conditions. Our findings offer, for the first time, the opportunity to explore the development of a dynamic ocean corridor for this protected species, illuminating a longstanding mystery in sea turtle ecology.

Overview

Global Surface Summary of the Day is derived from The Integrated Surface Hourly (ISH) dataset. The ISH dataset includes global data obtained from the USAF Climatology Center, located in the Federal Climate Complex with NCDC. The latest daily summary data are normally available 1-2 days after the date-time of the observations used in the daily summaries.

Content

Over 9000 stations' data are typically available.

The daily elements included in the dataset (as available from each station) are: Mean temperature (.1 Fahrenheit) Mean dew point (.1 Fahrenheit) Mean sea level pressure (.1 mb) Mean station pressure (.1 mb) Mean visibility (.1 miles) Mean wind speed (.1 knots) Maximum sustained wind speed (.1 knots) Maximum wind gust (.1 knots) Maximum temperature (.1 Fahrenheit) Minimum temperature (.1 Fahrenheit) Precipitation amount (.01 inches) Snow depth (.1 inches)

Indicator for occurrence of: Fog, Rain or Drizzle, Snow or Ice Pellets, Hail, Thunder, Tornado/Funnel

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.github_repos.[TABLENAME]. Fork this kernel to get started to learn how to safely manage analyzing large BigQuery datasets.

Acknowledgements

This public dataset was created by the National Oceanic and Atmospheric Administration (NOAA) and includes global data obtained from the USAF Climatology Center. This dataset covers GSOD data between 1929 and present, collected from over 9000 stations. Dataset Source: NOAA

Use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source — http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Photo by Allan Nygren on Unsplash

Here are a few use cases for this project:

1. Marine Traffic Monitoring: The ORDA2023CAPSTONE model could be used for surveillance and tracking of marine vessels. It can classify the boats, submarines and detect other relevant objects in the vicinity. This could be beneficial for port authorities, coast guard services, and maritime traffic controllers.

2. Search and Rescue Operations: This computer vision model could be employed during search and rescue operations at sea. It can identify not only various types of vessels but also people, which can assist in locating missing or stranded individuals more effectively.

3. Environment Monitoring: The model can be used for observing and identifying different forms of marine wildlife and objects contributing to pollution e.g. debris, bottles, etc. in the sea or coastal areas. This application could be helpful for environmental agencies and research institutions.

4. Water Sports Analysis: ORDA2023CAPSTONE can be used to analyze various water sports events such as sailboat races, surfing, or dinghy competitions. It can identify the type of water vessels, the athletes, and various equipment being used.

5. Maritime Education and Documentation: The model can be utilized for educational purposes, such as illustrating and explaining different types of naval vessels for students. It may also be handy for documentary creators focusing on marine life, naval technology, or maritime history, allowing them to accurately tag and sort footage.

Long-term data allow ecologists to assess trajectories of population abundance. Without this context, it is impossible to know whether a taxon is thriving or declining to extinction. For parasites of wildlife, there are few long-term data – a gap that creates an impediment to managing parasite biodiversity and infectious threats in a changing world. We produced a century-scale time series of metazoan parasite abundance and used it to test whether parasitism is changing in Puget Sound, USA and, if so, why. We performed parasitological dissection of fluid-preserved specimens held in natural history collections for eight fish species collected between 1880 and 2019. We found that parasite taxa using three or more obligately required host species – a group that comprised 52% of the parasite taxa we detected – declined in abundance at a rate of 10.9% per decade, whereas no change in abundance was detected for parasites using one or two obligately required host species. We tested several potential mechanisms for the decline in 3+-host parasites and found that parasite abundance was negatively correlated with sea surface temperature, diminishing at a rate of 38% for every 1°C increase. Although the temperature effect was strong, it did not explain all variability in parasite burden, suggesting that other factors may also have contributed to the long-term declines we observed. These data document one century of climate-associated parasite decline in Puget Sound – a massive loss of biodiversity, undetected until now. Methods Specimen selection and collection site history We focused on the metazoan parasites of eight fish species from Puget Sound, Washington, USA, selecting host species that were well-represented in natural history collections and that encompassed a broad range of trophic levels, body sizes, habitats, functional feeding groups, and vulnerability to human impacts (Supplementary Information Table S1). Specimens were sourced primarily from the University of Washington Fish Collection (UWFC) at the Burke Museum of Natural History and Culture, with additional specimens from other collections to increase temporal scope and resolution (Figure 1b; Supplementary Information Table S2). For each specimen examined, we noted locality data from the natural history collection databases and measured the specimen’s total length (TL) in cm. When the collection location was descriptive but lacked coordinates, we estimated the collection location in decimal degrees using Google Maps. Parasitological dissections Each fish was subjected to a comprehensive parasitological dissection (see details in Fiorenza et al. [2020] and Snover et al. [2005]). For each parasite identified, we noted its broad taxonomic grouping (Subclass Copepoda, Subclass Hirudinea, Class Monogenea, Class Trematoda, Class Cestoda, Phylum Nematoda, Class Acanthocephala; Supplementary Information Table S3). For flatworms, we stained and mounted specimens before identification (Cable et al. 1963). For nematodes, we cleared specimens before identification (Cable et al. 1963). We identified each parasite to the finest taxonomic resolution, which in most cases was family or genus level, and classified them into one of two transmission strategies: directly transmitted (i.e., parasites that can be transmitted between conspecific hosts) or complex life cycle (i.e., parasites that are transmitted from one host species to another host species in an obligately required sequence). For complex life cycle parasites, we also estimated the number of obligately required host species based on natural history information (Supplementary Information Table S3). Parasites that were identified to species and found in more than one host were recorded under the same parasite taxon name (e.g., Derogenes varicus). Larval nematodes of the genera Anisakis and Contracaecum are known to be host generalists for their fish intermediate/paratenic hosts (Mattiucci et al. 2006; Kuhn et al. 2013; Shamsi 2019); therefore, even though these worms could not be identified to species, we also recorded these under the same parasite taxon name across host species (i.e., Contracaecum sp., Anisakis sp.). For all other parasites that were not identified to species, we assumed that individuals found in one host were of a different species than those found in another host, and named them accordingly (Sasal et al. 1998; Benesh et al. 2021; e.g., Lepeophtheirus sp. of Walleye Pollock versus Lepeophtheirus sp. of Surf Smelt). Potential environmental drivers In a retrospective study like this one, it is not possible to definitively identify the causal drivers of change in parasite abundance. However, we had access to several long-term environmental datasets and sought to assess the correlation between environmental variables and parasite burden. Our environmental datasets included information on sea surface temperature (Wan 2022), heavy metal and organic pollutants (Brandenberger et al. 2008), and fish host density (Greene et al. 2015; Essington et al. 2021) within Puget Sound. The temperature and pollutant datasets reflect prevailing conditions in Puget Sound that might have affected all hosts and parasites, while fish density data reflect conditions pertaining primarily to parasites within that fish host. Data on sea surface temperature were from a continuous record (1921–2019) collected at Race Rocks lighthouse (Wan 2022); we extracted average monthly sea surface temperature in degrees Celsius, discarded any year in which more than one month was missing data (n = 4 of 98 years), and obtained an annual average for each year. For each host individual, we matched the year of the host’s collection to the corresponding year from the temperature dataset. Data on pollutants were from a continuous record (1774–2005) obtained by coring Puget Sound sediments (Brandenberger et al. 2008), which yielded values for the concentration of lead, arsenic, zinc, nickel, vanadium, chromium, copper, barium, and beryllium in micrograms per gram of sediment, as well as concentrations of lignin and soil biomarkers, which indicate inputs of terrestrial organic matter. We extracted annual values for each variable from two cores taken near Tacoma and Seattle, WA in Puget Sound (PS-1 near Tacoma = 47.347167, -122.409667; PS-4 near Seattle = 47.614967, -122.449017; Brandenberger et al. 2008), averaged values within each year across the two cores, and interpolated among years to bridge temporal gaps. Some of the 12 pollutant variables were collinear with one another (Supplementary Information Figure S3), so we performed a principal components analysis to reduce the dimensionality of the pollutant dataset and found that the first two principal components explained 74% of variation (Supplementary Information Figure S4). To account for annual measurement error, we ran a LOESS smoother on each principal component and then matched the year of each host individual’s collection to the corresponding year from the first and second principal components. We also had access to data on the density of the fish hosts for six of the eight host species we examined: G. chalcogrammus, H. colliei, M. productus, and P. vetulus from Essington et al. [2021] and C. pallasii and H. pretiosus from Greene et al. [2015]. Data from Essington et al. [2021] were annual projections of density based on historical data collected from 1946 to 1977, while data from Greene et al. [2015] were estimates of catch per unit effort (CPUE) for various Puget Sound basins sampled between 1972 and 2011. For Greene et al. [2015], estimates were averaged across basins within each year to obtain an annual estimate of abundance across Puget Sound. For each fish species’ time series, we interpolated among years to bridge temporal gaps and matched the year of each host individual’s collection to the corresponding year from the host density dataset, matching host species to corresponding parasites (i.e., P. vetulus density was recorded for P. vetulus parasites only). Literature cited

Brandenberger, J. M. et al. Reconstructing trends in hypoxia using multiple paleoecological indicators recorded in sediment cores from Puget Sound, WA. National Oceanic and Atmospheric Administration, Silver Spring, MD (2008). Benesh, D. P., Parker, G. A., Chubb, J. C. & Lafferty, K. D. Trade-offs with growth limit host range in complex life-cycle helminths. Am. Nat. 197, E40–E54 (2021). Cable, R. M. An Illustrated Laboratory Manual of Parasitology. Minneapolois, MN: Burgess Publishing Company (1963). Essington, T. et al. Historical reconstruction of the Puget Sound (USA) groundfish community. Mar. Ecol. Prog. Ser. 657, 173–189 (2021). Fiorenza, E. A. et al. Fluid preservation causes minimal reduction of parasite detectability in fish specimens: A new approach for reconstructing parasite communities of the past? Ecol. Evol. 10, 6449–6460 (2020). Greene, C., Kuehne, L., Rice, C., Fresh, K. & Penttila, D. Forty years of change in forage fish and jellyfish abundance across greater Puget Sound, Washington (USA): anthropogenic and climate associations. Mar. Ecol. Prog. Ser. 525, 153–170 (2015). Kuhn, T., Hailer, F., Palm, H. W. & Klimpel, S. Global assessment of molecularly identified Anisakis Dujardin, 1845 (Nematoda: Anisakidae) in their teleost intermediate hosts. Folia Parasitol. 60, 123–134 (2013). Mattiucci, S. & Nascetti, G. Molecular systematics, phylogeny and ecology of anisakid nematodes of the genus Anisakis Dujardin, 1845: an update. Parasite 13, 99–113 (2006). Sasal, S., Desdevises, Y. & Morand, S. Host-specialization and species diversity in fish parasites: Phylogenetic conservatism? Ecography 21, 639–643 (1998). Shamsi, S. Parasite loss or parasite gain? Story of Contracaecum nematodes in antipodean waters. Parasit. Epi. Cont. 4, e00087 (2019). Snover, A. K., Mote, P. W., Whitely Binder, L. C., Hamlet, A. F. & Mantua, N. J. Uncertain future: climate change and

Variation in both inter- and intra-specific traits affect community dynamics, yet we know little regarding the relative importance of external environmental filters vs internal biotic interactions that shape the functional space of communities along broad-scale environmental gradients, such as latitude, elevation or depth. We examined changes in several key aspects of functional alpha-diversity for marine fishes along depth and latitude gradients by quantifying intra- and inter-specific richness, dispersion and regularity in functional trait space. We derived eight functional traits related to food acquisition and locomotion, and calculated seven complementary indices of functional diversity for 144 species of marine ray-finned fishes along large-scale depth (50 m – 1200 m) and latitudinal gradients (29° – 51° S) in New Zealand waters. Traits were derived from morphological measurements taken directly from footage obtained using Baited Remote Underwater Stereo-Video systems and museum specimens. We partitioned functional variation into intra- and inter-specific components for the first time using a PERMANOVA approach. We also implemented two tree-based diversity metrics in a functional distance-based context for the first time: namely, the variance in pairwise functional distance, and the variance in nearest-neighbour distance. Functional alpha diversity increased with increasing depth, and decreased with increasing latitude. More specifically, the dispersion and mean nearest-neighbour distances among species in trait space, and intra-specific trait variability all increased with depth, whereas functional hypervolume (richness) was stable across depth. In contrast, functional hypervolume, dispersion and regularity indices all decreased with increasing latitude; however, intra-specific trait variation increased with latitude, suggesting that intra-specific trait variability becomes increasingly important at higher latitudes. These results suggest that competition within and among species are key processes shaping functional multi-dimensional space for fishes in the deep sea. Increasing morphological dissimilarity with increasing depth may facilitate niche partitioning to promote coexistence, whereas abiotic filtering may be the dominant process structuring communities with increasing latitude. Methods We observed 144 species of marine ray-finned fishes (Class Actinopterygii) on Baited Remote Underwater stereo-Video (stereo-BRUV) footage. We analysed data on the basis of the species present (observed in video footage) within each of the 47 depth-by-location cells. There were 144 species recorded, and 509 species-by-cell occurrences (many species naturally occurred in more than one cell). Our original dataset was comprised of a complete set of 15 raw morphological measurements for a total of 722 individuals (136 of these required some random-forest imputation, and missing traits were remeasured for 4 individuals) obtained directly from Stereo-BRUV footage. From this original dataset, we calculated all species-level functional metrics (i.e., FHV, MPFD, MNND, VPFD and VNND; see descriptions below). We created 100 tables of 509 unique species-by-cell occurrences (rows) for the 8 traits (columns; eye position, pectoral fin position, caudal peduncle throttling, elongation, eye size, oral gape position, jaw length/head length, and total length) by randomly drawing 1 individual from the list of all complete individuals for each species. To maintain any spatial structures in trait variability as well as possible, we drew an individual for each species within each cell from conspecific individuals that were (in order of preference): a) within that depth-by-location cell, b) at the same depth, or c) from anywhere within the Stereo-BRUV study design (n = 722) or d) from a museum specimen (n = 291). All species-level functional metrics were calculated for each replicate table, and we calculated the mean across all 100 tables for every metric for subsequent analyses. All functional metrics were calculated using 8 normalised continuous traits. We calculated the following species-level metrics for each depth-by-latitude cell, for each of the 100 species-by-trait (509 x 8) data matrices after calculating Euclidean distances: (i) mean pairwise functional distance (MPFD; (Clarke & Warwick 1998; Somerfield et al. 2008; Swenson 2014), (ii) mean nearest-neighbour distance (MNND; Swenson & Weiser 2014), (iii) variance in pairwise functional distance (VPFD; adapted from Clarke and Warwick (2001) and Somerfield et al. (2008)), and (iv) variance in nearest-neighbour distance (VNND; Swenson (2014). We also performed principal component analysis (PCA) on the normalised traits in order to calculate functional hypervolume (FHV; Blonder et al. 2014; Blonder et al. 2018). FHV was calculated using the first 4 principal component axes (which accounted for 70.2 – 74.4 % of the total variation in the 8D functional trait space across the 100 species-by-trait tables). We did not retain all 8 dimensions due to difficulties associated with calculating FHV when few species were present. FHV has been used as a proxy to estimate niche space, including high-dimensional, irregular spaces (Lamanna et al. 2014; Cooke, Eigenbrod & Bates 2019). For metrics focusing on intra-specific trait variability we used data solely from the in-situ stereo-BRUV footage (i.e., the dataset comprising 722 individuals). Due to the inability to measure every species observed on the stereo-BRUVs, this dataset represents a reduced number of species (62 out of 144), and cells (43 out of 47). Within this dataset, we were able to measure intraspecific trait variability for 42 species (2 or more individuals per species per cell). There were, on average, 3.34 species per cell (min = 1, max = 10, sd = 1.86) and 4.32 individuals per species per cell (min = 2, max = 15, sd = 2.5) to measure the intra-specific trait variability. We calculated mean pairwise functional distance (MPFD.I) directly, considering only the intra-specific distances. In addition, partitioning was done by performing a PERMANOVA on the Euclidean distances among all complete individuals separately within each cell. Different species were treated as different levels of the factor “Species”, and individuals within each species were treated as replicates in a one-factor design. Prop.I is equal to the proportion of total trait variation (within each cell) attributable to individual-level variation (i.e., Prop.I).Prop.I and MPFD.I could only be calculated when there were two or more individuals representing the same species within a depth-by-location cell.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on single levels from 1940 to present".

The OSPAR (https://www.ospar.org/convention) fish biodiversity assessment concerns sensitive and often rare species in the northeast Atlantic Ocean and marginal seas. All fish species captured during fishing on scientific groundfish (otter and beam trawl) surveys are recorded but not necessarily weighed and measured for length. The biodiversity assessment for sensitive and often rare species thus makes use of the occurrences (presence and absences) of a species only.

A list of sensitive fish species was developed to guide the work for the OSPAR fish biodiversity assessment. The list was created by first collating all sensitive fish species recorded on international and national hard law lists, Regional Seas Conventions lists, International Agreement Lists, relevant IUCN Red List species (classified as EX, CR, VU or EN) and all ICES and academic work to identify sensitive fish species. From this extensive list of species, species were removed if they did not occur in the OSPAR area of the Northeast Atlantic Ocean. Remaining species were divided by region; Bay of Biscay and Iberian Coast, Celtic Seas, Greater North Sea, Norwegian Sea and parts of Macaronesia where data was available. Those species whose normal distribution was at the very edge of an OSPAR region were not assessed within the region.

Species where ICES or ICCAT quantitative stock assessments are conducted in all or part of their distribution were retained on the list, but highlighted so as to avoid duplication of work. The list was then cross-referenced with the ICES WKCOFIBYC (ICES, 2021a) and WKABSENS (ICES, 2021b) sensitive species lists and reviewed by expert members of the OSPAR Fish subgroup to ensure all sensitive species were included.

To overcome the potential for species misidentification for those that are difficult to identify beyond the genus level, some species were grouped by genus for the assessment. These include Hippocampus spp. (combining Hippocampus hippocampus with H. guttulatus), Alosa spp. (combining Alosa alosa and A. fallax), Dipturus spp. (combining Dipturus batis complex, D. batis, D. flossada and D. intermedia), Mustelus spp. (combining Mustelus mustelus and M. asterias), Sebastes spp. (combining Sebastes marinus, S. mentella and S. norvegicus), Dasyatis spp. (combining Dasyatis pastinaca and D. tortonesei), Galeus spp. (combining Galeus melastomus and G. atlanticus), Coregonus spp. (combining Coregonus maraena and Coregonus oxyrinchus), Raja brachyura (including Bathyraja brachyurops) and Deania calcea (including D. profundorum). After grouping, a total of 102 unique taxonomic groups were retained on the OSPAR sensitive fish species list for four OSPAR regions (Greater North Sea, Celtic Sea, Bay of Biscay and Wider Atlantic).

Data file names reflect the OSPAR region sampled, country conducting the sampling, fishing gear and time of years of sampling (as defined by Greenstreet and Moriarty 2017), e.g.: BBICFraBT4 refers to Bay of Biscay and Iberian Coast data from France by a Beam Trawl survey in quarter 4 of the year andGNSIntOT3 refers to Greater North Sea data from International (multiple countries) sampling by an Otter Trawl survey in quarter 3 of the year etc.

Scientific trawl survey data are submitted to the ICES Database of Trawl Surveys (DATRAS):http://www.ices.dk/marine-data/data-portals/Pages/DATRAS.aspx The DATRAS reporting format is detailed online: https://datras.ices.dk/Data_products/ReportingFormat.aspx The metadata relating to the ICES co-ordinated surveys are available here: http://www.ices.dk/marine-data/data-portals/Pages/DATRAS-Docs.aspx

ICES Data Centre host the database of trawl surveys (DATRAS) for groundfish and beam trawl data. DATRAS has an integrated quality check utility. All data, before entering the database, have to pass an extensive quality check. Despite this errors and missing data arise, which are subsequently dealt with by the data submitters from the contributing countries as required. However, this screening process was implemented in 2009 for data from 2004 onwards. Since some survey time-series extend back to the 1960s, historic data (unless re-evaluated and re-submitted by contributing countries) may not have been subject to the same level of quality control as these more recent data. Furthermore, the type of information collected, the level of detail and resolution in the data, has gradually evolved over time. In order to derive a single format, quality assured monitoring programme data product covering the entire Northeast Atlantic region inconsistencies in the datasets required resolution. These corrections are detailed in ICES 2021a,b:Biological data for trawl surveys are downloaded directly from DATRAS in raw exchange format (known as “HL data”). Ancillary data were processed by ICES 2021a,b to create the "SweptAreaAssessmentOuput" (which replaces the “HH data”) and these were downloaded from the same location: https://datras.ices.dk/Data _ products/Download/Download _ Data _ public.aspx) For the biodiversity assessment of sensitive species, the data are processed to create a standalone dataproduct on species occurrence (presence and absence) and haul location. Initially, valid hauls are subset to determine the Standard Monitoring Programme (i.e. excluding hauls of duration shorter than 13 minutes or longer than 66 minutes) and these hauls are used to define the Standard Survey Area (excluding areas sampled infrequently over time) following the methods detailed in Greenstreet and Moriarty 2017). Biological data were accepted with ICES SpecVal of 1, 4, 5, 6, 7, 10 (see http://vocab.ices.dk/ for further information on SpecVal categories). Additional QA/QC is made at this step to determine if species identification issues are present in the raw biological data.

References: Greenstreet S P R and M Moriarty 2017. Manual for Version 3 of the Groundfish Survey Monitoring and Assessment Data Product. Scottish Marine and Freshwater Science Vol 8 No 18. Published by Marine Scotland. ISSN: 2043-7722. DOI: 10.7489/1986-1 ICES. 2021a. Workshop on Fish of Conservation and Bycatch Relevance (WKCOFIBYC). ICES Scientific Reports. 3:57. 125 pp. https://doi.org/10.17895/ices.pub.8194 ICES. 2021b. Workshop on the production of abundance estimates for sensitive species (WKABSENS). ICES Scientific Reports. 3:96. 128 pp. https://doi.org/10.17895/ices.pub.8299

1. SUDOANG PROJECT The SUDOANG project has provided common tools and assessment methods to managers to support the eel conservation in the SUDOE zone (Southern France, Spain and Portugal). One of the goals of the project was to develop an eel abundance and distribution atlas in the three countries, based on the results of the implementation of Eel Density Analysis (EDA). This model extrapolates eel abundance from a range of river segments sampled by electrofishing, to the whole river and lake network, by considering how eel abundance, size and sex vary according to different parameters related to eel habitat. To do this, we have created a dataset of "cumulated dam impact" which compiles different ways of calculating cumulated height from the sea.
2. SUDOANG DATABASE The dataset on cumulated impact was first derived from information on obstacles collected by the SUDOANG project. Obstacles data for the three countries were imported in the SUDOANG database (deliverable 2.2.1), whose structure is inherited from the DataBase for EEl (DBEEL), developed during a European research project (POSE - Pilot projects to estimate potential and actual escapement of silver eel, Walker et al., 2011). This database is designed to contain all data relative to eel biology and anthropogenic pressures applying to eel. During the course of SUDOANG, this database was used and ameliorated. In France the obstacles were compiled from three pre-existing different databases:

the Referential of flow obstacles (ROE) , the Information of Ecological Continuity (ICE) and the Flow Obstruction Database (Base de Données des Obstacles à l'Ecoulement, (BDOE). The data we have integrated into the SUDOANG 1.0.4. database came from a database dump of the 12th September 2019. The inventory includes bridges that have a significant impact on river continuity. In Spain, data came from:

the MITECO Ministry the Basque Water Agency (URA) - Basque Country the Catalan Water Agency (ACA) - Catalonia the University of Girona - Catalonia the University of Córdoba - Andalusia Xunta de Galicia, Consellería de Medio Ambiente, Territorio e Vivenda - Galicia the AMBER project In Portugal the data came from:

the Portuguese Water Agency (APA) MARE-ULisboa (University of Lisbon) CIIMAR, the University of Porto the AMBER project. In the case of the transboundary river Minho, the data came from:

CIIMAR, the University of Porto (Portuguese area) (report) EHEC, the University of Santiago de Compostela (Spanish area) (report) 3. DATA DESCRIPTION 3.1. Data collected on artificial obstacles Artificial obstacles were classified into 10 types according to the Adaptive Management of Barriers in European Rivers (AMBER) project. Some additional types (e.g., penstock pipes) were added to identify other obstacles in national databases that did not fit the AMBER classification (see the list below). Sometimes dams from different branches are connected, creating a dam-network. In those cases, we have only kept the dam(s) in the main course and use a hierarchical classification of the dams to only consider the cumulated height from the sea to the reference dam. We included only obstacles that are presently standing, i.e., not planned, under construction, or destroyed. Dikes, longitudinal control structures and grates were excluded. Obstacle classification according to the data collected and the AMBER project:

BR - Bridge: A structure that is built over a river to allow people or vehicles to cross CU - Culvert: A tunnel or pipe carrying a stream or open drain under a road or railway DI - Dike: An embankment used to hold back water DA - Dam: Structure that blocks the river and extends across the river bed to the flood plain FO - Ford: A shallow crossing-place in a river PP - Penstock: pipe Group of pipes that transport pressurised water from a reservoir (dam) to the turbines installed in a hydro-electric power plant RR - Rock ramp: A weir made of rocks WE - Weir: Structure across a river that does not extend to the flood plain OT - Other: Structure that is not covered by previous definitions UN - Unknown: Unknown We have projected obstacles on the SUDOANG river network at the nearest point within 300 m. To avoid projecting large obstacles in the wrong location in the southwestern France, SUDOANG experts have reviewed and corrected this information. We have also used an algorithm that extracts the best obstacle height data from the three existing databases in France. In the Iberian Peninsula, data providers validated and corrected obstacle location and height using a Shiny application developed by the project, in which they could directly correct the height of obstacles. The variables in the obstacles table (csv delimiter ",") are:

op_id: Identifier of the observation place name op_gis_layername: Original data source op_placename: Name of the dam op_op_id: If the dam is linked within a complex (e.g. when there are multiple channels for the same river) the name of the parent dam id_original: Original id of the dam (in the raw table) country: Country code ('SP', 'ES' or 'FR') dp_name: Name of the data provider obstruction_type_code: Type of obstruction (see table obstruction type code) obstruction_type_name: Name of the dam po_obstruction_height: Difference of level of water between the downstream and the upstream part of the dam po_presence_eel_pass: Presence of a pass suitable for eel (see paper) po_date_presence_eel_pass: Date of construction of the eel (or eel compatible) pass fishway_type_code: Code of the fishway type fishway_type_name: Name of the fishway type googlemapscoods: Link to google map x_espg_4326: Longitude (with ESPG 4326) y_espg_4326: Latitude (with ESPG 4326) 3.2. Modeling missing data and estimating the cumulative impact on obstacles For those obstacles missing height information, we have calculated height using a Generalized Linear Models (GLM of log transformed height, family = gaussian, link = identity. In France the model was based on river segment slope, river segment median flow and hydrographic basin. In the Iberian Peninsula, we have implemented a simpler model based on obstacle type, as information about flow or slope was not available for all river segments. The cumulated impact of obstacles was assessed by creating a table joining each river segment with all the dams located in the downstream course. Using this, various metrics were computed using different assumptions concerning the effect of obstacles. The heights were power transformed to test for a different effect of obstacle's height (the cumulated effect of two obstacles of 1 m might be different than the cumulated effect of a single obstacle of 2 m), and functions were developed to calculate cumulated obstacle transformed variables. Other variables were also tested. In fact, tests in France have shown that factors such as presence of a fish pass, and eel passability did not improve the model performance. For this reason, but also because in the Iberian Peninsula this type of information was too limited, we used dam height to model the cumulative height of obstacles at a given river segment. The variables in the cumulated_dam_impact_SUDOANG table (format Rdata - to be read with the R software, this will load as a data.frame called datadam) are:

cs_height_08_n: Cumulated height from the sea, dam height transformed with power 0.8, no prediction for missing values cs_height_08_n.: Same variable but truncated to 300 cs_height_08_p: Cumulated height from the sea, dam height transformed with power 0.8, with prediction for missing values cs_height_08_p.: Same variable but truncated to 300 cs_height_08_pps Cumulated height from the sea, dam height transformed with power 0.8, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel cs_height_10_FR: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from France are considered when building on a transnational water course cs_height_10_n: Cumulated height from the sea, no transformation, no prediction for missing values cs_height_10_n.: Same variable but truncated to 200 cs_height_10_p: Cumulated height from the sea, no transformation, missing height are extrapolated from two different models in France and the Iberian Peninsula cs_height_10_p.: Same variable but truncated to 200 cs_height_10_pass0: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams without pass are used to build the cumulated value cs_height_10_pass1: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams with pass are used to build the cumulated value cs_height_10_pp: Cumulated height from the sea, no transformation, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel cs_height_10_ppass0: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams without pass are used to build the cumulated value cs_height_10_ppass1: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams with pass are used to build the cumulated value cs_height_10_pps: Cumulated height from the sea, no transformation, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure cs_height_10_pscore0: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams without score are used to build the cumulated value cs_height_10_pscore1: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams with score (that have been expertised as no or small barrier for eel) are used to build the cumulated value cs_height_10_PT: Cumulated height from the sea, no transformation, no prediction for missing values, only

Sea Temperature From Oxygen Sensor (TEMP_DOXY) Profiles. EMODnet Physics data from a local source. cdm_data_type=Profile cdm_profile_variables=time,EP_PLATFORM_ID citation=Data are the property of the producer/owner distributed through EMODnet Physics. EMODnet Physics and the partners are not responsible for improper use comment=When missing DEPTH and PRES values are calculated on DEPTH or PRES variable data assuming 1 dbar = 1 m contact=contacts@emodnet-physics.eu Conventions=CF-1.6 OceanSITES-Manual-1.2 Copernicus-InSituTAC-SRD-1.3 Copernicus-InSituTAC-ParametersList-3.0.0, COARDS, ACDD-1.3 coriolis_platform_code=4802971 data_character_set=utf8 data_language=eng data_mode=R data_type=OceanSITES vertical profile date_update=2022-02-10T12:01:28Z defaultDataQuery=&time>=now-7day distribution_statement=Data used in this [type of derived work: e.g. publication/report/model/map…] was made available by the EMODnet Physics project, www.emodnet-physics.eu/map, funded by the European Commission Directorate General for Maritime Affairs and Fisheries. EMODnet Physics and data originators strive to provide access to data with the highest quality available, but the user must be aware that the quality of the different datasets can vary considerably. Users should also take note that all portals are still under development so some features may not be complete and new datasets, parameters and tools are made available regularly. For instance, estimates of data quality are progressively being incorporated along with the datasets, but it is the responsibility of the user to assess the adequacy of those data for his/her particular work purposes: neither EMODnet Physics nor the data originators are liable for any negative consequences following direct or indirect use of EMODnet portal information, services, products and/or data. Easternmost_Easting=168.17 ep_data_type=PR ep_parameter_group=Water conductivity/ BioGeoChemical featureType=Profile format_version=1.4 geospatial_lat_max=79.67188 geospatial_lat_min=-75.21251 geospatial_lat_units=degrees_north geospatial_lon_max=168.17 geospatial_lon_min=-122.1432 geospatial_lon_units=degrees_east geospatial_vertical_max=9.96921E36 geospatial_vertical_min=-100.0 geospatial_vertical_positive=down geospatial_vertical_units=m infoUrl=http://www.emodnet-physics.eu institution=EMODnet Physics keywords_vocabulary=GCMD Science Keywords last_date_observation=2021-11-30T23:52:53Z last_latitude_observation=39.08793 last_longitude_observation=-74.45278 metadata_character_set=utf8 metadata_language=eng naming_authority=Copernicus Marine In Situ netcdf_version=netCDF-4 classic model Northernmost_Northing=79.67188 qc_manual=Recommendations for in-situ data Near Real Time Quality Control https://doi.org/10.13155/36230 reference_system=EPSG:4326 references=http://marine.copernicus.eu http://www.marineinsitu.eu SDN=SDN:P01::OXYTAAOP sourceUrl=(local files) Southernmost_Northing=-75.21251 standard_name_vocabulary=CF Standard Name Table v29 subsetVariables=EP_PLATFORM_ID, EP_PLATFORM_CODE,EP_PLATFORM_TYPE testOutOfDate=now-7days time_coverage_end=2022-02-11T02:29:45Z time_coverage_start=2007-04-01T02:06:26Z update_interval=P1D Westernmost_Easting=-122.1432

Individuals often employ simple rules that can emergently synchronise behaviour. Some collective behaviours are intuitively beneficial, but others like mate signalling in leks occur across taxa despite theoretical individual costs. Whether disparate instances of synchronous signalling are similarly organised is unknown, largely due to challenges observing many individuals simultaneously. Recording field collectives and ex situ playback experiments, we describe principles of synchronous bioluminescent signals produced by marine ostracods (Crustacea; Luxorina) that seem behaviorally convergent with terrestrial fireflies, and with whom they last shared a common ancestor over 500 mya. Like synchronous fireflies, groups of signalling males use visual cues (intensity and duration of light) to decide when to signal. Individual ostracods also modulate their signal based on the distance to nearest neighbours. During peak darkness, luminescent "waves" of synchronous displays emerge and ripple acr..., Data are a mixture of types from different methods and experiments. They are most clearly outliend and used in conjunciton with the provided R code for context. Briefly, the majority of data are from observations of collective bioluminescent behaviors of wild, naturally kept, or experimentally manipulated ostracods (marine crustaceans). We used either cameras or human observations to record data. Camera data was either post-processed using computer vision or annotated by eye to record the number of bright pixels or number of behaviors, respectively. One data set was collected with a spectroradiometer, as described in the methods of the paper. Images in the dataset are demonstrative of the methods., , # Collective synchrony of mating signals modulated by ecological cues and social signals in bioluminescent sea fireflies

https://doi.org/10.5061/dryad.6m905qg6c

These data are different video or human annotations of behavioral data when observing collective behaviors of bioluminescent mating signals.

The goal of the R code and data include is to seamlessly recapitulate all analyses and figures from the manuscript. When downloading, as along as the paths to the datafile are changed to match the downloaded folder with the datasets (and proper dependencies are downloaded), everything else should run smoothly. Understanding the context and results of these data are best done in conjunction with the R code provided.

In order to prevent the R code from experiencing difficulties, the data are left with blank cells, missing columns names, etc. and instead manipulated, cleaned, and processed entirely within the R environment. Any blank cells or colu...

Populations of anadromous brown trout, also known as sea trout, have suffered recent marked declines in abundance due to multiple factors, including climate change and human activities. While much is known about their freshwater phase, less is known about the species’ marine feeding migrations. This situation is hindering the effective management and conservation of anadromous trout in the marine environment. Using a panel of 95 single nucleotide polymorphism markers we developed a genetic baseline, which demonstrated strong regional structuring of genetic diversity in trout populations around the English Channel and adjacent waters. Extensive baseline testing showed this structuring allowed the high-confidence assignment of known-origin individuals to the region of origin. This study presents new data on the movements of anadromous trout in the English Channel and southern North Sea. Assignment of anadromous trout sampled from 12 marine and estuarine localities highlighted contrasting ..., All individuals were genotyped at 95 biallelic single nucleotide polymorphism (SNP) loci (Osmond, King, Stockley, Launey, & Stevens, 2023) on the Fluidigm EP1 Genotyping System using 96.96 Dynamic Genotyping Arrays and scored using the Fluidigm SNP Genotyping analysis software., , # SNP genotype dataset from brown and anadromous trout

https://doi.org/10.5061/dryad.1ns1rn92w

Data for trout (Salmo trutta) samples genotyped at 95 single nucleotide polymorphism (SNP) markers (Osmond et al. 2023). Data consists of 3067 baseline trout samples, 435 known-origin individuals, and 371 anadromous trout caught in marine and estuarine areas.

Description of the data and file structure

The data file is a Microsoft Excel file, in GenAlEx format (Peakall & Smouse 2006, 2012), containing six tabs: Baseline_sample_info, Baseline, Known_origin_sample_info, Known_origin_samples, Marine&Estuarine_sample_info, and Marine&Extuarine.

SNP data is coded as 1 - A, 2 - C, 3 - G, and 4 - T. Missing data is coded as zero.

Tab: Baseline_sample_info

A table presenting details of the rivers and hatchery populations from which brown trout were sampled to construct the genetic baseline, including river code name, country, repo...

On behalf of Australia, and in support of the Malaysian accident investigation, the Australian Transport Safety Bureau (ATSB) led search operations for missing Malaysian Airlines flight MH370 in the Southern Indian Ocean. Geoscience Australia provided advice, expertise and support to the ATSB to facilitate marine surveys, which were undertaken to provide a detailed map of the sea floor topography and to aid navigation during the underwater search.

This dataset comprises Side Scan Sonar (SSS), Synthetic Aperture Sonar (SAS) and multibeam sonar backscatter data at 5 m resolution. Data was collected during Phase 2 marine surveys conducted by the Governments of Australia, Malaysia and the People’s Republic of China between September 2014 to January 2017. The data was acquired by Echo Surveyor 7 (Kongsberg AUV Hugin 1000), Edgetech 2400 Deep Tow and SLH PS-60 Synthetic Aperture Sonar Deep Tow deployed from the following vessels: Fugro Supporter, Fugro Equator, Fugro Discovery, Havila Harmony, Dong Hai Jiu 101 and Go Phoenix.

All material and data from this access point is subject to copyright. Please note the creative commons copyright notice and relating to the re-use of this material. Geoscience Australia's preference is that you attribute the datasets (and any material sourced from it) using the following wording: Source: Governments of Australia, Malaysia and the People's Republic of China, 2018. MH370 Phase 2 data. We honour the memory of those who have lost their lives and acknowledge the enormous loss felt by their loved ones.

Database with all the extant correspondencia (sea loan) contracts collected from the surviving notarial protocol books of Manila in the years between 1736 and 1800. It also include traces of the correspondencia contracts, such as cancellations, letters of payment, and the audit of fiscal Villacorta in 1752 (original documents in AGI). Data collected from the Microfilm copies stored in the Biblioteca Tomás Navarro Tomás in Madrid, open for consultation. Colección de Microfilmes de la Sección de Documentos Españoles del Archivo Nacional de Filipinas. Archivo del Centro de Ciencias Humanas y Sociales (ACCHS-CSIC). Original documents stored in National Archive of the Philippines.

The database includes information on each individual transaction, including year, takers, guarantors, investors, quantity, premium, destination, and the vessels in which it travels. It includes directories of individuals, travels, and institutions.

NOTE: The database is organized by individual transaction, it includes an index specifying the sources, and each transaction is referenced to the protocol book in which it is inscribed, but arranged by the year in which the transaction took place. On the folio references, the extant sources are in some cases very damaged. Since the sources I consulted were mostly the microfilms held in Madrid, I have annotated the folios based on the organization of the archive rather than the numbering of the protocol books themselves, since several pages are missing in many of them and others have their edges too damaged to read the original numbering.

NOTE: This is the improved version of the Manila Financial Instruments Database (MFID) - Correspondencias 1.0, published previously in Zenodo (10.5281/zenodo.10277780).

I shall keep expanding this database as new information becomes available. I am also looking for any collaboration or input of data on Manila correspondencias that cannot be found in the notarial archives.

This table contains the daily sea surface temperature observations taken at the Portobello Marine Laboratory wharf (LAT: -45.8160, LON: 170.6500). The first column is time in MATLAB datenum format. The second column is daily sea surface temperature recorded at 9am local time. Measurements are recorded to an accuracy of (\pm)0.1°C. Missing observations have been assigned the value -999. Additional station details and sampling information can be found in Chiswell and Grant (2018).

We acknowledge the foresight and dedication of the founders of this in situ dataset in the 1950s. We are grateful for all the people involved in the data collection. Notably these include

Doug Mackie (data acquisition and record maintenance)

Elizabeth (Betty) Batham who championed the long term climate sampling

All the researchers who have assisted with sampling