End product needs

Workshop participants, and specifically those engaged in image-based analysis, felt the most critical tools urgently required to support their work were in-situ photo-guides in book format (hard copy or e-book), a standard reference morphospecies taxonomic tree (or annotation scheme) that can be imported into different annotation software, and on-line user-friendly image reference catalogues that include information on characteristics used to classify animals as belonging to a particular OTU. The final database structure must therefore be such that these end-use products can be easily created from the database by a query using purpose-built web-accessible software as part of future developments.

Database structure

The final database structure consists of two tables that contain fields that map onto Darwin Core fields, together with additional fields for which no Darwin Core equivalent could be established. Table 2 is the OTU table. It documents the OTU, and primarily maps to fields from the Darwin Core classes “Taxon” and “Identification”. Table 3 is the image table. It documents the individual image file and maps onto fields from multiple Darwin Core classes, including “Occurrence”, “Identification”, “Event”, “Location”, “Record-level”, and “Organism”. The two tables are related via the “OTU” field. This structure allows a single OTU (one entry into Table 2) to be related to multiple example images of the OTU (many entries in Table 3).

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Table 2. The Operational Taxonomic Unit (OTU) table, one of two tables that make up the final database.

https://doi.org/10.1371/journal.pone.0218904.t002

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Table 3. The image table, one of two tables that make up the final database.

https://doi.org/10.1371/journal.pone.0218904.t003

The OTU table (Table 2) consists of a GUID field “Number”, the inclusion of which is standard practice in database tables. The “OTU” field is a unique number given to this taxon and is initially assigned by the user. The subsequent four fields: “scientificName”, “scientificNameID”, “scientificNameAuthorship”, “taxonRank”, provide the link to the WoRMS database. The link is via the “scientificNameID” field, which requires the user to input the appropriate Life Science Identifier (LSID) for the OTU drawn from the WoRMS database. Each taxon in WoRMS receives a unique and persistent identifier, known as the AphiaID. This AphiaID can be expanded to a LSID. WoRMS has implemented LSIDs for all its taxonomic names and they are displayed on each taxon page. The LSID integrates the AphiaID and so is the preferred option, of the two possible fields, to use as a link. The appropriate LSID for an OTU is the lowest formal taxonomic rank that can be assigned to an image. For some taxa, this may be at the species level; however, for many image-based identifications it will be at a higher taxonomic level, such as Family, Class or Phylum level. Use of the LSID field ensures that the OTU can be linked to standard taxonomic nomenclature and the related taxonomic hierarchy. Using this LSID, the other three fields within the database (“scientificName”, “scientificNameAuthorship”, “taxonRank”) can be auto-populated from WoRMS.

The “Morphospecies” field maps onto the “identificationQualifier” field in Darwin Core and allows the input of extra details distinguishing between different morphotypes; for example, Brisingidae msp1, or in the case of sponges, Porifera encrusting msp1, Porifera branching msp1. Thus, entries into this field will be of the form msp1, msp2, encrusting msp1, branching msp1, etc. The “CombinedNameID” field is then autopopulated by adding the “scientificName” and “Morphospecies” fields to give, for example, Brisingidae msp1, Porifera branching msp1. The “CombinedNameID” field can be mapped onto the “taxonconceptID” Darwin Core field. A recommended best practice for the standardisation of entries to the “identificationQualifier” field, specifically related to nomenclatural qualifiers used in image analyses is now in preparation. The “PreviousName” field is not intended to document recombinations of taxonomic nomenclature as this is captured and managed in WoRMS [90]. Rather, this field is to capture changes to the assigned identity of the OTU. For example, where Brisingidae msp1 was later confidently identified to a lower taxonomic level (e.g. Brisinga msp4). This field would capture its former “CombinedNameID”. The inclusion of the “IdentificationFeatures” free text field is intended to provide insight into the visual characteristics that observers are using to distinguish between morphospecies. It is hoped that over time this field will provide the material to start developing novel visual keys. The “IdentificationFeatures” free text field may map onto the Darwin Core “TaxonRemarks” field. Finally, the “IconicImage” field is used to identify the best example image of the OTU present in the database. This field determines the image that is supplied back to the WoRMS database for use on the appropriate taxon page.

The Image table (Table 3) also has a GUID field “Number”, followed by the “OTU” field, which provides the relational link to the OTU table (Table 2). The fields “InsituImageName” and “ExsituImageName” provide the relational link to the images that make up the morphospecies reference image catalogue, and are the name of the image file including the file extension (e.g. IMG10542.jpg). The “ImageCredits” field ensures the owners of the image are identified. We discussed at length how best to include in-situ and associated ex-situ images. While a strong argument was made around the need for good ex-situ images of taxa for use in developing guides for fisheries observer monitoring of bycatch, the group felt the focus of the database should be to provide a tool for the interpretation of in-situ image and video data. Therefore, ex-situ images should only be included in the database together with an accompanying in-situ image of the same individual. As a result, the “InsituImageName” field is required, while the “ExsituImageName” is optional. Where a physical sample has also been taken, this should be indicated in the “PhysicalSample” field as a simple yes or no. If this physical sample has been archived in a museum collection, the catalogue number should be included in the “catalogNumber” field. If it has been identified using molecular techniques, the Genbank or similar ID should be included in the “associatedSequences” field.

The fields pertaining to the Darwin Core class “Identification” concern the identification of the individual in the image, and are self-explanatory (“identifiedBy”, “dateIdentified”, “identificationRemarks”). The “identificationVerificationStatus” field is the indicator of the quality of the identification provided. Durden et al. [21] suggest three categories of image quality: 1 = Unconfirmed: the status of the organism is uncertain, pending field collection and further taxonomic investigation, or the description and naming of a new species, 2 = Provisional: the organism is very likely this species/taxon based on investigation (literature search, consultation with outside taxonomic experts, 3 = Certain: the organism has been collected and has been definitively identified by a taxonomic expert. We have modified these categories as follows: 1 = identified from image only, 2 = identified from image and physical specimens sampled from the same region, 3 = identified from image and physical specimen of the actual individual in the image. There are often instances where an organism has been identified from an image and a specimen collected that has not yet been identified. Under these circumstances the quality score would be 1, but the existence of a specimen noted in the “PhysicalSample” field. Once a specimen is identified the quality score for the image could be changed to 2 or 3.

The fields pertaining to the Darwin Core class “Location” concern where the image was taken. We recognise that for older image data archives, exact position data may not have been recorded. However, the importance of location and depth to field identification of taxa cannot be understated. We feel it is important to ensure that the terminology used to define location is consistent with a published standard. In addition, we want to ensure that, in the future, users will be able to construct local morphospecies reference image catalogues based on selection of an area through mapping software. The Marine Regions database [91] is ideally placed to provide this geospatial standard. Its use will also ensure compatibility with OBIS such that this database can share data with OBIS and vice versa. The required fields “locality” and “locationID” provide the link to the Marine Regions database. The user must input the appropriate “locality” and “locationID” for the image drawn from the Marine Regions database. The “locationRemarks” field is an optional free text field that allows users to capture more detailed location information that is not captured by the options available in the Marine Regions database. The fields “minimumDepthInMeters”, “maximumDepthInMeters” are also required as species distributions are structured with depth [92] and this characteristic is likely to be important in the development of future field guides. The remaining fields, “decimalLatitude”, “decimalLongitude”, are optional so as to accommodate older data and / or sensitive data, for example, from industry partners.

The fields pertaining to the Darwin Core class “Record-level” focus on ownership and origin of the image. Required information includes the name of the institution that owns the image (“institutionID”), a licence document (“dcterms:license”), the name of the person / institution managing right over the image (“dcterms:rightsHolder”), and the terms of access to the image (“dcterms:accessRights”). It is anticipated that a standard licencing arrangement can be agreed to upon submission of material to the database, whereby image ownership is retained by the organisation / individual submitting but use for scientific purposes is freely granted. Use of images for commercial gain would be prohibited. There are existing licencing models for WoRDSS and these can be replicated here. Optional fields allow the identification (“collectionID”) and citation (“bibliographicCitation”) of any previously published or in-house morphospecies reference image catalogues from which the image data have been drawn. The modified field is autopopulated and is the most recent date-time on which the resource was changed.

There are just two fields that relate to the image collection event via the Darwin Core class “Event”. These are the fields “RawImage”, which maps to the Darwin Core “eventID” field, and “habitat”. It is not the intention of this database to capture details of the research cruises, ROV dives, etc., on which the organism images were taken. These details are not overly important to the creation of a field guide. However, should this information be viewed as important in the future, we suggest that images are given the name of the original image from which the organism was cropped, and that this name be extended to consist of the following elements: CruiseNumber_StationNumber_timestamp_imagename. The “habitat” field is able to capture the geomorphological setting in which the organism was observed, e.g. seamount, canyon, mid-ocean ridge. We felt this information might be useful in the development of a field guide. The ideal situation would be to use standardised terms to describe these settings. We suggest the use of Greene et al. [93] as a standard reference; however, the European Nature Information System (EUNIS) [94, 95] or other classification systems may also provide a reasonable standard and the standard used could be indicated when data are submitted. One final field maps to the Darwin Core class “Organism” and is used to capture previous names that have been assigned to the organism in the image (“previousIdentifications”). As with the “PreviousName” field in the OTU table, this field is not used to capture taxonomic name changes, which are well recorded by WoRMS. It is used to capture changes in opinion on the identity of the organism in the image.

The remaining fields in the Image table do not map to Darwin Core fields but do provide additional information that is important to record. The “SubstrateType” field allows details of the substrate on which the organism was observed to be logged. Substrate is an important environmental factor that determines the distribution of species and can play a role in the field identification of taxa. As always though, it is preferable to use standard terminology to record substrate and there are many standards available. Among workshop participants, there was no consensus on methods of substrate interpretation from image data, and the terminology standards used. Some use EUNIS [94, 95], some a modified Folk [96] classification and others percentage of Wentworth [97] sediment size classes. The “SubstrateMethod” field allows the user to indicate the standard they have followed. The “Size” field, standardised to centimetres, is self-explanatory and may be useful in the future development of a field guide. The “ProjectName” field offers the opportunity to credit specific projects with provision of imagery, while the “Link to external database” field enables links to be made to source on-line morphospecies reference image catalogues.

The images are not stored within the table itself but should be provided as separate image files. Those with existing morphospecies reference image catalogues have tended to either paste images into Word or Power Point files, organise their data as Apple ibooks, or organise their images into Phylum or Class level folders. While this is useful at an individual level, and provides the end product required, it limits onward use and is not the appropriate format for a database.

A framework for coordination

While the database structure outlined above provides the means to archive data, the development of a unified morphospecies reference image catalogue requires a management structure to curate the database and manage new data submissions. The WoRMS database provides a model that can be adapted for use with this database. WoRMS is curated by teams who are responsible for different taxonomic groups. Each team is led by an editor who takes overall responsibility for that group. We suggest that the morphospecies reference image database is similarly managed by teams focused at the taxonomic grouping level. The appropriate taxonomic grouping will vary depending on variety represented by each phylogenetic level of the group, and expertise available. For example, Hexacorallia may have separate teams grouped at the Order level (e.g. Scleractinia, Actiniaria, Antipatharia), whereas Echinodermata may have separate teams grouped at Class level (e.g. Asteroidea, Echinoidea, etc.). Each team will consist of experts in taxonomy of the group plus ecologists engaged in field identification of organisms from imagery. We felt it was important to have both taxonomists and field ecologists working together, to ensure that the final database considers both taxonomic rigor and the practical use of the images. Each team will have a nominated lead, and leads will come together, as a steering committee, to ensure that a standard approach to data organisation and curation is achieved across the entire database.

We anticipate a two-stage process whereby an initial effort is made to collate and compile existing morphospecies reference image catalogues at a regional level using the new database structure described above as a data transfer format. In this format not all fields will need to be populated by those submitting data, for example the GUID fields, and other autopopulated fields. This initial effort to compile existing catalogues would be followed by new and on-going submissions of data, including from those encountering new organisms not in the existing database, and from those with higher quality images of organisms already listed in the database (Fig 2). We have committed to stage 1 of this process and morphospecies reference image databases held by all authors have been entered into this new database format and submitted to a central repository. Curation teams are now bringing these data submissions together into a single database.

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Fig 2. A conceptual model for how the developed framework will operate.

https://doi.org/10.1371/journal.pone.0218904.g002

Stage 2 of this process will involve the effort of the global community and could potentially be a focus for the up-coming UN Decade of Ocean Science for Sustainable Development (2021–2030). This could be a very light-touch involvement, where end users simply submit images of new organisms not currently present in the database to the database for inclusion (Fig 2). Or it could be a more targeted and active involvement aimed at raising the quality of the data already in the database. For example, principal investigators of research cruises could actively help to move taxa from “identificationVerificationStatus” 1 to level 3 by targeted in-situ imaging and collection of organisms on an opportunistic basis. Raising the quality of the data in the database should be a priority over the next decade, and with that the recognition of the importance of skills in taxonomy and species identification. A concerted effort to ground truth species identified from imagery only, should be made in order to provide robust tools with which to monitor ecosystems.

Ultimately, it is not the database per-se that end users require, but the end products (photo-guides in book format, taxonomic tree for annotation software, etc.) that can be pulled from the database. This will require the development of a web interface that draws on the underlying database to produce multiple end use formats (Fig 2). This aspect of the project represents the next stage of development and is anticipated to take place over the next two years.

Immediate advances enabled by the development of a common reference standard

We have proposed a common structure for a database from which a morphospecies reference image catalogue can be built. Our initial development is focused on the North Atlantic deep-sea benthos as a case study. However, the structure developed is applicable to any marine region or habitat, and may also be used for terrestrial ecosystems. Individuals need only adopt the structure and populate the tables with their own data. The Standardised Marine Taxon Reference Image Database (SMarTaR-ID) will enable different researchers to bring their data together in a common morphospecies reference image catalogue at an appropriate time. Within the North Atlantic deep sea that time is now. The implementation of coherent monitoring programmes to assess biological biodiversity in marine waters are mandatory under the EU Marine Strategy Framework Directive (MSFD 2008/56/EC), and all European nations are required to monitor sites of community importance every six years. An image catalogue, such as the one herein proposed, will be a powerful instrument to support monitoring efforts, particularly in poorly surveyed regions. We have outlined a framework by which data can be brought together, curated, and new submissions managed going forward, which follows a successful model already applied by WoRMS.

We anticipate the introduction of a common reference standard for the deep sea to enhance significantly our understanding of megafaunal biodiversity by enabling multiple researchers to combine existing datasets to address long-standing ecological questions. This is particularly the case for hard substrate habitats that dominate features, such as seamounts, ridges, banks, abyssal hills, canyons, and areas of the continental slope, and for which image-based techniques remain the only effective means of survey. Past exploration of the deep-sea epibenthic megafauna generated many paradigms, but these were largely built on data obtained using trawls and sledges. Video and still image-based tools have facilitated quantitative sampling of previously inaccessible habitats; and the resulting new findings are challenging the prevailing view of deep-sea ecosystems [98]. However, these new datasets are often limited to individual features or feature types (e.g. seamounts: [99, 100], abyssal hills: [101] slopes: [66, 102, 103] canyons: [48, 64, 104, 105, 106]; ridges: [107], fracture zones: [100], and hydrothermal vents: [108]) and thus limit our ability to generalise findings. In their review of major outstanding questions in deep-sea biogeography [109] concluded, among other things, that an integrated biogeographic framework of hard-substrate areas of the deep sea was required to yield more realistic estimates of endemism/cosmopolitanism. It has been repeatedly argued that concerted efforts to link existing independent data streams together to examine long‐standing questions of deep‐sea diversity are very much needed in order to move the field forward [109, 110]. The proposed database will facilitate these advances.

We anticipate that this common reference standard will provide an invaluable tool for environmental managers, industry and wider stakeholders. For environmental managers, it will, for example, enable the development of clearer descriptions and definitions of habitats of conservation concern. For example, deep-sea sponge aggregations potentially qualify as Vulnerable Marine Ecosystems (VME) under the United Nations General Assembly (UNGA) Resolution 61/105. They are also classed as a threatened and declining ecosystem under Annex V of the Oslo-Paris (OSPAR) Convention for the Protection of the Marine Environment of the North East Atlantic. However, comprehensive descriptions of deep-sea sponge aggregations, and specifically the component taxa that compose different types of aggregation, are lacking. In addition, basin-wide data on the distribution of sponge VME indicator taxa are only available for those species / genera whose appearance both in-situ and ex-situ are similar (e.g. Geodia, Hyalonema, Pheronema). For many sponge species, the lack of taxonomic resolution possible when identifying sponges from image data hinders progress in management and conservation of these taxa by limiting our ability to 1) effectively describe sponge VME composition and diversity, and 2) pool data to determine basin-wide distributions. A common morphospecies reference image catalogue will provide a standard reference to use in VME descriptions in the absence of confirmed taxonomic identification of species from physical samples. It will also facilitate the production of basin-wide models of the distribution of habitat forming sponge taxa to support spatial management decisions [111].

For industry, implementation of a standard approach to referencing morphospecies between industry and regulators will facilitate a much more effective impact assessment associated with licensing and consent processes, as well as subsequent monitoring approaches. Often in industry, a range of sub-contractors are used for routine survey and monitoring work by the various industry bodies. Therefore, morphotypes are produced per project with no consistency between sub-contractor or between years in long-term monitoring as data are rarely shared. This standardisation would increase industry and regulatory comparison across applications and across industries to facilitate cumulative impact assessments, thus allowing better understanding of impact at feature and site levels, as required in nature conservation legislation. For industry, this could also decrease levels of risk associated with the assessments as well as decreased analysis time and costs for survey data, and would be a particularly powerful tool if industry could include their own data in the database and play an active role in providing images and survey data.

The need for a standard approach in industry was recently highlighted by the development of the deep-sea mining industry in the Clarion Clipperton Zone (CCZ) of the central Pacific. Here, baseline data collection is taking place, commonly including seabed imaging-based assessments of megafauna [29, 47, 65, 112, 113]. Without a consistent morphospecies reference image catalogue it is difficult to compare studies and generate regional syntheses. This greatly hampers conservation and management efforts, which commonly rely on information on biodiversity, species ranges and behaviour–ecological properties that are difficult to assess without good quality and consistent identifications. Recent work to document megafaunal diversity will help (e.g. 47, 65), but widely adopted and regularly updated catalogues will be vital for improving scientific understanding and effective environmental management.

A common morphospecies reference image catalogue may also serve as a tool to support the identification of taxa from fisheries bycatch by fisheries observers (e.g. [114]. While our proposed database focuses on in-situ images of taxa, we advocate, and have provided for within the proposed database structure, the collection of ex-situ images of taxa. There are a number of existing image guides designed for use by fisheries observers that provide ex-situ images of VME indicator taxa (Table 1). This database could supplement existing guides by providing additional imagery. Interestingly, it may also provide a link between in-situ and ex-situ taxon identification, which may ultimately allow fisheries bycatch data to be pooled with in-situ image data, again broadening our understanding of species distributions (e.g. [115]).

Finally, the simple act of combining multiple existing morphospecies reference image catalogues will advance the overall quality of current identifications. It is important to remember that image data is no substitute for a physical sample, and a long term goal in this endeavour is that all image reference material has been ground truthed by a physical specimen identified by a professional taxonomist. At present different research groups have images of different species for which the “identificationVerificationStatus” level is 3 (the highest level, confirmed by physical specimen). By bringing these reference image sets together, we will collectively have more species that can be identified by reference to images in which we have the highest level of confidence of the animal’s identification.

Future advances enabled by the development of a common reference standard

The development of a common reference standard has the potential to advance significantly the field of offshore and deep-sea marine ecology. The ability to pool datasets across time and space will allow us to address a greater range of questions about the offshore and deep-sea benthic ecosystem than is currently possible. Critically, it will enable us to raise standards of identification from image data through the development of training materials and quality control measures. Efforts to develop such tools for shallow water have been undertaken by the UK’s National Marine Biological Association Quality Control scheme (NMBAQC). This programme is steered by a range of academic and governmental organisations, and provides guidance on best practice, as well as identification guides, taxonomic workshops, training exercises and quality control ring tests.

There will remain some potential shortcomings on the use of such catalogues related to uncertainties in species identification due to the method of image collection and scale. The ability to zoom-in on specific features of species with ROV cameras means ROVs may provide better imagery for identification than AUVs or drop-down cameras, particularly in cases where species look remarkably similar and occupy overlapping environmental niches. For example, the octocorals Acanthogorgia armata and Acanthogorgia hirsuta can only be distinguished if close up images of the polyps are taken, otherwise identifications have to be left at genus level. Nevertheless, the development of a common reference standard will expose these limitations to a wider audience, and help develop agreed international guidance around the taxonomic levels to which it is appropriate to identify when interpreting image data.

In the longer term, regional field keys are required for use in survey and monitoring of the deep-sea ecosystem. The construction of tools that allow others to identify taxa reliably and consistently in the field is perhaps one of the most underappreciated roles for taxonomists. It is also one of the most challenging roles as taxonomists are often not engaged in field identification, and therefore a gap exists between the generator and end user of a key [116]. The starting point for the development of any key is a standard reference against which to compare new observations. In traditional taxonomy, this is the type specimen, a physical specimen from which a species is described, that is subsequently archived in a museum. The development of dichotomous or polytomous keys is then achieved by measuring the variability in observable characteristics within examples of a taxon and between taxa, then selecting characteristics that best discriminate between taxa for a given region / group. These characteristics are then organised into pathways of character state choices (steps) that lead to identifications.

In order to move forward with the development of much-needed field keys to deep-water taxa, we must first develop an appropriate standard reference against which to assess new observations. This reference point remains the holotype specimen. Our proposed database will establish an image ‘similitype’ (or a series of images that contribute to the similitype) to accompany a physical specimen that has been identified with reference to the holotype of a species, or through matching DNA sequences to other specimens identified with reference to the holotype of a species, and thus link traditional taxonomy to field identification. This approach will provide a much-needed strategy to advance the taxonomic description of species based on multisource information collected by both ecologists and taxonomists [117, 118]. If researchers use and contribute to this common reference standard, a library of images with examples of each taxon will be built up over time. This library of image examples can then be used to understand both the observable characteristics within a species or higher taxonomic level grouping, and the variability in these characteristics in image-based data. Where possible it is desirable for these characteristics to be those used in traditional taxonomic keys. However, this will not be possible for all groups to all levels of the taxonomic hierarchy. For example, while it is possible to use traditional taxonomic features to determine the order of some coral taxa from image data, it is not possible to do this for anemone taxa, which rely on internal characteristics for positive identification. It is likely that novel characteristics, combinations of characteristics, as well as the use of circumstantial information (e.g. environmental characteristics), will be required to enable reliable and consistent field identification of organisms, but even then some morphotypes will remain as morphotypes.

Multi-access keys (also known as matrix based or free-access keys) may be more appropriate than dichotomous or polytomous keys (also known as single-access keys) for use with image data as they, by their nature, have multiple access points [116, 119]. Single-access keys place a logical order on the use of characteristics, with each step in the decision tree taking the user along a predefined pathway that progressively narrows the number of possibilities for the identification of the animal. If a characteristic is not visible at any step along this pathway, the choice required by the user is unanswerable and further progression is not possible. Views of organisms in in-situ image data can be highly variable, and it is likely that in any one image only some features will be visible. This may limit the utility of single-access keys with image data. Multi-access keys enable the user to determine the sequence of choices where the user can select from the list of characteristics offered in order to arrive at an identification. In the context of image data, this would allow the user to employ all visible characteristics (and potentially environmental information) to arrive at an identification. Multi-access keys are more suitable for computer-aided identification tools [116, 119]. This also makes them a promising tool to use with image data where analysis is computer based.

If we are to move forward with the application of AI and CV to the identification of taxa, we must have a common reference standard. Our proposed database aims to meet this need through future development that will enable the database to interface with image annotation software, such as Squidle [120] and BIIGLE 2.0 [121,122]. These annotation softwares enable users to mark the x,y position of organisms within an image and attribute this point / polygon with a taxon identification. This process of image annotation is the means by which ecologists extract semantic data from an image in order to then apply numerical and statistical analysis to these data and answer ecological questions. This annotated dataset is also the base data needed in the development of AI and CV algorithms. These algorithms require large numbers of images to “learn” the features that distinguish the different OTUs to which they have been exposed, and which of these features are characteristic of each OTU [123, 124]. If researchers are able to use a common reference standard, thus extracting the same information from an image regardless of who is annotating it, then collated datasets from various origins could reach the size needed to train and test CV algorithms (acknowledging challenges of observer bias). Their use within the field of deep-sea benthic ecology, will then increase exponentially through accumulation of data, skill and experience. This can only serve to facilitate the development of CV and bring us closer to automation of image annotation and data extraction.

Ultimately, standardisation of tools and methods is central to long-term monitoring and assessments of ocean health. Woodall et al. [125] recognised this and produced GOSSIP (General Ocean Survey and Sampling Iterative Protocol), which outlines a framework of 20 biological, chemical, physical, and socioeconomic parameters that allow marine scientists to generate comparable data on the function, health and resilience of the ocean. There are several international efforts underway to try and harmonise ocean observing in the areas of biology and ecology, including the efforts of the Group on Earth Observation–Biodiversity Observing Network (GEO-BON) and the Global Ocean Observing System (GOOS) panel on biology and ecosystem–Essential Ocean Variables (EOVs). These efforts are also being informed by international efforts, such as the Deep Ocean Observing Strategy (http://www.deepoceanobserving.org/), which is adding deep ocean context to GOOS EOV specifications. There are more than a dozen regional alliances internationally, which are implementing the GOOS vision with international coordination by the IOC. Together these organisations are forming a means for efforts from individual observers, as well as local to international bodies, to join together to realise the power of ‘big data’ in observing and understanding change. National-level data management and communications groups affiliated with GOOS are now working to include tools, such as automated image classification, into their information technology systems. The common reference image standard described above will therefore contribute to global efforts under GOOS.