dataesr/scientific-paragraphs-categorization huggingface dataset:
The GOOS in EBS Regions Because VOICE is motivated by the application of the FOO and as such is linked to GOOS, we first consider how ocean observing efforts in the EBS regions are visible on the global from the perspective of GOOS. To track long-term observing efforts, GOOS relies on the information collected and made available by the Joint Technical Commission for Oceanography and Marine Meteorology (JCOMM) in situ Observing Programs Support Centre (JCOMMOPS), which assembles information about when, where, and which observations have been executed and by whom. JCOMMOPS does not manage the actual observational data coming from various observational platforms but, rather, assembles all their metadata information (including the link to data centers that host the observational data). By making use of the metadata in JCOMMOPS, we present here a first assessment of EBS ocean observations.
JCOMMOPS operates at the boundary between "coordination of observational elements" and "data management and information products" (the second and third FOO pillars). This means that if missing information needed for analyses has not been incorporated into an integrated data aggregate or it is not adequately described in the metadata, then JCOMMOPS can coordinate the needed updates. Likewise, it could also be that the metadata are not complete or made available via the agreed data assembly centers. Note that our assessment only considers the GOOS observing networks: profiling drifters Argo, Ship-Based Hydrographic Investigation Program, Global Sea Level Observing System, global network of open ocean time series stations (OceanSITES), Ships Observations Team, and Data Buoy Cooperation Panel.
We use an example from the upwelling EBS region off West Africa in the Southeast Atlantic considering the VOICE relevant exclusive economic zones (EEZs) of the following countries: Republic of Congo, Republic of Angola (VOICE representative), Namibia (VOICE representative), and the relevant part of the South African EEZ (VOICE representative). JCOMMOPS lists for this region 46 observing platforms when we consider all possible levels of observing status  (probable, confirmed, registered, operational, closed, inactive). From the JCOMMOPS metadata, it is evident that the platforms include 25 Argo floats, 11 Ships Observations Team ships, one Global Sea Level Observing System sea level station, and nine Data Buoy Cooperation Panel surface drifters. The parameters observed (numbers in brackets denote the number of platforms in the JCOMMOPS metadata base) are subsurface pressure, temperature, salinity (25), bulk sea-surface temperature (8), air temperature (7), air pressure (7), relative humidity (7), and oxygen (1).
Likewise, the existence of the data and the observing efforts are not visible through JCOMMOPS and hence are not recognized as a contribution to the GOOS. Tchipalanga et al. (2018)  provide an example of a typical "bottle neck" case in the FOO context: they found that further time series research on physical data from RV Nansen cruises revealed new insight into fish stock dynamics, creating the potential for prediction and thus better management. In the FOO ocean observing value chain framework, the  Tchipalanga et al. (2018)  example demonstrates that by bringing the third FOO Pillar "Data Management and Information Products" from Pilot readiness levels 5 (verification) to readiness level 6 (demonstration of availability, use, interoperability), a different class of ocean observing product is made available in the Angolan EBS.
It was decided that the implementation plan must include a strategy for "data sharing standards" for multidisciplinary, multiplatform observations developing scientific strategies and also for creating suitable products to respond to the societal requirements identified for observing systems. As an outcome, an overarching question, linked to multiple societal drivers for OMZ observing (e. g. , fisheries, aquaculture, OMZ expansion, biodiversity changes, greenhouse gas emissions, carbon sequestration), was agreed upon for the proposed IMSOO-OMZ activity: "How do changing OMZs affect the spatiotemporal distribution, productivity, and trophic structure of benthic and pelagic communities?" 2 https://swot. jpl. nasa. gov/mission. htm 3 Implementation of Multidisciplinary Sustained Ocean Observations However, realizing that many ocean observing value chains are associated with the above question, more specific but related scientific questions helped to narrow down the assessment space: • What are the physical mechanisms controlling/influencing oxygen supply to OMZs? • What are the biological components controlling/influencing oxygen consumption? • How does bentho-pelagic coupling affect biogeochemical and ecological feedbacks? • What is the role of microbial community metabolism on the development of OMZs? • What is the bottom-up effect of changing OMZs on the trophic structure? • How does the fish biomass and community structure change in relation to a changing OMZ? From this overarching set of questions, the IMSOO-OMZ working group discussed approaches to propose fit-for-purpose, multidisciplinary ocean observing systems for OMZ regions.
For practical reasons, but acknowledging that the results will not be representative from a statistical point of view, we sent our questionnaire to a small sample (a dozen) of potential respondents that extended the initial IMSOO-OMZ group. Based on recommendations, one or two regional champions per EBS were selected that acted as representatives but without a formal application process. The questionnaires were completed by individuals or groups of two or three persons who had reasonable knowledge of the ocean observing efforts in the studied EBS regions. FOO design elements were assessed for requirement processes, coordination of observational elements, and data management and information products. All aspects of questionnaire design and its results are seen as contributions to the anticipated end product of the VOICE initiative, which is a blueprint of multidisciplinary sustained ocean observing in EBS regions.
Requirement Setting Processes: The First FOO Pillar The first FOO pillar (FOO1) seeks to assess the societal needs in terms of the environment or ecosystem information required to address specific observing objectives. This pillar also includes the observing approach to be used to respond to the observing objectives of relevance to VOICE. In other words, it asks, "What observing product is required?" and "How do we need to observe and integrate the data to create the product?" To assess the readiness level of the FOO1, a list of questions was formulated in the questionnaires (Table  2 ). The analysis was made by building a master answer file with all EBS regions and designing a graphical representation of the synthesized information (Figures  5, 6 ). We included in the questionnaire design all EOVs, 5  related to physics, climate, biogeochemistry, biology, and ecosystems.
g. , catch, fleet dynamics) related to their harvest. Often, the specific area or region of interest is based on the life cycle and biology of the population, such as migration patterns of different life stages (e. g. , eggs, larvae, juveniles, adults) and the degree of genetic mixing with other nearby populations of the same species. The fish and fisheries monitoring data are typically analyzed for the entire region or for subareas within the region and they indicate how the catch is reported (e. g. , reporting zones) and how the fishery is managed (e. g. , area closures). FIGURE 5 | Societal impacts and benefits are driving investments in a given oxygen minimum zone (OMZ) region of the world ocean. Shading indicates that these regions rely mostly on partnerships with foreign countries to carry out their monitoring activities.
Q2 What is the regional dataset or information product available from the variable measurements, if any? Q3 Is the data/information product freely available? FIGURE  9  | Current status of essential ocean variable (EOV) measurements with respect to phenomena being monitored (see Figure  8 ) in all oxygen minimum zone (OMZ) regions. Different icons represent different observing approaches. Please note that the same symbol is used for hydrographic surveys and ships of opportunity lines (grouped under "ship-based"), one symbol for all: "fixed-point" observations (buoys, moorings), and one symbol for all "remote sensing" (airborne, satellite, etc. ). phenomena listed using many of the biogeochemistry, biology, and ecosystem EOVs. Many phenomena are covered in the Peru-Chile Current system, West Africa, and the Northern Indian Ocean, utilizing the basic EOVs, i.
very few databases exist. About half of EBS regions apply QC measures on biogeochemical, biological, and ecosystems EOVs. Remotely sensed data such as sea surface height and ocean color are usually well utilized since these databases are open access. Upper trophic levels, live coral, macroalgal canopy, seagrass, and mangrove covers are the least monitored and seem not to be archived in any database. However, there are likely more activities related to variables that our questionnaire did not capture, for instance, the fisheries databases for large pelagic fish compiled by several regional fisheries management organizations (RFMOs) whose areas of competence encompass the EEZ part of the OMZ in studied EBS regions. With respect to information products listed in the questionnaires, some regions have produced either data climatologies (Peru, Namibia), parameter maps, data visualization tools, time series or parameter sections and figures, and indices (US West Coast, Peru, Cape Verde, and Namibia) and make modeling results available (Senegal, Namibia).
These include turtle, bird, mammal abundance, and distribution EOVs, fish EOVs, or habitat type EOVs, in which cases regional champions often did not manage to consult with specialists in the field. This is true despite the fact that all regions identified fisheries as their primary ocean observing objective. This bias is particularly important to note in the case of higher trophic level species as they constitute key indicators of marine ecosystem health. Future assessments should ensure wider consultation with local experts, RFMOs, and international networks, such as the Climate Impacts on Oceanic Top Predators-a regional program of the Integrated Marine Biosphere Research. The selected OMZ regions responded individually to the questionnaire for fish abundance and distribution EOV. For large pelagic fisheries, the Secretariats of the tuna RFMOs, namely, WCPFC (West Pacific), IATTC (East Pacific), ICCAT (Atlantic), and IOTC (Indian) compile catch, effort, and size data provided by the national administrations on the basis of scientific sampling.
Commercial fisheries data are available in aggregated format (1 • or 5 • spatial grid) through these RFMOs. High-resolution information is held in national databases but is not available for public use. Exchange agreements may have to be negotiated for access to these data. This may explain the lack of integration of oceanographic data from oceanographic programs with fisheries including biological and environmental data. These RFMOs may lack the purely oceanographic and biogeochemical expertise. Initiatives are under development in ICCAT and IOTC toward ecosystem assessments incorporating biophysical interactions  (Juan-Jorda et al. , 2018a,b). Added expertise based on the integration of relevant sustained oceanographic observations would be very welcome by these stakeholders. The issue of linkages between the three FOO pillars appeared to be a major weakness of the overall FOO concept making the readiness level assessment individually for each pillar a challenging task.
Gaps in "Data Management and Information Products" The third FOO pillar requires the largest human resources input to increase readiness levels in EBS regions (Figure  7 ). For the Angolan EBS,  Tchipalanga et al. (2018)  demonstrate how capacity development combined with a release of observational data can create new ocean observing products. The existence of databases, application of standardized QC procedures for real-time data, and development of data repositories or increased accessibility of data repositories are critical issues. Routes to be followed in addressing these issues will have to be discussed and agreed upon, considering national budget and staff constraints and varying policies but also international opportunities and cooperation. There is presently no simple and direct connection between the Food and Agriculture Organization and GOOS databases.
An assessment of readiness level for existing observing, data availability, and integration is provided and synthesized to inform local, regional, and even global stakeholders. We found that an observing strategy for fisheries and ecosystem management exists in all EBS regions, but maturity levels and observation capabilities differ. The readiness level for the VOICE elements is derived for the regions and points at system weaknesses. A major weakness is the difficulty to easily connect fisheries-related data and oceanographic databases. Potential routes for developing strategies for readiness level improvements include the need to provide interoperable oxygen and other environmental data that are colocated with fisheries data in space and time so they can be easily taken up in the fisheries management process and also by the marine aquaculture sector.
The vertical discontinuity of echotraces as recorded by research echosounders onboard scientific and fishing vessels can serve as a proxy of the oxycline depth providing high-resolution data of fish school abundances and habitat compression by the oxycline  (Bertrand et al. , 2011). If one can merge all available datasets together, more knowledge can be gleaned for the system under study. Oxygen and other environmental conditions can then be considered in the stock assessments in EBS regions and the information taken up to inform fisheries management. • Enhance the observing capacity for the particulate matter EOV by deploying more sediment traps on moorings. However, these require regular servicing and tend to integrate observations over time. Other options are to record particulate backscattering or beam attenuation with sensors implemented on the Biogeochemical Argo floats and/or to measure particulate organic carbon concentrations using Niskin bottles during ship surveys.
ACKNOWLEDGMENTS We thank all the  IMSOO-OMZ group  for engagement and input that was invaluable in conceiving the idea of VOICE. We are grateful to the  Monterey Bay Aquarium Research Institute (MBARI) in Monterey, CA, United States , for hosting the first  VOICE Science Plan Workshop , and to  GEOMAR in Kiel, Germany , for hosting the second  VOICE Science Plan Workshop. We thank the questionnaire respondents for their important contributions to the VOICE study. We also thank the two reviewers and the editor, LL, for their constructive comments which helped to improve the manuscript.
Next generation sequencing and mass spectrometry technologies have recently expanded the availability of whole transcriptomes and proteomes beyond classical model organisms in molecular biology, even in absence of an annotated genome. However, the fragmented nature of transcriptomic and proteomic data reduces the ability to interpret the data, notably in non-model organisms. Networkbased approaches may help extracting important biological information from -omics datasets. The reproductive cycle of the freshwater crustacean Gammarus fossarum. provides an excellent case study to test the relevance of a network analysis in non-model organisms. Here, we illustrated how the use of a co-expression network analysis (based on Weighted Gene Co-expression Network Analysis algorithm, WGCNA) allowed identifying protein modules whose expression profiles described germ cell maturation and embryonic development in the freshwater crustacean Gammarus fossarum.
Proteome datasets included testes, ovaries or embryos samples at different maturation or developmental stages, respectively. We identified an embryonic module correlated with mid-developmental stages corresponding to the organogenesis and it was characterized by enrichment in proteins involved in RNA editing and splicing. An ovarian module was enriched in vitellogenin-like proteins and clottable proteins, confirming the diversity of proteins belonging to the large lipid transfer family involved in oocytes maturations in this freshwater amphipod. Moreover, our results found evidence of a fine-tuned regulation between energy production by glycolysis and actin-myosin-dependent events in G. fossarum spermatogenesis. This study illustrates the importance of applying systems biology approaches to emergent animal models to improve the understanding of the molecular mechanisms regulating important physiological events with ecological relevance.
framework to investigate the rich content of information from omics datasets  3, 4. Weighted Gene Co-expression Network Analysis (WGCNA) is a systems biology method originally conceived for describing correlation patterns among genes across microarrays data  4. One of the main advantages of this statistical model is to be data driven and it represents a great opportunity in non-model organisms for which genome sequences, gene annotations, or functional pathways are not still available. WGCNA has been successfully used also for count-based (i. e. RNA-Seq based) gene expression data  5, 6. However, shotgun proteomics data have been poorly explored using WGCNA to date, despite the possibility to apply this approach to the spectral count metric largely used in this phenotypic methodology  7. Concurrently, gene co-expression network analyses of transcriptomic data have provided useful insights in understanding fundamental biological processes in many arthropod species.
In particular, we aimed to use a co-expression network analysis through a pooled proteomics dataset including testes, ovaries or embryos at different maturation or developmental stages to identify protein pathways regulating germ cell maturation and embryonic development of G. fossarum. This provides an excellent case study to test the relevance of a network analysis in non-model organisms, since the structure and the molecular physiology of sperms, oocytes, and embryos are highly differentiated.
Materials and Methods Proteomics datasets. We retrieved protein abundance data measured as spectral counts (SC) from two published shotgun proteomics datasets obtained by high-resolution tandem mass spectrometry using a LTQ-Orbitrap XL instrument. Only proteins validated with at least two different peptides were retained  2, 10. The datasets were merged in a matrix n × m, with n indicating the total number of proteins and m the total number of samples. Finally, the matrix consisted in 1,199 protein abundance data expressed as SC for 85 samples (Supplementary Data 1). The samples included G. fossarum ovaries (individual whole paired organs) and embryos (6 embryos per female) proteomes 10 at different stages of oocyte maturation and embryo development  10, 14. Five biological replicates for each stage (AB, C1, C2, D1, D2 for ovaries and S1 to S5 for embryos) were included in this dataset.
Testes were individually sampled from male organisms at 6 stages of spermatogenesis, namely at pre-copula stage (mature male in amplexus at the end of the spermatogenesis), and days after copulations (J0, J1, J2, J3, J4, J7)  2, 15. At J7 (7 days after copulation), the spermatogenesis cycle is completed. Five biological replicates for each spermatogenesis stage were included in this dataset. The annotation of the 1,199 proteins was updated compared with the original studies, by using a semi-automatic pipeline using DIAMOND  18. Briefly, protein sequences identified by peptides that matched an ORF entry of the GFOSS RNA-seq database 2 were blasted against the NCBInr and Swissprot databases. The closest predicted protein (indicated with NCBInr ID or SwissProt ID) and its E-value were associated with the corresponding GFOSS contig.
The Gene Ontology (GO) annotation of proteins homolog found in Swissprot database was performed using data provided by the GO Consortium through the GO website and the web application Amigo (http://amigo. geneontology. org). Two levels (1 and 2) of GO annotation were extracted. Data pre-processing and normalization. In order to identify possible outliers, sample clustering was performed using the hierarchical clustering function implemented in the lumi R package. One sample (AB-n1) was identified as an outlier and thus excluded from the final dataset. In particular, this sample was characterized by the lowest number of total measured SC. Principal Component Analysis (PCA) was used to analyze and identify the variables explaining the maximum variance associated to the proteomic data of the different developmental stages of G.
fossarum gonads and its embryos. In order to avoid the noise and poor reproducibility associated with low abundant proteins, proteins with less than three SC in the most abundant condition were filtered out  19. Finally, a dataset comprising 84 samples and 375 proteins was retained for further analyses. Before network modeling, counts need to be normalized across samples  4, 7. Normalization was performed using the calcNormFactors function from the R package edgeR  20. While this function has been originally implemented for RNA-Seq count data, it applies equally well to spectral count data  19. The calcNormFactors function automatically adjusts for the total number of spectral counts per sample (also known as original library size in RNA-Seq experiments) and normalizes for protein composition, taking into account the fact that highly abundant proteins tend to contribute more peptide/spectra than those lowly abundant  20.
calcNormFactors calculate a normalization factor that was applied to normalize SC values of each protein in each sample. Network analysis. The network properties of the proteome of the reproductive system of G. fossarum were analyzed using the R package Weighted Gene Coexpression Network Analysis (WGCNA)  3, 4. Shotgun proteomics gives access to a count-based quantification of peptides that can be aggregated to a contig/protein level when a species-specific RNA-Seq database is available, as it is the case for the datasets analyzed in this study. Thus, we used the WGCNA package to investigate the count-based proteomic database of gonadic and embryonal tissues obtained from the freshwater amphipod G. fossarum. WGCNA was used to construct a protein co-expression network, identify protein modules in the network and correlate the identified modules to external information, such as tissue and stage.
Module eigengenes are defined as the principal component of each module and they represent the protein expression profile in a module. Module eigengenes allow to explore how related the modules are and the correlation between modules and phenotypic traits, such as tissue type or maturation/developmental stages. Network properties, such as total (k total ) or intramodular (k within ) connectivity or module membership (ME) have been used to identify proteins that show the high degree of connectivity within a module (hub proteins). Due to their central position in the network, hub proteins are expected to play important biological role within their module. Reproducibility. An R script combining the whole analysis pipeline is available as Supplementary Document 1. Spectral count data and the associated metadata are available as Supplementary Data 1 and Supplementary Data 2, respectively.