dataesr/scientific-paragraphs-categorization huggingface dataset:
Colours indicate the number of stations available for each variable as a function of month and hemisphere of data acquisition ("N" -Northern Hemisphere; "S" -Southern Hemisphere). The empty (white) squares indicate no data for that month. cloud-free MERIS product; (2) an in situ reflectance data set where an in situ reflectance is available simultaneously with an in situ measurement of chlorophyll-a concentration and/or total suspended matter; and (3) a simulated data set where reflectances were generated by a radiative transfer model. This work used the match-up data set, which includes most of the in situ measurements, and is available at https://doi. org/10. 1594/PANGAEA. 841950 (Nechad et al. , 2015b). The match-up data set provides optical, biogeochemical, and physical data collections at 17 sites across the globe.
MARineEcosytem DATa (MAREDAT) MAREDAT database is a global assemblage of pigments measured by HPLC  (Peloquin et al. , 2013a)  from combination of 136 independent field data sets, solicited from investigators and databases. The database provides high quality measurements of taxonomic pigments including chlorophyll a and b, 19'-butanoyloxyfucoxanthin, 19'hexanoyloxyfucoxanthin, alloxanthin, divinyl chlorophyll-a, fucoxanthin, lutein, peridinin, prasinoxanthin, violaxanthin, and zeaxanthin. The database is available through PAN-GAEA (https://doi. org/10. 1594/PANGAEA. 793246, Peloquin et al. , 2013b). For this work, only measurements of total chlorophyll-a flagged with high quality were used. The time of day was unavailable and was set to 12:00:00 (UTC). These observations were flagged with "1" in column "flag_time".
data are key for the validation of the most recent ocean colour missions (e. g. Sentinel 2B and Sentinel 3B) and for other activities such as System Vicarious Calibration. Future improvements of this data collection could be made by continuing to analyse the available data from the projects, cruises, and archives described in the present work (namely SeaBASS archive which hosts many bio-optical in situ data) and find new data sources, while making sure that the already compiled data sets are the most updated ones following scientific advances and improved quality control measures. (grant OCE 09-26766). We thank Yves Dandonneau for allowing the use of GeP&CO data. We thank ICES database on the marine environment  (Copenhagen, Denmark, 2014)  for allowing the use of their archived data, and Marilynn Sørensen for the help with questions about the ICES data set.
Acknowledgements. This paper acknowledges funding from the  ESA OC-CCI  project (grant number  4000101437/10/I-LG ), the  EU-METSAT "Multi-mission Ocean Colour Algorithm Prototyping" (OMAPS) , and the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement  810139 :  Project Portugal Twinning for Innovation and Excellence in Marine Science and Earth Observation -PORTWIMS. This work is also a contribution to project  PEst-OE/MAR/UI0199/2014. We would like to thank the efforts of the teams responsible for collection of the data in the field and of the teams responsible for processing and storing the data in archives, without which this work would not be possible. We thank  Tamoghna Acharyya  at  Plymouth Marine Laboratory  for his initial contribution to this work.
We thank the  NOAA (US)  for making available the MOBY data and  Yong Sung Kim  for the help in questions about MOBY data. BOUSSOLE is supported and funded by the  European Space Agency (ESA) , the  Centre National d'Etudes Spatiales (CNES) , the  Centre National de la Recherche Scientifique (CNRS) , the  Institut National des Sciences de l'Univers (INSU) , the  Sorbonne Université (SU) , and the  Institut de la Mer de Villefranche (IMEV). We thank  ACRI-ST, ARGANS , and  ESA  for access to the MERMAID Database (http://hermes. acri. fr/mermaid,last access: 18 December 2022). We thank  Annelies Hommersom ,  Pierre Yves Deschamps , and  David Siegel  for allowing the use of MERMAID data for which they are Principal Investigators. The AMT is funded by the  UK Natural Environment Research Council  through its  National Capability Long-term Single Centre Science Programme, Climate Linked Atlantic Sector Science  (grant number  NE/R015953/1 ).
We also thank  Giuseppe Zibordi  for data quality control and  Brent Holben  and his team from  NASA Goddard Space Flight Centre  for making the data available online. The AWI data set was supported by the captain, crew, and other scientists at the specific  German RVs  ( Meteor, Heincke ,  Maria S. Merian ,  Polarstern , and Sonne) expeditions and funding by the  Helmholtz Association (HGF Innovative Network Funds Phytooptics, Helmholtz Infrastructure Initiative FRAM) , the  European Union  ( Seventh Framework Programme   SHIVA-226224FP7-ENV-2008-1020 1 ), the  BMBF  ( OASIS FK03G0235A  and  AS-TRA FK03G0243A ), the  DFG  (# 268020496-TRR 172  project  C03  within the  Transregional Collaborative Research Center Arc-tiC Amplication: Climate Relevant Atmospheric 1023 and Sur-faCe Processes , and  Feedback Mechanisms  (AC)3), the collaborative project  OLCI-PFT  (ACRI-AWI Offer # 209-180104 ), and the  ESA   656 S5P+Innovation Theme 7 Ocean Colour  ( S5POC )  project (no.
seadatanet. org,last access: 18 December 2022). We thank  Emmanuel Boss  for the TARA data. Funding for the collection and processing of the TARA data set was provided by  NASA   Ocean Biology and Biogeochemistry program  under grants  NNX11AQ14G ,  NNX09AU43G ,  NNX13AE58G , and  NNX15AC08G  to the  University of Maine. We would like to honour the memory of  Marcel Wernand ,  Tiffany Moisan , and  Trevor Platt , authors who contributed to the previous versions. Financial support. This paper received funding from the  ESA OC-CCI  project (grant number  4000101437/10/I-LG ), the  EU-METSAT "Multi-mission Ocean Colour Algorithm Prototyping" (OMAPS) , and the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement  810139 :  Project Portugal Twinning for Innovation and Excellence in Marine Science and Earth Observation -PORTWIMS.
Robert Arnone, Kevin Arrigo, William Balch, Ray Barlow, Mike Behrenfeld, Chris Brown, Douglas Capone, Ken Carder, Francisco Chavez, DennisClark, Herve Claustre, Jorge Corredor, Glenn  Cota, David Eslinger, Piotr Flatau, Robert Frouin,  Rick Gould, Larry Harding, Stanford B. Hooker, Oleg Kopelevich, Marlon Lewis, Antonio Mannino, John Marra, Mark Miller, Greg Mitchell, Tiffany Moisan, Ru Morrison, Frank Muller-Karger, James Nelson, Norman Nelson, David Siegel, Raymond Smith, Timothy Smyth, James Spinhirne, Dariusz Stramski, Rick Stumpf, Ajit Subramaniam, Ken- neth Voss MERIS Match- up In situ Database (MERMAID).
Sloyan, Paul Thomson and Tom Trull Bio-optical Database of Australian Waters. Available from the Australian Ocean Data Network (AODN). Com- piled standard variables: "chla_hplc", "chla_ fluor" MAREDAT Quality controlled global compilation of chla HPLC. Ray Barlow, Robert Bidigare, Herve Claustre, Available from PANGAEA. Compiled standard variable: Denise Cummings, Giacomo DiTullio, Chris Galli- "chla_hplc". enne, Ralf Goericke, Patrick Holligan, David Karl, Michael Landry, Michael Lomas, Michael Lucas, Jean-Claude Marty, Walker Smith, Denise Smythe- Wright, Rick Stumpf, Emilio Suarez, Koji Suzuki, Maria Vernet, Simon Wright PALMER "Palmer station Long-term Ecological Research" Ralf GoerickeCCELTERCruise data from "California Current Ecosystem Long Term Ecological Research". Available from CCELTER website.
Compiled standard variable: "chla_fluor". Ralf Goericke CIMT Sampling from the "Center for Integrated Marine Technology" (California). Available from CIMT website. Compiled standard variable: "chla_fluor". Raphael KudelaCOASTCOLOUR Quality controlled compilation of bio-optical data in several coastal sites. Available from PANGAEA. Compiled standard variables: "chla_ fluor", "chla_hplc", "rrs", "aph", "adg", "bbp", "tsm". (Antarctica). Available from PALMER website. Compiled standard variables: "chla_fluor", "chla_hplc". Oscar Schofield, Raymond Smith, Maria Vernet SEADATANET Global archive of in situ marine data. Available from SEADATANET website. Compiled standard variable: "chla_fluor". Not availableEarth Syst. Sci. Data, 14, 5737-5770, 2022 https://doi. org/10. 5194/essd-14-5737-2022.
This paper outlines an example method and trial scenario phases for extending the existing tools for maritime surveillance across the maritime regions of Europe. There are continuous dedicated efforts throughout European collaborations for advancing the features of Common Information Sharing Environment (CISE), which is a European Union (EU) initiative for ubiquitous information sharing across the maritime and land borders, as well as between the national sectors. The EFFECTOR EU project introduces a novel concept in the information exchange: the data lake, intended for providing a common smart data repository for diverse and comprehensive data analysis and storage that emanates from diverse sources within the local, regional, national, and international scope. Setting up of the data lake facilities and broadened information exchange is established and validated through early trials planned to be conducted in the project.
Administration for Maritime Safety and Port Management (AMSPM) is integrating maritime surveillance data from Montenegro in the EU information exchanges and vice versa. The EFFECTOR project's French maritime trial offers an implementation of the broadened scope of uses of advanced data exchanges, analysis storage, and retrieval. A case of retroactive data analysis and deductions is to be demonstrated through collaboration of AMSPM in the French maritime trial with other project partners. The trial scenario planning includes two vessels coming into the French waters near Marseille from distant ports, one via a stop-over in Montenegro and the other from Portugal.
This paper derives its content from the collaborative work in the EFFECTOR EU H2020 project (An end-to-end interoperability framework for maritime situational awareness at strategic and tactical operations)  [3] , which aims to boost collaborations between maritime stakeholders via an interoperability framework and associated data fusion and analytics services for maritime surveillance and border security. The paper's content is particularly focused on extracting the context and the activities of the Administration for Maritime Port Safety and Management of Montenegro (AMSPM) as a member of the EFFECTOR project. AMSPM is continuously enhancing its maritime surveillance capabilities through formally participating in the EU CISE collaboration projects and as a part of Montenegro's EU accession processes.
3. Multitude of standards implemented: data formats, interfaces and communication protocols are related to the systems in use and whether these deployments are using international standards or proprietary solutions. E. g. , for a maritime surveillance system at a specific level of communications these can be: NMEA, IVEF, ASTERIX, binary, GeoJSON,. xlsx, XML located at many layers of communications to name a few. This complexity of technologies and uses across the seas and maritime borders appropriately justifies efforts for advancing interoperability frameworks such as the CISE. The EFFECTOR project develops its solutions from the CISE fundamentals already developed in previous collaborations: its architecture framework composed of CISE Nodes and Adaptors, and the CISE infrastructure already in place at some maritime authorities who are project partners.
The project further aims to combine all the various surveillance systems, other interoperability frameworks (e. g. EUROSUR and the eCISE enhancements developed in the ANDROMEDA project  [9] ), data and standards by advancing data sharing and enhanced MSA at tactical and strategic levels. As its hallmark feature, the EFFECTOR project builds comprehensive data fusion and analytics tool including the implementation of a multi-layered data lake combining the information flows and usage at various levels and scopes across the EU. The project will test its solutions in France (i. e. French maritime trial with AMSPM as a participant), Portugal and Greece involving many of its partners.
B. The EFFECTOR Concepts As mentioned previously, the EFFECTOR project aims to facilitate bringing together many maritime surveillance systems, related initiatives and projects outcomes towards advanced interoperability using the CISE infrastructure as the facilitator of interconnections and data exchanges. The core concept being developed is a multi-layered data lake platform for data exploitation and end-to-end interoperability of various maritime surveillance systems with non-integrated and multi-standard data 5 , as shown in Figure  2. 5  In essence, the data lake is a Big Data-like data repository structure6 dedicated to storing and processing various maritime surveillance "raw" data (steams) coming from standardised and non-standardised data sources from and relevant to a country's maritime region.
A. AMSPM position in the CISE integrations AMSPM has participated in many regional cooperation schemes in the maritime affairs, e. g. Vessel Traffic Management Information System (VTMIS) with Albania, South Adriatic connectivity projects also including Italy, installation of National Maritime Single Window (NMSW) in Montenegro (  [7] [13]  [14] ) and recent H2020 EU projects COMPASS2020  [2]  and RESPOND-A  [15]  on tools for expedited maritime surveillance and responsivity. In fact, AMSPM just completed a participation in EU H2020 project ANDROMEDA  [6] [7]  [8]  working on enhancing the CISE data and services models (eCISE) and conducting trial implementations. In the ANDROMEDA project, AMSPM participated in the Adriatic-Ionian trial with partners from Greece and Italy (Italian Navy, Greek Maritime Affairs and Insular Policy Ministry, Hellenic Police etc.
IV. CONCLUSION Advancement of features of the integrated maritime surveillance across the European waters and borders is continuously being endorsed and conducted via collaborations, tests and deployments of novel sets of data exchange functionalities. One such large-scale effort is the maritime CISE Network facilitations and development, as conducted in the EFFECTOR EU project. AMSPM leads integration of Montenegro into the CISE and through participation in the EFFECTOR project ensures its operational readiness and preparation to become a formally integrated CISE member. This paper shows a case of trial planning to be conducted in the EFFECTOR project, where AMSPM participates in data exchanges using its data models and legacy systems. The French trial conducted in the project offers a specific example of data exchanges between European maritime authorities related to trustworthiness of vessels, that are stored in the multi-layered data lake and retrieved upon a critical situation at sea.
Data Availability Statement The DeepMIP simulation data can be accessed following  Lunt et al. (2021). In addition, temperature from the models can be downloaded directly from the Supporting Information of  Lunt et al. (2021). NorESM1-F is available from Guo, Bentsen, et al. (2019). The precipitation results of sensitivity experiments are available at https:// zenodo. org/record/6620024.
Organ-and body-scale cell atlases have the potential to transform our understanding of human biology. To capture the variability present in the population, these atlases must include diverse demographics such as age and ethnicity from both healthy and diseased individuals. The growth in both size and number of single-cell datasets, combined with recent advances in computational techniques, for the first time makes it possible to generate such comprehensive large-scale atlases through integration of multiple datasets. Here, we present the integrated Human Lung Cell Atlas (HLCA) combining 46 datasets of the human respiratory system into a single atlas spanning over 2. 2 million cells from 444 individuals across health and disease. The HLCA contains a consensus re-annotation of published and newly generated datasets, resolving under-or misannotation of 59% of cells in the original datasets.
The HLCA enables recovery of rare cell types, provides consensus marker genes for each cell type, and uncovers gene modules associated with demographic covariates and anatomical location within the respiratory system. To facilitate the use of the HLCA as a reference for single-cell lung research and allow rapid analysis of new data, we provide an interactive web portal to project datasets onto the HLCA. Finally, we demonstrate the value of the HLCA reference for interpreting disease-associated changes. Thus, the HLCA outlines a roadmap for the development and use of organ-scale cell atlases within the Human Cell Atlas.
INTRODUCTION Single-cell genomics has been enabling scientists to study tissues at unprecedented scale and resolution  [1] [2] [3]. Rapid technological improvements over the past decade have allowed datasets to grow both in size and number  4. This has led consortia, such as the Human Cell Atlas, to pursue the generation of large-scale reference atlases of human organs and the entire human body  5, 6. Such atlases, capturing the diversity of the cellular landscape of human tissues in health, development, normal aging, lifestyles, environmental exposures, and in disease, will advance insights into the molecular underpinnings of natural variation in healthy tissues, as well as mechanisms of disease inception, progression, remission, and treatment response  7. To capture diversity on the population scale, these atlases must include data from thousands of individuals.
The generation of atlases of this scale by a single group is currently not feasible. However, the cumulative datasets generated by the research community at large now contain hundreds of individual tissue donors, sampled at various anatomical locations within an organ. Integration of these disconnected studies into a single harmonized model may offer superior and much-needed 8 coverage of relevant donor demographic variables, which are expected to impact the molecular phenotypes of the cells in a tissue. The power of large-scale single-cell cross-dataset analysis has been demonstrated in several recent studies  9- 15. We previously performed the first single-cell meta-analysis of the respiratory system, aggregating expression data of three genes important for SARS-CoV-2 cell entry from 31 datasets, spanning over 200 individuals  11.
This study showed cell-type specific effects of sex, age and smoking status on gene expression, which could not have been recovered in any individual dataset. Especially when large numbers of individuals, rather than cells, are aggregated, it becomes possible to answer population-level questions using single-cell data. However, currently available integrated atlases are limited in the number of genes  11  , human samples  12, 14  , datasets  14  , or cell types  9, 12  , and include only non-harmonized or limited cell type annotations  [9] [10] [11] [12] [13]  and subject metadata  [9] [10] [11] [12] [13]  (e. g. age, BMI, smoking status). These limitations constrain the potential of atlases to serve as a "reference", as they fail to represent and catalog the diversity of cellular phenotypes within the organ and among individuals.
Moreover, choice of integration approach can impact the resulting reference  16  , and only a retinal atlas systematically assessed the quality of data integration  10. Overcoming batch effects through data integration is an essential step in building a high-quality atlas and remains one of the key challenges in the field. As each dataset represents a unique view of an organ, containing specific information and biases due to its experimental design, it is paramount to separate technical biases from biologically relevant information when integrating datasets. Successful integration of the available datasets into a single tissue atlas will therefore be a critical step to achieve the goals of the Human Cell Atlas  5. Here, we present the first integrated single-cell transcriptomic atlas of the human respiratory system, including the upper and lower airways, from published and newly generated datasets (fig.
1 ). The Human Lung Cell Atlas Core Reference (HLCA core) combines data from 14 datasets, 107 individuals, 168 healthy samples, and 584,000 cells to represent the cellular diversity of the human lung. The HLCA core contains detailed, consensusbased hierarchical cell type annotations with associated marker genes, recovers rare cell types from constituent datasets, and associates cell type-specific gene modules with covariates such as age, BMI, and anatomical location. Through transfer learning, the HLCA core can be used to annotate and interpret new datasets from healthy and diseased individuals, enabling discovery of novel cell types and states associated with normal variability and disease. We demonstrate the power of this approach by extending the HLCA to a total of 2. 2 million cells and 444 donors, mapping 32 new human lung datasets from healthy and diseased lungs to the HLCA core.
The HLCA thus forms the first consensus single-cell transcriptomic reference for healthy lung tissue. Harmonized cell annotations, raw count data, harmonized patient and sample metadata, and sample anatomical locations encoded into a common coordinate framework were collected and generated as input for the Human Lung Cell Atlas (HLCA) core (left). After integration of the core datasets, the atlas was extended by mapping 34 additional datasets, including disease samples, to the HLCA core, bringing the total number of cells in the extended HLCA to 2. 2 million. The HLCA core provides detailed consensus cell annotations with matched consensus cell type markers (right, top), gene modules associated with technical, demographic, and anatomical covariates in various cellular identities (right, middle), GWAS-based association of lung conditions with cell types (right, middle), a reference projection model to annotate new data (right, middle) and discover new cell types, transitional cell states, and disease-associated cell states (right, bottom).
Successful data integration into the Human Lung Cell Atlas establishes a core reference To build a foundation for the HLCA, we collected scRNA-seq data and detailed, harmonized technical, biological and demographic metadata from 14 different datasets (9 published, 5 unpublished)  1, 2, [17] [18] [19] [20] [21] [22]. These datasets include samples from 107 individuals, with diversity in age (ranging from age 10 to 76), sex (60% male, 40% female), ethnicity (65% white, 14% black, 2% latino, 2% mixed, 2% Asian, 14% unannotated), BMI (ranging from 20 to 49), and smoking status (52% never, 16% former, 15% active smoker, 17% unannotated) (fig. 2a ). Cells from the donors were obtained from 166 tissue samples, using a variety of sampling methods, experimental protocols, and sequencing platforms, and coming from different tissue donors (e.
Subjects/samples without annotation for a specific variable are shown under "NA" (gray bars). Anatomical region common coordinate framework (CCF) score ranges from 0 to 1, with 0 representing the most proximal part of the lung and airways (nose), and 1 the most distal (distal parenchyma). b, Overview of the HLCA core cell type composition, based on the 5-level hierarchical reference to which cell type labels of all datasets were mapped. Cell types were labeled up to the finest level of labeling available, and set to "None" if no cell type label was available at the level under consideration. Cell type composition is shown for level 1 (left), 2 (middle) and 3 (right). Level 4 and 5 annotations can be found in Supplementary Data Table  4. Each level is broken down into more detailed labels in the following level.