dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016; Jaume-Santero et al. , 2016) , the availability of additional BTP measurements and the possibility of assessing the continental heat fluxes in the context of the FluxNet measurements  (Gentine et al. , 2020)  require a comprehensive summary of all global ground heat fluxes and continental heat content estimates. The first estimates of continental heat content used borehole temperature versus depth profile data. However, the dataset in those analyses included borehole temperature profiles of a wide range of depths, as well as different data acquisition dates. That is, each borehole profile contained the record of the accumulation of heat in the subsurface for different time intervals. In addition, the borehole data were analyzed for a single ground surface temperature model using a single constant value for each of the subsurface thermal properties.
, 2019; Melo-Aguilar et al. , 2018; Stieglitz and Smerdon, 2007). In a novel attempt to reconcile continental heat content from soil heat-plate data from the FluxNet network with estimates from geothermal data and a deep bottom boundary land surface model simulation,  Gentine et al. (2020)  obtained a much larger magnitude from the global land heat flux than all previous estimates. Cuesta-Valero et al. (2020)  has recently updated the estimate of the global continental heat content using a larger borehole temperature database (1079 logs) that includes more recent measurements and a stricter data quality control. The updated estimate of continental heat content change also takes into account the differences in borehole logging time and restricts the data to the same depth range for each borehole temperature profile.
10 × 10 14 m 2 ). For comparison, the heat gain obtained in IPCC AR5 amounts to 274 ± 78 ZJ and 0. 4 W m -2 over the period 1971-2010 Although much lower, the second largest contributor is the storage of heat on land (orange shading), followed by the gain of heat to melt grounded and floating ice in the cryosphere (gray shading). Due to its low heat capacity, the atmosphere (magenta shading) makes a smaller contribution. Uncertainty in the ocean estimate also dominates the total uncertainty (dot-dashed lines derived from the standard deviations (2σ ) for the ocean, cryosphere and land; atmospheric uncertainty is comparably small). Deep ocean (> 2000 m) is assumed to be zero before 1990 (see Sect. 1 for more details). The dataset for the Earth heat inventory is published at the German Climate Computing Centre (DKRZ, https://www.
Atmospheric warming amounts to 1 % (2 %) in the Earth heat inventory, the land heat gain amounts to 6 % (5 %) and the heat uptake by the cryosphere amounts to 4 % (3 %). These results show general agreement with previous estimates (e. g. ,  Rhein et al. , 2013). Over the period 2010-2018, the EEI amounts to 0. 87 ± 0. 12 W m -2 , indicating a rapid increase in EEI over the past decade. Note that a nearglobal (60 • N-60 • S) area for the ocean heat uptake is used in this study, which could induce a slight underestimation, and needs further evaluation in the future (see Sect. 1). However, a test using a single dataset  (Cheng et al. , 2017b)  indicates that the ocean contribution within 1960-2018 can increase by 1 % if the full global ocean domain is used (not shown).
Data availability The time series of the Earth heat inventory are published at DKRZ (https://www. dkrz. de/, last access: 7 August 2020) under the DOI https://doi. org/10. 26050/WDCC/GCOS_EHI_EXP_v2  (von Schuckmann et al. , 2020). The data contain an updated international assessment of ocean warming estimates as well as new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period 1960-2018. This published dataset has been used to build the basis for Fig. 6  of this paper. The ocean warming estimate is based on an international assessment of 15 different in situ data-based ocean products as presented in Sect. 1. The new estimate of the atmospheric heat content is fully described in Sect. 2 and is based on a combined use of atmospheric reanalyses, multisatellite data and radiosonde records, and microwave sounding techniques.
For the global ocean observing system, the core Argo sampling needs to be sustained and complemented by remote sensing data. Extensions such as into the deep ocean layer need to be further fostered  (Desbruyères et al. , 2017; Johnson et al. , 2015) , and technical developments for the measurements under ice and in shallower areas need to be sustained and extended. Moreover, continued efforts are needed to further advance bias correction methodologies, uncertainty evaluations and data processing of the historical dataset. In order to allow for improvements on the present estimates of changes in the continental heat and to ensure that the database is continued into the future, an international, coordinated effort is needed to increase the number of subsurface temperature data from BTPs at additional locations around the world, in particular in the Southern Hemisphere.
Financial support. were supported by the  Met Office Hadley Centre Climate Programme  funded by the  BEIS  and  Defra. PML authors were supported by contribution number 5053. Catia M. Domingues  was supported by an  ARC Future Fellowship  ( FT130101532 ). Lijing Cheng  is supported by the  Key Deployment Project of Centre for Ocean Mega-Research of Science, CAS  ( COMS2019Q01 ). Maximilian Gorfer  was supported by  WEGC atmospheric  remote sensing and climate system research group young scientist funds. Michael Mayer  was supported by  Austrian Science Fund  project  P33177. This work was supported by grants from the  National Sciences and Engineering Research Council of Canada   Discovery Grant  ( NSERC DG 140576948 ) and the  Canada Research Chairs Program  ( CRC 230687 ) to  Hugo Beltrami.
Almudena García-García  and  Francisco José Cuesta-Valero  are funded by  Beltrami's CRC program , the  School of Graduate Studies at Memorial University of Newfoundland  and the  Research Office at St. Francis Xavier University. Fiamma Straneo  was supported by  NSF   OCE 1657601. Susheel Adusumilli  was supported by  NASA  grant  80NSSC18K1424. Review statement. This paper was edited by  David Carlson  and reviewed by two anonymous referees.
Uncertainty in the ocean estimate also dominates the total uncertainty (dot-dashed lines derived from the standard deviations (2σ ) for the ocean, cryosphere and land; atmospheric uncertainty is comparably small). Deep ocean (> 2000 m) is assumed to be zero before 1990 (see Sect. 1 for more details). The dataset for the Earth heat inventory is published at the German Climate Computing Centre (DKRZ, https://www. dkrz. de/) under the DOI https://doi. org/10. 26050/WDCC/GCOS_EHI_EXP_v2. The net flux at TOA from the NASA CERES program is shown in red (https://ceres. larc. nasa. gov/data/, last access: 7 August 2020; see also for exampleLoeb et al. , 2012)  for the period 2005-2018 to account for the golden period of best available estimates. We obtain a total heat gain of 358 ± 37 ZJ over the period 1971-2018, which is equivalent to a heating rate (i.
2, trivial; > 0. 2-0. 6, small; >0. 6-1. 2, moderate; >1. 2-2. 0, large; and >2. 0, very large  [32]. Intraclass correlation coefficients (ICCs) were obtained using a two-way (rater and subject) random effects model and interpreted as poor (0-0. 49), moderate (0. 5-0. 69), high (0. 7-0. 89), and very high (0. 9)  [33]. Statistical analyses were performed using SPSS software statistical package (SPSS Inc. , Chicago, IL, version. 16. 0).
The Cambrian 'Explosion', located by many authors between 540 and 520 million years ago (Ma), is considered to be an abrupt appearance in the fossil record of most animal phyla, with a sudden increase of complex morphologies across metazoan groups. In a few recent papers, the Great Ordovician Biodiversification 'Event' (GOBE) has similarly been restricted to a single dramatic biodiversification 'event' in the Darriwilian Stage of the Middle Ordovician Series, between 470 and 455 Ma, although historically the biodiversification is considered as an aggregation of radiation 'events' capturing a large and complex increase of taxonomic diversity of marine invertebrates covering the entire Ordovician. A review of biodiversity curves of marine organisms during the early Palaeozoic, including some based on data in the Paleobiology Database (PBDB) and the Geobiodiversity Database (GBDB), points towards a single, large-scale, long-term early Palaeozoic radiation of life that already started in the late Precambrian.
An abrupt 'explosion' of diversity in the Cambrian or a significant 'event' in the Ordovician are not visible in our biodiversity studies, because they are either regional, or only reflect a single group of organisms. It is evident that the datasets remain incomplete, in particular those for many geographical areas and for several fossil groups, that are not covered by the PBDB and GBDB; also, such areas remain so far poorly or entirely unstudied. Some recently published biodiversity curves have to be considered with care, as the truly global diversity estimates of marine organisms during the early Palaeozoic remain elusive. Here, we argue that published curves of taxonomic richness, which show distinct periods of diversification, cannot sufficiently be disentangled from biases. We therefore question the existence of a distinct Cambrian 'Explosion' and global Ordovician 'Event' in the global datasets.
The two major radiations (Palaeozoic and Mesozoic-Cenozoic) have subsequently been visible in almost all biodiversity curves produced during the last 150 years (e. g. ,  Erwin et al. , 1987). Both radiations are usually related to the fragmentation of supercontinents into microcontinents: the Palaeozoic radiation is related to the break-up of the supercontinent Pannotia and the Mesozoic-Cenozoic radiation to that of Pangaea (e. g. ,  Valentine and Moores, 1972; Valentine, 1973). Besides the Cambrian 'Explosion' and the Great Ordovician Biodiversification 'Event', a number of further terminologies have been introduced to describe various aspects of the early Palaeozoic radiation (Fig. 1 ). Bottjer et al. (2000)  introduced the term 'Cambrian Substrate Revolution' to refer to the evolutionary and ecological effects of substrate changes on benthic metazoans that took place during the Cambrian: the seafloor sediments that had well-developed microbial mats, which sporadically appeared during the Neoproterozoic, were replaced by seafloors with burrowing and grazing organisms and an increased vertically oriented component of bioturbation (see also  Buatois et al.
Can the Cambrian 'explosion' be recognized in biodiversity curves?  Na and Kiessling (2015)  measured the global genus-level diversity of marine animals from the Ediacaran to the earliest Ordovician based on the datasets from the Paleobiology Database (PBDB). These authors clearly observed a peak of genus-level diversity during Cambrian Stage 3. On the other hand, based on a different set of data from the Siberian Platform,  Zhuravlev and Wood (2018)  noted two phases of the Cambrian 'Explosion,' with a first 'phase' starting in the Ediacaran and continuing during the Terreneuvian, that was interrupted by an extinction during the Cambrian Stage 4, and a second 'phase' much later during the Cambrian Series 3 and 4 and extending into the Ordovician radiation. The precise location of the 'explosion' thus varies according to the datasets used by different authors (see below).
This IGCP project n • 410 was extremely successful and through this project, the terminology including the suffix 'event' became very popular. The origin and first use of the acronym 'GOBE' has never been clearly established, although it is commonly used today. The terminology 'event' can clearly be rooted in the societal context of geological studies of the 1990s. Many stratigraphers and palaeontologists were focused during this decade on 'event geology' or 'event stratigraphy,' as is clearly illustrated by the important publication of  Walliser (1995). A clear definition of the 'Great Ordovician Biodiversification Event' can also be identified and provenanced. It was  Webby (2004) , who not only summarized in his introductory chapter the important data compiled by many Ordovician palaeontologists in the  Webby et al.
Kröger and Lintulaakso (2017)  used the data from the PBDB to calculate diversity levels and concluded that the Darriwilian (late Middle Ordovician) was the time of a diversification maximum and the Sandbian (early Late Ordovician) the time of the Ordovician diversity climax, before a diversity fall beginning early in the Katian (Late Ordovician). Kröger (2018)  subsequently put the Ordovician radiation in the context of changes in the latitudinal diversity gradient (LDG) and the global cooling of Ordovician sea waters (e. g. ,  Trotter et al. , 2008; Nardin et al. , 2011; Rasmussen et al. , 2016). But only more recently the term 'event' was related to a much shorter period of time by a few authors, who based their interpretations on the PBDB. First,  Kröger et al. (2019, fig. 1)  restricted the term 'Great Ordovician Biodiversification Event' to the Darriwilian (when the diversity levels were the highest).
Rasmussen et al. (2019)  narrowed the concept of the 'GOBE' even more, considering a particular diversity increase in the PBDB dataset, taking place during an earlier part of the Darriwilian, as the 'event. ' This concept was also followed by  Stigall et al. (2019) , who recommended restricting the term 'GOBE' to this very short interval of diversification in the Darriwilian, while using the term 'Ordovician radiation' when referencing the sum of diversifications that occurred throughout the Ordovician Period. It becomes evident that the terminology 'Great Ordovician Biodiversification Event' needs a more focused redefinition, and consensus within the Ordovician community, by integrating different databases, not just the PBDB.
Databases and global biodiversity curves A number of global biodiversity curves covering the early Palaeozoic have been published. It should be noted that these biodiversity curves are essentially diagrammatic depictions of diversity measures against time, including measurements of fluctuations of taxonomic richness, resulting in diversity change curves (e. g. , turnover, temporal alpha or beta diversity). Some of these curves are compared here and illustrated in Fig. 2. These curves have been produced on the basis of datasets of fossil occurrences from all around the world, making them 'global' in the sense that they are not limited to a single country or continent. Nevertheless, none of these datasets provides accurate global coverage (see below). Furthermore, most of these different datasets are also presented as 'representative' in the sense that they include a wide range of fossil groups.
However, some fossil groups have much more 'weight' in a given database than others. In addition, many groups are simply absent in a number of datasets, that are usually limited to some abundant groups of invertebrate fossils, including the well-researched groups such as the brachiopods, trilobites, graptolites and echinoderms. The datasets used for 'global' biodiversity curves are thus neither complete in terms of geographical coverage nor in terms of taxonomic completeness.
Sepkoski's database finally listed approximately 37,000 genera, that allowed  Raup and Sepkoski (1982)  to recognize five major mass extinctions in the marine fossil record. Sepkoski's famous diversity curve (e. g. ,  Sepkoski, 1981)  was based on a simple digital database, where he recorded the first and last appearance of each group of marine invertebrates. The plotting of these occurrences revealed some interesting patterns in the diversity of marine invertebrates, including a rapid rise in the Cambrian and the Ordovician 'radiation' that led to a Palaeozoic 'plateau,' illustrating that between the end of the Ordovician and the end of the Permian, marine diversity remained almost constant, and was only interrupted by two extinction events, at the end of the Ordovician and at the end of the Devonian, before the much more severe Permian-Triassic mass extinction.
The Paleobiology Database Today, one of most frequently used databases for palaeobiodiversity studies (at least in the western world) is the Paleobiology Database, or PBDB (www. paleobiodb. org), created in the late 1990s. This collectionbased database is publicly accessible and free for anyone to use. It is maintained by an international non-governmental group of palaeontologists. Currently over 400 scientists from over 130 institutions in over 20 countries have contributed to the database, with clearly the majority of scientists from North America and western and northern Europe. Currently the database includes over 400,000 taxa from over 200,000 collections, and nearly 1. 5 million occurrences. The database covers many groups, not only marine invertebrates, but also vertebrate fossils, as well as plants.
Alroy (2000 Alroy ( , 2008) )  initially published several studies introducing new methods for quantifying macroevolutionary patterns. Concurrently, he also presented the first results of the PBDB. Alroy et al. (2001)  used subsampling methods developed earlier (e. g. ,  Foote, 1992)  to analyze the data of the PBDB, that can help to remove the variations in a biodiversity curve that result from uneven preservation and sampling effects. Alroy et al. (2008)  included Phanerozoic biodiversity curves of marine invertebrates, indicating the genus-level diversity, based on a sampling-standardized analysis of the fossil occurrence data compiled within the PBDB. The first curve  (Alroy et al. , 2008, fig. 1) , with 48 temporal bins of roughly equal length (averaging 11 myr), clearly showed a single long-term radiation from the Cambrian to the early Devonian, and removed the Palaeozoic plateau of the Sepkoski curve.
2B ). At present, many biodiversity curves have been generated from the PBDB, often published in journals of high-impact in terms of citation biometrics. Some authors have criticized the curves, partly because the dataset is incomplete, but also because of the questionable use of some particular measurement methods. For example,  Bush and Bambach (2015)  noted that previous standardized analyses (bias-corrected analyses on occurrence-level data in the PBDB) did not capture the Mesozoic-Cenozoic diversification of marine metazoans, simply because they were based on incomplete data. Prothero (2015)  also considered that the taxonomic and geographic data in the PBDB for most of the Miocene mammals of North America are so poor that any analysis of such data is premature at best. Close et al. (2020)  used the PBDB to analyze fossil occurrence data of Phanerozoic tetrapods.
These authors noted that a 'truly global' estimate of tetrapod diversity through geological time remains inaccessible because of the incompleteness of the fossil record. Most importantly,  Close et al. (2020)  noted that one to two thirds of the variations in 'global' biodiversity curves can be explained by changes in the palaeogeographical extent of sampled fossil localities, or, in short, the 'global' signal is not global. We have to bear in mind these conclusions when focusing on the early Palaeozoic datasets in the PBDB.
The Geobiodiversity Database The Geobiodiversity Database (GBDBwww. geobiodiversity. com), is an integrated system for the management and analysis of stratigraphic and palaeontological data (e. g. ,  Fan et al. , 2013 Fan et al. , , 2014)). It was started in 2006 and was first available online in 2007. It is a global, publicly accessible database that is section-based (and not collection-based, as the PBDB). It incorporates data not only from palaeontologists, but also from various disciplines of stratigraphy, including biostratigraphy. It allows regional and global scientific collaborations based on stratigraphical correlation and quantitative stratigraphy, and also on systematics, biodiversity dynamics, palaeogeography and palaeoecology. The GBDB became the formal database of the International Commission on Stratigraphy (ICS) in 2012  (Fan et al.
, 2013). The database, in addition to stratigraphical and palaeontological data, also started to include geochemical data  (Fan et al. , 2014). The rapid growth of data in the GBDB that included by 2012, data from over 35,000 collections with over 90,000 occurrences  (Fan et al. , 2013) , and currently data from about 125,000 collections with over 500,000 occurrences, from over 25,000 sections and over 50,000 formations. The database was originally developed at the Nanjing Institute of Geology and Paleontology, China, one of the major research institutes for stratigraphy and paleontology in the world, that hosts the server and the supercomputer (Tianhe II) to run the analyses. The database was first designed to compile all Chinese data, but the global coverage is an increasingly important facet of the GBDB.
The GBDB also includes an integrated GIS-based spatial database, to allow data visualization on maps and palaeogeographical reconstructions. Stratigraphical correlation is developed through different programmes, including CONOP (e. g. ,  Sadler, 2001; Sadler et al. , 2009). The GBDB is increasingly used for analysing stratigraphical research questions and biodiversity studies, mostly from China (e. g. ,  Fan et al. , 2014)  These biodiversity curves based on GBDB data also show the Cambrian and Ordovician radiations, but with different patterns than those observed in the PBDB; this is not unexpected, as both databases cover different palaeogeographical regions (see below). Both databases have a global scope, but differ in regional focus and method of sample collection and pooling.
Other databases Although the PBDB is highly visible, mostly due to many publications in high-impact journals (of publishers located in North America and western Europe), and besides the fact that the GBDB is becoming increasingly visible as a potential global player, it is important to note that there are a multitude of other databases that are currently under development, at a global, national, but very often also at a personal level. For example, Macrostrat (www. macrostrat. org) is an internet platform designed for the integration and distribution of various geological data that are relevant to the spatial and temporal distribution of different rock types. It includes currently over 1500 regional rock columns, from over 35,000 rock units. A major objective is to generate a globally comprehensive stratigraphic database  (Peters et al.
, 2018). Initial results, in relation to Macrostrat, indicate that there is a clear link between continental fragmentation and biodiversity over time (e. g. ,  Zaffos et al. , 2017). Macrostrat clearly provides evidence for a need of a quantitative spatio-temporal evaluation of the sedimentary rock record. However, its coverage is mainly limited to North America so far. Similarly, and as an example, the GeoStrat project (www. Dnr. mo. gov/geo logy/geostrat. htm), is designed to include all relevant data and metadata for stratigraphic-based data. Many other such initiatives are currently in progress. The major palaeontological collections, usually housed in national natural history and university museums, also have their own databases. For example, the 'Paleontology Database' of the American History of Natural History (New York, USA) is a dataset from a variety of legacy databases used in the four historical paleontology collections of the institution.