dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, there is an increasing population of satellites providing information about land surface conditions and changes in land cover, and several methods are also being developed to obtain accurate estimates of climate variables from satellite remote sensing observations in combination with land observational networks and numerical models  (Balsamo et al. , 2018). Expanding the permafrost areas considered here would be a priority in the next iterations of this analysis, particularly the inclusion of the Tibetan Plateau. Further sources of information to retrieve estimates of permafrost heat storage should also be considered in order to increase the confidence of the results obtained here. Ideally, monitoring of liquid water content in permafrost soils along with borehole temperature measurements would form a complementary dataset besides modelling for estimating permafrost heat storage.
, 2013; Slater & Lawrence, 2013; Burke et al. , 2020; Hermoso de Mendoza et al. , 2020; Steinert et al. , 2021). Furthermore, these land surface model components do not represent excess ice in the ground or ground subsidence, which bias the represented permafrost thawing (e. g. ,  Lee et al. , 2014; Rodenhizer et al. , 2020). Replacing ERA-Interim forcing by ERA5 data and forcing for the last decades of the 20 th century and the first decades of the 21 st century should also be considered. The inland water heat storage estimates could be refined using spatially varying morphometry characteristics to determine lake volumes per grid cell. The availability of new datasets like GLOBathy  (Khazaei et al. , 2022) , as well as new insights from the upcoming Surface Water and Ocean Topography (SWOT) mission would allow such an approach.
Data and code availability The subsurface temperature profiles from the Xibalbá dataset were used to derive ground heat fluxes and are available in Cuesta-Valero et al. (2021b). All ISIMIP2b global lake simulations used are publicly available through the ISIMIP repository (https://data. isimip. org/). The HydroLAKES dataset is available at https://www. hydrosheds. org/page/hydrolakes, GRanD at http://globaldamwatch. org/ and GLDB at http://www. lakemodel. net. The scripts used for the inland water heat storage calculations are available at: https://github. com/Ivanderkelen/inlandwater_heatuptake.
of ground heat flux by the new bootstrapping technique described above present slightly smaller values and a narrower uncertainty range than the results from previous estimates using the Xibalbá dataset(von Schuckmann et al. , 2020;   Cuesta-Valero et al. , 2021a). Previous analyses present a global heat flux of 97±6 mW m -2 for 1960-2018, in comparison with 84. 8±0. 8 mW m -2 for the period 1960-2020 in this study (Figure1a). Both heat flux estimates consider the same subsurface temperature profiles and the same singular value decomposition algorithm to produce inversions of individual logs. Nevertheless, the new bootstrap method used to aggregate inversions from individual profiles is conceptually different from the aggregation method used in von Schuckmann et al. (2020), which leads to slightly different values of global ground heat flux and to a narrower 95 % confidence interval (Cuesta-Valero et al.
These large differences between our results and those from Beltrami et al. (2002)  arise from the use of different inversion methods and from the higher number of more recent subsurface temperature profiles in the Xibalbá dataset than in Beltrami et al. (2002) , thus including the recent warming of the continental subsurface. Heat flux for inland water bodies reaches 16±27 mW m -2 for 1960-2020, displaying a large inter-annual variability and multimodel spread (Figure1a). This large inter-annual variability is explained by the smaller surface of global lakes and reservoirs in https://doi. org/10. 5194/esd-2022-32 Preprint. Discussion started: 8 August 2022 c Author(s) 2022. CC BY 4. 0 License. comparison with the global land and permafrost areas. Permafrost heat flux estimates for the Arctic region yield 60±80 mW m -2 for the period 1960-2020, thus lower than the ground and higher than inland water bodies during the same period of time.
Hand-over scientific software to a development team It can be beneficial to hand-over code to a software company, which then handles the software engineering tasks (e. g. user interface development, software development, maintenance, code testing, documentation) and the business aspects (e. g. licensing and distribution). Typical approaches for commercialisation are: (i) the development of fully integrated proprietary packages by a software company, (ii) proprietary packages set up for integration with external open-source packages, and (iii) the Independent Software Vendor (ISV) model, in which a software company offers a platform to ISVs (i. e. separate companies) that integrate their solutions in the platform (e. g. as packages) and offer it to their clients (e. g. ANSYS). ISVs are charged for the integration of their products in the platform, while they fully handle the development and the business aspects of their products  (Goldbeck, 2017).
g. Comsol 6 , ANSYS 7 ). These commercial solutions are typically accompanied by technical manuals, training, free student 8 download licenses and customer support. OpenFOAM 8  is an example of an open-source software under the General Public Licence (GPL). GPL allows for free use, modification and redistribution. In chemical engineering, one can find easy-to-use CAE tools for simulating processes in the energy, gas, chemical, petroleum and pharmaceutical industry (e. g. ProSim 9  ). In the food domain, CAE tools offer great assistance for tasks that can be partially automated such as processing (solving equations), post-processing and coding. However, current CAE solutions are unlikely to efficiently assist the modelling of solid food products (i. e. writing mathematical expressions that capture the physical system,  Datta.
Towards reusable scientific software In Europe, guidelines have been issued to incite scientists to adopt development practices that will make their software reusable  (Chue Hong. , 2014 , Fehr et al. , 2019). Additionally, sharing research data is well established as a key aspect of open science, which is expected to accelerate innovation in industry. It is typically realised using the European Commission program European Open Science Cloud 10 that implements the FAIR 11 principles (i. e. Findability, Accessibility, Interoperability, and Reusability), as well as related developments on (research) data management plans and domain protocols to share data. This section reports on initiatives that could encourage the reuse of scientific software in the food domain.
Infrastructures for reuse of scientific software Sustainable scientific software reuse should be based on a suitable infrastructure for storing, archiving and making the code accessible. Therefore, authors must deposit their software alongside other files (e. g. README, RUNME) into digital repositories and fill in registry forms that allow for curation by other users. Jackson (2018)  offers useful recommendations for the deposit of software, such as the delivery of a persistent digital identifier (e. g. Digital Object Identifier, DOI) that assures that the software can be cited and that the author gets credited for the work. Notice that, general purpose code repositories, such a Github, do not automatically provide persistent identifiers, although mitigating procedures exist 14. Certain repositories are recommended for specific developer communities.
For instance, CRAN 15  is well-known among users of R 16  as the repository that stores R packages with their documentation. Similarly, SciPy 17  is well known among Python 18  users. For any scientific software, it is important to consider archiving and referencing the software via registry services alongside the deposit of the code. In fact, registration and indexation might be more important for a scientific community than the code repository hosting service itself  (Gil et al. , 2016). General purpose digital repositories, such as FigShare and Zenodo, can be used for registering and archiving software (as for any other research products) and for making its title, DOI and licence citable  (Jackson, 2018). Finally, Software Heritage 19  is a recent international initiative that offers a service for indexing, organizing, making referenceable and accessible all the software that conveys technical and scientific knowledge.
Software Heritage archives and assigns an intrinsic and persistent identifier for digital objects (i. e. swh-id). One can also find domain-specific software repositories, driven by a community of scientists for collecting food safety models in their field, such as the Risk Assessment and Knowledge Integration Platform (RAKIP) 20   (Plaza-Rodrigez et al. , 2018; de Alba Aparicio et al. , 2018). They enable scientists to register their software via a set of metadata that capture relevant information for the domain. Interestingly, some of these repositories require compliance with a common modelling framework to favour data exchange between various modelling tools and the coupling of models in general  (Gil et al. , 2016). FSL-Lab is a graphical modelling platform for the integration of risk assessment models that goes a step further in term of model reusability (de Alba  Aparicio et al.
, 2018). One of the FSK-Lab key features is the support of a markup language for script-based or application-based models (Food Safety Knowledge Markup Language, FSK-ML), that is used to annotate models with metadata. FSK-ML allows for a harmonized writing and reading of mathematical models regardless of their sources, which facilitates greatly their integration and re-use in FSK-Lab. FSK-ML is expected to become a format for exchange of information broadly adopted by the microbial food safety community (i. e. regulatory agencies, food industries, consultancy companies, and food scientists) in order to facilitate the reusability of scientific models to improve risk assessment and decision making by food safety managers  (Haberbeck et al. , 2018). Software deposit solutions capture metadata about the software, but sometimes in an unstructured way, e.
g. as text in a README file. Additionally, the documentation provided in code repositories mostly focuses on the installation process  (Gil et al. , 2016). This makes it difficult for potential end-users to find software that matches their needs. Having the metadata captured in a software registry linked to the code makes the software searchable, discoverable and re-usable. This is an essential aspect of software sharing that can be addressed with software ontologies (i. e. controlled vocabularies that specify information about a software). The OntoSoft ontology  (Gil et al. , 2015)  is an example of a general software ontology centred on scientific software sharing. OntoSoft 21  captures six information items that can be queried by a user  (Gil et al. , 2015) : identifying the software, understanding and assessing software, executing the software, getting support, doing research, and updating.
Barriers for scientific software reuse The publication of datasets and software is a growing trend that is likely to become even more pronounced in the near future. This trend is encouraged by scientific publishers, funding agencies and research organisms that promote open science. Despite this, there is a risk that a great deal of datasets and software currently developed by researchers in food science remain unpublished and inaccessible to potential users. Main explanations for the limited adoption of practices that favour software reuse reported in other domains (e. g. Gil et al. , 2016 ) also apply to the food modelling community. They can be summarised in e. g. guidelines and tools. Traditionally, food science does not rely heavily on programming, therefore the lack of knowledge about practices and tools that favour software reuse is natural.
Collaboration through shared representations The code developed by researchers in the process of scientific discovery should be shared with the scientific community, like a research paper or a dataset. However, the primary goal behind the development of scientific code is generally not knowledge transfer. This section focuses on collaborative modelling techniques that aim at creating software usable by people other than scientists. Collaborating with other scientists, domain experts, stakeholders or even consumers, is not uncommon in the food sector. In general, the need to integrate expertise from various domains grows with the system scale and its complexity  (van Mil et al. , 2014). For instance, modelling the proofing of wheat dough i. e. the first fermentation operation of the bread-making process needs only knowledge of the dough rheology and of the leavening agent activity.
e. the so-called knowledge-level), leaving aside programming considerations, and (iii) adopting iterative and incremental development of the software. Not many papers have addressed this topic in the context of food. Ndiaye et al. (2009)  discusses the creation of a KBS for bread-making that captures the reasoning of bread technologists. Additionally, an incremental modelling approach is proposed to spur experts in providing feedback and informative critiques on the model structure  (Kansou et al. 2014). Thomopoulos et al. (2013)  presents an approach for learning interpretable data-driven models, which was applied to the processing and qualities of cereal foods. They used a domain ontology to select the factors from the dataset (e. g. cooking temperature) that would most likely affect the quality of the products (e.
, 2014). Kurtz et al. (2021)  propose an AI approach, based on the concept of collective attitude, to analyse a large-scale survey on from web debates and agent-based modelling to simulate opinion diffusion on vegetarian diets. Because food concerns everyone, food issues can spark the interest of many internet users that could provide information about their consumption or about their preferences. Open Food Facts 22  epitomizes the successful application of crowdsourcing in the food sector. The contributors (>1800) scan the information given on the product package (e. g. nutritional facts, allergens, ingredient list, barcodes) and send it to a server via a smartphone application. The data collected so far covers more than 75000 products from 150 countries and is available to the public as open data.
Education and training A considerable number of well-stablished European universities offer curricula addressing food science and technology, typically focussing on food engineering, microbiology and supply chain management. Several educational programs teach food modelling through learning-by-doing using general-purpose tools such as Matlab or Comsol. However, few institutions seem to put significant emphasis on advanced modelling and simulation techniques and scientific software in general. The availability of web-based course material appears also to be sparse. Instruments such as Massive Open Online Course (MOOCs) and Small Private Online Courses (SPOCs) have the potential to expand KT on to a large audience, while these can also be deployed to educate and train professionals. Below, three initiatives that illustrate this potential are highlighted.
Similarly, FEPSIM 35 provides a free web-based education tool that offers various physics-based modules (built on Matlab and ANSYS CFX/FLUENT) related to food engineering  (Koulouris et al. , 2015). MESTRAL 36 is another example of pedagogical material based on simulators enriched with related information  (Suciu et al. , 2021). MESTRAL converted actual research results into educational materials and is currently available for master and PhD students in food engineering. The online repository contains 15 modules. Each module is built along the same conceptual framework that includes a (i) simulator (that re-uses scientific software from previous research), (ii) contextual information, and (iii) background knowledge both captured in standardized conceptual maps (Cmaps, sheets A Cmap is a knowledge modelling technique using diagrams that represent semantic relationships between concepts.
Each Cmap in MESTRAL respects a template (i. e. meta-model) that imposes a tree-like organisation, a type of concept and a limitation on the number of concepts, to facilitate assimilation of the content. The digital material is composed of hypermedia that embed links from Cmaps towards (i) other Cmaps or the simulator, (ii) knowledge sheets that contain text, photos or videos, and (iii) external resources via URLs. The simulators run simulations based on case-study datasets that the user can display at will using sliders and plots.
Acknowledgements This paper is based on work carried out for the  COST Action CA15118 FoodMC  and supported by  European Cooperation in Science and Technology  (https://www6. inrae. fr/foodmc). The authors would like to acknowledge the following people for their support:  Christos Athanassiou ,  Serafim Bakalis ,  Dusanka Boskovic ,  Urska Vrabic Brodnjak ,  Maria Otília Carvalho ,  Stefano Curcio ,  Ferruh Erdogdu ,  Pop Eugen ,  Jasenka Gajdos ,  Jan van Impe ,  Mile Jovanov ,  Nickolas Kavallieratos ,  Diana Kopeva ,  Joao Miranda ,  Alexandru Moisescu ,  Maria Jose Oltra-Mestre ,  Ioan Sacala ,  Alexandrina Sirbu ,  Emil Stankov ,  Alberto Tonda ,  Vasilis Valdramidis ,  Heribert Watzke.
Finally, the likelihood to produce at least one juvenile and the hatching rate were tested using two Generalized linear models with binomial error distribution and corrected for overdispersion. In these models, the population, the range of temperatures and their interaction were entered as explanatory factors, while we used either the presence of at least one hatched juvenile as a binary response variable (1 or 0) or the hatching rate (the number of eggs that hatched divided by the total number of eggs produced) as a continuous response variable. We checked that the assumptions of the LMEs were met using the DHARMa package  (Hartig 2020). The analyses were conducted with the software R v4. 1. 1 (R Core Team, 2017) loaded with the packages car  (Fox and Weisberg 2019) , exactRankTests  (Hothorn and Hornik 2021) , emmeans (Lenth 2021) and DHARMa  (Hartig 2020).
Data Accessibility Data, scripts and codes are available online: https://doi. org/10. 5281/zenodo. 6338078 gene (658 bp) was amplified from each individual using routine barcoding primers LepF and LepR  (Hajibabaei et al. 2006). PCR amplifications was performed with the DreamTaq® PCR Master Mix Kit (Thermo Scientific) using an ESCO Swift Maxi® thermocycler with an initial denaturation step at 95°C (2 min) followed by 35 cycles at 95°C (45 sec), 52°C (60 sec) and 72°C (90 sec) and finally an extension step at 72°C (10 min). PCR products were purified, and Sanger sequenced in both direction using an ABI 3730XL sequencing system (Thermo Fisher Scientific) at Eurofins Genomics Company. Sequences obtained were corrected using Geneious® 9. 1. 8. The species status of each female was identified from NCBI databases using the BLAST tool  (Altschul et al.
1990 ). The BLAST results were ranked by percent identity and the reference sequences with at least 100% identity to our sequences were used to assign the species status. All sequences obtained in this study have been submitted to GenBank; their accession numbers are OL512959 to OL512964 for the 6 HNB females and OL512965 to OL512970 for the 6 SJNL females. The COI analyses revealed that all (6/6) HNB females belonged to the species "Forficula auricularia A" and all (6/6) SJNL females belonged to the species "Forficula auricularia B".
Acknowledgements We warmly thank  Wolf U. Blanckenhorn ,  Nicolas Sauvion , and  Anna Cohuet  ( Peer Community  in Zoology referees and recommender, respectively) for their very detailed and constructive comments on this manuscript. A preprint version of this article has been reviewed and recommended by  Peer Community In Zoology  (https://doi. org/10. 24072/pci. zool. 100012).
Despite these important hypotheses, there has been relatively little exploration of global biomass under past climates. Detailed studies of past vegetation dynamics generally explore the evolution of plant distribution and diversity, and periodical changes in ecology 3,22,23 but very few explore the magnitude of change in global plant productivity, especially over millions of years. The most progress to date on quantifying paleo-biomass has been achieved through use of the Sheffield Dynamic Global Vegetation Model (SDGVM) but this, and similar studies, have been restricted to post-Pangaea climates  16, 24. Here, we develop a simplified deep-time dynamic global vegetation model which can easily be run for a variety of past climates throughout the Phanerozoic to test the hypothesis that paleogeography itself has influenced the spread of plants across Earth's terrestrial land masses.
4 ). Our model also tends to slightly over-represent biomass in the northern high mid-latitudes and under-represent biomass in the southern high mid-latitudes. Such underrepresentation is possibly due to the absence of plant functional types that act as gradients between biomes and our restriction of a single best-adapted and non-evolving functional type to each gridcell. Additionally, existing biases present within such complex vegetation models (such as the LPJ-DGVM) may have been inherited. These include, but are not limited to, overestimations of the maximum carboxylation rate between plant functional types  36  and with changing CO 2 concentrations  37. Overall, the biomes in the model follow a similar geographic range to those on the present day Earth (Fig. S5 ). Figure  4  shows the longitudinal and latitudinal biomass comparisons and the relationship between the model and the global database  35.
These highlight that the model has reasonable capabilities in capturing the key trends, and again show the slight over-prediction of biomass in South-East Asia and under-prediction in tropical Africa. These differences may be attributable to the yearly-averaged datasets that are used as forcings. For example, seasonal changes in runoff and productivity are not captured which means monsoonal climates are not well-represented. Overall, we consider the model to be appropriate to the task it is designed for. It suitably reconstructs the major patterns of plant biomass on the present-day Earth.
Nevertheless, both the previous work, and our analysis agree that the breakup of Pangaea (Fig. S1 ) was accompanied by a substantial increase in the habitable space available for plants, most of it corresponding to our tropical biome (Fig. 7 ). The tropical biome is responsible for >50% of the total biomass throughout the Phanerozoic with its smallest relative mass occurring over the Carboniferous and Permian. In line with our hypothesis, the formation of Pangea and the spread of aridity over large areas around the equatorial belt shrunk the tropical biome more so than the boreal and temperate biomes that lie closer to the poles (Fig. 7 ). Under-and over-estimations of the biome contribution towards biomass may be present due to the lack of overlapping biomes within grid cells.
Precipitation and runoff decrease markedly as the amalgamation of Pangaea is completed, and the effects of the cooling in the late Paleozoic also reduced the habitable space for plants. Silurian mesofossils indicate the presence of lignified cell walls and tubular structures essential for water supply towards the peripheral regions of plants which were further developed towards the Devonian  40. Tracheophytes evolved between around 450-430 Ma  41  and the evolution of roots also fall between the Silurian-Devonian period, beginning with rhizoid structures and ending with extensive rooting systems  42, 43. Thus, this period of increasing aridity is associated with the circular evolution of morphological and physiological innovations in plants, focused towards water acquisition, transport and retention.