dataesr/scientific-paragraphs-categorization huggingface dataset:
Spatio-temporal patterns of metabarcodes and CCs CCs including only Syndiniales sequences unassigned at genus level were extracted from the network (4 245 CCs; 33. 6% of network and 47. 6% of unassigned network CCs at genus level, Fig. S7A , Fig. S8 ). Pielou index (J), using the vegan package) and statistical metrics (mean abundance per month) were computed. The Escoufier's equivalent vector method was applied on CCs present at least 5 times across each time series. This method was run with the package pastecs and sorted clusters according to their correlation to a principal component analysis (PCA)  [69]. The cumulated correlation level chosen was 75% in order to avoid retrieving clusters with negligible correlation (100% would result in retrieving the whole dataset). The rhythmicity of CCs across time was computed by the Lomb-Scargle Periodogram (LSP)  [70]  via the lomb package.
and supplemented with public oceanographic databases (cf. additional information in the next paragraph). The information contained 14 metadata variables: name of the campaign, sampled region, station (for oceanographic campaigns), sequencing technology, sampling date, year, month, season, depth (m), depth type (surface (depth 5 m), deep maximum chlorophyll (DCM), mesopelagic zone (depth 200 m), bathypelagic zone (depth 1 000 m)), sampled size fraction, latitude, longitude). The 3 temporal series datasets (ASTAN, SOLA, BBMO) were sampled only at surface, BioMarKs dataset was sampled at surface and DCM, while the 2 oceanographic campaigns (MOOSE and Malaspina) were sampled at surface, DCM, mesopelagic and bathypelagic zones (up to 2 000 m depth for MOOSE and 4 000 m for Malaspina). The sampled size fractions are: 0-0.
[68]distribution of clusters across marine environments and time was explored with R functions that were coded to extract the sequence attributes related to sampling data in each CC (location, dataset, depth, season month). A Redundancy Analysis (RDA) was performed on the abundance matrix of Syndiniales CCs using the metadata for Tropical/Subtropical Ocean and Mediterranean sea samples as explanatory variables. ANOVA tests were run to assess the robustness of the global RDA (all environmental variables included) and of the first two dimensions of the RDA with selected environmental variables. Both the RDA and ANOVA were run via the vegan package. Potential Syndiniales host communities were compared with the Jaccard dissimilarity index of the based on the Bray-Curtis compositional dissimilarity of abundances[68].
Models As specified in the THAI protocol  (Fauchez et al. 2020) , four GCMs were used in this study: • the Exoplanet Community Atmospheric Model (ExoCAM), a branch of the National Center for Atmospheric Research Community Earth System Model version 1. 2. 1. • the Laboratoire de Météorologie Dynamique -Generic model (LMD-Generic a. k. a. LMD-G). • the Resolving Orbital and Climate Keys of Earth and Extraterrestrial Environments with Dynamics (ROCKE-3D Planet_1. 0), • the Met Office Unified Model (UM, science version GA7. 0, code version 11. 6). In the following subsections, we provide a brief description of the model components related to the dynamics and dry physics (see Part 2 of the THAI intercomparison for details on the moist physics), with a focus on RT, large-scale dynamics (computed using so-called 'dynamical cores') and sub-grid scale dynamics (dry convection and turbulent mixing).
Radiative Transfer To take into account how the atmosphere, clouds and surface interact with the light emitted by the star and the planet, GCMs employ RT schemes. ExoCAM includes a two-stream correlated-k distribution RT scheme with a code lineage that traces back to  Urata & Toon (2013a,b)  and  Colaprete & Toon (2003)  with extensive modifications and reorganization occurring over time  (Wolf & Toon 2013; Kopparapu et al. 2017; Wolf et al. 2021). This RT scheme, known as ExoRT, has been separated from the 3D model and is provided on GitHub 1  in order to provide an accessible 1D column model version that facilitates more rapid development and testing. There are now several different RT versions available with ExoRT for use as part of ExoCAM that permit the simulation of different atmospheres.
While the longwave and shortwave stream bandpasses are customizable to optimize computational expense versus completeness, here we used a brute force approach and calculated the longwave and shortwave streams over the full spectrum in each case. (Note, for planets in the TRAPPIST-1 system, about 1% of the stellar energy received lies longward of 9 µm) Molecular absorption by CO 2 is included, with correlated-k distributions produced using the Atmospheric Environment Research Inc. line-by-line RT model (LBLRTM,  Clough et al. 2005 ) using the HITRAN 2004 spectroscopic database  (Rothman et al. 2005) , and Voigt line wings cut at 25 cm -1. Water vapor and CO 2 continuum absorption are included using MT_CKD version 2. 5, while assuming that CO 2 self-broadened continuum is 1. 3 times the foreign broadening continuum  (Kasting et al.
2004 ). LMD-G RT uses a two-stream scheme  (Toon et al. 1989 ) to take into account the scattering effects of the atmosphere (Rayleigh scattering) and the clouds (Mie scattering ; but note that this is only relevant for Hab 1 and Hab 2 simulations), using the method of  Hansen & Travis (1974). Both the UM and ROCKE-3D share a common RT module SOCRATES 2  , which is an open-source two-stream correlated-k RT model provided by the Met Office  (Edwards & Slingo 1996; Manners et al. 2012). SOCRATES is highly flexible. Spectral files, created for both longwave and shortwave streams separately, are tailored to specific atmospheric compositions and stellar spectra. The spectral files are then input to the GCMs at run-time, containing all information necessary to define the RT problem, including the number of spectral intervals, the absorbing gases, continua, and CIAs, the number of Gauss points used for each gas, the designation of dominant and minor absorbing species in each interval, and cloud and Rayleigh scattering properties.
Thus, dramatic changes to the RT can be achieved by careful pre-processing of the spectral files, with no changes to the hard-code being required. When run embedded in a GCM, SOCRATES uses the equivalent extinction method to treat multiple overlapping gas species  (Amundsen et al. 2017 ). In the equivalent extinction method the dominant absorbing gas, as determined from the total transmissivity in a test column, is treated with a full k-distribution, while all minor absorbing gases are treated as grey. The number of Gauss points used for the dominant gas in each spectral interval is determined based on a selected transmission error tolerance (usually 0. 001). For the Ben 1 simulations, spectral files were tailored to Earth-like atmospheric compositions, allowing for small amounts of CO 2 in N 2 dominated atmospheres.
The spectral files used by ROCKE-3D and the UM are identical (details provided in Table  3 ). Gas absorption coefficients are derived from the HITRAN 2012 spectroscopic database  (Rothman et al. 2013). For Ben 2 simulations, CO 2 absorption is treated following  Wordsworth et al. (2010)  with sub-Lorenztian line shape  (Perrin & Hartmann 1989)  out to 500 cm -1 with CO 2 -CO 2 CIA overlaid.
1983). The atmospheric vertical layers use a sigma coordinate system from the surface to 150 hPa. From there constant pressure layers are used to the TOA (0. 01 hPa or ≈ 65 km). For the THAI experiments the model was run with 72 × 46 grid points with each grid point being 5°in longitude by 4°i n latitude except at the poles where the latitude coordinate is only 2°in extent. The smaller polar grid boxes are necessary to maintain second-order accuracy of airmass fluxes, the pressure gradient force, and momentum advection terms  (Schmidt et al. 2006 ). The dynamical time-step used in the Ben 1 simulation was 225 s and 450 s in Ben 2. Both Hab 1 and Hab 2 simulations use a dynamical time-step of 450 s. For all 4 simulations the parameterized physics uses a time-step of ≈ 30 minutes (1756. 8 s to be exact), and less in some submodules, while the SOCRATES radiation was called every two physics time-steps (∼ 1 hour).
ACKNOWLEDGMENTS This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie Grant  Agreement No. 832738/ESCAPE. M. T. thanks the  Gruber Foundation  for its generous support to this research. M. T. thanks  Ray Pierrehumbert  and  Jeremy Leconte  for useful discussions on this work. M. T. was granted access to the  High-Performance Computing (HPC) resources of Centre Informatique National de l'Enseignement Supérieur (CINES)  under the allocations №  A0020101167  and  A0040110391  made by.
Despite the abundance of animal housing and outdoor manure storage emission measurements that have been carried out around the globe in the last decades, there is still considerable uncertainty about which factors have the largest influence on gaseous emissions  (Petersen at al. , 2018). The development of a database of all relevant published emission values on the manure management chain could help to improve the models and to address questions about the influencing factors. In addition, it would allow the refinement of emission factors for different livestock systems and climate zones. Improving knowledge of effluent emissions from housing and storage is not only an environmental issue; it is also an important resource from an agronomic and economic point of view. The DATAMAN project has enabled the development of this database that contains information from studies on CH 4 , N 2 O, and NH 3 emissions from livestock housing, grazing, and outdoor storage and field application of manure  (Beltran et al.
, 2021; van der Weerden et al. , 2021). It has global scope and was compiled and quality controlled by an international team of experts. The objective of the current study was to describe the housing and outdoor storage-based component of the DATAMAN database, with a specific focus on CH 4 , N 2 O, and NH 3 EFs for animal housing and manure storage from countries where these livestock farming systems are practiced. In addition to data collected from peer-reviewed research, the database includes information extracted from existing datasets, such as ELFE (ELevage et Facteurs d'Emission;  Vigan et al. , 2019) , data collated in connection with the 2019 refinement of the 2006 IPCC guidelines (IPCC, 2019), reviews (e. g. ,  Kupper et al. , 2020) , and many individual studies not captured in reviews or existing datasets.
Process of data collection All studies were checked for their suitability for inclusion in the database with the following criteria: (a) laboratory, pilot, or commercial scale studies conducted for 6 d or more and (b) measurements conducted with one of the following methods: tracer ratio, direct method, micrometeorological methods, or dynamic and static enclosures. We excluded all modeling studies because the focus was to collate only measurement data. Inclusion of relatively short-term or laboratory-scale experiments (for storage) broadens the potential use of the database beyond revised EFs toward identifying key drivers of emission. Data from laboratory-scale experiments were included in the assessment of EFs. The major objectives of the DATAMAN project were to determine revised EFs and assess key drivers of emissions.
Data in the scientific reports accessed were typically entered as rates of emission or cumulative emissions, with a wide range of units used. Consequently, additional work was required to either derive or estimate EFs using the information provided in a publication (derived) or using national data (estimated), where key information was omitted (e. g. , average kg N excretion per lactating dairy cow). This methodology, which was developed for the data conversion to EFs, can be found in  Webb et al. (2021)  and will not be discussed here. The housing and storage databases identify EFs in terms of whether they were supplied in the report, derived from data in the report, or estimated from data in the report and national default values. However, it was not possible to apply this methodology to all reported emission values due to insufficient key information (e.
Description of variables included in the housing and storage databases Housing and storage data were collated using templates developed in Microsoft Excel. The housing and the storage templates included 163 and 146 variables, respectively, which were grouped into different categories: "General," "Gas Measurement," "Animal Description," "Manure Description," "Climate," "Data Conversion," and "Housing Description" (the latter was limited to the housing tem-plate). The nature (qualitative or quantitative) of each variable varies in function. For example, some variables describe the methodology and treatments used in individual studies, whereas other variables relate to the measured data reported by individual studies. All these variables are presented and defined in the Glossary/Units section of the website that supports the database (https://dataman.
co. nz/ DataManGlossaries). Furthermore, the Web-based database includes a filtering function based on some of the key variables listed below, allowing users to examine and download subsets of the entire database. The "General" category is the only category that is identical in both templates. It contained 21 variables, including trial description, country, research institute that conducted the study, replicate number, latitude, longitude, database identification, online link to published research paper, reference of research paper, degree of variation in reported EF means, statistical method used to determine EF means, comments, and experiment identification. The "Gas Measurement" category contained 24 variables in the housing template and 21 variables in thee storage template, including gas measured, emission measurement technique, start and end times of gas measurements, measurement duration, number of measurements, cumulative emission, emission rate, and EF (supplied, estimated, or derived).
18) to provide data compilers with the option to capture the chemical and nutritional characteristics of up to two different feeds to take into account biphasic feeding. Variables concerning animal categories, animal growth, and breeding conditions are also given in the housing template. The "Manure" category contained 44 and 59 variables for housing and storage templates, respectively, including manure type, manure chemical composition (e. g. , dry matter content [DM], total N concentration, total ammoniacal N concentration [TAN], VS, ash content, C/N ratio, pH), manure treatment, and duration of storage. For housing, additional variables concerning C and N animal excretion and manure management (e. g. , removal, drying, use of additives, composting) were also included. For the storage template, storage conditions have been captured using variables such as storage type, storage facility dimension, and manure volume and weight at start and end of storage.
The "manure treatment" variable for storage referred to possible treatment of solid or liquid manure (e. g. , covering, compaction, aerobic treatment, solid separation, anaerobic digestion, composting, acidification, and the use of urease and/or nitrification inhibitors). We consulted the RAMIRAN glossary  (Pain & Menzi, 2011)  for guidance on definitions of manure type and storage type and included relevant descriptions in the Glossary/Units section of the database. "Climate" contains three variables for housing and 13 for storage. The common variables are climate zone, air relative humidity, and average wind speed during the trial. Climate zone was divided into four categories: temperate wet, temperate dry, tropical wet, and tropical dry, as defined by  Beltran et al. (2021). Other variables, such as the mean air and manure temperature, wind speed (over first 12 h and 3 d), and variables concerning rainfall (total rainfall during the trial, during the first hour, and during the first 6 h of storage) are requested for storage.
Quality control entry process The data obtained from existing databases (ELFE & IPCC refinement) had already undergone a quality control process when they were collated  (Vigan et al. , 2019; Kupper et al. , 2020; IPCC, 2019; B. Amon, personal communication, 14 June 2021). Nevertheless, for other inputs, and to check the consistency of the whole database, four different approaches were implemented for the quality control: (a) The collected variables are of the type character, integer, or numeric. To guide data compilers and provide an easy first-step visual quality control check, for many numeric or integer variables, a value range was given based on experts' judgement. For variables of type character, the maximum length of the expected string has been indicated. We also included dropdown lists for some character variables to homogenize the terms used to simplify the quality control process and facilitate the use of the collated information.
Summary of database per country, continent, and climate zone A higher number of EFs was observed for the housing database (Table  1 ; Figure  2 ) than the storage dataset, with 392, 1,281, and 351 EFs for CH 4 , NH 3 , and N 2 O, respectively. Europe is the continent with the most EFs in the database (71, 68, and 79% of the total number of Efs for CH 4 , NH 3 , and N 2 O, respectively). In Europe, EFs come from 18 countries spread over the whole continent. Ammonia is the most frequently observed gas with 875 EFs; 24% comes from France, 13% from The Netherlands, 16% from the United Kingdom, and 12% from Belgium. Most countries have data for all three gases, except for the Czech Republic, Ireland, Lithuania, Slovenia, and the United Kingdom, which only have NH 3 data for housing studies. The total number of EFs for N 2 O and CH 4 is similar (276 and 278), with high differences between countries for both gases; the number of EFs for N 2 O varies from 87 (for France) to 1 (for Germany) and from 77 (for Belgium) to 2 (for Hungary) for CH 4.
Variables collated in the CH 4 , NH 3 , and N 2 O housing dataset As for manure storage, none of the selected variables for housing was given in all publications. Table  3  presents the number of observations for a selection of quantitative variables relative to the total CH 4 , NH 3 , and N 2 O EFs for housing. Some of these variables are known to be key drivers  (Philippe et al. , 2011; Rigolot et al. , 2010) ; others are useful for deriving EF. Of the variables presented in Table  3 , the "number of animals" is the most widely reported, with >90% of EFs including this information. Average live weight is provided for about one-third of the emission values, regardless of gas type, but the distribution according to animal categories varies between the gases. For CH 4 , this variable is provided for 54% of dairy cattle EFs and 32% of swine EFs; for N 2 O, this variable is provided for 70% of dairy cattle EFs and 11% of swine EFs, and for NH 3 this variable is provided for 33% of dairy cattle EFs and 52% of swine EFs.
Concerning "average daily gain," >80% of the given values are for the swine category (data not shown). The variables C and N excreted are seldom included in published studies, which can make it difficult to derive EFs from the reported gas measurements when the EFs are not included in publications  (Webb et al. , 2021). Feed crude protein can be used to estimate N excretion for certain animal categories; however, this variable is also seldom reported. Data on "measurement duration" are important for evaluating the representativeness of the measured emissions over the rearing period. This information is provided for 71, 53, and 67% of the EFs for CH 4 , NH 3 , and N 2 O, respectively. Key variables (e. g. , egg or milk production) specific to certain livestock systems and useful for unit conversion were also collated in papers.
Summary of database per country and continent Table  4  and Figure  4  present a summary of the storage database, which contains 243, 654, and 421 EFs for CH 4 , NH 3 , and N 2 O, respectively. Europe is the continent with the most EFs for all gases (110 for CH 4 , 414 for NH 3 , and 199 for N 2 O). In North America, NH 3 is the most represented gas, probably because of North American regulations limiting NH 3 emissions for some farm categories  (Bjerg et al. , 2019)  and the implementation of   (Santonja et al. , 2017). Ammonia EFs were collated from 16 countries (though unfortunately none from African countries), CH 4 EFs from 13 countries, and N 2 O EFs from 17 countries in all continents. In Africa and Oceania, the EFs were collated from only one country (Kenya and New Zealand, respectively).
Type of animal and manure The number of CH 4 , NH 3 , and N 2 O EFs for manure storage were disaggregated by climate zone, animal, manure type, manure treatment, and storage type (Table  5 ). For all gases, dairy cattle and swine are the most represented animal categories. Dairy cattle represent 37% of the CH 4 dataset, 22% of the NH 3 dataset, and 35% of the N 2 O. Swine represent 39% of the CH 4 dataset, 48% of the NH 3 dataset, and 32% of the N 2 O dataset. The cattle category is also well represented for all three gases, with 12, 19, and 17% of the total number of CH 4 , NH 3 , and N 2 O EFs, respectively. Poultry represent about 10% of the EFs for the three gases, whereas other animal categories represented only a small percentage. Regarding manure type, slurry was the most common manure type for NH 3 and CH 4 (60 and 48% of the EFs, respectively), whereas solid manure was the most common  Weeping-wall 0 3 3 manure type for N 2 O, representing 32% of the EFs.
Variables collated in storage dataset Table  6  shows the occurrence of some selected variables (e. g. , manure pH, TAN content, wind speed, or air temperature) because they are known as parameters influencing emissions  (Pedersen et al. , 2021; Sommer et al. , 2017)  or useful for the conversion of emission data into EFs  (Webb et al. , 2021). None of the selected variables is given in all publications. Overall, the variables corresponding to manure characteristics were more commonly reported for slurry than for solid manure. However, for the N 2 O dataset, the total N, organic C, C/N ratio, and DM were more commonly reported in publications relating to solid manure than for slurry (data not shown). The most reported variables were manure DM, total N concentration, and pH (at the beginning of trials).
Manure VS was provided with 43% of CH 4 EFs (64% of these values concern slurry) but only with 21 and 11% of NH 3 and N 2 O EFs, respectively. The number of values for CH 4 is higher because VS concentration is a key parameter for CH 4 emissions  (Wang et al. , 2020). Average temperature, wind speed, storage facility, fouled surface area, and manure volume relate to the experimental conditions during measurement. These variables were reported for <60% of the EFs. Indeed, there were no wind speed data reported for the entire trial for any study. The same was true for the other two wind speed variables in the datasets: average wind speed over the first 12 h and average wind speed over the first 3 d. For the manure volume, this variable could be estimated by referring to the total storage volume of the storage facility, which is assumed to be given by authors as an indication of the stored volume during the experiments.