dataesr/scientific-paragraphs-categorization huggingface dataset:
| Simulation: Very good fit when the simulated sampling effort was cell-wise constant The estimated sampling effort had an R 2 of 0. 97 (for both alti and chbio_12) compared to the simulated sampling effort when the latter was constant within cells (columns x:alti H=+Inf and x:chbio 12 H=+Inf of Figure  2a ). This means that the estimate was almost collinear with the true sampling effort. Regarding species density estimates, the average R 2 over all species was 0. 95 for alti and 0. 88 for chbio_12 (columns x:alti H=+Inf and x:chbio 12 H=+Inf of Figure  2b ). This shows that the method recovers unbiased niches and sampling 2 http://doi. org/10. 5281/zenodo. 2635501 (4) ∼ 1 + etp + I(etp 2 ) + I(chbio_12 -etp) + I((chbio_12 -etp) 2 ) +chbio_1 + I(chbio_1 2 ) + chbio_5 + I(chbio_5 2 ) + awc_top +I(awc_top 2 ) + bs_top + I(bs_top 2 ).
| Method use guidelines This method should be useful for large datasets of opportunistic occurrences in which some species are highly observed: for instance, with data from large citizen science or naturalist programmes. The recommendations on model design and sample size below indicate the conditions under which the method is most potentially useful: 1. Include at least several tens of occurrences (all species included) per sampling cell. Otherwise discard the cells and their occurrences, and do not include any background points over these cells. Alternatively, the user can increase the size of cells or include more species, widely distributed and with many occurrences, to meet the condition. Indeed, the information gain on the sampling effort parameter in a cell is equal to the total number of occurrences in this cell (see Appendix A).
Scarce cells are a useless computational burden, as they need background points, and a potential source of variance. As the sampling effort in those cells is very uncertain, they consume degrees of liberty but do not contribute to reducing the variance in the species parameters. The method is not suited to contexts where the concentration of occurrences per sampling cell is too low: for example, herbarium datasets with few samples collected over large areas with very heterogeneous sampling effort. In such cases, the FactorBiasOut/TGB method  (Phillips et al. , 2009)  should be more reliable because it does not require many degrees of freedom to model sampling effort. For example, our test case had an average of 105 occurrences per cell. 2. There should be at least several tens of occurrences for each environmental feature for each species.
| Scalability This method can handle datasets that include a massive number of total occurrences over large geographical areas with many sampling cells, as shown by our test case with the Pl@ntNet data across France. This is favoured by the cell-wise constant sampling effort model and the use of a sparse design matrix. The memory load increases sublinearly with the number of sampling cells, but is roughly proportional to the inverse cell area of the highest resolution environmental raster. While there must be background points in all sampling cells, their total number just needs to enable a good screening of the overall environmental variability. The number of species may also be limiting as this linearly increases the memory load, independently of their number of occurrences. There is a room for improvement in reducing the need for memory in the fitting procedure: for instance, by optimizing the selection of background points or using a batch gradient descent algorithm.
DATA AVA I L A B I L I T Y S TAT E M E N T Following the FAIR principles, the datasets and source code used in this manuscript are provided at the URLS below: • Species occurrence data may be freely downloaded at http://doi. org/10. 5281/zenodo. 2634137 (Botella et al. , 2019). • Environmental rasters may be freely downloaded at http://doi. org/10. 5281/zenodo. 2635501 (Botella, 2019). • The R code for running simulations and real data illustration, as well as the list of modeled species are provided on the manuscript dedicated Github repository https://doi. org/10. 5281/ zenodo. 4455857  (Botella, 2020).
Method Sample size determination. An a priori power analysis was conducted using Gpower  (Erdfelder et al. , 1996). The analysis indicated that a sample size of 360 would be sufficient to detect a significant effect of our predictors with a power of 0. 95 and an alpha of 0. 05, based on an effect size (F 2 ) of 0. 05. Anticipating drop out and exclusion issues, we requested about 480 participants. Participants and design: A total of 479 American participants were recruited on the Amazon Mechanical Turk online data collection platform. Among the initial sample, 106 participants did not complete the questionnaire (N = 37) and/or failed (N = 69) to correctly answer the attention check. They were then removed from any further analysis, leaving a final sample of 373 participants (226 females, 1 "other"; Meanage = 37.
Method Sampling rationale: An a priori power analysis was conducted using Gpower  (Erdfelder et al. , 1996). The analysis indicated that a sample size of 460 would be required to observe a significant effect of our predictors with a power of 0. 95 and an alpha of 0. 05, based on an effect size (F 2 ) of 0. 05. Participants and design: A total of 496 American participants were recruited on the Amazon Mechanical Turk online data collection platform. Among the initial sample, 110 participants did not complete the questionnaire (N = 82) and/or failed to respond to the attention check question (N = 67). They were then removed from any further analysis, leaving a final sample of 386 participants (246 females, 1 "other"; Meanage = 40. 2, SDage = 13. 2) (see supplementary material S2 for detailed demographic information).
Method Sampling rationale: An a priori power analysis was conducted using Gpower  (Erdfelder et al. , 1996). The analysis indicated that a sample size of 251 would be required to reach a significant effect of our predictors with a power of 95 % and an alpha of 0. 05, based on medium effect size (F 2 ) of 0. 10. Based on prior studies, we expected that between hundred and hundred and fifty participants would be discarded from the sample due to attention check failure. Participants and design: A total of 397 American participants were recruited on the Amazon Mechanical Turk online data collection platform. Among the initial sample, 125 participants did not complete the questionnaire (N = 82) and/or failed to correctly answer the attention check question (N = 84). They were then removed from any further analysis, leaving a final sample of 272 participants (97 females; Meanage = 37.
However, the previous study did not incorporate this principle into an evolutionary dynamics of origin turnover. Thus, the important question arises of whether the tendency to avoid double stalls is related to origin gain and loss. To address this question, we defined a birth-death model, rooted in the experimental observations discussed in the previous section. This "double-stall aversion model", described in detail below, biases the turnover of replication origins in such a way that events (in particular birth events) leading to a decreasing double-stall probability are promoted, because they increase the fitness of the cell. In the double-stall aversion model, the extent to which the acquisition of a new origin where t is measured by protein-sequence divergence. The panels A and B are generated using data from approximately 320.
Data The experimental data used in this work come from ref. [22]. In particular, we made use of the data regarding the replication origins. For each origin in each of the ten Lachancea species, this dataset includes the chromosome coordinate and firing rate, and the inferred birth and death events occurred in the branches of the phylogenetic tree shown in Fig. 1  -Supplement 1. Focusing on the terminal branches of the tree and on the extant replication origins, this study defines three categories of origins: (i) "conserved" origins (which survived from the last ancestor) (ii) "newly gained" origins gained in the last branch of the phylogenetic tree, (iii) "lost" origins, which were present in the last ancestor species and are not present in the terminal branch. Properties of the lost origins (e.
Evolutionary model We defined origin birth-death models incorporating different evolutionary biases. In these models, the genome is described as a one-dimensional circle with discrete origin location x i , where the length of the genome is equal to the average genome length in Lachancea clade (10. 7M bp). We made use of a circular genome in order to avoid border effects. In the model, the set of origins change over evolution by three basic (stochastic) processes, birth of an origin in a certain genome region, origin death and change of origins firing rate. We have verified that choosing linear chromosome does not alter significantly our findings, although it affects the distances between origins close to chromosome ends (Fig. 3  -Supplement 1). Overall origin birth/death rates were estimated from the data as follows.
Simulations Code Availability. The code used to run the simulations, together with instructions to run it, was shared as a repository on Mendeley data, and is available at the url https: //data. mendeley. com/datasets/vg3r5355bj/2. Algorithm. The prediction of the different evolutionary models were derived numerically, making use of custom simulations written in C++, which implement the origin birth-death dynamics as a Gillespie algorithm  [30]. Every model variant was required to reproduce the experimental overall rates, b = 13. 5627M bp -1 t -1 for origin birth, d = 0. 612287t -1 for origin death and R = 0. 92t -1 for firing rate change. We simulated the three processes defining the model as follows. (i) The birth process has a common definition for the stall aversion and joint model. The algorithm first tests each subsequent inter-origin region, calculates the birth probability from Eq.
They are grouped into four datasets: hydrology, fluvial export of organic matter, greenhouse gas fluxes and meteorology/soil physics. The data from all sites follow a common processing chain from the sensors to the public repository. The raw data are stored on an FTP server. After operator or automatic processing, data are stored in a database, from which a web application extracts the data to make them available (https://data-snot. cnrs. fr/data-access/). Each year at least, an archive of each dataset is stored in Zenodo, with a digital object identifier (DOI) attribution (https://zenodo. org/communities/sno_tourbieres_data/).
| SENSOR CHARACTERISTICS AND DATA ACQUISITION An Orpheus Mini (OTT, pressure sensor) is used to measure the WTD in all sites (Table  1 ). The resolution of the sensor is 1 mm and its accuracy is 2 mm. It measures the water column pressure and the atmospheric pressure. The latter is subtracted from the former to obtain the water column height. Then the distance between the soil surface The same sensor is also used to assess the streamflow at the outlet of the catchment containing the peatlands. The outflow intensity is proportional to the water height. At low frequency, water outflow is measured using salt dilution and the water column height is simultaneously recorded. A calibration curve relating outflow and water height is established. This curve is used to produce a high frequency outflow time series (1 recording every 30 min).
icos-cp. eu/) for level 2 stations (http://www. icos-etc. eu/icos/documents/instructions). For carbon dioxide (CO 2 ) and water vapour (H 2 O) the sensor used is the LI-COR enclosed LI7200 IRGA (infrared gas analyser). For CO 2 , the calibration ranges from 0 to 3000 ppm, with an accuracy within 1% of the reading. For  dataset  (Knox et al. , 2019; Delwiche et al. , in prep. ).
| DATA AVAILABILITY The data of the SNO Tourbières are available through a web application developed with R-Shinny: https://data-snot. cnrs. fr/data-access/ (Figure  2 ). A tool to produce customized figures was developed by dreamRs, a company developing data science packages in R language (https://www. dreamrs. fr/). All the applications used in the SNO Tourbières information system are located in the CNRS servers in Orléans. EC fluxes and soil-meteorological data of La Guette peatland are also available through the ICOS carbon portal: https://www. icos-cp. eu/observations/carbon-portal. The CH 4 fluxes of this site are available at: https://fluxnet. org/doi/FLUXNET-CH4/FR-LGt (https://doi. org/10. 18140/FLX/1669641). Attributing a DOI to a dataset ensures its correct citation and long lasting archiving.
Each DOI is associated to a landing page that has to remain accessible in the long term. The strategy in the SNO Tourbières is to assign a fixed DOI to a dataset and apply a versioning to avoid too many landing pages. To do so, the chosen application is Zenodo (Figure  2 ), an open access data, research and software repository that generates a DOI and manages versioning. With the versioning, as soon as we have a new dataset, a new DOI is created with its associated metadata. This process makes the system flexible, traceable and manageable. The data are updated between one and three times per year. A SNO Tourbières data repository has been created: https://zenodo. org/communities/sno_tourbieres_data/. This application allows contribution from and to other data managing structures. An article can be written with a subset of data.
These data can be deposited in another data repository with the reference to the article. The SNO Tourbières dataset will mention if it contains this particular sub-dataset: 'HasPart' (e. g. ,  Rosset, 2019b; Rosset, Gandois, Le Roux, Teisserenc, Durantez Jimenez, Camboulive, & Binet, 2019). Furthermore, datasets of the SNO Tourbières can be contained in a larger data set (e. g. , Fluxnet, ICOS, OZCAR) and will mention it as: 'IsPartOf'.
The chamber flux data are collected manually at low frequency (6-12 times per year) with the Vaisala CARBOCAP GMP343 (measuring range: 0-2000 ppm, precision: ±5 ppm + 2% of the value). The chamber is 30 cm high with a diameter of 30 cm, portable, and removed between measurements. The probe is inserted into the chamber and left between 2 and 5 min. Flux is calculated from the slope of the CO 2 concentration against time (D'Angelo et al. , 2021). 4 | DATA FLOWFrom acquisition to accessibility, the data of the SNO Tourbières go through four stages (Figure2): (a) collection/storage; (b) processing;(c) integration into a database; (d) dissemination. The underlying principle of the SNO Tourbières information system is the findable, accessible, interoperable, re-usable (FAIR, Wilkinson et al. , 2016)  approach.
1. Collection and storage. The raw data (sensor output) are stored in the FTP server 'SRV-SO' located in CNRS -Orléans (SRV = server, SO = system of observation, Figure2). Data produced by automated stations are acquired at different frequencies, from 20 Hz (EC) to 1 measurement per 30 min. 2. Processing. Each dataset undergoes a quality check and data treatment. Calculations of high frequency time series are undertaken with scripts that may require specific information, such as the relationship between water height and water flow rate at the outlet obtained manually and at a low frequency. All the information (metadata) and scripts are stored in a second file. The processed data are stored in a third file (CSV format) with a measurement frequency of 1 per 30 min frequency for all data sets.
The data from this third file proceed to the next step (Figure2). 3. Integration into the database. All the operators working on data processing can store their data in the database at: https://datasnot. cnrs. fr. Before uploading a data set, the operator must fill in the information required by the system to allow the data to be uploaded to the database and to produce the metadata. This information is called 'reference data'. The core (database and web application) of the SNO Tourbières information system was adapted from the standard developed by the ECO-INFORMATIQUE team of the Institut National de la Recherche en Agriculture, Alimentation et environnement (INRAe, InfoSol unit in Orléans). The database management system used is PostgreSQL (https://www. postgresql. org/). 4. Dissemination.
The data are made accessible through web applications managed by the SNO Tourbières team and through data portals managed by research infrastructures (Observatoire de la Zone Critique Applications et Recherche: OZCAR, ICOS, FLUXNET-CH4, see data availability section below). 5 | FUNDING, CONTRIBUTORS AND DATA OWNERSHIP Recurring funding for the network coordination and some equipment is provided by the CNRS. The permanent staff cost is covered by the different universities and research institutes: CNRS, Universités d'Orléans, de Bourgogne Franche-Comté, de Rennes 1, Paul Sabatier and Jean Jaurès, INRAe. Equipment, operating costs and nonpermanent staff are funded through projects: (a) inter-regional European Union programme INTERREG: CARE-PEAT: Restoring the carbon storage capacity of peatlands (North West Europe, NWE 808), REPLIM: Red Pirenaica de Observatorios de ecosistemas vulnerables (lagos, turberas) al cambio climatico (Program Of Cooperation España-France-Andorra, POCTEFA, EFA056/15), PIRAGUA (POCTEFA, EFA210/16), OPCC (Observatoire Pyrénéen des Changements Climatiques, POCTEFA, EFA082/15); (b) State-Region Planning Agreement (Contrat Plan Etat-Région -CPER) project; (c) Agence National de la Recherche, ANR, projects: PEATWARM (ANR-07-VUL-010), TRAM (ANR JCJC 15-CE01-008), Laboratoires d'Excellences, LabEx DRHIMM (Dispositif de Recherche Interdisciplinaire sur les Interactions Hommes-Milieux, 11-LABX-0010), LabEx VOLTAIRE (VOLatils -Terre, Atmosphère et Interactions -Ressources et Environnement, 10-LABX-0100) or the; (d) Region Centre-Val de Loire (PIVOTS: Plateformes d'Innovation, de Valorisation, d'Optimisation Technologiques environnementaleS, CArBioDiv: restoring peatlands to promote Carbon sequestration and BioDiversity, CarEx: Carbon sequestration through Experimentation on vegetation), Bourgogne Franche Comté (CRITICAL Peat).
S. Gogo, F. Laggoun-Défarge, G. Bertrand, A. -J. Francez, D. Galop, L Gandois, D. Gilbert contributed as data producers and/or site managers and/or network coordinators. J. -B. Paroissien, L. Perdereau Y. Brossard, J. -P. Caudal, E. Lerigoleur contributed ton the building and/or the technical set-up (in situ and/or in relation to the server) of the information system. J. -M. Antoine, L. Bernard-Jannin, P. Binet, S. Binet, G. Bouger, T. Camboulive, S. Chevrier, G. Chiapiuso, B. D'Angelo, P. Durantez, C. Fléchard, C. Guimbaud, L. Hinault, A. Jacotot, F. Le Moing, G. Le Roux, F. Leroy, A. Lhosmot, Q. Li, E. Machado Da Silva, J. -S. Moquet, J. Mora-Gomez, T. Rosset, M. -L. Toussaint participated in producing the datasets (maintenance, data retrieval, calculations, funding). The data belong to the French State and can be used by anybody as long as the data are properly cited, either by acknowledging the SNO Tourbières and/or by citing their digital object identifier (DOI) when available.
ACKNOWLEDGEMENTS The SNO Tourbières team would like to thank  A. Schellenberger  and  P. Tcherniatinsky  from the  ECO-INFORMATIQUE team of the INFO SOL laboratory of the INRAe center in Orléans  for their help in adapting the initial database to our requirements. We also thank  V. Chaffard  and  C. Coussot  for helping us integrate our data in the OZCAR-THEIA information system. The SNO Tourbières is coordinated by the  Observatoire des Sciences de l'Univers en region Centre (OSUC -Université d'Orléans, CNRS, CNES). The SNO Tourbières is certified by the  CNRS. The SNO Tourbières is part of the  Research Infrastructure OZCAR  ( Observatoire de la Zone Critique, Application et Recherche ) and one site,  La Guette , is part of  ICOS (Integrated Carbon Observation System ) and another one, Bernadouze, is part the  Observatoire Hommes-Milieux (OHM -Laboratoire d'exellence Dispositif de Recherche Interdisciplinaire sur les Interactions Hommes-Milieux: LabEx DRIIHM).
Franz et al. , 2018)#/home. At the European level, La Guette peatland has been part of the French and European ICOS ecosystem network (https://www. icos-cp. eu/,Franzet al. , 2018)as an associated site since November 2019. At the international level, the methane flux of La Guette peatland is part of the FLUXNET-CH4 Auto or manual 1. Collection and storage framework are available geonetwork/srv at: SeRVer-System of http://193. 55. 175. 137:8080/ FTP SRV-SO (File Transfer Protocol Observation) 2. Processing Internaltreatment File formating Qualitycontrol OtherInformation Systems Data processing 3. Integration data-snot (système national Servers d'observation tourbières) CORE Information Databaseserver Materialisedview PostgreSQL (relationaldatabase management system) trigger Sytem* -Java (program language) -Hibernate (object-relational mapping) -Spring (application framework for Java platform) Shinyapp Pivot Metadata Application server 4.
Diffusion Zenodo DOI data-access F I G U R E 2 Data flow within the information system from the sensors to the public and the DOI attribution. The core information system used originated from the Service d'Observation d'Expérimentation au long term pour la recherche en environnement (SOERE, long term observation and experimental service for research on the environment) 6 | INVOLVEMENT OF THE SI SNO TOURBIÈRES IN NATIONAL AND INTERNATIONAL RESEARCH INFRASTRUCTURES At the national level, the SNO Tourbières is part of OZCAR, the French research infrastructure (RI) on the critical zone (https://www. Braud et al. , 2020)2018)et al. , 2018), and is registered in the OZCAR information system, THEIA-OZCAR (https://in-situ. theia-land. fr/;Braudet al. , 2020). The Bernadouze site is part of an interdisciplinary observatory Observatoire Hommes-Milieux (OHM -Laboratoire d'exellence Dispositif de Recherche Interdisciplinaire sur les Interactions Hommes-Milieux: LabEx DRIIHM).
Acknowledgements. This study is part of a collaboration between the  University Grenoble Alpes and Grenoble Alpes Métropole , the metropolitan authority of the Grenoble conurbation (deliberation 12 of the metropolitan council of 27 May 2016). This work is also part of both the  IND-EX  project and the  HYDRODEMO  project. Financial support. This research has been supported by  Grenoble Alpes Métropole , the metropolitan authority of the Grenoble conurbation. It is also supported by the  Rhône Alpes Region (France) , the  European Union  via the  FEDER-POIA program  and the French state via the  FNADT-CIMA program. Review statement. This paper was edited by  Christian M. Grams  and reviewed by two anonymous referees.
A. Blanc et al. :  Past evolution of western Europe large-scale circulation methodology to climate model projections hence appears as an interesting perspective. Code and data availability. The data analyses and plots were performed with the R language (https://www. R-project. org/, R Core Team, 2021) and Rstudio software (http://www. rstudio. com/, RStudio Team, 2021). The R code can be requested by email from the corresponding author. The ERA5 reanalysis is available on the Copernicus Climate Data Store (https://cds. climate. copernicus. eu/cdsapp#!/dataset/ reanalysis-era5-pressure-levels-preliminary-back-extension? tab=overview,  Bell et al. , 2020 ; https://cds. climate. copernicus. eu/ cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview,  Hersbach et al. , 2020). The ERA20C reanalysis is available at https://apps.
ecmwf. int/datasets/data/era20c-daily/ levtype=sfc/type=an/ (ECMWF, 2022). Information on how to download the 20CRv2c reanalysis is available at https://psl. noaa. gov/data/gridded/data. 20thC_ReanV2c. html (NOAA/OAR/ESRL PSL, 2022). The weather pattern classification and the SPAZM precipitation data set have been provided to the authors by Électricité de France for this research. They could be made available to other researchers under a specific research agreement. Requests should be sent to dtg-demande-donnees-hydro@edf. fr.