dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, all HMM profiles are concatenated and summarized using hmmpress to construct an HMM database. Then, for each NCBI CDS C i , hmmscan is used on the HMM database to get the best hits among the protoalignments-denoted bestAl(C i ). For each proto-alignment A j , the most similar sequences for each species S-denoted bestSeq(S, A j )-are detected via hmmsearch. Outputs from hmmsearch and hmmscan are discarded if the first hit score is not substantially better than the second (hit 2 < 0. 9 hit 1 ) and are combined in a best-reciprocal-hit fashion: Suppose that we are given a NCBI CDS sequence C i for species S k ; then denoting A j the best proto-alignment for C i , if the first sequence of bestSeq(S k , bestAl(C i )) equals C i and none of the other sequences C z 6 ¼ C i in bestSeq(A j ) is such that bestAl(S k , C z ) ¼A j , then C i is considered to belong to A j.
Brief Communication ß The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons. org/licenses/by-nc/4. 0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals. permissions@oup. com Open Access S i and the placental MRCA based on those two factors. When the linear regression prediction departs from the observed patristic distance (standardized residual >3), we consider the sequence as spurious and remove it. As in the previous versions of our database, we use exon positions in CDS alignments to infer exon orthology and alignments (see  Douzery et al.
2014 for details). Phylogenetic trees for each exon alignment are reconstructed as for CDSs. OrthoMaM v10 database is available at http://www. orthomam. univ-montp2. fr/orthomam_v10/. For each CDS and exon marker, we provide gene level information (gene name, GO annotation), full sequence traceability information (sequence identifier in Ensembl/NCBI, filtering details), nucleotide and amino acid alignments, phylogenetic trees, as well as several evolutionary indicators such as relative evolutionary rate and G þ C content. The improved web-interface (see fig. 1 ) provides a user-friendly way to query the database based on alignment content (number of sequences, number of species that should be represented, and alignment length), evolutionary indicators, or sequence similarity using blast. This new reactive web site, which has been completely redesigned to improve the user experience, the triplication of the number of species, the major update of the pipeline for orthology prediction and sequence filtering should make OrthoMaM v10 a central resource for anyone interested in mammalian comparative genomics.
Acknowledgments Our project largely benefited from the  Montpellier Bioinformatics Biodiversity  cluster computing platform. This work was supported by the  Agence Nationale de la Recherche  " Investissements d'avenir/Bioinformatique " ( ANR-10-BINF-01-02  project  Ancestrome ),  Labex CeMEB  " Centre M editerran een de l'Environnement et de la Biodiversit e"  ( ANR-10-LABX-0004 ), and the  European Research Council  ( ERC-2015-CoG-683257  project  ConvergeAnt ). This is contribution ISEM 2018-292 of the  Institut des Sciences de l'Evolution de Montpellier.
The Global Gridded Crop Model Intercomparison (GGCMI) phase 1 dataset of the Agricultural Model Intercomparison and Improvement Project (AgMIP) provides an unprecedentedly large dataset of crop model simulations covering the global ice-free land surface. The dataset consists of annual data fields at a spatial resolution of 0. 5 arc-degree longitude and latitude. Fourteen crop modeling groups provided output for up to 11 historical input datasets spanning 1901 to 2012, and for up to three different management harmonization levels. Each group submitted data for up to 15 different crops and for up to 14 output variables. All simulations were conducted for purely rainfed and near-perfectly irrigated conditions on all land areas irrespective of whether the crop or irrigation system is currently used there.
The potential to address these challenges is often explored with computer simulation models of agricultural productivity or outputs of such agricultural productivity models in combination with other models, e. g. economic models of the agricultural sector  5  or hydrology models  26. AgMIP (Agricultural Model Intercomparison and Improvement Project, see Table  1  for a complete list of abbreviations) was initiated to help improving agricultural modeling capacities across scales and aspects (soils, different crops, economics etc. ) by intercomparing models in simulation experiments using common protocols  27. The general idea of AgMIP is that different modeling groups around the world can participate, contributing data to the ensemble dataset. Protocols are developed to clearly describe important aspects in the configuration of the modeling experiments and all prescribed inputs are supplied to interested modelers.
The data were also used for economic assessments of climate change impacts on agricultural production systems  5, [33] [34] [35] [36]. However, this first global-scale simulation ensemble of AgMIP that was conducted as part of the ISIMIP project  3  revealed a broad range of GGCM results under different climate change scenarios and in response patterns  6, 31. This high level of uncertainty motivated the following GGCMI phase 1 to assess model performance and to identify general fields of model improvement. The protocol for GGCMI phase 1  37  thus designed a comprehensive modeling exercise aiming to understand GGCM skill in reproducing observed historic crop yield patterns in space and time. Besides inviting a broad group of modeling teams and models, the GGCMI phase 1 protocol also adds variants of management harmonization and weather input datasets.
Different assumptions on growing seasons across GGCMs had rendered the initial GGCM simulations  3  as difficult to compare. The broad availability of different historic weather data products, which also differ substantially in parts  37  , motivated the inclusion of different weather data products to address this source of uncertainty. A comprehensive initial model evaluation study based on the GGCMI phase 1 dataset  38  showed that GGCMs typically have better skill than statistical models to explain observed crop yield variability but also have little explanatory power in regions where yield variability is mostly driven by changes in management or pest outbreaks, rather than weather variability. None of the GGCMs proved to be generally superior to the others but differences in model skill were reported  38.
The output data from this set of simulations are described here. The GGCMI phase1 dataset provides an unprecedentedly large dataset of crop model simulations covering the global ice-free land surface. While the dataset has already served various analyses on crop yields  [38] [39] [40] [41]  , there are still many aspects unexplored; variables apart from crop yields have hardly been assessed so far, with the exception of actual growing season evapotranspiration by Wartenburger, et al. 42. The multi-dimensionality of the dataset (14 GGCMs, 11 input datasets, 3 harmonization levels, 4 crops, 14 output variables, time, and space) allows for further analyses and can also serve as an input to other models, the quantification of model uncertainties, and crop model emulation  43, 44. With the publication of the GGCMI phase 1 dataset we aim to promote further analyses and understanding of crop model performance, potential relationships between productivity and environmental impacts (e.
Methods The GGCMI phase 1 dataset 37 consists of the model output of 14 GGCMs (Table  2 ) for up to 11 weather datasets covering various time frames and up to 3 harmonization levels (default, fullharm, harmnon) for the 4 priority one (P1) crops (maize, wheat, rice, soybean), as well as for any number of additional crops (priority two, P2). Not all models have been able to simulate all P1 crops and a number of models provided several P2 crop simulations (see Table  3 ). The GGCMI phase 1 dataset has been compiled by 14 different crop modeling groups that have followed the protocol instructions  37  to achieve as much consistency as possible. It has to be noted, however, that not everything was or could have been harmonized across models. The focus of harmonization was on weather datasets to drive the models and on a few core crop management settings: the growing period and fertilizer inputs.
e. prescribed grid-cell-and crop-specific sowing and maturity dates) and fertilizer inputs (fullharm) and for the same harmonized growing seasons but with unlimited nutrient supply (harmnon, also referred to as harm-suffn in some publications, e. g. Müller, et al. 38  ). All simulations were conducted for purely rainfed (noirr) and for fully irrigated (firr) conditions as separate datasets so that crop yield could be aggregated with given crop-and irrigation masks to larger spatial entities in the post-processing  38, 47, 48. Global Gridded Crop Models. The GGCMs contributing to the GGCMI phase 1 data archive differ in model structure, input requirements, and processes covered and thus have implemented the simulations in different ways. We here first describe each individual GGCM with central references and a short description of the model setup and conduction of simulations.
Currently, no other yield-limiting factors (e. g. nutrients, pests, weeds, farm management) are taken into consideration. WOFOST has been embedded in the European CGMS that was developed within the framework of the MARS (Monitoring Agricultural ResourceS project) project of the Joint Research Centre of the European Commission. CGMS allows the regional application of WOFOST by providing a database framework that handles model input (e. g. weather, soil and crop parameters), model output (crop indicators such as total biomass and leaf area index), aggregation to statistical regions and yield forecasting. Model setup and protocol. The planting and harvest dates were taken from Elliott, et al. 37  for the different crops. For each crop and grid cell a pre-run was executed in order to determine the temperature sum requirements (phenological heat units: PHU) from planting to harvest based on the 30-year AgMERRA (Agriculture Modern-Era Retrospective Analysis for Research and Applications) weather forcings  52.
Following the spin up period, a 20th Century simulation was performed from 1901-2005 using transient meteorological forcing and atmospheric CO 2 generated from the same CESM simulation as used for the spin up. For AgMIP GGCMI all simulation configurations were started in 1978 with initial conditions provided for CLM crops from the 1978 state in the 20th Century simulation of Levis, et al. 58. The CLM Crop AgMIP simulations were performed over the 1978-2010 period for rainfed and irrigated versions of cotton, maize, rice, sugar cane, soy and wheat crops. Temperate and tropical versions of maize and soy were represented separated by latitude, with tropical versions from 30°S-30°N and temperate versions outside of those latitudes. Meteorological forcing was generated for CLM on a 6-hour time step from the daily values provided by the AgMERRA and WFDEI datasets.
EPIC-TAMU. General description. The EPIC version 1102 model is a further development of EPIC version 0810 described above for the EPIC-BOKU and EPIC-IIASA models, with the same fundamental routines for mechanistically simulating soil-plant-atmosphere dynamics. Additional model capabilities include improved soil water balance methods  71  , denitrification methods  72  , perennial crop growth routines  73  , and soil health impacts of biochar  74. Model setup and protocol. Data were computed for 40,500 pixels for which appropriate weather, soil and crop calendar data were available. A one-year spin up period was simulated in addition to the simulated years of each weather dataset. Planting occurs on the first day following the prescribed sowing date in which soil temperature is at least 2 °C above the 8 °C base temperature.
77  , but with a simplified classification. Grid cells with <5 °C in the coldest month are defined as temperature limited, all other grid cells as precipitation limited. In precipitation-limited grid cells, crops are planted on the first day of the month after which four consecutive months provide the highest precipitation throughout the year. In T limited grid cells, planting dates for summer crops are defined by cumulating PHU starting from the coldest month of the year until a crop-specific germination threshold is met. For winter crops, the same process is carried out backwards from the coldest month of the year. PHU were estimated from reported current sowing and harvest dates according to Sacks, et al. 64  for the default setup and from the datasets provided by GGCMI phase 1 for the harmonized runs based on long-term climate averages.
Winter wheat and spring wheat sowing areas change dynamically throughout the simulations period in each decade. For maize, a dataset of national human development index for the year 2000 (retrieved from http://geodata. grid. unep. ch) is used for distinguishing low-and high-input countries. Maize with a high potential harvest index (corresponding to current hybrids) of 0. 55 is planted in countries with a Human Development Index larger 0. 8 and maize with a lower harvest index of 0. 35 (corresponding to local conventional varieties  80  ) in all other countries. For rice, the harvest index and maximum leaf area index were modified based on current literature  81. All other crop growth parameters were left at the default values, which are based on literature and field trials  76, 80, 82.
ORCHIDEE-crop. General description. Simulations with ORCHIDEE-crop (Organising Carbon and Hydrology In Dynamic Ecosystems crop model) have been conducted using an improved version from Wu, et al. 94. The improvements include an allocation scheme resolving the source-sink regulation on biomass and yield, an irrigation scheme, and a fertilizer scheme. These updated developments are documented in Wang  95. Model setup and protocol. Simulations were performed over global land grids according to the land-sea mask of each climate input dataset. One-year spin-up was performed to balance the soil water budget. The time length of the spin-up was selected after testing the turnover time needed to balance the water budget of the 11-layer soil hydrology module in the model. No calibration was made for GGCMI simulations.
However, during development of ORCHIDEE-crop, the wheat and maize parameters were evaluated and calibrated against several agricultural eddy flux sites over Europe  94. The rice phenology parameters were calibrated against phenological observation networks in China  96. For the fullharm scenario, all input data instructed in the protocol were used. For the default simulation, the nitrogen fertilizer map was derived from a combination dataset of FAO and the International Fertilizer Association  97  , which is static and crop-specific. For the harmnon scenario, an over-saturation rate of 500 kgN ha -1 was applied in order to eliminate nitrogen constraints. paPSIM. General description. The pSIMS platform  98  leverages high-performance computing resources at the University of Chicago and Argonne National Lab.
For maize, grid cells described in the SPAM2000 (spatial allocation model) dataset  101  as "rainfed high input" or "irrigated" were assumed to use high-yielding hybrid cultivars, parameterized with 50% higher max grain number and 10% higher grain filling rate. Fertilizer levels in the default setting were the same as those used in the harmonized scenario (fullharm)  37  , with half applied at planting and half applied 40 days later. Wheat cultivar groups were selected based on mega-environments  102  and then phenology parameters were calibrated as with other crops. Soybean cultivars were selected based on standard maturity groups and were then calibrated to reproduce Sacks et al. 64  harvest dates  3. In the fullharm and harmnon settings, growing periods of all crops were calibrated in the same manner to reproduce given GGCMI growing periods  37.
Farm management practices represented in PEGASUS include irrigation and fertilizer application, decision of planting dates and choice of crop cultivars  105. For the GGCMI phase 1 simulations, PEGASUS version 1. 1 was used  106. Model setup and protocol. PEGASUS was calibrated to match average crop yields around the year 2000 of the Monfreda et al. dataset  107  , using a subset of the WATCH data (6 years from 1997 to 2002). Note that the calibration procedure in PEGASUS entails tuning only one global parameter, the light-use-efficiency coefficient (ε) as described in Deryng, et al. 105. For the default simulations, the calibrated version of PEGASUS from the ISIMIP fast-track 3 was used, making use of PEGASUS' internal algorithm to simulate planting date decision and choice of crop cultivars, as well as fertilizer data as referenced in Deryng, et al.
105. This means that the default configuration allows for progressive adaption of planting dates and choice cultivars according to annual mean climate conditions. For simulations of the fullharm and harmnon settings, a new calibrated version was used, using the same WATCH dataset as climate input and average crop yields around the year 2000 Monfreda et al. dataset  107  , but using: the harmonized crop calendar dataset and the harmonized fertilization application rates as specified by Elliott, et al. 37. However, this calibrated version differs only for wheat, for which ε was set to 0. 029 mol C m -2 s -1 APAR, instead of 0. 027 mol C m -2 s -1 APAR. APAR (mol quanta m -2 s -1 ) represents the daily average absorbed photosynthetically active radiation. ε = 0. 035 mol C m -2 s -1 APAR for maize and ε = 0.
011 mol C m -2 s -1 APAR for soybean was used for both default and fullharm versions. For this set of simulations, climate data were provided in one time-slice so that PEGASUS was run continuously over each time-period, including an initial 4-year spin-up. PEGASUS was run with downwelling longwave radiation input from the WFDEI dataset for AgMERRA and AgCFSR (Agriculture Climate Forecast System Reanalysis) simulations.
tamu. edu/software/), with input of planting date, growing season length, and monthly minimum and maximum temperature. In the simulation, different PHUs have been computed for each weather forcing dataset. For default setup, crop calendar data (planting and harvesting dates) were derived from Sacks, et al. 64  , and N and P fertilizer from FertiSTAT (database for statistics on fertilizer use by crop)  110  were used. For the harmonized setups (fullharm and harmnon), crop calendar, N, P and K fertilizer from GGCMI were used  37. For the simulation forced by each weather forcing dataset, 20 years were treated as model spin-up period. Automatic irrigation was used for irrigated cultivation with sufficient water supply (maximum value of 1000 mm). For default and fullharm scenarios, P was applied directly prior to planting and N was applied three times based on input data: first time before planting, second time one month after germination, third time two months after germination.
The PRYSBI2 output was interpolated to the requested 0. 5° resolution from its original 1. 125° resolution at which simulations were conducted. This means that the output of a 0. 5° grid was the same as the 1. 125° grid in which the 0. 5° grid was included. If a 0. 5° grid straddled multiple 1. 125° grids, the average value of these 1. 125° grids was used. PRYSBI2 data do not distinguish irrigated and rainfed production as irrigation is subsumed in the technology factor as described above. Model setup and protocol. The parameters relevant to the technological factor, including the temporal change rate of the technological factor and irrigation, were inversely estimated using historical crop yield data  117  for each grid cell and crop using the DREAM (DiffeRential Evolution Adaptive Metropolis) algorithm  118.
The number of Markov Chain Monte Carlo steps was set to 50,000 for each grid cell. This large amount of calculation (about 3 × 10 9 simulations in total) was executed on the super computer system of the Japan Agency for Marine-Earth Science and Technology (JAMSTEC). In PRIBY2, the parameter values for the grid cells for which the reference data do not exist were extrapolated using the relationship between (1) the parameter values estimated at the grid cells in which the reference data exist and (2) environmental factors, such as elevation, harvested area, latitude, longitude, irrigated area  119  , planting day  64  , and the value of gross domestic product. The dataset of Sacks, et al. 64  was used for parametrizing the planting date in the default setup. The parameter set that has the maximum likelihood to reproduce observed yield dynamics  115  for each grid and each crop was used for the default run.
An exception is PRYSBI2, which was extensively calibrated at grid-cell level. Generally, calibration of global-scale crop models is complicated by the lack of high-quality data and the absence of data on any aspect other than yield. Furthermore, calibration does not substantially improve model skill in global-scale applications, other than improving the reproduction of spatial patterns by imposing management-driven differences in yield levels in the calibration process  38. GGCMs have been evaluated in various forms: individually at field and global scale (see examples in Table  4 , but note that this list is far from exhaustive) or in model intercomparison exercises also at field  [91] [92] [93]  and global scale  38. Aspects other than yield have not been evaluated in GGCMI, even though some models have been assessed also for other output variables  65, 72, 83, 94, [123] [124] [125] [126] [127] [128] [129] [130].
Input data. All input data that have been supplied to modelers for the simulations has been described by Elliott, et al. 37  and are available for download at http://www. rdcep. org/research-projects/ggcmi. In addition to the nine weather datasets listed by Elliott, et al. 37  , modelers also supplied simulations for an updated version of the Princeton data (PGFv2) that span 1901 to 2012 as well as the GSWP3 (http://hydro. iis. u-tokyo. ac. jp/GSWP3/) dataset, which has been supplied by ISIMIP phase2a (Online-only Table  1 ). The complete set of weather datasets to drive the crop models thus comprises eleven historical datasets that are based on retrospective datasets and nine of these have been bias-corrected against different observation-based products, including CRU and GPCC (Global Precipitation Climatology Center).
This broad set of input data is meant to cover the uncertainty introduced from different reanalysis products and different bias-correction methods. An analysis of the role of different weather input data for GGCMs' skill to reproduce historic yield variability is still pending. All weather variables are bias-corrected individually, and against different data products. The 2-m temperature is typically bias-corrected against different versions of the CRU dataset, but precipitation can be bias-corrected against CRU, GPCC or other targets. The WFEDI bias-correction provides 2 sets, in which only the precipitation bias-correction differs  131  , denoted as WFDEI. CRU and WFDEI. GPCC respectively, no other subversions of weather forcing datasets are included or used here (Table  5 ). With this approach to bias-correct individual weather variables, the physical consistency between variables is not necessarily maintained.