dataesr/scientific-paragraphs-categorization huggingface dataset:
To formulate low-environmental impact feeds, it is necessary to incorporate data on environmental loads of feed ingredients into the feed formulation program. One alternative is to use a dataset based on consistent perimeters and methodologies for LCA. Bertoluci et al. [20]  emphasised that combining data from heterogeneous studies with different assumptions and perimeters can compromise the consistency of the dataset and the relevance of conclusions. Currently, few international databases include environmental impacts of feed ingredients  [21, 22]. The ecoinvent 1 database relies mainly on Swiss data and practices for the life cycle inventories (LCIs) of agricultural production and provides only a few feed-ingredient LCIs. The European database Agri-Footprint  [21]  contains standardised data (LCIs and environmental impacts) for several countries but not for all feed ingredients (e.
g. feed ingredients processed in France, amino acids). Using it to formulate feeds in France would require incorporating data from other sources for missing feed ingredients, thus increasing the uncertainty in results. Agri-Footprint is also mainly intended as a source of secondary or background data for LCI or LCA of animal products from the perspective of environmental labelling of food products  [21]. It is thus more appropriate for comparing animal production systems and mitigation options at the level of the animal unit subsystem. Consequently, an LCA database dedicated to feed formulation is needed, which requires greater data accuracy and model complexity for LCAs of feed ingredients  [23]. Feed formulation in France involves mostly French feed ingredients, which result from several cropping systems under a variety of soil and climate conditions and cropping practices.
A previous study  [18]  highlighted the need for homogeneously developed data on environmental impacts of feed ingredients to formulate low-impact feeds and investigate mitigation options. The ECOALIM dataset is the result of a collaborative project between research and extension institutes, with the participation of animal-nutrition companies. ECOALIM collects the most accurate and representative data available to date for LCIs of French feed ingredients. This dataset can be used in France and in countries importing French feed ingredients. This article presents the methodological choices and perimeters implemented to develop the ECOALIM dataset, as well as its potential application.
Goal and scope definition For the ECOALIM dataset, several system perimeters were defined (Fig 1 ): field gate, storageagency gate, plant gate, and harbour gate. The field gate is relevant for assessing impacts of onfarm feed production (i. e. crops produced and directly used on-farm). Feed formulation by feed companies requires additional perimeters: plant gate for the co-products of cereals, oilseeds and protein crops (e. g. meals) as well as for industrial products (e. g. amino acids) processed in France; storage-agency gate for cereals (perimeter includes grain drying); and harbour gate for imported feed ingredients. The impacts considered were climate change with (CCLUC) or without land-use change (CC), eutrophication (EU), acidification (AC), land occupation (LO), non-renewable (CEDNR) and total energy demand (CEDTOT) and P demand (PD).
Life cycle inventories French crops. Average LCIs for the following French crops were included in the ECOA-LIM dataset: maize, wheat, barley, oat, triticale, sorghum, rapeseed, sunflower, faba bean, flaxseed, pea, and soya bean (Table  1 ). LCIs for French crops were based on those in the French national database, Agribalyse 1 , for the main agricultural products  [24] , with updated emission factors for ammonia  [25]  and references for agricultural practices, when available. One major improvement compared to Agribalyse 1 is the distribution of the impacts associated with P fertilisation and nitrate leaching among the crops in the same crop rotation (according to crop requirements, P uptake and nitrate leaching among crops). For all French crops, average LCIs (yields; amounts of fertilisers, pesticides and seeds) were obtained from French agricultural data  [26] [27] [28]  (Table  2 ).
For maize, wheat, barley, rapeseed and sunflower, specific LCIs were constructed for systematic cover cropping, systematic organic fertilisation, and introduction of a protein crop in the crop rotation, based on data from the farm network of the French agricultural institute dedicated to cereals (ARVALIS-Institut du vegetal), to assess variability in the results due to crop management. Non-French crops. Average LCIs of the following non-French crops were included in the dataset: Brazilian soya bean; US soya bean, sorghum, and maize; UK wheat; Malaysian palm oil tree; Pakistani sugarcane; and Ukrainian sunflower (Table  3 ). Data for resources came from scientific publications  [19, 22, 29]  and statistics from national databases  [30] [31] [32] [33]  or FAO-STAT  [34]. Resources and emissions in LCIs were calculated according to previously published methodologies  [19, 35].
Industrial products. Industrial products in the dataset include products derived from crops and products that result from chemical synthesis, fermentation, mining extraction, and animal by-product processing. Co-products directly derived from crops are oils, meals, Dried Distilled Grains with Solubles (DDGS), molasses, pulps, and flours associated with extrusion processes, crushing, milling, distillation, etc. Products secondarily derived from crops are starch and gluten feeds. Processing factories were surveyed to collect the relevant underlying data in 2013-2014, except for French oils and meals and non-French feed ingredients  [36]. For other industrial products, LCIs are from Garcia-Launay et al. [17]  for amino acids, confidential industrial data for minerals, the French Technical Centre for Meat (ADIV) data for animal byproducts, and ecoinvent data adapted to the French context for vitamins  [36].
Impact assessment ECOALIM uses the ILCD characterisation method recommended by the Joint Research Centre  [43] , as well as the CML-IA characterisation method  [44] , which is the most popular in agricultural LCA. Energy demand is calculated according to the CED 1. 08 method  [44]. CC and CCLUC are expressed in kg CO 2 -eq, AC in both molc H + -eq (AC ILCD ) and kg SO 2eq (AC CML ), terrestrial EU in molc N-eq, freshwater EU in kg P-eq, marine EU in kg N-eq, EU CML in kg PO 4 3-eq, LO in m. y, and CEDNR and CEDTOT in MJ. PD is expressed in kg P and includes all P and phosphate inputs throughout the life cycle. Calculations were performed using SimaPro 1 software v8. 0. 5. 13 (PReConsultants, Amersfoort, The Netherlands) and the attributional ecoinvent v3. 1 database for background data  [36].
Statistical analysis The dataset was subject to a series of multivariate analyses using R statistical software  [45]  to describe variability in the impacts associated with each type of feedstuff. From all of the impact categories available in the dataset, we selected CCLUC, AC CML , EU CML , CEDNR, PD, and LO after examining the correlation coefficient between each pair of impacts and excluding impacts that were highly correlated. To address the high skewness of impact distributions, the quantitative variables CCLUC, AC CML , EU CML , CEDNR, PD, and LO were converted into qualitative variables with levels corresponding to the 4 quartiles of the distribution of each impact. The feed-ingredient typology was constructed with the active qualitative variables CCLUC, AC CML , EU CML , CEDNR, PD, and LO and the illustrative qualitative variable "Feedstuff Type" using multiple correspondence analysis (MCA), followed by hierarchical clustering (HC) on the 5 first dimensions of the MCA.
Typology of the feed ingredients The MCA and HC provided a general overview of the dataset. In the MCA, the 1 st and 2 nd dimensions distinguish the The HC built 6 classes that are separated on the 1 st to 4 th dimensions (Fig 4A  and 4B ). Class 1 contains 14 feed ingredients with the highest impacts for CCLUC, AC CML , EU CML , CEDNR, PD, and LO CML : industrial amino acids, fats and vitamins. This finding is consistent with previous LCAs on industrial amino acids  [46, 47]  and fats  [48, 49]. Class 2 contains 14 feed ingredients with the lowest impacts for CCLUC, AC ILCD , EU CML , CEDNR, PD, LO CML : minerals, co-products of plant origin and co-products from the bread industry. These low impacts are partly due to the economic allocation of impacts among co-products. Class 3 contains 14 feed ingredients with intermediate impacts for AC CML , EU CML , PD, LO CML , (2 nd quartile) and  The feed ingredient types in the ECOALIM dataset (Fig 3B  and 3D ) have different levels of environmental impacts.
Analysis of the dataset gives some highlights for feed formulation: • Some types have high impacts regardless of the ingredient (Class 1, e. g. amino acids) and are essential for formulating feeds. Their impacts are influenced by high energy inputs for industrial processes, such as those for amino acids, vitamins, and soya bean oil. Since these products are always incorporated in low quantities, they are not a mechanism for reducing feed impacts, except if amino acids are combined with cereals to replace soya bean meal associated with deforestation  [50]. • Other types, such as co-products of plant origin and from milling and bread industries, have low impacts because of economic allocation of impacts and the low-input processes used to produce them. They can be incorporated at higher percentages into feeds to reduce the latter's environmental burdens.
Comparison with other databases To our knowledge, only two other databases include French feed ingredients or crops: Agri-Footprint and ecoinvent 1. We focused comparison on the former (Fig The values of freshwater EU (Fig  5C ) are much higher (x100) in ECOALIM than in Agri-Footprint, and the ranking of crops is completely different. P leaching is calculated as a fixed proportion of P applied in Agri-Footprint, whereas the SALCA-P model used in ECOALIM accounts for the P applied (mineral and organic), crop duration (time between harvest of the considered crop and the preceding crop), soil erosion, and allocation of fertilisation among crops in the same rotation. AC impact  (Fig 5D)  is consistent between both datasets. In each case, rapeseed has the highest value, and oat and triticale have the lowest ones values.
The other crops have moderate differences in value between datasets, rendering their rankings different. When formulating feeds based on environmental impacts, these databases would yield different feeds. Feed formulation is an optimisation problem that relies on linear programming with nutritional constraints. Consequently, when minimising environmental impacts of feeds at a given nutritional level, incorporation of feed ingredients will follow the impact ranking of feed ingredients. Therefore, feed manufacturers need the most accurate data on environmental impacts that account for specific crop-management practices, data provided for the French context by the ECOALIM dataset.
Use, maintenance and updating of the dataset The ECOALIM dataset is the animal-nutrition subset of the AGRIBALYSE 1 database. Therefore, it is also available in the Agribalyse 1 v1. 3 database in SimaPro 1 , with the following name format for processes: • for unprocessed feed ingredients: "<Feed ingredient name>, <type of production>, <crop-management practice>, animal feed, <LCA perimeter> /FR U", e. g. Feed barley grain, conventional, national average, animal feed, at farm gate/FR U. • for processed feed ingredients: "<Feed ingredient name>, animal feed, <LCA perimeter> /FR U", e. g. Sunflower meal, with low dehulling, animal feed, at transformation plant/FR U. The ECOALIM dataset can be used to estimate environmental impacts of feeds, animal production systems and mitigation options (Fig  6 ).
Both feed manufacturers and LCA practitioners can formulate feeds according to the classic least-cost approach and calculate the environmental impacts of the resulting feeds using the ECOALIM dataset in Excel. To investigate mitigation options, they can also formulate "ecofeeds" based on both economic and environmental approaches by using the ECOALIM dataset in Excel in formulation software. In an initial application, eco-feeds formulated for pig production had impacts 6-15% (assuming current availability of feed ingredients) and 12-26% (assuming improved availability of some feed ingredients) lower than those of the least-cost formulation, depending on the impact considered  [54]. The ECOALIM dataset in the AGRI-BALYSE 1 database in SimaPro 1 can be used by LCA practitioners in more detailed farm LCIs to assess environmental impacts of animal products at the farm gate using either leastcost feed or eco-feed formulas.
For these applications, the ECOALIM dataset offers standardised, updated and reviewed data. It covers a range of feed ingredients that was not previously available in AGRIBALYSE 1 and is, to date, the only dataset that relies on reliable foreground data representative for France, collected from multiple surveys following a common framework. To our knowledge, ECOALIM is the first dataset available for feed ingredient LCIs and impacts that provides all feed ingredients used in conventional livestock production in France and specific LCIs for systematic cover cropping, systematic organic fertilisation, and introduction of a protein crop. It also applies to other European countries for several imported feed ingredients, as well as for French feed ingredients which are exported. This is a major addedvalue of the dataset because it provides reliable data to non-experts in LCA who may find it very difficult to gather properly data from various databases and publications  [20].
Conclusion The ECOALIM dataset provides life cycle impacts of feed ingredients used in French livestock production based on a standardised methodology and homogeneous foreground and background data. This approach avoids double-counting of environmental burdens. The approach was developed to be consistent with the AGRIBALYSE 1 database, which will allow future use of the ECOALIM dataset for feed formulation based on environmental impacts and environmental labelling of animal products. The ECOALIM dataset relies on representative and recent data that cover a wide range of production practices in France. Built with organisations such as feed manufacturers, raw material producers and R&D institutes, this dataset contains feed ingredients necessary to formulate complete feeds and several French feed ingredients for which no data were available before (i.
e. sorghum, flaxseed, and soya bean). The ECOALIM dataset will allow feed formulas to be developed which incorporate nutritional, economic and environmental constraints. This new approach to designing diets needs further research to evaluate its potential as an option for mitigating livestock-related impacts. It would be interesting to conduct a similar study in other countries with the same degree of accuracy in LCI data of feed ingredients. The framework developed in the AGRIBALYSE 1 database and ECOALIM dataset is adequate to further develop such databases.
5)because the latter relies mainly on Swiss data and practices for the LCIs of agricultural production and provides only a few feed-ingredient LCIs. Between ECOALIM and Agri-Footprint, rankings of CCLUC impacts of cereals, oil seeds and protein crops differ (Fig 5A)among crops (e. g. faba beans, oat, winter barley). Nevertheless, some consistency exists: rapeseed has the highest impact and sunflower and triticale have similar values in both datasets. Regarding CEDNR (Fig 5B), except for the lowest impact observed for faba beans in both datasets, ECOALIM and Agri-Footprint show different values for most crops: winter barley, wheat, maize grain, oat, rapeseed, and sunflower. Consequently, the ranking of the crops differs between the two datasets.
Acknowledgments The dataset described in this article was developed in the  ECOALIM  project, funded by the  French Ministry of Agriculture  (CASDAR partenariat et innovation-2012) and the  French Environment & Energy Management Agency  ( ADEME REACCTIF-2012 ,  1260C0061 ). The authors are very grateful to  Sarah Willmann ,  Morgane Magnin , and  Marie Laustriat  for helping to conceive and design the inventories; Paul Ponchant for reviewing the data; and to all the animal-nutrition organisations for sharing their data.
agreste. agriculture. gouv. fr/enquetes/statistique-agricole-annuelle-saa/ b Agreste, 2006. Enquête sur les pratiques culturales en 2006. Available from: http://agreste. agriculture. gouv. fr/publications/chiffres-et-donnees/article/ enquete-sur-les-pratiques c Online database (in French) available at: http://www. unip. fr/marches-et-reglementations/statistiques-france/surfaces-rendements-et-productions. html d UNIP: French agricultural technical institute dedicated to legume crops e ARVALIS: French agricultural technical institute dedicated to crops doi:10. 1371/journal. pone. 0167343. t002.
We used these markers to construct a genetic linkage map with Mapmaker software version 3. 0b  (Lander et al. , 1987) , using a LOD score threshold of 3. 0 and a maximum recombination fraction of 0. 3. Distances between markers were calculated with the Kosambi mapping function. For each linkage group (LG), marker order was checked with the 'ripple' command and markers were retained only if the LOD score value was greater than 2. 0. The LGs were assigned to pepper chromosomes on the basis of the positions of SSR and SNP markers common to the genetic maps for pepper published by  Alimi et al. (2013)  and  Quenouille et al. (2014).
, 2003). QTLs were detected by regression analysis, SIM, CIM, and non-parametric interval mapping (model = "np" in the R/qtl package) for the non-Gaussian phenotype distributions, although the residues were normal, making it possible to carry out a regression analysis to estimate additive and R 2 values. All the methods yielded similar results (the same QTLs at the same positions; data not shown), although the QTL peaks were slightly less sharp with the nonparametric procedure. A permutation test was performed with 1,000 replicates to determine the genome-wide LOD threshold empirically at the 5% probability level, for each phenotypic trait individually. The LOD threshold was estimated at 3. 6 for the three traits. For each QTL, the confidence interval (CI) was defined as a 2-LOD drop-off around the maximum LOD score.
ACKNOWLEDGMENTS We thank  Anne-Marie Sage-Palloix  and  Ghislaine Nemouchi  for plant breeding and for providing F 3 families seeds. We also acknowledge  Delphine Angella ,  Chami Kim ,  Cristina Martin Jimenez , and  Pierre Bautheac  for technical assistance. This work was financially supported by the  French Ministry of Higher Education and Research (MESR)  and the seed companies  Gautier Semences ,  Rijk Zwaan France ,  Sakata Vegetables Europe ,  Syngenta Seeds ,  Takii France , and  Vilmorin & Cie  in the frame of a CIFRE contract.
The objectives of quantitative discovery proteomics are to identify proteins in several biological samples that separate into at least two different biological conditions and to perform a relative quantification, so as to discriminate between the proteins which are significantly differentially abundant, and those which are not. This classically involves numerous steps: (i) protein extraction; (ii) proteins digestion into peptides; (iii) liquid chromatography and tandem mass spectrometry analysis; (iv) peptide identification on the basis of the fragmentation spectra; (v) peptide quantitation on the basis of the precursor chromatograms (XIC) and (vi) peptide aggregation into protein identity and abundance. The outcome of this analytical pipeline is a quantitative dataset that contains protein abundance across all replicates.
Once the quantitative dataset is available, the quantitative analysis may start. Its objective is to rely on an efficient and reproducible statistical pipeline to isolate the subset of proteins that are characteristic of the differences between the biological conditions, on which further more exhaustive wet-laboratory experiments will be performed. Numerous tools are available to perform such quantitative analysis, either as stand-alone tools (e. g. MSstats;  Choi et al. , 2014)  or as a module of a larger bioinformatics tool (e. g. Skyline;  MacLean et al. , 2010) , or as generic software that is not restricted to proteomics, but can be used in a wider omics context (e. g. InfernoRDN-Former DAnTE; Polpitiya et al. , 2008)  or even for general purpose statistics (e. g. JMP-http://www. jmp.
com/). It is also possible to sort the available tools according to their code being open (MSstats and more generally any R package) or not (Perseus-http://www. bio chem. mpg. de/5111810/perseus), as well as according to the presence of a graphical user interface (GUI) or not: generally most of the R packages are not fit with a GUI, while other software tools are. To date, the only software tool that is based on R and which is endowed with a GUI is InfernoRDN. However, the underlying R packages are not accessible, so that the code is not really open, and the GUI only works on Windows operating systems. As a result, to the best of the authors' knowledge, there is so far no software tool that is (i) devoted to proteomics; (ii) devoted to quantitative analysis; (iii) with open-source code that guarantees reproducibility, interoperability and quality control of the code; (iv) with a user-friendly GUI and (v) which can be operated on any operating system.
This lack has motivated the developments reported here. In general, quantitative analysis is composed of the following steps: 1. Filtering: Some peptides or proteins may be discarded, on the basis of several user-defined criteria (number of missing values within each or across all the biological condition(s), contaminant database, decoy sequences, etc. ). 2. Normalization: The protein abundances are rescaled (within or between conditions) to account for the variability between the analyses. Several algorithms can be used: quantile normalization  (Bolstad, 2007) , abundance normalization, scaling/centering (either globally applied or by condition), etc. 3. Imputation: To maximize the power of the statistical analysis, the missing values are imputed. This is achieved with one of the multiple available algorithms that accounts in a specific manner each for the specific nature of missing values (missing at random, or lower abundance censorship): k Nearest Neighbors  (Hastie et al.
DAPAR (differential analysis of protein abundance with R) is an R package that either proposes new algorithms for these five computational steps or simply binds the R packages implementing pre-existing state-of-the-art methods (refer to the ProStaR and DAPAR tutorial for an updated list of the available algorithms). The main feature of DAPAR is to gather in a single package, all the necessary statistical routines for quantitative analysis. Moreover, it is completely compatible with (i) the MSnbase package  (Gatto and Lilley, 2012) , which provides a standard format for quantitative datasets, as well as with (ii) any bioconductor package, so that its functionalities can be easily extended. However, as is, its use requires being comfortable with R programming, which is not the case for all proteomics practitioners.