dataesr/scientific-paragraphs-categorization huggingface dataset:
Quantification of the virus reads The fastq files corresponding of each experimental condition triplicate were used to determine the number and the percentage of total virus reads after a cleaning step and the mapping of cleaned reads on the genome sequence of EBLV-1 8918FRA strain (GenBank: EU293112) performed with CLC Assembly Cell (Qiagen, Hilden, Germany), in a dedicated workflow built on the Galaxy platform of Institut Pasteur  [65]  and adapted to single-read format  [66].
Differential expression and pathway analysis We analysed the differential expression of the RNA gene counts among the temperature and infection treated samples using R package DESeq2  [71]. The read counts were normalized around the median, with normalization scaling factors in the range of 0. 74 -1. 36. Negative bino- mial distribution generalised linear models were fitted in DESeq2 to obtain the model coefficients that can be interpreted as log 2 (FC) (fold change). Independent filtering was used to retrieve the results, which were then adjusted for multiple testing using Benjamini-Hochberg false discovery rate method (FDR). Genes that were significantly differentially expressed at p ≤ 0. 05 (after the FDR correction) and log 2 (FC) ≥ 1 were used for downstream analyses. We analysed the pathways involved in the bat cell line response to infection by EBLV-1 using manually curated Reactome databases and pathway analysis tool  [72].
Acknowledgements Image acquisition with the  Opera Phenix ® Plus High Content Screening System (Perkin Elmer ) and data analysis via the Columbus software (Perkin Elmer) were conducted at  Institut Pasteur Paris at UTechS Photonic BioImaging core , a member of the national infrastructure  France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ), kindly acknowledges the financial support of the  Région Ile-de France  (program DIM1Health) and the  Institut Pasteur.
Part of the LA region could be distinguished between dry or rainy seasons instead of winter or summer periods, although this speci city would limit comparisons across regions. Overall, this study shows the results of the rst large-scale monitoring initiative in LA and reveals an alarming situation for honey bees and native stingless bees in the region. Comparing honey bee colony losses between large-scale monitoring initiatives from the perspective of cross-continental analysis, we found that winter losses in LA places between the United States and Europe trends. This result con rms that honey bee colony losses are a global concern, and Latin America is not exempted. Although direct comparisons between datasets should be made with caution, since different sample sizes and methods were used for data collection, the results highlight the alarming situation of the LA.
To assess the representativeness of the data, we computed the proportion of the colonies included in the study regarding the number of managed honey bee colonies registered in each country and year based on the FAO data of the Food and Agriculture Organization of the United Nations  [42]. Note that no data on managed honey bee colonies was available in the FAO data for Bolivia, Panama, nor Peru. Moreover, FAO data focuses on honey bees and no such information was available for stingless bees. In order to compare the winter colony loss of honey bees occurring in LA with other parts of the world, we considered published data from two other large-scale monitoring initiatives from the United States (the BIP initiative) and Europe (the COLOSS initiative, which also includes non-European countries such as Algeria and Mexico).
Colony loss was modeled as a binomial response by comparing the number of alive and lost colonies per beekeeper (i. e. the statistical unit). Three models were built corresponding to summer, winter and annual losses. Stingless bee colony loss -In order to analyze whether meliponiculturists suffer similar or different losses in their activities than beekeepers, we focused on years and countries for which we collected data from both beekeepers and meliponiculturists. GLMs with a quasibinomial distribution of errors were used to analyze the effects of Country-Year (the combination of country identity and year of the survey as a unique xed factor, e. g. Bolivia 2017), bee type (i. e. stingless bee or honey bee, xed factor), operation size (log-transformed number of colonies, xed factor) and the interaction between Country-Year and bee type on colony losses (summer, winter and annual, response variables).
Acknowledgments: work was supported by  Eva Crane Trust  (www. evacranetrust. org); grant numbers  ECTA_20160903 ,  ECTA_20171216  and  ECTA_20230313 , and by  Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo (CYTED , through  Red COLMENA) ; grant number  122RT0125. The authors are grateful to  Nolberto Arismendi ,  Betina Blochtein ,  Guido Laercio Bragança Castagnino ,  Patricio Crespo ,  Shirley Cruz ,  Karen Escalante ,  Martín Geria ,  Ernesto Guzmán Novoa ,  Guillermo José Huerta ,  Celina María Junges ,  Nicolás Lepori ,  Santiago Marchini ,  Cristiano Menezes ,  Irina Muntaabski ,  Santiago Plischuk ,  Gustavo Riveros ,  Graciela Rodríguez ,  Wilams Gomes dos Santos ,  Michelle Santos da Silva ,  Antonio Saraiva ,  Aroni Satler ,  Modesto R. Seoane Rodríguez ,  Osvaldo Soruco Aponte ,  Marisol Vargas , and  José Ignacio Viera  for their help in contacting beekeepers and meliponiculturists.
Sequencing Data Processing, Diversity, and Statistical Analysis Diversity analyses were performed in order to characterize the composition and the structure of bacterial populations present on conveyor belt contact surfaces. As previously described by Shedleur-Bourguignon et al. , the cleaning and the analyzing of the sequences were executed using Mothur 1. 39. 5  [28, 31]. Alpha and beta diversity analysis were performed using RStudio 3. 6. 1. The lowest number of sequences in the samples was used as subsampling. For the alpha diversity, the coverage of the subsampling was measured as well as the number of operational taxonomic units (OTUs) in each sample, and their evenness was measured using the inverse Simpson and the Shannon indices. The indices were calculated by using the estimate_richness function of the phyloseq package version 1.
24. 2  [32]. Comparison statistics were performed between the samples from the different visits and between the different production line samples using Student t-tests and the Kruskal-Wallis test, both with a significance level of 0. 05. For the beta diversity, the Jaccard index based on the presence/absence of OTUs and the Bray-Curtis index based on the relative abundance of OTUs were used on the same subsampling. The distance matrices were plotted using 2D nonmetric multidimensional scaling graphs (NMDS) using the ordinate function of the phyloseq package and the ggplot2 package  [33] , and statistical variations between the different groups (visits and production lines) were assessed using permutational analysis of molecular variance (PERMANOVA) using the ADONIS function of the vegan package  [34].
The Bonferroni correction was used in order to correct the significance level in the presence of multiple comparisons. The impact of production lines on the beta diversity of the visits was taken into consideration by adding the production line factor in strata during statistical tests. The same procedure was used to remove the influence of the visits on the beta diversity of the production lines. The Multivariate Association with Linear Models method (MaAsLin version1) was used to identify OTUs that were significantly, positively, or negatively associated with each visit and with each production line in terms of relative abundance using the MaAsLin package  [35]. The two variables (visit and production line) were used as a fixed effect and were put into the boost formula to consider the influence of the variables on each other.
Evaluation of the Predictability of a Visit and a Production Line Using Random Forest Models The models were built in order to classify the samples according to the visit during which they were collected or according to the production line from which they came based on their microbiota. The high number of covariates (OTUs) implicated in the analysis as well as the potential interactions between them guided the choice of using a nonparametric random forest approach as a predictive tool  [36]. Two predictive models were built with RStudio 3. 6. 1 using the RandomForest and the caret packages  [37, 38]. For each of the two models, eight randomly chosen samples per visit and eight randomly chosen samples per production line were used as a test database to evaluate the performance of the models in order to avoid overfitting.
These results concur with several studies which have highlighted the presence of different microbiotas in the food production environment. Zwirzitz et al. applied the software SourceTracker on full-length 16S rRNA gene sequencing data from a swine slaughterhouse and revealed that contact surfaces such as the polishing tunnel and a railing used for the classification of the carcasses strongly contributed to the composition of the microbiota of meat samples  [4]. The study showed that many bacterial genera were unique to specific sites in a swine slaughterhouse, indicating that specific microorganisms occupy environmental niches in the facility. The authors suggested that the spatial distribution of the microorganisms is the result of specific transmission routes within the facility. Botta et al.
a b s t r a c t Due to societal concerns, assess the environmental impacts, address the issues and provide labelling to the consumer are growing issues for the agri-food sector. In this context, provide datasets specific to alternative systems is crucial to be able to take into account the variability between systems then address their issues and label them appropriately. This data paper compiles all the data used to produce the life cycle assessment (LCA) environmental of an organic lowinput apple value chain including the cultivation of apples at farm, the transformation of a part into juice and applesauce, the retail and the consumption stages. The raw data have mostly been obtained through interviews of the farmer and complemented by literature. They have been used to build a life cycle inventory (LCI), using Agribalyse 3.
0 and Ecoinvent 3. 8 as background databases. The dataset also compiles the life cycle impact assessment (LCIA) using the characterization method EF3. 0. As discussed in an associated scientific paper, this dataset participates in filling two gaps: integrate the variability between systems in the discussion and link upstream (at farm) and downstream (transformation, retail, consuming) impacts. This is done by  (1)  covering the entire value chain from cradle to grave when most papers found in literature focusses on one stage (e. g. the cultivation of apples) and (  2 ) applying LCA to a system that present specificities not well covered by LCA literature (e. g. low-input cultivation with no fertilization up to now.
Background The dataset has been built in support to a research article named: "Life Cycle Assessment of alternative apple value chain including fresh fruits, juice and applesauce". It aims to compute a life cycle inventory for a specific value chain that is not represented currently in the literature. Therefore, it has the objective to help scientists to better evaluate the environmental impacts of small-scale food value chain from cradle to grave. Specifically, it brings data for small-scale apple production, transformation and distribution. In addition, it provides life cycle impact assessment to feed discussions about alternative systems.
Data Description The dataset associated with this article contains files with information on inventory data used for the LCA and the LCIA results presented and discussed in the associated article: 1. (FAIRCHAIN)CS-BEL_raw_data. tab: raw numerical data about: apple production, calibration and storage, juice, applesauce and distribution. These data have been mostly collected face-to-face with the farmer. 2. (FAIRCHAIN)CS-BEL_LCI. tab: LCI for the baseline scenario for the whole value chain.
Life cycle inventory 4. 1. 2. 1. Agricultural stage. Most of the foreground data were collected in face-to-face interview of the farmer. Despite the very high variability involved in such small systems, the objective was to produce a dataset that is representative of one year of production. The surface of the orchard is 3 ha, producing 50 tons of apples a year. The tree density is 1125 trees/ha and the lifespan of the trees is estimated to minimum 25 years. The farmer provided data about mechanical work and inputs. Related emissions to inputs are calculated using Means in-out ( https://www6. inrae. fr/means ). Three inputs were absents from databases and proxies were proposed: Neem oil (proxy adapted from  [3]  ), potassium bicarbonate (proxy based on stoichiometry) and pheromones diffusers (proxy using only the major molecule).
The proposed inventories are available in (FAIRCHAIN)CS-BEL_dataset_complementary_LCI. pdf. A mix for the management of biowaste in France is also modelled. Two sensitivity analysis are provided for the cultivation part and discussed in the associated paper: -Sensitivity scenarios are proposed for fertilization: the same production with a supply of cattle manure in the first year (25 t/ha) and the same production with land preparation and nutrient management derived from  [ 4 ]. LCI was modelled using Means in-out ( https: //www6. inrae. fr/means ). -Sensitivity scenarios are proposed for the management of the biomass at orchard (feeding discussion on carbon sequestration. 4. 1. 2. 2. Transformation. After harvesting, apples are transported to a fruit cooperative then calibrated. From 50 tons, 38.
Retail and consuming. The farmer gave information about the location where the three products are sold. Fresh fruits are retailed in French organic shops all over the country, but mostly in the production area. Juice and applesauce are locally sold, in a 30 km circle around the farm. Energy consumption for cold storage of fresh fruits at retail is derived from literature  [5]. 5 % of fresh fruits are assumed lost at retail, following literature  [ 8 ]. A proxy inventory representing the French mix for biowaste treatment solutions has been modelled and used ((FAIRCHAIN)CS-BEL_dataset_complementary_LCI. pdf). Distances were calculated in consequence to the farmer assumptions on market and based on literature insights. The share of apples in the total basket were estimated from literature  [ 8, 9 ].
Details are available in (FAIRCHAIN)CS-BEL_raw_data. tab and (FAIRCHAIN)CS-BEL_LCI. tab. Juice and applesauce are considered to be stored in fridge by the consumer. Electricity consumption is derived from  [ 10 ]. At consuming, the apple waste is estimated to 25 % of the total mass  [ 11 ]. A proxy inventory representing the French mix for biowaste treatment solutions has been modelled and used ((FAIRCHAIN)CS-BEL_dataset_complementary_LCI. pdf). Finally, French waste scenarios from Ecoinvent 3. 8 were used to model the end-of-life of packaging. A sensitivity analysis is proposed on the management of apple waste. It is introduced and discussed in the associated paper. Details on the scenarios are provided in (FAIRCHAIN)CS-BEL_dataset_LCI_for_sensitivity_analysis. pdf and results in (FAIRCHAIN)CS-BEL_dataset_LCIA_results_of_sensitivity_analysis.
Acknowledgements The authors thank the members and colleagues of the project Fairchain funded by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 1010 0 0723. The authors thank  Erica Montemayor  for her support in building LCIs for specific organic inputs. The authors thank colleagues, including  Antonin Pépin  and  Hayo van der Werf , for fruitful discussions. The authors finally thank  Michelle  and  Michael Corson  for edit and proofread English language. This project received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 1010 0 0723. This document reflects only the authors' view. The European Commission is not responsible for any use that may be made of the information it contains.
Resource use, fossils, Resource use, minerals and metals, Climate change -Fossil, Climate change -Biogenic, Climate change -Land use and LU change, Human toxicity, non-cancer -organics, Human toxicity, non-cancer -inorganics, Human toxicity, non-cancer -metals, Human toxicity, cancer -organics, Human toxicity, cancer -inorganics, Human toxicity, cancer -metals, Ecotoxicity, freshwater -organics, Ecotoxicity, freshwater -inorganics, Ecotoxicity, freshwater -metals. 7. (FAIRCHAIN)CS-BEL_dataset_LCIA_by_product. tab: LCA results for 1 unit of product (i. e. 1 kg of fresh apples at consumer, 1 L bottle of apple juice at consumer and 250 g jar of apple applesauce at consumer). 8. (FAIRCHAIN)CS-BEL_dataset_LCIA_results_of_sensitivity_analysis. tab: Results of the sensitivity analysis. 3. (FAIRCHAIN)CS-BEL_dataset_complementary_LCI.
pdf: LCI proxies for inputs missing in databases for organic production (Neem oil, Potassium bicarbonate, Pheromones diffuser) and for the baseline scenario for biowaste treatment. 4. (FAIRCHAIN)CS-BEL_dataset_LCI_for_sensitivity_analysis. pdf: LCI for the sensitivity analysis presented in the associated paper: alternatives scenarios for fertilization, carbon seques- tration and apple waste management. 5. (FAIRCHAIN)CS-BEL_dataset_LCI_tables. pdf: LCI summarized in tables 6. (FAIRCHAIN)CS-BEL_dataset_LCIA_apple_value_chain. tab: LCA results calculated by the "EF 3. 0 method (adapted)" for the midpoint impact categories: Climate change, Ozone depletion, Ionising radiation, Photochemical ozone formation, Particulate matter, Human toxicity, non-cancer, Human toxicity, cancer, Acidification, Eutrophication, freshwater, Eutrophication, marine, Eutrophication, terrestrial, Ecotoxicity, freshwater, Land use, Water use,.
, 2019; Turetsky et al. , 2014; Yuan et al. , 2022) , but their importance varies substantially among wetlands and across time scales  (Knox et al. , 2021; Koebsch et al. , 2015). Furthermore, complex interactions hinder the use of simple correlation analyses for disentangling responses of CH 4 emissions to biophysical variables, leading to large uncertainties when interpreting observations  (Chang et al. , 2020; Knox et al. , 2021; Sturtevant et al. , 2016). Recently, the FLUXNET-CH 4 database was curated for supporting synthesis of wetland CH 4 emissions using the EC methods  (Delwiche et al. , 2021; Knox et al. , 2019)  and, for example, was used to evaluate inter-site variations in CH 4 emissions  (Chang et al. , 2021; Knox et al. , 2021; Yuan et al. , 2022). To improve the mechanistic understanding and accurate modeling of CH 4 emissions, the relative contributions of CH 4 emission pathways have been measured or estimated with various field measurements (Table  1 ).
| Dataset and model inputs We used daily EC CH 4 flux data archived in the FLUXNET-CH 4 database  (Delwiche et al. , 2021). We selected all mid-to high-latitude freshwater wetland sites from the database (Table  2 ) that contained all relevant forcing variables (i. e. , soil and air temperature, WTD, PA, and GPP). The selected 25 sites represent wetland types of bog (ombrotrophic), fen (minerotrophic), marsh, wet tundra, and rice paddy in temperate, boreal, and Arctic regions. The mean annual air temperature ranged from -5°C to 17°C across the sites, and minimum WTD ranged from -0. 62 to 0. 68 m. We used daily gap-filled CH 4 fluxes and the ancillary biophysical variables at the tower sites. The daily mean values of the gap-filled half-hourly variables were provided in the FLUXNET-CH 4 database  (Delwiche et al.
, 2021). We used two types of daily CH 4 fluxes (i. e. , FCH4_F and FCH4_F_ANN_median) in the database. FCH4_F was gap-filled using a multidimensional scaling (MDS) approach in REddyProc  (Delwiche et al. , 2021) , but still contained periods of time with long data gaps (<2 months). FCH4_F_ANN_median was gapfilled based on an artificial neural network method, which fills all data gaps  (Knox et al. , 2019). As input drivers from the FLUXNET-CH 4 database, daytime-based GPP (GPP_DT) in the database  (Lasslop et al. , 2010) , air temperature (TA_F), barometric pressure (PA_F), soil temperature (TS), and WTD (WTD_F) were used. The gaps in the meteorology (i. e. , TA_F, and PA_F) were filled using the ERA-Interim reanalysis data  (Vuichard & Papale, 2015) , whereas those of WTD and soil temperatures were filled using the MDS method.
We used soil temperature at two depths for representing the surface and deep layers in the model. For sites affected by permafrost (RU-Ch2, US-Ics, and US-Uaf), we assumed that the deepest soil temperature measurement was representative of the bottom of the active layer. Data for RU-Ch2, US-Ics, US-Bzf, and US-Bzb sites did not include WTD data in the FLUXNET-CH 4 database, but WTD data were directly provided from principal investigators. Since WTD for RU-Ch2 was based on discrete manual measurements, we linearly interpolated the data to the daily timescale. We prepared daily LAI as a model input based on satellite-based LAI smoothed using GPP. First, the four-day LAI data (MCD15A3H; collection 6) was downloaded from MODIS land products subsets. The spatial resolution of the product is 500 m.
Site The O 2 concentration was not included in the FLUXNET-CH 4 database, and thus was determined from WTD. When the water table position is above or below a soil layer, the layer is assumed to be anoxic or fully oxic, respectively. When WTD is within a soil layer, O 2 concentration in that layer is linearly related to that fraction of the layer that is inundated between fully oxic to anoxic conditions. To explore the underlying processes, the model contains 10 parameters and two initial values of the CH 4 pools in each soil layer (mol-CH 4 m -3 ), which are calibrated with data (Table  3 ). Ebullitive transport is calculated based on a concentration threshold scheme  (Peltola et al. , 2018) , which has two empirical parameters: nondimensional conductivity for bubble transport (p ebullition ) and a parameter for explaining episodic CH 4 bubble transport driven by barometric pressure changes (p pressure ; hPa -1 ).
05) except slightly high PSRF for two parameters for US-Uaf (PSRF <1. 12; Table  S3 ). Computational costs of the Bayesian inference ranged from 0. 35 h to 2. 5 days per site with an average of 6. 16 h (Table  S4 ). Model parameters were estimated using daily CH The sensitivities to the forcing variables were performed using the models successfully constrained for each site. First, we applied perturbations to the inputs of: (1) 1°C increase to the observed soil temperatures, (2) 10% increase in GPP and LAI, (3) 10 cm increase in WTD, and (4) 10 cm decrease in WTD with all other inputs held at measured conditions. Next, we examined the changes in modeled CH 4 emissions with unperturbed input (control experiment). We conducted the sensitivity analysis for sites spanning at least 3 years of data because the uncertainties are high in models constrained by short-term data  (Ueyama et al.
, 2022). To understand the variabilities in the estimated parameters across the sites, we applied principal component analysis (PCA) toward seven parameters: p production , Q 10production , p oxidation , p ebullition , p plant , p diffusion-water , and p pressure. The parameter for gas diffusion (p diffusion-gas ) was not included in the PCA because p diffusion-gas did not show a bell-shaped density curve at approximately half of the sites (Figure  S1 ). The parameters were first standardized with mean and SD and then compressed into two principal components (PC) using the scikit-learn library in python. We chose two principal components because they explained more than 70% of the variance in the parameters across the sites. oxidation were also consistent among the two models constrained with two data criteria, except for sites having low record numbers (e.