dataesr/scientific-paragraphs-categorization huggingface dataset:
Future European agriculture should achieve high productivity while limiting its impact on the environment. Legume-supported crop rotations could contribute to these goals, as they request less nitrogen (N) fertilizer inputs, show high resource use efficiency and support biodiversity. However, legumes grown for their grain (pulses) are not widely cultivated in Europe. To further expand their cultivation, it remains crucial to better understand how different cropping and environmental features affect pulses production in Europe. To address this gap, we collected the grain yields of the most cultivated legumes across European countries, from both published scientific papers and unpublished experiments of the European projects LegValue and Legato. Data were integrated into an open-source, easily updatable dataset, including 5229 yield observations for five major pulses: chickpea (Cicer arietinum L.
), faba bean (Vicia faba L. ), field pea (Pisum sativum L. ), lentil (Lens culinaris Medik. ), and soybean (Glycine max (L. ) Merr. ). These data were collected in 177 field experiments across 21 countries, from 37° N (southern Italy) to 63° N (Finland) of latitude, and from ca. 8° W (western Spain) to 47° E (Turkey), between 1980 and 2020. Our dataset can be used to quantify the effects of the soil, climate, and agronomic factors affecting pulses yields in Europe and could contribute to identifying the most suitable cropping areas in Europe to grow pulses.
It is thus important to build large datasets including yield data covering a wide range of cultivars, environments, and management practices to robustly identify suitable areas  23, 25, 27. Some existing datasets provide yield data for grain legumes in Europe, but none of them meets all the characteristics required for our purpose. For example, annual yield data of grain legumes are provided at the country level by the Food and Agriculture Organization (FAO) of the United Nations (see https://www. fao. org/faostat/ en/#data), but the spatial resolution is too coarse to capture local effects of variations in climate and soils on crop yield. The Global Dataset of Historical Yields for major crops provides annual yield data at a spatial resolution of 0. 5° (grid cells of 55 km), but only for soybean  28, 29.
The Spatial Production Allocation Model (SPAM) data provide yield data for 42 crops including grain legumes at the global scale at a spatial resolution of 0. 083°, with 10-km grid cells, but only for the year 2010  30. Moreover, available datasets mostly provide yield data where crops are grown, while observed actual yields in areas where grain legumes are not yet grown by farmers would be of interest as well, to identify areas where grain legumes could be potentially grown in the future. A previous dataset of legume yields from field experiments was made available by Cernay and co-authors  27. It covers the period 1967-2016 and includes records across 41 countries and 18 Köppen-Geiger climatic zones. But this dataset is focused on field experiments comparing several pulse species and does not include yield data collected in experiments including single pulse species.
This dataset thus missed numerous experimental pulse yield data obtained in Europe. To fill this gap, we present a new dataset on grain legume yields, gathering results from field experiments for the five dominant species in Europe: chickpea (Cicer arietinum L. ), faba bean (Vicia faba L. ), field pea (Pisum sativum L. ), lentil (Lens culinaris Medik. ), and soybean (Glycine max (L. ) Merr. ). This dataset, named the "European Grain Legume Dataset" (EGLD) includes 5229 yield observations from 177 field experiments across 21 countries, from 1980 to 2020. EGLD can prove to be useful for disentangling the effects of soil, climate and agronomic drivers of legume yields in Europe.
Methods Data were collected from three different sources (Fig. 1 ): (i) published experimental data from an online literature search; (ii) experimental data from trials conducted within the EU-FP7 LEGATO (LEGumes for the Agriculture of TOmorrow) Project (GA nr. 613551); (iii) experimental data from trials conducted within the EU-H2020 LEGVALUE (Fostering sustainable legume-based farming systems and agri-feed and food chains in the EU) Project (GA nr. 727672). Five pulses, namely chickpea, faba bean, field pea, lentil, and soybean have been searched, as the most cultivated in Europe.
Data collection from scientific papers. Concerning the first source of information, we used the Scopus collection to retrieve the foremost publications reporting peer-reviewed research on the selected pulse species. The systematic search of peer-reviewed journals was completed in June 2020. The search criteria included the following elements, searched for in the following order within article title, abstract, and keywords: At first, we did not set any restrictions on publication date and language and retrieved 2610 papers. The search results were refined to "Agricultural and Biological Sciences" and "Environmental Sciences" as subject areas in Scopus. After that, we further restricted the records to affiliation countries in Europe (EU-27 countries plus Turkey, Albania, Switzerland, United Kingdom, Norway, Ukraine, Serbia, Montenegro, North Macedonia, Kosovo, Moldova, Iceland, and Belarus).
Data collection from experimental trials. To expand the dataset, we sought out further yield data that met the following criteria: i) data coming from experimental trials; ii) at least one of the selected grain legume species grown as sole crop; iii) experimental site of the field experiment at one of the countries listed above; iv) study reporting grain yields; v) disposal of precise information on field site (region and site name and/or latitude and longitude coordinates); vi) main agronomic practices available and reported. Accordingly, we included also open data provided by the former EU-FP7 project LEGATO "LEGumes for the Agriculture of TOmorrow" (2014-2017), which has delivered the results of the 2-yr experimental activities performed on field pea, faba bean, chickpea, and lentil. The methodological aspects and the experimental details of the trials are openly available at https://intranet.
iamz. ciheam. org/forms/Legato/WP6/files/Field_Trial_Protokol_5. 1. 2016. pdf. The dataset was downloaded from https://intranet. iamz. ciheam. org/forms/Legato/WP6/index. php and eligible data, according to the criteria, were extracted and manually added to the dataset. Overall, data were produced in field experiments  in compliance with a common protocol. The field experiments were conducted on plots of 10 m 2 and replicated four times in space, according to a completely randomized design (CRD). Plant samples for grain yield assessment were collected on 1 m 2 sampling areas when the crops reached harvest maturity. The dataset was also complemented with the European H2020 LEGVALUE Task 1. 2 Partners (www. legvalue. eu) own data, which were generated both on-station and on-farm but always under well-determined field conditions.
The authors, as effective partners of the Project could deliver their not already published results. Data that met the eligibility criteria were added by each partner to the original dataset, following the related instructions (see Data records section). The trials were conducted according to site-specific experimental protocols, that were documented by the providing partners and are available in the dataset (Supplementary Table  S1 ). Those details on geographical site, pedoclimatic conditions, treatment replications, and agronomic management (i. e. , cultivar choice, tillage, plant density, fertilization, crop protection, growing cycle length) have been included in the dataset for each entry coming from both LEGATO and LEGVALUE experiments. Additional information on the experimental methodology of the trials of both LEGATO and LEGVALUE are summarized in Supplementary Table  S2.
Data records The dataset created with all the extracted data, named "European Grain Legumes Dataset" (EGLD) is available at figshare 177. Overview of data files. In the figshare repository, the following files are provided 177 : (i) "European Grain Legume Dataset. csv" that contains the data; (ii) "EGLD instructions. pdf " and "EGLD instructions. csv" that report the list and the definition of each column in the dataset (meta-data); (iii) "List of sources. pdf " and "List of sources. csv" that provide the list of the sources where the data were collected from. In "EGLD instructions" files we reported a brief description and the assumptions for each column (category) for the reader's guidance, to facilitate data entry operations as well as to inform the proper interpretation of the data. The files "List of sources" complement information about the founts from which the data has been acquired, together with the web link at which they are accessible and the responsible institution.
Overview of the structure of the dataset. The "European Grain Legume Dataset. csv" file contains all the data for the five selected pulse species, with variables as columns and entries as rows. As several papers and experiments reported multiple yield assessments, a unique ID was assigned to a single combination of year x site x crop species x experimental treatment level and was reported as a single entry (i. e. , a single row) in the dataset. This process led to a total of 5229 yield data for the five selected pulses all over Europe (Table  1  and Fig. 2 ), of which 2864 were collected from published papers, 1526 from non-published experimentations of the LEGVALUE project, and 839 from the LEGATO project. Records identified from published papers or the LEGATO Project or other experiments of the LEGVALUE Project were differently labeled in the database in the column "source" as Paper, Experiment LEGATO or Experiment LEGVALUE, respectively.
The coordinates of each yield data were extracted and included in "European Grain Legume Dataset. csv". All the sources from which data were recovered are reported in the file named "List of Sources. pdf ", available on the figshare repository 177. For data obtained from the LEGATO project or from experiments of the LEGVALUE project, a brief description of the experiment is delineated, while for published papers the full reference is reported. We also provide a web address where the sources can be accessed (through the digital identification number (doi) if available). Most of the published papers are publicly available under Open Access licenses. A few can be accessed by personal or institutional subscription, depending on each journal policy. All records enclose 72 columns that describe a corresponding number of variables with different types of information.
These variables can be grouped into four categories: the first provides information on the source of the data, the second on the experiment, the third on agricultural management practices, and the fourth category conveys figures about crop yield. The full list of variables included in the dataset, together with their detailed description and unit has been reported in Supplementary Table  1. Overview of the data. The EGLD 177 contains yield data from 21 countries, from ca. 37° N (southern Italy) to 63° N (Finland) of latitude, and from ca. 8° W (western Spain) to 47° E (Turkey) (Fig. 2 ), thus capturing a wide range of pedoclimatic conditions. With about one third (29%) of the data, Germany is the country with the highest number of observations, followed by Italy, Turkey, Spain, France, the UK, and Greece, each of these countries representing about 8% of the dataset (Table  1 ).
Other countries have lower numbers of observations, with Bulgaria, Croatia, Belgium, and Latvia having less than 30 observations each. Faba bean, pea, and soybean are well represented in the dataset as they account for 33%, 27%, and 23% of total number of observations, and are present in 19, 16, and 13 of the 21 countries, respectively. On the other hand, chickpea and lentil are much less represented as they account for only 9% and 8% of the whole dataset, and are present in 8 and 7 countries, respectively. Belgium and Croatia provide information only for one pulse (soybean), while Italy and Denmark have information about all the five species. The dataset captures a wide range of grain yield values, from complete crop failure (yield almost null) to very high yield (for all the species, the maximum yield was higher than 6 t ha -1 of dry matter) (Fig.
3 ). The most represented agronomic practice that has been evaluated in the experimental trials was found to be the genotype (cultivar) as it represents more than 50% of all the entries of the dataset. Tillage and weed control are the next, with 8 and 6% of total entries, respectively. Missing data are indicated as "NA" (not available) cells in the dataset. For example, cultivar was missing in 7% of entries, soil texture in 33% of the total, while the soil classification was not reported for more than half entries (about 62%), and tillage was missing in half (52%) of entries (Table  2 ).
Technical Validation After the data extraction, the quality check was further carried out by comparing the entire set of collected data against the corresponding original source (papers or experiments) to make sure the data were digitalized correctly. The formats of each column (numerical or string) were checked to correct misprinting. We visualized the data distribution for each numerical column and detected outliers, that were manually checked and validated by comparing them with the values reported in the original papers or experiments. Moreover, the values of crop yield reported in the database are consistent with the results of published research  1, 17, 23, [178] [179] [180]. Graphical exploration of the data was performed by using the package ggplot2 with the ggplot function of the statistical software R, version 4.
Usage Notes The yield dataset can be used for several purposes. First it is useful to improve our knowledge of the relationships between pedoclimatic factors, agronomic practices, and legume yield. The wide range of soil types (Fig. 4 ), climate zones (Fig. 1 ), and management practices (Fig. 5 ) covered by our dataset provides a unique opportunity to identify the main factors influencing yield of pulses  8, 181, 182. A better understanding of GxExM interactions impacts of the yield for grain legumes would contribute to improving our understanding of the determinants of productive performances of the cultivars tested in Europe and identify the most promising in different regions  15, 16. Second, the dataset can be used to map achievable yields of grain legumes over Europe under current and future climate scenarios.
This can be done by fitting statistical or machine learning models to predict grain yield from climate (and possibly other factors like soil and management practices) inputs, as done by Guilpart et al. for soybean  25. Thanks to the experiment geographical coordinates, it is possible to associate climate and soil information with the yield data included in our dataset  [183] [184] [185]. It would be then possible to train data-driven yield forecasting models and produce yield maps for the five pulse species under various hypothetical climate and management scenarios, as done for soybean  25. Such yield maps may be highly relevant to: (i) identify suitable areas for pulses cultivation in Europe under both current and future climate; (ii) simulate the impact of an increase of pulses growing area on the pulse production in Europe; (iii) support the definition of market scenarios as well as policies on protein/starch production in the EU, highlighting potentialities, environmental barriers, and constraints; (iv) identify pulses species and cultivars best adapted to local pedoclimatic conditions over Europe.
acknowledgements This work was done in the frame of Task 1. 2 of  LEGVALUE  project, which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement Nr. 727672. The Authors acknowledge all the LEGVALUE partners which contributed by sharing their experimental data: INRAE,  TERRES INOVIA , UNIPI, WU, PGRO, WR, SSSA,  INIAV ,  AICF ,  SEGES , LLKC,  FH-SWF , CRAN,  LAMMC ,  FiBL. The authors are also grateful to the partners of the  LEGATO Project "LEGumes for the Agriculture of TOmorrow ", project number  613551  funded by the  European Union  under the  FP7 Programme  for having kindly provided their experimental data for the database.
code availability Prisma Flow Diagram (http://www. prisma-statement. org/documents/PRISMA_2020_flow_diagram_new_SRs_ v2. docx) was used to generate Fig. 1. The statistical software R (version 4. 1. 2, 2021-11-01-"Bird Hippie" Copyright © 2021. The R Foundation for Statistical Computing) was used to produce Figs. 2-5. R scripts used to produce figures are openly available on the figshare repository.
Analysis of the microarray data revealed that 2286 goldfish genes out of the 52,362 genes on the microarray were differentially expressed between treated and control cells (fold change > 2). Additionally, hierarchical clustering analysis of differentially expressed genes (DEGs) showed a clear segregation between treated and control samples (Fig. 2A , upper dendrogram). This demonstrates that the treated cells transcriptome was modified by egg extract treatment and that the consequences were detectable after 8 days of culture. Differentially expressed genes between cultured treated and control cells showed a distribution into two clusters on the heatmap (Fig. 2A ). Cluster I gathers 872 genes (encompassing 38% of the DEG) that showed upregulation in the treated cultured cells. Cluster II comprises 1 414 genes (62% of the DEG) that were down regulated in the treated cells.
This highlights the importance of a careful characterization of the Xenopus egg extracts. Although the sample number in each category was low, we still performed a fold change analysis between the two groups. We observed that 83% of the Heatmap of the hierarchical clustering analysis by unsupervised approach using 52,362 goldfish genes (Java TreeView software; https:// bitbu cket. org/ TreeV iew3D ev/ treev iew3/ src/ master/). Control: control cultured cells (C1-5); Treated: treated cultured cells (T1-7). Each row represents a single gene. Differentially expressed genes (Fold Change > 2; False Discovery Rate (FDR) < 0. 05) between treated and control cells are shown on the heatmap (2,286 genes). Two clusters were identified. Cluster-I (872 genes) and cluster-II (1414 genes) contain the genes that were respectively up-and downregulated in treated cells compared to control cells.
Gene ontology (GO) analysis of the differentially expressed genes after egg extract treatment. GO analysis was a perquisite in order to process our DEG list into functions and biological significance. For this purpose, we had first to translate the goldfish gene identifiers into those of the closest species whose genome is well annotated in the GO databases, the zebrafish. This artificially reduced the number of DEG, because the zebrafish did not undergo the genome duplication reported in the Cyprininae sub-family, to which goldfish belongs. Only 1533 zebrafish genes (i. e. 67% of total goldfish DEG) were retained for subsequent annotations in GO. Of these, 591 annotated genes were up-regulated (cluster I) and 942 annotated genes were down regulated (cluster II) in the treated cells. GO analysis conducted with the WebGestalt web tool  36  showed that biological regulation and metabolic process were the most represented terms (Fig.
2B ) among biological process GO terms. Surprisingly, no straightforward reprogramming processes such as chromatin remodeling, stem cells, transcription factors, or pluripotency could be emphasized in GO terms after a statistical over-representation analysis. However, several other biological process terms significantly enriched in the GO terms list deserve specific attention. Deregulation of TGFβ and Wnt signaling pathways after egg extract treatment. Our work is reporting gene expression variation, but GO databases and related publications on gene function report mainly protein functions. It is therefore the protein writing nomenclature that will be used in the following sections dedicated to GO interpretation. The most significant GO term obtained from the cluster of up-regulated genes is the cell surface receptor signaling pathway (Fig.
3A ). The data mapping showed that this GO term was linked to highly significant child GO terms that are transforming growth factor beta (TGFβ) receptor signaling pathway, and Wnt signaling pathway together with regulation of canonical Wnt signaling pathway. This result was consistent with the KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis performed on the same set of DEG data, that also showed that both TGFβ and Wnt signaling pathways reached a significant level of enrichment among all the database terms (Fig. 3B ). To add on to the highlighting of these 2 specific pathways, we also observed an enrichment in the GO terms related to the MAPK/ERK cascade (Fig. 3A ), known to be one of the non-canonical pathways activated by TGFβ  37. Thus, the GO analysis based on the cluster of up-regulated genes clearly highlighted the TGFβ and Wnt signaling pathways as major ones being affected in the cells exposed to egg extract reprogramming factors.
Microarray analysis. Microarray preparation and hybridization. Agilent 8 × 60 K high-density oligonucleotide microarray (GEO platform no. GPL32340) was spotted with a set of 52,362 distinct goldfish oligonucleotides. Available goldfish NCBI sequences were blasted on the zebrafish genome, generating a list of zebrafish proteins identified as ENSDARP in the Ensembl database. The official symbol of each gene, its description and its Ensembl ID, called ENSDARG, were then extracted from the ENSDARPs using the Ensembl Biomart programm. Total DNA and RNA of the cultured cells were extracted simultaneously after cell lysis in RNAsin (1 µL) in Tri-Reagent, according the instructions for Miniprep DNA/RNA Direct-zol column extraction kit (Zymo Research, R2081). RNA labeling and hybridization were performed according to the manufacturer's instructions (Agilent "One-Color Microarray-Based Gene Expression Analysis (Low Input Quick Amp labeling)").
For each sample, 150 ng total RNA was amplified and labeled using Cy3-CTP. Yield (> 825 ng cRNA) and specific activity (> 6 pmol of Cy3 per μg of cRNA) of the obtained Cy3-cRNA were checked on Nanodrop. Cy3-cRNA (600 ng) from each sample was fragmented, and samples were hybridized on randomly chosen sub-arrays for 17 h at 65 °C. After microarray scanning (Agilent DNA Microarray Scanner, Agilent Technologies, Massy, France), data were obtained with the Agilent Feature Extraction software (10. 7. 3. 1) according to the appropriate GE protocol (GE1_107_Sep09) and imported into GeneSpring GX software (Agilent Technologies, Santa Clara, CA, USA) for analysis. Data were published at the NCBI's Gene Expression Omnibus  75  and are accessible through GEO series accession number GSE205854. Of the 16 cell samples laid on the microarray, only 12 samples passed the quality controls and were selected for analysis (n = 5 control; n = 7 treated with egg extract).
Differentially expressed genes identification and Gene Ontology analysis. The raw gene expression data were normalized and transformed into Log2 values using GeneSpring software (Agilent). Only genes displaying an expression value significantly higher than that of the background in at least 75% of the samples and in at least one of the two conditions were retained. Selection of differentially expressed genes relied on a Student's t-test with false discovery rate (FDR) correction and a fold change > 2 was applied. The significance level was set to FDR < 0. 05 and p-value < 0. 05. The DEGs were then classified according to their expression profile by unsupervised hierarchical clustering using Cluster 3. 0 software and were visualized by Java TreeView software (https:// bitbu cket. org/ TreeV iew3D ev/ treev iew3/ src/ master/).