dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Work on the Brain Imaging Data Structure began at a meeting of the INCF Neuroimaging Data Sharing Task Force (wiki. incf. org/mediawiki/index. php/Neuroimaging_Task_Force) held at Stanford University on January 27-30th 2015. While a flexible solution using the PROV W3C model (http://www. w3. org/TR/ prov-overview/) was first investigated, it was acknowledged that this technology would be only viable if tools were in place to write the associated metadata. Since experimental data are obtained from multiple tools, a solution accessible to most neuroimaging researchers was designed. An initial draft was heavily inspired by the data structure used by the OpenfMRI database, but soon evolved beyond backward compatibility. After the initial draft was formed, a series of discussions and public calls for feedback were conducted.
Feedback was solicited over Twitter, by presenting BIDS and distributing informational pamphlets at conferences (INCF, SfN), as well as by sending emails to SPM, FSL, Freesurfer, MRTrix, Slicer, Nipy, and HCP mailing lists (reaching over 5,000 researchers). Further refinement of the standard was facilitated by a meeting held during the OHBM conference in Honolulu in June 2015. The discussion over the standard involved domain researchers, computer scientists, MRI physicists, methods developers (FSL, SPM, Slicer, Nipy, PyMVPA, C-PAC, nilearn and aa), data curators (OpenfMRI, FCP/INDI, HCP, NKI, SchizConnect, ABIDE, DataLad, and BIRN) and database developers (COINS, LORIS, XNAT, NiDB and SciTran). The first Release Candidate was published on September 21st 2015 along with 22 example datasets, online and command line validation tools (https://github.
com/INCF/bids-validator), and a converter from OpenfMRI standard (https://github. com/INCF/openfmri2bids). The standard became official (version 1. 0. 0) with the publication of this manuscript and we expect to update and extend it through future releases (see Supplementary File 1). We encourage everyone to provide feedback on the standard as well as suggestion for new features and support for more data types. Proposed changes will be discussed publicly trying to accommodate the needs of the community. To facilitate this process we have created the http://bids. neuroimaging. io website. This work is licensed under a Creative Commons Attribution 4. 0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material.
Acknowledgements This work has been supported by the  International Neuroinformatics Coordinating Facility (INCF)  and by the  Laura and John Arnold Foundation. V. D. C. has been supported by in part by  NIH   P20GM103472. T. E. N. and C. M. have been supported by the  Wellcome Trust. N. N. has been supported by  NIH NIAAA  [ 1 U01 AA021697 ]. J. -B. P. , B. N. N. , and  R. A. P. have been supported by  NIH   NIAAA OD [1 U01 AA021697-04S1 ]. D. A. H. was supported by the  Intramural Research Program  of the  NIMH. M. H. was supported by the  German federal state of Sachsen-Anhalt  and the  European Regional Development Fund (ERDF) , project:  Center for Behavioral Brain Sciences (CBBS). T. A. was supported by the  Medical Research Council (United Kingdom)  [ MC-A060-53144 ]. Y. O. H. has been supported by in part by  NSF  [1429999].
The amplified fragments were separated and visualized on an ABI3130 sequencer (Applied Biosystems). Electropherograms were analyzed with GENEMAPPER V4. 0 software. For each primer pair, the reference panel used for allele calling in the progeny was based on the AFLP patterns of the three parental strains (presence/absence in one of the two homokaryons, presence in the hybrid). The allelic segregation at the mating-type locus (MAT) was determined by pairing each homokaryon E i h with the two homokaryons CA454-3 (MAT-2), and CA487-100 (MAT-4). The MAT allele of each Ei was deduced from unambiguous compatible reaction with one tester or the other. Mating tests were performed as described in  Thongklang et al. (2014a)  with two replicates per confrontation. Thus, the allele at the MAT locus could be determined for 74 E i (93.
Linkage map construction Genotypic data were independently scored by two experimenters to minimize scoring errors. Locus segregation was tested for deviation from expected Mendelian ratios 1:1 with a x 2 test. Linkage and mapping analysis was performed using MAPMAKER/EXP V3. 0b software  (Lander et al. 1987). The recombination frequency was converted into Kosambi centimorgan (cM) units. In a first round of mapping, markers having more than 30% of missing data, and/or markers with a skewed segregation ratio (P , 0. 05), were omitted to limit spurious linkage. AFLP markers were kept out of this first step, and those with unbalanced allelic segregation were definitely removed from the genotyping data set. A minimum LOD score of 4. 0 and maximum distance of 30 cM were set as thresholds for linkage groups (LG) determination with the 'GROUP' command.
The occurrence of crossovers for each LG and each individual was analyzed using Graphical Genotyping (GGT 2. 0) (van Berloo 2008). Estimated genome length L e was determined from the linkage data according to  Hulbert et al. (1988) , as modified by Chakravarti  (Chakravarti et al. 1991, Method 3). Map coverage was calculated using the formula P = 1 2 (1 2 2c/L e ) m , where P is the proportion of the genome within 2c cM of a marker, m is the number of informative markers on the map, and L e is the estimated genome length  (Beckmann and Soller 1983).
Data availability Strains are available upon request. Information about markers used or developed specifically for this project can be found in Table  A  and Table  B of File S1. The genotype dataset used to build the genetic map is available in Table  S1. The A. subrufescens EST sequences are accessioned in GenBank with the reference GBEJ00000000. 1. Coordinate data resulting from the alignment of these A. subrufescens EST sequences on the A. bisporus genome are available in Foulongne-Oriol et al. 2014 or upon request.
The first round of mapping included 106 markers representing the most confident and informative set of genotyped loci (only unbiased  codominant markers with less than one-third of missing data). With our mapping condition (LOD. 4, d max , 30 cM), a preliminary framework map was established with 97 markers distributed over 18 LG. The next rounds of mapping consisted in placing the remaining markers. The addition of markers allowed the merging of four groups into two larger ones. For the other groups, the sequential mapping procedure led to an increase of marker density and length. Twentytwo markers (14 AFLPs, two SSRs, and six CAPSs) were discarded from the current mapping dataset due to inconsistent localization or substantial increases of map length.
ACKNOWLEDGMENTS The authors acknowledge the technical assistance provided by  Ombeline Hueber. Manuela Brito  gratefully acknowledges the  Brazilian agencies "Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior" (CAPES)  and  "Programa de Doutorado Sanduiche no Exterior" (PDSE-CAPES)  for financial support. This work was supported by a research project funded by a bilateral cooperation between  Mexico  (project  115790   CONACYT) and France  ( ANR-09-BLAN-0391-01 ).
Grain legume crops are a significant component of the human diet and animal feed and have an important role in the environment, but the global diversity of agricultural legume species is currently underexploited. Experimental assessments of grain legume performances are required, to identify potential species with high yields. Here, we introduce a dataset including results of field experiments published in 173 articles. The selected experiments were carried out over five continents on 39 grain legume species. The dataset includes measurements of grain yield, aerial biomass, crop nitrogen content, residual soil nitrogen content and water use. When available, yields for cereals and oilseeds grown after grain legumes in the crop sequence are also included. The dataset is arranged into a relational database with nine structured tables and 198 standardized attributes.
A global dataset would provide us with a unique opportunity to analyze variability in grain legume performances across a large spectrum of environmental conditions, and to rank legume species of agricultural and economic interest according to several criteria. We introduce here a global dataset including the results of field experiments comparing 39 grain legume species grown as sole crops. Most of grain legume species included in the database correspond to species of significant agricultural and economic importance. We have selected only experiments comparing at least two grain legume species grown at the same field site during the same growing season, to prevent any confusion between species characteristics and environmental conditions. We excluded experiments on single grain legume species because, in such experiments, differences between species can be confounded with the effects of environmental factors.
Experimental data were extracted from 173 published articles  [2] [3] [4] 6, [14] [15] [16] [17] [18] [19]. In total, mea urements from 360 field sites were collected across 18 Köppen-Geiger climatic zones  186  in 41 countries (Fig. 1 ) over five continents (Table  1 ). The dataset contains 8,581 crop*field site*growing season*treatment combinations. Article references, field site locations, climatic conditions, soil types, yields, crop nitrogen contents, residual soil nitrogen contents    186  was used to link each field site to a grid size with a resolution of 0. 50 degrees of latitude by 0. 50 degrees of longitude. Eighteen Köppen-Geiger climatic zones are considered: equatorial climates (red), arid climates (orange), warm temperate climates (green) and snow climates (blue). Wit in each main Köppen-Geiger climatic zone, each Köppen-Geiger climatic subzone is indicated by a color gradient.
and management practices are systematically recorded for each crop*field site*growing season*treatment combination. When available, data on non-legume species grown at the same field site during the same growing season than grain legume species, and data on non-legume species grown after grain legumes in the crop sequence are also included. Most of these non-legume species correspond to cereals and oilseeds. The data are organized into a relational database with nine structured tables and 198 standardized attributes (Tables  2  and 3  (available online only)). The dataset can be used for two types of quantitative analysis. First, the dataset can be used to compare the crop production of a broad range of grain legume species, on the basis of experimental data with diverse criteria (e. g. , grain yield, aerial biomass and crop nitrogen content).
Second, the dataset can be used to assess the crop production of cereal and oilseed species following grain legume species cultivated as preceding crops in the same crop sequences, based on a consideration of field data for various criteria. The dataset is freely available to facilitate such analyses. It could easily be updated in the future, by adding the results of new experiments not originally included in the dataset. It might also be interesting to expand the dataset to include legumes grown for purposes other than grain production (e. g. , forage production) or legumes grown in intercropping systems. The global dataset should prove to be a useful support for experimental assessments of the agronomic and environmental performances of a large diversity of grain legumes.
We selected 223 eligible full-text articles that met these first six criteria (Fig. 2 ). Eligible full-text articles were then examined according to three additional criteria: (7) full-text article reporting raw data not duplicated in other articles or raw data that could be obtained by contacting authors, (8) full-text article reporting individual grain yield for each species and (9) full-text article reporting one or several experiments for which field site location or soil characteristics were precisely stated. We selected 60 full-text articles that met all nine criteria. This search was supplemented by screening the references cited in these 60 full-text articles. We also screened the references included in one meta-analysis about drought effects on food legume production  187  for eligibility.
Database structure All data are recorded in a relational database (Data Citation 1). The Structured Query Language (SQL) system is used to query and maintain the database. We used the open-access application Sequel Pro version 1. 0. 2 (http://www. sequelpro. com/). The data collected are grouped into nine related tables including 198 standardized attributes of five types: class, numerical, index, binary and date (Fig. 3  and Table  2 ). Within the database, the tables are organized according to a cascade path: each 'child' table is related to a 'mother' table. For instance, the 'Article' table is the 'mother' table for the 'child' 'Site' table (Fig. 3 ). The cascade path from each 'mother' table to each 'child' table is structured by a 'primary key' and a 'secondary key' (Fig. 3 ). A 'primary key' assigns an index to each row of the table, whether the table is a 'mother' table or a 'child' table.
A 'secondary key' assigns the 'primary key' of a 'mother' table to each row of a 'child' table. The cardinality from each 'mother' table to each 'child' table is based on 'one-to-one' and 'one-to-many' relationships (Fig. 3 ). The database is structured into nine separate but related tables, stored as CSV-formatted files (Data Citation 1). Tables are related to each other via primary and secondary keys, as explained in Fig. 3. The names, types and definitions of attributes included in the nine tables are listed in Table  3  (available online only). The 'Literature_Search' table describes each step in the literature search at which each original article was selected (e. g. , selection from the initial literature search or from references screening). The corresponding file is entitled 'Literature_Search.
The corresponding file is entitled 'Weed_Insect_Fungi. csv' (Data Citation 1), and includes 13 columns and 45,002 rows (including the row header for the names of attributes). The 'Irrigation' table describes irrigation management for each crop (e. g. , quantity of water applied and irrigation method). The corresponding file is entitled 'Irrigation. csv' (Data Citation 1), and includes 6 columns and 8,582 rows (including the row header for the names of attributes). In addition to the nine CSV-formatted files (tables), downloadable from Dryad Digital Repository (Data Citation 1), the entire content of the database is also stored in a SQL-formatted file. The corresponding file is entitled 'Database. sql', and is also downloadable from Dryad Digital Repository (Data Citation 1). Examples of SQL queries for extracting data for each table are stored in a TXT-formatted file.
The corresponding file is entitled 'Examples_SQL_Queries. txt', and is also downloadable from Dryad Digital Repository (Data Citation 1). The names, types, and definitions of the 198 attributes included in the nine tables are reported in Table  3  (available online only). The values (including error terms) and dates reported in graphics were digitized manually with the open-access application WebPlotDigitizer (http://arohatgi. info/WebPlotDigitizer/). The maximum error was estimated at 5. 0% for the digitization of low-resolution images, generally from articles published before 1990. 'NA' indicates that data were 'Not Available' for the cell concerned. 'NULL' indicates a logical absence of data for attributes included in the 'Crop', 'Tillage', 'Fertilization', 'Weed_Insect_Fungi', and 'Irrigation' tables.
When data were not reported for some attributes (e. g. , aerial biomass or water use) in the selected articles, we systematically collected data for related attributes (e. g. , harvest index or grain water use efficiency) in order to retrieve the missing data. For examples, aerial biomass can be deduced from grain yield and harvest index, and water use can be deduced from grain yield and grain water use efficiency. When data were not available for any related attributes, we contacted the authors of the selected articles, and we asked them to provide us with additional raw data when available.
'Crop_Sequence_Treatment_Name' attribute The name of each combination of crop sequences and management practices was based on the common names of the species, such as for both 'Crop_Sequence_Trt_Name' and 'Crop_Sequence_Trt_Species_ Order' attributes in the 'Crop_Sequence_Trt' table. For instance, the name of a legume-cereal sequence without application of nitrogen fertilizer (0N) could be 'Garden pea-Common wheat, 0N' where 'Garden pea' and 'Common wheat' are the common names listed in the United States Department of Agriculture Plants Database (http://plants. usda. gov/java/) for Pisum sativum and Triticum aestivum, respectively. Malik et al. 105  and McEwen et al. 108  described several crop sequences including grain legumes and crop sequences including barrelclover (Medicago truncatula) or common oat (Avena sativa), both preceding common wheat.
'Crop_Site_Growing_Season_ID' attribute This attribute is an index identifying each species grown at a given field site during one or several growing seasons. Identical raw data were found to have been duplicated in two pairs of articles: Muchow et al. 114  and Sinclair et al. 153  on the one hand, and Heenan et al. 71  and Armstrong et al. 2  on the other. The duplicated raw data from Sinclair et al. 153  and Heenan et al. 71  were excluded because the number of crop*field site*growing season*treatment combinations was smaller in these two articles than in their duplicates. 'Crop_Species_Scientific_Name' and 'Crop_Species_Common_Name' attributes These attributes give the scientific and common names of the species. The scientific name of each species was related to the common name listed in the United States Department of Agriculture Plants Database (http://plants.
'Crop_Date_From_Seeding_To_Harvest_Day_Number' attribute We calculated the number of days from seeding date to harvest date, with the open-access application Time and Date (http://www. timeanddate. com/). For data averaged across multiple growing seasons, we calculated the number of days from seeding date to harvest date for each growing season and then obtained the average by dividing by the total number of growing seasons. Some articles approximated seeding date and harvest date by describing these events as occurring in the 'early', 'middle' or 'late' part of the month. We defined 'early' as the first 15-day period of the month  (1st-15th), 'middle' as the 15th day of the month and 'last' as the second 15-day period of the month (15th-30th or 15th-31st). In these cases, the number of days from seeding to harvest was calculated by selecting the last day of the period concerned, i.
'Crop_Across_Treatment_Averaged_Value' and 'Crop_Across_Treatment_Averaged_Value_Type' attributes For species grown at the same field site during the same growing season, some articles reported only data averaged over combinations of treatments (e. g. , cultivar*seeding date*presence of irrigation). We included these data provided that each type of individual treatment was precisely defined in the article. In all cases, we systematically reported whether or not the data were averaged over combinations of Crop (1,n) (1,1) (1,n) (1,1) (1,n) (1,1) (1,n) (1,1) (1,1) (1,n with n 3) (1,1) (1,1) (1,1) (1,3) (1,1) (1,1) Figure  3. Relational model of the database. Each box represents one table. One 'primary key' and one 'secondary key' are assigned to each table (except for the 'Literature_Search' table, which is exclusively a 'mother' table).
Each table includes many attributes. For the sake of readability, attributes are indexed in italic from one 'mother' table to one or many 'child' tables along the cascade path of the database (Table  3  (available online only)). Arrows indicate relationships from one 'mother' table to one or many 'child' tables. For upward and backward matching between tables, each pair of numbers in brackets indicates the cardinality of the relationships between attributes. The cardinality may involve 'one-to-one' (i. e. , 1,1) relationship or 'one-to-many' (i. e. , 1,n) relationship. For upward matching, for instance, the cardinality (1,n) from the 'Article' table to the 'Site' table indicates that one article may have one or many field sites. For backward matching, the cardinality (1,1) from the 'Site' table to the 'Article' table indicates that each field site may belong to only one article.
Within each table, the names of primary and secondary keys are indicated in purple and blue, respectively. treatments. When data were averaged over combinations of treatments, the total number of replicates was calculated as the sum of the replicates for each of the treatments for which results were averaged. For articles reporting data for several cultivars of the same species but without data averaging, the data were reported separately for each cultivar. For articles reporting data averaged over several cultivars of the same species, only the averaged data were included in the dataset. The total number of replicates was calculated by multiplying the number of replicates of each cultivar by the total number of cultivars.
'Crop_Yield_Grain' attribute This attribute corresponds to grain yield data, with a few exceptions. For Brassica chinensis (pak choi), Citrullus lanatus (watermelon), Gossypium hirsutum (upland cotton), Ipomoea batatas (sweet potato) and Solanum lycopersicum (garden tomato), the yields reported are the economic yields. For Arachis hypogaea (peanut), pods are included in grain yields. In all other situations, the yield data given correspond to grain yields. Mutant non-nodulating legume cultivars, shading treatment and under-sowing treatment were excluded from the database. When grain yield data of following crops were confounded between the effect of preceding species and the effect of nitrogen fertilizer dose, these data were also excluded. Data were reported in 96% of all crop*field site*growing season*treatment combinations.
'Crop_N_Balance_Simplified' attribute This attribute corresponds to the simplified nitrogen balance originally calculated in the articles (e. g. , the difference between the quantity of nitrogen in grains and the quantity of fixed aerial nitrogen). Nitrogen balance data were only reported if the attributes used to calculate them were not directly available from raw data (e. g. , the quantity of nitrogen in grains and the quantity of fixed aerial nitrogen). This was the case for only three articles. 'Crop_N_Soil_Quantity_Percentage_Seeding' and 'Crop_N_Soil_Quantity_Percentage_ Harvest' attributes These two attributes correspond to the percentage or the quantity of soil nitrogen at seeding and at harvest, respectively. 'Crop_N_Soil_Quantity_Percentage_Seeding_Type', 'Crop_N_Soil_Quantity_Percentage_ Seeding_Depth', 'Crop_N_Soil_Quantity_Percentage_Seeding_Date', 'Crop_N_Soil_ Quantity_Percentage_Harvest_Type', 'Crop_N_Soil_Quantity_Percentage_Harvest_Depth' and 'Crop_N_Soil_Quantity_Percentage_Harvest_Date' attributes These attributes correspond to (i) the type of nitrogen (e.