dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2018). Second, we implemented two contrast analyses and a conjunction analysis to reveal unique and common neural correlations between DT and insight. A pooled dataset was created, whose computing parameters were the same as that in the single meta-analysis, combining foci from the two input datasets of DT and insight. Then, these pooled foci were randomly separated as two simulated datasets. Subtractions were conducted within the simulated datasets and input datasets, respectively. The subtraction results of the simulated datasets were compared with the input datasets using the p-value. Specific contrast analyses included DT > insight and insight > DT; conjunction analysis was DT \ insight. Parameterized details were as follows: p-value was calculated, and p <. 01. The minimum volume of a cluster is 100 mm 3 with a 3000 times permutation.
DT requires the involvement of crucial abilities to be involved in creative idea generation, such as flexibly retrieving long-term memory  (Silvia et al. , 2013) , constructing novel associations between remote concepts  (Benedek, Könen, et al. , 2012) , redirecting attention resources  (Dietrich & Kanso, 2010) , and conducting imaginative simulation  (Leboutillier & Marks, 2003; Zhang et al. , 2019). Our results F I G U R E 2 F I G U R E 3 Clusters activated significantly (p <. 01 with 3000 permutations, minimized volume of cluster forming threshold was 100 mm 3 ) in a contrast dataset analysis (DT > insight). A, anterior; I, inferior; IPL, inferior parietal lobe; L, left; LG, lingual gyrus; P, posterior; R, right; S, superior showed that the left IPL was active in DT. This observation is likely correlated with its roles in supporting autobiographical memory retrieval and semantic association combination under DT.
T A B L E 3 Brain activations of contrast and conjunction dataset analysis between divergent thinking (DT) and insight Note: DT \ Insight: No clusters were found. These two contrast data analyses were analyzed with a threshold at p <. 01 with 3000 times permutation, and the minimized volume of cluster forming threshold was 100 mm 3. Volume refers to the size of the cluster (mm 3 ). Montreal Neurological Institute coordinates were used. Abbreviations: BA, Brodmann area; Exp, experiment; L/R, left hemisphere or right hemisphere. Our data indicated that the activity of the MFG (extending to the IFG) cluster was correlated to DT. The left MFG/IFG appeared to be a task-switcher in supporting DT. Previous studies have suggested that these brain regions are specialized in cognitive flexibility  (Kleibeuker et al.
Therefore, it is hypothesized that the DMN may be the neural basis of autopilot and fast thinking modes embedded in the human brain  (Gronchi & Giovannelli, 2018; Raichle, 2015). Together, these data probably offer hints for the existence of System 1 when individuals undertake highly creative events. Therefore, our results indicate that DT mainly processes with an associative, autonomous, and fast thinking pattern (System 1).
| Insight > DT Insight is a high-level cognitive activity characterized by reflective progression steps such as breaking unwanted fixations, reorganizing representations, and forming novel associations  (Becker et al. , 2020; Luo & Niki, 2003). The problematic cases that induce insight tend to instruct subjects to concentrate on the current settings and exploit contextual rules. Unlike DT, successful insight is usually associated with self-generated emotional experience (i. e. , AHA response). These variations might propose its exclusive role in processing modes. Our data indicated that activation spread from the frontal to the parietal area. Most of these regions were located in the ECN, an area recognized to be important in rule-based creative problem-solving  (Beaty et al. , 2016). The ECN, with great emphasis on PFC's contribution, extensively contributes to the following cognitive processes: selective attention, information manipulation, response inhibition, and taskoriented representation  (Bunge & Souz, 2009; Seeley et al.
| Limitations and future directions This study has several limitations. First, the coordinate-based ALE technique uses activated coordinates and sample size across all the experiments included in our analyses. These collected data are tested for spatial convergence under a specific null space while the specific difference information is lost. Thus, as the effect size is inaccessible, a significant result can only be interpreted as the difference in convergence in contrast to the strength or decrease/increase of the observation  (Müller et al. , 2018). Second, to maintain the consistency of the null space to be test, ROI-based research was not considered. This approach limited external validation and may have induced some bias in our findings  (Müller et al. , 2018). Third, subjects finished experimental tasks with diverse response requirements (e.
The creative process is achieved through many integrated complex cognitive abilities. Further research should carefully substantiate its applicability in creativity mechanisms with neuroscientific evidence. Although our present meta-analysis is built upon richer data than previous ones, there is still plenty of room for future neurobiological imaging to answer key gaps in the knowledge regarding the multifaced nature of creativity. Future work could further define fig- ural and arithmetic creativity (e. g. , the matching problem) and their co-assisted networks. The current study just aims to reveal the common and distinctive localized consistent activation between DT and insight, so it misses out on valuable functional connectivity data. With functional connectivity analysis becoming more common, there is an increased opportunity to conduct meta-analysis connectivity modeling (MACA) research  (Lancaster et al.
The single dataset analyses indicated that the left IPL, right CUN, and left MFG were coherently involved in DT, while insight mainly recruited clusters in the left PreCG/IFG, left PG, left AMG, left PCUN, and right SFG. In contrast analyses, robust activation was significantly detected in the left IPL (key node of the DMN) and right LG in DT instead of insight activity. Conversely, areas like the left PreCG/IFG, right CG/MedFG, left SPL, and left PG/AMG were significantly activated during insight than DT. However, no overlapping clusters were observed in the conjunction analysis of these two creative events. These results could probably reflect that DT mainly demanded the DMN while insight might ask for the participation of the Executive Control Network (ECN) and the hippo-amygdala network for emotional responses.
This meta-analysis explored common and specific neural pathways involved in open-ended DT and closed-ended insight problems. Taken together, our data validate previous findings. In single-dataset analysis, significant cluster activity triggered by DT was localized in the IPL, CUN, and MFG. During insightful conditions, activated clusters were found in the PreCG/IFG/MFG, PCUN/SPL, PG/AMG, and SFG/CG. Critically, DT provoked stronger activation in the IPL and LG/CUN relative to insight, an observation that implicates the predominant involvement of the DMN. Conversely, insight produced significant activated patterns in the PreCG/IFG, CG/MedFG, SPL/AG, and PG/AMG, all closely linked with cognitive control and affective functions. However, we detected no common activation pattern from the contrast and conjunction analyses.
ACKNOWLEDGMENTS This work was supported by funding from the  Philosophy and Social Science Project of Guangdong Province  (Grant Number:  GD20YXL01 ), the  Guangdong Basic and Applied Basic Research Foundation  (Grant Number:  2020A1515110737 ), the  Project of Guangzhou Philosophies and Social Sciences  (Grant Number:  2020GZQN43 ), the  Humanities and Social Sciences Project of Guangzhou University of Chinese Medicine  (Grant Number:  2020SKYB07 ), the  China Postdoctoral Science Foundation  (Grant number:  2020M682682 ), and the  Guangdong Province Philosophy and Social Science Foundation for Youths  (Grant number:  GD21YXL02 ).
Brain activations of single dataset analysis in divergent thinking (DT) and insight Cluster Volume Peaks L/R Brain regions BA x y z ALE value Contributors to cluster DT > Control 1 1928 1 L Inferior parietal lobe (extending 40 À58 À30 40 0. 034 Abraham et al. (2012) to postcentral gyrus and Abraham et al. (2018) supramarginal gyrus) Beaty, Thakral, et al. (2018) Benedek, Beaty, et al. (2014) Benedek and Jauk (2018) Fink et al. (2010) Fink et al. (2015) Kleibeuker et al. (2013) O. Vartanian et al. (2018) 2 936 1 R Cuneus (extending to lingual 17 12 À88 12 0. 025 Abraham et al. (2018) gyrus) Fink et al. (2010) Green et al. (2014) Sun et al. (2016 Exp. 1) Sun et al. (2019) 3 808 1 L Middle frontal gyrus (extending 46 À48 36 14 0. 022 Mashal et al. (2007) to inferior frontal gyrus) Beaty and Mathias (2017) O.
The pathologists (all experienced in lung transplant pathology) discussed and defined the scoring system adopted for the different lesions, with the intention to evaluate its usefulness and reliability in research studies. An initial draft of the grid was circulated by email among the panel members; subsequent discussions and multiple revisions occurred by email and conference calls. The LASHA grid was finally drafted as a Word document where it was structured as multiple-choice questions, with number fields to be filled in to allow for standardization of results and easy transfer into a spreadsheet (Figure  1 ). This grid will be made available in an electronic format in REDCap (Research Electronic Data Capture, https://www. project-redcap. org/) which is a secure web application for building and managing online databases.
This application has several advantages, such as using a real DATABASE-SQL (MySQL), the availability of multiple forms connected to each other and different tools and customizations, easy management of multicenter studies, and the possibility of exporting data in an adequate format for statistical software (SAS, SPSS, R). A website will be set up with multisite access for managing the REDCap database. The writing workgroup was divided into subgroups covering the following topics: (1) introduction about the need for standardization of pathological diagnosis reports and the potential clinical impact, (2) methodology and description of the different lesions after a comprehensive literature search and review of each topic, (3) open issues and current/future perspectives.
Data Processing Observations used in this work are taken from the first set of data obtained by EMIRS. These data range from the start of the EMM science phase in May 2021 to the Mars solar conjunction in September 2021, equivalent to a solar longitude (L S ) range of 49°-100° of Martian Year (MY) 36. Two gaps exist at L S = 51°-57° and 93°-97° due to spacecraft safe mode events, so the continuous data at L S = 60°-90° are selected to avoid possible influence of these gaps (Figure  1a ). This is a dust-clear season in the late northern spring when Mars is near aphelion, and there are no significant seasonal variations of daily temperature anomalies (Text S1; Figures  S1-S3  in Supporting Information S1). Observations within this 30° of L S are considered together to improve statistics. The selected temperature profiles total ∼7.
Data Availability Statement Data from the Emirates Mars Mission (EMM) are freely and publicly available on the EMM Science Data Center (SDC, http://sdc. emiratesmarsmission. ae). This location is designated as the primary repository for all data products produced by the EMM team and is designated as long-term repository as required by the UAE Space Agency. The data available (http://sdc. emiratesmarsmission. ae/data) include ancillary spacecraft data, instrument telemetry, Level 1 (raw instrument data) to Level 3 (derived science products), quicklook products, and data users guides (https://sdc. emiratesmarsmission. ae/documentation) to assist in the analysis of the data. Following the creation of a free login, all EMM data are searchable via parameters such as product file name, solar longitude, acquisition time, sub-spacecraft latitude & longitude, instrument, data product level, and etc.
Emirates Mars Infrared Spectrometer (EMIRS) data and users guides are available at: https://sdc. emiratesmarsmission. ae/data/emirs. Data products can be browsed within the SDC via a standardized file system structure that follows the convention: /emm/data/<Instrument>/<DataLevel>/<Mode>/<Year>/<Month> Data product filenames follow a standard convention: emm_<Instrument>_<DataLevel><StartTimeUTC>_<OrbitNumber>_<Mode>_<Descrip-tion>_<KernelLevel>_<Version>. <FileType>. The Mars PCM output during L S = 0°-90° of MY 36 is available on the IPSL data server with https://doi. org/10. 14768/d49ef040-476c-4264-bf67-6b4b018b8620. Permission is granted to use these datasets in research and publications with appropriate acknowledgements that are presented on the data set websites.
As these metrics are scaled, their sources of data become different. Published data at the sub-field to field scales usually come from scientific investigations. Each data set from this scale will often report several of the metrics. When compiled across many such studies, data on all metrics can be collected and summarized. Published data at sub-country to country scales often come from surveys conducted by governmental or industrial organizations and usually contain only CPY, fertilizer consumption, and/or fertilizer application rates. Such data are typically collected at this scale to inform decisions about markets. Thus, to calculate nutrient offtake and uptake at larger scales, nutrient concentration data assembled from smaller scale studies are combined with unrelated, survey data conducted at larger scales.
Yield potential Crop yield potential was defined by  van Ittersum et al. (2013)  as the 'yield of a crop cultivar when grown with water and nutrients non limiting and biotic stress effectively controlled'. The yield potential estimated specifically for either rainfed or irrigated conditions will in this study be referred to as the 'Ypot' yield potential. Ypot values are estimated solely on crop growth determined by 'solar radiation, temperature, atmospheric CO 2 concentrations and genetic characteristics' (and water limitations in the case of rainfed or partially irrigated conditions) (www. yieldgap. org/glossary). Given how Ypot is estimated, it is therefore unsurprising that Ypot values are generally correlated to actual CPY values  (van Ittersum et al. , 2013). This offers an opportunity to use Ypot as an explanatory/predictive variable in the absence of, or in addition to, actual CPY data for predicting HI.
025-0. 012 kg N (kg DM) -1. These relationships have been supported by multiple studies  (Keulen and Heemst, 1982; Saidou et al. , 2003; Xu et al. , 2013; Shehu et al. , 2019). A direct relationship between CPY of maize and CPN or CRN has not been well defined. This can be partly explained by the fact that in many experiments, greater yield is achieved through greater application rates Fig. 3. Correlation (R 2 ) plot for a selection of variables from the maize dataset where variables are described in Table  1  (not all variables were included due to availability of data). Dependent variables are underlined.
Models in bold were chosen as predictor models. Model variables are described in Table  1. *Each data sub-selection included the same data for the variable listed in this column. * *Akaike Information Criterion (AIC) is a measure of the relative quality of statistical models for a set of data whereby a more negative value indicates a greater relative quality compared with another model  (Burnham and Anderson, 2002). This column indicates which model is best based on AIC for each sub-selection of the dataset, i. e. the sub-selection of the overall dataset used for the CPY, FN, Ypot and RY models. * **This is a conditional coefficient of determination for a linear mixed-effects model that is based on variance of the fixed and random effects (a value between 0 and 1)  (Nakagawa et al. , 2017).
Data from peer reviewed literature Data from a literature search were also included in the dataset for analysis. Terms used in Google Scholar (https://scholar. google. com/), CAB Abstracts (https://www. cabdirect. org/) and Ovid (https://ovidsp. ovid. com/) literature search platforms were as follows: (nitrogen) AND (maize OR corn) AND ("nutrient concentration" OR "nutrient content"). Some variations on these search terms were made so they were aligned to the formatting requirements of each search engine. Additionally, an email search alert was set up using these terms in Ovid and Google Scholar between the 1st of August 2020 and the 5th of October 2021. The list of articles was refined to include only articles from a peerreviewed journal or an accepted university thesis, and which included replicated field experiment results for fertilizer application rate, grain and/or residue yield, grain and/or residue nitrogen concentration, and experimental information such as location, year of experiment and experimental design.
In total, the queries from the multiple sources resulted in 91 articles (Appendix B). Data from the articles were manually converted into a standardized Excel format (Microsoft Office, Microsoft, Redmond, Washington, USA). Where data were not available in the publications as tables, data were extracted from figures using the GetData Graph Digitizer software (version 2. 26, http://www. getdata-g raph-digitizer. com/). Experiments included in the database covered a wide range of countries with the USA, India, China, Indonesia, Philippines, Argentina, and Hungary being countries with the most data (Fig. 1 ). Over 86% of total world maize grain production (for the 2018 reporting year) is represented by the 31 countries included in the dataset for this study  (FAOSTAT, 2020). H3_16 CPN CRN ~ CPN þ CPY þ FN þ FP þ FK Best AIC 0.
30 *Each data sub-selection included the same data for the variable listed in this column. * *Akaike Information Criterion (AIC) is a measure of the relative quality of statistical models for a set of data whereby a more negative value indicates a greater relative quality compared with another model  (Burnham and Anderson, 2002). This column indicates which model is best based on AIC for each sub-selection of the dataset, i. e. the sub-selection of the overall dataset used for the CPY, FN, Ypot and RY models. * **This is a conditional coefficient of determination for a linear mixed-effects model that is based on variance of the fixed and random effects (a value between 0 and 1)  (Nakagawa et al. , 2017). * ** *H3_7 was chosen instead of H3_8 (despite H3_8 having a better AIC) because there was little to no advantage in terms of Nakagawa R 2 values for including the FP variable.
Processing data before analysis Before analysis, data were first assessed for outliers. Summary statistics and visualizations using the "ggplot2" package in R version 4. 1. 0 (R Core Team, 2020) (herein referred to as 'R') were used to determine obvious errors or outliers in the data. From this process, only a limited set of (HI) data were categorized as being outliers as follows. HI values above 0. 65 were deemed biologically infeasible outliers and were excluded from analysis based on maximum HI values in a review by  Unkovich et al. (2006). Data collected from the literature were generally expressed as means across multiple replicates, whereas raw data from the data requests were values for each replicate. For a fair comparison of the data between the two data sources, the mean values were calculated with a weighting placed on the number of replicates and years of data from each source.
The RY for each location were calculated by dividing the actual CPY by the Ypot. Some sites did not have Ypot data available for maize in the Global Yield Gap Atlas. Sites without Ypot data were assigned (where possible) Ypot values predicted using random forest using growing degree days, aridity index, temperature seasonality, latitude, duration of growth from sowing to harvest, total seasonal precipitation, total available water capacity of the dominant soils in each area as predictor variables (Appendix C). These predictions were made using the "randomForest" package (version 4. 6-14) in R. For reference, a list of the relevant variables (and their abbreviations) in the combined dataset and the units is shown in Table  1.
Summary statistics As shown in Table  3 , the mean CPY across all countries included in the dataset (defined as the world mean values) was 7. 73 Mg DM ha -1 with a coefficient of variation (CV%) of 48%; mean HI was 0. 45 (CV%= 22%), mean CPN was 1. 32% (CV% =23%), and mean CRN was 0. 73% (CV%=39%). The region that had the greatest mean CPY was North America (10. 5 Mg DM ha -1 , CV%=27%), with the Africa region having the lowest mean CPY (3. 71 Mg DM ha -1 , CV% =47%). The region with the greatest CPN was West Asia (1. 7% DM as N, CV% =14%), while the lowest mean CPN was observed in Oceania (1. 13% DM as N, CV% = 12%). West Asia had the greatest CRN (1. 03% DM as N, CV% =1%), while the lowest mean CRN was in Latin America (0. 45% DM as N, CV% = 31%). However, it should be noted that the mean values are representative across all treatments in the data provided and may not represent averages for the typical farm management in each region.
Methods for testing prediction accuracy of models The data were randomly split into two subsets with 80% of data apportioned to a 'training' dataset, and the remaining 20% apportioned to a 'testing' dataset. For predictions using linear mixed-effects models, the lme4 package (version 1. 1-27. 1) of R was applied to the models listed in Table 2 (using the 80% of data set aside for training the model) to create predictions for the 20% of the data that was set aside for testing. For predictions using random forest the "randomForest" package  (version 4. 6-14)  in R was applied to the 80% training dataset with the 'trained' random forest model then applied to the test dataset. Predictions were compared with the actual (measured) values using linear regression analysis to determine the coefficient of determination (R 2 ).
Harvest index CPY was the best determinant of HI, when used in a (quadratic) linear mixed-effects model, or when used with Ypot (Table  4 ). In each table the 'best' model (based on AIC) is indicated for each sub-selection of the dataset. For HI, the Nakagawa conditional R 2 values (referred herein to as R 2 values) were up to 0. 57 across the range of models. Three linear mixed-effects HI models were chosen for use as a predictor model (see models in bold in Table  4 ) based on how parsimonious the models were (based on AIC) and their R 2 values. The model which had a log-transformation of CPY was chosen, despite the fact that R 2 values cannot be estimated for log-transformed models. It is important to note that the addition of fertilizer application rate information as an independent variable did not improve the AIC or R 2 values for the CPY models, indicating fertilizer application rate information was not good for inclusion in a prediction model of HI.
, 2016). On the other hand, from a practical and applied perspective, the random forest method has proven its ability to handle larger datasets and to improve predictions over time with a wide range of variables, and as more data become available  (Jiang et al. , 2021). If one is only interested in prediction accuracy, and the flexibility of adding new data or other variables to improve predictions, then random forest offers a feasible solution. However, if one wants to better understand the underlying causative factors that contribute to the predictions then the linear mixed-effects model could be the preferred method, despite its lower prediction accuracy. Although results of this study are based on maize, the methods described in this study can equally be applied to other crops and nutrients, subject to availability of data.