dataesr/scientific-paragraphs-categorization huggingface dataset:
This study addressed the challenge of automating a specific novel image recognition task, namely predicting stool consistency from a FIGURE 3. Bubble plots depicting agreement between participants' and the 2 independent researchers' scoring of photos across the 7 BITSS types. Bubble sizes were directly proportional to the number of score matches (agreement). The percentage of matches per BITSS type along the xaxis is depicted using a color gradient. A darker red color represents a higher percentage of scored photos among total images scored by participants at a given BITSS score (a score on the x-axis), whereas a darker blue color represents a lower percentage. BITSS ¼ Bristol Infant and Toddler Stool Scale. smartphone photo. This presented 2 major constraints. The first was a relatively small dataset of only 2687 photos, compared with hundreds of thousands of images typically used to train deep learning models from the ground up, such as inception models trained on the ImageNet dataset  (18).
The results of our pilot study show that automated assessment of stool consistency from smartphone photos is feasible and yields moderately high agreement with human raters. This method enables the quick and efficient re-analysis of complete datasets (eg, from clinical studies), which would not be possible with manual methods. For future use, the existing database of our study could readily be expanded with photos submitted for automated classification. Model performance could also be improved with an expanded photo database. It would also be of interest to validate the model by comparing its performance with that of expert clinicians. Importantly, this study also illustrates possibilities for using smartphones to rapidly generate and annotate photo datasets relevant for clinical use. Standardized assessments could be recorded and tracked easily over time, to examine the interplay of nutrition and stool consistency in large population samples.
database of more than 1 million images, and re-trained the classification layer to recognize the BITSS score from a stool photo. We modified an initial workflow (available at the TensorFlow for Poets git repository (19)) that uses an open source python library (TensorFlow, v. 1. 71) to perform k-fold crossvalidation (where k ¼ 10), maximizing the use of the limited training dataset. Each step used k-1 folds from the training set, reading the associated layer values and feeding them into the classification layer. At each step, the model's prediction was compared with the final researcher's BITSS score, and the results of this comparison were used to update the final layer's weights through backpropagation.
The customized model (poobao_v1. 1) based on Mobilenet_v1 was trained on 2478 labeled photos chosen by random sampling, and a holdout set of 209 photos was used to assess final model performance. The code based on the TensorFlow for Poets script using Python (2. 7. 12) and TensorFlow (v. 1. 71) is available at https:// github. com/poobao/stool_consistency. The repository contains the updated re-training script used to train the Poobao v1. 1 model.
( 18 ) 18 database of more than 1 million images, and re-trained the classification layer to recognize the BITSS score from a stool photo. We modified an initial workflow (available at the TensorFlow for Poets git repository (19)) that uses an open source python library (TensorFlow, v. 1. 71) to perform k-fold crossvalidation (where k ¼ 10), maximizing the use of the limited training dataset. Each step used k-1 folds from the training set, reading the associated layer values and feeding them into the classification layer. At each step, the model's prediction was compared with the final researcher's BITSS score, and the results of this comparison were used to update the final layer's weights through backpropagation.
MODIS LAI/FPAR are stage 3 standard land data products, which can be obtained free of charge from the Land Processes Distributed Active Archive Center (LP DAAC https://ladsweb. modaps. eosdis. nasa. gov/search/). They have undergone iterative updates from collection 4 (C4) to collection 6 (C6). C6 products have a spatial resolution of 500 m and a temporal resolution of 4 (MCD) or 8 (MOD and MYD) days. The MODIS official algorithm retrieves LAI/FPAR with a lookup table (LUT) inversion strategy based on the theory of three-dimensional radiative transfer (3D RT) and stochastic radiative transfer (SRT)  [19] [20] [21]. A detailed description of the MODIS LAI/FPAR algorithm can be found in references  [1, 22, 23]. The validity and reliability of the MODIS LAI/F-PAR products have been empirically validated  [2, 16, 20, 21, [24] [25] [26] [27] [28].
Therefore, this paper sought to fill this gap by developing a bibliometric analysis to summarize the progress of this research area over the past two decades and present an objective history development, applications, and research hotspots of the MODIS LAI/FPAR products. Based on literature from the Web of Science (WOS) core database and funding data from the retrieved literature, with the help of CiteSpace  [54]  and HistCite  [55]  software, we drew partnership maps of countries and institutions, a discipline map, a keyword co-occurrence map, and a timeline view of the MODIS LAI/FPAR products. We used statistical metrics, social network analysis (SNA), burst detection, and cluster analysis. Modularity and Betweenness Centrality Degree (BCD) were used as indicators of SNA, and burst strength was used to measure the burst citation of a publication.
Materials and Methods This study was divided into four parts: data collection, database construction, data analysis, and results interpretation (Figure  1 ). In the process of data collection, the literature data was retrieved from the WOS core database. The literature data were carefully and manually screened. We imported the "clean data" into HistCite  [55]  and CiteSpace  [54]  software to establish a local literature database. In HistCite and CiteSpace, we conducted publication volume analysis, cluster atlas analysis, burst citation analysis, and timeline view analysis. Finally, based on the results obtained in the previous steps, we interpreted the types of publications, discipline involvement, cocitations, and research hotspots.
Search Strategy and Data Sources. We selected articles and reviews from the core database in WOS. The search formula is "WOS: TS = MODIS LAI OR TS = MODIS "LEAF AREA INDEX" OR TS= MOD15 OR TS = MCD15 OR TS = MYD15 OR TS =MODIS FPAR) AND Languages: (English) AND Types: (Article OR Review). " The search period was between January 1, 1995, and July 15, 2020. A total of 1498 publications were retrieved. In the literature prereading and retrieval step, we found that many publications only mentioned the MODIS LAI/F-PAR data in the introduction or mentioned previous studies that used MODIS LAI/FPAR data instead of the literature itself using the data. Also, there were some publications that used MODIS data for LAI inversion and with no MODIS LAI/FPAR products for comparison. However, this paper only concerns about studies that used MODIS LAI/FPAR datasets or algorithms.
Therefore, in order to avoid the impact of data mixing on the analysis results, we carefully cleaned the filtered data by reading the abstracts, data use, and results descriptions. After elaborated data cleaning, 905 publications were left. Among these publications, 2020's (32 papers) were not used for trend analysis because the records were not complete for the year when we retrieved the data. The dataset containing these 905 publications can be download from https://github. com/DongxiaoZou/Bibliometrics-Data/blob/main/ download_All. txt.
Bibliometric Methods and Analytical Tools. This paper starts with the trends of funding, basic bibliometric metrics, coauthorship, discipline interactions, hotspots transformation, and algorithm development of the MODIS LAI/FPAR research. The methods and tools used in this paper are as follows. 2. 2. 1. Funding Information Acquisition. We obtained funding data from the retrieved literature data in the "FU" field. Detail funding data is attached to Table  S1    3 Journal of Remote Sensing  [55]. The basic statistics in this paper included number of publications by year, institute, country, and journal. These descriptive statistics draw an overall picture of the research using MODIS LAI/FPAR or its algorithms.
Core Research Country/Institution and Collaboration Mining. To analyze coauthor networks, we conducted a social network analysis (SNA). Known as a type of structural analysis, SNA is a quantitative method that considers the interdependence of individuals. It can intuitively display the overall structure of the network, the position of individuals in the network, and the relationship with other individuals  [56]. SNA is widely used in data mining, knowledge management, data visualization, information dissemination, and others  [57]. In a network, every node represents a country or an institution; an edge between two nodes represents a coauthorship between the two countries or two institutions. The thicker the edge, the stronger their collaboration relationship, which means that their coauthored publications are more numerous.
With time on the vertical axis and cluster on the horizontal axis, the documents in each cluster were sorted by publication time to provide a timeline view. Frequency directly indicates the citation counts of one publication. In this case, BCD measures the significance of a publication to other research. We also used burst detection to monitor the prominent literature. The algorithm for detecting emergence in Cite-Space is Kleinberg's burst monitoring algorithm  [63]. The algorithm aims to perform explosive monitoring of data streams by means of text data mining and can extract meaningful structural points from huge datasets, identifying those that have led to high growth of related topics over a period  [63]. In this article, this structural point is expressed as an explosive citation in a certain period, which drives a research hotspot and a rapid development of a subfield of literature.
Funding Analysis. Detailed foundation data is attached to Table  S1  in supplementary materials. Generally, we found that the number of funding reported from the literature of China grants greatly exceeded that of other countries (Figure  2 ). The reason for this phenomenon may be related to the fact that China started relatively late in this research area, and the government paid more and more attention to scientific research in the 21st century. The peak of China in 2009 may be connected to the releasing of a new collection of the MODIS LAI/FPAR products (C5). The decline in investment after this peak may be caused by the maturity of the instruments, algorithms, and applications. With a slight fluctuation, the funding trend of the US had been generally decreasing. The trends in other countries were not obvious.
Cocitation Analysis (1) Cluster Analysis. In cocitation clustering, some publications were "recalled" back to the clusters by CiteSpace automatically since they shared citation relationships with other publications in our dataset. The collection contained 834 publications according to clusters generated settings in 2. 2. 5, i. e. , selecting the top 60 most cited rather than all the publications for each year in our collection to generate the cluster map. The cluster labels were generated from the built-in algorithms  [70, 90, 91]  of CiteSpace. We applied the above three algorithms to the title, abstract, and keyword of the literature. We selected the top 50 most cited or occurred items from each slice to generate reference tags. Optimal tags on the right in Figure  8  were selected based on the labels generated by the above steps.
In this cluster, Myneni et al. had a seven-year outbreak with a burst strength of 9. 8  [72]. This paper proposed an algorithm for the estimation of LAI and FAPAR from atmospherically corrected NDVI observations  [72]. Although it did not directly use MODIS LAI/FPAR data, it provided an algorithm basis for the MODIS LAI/FPAR products' production. Knyazikhin et al. inversed the radiative transfer model with a LUT  [73, 74]. He retrieved LAI and FPAR from canopy reflectance data provided by the MISR and MODIS instruments. These studies have laid the research foundation for countless scholars who study radiation transmission and LAI retrieval. The paper by Myneni et al. has the strongest burst strength of all the burst literature  [1]. Its citation burst lasted from 2003 to 2010, and the outbreak lasted seven years.
In 2011, Mu et al. showed an 11. 7 strength  [86]. This paper identified problems in the ET algorithm in Mu et al. [31] , and improved the earlier algorithm. The authors improved the MODIS global terrestrial evapotranspiration algorithm with MOD15A2 FPAR as vegetation cover fraction data to partition the net radiation between the canopy and the soil surface. This improved MODIS ET algorithm was submitted to NASA and the dataset was updated through 2010  Every publication is published at the beginning of the blue line. The red segments represent the explosive citation period; one segment represents one year. A thicker line indicates a higher burst strength. The burst literature is highlighted in the timeline map with red nodes. 12 Journal of Remote Sensing Other clusters show additional research applications based on the MODIS LAI/FPAR products.
As the models improved enough, applications more closely connected to the nature of LAI and FPAR have appeared, and some algorithms for these applications have been developed. For a period, LAI/FPAR models and application algorithms have been perfected, and new applications have been derived. Afterward, with the tools and instruments established, there has been a gradual shift to holistic studies, algorithms upgrades, and MODIS LAI/FPAR product upgrades. As the data demands increased, researchers focused on data quality and generating LAI products with long time series, better temporal continuity, and spatial completeness. In this process, validations of algorithms and products prevailed. Some researchers worked on validation to improve the applicability of the data and to makes the accuracy explicit, which laid down a basis for the following research.
Following the keyword time zone view and tracing the variation of the keywords in the publications, we can find that the research derived from the algorithms is more and more extensive. Similar to the publication timeline view of Figure  8 , there is no clear delimitation between clusters in Figure  11. Because of the citing relationship, some publications not in our original dataset were added automatically by CiteSpace in this map, too. Adding those publications, the publication time is from 1986 to 2020, which is shown as the color bar from purple to yellow. Figure  11  contains the clusters with the closest cocitation relationship. The sporadic clusters around this main body were omitted to ensure the conciseness of the figure; so, the serial number of clusters in Figure  11  is discontinuous.
Similar studies and methods can be applied for other global moderate resolution LAI products, e. g. , GEOV * /GLASS/GLOBMAP, in order to derive an overall idea of the life cycle of the global LAI products. LAI algorithms, data evaluation and validation, and agricultural production assessment were high-frequency research hotspots related to MODIS LAI/FPAR. Broadening the research field both in disciplines and study areas, improving the algorithm, and breaking the existing difficulties will be the trend of future research. First, validation is a longlasting topic that shows a high correlation with the release of new data collections. Time series validation over LAI/F-PAR  [28]  and validation of LAI/FPAR with its application  [92, 93]  are being paid more and more attention. Validation research in different areas of terrain and vegetation coverage is always of significance.
) also seized more research attention. Thirdly, whether for simply pursuing algorithm improvement or applying LAI/FPAR as input to calculate other variables, the accuracy of LAI/FPAR products is positively correlated with the accuracy of the research. Therefore, algorithm research on LAI/FPAR will be a research hotspot for a period to come. At the same time, as we entered the big data era, with the rapid development of 15 Journal of Remote Sensing machine learning and artificial intelligence, algorithm research combined with machine learning may become another trend in this subresearch field. Further, the life of the MODIS sensor has far exceeded its designed specification. With the advancement of new sensors in multiple dimensions of time resolution, spatial resolution, and spectrum, it is also urgent to develop algorithms suitable for specific sensors.
Developing new algorithms is both a challenge and an opportunity for this area. Developing algorithms to better understand and model the scale dependence of LAI/F-PAR estimation using current data such as Sentinel-2/Sentinel-3 and Landsat could help to improve the data archives from medium resolution sensors. Another challenge is to ensure the long-term continuity of the valuable Earth System Data Records (ESDRs), NASA, and NOAA, and the military jointly launched the JPSS-VIIRS (JPSS: Joint Polar Satellite System, VIIRS: Visible Infrared Imaging Radiometer Suite) projects. The VIIRS instrument was designed with a strong MODIS heritage. The development of a continuous, consistent, and well-characterized VIIRS LAI/FPAR dataset is critical to continue the MODIS time series  [33, [108] [109] [110].
Some studies evaluated the VIIRS LAI/FPAR products through comparisons with the MODIS LAI/FPAR products in terms of both spatial and temporal patterns  [16, 33]. In this context, the new LAI/FPAR dataset will provide impetus to new research. The consistency and heritage of VIIRS LAI/FPAR and MODIS LAI/FPAR will keep the series products alive and provide valuable datasets for the scientific community. However, we recognize some limitations in this paper that mainly results from the research peculiarity and the limitations of the analysis tools. Even though we have reduced the data error by using a precision retrieval strategy and artificial data cleaning, there are still some "error publications" recalled by the analysis tool. Because they were highly cited by other papers in our dataset, these "error publications" were classified as the data that meet our needs.
These publications will not influence the statistical trends but will disturb the research trend analysis to some degree. Also, there might be some documents that have not been retrieved. We used the "TS" (means topic) as the field tag to retrieve papers in our search criteria. This tag could search the field of title, abstract, author keywords, and keywords plus within a record. As long as one of the search terms appearing in any of the above fields, the paper would be recorded in the result, whereas when the terms we used did not show up in any of the above fields but show up in the paper's main body, and the "needed paper" would escape from searching. Of course, as the "error publications" mentioned above, some of these omissive "right publications" could be "recalled" to our dataset in the same way.
Recs represents the literature count published in the journal. Total Local Citation Score (TLCS) represents the cited times in the collection of retrieved literature (905). Total Global Citation Score (TGCS) represents the cited times in the whole Web of Science (WOS) database. Impact Factor (IF) is a fiveyear IF from the Journal Citation Reports (JCR) of 2019. h-index is from Google Scholar Metrics, which means there are h publications with at least h citations in this journal, and it is not influenced by extreme values such as an average [64].
Acknowledgments This work was supported by the  National Natural Science Foundation of China  [grant number  41901298 ], the  Open Fund of State Key Laboratory of Remote Sensing Science  [grant number  OFSLRSS201924 ], the  Open Research Fund of Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences  [grant number  2018LDE002 ], and the  Fundamental Research Funds for the Central Universities  [grant number  2652018031 ].
104 * 104 Recs represents the literature count published in the journal. Total Local Citation Score (TLCS) represents the cited times in the collection of retrieved literature (905). Total Global Citation Score (TGCS) represents the cited times in the whole Web of Science (WOS) database. Impact Factor (IF) is a fiveyear IF from the Journal Citation Reports (JCR) of 2019. h-index is from Google Scholar Metrics, which means there are h publications with at least h citations in this journal, and it is not influenced by extreme values such as an average [64].
in supplementary materials. By visualizing the annual amount of money and number of funding, we obtained the variations of research period of different countries as a reference. Data collection Retrieve strategy Retrieve literature Clean data Output Database construction Import into Remove Build database duplicates database Data analysis Statistics Social network analysis Burst detection Cluster Timeline view Funding trend analysis Co-authorship Disciplines Hotspots Algorithm Literature analysis analysis analysis analysis trend analysis Result interpretation 2. 2. 2. Basic Bibliometric Metric Analysis. Based on biblio- metrics, this paper conducted data analysis on the literature related to the MODIS LAI/FPAR. The analysis of basic met- rics was conducted with CiteSpace [54] and HistCite software.
In this set of studies, the MODIS LAI/FPAR products were mainly used for primary productivity estimation. The cluster label implies that the studies were related to monitoring ecosystem dynamics with GPP retrieved from MODIS LAI/FPAR products. Additionally, inputting different LAI datasets into an ecosystem model to estimate GPP (or ET) can also be a way to validate the quality of different LAI products[92,93]. sionalMODIS products for four sites in North America, evaluated both the MODIS land cover product quality and MODIS LAI product quality in 2003[76]. In the following year, Fensholt et al. evaluated the MODIS LAI/FPAR and the relation between FAPAR and NDVI in a semiarid environ-ment using in situ measurements [79]. In the same year, Wang et al. evaluated the MODIS LAI algorithm at a coniferous forest site in Finland [78].
conducting inferences at a low computational cost, without preliminary selection of the relevant components of the ABC summary statistics, and bypassing the derivation of ABC tolerance levels. We have implemented a set of RF algorithms to process inferences using simulated data sets generated from an extended version of the population genetic simulator implemented in DIYABC v2. 1. 0. The resulting computer package, named DIYABC Random Forest v1. 0, integrates two functionalities into a user-friendly interface: the simulation under custom evolutionary scenarios of different types of molecular data (microsatellites, DNA sequences or SNPs) and RF treatments including statistical tools to evaluate the power and accuracy of inferences. We illustrate the functionalities of DIYABC Random Forest v1.
0 for both scenario choice and parameter estimation through the analysis of pseudo-observed and real data sets corresponding to pool-sequencing and individual-sequencing SNP data sets. Because of the properties inherent to the implemented RF methods and the large feature vector (including various summary statistics and their linear combinations) available for SNP data, DIYABC Random Forest v1. 0 can efficiently contribute to the analysis of large SNP data sets to make inferences about complex population genetic histories.