dataesr/scientific-paragraphs-categorization huggingface dataset:
Plant Materials and Fungal Pathogen Materials The materials used for GWAS were collected from all over the world, including spring, winter, and semiwinter ecotype accessions, and were cultivated under natural growing conditions. The phenotype data were collected in 2019. B. napus cultivar line ZS11, 888-5, M083, Westar, ZY821, and the 222 B. napus natural varieties all came from our laboratory and were planted in our experimental eld in Yangluo, Wuhan, Hubei province, China. Every year the natural varieties were planted in three replicates and developed by self-pollination, all the lines had been puri ed for many years. The S. sclerotiorum strain 1980 was preserved in our laboratory, and the mycelia were cultured on potato dextrose agar medium at 24°C in the dark. The advancing edge of growing mycelia was selected to make the mycelia plug for inoculation.
Identi cation of BaMLOs, BoMLOs, and BnMLOs Sequences of the 15 AtMLO proteins were downloaded from the Arabidopsis genome database (https://www. arabidopsis. org/) and were used as queries for the identi cation of MLO genes in B. rapa, B. oleracea, and B. napus. The genome information of B. napus, B. rapa, and B. oleracea were obtained from the BRAD (Brassicaceae Database) database (http://brassicadb. cn/#/) [51]. The HMMER3. 0 (http://www. hmmer. org/) was used to search for MLO genes (E value was set to 1e-5) [80].
Establishment of Phylogenetic Relationship The initial phylogenetic relationship tree was established through MLO protein sequences from Arabidopsis, B. rapa, B. oleracea, and B. napus using IQ-TREE software (http://www. iqtree. org/). The phylogenetic relationship tree then was beauti ed using EvolView, an online service tool (https://www. evolgenius. info/evolview/#login).
http://gsds. cbi. pku. edu. cn/index. php), an online service tool for gene feature visualization. Transcriptome Analysis in B. napus Cultivar Lines Transcriptome data of 35 different tissues or stages from B. napus cultivar line ZS11 were obtained from BnTIR online database (http://yanglaboratory. hzau. edu. cn/BnTIR). Weatsr and ZY821 were downloaded from another research of our laboratory [55], the GEO le (GSE81545) was downloaded from the National Center for Biotechnology Information (NCBI) database. Transcriptome data of 12 tissues of ZS11 was obtained from NCBI (SRA accession: PRJNA474576) [60]. Transcriptome data of 888-5 and M083 were not published. Clean reads were mapped onto the reference genome of B. napus 'Darmor-bzh' [56]. Heatmap was constructed using FPKM values through TBtools.
( http://gsds. cbi. pku. edu. cn/index. php), an online service tool for gene feature visualization. Transcriptome Analysis in B. napus Cultivar Lines Transcriptome data of 35 different tissues or stages from B. napus cultivar line ZS11 were obtained from BnTIR online database (http://yanglaboratory. hzau. edu. cn/BnTIR). Weatsr and ZY821 were downloaded from another research of our laboratory [55], the GEO le (GSE81545) was downloaded from the National Center for Biotechnology Information (NCBI) database. Transcriptome data of 12 tissues of ZS11 was obtained from NCBI (SRA accession: PRJNA474576) [60]. Transcriptome data of 888-5 and M083 were not published. Clean reads were mapped onto the reference genome of B. napus 'Darmor-bzh' [56]. Heatmap was constructed using FPKM values through TBtools.
Modelling of error estimates in the GRASP algorithm As mentioned in the introduction, in this work we make use of the GRASP (Generalized Retrieval of Atmosphere and Surface Properties) algorithm. It is a rigorous, versatile and open-source algorithm capable of providing information of aerosol properties from the measurements of different instruments and dynamic error estimates  (Dubovik et al. , 2011 (Dubovik et al. , , 2014 (Dubovik et al. , , 2021)). It is a flexible, generalized algorithm that relies on two independent modules, i. e. the forward model and the numerical inversion. The forward model contains the full description of the physical model, including various interactions of electromagnetic solar radiations, such as aerosol scattering, surface reflectance and gaseous absorption.
Aerosol retrieval from sun/sky radiometers alone All the tests and analyses in this study include the spectral observations by the ground-based sun/sky-scanning radiometers. These radiometers were used for more than 30 years by the worldwide AERONET project (https://aeronet. gsfc. nasa. gov, last access: 17 August 2022;  Holben et al. , 1998)  that unities a federation of a large number of ground-based remote sensing aerosol international networks. At present, AERONET observations are widely recognized as being a benchmark validation data set for satellite aerosol retrieval and as source of some unique information about detailed aerosol properties that are used in diverse studies on monitoring and predicting regional and global pollution evolution and climate change. The standard set of AERONET observations includes spectral direct sun measurements and spectral measurements of angular sky radiance obtained from sky scans by the radiometer.
This complementarity is well recognized by the research community, and a large number of joint observational sites with both radiometer and lidar observations have been established in last decade. In these regards, the European ACTRIS (Aerosols, Clouds and Trace gases Research Infrastructure Network) infrastructure (https://www. actris. eu, last access: 12 August 2022) is one of the good examples of networks emphasizing the acquisition of diverse complementary observations at each site. All ACTRIS observational supersites possess both sun/sky radiometric and complex multiwavelength lidar systems. GRASP retrieval has been successfully adapted by  Lopatin et al. (2013)  for processing such combined observations in an algorithm initially known as Generalized Aerosol Retrieval from Radiometer and Lidar Combined data (GARRLiC).
For example, AERONET start to establish so-called hybrid observational scenario during high SZA times  (Giles et al. , 2019). With regard to the performance of the error estimation, the effect of the bias in the sky radiances seems to be well captured by the GRASP error estimates. It should be also noted that we observe an anticorrelation between the radiance bias and the retrieval of the imaginary part of the refractive index. This effect is opposite to the one observed in the case of AOD and with significantly smaller differences. Thus, when the biases are positive (+3 % and +5 %), there is a decrease in the imaginary part of the refractive index. The fact that the value of AOD remains the same and that there is an increase in the value of the scattering is interpreted by the code as a decrease in the aerosol absorption.
The illustrations for aerosol retrieval from all simulated data sets were provided, using both approaches. However, the full statistical analysis  provided only the two conventional retrieval scenarios, i. e. AERONET-like, single-component retrieval from radiometric observations and GRASP bicomponent aerosol retrieval from combinations of radiometer and lidar data. The results of the tests showed that the complete set of aerosol parameters for each aerosol component can be robustly derived with acceptable accuracy in almost all considered situations. The retrieval of bicomponent aerosol was evaluated using radiometer-only simulated measurements and then adding lidar observations. These tests allowed us to observe that, by using the synergy of two instruments, there are some improvements in the retrieval of the aerosol properties of each component of the observed aerosol mixture and in the estimations of the retrieval errors.
pean Union. Tatyana Lapyonok has been supported by the Chemical and Physical Properties of the Atmosphere Project funded by the French National Research Agency through the Programme d'Investissement d'Avenir (contract no. ANR-11-LABX-0 0 05-01), the Regional Council, Hauts-de-France, the European Funds for Regional Economic Development (grant no. URF/1/2180-01-01), and the Combined Radiative and Air Quality Effects of Anthropogenic project. The authors also acknowledge AERONET and LALINET, for the scientific and technical support. We acknowledge the use of imagery from the NASA Worldview application (https: //worldview. earthdata. nasa. gov, last access: 22 July 2022), part of the NASA Earth Observing System Data and Information System (EOSDIS). The authors also would like to acknowledge the NOAA Air Resources Laboratory (ARL), for the provision of the HYS-PLIT transport and dispersion model (https://www.
ready. noaa. gov, last access: 22 July 2022) used in this publication. Financial support. This research has been supported by the European Metrology Programme for Innovation and Research (grant no. 19ENV04 MAPP) and the French National Research Agency (grant no. ANR-ll-LABX-0005-01). Review statement. This paper was edited by Daniel Perez-Ramirez and reviewed by Feng Xu and one anonymous referee.
Acknowledgements. The authors acknowledge the support from the  H2020 Marie Skłodowska-Curie RISE Actions  (grant no. 778349 ), and the authors acknowledge the support from the  European Metrology Program for Innovation and Research (EMPIR)  within the joint research project of  EMPIR 19ENV04 MAPP "Metrology for aerosol optical properties ". The EMPIR is jointly funded by the  EMPIR participating countries within EURAMET  and the Euro-.
Code availability. GRASP is an open-source software that is available, upon registration, from https://access-request. grasp-cloud. com/service/gitlab (last access: 14 August 2022; GRASP Open repository, 2022). Data availability. The data are available upon request to the corresponding author. AERONET data used for this study can be downloaded from NASA website https://aeronet. gsfc. nasa. gov/ (last access: 14 August 2022; NASA, 2022). LALINET data are available upon request.
Prevailing scholarly resource platforms like Google Scholar limit discovery by focussing only on publications, and favouring through their algorithm well-cited papers, English content, and disciplinespecific resources. A limitation in the context of cross-disciplinary and collaborative Open Science, such a service more specifically hinders discovery in the SSH. Characterized by a fragmented landscape, a variety of languages, data types, and outputs, research in the SSH requires services that fully exploit discovery potentialities. Moreover, a survey conducted within the TRIPLE project showed that most SSH researchers use Google Scholar as their starting point, and that they recognise the lack of control they have with this system. Beyond the extension of features and content, transparency is the.
Even common article types such as editorials and book reviews are out of scope 1. This means that while previous studies suggest better coverage for certain disciplines through GS content from disciplines with a high biblio-diversity such as social sciences and the humanities is more difficult to discover and remains sometimes hidden from GS users. It also means that additional output types from today's open science ecosystem, such as images, software, and datasets, cannot be found via GS. As such, GS lacks the ability to produce results that include relevant output and services from higher education institutions. This is a significant limitation because research outcomes beyond scientific publications are playing an increasing role in the context of digitisation of science  (European Commission, 2016).
Amendments from Version 1 The new version has been updated taking into account the reviewers comments. Substantial new paragraphs have been added to section "Why Google Scholar is not a complete discovery platform" and clarifications have been made to the sections "What is discovery?" and "Discoverability crisis issues: Why we need an open and inclusive discovery platform", where Figure  1  has been added. A concluding paragraph has been included as well. Any further responses from the reviewers can be found at the end of the article REVISED 1 See https://scholar. google. com/intl/de/scholar/inclusion. html#content language in which an output was written. This contrast shows how far GS has fallen behind when it comes to equity and transparency. Overall, while GS does seem to offer some marginally better equality than citation databases, it is clear from previous literature that the service presents several pitfalls.
Why Google Scholar is not a complete discovery platform With the increased availability of materials in digital form, identifying specific resources has become at the same time both easier and more difficult. While clearly scholars have many more opportunities to access material than before the advent of digital technologies, as  Cahoy (2018)  says 'Yet as the Web (and information technology in general) has matured, finding and retrieving information has become more fragmented. Users previously had two choices not so long ago-get it from the shelf or find it electronically via a database'. Traditionally, the main location for the identification of scholarly resources was the library, with its collection and catalogues. However, new digital technologies have brought changes to the way these operate  (Favaro & Hoadley, 2014) , and now the main sources for identifying resources have become others.
We have witnessed originally the emergence of citation databases, largely supported by editors, such as Scopus or the Web of Science (WoS). These operate largely by indexing publications. For some authors, these sources still remain "the main sources for citation data"  (Mongeon & Paul-Hus, 2016 ). An important innovation in this area has been, however, the introduction of GS. Unlike citation databases (such as Scopus or WoS), GS is a search engine that operates similarly to Google. According to  Lopez-Cozar et al. (2019)  it 'represents a break from this paradigm. Unlike traditional bibliographic databases, which are selective by nature, Google Scholar parses the entire academic web, indexing every scholarly document it finds regardless of its quality, and doesn't differentiate between peer-reviewed and non-peer-reviewed content'.
There are indeed several studies which have tried to assess the differences between the 'traditional citation databases' and GS for the identification and retrieval of resources (e. g. Harzing & van der Wal, 2008; Ştirbu et al. , 2015). Research has been conducted also to compare GS with open access repositories such as OpenAire and others  (Abad-Garcia et al. , 2018). Generally, there is no consensus on whether GS is a valid alternative to scholarly citation databases. For some authors (e. g. Haddaway et al. , 2015)  GS is not a valid replacement of services like WoS, where the former has been seen as missing relevant literature in the conduction of systematic analysis. Other authors,  (Harzing & Alakangas, 2016)  instead support the notion that GS offers a broader coverage of material than databases.
Notwithstanding the debate on the differences, advantages and disadvantages of GS over other services for conducting systematic literature analysis, it remains the fact that GS has become a main port of call for many literature searches  (Kramer & Bosman, 2016). It is a simple and free to use service and many start their literature search directly from GS. For instance, for  Jensenius et al. (2018)  says 'the advantages of Goggle Scholar stem primarily from its ease of use. '. The question is whether GS can be seen as the main resource for facilitating discovery. While the discussion on the differences in coverage with databases (Scopus, WoS) concentrates largely on the scholarly efficiency, other aspects need to be considered in the discussion. Several authors have compared GS with citation databases in other areas, for example on languages coverage, identification of grey literature, open access, disciplines, and diversity more generally.
Whilst it may be argued that academic databases could provide some edge for systematic literature retrieval, much literature converges on the idea that GS also fares better in this area than the academic databases. For example, GS also offers profiles for users, and the metrics contained therein have been argued to 'offer grounds to challenge unfounded perceptions and prejudices'  (Jensenius et al. , 2018)  that are widespread in research, for example, affecting women or minorities. In terms of language coverage,  Lopez-Cozar et al. (2019)  have shown empirically that 'while the percentage of documents published in English in WoS and Scopus is of 90% and 80% respectively, in Google Scholar the percentage is closer to 50%', concluding that GS offers better representation of languages other than English, when compared with academic databases.
This was also noted previously by Jacsó (  2008 ). An empirical investigation on the coverage of Chinese publications in GS and other citation databases shows that GS coverage is better than other services  (Zhang & Lun, 2019). This edge on the language coverage is of course due to the nature of the service offered by databases, which are products of academic private publishers that largely have publications in English, while GS, as a search engine, harvests the entire web. This also leads to better open access coverage than citation databases, as indeed, GS can also harvest open access versions (e. g. pre-prints) of papers that otherwise are not accessible because they are behind paywalls  (Jensenius et al. , 2018). In addition, GS has agreements with publishers to even gain access to closed access items for which no open access version exists  (López-Cózar et al.
, 2017). Finally, authors have argued that GS seems to offer better coverage of certain disciplines if compared to citation databases, in particular but not only, for social sciences  (Harzing & van der Wal, 2008). Overall, while the literature paints a picture of GS as a service more "equitable" than citation databases, it is difficult to assume that it offers a full and transparent solution. Although offered for free, GS still remains a closed service to a larger extent  (Coiffait, 2019; Kraker et al. , 2021). Indeed, several authors have pointed to the lack of transparency offered about how the service algorithms work, which to a large extent mirrors what happens with the main search engine Google (e. g. Rovira et al. , 2021). Other authors have pointed out the secrecy of the sources, from which GS retrieves the publications; 'There is no information about the publishers whose archive Google is allowed to search, let alone about the specific journals and the host sites covered by Google Scholar'  (Jacsó, 2005).
This notion was supported also by  Harzing & van der Wal (2008). Thus, the lack of openness about the algorithms and the sources used by GS makes this service unlikely to be the exemplar of equality and transparency. Despite the earlier noted better language coverage of GS if compared to citation databases, studies have also shown that GS still remains skewed towards English. This is especially due to the ranking algorithms, which perhaps is one of the components of the service that creates more inequality. The ranking algorithm puts the most cited papers at the top of the search results, and these normally tend to be publications in English: 'the ranking algorithm favors documents in English and handicaps documents written in other languages in searches that produce multilingual results'  (Rovira et al.
These publications tend again to offer a comparison between GS and established citation databases (e. g. Scopus) and pretty much highlight similar issues as those discussed earlier. Gardner and Eng (2005)  (in a perhaps not very recent paper) had shown that differences exist between GS and repositories/databases such as PsycINFO or the Social Sciences Citation Index, for retrieving social sciences literature on a specific topic ("homeschooling"). While GS appeared to offer a large variety of sources in relation to a specific research topic, it also appeared to retrieve publications which possess less academic "currency" than those that can be retrieved with established databases. Of course, the algorithm has changed a lot since this article. And it heavily relies on citations for ranking now.
However,  Prins et al. (2016)  have conducted a similar comparison, however focusing on the use of GS and other databases (such as Scopus) for the conduction of formal assessment/evaluation of research programs, with a focus on humanities (e. g. Anthropology). The authors concluded that GS could be used for such evaluations, but differently from repositories, the material retrieved by GS requires assessing the quality and whether it is reliable. From these two sources, it appears that reflection on the use of GS in SSH does concentrate much on the differences with other established databases in establishing what counts as relevant publications in the SSH broad area. Similarly to what seen earlier, GS does appear to offer a broader variety, thus supporting potentially the retrieval of less main-stream material, which however for purposes of e.
g. evaluation, systematic analysis may be less relevant or anyway requires much work for filtering out what is established content and what is not. This point of difference was raised, with a different spin, also by  Martin-Martin et al. (2019)  which, looking at the use of GS, Scopus and WoS in SSH, pointed that GS offers an approach which is more "inclusive and unsupervised" if compared to the "selective" approach of the databases Scopus and WoS. Indeed, whilst the latter pretty much index mostly journal papers, GS also includes a variety of other sources such as dissertations, book chapters etc. This last contribution thus seems to argue for the approach of GS as being more inclusive, however warning that there is a tradeoff, since we would then not be working with well-curated databases.
A considerable number then consolidate a Google Scholar search by looking at other academic databases. Some academics also use Twitter as an information source. Most issues affect the effective findability (not finding everything that you need when searching, especially difficult in interdisciplinary research, with a silo effect of research being published in specific disciplines which makes it more difficult to discover if the research is not so rigidly defined; differing keyword terms for similar topics used by different disciplines makes it harder to search; language differences when searching -different terms being used in different languages) and reusability (including the impossibility to access full texts, and to export resources in different formats). As part of the interview, interviewees were asked to express their desired features for a new platform to ease their discovery work practices and ultimately facilitate their research work.
A paper, a dataset or a researcher profile may lead us to several other materials. Thus, the keywords need to be broad yet specific -discovery should not imply browsing through an endless list of materials that are only marginally linked to our topic of interest. Moreover, finding what was not expected can mean the access to work in different languages (especially the ones we do not speak or do not normally use in the academic context), broadening the range of perspectives. Through finding by linking together we understand the interconnection between different kinds of resources that would often exist separately on more traditional search platforms. In discovering, more so than in conservative searching, one type of record can easily lead us to another. A dataset might be linked to its author, whose researcher profile will in turn bring the user to a relevant publication.