dataesr/scientific-paragraphs-categorization huggingface dataset:
Material and methods This section presents a detailed description of the data structure and the adopted methodology of this study. As mentioned in section 1, this study's primary data comprises social media public posts on EV-related communications. It is then evaluated based on the Political, Economic, Social, Technological, Legal and Environmental (PESTLE) framework to evaluate intersectionality and extract latent attributes using computational social sciences to contextualise EV policymaking in the United States. The computational social sciences methodology consisted of a concurrent application of social network analysis (SNA) and machine learning-based topic modelling (TM) using the Latent Dirichlet Allocation (LDA) algorithm. TM was used to complement SNA and improve its interpretability.
Data source This study's primary data are public posts in the English language with keywords 'EV/electric vehicle/electric cars' in the United States regional boundary and their total interactions on Facebook between the 1st of January and the October 31, 2020. The data was accessed through Facebook's CrowdTangle web-based platform. CrowdTangle is a public insight tool that tracks public content interactions from Facebook pages and groups, verified profiles. In addition, it does not include paid ads unless they began as organic, nonpaid posts that were subsequently "boosted" using Facebook's advertising tools. It does not include private accounts or posts made visible only to specific groups of followers  [60]. Posts from almost 13,764 public pages that referred to electric vehicle (EV) were collected for this study between the ten months.
The CrowdTangle team set the ten-month historical data limit. These posts ranged from news-media pages to public groups. Since each 'page' can contain multiple posts on related topics, we consolidated the posts with the same page ID to avoid redundancy errors in analysis. Therefore, the total number of posts extracted was around 36,000. While analysing these 36,000 posts, we were mindful of the limitations of Facebook posts as a data source that affect the generalisability of study results. First, specific demographic and socio-economic segments are not well represented in the dataset, especially people from lowincome backgrounds and lower education due to the Internet access divide  [61]. Second, not all with Internet access use Facebook and other social media sites. Third, it is unclear who can effectively use the Internet for information and knowledge and who cannot  [62].
Fourth, social media data presents the potential for self-selection bias. To counter the first three points and improve the generalisability of the results, we only use aggregated interaction data on public posts. This aggregated level provided a zoomed-out view of public communication on EV on the Facebook platform without weighing an individual's sentiment. We kept a wide bandwidth for PESTLE categorisation of the posts by randomly matching 40% of the post content with the categorical outcomes of the bibliometric analysis presented in section 2. 1. It added a degree of randomisation in sample selection that reduced self-selection bias (as recommended by Ref. [63] ). An 'interaction' can be the number of reactions, comments and shares made on a post. This metric does not include clicks on links or the post itself  [60].
We have used the 'total interaction' metric, the individual post links and the link description for constructing the nodes and edges for SNA (discussed in detail in section 3. 2). The data descriptive of the number of interactions on the EV-related posts per month is illustrated in Fig. 3. In Fig. 3a , the peaks represent average page interactions in a specific month. For most months, the peaks are in the range of 1-100 interactions. The central tendency of the dataset and the outliers are illustrated in Fig. 3b. The pages on EV-related communications from news agencies, public groups, academic communities, non-governmental organisations, and activist groups were in text. Thus, the cumulative text corpus extracted from these 36,000 posts was close to 600,000 words/terms. It served as the primary dataset for machine-learning-based topic modelling using LDA (see section 3.
Social network analysis (SNA) For our analysis, the downloaded CrowdTangle public page dataset (see section 3. 1) was categorised into PESTLE sectors (Political, Economic, Social, Technological, Legal and Environmental) based on each page's 'link description'. This categorisation was performed by referring to high-frequency keywords extracted through the bibliometric analysis in section 2. 1. The categorised datasets were cleaned for duplicate entries, and the blank fields were removed. Then, we tabulated the post link, description, identifier and total interactions into an Excel datasheet and converted them into comma-separated value files. Finally, we created nodes and edges datasheet in Excel, with each node representing a 'page' and a 'link description'. In our case, the edges connect the page and the corresponding link description.
Thus, the page became the 'source' in our dataset, and the link description became the 'target' for each PESTLE category. Important definitions regarding SNA are presented in the Appendix. We imported the nodes and edges dataset into Gephi version 0. 9. 2. It is open-source software for graph and network visualisation by automatically calculating modularity and the network diameters such as betweenness centrality and visualising the network structure by adjusting spatialization algorithms  [64]. In this study, modularity was calculated and applied to the nodes to divide them into differentiated community subgroups according to the closeness of their interactions and different colours were applied to distinguish these subgroups visually  [65]. Furthermore, we have used in-degree ranking (see Appendix for definitions) to visualise most frequently shared links in each PESTLE category.
'α' and 'β' are the hyperparameters of the Dirichlet distribution (see eq. (  1 )). A third hyperparameter 'γ' is needed to set the number of topics. We have used a cross-validation metric of perplexity scores  [86]  and the ldatuning (v0. 2. 0) algorithm in R  [87]  to determine the approximate number of topics under each PESTLE categories. Perplexity is a measure of how well a probability model fits a new set of data. It was calculated for cross-validation using the 'perplexity' function in the topicmodels (v0. 2-8) package in R  [88]. It included dividing the data into five subsets, and each subset gets one turn as the validation set and four turns as part of the training set. The ldatuning algorithm  [87]  used an iterative process to best-fit the model using the benchmarking criteria of Arun 2010, CaoJuan2009, Griffiths2004 and Deveaud2014.
The cross-validation results are presented in Appendix. A similar cross-validation approach was adopted by Refs. [76, 77, 79]. The analysis consisted of two steps. The first step was the preprocessing of the text corpus by removing all the stop words (e. g. , articles, such as "a," "an," and "the," and prepositions, such as "of," "by," and "from"), numbers, and punctuation characters and converted the text to lowercase. It also involved removing the inflectional endings of words and converting the grammatical form into its base/dictionary form. This step is usually referred to as lemmatisation in natural language processing  [89]. The second step was the conversation of the lemmatised corpus into document-term-matrix (DTM), using the tidytext (v0. 2. 0) package in R. The DTM was converted as per the tidydata rules presented by Silge and Robinson  [90].
Here, each sentence was treated as a document in the DTM that resulted in (M) unique documents that had w (words) and z (topics) as per eq  [1]. It was created for each PESTLE category. Once the appropriate topic clusters were extracted, the results were presented in a tabulated format with the probability values (β) of each word in each topic cluster. The extracted topics were further analysed and interpreted with the network of communities derived through the ForceAtlas2 algorithm in SNA, as mentioned in section 3. 2. The combined interpretation revealed critical intersectional focus points concerning EV-related crowdsourced data in the U. S. , and implications were drawn for contextualised policymaking.
Social media-based evidence presented here already shows a higher degree of sensitisation through outreach initiatives. It is essential to understand the influential actors of EV adoption at a community level, such that subscription-based and shared ownership schemes can be designed. It was observed that climate action, clean energy and pollution-free attributes of EV over ICEV were extensively interacted within the public posts, indicating that consumers react better to these sentiments. Future consumer policies accounting for such sentiments can influence more EV adoption. CrowdTangle's terms and conditions limited the granularity of the social media data used in this study. While it provided a cross-sectional and novel dataset of aggregated public interactions on EV-related posts, it did not provide any user-specific interactions.
Acknowledgement This study is in parts supported by  Bill & Melinda Gates Foundation  under the grant number [OPP1144] through the  Gates Cambridge Scholarship; Energy Transition Small Grant   2020  by the  Isaac Newton Trust ; and the Winter Fellowship -2020 by the  Michigan Conservative Energy Forum (MiCEF) , awarded to RD at the  University of Cambridge. The authors would like to thank the leadership council of the MiCEF, especially  Edward Rivet II ,  Jim MacInees  and  Denny Duchene  for their valuable comments and support. RD would also like to thank the  International Energy Agency  for hosting him as a visiting scholar during the preparation of this manuscript in their Digitalisation team at the  Energy Efficiency Division. Any findings, suggestion and conclusion presented in this study are that of the authors and do not represent the views of the affiliated organisations or funding bodies.
In the era of big and omics data, good organization, management, and description of experimental data are crucial for achieving high-quality datasets. This, in turn, is essential for the export of robust results, to publish reliable papers, make data more easily available, and unlock the huge potential of data reuse. Lately, more and more journals now require authors to share data and metadata according to the FAIR (Findable, Accessible, Interoperable, Reusable) principles. This work aims to provide a step-by-step guideline for the FAIR data and metadata management specific to grapevine and wine science. In detail, the guidelines include recommendations for the organization of data and metadata regarding (i) meaningful information on experimental design and phenotyping, (ii) sample collection, (iii) sample preparation, (iv) chemotype analysis, (v) data analysis (vi) metabolite annotation, and (vii) basic ontologies.
Very often, therefore, several different protocols need to be integrated to achieve the desired coverage of the metabolome within a single study  [15, 16]. Moreover, the data are presented in a myriad of formats. Data from targeted experiments are quantitative and can be expressed as milliequivalents of multiple reference standards, with reference to the fresh or dry weight, etc. The data from untargeted experiments are the results of a process where the analyzed metabolome is not defined a priori, and this includes several unknown metabolites. Different sample preparation, instrumental analysis, and data analysis protocols will deliver complementary (but not conflicting) datasets and, therefore, possibly slightly different conclusions. This higher complexity requires highly organized data and metadata management, and metabolomic data must be combined with a detailed set of metadata to be correctly read and reused outside the original experiment.
A few are specific to the field of metabolomics (e. g. , MetaboLights and Metabolomics Workbench), whereas others collect data from all the fields (e. g. , figshare). MetaboLights is an open-access, curated database for metabolomics experiments connected with their raw data and associated metadata  [21, 22]. The database is cross-species, cross-technique, and covers metabolite structures and their reference spectra as well as their biological roles, locations, and concentrations. It is part of the ELIXIR infrastructure  [23] , hosted by the European Bioinformatics Institute (EMBL-EBI), and is one of the recommended metabolomics repositories for several leading journals in the field. The Metabolomics Workbench  [24]  is another repository for metabolomics data and metadata, as well as metabolite standards, protocols with tutorials, training, and analysis tools.
The availability of both data and metadata is of the utmost importance in order to be able to adhere to the FAIR principles  [25]  that make data, experiments, and results Findable, Accessible, Interoperable, and Reusable. The goal of this work is to provide all researchers within the grapevine and wine science fields with specific guidelines to implement best practices, improve the quality, availability, and usefulness of the data and the associated metadata, and facilitate the indexing of the produced dataset. A second purpose is encouraging the grapevine and wine science community to follow these guidelines as much as possible and, finally, to harmonize the data and metadata resources into a common, shared, and user-friendly format.
Methods Each study created on public repositories has a unique alphanumeric study ID (e. g. , MTBLS000 or ST0000). The study ID cannot be modified, and it is needed when referencing the study in manuscripts or elsewhere, together with the relevant URL. Please note that the study ID can be used to retrieve the dataset in MetaboLights, but as it is only mentioned in the materials and method section, it is not sufficient to obtain the indexing of the dataset in the Data Citation Index (DCI) of Clarivate Web of Science, which would be desirable in order to connect any published paper to the FAIR data present in the public repository. In order to facilitate the automatic inclusion in the DCI, it is recommended to specifically insert this persistent, resolvable identifier in a citation in the manuscript when the data are published.
Moreover, one or multiple protocols describing the analyses performed are necessary. An example is a protocol for liquid or gas chromatography, followed by a second for mass spectrometry (Table  3 ). Other protocols are related to the steps taken for data transformation and, lastly, a protocol containing how the metabolites were annotated and quantified (Table  4 ). Finally, to adhere to the FAIR principle, the raw data, possibly in an open format, needs to be uploaded and linked to a study with the aim that other researchers can access it.
A set of recommendations on adequately describing a grapevine experiment to guarantee interoperability and standard ontologies between different datasets is based on the international metadata standard for Plant Phenotyping experiments (MIAPPE, www. miappe. org, accessed on 30 October 2021)  [26]. The MIAPPE checklist is a pdf file that lists all the items recommended to describe an experiment, from the general characteristics of an investigation/study to the biological samples proposing descriptors for experimental factors and environmental variables.
Intraspecific name(s) Three field codes might be necessary to identify the exact plant material used in an experiment. Field 1: code for the institution. Please refer to WIEWS codes from the FAO (http://www. fao. org/wiews/en/, accessed on 30 October 2021) or ROR codes (https://ror. org, accessed on 30 October 2021) for research organizations. Field 2: type of plant material. The most commonly used denomination for grapevine material is the variety name. We recommend using a standard name, such as the "prime name" extracted from the VIVC database (http://www. vivc. de, accessed on 30 October 2021). The type of plant material can be classified with (i) the five-digit VIVC code for identified varieties, (ii) "PRO" for genotypes from bi-parental crosses, (iii) "TL" for transgenic lines, (iv) "ESL" for lines regenerated from anthers or somatic tissues, or (v) nothing when the type of plant material is not characterized.
Data pre-processing The original or the converted data are often processed before the statistical analysis. For the MS data, the process might include peak picking, background subtraction, noise reduction, time or m/z filtration, alignment, spectral deconvolution, smoothing, binning, data reduction, filling missing peaks, etc. Report the software and its version used together with the parameters. The most popular software are MZmine, XCMS, MSdial, metaMS, Progenesis QI, and MetAlign.
Field Description Four levels annotation  [18]  This is the most common method used to report the annotation confidence in metabolomics. It includes the following levels of annotation: 1. Identified compounds. A minimum of two independent and orthogonal data relative to an authentic compound analyzed under identical experimental conditions is proposed as necessary to validate non-novel metabolite identifications (e. g. , retention time/index and mass spectrum, retention time and NMR spectrum, accurate mass and tandem MS, accurate mass and isotope pattern, full 1 H and/or 13 C NMR, 2-D NMR spectra). 2. Putatively annotated compounds. This level is applied when the annotation is made without chemical reference standards, based upon physicochemical properties and/or spectral similarity with public/commercial spectral libraries or literature.
Level 5: Exact mass (m/z) can be measured in a sample and be of specific interest for the investigation but lack information to assign even a formula. Screening and nontarget methods allow the tracing of these masses in other investigations, but level 5 indicates that no unequivocal information about the structure or formula exists. It is even possible to record the MS/MS of a level 5 mass and save it as an "unknown" spectrum in a database. This level should only apply to a few masses of specific interest since it would be counterproductive to label all masses in a sample as level 5. Blank measurements should be used to ensure the substance does not arise from sample preparation or measurement.
Results To the best of our knowledge, up to September 2021, we were able to retrieve fourteen publicly available grapevine metabolome datasets  [15, 16, [32] [33] [34] [35] [36] [37] [38] [39]  and seven others related to wine science  [40] [41] [42] [43] [44] [45] [46] , with the majority of such studies deposited in the repository MetaboLights. It is foreseeable that the number will quickly increase in the near future as the application of metabolomics to plant science increases. These guidelines are intended to assist the grapevine and wine metabolomics community in publishing their data in compliance with the FAIR protocol. In MTBLS2876  [32, 47] , there are data and metadata on the most important classes of grapevine leaf metabolites (primary compounds, lipids, phenols, and volatile organic compounds) detected at several time points after artificial inoculation of the oomycete Plasmopara viticola in resistant and susceptible grape varieties.
MTBLS784  [33, 48]  reported a study on the pre-harvest application of methyl jasmonate on Vitis labrusca grapes to verify the interaction with the phenolic compounds content by UPLC-HRMS-QTOF. MTBLS968  [34, 49]  is a study about the accumulation profiles of terpene metabolites in three Muscat table grape cultivars through HS-SPME-GCMS. The following three datasets (MTBLS898, MTBLS982, MTBLS984)  [16, [50] [51] [52]  comprehensively characterized the metabolic response of Merlot grape berries exposed to water deficit at different developmental stages by UPLC-MS/MS, SPME-GC-MS, and UPLC-DAD assays for the analysis of phenolic, volatiles and carotenoids compounds. The MTBLS392 study  [35, 53]  aimed to explore the core microbiota and metabolome of Vitis vinifera L. cv. Corvina grapes and musts during different vintages, by two-dimensional gas chromatography time-of-flight mass spectrometry.
Studies MTBLS897, MTBLS892, and MTBLS889  [15, [54] [55] [56]  represent the datasets of broad research investigating the impact of water deficit on the secondary metabolism of white grapes using large scale metabolites analyses in a season characterized by prolonged drought. The study with ID MTBLS346  [36, 57]  investigated the downstream metabolic changes of Silcora and Thompson seedless grape cultivars when genetically modified through the insertion of the DefH9-iaaM gene into their genome. The effect of the genetic modification upon the grape metabolome was evaluated by 1 H-NMR and exploratory data analysis and chemometrics methods. MTBLS209  [37, 58]  is a comparative study of the metabolome of the three grape berry tissues (skin, pulp, seeds) of three American Vitis species (Vitis cinerea, Vitis californica, Vitis arizonica) together with four interspecific hybrids, and seven Vitis vinifera cultivars.
The MTBLS137 dataset  [44, 65]  reported the UPLC-QTof MS untargeted analysis of Vitis vinifera L. leaves, collected in Italy and Germany from two fungus-resistant grape varieties (PIWI), Regent and Phoenix. It is an illustrative example of the use of the MetaDB pipeline. MetaDB was developed in order to combine with a user-friendly web-based, different bioinformatic tool used in metabolomics, which takes care of (a) metadata organization, (b) creation of randomized sequences including QC sample, (c) data quality evaluation, (d) data storage organization, (e) data analysis, and (f) submission to public repositories. MTBLS55  [45, 66]  is a study about the influence of storage condition and duration on the "chemical age" of red wines. And finally, study ST000006  [46, 67] , deposited at Metabolomics Workbench repository, reported the data of a GC-TOF analysis on seventeen white wines for investigating the chemical basis for mouthfeel properties in wine.
Discussion Although limited in number, the above-described public datasets cover a big part of the technological and scientific themes of the grapevine and wine community. Their most common aim was to investigate the cultivar/geographical differentiation, and second, to study the abiotic and biotic stresses. However, considering the scientific literature, there are still many fields that are widely present in the literature but are not covered by these datasets, from viticulture practices (rootstock, training systems, soil, leaf removal, etc. ) to winemaking techniques (enzymes/microorganism/coadjuvant, physical technologies, special wines, etc. ) and shelf life (packaging, exposure to oxygen, light, temperature, and the evolution during storage, etc. ). It is positive to see that the above-described datasets retrieved from public repositories were produced using an array of diverse, modern, or traditional instrumental/analytical techniques, such as gas/liquid chromatography, various mass spectrometers, nuclear magnetic resonance, and diode array.
Engaging in completely open science may be unrealistic and even undesirable for many researchers. However, the use of FAIR repositories is becoming mandatory by many funding agencies. We urge researchers working in viti-oenology to learn and adopt existing, robust tools that are immediately helpful and important to them in order to improve the visibility, quotability, and preservation of their data, which will be safely archived and ready to reuse. And we all know, and it is especially true in the era of big data, that reproducibility and transparency are key tenets of the scientific process that requires reliability and generality of results and methods  [68]. Of course, making a dataset publicly available is not enough since data and metadata management according to the FAIR principles is equally important.