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Danilo Bellini - GT6: Workshop sobre uso dos dados da base de dados SciELO

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A base de dados SciELO armazena e disponibiliza registros de metadados e de textos completos de aproximadamente 700 mil artigos de diferentes disciplinas e idiomas, originadas das coleções nacionais de periódicos da Rede SciELO de 16 países. Os registros de metadados contém os campos de dados das referências bibliográficas dos artigos (título, autor, periódico fonte, data, resumo e palavras chaves) e das referências dos documentos citados nos textos artigos (título, autor, fonte, data) que são disponibilizados pelo SciELO em acesso aberto com atribuição CC-BY.

Ao mesmo tempo, os metadados de todos os artigos indexados no SciELO, publicados nos últimos 10 anos, estão também armazenados e disponibilizados na base de dados do SciELO Citation Index na plataforma WoS e na base de dados Dimensions. Da mesma, forma estão disponíveis nas bases de dados Scopus e WoS os metadados dos artigos dos periódicos indexados por esta base.

O SciELO constitui portanto uma notável fonte de dados bibliométricos para o estudo das produções científicas dos países da Rede SciELO.

O escopo deste grupo de trabalho / workshop é compartilhar metodologias e tecnologias de acesso e exploração dos dados da base de dados SciELO, com destaque para a introdução ao uso de técnicas de ciência de dados com o concurso da linguagem Python, o acesso aos dados com a linguagem R com vistas a análises estatísticas e técnicas de acesso e uso dos dados das bases de dados SciELO Citation Index e Dimensions.

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Danilo Bellini - GT6: Workshop sobre uso dos dados da base de dados SciELO

  1. 1. WG6 Report SciELO 20 Years WG6 presentation title: Lecturer/speaker/rapporteur: Group coordinator: Executive secretary: Workshop on the use of datafrom the SciELO database Danilo J. S. Bellini Gustavo Fonseca Carolina Tanigushi WG6 date: Report date: 2018-09-24 2018-09-25 Venue: Tivoli Mofarrej São Paulo Hotel WG6 Report S c iE L O 20 Years 1 / 8
  2. 2. Summary During the workshop, these had been done: Brief introduction to the data analysis processes, emphasizing data access, data cleaning and exploratory data analysis Hands-on examples using Python and R, with emphasis in the Pandas library resources, showing how data munging, normalization, data visualization and other data analysis processes can beperformed Explanation of data analyses previously performed on data coming from SciELO and external sources Most of the time was spent on exploratory data analysis, interpretation of descriptive statistics and visualization. Some highlights of what had been studied in more depth include: Hirsch index Google Scholar’s h5-index and h5-median Calculation from raw Dimensions’ data SCImagoJR’s H index Field Citation Ratio (FCR) from Dimensions WG6 Report S c iE L O 20 Years 2 / 8
  3. 3. Tools Two programming languages had been used, besides several: Python IPython / Jupyter Notebook Python built-in modules (csv, s t a t i s t i c s , urllib, json, glob, os, re, collections, itertools, pprint) numpy pandas matplotlib seaborn openpyxl, to open XLSX files scipy.stats, to calculate the Pearson’s correlation coefficient NetworkX, a graph manipulation library including an API to draw graphs with matplotlib R R built-in modules (base, utils, stats, graphics) R Studio, an IDE for R, for creating R Markdown notebooks dplyr, to perform grouping operations similar to SQL’s GROUP BYand Pandas’ DataFrame.groupby WG6 Report S c iE L O 20 Years 3 / 8
  4. 4. Data sources Several analyses were performed before the workshop, whose processes and results were part of it. The data that had been studied in every analysis performed during and before the workshop came from thesesources: SciELO’s JSON APIs (RESTful) from: ArticleMeta, to get journal metadata Ratchet, to get access data SciELO’s articlemeta Python library, an alternative way to access the ArticleMeta API Reports from the SciELO Analytics SciELO Citation Index entries from the Web of Science Dimensions data regarding two journals: Nauplius and Brazilian Journal of Plant Physiology SCImagoJR’s CSV with all SJR and H indices for 2017 Scopus’ XLSX with all the data they make available WG6 Report S c iE L O 20 Years 4 / 8
  5. 5. Introduction to the analysis Besides: Identifying which collections have data in SciELO analytics (all certified and development collections, besides the active independent collections) Downloading all SciELO analytics reports Evaluating if the network reports have everything from the remaining reports Simplifying the column names Normalizing/cleaning the ISSN when dealing with multiple collections Normalizing the thematic area (dealing with unfilled data) It had been seenhow to plot data, with a strong emphasis on data interpretation and multiple types of plots (bar plots, line plots, box-and-whisker plots, heat maps, scatterplots, etc.), as well as subplot splitting/grouping. WG6 Report S c iE L O 20 Years 5 / 8
  6. 6. Previously prepared analyses Number of indexed journals in the SciELO network Deindexing reason in the SciELO Brazil collection Evaluating the daily access in the SciELO Brazil collection Three indices in Scopus 2017: CiteScore, SNIP and SJR SCImago Journal Rank in 2017, including SJR and H index FCR and H index in Dimensions Google Scholar indices Languages of research articles in SciELO Brazil, by thematic area, document publication year and journal indexing year Citations in the SciELO CI Proportion of Brazil in affiliation institutions in research articles from journals in the SciELO Brazil collection WG6 Report S c iE L O 20 Years 6 / 8
  7. 7. Results The proportion of Brazilian affiliations of research articles in the SciELO Brazil collection is decreasing Most citations of research articles in the SciELO network come from documents/journals that aren’t in the SciELO network. For research articles written in English, 76%of the received citations comes from documents external to the SciELO network The normalization step when calculating the FCR and its non-standard average calculation can easily push down the result (e.g. a journal with 10 documents receiving 15 citations and 3 documents with zero citations would have an average of citations of less than 7.5, before this number gets normalized by the year and field of research), making it an index best fit to evaluate older journals that are no longer publishing We should always look for the mathematics that defines an index, as that evaluation can already give us some insights regarding its bias towards some documents/journals WG6 Report S c iE L O 20 Years 7 / 8
  8. 8. Results Scopus indices should be taken with care: mixing the data from all countries makes it hard to compare data from SciELO and from other journals In SciELO Brazil, 95% of the journals marked asdeceased were actually just renamed to a new journal title/ISSN Matching data with external sources is difficult without a common standardized index such as the ISSN and DOI 0.8% of SciELO journals are marked in Scopus as not open, which seem to be an issue regarding Scopus data WG6 Report S c iE L O 20 Years 8 / 8

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