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. 2021 Sep 17;11(20):13920–13929. doi: 10.1002/ece3.8098

pyResearchInsights—An open‐source Python package for scientific text analysis

Sarthak J Shetty 1, Vijay Ramesh 2,✉
PMCID: PMC8525079  PMID: 34707828

Abstract

  1. With an increasing number of scientific articles published each year, there is a need to synthesize and obtain insights across ever‐growing volumes of literature. Here, we present pyResearchInsights, a novel open‐source automated content analysis package that can be used to analyze scientific abstracts within a natural language processing framework.

  2. The package collects abstracts from scientific repositories, identifies topics of research discussed in these abstracts, and presents interactive concept maps to visualize these research topics. To showcase the utilities of this package, we present two examples, specific to the field of ecology and conservation biology.

  3. First, we demonstrate the end‐to‐end functionality of the package by presenting topics of research discussed in 1,131 abstracts pertaining to birds of the Tropical Andes. Our results suggest that a large proportion of avian research in this biodiversity hotspot pertains to species distributions, climate change, and plant ecology.

  4. Second, we retrieved and analyzed 22,561 abstracts across eight journals in the field of conservation biology to identify twelve global topics of conservation research. Our analysis shows that conservation policy and landscape ecology are focal topics of research. We further examined how these conservation‐associated research topics varied across five biodiversity hotspots.

  5. Lastly, we compared the utilities of this package with existing tools that carry out automated content analysis, and we show that our open‐source package has wider functionality and provides end‐to‐end utilities that seldom exist across other tools.

Keywords: automated content analysis, exploratory analysis, natural language processing

With an increasing number of scientific articles being published each year, there is a need to synthesize and obtain insights across large volumes of literature. Hence, we developed pyResearchInsights, a novel open‐source, modular, end‐to‐end automated content analysis package that can be used to analyze scientific abstracts within a natural language processing framework.

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1. INTRODUCTION

Keeping track of conceptual and methodological developments in any scientific discipline is imperative to advance research. An exponential growth in published scientific literature has made it extremely difficult to keep track of scientific advancements (Roll et al., 2018). Within the field of ecology, we have observed a twofold increase in published literature over the last decade (Nunez‐Mir et al., 2016). As the volume of academic literature grows rapidly each year, it is difficult to methodically analyze and synthesize the extent of knowledge on multiple topics (Ferreira et al., 2016; Tejeda‐Lorente et al., 2014). Hence, there arises a need for automated tools that can analyze large volumes of academic literature.

Automated content analysis (ACA) tools are a suite of statistical analysis tools that can be used to identify the thematic composition of large volumes of text (Boumans & Trilling, 2016; Nunez‐Mir et al., 2016; Stockwell et al., 2009). Valuable insights with respect to the overall frequency of words and their relationships with other words, along with broad topics of research, can be ascertained using ACA tools and thereby serve as a crucial aid during the preliminary stages of exploratory research (Nunez‐Mir et al., 2016). For example, Fisher et al. (2011) used a combination of text analysis and Google Maps to analyze the spatial coverage of coral reef research. Similarly, Dyer (2015) used text mining and natural language processing to examine the content of manuscripts associated with the field of landscape genetics. Existing ACA tools are available on a subscription basis and have been used across publications ranging from topics of research in disease ecology and forestry research to analysis of global trends in ecological research (Han & Ostfeld, 2019; Nunez‐Mir et al., 2016; McCallen et al., 2019). However, many of these tools are expensive and inaccessible to a large portion of the scientific community, especially those from the global south (Reidpath & Allotey, 2020).

Here, we present pyResearchInsights, an open‐source end‐to‐end automated content analysis package, that (a) collects scientific abstracts, (b) cleans the texts collected, (c) performs ACA, and (d) presents interactive visualizations. Our package consists of five modular components: a Scraper (to collect published scientific abstracts, given a set of search words), a Cleaner (to get rid of formatting errors), an Analyzer (to measure the frequency of specific words), a natural language processing (NLP) engine (to perform topic modeling on the abstracts), and a Visualizer (to present the topic modeling results) (Figure 1). While ACA can be performed with existing packages, there is a lack of a truly open‐source end‐to‐end tool that can analyze scientific texts in this manner, without wrangling with and integrating multiple libraries and dependencies. The functionality of the package and each of its modules is presented in the case studies below.

FIGURE 1.

FIGURE 1

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A schematic of the pyResearchInsights pipeline is shown here. Scraper: This component collects publication abstracts from Springer containing the search words provided by the user. The user input to the Scraper is the search words to be queried from Springer. Cleaner: This component cleans the corpus of text retrieved and rids it of special characters and poor formatting. The input to the Cleaner is the raw abstracts collected by the Scraper. Analyzer: This component measures the temporal frequency of words occurring across the abstracts database. The input to the Analyzer is the cleaned abstracts generated by the Cleaner. NLP Engine: This component trains an LDA model using the words within the abstracts collected to identify research topics. The user can provide their own text files as input to the NLP Engine and analyze research topics, instead of relying on the Scraper to generate these text files. Visualizer: This component presents the topic modeling results in the form of LDA visualizations and frequency and weight charts. The input to the Visualizer is the cleaned, tokenized abstracts from the NLP Engine. Note: The LDA charts and bar plots presented above are for representation purposes only

2. CASE STUDY A: TOPICS OF AVIAN RESEARCH IN THE TROPICAL ANDES

In this case study, we illustrate the components of the pyResearchInsights pipeline by showcasing topics of research pertaining to a specific geographic area. We chose the example of avian research in the Tropical Andes because of the high diversity of bird species documented in the Neotropics, especially along the mountain ranges of the Andes (Quintero & Jetz, 2018). Since pyResearchInsights is open‐sourced and made available on pip, users can run the package on modern cloud environments such as Google Colab and Jupyter, leveraging powerful computational resources available on these platforms. In order to demonstrate this capability of pyResearchInsights ‐‐ the retrieval of publications, analysis, and visualization of topics presented in this case study was carried out entirely on a Google Colab notebook that can be accessed here: https://colab.research.google.com/drive/1gZjOKr5pfwVMuxCSaGYw20ldFpV4gVws?usp=sharing. We advise users to utilize the package in a Google Colab environment to ensure there are no dependency conflicts while installing pyResearchInsights on their local machines. Advanced users, however, can install the package on their local machines as well.

The process of ACA begins by supplying a list of search words. Here, we supplied the search words—“Tropical Andes Birds” to the Scraper component of the package, which uses the search words to collect a set of 1,131 abstracts from Springer (https://link.springer.com/). The Scraper collects only journal abstracts from Springer containing the exact search words provided by the user. Users can also query search words as Boolean strings in order to return abstracts containing specific combinations of search words. The Scraper component is solely an interface between the user and Springer for retrieving papers, analysis, and topic modeling. For example, if the search words “Tropical Andes Birds” are keyed into the package for abstract retrieval, abstracts containing all three words, “tropical”, “andes,” and “birds,” are retrieved for analysis by pyResearchInsights.

This module utilizes the Beautiful Soup (Richardson, 2007) and Urllib (urllib, 2008) open‐source Python web‐scraping packages to interface with Springer. In addition, the Scraper collects only publicly available data such as abstracts, author names, and date of publication (Fiesler et al., 2020). Furthermore, the data collected are not redistributed and used solely for the purposes of topic modeling and exploratory visualization.



searchwords = “Tropical Andes Birds”



abstracts_log_name, status_logger_name =


pre_processing(searchwords)



scraper_main(searchwords, abstracts_log_name, status_logger_name

abstracts_log_name is a time‐stamped folder that contains all the abstracts scraped during the session. The status_logger logs the functions executed during the code run for debugging in case of early termination of the code.

The Cleaner is then used to remove stop words, special characters, and symbols from the abstracts. The NLTK library (Bird et al., 2009) in Python has a list of stop words that are used to prune the corpus collected by the Scraper. This list of stop words can be expanded as well as we have done in Line 48 of the Cleaner script to improve the quality of the topic modeling results. Through analysis of the data retrieved from the Scraper, we noticed that the special characters that appear in texts collected by the package appear predominantly with the forward slash (“\”). Hence, we specifically searched for this special character while cleaning the corpus and got rid of strings containing it to ensure a clean corpus. The user can remove additional special characters from the corpus collected by modifying the dirty_elements list in Line 16 of the Cleaner.py script of the package. Consider the word “black\‐backed,” which is treated as a single word (since there's a hyphen connected “black\” and “backed”). When the Cleaner encounters the “\” while analyzing “black\‐backed” it removes the word from the corpus. The user can tweak this cleaning operation in Line 59 of the Cleaner script. Such errors occur due to poor formatting in manuscripts and do not hamper the performance of the NLP Engine significantly.



cleaner_main(abstracts_log_name, status_logger_name)

The cleaned corpus is passed through the Analyzer that generates a Pandas (McKinney, 2010) DataFrame to store the frequency of occurrence of various words in the abstracts in a.CSV file, which can be utilized by the user for further analysis. This DataFrame is not necessarily utilized by other modules of pyResearchInsights.



analyzer_main(abstracts_log_name, status_logger_name)

The cleaned corpus of text is also provided to the NLP Engine, which utilizes gensim (Řehůřek, & Sojka, 2010) and spaCy (Honnibal & Johnson, 2015) packages to train a Latent Dirichlet Allocation (LDA) language model (Blei, 2012; Blei et al., 2003) on bigrams and trigrams generated from the corpus (Tan et al., 2002 and Lafferty et al., 1992). An n‐gram is essentially a sequence of n‐words from a given sample of text. For example, consider the text “Tropical Andes is a biodiversity hotspot.” This text can be broken down into unigrams such as “tropical,” “andes,” “biodiversity,” or bigrams such as “tropical andes,” “biodiversity hotspot,” or trigrams such as “tropical andes biodiversity” or more.

First, the LDA model breaks the cleaned corpus of text into n‐grams as described above. Second, the model analyzes the frequency of each n‐gram word present in the corpus and assigns a weight. This weight is assigned on the basis of the frequency of occurrence of an n‐gram word and its co‐occurrence with other n‐gram words (Blei et al., 2003). During this weight assignment process, certain combinations of n‐gram words display high semantic associations (i.e., those words that closely occur together throughout the corpus of text). Such words are clustered together by the LDA model under the same topic, whereas those combinations of words that display poor semantic associations are weeded out (Blei et al., 2003). In addition, commonly used stop words are also removed from the corpus by the gensim package. This list of stop words can be modified by the user in the NLP Engine script. The user can also vary the number of topics generated by passing an argument to the NLP Engine, as shown below.



nlp_engine_main(abstracts_log_name, num_topics = 12,


status_logger_name)

Finally, this LDA model is passed to the Visualizer which generates a pyLDAvis (Mabey, 2015) visualization of the topic clusters (Figure 2). The user can now make a formal inference of the topic clusters by analyzing the frequency and weights of words within a topic. However, the assignment of topic labels by computer systems is an open‐ended research question in the field of natural language processing (Han Lau et al., 2011). Users can utilize their domain knowledge to assign labels to each topic by analyzing the words under each topic (Khandkar, 2009). For instance, exploratory analysis of the words and the topics of avian research across the Tropical Andes suggests that a number of publications are associated with climate change (Topic 3) and plant ecology (Topic 5) (Figure 2 and Figure 3).

FIGURE 2.

FIGURE 2

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The topics are presented using interactive concept maps as shown here. Topics are represented as circles on the left‐hand panel of the pyLDAvis visualization (intertopic distance map), where the area of the topic circles is proportional to their relative prevalence in the corpus (Sievert & Shirley, 2014; Mabey, 2015). On the right‐hand side of the pyLDAvis visualization are the words belonging to various topics. Clicking a word from this panel presents the distribution of that word among the topics. Clicking on a topic from the left‐hand panel presents the words belonging to that topic. When a sufficiently large corpus of text is provided to the LDA Model, the first few topics generated are general ideas discussed across the documents in the corpus

FIGURE 3.

FIGURE 3

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The Visualizer also generates word count and weight charts for each topic. The Y‐axes on the left and right represent the word count and weight, respectively, for each of the words on the X‐axis

3. CASE STUDY B: TOPICS OF CONSERVATION RESEARCH ACROSS BIODIVERSITY HOTSPOTS

In this case study, we used pyResearchInsights to identify topics of research within the broad domain of conservation biology and analyzed how these conservation‐associated research topics varied across biodiversity hotspots.

A total of 22,561 abstracts were downloaded (as of July 2019) from eight journals in the field of conservation biology, which span a wide range of literature associated with conservation biology (Table 1). We hypothesize that published literature in these journals will be representative of global conservation research topics. We used the Cleaner and Analyzer to remove special characters and obtain the frequency of words, respectively, across the abstracts.

TABLE 1.

Conservation science journals and number of abstracts scrapped

Journal Name Number of Abstracts
Conservation Biology 4,991
Biological Conservation 5,073
Biodiversity and Conservation 4,811
Environmental Conservation 5,350
Global Ecology and Conservation 730
Journal for Nature Conservation 789
Perspectives in Ecology and Conservation 95
Conservation Letters 722
Total 22,561
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To collect the corpus of abstracts for this case study, the authors utilized Columbia University's subscription to the 8 conservation journals mentioned since these abstract data are not publicly available. A total of 22,561 abstracts were collected across these 8 journals as of July 2019.

Using the NLP Engine, an LDA model was trained on these abstracts to generate 12 topics of global conservation research. The clustering of topics by language models is judged on the basis of “coherence” scores (He et al., 2008). A topic is said to be coherent if the words within the topic are semantically similar to each other. We calculated the coherence scores for our language model while varying the number of topics generated by the model from 2 to 40 topics. From this analysis (Figure 4), we found that the coherence score for our model in this case study peaks at around 12 topics, beyond which the performance of the model is imperceptible and generally leads to loss of coherence between topics. The 12 topics of research generated by the NLP Engine were presented using the Visualizer.

FIGURE 4.

FIGURE 4

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From our analysis, we found that the coherence score for our model on our dataset peaks at around 12 topics, as observed in the above chart. Beyond 12 topics, the improvement to the coherence score of the model is imperceptible and leads to loss of coherence between topics

The labeled topics and the top ten words clustered under these topics are presented in Table 2. Our results revealed that a large proportion of conservation‐associated research is centered around conservation policy, followed by landscape ecology, vegetation management, and other topics. Such a result gives the researcher an intuitive understanding of what abstracts and topics of research they might be interested in focusing on for further research and analysis.

TABLE 2.

The 12 Topics generated by the LDA model, labeled using open coding (Khandkar, 2009)

Topic 1

Conservation Policy

Topic 2

Landscape Ecology

Topic 3

Vegetation Management

Topic 4

Forest Ecology

Topic 5

Ecological Methods

Topic 6

Biodiversity Protection
Biodiversity Habitat Site Forest Datum Conservation
Conservation Landscape Vegetation Tree Model Protect
Management Richness Plant Composition Base Biodiversity
Research Diversity Grassland Stand Distribution Threaten
Knowledge Scale Cover Density Method Reserve
Approach Factor Type Fragment Information Threat
Policy Abundance Management Disturbance Estimate Include
Process Pattern Community Plantation Analysis Area
Develop Variable Composition Ant Predict Identify
Resource Influence Diversity Edge Result Priority

Topic 7

Climate Change

Topic 8

Population Dynamics

Topic 9

Species Diversity

Topic 10

Humans and Wildlife

Topic 11

Plant Diversity

Topic 12

Aquatic Ecology
Change Population Number Humans Plant Community
Increase Bird Diversity Land Distribution Soil
Impact Habitat Sample Local Genetic Ecosystem
Time Island Total Activity Region Water
Year Native Record Resource Diversity River
Effect Mammal Taxa Wildlife Range Environment
Decline Large Area Wild Show Wetland
Loss Density Region Traditional Pattern Fish
Climate change Individual Group Management Flora Invertebrate
Seed Size Richness People Endemic Coastal
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To expand on this case study, we examined how these 12 topics of conservation research varied across biodiversity hotspots. Biodiversity hotspots are areas of extraordinary biodiversity and yet are under tremendous anthropogenic pressures of habitat loss and climate change (Myers et al., 2000; Newbold et al., 2015). Obtaining an understanding of distribution of conservation research topics across hotspots can inform research and associated conservation efforts.

We used the Scraper to collect abstracts pertaining to five biodiversity hotspots from Springer, using the search words “Hotspot Name” and “Conservation.” We then analyzed how the 12 global conservation research topics, that were previously identified, varied across five biodiversity hotspots: Western Ghats‐Sri Lanka, East Melanesian Islands, Eastern Afromontane Forests, Eastern Himalayas, and the Tropical Andes (these hot spots were chosen in a random manner for the purposes of this case study).

We used the trained LDA model to identify how the 12 global conservation research topics are distributed in the hot spot‐specific literature. This distribution of topics across the five biodiversity hotspots is presented in Figure 5. We arrive at these topic distributions by using getdocument_topics() in gensim.

FIGURE 5.

FIGURE 5

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Distribution of global conservation research topics across five biodiversity hotspots

We observed that the prominent topics of conservation research across the Eastern Himalayas are landscape ecology (26.9%) and conservation policy (22.6%). On the other hand, a majority of conservation‐associated research in the Eastern Afromontane biodiversity hotspot were related to conservation policy (13.3%) and plant diversity (12.6%) (Figure 5).

4. COMPARISON WITH EXISTING AUTOMATED CONTENT ANALYSIS TOOLS

A key motivation behind developing pyResearchInsights is to provide researchers an open‐source package that performs automated content analysis (ACA). Existing tools and packages are often either pay‐walled or only offer a smaller set of functions. pyResearchInsights is the first open‐source end‐to‐end tool that offers a wide range of functions which can either be used for independent analyses or as part of the analysis showcased by the tool. To further illustrate the benefits of this tool over existing ACA tools, we compare several parameters of our tool with those present in existing tools. To do so, we carried out a thorough search of existing R and Python packages and showcased those tools that are comparable to pyResearchInsights in their utilities and those tools that are often used in the field of natural language processing.

The criteria for comparison of these existing packages with pyResearchInsights included the following: (a) end‐to‐end capability to collect and analyze scientific abstracts. Existing packages are fairly limited in their scraping capabilities, collecting publications from university repositories, or requiring periodic subscriptions. (b) Closed or open‐source availability of codebase. The availability of the codebase dictates whether users can change package settings and tailor process parameters to their liking. (c) Modularity of the package that would allow users to use specific functions for their use cases, instead of using the package as a whole. Through our search for existing tools that provide end‐to‐end analysis of scientific publications, we came across none that offered the end‐to‐end capability of pyResearchInsights, coupled with its open‐source codebase and modular functionality. Therefore, we compared pyResearchInsights to packages that offer some of these functionalities, either partially or fully in some cases.

As evident from Table 3, there are few tools that provide a complete end‐to‐end solution to the problem of analyzing large volumes of scientific texts. While a few packages offer some of the functionalities of pyResearchInsights, they lack other features. For example, packages such as Paperai (Mazetti, 2020) and EDA‐NLP (Bonhart, 2020) offer comprehensive scraping and visualization capabilities to users but lack a thorough cleaner module, because of which their visualizations are not completely representative of the input corpus of text. In contrast, the Cleaner and Analyzer modules of pyResearchInsights ensure commonly used stop words and symbols are weeded out of the corpus, prior to visualization.

TABLE 3.

Comparison of features of pyResearchInsights with those of existing open‐source and pay‐walled ACA tools: While plenty of packages offer some of the features of pyResearchInsights, there are none that are completely modular, open‐source and analyze scientific abstracts in an end‐to‐end manner

Package/Tool Environment Modular Open‐source End‐to‐End Analysis Jupyter Notebook Support Google Colab Support Pricing Model
ACA R ✓ ✓ x ✓ ✓ Free
quanteda R ✓ ✓ Δ ✓ ✓ Free
topicmodels R ✓ ✓ Δ ✓ ✓ Free
tm R x ✓ x ✓ ✓ Free
openNLP R ✓ ✓ Δ ✓ ✓ Free
R. TeMiS R ✓ ✓ Δ ✓ ✓ Free
litsearchr R ✓ ✓ Δ ✓ ✓ Free
EDA‐NLP Python x ✓ x ✓ ✓ Free
Paperai Python x ✓ ✓ ✓ ✓ Free
Leximancer ‐ x x Δ x x Subscription
pyResearchInsights Python ✓ ✓ ✓ ✓ ✓ Free
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The Δ symbol implies partial functionality of a given feature or parameter. End‐to‐end here implies the ability of a single package to collect texts from repositories, clean, analyze, and present their topic modeling results.

Packages such as tm (Feinerer et al., 2008), openNLP (Hornik, 2014), and R. TeMiS (Bouchet‐Valat & Bastin, 2018) provide users with extensive text mining capabilities such as tokenization, clustering, and analysis, but lack the interactive visualization techniques available in pyResearchInsights. These visualizations allow even novice users to explore and infer areas of research generated from the text files supplied. Such visualization techniques are lacking in quanteda (Benoit et al., 2018) as well, which allows users to clean, tokenize, and present only the temporal frequency of words in the corpus, similar in functionality to the Cleaner and Analyzer modules of our package, but lack the interactive pyLDAvis visualizations offered by pyResearchInsights’ NLP Engine and Visualizer. A package like litsearchr (Grames et al., 2019), on the other hand, offers the user a reproducible search strategy using text‐mining and word co‐occurrence networks to identify important terms to include in a search strategy, when conducting a systematic literature review. While litsearchr is particularly useful, it lacks a scraper module that can access scientific repositories such as pyResearchInsights and is limited to scraping only open‐access thesis repositories.

Pay‐walled tools such as Leximancer (Smith & Humphreys, 2006) have robust cleaning and visualization capabilities but lack abstract retrieval modules. Furthermore, such tools are available on a subscription basis, charging individual users approximately $560 per year, thereby making it inaccessible to researchers from less endowed programs. Given that these tools are closed‐source, users are limited by the kind of environments that they can be run on. A key advantage of open‐source R and Python packages is the ability to run them on Google Colab and Jupyter Notebooks, leveraging freely available powerful cloud computing for analysis, which is lacking in the case of closed‐source packages.

Although we compared features of pyResearchInsights with those of existing tools, it is important to note that our tool has its own set of limitations, despite offering a wide range of utilities. For example, we currently lack settings to fine‐tune the parameters of the content analysis process, such as pruning the dataset of duplicate texts and identifying and merging of synonymic words encountered during the training process. These settings prevent topic models from being trained on duplicate texts that are cluttered with repeated words and ensure that the topic models are representative of the texts provided by the user. While these options do not yet appear in our package, our open‐source alternative offers comparable features that exist in other tools, pay‐walled or otherwise.

5. CONCLUSIONS

pyResearchInsights is meant to be used as an aid to traditional literature survey techniques, carried out during the exploratory stages of research. The topic modeling results generated by the package should not be cited as conclusive proof of the significance or insignificance of a particular topic, relative to other topics in the literature. Lastly, the language used in scientific publications is linguistically nuanced, and hence, the results generated by the package should be interpreted accordingly.

CONFLICT OF INTEREST

None declared.

AUTHOR CONTRIBUTIONS

Sarthak J. Shetty: Data curation (lead); formal analysis (lead); investigation (lead); methodology (lead); writing–original draft (lead); writing–review and editing (equal). Vijay Ramesh: Conceptualization (lead); methodology (equal); project administration (lead); supervision (lead); writing–original draft (supporting); writing–review and editing (equal).

OPEN RESEARCH BADGES

This article has been awarded <Open Materials, Open Data, Preregistered Research Designs> Badges. All materials and data are publicly accessible via the Open Science Framework at URL: https://github.com/SarthakJShetty/pyResearchInsights; https://zenodo.org/record/4518917#.YSjkp45KhPY; Codebook: https://colab.research.google.com/drive/1gZjOKr5pfwVMuxCSaGYw20ldFpV4gVws?usp=sharing.

ACKNOWLEDGMENTS

We would like to thank Prof. Ruth DeFries and the Department of Ecology, Evolution and Environmental Biology, Columbia University, for funding this study. We would like to thank Dr. Barbara Han, who was kind enough to share the data and results from a previous study. This data helped us compare the results from our open‐source package with an existing pay‐walled tool. We would like to thank Dr. Vishwesha Guttal for providing access to computational facilities funded by DST‐FIST and DBT‐IISc Partnership Programme. Dr. Guttal also provided critical feedback on this manuscript. We acknowledge feedback from Dr. Anand Osuri, whose invaluable suggestions and feedback helped frame the study. We would like to sincerely thank an anonymous reviewer and Dr. Eliza Grames for providing a detailed review of our tool. By incorporating their suggestions, we are hopeful that our tool will be useful to a wide audience. Lastly, we acknowledge the feedback provided by the associate editor and Dr. Jennifer Firn, the editor‐in‐chief of Ecology and Evolution.

Shetty, S. J. , & Ramesh, V. (2021). pyResearchInsights—An open‐source Python package for scientific text analysis. Ecology and Evolution, 11, 13920–13929. 10.1002/ece3.8098

DATA AVAILABILITY STATEMENT

The topic modeling visualizations presented in each case study are generated as HTML files and can be viewed in a web browser. These visualizations can be found here: https://doi.org/10.6084/m9.figshare.13637171. The retrieval of publications and generation of topic modeling results presented in Case Study A were carried out end‐to‐end on a Google Colab Notebook that can be viewed here: https://colab.research.google.com/drive/1gZjOKr5pfwVMuxCSaGYw20ldFpV4gVws?usp=sharing.

REFERENCES

  1. Benoit, K. , Watanabe, K. , Wang, H. , Nulty, P. , Obeng, A. , Müller, S. , & Matsuo, A. (2018). quanteda: An R package for the quantitative analysis of textual data. Journal of Open Source Software, 3(30), 774. 10.21105/joss.00774 [DOI] [Google Scholar]
  2. Bird, S. , Loper, E. , & Klein, E. (2009). Natural language processing with python. O’Reilly Media Inc. 10.5555/1717171 [DOI] [Google Scholar]
  3. Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77–84. 10.1145/2133806.2133826 [DOI] [Google Scholar]
  4. Blei, D. M. , Ng, A. Y. , & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022. 10.5555/944919.944937 [DOI] [Google Scholar]
  5. Bonhart, O. (2020). EDA‐NLP‐PubMed‐and‐bioRxiv: Rapid analysis of scientific papers from bioRxiv and PubMed. Retrieved from https://github.com/OldBonhart/EDA‐NLP‐PubMed‐and‐bioRxiv
  6. Bouchet‐Valat, M. , & Bastin, G. (2018). RcmdrPlugin.temis: Graphical Integrated Text Mining Solution. R package: Retrieved from https://cran.r‐project.org/web/packages/RcmdrPlugin.temis/index.html [Google Scholar]
  7. Boumans, J. W. , & Trilling, D. (2016). Taking stock of the toolkit: An overview of relevant automated content analysis approaches and techniques for digital journalism scholars. Digital Journalism, 4(1), 8–23. 10.1080/21670811.2015.1096598 [DOI] [Google Scholar]
  8. Dyer, R. J. (2015). Is there such a thing as landscape genetics? Molecular Ecology, 24, 3518–3528. 10.1111/mec.13249 [DOI] [PubMed] [Google Scholar]
  9. Feinerer, I. , Hornik, K. , & Meyer, D. (2008). Text mining infrastructure in R. Journal of Statistical Software, 25(5), 1–54. 10.18637/jss.v025.i05 [DOI] [Google Scholar]
  10. Ferreira, C. , Bastille‐Rousseau, G. , Bennett, A. M. , Ellington, E. H. , Terwissen, C. , Austin, C. , Borlestean, A. , Boudreau, M. R. , Chan, K. , Forsythe, A. , Hossie, T. J. , Landolt, K. , Longhi, J. , Otis, J.‐A. , Peers, M. J. L. , Rae, J. , Seguin, J. , Watt, C. , Wehtje, M. , & Murray, D. L. (2016). The evolution of peer review as a basis for scientific publication: Directional selection towards a robust discipline? Biological Reviews, 91(3), 597–610. 10.1111/brv.12185 [DOI] [PubMed] [Google Scholar]
  11. Fiesler, C. , Beard, N. , & Keegan, B. C. (2020). No Robots, Spiders, or Scrapers: Legal and Ethical Regulation of Data Collection Methods in Social Media Terms of Service. In Proceedings of the Fourteenth International AAAI Conference on Web and Social Media (p. 14). [Google Scholar]
  12. Fisher, R. , Radford, B. T. , Knowlton, N. , Brainard, R. E. , Michaelis, F. B. , & Caley, M. J. (2011). Global mismatch between research effort and conservation needs of tropical coral reefs. Conservation Letters, 4, 64–72. 10.1111/j.1755-263X.2010.00146.x [DOI] [Google Scholar]
  13. Grames, E. M. , Stillman, A. N. , Tingley, M. W. , & Elphick, C. S. (2019). An automated approach to identifying search terms for systematic reviews using keyword co‐occurrence networks. Methods in Ecology and Evolution, 10(10), 1645–1654. 10.1111/2041-210X.13268 [DOI] [Google Scholar]
  14. Han, B. A. , & Ostfeld, R. S. (2019). Topic modeling of major research themes in disease ecology of mammals. Journal of Mammalogy, 100(3), 1008–1018. 10.1093/jmammal/gyy174 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Han Lau, J. , Grieser, K. , Newman, D. , & Baldwin, T. (2011). Automatic labelling of topic models. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (pp. 1536–1545). [Google Scholar]
  16. He, J. , Larson, M. , & De Rijke, M. (2008). Using coherence‐based measures to predict query difficulty. European Conference on Information Retrieval, Springer, Berlin, Heidelberg, 689–694. 10.1007/978-3-540-78646-7_80 [DOI] [Google Scholar]
  17. Honnibal, M. , & Johnson, M. (2015). An improved non‐monotonic transition system for dependency parsing. Proceedings of the 2015 Conference On Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Lisbon, Portugal (pp. 1373–1378). 10.18653/v1/D15-1162.8 [DOI] [Google Scholar]
  18. Hornik, K. (2014). openNLP: Apache opennlp tools interface. R Package, Retrieved from https://cran.r‐project.org/web/packages/openNLP/index.html [Google Scholar]
  19. Khandkar, S. H. (2009). Open coding (p. 23). University of Calgary. [Google Scholar]
  20. Lafferty, J. , Sleator, D. , & Temperley, D. (1992). Grammatical trigrams: A probabilistic model of link grammar (p. 56). School of Computer Science, Carnegie Mellon. [Google Scholar]
  21. Mabey, B. (2015). pyldavis. pyPI Package. Retrieved from https://pypi.org/project/pyLDAvis/ [Google Scholar]
  22. Mazetti, D. (2020). Paperai. NeuML package. Retrieved from https://github.com/neuml/paperai [Google Scholar]
  23. McCallen, E. , Knott, J. , Nunez‐Mir, G. , Taylor, B. , Jo, I. , & Fei, S. (2019). Trends in ecology: Shifts in ecological research themes over the past four decades. Frontiers in Ecology and the Environment, 17(2), 109–116. 10.1002/fee.1993 [DOI] [Google Scholar]
  24. McKinney, W. (2010). Data Structures for Statistical Computing in Python. In Proceedings of the 9th Python in Science Conference (pp. 56–61). [Google Scholar]
  25. Myers, N. , Mittermeier, R. , Mittermeier, C. , da Fonseca, G. A. B. , & Kent, J. (2000). Biodiversity hotspots for conservation priorities. Nature, 403, 853–858. 10.1038/35002501 [DOI] [PubMed] [Google Scholar]
  26. Newbold, T. , Hudson, L. N. , Hill, S. L. L. , Contu, S. , Lysenko, I. , Senior, R. A. , Börger, L. , Bennett, D. J. , Choimes, A. , Collen, B. , Day, J. , De Palma, A. , Díaz, S. , Echeverria‐Londoño, S. , Edgar, M. J. , Feldman, A. , Garon, M. , Harrison, M. L. K. , Alhusseini, T. , … Purvis, A. (2015). Global effects of land use on local terrestrial biodiversity. Nature, 520, 45–50. 10.1038/nature14324 [DOI] [PubMed] [Google Scholar]
  27. Nunez‐Mir, G. C. , Iannone, B. V. III , Pijanowski, B. C. , Kong, N. , & Fei, S. (2016). Automated content analysis: Addressing the big literature challenge in ecology and evolution. Methods in Ecology and Evolution, 7(11), 1262–1272. 10.1111/2041-210X.12602 [DOI] [Google Scholar]
  28. Roll, U. , Correia, R. A. , Berger‐tal, O. , & Ben‐gurion, M. (2018). Using machine learning to disentangle homonyms in large text corpora. Conservation Biology, 32(3), 716–724. 10.1111/cobi.13044 [DOI] [PubMed] [Google Scholar]
  29. Quintero, I. , & Jetz, W. (2018). Global elevational diversity and diversification of birds. Nature, 555(7695), 246–250. 10.1038/nature25794 [DOI] [PubMed] [Google Scholar]
  30. Řehůřek, R. , & Sojka, P. (2010). Software Framework for Topic Modelling with Large Corpora. In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, ELRA, Valletta, Malta (pp. 45–50). 10.13140/2.1.2393.1847 [DOI] [Google Scholar]
  31. Reidpath, D. , & Allotey, P. (2020). Preserve Global South's research capacity. Science, 368(6492), 725. 10.1126/science.abc2677 [DOI] [PubMed] [Google Scholar]
  32. Richardson, L. (2007). Beautiful Soup. pyPI Package. Retrieved from https://pypi.org/project/beautifulsoup4/ [Google Scholar]
  33. Sievert, C. , & Shirley, K. (2014). Ldavis: A method for visualizing and interpreting topics. In Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces (pp. 63–70). 10.3115/v1/W14-3110 [DOI] [Google Scholar]
  34. Smith, A. E. , & Humphreys, M. S. (2006). Evaluation of unsupervised semantic mapping of natural language with leximancer concept mapping. Behavior Research Methods, 38, 262–279. 10.3758/BF03192778 [DOI] [PubMed] [Google Scholar]
  35. Stockwell, P. , Colomb, R. M. , Smith, A. E. , & Wiles, J. (2009). Use of an automatic content analysis tool: A technique for seeing both local and global scope. International Journal of Human‐Computer Studies, 67(5), 424–436. 10.1016/j.ijhcs.2008.12.001 [DOI] [Google Scholar]
  36. Tan, C. M. , Wang, Y. F. , & Lee, C. D. (2002). The use of bigrams to enhance text categorization. Information Processing & Management, 38(4), 529–546. 10.1016/S0306-4573(01)00045-0 [DOI] [Google Scholar]
  37. Tejeda‐Lorente, Á. , Porcel, C. , Peis, E. , Sanz, R. , & Herrera‐Viedma, E. (2014). A quality based recommender system to disseminate information in a university digital library. Information Sciences, 261, 52–69. 10.1016/j.ins.2013.10.036 [DOI] [Google Scholar]
  38. urllib ‐ URL handling modules . (2008). Python 3 Package. Retrieved from https://docs.python.org/3/library/urllib.html [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The topic modeling visualizations presented in each case study are generated as HTML files and can be viewed in a web browser. These visualizations can be found here: https://doi.org/10.6084/m9.figshare.13637171. The retrieval of publications and generation of topic modeling results presented in Case Study A were carried out end‐to‐end on a Google Colab Notebook that can be viewed here: https://colab.research.google.com/drive/1gZjOKr5pfwVMuxCSaGYw20ldFpV4gVws?usp=sharing.

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