the appropriate software to use for creating research papers is

• Find and use open research software
• Research tools & techniques
• Digital Researcher Lab

As a researcher, you will likely use a range of tools and software programs to complete your research. You may need to investigate appropriate software to support your research, evaluate or even create software from scratch.

Find and use open research software  introduces you to the idea of open research software (ORS) to support your research tasks and workflows.

• About open software
• Find and assess open research software
• Popular open research tools
• Citing software
• Help and more information

1. About open software

Research software.

Research software includes tools used to conduct research and analysis, and software generated by researchers during the research process. Research software can be defined as: 

"software that includes source code files, algorithms, scripts, computational workflows and executables that were created during the research process or for a research process." Defining Research Software: a controversial discussion  

Research software includes the following: 

• An integral software product from a project 
• Custom algorithms or software central to the paper  
• Code that integrates with and builds on existing software 
• Scripts that automate the execution of a series of tasks in a given run-time environment 
• Executables, models, workflows or containers 
• Software services. 

Open-source and proprietary software

Software can be open source or proprietary. Open-source software allows you to:

• Use the software, usually at no cost 
• Study the code 
• Modify the software 
• Redistribute modified versions of the software.

It’s important to understand some of these key differences when considering open software for your research.

Benefits of open software

You can use ORS at any stage of your research. Some benefits of using existing ORS include: 

Improves efficiency and reproducibility via: 

• Publication of transparent data analysis processes that underpin the results 
• Wide and easy availability of the tools to re-run the analysis 
• Algorithmic transparency: the code can be analysed/evaluated by users 
• Building on existing tools (by extending or re-purposing them) 
• Access support from the software community (in reporting issues, writing documentation, contributing to bug fixes, reviewing or enhancing code) 
• Cost effective and accessible.
• Publish and share open research software
• Seaview Survey

Citing & publishing software: Publishing research software

• Publishing research software
• How to cite software

Introduction

Why should you publish software?

Publishing your research software helps ensure your software is citable, preserved, and accessible -- which supports scientific reproducibility; replication, and transparency. Additionally, it helps with gaining appropriate credit for your work. Publishing software also helps the community by enabling reuse of your code and methods. Moreover, it is increasingly required by funders who want researchers to make their data and workflows open and by publishers who aim to improve replicability. 

Where to publish software?

There are a number of different channels for publishing research software, including software papers, general source code repositories, and disciplinary archives. (Read more about options for software publishing …)

Where to publish depends on context...

• When the primary consideration is scholarly attribution, consider publishing in software journals first, or in general archives that enable the software to be cited.
• When the primary consideration is transparency and replicability, consider general replication archives.
• When the primary consideration is reuse consider publishing in a general replication archive, or disciplinary archive.
• When the primary consideration is preservation, consider an archive that provides an institutional commitment to persistence – this includes most general replication archives, and most institutional archives.

It is frequently appropriate to publish in multiple channels. For example, many creators of statistical software maintain development code in  Github  so that developers can contribute to it and reuse it ;  publish released versions in CRAN so that practitioners can find it and apply it; and vet major new functionality through publication in  The Journal of Statistical Software .

How to make your software more citable?

Making your software citable both gains credit for your work, and improves reproducibility of research that relies on the software. There are a variety of ways to make your software citable:

• Publishing your software in a software journal provides a citation with a persistent identifier, and provides peer review.
• Publishing your software in a major general replication archive provides a citation with a persistent identifier, and usually allows you to publish citable new versions of the software as you choose.
• If you publish software in Github, you can  create a citable archived version  whenever you choose, through Zenodo.
• If you publish software in a channel that does not directly support citation, you can  include a citation file  in the software itself.

Software Publishing Options

A software journal, such as  PLOS One or The Journal of Open Research Software,  allows submissions that "allow for submissions entirely focused on research software", enabling description and credit for research software ( Force11 software citation principles ). Like data journals, which focus on publishing papers that describe data sets, these journals support publishing papers that explain and describe research software packages. Publishing a software paper both serves as a way to extensively document the code, and to get publication credit for your work. 

Major source code repositories, such as Github, Codeplex, BitBucket, or Sourceforge  publish code, and provide many features to support ongoing development.

Disciplinary software repository such as The Comprehensive R Archive Network (CRAN) or NanoHub are often heavily used among specific research communities to locate software that can be applied in research.

General subject/replication archives such as Dataverse, Zenodo, or FigShare   publish individual versions of software and data in support of replication and reuse.

Institutional repositories such as DSPACE@MIT  archive papers, publications, and data in support of an institution's commitment to durable open access to the content produced by its community. 

• List of journals that publish software papers A long list of software journals and journals that accept software papers, by field
• Directory of Data Repositories This is a directory of data and software repositories, including many replication repositories and disciplinary repositories.

Archive Your Software and Making it Citable Using Zenodo

If your code is stored in GitHub , you can archive your repository and get a permanent citable DOI by archiving in either Zenodo or  Figshare . These are data repositories that allow management of all kinds of data, and are both free for researchers to use. Zenodo and Figshare can also be used to store research data.

Archiving your GitHub repository in Zenodo or Figshare: 

Creating a CITATION file for your software

The FORCE11  software citation principles  include the recommendation that software packages include a CITATION file along with other README files that documents exactly how the authors of the software would like to be cited by others. This ensures that users of the package know what information to include in their citations and makes citations of the code more likely, increasing the credit that you receive. 

Software citation files can be human readable CITATION files or machine readable files , and may vary depending on the language (for instance, R supports a citation() function as well as CITATION files ). Citation files should include:

• author(s) in the order you would like them to be credited
• title of the software package or code
• link to the location where the code can be downloaded or purchased 
• DOI if you have created one, or other unique identifier -- Force11 recommends that the identifier resolve to "a persistent landing page that contains metadata and a link to the software itself, rather than directly to the source code files, repository, or executable."
• version number
• release date

A citation file can also include contributor roles and the software license. Also consider including a BibTeX citation and the citation of any associated publications. 

Librarian for Electrical Engineering & Computer Science, IDSS, and Mathematics

• Preserving and sharing your data
• Citing sources: overview
• Citing data
• Next: How to cite software >>
• Last Updated: Dec 12, 2018 11:43 PM
• URL: https://libguides.mit.edu/software

30+ Essential Software for Researchers

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Are you stuck with inefficient research tools? Discover the best essential software for researchers to revolutionize your work.

Academic research can often feel like a complex puzzle. Every researcher knows the struggles of data crunching, project coordination, document writing, and intensive note-taking. 

But here’s the good news: online tools for researchers can turn these challenges into manageable tasks.

Table of Contents

Academia has transformed from a domain dominated by a survival-of-the-fittest mentality into an inclusive sphere of shared knowledge, growth, and discovery.

This paradigm shift signifies the evolution of academia from a space where only the intellectually elite thrive, to a nurturing environment that promotes intellectual curiosity and learning for all.

We encourage researchers to try out the tools mentioned below to find the perfect research tool.

Essential Software for Researchers

#1. google scholar: best for scholarly literature search and keeping up-to-date with research in your field.

• One of the top academic search engines
• Enables users to keep up-to-date with the latest research in their respective fields
• Provides citation data for each article, contributing to the ease of referencing

In the quest for identifying pertinent research problems and gaps, Google Scholar acts as your invaluable companion. This search engine is your lens into the cutting-edge developments in your field. 

It helps you pinpoint where your investigation could contribute to the existing body of knowledge. Here are other academic journal discovery platforms that can help you at this stage of research .

How much does it cost?

Source: https://scholar.google.com

#2. NVivo: Best for Designing and Conducting Qualitative Research

• Provides robust tools for data organization and analysis
• Encourages meaningful insights from qualitative data
• Promotes efficient coding, making it easier to sift through mountains of data

NVivo stands as a beacon of hope for qualitative researchers in the data fog. Its unique features categorize, analyze, and draw connections like a seasoned detective, unearthing meaningful insights with ease. 

With an intuitive interface, NVivo transforms complexity into a rich tapestry of knowledge. It empowers researchers to capture nuances, emotions, and subtleties, illuminating the essence of their study. 

As a guiding light, NVivo enhances the rigor of research and reveals profound insights that resonate with human experiences, making the journey through the data fog an exhilarating quest for wisdom.

• Free trial available
• Academic version costs $849 yearly

Source: Lumivero

#3. Qualtrics: Best for Survey Design and Distribution

• Comes equipped with sophisticated features for survey creation and distribution
• Includes advanced data collection methods
• Simplifies analysis with powerful analytics tools

When it comes to creating and distributing surveys, Qualtrics stands as the gold standard. This essential software is akin to having a personal survey consultant, streamlining every step of the process, from crafting engaging questionnaires to collecting and analyzing meaningful data. 

With its user-friendly interface and robust features, Qualtrics empowers researchers to gather valuable insights effortlessly. This powerful tool ensures that the journey from survey creation to data interpretation becomes a seamless and rewarding experience.

In the world of survey research, Qualtrics is your steadfast ally. It not only simplifies the process but also empowers you to glean meaningful insights from the data, adding immense value to your research. You can also check out other survey tools.

• Free version available
• Premium starts from $2,500 per year

Source: https://www.qualtrics.com

#4. SPSS: Best for Statistical Analysis and Data Interpretation

• Provides comprehensive tools for statistical analysis
• Simplifies data interpretation with an intuitive interface
• Supports a wide range of statistical tests

If statistical analysis is your battleground, SPSS becomes your formidable armor. This software doesn’t merely crunch numbers; it possesses the alchemical ability to transform them into comprehensible insights, making data interpretation a breeze rather than a battle. 

With its robust toolkit and advanced analytics, SPSS empowers researchers to extract meaningful patterns and correlations from complex data sets. 

SPSS takes the intimidation out of data analysis. With this robust software, you’re not just analyzing data; you’re demystifying it and transforming it into actionable insights that can drive your research forward. Here are other top data analysis software for researchers.

• Starts at $99

Source: https://www.ibm.com

#5. Tableau: Best for Data Visualization and Reporting

• An industry-leading tool for creating interactive, insightful data visualizations
• Empowers users to turn complex data into easy-to-understand, actionable information
• Streamlines reporting with customizable dashboards and real-time updates

Tableau dominates the field of data visualization and reporting as a true titan. It serves as a personal data storyteller, adeptly converting intricate raw data into visually captivating and easily comprehensible narratives. 

With its powerful features and user-friendly interface, Tableau empowers users to unlock valuable insights and make informed decisions from data that might otherwise be overwhelming. 

From interactive dashboards to dynamic charts, its versatility and effectiveness make it a go-to tool for businesses, analysts, and anyone seeking to extract meaning from data in an engaging manner.

• Starts from $180 per user yearly to $840 per year

Source: https://www.tableau.com

#6. Overleaf: Best for Collaborative Writing and LaTeX Editing

• A powerful platform for creating and editing LaTeX documents
• Enables seamless collaboration with real-time syncing and shared access
• Simplifies LaTeX editing with a user-friendly interface and pre-made templates

As your LaTeX guru, Overleaf offers an efficient, collaborative workspace tailored to crafting and editing LaTeX documents. Whether you’re a researcher, engineer, or academic, this platform empowers you to produce polished and professional papers effortlessly.

Say goodbye to the complexities of document preparation and welcome a seamless, intuitive experience that enhances productivity and fosters collaboration among peers.

Overleaf facilitates collaboration and simplifying the editing process, making crafting complex documents less daunting and more productive. You can learn more about LaTeX tutorials here.

• Standard: $199 per year
• Professional: $399 per year

Source: https://www.overleaf.com

#7. Grammarly: Best for Checking Grammar and Improving Writing Clarity

• A sophisticated tool for real-time grammar and spelling checks
• Enhances writing clarity and eliminates errors
• Provides personalized suggestions to improve your writing style

Grammarly is more than a proofreader; it’s your personal writing coach. This software is designed to guide you towards impeccable grammar, clear writing, and a refined style, ensuring your academic work shines.

With Grammarly at your side, you’re not just writing; you’re crafting compelling narratives. This tool helps ensure that your ideas shine brightly, unmarred by grammatical errors or unclear writing.

• $25 per monthly

Source: https://en.wikipedia.org

#8. Turnitin: Best for Plagiarism Checking and Originality Reports

• A plagiarism checker tool
• Provides detailed feedback to maintain academic integrity
• Supports multiple languages and file formats for broader accessibility

Turnitin stands at the forefront of safeguarding academic integrity. As a vigilant watchdog, this software diligently ensures the authenticity of your work and detects any inadvertent plagiarism, guaranteeing its originality. 

With Turnitin’s cutting-edge technology, students and educators can have the confidence that their academic pursuits maintain the highest standards of integrity and authenticity. 

By continuously refining its capabilities, Turnitin remains a trusted ally in upholding academic excellence and promoting a culture of originality in educational institutions worldwide.

Here are the best academic writing that can help you in your research.

• $3 per student per year

Source: https://www.turnitin.com

#9. Mendeley: Best for Discovering New Research and Collaborative Work

• One of the best reference management tools available
• Provides a platform to organize, share, and annotate research papers
• Facilitates easy referencing with a built-in citation tool

When navigating the extensive realm of academic research, Mendeley serves as your reliable compass. With its centralized hub, this tool facilitates the discovery of cutting-edge research, fosters collaborations among researchers, and efficiently organizes your ever-expanding library of academic papers. 

By offering seamless access to a wealth of knowledge, this reference management software empowers scholars to delve deeper into their fields of interest, stay up-to-date with the latest findings, and engage in meaningful academic endeavors.

You can compare collaborative writing tools here .

• Starts from $4.99 to $14.99

Source: https://www.mendeley.com

#10. Zotero: Best for Collecting, Organizing, and Citing Research Sources

• A comprehensive tool for collecting and organizing research sources
• Supports a wide variety of citation styles
• Integrates with numerous browsers and word processors for seamless usability

In the bustling marketplace of academic resources, Zotero stands out as a first-rate organizer. It helps you collect, manage, and cite your research sources, transforming a haphazard collection into a well-organized library.

Zotero is your personal library architect, ensuring your wealth of sources is well-structured and easily accessible. It doesn’t just simplify source management; it elevates your research process to a new level of efficiency.

Source: https://www.zotero.org

#11. Trello: Best for Research Project Management and Task Organization

• A robust platform for managing research projects and organizing tasks
• Facilitates team collaboration with shared boards, lists, and cards
• Enables tracking progress and deadlines for efficient project management

Trello is one of the best project management tools. This platform boasts a visually appealing and intuitive interface, facilitating seamless organization of tasks, progress tracking, and team collaboration.

Trello optimizes efficiency, enabling researchers to focus on their work, not administrative hassles. With an array of intuitive features, it remains an indispensable tool for coordinating and executing successful research endeavors.

Learn more about task management tools here.

• Standard: $5 per month paid yearly

Source: https://trello.com

#12. ResearchGate: Best for Connecting with Fellow Researchers and Sharing Publications

• A dedicated platform for networking with global researchers
• Enables sharing and discovery of academic papers and publications
• Provides a space for discussions, questions, and collaborative problem-solving

ResearchGate, a haven for those yearning for a community of like-minded researchers, offers a platform that facilitates connections among scholars. 

By joining ResearchGate, you can share your work, connect with fellow researchers, and discover new research that aligns with your interests. 

This dynamic environment empowers you to stay at the forefront of knowledge and contribute to the scientific community.

Source: https://www.researchgate.net

#13. Notion: Best for Comprehensive Note-Taking and Project Management

• A versatile tool for both note-taking and managing research projects
• Provides customizable templates for a tailored user experience
• Facilitates real-time collaboration among research teams

Isn’t it just exhilarating when you find a tool that simplifies your academic life? Well, that’s exactly what Notion is all about: a revolutionary digital workspace designed to merge the realms of in-depth note-taking and sophisticated project management.

With Notion, the tedious becomes straightforward, the overwhelming becomes manageable, and the complex becomes clear. It’s about getting the most out of your A-level studies, fostering a sense of achievement while making the process enjoyable. So, buckle up and let Notion revolutionize the way you work.

• Plus: $8 per month paid yearly
• Business: $15 per month paid yearly
• Enterprise: Custom price

Source: https://www.notion.so

#14. Quillbot: Best for Paraphrasing and Improving Writing Clarity

• Assists with paraphrasing and enhancing writing clarity
• Offers various writing modes to cater to different styles and tones
• Supports the construction of coherent and concise sentences

Quillbot is your personal wordsmith, adept at paraphrasing your text and enhancing its clarity. Its variety of writing modes cater to different styles and tones, and it can help in crafting concise and coherent sentences, making it an invaluable assistant in your research writing process.

Quillbot is your digital co-author. It helps in expressing your research findings in a clear and engaging manner, thereby improving your writing’s readability and impact. This is an essential companion in the quest to make your research more accessible and understandable. 

Here are other academic writing tools you may need.

• Premium: $9.95 monthly

Source: https://quillbot.com

#15. Jasper AI: Best for AI-Powered Writing Assistance

• Provides AI-driven assistance to enhance your writing quality
• Suggests improvements for clarity, coherence, and grammar
• Supports various writing styles and contexts, including academic research

Ever dreamt of having a personal writing mentor, constantly at your beck and call, simplifying the intricacies of academic writing for you? Welcome Jasper AI into your world – an exemplary writing companion that surpasses the functionalities of a typical digital assistant. 

This state-of-the-art tool propels your writing to a higher level. Gone are the days of laboring over endless edits and revisions. With Jasper AI, your writing process is streamlined, facilitating the creation of clear, compelling, and high-quality research work.

Here is a deep Jasper AI Review. Read it to learn more about how you can use Jasper

• Creator: $49 monthly
• Teams: $125 monthly
• Business: Custom price

Source: https://www.jasper.ai

#16. GanttPRO: Best for Project Scheduling and Time Management

• Offers robust tools for project scheduling and time management
• Provides a visual representation of your project timeline
• Supports team collaboration and task assignment

GanttPRO illuminates your project path with its visually appealing timeline representation. It becomes your dependable ally in planning tasks, monitoring progress, and optimizing your time management strategies. 

No longer do you have to wrestle with disorderly schedules and haphazard task allocations. GanttPRO simplifies the chaos and brings order to your project management landscape.

GanttPRO is not just a tool that assists in project scheduling; it’s your personal steward of time. This tool does more than just manage your project; it ensures your research endeavor is a resounding success.

• Basic: $7.99 monthly
• PRO: $12.99 monthly
• Business: $19.99 monthly

Source: https://ganttpro.com

#17. Scholarcy: Best for Quick Summarization of Academic Papers

• An efficient tool for extracting quick summaries from academic papers
• Supports in-depth understanding by highlighting key points
• Provides a reference list for further exploration

Scholarcy is your dedicated summarizer, rapidly transforming complex academic papers into digestible summaries. This amazing tool supports your understanding and equips you with a reference list for further research.

Scholarcy is your academic digest. It ensures you efficiently comprehend complex research papers, and ultimately, saves your precious time.

• $9.99 monthly

Source: https://www.scholarcy.com

#18. R Discovery: Best for Statistical Analysis and Data Visualization

• A potent tool for comprehensive statistical analysis
• Offers a robust platform for data visualization
• Supports reproducible research with code sharing and reusability

R Discovery provides a platform for comprehensive statistical analysis. It also facilitates data visualization. This supports you in presenting your research findings convincingly.

Overall, R Discovery is your statistician, your illustrator, and your collaborator. It aids you in understanding data, presenting it effectively, and maintaining research integrity. This way, it contributes significantly to your research quality.

Source: https://discovery.researcher.life

#19. Scopus: Best for Comprehensive Literature Search and Citation Tracking

• An extensive database for literature search across various fields
• Supports citation tracking for managing your bibliographies
• Offers analytical tools to assess the impact of research

Scopus is your academic searchlight, illuminating a vast landscape of scholarly literature. With its extensive database, citation tracking, and analytical tools, it aids you in finding relevant research, managing your references, and assessing your work’s impact.

Scopus is your scholarly sleuth and your research analyst. It equips you with the tools you need to conduct impactful research.

• Paid ranges from $500 to $1000

Source: https://www.scopus.com

#20. Journal Finder: Best for Identifying the Right Journals for Publishing Your Research

• A dedicated platform for pinpointing suitable journals for research publication
• Analyzes research abstract and keywords to suggest optimal matches
• Helps researchers avoid unnecessary rejections and hasten publication process

Journal Finder serves as your publishing compass, steering you towards the right journals to publish your research. This tool saves you from the guesswork, maximizing the chances of your work reaching the right audience.

With Journal Finder, you’re not merely seeking publication – you’re targeting impact. By leading you to the most suitable journals, it increases the visibility and influence of your research.

Source: https://journalfinder.elsevier.com

#21. Global Journal Database: Best for Accessing Information about Various Journals

• A comprehensive database of various academic journals
• Provides detailed information about the journals including impact factor
• Assists researchers in finding the right publication platform

Global Journal Database is your encyclopedic companion in the quest for the right publication platform. It provides you with detailed information about various journals, helping you make informed decisions about where to submit your research for publication.

The Global Journal Database does not just offer information—it provides clarity. This tool empowers you to choose the best platform for your research, maximizing its impact.

Source: https://researcher.life

#22. Citation Gecko: Best for Literature Review and Citation Network Exploration

• A specialized tool for exploring citation networks
• Aids in the literature review process by identifying key papers and authors
• Supports academic networking by linking researchers with similar interests

Citation Gecko is your guide in the maze of academic citations. It helps you identify key papers and authors in your field. This way, the tool supports your literature review process, and fostering academic networking.

Citation Gecko is not just a citation tool—it’s your academic navigator. It not only aids in your literature review but also fosters academic networking, broadening the horizons of your research.

Source: https://www.citationgecko.com

#23. OpenRefine: Best for Cleaning and Transforming Messy Data

• A powerful tool for cleaning up and transforming data into a usable format
• Facilitates the exploration of large data sets with ease
• Allows for batch editing and scripting for advanced data manipulation

OpenRefine is your personal data janitor, turning messy and inconsistent data into a clean, usable format. It gives you the power to explore, tidy up and transform large datasets, thus providing a robust foundation for your data analysis.

OpenRefine is not just a data cleaning tool—it’s your foundation for reliable data analysis. It provides you with clean, consistent data, which is vital for accurate results and insights in your research.

Source: https://openrefine.org

#24. MATLAB: Best for Complex Mathematical Calculations and Data Analysis

• Offers an advanced platform for complex mathematical calculations
• Supports high-level data analysis, visualization, and algorithm development
• Provides an integrated environment for multidisciplinary research

MATLAB is your math whiz, providing a platform for handling complex mathematical calculations and data analysis. It offers a comprehensive environment for calculations, algorithm development and visualization. This makes MATLAB a one-stop shop for researchers in quantitative fields.

Overall, MATLAB is more than a calculator—it’s a complete computational environment. It allows you to perform complex calculations, analyze data, and visualize your results.

• Premium: $860 yearly or $2150 for the perpetual license

Source: https://www.mathworks.com

#25. Amazon Drive: Best for Storing and Sharing Research Files

• A reliable solution for storing and sharing research files
• Ensures data safety with secure cloud storage
• Supports collaboration by allowing file sharing among research team members

Amazon Drive is your digital locker, providing a secure home for your precious research files. Its cloud storage solutions ensure data safety and allow you to share files with your research team, promoting collaboration and efficiency.

Amazon Drive is a storage tool and a guardian of your research. It provides secure storage and facilitates collaboration, making sure your research work remains safe, organized, and accessible, wherever you are.

• Ranges from $6.99 per month to $11.99 per month

Source: https://www.amazon.com

#26. Otter.ai: Best for Transcription of Interviews and Meetings

• Efficiently transcribes audio content from interviews and meetings
• Supports multiple languages and speakers
• Provides keyword search in transcriptions for easy data navigation

Otter.ai is your personal scribe, tirelessly transcribing your interviews and meetings into clear, accessible text. It recognizes multiple languages and speakers, and allows keyword search in transcriptions. This makes your data more manageable and the research process more effective.

Otter.ai does more than transcription—it simplifies your qualitative data analysis. By transforming audio into searchable text, it saves time and enhances data accuracy, which can significantly boost the quality of your research.

• Basic: Free
• Pro: $8.33 per month paid yearly
• Business: $20 per month paid yearly

Source: https://otter.ai

#27. LabView: Best for Data Acquisition and Instrument Control in Lab Environments

• Offers a platform for data acquisition and instrument control
• Facilitates lab automation by integrating hardware and software
• Allows real-time visualization of data for immediate analysis

LabView is your laboratory maestro, orchestrating a smooth interplay between data acquisition and instrument control. Its robust integration of hardware and software allows for lab automation, while real-time data visualization ensures immediate analysis, saving you valuable time and energy.

LabView is not just a lab tool—it’s a catalyst for efficiency and precision. By facilitating data acquisition, instrument control, and real-time analysis, it turns your lab into a hub of productivity, taking your research a notch higher.

• Starts from $493 to $2771

Source: https://www.ni.com

#28. SAS: Best for Advanced Statistical Analysis and Predictive Modeling

• Provides a platform for advanced statistical analysis and predictive modeling
• Supports data management and decision-making processes
• Offers visualization capabilities for better data understanding

SAS is your statistical powerhouse, offering advanced statistical analysis and predictive modeling capabilities. Its data management, decision-making support, and visualization tools make it a comprehensive solution for researchers looking to derive deep insights from their data.

SAS is more than a statistical tool—it’s a comprehensive solution for data-driven research. By enabling advanced analysis, predictive modeling, and data visualization, it empowers you to make the most of your data, thereby enhancing the quality and impact of your research.

• Paid ranges from $1000 to $5000

Source: https://www.sas.com

#29. BioRender: Best for Creating Scientific Figures and Illustrations

• Facilitates the creation of high-quality scientific figures and illustrations
• Offers a vast library of pre-made templates and icons
• Enables sharing and collaboration with peers and colleagues

BioRender is your personal scientific illustrator, providing a platform to create high-quality figures and illustrations. With a large library of templates and icons at your disposal, and collaborative capabilities, it empowers you to communicate your research visually.

BioRender is not just a graphics tool—it’s a bridge between your research and your audience. It aids in communicating your findings more effectively, amplifying the impact of your work.

• Individual: $35 monthly
• Lab: $99 monthly
• Institution: Custom price

Source: https://www.biorender.com

#30. Slack: Best for Team Communication and Collaboration

• Enables smooth team communication and collaboration
• Allows for organized discussions through channels and threads
• Integrates with other productivity tools for a cohesive work experience

Slack is your team’s digital huddle, fostering effective communication and collaboration. Its organization of discussions into channels and threads, along with integration capabilities with other productivity tools, ensures a seamless, efficient research process.

Slack isn’t just a communication tool—it’s your team’s virtual meeting room. By enabling efficient communication and collaboration, it brings your research team closer, improving productivity and fostering a cohesive research process.

• Pro: $7.25 per month
• Business: $12.50 per month

Source: https://slack.com

#31. RStudio: Best for Statistical Computing and Graphics in R

• Offers a dedicated environment for statistical computing in R
• Facilitates the creation of high-quality graphics for data visualization
• Supports the use of markdown for creating reproducible reports

RStudio is your personal statistician, providing a comprehensive environment for R, a popular language for statistical computing. 

This tool aids in creating high-quality graphics and supports markdown for reproducible reports, making it an essential tool for researchers dealing with statistical analysis.

RStudio isn’t just a programming tool—it’s your guide in the world of statistical computing. It aids in data analysis, visualization, and reproducibility, ensuring your research findings are accurate, compelling, and repeatable.

Source: https://posit.co

Academic research isn’t just about the pursuit of knowledge; it’s about leveraging the right tools to streamline that pursuit. As we’ve explored, these essential research software applications aren’t merely aids. 

They’re game-changers. They’re here to tackle challenges head-on, from organizing sources to research data analysis, and transform them into opportunities for growth and learning. 

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Five online tools that aim to save researchers time and trouble

From investigating a lab’s publication history to scanning manuscripts for statistical errors, these apps can help streamline some of the most time-consuming tasks.

Dalmeet Singh Chawla

Credit: erhui1979/Getty Images

23 June 2021

erhui1979/Getty Images

An expanding kit of digital tools and apps helps researchers automate certain processes and make others less burdensome.

Nature Index has selected five recently launched or revised tools that aim to make academic life easier, whether you’re thinking of relocating to a new lab, organizing your references or readying a manuscript for submission.

1) Choosing the right lab

Job-hunting can be tough, particularly if interstate or overseas relocation is on the cards. Before you commit to a new position, it’s useful to have some insight into your prospective supervisor, as well as the kind of lab culture you’d be stepping into .

A new tool called Super Researcher aims to make it easier for doctoral and postdoctoral candidates to compare lab leaders based on their productivity and impact.

Described in a bioRxiv paper published in February 2021, the app allows academics to run searches on specific researchers to see their numbers of annual publications, citation counts and details on their most frequent collaborators. The tool pulls publication data from the Scopus database.

The team behind the app, led by co-creator Sheah Lin Lee, a cancer researcher at the University of Southampton in the UK, is working to move it beyond the pilot stage. One challenge, she says, is that it’s hosted on a free (and sometimes unreliable) server, which means it’s prone to the occasional crash – something the team is hoping address in the future.

Lee says she hopes her tool will give researchers a ‘rough and ready’ indication of a lab’s publication culture, which could factor into their decision to take up a new position there. But she urges users to take other factors into consideration, too.

“We don’t think that people should judge whether you want to go to a lab solely based on publications,” says Lee.

2) A spell-checker for statistics

What if there were a way to automatically scan a manuscript for statistical errors while writing your manuscript ?

Statcheck, launched in 2015, aims to do so by recalculating p-values — a controversial but commonly used technique to measure statistical significance.

It initially received mixed reactions from academics, but has since gained more acceptance after a preprint study found that it was correct in more than 95% of its recalculations of p-values.

Statcheck has become a popular way to check manuscripts before submission to a journal, says co-creator Michèle Nuijten, who studies analytical methods at Tilburg University in the Netherlands.

It’s also being used by journals such as Psychological Science and the Journal of Experimental Social Psychology to weed out statistical mistakes during the peer-review process.

In 2020, Nuijten and her team expanded the functionality of statcheck by creating a free plugin to be used within Microsoft Word that works like a statistical spell-checker.

Nuijten cautions against using the tool as a means to imply fraud or wrongdoing, emphasizing how easy it can be, even for experienced researchers, to make mistakes in their calculations.

“We all make mistakes. It doesn’t mean we want to,” she says.

3) Spot the difference between preprint versions

Comparing different iterations of a manuscript on preprint servers such as arXiv can be a time-consuming process, says Sharvil Nanavati, a software engineer based in Mountain View, California.

After trying to find a tool to address this, Nanavati and Sergei Taguer, a software engineer in California, decided to build one themselves, which they launched in May 2021.

ArXiv Diff , which was built on top of an existing open-source tool, allows users to view manuscript updates by replacing the word “arxiv” in the URL of a paper to “arxivdiff” then clicking “Show Diff”.

So far, feedback on the new app has been mixed, Nanavati admits. Some academics have praised its usefulness, while others have pointed out that it doesn’t work on all manuscripts.

Nanavati says he’s tweaking the tool’s code to cater for cases where users flag errors. He says the tool, which is a labour of love, will continue to be available for free, but is limited to manuscripts posted to arXiv for now.

4) Find references to papers flagged on PubPeer

It’s becoming increasingly difficult to keep up with the commentary around new papers. Some of these discussions take place on Pubpeer, an online platform where researchers debate the veracity and robustness of specific papers.

While it’s possible to manually check a paper on PubPeer to see if people are talking about it, doing that for an entire reference list can be laborious.

In 2019, PubPeer launched a free plugin on Zotero, an open-source reference-management system that is popular among academics because it hosts a number of plugins with functions such as helping users find free versions of paywalled papers and flagging papers that have been retracted.

The new PubPeer plugin flags any references in a researcher’s paper shortlist — where they save studies that are potentially of interest and may be worthy of citing — that are being discussed on PubPeer, listing the number of comments.

PubPeer also has a browser extension that alerts researchers if they are citing a paper that is being discussed on the platform.

Boris Barbour, co-organizer of PubPeer and a neuroscientist at the Ecole Normale Supérieure’s Institute of Biology in France, says that while there are no immediate plans to expand the new plugin to reference management systems other than Zotero, “there are fairly significant incremental improvements and polishing that could be done”.

5) Scanning for predatory references

In May 2021, Edifix, a bibliographic referencing tool run by Boston-based publishing software firm Inera, expanded its capability to automatically flag references to papers published in predatory journals.

Inera teamed up with Cabell’s International , a scholarly-services firm headquartered in Beaumont, Texas, to access its list of predatory journals, which is usually pay-to-view .

When users check their references using Edifix, in addition to automatically formatting them and fixing any errors, the tool will flag any publications that Cabell’s has identified as predatory. Users can click on those references for an explanation of why the particular journal has been flagged as questionable .

However this new functionality will be free to use for Edifix subscribers until the end of 2021. After that, users will need to pay for a subscription to Cabell’s, says Elizabeth Blake, director of business development at Inera.

The decision to delete or retain references to flagged publications lies with the researcher, says Blake, as it’s possible for subpar journals to publish solid research, and there may be legitimate reasons to cite such work.

Edifix also highlights any references that have been retracted – which a growing number of other bots also do .

English Editing Research Services

Software Tools for Better, Faster Research Collaboration

Software tools have removed a lot of the manual, paper-based tasks of research. And they keep getting better and more innovative. Modern-day researchers can now focus more on the research itself, coordinate across distances, and keep everything together online.

Submission is all online, too, as is publication.

Edanz Smart Tools also speed up and empower your research. You can get going with them right away.

Let’s look at the main stages of research and the types of tools that can make your research a more time-efficient and enjoyable process.

What you’ll learn in this post

• How online tools can replace paper-based, time-consuming research collaboration processes.

• Ways to map and manage your research using cloud-based software.

• Recommended software for polishing up your scientific English and presenting it in a journal-ready format.

• Edanz’s own Smart Tools are designed for research and can be used, well, RIGHT NOW!

Brainstorming and idea-generating tools

Structured research almost always starts with idea generation. Collecting your and your team’s thoughts can get messy when you’re using sticky notes and whiteboards. There’s ample software to help research teams keep their ideas straight. So even if some team members are more “analog” and need to write things down, as long as you have someone taking notes, you can digitize and share the whole process.

Brainstorming and idea-generating software can be quite creative, and often crosses over into other uses, like life management and business.

Try these packages.

Coggle is a brainstorming app for collaboration, with sharable mind maps and flow charts. With a public gallery, teams can see and edit others’ diagrams allowing for a more connective experience. Coggle’s free plan allows for downloads as PDFs and unlimited public diagrams. It’s a good solution for mind-mapping and even the paid plans are very affordable.

MindMeister

MindMeister is cloud-based for real-time collaboration and working together when you can’t physically be together. The tool has an easy-to-use interface with minimal buttons to confuse newcomers. MindMeister has community mind maps, where other researchers can share premade mind maps specific to your research niche. The free plan is quite limited, meaning the paid plans are needed to get the most out of the software. But they’re modestly priced. This feature-rich option is a great teammate when you’re formulating your ideas.

MindMup perhaps excels best at getting you started. Using an online mind-mapping tool will be less familiar for most researchers compared with, say, word processing or SPSS. MindMup lets you quickly start in your first mind map for free. You can do structured writing, and storyboards, and save to PDF and PowerPoint, as well as Google Drive.

All three of these options offer similar features with different user experiences (UX). They’re all cheap add-ons to your research tool stack, as well, with monthly memberships under $10. Trial them, A-B-C test them, and add one of these (or another option) to organize your brainstorming process.

Effective English Writing

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Social networking tools

There’s plenty written on social media in the sciences, but beyond forums for sharing ideas and for self-promotion, these forms of social media are also power idea generators. By networking and participating, these tools expand your academic (and social) connections while opening your mind to new ideas and perspectives.

ResearchGate

ResearchGate is an academic networking tool where researchers connect, collaborate, and idea-create by uploading articles, papers, data, and code to an online database. Researchers receive analytics on their posts including the number of times their work‘s been read and cited by other users on ResearchGate. It’s one of the most active social networking tools in the academic sphere.

LinkedIn has become indispensable for most adults in much of the world. Some regions (especially North Asia) haven’t gotten over the amount of personal exposure and self-promotion, but even they are getting there. LinkedIn has evolved from an online resume and job hunting tool to a Facebook for professionals. This includes researchers.

Using LinkedIn as a place to output your research/work is a way to show off your skills and find connections with similar researchers. Posts about new advancements in your field are a good way to start academic conversations that can in turn benefit your understanding of what you’re researching.

If you’re doing exploratory research, you can use LinkedIn as a research engine. By searching the questions and answers page and seeing the interactions different topics gain, researchers can get insight into different industries. This is a good article on using LinkedIn as a research tool .

Academia (often called academia.edu) is a networking site dedicated to academics. Like ResearchGate, academia.edu has post analytics, allowing you to track your posts in real time. Academia.edu makes making connections easier by simply “Importing contacts” from other social networking sites like Facebook, Google, and Twitter.

Project management tools

Organizing your research and the deadlines that come with it is just as important as the research itself. Without project management tools, research can be chaotic and unorganized. Good research relies on collaboration and requires many revisions. Project management tools streamline this complex process.

The project management aspect of academic research can look a lot like project management in the corporate workplace. So these tools all have crossover with the working world.

Asana is a project management software for people and companies that prefer to work with lists instead of boards. The lists you can create are clean and present work straightforwardly. Often in research, all you need is an efficient set of lists to set out your ideas and thoughts. Asana is extremely easy to use, and implementation into your research process is quick and simple. It’s well suited for individuals and smaller tight-knit teams.

Trello uses the kanban (bulletin board) approach of visual boards and images instead of lists. The board, made up of “cards,” however, can still function like lists, or cards can contain lists. Trello has a social media feeling to its software and its style lends itself to researchers who prefer visual communication.

Trello works in real-time, allowing for continual updates and progression. The notification system broadcasts updates on the team’s progress. With a lot of members and moving parts on your project, this can really be helpful. Just tweak your notification settings or you’ll be waking up every morning to a mailbox full of Trello updates.

Infinity is mainly created for business use but works just as well with research. It offers a flexible and color kanban -style project management setup. It lets you create unlimited boards, folders, and items and then share them with collaborators. The number of templates for different situations can be adjusted to your needs.

ProofHub provides seamless collaboration with a large array of features, from Gannt charts to time-tracking. A ProofHub feature is its ability to edit tasks as they’re active without having to end them and then re-assign them, excellent for collaborating researchers. ProofHub is accessible on any device or computer, so no need to worry about where you’re working. It’s a premium paid software with no free version so make sure to use their free trial before buying.

Literature search tools

We cover literature search in a variety of places on Edanz, so this section want be new to many. And indeed, if you’re at any sort of academic institution, you’ll likely have access to a big library of powerful databases, like ProQuest and EBSCO. Still, there are times when you need a quick online search or a different perspective on your search.

Google Scholar

Google Scholar is the clear leader in the area of free, public literature searching. Yes, score one more for the Google behemoth (yawn), but it’s tough to argue with free.

Google Scholar is Google’s unique search engine applied to research papers and patents. It lets users find publications from all disciplines. The searching is free, and it will often turn up multiple locations of what you’re after. If it’s open-access or otherwise available, you’ll be able to save some of the heavier clicking and digging needed for the above mentioned powerhouse library databases.

Google Scholar has multiple export formats so you can almost always find the format needed for your citation style. Google Scholar’s main advantage is accessibility since you’re probably searching on Google anyway, so switching to the Scholar site becomes a natural progression. You can use the same Boolean operators and all sorts of filters. If nothing else, it’s a great “first step” for seeing what’s out there in there in the massive, seemingly infinite pool of literature.

Semantic Scholar

Semantic Scholar has substantially fewer articles in its database than its counterparts, but when used alongside Google Scholar it can offer different results. Its search engine is optimized to give more relevant and impactful results by finding hidden connections and links between papers/topics.

Connected Papers

We throw this one in as a wildcard, because it’s really quite brilliant and it does something the others don’t. It creates a visual map of linkages among the literature. Connected Papers is a bit of a different tool from the rest, as it is not a standalone database. It uses Google Scholar’s database and creates a unique visual map of all cited papers (and all papers since that have cited the original work).

By representing research citations as legs you get a feel of how interconnected research papers are. This tool is for getting a grasp of the research landscape of a particular topic. This sort of innovation is a wonderful contribution to academic society in general, so we give it a big thumbs up.

Reference management tools

Managing, organizing, and storing your references can be a massive job if done manually. Therefore, it is important to find a reference management tool that makes managing your references an easier process. For research projects large and small, a good reference manager can save many unnecessary hours and unneeded stress

Mendeley is a reference manager with a long track record. Mendeley can be accessed online and offline and works for both Windows and Mac devices, as well as portable devices (which is basically standard these days). It provides plugins for Firefox and Chrome that allow bookmarked websites to be put in your Mendeley library.

While this app used to be free, since its acquisition it’s become harder to access and coordinate across devices. But that doesn’t detract from its solid standing and reputation. If you pay to play, it should be more robust than ever.

PaperPile may be a less-familiar name, but it’s a very accessible solution. Its strengths are its simple cloud-based interface, low pricing, and easily cross-device ability. PaperPile’s design easily deals with large numbers of citations, with a robust search function. PaperPile automatically formats citations and bibliographies in the referencing style of your choice, but relies on metadata, like all in this category, so it may need some cleaning up.

EndNote is a more elaborate and complex software than the previous two. It lets you to search databases and import the citation information into Microsoft Word. It excels in its ability to edit the output citations to specific styles, often needed in Journal publications. This is a premium product and is rather expensive for a beginner. Use the free 30-day trial if you’re interested.

Word processing and document preparation tools

If you’re writing for journal submission, odds are you’ll be required to submit it as a Microsoft Word doc. This means Word remains the de facto word processor, but you’re not limited to it. word processors allow you to save in Word doc format, so you may find something better, cheaper, or more scientifically suitable for your team. Specialized STEM sciences such as math and physics may also welcome LaTeX format, for which there’s now a terrific cloud-based tool.

Microsoft Word

Although it may seem like a boring choice, Microsoft Word is a user-friendly and smooth word processing and document-creating software. Now part of the cloud-based 365 package, it’s no longer device-dependent. It provides many different templates, easy file sharing, and the ability to export as a PDF. Yes, it still comes with a bit of a hefty price tag, but there are different plans and most institutions and workplaces provide it for students and employees.

Google Docs

Google Docs made its mark by (1) being Google and (2) making all the functions of Microsoft Office completely cloud-based and tied to your Google account. It lets researchers collaborate on ideas in real time with commenting and suggestion features. And it lets documents be created in a quicker, more streamlined UX than Microsoft’s.

It’s good for brainstorming as well, though Google Workspace is always adding more tools suited to specific tasks (see Google’s own article on brainstorming here ). For a useful article on using Google Docs in research click here .

LaTeX (Overleaf)

LaTeX (pronounced LAY-tech ) is a long-standing typesetting and document preparation system mainly catering to academics. This owes to its ability to elegantly format long and complex equations. There is a coding element to LaTeX so there is a learning curve, but if you get past that the customization and speed to which you can create stunning documents makes it worthwhile.

You can download LaTeX to your computer via the LaTeX installer, allowing you to use LaTeX offline.

The process of downloading and using LaTeX can confusing and less than user-friendly. This is where Overleaf comes in. Overleaf is a cloud-based LaTeX editor that removes a lot of the coding aspects of LaTeX while keeping the style and equation formatting. It also easily accommodates teams and enables fast rendering to see what the final product will look like. For many scientists LaTeX (with or without a UI like Overleaf) is a must.

Pages is Apple’s take on Microsoft Word. Free on all Apple products but in typical Apple fashion, unavailable for everyone else. Pages offers virtually the same as what Microsoft Word does. The only difference is how it looks. Pages is a minimalist word processing software that helps declutter the space around your work and helps you focus on what you’re typing.

It also has a Word export option. As few (no?) journals accept submissions in Pages format, this is probably something you’ll need. For a Mac-based team, however, Pages may be an attractive option.

Table and figure creation tools

Well, there must be a load of them! So much so, we devoted an entire article to great software for creating scientific figures and tables – give it a read.

English language checking tools

Excellent research is often error-free, but proofreading can be time-consuming, and often missed errors sneak through. Therefore, a good language and grammar checker is essential in quality research writing. There are many to choose from, so a combination of multiple ones would give the best chance of an error-free piece of research.

Grammarly is probably the best-known online and plug-in-based language and grammar checker. This owes both to its aggressive marketing and its continually evolving tool. It’s come a long way and gives far fewer bad suggestions than it used to.

Grammarly’s free version checks for various language mistakes in real time, with popups and using its robust algorithm that scans your writing. It’s quite intuitive and easy to get used to. The paid version picks up on more intricate errors and writing tones. Grammarly also has its own plagiarism checker.

The mistake with Grammarly is accepting all its advice. If your English isn’t at a very high level, Grammarly can sometimes do more harm than good if it misreads your intent. If you have strong English, it can be a big help, even for native speakers.

Unlike the other language and grammar checkers presented, Hemingway doesn’t provide detailed grammar oversight. Hemingway instead comments on the overall readability and writing style of your work.

When writing research, you not only want it to be grammatically correct but also concise and flowing; i.e., readable. Hemingway’s alternative language checker can give your research the appropriate tone and help prevent verbosity. That, in turn, keeps readers’ attention. Hemingway does have a paid desktop app, but the free online app is feature-rich. Try that first.

ProWritingAid

Like Grammarly, ProWritingAid is an online grammar and spelling checker. It’s different in that it provides detailed reports to help improve specific writing skills. It integrates nicely with Google Docs, Microsoft Word, Gmail, and others, helping you work it into your overall routine. It has a mobile app, too. Writing up your research can often be unstructured and great research ideas can come to you at any time, so having a writing aid that’s so integrated is a bonus.

And that’s just for starters!

There are more steps in the process and more tools to help. Dig and dig. Try them out. If they don’t work, dig again. New options are continually emerging from creative minds. Don’t overlook Edanz Smart Tools , as well. These are specifically designed for the research cycle. And you can use them right now.

Training videos   |   Faqs

Popular Writing Tools and Software for Authors and Researchers

Overview   | Writing Software   |  Reference Management Software  |   Research Tools  |  Grammar Checking Tools

Whether you are writing an article, research paper, essay, blog, and dissertation or PhD thesis, it is important to choose an appropriate writing software tool for your work. The choice of writing software comes down to your personal taste. Everyday users are happy to shed a few dollars to purchase a well-known writing tool such as MS Word. Tech savvies welcome open source projects such as OpenOffice and LibreOffice. Whereas, research community is much more adventurous and have embraced the type-setting system, Latex as their writing medium. Blog writers and journalists use online writing tools such as GoogleDocs and DropBox Paper as they find these tools perfect for collaborating with others. Novel writers use more fancy writing tools such as Scrivener to organize their ideas and create a storyboard to help them write.  In this blog, we review some of the common writing tools and software used by writers.

1. Microsoft Word

MS Word is the most widely used tool in the writing community.  It comes with great features and keeps evolving with each version. Some of the popular features include: Grammar and spell checker, Thesaurus, text formatting and aligning, bullets and numbering, inserting watermarks, page numbers headers and footers, readymade templates and mail merge.  You can install plenty of third-party plugins and apps to enhance your experience. A good choice of plugins can save you a lot of time and effort with your writing.    

You can use office online for free and save your documents in the cloud; all you need is a Microsoft account. If you need a desktop version, then you have to purchase a copy. Some universities offer free copies to students. Office 365 offers a subscription model that allows you to install the latest version of MS office on up to 5 computers. You will also have access to the online version of MS Office and up to 1TB cloud storage.

Useful Links:

• Features of MS Word
• Advantages and disadvantages of MS Word
• Plugins and apps for MS Word
• About MS Office 365

LaTeX is a typesetting system for the communication and publication of scientific documents. It is free software. The writer writes in plain text and then adds markup tags to stylise text. Latex is widely used for publishing scientific papers, thesis, and books in many fields. Latex offers a wide variety of features including cross-referencing tables and figures, bibliography management, page layout, chapter and section headings, and numbering.  It has a steep learning curve, and beginners will take some time to build up expertise in Latex. People who have no or very little experience in programming will take a while to get used to Latex since it is similar to learning a new scripting language. Most publishers make a Latex template available alongside MS Word template as a part of the author submission instructions.

Complex equations can be beautifully formatted in Latex by inserting relevant tags. Latex produces a .tex file which in turn can be converted into a wide variety of output formats such as PDF, HTML, etc. using TEX distribution packages such as MikTex. The major disadvantages of using Latex is that it does not come with an inbuilt spell checker or graphical user interface.  Latex comes with several templates – book, report, article, letter or beamer. You should define the document class in your .tex file so that Latex can include all the necessary packages to produce the final output. One of the biggest advantages of using LateX is that you can concentrate on the writing and leave the formatting to Latex. You don’t have to worry about figures getting out of place because you hit a key by mistake. Everything is taken care of in the backend. One of the powerful features of Latex is bibliography management. Essentially the whole process of referencing and generating bibliography is automated using BibTeX or BibLaTeX.

• Latex tutorial
• Benefits of Latex
• Advantages and disadvantages of using Latex
• BibTex tutorial

3. Open Office

OpenOffice is an open-source product that mimics MS Office. It is completely free, and the suite of products it offers includes Writer (Word), Calc (Excel), Impress (Powerpoint) and Base (Access), plus a vector graphics editor, Draw (Visio). The default file format of OpenOffice is OpenDocument Format (ODF). However, you can open and save documents with DOC and DOCX extensions. OpenOffice is available for Windows, Linux, and macOS, and the tool is distributed under Apache License. Many paid features of MS Word such as PDF export are available for free in OpenOffice. There are no hidden charges for add-ins and upgrades. The tool supports over 40 languages and includes Grammar and spell checker.

OpenOffice might exhibit some formatting issues while working with DOC/DOCX files. These issues might be apparent while working with word files with a lot of pictures, columns, headers and fancy text alignments. OpenOffice remains quite popular as it is downloaded approximately 100,000 times a day. Another important advantage is that it is open source and you can customize the tool to your requirements. It is ideal for small businesses and startups if they are not so keen on shedding money for buying the MS Office site licenses.

• OpenOffice vs. MS Office
• Benefits of using OpenOffice
• OpenOffice Writing tips
• OpenOffice Writing training and ideas

4. LibreOffice

LibreOffice is a free open office suite that is similar to OpenOffice. LibreOffice project branched out from the OpenOffice project in 2010 and is maintained by ‘The Document Foundation’.  You might notice some minor differences in features between OpenOffice and LibreOffice. However they are broadly similar regarding layout and functionality. Both projects are well-maintained. LibreOffice team tends to release very frequent updates with minor feature increments, but OpenOffice project tends to release new versions with major feature updates. LibreOffice enjoys a better recognition among the Linux community as it comes packaged with Linux, whereas you must download and install OpenOffice manually.

• LibreOffice review
• OpenOffice vs LibreOffice
• OpenOffice and LibreOffice feature comparison
• Should you switch from OpenOffice to LibreOffice?

5. Scrivener

Scrivener is a writing tool that allows you write and manage a long document such as a Ph.D. Thesis or a novel with a lot of chapters, sections, and subsections. Scrivener is available for both Windows and Mac. One of the great features of Scrivener is that you can split your writing into small chunks or snippets. You can move these chunks around easily and reorganize your content. You can visualize your document using different views. One of the views displays a short message summarising each chapter as a sticky note stuck on a board. You can create folders and subfolders to store documents, images, PDFs, audio, video, and web pages that you need for your writing. You simply drag and drop these into your writing easily in a single click. After finishing the text, the user can export the project into a wide variety of formats. Scrivener is not a free software tool; you can get a copy for approximately $45.

• Scrivener for Dummies book
• Reasons for switching to Scrivener
• Benefits of Scrivener
• Scrivener online course and help

7. Google Docs

Google Docs is a web-based writing software offered by Google. The suite includes Google Docs, Google Sheets and Google Slides which are simplified versions of MS Word, MS Excel and MS PowerPoint respectively. You can create and edit files online. One of the powerful features of the Google docs is the ability to collaborate with other users online. The changes made by multiple users on the same documents are highlighted with a user-specific color. Google docs are extensively used in the publishing industry where the articles predominantly contain text and pictures with minimum formatting. This Google project is actively maintained, and there are frequent product updates.

There is a limit on the size of the documents you can create on the Google Docs. The documents cannot be larger than 50MB, spreadsheets have a limit of 2 million cells, and presentation cannot be larger than 100MB. The images in the documents and presentation slides cannot be larger than 50MB. Users can load and export documents in a wide variety of formats such as DOC, DOCX, TXT and ODF file formats. You can upload documents to Google Drive cloud storage. You can either download Google Drive App to your desktop or use the online interface to import and export documents. There are handy research tools that allow you to search for academic papers and quickly insert the appropriate footnotes or citations in a variety of citation formats to Google Docs. You can install third-party add-ons. Some of the popular add-ons include easy bibliography creator, diagramming tool, and table of contents generator.

• How to use Google Docs?
• Tips and tricks for Google Docs
• How to use Google drive?
• Google drive tips and tricks

8. DropBox Paper

Dropbox Paper, or simply Paper, is a web based document-editing service developed by Dropbox. Paper is broadly similar to Google Docs in functionality. It has a very lightweight interface and is capable of supporting a wide variety of content including images, Google spreadsheets, data from Github, YouTube videos, Spotify playlists, and plain old code. This makes DropBox Paper easy to customise for a wide variety of projects. Documents can be easily shared with others. Documents can be shared individually or added to a folder with group access. You can also invite people to edit (or view) a file via email or with a specific URL. It is so easy to create tables and image galleries in Dropbox Paper document. You can create a table by simply clicking on an icon and specifying the number of rows and columns. Image galleries can be created by simply dragging and dropping images. DropBox paper also offers plenty of shortcuts that will save you a lot of time. For example, typing # followed by space will create an H1 header, and typing a hyphen followed by a space will start an unordered list.   DropBox will notify the changes made to the DropBox paper documents via the bell icon in the top left-hand corner. The version control system is very good. You can see all the changes that have been made to the documents and who made the changes. It is easy to navigate to various sections of the paper as the headings are listed as links in the left-hand panel. One of the downsides of using DropBox paper is that DOCX and MD (markdown ) are the only file formats available for exporting documents at the moment. Also, DropBox Paper does not currently support spreadsheet and presentation formats. Hopefully, this will be included in the future updates.

• Google Docs vs. DropBox paper
• Reasons for choosing Dropbox paper
• Features of DropBox paper
• Tips and Tricks for using DropBox paper

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Given that the intro mentions all sorts of academic writing, there’s a severe lack of any mention of how these tools interact with referencing softwares. Mendeley, endnote, papers3, and many more I’m failing to mention…

Plugins are available almost exclusively for word and libreoffice, and little else. As beautiful as scrivener looks, and as much as I like the way it structures documents and lets you focus on the writing, without an easy way to cite-as-you-write and automatically generate a bibliography, it’s simply never going to make any huge in-roads as far as academia is concerned.

Timothy, For the purpose of an integrated software suite that incorporates every component of academic writing — research, writing, and citation management –you should look into Nota Bene (www.notabene.com). I note that I have no financial interest in the company.

most people use scrivener or scribus

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• Methodology

Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism, run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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Shona McCombes

Top 21 must-have digital tools for researchers

Last updated

12 May 2023

Reviewed by

Jean Kaluza

Research drives many decisions across various industries, including:

Uncovering customer motivations and behaviors to design better products

Assessing whether a market exists for your product or service

Running clinical studies to develop a medical breakthrough

Conducting effective and shareable research can be a painstaking process. Manual processes are sluggish and archaic, and they can also be inaccurate. That’s where advanced online tools can help. 

The right tools can enable businesses to lean into research for better forecasting, planning, and more reliable decisions. 

• Why do researchers need research tools?

Research is challenging and time-consuming. Analyzing data , running focus groups , reading research papers , and looking for useful insights take plenty of heavy lifting. 

These days, researchers can’t just rely on manual processes. Instead, they’re using advanced tools that:

Speed up the research process

Enable new ways of reaching customers

Improve organization and accuracy

Allow better monitoring throughout the process

Enhance collaboration across key stakeholders

• The most important digital tools for researchers

Some tools can help at every stage, making researching simpler and faster.

They ensure accurate and efficient information collection, management, referencing, and analysis. 

Some of the most important digital tools for researchers include:

Research management tools

Research management can be a complex and challenging process. Some tools address the various challenges that arise when referencing and managing papers. 

.css-10ptwjf{-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;background:transparent;border:0;color:inherit;cursor:pointer;-webkit-flex-shrink:0;-ms-flex-negative:0;flex-shrink:0;-webkit-text-decoration:underline;text-decoration:underline;}.css-10ptwjf:disabled{opacity:0.6;pointer-events:none;} Zotero

Coined as a personal research assistant, Zotero is a tool that brings efficiency to the research process. Zotero helps researchers collect, organize, annotate, and share research easily. 

Zotero integrates with internet browsers, so researchers can easily save an article, publication, or research study on the platform for later. 

The tool also has an advanced organizing system to allow users to label, tag, and categorize information for faster insights and a seamless analysis process. 

Messy paper stacks––digital or physical––are a thing of the past with Paperpile. This reference management tool integrates with Google Docs, saving users time with citations and paper management. 

Referencing, researching, and gaining insights is much cleaner and more productive, as all papers are in the same place. Plus, it’s easier to find a paper when you need it. 

Acting as a single source of truth (SSOT), Dovetail houses research from the entire organization in a simple-to-use place. Researchers can use the all-in-one platform to collate and store data from interviews , forms, surveys , focus groups, and more. 

Dovetail helps users quickly categorize and analyze data to uncover truly actionable insights . This helps organizations bring customer insights into every decision for better forecasting, planning, and decision-making. 

Dovetail integrates with other helpful tools like ​Slack, Atlassian, Notion, and Zapier for a truly efficient workflow.

Putting together papers and referencing sources can be a huge time consumer. EndNote claims that researchers waste 200,000 hours per year formatting citations. 

To address the issue, the tool formats citations automatically––simultaneously creating a bibliography while the user writes. 

EndNote is also a cloud-based system that allows remote working, multiple-user interaction and collaboration, and seamless working on different devices. 

Information survey tools

Surveys are a common way to gain data from customers. These tools can make the process simpler and more cost-effective. 

With ready-made survey templates––to collect NPS data, customer effort scores , five-star surveys, and more––getting going with Delighted is straightforward. 

Delighted helps teams collect and analyze survey feedback without needing any technical knowledge. The templates are customizable, so you can align the content with your brand. That way, the survey feels like it’s coming from your company, not a third party. 

SurveyMonkey

With millions of customers worldwide, SurveyMonkey is another leader in online surveys. SurveyMonkey offers hundreds of templates that researchers can use to set up and deploy surveys quickly. 

Whether your survey is about team performance, hotel feedback, post-event feedback, or an employee exit, SurveyMonkey has a ready-to-use template. 

Typeform offers free templates you can quickly embed, which comes with a point of difference: It designs forms and surveys with people in mind, focusing on customer enjoyment. 

Typeform employs the ‘one question at a time’ method to keep engagement rates and completions high. It focuses on surveys that feel more like conversations than a list of questions.

Web data analysis tools

Collecting data can take time––especially technical information. Some tools make that process simpler. 

For those conducting clinical research, data collection can be incredibly time-consuming. Teamscope provides an online platform to collect and manage data simply and easily. 

Researchers and medical professionals often collect clinical data through paper forms or digital means. Those are too easy to lose, tricky to manage, and challenging to collaborate on. 

With Teamscope, you can easily collect, store, and electronically analyze data like patient-reported outcomes and surveys. 

Heap is a digital insights platform providing context on the entire customer journey . This helps businesses improve customer feedback , conversion rates, and loyalty. 

Through Heap, you can seamlessly view and analyze the customer journey across all platforms and touchpoints, whether through the app or website. 

Another analytics tool, Smartlook, combines quantitative and qualitative analytics into one platform. This helps organizations understand user behavior and make crucial improvements. 

Smartlook is useful for analyzing web pages, purchasing flows, and optimizing conversion rates. 

Project management tools

Managing multiple research projects across many teams can be complex and challenging. Project management tools can ease the burden on researchers. 

Visual productivity tool Trello helps research teams manage their projects more efficiently. Trello makes product tracking easier with:

A range of workflow options

Unique project board layouts

Advanced descriptions

Integrations

Trello also works as an SSOT to stay on top of projects and collaborate effectively as a team. 

To connect research, workflows, and teams, Airtable provides a clean interactive interface. 

With Airtable, it’s simple to place research projects in a list view, workstream, or road map to synthesize information and quickly collaborate. The Sync feature makes it easy to link all your research data to one place for faster action. 

For product teams, Asana gathers development, copywriting, design, research teams, and product managers in one space. 

As a task management platform, Asana offers all the expected features and more, including time-tracking and Jira integration. The platform offers reporting alongside data collection methods , so it’s a favorite for product teams in the tech space.

Grammar checker tools

Grammar tools ensure your research projects are professional and proofed. 

No one’s perfect, especially when it comes to spelling, punctuation, and grammar. That’s where Grammarly can help. 

Grammarly’s AI-powered platform reviews your content and corrects any mistakes. Through helpful integrations with other platforms––such as Gmail, Google Docs, Twitter, and LinkedIn––it’s simple to spellcheck as you go. 

Another helpful grammar tool is Trinka AI. Trinka is specifically for technical and academic styles of writing. It doesn’t just correct mistakes in spelling, punctuation, and grammar; it also offers explanations and additional information when errors show. 

Researchers can also use Trinka to enhance their writing and:

Align it with technical and academic styles

Improve areas like syntax and word choice

Discover relevant suggestions based on the content topic

Plagiarism checker tools

Avoiding plagiarism is crucial for the integrity of research. Using checker tools can ensure your work is original. 

Plagiarism checker Quetext uses DeepSearch™ technology to quickly sort through online content to search for signs of plagiarism. 

With color coding, annotations, and an overall score, it’s easy to identify conflict areas and fix them accordingly. 

Duplichecker

Another helpful plagiarism tool is Duplichecker, which scans pieces of content for issues. The service is free for content up to 1000 words, with paid options available after that. 

If plagiarism occurs, a percentage identifies how much is duplicate content. However, the interface is relatively basic, offering little additional information.  

Journal finder tools

Finding the right journals for your project can be challenging––especially with the plethora of inaccurate or predatory content online. Journal finder tools can solve this issue. 

Enago Journal Finder

The Enago Open Access Journal Finder sorts through online journals to verify their legitimacy. Through Engao, you can discover pre-vetted, high-quality journals through a validated journal index. 

Enago’s search tool also helps users find relevant journals for their subject matter, speeding up the research process. 

JournalFinder

JournalFinder is another journal tool that’s popular with academics and researchers. It makes the process of discovering relevant journals fast by leaning into a machine-learning algorithm.

This is useful for discovering key information and finding the right journals to publish and share your work in. 

Social networking for researchers

Collaboration between researchers can improve the accuracy and sharing of information. Promoting research findings can also be essential for public health, safety, and more. 

While typical social networks exist, some are specifically designed for academics.

ResearchGate

Networking platform ResearchGate encourages researchers to connect, collaborate, and share within the scientific community. With 20 million researchers on the platform, it's a popular choice. 

ResearchGate is founded on an intention to advance research. The platform provides topic pages for easy connection within a field of expertise and access to millions of publications to help users stay up to date. 

Academia is another commonly used platform that connects 220 million academics and researchers within their specialties. 

The platform aims to accelerate research with discovery tools and grow a researcher’s audience to promote their ideas. 

On Academia, users can access 47 million PDFs for free. They cover topics from mechanical engineering to applied economics and child psychology. 

• Expedited research with the power of tools

For researchers, finding data and information can be time-consuming and complex to manage. That’s where the power of tools comes in. 

Manual processes are slow, outdated, and have a larger potential for inaccuracies. 

Leaning into tools can help researchers speed up their processes, conduct efficient research, boost their accuracy, and share their work effectively. 

With tools available for project and data management, web data collection, and journal finding, researchers have plenty of assistance at their disposal.

When it comes to connecting with customers, advanced tools boost customer connection while continually bringing their needs and wants into products and services.

What are primary research tools?

Primary research is data and information that you collect firsthand through surveys, customer interviews, or focus groups. 

Secondary research is data and information from other sources, such as journals, research bodies, or online content. 

Primary researcher tools use methods like surveys and customer interviews. You can use these tools to collect, store, or manage information effectively and uncover more accurate insights. 

What is the difference between tools and methods in research?

Research methods relate to how researchers gather information and data. 

For example, surveys, focus groups, customer interviews, and A/B testing are research methods that gather information. 

On the other hand, tools assist areas of research. Researchers may use tools to more efficiently gather data, store data securely, or uncover insights. 

Tools can improve research methods, ensuring efficiency and accuracy while reducing complexity.

Get started today

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Nine best practices for research software registries and repositories

Daniel garijo.

1 Universidad Politécnica de Madrid, Madrid, Spain

Hervé Ménager

2 Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, Paris, France

Lorraine Hwang

3 University of California, Davis, Davis, California, United States

Ana Trisovic

4 Harvard University, Boston, Massachusetts, United States

Michael Hucka

5 California Institute of Technology, Pasadena, California, United States

Thomas Morrell

Alice allen.

6 University of Maryland, College Park, MD, United States

Task Force on Best Practices for Software Registries

7 FORCE11 Software Citation Implementation Working Group

SciCodes Consortium

8 Consortium of Scientific Software Registries and Repositories

Associated Data

The following information was supplied regarding data availability:

There is no data or code associated with this publication.

Scientific software registries and repositories improve software findability and research transparency, provide information for software citations, and foster preservation of computational methods in a wide range of disciplines. Registries and repositories play a critical role by supporting research reproducibility and replicability, but developing them takes effort and few guidelines are available to help prospective creators of these resources. To address this need, the FORCE11 Software Citation Implementation Working Group convened a Task Force to distill the experiences of the managers of existing resources in setting expectations for all stakeholders. In this article, we describe the resultant best practices which include defining the scope, policies, and rules that govern individual registries and repositories, along with the background, examples, and collaborative work that went into their development. We believe that establishing specific policies such as those presented here will help other scientific software registries and repositories better serve their users and their disciplines.

Introduction

Research software is an essential constituent in scientific investigations ( Wilson et al., 2014 ; Momcheva & Tollerud, 2015 ; Hettrick, 2018 ; Lamprecht et al., 2020 ), as it is often used to transform and prepare data, perform novel analyses on data, automate manual processes, and visualize results reported in scientific publications ( Howison & Herbsleb, 2011 ). Research software is thus crucial for reproducibility and has been recognized by the scientific community as a research product in its own right—one that should be properly described, accessible, and credited by others ( Smith, Katz & Niemeyer, 2016 ; Chue Hong et al., 2021 ). As a result of the increasing importance of computational methods, communities such as Research Data Alliance (RDA) ( Berman & Crosas, 2020 ) ( https://www.rd-alliance.org/ ) and FORCE11 ( Bourne et al., 2012 ) ( https://www.force11.org/ ) emerged to enable collaboration and establish best practices. Numerous software services that enable open community development of and access to research source code, such as GitHub ( https://github.com/ ) and GitLab ( https://gitlab.com ), appeared and found a role in science. General-purpose repositories, such as Zenodo ( CERN & OpenAIRE, 2013 ) and FigShare ( Thelwall & Kousha, 2016 ), have expanded their scope beyond data to include software, and new repositories, such as Software Heritage ( Di Cosmo & Zacchiroli, 2017 ), have been developed specifically for software. A large number of domain-specific research software registries and repositories have emerged for different scientific disciplines to ensure dissemination and reuse among their communities ( Gentleman et al., 2004 ; Peckham, Hutton & Norris, 2013 ; Greuel & Sperber, 2014 ; Allen & Schmidt, 2015 ; Gil, Ratnakar & Garijo, 2015 ; Gil et al., 2016 ).

Research software registries are typically indexes or catalogs of software metadata, without any code stored in them; while in research software repositories , software is both indexed and stored ( Lamprecht et al., 2020 ). Both types of resource improve software discoverability and research transparency, provide information for software citations, and foster preservation of computational methods that might otherwise be lost over time, thereby supporting research reproducibility and replicability. Many provide or are integrated with other services, including indexing and archival services, that can be leveraged by librarians, digital archivists, journal editors and publishers, and researchers alike.

Transparency of the processes under which registries and repositories operate helps build trust with their user communities ( Yakel et al., 2013 ; Frank et al., 2017 ). However, many domain research software resources have been developed independently, and thus policies amongst such resources are often heterogeneous and some may be omitted. Having specific policies in place ensures that users and administrators have reference documents to help define a shared understanding of the scope, practices, and rules that govern these resources.

Though recommendations and best practices for many aspects of science have been developed, no best practices existed that addressed the operations of software registries and repositories. To address this need, a Best Practices for Software Registries Task Force was proposed in June 2018 to the FORCE11 Software Citation Implementation Working Group (SCIWG) ( https://github.com/force11/force11-sciwg ). In seeking to improve the services software resources provide, software repository maintainers came together to learn from each other and promote interoperability. Both common practices and missing practices unfolded in these exchanges. These practices led to the development of nine best practices that set expectations for both users and maintainers of the resource by defining management of its contents and allowed usages as well as clarifying positions on sensitive issues such as attribution.

In this article, we expand on our pre-print “Nine Best Practices for Research Software Registries and Repositories: A Concise Guide” ( Task Force on Best Practices for Software Registries et al., 2020 ) to describe our best practices and their development. Our guidelines are actionable, have a general purpose, and reflect the discussion of a community of more than 30 experts who handle over 14 resources (registries or repositories) across different scientific domains. Each guideline provides a rationale, suggestions, and examples based on existing repositories or registries. To reduce repetition, we refer to registries and repositories collectively as “resources.”

The remainder of the article is structured as follows. We first describe background and related efforts in “Background”, followed by the methodology we used when structuring the discussion for creating the guidelines (Methodology). We then describe the nine best practices in “Best Practices for Repositories and Registries”, followed by a discussion (Discussion). “Conclusions” concludes the article by summarizing current efforts to continue the adoption of the proposed practices. Those who contributed to the development of this article are listed in Appendix A, and links to example policies are given in Appendix B. Appendix C provides updated information about resources that have participated in crafting the best practices and an overview of their main attributes.

In the last decade, much was written about a reproducibility crisis in science ( Baker, 2016 ) stemming in large part from the lack of training in programming skills and the unavailability of computational resources used in publications ( Merali, 2010 ; Peng, 2011 ; Morin et al., 2012 ). On these grounds, national and international governments have increased their interest in releasing artifacts of publicly-funded research to the public ( Office of Science & Technology Policy, 2016 ; Directorate-General for Research & Innovation (European Commission), 2018 ; Australian Research Council, 2018 ; Chen et al., 2019 ; Ministère de l’Enseignement supérieur, de la Recherche et de l’Innovation, 2021 ), and scientists have appealed to colleagues in their field to release software to improve research transparency ( Weiner et al., 2009 ; Barnes, 2010 ; Ince, Hatton & Graham-Cumming, 2012 ) and efficiency ( Grosbol & Tody, 2010 ). Open Science initiatives such as RDA and FORCE11 have emerged as a response to these calls for greater transparency and reproducibility. Journals introduced policies encouraging (or even requiring) that data and software be openly available to others ( Editorial Staff, 2019 ; Fox et al., 2021 ). New tools have been developed to facilitate depositing research data and software in a repository ( Baruch, 2007 ; CERN & OpenAIRE, 2013 ; Di Cosmo & Zacchiroli, 2017 ; Clyburne-Sherin, Fei & Green, 2019 ; Brinckman et al., 2019 ; Trisovic et al., 2020 ) and consequently, make them citable so authors and other contributors gain recognition and credit for their work ( Soito & Hwang, 2017 ; Du et al., 2021 ).

Support for disseminating research outputs has been proposed with FAIR and FAIR4RS principles that state shared digital artifacts, such as data and software, should be Findable, Accessible, Interoperable, and Reusable ( Wilkinson et al., 2016 ; Lamprecht et al., 2020 ; Katz, Gruenpeter & Honeyman, 2021 ; Chue Hong et al., 2021 ). Conforming with the FAIR principles for published software ( Lamprecht et al., 2020 ) requires facilitating its discoverability, preferably in domain-specific resources ( Jiménez et al., 2017 ). These resources should contain machine-readable metadata to improve the discoverability (Findable) and accessibility (Accessible) of research software through search engines or from within the resource itself. Furthering interoperability in FAIR is aided through the adoption of community standards e.g ., schema.org ( Guha, Brickley & Macbeth, 2016 ) or the ability to translate from one resource to another. The CodeMeta initiative ( Jones et al., 2017 ) achieves this translation by creating a “Rosetta Stone” which maps the metadata terms used by each resource to a common schema. The CodeMeta schema ( https://codemeta.github.io/ ) is an extension of schema.org which adds ten new fields to represent software-specific metadata. To date, CodeMeta has been adopted for representing software metadata by many repositories ( https://hal.inria.fr/hal-01897934v3/codemeta ).

As the usage of computational methods continues to grow, recommendations for improving research software have been proposed ( Stodden et al., 2016 ) in many areas of science and software, as can be seen by the series of “Ten Simple Rules” articles offered by PLOS ( Dashnow, Lonsdale & Bourne, 2014 ), sites such as AstroBetter ( https://www.astrobetter.com/ ), courses to improve skills such as those offered by The Carpentries ( https://carpentries.org/ ), and attempts to measure the adoption of recognized best practices ( Serban et al., 2020 ; Trisovic et al., 2022 ). Our quest for best practices complements these efforts by providing guides to the specific needs of research software registries and repositories.

Methodology

The best practices presented in this article were developed by an international Task Force of the FORCE11 Software Citation Implementation Working Group (SCIWG). The Task Force was proposed in June 2018 by author Alice Allen, with the goal of developing a list of best practices for software registries and repositories. Working Group members and a broader group of managers of domain specific software resources formed the inaugural group. The resulting Task Force members were primarily managers and editors of resources from Europe, United States, and Australia. Due to the range in time zones, the Task Force held two meetings 7 h apart, with the expectation that, except for the meeting chair, participants would attend one of the two meetings. We generally refer to two meetings on the same day with the singular “meeting” in the discussions to follow.

The inaugural Task Force meeting (February, 2019) was attended by 18 people representing 14 different resources. Participants introduced themselves and provided some basic information about their resources, including repository name, starting year, number of records, and scope (discipline-specific or general purpose), as well as services provided by each resource ( e.g ., support of software citation, software deposits, and DOI minting). Table 1 presents an overview of the collected responses, which highlight the efforts of the Task Force chairs to bring together both discipline-specific and general purpose resources. The “Other” category indicates that the answer needed clarifying text ( e.g ., for the question “is the repository actively curated?” some repositories are not manually curated, but have validation checks). Appendix C provides additional information on the questions asked to resource managers ( Table C.1 ) and their responses ( Tables C.2 – C.4 ).

During the inaugural Task Force meeting, the chair laid out the goal of the Task Force, and the group was invited to brainstorm to identify commonalities for building a list of best practices. Participants also shared challenges they had faced in running their resources and policies they had enacted to manage these resources. The result of the brainstorming and discussion was a list of ideas collected in a common document.

Starting in May 2019 and continuing through the rest of 2019, the Task Force met on the third Thursday of each month and followed an iterative process to discuss, add to, and group ideas; refine and clarify the ideas into different practices, and define the practices more precisely. It was clear from the onset that, though our resources have goals in common, they are also very diverse and would be best served by best practices that were descriptive rather than prescriptive. We reached consensus on whether a practice should be a best practice through discussion and informal voting. Each best practice was given a title and a list of questions or needs that it addressed.

Our initial plan aimed at holding two Task Force meetings on the same day each month, in order to follow a common agenda with independent discussions built upon the previous month’s meeting. However, the later meeting was often advantaged by the earlier discussion. For instance, if the early meeting developed a list of examples for one of the guidelines, the late meeting then refined and added to the list. Hence, discussions were only duplicated when needed, e.g ., where there was no consensus in the early group, and often proceeded in different directions according to the group’s expertise and interest. Though we had not anticipated this, we found that holding two meetings each month on the same day accelerated the work, as work done in the second meeting of the day generally continued rather than repeating work done in the first meeting.

The resulting consensus from the meetings produced a list of the most broadly applicable practices, which became the initial list of best practices participants drew from during a two-day workshop, funded by the Sloan Foundation and held at the University of Maryland College Park, in November, 2019 ( Scientific Software Registry Collaboration Workshop ). A goal of the workshop was to develop the final recommendations on best practices for repositories and registries to the FORCE11 SCIWG. The workshop included participants outside the Task Force resulting in a broader set of contributions to the final list. In 2020, this group made additional refinements to the best practices during virtual meetings and through online collaborative writing producing in the guidelines described in the next section. The Task Force then transitioned into the SciCodes consortium ( http://scicodes.net ). SciCodes is a permanent community for research software registries and repositories with a particular focus on these best practices. SciCodes continued to collect information about involved registries and repositories, which are listed in Appendix C. We also include some analysis of the number of entries and date of creation of member resources. Appendix A lists the people who participated in these efforts.

Best practices for repositories and registries

Our recommendations are provided as nine separate policies or statements, each presented below with an explanation as to why we recommend the practice, what the practice describes, and specific considerations to take into account. The last paragraph of each best practice includes one or two examples and a link to Appendix B, which contains many examples from different registries and repositories.

These nine best practices, though not an exhaustive list, are applicable to the varied resources represented in the Task Force, so are likely to be broadly applicable to other scientific software repositories and registries. We believe that adopting these practices will help document, guide, and preserve these resources, and put them in a stronger position to serve their disciplines, users, and communities 1 .

Provide a public scope statement

The landscape of research software is diverse and complex due to the overlap between scientific domains, the variety of technical properties and environments, and the additional considerations resulting from funding, authors’ affiliation, or intellectual property. A scope statement clarifies the type of software contained in the repository or indexed in the registry. Precisely defining a scope, therefore, helps those users of the resource who are looking for software to better understand the results they obtained.

Moreover, given that many of these resources accept submission of software packages, providing a precise and accessible definition will help researchers determine whether they should register or deposit software, and curators by making clear what is out of scope for the resource. Overall, a public scope manages the expectations of the potential depositor as well as the software seeker. It informs both what the resource does and does not contain.

The scope statement should describe:

• What is accepted, and acceptable, based on criteria covering scientific discipline, technical characteristics, and administrative properties
• What is not accepted, i.e. , characteristics that preclude their incorporation in the resource
• Notable exceptions to these rules, if any

Particular criteria of relevance include the scientific community being served and the types of software listed in the registry or stored in the repository, such as source code, compiled executables, or software containers. The scope statement may also include criteria that must be satisfied by accepted software, such as whether certain software quality metrics must be fulfilled or whether a software project must be used in published research. Availability criteria can be considered, such as whether the code has to be publicly available, be in the public domain and/or have a license from a predefined set, or whether software registered in another registry or repository will be accepted.

An illustrating example of such a scope statement is the editorial policy ( https://ascl.net/wordpress/submissions/editiorial-policy/ ) published by the Astrophysics Source Code Library (ASCL) ( Allen et al., 2013 ), which states that it includes only software source code used in published astronomy and astrophysics research articles, and specifically excludes software available only as a binary or web service. Though the ASCL’s focus is on research documented in peer-reviewed journals, its policy also explicitly states that it accepts source code used in successful theses. Other examples of scope statements can be found in Appendix B.

Provide guidance for users

Users accessing a resource to search for entries and browse or retrieve the description(s) of one or more software entries have to understand how to perform such actions. Although this guideline potentially applies to many public online resources, especially research databases, the potential complexity of the stored metadata and the curation mechanisms can seriously impede the understandability and usage of software registries and repositories.

User guidance material may include:

• How to perform common user tasks, such as searching the resource, or accessing the details of an entry
• Answers to questions that are often asked or can be anticipated, e.g ., with Frequently Asked Questions or tips and tricks pages
• Who to contact for questions or help

A separate section in these guidelines on the Conditions of use policy covers terms of use of the resource and how best to cite records in a resource and the resource itself.

Guidance for users who wish to contribute software is covered in the next section, Provide guidance to software contributors .

When writing guidelines for users, it is advisable to identify the types of users your resource has or could potentially have and corresponding use cases. Guidance itself should be offered in multiple forms, such as in-field prompts, linked explanations, and completed examples. Any machine-readable access, such as an API, should be fully described directly in the interface or by providing a pointer to existing documentation, and should specify which formats are supported ( e.g ., JSON-LD, XML) through content negotiation if it is enabled.

Examples of such elements include, for instance, the bio.tools registry ( Ison et al., 2019 ) API user guide ( https://biotools.readthedocs.io/en/latest/api_usage_guide.html ), or the ORNL DAAC ( ORNL, 2013 ) instructions for data providers ( https://daac.ornl.gov/submit/ ). Additional examples of user guidance can be found in Appendix B.

Provide guidance to software contributors

Most software registries and repositories rely on a community model, whereby external contributors will provide software entries to the resource. The scope statement will already have explained what is accepted and what is not; the contributor policy addresses who can add or change software entries and the processes involved.

The contributor policy should therefore describe:

• Who can or cannot submit entries and/or metadata
• Required and optional metadata expected for deposited software
• Review process, if any
• Curation process, if any
• Procedures for updates ( e.g ., who can do it, when it is done, how is it done)

Topics to consider when writing a contributor policy include whether the author(s) of a software entry will be contacted if the contributor is not also an author and whether contact is a condition or side-effect of the submission. Additionally, a contributor policy should specify how persistent identifiers are assigned (if used) and should state that depositors must comply with all applicable laws and not be intentionally malicious.

Such material is provided in resources such as the Computational Infrastructure for Geodynamics ( Hwang & Kellogg, 2017 ) software contribution checklist ( https://github.com/geodynamics/best_practices/blob/master/ContributingChecklist.md#contributing-software ) and the CoMSES Net Computational Model Library ( Janssen et al., 2008 ) model archival tutorial ( https://forum.comses.net/t/archiving-your-model-1-gettingstarted/7377 ). Additional examples of guidance for software contributors can be found in Appendix B.

Establish an authorship policy

Because research software is often a research product, it is important to report authorship accurately, as it allows for proper scholarly credit and other types of attributions ( Smith, Katz & Niemeyer, 2016 ). However, even though authorship should be defined at the level of a given project, it can prove complicated to determine ( Alliez et al., 2019 ). Roles in software development can widely vary as contributors change with time and versions, and contributions are difficult to gauge beyond the “commit,” giving rise to complex situations. In this context, establishing a dedicated policy ensures that people are given due credit for their work. The policy also serves as a document that administrators can turn to in case disputes arise and allows proactive problem mitigation, rather than having to resort to reactive interpretation. Furthermore, having an authorship policy mirrors similar policies by journals and publishers and thus is part of a larger trend. Note that the authorship policy will be communicated at least partially to users through guidance provided to software contributors. Resource maintainers should ensure this policy remains consistent with the citation policies for the registry or repository (usually, the citation requirements for each piece of research software are under the authority of its owners).

The authorship policy should specify:

• How authorship is determined e.g ., a stated criteria by the contributors and/or the resource
• Policies around making changes to authorship
• The conflict resolution processes adopted to handle authorship disputes

When defining an authorship policy, resource maintainers should take into consideration whether those who are not coders, such as software testers or documentation maintainers, will be identified or credited as authors, as well as criteria for ordering the list of authors in cases of multiple authors, and how the resource handles large numbers of authors and group or consortium authorship. Resources may also include guidelines about how changes to authorship will be handled so each author receives proper credit for their contribution. Guidelines can help facilitate determining every contributors’ role. In particular, the use of a credit vocabulary, such as the Contributor Roles Taxonomy ( Allen, O’Connell & Kiermer, 2019 ), to describe authors’ contributions should be considered for this purpose ( http://credit.niso.org/ ).

An example of authorship policy is provided in the Ethics Guidelines ( https://joss.theoj.org/about#ethics ) and the submission guide authorship section ( https://joss.readthedocs.io/en/latest/submitting.html#authorship ) of the Journal of Open Source Software ( Katz, Niemeyer & Smith, 2018 ), which provides rules for inclusion in the authors list. Additional examples of authorship policies can be found in Appendix B.

Document and share your metadata schema

The structure and semantics of the information stored in registries and repositories is sometimes complex, which can hinder the clarity, discovery, and reuse of the entries included in these resources. Publicly posting the metadata schema used for the entries helps individual and organizational users interested in a resource’s information understand the structure and properties of the deposited information. The metadata structure helps to inform users how to interact with or ingest records in the resource. A metadata schema mapped to other schemas and an API specification can improve the interoperability between registries and repositories.

This practice should specify:

• The schema used and its version number. If a standard or community schema, such as CodeMeta ( Jones et al., 2017 ) or schema.org ( Guha, Brickley & Macbeth, 2016 ) is used, the resource should reference its documentation or official website. If a custom schema is used, formal documentation such as a description of the schema and/or a data dictionary should be provided.
• Expected metadata when submitting software, including which fields are required and which are optional, and the format of the content in each field.

To improve the readability of the metadata schema and facilitate its translation to other standards, resources may provide a mapping (from the metadata schema used in the resource) to published standard schemas, through the form of a “cross-walk” ( e.g ., the CodeMeta cross-walk ( https://codemeta.github.io/crosswalk/ )) and include an example entry from the repository that illustrates all the fields of the metadata schema. For instance, extensive documentation ( https://biotoolsschema.readthedocs.io/en/latest/ ) is available for the biotoolsSchema ( Ison et al., 2021 ) format, which is used in the bio.tools registry. Another example is the OntoSoft vocabulary ( http://ontosoft.org/software ), used by the OntoSoft registry ( Gil, Ratnakar & Garijo, 2015 ; Gil et al., 2016 ) and available in both machine-readable and human readable formats. Additional examples of metadata schemas can be found in Appendix B.

Stipulate conditions of use

The conditions of use document the terms under which users may use the contents provided by a website. In the case of software registries and repositories, these conditions should specifically state how the metadata regarding the entities of a resource can be used, attributed, and/or cited, and provide information about the licenses used for the code and binaries. This policy can forestall potential liabilities and difficulties that may arise, such as claims of damage for misinterpretation or misapplication of metadata. In addition, the conditions of use should clearly state how the metadata can and cannot be used, including for commercial purposes and in aggregate form.

This document should include:

• Legal disclaimers about the responsibility and liability borne by the registry or repository
• License and copyright information, both for individual entries and for the registry or repository as a whole
• Conditions for the use of the metadata, including prohibitions, if any
• Preferred format for citing software entries
• Preferred format for attributing or citing the resource itself

When writing conditions of use, resource maintainers might consider what license governs the metadata, if licensing requirements apply for findings and/or derivatives of the resource, and whether there are differences in the terms and license for commercial vs noncommercial use. Restrictions on the use of the metadata may also be included, as well as a statement to the effect that the registry or repository makes no guarantees about completeness and is not liable for any damages that could arise from the use of the information. Technical restrictions, such as conditions of use of the API (if one is available), may also be mentioned.

Conditions of use can be found for instance for DOE CODE ( Ensor et al., 2017 ), which in addition to the general conditions of use ( https://www.osti.gov/disclaim ) specifies that the rules for usage of the hosted code ( https://www.osti.gov/doecode/faq#are-there-restrictions ) are defined by their respective licenses. Additional examples of conditions of use policies can be found in Appendix B.

State a privacy policy

Privacy policies define how personal data about users are stored, processed, exchanged or removed. Having a privacy policy demonstrates a strong commitment to the privacy of users of the registry or repository and allows the resource to comply with the legal requirement of many countries in addition to those a home institution and/or funding agencies may impose.

The privacy policy of a resource should describe:

• What information is collected and how long it is retained
• How the information, especially any personal data, is used
• Whether tracking is done, what is tracked, and how ( e.g ., Google Analytics)
• Whether cookies are used

When writing a privacy policy, the specific personal data which are collected should be detailed, as well as the justification for their resource, and whether these data are sold and shared. Additionally, one should list explicitly the third-party tools used to collect analytic information and potentially reference their privacy policies. If users can receive emails as a result of visiting or downloading content, such potential solicitations or notifications should be announced. Measures taken to protect users’ privacy and whether the resource complies with the European Union Directive on General Data Protection Regulation ( https://gdpr-info.eu/ ) (GDPR) or other local laws, if applicable, should be explained 2 . As a precaution, the statement can reserve the right to make changes to this privacy policy. Finally, a mechanism by which users can request the removal of such information should be described.

For example, the SciCrunch’s ( Grethe et al., 2014 ) privacy policy ( https://scicrunch.org/page/privacy ) details what kind of personal information is collected, how it is collected, and how it may be reused, including by third-party websites through the use of cookies. Additional examples of privacy policies can be found in Appendix B.

Provide a retention policy

Many software registries and repositories aim to facilitate the discovery and accessibility of the objects they describe, e.g ., enabling search and citation, by making the corresponding records permanently accessible. However, for various reasons, even in such cases maintainers and curators may have to remove records. Common examples include removing entries that are outdated, no longer meet the scope of the registry, or are found to be in violation of policies. The resource should therefore document retention goals and procedures so that users and depositors are aware of them.

The retention policy should describe:

• The length of time metadata and/or files are expected to be retained;
• Under what conditions metadata and/or files are removed;
• Who has the responsibility and ability to remove information;
• Procedures to request that metadata and/or files be removed.

The policy should take into account whether best practices for persistent identifiers are followed, including resolvability, retention, and non-reuse of those identifiers. The retention time provided by the resource should not be too prescriptive ( e.g ., “for the next 10 years”), but rather it should fit within the context of the underlying organization(s) and its funding. This policy should also state who is allowed to edit metadata, delete records, or delete files, and how these changes are performed to preserve the broader consistency of the registry. Finally, the process by which data may be taken offline and archived as well as the process for its possible retrieval should be thoroughly documented.

As an example, Bioconductor ( Gentleman et al., 2004 ) has a deprecation process through which software packages are removed if they cannot be successfully built or tested, or upon specific request from the package maintainer. Their policy ( https://bioconductor.org/developers/package-end-of-life/ ) specifies who initiates this process and under which circumstances, as well as the successive steps that lead to the removal of the package. Additional examples of retention policies can be found in Appendix B.

Disclose your end-of-life policy

Despite their usefulness, the long-term maintenance, sustainability, and persistence of online scientific resources remains a challenge, and published web services or databases can disappear after a few years ( Veretnik, Fink & Bourne, 2008 ; Kern, Fehlmann & Keller, 2020 ). Sharing a clear end-of-life policy increases trust in the community served by a registry or repository. It demonstrates a thoughtful commitment to users by informing them that provisions for the resource have been considered should the resource close or otherwise end its services for its described artifacts. Such a policy sets expectations and provides reassurance as to how long the records within the registry will be findable and accessible in the future.

This policy should describe:

• Under what circumstances the resource might end its services;
• What consequences would result from closure;
• What will happen to the metadata and/or the software artifacts contained in the resource in the event of closure;
• If long-term preservation is expected, where metadata and/or software artifacts will be migrated for preservation;
• How a migration will be funded.

Publishing an end-of-life policy is an opportunity to consider, in the event a resource is closed, whether the records will remain available, and if so, how and for whom, and under which conditions, such as archived status or “read-only.” The restrictions applicable to this policy, if any, should be considered and detailed. Establishing a formal agreement or memorandum of understanding with another registry, repository, or institution to receive and preserve the data or project, if applicable, might help to prepare for such a liability.

Examples of such policies include the Zenodo end-of-life policy ( https://help.zenodo.org/ ), which states that if Zenodo ceases its services, the data hosted in the resource will be migrated and the DOIs provided would be updated to resolve to the new location (currently unspecified). Additional examples of end-of-life policies can be found in Appendix B.

A summary of the practices presented in this section can be found in Table 2 .

The best practices described above serve as a guide for repositories and registries to provide better service to their users, ranging from software developers and researchers to publishers and search engines, and enable greater transparency about the operation of their described resources. Implementing our practices provides users with significant information about how different resources operate, while preserving important institutional knowledge, standardizing expectations, and guiding user interactions.

For instance, a public scope statement and guidance for users may directly impact usability and, thus, the popularity of the repository. Resources including tools with a simple design and unambiguous commands, as well as infographic guides or video tutorials, ease the learning curve for new users. The guidance for software contributions, conditions of use, and sharing the metadata schema used may help eager users contribute new functionality or tools, which may also help in creating a community around a resource. A privacy policy has become a requirement across geographic boundaries and legal jurisdictions. An authorship policy is critical in facilitating collaborative work among researchers and minimizing the chances for disputes. Finally, retention and end-of-life policies increase the trust and integrity of a repository service.

Policies affecting a single community or domain were deliberately omitted when developing the best practices. First, an exhaustive list would have been a barrier to adoption and not applicable to every repository since each has a different perspective, audience, and motivation that drives policy development for their organization. Second, best practices that regulate the content of a resource are typically domain-specific to the artifact and left to resources to stipulate based on their needs. Participants in the 2019 Scientific Software Registry Collaboration Workshop were surprised to find that only four metadata elements were shared by all represented resources 3 . The diversity of our resources precludes prescriptive requirements, such as requiring specific metadata for records, so these were also deliberately omitted in the proposed best practices.

Hence, we focused on broadly applicable practices considered important by various resources. For example, amongst the participating registries and repositories, very few had codes of conduct that govern the behavior of community members. Codes of conduct are warranted if resources are run as part of a community, especially if comments and reviews are solicited for deposits. In contrast, a code of conduct would be less useful for resources whose primary purpose is to make software and software metadata available for reuse. However, this does not negate their importance and their inclusion as best practices in other arenas concerning software.

As noted by the FAIR4RS movement, software is different than data, motivating the need for a separate effort to address software resources ( Lamprecht et al., 2020 ; Katz et al., 2016 ). Even so, there are some similarities, and our effort complements and aligns well with recent guidelines developed in parallel to increase the transparency, responsibility, user focus, sustainability, and technology of data repositories. For example, both the TRUST Principles ( Lin et al., 2020 ) and CoreTrustSeal Requirements ( CoreTrustSeal, 2019 ) call for a repository to provide information on its scope and list the terms of use of its metadata to be considered compliant with TRUST or CoreTrustSeal, which aligns with our practices “ Provide a public scope statement ” and “ Stipulate conditions of use ”. CoreTrustSeal and TRUST also require that a repository consider continuity of access, which we have expressed as the practice to “ Disclosing your end-of-life policy ”. Our best practices differ in that they do not address, for example, staffing needs nor professional development for staff, as CoreTrustSeal requires, nor do our practices address protections against cyber or physical security threats, as the TRUST principles suggest. Inward-facing policies, such as documenting internal workflows and practices, are generally good in reducing operational risks, but internal management practices were considered out of scope of our guidelines.

Figure 1 shows the number of resources that support (partially or in their totality) each best practice. Though we see the proposed best practices as critical, many of the repositories that have actively participated in the discussions (14 resources in total) have yet to implement every one of them. We have observed that the first three practices (providing public scope statement, add guidance for users and for software contributors) have the widest adoption, while the retention, end-of-life, and authorship policy the least. Understanding the lag in the implementation across all of the best practices requires further engagement with the community.

Improving the adoption of our guidelines is one of the goals of SciCodes ( http://scicodes.net ), a recent consortium of scientific software registries and repositories. SciCodes evolved from the Task Force as a permanent community to continue the dialogue and share information between domains, including sharing of tools and ideas. SciCodes has also prioritized improving software citation (complementary to the efforts of the FORCE11 SCIWG) and tracking the impact of metadata and interoperability. In addition, SciCodes aims to understand barriers to implementing policies, ensure consistency between various best practices, and continue advocacy for software support by continuing dialogue between registries, repositories, researchers, and other stakeholders.

Conclusions

The dissemination and preservation of research material, where repositories and registries play a key role, lies at the heart of scientific advancement. This article introduces nine best practices for research software registries and repositories. The practices are an outcome of a Task Force of the FORCE11 Software Citation Implementation Working Group and reflect the discussion, collaborative experiences, and consensus of over 30 experts and 14 resources.

The best practices are non-prescriptive, broadly applicable, and include examples and guidelines for their adoption by a community. They specify establishing the working domain (scope) and guidance for both users and software contributors, address legal concerns with privacy, use, and authorship policies, enhance usability by encouraging metadata sharing, and set expectations with retention and end-of-life policies. However, we believe additional work is needed to raise awareness and adoption across resources from different scientific disciplines. Through the SciCodes consortium, our goal is to continue implementing these practices more uniformly in our own registries and repositories and reduce the burdens of adoption. In addition to completing the adoption of these best practices, SciCodes will address topics such as tracking the impact of good metadata, improving interoperability between registries, and making our metadata discoverable by search engines and services such as Google Scholar, ORCID, and discipline indexers.

APPENDIX A: CONTRIBUTORS

The following people contributed to the development of this article through participation in the Best Practices Task Force meetings, 2019 Scientific Software Registry Collaboration Workshop, and/or SciCodes Consortium meetings:

Alain Monteil , Inria, HAL ; Software Heritage

Alejandra Gonzalez-Beltran , Science and Technology Facilities Council, UK Research and Innovation, Science and Technology Facilities Council

Alexandros Ioannidis , CERN, Zenodo

Alice Allen , University of Maryland, College Park, Astrophysics Source Code Library

Allen Lee , Arizona State University, CoMSES Net Computational Model Library

Ana Trisovic , Harvard University, DataVerse

Anita Bandrowski , UCSD, SciCrunch

Bruce E. Wilson , Oak Ridge National Laboratory, ORNL Distributed Active Archive Center for Biogeochemical Dynamics

Bryce Mecum , NCEAS, UC Santa Barbara, CodeMeta

Caifan Du , iSchool, University of Texas at Austin, CiteAs

Carly Robinson , US Department of Energy, Office of Scientific and Technical Information, DOE CODE

Daniel Garijo , Universidad Politécnica de Madrid (formerly at Information Sciences Institute, University of Southern California), Ontosoft

Daniel S. Katz , University of Illinois at Urbana-Champaign, Associate EiC for JOSS, FORCE11 Software Citation Implementation Working Group , co-chair

David Long , Brigham Young University, IEEE GRS Remote Sensing Code Library

Genevieve Milliken , NYU Bobst Library, IASGE

Hervé Ménager , Hub de Bioinformatique et Biostatistique—Département Biologie Computationnelle, Institut Pasteur, ELIXIR bio.tools

Jessica Hausman , Jet Propulsion Laboratory, PO.DAAC

Jurriaan H. Spaaks , Netherlands eScience Center, Research Software Directory

Katrina Fenlon , University of Maryland, iSchool

Kristin Vanderbilt , Environmental Data Initiative, IMCR

Lorraine Hwang , University California Davis, Computational Infrastructure for Geodynamics

Lynn Davis , US Department of Energy, Office of Scientific and Technical Information, DOE CODE

Martin Fenner , Front Matter (formerly at DataCite), FORCE11 Software Citation Implementation Working Group , co-chair

Michael R. Crusoe , CWL, Debian-Med

Michael Hucka , California Institute of Technology, SBML ; COMBINE

Mingfang Wu , Australian Research Data Commons, Australian Research Data Commons

Morane Gruenpeter , Inria, Software Heritage

Moritz Schubotz , FIZ Karlsruhe - Leibniz-Institute for Information Infrastructure, swMATH

Neil Chue Hong , Software Sustainability Institute/University of Edinburgh, Software Sustainability Institute ; FORCE11 Software Citation Implementation Working Group , co-chair

Pete Meyer , Harvard Medical School, SBGrid ; BioGrids

Peter Teuben , University of Maryland, College Park, Astrophysics Source Code Library

Piotr Sliz , Harvard Medical School, SBGrid ; BioGrids

Sara Studwell , US Department of Energy, Office of Scientific and Technical Information, DOE CODE

Shelley Stall , American Geophysical Union, AGU Data Services

Stephan Druskat , German Aerospace Center (DLR)/University Jena/Humboldt-Universität zu Berlin, Citation File Format

Ted Carnevale, Neuroscience Department, Yale University, ModelDB

Tom Morrell , Caltech Library, CaltechDATA

Tom Pollard , MIT/PhysioNet, PhysioNet

APPENDIX B: POLICY EXAMPLES

Scope statement.

• • Astrophysics Source Code Library. (n.d.). Editorial policy .
• https://ascl.net/wordpress/submissions/editiorial-policy/
• • bio.tools. (n.d.). Curators Guide .
• https://biotools.readthedocs.io/en/latest/curators_guide.html
• • Caltech Library. (2017). Terms of Deposit .
• https://data.caltech.edu/terms
• • Caltech Library. (2019). CaltechDATA FAQ .
• https://www.library.caltech.edu/caltechdata/faq
• • Computational Infrastructure for Geodynamics. (n.d.). Code Donation .
• https://geodynamics.org/cig/dev/code-donation/
• • CoMSES Net Computational Model Library. (n.d.). Frequently Asked Questions .
• https://www.comses.net/about/faq/#model-library
• • ORNL DAAC for Biogeochemical Dynamics. (n.d.). Data Scope and Acceptance Policy .
• https://daac.ornl.gov/submit/
• • RDA Registry and Research Data Australia. (2018). Collection . ARDC Intranet.
• https://intranet.ands.org.au/display/DOC/Collection
• • Remote Sensing Code Library. (n.d.). Submit .
• https://rscl-grss.org/submit.php
• • SciCrunch. (n.d.). Curation Guide for SciCrunch Registry .
• https://scicrunch.org/page/Curation%20Guidelines
• • U.S. Department of Energy: Office of Scientific and Technical Information. (n.d.-a). DOE CODE: Software Policy . https://www.osti.gov/doecode/policy
• • U.S. Department of Energy: Office of Scientific and Technical Information. (n.d.-b). FAQs . OSTI.GOV.
• https://www.osti.gov/faqs

Guidance for users

• • Astrophysics Source Code Library. (2021). Q & A
• https://ascl.net/home/getwp/898
• • bio.tools. (2021). API Reference
• https://biotools.readthedocs.io/en/latest/api_reference.html
• • Harvard Dataverse. (n.d.). Curation and Data Management Services
• https://support.dataverse.harvard.edu/curation-services
• • OntoSoft. (n.d.). An Intelligent Assistant for Software Publication
• https://ontosoft.org/users.html
• • ORNL DAAC for Biogeochemical Dynamics. (n.d.). Learning
• https://daac.ornl.gov/resources/learning/
• • U.S. Department of Energy: Office of Scientific and Technical Information. (n.d.). FAQs . OSTI.GOV.
• https://www.osti.gov/doecode/faq

Guidance for software contributors

• • Astrophysics Source Code Library. (n.d.) Submit a code .
• https://ascl.net/code/submit
• • bio.tools. (n.d.) Quick Start Guide
• https://biotools.readthedocs.io/en/latest/quickstart_guide.html
• • Computational Infrastructure for Geodynamics. Contributing Software
• https://geodynamics.org/cig/dev/code-donation/checklist/
• • CoMSES Net Computational Model Library (2019) Archiving your model: 1. Getting Started
• https://forum.comses.net/t/archiving-your-model-1-getting-started/7377
• • Harvard Dataverse. (n.d.) For Journals .
• https://support.dataverse.harvard.edu/journals
• • Committee on Publication Ethics: COPE. (2020a). Authorship and contributorship .
• https://publicationethics.org/authorship
• • Committee on Publication Ethics: COPE. (2020b). Core practices .
• https://publicationethics.org/core-practices
• • Dagstuhl EAS Specification Draft. (2016). The Software Credit Ontology .
• https://dagstuhleas.github.io/SoftwareCreditRoles/doc/index-en.html#
• • Journal of Open Source Software. (n.d.). Ethics Guidelines .
• https://joss.theoj.org/about#ethics
• • ORNL DAAC (n.d) Authorship Policy .
• • PeerJ Journals. (n.d.-a). Author Policies .
• https://peerj.com/about/policies-and-procedures/#author-policies
• • PeerJ Journals. (n.d.-b). Publication Ethics .
• https://peerj.com/about/policies-and-procedures/#publication-ethics
• • PLOS ONE. (n.d.). Authorship .
• https://journals.plos.org/plosone/s/authorship
• • National Center for Data to Health. (2019). The Contributor Role Ontology.
• https://github.com/data2health/contributor-role-ontology

Metadata schema

• • ANDS: Australian National Data Service. (n.d.). Metadata . ANDS.
• https://www.ands.org.au/working-with-data/metadata
• • ANDS: Australian National Data Service. (2016). ANDS Guide: Metadata .
• https://www.ands.org.au/data/assets/pdf_file/0004/728041/Metadata-Workinglevel.pdf
• • Bernal, I. (2019). Metadata for Data Repositories .
• https://doi.org/10.5281/zenodo.3233486
• • bio.tools. (2020). Bio-tools/biotoolsSchema [HTML].
• https://github.com/bio-tools/biotoolsSchema (Original work published 2015)
• • bio.tools. (2019). BiotoolsSchema documentation .
• https://biotoolsschema.readthedocs.io/en/latest/
• • The CodeMeta crosswalks. (n.d.)
• https://codemeta.github.io/crosswalk/
• • Citation File Format (CFF). (n.d.)
• https://doi.org/10.5281/zenodo.1003149
• • The DataVerse Project. (2020). DataVerse 4.0+ Metadata Crosswalk.
• https://docs.google.com/spreadsheets/d/10Luzti7svVTVKTA-px27oq3RxCUM-QbiTkm8iMd5C54
• • OntoSoft. (2015). OntoSoft Ontology .
• https://ontosoft.org/ontology/software/
• • Zenodo. (n.d.-a). Schema for Depositing .
• https://zenodo.org/schemas/records/record-v1.0.0.json
• • Zenodo. (n.d.-b). Schema for Published Record .
• https://zenodo.org/schemas/deposits/records/legacyrecord.json

Conditions of use policy

• • Allen Institute. (n.d.). Terms of Use .
• https://alleninstitute.org/legal/terms-use/
• • Europeana. (n.d.). Usage Guidelines for Metadata . Europeana Collections.
• https://www.europeana.eu/portal/en/rights/metadata.html
• • U.S. Department of Energy: Office of Scientific and Technical Information. (n.d.). DOE CODE FAQ: Are there restrictions on the use of the material in DOE CODE?
• https://www.osti.gov/doecode/faq#are-there-restrictions
• • Zenodo. (n.d.). Terms of Use .
• https://about.zenodo.org/terms/

Privacy policy

• • Allen Institute. (n.d.). Privacy Policy .
• https://alleninstitute.org/legal/privacy-policy/
• • CoMSES Net. (n.d.). Data Privacy Policy .
• https://www.comses.net/about/data-privacy/
• • Nature. (2020). Privacy Policy .
• https://www.nature.com/info/privacy
• • Research Data Australia. (n.d.). Privacy Policy .
• https://researchdata.ands.org.au/page/privacy
• • SciCrunch. (2018). Privacy Policy . SciCrunch.
• https://scicrunch.org/page/privacy
• • Science Repository. (n.d.). Privacy Policies .
• https://www.sciencerepository.org/privacy
• • Zenodo. (n.d.). Privacy policy .
• https://about.zenodo.org/privacy-policy/

Retention policy

• • Bioconductor. (2020). Package End of Life Policy .
• https://bioconductor.org/developers/package-end-of-life/
• • Caltech Library. (n.d.). CaltechDATA FAQ .
• • CoMSES Net Computational Model Library. (n.d.). How long will models be stored in the Computational Model Library?
• https://www.comses.net/about/faq/
• • Dryad. (2020). Dryad FAQ - Publish and Preserve your Data .
• https://datadryad.org/stash/faq#preserved
• • Software Heritage. (n.d.). Content policy .
• https://www.softwareheritage.org/legal/content-policy/
• • Zenodo. (n.d.). General Policies v1.0 .
• https://about.zenodo.org/policies/

End-of-life policy

• • Figshare. (n.d.). Preservation and Continuity of Access Policy .
• https://knowledge.figshare.com/articles/item/preservation-and-continuity-of-access-policy
• • Open Science Framework. (2019). FAQs . OSF Guides.
• http://help.osf.io/hc/en-us/articles/360019737894-FAQs
• • NASA Earth Science Data Preservation Content Specification (n.d.)
• https://earthdata.nasa.gov/esdis/eso/standards-and-references/preservation-content-spec
• • Zenodo. (n.d.). Frequently Asked Questions .
• https://help.zenodo.org/

APPENDIX C: RESOURCE INFORMATION

Since the first Task Force meeting was held in 2019, we have asked new resource representatives joining our community to provide the information shown in Table C.1 . Thanks to this effort, the group has been able to learn about each resource, identify similarities and differences, and thus better inform our meeting discussions.

Tables C.2 – C.4 provide an updated overview of the main features of all resources currently involved in the discussion and implementation of the best practices (30 resources in total as of December, 2021). Participating resources are diverse, and belong to a variety of discipline-specific ( e.g. , neurosciences, biology, geosciences, etc .) and domain generic repositories. Curated resources tend to have a lower number of software entries. Most resources have been created in the last 20 years, with the oldest resource dating from 1991. Most resources accept a software deposit, support DOIs to identify their entries, are actively curated, and can be used to cite software.

Acknowledgments

The best practices presented here were proposed and developed by a Task Force of the FORCE11 Software Citation Implementation Working Group. The following authors, randomly ordered, contributed equally to discussion, conceptualization, writing, reviewing, and editing this article: Daniel Garijo, Lorraine Hwang, Hervé Ménager, Alice Allen, Michael Hucka, Thomas Morrell, and Ana Trisovic.

Task Force on Best Practices for Software Registries participants : Alain Monteil, Alejandra Gonzalez-Beltran, Alexandros Ioannidis, Alice Allen, Allen Lee, Andre Jackson, Bryce Mecum,Caifan Du, Carly Robinson, Daniel Garijo, Daniel Katz, Genevieve Milliken, Hervé Ménager, Jurriaan Spaaks, Katrina Fenlon, Kristin Vanderbilt, Lorraine Hwang, Michael Hucka, Neil Chue Hong, P. Wesley Ryan, Peter Teuben, Shelley Stall, Stephan Druskat, Ted Carnevale, Thomas Morrell.

SciCodes Consortium participants : Alain Monteil, Alejandra Gonzalez-Beltran, Alexandros Ioannidis, Alice Allen, Allen Lee, Ana Trisovic, Anita Bandrowski, Bruce Wilson, Bryce Mecum, Carly Robinson, Celine Sarr, Colin Smith, Daniel Garijo, David Long, Harry Bhadeshia, Hervé Mé nager, Jeanette M. Sperhac, Joy Ku, Jurriaan Spaaks, Kristin Vanderbilt, Lorraine Hwang, Matt Jones, Mercé Crosas, Michael R. Crusoe, Mike Hucka, Ming Fang Wu, Morane Gruenpeter, Moritz Schubotz, Olaf Teschke, Pete Meyer, Peter Teuben, Piotr Sliz, Sara Studwell, Shelley Stall, Ted Carnevale, Tom Morrell, Tom Pollard, Wolfram Sperber.

Funding Statement

This work was supported by the Alfred P. Sloan Foundation (Grant Number G-2019-12446), and the Heidelberg Institute of Theoretical Studies. Ana Trisovic is funded by the Alfred P. Sloan Foundation (Grant Number P-2020-13988). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Additional Information and Declarations

The authors declare that they have no competing interests.

Daniel Garijo conceived and designed the experiments, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Hervé Ménager conceived and designed the experiments, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Lorraine Hwang conceived and designed the experiments, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Ana Trisovic conceived and designed the experiments, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Michael Hucka conceived and designed the experiments, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Thomas Morrell conceived and designed the experiments, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Alice Allen conceived and designed the experiments, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.