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The UX Research Repository Landscape: What You Need to Know

Check out our comprehensive guide to choosing a research repository that suits your specific needs, with expert advice from research practitioners.

Welcome to our comprehensive guide to choosing a UX research repository that suits your organization’s needs. Grab a cup of tea , then dive into this treasure trove of priceless information and expert advice from research practitioners .

If you need evidence that research matters, look closely at the device you’re reading this on. Whether it’s Android, iOS or Windows (or Linux, we don’t discriminate), every detail of the user experience is likely steeped in research, and the titans of tech invest heavily in UX research each year.

Despite research’s growing importance, researchers today still grapple with age-old problems. User interviews take hours on end to tag properly and distill into key themes. New hires inherit a complex puzzle as they try to piece together fragmented findings across an organization. Without a central platform to elevate insights, user feedback and the value of research are drowned out for prospective audiences.

User research continues to be time- and resource-intensive. If only there were a solution to rid us of the dreaded drudgery of research…

Enter the UX research repository.

Summary of the UX Research Repository Landscape

• What is a research repository?
• Why you need a research repository
• How to organize a research repository
• Essential research repository features
• Choosing a research repository in 2023
• Benefits of a modern research repository tool
• Research repositories: Getting started

What is a Research Repository?

A research repository is a central archive for organizing and storing research, insights and user information. Think of a research repository as an encyclopedia, exclusively made up of a company’s findings — it brings all user research in one place so that you can easily conduct , analyze , organize and share customer insights across the organization.

A common misconception is that a research repository serves researchers exclusively. This couldn’t be farther from the truth — since research influences most departments within a company, a UX research repository acts as a knowledge bank for the entire organization.

So what type of knowledge goes into a research repository? The concept of ‘atomic research’ breaks it down into these four components:

At the end of the day, researchers provide a service for end users and key decision makers. Research repositories help you consolidate data, unearth patterns and trends across studies old and new, and facilitate informed business decisions.

Why You Need a UX Research Repository

Research seeks to identify customer needs, behaviors and goals to create better products and experiences for users. Repositories allow for easy mining and learning from insights, and bring efficiency to the research process. Let’s flip this around and see what industry professionals say happens when you don’t have a research repository in place:

Organizational silos

Whether they know it or not, every department conducts different types of research, amassing various forms of data in the process. Calls, surveys, social media posts and informal conversations (to name a few) all reveal customer perspectives. Usually, knowledge within departments remains just that – within departments. Seldom communicated across the company, information becomes siloed and insights are lost before they see the light of day. Lou Rosenfeld, founder of Rosenfeld Media, cautions that organizational silos lead to an incomplete and unbalanced understanding of customers and inhibit new learnings. He uses the fable of the elephant and the blind men to illustrate the importance of consolidating different perspectives to see the bigger picture.

Knowledge and insight burial

Ever download a useful file and forget where you saved it on your computer? Months go by and you can’t remember the file name or path — you spend hours trying to search by keyword, to no avail. The file has fallen into the dreaded black hole — just a microcosm of what happens with research files too.

The more data you create that doesn’t end up in a repository, the more data you ultimately lose. A centralized and organized space makes it easy to unearth and extract insights, avoiding the infernal black hole.

Effort duplication

Silos and lost insights lead to wasted time and effort. New researchers inevitably ask questions that have already been answered or looked into. Without means to access studies and the conclusions drawn from them, efforts are repeated and the perceived value of research suffers as a result. To keep track of various research initiatives, Gina Rahn, Director of User Experience & Design at LINQ leverages a research roadmap , keeping everyone on the same page. Research roadmaps provide project details and how they tie back to overarching business goals. A quick glance at the roadmap enables you to take stock of studies previously conducted, and ones that need to be undertaken. Repositories coupled with research roadmaps promote organization-wide awareness of research initiatives and insights.

Under-informed decision-making

According to Pawana Burlakoti, Head of Product at S&P Global, not consulting a research repository at the outset has dire consequences down the line. She’s adamant that you need validation at the beginning and during the course of your work. Pawana champions discovery research at S&P Global, which provides justification on whether to invest time and resources into something or not. Housing this information in a repository helps bring transparency around decision making and allocation of resources.

Regardless of the size of your company and stage of its life cycle, a research repository can help you avoid these unwanted scenarios. A real no-brainer. 

How to Organize a Research Repository

We’ve established that a repository is table stakes for anyone who collects customer data. So what types of data goes into a research repository?

Two kinds of people will access the repository for the above data — producers (researchers) and consumers (stakeholders). It’s important to consider the needs and navigation of all parties while organizing your repository. There isn’t a one-size-fits-all approach, but the guidelines below will show you how to set up your research repository for success.

Roles & Regulations

First provide ownership — who will own and manage the repository? They act as gatekeepers for the rest of the organization. Define roles by establishing who will use and access the repository on a regular basis — decision-makers, product managers, designers, developers, power users such as researchers and view-only users. Establish ground rules on correct usage of the repository and document them. Adherence is key to maintaining an organized repository.

Information architecture

Make sure you’ve conducted some research before you start thinking about organizing your data. It gives you an idea of how to structure your repository with appropriate taxonomy and metadata for your files. Taxonomy is nothing but a naming convention for your files and data. Stick to a consistent taxonomy and create a guide for new users to follow.

Tag your data! We can’t stress that enough. Tagging converts unstructured data into smaller, semi-structured nuggets. This makes it more manageable, easier for you to digest and analyze the most granular items. When tagging, the simpler the better. Tagging is an ongoing process, one you’ll continue to refine as you go along. Don’t create new tags without normalizing the existing set. Click here to learn more about tagging your data using Marvin.

Maintenance mode

Research repositories are living artifacts. Routinely update and maintain your repository to keep it organized. Devote time periodically for upkeep so that your repository remains easily accessible and searchable for users across your organization.

Head over here for more tips on how to structure and supercharge your research repository.

Essential Research Repository Features

What functionality do you need in a research repository? How do you discern between the essentials, and ‘nice to haves’? Each company prioritizes certain requirements, however some functions are non-negotiable. To make life easier, we’ve come up with a handy acronym to recall the laundry list of features to look out for in a research repository tool:

‘SAAS ASPIRE’

Remember it this way: You have certain aspirations for your research repository software (as a service).

These are listed in no particular order of importance ( other than giving you a catchy acronym ).

S earchable

If you’ve ever been asked, “What do we know about X?” or “Have we got any data on X?”, this feature is for you. The ability to search and filter by keyword, or slice and dice your data enables quick discovery, consolidates insights and saves time. This increases the importance of the tagging exercise (above). A visually appealing and engaging search feature helps too. 😉

A ccessible

Insights must be easily retrievable from the research repository. If I want to find out more about what customers said about Product A, I should be able to quickly search for “Product A” and “customer reviews”, for instance. Many platforms require people to have a separate login to access and simply view data or recordings. Tasked with creating a new account merely to view files, we’d rather not. A call for some simplicity, please.

A nalyzable

Silly as this may sound, a repository that only houses customer insights is a bit dated. Ideally, you want a tool that enables you to import different forms of data for deeper analysis. Marvin and numerous other tools have the capacity to house qualitative and quantitative data. This avoids constantly jumping from one analysis tool to another. All your analysis in one place. 

Perhaps the most important feature — we introduced organizational silos and the data vacuum above. Disseminating insights elevates the user voice within an organization, bringing everyone on the same page. Sharing text is one thing, but sharing data in its original formats (table, above) is compelling. Moreover, at companies where research is a new practice, sharing communicates its value to non-researchers. 

A rtificial Intelligence (AI)

Bill Gates recently said that the advent of AI is as revolutionary as the graphical user interface, used to power mobile phones and computers. As we grapple with something that might eventually replace us, it becomes even more crucial to be able to leverage this key technology moving forward. AI has a host of applications within a UX research repository – it can help transcribe interview recordings, summarize lengthy transcripts and so much more. Your choice of repository wouldn’t be future proof and forward thinking if it didn’t have at least some AI capabilities. 

Privacy is a fundamental human right. Corporations that have tracked and sold our data (a la Cambridge Analytica ) are now being forced to double down, due to emerging data and privacy legislation. Information housed in a repository is highly sensitive and must be anonymized, protecting users. Look out for adherence to one (or more) set of standards from this list . Better safe than sorry.

P ath traceability

Connect insights to raw data. For the sake of our acronym, we cheated by adding ‘path’, but it only drives our point home further. Refer to the atomic research diagram above. If presented with purely insights, without how they were arrived at or deduced, decision makers are certain to question their validity and reliability. Linking raw data (any format from table, above) to an insight illustrates how we arrived at a certain conclusion and enables viewers to make their own deductions, given the information presented.

I ntegrations

Standing alone, repositories are of little use. Companies use a host of applications and platforms such as a CRM, project management and an ERP system. You might use Slack for messaging, Notion for documentation and Qualtrics for quantitative analysis. A key feature of a repository is the ability to talk to apps that you already use regularly. Marvin’s seamless integration with Zoom turns your research repository into a UX powerhouse. Explore all of Marvin’s integrations here .

R eal-time efficiency

Video is the preferred format of choice for qualitative data collection. It’s more interactive and can reveal non-verbal cues such as body language, genuineness (although some might have a solid poker face!), tone and expressions. Unlike most tools, Marvin transcribes interviews or calls as they unfold, freeing up your time to stay engaged and concentrate on these non-verbal cues. Use the time stamped LiveNotes feature to collaborate with your team and take important notes as you conduct interviews. This makes revisiting insights and tagging a breeze.

E ase-of-Use

Last, but certainly not least. A repository tool must have an intuitive design and user-friendly interface. Learning the ropes should be quick and easy. We pointed out earlier that a repository is not exclusively for researchers — product managers carry out their own research and key decision makers will likely rely on the tool for vital information. It must be straightforward for different personas to navigate through the various functions. We’ve spoken to umpteen users who were turned off other tools due to the complexity in navigation. Self-serve is the name of the game.

Ultimately, it’s up to you and internal stakeholders to gauge requirements and prioritize mandatory features for your repository. Every listed feature is important, with anything over and above it a ‘nice to have’. Admittedly, we didn’t leave much wiggle room.

Choosing a Research Repository in 2023

Given the rather exhaustive feature checklist above, what are your choices when looking for a research repository? The way we look at it, you have two approaches to take when deciding on a repository tool:

• Homegrown – repurpose generic and free software. Why pay for something that you can get for free?
• Purpose-built – spring for a dedicated research repository tool. You get what you pay for.

Two very different schools of thought — let’s explore the first option in greater detail. 

Homegrown Approach to Building a UX Repository

The following software can be fashioned into a makeshift UX research repository tool.

The Cons of Using A Generic Research Repository

You’re spoiled for choice with applications or tools when taking the homegrown approach. And it will save you money as you build a custom solution that’s tailored to your company’s needs. But reader beware, you may find some major drawbacks when repurposing a generic tool into your UX research repository:

• Feature Compromise : Does your repurposed software tick all the boxes from our SAAS ASPIRE list? Highly unlikely — you’re going to have to make certain concessions somewhere.
• Scalability: Bear in mind, ‘free’ tools wouldn’t exist if they didn’t have paid versions as well. The moment you scale or add complexity, the ‘free’ element of these applications disappears and you have to spend money to keep up with continually growing scale or features. That’s how they get you.
• Inefficiency: Time is money. The price you pay with heavy customization is time. It’s the fundamental economic concept of opportunity cost. What’s the cost of the time spent creating the research repository infrastructure vs. the time saved if you opt for a dedicated tool?

The homegrown makeshift route certainly gets the job done, but you must continually evaluate whether it’s to your satisfaction. With database tools like Notion, trying to make qualitative data fit into a quantitative format is a classic case of “square peg, round hole.” 

The issue with a generic tool is that customization time and effort is high, and you don’t always end up with features that you really need. Don’t fall into the mousetrap of customizing an application consuming so much of your time, that it eventually reduces your overall productivity. We’re full of metaphors today — using freeware is like using anything but a hammer to get a nail in the wall. It might work, but not nearly as effectively as the real thing.

Benefits of a Modern Research Repository Tool

Above, we discussed the first of two different paths you can take when choosing your software. If you’re still reading, you likely chose not to explore the makeshift homegrown options. You clearly understand the value of supporting your research team with the best dedicated research repository tool.

Making research for everyone at your organization easier should be a priority. It will save you valuable time, resources and effort.

Speaking of, we’ll save you plenty of time, resource and effort right now with a short but comprehensive round-up:

• G2’s Top User Research Repositories ( based on real user reviews)
• A Thorough Comparison of Every User Research Repository Tool

Research Repositories: Getting Started

Not sure where to begin? Our panel of experts has their say on how to begin implementing and utilizing your research repository.

Unlock internal knowledge

Before jumping into using your repository, the first step is leveraging your existing knowledge base. Gina Rahn has seen numerous acquisitions take place during her time at LINQ, a K-12 educational software company. Whether new peers call it research or something else, they’re likely sitting on valuable information and knowledge. Gina tackled this problem head on — by treating coworkers as users, she was able to unearth utilizable insights from her internal research. 

Read more about how Gina employs design thinking using Marvin.

In a similar vein, Lou Rosenfeld recommends conducting an ethnographic study of your organization. Not only does it get everyone up to speed with research efforts, it enables a company-wide understanding of what research brings to the table. All these valuable internal insights go into your research repository.

Broadcast your findings

“You have to be your own marketer — you’re doing the work, sharing it, broadcasting it up and down.” These words of wisdom are from Claire Rowell, Lattice’s first researcher. 

Researchers like Claire often begin as one-person teams, so they must quickly perfect the art of communication. A research repository tool that facilitates easy sharing is a god-send for solitary researchers like Claire. The benefit of sharing insights across the organization is twofold — it elevates the voice of the user and simultaneously communicates the value of research. Claire shared more great advice for sole researchers beginning their journey.

Gina circulates a quarterly newsletter that informs everyone in the company of various research initiatives and developments. Colleagues tune in to video and sound bites from their users, fostering excitement around the product. This keeps the most important stakeholder at the forefront of everyone’s thoughts.

Orchestrate a culture shift

Easy enough, right? Sarcasm aside, to get buy-in from other teams and stakeholders it helps to give them access to the repository. Claire alludes to the fact that Marvin’s self-serve gets everyone energized about using an interesting repository tool. 

Learn more about how Claire and her colleague Jared use Marvin to create a research-driven culture at Lattice.

At S&P Global, Pawana is clear that the research culture dynamic must be conducive to risk taking – ‘fail fast, fail early’. Inevitably, researchers will explore some questions that have a dead end, prompting no further action. The repository acts as a question bank for these studies from yesteryear, ensuring that no efforts are duplicated down the line.

Twilio research leaders involve as many peers as possible throughout the research process. The idea is to empower them to conduct some form of their own research, ensuring everyone is laser focused on solving the customer problem.

Putting It All into Perspective

Research repositories are vital to a company leveraging its internal and acquired knowledge over time. Unlike its quantitative counterpart, processing qualitative data is convoluted and cumbersome. Qualitative data tends to be text-heavy — there aren’t many visualizations such as graphs. Sifting through volumes of text must be effortless and painless.

In looking for a research repository tool, you must consider the needs of users outside of the research team when making your decision. It must be easy to use for product teams, designers, developers, engineers and decision makers alike within your organization. Users must be able to search and navigate through the repository with ease.

To optimize information architecture, you need to treat your repository implementation as a UX project in itself, a constantly evolving proposition that you continue to refine. In our opinion, using a makeshift tool is the equivalent of bringing a knife to a gunfight (we told you — we’ve got the metaphors down) . You want a UX research repository that checks all the feature boxes (and there are plenty!)… one that’s built for scale and easy to use.

Set up a free demo and see how to centralize your research with Marvin today.

Photo by Adrien Converse on Unsplash

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Research repositories for tracking ux research and growing your researchops.

October 18, 2020 2020-10-18

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Every UX team needs to organize its user research in a research repository. I first worked on a research repository in the early 1990s. The lessons I learned then still hold true today, as the UX community gets serious about managing and growing user- research programs. These efforts now fall under the umbrella term “ Research Ops ” (with “Ops” being short for “operations” ) .

In This Article:

What is a research repository, relevant elements in a research repository, convenience and findability features in a repository.

A research repository is a shared collection of UX-research-related elements that should support the following functions at the organization level:

• grow UX awareness and participation in UX work among leadership, product owners, and the organization at large
• support UX research work, so UX professionals may be more productive as they plan and track research

There are two main types of content in a research repository:

• The input to doing UX research: information for planning and conducting research
• The output from doing UX research: study findings and reports

Before making a repository, analyze the UX-related processes and tools used (currently or in the near future) in your organization. Consider creating a mind map of how research gets done, or even a journey map or service blueprint of how research is initiated and results are used on development teams.

Some important components that can be housed in a research repository include:

Infrastructure

• Research team’s mission and vision communicate what the team is about, how it works, and how it hopes to work in the future. This information can help others to understand the team’s capabilities, what they can expect, and what they can request. An example mission is: The UX-research team provides user and customer research and guidance for all products, services, and systems at the organization in order to maximize usefulness, usability, efficiency, enjoyment, and support for the organization’s vision.
• Descriptions of research methods help the team learn or be reminded of a process and the reasons for different research types. Method descriptions and best practices can promote consistent high-quality work and even teach a less experienced researcher.
• Tools and templates for conducting and analyzing research , such as templates for test plans, protocols, reports, interview scripts, user tasks, consent forms, notetaking and tips for using remote-research or analysis tools could also be housed her

Research Planning

• Strategic research plans for the organization and for individual projects — like you might see in a research roadmap — can keep researchers and the rest of the team focused on the most important areas to research as opposed to every single product feature. When stored in a research repository these are easy to find and access
• Schedules make research accessible to everyone, by sharing the date, time, location, research method, and what’s being studied. Armed with this information, anyone can join or ask to join in on studies, or at least look for findings upon completion
• Detailed research plans communicate that research will be happening and how. When stored in a repository they serve as a vision document to align stakeholders and the rest of the team.
• Research requests enable product teams to request user research to be done. Depending on the research team’s size, mission, planning, and culture, research requests may not be available at all organizations. Research requests can give insight in the research needs at your organization and can drive UX-team growth.

Data and Insights

• Research reports tell what happened in the research study. They include overarching themes, detailed findings, and sometimes recommendations.
• Research insights are the detailed findings or chunks of information acquired from each research study. While findings also appear in reports, saving them as their own entities makes it easier to digest them, mark their severity , track their status, and link to specific design and development assignments in the backlog or project database. In other words, each insight is digestible and easy to see, and thus more likely to get addressed.
• Recordings and transcriptions stored in the repository or, alternatively, linked from the repository, They make user data easily accessible. Summarizing and transcribing each video allows teams to search for exactly what they're looking for. (Fun historical note: In the early 1990s, when usability-testing recordings were too large to store online, my team at Lotus created a video library. Developers could check out the physical videotapes as one would a book at a library. People were so dedicated that they borrowed them to watch the tests they had missed, and sometimes we had to make extra copies of tapes to meet the demand.)
• Raw notes and artifacts from research sessions are often trashed after they have been analyzed. But some teams keep the notes in case they might be useful for future analysis — for example, if a team was in a rush and focused on one area of the design at the time of the study,  later it may be able to revisit the notes to glean insights related to other aspects of the design. Those notes could help inform journey maps, personas, or other user-focused artifacts.

What Is NOT Always in a Research Repository

• UX-data analysis is usually done with specialized tools. The result of the analysis could be a text file (for example, for quantitative data analysis done in software such as R) or could be hosted online in a tool-specific format. If the latter, then the repository can link to the result of the analysis. For example, researchers may have conducted thematic analysis using Dovetail; the full research report can include a link to that board so team members can see the reasoning behind the findings.
• A participant repository or panel is usually not stored within a research repository, even though recruiting research participants is a core function of user research. That’s because the goals and audience for the two repositories tend to be quite different. But it can be helpful for them to link to one another.

There are many other components that research teams need to track internally but that are less likely to be part of a research repository, even though they may be linked from it: user stories in a backlog, participant recruiting tools, and budget tracking for research projects.

People should be able to easily find and discover information about research. Findable and accessible information makes it possible for the team to easily be part of a research project and feel ownership about the findings. Here are some repository attributes that make it easy to use:

• Supporting tags and metadata, to help people find items by the most granular topics
• Searchable by keyword (e.g. for research on a certain product feature), project, team, finding, severity, status, and more
• Hosted in a tool that people can easily access , use, learn and that matches the organization’s culture and mental model
• Portable, so that repository elements can be easily exported to other applications or formats

Research repositories store and organize information about UX research. They collect not only methodology-related documents, but also research results at various levels of granularity (from individual findings to reports). Their purpose is to streamline the work of the research team and also to make research widely available and easy to consume throughout the organization.

For more information about the growing ResearchOps community, see https://researchops.community/ .

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7 Best UX Research Repository Tools & Software for 2024

• December 13, 2023

If you’re a UX researcher, you’ll know why UX research repository tools are essential. Without them, organizing data becomes a nightmare – and user insights are lost to the void, never to be acted upon.

The best research repository software will allow you to preserve research and data for future use, while making a wide range of materials easily accessible for anyone who needs or wants to review them. The UX repository should act as your single-source-of-truth for anything related to user behaviour, personas, needs, pain points, preferences and feedback.

Well-documented and shareable insights = less frustration for user researchers!

@tldv.io We love constructive criticsm #productmanager #product #tech #productmanagement #corporatehumor #startup ♬ original sound – tldv.io – Meeting Recorder

Why Do We Need Specific UX Research Repository Tools?

Generally speaking, it’s a good idea to use a simple platform that everybody in your business can use – that includes  the technophobes (we’re looking at you, Karen). Tools like Notion allow all stakeholders to review user research without demanding they learn to navigate a complex platform. 

Keeping your UX repository simple could save you more money, time, and stress than you might first realize.

Companies with fewer than 50 employees have an average of 40 SaaS applications , while the figure for bigger companies (with more than 1,000 employees) is more like 200 tools and applications. That’s an average of $3570 spent on SaaS (Software as a Service) per employee!

To choose the best research repository tool, you should know in advance what you’ll mainly be using it for. This typically boils down to two general options:

• To provide transparency. If your goal is to be transparent with your research, sharing it among stakeholders and other colleagues, then it’s better to use a tool that is already widely used throughout the company, such as Notion, Miro, or Confluence. In this case, a more complex dedicated research repository, like Dovetail or Condens, is probably not for you. 
• To delve deeper into analysis.  If the goal of the research repository is to plumb the depths of your data, and it’ll be used only by a small team of researchers, then the limitations of widely popular tools, like Notion, are too much of a hinderance for your research. Instead, it’s advised to adopt more specialized user research tools like Dovetail or Condens.

Source: NNGroup

The Complementary Icing on the Cake

Whether you have a very broad scope, or you’re honing in on something so narrow that you need a microscope to get the fine details, tl;dv can be your best friend. It’s a remote UX research tool that makes your job a piece of cake. 

Ever wished you could go back and hear the voice of the customer again? Maybe you’ve even recorded meetings before, but rarely rewatch them because of the time-consuming effort to locate the right part and share it, not to mention the fact that most online meetings platforms take days to even let you see the recording. With tl;dv you can record all your meetings , get instant transcripts, and even timestamp and cut the video so that you can leave a hyperlink directly to the important part of your user interview.

tl;dv’s intuitive AI will provide automatic summaries at the end of each call, you’ll get highly accurate speaker recognition, and you can even create highlight reels out of a number of different clips, perfect for sharing your user research findings. It also integrates with 5,000+ different work apps , including those below.

Whichever research repository software you use, you can complement it with tl;dv , and link to your snippets of unbiased insights instantly.

So now you know why research repositories are needed, and which tool to use to enhance them, let’s get down to business, shall we? Here are the 7 best UX research repository tools for your user research .

7 UX Research Repository Tools for 2024 and Beyond

1. dovetail.

Briefly mentioned earlier, Dovetail is a data analyzing research tool that helps you create professional level research reports in minutes.

• Great for deep research among bigger user research teams that want their own space specifically for user research
• Store all materials in one place with great visualization and journey maps
• Discover research patterns in Dovetail’s powerful insights repository
• Easy to export data
• Unlimited storage for recordings, files, notes, photos, and reports of any kind
• Create professional research reports in moments
• Collaborate with multiple users and make use of flexible annotation tools
• Complex to learn so it’s not great for convincing stakeholders outside of user research
• Meetings need to be downloaded from an external recorder, then manually uploaded into Dovetail, taking up valuable time ( tl;dv has recordings and transcripts available as soon as the meeting ends and can be uploaded with a simple copy/paste)
• You must wait for the transcript to be generated, and there are no speaker tags or highlights (tl;dv’s speaker tags are added automatically and highlights are super easy to create )
• Limited file types: audio, video, and images are able to be uploaded but this may not cover all types of research
• Price: as a commercial software, Dovetail may not be affordable to everyone. tl;dv, on the other hand, fixes most of Dovetail’s cons for free…

Dovetail used to have a “permanent” free plan, but that has now gone. Instead, you can get free trials for the paid plans. This will help you get used to the software, but you’ll need to upgrade to one of their paid plans to use it more. Their Starter plan starts at $30 per month, while their Team plan will set yo back $375 per month. Their business plan, however, costs a staggering $1,800 per month or $21,600 when billed annually.

Harvard, Deliveroo, and Shopify all use Dovetail: the weight of those customer names should speak for itself. Generally, Dovetail is used by academic and government institutions, as well as product, service, design, and research agencies.

It enables you to conduct online usability tests, sharing your thoughts, insights, and expertise seamlessly. You can use Dovetail to get a leg up on your competition, helping you make sense of user patterns and empowering you to take decisive action.

Dovetail is a fine blend between being a specific research repository and being easy to learn and use. If you’re set on using a specialized UX research repository software but don’t want the steep learning curve, Dovetail might be for you. 

Having said that, Dovetail is not for everyone . Its strength is, in a way, also its biggest issue. Dovetail is so specialized toward user research prfoessionals that non-UX people are really going to struggle to adopt the platform. Unless your stakeholders are willing to put in the hours to learn how to navigate this sometimes non-intuitive software, they’re not going to derive useful insights from all that research. And that, well, kinda makes the research useless. User insights are most impactful when fed into product development, marketing and overall business strategy. Since Dovetail is not super accessible across a wider organization, any insights kept within this repository are likely to remain siloed.

Plus, we can’t summarize Dovetail without mentioning the secret black hole that sucks all your time away: you have to manually upload your user interviews. On top of that, the automated transcription takes ages to generate, and it doesn’t come with any speaker tags or options to highlight. Okay, that was actually 3 black holes. These things can cause huge delays – not to mention, it’s generally frustrating to do menial tasks for every interview. tl;dv, on the other hand, integrates smoothly into Google Meet, Zoom, and MS Teams,  then integrates your recordings smoothly into other platforms and repositories. We’re talking INSTANTLY. Plus, it’s free !

tl;dv may not be a research repository tool in and of itself, but it will make whichever tool you use 10 times easier. That’s because tl;dv integrates with Google Meet, Zoom, and MS Teams to enable you to record, transcribe and highlight the important moments during a live meeting. It makes your meeting moments instantly accessible after, so you can immediately capture and cluster the moments that matter.

For example, you want to store all the valuable insights from your user interviews on Notion or Miro? Or maybe Dovetail? No problem: the timestamps of tl;dv appear as hyperlinks, meaning you can just copy them and paste them directly into your research repository. Later on, when analyzing your research data, you can click the link to rewatch the exact moment where the user spoke the words that will help you improve your product.

Not only does it help you as a user researcher to avoid bias , it also makes it straightforward to share important and actionable insights with stakeholders and key decision makers . After all, the point of user research is to implement changes in your product or service that will benefit users, and in turn, your business. Right? So if your stakeholders can’t access your Condens repository or the research stored on Dovetail, they  can access your user interviews directly, without any hassle or fuss, via tl;dv. Let the customer do the talking.

• Free AI Integration : tl;dv utilizes top-of-the-range AI to provide summaries, notes, and automatic speaker detection.
• Free and Instant Translations : Live transcriptions in more than 25 languages for Zoom, MS Teams, and Google Meet.
• Incredible Free Plan : Unlimited free recordings of Zoom, MS Teams, and Google Meet calls.
• Easy to Use : tl;dv has an intuitive design that makes it super simple to use.
• Timestamps and Tagging Features : Timestamp your meeting highlights to quickly jump back to specific moments in the call. It also features the ability to tag colleagues at specific meeting moments so they get an email with a link straight to their inbox.
• Integrates With All Your Favorite Work Apps : Push timestamps and meeting clips directly to Notion, Slack, HubSpot, Salesforce, and more via third-party integrations.
• Create Clips and Highlights : Scan your library and make clips and highlight reels to condense insights.
• Intuitive Library : Search your meetings library for keywords in transcripts to quickly find what you’re looking for.
• Compatibility : Not yet available for WebEx or BlueJeans.
• Hyper specific: Not suitable for other types of research beyond live user research

With a free plan that defeats most paid plans, tl;dv is available for you to try out right now. For more advanced features, there are some paid plans that you can view here .

Free Plan Features

• Unlimited recordings
• Record Google Meet, Zoom, and MS Teams calls
• Transcribe ALL calls
• Transcribe in 25+ languages
• AI summaries and notes
• Set timestamps and highlights
• Create and share clips
• Set recording automations

The paid plans offer some crucial integrations, analytics, downloadable recordings, customizable share settings and priority customer support.

Regardless of which software you utilize for your research repository, tl;dv will enhance them. It’s not advised to use tl;dv on its own as its not intended to organize large swathes of multi-file-type data, but when complemented with the right research repository tool for your business, tl;dv can work wonders on your research, cut days out of your analysis, and convert user insights into decisive action with ease.

As you can see from the hero page above, Condens is here to “supercharge your UX research analysis”. If that doesn’t tell you exactly what it does, nothing will. But how does it work? And why is it better than its competitors?

To start with, you can use just one click to analyze a research session. There’s no coding required whatsoever, making it extremely easy to use, even for all the non-tech people at the back. 

UX designers, researchers, and consultants are the likely candidates to make use of this powerful tool. If, of course, they can afford to use it.

• Automatic transcription
• Video clips and highlight reels
• Organizing evidence with affinity clusters
• Share your work with clients and stakeholders in one place for easy collaboration
• Conduct user research remotely and reach a larger and more diverse participant base
• Advanced analytics and reporting features for uncovering greater insights
• Customization is limited 
• The tool is not necessarily suited for all types of research projects
• It’s on the pricey end of the spectrum as far as UX research tools go

Unlike tl;dv, Condens does not have a free plan. However, it does have a free trial which lasts 15 days. This is enough time to play around with the tool and learn if it’s the right one for you, but then it’s commit or nothing.

The lowest plan available is the individual plan, currently sitting at 15€ per month for one person only.

Individual Plan Features

• One researcher
• 12 hours of automated transcription per month
• Analysis features
• Basic integrations
• Unlimited projects
• Unlimited sharing of findings in read-only mode
• Personal support and onboarding

The next step up in pricing is hardly a step. You may need an elevator. It leaps from 15€ to 170€, increasing the researcher count up to 3 and unlocking a few extra features. For more information, check out Condens’s pricing .

There are also Business and Enterprise plans, setting you back 400€ and 1,000€ per month respectively.

While it’s a bit on the expensive side, Condens is used by big companies such as 02, Just Eat, and Eventbrite. In other words, it’s established a reputation for being one of the best at what it does.

It’s a great tool for remote UX research, and its automatic transcriptions can come in useful, but at the end of the day, we think Condens is a bit too expensive to place near the top of the list.

After all, tl;dv offers many of the same features , including instant and automated transcriptions in more than 25 languages, video clips and highlight reels, a video library for storing user interviews, and all this is included in the free plan alone… 

Miro is a popular tool among UX researchers, despite it not being a specialized research repository tool. More accurately, Miro is a digital whiteboard platform, allowing users to collaborate visually. It’s great for creating wireframes or flow diagrams.

However, as you can see in the image above, Miro now offers a template specifically for a UX research repository. This boosts it up the list dramatically as it means you can store all your user research in a way that’s visual and easy to make sense of.

• Your entire company can adopt Miro without any issues, allowing your stakeholders to view user research first-hand
• Real-time collaboration 
• Great visualization elements and easy to do Card Sorting
• Can be used for user interviews, usability tests, and surveys
• Integrated with many top of the range apps and tools
• Great UX reserach templates
• Doesn’t work well with recordings, nor transcribe meetings (luckily, tl;dv integrates with Miro to do the hard work)
• Limited analytical features
• Not great for organizing large amounts of data 
• Can be difficult to share with stakeholders not using Miro
• Lack of research-specific features

Like Notion, Miro isn’t as expensive as dedicated research repositories. Its free plan offers enough to fully get a feel for the platform.

• Single workspace with 3 editable boards
• 2,500+ community-made templates
• Integration with over 100 apps, including Slack, Zoom, and Google Drive
• Easily bring individuals to a specific area of the board and follow what they do

To summarize, Miro is a bit like Notion in that it can be used as a UX research repository software, even if that wasn’t its original purpose or intention. 

The main downside to Miro is that it can be difficult to organize large swathes of data. If you are doing  lots of user research, you may get lost and overwhelmed using Miro. 

However, for small amounts of separate research, Miro is a powerful choice that excels because of its unrivaled ability to display information visually.

While Notion is not a specialized research repository tool, you can still use it to store, organize and share your research. In fact, if you need to share it with stakeholders, clients, or other non-researcher colleagues, it might just be the easiest option.

Notion has an extremely low learning curve and is often used in businesses anyway. By collecting your data on Notion, you can save your team money and time that they would need to pay for and learn an entirely new system.

• Your entire company can adopt Notion without any issues, allowing your stakeholders to view user research first-hand
• Flexible and can be adjusted depending on your requirements
• Real-time collaboration allows multiple team members to contribute to the research repository simultaneously
• Integrated with lots of popular tools like Google Drive and Trello
• Powerful search function that helps you find valuable insights quicker than you can say “research repository”
• Doesn’t work well with recordings, nor transcribe meetings (luckily, tl;dv integrates with Notion to do the hard work)
• Limited analytics
• Limited visualization
• Limited research-specific features that could hold you back if you require some additional code or analysis
• Limited options for exporting data outside of Notion itself (not great if your stakeholders aren’t using Notion)

Notion has a generous free trial, but it is not great for large teams. Unlike most dedicated UX research repository tools, Notion’s prices are fair, even for the higher end plans. 

• Unlimited blocks for individuals and limited block trial for teams
• Collaborative workspace
• Integrate with Slack, GitHub and more…
• 7 day page history
• Invite 10 guests
• Basic analytics

For higher limits and more features, see Notion’s other plans , including their customizable Enterprise Plan.

If your team already uses Notion then trying it out for a research repository is a no-brainer. It’ll save you a lot of money as specialized research repository software is expensive. More than that, it’ll save you time. You already know how to use it, so now you’re just giving it an extra use case. 

But what about if your company doesn’t use Notion? Depending on what they do use, Notion could still be a good option. Obviously, if they’re already using a potential documentation tool that can be repurposed into a research repository, then that would be ideal. If not, Notion is not hard to learn and it’s not going to send you in the red either.

At the end of the day, it comes down to whether or not you want to use this research repository for sharing widely amongst your colleagues, stakeholders and clients, or whether it’s simply a dedicated hidey-hole for user researchers.

Maze is an intuitive tool, designed to make the product discovery phase continuous. It’s perfect for user-centric product teams that are conducting a lot of user interviews and usability tests .

Maze encourages teams to continuously collect insights from their users throughout the entire product development cycle, maximizing a product’s potential and making it truly stand out.

It’s particularly useful for product designers and UX researchers.

• Can conduct user research and testing from anywhere in the world
• User-friendly interface makes it easy for non-researchers to conduct research and view insights
• The ability to collect and store qualitative as well as quantitative data
• Integration with other tools such as Figma and Sketch
• Limited customization options
• The analytics and reporting features could be enhanced
• Customer support could be better
• The platform is limited to remote research which may not be useful for all research projects

Like the two UX research repository tools listed above, Maze has a free plan that we highly recommend you trial before diving headfirst into the paid versions. It’s not the best free plan in the world, but it’s enough to get a simple feel for the platform.

• 1 study / month
• Up to 7 blocks

For higher limits, as well as clips, Open and Closed Card Sorting, templates and more, see Maze’s paid options .

Used by Uber, Glovo, and Rappi, Maze is a UX researcher’s dream. It’s one of the best research repository platforms available as it’s specifically designed with user research in mind. 

The fact that it’s easy to learn, easy to use, and focuses on continuous user research, makes Maze a top contender for those in need of a repository solely for user research. Teams that were hoping to use their repository as a more general documentation tool may need to search for something else.

7. Productboard

Productboard is the easiest to use UX research repository software out there. As a product management platform, Productboard helps you get the right products to market, quicker, by understanding the needs of your customers. In doing so, it helps you build priorities and structure, aligning your whole team behind an intuitive roadmap.

Productboard is ideal for product managers, especially those in computer software or information technology services .

• Easily gather and organize customer feedback
• Features for prioritizing and organizing product developmental tasks
• Collaboration tools for async and remote work teams
• Integration with other tools such as Trello and Jira
• Can be difficult to learn for newcomers
• Difficult to customize 
• The customer support can be improved
• Can be costly for small businesses

Productboard doesn’t offer a free plan, but it does offer a free trial that we highly recommend you check out if you’re interested. It lets you test out its features for 15 days, before upgrading to one of the paid plans.

If you need unlimited products, features, and roadmaps, you’ll have to fork out for one of the paid plans .

Both Microsoft and Zoom use Productboard, so it’s easy to see its importance on business strategy.

As Productboard is a little niche, there can be a learning curve for newbies. As user researchers, you should know all about learnability, but not everybody in your workplace will, and not everyone will want to learn a complex new system just for their small role.

The use of Productboard is probably best for a select team of researchers, organizing their research data in a super convenient manner.

The Best UX Research Repository For You

At the end of the day, only you know what’s best for your business. If you have a team that already uses something like Notion or Miro, then there’s no point trying to get everyone to learn something more complicated. However, if the type of research you conduct is thorough and complex, it may require a more specialized research repository. 

Hopefully, the tools on this list have helped you understand more about what your team needs and what your stakeholders are most able to access. Remember: if the stakeholders can’t see your research, it’s effectively useless.

Also remember this: whichever UX research repository you choose to use, make sure you partner it with tl;dv for instant hyperlinks to user interviews that you won’t find elsewhere.

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Best UX Research Repository Tools in 2023 [Reviewed]

March 10, 2023.

Reem Abouemera

The way you analyze, categorize, and organize research data can make a big difference in the quality of insights and the key findings. In fact, it can draw the line between actionable insights and unusable data. And that’s why the choice of UX research repository tool shouldn't be taken lightly.

Today's landscape of UX research repository tools is more robust than ever, offering you a handful of options to choose from. To narrow down the list, we've tried and curated the best UX research repository tools in the market today. So if you've been on the hunt for a new UX research repository tool, you’ll find the best one that suits your needs here.

Alright, let’s get started. 

Specialized User Research Repository Tools

Specialized user research repository tools are designed specifically to help you save, organize, and analyze user research data. They’re made to help UX researchers manage their research projects effectively. 

Here are the best ones to try.

Grain helps researchers collect and organize user interviews while also enabling them to create and share research insights and findings—in visually appealing stories. 

When you conduct user interviews , add Grain to record, transcribe, tag, and organize your qualitative data. You can also choose to import your pre-recorded interviews from your Zoom Cloud or upload them manually—to parse and analyze your existing data. 

Grain, as a repository, allows you to add your team members, stakeholders, and collaborators to your workspace so that they'll be able to access all of your research data—anytime. 

Once you have the interview recordings in Grain, you have a variety of ways to slice and dice your data so that it's easy to share insights. Select the text in the transcript to clip and share key moments from a user interview or combine the insights from multiple interviews to create an engaging story. 

You can also copy & share the AI summary in a click. Need to share insights and key moments with other teams? Just copy and paste to embed Grain videos in your favorite communication tool (like Slack) and collaboration tools (like Miro or Notion).

Thanks to its native integration, you can also send research insights to your productboard directly from Grain. 

With Grain, forget about the tedious process of transcribing and organizing your user research data–let the tool do all the heavy lifting for you. Instead, focus on what you do best: uncovering actionable insights from your data.

Ready to try Grain? Here’s how to quickly set up a user research repository using Grain . 

Ideal For : Small to mid-sized research teams seeking a budget-friendly way to transcribe, organize, and share their qualitative research data.

2. Dovetail

If you're looking to pull and organize data from other sources than user interviews, Dovetail would be a good fit for you. Not only does it capture customer feedback from user interviews in one place, but it's also capable of importing emails, survey results, support tickets, tweets, Facebook posts, and app reviews. That means you can get a complete picture of your users' needs and wants and how they feel about your product using sentiment analysis.

Dovetail comes equipped with features like team collaboration, version control, and approval workflows—which in turn, makes it easy for teams to work together on research projects and ensure that everyone is always looking at the most up-to-date data.

Another thing that sets Dovetail apart is its ability to help you turn your data into reports. With features like custom dashboards and reporting templates, it's easy to create research reports that are both informative and visually appealing.

In a nutshell, Dovetail is a complete user research platform that gives you everything you need not just to collect and store data but also to analyze and report on it.

Ideal For: Large research teams that need a complete user research platform to analyze and organize both qualitative and quantitative data.  

What if you need a comprehensive user research platform just for qualitative data? That's precisely what Userbit offers. Along with enabling you to collect and store data from user interviews (with transcripts, tags, and highlights), the tool also comes with a suite of features to help you turn your data into insights.

For one, you can easily turn your transcripts into affinity diagrams or visual word clouds using Userbit's visualizations. This is a great way to quickly identify patterns and relationships in your data so that you can start generating insights.

Another useful tool that Userbit offers is the ability to generate user personas directly from your research data. This is a huge time-saver, as it means you don't have to manually create personas from scratch. You get a mental model of your users based on how they think and behave, which is invaluable for designing an intuitive user experience.

Userbit also makes it easy to share your findings with team members and stakeholders, allowing the whole team to collaborate on creating the design process and user path. 

‍ Ideal For : Large research teams that need a comprehensive qualitative user research platform with powerful data analysis and design tools.

If you want to structure and organize your user research data with minimal technical expertise, Condens is the tool for you. It's designed to be used by anyone–researchers, designers, product managers, and even those without a technical background. In a matter of minutes, you can create a research repository that's both well-organized and easy to use.

One of the things that set Condens apart is its visual interface. You can see all your data at a glance and quickly filter and search for specific items. This makes it easy to find what you're looking for, even if you have a large amount of data. Its AI-assisted transcription feature is also handy for quickly transcribing user interviews so that you can start analyzing the data right away.

Another great thing about Condens is that it offers a wide range of integrations. Whether you want to import data from Excel, Google Sheets, or another research repository tool, Condens makes it easy. This means you can get started using the tool without worrying about manually transferring your data.

So if easy onboarding and a visual interface are what you're after, Condens is worth checking out.

‍ Idea For: Researchers, designers, and product managers who want an easy-to-use research repository tool with a visual interface.

Generic Documentation Tools

General-purpose documentation tools can also be used to store and organize user research data. While these tools might not have all the bells and whistles of dedicated user research repository tools, they can still get the job done. 

Before we begin, it’s worth noting that generic tools are ideal only if you’re using them extensively along with other stakeholders and collaborators. If you’re planning to adopt a new tool, it’s better to opt-in for specialized repository software. 

Here are a few options to consider.

Notion is a versatile tool that can be used for everything from project management to documentation. It's great for storing user research data because it's easy to use, extremely flexible, and has a clean interface.

You can create custom databases in Notion, which is handy for organizing your user research data. You can also add rich media (like images, videos, and audio recordings) to your databases, making it easy to refer back to your research data later.

Notion also offers a wide range of integrations, so you can easily import data from other tools (like Excel or Google Sheets). This is handy if you want to consolidate all your user research data in one place. And with the help of extensions like Repo , you can even turn Notion into a dedicated user research repository tool with features like tagging and highlighting.

While Notion isn't a dedicated user research repository tool, it's still a great option for storing and organizing your research data.

Pro tip: You can enrich your research data in Notion by embedding key moments and videos from your interviews. 

Idea For : Researchers, designers, and product managers who want a versatile tool that can be used for a variety of purposes (including user research).

Jira is a project management tool often used by software development teams. However, it can also be used to store user research data and track user research projects.

Jira has a number of features that make it well-suited for user research. For example, you can use it to track user research tasks (such as interviews and user testing sessions) and create custom reports. This makes it easy to see how your user research is progressing and identify any areas that need improvement.

Plus, since you can add attachments to Jira tickets, storing and sharing user research data (like interview recordings and screenshots) is easy. You could also create a dedicated user research project in Jira, making it easy for your team to keep track of all the user research data in one place.

While Jira might be a bit overwhelming for those new to project management tools, it's still an alternative to consider if you're looking for a tool to store your user research data.

‍ Idea For: Mid-sized UX teams that don't want to leave their project management tool to store their user research data.

3. Airtable

Finally, Airtable is another database tool that can be used for various purposes, including user research. The tool offers a User Research template that saves you the trouble of setting up your database. And when combined with its User Feedback template , you can use Airtable to track your user research data and feedback in one place.

On Airtable, you can easily add attachments (like images, videos, and audio recordings) to your user research data. That way, you not only have form submissions related to your user research but also the actual data itself (like interview recordings).

Plus, Airtable has a number of features that make it easy to organize and find your user research data. For example, you can use views to filter and sort your data or create custom formulas to calculate things like net promoter score. You also have the option of visualizing your data in a variety of ways, from bar graphs to calendars.

All in all, Airtable is as close as you can get to a dedicated user research repository tool. And with its User Research and User Feedback templates, it's easy to get started with storing your user research data with it.

‍ Ideal For: UX researchers looking for a simple yet powerful tool to store their user research data–from submissions to interviews.

The Takeaway

Let's recap to help you choose the best user research repository tool for your needs. If you're a small to mid-sized research team with a heavy focus on qualitative data, Grain is your best bet. With its integrations with documentation tools like Notion and whiteboarding tools like Miro, it'll be a valuable addition to your toolkit.

On the other hand, if you're part of a large organization looking to invest heavily in a repository, Dovetail is worth considering. It's an all-in-one repository tool that works for both qualitative and quantitative data in equal measure. 

Userbit is quite similar to Dovetail but is only focused on qualitative data. So if you're not interested in any of Dovetail's quantitative features (like usability testing), Userbit might be a better option for you. And if all of these tools seem too technical for your needs and your priority is quick onboarding and easy data sharing, Condens may be the perfect fit.

Finally, if you're not interested in switching between tools and want to keep all of your data–user research and otherwise–in one place, Notion, Jira, and Airtable are all viable options. And because they're generic database tools, they're flexible and can be customized to fit your team’s needs. 

So, what's the best user research repository tool for you? We hope this guide has helped you make your final decision!

Related Articles

The 10 best ux research tools to try in 2023 [reviewed], how to update your research to make it work remotely, get started with grain.

How to build a research repository: a step-by-step guide to getting started

Research repositories have the potential to be incredibly powerful assets for any research-driven organisation. But when it comes to building one, it can be difficult to know where to start. In this post, we provide some practical tips to define a clear vision and strategy for your repository.

Done right, research repositories have the potential to be incredibly powerful assets for any research-driven organisation. But when it comes to building one, it can be difficult to know where to start.

As a result, we see tons of teams jumping in without clearly defining upfront what they actually hope to achieve with the repository, and ending up disappointed when it doesn't deliver the results.

Aside from being frustrating and demoralising for everyone involved, building an unused repository is a waste of money, time, and opportunity.

So how can you avoid this?

In this post, we provide some practical tips to define a clear vision and strategy for your repository in order to help you maximise your chances of success.

🚀 This post is also available as a free, interactive Miro template that you can use to work through each exercise outlined below - available for download here .

Defining the end goal for your repository

To start, you need to define your vision.

Only by setting a clear vision, can you start to map out the road towards realising it.

Your vision provides something you can hold yourself accountable to - acting as a north star. As you move forward with the development and roll out of your repository, this will help guide you through important decisions like what tool to use, and who to engage with along the way.

The reality is that building a research repository should be approached like any other product - aiming for progress, over perfection with each iteration of the solution.

Starting with a very simple question like "what do we hope to accomplish with our research repository within the first 12 months?" is a great starting point.

You need to be clear on the problems that you’re looking to solve - and the desired outcomes from building your repository - before deciding on the best approach.

Building a repository is an investment, so it’s important to consider not just what you want to achieve in the next few weeks or months, but also in the longer term to ensure your repository is scalable.

Whatever the ultimate goal (or goals), capturing the answer to this question will help you to focus on outcomes over output .

🔎 How to do this in practice…

1. complete some upfront discovery.

In a previous post we discussed how to conduct some upfront discovery to help with understanding today’s biggest challenges when it comes to accessing and leveraging research insights.

⏰ You should aim to complete your upfront discovery within a couple of hours, spending 20-30 mins interviewing each stakeholder (we recommend talking with at least 5 people, both researchers and non-researchers).

2. Prioritise the problems you want to solve

Start by spending some time reviewing the current challenges your team and organisation are facing when it comes to leveraging research and insights.

You can run a simple affinity mapping exercise to highlight the common themes from your discovery and prioritise the top 1-3 problems that you’d like to solve using your repository.

💡 Example challenges might include:

Struggling to understand what research has already been conducted to-date, leading to teams repeating previous research

Looking for better ways to capture and analyse raw data e.g. user interviews

Spending lots of time packaging up research findings for wider stakeholders

Drowning in research reports and artefacts, and in need of a better way to access and leverage existing insights

Lacking engagement in research from key decision makers across the organisation

⏰ You should aim to confirm what you want to focus on solving with your repository within 45-60 mins (based on a group of up to 6 people).

3. Consider what future success looks like

Next you want to take some time to think about what success looks like one year from now, casting your mind to the future and capturing what you’d like to achieve with your repository in this time.

A helpful exercise is to imagine the headline quotes for an internal company-wide newsletter talking about the impact that your new research repository has had across the business.

The ‘ Jobs to be done ’ framework provides a helpful way to format the outputs for this activity, helping you to empathise with what the end users of your repository might expect to experience by way of outcomes.

💡 Example headlines might include:

“When starting a new research project, people are clear on the research that’s already been conducted, so that we’re not repeating previous research” Research Manager

“During a study, we’re able to quickly identify and share the key insights from our user interviews to help increase confidence around what our customers are currently struggling with” Researcher

“Our designers are able to leverage key insights when designing the solution for a new user journey or product feature, helping us to derisk our most critical design decisions” Product Design Director

“Our product roadmap is driven by customer insights, and building new features based on opinion is now a thing of the past” Head of Product

“We’ve been able to use the key research findings from our research team to help us better articulate the benefits of our product and increase the number of new deals” Sales Lead

“Our research is being referenced regularly by C-level leadership at our quarterly townhall meetings, which has helped to raise the profile of our team and the research we’re conducting” Head of Research

Ask yourself what these headlines might read and add these to the front page of a newspaper image.

You then want to discuss each of these headlines across the group and fold these into a concise vision statement for your research repository - something memorable and inspirational that you can work towards achieving.

💡Example vision statements:

‘Our research repository makes it easy for anyone at our company to access the key learnings from our research, so that key decisions across the organisation are driven by insight’

‘Our research repository acts as a single source of truth for all of our research findings, so that we’re able to query all of our existing insights from one central place’

‘Our research repository helps researchers to analyse and synthesise the data captured from user interviews, so that we’re able to accelerate the discovery of actionable insights’

‘Our research repository is used to drive collaborative research across researchers and teams, helping to eliminate data silos, foster innovation and advance knowledge across disciplines’

‘Our research repository empowers people to make a meaningful impact with their research by providing a platform that enables the translation of research findings into remarkable products for our customers’

⏰ You should aim to agree the vision for your repository within 45-60 mins (based on a group of up to 6 people).

Creating a plan to realise your vision

Having a vision alone isn't going to make your repository a success. You also need to establish a set of short-term objectives, which you can use to plan a series of activities to help you make progress towards this.

Focus your thinking around the more immediate future, and what you want to achieve within the first 3 months of building your repository.

Alongside the short-term objectives you’re going to work towards, it’s also important to consider how you’ll measure your progress, so that you can understand what’s working well, and what might require further attention. 

Agreeing a set of success metrics is key to holding yourself accountable to making a positive impact with each new iteration. This also helps you to demonstrate progress to others from as early on in the process as possible.

1. Establish 1-3 short term objectives

Take your vision statement and consider the first 1-3 results that you want to achieve within the first 3 months of working towards this.

These objectives need to be realistic and achievable given the 3 month timeframe, so that you’re able to build some momentum and set yourself up for success from the very start of the process.

💡Example objectives:

Improve how insights are defined and captured by the research team

Revisit our existing research to identify what data we want to add to our new research repository

Improve how our research findings are organised, considering how our repository might be utilised by researchers and wider teams

Initial group of champions bought-in and actively using our research repository

Improve the level of engagement with our research from wider teams and stakeholders

Capture your 3 month objectives underneath your vision, leaving space to consider the activities that you need to complete in order to realise each of these.

2. Identify how to achieve each objective

Each activity that you commit to should be something that an individual or small group of people can comfortably achieve within the first 3 months of building your repository.

Come up with some ideas for each objective and then prioritise completing the activities that will result in the biggest impact, with the least effort first.

💡Example activities:

Agree a definition for strategic and tactical insights to help with identifying the previous data that we want to add to our new research repository

Revisit the past 6 months of research and capture the data we want to add to our repository as an initial body of knowledge

Create the first draft taxonomy for our research repository, testing this with a small group of wider stakeholders

Launch the repository with an initial body of knowledge to a group of wider repository champions

Start distributing a regular round up of key insights stored in the repository

You can add your activities to a simple kanban board , ordering your ‘To do’ column with the most impactful tasks up top, and using this to track your progress and make visible who’s working on which tasks throughout the initial build of your repository.

This is something you can come back to a revisit as you move throughout the wider roll out of your repository - adding any new activities into the board and moving these through to ‘Done’ as they’re completed.

⚠️ At this stage it’s also important to call out any risks or dependencies that could derail your progress towards completing each activity, such as capacity, or requiring support from other individuals or teams.

3. Agree how you’ll measure success

Lastly, you’ll need a way to measure success as you work on the activities you’ve associated with each of your short term objectives.

We recommend choosing 1-3 metrics that you can measure and track as you move forward with everything, considering ways to capture and review the data for each of these.

⚠️ Instead of thinking of these metrics as targets, we recommend using them to measure your progress - helping you to identify any activities that aren’t going so well and might require further attention.

💡Example success metrics:

Usage metrics - Number of insights captured, Active users of the repository, Number of searches performed, Number of insights viewed and shared

User feedback - Usability feedback for your repository, User satisfaction ( CSAT ), NPS aka how likely someone is to recommend using your repository

Research impact - Number of stakeholder requests for research, Time spent responding to requests, Level of confidence, Repeatable value of research, Amount of duplicated research, Time spent onboarding new joiners

Wider impact - Mentions of your research (and repository) internally, Links to your research findings from other initiatives e.g. discovery projects, product roadmaps, Customers praising solutions that were fuelled by your research

Think about how often you want to capture and communicate this information to the rest of the team, to help motivate everyone to keep making progress.

By establishing key metrics, you can track your progress and determine whether your repository is achieving its intended goals.

⏰ You should aim to create a measurable action plan for your repository within 60-90 mins (based on a group of up to 6 people). ‍ ‍

🚀 Why not use our free, downloadable Miro template to start putting all of this into action today - available for download here .

To summarise

As with the development of any product, the cost of investing time upfront to ensure you’re building the right thing for your end users, is far lower than the cost of building the wrong thing - repositories are no different!

A well-executed research repository can be an extremely valuable asset for your organisation, but building one requires consideration and planning - and defining a clear vision and strategy upfront will help to maximise your chances of success.

It’s important to not feel pressured to nail every objective that you set in the first few weeks or months. Like any product, the further you progress, the more your strategy will evolve and shift. The most important thing is getting started with the right foundations in place, and starting to drive some real impact.

We hope this practical guide will help you to get started on building an effective research repository for your organisation. Thanks and happy researching!

‍ Work with our team of experts

At Dualo we help teams to define a clear vision and strategy for their research repository as part of the ‘Discover, plan and set goals’ module facilitated by our Dualo Academy team.  If you’re interested in learning more about how we work with teams, book a short call with us to discuss how we can support you with the development of your research repository and knowledge management process.

Nick Russell

I'm one of the Co-Founders of Dualo, passionate about research, design, product, and AI. Always open to chatting with others about these topics.

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Inference code for CodeLlama models

meta-llama/codellama

Folders and files, repository files navigation, introducing code llama.

Code Llama is a family of large language models for code based on Llama 2 providing state-of-the-art performance among open models, infilling capabilities, support for large input contexts, and zero-shot instruction following ability for programming tasks. We provide multiple flavors to cover a wide range of applications: foundation models (Code Llama), Python specializations (Code Llama - Python), and instruction-following models (Code Llama - Instruct) with 7B, 13B and 34B parameters each. All models are trained on sequences of 16k tokens and show improvements on inputs with up to 100k tokens. 7B and 13B Code Llama and Code Llama - Instruct variants support infilling based on surrounding content. Code Llama was developed by fine-tuning Llama 2 using a higher sampling of code. As with Llama 2, we applied considerable safety mitigations to the fine-tuned versions of the model. For detailed information on model training, architecture and parameters, evaluations, responsible AI and safety refer to our research paper . Output generated by code generation features of the Llama Materials, including Code Llama, may be subject to third party licenses, including, without limitation, open source licenses.

We are unlocking the power of large language models and our latest version of Code Llama is now accessible to individuals, creators, researchers and businesses of all sizes so that they can experiment, innovate and scale their ideas responsibly. This release includes model weights and starting code for pretrained and fine-tuned Llama language models — ranging from 7B to 34B parameters.

This repository is intended as a minimal example to load Code Llama models and run inference.

In order to download the model weights and tokenizers, please visit the Meta website and accept our License.

Once your request is approved, you will receive a signed URL over email. Then run the download.sh script, passing the URL provided when prompted to start the download. Make sure that you copy the URL text itself, do not use the 'Copy link address' option when you right click the URL. If the copied URL text starts with: https://download.llamameta.net , you copied it correctly. If the copied URL text starts with: https://l.facebook.com , you copied it the wrong way.

Pre-requisites: make sure you have wget and md5sum installed. Then to run the script: bash download.sh .

Keep in mind that the links expire after 24 hours and a certain amount of downloads. If you start seeing errors such as 403: Forbidden , you can always re-request a link.

Model sizes

In a conda environment with PyTorch / CUDA available, clone the repo and run in the top-level directory:

Different models require different model-parallel (MP) values:

All models, except the 70B python and instruct versions, support sequence lengths up to 100,000 tokens, but we pre-allocate the cache according to max_seq_len and max_batch_size values. So set those according to your hardware and use-case.

Pretrained Code Models

The Code Llama and Code Llama - Python models are not fine-tuned to follow instructions. They should be prompted so that the expected answer is the natural continuation of the prompt.

See example_completion.py for some examples. To illustrate, see command below to run it with the CodeLlama-7b model ( nproc_per_node needs to be set to the MP value):

Pretrained code models are: the Code Llama models CodeLlama-7b , CodeLlama-13b , CodeLlama-34b , CodeLlama-70b and the Code Llama - Python models CodeLlama-7b-Python , CodeLlama-13b-Python , CodeLlama-34b-Python , CodeLlama-70b-Python .

Code Infilling

Code Llama and Code Llama - Instruct 7B and 13B models are capable of filling in code given the surrounding context.

See example_infilling.py for some examples. The CodeLlama-7b model can be run for infilling with the command below ( nproc_per_node needs to be set to the MP value):

Pretrained infilling models are: the Code Llama models CodeLlama-7b and CodeLlama-13b and the Code Llama - Instruct models CodeLlama-7b-Instruct , CodeLlama-13b-Instruct .

Fine-tuned Instruction Models

Code Llama - Instruct models are fine-tuned to follow instructions. To get the expected features and performance for the 7B, 13B and 34B variants, a specific formatting defined in chat_completion() needs to be followed, including the INST and <<SYS>> tags, BOS and EOS tokens, and the whitespaces and linebreaks in between (we recommend calling strip() on inputs to avoid double-spaces). CodeLlama-70b-Instruct requires a separate turn-based prompt format defined in dialog_prompt_tokens() . You can use chat_completion() directly to generate answers with all instruct models; it will automatically perform the required formatting.

You can also deploy additional classifiers for filtering out inputs and outputs that are deemed unsafe. See the llama-recipes repo for an example of how to add a safety checker to the inputs and outputs of your inference code.

Examples using CodeLlama-7b-Instruct :

Fine-tuned instruction-following models are: the Code Llama - Instruct models CodeLlama-7b-Instruct , CodeLlama-13b-Instruct , CodeLlama-34b-Instruct , CodeLlama-70b-Instruct .

Code Llama is a new technology that carries potential risks with use. Testing conducted to date has not — and could not — cover all scenarios. In order to help developers address these risks, we have created the Responsible Use Guide . More details can be found in our research papers as well.

Please report any software “bug”, or other problems with the models through one of the following means:

• Reporting issues with the model: github.com/facebookresearch/codellama
• Reporting risky content generated by the model: developers.facebook.com/llama_output_feedback
• Reporting bugs and security concerns: facebook.com/whitehat/info

See MODEL_CARD.md for the model card of Code Llama.

Our model and weights are licensed for both researchers and commercial entities, upholding the principles of openness. Our mission is to empower individuals, and industry through this opportunity, while fostering an environment of discovery and ethical AI advancements.

See the LICENSE file, as well as our accompanying Acceptable Use Policy

• Code Llama Research Paper
• Code Llama Blog Post

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