Data-Driven Web Applications
Applications that collect, manage, and present research or operational data over long time horizons.
Academic Research Technologies LLC partners with research groups, educational programs, and mission-driven organizations to build, maintain, and improve software over time. We’re flexible in how we engage and are accustomed to working within the practical constraints of research — timelines, budgets, and institutional processes.
Rather than focusing only on short-term feature delivery, our work balances immediate needs with long-term sustainability, usability, and institutional continuity.
Research and educational software often evolves over many years, across changing teams, funding sources, and technical environments. We work with groups at all points along that path — whether you’re starting something new, inheriting an existing system, or trying to keep important software running smoothly.
Our role is to meet teams where they are and provide support that fits their goals, constraints, and working style.
Our work typically includes:
We design, implement, and improve custom applications with a focus on clarity, usability, and accessibility. This often means working within existing systems and making incremental improvements rather than pushing for unnecessary rewrites.
We provide ongoing support for long-running systems, including security updates, dependency management, bug fixes, and infrastructure oversight — helping keep software stable, secure, and understandable as teams and technologies change.
We help teams think through technical decisions early and realistically. This includes sustainability planning, maintenance scoping, transition planning, and technical consultation, as well as preparing cost estimates, technical descriptions, and letters of support for grant proposals.
Across all of this work, we prioritize clear communication, reliability, and long-term viability — while staying flexible in how we engage.
Research and educational software takes many forms — from new ideas and pilot projects to long-running systems that have evolved across multiple grants, teams, and technologies.
We’re comfortable working with projects at any of these stages, including inherited systems, tight timelines, and well-defined improvement goals.
Examples of the kinds of projects we support include:
Applications that collect, manage, and present research or operational data over long time horizons.
Targeted improvements to existing software focused on usability, accessibility, responsiveness, and clarity. Common goals include improving usability, addressing accessibility issues aligned with WCAG guidance, updating outdated interfaces, and improving mobile and responsive behavior.
Custom data models and storage systems that support lab workflows, longitudinal studies, and evolving research needs.
Systems that connect research platforms and external services to automate data flow. This includes integrations with platforms such as REDCap and Qualtrics, as well as pipelines for ingesting, validating, transforming, and exporting data — including integrations with external and government APIs.
Public-facing platforms that support large, distributed audiences contributing data or participating over time.
Visual tools that help researchers, educators, and the public explore and understand complex datasets.
Applications designed to support teaching, learning, and instructional research.
Applications supporting data collection, study participation, or field-based research workflows.
Platforms focused on sharing research outputs, datasets, and findings with broader audiences.
APIs, authentication systems, background services, and other components that support reliable operation.
Incremental upgrades that reduce risk and improve maintainability while preserving existing workflows.
This list is illustrative rather than exhaustive; projects vary widely and are shaped by institutional context, project goals, and practical constraints.
We are technology-agnostic by design. Much of our work involves stepping into existing technical ecosystems and working within them — selecting tools based on project needs, institutional constraints, and long-term maintainability rather than preference for any single language or framework.
Our experience spans web and mobile applications, data management systems, integrations, infrastructure, and automation. As projects evolve, we’re comfortable onboarding into new languages, frameworks, and platforms as needed.
The emphasis is on clarity, reliability, and sustainability — while keeping momentum and avoiding unnecessary complexity.
We prioritize maintainability, clarity, and institutional fit. In practice, that often means choosing boring, well-supported options, working with what teams already know, and documenting decisions so systems remain operable as people and priorities change.
Research software often outlives its original grants, teams, and technical assumptions. Without ongoing care, even well-designed systems can become harder to use, less secure, or increasingly fragile over time.
We approach maintenance as practical, professional stewardship — focusing on keeping systems dependable and understandable as contexts change:
In practice, this work is about reducing risk, avoiding surprises, and helping teams focus on their research rather than their infrastructure.
Sustaining software over time often requires funding approaches beyond short-term grants. We work collaboratively with project teams and institutions to identify models that fit administrative realities, budgets, and timelines.
Common approaches include institutional maintenance agreements, restricted maintenance funds, membership support, and maintenance line items in new or follow-on grants. Many projects use a combination of these approaches over time, and we adapt to what works in each context.
Every project and research group operates a little differently. Engagements are scoped collaboratively to reflect your goals, timelines, funding realities, and institutional constraints — whether you need a short burst of focused help or an ongoing technical partner.
Common engagement patterns include:
Annual or multi-year agreements supporting long-running or public-facing systems. This model works well when you want a trusted partner available to handle maintenance, small improvements, and emerging issues over time.
Fixed-scope or time-boxed work for targeted enhancements, integrations, or modernization efforts. Projects are scoped to be realistic and achievable within available time and funding.
Short, focused engagements to help teams understand the current state of a system, identify risks and priorities, and prepare sustainability plans, cost estimates, and technical documentation for proposals.
Lightweight, ongoing consultation for teams with in-house development capacity who want a reliable, experienced sounding board for technical decisions, planning, and problem-solving.
Many teams start with one engagement type and evolve over time. We’re comfortable adjusting scope and approach as projects, funding, and priorities change. If you’re unsure which model fits, that’s normal — we’ll talk it through.
We work with organizations that support research, education, and public engagement and that rely on software systems over long periods of time.
This includes universities and academic units, nonprofit and educational organizations, and multi-institution collaborations. Engagements may be project-based, retainer-based, or advisory, depending on what’s most useful for the team.
Academic Research Technologies LLC operates independently and works alongside a wide range of institutions. Examples and references can be shared as part of initial conversations.
Initial conversations are intentionally lightweight and exploratory. The goal is simply to understand the project, the context it operates in, and what kind of support would actually be helpful.
A typical first conversation covers the current state of the software, who uses it, timing or funding constraints, and possible engagement models. If a project isn’t a good fit, that’s identified early and transparently.
If you’re interested in starting a conversation, it’s helpful to include:
This isn’t a formal intake — just enough context to make the conversation productive.