03 · What You Need to Know
How to Audit the Research Resources Available to Your Study
Start with what the methodology actually requires
Do not begin by browsing the facilities your institution happens to advertise. Begin with the study.
Translate the methodology into concrete resource requirements. If the study involves laboratory measurements, specify the equipment, consumables, environmental conditions, technical procedures, and throughput required. If it involves computational analysis, specify the software, storage, memory, processing, security, and specialist support needed. If you plan interviews, determine whether you require private rooms, recording equipment, transcription tools, secure storage, or remote interviewing infrastructure.
This produces a resource specification rather than a wish list.
The distinction matters because a university can have impressive research infrastructure that is entirely irrelevant to the particular bottleneck in your study.
Think beyond laboratories and equipment
Researchers sometimes interpret research resources primarily as physical facilities. Modern research can depend just as heavily on digital, informational, methodological, and human infrastructure.
| Resource category |
Examples |
What to verify |
| Physical facilities |
Laboratories, interview rooms, simulation spaces, clinics, testing rooms, field stations |
Suitability, privacy, capacity, booking, location, operating hours |
| Equipment |
Sensors, imaging systems, laboratory instruments, recording devices, specialized computers |
Specifications, availability, calibration, training, accessories, maintenance |
| Software |
Statistical, qualitative, GIS, survey, simulation, visualization, transcription, laboratory software |
License coverage, version, user eligibility, device limits, required modules |
| Computing |
High-performance computing, servers, GPUs, secure environments, cloud resources |
Capacity, quotas, compatibility, access procedures, technical support |
| Data infrastructure |
Secure storage, backups, repositories, databases, data-transfer systems |
Security, storage limits, retention, permissions, sharing and backup procedures |
| Research services |
Statistics, methodology, transcription, translation, laboratory services, instrument development |
Eligibility, expertise, waiting time, cost, scope of support |
| Information resources |
Databases, journals, archives, standards, specialized collections |
Subscription coverage, remote access, licensing, document availability |
| Human expertise |
Technicians, programmers, statisticians, methodologists, librarians, data managers |
Relevant expertise, availability, responsibilities, cost, timing |
A study can be constrained by any one of these categories. The missing resource may be a centrifuge, but it may just as easily be a secure server, a software module, or the only technician qualified to operate the equipment.
Institutional ownership is not the same as project access
A resource appearing on your university's website does not establish that you can use it.
Facilities may belong to a particular department, laboratory, center, grant, or research group. Access may be limited to staff, enrolled students in particular programs, approved projects, trained users, collaborators, or researchers who can pay internal service charges.
Institutional availability
The institution possesses or operates the resource somewhere within its research infrastructure.
Project accessibility
Your particular study and research team are permitted and practically able to use the resource under the conditions required by the methodology.
This distinction is especially important when resources are controlled outside your home department. Do not assume that institutional affiliation creates unrestricted access across the university.
Identify who actually controls the resource
Find the person, office, laboratory, center, or service responsible for access.
For equipment, this may be a laboratory manager or principal investigator. For software, it may be information technology or a licensing office. Secure storage may be managed by research computing or information security. Statistical consultation may operate through a methods center. Specialized archives may be controlled by a library or research collection.
Ask the responsible unit rather than relying solely on what a colleague remembers.
The same logic applied earlier to data access: the person who knows a resource exists is not necessarily the person who can authorize your use of it.
Check whether the resource actually meets your specifications
"The university has a microscope" is not enough if your study requires a particular imaging capability. "We have SPSS" does not help if your analysis depends on a module that is not included in the institutional license. "There is a GPU server" tells you little until you know the hardware, memory, queue limits, supported software, and access conditions.
Compare the resource with the methodological specification you developed earlier.
For equipment, examine model, accuracy, range, throughput, calibration, compatible materials, and required accessories where relevant. For software, verify the version, modules, operating system, licensing terms, and number of users. For computing, check storage, memory, processors or GPUs, permitted workloads, and security requirements.
A resource with the right general name may still be the wrong technical resource.
Check capacity, not just existence
A facility may be perfectly suitable and still lack enough capacity for your project.
Suppose your study requires 300 participants to complete a one-hour laboratory procedure. The laboratory can accommodate only one participant at a time and is available to you for ten hours each week. Even before setup, cleaning, cancellations, or equipment downtime, you have at least 30 weeks of laboratory use.
The resource exists. The capacity problem remains.
This should be compared with your recruitment and data-collection timeline. A resource that cannot process the required workload quickly enough can become the true bottleneck even when participants are readily available.
Check the booking system and competing demand
Shared research infrastructure rarely exists for your project alone.
Equipment may be heavily booked during teaching periods. Laboratories may prioritize funded projects. Computing clusters may have queues. Interview rooms may be unavailable during examinations. Technical staff may support several research groups simultaneously.
Ask how far in advance bookings can be made, whether recurring reservations are possible, whether priority rules apply, and what typical availability looks like during your planned data-collection period.
A resource with twenty available hours this week is not necessarily a resource with twenty available hours every week for the next six months.
Ask whether you need training before access is granted
Specialized equipment and facilities often require users to complete training, safety procedures, competency assessment, or supervised sessions before independent use.
Research computing systems may require account approval and technical orientation. Laboratories may require biosafety or equipment-specific training. Secure data environments may require information-security or data-protection training.
Include those requirements in the study timeline.
If only a trained technician can operate the equipment, the relevant resource is not merely the machine. It is the machine plus technician availability.
Check whether the necessary technical support exists
Equipment and software do not operate in a methodological vacuum.
You may need someone to calibrate an instrument, configure software, troubleshoot hardware, prepare samples, manage a computing environment, recover corrupted files, or explain how institutional systems should be used.
Ask who provides technical support, what that support covers, when it is available, and whether it involves fees.
This is particularly important for unfamiliar technology. If the study depends on equipment nobody on the research team knows how to operate and the institution provides no reliable technical support, institutional ownership alone does not solve the skills problem.
Check maintenance and calibration status
A piece of equipment can exist physically while being unusable scientifically.
It may be awaiting repair, overdue for calibration, operating intermittently, missing a necessary component, or approaching replacement. Laboratory equipment may also require certification or maintenance records relevant to particular procedures.
Ask whether the resource is currently operational and whether scheduled maintenance overlaps with your study period.
For measurements where calibration or quality control affects validity, determine who performs these procedures and how their status is documented.
Check the consumables that make the equipment usable
Institutional ownership of expensive equipment can create the impression that the major cost has already been solved.
The study may still require reagents, cartridges, sensors, electrodes, specimen containers, test kits, batteries, filters, calibration materials, or other consumables for every participant or run.
Determine whether these are supplied by the facility, charged to users, or must be purchased by the research project.
A free machine with a ₱2,000 disposable component per participant is not a free research procedure.
Check internal user fees
Shared facilities often recover part of their operating costs through internal charges.
Your university may own the equipment but charge researchers per hour, sample, scan, test, storage volume, computing unit, or technician hour. Some services distinguish between internal and external rates or between funded and student projects.
Ask for the current rate schedule and determine what the charge includes.
These costs belong in the full research budget. Institutional access can reduce costs substantially without necessarily reducing them to zero.
Software access needs its own audit
Software is easy to overlook because researchers often assume that a university license covers everyone.
Check whether students can use the license, whether access is limited to campus computers, whether remote use is permitted, how many simultaneous users are supported, when the license expires, and whether all required modules are included.
Some software may be available only in designated computer laboratories. Others may permit installation on institutional devices but not personal computers. Cloud-based tools may have storage or usage limits.
If your analysis depends on a specific package, verify that access will remain available throughout the period in which you need to prepare, analyze, revise, and reproduce the results.
Check whether collaborators can use the same software and systems
Your own access may not be enough.
If a statistician, research assistant, supervisor, or collaborator must work directly with the data or analytical files, determine whether that person can access the same software and computing environment.
Licensing or data-security restrictions may prevent files from being moved to another system. A collaborator outside your institution may not have access to your university's licensed software or secure server.
This can influence the software workflow, file formats, and division of analytical responsibilities.
Check storage requirements before data collection begins
Ordinary office documents require little storage. Research involving audio, video, imaging, sensor streams, genomic information, simulation output, or large administrative datasets can generate substantial volumes.
Estimate how much data the study will produce and where it will be stored.
Then verify whether institutional storage can accommodate the volume and whether it satisfies the security and retention requirements of the study.
Secure storage is not interchangeable with ordinary cloud storage
If your study involves sensitive, confidential, identifiable, proprietary, or restricted information, data may need to remain in an approved institutional environment.
Consumer cloud storage, personal email, portable drives, or ordinary collaboration tools may not satisfy applicable institutional or data-provider requirements.
Check what systems are approved for the data you intend to collect or receive, who can access them, how backups work, and what happens when the project ends.
A secure storage requirement can become a feasibility issue if the institution lacks sufficient capacity or if the approved environment does not support the software needed for analysis.
High-performance computing is useful only if your workload can actually run there
Projects involving machine learning, simulation, large-scale text analysis, imaging, genomics, or very large datasets may require more computing power than an ordinary laptop can provide.
Institutional high-performance computing can solve this problem, but investigate the details. What hardware is available? Which software and libraries are supported? Are GPUs available? Are there quotas or queues? Can packages be installed? How long can jobs run? Is sensitive data permitted?
Also ask what technical knowledge is required. A powerful computing cluster may be practically inaccessible if the workflow requires command-line, Linux, job-scheduling, or programming skills nobody on the project currently possesses.
Library resources can determine whether some studies are feasible
Not all infrastructure is technological.
Systematic reviews, bibliometric research, historical work, archival research, and evidence syntheses may depend on access to databases, full-text journals, citation indexes, standards, archival collections, or specialist literature.
Verify whether your institution subscribes to the databases required by the methodology rather than assuming that general library access is enough.
If a critical database is unavailable, investigate whether another legitimate access route exists through collaboration, interlibrary arrangements, public sources, or another institution.
Research services may have eligibility and waiting periods
Your institution may advertise statistical consultation, methodological support, data science, laboratory analysis, translation, transcription, instrument development, research computing, or other services.
Find out who can use them.
Some services support faculty-led funded projects but not student research. Others provide a limited number of free consultation hours. Some require supervisor referral or cost recovery. Waiting lists may be substantial near common thesis deadlines.
If your project depends on specialist support, verify availability early. Consider when specialist expertise should enter the research process, then confirm that the appropriate expertise will actually be available when your study needs it.
Check access outside normal working hours if your study requires it
Some studies need evening, weekend, or continuous access.
Participants may be available only after work. Laboratory procedures may run overnight. Long computational jobs may require monitoring. Field samples may need immediate processing. A study involving healthcare workers may need scheduling around shifts.
Determine the facility's operating hours and whether researchers can enter outside them. If access depends on staff presence, security personnel, or technical supervision, extended hours may not be possible.
A resource available from 9:00 a.m. to 5:00 p.m. is not fully available to a protocol that requires midnight measurements.
Check whether the resource will still exist throughout the study
Research timelines can extend across semesters or years. Institutional resources can change during that period.
Software licenses expire. Equipment is replaced. Laboratories relocate. Staff leave. Computing systems are upgraded. Service contracts end. A research center may reorganize.
You cannot eliminate all uncertainty, but ask about known changes when the project depends heavily on one resource.
This matters particularly for longitudinal research, where the same measurement procedures may need to remain available and comparable over an extended period.
Think about standardization when several resources or sites are involved
If multiple laboratories, devices, interviewers, software versions, or sites will produce data, determine whether their outputs are sufficiently comparable.
Two institutions may own equipment serving the same general purpose but use different models, calibration procedures, settings, or processing workflows. Software versions can also produce different defaults or capabilities.
Additional resources do not necessarily solve capacity problems if using them introduces measurement differences that the study cannot accommodate.
Where standardization matters, specify procedures and document the resource used for each observation.
Ask what happens if the resource becomes unavailable temporarily
Shared equipment can break. Servers can go offline. Software licenses can lapse. Facilities can close unexpectedly. Technical staff can become unavailable.
Estimate the consequences.
If the equipment is unavailable for one week, can data collection pause without affecting the study? If a specimen must be processed immediately, downtime may be more serious. If your analysis software becomes unavailable, can you move the workflow elsewhere without violating licensing or data-security requirements?
This leads directly to the next guide on what happens when a study depends on equipment, software, or services you cannot reliably access.
One critical resource can become a single point of failure
A study may have abundant participants, funding, time, and expertise but still depend on one irreplaceable instrument or facility.
If that resource fails, the study stops.
Replaceable resource
An appropriate alternative can be accessed without materially changing the methodology or compromising comparability.
Single point of failure
The study cannot collect, process, store, or analyze essential evidence if one particular resource becomes unavailable.
Identify these dependencies before the study begins. The more consequential the failure, the stronger the case for confirming availability, maintenance, technical support, and an appropriate alternative.
Do not assume your supervisor's access automatically becomes your access
Your supervisor may belong to a laboratory, hold a software license, have computing privileges, or collaborate with another institution. Those arrangements may help your project, but access rights do not always transfer automatically.
Verify whether students or collaborators can use the resource, whether your project must be registered, whether training is required, and whether any costs or approvals apply.
A sentence such as "My supervisor has access" should eventually become a concrete arrangement if the methodology depends on it.
Do not purchase a resource before checking institutional alternatives
When researchers discover that they need equipment or software, purchasing it can appear to be the simplest solution.
Before spending money, check whether the resource can be borrowed, shared, rented, accessed through a core facility, or replaced by an appropriate institutionally supported alternative.
This can substantially reduce the cost of an otherwise expensive study.
Purchase may still be appropriate when repeated access, scheduling, or technical requirements make shared resources impractical. The decision should follow an access and cost comparison rather than an assumption that ownership is necessary.
Document confirmed resources in the feasibility plan
Once a critical resource has been verified, record the details relevant to your study.
This might include the facility, responsible contact, access conditions, technical specifications, booking process, expected availability, fees, training requirements, maintenance arrangements, and any limitations.
For major dependencies, written confirmation can be useful where appropriate, particularly when another department or organization controls the resource.
The objective is not bureaucratic accumulation. It is to convert "I think the university has one" into evidence that the resource is realistically available to the project.
Watch Out
Do not list institutional equipment, software, laboratories, or specialist services as available simply because they appear on a university website or someone tells you the institution owns them. Verify that your project is eligible to use the resource, that it meets the required specifications, and that sufficient capacity exists during your actual research period.
Audit resources before the design becomes expensive to change
Resource verification should happen while the methodology is still flexible.
If a proposed measurement requires a specialized instrument, determine whether that instrument is realistically accessible before finalizing the measurement plan. If analysis requires high-performance computing, verify the computing environment before committing to a workflow that cannot run elsewhere.
Discovering after recruitment that the required laboratory can process only one quarter of your planned sample creates a much more difficult problem than discovering the same limitation during proposal development.
Facilities and infrastructure are therefore not merely implementation details. They are part of deciding whether the research question is feasible in your actual institutional environment.
