Custom app for managing measurement data in research projects
Appfront builds apps for research groups, knowledge institutions and R&D departments that take many measurements in their projects: every measurement recorded with instrument, institution, sample, date and who took it, the raw data stored unalterably, every edit traceable, and datasets shared with partners in line with the data management plan. So that a result can still be reproduced two years from now and nobody has to search through folders of files named definitief_v3_nieuw.
What is an app for measurement data management in research projects?
A measurement without context is just a number. To understand or reproduce a result later, you need to know which instrument, which setting, which sample, under which conditions, who took it, and what happened to the data afterwards. An app for measurement data management records this at the moment of measuring, not afterwards in a lab notebook.
Without such an app, raw files sit on the laptop attached to the instrument, edited versions sit in a shared folder, and the metadata lives in a notebook or spreadsheet that is not linked to the files. A PhD candidate who leaves takes with them the knowledge of which file is which. A partner in the consortium asks for a dataset and receives a zip file with an explanation in an email. And a funder who asks for a data management plan receives a plan that is not actually followed in practice.
We build custom solutions because the app has to fit your research: which instruments you use and which files they produce, which metadata is standard in your field, how you collaborate with partners, and which storage and repository your institution prescribes. A general document system stores files; it does not know the measurement behind them.
Measurement with metadata
With every import, the metadata according to the project's schema: instrument, institution, sample, conditions, date and researcher. A measurement without the required fields is not saved.
Raw locked, edits traceable
Raw data are stored unalterably. Every edit, such as calibration, filtering or averaging, gets a version with the method and source attached.
Sharing datasets
Datasets compiled per analysis or publication, shared with partners according to the rights set out in the project, and exported in open formats with the metadata included.
How we build your app for measurement data management in research projects
We start with the project: which instruments, which data streams, which partners, and what is in the data management plan that is not yet happening.
The instruments and their files, the metadata that is standard in your field, the partners and their rights, and the requirements from the data management plan.
Import from instruments and data loggers, the metadata schema per type of measurement, and unalterable storage of raw data.
Versions and edits with method and source, compiling datasets, and rights per partner and per dataset.
Export in open formats, integration with your institution's repository, reporting for the data management plan, and then ongoing maintenance.
What an app for measurement data management in research projects actually does
The components below come up in almost every research project. Which ones you need depends on your instruments and on who you collaborate with.
Instrument import
Files from instruments and data loggers are imported automatically or by drag and drop, with the format and instrument recognised.
Metadata schema
For each type of measurement, the required and optional fields, with controlled vocabularies from your field, so that metadata is comparable across measurements and researchers.
Raw data and versions
Raw data is stored unchanged with a checksum, and every processing step is saved as a new version with the method, the code or script, and the source.
Search and filtering
Search measurements by instrument, sample, period, conditions or researcher, across the whole project, without browsing through folders.
Datasets and permissions
Datasets per analysis or publication, with permissions per partner, and a fixed reference so that a publication points to the correct version.
Export and archiving
Export in open formats with the metadata included, to your institution's repository or an archive, ready for reuse in line with the FAIR principles.
Who we build a measurement data management app for in research projects
The app is intended for research where a great deal is measured and where data is currently spread across laptops, folders and emails.
Research groups at universities
PhD candidates who come and go, and funders who ask for a data management plan. The metadata schema and unchangeable storage are at the core.
Lectorates and practice-based research
Measurements in the field or at companies, often with students. Importing from data loggers and sharing with partners matter most.
R&D departments in companies
Test series for product development and confidential data. Permissions and the traceability of changes are what is needed.
Consortia with multiple partners
Data from different institutions within one project. Permissions per partner and shared datasets are at the core.
Test your idea first: a working prototype in 1 day
With OneDayBuild, we turn your idea into something tangible in one day for €1,150, so you can see whether further development is worth the investment. Decide to go ahead with the full build? Then we credit the full cost.
Explore OneDayBuild →Technology and integrations
This page is about measurement data in research projects. For samples and analyses in a laboratory, see our page on laboratory software, for instrument calibration see our page on calibration software, and for reports based on measurements see our page on an app for research reports. You can read about how we work at custom software development.
If you work with questionnaires full of routing, randomisation and experimental conditions, see our software for questionnaire routing and research design.
Why choose Appfront for your measurement data management app in research projects?
A result is only as good as the data beneath it. We build on that: every measurement with context, raw data that does not change, and processing steps that someone else can repeat.
Reproducible
From result back to the raw measurement, with every processing step in between. Anyone who wants to repeat it in two years' time can do so.
Knowledge stays in the project
Metadata is attached to the measurement, not held in the researcher's head. A departing PhD candidate leaves a dataset that someone else can understand.
The plan is followed
What is in the data management plan happens in the app: storage, metadata, permissions and archiving. Accountability to the funder comes from practice.
Security and privacy in a measurement data management app for research projects
The app holds research data, sometimes confidential or containing personal data. Access is organised per project, per partner and per dataset, and every view, download and change is logged with name and time. Data from participants can be stored pseudonymised, with the key kept separately.
The app runs in a European data centre or on your institution's infrastructure, with encrypted storage and daily back-ups. Raw data are stored with a checksum, so you can demonstrate they have not been altered.
Frequently asked questions about a measurement data management app for research projects
Questions researchers ask before they get started.
It reads measurements from instruments and data loggers, records the metadata for each measurement against a fixed schema, stores raw data unalterably, keeps every change traceable as a version, assembles datasets for each analysis or publication, and shares them with partners in line with the data management plan.
Yes. Funders often ask for a data management plan, and the app makes that plan workable: where data are stored, which metadata are recorded, who has access, and where they are archived. The reporting shows how the plan is being followed in practice.
Data should be findable, accessible, interoperable and reusable. The app ensures metadata follow a schema, datasets have fixed references, rights are explicitly recorded, and exports are in open formats, so a dataset can be used outside your group too.
In the first step, we look at which instruments you use and which files they produce. Import is straightforward for common formats; for a manufacturer's proprietary format, we write a reader or store the file with its metadata alongside it.
Participant data can be stored pseudonymised, with the key kept separately and access restricted. Access and use follow the terms of consent and the review by your institution.
No. The app is the working environment during the project; the repository is the archive afterwards. The app exports datasets with their metadata to the repository your institution prescribes.
Ask that question first. An electronic lab notebook records what was done, which is enough when the data themselves are small. Custom development makes sense when large volumes of measurements come from instruments, when metadata must be fixed for each type of measurement, or when partners in a consortium need to share datasets with their own rights.
Measurement data that will still mean something in two years' time?
Tell us which instruments you use, which partners you work with and what your data management plan says. We will show you what the import, the metadata schema and the datasets look like.