Service overview
Short communications, methods, protocol, data and software papers, registered reports and more. This service is part of that division and is scoped individually around what you need to learn, decide or publish.
A data paper describes a dataset so that others can find, understand, trust and reuse it. It reports how the data were collected, processed and checked, what the files contain and how to access them. Data papers are valued by communities that depend on shared data, and they give credit for effort that otherwise remains invisible.
A good data paper is not a results paper. It does not argue a scientific claim but documents a resource. The documentation must be complete: without it, data are unusable. Support concentrates on structuring the descriptor, documenting collection and processing, reporting quality control and advising on repositories and licences.
What this service covers
Problems this service can address
- Datasets with minimal documentation
- Unclear licensing and access terms
- Quality checks that are not reported
Methods and analytical approaches
- Data-descriptor structuring
- Collection and processing documentation
- Quality-control reporting
- Repository and licensing guidance
Applications
- Experimental datasets
- Simulation-output datasets
- Sensor datasets
Relevant engineering disciplines
Scope, inputs and deliverables
Scope of work
- Assessment of your material: data, methods, draft and target venue
- Structuring and drafting or editing strictly from your supplied results and methods
- Consistency and source checks on text, tables, figures and references
- Journal-formatted files and a submission checklist
- Feasibility check on: the dataset and its documentation
Typical inputs required from you
- The dataset and its documentation
- Repository plans
- Draft
Deliverables
- Data-paper manuscript
- Metadata checklist
- Reuse-statement drafting
Highly specialised requests are first checked for feasibility from this information. Please do not send confidential data through the public form; describe it first and agree secure transfer afterwards.
Project workflow and method
- 01Enquiry and requirement assessment
- 02Technical scoping and proposal
- 03Execution
- 04Quality review and delivery, with an agreed round of revisions
Read the full project workflow.
In depth: how the work is done
Value and scope of the dataset
The paper explains what the dataset contains, what question or application it supports and what makes it valuable: coverage, resolution, rarity or scale. We help authors describe this without exaggeration.
Data collection and acquisition
The paper details how data were obtained: instruments, sensors, sampling design, time and place, software, simulations or experimental protocols, and any ethical approvals. Calibration and uncertainty information are included. We review for omissions.
Processing and curation
Raw data usually undergo cleaning, calibration, filtering or transformation. These steps are documented so that users know what has been altered and can access raw data when possible. Versions of processing scripts are cited.
Quality control and validation
Evidence of quality includes consistency checks, comparison with independent measurements, outlier handling and known limitations. Technical validation is a standard section in many data journals. We help authors present checks and quantify uncertainty.
Structure, formats and metadata
Files, variables, units, formats and naming conventions are described, ideally in a data dictionary and in standard metadata formats. Open, non-proprietary formats aid reuse. We help organise the description.
Repositories, identifiers and licences
Data should be deposited in a trusted repository that issues a persistent identifier. A clear licence states permitted reuse. Sensitive data may require controlled access. We advise on these considerations; legal questions belong with qualified advisers.
Reuse potential and limitations
The paper suggests uses and states limits such as sampling bias, missing periods or measurement constraints, which guards against misuse. Example code showing how to load and use the data is valuable.
Citation and credit
Authors should state how to cite the dataset and the paper. Contributions of data collectors and curators are recognised in authorship or acknowledgements according to journal policy.
Preparing for reuse by different communities
Datasets are often reused by people outside the field that produced them. Documentation should therefore avoid unexplained jargon, state units and conventions and describe assumptions in plain terms. A short glossary and a diagram of file structure help. We help authors write documentation that a competent researcher in an adjacent field could use, which increases the dataset's reach and citations.
Ethical and legal checks before release
Before releasing data, authors should confirm that they have the right to share them, that third-party material is cleared, that personal or sensitive information is removed or protected and that any funder or institutional policy is met. Where doubt exists, authors should seek advice from their institution's data steward or legal office. We help word access and consent statements accurately based on documents authors provide.
Choosing the right journal or repository-linked venue
Some journals publish only data papers; others have data sections within a broader scope. Differences include peer-review focus, requirements for repositories and article processing charges. We help authors compare options on scope, data requirements and visibility, and prepare the descriptor to the chosen template. Authors verify current policies on the journal's site, since these change.
Quality control and good practice
Quality-control approach
- Every statement, number and reference is checked against your supplied data and the original sources.
- No data, results, citations or author contributions are invented; gaps are flagged to you.
- Authorship and contribution decisions stay with you and your co-authors, and the use of AI tools is disclosed where journals require it.
- Journal guidelines and reporting checklists are followed; acceptance is never promised.
Common pitfalls this service helps avoid
- Missing metadata. Data without documentation are not reusable.
- Undocumented processing. Users need to know what was changed.
- No quality evidence. Trust requires validation.
- Ambiguous licence. Users need clarity on permitted reuse.
What good reporting looks like
Support for a data paper is documented with a metadata completeness check, a quality-control summary, a repository and licence note and a reuse-limitations statement. Authors provide and verify all data and are responsible for rights and permissions. We do not create or alter data, and acceptance is not promised.
An illustrative scenario
Hypothetical example written for explanation. It is not a client case, a result or a testimonial.
Suppose a group has recorded vibration data from a test bridge over two years. The data paper describes sensors, sampling, environmental conditions, preprocessing and quality checks, including a comparison of redundant sensors. It documents files and variables, deposits data with a persistent identifier under an open licence and notes a three-week gap due to a power failure. Example scripts show how to compute basic modal parameters.
Questions to settle before you begin
- Is every file and variable documented?
- What processing was applied to the raw data?
- What evidence supports data quality?
- Where are the data deposited and under what licence?
- What are the known limitations?
Key terms
- Data dictionary
- A description of variables, units and formats.
- Metadata
- Data describing data.
- Persistent identifier
- A permanent reference such as a DOI.
- Technical validation
- Evidence of data quality.
- Licence
- Terms for reuse of data.
Working together on Specialised Scholarly Article Formats projects
Specialised article engagements begin by confirming which format suits your material, since short communications, methods, data, software, protocol and replication papers each have conventions and length limits. We ask for the results, code or datasets that the article describes, the target journal's guidelines and your deadline. Drafting follows the format's structure, and reporting checklists are applied where they exist. Statements about data and code availability reflect what you can actually share. We do not fabricate results, and we do not promise acceptance.
Frequently asked questions
Do you host my data?
No. Data should be deposited in a suitable repository chosen by you; we help document it.
Do data papers count as publications?
Yes, they are citable, though evaluation practices vary.
Can I publish a dataset containing personal data?
Only if consent, approvals and data protection requirements are met.
Which repositories are recommended?
Choose a trusted repository for your field or a general one such as Zenodo or Figshare, subject to journal requirements.
Which journals publish data papers?
Dedicated data journals and field journals with data sections.
Does ENGRexpert guarantee acceptance in a journal?
No. Editors and reviewers decide. We support the clarity, rigour and fit of your manuscript and never promise an outcome.
How is this service scoped and priced?
After your enquiry we review what you need, ask for missing information and send a written scope and quotation. Price depends on complexity, deliverables and timeline. See the project workflow.