This tool builds a Dryad-style metadata manifest for a dataset: title, authors, description, and a file list with sizes, output as structured JSON, plus file count and total size. Dryad is a generalist data repository, and its submission form asks for exactly this shape of information before any file moves. Stated plainly: nothing is uploaded by this tool — it assembles the package description locally, and you submit the manifest and files through Dryad yourself. Preparing the manifest first means the submission itself becomes mechanical instead of a scramble to remember file names and author spellings under a deadline.
How to use it
- Type the dataset title.
- List authors, comma-separated.
- Write a one- or two-sentence description.
- List files, one per line, as filename, size in KB.
- Click Build manifest and copy the JSON.
Worked example
A survey dataset has title “Survey responses 2026”, authors Ana Garcia and Bo Li, description “Anonymous survey responses from 200 participants”, and two files: responses.csv (340 KB) and codebook.pdf (120 KB). The manifest reports 2 files and 460 KB total, with every field in one structured block. When the researcher opens Dryad’s form, every answer is already written and checked — author order, file names, total size — instead of being reconstructed from memory while the upload waits. The manifest also becomes the lab’s own record of what was deposited, useful when a reviewer asks, a year later, which files went where.
Honest limits
The manifest follows Dryad’s general shape but is not Dryad’s official schema: field names, required fields, and accepted formats on the live form may differ, so check the current Dryad submission guidance before submitting and adjust field names as needed. The tool does not validate licences, embargoes, or funder requirements, and it cannot check that files are actually anonymized — it trusts your description. File sizes are as typed, not measured from real files, so a typo in size propagates into the manifest.
Who it is for
This tool is for researchers depositing survey, experimental, or observational data in Dryad, data managers assembling submission packages, and students learning what repository metadata looks like before their first deposit. It prepares the package; Dryad receives it.
Going further
Write the description for a stranger in five years, not for yourself today: name the population, the method, and the time frame in one sentence, and put anything a reuser would need — variable definitions, missing-value codes, collection context — in the codebook file rather than hoping the description carries it. A manifest is a promise about the package, and reviewers judge the deposit by whether the files keep that promise: if the manifest lists a codebook, the codebook must actually explain every column. Check the file list against the folder on disk before submitting, because the most common deposit error is not a wrong field but a missing file. Before submitting, read the manifest as if you were the reuser, not the author. Does the title say what the data are, or only what the project was called? Do the file names explain themselves, or do they require insider knowledge to decode? Is the description specific enough that someone could decide whether the data fit their question without downloading everything? Fixing these three things costs minutes and determines whether the deposit is ever reused, because reuse decisions are made from metadata alone in nearly every case. A dataset that cannot be understood from its manifest is, for practical purposes, not shared at all, however open its licence. The manifest is the dataset public face; write it for strangers. Finally, choose a licence deliberately rather than accepting a default without reading it, since the licence decides what reusers may legally do with the data, and an overly restrictive licence can quietly undo the openness the deposit was meant to achieve.
Related tools
Versión en español · Metadata XML Schema Checker · All free online tools.
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