> For the complete documentation index, see [llms.txt](https://pecan.gitbook.io/betydbdoc-dataentry/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://pecan.gitbook.io/betydbdoc-dataentry/finding-and-preparing-published-data.md).

# Finding and Preparing Published Data

BETYdb is designed for both previously published data and 'primary' data. Most of this documentation assumes that you have already identified a data set that you want to upload, or have a set of papers from which you would like to extract data and summary statistics.

## Meta-analyses

If you are planning to do a meta-analysis, even if this is not your first time, please read 'Uses and Misuses of Meta-analysis in Ecology" \cite{Koricheva\_2014}. Many texts are available, but the recent "Handbook of Meta-analysis in Ecology and Evolution" is probably the most comprehensive and specific for plant sciences.

For a meta-analysis, the first step is to find papers that contain the target data.

The easiest approach to use a search engine such as [Web of Science](http://apps.webofknowledge.com/), [Google Scholar](https://www.scholar.google.com), or [Microsoft Academic Search](http://academic.research.microsoft.com/). Starting with queries such as "*scientific name* + *trait*", and allowing these results to guide further queries. Often, the references (particularly of meta-analyses and reviews) and forward citations will point to other studies.

Another starting point for the programmatically inclined - which aids in documenting searches - is to submit queries programmatically. Carl Davidson wrote a [python script](https://github.com/PecanProject/pecan/blob/master/modules/meta.analysis/inst/citation_search.py) to search for citations based on species and trait name. In addition, the rOpenSci project has a [suite of R packages for searching publications](http://ropensci.org/packages/#literature).


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# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://pecan.gitbook.io/betydbdoc-dataentry/finding-and-preparing-published-data.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.