How good is your data? If you truly have amazing unpublished data that could readily form the basis for multiple top tier publications, then with some persistence you should be able to find academics who are interested in writing publications based on that data.
However, are you able to truly judge the suitability of your data for academic publications? In general, if you are not in academia, you are likely to have a poor to modest understanding of what makes a great dataset. For example, I sometimes work with industry datasets that look at the characteristics of workers (traits, job performance and so on). There are all sorts of academic reasons why a given dataset (that the consultant may think is interesting) will not yield a good publication: e.g., the sample size is too small; there were issues with data collection; the measures used are insufficiently reliable or valid; the measures used do not map onto the theoretical constructs in the literature; the questions that can answered by the dataset have been examined many times already in the literature; the meta data is incomplete.
Even if you have a dataset that could be used for a publication, there is still the issue of whether the dataset is good enough to persuade an academic to work on it. In general, more established academics have a large collection of studies and datasets sitting around waiting to be potentially written up. In this case, the academic is likely to strategically prioritise their publications in terms of some sort of effort-reward trade-off. So, your dataset needs to not only be publishable but sufficiently aligned and interesting to an academic to persuade them to work on it.
Thus, my advice would be to start to connect with a few academics in order to assess how suitable the datasets are for publication.
How to find a suitable academic? There are many strategies for finding a academic who might be interested in analysing your data for publication. Ideally, you'd have a sense of the kinds of publications that could be obtained from the data, and therefore which academics are working on these topics. A targeted email to some of these people explaining the data you have and your interests in sharing for publication should help to start a conversation. You can then have skype chat or face to face chats (with people in your city). If you're lucky you might have existing social networks with existing academics (e.g., where you did your studies, or in your town).
As you interact with these academics, you can get a better understanding of whether the data is suitable for publication. Also, if you have good data, but the first few academics are not interested in it, such academics may be able to refer you to other academics who are interested.
You also mention that you want "experienced researchers" to write the papers. In this regard, there is a trade-off. More high-profile researchers are more capable of getting quality work published. However, they also have many more opportunities. So there is a trade-off. For example, early career researchers may be more interested in your data, if they've had less time to obtain a broad range of research options. Your dataset might also be useful to form part of a PhD project.
How to negotiate co-authorship? Based on respected criteria for co-authorship (E.g., here), merely providing data is not sufficient to warrant co-authorship. You generally need to contribute to some degree to the writing and conception of the paper. That said, if you make co-authorship a condition of sharing your data, then academics are free to take or leave your proposal. If collecting the data in itself involved some degree of academic contribution and you also agree to contribute to the writing and analysis process (even if not leading), then such a proposal is likely to be more palatable to academics.
