--- dataset: - name: LinkOrgs Training Data tags: - data-linkage - record-linkage - organizations - LinkedIn-network - bipartite-network - Markov-network license: mit --- # Introduction _Data repository for:_ Brian Libgober, Connor T. Jerzak. Linking Datasets on Organizations Using Half-a-Billion Open-Collaborated Records. Political Science Methods and Research: 1-20, 2024. [doi.org/10.1017/psrm.2024.55](https://doi.org/10.1017/psrm.2024.55) ``` @article{libgober2024linking, title={Linking Datasets on Organizations Using Half a Billion Open-Collaborated Records}, author={Libgober, Brian and Connor T. Jerzak}, journal={Political Science Methods and Research}, year={2024}, pages={1-20}, publisher={} } ``` # Details This repository contains large-scale training data for improving linkage of data on organizations. `NegMatches_mat.csv` and `NegMatches_mat_hold.csv` refer to millions of negative name matches examples derived from the LinkedIn network (see paper for details). `PosMatches_mat.csv` and `PosMatches_mat_hold.csv` refer to millions of positive name matches examples derived from the LinkedIn network (see paper for details). Additionally, files with saved `*_bipartite` refer to the bipartite network representation of the LinkedIn network that we use for improving linkage. files with saved `*_bipartite` refer to the Markov network representation of the LinkedIn network that we use for improving linkage. Finally, data from all examples used in the paper are available in `Example*` folders. In each folder, the `x` and `y` data have linkage variables named `by_x` and `by_y` respectively, as does the merged `z` dataset. # Questions & Issues Direct questions to: `connor.jerzak@gmail.com` or open an [issue](https://github.com/cjerzak/LinkOrgs-software/issues). [](https://doi.org/10.1017/psrm.2024.55) [](https://doi.org/10.1017/psrm.2024.55) [](https://doi.org/10.1017/psrm.2024.55)