Traditionally, parsers are evaluated against gold standard test data. This can cause problems if there is a mismatch between the data structures and representations used by the parser and the gold standard. A particular case in point is German, for which two treebanks (TiGer and TüBa-D/Z) are available with highly different annotation schemes for the acquisition of (e.g.) PCFG parsers. The differences between the TiGer and TüBa-D/Z annotation schemes make fair and unbiased parser evaluation difficult [7, 9, 12]. The resource (TEPACOC) presented in this paper takes a different approach to parser evaluation: instead of providing evaluation data in a single annotation scheme, TEPACOC uses comparable sentences and their annotations for 5 selected key grammatical phenomena (with 20 sentences each per phenomena) from both TiGer and TüBa-D/Z resources. This provides a 2 times 100 sentence comparable testsuite which allows us to evaluate TiGer-trained parsers against the TiGer part of TEPACOC, and TüBa-D/Z-trained parsers against the TüBa-D/Z part of TEPACOC for key phenomena, instead of comparing them against a single (and potentially biased) gold standard. To overcome the problem of inconsistency in human evaluation and to bridge the gap between the two different annotation schemes, we provide an extensive error classification, which enables us to compare parser output across the two different treebanks. In the remaining part of the paper we present the testsuite and describe the grammatical phenomena covered in the data. We discuss the different annotation strategies used in the two treebanks to encode these phenomena and present our error classification of potential parser errors.