Data sources for results presented in Pruzhinskaya et al., 2022. Results from the Active Anomaly Discovery (AAD) algorithm and the feature data set extracted from ZTF DR3 light curves. "log/anomalies_feature_*.txt" files contain the list of OIDs classified by the expert as anomalies, i.e. supernova candidates, for each ZTF field. "log/answers_feature_*.csv" files contain answers to the AAD output given by the expert in order of their appearance. "log/fields.csv" contains supernova statistics for each ZTF field. "features/" directory represent the dataset we used for supernova search in ZTF photometric data with AAD. "feature_*.dat" files contain object-ordered light curve feature data, every object is built on 42 feature values, which are encoded as little endian single precision IEEE-754 float (32bit float) numbers. Feature code-names are the same for all three data sets and are listed in plain text files "feature_*.name", one code-name per line. "oid_*.dat" files contain ZTF DR object identifiers encoded as little endian 64-bit unsigned integer numbers. "oid_*.dat" and "feature_*.dat" have same object order, for example the first 8 bytes of "oid_796.dat" files contain the OID of the ZTF DR3 light curve which feature are presented in the first 168 bytes of "feature_796.dat" file. Note that only observations between 58194 ≤ MJD ≤ 58483 are used, see Malanchev et al. 2021 for features details. The sample Python code to access the data as Numpy arrays: import numpy as np oid = np.memmap('oid_796.dat', mode='r', dtype=np.uint64) with open('feature_796.name') as f: names = f.read().split() dtype = [(name, np.float32) for name in names] feature = np.memmap('feature_796.dat', mode='r', dtype=dtype, shape=oid.shape) idx = np.argmax(feature['amplitude']) print('Object {} has maximum amplitude {:.3f}'.format(oid[idx], feature['amplitude'][idx])) It should print "Object 796206400001779 has maximum amplitude 3.739"