This data set contains 30 million chess positions along with a label that indicates if the position is not check (0), check (1) or checkmate (2). In addition, we provide 3 reference explanations per data point consisting of 8×8 bit masks that highlight certain squares that are relevant for the decision. For each class, we identified one explanation type that characterizes it most accurately: - No check (0): All squares that are controlled by the enemy player, i.e., all squares that can be reached or captured on by any enemy piece. - Check (1): All squares (origin or target) of legal moves. As a checkmate is a check where the player under attack has no more legal moves, highlighting legal moves is sufficient to disprove a checkmate. - Checkmate (2): All squares with pieces that are essential for creating the checkmate. This includes attackers, friendly pieces blocking the King, enemy pieces guarding escape squares and enemy pieces protecting attackers.

The data is saved as a CSV file containing the chess positions in Forsyth–Edwards Notation (FEN) and the label (0-2) as columns. The FEN string can be read by most chess software packages and encodes the current piece setup, whose turn it is and some more game-specific information (castling rights, en-passant squares). The explanations are saved as 64-bit unsigned integers, which can be converted to SquareSet objects from the chess library. We provide code for converting between different data and explanation representations.

Our data set is based on the Lichess open database, which contains records of over 3 billion games of chess played online by human players on the free chess website Lichess. To read and process the games and to create the explanations, we used the Python package chess. We selected only those games that end in checkmate, excluding those that end by timeout or resignation. Also we skip the first ten moves, as they lead to lots of duplicate positions.

Venezuela Central Government: Expenditure: Ministry of Sanity and Social Attendance data was reported at 3,699.353 VEF th in Sep 2000. This records a decrease from the previous number of 4,524.244 VEF th for Jun 2000. Venezuela Central Government: Expenditure: Ministry of Sanity and Social Attendance data is updated quarterly, averaging 135,621.163 VEF th from Mar 1999 (Median) to Sep 2000, with 7 observations. The data reached an all-time high of 268,449.344 VEF th in Dec 1999 and a record low of 3,699.353 VEF th in Sep 2000. Venezuela Central Government: Expenditure: Ministry of Sanity and Social Attendance data remains active status in CEIC and is reported by Ministry of Economy, Finance and Public Banking. The data is categorized under Global Database’s Venezuela – Table VE.F005: Central Government: Expenditure: Quarterly.

Database for the web server DAnIEL: a user-friendly web server for fungal ITS amplicon sequencing data. This includes: DAnIEL interact: Manually curated table of fungal interactions based on abstracts from literature review. MeSH terms were used whenever applicable. DAnIEL clinical: Clinical samples of species involved in a suspicious fungal infection from the German National Reference Center for Invasive Fungal Infections (NRZMyk) Fungal projects in the NCBI SRA. Samples were sanity-checked for presence of ITS sequences.