The Large sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) general survey is a spectroscopic survey that will eventually cover approximately half of the celestial sphere and collect 10 million spectra of stars, galaxies and QSOs. Objects in both the pilot survey and the first year regular survey are included in the LAMOST DR1. The pilot survey started in October 2011 and ended in June 2012, and the data have been released to the public as the LAMOST Pilot Data Release in August 2012. The regular survey started in September 2012, and completed its first year of operation in June 2013. The LAMOST DR1 includes a total of 1202 plates containing 2 955 336 spectra, of which 1 790 879 spectra have observed signalto-noise ratio (SNR) ≥ 10. All data with SNR ≥ 2 are formally released as LAMOST DR1 under the LAMOST data policy. This data release contains a total of 2 204 696 spectra, of which 1 944 329 are stellar spectra, 12 082 are galaxy spectra and 5017 are quasars. The DR1 not only includes spectra, but also three stellar catalogs with measured parameters: late A,FGK-type stars with high quality spectra (1 061 918 entries), A-type stars (100 073 entries), and M-type stars (121 522 entries). This paper introduces the survey design, the observational and instrumental limitations, data reduction and analysis, and some caveats. A description of the FITS structure of spectral files and parameter catalogs is also provided.

This paper describes the data release of the LAMOST pilot survey, which includes data reduction, calibration, spectral analysis, data products and data access. The accuracy of the released data and the information about the FITS headers of spectra are also introduced. The released data set includes 319 000 spectra and a catalog of these objects.

LAMOST DR5 released more than 200,000 low resolution spectra of early-type stars with S/N>50. Searching for metallic-line (Am) stars in such a large database and study of their statistical properties are presented in this paper. Six machine learning algorithms were experimented with using known Am spectra, and both the empirical criteria method (Hou et al. 2015) and the MKCLASS package(Gray et al. 2016) were also investigated. Comparing their performance, the random forest (RF) algorithm won, not only because RF has high successful rate but also it can derives and ranks features. Then the RF was applied to the early type stars of DR5, and 15,269 Am candidates were picked out. Manual identification was conducted based on the spectral features derived from the RF algorithm and verified by experts. After manual identification, 9,372 Am stars and 1,131 Ap candidates were compiled into a catalog. Statistical studies were conducted including temperature distribution, space distribution, and infrared photometry. The spectral types of Am stars are mainly between F0 and A4 with a peak around A7, which is similar to previous works. With the Gaia distances, we calculated the vertical height Z from the Galactic plane for each Am star. The distribution of Z suggests that the incidence rate of Am stars shows a descending gradient with increasing |Z|. On the other hand, Am stars do not show a noteworthy pattern in the infrared band. As wavelength gets longer, the infrared excess of Am stars decreases, until little or no excess in W1 and W2 bands.