Update (Version 2): Added the quality-flag column Q (1–3). Distance values and uncertainties remain unchanged. This dataset provides catalogues of extinction-based distances for Galactic planetary nebulae (PNe), derived using an optimised Gaia-based extinction–distance method, as described in: Deng, Wang & Jiang (2026), “The extinction distances for over one thousand Planetary Nebulae with Gaia measurements”.BackgroundAs key tracers of stellar evolution, chemical enrichment, and the interstellar medium, accurate distances to PNe are crucial for determining their intrinsic properties. However, obtaining such distances has long been challenging, as existing methods rarely achieve both broad applicability and high reliability. Despite Gaia's identification of central stars (CSPNe) for ∼70% of known PNe, reliable distances remain scarce: fewer than 25% have accurate parallaxes for deriving distances. To address this limitation, we develop an optimized Gaia-based extinction–distance method for PNe with identified CSPNe, which allows distances to be estimated for over one thousand objects and serves as a complementary approach when parallaxes are uncertain or missing.Data filesDistance_for_1066PNe_withQ.csvExtinction-based distance for 1,066 Galactic PNe.Columns:PNG: PN identifier (HASH format)Name: Common PN nameD: Distance (pc)D_err: Uncertainty (pc)Q: Quality flag (1–3) indicating the degree of independent external support for the adopted distanceQuality flag definition:Q = 3 – Supported by CSPN-based distance estimate(s) and at least one additional independent indicatorQ = 2 – Supported either by CSPN-based estimate(s) only or by at least two independent non-CSPN indicatorsQ = 1 – Supported by a single independent indicatorThe definition of Q follows Section 5.1 of Deng, Wang & Jiang (2026).CSPN_for_15PNe.csvResults for 15 PNe with disputed CSPN identifications.Columns:PNG: PN identifierName: PN namesource_id: Gaia DR3 source_id of adopted CSPNUsageData are provided as CSV tables and can be easily accessed in Python, for example with pandas: import pandas as pdpn_catalog = pd.read_csv("Distance_for_1066PNe_withQ.csv")CitationIf you use this dataset, please cite:   http://arxiv.org/abs/2603.15139

Update (Version 2): Added the quality-flag column Q (1–3). Distance values and uncertainties remain unchanged. This dataset provides catalogues of extinction-based distances for Galactic planetary nebulae (PNe), derived using an optimised Gaia-based extinction–distance method, as described in: Deng, Wang & Jiang (2026), “The extinction distances for over one thousand Planetary Nebulae with Gaia measurements”.BackgroundAs key tracers of stellar evolution, chemical enrichment, and the interstellar medium, accurate distances to PNe are crucial for determining their intrinsic properties. However, obtaining such distances has long been challenging, as existing methods rarely achieve both broad applicability and high reliability. Despite Gaia's identification of central stars (CSPNe) for ∼70% of known PNe, reliable distances remain scarce: fewer than 25% have accurate parallaxes for deriving distances. To address this limitation, we develop an optimized Gaia-based extinction–distance method for PNe with identified CSPNe, which allows distances to be estimated for over one thousand objects and serves as a complementary approach when parallaxes are uncertain or missing.Data filesDistance_for_1066PNe_withQ.csvExtinction-based distance for 1,066 Galactic PNe.Columns:PNG: PN identifier (HASH format)Name: Common PN nameD: Distance (pc)D_err: Uncertainty (pc)Q: Quality flag (1–3) indicating the degree of independent external support for the adopted distanceQuality flag definition:Q = 3 – Supported by CSPN-based distance estimate(s) and at least one additional independent indicatorQ = 2 – Supported either by CSPN-based estimate(s) only or by at least two independent non-CSPN indicatorsQ = 1 – Supported by a single independent indicatorThe definition of Q follows Section 5.1 of Deng, Wang & Jiang (2026).CSPN_for_15PNe.csvResults for 15 PNe with disputed CSPN identifications.Columns:PNG: PN identifierName: PN namesource_id: Gaia DR3 source_id of adopted CSPNUsageData are provided as CSV tables and can be easily accessed in Python, for example with pandas: import pandas as pdpn_catalog = pd.read_csv("Distance_for_1066PNe_withQ.csv")CitationIf you use this dataset, please cite:   http://arxiv.org/abs/2603.15139

This dataset provides catalogs of extinction distances for Galactic planetary nebulae (PNe) derived with an optimized extinction-distance method, as described in:Deng, Wang & Jiang (2026), "The extinction distances for over thousand Planetary Nebulae with the Gaia measurements"BackgroundAs key tracers of stellar evolution, chemical enrichment, and the interstellar medium, accurate distances to PNe are crucial for determining their intrinsic properties. However, obtaining such distances has long been challenging, as existing methods rarely achieve both broad applicability and high reliability. Despite Gaia's identification of central stars (CSPNe) for ∼70% of known PNe, reliable distances remain scarce: fewer than 25% have accurate parallaxes for deriving distances. To address this limitation, we develop an optimized Gaia-based extinction–distance method for PNe with identified CSPNe, which allows distances to be estimated for over one thousand objects and serves as a complementary approach when parallaxes are uncertain or missing.Data filesDistance_for_1066PNe.csvExtinction distances for 1,066 Galactic PNe.Columns:PNG: PN identifier (HASH format)Name: Common PN nameD: Distance (pc)D_err: Uncertainty (pc)CSPN_for_15PNe.csvResults for 15 PNe with disputed CSPN identifications.Columns:PNG: PN identifierName: PN namesource_id: Gaia DR3 source_id of adopted CSPNUsageData are provided as CSV tables and can be easily accessed in Python, for example with pandas: import pandas as pdpn_catalog = pd.read_csv("Distance_for_1066PNe.csv")CitationIf you use this dataset, please cite:  http://arxiv.org/abs/2603.15139

The number of known periodic variables has grown rapidly in recent years. Thanks to its large field of view and faint limiting magnitude, the Zwicky Transient Facility (ZTF) offers a unique opportunity to detect variable stars in the northern sky. Here, we exploit ZTF Data Release 2 (DR2) to search for and classify variables down tor ∼ 20.6 mag. We classify 781,602 periodic variables into 11 main types using an improved classification method. Comparison with previously published catalogs shows that 621,702 objects (79.5%) are newly discovered or newly classified, including ∼700 Cepheids, ∼5000 RR Lyrae stars, ∼15,000 δ Scuti variables, ∼350,000 eclipsing binaries,∼100,000 long-period variables, and about 150,000 rotational variables. The typical misclassification rate and period accuracy are on the order of 2% and 99%, respectively. 74% of our variables are located at Galactic latitudes, |b| < 10◦. This large sample of Cepheids, RR Lyrae, δ Scuti stars, and contact (EW-type) eclipsing binaries is helpful to investigate the Galaxy’s disk structure and evolution with an improved completeness, areal coverage, and age resolution. Specifically, the northern warp and the disk’s edge at distances of 15–20 kpc are significantly better covered than previously. Among rotational variables, RS Canum Venaticorum and BY Draconis-type variables can be separated easily. Our knowledge of stellar chromospheric activity would benefit greatly from a statistical analysis of these types of variables. These supplementary materials contain g and r bands single-exposure photometry for 781,602 periodic variables in Table 2 of the paper.
SourceID is the internal source identifier joins these attachments to Table 2. Example: For variable star ZTFJ000000.19+320847.2 in Table 2, the SourceID=3 is adopted to search corresponding single-exposure information in both 'ztf2g' and ''ztf2r'. File Description: ztf2g g band single exposure photometry data of variables from ZTF2. SourceID, RAdeg, DEdeg, HJD, gmag, e_gmag, g_flag ztf2r r band single exposure photometry data of variables from ZTF2. SourceID, RAdeg, DEdeg, HJD, rmag, e_rmag, r_flag Table2 ZTF Variables Catalog. Table6 ZTF Suspected Variables Catalog.

The number of known periodic variables has grown rapidly in recent years. Thanks to its large field of view and faint limiting magnitude, the Zwicky Transient Facility (ZTF) offers a unique opportunity to detect variable stars in the northern sky. Here, we exploit ZTF Data Release 2 (DR2) to search for and classify variables down tor ∼ 20.6 mag. We classify 781,602 periodic variables into 11 main types using an improved classification method. Comparison with previously published catalogs shows that 621,702 objects (79.5%) are newly discovered or newly classified, including ∼700 Cepheids, ∼5000 RR Lyrae stars, ∼15,000 δ Scuti variables, ∼350,000 eclipsing binaries,∼100,000 long-period variables, and about 150,000 rotational variables. The typical misclassification rate and period accuracy are on the order of 2% and 99%, respectively. 74% of our variables are located at Galactic latitudes, |b| < 10◦. This large sample of Cepheids, RR Lyrae, δ Scuti stars, and contact (EW-type) eclipsing binaries is helpful to investigate the Galaxy’s disk structure and evolution with an improved completeness, areal coverage, and age resolution. Specifically, the northern warp and the disk’s edge at distances of 15–20 kpc are significantly better covered than previously. Among rotational variables, RS Canum Venaticorum and BY Draconis-type variables can be separated easily. Our knowledge of stellar chromospheric activity would benefit greatly from a statistical analysis of these types of variables. These supplementary materials contain g and r bands single-exposure photometry for 781,602 periodic variables in Table 2 of the paper.
SourceID is the internal source identifier joins these attachments to Table 2. Example: For variable star ZTFJ000000.19+320847.2 in Table 2, the SourceID=3 is adopted to search corresponding single-exposure information in both 'ztf2g' and ''ztf2r'. File Description: ztf2g g band single exposure photometry data of variables from ZTF2. SourceID, RAdeg, DEdeg, HJD, gmag, e_gmag, g_flag ztf2r r band single exposure photometry data of variables from ZTF2. SourceID, RAdeg, DEdeg, HJD, rmag, e_rmag, r_flag Table2 ZTF Variables Catalog. Table6 ZTF Suspected Variables Catalog.