Science data products
Many KiDS papers are accompanied by the release of highlevel scientific data, such as MCMC chains, specific catalogs, or software code. This page provides links to such data products as well as accompanying information.
Acknowledgements
Users of any KiDS data must include proper acknowledgements in their publications. In the tables below the Acknowledgements section indicates which papers/data sets should be acknowledged, and the exact text to use is specified on the Acknowledgements page.
Cosmological analyses
KiDS1000: Cosmic shear with enhanced redshift calibration
van den Busch et al., 2022, A&A 664, A170
We present a cosmic shear analysis with an improved redshift calibration for the fourth data release of the KiloDegree Survey (KiDS1000) using selforganising maps (SOMs). Compared to the previous analysis of the KiDS1000 data, we expand the redshift calibration sample to more than twice its size, now consisting of data of 17 spectroscopic redshift campaigns, and significantly extending the fraction of KiDS galaxies we are able to calibrate with our SOM redshift methodology. We then enhanced the calibration sample with precision photometric redshifts from COSMOS2015 and the Physics of the Accelerated Universe Survey (PAUS), allowing us to fill gaps in the spectroscopic coverage of the KiDS data. Finally we performed a Complete Orthogonal Sets of E/BIntegrals (COSEBIs) cosmic shear analysis of the newly calibrated KiDS sample to show the robustness of the cosmological constraints with respect to the choice of redshift calibration data.
This data release contains the redshift distributions, COSEBI data vectors and covariance matrix, and MCMC chains for the gold samples, see the README.md file included in the tarball.
Links 
→ Paper on arXiv → Recorded talk for Cosmology from Home (2022) 

Data 
For each gold sample: sampled posteriors in the form of Multinest chains; data vectors, covariance matrices and redshift distributions; cosmosis configuration files; example plotting script 
gzipped tarball (34 MB) 
Software  CosmoWrapper based on KCAP  CosmoWrapper 
Acknowledge 
van den Busch et al. (2022), A&A 664, A170 KiDS1000 Weak lensing data 
KiDS1000 Cosmology: Constraints beyond flat ΛCDM
Tröster et al., 2021, A&A 649, A88
We present constraints derived from the KiDS1000 cosmic shear and 3x2pt data on models beyond flat ΛCDM. The links to the paper along with the associated data products can be found below.
Links  → Paper on arXiv  
Data  MultiNest nested sampling chains for the extended cosmological models considered in the paper, as well the chains for the joint analyses with SNe and CMB lensing. 
gzipped tarball (126 MB) README 
Software  KiDS Cosmology Analysis Pipeline  KCAP 
Acknowledge  Tröster et al., 2021, A&A 649, A88 
The Weak Lensing Radial Acceleration Relation: Constraining Modified Gravity and Cold Dark Matter theories with KiDS1000
Brouwer et al., 2021, A&A, 650, A113
We present measurements of the radial gravitational acceleration around isolated galaxies, comparing the expected gravitational acceleration given the baryonic matter in the system (g_bar) with the observed gravitational acceleration (g_obs), using weak lensing measurements from the fourth data release of the KiloDegree Survey (KiDS1000). These measurements extend the radial acceleration relation (RAR), traditionally measured using galaxy rotation curves, by 2 decades in g_obs into the low acceleration regime beyond the outskirts of the observable galaxy.
In this data release, each text file contains one Excess Surface Density (ESD) profile obtained using weak gravitational lensing with KiDS1000. These ESD profiles correspond to the lensing Radial Acceleration Relation (RAR) results shown in the respective figures of Brouwer et al. (2021) as explained in the README file.
Links  → Paper on arXiv  
Data  Weak lensing ESD profiles corresponding to the RAR results of Brouwer et al. (2021). 
tarball (2.3 MB) README 
Software  The KiDS GalaxyGalaxy Lensing (GGL) pipeline (for access contact Cristóbal Sifón: cristobal_._sifon_at_pucv_._cl)  KiDSGGL 
Acknowledge  See Acknowledgements section of Brouwer et al. (2021) 
KiDS1000 cosmology: Cosmic shear constraints and comparison between two point statistics
Asgari, et al., 2020, A&A, 645, A104
We present cosmological constraints from a cosmic shear analysis of the fourth data release of the KiloDegree Survey (KiDS1000). The links to the paper along with the associated data products can be found below.
Links 
→ Paper on arXiv → KiDS1000 cosmic shear constraints webpage 

Data  Sampled posteriors in the form of Multinest chains; data vectors, covariance matrices and redshift distribution of galaxies; covariance matrix of the uncertainty in the redshift distributions; configuration files and plotting script  compressed tarball (16 MB) 
Software  KiDS Cosmology Analysis Pipeline  KCAP 
Plotting script  chainconsumer  
Acknowledge 
Asgari, et al., 2020, A&A, 645, A104 KiDS1000 Weak lensing data 
KiDS1000 Cosmology: Multiprobe weak gravitational lensing and spectroscopic galaxy clustering constraints
Heymans, et al., 2021, A&A, 646, A140
In this paper we present the 3x2pt analysis of KiDS1000 with BOSS and 2dFLenS. The links to the paper along with the associated data products can be found below.
Links 
→ Paper on arXiv → 3x2pt cosmology with KiDS1000, BOSS and 2dFLenS webpage 

Data  sampled posteriors in the form of Multinest chains for the KiDS1000 bandpower cosmic shear analysis; BOSSDR12 galaxy clustering analysis; different 2x2pt combinations and the fiducial 3x2pt analysis 
gzipped tarball (51 MB) README 
Software  Open source software and data repository  KiDSWL 
Acknowledge 
Heymans, et al., 2020, A&A, 646, A140 KiDS1000 Weak lensing data 
KiDS+VIKING450 and DESY1 combined: Cosmology with cosmic shear
Joudaki, et al., 2020, A&A, 638, L1
We present a combined tomographic weak gravitational lensing analysis of the Kilo Degree Survey (KV450) and the Dark Energy Survey (DESY1). This includes homogenizing the analysis framework by adopting consistent priors, nonlinear modeling, and calibration of the redshift distributions. The links to the paper along with the associated data products can be found below.
Links  → Paper on arXiv  
Data  Primary Monte Carlo Markov Chains 
gzipped tarball (399 MB) README 
Software  Likelihood code  KiDS+DES 
Acknowledge 
Joudaki, et al., 2020, A&A, 638, L1 KiDS450 
KiDS+VIKING450: Cosmic shear tomography with optical+infrared data
Hildebrandt et al. 2020, A&A, 633, A69
The first combined cosmological measurements of KiDS and VIKING were published in Hildebrandt et al. (2019), based on 450 sq.deg. of KiDS+VIKING data. Based on greatly enhanced 9band photometric redshifts and new image simulations (Kannawadi et al. 2018) this measurement updates the results from Hildebrandt et al. (2017) and significantly increases the systematic robustness.
Links 
→ KiDSVIKING450 Cosmic Shear webpage → Paper on arXiv 

Data  Primary Monte Carlo Markov Chains 
gzipped tarball (1.7 MB) FITS (1.8 MB) README 
Data Vector (tomographic twopoint correlation function), Covariance Matrix, different versions of the redshift distribution 
gzipped tarball (1.1 MB) README 

Software  Likelihood code  kv450_cf_likelihood_public 
Acknowledge  See Acknowledgements section on KiDS+VIKING450: Cosmic shear tomography with optical+infrared data 
Euclidera cosmology for everyone: Neural net assisted MCMC sampling for the joint 3x2 likelihood
ManriqueYus & Sellentin, 2020, MNRAS, 491, 2655
We develop a fully noninvasive use of machine learning in order to enable open research on Euclidsized data sets. Our algorithm leaves complete control over theory and data analysis, unlike many blackbox like uses of machine learning. Focusing on a `3x2 analysis' which combines cosmic shear, galaxy clustering and tangential shear at a Euclidlike sky coverage, we arrange a total of 348000 data points into data matrices whose structure permits not only an easy prediction by neural nets, but it additionally permits the essential removal from the data of patterns which the neural nets could not `understand'. The latter provides an often lacking mechanism to control and debias the inference of physics. The theoretical backbone to our neural net training can be any conventional (deterministic) theory code, where we chose CLASS. After training, we infer the seven parameters of a wCDM cosmology by Monte Carlo Markov sampling posteriors at Euclidlike precision within a day. We publicly provide the neural nets which memorise and output all 3x2 power spectra at a Euclidlike sky coverage and redshift binning.
Links 
→ Paper on ADS 

Data  Trained models 
gzipped tarball (452 MB) 
Reference power spectra 
refeuclid.npy 

Software  Loading explanatory module to use the trained models and plot power spectra  Load_Models.py 
Acknowledge  See Acknowledgements section in ManriqueYus & Sellentin (2020) 
KiDS+VIKING450 and DESY1 combined: Mitigating baryon feedback uncertainty with COSEBIs
Asgari et al. 2020, A&A 634, 127
Cosmological constraints from a joint cosmic shear analysis of the KiloDegree Survey (KV450) and the Dark Energy Survey (DESY1), using Complete Orthogonal Sets of E/BIntegrals (COSEBIs). With COSEBIs we isolate any Bmodes which have a noncosmic shear origin and demonstrate the robustness of our cosmological Emode analysis as no significant Bmodes are detected. We highlight how COSEBIs are fairly insensitive to the amplitude of the nonlinear matter power spectrum at high kscales, mitigating the uncertain impact of baryon feedback in our analysis. COSEBIs, therefore, allow us to utilise additional smallscale information, improving the DESY1 joint constraints on S8=σ8(Ωm/0.3)0.5 and Ωm by 20%. Adopting a flat ΛCDM model we find S8=0.755+0.019−0.021, which is in 3.2σ tension with the Planck Legacy analysis of the cosmic microwave background.
Links 
→ Paper on ADS 

Data 
Primary chains (multinest and emcee) Data, Covariance matrix and redshift distributions Plotting script for the chains 
gzipped tarball (205 MB) README 
Acknowledge 
Asgari et al., 2020, A&A 634, 127 KiDS450 
A Bayesian quantification of consistency in correlated datasets
Köhlinger et al. 2019, MNRAS, 484, 3126
We present a novel suite of Bayesian consistency tests for correlated datasets. Without loss of generality we focus on mutually exclusive, correlated subsets of the same dataset in this work. An example for such a dataset are the twopoint weak lensing shear correlation functions measured from KiDS450 data (see Hildebrandt et al. 2017 below). Applying these consistency tests then to the KiDS450 data, we do not find any evidence for significant internal tension, with significances below 3 σ in all cases.
Links  → Full paper at ADS  
Data  Data Vector (tomographic twopoint correlation function), Covariance Matrix, DIR and CC redshift distributions 
gzipped tarball (3.8 MB) README 
Software  Likelihood module to be used within MONTE PYTHON  kids450_cf_likelihood_public 
Modified '2cosmos' MONTE PYTHON (incl. '2cosmos' likelihood for KiDS450 correlation function data)  montepython_2cosmos_public  
Acknowledge 
For the software: Köhlinger et al. 2019 (arXiv:1809.01406) For the data: Hildebrandt et al. 2017 (MNRAS, 465, 1454) KiDS450 
KiDS+GAMA: Cosmology constraints from a joint analysis of cosmic shear, galaxygalaxy lensing and angular clustering
van Uitert et al. 2018, MNRAS, 476, 4662
Based on 450 square degrees of survey data, this is a joint cosmological analysis of cosmic shear, galaxygalaxy lensing, and angular galaxy clustering. All probes are measured in terms of tomographic band powers integrated over correlation functions. A number of scientific data products are available, including MCMC chains, data vectors, the covariance matrix, and redshift distributions.All data files are accompanied by detailed README files.
Links 
→ Paper on ADS 

Data  Primary Monte Carlo Markov Chains (cosmic shear: ee; galaxygalaxy lensing: en; angular galaxy clustering: nn) 
vUitert18_KiDS450_chains_ee.tar.gz vUitert18_KiDS450_chains_ee_en.tar.gz vUitert18_KiDS450_chains_ee_en_nn.tar.gz vUitert18_KiDS450_chains_ee_nn.tar.gz vUitert18_KiDS450_chains_en_nn.tar.gz README 
Data Vector (tomographic band power spectra), covariance matrix, redshift distributions 
vUitert18_KiDS450_data.tar.gz README 

Acknowledge 
van Uitert et al. 2018, MNRAS, 476, 4662 KiDS450 
KiDS450 + 2dFLenS: Cosmological parameter constraints from weak gravitational lensing tomography and overlapping redshiftspace galaxy clustering
Joudaki et al. 2018, MNRAS, 474, 4894
We perform a combined analysis of cosmic shear tomography, galaxygalaxy lensing tomography, and redshiftspace multipole power spectra (monopole and quadrupole) using 450 sq deg of imaging data by KiDS overlapping with two spectroscopic surveys: the 2degree Field Lensing Survey (2dFLenS) and the Baryon Oscillation Spectroscopic Survey (BOSS). A number of scientific data products are available, including MCMC chains, data vectors, the covariance matrix, redshift distributions, and convolution matrices. The likelihood code is also publicly available. More details can be found in the paper linked below and all data files are accompanied by detailed README files.
Links 
→ Paper on ADS → 2dFLenS data page 

Data  Primary Monte Carlo Markov Chains 
kids2dflenschains.tar.gz (112 MB) README 
Data Vector (tomographic twopoint correlation functions, redshiftspace multipole power spectra), covariance matrix, redshift distributions, convolution matrices 
kids2dflensdata.tar.gz (4.3 MB) README 

Software  Likelihood code  CosmoLSS 
Acknowledge 
Joudaki et al. 2018, MNRAS, 474, 4894 KiDS450 
KiDS450: weak lensing power spectrum
Köhlinger et al. 2017, MNRAS, 471, 4412
Based on 450 square degrees of survey data, an analysis of the weak gravitational lensing shear power spectrum was published in KiDS450: the tomographic weak lensing power spectrum and constraints on cosmological parameters (Köhlinger et al. 2017, MNRAS, 471, 4412).
A number of scientific data products are available, including MCMC chains, data vectors, the covariance matrix, and redshift distributions, as well as software code specifically written for the analysis.
More details can be found in the paper (linked below) and all data files are accompanied by detailed README files.
Links 
→ Paper on ADS 

Data  Primary Monte Carlo Markov Chains 
3z bin analysis: gzipped tarball (1.9 MB) FITS (1.9 MB) 2z bin analysis: gzipped tarball (1.3 MB) FITS (1.4 MB) README 
Data Vectors (tomographic Emode and Bmode band powers for the 2 and 3 zbin analyses), Covariance Matrices, DIR distributions, and additional calibration data 
gzipped tarball (5.9 MB) README 

Software  Quadratic estimator  qe_public 
Likelihood module to be used within MONTE PYTHON  kids450_qe_likelihood_public  
Acknowledge 
Köhlinger et al. 2017, MNRAS, 471, 4412 KiDS450 
KiDS450: Cosmological parameter constraints
Hildebrandt & Viola et al. 2017, MNRAS, 465, 1454
Based on 450 square degrees of survey data, the first cosmological parameter constraints from KiDS were published in KiDS450: Cosmological parameter constraints from tomographic weak gravitational lensing (Hildebrandt & Viola et al. 2017, MNRAS, 465, 1454).
A number of scientific data products are available, including MCMC chains, data vectors, the covariance matrix, and redshift distributions.
More details can be found on the page linked below and all data files are accompanied by detailed README files.
Links 
→ KiDS450 Cosmic Shear webpage → Paper PDF → Paper on ADS 

Data  Primary Monte Carlo Markov Chain 
gzipped tarball (91 MB) FITS (60 MB) README 
Data Vector (tomographic twopoint correlation function), Covariance Matrix, DIR and CC redshift distributions 
gzipped tarball (3.8 MB) README 

Software  Likelihood code  KiDS450 
Acknowledge 
Hildebrandt & Viola, 2017, MNRAS, 465, 1454 KiDS450 
Catalogs
High quality strong lens candidates
This catalog combines high quality strong lens candidates from several recent strong lens search projects using KiDS data (Petrillo et al. 2019, Li et al. 2020, Li et al. 2021).
Links 
→ KiDS high quality strong lens candidates webpage 

Acknowledge 
Petrillo et al. (2019, MNRAS, 484, 3879) Li et al. (2020, ApJ, 899, L30) Li et al. (2021, ApJ, in press, arXiv:2110.01905) KiDS DR4 
KiDS DR4 bright galaxy catalog
This catalog contains galaxies fluxlimited to r<20 mag from the ~1000 deg^{2} KiDS Data Release 4 and provides a highly pure and complete dataset of about 1 million galaxies with photometric redshifts and physical properties.
Links 
→ KiDS DR4 bright galaxy catalog webpage 

Acknowledge 
Bilicki et al. (2021, A&A in press, arXiv:2101.06010) KiDS DR4 
KiDS1000 weak lensing SOMgold catalogue
The KiDS1000 data set encompasses 1006 survey tiles. Only galaxies with reliable shape and redshift measurements, our "gold sample", are included in this catalogue. The catalogue contains a total of 21,262,011 sources, and is presented as a single 16GB FITS table.
Links 
→ KiDS1000 weak lensing SOMgold catalogue webpage 

Acknowledge  See KiDS1000 weak lensing SOMgold catalogue webpage 
KiDS DR4 quasar catalog
Based on the KiDS DR4 data a catalog of over 1 million quasar candidates was constructed, using both the optical KiDS and the nearIR VIKING data.
Links 
→ KiDS DR4 quasar catalog webpage 

Acknowledge 
Nakoneczny et al. 2021, A&A, 649, A81 KiDS DR4 
KiDS+VIKING450 catalog
The KiDS+VIKING450 weak lensing shear catalog formed the basis of the first tomographic weak lensing analysis based on a combination of KiDS and VIKING data. Based on the same data set as KiDS DR3, spanning 450 square degrees, although the released shear catalog is filtered to include only sources with reliable shear measurements. Details regarding the catalog can be found on the dedicated webpage linked below.
Links 
→ KiDS+VIKING450 catalog webpage 

Acknowledge  See KiDS+VIKING450 catalog webpage 
KiDS DR3 quasar catalog
Based on the KiDS DR3 data a catalog of 190,000 quasars was constructed, covering an area of ≈400 sq. degrees.
Links 
→ KiDS DR3 quasar catalog webpage 

Acknowledge 
Nakoneczny et al. 2019, A&A, 624, A13 KiDS DR3 
KiDS DR2 cluster catalog
Based on the KiDS data released in DR1 and DR2 a catalog of 1543 candidate galaxy clusters was constructed, covering an area of 114 sq. degrees, in the redshift range 0 ≤ z ≤ 0.7.
Links 
→ KiDS DR2 cluster catalog webpage 

Acknowledge 
Radovich et al. 2017, A&A, 598, A107 KiDS DR1/DR2 
KiDS450 lensing catalogs
The KiDS450 weak lensing shear catalog formed the basis of the first tomographic weak lensing analyses from KiDS and the first constraints on cosmological parameters. Based on the same data set as KiDS DR3, spanning 450 square degrees, although the released shear catalog is filtered to include only sources with reliable shear measurements. Details regarding the catalog can be found on the dedicated webpage linked below.
Links 
→ KiDS450 Weak lensing data webpage 

Acknowledge  KiDS450 
Lensing catalogs 2015
The first KiDS weak lensing results, published in 2015 and 2016, made use of shear catalogs that were based on imaging data from KiDS DR1 and DR2. Some information related to the catalogs and the papers based on them can be found on the webpage linked below.
Links 
→ Lensing catalogs 2015 webpage 

Acknowledge  Lensing catalogs 2015 