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Additional file 1: Data S1. Participant characteristics. S1a: Characteristics of individuals with CSF Aβ1-42 and tau measurements available; S1b: Characteristics of individuals with CSF proteomic data. Data S2. Protein annotation and statistics of group comparisons of protein levels. Data S3a. Full list of GO biological processes associated with proteins that differ according to group and clinical stage. Data S3b. SynGO enriched synaptic cellular components and biological processes that differ according to group. Data S4a. Estimated marginal means of AD GWAS-based polygenic risk scores in controls, AD individuals with increased t-tau and AD individuals with normal t-tau. Data S4b. Top 1000 SNPS from GWAS on AD individuals with increased t-tau and normal t-tau in pooled ADNI and EMIF-AD MBD cohorts. Data S4c. Difference in MAGMA gene score between AD individuals with increased t-tau and normal t-tau based on t-tau GWAS in pooled ADNI and EMIF-AD MBD cohorts. Data S4d. Difference in GO biological process MAGMA geneset score between AD individuals with increased t-tau and normal t-tau based on t-tau GWAS in pooled ADNI and EMIF-AD MBD cohorts. Data S5a. Correlation between genetic risk score and CSF protein level in individuals with abnormal Aβ1-42. Data S5b. Association of the number of GMNC rs9877502-A risk alleles and number of APOE-e4 alleles with CSF protein concentrations in a linear model in individuals with AD. Data S5c. GO-BP processes enriched for proteins that have a positive or negative association with the number of rs9877502-A risk alleles in an additive model. Data S6. Annual change in imaging measures.

Additional file 3: Summary of Zenodo view and download statistics, plus citations (CSV format) as of 28 April 2022 [235].

In this data release from the ongoing LOw-Frequency ARray (LOFAR) Two-metre Sky Survey (LoTSS) we present 120-168MHz images covering 27% of the northern sky. Our coverage is split into two regions centred at approximately 12h45m +44{deg}30' and 1h00m +28{deg}00' and spanning 4178 and 1457 square degrees respectively. The images were derived from 3,451 hrs (7.6 PB) of LOFAR High Band Antenna data which were corrected for the direction-independent instrumental properties as well as direction-dependent ionospheric distortions during extensive, but fully automated, data processing. A catalogue of 4396228 radio sources is derived from our total intensity (Stokes I) maps, where the majority of these have never been detected at radio wavelengths before. At 6"resolution, our full bandwidth Stokes I continuum maps with a central frequency of 144MHz have: a median rms sensitivity of 83mJy/beam; a flux density scale accuracy of approximately 10%; an astrometric accuracy of 0.2"; and we estimate the point-source completeness to be 90% at a peak brightness of 0.8mJy/beam. By creating three 16MHz bandwidth images across the band we are able to measure the in-band spectral index of many sources, albeit with an error on the derived spectral index of >+/-0.2 which is a consequence of our flux-density scale accuracy and small fractional bandwidth. Our circular polarisation (Stokes V) 20" resolution 120-168 MHz continuum images have a median rms sensitivity of 95uJy/beam, and we estimate a Stokes I to Stokes V leakage of 0.056%. Our linear polarisation (Stokes Q and Stokes U) image cubes consist of 480x97.6kHz wide planes and have a median rms sensitivity per plane of 10.8mJy/beam at 4' and 2.2mJy/beam at 20"; we estimate the Stokes I to Stokes Q/U leakage to be approximately 0.2%. Here we characterise and publicly release our Stokes I, Q, U and V images in addition to the calibrated uv-data to facilitate the thorough scientific exploitation of this unique dataset. Cone search capability for table J/A+A/659/A1/catalog (Raw radio component catalogue of LoTSS-DR2)

Posterior sample files associated with "A NICER View of PSR J0030+0451: Millisecond Pulsar Parameter Estimation" by Riley et al. (2019; DOI: 10.3847/2041-8213/ab481c). Also included are model modules and scripts in the Python language using the X-PSI framework. Please refer to the README for detailed information.

The prominent radio source Hercules A features complex structures in its radio lobes. Despite being one of the most comprehensively studied sources in the radio sky, the origin of the ring structures in the Hercules A radio lobes remains an open question. We present the first sub-arcsecond angular resolution images at low frequencies (<300MHZ) of Hercules A, made with the International LOFAR Telescope. With the addition of data from the Karl G. Jansky Very Large Array, we map the structure of the lobes from 144MHz to 7GHz. We explore the origin of the rings within the lobes of Hercules A, and test whether their properties are best described by a shock model where shock waves are produced by the jet propagating in the radio lobe, or an inner-lobe model in which the rings are formed by decelerated jetted plasma. From spectral index mapping, our large frequency coverage reveals that the curvature of the different ring spectra increases with distance away from the central active galactic nucleus. We demonstrate that the spectral shape of the rings is consistent with synchrotron aging, which speaks in favor of an inner-lobe model, where the rings are formed from the deposition of material from past periods of intermittent core activity. Cone search capability for table J/A+A/658/A5/list (List of fits images) Associated data

This project aimed to produce the first wall-to-wall estimate of C stocks in plants and soils of Canada at 250 m spatial resolution. This dataset contains the map with total C stored in plants of forested areas in Canada (AGB, BGB and dead plants) in kg/m² and C stock uncertainty. To estimate the C stored in plants of forest areas, we used 47,967 ground measurements of AGB measures and 58 covariates mainly composed of optical data, terrain parameters, structural parameters (e.g., SAR data, clump index, canopy heights – generated from satellite LiDAR- included in the other dataset), soil type map and radiation flux data. Different models were trained using a recursive feature elimination, random forest scheme and a 5-fold cross-validation assessment. The model with higher R² and lowest root mean square error (RMSE) was used for spatial prediction of AGB in forest areas while 1st and 3rd quantiles of RF quantile regression were used to build the uncertainty map. After generating the AGB map, the root biomass of forest areas was computed by its relationship to AGB according to forest type. The dead plant materials were computed by a linear regression between live and dead AGB defined with ground measurements. Ultimately, the AGB as well as dead plant materials and BGB were multiplied by 0.5 to provide the maps in kg C m-2.

Additional file 4: Summary of Zenodo citations plus DOIs per list (CSV format) as of 1 May 2022 [236].