BUSCADOR RECOLECTA

This dataset is a major update to the quasi-global, high-frequency (at least hourly) sea level dataset known as GESLA (Global Extreme Sea Level Analysis). Versions 1 (released 2009) and 2 (released 2016) of the dataset has been used in many published studies, across a wide range of oceanographic and coastal engineering-related investigations concerned with evaluating tides, storm surges, extreme sea levels and other oceanographic processes. The third version of the dataset (released 2021), presented here, contains twice the number of station-years of data (91021), and nearly four times the number of station records (5119), compared to version 2. The dataset consists of records obtained from 36 sources around the world, including some data archaeology efforts. The oldest record dates from year 1805 (spanning 217 years), followed by a few other stations starting during the 1840s and 1850s. However, the vast majority of records start during the 1950s. Data have been updated until mid-2021 whenever possible. We have archived the dataset into two parts. This second part contains the 592 records that are can be used for research purposes, but not consultancy. The higher-frequency sea-level dataset in this second part of GESLA-3 was obtained from 3 international and national data providers, specifically: City of Venice, Tide Forecasts and Reporting Center, University of Zagreb and the Copernicus Marine Environment Monitoring Service. These data are made available under the creative commons BY-NC 4.0 license., Peer reviewed

Table S1. Characteristics of the 11 studied ST274-Pseudomonas aeruginosa recovered from different origins; Table S2. Characteristics of the genomes of 14 reference ST274-P. aeruginosa strains and control strains PAO1 and PA14 downloaded from the NCBI database; Table S3. Single-nucleotide polymorphisms (number of SNPs) in the core genome among the 11 studied ST274-P. aeruginosa, the 14 reference ST274-P. aeruginosa genomes, and the two control genomes; Table S4. Pangenome determined using Roary in all 25 ST274-P. aeruginosa strains and the control strains PAO1 and PA14; Table S5. Acquired genes in ST274-P. aeruginosa with Resfinder prediction; Table S6. Dataset and function of the 170 antimicrobial resistance genes analyzed to determine the mutational resistome of the ST274-P. aeruginosa strains; Table S7. Mutational resistome of the 25 studied ST274-P. aeruginosa strains with respect to PAO1 genome; Table S8. Detection of alterations in the mutome genes (involved in the mutator phenotype) of the 11 studied ST274-P. aeruginosa strains and the 14 reference ST274-P. aeruginosa genomes, with respect to PAO1 genome; Table S9. Dataset and functions of the 250 virulence genes analyzed to determine the virulome of the ST274-P. aeruginosa strains; Table S10. Amino acid changes in virulome of the 25 studied ST274-P. aeruginosa strains with respect to the PAO1 genome., Peer reviewed

Additional file 1: Table S1. Requested and simulated transcript models and read counts for ONT and PacBio datasets used in SQANTI-SIM validation. Table S2 Simulated cDNA ONT and PacBio datasets used for pipeline benchmarking. Additional file 2: Figure S1. Number of detected true (TP) and false positives (FP) for different types of novelty (ISM, NIC, and NNC). Figure S2. Relationship between true positives (TP), false negatives (FN), and false positives (FP) with (a) transcript length, (b) number of exons, and (c) simulated expression level. Figure S3. SQANTI-SIM characterization of CAGE peak data. Additional file 3: Supplementary methods and code. Additional file 4: Peer review history., Peer reviewed

PN40024, a highly homozygous Pinot Noir inbred line, was used for T2T genome assembly. In total, we generated 39.12Gb (~65X coverage) HiFi reads by the PacBio platform. The preliminary assembly were conducted using Hifiasm on HiFi reads, and Mumer was used to order and orient the contig-level assemblies using the PN40024.v3 genome as the reference, forming 169 contigs representing 19 chromosomes. The PN_T2T genome size (494.87M) is longer than that of 12X.v0 (426.18M). Due to the accuracy of HiFi long reads, the N50 length PN_T2T of (26.89 Mb) is 260 times higher than PN_v3 (~102Kb). For all 9423 gaps in 12X.v0 assembly, PN_T2T assembly is the gap-free grape genome.To validate the quality of our assembly, K-mer and BUSCO were conducted. We used K-mer to evaluate genomic heterozygosity, estimated 99.8%. BUSCO to evaluate genomic completeness, about 98.5% of the core conserved plant genes were found complete in the genome assembly. The PN40024.T2T genome assembly: PN40024.T2T.fa The PN40024.T2T gene annotation: PN40024.gff3 The PN40024.T2T TE annotation: PN40024.TE.gff The PN40024.T2T centromere annotation: PN40024.trf.gff3 The PN40024.T2T protein sequence: PN40024.protein.fa The PN40024.T2T cds sequence: PN40024.cds.fa Comparison of gene annotation among PN_T2T and 12X.v0, 12X.v2, PN40024.v4, PN40024.v4.1: correlation.list, Peer reviewed

Description of the data files: 'CH_Female_Multi_2004_6events_v3.txt': Capture-recapture multievent data for females; 'CH_Female_Multi_2004_z_init_Nimble.txt': Initial values for latent z states and state at first encounter for females; 'CH_headed_Male_Multi_2004.txt': Capture-recapture multievent data for males; 'CH_headed_Male_Multi_2004_z_init.txt': Initial values for latent z states and state at first encounter for males; 'count_data.RData': Population count data; Model code: 'IPM_10.txt' All files are called in the online supplementary code for running the integrated population model., Peer reviewed

Excel dataset., Peer reviewed

R data format, Peer reviewed

This zipped folder includes all data and the scripts to replicate the analyses from the study by Santangeli et al. Mixed effects of protected areas on terrestrial and freshwater biodiversity. Specifically, we include in the main folder the script (R file, suitable for R software - "SiteMatchingBirdExample") and site level data ("BirdData.csv") used to run the matching analyses of protected and unportected sites (data and script provided for the birds data only due to data ownership agreements). This example is easily applicable to the other three taxonomic groups. We also include the data and scripts to run the Joint Species Distribution Modeling on the occurrence data separately for each of the four taxonomic groups studied in the folder "HMSC_models". Here the user will find four separate sub-folders, each including in turn a folder named data which hosts the occurrence data from sites selected after matching for that taxon (CSV file named SXY, includes also site level information), as well as a script file (named e.g. birds_HMSC in the case of birds folder) that can be used to replicate the joint species distribution model on that specific taxonomic group., European Commission through the Horizon 2020 Marie Skłodowska-Curie Actions individual fellowships (Grant no. 101027534), Jane and Aatos Erkko Foundation., Peer reviewed

Datasets from HAB monitoring program in Mallorca., Peer reviewed

Table 1S. Samples’ and results’ description.-- Figure 1S. Occupacy and detection probabilities.-- Figure 2S. Matrices of pairwise comparisons between positives.-- Table 2S. Modelling site-occupancy.-- Table 3S. Effect of processing times.-- Table 4S. Modelling the effect of site-dependent covariates on site-occupancy.-- Figure 3S. Sample processing intervals graphs.-- Figure 4S. Summary of results discussed by geographic sector. Appendix 1S Partners of the 2021 Spot the Monk campaign, Peer reviewed